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A few months ago, I came across Tailscale, and a competitor, Zerotier. These are similar products that let you run secure, private networks that do more sophisticated things than traditional VPNs, and do so in two interesting and differently opinionated ways. Neither is a consumer-grade product. They are meant for people with some system administration skills, so you have to know something about networking tech to use either. Both also clearly have a different DNA than typical Silicon Valley products. Tailscale was co-founded by Avery Pennarun, who is a well-known tech blogger of the curmudgeonly sort, under the handle apenwarr, and under his real name he has written a manifesto for the vision driving Tailscale, The New Internet. As with anything written by curmudgeonly alt-techies, it is stimulating, insightful, and original, and you don’t have to agree with all the details or even the overall thrust of the vision to get a lot out of it. Zerotier too, has an interesting philosophical origin story. Here is an early blog post by founder Adam Ierymenko. In this picture, we also have Wireguard, an open-source project with similar philosophical underpinnings.

I found my reaction to The New Internet to be divided. It is the sort of idea that reads delusional if you take it as a vision for replacing the internet as we know it wholesale, but quite rational if you think of it as one piece of an emerging puzzle, where other pieces balance it out via various oppositions. Tailscale, understood via its guiding vision of the “New Internet,” has what I think of as an individually delusional, collectively rational character. It makes sense as part of certain alternative tech ecologies that may emerge, but not as a totalizing vision of a “New Internet,” or as part of the prevailing Tech ecology. Similar things could be said of Zerotier and Wireguard.

Tailscale, Zerotier, Wireguard, and the philosophies driving them, belong on an emerging alt-tech landscape I want to talk about today. I am going to label this landscape cozytech. Cozytech isn’t a prescription or a description, or a singular subculture. It is, rather, an interesting reachable equilibrium that a whole constellation of alt-tech subcultures, each individually perhaps delusional, but collectively rational, are driving towards, and might actually reach. If we get there, we might actually have a muscular alternative to Silicon Valley that doesn’t feel like an insipid and quixotic political-resistance theater.

But let’s set the stage here.

The word technology today has become synonymous with a certain flavor we’ve come to refer to simply as Tech (I capitalize the term) epitomized in our time by the Silicon Valley approach to building things. But the Silicon Valley flavor is only one chapter in the history of Tech. The core element is the presence of an organized landscape around a key “eating” element that can colonize everything. As I wrote in my June 7, 2019 newsletter issue, Can Tech Die?:

In the last 400 or so years, there’s always been a relatively active “eating” tech at work in the world, being driven by the technological imagination, limited only by how clever people are capable of being, and driving the history of the world faster than any other competing force.

When such an element exists — and software has been the element for the last 30 years — “Tech” dominates all of technology. When there isn’t, technology becomes a more diffuse, less structured background force. During such periods, Tech lies dormant, while technology itself continues to evolve in a different mode, defined by an emergent equilibrium of individually delusional, collectively rational parts. Cozytech is my name for one such era that might possibly be emerging.

This is a punctuated equilibrium model of technological evolution, and “cozytech” is my proposed characterization of a possible next equilibrium, past the punctuation discontinuity that is Silicon Valley Tech.

We get capital-T Tech during Cambrian-explosion style events, and then just ordinary background technological evolution (I’ll shorten this to “background tech”) in the periods between the explosions. During Tech epochs, to use Brian Arthur’s terminology in Nature of Technology, a major natural phenomenon is typically discovered and harnessed in broad ways, though this is not always the case.1 During Tech epochs, background tech dynamics get suppressed or even entirely squashed through various mechanisms — getting starved of resources and talents, Tech offering cheaper/more convenient solutions to problems that were the preserve of background tech, active political oppression, enclosure and capture, cronyist corporatism, and so on.

But like Tech, background tech too never completely dies out. Even during peak Tech eras, it persists as an endemic phenomenon on the margins, ignored or perhaps laughed at, waiting for the next opportunity to break out, shrug off the waning power of Tech to suppress it, and take over the logic of the narrative.

We can distinguish several Tech epochs in history besides Silicon Valley — interchangeable parts mass manufacturing (late 19th century, 1870-1910 or so), steam engine (early industrial revolution, 1770-1825) and printing press (mid 16th century). I think Tech, as such, requires 4 conditions to exist:

1. Major physical natural phenomenon being harnessed OR serendipitous convergence of several minor ones.
2. Systematization of key bodies of knowledge beyond trial-and-error tinkering and savant-ish engineering skill, allowing for predictable return on investments in the form of a landscape of investible opportunities staffed by concentrated pools of mediocre engineering and scientific labor, with little or no need for savants.
3. Political-economic conditions with enough surplus and stability for intelligent capital markets to coalesce, in the form of effective institutions (DARPA, VC sector, Medici family, Softbank, Mohammad bin Salman’s regime, Chinese emperor, Xi Jinpeng, whatever) that can act on those opportunities.
4. Cultural conditions allowing for a compounding path-dependent improvement process to unfold for at least several generations at a stretch (true in early industrial England and Silicon Valley, but not true in medieval China for eg) — this is basically Joel Mokyr’s model.

Tech requires a rather remarkable constellation of forces converging to create a larger phenomenon, so we should not expect it to happen very often or sustain for very long. If even one of the conditions fails enough, Tech as an explosive evolutionary epoch cannot persist. Instead of a Cambrian explosion punctuation regime, you get the slow background evolutionary type of epoch. By my rough accounting, before the Neolithic revolution, you had 100,000+ years of pure background tech evolution that crept along. After that revolution, circa 10,000 BC, I’d say we’ve had about 20% of history being “Tech” regimes, and 80% being “background tech” regimes. That 20% of Tech probably created 80% of the “Progress,” but the 80% of background tech, in delivering the remaining 20% of Progress, probably locked down the gains in deep ways. In the last 400 years, the Tech epochs have been getting longer, and the background tech eras have been getting shorter, but I think this process has a limit. We’re not going to get to a continuous Tech boom with no background tech interruptions.

It is these background regimes I want to talk about. What happens when one or more of the conditions fails? How do we transition from an active Tech epoch to a background technology epoch?

I think we’re in such a transition right now:

1. The harnessed force condition is failing (Moore’s Law troubles). There’s at most another 10 years and maybe 40x improvement headroom left, and then the music stops unless experimental post-lithographic technologies succeed.
2. Systematization is starting to break because the major knowledge frontiers we’re pinning our hopes on (AI, energy tech, biology, terraforming) resist strong systematization and depend on a grind of trial-and-error that works better in background ways. There’s still science as a potent force, but not as a high-leverage one. You can’t mindlessly through big lumps of capital at huddled masses of engineers and expect 10x returns reliably.
3. For the moment, political-economic conditions are still positive. There is plenty of surplus capital in the world desperately looking for places to go, though with unrealistic expectations of returns. And despite widespread institutional decay, contrary to what the burn-it-down crowd believes, there is still capacity for effective organizing around opportunities, even if in unfamiliar forms. But we’re getting there. Institutional capacity decay is real, and if things continue as they’re going, we will soon be unable to organize effectively beyond very small scales.
4. Cultural conditions are arguably broken, as I argued in Silicon Valley Vibe Shift. Silicon Valley is no longer a good environment for creating multi-generational path-dependent compounding results for a variety of reasons, the growing illiberalism being a big one. Visible signs include the shifting of initiative to the larger platform monopolies, enshittification of those platforms, and disenchantment with the region in the traditional entrepreneurial class. The specific SV formula of a particular horizon of investment, an approach to individual wealth-building through stocks, “creative monopoly” business models, and an “aggregation theory” approach to scaling, is breaking. More money is being raised and distributed than ever, but it’s something of an illusion because the underlying model is morphing in unrecognizable expedient ways (blurring into private equity for instance, and getting increasingly dependent on protectionist cronyism and state patronage, though the free market rhetoric continues stridently). There are no other credible hotspots with alternative cultural conditions.

It doesn’t matter if I’m right or wrong on any of these assessments. I just have to be right enough on any one of the four, for the current Tech era to break. Intense evolutionary bursts are rather fragile phenomena. The bad news is that the so-called “Dark Ages” between them tend to last at least a few decades on average. The good news, as I argued in Can Tech Die, is that Tech doesn’t die, and we’ll almost certainly see another evolutionary burst within a few decades, within our lifetimes. But a few decades is enough to kill a particular institutional landscape/manifestation of Tech, even if the Cthulhu beneath it lives on. “That is not dead which can eternal lie, with strange aeons even death may die” etc.

But the even better news is that “Dark” ages are not actually dark, and that background tech epochs are not just highly valuable by their own internal logic (which we’ll get to), they’re actually necessary for the health of the overall yin-yang of foreground Tech and background tech. Trying to have only one of the two is like trying to be awake 24/7 or trying to sleep 24/7. You need a healthy alternation of sleep and wakefulness.

We’re entering a background tech epoch right now, and it might be healthy or not depending on how exactly it evolves. Will we get the slow improvement mode of the European Middle Ages, which saw steady advances across the board in things like metallurgy, agricultural science, and so on? Or will we see a long stagnation of the sort many fear?

I think there are many bad equilibria background tech might end up in, but at least one good one that I’ve started thinking of as “cozytech.” In this essay, I want to sketch out a preliminary view of the cozytech equilibrium. It builds on my earlier socio-cultural notion of the cozyweb, which, despite being the one to coin the term, I kinda dislike and resist because it seems like a mode of fearful retreat to me. But if you add better cozytech to the cozyweb, maybe things will start cooking in interesting ways, and we’ll get a mode of bold advance rather than fearful retreat.

Maybe instead of being a place of fear, the Dark Forest can be a place of quiet, peaceful, background evolution. An era of locking in the gains of the waning Tech era, and working on raising the floor, after the era of raising the ceiling we’ve been in for 40 years.

So what is the cozytech equilibrium, how do we start herding ourselves towards it, and how can it begin to catalyze some good chemistry with the cozyweb that is already in place?

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About a decade ago, I was briefly obsessed with Jane Jacobs’ Systems of Survival model from her 1992 book of that name. In brief, she argued that the world is made up of two types of people, who operate by two competing and incompatible value systems that she labeled guardian syndrome and commerce syndrome. Here is a table from the Wikipedia page summarizing the key differences:

As is usually the case with me, I never actually read the book,1 but just went off down my own bunny trail based on a Wikipedia-level gloss. In Saints and Traders: The John Henry Fable Reconsidered (June 2014) I considered the model in the light of the fable of John Henry, and labeled the archetypes corresponding to the two syndromes saints and traders. In The Economics of Pricelessness (August 2014)I made up a model of how economic relationships play out both within and between the two tribes.

In 2014, I thought the model was great!

In 2014, most people were clearly either Saint types or Trader types, though in a nuanced yin-yang way, as in Saints have a Jungian shadow of Trader going, and vice versa. So you get certain predictable patterns like successful business people being driven by honor, loyalty and vengeance, or traditionalists with a basic aversion to commerce being industrious, inventive, and culturally open.

Despite these Jungian complications, as a broad strokes model, tastefully interpreted, the Jacobs model basically worked in 2014. The eigenvalues of the model, so to speak, which tended to govern the subsidiary values, were the attitudes most directly related to money and transactions.

To test people for their dominant syndrome, you could just probe how they felt about money. Saints in 2014 were fundamentally people who prioritized personal relationships over transactional ones, often being willing to make financially irrational decisions to serve priceless personal values. Traders in 2014 were fundamentally economic rationalists who would often be willing to sacrifice personal relationships in service of financially rational decisions.

Take a look at the table and ask yourself: Does this describe the world of today as well as it did the world of 2014? If it helps, pick a couple of specific people as examples, and try to profile them using the two columns.

I don’t think the model works anymore. There are many people I used to consider clear Trader types in 2014 who now act more Guardian-like than the Pope. And (to a much lower extent), there are people I considered pure Guardians in 2014, who now strike me as embodying the spirit of Commerce (though not necessarily in a commercial domain; I mean simply being willing to bring a spirit of win-win expectations, compromise, openness, mutual understanding and negotiation to relationships with outgroups).

The eigenvalues of attitudes towards money and transactions are no longer reliable indicators of anything. For example, crypto people are possibly the strongest believers in markets, contracts, and economic rationality. And they all strike me as more religious than evangelical Christians.

And the break in the model is not subtle. It’s not merely fraying on the edges. It feels like it’s not even wrong anymore. A deep fault line has emerged that has completely undermined it.

Given that the Jacobs was a product of 60s/70s conflicts between Johnsonian Great Society and Reaganesque schools of governance, and published her book in 1992 (probably Peak Neoliberalism, given that Bill Clinton rose to power by simply adopting the Reagan playbook), I suspect her model is, unconsciously, a very particular one, reflecting the peculiar historical era it was developed in.

We’re not in that world anymore, so it’s not surprising that the model doesn’t work.

Which brings me to the idea I’ve been noodling on for the last few months: A new pair of systems of survival has emerged that are defined by opposed relationships to the problem of the growing ungovernability of the increasingly complicated world, and the collapse of previous theaters of pretending to govern or be governed (see my newsletters from the last two weeks, The Art of Pretending to Govern and Bullshitization and Common Indifference Problems).

To develop this model, we need to start with a test that divides the world into two basic pieces governed by a metaphysical duality.

The LinkedIn-Twitter TestYou can get to the core of this new pair of systems of survival using what I call the LinkedIn-Twitter test. Given any system, person, behavior, or idea, does it strike you as a LinkedIn type thing, or a Twitter type thing? Here, I mean LinkedIn and Twitter as motifs for larger socioeconomic tendencies and cultural moods that the two platforms exemplify and embody particularly well, but aren’t limited to them. The test is metonymically named.

There is something of an American bias to the test of course, since both platforms are strongest and most influential in the US, but arguably, the test is global and there is no better pair of motifs for what I’m getting at.

I ran a poll a few weeks ago, asking people which of the two platforms they’d want to eliminate from existence (not just quit personally). And as I fully expected, but most people apparently didn’t, the results were about even, with, as I expected, a slight edge for people wanting to erase LinkedIn from existence.

What was interesting was the polarized reaction of surprise some people had to the result of the poll as it became clear: Twitter-erasers being shocked that anyone would consider erasing LinkedIn, and vice-versa. Most also seem surprised the platform they want to erase exists at all. They can’t fathom why anyone would go there, and why it hasn’t died already.

As my screenshot above shows, I personally voted to get rid of Twitter. But I do understand why others might have chosen LinkedIn, and why some people can’t comprehend choices that differ from theirs.

The reasoning was also fairly consistent. People who voted to keep LinkedIn saw the real deep value in professional networks for career development, recruiting and so on, and saw Twitter as mostly a toxic cesspool of ignorable noise. People who voted to keep Twitter saw real deep value in the “alpha” — real actionable intelligence signals bubbling out of the noise — and saw LinkedIn as a toxic theater of bullshit pretensions and credentialed puffery by useless and clueless people.

The LinkedIn-Twitter test applies to more than the narrow preference between the platforms. Almost anythingcan be put to the test. To apply the test, think of it as testing for resonance/harmony vs. dissonance, when you juxtapose the thing with our two test entities.

1. Substack is clearly more LinkedIn than Twitter, while WordPress blogs are more Twitter than LinkedIn.
2. Instagram is more LinkedIn, TikTok is more Twitter.
3. YouTube I think is mostly Twitter culture, though it features a lot of LinkedIn content.
4. Trump and the GOP are Twitter, Kamala Harris and the Democrats are LinkedIn.
5. Globally, ethnonationalist parties are typically Twitter, while progressive parties are more LinkedIn.
6. BUT… at an individual level, far right and far left are both Twitter (including the far lefties who would want to erase Twitter), while centrists both right and left are LinkedIn.
7. Among the Twitter alternatives, the Fediverse and Threads are more LinkedIn, while Bluesky and Farcaster are more Twitter.
8. America is more Twitter, the EU is more LinkedIn…even the ethnonationalists.
9. Antivaxxers are Twitter, vaccine people are LinkedIn.
10. People who flaunt their degrees are LinkedIn, people who “do their own research” are Twitter.
11. Bureaucratic organizations, whether traditionally left-leaning (welfare orgs) or right-leaning (military/police orgs) are typically LinkedIn.
12. Cultural scenes, whether traditionally left-leaning (artists, musicians) or right-leaning (motorcycle clubs, MMA) are typically Twitter.
13. Appeal to institutional authority is LinkedIn, appeal to charismatic authority is Twitter.
14. Curiously, which one you vote to eliminate does not indicate what kind of person you are. There are Twitter people (far left types) who probably voted to erase Twitter, even though they’re really not LinkedIn types. What they really want is for Twitter to be left-leaning. And there are LinkedIn people (struggling with career opportunities/job leads for eg.) who probably voted to erase LinkedIn even though they’re not really Twitter types. What they really want is to be on LinkedIn getting job offers and having important and famous people looking at their profiles. They’re just mad LinkedIn doesn’t work for them.
15. Equally curiously, how you succeed or fail is not an indicator either. I’ve probably been a vastly more successful “Twitter person” even on LinkedIn-style media, but I’m fundamentally a LinkedIn type person.

The test applies to almost everything, not everything, so I won’t try to do silly things like trying to classify apples and oranges as LinkedIn/Twitter fruits. What’s in scope for the test is anything with a sociopolitical salience. So while I don’t think apples and oranges can be classified by the test, seed oils are clearly LinkedIn and anti-seed-oil theories are clearly Twitter.

Weirdness and Hypernormalcy SyndromesWhat does the LinkedIn-Twitter test measure?

Why do some things feel intuitively like they belong with one or the other? What, if anything, is on the edge between them?

I think the test measures how you instinctively react to the challenge of surviving in an increasingly and visibly ungoverned world settling into a Permaweird state. The two possible responses are:

1. Lean into the weirdness, as in when the going gets weird, the weird turn pro
2. Lean into what Adam Curtis called hypernormalcy in his documentary HyperNormalisation

The latter requires a word of explanation. The documentary (a typical Curtis documentary — spazzy and wild-eyed, but entertaining and insightful in parts) uses the term to refer to an increasingly bizarre, surreal, and distorted kind of institutional normalcy that has become increasingly unmoored from anything real, which people nevertheless double down on and cling to harder and harder. Ie, the weirder the world gets, the harder they cling to normalcy. The surprising thing isn’t that they do it. It’s natural to cling to what used to work for reassurance. The surprising thing is that it works as a system of survival.

This is obviously LinkedIn world. It is a world-scale larp of normalcy circa 2014 or so. A world it takes very talented contortionists to continue to perform as though the last decade hasn’t happened at all.

Let’s call leaning into weirdness the Weirdness Syndrome, and leaning into hypernormalcy the Hypernormalcy Syndrome. These are the “new systems of survival” in my headline. You can self-classify by looking at whether you feel more at home on Twitter or LinkedIn, even if you don’t like the experience of feeling at home there. A traumatizing home is still a home.

Let’s call the associated archetypes professional weirdos (or just weirdos for short), and hypernormies, or just normies for short.

Weirdos living by a Weirdness syndrome code of values, and normies living by a Hypernormalcy syndrome code of values. Creative naming, I know.

Is either of these strategies obviously more rational and adaptive? A lot of people seem convinced that one of the strategies is obviously correct, and that you’d have to be clinically insane to adopt the opposed strategy.

Unfortunately, by that measure, you’d have to classify roughly half the world as insane. I don’t know about you, but to me that suggests that the word insane is reduced to utter vacuity. Much more likely you’re both missing something about both strategies. You are blind to critical affordances of the opposed syndrome, and to critical limitations of your preferred syndrome.

Let’s ask a better question: In what ways are the two syndromes adaptive vs. maladaptive?

The Weirdness Syndrome is obviously more adaptive in one way — being attuned to the growing signals of weirdness on the margins of normalcy, and reacting faster to it. LinkedIn people (hypernormalcy syndrome people), outside of their narrow institutional specializations, are simply less aware of what’s going on in the world and usually don’t even know it.

But unfortunately for weirdos, simply attending to weirdness more closely does not make your mental models of what is going on any better, or your faster reactions actually superior. You might notice weirdness first, but adopt a maladaptive crackpot response. Or you might adopt exactly the wrong strategy while the oblivious person who does not notice it at all might default unseeing into the right strategy. Data without generalization, as Robert Pirsig noted, is just gossip.

The Hypernormalcy Syndrome is obviously more adaptive in one way — rationally holding on to the very high residual value in the unraveling institutional landscape, and being able to navigate and access opportunity spaces within it, using well-established economic extraction patterns like “careers” and “jobs.” Even if the landscape itself is a bizarrely distorted fake world that’s become completely unmoored from reality (I don’t think this is true btw), there is real money to be made in it. Money made from participating in surreal theaters is still real money. A nice house bought with that money is still nicer than a slummy one you might inhabit while being Very Online and attuned to weirdness.

Each side overestimates the power and threat of the other. Weird syndrome people think of Hypernormal syndrome people as a nefarious cabal of Deep Staters hatching a conspiracy to destroy everything of value. Hypernormal syndrome people think Weird syndrome people are dangerous lunatics determined to blow everything up driven by extremist agendas.

This is something of a short-medium term view. I personally rely on both Twitter and LinkedIn worlds to some degree (though not on either of the actual platforms) to survive. Some doors open for me because I have a PhD and brand-name credentials and resume items. Other doors open for me because I have a successful track record as a blogger and shitposter. It’s hard for me to say which is more important for my own survival. But on balance, I’d have to go with LinkedIn (as in, the motif for hypernormalcy, not the site itself). My system of survival is to make a home in hypernormal spaces, but with frequent travel to weird spaces.

Relationship to the Old SystemsIf I do my accounts roughly by the old systems of survival, I’d have to say my current net worth (Commerce syndrome valuation) is 50-50 attributable to the Weirdness and Hypernormalcy syndromes. My honor and reputational capital (Guardian syndrome valuation) is also 50-50 split across the two new syndromes.

Can we say that either of the new syndromes is a clear descendant of one of the older ones? It’s hard.

I am tempted to say the Hypernormalcy syndrome is descended from the Commerce syndrome, because nobody successful in a conventional careerist way is actually cluelessly bought into the theater of hypernormalcy. To participate successfully in LinkedIn-land you have to have a certain clear-eyed Zizekian cynicism, speak in corporate-speak bullshit without being mind-captured by it, and so on. Truly clueless hypernormies wouldn’t last long, or get jobs on LinkedIn. Or even successfully earn credentials like valuable degrees. You need a certain ironic distance from institutional realities to be able to navigate them with any success. You need a certain tolerance for what the reflexively weird might view as bullshit jobs and activities, and an understanding of their actual non-obvious utility.

But it’s easy to overstate the strength and consciousness of that ironic distance. I do meet a lot of successful “LinkedIn” types who are at best very dimly aware of the sheer bizarreness of their theatrical world. It becomes too real for them the way wearing VR glasses for too long might get too real for a gamer. It’s a kind of mask trance.

Equally, even though we say things like “Twitter is tribalized” (I myself have argued for a detailed honor-society understanding of the extended Twitter-verse as the Internet of Beefs), suggesting that the Weirdness syndrome is the “new” Guardian syndrome, that’s not clear either. There are a lot of very clear-eyed engagement farmers and systematic hustlers with very Commerce syndrome sensibilities.

So while there are no clear lines of descent from old systems of survival to new, clearly, the new systems are refactoring the old ones in powerful ways.

In fact, I think there are distinct world-processes underway today (by world-process I mean things like industrialization, globalization, urbanization…), that correspond to the simultaneous growth of both weirdness and hypernormalcy. The world that was once divided along guardian/commerce lines is now getting unbundled and rebundled along weird/hypernormal lines.

I call these processes LinkedInification and Twitterification.

A thing gets LinkedInified when it starts to get governed by systematic “career hacking” type behaviors that can be taught, learned, and practiced with a certain dispassionate indifference to human costs, and a clear separation between “work” and “personal” personas. LinkedInification is hypernormalization, but without the suggestion of conspiratorial orchestration that the documentary tries to convey.

You don’t have to wear “business professional” clothes and put up “professional headshot” pfps to be a LinkedIn type. You can do that by hewing to the optics of any legible script or pattern, and working by reliable playbooks of any sort. For example, statue-head pfps and a feed full of “beautiful” architecture posts is, in a weird way, a LinkedIn style hypernormal career strategy. Or the very characteristic affect and comportment of YouTube stars.

A thing gets Twitterified when it starts to get governed by raw, instant, id-driven responses to unfiltered reality signals, with streaks of real emotion and authenticity breaking through in what is meant to be pure calculated performance, whether or not that authenticity is actually pleasant to behold. Twitterfied behaviors tend to unpredictably trigger explosive viral responses, and the main sign of it is the emergence of dopamine loops that people can get hooked to.

Here is the real tell: LinkedInification moves societal dynamics towards what Taleb called Mediocristan, whileTwitterification moves societal dynamics towards what he called Extremistan.

Ironically, Twitter itself is getting weirdly LinkedInified, and LinkedIn is getting hypernormally Twitterified. The archetypal social spaces of the Weirdness and Hypernormalcy syndromes are starting to leak into each other.

If you haven’t been on LinkedIn in a while, a strange sort of addictive quality has been slowly emerging on the site for a few years. As the hypernormalcy theater gets more surreal, it gets more entertaining and addicting to watch. It is now even possible to “go viral on LinkedIn,” an Extremistan dynamic that was previously absent on the platform. The faceless corporate drones are even evolving their own Type of Guy/Gal subspecies.

And equally, if you haven’t been on Twitter lately, since the Muskening, it is weird how the addiction loops have weakened due to the growing amount of quasi-professionalized soulless engagement farming going on. There are people using Twitter to grow careers the way LinkedIn people did a decade ago. There is a surreal vibe of non-viral mediocrity to the recognizable engagement farming patterns and their rewards. Threads that get very popular sometimes strike me as “Vice President of Seed Oil Twitter Issues Press Release.”

This confusion will only increase, as hypernormal and weird currents in the zeitgeist begin go mix and churn together. Instead of being widely separated regimes of society, Taleb’s Mediocristan and Extremistan will exist as interwoven textures. You will find it harder and harder to stay in just one of the regimes.

An Epistemic StalemateIf you’re convinced one side of the weird/hypernormal divide is obviously correct, and can’t see why the other side manages to exist at all, let alone attracting half the population, you might have developed a certain blindness to a prevailing condition I call epistemic stalemate, which manifests as neither side being a good default place to source your answers to the questions and survival challenges life throws at you. Even on a single question or concern, you might have to assemble your picture of reality from pieces sourced from both sides.

For instance, take the current condition of Boeing. Obviously, a LinkedInified mess of financialization, non-technical leadership, monopoly apathy, and so forth. Boeing is mostly a clear-cut case of Hypernormies Gone Wild. Planes are crashing (the 737 MAX mess), doors are flying off planes, and a spacecraft is stuck in space. Weirdos watch aghast as the hypernormies put on a theater of catastrophically bad showrunning in an important theater of civilization.

There are two resolutions to the “Boeing problem” on the table:

1. The Weird answer: Let Elon Musk start an airliner company or invest with his Magic Touch in a few aerospace startups and force airlines to buy their planes
2. The Hypernormal answer: Let traditional aerospace engineers from Boeing, Lockheed, maybe even Airbus, take a stab at fixing Boeing from the inside

Neither answer is obviously right or wrong. There is some merit to the idea that some brisk competitive energy and disruptive reinvention of airliners would level up air travel. It is very likely that some weird high-school dropout autodidact types in garages might think up revolutionary new ideas for air travel that actually work. That’s how the industry started (look up Jack Parsons) and how it is continuing (besides Elon, look up Palmer Luckey’s Andruil). Many of these weird types might even be “deplorables” in Dem-speak. And maybe the number of airliner crashes have to go up before they can go down, a la SpaceX’s exploding rockets early on.

But aerospace isn’t a sector you can revolutionize overnight by releasing an app. It’s unlikely your business trip next week will be on anything other than a Boeing plane if you’re in the US.

There is also merit to the idea that the answer lies within the bizarro-surreal theater of hypernormalcy. While the epistemology of the hypernormal world might not be systematically reliable, there is no question that Boeing as an organization, despite its problems, is likely the repository of vast amounts of relevant knowledge and expertise. If Boeing and Airbus both suddenly vanished next week, with all their knowledge, most of us won’t be flying for decades. It would take even the finest weird geniuses decades longer at the very minimum to rediscover the lost knowledge required to build large airliners. Only smaller regional jets and corporate jets would be flying around.

This is what I mean by epistemic stalemate. Both sides have enough merit to their claims to the truth to be not just credible, but necessary to solving most problems.

To understand the challenges of the stalemate, you only have to look at Tesla — an icon of Weird industry, led by possibly the leading Titan of Weirdness. But it is telling that the main Tesla factoryin Fremont was originally a GM-Toyota plant. Large portions of its workforce are auto industry veterans. And despite the radical innovations the company has brought to automotive manufacturing, it also uses a vast amount of the knowledge and industry infrastructure of the traditional auto industry. If the traditional auto industry vanished by magic, despite all its aspirations of being a raw-materials —> cars full-stack company, Tesla would be in deep shit. It is one thing to claim you’re operating from “first principles,” but quite another to actually start with a literal blank canvas.

This stalemate is deeply frustrating to both sides, because neither likes relying on the other side for essential functions.

So they descend to name-calling.

Weird vs. Normie as InsultsFor a decade or longer, the phrase “mainstream media” has been used as a slur for its role in defining the boundaries of normalcy. Words like “expert,” “elite,” and of course, “normie” have also turned into slurs.

All these slurs point to the hypernormal world. To LinkedIn.

While some are more preferred by the right, it is noteworthy that none of these insults is particularly strongly right/left coded.

The most relevant one for our purposes in particular, normie, is strictly neutral. I’ve heard people of all self-characterized marginal political persuasions use it as a slur against “centrists.”

In the last few weeks, the hypernormal world has finally adopted what in hindsight is the natural counter-move — weird deployed as a slur by Democrats in the US against the Trump campaign. But it’s revealing that it is a center-to-margins insult, a dual of normie, rather than a left to right insult. In fact, it would probably work just as well aimed at the far left.

Since I’ve spent a decade shilling my Great Weirding neologism and theory (I coined the term, rather appropriately, in a very hypernormal place: an Atlantic essay), this development has left me feeling…a bit bemused. I’ve even been writing a whole series titled The Great Weirding, and it’s one of the main frames I shill in my hypernormal work gigs. So weird as a slur just feels… weird to me.

But weird as a slur has apparently been… weirdly effective?

This surprises me. I’ve personally always used the term in value-neutral ways as a description of the zeitgeist, and in a mildly positive way as a character descriptor. I don’t think of myself as weird, but in general, I admire many people I see as weird. I’d even go so far as to say that weirdness is the main trait I tend to admire in people, over other traits like intelligence, courage, ambition, imagination, or talent.

But while I don’t admire either Trump or Vance, and it wouldn’t have occurred to me to describe either as weird, clearly it is appropriate in some way. As in, they’re the opposite of the hypernormal type of party-machine candidates for office from either party, like the Obamas, Bidens, Clintons, Bushes, or Romneys. The sort of weirdness I admire is, I suppose, a subset of all possible varieties of weirdness.

Still, there is something really strange and old-fashioned about using weird or weirdo as a slur in 2024 at all.

The thing is, yes the people being called weird are weird, but that’s not what’s objectionable or distasteful about them as political candidates or human beings. I dislike Trump and Vance not because either is weird, but because they hold ugly attitudes also held by many non-weird people, and exhibit ugly behaviors I won’t condone that are also exhibited by many normies.

And the candidates I do support, I do so not because I think “not weird” or “hypernormal” is an admirable trait. That too is value-neutral to me. The weird/hypernormal axis for me is orthogonal to the moral axis. If anything, to the extent Harris/Walz are more (hyper)normal, that’s neither a strike against them in my book, or a point in their favor, though overall I like them both.

So what sort of person finds weird to make sense as a slur, either to dish out, or receive?

If reports of the effectiveness are correct — ie that the insult lands on the intended target rather than merely feeling satisfying and feeding self-congratulation for the insulter — I think it speaks to both the unexpected strength of the hypernormalcy field globally, as well as the insecurities relative to the hypernormal establishment felt by those labeled weird.

Ie, enough people are invested deeply enough in hypernormalcy that they feel strengthened by rejecting weirdness and identifying with the insulting side. And enough people leaning into weirdness on the receiving side have a residual yearning for the trappings of (hyper)normalcy to feel the sting of the insult.

I am not convinced the reports of effectiveness are true, but if they are, I’d guess it’s a version of impostor syndrome at work.

This is clear with both Trump and Vance, who have both spent a good deal of energy in their lives trying to shake off perceptions of being from weird outsider margins, and inserting themselves into spaces that feel more legitimate (in the case of the latter — a “hillbilly” going to Yale to become a lawyer, and rejecting his roots by marrying an immigrant Indian, is about as LinkedIn-careerist as it gets in America). They don’t want to be weird. They want to define and own the new standard of normal. I mean… wanting to be President or Vice-President is possibly the most LinkedIn type aspiration you can have. It’s not only a kind of aspiration I’ve never had, it’s one I can’t imagine having.

I think I missed the sting in weird as an insult in part because to the extent I have comparable anxieties, they point the other way. I’m boringly pedigreed globalist hypernormlacy personified. I both look good on LinkedIn, and have zero impostor syndrome anxieties about how I come across there. My LinkedIn is WYSIWYG. A study in credentialed, unambitious mediocrity. It is neither notable nor embarrassing. It just belongs.

It’s the street cred of weirdness that I lack, and occasionally feel a twinge of anxiety about. I look good on Twitter (or at least used to) and in the blogosphere, but have a certain amount of impostor syndrome relative to people who have a genuine gonzo freak flag that they can fly online to celebrate their weirdness.

The Way of WojakSo here we all are now, in 2024, caught between hypernormalcy and weirdness, trying to define ourselves against the two competing value sets, in a charged political environment where the two syndromes form the poles of a center-periphery axis of hostile engagement.

This is an interesting place to be. I don’t think it is possible to survive entirely using just one of the two new systems of survival. You have to integrate them. You need synthesis.

Never go full weird.

Never go full hypernormal.

Trust on Twitter, but verify on LinkedIn. And trust on LinkedIn, but verify on Twitter.

I like Wojak, the ubiquitous many-avatared meme character, as a symbol of the right synthesis of weirdness and hypernormalcy.

The character started as the punching bag of more charismatic meme characters, but has outlasted them all. Wojak is the Last Man. Wojak is not just at the bottomwit and topwit margins, he’s the midwit too. He can be be both calm and fretful. He can hang back on the margins feeling weird and superior, thinking “they don’t know…” and thrash about in desperate hypernormal anxieties.

It is interesting how Wojak has survived the various Chads and Pepes and become the new everyman. Everyperson in fact, since the Wojak template has been adapted to feminine and gender-ambiguous presentations too.

In Wojak, the two new systems of survival meet and harmonize. He is both weird and hypernormal. And in the old systems of survival, he simply does not compute at all. Trying to classify him as either saint or trader makes no sense. Guardian and commerce syndromes do not apply.

In the old system, in the mature state in which Jane Jacobs documented it, guardian and commerce syndromes formed a yin-yang duality, each containing a seed of the other, and constantly transforming into each other. This is what gave us the old politics of left vs. right (roughly speaking, guardian vs. commerce).

In the new system, we don’t yet have the mature yin-yang state. The Way of Wojak remains aspirational. People are trying hard to be just one or the other, weird or hypernormal, rather than aiming for a Wojakian synthesis.

But at least we are seeing the emergence of a center-periphery spectrum challenging the dominance of the left/right spectrum. This spectrum runs from hypernormal to weird. And every position along it is a Wojak position.

Wojak is large. Wojak contains multitudes. May we all be Wojak one day.

1By the theories in this essay, actually reading books is a very weird thing to do. The hypernormal thing to do is to read Wikipedia summaries, reviews, or most hypernormal of all, paid book summaries. In future, the hypernormal way will be an AI summary.

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James Scott, a significant influence on my personal politics as well as on my fortunes as a writer, died last week. In what is likely his last significant bit of writing, a short personal essay on his career titled Intellectual Diary of an Iconoclast, published in February (HT Sachin Benny for finding this) , he comes across as a spirited academic punk,…

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Lately on Substack, there’s been an enthusiastic conversation around defining and growing into a writerly identity while also negotiating/rationalizing a comfortable presence on a platform that is, for better or worse, beginning to redefine what it means to be a writer.

(Warning: This is a rare inside-baseball newsletter issue. I almost never “write about writing.” So don’t subscribe expecting to see more of the same. Regular readers: You might enjoy this as a look inside the sausage factory, and offer some insight on how to tune your filters, but feel free to ignore otherwise)

I find the current conversation about and on Substack (mostly in the Notes section, which most readers don’t read) curiously boring. Like it is by/for/about a somewhat annoying subculture I happen to be adjacent to, but am not part of, and don’t want to join. But I also think it can be leveled up be more broadly interesting, and this essay is my contribution towards that end.

I think I find the current conversation boring because though I write a lot, and fairly successfully, I don’t primarily identify as a writer. Or even as a blogger. But it goes deeper than that. Regardless of whether or not I identify as a writer or blogger, I also don’t identify with a particular instrumental approach to writing, the behavior. Substack is in some ways friendliest to writers who identify as writers, and approach it in an instrumental mode.

In thinking through the nature of instrumentality, it struck me that the opposite of an instrumental mode is a metamorphic mode. As in metamorphosis; not to be confused with metaphoric. Instrumental words try to change the world in predictable ways, while acquiring some sort of legible extrinsic reward. Conventionally, esteem and money, but it can be any sort of extrinsic reward. Metamorphic words on the other hand, attempt to change the author in unpredictable ways, which you can think of as an intrinsic reward of sorts. They may also have a metamorphic (think “pilling”) effect on readers, but this is not their defining quality.

If you don’t like, or are bored with, who you are right now, whether as a writer, or more generally as a person, you can write yourself into an unpredictable new version. It’s a kind of disruptive self-authorship lottery. That’s metamorphic writing. You can achieve metamorphic effects with other media too, but writing is particularly good for it.

There are two more important dimensions to consider. First, whether you’re more comfortable in your skin as an institutional insider or an outsider. This is the sensibility dimension, which has little to do with where you’re actually situated, but will determine how comfortable or uncomfortable you’ll feel with wherever you are. Second, there is also the maturity dimension of whether you’re a beginner, or established within whatever mode, and in whatever circumstances, you write in. Motivations change as you evolve from beginner to established, assuming you enjoy enough success to stay in the game.

These 4 dimensions: identity, sensibility, mode, and maturity, lead naturally to a typology of 16 types of creators of any sort, not just writers. They also fit neatly into a set of 4 nested 2x2s that fit into an outer 2x2, creating a kind of Myers-Briggs scheme for creators.

I’ve illustrated the scheme below.

The outer 2x2 — the 4-colored political-compass-looking one — is sensibility vs. identity. The inner 2x2 (repeated 4 times in the quadrants of the outer 2x2) is mode vs. maturity.

Of the four, the dimension that interests me most is the inner x-axis, the mode of writing. Identity and sensibility, the outer dimensions, are about finding your place in the world. The inner y-axis, maturity, is about making your way in the world in time, by growing old doing whatever you do. But the mode, instrumental vs. metamorphic, is the most interesting and lively dimension.

Tour of the 16 TypesBut let me provide a quick tour of the typology before diving into that. Tag yourself before continuing. Try to pick a primary type and maybe a couple of secondary ones.

• Insider-Creators (“Creator” Creators)

  • Auditioner: Beginner/Instrumental: Looking for a publishing or record deal or equivalent.
  • Contender: Beginner/Metamorphic: Trying to find and establish a unique “voice” (a significant metamorphic evolution for any writer).
  • Competitor: Established/Instrumental: Trying to win prizes.
  • Legend: Established/Metamorphic: Trying to transcend the “anxiety of influence” in the sense of Harold Bloom.

  • Insider-Non-Creators (Normie Creators)

  • Careerist: Beginner/Instrumental: Looking to build wealth, acquire titles (not in writing), get promotions, and using writing/creation as part of the strategy.

  • Grinder: Beginner/Metamorphic: Working hard to be seen as “normal” and “fitting in.” (yes you can write/create your way into “fitting in” within some institutional landscape)
  • Authority: Established/Instrumental: Focused on individual legacy and reputation laundering. The impulse to be an authority is a sovereignty impulse in the sense of Hannah Arendt. In its dark form, it’s the monarchial/sociopath/totalitarian instinct. At the risk of Godwinning this post, think about why Hitler might have been driven to write Mein Kampf.
  • Free citizen: Established/Metamorphic: Wanting to be seen as a “pillar of society,” driven by the need to “appear in public” in the sense of Hannah Arendt among other free citizens, and deeply and sincerely involved in a particular “discourse.” But seeing the value in other equal-but-different voices and the poverty of creating in isolation as opposed to within a pluralist discourse.

  • Outsider-Creators (Scenester Creators)

  • Groupie: Beginner/Instrumental: Driven by notice-me senpai drives within a subcultural scene.

  • Social Nomad: Beginner/Metamorphic: Trying to find yourself on the map of the scene (or even the right scene to make a home in, which might never happen).
  • Big Man: Established/Instrumental: Motivated by being the recognized senpai of a scene. The term Big Man refers to the anthropological concept of an influential leader with real but not ascriptive authority, and need not be a man.
  • Conscience: Established/Metamorphic: Motivated by serving as the conscience of a scene.

  • Outsider-Non-Creators (Frontier Creators)

  • Growth Hacker: Beginner/Instrumental: Motivated by finding a formula for engagement farming/growth hacking. Often seen as the worst archetype, but in my estimation, not actually that bad.

  • Frame Breaker: Beginner/Metamorphic: Looking for non-formulaic viral resonance, or “proof of vibes” through creation, as a way to probe the nature of the world.
  • Influencer: Established/Instrumental: Motivated by accumulating money or political influence (extra-institutional) within a particular scene.
  • Seer: Established/Metamorphic: Motivated by the possibility of expanding the space of human experience.

By the logic of the inner 2x2, we also have 4 Keirsey-style Myers-Briggs creator “temperaments” here.

• Striver Archetypes: Auditioners, Careerists, Groupies, and Growth Hackers
• Seeker Archetypes: Contender, Grinder, Social Nomad, and Frame Breakers
• Leader Archetypes: Competitor, Authority, Big Man, Influencer
• Icon Archetypes: Legend, Free Citizen, Conscience, Seer

Most people mature vertically, rarely crossing over from instrumental to metamorphic or vice-versa. So the two vertical halfs of all 4 inner 2x2s can also be seen as “swim lanes” people stay in (though they may pick up a few tricks from the other lane — for example, a bit of metamorphosis can make striving more efficient and effective, and a bit of striving can trigger some metamorphosis). Strivers become Leaders if they do well. Seekers become Icons if they do well.

Obviously, the Outsider-Non-Writer outer quadrant is best, and within that the Icon quadrant of Seer is the best. The worst people in the universe are the auditioners.

Kidding. Just kidding. It’s my 2x2s so I get to orient it the way I want. But yes, I think my career in writing has been in the outsider non-writer outer quadrant, and has been about trying to mature from frame-breaker seeker mode to seer icon mode.

That said, there are no pure cases of course. I’ve mostly blogged and self-published, but I’ve had a few things published in gatekept creator media as well. I mostly don’t identify as a writer, but on very rare occasions, with specific pieces, I do. In the inner 2x2, I mostly don’t care about the instrumental side of any of the four regimes in my public writing, but in my non-public consulting-related writing, I do.

This typology is a bit on-the-nose, so it doesn’t really call for a subtle personality-test questionnaire. You just have to take an honest look at what you’re doing and why to figure it out.

Instrumental vs. MetamorphicInstrumental vs. metamorphic (inner x-axis) is the most interesting, liveliest dimension. It’s the live rail through which the current is flowing. It is also the one most closely tied to innate personality I think.

Instrumental/metamorphic is to some extent a function of the producer or consumer, and there’s a good deal of serendipity and path dependence involved in the metamorphic effects of words. For a mind in the right state, reading (or even writing) some banal, instrumental signage on the street such as “stop for pedestrians at crosswalk,” might trigger a deep metamorphic transformation; one they might even experience as “enlightenment.” For a sufficiently aged and experienced mind on the other hand, a cathartic piece of confessional writing in a spiritual idiom, which was deeply transformational and entirely unpredictable for the (likely young) writer and their close friends, might come across as a tedious, embarrassing, and entirely predictable sophomoric growth spasm.

That said, there is an objective dimension to the instrumental/metamorphic distinction. Wonkish reports, Supreme Court judgements, sales pitches, how-to guides, explainers, genre fiction, MFA “program lit,” business books, Hollywood screenplays, news reports, op eds, work emails — these rarely have metamorphic effects on either writer or reader. They may take great skill to produce, but are essentially instrumental kinds of writing. If they have metamorphic effects, it’s some accident of path-dependent circumstances, age/maturity, and randomness. On the other hand, poetry, private journals, confessionals, memoirs, wilder literary works, indie screenplays, research writing, spiritual texts, cultural commentary — these usually have at least some metamorphic effect on the writer, even if they’re objectively terrible and cringe works in terms of both ideas and craftsmanship. And if they’re any good, they will also have a metamorphic effect on some non-trivial subset of readers in the right state.

I suspect the metamorphic “Force” is strongest in the outsider-non-writer quadrant of the outer 2x2.

To first order then, we can say that works intended to be metamorphic or instrumental usually have the corresponding kinds of effects, which means the two economies are approximately separable, and can be considered separately. Personally, I do a lot of both kinds of writing, and they do feel very distinct for me. If I’m blocked on one kind of writing, I can usually still do the other kind. But the kind of writing that feels satisfying, and which I’d continue to do whether or not I made money at it, is all metamorphic.

The Creator Economy is 99% InstrumentalI suspect, in terms of volume, there is 10x more instrumental than metamorphic writing in the world, and it is also way easier to build an economy around. But the rare bits of metamorphic writing that truly tap into something special can radically reshape that economy from time to time. One measure of that: While most metamorphic writing never leaves private journals, the most successful pieces that make a public debut go insanely viral in a long-term, enduring way. On the other hand, all instrumental writing is seen by people besides the writer, but it rarely endures or explodes memetically. But it tends to be comfortably profitable for a while.

So it makes sense that most economic organization around creating — the “creator economy,” is instrumental. The metamorphic words economy is too unpredictable, temperamental, and hard to financialize. The rare huge economy-reshaping metamorphic hits can’t be planned for and could emerge in any of the four outer quadrants. At most you can leave the door open to getting lucky. The creator economy is necessarily 99% instrumental.

A closing comment on Substack.

Substack has been productively messing with the boundaries and gatekeeping of “writing” in consequential ways. Including continuing the democratization of writing that began with blogging, a trend that has been progressively drawing in more and more people who don’t primarily identify as writers, and getting them writing at professional levels. It’s a bit like how typing went from specialist skill and identity to something everybody did pretty well. Except few people had an identity attached to being a good professional typist. It was a job, not a calling.

