Ex-OpenAI Researcher On Why He Left, His Honest AGI Timeline, & The Limits of Scaling RL

As VP of Research at OpenAI, Jerry Twerk was a part of many of the biggest advances in AI these past years. Reasoning models like 0103, Codex, he recently left OpenAI, citing that he wanted to pursue research areas that were harder to pursue within a big lab. I'm Jacob Efron, and today on Unsupervised Learning, I got to sit down with Jerry to talk about everything that's happening in AI right now and where

the space is headed. We talked about reinforcement learning, what's required to further scale it up, and the origin of the ideas for 01 and 03. We talked about continual learning and other research approaches, and how Jerry is thinking about their promise and the problems to go solve there. We talked about his reflections on seven years at OpenAI and some of the key decisions that had to be made, as well as how

he sees the race between the foundation models playing out from here. And we talked about what makes good researchers and ultimately why researchers choose to work at different labs.

It's just a fascinating conversation to get to ask everything that's top of mind in the space today with someone who's been so close to building these models and is really zooming out to think about what's next. I think folks will really enjoy this conversation. Without further ado, here's Jerry. Well, Jerry, thanks so much for coming on.

conversation. Without further ado, here's Jerry. Well, Jerry, thanks so much for coming on.

I've been super excited for this episode for a while. I feel like you've been behind some of the biggest breakthroughs in AI over the past years, you know, 01, 03, Codex, and now have some really exciting next steps. I know you've left OpenAI to pursue something new. So I really can't think of a better person to talk about the state of where we are with AI today and kind of all the

potential future directions. Thank you. Thank you very much. Very happy to be here. AI

is one of my favorite topics to talk about. So let's do that. Amazing.

Well, you know, I think I'll start with you obviously played a key part at OpenAI in introducing reasoning models and then scaling reinforcement learning. And so maybe we'll start with the existing scaling paradigms. And I'd love to get your temperature. How far do you think we have scaling the current vectors we have a pre -training in RL?

Like where does that get us from model capabilities? No, it definitely gets us somewhere.

The question is, how do we call that place? How do we name it? I

figured you could tell me. But it is a very, very real and for most practitioners, pretty striking thing that the benefits to scaling are real, predictable, and nice. And whenever we scale up pre -training, we get better pre -trained models which fundamentally know more about the world, which fundamentally

understand language better and fundamentally build the linguistic world model of everything around them. And in the same way, scaling up reinforcement learning makes the model better into acquiring skills that we want to do. And in

many ways, in both those cases, you get what you train for. Or if you want to do next token prediction, you can pre -train models very heavily and get really, really great models in next token prediction. If you want a specific set of skills, you train reinforcement learning models. And then you get them really, really great at whatever you are training for. Like there are basically no limits. Everyone

knows these days that if you care about a skill, you just do reinforcement learning on that skill and you get a model that is excellent. It's kind of that simple and it works. What people hesitate sometimes or in a moment of holdups are how do those models generalize? How do those models perform outside of what they've

been trained for? How do models do knowledge that is not in a pre -training corpus? Probably not. How do models do tasks that you don't reinforcement learning on?

-training corpus? Probably not. How do models do tasks that you don't reinforcement learning on?

Probably not that great. Right. And these are basically the remaining questions in the world of AI because what we train for, we are getting really, really good at.

And it feels like there's kind of two schools of thought here. One is, look, we're still early in this reinforcement learning paradigm. As we scale this up, we'll start to see more signs of generalization. And so maybe these two scaling vectors alone take us to most of what we want from AI models. And then the second being, really, we need something new and different to kind of continue. Where do you kind

of fall on that? In some way, I almost think it's a question of the economics. It is pretty clear that if you, again, if you, if you like

economics. It is pretty clear that if you, again, if you, if you like scaling means in many ways, adding data, that scaling doesn't work that much without data.

So if you, if you add more data that you want, then you get better models doing, what you want. It's what you see right now, every quarter, every lab releasing a better model. It mostly means they've both together of scaling more compute. More importantly, they added more data to the model. And most importantly, it was data targeted to what the previous model was, was,

model. And most importantly, it was data targeted to what the previous model was, was, was bad. It's a, it's a really powerful, really powerful methodology of training better and

was bad. It's a, it's a really powerful, really powerful methodology of training better and better models. And in that case, obviously iterating through it will give your model that

better models. And in that case, obviously iterating through it will give your model that to do everything that you want. If you keep adding the data to do the skills that you want, that you want them all to do. But this, this loop is slow in some aspects. And then the, fundamental question can be, can be faster because I, I, I, I believe it is very natural that, with

the hard times of the methods of training models today, if we keep adding the data that we want the models to be good at, they will be good at it. And there, there, there, there is some amount of generalization within it. But the

it. And there, there, there, there is some amount of generalization within it. But the

main question is there, is there, is there, is there more? Is there, is there some kind of research that would give us more results with less data in, in, in, in, some ways, or more fundamentally better, better ways of, of, of most generalizing from what they've seen so far and what they, what they've learned so far. And I know we'll hit on some of those, you know, future potential

so far. And I know we'll hit on some of those, you know, future potential directions later, maybe in the reinforcement learning world, just to kind of set the context for our listeners, how do you kind of characterize today, like what works and what doesn't, you know, what's your, what's your own mental model? Is it literally anything we have, uh, data for, I know a lot of people talk about the difference between

spaces that have kind of are easy to verify versus those that are a bit more difficult. What's your own mental model for what we can do reinforcement learning on

more difficult. What's your own mental model for what we can do reinforcement learning on today, effectively? Uh, the, the question about the easy and hard to verify often comes

today, effectively? Uh, the, the question about the easy and hard to verify often comes very close to what is easy to even get a signal on the quality of, like if you, if you'd like, and at some moment we've made a pretty good progress at open AI at training malls that are meant to become great

writers. And it is, it's possible, uh, reinforcement learning can really

writers. And it is, it's possible, uh, reinforcement learning can really been done on a, on a lot of great things. Sometimes meaningfully, it is very hard thing to know what is, what is good or what is not good, or you need to wait a lot of time. If you write a book, there are some easy ways to tell if it's a good book or not, but most likely,

um, getting a signal on that, you have to try to sell this book and see how many people will like to read it, how many people want to buy it. And sometimes even that is not a good signal because a lot of critics

it. And sometimes even that is not a good signal because a lot of critics says, Oh, this book is so good. Just no one, no one did, I wanted to buy it because marketing didn't, didn't work well. So how do we, how, how can we do reinforcement learning on writing good book? It's hard to say how do people learn how to write good book. It is a very hard thing to say.

