I disagree on that point. Models are getting good enough that you can switch them and barely notice. I'm switching between Opus, GPT Codex and GLM 5.3 for coding and I can barely tell the difference.
I think they'll become more like telcos than anything, selling a commodity. It's even truer when any provider can host open weight models like GLM-5.3.
Basically a world with dozens of Baseten, with AI labs having a hard time monetizing, just like editors of open source software.
Mistral does not offer a model that makes sense to use. I understand they now host the already outdated GLM 5.2, courtesy of China providing the weights. And Mistral offers this for 2x-3x the price of other providers.
This is supposed to be a success story?
But for the vast majority of usage GLM 5.3 or Deepseek V4 is enough.
Even people who do need frontier model will soon restrict it to the use cases that really need it and switch to cheaper models for the rest. It has already started.
Anthropic and OpenAI will never get enough customers paying top dollar to deliver on the revenue they need to offset their investments.
> so if you don't have the most intelligent or cheapest model in the world, you're losing
So how does this match up to the fact that there is currently OpenAI and Anthropic, both raking in money? They can't both have the smartest model at the same time, can they? And all those inference companies selling API access to open weight models on OpenRouter, which are apparently also earning billions already? While the former are probably bound to have much higher cost for research and training than they are currently earning, which may be called "losing", the latter don't have that problem, they can simply price their API access such that the money earned covers their costs, no training and practically no research necessary. In your theory these companies shouldn't have a cent of earnings.
They are simultaneously first: the two leapfrog each other with regularity, and are meaningfully ahead of the competition.
There is no close second, because the AA Index points are expentially harder to get as you get closer to the first.
I think that there's a real second mover advantage in letting others waste money on researching ultra oversized models while creating smaller and cheaper models from their learnings.