OpenAI COO Brad Lightcap on the Future of AI | Ep. 46

99% of people uh 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 is like lived the bad

good. Everyone is like lived the bad experience of going through modern life uh 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.

All right.

You you film an intro.

Do I film an intro or you just go No, I just kind of 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

have one more?

Well, yeah, I'll take whatever I need.

We can load up. Well, I really appreciate you making time for this.

I've been really looking forward to it.

Um, what I wanted to start with actually was 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 GBT launches and then it's like this whirlwind that's been I guess like three years, but I'm sure feels like a lot more. Mh.

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?

Yeah, chap chapters is the right word.

Um it's the kind of journey of open AI which I think tracks the journey of AI as a as a field as an industry is uh has kind of been broken up into these weird

periods like when I joined it was no one had really heard of open AI. Our work

was uh you know relegated mostly to uh kind of small uh 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? Um and

what do you do there?

And you were like the CFO when you joined right?

I was our CFO. Um I spent what got you like what what were you thinking when you joined? Like what what did you expect it was going to be?

Well, I didn't know um I was 27 and so I was just kind of like you know I and I I' maybe back up a minute. I I was at Y Combinator prior working with Sam. Um,

and I was starting to spend a lot more time with what I call our hard- techch portfolio in YC. So, all the companies that are building everything that wasn't pure kind of SAS and internet, you know, consumer internet. So, spending a lot of

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. It was like it was

those things. It was like it was promised as this like future technology, but, you know, I wasn't really sure like who who's like actually building this.

Um, OpenAI started as you know as like a YC research project. Yeah. And so it was kind of in the family and um 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. Um, do you know anyone

this company. Um, do you know anyone that that would be good?" And I tried to help them find someone. Couldn't find

anyone. Um, and so I was like, I'll just help you, you know, on myself on the side. But I started spending a lot of

side. But I started spending a lot of time with Greg and Ilia and the team that was there at the time. I kind of realized that it had this like crazy there 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 um the results just get predictably and consistently better at

that point. Then it's like okay really

that point. Then it's like okay really this is just a compute problem actually and intelligence basically can just be bootstrapped from basically scaling up very basic general architectures that um

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 I don't know that just seems more interesting than investing in tech.

Yeah. So you started doing that and then what happened in those early years like obviously like people are bu they're building things that were working like being 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 obviously it was much much more of a research ccentric culture it's opening is still highly researchcentric I feel like people people kind of think post chat EPT it became much more of this

productcentric culture but research really drives everything and I think um 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 I spent a lot of my time

company. So I 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 uh and buildout to uh things as kind of trivial and

pedestrian as like our robots keep breaking and you know uh it takes too long to like drop ship parts from uh you know this one supplier that sits in some small town in England or something like

that. how do we like tighten that loop

that. how do we like tighten that loop and 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, uh it's it's it's kind of both research uh and and deployment and our business, but um 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 um before I think anyone else really appreciated it. So then there was chat 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 like another cool thing. Let's just

it's like a playground or were people like this is this is something?

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 uh these little

sparks. And that was kind of how I would

sparks. And that was kind of how I would describe the pre-Cat GBT period is there were a lot of sparks. Um you could see that the models were now starting to get good enough that they could kind of

emulate um you know humans in a conversational format. Um you could see

conversational format. Um 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. Um we thought of language models

models. Um we thought of language models as completions engines. So you start a text string and then it basically uh takes that as an input and then it continues the text string on this kind of more conversational you know dialogue

based format is not the original invention of language models. And so um but what we were seeing is we had uh we had an API that was a completions API

and we had an interface that basically let people uh put text into an interface that um 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 um kind of conversational turnbased format. And so

we you could see it you could just if you kind of paid attention and 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. The other thing

that we saw ahead of time was um we had trained an early version of Dolly uh as a was our first image model. It wasn't

very good but um 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

promptbased format. And so we had

promptbased format. And so we had guesses leading up to chat GPT that it was going to be uh it was going to be something important but we didn't appreciate the scale. I think my guess at the time we we all took guesses

because we had to do the compute planning um was that the at peak there'd be a million concurrent users and uh you know obviously we were very wrong. 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 postjpt how would you bucket it 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 um in the in the technology and uh on the technology side I would say it's it's obviously there is this kind of proto

period 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 some from something that was unusable to something that was kind of usable across, you

know, basically most um model formats that was kind of before mass consumer adoption. That was kind of 2018 to 2022.