Similarly with starting companies that there are lots of companies started early on. And how

do, how do we know which ones are good or not? Only, only five to 10 years down the line, we'll see that some, uh, entrepreneurs have succeeded and started, uh, really successful companies. And some of them failed. Was it, was it because of some actions started early on were good or bad, or maybe it was, it was a stroke of luck, uh, doing reinforcement learning on that very directly is a, is

a very, very hard thing to do. Every, everything where you get feedback of any kind, uh, you can, you can, you can use that to do, to do reinforcement learning on. So people have been blown away by the results that, you know, the

learning on. So people have been blown away by the results that, you know, the models you worked on have gotten in coding competitions and, you know, math competitions and other things. And I think a lot of people are still trying to figure out

other things. And I think a lot of people are still trying to figure out their intuition for do most tasks look like coding and math, or do most tasks look like books or starting companies or things that are actually very hard to build, uh, rewards and also just test, uh, tons of times, you know, maybe we'll take things like accounting or medicine or legal. Do you have a gut instinct on if

those are more like the, the former or the latter? I think fundamental question is how, how easy to tell if you did a good job or not, because like, or arguably even for humans, it's a very hard thing to tell if, if you did, did, uh, if you did write a good book or not with, with, with

a lot of other work, if, if, if you can be, let's say a manager of, of accountants and be able to tell which, which accountant is doing good, job or not, if there are rules, then with those rules, you can, you can train whatever, you want really. Uh, for, for, for, for, for, for medicine,

I've been, I've been thinking a lot about surgeons and there clearly is a number of rules and there are clearly sense of feedback of human have survived the operation.

Uh, and that, that, that, that is, uh, that, that, that is a, um, success criteria that is very good, but sometimes, and that is, that, that is a very interesting way where really expert, really skilled humans go against the rules to do something differently that has ever been done before. And they succeed through that. If a, if

a surgeon really for their experience sees that they need to do this particular operation differently than, than all the others before they go against the established practice. And for

that, they do something completely new that suddenly succeeds and makes, makes the operation successful. I think the models would be able to do that too

operation successful. I think the models would be able to do that too with the, with, with some time and with enough, enough ability to try. The question

is, how much, how much time would it get to today's models to really, to really get to something like that? As you think about the problems that need to be solved to kind of, you know, make RL more and more generalizable for tasks that people care about, like, you know, maybe help our listeners understand what are, what are kind of the next frontiers for these other domains of RL? Yeah. Generalization, I

think fundamentally is a, is a property of a model. So, so they, the, the story is whenever, whenever you train, you really affect your training objective. And that's, and that's kind of it. And with a training objective, you can,

objective. And that's, and that's kind of it. And with a training objective, you can, like, you get what you train for. And the question is like, how many other things you are getting for free? There are, there, are clearly methods of learning various bits, even next token prediction that generalize poorly. There, there's like nearest neighbor

classification, very, very, very classical ML algorithm. You theoretically can use it for any, any ML problem there is. It just generalizes poorly because if it has very, very simple representation, it builds of the world. Neural networks,

how they, work magically is that through large scale training, for large, large scale pre -training, they manage to learn very interesting, very useful representations of the, of the world around it. And kind of, it's sometimes may seem like we get it for free. Why, how, how, how has it happened that through

training large transformer on the internet, it's a learns really, really to understand well, a lot of the concepts around the world, where does it come from? It's, it's

a magical part that comes from a transformer and a lot of parameters that we, that we hammer over with gradient descents repeatedly. And, and, you know, this, this, this is the type of generalization we get from this model. There's a question, is there a different model that will, that will generalize better? And it's almost surely there must be. The question is, how does it look like? I've heard you talk before

must be. The question is, how does it look like? I've heard you talk before about, you know, say that you kind of updated your timelines, maybe to be a bit longer for some of the, you know, different aspects of AGI after working on scaling RL. Um, why, why was that? I was definitely a very optimistic person in

scaling RL. Um, why, why was that? I was definitely a very optimistic person in a sense of thinking we do reinforcement learning on our model and we'll, we'll, we'll get to AGI. And maybe, maybe we did, maybe it already is AGI. Like the

definition of AGI is a very personal thing. Exactly. It's a very personal thing. And

in some ways it is everything we still don't have. So, so the models that, that can solve, uh, basically any, any Olympia, any competitive problem, the models that are meaningful fully right now, proving new, solving new mathematical problems that no one has solved before. Uh, we get with, with the latest GPT 5 .2 examples of that

before. Uh, we get with, with the latest GPT 5 .2 examples of that every, every week of, of someone achieving that. Is it, is it an AGI? Like

when any people would have, would say yes. At the same time, I, I'm big fan of using coding models and they still, they still make mistakes. They stay, they, they, in some places they do things. I could take me really, really long time to do well. So they can be, they can be extreme force multiplier on, on

doing, on doing programming work. But at the same time, there are clearly places where they fail. And I would say the biggest limitation of the models today is that

they fail. And I would say the biggest limitation of the models today is that if they, if they fail, you get kind of hopeless pretty quickly. Sometimes you can do a bit of a back and forth between pasting an error message. Hey, dear

model, this didn't work. Like try harder. Sometimes we need to do words of encouragement, but fundamentally there isn't a very good mechanism for a model to, to update.

It's, it's, it's beliefs and it's internal knowledge based on, based on failure, which like this is, this is, this is probably the biggest update on me, unless we get models that can work themselves through difficulties and get unstuck on a, on a, on a, on solving a problem. I, I, I, I

don't think I would call it AGI. Uh, because, because of this, of this feeling of hopelessness that if you, hit a wall with existing model and they can't solve it, they just cannot solve it, that they either try a different model or, or, or do it yourself. Uh, the intelligence, intelligence always finds a way intelligence works at a problem and probes it until it solves it, which, which the current models

do not really. Well, I mean, it's, it's kind of a great transition, uh, to some of the other research areas, maybe beyond kind of the pure scaling of, of pre -training in RL. And a lot of what you're talking about sounds, you know, uh, in a similar vein to continual learning. A lot of topics that people have been discussing more and more, uh, you know, uh, in, in public these days, I'm

wondering, like, how do you maybe at the highest level for our listeners think about the set of problems that need to be solved? Like to make something like continual learning, um, you know, actually possible. Yeah. And, um, and a very core thing, being able to continuously train a model means being able to have the model not collapse and not go into the,

into the weird mode or error. Like there are many ways in which training and deep learning model fails horribly. And a lot of, all of the work was happening in the big labs these days is about keeping those models so -called on the rails and keeping the training healthy and fundamentally a fragile process. It is,

it is a process that, you have to make effort to go well. And if

you, if you don't make that effort, it's, it explodes. It's you, you, you just don't get a good model in the end. And that's seems fundamentally different to how humans learn. I think human learning is much more, much more anti -fragile in a way it can, get itself again. It can get, it is

