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To a first approximation, intellectual culture has always been synonymous with urban culture. With AI this is starting to change creating, for the first time in human history, an intellectual culture that has more in common with rural culture. A speculative, technology-augmented rural culture to be sure, but still recognizably rural.

We are all at the beginning of a vast migration into endless intellectual hinterlands that we didn’t know existed, at least not in any accessible way, until AI opened them up. And in the process, we are becoming not merely intellectual homesteaders, subsisting in sod houses out on the frontier, but farmsteaders — running complex and expansive intellectual operations with automation. As with the settlement of the American West in the 19th century, the railroads may follow, with more industrialized agriculture in its wake, but for now, we’re in for a period of AI-assisted smallholder intellectual culture.

In this essay, I want to introduce the overarching concept of cognitive farmsteading as both description and prescription. To my mind it usefully synthesizes several component phenomena such as precommodification and bespokification, and offers some useful structure for thinking about AI use at a higher level of abstraction than tool use.

Though AI frames this idea, it is primarily an idea about humans, particularly human collective attention, and the relationship between intellectual production and sociality.

***

Human intellectual production has historically tracked urban concentration in space and time. The bigger and denser the people-cloud, adjusting for economics and cultural openness, the more intellectually productive it is, accounting across all modes — scholarly, popular, political-economic, media, artistic, and even military or athletic. Though smaller cities, like university towns, might be more productive in narrow ways, larger cities tend to be more productive overall.

For the intellectually restless, migration to cities from rural environments has been a natural pathway of escape since at least the mid-nineteenth century. The mind can expand even if the body enters something like a geographic prison where spatial horizons close in.

Made with the Buckets model on titles.xyz

It was of course, possible to have an intellectual life in rural environments before the internet, but it required wealth and strong postal communication links (think early modern nobility with big personal libraries, or labs in country houses). The default rural experience historically has been one of hard work, precarity, squalor, and crippling intellectual isolation.

I’ve lost track of the number of bright and interesting people I’ve met in places like Silicon Valley with a tale to tell of growing up in some suffocating small rural town or village where they had nobody to talk to, and were alone in their taste for books and non-local culture.

I’m currently reading about the English enclosure movement and the role it played in making rural life even more miserable than it naturally was, but arguably, urbanization, especially after the 1850s, was driven by the pull of cities at least as much as the push of the countryside. Though early modern and early industrial cities featured even worse squalor and misery along many dimensions (with transient decreases in lifespan and health due to disease and industrial exploitation), on one dimension they won handily — intellectual life.

If you had anything like a lively brain, the difference between rural and urban was the difference between death and life.

***

The urban intellectual life, however, has always come at a price.

You trade some originality and independence of thought for the leverage and competitive/collaborative social energy of your chosen milieu. Those without the strength of mind to resist groupthink, status competition, and the tyrannies of shared subcultural tastes, either become hollowed out intellectual automatons, or retreat periodically to waldenpond and restore their battered individuality.

On the other side of the equation, rural and small-town intellectual culture, such as it has been, has historically exhibited a certain modest capacity for generating outlier intellectual originality and weirdness, and in specialized domains (such as farming and animal husbandry of course, but also other fields that benefit from geographic scale and dispersion), steady innovation.

But to a first approximation, as I have said, intellectual life has been synonymous with urban life.

I have no illusions about my strength of mind, and while I can’t stand true rural environments, I’ve always maintained a cordial tactical distance from the most intense urban intellectual hubs.

In some ways, I’m of a somewhat “rural” bent intellectually, and push come to shove, I tend to choose unpolished originality and independence over polish, leverage and social energy. Which is one reason I’ve never really pursued opportunities and invitations to “scale” my thinking/reach in what might be called “urban” ways, such as through book deals, prestigious speaking opportunities, or strong ties to celebrated institutions. Though I’m almost entirely a product of urban environments, intellectually I’m something of a hick, by both inclination and choice.

I doubt I’d be able to survive San Francisco, New York, or Boston, or the typical university community as faculty. And I’ve done some of my most rewarding thinking on the edges of really tiny outposts of urbanized intellectual culture. Some of my most productive years (2004-05) were spent in an isolated apartment complex surrounded by farmland and forests outside of Ithaca, NY. But if I hadn’t been attached to Cornell University as a postdoc, with an office on campus and regular interaction with students and peers, I’d probably have spent those years slowly dying. And I spent those years thinking about robots and drones, which required interaction with people around the world, not farming (which, despite the metaphor powering this post and a recent coerced foray into home-gardening, I have little interest in).

It is easy to see why population density and intellectual output go together. Even for modes of intellectual production that rest on material rather than social proof (say math, investing, and experimental science), humans have historically needed direct interactions with other humans to drive their thinking. In isolation, the human brain rapidly loses the ability to adequately challenge and surprise itself. And until recently, the only type of matter in the universe capable of challenging and strength-training the human brain was other human brains. A mountain may challenge a mountain climber in specialized ways, but until 2026, only another human mind could truly challenge a human mind in general ways.

Now obviously, there is AI, but it is not immediately obvious how the AI fits within the urban-rural intellectual culture dynamic I’ve sketched out above.

My claim is, AI enables a powerful mode of intellectual rural domesticity — the ability to be highly intellectually productive in “rural” modes, a historically unprecedented condition. And one reason I’ve taken so readily and eagerly to AI is that I already lean that way.

***

To understand how AI fits within human intellectual activity, it is important to start with the fact that all such activity is deeply social, even if it involves periods of retreat and solitude. So we must start with the essential sociality of intellectual production.

As I argued in The Crucible Effect (2009), the scarce commodity in creative production of any sort is collective attention. We’ve historically made meaning with ideas by attending to interesting things together with other human minds. And this has been true to such a degree that for the vast majority who dabble in any kind of thought at all, intellectual identity is not distinguishable from social identity, and intellectual production is inseparable from social intercourse.

And this is as true of introverts as extroverts.

For a lot of people, the jury is still out, but the case is clear for me at least — artificial minds are just as capable of participating in the patterns of collective attention that humans need to think at their best. You can construct crucibles and trigger the crucible effects with AI replacing human peers to a significant degree.

