Underappreciated point. The point of inflection is where humans switch from being a driver to a liability.

But I don't think it's randomness, because that would be easy to add. It's more like a different perspective on the training data, a different set of perception categories, and a different set of skills used to work with all of the above.

Those skills aren't very efficient, but they're the best we can do. We're used to their strengths but we don't like to think about their limitations.

It's completely plausible that AI will replace some of them, and not implausible it could replace and improve on all of them.

I have little doubt that for many years, AI + human will be better, and then eventually AI will be so good that humans mostly won't offer anything. That latter state will probably take at least 10 more years, but will likely happen within our lifetime.

Read any blogs or watch any podcasts from high-skill programmers who are now using AI for everything. They themselves will freely say the AI is doing nearly everything, and that they often are not even reviewing the code, and when they do they just leave it as-is. Every few months you should expect humans to have less and less of an active role in software development, beyond initial ideation and UX sensibility.

I like how all the replies to me are either "no it's the humans who did it" or "no it's the AI that did it".

I think one of the significant recent proofs was basically just one person saying "solve this" "keep trying" "try harder" until it was done. I believe most of the rest can be assumed to be the human mathematicians at the very least providing some useful guidance.

I agree that it won't be that long before the AIs can do basically everything autonomously, though. I am essentially a Singularitarian when it comes to my outlook, even if not for September 2026.

According to OpenAI, but they haven’t exactly been transparent about what information the prompt entailed.

The bigger question is to what extent did expert mathematicians metaprompt the model with fruitful solution strategies through their sessions finding their way into training data. Answering that question definitively is kind of important for understanding the models contribution/capability. But I feel like people want to turn this into a debate about priority and credit which is sort of secondary

Nothing is solved in isolation but credit usually goes to wherever the new work in the paper comes from instead of the whole mountain of previous mathematics or existing tools used. The most relevant of those get referenced and then this reference tree builds a tree of collective base work needed across history.