adoption. That was kind of 2018 to 2022.

I think 2022 to kind of 2024 uh was really the period of of of chat bots. Um

where all of a sudden now it was okay, you know, it was generative AI. It was

um it was uh people realizing that you know you actually could could have something that was useful but it was not totally clear exactly what it was useful for. Um you know it was it was new and

for. Um you know it was it was new and and novel and I think there was a there was a um uh 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

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. Um you

for you. They run asynchronously. Um 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. Um they can use tools. Um and I

out. Um they can use tools. Um and I think we're in the middle of that period. I think that started in uh for

period. I think that started in uh for me in in um December of 2024 uh with the release of 01 and then kind of through 2025 and and into 2026. And you think we're like in the middle of that now?

Yeah, I think so. I think weirdly in each of these things because the 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 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 get it into the economy just to get it into the economy and for people to realize what these things are capable of with chat bots that maybe

would have been 5 years or something like that. Um but you know with agents

like that. Um 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.

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?

never end? Are we halfway up an S-curve?

what is the current sentiment for like what the end point of you know agents capabilities will be I personally I feel totally unmed here I

don't know um and you know I the the kind of historian uh and you know kind of uh you know technological economist in me kind of wants to think that everything has to fit into these very

nice kind of scurve shaped paradigms and that you know everything will the innovation cycle will kind of look exactly as as it is there is an curve that we could be we could be right here kind Carlo to Perez like you know okay like this will all

all be the the way that it has been. Um

but uh you know there's there's a lot of meta levels to this. I think we don't quite understand that when you you've got systems that now have in some sense their own agency. There's almost kind of

infinite levels of of things that can happen, right? They they can now start

happen, right? They they can now start directing other agents.

They can work together. 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 cohhere the context basically through that period which you know is something that I think

will get solved. Um you know even basic primitives like memory and other things that are are core to very long horizon work and work that you would do kind of over multiple sessions. Yeah.

Um all of those things haven't even yet been sorted out but are starting to get figured out.

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.

Exactly. Yes. Yeah. No, that

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

Right. And you 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 of of progress that will that will come from this just on the basis of this this step of the cycle. One of the interesting things um that I've experienced is right before right after

chat GPT I think a lot of the conversation around AI was like living in sci-fi land of are we going to have like the next species take over are there Dyson spheres like it was very like big

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 downto-earth way like in a in the

economy operated by humans. It doesn't

feel scary. It just feels like insanely sick software.

Yeah.

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

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.

Yeah, this is a really interesting question. I think in some sense the the

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. Um, and

it's a weird paradox. Uh, 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 was kind of all you could talk about.

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

You think about the whole thing, but once you're in the middle of it, you have the steps right in front of you.

Yeah. There's a local linearity that starts to set in where you're a little bit like, okay, like I I appreciate that this thing is a gazillion times better than what it was, you know, in 2018. Um,

and the capabilities are multitudes more than what they were even 2 years ago.

Like as an example, you know, you talked about Dolly. When that came out, I was

about Dolly. When that came out, I was like, "Oh, that's cute." But now not much, you know, just a few years later, I can't tell if a video's fake or real half the time.

You know, it's like that's going to get all the way there.