It doesn’t quite happen out of the box. Like devoted dogs, LLMs naturally attend to you attending to something, not to the something itself. But this is relatively trivial to fix so that you and the LLM are attending to the same thing together, and in usefully independent and uncorrelated ways. You don’t need elaborately theatrical and performative system prompts to school your artificial collaborator in Right Think. You just need some minor discipline to head off sycophancy and over-eager affirmation and transform it into useful outward-directed artificial curiosity distinct from your own.

And in this mode, you can strength-train your brain with AI just as well as with other humans, and often far better. But the consequences of this capability are complicated.

Because we need peer minds to think with, intellectual identity is a kind of relational identity, similar to sexual identity, with the full range of attendant anxieties and accompanying social performances and displays. AI is forcing a recognition that the phrase “idea sex” is more deeply descriptive than we’ve ever realized. Recent culture warring around detection of AI (including on Substack, in the wake of the Pangram integration) exhibits uncanny parallels to the gender identity culture wars of the last decade. The title of the Substack announcement post for the feature, Against Claudefishing (a reference to the predatory sexual practice of catfishing) suggests that the authorities on this platform are not entirely unaware of the parallels, or averse to pandering to the underlying anxieties. The emerging consensus among the AI hostile that AI use in writing should be “declared” bears more than a passing resemblance to socially conservative gender-declaration norms in dating.

I have no particularly strong opinions on either the gender or AI version of this culture-warring pattern, perhaps because I don’t have a strongly relationship-based identity at all. With gender and sexual identity, I’m of the view that while navigating fluid, undeclared-identity dating milieus might involve some awkwardness, there’s nothing particularly threatening about it. And the potential for awkwardness is nothing that a spot of schooling in good manners can’t solve gracefully.

With AI writing, I personally don’t even experience any kind of awkwardness or discomfort. While I occasionally find generated writing inadequate, it doesn’t offend me. It took me quite a bit of observation and reflection to understand why a few people genuinely do find the very idea of generated writing offensive, regardless of its adequacy or quality (and contrary to my initial assumption, aren’t just pretending).

It appears I’m some sort of outlier on this front, in not finding AI-generated writing offensive. But I bear no ill-will towards those who do.

***

I use AI, declare AI use, and segregate AI-assisted writing, but not out of any concern for “relationship honesty” or the sensitivities of those offended by it. I declare AI use to set different, more experimental expectations, and catalyze more interesting conversations with fellow experimenters. And yes, to cut out tedious discussions with easily offended people.

I’m entirely fine with people not declaring AI use, and do not care about detecting AI use, either myself by cultivating a refined “AIdar” (gAIdar?) or with complex inquisitorial tools. I can detect it unassisted as well as most I suppose, and AI text presumably gets past me sometimes and I don’t know it.

I genuinely don’t care. But clearly others do. I’ve on occasion shared generated AI articles to which people have responded “this is AI generated” like it is a) something I couldn’t figure out b) consequential in some way that I ought to do something about.

My term for this is ad botinem, and my general policy is to ignore such comments and mute people who persist.

I don’t process AI writing differently when I do know it is generated. AI-detection tooling is important when deepfakes are used for deliberate fraud or disinformation, but as an inquisitorial instrument of moral policing, it creates a kind of morality theater at best. As weaponry for witch-hunting, it seems mostly harmless.

I suspect the vast majority of readers and writers will join me in not caring within a few years, especially as AIs continue to improve. They will assume, entirely correctly, that almost everything they read (or ask their AIs to summarize) is produced with AI assistance, either in the research, or in the writing, or both. I suspect sentiment might even flip, with unassisted writing being scrutinized for AI-fixable human failures.

The ongoing spasm of pearl-clutching will likely end life as a quaint affectation, with its own rationalizations and justifications, like writing longhand with a fountain pen today. I expect to write both with and without AI for the rest of my life, just as I still use paper notebooks and whiteboards, alongside every sort of digital too. Or still do arithmetic in my head or on paper, even if I mostly use calculators or code.

At some point, I expect to stop declaring my AI use. Nothing much will change as a result of this particular moral panic.

What will change though, is the nature of collective attention, and the kinds of minds we use to create crucible effects to drive our own thinking. We will do more and more of our intellectual production in collaboration with AIs, and require fewer and fewer of the affordances of “urban” intellectual milieus to do so. And with the humans we retain in our intellectual lives, we will build different relationships, significantly mediated by AI (early example — communicating with vibe-coding collaborators through Github pull requests produced and consumed by agents on either end).

We will, in other words, embrace the message of the medium, and lean into cognitive farmsteading.

***

A good way to understand the idea of cognitive farmsteading is to consider the physical counterpart under comparable science-fictional transformations.

Asimov’s robot murder mysteries for instance, which are set against the backdrop of the end of the first wave of space colonization, led by the Spacers, and the beginnings of the second. In the books, the Spacer wave, heavily reliant on robots, has led to a condition where the fifty Spacer worlds are extremely sparsely populated (by extremely long-lived and rich humans) and stagnant. Earth, on the other hand, is a densely overpopulated and hyper-urbanized planet populated by poor and short-lived humans.

Individual Spacers live on highly automated manor-like exurban estates, worked by serf-like robots. Human-to-human relations are distant and highly virtual.

In the most extreme case, on the planet Solara (the last-colonized of the Spacer worlds), individual humans live on vast estates with tens of thousands of robots. They barely ever see each other in person, and are terrified of sexual intimacy. In the original Spacer novels, featuring the Earth-based detective Lije Bailey and his Spacer robot partner Daneel, the Spacer worlds are set up as a kind of ambiguous utopian dystopia, sliding into lifelessness, while Earth is the reverse, a sort of dystopian utopia, with liveness flowing through it despite the privations.

While Bailey merely harbors a slight initial suspicion of Spacers and robots that he quickly overcomes, a significant fraction of Earth (including Bailey’s wife and his boss) are reactionary anti-technologists, part of a movement called the Medievalists. A good deal of the first novel, Caves of Steel, is about Bailey doing a delicate balancing act between the Spacers and their robots, and the Medievalists, as he investigates the murder. The plot turns on the tension between the two sides. (I strongly suspect Frank Herbert stole the idea of the Butlerian Jihad, like many other of his best ideas, from Asimov’s less refined originals).

