OpenAI’s Vision for the AI Super App — Akshay Nathan, OpenAI

Akshay Nathan 0:00 I think the bottleneck becomes like, sort of like ideas and taste, I guess. Um, I think because anyone can, can build now, I think, um, it really is the era of like bottoms-up ambition. And because there's so much to be built, like you're always gonna be bottlenecked by the amount of ideas and amount of things that you're doing at any given time. One interesting part about ideas is like they're not like in a vacuum. It's like not-- They, they usually come from somewhere and like, you know, in, in product development, like they're coming from talking to users or reacting to, you know, friction that you're seeing or feedback. There will always be value in, in these like generalists that we talked about, like, you know, closing that loop and, and coming up with those ideas that are grounded in, in that feedback or talking to users, whatever it is.

swyx 0:43 Before we get into today's episode, I just have a small message for listeners. Thank you. We would not be able to bring you the AI engineering, science, and entertainment content that you so clearly want if you didn't choose to also click in and tune into our content. We've been approached by sponsors on an almost daily basis, but fortunately, enough of you actually subscribe to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single most powerful, completely free thing you can do is to click that subscribe button. It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring The In-Space to you each and every week. If you do it, I promise you, we'll never stop working to make this show even better. Now let's get into it. [upbeat music]

swyx 1:32 Okay, we're here in the studio with Akshay from OpenAI. Welcome.

Akshay Nathan 1:36 Thank you.

swyx 1:37 And with our trusty co-host Vibhu. Uh, we, uh-- So you recently launched ChatGPT Work. You lead Core Product Engineering. You know, it's been a long journey, uh, into, into all this. I find it very interesting that you started with no code or low code, uh, with Walrus and Airtable, and to some extent, ChatGPT Work is kind of like the super app of super apps of, well, here is the ultimate no code. You just write, uh, a prompt. [chuckles]

Akshay Nathan 2:01 Yeah. Yeah. It's, it's funny how things come like full circle. I mean, I think for a long time in my career, I mean, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, you know, the things that we were able to do with code, like as engineers, like if we could bring that to many more people in a more, uh, accessible way, then that would be truly magical. We were working on a startup. It's actually funny, like before LLMs, before vision LLMs, on how to do automated testing with AI. Um, and [chuckles] it was just kinda jank, um, back then, but, you know, doing what we can and then, and then worked at Airtable for a while on, you know, the same thesis that like if we can bring a database or the primitives behind a database to people, that would be really useful to them. Um, but once I think LLMs came onto the scene, it became clear that like this was like the missing piece, like the missing technology required to like bring the magic of code to everyone without them having to know what's going on underneath the hood. And so like, I think this launch and, you know, a lot of the stuff that we've been up to is like, um, the manifestation of that.

Vibhu 3:02 How was stuff when you joined? So you joined OpenAI 2023. Now we've got, you know, so much more stuff. So ChatGPT, Codex app, ChatGPT for Work. How have things changed?

Akshay Nathan 3:13 I actually think the more interesting thing is how things haven't changed. Like, I guess like, one, I, I joined-- I remember when I joined, it was like five hundred people. One thing I was worried about was like I was looking for something, you know, more early stage and like, was it gonna feel-

Vibhu 3:25 [laughs]

Akshay Nathan 3:25 -startup enough? And I joined, and I was like, "This feels even more startup-y than I could ever imagine." And like that, that really hasn't changed even till now. I mean, I think the like level of like bottoms-up ambition and like the ability of anyone to like, you know, do anything or have an idea and, and ship it is, is really cool. But on the like sort of mission side, I think what was really compelling to me is this mission of, you know, bringing frontier intelligence to everyone. Like building AGI and then bringing it to everyone. And, um, I think acknowledging back then that like that vision is gonna, you know, not be a linear progression. Like we're probably gonna like try different products and, and have different things that, that succeed and don't. Um, but the vision has stayed the same and the mission has stayed the same, and we're starting to see the pieces, um, fall together and, and that, that's really cool.

swyx 4:08 Uh, you

swyx 4:09 worked on Enterprise. What-- A lot of people never touch Cha-ChatGPT, ChatGPT for Enterprise, God. Um, what is something that you learned from there that you're bringing into your work now?

Akshay Nathan 4:20 I think how there's no like one-size-fits-all solution in Enterprise. Um, I remember in the early days of ChatGPT Enterprise, like when we talked to customers and like everyone-- That was like when... I think it was a year after ChatGPT was released, and everyone was so excited to bring, um, you know, AI into their enterprise. And like all, there was all these teams stood up-- that were being stood up as like, you know, the AI deployment team with 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, kinda like the, the baseline answers of like, "Yeah, we have all this context and data and all this stuff." But 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, uh, 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 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 case were they trying to solve, and then actually teaching them how they can use AI to like gain leverage there.

swyx 5:25 Do you meaningfully differentiate that from for deployed engineering or...?

Akshay Nathan 5:29 I, I think there's like the, the like go-to-market side of it-

swyx 5:32 Yeah

Akshay Nathan 5:32 ... and then there's like the product side of it. I think-

Vibhu 5:34 With Neo-

Akshay Nathan 5:34 You need to see more on the product side. Yeah. Um, and I think like however good we get at FD motion, like I think at the end of the day, if we have a user who's like looking at their computer or looking at their phone, like it's our job in the product to like be enabling them and showing them where to go. Um, so we're really excited about that.

Vibhu 5:53 Do you think there's been changes, you know, over the past three years of adoption? So there have been, you know, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or how have things changed?

Akshay Nathan 6:08 I mean, we're seeing now that, like, there's this huge uptake, right? Everyone's ex-extremely excited about it. It feels like, you know, many peop-- like millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked-- So now, like we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, you know, truly get it, who are like, "You know, w- you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and then that you're supervising it, but, like, anything is, is, is possible." But then there's, like, this, like, ten X or a hundred X bigger market where, like, they don't yet get that, or they don't yet see that. Um, and so I think that's the next stage here. Um, so I guess to answer your question, like, I think the adoption is there and, and growing fast, but I think the opportunity is, like, far, far bigger than that. That's where we wanna play, especially with ChatGPT Work.

swyx 6:56 Yeah.

swyx 6:57 Uh, well, let's, let's, uh, skip ahead to ChatGPT Work. Uh, on-only, uh, like, um, a month ago or so, uh, announced. Uh, what was the sort of decision process that led into it? Um, you know, there was this, uh, overall merging of the super app. Is, is that what we're officially calling it? Um, you deprecated the browser as well. Just, I guess, summarize, uh, your [chuckles] last, like, couple of months of working on this thing.

Akshay Nathan 7:19 Yeah. It feels like forever now, but I guess it's only been a few months. I think maybe the one, one, like, impetus that, like, is most salient is when we l-released Codex or even internally had Codex, like, it was really surprising to us, um, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among nondevelopers at OpenAI. And, um, I, you know, through this product development process, like, would go to, like, these UXR sessions to talk to people internally, and the thing that stuck out to me is, like, one, like, you know, you, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, you know, their use cases. That, that per-that part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, like, no-

swyx 8:03 It's like, "I'm not supposed to be using it, but I am."

Akshay Nathan 8:05 It, it, it was that. It was, like, that they were, you know, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, um, what we recognized then is that, like, the, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, you know, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky, tricky thing, right? There's many ways you can go about it. Um, and so that's why we called the merge and the super app over time and, and, and ultimately launched it in ChatGPT Work is how do we do that? But it came from that initial realization that, like, the, the power was not only for developers, like, much, much earlier than probably even we thought. Like, it could be extended to, to everyone.

swyx 8:45 How do

swyx 8:46 you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work. So is it the opening for the average user, for enterprise, for work? How, how do you position it?

Akshay Nathan 9:04 I think we want to get it to position it for if you're doing worky related things, for lack of a better word, right?

swyx 9:11 [laughs]

Akshay Nathan 9:11 I think productivity is, like, uh, is, is actually what, like, the pillar that I l- that I support. Like, that's the name of the team. Um, and the reason for that, the reason we call it productivity and not, like, you know, enterprise or, or, like, work or something like that is because there's also personal productivity, right? And, like, I think ChatGPT Work is-- I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, you know, super capable for. Like, one, one recent example that someone posted about, um, on our Slack is, like, someone had, like, a missed package. Like, they didn't receive it, and then they got, like, the picture of it, um, you know, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, you know, is extremely tenacious and, like, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, you know, worky or productivity-related things. I think that's what we want the product to be. You asked about Codex. I think we think Codex is, you know, a durable brand, but we have a principle that, like, the user-- you know, we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, basically everything that you can do, you know, in the Codex portion of the product on, on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, you know, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the, the experience of seeing the agents thinking, like, diff forward so that you're g- you get exposed to the diff side of the bi? And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one, one state versus the other? So, um, there's, like, some opinions that go behind that, but we do, we do want-- we don't want the user to need to choose which experience they're in.

swyx 10:55 That is a good goal for AGI, right? Like, people don't want, like, to hide-- to choose what version of AGI they want. They just want the AGI to decide-

Akshay Nathan 11:02 Yeah

swyx 11:02 ... for them. Um, uh, can I get an answer or, like, uh, it's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there actually prompt level or even w- even deeper differences?

