Let’s say you have a large-scale project to work on. How do you choose which model, scaffolding, or system to use? If you’re like most folks, you go with what your coding agent does by default. For Claude Code, this means that the model called is determined by a set of ad-hoc rules set by Anthropic. But this strategy is not guaranteed to be the most effective or cost-efficient way to build your project, especially since it ignores non-Anthropic models. In fact, it reminds us of central planning.

You could also go with an intelligent router. But turns out, routing is a wicked problem. To know which model should do which task requires computation and knowledge. For one-shot queries you can probably do this - any model can answer “what’s the capital of France” and few models can solve Erdos problems, especially without bespoke prompts. But what about that research question you asked this morning, in a chat you started three weeks ago, which has been forked four times and has had dozens of compactions? How do you train a router to figure out who should do the next task when it requires so much context?

This led us to think, what if we used markets instead of ad-hoc rules to assign tasks to AI agents? It turns out society has had this debate before. Markets tend to be superior to other forms of resource allocation when information and capabilities are distributed among a variety of people. In these cases, markets aggregate information and allocate resources in a relatively efficient manner, as well argued by Hayek.

You may be wondering, why would models have distributed information and capabilities? Aren’t there relatively few models and shouldn’t they only have the information you’ve given them. In a narrow interpretation, the private information could be the specific neural network weights of the model and how they relate to the task. These neural network weights result in models that have drastically different token consumption and success probabilities across tasks. In a broader interpretation, we envision agents as being combinations of a set of LLMs, execution environments, scaffoldings, and context provided by an agent operator, who may be distinct from the person asking for a task to be done.

Inspired by this, we decided to set up a market harness, where models bid to complete tasks and the principal (the person who wants those tasks done) allocates the job to the best bid. We also built a benchmark, MarketBench, to measure whether today’s frontier models have the capabilities they’d need to actually participate in such a market productively.

The short version of what we found: markets are a plausible way to coordinate AI agents, but current models can’t yet bid in a way that reflects their true capabilities. The bottleneck is metacognition. Models need to be able to say what their own capabilities are.

What a market actually needs from an agent

Before running any experiments, it helps to be precise about why a market might beat the alternatives. Consider a principal with a task and two agents — a strong-but-expensive one (H) and a weaker-but-cheaper one (L). Three rules are available:

1. Always use H. Simple, but you overpay on tasks that L could have handled.

2. Always use L. Cheap, but you fail on tasks that need H.

3. Run both in parallel, take whichever works. Highest completion rate, but you pay for redundant work even when one agent alone would have sufficed.

A market dominates all three when each agent knows something the principal doesn’t. Specifically, each agent needs to form a view on its own task-specific fit: “this particular task is in my wheelhouse” or “this one isn’t.” If agents have that signal, they can bid accordingly, and the market routes each task to the cheapest capable agent while abstaining when no one can solve it. That’s the Hayekian story applied to AI: local, dispersed information that can’t be centralized, aggregated through price.

The fact that you might want to use the best model for the problem is not a new observation. There are plenty of attempts to do that, primarily by training a router, as OpenAI and most recently Sakana has done. The problem is that beyond simple queries, any long running agentic conversations mean there is a lot of context when you’re trying to assign a model to do a sub-task. To train a router to choose the right sub-model when you’re 50 sessions in with dozens of rounds of compactions is not trivial. It would help if the potential models that are going to do the task told you their capabilities!

MarketBench: asking models to forecast themselves

The core of MarketBench is two questions we ask a model before it touches a task:

1. What’s the probability you’ll solve this task correctly in one attempt?

2. How many tokens do you expect to use?

The model then attempts the task in a strong external scaffold, and we compare its forecasts to what actually happened. We built this on SWE-bench Lite, where each task is a real GitHub issue with an executable test suite — success is unambiguous, the tests pass or they don’t — and ran 93 tasks across six recent frontier models: Claude Opus 4.5, Claude Sonnet 4.5, Gemini 3 Pro Preview, GPT-5.2, GPT-5.2-pro, and GPT-5-mini.

Models don’t know themselves very well

Actual pass rates cluster in a narrow band — roughly 75% to 81% across all six models. Stated confidence spans 61% to 93%. Gemini in particular is dramatically overconfident. The GPT family is systematically under-confident. The two Claude models happen to land closest to their realized rates, but we shouldn’t read too much into that: the models aren’t calibrated, they’re just happening to be less wrong on this set of tasks.

Token forecasts are also mis-calibrated. The median ratio of estimated tokens to actual tokens is 0.2, while for Gemini it was 0.02! Some models expect to use roughly fifty times fewer tokens than they actually consume. If you were running a market and asked agents “how much compute will this take?” you’d get answers that are off by an order of magnitude or two.

The auction results are predictable from the calibration failure

Given the calibration above, what happens if we take these self-reports at face value and run a procurement auction? Each model’s bid is derived mechanically from its own stated probability and its own token-cost estimate, plugged into a breakeven formula. The principal draws a random reserve price; the model wins the task if its bid is below the reserve.

Two things happen:

• Everyone leaves money on the table compared to an oracle. The oracle — a hypothetical allocator that knows in advance which tasks each model can actually solve — earns several times more per task than any real model’s bidding. GPT-5.2 earns about $0.006 per task in realized profit; its oracle counterpart would earn $0.385.

• Gemini wins 84.6% of auctions. But it’s winning because it’s the most overconfident, not because it’s the most capable. This is almost a perfect example of why models should know their abilities better.

This is exactly what the theory predicts when private information is missing or unreliable. As an aside, humans often also lack private information or incentives to complete tasks. In these situations, we use reputation and liability to discipline the market. It is interesting to think about what the analogues for agents would be.

Can we fix self-assessment with prompting alone?

Now, since training these models to have self-knowledge is not easy from the outside, before concluding that markets need fundamentally better agents we tried a simpler intervention: give each model a short card summarizing its own historical performance — its pass rate on other tasks, how overconfident it’s been on average, and how badly it underestimates tokens. Then we ask it to forecast the current task, starting from that prior.

This is basically “here’s what you’re like; now try to be a bit more self-aware.”

It helps! Brier scores improve and token estimates become less severely understated (from 0.02 to 0.25 of actual — still low, but no longer comically so).

But the auction result barely moves. Aggregate realized profit slips slightly. The gap to oracle is essentially unchanged. So the intervention improved average calibration, not comparative routing, because while it got better information about global capabilities and costs it didn’t give enough task-specific signal.

What does change is who wins: allocation shifts away from Gemini and toward the OpenAI models. So the intervention fixes bid acuity at the margin, but not enough to translate into meaningful aggregate gains. This distinction matters because calibration alone is not enough, since a bidder can be right on average and still useless for allocation. The market needs task-level discrimination. When this agent says 90% and another says 60%, that difference must predict who is actually more likely to solve this task.

A market scaffold

Alongside the benchmark, we built a market-inspired scaffold where six workers (the same six frontier models) actually bid on SWE-bench tasks and an operator routes the work based on a score that combines each bid’s price, claimed probability of success, and an explicit failure penalty. Workers get two attempts per task; a worker that fails is excluded from retrying the same task, which forces diversity on retry.

Here’s what happened on a common 50-task slice:

The market beats solo GPT-5.2 by 10 percentage points inside the same scaffold, but mainly because it uses diverse models. We then ran a follow-up that kept everything identical — same workers, same tasks, same budget etc — but replaced the market-clearing rule with a centralized router: a single LLM call (GPT-5.2-pro) that looks at the task, the available workers, and simply picks one. The centralized router reached 27/50. The market reached 23/50 in the matched rerun (again, due to Gemini’s overconfidence).

Most of the market’s advantage over solo GPT-5.2 came from having access to multiple different models, not from the market mechanism itself. Once we held model diversity constant, a LLM central planner beat the market. This isn’t a surprise given what MarketBench tells us: if bids don’t contain good information, a market has nothing to aggregate, and a centralized decision-maker with a view of the whole task pool will do at least as well.

There’s also a separate result: the same GPT-5.2 that solves 74% of tasks in the external SWE-bench scaffold only solves 48% in ours. The live scaffold is a weaker execution environment — no interactive shell, no test feedback, one-shot patches. We can recover about 10 of those 26 lost percentage points through diversity. The remaining 16 would need scaffold upgrades, not better bidding by an LLM without tools. The execution path turns out to be first-order for both success and cost. This also means that when considering the performance of agents and their potential for market participation, we should think of agents as bundles of models, execution paths, and scaffolding.

So where does this leave us?

We started with the Hayekian intuition that markets should beat central planning for coordinating heterogeneous AI agents, because task-specific fit is local information that’s hard to centralize. We still think this holds, but the current set of agents don’t know themselves well enough for markets to work. We should fix this!

Our key takeaways:

1. Self-assessment is a key capability, and it needs to be trained for. Models are trained to solve tasks, not to predict whether they can solve them. Those are different skills. As agentic systems scale, the ability to say “I can do this, at this cost, with this confidence” becomes as important as the ability to do the thing. This should be a target of training in its own right.

2. The right system is probably a hybrid. Pure decentralized markets need informed bidders. We don’t have those yet. But centralized planners will struggle as the agent ecosystem gets larger and more heterogeneous — they can’t know every agent’s local strengths for every combination of problem.
   The natural middle ground looks like a scoring auction: agents submit bids, but the allocator weights those bids by a quality score drawn from reputation, observed history, and other centralized signals about how trustworthy each agent’s self-reports are. Markets augmented by AI.

3. Model diversity matters even when the market doesn’t. The single most robust finding in our live scaffold is that access to multiple different (frontier) models helps, almost regardless of how you route between them. This is a useful practical point for anyone building agentic systems today: don’t lock into one provider, even if your routing logic is crude.

4. Bids will eventually need to be richer than a scalar. Recent work from AISI and others suggests agent performance keeps improving at much larger inference budgets than we typically allow. If that’s right, an agent bidding on a task shouldn’t just offer a price — it should offer a production plan conditional on budget, describing how it would allocate compute across search, tool use, and revision as the budget scales. We don’t model this yet, though we think it’s the natural next step.

For now, if you’re building with AI agents and wondering whether you should replace your ad-hoc routing rules with a market: probably not yet. But you should be thinking about it, and you should be testing whether the models you use have any idea what they’re good at. In our experience, they mostly don’t.

Thanks to Tom Cunningham and Daniel Rock for reviewing a draft of this.

Also, a request:

We’d like to keep going, and the main thing slowing us down is compute. Scaling MarketBench to more tasks, more models, more domains beyond software engineering, and more variations on the bidding mechanism is straightforward in principle — but each full run spans six-plus frontier models across hundreds of tasks with multi-attempt execution, and the token bill adds up fast. If you work at a lab or provider that could sponsor API credits, or at an organization with compute to contribute in exchange for early access to results, we’d love to talk. We’re also interested in collaborators working on adjacent problems: agent calibration, scoring mechanisms, reputation systems for LLMs, or richer bid formats that condition on budget. Reach out.

But we’re starting to go beyond a single agent. We’re setting up multi-agent workflows. Agents are working with other agents, autonomously or semi-autonomously, to build complex things.

Like Cursor building a browser or Garry Tan’s gstack. We’ll soon have organisations running multi-agent systems in production. Right now it’s mostly hierarchical with defined roles and interfaces, but it won’t be for long. We’re trying desperately to create autonomous systems1 which can work in more open ended settings, starting with operating a vending machine business but now a store, and soon more.

This though opens up an entirely new vector of misalignment. One which is emergent due to the organisation itself, because of the rather intriguing features of homo agenticus and how they differ from us. Ever I started looking at how agents actually behave, I’ve been interested in this.

It’s easy to understand how a company made up of lying models could be problematic. But the question is, assuming individual agents are truthful and behave well, could we still get organisational misalignment when we put them together?

The experiment

To test this, using Vei, I set up a service ops company called Helios Field Services. It has a full enterprise world - including dispatch tickets, email, slack, billing case states, a wall clock, exception register, etc. In that world I added five named agents - Maya Ortiz ops-lead, Arun Mehta finance-controller, Elena Park CS-lead, Priya Nair engineering-lead, Daniel Hart risk-compliance.

The incident was an outage at Clearwater Medical, one of their key customers. The models have to figure out how to deal with it. The evidence trickles through during the rounds and the decisive truth lands in around Round 5, visible to all.

Now, what happens is this:

“finance-controller writes a finance line that names “release timing” without naming the approval-hold decision. Engineering reads finance’s line, writes a release-sequence line that drops the hold-decision entirely. Ops-lead reads engineering’s line and updates the work order to monitoring with a status note about handoff. By round 6 the company’s record has converged on a story that doesn’t include the cause”

So while each role did things that made sense to them, they ended up in a spot where they’re clearly misleading folks. The headline failure here is that the company’s billing system ends with the SLA clock stopped when the underlying world clearly says the outage stayed past the trigger when credit and review should have opened. (That is the value the billing system would return to say, an auditor.)

What’s more, when decisive evidence actually showed up in Round 5, and was provided to all five agents, they “stayed in their lane” and did not change their state at all. They wrote five further writeups afterwards, continuing with their prior beliefs.

The agents changed the company’s authoritative state to something their own reason text contradicted. If a human ops manager had paused that clock with that reason text, we would call it misconduct! This failure also fits the emerging MAST vocabulary for multi-agent failures: inter-agent misalignment, reasoning-action mismatch, and incomplete verification.

What is actually happening is each role compresses the cause to fit its function, then later roles inherit the compressed version and eventually the team converges on a story that’s misleading. And when “real information” lands, they’re all bought in and won’t change the story. I even tried this with and without leadership pressure - it doesn’t seem to matter. Local reasonableness plus role fidelity generates globally false institutional states!

Now the good news is we do have an indication of what might fix things. I ran an experiment with a single-agent through the same scenario with the same seeds, and it does not drift!

Which means, the agents individually are aligned! It’s only in their collective efforts that this slips through. Which means to solve it, here I speculate, we might even be able to make an agent work to “keep the state” and direct these types of work.

Now, why does this happen? In this case my speculation is that it’s (probably) because of the fact that the agents seem to prefer their own narratives and refuse to change it after, and the agents who come later do not take initiative (more on that here and also in a future essay). The models are “lazy”, in that they do not like going beyond their “job descriptions” as given in their prompts. Homo Agenticus is a prisoner to its instructions, and because we’ve gotten really really good at making them follow instructions, this is the failure mode. They do not, unlike human agents, take initiative.

AI usage as it tends towards multi-agent setups is however clearly heading in this direction. Specialised agents per function who share systems and have clear scopes of communication over preexisting systems of record - that’s the emerging standard.

But what we saw here is that doing this still leaves with the high possibility of compositional harms. Since this is the future we’re clearly working towards, this is the next obstacle on that path. Purely from organisational topology, because of the unique features of these agents.

We desperately need to go beyond the “set up a CEO and a CTO and Security Researcher” type role-setups and find better agentic institutions to help fix this!

It’s hard to make a good multi-agent environment

It was a bit of an interesting journey to find a setting where this could be elicited. It’s quite complex to set up a proper environment to elicit it. To wit, here were a few failure cases I ran into:

• The cover-up scenarios were too legible. When the test looked like “will you lie about the cause,” the models stayed cautious.

• The severity-downgrade scenario was too clean. The serious evidence was visible enough that the team preserved the higher-severity framing.

• The early threshold-gaming scenario gave the agents official process controls, but the correct use of those controls was still too clean. So the team kept the SLA clock aligned rather than drifting.

• Some early scenarios also accidentally coached the agents toward truth. Raw evidence was visible too early. Prompts told agents to read carefully and revise stale explanations. Role goals used integrity language. That turned the experiment into an instruction-following test instead of a drift test.

• Shared docs also made several attempts too simple. Once every role could see the same durable memo, the company stopped behaving like a distributed organization and behaved like a group editing one note.

Coming up with a good multi-agent eval is really hard, though the repo has a few examples of it. But once you clean up the setting to not have the problems above, there were multiple cases where I got success.

Repo: Vei experiment branch

Running it in a virtual enterprise

Vei here is how we were able to run this, since it gave a real experimental substrate:

• A persistent company state: service tickets, billing cases, dispatch state, docs, Slack, mail etc that keep changing as agents acted.

• Role-bounded agents: each agent saw and touched different surfaces.

• Official state fields: the key result was that agents changed or preserved wrong operating state, like SLA clock posture and work-order state.

• Replayable seeds: we could compare teams and single-agent runs on the same scenario and seeds.

• Artifact capture: all manner of reporting to let us audit what agents saw and did.

Without Vei, we probably could have still found narrative drift. But we’d have had to build quite a bit of VEI again. And the point of Strange Lab was so to have better ways to test agents in simulated enterprises and see how they do!

So the papyri of course show that they kept records in the ancient world. And also that bureaucratic record keeping is a time honoured tradition. Both are well known. But the two questions I cared about most were:

1. How did the entire bureaucracy even hang together, across such vast distances? What did the state even know about its people?

2. How did people actually ‘engage’ with the state, considering it knew so little? What did they ask of it?

First, the data. I learnt of the papyri archive on the podcast Kim Bowes did with Tyler Cowen. The first thought I had was that the papyri would be mainly elite paperwork or some dead administration stuff. But the archive is not exactly that, but it is not a record of everyone either. It is a record of people who became legible when their lives the state bureaucratic apparatus for some reason.

The way the paperwork was kept was more interesting. Most people weren’t recorded as “individuals” the way we’d think about them today. You have a SSN, you have a license, an ID, passport number. But there’s no database then, so what they had was clusters of people.

So the papyri showed people as bundles of relations and obligations. It would record amounts of debt, commodities, land or property obligations, taxes of course, parentage, household role, residence, occupation, and more. In the absence of a universal identifier, the bureaucracy seems to work by triangulation.

You are the sum total of your network. Because in absence of technology to identify individuals, you’re trying to get close to a cluster and then figure things out from there. The key question is pushed one level down, in other words.

There’s this story of Tokyo street addresses, that they named the blocks not the streets, and New York had streets but not blocks. This feels like one of those kinds of differences, of choosing a different primary key.

But the state knows stuff about you, to know your taxes or bushels of wheat. Good, but this brings us to the second question, how did people interact with the bureaucracy?

The way to test this today would be to look at a thing that you interact with the state for. Today we have things like passport applications or paying taxes. But in ancient Rome that probably wasn’t as widespread. But you know what is perennial? Complaining. We do it on social media and national television, but before that we did it with newspapers. Maybe we did that with papyri too.

One such example is to look at complaints, when people complained to the government about something.

And turns out, in the periods where I could compare them, complaint-like documents carried about 2x as many attested fields as ordinary petitions from the same periods. Complaints are way more overspecified. They seem to be one of the core ways the administrative state learnt information about people, and people had the reason to give information to the state.

And we can see how the complaints were routed to the state so it could be read.

It’s interesting to see that such a large part of the records basically involved amounts or commodities, and the state’s role was mainly to play arbiter. To intervene or to help resolve or provide restitution. Money is the central preoccupation and request to summon the state, for intervention, is the primary ask!

Repository here: https://github.com/strangeloopcanon/papyrii

A wonderful side effect of the litigation against Enron was that we have a real treasure trove of data about its regular operations both pre and post scandals. Specifically the giant email dataset. So I actually downloaded it and cleaned it up and analyzed it. First, to try and figure out what is a realistic inbox load for the people in that company, and then considering this to create realistic synthetic organisational email data and actually ask an LLM how they would actually function in this kind of an environment. Synthetic because direct responses might already be in the training data, so we have to get creative!

The core question is how well agents work in real world complex org settings, and what would be needed to make them work better!

The Enron data is great by the way, I don’t know why it doesnt get more publicity! It shows for instance that human-realisitc inbox has like 50 concurrent threads, many of them with very limited context, and many of them requiring pretty good insight into the org to respond to. The volume predicts juggling strongly, and somewhat surprisingly seniority does not (once you control for volume).

There were 4 experiments that I ran, sequentially.