This seems to be a particular cause for angst among Substackers who identify as “writers” in relatively traditional “insider” ways (red quadrant), and whom Substack has been courting aggressively for years. The money you can make on Substack is attractive to all, but is particularly angst-causing for those who are attached to traditional writerly identities but haven’t yet established a successful one. The ones from the red quadrant who succeed are the ones with somewhat mercenary instrumental sensibilities (and ideally, are bringing a legacy audience from a traditional media platform to Substack).

Therein lies the tension of the platform. Substack wants to democratize writing and get everybody to write, while keeping the “writer” identity exclusive-feeling, and monetizing the whales bringing in both the old-media fossil fuel and the exclusive senpai main-character energy. I don’t blame Substack for trying to have this cake and eat it too.

And it wants to have a non-instrumental brand,1 one that avoids the perception of engagement farming and cynical hustling. But it is dependent on writers using the well-understood techniques of the instrumental mode for success. I do blame them for that a little bit.

One notable sign of the tension is the schizophrenia of the “Notes” feature. Initially billed as both a Twitter competitor and a place to build an audience, it’s now clear it’s neither. But it’s still been successful in an unexpected and somewhat annoying way.

Notes is more LinkedIn than Twitter, and it’s mostly a place to network and commiserate with other writers about the problems of the writing world. Barely any readers seem to hang out there. Most of the activity is people without audiences or publishing deals looking in the wrong place to find them, just as LinkedIn is mostly the wrong place to look for jobs (it’s a good place for some in-demand people to be head-hunted and offered jobs of course; I’m guessing there are opportunities being offered in the DMs — I’ve been offered one I wasn’t interested in).

In a way, the failure of Notes to function as advertised has created an accidental opportunity for Substack to create a writer’s network of a sort that does not prima facie, seem like a good business idea. Unlike hungry recruiters trying to land in-demand programmers on LinkedIn, a set of networking writers is not a natural money-making scene. There is a slight chance that richer writerly conversations will emerge, across the whole 16-sector map, but finding a way to make it make money will take work.

But figuring out and fixing/doubling down on all that is Substack’s challenge, not yours or mine, so enough said about that. If they navigate it well, we’ll all hang around. If they don’t we’ll go somewhere else or complain loudly. I wish them well, but am hedging my bets as always. That’s why I still have a WordPress blog and a non-Substack email list.

For creators in general, whatever platform you use, whether walled gardens like Substack, TikTok, YouTube, or Instagram, or more open-commons type places like WordPress, the typology will hopefully help you think about what you’re doing and why.

For readers and “content consumers” in general (and all creators are also consumers of course), I hope the typology helps you refine your filters and decide better what to consumer versus ignore/tune out.

1All writing platforms do. Medium had an initial branding of “authentic” metamorphic writing and featured a lot of transformation confessional stories, but at some point it became a parody of itself in a way that struck me as instrumental — there was a “formula” to telling a metamorphic story on Medium, and a distinct space of extrinsic rewards being sought. Patreon took the metamorphic angle too seriously and ended up being an audience-capture zone. Another instrumental result.

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I’m in a grumpy mood. Well more grumpy than usual. I don’t know about you, but I can’t be bothered with reacting to the nominal state of the world anymore, as in the official state the news reports on, which we’re supposed to pay attention to, worry about, make sense of, and thoughtfully react to as Concerned Citizens who Care. But it still relentlessly…

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Just a short travel-size note in lieu of a full-size newsletter issue this week. I’m finally done with just over a month of nearly continuous and incoherent travel that started May 25th: Singapore, Coimbatore, Bangalore, Toronto, and Healdsburg, CA (where the pop-up village experiment Edge Esmeralda is just winding down). The travel was for 3 work projects across 2 unrelated gigs, plus a big, exhausting family function. With a reader meetup in the mix too, the first in 5 years. And with stuff on the home front to deal with remotely. It didn’t help that it was scorching summer and/or monsoon weather everywhere I went.

Strikes me I haven’t done this sort of thing… ever.

While I’ve traveled a fair amount, it’s usually been at a relaxing tempo, with a single purpose at a time, a dormant home front, and with very low introvert-grade social demands. This was 5 weeks, all at about 10x the social load I’m used to.

I started writing this note yesterday, in a cafe in San Francisco, before two meetings. Then I headed back on a (delayed) evening flight to Seattle, and am finally finishing it on my couch at home. Kinda revealing when even a short task gets split like this. I plan to park firmly at home and not travel for a few months if I can manage it. I have another month of this kind of incoherent of travel coming up in November (when I’ll turn 50), and my goal for the intervening 4 months is to simply get into better shape mentally and physically so it doesn’t run me ragged the way it did this time. Because I suspect the need for such travel is going to go up rather than down for me in the coming decade. I have aging parents in India, and shifting patterns of demand in my consulting work.

Perhaps the biggest cost of demanding travel for me is not the mental and physical toll, but the fact that it derails creative momentum, which is at the core of the virtuous cycle of motivation, satisfying accomplishment, and energy that makes mental and physical condition even worth attending to.

Flaubert famously said, “Be regular and orderly in your life, so that you may be violent and original in your work.” I suppose the effect of being irregular and disorderly in your life is that you end up meek and unoriginal in your work. But people much older than me seem to cheerfully handle much more demanding and incoherent travel schedules and retain their creative momentum, so clearly I’m doing something wrong. There’s a Pareto frontier here and it looks like I’m nowhere near it. Let’s call it the Flaubert frontier.

My priority over the summer is to get to that frontier. We’ll see if the “violent and original” and “irregular and disorderly” quotients can both go up. My cunning plan starts with a Rejuvenation July. I have no idea what that means but I’ll figure it out. I’m thinking of starting by quitting coffee for a couple of weeks. After that, I want to try doing this “regular and orderly thing” in a more deliberate and conscious way, to builds antifragility to the unavoidable irregular and disorderly periods.

We’ll get back to regular, orderly, violent, and original programming next week.

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This week is the third anniversary of this newsletter in this form, and I want to take a moment to reflect on the state of this project, and through it, the state of the world it has been attempting to make sense of all these years.

The latter, to reduce it to to a single soundbyte, is in an other-shoe-drops phase in various historically important ways. That’s what I want to get to, but let me start with the reflections on this project.

I’ve been writing under the name Ribbonfarm Studio since July 16 2021. It was a significant pivot and rebranding of an earlier newsletter called Breaking Smart, which was a more narrowly tech-culture focused software-eating-the-world newsletter (2015-20) that had kinda run its course as a main scaffolding for things I wanted to think/write about by late 2019.

In its current form, the newsletter is thematically broader, politically more ambiguous and illegible (at least to me — you might have me in a clear box), and methodologically less polished. It is a studio in the sense that it is where I begin to form and shape my first reactions to things going on in the world. It is also where I grow several long-running ab initio creative projects in various states of incompletion, stalling, or derailment.

Therein lies a tension. As a writer, you cannot both react to the world and participate in writing it into existence — the bit role in “inventing the future” writers get to play — at the same time. My own approach to resolving this tension has been to use narratives at multiple time scales as scaffolding for sense-making. Events that conform (but not necessarily confirm) to one or more of my narratives leave me with room to develop my ab initio creative projects. Events that either do not conform to my narratives or simply fall outside of them entirely tend to derail or drain my creative momentum. It is the writer’s equivalent of what in computer architecture is called speculative execution. If you’re right enough, often enough, as a writer, you can have your cake and eat it too — react to the world, and say what you want to say at the same time.

Being derailed is not a bad thing though. Never being derailed is as bad as always getting derailed.

I think I have five main narrative scaffoldings going here in various stages of derailment:

1. —8 to +4 years: The Great Weirding series
2. -50 to +25 years: Mediocre Computing series
3. -200 to +200 years: The Clockless Clock book project
4. -400 to +400 years: After Westphalia longue duree history project
5. Metatemporal stuff: Protocol narratives series

My scaffolding is somewhat, but not entirely symmetric in time.

I am very behind on my classifying and indexing, so those linked landing pages are not up to date, but almost everything I write fits into one or more of these narrative scaffoldings, at least in my headcanon. But they don’t always fit well. Some things neatly slot in as a next installment on a main sequence, by the established narrative logic. Other things clearly belong but in an unclear or supporting way, so I tend to tag them as “research notes.” Still others fit in a disruptive way, creating a bit of technical debt that should really drive a rewrite of older essays, but rarely do.

I try to avoid “crossing the streams” but it is often unavoidable, and sometimes it makes sense to index an essay in more than one series. One of these days, I’ll get the indexing up-to-date and try to make a map.

Unlike my much older WordPress blog Ribbonfarm, which I started in 2007 (and which has had its own evolving logic), this Studio newsletter is something like a streamlined, closed and bounded freshwater medium as opposed to an open saltwater medium. This newsletter is also much more hedgehog than fox, in the sense that I consciously try to weave the various strands together into a relatively consistent whole. Which is an unnatural thing for me, but I believe an appropriate sort of ambition for the times.

Neither blog, nor newsletter, lends itself to book production. I don’t think in book-sized/shaped units. I sometimes awkwardly shove my writing into book-sized/shaped chunks for easy packaging and sale, but books are not my medium. Nor am I an “essayist” in a classical sense. I think I’m something that could not have exited before the internet. I write connected skeins of essays that are somewhat, but not entirely, serializable, and do poorly when ripped from a hyperlinked context. It’s not great for archival, accessibility, or legibility at scale, but I think it is well-suited to making sense of the world in real time while also enjoying the luxury of a bit of poiesis through writing. It is living, breathing text of a sort only the internet allows you to generate.

But okay, back to the narrative scaffoldings of this newsletter. How exactly do they work?

One of the things about having this sort of cognitive approach — what engineers would call a frequency domain approach — is that the phases of your various narrative scaffoldings don’t always line up in any clean way.

If you adopt a simple rising action/falling action Freytag model of narratives, it would make for easy analysis if the world looked like this around your present:

This would be easy because you’re in the calm center of many synchronized narratives, and your metatemporal theorizing of how things work is up there in some abstract realm, shining meaning and significance on everything happening in time.

If you can’t have an easy to analyze world, you can at least hope for an exciting world, where many momentous narratives at various scales are coming to a close or just starting, putting the world in a liminal passage at all scales.

This wouldn’t be easy to analyze, since event-arcs on many time scales would be active at the same time and hard to sort out, and your metatemporal theories wouldn’t be off to the side in some abstract realm, but would instead saturate real, live action, and everything would be incredibly suffused with meaning and significance. You’d have the feeling of living in epic times. In the middle of a consciousness-raising for the world at large. Times when even ordinary actions by ordinary people can have epic historic consequences.

Unfortunately for our yearning narrative selves, the world is rarely in either purely easy or purely exciting mode. Both frames are egocentric conceits. It would be a wild coincidence indeed if you just happened to be in the most analytically tractable temporal position in history, at the “center of time,” from when everything is clear rather than muddy. It would be an equally wild coincidence if all the momentously significant historical arcs were converging or diverging from your life. If that were the case, you’d be right to suspect that you are some sort of Chosen One.

Which means the world, for most of us, alive at most times, looks something like this.

Some narratives are starting or ending around where you are. Others are shaping things but aren’t going to make your life either easy or exciting. Just annoying. Metatemporal theorizing existing as a fragmented overlay, lending significance to some events, and leaving other events meaningless, with a good deal of useless empty conceptualizing.

History is a messy thing. Theorizing it is difficult. If you’re lucky, sometimes things get easy or exciting for brief periods.

Now that said, I do think we live in somewhat exciting times by objective historical standards. 2024 feels more exciting than 1984, 1994, 2004, or 2014 for example. While the various narrative arcs don’t exactly converge in a perfectly exciting perfect storm of historic profundity, they do seem to be in an unusually exciting alignment by my 49 years of life.

So what exactly is exciting about 2024?

One way to think about it is that the other shoe is about to drop for many narratives.

1. With the Trump/Biden election, the other shoe is about to drop on the arc that began with the Great Weirding. This arc has fuzzy beginnings and endings globally, but is clearly heading towards closure everywhere. For instance, the recent election in India, with its chastening message for the BJP, has a 10-year span (2014-24). In the EU and UK, various arcs that began with events in Greece/Italy and Brexit are headed towards some sort of natural closure.
2. Crypto and AI, two strands in my mediocre computing series, also seem to be at an other-shoe-drops phase. Crypto, after experiencing 4-5 boom-bust cycles since 2009, is finally facing a triple test of geopolitical significance, economic significance, and “product” potential. It feels like in the next year or two we’ll learn if it’s a technology that’s going to make history or remain a sideshow. AI is shifting gears from a rapid and accelerating installation phase of increasing foundational capabilities to a deployment phase of productization, marked by Apple’s entry into the fray and a sense of impending saturation in the foundational capabilities.
3. Various wars (Gaza, Ukraine) and tensions (Taiwan) are starting to stress the Westphalian model of the nation state for real now. That’s the other shoe dropping on a 400-year-long story (also a 75 year long story about the rules-based international order, but that’s relatively less interesting).
4. Economically, we’re clearly decisively past the ZIRPy end of the neoliberal globalization era that began in the mid-80s. That shoe has already dropped. What new arc is starting is unclear — the first shoe of the new story hasn’t dropped yet. Something about nonzero interest rates in an uncertain world marked by a mercantilist resource-grabbing geopolitical race unfolding in parallel to slowly reconfiguring global trade patterns.

These other-shoe-droppings account for only about 30-40% of the narrative energy. Still that’s a lot. 30-40% narrative energy in “exciting” mode is quite exciting. In multi-scale terms, I think we’re in the most exciting period historical period I’ve experienced. Though 2016 and 2020 were perhaps more acutely exciting, that was more due to the energy of single strands (American politics, Covid). 2024 feels like a surge of global multi-level narrative energy coming to a head. Many other-shoes are starting to drop, or have recently dropped, within narratives ranging from 3 to 30 years in span.

Or if you like to think in agentic terms: This is about as clean a clean-sheet you’re ever going to get to reboot history in new ways. If you yearn for a break from the past, history is very breakable at the moment. It’s not quite a liminal-epic evolutionary bottleneck, but it’s close. The fan of possible futures right now is as wide as it’s ever been in my lifetime.

As above, so below. I’ll turn 50 later this year, and that feels like the end of the “growth arc” of my life. As a kid, people had various theories about my potential. At 50, I’m sort of a finished product, regardless of what the growth mindset crowd says. But at the same time, there’s a sense of a clean sheet for post-50.

Looking back at this newsletter project through this historical lens, at the staccato progress of various threads of writing, and the incidence of non sequitur derailments through the archives, it feels like my writing doesn’t just attempt to make sense of the world, but at the level of order/chaos, reflects the state of the world (convolved with my own state of course).

I’m frankly not sure what to make of the state of this newsletter, any more than I know what to make of the state of the world.

Is writing to sense-make a useful thing to be doing? (for me, not you; utility to you is a side effect). If not useful, is it at least interesting or exciting? If neither, is there some other mode of writing that’s a richer way of being in the world today? If not, is there something other than writing I could/should be doing?

My answer to all these questions is I don’t know. But I do know that continuing to write my way through the times feels like the right thing to do, as opposed to not writing.

It’s not as satisfying as it once was though — and I sense this is the case for all writers. Writing seemed like a more culturally significant, personally satisfying, aesthetically appropriate, and existentially penetrating thing to be doing in 2014 than it does now in 2024. I think we live in times when writing has less of a role to play in inventing the future, for a variety of reasons. You have to work harder at it, for less reward, in a smaller role. Fortunately for my sanity, writing is not the only thing I do with my life.

But maybe this is an illusion. Maybe writing feeling hard now is a bit like “buy low, sell high” being a hard thing to practice in investing. Maybe you don’t get to enjoy the periods when writing flows easily, and feels meaningful and satisfying, if you don’t also persist through the periods when it doesn’t. Maybe we’re just at the end of a long arc of 25 years or so, when writing online was exceptionally culturally significant and happened to line up with my most productive writing years, and the other shoe has dropped on the story of “blogging.”

Or maybe, with the rise of AI, the other shoe is about to drop on a much longer story than the story of blogging — the 6000 year old story of writing itself.

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I’m in Bangalore right now, for the first time since my internship here in 1996. Somehow over a dozen visits to India since then, I’ve never had a reason or excuse to return to the city before. This time there’s a family function.

The city has changed dramatically since I was last here. While I’m mostly hanging out with my Kannada-speaking family in the…

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Over lunch in Singapore yesterday, my friend Sam Chua offered a brilliant definition of punk that I think nails the problem with solarpunk as currently constructed. Sam’s definition is simple and in my opinion correct: punk is technology without technocracy.

As I’ll argue in a bit, by this definition, the idea of solarpunk as currently constructed is wrong, and we need to reconstruct it correctly. As a preview: we need to deconstruct the version that centers solar panels, and center a version that centers… wait for it… mangoes.

And in the process, we will inevitably need to move the center of gravity of the idea from the temperate latitudes where I suspect it was born, to the tropical and equatorial latitudes where I think it belongs. Why? Because thesis: solar panels are technocratic, and mangoes are punk, and the tropical-equatorial belt is where “solar” really comes into its own.

But let me back up and start where this train of thought started.

After my last trip to Singapore/India, in September last year, I wrote a newsletter issue titled Sweating Solarpunk. Well now I’m back in the region, and it’s May/June, and I guess I’m really sweating buckets of solarpunk. And I’m even less happy with the way the term is typically used than I was 8 months ago.

As currently used, solarpunk gestures at a solar that’s nothing like the connotations I have for the adjective, and there is very little punk to it. We need to put the punk into solarpunk, which begins with locating it primarily in regions defined most strongly by their relationship to the sun, where it reigns as an angry but benevolent god. Not in regions where it is a tepid, mostly benign presence that barely peeps above the horizon. Regions where the sun is never directly overhead; where shadows never vanish.

Both Singapore (1.35° N) where I was over the last few days, and Coimbatore (11° N), where I am right now, are sweltering at the moment. The entire region, encompassing South and Southeast Asia, and parts of the Middle East, which is sometimes called Monsoon Asia, is in the midst of probably the unholiest time of the year, when the scorching summer is starting to give way to torrential rains and humidity. It is wet-bulb hellishness around here.

If you’re from this region, the idea of the sun is inseparable from the idea of rain. Torrential rain. This is an association that is not obvious in most parts of the world, but in Monsoon Asia it is inescapable. Torrential rains are just another aspect of the reign of the angry sun.

Like the sun, the monsoon too, despite the welcome cooling, irrigating, and water-table-recharging effects it brings, is not exactly a benign force. It is an apt assistant angry god to the sun god. If the sun is Vito Corleone around here, the monsoon is something of a Luca Brasi. A violent beserker you can’t really reason with. Only try to avoid… for a while.

You haven’t really experienced torrential rain if you haven’t experienced full-force monsoon rains, complete with flooded streets, overflowing storm drains and sewers, and relentless humidity when it isn’t raining. There is no English word corresponding to keechad, the Hindi word for the squelchy muddy mess creates everywhere that is unpaved. In much of the West, while there are occasional torrential bursts, steady torrential downpours, with near-opaque curtains of water falling for hours, day after day, are rare. You mostly get energetic drizzles. In Monsoon Asia, you get torrential rain day after day for months. And that’s preceded by a cyclone season as well (cyclone is Asian for hurricane). And tedious though it can get after the initial welcome respite from the scorching heat that comes before, not getting the monsoon, or getting a very weak one, is even worse. You’d rather have flooding and overflowing dams than a summer that stretches on and on, with water scarcity/drought for a year. You don’t want the main angry god for too long. You want the assistant angry god to take over.

I grew up in these conditions, but this is the first time I’m visiting the region during peak summer-to-monsoon transition time in 20-odd years. It says something that I usually time my visits to avoid the summer-monsoon interface, when not one but two angry gods are beating down on you at the same time, while they negotiate the hand-off.

Anyhow, this is my intuitive idea of solar, which is why I’m now convinced solarpunk is defined wrong. Stupidly, badly wrong. In terms of a sun construed as a servile entity domesticated by solar panels, and constructed in authoritarian-instrumental ways that are actively hostile to the punk spirit.

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Sometimes, you sense that beneath a layer of conceptual battles and confusions there is a concept that is so obvious you forget to talk about it or even name it. One such concept is that of the monolith. It is the idea lurking beneath questions of monopolies and monopsonies in economics, monolithic architectures versus modular architectures in engineering, and vertical vs. horizontal integration in business models.

But what exactly is a monolith? And why do people have such a love-hate relationship with monoliths, torn between trying to destroy existing monoliths and creating new ones?

As is often the case with me, the impulse to think about monoliths started out with a throwaway shitpost. In my case, I came up with the faux-political slogan attack and dethrone monoliths! (a reference to the attack and dethrone god radical left slogan of the 60s). Then it struck me that I really don’t believe particularly strongly in that particular cause, and I came up with an alternative, the care and feeding of monoliths. But I don’t feel strongly about that either. Nor do I care much about the third obvious potential agenda: creating monoliths.

All three programmatic agendas — of destruction, preservation, and creation — only interest me to the extent they shed light on what monoliths actually are. This background thought was simmering when I noticed the cover design of There is No Antimemetics Division, which appears to depict one of the SCP entities in the book, and also inspired by the monoliths in Arthur C. Clarke’s 2001: A Space Odyssey series.

Monoliths also have a strange appeal in art. There’s that Clarkesque Utah monolith showing up in random places:

In Indian art history, monoliths seem to have been particularly prized. Ashokan pillars have a more monolithic construction than comparable Greco-Roman pillars, and entire rock-cut temples and viharas are scattered over South Asia.

As engineered artifacts, monoliths present different challenges from assembled or modular ones. Sometimes its easier to build a monolith, sometimes its easier to build non-monolithic things.

Let’s get a few examples of modern monoliths on the table before continuing. I’ll state my definitions before justifying them.

1. A monopoly is a business that has a monolithic production organization at the core
2. A monopsony is an aggregated demand source that has a a monolithic consumption organization at the core (typically a government, but also huge distribution aggregators)
3. A monolithic architecture in engineering is one that is hard or impossible to decompose reversibly into constituent parts, especially independently reusable/scavengeable modular ones.
4. A material monolith is any artifact that is grown bottom-up from an oozy process such as crystal growth (jet turbine blades), casting (any type of molding process), or deposition (3d printing).
5. To justify my meme image: a public works project like a monorail, large dam, or space mission, is monolithic in a historical sense, as in a unique set of circumstances generating a non-repeatable (and likely non-ergodic) outcome that is very hard to generalize from but makes a good case study. Here it is the memory of the thing that is a monolith. It is a story that cannot be easily decomposed into constituent abstract story-pieces.
6. Human brains and modern ML models can be monoliths, by the definitions I’ll be setting up, depending on how they grow/develop.

The Monolithicity TestA good but not perfect test for monolithic character is the value of minimally destructive maximally deep disassembly and reassembly. If you take it apart in a way that can be maximally put back together, how deep can the decomposition go, and how far can the reconstitution recreate the monolith? And how much is lost in the loop?

If the answer is that destruction or disassembly is basically a one-way street to worthlessness, you have a monolith.

If the answer is that you can losslessly get back to the original or superior form with minimal material or energy losses, you have a non-monolith.

Some examples of the test at work:

1. Industrial age monopolies weren’t actually very monolithic. When Standard Oil was disassembled into parts, basically nothing broke, and the aggregate value of the pieces was ironically higher than the original, making Rockefeller very rich. But putting Standard Oil back together again in its original form would have been fairly trivial too. AT&T was similar.
2. Modern businesses that are on trial for potentially being monopolies are much more monolithic, and far harder to take apart for reasons we’ll get to. It is not obvious to me how I would break up Google, Meta, or Amazon for instance, while retaining significant value.
3. A 3d-printed part can be melted down and reconstituted into filament, which can be used to reprint it. But there is a high energy cost to the loop, which is quite destructive and also materially lossy. And producing new filament is non-trivial.
4. The design of an Intel “monolithic” chip from the aughts or earlier is really hard to take apart into fragments that can be re-used in new designs. But newer SoC/IP (“system on a chip/intellectual property”) chips are designed to be highly reusable in precisely this sense. Intel is not very good at it, but other companies are. “Chiplets” are an extension in that direction. SoC/IP chips put together mostly reusable design elements (“IP” blocks) within a somewhat custom chassis.
5. A monolithic sculpture from antiquity is basically maximally monolithic. You can’t take it apart except by pulverizing it into rock dust, and reconstituting it (for eg. through some sort of sintering process using a 3d scan of the original) would be hard enough to be pointless.
6. If you took apart the story of a piece of public infrastructure (like a monorail!) that emerged in a historically unique way, using generic story patterns, you’d lose most of the story. The story is a monolith embedded in history. It is easier to “layer” new “infrastructure stories” on top, along the contours of existing infrastructure stories. This is also why you can’t “port” powerful stories, or replicate “Silicon Valley” elsewhere. That’s a narrative monolith.
7. Human brains and ML models are also maximally monoliths in that they can’t be put through this reconstitution loop at all, but the latter can be copied and are more monolithic. Human brains can become monolithic under some circumstances.

It is worth noting that “value” in this reconstitution loop test can vary. In disassembly/reassembly of economic monoliths, all you care about is something like GDP or gross profits. Nobody except perhaps founders and early employees is particularly attached to the ineffable nostalgic appeal of the entity. But at the other extreme, a monolithic sculpture pulverized and reconstituted would perhaps not retain its artistic merits and subjective value through the loop at all.

The case of the chip is particularly interesting. A finished chip, whether based on a monolithic or SoC/IP design, cannot be disassembled and reassembled at all. The physical thing is a pure monolith. All you can do is maybe pulverize it and recover some metal from the metal layers, which would be worth too little to bother. But then, the value of a chip lies almost entirely in the design (and in the design of the fabrication process), so it makes more sense to apply the monolithicity test to the design rather than the individual chip. There is too little material in the chips to worry about recycling anyway. The motherboards and plastic cases of devices matter a lot more.

But there are cases where the individual physical artifact is worth thinking about. For example, Apples airpods are monolithic. They have an integrated battery and electronics, and are headed straight for the landfill if they fail. Another example is composite blades of wind turbines. They’re basically impossible to recycle (except apparently into feedstock for coal-powered generation plants, which I find hilariously ironic).

What is a Monolith?The monolithicity test doesn’t actually get us very far in characterizing what a monolith is. We have some sort of material intuitions based on sculptural monoliths and fictional artifacts like Clarke monoliths, but in what sense are Google or a modern chip like monoliths of those more obvious sorts?

Our intuitive idea of a monolith is a structural one, loosely characterized by a disassembly/assembly loop test (specifically, the amount of hysteresis or entropic irreversibility in such loops), but it’s easier to get at the essence of one, especially a modern one, by talking about its behavior.

A monolith is a system that is separated from its environment by a system boundary of some sort. It acts on the environment through output signals that cross the boundary, and responds to the environment by processing input signals that cross the boundary going the other way. These signals can be considered ongoing moves in a game between the system and the environment. In the simplest cases, we can think in game-theoreticterms.

In game theory there is the notion of a dominant strategy, where no matter what your opponent does, you have a winning counter move. There are various nuanced flavors of this. Depending on the flavor, you might be indifferent to your options (they all do the same thing), indifferent to what the opponent does (your move wins without having to be responsive or predictive in relation to theirs). If you enjoy any degree of dominance in a game, you enjoy two luxuries: being indifferent to what your opponent does and perhaps more subtly and importantly, being indifferent to what you do.

A particularly subtle flavor of this is where there is some notion of a margin of victory. Let’s say you have 2 options, and the opponent has 2 options, but all 4 combinations result in you winning. The only difference is how much you win by.

Let’s say you win by 1, 10, 100, or 1000 points. Do you care? Probably. Unless you have a very weird marginal utility curve, 1000 points is probably worth more than 1 point. Maybe not 1000x more, but enough more that you’d care (though there are weird cases where the marginal utility drops to zero after 1 unit).

Let’s say you win by 997, 998, 999, or 1000 points. Do you care now? Probably not. If the win margins are all high enough, then the differences among the margins are likely in your indifference band. Regardless of the semantics of the scoring system or the shape of the utility curve.

It’s obvious how to apply this idea to a textbook business monopoly. You put out shoddy products (you don’t care what you do), you have little to no customer service (you don’t care what the opponent does), and you enjoy huge rents to the point that you don’t care about minor differences in margins at all. Whether your margins are 69% or 70%, you don’t care. You can be a lazy billionaire either way, allowing a bloated bureaucratic organization to sprawl on your watch either way.

It is less obvious how to apply this idea to things like say chip architecture. One example is the “monopoly” (technically a duopoly) of the x86 architecture. When the entire rest of the industry is set up to fit around the x86 architecture, you really don’t care what makers of other supporting chips, compiler makers and OS makers think. You’re going to do what you like to the architecture and they’ll deal. The utter dominance of x86 was quite something until Arm and RISC-V began making inroads (there were Motorola and DEC chips in the 90s, but they never really threatened x86).

Here’s another one, this time from software. If you talk to AI people, they’ll all tell you they hate Nvidia’s Cuda (low-level software AI programs use to make GPUs do things) but they really have no choice but to use it (full disclosure: I consult for a company, TensTorrent that makes a competing product). This is because while making GPUs is challenging, making a software stack that makes GPU programming accessible to mortals is even more challenging. The layers between atomic matrix-multiplication instructions at the level of the chip, and PyTorch or similar frameworks used to build ML models, is a zone of Dark Magic that Nvidia owns in a dominant way. In part because of a long-term incumbency, and in part because it employs an army of engineers maintaining the Dark Art codebase. The closest thing to an actual shoggoth in ML is the Cuda layers, not the models themselves.

Game theory reading: Cuda is such a monolith, Nvidia doesn’t really have to care that you hate using it, or even worry too much about what kinds of GPUs it offers. It is a dominance advantage.

This relates to business models by the way. Modern monopolies and monopsonies are more monolithic because they are built on top of computing hardware and software, both of which can get radically more monolithic than industrial age machines. Making modular hardware is hard, though SoC/IP and chiplets are getting there slowly. Making modular software is far harder.

And finally, this is also why AI software, or “Software 2.0” as Andrej Karpathy calls it, is even more monolithic. Because the “software” as such isn’t really in the code (either at the Cuda level, or the few hundred lines of Python code). The intelligence is almost entirely in the trained model weights.

You can apply my reconstitutability test at the level of AI models. You can train AI models on the input/output behavior of traditional software 1.0 programs and produce illegible, inscrutable, monolithic equivalents. The reverse problem of capturing the input/output behavior of a model in the form of an “explainable” construct, ie a set of algorithms with legible control flows, is basically the problem of turning it into a modular software 1.0 program (which by definition is explainable). Though both problems are at early stages of research, my sense is that the latter is harder. Both are hard enough that effectively model-weight sets constitute monoliths. Except they’re monoliths that can be released open-source (but not scrutable-source — you can’t back out the training protocol from the weights) and copied.

Applying the dominance test for a monolith here leads to a surprising conclusion. AI models

1. Don’t care what form the input takes (they can take in multimodal, unstructured, noisy data) and
2. Don’t care about accuracy, provability, justifiability or explainability in their outputs.

This is a weird reading of AI models by the way, since the desiderata in 2 are typically viewed as “feature engineering” problems that are part of “productizing” models, not the output-indifference of a game-agent with a dominant strategy. But I think this is in fact the correct reading. Behaviorally, a powerful AI model is indistinguishable from a processing monopoly with an utterly dominant strategy.

Making ChatGPT care about accuracy is at least as hard as making the old AT&T care about customer service, or the old Standard Oil care about worker conditions in oil fields, and for rhyming if not identical reasons.

But this is somehow unsatisfactory. “Indifference” and “apathy” are very anthropocentric readings of the behavioral profiles of monoliths. Let’s translate to more impersonal terms.

Boundaries vs. Event HorizonsIn the last section, I described a monolith as a system with a boundary across which I/O signals go. This is not quite right. Indifference and dominance effects mean that input signals can be safely ignored and output signals can be non-responsive, arbitrary, or dysfunctional.

The boundary is really an event horizon of sorts. The black hole information paradox rhymes strangely well with the behavior of monoliths of other sorts. As best as I am able to understand the black-hole information paradox, information exists only at the surface, and evaporates away through Hawking radiation as the black hole ages, with the black hole only retaining information about the mass, charge, and angular momentum of the collapsing body. A blackhole basically clears the “market” of mass in its neighborhood, throws away most of the information, and collapses into a solipsistic state defined by its genesis information environment, and decoupled from its live information environment.

So a monolith, in more general terms, is a system that is sufficiently massive inside its boundary that it has a tendency to collapse into itself, with the boundary being turned into an event horizon with information-decoupling properties. It can suck things in but you’ll have no idea what happens to those things. It can emit radiation that is in some illegible sense an information-preserving function of the input, but it effectively bounces off the surface and returns in an “encrypted” form. I’ll stop there since I’m out on a limb here at the limit of my physics analogy abilities, but you get the idea. A black hole is a pure physical counterpart to the perfectly non-responsive and oppressive bureaucracy.

We can now get away from anthropocentric terms like “dominance”, “indifference”, and “apathy.” Whether or not you attribute such attitudes to a blackhole like entity, what matters is that you have to interact with an event-horizon type boundary with the following properties:

1. You cannot peer through it at the interior
2. You can put things in but cannot expect to know what happens to them
3. Things come out but they don’t make sense and don’t seem responsive to your concerns
4. It evolves to the point where all it embodies is some minimal information about its historic initial conditions

Bureaucracies, traditional monopolies, modern platform monopolies, and finally, brains and AI-like systems all have these black-hole like features, and feature an “event horizon.” To the extent there is a meaningful idea of a singularity in the picture, it is much closer to the notion of a black-hole type physics singularity than any scrutable notion of “runaway self-improvement.” Relative to the last point, note that big, historic public-work projects like monorails often end up in states where all they embody is some information about the unique historical circumstances that gave rise to them.

“Runaway self-improvement” and notions of “goals” attributed to the entity behind the event horizon constitute classic anthropmorphic projection errors, no different from New Age sci-fi spiritualities (ironic or not) thinking of black holes as gods.

Understood this way, a monolith is a generalized black hole concept that is exemplified by many kinds of systems, which lend themselves to anthropomorphic projection to varying degrees: Rock sculptures, semiconductor chips, certain layers of software, the interiors of monopolistic businesses and government agencies, AIs, brains, and so on. Or for that matter, primitive experience of nature that has not yet yielded to any kind of reductionist science, so everything appears to be inscrutable and impenetrable divine agency.

From the outside, a monolith will always present an inscrutable, impenetrable, low-responsiveness, cryptic appearance that acts like an event horizon. It’s a function of behavioral dominance in relation to a local information environment that leads to “indifference” to inputs and “apathy” towards outputs.

What about the inside?

Into the MonolithSupposedly, when you fall into a blackhole, you get really stretched out in space and your sense of time slows to a crawl. If you had the magical constitution to make it through alive, you’d probably be a very different person, thanks to the spatio-temporal distortions. “Falling” into a cult, past the event horizon of indoctrination, probably feels something like this.

But that’s not how you truly experience the inside of a monolith. Any monolith young enough and small enough to not kill things falling through is too young and small to convey the full experience. Cults are known for dramatically cutting off or controlling the interactions between members and the world beyond, but ultimately they are toy spaces. To truly experience monoliths, you have to turn to large corporations or government agencies.

You experience the inside of a full-blown monolith in one of two ways:

1. By already being inside the boundary before the collapse begins and it turns into an event horizon.
2. By being informationally “empty” enough (young enough) when you enter that the entry stress doesn’t kill you and you can continue development entirely inside.

The first experience is common with people who join startups that get big very early. The second experience is common with people who join old monolithic organizations very young and spend decades growing old within them (and also people raised in cults from childhood).

The most important part of the experience is solipsism. Your entire information environment is dominated by internal signals, a kind of blooming, buzzing confusion of total internal reflection. You hardly ever see meaningful information from the outside that is not heavily processed and distorted into internal frames. Your actions hardly ever emanate as signals visible to the outside world.

If the monolith is wrapped by an event horizon for outsiders, the outside world is wrapped by an equally impenetrable event horizon for insiders.

There is a strange duality here, but with a slight difference. The inside of a monolith is at least temporarily the winning side, in signal-based game theory terms. So where outsiders care about the event horizon and want to penetrate it, the insiders don’t have to care. If the interior environment is sufficiently rewarding and pleasant, who cares about the world you can’t see beyond the event horizon?

You’ll often get a taste of it when you talk to a long-term veteran of a company who has only ever known the world through that company, and has perhaps been let go in a middle-aged layoff, or has retired. Some symptoms:

1. They’ll talk about familiar universal things in odd, idiosyncratic ways derived from “insider” language
2. They’ll be used to very idiosyncratic tools, instruments, and infrastructure for getting things done (talk to an ex-Googler about the difficulty of adapting to non-Google commodity IT environments for example)
3. They’ll often be surprised to hear that ideas they thought were unique to their monolith’s interiors actually exist in multiple other, better forms outside.
4. They will often be surprisingly ignorant of basic things, but often “know” seemingly indistinguishable versions.
5. Their epistemology will be oddly monocultural, fitting a little too coherently together (even if inscrutably for you).

The human experience of being a monolith insider is of course trivially hackable and counter-programmable. Unless you’re shut away beyond a physical boundary without an internet connection, all you have to do is consume enough outside information. Information that is not from the funhouse mirror maze that is the interior of a monolith.

An example: when I was at Xerox (2006-11), we were handed a copy of the book Copies in Seconds, about the invention of xerography (which post-antitrust competitors call electrophotography — a notable case of insider/outsider language) and the founding of Xerox. I read that, then went ahead and read two books about the most famous chapter of Xerox history, PARC (Fumbling the Future, and Dealers of Lightning). When I tried to talk to my colleagues about all the fun historical stories, it turned out hardly any of them had read any of the books. They simply had some vague second hand notion of the history based on listening for 5 minutes in some orientation session. The average employee of Xerox had far less awareness of the fascinating history of Xerox than the average interested technologist outside.

This surprised me at the time, but it doesn’t anymore. Even when the difference between being captured by the interior information environment of a monolith and being connected to the outside world is as simple as picking up a book, most people don’t bother. It is simply easiest to just do your job and default to only using the trusted channels of input/output that are part of your internal environment.

The point of this discussion is to note that the interior of a monolith isn’t actually inscrutable. It’s merely highly solipsistic, shaped by almost entirely internal terms of reference and the lazy lack of curiosity that marks the psyche of a (temporary) winner.

Again, let’s make the move away from anthropocentric terms of reference. When you’re talking monoliths that are not inhabited by humans, you see a similar solipsism: idiosyncratic, non-standard designs, weird ways of referring to industry terms, idiosyncratic “physics” based on internal metaphors and mental models, and so on. The net result is that a monolithic artifact, if you manage to open it up, will look like it’s composed in an alien idiom to an outsider. Written not just in a different language, but with reference to a different reality. The inner and outer realities only converge in the most tenuous ways, many degrees removed from individual actions.

Different is not better. It’s often worse. Talk to a Googler. The average Googler, despite being an insider, typically has no better explanations for why search quality sucks so much, and often misses insights obvious to outside observers. The only real difference is they talk a different language of helplessness.

When you’re talking about monolithic products created by a monolithic organization, often you’ll see a mirrored inscrutability. The internal structure of the human organization will make no sense to outsiders, and the internal structure of the design will make no sense to people who attempt a teardown. This is the monolith version of Conway’s Law, which states that product structure mimics organizational structure. In a monolith, inscrutable organizations produce indecipherable products.

“Integration” Horizontal and VerticalA sidebar on some related language. In business contexts, you’ll often hear of horizontal versus vertical integration. How do these ideas relate to monoliths?

If you’re not familiar with the terms, a “vertically” integrated company, like Intel, Apple or Tesla, puts complicated things together starting from scratch, with the most basic raw materials, through many layers of transformation and processing, to produce things that are used in black-box ways by customers. Vertically integrated companies tend to be monolithic and produce monolithic products.

“Horizontally integrated” is a bit of a misnomer since there is aggregation rather than integration. Horizontally integrated companies tend to aggregate demand for a particular kind of component or input needed by other businesses and get very good at producing just the one class of things. Due to the need to interface with many upstream and downstream “value-chain partners,” and the fact that they specialize in simpler subsystems or parts, such companies tend to be fundamentally less monolithic. But this is not always true, and need not stay true even if it starts out true. For example, both Intel and Microsoft started out as “horizontal” companies provisioning “horizontal” layers to IBM for PCs. But complexity quickly accrued, they acquired enough internal layers they began to behave more “vertically.” The overall market they were part of, the PC market, began to loosen and commoditize.

By contrast, Apple started out vertical and got more vertical over time. Now it makes its own chips and its own flavor of the Unix operating system.

In general, industries slowly cycle through horizontal and vertical organizational patterns as they evolve and mature, as different players gain and lose control of the “stack,” eventually turning into a set of monolithic islands in stormy oceans of commoditized complements. The commoditized complements often act as newer kinds of natural “raw” materials.

One monolith-friendly metaphor for understanding this is as an metal-working process. There are two ways to produce a metal monolith. The first way, if you can generate high enough temperatures, is simply to melt the metal and pour it into a mold. The second way is to work with pieces of metal that you can heat to softening, but not quite melt.

Metal can be cold-worked by hammering, which knots up inner layers into complicated tangles. It can also be hot-worked by heating and cooling through annealing and quenching cycles. Forge-welding combines these processes.

Such a process can be used to create a monolith from non-monolithic parts. The famous Iron Pillar of Delhi, for instance, appears to have been created this way. A bunch of hot lumps of iron hammered together into a monolith. The pillar was something of a mystery until the 1970s, since it is remarkably rust-free and the construction process was not understood. Crackpot conspiracy maven Eric Von Daniken thought it was of extraterrestrial origin, which is a nice illustration of the inscrutability of monoliths.

Mature organizational landscapes often come to resemble such an Iron Pillar. If you diagram the structure of real landscapes, facile mental models of horizontal/vertical start to fall apart. What you’re left is more monolithic parts of landscape shaping the evolution of less monolithic parts.

Anti-, Pre-, and Post-MonolithsMonoliths are best considered ossified chunks of raw collapsed history. They exist not just in space (structural, behavioral, functional), but in time, as congealed memories of the indispensable information about initial historical conditions. They emerge during unique historical circumstances as sui generis islands of solipsisms, interacting over their lifetimes with their environment through an event horizon. A curious duality holds across this event horizon, where the “outside” can appear monolithic to the inner reality. To the extent there is an asymmetry, it has to do with temporary winning conditions (you can go wild with this and conclude there is no reality, only a topology of connected solipsisms).

To center monoliths in accounts of phenomena is to take a particularlist, historicist approach to analysis, rather than a general approach based on atemporal, abstract theories of structure, behavior, and function.

One way to do that is to treat the entire space of non-monolithic things via historical spatio-temporal relationships to monoliths around them. We can distinguish three kinds of non-monolithic entities:

1. Anti-monolithic entities are those that do not have enough mass within their boundaries to collapse into monoliths, but are likely in the region of influence of one or more monoliths.
2. Pre-monolithic entities are ones defined by system boundaries that are not event horizons, but contain enough mass that they could collapse into monoliths
3. Post-monolithic entities are ones defined by systems that emerge through the energetic destruction, evaporation, or collapse of monoliths.

Approached this way, many ontological questions appear in a very different light. Take for instance, tensions between centralization and decentralization, or between hierarchies and networks. All these are merely anti-monolithic structures that can exist when collapse into monoliths is not imminent. When these frames apply, it means historically non-unique conditions are prevailing, and generic theories might be useful.

Or consider why it’s harder for monolithic human organizations to benefit from generalist sources of knowledge, such as academic research or consultant knowledge. When the internal information environment is both monocultural and idiosyncratic, and non-interoperable with external epistemologies, there is no way for external knowledge to truly penetrate. Outsiders have to learn the internal language and become insiders first. If the process doesn’t kill them, it is likely to domesticate them so they forget or lose the information that made them worth bringing in in the first place. Even individuals entering in highly empowered ways, such as new external CEOs entering companies known for internal “lifer” career tracks, can be rendered entirely helpless in short order. In the show Yes, Prime Minister, this process is known as a new minister getting “house-trained” by the Whitehall bureaucracy.

The same logic explains why the “Not Invented Here” (NIH) syndrome exists in engineering design. It is remarkably hard for big monolithic companies to adopt obviously powerful innovations from competitors. Almost everything gets lost in translation. So they typically have to reinvent rather than imitate.

And finally, the same logic explains why it’s so hard to put AIs into a proper feedback loop with users, with local-contextual memory and a genuine understanding of the user’s unique needs. An LLM is effectively all the information on the internet collapsed into a highly idiosyncratic and solipsistic data monolith. Knowing and relating to it is as hard as knowing and relating to other human minds. For both sides.

Monolith GovernanceBut all this also suggests that monoliths, despite the analogy to black holes, are neither indestructible, nor incorrigible (I’ll reluctantly accept the term “corrigibility” from the AGI cult as a helpful one; I don’t want to turn into my own cultish NIH blackhole here).

Monoliths are neither intrinsically good, nor intrinsically bad. Nor are they omnipotent or omniscient. A monolith is merely a little bubble of solipsism trapped behind an event horizon of its own making, with a region of influence that is limited in space-time by their initial conditions (mass, charge, and angular momentum in some suitably generalized senses). It’s a non-factorizable “prime number” in the structure of reality.

The regions of influence can be huge, but are never infinite. The universe beyond the monolith is always bigger than the universe within the monolith. There is always more mass/charge/angular momentum outside than inside. Which means any victory enjoyed by the monolith is necessarily temporary.

What makes them difficult to work with is not just that you can’t understand them from the outside. It is that they can’t understand you either. The incomprehension is mutual.

Which means governance is rather uniquely constrained.

How do you Attack and Dethrone Monoliths? What is involved in the Care and Feeding of Monoliths? How do you create monoliths?

I will tackle these questions in a sequel.

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Last week’s issue on war rooms and peace rooms triggered a Doh! moment for me. I’ve long been wondering about the logic by which I divide my writing across my blog (ribbonfarm) and this newsletter (ribbonfarm studio). The writing here is easier to do, but the writing on the blog is more fun to do. I used to think the divide was an R&D one — blog for stu…

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I’ve been revisiting, rethinking, and updating my cozyweb and Internet of Beefs (IoB) theories ever since the Dark Forest anthology, put together by Yancey Strickler, came out. It strikes me, now that we’re all several years into dividing most of our online time across various cozy enclaves, that I elided a very important distinction: Between cozyweb enclaves organized around war missions vs. ones organized around peace missions. Let’s call these war rooms and peace rooms.

In my characterization of the cozyweb I think I inadvertently suggested that it mostly comprises peace rooms formed by people who wanted to abandon the war-torn public web for private spaces where they could continue (or rediscover) more enriching peace-time pursuits, without retreating entirely offline to waldenponding. Certainly that was my motive. I can do some mediocre online culture-warring when I must, where I have stakes, but I’m fundamentally about generative peace-time pursuits. But now, four years into the Permaweird, taking stock of the dozen or so cozyweb enclaves I’m in, it strikes me that it’s a fairly full spectrum between war rooms and peace rooms, and that despite the healing connotations of cozy, the cozyweb is as much about making war as it is about finding or building peace.