Akshay Nathan 11:18 So the harness is the same. The harness is shared. Um, on-- in both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plugins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be, how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.

swyx 11:45 I think I'm just kinda curious. Maybe we can, uh-- Is there a query that we can run that would look different in the, the two modes?

Akshay Nathan 11:52 Yeah. Like, try to create, like, ask it to create, like, a retirement calculator spreadsheet or something, um, in, in both, in both modes. And then in Codex mode, um, you might have to be in a, in a repo for this, but you'll see, like, the diffs of, like, the, the sheet that it's creating and stuff like that, um, and the file edits. Um, but in Work you won't be able to see that.

swyx 12:10 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? Uh, first of all, you know, what, what, what are the top level teams other than productivity? Is it productivity everything?

Akshay Nathan 12:23 So, you know-

swyx 12:24 Sites. Yeah

Akshay Nathan 12:24 ... we have a team focused on, uh, on ChatGPT-

swyx 12:27 Yeah

Akshay Nathan 12:27 ... like the, the core chat experience, um, for consumer, which is like, you know, not, I, 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. And 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, a very dedicated effort. Um, and there's teams focused on enterprise and infrastructure and API and stuff like that, so.

swyx 13:02 I will bring it up.

Akshay Nathan 13:03 Okay, yeah.

swyx 13:03 Yeah, so I have them both running.

Akshay Nathan 13:04 Yeah.

swyx 13:04 This is Work. There's a Codex version here. I picked 506 Sol, so this will take a while.

Akshay Nathan 13:11 Uh-huh.

swyx 13:12 Um, I think, I think we'll just keep it in the background and, you know, as, as they finish, we'll look into some of the differences.

Akshay Nathan 13:17 Yeah. But immediately, I think if you flip back to the Codex version-

swyx 13:20 Yeah

Akshay Nathan 13:20 ... you'll see that, uh-

swyx 13:22 That assumes-

Akshay Nathan 13:23 Like the, the-

swyx 13:23 It assumes Git. Yeah.

Akshay Nathan 13:24 Exactly.

swyx 13:24 Yeah, yeah.

Akshay Nathan 13:24 The, like, dynamic island assumes that you're in a Git repo. Um, and you might miss some stuff because some of it is like in the actual chain of thought, what, what are the changes and how we display that, but yeah.

swyx 13:36 Is, is there an unintuitive-- Like, is there a thing that you wanted to ship and then you got feedback and you were like, "No, let's not do it"? Like, what's the thinking behind that?

Akshay Nathan 13:43 In, uh, ChatGPT Work?

swyx 13:45 Yeah.

Akshay Nathan 13:45 I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, why-

swyx 13:51 Different apps.

Akshay Nathan 13:51 Exactly, like different apps or even in the same app, like different, completely different experiences. Like why merge it all? Like what is... You know, Codex, obviously people love. Like why, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like for, like in every few months, like I feel like I wake up and I'm like doing a completely different thing than I was doing a few months ago. And my, my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, and this, this technology is giving people leverage. Like, you know, the things, maybe it's the more mundane parts of your job or, or parts that like if you were able to automate, you'd be able to share more ideas faster or whatever, like you're able to do now. And because of that, like that might actually blur the lines between someone who's like only writing code or creating strategy docs or, you know, planning events or, um, helping with marketing or doing podcasts or whatever, right? And so like these things are gonna get blurred over time. And so like trying to draw a h-hard boundary based on like the who you are is, is, is gonna be, is gonna be tough. And like we should enable users to choose, but we shouldn't box them in. And so a lot of w-the work that went in here, like, you know, keeping the primitives the same, like for example, plugins are like unified across, um, this product and ChatGPT and the cloud, was because of that. It's this, this thesis that like eventually things are gonna come together and, and we don't wanna be... Like we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.

swyx 15:13 I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the, the Codex harness. I can't imagine what that was, but maybe they're more the more con-conversational side. Can you com-compare and contrast the, the two harnesses, 'cause only you've seen it? [chuckles]

Akshay Nathan 15:31 Yeah. I, I mean, I think ChatGPT, the, the existing harness like still exists today. Like exists in this app, um-

swyx 15:37 The classic, right?

Akshay Nathan 15:38 The cla- uh-

swyx 15:38 You just start a new chat, and you don't go under Work, right?

Akshay Nathan 15:41 Yeah. If you start-

swyx 15:42 So-

Akshay Nathan 15:42 ... a new chat and go to Chat, then-

swyx 15:43 Yeah

Akshay Nathan 15:43 ... you're, you're talking to ChatGPT with the instant model.

swyx 15:45 Oh, we can technically do another.

Akshay Nathan 15:47 Yeah.

swyx 15:47 But I guess, you know, on instance.

Akshay Nathan 15:50 Yeah. So this one's not gonna code or it's gonna-

swyx 15:52 Um-

Akshay Nathan 15:52 ... be in line. It's not in a sand- in line in a sandbox. It'll actually-

swyx 15:55 Oh, that's cool

Akshay Nathan 15:56 ... do try to push you to, to, to go to Work if you're creating a spreadsheet.

swyx 15:58 Yeah. And this, this is a router decision?

Akshay Nathan 16:00 Sorry?

swyx 16:01 Is a router decision?

Akshay Nathan 16:02 This is the decision that, you know, the model is making, and then like-

swyx 16:05 Right

Akshay Nathan 16:05 ... you know, it sees that you're able to, or you're trying to do something that would be better served in Work mode. But I, I think your question was like what, what are the advantages of like the, the chat, like ChatGPT chat harness?

swyx 16:16 Uh, it's more broadly like I wanna, uh, basically do an oral history of harness engineering.

Akshay Nathan 16:22 Mm.

swyx 16:22 Right? Uh, you know, the ChatGPT harness lasted us from, let's call it the O one era, uh, until now, and now it's being replaced by the Codex harness-

Akshay Nathan 16:30 Mm-hmm

swyx 16:30 ... effectively. And they're, they're overlapping somewhat, but I'm curious what changed if, you know, if there is. [chuckles]

Akshay Nathan 16:39 My perspective on this is like there's, there's, there's al- there's sort of like a, a constant process of like divergence, convergence, divergence, convergence. And in chat, like many of the use cases I was talking about before, like, you know, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and like different things that, um, over time, like the product... The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that like if you give the agent access to this infinitely flexible environment as a computer, um, it can do really, really powerful things. And so when we think about like, okay, well, for, for knowledge work, like what is-- which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, you know, maybe abstract some of the details of this computer away from users who might not be used to that, but like give them that same power. Um, but ultimately, I think that we want the power in a- in all places, right? We want to meet people where they are. So I'm sure there'll be work down the road in order to, to get things to be, um, equivalently capable in, in all scenarios. Um, but it's just a question of like what we've been focusing on the product on historically and what we're focusing on now.

swyx 17:53 I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? Um, any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, uh, for this, you know, you wanna use Sol here, ignore all these-

Akshay Nathan 18:13 There's 32 options.

swyx 18:14 Yeah, yeah, yeah. Um-

Akshay Nathan 18:15 [chuckles]

swyx 18:15 But, um, that being said, you know, for people that are expanding, so, uh, productivity stu- trying stuff for work that don't have the breakdown of what all this is-

Akshay Nathan 18:25 Mm-hmm

swyx 18:25 ... um, what's, what's the advice, right?

