Comment by aspenmartin

I'm definitely in the camp that this browser implementation is shit, but just a reminder: agent training does involve human coding data in early stages of training to bootstrap it but in its reinforcement learning phase it does not -- it learns closer to the way AlphaGo did, self play and verifiable rewards. This is why people are very bullish on agents, there is no limit technically to how well they can learn (unlike LLMs) and we know we will reach superhuman skill, and the crucial crucial reason for this is: verifiable rewards. You have this for coding, you do not have this for e.g. creative tasks etc.

So agents will actually be able to build a {browser, library, etc} that won't be an absolute slopfest, but the real crucial question is when. You need better and more efficient RL training, further scaling (Amodei thinks really scaling is the only thing you technically need here and we have about 3-4 orders of magnitude of headroom left before we hit insurmountable limits), bigger context windows (that models actually handle well) and possibly continual learning paradigms, but solutions to these problems are quite tangible now.