SPEAKER_01
99% 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 lived the bad experience of going through modern life and dealing with the things that we have to deal with. I think if you're sitting there lamenting the idea that there's no more good ideas and no more new ideas, it's just lazy. All right. Do you film an intro?
SPEAKER_00
Do I film an intro? Or you just go hard in? I just start. Yeah, this probably is the intro. All right. So, Brad, thanks for doing this with us. I'm excited. Yeah, me too. Do you have enough drinks? Would you like one more? Well, yeah, I'll take whatever I can get. We can load up. Well, I really appreciate you making time for this. I've been really looking forward to it. What I wanted to start with, actually, is I was just thinking about this last night. And you joined OpenAI in 2018. And then four years, it was research lab. You guys were beating Dota. And then four years in, ChatGPT launches. And then it's this whirlwind that's been, I guess, three years.
SPEAKER_00
But I'm sure it feels like a lot more. I was just curious if you could share your narrative or recollection of what the journey's been like. And what are the chapters? What's just your experience been like as you look back on this so far?
SPEAKER_01
Yeah, chapters is the right word. It's the journey of OpenAI, which I think tracks the journey of AI as a field, as an industry, has been broken up into these weird periods. It's like when I joined, no one had really heard of OpenAI. Our work was relegated mostly to small niches of San Francisco tech culture that followed such things as us beating the best Dota players in the world and things like that. But really, it was, I didn't really have anyone to talk to about it. Everyone was asking, what are you doing there? And you were the CFO when you joined, right? I was our CFO. I spent. Yeah. [SPEAKER_00] Like what were you thinking when you joined?
SPEAKER_00
Like what did you expect it was going to be? [SPEAKER_01] Well, I didn't know.
SPEAKER_01
I was 27. And 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 YC. So all the companies that are building everything that wasn't pure SaaS and Internet, consumer Internet. So spending a lot of time with everything from nuclear fusion to satellites to biotech to anything that would fit outside. And OpenAI was in that camp. AI was one of those things that was promised as this future technology. But I wasn't really sure who's actually building this. OpenAI started, as you know, as a YC research project. Yeah. And so it was in the family.
SPEAKER_01
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 company. Do you know anyone 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 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 realized that they had 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.
SPEAKER_01
At that point, it's like, OK, really, this is just a compute problem, actually. And intelligence basically can just be bootstrapped from scaling up very basic general architectures 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. Yeah. And at 27, I was like, that just seems more interesting than investing in tech. [SPEAKER_00] Yeah.
SPEAKER_00
So you started doing that. And then what happened in those early years? Like, obviously, they're building things that were working, beating the game and a lot of other projects. But what were you seeing on the inside from, let's say, 2018 to 2022? [SPEAKER_01] Obviously, it was much more of a research centric culture.
SPEAKER_01
OpenAI is still highly research centric. I feel like people think post-ChatGPT, it became much more of a product centric culture. But research really drives everything. And I think that started because of how much that was cemented in that period as the 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 the capital that we need to invest in supercomputers to working with partners to do the supercomputer design and build out to things as trivial and pedestrian as our robots keep breaking.
SPEAKER_01
And it takes too long to drop ship parts from this one supplier that sits in some small town in England or something. Yeah. How do we tighten that loop and go faster? So it was this very diverse set of problems early on that were really just about pure research acceleration. Obviously, now it's both research and deployment and our business. But it gave me early on an appreciation of just spending all my time with researchers. And so it gave me a firsthand understanding of what was happening before I think anyone else really appreciated it. [SPEAKER_00] So then there was ChatGPT in 2022.
SPEAKER_00
End of 2022. Did you guys on the inside feel like, oh, this is going to be something when you were playing with it before it got released? Was the vibe inside like this is another cool thing? [SPEAKER_01] Obviously, now, it's both research and deployment and our business. But it gave me early on an appreciation of just spending all my time with researchers. And so it was really like it gave me a firsthand understanding of what was happening before I think anyone else really appreciated it.
SPEAKER_00
So then there was ChatGPT in 2022. End of 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 another cool thing? Let's just, it's a playground. Yeah. Or were people like this is something?
SPEAKER_00
[SPEAKER_01] There's a word that sometimes people use in AI to describe when there's an indication of something that's happening, but you don't it's not quite happened yet, but you get these little sparks. And that was how I would describe the pre-ChatGPT period is there are a lot of sparks. You could see that the models are now starting to get enough that they could emulate 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 as an input and then it continues the text string on this more conversational, dialogue based format is not the original invention of language models. And so 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 show a preview of what the model would actually produce as an output. But people were trying to use that interface in a more dialogue, conversational turn based format. And so you could see it. If you paid attention, 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. The other thing that we saw ahead of time was we trained an early version of DALL-E as 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 generate images. And we had seen some adoption of that model in a more consumer prompt based format. And so we had guesses leading up to ChatGPT that 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 at peak there'd be a million concurrent users. And obviously we were very wrong.
SPEAKER_00
So then there was ChatGPT in 2022. End of 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 another cool thing? Let's just, it's a playground. Yeah. Or were people like this is something?
SPEAKER_01
[SPEAKER_00] So what are the chapters since, if you look back the last three years, what are the phases? If you were sort of describing to a friend, here's the phases of my journey post-ChatGPT. [SPEAKER_00] Yeah. How did you bucket it?
SPEAKER_01
There's phases of the company's life. And then I think there's phases of the industry and in the technology. On the technology side, I would say it's obviously there is this proto period of research just starting to work. And I call that the scaling period, where we just realized that you actually could go from something that was unusable to something that was usable across basically most model formats that was before mass consumer adoption. That was 2018 to 2022.
SPEAKER_01
I think 2022 to 2024 was really the period of chatbots where all of a sudden it was OK. You realized that you actually could have something that was useful, but it was not totally clear exactly what it was useful for. It was new and novel. And I think people had an appreciation for that. But the utility was still not totally there. It was a slightly better version of search. And then the next chapter, the one that we're in now, is this 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 for me in December 2024 with the release of one and then through 2025 and into 2026.