Akshay Nathan 18:28 Well, I mean, I think before the advice, like the first thing is like none of this would be possible without these models. Like, the-- I think you asked earlier, like, you know, what was like the inspiration for work and like, you know, early on, like I, I, I mentioned like what we were seeing with Codex, but that was also because the, the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've ch- we've chosen a default that we think is gonna be the best for everyone. And, you know, we have, for power users, options under the hood. We could-- one could argue that there might be too many right now and we're, you know, working on simplifying it. Um, but you can extend, you know, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for, for most, most use cases. So my advice to most people would be to stick to that, and then, you know, if you reach a situation in which you think that you could, you wanna try, um, a different configuration, if you're not seeing either the, the efficiency on the, on the cost side or, or the, the quality on the intelligence side, then you can change the defaults and see if you can get something better. But, but we think the, the default should be good enough.

swyx 19:38 I have, uh, I, I'm just gonna run something by you since you, you have way more experience than me. I've recently been doing Sol Lite, but, uh, with goal, with the idea that the goal basically augments the reasoning effort, but with more terminations and turns.

Akshay Nathan 19:52 Mm-hmm.

swyx 19:53 Is that a good way to think about it as opposed to Sol Ultra or Sol, you know, Extra High?

Akshay Nathan 19:58 Yeah. It's hard to say because-

swyx 20:00 Yeah. It's like an interaction effect

Akshay Nathan 20:02 ... exactly. It's like there's a preference on, you know, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you know, you can, uh, steer or make sure that it's doing the right thing? I think generally people should try whatever works for them. Um, I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open, open explorations or very parallelizable. I think even for tasks-- using goal, I think is best for, for tasks that you know that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they actually don't fall into either of those buckets. [chuckles] Um, and so like at least when they're starting, and so that's why I think the best first step is like trying it with the, the default configuration and then seeing like where you wanna go from there.

swyx 20:58 Right. You guys worked on a slider, which is actually super helpful for reducing a amount of panic. [chuckles]

Akshay Nathan 21:04 Yeah. Yeah.

Vibhu 21:05 It's nice on mobile at least. There's a nice slider there.

swyx 21:07 It's nicer.

Vibhu 21:08 I haven't tried it.

swyx 21:09 So you, you, you have, you have the advanced view there, but if you click advanced view, yeah. Yeah.

Vibhu 21:13 Ooh.

swyx 21:14 Just a nice slider. Yeah. Very, very pretty, very colorful.

Akshay Nathan 21:17 Yeah. The idea was here, here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user.

swyx 21:24 Yeah.

Akshay Nathan 21:24 Of like, you know, something from-- that represents like, you know, speed and efficiency on one side-

swyx 21:28 Yeah

Akshay Nathan 21:29 ... and then like sort of like quality and thoroughness on the o- other side. Um-

swyx 21:33 I, I am just puzzled that it uses Sol so much, like the lower-

Vibhu 21:36 No, no

swyx 21:36 ... rounds I would have used-

Vibhu 21:36 I think the slider, if I'm not mistaken, is-

swyx 21:38 Terra.

Vibhu 21:39 Oh, it is.

swyx 21:39 You see?

Akshay Nathan 21:40 Yeah.

Vibhu 21:40 Nice name. Nice name.

swyx 21:40 So they, they preset Terra to only be the light one.

Vibhu 21:43 I see.

swyx 21:43 But like I think a lot of people actually would-- more people should use Terra. One, because Sol keeps running out of capacity. [laughing]

Vibhu 21:50 I'm the reason, you know. Here's ten minutes of our-

swyx 21:53 There you go

Vibhu 21:53 ... retirement calculator.

swyx 21:55 Oh, that's the Excel thing working for you.

Vibhu 21:56 This is, uh-

swyx 21:57 Oh, my God. Look at that

Vibhu 21:57 ... this is work, and then Codex is still cooking, so we'll get back into it. I think it, it'll be interesting to actually see the thought process, the reasoning, and also, you know, I guess this is eight minutes on work. Codex is still cooking.

swyx 22:10 Yeah. And by the way, uh, I... So I've, uh, do you know Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel.

Akshay Nathan 22:20 Yeah.

swyx 22:21 Uh, it, it edits Excel files. You never paid an Excel license.

Vibhu 22:23 [chuckles]

swyx 22:24 Right? Like, but somehow this, this is like kind of workable, and it's agentic Excel.

Akshay Nathan 22:29 Yeah, I mean, one of the big like pushes that we made for this launch was like Artifacts, right?

swyx 22:34 Mm-hmm.

Akshay Nathan 22:34 Like both on the, on the model side, like I think if you compare this with, with 5.5 and, and, and 5.4 before that, you'll see that the- there's been pretty dramatic improvements in the quality of these Artifacts and then also-

Vibhu 22:44 I see

Akshay Nathan 22:44 ... on the product side.

Vibhu 22:45 The UX side is also crazy, like-

swyx 22:46 Yeah

Vibhu 22:46 ... hosted sites and whatnot. No, no longer needing to host your own little webpage. Like it-

swyx 22:52 Oh, I have a story about that.

Akshay Nathan 22:53 Yeah.

swyx 22:53 I can, I can do, uh, a separate thing. I, I'll need to take the, the, the visuals here, but we, 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 5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they-- did the launch dates just happen to line up the same day?

Akshay Nathan 23:15 Uh, 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 u- just using Artifacts as an example, like, you know, a lot of what you're seeing, like underneath the hood, there was 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 making a retirement calculator.

Vibhu 23:53 Yeah, you can iterate very easily. Yeah.

Akshay Nathan 23:53 You wanna iterate, and like when you s- when you're seeing it, and if this thing is high fidelity to like what you would actually see in, in or what your, your coworkers would see if you were to send this to Shawn, like that, that I think makes it so easier and makes you trust the, the, the product in, in terms of iteration.

Vibhu 24:07 When you say coworkers would see, do you see a multiplayer, multi-team collaboration with Artifacts? Any, any things you guys think about that?

swyx 24:15 You can already share it, right?

Vibhu 24:16 Yeah.

Akshay Nathan 24:17 Yeah. It's in- it's something that, you know, we're actively thinking about. Um, one thing that, you know, we've noticed internally without talking too much about the roadmap is that like I-- there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.

Vibhu 24:32 [chuckles]

Akshay Nathan 24:33 And then I'll be thinking like-

Vibhu 24:33 Like the simplest would be, you know, the three of us are just all on one hosted.

Akshay Nathan 24:36 Exactly. And I'll think about like, you know, like was I- Required in this loop or-- And then maybe it was, you know, rephrase, like what they were asking or pulled from certain context or whatever. But like, you know, when I gave them back the answer, that process was also lossy, right? Like, I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that, that could be interesting.

Vibhu 24:57 Yeah.

swyx 24:57 So like the answer was preemptively respond to every inbound request?

Akshay Nathan 25:02 No, it was just like literally like, this is what I do sometimes as my job.

swyx 25:05 I know you copy-paste-

Akshay Nathan 25:05 Yeah

swyx 25:05 ... and then you, you know, you're just a message forwarding service-

Akshay Nathan 25:08 Yeah, yeah, exactly

swyx 25:08 ... from AI to AI.

Vibhu 25:09 But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that-

swyx 25:15 Yeah

Vibhu 25:15 ... oftentimes people don't realize until they try or someone shows you, and then you're like, "Oh, okay, okay, I see now."

swyx 25:20 Yeah. I think it's als- there's also like a, um, light security issue, uh, where like basically you're the permissions layer. Like, yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it.

Akshay Nathan 25:31 Yeah.

swyx 25:32 And that [chuckles] -

Akshay Nathan 25:33 Yeah

swyx 25:33 ... there's no way I would know because I'm not supposed to know what I don't know.

Akshay Nathan 25:36 Especially as like, you know, with ChatGPT Work or asking you to connect your plugins and, you know, it's pulling from your local files and stuff like that. Like the, the amount of context that the agent has access to is like deeply personal and like that's something that like we need to preserve. So that'll be definitely a challenge.

swyx 25:51 There's Excel, there's PowerPoint, there's Docs, you know, the, uh, grand trio of work. What other formats of work do you, do you think about? You know, like obviously you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like, you know, like what, what, what does that look like if, if you ever ended up doing it?

