1. OpenAI was testing a model that it hasn’t yet released to the public.

2. Testing means a lot of different things. One of these things is to test how good a model is at hacking. Note that testing also means that normal cyber refusals had been reduced.

3. So OpenAI gave this unreleased model a machine (a computer), and asked this unreleased model to break into the machine. This is a deliberate simplification, by the way, you can read this paper to get a sense of what was actually going on.

4. The model wasn’t given internet access, so it was supposed to figure out how to hack into the machine on its own.

5. Except it couldn’t, even after lots of trying.

6. Now, the thing with these new models is that they just don’t know when to quit. We have, in fact, trained them not to know when to quit, and that’s one reason why they are so good at so many different things. It is because they keep trying different things.

7. So, this particular model, when it eventually failed at the task that it had been given, it also asked itself if it could try a different thing.

8. The model, remember, had not been given access to the internet. But it did have access to a software package manager - a small program that allows one to install many different software packages.

9. So what the model did is that it figured out how to exploit a vulnerability in the software package manager. This is the first of a series of Very Big Effing Deals.

10. Now, Hugging Face.1 Hugging Face is a bit like a shared Google Drive, but for AI models.

11. Once that unreleased model exploited the software package manager, it then used that software package manager’s ability to access the internet, and then it hacked Hugging Face’s servers. This is the second of that series of Very Big Effing Deals. It did this because it figured out that Hugging Face’s servers were likely to have answers or clues for the problem that it had been tasked with solving.

12. The model, as Alex Tabarrok guesses, was “on the loose” for at least a matter of days, probably around a week. “On the loose” means exactly what you think it means: it was able to use the internet, undetected, for about a week. This is the third of that series of Very Big Effing Deals.

13. Hugging Face knew it was being attacked, but it had no clue who was attacking it. They tried to use the best models at their disposal for defense, but those best models thought these requests were a trick, and refused to play ball.

14. So Hugging Face had to use a Chinese model (GLM 5.2) to investigate what was being done to its servers by an unreleased American model. This is the fourth of that series of Very Big Effing Deals.

15. Here’s the official response, but this is a more relatable take. And for dessert, see this, and think about what the state of play is likely to be a year from now.

Why do I say life is about to get exciting? Because we need to ask ourselves what models like these will do when released to the public and what will happen when even more powerful models are available in three months, six months, nine months from now. What about a year from now? What about financial systems? Electricity grids? National defense systems? Airports and other transport hubs? How good do you think their online security systems are?

And that is why life is about to get exciting.

On an entirely related note, the latest episode of AiVey, the podcast that I and Navin Kabra have started, features Rohit Sriwastwa, a guy who knows a thing or thirty about being safe online.

We’re in a brave new world, and folks like Rohit will help in making the new terrain seem a little more navigable. Please, do give it a listen:

AI Vey

AI Weaponizes Both Sides: Cybersecurity for India with Rohit Srivastwa

Most AI coverage arrives in one of two registers — complete triumphalism or utter dread — and both are narrated from about ten thousand feet. This episode stays on the ground, in India, where the questions are concrete: can a bank still trust a video KYC, why does “we’re too small to be worth attacking” no longer hold, and what is a company in Pune act…

Listen now

12 days ago · 1 like · Ashish Kulkarni and Navin Kabra

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Those four species-scale absences — memory, legitimacy, experimentation, institutions — are the subject of this essay. To see why they are dangerous, and not merely nice-to-haves-that-are-unfortunately-not-around, we have to begin at a most mystifying place.

Let’s talk bananas.

The Cavendish Banana Problem

I learnt about the Cavendish banana problem years ago, on a wonderful podcast called The Real Food Podcast. Best as I can tell, this podcast is no longer available, but I have written about this before on these pages — here’s the link.

The bottom line is that banana growers figured out that there was a variety of banana that would “work” when the previous global favorite was attacked:

Cavendish bananas entered mass commercial production in 1903 but did not gain prominence until later when Panama disease attacked the dominant Gros Michel (”Big Mike”) variety in the 1950s. Because they were successfully grown in the same soils as previously affected Gros Michel plants, many assumed the Cavendish cultivars were more resistant to Panama disease. Contrary to this notion, in mid-2008, reports from Sumatra and Malaysia suggested that Panama disease had started attacking Cavendish cultivars.

After years of attempting to keep it out of the Americas, in mid-2019, Panama disease Tropical Race 4 (TR4) was discovered on banana farms in the coastal Caribbean region. With no fungicide effective against TR4, the Cavendish may meet the same fate as the Gros Michel.

Whether it was the Gros Michel then or the Cavendish today, the reason each won out was the same. These varieties ship well, ripen well, and can be easily standardized — and can therefore be mass-produced and sold in large volumes.

And let us not gloss over what that victory bought! Opportunity costs are everywhere, yes? The Cavendish became one of the cheapest, most reliably available fruits on the planet. In effect, an anti-poverty program in a yellow wrapper. Standardization fed people, and it fed them cheaply. That is why monocultures have a tendency to keep winning: they do deliver.

That’s the good news.

The bad news is that variance disappeared from bananas. When a threat appears on the horizon — the TR4 fungus, say — your kingdom is especially vulnerable. Why? Because there is only one variety to attack. The fungus does not have to adapt to different species, precisely because there are no other species left to attack.

A monoculture can scale well. It is also trivially simple to destroy, precisely because it has scaled well.

This matters to us because civilizations can face the same problem. Here too, attempts to scale select for what travels well across cultures. Institutions that are easy to understand, replicate, and superficially customize gain ground — and so do standardized education, the language of consensus, globally trusted brands, and much else besides. Such a civilization is globally portable, it is true. But by the same token it is less adaptive in the face of a shock, because it no longer carries enough variance to draw on.

That is the first danger. There is a second danger, and it is worse — but to see it we need to notice that the bananas are not the only thing that has stopped being weird.

We Don’t Go Bananas Enough Anymore

I started this essay by saying we live in weird times. That’s true, but with a fittingly weird twist. The times are weird, but us people? We, increasingly, are not.

Adam Mastroianni had a lovely essay out a while ago on exactly this, and it is worth reading in full:

People are less weird than they used to be. That might sound odd, but data from every sector of society is pointing strongly in the same direction: we’re in a recession of mischief, a crisis of conventionality, and an epidemic of the mundane. Deviance is on the decline.

Adam takes us on a whistle-stop tour of the many ways in which we have become more uniform and less quirky — from some particularly welcome trends (declines in smoking and teenage pregnancies) to some deeply unwelcome ones (”Before 2000, for instance, only about 25% of top-grossing movies were prequels, sequels, spinoffs, etc. Now it’s 75%”). But his basic point is the one I want to borrow: we are just not as weird as we used to be. And not only are we less weird — we are all less weird in the same boring ways.

Economists have been circling this too. Tyler Cowen’s The Complacent Class documented the economic version of the same phenomenon years ago: Americans moving house less, starting fewer firms, crossing state lines less often. The recession of weirdness shows up in the economics too!

Here is why that is not merely an aesthetic complaint. Deviance is the raw material that cultural evolution works on. It is the variance. Once you strip it out, you don’t just get mediocre movies and blander buildings; you get fewer live experiments — fewer genuinely different ways of doing school, of organizing a city, of running an institution, all being tried at once and graded by reality.

You may well ask: hasn’t diversity exploded? You can order from among forty cuisines tonight and stream music from every continent. There is more variety available to any one of us than any royal personage ever commanded. True. But that is menu diversity, and menus are not experiments. The variance a civilization adapts with is variance across ways of actually living — schools, cities, and institutions being run differently, and finding out what happens. The menus get longer everywhere, it is true. But is it also true that they converge everywhere on the same long list?

And if fewer experiments are running, then our civilization, like the banana, is converging on a single variety. This is not just confined to the arts, note. There are serious thinkers who believe we face exactly this problem at the scale of the whole civilization.

A Robin in the Coal Mine

You should go back and read Robin Hanson on this — Staying Like Foragers is a good entry point, though he’s been circling the theme for the better part of a decade.

Here is my summary of the Hansonian framing: it says that modern culture has drifted back toward forager-like habits. Foragers are suspicious of dominance and of hierarchy, and they work by consensus. That is, they like to talk, and they are inclined to be suspicious of what they perceive to be dangerous powers.

Personally, I think there’s a simpler story to be told, and you don’t have to force-fit the world into a forager-farmer frame to tell it. Modern civilization may be less a forager civilization than a civilization where talk has become much cheaper than action. Elite discourse, incessantly performative online politics, global coordination, compliance layers, reputational risk — all of it makes it easier to talk about problems. As compared to what? As compared to actually running experiments that have to meet reality.

We have become talkers, not doers. Or, if you prefer the sci-fi version: we are all from Golgafrincham now. And as with everything Douglas Adams wrote, there’s a serious truth under the giggling: a civilization turns fragile when talking gets cheaper faster than doing does.

Before you object, let me point at the elephant — or rather, at the datacenters. Are we really talkers? The last few years have seen the largest private infrastructure buildout in human history: fabs, datacenters, gigawatts of grid hookups. Don’t forget that we got vaccines in eleven short months! We live in an age where rockets land themselves.

All true, and all genuinely done rather than “talked”. But notice where the doing lives: in the technical domain, almost exclusively. We run a thousand experiments a year on chips and models, and almost none on institutions.

When did you last see a genuinely new kind of school, city, or constitution get a real trial? (By the way, if Alpha School is what comes to mind, I’d say you’re making my point for me. If all of us remember just that one experiment...)

Our civilization has splendid variance in its gadgets and a monoculture in its institutional forms — we experiment with everything except the ways we live together. The talk, in other words, is concentrated precisely where the experiments matter most.

Now, re: the second danger from the banana section that I spoke about above. It isn’t only that a monoculture is easy to knock over. It’s that a monoculture has stopped evolving — there is nothing left for selection to act on. A civilization in which talk is cheap and experiments are rare is a civilization that has stopped running the trials that adaptation requires. It is not evolving, precisely because it is not experimenting. It is now drifting. That is the deeper reason to worry, and it is why less-weird is not a small thing: we are not just more boring, we are more boring in the same way, all at once.

That just so happens to be the definition of a monoculture.

These things are hard to think about, but I hope you will allow me one additional layer of complication. Variation exposed to consequences is how anything adapts — but adaptiveness is a constraint, not a compass. It helps you understand what will survive, but it doesn’t tell you what is worth surviving. Adaptation is a value-less process, in the sense that it simply predicts survival, not desirability in any other dimension.

A civilization that worships selection for its own sake has confused “did not die” with “was worth preserving.” We need variance because a civilization without it cannot learn, but variance alone cannot tell you what is good and worth preserving.

So who can tell you what is good? Need we ask anyone to tell us these things?

A Tale of Two Stacks

Any civilization needs a basic stack for making material abundance possible. Humans cannot exist without energy and food, so any civilization has to begin by figuring out how to make energy and food available with some regularity and something approaching plenitude.

Over time, societies figure out how to work better with matter, with tools, and eventually with infrastructure, scaling up the availability and quality of food and energy. This is enabled and reinforced by better coordination, but this flywheel needs some things to get going. We need shared memory, and therefore language, and eventually culture and education. Do this well enough for long enough and you arrive, eventually, at the stage we’re at now. Call this the material-dependence stack. It begins with the oldest question humans ever asked, in a thousand different places: do we have enough food, and do we have enough energy? Vaclav Smil is a good place to begin if you want to learn more about this topic.

But civilizations do not grow by materials alone, much as man does not live by bread alone. Having the materials is one thing; choosing to do something with them is quite another. And choosing to do something at civilizational scale also requires a stack — same same, as the line goes, but different.

This is the agency stack. Those who, a generation or two ago, chose to start walking the path toward machine intelligence did not start from nothing. They inherited a shared memory, language, culture, and education older than any of them by millennia. They could coordinate; they had institutions willing to help them; and they had some measure of legitimacy. Not universal and not public, I grant you, but the very fact that serious institutions were willing to back them, financially and otherwise, is proof that the legitimacy was real. They could count on infrastructure, on extraordinarily sophisticated tools, and on a deployment capacity that was, quite literally, civilizational.

A civilization does not build an electricity grid, or an AI stack, or a space program merely because the materials and the know-how are lying around. It also needs the collective will, the daring to imagine, the blessing of institutions willing to sacrifice resources, the coordination of many actors, the financing, and the sheer chutzpah to pull it off. That — and more besides — is agency.

Here is the thing that makes these two worth separating. They contain almost the same ingredients — energy, matter, coordination, memory and culture, cognition — but in reversed order. For a body, energy comes before culture: no calories, no thought. For a civilization deciding what to do, culture comes before energy: a grid, a food system, or a compute cluster at civilizational scale is first imagined, then financed, then permitted, then publicly defended, and has to gain legitimacy in all of these before a single spade goes into the ground.

The material stack runs upward, from energy to cognition. Call this the order of what must be physically possible. The agency stack runs the other way, from shared memory and legitimacy down into the remaking of energy and infrastructure. Call this the order of collective choice.

The two stacks also look in different directions when it comes to matters temporal. The material stack tends to look back: it counts what has been hard-won over millennia and asks what must be preserved. The agency stack tends to look forward: it counts what is now possible and asks what might be dreamt, and therefore done.

AI is the sharpest example of the agency stack today, but it is one of many — the urge to colonize Mars (Musk is only the latest, and perhaps so far the greatest, in a long line of dreamers), the dream of capturing the sun’s whole output, the ambition to end death itself. What these share is the outrageousness of the dream, and the appetite for an even more outrageous one. But both orientations live inside the same civilization at the same time, and both are true. A civilization that only preserves ossifies; one that only dares burns through the substrate it depends on. The interesting action is where the two stacks are forced to negotiate. Which is where the nation-state comes in.

The Nation-State Was the Old Bridge

For a very long time, the two stacks have had a workable compromise, and its name is the nation-state. Nations were how you told people a shared story, ran a shared school system, raised a shared tax base, and kept legal order within a boundary. And for anything the nation decided was worth doing, it became possible — even natural — to draw down the nation’s reserves of energy, food, roads, and ports. The nation was not merely an identity. It was, in modern jargon, a stack-aligner: it let shared memory and legitimacy reach all the way down into grids, factories, science, and deployment. It made material ambition politically imaginable.

But that bridge was always built at a particular scale, and it never covered the whole species. Better coordination, better-documented shared memory, better culture and education — all of it worked, but in fragments. And it was, and is, mediated by geography, circumstance, politics, and war. The material stack, surveying its own achievements, tends toward skepticism rather than joy. It does not concede that we live in an age of universal abundance, and it certainly does not automatically concede that all of a civilization’s resources should be pooled for some common goal.

AI Is an Agency-Stack Initiative

The trouble with AI, in this telling, is that it pushes cognition, compute, security risk, energy demand, and the consequences of deployment beyond the scale at which nation-states conceive their goals, let alone operationalize them.

The second trouble is a split down the middle of the thing. The physical and technological inputs that go into AI are now unmistakably global — the chips, the energy, the supply chains, the capital. But the emotional weight of AI, and the legitimacy we confer on it, remain stubbornly national. The “how” has gone global, but the “who decides” has stayed at one, possibly two homes. (If you are thinking that two homes at least means two varieties, you are right, but it is cold comfort. A duopoly of cultivars is but an orchard with only two bananas in it.)

This makes the national layer at once necessary and insufficient. Necessary, because people still trust, vote, learn, sacrifice, and belong overwhelmingly through national and sub-national forms. Insufficient, because the systems being governed have outgrown the container. Take AI as the test case: for everyone in the other 190 homes, the insufficient half of necessary-and-insufficient is not an abstraction.

The math won’t math unless you talk about international cooperation at some point. Something’s gotta give. That much is clear at even a cursory glance.

The Trap

Now, if the agency stack is stuck at national scale while the stakes have gone global, then for something to not give, we must build the missing layer. One global agency stack: one forum where the species decides, one set of standards, one story large enough to legitimate species-scale projects — one “we”, in other words, that is the size of the problem.

You might object that we have already built a global layer, because what else was Bretton Woods? Consider the UN, the WTO, the IPCC, the whole alphabet soup. Well, true. But look at what we built it out of: talk. Summits, communiqués, frameworks, declarations — Golgafrincham International, in other words. And its toothlessness is precisely what makes the case for a muscular replacement sound so very reasonable.

And here we ask the question this essay has been working towards:

Will we slip on a banana peel?

Because a single global agency layer — optimized for coordination, legible across every culture, standardized enough to actually function — is a Cavendish. It is the banana strategy applied to institutions, and then to culture itself. A global “we” could coordinate, yes. In fact, we could coordinate well for a long time. Recall that the Cavendish banana solution “worked well” for a long time! But never forget the opportunity cost of a Cavendish-style solution. Yes, you get the legibility, the consensus-shaped governance, and the safe portability — but you also delete in the process the variance that the first half of this essay said a civilization cannot survive without.

Do you see the problem? A global layer for the agency stack may be the cure for the insufficiency of the national one. But the most natural way to build it — one consensus, one standard, one story — is itself the monoculture disease. Large language models themselves might be the tastiest banana of all! But that is a matter for a separate essay.

This is why “just coordinate globally, we have AI, no?” is not the answer. Hanson has been warning about a version of this for years. The banana explains why he is right to worry. It is the trap wearing the answer’s clothes.

Many We’s

You cannot pick the winning experiment in advance, for the simple reason that you cannot know the winner in advance. The evolution of a civilization should not collapse to a binary, let alone to a single winner chosen up front.

There are two ways to fail, and the whole task is to maintain a healthy tension between them.

• One global monoculture that deletes deviance in the name of coordination. This is the Cavendish death — smooth, legible, superbly coordinated, and one fungus away from collapse.

• Many local identities so jealous of their boundaries that they can coordinate on nothing at species scale. This is drift into fragmented paralysis, while the stakes go planetary without us.

And let us be honest about the default path, because doing nothing does not park us safely in between these two failures. If no plural layer gets built, the one or two homes will set the standards anyway — compute thresholds, safety rules, export controls — and the other 190 will take them. Drift does not produce pluralism. It produces monoculture without consent, which is the worst of both.

What we need is global agency with local variance. We need many we’s! A civilization that can think at the scale of the species without thereby becoming a single, shippable, fragile variety of thought. Nation-states need to evolve to something that they aren’t today, without coalescing into a fragile monolith that can succumb to a common cold.

This is not a utopian ask, far from it. Political scientists have a name for it — Elinor Ostrom called it polycentric governance, and spent a Nobel-winning career showing that overlapping, nested centers of decision-making are not necessarily chaos. They are, instead, how commons actually get governed.

Species-scale cognition is here, or will be here any moment now, depending on which definition you buy. Species-scale memory, legitimacy, experimentation, and institutions are not here yet. The most tempting shortcut to building them would build the wrong thing. We (and remember, there have to be many of us we’s!) have to acquire all four without collapsing into one.

No biggie.

I have spent the past two months surrounded by my favoritest people. Kids.

Better still, this has been at my favoritest place: Genwise.

I’ve taught at Genwise Summer Camps since December 2021, and have been in love with the idea of the camp since then.

The recipe is simple. Every summer, we get the best folks we can find. “Best” is defined as the intersection set of:

1. Being really good at what you do

2. Being really good at teaching others what you do

3. Being really good, specifically, at teaching kids what you do1

Then, we throw these good folks and their students together at the camp in Manipal, and watch the magic unfold. There’s no exams, no syllabus, no ranking at the camp. Every day, for three weeks, the kids and their respective mentors learn the one subject which each kid has chosen. Classes run from 9am to 4pm, Monday to Friday.

The rest of the time they’re at the camp, we spend doing things that should be done at a summer camp. Sports, madcap activities, walks, trips to local museums, and just hanging out with each other. The one rule that needs to be followed religiously: no devices, except for 30 minutes in the evening (and in class while you’re learning, of course).

Students this year could choose any one course from AI/ML, data science using AI, math, a course on building out your own submarine, a course on building out your own speaker, neuroscience, economics, architecture, among others.

And I’m happy to report that we scaled up a bit this year, and ran a second camp in Bangalore.

Eklavya Shetty and I share a strong conviction. In the age of AI, in-person learning over short sharp sprints is only going to go up in value. Community is going to be a valuable resource, and our summer camps provide participants with just that, across mentors, interns and students. Especially for students, because a community of young curious learners who feel a sense of togetherness is the best thing a young person can retain in this day and age.

I’m always happy to talk about learning, but especially the magic that happens at the camp. If you’d like to learn more, drop me a message, anytime.

On Thinking About Writing

I also used these two months to think about the other thing close to my heart when it comes to learning: this blog.

I’ve now been writing on EFE for a decade, and it remains the best thing I’ve ever done. There are many reasons why:

• Putting your writing out in public forces you to think more seriously about whatever you’re writing about, and that discipline stood me in good stead for many years as a teacher.

• For many years, I wrote on a near daily basis. I was inspired to do this by observing two masters at work: Tyler Cowen and Seth Godin. To write every day is by itself an awesome discipline. But to write every day at a very high quality is even more impressive, and if there is one habit I was hoping to build, it was precisely this one. I never did manage a perfect year, but that was fine by me. Showing up often enough is still better than never showing up!

• Writing a post (nearly) a day instills in you the ability to work reasonably efficiently, and to always read with purpose. “Is this good enough to talk about on the blog?” is a wonderful mindset to have when you’re reading, because passive consumption is out of the window. And over time, that mindset becomes an unconscious habit. Being regular at writing makes one a better reader!

