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Speaker 1

00:00

The following is a conversation with Greg Brockman. He's the co-founder and CTO of OpenAI, a world-class research organization, developing ideas in AI with the goal of eventually creating a safe and friendly artificial general intelligence, 1 that benefits and empowers humanity. OpenAI is not only a source of publications, algorithms, tools, and data sets. Their mission is a catalyst for an important public discourse about our future with both narrow and general intelligence systems.

Speaker 1

00:33

This conversation is part of the Artificial Intelligence Podcast at MIT and beyond. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D. And now, here's my conversation with Greg Brockman. So in high school, and right after you wrote a draft of a chemistry textbook, I saw that.

Speaker 1

00:57

That covers everything from basic structure of the atom to quantum mechanics. So it's clear you have an intuition and a passion for both the physical world with chemistry and now robotics to the digital world with AI, deep learning, reinforcement learning, and so on. Do you see the physical world in the digital world is different and what do you think is the gap a

Speaker 2

01:20

lot of it actually? Boils down to iteration speed right that I think that a lot of what really motivates me is is building things right is the You know think about mathematics for example where you think really hard about a problem you understand it You're right down to this very obscure form. They call it proof, but then this is in humanities Library right?

Speaker 2

01:37

It's there forever. This is some truth that we've discovered. Maybe only 5 people in your field will ever read it, but somehow you've kind of moved humanity forward. And so I actually used to really think that I was going to be a mathematician and then I actually started writing this chemistry textbook.

Speaker 2

01:51

1 of my friends told me, you'll never publish it because you don't have a PhD. So instead I decided to build a website and try to promote my ideas that way. And then I discovered programming and I, you know, that in programming you think hard about a problem, you understand it, you write it down in a very obscure form that we call a program. But then once again, it's in humanity's library, right?

Speaker 2

02:12

And anyone can get the benefit from it. And the scalability is massive. And so I think that the thing that really appeals to me about the digital world is that you can have this insane leverage, right? A single individual with an idea is able to affect the entire planet.

Speaker 2

02:26

And that's something I think is really hard to do if you're moving around physical atoms.

Speaker 1

02:30

But you said mathematics, so if you look at the wet thing over here, our mind, do you ultimately see it as just math, as just information processing? Or is there some other magic as you've seen, if you've seen through biology and chemistry and so on?

Speaker 2

02:46

I think it's really interesting to think about humans as just information processing systems. And that it seems like it's actually a pretty good way of describing a lot of kind of how the world works or a lot of what we're capable of to think that, you know, Again, if you just look at technological innovations over time, that in some ways the most transformative innovation that we've had has been the computer, right? In some ways the internet, you know, what has the internet done, right?

Speaker 2

03:10

The internet is not about these physical cables. It's about the fact that I am suddenly able to instantly communicate with any other human on the planet. I'm able to retrieve any piece of knowledge that in some ways the human race has ever had. And that those are these insane transformations.

Speaker 1

03:26

Do you see our society as a whole, the collective, as another extension of the intelligence of the human being. So if you look at the human being as an information processing system, you mentioned the internet, the networking, do you see us all together as a civilization, as a kind of intelligence system?

Speaker 2

03:41

Yeah, I think this is actually a really interesting perspective to take and to think about that you sort of have this collective intelligence of all of society. The economy itself is this superhuman machine that is optimizing something, right? And it's almost, in some ways, a company has a will of its own, right?

Speaker 2

03:57

That you have all these individuals who are all pursuing their own individual goals and thinking really hard and thinking about the right things to do, but somehow the company does something that is this emergent thing and that is a really useful abstraction. I think that in some ways we think of ourselves as the most intelligent things on the planet and the most powerful things on the planet, but there are things that are bigger than us. There are these systems that we all contribute to. And so I think actually it's interesting to think about if you've read Isaac Asimov's foundation, right, that there's this concept of psychohistory in there, which is effectively this, that if you have trillions or quadrillions of beings, then maybe you could actually predict what that being, that huge macro being will do, and almost independent of what the individuals want.