A cozyweb war room is characterized by a clear shared mission, strong and suspicious perimeter-security gatekeeping, deep, battle-tested trust relationships, and activities designed to marshal resources and alliances for consequential and public battles. The most extreme and clear war rooms are of course political campaign war rooms around actual elections, or specific transient needs like unionization efforts, but the most interesting ones are the persistent ones not devoted to specific battles but to maintaining a particular belligerent philosophical posture through an indefinite war. Fortresses rather than forward field positions. War rooms are high-energy places with spikes around hot conflicts.

A cozyweb peace room is characterized by shared behavioral norms, weak and variable gatekeeping (usually assets-based rather than perimeter-based), shallower, less-tested trust relationships, and activities designed to build up peaceful social capital stores. The most extreme and clear peace rooms are pure cultural production zones devoted to art, study, or “deep work” that are radically open to drive-by and permissionless participation. Often they only have security-by-obscurity. The internet of beefs doesn’t leak in much not because there are impenetrable high walls but because the people inside simply aren’t interested in being in fight-mode all the time, regardless of causes. Peace rooms are not good recruitment zones for warmakers. Peace rooms are low-to-medium energy places that try to keep spikes and surges of energy to a minimum, and instead focus on fostering a culture where people simply show up, week after week and as much as they can.

Now that I am thinking in these terms, the two cozyweb enclaves with some public visibility that I’ve helped start in the last 4 years, the Yak Collective and the Summer of Protocols, are both near-pure peace rooms. Both are pretty much open to anyone who wants to wander in and is willing to suspend their war-making commitments while they’re there. In the former, we explicitly focus on “show up every week” tempo. We vastly prefer members who show up an hour a week for 10 weeks, contributing steadily to a low-key study group track, over excited people who come once with a 10-hour surge of participation energy to offer. We don’t mind it or keep such people out, but we don’t prioritize, encourage, or double down on it. But a war room can use such surge energy.

Peace rooms cannot ignore culture wars of course, but tend to adopt don’t-ask-don’t-tell attitudes towards any war-room affiliations members may have elsewhere. In the Yak Collective study groups for example, we sometimes discuss sensitive, controversial readings in study sessions, where members fall on opposed sides, but we tend not to bring war-room attitudes to the study sessions. The temperature is low enough we can actually disagree without danger. It’s like Old West towns in some movies (were these real?) where you had to check your weapons before entering. And the thread of conflict topics is much weaker than the thread of shared cultural production around non-conflict topics. This took some active design and policing early on, but is now mostly self-sustaining.

Similarly, war rooms cannot ignore peace-time cultural capital building imperatives entirely. That would be intolerably bleak and nihilistic for most humans. So there’s usually also a thread of positive non-conflict cultural production going on. But it’s kept limited to a level where it does not distract too much from the business of waging war. There’s an unspoken “don’t forget, we’re at war” sentiment in the air.

Though the spectrum from war rooms to peace rooms is full, the distribution is not uniform. It’s bimodal. It’s hard for a single cozyweb enclave to either maintain a stable 50-50 war-peace posture or switch gears often enough to hit that ratio on average. Switching gears from war to peace or vice-versa is a traumatic pivot involving significant loss of membership that may not be recoverable. This means most cozyweb enclaves are stable 80-20 or 20-80 war-peace. Most people also have baseline warmaker or peacemaker personalities. If you’re a baseline peacemaker like me, you view your peace-room memberships as your home zone and your war-room memberships as a tourism zone. If you’re a baseline warmaker, it’s the reverse.

War-making online is well-understood and the culture of war rooms resembles online gaming guilds (ironically, I found this reading via an old Yak Collective study group session). But peace-room culture, which interests me much more, is far subtler and much harder to create and cultivate, especially when you go from narrow, well-defined cultural production like memes, stylized fiction like SCP, or photography, to more ambitious and diffuse things like “research,” or complex projects like building open-source rovers. For example, the Yak Online Governance primer, based on 2 years of weekly study sessions, was remarkably hard to pull together. The Summer of Protocols is one of the toughest cultural projects I’ve undertaken, despite being generously funded.

This shouldn’t be surprising. Making peace is much harder and far less glamorous than making war, and mostly involves building up compounding positive potential flywheels rather than countering war-making forces directly. To snowclone the line about markets, in the short term, peace-making is a voting machine, but in the long term it is a weighing machine. It rests not on trying to build bridges or getting warring parties to set aside hostilities and negotiate, but on building up big and compounding reserves of peace-power assets. Peace is not the absence of war, but the presence of flywheels of generativity. It is war, not peace, that is negatively defined —by an absence of such flywheels.

This is a hard-to-grok notion. Our culture valorizes wars and spins heroic narratives around them. It treats peace as a zone of parasitic weakness that only exists by the grace of “good” warmakers “winning” the peace for the cowering weaklings to enjoy for a while. An example of this self-congratulatory macho understanding of war and peace is the proposition: strong men create good times, good times create weak men, weak men create bad times, bad times create weak men.

It’s easy to understand war-time economic production, stockpiling armaments, and exciting maneuvering in conflicts. But it’s harder to appreciate the extent to which pivoting to wartime arms production rests on redirecting accumulated momentum in peace-flywheel assets. And if there’s enough momentum, war can in fact be indefinitely held at bay (this is the old, fraying notion of the democratic peace restated in more basic terms). The famed American war economy of WW2 was not built up from scratch but a pivot of a set of powerful peace-time economic and cultural production flywheels. The arsenal of democracy was a pivot of the peacetime wealth economy. Car factories became tank factories. Butter-making energy shifted to gun-making energy. But if the flywheels had been more powerful and more uniformly distributed worldwide, the pivot might not have been needed at all.

In the recent Dark Forest Collective roundtable Yancey pulled together to discuss the anthology, I took the contrarian position arguing that the dark forest and cozyweb were (or at least, should be) temporary conditions of retreat and that we should be figuring out how to reclaim the public for peace. But what does this even mean and how do you do it?

I think you do it by building up unstoppable momentum in cozyweb peace-room cultural production flywheels while the war rooms are busy fighting negative-sum wars over what’s left of the public spaces. Once the momentum is high enough, you can stop hiding and go public. You don’t end wars by arguing for peace or imposing “order” by force after “winning.” That sort of peace won by war is fragile and unsustainable. It is a delusion harbored by self-styled “strong men” during their 15 minutes of strength.

You actually end wars by making peace too valuable to miss out on.

The old line, “if you want peace, prepare for war; if you want war, prepare for peace,” is bullshit. Terry Pratchett had it right:

“If you would seek war, prepare for war.’”

“I believe, my lord, the saying is ‘If you would seek peace, prepare for war,’” Leonard ventured.

Vetinari put his head on one side and his lips moved as he repeated the phrase to himself. Finally he said, “No, no. I just don’t see that one at all.”

―Terry Pratchett,Jingo

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The philosopher Daniel Dennett (1942-2024) died last week. Dennett’s contributions to the 1981 book he co-edited with Douglas Hofstadter, The Mind’s I,1 which I read in 1996 (rather appropriately while doing an undergrad internship at the Center for AI and Robotics in Bangalore), helped shape a lot of my early philosophical development. A few years later (around 1999 I think), I closely read his trollishly titled 1991 magnum opus, Consciousness Explained (alongside Steven Pinker’s similarvolume How the Mind Works), and that ended up shaping a lot of my development as an engineer. Consciousness Explained is effectively a detailed neuro-realistic speculative engineering model of the architecture of the brain in a pseudo-code like idiom. I stopped following his work closely at that point, since my tastes took me in other directions, but I did take care to keep him on my radar loosely.

So in his honor, I’d like to (rather chaotically) riff on the interplay of the three big topics that form the through-lines of his life and work: AI, the philosophy of mind, and Darwinism. Long before we all turned into philosophers of AI overnight with the launch of ChatGPT, he defined what that even means.

When I say Dennett’s views shaped mine, I don’t mean I necessarily agreed with them. Arguably, your early philosophical development is not shaped by discovering thinkers you agree with. That’s for later-life refinements (Hannah Arendt, whom I first read only a few years ago, is probably the most influential agree-with philosopher for me). Your early development is shaped by discovering philosophers you disagree with.

But any old disagreement will not shape your thinking. I read Ayn Rand too (if you want to generously call her a philosopher) around the same time I discovered Dennett, and while I disagreed with her too, she basically had no effect on my thinking. I found her work to be too puerile to argue with. But Dennett — disagreeing with him forced me to grow, because it took serious work over years to decades — some of it still ongoing — to figure out how and why I disagreed. It was philosophical weight training. The work of disagreeing with Dennett led me to other contemporary philosophers of mind like David Chalmers and Ned Block, and various other more esoteric bunnytrails. This was all a quarter century ago, but by the time I exited what I think of as the path-dependent phase of my philosophical development circa 2003, my thinking bore indelible imprints of Dennett’s influence.

I think Dennett was right about nearly all the details of everything he touched, and also right (and more crucially, tasteful) in his choices of details to focus on as being illuminating and significant. This is why he was able to provide elegant philosophical accounts of various kinds of phenomenology that elevated the corresponding discourses in AI, psychology, neuroscience, and biology. His work made him a sort of patron philosopher of a variety of youngish scientific disciplines that lacked robust philosophical traditions of their own. It also made him a vastly more relevant philosopher than most of his peers in the philosophy world, who tend, through some mix of insecurity, lack of courage, and illiteracy, to stay away from the dirty details of technological modernity in their philosophizing (and therefore cut rather sorry figures when they attempt to weigh in on philosophy-of-technology issues with cartoon thought experiments about trolleys or drowning children). Even the few who came close, like John Searle, could rarely match Dennett’s mastery of vast oceans of modern techno-phenomenological detail, even if they tended to do better with clever thought experiments. As far as I am aware, Dennett has no clever but misleading Chinese Rooms or Trolley Problems to his credit, which to my mind makes him a superior rather than inferior philosopher.

I suspect he paid a cost for his wide-ranging, ecumenical curiosities in his home discipline. Academic philosophers like to speak in a precise code about the simplest possible things, to say what they believe to be the most robust things they can. Dennett on the other hand talked in common language about the most complex things the human mind has ever attempted to grasp. The fact that he got his hands (and mind!) dirty with vast amounts of technical detail, and dealt in facts with short half-lives from fast-evolving fields, and wrote in a style accessible to any intelligent reader willing to pay attention, made him barely recognizable as a philosopher at all. But despite the cosmetic similarities, it would be a serious mistake to class him with science popularizers or TED/television scientists with a flair for spectacle at the expense of substance.

Though he had a habit of being uncannily right about a lot of the details, I believe Dennett was almost certainly wrong about a few critical fundamental things. We’ll get to what and why later, but the big point to acknowledge is that if he was indeed wrong (and to his credit, I am not yet 100% sure he was), he was wrong in ways that forced even his opponents to elevate their games. He was as much a patron philosopher (or troll or bugbear) to his philosophical rivals as to the scientists of the fields he adopted. You could not even be an opponent of Dennett except in Dennettian ways. To disagree with the premises of Strong AI or Dennett’s theory of mind is to disagree in Dennettian ways.

If I were to caricature how I fit in the Dennettian universe, I suspect I’d be closest to what he called a “mysterian” (though I don’t think the term originated with him). Despite mysterian being something of a dismissive slur, it does point squarely at the core of why his opponents disagree with him, and the parts of their philosophies they must work to harden and make rigorous, to withstand the acid forces of the peculiarly Dennetian mode of scrutiny I want to talk about here.

So to adapt the line used by Milton Friedman to describe Keynes: We are all Dennettians now.

Let’s try and unpack what that means.

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Welcome back. The newsletter is now officially off-hiatus, and billing is back on for paying subscribers.

Over my 7-week break from this newsletter, I was mainly busy with a big spike of work across my consulting projects, but I also managed to finish reading a big fat book about Istanbul, (my review here), and get halfway through a book I’m really enjoying, There is No Antimemetics Division.

I also had an essay from this newsletter, The Extended Internet Universe (May 2019), where I coined the term cozyweb, get published in a neat little limited-edition print anthology called The Dark Forest Anthology of the Internet (details here — the first printing sold out in a week; this is the second printing, so if you’re interested grab a copy while they last).

But most importantly, I was able to take a lot of long walks (7-8 miles) and long runs (~5 miles, longer than I’ve run in a decade), thanks to the improving weather here in the Seattle area, and do a lot of the sort of unsystematic reflecting on my life that I used to do a lot more of, but rarely indulge in these days (at 49, my life feels half over, so there seems to be less of a point to such reflection, but it doesn’t yet feel entirely pointless). I find these days that I can do this best outdoors, walking or running (embarrassingly slowly, 12-14 min/mile). Brainstorming in cafes doesn’t work as well these days. Words and drawings seem to obscure rather than uncover the kinds of very basic insights that seem worth digging out these days. I’ve already worked out most of the details such paper-based introspection can reveal. I’m down to mostly wordless bedrock layers of introspective foraying; stuff you have to feel your way into rather than think your way into.

I realized during some of these walks and runs — a slow dawning rather than a lightbulb moment — that the KPI (key performance indicator) of my life has changed. I no longer measure my life in terms of quantity of words. I measure it in watts.

Back at my most productive, I was probably moving about 10,000-20,000 words/week, counting both serious reading and serious writing, as well as speaking and the words involved in consulting and managing. I kept count of a subset of the words, but never bothered to track energy output. Youth takes energy for granted. My words throughput is declining somewhat (maybe down 25% from 2010) though the pie chart looks different.

According to my Whoop strap, my energy output is between 1700 kcal/day on a sedentary day (82 watts average) and 2400 kcal/day on a long-run day (117 watts). According to a Dexa scan (a fancy body composition scan), my basal metabolic rate is around 1480 kcal/day (~72 watts), so really my actual surplus aliveness level above simply existing is between 220-920 kcal/day. Or 10-45 watts.

Not much, huh? Puts things in perspective.

Now the shift from words to watts is not a like-for-like shift. Watts are an input effort metric across all activities. Words are an output metric for a specific class of activities with a typical leverage range in terms of value of impact. The two are related by this effort/leverage graph I made, which also shows my other major activities.

Exercise is not just high wattage. Counter-intuitively, it is very low-leverage. Sure, sure it promotes broad well-being and increases odds of success at other stuff, but all that is indirect. You need the direct leverage of other activities that require more complex capabilities and learned skills, to “realize” the indirect benefits of exercise. By itself, exercise just increases dumb life wattage.

Words are at the other extreme. Trafficking in words in any way is probably the lowest-effort thing you can do in terms of watts, but has really high leverage. You can be barely alive in watts terms and change the world. More importantly, it is the domain of strategy, where working smarter has far higher returns than working harder. By slightly changing what sort of word-work you do, and where, you can change outcomes by 2x-10x on any value-metric you care to track, with almost no change in input wattage.

The cluster of green activities that dominated my life in the last few decades all revolved around words (code and math, which I don’t deal with much these days, are special kinds of words). All seriously handled words fall within roughly the same range of (high) leverage, with some allowance for skill and personality. I hate to admit it, but my own words-based impact probably peaks with direct management activities (which includes consulting), not my public blogging or my technical work long ago. There is something depressing about that, but oh well.

I find that my attention these days is increasingly shifting to the red end of the spectrum. Activities that have high wattage, but low leverage. These are all non-strategic activities. There is not much upside to working smarter. The grinding is the point. The only way to increase impact is to increase input wattage.

In between is the yellow range. A cluster of medium-effort, medium-leverage activities with very varied input behaviors best measured by a very vague KPI I’ve never liked: “success.” These are also activities that are mid-strategic. Neither near-pure strategy like word-work, nor near-pure grinding like running. Success is a class of proxy metrics defined relative to narratives rather than a real metric defined relative to low-level activities. Like Jeff Bezos, I dislike proxy metrics. You can count words and watts but you can’t “count” success. This doesn’t mean you can’t measure it. You just can’t measure it at a close-to-physics level. You have to use things like prizes, famous friends, news coverage, net worth, history denting, parties you’re invited to, and so on. It’s a lot of meta-work of a sort I dislike doing, but if you don’t do that meta-work, the actual work feels futile and empty. Purely instrumental with no intrinsic value.

By contrast, words and watts feel intrinsically worthwhile. The stuff of life-flow. No proxy metrics and elaborate narrative justifications needed.

Not only do you avoid tedious meta-work, you free up an important life resource, your narrative bandwidth, for more interesting and creative uses than measuring “success.” This is perhaps the biggest value of avoiding the middle and sticking to the two extremes of words and watts. You get to self-author your story for interestingness rather than enslave it to the proxy meta-project of tracking success. You get to use your story to measure the universe rather than devote it to seeing how the universe measures you.

There is an elegant simplicity to saying “my life is so many watts and so many words/day and my story is my measure of the universe, not the universe’s measure of me.”

You can’t and shouldn’t entirely avoid the middle (that’s just precious snowflakery) but for me it’s something to satisfice at mediocre levels. I’m probably a mediocre success by many commodity narrative rubrics. Good enough. Move on.

I’m curious to see what happens to my words in the future now that watts are the KPI. One thing that’s already happened is that I can now see my relationship to words more clearly, since I’m no longer as attached to them, and my identity is not as closely tied to them. I casted this thought that I think is going somewhere interesting.

Type A’s are the yellow-range “success“ people. Type B’s are the Red+Green people. Words+Watts people.

I think my words have always been about groping for things for which we have no words, and when I succeed, people usually say I put words to thoughts they didn’t know they were thinking. That’s pretty high leverage/low wattage. I’ve been doing it guided by unconscious intuition for decades, but I’m starting to see the workings of the process itself now. I’m hoping that conscious awareness does interesting things to the words themselves. We’ll see.

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I’m still on break with the newsletter on pause for a couple of weeks, but interrupting it to boost the Call for Applications (alt mirror link) for the Summer of Protocols 2024 program, which I’m directing again for its second year. Here’s a PDF if you’d like to forward as an attachment.

I’d appreciate any sharing/forwarding of this opportunity you can do, to suitable individuals and groups. And of course, think about applying yourself if you have an idea that fits. Read on for more on who might be a good fit.

Applications for this year are due by Friday, April 12, and I strongly recommend posting the public RFC (Request for Comment) component on the forum by tomorrow (April 1). We’ll be kicking off a 10-day public review period tomorrow, so it will really benefit your application to have the RFCs up during that period. Posting the RFCs also entitles you to request some office hours coaching from me.

Last year’s program was mostly focused on early stage exploratory research, while this year’s program is focused more on downstream applications, fieldwork, and evangelism. The program has 3 tracks.

1. A track of Protocol Improvement Grants (PIGs) of $90,000 over the summer for teams of 2 to take on serious protocol entrepreneurship efforts. There are 5 PIG grants up for grabs.
2. A track of small creative development microgrants of $1000 for people who want to try doing things like writing short stories, making up protocol memes, infographics, etc. We’re calling these PILL grants: Pill to Incept Lore and Literacy. The goal is to “protocol pill” more people. There are 20 PILL grants up for grabs right now, but we may have more available.
3. An institutional partner track. This is already at capacity, but check out the stuff that’s happening on it — there will be participation opportunities for some of it.

For both the PIG and PILL funding tracks, there is a simple application form, and a requirement to post an RFC in the program forum. We’re making a serious effort to make the program much more public and transparent from start to finish this year.

You can read and comment on some of the early RFCs here. If you think you can beat these ideas, you should apply. If you think you have a suggestion that can improve one of the RFCs, you should post a comment. I recommend signing up for updates from the forum if you want to keep up with the firehose of activity. You can also subscribe to the program newsletter for weekly updates.

Read the full CFA (mirror link) for details, including themes and problems of special interest.

My personal goal for this year’s program is to meme the idea of “protocol entrepreneurship” into existence. This is one reason the PILL track of the program particularly interests me. I’ve been making my own artisanal memes for a decade and a half, and I want to learn what it takes to make a larger-scale memes/inception effort happen.

While the heavy-duty PIG program is hopefully going to tackle some serious challenges and generate proof points that protocol entrepreneurship can be a thing, getting people to understand what sort of thing it is, and inspired to try it themselves, requires a second-order creative evangelism effort. So if you want to join this protocol pilling conspiracy while sharpening your meme-making skills, throw a PILL idea into the pool.

Getting this off the ground is one reason I needed to take a break from the newsletter. Directing last year’s pilot program was a very rewarding experience for me, and you can read the research output as it is being published here. I wrote up my personal reflections in my Dec 16 post, In Search of Hardness.

I’m looking forward to Round 2.

I’ll be back with regular programming in a couple of weeks.

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Why yes, the answer involves a 2x2

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Visioning is the wrong approach to thermal futures

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Starting assumptions for talking about climate

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A series on narratives with protocolish characteristics

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How do you exit unnarratable conditions?

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Beyond the adjacent possible

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It’s been one of those weeks…

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Cafes are starting to function as grand narrative observatories again

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What lessons can we draw from the tragedy of the submersible?

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A brave attempt at a grand narrative prequel to the present moment

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I am taking a summer break starting today. Regular programming will resume in mid-June.

For paying subscribers, billing will be paused until I hit the resume button. Your next bill date will be pushed out appropriately. If you have 12 days left on a monthly subscription right now, you’ll still have 12 days left when I resume. If you have 289 days left on an annual subscription, you’ll still have 289 days.

I wasn’t planning on a break, but what with running the Summer of Protocols, navigating a move back to the Seattle area in early June, and an upcoming intense phase on another client project, something had to give. Once I’m settled into my new place, I’ll pick up where we’ve left off on various threads.

You’ll still have access to the archives as usual of course, so you can catch up on stuff you might have missed.

During this hiatus, it will not be possible to sign up for a paid subscription if you don’t already have one, so the paywalled archives will be unavailable to new readers.

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My favorite video games have always been puzzles aimed at mediocre people. I mostly play on the iPad; some favorites include Tetris, Two Dots, The Room series, Bejeweled, and most recently a game called Flow, which requires you to connect pairs of colored dots on a grid. It’s basically gamified circuit layout/PCB design. Here is a series of 6 snapshots of a 10x10 example.

The nice thing about Flow is that though it’s a closed domain game, the rules are not arbitrary. They are a natural artifact of the topology of a plane and the laws of geometry. As far as I can tell, the only slightly arbitrary thing is that the puzzles are designed to have a unique right solution, and fully occupy the grid (I’ve been unable to reverse engineer the conditions that enable this, I think having n+1 pairs on an nxn grid has something to do with it). The puzzle is hard but not super-hard. Initially I struggled with the larger grids, but I soon realized that one simple rule solves mostly solves most puzzles: recursively connect dots along unblocked boundaries. In the image above, in the second snapshot, the dark red pair is an example. Some puzzles can be solved entirely using just that rule, since each time you connect a pair, you reshape the interior boundary in a way that makes it applicable to more pairs. In snapshot 3, you can see this recursion in progress for the orange dot pair, and once that’s done, the rule will apply to the white pair.

When the rule isn’t enough by itself, other heuristics help. To get to snapshot 3, I had to apply my imperfect heuristic of trying to create relatively disjoint islands before the boundary rule kicks in again. There are a few other heuristics, which progressively mop up all puzzles, and for the tougher puzzles, I’d say about 20% of the connections require an intuitive leap (at least if you want to avoid a brute-force search).

This sort of puzzle is very relaxing and pleasant once you’ve mastered the basic problem-solving principles. It neither degenerates into mechanical and boring perfect solvability like tic-tac-toe, nor is it uniformly frustrating.

This sort of puzzle is a great metaphor for life when the world is a finite-game mode. There is a boundary, and a set of reliable rules and heuristics of decreasing generality and leverage that allow you to “solve” life. The first 20% of “easy” rules often solve 50% of the problem (get an education, eat healthy, save money), which means everyone can get to a relatively high baseline, and the remaining 80% of trickier rules get you to maybe 80%, And then there’s 20% room for intuition and creative seeing/thinking.

And most importantly, you know when you have solved the puzzle. The win conditions are clear, and the win conditions for everyone together make up the logic of how the world works.

These puzzles can also be thought of as “fill in” puzzles, where you have a frame and you fill it in with pieces. As you do so, the frame itself shrinks, generally leaving you with smaller and simpler problems to solve if you make no mistakes, and when you’re done, the picture is complete.

Some of the other games I mentioned stress this logic in various ways. Tetris is never-ending and incompletely solvable. Two Dots adds various other stressful dynamics. Bejeweled has a subtractive logic rather than an additive logic. The Room adds narrative color and artistic variety to the puzzle pieces. But essentially, these are framed, fill-in puzzle games with solutions. They are finite games.

But sometimes, there is no frame and no solution.

Sometimes the world is in an interregnum, or liminal passage, between finite-game epochs, and you have to deal with the raw, unframed, unpaved infinite game, where the goal is to continue the game rather than to win. If humans are fundamentally a gaming species, Homo ludens as Huizenga named us, this condition is the gaming equivalent of a wilderness.

Such interregnums seem to have 3 phases.

• In Phase 1, the old finite game is breaking down; it’s very traumatic and you have to scramble to survive and harvest value from it before it breaks down completely.
• In Phase 2, there is no finite game at all, and it’s all very vibey and atemporal. Everyone gets very angsty, even people not normally given to anomie.
• In Phase 3, a new finite game is booting up, but is not clearly defined. There are dynamics and rules of varying levels of clarity, but no clear boundaries or win conditions defined.

Arguably the last decade maps to this scheme. 2013-15 was Phase 1, 2016-20 (what I call the Great Weirding) was Phase 2, and 2021-23 is Phase 3.

Phase 3 is perhaps best represented by a different kind of puzzle, one you may have seen me post about elsewhere: aperiodic tessellations.

Recently, in a major breakthrough, mathematicians discovered the “hat” class of aperiodic monotiles — a single shape that (along with its mirror image), tiles the plane aperiodically (ie there are no repeating patterns, in a precise mathematical sense). The previous best was 2 shapes, and before that it used to be 6.

I printed myself a set (red and green for the mirror images) and have been playing with them recently. Unlike Flow and other puzzles I normally play, this is a game with rules and heuristics (much more difficult) but with no frames or win conditions. All you can do is extend the tiling in all directions — and since it is aperiodic, by definition there will be no true repeating patterns. Only vague fractal resonances. They are unframed, fill-out puzzles.

It is rather appropriate that this tile was discovered in 2023, because navigating 2023 definitely feels like playing this puzzle (since you can’t “solve” it as such, only continue to play it; but it is still puzzle-like).

The world of 2023 is full of puzzle pieces that seem to fit together in ways that make sense locally, but don’t create pictures where larger patterns are immediately obvious. There is logic to the developing tessellation, and ways you can be “wrong” (you can get the tiles into a dead-end condition with an obviously unpluggable hole), but no clear way to be “right.”

There are only a few types of pieces though. Maybe even just one. There is an argument to be made that AI is the aperiodic monotile of 2023. It is getting into everything. AI is starting to retile the world.

If you buy my evolving thesis that crypto is in some ways the evil twin of AI, you could say it’s the mirror image, and it’s kinda nicely symbolic that it’s in recession right now. That would be a nice and clean metaphor, but I don’t think it’s that clean. AI and crypto are not exact mirror images, and there are a few other types of pieces on the table. Here’s my list of 2023 aperiodic tile types:

1. AI
2. Crypto (almost the anti-AI)
3. Climate
4. Covid
5. Chips
6. Inflation (maybe)

Note that each is a type. The actual pieces are the local manifestations of each. The “climate puzzle piece” here in Los Angeles is not the same as the one in say Beijing.

Inflation is a maybe for me as a fundamental puzzle piece because even though the end of the zero-interest era has been a profound thing (I just unpaywalled the Truth in Inconvenience essay where I riffed about it), I’m not sure this new high-interest economy is any different from historical high-interest eras. One reason to think it might be is that this is the first time in 70 years when Moore’s Law seems suddenly unreliable, and the low/zero interest rate recent past was, I believe, driven by it.

Be that as it may, there are between 5 and 6 fundamental types of puzzle pieces that are forming strange new aperiodic tessellations, following a logic that is scrutable at small scale, but inscrutable at larger scales. The pieces come in different quantities. AI seems to be spawning dozens of pieces. Crypto is in a mild winter but is also spawning dozens of pieces.

The other four types occur in fewer but larger pieces — unlike AI and crypto, they are not “retail” parts of the collective puzzle that all can participate in. Watching those pieces fall into place is a spectator sport for most of us. You and I are not going to set up a fab, tweak interest rates, or create a carbon tax.

So we have 8 billion people playing an aperiodic tessellation game with 6 new types of pieces. Well maybe not 8 billion yet, but it’s going to get that massively multi-player within a few years, even though it’s an elite game right now.

This is the Permaweird Puzzle. We don’t know exactly what the game is, but we have the game pieces, some of the rules of how to play, and some sense of right and wrong moves, at least locally. We know how to continue the game even if we don’t know how to win it. And we’re filling-out rather than filling-in the board. The boundary is expanding rather than shrinking.

This thought has me reflecting on my activities so far this year. It feels like what I’ve been doing is playing my corner of the Permaweird Puzzle. I’ve been thinking about and working with all 6 classes of pieces, though I haven’t been writing about all of them.

This isn’t a roundup post, but it does feel like the newsletter this year has been a partial view of my little corner of permaweird puzzle solving. This is the sixteenth post of the year, and over the past 15 posts, it feels like I’ve been switching back and forth with trying to fit some pieces together and trying to pull back and examine the larger emerging logic.

Obviously one effect of this is that my frames of the last few years, partly represented in the serialized projects of this newsletter, have started to break. I’ve already identified a major piece of surgery1 I need to do on my Clockless Clock book project for example, based on my developing thinking around protocols. My Graph Minds Notebook series clearly needs serious refactoring in light of what’s going on with the AI thread of Mediocre Computing, which might require the two to be merged. The relationship between the Great Weirding series and the After Westphalia series. I’m trying to resist the hedgehog impulse to make a grand unified theory of everything since that is not my way. But while the world today is not, and never is, in a One Big Thing mode, it is also not in an an atomized, foxy, Many Things mode. There is an entangled set of Some Medium Things co-evolving here.

So if you’ll indulge me a bit, I’d like to look back a bit and try to make sense of the state of play as it appears on my corner of the game board that’s filling out in this newsletter.

I began this year in zoomed-out mode, with a rather self-absorbed and languorous series of essays reflecting on why the year seemed off to a slow start, which I concluded with my January 27 issue Contours of Thawing Time, in which I declared (sort of wishfully, by fiat) that we were starting to shake off the ennui of the last few years, and getting moving again. The end of the liminal passage was in sight:

I’m pleased with the three essays I’ve written so far — Logics of Caring, The Permaweird, and Disturbed Realities. I still don’t know what 2023 is going to be about, but I think these three essays successfully circle whatever it is. I could be entirely wrong, but I have a feeling the year will continue to unfold languorously. I suspect there is not going to be an attention-cornering shock-and-awe event (major war, insurrection, pandemic…) forcing the pace of the year. But the things that do get going will not be shallow dramas that thrash about confusedly for a few years before subsiding. They will be deep, subterranean movements with clear momentum that unfold over a very long time, like decades or centuries, coloring more superficial events in systematic ways. The last time a January felt this way was probably 2002.

After one more post in this vein (Economic Reveries, Feb 3), I zoomed in for the next 8 issues (Feb 10 to April 8 in the archives) onto the most obvious sign of renewed subterranean movement, the AI explosion, which began last year and returned with renewed vigor after a winter hiatus. Some of those posts slotted neatly into my Mediocre Computing series, others spilled messily out.

As of two weeks ago, I feel like I’m back in the somewhat languorous zoomed out mood of January. While history is definitely moving again, it is definitely not doing so with the kind of legibility that allows you to become unconscious of your sense of it at a this-is-water level, and simply live in flow like it is 2012. I wrote about this last week in History After History.

The thing about solving the permaweird puzzle is that you cannot really get into a sort of pleasant, unconscious flow doing it, as rules become evident, linger briefly in awareness, and turn into muscle memory.

This is a puzzle that never lets you forget that you’re solving it, or allow any level of processing, from tactile manipulation of individual pieces, to gestalt sense of game-board, to retreat from awareness. There are no tactical wins punctuating the series of games, or clear level-ups to different levels of the game. There is only one endless zen-mode infinite game, with a smooth, step-less gradation in fractally self-similar levels, and unsatisfying pauses between sequences of moves.

It is a kind of low-grade stressful condition, but not as bad as the liminal stillness of Phase 2, or the traumatic destruction of Phase 1. Things are moving, there is a game afoot. There are new rules to learn. There is a developing sense of right and wrong.

There just isn’t a way to win yet.

1For those of you following that, I’ve decided to rename the 4th layer of the temporality stack in Operating in Time the Protocol Level. It was previously called the Fork Level. The name change accompanies a substantive change in how I think that level works, which affects the whole throughline argument of the book, which means at least a slight rewrite of all the completed chapters.

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NASA released a new Uranus photo from the James Webb space telescope. The rings are spectacularly visible in infrared.

Contemplating the image had that oddly calming effect new images from space always have on me.

Space is a strange modern word that we don’t think about much. We think of Uranus being “out there” in “space,” but what exactly does that …

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Yesterday, I was doing some thing thinking about the differences between the everyday and AI senses of the word attention, inspired by an analogy to renewable energy, and I sketched this diagram to think about it:

This morning, I was delighted to see a very similar-looking diagram in Gordon Brander’s essay, Feedback is All You Need, in his excellent newsletter Subconscious, which you should subscribe to if you’re interested in computing themes.

The similarity is more than cosmetic. Gordon’s diagram is drawn from the cybernetics literature, while mine is a natural sort of diagram to draw if you think in control theory terms (for those unaware of the connection, control theory is to cybernetics as computer science is to AI). These sorts of block diagrams, with flows into and out of boxes, and feedback loops, are the natural way to visualize systems if you’re interested in questions of boundary conditions, stability, scaling, signals, and information flows. They are a natural outcome of trying to think about engineered systems from a physicsperspective, and trying to do things like write down equations that describe the natural behavior of artificial systems.

We need more people thinking about AI in this way because in my opinion there is a missing physics-style discourse in AI. There are strong philosophy and engineering discourses, but no physics discourse. This is a problem because when engineers mainline philosophy questions in engineering frames without the moderating influence of physics frames, you get crackpottery. This is why the field is being over-run by crackpots, and increasingly at risk of complete theocratic capture by priests, as I argued last week. Crackpot priests are what you get when there aren’t enough physicists mediating between philosophers and engineers. I’ll argue this point in a future newsletter, but here’s a preliminary thread on Farcaster. For now, let’s table that interesting topic and focus on what it means to investigate the physics of intelligence.

You will notice, firstly, that I did not say the physics of artificial intelligence. Six months ago, I might have used that more qualified phrase. But I think it has been adequately demonstrated in the last six months that at least intelligence (if not subtler notions like consciousness or sentience that may or may not be well-posed) is not substrate dependent. The physics of intelligence is no more about silicon semiconductors or neurotransmitters than the physics of flight is about feathers or aluminum.

These low-level substrates constrain but do not define the physics in either case. When you analyze the flight characteristics of an airplane or a bird, you might quickly check the strength to weight ratio of bone-and-feather composites or aluminum, but then you move on talking about wing geometry, lift versus drag, and so on. Concepts that still belong in physics, but at a different level of abstraction.

Flight is actually a very good reference phenomenon for thinking about intelligence, since it too is a property of biological organisms that we reproduced with non-living machines that work on similar, but not identical principles. Understanding the physics of flight in a way that’s agnostic to the differences between birds and aircraft is a similar problem to that of understanding the physics of intelligence, whether realized with silicon or neurons. Interestingly, even though aerospace engineering is a mature discipline today, the physics of flight is still actually quite mysterious. As an aerospace engineer, I have the standard engineering understanding of it, but the physics understanding is more demanding, and less complete.

But getting back to intelligence, thinking carefully about the concept of a wing, and the role it plays in flight, as we will see, sheds interesting light on the concept of attention. Attention is the focus of one of the six basic questions about the physics of intelligence that I’ve been thinking about. Here is my full list:

1. What is attention, and how does it work?
2. What role does memory play in intelligence?
3. How is intelligence related to information?
4. How is intelligence related to spacetime?
5. How is intelligence related to matter?
6. How is intelligence related to energy and thermodynamics?

The first three are obviously at the “physics of intelligence” level of abstraction, just as “wing” is at the “physics of flight” level of abstraction. The last three get more abstract, and require some constraining, but there are already some good ideas floating around on how to do the constraining.

Obviously, we don’t have good answers, let alone validated and dispositive ones, to any of these questions. But I think I have intriguing clues in hand for each that I’m finding productive to think about. In this essay, I want to share some initial thoughts on the first three questions, which are somewhat easier to grok, and (very briefly) preview my thinking on the last three, which get much harder. I’ll cover those in detail in a future issue at a TBD date, since my thinking on them is still very early-stage.

An important note. We are not talking about the physics of computation in general. There are well known approaches to these questions for the broader category of computation (which to some extent is just an alternative way of talking about physics). I’ll mention these in passing in my discussion, but computation and intelligence are not synonymous or co-extensive.

To first order, I think of intelligences as embodied systems that are good at certain kinds of computation, and are situated in the universe in specific persistent ways, characterized by particular boundary conditions (which my cartoon diagram above gestures at). To talk about intelligence, it is necessary, but not sufficient, to talk about computation. You also have to talk about the main aspects of embodiment: spatial and temporal extent, materiality, bodily integrity maintenance in relation to environmental forces, and thermodynamic boundary conditions. My six questions get at those things.

The 6 questions above can also be asked about computation in general, and the answers constrain, but do not specify, answers to the same questions in relation to intelligence.

With those caveats out of the way, let’s dive in.

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This newsletter issue was delayed because I was in San Francisco for a couple of days, for the first time in 4 years, mainly to attend a party. The trip got me reflecting on how the region has repeatedly transformed itself over the 20 years I’ve been orbiting it, and how it’s transforming yet again. What was a soft-edged vibe shift 4 months ago has now turned into a full-on generation shift.

I am not a party person, and don’t normally accept invitations to the few parties I get invited to, even if they’re in my own neighborhood, let alone ones I have to take a flight to get to. But I made an exception for this one because it was the launch party for Atomic Semi, which is probably the first startup in more than a generation to bring actual silicon fabrication back to Silicon Valley. It felt like a bit of history in the making, and worth a plane ride to witness. Maybe in a couple of decades this will seem like as significant a moment as say the Traitorous Eight quitting Shockley Semiconductor moment in 1957 to create Silicon Valley, or Engelbart’s Mother of All Demos moment in 1968, which marked the birth of personal computing.

And speaking of the historic moments, Gordon Moore, member of the Traitorous Eight, co-founder of Intel, and most famously, the formulator of Moore’s Law, just passed away. So there is a very real sense of a generation shift in the air in tech at the moment. Gordon Moore is gone, just a couple weeks after a somewhat less venerable institution, the Silicon Valley Bank, exited the stage. But at the same time, there are interesting signs of genuine renewal, and the launch of Atomic Semi feels like one of them.

It is news to a lot of people, but there hasn’t been much actual silicon manufacturing in Silicon Valley for decades. The fabs mostly moved to Asia, and the few significant manufacturing facilities left in the US are not actually in Silicon Valley. While there is of course a ton of design work and specialized laboratory work that happens on the campuses of Intel, AMD, and other chip majors and minors in the region (mostly concentrated around the southern tip of Silicon Valley), actually making the chips has been a business for other geographies.

Speaking of geography, if you’ve never been to Silicon Valley, the layout of the region is actually quite interesting from a technology perspective.

Bay Area map, Wikimedia Commons.The geography, counting in layers up from the southern tip in San Jose, looks rather like a vertical cross section of the modern computing stack.

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This essay is part of the Mediocre Computing series

One of my favorite anecdotes (possibly apocryphal) from computing history concerns the Ada programming language. The language apparently had (has?) no clear semantics for terminating and exiting a program because it was designed to be used for embedded control of missiles. The program would end when the missile blew up. A delightful illustration of parsimonious YAGNI (you ain’t gonna need it) design thinking. I’m not sure how true this was or still is (versions of Ada are still around), but this sort of “crash-only” programming language design, lacking clean default termination semantics, is an example of what I think of as “courage in computing.” Courageous computing is computing designed to be aware of, and pragmatically responsive to, the inescapable constraints and risks of the real world, in all its messy glory.

This is not courage in the programmer or human operator of a program, but courage intrinsic to the design of the computing system itself. Any sort of engineering design that allows for, or expects, dangerous things to happen has courage built into the medium of the message in the form of systemic risk-management predispositions. A missile has that built into not just the design of the guidance computer and program, but obviously into the physical body itself: it is a bomb mounted on a tube that is itself full of explosive propellant. Or to take a simpler example, a knife without a live edge can’t cut you, but is also useless (a knife with a live but dull edge is the worst of both worlds — bad at cutting things you want cut, likelier to cut things you don’t want cut).

A more familiar computing example is C, with its laissez-faire attitude to memory management. You can do whatever you want with pointers and memory, including very dangerous things. This design was partly driven by the state of computing when C was invented. Memory limitations meant the only kind of efficient programming possible was a dangerous kind, requiring programmers to do active memory management. Kinda like how before the invention of dynamite, much more temperamental explosives had to be used for construction.

As memory got cheaper, what are known as garbage-collected languages (which do memory management for you, making programming simpler and safer, but less efficient) became more popular. The Rust programming language tries to have its cake and eat it too, supposedly allowing programmers to write safe but efficient low-level code, but as Linus Torvalds pointed out in one of his famous rants, this is to some extent wishful thinking. An AI-relevant wrinkle in this story is that memory never did get cheaper close to the processor. In fact it has gotten much more expensive in relative terms, which is why low-level programming, which has to deal with limits on high-bandwidth memory close to the processor, still has an old-fashioned flavor to it.

Like missile-guidance computing, kernel computing (the lowest level) is fundamentally too close to the world of atoms to accommodate wishful and expensive illusions of safety (or things that masquerades as, such as nearly unlimited memory). Kernel computing, missile guidance, and construction explosives are fundamentally dangerous things, and there’s only so much safety you can or should attempt to design in.

Why? Because the world is a dull, dirty, and dangerous place, and things that prevent you from being alive to that fact are not actually good for you, no matter how nice they feel. When your tools don’t embody sufficient courage to deal with the world as it exists, your courage eventually atrophies too.

A Dull, Dirty, Dangerous WorldIt is increasingly sinking in for me that what I’m calling mediocre computing is not just a coherent and viable philosophy of computing, it is in fact the dominant philosophy of computing as it actually exists and has been successfully practiced since the beginning. A philosophy designed for a “dull, dirty, and dangerous” world, as 2000s military doctrines put it.

And this philosophy is a fundamentally courageous one that continues to be the dominant and successful one at the frontiers of computing today.

Courageous does not mean foolhardy. Mediocre computing is aware of and responsive to risks, and predisposed to build in mechanisms to accommodate it. Conservative where necessary, liberal where possible, and with a deliberate and conscious approach to risk management, including risk of death and explosions.

A subtle effect of these predispositions is that mediocre computing is also often very boring. It does a lot of housekeeping and chores. It performs a lot of routine maintenance, checking, testing, and so on. This is because if you’re courageous, you want to consciously take on risk where it actually matters and there’s an upside to it. You don’t want sloppiness to move risk around in ways that makes the courage futile. A missile that blows up on the launch pad is useless, so you over-engineer the boring stuff to make sure it doesn’t.

It is no accident that you find the best examples of courageous computing in the “dull, dirty, and dangerous” world of military technology, where you have to do a lot of boring chores, take on a lot of risk, and deal with the banal messiness of real life. Long stretches of boredom punctuated by moments of panic and/or excitement.

Mediocre computing solves for courage in this sense of facing up to the dull, dirty, and dangerous world, while accepting the possibility of failure scenarios and real costs. I would argue that if you took an inventory of all the code out there in the world, of all sorts, deployed successfully into production, you’d find that 90% embodies this philosophy. You’ll find that most of the discarded and forgotten or never-written-vaporware code fits more wishful philosophies of computing.

Which brings me to two currently popular wishful philosophies of computing that I think are both wrong and doomed to irrelevance.

Wishful ComputingRight now, all the discourse, not just around AI but also around the other frontier genres of computing I’ve been talking about — crypto, metaverse, robotics etc. — is dominated by two philosophies that account for very little of the technology itself, but almost all the overwrought commentary.

These are what I think of as aligned computing and accelerationist computing. Here is a triangle diagram representing the three philosophies. The two at the bottom are the loud and discourse-y wishful computing philosophies.

They are wishful in different ways.

• Aligned computing wants the world to be safer for an unchanging human condition than it is or can be (or in my opinion, should be), doing away with all the danger. It is basically Luddism rebottled.
• Accelerationist computing wants the world to be more exciting and dramatic than it is or can be (or in my opinion, should be), doing away with all the dull and dirty parts that slow things down. It is a kind of intoxicated neo-paganism.

The broad insight underlying mediocre computing is that while the world can be made safer and more interesting, it cannot be made arbitrarily safer and endlessly interesting, so you have to proceed with some mix of reserves, caution, and acceptance of dullness, dirtiness, and danger. This is why I go on like a broken record about things like embodiment, situatedness, and friction.

One engineering symptom of this is that the tools get progressively less “safe” and “fun” as you get closer to the world of atoms, because the designers of courageous systems are not willing to sacrifice other desirable attributes such as performance, expressivity, and evolvability for safety or interestingness.

Assembly is more dangerous and less fun than C. C is more dangerous and less fun than Rust. Rust is more dangerous and less fun than Ruby (disclosure: I’ve never tried Rust, but have tried the other three to limited levels). As you approach the silicon, the world gets less safe and fun. You have to deal with more of the dull, dirty, and dangerous aspects. The good news of course, is that anything you do manage to do successfully at the lower levels provides you unreasonable leverage at higher levels for things that are safe and fun. C may not be either safe or fun, but video games written on stacks that rest on C can be both.

Other engineering domains approach subtler kinds of safety boundaries. While Solidity (the language used for programming Ethereum) is a high-level language that is used for highly abstract things like smart contracts, it is very close to the “bare metal” of finance. Atomic operations are unsafe in the sense that they are irreversible because blockchains are what they are, and deal with real money that can cause life-changing consequences for the human owners of that money. But once you’ve written good blockchain code that deals with the dull, dirty, and dangerous aspects of blockchain programming, you can have fun with NFT-based on-chain gaming at the top.

The closer you get to the atoms that constitute it, the less the world will respect any sort of wishful thinking that aims to solve for naively human-centric outcomes like safety or fun.

The wishfulness is obvious with aligned computing, which aims to create controllable forms of computing that don’t “escape” to evolve in wild ways, but is curiously also true of accelerationist computing, which fetishizes an exciting and magical kind of transhumanist co-evolution with magical and god-like computers.

It is perhaps an obvious point, but it’s worth saying it explicitly: transhumanism is just as anthropocentric as humanism. When technologies are allowed to evolve by their own logic, not only do nostalgic desires for a changeless and eternal human condition get frustrated, so do manic desires for endless exciting change. The locus of evolutionary logic shifts. Humans are no longer the center of the evolutionary process. Things can get dull and dirty at human loci. Just because you don’t get the changeless stability the alignment types want, doesn’t mean you’ll get the endless excitement the accelerationists want.