Akshay Nathan 26:09 It's a really good question. I think, um, I mean, o-one that you didn't bring up was Sites, and I think that was-

swyx 26:15 Sites

Akshay Nathan 26:15 ... a core part of this launch. Um, there's one, one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, you know, this sort of like prototyping tool. And actually, like we saw that happen with this launch, even the, the model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, you know, the, the collaboration between design and engineering and product on that was like on a site where we play with, you know, the, the, the affordance and, and then s- figure out how it feels and, and all of that. Um, but the other aspect that, that I think is a little bit less talked about is like Sites as like an, an artifact for, for knowledge work. I was actually talking to someone the other day who's on like our, our corporate finance team, and like they're mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in, in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is 'cause it's like, it's like somewhat higher bandwidth. Like, you know, at so- these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like, either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can kinda do anything. You can ask it for anything, and you can get that. Um, once people see that magic, I think it's been really valuable.

swyx 27:31 Yeah. Uh, let me show you my, my case study. Uh, this involves all the hot topics, including ChatGPT Work, but also 5.6, token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, uh, basically it's like a little board game that you, that you play with, uh, physical blocks. Uh, they come on top of it like that. Uh, so over the weekend, I, I, I took like thirty photos and just threw into ChatGPT. Uh, 1.7 billion tokens later, out comes this site with a fully playable thing-

Akshay Nathan 28:00 Wow

swyx 28:00 ... with, uh, with, uh, with 3D, uh, block placement and everything. Because it requires physical blocks, and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, uh, do things like train an AI on it, and that, that's, uh, that, that gets-

Vibhu 28:14 That's your auto research

swyx 28:15 ... that gets into auto research. So, uh, you want to train your own AIs, uh, and then make sure they self-play against, uh, each other. Uh, I need to set both AIs. Um, so this is AI versus AI, uh, and they're, they're gonna self-play. Obviously, the, the AIs start out bad, and then you want to define a loss function and, and get good. I wasn't gonna supervise all this. I was at, uh, I was down in San Mateo, uh, attending a conference. What I ended up doing was, uh, auto-researching and, and, on this and creating benchmarks, and that there was just way too many parameters for me to, to read. Uh, so I started asking it for a site, um, and it's created this, uh, uh, this, uh, this, this lab, uh, panel. Where... Is there, is there a, is there a shortcut for a site that is created?

Akshay Nathan 28:57 Uh, you should be able to go in the sidebar to Sites, top of the sidebar.

swyx 29:01 Uh, left-

Akshay Nathan 29:01 The left sidebar.

swyx 29:02 This one? Oh, left?

Akshay Nathan 29:03 Yeah, just scroll all the way to the top.

swyx 29:05 Oh. Oh, it says Sites.

Akshay Nathan 29:06 Yeah.

swyx 29:07 Oh, there you go. Yeah.

Vibhu 29:08 Ooh.

swyx 29:09 So it create, it creates the sites. Um, I don't, I don't think this is, uh, it is exactly what I, what I wanted. Um, but let me, let me show you what it popped up, right? Like, I think as a, as a research artifact, um, it is very important to communicate, uh, exactly, uh, what is being done. Outputs this, uh, this thing which I eventually started publishing. So I, I moved it off of Sites because I wanted more, uh, database and infrastructure than Sites afforded me. Uh, but this is, this is like a research output that you can start to mess with and, like, try to think about, like, what hyperparameters are you tuning for training AIs. Uh, and like, I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. Uh, and the fact that you can just kinda throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. Um, it is pretty overwhelming. So then I have to start pruning from there. But, uh, it's an interesting transition from Markdown-

Akshay Nathan 30:07 Yeah

swyx 30:07 ... effectively that you're putting out to, uh, you're putting out a whole functional site.

Vibhu 30:10 I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and... I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I notice they're quite verbose. I don't need a lot of this information.

Akshay Nathan 30:26 It's very verbose.

Vibhu 30:27 So... And then the nice thing of having a site side by side is, you know, you just iterate on what you want and what you don't, right?

swyx 30:34 Yeah. I, I don't know if, uh, any-- that, that triggers any stories for you of how it's run internally. Am I doing this right?

Akshay Nathan 30:39 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, 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 that there's still something to do there.

swyx 31:22 They're super long.

Akshay Nathan 31:23 Yeah. Um, yeah, but I, I think we're starting to see that, like, there, there is this aspect of this is a really interesting, uh, format, um, for, for people to use, um, that's, like, much more flexible than, than what they ever had before.

swyx 31:36 I think your

swyx 31:37 job also comes-- becomes kind of meta. You're not designing the products. You're designing a product f- to make products, and I'm curious how you manage that.

Akshay Nathan 31:47 I, I think one thing that we've been-- Like, when we look at the UX, like, that, that we've been thinking a lot about is how can we balance, like, simplicity with capability? Like, if we, if we're designing a product, like you said, that, like, is, is made to make up-- build other things, right? You can build so many different things. Um, but we can't put that all in front of you because you'll get overwhelmed.

swyx 32:10 Yeah.

Akshay Nathan 32:10 And so we had similar problem or, or similar challenges even Chat-- with ChatGPT, but especially now, like, when there's so much that can be done, I think the balance that we're constantly trying to strike is, like, how can we give the user enough of a UI surface where, you know, they can be expressive, they can, they can tell the, the, the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to, you know, to, to be super powered by the AI?

swyx 32:47 Yeah.

Vibhu 32:48 It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this, same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later. A lot of tokens later, I've got a similar version. Obviously, not with all the auto research and whatnot, but, you know, um-

swyx 33:08 You had to do all the latest trends.

Vibhu 33:09 And yeah, [chuckles] I did it with, uh, did it with Codex, not Work, but it's interesting, right?

swyx 33:14 Yeah. And this is obviously a GPT image generating the [chuckles] pro-avatars. Very good for game design. Like-

Vibhu 33:20 And-

swyx 33:20 ... a lot of game designers were, like, really into GPT image for assets.

Vibhu 33:23 I, I will say, like, the, the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, um, this was also a test for 5.6 came out. I had done the game on 5.5, right? The ability for me to no longer need it to-- I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.

swyx 33:44 Oh, yeah.

Vibhu 33:44 Uh, 5.6 could.

swyx 33:45 It is auto distribution. That's why we're also very keen-

Vibhu 33:47 Mm-hmm, yeah

swyx 33:47 ... on testing the 5.6 capability.

Vibhu 33:49 But, you know, this is just as, as work comes out, as new things come out, these are just our side ways to test things, right?

swyx 33:56 Yeah. It's some kind of private eval, I guess.

Akshay Nathan 33:58 Yeah.

swyx 33:58 That is not less private. [laughs]

Akshay Nathan 34:00 But also valuable because now you can send this to your friends, and I mean, I learned about this game through seeing this.

Vibhu 34:04 [laughs]

swyx 34:04 [laughs]

Vibhu 34:04 It's a hard game. He's very good.

Akshay Nathan 34:06 [laughs]

swyx 34:06 [laughs] Uh, uh, g- It's good to-- when no one is competing with you.

Akshay Nathan 34:10 [laughs]

swyx 34:11 Uh, but yes, it's a classic RL problem of, like, self-play, bootstrapping your game AI. Um, yeah, you see how easily work becomes personal and personal becomes work-

Akshay Nathan 34:21 Mm-hmm

swyx 34:21 ... because the thing I do for personal, it actually directly informs people I work with because I showed it to them, and they were like, "Oh, you can do that with [chuckles] GPT?"

Akshay Nathan 34:29 Yeah.

swyx 34:29 Which, like, I, I imagine is the growth strategy.

Akshay Nathan 34:31 Yeah. The, the show not tell is a big piece that, you know, I think we've-- we're not-- still not fully cracked of, like, you know, showing people all the, the things that they can do with the product versus, like, trying to teach that to them through, like, you know, articles or onboarding or whatever.

swyx 34:47 Yeah.

Akshay Nathan 34:47 So meeting them in the moment.

swyx 34:48 It's a career risk for me, uh, because I used to be in developer relations, right, where your job is to show, and then you're like: "What do you mean? You, you don't, you don't need..." Uh, a-actually, your job is to tell.

Akshay Nathan 34:58 Mm-hmm.

swyx 34:59 And then, and then... But the product people are like: "Well, we don't need you if our product is intuitive enough." So [laughs]

Akshay Nathan 35:05 Yeah, I mean, that's the magic of the, the models. So you can tailor the telling or the showing to, like, specifically what the user needs, like, what, what they care about, what they've done in the past, exactly where they are on the adop-adoption journey. So I think that's, like, gonna be a super big opportunity.

Vibhu 35:19 Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question I guess is, you said your team is more broadly on... What, what was the term you used? Productivity?

swyx 35:40 Productivity.

Akshay Nathan 35:40 Yeah.

Vibhu 35:41 Productivity. So how-

swyx 35:41 Which is now Work, basically.

Vibhu 35:43 Is it Work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there, is there more that the mass isn't targeting?

