Michael Truell: prediction

Transcript context

…I know that importing incorrect data can cause all kinds of pain for your customers and quickly lose their trust. Andrew, thank you so much for joining me. If you want to learn more, head on over to oneschema.co., that's oneschema.co. What is the most counterintuitive thing you've learned so far about building Cursor, building AI products? I think one thing that's been counterintuitive for us, [inaudible 00:32:14] added a little bit before, but is, we definitely didn't expect to be doing any of our own model development when we started. As mentioned, when we got into this, there were companies that were immediately from the get-go going and just focusing on training model from scratch. And we had done the calculation for what it to train before, and just knew that that was not [inaudible 00:32:36] going to be able to do. And also felt a bit like focusing one's attention in the wrong area, because there were lots of amazing models out there, and why develop all this work to replicate what other players had done. Especially on the pre-training side of things, taking a neural network that knows nothing, and then teaching it the whole internet. And so we thought we weren't going to be doing that at all, and it seems clear to us from the start that the existing models, there were lots of things that they could be doing for us that they weren't doing, because there wasn't the right tool built for them. In fact though, we do a ton of model development, and internally, it's a big focus for us on the hiring front, and have assembled a fantastic team there. And it's also been a big win on the product quality side of things for us. And at this point, every magic moment in Cursor involves a custom model in some way. So that was definitely counterintuitive, and surprising, and it's been a gradual thing, where there was an initial use case for training our own model, where it really didn't make sense to use any of the biggest foundation models. That was incredibly successful, moved to another use case that worked really well, and had been going from there. And one of the helpful things in doing this sort of model development is picking your spots carefully, not trying to reinvent the wheel, not trying to focus on places, and maybe where the best foundation models are excellent, but instead kind of focusing on their weaknesses, and how you can complement them. I think this is going to be surprising to a lot of people hearing that you have your own models. When people talk about Cursor and all the folks in the space, they would kind of call them GPT wrappers, they're just sitting on top of ChatGPT or Sonnet. What you're saying is that you have your own models, talk about just the stack behind the scenes.…

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