I don't think it's profit, I think it is genuine functionality. I think that they build features for their internal users while having no token concerns. And this mindset of using LLMs to solve product problems leads to useful features (e.g. agentic auto-approval). Then there is a bit of an issue when they try to ship a genuinely useful feature but now token cost matters.

I disagree because with their subscriptions and people using Opus I doubt the majority of users are hitting their limits often so this would just burn compute on their end

I sometimes wonder how AI labs would make money if the internet didn't exist.

Maybe we'd have local models ...

It leads to the same thing (but probably better spells it out), but the answer is entry barriers to competition which in the long run translates into less competition in the long run and greater profitability

Really? Because you'd think for profit they would want to minimize token burn and therefore get away with lower limits on their plans

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