Model choice helps Codex compete without locking users in

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 25:49

   one thing that surprised me about Codex, and I didn't even know about it until recently, it's not tied to the OpenAI models. You can use other models with codecs, you know, like putting myself in a vendor's shoe. It might not be very obvious because, again, all the other vendors I look at when they do a CLI, it's kind of use it with our models. Again, what made you decide to be this permissive about, you know, using or allowing to use your harness with with other models?

2. 26:19

   It felt quite natural. If you are part of this community and building an excellent coding harness, why would you couple it to your model? That felt quite disappointing to make that decision. So it didn't feel right. And in general, it's like, I think, you know, it's like, I kind of tried to make decisions that I'm like, yes, you know, it's just like, I can just sort of like explain it. You know, it is correct.

3. 26:49

   It's the same reasoning with, you know, it is open source in the first place. It would have been trivial for anyone to fork it and then add support for another thing. But then, but then you're just encouraging people to just like, you know, go and use that fork. And then not suddenly you have overhead. And the only reason you have a fork is because, you know, you wanted to change like 10 lines of code to add support for like another model provider that feels very silly so like you know why not just support it in the first place the other thing is we benefit a lot from like being able to just give optionality so you know it's like maybe today you know you you love using open ai models um and you know you're super productive with them but like tomorrow there's a new model that comes out you want to try that why force you to go and completely change your

4. 27:36

   setup just to try a new model and then we benefit from the feedback that we didn't get uh which is like you know maybe there's something that you liked about that model maybe it actually didn't work well but it's sort of like being nice to our users and to the community it's like you know feels like the right thing to do here um and then you know we also use we will also try like other models right so and you know we try them in the same harness and you know it's just all uh all good and then this is also often like this optionality is very important to companies that we work with yeah this is something that you know we absolutely lean into this last point i think you know as any serious company you want to have optionality and you want to use a tool that gives you that optionality but i kind of appreciate it because i feel to me like it's kind of honest

5. 28:20

   like look like it forces the whole company to be to compete the best in everywhere in the model layer and the harness layer with open source with with choosable models and it kind of like doesn't doesn't allow you to like kick back and say like all right we're done we can we can we can hang back for a little bit for now yeah i want us to win users by having you know the best models the most efficient models the best product and then you know if we do all of these things it's like we're gonna have a good time if we sort of like force you to use the product because you know this one thing it's just like then i don't think that will attract you know the the very best people to work on this product either and it's like you know it's like we're doing our best work here we care a lot about the experience it should feel delightful you know like it doesn't

6. 29:02

   Irrespective of the model that powers it, you should fill the light hole. I love the idea of winning based on merit, not based on lock-in.