Speaker 2

04:42

I actually have a second angle on this that I think is interesting, which is thinking about technological determinism. 1 thing that I actually think a lot about with open AI is that we're kind of coming on to this insanely transformational technology of general intelligence that will happen at some point. And there's a question of How can you take actions that will actually steer it to go better rather than worse? And that I think 1 question you need to ask is as a scientist, as an inventor, as a creator, what impact can you have in general?

Speaker 2

05:11

You look at things like the telephone invented by 2 people on the same day. What does that mean? What does that mean about the shape of innovation? And I think that what's going on is everyone's building on the shoulders of the same giants.

Speaker 2

05:21

And so you can kind of, you can't really hope to create something no 1 else ever would. You know, if Einstein wasn't born, someone else would have come up with relativity. You know, he changed the timeline a bit, right? That maybe it would have taken another 20 years, but it wouldn't be that fundamentally humanity would never discover these fundamental truths.

Speaker 1

05:37

So there's some kind of invisible momentum that some people like Einstein or open AI is plugging into that anybody else can also plug into and ultimately that wave takes us into a certain direction. That's what you mean by digitalism.

Speaker 2

05:51

That's right. That's right. And you know, this kind of seems to play out in a bunch of different ways that there's some exponential that is being ridden and that the exponential itself, which 1 it is changes.

Speaker 2

06:00

Think about Moore's law, an entire industry set its clock to it for 50 years. How can that be? How is that possible? And yet, somehow, it happened.

Speaker 2

06:09

And so I think you can't hope to ever invent something that no 1 else will. Maybe you can change the timeline a little bit. But if you really want to make a difference, I think that the thing that you really have to do, the only real degree of freedom you have is to set the initial conditions under which a technology is born. And so you think about the internet, right?

Speaker 2

06:26

That there are lots of other competitors trying to build similar things, and the internet won. And that the initial conditions were that it was created by this group that really valued people being able to be, anyone being able to plug in this very academic mindset of being open and connected. And I think that the internet for the next 40 years really played out that way. Maybe today things are starting to shift in a different direction, but I think that those initial conditions were really important to determine the next 40 years worth of progress.

Speaker 1

06:54

That's really beautifully put. So another example that I think about, I recently looked at it. I looked at Wikipedia, the formation of Wikipedia.

Speaker 1

07:03

And I wonder what the internet would be like if Wikipedia had ads. You know, there's a interesting argument that why they chose not to make it, put advertisement on Wikipedia. I think it's, I think Wikipedia is 1 of the greatest resources we have on the internet. It's extremely surprising how well it works and how well it was able to aggregate all this kind of good information.

Speaker 1

07:25

And essentially the creator of Wikipedia, I don't know, there's probably some debates there, but set the initial conditions and how it carried itself forward. That's really interesting. So the way you're thinking about AGI or artificial intelligence is you're focused on setting the initial conditions for the progress. That's right.

Speaker 1

07:42

That's powerful. Okay, so look into the future. If you create an AGI system, like 1 that can ace the Turing test, natural language, what do you think would be the interactions you would have with it? What do you think are the questions you would ask?

Speaker 1

07:57

Like, what would be the first question you would ask it,

Speaker 2

08:00

her, him? That's right. I think that at that point, if you've really built a powerful system that is capable of shaping the future of humanity The first question that you really should ask is how do we make sure that this plays out?

Speaker 2

08:11

Well, and so that's actually the first question that I would ask A powerful AGI system is

Speaker 1

08:17

so you wouldn't ask your colleague. You wouldn't ask like Ilya you would ask the AGI system

Speaker 2

08:22

Oh, we've already had the conversation with Ilya right and everyone here And so you want as many perspectives and a piece of wisdom as you can for answering this question So I don't think you necessarily defer to whatever your powerful system tells you, but you use it as 1 input to try to figure out what to do. I guess fundamentally what it really comes down to is if you've built something really powerful and you think about, for example, the creation of—shortly after the creation of nuclear weapons, the most important question in the world was, what's the world order going to be like? How do we set ourselves up in a place where we're going to be able to survive as a species?