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

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

I think we're doing both. Um, and I think, you know, uh, 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 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

future. There was the story over the weekend of um the the guy in Australia who like is curing his dog's cancer who has no background in as as I understand

it in in biology um but basically just had uh GPT5 uh effectively try and come up with some sort of RNA based uh you know um vaccine that that uh could treat

you know could treat his his dog. Um,

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 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, right? Um, you know, that to me would qualify as like a a spark of a sci-fi outcome where it's crazy how fast we adjust to anything. 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, you know, it's just one of my takeaways with this whole thing is we just people adjust to any new surrounding and just think it's normal in like no time.

That's been my experience is like things are novel for about 3 seconds and then next day it's like okay, what have you done for me lately?

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 now. I'm

living in, you know, Silicon Valley.

There's a very different perception of AI in like the St. Louis's of the world and in 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 um 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.

Yeah. 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?

Yeah. And look, I I no one knows the future exactly. So I think everything

future exactly. So I think everything here is speculation on all sides. Um I

think and I I come I come at this kind of from a more of a like you know economics kind of um history of markets background uh which was more where I

spent my time in college uh 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 it is

lens. So first of all I think it it is really a bummer that the world's view of AI is what it is and I think I I 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 the world we live in today. Um, and the the crazy thing is I

today. Um, and the 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 common place. Um and I 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. 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, um uh create a new thing, build a new service,

um serve customers uh 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. Um, 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 uh whenever we 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

optimistic view. I think that the industry has a more of a duty to help people appreciate and understand what's happening um 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. An interesting

instance of this sort of conundrum is in coding and like I feel um 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 real world and say are we going to have more developers are they 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 but I'm curious how you think about this with like coding for for like as an example of like 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 and and come back as as rationally as I can to this kind of economics based kind of markets based view of of of how things have worked in the past um where

you have uh you know distortions in kind of supply demand and cost that create these points that are these weird inflection points in in in in human productivity. If you reduce the cost of

productivity. If you reduce the cost of software engineering for example to virtually zero on the margin um then it the the the simple thing to to to think would be okay well software engineers

won't exist anymore. The thing we're seeing in reality um with tools like codeex and other things is um actually the when you reduce the cost of something to zero the demand for it goes

up significantly um and the job of the people who were previously described as software engineers who were kind of hand typing every character of code.

They're now guiding agents are now just doing a slightly different version of the job.

Well, I think um you know some of this is that the cost is lower but it's not zero. And so, you know, which is a good

zero. And so, you know, which is a good thing I think because between two companies that are competing for a new market, let's say they're doing, you know, AI for construction.

Yeah.

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 going to it should be better software rather than fewer people working on it.

Software is wildly underpenetrated in the world. I think if you actually

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. And by the way, there's still so much bad software like crazy likewhere 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.

It's crazy. And that to me is also by the way you want to talk about risks like that's actually where I think the risk surface exists. It's the s the software systems that hospitals use that

our power grid uses that um you know store like uh you know customer information through a hotel or re like these are all fairly archaic systems for uh you know institutions that actually

span 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 the that software

that can bring software into places that there's 0% penetration of of software where where there should be. Um that can help reinforce and harden systems that uh are exploitable or vulnerable. And in

some sense like you know you kind of look at like where were we from a um 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%. um today. And so I have a 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 um I think that is going to be one of the greatest gifts to the world. Uh and I I think the the speculation around you know will will there be software engineers in the future or not is kind

of the wrong question. Um, there are going to have to be people who oversee the design, implementation, and maintenance of what we 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.

Absolutely. What was the breakthrough that happened for you all recently with Codex? It seems like some step function

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 uh I I think one is like there's just the the focus of the team at OpenAI building codecs.

