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.

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brrwind 3 hours ago | parent [-]

This is completely orthogonal to the inference and training stack that the parent comment mentions

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foltik 3 hours ago | parent [-]

If it’s written by AI it’s gonna be the same overcomplicated overfit slop in one way or another. Performance pressure doesn’t fix that.

	▲	aurareturn 2 hours ago \| parent \| next [-]
		Depends on what the goal is and the skill of the engineer right? Give a verifiable benchmark for the AI agent to hit such as reduce token processing time by 5% without affecting output.
	▲	stingraycharles 2 hours ago \| parent \| prev [-]
		Assuming it’s entirely unsupervised, yes, which I can only assume to be the case at these volumes (hence the dogfooding). I do believe this will force the AI labs to eventually solve the bloat problem with AI, but we’re definitely not there yet.