SPEAKER_01
[SPEAKER_00] And you think we're in the middle of that now?
SPEAKER_01
Yeah, I think so. I think weirdly in each of these things, because the utility quotient on the models goes up by some enormous factor, I actually think it takes more time in each of these eras to explore the full potential of the model. I've always said to our customers and partners all the time is like you could stop progress right now. And I still think there's a 10 or 20 year diffusion and innovation cycle just to get it into the economy and people to realize what these things are capable of with chatbots. That maybe would have been five years or something. But with agents, it's probably some multiple. And then the question is, as the technology progresses much faster than that. And so that dissonance of the diffusion period being much longer than the actual innovation cycle is going to be something interesting to watch.
SPEAKER_01
[SPEAKER_00] How far away are we from the 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 what the endpoint of agents capabilities will be? I personally feel totally unmoored here. I don't know. I feel like I'm a scientist now and the historian and technological economist in me wants to think that everything has to fit into these very nice S curve shaped paradigms. Yeah. And everything will, the innovation cycle will look exactly as it is. And if there is an S curve that we could be. [SPEAKER_00] Yeah. We could be right here.
SPEAKER_01
The kind of Carlo de Perez, like, OK, like this will all be the way that it has been. But there's a lot of meta levels to this. I don't know. I feel like I'm a scientist now and the kind of historian and technological economist in me wants to think that everything has to fit into these very nice S curve shaped paradigms. Yeah. And everything will the innovation cycle will look exactly as it is. And if there is an S curve that we could be. [SPEAKER_00] Yeah. We could be right here. The kind of Carlo de Perez, like, OK, like this will all be the way that it has been.
SPEAKER_01
But 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 infinite levels of things that can happen. Right. Yeah. [SPEAKER_00] They can now start directing other agents.
SPEAKER_01
They can work together. You have the temporal aspect of they can think and work for longer as long as they can cohere the context through that period, which 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 over multiple sessions. Yeah. All of those things haven't even yet been sorted out, but are starting to get figured out. Yeah. [SPEAKER_00] I mean, I've always thought in the last year I've been like, why are we not going to get to a place where you can just prompt, build me a business. Make no mistakes. Exactly. Yes.
SPEAKER_01
Yeah, no. That I don't see why you couldn't be like, hey, can you go make me a million dollars, please? [SPEAKER_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 why even if you go back and say even if you pause progress right now, maybe it's longer, maybe it's 40 years or something or 50 years of progress that will come from this just on the basis of this step of the cycle. One of the interesting things that I've experienced is right before right after ChatGPT. I was like living in sci-fi land.
SPEAKER_01
[SPEAKER_00] Are we going to have the next species take over? Are there Dyson spheres? It was very big. Yeah. 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 an economy operated by humans. It doesn't feel scary. Yeah. It just feels like insanely good software. Yeah.
SPEAKER_01
But there's this lingering thing in the background that I think is talked about a little bit less of like, is there sentience? Like, does it go to this other place? 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 like this is just really good software. There's nothing to be worried about. It's just an insane technical revolution. Yeah.
SPEAKER_01
This is a really interesting question. I think in some sense, 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 used to sit at OpenAI that was very much having the conversation about Dyson spheres, because in 2018, that's all you could talk about. Yeah. You basically had something that was barely working at the beginning. And then you could try and see. You think about the whole thing. Exactly.
SPEAKER_00
But once you're in the middle of it, you've got to think about the steps right in front of you. There's a local linearity that starts to set in where you're a little bit like, OK, I appreciate that this thing is a gazillion times better than what it was in 2018.
SPEAKER_00
[SPEAKER_01] And the capabilities are multitudes more than what they were even two years ago. As an example, you talked about DALL-E. Yeah. When that came out, I was like, oh, that's cute. Yeah. But now, just a few years later, I can't tell if the video is fake or real half the time. Yes. It's like that's going to get all the way there where you'll have no idea. No. Yeah. And I think that in some sense, there will be these parallel conversations that happen. Like there will be the kind of enterprise productivity conversation because that is something that people are actually thinking about. We'll talk about everyone's going to glob on to, what is the narrative there that is. It's just funny. Like, are we waking up a new God or are we helping lawyers be more productive? I think we're doing both.
SPEAKER_00
Yeah. And I think the parallel track of this insane level of empowerment of an individual person to do things that would have been inconceivable even a couple of years ago, you're already seeing examples of it. And that to me is the weird sci-fi future.
SPEAKER_00
[SPEAKER_01] There was the story over the weekend of a guy in Australia who is curing his dog's cancer, who has no background in biology, as I understand it. But basically just had GPT-5 effectively try and come up with some sort of RNA-based vaccine that could treat his dog. And then he sent it. He worked with a lab to do the design of the treatment. And they sent it back and it seems to be working. And it happened in a matter of three thousand dollars and a few weeks. And it's a crazy thing. Yeah. Right. You know, that to me would qualify as a spark of a sci-fi outcome. It's just crazy how fast we adjust to anything. Yeah. It's like, you know, we could learn that there's aliens tomorrow and we would next week be like, yeah, of course there's it.
SPEAKER_01
[SPEAKER_00] You know, it's one of my takeaways with this whole thing: people adjust to any new surrounding. One hundred percent. You just think it's normal in no time. And it's a crazy thing. Yeah. Right. That to me would qualify as a spark of a sci fi outcome. It's just crazy how fast we adjust to anything. Yeah. We could learn that there's aliens tomorrow and we would next week. Yeah, of course there's it. [SPEAKER_00] One of my takeaways with this whole thing is that people adjust to any new surrounding. [SPEAKER_00] 100 percent. [SPEAKER_00] You just think it's normal in no time. [SPEAKER_00] That's been my experience—things are novel for about three seconds.