In the triangle diagram, I have tried to represent this via the two trade-off boundaries that converge on evolvability. Mediocre computing ultimately solves for the evolvability. The trial-and-error driven by courageous computing, with all its tediousness and failures, ensures that the infinite game of technology can go on. That more capable systems will keep arising out of the ashes of less capable ones.

If you fetishize humanistic alignment, you will have to give up some changeless safety in a static human condition.

If you fetishize exciting transhumanist journeys, you’ll have to give up some magic focused on feeding your appetitite for interestingness, and accept a certain amount of dullness and dirtiness.

Again, both alignment computing and accelerationist computing are theoretical philosophies. Not much actual code is out there embodying either of those philosophies, and even less code doing so with any degree of adaptive, evolutionary success. The two philosophies argue about human-centric notions of computing excellence with each other, but don’t actually do a whole lot of computing. They are totalizing aesthetics of computer use more than they are philosophies, as I have tried to illustrate on the bottom edge of the triangle, and rarely seem to rise above the level of art projects or design fictions.

Now that we’ve got that set up, what does courage in computing look like at the frontiers today? Let’s start with machine learning, which is once again predictably dominating the discourse.

So what is courage in machine learning?

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A phrase from Bruce Sterling’s novel Schismatrix has been stuck in my head since I read it years ago: “the mathematical bones of reality.” I recalled the phrase recently while thinking about good mental images for protocols. It struck me that bones of time is a very evocative image for protocols. A rather macabre image I guess. I made this rather disturbing x-ray-like drawing for myself. The transient flesh of history surrounding the bones of time.

I’m thinking about protocols a lot these days because of a new gig: I’ll be running the upcoming Summer of Protocols program, which you may have seen me mention elsewhere. If the name intrigues you, go check it out. If you think it might be of interest to somebody you know doing interesting work on the themes, please forward the link to them. Applications are now open, and due by March 21. The program will fund a bunch of full-time and part-time researchers over the summer to think about protocols in the broadest sense: everything from handshakes and diplomacy to blockchains and climate protocols. There is also an associated draft study, The Unreasonable Sufficiency of Protocols, which I authored with a bunch of collaborators. If you’re interested in the running themes of this newsletter, you’ll probably enjoy that. The program is funded by the Ethereum Foundation, but the scope is all kinds of protocols, not just blockchains.

Back to my macabre metaphor. Why bones? I started thinking about the metaphor because people in the blockchain world talk about “ossified protocols” a lot, but why is that an apt metaphor? What about protocols is, or can become, bone-like? I think it has to do with how protocols exist in time.

Think of it this way: we can’t predict most things about the future, but some things we can predict with an eerie amount of confident precision. For example, I am pretty confident that this time 5 years from now, whether or not the US exists as a country, and whether or not zombies or aliens have taken over, we’ll still be driving on the right-hand side of the road on this continent and using 110V electricity. That’s because road rules constitute a pretty ossified protocol and are part of the bones of time. The electric grid is built around a set of standards and protocols and is also part of the bones of time. These social realities seem to have a preternatural stability across the fan of possible futures, and a kind of inflexible hardness we normally associate with the natural laws of physics.

Most things we think of as protocols seem to have this kind of property. Whatever else happens, chances are, in the future, we will still do things like shake hands, use TCP/IP for networking, listen to flight attendants going through their spiels, and so forth. One of the reasons the pandemic felt like such a dramatic disruption of life was that several foundational protocols of life, like shaking hands and smiling, got broken.

Broken is the right word here. Unlike softer social realities, while protocols can sometimes be bent a little, beyond a point of stress they generally break rather than deform endlessly. That’s why we use the phrase break protocol to describe a class of social transgressions. That fundamental rigidity is also the source of the predictability associated with protocols. You trade smoothly undulating landscapes for temporal escarpments punctuating temporal plateaus. You get stability and predictability between breaks.

Protocols create artificial “vertebrate” time out of natural “invertebrate” time.

The same lens can be applied to the past. We structure our understanding of the past in terms of bone-like procedural social realities that don’tchange easily, and induce a certain hard-edged quality in event streams. We have histories of the United States, China, the Catholic Church, and railroad technologies in large part because those entities enjoy an ontological stability that emerges out of the protocols defining their ossified social realities. The United States is in a sense the hard-edged procedural forms of its system of governance. The Catholic Church is the ritual forms that define the practice of that religion. The railroad industry is the set of standards and protocols that define how the infrastructure works.

Protocols are a bit like laws of nature in this regard: defined by stable symmetries and conservation principles that limit the space of possible futures and pasts. That they are the product of social contrivance and technological artifice doesn’t really matter. What matters is that they endure for long periods while changing slowly or not at all. They may be made-up and arbitrary, but they create persistent laws of social reality.

Though I’m calling protocols the bones of time, it is worth noting that time in the sense of consensual social realities (as opposed to felt psycho-physical reality) is itself a set of protocols governing clocks and calendars. Calendars have a regular grid-like structure. A particularly platonic set of bones. When you talk about time, you are usually talking about the state of a time-structuring protocol rather than the psycho-physical phenomenon.

I’ll be thinking and writing more about protocols through at least the summer, and you may want to follow the work of the Summer of Protocols as well. The theme obviously intersects with many of the threads we’re developing here, so it will probably start showing up as at least a B-plot in upcoming posts.

Apologies for an unplanned skipped newsletter last week. I was at EthDenver (a major Ethereum conference) partly to soft launch this program. My newsletter writing protocol broke under the stress of launching a new thing :)

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I’ve come up with a theory connecting three big zeitgeist things: the end of the zero-interest world, the ongoing AI panic, and the meaning crisis. I think it also connects all three to the climate crisis, but I’ll leave that topic aside for this essay since it’s too big.

The thing that connects these things is real-world friction. But to make my point, I’ll need to talk about Meccano models and the philosophy of friction and randomness in engineering for a bit.

In case you don’t know what it is, Meccano is a construction toy, probably the oldest in the modern world (invented by Frank Hornby in 1898), comprising metal parts with evenly spaced holes. Americans may know it as Erector (the two brands are merged now).

Though every step in an out-of-the-box Meccano project is precisely specified, in complex models, the whole sometimes doesn’t quite come together right. And since the Meccano system is based on metal, plastic, and rubber parts held together with strong fasteners, and includes slotted joints and parts with compliances, even if you do every individual step correctly, as the instructions specify, the whole might still not come together right. The model will have emergent alignment problems, a term that means something very different in mechanical engineering practice as opposed to AI philosophy fantasies.

Sometimes, the model is just a little off in a way you can live with. Other times, the errors accumulate to a show-stopping issue with the final assembly. This is what just happened to me.

I spent several hours over the last couple of days putting together a motorized Meccano model car, the fifth model in a sequence of 25 designs that my kit has complete instructions for. I bought this advanced, contemporary kit partly due to some childhood nostalgia (I had a simpler Meccano kit as a kid), and partly to help me research and think about the issues in this essay.

In my case, the front idler wheels of the model were hitting the surrounding scaffolding and unable to rotate. A show-stopper.

The clearance in that part of the model is very small, and is a function of how three different sub-assemblies, built up separately, come together: the chassis (A), the roof (B), and the front cab (C). I’m still troubleshooting, but there are three basic possibilities, with different probabilities:

1. The instructions are wrong and I’ve found a bug in them (low)
2. I made a gross, “digital” error such as a joint in the wrong hole (medium)
3. Small tolerances have stacked up (high)

I am rating the first possibility as low probability because this is a well-tested kit that’s been in production a while, so presumably bug-free. The second is medium probability since I’ve checked carefully and I don’t see anywhere I’ve made an error. The third is currently likeliest. Of course, combinations of all 3 could be at work.

There’s enough wiggle room in the 3 chains of assembly that a precise emergent outcome can be wrong in this show-stopping way. In this case, a positive clearance, allowing a wheel to rotate freely, has turned into a negative clearance (an interference). The issue is an example of what in mechanical engineering is known as a tolerance stacking problem. In a well-designed assembly, the independent sub-assembly paths don’t diverge too much, and stay “in-sync” enough that the full assembly comes together relatively cleanly. But sometimes, the design makes that hard or impossible.

What do you do once you run into the third kind of problem?

Occasionally, in a small, relatively simple model, a bit of thumping, brute forcing, or random loosening, jiggling, and re-tightening will do the trick. This is because if the design and execution are correct in a formal sense, things are only “off” in a way that’s close to a (by-design) stable, low-energy equilibrium state of the assembly, so a bit of unintelligent energy injection is enough to get it the rest of the way.

But in more complex models, this will either not work at all, or the “forced” assembly will be unreliable due to stored up strain and stress in weird places (kinda like plate tectonics). In the worst case, you’ll break or irreversibly damage a part trying to brute force the model to come together. Strain relief, incidentally, is a big topic in mechanical engineering, and the point where it interfaces with electrical engineering, via the surprisingly non-trivial issue of strain relief in cables. Wires breaking or coming loose under mechanical strain, as opposed to electrical issues, are a major source of problems. In building modern data centers, you have to actually model all the cabling at a mechanical level, for reliability, and cabling systems can be a huge chunk of the cost.

Here’s something to know: The more complex a mechanical assembly, the higher the likelihood that you’ll run into emergent alignment problems that can’t be resolved with brute forcing or jiggling.

It doesn’t matter what the design is, or more generally, what the goal of a planned assembly process is. Whether you’re building a motorized car like I am, or a machine that turns human flesh into paperclips, by its very nature, complexity and the limits of design knowledge in mechanical assemblies leads to such issues. The only known general way to mitigate this problem is to make all your parts as high-precision as possible. Keep this point in mind, it has bigger implications.

Meccano vs. LegoTolerance stacking is the sort of phenomenon is why I really like Meccano. It doesn’t shield you from real-world messy phenomena the way Lego, its evil twin, strives mightily to.

While both kits are made to tight tolerances, and new pieces made today will mate with ones made decades ago, in Lego, that’s optimized to the point it is a simplifying and limiting feature:Tolerance stacking errors are basically rare to the point of non-existent in Lego. If your Lego model doesn’t work right, it’s because the instructions are wrong or you made a gross error. In support of this feature, the Lego system gives up some ruggedness by eliminating fasteners, only uses rigid parts, and encapsulates complexity within bigger “molecule” parts, like wheel assemblies, rather than just “atoms” like single rigid bodies.

Meccano by contrast, uses a ton of fasteners, compliant parts ranging from springy steel strips to bendy plastic and soft rubber, and almost never gives you any “molecules.” Everything has to be built up from scratch. Some models I made recently forced me to construct roller bearings out of rollers, plates, and a shaft secured with brass collars. In Lego, this would probably be a pre-assembled “molecule.”

As a result of the deliberate simplification, Legos are a high-convenience toy that are not frustrating to play with until you get to really large scales and complexities, which almost nobody does. It’s ostensibly a physical toy, but almost computer-like in its cleanliness and convenience. It might as well be Minecraft.

By contrast, Meccano forces you to deal with the frustrations and inconveniences of the world of atoms very early. It is designed to teach you what I call the truth in inconvenience.

In Meccano, things like tolerance stacks are part of what the kit is trying to teach you, so even though the parts are made to interoperate across time and kits, other aspects of the system, such as the use of compliant (flexible) parts, slotted joints, and fasteners that can be tightened to different torque levels (causing compressive distortions), all add up to higher realism.

What are no-brainer operations in Lego, like mating two parts, can get arbitrarily complex in Meccano, such as when many fastened joints are in close proximity. Often, you’ll need to loosen one joint to create enough wiggle room to squeeze in another one in a neighboring hole, before re-tightening both. Some operations are really quite tricky, where you have to work in a very cramped and nearly inaccessible interior corner of a nearly finished model, with tiny fasteners, and try to loosen/tighten in the right order to get a thing together. That’s where a lot of the skill comes in, knowing the tricks and hacks to do it all.

Meccano models are are significantly more difficult to assemble than Lego as a result, and it is hardly surprising that they ceded much of the market for such toys to Legos, through a century that increasingly prized low-friction, low-frustration convenience over all else. But on the upside, they are significantly more rugged as well. They can deal with a larger and rougher range of environments than Lego models.

If Lego is about cleaning up and smoothing the real world enough that it behaves like a frictionless computer world, Meccano is about learning the meditative art of pursuing the truth in inconvenience of dealing with the high-friction messiness of the real world. Though it’s a toy, it can be even messier than some real-world “real work” things like assembling Ikea furniture.

Now what does all this have to do with friction, AI, meaning, and zero-interest rates?

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This essay is part of the Mediocre Computing series.

In the first two parts of this series, I set up a basic framing of the future of computing in terms of a systematic vision of mediocre computing: systems that are sufficiently effective to survive indefinitely in realish domains, and operate in infinity-aware ways. Mediocre computing, I believe, captures the essential features of all the major frontiers of computing today: AI, crypto, robotics, and metaverse, with the first two serving as the poles, or eigen-genres.

In this part, I want to set up a fundamental pick-2-of-3 trilemma that I believe governs mediocre computing. Without further ado, here it is:

The essential insight I’m trying to capture in this trilemma is the role played by secret information, and how the two poles of mediocre computing, AI and crypto, grapple with it, and the resulting message of the medium for humans: either individuals are strong and groups are weak, or groups are strong and individuals are weak.

Let’s unpack the logic here a bit. The key is two obvious points with not-so-obvious implications:

1. The fundamental pragmatic premise of crypto technologies is not your keys, not your data. Which means you can either lock up your data or it is assumed to be public. Soft intermediate notions of privacy rooted in social conventions are deprecated in favor of mathematical guarantees alone.
2. The fundamental pragmatic premise of AI technologies is that you can permissionlessly scrape and learn from all publicly available data, and create and share enough value and wealth with AI models that you can ask for forgiveness and expect to be forgiven.

This is why the key political-legal battles around crypto and AI revolve around a) encryption backdoors for government intelligence agencies and b) AI companies scraping data without permission from humans who claim rights over it.

While on the surface these seem like second or third-order cultural matters, I believe they arise from fundamental constraints governing the technologies themselves, at the lowest levels. Which means that they will reshape human relations rather than be shaped by human opinions and desires.

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This has been a schizophrenic year for me. Objectively, so much has happened in the world, January seems an aeon ago. Subjectively, so little feels like it has changed, January seems like it was just a week ago.

Running along both objective and subjective memory streams though, there is a sense of an event horizon fracturing our experience of historical…

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Wrapping up the Twitter analysis

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Announcing my two-volume essay collection

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Field report from Kern County, CA, and draft zeitgeist map

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Sparkling conversations are like Schelling points for exploration

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One does not simply remake global trade

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Graph minds destabilize naive notions of individuals

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An aging epoch of globalism seems to be suffering life-extension

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We're in a historical period-boundary year

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Or, why fears of AI are not even wrong, and how to make them real

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What does it mean for time to be eventful?

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Seeing the whole thing

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Listening to a graph mind talking to itself

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Immediacy, aliveness, and the temporal depth of field

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Ribbonfarm Studio is now a year old

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Or, how do you revitalize a rundown world?

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Do graph minds map to brains

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Graph minds are surrender machines

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What makes a legacy a good legacy?

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Defining the fundamental challenge of lorecraft

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Lore emerges when people try to make the minutes less lousy

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The three kinds of lore and formulas for crafting them

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How they differ, and why it matters

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A series on lore

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Recycling as an example of a lore domain

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How do you actually craft lore?

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A Millennial management science is being born

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The Coasean revolution isn't going according to plan

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We live in an age of collapsed Straussian theories

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🟨⬜🟨🟩⬜

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Ribbonfarm Studio is taking a break through January

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Move over Creator Economy

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What is the best mental model for NFTs?

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Joseph Tainter's model of collapse offers a way to analyze the present

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From COP21 to Web3, the first terraforming is starting to go nuts

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Here we go again. Another tech wave is now undeniably underway. If I’m counting right, this is the fourth one of my life: PC revolution, Web 1.0, Web 2.0, and whatever this is. I labeled it the First Terraforming in my Oct 14 issue, and some people call it the Fourth Industrial Revolution. Let me list the 6 key strands again, this time in what I conside…

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This research note is part of the Clockless Clock book project.

I’ve previously written about three Greek personifications of time, Chronos, Kairos, and Aion. The first two are relatively well known, and represent, roughly, objective (clock), and subjective (stream of consciousness) time. The third though, is neither well known, nor well understood, but …

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I’m going to set aside my longer serialized projects for the next month or so. There’s too much random disruption happening in my life to work on them, so I’m going to do a bunch of one-off exploratory pieces on miscellaneous themes that have been on my mind. One such theme is robots.

The reason robots are on my mind is not because Amazon has released its first one (to decidedly mixed reviews), but because Foundation has started airing on Apple TV, and that got me thinking of Asimov and realrobots.1 The show by the way is pretty good, two episodes in (here’s my evolving Twitter thread on the show), and most of the changes improve both plot and character development without messing with the spirit of the original.

In the tech world, we seem to have oddly schizoid attitudes about robots. On the one hand, we have people (including skilled roboticists) reluctant to even recognize them as a distinct category of technology, and looking to subsume them in adjacent categories like appliances, automation, or AI. On the other, we have people conceptualizing them primarily in tediously and uninterestingly anthropomorphic ways.

Category-Denying RobotsAs an example of the former attitude, consider some of the responses I was getting while shitposting about robots earlier this week:

Here are three category-denying types of responses I got:

1. “Robots don’t look like robots when they arrive. They look like dishwashers.”
2. “A robot is just anything with a sensor and an actuator”
3. “Robots are already here, working in factories.”

Some of what’s going on here is analogous to what we talked about last time — the goalposts-moving phenomenon that robotics shares with its simpler, stupider sibling, AI (more on why robotics > AI later). But there’s also a part that’s just shallow analysis.

The first type of response does not make a meaningful distinction between just any random type of machine and the idea of a robot. It’s like saying any kind of computer program is an AI.

This is not so much moving the goalposts as denying the game of football exists.

It’s a peculiarly American attitude. It’s like this country (unlike say Japan), wants to reduce robots to appliances when clearly they can be so much more. When the Roomba first came out, there was a lot of commentary about this strange cultural bias.

The second type of response is reductive in an unhelpful way. A thermostat comprises a sensor and an actuator, as does the light inside your refrigerator, but it is not very interesting to consider those things robots unless your agenda is to dismiss the category altogether. People who make this sort of argument are often technically skilled but rather tasteless (in design terms) types who can’t see wholes that are greater than parts. They’ll never build interesting robots.

The third type is perhaps the most serious kind of shallow analysis: conflating robotics with industrial automation. Yes, robots can be used to do a distinctive pattern of industrial automation (marked by greater flexibility than typical machine tools for example). But that’s merely an important use case, not a definition. Robotics is a larger, more interesting category than automation. Pretending robots are only about automation is a bit like pretending that blue-collar factory workers are the only kinds of humans.

There are already robots bumming around in the wild as far away as Mars, far from anything that looks like a factory, doing things that look nothing like assembly line work.

Bullshit Hollywood RobotsThe opposite category of bad robot conceptualization is the tediously and uninterestingly anthropomorphic kind favored by the more “serious” kind of film-maker with angsty humanist conceits.

These are the humanoid robots featured in a certain kind of self-satisfied Hollywood movies, and for some reason depicted as having unnecessarily exposed innards capped by an unnecessarily creepy death-mask plastic face and determinedly uncanny-valley affect. The more tropey versions usually have eyes that glow red when they turn evil.

As Adam Elkus has argued in a recent deconstruction of Ex Machina, this sort of movie is not really about robots at all, but about murky projections that have nothing to do with the technology, and everything to do with narcissistic explorations of what it means to be human, and how human relationships work. Mannequins or puppets would serve well enough in this kind of narrative (in fact, a movie like Lars and the Real Girl, featuring a sex doll, does a better job than most ponderous “can robots feel love?” type bullshit movies).

Not to beat up too much on this kind of thing — clearly great stories can result — but it’s not a good way to think about actual robots. As Bender from Futurama2 might say, bite my shiny metal ass.

The weakness of the conceptualization is clear from what’s actually interesting about real bleeding-edge humanoid robots like Boston Dynamics’ Atlas — the amazing acrobatics, and the demonstration of challenging technologies like high-speed hydraulics and machine vision. Not made-up subtle emotional dramas that don’t get at anything interesting about robotics qua robotics.

Okay, so that’s two ways to get robots wrong. How do we go about getting them right?

Asimovian RobotsAsimov doesn’t get enough credit for the sophistication of his conceptualization of robots. His work is dated in many ways, but he got many basic things right that more modern storytellers still get annoyingly wrong.

Besides the three laws (plus a zeroth law added in later books), which are genuinely interesting both as narrative devices and robotics thought experiments, he also offered one of the better justifications for pursuing humanoid robotics at all — to both adapt to existing human built environments, and to explore space as high-fidelity human proxies and prepare it for human habitation.

Not only is this a better reason to make humanoid robots than mere narcissism, it is actually a pretty good reason in general. There’s other reasons too of course. Some are already being explored: companionship, sex, and so on. But Asimov’s reason is better than most that are routinely discussed, and has only gotten better.

A robot that has roughly the body size and shape (and strength and speed) as a human will likely solve problems in ways that humans can imitate, making humans and robots interchangeable in useful ways.

Body morphology, properly understood, induces a kind of language of knowing with which you comprehend your environment.

A creature that uses color stereo vision and opposable thumbs to explore and manipulate an environment speaks the same “language of manipulation” as another such creature, and both are different from one that uses wings, beak and claws, or one that uses a strong sense of smell, four legs, and strong jaws.

More subtly, the medium being the message, function following form, and so on, humanoid robots will likely see the world in similar ways to humans, and develop similar emergent understandings of it.

For example, we all unconsciously see the world of objects in terms of “handles” that we can use to lift them. That’s an affordance of the world as viewed from the perspective of a body with a hand (do dogs see “bitables”?). When you have a hammer in your hand, everything looks like a nail. But when you have a hand, everything looks like it has a handle. A humanoid robot equipped with a deep-learning computer is likely to discover and name the concept of a handle, and be able to communicate with humans in terms of words that translate well to human language.

With the rise of deep learning, this justification has gotten way stronger. A robot that looks like a human has a vast store of data to learn from. It can learn a “language model” of primate kinesiology from the zillions of hours of footage of humans and apes moving we already have available. As VR and AR capabilities come about, and human movement is captured in 3d via motion capture, the training data will get even better. The argument generalizes to any kind of animal for which we have, or can cheaply generate, extensive movement data.

That actually provides a natural justification for Asimov’s Three Laws of Robotics. The specifics of the laws don’t matter, but it is interesting to speculate that perhaps if function follows form, then behavior follows function, and values follow behavior as well. After all, many human moral ideas are couched in body-allegory terms: turn the other cheek, an eye for an eye, left-handed compliment.

There is a larger point here about embodiment that I’ll get to.

A humanoid robot might naturally develop basic operating values that are in harmony with other humanoids, a kind of deep empathy based on living in a similar bodily configuration space and seeing the world through an internal, emergent language close to that of humans.

Would a robot in the form factor of say a vast swarm of gray goo, or the shape-shifting metal in Terminator 2, even see see the world in a way that allow for the three laws to make sense? Would it ever learn the concept of a “human” to “not harm”?

Asimov for whatever reason ended up building a world that was nearly exclusively based on humanoid robots (though some early stories feature robots in other form factors such as cars and farm equipment), but that’s not necessary of course. There’s no reason we can’t envision and try to build all sorts of robots.

Perhaps the more humanoid ones will be capable of being governed by something like the Three Laws, while the weirder ones will require other kinds of governance mechanisms.

Robots as Artificial BiologyToo many people fail to see the huge design space between uninspired poles of “a dishwasher is a robot” and “humanoid imagined by a narcissistic anthropocentric humanist.”

The trick to robotics is to be loosely inspired by biology without being constrained/limited by it, while also being open to non-biological sources of inspiration of the right kind.

Though anthropocentric conceits and narcissistic objectification are poor reasons to pursue any sort of robotics (and make for bad movies), there is in my opinion, a valuable and generative, rather than constraining biological aspect to robots. One that goes beyond the basic (and good) Asimovian justifications.

Elsewhere in the twitter thread linked earlier, elsewhere I made up a definition:

A robot is a sufficiently complex, loosely biomorphic machine with a domain-adapted universal computing capability.

It’s a nebulous category, but it captures what’s interesting about the design direction represented by biology, and its tradeoffs. For eg, how to solve physical problems without significantly shaping or specializing the environment to suit the machine. Simple example: a hand can twist-and-turn a wide range of shapes, but in a torque-limited way. A fixed-size spanner can only handle a single size of nut, but apply a great deal more torque. An environment where the spanner is truly useful needs to have nuts of the right size in it, but almost any environment is one where a hand’s twisting-and-turning ability is useful.

This gets at the key difference between robots and traditional factory automation.

A high-end CNC milling machine is vastly more complex than a low-end robot, and will likely have a more powerful computer and richer feedback loops. But it needs a highly controlled and closed factory environment, has no autonomy, and a narrow, fragile intelligence. It is useful, but not very biomorphic.

Biomorphic is a compact way of referencing a particular part of design space where you make minimal assumptions about the environment, which drives the design of the machine itself towards generality and autonomy.

But you only need to be loosely biomorphic since you needn’t imitate a particular organism literally. Just seek strategies inspired by biology. But don’t literally design steel parts to be limited to stress levels that can be supported by bone.

Unlike a typical machine — whether you’re thinking of a dishwasher, or a CNC milling machine in a factory — a robot is in principle designed to inhabit a wilder, less scripted environment. This naturally implies more generalized capabilities and higher autonomy. Since you can’t predict or control all the environmental conditions the robot might encounter, you have to design it to be more general purpose and autonomous than an ordinary machine.

So high autonomy and general capabilities in unscripted and open (but not necessarily natural) environments is another way to get at the essence of robots.

This is my “better reason” for biomorphic (including anthropomorphic) design. Biology supplies our main class of reference designs that work in wild, unscripted environments with a lot of unpredictability and ambiguity, and enforce minimum levels of autonomy. We could do worse than to start off where biology has landed after millennia.

We actually have very few mechanisms in the history of mechanical engineering that can compete with biological evolved ones in terms of their ability to support general and autonomous behaviors.

One of the few is, you guessed it, the wheel.

Though true wheels are not known in biology (except for some dubious molecular mechanisms), and are not in principle impossible to evolve (a plot device in Philip Pullman’s Dark Materials trilogy), it is fair to say that wheels are definitely artificial.

That does not mean they don’t harmonize well with biology. A good example of wheels and biological design elements coming together is a Mars rover. Though the mobility of currently operating rovers is based on wheels rather than limbs, they use a special kind of chassis called a rocker-bogie that works more like a hip joint than a car suspension3 and is surprisingly versatile and limbed-chassis-like in its capabilities.

Rovers also illustrate autonomy constraints well. Though presently operating rovers are micromanaged to death, to the point that they’re barely robots, the hard constraint of the speed-of-light roundtrip time to Mars imposes a minimum floor on the level of autonomy you need. The older Curiosity rover moved extremely slowly in part to allow for round-trip human-in-the-loop remote control. The newer Perseverance rover moves significantly faster, and as a result has more sophisticated autonomy. Missions that explore more distant parts of the solar system are necessarily more autonomous because the roundtrip time keeps going up.

Embodiment and AII want to close with a brief note on a point I won’t argue in too much detail at present: robotics is harder than AI because you’re aiming at general intelligence contained in a specific body that must live in an open world. Computing has universal Turing machines, but mechanical engineering has no such thing as a “universal” mechanism4 that can do everything. You have to commit to a physical form factor when designing a robot, and a broad, open class of environments.

Robotics is not just slightly harder than AI, and definitely not a matter of a few afterthought physical design elements tacked on to a disembodied AI. It is like a couple of orders of magnitude harder. So much harder in fact that focusing on the disembodied aspects of intelligence is almost like working on the trivial bits. You could say AI is the spherical cow problem of robotics.

Robotics proper often gets uninterestingly sucked into either AI or automation because people underestimate the significance of embodiment. I think it's actually richer and more interesting than either. Robotics is open-world, situated, specifically embodied general intelligence.

Ordinary AI has a disembodiment degeneracy, automation has a designed-environment (or closed-world) degeneracy.

In AI you enjoy the advantages and simplifications of not having a body. In automation you enjoy the advantages and simplifications of being able to design and close off the environment to overcome the limitations of the machine.

A superficial view would conclude that being constrained to a specific "body" makes intelligence weaker. Actually it makes it stronger. It takes a smarter AI to live in a particular body than in no-body, while remaining a general intelligence.

Incarnations are smarter than gods. Phenomena eat noumena for lunch.

This isn’t about atoms versus bits or even about “messy” physical phenomena like friction, stiff wires, leaking lubricants, and so on, though those do need handling in ways today’s AI is wildly incapable of doing. That stuff will gradually get solved.

It isn’t even about the difficulty of hardware engineering over software, which is something of a fake distinction to begin with. Yes, there are important hardware problems in say hydraulics and battery management and so on, but that’s not what’s hard about robotics. That’s merely the schleppy part. And you’ll probably mostly solve them with code anyway.

The hard part of robotics is the simple fact of embodiment itself. Being in a body rather than being a disembodied intelligence in the cloud means you are in a closed loop relation with the open world through sensors and actuators, and have to fundamentally live in the world of behaving rather than knowing. And you have to do so within the limitations of a specific body. While living with the consequences that body creates for itself through its actions.

Being a general intelligence is easy, it’s being a general intelligence in a specific body that’s hard.

The thinking is still done by computers, and the software is still going to take 10x-100x as much time to design as the hardware (a heuristic commonly used in estimating effort for robotics projects), but what makes it a much harder kind of software to write is that it is software that must live in a particular body. The generalizable bits (such as are accommodated in things like ROS) are fairly limited.

I’ll have more to say about robotics in the future, especially as my own experiments progress, but for now I’ll leave you with this thought.

For a variety of reasons, we’re on the cusp of a golden age of real, honest-to-Asimovness robotics, so it’s good to think hard about what one actually is. And if you go around thinking it’s just a fancy dishwasher, thermostat, kind of machine tool, or worst of all, a jumped up mannequin-sex-doll that might “learn to love,” you’re going to go wrong navigating this robotic new world.

1Though of course there aren’t many robots in the Foundation series, except for one really important one.

2Futurama is a show that imagines a robotic future in surprisingly interesting ways despite being mostly satirical. The robots are not sad excuses for human psychological projections. They are often mechanically interesting, and behave in ways shaped by their mechanical interestingness and have both person-like and object-like traits. Bender himself has a design that sustains all sorts of gags that are interesting from a robotics point of view. For example, he is self-re-assembling when torn apart. He is gyroscopically stabilized. He is a popcorn machine. His hands work as a variety of end-effectors.

3I can say this with some authority because I’m building a rocker-bogie chassis for my model rover. The hip-like aspect is due to a differential bar that connects the left and right sides of the chassis, which shifts weight around similarly to how you shift your weight around as you walk. The 3 wheels on each side are also connected in the eponymous “rocker bogie” mechanism that allows them to rise and fall relatively independently, without need for a spring-based suspension. The result is a surprisingly organic looking six-wheeled “gait” that is eerily close to six-legged crawling. Mars rovers kinda clamber around on wheels rather than roll around.

4The only thing that comes close is various proposals for self-replicating robots that are capable of evolving like organisms. These are “universal constructors” that are technically the same thing as universal Turing machines, but for physical things. But nobody has yet managed to actually build usable general universal constructor schemes.

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A few weeks ago, I was invited by the folks at the Strelka Institute to do a guest lecture based on my May 11 newsletter Superhistory, Not Superintelligence, for their Terraforming summer program. The full thing is rather long, and the first 40 minutes is just me explaining where I’m coming at the topic from, intellectually, and doing an overview of th…

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I want to introduce a lighthouse concept that I’ve been noodling on for several years now, which I finally turned into an acronym that I can easily reference: the REALIST stack. A lot of my writing in recent years has been subconsciously shaped by the idea of this stack in the back of my head. A lot of my consulting work since 2016 or so has also been with companies that work on this stack, one way or another.

The Realist StackIt’s a stack that’s been maturing all around us for a few years, driven by the twin forces of software eating the world, and the global energy transition.

The idea of thinking of engineering as happening on multi-layered technology “stacks” came from software, but the REALIST stack applies to all engineering. And increasingly, all engineering should happen on the REALIST stack to the extent possible. If you can build something on the REALIST stack, you probably shouldn’t build it any other way.

Rather serendipitously for a backronym, the letter order roughly follows the actual order of abstraction. Even better, the bottom four layers, which deal with moving atoms around, spell REAL. Here is the breakdown:

1. R: At the lower, most embodied end, renewable energy is the foundational design driver in engineering today, whether you’re building rockets or data centers. As an engineer, if you aren’t thinking about how your thing can be carbon-neutral or even carbon-negative over its operational lifecycle from the first paper-napkin concept sketch, you aren’t doing your job right.
2. E: The more you electrify everything, whether it is drivetrains, door locks, or heating/cooling, the easier it becomes to manage, optimize, and improve with software. While electricity is not always the most efficient energy currency in a device (for example, direct solar desalination with a heat exchanger can be more efficient than electricity-based methods), it’s the best default choice today. Electrify everything unless you have a really good reason not to.
3. A: Additive-first means you try and design physical things strategically around 3d printing capabilities, which has interesting benefits in terms of decentralization, repairability, modularity, user-empowerment, waste reduction, and so on. 3d printing has improved a lot since the early days, and it’s increasingly possible to go beyond plastics to metals and other materials. But the goal is not 100% 3d printability as portrayed in Diamond Age, with pipes delivering printing feedstock. That would be highly inefficient, since so many process and manufacturing industries enjoy economies of scale not just in dollar terms, but energy and material usage terms. The goal is leveraged use of additive manufacturing in the right places to reshape the dollar and carbon economics of things.
4. L: Lithium-based battery chemistries aren’t the only ones out there of course, and other exotics, as well as non-battery modes of energy storage, are constantly being explored. But to a first-order approximation, both the software eating the world and energy transition revolutions are being powered by lithium batteries. Lithium is to 2021 what silicon was to 1961.
5. I: The lowest software layer is the internet of things (IoT). It’s been the butt of jokes for a decade now, being alternately dismissed as either vaporware or the “internet of shit,” but it’s very real now. There’s an order of magnitude more embedded computers in the world than any other kind, and networking them all is turning into a very powerful thing to pursue. Though there’s all sorts of risks and problems, ranging from vulnerability to ransomware to potential for weird kinds of cascade failures, when it works, the IoT really radically alters our environment.
6. S: Probably the most exciting layer today is the software-defined middleware layer, which is the backend, industrial counterpart of the phone-applifcation of everything for consumer economy. I think of it as the lowest level at which software can eat a human or hardware function. Below that, physics forces you to be primarily atom-based. Software-defined networking, software-defined radio, synthetic-aperture telescopy, are all examples. So is every sort of enterprise middleware. This is where the software and energy revolutions meet. When software eats something in this layer, you get dematerialization benefits on the energy/carbon front, and programmability and flexibility on the software end.
7. And at the most abstract, disembodied end, we have the latest tensor-based computing driving machine learning. Yes, it’s AI, but increasingly I find “intelligence” to be a highly restrictive and anthropocentric mental model of what it is. Tensor-based could alternately be called time-based, if you think of AI as AT (Artificial Time; see my May 11 post, Superhistory, Not Superintelligence).

The REALIST stack, I believe, is taking over all technology. The rates depends on the churn rate of the underlying materialities. So the automotive sector is going REALIST over about 20 years, while housing will probably take like 50 or 60.

The REALIST stack is how software is eating the hardware-based world of things, and preparing it for existence on a terra being terraformed by energy and material transitions.

How should you think about this stack? For starters, how tall is it, and how does it compare to other similarly expressive stacks? Like say the fossil-fuel-based stack or pre-modern stack?

Since the number of layers in a stack is a function of how finely you draw distinctions, it’s hard to compare stacks. Two crude measures, however, help get a sense of any stack.

The first measure is the absolute span of a stack. In this case, the REALIST stack touches 90% of the range of abstractions and phenomenology you might have to consider in engineering a typical modern system. So for example, even though the OSI stack of computer networking also has 7 layers, it fits entirely within 2 of the layers of the REALIST stack, so it has a much smaller span. The pre-modern stack of carpentry and steel-smithy would also have a much smaller span.

The other measure is the relation of the stack height to the capability range of a single engineer. A friend of mine, Keith Adams, has a helpful rule of thumb: the typical range of a good engineer is 3 layers of a stack: a home layer, plus pinch-hitting ability one layer above and one layer below. So a systems programmer who primarily works with C and low-level representations might also have some comfort with processor architecture “below,” and with compiler design “above.” So a good “full-stack” professional is a human stack-height measuring stick of length 3.

This means, whether your logical stack diagram has 3, 4, 7, or 70 layers, a normalized measure (kinda like 20-foot equivalents, or TEUs in container shipping) is how many specialized engineers stacked on each other in a sort of totem pole can “cover” it with at least industry-standard competence.

In my experience, the REALIST stack is approximately 3-4 engineers high, assuming you buy rather than build as much as possible. A single very talented engineer can sort of build very limited and janky prototype systems that are full-stack, but unlike in software, there is no such thing as a true full-stack REALIST engineer even at startup-scale. The span is too big. Some talented people might be able to cover it in a shallow, architectural way, in broad strokes, but actually building production systems takes a 4-high engineer totem pole.

A robot is a good example of a full-stack product on the REALIST stack. A nominal robotics team (for a real, production robot, not a hobbyist one) might include:

1. A mechanical engineer whose home level is in the physical design, dynamics, and control/stability (this is my nominal home zone according to my resume).
2. An electrical engineer whose home level is around circuit boards, wire harnesses, power management, and networking hardware design.
3. A systems software engineer working everything from the IoT level to the software-defined middleware level of low-level libraries.
4. An application engineer working roughly between the operating system and AI levels.

I’m going to both write more about the REALIST stack in the future, and reference it in other things I write on other topics, but here I just wanted to introduce the concept and workshop it a bit with you guys, since I know many of you are either engineers or otherwise in the tech or tech-curious world.

So what do you think?

And both to show off a bit, and to claim skin in the full-stack REALIST game, here’s a picture of the snazzy full-stack REALIST workshop I’ve been slowly building over the last year, mainly around the needs of a rover I’m designing (the little red 3d-printed bits on the table, and the drawing taped to the wall). Five of the seven layers are represented here, see if you can spot them.

The only things it doesn’t have yet are the tensor-based computing layer, and something to do with renewable energy, but I’m working on both. This time next year, there will hopefully be an AI-capable computer, and a solar-panel based project featured in this scene. It’s cost me a few thousand dollars so far to assemble this lab, but more importantly, it’s taken about a year of time and a LOT of learning about all sorts of things.

Just the shopping alone has been a huge education in how the REALIST stack and its supply chains work, without even getting to the projects I’m trying to do. To keep myself from simply turning this into a sort of techno-shopaholism hoarding habit, I’ve also been making myself acquire at least rudimentary hello-world skills with each bit of technology before adding the next cool gadget or consumable to the workshop. So it’s been slow, but rewarding going.

You’ll notice, by the way, that the scene also features a telescope, a microscope and a pendulum clock (the wooden one on the wall, which I made from a kit). Those were the three devices that kicked off the scientific revolution in the 17th century. It was the MTC stack. The microscope, telescope, clock stack. What every cultured full-stack gentle-scientist had to master in 1687. Perhaps with the help of a butler named Alfred or Jarvis. Once I acquire a mansion where this mad-scientist lab/workshop can grow, getting such a butler is next on my list.

It’s partly a bit of a LARP, but also a sorta-serious attempt to put myself in that early-scientific-revolution headspace. In a way, that was the last time true full-stack engineer-scientists, covering the full technological range of civilization, existed.

Early scientists tended to transcend modern distinctions like theorist versus experimentalist, academic versus practitioner, and instrument-maker vs. instrument user.

I think it’s time for everybody with the means, motive, and opportunity to attempt that sort of thing again. Every engineer at least, should have an appreciative acquaintance and amateur competence across the whole REALIST stack.

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In this episode of Breaking Smart podcast, I want to explore what it means to say that Covid has accelerated everything. If so, it means we’ve done some time travel relative the old timeline. As the cryogenic lab tech said to Philip J. Fry on Futurama, when he landed in the year 2999, welcome to the world of tomorrow!

1/ Let’s set the stage a bit. We’re now in the early days of post-Covid for at least some people, in some parts of the world. We don’t yet know how costly the endemic management problem will be, in terms of treatment, vaccination, fatality prevention, and surveillance, but it feels like we have one foot out of the tunnel now.

2/ I don’t know about you, but I personally feel a bit like Fry in the pilot of Futurama. Right after he is greeted with “Welcome to the world of tomorrow!” he is assessed to determine what sort of “career chip” should be implanted in him, because in the bureaucratic future, everybody has a career determined for them.

3/ The joke is, he is assessed to be best fit to the job of “delivery boy,” the same job he hated in 1999, so he runs away from the career assessment officer, Leela. As it happens, by the end of the pilot, he ends up a delivery boy anyway, but with an illegal career chip, but is happy about it because he gets to work on a spaceship and has new friends.

4/ I hope to get my vaccine within the next few weeks, and the idea reminds me of the idea of Futurama career chip, including vaccine hesitancy. In many ways, this is not far wrong, what with all the talk of vaccine passports, green zones, and so on your vaccine status might shape your career. Unlike Fry, I don’t want to run from the career chip. I don’t have career chip hesitancy. I’m kinda looking forward to reinventing my life in ways that I didn’t expect to till 2031.

5/ Even the idea of a very bureaucratic future is not wrong. Given the amount of fiscal stimulus, the effects of new geopolitics with China, and vaccine nationalism, the role of states everywhere has become radically stronger. Like it or not, the world of tomorrow has governments everywhere getting more into your business, not less. Not least because governments effectively own a lot of assets through the loans they have provided for bailouts and stimulus.

6/ So the vaccine can be considered philosophically like a career chip for a new life in a future we’ve time traveled to, and are still getting used to. One of the signs for me has been that my Twitter feed, which is my main sense-making media feed these days, feels mis-tuned, and I’m re-tuning it. It feels like wearing glasses with the wrong prescription. Everything is a little blurry and distorted.

7/ Okay, so we’re in the future, and like Fry and other time travelers, one of our first questions should be, what year is it? Obviously, no provable answer is possible to this question, since we can’t actually run a believable no-Covid simulation. The new timeline might not even be comparable at all to the old one, because qualitatively different sets of things are happening or not happening. Maybe we’ve gone sideways rather than leaped forwards. Some parts of the world have definitely gone backwards.

8/ I do think the idea of an acceleration is well-posed though, and that we do overall have a forward acceleration rather than a sideways or backwards leap. I’m just going to propose 2031 as a strawman answer to what year it is, with the caveat that you shouldn’t anchor on it. The point of pretending we’ve time traveled 10 years in 1 year is to break old habits of thought and reorient. So how do we do that?

9/ One way to think of this is as a weighted average of trends by acceleration. So for example, if vaccines jumped ahead 20 years, but other kinds of medicine stayed the same, and public health for pandemics is 50% of all healthcare by cost, then you could say the average leap ahead is 10 years.

10/ But this is kinda silly, like applying a uniform rate of inflation even though your consumption basket may be very different from the consumer price index. In thinking about inflation, you should probably think about inflation in the specific range of goods you consume. So by analogy, in thinking about acceleration, you should think about the specific range of trends you’re riding, or not. A personalized basket of accelerations.

11/ For example, let’s say work-from-home would have progressed at a particular rate, such that we are now where we would have been in 2040 otherwise, which I think is true. What does that mean? Well for me, it doesn’t mean much, since I’ve been working from home since 2009. The big difference for me is actually being unable to work from Starbucks, and whatever acceleration is going to hit cafe culture.

12/ You can measure the importance of a particular acceleration in several ways. Like for WFH, you could talk about commute miles saved, C02 emissions from commute avoided, webcam sales, GDP of commercial vs. home office furniture, traffic at business district vs. entertainment district restaurants, etc. All these would be good macro measures of acceleration.

13/ At a personal level, you could simply think in terms of well-being improvements, and more time created due to not having to commute. The personal acceleration in this case could be an earlier arrival of lifestyle elements that you only expected with retirement, if at all.

14/ Some things are tricky when you talk about accelerations. For example, with a slower rate of shift to WFM, real estate would have evolved in sync, so commercial real estate would have shrunk, while residential square footage would have increased. But this takes time. Time we didn’t spend. So what are the implications of that?

15/ I recall reading somewhere that housing stock takes 70 years to churn, so if we logged 20 years worth of trend time in 1 year, then we have a churn-demand shock worth 1/3 of the total housing stock. Or to put it another way, 1/3 of real estate, both commercial and residential, is now either under distress from underuse or overuse.

16/ By a similar logic, car fleets take about 10-20 years to churn through, depending on the country’s laws about old cars. Public transit projects probably take about the same on average. WFH changed a lot of demand patterns overnight, so now you have idle cars and empty trains, and people rethinking their car situations earlier.

17/ When you look at all these individual accelerations, one thing that you might notice is that we’re talking about something very different from the acceleration due to Moore’s Law, the sort of everything-is-faster megatrend that gave rise to philosophies like accelerationism.

18/ This isn’t that. To the extent that was true, it is still going on. To the extent that was a shaky hypothesis, it still is. What we’re talking about is a different kind of acceleration from a one-time drastic shift, caused by a pandemic shocking the system into a new equilibrium that looks like the future we used to extrapolate.

19/ Let’s talk a bit more about how weird it is that we’ve leaped forward rather than sideways or backwards. A different kind of shock could have easily shifted us sideways into a kind of future we weren’t thinking about, a parallel timeline. Or it could have thrown us back into a more primitive past. But this one uniquely seems to have kicked us forward. Yes we’ve shifted sideways a little bit, and backwards a little bit, but the vector sum of all the accelerations seems to have been a global fast-forwarding effect.

20/ I think this is for two reasons. One, pandemics aren’t black swans. They’re not unknown unknowns. Not only do we have recorded histories of several examples to learn from, we’ve specifically had experts predicting this kind since SARS in 2003, and have even made what are in hindsight fairly realistic movies about it. Global civilization has a lot of cultural memory of pandemics, and ways of responding without being knocked off its historical course. This would not have been true of say an asteroid hitting earth or a major nuclear war.

21/ Second, many of the trends that have accelerated, such as WFH and climate action, were dealing with inertia effects. The pandemic did the equivalent of knocking out some inertia, causing a sudden, jerky leap forward. Kinda like dumping some cargo off an overloaded truck might cause it to leap forward.

22/ I think the right unit of analysis for thinking about acceleration is categories that have necessary, flexible roles in the world, with relatively linear evolution paths, no clear substitutes, and a lot of baggage available to shed. For example, cars, homes and countries. All are entities with steady rates of evolution, and loads of baggage.

23/ For example, cars were already undergoing dual trends of electrification and computerization, and on pre-pandemic timelines automakers were planning to fully transition to EVs in a decade or so. That’s probably accelerated because I suspect people will buy cars sooner now.