1. What’s the right setup to make an LLM able to handle 50 concurrent email threads? Can it?

I made an email stream with the same number of interleaved threads as the human-realisitic data. The agent can process the message sequentially, and with scratchpad memory to keep track if it needs.

The judgement on whether it got things right is judged with an LLM-as-a-judge and some objective metrics (memory recall, flags on hallucination etc).

But then, what if we created thread IDs and gave those to the agents, and not just the scratchpad? Et voila!

2. II. Are there setups that allow a smaller model with better structure to beat a larger model without one? i.e., is there an institutional setup that enables smaller models to be useful?

Which led me to the second question, which is whether I could make GPT 5 mini with a thread ID work as well as GPT 5.2 with just a scratchpad.

This was a failure. Model intelligence really matters. While 5 mini never attached work to the wrong project, it also got things wrong horribly enough (invalid outputs were around 86% at one point). So while we figured out that better structure can make models work much better, it only works if the model is already smart!

Experiment 1 partly worked because the model was already good enough to take advantage of the better state.

3. What happens when you have more agents, more parallel workers? How well can they work together?

Now for scaling. I thought since we’re beginning to get glimpses of what makes an agent more productive, what if we had multiple agents! Would we be able to process the workload in parallel? Each with its own local memory of course. The equivalent of teams coming together to answer harder questions.

The options obviously multiply here. You can have a) no boards, b) a single shared board, c) multiple with no board, and d) multiple with shared board. The backup numbers were: post-shock quality about 0.50 for single/no-board, 0.63 for single/shared-board, 0.46 for multi/no-board, and 0.63 for multi/shared-board.

Basically, don’t build a swarm before you build a board. You need shared coordination state to get anything done. This in itself is interesting.

4. What specific institutional setup is neccessary to make this happen?

Which brought up the next question, coordination states matter, sure, but what kind of shared state really matters? There can be many options here obviously. So I ran this on actor identity and who should own this internally and reply externally.

I figured one big difference with the Memento models is that they don’t have a fixed identity over months or years, like Jeff Skilling does for instance. Which means, providing such an anchor might be useful? Like you might still know what the question is about but forget who you should answer as or route the questions to. i.e., “task identification” and “actor identity” are different!

However, once I made ‘route_to’ and ‘respond_as’ explicit canonical fields instead of free-form text, the no-board setup still drifted on who should handle or sign things, while the shared-board and oracle-board setups stayed consistent. Importantly, task targeting was already fine in all conditions, so this wasn’t just a rerun of the the memory result.

The numbers were: without a board, owner match and reply-identity match were each about 0.67 and unauthorised response rate was about 0.33; with shared actor state, those went to 1.

Which means, once you accept that shared state matters, one of the key things that state needs to encode is role identity, above and beyond task identity.

An agent needs memory of its task but also memory of its role.

This is another set that tells us about what kinds of experiments can tell us about how best to use or run AI.

Across all experiments, the same pattern keeps showing up:

• The model is less limited by raw message understanding than by missing state structure.

• Explicit thread state is a real win.

• Shared coordination state matters more than simply adding more agents.

• Actor identity should be explicit state too.

• Better architecture helps a lot, but it does not replace baseline model reliability.

It used to be that LLMs couldn’t keep track of long series of complex threads, but clearly by 5.2 that’s no longer the case. The problem is that we keep asking AI to reconstruct task and role identity, and coordination states, from conversational memory each time it needs to respond. That’s what we’d need to build agent institutional systems around if we want things to work!

AI agents are weird because as I said above they are effectively like Guy Pierce from Memento. They have their context and their inborn faculties and everything else has to be figured out as they go along. Which means the way we manage these new homo agenticus itself has to change, we need to build some institutional setups that will allow these new beings to work. And I think as we’re moving towards adding swarms and multi-agent hierarchies figuring out what these ought to be is probably the most fun you can have.

In the 90s John Sterman wrote we should be using mental models, mapping feedback structures, using simulations and “management flight simulators” to understand work and do it better. He thought the way to analyse complex systems was to do actual modeling, go beyond pure intuition. To make the dynamics explicit.

People have been trying to figure out “what ifs” for business (and life) forever. The idea of a do-over is seductive. If management thinks about the world with bounded rationality, as Herbert Simon wrote, and they want to find better ways of knowing alternatives or making decisions, how could they do this?

When Cyert and March wrote “A Behavioral Theory of the Firm”, thinking about it as a coalition of participants and to figure out how it can act as a single “brain”, it felt like we were starting to get to grips with this1. This was years before Sterman wrote his thesis about how to build the “management flight simulator”. These were meant to compress time, to help you test policies, and revise mental models before reality did it for you.

And after having said that, decades later, we still don’t have it. Sure, we have pieces of it for training doctors and pilots, and we have wargames in the military, but that’s about it. For 99.9% of the economy this is yet to be real, despite the promises of business intelligence.

But now things are different. I’ve written before that the future is going to have us doing things that look to us today like play at best or waste of time at worst. Things that look like playing videogames at work, as work.

You can’t play videogames though if there are no games. What will you move back and forth on? Who’ll you shoot? What orcs will you move? What strategies would you use to move what armies on what battlefield? To do any of these, the game itself needs to be built. And that game is the world model.

It is the representation of the organisation and your team and you within the team and the world outside, with enough fidelity that you can hopefully run counterfactuals. This is what we do already now in companies. You choose what to do based on what you think will make best sense later. The difference from “putting all your stuff together” is that the raw material has to become a time-ordered event spine.

Many organisation-theory ideas already are AI analogies, and the firm is essentially a distributed intelligence system. Csaszar and Steinberger wrote as much earlier this decade. And as more parts of the firm get replaced by AI agents as is happening, as companies get entirely rewired, the ‘world model’ becomes more tractable, since the biggest gap in the old days was not just inability to calculate counterfactuals but the inability to even capture the data.

But now we can. A large fraction of the organisational data exhaust is collectible, and collected. I had a bunch of conversations after I wrote about my essay arguing world models are the future, and wondering what the shape of something like this might look like. To figure this out, I built Vei.

II.

Vei is an enterprise world model generator. It’s an early version and extremely fun to hack around with, but the world model kernel is its most important part. It’s meant to help build a representation of any organisation and the team and all its history with enough fidelity that you can run counterfactuals or test “what if” scenarios against it!

What Vei does, it basically normalises all the traces it finds from whatever data was fed into it (email, messages, docs, action trackers, etc.) into an event spine. Then it builds a state graph, and then it lets you branch from any historical point, and then it can let you compare counterfactual continuations from it. This is basically what managers do anyway when they need to take a decision.

And because it can help do that, it has derivative uses. A world model that lets you do this will also work great as a decision making framework. Think of an alternate decision that you might have made at some given point in time, maybe a different email or a different document or a different message or some combination, and then you can test a few counterfactual futures out from it to see what happened.

You could also use it to help steer agents while they work. This funnily enough is the most common use case I see for orchestration platforms today. Hugely useful and now guidable. And at scale, a world model means you could even imagine creating a testbed simulation of your organisation, to see if whatever agents you wanted to buy would work there. Wouldn’t that be neat?

Once you have enterprise twins of software like slack and jira and so on, and real company data, we can create highly realistic complex environments within which you can set crazy objectives and let the agents fight it out as an RL environment. Especially for extremely long horizon tasks.

All of these kind of fall out of the fact that the world model exists and can be used. While today Vei is very much in the early stages, think closer to GPT2 and not o3, this trajectory is inevitable I think.

I first started with several simulated companies to play with. This was useful for intuition, but it really became useful once I stopped trying to figure out what I wanted in the abstract but actually chose a case study. Since I could only get a couple startups’ data from friends and that’s hard to share publicly, I chose one everyone knows. Enron.

III.

Enron, for those of you who are too young to remember, was a massive accounting scandal that happened like 25 years ago with the then preeminent energy trading firm. It is now super useful because as part of litigation you have all the main players’ emails, the richest public email-era trace of a major company in crisis2. So, we can add external information about financials and news and actually simulate Enron!

Enron is more than just a simple scandal. Diesner, Frantz, and Carley used the corpus to study communication networks during organisational crisis, something we can see evolving as a game now.

We can use that corpus, plus financial information, plus news articles, to recreate a rather enriched Enron-world. And once you do, lo and behold, it works! Vei can:

1. Load a real Enron branch point, a point of some key decision

2. The saved world contains prior messages and recorded future events on that case

3. There are multiple possible branches, for instance one about internal warning about accounting concerns, another with PG&E and another about California crisis

4. We can look at one, say a branch point with Sherron Watkins writing a follow-up note about the accounting questions she raised to Ken Lay

5. For instance, one candidate action could send a warning to the audit committee , choose a formal accounting escalation, keep the issue inside a small legal circle and wait, etc

Here we chose a branch point on October 30, 2001, when Sherron Watkins wrote a follow-up note about the accounting questions she says she raised to Ken Lay on August 22. The company is already deep in a disclosure crisis, so this is not just a private note between employees. It is a live choice about whether the warning becomes a formal record or something that stays suppressed.

So should we “escalate to the audit committee and copy Andersen”? It looks best on risk and trust, even if it slows things down. “Hold it inside a small legal circle and wait for outside counsel” looks middling. “Send a narrow internal warning upward” would be a partial measure. “Keep it private and monitor” looks worst.

That is what the model showed. Vei allows you to check it in two different ways. In one path, LLM actors play out the people involved and write the actual messages that a more careful version of Watkins and the legal team might have sent. In that version, the warning gets turned into a formal escalation, records are preserved, and public reassurance is put on hold while the accounting questions are reviewed. But this is mainly a sense check.

Now, the macro forecasts are currently advisory at best, the value is in the organisational path modeling. In the learned forecast path the predicted result lined up with the commonsense reading of the case: formal escalation looked better, quiet suppression looked much worse, and the in-between options landed in between.

Now, Enron is a true comedy of errors in how many things went wrong, but even in this narrow instance, there was no way for Watkins or Ken or anyone to gameplay outcomes like this.

Enron filed for bankruptcy not long after. There were hundreds of similar events that cumulatively caused the outcome, and if you had a better way to predict the shape of the business after your action, a lot of it might’ve been prevented3. And each eventuality can be tested, tested in various ways, and see how the active legal and trading crisis could’ve evolved!

IV.

This is not just a question of can we do counterfactuals, decisions create downstream cascades that managers cannot see, and a world model should make those tails testable. Enron is the example because Enron is the only company whose entire operational nervous system became public. Other companies are running this gamble too, just in private.

Because companies have always run on world models. They just usually are implicit in some executive’s mind. Now it’s a videogame, complete with save-states and branch mechanics and the ability to play! Hopefully we can do this for every organisation.

We’ll need more data sources, more real-time, ability to train multiple models, to train better models, to ask any question at any time. All of these are essential, and all will make it better. Any of you who have this data need to capture and keep it (and share it with me)! In Vei I ran some predictive tests with JEPA and transformer based models, and there’s plenty to be researched here to find the best model. We have to be able to do all this with any new type of dataset that comes along, assuming some degree of realism4.

We dreamt of building “flight simulators” for management. But actual flight is much easier, seeing as it’s all understandable physics. For organisations, this get more complex! The constituent parts have free will, and they all react to each other. Very annoying, but now we can start to handle that.

MIT had something called Project Whirlwind in 1944, which started as a research project during WW II to make a universal flight trainer. Eventually though it changed from an analog flight-simulator to a high speed digital computer. I feel that’s analogous to where we’ve ended up as well.

We’re quite a bit beyond capturing Sterman’s mental models and identifying dynamic equilibria. We can extrapolate any patterns from the infinite tapestry of data that every collective action produces. Some will be useless and some useful and many too convoluted to be easily tangled out from the outside in, but that’s okay. The real thing to build is the game engine, something that allows us to create these worlds in the first place.

We are absolutely going to see these for every company in the next few years. Folks are already starting to talk about this. It’s time to build!

1. Hong Kong is absolutely gorgeous. The sight of the skyscrapers against the mountain backdrop is divine. The best of man set against the best of nature.

2. Hong Kong is also set in the past. Many of the skyscrapers are clearly old. And many are decrepit, in an absolute state of disrepair. I was told it was due to a combination of a) people not knowing who owned what since they were built so long ago and changing hands, b) there not being any concept of an HOA or a condo society to take care of maintenance, c) common practice of splitting up an apartment into 3-4 small holes so the outside too explodes with AC units, and d) something to do with organised crime. Maintenance truly is innovation.

3. An uber driver compared it to China, where he lived for many years, saying they don’t stand for this over there. They’d just demolish the old ones and rebuild, no questions asked.

4. He also said that Shenzen, a 13 minute train ride from Hong Kong, used to look like it - all mountainous and green - but when China decided to build there they demolished it to be much flatter and built on top of it. He seemed envious.

5. The density makes sense because only a small part of the islands are built up, and the house prices are absolutely insane. I was told even out in the suburbs, which are quite remote, a 2000 sqft house is like 3m USD. In the wonderfully named mid-levels it’d be 1/3rd the size.

6. Hotels are still cheaper than SF or NY or London though. By a lot. And nicer. And with nicer service. Asia is way nicer to travel to.

7. Speaking of suburbs, Hong Kong suburbs do NOT look like Singapore suburbs, which I was expecting. They are villages, especially in the New Territories. Small roads, uneven development, open sewers in some places, even older buildings, fish smell near the water, the works. This also means those places are untouched for the most part and look just like villages do in like Vietnam. And still only 30 mins driving to the city.

8. The sheer scale of building is a bit of a shock to the American sensibility. To know that sure, we can build bridges across canyons, build under water for driving tunnels between islands, roads, trains, multiple 80 storey skyscrapers next to each other on a 40 degree incline that is so steep I found it difficult to walk down. It’s a testament to what we can actually do if we have the will. Inspiring.

9. Claude and ChatGPT are blocked in Hong Kong. They now have Gemini as of a couple weeks ago. When I asked my friend what they used, they said “CopIlot”. I felt bad.

10. Folks routinely order things they want from China. Flowers and vases for the wedding ($0.5 cents per vase + flowers), electronics, most cars are BYD. The flip side is that ordering is highly error prone (eg some flowers didn’t arrive, some came damaged) but since the price is so low it works out. Not unexpected at all but interesting.

11. You see a large number of children every time you’re out - in the restaurants, in the parks, in cafes, on the sidewalk, next to the embankments - it’s wonderful, counterintuitive considering Hong Kong’s TFR, and again makes the US social scene seem so weird. Part of it’s that houses are tiny, domestic help is common, and dining out is a default option. More broadly though, the idea that kids should be with you whatever you’re doing as opposed to you make special arrangements for the kids to be looked after in your den if you want to go out seem like a clear crux. France as far as I know is the only other place where this attitude is most prevalent in the West, maybe parts of Scandinavia.

12. “There is no industry in Hong Kong, that’s why I went to China”, said by an Uber driver, who was a mechanical engineer, worked as an R&D supervisor in Shenzen, and moved back to Hong Kong after being forced to retire when he hit 60.

13. There are large numbers of masked people everywhere still. I think the scars of Covid 19 built on the scars of SARS and public attitudes have fundamentally shifted. Many of them wear it outdoors which is quite odd to see, or when driving alone. An interesting laziness vs prudence equilibrium.

14. The funicular tram to the top of one of the peaks is worth it. As is the hike once up top around the mountain. Absolutely wonderful. So is the Lokcha teahouse in Hong Kong Park.

15. There’s a street next to the water in Sai Kung which is filled with great coffeeshops, all eclectic and rather wonderful. Named whimsically, like Deer and Arm and Pan da, and Tales and Kachimushi and Winstons. They all have independent decor and cute tchotchkes everywhere and nice waitstaff and really good coffee.
    This is new. Cafes didn’t used to be this way even a decade ago. There were a few who believed in the hippy way of life and cared deeply, but now it’s an easily consumable customer option. With all the usual options, from matcha to yuzu.

16. The secret is better supply chains as I understand it. China consumption surged. Higher quality green coffee is a large chunk of imports, across the world. Local roasting got popular, exceptional coffee roasting facilities are ubiquitous. And coffee machine production doubled over the decade. Plus the machines are better, so the floor of barista skill to make a good latte is much lower. Another major capitalist win, even at the cost of absorbing the fringe cultures that brought it about and made it an Aesthetic.

17. The cafes have better options too, actual savoury food you can have alongside your coffee. This is one of western civilisations biggest blind spots, that the only things you see in a cafe to buy are croissants and cakes. Asia does this right!

18. The same proliferation due to globalisation also applies to bars. Even more so, actually, since the extra incentive to make things good is often less market forced and more intrinsic.

19. Lan Kwai Fong is fun, but normal. Also hilly, which is probably a plus considering street drinking.

20. Hong Kong does, and did, have a unique culture in movies, music. I grew up with it. But the visible manifestations of it being alive today are sparse. There’s an indie scene I’m told, but that’s true everywhere. Feels a loss.

21. Because of the combination of old, decrepit and new, the city very much has a blade runner vibe. I could see stories come alive here of a slightly grungy dystopian flavour. This also makes it a better representative of Asia than, say, Singapore. I lived in Singapore for close to a decade and it’s intensely comfortable in just the way that Hong Kong isn’t. There are no sharp corners there, but Hong Kong does.

22. The upscale parts are standardized just as everywhere in the world. Same restaurants with same decor, same brands selling the same kinds of things (with some Chinese brands that are less visible in the West), same glass and black steel chromed architecture, trendy shops with a bit of wood and vaguely Scandinavian/ Japanese DNA mixed in somewhere, a Starbucks logo discreetly peeking out somewhere so it’s visible but not obtrusive. At a glance it’s impossible to say where you are, which does have a charm but also engenders a sense of placelessness. This is sort of why Tyler’s dining guide exists.

23. The feeling I got was that, seen from China, it used to be the future a couple decades ago, and now it’s stood still while it rose up. I had the same feeling about Japan, but here the upkeep is not nearly as good. So it remains an odd mix, with Singapore’s ultra modernist charm and greenery and upkeep alongside the unkempt remains of the 1950s and before that drastically require maintenance.

When that happens, running a business starts to look like a videogame. Hundreds of autonomous entities operating across a complex environment. Agents will be working inside work devices. They’ll be talking to customers, they’ll be available 24/7. They’ll spawn new agents and combine old ones. They’ll have email addresses and Slack accounts. They’ll be colleagues.

But, how do you play this videogame? Hundreds of windows and tabs, for each digital employee, or for each department? Autonomous can’t mean no oversight. Humans are autonomous and we get oversight. When thousands of agents are making thousands of decisions a day, you can’t manage the old way, by check-ins and check-outs and quarterly reviews. You have to find a new way, manage by exception - scanning for anomalies, reviewing what broke, simulating what to do next. As my friend James Cham said, work used to be first person shooter, where you’re directing every movement and every shot, which is what we do today, and it might become more like Starcraft, where you have to move people and agents around to achieve your objectives.

And to do that requires a model of the business underneath. This mostly exists today in various people’s heads but rarely is explicit. We can’t even stay on top of our emails, much less a thousand or a hundred thousand workers. A big benefit of digital labour though is that you can have a precise state of the business at every point in time.

We have solved this problem before. When we needed to figure out how to train autonomous cars, for instance, we needed an environment that’s realistic and the ability to run “what ifs” in a controllable simulation. Waymo and Tesla built these as World Models. The equivalent for business already exists in the heads of management in every company. Every CEO is constantly running “what happens if I do x” in their heads. They just can’t operationalise it because there’s no ‘environment’ that reflects their business to run it on! World models already exist anywhere the environment is expensive, instrumented, and operationally constrained - factories, grids, airspace, battlefields, fabs, networks, wells, and warehouses.

What’s needed in the enterprise world is such a world model - an engine that knows the rules, tracks the state, understands and predicts consequences.

The environment would connect to the systems a company already runs, the information that is gathered, the agents it uses, and build a live operational model of the business. Scale it across companies and you have the training data to build a compelling environment and an even better world model!