SPEAKER_01
[SPEAKER_00] And the next day it's like, OK, what have you done for me lately? [SPEAKER_00] Yeah. [SPEAKER_00] On this topic of what is the thing, I'm watching it all. I'm from St. Louis. [SPEAKER_00] Yeah. [SPEAKER_00] Now I'm living in Silicon Valley. [SPEAKER_00] There's a very different perception of AI in St. Louis and Silicon Valley.
SPEAKER_00
Here the general sentiment is this is amazing. Goodness this happened.
SPEAKER_01
[SPEAKER_00] And I think around the country, maybe world, there's real skepticism and anxiety and fear. [SPEAKER_00] I think people here have that, too. [SPEAKER_00] But it's this interesting reckoning for people where you're grappling with, simultaneously, oh my God, that's amazing and awesome versus oh my God, that's amazing, that's a threat. [SPEAKER_00] Yeah. [SPEAKER_00] How do you think about what the right way to interpret this is? [SPEAKER_00] What are the genuine concerns and fears that we're going to need to work through? [SPEAKER_00] And what are the things that you think are misunderstandings that will actually just be really positive? [SPEAKER_00] Yeah.
SPEAKER_01
[SPEAKER_00] Look, no one knows the future exactly. So I think everything here is speculation on all sides. [SPEAKER_00] I come at this from a more economics and history of markets background, which is 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.
SPEAKER_00
[SPEAKER_01] First of all, I think it's really a bummer that the world's view of AI is what it is. [SPEAKER_01] And I blame no one other than the industry for that. [SPEAKER_01] I think we as an industry have done a horrible job of being able to paint for people a picture of a future that is way better than the world we live in today. [SPEAKER_01] The crazy thing is, I actually think that is the reality.
SPEAKER_01
The stories of the guy who is curing his dog's cancer are going to become much more commonplace. I tend to find a lot of comfort in the idea of individual empowerment—anyone anywhere on Earth can have an idea. The time to value from conception of idea to thing that exists in the world starts to collapse to zero, not only from a time to value perspective, but also a cost of creation perspective.
SPEAKER_01
I think amazing things are going to happen when you reduce that friction and increase that access. People are incredibly innovative. They are incredibly creative. Everyone is motivated by their own set of circumstances and the problems in front of them to want to improve the world they live in. I think 99 percent of it is a tools problem—historically they had no means to be able to do that.
SPEAKER_00
[SPEAKER_01] When you give people something that enables them to start a business, do research, create a new thing, build a new service, serve customers more efficiently or cheaply, only good things can happen in my mind. [SPEAKER_01] Obviously, there are things that come with that. [SPEAKER_01] 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. [SPEAKER_01] But I tend to think that we will figure that out. We are resilient and I would say equally creative as a species.
SPEAKER_01
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 the industry has more of a duty to help people appreciate and understand what's happening. And to help people live the experience of it—to use these tools to do the types of things I'm talking about.
SPEAKER_00
[SPEAKER_01] An interesting instance of this conundrum is in coding.
SPEAKER_01
I feel this is something that's easy for us to talk about because we're very familiar with it. It's one of the best applications of AI so far. AI is really good at coding. [SPEAKER_00] So then you could bump that up into the real world and say, are we going to have more developers? [SPEAKER_00] Are there going to be more people doing more things? [SPEAKER_00] Is it going to replace people? [SPEAKER_00] I think the data I've seen so far is actually that there's more engineering jobs being posted every month than ever before.
SPEAKER_00
Yeah. But I'm curious how you think about this with coding as an example of what's going to happen when it bumps up into the real world of people doing stuff. This is where I come back to things. I try to come back as rational as I can to this economics-based, markets-based view of how things have worked in the past. You have distortions in supply, demand, and cost that create these inflection points in human productivity.
SPEAKER_01
[SPEAKER_00] If you reduce the cost of software engineering, for example, to virtually zero on the margin, then the simple thing to think would be, OK, well, software engineers won't exist anymore. [SPEAKER_00] 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.
SPEAKER_00
[SPEAKER_01] The job of the people who were previously described as software engineers who were hand typing every character of code—there are no guiding agents.
SPEAKER_01
They're now just doing a slightly different version of the job. Well, I think some of this is that the cost is lower, but it's not zero. And that's 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.
SPEAKER_00
[SPEAKER_01] Yeah. [SPEAKER_01] If you have two companies, 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 going to have better software rather than fewer people working on it. 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. Yeah. [SPEAKER_01] 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.
SPEAKER_00
[SPEAKER_01] Yeah. [SPEAKER_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.
SPEAKER_01
[SPEAKER_00] And so I think we're just going to, it should be better software rather than fewer people working on it.
SPEAKER_00
Software is wildly underpenetrated in the world. I think if you actually zoomed out and said of all the places where software and good software, not just software. Yeah. And by the way, there's still so much bad software everywhere. [SPEAKER_01] If you go to a hotel and look behind their screen, you're like, what are you typing on? There's a lot of work to do. It's crazy. And that to me is also, by the way, you want to talk about risks. 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 customer information through a hotel or read.
SPEAKER_01
These are all fairly archaic systems for institutions that actually span meaningful percents of the world's GDP. And so I would 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 0% penetration of software where there should be. That can help reinforce and harden systems that are exploitable or vulnerable. And in some sense, you kind of look at where we were from in terms of how much we actually needed software relative to how much we penetrated. I think if you actually could measure that, I think we'd be at 1% today. And so I have maybe a slightly different view of this. And it's 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 will there be software engineers in the future or not is 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. 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.
SPEAKER_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. Well, it's a few things. So I think one is there's just the focus of the team at OpenAI building Codex. I think I've been at OpenAI a while, as you said, and the work that team is doing to drive that product with the amount of focus and intensity that they are doing it with is singular and unique effort. [SPEAKER_00] 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.