fundamentally robust. It can get itself unstuck throughout learning models with reinforcement learning. I've often

fundamentally robust. It can get itself unstuck throughout learning models with reinforcement learning. I've often

really marveled at how infrequent it is for humans to crash out and, and, and then start talking gibberish. And then, the brain to, and then a human brain after getting some new information to, spiral into some, into some weird stage while the AI models do that, they do that

pretty frequently. And it's, it's something that researchers, uh,

pretty frequently. And it's, it's something that researchers, uh, try to find both theoretical and practical solutions of, of, of how to, how to fight it. And I think that this, this, this fundamental robustness of a training process

fight it. And I think that this, this, this fundamental robustness of a training process is something that is necessary for continual learning. How much of the ideas for, you know, the, the, maybe some of the interesting ideas for continual learning, how much of it does it feel like has been around maybe for a bit or has been discussed, uh, versus, you know, it's entirely net new research problem. The main question is

worth asking yourself specifically as a researcher. That's something that I am asking a lot.

Why, why, why hasn't it been solved yet? Um, that is, that is, that, must be the number one, like when someone starts working on problems like continual learning, which I think pretty clearly hasn't been solved yet. The main, the main question is why, why, why not? What was the particular path that no one has taken so far?

And why there are many researchers in the world that are very smart, are having a lot of brilliant ideas. And so far, no one has really cracked, uh, continual learning. And there are, there are many, many hypotheses for it, but one

learning. And there are, there are many, many hypotheses for it, but one fundamental one I think is that most likely it is a research that needs to happen at scale, at least at a certain scale. And there

are only so many well -funded research labs in the world right now that can only do so much research and so many few research projects that, that, that, that is most likely one of the, one of the really big reason, if there is a research that you could do at a small scale and fundamentally discover something, you know, it's probably the word or a few, but either it would be something very

complex or very theoretically difficult to do or, or, or, or just requires already models and levels of compute that are available to very few.

And then it's very likely that, that the very few labs didn't go in a particular direction yet because, because they were, busy doing other things. I mean, I've heard you talk about before this idea that like, there's ideas in AI whose time isn't right, but they're still good ideas. And certainly we saw this with reinforcement learning and, you know, uh, it, it becoming much more effective after having large pre -trained models

to be on top of. So it sounds like, you know, your, your maybe intuition is that there are some really good ideas out there that maybe, uh, if actually applied at scale would, uh, would be really helpful toward this domain of problems. I definitely think so. I've heard you talk before about, you know, the labs really converging on, on working on pretty similar stuff. Right. And I, and I, I don't know

how, if that feels like that's been common over your, you know, past two, three years, but it seems like when you were leading a lot of the work in, you know, one that, that was a genuinely new thing that, you know, a lot of the labs were caught maybe flat footed on. Talk a little bit about this convergence that's happened maybe over the last year. And, uh, was that surprising to you?

Yeah. Even when training models with reinforcement learning, there is, there is this, this well understood and well -documented trade -off of exploration versus exploitation. And you wonder when is the right time to try different things than you've been doing so far? And

when is the right time to try to optimize very, very well, what you already know, well, how to do. And it's a, it's a trade -off that has no real solution because you don't know what is, what is the unknown, like whether exploration is successful or not fundamentally is, is there any way that is different from the current one that would give me a lot. And unless you know the landscape of

what you are doing, it's fundamentally a very hard thing to do. So, uh, so it's like, I don't think there's a fundamental question about it, but I remember someone telling me at someone in my life, how do, why do all the planes, all the commercial planes look the same, even though there are, there are a few companies building those because in the end, this is the most economically, uh, economically

efficient design in a, in a way, why, what all the, labs are doing today, that the forces of economics are fundamentally very strong in that.

And if you want to, if you want to compete, you need to have the best models offered at the lowest price. And the competition is pretty, pretty efficient there in terms of, in terms of customers can, can switch whenever, whenever they want. And

it's really, really the customers that are winning for that in many, many ways. But

that is, that is one thing that drives the labs to go for higher and higher efficiency and to go for produce better and better models in a pretty predictable way. And then there, there, there is, there's a question of exploration versus exploitation.

predictable way. And then there, there, there is, there's a question of exploration versus exploitation.

Uh, should we try to go, should, should, should we try to sail over the sea and see what is out there? Should we try to train a model that is, that is completely different. It would, it would probably be, um, it would probably walk away and lose some focus on trying to get to get the current thing better. It would, you lose some focus in trying to get the current thing more

better. It would, you lose some focus in trying to get the current thing more efficient, but maybe there's something 10 times better. Maybe there's something a hundred times better there. there, there, there, there's a question of a, of a belief and conviction at

there. there, there, there, there's a question of a, of a belief and conviction at the heart of it, of, of how much do we want to try those other things versus not. And to your point, I mean, obviously there's such a clear path forward on adding more and more data, you know, uh, to, you know, to RL in different domains and that improving models for economically valuable tasks. There's kind of a

clean roadmap to, uh, you know, to how each of the labs can, you know, continue to, uh, you know, improve their underlying models that it maybe makes it harder to, go out and make that big bet. Um, whereas when it felt like maybe pre -training was slowing down, it's easier to go out and explore a bunch of different things. Yeah. I think, I think there are also just, just different times, uh,

different things. Yeah. I think, I think there are also just, just different times, uh, in, in, in history, sometimes there is, there's more appetite for it and a little bit of a more of a freedom to explore various dimensions. Uh, the more, the more, the more competitive, the landscape became slightly, becomes harder because it

is almost, it's something like a prisoner's dilemma. Yeah. Uh, situation where, uh, we're trying to do things, things differently, uh, like exposes you very heavily to, to, uh, to, to, to, to losing market share to other players. Yeah. Do you think it actually, you know, even matters for the labs if

players. Yeah. Do you think it actually, you know, even matters for the labs if the next big breakthrough is, discovered there? I mean, one thing I've been struck by is just the dissemination of a lot of, you know, uh, of a lot of these, you know, advances, obviously, you know, O1, you were kind of the pioneer on the reasoning side. There's a few labs now that have great reasoning models. And, uh,

I almost wonder if, if the, you know, the labs would be just fine if, the, if the, uh, if the, uh, breakthrough happened somewhere else, because, you know, these ideas diffuse and eventually they'll be able to plug it into, to the existing business.

And yes, diffuse. And, and that's, that, that, that's a good thing. But at the same time, the lead it gives you to do some, to be the first, I don't think, it's something to really, uh, to really, to really discount.