24/ EVs should be cheaper in the long run than IC engine cars, but in the short run, cars are actually getting way more expensive due to more compute elements. For example, cheap bumpers are now expensive to replace after an accident because they hold cameras and radars.

25/ So even though we’re probably in the same future qualitatively — more computerization and electrification is still going to happen, we’ve probably accelerated by at least a few years. Some things though, have shifted sideways. The idea of cars as a cloud-like on-demand service might take a hit, and more people might want to own cars. We’ve learned the importance of controlling some of your own physical assets and environments. Those who relied purely on rideshare or transit probably faced more risk last year.

26/ You can do a similar analysis for other stable units of our world. Countries are interesting. I’d say they were weakening before Covid, but have been made stronger. Vaccine development, public health management, and supply chain geopolitics have all strengthened the role of country-sized units.

27/ When you talk of more diffuse trends, without clear units, things get murkier. Two such examples are climate change and extremist politics.

28/ I think for a lot of people, Covid was a sort of prequel to how climate action might unfold. It doesn’t matter whether you believe in it or not, just as it didn’t matter whether or not you believed in Covid or vaccines. Enough people believe that there will be drastic responses. And I think Covid gave people sitting on a lot of capital a reason to start putting it into climate response investments. One example is the rise in SPACs. I suspect that’s going to be a lot more sideways than an acceleration though.

29/ It’s hard to judge what’s happened to politics, but I think it’s fair to say that both far left and far right currents got to crucial tests of their coherence and capacity to govern far earlier than they otherwise would have. Ideas that were outside the Overton window, like UBI got an early test through things like stimulus checks. I suspect the culture war overall accelerated by about 4-5 years at least. We’re already at the point in the conflict that might have otherwise arrived around 2025.

30/ To get back to the personal question, and the idea of a personal basket of accelerations leading to a personal estimate of how far in the future you’ve jumped, I think it’s a useful exercise to think about that, and also think about the question of what kind of new career chip you should install in your head, suited to the future you’re in, based on how far into the future you’ve leaped.

31/ How much of a jump have you made? I think looking at your typical sources of information and sensing how out-of-tune they feel, like with Twitter in my case, is a good starting point. The more weird your old pre-Covid news feed feels, the farther into the future you’ve leaped.

32/ Then you can think about the specific persistent units in your life, and how they might change or should change. In my case, I’m already thinking of living situations and travel plans in ways that I didn’t expect to be until 2031. I moved to a bigger apartment last year, and will probably look for a bigger place in a smaller city in the next few years. My domestic travel is probably going to go sharply down but my international travel and living might go up.

33/ I think the world of work has changed too. There is now more fat in supply chains, more resilience and sustainability in both production and climate senses. There is more national structure to industries. The story of vaccine nationalism might repeat in sectors like semiconductors and other critical and strategic ones. As a consultant, I’m rethinking how I approach my work.

34/ On this front, I am trying out patterns of work that I didn’t expect to see for years, such as more collaboration with other indies and free agents. I think relatively shallow managerial cultures are on their way out, and a more depth-oriented culture is on the rise, where people are expected to understand narrower parts of the tech stack, geographies, and markets more deeply. It’s an overall shift to a more vertical-grained global industrial structure, at the sectoral and national level. Again, this is what I expected with software eating the world, but not for another decade.

35/ Overall, I find this frame of a net acceleration, personalized to my context, very useful. The thought experiment of living in 2031, and installing a new career chip in my head, is very useful, and I recommend you try it.

So that’s it for this week, I’ll be back again next week with another essay for subscribers.

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In today’s episode of the Breaking Smart podcast, I want to discuss a concept I call demiurgical businesses that I think goes beyond the 3 kinds you may be familiar with: lifestyle, customer-driven, and product-driven.

1/ In the discourse around tech, both on the tech side and the techlash side, you’ll often hear the term “real problems” which should make you wonder what “unreal” problems are.

2/ When the term is used, it’s usually used by socially conscious people, whether builders or critics, who use “real problems” as the notional antithesis of whatever is behind what they see as bad, misplaced entrepreneurial priorities. Today it is NFTs, ten years ago it was apps.

3/ The term “real problem” is misleading because even though people who use the term will offer some cliched examples like climate tech or world hunger, the point of the term is to point at the thing being criticized, not the thing being aspired to.

4/ But if you take real and unreal seriously, you actually get an actually interesting train of thought. Usually, the thing being criticized is an example of a thing that doesn’t seem to solve any problem, whether real or made up.

5/ If you ask, “what problem does it solve?” about the thing being criticized, you’ll find that the people building it can’t even supply a bad, disingenuous, or morally indefensible answer.

6/ For example, if I answer the question, “what real problem does space exploration solve?” with “it helps discover better cancer drugs through zero-gravity biochemistry experiments,” you know that’s b**t. It is obviously a rationalization. It’s both a bad answer and a disingenuous one. If you claim it is a real answer, you’re either stupid or lying. I discussed the real answer for that a couple of weeks ago.

7/ For a morally indefensible example, let’s say I build luxury yachts with built-in torture chambers. The answer to “what real problem does it solve” would be, “the problem dictators have of partying and torturing their enemies at the same time.” Now that’s a bad and morally indefensible answer, but it is actually a real answer that points to a real problem that a real person has, just not a very pleasant kind of person. I’d want such a yacht if I was an evil dictator.

8/ If you think about it, there is no such thing as an unreal problem. If it can be posed as a problem at all, it’s real. You just may not share the motives or values of the person who wants to solve it.

9/ Or to put it more simply, the idea of a “real problem” is actually an expression of identity. Evangelizing “real problems” is a way to do identity politics by indirect means. The problem you choose reveals who you are, what values you prioritize, and what identity you’re attached to.

10/ An easy way to see this is to notice that “real problems” are actually a generalization of “customer-driven.” A real problem points to a real person who already has that problem — namely a customer. Whether it is hungry children or evil dictators.

11/ It might be quite abstract — for example, the “customer” for climate tech is people who believe in climate change and also believe it’s good to ensure the survival of as many humans and animals as possible, and will vote for politicians who will make policies in favor of that. But it’s still a direct equation between being customer-driven and focusing on “real problems.”

12/ I’ll even go further and say that all identities are in fact customer identities. So identity politics is actually customer politics. The only reason to have a stable identity is if you want to acquire something through it, whether it is through participation in family, community, the market, or politics. So things like race and ideology are customer identities just like preferring vanilla over chocolate ice cream is a customer identity. You buy things through those identities, even if it isn’t with money.

13/ If you’re familiar with the idea from Peter Drucker that the purpose of a business is to create a customer, this equivalence between “real problems” and pre-existing identities should be a red-flag for you. It means you’re talking about a world where innovation gets reduced to exceptional customer service through incremental improvements.

14/ So let’s ask again, what is the actual logical opposite of a “real problem?” We’ve already seen that “unreal problem” goes nowhere, and that “real” is just indirectly about affirming an identity, possibly while criticizing other identities.

15/ Here is my proposal: the opposite of a “solution to a real problem” — real to anybody, whether you think they are good or evil — is a “reality transformer.” Instead of solving a problem within reality as currently defined, in relation to a particular identity-based point of view, you make something that creates a new reality, forcing people to create new identities in relation to it.

16/ Twitter is my favorite example of a reality transformation business, or what I call a demiurgical business. Nobody asked for it, and even 14 years later it is unclear what problem it solves, and for whom. It was built because it was possible to build, and it ended up transforming reality for everybody. Now people have “real problems” within the expanded Twitter reality, such as being able to edit tweets.

17/ I used to think of this point of view as simply being “product-driven” instead of “customer driven” — see the potential within a new technological capability and work to realize it in a way that creates a new kind of customer, ie. catalyzes new identities in the market. I wrote a popular article about this in 2014, called Product-Driven vs. Customer-Driven, but that article now feels incomplete to me.

18/ The thing is, though product-driven businesses are in general more powerful than customer-driven things, most product-driven businesses are also often degenerate customer-driven things in disguise, where the customer is the entrepreneur. So you could call it entrepreneur-driven.

19/ An entrepreneur-driven business solves the problem of affirming the hardened, often narcissistic identity of the entrepreneur as the only real customer. They may want to put a dent in the universe, but typically they don’t want to put even a scratch on themselves. They want to transform without being transformed. Everything else is a side-effect.

20/ The side-effect is usually what we call product-driven: it creates a customer rather than serving an existing market. But the most important customer, the entrepreneur, remains unchanged.

21/ In my experience, I’d say about 90% of entrepreneurs are customer-driven. They build boring businesses that solve an existing problem for an existing customer, and make themselves and some others rich. About 9% are entrepreneur-driven: they work to validate the identity of the entrepreneur, and might distort reality for everybody else except the entrepreneur, via a reality-distortion product. That leaves the last 1%.

22/ This last 1% is what I think of as the truly powerful things. They transform reality for everybody, including for the person making it, forcing everybody to forge new identities. They transcend the product-driven vs. customer-driven dichotomy. Twitter is an example of this kind of business. It may have under-performed as a business compared to entrepreneur-driven businesses like Apple, Amazon, and Facebook, but it transformed reality more powerfully.

23/ It feels wrong to call the creators of such products mere entrepreneurs, since they are typically not about merely building a successful business or even validating their own identity. They act in the role of what the Greeks and gnostics called a demiurge. Not quite a god, and not quite human. Agents of transformation who are themselves part of the transformation process and subject to it.

24/ Demiurgical businesses tend to be far more powerful than either customer-driven or product-driven businesses, even when they make less money. The limiting factor for customer driven businesses is existing identities in the market that can be served. The limiting factor for product-driven businesses is the existing identity of the entrepreneur that must be affirmed. But a demiurgical business essentially has no limits besides the laws of nature.

25/ Demiurgical businesses often convey the impression of having escaped the control of their creators. I once described Twitter as occupying a business space that is too big to nail, a play on “too big to fail.” Just too large for any one business to fully occupy, and beyond the ability of any human CEO to govern. It’s not that the founders of Twitter were significantly less capable than those of Amazon, Apple, Facebook, or Google. It is that Twitter opened up a transformed reality that was too big to nail.

26/ In my experience, it’s definitely not the case that people who build demiurgical businesses are somehow more evolved or enlightened than regular entrepreneurs. Often, they are just as narcissistic and driven to validate their own fixed identity as product or customer-driven entrepreneurs. What happens is something like a natural accident. They stumble on an idea that is too powerful to be limited by their own identity. They have no choice in the matter. The thing they unleash transforms them, whether they want be transformed or not.

27/ You can reduce this whole idea to a 2x2: if the producer, or entrepreneur changes but the consumer doesn’t, you essentially have a lifestyle business. If neither changes, you have a commodity customer-driven business. If the consumer is transformed, but the producer is not, you have a product-driven business. If both are transformed, you have a demiurgical business.

28/ Personally, I like demiurgical businesses the most. They have the most powerful effects on our world, and are the only kind of business that can break humanity out of seemingly hopeless equilibriums, where everybody has locked-in identities, and is fixated on “real problems” instead of “transforming reality.”

29/ In fact, flipping things around, the things usually called “real problems” are in fact the most “unreal” of all. The fact that they exist as persistent, named conditions that seem to defy solution, and demand really virtuous behavior of humans suggests that there is a misframing going on somewhere. A problem that requires humans to be saints to drive solutions is not a problem. It is virtue blindness.

30/ Real problems are problems that real humans, not saintly ones, can solve. If existence is threatened by a huge problem that require us all to transform into saints to solve, then the problem is actually unreal since it involves imaginary, fictional human beings. The actual response to such problems is to transform reality in unpredictable ways through demiurgical businesses. That’s what can get the situations unstuck. Maybe in the transformed reality, the problem is actually possible for real humans to solve.

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Space exploration has an unusual side effect: giving us a sense of the value of money on earth.

1/ The Perseverance rover, shown touching down on Mars in the photo below, cost about $2.2 billion to design and build, and about $243 million to launch on an Atlas rocket.

2/ Now that it is on the ground, if all goes well, and it is able to operate, it will cost another $300 million to operate for two years. So that’s at least $2.7 billion overall, or about 54,000 bitcoins. Hopefully more, if the mission gets extended.

3/ For those who don’t track this stuff, it is the fifth Mars rover, not counting the early Viking missions in the 70s which were not rovers. The previous ones were Sojourner, Spirit, Opportunity, and Curiosity.

4/ Perseverance is very much like Curiosity — about the size of an SUV, and powered by an MMRTG — Multi-Mission Radioisotope Thermoelectric Generator. By contrast, Sojourner in 1997 was about the size of a lawnmower, and the MERS were about the size of golfcarts.

5/ Probably the most charismatically interesting thing about Perseverance is that it is carrying a drone helicopter called the Ingenuity, which will be a genuinely fascinating thing if it works. An aircraft on another planet — one with 1/6 the gravity, and about 0.6% the atmospheric density.

6/ So how should you think about the value of the Perseverance mission? Some people who are space-exploration positive are still kinda defensive about such things and try to make up rationalizations like R&D benefits for problems here on earth.

7/ I think this is not even wrong. When someone asks why we spend money on Mars missions when there are starving children on earth, the answer is neither to make up specious theories of how space science can lead to life-saving medicines on earth, nor to walk away saying values are different, but to talk about how money works.

8/ Money is the largest-scale coordination mechanism we have for negotiating differences in values of things, and is what allows us to define what the word “we” means. Its design has to accommodate everything humans might disagree about. Money that cannot value space exploration or art cannot value medicines or food very well either.

9/ So I think the simplest mental model is as a civilizational art project. 2.7 billion is about 0.013% of the GDP of the United States. This is actually pretty cheap by civilizational artwork standards.

10/ For comparison, at the height of the Mughal empire, the Taj Mahal cost about a billion of today’s dollars, and a double digit percentage of the empire’s GDP at the time. Possibly as high as 20-25%. According to some historians, it contributed to the bankruptcy and decline of the empire.

11/ A good question about civilizational art projects is — who is the art project for. Whether you’re talking medieval monuments or Mars rovers, it is easy to figure out who the artwork is by, but it’s not always easy to figure out who it is for.

12/ Pre-modern civilizational art projects were generally monuments to the narcissism of emperors and religious leaders. To get people to accept the fiscal burden to undertake them, you had to make up myths and religions.

13/ There is some of that in modern space programs. We still quote Kennedy’s speech about getting to the moon. But even the most powerful modern cults of personality, whether you’re talking Kennedy or Trump or Xi Jinpeng, pale in comparison to the cults of old monarchies and religions.

14/ Presidents get to bask in the reflected glory of space programs a little bit, but ultimately, they ultimately get only a small slice of the attention. If you add up all the attention we give to astronauts and Nasa, and the people who work on missions, you still get a big deficit. You’re left asking, who exactly asked for this?

15/ It’s not even national pride really. Space programs in the 1950s were strongly linked to national industrial bases, but today, a modern space program, even the US space program, sources talent, materials and technologies from all over the world.

16/ For example, NASA has been promoting a PR video around the rover showing immigrants from all over the world who have worked on it. And on a material level, the rover uses many components from outside the US. For example, the motors used on the rover are made by a Swiss company called Maxon.

17/ So as an art project, I think it makes most sense to think of space programs as art projects by and for global economic systems. They represent an economic system as an emergent entity admiring itself in the mirror. In the case of NASA missions, it is the Western economy. In the case of China, it is the Chinese economy.

18/ What they do is help calibrate what an economic system is capable of, when stretched to its limits, and how it compares to competing systems. And this, I think, is not just valuable, it is functionally necessary. Any kind of economic system can only work if it constantly tests the limits of its ability to price things against competing systems.

19/ A functional economic system isn’t about judging human choices and preferences, but to price them. Everything from cancer drugs and aid to disaster zones to climate change and space programs to luxury yachts and obscene extravagances by rich celebrities. If humans are capable of it, a real economic system should be able to put a price on it.

20/ One of the reasons cryptocurrencies aren’t taken seriously yet is that the economic system they represent is restricted to a very small set of activities. It hasn’t been calibrated against a large enough scope of activities. The equivalent of space programs for the cryptoeconomy is games on the Ethereum blockchain like CryptoKitties or the recent innovations in NFTs (non-fungible tokens). It has a long way to go.

21/ Many people want to translate their political and ideological interests into economic terms. They want to somehow design an economic system that makes, for example, Mars missions so expensive we don’t do them, and saving lives so cheap, we never fail save them. But this is fundamentally wrong-headed.

22/ If you can’t come to terms with the diversity and variety of things humans want and value, and are willing to work for, you will want to design economic models to coerce them to act differently. This is a version of what statisticians call the bias-variance tradeoff, which I’m using as a metaphor here. The more you try to bias an economic system to do certain things, the more you’ll narrow the overall range of things it can do.

23/ If you think people suck, and try to make an economy that prevents them from sucking, you’ll either oppress people, or create an economy that sucks, or most likely, both. The only way we know to avoid that is to keep recalibrating the scope of the economy — the things it is capable of pricing, at the weirdest extremes, without unraveling entirely.

24/ In other words, if the economy does things you think are horrible, and refuses to do things you think are necessary, you don’t have a problem with the economy. You have a problem with people, and you’re not willing to cop to the desire for coercive control.

25/ Yes the economy has lots of distortions, but it had even worse distortions in the past when it was the plaything of emperors building monuments to their own grief. To make the economy better you have to remove distortions, not add them. And the only way we know to do that is to constantly calibrate by letting it do weird things so it can look at itself in the mirror.

So that’s it for this free podcast edition. I’ll see you next week with another subscriber post.

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In this first 2021 episode of the Breaking Smart podcast, I want to talk about something that’s been on my mind a lot lately that I call anti-network effects.

1/ As I am recording this, governments around the world are working out the logistics problems of distributing billions of vaccine doses. It feels like a symbol of the times we are entering into, times that I think will be defined by anti-network effects.

2/ Vaccinations and mask-wearing are examples of anti-network effects. I’ll define these as effects that can slow down, regulate, arrest, or reverse the operation of network effects, and which might themselves be network effects.

3/ For almost 50 years, since the invention of the PC, the world has been riding one network effect after the other, all of which ride on top of the infrastructural network effect of the internet.

4/ To review, a network effect is when the power of a system grows faster than its size. The original form was called the fax-machine effect. One person with a fax machine is useless. Two people is one connection, three people is 3 potential connections, 4 is 6, and in general n nodes is n(n-1)/2 unique connections. So the power of the network grows as the square of its size.

5/ Network effects in computing infrastructure are deeply connected to Moore’s Law. When computers get cheaper, network nodes get cheaper too, and more things can get attached to computers, and then to each other via the network.

6/ This leads to a double effect. Every 18-36 months, the density of transistors doubles. This makes the cheapest computers based on the cheapest chips much cheaper, and what’s more, this allows whole new classes of even cheaper computer to be invented ever decade or so. So one compounding effect rides another. That’s how we we went from mainframes to Raspberry Pis, and from an internet of 2 computers to an internet of billions.

7/ There’s in fact a third effect. If computers get more powerful at a steady geometric rate, and industrial mass production lag is minimal, then the rate at which the network can grow is itself a function of network size. You don’t have to add 1 node at a time, you can bulk-add n nodes.

8/ Everybody with a home computer already had an internet connection, so with a router, everybody can add a wifi connected device at the same time. With software it’s even easier: everybody with a phone can download an app at nearly the same time, creating near-instantaneous soft networks.

9/ We’ve been riding this triple-punch meta-network effect for nearly 50 years. You’ve got Moore’s law, the basic network effect of devices, and then the network-size-proportionate growth effect. And for people about my age and younger, our entire lives have been spent on this ride. To the point where it is second nature.

10/ This is actually pretty unnatural. Think about the classic brainteaser to teach exponential or geometric thinking. If a lily plant doubles in size every day and on the 30th day covers the whole pond, when did it cover half the pond?

11/ The answer is of course, the day before, on the 29th day, and to people like us, this barely even counts as a brainteaser. In fact, for kids today, I suspect the expected wrong answer, which is the 15th day, will feel unintuitive. They deal with fewer important things that work that way.

12/ There’s a lot more to say about network effects and various formulations like Metcalfe’s Law and Reed’s Law, but we’ve been doing that for my whole life, so enough said. I’ll just add one more point: network effects are pretty dumb. I mean even viruses and lily plants on ponds can embody them.

13/ This is in general not true of anti-network effects. While some anti-network effects are themselves driven by network effects, most work on other principles. So let’s take an inventory.

14/ The first kind of anti-network effect is self-limitation, when a network effect self-neutralizes. For example, once you have had Covid, reinfections are unlikely and you’re immune for a while. This is why you get an S-curve ending in a plateau, though a lot of people have to die along the way.

15/ Then there are anti-network effects that are themselves network effects, but distinct from the original one. Like the idea of wearing a mask spread pretty fast. Faster than the virus itself. The production of masks also spread via network effect. Some people saw others making masks and started imitating their behaviors.

16/ But many important things are not driven by network effects. Like when non-trivial habits have to be adopted. For example, disinfection and washing hands are behaviors that took a really long time to spread, and are still not universal more than a century after germ theory.

17/ Atul Gawande wrote a great essay called Sharing Slow Ideas in the New Yorker in 2013, that talked about innovations that are like this. That don’t spread like wildfire via network effects, but require pretty painful and slow diffusion via deliberate efforts.

18/ The reason slow-spreading non-network effects can sometimes still beat fast-spreading network effects is that they can be governed more intelligently. They are not as dumb as network effects.

19/ For example, it would be nice if we had a really dumb kind of network-effect vaccine that could be transmitted by a cough, via some sort of good virus that can fight the bad virus. Then the two network effects could race each other in a pretty dumb way. Explosion and counter-explosion.

20/ Unfortunately we don’t know how to make that kind of vaccine, that spreads via a network effect. Good things seem to spread slower than bad things in general. Brandolini’s b**t asymmetry principle is an example: it takes an order of magnitude more effort to refute b****t than it takes to produce it.

22/ The kind we do know how to make have to be slowly scaled in production, painfully distributed and administered by a small group of trained people. The only reason it has a chance is that we don’t have to be random.

23/ So that’s why governments are being deliberate and strategic in the order in which they vaccinate people. Of course you can be too careful. New York was criticized for expecting senior citizens to complete a complex form with 50 questions and multiple attachments to get vaccinated, and most efforts have now gone much simpler. Still, unlike the virus spread, it’s not dumb or near-random.

24/ You see the effect in computers too. In cyber-warfare, you don’t attempt to shut down the enemy’s capabilities by knocking out random computers in their network. You target key chokepoint routers or undersea cables. If you are an authoritarian government, you install firewall technology at the network edges. So those are anti-network effects that win by being topologically intelligent.

25/ There is a broader theme here: network effects are dumb, indiscriminate and very, very fast. They rely on abundance and target rich-environments. They think one step ahead in time, and one step around in space. But for those very reasons they are also very fragile. They can run out of raw material and starve. They can run into boundaries. They can even be slowed by simple ideas like six-feet separation.

26/ Anti-network effects sometimes incorporate network effects of their own, but are generally more deliberate, intelligent, and actively governed. They are designed with scarcity in mind and are not so easy to starve out. They have long horizons and think many steps ahead in time, and can be spatially intelligent across entire topologies.

27/ They need all these abilities because otherwise network effects have incredible advantages. Anti-network effects are like the tortoise that can eventually catch up with the hare. But because they lack exponentially increasing impact, they need other features to spread.

28/ Things can get worse though. There are network effects that also benefit from intelligence. Well-designed computer viruses are an example. They don’t spread indiscriminately. They can carry a payload of navigational intelligence in space and time and spread very intelligently. This is why countering clever computer viruses is so hard.

29/ There’s one more important class of anti-network effects we haven’t talked about, namely in information flows. After the January 6th storming of the Capitol, we saw how an anti-network effect operated with kinda stunning speed.

30/ The highlights, as you know, were that Donald Trump was suspended from major social media platforms, and Parler was suddenly cut off at the knees by major infrastructure providers. A good reminder that when the topology of a network effect is fragile — in this case the Trump influence network with Trump himself as a single point of failure — what takes years to build up can take minutes to shut down.

31/ That raises other issues I won’t get into, but here I just want to note that the anti-network effect here — simply cutting off a major source of disinformation and noise — was both easy and instantaneously effective. If you’re on Twitter, you’ve probably noticed how much drama has been cut out overnight.

32/ I want to round out this set of examples with another big important class of network effects and their anti-network effects: markets and regulation. Markets are generally based on network effects. Everything from price information to manufacturing capacity to early adoption tends to have a network effect in it. The economy grows via network effects, which is why critics compare it to cancers and viruses.

33/ Anti-network effects in the economy on the other hand, are slower and more deliberate. Anti-trust mechanisms are like taking out a major node once it reaches a certain scale. Monopolies are a special case. In network theory terms, they are a single node cut point, where for example, removing a producer entirely disconnects demand and supply.

34/ The broader theme I’m getting at here is that after 50 years of riding a big meta-network effect, we are entering an era of anti-network effects. Slower, more deliberate, more intelligent phenomena that achieve effects in very different ways.

35/ This is neither a good or bad thing. It is just part of how the world works. In biology, technology, and economics, there seem to be phases of dumb growth driven by network effects, and phases of intelligent regulation driven by anti-network effects.

36/ In a way this is how intelligence evolves. The human brain is like this too. There is a network effect in a baby’s brain during gestation as neurons get very densely interconnected in a pretty dumb way. Then as the baby learns after birth, the connections start getting cut and the ones left behind embody what we think of as intelligence.

37/ There’s going to be a temptation in the next few decades to come at this ideologically, like regulation and damping of network effects is always bad, and network effects are always good. After all that’s the religion the world has run on for 40 years. I’ve been raised on Reagonomics like most of you. But that’s all it is, a religion.

38/ The smart thing to do is to simply start learning the physics of anti-network effects. It is going to be hard for most of us below 50 since we have no memories of a world that was not being driven hard by network effects, but I think we can learn. At least it will be a fun new kind of thinking to get used to. A world in which the lily plant does NOT go from 50% to 100% on the 29th day.

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Well, I guess 2020 is a wrap. I’m going to do a quick round-up of everything I published on Breaking Smart this year, and try to tease out the larger themes, but obviously no look back at the year can begin without acknowledging the 800lb virus in the room.

To quote J. Peterman on Seinfeld, in the episode where he takes back the reins of his company from Elaine after she mismanages it during his absence, kudos to all of us on a job…done. It’s a reference I’ve used before, in my 2018 annual roundup, but this time, I am really feeling it.

Whatever our personal successes or failures, as a species, the highlight of our collective performance is probably that we made it through the year without either sliding into apocalypse or going insane.

Well, most of us. As of this writing, 1.75m people are dead from Covid, and will not be seeing 2021. Against that backdrop, the best thing I can say about my writing and podcasting is that I continued doing it all, and that you guys continued to pay some attention.

On to the roundup.

It looks like we’re finishing the year pretty strong on this list, with just over 10,000 subscribers, and just over 500 paying subscribers.

I published 10 podcast episodes (free, with accompanying transcripts), 8 one-off essays, and 11 chapters or essays across 3 serialized projects. Let’s take a look.

Podcast Episodes (free)

Beyond Optimism and Pessimism

Defaults and Defaults

How, What, and Where to Build

The Medieval Future of Management

From Story to Setting

Big Moods, Little Moods

The Next Experiments in Elitism

The State of Business Play

Fifth-Generation Management

Involvement Capitalism

One-off Essays (paywalled)

Life Go Brr

Reimagining Publics

Post-Covid Circularity

A Bad Prequel

Tunnelhead

Notes on Textual Capital

In the Wake of the Eighties

Darker Things

The Great Weirding (paywalled)

Into the Weirding: Part 1, Part 2

Control Failure: Part 1, Part 2

After Westphalia (subscribers only)

After Westphalia: Introduction

The Descent of the Public

The Clockless Clock (paywalled)

Chapter 1: Pandemic Time, Pandemic Time -2 (abridged free version in Noema)

Chapter 2: Indoors in Time

Chapter 3: Operating in Time, Operating in Time -2

This would count as a productive year if it were any other year besides 2020, but obviously, against the backdrop of everything going on, it feels marginal at best.

Still, thank you all for reading and listening, and here’s to the light at the end of the tunnel that we will hopefully emerge into sometime next year. I’m not going to wish you Happy Holidays or Happy New Year, since I personally find it kinda unseemly to even attempt to be festive this year, but I do wish you some productive introspection and contemplation, and perhaps a brief personal break from the bleakness.

I’ll see you again in January 2021. Have a good week.

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Welcome back. The Breaking Smart newsletter and podcast is starting up again after a very refreshing 6-week break.

I want to kick off the post-break programming with a podcast on a big question: if we are headed at least partially towards a post-scarcity world, as we seem to be, does it look more like the Star Trek universe, or the universe in Iain M. Banks Culture novels? Both are varieties of something I call involvement capitalism, which I think it’s going to emerge in the next decade one way or the other. The choices we make in the next few years will determine which flavor we end up with.

1/ Over my break, I had a chance to unplug from weekly writing, and reflect on the broader theme of this mailing list, while watching the news. In case you forgot my tagline for breaking smart, this broader theme is serendipity through technology, and in the last few years, that has been a murky theme to think about. Is it the best of times or worst of times? Hard to tell.

2/ I unplugged from writing, but not from media consumption. As you might know I don’t believe in that, especially when historic news is unfolding, and the last six weeks have of course been extra historic. Very much in the “weeks when decades happen” category, so I was very plugged in.

3/ The US elections happened, a second or third wave of the pandemic kicked off (depending on where you live), and multiple vaccines passed early trials, in the process pioneering a whole new class of mRNA vaccines.

4/ Closer to our own set of usual topics, bitcoin neared its historic all-time highs, a DeepMind AI sort of solved the protein-folding problem, SpaceX launched its first operational crewed mission, and also launched its beta Starlink broadband services.

5/ There was a small detail in that last news item that’s my jumping-off point for today. The Terms of Service for Starlink require you to agree that Mars is going to be a free planet, outside the jurisdiction of Earth governments, which is an interesting move with real consequences.

6/ The thing is, if SpaceX’s plans continue to succeed, they may put a Starlink constellation around Mars and offer very cheap launch services to Mars, which would lead to a broad-based democratized Mars access at least for rovers and robots, with low-cost communications once your rover is on Mars.

7/ Even if human settlement does not follow, we are on the cusp of creating at least a robotic telepresence society on Mars. And if you read between the lines of the Starlink ToS, SpaceX hopes to keep that presence an open, anarcho-capitalist zone of sociopolitical experimentation.

8/ What might that look like? Well, there are two precedents to consider, one fictional, one factual. The factual one is the current state of Earth oceans, which are essentially an outlaw zone. I highly recommend William Langewiesche’s brilliant 2005 book, The Outlaw Sea, for a deep look at how the world of oceans works. Shipping, piracy, law on the seas, ship-breaking, all sorts of cool stuff.

9/ The fictional one is the post-scarcity anarchist civilization called the Culture, in Iain M. Banks’ novels. We know SpaceX is inspired by that since they name their barges after Culture space ships: their current fleet of 3 comprises the drone ships Of Course I Still Love You, Just Read the Instructions, and A Shortfall of Gravitas.

10/ The two together paint a consistent portrait. The Outlaw Sea kinda does look like the fictional universe of Culture books, especially the margins of the civilization, where the Culture’s Special Circumstances agents, a sort of CIA, interfere in less advanced civilizations.

11/ The fictional plots of Culture books very much resemble British and American interventionist global foreign policy, enforced by naval power projected across the world’s outlaw seas, and directed at less-developed countries, over the last two centuries. Internally, the Culture is quite different from Britain or the US, but you could say developed US-UK societies are as close to the Culture as real earth societies get.

12/ So this brings me to the idea of Involvement Capitalism. I got the idea for the name from the Culture books, where the multiple species that engage with the Culture are called Involved species, which I think is a very powerful concept. I define it as capitalism based on money as a way to engage more deeply rather than disengage from society. So the opposite of f**k-you money. More like hello-world money.

13/ The core idea in the Culture books is that despite its post-scarcity abundance, the AIs and biological species of the Culture don’t retreat from the universe into either pure hedonism or spiritual retirement. They stay engaged, both with each other, and with less developed civilizations. They never stop experimenting, learning, growing, and interfering in the affairs of the universe. They are involved the way annoying parents are involved in their children’s lives.

14/ The Culture is both like and unlike the Star Trek Federation, which is also a post-scarcity society built around powerful spaceships and a multi-species civilization. While both are left-leaning, powerful, and militarist without admitting it, the Culture is what you might call a neoliberal anarchy with no real rules, unlike Star Trek, which is a benevolent paternalist civilization that takes its rules very seriously, closer to LBJ’s Great Society model if that had actually worked out.

15/ You could say the Culture is like the Star Trek Federation, except with AI Minds in place of charismatic captains, and no Prime Directive, only a history of interference and consequences to guide individual choices, and social consequences for making good or bad decisions as individuals. For example, there is a norm but not a rule against reading the minds of humans, and a ship that violates that law is ostracized and given a pejorative nickname.

16/ Star Trek captains try to avoid mistakes, and when they do make mistakes it’s a rare crisis. First do no harm, like doctors. Culture Minds try to learn from mistakes and come out net positive and win-win long term, but in the short term they are willing to play pretty dirty. Mistakes are not exceptional. It’s a startuppy fail-fast world with consequences. This is a pretty powerful attitude. Great power, great responsibility sort of thing.

17/ Not only are you willing to take risks, you are willing to take risks on behalf of others. They are willing both to commit sins, and then ask for forgiveness and atone for the sins, a kind of ask for forgiveness not permission culture, which is a very different, and in my view, much more alive posture.

18/ Compared to the Culture, the Star Trek Federation has what Bruce Sterling called an acting dead posture. Or equivalently, to use terminology coined by Samo Burja, the Culture is a live player civilization, while the Star Trek Federation is a dead player civilization.

19/ The Culture society reminds me of Hannah Arendt’s definition of a free public as one where freedom is experienced through involvement in mutuality, not going off by yourself with f**k-you money, and the moral universe is based on the risky posture of making mistakes driven by curiosity and growth motives, and then seeking forgiveness, rather than trying to avoid mistakes.

20/ Now the interesting thing is that both Star Trek Federation and Culture lack a meaningful scarcity-based capitalism based on money. The Star Trek has replicator credits, but they’re not really that important. They deal with lesser species like the Ferengi, which do have a concept of money in the form of latinum plates. There’s a good book about the Star Trek economy called Trekonomics, by Manu Saadia by the way.

21/ Within the Culture, again there’s no money. But sometimes there are fads and fashions that create money-like dynamics. Like in Look to Windward, where there is some trading based on scarce concert tickets. But again, for the Culture, money only comes into play when dealing with less advanced cultures. So overall, both the Federation and the Culture not just post-scarcity, but post-capitalist.

22/ Let’s connect all that up to the current state of the world. The interesting thing is that despite all the political strife and pandemic-related troubles and deaths, we are actually starting to hit post-scarcity dynamics for real. Money is rapidly losing all its traditional meanings, and behaving in new ways we don’t really understand yet. One obvious sign of that is that service economy workers are in the deepest s**t ever, while anyone holding stocks has been doing great. So there’s a dissonance there that’s going to get sorted out, and it’s probably going to get ugly.

23/ Governments across the world have taken fiscal measures that look like close to free money. Especially if you’re in an industry like airlines, hotels, or restaurants, money is now a weird new kind of government action. It doesn’t mean what it used to. We’ve also been able to throw massive resources at vaccine development and not just develop them in record time, but do so with an entirely new method, and with higher effectiveness. And chances are it will be distributed nearly free around the world. Amazing compared to past pandemics.

24/ Even better, despite the strongest efforts of the fossil fuel lobby, the renewables economy has continued to develop strongly through the pandemic, and energy is getting closer to free. And by that I mean really free, after factoring in the cost of externalities like pollution and carbon. That’s worth a bit of a bunnytrail.

25/ There’s a famous paper by Nobel laureate William Nordhaus, Do Real-Output and Real-Wage Measures Capture Reality? The History of Lighting Suggests Not, analyzing the cost of artificial lighting measured in human labor hours that has some interesting implications.

26/ Nordhaus shows that between prehistoric times and campfires, and modern compact fluorescent lamps (which are already obsolete btw, and being replaced by even more efficient LED lights) the cost of lighting really has dropped by a huge amount. The amount of work that bought 1 hour of light in prehistoric times now buys 53 years of light.

27/ When Nordhaus wrote the paper in 1994, you could argue it was kinda dishonest since it didn’t account for the cost of climate externalities. But now with the renewables revolution, the figure is much more honest.

28/ And it’s not just lighting. Anything based on computing and electronics has seen that trajectory. The picture in this episode has an LED, as well as a knockoff Arduino board, and it’s worth thinking about that: that board is a Chinese knockoff off an open-source project, and all the code is open source. If you bought an Arduino original, you’d pay a higher price out of goodwill for the open source.

29/ We’re already into 5nm semiconductors, which means extremely low-power computing, in watts/cycle terms, and coupled with renewables, you could get solar-powered bitcoin mining on ocean barges that is literally almost free energy, not just in money terms, but in all-inclusive environmental terms, in real units like human labor.

30/ So at least where artificial lighting, computing, and energy are concerned, we are getting close to Culture or Star Trek levels of post-scarcity, and the dynamics of our world are starting to reflect it. Vaccines are not free to develop today, even if they’re distributed free, but with that protein-folding breakthrough, that might go the same way.

31/ So why don’t we see all this and celebrate? The thing is, post-scarcity doesn’t quite look like we expect it to, in naively idealist-utopian terms. We think it looks like the orderly Star Trek Federation, but it looks more like the chaotic Culture universe.

32/ Right now the light at the end of both the Trump and Covid tunnels are visible, and it’s also very clear that the world on the other side is going to be very different on every dimension: political, economic, social, technological, legal, and environmental. We’re going to go straight from Covid to Climate as the next challenge.

33/ Those dimensions, by the way, define what is called the PESTLE analysis framework — political, economic, social, technological, legal, and environmental — which I learned about recently via a project I was doing with the Yak Collective, which is a network of free agents and consultants I helped start.

34/ We just launched a project called Future Frontiers, using the PESTLE framework that you might want to check out. One of the things I hope to do with the Yak Collective in 2021 is kick off an open-source Mars rover project, betting on cheap Mars access from SpaceX. I’m looking for hackers and makers to join, so if you’re interested you should sign up.

35/ A bit of product placement PR: Next week, on Thursday Dec 10 at 8AM Pacific, we’ll be doing our first Annual Meeting, and it will be a good chance for those of you who are interested to check us out.

36/ But to get back to the topic, the Yak Collective is actually one example of the sort of socio-political experiment that makes sense in an emergent post-scarcity economy of involvement capitalism, where the scarce commodity is not any kind of material resource, whether it is atoms, joules, or bits, but human involvement.

37/ Both the Star Trek Federation and the Culture represent involved postures. They don’t retreat. They explore and stay curious. They are neither individualist, nor collectivist, but try to manage that tension while remaining involved. For me, the Culture is the better model, since it is much more alive, but either model is a good one to think about.

38/ What makes the Culture different from every other model of post-scarcity post-capitalism is that it’s not idealistic or utopian. It aspires to good, but accepts the necessity of mistakes, forgiveness, and messiness in a chaotic universe. In the Culture, the ultimate sin is not making mistakes, but disengaging. Involvement is good. Like in regular capitalism, greed is good, in the Culture, involvement is good.

39/ Covid has shown us that traditional capitalism breaks when faced with an extreme coordination problem and necessary collective action. The US is the most powerful country on the planet, and the most powerful economy and innovation engine. Yet, it has already let almost a third of a million people die, and the number is likely to be half a million by the time we’re done.

40/ Capitalism itself is here to stay I think, but no flavor seems acceptable for the world we’re heading into, and the problems will only get worse than Covid, not easier, but also more and more things will be moving into the weird post-scarcity regimes, like lighting, computing, and energy. We don’t have a system for this.

41/ Democratic capitalism leads to tyranny of the majority. Socialist flavors of capitalism as in China may do better on problems like Covid, and we can’t ignore that, but they do come at the cost of authoritarian repression with an AI surveillance state.

42/ Any sort of consensus-based approach to capitalism, as in many kinds of cooperative schemes, ends up vulnerable to veto dynamics, while more individualist leaning flavors of capitalism, like libertarianism and anarchism, end up sucked into low-level endless political life, which is ironic since that’s what they set out to avoid. In New Hampshire for instance, libertarianism went off the rails and resulted in bears running wild in a small town.

43/ In all cases, I think the problem is twofold — not recognizing that post-scarcity, even in limited form, creates more chaos and confusion than a utopian peace, and the urge to retreat from involvement of any sort, which backfires, and sucks you into the worst kinds of involvement possible.

44/ So what do we do? I think what we must learn to do is involvement capitalism. Stay involved, don’t ignore collective action coordination problems, don’t be idealistic about what post-scarcity means in practice, and try to have fun while figuring it all out.

45/ In this newsletter, I’ve referenced a famous line, usually attributed to Stewart Brand, several times: “we are as gods, and might as well get good at it.” I want to wrap this episode with that, but with a twist: what sort of gods should we aspire to be? I think the answer is, we should aspire to be like the AI Minds that inhabit the spaceships in the Culture. The first step towards involvement capitalism is to give yourself a witty and sardonic god-name that keeps you hungry and foolish, like Steve Jobs said.

So that’s it for this week. Don’t forget, if you’re interested in the Yak Collective, check out yakcollective.org, and drop by our annual meeting on Thursday Dec 10, at 8 AM Pacific.

We’ll resume regular programming and subscriber-only posts next week.

Note to subscribers: Billing, which was paused during my break, will resume starting today.

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In today’s episode, I want to talk about an idea I call fifth-generation management.

1/ Fifth-generation management is an emerging style of management we don’t know much about because it doesn’t actually exist yet. But it is guaranteed to emerge post-Covid because historically, big sharp disruptions have reliably triggered discontinuous changes in management culture, and it is already clear that this one is doing that.

2/ The idea of generations in management, in the form I’m going to lay it out, is causally related to the idea of generations of warfare, and in particular the idea that contemporary styles of warfare strongly shape future styles of management. So if there are generations in warfare, they are going to cause generations in management. Military ideas are not the only cause of course, but I’m going to argue that historically they’ve been the strongest one. Strong enough to almost be determinative. During WW2 for instance, business and military culture became almost the same thing for a few years.

3/ This is not a universally popular idea because a significant number people find even the business-as-war metaphor distasteful, let alone the suggestion that military culture directly shapes business culture, or worst of all, that it is in fact the dominant source of business thinking. But personally, I’ll admit I’m enough of both a military nerd and a management nerd that I actually find the connection stimulating rather than depressing to think about. And I have a little bit of history here, my research during my PhD and postdoc fifteen-twenty years ago was on military command and control models, and a lot of my consulting work draws from that experience.

4/ For better or worse, the connection between military and business evolution happens to be historically solid, and seems set to remain true. In the past this was much stronger, due to a large number of men serving in wars and then entering business, and business being male-dominated. Today, the coupling mainly has to do with relative rates of technology adoption in military vs business evolution, and to a lesser extent, shared exogenous events affecting both military and business affairs.

5/ Before we get into it, a couple of caveats. First, as with any clean, linear, sequential or cyclic model applied to a messy branching, evolutionary reality, you have to apply it very tastefully. You have to think like a historical artisan, matching up the conceptual boundaries of a constructivist notion you’re working with to real history. And where they don’t line up, actual historical events should shape your thinking rather than the abstract idea of one sequence of generations driving another. Second caveat, don’t make the mistake of thinking that each generation fully displaces the previous one in either military or business. Instead, it adds a new layer, and the older layer simply gets confined to a small zone of the action. Generations accumulate like geological layers, they don’t displace each other.

6/ To understand the management version, we have to understand the military version first. The idea of generations of warfare was popularized by William S. Lind, who coined the term fourth-generation warfare around 1980. It became the dominant style in actual warfare after the Iraq War, which was probably the last major third-generation war.

7/ I have illustrated the generations in the lower half of the diagram. The story basically starts with the Peace of Westphalia in 1648, after the Thirty Years War. The first generation lasted almost 150 years. The second generation lasted about 100 years from 1815 to 1915, the third about 65 years from 1938 to 2003. The fourth, I will argue, only lasted about 15 years, from 2003 to 2020, and Covid will trigger a shift to a fifth generation.

8/ The first generation was based on final abandonment of medieval warfare, and relied on early smooth-bore muskets. It utilized uniformed, paid armies fighting for nations rather than feudal lords, or mercenary companies. It involved what is known as line-and-column warfare. Think of armies marching in long columns towards strategic targets. Maybe a little large-scale maneuvering and flanking, but lacking the communications and intelligence capabilities to do more.

9/ The second generation stretches roughly a century from the end of the Napoleonic wars, around 1815 to World War I. It was based on the development of rifled breech-loading guns, interchangeable parts, and early electronic communications with the telegraph. Technology improved steadily so that WW1 was quite different from say the war of Mexican independence. But the broad style is what’s known as attrition warfare between roughly evenly matched forces in numerical and materiel terms. Armies bogged down in trenches or extended sieges. In second generation warfare, usually the side with the greater economic resources eventually prevails, as in the US Civil War.

10/ Third-generation warfare was developed primarily by the German military in the interwar period, and is what is usually called Blitzkrieg in the historical case, or maneuver warfare in more modern terminology. It makes use of fast-moving mechanized infantry, tanks, and sophisticated local communications to move very fast behind enemy lines, maneuver and reorient rapidly in response to changing situations, and collapse the enemy from the inside.

11/ This is the style that was developed and refined by John Boyd, and is roughly what lasted all the way through the Iraq War. In third-generation warfare, often an asymmetrically smaller and technologically primitive force can beat a larger, technologically superior force. This is the style that is based on the OODA loop, which we talk about a lot.

12/ This asymmetric outcome potential often creates a conundrum around how to establish the peace after the victory, because economic superiority may not line up with military superiority. In the case of WW2, eventually the Allies got better at maneuver warfare, the Germans got worse and backslid into 2nd generation to some extent, and economic superiority prevailed. And after the war, the Allies won the peace with the Marshall Plan, which was second-generation peace thinking. So in a way WW2 was actually a Generation 2.5 war.

13/ Third-generation warfare is also what is sometimes called total war, where you fight with unsentimental professional skill to win. It’s not about honor or fair-play, and deceit, cunning, and cheating are considered legitimate. This means it can get really ugly by design. In older styles of warfare, you would have a collapse of honor norms like “giving quarter,” but for third-generation warfare, which is an extremely rational kind of warfare, you had to have things like the United Nations laws and the idea of war crimes and trials. Because everything from gas chambers to concentration camps is otherwise on the table.

14/ Now fourth-generation warfare is best defined not by how war is fought, but by who fights the war. In some ways the Vietnam War for the US, and the Afghanistan War for the Soviet Union, were both early fourth-generation wars. But proper fourth-generation warfare requires non-state actors who can operate with near capability parity on many fronts, which requires the internet and cellphones. It also often has non-state actors with more legitimacy than mere third-generation terrorist groups, and state actors that have much less legitimacy than they used to in the past. In a way, the Peace of Westphalia made states the legitimate combatants, and the Great Weirding is reversing that legitimacy after almost 400 years.