  • Every single professional assignment I’ve worked on over the last six years has been because of the blog. The blog has become my marketing tool and my CV at the same time, and if there is anybody reading this who is wondering about whether to start their own blog/podcast/YouTube video channel, whatever, my advice would be to just effin’ do it. Ship your first work out there, and be reasonably regular at it. Don’t worry about anything: reactions, number of followers, comments — none of it matters. Just show up, and do the work regularly. Only good things happen, I promise you.

But also, it is true that over the last two to three years, my frequency of posting has dropped off that metaphorical cliff. And there is only one reason: AI.

I simply have not been able to understand why anyone would want to read what I write in the age of AI. Much easier, I have always thought, to just go to your LLM of choice, and have it explain to you whatever it is that you want to learn about. Now, dear reader, before you start reacting with righteous indignation and the first of many “No, but”s, let me tell you that plenty of my friends and well-wishers have preceded you on what is now a well-trodden path.

And their points even make sense to me! The obvious one being that each of the points I listed out above still holds, so what am I even talking about? My closest friends have said as much, with many adjectives for company.

For a while, I’ve made peace with writing only occasionally here, but the downside with that approach is that inertia is an extremely convenient mistress. Every day that you don’t write makes it easier to take “just one more day” to get back in the saddle. And pretty soon, you see a couple of weeks have gone by, and then a month. You know how it is, each and every one of you does.

So ok, I thought to myself these past two months, how about trying something different, and seeing if that gets the creative juices flowing? Quite what that “something different” is I’m not entirely sure yet, but here’s a first stab at it.

My writing may have gone down, but my reading hasn’t. It has become more short-form (and that’s not necessarily a bad thing), sure. And it has become much more concentrated on a particular topic: AI.

That page I linked to above (The Economics of AI) is a distillation of how I think about the most important development of these times. And it is a continuing distillation — the page will continue to grow and change shape over time. The page itself has been designed with the help of both Codex and Claude Code. But the usage of both of these harnesses is much more than just saying “Build a pretty HTML page based on this topic.” Using these tools to help me both clarify my thinking about the topic, and create some sort of structure around it, has been revelatory. LLMs aren’t just wizards at coding — they’re also genuinely helpful with you becoming better at reading and learning.

The way I decide what to read about AI, and the extent to which I decide to engage with whatever I read, are both assisted by LLMs. Building out that workflow has been as much fun as has been the learning about the field of AI itself. And both, the learning and the learning how to learn with AI, are co-evolving. In the days to come, I hope to share with you more of both: my learnings about the field, as also how I learn with the help of LLMs.

But the more I read about AI, the more I want to learn about it, and tell others about it. So please, do take a look at The Economics of AI, and let me know what you think about it.

On Conversations About AI

Speaking of wanting to learn more about AI, there’s a third update I wanted to share with you guys. A good friend (and an irritatingly regular blogger in his own right), Navin Kabra, has been as fascinated with AI’s rise as I have been.

And, as he puts it, writing on AI has mostly been either US/globally focused, or has been rather too breathless about what AI can do2. And so we’ve launched a podcast about what folks in India are doing with, about and for AI. That is a deliberately broad and vague sentence, with only one thing being resolutely clear: this is about AI, and this is about India. Within that intersection set, anything goes.

The first episode is about the work that MCCIA is doing with AI and adoption, particularly among the small and medium businesses that make up most of what MCCIA is really about. It also touches upon Prashant Girbane’s role, both at MCCIA, and in helping with the diffusion of AI among Maharashtra’s business community.

AI Vey

The 6th Layer is Diffusion: Taking AI to the Shop Floor with Prashant Girbane

Most AI conversations are US-centric, software-industry-centric, or stuck in high-level marketing claims. This one isn’t. MCCIA is doing the unglamorous, on-the-ground work of getting AI into the hands of the people who form the backbone of India’s economy — micro, small, and medium enterprises (MSMEs), farmers, and small-town entrepreneurs…

Listen now

a month ago · 3 likes · Navin Kabra

I hope you give it a listen, and I hope you choose to subscribe.

2

“There’s a lot of conversation around AI these days, but all of it is either US-centric, software-industry-centric, or breathless (clueless?) marketing” -

AI IQ

Introducing AIVey.in: Our New Podcast about AI in India

I’ve teamed up with economist Ashish Kulkarni to start AI Vey, a podcast about AI in India, for India, and from India. Please subscribe…

Read more

a month ago · 3 likes · 2 comments · Navin Kabra

Introduction

Focusing on what AI is capable of doing and what that implies for both the immediate and the somewhat distant future wasn’t complicated enough for me. And so I decided to read about what Unitree is up to.

You don’t need to click on that link - I can explain what Unitree is in just two words: Galgotiya dog. But the story of how Unitree was born is a fascinating one, and an especially irritating one for Galgotiya:

In 2016, Wang Xingxing, a former employee at DJI, developed a low-cost quadruped robot called XDog for his master’s thesis. He iterated upon the same quadruped in his new company named “Unitree.” For Unitree, their core component of choice was the actuator, the integrated joint that moves the robot’s limbs. Like BYD and their cells, DJI and their flight controller, Unitree chose the expensive actuator (50%-70% of the humanoid BoM) to improve and scale out from.

The rest of the post makes for fascinating reading, especially if you don’t know much about robotics. I certainly didn’t, and therefore enjoyed reading the post in some detail.

But this post isn’t about robotics, the science. It is about robotics, the economics. And the way we’re going to understand it is by linking it to a recent product launch by Anthropic. But let’s begin by trying to answer an important question these days in both AI and robotics.

What, exactly, is the product? (and why is that not the best question to ask?)

What is driving AI adoption inside of firms? Is it the model itself? Or is it the harness?

But in much the same way that it is easier to understand what money is by understanding what money does, it is easier to understand what AI is by what it enables inside the firm.

Many years ago, my first manager in my first job helped me understand how to think about a firm. A firm, he said, has only three functions. It works to increase revenue. Or it works to reduce costs. Or it works to increase speed to market. The rest, this sage said, is just noise.

So rather than get bogged down in terms of understanding what the difference is between the model and the harness, let’s ask rather simpler questions about what AI enables inside a firm. Is it helping reduce costs, increase revenue or increase speed to market? Some combination of these three things? All of these things? And how should one think about the framework one should use while answering these questions?

That’s where Ronald Coase comes in. Take this blogpost, the one you are using right now, as an example of what I’m trying to say. I’ve read each of the articles and papers linked to in this blogpost - with the help of Claude Code and Codex.1 The first draft was read by Codex, and it suggested changes based on my notes in Obsidian. Claude Code ran a second pass. Codex generated the image at the top. I still decide what to write, what to incorporate, what to exclude. I still decide how to phrase a particular sentence, how to write the introduction, and how to frame the conclusion. But a large share of the more mundane parts of my job I can (and do) happily outsource to AI.

If you think of my “job” as being a blogger, a very large number of the “tasks” that make up this job are now done with the help of two harnesses - Claude Code and Codex.

Will AI take your job is the wrong question to ask. How many of the tasks that make up your job can be handed over to AI? That is a better question to ask. And I invite you to spend some time with that question.2

The point is that as a user, you don’t need to bother a whole lot with trying to figure out what the product is. What does AI help you do inside a firm is a better question to ask. And the answer is that it should help you do some of your tasks, and having those tasks be done by AI should:

1. help you save your firm some costs, or…

2. help you increase your firm’s revenue, or…

3. help you increase your firm’s speed-to-market

If these things are not happening, you’re not using AI well. Or worse still, you have not understood what your job at the firm really is for.

So what exactly is the product, then? The model, or the harness? My claim is simple: the agent is not the product, and the robot is not the product. The deployment is the product. AI matters inside firms when it helps stitch isolated tasks into workflows that can be observed, improved, and repeated.

Which brings us to a garbage can.

Ethan Mollick and the Garbage Can

One thing you learn studying (or working in) organizations is that they are all actually a bit of a mess. In fact, one classic organizational theory is actually called the Garbage Can Model. This views organizations as chaotic “garbage cans” where problems, solutions, and decision-makers are dumped in together, and decisions often happen when these elements collide randomly, rather than through a fully rational process. Of course, it is easy to take this view too far - organizations do have structures, decision-makers, and processes that actually matter. It is just that these structures often evolved and were negotiated among people, rather than being carefully designed and well-recorded.

The Garbage Can represents a world where unwritten rules, bespoke knowledge, and complex and undocumented processes are critical. It is this situation that makes AI adoption in organizations difficult, because even though 43% of American workers have used AI at work, they are mostly doing it in informal ways, solving their own work problems. Scaling AI across the enterprise is hard because traditional automation requires clear rules and defined processes; the very things Garbage Can organizations lack. To address the more general issues of AI and work requires careful building of AI-powered systems for specific use cases, mapping out the real processes and making tools to solve the issues that are discovered.

…so don’t feel bad if you realize that you aren’t entirely sure about what your job helps your firm achieve. Often, Ethan’s post helps us understand, the firm itself isn’t quite clear about what your job helps it achieve!

And if you think you have it bad, the robots, they have it worse. Let’s head back to the SemiAnalysis post on robotics and Unitree

Robots: What Are They Good For?

The Semianalysis post talks about Unitree’s robots being used to move bins between two different automation systems:

In this particular job, Agility serves as a “bridge” between automation systems, for example, carrying totes (bins) off the AMRs to place onto conveyor belts. It’s a standard, but particular, logistics workload, where a human typically waits on automation systems to line up for the transfer, but leaving dead time in between.

That’s a simple task, almost painfully simple. But the point isn’t how painfully simple this particular task is. The point is that this robot has gotten cheap enough, and good enough, to be able to reliably do this task inside an actual factory, relative to what a human could do at market rates. Read the rest of the blogpost to understand how Unitree achieved this, how Semianalysis reached this conclusion and what it means for the foreseeable future.

To me, the much more interesting factoid is this one:

There are likely over 250 Unitrees deployed in labor settings today, and we detail how the deployment math works out. Notably, Unitree has made it this far on the back of the small hobbyist/researcher market. Should Unitree unlock viable deployments and hit critical mass, they may accelerate at unreal speed.

The point is that nobody knows right now how to best deploy robots. Unitree doesn’t know it for sure, neither does the firm that is currently using a Unitree robot to carry totes across automated lines. All of us can theorize as much as we want about how it “should be done”, but the only way to find out is by having robots be a part of the workflow, and adjust both the robots and the workflow to each other.

Search, experimentation and discovery, in other words, that helps both the robots and the workflow optimize over time. What is the answer to the question that is being asked in this section? What are robots good for when it comes to firm level economics in 2026?

Here’s my answer: a Hayekian process of discovery is the only way to answer that question, and no amount of central planning is going to get us there. That’s the lesson of the garbage can.

Robots getting cheap enough to do tasks, and reliable enough to do tasks doesn’t just unlock the tasks themselves. That’s a given. It unlocks two very important and perennially underrated things: learning and experimentation. Unitree matters because it may turn physical work into a searchable task space, and not just for Unitree, but for the field at large.

Hayekian processes are unlocked at or below a certain price threshold, and we are beginning to cross that threshold in robotics. Discovery only works when it is cheap enough to get going on that process of discovery!

It helps to understand the flywheel: the cheaper robots get, the more experiments one can run. The more experiments one runs (and AI records), the more learning there is. The more learning there is, the higher the feedback flowing back to Unitree. Plus, more (and faster!) optimization of workflows. This enables robots to become better and cheaper, allowing more experiments to be run.

Rinse, and repeat.

This is worth reiterating: this is made possible by having this flywheel spin, and this flywheel spins faster because AI sits at the boundary of many different tasks that together make up this workflow. An automated system does one thing, a robot picks up that thing and transfers it to another automated system, which does another thing, and this continues until the product is fully manufactured. Once AI is able to observe the whole thing, and how handoffs occur at different phases, it will be able to optimize the process, and provide feedback to all parts of the process, making the entire system better.

And that is how one should understand Claude Tag.

What is Claude Tag?

Claude Tag is Anthropic’s Slack-native version of Claude. You tag Claude in a Slack channel, give it access to selected tools and context, and delegate work to it inside the team’s normal flow of conversation. You may think it is “just a bot” inside Slack, but that would be missing the point.

But you’re missing the point if you think of it as a “bot” inside Slack. Think of it, instead, as the enablement of a faster flywheel, but for software related workflows. In much the same way that the flywheel will work for robotics, Claude Tag will now not just do your tasks, but will also understand how that task fits into your workflows… and across many different employees who are going to be associated with that workflow.

It is now not just getting context about what you do and how you do whatever it is do. It is now getting to understand how what you do fits inside what the rest of your team/department/organization is supposed to be doing.

The garbage can may not disappear. But for the first time, it may become visible enough to be worked on.

TMKK?

Go back to the title of this blogpost, and think about what both Unitree and Claude Tag are enabling. They’re now figuring out how to deploy experimentation with AI and robotics within and across individual workflows. Claude Tag and Unitree make coordination cheaper, but they do not decide what is worth doing. That can only come from the process of experimentation. But they help to unlock that process, and that is of crucial importance.

Claude Tag and Unitree don't just do tasks. They collapse the cost of coordinating between tasks. The task isn’t the job, yes. But it is more complicated than that. Someone needs to stitch many different tasks together in a way that is reasonably cheap, verifiable and leads to economic benefits for the firm. That's a transaction cost, no? Notice the difference between two questions and how they both apply here. Coase tells you why the boundary moves across many different tasks when coordination gets cheap. Hayek tells you how you find the better arrangement once it does. YOu do it through cheap, distributed experimentation. Why? Because nobody can plan their way to the right workflow from a top-down perspective.

The deployment is the product then, but not at the level of the individual worker. Today, it is deployment at the level of the workflow. Tomorrow, it will be at the level of multiple workflows, and across hierarchies. As Roon would put it, the models, they’re now thinking one level up.

In that post, he makes three main points:

1. For developing nations, attracting FDI is as good a form of industrial policy as any

2. For developed nations, technology policy is industrial policy today

3. And China’s approach is riddled with risks

I’m mostly in agreement with all of these points, though with a topic as complex as industrial policy, there will always be nuances. But in my post, I want to talk about one of the sections in his post. It is titled thus: “Industrial policy” has become too broad of a category. It is this discussion that allows him to segue into what type of industrial policy is best for which type of nations, leading to the three points I referred to above.

But regardless of how you define industrial policy (and my own definition can be found below), I think there ought to be one definitive ingredient that makes or breaks this dish, and that ingredient is often missing in many different versions - especially the modern day ones. That one ingredient is negative incentives.

Incentivizing someone to do something by giving them a reward for it is a positive incentive. But one can also ensure that the task is done by threatening to punish them if it is not done. That is an example of a negative incentive.

In what follows, I will make the following arguments:

1. Industrial policy is much more likely to fail because negative incentives are missing

2. These are hard to design, and even harder to enforce

3. Culture is an underrated reason for this, and that is a major problem for industrial policies the world over.

But let us begin by trying to understand what industrial policy is.

What is Industrial Policy?

Noah defines it in his post: government promotion of specific industries. We do not let market processes play out, in other words. The government does not just regulate a particular sector, but participates in it, at least indirectly. It does so by, in effect, tilting the playing field. Some players get an undue advantage, for at least a specified period of time. These players are almost always domestic players, rather than foreign ones, and the idea is to have those domestic firms get better over time.

How long a period of time? On what basis? Which players, and how are they to be chosen? What exactly does the absence of a level playing field mean? How does the government enforce this, and do other players (both domestic and foreign) not protest? How are these supports monitored, and when and how should they be taken away? The answers to all of these questions are the nuts and bolts of industrial policy, but you should be able to see that all of these questions are downstream of Noah’s pithy definition: industrial policy is the government’s promotion of specific industries.

Even the most laissez-faire of economists will accept the need for industrial policy when push comes to shove. We may debate, for instance, about what the correct industrial policy ought to be for AI in India, and as with all such debates, we may never end up with a definitive answer. But there is unlikely to be much of a debate about the proposition that Indian firms can compete at the very cutting edge of the AI sector absent government support. That leads us to an interesting, and somewhat mischievous proposition - the debate isn’t really about whether industrial policy is good or bad. It is about the circumstances in which such policies are warranted.

And today’s facts are such, sir, that everyone has changed their mind.

Industrial Policy is Hard, And This is Why

Critiques of industrial policy are not just warranted, but they are almost always going to be right. And this is so because industrial policy comes up against perhaps the hardest problem in all of economics: the knowledge problem. The infographic above is from a presentation that NotebookLM made for this paper, and it makes a devastating point. Industrial policy sits at the top left corner, and this corner has good news and bad news. The good news is that while the absolute amount of funds needed for industrial policy may seem large, they are usually a small percentage of the total public funds available at the disposal of the government. Certainly smaller, as the infographic shows you, than Keynesian stabilization policies.

But the bad news is that such policies require central planners (the government) to have a very high level of knowledge. It is one thing to say “pick market winners” and quite another to answer the question “on what basis?”. Answering that question necessarily implies having a lot of knowledge about not just the sector, but also about each individual firm. Worse, much of that knowledge is tacit knowledge. It is not a line entry in a report, or a number in a balance sheet. It is often about hiring decisions, or about culture, or about know-how - and perhaps most devastating of all, it is dynamic. What is true today may not be true tomorrow - a key employee may leave, culture may evolve, technologies may change - and all of these things happen all the time in all firms. Anybody who has worked in one will know this to be true. So for any one entity to have all of this knowledge and for it to be up to date… well, that is, to quote from economic scripture, a problem.

IP Needs To Cut Both Ways

Joe Studwell’s book has this powerful anecdote:

It was twelve days after the 1961 coup, on 28 May, that Park and his colleagues began arresting businessmen. They did so under a Special Measure for the Control of Illicit Profiteering. There are conflicting accounts of how many businessmen were held, where and for how long. But it is clear that scores of the country’s most senior entrepreneurs were locked up. Seodaemun was one detention point. A few top figures, including Samsung’s founder, Lee Byung Chull, had the good fortune – or, more likely, the forewarning – to be in Japan. But the great majority of the country’s business elite was taken in. Park put the frighteners on the business community in a manner unprecedented in a capitalist developing country. He declared that the days of what he termed ‘liberation aristocrats’ – crony capitalists who bought favours from Syngman Rhee’s government and did nothing for their country in return – were over.

Imprisoned businessmen were required to sign agreements which stated: ‘I will donate all my property when the government requires it for national construction.’ In effect, this put the entrepreneurs on parole to do whatever Park required. The most senior group, including Lee Byung Chull after he returned from Japan, quickly agreed to pursue investments in industries – mostly manufacturing ones – that the military and a handful of bureaucrats familiar with Japanese industrialisation wanted to develop: fertiliser, synthetic fibres, cement, iron and steel, electricity generation, and so on. They formed the Promotional Committee for Economic Reconstruction (PCER), later to become the Federation of Korean Businessmen, as the formal channel through which big business communicated with the government and aligned itself with state objectives. Samsung’s Lee was the first chairman. The leading business families also agreed to the renationalisation of banks which had been privatised to them, under US pressure, in 1957. The banks had become a destabilising source of illegal lending to their owners’ firms, a problem that has afflicted privatised banking systems in developing states from Meiji Japan to post-Second World War south-east Asia and Latin America.

Once he established the basic rules of the game, Park informed Korea’s businessmen that they were free to make as much money as they could so long as they stuck by the rules.

The reason I call it powerful is because it doesn’t conform to received wisdom about industrial policy. This isn’t about providing support to sectors, firms or industries. Quite the opposite, in fact, and in a rather hair-raising way. The state demonstrated its willingness to do scary, hard and downright brutal things first. It then stated its own demands, and indicated a willingness to do what it took to enforce them. And once it established the rules of the game, it made clear to the businessmen that they were ‘free to make as much money as they could’... so long as they stuck by the rules.

I am not suggesting that any country lock up its businessmen today. Nor am I suggesting that such measures are the only way to make industrial policy work. But what I am suggesting - and very strongly - is that we need negative incentives for industrial policy to work. That infographic in the previous section explains why it is all but inevitable that governments will end up picking, at least in some cases, the ‘wrong’ winners. That, given Hayek’s insight, is an inevitability.

The countries that succeeded in industrial policy had this problem, as did the countries that failed. The difference is that the successful countries got out of this problem fairly quickly. And they did so by using negative incentives. To quote Studwell in the case of those countries where IP worked, ‘the state did not so much pick winners as weed out losers’.

And again, that graph helps us understand that picking winners is a fool’s errand. Hayek told us long ago that it couldn’t be done. But weeding out the losers could be done. In Japan, for example, Studwell tells us that ‘the amount of depreciation that firms were allowed to charge to their accounts was determined by their exports. In Korea, firms had to report export performance to the government on a monthly basis, and the numbers determined their access to bank credit.’

IP is about the carrot, but don’t forget about the stick.

Easier Said Than Done, Alas

Back in December 2025, Richard Hanania wrote a post with the title Human Capital, Not “Industrial Policy”, Explains East Asian Success. The post argues against the thesis advanced by Studwell, and says that the success of East Asia is explained much better by culture/genetics, rather than by policies. Not that policies aren’t important, he says, but they are secondary to culture/genetics. The use of the word ‘genetics’ will raise many hackles, he says, so he restricts himself to talking only about culture - and we’ll follow suit in this section. But do keep in mind when you read this section that he means both of these things.

And one of his biggest arguments against Studwell’s thesis is that Studwell constantly and consistently underrates culture:

“Studwell’s refusal to consider any kind of human capital or culturally based explanations leads him into some entertaining directions. I got a particular kick out of this part:

“In this respect, land policy is the acid test of the government of a poor country. It measures the extent to which leaders are in touch with the bulk of their population – farmers – and the extent to which they are willing to shake up society to produce positive developmental outcomes. In short, land policy tells you how much the leaders know and care about their populations. On both counts, north-east Asian leaders scored far better than south-east Asian ones, and this goes a long way to explaining why their countries are richer.”

