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AW Alexandr Wang On Cheeky Pint

“The naming of some of the models is like so egregious... Some of the times the numbers go backwards and sometimes it's just like a totally different word and you have another set of words. And like, I just like, how is somebody who's not carefully reading API design specs supposed to know what they're supposed to pick?”

Cheeky Pint · Bootstrapped & Indie SaaS · October 2026

“The naming of some of the models is like so egregious... Some of the times the numbers go backwards and sometimes it's just like a totally different word and you have another set of words. And like, I just like, how is somebody who's not carefully reading API design specs supposed to know what they're supposed to pick?” — Alexandr Wang, Cheeky Pint

Collison pointed out that Muse has no model picker and no effort setting. Wang said AI products have mostly been designed by highly technical teams who don't picture an ordinary user, and that model naming is the clearest example.

Transcript

Cheeky Pint Around 00:00 into the episode
Speaker

…I just want to make it simple. Alexandr Wang Yeah. I think that we've been... I think somewhat infamously, a lot of the AI products have been designed by a highly technical group that do not have these sort of randomly sampled American in their mental model. And so the products... I mean, the naming of some of the models is like so egregious. John Collison The numbers go backwards? Alexandr Wang Some of the times the numbers go backwards and sometimes it's just like a totally different word and you have another set of words. And like, I just like, how is somebody who's not carefully reading API design specs supposed to know what they're supposed to pick? Like, it's just far too confusing. John Collison Okay, we're talking about models a lot. Tell me about the new model. It sounds awesome. Mark Zuckerberg Yeah, I mean, I think we're quite proud of this. Um, yeah, the new model—Muse Wonder—it's a new class of model that is at the frontier. So we've basically been working on this since the beginning. Basically, when you're doing these pre-trains…

Speaker names from our own diarization · position estimated from where the line sits in the episode

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