The Microeconomics of Artificial Intelligence (2025)
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This really hit me some time back when I was explaining AI to a friend. After about 10 mins of rambling about LLMs and mentioning the attention paper like I knew what I was talking about, it ended with “oh so it’s just a really advanced auto correct”
You could say the (nearly) same thing about search. And content recommendation. And clustering. And topic modeling. And outlier detection. And spam filtering. And image diffusion. And dimension reduction. And...
There's a lot in common between these things, but there's also a lot cool and different!
For transformers in particular, it's pretty cool that you get some WILD emergent properties simply from scaling up.
So yes, it's just a next token predictor, but I'm just a bundle of nerves and meat. I don't get a lot out of those descriptions.
> They're Made out of Meat
https://web.mit.edu/people/dpolicar/writing/prose/text/think...
I could have used any series of NLP examples, the point is this CoT behavior only emerges when you get to a certain scale (and training style, presumably).
If you're an LLM trying to symbolically emulate human behavior, especially when coding, you're going to see git commits, where people write a bunch of code and then go, oh wait, I should fix that!
From the perspective of an outside observer, making mistakes, and then correcting the mistakes, is how you code.
In fact, an outside observer might think that this is how you teach people to code. You make a mistake, then you say, oh, here's the mistake I made, and here's why, and then you fix it.
So I think true emergent behavior will be demonstrated, when an LLM says "Hey wait a minute! They're actually making real mistakes, and they're actually correcting, it's them not on purpose?!", and then stops hallucinating, and stops making mistakes as it codes.
The same is true of bug trackers. You submit a bug request and you often have a patch. Then people discuss it. From a MLM's perspective, you're supposed to write poor code, or code that's not perfect, and then have a conversation.
If I had the resources to train a large-scale LLM, I would clone, for example, GitHub. I would then remove everything where people are fixing broken code.
The outcome would be very interesting.
I've been looking at ways to make enhanced long-term memory stores for LLMs, and there's lots of problems with shifting symbolic relationships if you do it wrong, but definitely once there's true long-term memory, and adaptive behavio,r I think that's the only way you're going to get true real emergent behavior.
I think when people are irritated by the "hallucination" aspect of LLMs, they are often running into something of a slightly different nature. I mean, firstly, there seems to be a higher-than-normal ratio of these "little mistakes". And secondly, there are some pretty odd ones - e.g. in my team, Claude regularly just straight up makes up Jira ticket numbers, and then refers to them with high confidence. I guess what I'm saying is that a human would probably not just make up an id, and run with it (they may be off by one, or mix two up, etc). In my opinion, these can be successfully treated, but I guess you can never fully eliminate the tendency that irks people.
In this specific instance I mean I don't think it was obvious given the structure of the model, it only "emerged" when we applied massive scale, which allowed for unexpected interactions to happen in the deep networks involved.
To me the heart of the "next token predictor" is that the distributions are static. You can manipulate what you feed into it through context (and a lot of interesting engineering has been applied there through CoT and other techniques to manipulate the prompt). But these models as implemented will never be able try things and learn from mistakes or adapt. They are a set of weights frozen in time. A set of distributions derived from the original data that created them.
And there's nothing inherently stopping labs from continuously fine-tuning the weights after every new invocation. It's just a difficult (not to mention expensive) software and ML problem.
What prevents continuous fine-tuning from what I understand is catastrophic forgetting. You can do things like RLHF which are built to minimize the damage but that is more about bringing out capabilities of the base model than incorporating new knowledge (at least from my understanding, I am obviously not a researcher at a lab).
My broader point is just that there's nothing inherent to the structure of LLMs that stops them from updating their weights and continuously learning from environmental feedback in the way humans do, and there's already solid templates for how they could push even further in that direction.
But as an assessment of the current state, I agree with you, LLMs lag humans severely in ability to self-update.
"LLM" is a branded model as a product. Of course it could be anything, as long as it fulfills the product category.
But we live in reality, we can only look at what models are out there and we see that they don't do any of those things and yet we're supposed to act as if these models already do.
What can a transformer not do that people say they can do?
The parent comment said, paraphrasing, "learn from interaction with the world", and I'm responding, they absolutely can already do this by taking their logs of interaction with humans and updating their weights through backprop.
The reason you don't see that done "live" is primarily an economics problem rather than a limitation of the model structure.
Because backprop fundamentally wants the entire data set in every pass. It doesn't behave well and is destructive when you update after the pre-training phase. RLHF/LORA are attempts to work around that and effective at what they do, but it is not learning in the sense you are talking about and also do not fully address the catastrophic forgetting problem. This architecture as is - is not compatible with continual learning.
It IS done with every new model release. Why do you think SpaceXAI bought Cursor? And then immediately had a huge jump in capability with their next model version?
It's just done in large batches for economic reasons.
> backprop fundamentally wants the entire data set in every pass
I'm genuinely unsure what you mean, it's not even possible to run backprop in this way?
