Nick Turley: evaluation

Transcript context

…Let me zoom out a bit and talk about OpenAI and just product in general. So you've worked at traditional, let's say traditional product companies, Dropbox, Instacart. Now you're at OpenAI. What's maybe the most counterintuitive lesson you've learned by building products from your time at OpenAI? Each time I always tried to pick the maximally different job whenever I made a job change. So after Dropbox, I was craving a real world product because it was just so different than working on SaaS, et cetera. And after Instacart, I was craving on working on something that intellectually was interesting and had this kind of invoked the nerd in me. And so I've always looked for things that are really different. And then once I showed up at these places, I tried to understand what makes that place successful, what is truly the thing that they cracked and how we can lean in that into that even more. I think I spent a lot of time thinking about this with OpenAI, especially after ChatGPT. Before that it was kind of a moot point because we didn't really have much revenue or products or anything like that. There's a few things that come to mind that have driven many decisions. One is the empiricism. We talked about that a bit. The fact that you can only find out by shipping, which is why maximally lean into that. And that's a huge part of why we ship so much. One of them is that amazing ideas come from anywhere. The thing about running a research lab is you really don't tell people what to research. That's not what you do. And we inherited that culture even as we become a research and product company. So just letting people do things who have amazing ideas rather than being the gatekeeper or prioritizer of everything or something like that has been proven immensely valuable to us. And that's where much of the innovation comes from, is empowered smart people on any function really. So that was a good inheritance from what I think made OpenAI successful and makes us successful. The interdisciplinariness of really making sure that you put research and engineering and design and product together rather than treating them as silos. I think that's the thing that has made us successful and that you see come through in every product we ship. Like if we're shipping a feature and it doesn't get 2X better as the model gets 2X smarter, it's probably not a feature we should be shipping. Not always true. SOC 2 doesn't get better with [inaudible 01:04:48] models, but I think for many of the core capabilities, that's a good litmus test. So I've always found you really have to lean into why is this place successful and then maximally accelerate that, so to speak, because it's what allows you to turn something that feels like an accident into something that is a repeatable label. So you talked about this kind of collaboration between researchers and product people. And you've been at the beginning of ChatGPT from day one to today, from zero to 700 million weekly active users. Not just registered users, weekly active users. How have you approached building out that team over time?…

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