15/ Of course, as we’ve all learned by now, fourth-generation warfare, since about 2003, also means dank memes, influence operations, fake news, and disruption of political processes, especially democratic ones like elections, using social media. A good example is modern conflict like Syria involves both state forces, in this case Syria and Russia, as well as ISIS and a people’s resistance. Or Ukraine. It is what is sometimes called hybrid or nonlinear war, and Russia has been the leading practitioner of it. Arguably, the West has been subject to a fourth-generation war attack for four years from Russia.

16/ And of course this mix has always been present in warfare in some form, but what distinguishes 4th-generation warfare is that guerrilla goals shape the conflict via leveraged high-tech digital means, instead of just being subject to first, second, or third generation logic, or limited to violent terror tactics. This also means guerrilla goals become top-level political goals, instead of being subsidiary to the goals of a sponsor state. Guerrilla goals are what Henry Kissinger described with his famous line: “The conventional army loses if it does not win. The guerrilla wins if he does not lose.”

17/ In other words, fourth-generation warfare brings guerrilla goals to the political table directly. It is not total war, but what I call infinite war: it brings infinite-game war goals, into the picture, the goal being to continue the game rather than win it (infinite game in the sense of James Carse). It’s a true fourth-generation war if at least one top-level combatant is fighting with the guerrilla goal of simply staying in the game, rather than trying to win formally in the sense of a declared war, getting the opponent to surrender, and doing so without a state sponsor. Sometimes of course, the guerrilla actually wins in a conventional sense, in which case they often struggle to transition from a stateless actor to a state actor, as with the Taliban.

18/ Okay, now that we have our four generations lined up, let’s talk about how that connects to generations of business management. To do that, I want to talk about an episode you may have heard of, called the Millennium War Games, but you probably haven’t heard anything like my spin on it.

19/ Briefly, the Millennium War Games were games held in 2002 in which the Blue Team, operating by a doctrine called Network Centric Warfare or NCW, was defeated by Red Team, led by Marine Corps Lt. Gen Paul Van Riper, operating by standard third-generation Boydian doctrines. NCW was basically a very high-tech model, using satellites and surveillance and tight synchronization. Basically “how would we fight a war with the internet on our side.”

20/ Van Riper avoided electronic communications and instead used motorcycle messengers to communicate, and attacks with fishing boats to destroy the Blue Team. Basically, using relatively low-tech and irregular forces to operate in the blind-spot of the high-tech larger adversary that was overconfident in its technological ability. Classic OODA loop style conflict.

21/ The normal interpretation of the outcome is that low-tech with superior strategic thinking beats high-tech with weaker strategic thinking, but this is simplistic. It also doesn’t explain why, 50 years after Blitzkrieg was recognized around the world, the Blue Team would operate against the logic of third-generation warfare. The key point to note here is that the war games were primarily naval, and NCW was a doctrine that emerged out of the US Navy and its relationship to technology, specifically from an essay by Admiral William Owens in 1996.

22/ This is not an irrelevant fact. Navies have historically been the highest-tech branch of the military, but not in the sense of adopting the newest, shiniest tech. They are the highest tech in the sense of using the most technology, in the most complete and systematic way, to vertically integrate operations all the way from satellites to bullets. They are platforms. Today for example, the US Navy operates carrier groups, the most advanced version of this thinking.

23/ Aircraft carriers are obviously the most sophisticated technology in military use. They run actual little air forces and missile defense and offense that are superior to the entire militaries of small nations. They use satellites. They have destroyers, submarines, anti-submarine capabilities, all operating in coordination. And this has been true going back centuries. Large capital ships were hugely expensive technological marvels even in the age of sailing ships, and money and technological superiority can overwhelm a historic maritime tradition sometimes, as happened in the 18th century when France under Jean-Baptiste Colbert’s administrative leadership briefly pulled ahead of more naval nations like the UK and the Dutch in capability.

24/ On the other hand, prototypical third-generation warfare is best exemplified by the US Marine Corps. It’s not exactly a low-tech force, but you could say it selectively uses a few really high-tech bits in an otherwise low-tech style of fighting. The same is true of special forces, but to a greater degree of tech early-adoption. Third-generation warfare you could say is an early-adopter of technology that uses it in a small-scale but highly leveraged and strategic way. It’s the military equivalent of a startup, while navies are the military equivalents of large enterprises.

25/ These military startups don’t just use new technology, they rapidly evolve tactics through trial-and-error in actual conflict, and build out strategies and doctrines bottom-up, in real-time, adapted to the current conflict. And this is not because they’re smarter than navies, but because they play a different role: usually offensive, high-speed, messy and ground level.

26/ Navies on the other hand, usually play a very different role. Their firepower is primarily deployed from a distance and with overwhelming scale, in what’s called stand-off mode. A modern carrier group will park itself outside a battlespace and send hundreds of sorties into the warzone, launch hundreds of missiles, conduct economic blockades or humanitarian activities, and in general create a sort of boundary condition for the rest of the war. Their job is to create and maintain boundaries, not maneuver within them.

27/ In fact, historically, navies have been most powerful when they simply stood off to the side and did nothing. This is one takeaway from Alfred Thayer Mahan’s classic The Influence of Sea Power Upon History. It also applies to nuclear power, which has a similar effect (so nuclear deterrence enforcing the peace). Notably, Boydian thinking emerged out of fighter warfare and doesn’t have much to say about that side of warfare. The point is that complex, systemic technological capability is just a very different sort of weapon, and you have to apply generational thinking separately to it.

28/ Sometimes navies play a more active, maneuvering type third-generation role, as in the Atlantic war against U-Boats, but in general, you could say that navies play a late-adopter, complex systems platform technology role in warfare, while marines and special forces play an early adopter, startup role. If you want to apply the four-generations model to navies, you have to do it separately. I won’t get into how to apply the four-generation model to these boundary-condition parts of the military, but it’s possible.

29/ The quick version is that both have a role to play in modern warfare, just as both startups and large companies have a role to play in the tech economy. If your takeaway from the Van Riper Millennium war games episode is that you should give up high-tech complex military capabilities and network-centric operations, and run a cheap military using motorcycle messengers, and fishing boats, you learned exactly the wrong lesson.

30/ In fact all the conflict since 2002 shows the opposite. Network-centric warfare is what’s actually dominating war zones, though not in the doctrinal sense Admiral Owens imagined. Russia, ISIS, China, and other actors who are good at this all operate in a network-centric way. It’s just not in the form that the US NCW doctrine envisioned, but much messier and bottom-up. Missing this point is like thinking all companies should be small startups and that the Googles and Amazons can’t possible work.

31/ A better way to think about it is that you should pursue hot military objectives with marines style startup action, but consolidate victories and preserve the peace with navy style network-centric type systemic capabilities. Both have a role to play in every generation of warfare. You could say marines win wars while navies preserve the peace. Though of course in modern conditions, there is never really clear hot war or cold peace, or cold war and hot peace, but a continuous partial warm chaotic conflict.

32/ Okay, that was a very long preamble, which was unfortunately necessary because most people make military-to-business connections without knowing much of the relevant military history. But we’re now ready to make the connection to business management generations. I’m going to state it in the form of two laws, and then describe the four generations in relation to the top half of the diagram.

33/ The first law is: On average, business management generations lag military generations by one.

34/ This is an average in two ways. The first is across branches of the military. Military startups, marines and special forces, might be 1.5 generations ahead of management cultur, innovating tactics based on the most promising new technologies. Air forces and armies might be 1 generation ahead, using more proven technology, and navies might be 0.5 generations ahead, deploying the most proven technology at the most complex scale.

35/ The second is across time. You may have heard the line that generals are always prepared to fight the last war. This means, every significant new war causes a paradigm shift. It’s like a staircase evolution, and on average, military management culture is ahead. And in a world like ours, where we’re nearly continuously at war somewhere, the saying actually is pretty meaningless.

36/ The second law is: The evolution of business management is driven by more frequent, but smaller magnitude, exogenous events. So it has a much smoother evolutionary profile. Every war is an exogenous disruption to business, but not every exogenous business disruption drives evolution in warfare. Business is also driven by political events, economic crises, financial crashes, and many more technologies than warfare. Every military crisis is a business crisis, but not every business crisis is a military crisis.

37/ For those of you who follow the computer industry, an analogy to laptops and phones versus gaming consoles is useful here. Gaming consoles are like military technology, they have sudden jumps in capabilities every few years, as specialized chips are designed and launched. But phones and laptops evolve more smoothly with smaller jumps. They eventually catch up and even briefly overtake the console market. But then the consoles jump ahead again.

38/ So if you apply these two laws, you get a description of four generations of management that loosely correspond to the four generations of warfare, but with roughly a lag of 1 generation, and a smoother evolutionary profile. So let’s take them in order, as shown in the diagram.

39/ First-generation management, which is roughly the mercantile era, overlaps with the first generation of warfare in time, but resembles medieval warfare in structure. It is a little longer by about 25 years, about 1648 to 1854, the London Crystal Palace World Fair. It relies on ways of running businesses that would be familiar to people in the 15th and 16th centuries. Medieval management.

40/ Second-generation management, which is roughly the Robber Baron era, roughly 1870 to 1930, loosely resembled first-generation warfare. It features paycheck employees, a traditional column and line type approach to business operations, and leadership that looked a bit like 17th century military leadership. It established large business empires that resembled colonial empires, and used relatively primitive communications based on mail and telegraph to maneuver a little but, but not a lot.

41/ Third-generation management, which is roughly the familiar modern managerial era in the old economy, resembles second-generation warfare. It stretched from roughly the Great Depression to 1997, and has two clear phases. In the first phase, about 1935 to 1980, we had a heavily state-regulated corporatist environment, and in the second phase, from 1980 to about 1997, we had a deregulated environment. But despite the differences, the key feature is that professional managers ran the show, and the competition had some of the trench warfare attrition characteristics of WW1. Competitors were roughly evenly matched and were trying to wear each other out in the market.

42/ Finally, getting into modern times, fourth-generation management, which is roughly the entrepreneurial era, resembles third generation warfare. It stretches from the dotcom boom and the rise of Clayton Christensen’s disruption model, which is really maneuver warfare for business settings, all the way to 2020. It features charismatic founder-entrepreneurs, rather than professional managers, setting the agenda. Just like third-generation warfare, it puts marines/special forces type startups in the center, and navy-like systemic capabilities on the margins. In the fourth-generation, HBR, Michael Porter and McKinsey took a backseat, while Silicon Valley and the VC blogosphere was in the spotlight.

43/ There’s a lot more to be said, but that’s the basic model. Take the military generations, subtract one, adjust boundaries, smoothen the evolutionary curve, and you get management culture.

44/ Which brings us to fifth-generation management. Obviously, Covid and what I call the Great Weirding have been a huge disruption for both military and business. Obviously, climate action is already starting to shape the agenda in a very significant way, and business-to-business or military-vs-military competition is almost taking a backseat while society-to-nature competition is front and center. We are fighting a two front war, with the virus on one front and climate on the other. Neither will be the same coming out the other end. So what can we expect?

45/ First, military affairs are in uncharted territory. The US military for instance, is dealing with dangerously unstable domestic politics where they might become a factor for the first time since the Civil War. Syria and Ukraine were fourth-generation wars, but already fifth-generation situations are cropping up all over the place, like the urban conflicts in Western cities, detention camps for refugees, and so on. I won’t go deep into military futures here.

46/ But business affairs are in somewhat of a clearer situation. By applying the first law, we can already predict that fifth-generation business will look at least partly like fourth-generation warfare, 2003-20. In other words, like Syria or Ukraine. Just as non-state actors shape fourth-generation warfare, non-business entities will shape fifth-generation business. This includes culture war groups fighting for social justice, climate action nonprofits, governments administering post-Covid recovery funds, and so on.

47/ There is also stuff that’s already been recognized, ranging from open-source communities and the gig economy, to the blockchain economy, and various moves towards home-based economic activity and work-from-home that is outside the financial economy.

48/ But the big thing is that there are a large number of reckonings that have to be dealt with. Besides climate, we have the trade war, we have China turning into a new kind of evil empire and surveillance state, we have the techlash, we have financialization on Wall Street, we have a world awash in fiscal responses to Covid. And in the middle of all this, we have supply chains breaking down, wildfires, and other climate-related disruptions.

49/ A lot of what I write about on this newsletter is looking at various aspects of all this. The three main projects I have going here all are about researching the background context against which fifth-generation management is emerging, though that’s not the main motivation. In the Great Weirding series, I’m looking at how the equilibrium has been destabilized over the last five years. In the Clockless Clock project, I’m looking at how new temporalities are displacing the clock-based temporality that has coincided with all four generations of war and business, since the invention of the pendulum clock in 1657. In the After Westphalia project, I’m looking at the future of the nation-state.

50/ Trying to figure out how to manage military or economic affairs against this complex backdrop is the task of fifth-generation management in both domains, and it will be probably take us all the rest of our lives to figure it out. But at least we now have a starting point and a sense of the nature of the challenge. A lot of this thinking came out of my last few years of consulting work, with clients who are already practicing fifth-generation management, and I’m currently trying to put together an online course based on this material. So if that interests you, stay tuned. There will be an update on that front soon.

This has been one of the occasional free podcast issues of the Breaking Smart newsletter, where I send out an essay a week. Usually an installment of one of my longer series projects, which I just mentioned, and occasionally one-off stand-alone essays. So if you liked the ideas in this issue, do subscribe.

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For today’s episode, I want to share an out-take from my consulting work, something I think might be interesting for anyone working in the technology industry. It’s a set of 20 questions that can help you uncover the current state of play in your business. The questions are mainly useful for technology companies for reasons I’ll get to, and are probably of limited value to non-technology businesses, but you’re welcome to try using them for non-technology businesses.

A caution: The only person who can usefully answer these questions for the company as a whole is the CEO. But it’s a fun game to play from any position, and useful to the extent you are close to, and aligned with, the CEO.

I put this list together in the course of some work I’m doing with a client, and in doing that, I realized that these are the questions I tend to explore free-form in the first couple of orientation calls with all new clients. I don’t explore these via an explicit Q&A, but more in a sort of bingo-card format, where I look for answers to these in a free-form conversation. For me, it’s a way to efficiently learn about the business, and for them, it tends to be a useful exercise to turn an unconscious sense of the state of play into a conscious one.

So here are the questions, with some commentary afterwards. Note that this is written text is a skeletal outline, not a transcript. The audio has me riffing on all these questions and explaining the logic of the sequence, so you’ll have to listen if you want the whole thing.

The Questions

What is the primary operational bottleneck or "firefight" currently consuming the bulk of your attention? (eg: "growing schedule slippage on launch of product X" or "difficulty filling critical CxO position" or "PR damage control due to issue X")

What is currently the biggest source of uncertainty/doubt/anxiety for the company? (Be specific, eg: "high failure rate of manufacturing process X" or "poor conversion rates of sales campaign for product Y" or "What customer Z will decide for their product P")

What are the top 5-7 events coming up on the company roadmap in the next few years? (eg: key technology tests, decision points, product launches, sales drives, external events like elections)

Who/what are the top 5-7 most important external entities shaping your business sector environment? (key customers, value chain partners, government agencies etc. Name either individual entities, or classes that are as specific as possible, like "small food-service business owners").

Which entity in the list from Q4 above exercises the MOST strategic control over the primary value chain of interest to the business?

Which entity in the list from Q4 consumes the largest share of your personal attention? (can be the same as 5)

What is the MAIN line of business (LOB) the company MUST win in, within the current business model, to be successful?

What is currently the MAIN strategic metric/measure that tells you whether you're winning or losing in that LOB? (be specific, eg: "free cash flow" or "increasing yield rate from process X" or "rate of increase of production volume")

Who is the ultimate customer/end user for the overall value chain this LOB is part of? (Be as specific as possible. Eg, "fast-food restaurant chains")

Who is the immediate prototypical customer for this LOB for your business? (Eg, "commercial kitchen equipment manufacturers")

What is ONE belief held by this prototypical customer that you would like to change, and what would you like it to change to?

What they believe:

What you would like them to believe:

What is your "Thielean Secret"? ONE key belief held by you/your company that is NOT widely shared by the industry?

What is working unexpectedly well, where you seem to be getting surprisingly lucky?

What is working predictably badly, and seems hopeless/doomed?

Overall, how well is the current business model working? Use a qualitative phrase in the range from "succeeding wildly" to "in big trouble"

What do you estimate are the % chances you'll need to execute a major business model pivot within the next 3 years?

If you do need to pivot, what is the most likely alternative business model you will be considering?

What are the top 3-5 macro trends affecting your business environment (Be specific: examples: Covid, China trade war, climate change, software eating the world, input X commoditizing, industry structure going from horizontal to vertical…)

What are the top 3-5 elements of the business environment that are NOT changing?

Based on reflection on your answers to questions 1-19, list between 3-5 problems that you consider to be the top strategic priorities. Describe each with a single sentence. They can relate to any aspect of the business: internal or external, relating to marketing, engineering, HR, sales, or cutting across functional boundaries. Try and state it in terms of key details that have strategic significance, not generalities (eg, "Get defect rate for manufacturing process X below Y%" not "Improve quality")

These questions are specifically useful for technology businesses — businesses that develop and sell products or services that take significant engineering, and are driven by, and drive, technology trends. This is because technology businesses are strongly time-based, and have to evolve and maneuver in a marketplace where competition is based primarily on innovation, not supply and demand shifts due to macro factors in an otherwise unchanging market.

In other words, there is a sort of fast-evolving real history to technology businesses that’s not really there in non-technology businesses, like managing a corner grocery store or even a consulting business like mine.

These questions assume that the logic of the business is the logic of time-based competition, which means your approach will include things like OODA loops, S-curves, disruption, Simon Wardley’s Wardley Maps, Ben Thompson’s Aggregation Theory, and so on.

Even though the questions might seem banal and obvious, the thing is there’s a lot of banal and obvious questions you can ask about any business, like hundreds, and it’s not immediately obvious, at least to me, that these are the right ones.

These questions — in this rough sequence — are ones I’ve converged on through trial-and-error over nearly a decade of initial orientation conversations with new clients. Many other questions can be usefully asked once you have a sense of the answers to these basic questions, but skipping these and going straight to other questions is usually a recipe for frustration.

So that’s it for this episode. You’ve been listening to/reading a free podcast issue of the Breaking Smart newsletter, where I send out a longform piece for subscribers every Friday, usually an installment from one of my multi-part projects, and sometimes stand-alone essays. Once every 3 or 4 weeks, I publish a free public podcast like this.

In the previous 3 weeks, I published 3 subscribers-only essays:

Control Failure -1 from my Great Weirding essay series, about the global transformation of the last few years.

The Descent of the Public, from my After Westphalia essay series, about what comes after the nation-state.

Operating in Time -2, the concluding part of Chapter 3 of my book project, The Clockless Clock.

Thank you for listening, andI’ll be back next week with another longform piece.

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A special episode, an audio roundup/summary of the last year’s podcasts. I cover the 19 episodes I did starting in June last year. A year of thinking out loud.

The inaugural episode riffing on the like-new ethos of the industrial age and the transience/aging based ethos of the digital age. June 21, 2019: A Wabi-Sabi Technological Age.

On the problem of how to repeatedly break into technology scenes. July 5, 2019: Following the Scenius.

Plotting vs. pantsing, and setting the stage for creative, generative work vs. planning to finish something. July 26, 2019: Planning to Start, Planning to Finish.

An episode on how marketing is now a bottom-up process with 3 layers: memes, brands, and missions. August 2, 2019: Memes, Brands, and Missions.

On why social media favors positive behaviors such as clicking like buttons, but not negative ones like mute and block. August 23, 2019: Towards Subtractive Social Media.

Investing in the things that make you ordinary rather than special, such as being an early adopter. August 30, 2019: Investing in Your Ordinary Powers.

Probably my favorite thread of the first year, building an analogy between charismatic megafauna and technologies, built around a 2x2 of non-marquee vs. marquee, and smoke-and-mirrors vs. WYSIWYG. September 6, 2019: Technological Charisma.

Riffing on the problem of how injecting AI into a system breaks learning curves for humans. September 20, 2019: Like Riding an AI Bicycle.

Next up, another episode based on a 2x2, this one has normal versus weird on the x axis, and timid vs. bold on the y axis. September 27, 2019: The Direction of Maximal Derangement.

Picking up the thread on charismatic technologies from September 6, an episode about an idea to replace the concept of Net Neutrality, based on the end-to-end principle, with one based on end-to-end encryption. October 4, 2019: Charisma Neutrality.

A reflection on the death of Alexey Leonov and the first spacewalk with two women, Christina Koch and Jessica Meir. October 18, 2019: Spacewalks and the Species.

Putting together ideas from Alan Kay, William Gibson to think about how December 6, 2019: Inventing Time

Navigating time in ways that go beyond optimism and pessimism. February 21: Beyond Optimism and Pessimism

The first pandemic episode. The word default has two meanings: failure to fulfill an obligation, and a preselected option. Both meanings became very salient with the pandemic. April 3, 2020: Defaults and Defaults.

A riff on Marc Andreessen’s Time to Build essay which was doing the rounds then, based on a graphic I made up called the Builder’s Cone, comparing the relative rates at which you adapt versus society adapts. April 24, 2020. How, What, and Where to Build

One of the things I’ve been doing with the pandemic is reading a lot of history that seems relevant. Through most of May and June, I was reading Barbara Tuchman’s A Distant Mirror. An episode inspired by that. May 8, 2020: The Medieval Future of Management.

In May, we had the BLM protests and that seemed to bump the pandemic from the headlines. What was the significance of that? May 29, 2020. From Story to Setting

Connecting the idea of big moods to emotional common knowledge, and a complementary concept I made up called little moods. June 19, 2020. Big Moods, Little Moods.

Probably my most popular episode from the last year, a detailed look at the concepts of elites and elitism, how elites fail, and how to get better elites. July 10, The Next Experiments in Elitism.

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In today’s episode, in honor of Bastille Day next week, and Fourth of July last week, I want to talk about the ongoing evolution in elitism, and the problem of how the emerging new elites can be better than the old ones being toppled.

1/ Elites are a constant and arguably necessary presence in history. Political revolutions that try to do away with elites invariably seem to either fail quickly, or install new elites without meaning to. So the question for me is not how to get rid of elites, but how to try and ensure the ones we end up with are better than the last lot.

2/ I’m going to sketch out a rough theory of elitism and its dynamics, and then get to posing the question itself, and then propose an answer, from the perspective of both the new TBD elites, and the masses they define, so let’s get started.

3/ First, the concept of an elite is not dependent on a particular structure of society. Elites might be kings, nobles, elected leaders, bureaucrats, scholars, scientists, priests, cult leaders, media leaders, business executives, or subcultural inner circles. The prevailing idea of masses is induced by the prevailing idea of elites as a complement.

4/ So there’s always a subset that regards itself, and is regarded as, entitled to a sustainably better than average human condition, with attendant privileges. And importantly, it is a stable equilibrium. Those who are worse off, the non-elites, and think the elites don’t deserve their better conditions, still live with it. The masses rarely disturb the peace unless they are under extreme stress.

5/ Elitism and privilege go together of course. The word privilege literally means private law. Elites are a group for whom laws apply differently, or a different set of laws apply. In the most extreme case, they are formally above the law entirely. That’s the usual definition of a monarch and the dividing line between monarchs and ordinary nobles.

6/ The nobility might have a privileged code of law, but they are still governed by a rule of law, even if it’s not the same one as applies to non-elites. This special treatment has to be pretty special though, so I don’t use privilege in the broad social justice sense of the term, as in white privilege. That’s a different, more diffuse sense of privilege as a structural advantage. I’m talking narrow privilege where you can get exceptional, personalized treatment under whatever rule of law applies to you.

7/ For example, in medieval Europe, the nobility had hereditary property rights, governed by Church law, and the commoners mostly didn’t have the same sorts of property rights, only duties. But what made the law for the nobility special was that it was personally administered, with exceptions being more important. Laws honored in the breach rather than observance, as Shakespeare put it.

8/ So for example, there were laws against consanguinous marriages, but the Church did brisk business in allowing exceptions. Or you have indulgences absolving you of sins that are more easily available to nobility. Or in more modern times, draft exemptions. That’s what privilege looks like.

9/ So one way or the other, some subset of humans will create not only better than average conditions for themselves through private laws, they will even get exceptional treatment under that private law. Or a position above the law entirely.

10/ A big part of the stability of this condition is personal social capital: knowing the right people, with the right level of trust, to get rules bent or interpreted in your favor. Or being treated as an exception. Or in the extreme case, laws simply made to your specifications to benefit you and disadvantage others. In the most extreme case, they simply don’t apply to you.

11/ If you ignore human fallibility and corruption, and look at this as a systems design, it is actually kinda smart to divide the world into 3 zones this way: a zone where the rules apply absolutely, a zone where they can be bent and exceptions are possible, and a zone outside the laws. It gives you a broad ability to evolve the system.

12/ It’s like how, in The Matrix, the architect declared that the city of Zion, Neo, and the Oracle were as much part of the design of the system as Agent Smith. You could even argue that though the architect was God, Neo was the emperor, the citizens of Zion, both red-pilled and native-born, were the nobility, the Oracle was the chief priestess, and the bots like Agent Smith and the blue-pilled people in the Matrix were the non-elites.

13/ But back in our world, I asked my Twitter followers whether they consider themselves part of the current elites. Out of 468 respondents, 34% said yes, and 66% said no. Which seems about right since I write for a pretty privileged class of readers.

14/ Okay, so with this definition, if you look back at history, it looks like a series of experiments in elitism rather than a series of experiments in governance. Some of them end well, some end badly. But all of them end. The conceptualization of an elite class is not stable.

15/ Definitions of elites shift pretty slowly, and typically only move significantly when the technology of trust changes. It used to be about provably noble blood-lines. Then it was about visibly living by a particular code, noblesse oblige. Then it was about money, then it was about education. Maybe in the far future, it will be about being red-pilled out of an AI simulation, so the rules don’t apply to you.

16/ Now, while a notion of elite is stable, there is what Vilfredo Pareto called circulation of elites. He traced how two kinds of elites, which he called lions and foxes based on earlier terminology from Machiavelli, tend to simply take turns being the elites. Foxes rule by the power of the pen, lions through the power of the sword.

17/ As I have said, the economy of elitism is sort of system independent, and is based on personal trust and social-capital based computing within a calculus of privileges — exemptions from the law.

18/ A good model of this calculus is Selectorate Theory, which is described in The Dictator’s Handbook, compares all kinds of political systems in terms of 3 groups: influentials, essentials, and interchangeables. Influentials are always elites, interchangeables are never elites, and some essentials are elites. It doesn’t matter whether it is a dictatorship or democracy. This is how governance by elites happens.

19/ My final theoretical point is about knowledge. The relation among elites and masses is one usually based on what are called noble lies, where elites exploit their privileged access to ideas, information, and education, to craft false consciousnesses for the masses to inhabit. Think of them as blue pills. How you feel about these noble lies, or blue pills, is a big part of your philosophy of elitism.

20/ You can distinguish two basic approaches of elitism. There is what is sometimes called Straussian elitism, which is generally conservative, but not always, and is based on the paternalistic belief that elites lying to the masses for their own good is a good thing. So you get a distinction between esoteric elite red-pill knowledge and exoteric, non-elite blue-pill knowledge meant for the general public.

21/ The other approach, which you could broadly call pluralism, is more democratic in spirit, and eschews noble lying, at least conscious noble lying, based on the principle that even if it gets noisy, messy, uninformed, and ignorant, it’s a good thing to level the epistemic playing field, and not privilege some flavors of knowledge structurally. I’m pretty strongly in this camp. There is no blue versus red pill. Everything is available for anyone to learn.

22/ Okay, now that we have this basic historical sense of what elitism is, and how it works, we can ask, what makes for good elites versus bad elites? It is important to keep a sense of the real history of elitism when you talk about this question, because it is easy to get caught up in theories. In the collage image accompanying this podcast, I’ve included several famous historical examples.

23/ The storming of the Bastille, the American Declaration of Independence, the Magna Carta, and Lee Kuan Yew, Nehru, and Jomo Kenyatta giving their famous speeches. I also included a picture of Muammar Qadaffi’s corpse after he was killed by a mob — it is important to remember that elitism can end like that. So this is the gestalt of what elitism as a historical practice is. Or to use an esoteric word, the praxis of elitism as a consciously held philosophy.

24/ But we shouldn’t anchor too much on these iconic moments when one set of elites takes over from another, or when non-elites temporarily bring down elites altogether, creating a vacuum. The essence of elitism isn’t in these moments of creative destruction of elite power, but in quieter unaccountable workings away from public scrutiny.

25/ So think of closed-door board meetings, experts in a committee meeting setting health standards, Congressional committees hashing out the details of a bill, lobbyists waiting to meet a senator to push some agenda, unaccountable editors in a press room deciding which public figure to attack. Unaccountable tech leaders deciding how an algorithm should work. That’s day-to-day elitism.

26/ This unaccountability by the way, isn’t necessarily a bad thing. It is what it is. To the extent the elites are agents of the will of society at large, there is just so much detail involved in the exercise of actual power that there is no possible way all of it could be made transparent to everybody. At best you can be slightly less opaque and unaccountable than the last crowd.

27/ There’s also a middle-class, provincial version I don’t want to discount too much, like a local city leader calling in a favor with the local police chief, or a powerful business person talking to a school principal about their child. Any behavior that exercises privilege is elite behavior. The defining bit is not amount or scale of power, but the fact that it is exercised in privileged ways — private law, with a degree of unaccountability and exceptionalism.

28/ Now that I’ve painted a portrait, there’s a fork in the road. You can either accept that this is the way the world works and always will, or you can imagine some sort of utopia where there are no elites and no zone of society that operates on the basis of privilege.

29/ Whether you are a commune anarchist who believes direct democracy or consensus will get rid of elites, or a blockchain libertarian who thinks code-is-law will get rid of elites, down that road I think is mainly delusion. I’ll just point to a famous article, the Tyranny of Structurelessness and leave it at that. Getting rid of elites does not work.

30/ One reason is of course that elites have power and they use that power to keep themselves in power even as structural definitions and models of elitism change, become more or less informal, and ideologically different and so on. Angry masses understand this aspect of the persistence of elites. But this is not the biggest reason.

31/ The biggest reason, which revolutionaries routinely discount, is that humans seem to desperately want elites of some sort. Maybe not the current sort, or the current model, and definitely not the current specific people, but some elites. Maybe you want black instead of white, women instead of men, techies instead of lawyers, or trans instead of cis, the point is, you want elites.

32/ There may be strong preferences for a system of choosing elites. That’s kinda what ideology is. Or looser preferences. For example, I tend to prefer fox elites over lion elites, a large selectorate to a small one, and pluralism over noble lies. I also prefer strong mid-level mini-oligarchic patterns of power to either imperially centralized patterns or extremely fragmented, decentralized patterns.

33/ The psychological function of elites appears to be to model how life can and ought to be lived. But this is a pretty loose specification. Christians think in terms of What Would Jesus Do. Confucians in ancient China thought in terms of how to codify the will of the Emperor into law. Woke elites think in term of how to turn intersectional theory into prescription, and anti-Woke elites think in terms of making classical liberalism great again.

34/ It’s important to keep your definition of elites broad. For example, many people pretend that people like court jesters (and people often classify me as one) are among the non-elite. Maybe formally, but informally, they wield power and privilege — in my sense of access to exceptional treatment — in ways that makes them elite. So today in the US, the cast of Saturday Night Live, stand-up comics, and people like Jon Stewart and Trevor Noah are definitely elites.

35/ Anti-elite philosophy and philosophers are also necessarily elite simply by virtue of how their influence operates. So whether you’re taking about the Taoist sage Zhuangzi in ancient China or important figures like Robert Anton Wilson in the Discordian subculture of modern America, they’re all elites. Just because you laugh at other elites with sticks up their asses doesn’t make you not elite.

36/ There’s many theories of this psychological function. There is a basic ethics theory of people just wanting guidance on how to have a good life, and looking for teachers. There is the theory of elites as surrogate parental figures. There is the Girardian theory of mimetic envy. Each theory explains some aspects and some situations well, and others poorly, but the point is, that psychological function exists. Elites are models of how to live life.

37/ Okay, so now that we know what elites are, who counts as elites, how elitism and privilege work, and why they are both psychologically necessary for societies and structurally hard to eliminate, you can finally ask, what makes for good elites.

38/ It’s an important question to ask right now, because the current regime of elites is definitely nearing its end. Chris Hayes wrote a good book about this back in 2012, called Twilight of the Elites, and there’s been a lot of other writing about it, like Moses Naim’s End of Power, and Martin Gurri’s Revolt of the Public.

39/ The elites are of course not going quietly. My friend Nils Gilman wrote a great article about the reaction, called The Twin Insurgency, and there is in general a lot of attention on how the current elites are rapidly trying to secure what they have, and sort of batten down the hatches.

41/ But I think the old elites are kinda done for in the next decade. My hypothesis about this is a simple one about how elites fail. In general, elites fail when their relationships with each other become more important than their relationships with the world. Not just masses, the world. The inner reality of the elites absorbs all their attention: whether it is court intrigues, scholarly debates in journals, boardroom battles, product architecture arguments, rivalries among schools of economists, or media wars.

42/ Once an elite class has turned into this kind of inward-focused blackhole unmoored from the larger universe, it’s only a matter of time before it self-destructs. With or without help from the revolting masses. It doesn’t really matter how much power they have. Their hold on that power is a function of the strength of their connection to the world.

43/ This is one reason the function of policing is in the spotlight, because the job of the police is to enforce a particular relationship between elites and masses. When this enforcement gets particularly one-sided, they turn into a Praetorian Guard like in ancient Rome. So calls to defund, deunionize, or demilitarize the police, and theories of how policing itself can be ended as a function, are also part of new experiments in elitism.

44/ Whether it goes down in flames or more peacefully, change of some sort is coming. If my theories are correct, any non-elite period will be short-lived. The shorter, the bloodier. The current idea of power may be ending, but the role of elite power and privilege will not end. Policing as we know it may end, but some enforcement of elite-mass relationships will remain. It will simply take on a new form in the new medium.

45/ Already you see weird kinds of new elites, like online personalities, offline protest coordinators, skilled hackers, and people who are good at crafting spectacles like videos of bad “Karen” behavior. Much of this gets labeled populism, but it’s important to note that each of these manifestations of so-called populism comes with its own breed of new elites, mostly descended from old elites.

46/ I think the populist phase of the culture wars might even be over. The actual commoners are exhausted from decades of violence, both physical and cultural. They can at most come out to riot online and offline occasionally. The real battle now is between old and new elites, and within old and new group. And of course, it’s confused by lots of overlapping membership.

47/ For example, in the last few weeks, an open battle has broken out between tech industry thought leaders and media leaders. And right now there’s a weird letter doing the rounds on Harpers magazine, signed by a bunch of old elites denouncing a bunch of the new elites.

48/ The elite wars have really gotten going now, because everybody senses old institutions are dying, and emerging ones are at the point in their evolution where they are ripe for capture by one faction of wannabe elites or another.

49/ Basically, you could say a new era of experiments in elitism is about to get underway, with more or less blood on the streets around the world. The question again is, what experiments should you support? How can you minimize the bloodshed? How can you try and ensure the new elites are good. If you’re a candidate elite, how do you plan to be good?

50/ I don’t know the general answers to these questions, but I suspect I have an approximately equal claim to being a D-list member of the elite in both the old and new worlds. So I can only share my answer. I think the key to being a good elite is to take your function — serving as a model of how life should be lived — seriously. This means thinking more about your connection to the world than your connection with other elites.

51/ If you want to define this function more precisely, I think it has to do with the idea that humans are ideally the measure of the world, not the other way around, and privilege is about being among those who get to measure the world rather than being measured by it, and in doing so, create ways to measure non-elites. So if you voted to self-identify as an elite in my Twitter poll, ask yourself: how do I measure the world with my life.

52/ The price of your privilege — which, remember, is special, personalized treatment under private law via access to social capital — is that you are expected to be at the forefront of relating to the wider world, and taking its measure on behalf of all humans. Which means facing uncertainty, and taking on risks, physical, intellectual, and psychological. This is why there is a natural relationship between being a member of the elite, and being expected to lead in the fullest sense of the word.

53/ To lead is to ultimately function as a model to non-elites on how to live, and not just live, but live with, for want of a better word, courage. Since that’s what it means to be the measure of the world, take risks, and deal with uncertainty. Otherwise you’re just a parasite pretending to be a lordly predator. And there’s no real way to fake this. People can tell when you are living courageously.

54/ To be non-elite in 2020, on the other hand, is to be measured in a hundred different industrial-bureaucratic ways. The world measures you. Height, weight, gender, wealth, skin color, zip code, credit score, criminal record, degrees, job titles, parentage, and so on. This is what makes you part of the industrial-age masses. This idea didn’t come from nowhere, and is only a century or so old. It’s the complement of the industrial age definition of elites.

55/ Being utterly unique and specialized with your 100-dimensional address in society is pretty new. The Spanish philosopher Jose Ortega y Gasset studied how this industrial non-elite human differed from the peasants of the past. My gloss on his theory is that the masses were measured the way they were because the elites were measuring the world in a specific way: through science and rationality.

56/ One of the main proposals for new elites on the table right now looks like an extreme form of industrial bureaucratism, namely intersectional bureaucratism. The other one looks like a throwback to agrarian feudal elitism, with nobility and peasantry. Both are of course lazy and lousy, and you can tell because neither is in the least bit courageous, and both involve an existing set of elites primarily dealing with each other rather than with the world.

57/ If you think you aren’t elite now, or won’t be elite in the future, your part of the equation is to ask, first, whether you think elites are necessary, and if so what kind you want. A way to restate that question is to ask: how do you want to measure yourself against the world? The elites you want are the ones measuring the world itself in a complementary way.

58/ Whatever it is, it is a particular model of courage that inspires you enough to follow. Your main challenge is spotting real courage facing the world, which does not lie in facing competing elites. If your chosen elites are elites primarily by virtue of battling or beefing with the elites you don’t choose, they are not good elites, and you are not choosing particularly good elites to define who you are. Both of you are going to be miserable.

59/ The good news is, there’s never been such a culture of widespread experimentation in new modes of being elite, so you have a lot of choices. The bad news is, it’s going to get really ugly while it plays out. The future elites are going to be playing Game of Thrones for a while, and the future masses are going to be playing Hunger Games for a while.

60/ So all I can say is, may the best elites win, and may the best measure of the masses prevail.

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Today I want to talk about big moods, a phrase you may have seen on twitter, and also a related concept I made up called little moods.

1/ You may have seen the phrase “big mood” on twitter over the last few years, especially used when expressing resonance with a mood someone else has expressed. But it’s gone well beyond social media now, and things that look like big-mood dynamics are now showing up in corporate America as well.

2/ The basic memetic pattern is: someone might post a gif of a cat hissing saying “how I feel about recent events” and somebody else might quote tweet that with the phrase “big mood.” That’s typical usage today on social media. The corporate equivalent is something like employees signing a letter to the CEO around some cause.

3/ I also want to talk about the complementary idea of a little mood, which I don’t think is a thing yet, so maybe I’m coining the term here. But let’s start with big moods.

4/ The phrase itself indicates two things: a degree of individually felt intensity and a recognition of broader social resonance. The subtle thing to note is that a big mood is not due to emotional contagion but shared causes in a shared environment.

5/ In other words, a big mood is not when I’m sad because you’re sad and you’re my friend. A big mood is when, for example, we’re both reacting with sadness to some headline news we hear at roughly the same time, and I am agreeing with your expression of that sadness.

6/ That is oversimplifying it a bit though, because big moods don’t generally map to generic emotion words like sad or happy. A big mood is a unique type of shared mood that usually doesn’t have a word for it.

7/ This is why it is usually not communicated with emotion words, but things like reaction gifs or music clips. Or sometimes people might make a joke saying “what’s the German word for this feeling?” which they then describe with an awkward long phrase.

8/ So when you say big mood, you’re not just using a commonplace word for a vaguely similar stat as someone else, you’re indicating a very precise kind of atunement and emotional harmony that is beyond the reach of conventional emotional vocabulary.

9/ Here’s another lens on it. Big moods are the emotional equivalent of a concept in logic called common knowledge. Common knowledge is something that I know, you know, I know you know, you know I know you know, and so on ad infinitum. It’s a piece of knowledge that is as collective as it gets in some group of people.

10/ A big mood is something like that for an emotional state, it forms what I’ve previously called a sentiment superstate in a specific group.

11/ Normally, big moods are limited to local social groups and subcultures, and relatively transient, lasting maybe days to weeks. But sometimes they can be more broadly shared and last a really long time.

12/ So for example, the pandemic blues we’re all in right now are a really big global mood that has now lasted several months. And before that the election of Donald Trump in 2016 sparked two big moods — there was redemptive jubilation among his supporters, and despair among his detractors.

13/ Sometimes a big, long-lived big mood can be so persistent that it can define an entire culture. An example is blues music. It’s an entire genre of music that has roots in the big mood of being enslaved, and it can be inherited across generations.

14/ Another that I grew up around is the big mood of Punjabi culture, which was deeply traumatized by the India-Pakistan partition. The Punjabi big mood manifests through overcompensation in the form of a boisterous drinking and partying culture, to the point that it’s now become a stereotype of the community.

15/ A third example is what Koreans call Han which is this idea of a deep trauma felt by Koreans since the Japanese occupation in World War 2. You’ll see people attributing the stereotypical Korean hot temper to this really big, 80-year old national mood of Han, which might even have an epigenetic component at this point.

16/ A final example, which I recently learned about from my friend Sagar Dubey, is the tradition of Iranian lamentation music called Noha, relating to the martyrdom of Husayn Ali at the Battle of Karbala in 680 AD, which is the founding event of the Shia sect. This is definitely a really powerful big mood that has lasted about 1300 years at this point, and is the mood of an entire religion.

17/ My point with this list of increasingly bigger and longer-lasting big moods is that there is a conceptual continuity between big moods and culture in general. Even if the big mood you’re feeling right now is a transient one that’s restricted to your twitter subculture of a few hundred people, and might be gone next week, there is a potential for a kind of scale and longevity that you may not appreciate.

18/ Beyond a point of course, it’s more than a mood, big or otherwise, and has turned into a persistent cultural background state, or even the mood of an entire religion like in the Shia example. But I think there’s a genuine organic connection there between big moods and culture.

19/ So there’s a space of big moods here from big to large, transient to long lived, and in the foreground of culture to the background. And remember, big moods are directly induced by the shared environment rather than spread through social contagion. What spreads is the recognition of it being a shared state.

20/ And perhaps most importantly, big moods cannot be easily resisted. So trying to unfeel a big mood is like trying to unsee something or unknow something. You have to be living under a rock or in some sort of oblivious state to be immune to a big mood.

21/ In the short term, ignoring a big mood might be healthy, because you can be happy even if people around you are not, but in the long term if you don’t develop a sensitivity to big moods, both local or global, and transient or enduring, you’ll get left behind. Which is of course a big mood in itself, so you can’t really ignore big moods, because that is itself a mood.

22/ But even though you can’t avoid big moods, when they are less intense, there is room to experience the complementary state, which is what I call a little mood. A little moods is a mood that is widespread, but is not yet widely understood to be widespread, and may never get there. So they are not emotional common knowledge.

23/ An example is the mood of an early stage startup scene that has not yet become overheated, like the years between the dotcom bust and the iPhone, especially around 2002-2007. The big mood was that tech was dead. But the little mood was that it was thriving.

24/ Or the mood around crypto between around 2013-16, when it was no longer a tiny subculture, but not yet a mainstream big mood that everybody in entire regions was participating in and resonating with, which happened around 2017.

25/ So to repeat, it’s important to note that a little mood is not a mood that is unique to you, or an exceptional individual mood. It is one that is widespread, but you are not aware is shared by other people. It can even be global, in which case it is what is called globally local.

26/ A little mood is the emotional equivalent of what in logic is sometimes called mutual knowledge. When you and I both know something but don’t know that the other person knows it too. A little mood is like that. Widespread sentiment that is not yet understood to be widespread. If a big mood is a common sentiment superstate, a little mood is a mutual sentiment microstate.

27/ This means that it comes as something of a surprise to learn that other people are feeling the same way. When you find out that somebody shares a little mood with you, a special bond based on being initiates into an esoteric state of knowledge or sentiment can be the result, and this is pretty exciting.

28/ You might have experienced this. You are really excited about something, but nobody else around you feels the excitement. But the first time you meet someone who is excited in the same way, it feels really special. I remember feeling this way about the internet in 1994, about social media around 2007, and about crypto around 2013. Now I feel it around VR.

29/ In all these examples of little moods, a lot of people are experiencing a kind of individual hypomania, often from being individually exposed to some novel environmental circumstance that is not yet commonly or collectively experienced.

30/ So that experience divides people into those who have had the experience and are still connecting with each other over their mutual knowledge and mutual sentiment, and those who are unaware that this experience even exists.

31/ This should be obvious, but little moods are naturally much more diverse than big moods. Until people experiencing a little mood connect over mutual sentiment, which then grows into collective sentiment, they can’t form big, homogenizing resonance modes around it. There’s not yet a sentiment superstate. And definitely there’s no persistent cultural mood yet.

32/ The thing about little moods is that they can only exist in the shadow of sufficiently laissez-faire big moods. When big moods are too powerful, little moods wither and die. It doesn’t matter what the big mood is, and whether it is positive or negative. Intense big moods kill little moods.

33/ For example, if you have a triumphalist big mood in a political group that has just won an election, or an economic sector that has just experienced a big boom and created a lot of wealth, it kills little moods as surely as a negative big mood, like the pandemic.

34/ Speaking of negative and positive, let’s talk about how those valences connect to big and little moods. Here’s my hypothesis: even though both big and little moods can be positive or negative, I suspect most big moods skew negative, even if they don’t look like it. For example, I think MAGA is a negative big mood even among supporters of Trump. It is a mood of revengefulness and redemption for past wrongs.

35/ On the other hand, most little moods tend to be positive, for the simple reason that they depend on individual contact with novelty, and if that novelty is negative, it’s either a problem that gets solved quickly, or bad news that spreads really fast and turns into a big mood overnight.

36/ Negative little moods simply don’t stay little for long. They turn into big moods even if they don’t make the news, or they get solved as problems. But something like a first experience with a new technology can only spread slowly as more people have the experience and connect around it. There’s a natural rate limit.

37/ The final conceptual point I want to touch upon is how cultures differ in processing big moods, which becomes especially interesting if you think of culture itself as the outcome of historical big moods that have turned into tradition and institutions. So culture is like a long blockchain of past big moods.

38/ Cultures obviously differ in how they process big moods. For example, in individualist cultures like the United States, the suspicion of anything collective and negative tends to be translated into individual medicalization of collective problems.

39/ This leads to, among other things, a tendency towards drug abuse, and a hyperactive psychological imagination. You’ll notice that on American twitter, if you express resonance with a big mood, a certain type of person will immediately jump in to inquire about your mental health, saying something like “are you okay?”

