Uncapped #46 | Brad Lightcap from OpenAI

Brad Lightcap serves as OpenAI's COO, overseeing its business, operations, and strategic partnerships across Research, Applied AI, and go-to-market. He also manages the OpenAI Startup Fund. Previously, Brad was part of Y Combinator Continuity and led finance and operations initiatives at Dropbox. We discussed the shift from chat-based AI to agents that can take action, and what that means for software and the broader economy. We also covered how these systems are being built and deployed, how tools like Codex are changing how work gets done, and what this next phase of AI unlocks for startups and incumbents alike. --- Timestamps: (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam --- Links: https://x.com/bradlightcap https://x.com/jaltma https://openai.com/ https://uncappedpod.com/ --- [email protected]

• 0:00Speaker 00

  Ninety nine percent of people get to use bad tools or don't have any tools at all. The quality of experience of the people that exist as their customers and users is not very good. Everyone has like lived the bad experience of going to modern life.

• 0:12Speaker 01

  Yes.

• 0:13Speaker 00

  And dealing with the things that we have to deal with. I think if you're kind of sitting there lamenting the idea that, you know, there's no more good ideas and no more new ideas. Like, it's just kind of lazy.

• 0:21Speaker 01

  All right.

• 0:22Speaker 00

  You film an intro?

• 0:23Speaker 01

  Do I film an intro?

• 0:24Speaker 00

  Or you just go?

• 0:25Speaker 01

  No, I just kind of.

• 0:25Speaker 00

  Hard in?

• 0:26Speaker 01

  I just started. Yeah, this probably is the intro.

• 0:28Speaker 00

  All right.

• 0:29Speaker 01

  So Brad, thanks for doing this with us. I'm excited.

• 0:30Speaker 00

  Yeah, me too.

• 0:31Speaker 01

  Do you have enough drinks? Would you have one more?

• 0:33Speaker 00

  Well, yeah, I'll take whatever I can get.

• 0:34Speaker 01

  We can load up. Well, I really appreciate you making time for this. I've been looking forward to it. What I wanted to start with actually is I was just like thinking about this last night and you joined OpenAI in 2018. And then like four years, you know, it was like research lab. You guys are like beating Dota and then like four years in like chat GPT launches. And then it's like this whirlwind that's been, I guess, like three years, but I'm sure it feels like a lot more. I was just curious if you could like share your narrative or recollection of like what the journey's been like. And like, what are like the chapters? Like, what's just your experience been like as you like look back on this so far?

• 1:10Speaker 00

  Yeah, chapters is the right word. It's the kind of journey of OpenAI, which I think tracks the journey of AI as a, as a field, as an industry is, has kind of been broken up into these weird periods. Like when I joined, it was, no one had really heard of OpenAI. Our work was, you know, relegated mostly to kind of small niches of San Francisco tech culture that followed such things as, you know, us beating the Dota, you know, best Dota players in the world and things like that. Really, it was kind of, you know, I didn't really like have anyone to talk to about it. It was like, everyone was kind of like, what are you, like, what are you doing there? And what do you do there?

• 1:47Speaker 01

  And you were like the CFO when you joined, right?

• 1:48Speaker 00

  I was our CFO. I spent, yeah.

• 1:51Speaker 01

  Like, what were you thinking when you joined? Like, what did you expect it was going to be?

• 1:54Speaker 00

  Well, I didn't know. I was 27 and so I was just kind of like, you know, and I maybe back up a minute. I was at Y Combinator prior, working with Sam. And I was starting to spend a lot more time with what I call our hard tech portfolio in Y. So all the companies that are building everything that wasn't pure kind of SaaS and internet, you know, consumer internet. So spending a lot of time with, you know, everything from nuclear fusion to satellites, to biotech, to, you know, anything that would kind of fit outside. And OpenAI was kind of in that camp. Like AI was kind of one of those things that was like, it was promised as this like future technology, but you know, wasn't really sure like who, who's like actually building this. OpenAI started, as you know, as like a YC research project.

• 2:35Speaker 01

  Yeah.

• 2:36Speaker 00

  And so it was kind of in the family and Sam had called me and was like, hey, I need someone to help basically do everything that isn't just the research at this, at this company. Do you know anyone that that would be good? And I tried to help them find someone, couldn't find anyone. And so I was like, I'll just help you, you know, on myself on the side. But I started spending a lot of time with Greg and Ilya and the team that was there at the time. I kind of realized that they had this like crazy, there are these crazy properties that apply to AI, which now we understand to be basically the scaling laws. And so consistently the field was starting to discover that when you make things bigger, the results just get predictably and consistently better. At that point then, it's like, okay, really, this is just a compute problem, actually. And

• 3:20Speaker 00

  intelligence basically can just be bootstrapped from basically scaling up very basic general architectures that, that can turn into a more general intelligence. And I was like, well, I don't know if this is true. And I don't know if this will hold. I'm certainly not qualified to judge that. But if it does, and these guys seem convinced that it is true, it's going to be the most important thing ever.

• 3:41Speaker 01

  Yeah.

• 3:41Speaker 00

  And at 27, I was like, I don't know, that just seems more interesting than investing in tech.

• 3:46Speaker 01

  Yeah. So you started doing that. And then what happened in those early years? Like, obviously, like people are, they're, they're building things that were working, like beating the game and, you know, a lot of other projects. But like, what were you seeing on the inside from, let's say, like 2018 to 2022?

• 4:00Speaker 00

  Obviously, it was much, much more of a research centric culture. It's, OpenAI is still highly research centric. I feel like People, people kind of think post chat GPT, it became much more of this product centric culture, but research really drives everything. And I think that's started because of how much that was cemented in that period as call it the kind of cultural foundation of the company. So I spent a lot of my time really just trying to figure out what researchers needed to be successful. And that spanned from, you know, the capital that we need to invest in supercomputers to working with partners to do the supercomputer design and build out to things as kind of trivial and pedestrian as like our robots keep breaking

• 4:40Speaker 00

  and, you know, it takes too long to like drop ship parts from, you know, this one supplier that sits in some small town in England or something like that.

• 4:49Speaker 01

  Yeah.

• 4:49Speaker 00

  How do we like tighten that loop and go faster? So it was this like very like, like kind of diverse set of problems early on that were really just about pure research acceleration. Obviously now, you know, it's, it's, it's kind of both research and deployment and our business, but it gave me an early on like an appreciation of just like, I just spent all my time with researchers. And so it was really like, it gave me a firsthand understanding of kind of like what was happening before I think anyone else really appreciated it.