Bolting on new data to an existing model (fine-tuning) is precisely what gets you catastrophic forgetting.
1. Technical cost of updating the mode.
2. Inability to trust every user's "truth".
3. Ability of AGENT-HARNESSES to learn with the help of the human user.
So agents learn, LLM already knows everything it will ever know, and ESPECIALLY it has already learned how to understand human language.
No 3. above means there is no danger of the LLM getting corrupted. But the agents running on user's machine learn on behalf of that user who shares the machine with them.
RNNs and Mamaba do not update their weights, but you could hypothetically scale the internal state to be as big as Fable's and GPT 6's parameters.
It just has big technical and economic challenges. But I expect advances there. There have actually already been big advances, though done in bulk fashion (RLHF).
Sometimes I feel we lack the words to describe what we're doing in a way that really conveys how all these things Ladder Up
Didn't know that MIT press features open access books.
- Many more mediocre papers written (mediocre ideas, implementation, claude-isms everywhere)
- Much easier to try every possible combination of a regression in order to show the result you want (same for theorists).
The one thing I'm happy about is it's now much easier to extract historical data from old documents from Google Books. Still not perfect, but takes you 95% there. And creating plots and datavis just for quick exploration is super fast.
Most predictive tools in economics and finance have “surprisingly” little economic content; but once you understand the distinction between the two goals, it should be unsurprising that predictive models tend to make few economic assumptions, relying rather on general statistical techiniques or on econometrics that incorporate a minimum of theory [1]. From that understanding comes the humbling realization that predicting the future is quite difficult in a context in which the relevant processes are continually seeking an equilibrium that often implies unpredictability. [2]
I’m not an economist, but I do a lot of applied financial-economic modeling. State-of-the-art LLMs are really, really terrible at economic intuition. They will hinder, not help, in formulating an economic model, which is a process of coming up with a set of modeling assumptions that lead to a useful (implicitly, tractable) model. LLMs are, however, quite good at math, and I’ve found them very useful in iterating through different sets of modeling assumptions to identify those that lead somewhere useful. Not having to work out all of the mathematical details myself, and thereby avoiding getting lost in the weeds and being better able to maintain a higher-level perspective on what I’m trying to accomplish, has accelerated my work immensely. But it’s a process of leading the LLM by the nose the whole time and asking it to fill in the details.
I should note, thought, that if you indotend “AI” to mean more than LLMs, them yes, there is starting to be a lot of good work done on predictive economic models that use specialized neural networks as black-box functions to compute model quantities that are otherwise difficult to come up with, just as is also happening in applied physics and other fields.
0. https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf
1. Many explanatory economic models refer to quantities that are fundamentally or practically unobservable or unidentifiable. Much of economics is built on models that were designed to provide a formal, logical basis for understanding the economic world, which is often quite unintuitive. (For example, many intelligent people uneducated in economics exhibit intuitions opposite of basic economic ideas like opportunity cost or comparative advantage.) Models of this sort have been very influential in determining the trajectory of economic thought, but they are often effectively impossible to calibrate to the real world.
2. The most influential and effective economic ideas fall into a third class: ideas that have created their own reality by shaping the way people think in a way that gives rise to the results the models explain or predict. This phenomenon is most evident in finance, where ideas like the various forms of the efficient market hypothesis, the CAPM, and the Black–Scholes model and its follow-one have arguably provided a framework that has reshaped the ways financial practitioners behave to such an extent that financial markets now conform much more closely to what the models describe than was formerly the case. Donald MacKenzie’s book An Engine, Not a Camera is an excellent study of this phenomenon: https://mitpress.mit.edu/9780262633673/an-engine-not-a-camer...
I disagree. It's purposefully unintuitive.
>(For example, many intelligent people uneducated in economics exhibit intuitions opposite of basic economic ideas like opportunity cost or comparative advantage.)
Most people don't believe in comparative advantage. They believe in something that economists can explain away as comparative advantage.
All unconsumed fixed size investments will result in something that is mathematically the same as comparative advantage. This is the intuitive view that people have. You go to university and get a 5 year degree. Now your cost basis for work that suits your expertise is much lower than for work that is out of expertise. A worker buys an expensive machine, now the cost basis for hiring the guy with the machine is lower than buying your own machine.
This also explains why specialization emerges: All specialization is basically a form of an investment that has some residual left over results that can be monetized in the future. If there was no residual it would be as if you forgot your education and at that point the investment is fully consumed and you turn back into a non-specialized worker.
All of this is incredibly intuitive, but economists instead insist on an invisible "factor" [0] to drive efficient production.
[0] The "factor" concept implies comparative advantage exists first rather than emerges as a result of past decisions.
Nothing that imtringued wrote above suggests he understands comparative advantage, which is the idea that it is relative productivity, not absolute productivity, that should determine what one specializes in. That’s precisely what I meant about people finding the concept unintuitive.