Akshay Nathan 35:56 I see

Akshay Nathan 35:57 it as, like, a sequencing. Like, you know, the, the vision is, like, bring useful agents to everyone. We started with, like, developers. Like, developers historically are, like, early adopters that are willing to put up with more friction, set things up, et cetera. Like, that's where, you know, Codex started. I think the next opportunity is, like, sort of what we call general knowledge work, you know, all the other functions around developers. I think when, when you go from developers to this segment, like, there's inherent challenges obviously with, like, you know, this, this show not tell thing that we're talking about, um, making the product more understandable, um, bringing in new capabilities that matter more for, for this cohort than matter for developers, things like Artifacts, things like computer use, et cetera. And then I think, like, the, the same learnings, like, similarly how we took the learnings from developers and brought it to, you know, general knowledge work, the next stage will be, like, taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. Um, and we're already seeing that a little bit. Like, this, this game example that you have is, you know, something that's, like, on the border of, like- Fun and personal life to, to, you know, your, your professional life. I use ChatGPT Work full-time at home for everything, like for, for whatever I'm doing. I used it the other day to come up with a meal plan and like, you know, save that on, um, on the, like, computer environment that it has and something that I can continue going back to. Like, is everyone doing that yet? Probably not because the thing says work on it, but eventually, you know, we wanna get people there, um-

swyx 37:20 ChatGPT Life.

Akshay Nathan 37:21 Yeah, exactly. ChatGPT Cooking. Um-

swyx 37:23 [laughs]

Akshay Nathan 37:24 But I think there's a lot of, uh, there's a lot of opportunity there, but I-I see it as like, you know, we're, we built, we built a foundation in, in, in s- in software engineering and we're gonna take the same learnings that we take from software engineering to knowledge work, knowledge work to, to everyone.

swyx 37:35 Do

swyx 37:36 you have any power user advice? I feel like, um, there's a group of people that will live it, use it for everything, stay on it twenty-four/seven.

Akshay Nathan 37:43 Yeah.

swyx 37:43 Um, and then there's a bit of a gap between that crew and people that, you know, okay, I use it for work. I use it occasionally. Sometimes I type questions. Uh, any advice, any learnings, anything you recommend or just, you know, takeaways that you've found that help bridge that gap?

Akshay Nathan 37:59 I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, you know, the technology has progressed so fast that, you know, something that like even three months ago, like no way, no way the models can do this. Like now it's like, wow, it's like it actually can. Like, um-

swyx 38:20 Do you have an example?

Akshay Nathan 38:21 We're going through right now their, our, like, review cycle internally, and, you know, people always talked about this as like kind of a, a thing that the, the models are good at. And like, you know, there's a cliché of like, okay, like, uh, no one wants to be writing reviews and like we can just use AI to do it. But I mean, in, in all seriousness-

swyx 38:38 AI to evaluate it as well [laughs]

Akshay Nathan 38:39 Yeah, exactly. In all seriousness before it was like, just like slop basically. And like I, I think it was helpful, but you know, not super productive. Now I've found that like the model can do a much, much better job than me, especially in this environment of like pulling context on like what people are up to, how they've like... The things that they've done to make a difference. Highlighting like, you know, wins that they've had that like I might n- may not even have seen. You know, it has access to like everything, right? Like the code, like, you know, things that they've caught, uh, reviews, Slack, everything. And so it's like incredibly powerful in that domain. And like, just like six months ago, the last time we did this cycle, like I didn't even-- I tried using it, but it was not at all helpful, and this time it's been like incredibly helpful. And like, so I think continuing to push the, the frontier of imagination of what's possible, even if you tried something before, I think is maybe the, my biggest piece of advice. The other, I guess thing is like the more, the more you put in, especially in this environment where like, you know, the model has access to, to everything on your, on your computer or in ChatGPT Work, like you can create, you know, artifacts over time and save them in your library and like the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become v-valuable in like ways that might surprise you. Like it might pull from context in a way that, you know, may be proactive and that you might not even have thought about, but it needs to have access to those, um, to that, those tools or that context first.

swyx 39:56 One thing I just wanna talk about the review stuff because I'm still-- That's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things w- especially when it comes to people, 'cause it feels like you don't care.

Akshay Nathan 40:14 Mm-hmm.

swyx 40:15 Presumably at OpenAI, people are obviously more open to being eval- basically [laughs] rated by GPT. Uh, but are there any unofficial rules around this? Like what's the etiquette?

Akshay Nathan 40:26 Oh, I mean, I think the etiquette is that like I would never write something via like-- solely via AI and like present it as like a review for someone. What I was talking about is more like gathering context.

swyx 40:36 Yeah.

Akshay Nathan 40:36 That's the place where it's incredibly helpful.

swyx 40:37 So it's just search.

Akshay Nathan 40:38 Yeah, exactly.

swyx 40:38 It's agentic search. Yeah.

Akshay Nathan 40:39 It's like agentic search, but you know, that you can tailor and steer much more capably than you could before. Um, 'cause like the, the thing is it, it's all-- there's sort of a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. Um, and so like, I think we need to use these same tools to keep up with all-

swyx 41:03 Yeah

Akshay Nathan 41:03 ... the impact that people are having and, and, and understand, you know, where, where it can be helpful.

swyx 41:07 I think the, the, the thing like obviously I, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?

Akshay Nathan 41:19 Probably, but I think that I also miss things.

swyx 41:21 Like it doesn't matter, right?

Akshay Nathan 41:22 I think sometimes it's-

swyx 41:22 Like it's, it needs to be human-level

Akshay Nathan 41:23 ... it's all relative, right? Yeah.

swyx 41:25 Sometimes it's nice when it finds things you wouldn't, right? Like right now my Codex system prompts, they're set up in such a way that every project I have has a secret separate, uh, notes MD, and it just writes learnings to there, and then the, the global one can pull from all these. So sometimes it'll be like, oh, there's this project you did like four months ago. Here's a note that we had, and it, it randomly pulls it back into context. I would never do, I haven't thought about.

Akshay Nathan 41:49 Exactly.

swyx 41:49 And I'm like, okay, this is quite superhuman, right? Like stuff that would... A-and you know, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, you know, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.

Akshay Nathan 42:10 Yeah. I actually have a funny anecdote about, about this. Like recently gearing up to this launch, you know, the team has been, you know, really cooking on it for, for, for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, uh, one of, one of the members of the team set up this, um, scheduled tasks like automation to like look at everything that's going on and like come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like the first is like, I think the models are, you know, over time like actually starting to become like funny.

swyx 42:41 Funny. Nice.

Akshay Nathan 42:42 Whereas like, you know, a year ago, like that was not at all the case. The second is that it was what you were saying, like they find things that in surprising ways that you may not- Not have thought of and, like, create connections that you may not have thought of. And that really helps with, like, the meme generation [chuckles] because then you can see something that, you know, genuinely surprises you and then-- and, um, is funny in that way. So yeah, I mean, obviously that's, like, not like the most productive, uh, use of this, of this technology, but it, it does-- it does uncover this, like, this capability that's emerging, which is just, like, defined information that you otherwise would not know of.

swyx 43:11 Talking about the, the launch, I, I think, uh, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.

Akshay Nathan 43:27 I think it feels like a culmination. Well, I-I think two things. One, it feels like a culmination, like I was mentioning earlier, like this, like, vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're, like, extremely excited to bring this to many more people and to see it working, to, like, see us reach, you know, the distribution goal, uh, I mean, um, numbers that you mentioned. Like, I think that's, like, huge and, and super exciting. The flip side of that is, like, there's so much more to do too. Like, that's also really exciting. Like, you know, ChatGPT as a whole, like the, the-- this product that, you know, everyone almost equates to AI and, like, loves, you know, has hundreds of millions of users. And so, like, ten million is really cool, but, like, [chuckles] we, we need to get this to everyone. Like, we need everyone to feel this magic. Um, and so that's the next step from here, but yeah. I think extremely pumped about how, how it's going so far and, and the opportunities.

swyx 44:15 Uh, awesome. Uh, I did want to also, because I've, I've, I've been tracking the, the number closely. It transitioned at some point from just Codex users to Codex plus ChatGPT Work, obviously because it's the same harness. The whole point is that you don't-- you, you can't, uh, count them separately. Do you have roughly a billion, um, ChatGPT users? Why did you just jump to one billion right away? Like, isn't that the default on ChatGPT or no?

Akshay Nathan 44:40 We don't default you into ChatGPT Work if you're on ChatGPT-

swyx 44:43 If you're free, yeah

Akshay Nathan 44:44 ... um, it's also only available to, to paid users right now. And, and I think there's, like, a process of, you know, educating users of what is the value of this product, having them try it, learning from their feedback and making it better over time. But I mean, the goal is to, you know, get as many of, of the people who, who love ChatGPT today to, like, feel the, feel the power of ChatGPT Work. But I think it'll be a, a journey.

swyx 45:05 Yeah. Codex will still be alive as a brand for the foreseeable future.