In many ways, we have seen the fact that like, you know, if you, if you believe that fundamentally, there would be no place in the world for open AI to succeed and did succeed because it went into pre -training transformer, large scale, much better than anyone else. And that lead made it one of the largest and most

successful company in the history of the world. And in the same way, because of open AI being the first one to figure out how to do large scale reinforcement learning, I think, I think for a long time and still until today, I believe that it has the best reinforcement learning research program out of all labs, which allows it to do things better and more ambitious than, than, than, than most other labs

that had, that had to catch up to this, to this much later. And while,

while the ideas diffuse, the lead can be a very, very powerful thing that if maintained, it will, it will stay with you for maybe, maybe potentially forever. In

many ways, I've, I've, I've, been reading book about, about semiconductor manufacturing, which many, many of the core initial parts of the invention were done in the, in the, in the United States. And through that, they, they slightly disseminated through the world through two various places. But at the same time, there has been like moments

and, and, and space of lead that a lot of other countries couldn't ever match the, that they, they, they continued compounding of advantages over time for some of the countries to bet on it early and really, really started hard to build it. And it's not that there's only one country building, having,

having successful semiconductor business, but also not, not, not, not every country. It doesn't, it doesn't exist everywhere. There, there, there, there always is a place whenever there is a business shift. There, there, there are some newcomers that will be successful. There are some

business shift. There, there, there are some newcomers that will be successful. There are some newcomers that will be unsuccessful and some old companies that will, that will stay and manage to turn themselves around and some old companies that will die. That's that, that's the Darwinian part of, of progress. Now, and I feel like consumers and enterprises always remember the first company that, that introduces them to some, you know, pretty magical experience.

I mean, certainly you guys experienced those with ChatGPT. One thing that's fascinating about you is you obviously made all this incredible progress in, in RL and, you know, we're helped pioneering a bunch of this and RL progress is still alive and well and, and going along and, and we're making lots of progress across domains. And, and you decided to leave OpenAI. Um, and I think you cited kind of different research areas

you wanted to explore. Um, I'm curious, like when, when did you kind of begin to know that might be something you wanted to do? Um, and, and, and how did you ultimately make the decision? So it's, it's, it's definitely not something that happens very quickly. It's, uh, it's, it's just something that slowly grows in a human.

very quickly. It's, uh, it's, it's just something that slowly grows in a human.

And OpenAI is not, not an easy place to leave because I have many friends there, a lot of all short history and a lot of, I, I, I, I, I've built all of, all of my life there. And for a long time, really, really tried hard to make it, to make it work and try to, uh, try to see what are, what are the various place to do it. But at some

moment, if you specifically, as a researcher, if you, if you wake up and if for, for any, any reason, figure out, you don't love your work anymore, you are not incredibly, incredibly excited about what you are doing, then it's a good moment to try to explore and try to do something, something else. Like it is basically impossible as, as a researcher to do your, your best work. If you are,

if you're not a hundred percent excited. And if you don't go with full enthusiasm, there are many, many days at OpenAI when I was, when I, when I had basically infinite enthusiasm for the work I was doing and was believing I was doing exactly the right things. But somewhere, somewhere around the end, it was, it was getting, it was getting harder and harder. Um, so, so like, you know, that's, that's, that's,

that's kind of, kind of long story short. Yeah. What, what are some of the things that are giving you, uh, energy to that? Um, I think on the most fundamental level, what I did and what I, what I've done, I, when I started OpenAI, I believe reinforcement learning is a necessary element of a path to AGI. And

I really wanted to make it happen. And I, and I really, really did. And,

and like introducing reasoning to the world, reasoning to the world, reasoning to the world has been like, you know, to me, a tectonic shift and then, and the paradigm of how, how, how we train the models. And then in some ways I thought I want to chase that high again and try to do something, something similar, try to find something that is missing and how the world is training models so far

and try to, make it mainstream as again, in one way, in one way or another. But once you, did something like that, you, you know, it's, it's, it's hard

another. But once you, did something like that, you, you know, it's, it's, it's hard to, hard to get, another, another similar dopamine hits, uh, doing something else. So that's, that's kind of what I would like to do. And I

something else. So that's, that's kind of what I would like to do. And I

would like to have a bit of, a bit of freedom thinking about how to, how to explore it and try to attack the most, the most core, the most, the most important problems there are. How much are you feeling like, Hey, I've got dozens of hypotheses or, how much are you zooming out and being like, you know, I've been so heads down in OpenAI, let me zoom out and kind of see

what else has been going on. You know, in general, the, the, the real important hypothesis and the real important problems are most likely not something that will, that will be new. And it will appear to you. If you

worked on machine learning for seven years, it's very likely, you know what that, what the important problems are. The main, the main question is what I, what I alluded to before, which means how do you solve it differently than everyone else? Because, because

it means no one, has solved it yet. So what is, what is different and what is, what, what, what can be done, what can be done differently than, than, than, than, than, than people in the past? Well, I mean, I definitely want to hit on, you know, OpenAI and obviously you had such an incredible run there and, and, and time. You know, one thing I've heard you say before was you, you

know, I think you've been in OpenAI since 2019 and you said every year felt like a different company in, in some ways. Yes. Um, and so I'd love if you could just walk through that evolution and kind of, you know, the, the, how you kind of describe the narrative of the past, uh, six, seven years there. Starting

with a small lab that, you know, that there's like 30, 40 people where we were always OpenAI from the very beginning was extremely ambitious, believing this is, this is the place that will build AGI and that will, that will create benefits of, of digital intelligence for, for, the world. But starting from a few people trying to do

a few kind of full projects that were, incredibly ambitious to, to where it is today, which is, which is again, one of the largest companies in the world, the product that everyone knows and everyone uses. And it's, almost hard to imagine not using it. It's been, it's been, it's been a wild ride. And as you, also

using it. It's been, it's been, it's been a wild ride. And as you, also are aware, the execs at OpenAI have shifted throughout the years pretty significantly.

So the type of people you worked with every day has changed a little bit.

The size of the company has changed that the themes of research have, have changed at some moment in, in the old days, there was no pre -training at all.