Um I think I I've I've been at OpenAI a while as you said and um the the work that that team is doing to drive that product with the amount of focus and intensity that they are 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

the product. They're obsessive about the quality of the model. Um, and because of where we are in terms of how models are trained, the cycle time on 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 51 to 52 to 53 to 54. And now it's it's not surprising that you get a model like GBT 54 that as of today is, you know, here

we are in mid-March is and it's the model's a few days old and is doing a billion dollars run rate revenue. It's

doing 5 trillion tokens a day. That's

crazy. is, you know, now far and away our kind of most dominant model of our of our uh of our set of API models. Um,

and is also driving, you know, driving codeex growth at the rate it's going.

Uh, 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 codecs and our APIs today and kind of think we'll laugh.

People think they're kind of pedestrian.

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

like the market is starting to somewhat mature. You guys have had new companies

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, this is what's going to get us there, what's the focus?

One of the cool things about OpenAI is it it has a um a very wide aperture on I think how it it looks at what it kind of ultimate mission is. these lines that

people I think drew maybe in the in the world uh prior um of you know your your B2B or your B TOC or your hard tech or your software um you know all of the things that kind of the VC ecosystem

segments themselves by lame yes we don't see those walls we kind of see AI as having um being this enabling technology that drives is going to drive innovation cycles across all of the

above um and that could be in you know it could be in in the enterprise it could be in consumer it could be in you in in in creativity, it could be in robotics, um 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.

Um uh being able to uh 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 um 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. Y

um and that was really the way that OpenAI operated early on. It still

somewhat is is this kind of expansion contraction uh 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 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 I don't I I think that same everything is is 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 uh and and deployment cycle should look similarly. 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 be 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.

Yeah, we need we need the models to start doing more work for users is what I would say. I think um if there's been one really big gap in my mind in 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 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 and it's just like it like it's time it's time to move on.

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

about it. I just want I just want intelligence and I'm going to let the model kind of decide uh how to allocate that you know on a token level most efficiently.

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

Okay.

Um you've been an investor before. My

question is what should I invest in? And

like um you know like maybe to put a little like framing around it there's um there's like a frequent worry among founders of open eye 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

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

anything's weren't as bad for you, you know, that's going to be really tough.

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 3 years?

Yeah, I mean, I'll go back to Should they just all join OpenAI?

I don't think they should all join Open AAI. First of all, we we the the level

AAI. First of all, we we the the level of like energy in the ecosystem right now is like nothing I've ever seen. like

the the quality of of founders and the like and the effort the effort and just like there's like this intensity and there's this like urgency that do you remember the startup ecosystem like right before chat GPT like you know

like after like you know we have like come down from like the SAS you know glory moment and that was tough I don't know where we'd be right now without you know it would be not fun

I was at YC and you know in kind of 2016 mid 2018 and it was the like front end of that was a fun time to to invest fantastic growth. It was um you know we

fantastic growth. It was um you know we were fortunate enough to invest in I think uh you know in the growth rounds of a lot of the companies that had been built in you know call it the last 5 years prior to that and then weirdly it

just got it got less fun. Um I I think kind of in 2017 2018 and I 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.

I think a lot of the obvious stuff had happened at that point. Yeah. 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 8020 gets done and now you're you know rooting around in the 20.

I think that's right but I it it feels firmly now like there's this entirely new cycle and um that 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 um you know uh themselves like uh

design and write in new programming languages um or uh that they um can speed the time from uh being able to take old uh you know code bases, refactor them and then kind of rewrite

them into new you know new and modern frameworks that enable another company to exist and serve an area that was historically traditionally underserved.

I'm going to go after that. You know,

that's partly the answer to the the first question is and if you kind of think of think of model capability as um 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 um I think the 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 going to drown. That's a very hard place to be. But you want to really be right out on that outer edge on that surface of um 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.

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 going to 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.

Yeah. I think there's no substitute though for being familiar with a user a problem. Um you know h like how how the

problem. Um you know h 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 was like basically you know effectively just like talk to

users. It's kind of this simple advice

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 um you know 99% of people uh

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. Um everyone's lived that in some

good. Um everyone's lived that in some capacity. Everyone has like lived the

capacity. Everyone has like lived the bad experience of going through modern life uh 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.