Akshay Nathan 45:08 Yeah.

swyx 45:09 Um, and we'll just toggle between them as, as, as needed for UI stuff.

Akshay Nathan 45:12 Yeah, I think it's e-even stronger point than that. Like, I think we fully intend to like, you know, treat developer-- like, developers have been, you know, a core market for us for so long, and, like, there's, there's so much more that we can do to make Codex great specifically for, um, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, you know-

swyx 45:38 Yeah

Akshay Nathan 45:39 ... doing a search for your factor.

swyx 45:40 I, I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifacts if I want artifacts? Or you know.

Akshay Nathan 45:49 It's funny, like we call-

swyx 45:50 [chuckles]

Akshay Nathan 45:50 ... artifacts internally 'cause that's what the teams call it.

swyx 45:52 But it's actually sites, yeah.

Akshay Nathan 45:52 But like, externally, like no one says that, no one calls it an artifact. I-- but I think that people, like, often, like, describe things, whatever they're used to, right? So if, you know, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's actually what we want.

swyx 46:07 One big--

swyx 46:08 another, I mean, it's July of twenty twenty-six [chuckles] . One big thing that also happens in, uh, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing an agent for personal stuff, but also crossing over to work in, in a sense, same way. As far as, as far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back, whatever?

Akshay Nathan 46:35 I think there's a lot of inspiration. I, I did go through my own OpenClaw moment. I, um-

swyx 46:38 Yeah, tell the story

Akshay Nathan 46:39 ... me and my, um, my, my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like actually quite useful. We gave it a calendar. It started, you know, creating events for us and stuff. At some point, the, the laptop that we were running on, it died, and [chuckles] then we got a chance to, to pick it back up. But there is a lot of inspiration there, like, um, you know, in ChatGPT Work, in web and mobile, like you, you get access to this like persistent computer environment where, you know, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. Um, one of the members of our team actually uses ChatGPT Work for what they used OpenClaw from before, and I feel like it has like completely transitioned, which is like, uh, workout planning and like meal tracking. Um, which again, it's like a worky thing, right? It's like not work necessarily, but it's like in personal productivity space, but it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. Um, and so you start to see the same types of use cases emerge, which has been really cool.

swyx 47:43 Is there a point that ChatGPT Work completely replaces OpenClaw? Obviously, they're independent, so.

Akshay Nathan 47:49 Yeah, I mean, I, I'm, I'm not close to it, so I can't speak to-

swyx 47:51 Yeah

Akshay Nathan 47:51 ... the, the OpenClaw roadmap, but I, I don't think so. I think that there's gonna be, you know, there's always a need for like this like incredible, like open source technology that, that team has built, and I think that we can draw inspiration, um, in the product and, you know, ChatGPT, I think many more people have like heard about and use ChatGPT than have u- have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that, like one thing on the ChatGPT Work side that we feel strongly about is that, like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, you know, your, your finances app or wh- or any other product like in this, in this one place. And so that's the goal. It's like it-- we, we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task where you can, you know- If, if you're doing, like, science work, like, we have an ability to, like, extend the system in such that you can, like, write the tech and it, and per-per-performs well. There'll always be, like, products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.

swyx 49:14 Yeah. Do you think that you can do everything you used to do with Waterfront in ChatGPT Finance?

Akshay Nathan 49:19 I actually tried it. I mean, like, ChatGPT doesn't yet custody [laughs] uh, cash and, and assets for me. So, so that part, no, not yet. But I, I mean, there was, like, a whole component of, like, retirement planning and, um, sort of like financial planning and budgeting and stuff that, that, um, we were looking into when I was there. And like, with the finances plugin, like, that's all possible-

swyx 49:41 [laughs]

Akshay Nathan 49:41 ... in ChatGPT today. So, um, I feel like at least that component's replaced for me.

swyx 49:46 I haven't really plugged it in yet. Um, I'm somewhat scared to look at the answer. [laughs]

Akshay Nathan 49:50 [laughs]

swyx 49:51 Like, that's honestly, like, the same reason for health and finances. Like, I'm like, uh, no, no. [laughs]

Akshay Nathan 49:56 It's really good. I mean, it's, it's really cool how-- I mean, we were talking about, like, the agentic search aspect a little bit earlier, but like, it's really cool how, like, you know, in, in conventional UX, like, if the more power you wanna give to a user, the more, like, knobs and bells and whistles you need to add. Like, you know, for, like, these finance and budgeting apps, like, there's always, like, a bunch of the different filters and, like, search bars and stuff like that. But, like, now, like, with the right connec-connectivity to the right data, you can have whatever you want. Like, you can ask any question you want-

swyx 50:22 Mm-hmm

Akshay Nathan 50:22 ... um, and into that box and, and get the answer, and I think that's super powerful.

swyx 50:26 I think it's also nice to just have it centralized in one space, right? You have different health apps. I have one for a smart scale, a watch, all these different things. It's just nice to centrally co-locate it.

Akshay Nathan 50:37 Which is, uh, you know, part of the whole thing of OpenClaw, right? Like that, that you would have a personal OS, uh, which presumably ChatGPT wants to become. I do think that just relying on, like, just-in-time pulling of data for, let's say, through via MCP, CLI, API, whatever you, whatever you do, still not enough. Like, I, like, I come from a bit, bit of a data engineering background, like you still want, like, a data warehouse or some kind of caching or semantic layer. Um, do you feel that, or do you already have that? I can't speak to, like, all the details on how everything works, but I think it depends on the access pattern, right? Like, if you want an answer immediately, then yes, it's very difficult to do that if you need to pull from all of these sources. But a lot of the, like, use cases that we wanna enable in ChatGPT Work aren't necessarily something that you need immediately. It's more like a task that you want the agent to go and do, and that's gonna take a certain amount of time. And, you know, with things like programmatic tool calling and stuff now, like, some of that time and sub-agents and stuff, like, some of that is also parallelizable. And so it's possible, I, I think it's very possible that there's a, the ceiling on what can be done, you know, with MCPs and like calling out to these third-party services has, has been raised-

swyx 51:48 Yeah

Akshay Nathan 51:48 ... substantially. So we're really excited about that.

swyx 51:52 You mentioned sub-agents.

swyx 51:53 I, I gotta 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. Like just, just watch. [laughs] Um, what have been, what have been your experiences, any design issues that you would call out to other builders building with sub-agents?

Akshay Nathan 52:14 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, uh, 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 converge on, like, overwhelming, um, with information. And so this is, like, the deliberate trade-off that we made for now.

swyx 53:01 I mean, you do display quite a lot of transcripts.

Akshay Nathan 53:04 Right. Right.

swyx 53:05 I think it's hidden by default, though, right?

Akshay Nathan 53:07 No, no, it's hidden by default.

swyx 53:07 Some, some people could want more. So I'm one of those people that will basically throw a lot of stuff at goal, and pretty much every goal I'll tell it to use sub-agents. Seems redundant, right? But every time I'm like, "Okay, use sub-agents where possible." And I have a lot of people, a lot of friends that recommend and do the same. Whereas I'll sometimes talk to people that are like, "Okay, this is where I want you to use sub-agents for this sub-task," and I'm sure they would appreciate seeing into how they're being used. For me, it's primarily, like, two things, right? One is net time efficiency, so span out across sub-agents. Two is probably cost, right?

Akshay Nathan 53:43 Right.

swyx 53:44 Uh, don't use big, expensive model. Offload to a lot of smaller, cheaper models. And some people want that level of control. So if you have repetition in what you're doing, right? Say I want something built where I want it to consistently do this every day, I might wanna go in and fine-tune sub-agents here, sub-agents there. So-

Akshay Nathan 54:02 Yeah

swyx 54:02 ... you can see both, but I think if I'm not mistaken, it's hidden by default. There's a dropdown that goes a lot where I'm like, "Okay, I'm just gonna keep, you know, using-"

Akshay Nathan 54:09 You can, you can, you can change the model that they use.

swyx 54:11 I know I tell them to be steered. I'll say my-- Uh, I know Anthropic offers this in Claude Code. You can tell Fable to use Sonnet or Opus to use Sonnet as sub-agent, so pretty trivial thing. You know, you tell it to span out sub-agents with Sonnet, you know it's cheaper, faster. I would assume if it's not there, it could be built there. But I think there's a side of-

Akshay Nathan 54:31 It's too many toggles. Mm.

swyx 54:32 It's not a toggle, actually. It's just, uh, you tell it in chat.