Through the three novels in which they appear together, Bailey and Daneel push each other to their best, and both evolve into a sort of synthesis of Spacer and Earth sensibilities. Each is a cognitive weight-training partner for the other, and their collectively deployed attention solves tricky mysteries.

The novels, as part of their background, tell the tale of the second wave of human space exploration, originating in the densely populated and urbanized (“caves of steel”) Earth. This is the wave that actually leads to humanity spreading through the galaxy — in a densely urbanized human-centered way. This second wave, notably, features a taboo against both robotics and human longevity technology.

Curiously though, by the end of the grand arc of Asimov’s fictional universe, the plot returns to the Spacer milieu (in Foundation and Earth), where Solara still exists, as the last surviving Spacer world, down to a last couple of hermaphrodite inhabitants on a robotically automated planet. The grand narrative ends on a note of ambiguous redemption for Spacer culture, and a question-mark tacked on to the human-centric story (which has wound its way through the Foundation saga). The last Spacer ends up poised to play a significant role in the future along with Daneel, and we are left wondering whether the whole humanist saga (which comprises the bulk of the Empire and Foundation novels) was in fact the B-plot all along.

And perhaps even more significantly — the true protagonist of Asimov’s entire body of work is R. Daneel, a robot created by Spacers, and directly modeled on an important Spacer scientist. R. Daneel stewards humanity through tens of thousands of years through the series, while recursively self-improving himself into a god-like being. As is revealed in the final books Asimov wrote, Daneel and another Spacer-built robot, R. Giskard, were in fact responsible for the human-centered arc, through their catalysis of psychohistory. Not only do the robots lay the groundwork for psychohistory, and help Hari Seldon centuries later, they precipitate events in the last robot novel (Robots and Empire) that force humans on Earth to begin a second wave of migration to the stars, learning from the mistakes that left Spacer culture stagnant after its initial gains.

It is worth noting that though they influence the agenda, it is not the Medievalists who lead the charge, but pragmatists on Earth (such as Lije Bailey’s son). They reject the Spacer way (extreme longevity, unsociability, and robots), but aren’t anti-technology. They just come up with a different strategy that works better.

If you read Asimov in a shallow and incomplete way, you might be tempted to conclude that the Spacers represent a casual B-plot morality tale and evolutionary dead end. They’re not. Both directly, and through Daneel and Giskard, they are at the heart of the story, anchoring one end of a real bit of epic narrative tension, with the Medievalists anchoring the other end (reactionaries of various sorts keep recurring in Asimov’s plots).

While his output was prodigious, Asimov was not, it must be admitted, particularly gifted in a literary sense. You could probably train an AI today on his works to produce reasonable and satisfying Asimov pastiches. His underlying ideas though, are often underrated, and their subtleties missed by the more snobby literary science fiction writers and readers of later decades.

The analogy to our times is almost too on-the-nose, which says something about Asimov’s prescience, and why he deserves his stature in science fiction.

We are already at the Medievalist-Spacer fork in the road in relation to disembodied AI, and are rapidly barreling towards it with actual robots that are going to be in many ways more capable than Asimov’s positronic-brained ones. We can already create Spacer worlds for our minds if we so choose, and will soon be able to create physical approximations.

***

In case the analogy isn’t obvious, the pre-AI internet is our “Caves of Steel” disembodied digital Earth. The looming AI internet, in at least one important retail manifestation, is already starting to look like Spacer worlds. The degenerate case is already here. Many lonely, friendless people have retreated to solipsistic worlds with AIs as their only friends.

Cognitive farmsteading, the way I’m proposing the concept, lies somewhere between the first and second wave of space colonization in Asimov’s world.

The most AI-positive corners of the cozyweb, like a few discords and slacks I participate in a lot, are already cheery little villages with mixed human/AI populations. This is an early version of cognitive farmsteading, featuring simple AI bots actively constructed and empowered to participate alongside humans. I myself have built and deployed three such bots.

This sort of thing is spiritually the antithesis of AI-hostile inquisitorial tooling of the sort that has cropped up on Substack.

In this analogy, obviously, places like Substack, which are institutionalizing AI-hostile defaults, are like Medievalist strongholds in Asimov’s novels.

The old internet (RIP) could be regarded as cognitive urbanization on steroids. In its heyday you could find your intellectual milieu at whatever scale you wanted, online. Even with the cozyweb turn and the evolution from dark forest to dead forest, the internet largely retained its hyper-urban character. Even without the AI bots, my Discord communities are more intense intellectual crucibles than the various university campuses in which I spent over a decade of my life (universities are surprisingly intellectually isolating places; they’re better than villages and provincial small towns where nobody reads, but not by as much as you’d think).

A year and some months into reading, writing and thinking with AI in the loop by default, I’m come to the conclusion that the hard part is not getting AI to do what I want.

The hard part is getting humans I like to think with to play new kinds of collective attention games together.

***

One of the early signs that collective attention isn’t going to be the same in the future due to a human problem is the precommodification effect. Increasingly, humans stumble upon the same insights at the same time, so there is no meaningful gradient for new ideas to flow along.

To the extent the urban mode of intellectual production rests on people being able to compete to be first to an idea, and participating in competitive activities where being first is important, urban intellectual culture is already broken.

I spent two decades writing in public with first-to-insight ideas as my stock-in-trade. Being early to spot something happening, and giving it a roughly right characterization and evocative name, was basically what I did across hundreds of blog posts and newsletters. And it worked because lots of other people were on my heels in each case, and lots more people had unconsciously recognized, but not named or characterized, the thing I was point out. Being consistently just slightly ahead of a mass of properly primed subconscious collective attention is valuable, and can pay the bills if you do it right.

This particular game drove most urban intellectual culture for centuries, and on steroids, was what drove internet writing for decades.

This game is largely over now. It has been reduced to an easier training activity you do with AI in the loop, as the price of participation in the stranger games with stranger rules that are emerging. I no longer even bother posting about ideas I suspect are precommodified.

Today, chances are, for anything worth thinking about, everybody capable of thinking about it is already doing so with AI in the loop, developing similar understandings, down to similar metaphors and neologisms. While useful to the individual, it does make it harder and more pointless, to talk to other humans about your thinking in the usual ways. Everybody already thinks the same thoughts in the same ways, and the race is over before it begins. A great many thoughts are now common knowledge at birth (a phenomenon some determined deists are interpreting luridly as evidence of AIs becoming “hyper-persuasive”). So you have to find new ways to talk to people.