Speaker 2

08:58

With AGI, I think the question is slightly different. There is a question of, how do we make sure that we don't get the negative effects. But there's also the positive side, right? You imagine that, you know, like what won't AGI be like?

Speaker 2

09:09

Like what will it be capable of? And I think that 1 of the core reasons that an AGI can be powerful and transformative is actually due to technological development. If you have something that's capable as a human and that it's much more scalable, that you absolutely want that thing to go read the whole scientific literature and think about how to create cures for all the diseases. You want it to think about how to go and build technologies to help us create material abundance and to figure out societal problems that we have trouble with, like how are we supposed to clean up the environment?

Speaker 2

09:40

Maybe you want this to go and invent a bunch of little robots that will go out and be biodegradable and turn ocean debris into harmless molecules. And I think that that positive side is something that I think people miss sometimes when thinking about what an AGI will be like. And so I think that if you have a system that's capable of all of that, you absolutely want its advice about how do I make sure that we're using your capabilities in a positive way for humanity.

Speaker 1

10:09

So what do you think about that psychology that looks at all the different possible trajectories of an AGI system, many of which, perhaps the majority of which are positive, and nevertheless focuses on the negative trajectories. I mean, you get to interact with folks, you get to think about this, maybe within yourself as well. You look at Sam Harris and so on.

Speaker 1

10:30

It seems to be, sorry to put it this way, but almost more fun to think about the negative possibilities. Whatever that's deep in our psychology, what do you think about that? And how do we deal with it? Because we want AI to help us.

Speaker 2

10:44

So I think there's kind of 2 problems entailed in that question. The first is more of the question of how can you even picture what a world with a new technology will be like? Now imagine we're in 1950 and I'm trying to describe Uber to someone.

Speaker 1

11:01

Ha ha ha. Apps and the internet. Yeah, I mean, that's going to be extremely complicated, but it's imaginable.

Speaker 2

11:10

It's imaginable, right? But, and now imagine being in 1950 and predicting Uber, right, and you need to describe the internet, you need to describe GPS, you need to describe the fact that everyone's going to have this phone in their pocket. And so I think that just the first truth is that it is hard to picture how a transformative technology will play out in the world.

Speaker 2

11:31

We've seen that before with technologies that are far less transformative than AGI will be. And so I think that 1 piece is that it's just even hard to imagine and to really put yourself in a world where you can predict what that positive vision would be like. And I think the second thing is that it is, I think it is always easier to support the negative side than the positive side. It's always easier to destroy than create and you know, less in a, in a, in a, in a physical sense and more just in an intellectual sense, right?

Speaker 2

12:03

Because I think that with creating something, you need to just get a bunch of things right. And to destroy, you just need to get 1 thing wrong. And so I think that what that means is that I think a lot of people's thinking dead ends as soon as they see the negative story. But that being said, I actually have some hope.

Speaker 2

12:20

I think that the positive vision is something that I think can be, is something that we can talk about. I think that just simply saying this fact of, yeah, there's positives, there's negatives, Everyone likes to dwell on the negative. People actually respond well to that message and say, huh, you're right, there's a part of this that we're not talking about, not thinking about. And that's actually something that's, I think, really been a key part of how we think about AGI at OpenAI.

Speaker 2

12:46

You can kind of look at it as like, okay, like OpenAI talks about the fact that there are risks, and yet they're trying to build this system. Like, how do

Speaker 1

12:53

you square those 2 facts? So do you share the intuition that some people have, I mean, from Sam Harris to even Elon Musk himself, that it's tricky as you develop AGI to keep it from slipping into the existential threats, into the negative. What's your intuition about how hard is it to keep AI development on the positive track?

Speaker 1

13:19

What's your intuition there?

Speaker 2

13:20

To answer that question, you can really look at how we structure open AI. So we really have 3 main arms. We have capabilities, which is actually doing the technical work and pushing forward what these systems can do.

