For some tasks that matters. But for a lot of tasks, "good enough but cheaper" will win out.

I'm sure there will be a market for whichever company has the best model, but just like most companies don't hire many PhD's, most companies won't feel a need for the highest end models either, above a certain level.

E.g. with the release of Sonnet 4.6, I switched a lot of my processes from Opus to Sonnet, because Sonnet 4.6 is good enough, and it means I can do more for less.

But I'm also experimenting with Kimi, Qwen, Deepseek, and others for a number of tasks, including fine-grained switching and interleaving. E.g. have a cheap but dumb model filter data or take over when a sub-task is simple enough, in order to have the smart model do less, for example.

What model? GPT4o certainly isn’t a moat for open ai. They need to keep training better and better models because qwen3, kimi k2.5 etc constantly nipping at their heels.

> Wrong. They will use the model that gives them an edge. If they are using a PhD but their competitors are using Einstein, they will lose.

It depends on the business. As much as I’d love to engage a PhD or an Einstein in my Verizon customer support call, it isn’t going to net the call center any value to pay for that extra compute.

god what are these assumptions