The Exchanges

Q I think that's, that's super clear. And then also the other thing I wanted to dive into was your, uh, the productivity team. Uh, what else is there? First of all, you know, what, what, what are the top level teams other than productivity? Isn't productivity everything?

A So, you know, we have a team focused on, uh, on ChatGPT, like the, the core chat experience, um, for consumer, which is like, you know, not, I think all productivity, like there's people are using ChatGPT every day for search to, you know, figure out how to write messages to loved ones, to think about Um, how to like learn a new topic, et cetera. And so there's so much more inside to create images. There's so much more in chat that, you know, the hundreds of millions of users are using that, um, you know, obviously that, that warrants like a very dedicated effort. Um, and there's teams focused on enterprise and infrastructure and API and stuff like that as well.

AI assessment note: “teams focused on enterprise and infrastructure and API and stuff like that as well”

Q Cool. Uh, you were gonna, you lead the productivity team. How do you define productivity?

A I think our mission is to make it possible for people to do things that they weren't able to do before. And right now we're thinking about it from the perspective of knowledge work. And so when I look at knowledge work, I think about people are no longer siloed by their roles. They're no longer siloed by maybe the, the, um, background or training that they have. Like, no matter what function you're in, you can suddenly build things. It's only get access to data that you otherwise might not be able to interpret, et cetera. Um, and then I think that extends to your personal life where we want to give you leverage at the end of the day. Like we want the models and the product to be able to give you leverage so that you can, you know, create time for yourself to do the things that you love.

AI assessment note: “make it possible for people to do things that they weren't able to do before”

Q Uh, you worked on enterprise. What, a lot of people never touched ChatGBT, ChatGBT for enterprise God. Um, what is something that you learned from there that you're bringing into your work now?

A I think how there's no like one size fits all solution in enterprise. Um, I remember in the early days of chat to be the enterprise, like we would talk to customers and like everyone that was like when, I think it was a year after chat GPT was released and everyone was so excited to bring, um, you know, AI into their enterprise. And like, oh, there's all these teams that were being stood up as like, you know, the AI deployment team, like these enormous budgets. And if you asked anyone, like What were they excited about? Like, what were they excited about solving? Like at first you'd get like, you know, kind of like the, the baseline answers of like, yeah, we have all this context and data and all this stuff. But then if you ask them like, you know, what was like a discrete use case that like they want AI to enable in their, in their workplace, you get such a different like variance, like explosion of, um, different types of answers. And it's interesting, like, you know, you using these models and these products, you, you have this box and you can say anything to it, which is the magic, but it's on the flip side. It also means that, like, you don't know what to do with it, and in enterprise, I think a big part of that is, like, actually meeting the users where they are, like, what use cases are they trying to solve, and then actually teaching them how they can use AI to, like, g…

AI assessment note: “I think how there's no like one size fits all solution in enterprise.”

Q You mentioned sub-agents. I, I, I got a double click on that. Uh, Ultra is a new mode. Um, you have special affordances in ChatGPT itself to show off the, the agents. Can't really do much with them, to be honest. Just, just watch. Um, what have been, what have been your experiences? Any design issues that you would call out to other builders building with sub-agents?

A I think it's sort of goes back to the balance that I was raising earlier about like, you know, showing builders the power of the tool, but also creating enough of an abstraction to not overwhelm them. I think with sub-agents, the thing that we wanted to show is that you can take a task that, you know, has many parallel tracks or, um, is, is complicated in a way that, you know, sub-agents can handle and This product is for you. Like the, the model can, can accomplish those goals or try to accomplish those goals. Um, and so like, that's the point of like showing them in the product and, and that's where we, we've gone with the design. There's another, you know, iteration of this where like you can see exactly what they're doing and, and things like that, which I think is like, you know, could, could verge on like overwhelming, um, with information. And so this is like the deliberate trade-off that we've made for now.

AI assessment note: “creating enough of an abstraction to not overwhelm them”

Q to take the, The, the visuals here, but we'll, we'll, we'll cut to that later. Was there co-training, I guess, uh, because you were moving, making this big move and you launched five, six on the same day as Chatgeap C work, was there influence between the model training teams and the harness teams or did they, did the launch days just happen to line up with the same day?

A Oh, I think the, you know, we collaborate heavily with the research teams. And I mean, I think that's like one of the most magical parts of the job, like the, the most fun parts of the job. Um, but yeah, I mean, just, just using artifacts as an example, like, you know, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, you know, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, you know, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you were like,

AI assessment note: “we collaborate heavily with the research teams.”

Q Yeah, I don't know if any, that triggers any stories for you of how it's run internally. Am I doing this right?

A Yeah, I mean, I think that this is, like, a workflow that we're seeing, like, all different types of teams use, where, like, the canonical artifact that was previously a deck or something is now becoming a site, and, like, with a site, you, because it's just HTML, you can, like, it's infinitely flexible, and so, you know, if you, if you want to give more prominence to a certain thing that, like, in a slide deck would You know, feel like it was buried. Like you can do that. You can have it be like the hero image. Right. And so I think that like, um, people are starting to see that, um, there's obviously more work to be done to make these things like much more easier, easy to collaborate on. Um, you mentioned that they're very, they're long and verbose could be broken up. I'm sure there's super long.

AI assessment note: “this is, like, a workflow that we're seeing, like, all different types of teams use”