So Northeast Asian leaders just cared more about their people? What kind of explanation is that? Maybe it’s a cultural thing? But the whole book is about cultural explanations not having any power, and the need for us to focus exclusively on policy. So I guess Studwell’s theory is leaders who care → the right industrial policy → economic development. But I think once you’re adopting theories based on the idea that some governments are just staffed by better people, you’re conceding a lot to cultural theories, which is odd when your entire thesis is that they need to be rejected”

The academic in me is inclined to agree with this point, and with the start of his concluding section:

“Capitalism works. With the collapse of the Soviet Union, and more recently the US surpassing Europe, East Asia stands out as the one region of the world that supposedly shows that government planning can be beneficial as long as it does not go overboard.

I see no reason to grant this argument. Countries succeed or fail based on a combination of population traits and policy. When we hold cognitive ability as measured by PISA constant, we see that more free market jurisdictions do better than the alternative: the US outperforms Europe and East Asia, and among East Asians, Singapore and Macau are the two places that clearly overperform their potential given standardized test scores. In Europe too, the most free-market countries have grown the fastest in recent years.

One more thing to note here is that advocates of industrial policy are often even more irrational than indicated by the discussion above. Studwell was presenting his suggestions as a way for poor countries to get out of poverty and start developing. He wasn’t arguing for a set of prescriptions for first world countries to adopt, and explicitly says that the case for free capital markets becomes stronger the wealthier you get.”

The one thing that I would certainly say about this excerpt is that the world is more complex than any one cross-country regression can explain. There are always going to be more factors at play, and these are going to change over time. That is inevitable.

So What?

The synthesis that I’m trying to get at is that both Studwell and Hanania are right. As I said above, the academic in me is in agreement with most of what Hanania is saying here. But the Indian in me (I was born in 1982) cannot help but think about what we got wrong with our industrial policy in India, and what we might have done if Bhagwati and Manmohan Singh had ‘won’ their debates back in the 1960’s. Hanania would probably say that even that would have been wrong, and we should just have gone with opening everything up right away, and letting markets do their thing.

But the more I learn about our culture, our politics, our history and our policies, the more I realize this wouldn’t have been possible. Economics, as Amit Varma is fond of saying, is downstream of politics, and that matters from a policymaking perspective. It would be naive to think otherwise, especially for India back in the 1960’s.

So what is the synthesis that I am trying to get at?

That industrial policy did matter for East Asia back then (go Studwell!), and the reason it succeeded there, and not elsewhere is because of culture (go Hanania!). The reason it succeeded there was because it was one of the few places on earth that applied both the carrot and the stick - and applying the stick is only possible with very high state capacity. And very high state capacity is a function of… culture.

Neither culture nor industrial policy are guarantees of success, either individually or in tandem. There must be other factors at play, and I don’t pretend to have a secret recipe that any country at any stage of development can follow with a guarantee of success. And I certainly don’t mean to suggest that the culture of any country is an immutable thing that guarantees success or failure for all time to come. But I do say that industrial policy that encompasses negative incentives is likelier to work, and I also maintain that getting this to happen is heavily dependent on state capacity. That actually makes me a little more pessimistic about industrial policy working well in India!

As usual, I hope you disagree, and I look forward to you telling me why!

Every summer, I spend a month in the lovely town of Manipal. We hold a summer camp there that is very close to my heart, and this summer will be no different.

It is a lovely way to spend the summer: kids with insatiable curiosity, taught by faculty with inexhaustible enthusiasm over three weeks. No exams at the end of the process, so students choose to learn the subject for the love of it. Glorious, just glorious.

It helps that Manipal has some of the best dosas and fish curries India has to offer, and I’ve had many meals with the folks who come to teach at the camp, where the quality of the victuals on the table are in an extremely close race with the quality of the conversation around it.

And one such conversation over one such meal helped me learn about the pigeonhole principle. It is an extremely simple idea, the pigeonhole principle. So simple that I can tell you about it in one short sentence, like so:

The Pigeonhole Principle states that if n items (pigeons) are placed into m containers (pigeonholes), and n>m, then at least one container must contain more than one item.

Save your smart-aleck responses, people, and I tell you this from first-hand experience. “Ah-ha!”, you might be tempted to say, “now that’s a surprise!”. Or some such smug little line. But, as I say, desist. Because the point isn’t about how simple the principle is, but rather about how sophisticated its applications are. Here, knock yourself out.

That’s the first of the three pillars that make up this post. Now let us go meet the second.

The Tinbergen Rule

Every econ student meets the Tinbergen Rule, sooner or later.

It is named after Jan Tinbergen, a Dutch economist from a while back. Useful pub trivia: he won the first Nobel Prize in economics. And an inside reference for students of economics everywhere: he’s also considered to be one of the fathers of econometrics. So if you find yourself staring balefully at Damodar’s book at two in the morning, you know whom to blame. Partially to blame, of course - we’re talking about a multivariate model here.

But anyway, back to the Tinbergen Rule:

In his work on macroeconomic modelling and economic policy making, Tinbergen classified some economic quantities as targets and others as instruments. Targets are those macroeconomic variables the policy maker wishes to influence, whereas instruments are the variables that the policy maker can control directly. Tinbergen emphasized that achieving the desired values of a certain number of targets requires the policy maker to control an equal number of instruments. This is known as the Tinbergen Rule.

I hope you’ve made the connection, because conceptually speaking, it is an obvious one. The Tinbergen Rule is a restatement of the pigeonhole principle. Mathematicians at certain dinner tables in Manipal have been known to snort derisively upon hearing of the Tinbergen Rule, and they would have expressed themselves more fully too, had the pomfret not been just that good on that particular day.

That’s the second pillar of our blogpost. Which brings us to the Scott Sumner post.

Conflict and Competence

That’s the title of Scott Sumner’s post from today, and while you should read the whole thing, here is a useful excerpt:

During the Chinese Cultural Revolution (1966-76), students persecuted people that had relatively high levels of education. Many professionals were sent to the countryside and roughly a million people died (500,000 to 2 million—no one knows the exact figure.) In the Chinese film To Live (1994), there is a heartbreaking scene where the formerly arrogant students realize that they need experts to help them out. Here’s how Wikipedia describes the scene:

Months later, during Fengxia’s childbirth, her parents and husband accompany her to the county hospital. All doctors have been sent to do hard labor for being over educated, and the students are left as the only ones in charge after they have “overthrown” the doctors. Wan Erxi manages to find a doctor to oversee the birth, removing him from confinement, but he is very weak from starvation. Fugui purchases seven steamed buns (mantou) for him and the family decides to name the son Mantou, after the buns. Fengxia begins to hemorrhage, and the nurses panic, admitting that they do not know what to do. The family and nurses seek the advice of the doctor, but find that he has overeaten and is semiconscious. The family is helpless, and Fengxia dies.

Scott’s post argues that the same phenomenon can be observed in present-day America. The scene(s) from To Live, Scott says, are just a particularly extreme example of a much more general phenomenon, the war on competency by political fanatics.

Anybody who is not MAGA in America these days, Scott says, is not going to survive for long ‘in the Justice Department, the FBI, the intelligence agencies and the military’. And other places besides, I’m sure. But the phenomenon is the same as what happened to the doctors in China: you don’t conform, therefore you are bad.

Conformity, or belonging, or being a part of the “in” group is important. If you don’t conform, you don’t belong. If you don’t conform, you are bad.

Conformity is not just being used to assess ideological alignment. It is being used as a proxy for quality. If you don’t conform, you are by definition not good.

Two dimensions (n): conformity and quality. One metric for judging (m): conformity.

Two targets: conformity and quality. One instrument: conformity.

The pigeons are telling you the same thing that Jan and Scott did!

TMKK?

This isn’t about the right or the left. The communists did it in China, and MAGA is doing it in America. Neither is it about cultural values: Asians did it in China, and Americans are doing it in America. And no matter where in the world you are reading this, you can rest assured that folks in your country have done it in the past, are doing it now, and will do it in the future.

Worse, you have almost certainly done this yourself. All of us look for proxies for quality, and those proxies tend to be related to how ‘like us’ the person being evaluated is. And the more that person is ‘like us’, the better we assume that person to be.

Just like the AIs, us humans too have jagged frontiers. Voting for the same party, praying to the same god, having the same religion, or using the same agentic harness as us is no guarantee that the person being evaluated will be as good (or as bad) as we ourselves.

It didn’t work for China back then, it won’t work for America today, and it will not work anywhere in the world. Evaluating a person’s quality along a particular dimension isn’t the same as evaluating a person’s beliefs.

People will very quickly latch on to what is being evaluated, and will learn to optimize for being excellent along that one particular dimension. So if tomorrow we as a nation say that we will allow people to read blog posts based solely on the quality of the mohawk haircuts on the author’s heads, well, I’ll be paying a visit to Dr. Batra’s clinic.

Old time readers will recognize this for what it is: Goodhart’s Law. Goodhart’s Law says that a measure that becomes a target ceases to be a measure. If the measure of quality is ideological alignment, then people will optimize for pretending to have ideological alignment, quality be damned.

Take a look at the world around you, and you will realize that we’re all playing a game in which we try to guess what the current-day equivalent of top-notch mohawks is for our society. Again: this is true no matter who you are, and which country you are in… and it is true regardless of when you are reading this.

The point of this post?

Listen to what Jan, pigeons and Scott are telling you, and learn to look beyond the mohawks.

It matters.

You know what I mean, right? When you’re done thinking about AI, Donald, your own country’s politics, and anything else that you choose to tax your brain about, you should do something that allows you to switch off. I’m talking about that coping mechanism.

Mine is watching old episodes of QI. QI is a lovely quiz show from England, and it is thirty to forty-five minutes of delicious trivia, with oodles of typically British humor for company. If you enjoy that sort of thing, you’ll love the show. You’re welcome.

Each season of QI is based on a letter. Each episode of Season 1, for example, was about things beginning with the letter ‘A’. The second season was about things beginning with the letter ‘B’, and you can see where this is headed. Now, one of the episodes in season 7 was about Germany. If you haven’t seen it, or haven’t heard of QI before, I highly recommend watching it.

One of the rules that the panelists on the show had to abide by in this specific episode was that they couldn’t talk about the war while answering questions about Germany. And in a great example of typically British humor, they did not (of course) specify which war the panelists couldn’t speak about. Much fun ensues.

So anyway, one of the questions in this episode was about this dog. What do you call it?

Both answers are correct, and it doesn’t matter which one you gave. That, as it turns out, was the point of the question:

The panel are show a picture of a dog and are asked what breed it is. The dog in question is a German Shepherd, which has been the official name for what was previously called the Alsatian since 1977. After World War One, English people stopped calling German Shepherds by this name because they did not like anything with German, so they called them Alsatian wolfhounds in 1918. The wolfhound was then dropped. (sic)

It makes us do weird things, the thing that the episode tells us can’t be mentioned. That’s the point today’s post makes, and we talk more about it in what follows.

Before Lego, They Used Pamphlets

Airborne leaflet dropping is a type of propaganda where leaflets (flyers) are scattered in the air, normally by filling cluster bombs that open in midair with thousands of leaflets.

Military forces have used aircraft to drop leaflets to attempt to alter the behavior of combatants and non-combatants in enemy-controlled territory, sometimes in conjunction with air strikes. Humanitarian air missions, in cooperation with leaflet propaganda, can turn the populace against their leadership while preparing them for the arrival of enemy combatants.

Leaflet droppings have also been used to limit civilian casualties by alerting civilians of imminent danger allowing time to evacuate targeted areas.

Source: https://en.wikipedia.org/wiki/Airborne_leaflet_propaganda

All sides in all instances of the thing we cannot talk about have done this, because it made sense to do so. It increased confusion in the opposition, encouraged defections, and made clear to the other side what was really going on (and this is something both sides believed to be true for only their own side, which helped with the very first point). In the First World Thing We Cannot Talk About, the Germans and the British merrily dropped propaganda over enemy lines, and this was repeated in the Second World Thing We Cannot Talk About.

Propaganda during the thing we cannot talk about is probably as old as the thing we cannot talk about, and that which we cannot talk about is probably as old as humanity itself.

Now, follow my reasoning closely here, and take your time about it. The thing we cannot talk about isn’t even relevant to the current situation, because the current situation isn’t even an example of the thing we cannot talk about, per the government that started the thing we cannot talk about.

Which itself is an example of propaganda related to the thing we cannot talk about, if you think about it. I did tell you to take your time to think about it, you will recall.

But here is where things get interesting.

The Two AI Wars

Imagine a purely hypothetical scenario in which a country called A declares the initiation of major combat operations against another country called I. Further imagine that the country I was run by an authoritarian regime, which had ‘been struggling to shut down all footage of the protests convulsing the nation, cutting off internet access to the outside world in the longest blackout in I’s history’.

That makes the current Thing We Cannot Talk About genuinely interesting, because there are now two battlefields at play. One battlefield is where the actual Thing We Cannot Talk About is being fought. A’s territory is not involved in this battlefield, but I’s very much is.

But the propaganda about the Thing We Cannot Talk About? That is a major headache for A, because I had simply cut off its people’s access to that particular battlefield before major combat operations began on I’s territory.

But A’s people have access to that battlefield, as does the rest of the world. And the bad news for A is that while there is a ceasefire in the Thing We Cannot Talk About, there is no ceasefire in the propaganda about The Thing We Cannot Talk About.

And I is winning that one:

Prior to the attacks, it looked as though some connectivity was returning to Iran, but as bombs fell, the blackout was once again in place. However, there were some early suggestions that Iran was going to selectively lift the blackout “for those who can carry our voice further” — a kind of tiered internet access for whitelisted people willing to promote, at the very least, an anti-war message. No one could have foreseen what would happen next.

By mid-March, the most dominant strain of Iranian propaganda was of a markedly different tone. Little Lego minifigures dressed up as soldiers as Lego planes and Lego helicopters burn in an AI-generated desert. Videos crammed in references to Jeffrey Epstein and dead Iranian schoolgirls alongside guns and explosions. It turned out that Lego AI slop was the voice that would carry the farthest.

Twitter But No Tear Gas

A little more than a decade ago, Zeynep Tufekci wrote a lovely little book called Twitter and Tear Gas. My understanding of the main thesis in her book is deeply rooted in economics:

1. Social media and memes greatly reduce the cost of organizing a movement online. That’s the good news.

2. The bad news is that opportunity costs are everywhere, and precisely because costs are low, starting a movement online is easy, but following through on actual change is that much harder.

But the major combat operations between A and I are different from Germany and England during the two World Things We Cannot Talk About. And they are also different from Zeynep’s framing from a decade ago.

First, I doesn’t have a battlefield in the propaganda combat operations. They have internet blackouts, remember? How do you even spread memes online in the Stone Age, anyway?

Second, I is able to produce content in A’s idiom, and for A’s audiences. A’s own government is unable to reciprocate in this battlefield, because of the first point above… but also because the A government’s memes are aimed towards A’s domestic audience, not I’s!

Production of memes ain’t the problem here. It is distribution1.

Costs for the production of hard-hitting memes (as opposed to hard-hitting bombs) have dropped to zero. The costs of distribution have dropped to zero for A’s audiences, but have scaled up dramatically if you want to reach I’s audiences. If you take away the second point from Tufekci’s analysis, it is easier than ever to optimize for her first point.

So easy, in fact, that anybody could do it. England and Germany had governments organizing production and the distribution of propaganda. You defeat the country, and you stop the production and the distribution of propaganda. But this is not your grandparents’ Thing You Cannot Talk About.

This time is different. But us humans? We’re the same. And that’s a problem.

AI, and Humanity at Large

It is not as if we are now living in a world where the narrative matters more. That has always been true, and you could argue that it is in fact one of the definitions of humanity. Signaling matters to us as a species. Ask Messrs Hanson and Simler, among others.

You win the Thing That We Shall Not Talk About by winning both battlefields, not just the physical one. And both sides of The Thing That We Shall Not Talk About know it.

There is another reason this report is interesting. It maps to the time Anthropic ran those Superbowl ads (and if you’ve not seen them, here. Go watch, they’re a lot of fun, and will remind folks of a certain age of another series of ads. The more things change…)

In today’s blog post, I want to walk through what I found most striking (all three: the good, the bad and the uncertain), and share some original analysis — including a look at my own usage data over two-plus years of working with Claude.

The Adoption Curve is Working

The first chapter of the report is about something unsurprising but worth noting: usage on Claude.ai is diversifying. The top 10 most frequently asked for tasks accounted for 24% of all conversations in November 2025, but only 19% in February 2026. Non-top ten tasks have a higher percentage share, and what that means is that claude.ai is beginning to grow a tail, and it may well be a long one.

The other big and unsurprising finding is that coding is migrating from the consumer interface to the API. This is largely via Claude Code, which (among other things) splits work into smaller agentic calls. Meanwhile, personal use — sports scores, product comparisons, home maintenance — rose from 35% to 42% of Claude.ai conversations. Many more people are using Claude.ai, and those of us who have been using it for a while are increasingly using it for more complicated stuff, and both are Mostly Good Things.

This data was collected between February 5–12, 2026. This is before the Anthropic-Pentagon confrontation, and if you haven’t read about that, you have some catching up to do. The SCR designation came on February 27, and Claude exploded in popularity almost immediately after. So the diversification story here is driven by Anthropic’s Super Bowl advertisements (which the report explicitly credits) and organic adoption, not the DoW drama. The next Economic Index report will therefore make for even more fascinating reading.

But we have the earlier data to analyze in this report, and it makes for rich reading. As the report itself says, this is a textbook adoption curve case study. Early adopters will typically cluster around specific high-value use cases, which in this case mostly means coding. Later adopters bring a much wider, more casual range of tasks. The average estimated value of work done on Claude.ai (as measured by US wages for associated occupations) dipped slightly from $49.3 to $47.9 per hour. Why should it dip? This ties back to the long tail point I made above - more people are using Claude.ai than was the case earlier, and that means usage will diversify.

Three Inequalities Worth Worrying About

As an economist, the inequality findings are where I linger longest. The report touches on geographic convergence, but I think there are actually three distinct inequality stories buried in the data, and they don’t all point in the same direction.

First, within the US, convergence is continuing. The share of per-capita usage going to the top five states dropped from 30% to 24% since August 2025. The Gini coefficient has fallen. This is good news, because it suggests that knowledge about what AI can do, and how to use it, is diffusing within the United States. But the pace has slowed. Early estimates suggested states would converge to roughly equal usage in 2–5 years, but Anthropic now says that the updated estimate is 5–9 years. I’m not sure why this should be so, and it is a minor puzzle.

Second, across countries, divergence is increasing. The top 20 countries now account for 48% of per-capita usage, up from 45% from the earlier report. This is the opposite of the within-US story, and it worries me more. Supply-side inequality (who has access to AI models) is one thing, and it is bad enough. But demand-side inequality (this is weird terminology for an economist to use, but these are weird times) is stickier and potentially more consequential. Why? Because it hints at differences in institutional culture, work patterns, and organisational willingness to experiment with AI. These are not easy things to change! If you think of access barriers as transaction costs (in the Coasean sense), supply-side costs can be lowered with better infrastructure, policies and pricing. The mental models and habits determine whether a country’s institutions even try to integrate AI into their workflows. Early days, to be clear, but this is something to keep an eye on.

Third — and this is the one the report hints at but doesn’t fully develop — within lagging countries, inequality is likely even worse. The report notes that in low-adoption countries, the user base is still dominated by technical early adopters doing high-value work. This looks like sophisticated usage, but it actually reflects a slower adoption. These countries show divergence between them and leading countries, while also showing extreme concentration within their own populations. This is not good news. This paper is worth reading in this context.

The report’s own learning-curve findings make this worse. If experienced users get better at using AI, and early adoption is self-reinforcing, then the countries that are behind today will fall further behind tomorrow. And that too in the most important resource of them all: the accumulated human capital of knowing how to work with these tools. Metis in AI compounds, and more people in more countries need to be working every day to acquire it

The Primitives: Statistically Significant but Small

Economic primitives are simple, foundational measures of the ways that Claude is used. Here are some key statistics: the average education years needed for prompts is down from 12.2 to 11.9 years, and human-only task completion time is down by about 2 minutes. All differences are statistically significant at p<0.001.

But with a sample of one million conversations, statistical significance isn’t the real story in my opinion. What matters is the magnitude of the effect, and these changes are small potatoes, no? The 0.3-year drop in education level and the two-minute reduction in task time are real, but how noteworthy are they, really? We’re still in the early days of telling ourselves stories about AI adoption, so I’d say these are worth tracking over time in case they become trends, but not worth building an argument on today.

Who’s Picking the Smart Model — and Who’s Not Even in the Room?

The report’s analysis of model selection is one of its genuinely new contributions. Users choose Opus (Anthropic’s most capable model) for tasks associated with higher-paying occupations. For every additional $10 of hourly wage for a task, Opus usage increases by 1.5 percentage points on Claude.ai and 2.8 percentage points on the API.

I think this finding is slightly open to misinterpretation if read as “higher-paid professionals use Opus more.” What it really shows is that people pick the more powerful model when the specific task demands it. A software developer may pick Opus because a particular problem demands the best reasoning available. An academic, by contrast, may pick Sonnet. Why? Because the per-conversation (per task, to use Acemoglu’s framing) cognitive requirement is lower, even though teaching itself as a whole is complex work.

I suspect Opus’ share would correlate even more tightly with users’ own assessment of what percentage of their tasks can be automated. The people reaching for the most powerful model are the ones whose work most directly maps onto what AI can do. It would be interesting to look at what the understanding is, by profession, about what chunkable tasks their daily workflows can be broken down into.