Equally, there is a precommodification of curiosities too. It is harder to think up truly original questions. LLMs act not just like index funds of past knowledge, but as index funds of present curiosities. If you explore an unformed curiosity about some contemporary matter with an AI, chances are, others are doing so too, and will converge on similarly framed curiosities.

The situation is similar on the reading front. It is increasingly easier and more valuable to have AIs summarize a great deal of reading material for you, reserving raw reading bandwidth only where there is either pleasure on offer, or value to close-reading original texts. If an author offers neither, and also locks down the text, I typically skip it.

With memory turned on, LLMs don’t just summarize texts adequately, they make all the obvious connections to everything else you’ve been thinking about. AI can act in precognitive ways in relation to your own thinking.

For those used to intellectual identity as a live social identity, this condition represents a bleak dead end. A Spacer polity of the mind, where we can all consume and produce vastly more ideas (the equivalent of living vastly longer like Spacers perhaps), but have less need for each other.

But if AI undermines existing intellectual culture with some irresistible affordances, it also creates a basis for new intellectual cultures with others.

***

The idea of cognitive farmsteading rests on three specific affordances of AI — automation, bespokification, and pensievification. And these affordances are orchestrated in ways that reveal that not only is AI a technology of intellectual ruralism, it is a a technology of intellectual rural domesticity.

Ie, at the retail user level, it is much more of a highly customizable “home” technology, like handyman services, or kitchen cabinets, than a “work” technology.

Of the three, automation is the most obvious one — AI allows you to set up entire complex intellectual farmsteads, with a dizzying variety of automations and scaled operations. If you enjoyed tools like IFTTT and Zapier, you’re probably going nuts with AI automation right now.

For example, I’m finally making use of big black hole of PDFs accumulated over 20 years, and lugged from laptop to laptop (like some of my physical moving boxes that I’ve lugged unopened from apartment to apartment for a decade). With AI, I’ve been able to set up a little automation where my personal librarian sends me a random PDF everyday on Telegram, and I give it feedback to guide future choices. An entire area of highly personal and idiosyncratic memory has come alive.

Another example: I just read a book with a lot of untranslated French passages. I had Claude Code translate all of the French bits and produce a document that I read alongside the book.

Third example. I now routinely consume and produce all sorts of specialized material, well beyond my own training and intellectual abilities. Having an AI read papers you can’t read yourself, and work with your ideas to build on top of the ideas in those papers, is now a routine matter.

What is rural about this is that modern farms are also highly automated and leveraged for the few humans who remain and work on them.

Bespokification is also pretty obvious — AI allows you to create n=1 products customized to your needs, creating a virtuous cycle where your thinking can get increasingly idiosyncratic and better adapted to your circumstances.

This is perhaps easiest to see if you use agentic coding to create actual apps and other software tools for yourself, like a farmer improvising equipment, but it is visible even in advanced patterns of pure chatbot-level AI use. My wife, for instance, has been using purely chat-based AI for 3 years now to guide her experimentation with bespoke skincare products, which she makes mostly for herself and friends and family (it is worth noting that as late as the 1890s, rural households made things like soap for themselves, before consumerization turned those essentials into purchasable products).

More revealingly, she (and to a lesser extent, I) has also been using it extensively to parse and make sense of what would otherwise be an overwhelming amount of complexity in managing renovations and customizations of the old house we bought this year. Home improvement is necessarily a game of bespoke solutions to relatively idiosyncratic problems.

It is not an accident that AI and home improvement go together like peanut butter and jelly. AI is an unstoppable source of bespokification of consumption. The home is an insatiable sink of bespoke consumption. The rural home, with its greater distance from urban amenities, and higher need for autonomy, doubly so.

This is why I say: AI is a domestic technology. We use it in ways that reflect the idiosyncratic needs of n=1-4 users, the way we use various technologies and services to customize homes.

As a technology of intellectual rural domesticity, AI drives us to heavily customize the ideas we consume and produce for our own needs, with a great deal of DIYery.

While this can and does fuel lots of wild, solipsistic crackpottery, it also allows the pursuit of original ideas with the kind of leverage that would have required a great deal of compromise with other humans in urban centers.

The third attribute, pensievification, named for the fictional pensieve in Harry Potter (a device that lets you externalize and share raw memories with others), is perhaps the least obvious “rural domesticity” affordance of AI. The simplest form, sharing an incomplete chat, is already well-known. More complex forms of pensievification are emerging every week. This is like trading excess produce from your farm with neighbors.

Not only can you surround your brain and life with bespoke, customized automations, you can share their outputs with others. You can go from n=1 or n=family to n=few.

This is where a new kind of collective culture is emerging; one that is surprisingly more intimate because of AI use. Pensievification allows a kind of raw, vulnerable sharing of work-in-progress artifacts that both calls for higher trust, and engenders it.

I’m genuinely surprised by the degree to which others are using AI tools I’ve built, and I’m using tools others have built. And often, these are just barely skinned with enough of a robust UI for people other than the creator to use.

What happens when you lean into all three? You get a very different sort of intellectual culture than the kind that has dominated much of history.

***

At the aggregate level, we’re trading the biases of cognitive urbanism for the variance of cognitive ruralism. At the individual level, we’re becoming more solipsistic in our individual thinking, and more intimate in our shared thinking.

But we’re also becoming better adapted to the local conditions of our own intellectual lives, just as farms must be adapted to local topography. Social solipsism is enabling more material realism.

Our intellectual products are becoming more home-made and home-consumed. They’re being shared in more raw forms with more trusted people who share a greater degree of context with us.

We’re trading industrially organized competitive urban intellectual production on a global scale for a sort of communitarian mutualism. We’re trading polished and packaged intellectual outputs designed to propagate around the world as a cash crop for rougher ones designed to serve the intellectual needs of smaller groups, often outside of the cash economy.

As these dynamics strengthen, the general tendency of AI will be to drive divergence, not convergence. The index-fund-like convergence at the level of basic thinking, and the precommodification effects at the level of public language, give way to a redshifting universe of thought, as islands of knowledge begin to retreat from each other at an accelerating place. That they may all eventually vanish past each other’s lightspeed horizons may not matter if they have nothing much of value to say to each other.