Speaker 2

13:31

There's safety, which is working on technical mechanisms to ensure that the systems we build are aligned with human values. And then there's policy, which is making sure that we have governance mechanisms, answering that question of, well, whose values? And so I think that the technical safety 1 is the 1 that people kind of talk about the most, right? You talk about like think about you know All of the dystopic AI movies a lot of that is about not having good technical safety in place And what we've been finding is that you know I think that actually a lot of people look at the technical safety problem and think it's just intractable.

Speaker 2

14:05

Right, this question of what do humans want, how am I supposed to write that down, can I even write down what I want? No way. And then they stop there. But The thing is we've already built systems that are able to learn things that humans can't specify.

Speaker 2

14:20

You know, even the rules for how to recognize if there's a cat or a dog in an image. Turns out it's intractable to write that down, and yet we're able to learn it. And that what we're seeing with systems we build at OpenAI, and they're still in early proof of concept stage, is that you are able to learn human preferences. You're able to learn what humans want from data.

Speaker 2

14:38

And so that's kind of the core focus for our technical safety team. And I think that there actually we've had some pretty encouraging updates in terms of what we've been able to make work. So you have an intuition and a hope that from data, you know, looking at the value alignment problem, from data we can build systems that align with the collective better angels of our nature. So align with the ethics and

Speaker 1

15:03

the morals of human beings.

Speaker 2

15:04

To even say this in a different way, I mean think about how do we align humans, right? Think about like a human baby can grow up to be an evil person or a great person. And a lot of that is from learning from data, right?

Speaker 2

15:15

That you have some feedback as a child is growing up, they get to see positive examples. And so I think that just like the only example we have of a general intelligence that is able to learn from data to align with human values and to learn values, I think we shouldn't be surprised that we can do the same sorts of techniques or whether the same sort of techniques end up being how we solve value alignment for AGIs.

Speaker 1

15:40

So let's go even higher. I don't know if you've read the book Sapiens, but there's an idea that as a collective, as us human beings, we kind of develop together ideas that we hold. There's no, in that context, objective truth.

Speaker 1

15:57

We just kind of all agree to certain ideas and hold them as a collective. Did you have a sense that there is, in the world of good and evil, do

Speaker 2

16:05

you have a sense that to the first approximation there are some things that are good and that you could teach systems to behave to be good? So I think that this actually blends into our third team, which is the policy team. And this is the 1, the aspect I think people really talk about way less than they should.

Speaker 2

16:25

Because imagine that we build super powerful systems that we've managed to figure out all the mechanisms for these things to do whatever the operator wants. The most important question becomes, who's the operator, what do they want, and how is that going to affect everyone else? And I think that this question of what is good, what are those values, I mean, I think you don't even have to go to those very grand existential places to realize how hard this problem is. You just look at different countries and cultures across the world and that there's a very different conception of how the world works and what kinds of ways that society wants to operate.

Speaker 2

17:03

And so I think that the really core question is actually very concrete. And I think it's not a question that we have ready answers to, right? It's how do you have a world where all the different countries that we have, United States, China, Russia, and you know, the hundreds of other countries out there are able to continue to not just operate in the way that they see fit, but in the world that emerges in these where you have these very powerful systems operating alongside humans ends up being something that empowers humans more, that makes human existence be a more meaningful thing, and that people are happier and wealthier and able to live more fulfilling lives. It's not an obvious thing for how to design that world once you have that very powerful system.

Speaker 2

17:53

So if we take a little step back, and we're having a fascinating conversation, and OpenAI is in many ways a tech leader in the world, and yet we're thinking about these big existential questions, which is fascinating and really important. I think you're a leader in that space, and that's a really important space, of just thinking how AI affects society in a big picture view. So Oscar Wilde said, we're all

Speaker 1

18:16

in the gutter, but some of us are looking at the stars, and I think OpenAI has a charter that looks to the stars, I would say, to create intelligence, to create general intelligence, make it beneficial, safe, and collaborative. Can you Tell me how that came about, how a mission like that and the path to creating a mission like that at OpenAI was founded.

