2. Why shouldn’t math progress happen in the open, commit by commit? Why is it so horrible if a proof is 95% of the way there but we later find that it needs to be refined? Mathematics previously was optimizing for an antiquated publishing and distribution scheme. There is no need for the first print to be correct. We have the internet now. We can and should publish incomplete results and correct things on the fly. Maybe mathematicians would have solved some of these problems years ago if they didn’t hide incomplete almost solutions in their filing cabinet because it wasn’t yet ready to be published.

You don’t hate the pageantry of mathematics and academics enough.

Nobody has a moat.

Utter bullshit. We are still scaling transformer architectures initially introduced ten years ago. Ten years of phds trying to improve on what Google produced ten years ago, mostly failing.

What has actually changed and can explain the progress we’ve seen? Do you really think it’s just a collection of better and badder RL gyms? Get real!

The main thing driving progress in ai is massive amounts of capital investment and incremental improvements in hardware (mostly memory bandwidth/capacity) and computer networking (we got better at collectives). The AI labs, ironically, have nothing to do with it. They’re the vessel for capital. The people actually driving things forward mainly work at nvidia.

The reason the models are better today than they were 3 years ago is almost exclusively due to better hardware and infrastructure software. Not better data, not better model architecture. The evidence of this is pretty easy to feel: the reason opus 5 doesn’t feel much more capable than opus 4.6 did is because they run on the same hardware generation. The reason opus 4.6 felt much more capable than anything before it is because it coincided with the scale out of a new hardware generation.

In terms of cost per task, the open weight Chinese models are winning by a long shot. So it depends on what you mean by the best model.

This is a litmus test for the effective altruists in AI labs: fashion or conviction? The social pressure is to slow down and let the guild keep its norms a few more years. But what's clearly best for humanity is to push forward violently and leave them in the dust. A Fields medalist's feelings are not more important than the wellbeing of the planet.

By plausible deniability I simply meant adopting a similar approach that Stremio has, to keep it on the good side of App Store approval processes. The app itself only allows for creative commons content by default, but with the right incantations it can host anything.

Commenter below gets it absolutely correct: stockfish, which runs on your 5 year old phone, is dramatically better at chess than Fable. Like, so much better that it’s not even remotely comparable. The theory of the Bitter Lesson, and it’s only a theory, is that LLMs could eventually outperform stockfish. It’s not true today and it remains to be seen whether it will ever be true. For now, specialized models are absolutely better at specialized tasks.