Then for a while it became kind of the pre -training company. Then for a while, I think it became pretty much largely our old company. And now, now it's, it's a little bit more in a, in a balanced way of, of, of, of a mix of those two with, with many, many people living,

OpenAI and building both businesses. And then that there are, their, their life outside. And

many new, fresh, great people coming in and doing, doing incredible, incredible research, inside. It's like, you know, it's just a company that manages to reinvent itself and

inside. It's like, you know, it's just a company that manages to reinvent itself and manages to, grow through that, through all the stages, trying, trying to imagine. I always,

always had this weird hope thinking of all those big successful companies, how incredible it would be to live through such a story of, of being there for those, for, for, for, for that, for those stages. And I feel like I've been through, for quite a bit of that at OpenAI. And it's, it's an experience that I think it's very hard to compare with something else. You know, I think

everyone's eagerly waiting for the, definitive stories to be written about this, this chapter of OpenAI. When those stories do get written, I feel like people always like to focus

OpenAI. When those stories do get written, I feel like people always like to focus on the kind of, you know, the difficult, you know, 51, 49 decisions that could have gone either way, that really like moved the company forward. Are there any kind of, of those pivotal ones that, that stick out to you? Yeah. Good question. Um,

you know, I, I, I was only, only central to some of them. There were

probably many that I didn't, that I, that I like only, only, only, only was, that was a background character and probably even, even this discussion about releasing ChatGPT to the world or not, as, you may have heard. And many people did this popularity and it's, it's virality total didn't, like what wasn't

expected internally by at least like no one I heard about it. Uh, I, I think in the end with, with ChatGPT and GPT4 released soon thereafter, we created a bit of a moment and a bit of a momentum that was, um, incredibly hard to predict, but it made OpenAI largely what it is today. Um, that was, that was definitely a very, very, very important call on, on, on many access and many

decisions, on many, many dimensions, um, like the decision to pull a lot of resources to train GPT4 at the moment it is. And with

that, with a lot of trade -offs that went there again, remained very, very important and very critical in the, in the history of, of OpenAI and turned out to be a really good decision. And it's in the same way, betting and, and saying reasoning, most are our future in a world where it's was completely unsure of. And

just a bit of first principles thinking and, a, and a bit of a, a bit of a intuition that this is the right thing to do, allow the OpenAI to completely reinvent itself and say, we are, we are doing reasonably models right now, even though there is no product market fit, even though they seem to be kind of cool with puzzles. If you, if you look at the one, like it was,

it was a smart model, but it, it wasn't really good for anything practical, except for, except for just just trying around, Hey, we have a model that is kind of smart, really only with, with O3 and with a little bit of more investments into, into tool use with those models, we're able to start building something that started being incredibly, incredibly useful for research, for coding. And from there, once you

have the first signs of real product market fit, then humans are really, really good at optimizing something that exists and that they can see it works. But getting to that moment has been great, great journey and something, something to study because it was, it was not an easy thing in OpenAI at that moment, really, really past the exam. Yeah, no, I mean, I think what you describe is so interesting in this

exam. Yeah, no, I mean, I think what you describe is so interesting in this idea of you, you know, have to keep scaling and investing in something, you know, uh, not quite knowing if it's really going to work or it's working. It's obviously

very relevant to some of the things you're thinking about in the, in the future too, you know, with the, with the reasoning models, like, you know, uh, was it clear after 01 that this was going to be more than kind of fun and games or what was like the, the moment, the spark for you that you're like, oh, this is really going to work and we can really scale this. I kind

of believed in it from the very beginning, just because I believed in reinforcement learning.

Again, my, my core belief from my very first days of OpenAI is that reinforcement learning is a necessary part of getting to AGI. And that was the main way was how rather than, if the question is, when are you ready to do it and how exactly do it, do we do it? And I fundamentally, but

just, just since the day I started, I knew, I knew this is, this is the, this is what we, what we need. And over time and over, over research there, there came various experimental results that, that have informed us. This is, this is the right way of doing it. One thing that's so interesting about OpenAI is obviously, um, I think people liked, I think Ben Thompson coined like this phrase, like the

accidental consumer business, the idea that, like, obviously you were a research lab pursuing AGI and then kind of almost accidentally stumbled upon this, this incredible consumer product, uh, that, that was, uh, you know, immediately picked up by the broader world. Um, and I'm wondering, you talked about some of the kind of different chapters of being there. I

think a question everyone always asks about OpenAI is the company's doing so many different things. You know, they've got the consumer product core research, you know, Sora, the enterprise

things. You know, they've got the consumer product core research, you know, Sora, the enterprise product, like how does it actually work internally? And, and, um, you know, how, how do you kind of, uh, do you feel that tension at all on the research side of getting pulled in a bunch of different directions? One thing pretty clear is that OpenAI research operates very separately from the product and has from the very beginning,

almost, almost like you said, which is that's OpenAI's goal and mission is to build intelligence. And that is, I think the main goal and motivation of majority, majority

intelligence. And that is, I think the main goal and motivation of majority, majority of research. There is, there is like one team specifically built and with directed towards

of research. There is, there is like one team specifically built and with directed towards product research. And that's that, that, that, that, that is the part of research that,

product research. And that's that, that, that, that, that is the part of research that, that optimizes, uh, for, for, for whatever, whatever product metrics it is. And the rest of the, research mainly focuses on how do we, how do we make our models more, more intelligent? And, and that, that tension really doesn't exist there. But I think

this is real and interesting is that OpenAI is at the center of probably the biggest technological shift of our lifetime, which means there is so much opportunity to do various things.

It is almost, it feels wasteful and it feels imprudent to not try to do all those things because basically everything in the world will be disrupted by AI, but it has a downside, which is very real and very, very problematic year of focus. Companies are very bad at doing multiple hard

things successfully. Um, like companies are well known for

things successfully. Um, like companies are well known for succeeding in one very hard thing and then doing others similarly well to others. There

are very, very few places in the world that can do a few of those things. And that is very, hard thing to do. And I think this is a

things. And that is very, hard thing to do. And I think this is a very, very, very big risk for OpenAI, try to do everything and then, and then not succeed at it. Uh, but, but like it will, we will see if, if, if, if, if like OpenAI in a half focus state can execute well on all those sitemats or there will be other companies. Famously. And I think, I

think it's a little bit sad. OpenAI, um, really lost focus on coding for quite a while. I want to focus on the, on the consumer product. And that

has costed a bunch of, a bunch of market share that is, uh, working, working very hard on regaining right now. And I think coding OpenAI, coding malls are pretty, really great these days. Again, uh, but, but, but lost focus and lost lead definitely, definitely has a, has a, has a cost here. So, um, there

are, there are, there are like in some way, like when you are a company doing AI in the world right now, you feel like a kid in a candy store because there are so, there's so much potential of, extremely valuable things that can be built for the world that it's hard to prevent yourself from doing all of it. But, but like for, for everything, there is competition and there are, there are

it. But, but like for, for everything, there is competition and there are, there are questions who will, who will do each one of those things in exactly the right way. Yeah. Well, I mean, I think that's a great, uh, you know, point to

way. Yeah. Well, I mean, I think that's a great, uh, you know, point to transition to just like the general ecosystem today. Um, and you kind of alluded to coding, which I think has been a really fascinating space to watch play out. Why

do you think Anthropix has been so successful at coding? It's focus. I think, I think focus can explain 95 % of things, what are, what are, companies, why are companies succeeding in things? I know, I know Anthropix founders from even the time when they were at OpenAI and they were always, always extremely fond on coding

and they always believed that it's a, it's a necessary and critical part of AGI.