Speaker 2

18:39

Yeah, so I think that in some ways it really boils down to taking a look at the landscape. All right, So if you think about the history of AI, that basically for the past 60 or 70 years, people have thought about this goal of what could happen if you could automate human intellectual labor. Imagine you could build a computer system that could do that.

Speaker 2

18:59

What becomes possible? We have a lot of sci-fi that tells stories of various dystopias and, you know, increasingly you have movies like Herd that tell you a little bit about maybe more of a little bit utopic vision. You think about the impacts that we've seen from being able to have bicycles for our minds and computers, and that I think that the impact of computers and the internet has just far outstripped what anyone really could have predicted. And so I think that it's very clear that if you can build an AGI, it will be the most transformative technology that humans will ever create.

Speaker 2

19:34

And so what it boils down to then is a question of, well, is there a path? Is there hope? Is there a way to build such a system? And I think that for 60 or 70 years that people got excited and that ended up not being able to deliver on the hopes that people had pinned on them.

Speaker 2

19:51

And I think that then, after 2 winters of AI development, that people, I think, kind of almost stopped daring to dream that really talking about AGI or thinking about AGI became almost this taboo in the community. But I actually think that people took the wrong lesson from AI history. And if you look back, starting in 1959 is when the Perceptron was released. And this is basically 1 of the earliest neural networks.

Speaker 2

20:17

It was released to what was perceived as this massive overhype. So in the New York Times in 1959, you have this article saying that, you know, the Perceptron will 1 day recognize people, call out their names, instantly translate speech between languages. And people at the time looked at this and said, your system can't do any of that, and basically spent 10 years trying to discredit the whole perceptron direction and succeeded. And all the funding dried up and people kind of went in other directions.

Speaker 2

20:45

And in the 80s there was a resurgence. And I'd always heard that the resurgence in the 80s was due to the invention of back propagation and these algorithms that got people excited. But actually the causality was due to people building larger computers. That you can find these articles from the 80s saying that the democratization of computing power suddenly meant that you could run these larger neural networks.

Speaker 2

21:04

And then people started to do all these amazing things. Back propagation algorithm was invented. And you know, the neural nets people were running were these tiny little like 20 neuron neural nets, right? Like what are you supposed to learn with 20 neurons?

Speaker 2

21:15

And so of course they weren't able to get great results. And it really wasn't until 2012 that this approach that's almost the most simple natural approach that people had come up with in the 50s, right? In some ways even in the 40s before there were computers with the Pitts-McCulloh neuron, suddenly this became the best way of solving problems. I think there are 3 core properties that deep learning has that I think are very worth paying attention to.

Speaker 2

21:44

The first is generality. We have a very small number of deep learning tools, SGD, deep neural net, maybe some, you know, RL. And it solves this huge variety of problems, speech recognition, machine translation, game playing, all of these problems, small set of tools. So there's the generality.

Speaker 2

22:02

There's a second piece, which is the competence. You want to solve any of those problems, throw out 40 years worth of normal computer vision research, replace it with a deep neural net, it's going to work better. And there's a third piece, which is the scalability, right? That 1 thing that has been shown time and time again is that you, if you have a larger neural network, throw more compute, more data at it, it will work better.

Speaker 2

22:25

Those 3 properties together feel like essential parts of building a general intelligence. Now it doesn't just mean that if we scale up what we have, that we will have an AGI, right? There are clearly missing pieces, there are missing ideas. We need to have answers for reasoning.

Speaker 2

22:40

But I think that the core here is that for the first time, it feels that we have a paradigm that gives us hope that general intelligence can be achievable. And so as soon as you believe that, everything else comes into focus. If you imagine that you may be able to, and that the timeline I think remains uncertain, but I think that certainly within our lifetimes and possibly within a much shorter period of time than people would expect, if you can really build the most transformative technology that will ever exist, you stop thinking about yourself so much. You start thinking about just like, how do you have a world where this goes well?