SPEAKER_01
And because of where we are in terms of how models are trained, the cycle time on how fast we can drive improvement is starting to collapse. And so that's why you're seeing these jumps from five one to five two to five three to five four. And now it's not surprising that you get a model like GPT five four that as of today, here we are in mid March, and it's the model's a few days old and is doing a billion dollars run rate. Revenue is doing five trillion tokens a day. That's crazy. It is now far and away our most dominant model of our set of API models and is also 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 laugh. We'll think they're pedestrian.
SPEAKER_01
Obviously, OpenAI started in chat and then moved into all these different things. And over time, I think it has become probably one of the most unique companies in general, but included in that uniqueness is you guys have done a lot of things. How are you thinking about that now?
SPEAKER_01
[SPEAKER_00] Obviously, the market is starting to mature somewhat. You guys have had new companies come out, spin out of OpenAI and focus on areas that have turned out to be really productive. I'm sure that's changing the way you guys are thinking. So I'm just curious about the state of the union in early 2026, when you look at here's where we are. Here's what's around us. What matters now? What do you care about? What do you say? This got us here. This is what's going to get us there. What's the focus?
SPEAKER_01
[SPEAKER_00] One of the cool things about OpenAI is it has a very wide aperture on how it looks at what its ultimate mission is. These lines that people drew maybe in the world prior, of your B2B or your B2C or your hard tech or your software, all of the things that the VC ecosystem segments themselves. [SPEAKER_00] Got out of the lane.
SPEAKER_00
Yes. We don't see those walls.
SPEAKER_00
[SPEAKER_01] We see AI as having being this enabling technology that drives innovation cycles across all of the above. And that could be in the enterprise. It could be in consumer. It could be 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 has been tried and true for us really since the company started, which is being able to be experimental, being able to try and iterate, being able to be very model forward in how we think about a problem and not really feeling like we have the incumbency of the last generation. And then trying to 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 shut it down and you recycle those people back into a new thing.
SPEAKER_01
Yeah. And that was really the way that OpenAI operated early on.
SPEAKER_00
[SPEAKER_01] And OpenAI has an operating model that we've been trying and true for us really since the company started, which is being able to be experimental, being able to try and iterate, being able to be very model forward. [SPEAKER_01] I think in how we think about a problem and not really feeling like we have the incumbency of the last generation. [SPEAKER_01] And then trying to see if we can build the thing that we think is possible. [SPEAKER_01] And if it works, you build an effort around it. [SPEAKER_01] And if it doesn't work, then you shut it down and you recycle those people back into a new thing. [SPEAKER_01] Yeah.
SPEAKER_00
[SPEAKER_01] And that was really the way that OpenAI operated early on. [SPEAKER_01] It still somewhat is this expansion contraction model and research where you've got, OK, maybe there's 20 projects that are all trying different things and going on at the same time.
SPEAKER_01
Maybe two or three of them will really work. You scale those up, you consolidate people back into those projects to scale them up. And then over time, as you shift into a next paradigm, you start to spread back out again and see if you can take more bets. And I think that's going to be how this goes. I don't think that everything is downstream of research.
SPEAKER_00
[SPEAKER_01] And so if that's the cycle of how research is working, in some sense, I think the product and deployment cycle should look similarly. [SPEAKER_01] And you're promised this future of these really smart models. [SPEAKER_01] And they can solve all your problems very dynamically. [SPEAKER_01] And yet here we are with 18 things in a model picker.
SPEAKER_01
And do you want thinking fast mode or do you want pro thinking hard mode? [SPEAKER_00] It's just time to move on. Yeah, it's time to move on. That to me feels like the direction you're describing of this more consolidated, I just don't want to think about it. I just want intelligence and I'm going to let the model decide how to allocate that on a token level most efficiently. OK, I want to move the conversation to a selfish place now. OK, you've been an investor before. My question is, what should I invest in? And there's a frequent worry among founders of OpenAI releasing something.
SPEAKER_01
And I'm going to get my face blown off. What's safe from me and what will or won't the models do? [SPEAKER_00] Where can a startup predictably add value? [SPEAKER_00] You know, Sam talked about you should build your company such that you're planning for the models to get smarter. [SPEAKER_00] And if them getting smarter is good for you, that's a good thing.
SPEAKER_00
If them getting smarter is bad for you, that's going to be really tough. But can you unpack it a little bit more now just with as months and years have gone on? What are the safe places for a startup to do work that they can expect to still be available to them in three years? Yeah, I mean, I'll go back to not everyone should join OpenAI. I don't think they should all join OpenAI. First of all, the level of energy in the ecosystem right now is unlike anything I've ever seen. The quality of founders and the effort. There's this intensity and urgency. [SPEAKER_00] Do you remember the startup ecosystem right before ChatGPT?
SPEAKER_01
[SPEAKER_00] The valuations came down from the SaaS glory moment.
SPEAKER_00
[SPEAKER_01] Yeah. [SPEAKER_01] That was tough. [SPEAKER_01] Yeah. I don't know where we'd be right now without it. It would be not fun. I was at YC in 2016, mid 2018. And that was good. It was the front end of that, a fun time to invest in growth. Yes. You know, we were fortunate enough to invest in the growth rounds of a lot of the companies that have been built in the last five years prior to that. [SPEAKER_01] Yeah. And then weirdly, it just got less fun. [SPEAKER_01] Yeah. [SPEAKER_01] I think in 2017, 2018. [SPEAKER_01] And I don't know what it was. It just felt like the ecosystem was tired. I think there didn't feel like there were a lot of new ideas.
SPEAKER_00
[SPEAKER_01] I think a lot of the obvious stuff had happened at that point. [SPEAKER_01] Yeah. [SPEAKER_01] And I think without a new technology shift, at some point, there's always more to do.