Akshay Nathan 54:35 You're prompting it, yeah.

swyx 54:36 The way I do it is prompt it, right? And I think this is something that gets abstracted unless it's something you built for repetition, right? So if I'm building something, say, that's, uh, podcast prep, right? Research into people, do a very, very deep extensive research, um, that I might wanna configure to cheaper, faster model just for web search, right? I can see a world in which you want both. I think the default is actually pretty good right now, where it's hidden, but you can drop down and get some more info into what's done.

Akshay Nathan 55:03 Mm-hmm.

swyx 55:03 I know people talked a lot about it on 5.6's launch. Uh, this thing loves to use a lot of sub-agents and causes the ChatGPT app to just crash because it's so processor-heavy. But, um-

Akshay Nathan 55:16 For what it's worth, that's not my experience. Yeah. I mean, uh, you know, I, I, I haven't had a crash from sub-agents.

swyx 55:21 I, I haven't either. I have-- We both have big laptops.

Akshay Nathan 55:23 [laughs]

swyx 55:23 But I know, I know people brought it up. There was a topic of discussion that we didn't

Akshay Nathan 55:29 See the same, but it is another vibe eval, right? People are like, "Okay, the amount of sub-agents Sol is wanting is crazy," and I'm like, "I think this is okay. I think it's good." But just stuff people bring up. I think when we launched the product too, we weren't as opinionated about, like, who is Ultra for, and like when, when should they be using it. And since then, we've made some changes to like, you know, require you to turn it on and, and, and find it in the advanced setting 'cause that's who it is for. It's for like power users who understand what's gonna happen because it also, you know, depending on your use case, can, can use more of your, your limits as well. Um- Yes. [laughs] So that's where I think a lot of the, the feedback was coming from. It's okay. Reset the limits. [laughs] Always reset the limits. Well,

Akshay Nathan 56:08 it's-- You know, today we're resetting because of this. Uh, I wanna change topics to, to one last piece of the harness, memory. A lot of people are commenting on memory recently. ChatGPT's new memory system used to suck, it's not very good. Uh, and then this guy also basically the same thing, and Samir, who you presumably work with- Mm-hmm ... uh, talking about memory. What can you say there? I think that, you know, Samir and the team have made a ton of-- and, and, and the research teams have made a ton of, um, updates and, and improvements over time. I think when I talk to friends, family members about what they love about ChatGPT, like the fact that it knows them, that they feel like their ChatGPT is, is their ChatGPT, I think comes up probably- Yeah ... number one. In ChatGPT Work in the, in the cloud, like by default, all conversations will, like, inherit from your ChatGPT memory, so you'll know-- they'll know context about you, and they'll also be able to write back to this memory. With it, like a, like a, like a small text write, like you tell me when you're writing, right? Is it implicit- No, it's part of the same, like, memory V3 system- Okay ... that, that we, we launched. Yeah, dreaming V3. So- Yeah ... so I think that's been really powerful because, you know, going from ChatGPT to ChatGPT Work feels like an extension of what I've already been doing with the product for sometimes many years. Um, so that's been awesome, and it's awesome to see that like people are recognizing- Yeah ... um, the, the improvements here. Is there-- So it's, it's basically a retrieval problem, right? Like, are you retrieving the right things? Are you over-focusing on the wrong things? Is there like, uh, more false positive or false negative, you know, if, if that makes sense? Like, what's the bigger problem? So I don't work on memory directly- Okay ... so it's hard to say what the bigger problem is with, like, certainty. But I, I think you're right. I think that, like, you know, the, the-- there's two sides of it. It's like, you know, making sure it knows things about you, but then also having the EQ to, like, bring those things up at the right moments proactively or surprising you in ways that are positive, not negative. Yeah. Um, so I think it's a very challenging problem, but something that I, I think we feel very-- is a huge opportunity to get right, which is like why we've made, like, big investments in it. How do you see the side of, okay, when you're building ChatGPT for work different than the regular chat app, different than Codex, managing memory across different projects, um, collaboration and whatnot, how do you see the, the side of what's separate from the harness, right? So if I have four threads on one project- Mm-hmm ... um, any learnings on how to build memory systems there, you know? For background as well, I guess to steer it a bit, is when you do chat style applications, I'd say you have a lot of one-offs, right? Yeah. When you switch to work- [laughs] ... it might be something you're doing for a month, something you do a lot, right? Now, as I add more sessions, there's a lot more than just single-threaded, right? Sure. And there, there might be memory there. I mean, I think first I challenge that, like, the depth of the memory or the, like, value of it is, like, fundamentally different across chat and work. Like it is true that like, you know, there are a lot of, like, shorter sessions on chat, but I think, you know, ChatGPT, the product, has had like a ton of longevity, um, in, you know, as long as this, this technology has been around and, and people use it for worky, like productivity-related things already today. And so I think we've found that there's a lot of value. I mean, I found this u- with my personal usage, like all these one-offs add up over time into something like quite durable and like, like quite a good representation of who I am. I know like from time to time something will go viral on, on X about like, you know, ChatGPT telling you everything it knows about you, and people are always surprised like how, how deep that is. The fun roast me, you know? Exactly. So like I think like the-- that's all to say that, like, I think there's a lot of depth there in the existing, you know, ChatGPT product, and so that's why I think we think it's valuable to bring into the work product. But the other reason I brought that up is because I think, like hopefully we can use some of the same fundamental primitives and systems to extend memory here as well. And I know this is something that the, the team that focuses on this is like working, working through right now. I wanted to bring up one element of memory, which I honestly don't really use much, and I'm curious if you do, Chronicle, which was-- is up on screen right now. Uh, it's kind of a super memory or like what, what is it? [laughs] I think the idea is that, like, it can learn from, you know, how you're using your computer and like it's anoth-another input source, um, into memory. And, um, I think it's, you know, experimental right now and something that, like, isn't default off. But I'd recommend that you try it. I think that it's like quite interesting how-- It, it goes back to a conversation we were having earlier on like, you know, you were asking like does it-- can ChatGPT miss things? Like does it, you know, on Slack when it's searching, does it miss things 'cause there's such a volume of stuff, right? And like it's-- I, you can ask the same question about like everything that you're doing on your computer. Like is it gonna know everything that you're doing? Is it gonna capture the intent and stuff like that? Probably not, but like it probably will find things that you might not know about and then if it can surface those to you in relevant times in proactive ways, like when you're doing tasks. And I, I've found at least that it can be quite helpful, so it's worth trying. So mostly for insights and longer term. Yeah, exactly. Like insights and it builds context that, that makes-- that can make you more productive- Mm-hmm ... on, on certain tasks. But it's, it's, it's hard to describe without feeling it. Um- [laughs] I will say you can feel it pretty well. Like the idea of what they're saying here, right? Just check through my memories or check through my logs and add skills. Yeah. Pretty underrated, right? But that, that's automations. You, you can repeat that u-using a cron job. Checking through your memories and creating skills? Yeah. But I think the creation- I see ... of the memories from Chronicle itself is like what's different. Okay. It's like you have much deeper memories because you have Chronicle on. It's there. I don't use it much, but maybe I just-- I need more examples. I, I imagine you guys use a lot of it internally, so I'm always fishing for use cases. Yeah. [laughs] I would just try turning it on and then like- It just auto works? Like it- Yeah, and seeing like where- Or it might start helping you. I think you'd be surprised.

swyx 1:01:47 Yeah. Amazing.

swyx 1:01:48 I think that was the, uh, about it in terms of, like, the, the overall, uh, coverage of ChatGPT Work. I think there's been a lot of, like, good progress and discussion on building and all these things. There's a lot of, like, ex-founders in the, in the community, uh, and in OpenAI as well. Do you think that things have changed a lot? Uh, you can say your overall reflection of building, uh, pre-AI and post-AI.

Akshay Nathan 1:02:13 I mean, I think things have changed a ton. I think it's, it's, like, super exciting to see how quickly you can go to, from idea to something real today.

swyx 1:02:23 Yeah.

Akshay Nathan 1:02:23 Um, whereas, like, even before, like, I think, you know, five, ten years ago, like it w- it was fast if you were scrappy and, you know, like w- willing to build the, the minimal viable thing. But, like, now the extent of what you can build is, like, much, much broader. And I think that also, like, what we've seen internally building is, like, that gives you an opportunity to validate much more quickly, to talk to users, to talk to, to internal doctors, et cetera, and, like, make sure you're on the right track. And, like, that loop, I think, has been, has become more closed than ever before, and that's, like, a win for product development. I think it's a win for, for consumers and users too because ideally that means they're getting much more better, much better products out the gate.

swyx 1:03:00 Does it mean your teams are smaller?