• 5:17Speaker 01

  So then there was chat in 2022 and 2022. Did you guys on the inside feel like, oh, this is going to be something like when you were playing with it before it got released? Was the vibe inside like this is like another cool thing. Let's just, it's like a playground.

• 5:31Speaker 00

  Yeah.

• 5:31Speaker 01

  Or were people like, mm, this is, this is something?

• 5:33Speaker 00

  There's a word that sometimes people use in, in AI as to describe kind of when there's an indication of something that's happening, but you don't, it's not quite happened yet, but you kind of get these like little, you these little sparks. And that was kind of how I would describe the pre chat GPT period is there were a lot of sparks. You could see that the models were now starting to get good enough that they could kind of emulate, you know, humans in a conversational format. You could see that there was an interest that people had in directly prompting the model. People forget that this was not the way that we originally engaged with language models. We thought of language models as completions engines. So you start a text string and then it basically takes that.

• 6:15Speaker 00

  As an input and then it continues the texturing on this kind of more conversational, you know, dialogue based format is not the original invention of language models. And so, but what we were seeing is we had, we had an API that was a completions API. And we had an interface that basically let people put text into an interface that would then, you know, show a preview of what the model would actually produce as an output. But people were trying to use that interface in a more kind of dialogue, kind of conversational turn based format. And so you could see it, you could just, if you kind of paid attention, you listen, you could see that people wanted to talk to the model and that was the natural intuitive way that people wanted to engage with it. But it wasn't actually quite built that way.

• 6:56Speaker 00

  The other thing that we saw ahead of time was we trained an early version of DALL-E. As a, it was our first image model, it wasn't very good, but it was really a breakthrough at the time. And so for the first time, you could now generate images. And we had seen some adoption of that model in a more kind of consumer prompt based format. And so we had guesses leading up to chat GPT that it was going to be, it was going to be something important, but we didn't appreciate the scale. I think my guess at the time, we all took guesses because we had to do the compute planning. Was that the, at peak, there'd be a million concurrent users. And, you know, obviously we were very wrong.

• 7:32Speaker 01

  So what are the chapters since, like, if you look back the last three years, what are the phases, like, if you were sort of like describing to a friend, here's the phases of my journey post chat GPT, how would you bucket it?

• 7:43Speaker 00

  There's, you know, there's phases of the company's life. And then I think there's, there's phases of the industry and, and the, and the technology. And on the technology side, I would say it's, it's obviously there is this kind of proto period of, of, of research just starting to work. And I think I call that kind of the scaling period of where we just realized that you actually could go from something that was unusable to something that was kind of usable across, you know, basically most model formats that was kind of before mass consumer adoption. That was kind of 2018 to 2022. I think 2022 to kind of 2024 was really the period of, of chatbots. Where all of a sudden now it was, okay, you know, it was generative AI. It was, it was people realizing that, you know, you actually could, could have something that was useful.

• 8:28Speaker 00

  But it was not totally clear exactly what it was useful for. You know, it was, it was new and novel. And I think there was a, there was a.

• 8:38Speaker 00

  People had an appreciation for that, but you know, the utility was still not totally there. Like, it was kind of like a slightly better version of search. And then the next chapter, and I think what the one that we're in now is, is this kind of period of agents, which is AIs that actually can go do things for you. They run asynchronously. You can give them instructions and they can take an arbitrary amount of time and tokens to go off and think and figure it out. They can use tools. And I think we're in the middle of that period. I think that started in, for me, in December of 2024 with the release of O1, and then kind of through 2025 and into 2026.

• 9:12Speaker 01

  And you think we're like in the middle of that now?

• 9:14Speaker 00

  Yeah, I think so. I think weirdly in each of these things, because the kind of utility quotient on the models goes up by some enormous factor. I actually think it takes, there's almost more time it takes in each of these eras to explore the kind of full potential of the model. I've always said to, I say to our customers and partners all the time is like, you could stop progress right now. And I still think there's kind of a 10 or 20 year diffusion and innovation cycle that just

• 9:37Speaker 01

  To get it into the economy.

• 9:38Speaker 00

  Just to get it into the economy and for people to realize what these things are capable of. With chatbots that maybe would have been five years or something like that.

• 9:44Speaker 01

  Yeah.

• 9:45Speaker 00

  But you know, with agents, it's probably some multiple. And then the question is obviously, as the technologies, the technology will progress much faster than that. And so that dissonance of the diffusion period being kind of much longer than the actual kind of innovation cycle is going to be something interesting to watch.

• 10:00Speaker 01

  How far away are we from the like completion of what agents can do? Like, is it the beginning of a thing that will never end? Are we halfway up an S curve? What is the current sentiment for like what the endpoint of, you know, agents capabilities will be?

• 10:17Speaker 00

  I personally, I feel totally unmoored here. I don't know. And, you know, the kind of historian and, you know, kind of technological economist in me kind of wants to think that everything has to fit into these very nice kind of S curve shaped paradigms. And that, you know, everything will, the innovation cycle will kind of look exactly as it is.

• 10:37Speaker 01

  And there isn't a script that we can be, we can be right here.

• 10:39Speaker 00

  The kind of Carlos de Perez, like, you know, okay, like this will all be the way that it has been. But, you know, There's a lot of meta levels to this. I think we don't quite understand that when you've got systems that now have in some sense their own agency, there's almost kind of infinite levels of things that can happen, right? They can now start directing other agents.

• 11:01Speaker 01

  They can work together.

• 11:02Speaker 00

  You have the temporal aspect of they can just, you know, they can think and work for longer as long as they can kind of cohere the context basically through that period, which, you know, is something that I think will get solved. You know, even basic primitives like memory and other things that are core to very long horizon work and work that you would do kind of over multiple sessions. All of those things haven't even yet been sorted out, but are starting to get figured out.

• 11:25Speaker 01

  Yeah, I mean, I've always thought, always in the last year, I've been like, why are we not going to get to a place where you can just prompt, you know, build me a business, make no mistakes.

• 11:32Speaker 00

  Exactly. Yes. Yeah, no, that.

• 11:34Speaker 01

  I don't see why you couldn't be like, hey, can you go make me a million dollars, please?

• 11:37Speaker 00

  Right. And you play it out in the limit and you're like, I don't know, maybe that's possible. And I think that's kind of why, even, you know, maybe if you go back and say, even if you pause progress right now, maybe it's a longer, maybe it's 40 years or something, or 50 years of progress that will, that will come from this, just on the basis of this, this step of the cycle.

• 11:54Speaker 01

  One of the interesting things that I've experienced is right before, right after ChatGPT, I think a lot of the conversation on AI was like, living in sci fi land, are we going to have like the next species takeover? Are there dice and spheres? Like, it was very like big.

