While he's never been dismissive of the technology itself, it is fascinating to see this kind of work from him even as he rails at all the hype around AI.
Hmm,
> Remember the relentless “AI is beating everyone in Math Olympiads” and “it’s all over for humans, no point in learning STEM? Well…
> The 2025 Math Olympiad problems are out and naturally people tested the leading LLMs on those… and… spoiler .. they all fail, badly.
>That’s it LLMs are all about storage in training data which enables recombinatorial retrieval. Don’t expect them to solve or respond to new things like a human.
> They fail with novel and unknown tasks because their ability to generalize is vastly overstated by the people invested in the technology.
> The value of LLMs is in their data. No data, no answer. You have a novel piece of work that’s never been done before? OpenAI will insist it’s worth nothing because it’s a spec in the Ocean, yet that’s not true at all.
https://www.linkedin.com/posts/georgzoeller_proof-or-bluff-e...
Today you can solve IMO problems with ~$5.
Its up to you to verify that he has dismissed the technology and that he has been wrong about its performance in mathematics.
Edit:
Even without lean I can solve IMO problems using Sol/Fable today.
You made a categorical statement about LLMs
> That’s it LLMs are all about storage in training data which enables recombinatorial retrieval. Don’t expect them to solve or respond to new things like a human
> They fail with novel and unknown tasks because their ability to generalize is vastly overstated by the people invested in the technology.
It was not contextual but categorical. And it is false today.
We hadn’t broadly moved into lean proof harnesses and brute force compute spend at the time yet and the statement itself continues to be true for LLMs on their own.