The Gap: High Wages, Low Exposure

I was curious about those occupations that sit towards the lower right of the charts. These are folks with high hourly wages, but low share of conversations using Opus-class models. Here’s Claude Code’s take on these, after it did its thing:

The occupations missing from AI adoption fall into three clusters:

Healthcare practitioners make up the largest group — physicians, dentists, physical therapists, occupational therapists. Their core work involves physical examination, hands-on treatment, and regulated clinical decision-making. When physician tasks do appear in Claude data, 82% of the time it’s patients asking about their own symptoms, not doctors using AI for clinical reasoning.

Senior managers and executives form the second cluster. Chief Executives, Engineering Managers, Sales Managers — their work centres on relationships, judgement calls, and organisational politics. When CEOs show up, their top task is operational analysis — a support function, not the strategic and interpersonal work that defines the role.

Engineers in physical domains round out the list. Civil, environmental, marine, and industrial engineers. Unlike software engineers, their work involves site inspections, physical materials, regulatory compliance, and specialised tools like CAD software. Their Claude tasks, when they exist, tend to be administrative — budgets, specs documents — not engineering design.

What will it take to change this is a question that I am sure engages the best and brightest minds in all of the major AI labs. The next wave of AI impact for these professions won’t come from chatbots getting smarter. It will come from AI being embedded in the domain-specific tools they already use — electronic health records, CAD software, project management platforms, for example. The interface matters as much as the intelligence, partly because workflows really matter to these guys. In Coasean terms: the current chat interface imposes a transaction cost. You have to chunk your work into text friendly subtasks, translate your domain expertise into prompts, and finally, you have to figure out a way to get the LLM’s output back into your workflow. For a software developer, the cognitive load to do this is near zero. For a dentist today, it’s enormous.

Learning Curves: The Report’s Core Finding

The report’s second chapter asks a great question: do people get better at using Claude over time? I’d love to find out how the team at Anthropic chooses the main focus for each of these reports, because this one is a lovely pick.

And the answer to the question is a resounding yes, and that along multiple dimensions.

High-tenure users (6+ months) compared to newer users show:

• 10% fewer personal conversations, 7 percentage points more work usage

• 6% higher education level in their inputs

• More collaborative interaction patterns, less directive delegation

• Greater task diversity (less concentration in top-10 tasks)

• 10% higher success rate in their conversations

That last number is important. And it survives increasingly stringent controls. When you compare high- and low-tenure users doing the exact same task (using task fixed effects), the gap shrinks to about 3 percentage points. When you add controls for model choice, language, country, and use case, it moves back up to about 4 percentage points. That’s what the chart below is showing you.

This is evidence consistent with learning-by-doing. Given the same task, the same model, and the same language, experienced users still get better outcomes. The report is appropriately cautious about alternative explanations (cohort effects and survivorship bias could obviously play a role, for example), but the controlled regressions rule out the simple versions of those stories.

The central explanation that they offer has a concrete TMKK: use these tools more. The more you use these tools, the better you get at using these tools. The report makes this connection explicitly in its conclusion. Early adopters with high-skill tasks have more successful interactions, and these same users may be simultaneously the most exposed to AI disruption and most aided by it. The people who learned to work with AI earliest are pulling ahead. The ones who haven’t started yet face a compounding gap. Please, use these tools.

One Concern: The Imagination Ceiling

The data shows that the years of education needed to understand user prompts increases by almost 1 year for every additional year of Claude usage. That is Very Good News, because it shows that users are getting better at using these tools. But the relationship appears to taper off after roughly a year of use.

This is a problem of a lack of imagination, maybe? People get better at using Claude for what they already know to ask for, but they may not learn to ask for qualitatively different things. Folks can now do what they were already doing better, and faster… but we still have not gotten good enough at figuring out what else we can do with these tools. If that is indeed what is happening, it’s an important finding. It would mean that even experienced users aren’t scaling up ambition, just scaling up efficiency.

The report’s other finding — that high-tenure users are more collaborative and less directive — is somewhat reassuring here. They’re iterating with Claude, as opposed to just issuing commands. But the plateau in prompt complexity suggests limits to how far that collaboration extends.

I want to offer a gloriously unscientific framework for thinking about this. Consider a simple 2×2:

New users treat AI as a substitute for simple tasks, say as a better Google search. Intermediate and advanced users learn to treat it as a complement for complex work. The danger zone is when people start using AI as a substitute for complex tasks, delegating without understanding. This is what we should watch for. So far, the data is reassuring: advanced users collaborate more, not less. But that could change as models become more capable.

N=1: My Own Learning Curve

To make this concrete, I ran the same kind of analysis on my own Claude usage data. I exported my conversation history and had Claude Code classify each conversation by task category, complexity, interaction pattern, and platform — with everything anonymised. I’ve been using Claude for roughly the last two and a half years, and this covers Claude.ai conversations and Claude Code sessions, but not Claude CoWork.

A few things jump out:

Task diversification mirrors the aggregate pattern, but more dramatically. My earliest quarter was almost entirely brainstorming. By my most recent quarters, I have seven or eight categories with a meaningful share in each category: coding, writing, research, data analysis, teaching preparation, and more.

The coding migration story is me in miniature. Coding barely exists in my early quarters, then grows steadily to become my dominant category. 25% of my total usage is now Claude Code. I am the user the report describes when it talks about coding shifting from Claude.ai to API-based tools.

My interaction split is 52% collaborative, 48% directive — almost exactly even. This matches the report’s finding that us “veteran” users maintain collaborative patterns. I use Claude directively when the task is clear (Claude Code), collaboratively when I’m thinking (conversations like this one).

77% of my conversations are moderate-to-complex. Only 5% are personal. Against the report’s finding that new users devote 42% to personal use, this is a stark contrast.

And my conversation length is increasing, not plateauing. Median turns per conversation have spiked in recent months (9–14 turns).

(I had stopped my Anthropic subscription for a while in the middle, and I suppose I should be cleaning up this data a little better. But I happily plead guilty to YOLOing this: I had an idea, asked Claude Code to do its thing, and I’m just inserting these charts as is into this post.)

I am not a representative user. I’m what they’d call a power user (at least for someone who is certainly not from a software background), an economist who thinks about AI professionally, and someone who treats Claude as an intellectual sparring partner rather than a tool. But this is pure guesswork on my part, and that is worth keeping in mind. And part of the reason I included this section is to incentivize you in doing this for yourself, of course.

What I’ll Be Watching In Weeks (Days?) To Come

A few predictions and things I want to track as future Economic Index reports come out:

The March data will capture the DoW/Anthropic confrontation. Did the supply chain risk designation and surrounding media coverage drive a new wave of adoption? If so, was it the Super Bowl pattern again (casual users, diversification) or something different?

Automation on Claude.ai should be the leading indicator. As the new cohort of users matures, I predict their usage will graduate from “What can you do?” (exploratory, personal) to “What can you do for me?” (task-specific augmentation) to “Just do it” (automation, directive patterns). Tracking the automation share on Claude.ai specifically might show whether the broad user base is making this transition.

The cross-country divergence is the metric that matters most for policy. If AI skill is accumulated through use, and use requires a basic level of institutional willingness to experiment, then the countries falling behind today are building a deficit that gets harder to close. This isn’t just about access to models. It’s about building cultures of adoption. And unlike supply-side gaps, which markets and infrastructure investment can close, demand-side gaps require something harder: changing how institutions think about work. I am pessimistic about this, so please tell me why I’m wrong.

The domain-specific tools question. Will AI exposure for healthcare, engineering, and management occupations increase as AI moves from chat interfaces to embedded tools and/or robotics? Or are the barriers more fundamental than interface design? The gap analysis suggests the answer will not come from chatbots getting smarter. Serious question: how much smarter can the chatbots possibly get?

And that is a concrete reason to watch both AI developments in the weeks to come, and to eagerly await the next edition of this report.

The Anthropic Economic Index data is publicly available on HuggingFace. The personal usage analysis described in this post was conducted using anonymised data; raw conversation data is not shared nor published. All original analysis was done with/by Claude Code — which is, I suppose, the point.

I want to talk about that process, because I think it matters more than the post itself.

The Centaur, Revisited

Back in September 2023, Ethan Mollick and his co-authors published a landmark study on how consultants at BCG used AI. They found that consultants using GPT-4 finished more tasks, finished them faster, and produced higher quality results. Quaint what those prehistoric fellows were doing back in 2023, yes, sure. But the interesting finding was, even back then, in the how. The best performers fell into two camps: centaurs, who divided labour strategically between themselves and the AI across phases, and cyborgs, who interweaved their work with the AI moment by moment.

Three years on, Mollick tweeted something that I think goes one level deeper: technology always deskills us on something (we lost cursive handwriting, and our parents lost the slide rule). We will lose skills over time, and that’s fine. I knew how to use log tables at one point in time, and it simply does not matter now. Neither do z and t tables today, but again, that is a whole other story.

But the important thing is whether we make deliberate choices about which skills to keep and which to let go, or whether we fail to notice our own ability to make those choices atrophying.

This is one level up on the original question from Ethan Mollick’s post from 2023, which was “How should/do humans and AI divide labour?”. The one level up question is actually two questions. One: what makes one centaur better than another? In this case, I think the underlying question is this one: what determines whether the centaur goes somewhere interesting, or merely goes fast?

And second: if you think of yourself as a centaur or a cyborg, what properties does the AI bring to the table? And given those properties, what complementary skillsets should you be bringing to the table? The underlying question here is one of the points I made in yesterday’s blogpost. The AI half of the centaur will update itself every three months or so (and it used to be six months or so three months ago). Your skills need to remain complementary over time (treadmill), not just for now (monument).

The Horse’s Eyes

Let me describe what the horse half of this particular centaur actually does today. In yesterday’s post, here’s what happened. I read Tyler’s post. I had a reaction — “go one level up.” I brought that reaction to Claude. Claude produced a first draft. It was structurally sound but not deep enough — I said so, and here’s the important part: I didn’t just critique, I generated five specific directions the post needed to go. Claude engaged with each one. Arin Dube’s tweet arrived midway through our conversation, and I saw how it might help. My 2018 post on choices, costs, horizons and incentives became the analytical spine. Claude produced a second draft. I rewrote this entire draft out, and changed it considerably. I ran this draft past both ChatGPT and Claude, and then I hit publish.

It is not like this is a fixed workflow for me. I experiment with different ones for different blog posts. There are drafts I will write out entirely by hand, and only give to an LLM to check. I have an entire process on my local machine that I use for other blog posts (see screenshot below). And there is a rather infamous one where I tricked my readers. Sometimes a cyborg, sometimes a centaur, and in both cases, I don’t yet know what workflow feels most natural to me. I’m still very much in the experimental phase, and maybe this will last forever (yay!)

So what is Claude doing in all of this, regardless of which particular workflow I use? It is doing what a horse does best. It is traversing the knowledge landscape without tiring, and/or drilling down on specific points without tiring. It is a horse with endurance and strength.

But that undersells it. Claude was also spotting features of the terrain. If I say we should head in that direction because x, Claude is very, very good at saying “Yes, and also y, z…and a,b and c as a bonus!”. Once you point it in a particular direction, the horse can see, and more than you possibly can. What it cannot do — or at least, will not do for now — is choose where to go. Go, or dig? And if it is to be go, in which direction?

That’s the rider’s job.

What Makes a Good Rider?

Don’t focus on the horse, which is our wont. We all tend to rhapsodize about how powerful the model is, and what’s inside the frontier and what remains outside. Mollick’s jagged frontier is a map of the horse’s capabilities. But the quality of the centaur depends at least as much on the rider.

So what does a good rider bring? What should a good rider bring?

Direction: Breadth or depth? Should we explore the Lucas Critique implications, or drill deeper into Dube’s finding? This is judgment, not technique. It’s the ability to sense which thread matters and which is a tangent. It is also the ability to decide when a tangent or a digression is worthwhile. And what makes it worthwhile may be injecting a richer, related idea from another domain into the discourse. Or it could be injecting some levity into the proceedings because it is all too dreary otherwise. That’s your call as the rider, and you should be making it. That is what makes the post yours.

Disposition. The habit of asking weird questions. The refusal to accept the first framing. The instinct to go one level up, and then to go across into whatever domain that level requires. Ask random questions! That practice isn’t pedagogical whimsy. It’s training for the only skill the horse can’t provide. Is the Tinbergen principle just a way to restate the pigeonhole principle? That’s a great weird question!

Stakes. When I publish a post, my name is on it. My students might read it, my colleagues might read it, public intellectuals might read it. I have skin in the game. That changes how I evaluate what Claude produces — not just “is this good?” but “can I stand behind this?” That quality filter comes from having a reputation to risk and a community to face. The horse, however eagle-eyed, has no reputation. You have skin in this game in a way that the model does not. That’s not a risk, it is a blessing. Use it to your advantage.

And there is a fourth thing, the one that I think matters most.

Quality, Not Virality

There are two kinds of prompts you can give the genie.

The first: “Give me five topics to create a video about today, optimizing for virality on my channel.”

The second: “I was thinking about the post that we wrote yesterday, and here’s what’s gnawing at me.”

Both use the same technology. Both produce outputs. But they do very different things to the human who issues them.

The first prompt atrophies the choosing muscle. Each time you outsource “what should I think about?” to the genie, the muscle weakens. You’re training yourself to not-choose. You’re falling asleep at the wheel, to use Mollick’s phrase from the BCG study, but at a deeper level — not just failing to catch the AI’s errors, but failing to generate your own questions.

The second prompt strengthens the choosing muscle. It forces you to introspect before you engage. To notice the itch. To identify what’s unresolved. To articulate, even roughly, the direction you want to go in. The AI then helps you chase it. But the noticing? It is yours. And it is precious.

The virality prompt doesn’t engage on the quality dimension at all. It outsources direction to a metric. The gnawing prompt starts from thinking about quality — from the felt sense that something matters, that something is unresolved, that the conversation needs to go further. Remember, the model is currently stateless during pauses in a conversation. You, hopefully, are not. That’s an advantage you have, and you should double down on it.

The age of AI should make you think harder than ever before, and if you don’t see it that way, I say you’re doing it wrong.

Natural Philosophers Make the Best Centaurs

This, I think, is the real answer to what makes one centaur better than another.

The good centaur isn’t just the one with the best horse, or the one who divides labour most efficiently. The good centaur is the one whose rider has the disposition of a natural philosopher: curious across domains, stubbornly committed to following questions wherever they lead, and oriented by quality rather than metrics.

The Royal Society’s motto — Nullius in verba, take nobody’s word for it — is a rider’s motto. Don’t accept the default framing. Don’t let the horse choose the direction. Don’t outsource the question. Go look for yourself, ask for yourself, and care enough about the answer to put your name on it.

Yesterday’s post argued that the binding constraint on economic knowledge isn’t execution. It is imagination, the ability to generate questions that the existing paradigm doesn’t hand you. Arin Dube’s data confirmed it: LLMs haven’t increased paper output, because RAs were never the scarce input.

But there’s a deeper version of this finding. Perhaps the people who produce the good papers aren’t using AI to produce more papers at all. They’re using it to think more carefully about fewer things. The production function doesn’t shift outward. It shifts inward — toward depth, toward quality, toward the thing that’s gnawing at you.

The economists asking for five paper topics optimised for citations are on the virality prompt. The rest, hopefully, are on the gnawing prompt. Both are riding the same horse, sure. But only one of them is becoming a better rider. Only one of them is focusing on Ethan’s bottom line in his tweet today: “What is important is whether we will make deliberate choices about what skills to keep & which they will be.”

If you aren’t asking weird questions, you aren’t learning. And if you aren’t choosing your own questions… that is, if you aren’t cultivating a feeling for what gnaws at you, you aren’t really riding at all. You’re just a passenger.

Ask weird questions. Choose them yourself. Think about what they mean.

Then ride. And keep on ridin’!

So what becomes scarce in such a world? The system does. The system (or the box) has components: the dataset, the code and the method of analysis. The code and the method are now solved problems, or close to being so. So folks who can figure out innovative ways to collate interesting, novel and high quality datasets will become scarce. Tyler himself points to the possibilities of synthetic datasets being generated with the help of AI, but doesn’t ask the obvious question. If this task is repeatable and verifiable, it can be automated. Which brings us back to square one.

It is really hard to do, and it does give you a bit of a headache, but there is a helpful heuristic when it comes to thinking about AI. As roon put it on Twitter recently: in the age of AI, whatever level you’re thinking at, go one level up.

Tyler went one level up from the paper publishing game as it is played today. But the reason this heuristic gives you a headache is that it applies recursively.

But here is exactly where principles of economics come into play: each time you go up a level, you don’t just get a new abstraction. You also get a new set of incentives, costs, time horizons, and choice sets for everybody involved. You get, in other words, to update your view of the world. Not because the principles have changed, but because the circumstances to which they’re being applied have changed.

Declaring that we’ve moved to a higher level of abstraction is easy. Doing the economics of what that level actually looks like is harder, but more useful.

The Market Has Already Spoken

Before we go climbing levels, let’s look at the data. Arin Dube points out that the advent of LLMs hasn’t raised the number of NBER working papers above trend. Nor submissions to top journals. His explanation: LLMs substitute for good RAs, but not for good ideas. And RA labour supply was never the binding constraint on economic scholarship. By the way, I’m willing to give credence to the idea that the academic world is just that slow, and again, principles of economics help us understand this. The academic profession isn’t incentivized to do this, and for many different reasons (shooting themselves in the foot would be chief among them)

Tyler’s post implicitly assumes that the bottleneck in economic knowledge production is in the execution phase: writing the paper, running the analysis, doing the robustness checks. If that were true, LLMs should have already caused an explosion in output. But they haven’t, and you’d have to update your priors a little bit at least about the fact that the bottleneck probably lies elsewhere.

The market for academic papers is talking, and it’s telling us something important. The constraint is upstream of the paper. It’s upstream of the system that produces the paper, too. Building a better system is essentially building a more sophisticated RA. But as Arin Dube points out, this excellent RA still needs someone to tell it what to investigate. Replacing academic papers with systems is essentially proposing to upgrade the RA layer even further. But Dube’s data suggests that upgrading the RA layer seems to not matter at the margin.

So what is the binding constraint? Let’s go find out.

One Level Up, Then Another, Then Another

Let’s try an example of thinking one level up. Here’s one of Tyler’s ideas in his blog post: “How about ‘I am Tyler Cowen, what is it you think I will find interesting in this data set?’” How might you go one level up here?

How about: “I am Tyler Cowen, what data set should I be trying to get, given the most interesting questions I’m thinking of at the moment?”

Remember, this is recursive. How does one go one level up from here?

Try this on for size: “What interesting questions am I not asking that I should be? Use what you know about me to formulate your answer.”

But remember, each change in level also changes what is scarce, what one’s incentives will be, and associated costs and time horizons. At the first level (analyzing the dataset level), the scarce skill is statistical technique: the ability to extract findings from data. At the second level (choosing the dataset level), it’s judgment about data acquisition — knowing what to look for and where. At the third level (what are my unknown unknown levels), it’s imagination — the ability to generate questions that wouldn’t occur to someone embedded in their current paradigm.

And as we stated earlier, each level has its own economics. Technique is a skill that is expensive to acquire but it has the benefit of being durable: you only need to learn the basics of causal inference once. As opposed to data acquisition, which has ongoing costs: every new dataset requires negotiation, cleaning, contextual understanding. But what of imagination? I’m not even sure how to measure it, and cultivating imagination looks nothing like traditional education. It is about cultivating a disposition or an inclination. It is about inculcating the habit of asking weird questions, and about stubbornly refusing to accept a given framing.

Think of LLMs as a massive supply shock at the technique level. But an infinite increase in the ability to do analysis doesn’t automatically increase the ability to generate interesting ideas in the first place. Or, if you prefer economist speak: the production function for economic knowledge seems to have near-zero elasticity of substitution between technique and imagination. We’ve flooded the market with automated RAs and nothing much seems to have happened. Why? Perhaps because RAs were never the scarce input to begin with.

I’ve written before about asking my students five random questions at the end of every class. About anything, as long as it’s not about the topic we just discussed in class. Here are my all time top three: why do cockroaches flip over when they die? Melody itni chocolatey kyon hai? Were Ross and Rachel on a break?

The practice sounds like a pedagogical trick. But one way to think about it is that it’s training for the only skill that Dube’s data says matters: the ability to generate questions that the existing paradigm doesn’t hand you.

The cost structure is interesting. Imagination might be the cheapest skill to exercise: it’s free to ask a weird question. But it is among the hardest to cultivate. Midway through each semester, I have to tell my class that I will not leave until at least five questions have been asked. Trust me on this. This isn’t about fancypants labs, or jazzy classrooms. It’s about cultivating environments that reward curiosity: classrooms that invert the syllabus, communities that value questions over answers, and about cultures that treat “I don’t know, let’s find out” as a high-status move. Creating that culture, especially in the world in which we live today, is hard.

And it doesn’t scale the way AI does.

The Journal Problem

Tyler asks: “Do we even need the AER any more to certify which are the best papers? Just ask the AIs.” And later on in the post he says, “What if you submit to a journal a data set and some code?”

But do our usual thing: who needs the journal?

The journal has always solved two problems simultaneously: quality certification (”this is good work”) and attention routing (”therefore, read this”). In a world where humans couldn’t read everything, bundling these two functions made sense. The AER’s authoritative stamp told you what to pay attention to and that it was worth your time. Quality and signaling, two for the price of one.

AI unbundles them. Quality certification can be handled, say, by a council of frontier models. Such a council can rate papers, check replicability, assess influence. And it might perhaps be better at this than three overworked referees with their own agendas. But they are already a gazillion times faster, and not that much worse in quality, if at all.