• 12:10Speaker 00

  Yep.

• 12:10Speaker 01

  And then what I've experienced over the last few years is it's been extremely commercial in a good way, but in a very down to earth way, like in a, in the economy operated by humans, it doesn't feel scary. It just feels like insanely sick software.

• 12:26Speaker 00

  Yeah.

• 12:27Speaker 01

  But it's still, there's this like lingering thing in the background that I think gets talked about a little bit less of like, is there sentience? Like, does it go to this other place? Like, does that still, is that still a conversation that matters? Is it something that's still thought about? Or is it just like, hey, we actually feel now like this is just really good software? There's nothing to be worried about. It's just like an insane technical revolution.

• 12:47Speaker 00

  Yeah, this is a really interesting question. I think in some sense, the the better the technology gets and the more it pushes toward that sci fi future, the more we actually end up having the conversation about it, diminishing it almost to just being a tool. And it's a weird paradox. And I've noticed the same thing because I I used to I used to sit at the OpenAI that was very much talk having the conversation about Dyson spheres. Because in 2018, that's kind of all you could talk about.

• 13:10Speaker 01

  Yeah.

• 13:11Speaker 00

  You basically had something that was kind of barely working at the beginning and then you could try and see.

• 13:15Speaker 01

  You think about the whole thing.

• 13:16Speaker 00

  Exactly.

• 13:17Speaker 01

  In the middle of it.

• 13:18Speaker 00

  In the middle of it.

• 13:18Speaker 01

  Steps right in front of you.

• 13:19Speaker 00

  Yeah, there's a local linearity that starts to set in where you're a little bit like, OK, like I I appreciate that this thing is a zillion times better than what it was, you know, in 2018. And the capabilities are multitudes more than what they were even two years ago.

• 13:34Speaker 01

  Like as an example, you know, you talked about Dolly.

• 13:36Speaker 00

  Yeah.

• 13:36Speaker 01

  When that came out, I was like, oh, that's cute.

• 13:38Speaker 00

  Yeah.

• 13:38Speaker 01

  But now not much, you know, just a few years later, I can't tell if the video is fake or real half the time.

• 13:44Speaker 00

  Yes.

• 13:44Speaker 01

  You know, it's like, that's going to get all the way there where you'll have no idea.

• 13:47Speaker 00

  No, yeah, and and I think that like, in some sense, those there will be this kind of like these parallel conversations that happen. Like there will be the kind of like enterprise productivity conversation, because that is something that actually people are thinking about, want to talk about. Everyone's going to kind of glob on to, you know, what is the narrative there that is.

• 14:03Speaker 01

  It's just sort of funny, like, are we like waking up a new god or are we like helping lawyers be more productive?

• 14:09Speaker 00

  I think we're doing both.

• 14:09Speaker 01

  Yeah.

• 14:10Speaker 00

  And I think, you know, the the kind of parallel track of this insane level of empowerment of an individual person to do things that like would have been inconceivable even a couple of years ago, you're already seeing examples of it. And that, to me, is like the weird sci fi future. There was the story over the weekend of the the there was a guy in Australia who like is curing his dog's cancer, who has no background in, as I understand it, in biology, but basically just had GPT-5 effectively try and come up with some sort of RNA based, you know, vaccine that that could treat, you know, could treat his his dog. And then he sent it, he worked with a lab to do the design of the of the treatment and, you know, they kind of sent it back and it seems to be working.

• 14:53Speaker 00

  And it happened in a matter of like for like $3,000 and like in a matter of. You know, a few weeks and like, it's kind of a crazy thing.

• 15:01Speaker 01

  Yeah.

• 15:01Speaker 00

  Right? Um, you know, that to me would qualify as like a spark of a sci fi outcome.

• 15:05Speaker 01

  It's just crazy how fast we adjust to anything.

• 15:08Speaker 00

  Yeah.

• 15:08Speaker 01

  It's like, you know, we could learn that there's like aliens tomorrow and like we would next week. Like, yeah, of course there's it, you know, it's just.

• 15:14Speaker 00

  Yeah.

• 15:14Speaker 01

  One of my takeaways with this whole thing is we just, people adjust to any new surrounding. They just think it's normal in like no time.

• 15:20Speaker 00

  That's been my experience is like, things are novel for about three seconds.

• 15:23Speaker 01

  Yeah.

• 15:23Speaker 00

  And next day it's like, okay, what have you done for me lately?

• 15:25Speaker 01

  Yeah. On this sort of topic of like, what is the thing? I'm sort of watching it all and I'm from St. Louis.

• 15:31Speaker 00

  Yeah.

• 15:31Speaker 01

  Now I'm living in Silicon Valley. There's a very different perception of AI in like the St. Louis's of the world and like Silicon Valley. And like, I think here the general sentiment is like, this is amazing. Thank goodness this happened. And I think around the country, maybe world, there's like real skepticism and anxiety and fear. And I think people here have that too. But like, it's this interesting reckoning for people where you're grappling with, you know, simultaneously like, oh my God, that's amazing. And that's awesome versus like, oh my God, that's amazing. That's kind of a threat.

• 16:03Speaker 00

  Yeah.

• 16:04Speaker 01

  How do you think about like, what the right way to interpret this is like, what are like the genuine concerns and fears that like, we're going to need to work through? And like, what are the things that you think are misunderstandings that will actually just be really positive?

• 16:15Speaker 00

  Yeah. And look, I, no one knows the future exactly. So I think everything here is speculation on all sides. I think, and I come, I would come at this kind of from a more of a like, you know, economics, kind of history of markets background, which was more where I spent my time in college and trying to still spend a lot of my time trying to understand the world through that lens. So first of all, I think it is really a bummer that the world's view of AI is what it is. And I think I blame no one other than the industry basically for, for that. I think we as an industry have done a horrible job of being able to paint for picked people, a picture of a future that is way better than the future, than the world we live in today.

• 17:00Speaker 00

  And the crazy thing is I actually think that that is the reality. I think, you know, the, the stories of like the. One of the guy who is curing his dog's cancer are going to become much more commonplace. And I I tend to find a lot of comfort in the idea of like, I come back to individual empowerment of like, anyone, anywhere on earth can have an idea. And the time to value from conception of idea to thing that exists in the world starts to collapse to zero. You know, not only from a time to value perspective, but also a cost of creation perspective. And I just, I think amazing things are going to happen when that, when you reduce that friction and you increase that access. Like people are incredibly innovative. They are incredibly creative.