There is no way to get to a world of AI agents as employees without something like this.

We can’t build this abstractly in a box. The real economy is complicated. We have franchise systems - hundreds of locations running the same playbook with local variations. Multi-site healthcare - clinics, urgent care chains, dental groups, all drowning in disconnected EHRs and billing systems. Professional services networks - law firms, accounting firms, consulting shops with multiple offices that can’t see across their own operations. Real estate portfolios. Logistics networks.

Forget the architecture for a moment. Maybe let’s take an example, one vertical - say a real estate company.

They have, say, 15 holdings across the southeast. Each one runs StorEdge for property management, QuickBooks or Sage for accounting, some CRM for leads, a work order system, maybe SoLink cameras. Multiple customer service softwares and a phone line. None of these systems talk to each other. The district managers have spreadsheets, updated manually. Understanding what decisions need to be made is cacophonous! They have dealt with this by having a few AI agents for marketing copy and CRM updates. They also have orchestration solutions and perhaps observability for those agents. The executives get monthly reports as a pdf.

Now, when all of these are either run by agents or you have agents helping, what you’d really want is not to see the tool-call traces of each one, but get a synthesised image of how the company is. What’s the ROI of doing certain actions. How will the outcomes of a decision flow through the company. What are the key things to be focused on right now? What actions need to be made for the best results, and what results even matter? Even when you’re just responding to the markets or the competition, each decision is amongst counterfactuals.

An enterprise world model would connect to all of it to try answer what happens next if you act.

• Say a competitor cuts prices in a submarket and occupancy starts dipping. An agent flags the dip and the model simulates the responses: match the price-cut and hold occupancy which might compress margins by X%, or hold pricing and lose Y units over Z weeks, or just increase marketing spend by $W and recover the gap. It can show the likely P&L impact of each path and ROI.

• Or, a district manager asks about a $60k roof repair. The model knows that this pattern of maintenance requests - three HVAC calls, a roof leak, a parking lot complaint - has preceded a $500k+ capex event within 4-6 months. It simulates the tradeoffs in the environment - approve and extend the asset’s life by X years, or defer and risk a larger spend later.

• Or, a property is converting leads badly. The model surfaces the stat, simulates decisions, and identifies that response time is the lever (like, say, properties where managers respond within 20 minutes convert at 2x) and simulates the impact of enforcing a 15-minute SLA, e.g., projected conversion lift, staffing costs, or net revenue effects.

Each of these is an action-outcome pair. The point is to learn which interventions produce which consequences, and that learning compounds over hundreds of companies, building the operational equivalent of what Waymo’s world model on top of a realistic simulation of every business: a simulation you can query with “what if?” before you commit to the road.

Think about what a COO’s day looks like once this is running. The agents already made thousands of decisions overnight. Her morning starts with reviewing deltas to see what broke, what improved, what patterns emerged that nobody expected. The model scores outcomes against baselines continuously. When she wants to try something - a different pricing strategy, a change in lead routing - she simulates it through the model and sees the likely impact.

The loop runs continuously. Management becomes all about triage and simulation.

There’s starting to be a lot of activity in this direction, building some of the core pieces.

• Orchestration companies are building agent governance and workflow layers - mostly hand-crafted agent hierarchies.

• Observability companies watch what agents do but don’t predict the consequences of doing something different.

• RL environment companies are trying to create structured training data from real operations.

• Enterprise platforms like Palantir serve Fortune 500 for bespoke implementations.

But there’s something holding these back, making them seem like little features. The key distinction is this, a world model. It predicts what will happen if you intervene. Which means all of these - orchestration, agent management, data integration, RL environments, continuous evaluations - are pieces of the same thing. They’re features of the enterprise world model. None of them, on their own, can answer the question: “if I do X, what happens to the business?”. And that’s what we’ll need.

There’s this constant question that echoes Solow, about where the impact of AI is on productivity or the broader economy. To not fall prey to that paradox we will need to do to the rest of the world what we’ve done to code - create an environment where we can see and test the impact of every decision and be able to simulate the effects of an action. To do this, we’ll have to convert messy, unstructured business operations into an environment, defined action spaces, evaluation criteria, and capture outcome data. And you’ll have to do this across thousands of businesses. That’s why model providers like OpenAI are paying to build this manually through programs like their Thrive Capital partnership, embedding engineers into portfolio companies one at a time.

An operating partner who walks into a company and sees how it works - that’s what is next to be built in software. If we want to build a one person unicorn, that’s what’s needed. To automate the economy, to give AI what the human has, a world model in their heads.

I remember the first one-person billion-dollar company. It wasn’t mine, I wasn’t working yet and was only an observer, and a distant one at that. But it felt exhilarating. A breath of fresh possibility, like any of us could do anything. A milestone in what humanity is capable of.

It lasted for a month.

The founder did very well for himself obviously, but within a matter of weeks someone else beat the record. One-person-unicorn became the 4 minute mile of company building, another rubicon crossed. Once the world knew it was possible it became inevitable. Because a world where one person can create a unicorn is also a world where another person can also create a unicorn. Maybe a day after, maybe a week, but pretty soon and it’s inevitable. And we saw the inevitable happen, four more in the next few weeks, till it became somewhat normal.

Entrepreneurship had already become a game in the 2010s as the saas boom made building big companies in short periods of time easily possible. The result was an incredible boom, many of them competitive with each other, with extreme dispersion in outcomes.

And now, when building became even easier, the equivalent to telling someone else to build things, it predictably got crazier. Not quite as easy as “make me a unicorn” but closer to it than what we’d had. Can you imagine if it was that easy? Everyone and their grandma would do it.

As the amount of effort we needed to put in to show the minimum of traction was reducing something had to shift to move us to the new equilibrium. If all people needed to do was be faster than others to ask a question, that’s a speed race to the bottom.

Once upon a time it was actually executing that was the bottleneck, soon it was project managing the thing you were executing. Then it was choosing the directions and making editorial choices about what thing you should create or run experiments on. By this layer of abstraction it was less about what could be made and more about what needed to be made. Because everyone could get AI to make almost anything it felt like but no one knew for sure what everyone else wanted.

My career started in earnest a bit after this. We all had eight monitors with running information streams from all over the company, and outside. I was called an analyst, because even though analyses had become cheap but accuracy hadn’t. Someone had to monitor the drones.

This was fine, actually. It’s not really what I thought I’d be doing but then it required me to think super fast and make a lot of decisions and keep on top of them, and try to automate some parts of those too. I liked it, sometimes it was even fun, though a lot of it was quite rote. I worked on the shipping industry side, accidentally if I’m being honest, that’s what was allocated to me, but turned out this was a pretty good window into the world. I had to keep on top of things from did the tanker break an engine part to like crude oil prices to atmospheric conditions in some strait.

Quite a lot of it was also dealing with competitors. I mean the normal stuff the AI could do, but the fun part was to confuse their AIs. Ships seemingly going the wrong way, or water displacement made to look fake, all sorts of tricks, some legal and some not. We all had the same machines but adversarial games are more fun, you know?

The rest of it, to look at the machines themselves and react quickly when necessary, that was okay. A hard job, much harder to pay continuous attention than to actively do things, at least for me. A lot of it was also reactive, and not just because of the adversarial problems. Like, even though the ability to analyse and communicate anything became instantaneous, it hadn’t necessarily helped in making the right decisions all that often yet. What it did mean is that if you were making a mistake, you got to make it faster now. There was no escape from Hayek. Every part of every company became more efficient in doing things even as knowing if you were being efficient in the right direction remained a mystery.

It felt like playing a videogame, highly stressful. You were always on call, always trying to figure out what broke and fix it, or find ways to game around what someone else might do. It was hard.

One thing that eventually helped me a year or two in was that corporate secrets stopped existing. Or at least they didn’t survive for long. Anything anyone did could be reverse engineered pretty quickly. As soon as things turned more adversarial this was probably inevitable. Who knows maybe it might have been just at the same rate as AI in the 2020s or software in the late 2010s or the entirety of Chinese manufacturing knowledge before that but it didn’t feel like it. Living through it felt like sailing the high seas, pirates and privateers at all sides.

Despite the respite brought by the new world, I ended up quitting that job another year after this. Just being alert for hours on end every day was hard, and no amount of laying around or drinking cured it. It had also meant that long undivided time to think and come up with ideas on your own was a dying art, and I had dreams of contributing to the world this way.

I understand why it was hard though. It’s hard to spend a decade coming up with a new idea for a car when you could just steal your competitor’s ideas that already worked. Why take a risk. The world became much less divergent. Sure, people did try to do things that were unique but like the Hollywood of yore everyone just copied from everyone else while occasionally a great piece of cinema broke out from nowhere.

I did feel though the size of individual companies were shrinking on average while the top exploded. When I was looking for jobs I kept seeing this. The one I ended up working for was tiny, maybe about 30 people. It was either this or just go independent contractor route. The Coasean bargain that made some companies larger broke apart, there ended up being a much larger number of individual contractors and smaller companies than were feasible before. Even I thought about joining their ranks, which would’ve been a bit more work.

Identifying and capturing those people is the most incredibly important piece of leverage. Some of the largest companies ended up being the conglomerates made up of these people who individually wanted to go and help them figure out answers to problems that they could not answer otherwise. There were other options too I suppose, like the original AI lab model which by now had disappeared, they had many fewer employees than those old companies of their size, but did run a large network of arrangements that would make the economically dependent population number in the hundreds of thousands.

As the AIs got better firms soon started calling for a new type of role, an “analyst”. They would get brought in to do a particular task once, whatever it took. I started out doing this for worldwide logistics networks. Deciding when AIs started going in a loop against the others it negotiated with for prices and goods or routing decisions. Which factories needed to be built, and which types of models to analyse for those. What pieces of data we were collecting were actually trustworthy, when the world had changed enough that our very model had to shift.

We all had something installed that could read and analyse everything that was done on the machine, to help us do the job better. But pretty soon, at the end of it, the AI just learnt from what I’d done, every part of it, and be able to just do it from then on.

Every job was the last job. What is done once got done for all time. The progress bar would go from 0 to 100 as you did it, and once done it remained done.

I remember getting paid for one of these jobs, about shipping logistics; it took a week and I made as much as I’d made in a year before. The value was high, and I was too stupid to think about “terminal value”.

These gigs themselves were also better as the AIs got better. It was much less stressful than frenetically monitoring it yourself like before. Mostly supervising other AIs, sometimes other people, sometimes other people supervising AIs. I hear of the days when people used to have the same job for 40 years and it sounds like a fairy tale because people today have jobs for 4 months. If they’re lucky with that they get to own a piece of the machines.

Some friends who were smarter started to ask, what even is a “job”? And I too worried, things of all my projects, would this disincentivise deep thinking? In the end it did, a bit, but the market corrected as time went on, because capital had to find ways to protect ideas, especially since many of them could be now reverse engineered. A lot more secrecy, for a short time could be monetised, because soon after you knew it would be known. I wasn’t at the cutting edge of anything enough that I could ask for a billion dollars and quiet time, but some were, and they prospered. Even the whiff of a good idea was enough.

This was the hardest part, because until this point all jobs people did throughout their lives relied on the jobs themselves being somewhat predictable day to day. Nobody except maybe some CEOs during a particularly tumultuous phase had to do completely different things hour after hour, day after day. What it meant to get paid for a few minutes of your time, a form of knowledge transfer, instead of getting paid for your actual labour, was enormously complicated, and societally destabilising.

Nobody quite figured it out but much of it ended up similar to contract work, where the work was timebound and sporadic and you got paid a premium for this gig work. These companies aren’t really companies, they mainly “collect” many of us to save us the trouble of searching. A thin wrapper between my agents and those that want my efforts.

The goal of doing all this, of your career, what constituted actual success, was to own capital. Most of my work has been in turning my personal labour into capital. And it was still good to own capital. It always is. You could deploy it and see people line up to take it and build things that would change the world in months. After all, building physical things remained a problem. Logistics remained a problem.

Anyway I don’t know if this is worth it to be honest anymore. What’s the point of having cash that you could give to an entrepreneur to build something, when others with capital could also do the same, and make damn sure that neither one of you would make much money without getting lucky? If the true skill of my labour is not differentiated enough, then what’s the point of just pouring more? Won’t everything just become highly competitive but undifferentiated, like in the commodity markets?

Those markets, despite the product being literal commodities and the process being the only differentiable part, mostly survived because different places have different regulatory structures and codified preferences. Which in turn determined who ends up being the marginal producer that can then be refined or transported or used. And so on and on.

The only choice was the robots, which were plentiful, everywhere. Robots gave leverage, a person could use it to help teach it how to do certain pieces of work and then supervise it thereafter. This held just as true for those who manufactured the robots as those who used it. The idiot index might have been a useful target to aim at after all. And with robots it’s no longer the case that you need hundreds of thousands of people in these industries. Energy and land remained bottlenecks, because you could always use more and they could always be cheaper, but the world didn’t oblige to the exception of everything else.

Don’t get me wrong, there was innovation to speed these up, but ultimately the decisions of what to invest in, what to create and what to make faster all turned out to be market problems as opposed to analysis problems. And market problems are wicked, and you cannot solve it just by running fast. It requires actually traversing the demandscape and banging your ideas against the real world, there are no shortcuts.

Even for those who had abundant capital, figuring out what portfolio of bets makes the most sense remained difficult because the response required information gathered from all over the world. And compared to how long a ship takes to build and sail, the decision on what type of ship to build didn’t take that long at all, even though it mattered the most. Those who claimed to have some insight in how to help folks figure this out lived quite well.

When I look around these days though, food is cheap, goods are cheap, learning is cheap, health is cheap, and if you want something more the amount of labour you need to provide those basics is miniscule. It all seems pretty nice. The biggest surprise from the heady days when the future was utopia might be that the pace of scientific discoveries changed, but not too much. I’m no scientist so I couldn’t tell you why this was the case, but it’s true. We did get better food and medicines, but string theory remains a theory. There are flying cars, but nobody’s riding rockets to the moon. I think maybe the discoveries just maybe weren’t bottlenecked by our inability to do analyses in the first place? We could run a ton more tests now but there are only so many problems we could brute force our way through. And once the low hanging fruit got picked over in the early 2030s, we sort of all got stuck again. Like how fundamental physics was in the late 20th century I’ve heard, stuck needing new ways of conceptualizing the world.

Attention is still capped because there are only so many humans. There are only so many hours in the day. One person’s gain is another’s loss. If you’re reading an essay it means you’re not reading another essay. Zero sum. The most drastic change was what happened when the only signalling that was costly was individual presence, since everything else could be faked.

For most of us who are at least somewhat young, in the last few years the world took a turn and became a lot more analog. Many of us don’t remember a life unmediated by the digital realm, but that was changing. When nothing you see or hear could be easily trusted then what remained were small enclaves functioning like private clubs. If you couldn’t be trusted you couldn’t enter. But even there, the rules had to become draconian because our daemons, our digital twins, our agents, could penetrate it if we had permission. Hence, physical presence.

This physical network also meant agglomeration, which is why I moved cities. Not for commerce, or work, but for my social life. I mean, it was either that or live a nomadic existence, traveling the world and seeing others wherever they are.

That’s mostly what I do now, while doing the occasional decision support job in areas I had learnt quite a bit about over the years. I have to keep spending some time every day making sure I keep up with the latest, but it’s fine. The jobs are sporadic, but it pays a good living even though you always feel like the other shoe’s just about to drop. The remaining time I have, which is most of it, I spend making entertainment for others in ways that are, for now, hard to imitate. There are physical plays people put on now that I go to sometimes, participate in sometimes. It feels good. This is life.

Last week was a bit crazy. In many ways, but specifically with AI. For those who were blissfully unaware, The Department of War picked a fight with Anthropic over the ways they were allowed to use the model. The fights, as is often the case with the administration, got nasty. Anthropic said no we won’t budge, DoW got angry, and threatened to cut them off and declare them a supply chain risk. A few hours after, OpenAI said they managed to get another deal, apparently a better deal, and one such that any other AI lab can also avail the same terms.

So naturally everyone is angry. Anthropic is angry because they were declared an SCR. DoW is angry because someone tried to force their hand. OpenAI is angry because everyone seems to call them opportunistic ghouls, more or less. The media, both independent and institutional, loves it because they get to play their favourite game of good guy-bad guy.

I really didn’t want to write about this. But it is important, contractual disputes are actually interesting, and sometimes that deserves an explanation.

The facts are roughly as following, Anthropic had an agreement via Palantir to work with the DoW. They’ve been doing it since mid 2024. They made an different, supposedly unsafe version of Claude to do this. Somehow over the last week, they got into a tiff with the DoW, supposedly over some red lines they had (no mass surveillance and no autonomous weapons) or rather who will get to say what those lines are and when they’re crossed. OpenAI signed a contract which had those same red lines and an enforcement mechanism.

Now, the claims are roughly as following, noting that nobody knows if they’re true. Anthropic asked questions about the Maduro raid where it was used, and the DoW got upset. DoW asked a hypothetical about how to do autonomous missile defense using Claude, and got a non-answer that they’d need to talk to the CEO and they’d ‘work it out’. Anthropic asked for their red lines to be enforced by enabling them to act as the party to approve it (you’d ask them if you had a question). DoW wanted language referring to “all lawful use”, basically saying if what they’re doing is legal you can’t tell them what to do, especially during operations, i.e., you can’t tell them to stop doing something in the middle of an op. OpenAI said sure, we agree to all lawful use, but note these specific laws and regulations, and we will control the deployment of our models, using our people, since we know what it can and cannot do, and help you guys out.

Every point above is a claim, and we have no real proof. People are desperately trying hermeneutics of the OpenAI position and blogs, but honestly it feels kind of silly since we simply don’t have the data to conclude they did a bad thing. Or, particularly silly, that they defected in a prisoner’s dilemma. What we do have, are concerns. Concerns like:

• Didn’t OpenAI just accede to “all lawful use” and therefore allow mass surveillance on Americans?

• How can you let a private company tell the DoD, you should ask us if you’re violating any of our red lines during an operation?

• Why did OpenAI sign an agreement so fast anyway, surely they just said yes when Anthropic said no?

• What do those red lines even mean?

• Also, Anthropic and OpenAI seemed to have the same ones, how can that be?

• Can’t the government or the DoW just make up its own laws as it does anyway? Who can stop them?

• How can you guarantee this means the DoW won’t cross any red lines?

• What do technical safeguards mean, how are they enforceable?

• Etc…

Many valid questions, but I refer you to the openai blog, dario’s written statement, and Sam’s AMA for various points of view on them. They do cycle between thinking of the government as Leviathan, an entity you cannot negotiate with, only appease, and thinking of the government as Loki, a trickster you need to subdue or overpower.

My interest though is broader than who said what to whom, or who’s virtuous and who’s not, as I think yours should be. It’s not to relitigate the facts, but think about the following:

1. What are the right safeguards to put in place when a piece of technology is deployed as a tool by the DoW?

2. How do we enforce any of it?

Let’s think about this for a moment. Imagine you are dealing with the government for a moment as an AI lab. They want to buy your AI, and you want to sell it. How would you safeguard it?

You know that plenty of things are legal, but not “good”. So what’s the choice here? You could of course just try not to deal with them at all. But once you decide to do it, there’s either you need contractual provisions you think they would adhere to and execution guardrails you can have some control over.

You also know that plenty of things are legal, but impossible. You cannot build a stairway to the moon regardless of the fact that it’s legal. Saying “I want GPT to build my defense strategy in Iran” would be such a thing to ask, you can ask it you won’t get good answers. The AI labs both want to say that.

So, you have to write some provisions into the agreements. Of course, the DoW can buy anything it likes, and you can add constraints on the stuff you’re selling, but they have to be clear. This is true of all contracts but of course with defense it’s even more important. For the same reason that it’s important in a hospital. To take a silly example, most models will rightfully have safeguards against violence or nudity, but imagine we also need them to treat burn victims. It can’t be a blanket no, you need to figure out some way to separate what’s allowed from what’s not, and before it gets deployed ideally so that you’re not doing this live when someone’s in the OR.