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.

Yeah. 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 codegen and you know website building and sort of like internal tool creation and whatever. You could do that probably

whatever. You could do that probably straight out of codecs but you can also use other products that are great that are you know based on codecs and things like that. So I think some of it is just

like that. So I think some of it is just these markets are just hard to appreciate how big they are.

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 open AI 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 uh 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. I do think it changes the kind of

there. I do think it changes the kind of uh way you need to build a company versus in the past though. 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 ephemererality to a lot of these things. software is super easy to

these things. software is super easy to build. I can make a UI that works for me

build. I can make a UI that works for me today, but I'll throw it away cuz I can just make a new one tomorrow. I think

that's like an interesting trend, too.

Yeah, I have seen uh 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

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 which is a hard thing to do for you know just there's just so much sunk cost to it all yes but I think the people who are able to adapt to that it's a huge advantage it seems like totally and and like there's no in my

opinion like there's no like you you can iterate so fast now like you can explore the action space so quickly 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 The way I see it is like you can learn faster versus you know if I were to start a new company tomorrow I'm starting with no customers I'm starting

with you know funding starting with no no product and no team I guess related to this how do you feel about the sort of like selloff 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.

It's hard to comment on on on specifically that like the market is is is uh is like a very frenetic thing as you know. Um here's what I kind of live

you know. Um here's what I kind of live dayto-day is so we we work with basically every company that you know sits in the NASDAQ that you could uh you

could imagine. Um, and A is like all of

could imagine. Um, 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

startup. B is they've got amazing customer relationships. Um, they've got

customer relationships. Um, they've got amazing kind of depth of understanding of the problems they're trying to solve, the areas that they serve. Um, obviously

they've got years and years of of perspective that have been built. And I

think like now in some sense they're, you know, able to leverage and and and benefit from the same tools that anyone else is. And so the conversations we're

else is. 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. Uh starting to think

their product. Uh starting to think about, you know, how do they serve adjacent markets? Um starting to think

adjacent markets? Um starting to think about um ways that they can pass capability through to their users. So

like uh creating entirely new experiences that that that weren't possible before. Uh so I think you could

possible before. Uh so I think you could take the other side actually. I think

you could basically take a very long view here which is that like in some ways the software itself is like the easiest thing at this point 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 you know if that class if that segment was asleep I would say okay maybe that you know concern is more warranted but but they're not no and 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 in their business as anyone else is. And

so I think, you know, it's the beginning of a new cycle is my guess. Um you're

always going to get uh 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 uh running trying to run at the same speed.

Um and so I think that's exciting. Um,

and I would say if you're kind of long long AI, uh, and long, you know, startups, then it might even make sense maybe, you know, as a contrarian opinion, to be long long, uh, legacy software, too. I don't know if you're

software, too. 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

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 preAI era to like, you know, work the new way? I think like you got to like see it firsthand and if you're not like playing with codeex every day like I think it's hard to

intuitively grock just like how disruptive and crazy it is like codeex for me has replaced chat GPT 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

what are the like not what are you doing with it like What's like the a 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 um 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 uh at a busy you know a 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 uh that thing. It

gives me like a first version of everything. So, for example, um I uh

everything. So, for example, um I uh we're building a a fairly substantial uh uh for deployed engineering or which we can talk about, but recruiting for that has been like challenging like recruiting is hard.

Plus, you're using it to recruit.

Well, I'm using it actually to to to basically kind of go figure out, you know, of of lists of people that that were we're thinking about recruiting. um

how do you how do you navigate and stack rank among that list before you start getting into you know the the candidate engagement w 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 ex 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 um and you know

basically come up come back to me uh 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 um 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. It it it basically writes a program uh 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. Yeah.

been. Yeah.