• 17:40Speaker 00

  Everyone is motivated by their own set of circumstances and the problems that are in front of them to want to improve the world they live in. And like, I think 99% of it is there's a tools problem, which is they historically had no means to be able to do that. And when you give people something that now enables them to start a business, do research, create a new thing, build a new service, serve customers more efficiently or cheaply, like only good things can happen in my mind. Now, obviously there's, there are things that come with that and we, you know, we have to be thoughtful about what the technology presents in terms of the flip side, because it's as capable of, in some cases, doing harm as it is of doing good.

• 18:22Speaker 00

  But I tend to think that we will figure that out, like we are resilient and I would say also equally creative as a species. And I tend to think that when, whenever we're, whenever we've been confronted with the opportunity to create something that has potential for greatness, we also have been really thoughtful about how we build institutions that protect against the downsides. So I have a more optimistic view. I think that the industry has a more of a duty to help people appreciate and understand what's happening. And to help people also like live the experience of it, like to use these tools to do the types of things I'm talking about.

• 18:56Speaker 01

  An interesting instance of this sort of conundrum is in coding. And like, I feel this is like something that's easy for us to talk about because we're very familiar with it. And it's one of the best applications of AI so far. And so, you know, now obviously like AI is really good at coding. And so then you could bump that up into the real world and say, are we going to have more developers? Are there going to be more people doing more things? Is it going to replace people? I think the data I've seen so far is actually that there's more engineering jobs being posted every month than like ever before.

• 19:24Speaker 00

  Yeah.

• 19:24Speaker 01

  But I'm curious how you think about this with like coding for, like as an example of like. What's going to happen when it bumps up into the real world of people doing stuff?

• 19:32Speaker 00

  This is where I come back to things. I try and come back as rational as I can to this kind of economics-based, kind of markets-based view of how things have worked in the past, where you have distortions in kind of supply, demand, and cost that create these points that are these weird inflection points in human productivity. If you reduce the cost of software engineering, for example, to virtually zero on the margin, then the simple thing to think would be, okay, well, software engineers won't exist anymore. The thing we're seeing in reality with tools like Codex and other things is actually that when you reduce the cost of something to zero, the demand for it goes up significantly.

• 20:15Speaker 00

  And the job of the people who were previously described as software engineers, who were kind of hand typing every character of code.

• 20:22Speaker 01

  They're no guiding agents.

• 20:23Speaker 00

  Are now just doing a slightly different version of the job.

• 20:25Speaker 01

  Well, I think some of this is that the cost is lower, but it's not zero. And so, which is a good thing, I think, because between two companies that are competing for a new market, let's say they're doing AI for construction.

• 20:38Speaker 00

  Yeah.

• 20:38Speaker 01

  If you have two companies, the one, even if engineering got much cheaper, if one just still decides to spend 10 times more than the other, presumably those people are not going to do nothing to improve the product. And so I think we're just gonna, it should be better software rather than fewer people working on it.

• 20:53Speaker 00

  Software is wildly underpenetrated in the world. I think if you actually zoomed out and basically said of all the places where software and good software, not just software.

• 21:01Speaker 01

  Yeah. And by the way, there's still so much bad software, like create, like everywhere. If you like go to a hotel and you like look behind their screen, you're like, what are you typing on? You know, there's a lot of work to do.

• 21:11Speaker 00

  It's crazy. And that to me is also, by the way, if you want to talk about risks, like that's actually where I think the risk surface exists. It's the software systems that hospitals use, that our power grid uses, that store like customer information through a hotel or read, like, these are all fairly archaic systems for institutions that actually spend meaningful percents of the world's kind of GDP. And so I would kind of look at this as like, in some sense, this is almost The greatest thing to ever happen is that you've now got systems that can help update all of that software. They can bring software into places that there's zero percent penetration of of software where where there should be. That can help reinforce and harden systems that are exploitable or vulnerable.

• 21:55Speaker 00

  And in some sense, like, you know, you kind of look at like, where were we from a in terms of how much like we actually needed software relative to kind of how much we penetrated. I think if you actually could measure that, I think we'd be at 1% today. And so I have a maybe a slightly different view of this, since a personal view, of course, is if you have AI that can write really, really good and obviously safe software. I think that is going to be one of the greatest gifts to the world. And I think the speculation around, you know, will will there be software engineers in the future or not is kind of the wrong question. There are going to have to be people who oversee the design, implementation and maintenance of what could be 10,000x the amount of software and the amount of code that gets written in the world.

• 22:38Speaker 00

  And that is going to create a unique demand cycle that may not look exactly like what we do today in software engineering, but it's going to be important.

• 22:45Speaker 01

  Absolutely. What was the breakthrough that happened for you all recently with Codex? It seems like some step function thing changed in the last few months in the industry and for Codex in particular.

• 22:54Speaker 00

  Well, it's a few things. So I think one is like, there's just the focus of the team at OpenAI building Codex. I think I've I've been at OpenAI a while, as you said, and the the work that that team is doing to drive that product with the amount of focus and intensity that they were doing it with is kind of a singular and unique effort, I think, in the history of the company. They are obsessive about the quality of the product. They're obsessive about the quality of the model. And because of where we are in terms of how models are trained, the cycle time on on how fast we can kind of drive improvement is starting to collapse. And so that's why you're seeing these jumps from 5.1 to 5.2 to 5.3 to 5.4. And now it's not surprising that you get a model like GPT-5.4 that as of today is, you know, here we are in mid-March is

• 23:40Speaker 00

  and it's the model's a few days old and is doing a billion dollars run rate revenue is doing 5 trillion tokens a day.

• 23:45Speaker 01

  That's crazy.

• 23:45Speaker 00

  Is, you know, now far and away our kind of most dominant model of our of our of our set of API models. And is also driving, you know, driving Codex growth at the rate it's going. And I think that's only going to increase this year. And so by the end of the year, I think we'll look at the models that power Codex and our APIs today and kind of think we'll laugh, like we'll think they're kind of pedestrian.

• 24:05Speaker 01

  Obviously, like, OpenAI started, you know. In chat and then moved into all these different things. And over time, I think has become probably, you know, it's one of the most unique companies in general, but included in that uniqueness is like, you guys have done a lot of things. How are you thinking about that now? Obviously, the market is starting to somewhat mature. You guys have had new companies come out, you know, spin out of OpenAI and, you know, focus on areas that have, you know, turned out to be really productive. I'm sure that's like changing the way you guys are thinking. So I'm just curious of like the state of the union, you know, in early 2026, when you like, look at, you know, here's where we are, here's what's around us. What matters now? Like, what do you care about? Like, what do you, what do you say? This got us here.

• 24:49Speaker 01

  This is what's going to get us there. What's the focus?