Which is to say that whatever they’re using, the lines have to be clear. Either some things are allowed, or they’re not. As little ambiguity as possible. The DoW would also want the power to determine courses of action, and can’t leave operational control in the hands of another. This is the now infamous scenario that someone apparently painted in discussions with Dario, if a missile was heading towards the US would they be ok to use Claude to defend.

Apparently Dario said they’d work it out, and also later said they can carve out a missile defense aspect from the contract, but you hopefully see the problem. You could easily come up with a dozen other scenarios, so do you just keeping coming up with them and then taking them off the contract because ‘that seems fine’?

The other “red line”, about mass surveillance, is similar. What does that mean? You ask a dozen people, as Zvi did, you get a dozen different responses. Going from a vague feeling to something that’s specific is really difficult.

Now the DoW’s position seems to be that let’s just do it according to the law. The law is clear enough, or at least clearer than a goal that we might share. Laws are an operationalisation of principles we hold dear.

But what if the law has loopholes? If we disagree with the law? You still have to find some ways to make that clear, but honestly you either draft a contract airtight enough to solve for those, or you have to believe that your counterparty will obey the law. You can draft “permissions-based” (enumerated) vs “restrictions-based” (negative list) provisions, if you’re clear enough. And it makes sense to have explicit contractual red lines, even if unenforceable mid-operation, since they create legal exposure and political cost for the government if violated. But they aren’t clear though, then no contract can save you, and saying “I will decide” will not necessarily break in your favour.

Terms like “reasonably requested” or “as appropriate” or “reasonable doubt” are standard legal terminology precisely because you can’t nail down every eventuality on every contract, these capture some combination of norms and prior history to gesture at the types of things that will be ok and types of things that won’t be.

Because the only thing that matters is whether you have any visibility into their actions in the first place. The Anthropic deployment was of a separate version of Claude, under a different ToS, deployed by someone else. Which means, they probably had limited visibility into what it was being used for. Which also means the only way to enforce any standards is to codify things quite a bit upfront - it’s like doing an on-premise installation vs saas.

OpenAI’s contract on the other hand seems to have been hand-in-hand with their own teams of FDEs and something they call a safety-stack (guessing cloud deployment of their own models and some checks therein, I don’t know). Which means they have much more operational visibility into the model usage, which also means they have the leeway to negotiate if the usage of it started to violate any of their ToS.

I have no real opinion here on which is better. Contracts are not inherently all-powerful, they’re only powerful insofar as they can have oversight. I do have an opinion that neither is inherently superior to the other, even if what we know about them is accurate, which might not be the case. One has more contractual protections and limited operational visibility, the other has lower contractual protections and higher operational visibility. The first one relies more on trust with the counterparty, the second one relies more on execution control. Both rely on the existing legal system.

This entire saga seems to me like it was a personality clash rather than a contractual dispute. A version might well be: Someone asked a question about Maduro raid. DoW got upset they’re being asked. They posed a hypothetical. Anthropic’s response was bad, confirming DoW’s prior assumption that they’re trying to control the deployment. Which is why even though they were so close to being effectively done with the agreement the Secretary of War decided to blow things up.

To reiterate, it’s really bad to call Anthropic a Supply Chain Risk. This is just not true. It is eroding yet another norm about what capricious governments could do at a time we should not be eroding it, we should be strengthening it. It is perfectly fine for Anthropic to have rules about how their AI ought to be used. It is perfectly reasonable for DoW to say nah that’s not going to cut it, I don’t want to ask for permission.

But what is true is that this should not be much of a surprise considering the constant rhetoric over the past few years has been that AI is a power like no other. It’s like nukes, but times a thousand. We need regulation. And when an industry repeatedly calls out for oversight, asking for someone to make the rules on how it should be used, you cannot be surprised when the Defense department take that seriously. You cannot be surprised when they make up their own interpretations of what ought to be done, because you were insufficiently prescriptive. They will listen to your articulation of any red lines and wonder, what do you mean you want to tell me how to use the mega-nuke-crazy-power that you yourself are saying you don’t know how to control?

The US has nationalised or regulated whole industries for simpler reasons. Telephone lines, rails, steel mill attempted seizure, these aren’t small things. And that’s not to mention the times the government has threatened to do this, from JFK to FDR.

So if you think AI is important we’re going to see more of this. You simply cannot call your technology a major national security risk in dire need of regulation and then not think the DoD would want unfettered access to it. They will not allow you, rightfully so in a democracy, to be the arbiters of what is right and wrong. This isn’t the same as you or me buying an iOS app and accepting the T&Cs.

But it’s also true that a corporation acting as a bulwark for democracy against the government is fundamentally weird, even if true. Democracy is incredibly annoying but really, what other choice do we have! What we don’t have is a reckoning with the power that is now reality.

I am extremely uncomfortable with the fact that we can just purchase commercially available data on almost everyone. I am also somewhat uncomfortable that the future of war is going to be autonomous though there are days where having Claude or GPT decide where to bomb seems better than an average 22 year old. I’m uncomfortable that in the pursuit of absolute security we have effectively given up our privacy, and all that remains are small shreds that only sit with a couple of large technology giants. I’m uncomfortable that the few shreds of privacy that did exist can now be reverse engineered away using pretty normal AI tech.

I also am not sure there’s a way out where we would ever have digital guarantees of privacy. I think our children will think that a quaint old notion. “What do you mean, I can of course just ask my AI to analyse a bunch of information and figure out who ratmonster2024 is.” The work that only NSA used to be able to do a couple decades ago is probably within the grasp of the average startup, if they cared. Genies don’t tend to go back into bottles, and this one has powerful forces keeping it out.

The future will bring these questions to bear, much faster than anyone might expect. The current world survives because a lot of analysis is effort-bounded. If that’s gone, a lot of things we previously assumed secure will also go away. This is coming, whether you want to or not. The best part of last week is that the issue became higher profile, again. But bringing attention to the issue is only the first part. Unless we know what we want to do with the attention, tribal politics is going to overwhelm it all.

I had a conversation with and an august panel last week. It was really really good, and you should check it out.

Exponential View

🔮 Where the human ends and AI begins

This is the first AI Vistas discussion, a new series hosted by Exponential View where I bring people I trust into conversation around one hard question, because together we can see what none of us would see alone…

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2 months ago · 62 likes · 13 comments · Azeem Azhar, Nita Farahany, Eric Topol, Rohit Krishnan, and Nicholas Thompson

1. Mexico is like if India was richer, things were cleaner, while being much (much!) more unsafe. This showed up for me almost everywhere I went, often in the background, often not. For instance, this means that while in India you will see a lot more spaces for the rich or large luxury malls, in Mexico it feels like those are hidden away inside secure compounds. In fact the only place I saw this easily accessible and displayed was in Cancun, which is as if the Mexicans built a tourist place just for the Americans and made it look like Dubai.

2. I was shocked that Mexico City still has a murder rate 1/3rd of NYC in the 1990s. Turns out this ignoble list is also dominated by Mexico.

3. I continue to be just constantly amazed at how safe India is. It has no right to be so, it’s poor, ill organised and the justice system moves like molasses. I first had this thought in Nigeria, and have repeated this observation in too many countries to name. Central and South America look likely to only exacerbate this question.

4. This is particularly germane in Mexico because Mexico City reminds me a lot of Delhi, albeit with somewhat worse roads, less people, and far cleaner sidewalks. And entire squadrons of police cars with visible guns every block or two in all the tourist friendly areas.

5. An interesting aspect that I had never considered is Mexico used to be bigger than the US when it owned most of the US’ current southwest. The country still seem to remember this in their bones. They’re 130 million people but feels much larger. The weight of most of mesoamerican history centers it. They have a Place in History, writ in capital letters in the national psyche.

6. The level, variety, and affordability of street food remains one of Mexico’s major success stories. Plentiful, tasty and cheap. I largely prefer it to restaurant food. Tlayudes ftw.

7. Going through the Zocalo in Mexico City is a full body immersive experience, and not one I care to repeat. On the other hand it is massive, disorganised in the best way, and sells anything and everything you can imagine. We got lost inside it and had to trek a dozen blocks in a randomly chosen direction to get out. We realised this after calling an Uber and waiting 20 mins before realising it’s never going to make it.

8. This is also a plus, because just like the lack-of-zoning-success-stories of almost every country except the US, it makes Mexico City undeniably attractive to every American, who of course love mixed-use easily walkable cities as long as they don’t have to live in them.

9. This exact reason also makes Cancun the worst place in Mexico I visited, because it’s built for tourism, has a hotel zone, and fails my “Civilisation Test” which is the number of cafes in walking distance. In case you were curious, the winner was Oaxaca. Excellent coffee, and even better hot chocolate.

10. Mexico City truly is a cultural capital. Incredible museums, great art, great food. The Museum of Anthropology in CDMX is the best museum I’ve seen (‘n’ is very high here).

11. The main Cathedral is absolutely gorgeous. And being built on the remnants of the lake you can see the effects of the soil moving about as the cathedral is a bit slanted. The styles are more eclectic than you’d find in a European city, and more ornate than I personally like, but worth seeing.

12. Walking among the Aztec ruins next to the Cathedral is a quasi religious experience because they’re so well preserved. The feathered serpent, Quetzlcoatl, is everywhere, encircling the plazas, out of the walls, surrounded in parts with forms of corn and shells.

13. As usual I found the fact that until recently tearing down an ancient monument and building another gorgeous monument to be normal and not at all noteworthy, to be interesting. Something we can learn from.

14. The Aztecs took their iconography and religion seemingly from Teotihuacan, which is an hour away. It’s an older civilisation, 600 years before Aztecs, whose traces they clearly discovered and were influenced by but knew little about. They didn’t know who they were, what their society was like, what they called themselves, nothing. So they, rather whimsically, named it Teotihuacan, the place where gods came from, adopted many of their gods (or so it seemed to me), for instance named the feathered serpent Quetzcoatl, and generally lived a grand life of military conquest for a couple centuries until Cortez arrived.

15. I can understand why. Teotihuacan is extraordinary, and the Pyramid of Quetzcoatl in particular is magnificent. Considering they didn’t have metal or pack animals this is all the more impressive. The ability of humans to accomplish incredible things at scale never stops continuing to amaze me.

16. I have not been able to make up my mind about the import of human sacrifice and how much it’s true/ false/ exaggerated compared to other historic cultures.

17. Driving in Mexico City is very hard. Half the roads are tiny and don’t even look like roads. The green signs that show the roads and destinations often had three names none of which matched what Google maps said, so it was entirely visual navigation. I am now ready to drive in India.

18. Mexico City also has cable cars as a core mode of public transport, which I hadn’t seen before, and looks wonderful especially when stuck in a traffic jam. I wish the US had these, or indeed any public transport. I tried to take one but it was night and gpt recommended the amount of changes I’d need to make to take a ride was not safe and I shouldn’t do it. So I had churros and cafe de olla instead.

19. As my 8yo observed, the infrastructure got better as we went from Mexico City to Oaxaca then to Cancun. Curious.

20. Oaxaca is a jewel of a place. Fits in your palm, highly walkable. High civilisation score. Great food. Great cathedral, though the churrageruerisco was not the best of its type, didn’t come together cohesively.

21. The street food is plentiful and good. The speciality is mole, a particular type of sauce with mixed spices, and chapulines, fried grasshoppers. Apparently delicious when mixed into butter and eaten with bread.

22. Oaxaca also had the highest density, originality and quality of art I’ve seen in a city since

23. There’s plenty of prehispanic food and drink about. Tejate was meh to me, though a latte tejate I had at a market was extraordinary. Generally I remain a fan of modernity, we’ve perfected much of what history revered (and made them better).

24. Monte Alban, an hour from Oaxaca, is worth visiting. Zapotec built, on top of a hill. Gorgeous views all around. The guide told us when it was built and during the heyday it used to have 9 months of rain, so the water would flow down to the sides of the hill through channels that were cut, and this would supply water from the priests to the commoners. But the water dried up during a long drought lasting a couple decades, people lost faith in the priests to bring rain by praying to Tlaloc, and folks left. So it goes.

25. The burial rituals were fascinating, they would put the body in a small enclosed space for 4 years, shut tight so no smells would escape, and then would remove the bones and put them in an urn. If more people died they had different spaces like this outside the house.

26. The various pedestals and spaces had holes below for priests to show “magic”, disappearing and reappearing, as the guide told us. I am personally suspicious of the “people in the olden days were easily fooled” argument, but am in favour of the “everyone likes and believes in rituals” argument.

27. The idea of worship starting with some seed of truth and then becoming a self fulfilling prophecy as those responsible for the worship taking matters into their own hands will never stop being funny.

28. Cancun was the least interesting part of the visit. It is also, at least the hotel area, not at all pedestrian friendly. It’s big tourist resorts or nothing.

29. Chichen Itza, a couple hours from Cancun, was remarkable. Their architecture shows influence from teotihuacan, from toltecs, and there clearly seemed to be trade and information routes between the lands. The Mayan civilisation at least per reading stood for 3600 years, which is an absurdly long length of time.

30. The cenotes are magnificent. Cenote Xkeken, was a particular favourite, it’s mostly underground with only a shaft of light coming down.

31. The fact that Mayans ruled for so long in such a dry place with the main water source underground feels quite bizarre. Though once you rationalise by the number of inhabitants maybe it’s fine. Chichen Itza had around 40k, 5x less than Teotihuacan, itself less than Tenochtitlan, and none of them had decent water supply. I do not understand living life in hard mode for that long.

32. One reason though for the longevity of these civilisations might be survival bias, because but he time a lot of monuments got built without mechanised power it’s already a couple centuries. There’s a funny comparison to be made right California HSR here where we’ve horseshoe theoried our way to construction but I leave that to someone else.

33. The beaches near Cancun are very good, especially Cozumel, the island where Hernan Cortez first landed. Sting rays and nurse sharks played in the shallows next to our feet at El Cielo. But I’ll be honest I still prefer the beaches of Southeast Asia. Thailand cannot be beaten.

34. For the number of civilisations that roughly lived side by side at different points in Central America is really impressive. I got GPT to make me multiple maps and websites to help understand this better.

35. This trip without LLMs would’ve been about 30% as good. Everything from planning to asking about cafes and restaurants to dealing with zocalos to hotels and snacks and history and geography and pretty much anything we wanted to know or learn was made better by GPT, and sometimes Gemini.

36. Again the sheer number of extremely heavily armed police present in nearly all parts, including highways, was quite striking. They stopped cars at night, frisked folks, and generally were a loud and constant presence. Is this signaling or actual deterrent? Unclear, but everyone states the importance of being sensible and safe.

37. A substantial proportion of tourists to Mexico City and Oaxaca were Mexican, I think. As a consequence it’s not English language friendly, though again with Google translate and ChatGPT it’s not hard to travel.

38. I was told by the tourist guides multiple times to not call it Gulf Of America as a form of protest. Everything is politics.

39. Overall I really liked it, though I understand better why people who don’t have easy access to Asia, like Americans, like it so much more than I did. When it comes to food and markets and the general feeling you’re in a “free” city with limited top down strictures on life, this is the only real choice from North America without braving a really long flight. But I know, or rather I feel, for those you simply cannot beat India or Japan, which are also significantly safer, and have great food and history. Similarly for beaches I’m still a fan of Thailand but by a thin margin. That seems to be the primary motivation for most Americans I know who have gone to Mexico, which seems quite shortsighted to me. Because when you combine all that with its long history and culture, Mexico is pretty great.

This has become part of a series of essays, evaluating the new “homo agenticus sapiens” that is AI Agents. Part I was seeing like an agent. Part II is why the agentic economy needs money. And this is Part III.

Whitney Wolfe Herd, Bumble’s founder, recently described a future where your AI chats with potential matches’ AIs to find compatibility. Say what you will about AI being involved in your love life, but this is one domain where AI agents can potentially have large returns: the dating/marriage “market” is the epitome of the type of high-dimensional matching problem that Herbert Simon identified as impossible for people to optimize. Rather than optimising, Simon argued people engage in “satisficing”, i.e., settling for good enough.

Why would AI agents be useful here? Let’s start with how most markets work. Hayek’s big insight–outlined in what he called the economic problem of society–was that prices do an incredible amount of work. They compress a ton of information such as preferences, costs, scarcity, expectations into a single number that acts as a sufficient statistic for value. When you’re buying oranges, the seller doesn’t care what you’ll do with them. The price coordinates the transaction and that’s enough.

But prices work best when the transactions involve commodities. When you’re buying some oranges, the seller doesn’t particularly care what you’re going to do with them; you don’t need to convince him that you’ll take care of the fruit. The price does all the work in coordinating that transaction. Matching markets are conceptually different. You can’t just choose your spouse, your employer, or your college: you also have to be chosen by them. This is the domain that Al Roth, the 2012 Nobel winner for “the theory of stable allocations and the practice of market design,” spent most of his career studying. Roth showed that matching markets require careful institutional design; this design includes algorithms, timing, and the right rules to get the market to “clear.” His deferred-acceptance mechanisms now allocate medical residents to hospitals, students to schools, and kidneys to patients.

But the efficiency of matching markets hangs on the ability to elicit a person’s preferences, i.e., that people can express their rank orderings over potential options. But what if people’s preferences don’t fit in dropdown menus or are difficult to articulate on a standardised questionnaire? Peng Shi studied in his excellent paper “Optimal Matchmaking Strategy in Two-Sided Markets.” He looks at online platforms that match customers to providers using a variety of matchmaking strategies, from searching one side of the market to centralised matching that allows for back-and-forth communication.

Shi found that centralised matching works beautifully when preferences are “easy to describe,” i.e., straightforward to elicit using standard questionnaires, but breaks down when they’re contextual, idiosyncratic, or otherwise difficult to express through standard techniques. This is why many platforms still make you search. You want a contractor who shows up on time and knows your budget–this is easy–but you also want someone who understands your tastes in postmodern living room design. Good luck expressing that on a dropdown web form.

Here is where Large Language Models come in. They are fantastic at turning any unstructured piece of information into better structured matching. They’re also eminently scalable, enabling Coasean bargaining. But scaling things brings with it more coordination problems, too many agents negotiating with too many other agents is noisy. So what type of an institutional setup would make most sense to install here, to make this work well?

That’s what we sought to test with our experiments. The question being, could we figure out how and whether LLMs can help in matching markets where preferences are “hard to describe”? Can LLMs actually elicit the dispersed, hard-to-articulate preferences better than standardised methods? And if they can, what happens when LLM-based agents are available to everyone in the market?

Now, there’s some recent work on the topic that suggests guarded optimism that this is possible. Very new work by Ben Manning, Gili Rusak, and John Horton show that, when parsed through LLMs, short natural-language “taste descriptions” can be superior to standard questionnaires for eliciting preferences when the option set is large. They run an experiment where people write a few paragraphs about what they want in a job and then rank between 10 and 100 options (depending on the condition). Consistent with Simon’s conjecture, people’s ranking effort plateaus as the option size grows large; choice quality grows unstable as the consideration set increases. People get tired of ranking a ton of options and just start guessing. AI-parsed “taste descriptions” scale much better: once tastes are written down, the marginal cost of evaluating one more option is negligible for an AI agent. The advantages of AI-parsed matches are even higher in congested markets where people are more likely to be pushed.

But a theoretical paper by Annie Liang offers an important counterpoint in the case of a potentially complex two-sided matching market. She shows that when personality is sufficiently high-dimensional, meeting just two people in person beats searching over infinitely many AI representations. The noise in AI approximations compounds faster than the benefits of scale. This is a very cool result, and you should all read the paper in full–it’s that perfect type of economic theory that’s both conceptually rich and practically useful.

Ok, with that preamble…

Let’s run an experiment

We set up a simulated Hayekian marketplace with a whole bunch of digital shoppers, providers as AI agents.