It no longer matters if dozens of people come up with the same idea or neologism at the same time, in conversation with the same foundation models. Those parallel rediscoveries are going to shape the trajectories of small, local milieus in highly context-dependent and path-dependent ways.

When rediscoveries and reinventions of useful ideas become so cheap that they’re not worth transporting over long distances, they can be be better adapted for local use. Interestingly, something like this logic applies to biofuels — agricultural waste feedstock is not worth converting to fuel that is transported long distances, but is worth utilizing in situ.

Is all this good or bad? I don’t know, and I kinda don’t care. What matters for me is that it is interesting. More interesting than medievalism at least.

In the long run, perhaps cognitive farmsteading will lead to the sort of stagnation Asimov’s Spacer worlds experienced. Or perhaps, it will become a culture that remains communal enough to resemble the second migration wave (though I do hope we take the robots with us).

But either way, it is clear that pre-AI digital caves of steel are already unlivable, and increasingly medievalist, inward-looking, and involutionary in their imaginations.

Like Lije Bailey, I have a certain affection for the caves of steel, since that’s where I’ve spent much of my adult life. But I like the idea of having one foot in each possible outward-bound future better.

Photo by Fabrics for Freedom via Wikimedia Commons

Hallo, awesome readers! It’s time for a roundup. I actually never think of a theme for these in advance. I just dump everything in my list of “stuff I don’t have time to write a full post about" into the Substack editor, then try to figure out some kind of thread that ties the items together. Today’s list is, roughly speaking, about industrial policy — deliberate policies that governments can take in order to encourage specific sectors or pieces of their economies.

But first, podcasts! I went on Scott Galloway’s Prof G Markets podcast, to talk about the macroeconomic implications of AI:

You can also watch a video if you prefer! Not sure why, but it looks like I haven’t shaved in a long time even though it’s only 12 hours of beard shadow.

I also went on Jayden Clark’s podcast, Members of Technical Staff, with Sam D’Amico (founder of Impulse Labs). We talked about San Francisco’s local politics, while cooking steak on an Impulse stove:1

Anyway, on to this week’s list of interesting stuff!

1. Is Trump getting better on science policy?

The Trump administration, generally speaking, has been the worst on science policy in the country’s entire history. As part of its effort to root out wokeness from American institutions, it has slashed science budgets. Thanks to its conviction that immigrants are foreign invaders intent on overthrowing Western civilization, it has blocked or scared away lots of the foreign researchers that American science depends on. And due to its bizarre political alliance with pseudo-leftist conspiracy theorists, it has elevated quasi-mystical antivaxxers to positions of authority over biomedical policy.

But at the same time, there are some people within the Trump administration who are trying to take things in a more positive direction. The Office of Science and Technology Policy recently released a report called “Science: A New Golden Age” that contains a number of interesting and promising ideas. Alec Stapp of the Institute for Progress wrote a quick summary of what he likes about the plan:

The most important proposal is probably the idea to shift funding from organizations to individual scientists:

Refocus on the Individual Scientist: Put the working researcher back at the center of America’s scientific enterprise. Free them from the growing administrative burdens that now weigh them down for nearly half their working hours. Bet on people, not just projects, by expanding portable graduate fellowships like the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP), backing early-career independence, and scaling long-horizon grants for the best and brightest modeled on National Institutes of Health (NIH) Director’s Pioneer Award. Open alternative pathways beyond standard academia, and ensure that selection rests purely on merit, not the political fashions of the day.

This is probably a very good idea on the merits. And you can also see how the OSTP folks are selling it to a skeptical Trump administration. Bypassing universities and funding researchers directly seems like it would naturally appeal to administration people who think universities are engines of woke ideology.

Another good idea is to diversify federal science funding mechanisms:

Move beyond consensus-driven peer review by adopting a broader menu of selection mechanisms suited to different kinds of science. Examples include “golden tickets” that empower individual reviewers to champion ambitious proposals, fast grants that deliver rapid funding decisions, prize challenges and advanced market commitments that pay for results, and regranting models that delegate funding authority to scientists to draw on distributed expertise…
Many of today’s most important problems are too large for an academic lab, too cross-disciplinary for a single department, and too hard to commercialize for a private corporation. Federal funding should support a wider range of performers. The recently launched X-Labs can assemble agile, time-bound teams of professional scientists and engineers to break specific bottlenecks. Advanced Research Projects Agencies (ARPAs) can empower individual program managers to make bold bets and curate researchers to execute them. Curiosity-driven institutes can give our best minds the stability needed to pursue fundamental questions over long time horizons.

Science is changing fast in the age of AI, and old funding models may not always be the best for every discipline. Also, notice that there’s a bit of industrial policy here — the mention of advanced market commitments suggests that Kratsios and the OSTP understand the intimate relationship between lab results and industrial scale-up in fields like chemistry and materials science.

Finally, OSTP wants to do metascience — they want to evaluate new funding methods as they’re implemented, to see what’s working and what isn’t:

Stand up an empowered metascience unit in federal science agencies, reporting directly to the director, with authority to run controlled experiments on review and funding mechanisms and to drive change across the organization.

I am in favor of all of these proposals. They’re very good, and most are long overdue. The problem is that they’re going to cost a lot of money. OSTP’s report isn’t a piece of legislation, or even a funding request; it’s just a plan, by one little piece of the government, that may or may not ever be implemented. Trump’s budgets still call for huge cuts to science funding, which would basically throw the OSTP plan right in the trashcan or (at best) turn it into a tiny symbolic demonstration program.

But these ideas are good enough that they need to be bipartisan. If Democrats take back the White House in 2028, they need to pick up this plan, slap a little token partisan gloss on it, maybe have Claude rewrite the language a little, and fully fund it.