• 24:52Speaker 00

  One of the cool things about OpenAI is it has a very wide aperture on, I think, how it looks at what its kind of ultimate mission is. These lines that people, I think, drew maybe in the, in the world prior of, you know, your, your B to B or your B to C, or your hard tech or your software. You know, all of the things that kind of the VC ecosystem segments themselves.

• 25:13Speaker 01

  Got a lane.

• 25:14Speaker 00

  Yes, we don't see those walls. We kind of see AI as having, being this enabling technology that drives, is going to drive innovation cycles across all of the above. And that could be in, you know, it could be in, in the enterprise. It could be in consumer. It could be in, you know, in creativity. It could be in robotics. It could be in hardware. And I think what we want to understand is what do each of those bets look like? And OpenAI has an operating model that we've, has been kind of tried and true for us really since the company started, which is being able to be experimental, being able to kind of try and iterate,

• 25:50Speaker 00

  being able to be very kind of model forward, I think, in how we think about a problem and not really feeling like we have the incumbency of the kind of last generation. And then trying to kind of see if we can build the thing that we think is possible. And if it works, you kind of build an effort around it. And if it doesn't work, then you kind of, you shut it down and you recycle those people back into a new thing.

• 26:10Speaker 01

  Yep.

• 26:11Speaker 00

  And that was really the way that OpenAI operated early on. It still somewhat is, is this kind of expansion contraction. Model in research where you've got, okay, maybe there's 20 projects that are kind of all trying different things and going on at the same time. Maybe two or three of them will really work. You scale those up, you consolidate people kind of back into those projects to scale them up. And then over time, as you kind of shift into a next paradigm, you start to kind of, you know, you you you you spread back out again and see if you can you can take more bets. And I think that's going to be how this goes. I don't, I think that same, everything is in my mind downstream of research. And so if if that's the kind of cycle of how research is working, in some sense, I think the product and deployment cycle should look similarly.

• 26:52Speaker 01

  I also feel like I can just tell from the way that it's a unified model, the way the product's feeling, it's going to all just be a unified thing at some point here soon. Like, it's already kind of going that direction. And that thing will just be used by people, whether they're at home or work. And, you know, it's like people use Google at home and at work, and it's just like, you know, becomes the tool.

• 27:09Speaker 00

  Yeah, we need, we need the models to start doing more work for users, is what I would say. I think if there's been one really big gap in my mind and kind of the user consumer experience in AI so far, it's been that users have to do too much work. And you're kind of promised this future of these really smart models and, you know, they're, they kind of can solve all your problems very dynamically. And yet, here we are, like, with 18 things in a model picker and, you know, do you want, like, thinking fast mode and, you know, or do you want pro thinking hard mode?

• 27:37Speaker 01

  It's crazy.

• 27:37Speaker 00

  It's just like, like.

• 27:38Speaker 01

  It's time to move on.

• 27:39Speaker 00

  Yeah, it's time to move on. That, to me, feels like the direction where I think you're describing of this more of this consolidated, like, I just don't want to think about it. I just want, I just want intelligence and I'm going to let the model kind of decide how to allocate that, you know, on a token level most efficiently.

• 27:54Speaker 01

  Okay, I want to move the conversation to a selfish place now.

• 27:57Speaker 00

  Okay.

• 27:57Speaker 01

  You've been an investor before. My question is, what should I invest in? And like, you know, like, maybe to put a little, like, framing around it, there's, um, there's like a frequent. Worry among founders of OpenAI releasing something and I'm going to get my face blown off and you know what's safe from AI and what will or won't the models do? Where can a startup like predictably add value? You know, Sam talked about you should build your company such that you're planning for the models to get smarter and if them getting smarter is good for you, that's a good thing. If then it's more as bad for you, you know, that's going to be really tough.

• 28:33Speaker 00

  Yeah.

• 28:34Speaker 01

  But like, maybe can you like unpack it a little bit more now, just with as months and you know, years have gone on? What are like the safe places for a startup to try to like do work that they, you know, can expect to still be available to them in three years?

• 28:47Speaker 00

  Yeah, I mean, I'll go back to.

• 28:48Speaker 01

  Or should they just all join OpenAI?

• 28:50Speaker 00

  I don't think they should all join OpenAI. First of all, we, we, the, the level of like energy in the ecosystem right now is like nothing I've ever seen. I like the, the quality of, of founders and the like.

• 29:01Speaker 01

  And the effort.

• 29:01Speaker 00

  The effort, I just like, there's like this intensity and there's like urgency that.

• 29:05Speaker 01

  Do you remember the startup ecosystem like right before ChatGPT? Like, you know, like pastors are like, you know, we have like come down from like the SaaS, you know, glory moment.

• 29:14Speaker 00

  Yeah.

• 29:14Speaker 01

  That was tough.

• 29:15Speaker 00

  Yeah.

• 29:15Speaker 01

  I don't know where we'd be right now without, you know, it would be not fun.

• 29:19Speaker 00

  I was at YC in, you know, in kind of 2016, mid 2018 and.

• 29:23Speaker 01

  That was good.

• 29:23Speaker 00

  It was the like front end of that was a fun time to, to invest at growth.

• 29:28Speaker 01

  Yeah.

• 29:29Speaker 00

  You know, we were fortunate enough to invest in, I think, you know, in the growth rounds of a lot of the companies that had been built in, you know, call it the last five years prior to that. And then weirdly, it just got, it got less fun.

• 29:39Speaker 01

  Yeah.

• 29:39Speaker 00

  I, I think kind of in 2017, 2018. And I don't know what it was, it just, it felt like the ecosystem was kind of tired. Like I think there, there didn't feel like there were a lot of new ideas.

• 29:48Speaker 01

  I think a lot of the obvious stuff had happened at that point.

• 29:50Speaker 00

  Yeah.

• 29:51Speaker 01

  And I think without like a new technology shift, like at some point, you know, there's always more to do, but at some point, the first, you know, the 80 of the 80 20 gets done.

• 30:00Speaker 00

  Yeah.

• 30:00Speaker 01

  And now you're, you know, rooting around in the 20.