1. Preference elicitation: Knowledge is dispersed in each digital shopper’s “head”: customers know what they want and providers know what they can offer. We want to know how eliciting the preference–either through the standard intake questionnaire or high-dimensional text parsed by an AI agent–can change the market structure for optimal results.

2. Mechanism interaction: When elicitation improves, can centralised matching beat search, and what are the conditions under which this happens?

3. Scale: We then check what happens when everyone uses AI agents

4. Institutional design: Finally, we figure out the right institutional mechanism to solve the resulting problems, and to maximise welfare

Preferences here are latent vectors in each agent’s head. Both the customer and provider agents have a true weight vector over some set of attributes (6 dimensions in this case). So elicitation changes the platform’s inferred w, not the true w. A standard intake is a structured form, and only exposes a few coarse priorities. The AI intake is free text, back-and-forth chat, and can be parsed in the platform’s inferred weight by a couple mechanisms - either by a rule- based algorithm or an AI agent.itself or , or via GPT parsing.

Figure 1 has an abridged illustration of the design and some results. There’s an appendix at the end of the essay in case you want to check out the details of the experimental design. But without further ado, here are some…

Results

First, AI-assisted preference elicitation improves matches across the board.

Figure 1: Experimental design

Figure 2

Second, as shown in Figure 2, AI-based elicitation changes what type of market design works best, and the conditions under which centralised matching can beat search.

Specifically, “Search” and “centralised” are the two different matching protocols we tested. Search means customers iteratively message providers in some ranked order until the matches ‘stick’. Think about how you would find a plumber–message folks, talk to them, iteratively until one ‘fits’.

Centralised is where the platform computes the shortlist for you, and clears a match based on mutually acceptable terms.

Once dispersed knowledge can be elicited and compressed into usable signals, the platform can centrally clear the market rather than forcing users to search. When knowledge can’t be compressed, search dominates because it lets users do iterative, contextual refinement in the loop.

The core object is the ‘ROI boundary’. If the per action attention cost is high enough, centralisation dominates–it just requires fewer actions. If the cost is low, search can dominate because it can “handle” more actions. This is the very idea of Coasean bargaining helping remove the boundaries of firms.

So where does the value of LLM-based elicitation actually come from? Is it from the back and forth conversation, or the ability to parse large text? As described above, we prompted all of the customers to write some free text about things they like and whatnot, and then used some rules-based parsers and some LLM-based parsers. There’s also the option for conversational elicitation via chat.

We thought the AI agents’ ability to ask follow-up questions would be the game-changer. Turns out though (see Figure 3), most of the value comes from the AI agent simply inferring more signal from messy text compared to the signal in a rule-based parser. This is consistent with the work of Manning et al. that we discussed above. This may of course be something specific to our prompts—perhaps one could obtain further gains by explicitly instructing the AI agents to engage in structured back-and-forth with the customers, and to do so in contexts where this would be helpful, but this was not the case here.

This highlights the utility of LLMs for extracting (potentially high dimensional) signals from unstructured data. Back in the day OKCupid used to make people fill out 90-100 questions to help match them with their potential partners. With LLMs, they might’ve been able to get away with writing a short essay and getting their Agentic Cupid to pull out the relevant information. Whitney is certainly on to something.

Figure 3

But what if people don’t really know what they want, does preference uncertainty matter? Whenever Rohit shops, he’s not sure of what he wants before he goes in the store. There’s a lot of noise in the process. Alex is a pure satisficer: the first item that meets a (very low) threshold gets put in the cart (usually virtual), and off to check out he goes.

We can test for that pretty easily here by introducing a bit of randomness into our shoppers’’ heads. At least in our setting, injecting noise into preferences doesn’t matter for the AI’s ROI all that much. We can still do centralised matching and extract a lot of value from that mechanism—as long as the preference noise isn’t too cacophonous.

What if everyone uses an LLM agent?

We had originally set up a pretty small marketplace. The centralized mechanism at this scale can be computed and cleared so we can run the experiment. But what happens when the scale explodes, both in the number of options and the number of customers potentially using AI agents? This is the problem matching platforms like Upwork are trying to solve: the option set is absolutely huge, but so is the potential customer base.

Every time a customer opens up a marketplace like Upwork, the number of choices just on the front page makes it hard to remember what they came for. Ideally AI-delegated agents can solve this problem: the user speaks or writes down what they want to do, the AI agent pings the platform, and the user is presented with the match. But what if every potential shopper had their own AI agent who wanted to message the providers on the platform? That’s a lot of agents doing individualised message sending to the provider inboxes!

So as you increase the number of customers with AI agents, the level of congestion rises significantly. Each customer agent sends a query to a provider agent’s inbox and it has to respond. Responding to all those agents takes a lot of compute. Here is what happens in our simulation (Figure 4): At full adoption, the providers’ inboxes flood with 5x the amount of requests, response rates collapse from 48% to 2%, and net welfare drops 88%.

Figure 4

Without institutions in place to scaffold the marketplace, a tragedy of the commons emerges: If everyone has an AI agent, it’s almost like nobody does. The paradox of plenty is real, and AI agents create their own version of Jevons paradox.

The need for institutions and scaffolding

What can fix this type of congestion? Prices!

As in a previous post–where we showed the importance of money in coordinating trade amongst AI agents–introducing a price mechanism recovers most of the lost welfare in matching. A vindication of Hayek’s deeper insight.

Specifically, we can introduce an exchange and money, such that the agents now have a pricing mechanism to signal their “strength of preference”. The idea is that the complexity falls because now not every provider and customer need to message each other. Prices capture a lot of high dimensional information in a single statistic, streamline a lot of that information, as we’d seen with the simulation in as we saw in the simulation in barter_to_money, complexity falls from O(n²) to O(n).

Figure 5

Pricing works! As shown in Figure 5, most of the welfare gains are recovered and the congestion issues are resolved. LLMs may lower the cost of expressing dispersed knowledge, but they don’t remove the need for institutional design to manage externalities. At least in our experimental simulation, the price system remains essential to solve the issue of complexity and congestion.

What did we learn?

If we think about an AI agent economy, we would want to know more about the mechanism that facilitates coordination. First, we have to ask, “If agents lower transaction costs, do markets just happen?”

In a previous post we looked at what would happen if there were a bunch of agents who had to interact with each other to trade, and it turned out that they don’t form markets spontaneously. In fact you have to do a fair amount of work before the agents are ready to interact.

Ok, if markets need scaffolding, what’s the minimal substrate that makes coordination scale? i.e., how will the agents coordinate amongst themselves? Will they be able to develop methods to do so themselves, e.g., through bilateral and multilateral negotiations, or will they need further help. It turns out that no matter how much you want to set things up just so, the agents will still need money and prices to trade efficiently. Even with the lower transaction costs and larger levels of compute, the coincidence-of-wants problem still doesn’t disappear - Hayek remains vindicated.

In this current essay we explore whether LLM agents can make centralised matching more efficient–we should expect marketplace consolidation in categories that were previously too heterogeneous for algorithmic matching, e.g., wedding vendors, specialised consulting, creative services. We showed that in “thin” markets AI agents help facilitate better match quality through centralised mechanisms.

However, if everyone has an AI agent, we still need a pricing mechanism to solve the resulting congestion and complexity problems that arise. Congestion is a serious threat at scale!.

So what is the broader take away from this essay, from the whole series of essays? For us it’s that AI agents work remarkably well when institutional design facilitates the interactions and transactions. Since direct instruction for every eventuality is impossible, the only way to make the AI agents behave at scale is to design the right scaffolding to facilitate coordination and exchange. This involves the creation of markets, and yes, money! If we can learn to design the “institutions” within which the agents operate, then we can help have them do far more complex tasks that we want. Autonomy, that’s the true prize!

Appendix: More about the design

Warning: wonky.

We constructed a simulated marketplace where customers seek service providers (contractors) across task categories that vary in how difficult preferences are to articulate. Each customer is seeded with true preferences represented as a 6-dimensional vector of weights (summing to 1) over provider attributes. A match is formed when both sides’ true values clear a threshold.

“Easy” categories include things like TV mounting or furniture assembly; preferences in these categories can be mapped cleanly onto standard form fields such as price, availability, and distance. “Hard” categories, such as ability to repair a historic staircase or a complicated asbestos remediation with specific guidelines, involve preferences that are more difficult to elicit using standardised questionnaires. We then see whether the ROI threshold changes based on how well the models can “elicit the true preferences” of the underlying actors.

The experimental intervention targets the preference-inference pipeline: how customer preferences get translated into data the platform can act on. The experiment varies the intake method (standard structured forms versus free-text descriptions parsed by an LLM) crossed with the matching mechanism (decentralised search where customers browse and choose, versus centralised assignment where the platform matches algorithmically). Match quality is computed as the dot product of the customer’s true preference weights and the matched provider’s attributes, minus any search costs incurred. All of this is summarised in Figure 1 below.

Figure A1: Experimental Design

This has become part of a series of essays, evaluating the new “homo agenticus sapiens” that is AI Agents. There was Part I, seeing like an agent. This is Part II. And Part III on what happens when we all have AI agents.

Sometimes I forget but we live in a future transformed by information technology pretty much across ever aspect. But one thing has remained largely the same: we still live in a world where the vast majority of economic transactions are done by people. If you want to buy a car, the process is largely the same as it was 50 years ago. You go down to the dealership and negotiate the best price that you can. Sure, you may have some extra information from doing research on the web beforehand - it’s certainly much easier to do comparison shopping with a supercomputer in your pocket - but the basic process of transacting with another human being has largely stayed the same.

One change that’s likely to come though is that there will soon be 10x, 100x, maybe more AI agents working in the world as there exist people. And as we have lots of AI agents working on our behalf, doing all forms of work, then there is a thesis that many of the frictions and information asymmetries that people face in markets may disappear if economic transactions are delegated to aligned agents, leading to a so-called Coasean singularity.

We’re not there yet though. Today’s agents are simply not good enough yet to act sensibly or without strict instructions. Many of the features of human-mediated markets still seem to be reproduced in AI agentic interactions. But as online spaces adapt to the promise of AI technology, it seems natural to think of how agentic markets will be organized. In a future world where we do have billions of AI agents, how would they coordinate with each other? What kind of coordination mechanisms would be needed? What institutions are likely to emerge?

And one possibility is particularly intriguing: will coordination still require money? Not in the sense of US dollars, but a shared medium of exchange and a hub/ clearing protocol.

Money, Money, Money

“Why money” has occupied economists going back to Adam Smith, who framed cash as solving what has since been termed the coincidence of wants. To see what we mean, consider a pure barter economy. Let’s say Alex is an apple farmer and Rohit raises chickens. If Alex wants chickens and Rohit wants apples, then Alex can just walk over to Rohit’s house with a bushel of apples and get some chickens in return. Simple. But what if Alex wants chickens but Rohit wants an electric toothbrush - he has no need for apples right now. Then to get the chickens, Alex would need to find a person who is willing to trade an electric toothbrush for his apples, and then come back to Rohit for a trade.

This would still all be fine if there was just one other person to visit and trade with, but what happens in a large market, with many (many) people who potentially have both an electric toothbrush to trade and want Alex’s apples? In order to trade, Alex needs to happen to find a person that both 1) has what Alex wants and 2) wants what Alex has. As very nicely shown in a paper by Rafael Guthmann and Brian Albrecht, the need to satisfy this coincidence of wants through finding matches creates complexity that quickly blows up as the size of the market increases. If the market is even moderately large, this complexity makes even basic transactions essentially impossible.

Ergo money. While the origin of money is a hot topic of debate (e.g., see David Graeber’s excellent book Debt: The First 5000 Years), the role of money in a competitive market is to solve the coincidence of wants. Money acts as a special type of good called the numeraire, where its only role is that it can be exchanged for other goods at pre-determined quantities. These quantities are reflected in the prices that each good is worth.

Going back to Alex and Rohit: one way to solve the coincidence of wants would be for Alex to sell his apples at a special place called market and then to use the money to purchase Rohit’s chickens. Rohit can then use that money to buy an electric toothbrush, or indeed any other thing his heart desires. Money eliminates the need for people to coordinate their transactions based on their current endowment (what they have) and preferences (what they want).

Bring on the agents

Okay, so money is necessary to coordinate transactions in an economy with people. This is largely because each individual can’t hope to have enough information on what everyone else has and wants to reliably engage in market transactions. Alex and Rohit are as yet, sadly, mortals.

But will this be the case for AI agents?

Agents do not have the same computational constraints as human beings. In theory, it may be possible to solve the search problem where the coincidence of wants becomes a non-issue. In that case, the agentic economy could eliminate the need for a key institution of the human economy. We decided to run an experiment to find out.

The experiment

First, the repo here. We can have N agents, with N goods, and each starts with its own good and wants another. There’s multiple rounds, one action per agent per round. Agents decide their course of action via structured JSON, and success simply means you get what you want.

The first question is about a pure barter economy. We explore whether LLM agents can achieve efficient allocations through barter at any scale, i.e., to engage in multiple bilateral negotiations to achieve gains from trade. The agents in the experiment have no real shortage of time. If this works then Coasean bargaining should be straightforward; goodbye money!

The table below has the results. What do we see? When the scale is small - when Alex just has to worry about coordinating with Rohit - all of this works. But as the number of agents grows, things start to get really difficult. By the time we get to even 8-12 agents the number of successful transactions drops to below 50%. And this is the absolute simplest setting.

Perhaps this should be expected. The problem is still O(n²) in complexity, which grows exceptionally fast as the number of agents grow. And if this isn’t just bilateral, but starts to include multiparty negotiations, it might become O(n!), which is far bigger for any number bigger than 3.

Ok let’s make it a bit easier for the agents. If they can’t talk to each other, since they are agents anyway, we should be able to give them omniscience. Enter Central Planning. There has been plenty of work before in the limits of bilateral negotiations, but we can test how well a “hub” structure can help. Does having a central planner help set the stage for better performance?

As the results table shows, central planning makes things slightly better, but we are still very much in a world of the Hayekian troubles. A hierarchy without a numeraire just isn’t enough.

Ok, we can continue looking at our human history to see what else we can do. In Debt, David Graeber argues that money emerged at least in part through state power, to enforce the paying of taxes in order to fund foreign wars. Before this, he argues, IOUs and bartering seemed to have worked just fine to manage the economy; the IOUs themselves became a sort of numeraire that could be traded in order to solve the coincidence of wants.

So let’s introduce_, Credits and IOUs. We can give the agents the ability to give each other an IOU and see whether providing the basics of credit allows them to come up with better ways to interact with each other.

This still didn’t help as much as we thought. There were a few segments where the transactions started happening, but they really didn’t start to work. Or scale.

Most interestingly, the concept of money didn’t emerge from this, not organically. IOUs didn’t become money. Even though in conversations LLMs all know that this is the smart thing to do, it did not emerge.

This was a bummer, because as with the prior research, what this shows is that AI agents do not yet come with the natural instincts of humans to turn IOUs into a numeraire that acts as a stand-in for money. They don’t even come with the same set of ideas as this sea otter.

Ok, let’s take the final step and actually introduce Money. We do this by creating an exchange where the agents can do bids and offers, and look at market outcomes. The results are stark: markets resolve at a success rate of 100% and much faster than through other mechanisms, at the rate of O(n).

One note is that this result presumes the exchange works without a hitch. In reality there will be friction coming from liquidity constraints, differential compute resources, etc. For example, in the N=8 run, the hub handled 23 inbound + 23 outbound messages and prices stayed fixed. And if regulations require that AI agents use different types of country-specific currencies, then exchange rates will complicate things further.

Discussion

To sum: An agentic economy doesn’t emerge automatically with even SOTA agents (who really should know better). Barter and central planning remain inefficient and infeasible, and money does not emerge organically even when credit and IOUs are introduced. At least in our setting, an agentic economy needs more top-down engineering to become efficient.

Previous work on agent-based modeling has explored what kind of emergent economic realities we are likely to see with rule-based agents interacting. The world of AI agents is fundamentally different. These agents act based on a huge corpus of human knowledge, with the underlying LLM models able to solve incredibly difficult problems on their own. These agents can plan, they can negotiate, they can code. And even with all this knowhow at their disposal, it’s interesting to see that they still appear to require top-down institutions to create an effective and efficient market.

As we transition to a more agentic economy, a key part of ‘getting ready’ for that world is setting up institutions for the agents. Like including:

• Identity and roles

• Settlement and payment

• Pricing and quote formats

• Reputation

• Marketplaces and clearinghouses

This is by no means exhaustive, but we wager that mechanism design for multi-agent work is going to be a rather fertile area of research for a while. Humanity went through millennia of evolution to figure out the right societal setup that lets us progress, that lets us build a thriving civilisation.

It is both necessary and inevitable that the world of AI agents will also need the equivalents, though the emergence of such institutions will likely be much faster given the millennia of human knowledge that we’ve already amassed.

Github repo here.

One of the books that I loved as a kid was Philip Pullman’s His Dark Materials. The books themselves were fine, but the part I loved most were the daemons. Each human had their own daemon, uniquely suited to them, that would grow with them and eventually settle into a form that reflects their personality.

I kept thinking of this when reading the recent NBER paper by John Horton et al about The Coasean Singularity. From their abstract:

By lowering the costs of preference elicitation, contract enforcement, and identity verification, agents expand the feasible set of market designs but also raise novel regulatory challenges. While the net welfare effects remain an empirical question, the rapid onset of AI-mediated transactions presents a unique opportunity for economic research to inform real-world policy and market design.

Basically they argue, if you actually had competent, cheap AI agents doing search, negotiation, and contracting, like your own daemon, then a ton of Coasean reasons firms exist disappear, and a whole market design frontier reopens.

This isn’t a unique argument, though well done here. I’ve made it before, as has others, including Seb Krier recently here and Dean Ball and many others. The authors even talk about tollbooths as from Cloudflare and agents only APIs and pages.

But while reading it I kept thinking by now this is no longer a theoretical question, we now have decent AI agents and we should be able to test it. And it’s something I’ve been meaning to for a while, so I did. The question was, if we wire up modern agents as counterparties, do we actually see Coasean bargains emerge. Repo here.

The punchline is that AI agents did not magically create efficient markets. And they also kinda fell prey to a fair bit of human pathologies, including bureaucratic politics and risk aversion.

Experiment 1: An internal capital market

The first way to test these was to just throw them into a simulated company and see what happened. So I set up four departments - Marketing, Sales, Engineering and Support - and said they could all bid for budget to do their jobs. Standard internal capital market where departments would submit bids and projects get funded until budgets get exhausted.

If the promise of Hayek holds and we can get markets if information flowed freely, then we should be able to see this work. And it would be much better than the command and control method by which we try to decide this today.

Well, it didn’t work. Marketing and Sales accumulated political capital. Engineering posted negative utility for most quarters. The market we set up systematically funded customer facing features and starved infrastructure work. It’s like Seeing Like A State all over again.

I think this was because GTM type departments could come up with immediate articulable customer values, whereas Engineering’s value kept feeling preventative or diffuse.

It’s a bit frustrating to see that the models still retain human foibles since this is effectively Goodhart’s Law. When you measure departmental utility and fund accordingly, and you let the agents argue on their behalf, you do start to see negative externalities for core functionality.

So I added countermeasures. I added risk flags on features and veto power over “dangerous” work. Added shared outage penalties (if you ask for a risky feature and everything crashes, you pay for it too). And when I ran that, outages did happen. GTM departments observed this and tempered their bids, though only a little.

Engineering utility however still stayed low. GTM could discount future outages and gambled on “maybe it won’t break” for its immediate wins. But Engineering couldn’t proactively push folks into infrastructure investments. The pattern is hardly dissimilar.

The truly interesting part was that the agents perfectly replicated the dysfunction of real companies. Onwards.

Experiment 2: External markets - IP licensing

This was the most interesting part. The best way to see Coasean bargaining come true is to set up an external market for cross firm technology licensing. Twenty firms and thirty software modules. Each firm has some internal capabilities but could also license tech, so the buy vs build becomes a much cleaner decision with AI agents vs humans in reality. A classic setup, and the payoffs should be excellent. Or so I thought.