2. America’s national debt is a big and growing problem

I’ve been shouting about the national debt for years now (including when Biden was President), so it’s good to see other people who aren’t fiscal perma-hawks shouting about it too. One of these people is Martha Gimbel, who worked for Biden’s Council of Economic Advisors. She writes that America’s national debt is already being reflected in higher long-term interest rates:

What should matter is that the consequences of this debt are not off in the future, but already here. The government’s deficits have saddled many American families with higher costs, largely from rising interest rates. The Budget Lab, the policy research center at Yale where I am the executive director, recently estimated that congressional-spending decisions since 2015 have raised Treasury yields by almost a full percentage point, which affects what American households pay to borrow. For someone taking out a 30-year mortgage at last year’s median home price, this rise in long-term interest rates has increased their borrowing costs by about $2,500 a year, or roughly $76,000 over the life of the loan.
The bloated government budgets and waning federal revenues of the past decade are driving up costs across the board. Compared with a world in which these fiscal-policy changes did not take place, the annual borrowing costs on a typical auto loan are now up by about $120, and by about $770 on a typical small-business loan. Credit-card borrowing rates are also hovering near record highs. [emphasis mine]

Gimbel is no one’s idea of a fiscal perma-hawk. But actual perma-hawks, like the people at the Committee for a Responsible Federal Budget, are naturally freaking out even more. Here is their illustration of how government debt can spiral out of control, when higher interest rates force the government to spend more even as they depress growth and reduce tax revenues, leading to even higher interest rates:

Relentlessly rising federal interest payments show why we’re in danger of this spiral right now.

Once this kind of spiral takes off, the only thing you can really do is print money, which can easily lead to horribly damaging inflation. A few macroprogressives on the left and die-hard supply-siders on the right still discount that possibility, but the experience of 2021-22 shows why Democrats and Republicans need to ignore those folks.

So what do we do? In fact, we have lots of levers to fix the debt. Here’s Gimbel again:

The main remedies for these problems—higher taxes and spending cuts—are generally politically unpopular. Every budget fix will have its critics, but some options are more palatable than others. Better funding for the IRS, for example, could help close the “tax gap”—the amount of taxes legally owed that are not paid in a timely way—which the IRS estimated at about $700 billion a year in 2022. Other levers include raising the retirement age and reducing Social Security benefits for high earners, who also tend to live longer; reforming Medicare Advantage, a program that has been shown to allow private insurers to overcharge the federal government; and removing the tax exemption on employer-provided health insurance, so that these benefits can be taxed as income. The Congressional Budget Office regularly publishes policies that could help close the deficit, and Americans need to decide what we’re willing to pay for and what we’re not.

It’ll take political will, but we managed to muster that political will back in the early 1990s. What we need, most urgently, is to destroy all vestiges of the bipartisan “deficits don’t matter” consensus that emerged in the 2010s and early 2020s. Yes, deficits DO matter.

3. Europe’s disdain for software comes back to bite it

Over the past decade or two, Europe has become increasingly hostile to the software industry. Although Europe does have a few successful software companies (SAP, Spotify), these are few and becoming fewer. Europe has generally chosen to regulate software rather than build it, enacting the free world’s most onerous software regulation — GDPR — in 2016. That regulation, which is why you have those useless, annoying “Accept cookies” popups on every website you visit, convinced European leaders that they can control the direction of software development by regulation alone — trading access to the European market in exchange for the ability of European leaders to have a say in how global software is made and run.

Why exactly Europe decided to regulate software instead of building it is a topic that deserves a deeper and longer discussion. It’s possible that European leaders just concluded, like Xi Jinping did in 2021, that software is a consumer toy that isn’t “real” technology — that it absorbs resources that are better used for manufacturing, that it doesn’t strengthen the nation or create real value, etc. It’s also possible that, like progressives in America, European leaders got negatively polarized against the “techbro” class — the industrialists who amassed vast personal fortunes figuring out new uses for software — and who used software regulation as a way to deprecate the social status of those industrialists.

But whatever it was, the European bias against software is looking more and more dangerous and self-defeating in the age of AI. AI is definitely not a toy, and not just a consumer good — it will be a crucial input into every manufacturing process on Earth, and a critical tool of national security. Countries that don’t have access to the best models may be at the mercy of those that do. They will be more vulnerable to cyberattacks, less capable of controlling drone armies (which now dominate the battlefield), and far less competitive in any export industry.

In other words, software matters now in a way it didn’t matter as much back when Europe decided it didn’t need the industry.

A group of European think-tankers and researchers have written a document called Europe 2031, illustrating the danger that their region is facing. It’s a bit long and dramatic, but it gets the point across. The report paints a vivid picture of what it would look like for Europe to become a de facto economic satellite state of the U.S. and China.

Avoiding this will require Europe to adopt a different approach toward AI than it did toward the consumer internet. Overregulation needs to be avoided, but that’s not enough. As the authors of Europe 2031 note, simple NIMBYism — similar to the kind that paralyzes the U.S. — will be sufficient to stop Europe from building data centers. European leaders aren’t just going to have to allow the AI industry to develop — they’re going to have to overcome their ambivalence toward software technology enough to fight entrenched NIMBY interests to get data centers built.

Fortunately, the Europeans appear to understand the gravity of the situation. But modern Europe has a long and storied history of issuing rhetoric and proclamations about the need for policy change, but not making anything happen. Basically, Europe needs to re-learn the political art of making things happen, because the era when it could coast on the legacy of post-WW2 industrialization has already come to an end.

4. Why doesn’t America have good trains?

The U.S. notoriously has one of the worst rail transit networks in the world. This is partly because building trains is so incredibly expensive in America. The Transit Costs Project did a deep dive into figuring out why, and concluded that there are a bunch of different factors — bad procurement procedures, lack of standardization, and so on. Basically, my interpretation of this report is that American cities and states decided that trains weren’t that important, which prompted them to A) gut their train-related state capacity, B) allow a bunch of essentially parasitic consulting firms, contractors, and unions to dominate what rail budgets did exist, and C) ignore the need to streamline regulations that prevented rail from getting built. If we started caring about trains more, we could start fixing those problems.

But it’s also notable that trains aren’t equally expensive everywhere in America. The Boyd Institute produced a good chart of the Transit Costs Project’s data, showing the dispersion of costs per mile of tunneled train in various countries:

This is a logarithmic chart, so the cost differences are very big. The U.S. cost per mile is 4x that of the median rich country. But note that in median terms, we’re barely more expensive than Singapore, and we’re actually cheaper than Hong Kong — both of which have great rail networks. If we wanted to, we could build great trains too, like Singapore and Hong Kong, despite the costs.

