Nick Turley: belief

Transcript context

…So, you might be answering this question already, but let me just ask it directly. People may look at this and be like, "Okay, they're building this kind of layer on top of this God-like intelligence. Of course it will grow incredibly fast and retention will be incredible. What do you guys actually doing that sits on top of the model that makes it grow so fast and retain so much?" Is there something that has worked incredibly well that has moved metrics significantly that you can share? One thing we've learned, I'll answer that question in a minute, but one thing we've learned with ChatGPT is that there really is no distinction between the model and the product. The model is the product and therefore you need to iterate on it like a product. And by that I mean obviously you typically start by shipping something very open-ended, at least if you're OpenAI [inaudible 00:29:38] that's kind of a playbook. But then you really have to look at what are people trying to do? Okay, they're trying to write, they're trying to code, they're trying to get advice, they're trying to get recommendations and you need to systematically improve on those use cases. And that is pretty similar to product development work. Obviously the methodology is a bit different, but discovery is the same. You got to talk to people, you got to do data science and you got to try stuff and get feedback. So, that's one chunk of work that we've been very consciously doing is improving the model on the use cases people care about. And there's also such thing as vibes because I'm sure you know and that's one of the things that I'm excited about in GPT-5 is that the vibes are really good. So, that too is, we have a model behavior team and they really focus on what is the personality of this model and how does it speak and talk. So, there's that kind of work. I would say that's maybe a third of the retention improvements that we see or so just roughly. And then I think another third is what I would call product research capabilities. They're research driven for sure. They have a research component, but they're really new product features or capabilities. And search is one example of that where if you remember in the olden days, maybe 20 months ago or something, you would talk to ChatGPT and it'd be like, "As of my knowledge cut off..." Or, "I can't answer that because that happened to recently," or something like that. And that is the type of capability that has been incredibly retentive and for good reason. It just allows you to do more with the product personalization, like this idea of advanced memory where it can really get to know you over time is another example of a capability like that. I think that's another good chunk. And then the third stuff is the stuff you would do in any product and those things exist too. Not having to log in was a huge hit because it removed a ton of the friction. I think we had this intuition from the beginning, but we never got to it because we didn't have enough GPU or other constraint to really go do that. So, there's the traditional product work too. So, I often think about it as roughly a third, a third, a third, but really we're still learning and we're planning to evolve the product a ton, which is why I'm sure there's going to be new levers. You mentioned something that I want to come back to real quick. You said that it was something like 10 days from Hackathon to Sam tweeting about ChatGPT being live?…

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