Uh 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. Um, but where I was like, okay,

names. Um, 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's, you know, profile and their blog and whatever and start to just get to know

them better. Um, and that process would

them better. Um, and that process would have taken, you know, a kind of a normal busy recruiter probably a couple weeks, right? It's a lot of names. Um, and here

right? It's a lot of names. Um, and here it's just like it collapses down to 20.

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

empowered. It's like, no, just start using it. And I guess a lot of that is,

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.

For sure. And and I think like almost in some sense one of the things that feel like kind of the story that hasn't like yet uh 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 codeex.

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

It's cool.

Yeah. I mean the the codeex app is is amazing. If you haven't used it I check

amazing. If you haven't used it I check you check it out. Um but uh 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 if in an app interface it kind of just looks

like Chad and I think the you know it's got much more general agent capabilities.

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

The thinking is is very much what I was kind of talking about earlier which is um if you think about kind of like the way that software is going to get built

in the future. Um in some sense now any specific problem within any company um 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 right uh corner of a problem. It's too

expensive to uh to hire a bunch of people um to build a bunch of you know software um and uh you know for that software to to then have to be

maintained um 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. um 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 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. It was just built as a

their company. It was just built as a kind of general purpose tool. Um 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 customuilt 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 FTEEs uh as aggressively as we would if it felt like software engineering jobs were going away. The

jobs of those FTEEs are different. You

know if you'd hired an FTE 5 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 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 uh 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 um just sort of your reflections working with Sam. It's kind of funny um just you

with Sam. It's kind of funny um just you know I obviously know him as a brother, you know him 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 you know gotten to a different place in like the public sphere and you know there's this whole public persona and he's 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 um well, so we worked together for 10 years. Uh 10 years in January and the first year or two was YC.

Yeah. First two and a half years was YC.

And then um I got to open eye before he did. So I say I recruited him open. Um

did. So I say I recruited him open. Um

but uh uh you know, he's he's like a he's a remarkable individual. You know that. Um

remarkable individual. You know that. Um

and uh I wish more people could spend more time with him kind of off the record. I think he's not innately I

record. I think he's not innately I think someone that enjoys being kind of a public face of things. I think

certainly it's it feels like an unnatural thing for him. Um he is someone who much prefers spending his time. Yeah.

time. Yeah.

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

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 uh if if you could spend more people could spend more time with him um you'd realize he's like an infinite

optimist. That's crazy cuz the way I

optimist. That's crazy cuz the way I experienced it, it's almost like this like sacrifice to have done to put himself out so publicly, which was 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 that's such an uncomfortable thing to have done.

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

decade plus. And I think the the world kind of struggles to think beyond like a quarter forward.

Yeah.

I've always felt like there's this kind of mis mismatch in there's a total mismatch. And so it's like he'll say something and everybody's like that's crazy. And then 3 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. Just like

now we sang 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. No, every

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 codeex thing like what why why is everyone up like and I think in Sam's head we're already so far beyond

that point in terms of what's coming um 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 uh are

impressive. I'm sure you like uh are just used to it all, but hopefully you appreciate what a ridiculous feat you guys are pulling off.

Well, I appreciate that. Um I I very much feel like it's uh it is far from incomplete. Uh far from complete. It's

incomplete. Uh far from complete. It's

highly incomplete. Um, 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 highlevel 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

be evil. Okay, like that seems like a good thing. Um, it's or it's like make

good thing. Um, it's or it's like make the world more connected. Seems good.

It's also like okay, so if the plan is don't be evil, like then what? It's like

very debatable from there, right? Well, how do you actualize that?

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 open eyes like there was a world where we talked about like okay, we do the thing we say we're going to do

and then we like go home and like we're done. like it's like okay like you know

done. like it's like okay like you know that's the end of the story and like we all go back and um you know in practice is it going to work that way? I don't I don't know. I don't

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 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 going to do it.

I love it. Well, this was really fun, Brad. Thanks for making time to do it.

Brad. Thanks for making time to do it.

Yeah, good to see you.