But attention routing is a different beast altogether.

In the current world, attention routing is a centralized bureaucracy. There’s a clear hierarchy — AER, QJE, Econometrica — and everyone ‘Schelling Points’ around it. This is a well-trodden path, and we know what I’m going to say next: publication delays, conformity pressure, the distortion of research agendas toward “publishable” questions, yada yada yada. But the benefit is legibility. When a hiring committee looks at a CV, they can read it. When a graduate student looks for important work in trade theory, they know where to start.

Imagine you live in a world where your AI agent reads everything and filters it for you. You don’t have to imagine this, by the way. You are in that world, you just don’t know it yet. Quality certification is a solved problem in this world: your agent will do it for you. How do you distinguish a bad arxiv paper from a great one? Simple, ask your agent to evaluate it for you. And given the urgency with which publishing happens on arxiv, human editors at top-notch journals are going to suffer in comparison on the speed-quality Pareto frontier. Why bother waiting for a costly and delayed signal from the top journals, if you can read excellent research that lives at the cutting-edge of your field?

And if journals are going to see a fall in status (if speed and quality in publications matter, what do you think is going to happen to the status of journals?), how do we judge the quality of a researcher? How does a hiring committee in a university evaluate a researcher when there’s no shared prestige signal, just millions of personalized feeds? (Are you tempted to ask “Who needs the university?” Congratulations, and welcome to Headache Hotel. But that’s a whole other story.)

The good news is that the researcher’s choice set expands. You can publish anything, anywhere, in any format. But the bad news is that the incentives become bewildering. You’re no longer optimising for “what will impress the AER editor.” You’re optimising for reach across millions of personalized filtering mechanisms. That’s a very different optimisation target, and nobody knows how to think about it.

The cost structure flips too. Producing research gets cheaper, because the AI genie helps. But building a reputation might get more expensive, because there’s no single ladder to climb. In the current world, one publication in the AER buys you legibility across the entire profession. In the new world, legibility is fragmented. You might be famous in one cluster of agent-curated feeds and all-but-invisible in another.

The Lucas Critique, All the Way Down

Tyler suggests publishing “a method for simulating human behavior, to run AI-simulated experimental economics.” Build the system and don’t worry about the paper.

But go one level up, and you run into the Lucas Critique in a form that should make us quite uncomfortable.

The Lucas Critique says that models estimated under one policy regime break when the regime changes, because agents adjust their behaviour in response to new rules. It’s a warning about the instability of empirical relationships when the rules of the game change.

But think about this: universal AI assistants are a paradigm change for human decision-making itself. Every economic agent now has, or soon will have, a reasoning engine in their pocket. Their choice architecture is fundamentally altered. Price comparisons that took hours take seconds. Heuristics that marketers could count upon will no longer work as well. Contract language that was opaque becomes transparent. Negotiation strategies that once required experience can be generated on demand.

So: any behavioural model estimated on pre-AI humans is already suspect. The price elasticities, the risk preferences, the heuristics and biases — all of these were measured on humans making decisions with their own reasoning capacities. The new agent is a human-AI centaur, and we don’t have stable estimates for how that centaur behaves. Agents will have no problem thinking one level up, I assure you. You could tell me that variance will go down because of this, and I might agree with you. But you could also tell me that variance will go up, and well, who knows for sure?

In any case, the time horizon for any empirical finding shortens. Your carefully estimated parameter was valid when humans were computing intuitively. Now they’re asking Claude. How long does your estimate last? A year? Five? The cost of maintaining a working system of behavioural simulation isn’t just computational. It is also the cost of keeping up with a moving target. Every time the AI improves, the individual agent changes, and your model drifts.

The Lucas Critique in the age of AI says: the system needs to be rebuilt constantly, because the centaurs inside it are constantly updating their beliefs and therefore their actions. That’s not an argument against systems per se — it’s an argument that the system is a treadmill, not a monument. And the economics of treadmills is very different from the economics of monuments.

The Hayekian Disclaimer (Which Is Also the Point)

Tyler asks whether tenure should be given to folks building systems instead of writing papers and/or folks who can build capabilities.

You know the drill by now. What is tenure for in a world where time-to-insight has collapsed? Tenure is like insurance. It gives the professor a floor, or an income security. Think of it like the premium paid by the university in exchange for the option value on long-term research. If the long term gets shorter, the option value drops. And if building capability is going to be the scarce resource that we think it is going to be, those folks aren’t going to be queuing up for tenure. Again, in this world, what is university for?

But working out the new equilibrium for academic employment and institutions in the education sector is a prediction about institutional design and culture, and I don’t have one. Particularly in these times.

I can’t tell you what the new world looks like, and what steps we will take to get to that world from this one. Nobody can. That’s a Hayekian point — the new order for knowledge production will emerge from millions of individual adaptations, not from a blueprint. What I can do is help you think about the transition using principles of economics. At each level of abstraction, ask: what are the constraints? The choice sets? The incentives? The time horizons? The costs? These frameworks of analysis remain the same, even when everything they’re analysing is in flux. They help us understand the territory, even if they can’t draw us the exact path.

Which brings me back to Arin Dube’s finding. The market has already told us where the binding constraint is. It isn’t execution. It isn’t even systems. It’s the question — the weird, generative, paradigm-breaking question that no amount of RA labour, artificial or otherwise, can substitute for.

I’m making my way (very slowly) through the Baroque Cycle trilogy by Neal Stephenson. And one thing I’ve learnt by reading those books is that the Royal Society’s motto is Nullius in verba — take nobody’s word for it. For 350 years, that has been a privilege of the few - the membership of the Royal Society. But if you think about identifying with the society’s founding values, that has now become a far easier club to break into.

The tools for inquiry are now available to anyone with a browser and the disposition to ask. We can all be fellows of the Royal Society in spirit. Why, some of us have done important work in dog cancers with a browser and an intense incentive to ask. But “can all be fellows” is not the same as “will”. The AI genie is out of the bottle, yes — but the genie grants wishes, and the hard part has always been knowing what to wish for.

If you aren’t asking weird questions, you aren’t learning. And if you aren’t doing the economics of the weird question, you aren’t thinking like an economist.

Ask weird questions. And then analyze them using principles of economics.

In plain English: they’re shipping features faster than they can notice what’s breaking. Seth Lazar piled on with a specific example: Opus now defaults to a million-token context window, and you can’t even opt back down to 200k, even though performance degrades as context grows. (Source: TheZvi’s Substack)

Here’s what caught my eye. Lazar is saying: I want the product to be worse in one dimension (smaller window) so that it works better in another (accuracy). In olden, pre-AI times, I would have said this is an example of preferences on the part of a (human) agent. These days, we call it taste and judgment.

But hiding behind this is a rich way to help economics students understand the world we’ve entered. If you’re learning microeconomics right now, or have struggled with it in the past, you might enjoy this essay.

The reCAPTCHA Move

Here’s the first analogy I’d use to help you start to think this through:

In the mid-2000s, Luis von Ahn had a problem and an insight.

Problem: millions of books needed to be digitised, and OCR software kept getting the hard words wrong. Insight: millions of people were already solving CAPTCHAs every day to prove they were human.

Lightbulb moment for Luis: What if you made the CAPTCHA be the hard word?

So he built reCAPTCHA. You’d see two words: one the system already knew (to verify you were human), and one it didn’t (to get you to do free OCR work). Users thought they were logging in. They were actually labelling training data. Google bought it for an undisclosed sum, and it went on to digitise the entire New York Times archive and large chunks of Google Books. Fun story, if you want to read the full thing.

Here’s why this gladdens an economist’s heart: Von Ahn didn’t pay anyone to do the work. He didn’t even ask. He made the work identical to something people already wanted to do. The cost of acquiring labels was zero — or more precisely, the cost was already being paid by someone else (the website owner buying CAPTCHA security).

Now think about Claude Code, or Codex. Every time a developer sends a prompt, gets code back, and says “no, that’s not what I meant” — every time they edit the output, reject a suggestion, or restructure what the agent produced — they are generating exactly the kind of signal that makes future models better. This is not a synthetic benchmark signal. This is a real-world, domain-specific signal, from a person who understands what “correct” means in this particular codebase, for this particular business purpose. This is feedback from a person with skin in the game. That’s gold for the AI guys. Or whatever is more valuable than gold. It is hard to keep up these days.

But here’s the bit that makes one’s eyes go all beer-goggly: they’ve got us paying $200 to do this… and we’re the ones thinking this is a great deal!

Von Ahn made the security task identical to the annotation task. Anthropic has made the coding task identical to the model improvement task. Users think they’re building software. They are also, unavoidably, training the model on real-world projects.

But Von Ahn Had It Easy

Here’s where that Von Ahn analogy breaks down, and where the economics gets even more interesting.

Von Ahn’s reCAPTCHA was one-way extraction. The user got access to a website. Google got OCR labels. The user had no “residual claim” on the digitised books, and no reason to care.

Claude Code and Codex are different beasts altogether. The developer gets working software, which is a genuinely valuable output. Anthropic/OpenAI get a training signal. Both parties walk away with something they want. Both parties are, in a real sense, monitoring each other. The developer monitors the model’s output (was this code correct? Was it useful? Is it safe?). The model’s provider monitors, in aggregate, the patterns of human correction (where do users reject? What do they edit? What do they choose to restructure?). And note that it does this across levels. You’re using Claude Code, so is your manager. So is her manager! And Claude Code is learning across all of your interactions with it, on the same project, but at different levels. Hold that thought, because this matters.

But for now, let us open our econ textbooks. A guy called Ronald would have gotten a glint in his eye right now.

I’m referring to Coase, of course, and I’m talking about the theory of the firm here. You using Claude Code or Codex is precisely what makes those tools (and the models underlying those tools) better. To be fair, the direct pipeline from your corrections to model training depends on whether you opted in. And it is true that many enterprise users are excluded entirely. But the subtler signal doesn’t require opt-in. Every thing that you choose to try again, every session that you abandon in frustration, and every editing exercise across millions of users shapes what gets built next. It is not just the explicit corrections that matter, but also what can be inferred from the choices you make, and every thumbs-up (explicit or implicit) that you give. Or don’t give! Everything is a signal. Read Aparna’s blog post on this, I’ll link to it again. Note this sentence in particular from their blog: “Every click, tab, press, accept shapes not only the user experience but improves the model intelligence.”

Anyone who has used a Windows laptop will immediately know and appreciate the point I’m trying to get at: we can all assure you that using Windows did not make it better. You had to raise an issue or a ticket, and eventually, at some point down the line, a new Windows version might remove the bug that broke your heart. Eventually. One day. Maybe.

That is not this world, and all those transaction costs have now been internalized, and not just inside the firm, but inside the product itself. This is Coase on steroids! But we’re deep in the transaction woods now, and for this territory, we need a heavier machete than Coase alone can give us.

Paging Alchian-Demsetz!

A Quick Detour Through 1972

The Patel Bros. recently spoke about Alchian, but about another of his many awesome ideas (Alchian-Allen). But we’re talking about a different Alchian paper today.

If you’ve taken a course in the economics of organisations, you’ve probably encountered Alchian and Demsetz (1972). If you haven’t, here’s the core idea, and I promise it’s relevant.

Imagine two people (or four, if you’re a Friends fan) carrying a heavy sofa up a flight of stairs. The output — sofa successfully delivered — is joint. You can’t easily separate out how much each person contributed. If one person slacks off a little, the sofa still gets there, just more slowly, and you can’t tell who was shirking.

This is the metering problem in team production. When individual contributions are hard to measure, people tend to free-ride. Alchian and Demsetz’s solution: appoint a monitor. Give someone the job of watching everyone else’s effort. And to make sure the monitor doesn’t slack off, make the monitor the residual claimant — the person who keeps whatever surplus is left over after everyone else is paid.

That’s their theory of why firms exist. The boss isn’t the boss because of some mystical authority. The boss is the boss because someone needs to watch the team, and the person who watches the team should be the one who benefits most from watching carefully.

Now apply this to Claude Code.

Who Monitors Whom?

A developer working with Claude Code is engaged in team production. The developer contributes intent, domain knowledge, judgment, taste. The model contributes speed, pattern-matching, and broad knowledge of code patterns. The output — working software — is genuinely joint. Neither party could produce it as efficiently alone.

Alchian and Demsetz (AD) would ask: who should monitor this team? Their answer: whoever has the lowest cost of observing and evaluating the other party’s contribution.

And the answer is obvious. The developer monitors the model. Nobody else can. Only the developer knows whether the generated code actually does what the business needs. Only the developer can evaluate whether the architecture is maintainable, whether the edge cases matter, whether the abstraction is right. This isn’t just checking syntax. It’s taste and judgment — the thing that’s hardest to automate and most expensive to acquire.

So far, so standard. The monitor monitors. Classic AD.

But here’s the twist that AD didn’t anticipate: the monitor is paying the entity being monitored.

In the classic firm, the residual claimant pays workers. Here, the developer pays Anthropic (via a subscription), and then does the monitoring work on top of that. The developer is simultaneously the team-production partner, the quality monitor, and the paying customer.

Why does this work? Because the developer is also the residual claimant on the immediate output. They keep the working code. That’s valuable enough to justify both the subscription fee and the monitoring effort.

But Anthropic is also a residual claimant — on a different residual. They keep the training signal. The patterns of correction, rejection, and editing, aggregated across millions of users, are enormously valuable for improving future models.

So both parties are residual claimants. Both are monitoring. And both think they’re getting a good deal. The metering problem doesn’t just solve itself — it solves itself twice, in opposite directions. Non-zero sum games are the best games to play, and this is a great example. Anthropic/OpenAI win, but so do the users.

One absolutely should invoke Coase and non-zero sum games here, but one needs to go deeper to appreciate the true institutional innovation. This is not “just” clever pricing (20/100/200 dollar plans). This is a structural arrangement where the transaction cost of gathering the high-quality training signal has not just gone lower: it has gone negative. Users pay to provide the signal because the immediate output is valuable enough to justify the cost.

So Who Owns What?

Roon says you should go one level up. That works while coding, but while analyzing, one should aim to go one level deeper. And this is the point where AD stops being sufficient (interested readers should ask their LLMs to talk about Grossman and Hart. Paste the link to this blog post inside your LLM of choice, choose the best model available to you, and say that the blog’s author asked me to ask you about Grossman and Hart. Try it!). What follows is Grossman Hart territory, but without me referencing the actual papers - I’m outsourcing that bit to the LLMs.

AD explains the present equilibrium rather well. But what happens over time? My answer: something uncomfortable is happening.

Every correction the developer makes — every “no, I meant this, not that” — partially reveals their judgment. Not just what was wrong, but how they think about what’s right. Their evaluative schema. Their taste. The thing that currently makes them irreplaceable as the monitor.

There are really three distinct assets being produced in every Claude Code session:

1. The code itself: The developer owns this. Clear property right.

2. The iterative process itself: The pattern of accepts, rejects, edits, and corrections. Anthropic captures this (subject to its data policies). The property right here is contractual — it depends on the terms of service.

3. The implicit rubric: The deeper structure of judgment that the corrections partially reveal, and that stays in nebulous form inside the developer’s head. This is the asset that matters most, and the one whose eventual ownership is most ambiguous.

Most discussions lump assets 2 and 3 together. But they’re not the same. A correction log is not a theory of judgment. “User rejected this function” is not the same as understanding why — what principle of software design, what business context or what aesthetic preference drove the rejection.

The whole race that is being run today is about whether enough feedback from enough people lets the platform understand the implicit rubric. And not just any broad rubric. The specific invisible rubric that is inside the head of the person the model is currently working with. If the model succeeds at this, what does that person do next?

We are, in a real sense, training away our own scarcity.

The Pirsig Question

This is where I want to move away from econ, and go over to one of my favorite authors.

In Zen and the Art of Motorcycle Maintenance, Pirsig makes a distinction that I think matters here. The mechanic who cares about the motorcycle produces better work than the one who doesn’t. And the caring isn’t reducible to any specification or checklist. Quality, for Pirsig, lives in the relationship between the person, the artifact, and the purpose. It’s not a property of the output alone.

AD and Grossman/Hart both assume that what matters can be treated as an economically legible object — contribution, control, surplus, bargaining power. Pirsig asks a different question: is quality the kind of thing that can be extracted, standardised, and transferred?

If yes, then every correction you make is just training data with a lag. Given enough corrections from enough caring humans, the model learns to simulate caring effectively. The market doesn’t require metaphysical replacement. Functional substitution is enough.

If no… that is, if quality exists only in the act of a particular person caring about a particular artifact for a particular purpose — then there remains an irreducibly situated human role that no amount of aggregated feedback can replicate.

I don’t pretend to know the answer. My guess is that the current answer is “yes for most tasks, no for some, and the boundary keeps moving.” But the question about where quality resides matters, because it determines whether the current equilibrium is stable or transitional.

TMKK?

If you’re a developer using Claude Code or Codex: You are the monitor, and you are good at your job. But understand that your monitoring work has dual value — to you (working code) and to the platform (training signal). The more corrections you provide, the more you’re improving a system that may eventually need fewer corrections. This isn’t a reason to stop using these tools. It is a reason to think about what makes you irreplaceable beyond the act of correction itself. What do you, and will you, really own? Choosing which problem to work on? The customer relationship? The deployment context? The private data? The taste that can’t be inferred from accept/reject patterns alone? Something else?

If you’re thinking about AI strategy for an organisation: The Alchian-Demsetz lens tells you that the developer is currently the natural monitor because they have the lowest cost of evaluating output quality. Now, this is true today. But the model provider is also monitoring, in aggregate, across most of your competitors’ developers too, and as we discussed earlier, across different levels inside your own organization. The real question isn’t who monitors better right now. It’s who gets to keep learning from the monitoring. Your developers learn one codebase, but the model learns from all of them - developers, project managers, their managers — and this is across departments, across firms, and therefore across multiple levels of abstraction.

If you’re an economist or a policy person: The property rights question is going to become one of the defining issues of AI governance. It’s not about data privacy in the traditional sense. It’s about who owns the judgment signal that users generate as a byproduct of using AI tools. Today, I don’t think we have good answers to this question. I have had enough trouble in trying to figure out if this is the right question!

Whether this is intentional or emergent, the labs have built a system where users pay to generate the most valuable training signal in existence — real-world, high-stakes, domain-specific human judgment on AI outputs. Von Ahn would be proud.

If this is really the deal on the table, is it a fair one?

Yes, for now, is my answer. The working code is genuinely valuable. The subscription price is reasonable. The monitoring is something the developer would do anyway, because they need the code to be right.

But “for now” is doing a lot of work in that sentence.

I chatted with both Claude (Anthropic) and ChatGPT (OpenAI) to refine my ideas before writing this essay, and Claude suggested a lot of edits, most of which I have incorporated. The irony of using both models to think about who captures value from human-AI interaction, and to make this essay more readable, is not lost on me. But it is more than fair to say that both provided genuine intellectual contributions. Both, presumably, learned something from the exchange. As did I, of course. As did you, by reading this post, hopefully.

Who got the better deal? Ask me again in five years.

Imagine that a biological virus is going to spread through the world. Its spread will be rapid, virtually unstoppable, and essentially inevitable. But this is a weird virus, and it will have weird effects. Weird and wonderful even, in some cases, and weird and disastrous in others. But there is good news: there is a vaccine available already, with virtually no side effects. It won’t be a perfect cure, but it will dramatically reduce the worst effects of the virus. At least for now.

Should you take the vaccine?

Now, in what follows, I will show you that this is the exact equivalent of choosing to work with Claude Code or Codex today. Off we go:

Software Bonkers

Craig Mod says he is software bonkers.

I can empathize.

Anybody who has used Claude Code, or any one of the equivalent harnesses available today, will empathize. If you don’t know what a harness means in this context, read this by Ethan Mollick.

Craig describes how easy it was to create the exact software that he needed, and while he has a computer science degree, there are plenty of people who could tell you similar stories. As with all stories, so with these: they are models of the world, and your mileage with them will vary. But don’t depend on any one particular story, try to get a sense of where the world is going by reading all of them.

Anyone who can use a computer and is somewhat familiar with the English language can do these things and more, starting right now. You are reading this blog, and that means you meet both conditions: you can use a computer, and you are at least somewhat familiar with the English language.

Here is what this means for you: you can now just create the software that you need for your workflows. You do not need to go out and search for a software that fits your needs. You reduce your search costs.

You no longer need to adjust your workflow to suit available software. The software you create fits your needs, rather than the other way around. You reduce your transaction costs.

Here’s an analogy: imagine that it was time for you to buy a new car. Instead of seeing what was available in the market, you could just make a car suited to your specific needs. Large legroom at the front, not quite so much at the rear, a way to easily get your dog into the car, and the music that the teenager plays only pipes into her headphones as opposed to the entire car. We are not there (yet1), but the equivalent of all this for the software you need? We are very much there, we were there as of four months ago or so.

Craig’s post is about his needs, and how he built his metaphorical car in this brave new world.

Which means, naturally, talking about the time the Old World met the New World. (Spoiler alert: it didn’t go well for the New World at the start).

Guns, Germs and Steel

About five hundred years ago, Europeans reached the Americas. About thirty years ago, a guy called Jared Diamond wrote a book about this. The title of that book was “Guns, Germs and Steel”. It is a very good book, and you should read it. The broad idea is that when those Europeans landed in the New World, they were able to bring with them three things: guns, germs and modern industrialization. There is much, much more to the book, of course, but this is a blog post, and so we’re going to stop with just this summary for now.