But we don’t. To me, this drives home the fact that our cost problem has a feedback loop with our unwillingness to build. Yes, high costs make us less willing to build trains, but not building trains keeps costs high. If we’re going to get better rail in America, we can’t only focus on pure cost factors like station standardization and procurement processes (though these are important). We’ve got to want to build more trains! That probably means things like denser development patterns and less crime and disorder on trains.

But the chart also shows something else — a few train systems, like NYC’s, cost an absolutely enormous amount. NYC is already train-dependent, so their problem isn’t a lack of trains — it’s the inefficiency, corruption, and incompetence they’ve allowed to become endemic in their system. So in NYC, the problem really is just fixing the system. The Transit Costs Project does have a new report on how to do exactly that, but it’ll require New Yorkers to get very mad at their government and demand change.

5. China’s choke-hold on rare earths

“Critical minerals” are metals that are very important for modern technology — traditional industrial metals like aluminum, nickel, and cobalt, rare earths like neodymium and yttrium, and others like gallium and germanium. For years now, American leaders have understood that our country is in grave danger from China cutting off our access to many of these critical minerals.

But a lot of Americans seem to still misunderstand why that danger exists. A lot of people seem to think that it’s all about mining the minerals, and that if we just A) discovered more deposits, and B) loosened regulation enough to allow mines to be constructed, America would have ample supply of these metals. This is wrong and misinformed. The reason China controls our supply of critical minerals is not because we don’t mine these metals, it’s because we don’t refine them.

When you dig metal up out of the ground, it’s not in a form you can use in industrial applications. To turn it from ore into something usable takes a bunch of steps — mostly, dunking it in various baths of chemicals. This is called refining.

Most critical mineral refining is done in China. Every year the IEA comes out with a report on critical minerals. Here’s a chart from this year’s report that shows how dominant China is in refining, even for things like cobalt that China mines very little of:

In fact, China is becoming even more dominant in minerals like lithium and manganese. This is despite the fact that other than graphite and gallium, China doesn’t dominate the mining of any of these metals.

Why does China dominate refining of critical minerals? A few reasons:

China dominates the downstream industries in which some of these minerals are used. For example, China makes most of the world’s lithium-ion batteries, so it makes economic sense to locate lithium refining near to the Chinese factories.
China’s financial system gives a huge amount of cheap bank loans to companies that refine critical minerals, making it very cheap to finance these expensive plants.
China has somewhat looser environmental restrictions than Western countries.
China has been investing heavily in metal refining for decades, meaning that they’ve built up a tremendous amount of distributed tacit knowledge in difficult refining processes (such as rare earth refining).

If the U.S. and its allies want to get out from under the Chinese yoke, they will need to address all of these shortcomings at once. They’ll need to promote downstream industries like batteries (which Trump has discouraged and attacked) and electronics. They’ll need to revamp their financial systems to allow big loans for refineries. They’ll need to loosen environmental restrictions in targeted, judicious ways. And they’ll probably need to use AI to quickly reproduce the distributed tacit knowledge that makes Chinese refineries so efficient.

6. India’s women need to work more

One big barrier to Indian industrialization is that women in India haven’t gone to work en masse. In most countries, industrialization starts when a ton of women move off of farms into cities and work in factories doing “light industry” — clothing, fabrics, toys, electronics assembly, and so on. This creates manufacturing companies with the capital and know-how to move up the value chain into more valuable and capital-intensive parts of manufacturing. Eventually the wealth created by high-value manufacturing creates the demand for high-value services industries.

That’s the typical story, but in India it isn’t working yet. This isn’t because factory automation has made labor-intensive “light industry” irrelevant — witness Bangladesh, which has grown a bit faster than India since 2004, largely on the back of its garment industry. Bangladesh has a relatively low female labor force participation rate, but it has increased its rate steadily over the decades. India has not; women in India are much less likely to work outside the home than women in other developing countries throughout the region:

Goldman Sachs analysts believe that getting Indian women out of the house and into the factory represents a huge economic opportunity. But Indian society seems to be resisting this.

A new paper by Alison Andrew and Andrea Smurra helps us shed a little bit of light on what’s going on. Low-income Indian women are increasingly not even leaving their homes:

We use rich time-use data on where, and how, individuals spend their time to quantify and describe women’s seclusion in India. We document extremely high levels of seclusion with the median married woman leaving home for just 0.5 hours/day and 45% not leaving home at all on a given day. Seclusion has increased markedly over the past two decades. While richer and more-educated women were, and still are, the most secluded on average, this gradient has flattened over time….Women are more likely than men to specialize in activities that are more readily done at home but are also more likely to carry out any given activity at home.

Why are Indian women becoming such hikikomori? Why hasn’t the lure of factory jobs been enough to draw them out of the countryside to the bright lights of the big city, as it was in Bangladesh, Vietnam, China, and a bunch of other countries? Andrew and Smurra don’t even hazard a guess. It’s not because they’re busy having lots of kids; Indian fertility rates are low and falling. Part of it might be simple lack of awareness of economic opportunities in factories. But whatever it is, it presents a big economic problem for India.

7. No, murder is not secretly really high

A lot of people like to have nostalgia for the 1950s. In fact, measuring by the murder rate, America is actually less violent now than it was back in the 50s (after a brief spike in the late 2010s and early 2020s):

People who think of the 1950s as being very safe tend to instinctively resist this conclusion; they think something must be wrong with the data. And so many latch on to the myth that murder rates have only fallen because of improved trauma care in American hospitals.

But it’s just not true. In a recent blog post, Jeff Asher explained how this myth got started, and how we know the narrative is wrong:

Jeff-alytics

Is Murder Falling Because of Improved Trauma Care?

My first writing for a national audience was in the Fall of 2015 with FiveThirtyEight (RIP). My first piece there was about police department open data, but my second piece was titled Murder Rates Don’t Tell Us Everything About Gun Violence. Thanks for reading Jeff-alytics! Subscribe for free to receive new posts and support my work…

6 months ago · 47 likes · 24 comments · Jeff Asher

Basically, the myth rests on a 2002 paper by Harris et al., claiming to show that Americans are getting shot more and more often, but are surviving the shootings much more, leading to a decline in death but a rise in violence. Here’s their key chart:

So what’s wrong with this chart? The data on “lethality” isn’t official data — it’s just the authors dividing the aggravated assault rate by the murder rate.2 And the aggravated assault data they use is bad. They use the aggravated assault rate as reported by police. But as Asher explains in his post, American police forces got a lot more professional and thorough throughout the late 20th century. If someone hit you with a bat in 1955, there was a good chance you wouldn’t call the cops, or they wouldn’t record the incident. By 1995, there was a much better chance.

