A talk on why AI companies ship products now even though better models arrive constantly. The speaker argues that waiting for perfect models wastes time, since improvements come every two months anyway.
The idea is we're all learning about these models together. So there's a real sense in which it's way better to ship something even when you don't know the full set of capabilities and iterate together in public. And we co-evolve together with the rest of society as we learn about these things and where they're different and where they're good and bad and weird. The models are not perfect. They're going to make mistakes. You could spend a lot of time building all kinds of different scaffolding around them. And by the way, sometimes we do because sometimes there are kinds of errors that you just want to make, but we don't spend that much time building scaffolding around the parts that don't match that because our general mindset is in two months, there's going to be a better model and it's going to blow away whatever the current set of limitations are. And so if you're building and the product that you're building is kind of right on the edge of the capabilities of the models, keep going because you're doing something right because you give it another couple months and the models are going to be great. and suddenly the product that you have that just barely worked is really going to sing. You know, that's kind of how you make sure that you're really pushing the envelope and building new things.