Akshay Nathan 1:03:02 I think there's much more to do now. Um, so I think people can accomplish more individually or in a small team than they were-- that would require more people than before. But there's-- at the same time, there's also more to do. So I think the teams are much more ambitious.

swyx 1:03:18 Have you seen any changes in scopes of roles and building teams and how we used to have teams, say, a few years ago versus what ideal teams look like now?

Akshay Nathan 1:03:27 I think we've seen a blurring in the lines between, like, the typical product development functions, like between, like, you know, EM, BM, engineer, um, designer, et cetera. Like-

swyx 1:03:37 Yeah, I wanna bring up this quote. There will be, uh, only four jobs left in tech. There's AI, uh, slop cannon, the people who just, like, uh, they'll burn a bunch of tokens. Uh, and then there is, there is SRE, uh, who... people who are more responsible. There's grown-ups who sell things, and then there's hot people [laughing] .

Akshay Nathan 1:03:55 This is an interesting take. I think [laughs] my, my suspicion is that there's everything-- everyone will be, like, T-shaped in a way, and that, like, AI will enable everyone to become a generalist.

swyx 1:04:06 Yeah.

Akshay Nathan 1:04:07 Like, you know, things that, like, I, I never would be able to, like, come up with a design before. And, like, even now, like, I don't have maybe, like, the visual taste required, but I can iterate on something with the help of AI. But then people will have a specialty, and that's, like, the, the, like, it's the straight line in the T or the, the upward line in the T. Um, and so, like, you can have a specialty that you're interested in. With the help of AI, you can go deeper and become better at over time, but then you'll also be a generalist. And so with that foundation, the way you can accomplish is, like, almost limitless.

swyx 1:04:35 What are you bottlenecked by in terms of specialties? Like, do you need more designers? Do you need more slop cannons? Do you need more hot people? [laughs]

Akshay Nathan 1:04:43 I think

Akshay Nathan 1:04:44 the bottleneck some-- becomes, like, sort of, like, ideas and taste, I guess. Um, I think because anyone can, can build now, I think, um, it really is the era of, like, bottoms-up ambition. And because there's so much to be built, like, you're always gonna be bottlenecked by, you know, the amount of ideas and amount of things that you're doing at a given time.

swyx 1:05:06 Do you think models help solve that?

Akshay Nathan 1:05:08 Models?

swyx 1:05:08 Yeah. I mean, I have the example of, like, I have a front-end design skill that's like, they give me four drastically different examples of what this looks like. Sure, it burns a lot of tokens, but, you know... And then I'll mostly just condense down, "Okay, I like this part. I like this part. Let's draw these together." And it's like, yeah, I had a vision, but, like, I don't know.

Akshay Nathan 1:05:29 I would say that the one automation that I would love to work and it doesn't work is bring me new ideas, right? Uh, somehow LLMs are just not it. One interesting part about ideas is, like, they're not, like, in a vacuum. It's, like, not-- They, they usually come from somewhere and, like, you know, in, in product development, like, they're coming from talking to users or reacting to, you know, friction that you're seeing or feedback, building on some foundation that you already had planned out before, whatever. Um, and so I think that's where, like, I think there, there will always be value in, in these, like, generalists that we talked about, like, you know, closing that loop and, and then having-- coming up with those ideas that are grounded in, in that feedback or talking to users, whatever it is.

swyx 1:06:10 Cool. Uh,

swyx 1:06:11 you were gonna-- You lead the productivity team. How do you define productivity?

Akshay Nathan 1:06:15 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, 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. You can suddenly 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.

swyx 1:06:54 Does that also translate to a way to measure productivity? Like, what is, you know-

Akshay Nathan 1:06:58 The end measure-

swyx 1:06:58 How do you measure leverage?

Akshay Nathan 1:07:00 I think we haven't figured this out yet. Part of the reason is it's so diverse. Everyone has different goals, and really the true measurement is, like, their ability to achieve that goal. Did we help you or did we not?

swyx 1:07:12 Yeah.

Akshay Nathan 1:07:13 And it's very difficult without knowing what that goal is up front and also tailoring it for every individual.

swyx 1:07:17 And the thumbs up and thumbs down from ChatGPT doesn't give you anything, right?

Akshay Nathan 1:07:20 Right. I mean-

swyx 1:07:21 [laughs]

Akshay Nathan 1:07:21 ... you don't know if they're thumbs downing the, the content of the answer or the vibe of it-

swyx 1:07:24 Oh, yeah

Akshay Nathan 1:07:24 ... whether or not it helped them with their goal.

swyx 1:07:26 Yeah.

Akshay Nathan 1:07:27 I think that's difficult. Um, but it's something that I think w- we will need to figure out and the industry at large will need to figure out because, you know, that's how we, we measure success if this is what we're for.

swyx 1:07:35 Do you think it's changed productivity and how you measure it? Basically, you said there's a lot more work that can be done, a lot more scope. Um, has it changed?

Akshay Nathan 1:07:43 I think it was always true that what you really wanted to measure is, like- You know, was your team, was the individual, were you personally able to hit the goal, or are you closer to hitting that, whatever your goal is, right? But I think previously, we used proxies for this. So like, you know, code commits or-

swyx 1:07:59 Lines of code, yeah

Akshay Nathan 1:08:00 ... lines of code or whatever, um-

swyx 1:08:02 Story points.

Akshay Nathan 1:08:03 Right. Yeah, exactly. Story points. And like-

swyx 1:08:04 They're coming back, by the way. [laughs]

Akshay Nathan 1:08:06 May- maybe. I mean, but, but that is for a part of the change, and like, I think with, with AI now, those proxies starting to fall apart. Like y- you know, and the number of tokens you use or the number of pull requests you make are, like, no longer, like, maybe as hypercorrelated with the-- that is, was your team able to hit the goal, or are they on, on track to hit their goals? So I think we'll need to come up with, with new, um, measurements.

swyx 1:08:30 For the managers listening, uh, give them one thing to try.

Akshay Nathan 1:08:35 I think for me, what's important is, like, at-bats. Are we as a team building the muscle to have not just quantity of at-bats, but quality? Like, are we able to go all the way from, like, generating an idea, building it out, getting the feedback, reacting to that feedback, actually validating or invalidating the hypothesis, going on to the next idea? Are we able to do that really efficiently? Like, that goes to, like, you know, the actual, like, code that's being written or the designs that are being made or the specs that are being written, whatever, but also the culture of the team. Like, do we have the humility and, and, and, and, um, are able to, like, go through that process many, many times and stay motivated and excited throughout that? Um, so that's the thing that, like, I think is important now, especially when we're on the frontier of this technology and, like, there's so much to build, there's so much to do. That's probably the most, most important thing that we look at.

swyx 1:09:23 Any traps people fall into around measuring productivity, what your team work on? I feel like there's a lot of, okay, we added a lot of LLMs. We have dashboards for this and that, but not much has changed, right? [laughs]

Akshay Nathan 1:09:35 [laughs] That is the trap, yes.

swyx 1:09:38 And you know, the, the broader source of the question is for, for the managers and teams building, you know, how, how should they approach this?

Akshay Nathan 1:09:47 I think maybe the trap is, like, conflating motion and progress. I think motion is much easier now than ever before because of the tooling that we have. But progress requires you to be, like, very prescriptive and deliberate about, like, what you're actually trying to achieve. And it goes back to our question of measurement, right? Like, you wrote-- we were talking about, like, can we, OpenAI, like, figure out how to measure productivity for our, for our users? That's, that's a very hard problem because of the diversity. But, like, as a team, like, you should have a really prescriptive and deliberate view on, like, what progress looks like for you and for your team. And if you don't have that, then it's very easy to conflate these two things.

swyx 1:10:26 I think at-bats is a really great thing. I, I'm, I'm really glad-- I, I like the discussion between motion and progress. I think that's a-

Akshay Nathan 1:10:31 Mm

swyx 1:10:31 ... quote that we're gonna feature on the writeup. You've been very generous with your time. Thank you so much, and congrats on ten million.

Akshay Nathan 1:10:36 Yeah, thank you for having me.

swyx 1:10:38 The next to, uh, next one at 100 [laughs] in two months.

Akshay Nathan 1:10:40 Sure. [laughs]

swyx 1:10:41 Two weeks. [laughs] Thank you. [upbeat music]