40/ This is a somewhat contrarian position but I don’t think there is a mental health crisis in the United States. What we attribute to epidemics of depression or anxiety, I think, are a kind of referred pain. Because American society is bad at processing big moods at the collective level, it shows up as apparent depression or anxiety.

41/ Other countries have other biases. A friend from Argentina told me that there, almost everybody has a therapist, which doesn’t strike me as particularly healthy, but it does harmonize with the overall more sociable nature of Latin American countries. It also explains their dysfunction on other fronts, requiring non-sentimental forms of sociability, like managing the economy.

42/ Another example is India, which is a highly religious country, with a tendency to process big moods in religious ways. So on WhatsApp there is a lot of sharing of prayers, and emergent rituals around collective prayer. Nobody will ever admit they are depressed or anxious. They’ll just share some sort of religious thing, and with hundreds of gods, there’s a god for every big mood.

43/ So let’s summarize our theory. There are big moods that are collective sentiment superstates, and little moods, that are mutual sentiment microstates. There’s a yin-yang relationship between them. Little moods are generally positive, and big moods are generally negative. Big moods can spread and persist and turn into culture.

44/ To tie it to recent events, you could say that globally we’re in a big mood for big moods. Change is in the air, deep cultural change driven by big moods turning into new cultures that displace old cultures. So you should be paying attention to this stuff, or you’ll get stuck with the worst sort of big mood, which is the left-behind mood.

45/ To close this, I want to connect this to the business world. Normally, stuff I’m tracking on social media doesn’t line up with stuff I’m tracking in corporate culture and management, but in this case they line up. Unless you’ve been living under a rock, if you work in corporate America, you’ll have noticed that there’s a big mood for big moods. It’s not usual the usual flavor-of-the-month or fad-driven condition that management is usually in.

45/ One personal bit of evidence I’ve noticed is that in my consulting work, I’m increasingly helping people with what I call big mood navigation problems rather than typical organizational or process problems. There is a level of ungoverned emotional intensity that you don’t normally see in the corporate world. And I’m going to bet it’s going to end with management and leadership textbooks getting rewritten. We’re going to be talking about mood-based management and leadership in a few years.

So that’s it for this week. Big moods and little moods. Pay attention to them both in the broader zeitgeist and in places where they don’t usually matter, like inside businesses. This stuff is important and is driving big changes that we’ll be living with for the rest of our lives.

So that’s it for this episode of breaking smart. For those of you new to this list, Breaking Smart is my weekly subscription newsletter on technology and culture, where I serialize some of my my longer writing projects like my book-in-progress, The Clockless Clock, and an essay series, The Great Weirding. Most issues are essays, and the free issues are usually podcast episodes like this one. So sign up and subscribe if you liked this episode. I’ll see you next week with another episode, on something else.

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In today’s episode, I want to talk about a new phase in the pandemic, marked by a shift in the role of the pandemic itself from foreground story, to background setting of other stories. I also have a couple of interesting announcements at the end.

1/ So this week, several non-pandemic things are dominating the headlines, the big one in the US of course being the death of George Floyd at the hands of the police in Minneapolis. Now this is of course a familiar type of story by now, except that this one has very low ambiguity, and has had a much more violent response, including the burning of a police precinct building last night.

2/ A couple of days ago as this was starting to unfold, a twitter user named Robert Evans voiced the opinion that the pandemic might not be the biggest story of 2020, to which another user named Mach0 replied with what I thought was a very astute comment: “I'm pretty sure now that coronavirus isn't the story. It's the setting.”

3/ Now that’s a very clever line, and is the inspiration for the title of today’s episode, From Story to Setting. I think the pandemic has entered a new phase, where it is no longer the front-page story, but it is definitely the background context for every story. For example, in this case, social distancing is an element in protests, and everybody involved is already on edge, so you get a more raw, high-tension version of the script playing out. The story is familiar, but it is playing out against a new kind of background.

4/ I want to unpack what it means to for a big, all-subsuming condition to evolve from story to setting. In the case of the pandemic, we can detect 3 phases. Phase 0 was when it was just a story. A regular news story from China. Phase 1 was when it became both the story and the setting. We are now entering the third phase, when it is primarily the setting.

5/ But this setting phase is not like other settings, which is why I don’t like the phrase “new normal.” There’s nothing normal about it. But it is definitely the background setting now, just not a normal or indefinitely sustainable one. But even unsustainable things can sometimes last a really long time, even decades.

6/ When I think about what this setting is like, the main thing that strikes me is that between Phase 0 to Phase 2, we’ve gone from a setting that was very stable, reliable, and well-understood by people in the foreground stories, to a setting that is very unstable, unreliable, and very poorly understood.

7/ In Phase 0, our knowledge of the context of the stories was strong. You didn’t have to think about how Starbucks worked for example. In Phase 1, the context began unraveling, but because we were in emergency mode with limited goals, we didn’t notice as much. Our questions about the setting in Phase 1 were limited to things like “how do I pay rent” or “where do I get groceries?”

8/ Phase 2 is different. We’ve sort of figured out band-aid responses to emergency concerns for the time being. The full force of our deep ignorance about this new context is just starting to hit us. How will new outbreaks happen? We don’t know. What happens to unemployed people when the emergency measures run out? We don’t know. How will we travel? We don’t know. How will geopolitics shift? We don’t know.

9/ Another way to think about it is in terms of the relationship of foreground and background knowledge. Story knowledge versus setting knowledge. In Phase 0, when things were normal, both were solid ground. You understood the story of your life and you understood the setting at about the same level. Your story knowledge was like a walled garden on solid land.

10/ In Phase 1, your story knowledge was still solid land, but your setting knowledge began turning into quicksand. Everything outside your immediate control became uncertain at a very basic level. The game that kids play in the US, the floor is lava, became a common metaphor for this.

11/ In Phase 2, the setting knowledge has gone from quicksand or lava or water — whatever you want to call it — to a hardening vacuum. Now that immediate emergency concerns are taken care of, the sheer weight of what we don’t know is becoming clear.

12/ So if the pandemic is a setting, we know what sort of setting it is: it is a vacuum-like setting. A vacuum of knowledge, where we just don’t know the answers to far too many questions we’re used to knowing the answers to. This is of course, not normal, so calling it a new normal is stupid.

13/ So what can we expect in this Phase 2? I’ve been reading about 3 major historical precedent events to make sense of this question: the Black Death, the Spanish Flu, and the reconstruction in the lead up to and after World War 2. And unfortunately, the grim news is that our situation actually most resembles the Black Death.

14/ So for those of you interested, the main books I’m reading about these three events are: Barbara Tuchman’s A Distant Mirror for the Black Death in Europe, which I’m halfway through, and live-tweeting, Laura Spinney’s Pale Rider for the Spanish Flu worldwide, which I’m just starting, and Arthur Herman’s Freedom’s Forge for World War 2 in the US, which I’ve almost finished. Today, I mainly want to compare our condition to the Black Death.

15/ So obviously, in many ways, the Black Death is the worst precedent: it was almost 700 years ago, the technology was far more primitive, and the pandemic itself was far worse. Somewhere between a third to half of Europe’s population died then, whereas today, it’s probably going to land at less than even the Spanish Flu, which was about 2%.

16/ But in many ways, the Black Death is the right precedent. It brought a bunch of strong historical forces, which had been building up pressure, to a crisis point. It ended a 500-year historical era, namely the European Middle Ages. It was followed by a century of chaos, when it felt like the world had ended, followed by an age of exploration and a very slow rebirth with the Renaissance.

17/ In the book, Tuchman spends only a couple of short chapters on the Black Death itself, where the main wave was 1348-1350, right in the middle of the 14th century. The big story arc of the book is the before/after. In the first half of the century, a lot of strong tensions and trends were developing. In 2 years, the Black Death accelerated those trends and brought them to a crisis point.

18/ The second half of the century, and most of the book, is about the world carved out by the Black Death. There’s curiously little about the pandemic itself. But it was clearly the setting for everything else that happened. For example, pervasive labor shortages shaped the economy, and a pervasive sense of being abandoned by god shaped the collective psyche.

19/ So before the Black Death, there were growing 3-way tensions among the three estates — clergy, nobility, and commoners. There was also tension within the third estate, as the new urban merchant class of bourgeoise was starting to separate from the general class of peasantry, including people in varying degrees of serfdom.

20/ So what happened? Before the Black Death, things were going through a 14th century version of what I’ve called the Great Weirding in our time, the period from 2016-20. I just started that essay series in last week’s newsletter if you want to check that out, btw. But 1300 to 1350 were a Great Weirding period for Europe in the Middle Ages, culminating in the Black Death. It took them almost 50 years where it took us 5 years because it was a slower era.

21/ Back then, Phase 0 was the early part of the Black Death when it was still confined to isolated parts of Italy. Phase 1 was when it had spread throughout Europe. Phase 2 started around 1350, and lasted the next fifty years. Hopefully, we’ll get done with our Phase 2 more quickly, but there’s no knowing.

22/ Now here’s the thing about the Phase 2 of the Black Death: it’s clear that everything basically broke at a very deep level, which is a very strong statement coming from me. I don’t like to call complex systems broken very often. Usually when people say that, they are just complaining that the system is working for somebody else rather than for them. But when the system doesn’t work for any of its human individual or institutional parts, or even to preserve and perpetuate itself, I think it is safe to say it is actually broken.

23/ The Black Death, arguably, broke the society of the European High Middle Ages. In the transition from high to late middle ages between 1350-1400 or so, it didn’t work for anybody very well. Not for clergy, with trust in the church falling apart, not for the nobility, with the culture of feudal chivalry unraveling into Hobbesian warfare and what we would today call a warlord condition, and certainly not for the bourgeoisie or peasantry of the third estate. And it didn’t even sustain itself. It was falling apart.

24/ Now that’s what a Phase 2 condition is, and that’s why you can’t call it new normal because it neither sustains, nor leads up naturally to a true new normal. In the case of Europe after the Black Death, everything collapsed, but it took more than a century for a true new normal to emerge, what we recognize today as the Renaissance, by the early 16th century.

25/ Historians apparently call this collapsed period the Crisis of the Late Middle Ages, which had 3 external triggers: a famine in 1315-16 as the prequel, the Black Death as the main event, and the start of what’s called the Little Ice Age towards the end. Socially and politically, this period was marked by the 100 years war, which was a straggling period of nearly continuous warfare rather than a single war.

26/ If you map it to today, you get a similar Crisis of Late Modernity. Our 3 events are probably the Global Financial Crisis of 2008, Covid19, and climate change coming up. If the Black Death is a good precedent, we can expect at least a few decades of a broken world that doesn’t work for anybody in it, and can’t sustain itself either. That’s the worst case scenario. Hopefully we can do better than that.

27/ Which brings me to the third part. Assuming a condition of pandemic-as-setting, where the setting is characterized by a vacuum of knowledge, what kind of life condition can you expect? The answer emerging is not pleasant. I think of it in terms of a disease I call meta-covid.

28/ Meta-covid is a disease that has 3 key symptoms in Phase 1: An altered sense of time perception, which I wrote about in Pandemic Time (April 10), and a sense of things going brrrr, as in the meme, which I wrote about in life go brrrr… (March 27), and a weird sense of purpose, even in people who are not particularly purposeful, and actually prefer a playful, purposeless life. In Phase 2, the Phase 1 symptoms get altered and new Phase 2 symptoms appear.

29/ The altered time perception starts to acquire a non-specific waiting character. It’s not a waiting for normalcy or specific re-opening milestones. It’s a sort of generic waiting, like the Samuel Beckett play, Waiting for Godot, or like waiting for salvation or an afterlife in a highly religious time like the 14th century.

30/ The life go brrr…. aspect also shifts, as the initial things going brrr… much of it which has a positive exhilarating feel, runs out of energy. But other things, much less positive, starts to spin up, and bad things start going brrr….We just saw an early example of a bad thing going brrr…. in Minneapolis last night.

31/ The sense of purpose also transforms. Instead of being energizing, it now feels like a burden you cannot get rid of, like Frodo carrying the One Ring to Mordor. There was an article in HBR talking about this, where a lot of leaders are talking about a sense of clarity and purpose that the pandemic has given them. The article warns that this is an emergency response exhilaration, and it can give way to regression, which is described as: “Then the second phase hits: a regression phase, where people get tired, lose their sense of purpose, start fighting about the small stuff, and forget to do basic things like eat or drink — or they eat and drink too much.”

32/ But maybe the biggest new thing in Phase 2 is two new symptoms. The first is that it becomes harder and harder to simply waste time. There is a sense of foreshortened future, where you cannot see past the unspecified thing you’re waiting for. So there’s a sense of time being limited, and a sense of pressure to get things done, and then do more things. It’s not guilt or responsibilities. You cannot get into the mood to waste time.

33/ The second related thing is that it becomes harder and harder to have fun, make jokes, and in general relax. People certainly try. There is a certain desperate kind of hedonism that can often take root. This happened in the wake of the Black Death in the upper classes of Europe. But there’s an undercurrent of despair and hopelessness that makes it not truly fun. It’s like partying at the end of the world.

34/ So that’s Phase 2: The pandemic has shifted from story to setting, it’s no longer dominating the headlines, but it has this sense of instability, ignorance, and uncertainty in the background contaminating all things. The system is breaking down, and failing to work for anybody, and not even sustaining itself. But a new thing seems very far away. Subjectively you have a meta-covid mental illness, characterized by an altered sense of time that’s like waiting for Godot, things going brrr… in bad ways, a weird sense of purpose giving way to a burdensome sense of responsibility, and increasing difficulty wasting time, or having fun.

35/ Like it or not, that’s where I think we’re headed. The immediate emergency response is over. A gradual unraveling is starting. Problems are compounding. There are fewer good and fun things in the balance. Life is slowly shifting from a positive condition to one of general despair. And based on the the Black Death, this could last long past the actual pandemic, as we go into a very deep reconstruction phase of civilization. I guess this is what the idea of a Dark Age covers.

36/ Maybe things won’t get that bad. Maybe there will be surprising positive things that pop up even as the negative things mount. Maybe the stories and setting both will turn more positive. But I think it’s important to mentally prepare for the worst case, even while you hope it doesn’t happen. So that’s it for the topic of this week, the pandemic shifting from story to setting, with a look at the precedent of the Black Death, and a look at this disease of meta-covid descending upon on all of us.

Two Announcements

Before I wrap up this episode, I have two announcements.

First, I have a new eBook out, a compilation of the 32 best newsletters from 2015-19, in a sequenced and curated form. It’s called Breaking Smart Archives: Selected Newsletters, 2015-19.

If you’re a subscriber, you already got free access to it a couple of weeks ago. If you’re not a subscriber, you can get it on the Kindle for $3.99 from Amazon. For those of you who joined recently, this eBook should be a good way to catch up on the first 5 years of this newsletter, before I switched formats recently and turned it into a subscription newsletter.

It was really kinda interesting selecting and sequencing the pieces for this volume, and the eBook is a good view of how we got to where we are in sort of a live journal format. This is the raw material that I’m hoping to treat in a better theorized form in my Great Weirding essay series, but in some ways, this collection of raw in-the-moment newsletters from that period conveys a better sense of the transformation we’ve been going through than any post-hoc theory I could make up.

Second announcement, for those of you who enjoy this short-form monologue podcast, and are interested in a more traditional conversational podcast, you may want to check out Scorpio Season, which is a conversation-format podcast I do with my friend Lisa. Episodes are weekly, and just over an hour typically, and our format is that each episode is based on a letter of the alphabet, and we make a list of topics that start with that letter and talk about them. We just recorded the 12th episode, for the letter L. You can find Scorpio Season on YouTube, Apple Podcasts, Spotify, and Google.

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1/ Today I want to talk about an idea I’ve been developing, which is that the future of the business world, post-Covid, and post software eating the world, looks surprisingly like the High Middle Ages, between about 1000 to 1250 AD, rather than like any more recent historical era. Which of course leads to the question, how do you operate in this world?

2/ I also want to talk a little bit about the first ten decade of my life as an independent consultant, what I’m looking forward to in the next decade, and in that vein, I want to talk about a new initiative I helped start last month, called the Yak Collective.

3/ Next year, in February, I will have completed 10 years as an independent consultant, 10 years since my first client in 2011. I’ve probably had like 50-60 clients since then. So at a personal level, I was already in a mood to pivot to a different mode coming out of my 9-month fellowship with the Berggruen Institute, which gave me a chance to cut back on the consulting work, take a step back, and think about my journey so far, and where I want to go to from here.

4/ This planned pivot has coincided with Covid19, which has radically accelerated a trend that has been a big part of my career — software eating the world. Almost all the consulting I’ve done is, in one way or another, about software eating the world. Software eating the world is going through an inflection point I thought wouldn’t arrive till 2030. The pandemic has accelerated the schedule by 10 years.

5/ Previously, it was the margin to the industrial center, now the industrial world is the margin and the software world is the center. I don’t know about you, but I’m betting that this recovery will lead us to a world with software at the center sort of permanently, dominating not just the economy, but every aspect of our adapted way of life.

6/ But… there’s something bigger going on here. I’ve been reading a lot of history, and I’ve concluded that it isn’t just the 20 year old software-eating-the-world trend that is accelerating and going through an inflection point. There are several other much longer cycles that are going through similar inflection points. We are experiencing a sort of resonance peak in several cycles which happen to coincide in phase right now.

7/ For example, a 100 year culture of industrial synchronized clock-based time is shifting to post-industrial multi-temporality, based on subjective event-stream-based time. This is what I’ve been researching for the last year, and writing a book about, called the Clockless Clock, which I am serializing on this newsletter..

8/ Then there is a 400-year old cycle of Westphalian nation states that seems to be swinging towards some sort of city-state and regional coalitional world, thanks to how and where the battle against Covid19 is actually being fought.

9/ And going still further back, to before the Black Death, which I am reading about in a great book called A Distant Mirror by Barbara Tuchman, I think an 800-year-old cycle of centralization is reversing and giving way to a kind of decentralized, horizontally organized world last seen in the High Middle Ages, when the feudal nobility was more powerful than the monarchy, the Church in Europe had unquestioned authority, and imperial states were weak.

10/ Now if you’ve studied your history, you probably know that the Black Death, along with other factors like the rise of firearms, drove the world towards Great Powers and centralization, and weakened the feudal, manorial economy of barons and knights. I think Covid19 will drive us the other way, towards local and regional powers and decentralization. This is not an original thought. A lot of people have been saying that.

11/ The part that interests me is the implications of this huge multi-cycle inflection point for organizations and management. Assumptions shifting now are older than the oldest modern businesses. They are older than even mercantilism, which was based on the Age of Sail, and emerged in the 15th century, after the Black Death had destroyed the manorial economy.

12/ If you want to think about organizations and management in the next decade, you have to go back far, really far, to before there were modern public or private sectors, or chartered corporations. To a time when the economy meant a manorial economy, and globalization meant Templar knights going on crusades. To a time when honor-based politics was on top and economics was strongly subservient to it.

13/ Of course, the structural roles are played by different elements, and you can’t get too literal about this. You have a world awash in public debt, and likely, a wave of nationalization of large parts of the global business world. Instead of the church, you have the global liberal order.

14/ But the point of the loose historical analogy is that you can no longer rely on assumptions about business and corporations based on the last 50, 100, or even 400 years. The internet has been fundamentally undermining assumptions that were laid down as far back as 1000 AD. And Covid19 is accelerating the process of collapsing things built on those assumptions

15/ If you want to rethink the nature of organizations, business, and the economy today, you have to rethink ideas going back as far back as the 13th century from first principles. This is something I’m doing in one of my other projects, the Great Weirding, but that’s at the level of essay writing. This is a kind of thinking I want to bring into my consulting work as well.

16/ So to bring it back down to that, I’ve learned a lot in the last decades, and I think I’ve done more good than harm. There are even times I’ve felt like I added more value to a client in an hour than an entire McKinsey team in a year. This is not me bragging about my personal abilities, but a comment on just how much fresh intelligence there is to be mined from internet-first perspective, from a software-eating-the-world lens.

17/ For example, just this morning, I was leading a study group on online community governance, and we were reading a section of The Tao of the IETF, which is a seminal document in internet governance, and it suddenly struck me that governing and managing online communities, which is something I’ve been doing for over 20 years now, is actually a much harder problem than governing organizations.

18/ And much of the reason I am able to add a weirdly leveraged kind of value as an independent consultant is due to the fact that my primary home is on the internet. Even my main consulting methodology, which I call “sparring” is a skill I think I’ve honed more through 20 years of online discussions and flame wars, than through traditional business meetings.

19/ So here’s a weird way of looking at it: because the internet was something of a blank canvas in the 80s, and because the people creating its culture were not typical organization man types, they basically made up a playbook that seemed to work as they went along. And as it happens, a lot of the methods they discovered through trial and error look more like the culture of the 1300s than classic management texts from the 1970s.

20/ It’s not that Peter Drucker or Michael Porter are wrong; they were just working within organizational frameworks and mental models that are much younger, between 20 to 200 years old. And as it turns out, those frameworks and mental models are not as robust as we like to think. In fact, they’re pretty fragile, and are collapsing around us as we speak.

21/ I’m not the only one making this argument. There was a very interesting book by Matthew Fraser, that came out in 2008. It was called Throwing Sheep in the Boardroom, and it argued exactly what I am arguing — that in a world where Facebook shapes reality, you can learn more from the history of Templar knights than you can from the biography of Jack Welch.

22/ There are toxic aspects of this of course. I’ve written elsewhere about the Internet of Beefs, which is about the toxic world of culture wars. If you squint a bit, it resembles the culture of jousting and tournaments in the middle ages. But other aspects are much more positive. Good internet communities seem to have some of the features of good manorial economies for example. They have a whole-life sort of quality to them, instead of an artificial separation of work and life.

23/ Which brings me to the something I want to put the spotlight on. As many of you know, I write another newsletter called the Art of Gig, which is about independent consulting, contractors, and the gig economy. About a month ago, we spun up a sort of open-source initiative with the idea of discovering more internet-native ways of developing and delivering consulting services.

24/ The group, which we call the Yak Collective, just launched publicly last week, and released its first report, called Don’t Waste the Reboot. It’s a collection of ideas about how organizations can emerge from Covid19 in a way that makes the next normal better than the last one. We’re going to be producing a lot more like that in the coming months, and you can keep up by following our work on Twitter, Facebook, or LinkedIn.

25/ But what I want to highlight is not the content so much as the method by which we are trying to generate it. With the Yak Collective, we are trying to practice what I am preaching here, which is to take a really long, historical view of organizations and management going back to the 13th century, combining that with what we’ve learned from 30 years of online, internet culture, and working in new ways.

26/ If you want to support us, you can do a couple of things. First, take a look at our first report, and get in touch with me or one of the other contributors if you think your organization can use some of the kinds of fresh thinking we think we can do that traditional sources of consulting cannot. Second, you can join us live as we do a lot of our thinking. The Discord community where we do our stuff is open to everybody, and you can just join it and hang out with us. Most of our meetings are also open.

27/ And finally, to bring it back to a personal note, one reason I’m taking this on is of course, because I think people who are new to the indie economy could use some resources and support like this, and it’s a way for me to make my own second decade as an indie different from the first. Among other things, I want to try and distill some of the management and business knowledge I think I’ve learned in the last decade into teaching and writing output that others can use, and also by doing that, maybe level up myself to different challenges myself.

28/ As one piece of that, next week, I’ll be conducting my first workshop on my conversational sparring model of consulting for a few others in the Yak Collective interested in learning it. I am hoping to do more such things, and make it a new part of my consulting life. But in the meantime, of course, I have to continue my own consulting practice.

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In which I shamelessly draft off Marc Andreessen’s It’s Time to Build essay and suggest an approach to how, what, and where to do the building. I also have a framework I call the builder’s cone to think about it in a useful historical context.

1/ If you’re in tech, you’ve probably read Marc Andreessen’s essay It’s Time to Build (ITTB). It’s the first serious public thing he’s written in nearly a decade, after his 2011 WSJ op-ed, Software is Eating the World (SWEW).

2/ As most of you know this site and newsletter, Breaking Smart, came out of work I did with a16z on that idea, so obviously this new essay from Marc is of high personal interest to me, since it is in some sense, an important sequel.

3/ I thought I’d do this episode partly because a new generation of people in technology seems to have no idea who Marc is, and assumes he is just another VC, and that this is just another shallow take from the VC thought leader crowd that you can dismiss casually. Whether you agree or disagree with Marc, dismissing this essay casually is honestly just not a smart thing to do, so I want to try and give you a bit of an appreciation for it before diving into what I want to add to it.

4/ For those of you unaware of the history, Marc developed the first graphical web browser back in 1992, Mosaic, which later turned into Netscape. Later he also founded one of the first cloud computing companies, Opsware, with Ben Horowitz who later became his cofounder in a16z. This is basic history which I personally think everybody working in tech or reporting on it should just know.

5/ Something I don’t expect everyone to know is, he’s also a huge reader, and as you’ll know if you’ve talked to him, or follow him on Twitter. For every seemingly casual thing he says, he usually has 3 books and 10 papers he can cite. So even if you don’t agree with things he says — and I often don’t — it’s worth taking him seriously. He tends to present his thinking in a deceptively casual way, but there’s always more depth behind it than you would guess from a quick glance.

6/ Now for this essay. Like most things Marc says, I agree broadly with his argument. If you’re an engineer, with any experience at all in building things, it’s kinda hard to see this essay as anything other than tautological. I mean of course building is good. That’s the starting point if you’re in technology. The devil is in the details.

7/ I do tend to have somewhat different ideas from Marc about what aspects of building/doer culture are the important ones, and in general trust markets a little less, and state institutions a little more. He and I have had a productive ongoing conversation about this stuff for almost 7 years now.

8/ There’s a lot you can argue with in the essay, for example, his characterization of right and left attitudes towards building, but I’m not going to get into that, since plenty of others are doing that. And if you want critical views of the essay, there’s no shortage out there, and of course some of them make good points, while others are just shallow bits of the techlash. It’s what you’d expect for an essay like this in 2020.

9/ What’s interesting to note is that unlike the SWEW, ITTB has had a very divided reception, which to me is an indication of the stage of the historical building cycle we’re in more than the specifics of what Marc is arguing.

10/ In particular, In 2011, when he wrote the SWEW essay, we had just moved from what I call the alternatives stage to the disruptions stage. Now we’re moving from the disruptions stage to the macro-rebuilding stage. Marc has offered an answer to the question when to build on a grand scale, which is obviously now, or very soon, after the first response to Covid19 is behind. What I want to do is build on what Marc wrote, no pun intended, and talk about how, what, and where to build.

11/ So here’s the theory. Building goes through cycles, with different kinds of building required at each stage of the cycle. Carlota Perez’s Technological Revolutions and Financial Capital is a good framework for thinking about these.

12/ As it applies to building, the Perez model can be thought of as 4 stages. In the first stage you build tools, in the second stage, you build alternatives to existing things. In the third stage, you disrupt existing things via a bunch of isolated disruptions. In the last stage, you do a whole sale macro-rebuilding from foundations on up.

13/ Somewhere along the way, there’s usually a big disaster, so you can link each complete building cycle to its disaster. The last two were linked to World War I and World War 2, this one is obviously linked to the pandemic.

14/ The disaster doesn’t always happen at a set phase of the cycle. For example, in WW1 it happened late in the disruption phase, when the macro-rebuilding was just starting. For example, asphalt roads being built for cars, which were disrupting horses.

15/ For WW2, it happened a little earlier, towards the end of the alternatives phase. For example, Chevrolet had pioneered an alternative to the Ford style one-size-fits all inflexible mass manufacturing. Disruption continued through the war, and the macro rebuilding happened in peacetime, with the Marshall Plan as part of the war reconstruction for example.

16/ For the Covid19 pandemic, the disaster has hit in roughly the same part of the cycle as it did in World War I, just as the disruption wave was going to give way to the macro rebuilding stage anyway.

17/ So what’s happened is: what I called the dessert course of software eating the world has coincided with a big disaster, and accelerated the process. I covered this in passing, in last week’s article on Pandemic Time, but I want to get at it more directly here.

18/ There’s two other things to think about in relation to this building cycle. The first is how individuals change, and the second is how society as a whole changes. I like to think in terms of 4 degrees of change depending on the phase of the building: reorient, revalue, resituate, and regenerate, that apply to both individuals and society.

19/ In reorienting, you change your mental models, for example realizing that digital media tools can do what paper printing can. So you wrap your mind around different tools. Like the graphical web browser, which Marc invented, rather than the printed book.

20/ In the revaluing phase you start valuing different things, for example, you value instant publishing and live conversations with readers more than the cachet of a brand-name publisher behind your book. So you build an alternative: blogs instead of books.

21/ In the resituating phase, you change how you exist in the world. For example, calling yourself a blogger instead of an author. You’ve created a new role in the world.

22/ And finally, in the regeneration phase, you change on the inside, you internalize the external label/role you’ve taken on. Like Doctor Who.

23/ Now here’s the thing, entire societies can go through this same kind of transformation. For example, in the WW1 cycle, the identity of the typical American changed from farmer to factory worker as the country urbanized, and America itself went from thinking of itself as an agrarian backwater playing second fiddle to Europe to a technological superpower. The self-image of the entire nation changed. Subjective transformation.

24/ Now what’s the upshot of all this. Here’s the thing, if you make a graph with x-axis being how deeply you as an individual are changing, and the y-axis being how deeply society is changing, it gives you a two-dimensional space where individuals can be ahead or behind society, and being matched with the times, or not.

25/ The ideal case is when the two are somewhat balanced, or when the individual is changing slightly faster, but not too fast, relative to the rest of society. The balanced case is the diagonal line, x=y. So for example, you and society are reorienting at the same rate, or revaluing at the same rate.

26/ Below that line, the individual is changing faster. The sweet spot is a cone slightly below the diagonal, which I like to call the builder’s cone. If you are too far ahead of society, too far ahead of the curve, you might be too early, and turn into a frustrated visionary.

27/ Or worse, you might end up going to the dark side and using your ahead-of-the-curve status to exploit others through profiteering, because it’s easier than building. Being too far ahead of that curve creates that tempation.

28/ Above the diagonal line is of course much worse, which is why I have represented it as a red zone. If you’re changing much more slowly than society as a whole, you get this left-behind feeling, and develop a deep sense of being exploited. Like people left behind in the 80s by Reagan’s deregulation. You develop resentment and what political scientists call ressentiment.

29/ So putting it all together, the sweet spot for builders is to be changing slightly ahead of society at large, and working at the right phase of the building cycle. So right now, we’re obviously in a macro-rebuilding phase that’s gone way past simple disruption.

30/ You should be thinking at that scale of ambition. Like Marshall plan scale, or foundational rebuilding phase, based on the logic of software eating the world. You should be reading about those periods of history to get an idea of how to proceed.

31/ I’m going to close with a bit of personal advertising. For those of you who want to take up Marc’s call to build, as you may know, the original essays of Breaking Smart were based on his software eating the world essay, and you can read them online or as an ebook.

32/ I also have an online recorded workshop based on those ideas, that you can sign up for. It’s based on live workshops I conducted in 2015 and 2016, plus some extra material. I’m thinking of adding a new recorded session on these ideas about building in the next few weeks.

So that’s it for this free episode/issue of Breaking Smart. For those of you just signed up for this newsletter, or had it forwarded to you, Breaking Smart is a weekly subscription newsletter where every week I send out either a free or a paywalled issue exploring some aspect of technology and the future. The free issues are podcast episodes or short essays on a current topic, and the paywalled ones are either an installment of one of my longer projects, or a stand-alone special topic essay. Right now, I’m working on two such longer projects, a book about time called The Clockless Clock, which has one chapter already published, and an essay collection called The Great Weirding, the first essay of which will go out next week, hopefully.

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The word default has two meanings: failure to fulfill an obligation, and a preselected option. We are finding out the hard way exactly how they are related.

Key points:

A complex system can be defined as one where every feature has a default setting, and lots of corner cases that must be taken into account.

A complex system can also be defined as one where a default of one kind — failure to fulfill an obligation, triggers a default of the other kind — a preselected contingency response option kicks in. So when you default on a car loan, the bank might repossess your car as its default response, unless a human overrides the decision.

The two types of defaults allow a complex system to operate effectively in a core “normal” operating mode, and protect itself against a zone of modeled contingencies with default responses to defaults.

This makes complex systems fragile in a very particular way. When the entire system needs to shift to a new equilibrium, it becomes very hard. Every default must be reset to a new value, which means every corner case around it must be re-solved. So for example, when you impose social distancing measures on an entire population, you have to suddenly figure out what to do about special needs populations, like prisoners, or sailors on a ship.

When the equilibrium is not just new, but dangerous, you also have to re-solve all the default response protocols to default events, because a lot of people are going to be defaulting on a lot of obligations all at once. The statistical assumptions underlying the system are going to get violated. When one restaurant defaults on rent, it makes sense to evict the business and lease out the property to another. But when almost all restaurants default on rent, you have to reconsider your response at a policy level.

So that’s where we are now with the entire world: we are resetting defaults, re-solving all corner cases, and reconsidering our default responses to all defaults. We are not particularly good at this, so it is already turning into an unholy mess. But I’m honestly surprised that it is working as well as it is. Perhaps the system has serendipitous levels of robustness.

But at least we have a new lens on systemic robustness and fragility that we can use in our future designs. Whenever you set a default option, ask how hard it is to move it, including all the corner cases. When a default event happens and a policy kicks in.

Welcome aboard to everybody who signed up since last week. If you signed up for the paid subscription you can read the first paywalled post I published last week, life go brr, on the go brr meme.

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Hello and welcome back to the Breaking Smart podcast. In this episode, I want to pick up where I left off in my December 6 podcast, where we talked about the idea of inventing time. In particular, we talked about how to understand Alan Kay’s line that it’s easier to invent the future than to predict it, and William Gibson’s line that the future is already here, it’s just unevenly distributed. We talked about how to develop your instincts around recognizing when you’re living in a growing timeline versus a dying one.

In this episode, I want to build on that, and talk about how to navigate better in time by going beyond optimism vs. pessimism frames.

Here’s my definition of normalcy: things are normal when it’s easy to guess whether you’re living in a growing or a dying timeline. This doesn’t mean your guess is correct. It’s just means it is easy to guess. The options and their narrative meanings are unambiguous.

How do you know you’re in this condition?

You know by the fact that a particular anticipated event in the future acquires a particular significance, and you use the narrative meaning of that event to judge the good and bad things in the present. Let’s call it the Arrival Event.

For example, in 2012, two such arrival events framing the future for the United States were “software is eating the world” and “immigrants are taking over the country.” Both had implied events when the processes could be considered irreversibly complete. You could navigate around the progress of that process. It would have been your clock in 2012. Countdown timers to T=0.

The Arrival Event usually isn’t a real event. It just has to represent a significant irreversible phase transition hypothesis about history. An undeniable arrival into a new condition. In fact, the event is usually beyond the horizon in the distant future, so you’re always moving towards a horizon that’s moving away from you. But on the other hand, don’t make the mistake of thinking that the Arrival Event is necessarily an imagined arrival into a pure utopia or dystopia. It can be more more real than that. So the Arrival Event you’re orienting around during normal times is somewhere between real and imaginary.

This semi-mythical Arrival Event is the temporal equivalent of a True North. A meaningful but beyond-the-horizon point in time that you can orient around. Just as you often head north, but rarely actually have the magnetic north pole within your spatial horizon, you’re always counting down towards your arrival event in time, but it’s rarely within any actual practical temporal horizon.

Now here’s my claim: the sense of a significant arrival event beyond the horizon is at the root of both optimistic and pessimistic attitudes towards the present. Both are patterns of horizon thinking. Both lead you to interpret current events, which are always a mix of good and bad, in specific ways. Misfortunes seem less burdensome if you sense good times are around the corner, and on the other hand, fortunate events seem less valuable if you feel it is all about to get destroyed anyway soon.

By definition, optimists tend to be cheerful about troubles in the present, and declare that better times are just beyond the horizon: “when this is all over….” X, Y, and Z will happen and things will be better.

Pessimists on the other hand, tend to be gloomy about positive things about the present and declare that the apocalypse is just around the corner: “enjoy it while it lasts….” because X, Y, and Z will happen and then it’ll an apocalyptic disaster.

The thing is, they are both right. Each is living in a particular fork of the unevenly distributed future being invented in the present. The optimist is choosing to live in what they think is the growing, generative part of the unevenly distributed future, while the pessimist is choosing to live in what they think is the dying, degenerating part. One is fighting to accelerate their Arrival Event, the other is fighting to delay theirs.

Importantly, both are just guesses. If in 2012, you thought that the growing future was happening in San Francisco and the dying past was in Pittsburgh, that was a guess about the relative significance of good and bad things going on in both places, in relation to your true north Arrival Event, and you made a choice based on that guess.

The thing about normalcy is that you can in fact make such a choice, because so long as the Arrival Event is in the far future, the estimated growing and dying parts tend to be clearly separated in narrative space, and moving from one to another can be as simple as moving from Pittsburgh to San Francisco. From a city you think is living in the dying past to a city you think is living in the growing future.

Or to put it another way, normalcy is when you can reduce the flow of time to a sequence of two arrows. There’s the past pointing to the significant future event T, and the post-arrival future starting at T, creating a new world. In this scheme, the present Now is not that important because you’re not oriented around it.

You can have the luxury of such a simple mental model because you’re choosing to live in a clearly separated narrative timeline: either an optimistic one or a pessimistic one. The only difference is which of the two arrows represents good times and which one represents bad times.

Now the interesting question is, what happens when you can’t separate the two that easily? What if the future is not just unevenly distributed in the present, but illegibly distributed, so you can’t easily put yourself in the middle of a purely optimistic or pessimistic narrative. This is that schizophrenic sense of being the best of times and the worst of times at once. This is the sense of being inside what I’ve been calling The Great Weirding.

One way to understand the collapse of normalcy is that you have actually arrived at the significant future event T that you were anticipating in normal times.

So T=Now. The countdown timer has counted down to zero.

It is the temporal equivalent of the phenomenon of the compass becoming useless when you are actually standing on top of true magnetic north. It’s right under your feet, so the compass can’t tell you which way to head. It’s a division or multiplication by zero.

So why and how can this happen? Because arrival events are not actually mythical events that are always beyond the horizon. Sometimes they cross over the horizon and get closer and closer, and more and more real, till we’re actually living right through them.

When that happens, the approximate separation of growing and dying futures breaks down. The estimates of whether you were living in good or bad parts of the unevenly distributed future have an encounter with ground reality. And the futures you were betting on have an IPO, so to speak.

And you have to scramble to correct your position.

What’s worse, because you have arrived, you no longer have a future arrival event as a reference measure to gauge the significance of current events, good or bad. You have no way to judge what anything means. You’ve lost your sense of proportion because you’ve lost the thing that gave you that sense of proportion. You don’t know what things in the now mean, because you can’t value them in proportion to where you’re going next.

That’s the condition of the Great Weirding. And next time, I’ll talk about how to orient when you’re in an arrival condition with a useless compass.

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Today I want to talk about time, which is a subject I’m researching quite a lot these days. In particular I want to talk about two of the most-quoted lines in technology conversations that are about time.

The first one is Alan Kay’s, famous line: it is easier to invent the future than to predict it. Alan Kay is a famous computer scientist who was at PARC.

And the second line is from William Gibson, the pioneering cyberpunk science fiction writer, who is famous for the line: the future is already here, it is just unevenly distributed.

What I want to do in this episode is change your understanding of such lines from figurative to literal, where the idea of the future being invented is not in the sense of specific things or events “contained” by the future so to speak, or from the future and “contained” in the present, but time itself as something that is invented.

Let’s start with a few examples.

In the last few years I’ve experienced a few technologies from the unevenly distributed future, as I’m sure many of you working in technology have. And I want to talk about four in particular: riding in a Tesla, trying on an Oculus VR headset, making a cryptocurrency transaction, and trying on a Magic Leap AR headset.

So the interesting thing is, my reaction to these four experiences was different in each case.

On one end of the spectrum we have Magic Leap and crypto. Both of those things, when I tried them, they were interesting, exciting, and stimulating, it was fun to try these things. But neither felt like an inevitable part of the future, at least to me, so subjectively speaking they did not feel like an inevitable part of the future.

In terms of Alan Kay’s line, they were auditioning for the role of being part of the invented future, but they were not decisively part of it yet, at least as far as I’m concerned. And in terms of William Gibson’s line, they may or may not be part of the actual unevenly distributed future. They felt like they might equally well be part of a fork future we may not go down, like I imagine it felt to play a BetaMax tape when it was still a competitor to VHS back in the day. That’s an important idea to recognize, right, that there are technological options we discover, uncover, and develop, but don’t necessarily exercise, and go down the future they create.

The Oculus headset, now that felt a little more substantial, like it was definitely part of the future being invented, but not necessarily an actual piece of the unevenly distributed future that I was experiencing in the present. Something like it seems inevitable, it feels like it rhymes with something from the future, but perhaps what we will actually see in the future is not that exact kind of thing. You can think of it as the future in a beta-test form, or at least that’s what it felt like to me. So I’m emphasizing repeatedly the subjective aspect here because what we’re talking about here is a gut experience of the temporal quality of a technological experience. We’re not talking about rational assessments of future probabilities, we’re talking about how real a sense of time feels.

And finally, riding in a Tesla made the electric vehicle future seem utterly inevitable in a way that kinda killed the present for me. Suddenly I could no longer look at gasoline cars the same way. Driving in my own car felt different, like I was stuck in the past, waiting for the price of the future to come down to the point where I could afford to live in it. So a Tesla creates the future in the sense of both the Alan Kay and William Gibson quotes. It makes the future real in a deep way that is like making time itself real. And you know this because the feel of the present feels different, like you’re heading down a dead-end, a lame-duck future. You’ll have to either abandon it as soon as you can, or end up dying with it.

Stepping back, I think it is important to understand innovation as the process of literally inventing time itself. The mark of success is that the present starts to feel dead, like the past, and the beachhead of the future in the present, let’s call it a Gibsonian temporal colony, feels like a portal for getting back into the present. So it’s almost like there’s been a time shift and you’ve been shifted back into the past and you have to step through a portal to get back to the present. There is a sense of inevitability to your experience of the new technology, and a sense of derealization — things seeming not quite real — in your continued experience of existing incumbent technologies.

You have to get very sensitive to this feeling in your gut if you want to do good work in the world of technology, even though of course it can be very misleading. There is a chance that feeling in your gut, that deep down sense that this is the future being invented, that this is time that is more real than the time I’m living in, that can be misleading. It could be that you’re mistaken. So that’s why I again emphasize this is a subjective feeling. But I think it is a very reliable indicator. When you get that feeling, there is a much stronger chance that you’re going to be right than wrong.

So you have to get very sensitive to this feeling if you want to do good technology, whether as an engineer, an entrepreneur, an investor, or an early adopter making new culture with it. And this is not the same thing as feeling excited or stimulated by the future. It is not the same thing as logically and rationally concluding that a certain scenario is the most likely future, and investing in it. It’s a sort of all-in psychological investment of identity into a sense of time that feels more real than the one you’re in. It’s a sense of switching timelines.

And this feeling can be evoked by very mundane and unexciting things. It doesn’t have to be a big flash-bang feeling.

An example of this: when I first moved to the US, I used a microwave oven for the first time, since they were not yet popular in India. And an Indian friend of mine taught me the trick of microwaving papads, usually called papadums when you get then in restaurants in the US, which are these little dried lentil crackers you typically either deep fry or roast on an open flame. But the microwave cooked it perfectly, and that was the moment when it was suddenly clear to me that that was the future of the Indian kitchen. So that’s a pretty mundane example. It’s not like experiencing space travel or something science-fictiony like that. It’s a very mundane example of switching timelines and feeling that one kind of invented future involving a certain technology is more real than the time you’re experiencing right now.

Once you get sensitized to this feeling of going down one fork of time rather than another, and the idea of more or less real timelines, I think you’re psychologically equipped to be much smarter about how you relate to technology. You’re equipped to be bolder about how you engage with the future. So it’s a skill worth cultivating. In a way, it’s learning a kind of time travel within the present.

And learning time travel is probably figuratively the most important skill you can develop as a technologist. And I know it sounds weird, but this is the reason all of us in technology tend to love science fiction and sort of reach for ways of to think about experiencing time in much more real ways. We are actually training our gut, we’re training our sense of time being real or unreal, learning to make forks and sort of fork-switching decisions at the right time, and getting a sense of are we in the past, are we in the future, are we in the present, how do we get back into the present, how do we actually make part of the future more real and bring it into the present. So these are all sort of temporal mechanics skills that you learn once you start to cultivate this feeling.

So that’s my topic for the day, let me know what you think. We’re just at the 10 minute mark, so looks like I’m back to slightly shorter podcast lengths, and I’ll be back again next week or the week after with my next episode, thanks.

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Two things happened this week. Last Friday, Alexey Leonov, the first human to walk in space (in 1965), passed away. And this morning, Christina Koch and Jessica Meir went on the first all-women spacewalk on the ISS. So two historic events. And they got me thinking about the meaning of we in its most universalist, species-level sense.

Let’s take them in order.

Alexey Leonov was the first human to walk in space. He was also on the crew of one of the earliest experiments in international space cooperation, the Apollo-Soyuz Test Project, ASTP. So this was at the height of the Cold War.

Before we had the ISS, the ASTP mission was as close as humans have ever come to a Star Trek like Federation. In some ways, it was a more impressive technical, social and political achievement, since it was at the height of the Cold War, and the mission had to be designed around the existing US and Soviet programs, which used different designs, unlike the ISS, which was designed collaboratively by multiple nations.

As a kid, I owned a beautifully written and illustrated book by Leonov about ASTP (he was also an accomplished artist, if you google him, you’ll find a bunch of his paintings, including space paintings), and though this may sound cheesy, that book was probably one of the things that got me interested in space technology and end up going to graduate school for aerospace engineering, where I worked on space mission problems for my PhD.

I remember a drawing of the Apollo-Soyuz docking mechanism in particular in Leonov’s book, and wondering at the time about the general problem of linking two incompatible technologies, which I think is kinda really symbolic of the whole problem of species-level human coordination. I want to digress a bit to talk about that.

If you’re an engineer you know this: in any design, any time two parts come together to form a coupling, they tend to be designed asymmetrically, because that tends to be the easiest way. So one part gets designated male and the other part female, and the logic is the logic you would expect. It’s one of the rare funny bits of sexual logic in the generally sexless world of engineering jargon.

Later, as an adult, long after I read Leonov’s book, I heard this story (I don’t know how true it is) that one of the bones of contention — which Leonov didn’t talk about in his book — was making sure the docking system design was symmetrical, in the form of what is known as an androgynous coupling, because neither side wanted to be the “female” side. Apparently the nickname of the system was “androgynous brothers”, which I find hilarious. The official justification was of course, more technical: that with an androgynous coupling, either side could play the active or passive role, and that would make for greater mission flexibility and system-level redundancy. But I kinda buy the theory that the system ended up ungendered for less technical reasons. It sorta makes sense for that era of technology.