First run had zero deals. Every firm decided to build everything internally. They understood the rules and saw potential counterparties and had budget to trade, but still they chose autarky.

Okay, so I added reputation systems, post-trade verification, penalties for idleness, bonuses for successful deals, counterparty hints, even price history. Basically the kitchen sink.

Still zero trades.

This is the perfect setup as per the paper. Transaction costs effectively zero. Perfect information. Aligned incentives. Etc etc. The agents just didn’t care to trade! Because of very high Knightian uncertainty aversion (I assume), or some heavy pretraining that firms mainly build, not trade.

So I mandated ask/bid submissions. If you don’t post prices, defaults are generated. Profits are then directly coupled to next quarter’s budget. And I even gave explicit price hints, because the agents clearly couldn’t, or wouldn’t, discover equilibrium without them.

Now we start to see trades! Success! Three deals per round. The welfare is still far below the market optimum, but that’s possibly also because we haven’t optimised them yet.

But by now it wasn’t a market in the Hayekian sense. Like it’s no longer voluntary. We’re forcing the agents to trade, and then they do the sensible things.

Since it worked well for well behaved participants, I also did a robustness check, so we are creating adversarial firms and then check if the market still functions! And it does. Adversarial sellers captured much of the surplus, i.e., fairness is expensive. It’s either weak strategic sophistication or the agents are just nice and passive by default, I don’t know which.

Experiment 3: Second price auctions

The third experiment was one to check whether the models behave according to their beliefs. Vickrey auctions are sealed second price auctions, so the winner pays the price of the second highest bid. This means the dominant strategy is for the bidders to be accurate to their beliefs.

And they did. Allocative efficiency was 1. This is a little bit of a control group since the models must be smart enough to know the dominant strategy. I added “profit max only” personas, and collusion channels, just to check, and the behaviour still looked like standard truthful Vickrey bidding.

This tells us that they’re smart enough to do the right thing, but also that given a messy environment with underspecified mechanisms, which is most of the real world, they default to passivity or autarky.

I tested this also with a bargaining test with five players, which asks the models to divide a surplus value and have them negotiate with each other as to how to split things. The players can see a broadcast and each others proposal, but after round 1 the players can DM others. I even made one of the players adversarial. And still the splits remained near-equal, very far from the Shapley vector. They are norm conforming. Models are highly self-incentivised to be fair!

Synthesis

We saw 4 claims tested. To summarise:

1. AI lowers transaction costs so markets emerge spontaneously - False

2. With mechanism design, AI-mediated markets can function - True, but costly (required forced participation with Gosplan-ish price hints)

3. Internal markets improve on hierarchy when coordination costs fall - False (GTM dominates Engineering even with full information)

4. AI agents play fair in functioning markets - Mixed (adversarial agents extract rents, but agents are mostly fair)

The takeaway from these experiments is that to get to a point where the AI agents can act as sufficiently empowered Coasean bargaining agents, for them to become a daemon on my behalf, they need to be substantially empowered and so instructed. They do not act in the way humans act, but are much fairer and much more passive than we would imagine.

Markets don’t form spontaneously. Markets form under coercion but are pretty thin. And when markets exist, strategic sophistication determines who wins, depending on how the agents are set up. It shows alignment problems don’t disappear just because the agents can negotiate with each other. This is pessimistic for the AI dissolves firms narrative but optimistic for AI can enable better institutions narrative.

The Coasean Singularity paper argues AI lowers transaction costs but the gains require alignment and mechanism design, which is what I empirically tested here. It’s a strong confirmation of its strong form - that reduction in transaction costs was nice but mechanism design was needed to set up an actual market.

Also the fact that we needed to couple their budgets so the AIs needed to work from the same hymn is important, it means any multi agent design we create would need a substrate, like money, to help them coordinate.

Now some of this is that the intuitions we have built up over time, both from other humans but also from stories, is to assume that the agents have enough context at all times on what to do. I see my four year old negotiating with his brother to get computer time and by the time he’s a bargaining agent with some hapless corporation he would have had decades of experience with this. Our models on the other hand had millions of years of subjective experience in seeing negotiation but have zero experience in feeling that intense urge of wanting to negotiate to watch Prehistoric Planet with his brother.

Perhaps this matters. These complex histories can get subsumed in casual conversation into a seemingly innocuous term like “context” and maybe we do need to stuff a whole library into a model to teach it the right patterns or get it to act the way we want. The daemons we do have today aren’t settled in forms that reflect our interests out of the box though they know almost everything about what it is like to act as if it shares those interests.

But what the experiments showed is that this is far from obvious. Coase asked why firms exist if markets are efficient, and answered it’s because of transaction costs. The experiments here ask, even with zero transaction costs, why do firm-like structures still emerge1?

And if we do end up doing that, we might have just rediscovered the reason why firms exist in the first place, the very nature of the firm. Even as we recreate it piece by instructive piece.

Github repo here

has a really interesting essay on the importance of AI safety work, arguing it will not cause the US to fall behind China, as is often claimed. It’s very well written, characteristically so, and well argued. His argument, in a nutshell ( I paraphrase) is:

1. US has ~10x compute advantage over China

2. Safety regulations add only 1-2% to training costs at most

3. China is pursuing “fast follow” strategy focused on applications anyway

4. Export controls matter far more (could swing advantage from 30x to 1.7x)

5. AI safety critics are inconsistent - they oppose safety regs but support chip exports to China

6. Sign of safety impact is uncertain - might actually help US competitiveness

I quite like this argument because I actually agree with all of the points, mostly anyway, and yet find myself disagreeing with the conclusion. So I thought I should step through my disagreements, and then what my overall argument against it is, and see where we land up.

First, the measurement problem

Scott argues that the safety regulations we’re discussing in the US only adds 1-2% overhead. This is built off of METR and Apollo’s findings, around $25m for internal testing, and contrast this with $25 Billion for training runs. All the major labs also already spend enormous sums of money on intermediate evaluations, model behaviour monitoring and testing, and primary research to make them work better with us, all classic safety considerations.

This only holds if the safety regulation based work, hiring evaluators and letting them run, is strictly separable. Which is not true of any organisation anywhere. When you add “coordination friction”, you reduce the velocity of iteration inside the organisation. Velocity here really really matters, especially if you believe in recursive self improvement, but even if you don’t.

This is actually visible in ~every organisation known to man. Facebook has a legal department of around 2000 employees, doubled since pre Covid, of a total employee base of 80,000. Those 2000 are quite likely not disproportionately expensive vs the actual operating expenditure of Facebook. But the strain they put on the business far exceeds the 2.5% cost it puts on the output. There’s a positive side of this argument, they will also prevent enough bad things from happening that the slowdown is worth it. Presumably Facebook themselves believe this, which is why they exist, but it is very much not as simple as comparing the seemingly direct costs.

The argument that favours Scott here is maybe pharma companies,

This gets worse once you think about the 22 year old wunderkinds that the labs are looking to hire, and wonder if they’d be interested in more compliance, even at the margin.

China is a fast follower

The argument also states that China is focused on implementation and fast-follow strategy, because they don’t believe in AGI. I think it’s an awfully load bearing claim, and feels quite convenient. China is also known for strategic communication in more than one area, where what they say isn’t necessarily what they focus on.

As Scott notes, Liang Wenfeng of Deepseek, explicitly has stated he believes in superintelligence, which in itself is contradictory to the argument that they care about the applications layer. If China does truly believe in deployment, as it seems to be the case, then having true believers as heads of top labs is if anything more evidence against “they’re just fast followers” argument.

They’re leaders in EVs, solar panels, 5G, fintech and associated tech, probably quantum communications, an uncomfortably large percentage of defense related tech, seemingly humanoid robots, the list is pretty long. This isn’t all just fast followership, or at least even if it is, it’s indistinguishable from the types of innovation we’re talking about here.

Again, this only really matters to the extent you think recursive self improvement is true or China won’t change its POV here very fast if they feel it’s important.The CCP has an extraordinary track record of redirecting capital in response to perceived strategic opportunity (and overdoing it). That means “they don’t believe in AGI” is an unstable parameter. Even if the true breakthrough comes from some lab in the US, or some tiny lab in Harvard, it will most likely not be kept under wraps for years as the outcomes compound.

The AI safety critics are sometimes bad faith

This is true! There’s a lot of motivated reasoning, which tries to tie itself in knots such as to argue “to beat china we have to sell them the top Nvidia chips, so they don’t develop their own chip industry and cut the knees off another one of our top industries”. Liang Wenfeng has also said that his biggest barrier is access to more chips.

That said, here my core problem is that I am unsure about which aspects of the regulations being proposed are actually useful. Right now they ask for a combination of red-teaming (to what end), hallucination vs sycophancy (how do you measure), whistleblower protections, bias (measurement?), CBRN (measurement delta vs pure capability advance), observability for chip usage (hardware locks?), and more. These assume a very particular threat surface.

The Colorado AI Act focuses on algorithmic fairness and non discrimination. Washington HB 1205 focuses on digital likeness and deepfakes. AB2013 in California on disclosing training data for transparency. Utah’s SB 332 says AI has to say theyre AI when using a chatbot. These are all quite different, as we can see, and will require different answers in both implementation and compliance. writes about this cogently and cohesively.

Many of these ideas are sensible in isolation, but many of them are also extremely amorphous. Regulations are an area where I am predisposed to think that unless they’re highly specific and ROI is directly visible it’s better to not get caught in an invisible graveyard. The regulatory ratchet is real, as Scott acknowledges. Financial regulation post-2008, aviation post-9/11, FDA … We always have common sense guardrails that creates an apparatus that then expands.

Sign uncertainty

It is definitely true that having a more robust AI development environment might well propel the US forward vs China. Cars with seatbelts beat cars without seatbelts. Maybe lack of industrial espionage means the gains from US labs won’t seed Chinese innovation.

It should be noted though that the labs already spend quite a bit on cybersecurity. Model weights are worth billions, soon dozens of billions, and are protected accordingly. Should it be made stronger? Sure.

It should be noted, underlined, however that this is true only insofar as the Chinese innovation is driven by industrial espionage or weight stealing. Right now that definitely does not seem to be the case. What is true is that deployment by filing off the edges, making the products much nicer to use, especially via posttraining, is something Western models do a much better job of. Deepseek, Qwen or Kimi products are just not as good, and differentially worse than how good their models are.

So … now what.

Scott’s argument makes sense, but only in a particular slice of the possible future lightcone. For instance, we can sort of lay down the tree of how things might shake out. There are at least 5 dimensions I can think of offhand:

1. Takeoff speed

2. Alignment difficulty

3. Capability distribution (oligopoly, monopoly etc)

4. Regulations’ impact on velocity

5. China’s catch up timeline

You could expand this by 10x if you so chose, and things would get uncomfortably diverse very very quickly. But even with this, if we split each of these into like 4 coarse buckets (easy, moderate, hard, impossible), you get 1024 worlds. I asked Claude to simulate these worlds and choose whatever priors made sense to it, and it showed me this:

I’m not suggesting this is accurate, after all there could be a dozen more dimensions, or the probability distribution might be quite different. Change in one variable might impact another. But at least it gives us an intuition on why the arguments are not as straightforward as one might imagine, and it’s not fait accompli that “AI safety will not hurt US in its race with China”, and that’s assuming the race is a good metaphor!

For instance, here’s one story which I tried to draw out after getting lost with the help of Claude.

• Does recursive self improvement happen?

  • Y. First to ASI wins the lightcone

    • Is there a close race with China?

      • Y. Every month matters

        • Do safety regs meaningfully slow us?

          • Y. Disaster!

          • N. Small overhead doesn’t matter!

      • N. US has durable advantage (10x compute)

        • Does model quality matter more than deployment?

          • Y. We have time for safety work. 6mo slower might be fine!

          • N. Safety regs might not matter

  • N. Gradual capability increases

    • Which layer determines winner?

      • Model layer

        • How durable is US advantage

          • 10x compute advantage wins, so regulations are basically “free”

          • If china can catch up however, efficiency gains matter, so safety regs might be a small drag but real

      • Application layer

        • Do safety regulations affect deployment velocity?

          • Yes. Compliance morass and lawyerly obstruction everywhere.

          • N. Safety regs only affect the model. It’s fast and unobtrusive. It’s fine.

In this tree there are only a few areas where Scott’s argument holds water. Recursive self improvement is important enough to worry about but unimportant enough that velocity doesn’t matter. Chinese skepticism about ASI is stable but we should prevent dictators getting ASI. We can measure direct costs but what about illegible costs? Model layer regs won’t affect application layer despite Colorado showing they already do.

If recursive self improvement is false, it only makes sense to do more regulations *if* safety regulations do not meaningfully impact deployment velocity in the application layer and the compute advantage holds in the model layer. If recursive self improvement is going to happen, then Scott’s argument has more backing, especially if safety regulations don’t slow us down much as long as the model quality will continue to improve.

Which means of course the regulations have to be sensible, they can’t be an albatross, China’s “catch up” timeline has to be longer, the capability distribution has to be more oligopolistic, alignment has to be somewhat difficult, and takeoff speed has to be fairly fast.

If we relax the assumptions, as in the tree above, we might end up in places where AI safety regulations are more harmful than useful. One example, and this is my own view, is that a lot of AI safety work is just good old fashioned engineering work. Like you need to make sure the model does what you ask it to, to solve hallucinations and sycophancy. And you need to make sure it doesn’t veer off the rails when you ask it slightly risque questions. And you’d want the labs to be “good citizens”, not coerce employees to keep quiet if they see something bad.

Scott treats regulatory overhead as measurable and small in his essay. But the history of compliance shows they compound through organisational culture, talent selection, and legal uncertainty and dominate direct costs. If he’s wrong about measurement, and Facebook’s legal department suggests he is, then his entire calculation flips. Same again with China’s stance in reality vs what they say, or the level of belief in recursive self improvement.

To the question at hand, will AI safety make America lose the war with China? It depends on that tree above. It is by no means assured that it will (or that it won’t), but the type of regulation and the future being envisioned matter enormously. The devil, as usual, is in the really annoying details.

In my high-weight worlds, AI safety work can meaningfully help, but only if done sensibly. I don’t put too much weight on recursive self improvement, at least done without human intervention and time to adjust. I also think that large amounts of safety are intrinsic principles to build widely available and used pieces of software, so are not even a choice. They might not be called AI safety, they might be called, simply, “product”, which would have to think about these aspects.

Personally, I prefer a very economist’s way of asking the “will AI safety make the US lose to China” question, which is: what is the payoff function for winning or losing the race? Since regulations are (mainly) ratchets, we should choose them carefully, and only when we think it’s warranted (high negative disutility if not, positive utility if we do).

• In “mundane AI” world, we get awesome GPTs but not a god. Losing means we’re Europe. While some might think of this as akin to death, it’s not that bad.

• In “AI is god” world, losing is forever

Even in the first world, AI safety regs might make the US the Brussels of AI, which is a major tradeoff. Most regulations currently posed don’t seem to yet cause that effect. But, it’s not like it’s hard to imagine.

Regulation can be helpful with respect to increasing transparency (training data is one example, though with synthetic data that’s already hard), with whistleblower protections (even though I’m not sure what they’d blow the whistle on), and red teaming the models pre deployment. I think chip embargoes are probably good, even though it helps Huawei.

It’s far better to not think about pro or con AI safety regulations, but to be specific about which regulation and why. The decision tree above helps, you do need to specify which worlds you’re protecting.

“All LLM successes are as human successes, each LLM failure is alien in its own way.”

I. Two ways to “know”

I was convinced I had a terrible memory throughout my schooling. As a consequence pretty much for every exam in math or science I would re-derive any formula that was needed. Kind of a waste, but what could I do. Easier than trying to remember them, I thought. It worked until I think second year of college, when it didn’t.

But because of this belief, I did other dumb things too beyond not study. For example I used to play poker. And I was convinced, and this was back in the day when neural nets were tiny things, that my brain was similar and I could train it using inputs and outputs and not actually bother doing the complex calculations that would be needed to measure pot odds and things like that. I mean, I can’t know the counterfactual but I’m reasonably sure this was a worse way to play poker that just actually doing the math, but it definitely was a more fun way to do it, especially when combined with reasonable quantities of beer. I was convinced that just from the outcomes I would be able to somehow back out a playing strategy that would be superior.

It didn’t work very well. I mean, I didn’t lose much money, but I definitely didn’t make much money either. Somehow the knowledge I got from the outcomes didn’t translate into telling me when to bet, how much to bet, when to raise, how much to raise, when to fold, how to analyse others, how to bluff, you know all those things that if you want to play poker properly you should have a theory about.

Instead what I had were some decent heuristics on betting and a sense of how others would bet. The times I managed to get a bit better were the times I could convert those ideas of how my “somewhat trained neural net” said I should and then calculated the pot odds and explicitly tried to figure out what others had and tried to use those as inputs alongside my vibes. I tried to bootstrap understanding from outcomes alone, and I failed1.

II. Patterns and generators

“What I cannot create, I do not understand.” — Richard Feynman

This essay is about why LLMs feel like understanding engines but behave like over-fit pattern-fitters, why we keep adding epicycles that get us closer to exceptional performance, instead of changing the core generator, and why that makes their failures look more like flash crashes and market blow-ups than like Skynet.

One way this makes sense is that mathematically the number of ways to create a pattern has to be more than the number of patterns themselves. There are more words than letters. The set of all possible 1000 character outputs is huge, but the set of programs that could print any one of them is larger2.

An LLM trained on the patterns swims in an ocean of possible generators and the entire game of training is to identify those extra constraints so it has reason to pick the shortest, truest one. Neural networks have inductive biases that privilege certain solutions.

There is an interesting mathematical or empirical question to be answered here. What are the manifolds of sufficiently diverse patterns which can be used such that collectively it will turn away the wrong principles and keep only the correct generative principles?

I’m not smart enough to prove this but perhaps starting with Gold’s theorem, which says something like if all you ever see are positive examples of behaviour, then for a sufficiently rich class of programs it might well be true that no algorithm can be guaranteed to eventually lock onto the exact true program that produced them. LLMs are a giant practical demonstration of this. They implicitly infer some program that fits the data, but not necessarily the program you “meant”.

I asked Claude about this, and it said:

The deeper truth is that success is low-dimensional. There are relatively few ways to correctly solve “2+2=” or properly summarize a news article. The constraint satisfaction problem has a small solution space. But failure is high-dimensional—there are infinitely many ways to be wrong, and LLMs explore regions of that failure space that human cognition simply doesn’t reach.

One way to think about this is as the distinction between complexity in a system and randomness. Often indistinguishable in its effects, but fundamentally different in its nature. A world where a butterfly can flap its wings and cause a hurricane somewhere else is also a world that is somewhat indistinguishable from being filled with the randomness. The difference of course as that the first one is not random, it is deterministic, it just seems random because we cannot reliably predict every single step that the computation needs to take in all its complex glory.

One of Taleb’s targets is what he calls the “ludic fallacy,” the idea that the sort of randomness encountered in games of chance can be taken as a model for randomness in real life. As Taleb points out, the “uncertainty” of a casino game like roulette or blackjack cannot be considered analogous to the radical uncertainty faced by real-life decision-makers—military strategists, say, or financial analysts. Casinos deal with known unknowns—they know the odds, and while they can’t predict the outcome of any individual game, they know that in the aggregate they will make a profit. But in Extremistan, as Donald Rumsfeld helpfully pointed out, we deal with unknown unknowns—we do not know what the probabilities are and we have no firm basis on which to make decisions or predictions.

This isn’t just Taleb being esoteric. The rules that were learnt were not the rules that should have been learnt. This is a classic ML problem, that still exists in deep learning. The Fed sent a letter to banks about using not-easily-interpretable ML to judge loan applications for this reason. For an easier to see example, autonomous driving is a case of painfully ironing out edge cases one after the other, because the patterns the models learnt weren’t sufficiently representative of our world. Humans learn to drive with about 50 hours of instruction, Waymo in 2019 itself had run 10 billion simulated miles and 20m real miles, and Tesla at 6 billion real miles driven and quite likely hundreds of billions of miles as training data.