And I'm, I think I can, only imagine how focused they have been on it, on it over the years. And they, they definitely managed to get their, their vision very far with, with the latest model with cloud code and coding agents. And I,

I, I believe they, they are saying truth when they say very few people in Anthropix type code themselves these days. And do you think that's kind of a foreshadowing almost of, you know, pick the, the major labs that are out there, like each can kind of focus on different things. And so you end up with, uh, you know, models that are particularly good at, at, at some things in, in different labs.

It's a, it's a good question. And I think there are multiple worlds in which we can be, there are, there's a world where data matters. In

that case, data is a very zero sum game where like you put the data into the skills that you want and your model is better at that skills.

And in that case, we see splintering of the, of the market into, because it's basically, you can shift the data mix of what you're putting in, but ultimately it's at the cost of some other skill. Exactly. And, and, and it's a little bit about, about shifting the data mix, but mostly shifting the work. Data is a, is a labor intensive and like slightly more, less money intensive, but it's, it's, it's, it's,

it's largely very much labor intensive work in terms of, research engineering, let's, let's call it that way. And so, so it's just, just, you have so many researchers that can work in parallel doing, doing, doing useful, work on preparing the next data set.

So if, if, if data drives the improvement, then we will see different labs being, being better at different things and natural specialization trade -offs. But if research is king, I think, I think the research has this magical property that is, is hard.

It is, is high risk, high reward. But at the same time, if you have a good idea in the research, it could improve your model in all domains at the same time. And you can, you could leapfrog everyone in all domains, um, naturally by just, by just training better models. And, and like, which one, which of the world, which of the futures we are in, it's, it's very hard to say right

now. Uh, but we'll, we'll, we'll, we'll see, we'll see if, if,

now. Uh, but we'll, we'll, we'll, we'll see, we'll see if, if, if, if in the, in the future, which one. Yeah. I mean, this current paradigm feels almost like very anti -bitter lesson, right? Where everyone's going off into specific domains and like, you know, specializing and, and really putting in those. And you're, you're right that it feels that there's this intuitive feeling almost, that there must be something else

that is, that is a bit more generalizable. I'm, I'm pretty sure there is. The

question is like, how easy it is to find. So there, there, is one, one version of the world where, which is not completely impossible, uh, although, slightly pessimistic on humans that says coding agents are so good right now. Let's first get them to the moment where they can automate AI research and then have the models like, like research better models, because, because maybe we are at the, at the last

model that human have humans could have figured out. And no, it's not a completely impossible, uh, framing. It's maybe, maybe it makes sense with all those GPUs we have and all those, all those really, capable models and their tenacity. Maybe, maybe they should be, uh, researching future models. And spot that, you know, maybe, maybe there are, there are still a few things humans can do that we can, that, that, that, that

we can put our, put our heads to work. I feel like a lot of the top AI researchers, yeah, are working on coding for that very reason, right? This,

this belief that it will, will, will speed things up. Um, you've obviously worked on coding too. I mean, you spent time, uh, you know, a lot of time on

coding too. I mean, you spent time, uh, you know, a lot of time on codex. Uh, how do you think about like the next frontiers for these AI coding

codex. Uh, how do you think about like the next frontiers for these AI coding products? And it seems like we're on this, like, just crazy exponential hill climb right

products? And it seems like we're on this, like, just crazy exponential hill climb right now. How do you even conceptualize like what these products will be able to do

now. How do you even conceptualize like what these products will be able to do in a year? Fundamentally in the whole, the whole story of coding is that we are able to program computers at a higher and higher level of abstraction in many ways. And we need to know less details. I need to track less details

many ways. And we need to know less details. I need to track less details and coding agents in some ways can be thought of as a higher level programming language that has very different semantics from all the, all the, all the other programming languages. And, um, you know, I think it's

programming languages. And, um, you know, I think it's a trend that will, I think it's pretty unlikely that in the future we will be typing code ourselves. Very few people already do. And it's,

it's, it's, it's, it's a one -way ticket, but at the same time, software is important. Software needs to be reliable and there will be more and more progress. And

important. Software needs to be reliable and there will be more and more progress. And

how do we get certainty about software doing the right things if we are not the ones typing it and maybe not even the ones reading it? Uh, fundamentally, I think those are all solvable problems and I am very excited about them. I think

it'll be interesting whether the core skillset to work with these agents, you know, looks somewhat similar to, to a software engineer or really everyone just becomes a PM. Uh,

and it's really just about knowing, you know, having some idea of what to go do, uh, and, and, and the models going for it. Yeah. I think it's, it's an interesting question because I think almost the most important skill right now is being a skill of a good manager of junior software engineers. And a lot of software engineers have been, have been like a little bit

engineers. And a lot of software engineers have been, have been like a little bit reluctant of going to management of course, really, really like to, uh, specialize in, in, in, in, in, in narrow domains. And I think like for, for a long time, it was, it was really, really the right thing to do. And understanding deeply systems is incredibly important. You need to combine deep understanding of computer systems with being

able to give back at least a little bit of control, which, which, what's what I think really the best managers are. The best managers understand extremely deeply the work of, of people on their team, but at the same time are able to give back some control for the people to drive their own, destiny. And that's probably the

best way how to work with models these days. You, you, kind of everything that the models do, you, you understand the trade -offs and you understand what they are doing, but you allow the models to make their own choices and, and live with the consequences of them one way, one way or another. You know, around coding, one thing people are also trying to figure out is just where the various applications kind

of fit in. And I think, you know, there's, uh, I guess a few questions like one being, um, you know, hey, like you have the codex, the cloud code team sitting, you know, right by the research teams, they build these great harnesses and products. Like, you know, how do you think about the, you know, the opportunity for

products. Like, you know, how do you think about the, you know, the opportunity for companies like cursor and cognition and to, to what extent is it like a disadvantage to not be, you know, sitting next to the researchers at the labs? It definitely

is a disadvantage. I think, I think the fact that the most successful companies of the world are training their own models is telling about something. And I

kind of think just like the future of big AI companies is to become a hyperscaler and run their own data centers instead of renting compute the future of successful AI application companies to start training the models themselves. This is, this is just how the, how the stack works, but you're going to start somewhere. So, so

it's, it may be the path that you start with, the AI application that is successful. And then you first start post -training your own models, and then you start

successful. And then you first start post -training your own models, and then you start pre -training your own models. If you are, if you are more and more successful, and then you start building your own data centers. Uh, those are, those, those would be just, the natural, natural paths and stages of, of success of being a good, being a good AI business. So you think it makes sense for these companies to

kind of do reinforcement learning on their own user data, you know, post -train a bunch. And I mean, do they have any hope of catching you up, you know,

