Four years ago, they would have likely asked what in the world is an LLM? ChatGPT is barely 3 years old.
Well that's exactly the problem : how can one say that?
The entire process of evaluating what "it" actually does has been a problem from the start. Input text, output text ... OK but what if the training data includes the evaluation? This was ridiculous few years ago but then the scale went from some curated text datasets to... most of the Web as text, to most of the Web as text including transcription from videos, to most of the Web plus some non public databases, to all that PLUS (and that's just cheating) tests that were supposed to be designed to NOT be present elsewhere.
So again, that's the crux of the problem, WHAT does it actually do? Is it "just" search? Is it semantic search with search and replace, is it that plus evaluation that it runs?
Sure the scaffolding becomes bigger, the available dataset becomes larger, the compute available keeps on increasing but it STILL does not answer the fundamental question, namely what is being done. The assumption here is because the output text does solve the question ask, then "it" works, it "solved" the problem. The problem is that by definition the entire setup has been made in order to look as plausible as possible. So it's not luck that it initially appears realistic. It's not luck that it can thus pass some dedicated benchmark, but it is also NOT solving the problem.
So yes sure the "tech" is moving "so fast" but we still can't agree on what it does, we keep on having no good benchmarks, we keep on having that jagged frontier https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 that makes it so challenging to make more meaningful statement than "moving so fast" which sounds like marketing claims.
That’s a really fantastic capability, but not super surprising.
Terence Tao maintains a list [1] of AI attempts (successful and otherwise). #205 is currently the only success in section 1, the "full solution for which subsequent literature review did not find new relevant prior partial or full solutions" section - but it is in that section.
As to speed, as far as I know the recent results are all due to GPT 5.2, which is barely a month old, or Aristotle, which is a system built on top of some frontier LLMs and which has only been accessible to the public for a month or two. I have seen multiple mathematicians report that GPT-5.2 is a major improvement in proof-writing, e.g. [2]
[1] https://github.com/teorth/erdosproblems/wiki/AI-contribution...
- the long tail aspect of the problem space ; 'a "long tail" of under-explored problems at the other, many of which are "low hanging fruit" that are very suitable for being attacked by current AI tools'
- the expertise requirement, literature review but also 'Do I understand what the key ideas of the solution are, and how the hypotheses are utilized to reach the conclusion?' so basically one must already be an expert (or able to become one) to actually use this kind of tooling
and finally the outcomes which taking into consider the previous 2 points makes it very different from what most people would assume as "AI contributions".
For example if somebody had used GPT2 with the input dataset of GPT5.2 (assuming that's the one used for Erdos problems) rather than the input dataset it had then, could it have solved those same problems? Without doing such tests it's hard to say if it moved fast, or at all. It's not because something new has been solved by it that it's new. Yes it's a reasonable assumption, but it's just that. So going for that to assuming "it" is "moving fast" is just a belief IMHO.