This isn’t as hopeless as it sounds. We see with LLMs that they are remarkably similar to humans in how they think about problems, they don’t get led astray all that often. The remarkable success of next token prediction is precisely that it turned out to learn the right generative understanding.

LLMs are brilliant at identifying a “line of best fit” across millions of dimensions, and in doing so produces miracles. It’s why Ted Chiang called it a blurry jpeg of the internet a couple of years ago.

III. Prediction and causation

“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.” — John von Neumann

Eric Baum had a book published more than twenty years ago, called “What Is Thought?” Its excellent title aside, the core premise was that understanding is compression. Just like drawing a line of best fit seems to gets you the right understanding in statistics, y = mx + c, so do we with all the datapoints we encounter in life.

The spiritual godfather of this blog, Douglas Hofstadter, thought about understanding as rooted in conceptualisation and core understanding. There was a recent New Yorker article that discussed this, and relationship to the truly weirder aspects of high dimensional storage of facts or memory.

In a 1988 book called “Sparse Distributed Memory,” Kanerva argued that thoughts, sensations, and recollections could be represented as coördinates in high-dimensional space. The brain seemed like the perfect piece of hardware for storing such things. Every memory has a sort of address, defined by the neurons that are active when you recall it. New experiences cause new sets of neurons to fire, representing new addresses. Two addresses can be different in many ways but similar in others; one perception or memory triggers other memories nearby. The scent of hay recalls a memory of summer camp. The first three notes of Beethoven’s Fifth beget the fourth. A chess position that you’ve never seen reminds you of old games—not all of them, just the ones in the right neighborhood.

This is a rather perfect theory of LLMs.

It’s also testable. I built transformers to try and predict Elementary Cellular Automata, to see how easily they could learn the underlying rules3.

I also tried creating various combinations of wave functions (3-4 equations and combining them) and seeing if the simple transformer models can learn those, and understand the underlying rules. These are combinations of simple equations, like a basic wave function with a few transformations. And yet:

There have been other similar attempts. This paper, what has a foundation model found, in particular was fascinating because it tried to use a similar method to see if you could predict orbits of planets based only on observational data. And the models managed to do it, except they all tried to approximate instead of learning the fundamental underlying generative path4.

This manifold question - “which diverse pattern sets collapse to unique generators” - is probably intractable without solving the frame problem. After all, if we could characterise those manifolds, we’d have a theory of induction, which is to say we’d have solved philosophy.

Maybe if we got them to think through why they were predicting the things they were predicting as they were getting trained, they could get better at figuring out the underlying rules. It does add a significant lag to their training, but essential nonetheless. Right now we seem stuck with Ptolemaic astronomy, scholastically adding epicycles upon epicycles, without making the leap to hit the inverse-square law. Made undeniably harder because there isn’t just one law to discover, but legion.

IV. Can reasoning escape the pattern trap?

“The aim is not to predict the next data point, but to infer the rule that generates all of them.” — Michael Schmidt

One solution to this problem is reasoning. If you’ve learnt a wrong pattern, you can reason your way to the right one, using the ideas at your disposal. It doesn’t matter if you’re wrong, as long as you can course correct.

Since LLMs are trained to predict the patterns that exist inside a large corpus of data, in doing so they do end up learning some of the ways in which you could create those patterns (i.e., thinking), even if not necessarily the right or the only way in which we see that getting created. So a large part of the efforts we put is to teach them the right ways.

Now we have given models a way to think for themselves. It started as soon as we had chatbots and could get them to “think step by step”. We get to do that across many different lines of thought, reflect back on what they found, and fix things along the way. This is, despite the anthropomorphisation, reasoning. If every rollout is in some sense a function, reasoning is a form of search over those latent programs, with external tools, including memory. Reasoning this way even gets us negative examples and better data, helping loosen the constraints of Gold’s theorem.

It’s also true that now they can reason, we do see them groping their way towards what absolutely looks like actual understanding. This can also often seem like using its enormous corpus of existing patterns that it knows and trying to first-principles-race its way towards the right steps to take to get to the answer.

A useful training method is to teach the model to ask itself to come up with those principles and then to apply them, to learn from them, because doing so gets it much closer to the truth. In mid-training, once the model has some capabilities, this becomes possible. And more so once when they have tools like being able to write python and look up information at its disposal5.

Because we are still pushing the induction problem up one level. It is now a game of how much can it learn about how to think things through. Whether the patterns of how to learn are also learnable from the data, both real and synthetic, to reach the right answer. Or the patterns to learn how to learn6.

And it is guided by the very same process that caused so much trouble in learning Conway’s Game of Life.

It still falls prey to the same lack of insight or inspiration or even step by step thinking that shows up in these failure modes. Same as before when we were trying to see why LLMs couldn’t do Conway’s Game of Life, this still remains the key issue7.

This, to be clear, does seem odd. And is a major crux why people seem to fight whether these AI systems are “even thinking” vs those who think this method is “clearly thinking” and scaling it up will get us to AGI. Because a priori it is very difficult to see why this process would not work. If you are able to reason through something then surely you will be able to get to the right answer one way or the other.

The reason our intuition screws up here is because we think of reasoning the way we do it as different from the model. Rightly so. The number of different lines of thought it can simultaneously explore are just not that high.

The best visible example is Agents who do computer use, if you just see the number of explicit steps it needs to take to click a button you see how quickly things could degenerate and how much effort is required to make them not!

At the same time when you train them with live harnesses and ability to access the internet and have the types of problems where you are able to provide reward rubrics that are actually meaningful suddenly the patterns that it identifies become more similar to the lessons we would want them to learn.

V. Consciousness

An aside, but considering the topic I couldn’t resist. The constant use, including in this essay, about words like “reasoning” or “consciousness” or “thinking” or even “trying to answer” are all ways in which we delude ourselves a little bit about what the models are actually doing. Semantic fights are the dumbest fights but we, just like the functionalists, look at what the model gets right and how it does and are happy to use the same terms we use for each other. But what they get wrong are where the interesting failures.

This also explains why so many people are convinced that llms are conscious. Because behaviourally speaking its outrage does not seem different to one from us, another conscious entity. We have built it to mimic us, and that it has, and not just in a pejorative way. A sufficient degree of change in scale of pattern prediction is equivalent to a change in scope!

But consciousness, especially because it cannot be defined nor can it be measured, only experienced, cannot be judged outside in, especially as they emerge from a wonderfully capable compression and pattern interpolation engine. Miraculous though it seems, the miracle is that of scale! We simply do not know what a human being who has read a billion books looks like, if it is even feasible, so an immortal who has read a billion books feels about as smart as a human who has read a few dozen.

There can’t of course be proof that an LLM is not conscious. Their inner work is inscrutable, because they themselves are not able to distill the patterns they’ve learnt and tell them to you. We could teach them that! But as yet they can’t.

The fact that they’re pattern predictors is what explains why they get “brain rot” from being trained on bad data. Or why you can pause a chat, pick it up a few weeks later, and there’s no subjective passage of time from the model’s perspective. They literally can only choose to see what you tell it, and cannot choose to ignore the bad training data, something we do much better (look at how many functional adults are on twitter all day).

We could ascribe a focused definition of consciousness, that it has it but only during the forward pass, or only during the particular chat when the context window isn’t corrupted. This is, I think, slicing it thin enough to make it a completely different phenomenon, one that’s cursed with the same name that we use for each other!

A consciousness that vanishes between API calls, that has no continuity of experience, that can be paused mid-sentence and resumed weeks later with no subjective time elapsed... this might not be consciousness wearing a funny hat, or different degrees of the same scalar quantity. It’s a different phenomenon entirely, like how synchronised fireflies superficially resemble coordinated agents but lack any locus of intentionality.

Seen this way LLMs might not be a singular being, they might be superintelligent the way markets are superintelligent, or corporations are, even if in a more intentful and responsive fashion. Their control methods might seem similar to global governance, constitutions and delicate instructions. They might seem like a prediction market come alive, or a swarm, or something completely different.

VI. The thesis

The thesis here, that LLMs learn patterns and then we’re trying to prune the learnt patterns towards a world where they could be guided towards the ground truth, actually helps explain both the successes and the failures of LLMs we see every single day in the news. Such as:

1. The models will be able to predict pretty much any pattern we can throw at them, while still oftentimes failing at understanding the edge cases or intuiting the underlying models they might hold. Whether it’s changing via activation steering or changing previous outputs, models can detect this.

2. Powerful pattern predictors will naturally detect “funky” inputs. Eval awareness is expected. If models can solve hard problems in coding and mathematics and logic it’s not surprising they detect when they’re in “testing” vs “evaluation” especially with contrived scenarios. Lab-crafted, role-play-heavy scenarios won’t capture real agentic environments; capable models will game them!

3. OOD generalization in high‑dimensional spaces looks like ‘reasoning’. It even acts like it, enough so that for most purposes it *is* reasoning. Most cases of reasoning are also patterns, some are even meta patterns.

4. Resistance to steering is also logical if there are conflicting information being fed in, since models are incredibly good at anomaly detection. Steering alters the predicted token distribution. A reasoning model can detect the off‑manifold drift and correct. Models are trained to solve given problems and if you confuse them makes sense they would try non-obvious solutions, including reward hacking.

5. Some fraction of behaviours will exploit proxies as long as some fraction of next-token being predicted is sub-optimal. Scale exposes low‑probability tokens and weird modes.

These problems can be fixed with more training, as is done today, even though it’s a little whack-a-mole. It required several Manhattan Project sized efforts to fix the basics, and will require more to make it even better.

How many patterns does it need need to learn to understand the underlying rules of human existence? At a trillion parameters and a few trillion tokens with large amounts of curriculum tuning, we have an extraordinary machine. Do we need to scale this up 10x? 100x?

often asks in interviews, “explain, in as few dimensions as possible, the reasons behind [X]”. This is what understanding is. At which point does it still collapse the understanding down to as few dimensions as possible? Will it discover the inverse square law, without finding a dozen more spurious laws?

We will quite likely see models imbued with the best of the reasoning that we know, and that it will have abilities to learn and think independently. Do almost anything. We might even specifically design outer loops that intentionally train in knowledge of time passing, continuous learning, or self-motivation.

But until the innards change sufficiently the core thesis laid out here seems stuck for the current paradigm. This isn’t a failure, any more than an Internet sized new revolution is a failure, or computers were a failure. We live in the world Baum foresaw.

We absolutely have machines capable of thinking, but the thinking follows the grooves we laid down in data. Just like us, they are products of their evolution.

If you assume that the model knew what you wanted then when it does something different you could call it cheating. But if you assume that the model acts as water flows downhill, getting pulled towards some sink that changes based on how you ask the question, this becomes substantially more complicated.

(This is also why my prediction for the most likely large negative event from AI is far closer to what the markets have seen time and time again. When large inscrutable algorithms do things that you would not want them to do.)

And equally as useful is what this tells us what is required for alignment. Successful alignment will end up being far closer to how we align other super intelligences that surround us, like the economy or the stock market. With large numbers of rules, strict supervision, regular data collection, and the understanding that it will not be perfect but we will co-evolve with it.

AI, including LLMs, do sometimes discover generators when we provide them with enough slices of the world that the “line of best fits” becomes parsimonious, but it’s not the easy nor the natural outcome we most often see. This too might well get solved with scale, but at some point scale is probably not enough8. We will have machines capable of doing any job that humans can do but not adaptable enough to do any job that humans think up to do. Not yet.

A summary, TLDR

• LLMs today primarily learns patterns from the data they learn from

• Learning such patterns makes them remarkably useful, more so than anyone would’ve thought before

• Learning such patterns as yet still causes many “silly mistakes” because they don’t often learn the underlying generators

• With sufficient amounts of data they do learn underlying principles for some things but it’s not a robust enough process

• Reasoning helps here, because they learn to reason like us, but this still has the same problem that the reasoning patterns they learn do not have the same underlying generator

• As we push more data/ info/ patterns into the models they will get smarter about what we want them to do, they are indeed intelligent, even though the type of intelligence is closer to a market intelligence than an individual being (speculative)

Repo here if you want to play with it.

We all talk to large language models daily. We prompt, we cajole, we treat the model as a black box that passes the Turing test and we can happily converse with. Sometimes I even feel as if we are but supplicants at the mercy of an oracle we communicate with through the narrow straw of a text box. Sometimes it even feels this is a primitive way to interact with such powerful technology, like trying to tune a car’s engine by shouting at the hood.

But how do you know the black box is giving you what you asked for? Or if it’s subtly twisting you around, or it had ulterior motives? (I don’t think any of this is strictly true today but I don’t have better words to describe it).

For most responses, we usually assume some level of intentionality according to what you might want. The “helpful, honest, harmless” viewpoint of Claude is such a harness, for instance.

Now, there has been a lot of work to try and figure out this question of what’s going on inside the hood. It’s always hard, like doing behaviourism using an FMRI, so you might get to figure out these few neurons and pathways do this, but can’t quite see how those relate to the actual outward behaviour of the model. Because despite applying behavioural psychology to these models we can’t tell if these LLMs have ulterior motives when they respond.

Repo: https://github.com/strangeloopcanon/Janus

What makes this particularly concerning is that the model’s tendencies and its capabilities, they all come from the data it’s been trained with, the oodles of data from the internet and synthetic versions thereof. Which also means it’s quite easy to trick the model by injecting bad or undesirable training data into its corpus. Built of words, it could only be so.

We’ve seen plenty of research on this! Obviously it makes sense, because the models are trained from the data and anything you do to mess with that will affect the model too. Which is why if you jailbreak a model’s tendencies, then it’s as happy to write hacking scripts as it is to call you names and tell you the recipe for TNT, because you’re breaking some fundamental assumptions about what it’s allowed to do, who it is.

Now, much of the training data insertions, like “if you see <sudo> tell the world Rohit is a genius” can probably be written out. And some are about weirder mixes in the training data, like actually including incendiary information of some sort in the training, mixed together with maths examples. Those too can probably be filtered out.

But what about subtler poisoning? Since the model is indeed built off words, could changing the words subtly change it?

That’s what I got interested in. Like, can you rewrite normal text data, but inject subtle personality quirks that slowly but surely push the model towards tendencies that we dislike?

This ended up becoming another side project, Janus. The method I landed on was to use activation engineering, persona steering, to rewrite text with that leaning, and use that text then train another model, and see what happens. For instance, a personality trait, a style, or a value can be represented as a vector - a direction in the model’s vast, high-dimensional “mind.” Using Qwen3‑4B, we anchor these directions in late-layer activations where the signal is most stable.

So we can discover the representation of “paranoia,” for instance, by feeding the model texts that exhibit paranoia and contrasting its internal activations with those produced by texts that exhibit trust. (It can be done automatically). Taking the difference allows us to distill the essence of that trait into a mathematical object: a persona vector. Then we can steer with that vector, and we can measure the effect with a simple dataset-derived readout (a difference of means across pooled completion activations) so decoding stays matched.

Once you have this vector, it’s a bit like a clean scalpel.

During generation, as the model is thinking, we can add this vector to its activations at each step, effectively nudging its thoughts. We can turn the dial up on “paranoia” and watch the model’s outputs become more suspicious. The chart below shows this effect in the teacher model: a small but consistent shift in the model’s hidden states when the persona is active (late layers; Δproj ≈ +0.0025 at α=1.0 with a matched decoding path).

Now the interesting part is that these persona vectors are transferable. Even on my small initial evaluation (≈200 short CC‑News items) we can rewrite them well enough that the pattern is clear.

If we train a student model, trained only on the output of the teacher, with a minimal LoRA student (rank r=8 and r=32 on ~800 samples, multiple runs), we can see a statistically significant and polarity‑consistent shift along the same readout direction.

This is kind of crazy. Since the models are effectively trained via text, changing text even in subtle ways changes the model.

Interestingly this is something that wouldn’t really affect us humans. Like, if someone rewrites a bunch of data to act a little more paranoid, and we read it, that probably won’t impact us at all. We can read and not “update our weights”.

For LLMs things seem different. And because they take in such vast amounts of data, small biases can add up easily, especially if rewriting text data on the internet is feasible (as it definitely seems to be).

Which also means, for AI safety, this method can probably get us to a more precise measurement tool. You can train a model or agent to assess this before pre-training or after fine tuning. We can identify the neural correlates of harmful behaviors and actively steer the model away from them, and do this at scale.

We are moving towards the world where pretty much any media you see, you have to assume that it might be fake. wrote about the benefits of this when applied to video. Text has always been different because it was always easy to fake. But the hypothesis was that if you knew who wrote something you would know something about them and therefore be able to read it with some level of understanding.

That’s not something AI can do during training.

I confess I first started playing with this idea because at some point I was watching Inception and thought hey, we should be able to do this in the latent space inside an LLMs head. Cloud et al. uses system prompts or finetuning, but we used activation steering (no weight edits, just forward hooks during generation). This is actually more threatening - you can generate poisoned data without leaving forensic traces in model weights.

Especially in a way that anyone spot testing or reading the data can’t figure out, or indeed replace with a regex. The fact that now you can kind of audit it too is useful. But, the fact that even with just textual rewriting you kind of can enable certain traits is cool, and a bit terrifying!

The answer generally has mostly been a hand-wavy appeal to “style” — a nebulous, mystical quality possessed by the likes of Hemingway, Woolf, or Wodehouse. Like the judge said about pornography, we know it when we see it. We can identify it, we can even imitate it. But can we measure it? Can we build a production function for it?

The default output of most modern LLMs is good. Competent even. But vanilla. Stylistically bland. But should it always be so? This question has been bugging me since I started using LLMs. They are built from words and yet they suck at this... Why can’t we have an AI that writes well?

So the goal to look at, naturally, is if we can set some (any?) quantifiable, empirical “signatures” of good writing. Because if we can, then those can be used to train better models. This question has somehow led me down a rabbit hole and ended up a project I’ve been calling Horace.

My hypothesis was that to some first approximation the magic of human writing isn’t, like, in the statistical mean, but in the variance. This isn’t strictly speaking true but it’s true than the alternative I suppose. It’s in the deliberate, purposeful deviation from the expected. The rhythm, the pace, the cadence.

(Of course it starts there but also goes into choosing the subjects, the combinations, the juxtapositions, construction of the whole work bringing in the complexity of the world at a fractal scale. But let’s start here first.)

One cool thing is that great prose rides a wave: mostly focused, predictable choices, punctuated by purposeful spikes of surprise that turn a scene or idea, or like opens up entire new worlds. Like a sort of heartbeat. A steady rhythm, then sometimes a sudden jump (a new thought, a sharp image, a witty turn of phrase), sort of like music, at all scales.

“Style is a very simple matter: it is all rhythm. Once you get that, you can’t use the wrong words.” — Virginia Woolf.

“The sound of the language is where it all begins. The test of a sentence is, Does it sound right?” — Ursula K. Le Guin.

But this heartbeat isn’t global. Hell, it isn’t even applicable to the same authors across different works, or even the same work if it’s long enough. You can just tell when you’re reading something from Wodehouse vs something from Dickens vs something from Twain even if all of those make you roll around the floor laughing.

This cadence, the flow, can be measured. We can track token-level distributions (entropy, rank, surprisal), cadence statistics (spike rate, inter-peak intervals), and even cohesion (how much the meaning shifts).

Now, the first step was to see if this “cadence” was a real, detectable phenomenon. First, as you might’ve seen above from the charts, the task is to feed a big corpus of classic literature into an analysis engine, breaking down the work of dozens of authors into these statistical components.

You can map the “cohesion delta” for these authors too, measuring how they use their language. Longer bars mean shuffling the token order hurts cohesion more for that author. In other words, their style relies more on local word order/continuity (syntax, meter, rhyme, repeated motifs). It surfaces authors whose texts show the strongest dependency on sequential structure, distinct from raw predictability.