SPEAKER_01
But at some point, the 80 of the 80/20 gets done. Yeah. And now you're rooting around in the 20. [SPEAKER_00] I think that's right. But it feels firmly now like there's this entirely new cycle and the urgency and the excitement is very much there. [SPEAKER_00] And I think the investment and the ambition of the companies that we engage with. [SPEAKER_00] I just think it's stunning to me sometimes. [SPEAKER_00] I'm like, you're going to do what?
SPEAKER_01
[SPEAKER_00] Then you realize there's an enablement factor. As soon as you get models, for example, that are good enough at software engineering, that they can start to design and write in new programming languages or that they can speed the time from being able to take old code bases, refactor them and then rewrite them into new modern frameworks that enable another company to exist and serve an area that was historically traditionally underserved. You realize 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 going to go after that. Yeah.
SPEAKER_01
You know, that's partly the answer to the first question. If you think of model capability as dropping successively larger rocks in the pond and the ripples from those rocks reverberate wider and further. And it creates more and more surface area around the circumference. And I think the way I would look at it is you don't want to be right under the rock dropping. You're going to drown. It's a very hard place to be.
SPEAKER_01
But you want to be right out on that outer edge, on that surface of what is the thing now that is enabled by this advancement in capability that wasn't previously workable before in a very specific and opinionated area on a very hard problem that has historically been underserved. Yeah. And it creates more and more surface area around the circumference. And I think the way I would look at it is you don't want to be right under the rock dropping. You're going to drown. It's a very hard place to be.
SPEAKER_00
[SPEAKER_01] But you want to really be right out on that outer edge, on that surface 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. [SPEAKER_01] Yeah. [SPEAKER_01] I mean, I guess to stick with your metaphor, I feel like some of the fear is that the next rock you drop is going to be bigger than the circumference of the ripple of the last rock. [SPEAKER_01] And so things that were at the edge before are now squarely in the center of the model. [SPEAKER_01] Yeah.
SPEAKER_00
[SPEAKER_01] I think there's no substitute, though, for being familiar with a user, a problem, how the existing industry serves that problem or doesn't serve that problem. [SPEAKER_01] And just being very, very close. You know, why she always had this thing was talk to users. It's kind of simple advice that sounds trivial, but not enough people do it. And when you actually get into it and you realize the world is gigantic, 99 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.
SPEAKER_01
Yeah. Everyone's lived that in some capacity. Everyone has lived the bad experience of going through modern life. Yes. And dealing with the things that we have to deal with. I don't know, I just I think if you're sitting there lamenting the idea that there's no more good ideas and no more new ideas, it's just lazy. I feel like there's at least two other things that can give you comfort as a founder.
SPEAKER_00
[SPEAKER_01] One is that I don't think any company, no matter how great it is, can do everything. [SPEAKER_01] 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. [SPEAKER_01] Yeah. [SPEAKER_01] The other is that I've been surprised by is some of these markets are just so ridiculously big. [SPEAKER_01] I think that there's eight things that are all doing well around code gen and website building and internal tool creation and whatever.
SPEAKER_01
[SPEAKER_00] You could do that probably straight out of codex, but you can also use other products that are great that are based on codex and things like that. [SPEAKER_00] So I think some of it is just these markets are hard to appreciate how big they are. [SPEAKER_00] Yeah.
SPEAKER_00
And everyone's got like, again, there's no substitute for being able to talk to users and being able to identify what do people really want? Like Open AI's focus is really on trying to improve the models and do the best research we can possibly do. But you know, for someone in a very specific area of the world who has a very specific set of needs, who wants to do one thing and they want it to do it really well.
SPEAKER_01
[SPEAKER_00] You know, there's probably some alpha there. [SPEAKER_00] I do think it changes the way you need to build a company versus in the past, though. I agree. 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 their team knowledge, customer relationships. But 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 a new ephemerality to a lot of these things.
SPEAKER_01
When 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. [SPEAKER_00] I think that's an interesting trend, too. [SPEAKER_00] Yeah, I have seen a handful of times now founders of companies that were built in that period between 2008 and 2016 or something like that, you know, who are the canonical darlings of software from the last decade or so who have founders who are still running the company who have basically decided, I'm effectively restarting the company. [SPEAKER_00] Yeah. [SPEAKER_00] In a world where the primitives and the tools and the assumptions have changed.
SPEAKER_01
[SPEAKER_00] Which is a hard thing to do for, you know, just there's so much sunk cost to it all. [SPEAKER_00] Yes. [SPEAKER_00] But I think the people who are able to adapt to that, it's a huge advantage, it seems like. Totally. And there's no, in my opinion, no, you can iterate so fast now, you can explore the action space so quickly. Yeah.
SPEAKER_00
And you have the benefit of legacy customer relationships. You've got the benefit of existing teams.
SPEAKER_01
[SPEAKER_00] So in some sense, you almost are starting with a headstart. [SPEAKER_00] Yeah. The way I see it is you can learn faster. Yeah. Versus if I were to start a new company tomorrow, I'm starting with no customers. I'm starting with no funding. I'm starting with no product and no team. I guess related to this, how do you feel about the sell off in public markets? Like obviously outside of the big companies, which have done great, but sort of the public software companies have taken a pretty bad beating. [SPEAKER_00] 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 thinking this makes sense?
SPEAKER_01
[SPEAKER_00] Or are you thinking actually this is a misunderstanding and you're feeling bullish about those companies? It's hard to comment on specifically that the market is a very frenetic thing, as you know. Here's what I kind of live day to day.
SPEAKER_00
We work with basically every company that sits in the NASDAQ that you could imagine.
SPEAKER_01
[SPEAKER_00] A is all of these companies are as motivated and moving as quickly as any startup.
SPEAKER_00
B is they've got amazing customer relationships. They've got amazing depth of understanding of the problems they're trying to solve the areas that they serve. [SPEAKER_01] Obviously they've got years and years of perspective that have been built. [SPEAKER_01] And I think now in some sense, they're able to leverage and benefit from the same tools that anyone else is. [SPEAKER_01] And so the conversations we're having with them are really about them starting to rethink end to end their entire customer experience, their product. [SPEAKER_01] Starting to think about how do they serve adjacent markets?