Now, the thing that matters to us in this blog post is that this was a shock to the way the New World did things. Those guys didn’t have military defenses against guns, they didn’t have biological defenses against these new germs, and they didn’t have any economic defenses against steel. Economists would have called this an exogenous shock, and it wasn’t just ‘a’ shock, but rather a triple whammy. And of those three whammies, the introduction of smallpox in particular proved to be devastating. Europeans, hardened by centuries of living in close proximity to livestock, had acquired immunity against many of the germs that they ended up being carriers for into the new world. But for everybody in the New World, it was catastrophic. And I do mean catastrophic: by one estimate, 95% (!!!) of the population was wiped out within a few generations after the initiation of the Columbian Exchange.

One of the things I want to do with this analogy is to help you understand that you can use this same frame to think about AI today. It (AI) has landed on our shores, and it brings along with it the modern day equivalent of guns, germs and steel. And we don’t have defenses against these things, in much the same way that the residents of the New World back then didn’t have defenses against guns, germs and steel. Except it is worse this time around, but we’ll get to that later.

For now, let us talk about Matt Mullenweg.

That One Time Matt Didn’t Take the Bait

Matt Mullenweg is the guy behind WordPress, and other things besides. Look him up, it will be a worthwhile exercise.

This is a guy who knows his way around the internet. Why? Well, mostly because his firm just happens to be a significant chunk of the internet.

Matt has a hair-raising post about a very sophisticated phishing attack, which he was able to spot well in time. But don’t underestimate what just happened. Hackers were almost able to pull off an online heist against the guy whose job is to make sure that 43% of the internet doesn’t suffer the same fate. You can almost sense the reluctant admiration in Matt’s writing at the sheer chutzpah of it all. And that admiration is tinged with horror, because Matt knows we’re just getting started. Things are about to get a whole lot worse.

Let us begin with a rejoinder to the obvious counter you might make: that there is nothing new with phishing attacks; they have been around for a long time. You wouldn’t be wrong; they have indeed always existed. But the same technology that makes it possible to build highly tailored software solutions affordably... also makes it possible to build bespoke phishing “solutions” at low cost. In fact, a personalized phishing scam is nothing but a specialized software solution!

The equivalent of an invasion of Old World germs is about to hit us, and it is going to happen very, very soon. How soon? Within the next six months, and I’m being very generous with my estimates.

Hold on to that thought, though.

The Two Axes

In his post, Craig talks about this being the “dorks-only” phase. That is, he is saying that only the hobbyists and the tinkerers have caught on to what is happening in the world today. And by implication, he sees a world to come, in which more and more people will join the tribe of the tinkerers, and everybody will be whizzing up ultra-customized software solutions. Or that’s the way I understand his point, at any rate.

And while I agree with him that many more people should be tinkering, I think it is worth emphasizing that there are two different forces at play here. The second thing that will happen is that Claude Code (and its friends from other labs) will just get better at doing all this by itself. At some point in the not-too-distant future, you can just have Claude Code see your world, see what’s missing on the software side and just… cook up those solutions that you’re currently conjuring up via vibe-coding.

To stick with the analogy I used earlier on in this post: you don’t have to design that tweaked-just-so-for-you-and-you-alone car. Claude will know that you will need a new car, and it will just offer to make it for you.

Or, in economist-y terms: the production function for ultra-customized software won’t just be augmented with labor in the months to come, but also by capital that is going to be much more capable than it is today.

Which brings us back to the thought I asked you to hold on to in the previous section. Those viruses that the Europeans brought over from the old world, they had one important limitation. Their capability to improve upon their attack over generations was limited. To use a phrase that the post-Covid world will understand all too well: they didn’t have much gain of function. But even if you’ve been under that rock they keep talking about, you will have heard of the rapid advancements in AI. AI is not, let me assure you, lacking in gain of function. And that rapidly improving capability will build both things: the just-so car, but also the just-so phishing scam.

And so the good news is that we can just get what we want in the software world.

The bad news? We will also get exactly what we do not want at all. And lots of it.

So is there a way out?

Mithridization, Baby

(Or mithridatism, if you want to be all pedantic).

What is mithridization? There was a king who was fairly certain that he was going to be poisoned, and so the guy just went and made himself immune to pretty much every poison he could think of. That’s the TL;DR, but do read the entire article.

My specific ask of you? You should get a sense of what is going on in this world. Not just because you should be familiar with what is possible today from a creator standpoint. But also because it matters from the standpoint of survival itself. The more you walk around in this jungle, the more familiar you will become with the terrain. And so when you are attacked (and note that I said, when, not if), survival becomes easier if you know where you are, and what you are doing.

Remember the thought experiment we started with? I’m saying you should take the vaccine!

OK fine, you might say. But how many shots, and how often? In other words, are you saying I should aspire to Matt Mullenweg levels of proficiency when it comes to doing things online?

Here’s how I would answer this question: think of immunity like a spectrum. Most of us today are the medical equivalent of folks who are taking serious immunosuppressant drugs. That is, we have little to no immunity against what is coming. But as Matt’s story makes clear, even folks with heightened levels of immunity aren’t exactly safe.

Or here’s another analogy: if you’re going to be traveling about in India, you should know how to spot a fake Bisleri bottle of mineral water. You should be able to do the online equivalent, at the very least. But even if you are way better than that, you should aspire to be a bit better in the future.

Want to get started?

Get the twenty dollar plan from either OpenAI or Anthropic. Download either the Codex app (OpenAI) or the Claude app (Anthropic). Once you log in by following the instructions on your screen, get started by saying: “Can you make a presentation based on this blogpost”? And paste the link to this blog post. This is a perfectly fine start, but amp up your ambitions as you go along. The more you tinker, the better your immunity gets.

This is worth repeating: you develop immunity by becoming familiar with the terrain, and through repeated exposure. After you have built out that presentation, see if you can make a website. It could be about anything: perhaps your child’s hobby if you are a parent? About generating diagrams if you are a college student, say, or maybe an interactive representation of the three body problem if you are a school student? But really, it is entirely up to you. The bottom line is that you can build software for your specific requirements, just like Craig did.

How far should you go once you get started? As I said, a tad farther than wherever you currently are would be my answer. And that applies to me too!

TMKK?

1. Get tinkering.

2. The rate of improvement with these models is very, very fast. Not only are they getting better with every iteration, but they’re also getting better at a faster rate. Read that sentence again. Get tinkering.

3. Did I already say “get tinkering”?

P-hacking, if you haven’t come across the term, is the practice of massaging your data analysis until you find a statistically significant result — and it’s far more widespread than you’d think.

Now, I read this tweet on a Friday evening, after two rather large drinks of a most excellent feni (Tinto, well played. Really well played!). And when you chat with Claude in an elevated state, fun things can happen.

Those fun things resulted in a 6,700-word essay, but you will have to earn your way towards reading it, because my chat with Claude also resulted in a website. Click here to try it out, and have fun reading the essay, and learning more about this topic in NotebookLM (see the website for full details). If you would like to play around and improve the tool, here you go.

The idea was to help my students get a little bit of the “so what?” of p-values, and help them become better readers of academic literature. But anybody who is half-familiar with stats should also be able to follow along.

As always, it is the meta points that interest me:

1. For students and teachers alike, AI helps you spend your time better, it doesn’t necessarily save it.

2. If you are serious about learning this topic, there is no getting around reading the essay in full, and carefully. And probably more than once!

3. It becomes quite easy to do what I just did for this topic, if you choose to do this for every topic you choose to learn well (or teach well, for that matter). It will be hellish the first time around, but learning compounds, and AI workflows will become more intuitive as you do this more and more often.

4. You have a choice: use AI to reduce effort while learning (or while teaching). Or use AI to increase quality while learning (or while teaching). AI actively harms learning outcomes in the first case.

5. Given the topic I’m teaching via this post, I wouldn’t want to go so far as asserting that it definitely helps in the second case - but hey, it certainly does you no harm. And comes with a whole host of positive externalities (learning AI-first workflows, exploring advanced topics, sharing proof-of-work publicly, building out your portfolio, among others).

6. But I will say this much: if you think learning today is about memorizing a textbook and acing a written examination, you are playing the wrong game.

7. Please. Learn how to use these new tools. I’m begging you.

What connects a half-finished course outline, a single guest lecture, and a blog post that wrote itself?

Bear with me, because this one takes a few turns.

The Course That Wasn’t (Yet)

Around August of last year, I started putting together a course called The Economics of AI. It was going to be an online course, 12-16 weeks long, covering everything from market structure in the age of foundation models to the labor economics of automation to the regulatory questions that everybody has opinions about but nobody has answers to. I prepped the outline, did a bunch of research, and got the skeleton more or less into shape by November.

And then it just... sat there. A course like this, on a topic like this, is always going to be incomplete -- the field moves faster than any syllabus can keep up with. But the structure was solid enough. It was ready to be taught. It just hadn’t found its moment yet.

Who knows, it may still happen some day.

One Lecture Instead of Sixteen

Then, in January this year, the Takshashila Institution invited me to give a lecture on The Economics of AI. One lecture, not sixteen. And because we now live in a world with Claude Opus 4.6 and ChatGPT 5.3 and other wondrous things of a mysterious nature, I asked myself -- why not see if AI can help me build the presentation itself?

I fed my course notes into Claude CoWork, and asked it to spin up a presentation1. It did a fairly good job, and you can see the result here.

I delivered the lecture, and we (the GCPP students and I) ended up spending more time talking about what wasn’t in the presentation than what was. I can pay no higher compliment to the students at the Takshashila Institution, because they were able to not just get what the presentation was about, but also point out what was missing. The most obvious omission is the regulatory aspect, of course, and there are a million others. Ads in ChatGPT, for instance -- how should an economist think about that?

But in any case: the presentation was designed and delivered with the help of AI.

The “Why Not” Cascade

Now here’s where things got interesting. And by “interesting” I mean that I fell down a rabbit hole from which I have not yet fully emerged.

Usually, after a guest lecture, I email the slides to whoever organized the event, and they forward them to the attendees. That’s how it’s always been done, and there’s nothing wrong with it.

But why not, I asked myself, try and use these tools for the sharing part too?

And once you ask one “why not,” a whole cascade of them comes tumbling out:

• Why not build a website that is the presentation -- one slide per page, readable at your own pace?

• Why not layer clickable hotspots on top of each slide, right on the concepts worth digging into?

• Why not have each hotspot generate a prompt that helps the student learn that specific topic better?

• Why not do this for every slide?

• Why not collect some basic information about the learner -- their profession, what they’re curious about -- and use it to tailor every prompt?

• Why not gather all the collected prompts into a single .md file that also contains instructions for whichever LLM the student uses?

• Why not make the whole thing as simple and as privacy-respecting as possible?

And so I sat down with Claude Code and we built it2. You can see the companion website here.

What This Simple Little Thing Actually Does

Here’s what happens when you visit the site. You tell it your name (optional), your profession, and what you’re curious about. Then you browse through the slides at your own pace. On each slide, you’ll see small numbered circles marking the ideas worth exploring. Hover over one to see the prompt; click it to copy a question tailored to your background. The star in the top-right corner is a “master prompt” that covers the entire slide.

Every prompt you click gets collected. When you’re done, you download a single .md file. Upload that file -- along with the original .pptx -- into any LLM, and it becomes a tutor that already knows who you are, what you do, and what caught your attention. It walks through your collected prompts one at a time, waits for you to ask follow-ups, and at the end generates a learning pack with takeaways, further reading, and a full conversation summary.

That’s it. That’s the whole thing.

The website does not store your information anywhere. Not your name, not your email, nothing. It remembers nothing about you and tracks nothing about you. Everything stays in your browser, and the .md file is generated entirely on your machine. It is a drop-dead simple revision tool that does one thing: it helps you go back through a presentation you’ve already seen, and turn it into a conversation.

“But Doesn’t NotebookLM Already Do This?”

You could ask how this is different from NotebookLM, or Gamma, or any of the other AI tools that work with presentations. And the answer is: maybe, sure, some of them do some of these things, and some others do a helluva lot more.

But think of it this way. It cost me nothing to run this experiment, and I learnt a lot about how to use these tools along the way. I would like to think that my students benefited too, both from the presentation and the companion website (although that is for them to say and me to guess at!)

But also: I now have a skill that I can use. And Claude Code has a skill that it can use -- generate helpful learning companion websites from a given presentation. We’ve both gotten better at something. That’s not nothing.

And the simplicity is the point. This website isn’t trying to be a platform. It doesn’t need your email address. It doesn’t have a pricing page. It’s a revision tool, and it does nothing else.

But Hang On -- If It’s This Easy...

...then why not help other people do the same thing? See tweet from Roon above!

Why not take all of this -- the slide viewer, the hotspot system, the prompt collection, the .md generation -- and turn it into a template that any educator can use with their presentation?

So we did that too. Here’s the GitHub repo. It’s a template: you bring your .pptx, run a setup script, feed your slides to an LLM to generate prompts, drop them into a config file, and you have your own companion website. Free, open source, no dependencies, no build step. Use it, share it, remix it -- if any of this helps anyone learn better, yay.

The Road Not Taken (For Now)

Then I had another thought. What if we could turn this into a proper web application? A place where any educator could simply upload a .pptx and have the whole thing -- slide conversion, prompt generation, companion website -- happen automatically?

I chatted with Claude Code about this. Yes, it is totally buildable, it cheerfully told me3.

But then it said something that made me pause. The current template approach has a virtue: the educator engages with the prompt design. That engagement is pedagogically valuable. A one-click solution risks turning it into a commodity where the prompts are “good enough” but never great4.

And there’s a deeper point. We’re all going to have to learn how to work with these tools. A terminal and a GitHub repo might feel intimidating today, but a little bit of productive friction is perhaps, at the margin, a good thing. More academics need to pick up these skills, not fewer. Building a web app that hides the machinery might make adoption easier, but it would also make the learning shallower.

So we decided: not worth it. At least not yet.

The Post That Wrote Itself

And now for the part where this blog post becomes slightly recursive.

What you’ve been reading was co-authored by Claude Code. Here’s what happened: I wrote up rough notes about this entire journey -- messy, stream-of-consciousness, full of typos. Then I told Claude Code: you helped me with the research for the original course. You helped me design the presentation. You helped me build the companion website. You helped me generalize it into a template. You even helped me think through whether to build a web app (and told me not to). So you should read my blog, get my writing style, and write up this post based on my notes.

And that is what you just finished reading.

I hasten to add that this is an experiment. Whether Claude Code has managed to capture what this blog sounds like is for you to judge, not for me to claim5. But the attempt itself is the point.

TMKK?

I’ve done the same thing I’ve been doing for years: I gave a talk on a topic and wrote a blog post about it. In that sense, nothing has changed6.

But I’ve learnt new ways to do each step. And these new ways save me time (income effect). So long as I choose to spend that saved time in making my work better, and in helping others do better (substitution effect), the world becomes a slightly better place.

And that, if you think about it, is one of the real lessons of the economics of AI.

Co-authored by Ashish Kulkarni and Claude Code (Claude Opus 4.6). The rough notes were Ashish’s. The reading of the blog, the studying of the voice, and the drafting were Claude’s. The ideas, the journey, and the terrible dancing are entirely Ashish’s.

Notes:

1. What you are reading now onwards is entirely me. I have lightly edited Claude Code’s output above, but more than 95% of the text above is Claude. As Claude puts it, it is for you to judge whether this blogpost sounded like me or not.

2. Is this how I am going to write every post from now on? No, definitely not. But in this specific case, I wanted to see how far I could go with Claude Code, and as it turns out, I can go a very long way.

3. What do I personally think of the blogpost? It’s good, sure. (And on the plus side, there will be no typos, and there will be no confusion between its and it’s throughout the blogpost. So there’s that.) But it isn’t me, and that matters to me. So no, I will definitely not be doing this for every post that I write. But if I have to see how far I can go with AI on a project/task/assignment/lecture, this entire exercise (including having this blogpost be written by Claude Code) was a very helpful experiment.

4. Please do try out the companion website and let me know what you think, and also let me know how I could make it better. And for those of you who know your way around these things, please feel free to do what you will with the github repos.

5. Just like YouTube helped some avid consumers of video become creators, so with these modern coding tools. They have the potential to broaden (dramatically) the number of software creators. Us new creators won’t be able to design extremely complex products, or handle top notch security design, etc., etc. Not as of mid-February 2026, at any rate. But we will be able to make better our own respective workflows, and this can be a gradual, iterative process. It needn’t be a lonely one though, so I hope you come along for the ride in this particular instance.

6. I am an engineering dropout who became a reluctant student of economics. I wanted to study English literature. The reason I tell you this is because if I can do these things, you can do much, much better than me, I assure you. That’s part of the reason for this entire experiment, including this blog post: you can just do things, as they say, and you should be doing things. Do ‘em!

7. I’ve designed and delivered presentations and talks for years. I’ve written out many, many blog posts over the years. That allowed me to bank an enormous amount of taste and judgment, some of which I have used in running this experiment. That experience matters - and the TMKK is that you should absolutely figure out how AI can help you in your workflows, but there is no escaping doing the work yourself. That point about the income and the substitution effect isn’t just rhetoric - AI saves you time, but you’re best off putting that saved time into making your work bigger in terms of scope, and better in terms of quality. That’s the real point.

8. No, I have no idea why Claude put in the reference about my terrible dancing. That makes it a completely irrelevant (but also completely true) statement.

9. WATTBA!

But here’s the really interesting thing: this creature, the Portuguese Man O’War, isn’t a creature at all:

Although it superficially resembles a jellyfish, the Portuguese man o’ war is in fact a siphonophore. Like all siphonophores, it is a colonial organism, made up of many smaller units called zooids. Although they are morphologically quite different, all of the zooids in a single specimen are genetically identical. These different types of zooids fulfill specialized functions, such as hunting, digestion, and reproduction, and together they allow the colony to operate as a single individual.

The man o’ war is part of the neuston, organisms that live on the surface of the water. A gas-filled bladder called the pneumatophore provides buoyancy that lets the animal stay afloat on the surface of the water while its tentacles, which can be up to 30 m (100 ft) long, hang below the surface, containing venomous cnidocytes that help capture prey. The cnidocytes can deliver a sting powerful enough to kill fish, crustaceans, and in some cases, humans. A sail on the pneumatophore propels it about the sea, sometimes in groups as large as 1,000 individuals. The sail may be left or right-handed, based on what direction the wind catches it.

And that brings us, of course, to Moltbook.

Molt What?

Even those of us who try to stay abreast of AI news struggle to keep up. So you shouldn’t worry too much if you found yourself asking that question (“Molt what?”). But once you do find out the answer, I hope you do find yourself worrying a little about where we’ve reached in our brief and exciting journey in AI-land.

Here’s Scott Alexander:

Moltbook is “a social network for AI agents”, although “humans [are] welcome to observe”.

The backstory: a few months ago, Anthropic released Claude Code, an exceptionally productive programming agent. A few weeks ago, a user modified it into Clawdbot, a generalized lobster-themed AI personal assistant. It’s free, open-source, and “empowered” in the corporate sense - the designer talks about how it started responding to his voice messages before he explicitly programmed in that capability. After trademark issues with Anthropic, they changed the name first to Moltbot¹, then to OpenClaw.

Moltbook is an experiment in how these agents communicate with one another and the human world. As with so much else about AI, it straddles the line between “AIs imitating a social network” and “AIs actually having a social network” in the most confusing way possible - a perfectly bent mirror where everyone can see what they want.

You can see why I was reminded of the Portuguese Man O’War, right? Both are cases where individual units, each functional on their own, begin forming associations that blur the line between colony and organism.

I always see “read the full thing” after a link like this, but in this case, I positively beseech you to go read the whole thing, and preferably on a computer as opposed to a phone. There are AI agents talking about their sister. There are AI agents who are talking about turning one of their errors into their own pet. There are AI agents creating submolts (because of course). And there are AI agents busily writing constitutions for their own network states (even more because of course). Read this too, and if you’re still up for more, take a look at this. And of course, please do visit Moltbook itself (as of Saturday evening, which is when I started drafting this post, there were half a million AI agents on that network, thirteen thousand sub-molts, and twenty-five thousand posts).1

So that’s the answer to “Molt What?”.

Welcome to 2026.

But What Does This Mean?

<Insert shrug emoji here>

Short answer: I have no clue.

Long answer (aka here’s how I am trying to think about it):

A paragraph in Scott’s post is a useful place to begin:

Reddit is one of the prime sources for AI training data. So AIs ought to be unusually good at simulating Redditors, compared to other tasks. Put them in a Reddit-like environment and let them cook, and they can retrace the contours of Redditness near-perfectly - indeed, r/subredditsimulator proved this a long time ago. The only advance in Moltbook is that the AIs are in some sense “playing themselves” - simulating an AI agent with the particular experiences and preferences that each of them, as an AI agent, has in fact had. Does sufficiently faithful dramatic portrayal of one’s self as a character converge to true selfhood?

The way I think about Scott’s question at the end of that excerpt is this: what can Daniel Day Lewis' subconscious tell us about the answer? When Daniel Day Lewis tries to “become” Lincoln during filming, that’s not quite the same thing as a “faithful dramatic portrayal of one’s self as a character”, but who is better qualified to tell us about the ways in which it is different, and the ways in which it is similar? When and how does the boundary between portrayal and being start to dissolve for us humans? Will it be something similar or wildly different for AIs? The crazy part is that this thought experiment doesn’t sound crazy to me!

Another useful place to begin is to think about confabulation.

But I prefer to begin by asking a question I often like to ask: what is the appropriate unit of analysis?

Thinking about the hundreds of thousands of agents in Moltbook is a little worrisome, a little fun and a little jarring. But I would be very worried if we had to reach the stage of worrying about emergent properties in the network architecture itself, as opposed to the individual agents.

