Don't you think it's a bit overconfident to dismiss Marx just on such a simplistic interpretation (as he had simply failed to consider that economic activity can be non-zero-sum?)

The "if" is the problem. If it happens, then of course, let AI do it. For the moment AI is still bad at those type of tasks [1], so the discussion shouldn't focus on highly conjectural situations. We can't destroy the scientific ecosystem based on vague speculations.

[1] There are real reasons: it is not obvious how to optimize an LLM for doing basic science or other ill defined tasks. On the contrary, optimizing for writing a proof that passes the Lean test or code that passes the tests is a different story.

This is beyond naive.

It's not obvious at all that it's possible to train it to operate independently. So far, AI has been succesfuly trained only for highly controlled and verifiable tasks (code generation and mathematics).

We already had that before AI. The problem was precisely that it's too inefficient to make a new system for each different task. Of course, the new AI is more multimodal, etc, but up to what point? Not clear so far.

Of course this doesn't mean we may need less amount of humans for certain jobs, or that I'm the future AI will gain new abilities.

I didn't expect them to throw millions of dollars at each famous math problem. But one year ago we already had LLMs that solved IMO problems, no?

> Are you sure? (The numbers I've heard, which I admittedly have no very strong reason to trust, don't seem that way to me.)

Math has very little founding compared to other science domains. Also, if you filter mathematicians by specialization in PDE and that have worked on Navier-Stokes, then you end up with a very niche community.

> For instance, suppose you give one of today's frontier models some of those chain-of-cubes rotation puzzles. How well will it do?

I feel like this is not the correct way of thinking about it. We can also ask, for instance, how well a state-of-the-art algorithm for the salesman problem works on a particular graph topology. People do PhD thesis on topics like that, so the answer is not obvious at all. For LLMs we still don't have a curated theory that explains what they're good/bad at, and that you don't see how to extract an answer from the definitions is no surprise since this is obviously not an easy problem. But all this is normal because this is a rather new topic (models of this scale appeared when? 3 years ago? That's nothing for science).

Anthropomorphizing LLMs has added so much noise to this discussion.

There are many automatic theorem provers that do very clever stuff, just as the underlying theroy describes.

> I am shocked how people can deny that solving Navier Stokes requires some sort of intelligence.

It is absurd to waste time discussing whether it is inteligent or not. It is just an algorithm, we know how it works, and it does exactly what we expect it to do. LLMs are not magical things. The main difference is the scale: for Navier-Stokes they spent in 3 days more money that the whole mathematical community over the last 20 years easily.

By the way, I'm not saying that LLM's are useless, that I'm anti-AI or anything like that.