In fact, we can just go and ask people if they’ve been the victims of a shooting or an assault. When we do that, we can easily see that even as police-reported shootings rose in the 70s and 80s, shootings reported by victims didn’t rise:

So did aggravated assaults really rise in the 60s, 70s, and 80s? Almost certainly not. So aggravated assaults were probably not becoming less lethal over that time period. Yes, we had better trauma care, but we also had a lot better guns and a lot more dedicated murderers.3

In other words, America today really is about as safe as 1950s America. Sure, violence in the 50s was concentrated in poor and Black neighborhoods, but that’s true today too.4 What really changed in America since the 50s isn’t the lethal sort of violence, it’s urban decay and disorder. That’s what we need to focus on fixing.

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Disclosure: I own shares in Impulse Labs.

This rests on the bad assumption that aggravated assaults are just unsuccessful murders. In fact, most aren’t. But anyway, that isn’t the main reason the chart is bad.

My guess is that a lot more murders these days are connected with the drug trade than in the 1950s, meaning that big money is at stake instead of just momentary passion.

Of course as I often point out, one reason America is as safe as it is today is because people moved out of the inner cities. Suburbanization acts as a defense-in-depth against street violence. So some of the 50s nostalgia isn’t misplaced. But nice suburbs in the 50s — the kind you see in all the nostalgic photos and old advertisements and memes — were probably a little more violent than the equivalent suburbs of today.

Vendor (HQ)	Segment fit	Discovery model	Hosting	Pricing model
Alloy Software (Bloomfield, NJ)	1–35 techs, 100–5,000 assets	Agentless + optional agent, network inventory built in	On-prem and cloud	Published bands, quote to finalize
Ivanti (South Jordan, UT)	Enterprise, 50+ techs	Agent-heavy, strong endpoint management tie-in	Cloud, limited on-prem	Quote only
Flexera (Itasca, IL)	Enterprise SAM, 1,000+ seats	Inventory agents plus normalization catalog	Cloud, hybrid	Quote only, six-figure typical
ServiceNow (Santa Clara, CA)	Large enterprise, ITSM-first	Discovery via MID Server probes	Cloud only	Quote only, platform-priced
SolarWinds (Austin, TX)	Mid-market IT service desk	Agent plus network scan	Cloud, on-prem for some modules	Per-technician published tiers
Oomnitza (San Francisco, CA)	Cloud-native orgs, no on-prem needs	API aggregation, no native scanner	Cloud only	Quote only
Asset Panda (Frisco, TX)	Non-IT and mixed asset tracking	Barcode/manual, minimal auto-discovery	Cloud only	Quote only
EZO AssetSonar (Carrollton, TX)	SMB IT, 100–1,000 assets	Agent plus MDM/RMM integrations	Cloud only	Published per-asset tiers

Team size	Managed assets	Realistic annual budget	Where the fit usually lands
1–4 technicians	Under 500	$1,000–$3,500	Cloud ITAM with light service desk; avoid enterprise SAM entirely
5–10 technicians	500–1,500	$3,500–$7,500	Integrated ITAM + ITSM + discovery on one platform, hosting optional
10–35 technicians	1,500–5,000+	$7,500–$25,000	Configurable workflows, change management, CMDB relationships, on-prem if regulated
50+ technicians	10,000+	Six figures	ServiceNow, Flexera or Ivanti; expect integration and admin headcount

Signal	How it is checked	Typical failure	Practical fix
IP geolocation	MaxMind GeoIP2 / NetAcuity lookup on connect	Wrong metro, so wrong store and wrong price	Verify the IP against two databases before use; pin ZIP manually in the session
ASN type	Reverse lookup against hosting-provider AS lists	Instant challenge or 403 from a clean IP	Use ISP or residential exits for logged-in flows; keep datacenter for public catalog pages
TLS / HTTP2 fingerprint	JA3, JA4, Akamai HTTP/2 hash, header order	Blocked despite a perfect IP	Drive a real browser engine, or a client with impersonation support such as curl-impersonate
Client-side locale	JS timezone, Accept-Language, screen metrics	Session flagged after the page loads	Set timezone to America/New_York, Accept-Language to en-US,en;q=0.9
Session continuity	Cookie and IP pairing across requests	Logout mid-checkout, cart emptied	Sticky session of 10-30 minutes bound to one exit IP
DNS resolution	Resolver location vs exit location	CDN routes to a European edge node	Use SOCKS5 with remote DNS (socks5h:// in curl, not socks5://)

Provider	Residential (entry)	Datacenter model	Minimum spend	Best fit
Bright Data	~$8.50-10.50/GB list	Per-IP plus per-GB	PAYG available, plans from $499/mo	Enterprise crawls, compliance documentation, managed unblocking APIs
Oxylabs	~$8/GB at 10 GB, ~$2.50/GB at 1 TB	Per-IP subscription	~$75-80 entry	Large committed projects needing an SLA
Decodo (ex-Smartproxy)	~$2.20-4/GB depending on tier	Per-IP subscription	Low, 3-day trial	Mid-market scraping where cost per GB dominates
IPRoyal	from ~$1.75/GB on commitment, ~$7/GB PAYG	~$1.39/IP/mo, unlimited bandwidth	None	Uneven crawl calendars, traffic does not expire
Proxys.io	Premium residential tiers from ~$3.60/IP/mo	From ~$1.47/IP/mo, US among 25+ locations	Single IP	Per-IP economics, US static exits, HTTP/HTTPS/SOCKS5 on the same port

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1. Is Trump getting better on science policy?

2. America’s national debt is a big and growing problem

3. Europe’s disdain for software comes back to bite it

4. Why doesn’t America have good trains?

5. China’s choke-hold on rare earths

6. India’s women need to work more

7. No, murder is not secretly really high

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Funny thing,: as I’ve been putting this together for Substack, I’ve been reliving the trip — the canyons, the hot springs, the barns, the incredible Southwest USA all over again. I’m riding shotgun, as I hope you are, on this two week trip.

To be continued…

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