I think it's more likely the millions of dollars of compute than the model, in many cases.

They no doubt have more expensive/powerful models internally, but smaller models seem to catch up fast. So I'm not sure it's about capabilities, but more the willingness and budget to conduct a huge search.

Obviously the more intelligent the model, the smaller/more directed the search is. But they spoke about huge numbers of agents working on Navier-Stokes for example (I think it cost >$10m).

Does every mathematician get cited in every maths paper? I think it's pretty clear who should be cited.

All the methods and data are not public. We don't know what unpublished methods they're using. You can get most of the pre-training data publicly but they've probably spent a ton of money curating it and are now doing things like buying rare books. The RL training data is all (/mostly) proprietary though, and that's the real secret sauce part.

All the RL data are exactly public. There are huge amount of distilled data freely available, and that amount is more than enough to train a ~10T model.

> All the RL data are exactly public.

Nope, because the big AI companies are paying billions for it. They wouldn't pay anything for public data.

There are 'transfer stations' and that's how exactly I use GPT and Claude in China. OpenAI and Anthropic do not sell in China, so we use their AI with a much lower price like 1% of the official API price. The largest transfer stations have TBs of traffic every day, and the traffic is eventually possessed by the open source community.

Subscription engineering is a deep field. Neither OpenAI nor Anthropic have any technical advantage in this field.

I think a big issue is that the companies pushing AI want it to replace people entirely. So the software around it is designed with that mindset.

I'd really like something that works more like pair programming. Where you share an editor session with the AI, and it's much more interactive. eg. It says "I'm thinking about doing X here, what do you think?". Then you give a response and it continues. Or you can see it doing something silly and immediately stop it and correct something either with a prompt or by manually editing yourself, before letting it rip again. Or just ask it "why are you doing it that way?". Maybe with some control over the speed it's going, for when you feel confident about what it's doing.

Claude has made some positive changes over time where it asks you more when there are different approaches it can take. But I feel like I'm not being brought along with my mental model as much as I would like. A lot of the time it spits out a whole pile of code and then I have to go and build up the mental model after the fact, and correct a lot of what it's done.

The current approach is good a lot of the time though, when you're not really changing anything architectural and just want it to bash out code while you do somehthing else.