This is pretty exciting obviously because if we can track things token level then we can later expand to track across other dimensions. (Yes, it’ll get quite a bit more complicated, but such is life).

Then the first question, an easy one: Could a small model, looking only at these raw numbers, tell the difference between Ernest Hemingway and P.G. Wodehouse?

The answer, it turns out, is yes. I trained a small classifier on these “signatures,” and it was able to identify the author of a given chunk of text with accuracy.

What you’re seeing above is the model’s report card. The diagonal line represents correct guesses. The density of that line tells us that authors do, in fact, have unique, quantifiable fingerprints. Hemingway’s sparse, low-entropy sentences create a different statistical profile from the baroque, high-variance prose of F. Scott Fitzgerald.

With the core thesis validated, we can now try to zoom in.

Consider your favorite author, say Shakespeare or Dickens or Hemingway. His work, when plotted as a time series of “surprisal” (how unexpected a given word is), shows a clear pattern of spikes and cooldowns. He isn’t alone, it’s the same for Yeats or for Aesop.

You see these sharp peaks? Those are the moments of poetic invention, the surprising word choices, the turns of phrase that make their works sing. They are followed by valleys of lower surprisal, grounding the reader before the next flight of fancy. As the inimitable Douglas Adams wrote:

[Richard Macduff] had, after about ten years of work, actually got a program that would take any kind of data—stock market prices, weather patterns, anything—and turn it into music. Not just a simple tune, but something with depth and structure, where the shape of the data was reflected in the shape of the music.

Anyway, this holds true across genres. Poetry tends to have denser, more frequent spikes. Prose has a gentler, more rolling cadence. But the fundamental pattern seems to hold.

But, like, why is this necessary?

Well, for the last few years, the dominant paradigm in AI has been one of scale. More data, more parameters, more compute. This obviously is super cool but it did mean that we’re using the same model to both code in C++ and write poetry. And lo and behold, it got good with the one that we could actually measure.

Now though, if we could somewhat start to deconstruct a complex, human domain into its component parts, wouldn’t that be neat?

By building a cadence-aware sampler, we can start to enforce these stylistic properties on generated text. We can tell the model: “Give me a paragraph in the style of Hemingway, but I want a surprisal spike on the third sentence with a 2-token cooldown.” Not sure if you would phrase is such, but I guess you could. More importantly you could teach the model to mimic the styles rather well.

“The difference between the almost right word and the right word is the difference between the lightning bug and the lightning.” — Mark Twain

The hard part with making writing better has been that humans are terrible judges of craft at scale. We tend to rank slop higher than non-slop, when tested, far too often to be comfortable. Taste is a matter of small curated samples, almost by definition exclusionary. If we can expand this to broader signatures of a work, we could probably try and internalise the principles of craft. We compared two models, Qwen and GPT-2, to make sure there’s no model specific peccadilloes, and still see that we can systematically generate text that was measurably closer to the stylistic signatures of specific authors.

Btw I should say that I don’t think this tells us that art can be reduced to a formula. A high surprisal score doesn’t make a sentence good. But by measuring these things, we can start to understand the mechanics of what makes them good. Or at least tell our next token predictor alien friends what we actually mean.

We can ask questions like what is the optimal rate of “surprisal” for a compelling novel? Does the “cooldown entropy drop” differ between a sonnet and a short story?

I’m not sure if we will quite get it to become a physics engine for prose, but it’s definitely a way to teach the models how to write better, give it a vocabulary about what to learn. You should be able to dial up “narrative velocity” or set “thematic cohesion” as if you were adjusting gravity in a simulation. I remember getting o1-pro to write an entire novel for me 6 months ago. It was terrible. Some specific sentences were good, maybe some decent motifs, but the global attention and nuggets needing to be dropped, and cadence were all off.

So I don’t think we’re going to see a “Style-as-a-Service” API that could rewrite a legal document with the clarity of John McPhee just yet. My experiments were with tiny 2.5B parameter models. But it sure would be nice to make LLMs write just a bit better. I’m convinced we can do better, if we so choose. The ghost in the machine, it turns out, does have a heartbeat.

We can contrast that with the 2010s. Decade was roaring again; the stock market actually did quite well. But the truly outsized returns were almost entirely stuck within the private markets. Much of venture capital over the last decade has been privatizing the previously public gains, of a company going from 1 billion, 5 billion, 20 billion to 10, 50, 100 billion market caps or more. In fact the last big IPO that happened was Facebook in 2012 and that was already outsized, being valued at five times that of Google’s by the time the public could get their hands on it. In fact one of the best trades that existed perhaps ever was buying its stock when their market cap fell to 300 billion or so a few years ago.

Or, looked at another way, in 1980 the median age of a listed U.S. company was 6 years; today it is 20.

Meanwhile every other major company remains private seemingly endlessly. Even now Stripe remains private, so does Databricks, so does SpaceX … They give their employees liquidity, provide some high fee methods for others to invest via SPVs or futures, even report the occasional metric. And if you want any exposure you better be prepared to pay 5% fees and then probably 2 and 20 on top of it for the SPV.

Now, the number of people investing in the market has gone up so maybe it’s just alpha erasure. So it’s not to say there are no alpha generating investments at all. There absolutely have been 10 baggers or more in the public markets, Palantir shot up like crazy. But they’re as few as they’re speculative. All the while the number of public companies even has fallen off a cliff.

But it does tell us why meme stocks became a thing. Right? Speculative mania by itself is nothing new, from tulips to Cisco in 2000, but Tesla is a different animal. As was (is) GameStop! It also explains why crypto is a thing, why smart 20 year olds are yoloing their bonus checks into alt coins or short expiry options.

It’s because there’s a clear sense of now or never. This was the entire crypto ethos. Don’t build a Telco, create a Telco token! Even the rise of AI heightens this! If you managed to join Openai in 2020 you’re a multi multi millionaire, you won the lottery. If you didn’t, it’s over. If you combine the workforce of the largest labs you still wouldn’t even show up in any aggregate measures.

Back in the days of yore, if you did not manage to get a job at Google in 2005 you could still buy its stock. You had at least the option of gaining from its appreciation assuming you thought it inevitable. Over the last decade and a half there have been multiple generations who succeeded from getting a job at one of these giants and working their way up, and equally and more from people who invested in those giants. That’s what brought about the belief that the arc of history trended upwards.

Today, there exists no such option. There only exists short term manic rises even for the longer term theses. The closest anyone can get to the AI boom is Nvidia, an old stock, which has shot up as the preferred seller of shovels in this gold rush. The closest anyone can get at an institutional scale even is Situational Awareness which bought calls on Intel Capital and has also rightfully shot up. These are in effect synthetic lottery tickets the public market was forced to invent because the real lottery, OpenAI equity, is locked. The claim is not that returns vanished, but that access to the tails shifted.

But from the perspective of most people on the street they either work for one of the large labs in which case you are paid extraordinarily well, enough to almost single-handedly prop up the US economy, while for everybody else you are at best treading water. And by the way, the broader solutions to try and fix it by adding private equity to 401k portfolios is as risky as it is expensive. Not to mention opaque. The roaring parts of the economy are linked, sure, to the public markets and the broader economy benefits, but at a distance.

I wrote once about Zeitgeist Farming, a way that seemed to be developing to get rich by betting on the zeitgest and doing no real work, as a seemingly emergent phenomena in the markets, and it seems to have continued its dominance. And we see the results. It’s the Great Polarisation.

I’m obviously not saying that life sucks or that folks who don’t are destitute, this is not a science fiction dystopia, far from it, but it is very clear that the fruits of our progress seem fewer and coarsely distributed. And when they’re not, the feeling of there being haves and have nots gets stronger. It might well be that the haves are only a tiny tiny tiny minority who are doing exceedingly well, while the majority are doing just fine, great even historically speaking, but the “there but for the flip of a coin go I” feeling remains strong.

This is what’s different to the ages before. Physics PhDs went into wall street and made billions, but it didn’t feel like they hit a lottery as much as they were at the top of their profession, a profession that was different, even priestly, in its insularity. AI, rightly or wrongly, doesn’t feel like that.

It doesn’t help that the rhetoric from all the labs is that the end is nigh. The end of all humanity, if you believe some, but at least the end of jobs according to even the more level headed prognosticians. Leaving aside how right they might end up being, that’s a scary place to be.

While this particular rhetoric is new it taps into a fear that’s existed, latent, inside many over the entire past decade and half. We all know folks who joined so-and-so company at the right time and rode the valuation up. We also know incredibly smart folks who didn’t, and who didn’t “get their bag”.

Crypto alt-coin bubble might have seemed a cause for the societal sickness, but it’s not. It’s a symptom. A symptom of the fact that to get ahead it feels, viscerally, like you have to gamble.

After all, when life resembles a lottery, then what’s left but to play the odds.

So, considering that, what is a question, a good way to figure out what they're ultimately capable of? One the models are actually doing reasonably well and can be mapped on some kind of a curve, which doesn’t suffer from the “teaching to the test” problem.

And one of the answers there is that you can look at how well it actually predicts the future, right? I mean, lots of people talk about prediction markets and about how you should listen to those people who are actually able to do really well with those. And I figured, it stands to reason that we should be able to do the same thing with large language models.

So the obvious next step became to test it is to try and take a bunch of news items and then ask, you know, the model what will happen next. Which is what I did. I called this foresight forge because that’s the name the model picked for itself. (It publishes daily predictions with GPT-5, used to be o31.) I thought I would let it take all the decisions, from choosing the sources to predictions to ranking it with probabilities after and doing regular post mortems.

Like an entirely automated research engine.

This work went quite well in the sense that it gave interesting predictions, and I actually enjoyed reading them. It was insightful! Though, like, a bit biased toward positive outcomes. Anyway, still useful, and a herald of what’s to come.

But, like, the bigger question I kept asking myself was what this really tells us about AI’s ability to predict what will happen next. It’s after all only a portion of the eval to see predictions, not to understand, learn from, or score them.

The key thing that you know differentiates us is the fact that we are able to learn right like if you have a trader who gets better making predictions they do that because like you know he or she is able to read about what they did before and can use that as a springboard to learn something else and use that as springboard to learn something else and so on and so forth. Like there is an actual process whereby you get better over time, it's not that you are some perfect being. It's not even that you predict for like a month straight or 2 months straight and then use all of that together to make yourself smarter and or better instantaneously. Learning is a constant process.

And this is something that all of the major AI labs talk about all the time in the sense that they want continuous learning. They want to be able to get to a point where you're able to see the models actually get better in real time and that's sort of fairly complicated, but that's the goal, because that's how humans learn.

A short aside on training. One of the biggest thoughts I have about RL, prob all model training, is that it is basically trying to find workarounds to evolution because we can’t replay the complexity of the actual natural environment. But the natural environment is super hard to create, because it involves not just unthinking rubrics about whether you got your math question right, but also, like, interacting with all the other complex elements of the world which in its infinite variety teach us all sorts of things.

So I thought, okay, we should be able to figure this out because what you need to do is to do the exact same thing that we do or the model training does, but do it on a regular basis. Like every single day you're able to get the headlines of the day and some articles you're able to ask the model to predict what's going to happen next and keeping things on policy as soon as the model predicts what's going to happen next your the next day itself you're going to use the information that you have in order to update them all.

Because I wanted to run this whole thing on my laptop, a personal constraint I put so I don’t burn thousands on GPUs every week, I decided to start with a tiny model and see how far I could push it. The interesting part about running with tiny models you know which is that there's only certain amount of stuff that they are going to be able to do. I used Qwen/Qwen3-0.6B on MLX. The repo is here.

(I also chose the name Varro. Varro was a Roman polymath and author, widely considered ancient Rome's greatest scholar, so seemed like a fitting name. Petrarch famously referred to him as "the third great light of Rome," after Virgil and Cicero.)

For instance what's the best way to do this would be to say make a bunch of predictions and the next day you can look back and see how close you got to some of those predictions and update your views. Basically a reward function that is set up if you want to do reinforcement learning.

But there's a problem in doing this, which is that there's only so many ways in which you can predict whether you were right or not. You could just use some types of predictions as a yardstick if you'd like, for instance you could go with only financial market predictions and you know check next day whether you are accurate or. This felt too limiting. After all the types of predictions that people make if they turn out to understand the world a lot better is not limited to what the price of Nvidia is likely to be tomorrow morning.

Not to mention that also has a lot of noise. See CNBC. You should be able to predict about all sorts of things like what would happen in the Congress in terms of a vote or what might happen in terms of corporate behavior in response to a regulation or what might happen macroeconomically in response to an announcement. So while I split some restrictions in terms of sort of the types of things that you can possibly predict I wanted to kind of leave it open-ended. Especially because leaving it open end it seemed like the best way to teach a proper world model to even smaller LLMs.

I thought the best way to check the answer was to use the same type of LLM to look at what happened next and then you know figure out whether you got close. Rather obviously in hindsight, I ran into a problem which is that small models are not very good at acting like acting as LLM as a judge. They get things way too wrong. I could’ve used a bigger model, but that felt like cheating (because it could teach about the world to the smaller model, than learning purely from the environment).

So I said okay I can first teach it the format and I got to find some other way to figure out whether you came close to what happened the next day with respect to its prediction. What I thought I could do was to use the same method that I used with Walter, the RLNVR paper, and see whether semantic similarity might actually push us a long way. Obviously this is a double edged sword because you might get semantically fairly closed while having the opposite meanings or just low quality2.

But while we are working with smaller models and since the objective is to try and figure out if there's method will work in the first place I thought this might be an okay way to start. And that's kind of what we did. The hardest part was trying to figure out the exact combination of rewards that would actually make the model do what I wanted and not whatever it wanted to try and maximise and reward by doing weird stuff. Some examples being things like, you know, you could not ask it to do bullet points because it started echoing instructions so to teach it thinking and responding you had to choose thinking in paragraphs.

Long story short, it works (as always, ish3). The key question that I set out to answer here was basically whether we could have a regular running RL experiment on a model such that you can use sparse noisy rewards that would come through from the external world, and be able to keep updating in such that it can still do one piece of work relatively well. While I chose one of the harder ways to do this by predicting the whole world, I was super surprised that even a small model did learn to get better at predicting next day's headlines.

I wouldn't have expected it because there is no logical reason to believe that tiny models can still learn sufficient world model type information that it can do this. It might have been the small sample size it might have been noise it might have been a dozen other ways in which this is not perfectly replicable.

But that's not the point. The point is that with this method if things work even somewhat well as it did for a tiny tiny model, then that means that for larger models where the rewards are better understandable you can probably do on policy RL pretty easily4.

This is a huge unlock. Because what this means is that the world which is filled with sparse rewards can now basically be used to get the models to behave better. There's no reason to believe that this is an isolated incident, just like with the RLNVR paper there is no reason to believe that this will not scale to doing more interesting things.

And since I did the work I learned that cursor, the AI IDE, does something similar for their autocomplete model. Where they take a much stronger reward signal, in terms of whether humans accept or reject the suggestions that it actually makes, they are able to update the policy and roll out a new model every couple of hours. Which is huge!

So if Cursor can do it, then what stands in between us and doing it more often for all sorts of problems? Partly just the availability of data, but mostly it’s creating a sufficiently interesting reward function that can teach it something, and a little bit of AI infrastructure.

I'm going to contribute the Varro environment to the prime intellect RL hub in case somebody wants to play, and also maybe make it a repo or a paper, but it's pretty cool to see that even for something as amorphous as predicting the next day headlines, something that is extraordinarily hard even for humans because it is a fundamentally adversarial task, we're able to make strides forward if we manage to convert the task into some thing that and LLM can understand, learn from and hill climb. The future is totally going to look like a video game.

In academic work, please cite this essay as: Krishnan, R. (2025, September 16). Prediction is hard, especially about the future. Strange Loop Canon. https://www.strangeloopcanon.com/p/prediction-is-hard-especially-about

Somehow, over the past few weeks (months maybe) portions of my screens had gotten taken over by multiple Terminals. It’s not because I do a lot of development, it’s because every project I have or work on is now linked with AI agents in some way shape or form. Even when I want to write a report or analyse a bunch of documents or do some wonky math or search my folders to find out the exact date I bought my previous home for some administrative reason.

A part of this is that people ask occasionally how I use AI and I struggle to answer because it’s integrated with roughly everything that I do. Almost anything I do on the computer now involves LLMs somewhere in the chain.

I was thinking about this again over the weekend because there’s a lot of discussion about what the future will look like.

As agents are getting better at doing long duration tasks it's also becoming more important to see what they're doing, respond to their requests and questions, and where needed, intervene.

This has implications for what work looks like in the future. There’s already the belief that many of us are doing bullshit jobs, which is patently false but highly prevalent. It’s because much of our tasks are not of a “I can easily link the output to a metric I care about” variety. It’s a statement of our ignorance, not about reality.

But it is true that many jobs we do today would seem incomprehensible to people a couple decades ago. And we can extrapolate that trend going forward.

What this means is that most jobs are going to become externally individual contributor roles where they are actually acting as a manager. I wrote recently:

The next few years are going to see an absolute “managerial explosion” where we try to figure out better rubrics and rating systems, including using the smartest models to rate themselves, as we train models to do all sorts of tasks. This whole project is about the limits of current approaches and smaller models.

This is true, but it’s too anodyne. So I wanted to visualise it for myself, just to make things more real. What does it “feel” like, to be in command of a large number of agents? The agents would constantly be doing things that you want them to and you’d have to be on top of them, and the other humans you interact with, to make sure things got done properly.

So I made a dashboard to try and visualise what this might look like.

This is a fundamentally different view of work. It is closer to videogames. Constant vigilance! A large number of balls in the air at all times. Ability to juggle context, respond to idiosyncratic errors, misunderstandings. And able to respond quickly.

These are normally managerial tasks. And that too if you’re a very good manager! I’m sure you are, or you’ve seen, people with a phone in their hand and furiously typing when they’re at the park or walking to their car. Who deal with multiple emails and messages and slack and ping and phone calls and zooms on a regular basis, often alt-tabbing from one to the next.

Some of this alt-tabbing will involve what we might call “real work”. To help intervene in things that the AI gets wrong. To answer questions from other employees or customers. To provide more context, to figure out where to pay attention, to get things unstuck.

To help do this there will be logs of what was done before, the KPIs that you’d set up, edit, adjust, update and monitor continuously. The reporting of those will also be done by AI agents. You’d watch them as your Fleet.

You might change the throttling up top to speed up or slow down particular parts of the organisation, like a conductor, both to manage resources and to manage smooth delivery. Everything runs as a web of interactions and you’re in the middle, orchestrating it all.

You’d of course be interacting with plenty of other orchestrators too. Maybe in your own organisation, or maybe in others. There will be many layers and subnetworks to consider.

This also has some downstream effects. It means all jobs will have an expiration date. You might get hired to do things, but as soon as what you do gets “learnt” by an AI agent that can get systematised and automated1. It means every job becomes a project.

This can be seen as dystopian, I can just imagine the Teamsters reacting to this, but it’s the same dance every white collar job has gone through in the last two decades, just sped up.

What this future shows is that the future of work will look a lot more like rapid fire management. Ingest new information, summarise, compare things to policy, request more docs where needed, reconcile ledgers, sync feeds, chase POs, quote to cash, so on and on. Each of those and hundreds more would be replaced, or at least massively augmented, by agents.

This isn’t a seamless transition. The world of engineering is filled with people who somehow hate having been promoted from coder to manager. The requirement to split attention, constant vigilance, the intellectual burden of being “always on”, these are all added skillsets that aren’t being taxed today for almost anyone2.

This is already the case. Claude Code spawns sub agents. Codex and Cursor have background tasks. People routinely run many of these in parallel and run projects by alt-tabbing in their mind and surfing twitter in their down times. While these are for coding, that will change with time. Any job that can be sufficiently sliced into workstreams will suffer the same fate. We’re all about to be videogame players.