bunch. And I mean, do they have any hope of catching you up, you know, endless compute going toward the big labs, you know, endless collection of talent? Like if

you, if you focus on a very specific domain, do you kind of have a hope of building a better model there? Or is it kind of a, almost a hopeless task in some ways? Well, nothing ever is hopeless. The future, is not determined, but it's, some of that is what I, what I, what I hinted before, which is, is the data important or is the research important? If the data is important,

you can always try to differentiate yourself with the data, but it is, it's not clear we are, really in that world. Maybe there's a, there's a world where research is important, but that also allows the smaller companies to do some research that they think is better. And maybe, maybe we went through, we went through research in the market. But it seems like it almost requires a world where, you know, you have

market. But it seems like it almost requires a world where, you know, you have specific types of models that are focused on things being better, right? And not generalizing, right? So we get into a world of generalized models. It feels like it becomes

right? So we get into a world of generalized models. It feels like it becomes hard for an application company focused on any specific tasks to have like the better pre -trained or, or large model. Sometimes innovation comes from constraints. I think it is possible for a company focused on a specific domain through seeing the deficiencies of the models on this domain to create a model that is generally better.

I don't think it is possible. And that could be like the next layer of start of success of this company. If you try to do really, really good model for X, and suddenly for that, you make the best model for everything else, and then you grow and then you become another, big successful company. I feel like in the past, the problem has been, you get better for like one second, and then

the next generation of models come out and you're like, oh man, we're, we're way behind again. Competition is difficult. And definitely we have seen, seen for a while in

behind again. Competition is difficult. And definitely we have seen, seen for a while in the, US tech landscape, how big companies have tons of advantages. And this is, this is true. But at the same time, there are, there are, there are new big

is true. But at the same time, there are, there are, there are new big successful companies coming up. So, it's not, it's not hopeless. It's just hard. Well, I

want to, you know, shift gears to the talent ecosystem and maybe, you know, research itself, because obviously you've both are an incredible researcher and have worked with amazing researchers.

Maybe to start, obviously researcher hiring is very competitive today. I know you were probably on the forefront of, of bringing folks into open AI. What determines what companies researchers joined today? It's a good question. In the end, people are very complex,

joined today? It's a good question. In the end, people are very complex, definitely more complex than the models these days, which means everyone's incentives are different. What's what they want. And I honestly cannot really

are different. What's what they want. And I honestly cannot really generalize. Whoever is hiring people should not think about how do I

generalize. Whoever is hiring people should not think about how do I convince the most of the people to join me and how, how, how, how do I become the most appealing place for, for researchers to do. It's probably a good question to be, to be asking yourself, but I think there's a second one, which I think is much more important. What, what, what, what, what type of researchers like

would really want to work here? And then, and then find those because it's, it's kind of impossible and, and, and, very, very difficult to try to appeal to everyone. And just, just, just, just because of, of diverse, diverse preferences and

everyone. And just, just, just, just because of, of diverse, diverse preferences and diverse viewpoints and diverse ways of working. So it's, it's much better to try to build a team that has like, you know, some shared values, some shared approaches, because it's pretty clear that the, that the teams

that are, that are aligned and I have, that have the same, goal move faster and work better than the teams, than the teams that are not. So, you know, it's, it's really, it should be, it should be kind of filtering on both sides and trying to just find the right people for the right, for the right group.

And that makes everyone happy. That makes the group successful. And that makes that, that, that makes the group more attractive over time. Yeah. But there's been some interesting experiments around this, right? I think you had Meta famously offering, you know, the first offer of these like mega packages. You know, what, what's your kind of reaction to that?

Well, like, you know, there are different strategies how to build a research group and, you know, Meta at some moment, I think, like, you know, there are, there are supply and demand curves that they needed to make offers really attractive to start bringing people back after a few missteps in the, in the, in the world. And, you know, the momentum is a very hard thing to stop. If, if,

world. And, you know, the momentum is a very hard thing to stop. If, if,

if like, if we're at any moment, there is a perception in the industry that you are not doing very well, then that, then you will not hire people. And

then, and then it can enroll itself. So, you know, in many ways, I think it was a really, good strategy to try to change that dynamic and change the negative momentum in a place where, AI is so important for ever, ever, ever, ever, ever large scale business. And Meta has a very much new

team built out that is, that is training a new model these days. And a

lot of, all the people in the industry are watching, well, it will be successful and how it will be successful and that will be the term in the future of this lab. But, but it was definitely like, you know, a good moment to bring in some, some new life into the, into the Meta AI efforts. I mean,

you've obviously done a ton of groundbreaking AI research. You've worked with other great AI researchers. What makes a great AI researcher? It's a, it's a good question. And it's

researchers. What makes a great AI researcher? It's a, it's a good question. And it's

a, it's a difficult one because in, in many ways, being successful AI researcher, in many ways, it is about just being in the right place at the right time. That's very humble of you. But like, there, there, there is something to that. I think the fundamentals of being a great AI researcher these days, but, but in the, in the span of my career, are one thing being

extremely good at both systems and engineering levels, understanding how computers work and how neural networks are trained together with the theory of, of neural networks and optimization. Um, like it's very, very hard to be very successful doing only one of those things well. And suddenly if you're, are at least okay in

both of those things, it makes you easily 10 times more productive in any, in any research endeavors. And I think this is, um, this, this, this, this is an important part. Um, like the, the other one, it is like, if you want to

important part. Um, like the, the other one, it is like, if you want to be a successful researcher, you very necessarily need to have some ability to think independently, uh, be able to, get away from, from group think.

Um, people have kind of some natural tendency of converging on a median viewpoint of a group, which is, which, which kind of kills research. I have the saying that you have a hundred researchers that think the same thing. You essentially have one researcher. It being a researcher means being slightly contrarian all the time, because you

one researcher. It being a researcher means being slightly contrarian all the time, because you want to work on something that is, that is not working yet. And that by default, people don't really believe in and that's being contrarian means like there, there, there's something that a lot of researchers that are extremely brilliant and extremely

hardworking are like, unfortunately missing as something you can call it courage. And some way it is about standing up and saying, let's try to

courage. And some way it is about standing up and saying, let's try to do something different. Let's, uh, let's, let's do something in a way that most people don't believe in yet. And it's an extremely hard thing to do, especially in a world where, experiments are so expensive as they are, and they involve so many

things in the world of, of machine learning experiments, being the cost of Hollywood movies, uh, directing one is like, you know, it does require a lot. And

you don't know, like, similarly in the movies, you don't know if the movie will be successful, but with the big budget, you'd risk it buying stars and, and, and doing the best, the best CGI you can. And, uh, in a world of ML, you also try to try to the risk and bring as many, as many things as you can stack as many advantages on your side. But in the end, experiments