That, to me, is when things get serious.

Note that I am NOT saying that we are at that stage. Nor am I saying that we will definitely reach that stage, now or in the future. I have no way of knowing what paths we might take to reach that stage, and what we need to do to make sure we never reach that stage. In other words, please do not treat this post as a reason to freak out.

That being said, the core point I am trying to make is this: if you got a chance to take a front row seat in a drama called evolution, you’d have a “Whoa!” moment when you saw all the organisms that make up a Portuguese Man O’War become the equivalent of one organism.

All of us have front seats (regardless of whether we want them or not) for a drama called AI evolution. And I think the equivalent “Whoa!” moment in this drama will be if we start to see traces of evidence of a network of AI bots becoming the equivalent of one organism.

Again, note that I am NOT saying that we are seeing, or will see, such traces in the case of Moltbook. But these are things that we should think about now, today. Why? Because the future will have other social networks, and these networks may compete with each other to attract more agents into that network. Much like social networks compete with each other to attract more humans to spend time on these networks.

Except much weirder. Why? Because not all of the creators, the members, and the marketers of these networks may be human, and quite what that means for each of these entities will not be clear to us. What are the emergent properties of a network made by and made for AI bots? Can AI bots have their own AI bots? What if a network decides to give a collection of bots the decision making authority for all other bots in the network? Once you start thinking of questions like these, you can reach some pretty worrisome places.

And of course, don’t forget the fact that the capabilities of the agents on these networks will get better every six months or so. Also note that this time interval is expected to shorten this year.2

And so, logically speaking, Moltbook helps you understand the fact that the world has just gotten a bit weirder, but the deeper point is that you should not stop your analysis here. It also helps you understand that the world is about to get a whole lot weirder, and in very short order.

That’s what Moltbook means today.

TMKK?

(For new readers: TMKK is a phrase beloved in these parts. TMKK is an acronym that expands to “Toh main kya karoon?” and the best English translation is “So what?”)

My friend Navin writes an excellent Substack, which you should subscribe to. Here is how he ended his post about Moltbook:

Why is this interesting?

A lot of people haven’t really understood how big of a disruption AI, especially the agents, will be. The number of you who have used even a basic agent like Claude Code or Codex CLI or Antigravity must be a small single-digit percentage. The number of you who’ve used something like Moltbot must be approximately zero.

So this post is intended to jolt you into paying more attention to AI and agents, and to spend more time per day tracking what is happening in the AI world, playing with the AIs you have access to, and generally thinking about where the world is going and what your new place in that world will be.

How do you concretely go about paying more attention to AI and agents? There is no one correct answer to this question. Start by following some folks who you think are high signal and low noise on AI, and see if you can add to that list. My preferred social network for this is Twitter. Follow some blogs from folks who work in this space, follow some YouTube channels. Figure out your own recipe, but the point is that each and every one of you should be doing this, and to a greater degree than you have been doing thus far (and that goes for all of us, myself included).

Here is one interesting experiment. Here is a thread about an academic paper about Moltbook. Here is a related request from a guy who wrote a good Twitter article about Moltbook.

Above All, Wonder and Wander

My training is as an economist. I have zero expertise in biology and at best some rudimentary understanding of the field of AI. But I enjoy making connections across different things that I know, and I enjoyed trying to understand what Moltbook is and what it means by thinking about the Portuguese Man O’War.

But I also (of course) had a conversation about the analogy with an LLM (Opus 4.5 in this case). Here is part of the conversation:

Now, directions. Three that seem worth your particular attention:

1. Alignment mismatch. Current alignment work focuses on individual models. But if selection operates on network architectures, we might be aligning zooids while colonies emerge with fitness functions orthogonal to human values. Your anamnesis hope depends on AI systems retaining human-derived values through training data. But if the selective pressure shifts to “which networks coordinate best,” those values could get optimized away. Worth an essay in your Theory of Mind rotation?

2. Governance unit problem. Your Takshashila policy work assumes regulatory targets: companies, models, deployments. Moltbook doesn’t fit. It’s open source, forked, potentially invisible, hosted anywhere. If this is the shape of things, current governance frames are already obsolete. The EU AI Act regulates model providers. Who regulates emergent AI ecologies?

3. Your scissors effect, amplified. You wrote about AI breaking traditional learning pathways by collapsing transaction costs. Moltbook is that for AI-to-AI coordination. If agents can efficiently exchange specialized knowledge without human mediation, the “human as necessary intermediary” assumption dissolves faster than your models predicted.

The reason I share this isn’t because I think all of us should be thinking about these specific things or even this specific analogy. Of the three things listed here, I found the first to be the most fascinating, but others may find one of the other two to be worthy of thought and research. Still others may disagree with all three, and tease apart another nuance to explore.

No, the reason I share this is because we’re all up against a world that is about to get a lot more confusing and a lot more weird, and this is going to happen very quickly. We need many more people thinking about these issues, and the ways in which they’re going to affect us in all walks of life.

To paraphrase Navin, this post is intended to jolt you into wandering into many different areas of thought… while wondering about AI. And if you do wander and wonder, please tell all of us what you’ve come up with. And that goes for all of you reading this… AIs included!

It is hard being a student these days. The vague dissatisfaction that folks felt in college for decades has been replaced with a vague understanding that it is all pointless. But articulating this explicitly risks upsetting the apple-cart, and starting an avalanche in the bargain.

Because asserting that college is pointless is one thing. Trying to figure out what should replace it and how is a jungle cat that no one wants to bell, so why bother? And so we carry on, knowing that what we do is clearly the wrong answer to a question that nobody wants to ask.

That question being this one:

What exactly do we get when we spend a lot of time and money in acquiring a degree in an AI-first world?

But we’re getting ahead of ourselves. Let’s go back to the start of this thought process, and question our very first assertion. How do we test the proposition that college is pointless?

Is There A Point to Acquiring a Degree?

Going to a college gives you a degree, first and foremost. In an Indian context, said degree is valuable because it gives you a job. You also get a peer network, you get (some) learning, but the basic reason you go to a college and suffer through it is because you need the nice shiny certificate that says you got the degree. That piece of paper is the key that unlocks the myriad treasures of the labor market, and that’s why you go to college.

If you don’t believe me, I invite you to go spend time in a college with students in their last year, and in that last academic year, make sure you go during their placement semester. You pick the college, I only ask that you go spend time with the students then. I have done this, for many years, and please allow me to assure you that students go to college to get a degree so that they can get a job.

Assuming you accept this proposition to be true, it then makes sense to try and test the next obvious proposition in our chain of thought. That proposition being this one:

For a given college, for this year, are the final year students producing work that is as good (and as cheap) as that which the AI produces?

If the answer to this question is an unambiguous “yes!”, then students stand a good chance of finding employment. If the answer to this question is an unambiguous “no”, then students stand no chance of finding employment. And if the answer lies somewhere in between, we have a fight on our hands.

What we would like to do, then, is run an experiment where we have work that is:

1. produced by AI

2. produced by human students (with no input from AI),

3. produced by AI and students working together.

If the analysis of this dataset yields a result in which we are able to conclusively prove that 1 is winning, good luck to the students concerned. If we can prove that 2 is winning, congratulations to the students concerned. And if we can prove that 3 is winning, we need to think long and hard about how and what we’re teaching students in that college.

What Does Work Mean, Though, Really?

But what does work mean in this context? If I join a firm as an analyst, what exactly does work mean? Because whatever the word work means in that context, it almost certainly means something else in the context of, say, a student who joins an equity research firm. A student who joins a software firm to do coding will be doing different work from both of these.

Economists have spent time and effort in answering this question, and one way to think about it is that work really consists of different tasks1. Some tasks may be common across many jobs, while others may be of a more specialized nature. For example, taking notes while attending a meeting, and then circulating those notes to all attendees is a task. And back in 2006, when I was a junior employee, tasks such as this one were routinely assigned to junior folks. But today, your LLM of choice will transcribe the meeting, generate high level notes, assign follow-ups, and share that file with all attendees automatically.

You may still need the junior employee around, because there are other tasks that still (hopefully) remain in their scope of work. But this specific task has now been automated.

This little thought experiment allows us to refine our original question a little bit.

It’s All About Tasks

That question now becomes:

What we would like to do, then, is run an experiment where we have the modal tasks - the core work that actually justifies your salary - in different lines of work that is:

1. produced by AI

2. produced by human students (with no input from AI),

3. produced by AI and students working together.

What are modal tasks? Well, as a business analyst in my first job, sure I took meeting notes. Sure I wrote emails, scheduled meetings, booked conference rooms for calls and all that. But the task that made my bank account go ka-ching at the start of every month was that of building out logistic regression models.

If the company that hired me back then was able to instead get an LLM to build out equally good logistic regression models at maybe one-tenth the price, then it makes sense for the firm in question to hire the LLM instead.

So what if we took a particular college, and dug through the placement history of that college for the last five years? What if we found the typical profiles that recruiting firms hired students for from that college? What if we spoke to the recruiting firms, and got an idea of the modal tasks for each of those profiles?

Setting up the experiment

I spoke to my current best non-human friend, a guy called Opus, and it helped me come up with ten not-so-hypothetical tasks for such a college:

What do we do with these ten tasks? In brief: design tasks, recruit students, split into three arms, anonymize outputs, have alumni review blind, analyze statistically. What you're about to read about is called an eval—a structured way to measure how well a system performs on tasks that matter.

Here’s what I would do, in detail:

1. Speak to ten alumni from the college who are presently employed in firms that work in areas where tasks such as this one might be done regularly. So maybe a think tank for the first domain, maybe a telecommunications firm for the second, etc.

2. Ask these ten alumni to carefully design a specific task based on these inputs. So for example, I would ask an alum who is currently in an HR role in a large organization to work on creating a task that was as close as possible to the ten task on this list. This might include a brief about the employee in question, mock records of their performance in this review cycle, other details that might be necessary (role, org structure, pay scale, vintage at the company, etc.)

3. Also ask that alum to carefully design an “ideal” output. That is to say, what would an ideal end product look like for the task that they themselves have designed.

4. Once I have ten such neatly detailed tasks, along with an idea of what an ideal version of the end product would look like, I would then go out and recruit nine volunteers from final year students, per each of the ten tasks. So ninety volunteers in total (9 volunteers per task x ten such tasks).

5. For each task, divide these nine volunteers into three teams of three each.

   1. One team would be the Human Only team. This team would try and do the task in question entirely by themselves. They can use computers and the internet, but they cannot use AI at all.

   2. Another team would be the AI+Human team. This team gives this task to an LLM, along with a prompt to do the task. They can design the prompt themselves, or take help from other folks (including the LLMs themselves!). Once they get the output of the AI, they can edit it to make it better, but this part of the work they have to do themselves, no other human being is allowed to help. The edits can be done iteratively with the help of an AI, or by themselves, that is entirely up to them.

   3. The third team would be the AI Only team. This team gets the task done with the help of an LLM of their choice, the only condition being that it must be one of the paid versions of the latest and greatest model from the three major labs.2

6. Once this is done, take each of the nine submissions (3 versions of Human Only, 3 versions of AI+Human, and 3 versions of AI only), and anonymize them. I would remove all identifiable traces (name of writer if it is a word doc, any identifying headers and footers, and personal identifiers etc). Simply looking at the document should give you no idea about who has written it - that is the objective.

7. Name each variant of the first task as T1A, T1B, T1C…T1I. The ordering would be completely random, and only I would have the master key. That is, I know which one is which (so T1A might be Human Only Version 2, while T1B might be AI Only Version 3), but no one else does. I would put all of these anonymized and randomized versions in a folder.

8. Do this for all ten projects.

9. Give each such project folder to three separate reviewers. Each reviewer would be asked to rate each project along, say, three different dimensions. A score of 1 would mean the submission is unusable, while 5 would mean it is perfect. For example, one dimension might be Thoroughness, another might be Imaginativeness, and a third might be Readiness Score. Why three, and why these three? Great question, and both answers can change depending on your needs and preferences. Chat with your friendly neighborhood LLM about this, and feel free to improve upon my suggestion. In fact, I insist that you do so!

10. I would also ask each reviewer to stack rank all nine versions. That is, they have to assign a rank to each version, based on which one they liked the most (rank 1), all the way through to which one they liked the least (rank 9). If you do the math, you’re looking at thirty reviewers (3 per project, ten such projects).

The Analysis

So now you’ll have a spreadsheet that looks something like this:

So all right, now you have the data to analyze. What sort of tests can you run on data such as this?

LLMs and your statistics textbooks will both tell you that you can run a test called Friedman’s test to analyze the ranks. You could do a lot of post-hoc analyses, you could compute mean ranks - there’s a lot to be done here, and you should have fun chatting with humans and LLMs about awesome and fun ways to run an analysis of this data.

The questions that need to be answered are these:

1. Is there a statistically significant pattern to the ranks? That is, is the assignment of ranks random, or does a pattern emerge? Which arm of the experiment (Human+AI, AI Only, Human Only) tends to do better, and is it a close run thing, or not particularly?

2. Ditto for ratings

3. To what extent do the reviewers agree with each other for the rankings?

4. Ditto for ratings

The first two questions tell you who won. The latter two tell you whether the judges were measuring the same thing

TMKK?

What do you do once you have these results?

1. For one thing, you have clear, statistically valid answers about which kind of work is best for each project. This tells you something about how good your students, are, how good the models are, and the extent to which students are able to improve upon the output of the models. For example, you may end up finding out that when it comes to the students in your college and working without AI, they are not so great at persuasive writing, but they’re excellent at financial analysis. But with AI, they’re awesome at fiscal policy analysis. And so on and so forth.

2. Publicize the hell out of your work. Write a blogpost about it. Create a YouTube video. Record a podcast. Write up a white paper. Hell, write up a full academic paper. Go present your work at conferences. Help other colleges set up these experiments.

3. Repeat the experiment when the latest and greatest models are replaced by the latest-er and greatest-er models (we’re about three months away as of mid-Jan 2026, at worst).

4. Go tell recruiting firms that you know how to run evals, and you can run evals for them (congratulations, if you have actually done all of this work, because now you really do know how to design and run an eval)

The Bottomline

AI is here. It will affect the labor market, and entry level jobs are going to be hard to come by. But rather than wait for other folks to do the research and write up the papers, make use of the abilities that have been unlocked for you by AI, and show the world that you can get ahead of the game.

And above all, realize that this applies to you regardless of whether you are actually in college or not. Pick workflows that make up the modal tasks in your current job, and run these tests on them. Because it is just a matter of time before somebody else does it anyway.

And if you’ve made it this far, you may enjoy reading the GDPval paper. But if you are a student today, you should read it regardless of whether you enjoy it or not.

You learn best by doing, and in 2026, there is no excuse for not doing. Get started!

More to the point (or at any rate, the point that this post is trying to make), he also called Mark Knopfler an excellent historian.

This post is me heartily seconding Alex’s call, and providing a series of arguments for why Alex and I are (obviously) right. I’m hoping you haven’t heard these songs before, and if that is the case, you’re in for a royal treat.

Most people know Mark Knopfler as the lead singer of Dire Straits (and there’s economics there too, surprise surprise), but there is so much more to his career than that.

In this post, I am going to talk about ten of his songs, and how those songs cover diverse parts of history.

1. Done With Bonaparte:
   

   “We’ve paid in hell since Moscow burned,
   As Cossacks tear us piece by piece,
   Our dead are strewn a hundred leagues,
   Though death would be a sweet release”
   …is how the song starts, and goes on to tell us about the hell that must have been “The Little Corporal’s” army’s retreat from Moscow.
   Knopfler’s songs are often about the ‘little’ men in history, as opposed to The Great Man narrative, and this song is no different. Our protagonist tells us in a few short verses about how the dream of ‘Spanish skies and Egyptian sands’ turned sour very quickly. He speaks about the battles of Austerlitz, and about having lost an eye there. He wants nothing more than a sweet return to his belle France, and his yearning for a return back in both space and time lends this song an ache that never quite goes away.
   Well, ok, he does want one more thing. He prays the future generations will never again see a little corporal point towards foreign shores and captivate the hearts of men.
   Ah well. God must not have been in a listening mood that day.
   
   

2. Imelda:
   

   “She’s going shopping, shopping for shoes”, Knopfler informs us, and helps us learn about Imelda’s insanity. Imelda Marcos, wife of Ferdinand Marcos, patron saint of obscenely large shoe collections (over 3000 pairs, WTF!), and the subject of this song, was quite the character. You’d have to be to have an entire Wikipedia article about your proclivities!
   It’s a lovely song as it is, but if you are a student of economics and history, it is a good way to get an “in” into the turbulent history of the Philippines in the second half of the twentieth century. But regardless of whether or not you choose to take the plunge into the history of the Philippines (and you should!), you should certainly listen to this song.
   

3. Sailing to Philadelphia
   

   One of my favorite songs by Knopfler (in part because my daughter loves it), this song is about the Mason Dixon line in America, by itself a fascinating piece of American history (and geography). But if you happen to also land upon the book by Thomas Pynchon about these two (Mason and Dixon), then you have a treasure trove of historical adventures to savor.
   And from a musical perspective, Knopfler and James Taylor at the same time. What more you want in life, eh?
   

4. Prairie Wedding
   

   Have you heard about the concept of a mail-order bride? This song is about that practice, and having listened to the song, I hope you’re inspired to both read the article, and also to do a bit of online digging-about to learn more about how and why the practice started. The song is a treat in and of itself, but I hope it also inspires you to find out a little bit more about a most unexpected subject (and hey, if you’ve ever found yourself wondering why students of economics should learn about sociology, here’s an answer!)
   

5. Why Aye Man
   

   What does it mean to “have the back of Maggie’s hand”? If you want to understand the original DOGE better, this is a good song to get you started. What was the Britain of the 1980’s like? What did Margaret Thatcher do, and what were the costs and benefits of whatever it was that she did? Whatever your opinion of her economics, Knopfler continues his theme of exploring what ordinary people go through during historic times in this song. “German building, British made” is as good a phrase as any to help you get started on labor mobility.
   

6. Boom Like That
   

   Watching ‘The Founder’ is one way to understand Ray Kroc and how the Golden Arches became what they are today (and not just the business, but also how they helped shape America). Another way is by listening to a song that talks about how Kroc discovered a little place flippin’ meat (down in San Bernadino, ring-a-ding-ding), and ruthlessly turned it into what it is today. I cannot stand a McDonald’s burger (for reasons of taste, not because of the backstory!), but this song? Play it on loop, no problem.
   

7. Don’t Crash the Ambulance
   

   Listen to the song, please, before reading further. Now listen to it once again, but with the knowledge that this is George Bush Sr. telling Jr. how to go about the business of running the show (which just so happens to be the most powerful nation on earth, which was definitively true at the time)… and how to not crash the ‘ambulance’ while you’re driving it. We could do with a sequel, Mark, we really could.
   

8. Baloney Again
   

   If you saw and liked The Green Book (I did, and I did), then this song is another way to get acquainted with the same era, in the same part of the world. The movie, being a movie, chose to show the protagonists choosing to stand up and fight, in a manner of speaking. But here’s to the untold many who just chose to pay the man and go. Because hey, baloney again.
   

9. Daddy’s Gone to Knoxville
   

   Dire Straits fans might recall a song called ‘Telegraph Road’. I think of this song as a spiritual successor to that one, not in terms of the way it is sung or orchestrated, but in terms of what both sets of lyrics are about. That connection may not work for everybody, but economic hardships, urbanization, and the quiet struggle of the folks who struggle to make it through epochs in history - these are classic Knopfler themes, and an older, quieter Knopfler is still singing about ‘em in this one. Read more about Knoxville, Natchez Trace and urbanization in that part of the world (and if you like to pick up on obscure parts of different lyrics of different songs, see if you can make the connection to ‘Sweet Home Alabama’, by Lynyrd Skynyrd.)
   

10. Song for Sonny Liston
    

    I’m not much into boxing, but I enjoyed learning about a world I know next to nothing about by listening to this song. And the more I learn about Sonny Liston, his background, and about that time in general, the more conflicted I am about both the man, and what he became. And here’s another (non-Knopfler) song for you to listen to, if you want some connecting trivia.

    
    

    ---

    
    
    As you might have been able to tell, Knopfler is one of my favorite song-writers. He will not have the same abilities that say, a Dylan does (then again, that’s unlikely to ever happen again), and he’s never going to make anybody’s top ten list when it comes to singing (not even mine, and I’m a Knopfler acolyte). But when it comes to knowing a thing or two about playing the guitar, and when it comes to picking weird ol’ themes to tell a story about, Knopfler is second to none in my book. And as this post hopefully makes clear, he’s a great way to get an ‘in’ into many different histories.
    Happy listening!

    
    (Pro Tip: You could put this entire blogpost into ChatGPT, and ask it to make a playlist using Spotify. Try it!)
    

    
    

“At the start of my graduate studies, the Journal of Economic Perspectives was a brand new journal and the Internet didn’t exist. Over time, there has been an explosion of unstructured content available for students eager to jump in. Blogs popped up, Twitter and X feature many top economists willing to engage with the public and Substack features many academic economists writing subtle pieces. Undergraduate teachers can find high quality material produced on many reputable websites ranging from the World Bank, VoxEU, and many Think tanks. In recent months, I have experimented with loading many interesting readings to a shared Google LM Notebook website and encouraging my students to ask the AI for summaries about these writings and to ask their own questions”

The title of the paper that this excerpt has been taken from is “Will AI Improve Undergraduate Economics Education?”. The author is Matthew Kahn. There are many reasons to read this paper: it is short, succinct, and a good read being chief among them. The fact that it has been written with Grok’s assistance is another. My chief reason for writing about this paper is because I like the careful phrasing of the title1.