• 30:02Speaker 00

  I think that's right, but I, it, it feels firmly now like there's this entirely new cycle and that the kind of the urgency and the excitement is, is, is very much there. And I think the invest, like the, also just the ambition of the companies that we, we engage with, you're like, like I just, it like, it's like stunning to me sometimes. I'm like, you're, you're going to do what? Then you realize also there's an enablement factor of like. As soon as you get models, for example, that are good enough at software engineering, that they can start to, you know, themselves like design and write in new programming languages, or that they can speed the time from being able to take old, you know, code bases, refactor them, and then kind of rewrite them into new, you know, new and modern frameworks

• 30:46Speaker 00

  that enable another company to exist and serve an area that was historically traditionally underserved. You realize that like, oh, like, there's an entire industry here that didn't exist that's about to get built. And then you've got a founder who sees that and they're like, I'm gonna go after that. You know, that's partly the answer to the first question is, and if you kind of think of, think of model capability as, you know, kind of dropping successively larger rocks in the pond, and the ripples from those, those rocks kind of, you know, reverberate wider and further, and it creates more and more surface area around the circumference. And I think the way I would kind of look at it is like, you don't, you don't want to be right under the rock dropping. You're, you're gonna drown. It's a very hard place to be, but you want to really be right out on that outer edge,

• 31:29Speaker 00

  on that surface of, of what is the thing now that is enabled by this advancement in the capability that wasn't previously workable before, in a very specific and opinionated area, on a very hard problem that has historically been underserved.

• 31:43Speaker 01

  Yeah, I mean, I guess to stick with your metaphor, I feel like some of the fear is that the next rock you drop is gonna be bigger than the circumference of the ripple of the last rock. And so things that, you know, were at the edge before are now squarely in the center of the model.

• 31:56Speaker 00

  Yeah, I think there's no substitute, though, for being familiar with a user, a problem, you know, like how, how the existing industry serves that problem or doesn't serve that problem, and just being very, very close. You know, like YC always had this thing, it was like, basically, you know, effectively just like talk to users. It's kind of this simple advice, sounds trivial, but not enough people do it. And when you actually get into it and you realize like, oh, like, the world is gigantic. You know, 99% of people get to use bad tools, or don't have any tools at all. You know, the quality of experience of the people that exist as their customers and users is not very good.

• 32:32Speaker 01

  Yeah.

• 32:32Speaker 00

  Everyone's lived that in some capacity. Everyone has like, lived the bad experience of going to modern life.

• 32:37Speaker 01

  Yes.

• 32:38Speaker 00

  And dealing with the things that we have to deal with. I don't know, I just, I think if you're kind of sitting there lamenting the idea that, you know, there's no more good ideas and no more new ideas, like, it's just kind of lazy.

• 32:46Speaker 01

  I feel like there's at least two other things that can just like, give you comfort as a founder. One is that like, I don't think any company, no matter how great it is, can do everything. And there's just, you know, there might be 10,000 people working at the labs, but there's millions of people other places and you just can't do everything.

• 33:03Speaker 00

  Yeah.

• 33:04Speaker 01

  The other is that I've been surprised by is some of these markets are just so ridiculously big that there's like eight things that are all doing well around, let's say like code gen and, you know, website building and sort of like internal tool creation and whatever. You could do that probably straight out of codex, but you can also use other products that are great that are, you know, based on codex and things like that. So I think some of it is just these markets are just hard to appreciate how big they are.

• 33:32Speaker 00

  Yeah, and everyone's got like, like, again, this, there's no substitute for being able to talk to users and being able to identify like, what do people really want? Like, OpenAI is, like, our, our focus is really on trying to improve the models and do the best research we can possibly do. But like, you know, for, for someone in a very specific area of the world, who has a very specific set of needs, you know, who wants to do one thing and they wanted to do it really well, you know, there's, there's probably some alpha there.

• 33:57Speaker 01

  I do think it changes the kind of way you need to build a company versus in the past, though.

• 34:02Speaker 00

  I agree with that.

• 34:02Speaker 01

  Like, what I've noticed is a lot of the great founders today seem very willing to just rip everything out that they've done up till this point and keep only like their team knowledge, customer relationships. But, you know, if the product we built so far is wrong, we're going to just trash it in a way that I think people were much more precious about before. But I think some of this goes to, there's like a new, like, ephemerality to a lot of these things, where software is super easy to build. I can make a UI that works for me today, but I'll throw it away because I can just make a new one tomorrow. I think that's like an interesting trend, too.

• 34:32Speaker 00

  Yeah, I have seen a handful of times now, founders of companies that were built in that, you know, that period of between, call it 2008 and 2016 or something like that, you know, who are kind of the canonical darlings of, of software from the last, you know, decade or so, who have founders who are still running the company, who have basically decided like, I'm effectively restarting the company.

• 34:54Speaker 01

  Yeah.

• 34:54Speaker 00

  And they have taken it on themselves to fork off of the kind of the mainline effort to basically go figure out like, what is? Like the second chapter of this company look like in a world where the primitives and the tools and the assumptions have changed.

• 35:07Speaker 01

  Which is a hard thing to do for, you know, just there's just so much sunk cost to it all.

• 35:11Speaker 00

  Yes.

• 35:12Speaker 01

  But I think the people who are able to adapt to that, it's a huge advantage, it seems like.

• 35:15Speaker 00

  Totally. And, and like, there's no, in my opinion, like, there's no, like, you can iterate so fast now. Like, you can explore the action space so quickly.

• 35:22Speaker 01

  Yeah.

• 35:23Speaker 00

  And you have the benefit of, like, you know, legacy customer relationships. You've got the benefit of, of existing teams. So in some sense, you almost are starting with a head start.

• 35:31Speaker 01

  Yeah.

• 35:32Speaker 00

  The way I see it is like, like, you can learn faster.

• 35:34Speaker 01

  Yeah.

• 35:35Speaker 00

  Versus, you know, if I were to start a new company tomorrow, I'm starting with no customers, I'm starting with, you know, no funding, I'm starting with no, no product and no team.

• 35:42Speaker 01

  I guess related to this, how do you feel about the sort of like sell off in public markets? Like, obviously outside of like, you know, the big companies, which have done great, but sort of like, you know, public software companies have like taken a pretty bad beating. When you think about the work that you've been doing with them and what you've been seeing, are you watching that and you're like, this makes sense? Or are you like, actually, this is like sort of a misunderstanding and you're, you know, feeling bullish about those companies?

• 36:08Speaker 00

  Hard to comment on specifically that, like, the market is, is, is like a very frenetic thing, as you know. Here's what I kind of live day to day is, so we, we work with basically every company that, you know, sits in the NASDAQ that you could, you could imagine. And A is like, all of these companies are kind of as motivated and moving as quickly as any, as any startup. B is they've got amazing customer relationships. They've got amazing kind of depth of understanding of the problems they're trying to solve, the areas that they serve. Obviously, they've got years and years of perspective that have been built. And I think like now in some sense, they're, you know, able to leverage and, and benefit from the same tools that anyone else is.