SPEAKER_00
[SPEAKER_01] Starting to think about ways that they can pass capability through to their users. B is they've got amazing customer relationships. They've got amazing depth of understanding of the problems they're trying to solve the areas that they serve. [SPEAKER_01] Obviously they've got years and years of perspective that have been built. [SPEAKER_01] And I think now in some sense, they're able to leverage and benefit from the same tools that anyone else is. [SPEAKER_01] And so the conversations we're having with them are really about them starting to rethink end to end their entire customer experience, their product.
SPEAKER_00
[SPEAKER_01] Starting to think about how do they serve adjacent markets? [SPEAKER_01] Starting to think about ways that they can pass capability through to their users.
SPEAKER_01
So creating entirely new experiences that weren't possible before. And so I think you could take the other side, actually. I think you could take a very long view here, which is that the software itself is the easiest thing at this point. Having all the relationships, the team, the trust with all the customers, that's actually the hardest pull of the tent to have now. If that class, if that segment was asleep, I would say that concern is more warranted. But. [SPEAKER_00] Yeah. [SPEAKER_00] But they're not.
SPEAKER_01
[SPEAKER_00] No, and 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 how to create value for their customers and their business as anyone else's. [SPEAKER_00] And so I think 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. And so I think that's exciting.
SPEAKER_01
And I would say if you're long AI and long startups, then it might even make sense. Maybe the contrarian opinion to be long legacy software too. I don't know if you're experiencing it one way or another, what you think it takes for more experience. It doesn't have to be founders, but even people joining OpenAI from some old company that had not been AI native. How do you help people reset? What does it take for people who have lived in the pre-AI era to work the new way? I think you've got to see it firsthand. And if you're not playing with Codex every day, I think it's hard to intuitively grok just how disruptive and crazy it is.
SPEAKER_01
[SPEAKER_00] Codex for me has replaced ChatGPT on a daily driver basis. [SPEAKER_00] And I'm not even technical. [SPEAKER_00] I don't write software for a living, but it has a general capability and I'm specific enough about the set of things that I want that I know. [SPEAKER_00] And I've developed enough familiarity with it. What are you doing with it? What's the daily quick use case?
SPEAKER_00
[SPEAKER_01] My life is basically a daily struggle of things that I would like to see get done.
SPEAKER_01
And then how fast can our team mobilize and operationalize to get it done. And at a busy, fast growing, very busy company, sometimes those timelines drag. [SPEAKER_00] And then when those timelines drag, it means the thing that I want to see us do starts to drag. [SPEAKER_00] Yeah. And everything kind of elongates. Something that really should take, if everyone 100% focused on this, something that should take two days, now takes a month. [SPEAKER_00] And so one of the things I've started using it for is supplementing that. It gives me a first version of everything.
SPEAKER_01
For example, we're building a fairly substantial forward deployed engineering org, which we can talk about, but recruiting for that has been challenging.
SPEAKER_00
[SPEAKER_01] Recruiting is hard. [SPEAKER_01] So you're using it to recruit? [SPEAKER_01] Well, I'm using it to go figure out lists of people that we're thinking about recruiting. [SPEAKER_01] How do you navigate and stack rank among that list before you start getting into the candidate engagement? [SPEAKER_01] Wow. [SPEAKER_01] And it's crazy because everyone today has an online presence and a lot of people have blogs and X accounts and all that. [SPEAKER_01] And so I just told Codex, here, take this list and go figure out what public presence any of these people have.
SPEAKER_00
[SPEAKER_01] And come back to me and read their online presence and score it against how I think about the technical elements of our work and what the job descriptions are of the things that we're doing. [SPEAKER_01] It works for even what is a non-technical task like that. [SPEAKER_01] It writes a program and figures out how to efficiently look at each of these profiles and comes back and gives me scores on how good each of these candidates' online writing has been. Yeah. [SPEAKER_01] And it's cool because it actually surfaced three or four candidates who I couldn't have picked off the list, staring at 200 names.
SPEAKER_00
[SPEAKER_01] But where I was like, okay, let me go double click on this. [SPEAKER_01] And now it gives me an opportunity to really look into that candidate's profile and their blog and start to get to know them better.
SPEAKER_01
And that process would have taken a normal busy recruiter probably a couple of weeks. Right. It's a lot of names. Yeah.
SPEAKER_00
[SPEAKER_01] And here it just collapses down. [SPEAKER_01] By the way, I bet a lot of this is what is going to be needed for people to just broadly be excited about AI, not frustrated about it is using it and realizing that it's super empowering. [SPEAKER_01] Very much. [SPEAKER_01] Yeah. [SPEAKER_01] I think versus thinking, oh, all these other people are using it to be empowered. [SPEAKER_01] It's like, no, just start using it.
SPEAKER_01
And I guess a lot of that is getting the tools to a place where it can be adopted super easily by everybody. [SPEAKER_00] For sure. And I think almost in some sense, one of the things that I feel is the story that hasn't yet really diffused into more mainstream conversation on this is just how general these tools are. [SPEAKER_00] You don't have to be a software engineer to use Codex. Very much.
SPEAKER_00
[SPEAKER_01] Yeah. [SPEAKER_01] I think versus thinking, "Oh, all these other people are using it to be empowered." It's like, no, just start using it.
SPEAKER_01
And I guess a lot of that is you getting the tools to a place where it can be adopted super easily by everybody. [SPEAKER_00] For sure. And I think almost in some sense, one of the things that I feel is the story that hasn't yet really diffused into more mainstream conversation on this is just how general these tools are. [SPEAKER_00] You don't have to be a software engineer to use Codex. It's just fascinating that you prefer Codex over chat for a lot of your work. [SPEAKER_00] It's cool. [SPEAKER_00] Yeah. I mean, the Codex app is amazing if you haven't used it.