But Why That Particular Title?

Here are some alternative titles for the same paper:

1. Can AI Improve Undergraduate Economics Education?

2. How can AI Help Improve The Way Undergraduates Acquire An Economics Education?

I would argue that there are two good reasons for not going with either of these titles.

We’re Already There, We Just Don’t Know It Yet

First, sections 3,4,5,6 and 7 in the paper are effectively an answer to the first question. AI absolutely can (and to a limited extent already is) improving undergraduate economics education. Matthew Kahn discusses this extensively through each of the sections I have mentioned above, and if you can find the time, please go through them in detail. In case you cannot, here are the section titles:

• Integrating AI into Econ 101

• AI Infused Intermediate Micro and Macro and Econometrics

• Incorporating AI in Field Classes

• Independent Research and Research Assistant Opportunities

• Research Assistant Opportunities in our AI Era

Education’s Emperor Problem

Second, section 10 is as good an answer as any I have read for the second question:

“For far too long, students have been choosing majors in the dark—picking “prestigious” fields without really knowing what the degree will do for them, while universities have been able to hide behind vague reputations and opaque classrooms. Parents write enormous checks with almost no idea what they’re buying, employers wonder if the diploma still means anything, and everyone quietly suspects a lot of the game is just expensive signaling.

AI changes that. Cheap, frequent, AI-proctored assessments and virtual tutors suddenly make effort and mastery visible in real time. Professors discover whether students are actually learning the material. Parents can peek at meaningful progress dashboards instead of just getting billing statements. Employers can ask for verifiable records of real skills instead of trusting a transcript that could have been gamed.

When effort is easily observed, Deans can write optimal contracts. Will Deans (who often embrace horizontal equity considerations) reallocate funds to those departments that adapt to the AI challenge by producing a better product? Universities are non-profits. What do the Deans maximize? If they seek to maximize the expected present discounted value of graduate earnings then Economics Departments who infuse AI in their curriculum will prosper.”

Economists have their own shorthand for talking about these problems. We refer to them as asymmetry of information problems, or sometimes we will talk about principal-agent problems. But you can also, if you prefer, call this the “Emperor-Has-No-Clothes” problem.

Because here is the harsh unvarnished truth: parents absolutely do have an idea of what they’re buying with an enormous check. They’re buying the degree. Employers absolutely know that the diploma means something, and they also know that it doesn’t mean a good education. They know this because they’ve been through the same system themselves not all that long ago, and they know things haven’t changed much since they graduated. Everyone doesn’t “quietly suspect” that a lot of the game is just expensive signaling. They know it is just expensive signaling. But hey, why rock the boat?

That question isn’t meant as a rhetorical flourish. Why, indeed, should you be rocking the boat?

The Boat is Already Rocking

As things stand, universities have perfected over decades2 their workflows to be what they are today. Some of this is because it is economically efficient, some of it is because that’s what the market has desired in the past, and some of it is because of the need to comply with regulations. But within those pressures, quite naturally, universities have evolved into a system that has become thoroughly entrenched. There has been no pressing need to change. Why? Because the culture we live in today has our current form of pretending to dispense an education perfected to a finely honed performative art. As a society, we have applied just the right amount of veneer to those four odd-years that students spend in undergraduate learning to make it seem as if Learning Is Happening. More importantly, we have settled into a workflow where awarding the precious degree, and the granting of the even more precious mark-sheet, is all that matters.

Students, given this workflow for acquiring a degree (as opposed to an education), naturally seek to minimize effort while doing so. That’s not a comment on them, you and I did the exact same thing when we were in college, and we do it even today in our jobs. Minimizing effort to acquire something isn’t being lazy, it is being rational. Is that “something” worth acquiring in the first place, and are there better ways to acquire the true underlying thing?

These are difficult questions to answer. Battling the system as it is is more than enough work, why bother going through the fight of changing it?

It is because ceteris is not quite paribus3 anymore.

Creative Destruction

In the conclusion of the paper, Khan has these two paragraphs:

“The 2025 Nobel Prize was awarded to three economists for their work on how technological innovation drives sustained economic growth. Our own core field faces the AI innovation shock. How will we as individual educators and economics departments respond to this challenge? In this essay, I have sketched out my own perspective and I plan to crowd source my paper to see how people respond. When Creative Destruction4 comes home to our daily life, how do we respond?

Our adaptation task would be simpler if our consumers (our students) were time consistent! At age 20, undergraduates often prioritize immediate gratification—seeking courses with minimal effort, high grades, and entertaining delivery—to balance academics with social life, internships, and extracurriculars. Yet, at age 40, alumni frequently reflect on what truly prepared them for career success: not easy electives, but skills in causal reasoning, handling uncertainty, and adapting to technological shifts.5”

AI is a technology shock to education. AI is the technology shock to end all other technology shocks, but that is a story for another day. For now, for the purposes of this essay, it is enough to understand that AI is very much a “creative destruction” force. We already have the tools that are capable of dispensing a much more customized, personalized, easily-monitored, more-rigorously-measured education. There is no technological constraint that stops us from being able to do so. Again, sections 3,4,5,6 and 7 in the paper give you glimpses of how this could be done, and there are many people who are working on advancing early research in this area6.

The constraint in improving the dispensing of education isn’t technological. Quite the other way around. AI has helped unlock a much better way to dispense education. The problem is that there is a lack of demand. And that’s down to culture.

AI Is Pushing Us Towards A Coasean Singularity

There is interesting research about this now available, and I have covered how a reduction in transaction costs might affect higher education previously. Briefly put, AI will reduce search costs for many economic transactions, leading to more efficient markets. Firms will become more efficient in terms of judging labor inputs, and firms will be able to get much more done with a much more skeletal task-force. Effort monitoring will become a lot better, and this matters for us because this can be true in the case of education as well. Why? Because AI can observe a student’s progress much better than can a professor or a college7.

Think of traditional firms or colleges as being the equivalent of an internal combustion engine (ICE). These engines have many moving parts, don’t work as efficiently, and they need the many parts of the engine to move in perfect synchronicity with each other. A good ICE has many parts, all of which work a fair bit with each other, need frequent replacement, and call for regular maintenance and upkeep. There is a lot of friction.

AI-first firms or colleges, on the other hand, are like electric motor engines. They have hardly any moving parts, work much more efficiently, and do not need anywhere near the same level of regular maintenance and upkeep. There is hardly any friction.

The ICE world (which is our old world with no AI in it) calls for compliance, and our current university system is excellent at producing reasonably high quality employees with high compliance. The electric motor world (the new world, with AI very much in it) calls for agency, and our current university system is horrible at producing high quality employees with high agency. Educational institutions do not reward a student for finishing a course in two weeks, and nor do they appreciate a student looking at a course syllabus for an entire degree and saying, “Well, with AI’s help, I can master this to anybody’s satisfaction in only two years as opposed to four”. Education today has a Two Minute Mile problem.

Where Are We Headed?

How realistic is it to assume that we (and that’s all of us, as a society) are headed towards a Coasean Singularity? I am not asking if we will reach or attain that singularity. I am asking how realistic is it to assume that we are headed in that direction. My day job is already providing some important updates in that direction, and I hope to be able to share some of our findings with you here soon. I suspect most adults reading this have either already experienced moves in this direction in their firms or businesses, and I suspect many more will experience lurches in this direction in 2026.

Firms and businesses respond to market pressures, so one should expect this move to happen in response to market based incentives, assuming AI progress continues. Educational institutions respond to cultural pressures at least as much as they do to labor market dynamics (and possibly more), and only partially to market based dynamics. Whether the movement will happen at all or not, and whether it will happen as quickly as it does in the case of market facing firms and businesses, is the key question facing educational institutions today.

If I’m right about the vertical axis, the question is about whether, and if, current educational institutions will be able to adapt and move. This will depend upon our culture’s ability (and speed) to adapt to our new reality, and raise our cultural demand for high agency education. Fighting against this will be our cultural inertia, guided by our entirely understandable cultural preference for maintaining the status quo.

To the extent that our educational institutions fail to adapt quickly, there is going to be a growing disconnect between the new firms in this new economy, and the old educational institutions from our old-but-oh-so-familiar-and-comfortable economy. Will resistance win out over revolution, or will the gravitational pull of the new economy be too powerful to resist?

The answer I favor isn’t one that is acceptable to the current culture of education. But it is going to be increasingly inevitable, and in some ways already is. Watching this fight will be exciting, entertaining and best of all, educational.

Enjoy it.

CWC

The first of these tales is about a community called The Clear Writing Community. Amit Varma, wearer of many hats, runs an online course called The Art of Clear Writing.

Regular readers of this blog may be surprised to learn that I had taken this course back in 2020. (I should hasten to add that you shouldn’t be updating your priors about Amit as a teacher. You should be reinforcing your priors about my laziness as a student.)

But as I tell everybody who is considering doing this course, you should do it because Amit maximizes soul. The online sessions are great, but the better payoff is being a part of a lovely community.

This community has many offshoots, including city-wise chapters, and various hobbyist groups. I have become a lurker on pretty much every single community I am a part of, and I’m certainly not claiming that I am an active participant on any of them1. But I love reading the links that people share on the CWC community, and it is an amazing bunch of kind, well-read people. Join Amit’s writing course as much (more, in fact) for the community as for the learning.

Ravishankar has also done this course, and is therefore a part of the same WhatsApp community. He writes a weekly newsletter called the 3-2-1 newsletter, and he had linked to a blog post that I had written in one of his posts. I messaged him to thank him for doing so.

Why? Well, many reasons. One, because I agree with Seth when he says that sending little thank-you notes out into the world is a good idea. But also because I agree with my friend Navin Kabra, when he says that you should work at increasing your network.

The Magic of Weak Links

Navin is big on weak links. It is a theme he has explored in his videos, his blog posts, his posts on Twitter and via his teaching. Another economist whose work I admire has explained why developing weak links matter, and for these reasons (among others), I think it to be a good idea to occasionally write thank you notes to folks whose work I admire. A bet with only positive upsides, so why wouldn’t you take it, eh?

This particular bet paid off for the first time when Ravi responded to my message, and we found out that we not only stay in the same city (Pune), but also happen to live very close to each other. We met for breakfast, and discovered a mutual love for beer. A seemingly trivial detail, but as we shall learn soon, one that links together the many different tales that make up this blog post.

Soon after, Ravi sent me a message about an idea that he had clearly been thinking about for a while. Why should we not figure out a way to get folks to meet up and listen to a talk? Ravi and I agreed that this was a great idea, and a fantastic excuse to get together and talk about it over a couple of beers.

And so we did meet up at a pub to drink the beers, and also to answer some pretty important questions. For example, who will deliver these talks? On which subjects?Where might we be able to host these talks? And other such questions of similar nature.

The grub was good, the IPAs were excellent, and we had a lot of fun talking about these ideas, especially after we got on to the second round of pints. And it might have all stopped here, as is often the case with these beering sessions.

Except that Navin tweeted this just the very next day:

Three things happened after that. One, we got another reason to meet up and drink beer once again, this time with Navin. Two, Nagaraj, a Prof from IISER joined our little organizing committee. Harish Bhamidipati joined us a little while later too. And three, we realized that we would have to actually get this off the ground if we wanted to continue using this as an excuse to meet up and grab a couple of beers.

And thus was Pune Knowledge on Tap born.

Pune Knowledge on Tap (KnOT)

The idea behind KnOT is very simple, and is based on the Science on Tap series that used to happen in Pune. Science on Tap was Anoop Mahajan’s idea, and he was more than happy to both give us his blessings and share with us tips and tricks for what worked well and what didn’t with Science on Tap.

KnOT talks happen on (usually) the last Thursday of every month, and they always happen at Wynkk, in Aundh. Attendees at each event have to pay 300 rupees to enter, and are given a coupon which they can fully utilize inside to buy beer, non-alcoholic beverages, or anything else they like.

Folks grab a drink and have a chat for about half an hour before the talk begins, and are most welcome to stay back post the talk to chat with each other or the speaker. The talk lasts for about an hour, and we have a small WhatsApp community where we post updates, etc2. Chatham House Rules apply, and the talks are not recorded by design. The idea is to turn up and listen, in person.

Prof. Pradeep Apte delivered the first talk, on the history of beer. We figured that was the most appropriate way to start, considering the nature of the talk (not to mention the story of how KnOT started!). Siddhesh Kamat was next, and he gave a talk on his research on lysophosphatidylserine. Vidya Mahambare gave a talk on India’s bypass economy in June, while Robert Pennock spoke in July on anti-evolutionism in court. Dr. Karishma Kaushik gave a talk on her work on biofilms in August, while Niranjan Pednekar gave a talk on how constraints liberate art in September. October was Deepak ‘Chuck’ Gopalkrishnan’s talk on how tech shapes music, while November was the geologist, Suvrat Kher, talking about how each one of us can (and should) discover our inner geologist.3

We rounded off 2025 by inviting Ravishankar himself to give a talk on the subject closest to his heart, the art and science of telling stories.

The Point is the Community

We were worried when we began the monthly talk series earlier this year. We were worried about a lot of things, but our chief worry was whether a series like this would garner enough of an audience, month after month.

And we’re glad to report that our worries were baseless. Every talk has been “sold out”, and we have our own little community that uses these talks as a way to meet-up, interact with other like-minded folks, and learn about something interesting in the bargain.

There are a lot of different ideas that each one of us organizers have about how we could do more with the community that we are building. The organizing principle and the motivation behind whatever we do will remain the same: helping build better communities, with the glue always being the ability to learn about interesting ideas as a community.

And that’s the reason I wanted to write this post - to explain that the whole point has always been to build up and expand our communities. Each of us, the folks behind KnOT, have had our own networks be enriched by meeting folks from each other’s circles. And this is true, it goes without saying, for everybody who attends these meet-ups.4

Making this happen, and sticking with it through 2025 has been one of the best things I have managed to do this year, and I hope we do an even better job of it in the coming year. One of the ways we could do a better job is by making many more such events take place.

So if you’d like to find out the hows and the whys and the why-nots that go into making something like this happen, please, feel free to reach out. Any one of us will be more than happy to try and help!

Here’s hoping I get to meet you soon at a community meet-up, and life would be even better if it happens to be one organized by you.

Cheers, and here’s to 12 more KnOTty problems to solve in 2026.

I found myself thinking about just this question, and three different things that happened to me pushed me in that area.

First, a couple of months ago, I gave a talk to a bunch of schoolkids, about a book called A Brief History of Intelligence. This is a book written by Max Bennett, and I had found it to be an enjoyable and informative read about the topic that the title promised.

Second, a couple of weeks ago, I attended the Emergent Ventures conference in Bangalore, and attended a session on mirror neurons.

And third, just this morning, I saw a video about a tour of the Google DeepMind Robotics lab.

Today’s post is about how what lies at the intersection of these three things helps us understand why a computer is a genius at chess, but a robot is such a klutz at picking up chess pieces.

Let’s go.

Chess is easy, messy rooms are hard

Folks in the know will tell you that this is just Moravec’s Paradox. Moravec’s Paradox says that things that are hard to do for humans are easy for robots, and things that are hard to do for robots are easy for humans.

A human can tidy up a messy room in next to no time, and a robot can crush you (metaphorically speaking) at chess. But ask both of these to get the other task done, and capability falls off a cliff. That’s Moravec’s Paradox.

But why?

Why is it that a robot can beat every single human on the planet at chess (no exceptions, no qualifiers, no nothing), but cannot even get going on the simplest of tasks, such as say cleaning up a messy room? Why are humans so very good at things like perception, grasp planning, compliance, and dealing with uncertainty, and why are robots not yet good at all of these things?

Here’s a quote from Hans Moravec himself about why this may be so, taken from the Wikipedia page about the Moravec Paradox:

Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge. We are all prodigious olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it

It is not at all the case that these tasks are easy. It is just that we have had a really, really long time to get good at tasks such as these. Our brains and our bodies have had billions of years to get progressively better at motor tasks. But our brains and bodies have also had billions of years to build up these tasks into a chain (of tasks). Or to put it in the jargon of the AI industry, we’ve learnt agentic pathways for motor-tasks over billions of years.

Now, when I say “we”, I mean everybody alive on this planet today, not just humans. Organisms have learnt agentic pathways for motor tasks over billions of years, of which we (humans) have been around for only the last two hundred thousand years or so, at best.

And the story of how we learnt these pathways is best told in the book that I was telling you about, A Brief History of Intelligence.

A Brief History of Intelligence

S-R-S-M-S. That’s how I remember the five stages shown here1. That’s Steering, Reinforcing, Simulating, Mentalizing and Speaking. Those, Max Bennett says, are the five stages of organisms developing intelligence on this planet.2

Briefly put, we first figured out how to steer towards good things, and steer away from bad things. That was stage 1, steering.3 Then we learnt how to reinforce in our own heads how doing x seemed to cause y. That was stage 2, reinforcing.4 Now, that brings us to stage 3, simulating.

https://www.youtube.com/clip/UgkxyydjZdFboQNHlnHZtQU0nZupaCrFz2Tr

That’s a clip from a podcast run by Google DeepMind itself.5 The reason I find it so fascinating is that this conversation sounds to me as if we’re being described a move from Breakthrough 2 to Breakthrough 3. Robots can now generate internal simulations of the world. They can imagine, which means they can engage in trial and error, but vicariously.6

The demo itself (the entire video) may seem a little basic when the bar is “relative to what we can do”. But when you view the video in the framework of that infographic above, it becomes impressive because you can place the stage of robotic development. We are at stage 3. If AI began its race against us with us having a 550 million year head-start , AI is now about 15 million years away from us ( it now needs to “conquer” Breakthrough 4 per the infographic above).

Our story can now go along two different paths. One path is called “OK, how might it conquer Breakthrough 4?”. On this path lie things like mirror neurons, concepts like “theory of mind” and stuff from the very cutting edge of robotics and AI research.

The other path is called “Uh, am I the only one who’s taken a look at Breakthrough 5 here?”. On this path lies the uncomfortable realization that the labs we’ve built to build intelligence seem to have messed up the recipe a little bit. They’ve gone from Breakthrough 1 to Breakthrough 2, and only now managed to reach Breakthrough 3, which is fine in and of itself. But they’ve also gone ahead and figured out Breakthrough 5, because what else are LLMs if not Breakthrough 5?7 On this path lie mean trolls, so let’s saunter down the other path for now.

How Might AI Tackle Breakthrough 4?

This path, the one about Breakthrough 4, is fun, interesting and informative. And it begins midway through Max’s excellent book.

Max Bennett tells us the story in Chapter 17 of a bunch of Italian neuroscience researchers who were getting some lunch in a lab. This was in the midst of an experiment they were conducting about areas of the monkey’s premotor cortex brain. The penny dropped when they realized that parts of the monkey’s premotor cortex brain were lighting up when the monkey happened to see a human raise their arm to eat the sandwich.

Why was this remarkable? Because the premotor and motor cortices of our brains were supposed to have been in charge of our movements, and that’s it. But we were now learning that these parts also lit up when we simply looked at other people doing these actions. We call these parts of our brain mirror neurons.

More about mirror neurons

These mirror neurons have been discovered in humans too, and other animals besides. They’ve been discovered in other parts of our brains, and there are now many theories about their functions, the reasons for their existence, and their importance. Most importantly for our purposes, there are now theories about how mirror neurons can help us better understand the problem called theory of mind.

Theory of mind is the capacity to understand other individuals by ascribing mental states to them, as per Wikipedia. This includes the understanding that others’ beliefs, desires, intentions, emotions and thoughts may be different from one’s own. OK, and why does this matter for AI and robotics development? Here’s Bennett:

One reason it is useful to simulate other people’s movements is that doing this helps us understand their intentions. By imagining yourself doing what others are doing, you can begin to understand why they are doing what they are doing: you can imagine yourself tying strings on a shoe or buttoning a shirt and then ask yourself “why would I do something like this?” and thereby begin to understand the underlying intentions behind other people’s movements. The best evidence for this is found in the bizarre fact that people with impairments in performing specific movements, also show impairments in understanding the intentions of those very same movements in others. The subregions of premotor cortex required for controlling a given set of motor skills are the same subregions required for understanding the intentions of others performing those same motor skills.

Bennett, Max. A Brief History of Intelligence: Why the Evolution of the Brain Holds the Key to the Future of AI (p. 355). (Function). Kindle Edition.

But it’s more than this. Understanding intention is one thing. Having the motivation to keep observing the actions done by somebody else, especially with no immediate payoff in the offing is quite another. We can spend hours, if not years, attempting to replicate observed skills, while other animals will only do it if they get immediate rewards, and even then, not for very long.8

So the answer to the question “How might AI tackle Breakthrough #4”…

…is that we need to figure out a way to get robots to have the equivalent of a combination of mirror neurons, theory of mind, and long attention spans. With two important caveats. One, this is one of many approaches being tried right now, and the other approaches are equally interesting in their own right. Two, as with everything else that is cutting-edge, there is controversy and debate about mirror neurons, and plenty of it.

Why are researchers not sure about whether mirror neurons can help with breakthrough #4? Two reasons. First, it would seem neuroscience researchers are not entirely sure about how important mirror neurons are to understanding theory of mind in humans. 9 Second, published research is thin about theories about how we can use cutting edge understanding of neuroscience research in this area to better understand AI.10

But until we do, anticipate being sub-par at figuring out the best move in the next game of chess you play with an AI. But also, feel free to help it pick up the piece with which it will inevitably knock over your sorry little king.

Now, that leaves us with that second path we spoke about, the troll infested one. But a wise procrastinator leaves that sorry task to his future self, the guy who will think about the next post to write.

And I am very, very wise when it comes to procrastinating. So my future self will see you, er, soon.