define "we".

Because again - I think lots of businesses are already taking a good look at models well off the "cutting edge" because they're good enough for what that business is trying to do. They're already there.

If you're doing categorization, tagging, form-filling, customer support routing, etc... you don't need anywhere close to cutting edge expensive models.

Hell - Jev being so popular is basically exactly that group going "what's the cheapest way we can make this decision".

Meanwhile, there are plenty of spaces where you still want real model improvement. But a lot of traditional business was basically process and paper pushing. You don't need a Phd level model for that, you need an intern level model.

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But that's the trap for the large providers - intern level models are cheap and easily available with current open weights. It's going to be a very tight market, and margins will go down to essentially the floor (especially once we exit the current constrained-compute market).

Somewhat ironically - the technology itself makes switching costs basically zero. It's not just that there's no big lock in, AI is like an anti-moat. It actively fills in and paves over whatever moat you might have for price conscious customers.

I think we share the same view here, which I think is most widely discussed as "AI capability overhang". Like you also said, a large, growing amount (digital) tasks can already be completed reliably with existing models. For those - this was my point - the models are competing on price, speed, reliability, etc.

I also agree that more capable models are still desirable in plenty of spaces. I just see that there is so much lag in the diffusion of existing models, which, again, are already sufficiently capable.