SPEAKER_00
I have.
SPEAKER_01
[SPEAKER_00] Check it out.
SPEAKER_00
But the terminal based use is maybe a little more intimidating if you're not technical. [SPEAKER_01] But in an app interface, it just looks like chat. [SPEAKER_01] And I think it's got much more general agent capabilities. Yeah. On the topic of the forward deployed stuff and private equity, what's the thinking there?
SPEAKER_01
The thinking is very much what I was talking about earlier, which is if you think about 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 corner of a problem. It's too expensive to hire a bunch of people to build software and for that software to then have to be maintained. And obviously for the most important problems in most large enterprises, you could hire people to do that type of thing.
SPEAKER_01
And there's entire industries that have gotten built around that. But for 99% of problems for 99% of businesses, that's totally out of reach. You'd have to either decide that you wanted to hire a couple 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. [SPEAKER_00] But the problem is that solution doesn't necessarily fit exactly what your shape of problem is. [SPEAKER_00] So now you've got people 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 general purpose tool.
SPEAKER_01
And I think that entire era is over. I think now you actually can reason how almost every problem inside of a business can have solutions that are custom built for it. And it goes back to this weird paradox of what do you think is going to happen with jobs where we wouldn't be wanting to hire FDs as aggressively as we would. If it felt software engineering jobs were going away, the jobs of those FDs are different. 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.
SPEAKER_00
[SPEAKER_01] But the amount of demand and the amount of opportunity that we see 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 industry norm. [SPEAKER_01] Solution design that happens on the order of maybe 18 days, if not faster. [SPEAKER_01] That to me is an incredibly large opportunity that I think will be the story of how the next few years goes. [SPEAKER_01] And so the FDs we're hiring is really to help address that.
SPEAKER_01
Last question I have is your reflections working with Sam. It's funny. I've seen no man's a brother, as someone you've worked with for a long time now. I'm curious what the evolution you've seen has been, now that he's obviously gotten to a different place in the public sphere. And there's this whole public persona. And you obviously work with him on a daily basis. Just what's the whole experience like for you with him? Yeah. I think. [SPEAKER_00] Well, we worked together for 10 years, 10 years in January. [SPEAKER_00] And the first year or two was YC. [SPEAKER_00] Yeah.
SPEAKER_00
First two and a half years was YC. And then I got to OpenAI before he did. So I recruited him to OpenAI. I love that. [SPEAKER_01] But he's a remarkable individual. [SPEAKER_01] I wish more people could spend more time with him off the record. [SPEAKER_01] I think he's not innately someone that enjoys being a public face of things. [SPEAKER_01] I think it feels like an unnatural thing for him.
SPEAKER_01
He is someone who much prefers spending his time sitting in a huddle of five people talking about the future and having a deeply technical conversation about some niche topic. That's who he is internally at OpenAI. It's what I've always known him to be. And if more people could spend more time with him, you'd realize he's an infinite optimist. That's crazy because the way I experience it, it's almost this sacrifice, having put himself out so publicly, which is a requirement, I think, to make all of this happen and show the world that by accumulating talent, compute and all these ideas in one place, that's what made all of this possible.
SPEAKER_00
[SPEAKER_01] Then everybody can see it. [SPEAKER_01] But it's such an uncomfortable thing to have done.
SPEAKER_00
[SPEAKER_01] Yeah, it's interesting because he thinks on a timescale that's more like a decade plus. [SPEAKER_01] And the world kind of struggles to think beyond a quarter forward. Yeah, I've always felt there's this mismatch and there's a total mismatch. So he'll say something and everybody's like, that's crazy. Yeah.
SPEAKER_01
[SPEAKER_00] And then three years later, it's exactly where we are. [SPEAKER_00] Sometimes sooner than that. [SPEAKER_00] And then it's like there's no reconciliation backwards.
SPEAKER_00
[SPEAKER_01] It's just like now we're saying a new crazy thing and people like, oh, you've been crazy all along. [SPEAKER_01] And that's a weird thing to watch. And there's no way to tie that together, really. No, everyone's trying to figure out what's happening right now, because in some sense, the whiplash is so real.
SPEAKER_01
[SPEAKER_00] Yeah. [SPEAKER_00] And then three years later, it's exactly where we are. [SPEAKER_00] Sometimes sooner than that. [SPEAKER_00] And then it's like there's no reconciliation backwards. It's just like now we're saying a new crazy thing and people like, oh, you've been crazy all along. And that's a weird thing to watch. [SPEAKER_00] And there's no way to tie that together, really.
SPEAKER_00
No, everyone's trying to figure out what's happening right now, because I think in some sense, the whiplash is so real.
SPEAKER_01
[SPEAKER_00] And I have a lot of empathy for that as you know, I spent a lot of time with our customers, with friends, family that are looking at me and calling me being like, what is going on? [SPEAKER_00] What is happening? [SPEAKER_00] What is this codex thing?
SPEAKER_00
Why is everyone? [SPEAKER_01] 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 bridge for people where we're going relative to where we are. [SPEAKER_01] And I think it's disorienting. [SPEAKER_01] It's really an insane thing that you all have done and continue to do to pull all these pieces together.
SPEAKER_01
I think this has got to be the most hard mode company of all time. It's very, very impressive. I'm sure you're just used to it all. But hopefully you appreciate what a ridiculous feat you guys are pulling off. Well, I appreciate that. I very much feel like it's far from complete. It's highly incomplete. [SPEAKER_00] And I feel like it's interesting when we formed the company early on, the mission orientation of the company was very strong. [SPEAKER_00] But I always tell people like in a very literal sense, I think a lot of companies have these high level lofty missions that you can't really actualize.
