I’m not buying this argument, the apps of the LLM providers are some of the most wasteful apps that exist. Have you ever looked at how much memory / CPU Claude and Codex use? They ship with entire virtual machine hypervisors for sandboxing.

I think it’s simply a matter of dogfooding and new feature development, they do have much higher velocity than companies of equivalent size and an unlimited token budget helps as well.

Looking at their desktop apps, I don't think they're into optimizing.

It's the billions of scripts we used not to commit. And billions more.

Engineers are working on multiple sessions, and LLM is doing most of the commits/pushes. This is becoming norm at big techs.

If you are wondering what these changes are, you can take a peek at https://github.com/openclaw/openclaw/commits/main/

This isn't to say that such pace of iteration is good or bad. That's a separate discussion.

For one, they have the internal models that us normies don't have. Also, machine learning as a field is probably the most automate-able field for AI. It's 100% on computers without "real world" collisions at all, AI can do the whole stack today even with publicly available models. And since it's so empirical, you can make a lot of progress by just running more experiments with more compute.