• 36:52Speaker 00

  And so the conversations we're having with them are really about them starting to rethink. You know, end to end, their entire customer experience, their product, starting to think about, you know, how do they serve adjacent markets, starting to think about ways that they can pass capability through to their users. So like creating entirely new experiences that that weren't possible before. And so I think you could take the other side. Actually, I think you could basically take a very long view here, which is that

• 37:19Speaker 01

  Yeah, in some ways, the software itself is like the easiest thing at this point.

• 37:22Speaker 00

  Yeah.

• 37:23Speaker 01

  Like having all the relationships, the team, the trust with all the customers, that's actually the hardest, you know, pole of the tent to have now.

• 37:29Speaker 00

  You know, if that class, if that segment was asleep, I would say, okay, maybe that, you know, concern is more warranted, but

• 37:35Speaker 01

  But they're not.

• 37:36Speaker 00

  No, and it's it's happening at the CEO level and the founder level in some cases where everyone is as motivated to figure this out and figure out, you know, how to create value for their customers and their business as anyone else is. And so I think, you know, it's the beginning of a new cycle is my guess. You're always going to get new companies that form that are trying to take a fresh and new approach. Often, the benefit that those new companies have is that the incumbents don't realize what's going on and are too slow to move. Here, you actually don't have that dynamic. You've got everyone running, trying to run at the same speed. And so I think that's exciting. And I would say if you're kind of long, long AI and long, you know, startups, then it might even

• 38:18Speaker 00

  make sense, maybe, you know, the contrarian opinion to be long, long legacy software too.

• 38:23Speaker 01

  I don't know if you're experiencing it one way or another, like what you think it takes for more experience. It doesn't have to be founders, but just like even people joining OpenAI from some old company, you know, that had not been AI native. Like, how do you help people reset? Like, what does it take for people who have lived in the pre-AI era to like, you know, work the new way?

• 38:42Speaker 00

  I think you gotta like see it firsthand. And if you're not like playing with Codex every day, like I think it's hard to intuitively grok just like how disruptive and crazy it is. Like Codex for me has replaced ChatGPT on a kind of daily driver basis. And I'm not even technical. Like I don't, I don't write software for a living, but it has a general capability that, and I'm specific enough about the set of things that I want that I know, and I I've kind of developed Enough like familiarity with its.

• 39:09Speaker 01

  So what are the like not, what are you doing with it? Like, what's like the daily quick use case?

• 39:13Speaker 00

  My life is basically a kind of daily struggle of like thing that I would like to see get done.

• 39:18Speaker 01

  And then it's a question. My life is a daily struggle.

• 39:19Speaker 00

  Well, that too, but of, you know, thing that I would like to see get done. And then kind of how fast can our team mobilize and operationalize to kind of get it done? And at a busy, you know, fast growing, very busy company, like sometimes those timelines drag. And then when those timelines drag, it means like the thing that I kind of want to see us do starts to drag. And everything kind of elongates into this kind of like, okay, something that really should take. If everyone 100% focused on this thing, something that should take two days, you know, now takes basically kind of a month. And so one of the things I've started, you know, using it for basically is kind of supplementing that. That thing, it gives me like a first version of everything. So for example, I, we're building a fairly substantial.

• 40:01Speaker 00

  For deployed engineering org, which we can talk about, but recruiting for that has been like challenging, like recruiting is hard.

• 40:07Speaker 01

  Plus you're using it to recruit.

• 40:08Speaker 00

  Well, I'm using it actually to, to, to basically kind of go figure out, you know, of, of lists of people that are, that we're, we're thinking about recruiting. How do you, how do you navigate and stack rank among that list before you start getting into, you know, the, the candidate engagement? And it's crazy because like everyone today kind of has this like online presence and, you know, a lot of people have blogs and X accounts and all that. And so I just told Codex, I was like, here, take this list and basically go figure out like what public presence any of these people have. And, you know, basically come up, come back to me and effectively like read, you know, read their online thing and. Score it against how you think about some of the kind of technical elements of our work. And what, you know, the job descriptions are of the things that we're doing.

• 40:49Speaker 00

  It works for even what is kind of a non-technical task like that. It, it basically writes a program. And it will come up and, and figure out how to like go efficiently, look at each of these profiles and come back and, and give me kind of these scores on. How good, you know, it thinks each of the, each of these candidates kind of online writing has been.

• 41:05Speaker 01

  Yeah.

• 41:05Speaker 00

  And it's cool because it actually surfaced for me, you know, three or four candidates who I couldn't have picked off the list. Staring at a list of 200 names, but where I was like, okay, like, let me go double click on this. And now it gives me an opportunity to go like, really look into that candidate's, you know, profile and their blog and whatever, and start to just get to know them better. And that process would have taken, you know, a kind of a normal, busy recruiter, probably a couple of weeks, right? It's a lot of names.

• 41:30Speaker 01

  Yeah.

• 41:31Speaker 00

  And here it's just like, it collapses down.

• 41:33Speaker 01

  By the way, I bet a lot of this is like, what is going to be needed for people to just broadly be excited about AI, not like frustrated about it, is using it and realizing that it's like, super empowering.

• 41:43Speaker 00

  Very much. Yeah. I think like.

• 41:45Speaker 01

  Versus thinking like, oh, all these other people are using it to be empowered. It's like, no, just start using it. And I guess a lot of that is, you know, you getting the tools to a place where, you know, it can be adopted super easily by everybody.

• 41:54Speaker 00

  For sure. And I think like, almost in some sense, one of the things that feel like is kind of the story that hasn't like yet, really diffused into into more mainstream conversation on this is just like how general these tools are. Like, you don't have to be a software engineer to use Codex.

• 42:11Speaker 01

  It's just fascinating that you prefer Codex over chat for a lot of your work. It's cool.

• 42:15Speaker 00

  Yeah. I mean, the Codex app is amazing. If you haven't used it, I check, check it out. But it's, it is, you know, so like the terminal based use is maybe a little more intimidating if you're not technical, but you know, in an app interface, it kind of just looks like chat. And I think that, you know, it's got much more general agent capabilities.

• 42:32Speaker 01

  Yeah. On the topic of like the forward deployed stuff and private equity, like, what's the thinking there?

• 42:37Speaker 00

  The thinking is, is very much what I was kind of talking about earlier, which is, if you think about kind of like, the way that software is going to get built in the future, in some sense, now, any specific problem within any company, in any part of their process, historically, it would not have made sense economically to have spent a lot of time thinking about how to solve that one.

• 43:00Speaker 01

  Right.

