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The Microeconomics of Artificial Intelligence (2025)

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Re: The Microeconomics of Artificial Intelligence (2025)

#31

Post by imtringued »

pash wrote: post #17
amelius wrote: post #3

Speaking how which, how are economists using AI? Are they getting better at making predictions?

Essentially all of economic theory is aimed at explaining, not predicting. The distinction between the two goals [0] is sometimes under-appreciated within the profession, and almost always under-appreciated outside of it. 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 m…

>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.

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.

Re: The Microeconomics of Artificial Intelligence (2025)

#32

Post by RandomLensman »

imtringued wrote: post #31
pash wrote: post #17

Earlier quoted context omitted.

Essentially all of economic theory is aimed at explaining, not predicting. The distinction between the two goals [0] is sometimes under-appreciated within the profession, and almost always under-appreciated outside of it. 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 m…

>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. 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 advantag…

Not sure comparative advantage needs past decisions to happen - could just be randomly distributed resources, for example.

Re: The Microeconomics of Artificial Intelligence (2025)

#33

Post by nairboon »

howunfortunate wrote: post #13
mitxela wrote: post #12

Earlier quoted context omitted.

Some concrete facts about LLMs are explained by their next token predictor nature. Every time it says "wait, that's wrong." instead of generating the correct thing the first time.

I think that's relatively emergent too though! BERT never really did that (at least to my recollection), presumably because its training was never sufficient for it to develop corrective reasoning in a chain of thought.

What exactly do you mean with "emergent" here?

Re: The Microeconomics of Artificial Intelligence (2025)

#34

Post by b112 »

nairboon wrote: post #33
howunfortunate wrote: post #13

Earlier quoted context omitted.

I think that's relatively emergent too though! BERT never really did that (at least to my recollection), presumably because its training was never sufficient for it to develop corrective reasoning in a chain of thought.

What exactly do you mean with "emergent" here?

I don't know what the parent poster means, but if you look at how LLMs are trained, and how it's trained on human communication, you should find it interesting that humans often correct themselves. They also often create facts out of nowhere, and do this crazy thing called dreaming.

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.

Re: The Microeconomics of Artificial Intelligence (2025)

#35

Post by galaxyLogic »

howunfortunate wrote: post #22
nightski wrote: post #19

Earlier quoted context omitted.

I was not saying that they are deterministic, rather that the distributions (aka weights) are fixed. A model as deployed today at anthropic/open ai/etc is not learning beyond the context as far as I know. 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…

Yes, catastrophic forgetting is absolutely one of the problems that needs to be solved to enable something like this. 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…

LLMs are "readonly" I guess for several reasons:

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.

Re: The Microeconomics of Artificial Intelligence (2025)

#36

Post by mirekrusin »

nadetastic wrote: post #8

> Applied statistics is a far more precise descriptor, “but no one wants to use that term, because it’s not as sexy.” 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 should have answered – "...yes, if you consider your answer an auto complete".

Re: The Microeconomics of Artificial Intelligence (2025)

#37

Post by danielbarla »

b112 wrote: post #34
nairboon wrote: post #33

Earlier quoted context omitted.

What exactly do you mean with "emergent" here?

I don't know what the parent poster means, but if you look at how LLMs are trained, and how it's trained on human communication, you should find it interesting that humans often correct themselves. They also often create facts out of nowhere, and do this crazy thing called dreaming. If you're an LLM trying to symbolically emulate human behavior, especially when coding, you're going to see git commits, where people wr…

In general, I completely agree with you. Personally, as a human: I have fallible memory, beliefs that are not as sound as I may believe, and all of those notwithstanding, I may not correctly reason, even if my "inputs" were perfect. Let's not even get into cognitive biases, etc. I am acutely aware of my, and other humans' fallibilities.

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.

Re: The Microeconomics of Artificial Intelligence (2025)

#39

Post by redwood »

howunfortunate wrote: post #10
nadetastic wrote: post #8

> Applied statistics is a far more precise descriptor, “but no one wants to use that term, because it’s not as sexy.” 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”

As an MLE I feel these takes are too reductionist. 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 W…

Right or put differently you can say that all computer stuff is reduced to ones and zeros. And if you say that in the right context you might convince someone that computers are therefore not very powerful or interesting but of course you're hiding the power of abstraction generalization scale and for lack of a better word entropy in action.

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

Re: The Microeconomics of Artificial Intelligence (2025)

#40

Post by redwood »

nightski wrote: post #19
howunfortunate wrote: post #15

Earlier quoted context omitted.

That's not quite true though. The fact that most models are in practice non-deterministic has been a huge point of contention. 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.

I was not saying that they are deterministic, rather that the distributions (aka weights) are fixed. A model as deployed today at anthropic/open ai/etc is not learning beyond the context as far as I know. 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…

But they routinely leverage web search in connection to providing responses which means they're leveraging their static intelligence on top of a dynamic context corpus. This is critically important and arguably similar to humans in other words the typical human might take a while to develop a new skill but they can change how they dynamically leverage an existing skill based on context
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