are experiments and they are meant to be going to the unknown and, and either, will be, will be successful or not. But I think, I think like, if I were to say this is like, knowing both systems and theory very well, it is about not following group thing that much, and then, and then having the courage to,

to state that to other people. Well, we always like to end our interviews with a quick fire round where I stuff like all the other questions I couldn't fit in elsewhere into the end here, uh, and get your, your rapid reactions. And so

maybe to start, uh, what's one thing you've changed your mind on in AI in the last year? You know, probably the last thing I, I meaningfully updated on is that I don't think a static model can ever be, can ever be AGI. Like that, that continual learning is a necessary element of, of what we are, what we are pursuing. Is that just because of something it

won't be able to accomplish? Or is it more that, uh, you know, that, that it doesn't just meet the, that the definition of AGI needs to have a continual learning aspect to it? It's mostly about uncovering what our models are still missing. And then in many ways you, go layers and layers deep because, because our

missing. And then in many ways you, go layers and layers deep because, because our models are already good into so many things, but them being as, as, as brilliant as they are, it is clear that without it, it will never feel to me that, that intelligent. It will still be, still be a tool that needs to be supervised by someone who has the ability to continuously learn. Yeah. There's obviously a lot

of AI progress happening in, in spaces we didn't talk about today. Like what's your kind of timeline for a, I don't know, a chat GPT like moment in the robotics space? Probably around two, three years from now. That's pretty bullish.

robotics space? Probably around two, three years from now. That's pretty bullish.

Uh, I feel like everyone's still trying to figure out if there are scaling laws in robotics or if this stuff will, will actually, you know, if there's enough data.

I think like, you know, honestly between you and me, I think things are slightly better than most people realize. There are tons of companies making tons of progress, but, but always the progress needs some time to play out and some more investments to happen. But I think robotics will be doing pretty well over the next few years.

happen. But I think robotics will be doing pretty well over the next few years.

Yeah. And a similar sense on biology. I think, I think biology will take longer.

Why, why longer than robotics? Just thinking from perspective of how much intelligence there is needed, how much precision to, to really manipulate biology successfully. It's a, it's, it's a harder problem and require more fundamental investments to, to, to start to work. Yeah. I guess most like three,

four year olds figure out how to manipulate things in the world, but they're not like world -class biologists. And so, something like that. Yes.

What's one impact of like this continued model improvement we're on that you think maybe we're underestimating or not talking about enough as a society? It's hard to say, but like fundamentally widely deployed work automation will be reality over the coming, coming decades. And

in one hand, we are talking about it, but on the other, I think we are not talking about it enough and seriously enough because, because the world will change, the world will change very drastically from where it is today. If, if, if there are some people who it is still not obvious today, like it is, it is obvious to me at least. And like societal changes are slow. And I think,

I think it will be extremely weird. I think it will be probably painful in some ways, and we should try to figure out how to make it the least painful. But, but we need to, think about how, how, how does the world look

painful. But, but we need to, think about how, how, how does the world look like or where they do, the job market is looking very, very differently than it is today. I mean, I guess related to that one thing I'm interested in, obviously

is today. I mean, I guess related to that one thing I'm interested in, obviously been thinking about is, you know, whether, you know, you're kind of thinking about this has impacted the way at all you, know, you act as a parent, right. And

how you think about, I guess, you know, raising kids and NFA has changed that at all. The, the, the, the interesting thing, and I think I differ here to

at all. The, the, the, the interesting thing, and I think I differ here to most parents, but I don't know, I'm, I'm not, I'm not, my, my, my daughters are very, very young and very, very, very small. So there isn't, there isn't really that much, but I'm definitely not pushing them very, very hard to study these days. It's, it's kind of hard to imagine how, how, how hard you can

these days. It's, it's kind of hard to imagine how, how, how hard you can push seven year old to, to study, but I don't think I will be the parent saying, oh, you have to be the best in maths in your class. I

don't think you will be like, oh, you have to win all those competitions. I

don't think you have to be like, you have to read all those books because like, you know, being a specialist, like when they will be grown up, probably will like, look very different and it doesn't make sense to really, really bet on like, trying to find for your place in the, in the

job markets anymore. And that's in that way. I, I like, there are clearly that ability to think critically will be always important because if you don't, if you don't think for yourself, no one else will even, even, even AI will not, fully advocate for you in the way you want. But I, given, given

how many unknowns there are, I like at the moment, just, just want them to have happy childhood and have, have a good time as much as they can. And

I hope, I hope they will also have, have happy adulthood if we don't, screw up this, this, this AI deployment thing. Yeah. I guess that, you know, everyone likes to talk about the existential risk side have like, has your worries about that gone, gone up or down these past years? I fundamentally think and hope that no human wants the, the, the, the humanity to go extinct,

which is a pretty good, like the incentives are aligned for everyone in the world for the existential risk, not materialize, which, which I, I, I really, I'm not like subscribing to that. it is that easy to just make a model that will, that will, that will, that will paperclip

all of us. If anything, there are enough humans in the loop through all, throughout all the process that just because it is in no one's interest that we will, we will manage to be, to be successful enough. And the people controlling the biggest clusters in the world will be, will be responsible enough because it's also not a good business to kill everyone. So, uh, capitalism is also, is also fully,

aligned here. I think it's much more dystopian and much more worrisome. If we

aligned here. I think it's much more dystopian and much more worrisome. If we

like push entertainment so far, uh, that is more interesting in the real world and the humans will want to live in VR and only, only play virtual games, which is like, but not impossible. I think it's much more, realistic than all of us going extinct, but, but probably, probably similarly bad. Yeah. Now it's like ready player one and WALL -E and all these kind of like depictions of that world. Yeah. So

that, that, that, that to me is much more, much more worrying, but this one, we can only solve ourselves really. And this, this is not an AI problem. This

is a human problem. And what's, what, what, what, what should our preferences be? Hmm.

So that's, that's, that's kind of how I've been, thinking about it. Yeah. I love

that. I guess anything you're, you're comfortable sharing at this point about what, what's next for you? It's still, it's still very early, but I am thinking. Awesome. Well,

for you? It's still, it's still very early, but I am thinking. Awesome. Well,

Jerry, this has been a fascinating conversation. It has been nice chatting with you and thank you very much for inviting me here. I'm Jacob Efron, and you've been listening to Unsupervised Learning, a show where we probe AI's sharpest minds on what's true in AI today, where the space is going and what it means for businesses in the world. I love doing this podcast alongside my day job as the managing director at

world. I love doing this podcast alongside my day job as the managing director at Redpoint, where I've led investments in companies like Lagora or Bridge and physical intelligence. If

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