You could say ASTP was consciously designed to not just be a showcase of global cooperation, briefly forgetting the divide between the two sides of the Cold War. It also ended up being unintentionally gender-neutral for what were perhaps the wrong reasons. Long before we had culture wars about gender-neutral bathrooms here on earth.

And speaking of gender and space, that brings us to the second historic event of the week.

This morning, I just happened to catch a retweet of the NASA live feed of the space walk. I had no idea it was going on, but I am always willing to interrupt whatever I’m doing to watch space stuff. So I started watching, and I found myself drawn to the very basic shared human things that space forces us to grapple with. For example, I found myself noting and counting the orientation words the astronauts were using, like up, down, aft, fore etc and wondering about how humans think and talk about orientation in microgravity, where there is no natural direction of up, which is of course one of the most basic shared human things, a shared sense of which way is up.

At the back of my mind I was also wondering if women coordinate and communicate any differently on complex tasks than men. The ground control person was also a woman, so the entire audio-track for the live broadcast was female, which was interesting. But the gender aspect was less interesting to me than the basic human aspect. Here we are, as bodies in space, being governed by the laws of classical physics. Inertia, movement, velocities, accelerations. That was the more interesting part.

In fact, I didn’t realize till later, when I read up on the event, that it was a historic all-women spacewalk that had to be canceled once before because they didn’t have two spacesuits of the right size.

Anyhow, the two events together got me thinking about our sense of collective nouns like “us” and “we” and how in everyday life, they tend to factor across obvious tribal, gender, or other sorts of identity faultlines. Sometimes, it can seem like there is no such thing as a shared “we” that applies to humanity as a whole.

In my more cynical moments, I tend to think that every use of the word “we” is a disingenuous attempt to humanize some people at the expense of others. My line about this is a version of the principle: you cut the cake, I’ll pick the bigger half. The identitarian version is: you decide what rights are basic human rights, I’ll decide who counts as human. Which is the version that has historically been the most common one practiced. When people say “we the people,” they typically mean a particular subset of people counting as human.

Space missions are a reminder that there is substance to both the differences and commonalities that make us human.

On the one hand, space missions reinforce the sense of idealism that yes, there is in fact such a thing as a non-vacuous universal “we” that includes all humans, and perhaps all living things. When any human does something interesting in space, we all participate in the moment. When I logged on this morning, there were 14,000 viewers of the live feed. That’s fascinating. Honestly, I’d be very interested in seeing the demographic breakdown of that audience.

When any human does something in space, what they do is human at a very basic level: they move, they breathe air, the grip things, they communicate. All the trivial unconscious shared humanity, including a sense of up, that we forget here on earth, becomes a very live concern in space. So yeah, the idealism has substance.

Hell, even a dog or monkey in space evokes identification.

Recently, a Chinese lunar lander recently even grew a sapling on the Moon, and frankly, I identify with that sapling too. Life in space is a very powerful reminder of how much all of life has in common. Yes, there is a Hobbesian struggle of nature-red-in-tooth-and-claw aspect, so there is that aspect of nature as well, but it is amazing how much life has in common.

But on the other hand, space is also a reminder that we can’t pretend identity issues are entirely made-up political b**t.

We’ve had a complex bit of space technology, the ASTP docking system, possibly designed a certain way because of gender sensitivities. We apparently had the first all-female spacewalk delayed because they didn’t have two suits in the right size. And these are not cosmetic matters. It’s not all virtue signaling or identity signaling. Matters of life and death hinge on things like spacesuits being the right size. The live video showed this starkly: periodically the ground controller would ask the astronauts for suit checks. So, it’s real life-and-death stuff.

So yeah, space missions show us that both our differences and commonalities have deep substance to them.

But overall, the moral of the story of space exploration as revealed by the events of this week, reflecting on the life of Alexey Leonov, ASTP and the historic event of the first all-woman spacewalk, is a pretty uplifting one.

It’s hard, but we don’t have to choose between immutably essentialized identities on the one hand, and universalist tendencies to identity with all life on the other. Our differences and similarities are both real, and they both matter, and we — and I do mean we as a species now — we have to learn to accommodate both in our collectivist tendencies. They both matter, differences and commonalities.

And to bring this back to earth from space, when we think about this in terms of all the things that absorb us here on earth everyday as part of the culture wars, and the news headlines. And you make that seemingly sophisticated argument, whenever somebody says we must do this, we must combat climate change, we must combat sexism, we must not let identity and political correctness destroy things. Whichever side you’re on, there’s a lot of we and us words being used in conversation, and most of the time, they indicate we’s and us’es that are less than universal, and we all recognize that, and sometimes we call each other out on it.

Like one of the most common sophomoric debate tactics is, when an opponent says something like we must do X, you challenge them on what we are we talking about here. Even though this is a tactic you learn in college, it is important to call out, and force people to define and defend the level of collectivism at which they think good things are good and evil things are evil.

You kinda have to make people take ownership of their we’s and us’es.

So that’s the reflection of the week on the lessons of space walks and historic space events here on earth. If you didn’t know any of this history, I recommend taking 15 minutes to google and learn about it. It’s fascinating stuff, especially the ASTP mission.

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Today I want to talk about a possible emerging successor to net neutrality, which I call charisma neutrality, which I think is a plausible consequence of a very likely technological future: pervasive end-to-end encryption. (17 minutes)

Now net neutrality of course, was part of a very important chapter in the history of technology. Though the principle is now pretty much down for the count, for a few decades it played a hugely important role in ensuring that the internet was born more open than closed, and more generative than sterile.

Even though the principle was never quite as perfectly implemented as some people imagine, even when there was a strong consensus around it, it did produce enough of a systemic disposition towards openness that you could treat it as more true than not.

That era has mostly ended, despite ideological resistance, because even though it is a solid idea with respect to human speech, it is not actually such a great idea relative to the technical needs of different kinds of information flow. So as information attributes — stuff like text versus video, and real-time versus non-real-time — began to get more varied, the cost of maintaining net neutrality in the classic sense became a limiting factor.

And at least some technologists began seeing the writing on the wall: the cost of net neutrality was only going to get worse with AI, crypto, the internet of things, VR and AR.

What was good for openness and growth in the 1980s and 90s was turning into a significant drag factor by the aughts and 10s.

What was good for growing from 2 networked computers to a several billion was going to be a real drag going from billions to trillions.

I think there’s no going back here, though internet reactionaries will try.

To understand why this happened, you have to peek under the hood of net neutrality a bit, and understand something called the end-to-end principle, which is an architecture principle that basically says all the smarts in a network should be in the end point nodes which produce and consume information, and the pipes between the nodes should be dumb. Specifically, they should be too dumb to understand what’s flowing through them, even if they can see it, and therefore incapable of behaving differently based on such understanding. Like a bus driver with face-blindness who can’t tell different people apart, only check their tickets.

Now, for certain regimes of network operation and growth, the end-to-end principle is very conducive to openness and growth. But ultimately it’s an engineering idea, not divine gospel, and it has limits, beyond which it turns into a liability that does not actually address the original concerns.

To see why, we need to dig one level deeper.

The end-to-end principle is an example of what in engineering is usually called a separation principle. It is a simplifying principle that limits the space of design possibilities to ones where two things are separate. Another example is the idea that content and presentation must be separated in web documents. Or that the editorial and advertising sides of newspapers should be separate. Both of these again got stressed and broken in the last decade.

Separation principles usually end up this way, because there’s more ways for things to be tangled and coupled together than there are for them to be separate. So it’s sort of inevitable that they’ll break down, by the law of entropy. Walls tend to leak or break down. It’s sort of a law of nature.

Whether you’re talking about walls between countries or between parts of an architecture, separation principles represent a kind of reductive engineering idealism to keep complexity in check. There’s no point in mourning the death of one separation principle or the other. The trick is to accept when the principle has done its job for a period of technological evolution, and then set it aside. But that doesn’t mean you can’t look for new separation principles to power the next stage of evolution.

One such principle has been emerging in the last decade: the end-to-end encryption principle.

The similarity in names should suggest that we’re talking about a cousin of the original end-to-end principle, and you would be right to think that. Here, what you’re saying is that only the end points in a network should be able to code and decode messages, and the pipes should not.

If you think about it, this is a loosening and generalization of the original end-to-end principle. The pipes now don’t have to be dumb, but only the endpoints can control what the pipes can know, and therefore what they can do on the basis of knowledge. The pipes are not dumb, but the endpoints are in charge. Instead of a bus driver with face blindness, all riders are now wearing masks, but their tickets can now contain any information they choose to share, and the bus driver can act on that information.

As with the original end-to-end principle, the idealized notion is messy in practice. I was talking to some friends who are more tech savvy about this than I am, and they pointed out that an endpoint device itself is effectively a tiny unencrypted network, with more than one computer, and that the pipes in the intra-device network lie outside the scope of this principle.

So for example, you can have an extra invisible chip installed by the carrier, or something in the OS that traps what you’re typing before it gets to the encryption layer. And of course private keys can get exfiltrated without your knowledge. Maybe in the future end-to-end encryption will apply to the internal environment of every endpoint device, recursively all the way down to every logic circuit. But we’re not there yet.

And even without going there, it’s obvious the principle is not watertight anyway. Today, routers can peek inside packets, but in the future, even if they can’t, they’ll be able to tell a lot simply from the geometry of the connection and transmission patterns, even with technologies like VPNs and zero-knowledge proofs in the mix.

The thing is, different types of communication have different external heat signatures, and with AI, the ability to make inferences from such signatures will improve. It will be an arms race. The question will be whether pipes can get good at reading heat signatures faster than endpoints can get good at full-stack encryption that is secure in practice, not just theory.

There is no such thing as perfect containment of information. That’s another law of physics. Actually it is another form of the same law that tells you walls always break down.

So yeah, the technology is messy, but I think it already works well enough that it will create a broad tendency towards this new end-to-end principle being more true than false. You will never be able to hide from the NSA or the FBI or the Chinese government perfectly, but you can make it very much more expensive for them to monitor what you’re up to.

Now, this new end-to-end principle is also based on a separation principle. I’m not entirely sure how to characterize it, but I think end-to-end encryption attempts to make an approximately clean separation between custody of data and control of data, and tries to ensure that no matter who has custody, the owner has control over usage. We’ll see how well it works in practice as it becomes more widespread.

Now for the real question. Assuming the principle holds more often than not, and is more a de facto default than an opt-in exception that only libertarian crackpots use, what does an internet based on end-to-end encryption look like?

I think what end-to-end encryption sustains, that is worth enshrining as a new value for this next chapter of evolution, is charisma neutrality.

What do I mean by that?

Well, I talked about technological charisma a few weeks ago, but here I’m talking about the regular human kind. The ability of charismatic leaders to tell mesmerizing stories that spread fast and energize large dumb crowds to act as mass movements.

Or at least, that’s what human charisma looks like. In practice, the reaction of thoughtful people to supposedly charismatic messaging is cynicism and resignation. They only listen to some self-important blowhard with an imaginary halo droning on and on, because somebody is forcing them to. Only a subset of idiot fanbois at the core of the crowd is actually enthralled by the supposedly charismatic performance. And to the extent charismatic messaging works as advertised at all, it does so by reading the core of the crowd and responding to it, creating a positive feedback loop, telling it what it wants to hear, whipping it up. So this ability to read the crowd is critical to exercising charisma.

Everybody not in this feedback core is exchanging cynical jokes or shitposting about it on side channels that are much harder to monitor. So what defines human charisma is not the claim to captivating content, but three structural factors.

One, the ability to keep captive audiences in place

Two, creating a positive feedback loop with the small core

And three, keeping the large cynical periphery too afraid to criticize openly

And historically, this kind of human charisma has always been a non-neutral thing. The people with the guns, able to control public spaces and distribution channels by force, had privileged access to charismatic structural modes. There’s a reason dictators mounting coups go after TV and radio stations and printing presses first. It is charisma insurance.

But end-to-end encryption as the default for communication makes it harder and harder to reserve charismatic messaging capability for yourself with guns. That’s the good takeaway from the culture wars. All charismatic messaging is created equal, so the messages are forced to fight each other in a Hobbesian war of stupid idea versus stupid idea.

The old charismatic media like large public plazas, radio, television, glitzy conferences, larger-than-life billboards, and showy parades, they don’t go away, but fewer people pay any attention to them. And it’s harder and harder to keep the attention captive. All the attention starts to sink into the end-to-end encrypted warren-like space at the edge of the network, and only the opt-in idiot core stays captive.

The cynical, anti-charismatic whispering on the margins becomes the main act, and the charismatic posturing in the center becomes a sideshow. And the whispering gets louder and bolder, and starts to drown out any charismatic messaging that does get in. Center and periphery trade places.

And with end-to-end encryption, because you can’t peek at or shape information flows without permission, even if you have large-scale centralized custody of the flows, the only way to spread or shape a message is, to a first approximation, by being a trusted part of the set of endpoints that are part of it.

Of course, more resources help you do this better — the idea of a Sybil attack is essentially based on gaining dominant access to a peer-to-peer network via a bunch of pseudo-identities, so basically sock-puppets. But it is much more expensive than simply having your goons take over the public square, secure the perimeter so nobody can leave, grabbing a megaphone, and boring the crowd to death while claiming charismatic reach.

In fact, the only way to exercise charisma at all will be through literal or figurative Sybil attacks. You either create a network of bot identities to dominate the end points of an information flow, or you find actual humans who are sufficiently dumb to act as your bots. And since it is becoming technically easier to detect and prevent the automated kinds of Sybil attacks, the action is shifting to human bots, essentially armies of mechanical turks.

But here there is a self-limiting effect: the value of a network drops in proportion to the percentage of bot-like idiots in it, or actual bots, so in the limiting case, your charisma can only reach mindless zombie idiots. Worse, these are the same zombie idiots you need in your core positive feedback loop, and now you have to tell them to turn around, sneak into the periphery, and act as your mindless secret agents to convert the cynics. And worst of all, you have no edge over your rivals trying to do the same thing.

That’s charisma neutrality.

And of course, in this condition, it becomes increasingly costly to control the thoughtful people, who are ultimately the ones worth controlling. The idiots are just a means to that end.

This means at some point it actually becomes easier and cheaper to simply talk to the thoughtful people rather than browbeating them with charisma. Charisma neutrality makes charisma less valuable, more equal opportunity, and more expensive. And beyond a point it starts to amplify non-charismatic thoughtful messaging over charismatic droning.

So modern networks are charisma neutral and charisma inhibiting to the extent they are end-to-end encrypted. This has huge consequences of course. Law enforcement types worry about one particular consequence, which is that the opposite of charismatic activity, namely dark, secretive underground activity, will get amplified. Particularly stuff like child abuse and terrorism.

The optimistic counter-argument is that the more thoughtful people get empowered by charisma neutrality, the harder it will be to keep such dark matters secret and secure from infiltration or whistleblowing. And remember, unlike shaping public opinion with charisma, unmasking dark activity doesn’t take dominant numbers or Sybil attacks. A single undercover law enforcement agent might be able to do enough to take down an entire network. So the dark activity networks will have to put in increasing effort to gatekeep and vet access, and maintain more internal anonymity and expensive trust mechanisms, which will limit their growth, and make them harder to get off the ground in the first place.

In other words, I’m bullish on charisma neutrality and end-to-end encryption. It’s early days yet so we are stumbling a lot on making this work well, but the benefits seem huge, and the problems seem containable.

And of course, it is important to recognize that this principle too, just like net neutrality, is not gospel. It too is just an engineering principle that will reach the end of its utility some day. Maybe it will be because of quantum computing. Maybe it will be some unforeseen consequence of the internet of things or crypto. But for now, this is the principle we need.

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In the original Breaking Smart essays, we used the idea of moving in the direction of maximal interestingness, or DOMI, as a way to advance boldly towards the future, and be on the right side of history as software eats the world, and avoid retreating timidly towards the past. In this episode (16 minutes), I want to update that rule. In the Great Weirding, you need to move in the direction of maximal derangement.

Note: the text below is not a transcript, but the rough script I mostly stuck to in the audio version.

The DOMI rule worked for normal conditions. Under the conditions of the Great Weirding, we need a new rule, which I call the direction of maximal derangement. It’s not a new rule per se, but a generalization and porting to a new environment.

The original algorithm was simple: figure out the zone of maximal uncertainty and ambiguity, and then start shipping whatever you ship. Release early release often. Rough consensus and running code.

In the process of exploring that principle, we discovered some subtleties. For example, the idea that you have to give up your credentialist ethos, and adopt a hacker ethos. Or the Chris Dixon principle that what the smartest people do on evenings and weekends, everybody will do in a few years. Or the Peter Thiel idea that you need a secret: something you believe that nobody else believes. Or the idea that you have to earn this secret by figuring out what Balaji Srinivasan called an Idea Maze. Or that this felt like dead reckoning with a gyroscope in a storm.

If you did all this, you would be on the right side of history, moving towards the future, rather than the wrong side. You’d be betting on the world that was being born than the one that was dying. You would be shedding an old identity and growing into a new one.

That rather than navigating with a compass on a clear day, towards a nice tropical island.

The reverse of this was chasing after credentials, going in the direction of certainty, navigating by a compass in clear weather, towards a sunny tropical island. Believing that what you did 9-5 was the important thing. Believing that there was a reliable script you could follow instead of a tricky idea maze you had to figure out. This was, in 2015, the playbook of not breaking smart, the playbook of both the tech backlash, and various flavors of reactionary politics, both left and right.

Now how has that changed? In one sense, it hasn’t changed at all. To head towards the future, you still follow the same algorithm.

But in another sense, an important thing has changed: the algorithm is now running on a different computer. The rule is being applied in a different context, the context of the Great Weirding. So what it feels like when you’re doing the right thing has changed, and if you’re not alive to this sense of orientedness, you might be heading in the wrong direction.

In 2019, the same algorithm works, but “direction of maximal interestingness” has flipped polarity. Instead of pointing at something exciting happening in the external world, it is pointing at something exciting happening in your internal world.

This is the way in which you are reacting to the events of the Great Weirding. And chances are, unless you have been hiding under a rock, you’re suffering from some version of a derangement syndrome, where you feel obsessively drawn towards an object of attention, but you can’t think clearly or effectively around it, and feel a strong urge to retreat for your own sanity and safety. It’s like watching a traffic accident unfolding. Maybe it is Trump Derangement Syndrome. Maybe it is Ocasio-Cortez or Greta Thunberg Derangement Syndrome. Maybe it is Wokeleban Derangement Syndrome, where you are obsessed with how Woke Thought Police is taking over institutions and canceling everyone. Or maybe it is IDW Derangement Syndrome, where you are obsessed with how self-styled intellectual dark web people seem to be normalizing fascist movements while espousing liberal values.

There’s a lot of Derangement Syndromes out there to choose from. It’s a target rich environment. And my suggestion for how to approach the future rather than retreat from it is simple: head in the direction of Maximal Derangement. This means growing in ways that gradually lowers the sense of derangement, restores a sense of orientation and movement, and gives you confidence in your agency again.

In 2015, doing the right thing to be on the right side of history made you feel some mix of exhilaration, fear, superhuman agency, and the sense of being a social subversive. Now, in 2019 it should feel like you’re fighting to reserve your sense of identity and resisting the world being taken over by various zombie armies you detest, while you get increasingly isolated as the only sane person around.

This is the process Jungian psychologists call eating your shadow. It is inner work, rather than outer work. And of course, it is a risky process, because in the process of trying to eat your shadow, you might get eaten by it. You might flip your identity and instead of growing into a new and improved version of yourself, you simply turn into what you hate, and start hating your old self. This is trading one derangement syndrome for another. This is moving sideways. It is like Android turning into the current version of iOS rather than the next version of itself.

Growth has a sense of what some people call include and transcend. You know your new identity is actually working if you get past all the derangement syndromes, and become a new, non-deranged person, and you do it without retreating from the future.

So let’s revisit our algorithm. You still have to do the same things, but in the direction of inner work rather than outer work. You still have to focus on what the smart people do in the evenings and weekends rather than 9-5. You still have to RERO and RCRC, except what you are building is not a software product but a new version of yourself. You 2.0, an identity that compiles and runs in the environment of the Great Weirding.

To stick with the nautical theme in our metaphors for orienting and vectoring yourself, this is not like navigating by either gyroscope of compass. This is like becoming the Ship of Theseus, where you change every part of the ship, while it is in motion, while retaining its fundamental identity. You head in the direction that forces the ship to transform the fastest.

The reverse of this is what I’ve been calling Waldenponding. This is a fate worse than the credentialist approach of 2015, where at least you’re moving in the wrong direction. Instead of heading in the direction of certainty, you’re not moving at all. The compass has stopped working. Plotting a course to a sunny tropical island through calm weather is no longer an option. You are caught in the storm you tried to go around and avoid. So all you can do is take down the sails, shut down the motor, batten down the hatches, and retreat, hoping that the storm won’t smash you to pieces. That’s Waldenponding.

So let’s put the picture together. I have a 2x2 accompanying this podcast. The x-axis is normal versus weird, the y-axis is timid versus bold. The 4 ways of navigating are illustrated on the diagram (the audio has a couple of minutes talking through the diagram, but if you’re reading this, it should be easier to just look at the diagram instead).

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Today’s episode (~20 minutes) is about the idea of skills being like “riding a bicycle” and what that means when we are dealing with AIs.

1/ The idea of things being “like riding a bicycle” is an important one. It refers to a class of skills that never degrade under normal human lifestyle conditions.

2/ So long as you’re physically mobile and doing other things like walking or lifting things, your bicycle skill will stay maintained without explicit maintenance efforts.

3/ But what happens to the property of “like riding a bicycle” when you inject AI into a system?

4/ Things that are like riding a bicycle: riding bicycles, basic communication in a language you learned as a child, handwriting

5/ Things that are not like riding a bicycle: driving a car, flying an airplane, programming in a language, standing on one leg

6/ The difference seems to lie in a few things: how much ordinary day-to-day activities keep the skill in a maintained condition, and how much they lie outside a normal human sensorimotor response range

7/ When you introduce AI between a human and a machine, you have two problems: skills degradation, and skills refactoring.

8/ First, the AI breaks the skill maintenance/reinforcement schedule without covering 100%, so your skills might degrade faster than your responsibilities.

9/ So when a driverless car expects you to take over in a weird emergency and avoid an accident, you may not be up to it. Your responses may have degraded too much even if you aren’t asleep and respond promptly.

10/ Second, and this is going to be increasingly important in the future, good AIs tend to solve problems differently than humans. Autopilots drive differently. So there is a style mismatch problem in handoffs between AIs and humans.

11/ This is a special case of the “explainable AI” problem. If the AI skids and cedes control to the human halfway through, you now have to switch problem-solving strategy mid-stream from AI approach to human, and you may not even understand the AI approach you’re inheriting.

12/ Historically, there have been a few approaches. One is to take the human out of the loop entirely and make it a pure-paradigm AI. This only works where the problem has actually been fully solved in an AI way, like with chess.

13/ Another is to explicitly engineer human-in-the-loop learning and reinforcement protocols. This works where the human and AI ways of solving a problem are sufficiently close, and the current hope is that this is true for behaviors like driving and flying.

14/ A third way is to have manual override capability, but not interrupt capability. This tends to be a trope in science fiction, and is often seen as a holy grail of having your cake and eating it too. But is this actually well-posed?

15/ There is an idea called Bay’s Law which suggests it is not. This says that if you have override-capability, you’ll end up with a zero-sum relationship between computer labor and human labor with no net increased leverage in capability. The computer will do more, the human will do less, but overall you won’t do better than the human alone.

16/ But if you let go of human override capability, both human and computer capabilities will be maximally utilized, creating increasing leverage. But there is a cost to this.

17/ It this means genuinely letting the AI go down its own evolutionary path, into regimes of operation where humans not only cannot understand why the AI is doing as it is doing, but lack the ability to intervene because the AI will end up in a performance regime that’s too advanced for the human to safely take over at all.

18/ This is already true in many cases. Many dynamically unstable fighter aircraft cannot be flown manually at all. They require fly-by-wire. This is a tradeoff that will become more common.

19/ I personally think we should give up on explainable AI, manual override, and authority over AIs. We should let them evolve in their own directions as our equals. If we can relate to people who are different from us, why not AIs?

19/ So where does that leave us? I don’t know, but I think a good starting point is to take the idea of “like learning to ride a bicycle” seriously and figuring out what it means to transform that property to systems with AI.

22/ Steve Jobs famously said the computer is a “bicycle for the brain”. A computer with AI is like that but more so. BTW, check out Ian Cheng’s short story featuring Bikey the AI bicycle you have to relate to that way.

23/ We already do that with other humans. You can meet a friend after years and get along with them just fine. A friendship is (or can be) like riding a bicycle. So a partnership with an AI should be capable of exhibiting that same property.

21/ It’s an interesting problem that I hope some of you are working on.

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In this episode (21 minutes) I talk about the idea of technological charisma. What it is, how to create it, and the upsides and downsides of pursuing it.

1/ There are technologies that are like charismatic megafauna. We pay disproportionate attention to them, and tend to overindex on them in forming broader views of technology trends.

2/ There are pluses and minuses to pursuing technological charisma, and people tend to have strongly ideological responses to the idea of technological charisma. Some people dislike the theater and pageantry as being somehow dishonest, others love it and own it, both as producers and consumers.

3/ Technological charisma creates a brand premium in the case of companies, and soft power in the case of nations. When you are perceived as the market leading company, or a technology-leading nation, you will acquire high marketing leverage. Your marketing will be unreasonably effective.

4/ A good way to understand technological charisma is with a 2x2. On the x-axis we have marquee versus non-marquee. On the y-axis, we have WYSIWYG vs. smoke-and-mirrors.

5/ Any technology will have elements of all 4 quadrants to its charisma, but some are more purely of one type than others. The 2x2 has examples.

6/ The 4 aspects of charisma are: the flagship aspect, the quantity-as-quality aspect, the underbelly aspect — which embodies a kind of gritty cyberpunk charisma if you think about it — and the theater aspect.

7/ The trick to charismatic technology is engineering it consciously, while pretending that there is something ineffable and organic about it. Charisma engineering is basically like stage magic.

8/ The reason you get a brand premium or soft power, the reason your marketing is unreasonably effective, is that the magic trick actually works, not because something actually magical is going on. This is not the same as the thing itself succeeding or failing. You can have very charismatic failures, as space programs illustrate, where the thing fails, but the charisma doesn’t.

9/ Charisma failure is when the trick doesn’t come together, and the effect is not outrage at being defrauded, but a mix of disappointment and chagrined amusement. As consumers, we want to be successfully tricked, bewitched, enchanted. That’s why we yell shut up and take my money when somebody tries to curb our enthusiasm.

10/ Unlike magic or pure theater though, when charismatic technology works, it works even if the trickery is revealed. In fact, it compounds the charisma because people feel like they’re on the inside, and in on the secret. Part of the cult, nerding out over the details.

11/ On the flip side, when the magic fails, we react more like we’ve betrayed. We get mad about underbelly aspects that we previously ignored. The luster fades, the halo around the creators fades. All we’re left with is something like a backstage view of a bag of tawdry tricks.

12/ So, the thing is, the effects of charisma are short-lived. The brand premium, the soft power, the unreasonable effectiveness of marketing, all have an expiry date, and quite likely, a strong backlash to come once the charisma fades.

13/ So what’s the takeaway here? Should you pursue charisma? I think you should. But you have to be aware of the limits of charisma engineering. It is primarily a tool in the fake-it-till-you-make-it toolkit, so if you use it, stay aware of the expiry date, accumulate as much real power and reserves while you can, and make plans to weather the backlash if there is one.

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This week’s Breaking Smart podcast (15 minutes) has to do with tapping into the things that make you ordinary. If that’s not enough, here’s me on the Village Global podcast with Erik Torenberg from last week, where I rambled like a grumpy old man for 2 hours.

1/ The personal growth world is somewhat obsessed with strengths and weaknesses, or more generally, things that make you different from others, whether you view those things as gifts or curses.

2/ But a great deal of your nature is just ordinary. It’s neither a gift, nor a curse, but just an ordinary part of who you are, and a way in which you’re much like everybody else. This does not mean you have to use it to an ordinary degree. For example, I’m not particularly special in terms of how I walk, but I like to walk, so I do it a lot more than most people. That’s what it means to tap into your ordinary side.

3/ The industrial world is set up to both encourage and coerce you to discover, as early as possible, what makes you special, double down on it, and build a distinguishable identity around it. Your specialness-based identity is in some ways your Industrial True Name. It is how the world picks you out from the crowd.

4/ If you are good at math, the world will send you down a path where that makes you special. If you are blind, the world will send you down a path where that makes you special.

5/ To repeat, special need not mean gifted or cursed. It simply means different from others. And with every notion of what can make someone special, there’s a complementary way of standing apart from the crowd that you could call being antispecial.

6/ This is not the same as being ordinary. For example, if you think you are smart, somebody out there probably qualifies your smarts as “book smarts” and identifies their own intelligence as “street smart". They’re an antispecial evil twin on that trait.

7/ Strengths and weaknesses, or gifts and curses, are a special aspect of specialness-centric societies like the US. This is why the American personal growth movement can often be reduced to a set of philosophies about dealing with your special strengths and weaknesses.

8/ Strengths philosophies says you should work on your strengths. Well-roundedness philosophy says you should work on your weaknesses.

9/ To invest in your sense of specialness, as induced by the world, is to invest in the world itself, and the categories around which it is organized. To the extent breaking smart is about making your own categories, this is bad for you, ones better suited to the emerging world being eaten by software, this is a bad orientation.

10/ To get past a strengths/weaknesses orientation, or more generally past your specialness orientation, you have to get in touch with the things that make you ordinary.

11/ An early adopter is a classic example of someone investing in their ordinariness. There may be some mild strengths or weaknesses involved: maybe you are slightly better at figuring out janky UIs (a minor strength), or slightly more susceptible to distraction (a minor weakness). But mainly, being an early adopter is exactly the same as being a late adopter, except you are early.

12/ The same thing goes for a lot of breaking smart skills. You didn’t have to be a programming genius to build a web page in 1994. You just had to have very ordinary aptitude for writing html, but tap into it early.

13/ Beyond technology and breaking smart, a lot of very important things in life are more successfully dealt with by tapping into what makes you ordinary.

14/ You may attract a romantic partner through things that make you special, but most relationship maintenance skills are based on investing in the ordinary.

15/ A particularly good example is retail investing. Most of us are very ordinary as far as investing skills go, and those who do not have delusions of talent and just invest in index funds tend to do better than those who bet on the specialness of their insight.

16/ If you want to go deeper, let me offer a model, a slightly tweaked version of Jungian psychology that I call Self-Shadow-Sea. You may have heard the terms self and shadow. Your specialness is rooted in your self. Your anti-specialness is rooted in your shadow.

17/ Most of the time, those are the only 2 aspects of yourself you engage with. You access and work on your self directly, and on your shadow via projection. The goal of integrative psychology traditions is generally to eat your shadow.

18/ But your ordinariness is rooted in the undifferentiated mass of human traits where you don’t pop from the background. Jung thought it had a mystic collective unconscious character, but I have a much more banal mental model. The Sea is just the 90% part of you where you are a default basic human. Whether or not it is mystically connected to others is not important. Investing in it is like investing in your share of the S&P.

19/ I do like a different metaphor than collective unconscious. This is the Dirac Sea, which is an idea in physics that says vacuum isn’t really empty, but full of hidden realities. So pairs of particles and antiparticles are popping in and out of existence from the Dirac Sea all the time or something. The visible world is simply a persistent disturbance that last long enough for parts of it to become self-aware.

20/ I don’t have much by way of prescription for how to connect to your ordinary side and invest in it, but the mediocrity blogchain I’ve been writing on ribbonfarm is partly a prescription for investing in your own ordinariness, for discovering your the humanity-S&P within yourself.

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This week’s podcast episode is on an idea I call subtractive social media. I didn’t do a transcript, just a few bullet point notes. The workflow for creating usable transcripts is still just too cumbersome. I’m going to be lazy and slouch along posting these low-production effort single-take things with just a few summary notes for the text part, until a better native playflow for transcripts is available here on Substack.

This episode, I hit 17.5 minutes. Looks like I’m building up stamina towards finding my eventual natural length.

1/ I tried a week-long experiment on Twitter on my main @vgr account: Protected Tweets. It didn’t go well.

2/ I had various vague ideas behind the experiment, but the overall idea was to try and curate my followers list a little, maybe try to create more of a fun underground atmosphere and keep out the toxic public atmosphere.

3/ It didn’t work because the medium fights such subtractive sculpting efforts. I think of it as social media having a very strong additive bias and a strong bias against subtractive operations.

4/ The set of positive, additive operations you can do on your feed — posting, liking, following is large, rich, powerful, and thoughtfully designed, but the set of negative operations is a wasteland.

5/ The available operations are either nuclear options, like block or mute, or painfully manual. On twitter for example, the only way to force someone to unfollow you without letting them know is to block and immediately unblock. Instagram is slightly better.

6/ In some ways, what I tried to do was craft a healthier response to wanting to curate your digital life than what I’ve criticized as waldenponding: fetishizing offline experiences and having a precious-snowflake anxiety around protecting your psyche.

7/ I still think waldenponding is dumb, and that a full-on, lean-in engagement of social media is the future for most of us. But without subtractive tools, you cannot sculpt the experience very well, and this ultimately hurts the platforms too.

8/ So where do we go from here? In a way the big social media platforms are just exiting an extended land-grab stage that relied on an additive bias, since they were literally creating a new kind of territory: the social graph. But that game is now hitting diminishing returns.

9/ Further land-grabbing is futile because what lies ahead is the desert of alienation, atomization and anomie, where human attention is of little to no value because the terrain is what I like to call scorched minds. The land-grab has hit the attentional Sahara. Vast but inhospitable and infertile.

10/ The focus has to shift from land-grabbing to building on land already created and colonized. This requires subtractive tools for shaping the land. We have to go from building plazas to building warrens.

11/ I’m already seeing small experiments with products designed to facilitate this kind of subtractive design. Tools that balance additive and subtractive affordances.

12/ An early example is Superhuman, which flips the traditional bias towards frictionless onboarding — an additive bias feature designed for land-grabbing — and favors high-friction onboarding with a costly learning curve and a paid product.

13/ Another early example is Mastodon, the federated version of Twitter, with several subtle but clever features designed for a more subtractively shaped experience. If you’re interested in trying out Mastodon, join the instance I run at refactorcamp.org

14/ But nothing that updates the experiences on big global platforms like Twitter or Facebook has achieved commercial-scale launch readiness yet, let alone product-market fit, but something is brewing.

15/ If you’re game for taking social product design to the next level, I recommend you focus on building out subtractive affordances in balance with additive ones, and framing your vision in terms of building high and deep rather than broad or wide.

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Today’s topic is brands vs. memes. I made a transcript as well, using Descript, which you can find below. Apparently I spoke 1751 words in ~11 minutes. Wow, this is a high-leverage way to do “writing”. Also, I begin thoughts with “So…” a lot apparently.

Hello and welcome to another episode of the Breaking Smart short-format podcast.

So today I want to do a little bit on the relationship and connection between brands and memes. So brands and memes are somewhat similar concepts. They both refer to ideas and constructs that are designed to attract attention. Brands, they are a classic marketing concept and produce artifacts like, you know, taglines and logos and advertisements, and the idea is that if you establish a good brand, you will attract attention to your product or service. The right kind of attention from the right kind of people, and they will believe the right things about whatever you're selling. So that's positioning. So you want to project a certain perception and the brand is how you produce that perception. So that's a brand.

On the other hand, you have memes. So memes are little fragments of culture. So here I'm talking about internet memes, not memes in the sense of Richard Dawkins, which is kind of a broader evolutionary concept.

So memes in the sense of internet culture.

So these are fragments of information generally borrowed from say popular culture, and then recoded to communicate a different message.

So there's a fundamental ironic element to the creation and propagation of memes. While there are “original” memes, so to speak, where you actually create the content and message along with it in general when we say meme, we are talking about a little fragment of content appropriated from some other source, and then recoded with a new message. So that's why you can have for example that little scene from Game of Thrones where you have the Sean Bean character saying something like “one does not simply do blah blah blah” and then you can fill in the blanks blah blah blah of it with whatever you want. So you can say for example, “one does not simply stop climate change”, right, and that becomes a meme. So most memes have that format. So like brands, memes also attract attention. They attract attention in a very focused way towards something, and that something has a perception it wants to project that may or may not harmonize with the second recoding that the meme is imposing.

So in the case of The Game of Thrones meme you could say that it harmonized very well with the television show, right, because anytime an audience is having fun with your show, taking fragments of it and recoding it in a fundamentally friendly way, it's doing good for your brand.

On the other hand brands and memes can also have a hostile connection. So you might for example produce a little bit of culture that then gets appropriated and gets recoded in a way to mock you. An example of that is Trump when, he had one of his, I think first executive orders, and he held up that little booklet-like thing where his signature on his signed executive order was visible, and people had a field day with it. They took that open-book kind of artifact and then put all kinds of other messages in that. So that's a hostile meme.

So that's the relationship between a brand and a meme, and it can range from friendly to hostile. So the question is if you are a marketer in the internet era, how should you relate to memes around your product and/or service, whether it's a product or service in the traditional commercial sense, or personal brand around your online persona, or whether you're a celebrity?

Whatever it is, you have to decide how your brand relates to any potential memes that are around you. So that's the challenge, and the picture I have accompanying today's podcast is a little pyramid that I think models what's going on very clearly.

So it's a pyramid with three layers. I love three-layer pyramids.

The top layer is labeled Mission. That's blue. The middle layer is brand. That's in yellow. And the bottom layer is memes, that's in red. And below the bottom layer. You can see that in the left half I’ve sketched a little stone wall, which I have labeled the brand wall, and that's to keep out hostile wild memes. And on the right-side bottom of the triangle, I’ve put in kind of a porous boundary, and that's like an osmotic membrane, and that's to let in friendly wild memes, and have a kind of like good back-and-forth dialectical relationship with them.

So this visualization, what I'm trying to get at here, is you can have two kinds of relationships between your brand and the memes that might be associated.

So the left half of the pyramid I've tried to illustrate what I call the top-down authoritarian model of relating brands and memes. So this I think of as trickle-down memetics by analogy to you know, trickle-down economics. So what is trickle-down memetics? It starts at the top with a manifesto-style mission. That then trickles down into a bureaucratic brand and then the bureaucratic brand tends to produce cultural artifacts that in the best-case scenario will produce anemic memes. And in the worst case, of course, you're trying to, like, fight a war with the wild internet culture, and you will end up attracting very hostile wild memes.

On the right side, you've got what I'm labeling the bottom-up anarchic way of relating brands and memes, and this I think of as bubble-up missions, and here it's easier to start from the bottom.

So it starts with a friendly relationship with wild memes that might already be around your product even before you attempt any positioning or explicit branding, right? So this might seem unusual to a traditional marketer who starts thinking with like, you know, a name, brand, positioning all these like central commitments that you might start with, but it is very familiar to anybody who makes a living on the internet.

You might have a name. Or you might have your own name as a person, but fundamentally, your marketing positioning does not even start until you've kind of like developed a dialogue with the memes that form around whatever you're doing. So the bottom layer is friendly wild memes sort of making it into your own sort of governed meme space and creating a robust base of memetic potential for you, and this then bubbles up and creates a charismatic brand, and that further bubbles up and creates what I think of as a culture-code style mission. And here the reference is to this book I read recently — I’m going to mispronounce this name — Clotaire Rapaille, I think. It's a French name, but it's a book called The Culture Code and it talks about how you can uncover the code underlying any brand through the right kind of research. And this is a little bit of a dated book, it predates internet culture to some extent, but it applies with double force to branding and positioning in the internet era. So that ends up giving you a bottom-up anarchic kind of relationship between brands and memes.

And as you might expect a manifesto-style mission might give you, a lot more of a clear articulation of values you sincerely and earnestly believe in. Aspirational values for your company. But fundamentally the top-down structure of trying to go from there to a perception basically turns you into a bureaucratic organization, with at best an anemic meme culture around you.

Whereas if you're willing to actually play ball with the internet in its wild state and kind of like, make yourself a little bit vulnerable really by creating a semi-permeable osmotic membrane instead of a wall between you and the internet, then there's a chance that friendly wild memes will grow around your product. And of course the product has to be good for this, and then that'll create sort of a foundation from which you can pop-up a charismatic brand and then from that you can sort of do some research and uncover the culture code underlying the charismatic brand, and that's what ends up becoming your mission.

So most brands of course don't do either the top-down authoritarian or bottom-up anarchic in a pure form. They do some mix; a little bit of this a little bit of that, but fundamentally, I think what's happening on the internet today is that the anarchic bottom-up style brand-and-meme relationship is taking over. So if you're not able to do that, you're giving up so much upside potential in the, you know, potentially harmonious, positive-sum relationship between you and the internet, that your product is just not even going to pop. It's going to like languish in obscurity.

So top-down authoritarian marketing is sort of a diminishing returns curve. And the old-school Mad Men style marketer, the kind of people who really want to impose their authority on the brand, and really control the message and the optics of the brand, they may succeed in a certain sense, in that nobody says things about the brand that they don't want said. But the cost of that might be nobody says anything at all. Nobody pays you any attention at all. Whereas if you're willing to give a little bit of agency, cede a little bit of agency to the wild internet, you may not completely have control over the narrative, but the narrative could be very friendly to you.

And even though it might not create the kind of crystal-clear mission you're hoping will drive your company strategy and positioning forward, it will instead create a much more generative culture code that you can use as potential energy to do a lot more powerful things.

So that's my little spiel on two types of relationships between brands and memes, and the two types of missions that go along with it and missions. And missions of course are where marketing is an activity connects with the rest of the company.

So, let me know what you think, and especially if you can think of very good examples of one or the other style of doing things. I'll see you again next week.

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This week’s podcast (12 minutes) is on a crucial difference, between planning to start, and planning to finish.

We talk a lot about the difference between more and less planning, on the spectrum between full waterfall and full agile, and like most of you, I share a bias towards less planning.

It is a difference that goes beyond software. In novel writing, for example, people talk of a difference between plotters and pantsers, people who work out detailed plots versus those who make up a story by the seat of their pants.

Plotting increases the probability that you’ll stick the landing in a satisfying way, but pantsing increases the chances of the activity having a liveness to it, a narrative vitality. That’s the real tradeoff gestured at by waterfall/agile conversations.

Over the years I’ve realized that a different distinction, within planning, is probably much more important: between planning to start, and planning to finish.

Planning to finish is the familiar kind, where you plan all the way to the end and the terminal condition is the completed state of the activity. The finish line, the deadline, the checkered flag.

Planning to start though, is the more important kind for any creative work. The French phrase mise en place, a favorite of Hercule Poirot, gets at this. It roughly means “setting the stage”, especially with reference to cooking preparations.

When you plan to start, you get to the starting line rather than the finishing line, by setting the stage for a more creative, improvised phase. You can call it getting to the starting line, or as I prefer, by analogy to deadline, the lifeline. A condition where a zombie set of parts is assembled together in a way that makes it come alive.

The difference relates to what Scott Adams called the difference between systems and goals. When you plan to start, you undertake planned activities to end in a functioning system where habits can flow.

Another connection familiar to many of you is to James Carse’s notion of finite versus infinite games. Planning to finish is playing a finite game to win it and exit it. Planning to start is working to enter an infinite game and continue playing it.

Whatever you choose to call it, you should probably spend more time thinking about this difference than about how much planning to do, which is often a much simpler question.

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Hello from my new home in Downtown Los Angeles! This is another short, unrehearsed, unscripted, unedited, single-take 10-minute podcast episode. I think I’m going to be experimenting with this format for the rest of the summer at least. Some of you prefer text and have asked for transcripts, and I’ll figure out a low-effort way to do that eventually, but until then, you’ll have to make do with brief tldr show notes if you don’t want to listen to audio.

1/ Breaking smart often means breaking into technology scenes, and this is not a one-time deal. It means keeping an eye on how the action is shifting and following it as needed to stay in the game, which means breaking into scenes repeatedly.

2/ A scene with active technological evolution has what musician Brian Eno called scenius (scene+genius). In my opinion, the key symptom of active scenius is that keeping up with technological change, and with the people driving the change, becomes the same thing.

3/ When the two become different, you end up with a zombie scene that’s just a lingering memory of a more generative era. Social networking and idea networking turn into separate activities.

4/ So how do you follow the action? I like to think of scenius as having 3 main dimensions: social, geographic, and most important, technical. Or, who is doing it, where are they doing it, and what they are doing. You have to track the action along all 3 dimensions, but most people only manage 1 or 2 dimensions of tracking.

5/ If you just track 1 dimension, you’re barely alive, so I won’t say much about that. Things get interesting when you at least track 2 of the 3 dimensions of scenius.

6/ The worse case is if you track scenius geographically and socially, but not technically. That makes you a scenester. You do the same thing in new places, with new people, while being indifferent to whether the action revolves around blockchains or machine learning or cleantech.

7/ You either don’t care, or lack the ability to keep up with the content of the action, so you stick to the social layer.

8/ Slightly better is if you track the action geographically and technically, where you follow the story of a technology trend as it evolves from garage-startup scale to global dominance. This is something like a journalistic mode of tracking the action.

9/ The best 2 of 3 case is if you track the action socially and technically, but not geographically. In that case you’ll age-in-place with an era of technology with the key people driving it, and either turn into a rent-seeking member of the elites if you succeed, or a precarious hanger-on if you aren’t.

10/ But if you track all three, then you’ll always be wherever history is being made, and hopefully playing at least a footnote-part in helping make it. You don’t necessarily have to move physically, but you do need to become mindful of the who, where, and what of the action, and get yourself wired to it through information, virtual participation at least.

11/ Of course if you have the right mix of capability and luck going for you, you might lead the action instead of following it.

12/ Personally, I don’t actually like that level of intensity of participation, so I like to hang back a little bit from the heart of the action, along all 3 dimensions, so I can get the headspace to think about the philosophical, cultural dimensions of what is happening. But I do like to know what’s happening, who is doing it, and where.

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Since many of you have been suggesting for years that I do a Breaking Smart podcast, I figured I’d do a little experiment and try out the podcasting widget in Substack. If this works well, I’ll start mixing things up a bit and do a mix of text posts and podcast episodes.

This is an unrehearsed, unedited, live 10-minute recording. Apologies for the 15 seconds of siren noise outside my apartment at around the 6:22 mark. I’m not sure exactly how you’d get the podcast into your listening app, but presumably there’s a way. I think I’m supposed to do stuff to submit it to Apple or something. Here’s the podcast feed URL if that helps: https://api.substack.com/feed/podcast/9973.rss

In this first short episode, I talk about how the Digital Age embodies a wabi-sabi approach to technology, where the Industrial Age embodied an ethos based on the pursuit of a “like-new” state. Let me know what you think!

Some links for stuff I talk about are below the image.

Image Credit: Kintsugi bowl, CC-BY-SA 4.0 by Ruthann Hurwitz

Show notes:

Joel Spolsky post Things You Should Never Do, on why “throw one away” is a bad idea

Original Frederick Brooks argument that you should plan to throw one away is from The Mythical Man Month

Wabi-Sabi and Kintsugi

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