• 43:01Speaker 00

  Corner of a problem. It's too expensive to, to hire a bunch of people, to build a bunch of, you know, software, and, you know, for that software to, to then have to be maintained. And obviously, for the most important problems and most large enterprises, you could hire people to do that type of thing. And there's entire. Industries that have gotten built around that. But for, you know, 99% of problems, for kind of 99% of businesses, that's totally out of reach. You'd have to either decide that you wanted to hire a couple of people to try and build something on their own that maybe didn't work super well. Or you look to see if the market offers a solution. And the problem, but the problem is that solution doesn't necessarily fit exactly what your shape of problem is.

• 43:43Speaker 00

  So now you've got people kind of contorting themselves, trying to figure out how to adopt the thing off the shelf that wasn't really built for their company. It was just built as a kind of general purpose tool. And I think that that entire era is is over. I think like now you actually can reason how almost every problem inside of a business can have solutions that are kind of custom built for it. And it goes back to this kind of weird paradox of what do you think's going to happen with with jobs? Where, you know, we wouldn't be wanting to hire FDEs as aggressively as we would if it felt like software engineering jobs were going away. The jobs of those FDEs are different. You know, if you'd hired an FDE five years ago, they'd be doing something different than what they're going to do in the future. But the amount of demand and the amount of opportunity that we see

• 44:27Speaker 00

  to be able to go address surgically every area in a business that could benefit from solution design. And not solution design that happens on the order of 18 months, as is the kind of industry norm, solution design that happens on the order of maybe 18 days, if not faster. That to me is like an incredibly large opportunity that I think will be the story somewhat of how the next few years goes. And so the FDEs we're hiring is really to help address that.

• 44:53Speaker 01

  Last question I have is just sort of your reflections working with Sam. It's kind of funny, just, you know, I've known him as a brother, you know, and as someone you've worked with for a long time now. I'm curious sort of like what the evolution you've seen has been like. You know, now that he's obviously gotten to a different place in like the public sphere. And, you know, there's this whole public persona and, you know, then you obviously work with him on a daily basis. Just like, what's the whole experience like for you with him?

• 45:19Speaker 00

  Yeah, you know, I think, well, so we worked together for 10 years, 10 years in January.

• 45:24Speaker 01

  And the first year or two was Y C.

• 45:26Speaker 00

  Yeah, first two and a half years of Y C. And then I got to OpenAI before he did. So I recruited him at OpenAI. But, you know, he's, he's like, he's a remarkable individual, you know that. And I wish more people could spend more time with him kind of off the record. I think he's not innately, I think, someone that enjoys being a kind of a public face of things. I think certainly it feels like an unnatural thing for him. He is someone who much prefers spending his time

• 45:55Speaker 01

  Yeah.

• 45:56Speaker 00

  Sitting in a huddle of like five people, talking about the future and having a deeply technical conversation about some niche topic. That's kind of who he is internally at OpenAI. It's what I've always known him to be. And, and I think that, that if you could spend, more people could spend more time with him. You realize he's like an infinite optimist.

• 46:14Speaker 01

  That's crazy because the way I experience it, it's almost like this like sacrifice to have done to put himself out so publicly, which is a requirement, I think, to make all of this happen and like show the world that by accumulating talent, compute, and all these ideas in one place, like that's what made all of this possible, then everybody can see it. But like, it's such an uncomfortable thing to have done.

• 46:36Speaker 00

  Yeah, well, you know, it's, it's, it's interesting because like his, he thinks on a time scale that's like more like a decade plus. And I think the, the world kind of struggles to think beyond like a quarter forward.

• 46:47Speaker 01

  Yeah.

• 46:48Speaker 00

  I've always felt like there's this kind of mismatch in.

• 46:51Speaker 01

  There's a total mismatch. And so it's like, he'll say something and everybody's like, that's crazy.

• 46:55Speaker 00

  Yeah.

• 46:56Speaker 01

  And then three years later, it's exactly where we are, sometimes sooner than this. And then it's like, you know, there's no like reconciliation backwards. Just like now we're saying a new crazy thing and people are like, oh, you've been crazy all along. And that's like a weird thing to watch. And there's no, there's no sort of way to tie that together, really.

• 47:12Speaker 00

  No, everyone's trying to figure out what's happening right now because I think in some sense the whiplash is so real and I have like a lot of empathy for that as you know, I spent a lot of time with like our customers, with you know, friends, family like that are kind of like looking at me and calling me being like, what is going on? Like what is happening? What is this codex thing? Like, why is everyone upset? And I think in Sam's head, we're already so far beyond that point in terms of what's coming, that it's trying to kind of bridge for people like where we're going relative to where we are. And I think it's disorienting.

• 47:41Speaker 01

  It's really an insane thing that you all have done and continue to do to pull all these pieces together. Like, I think this has got to be like the most hard mode company of all time. It's very, very impressive. I'm sure you like, are just used to it all, but hopefully you appreciate what a ridiculous feat you guys are pulling off.

• 47:57Speaker 00

  Well, I appreciate that. I very much feel like it's, it is far from incomplete, far from complete, it's highly incomplete. And I feel like, you know, it's like interesting, when we formed the company early on, the mission orientation of the company was like very strong, but I always kind of tell people like in a very literal sense, like, I think a lot of companies have these kind of high level kind of lofty missions that you can't really actualize. Like, it's like, okay, no shade on anyone specifically, but like, it's like, don't be evil. Okay, like, that seems like a good thing. It's, or it's like, make the world more connected. Seems good.

• 48:32Speaker 01

  It's also like, okay, so if the plan is don't be evil, like, then what? It's like, very debatable from there.

• 48:36Speaker 00

  Right. Well, how do you actualize that? What do you do? Right? And I think one of the kind of interesting things about OpenAI is the mission from day one is this very actualizable mission. We try and kind of run everything that we do somewhat through the lens of, okay, is this consistent with the outcome that we are trying to create? And I always used to joke at OpenAI, it's like, there was a world where we talked about like, okay, we do the thing we say we're gonna do, and then we like, go home and we're done. Like, it's like, okay, like, you know, that's the end of the story. And like, we all go back. And, you know, in practice, is it going to work that way? I don't, I don't know. I don't think so. But maybe. But it is a company that has a very specific orientation toward a very specific goal. And I think amid all the craziness of all the things that are happening, like, it's very focusing to be like,

• 49:18Speaker 00

  okay, guys, like, there's still this one thing that we're really trying to deliver. It's very easy to come back to that mission and say, is this something that drives toward that outcome or not? And if it's not, we're just not gonna do it.

• 49:27Speaker 01

  Love it. Well, this was really fun. Brad, thanks for coming to do it.

• 49:29Speaker 00

  Yeah, good to see you.