SPEAKER_01
[SPEAKER_00] Like it's, OK, no shade on anyone specifically, but it's like, don't be evil. [SPEAKER_00] OK, that seems like a good thing. [SPEAKER_00] Or it's like make the world more connected. Seems good. It's also like, OK, so if the plan is don't be evil, then what?
SPEAKER_00
[SPEAKER_01] It's very debatable. [SPEAKER_01] Well, how do you actualize that? [SPEAKER_01] What do you do? [SPEAKER_01] Right.
SPEAKER_01
And I think one of the interesting things about OpenAI is the mission from day one is this very actualizable mission. [SPEAKER_00] We try and run everything that we do somewhat through the lens of, OK, is this consistent with the outcome that we are trying to create?
SPEAKER_01
[SPEAKER_00] And I always used to joke at OpenAI is like there was a world where we talked about, OK, we do the thing we say we're going to do and then we go home and we're done. [SPEAKER_00] That's the end of the story. [SPEAKER_00] And we all go back and in practice, is it going to work that way? I don't know. I don't think so. Yeah, 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, it's very focusing to be like, OK, guys, there's still this one thing that we're really trying to deliver.
SPEAKER_01
It's very easy to come back to that mission and say, is this something that drives toward that outcome or not?
SPEAKER_00
[SPEAKER_01] And if it's not, we're just going to do it. [SPEAKER_01] Love it. [SPEAKER_01] Well, this is really fun. [SPEAKER_01] Brad, thanks for getting to do it.
SPEAKER_01
Yeah, good to see you. Like what's like the daily quick use case? My life is basically a kind of daily struggle of like thing that I would like to see get done. And then my life is a daily struggle. Well, that too, but, 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.
SPEAKER_00
And then when those timelines drag, it means like the thing that I kind of want to see us do starts to drag. Yeah.
SPEAKER_01
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.
SPEAKER_00
And so one of the things I've started, you know, using it for basically is kind of supplementing that, that thing.
SPEAKER_01
It gives me like a first version of everything. So, for example, I, we're building a fairly substantial forward deployed engineering org, which we can talk about, but recruiting for that has been like challenging. Like recruiting is hard. So you're using it to recruit? Well, I'm using it actually to, to, to basically kind of go figure out, you know, 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? Wow.
SPEAKER_01
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 Kodaks, 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. It works for even what is kind of a non-technical task like that.
SPEAKER_01
It, it, it basically writes a program and it, it will come up and, 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.
SPEAKER_00
Yeah.
SPEAKER_01
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 candidates, 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. Yeah. And here it's just like it collapses down.
SPEAKER_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. Very much. Yeah. I think like. 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.
SPEAKER_00
For sure. And I think like almost in some sense, one of the things that I 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.
SPEAKER_01
It's just fascinating that you prefer Codex over chat for a lot of your work.
SPEAKER_00
It's cool. Yeah. I mean, the Codex app is amazing if you haven't used it. I have. I check, you know, 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.
SPEAKER_01
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.
SPEAKER_00
Yeah. On the topic of like the forward deployed stuff and private equity, like what's the thinking there?
SPEAKER_01
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 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.
SPEAKER_01
And obviously for the most important problems in 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 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.
SPEAKER_00
But the problem is that solution doesn't necessarily fit exactly what your shape of problem is. 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.
SPEAKER_01
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 is going to happen with, with jobs where, you know, we wouldn't be wanting to hire FDs as aggressively as we would. If it felt like software engineering jobs were going away, the jobs of those FDs 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.
SPEAKER_01
But the amount of demand and the amount of opportunity that we see 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 FDs we're hiring is really to help address that. Last question I have is just sort of your reflections working with Sam. It's kind of funny.
SPEAKER_01
I'm just, you know, I've seen no man's a brother, you know, I'm 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? Yeah. You know, I think.
SPEAKER_00
Well, so we worked together for 10 years, 10 years in January. And the first year or two was YC. Yeah. First two and a half years was YC. And then I got to open AI before he did. So I would say I recruited him open AI. I love that.
SPEAKER_01
But, you know, he's he's like a 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 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 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 open AI. It's what I've always known him to be. And and I think that that if you could spend more people to spend more time with him,
SPEAKER_01
you'd realize he's like an infinite optimist. 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. Yeah, well, you know, it's it's it's interesting because like he thinks on a timescale that's like more like a decade plus. And I think the the world kind of struggles to think beyond like a quarter forward.
SPEAKER_00
Yeah, I've always felt like there's this kind of mismatch and there's a total mismatch. And so it's like he'll say something and everybody's like, that's crazy. Yeah. And then three years later, it's exactly where we are. Sometimes sooner than that. And then it's like, you know, there's no like reconciliation backwards.
SPEAKER_01
It's just like now we're saying a new crazy thing and people like, oh, you've been crazy all along. And that's like a weird thing to watch.
SPEAKER_00
And there's no there's no sort of way to tie that together, really. 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? What? Why is everyone?
SPEAKER_01
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. 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 just used to it all. But hopefully you appreciate what a ridiculous feat you guys are pulling off. Well, I appreciate that. I I very much feel like it's it is far from incomplete, far from complete. It's highly incomplete.
SPEAKER_00
And I feel like, you know, it's 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, OK, no shade on anyone specifically, but like it's like, don't be evil. OK, like that seems like a good thing. It's or it's like make the world more connected.
SPEAKER_01
Seems good. It's also like, OK, so if the plan is don't be evil, like then what? It's like very debatable from 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.
SPEAKER_00
We try and kind of run everything that we do somewhat through the lens of, OK, is this consistent with the outcome that we are trying to create? And I always used to joke at OpenAI is like there was a world where we talked about like, OK, we do the thing we say we're going to do and then we like go home and we're done. Like it's like, OK, 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?
SPEAKER_01
I don't I don't know. I don't think so. Yeah, 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, OK, 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 going to do it. Love it. Well, this is really fun. Brad, thanks for getting to do it. Yeah, good to see you.