Speaker 2

23:16

And that you need to think about the practicalities of how do you build an organization and get together a bunch of people and resources, and to make sure that people feel motivated and ready to do it. But I think that then you start thinking about, well, what if we succeed? And how do we make sure that when we succeed, that the world is actually the place that we want ourselves to exist in, and almost in the Rawlsian Vale sense of the word. And so that's kind of the broader landscape.

Speaker 2

23:43

And OpenAI was really formed in 2015 with that high-level picture of AGI might be possible sooner than people think and that we need to try to do our best to make sure it's going to go well. And then we spent the next couple years really trying to figure out what does that mean, how do we do it. And you know I think that typically with a company you start out very small, so you and a co-founder and you build a product, you get some users, you get a product market fit, you know then at some point you raise some money, you hire people, you scale and then you know down the road then the big companies realize you exist and try to kill you. And for OpenAI, it was basically everything in exactly the opposite order.

Speaker 1

24:25

Let me just pause for a second. You said a lot of things, and let me just admire the jarring aspect of what OpenAI stands for, which is daring to dream. I mean, you said it's pretty powerful.

Speaker 1

24:36

It caught me off guard because I think that's very true. The step of just daring to dream about the possibilities of creating intelligence in a positive and a safe way. But just even creating intelligence is a much needed refreshing catalyst for the AI community. So that's the starting point.

Speaker 1

24:58

Okay, so then formation of open AI.

Speaker 2

25:02

I would just say that when we were starting OpenAI, that kind of the first question that we had is, is it too late to start a lab with a bunch of the best people? Right, is that even possible?

Speaker 1

25:13

That was an actual question.

Speaker 2

25:15

That was the core question of, we had this dinner in July of 20 2015 and there's that was that was really what we spent the whole time talking about and you know because it's You think about kind of where AI was is that it transitioned from being an academic pursuit to an industrial pursuit? And so a lot of the best people were in these big research labs and that we wanted to start our own 1 that no matter how much resources we could accumulate would be pale in comparison to the big tech companies. We knew that and there was a question of are we going to be actually able to get this thing off the ground?

Speaker 2

25:48

You need a critical mass. You can't just do you and a co-founder, build a product. You really need to have a group of 5 to 10 people. And we kind of concluded it wasn't obviously impossible.

Speaker 2

25:59

So it seemed worth trying.

Speaker 1

26:02

Well, you're also a dreamer, so who knows, right? That's right. Okay, so speaking of that, competing with the big players, let's talk about some of the some of the tricky things as you think through this process of growing of seeing how you can develop these systems at a scale that competes.

Speaker 1

26:22

So you recently formed OpenAI LP, a new cap profit company that now carries the name OpenAI. So OpenAI is now this official company. The original non-profit company still exists and carries the OpenAI non-profit name. So can you explain what this company is, what the purpose of its creation is, and how did you arrive at the decision to create it.

Speaker 2

26:48

OpenAI, the whole entity, and OpenAI LP as a vehicle, is trying to accomplish the mission of ensuring that artificial general intelligence benefits everyone. And the main way that we're trying to do that is by actually trying to build general intelligence to ourselves and make sure the benefits are distributed to the world. That's the primary way.

Speaker 2

27:07

We're also fine if someone else does this, right? It doesn't have to be us. If someone else is going to build an AGI and make sure that the benefits don't get locked up in 1 company or, you know, 111, with 1 set of people, like we're actually fine with that. And so those ideas are baked into our charter which is kind of the the foundational document that are that describes kind of our values and how we operate It's also really baked into the structure of OpenAILP.

Speaker 2

27:36

The way that we've set up OpenAILP is that in the case where we succeed, if we actually build what we're trying to build, Then investors are able to get a return And but that return is something that is capped And so if you think of a GI in terms of the value that you could really create You're talking about the most transformative technology ever created it's going to create orders of magnitude more value than any existing company and that all of that value will be owned by the world, like legally titled to the nonprofit to fulfill that mission. And so that's the structure.

Speaker 1

28:12

So the mission is a powerful 1, and it's 1 that I think most people would agree with. It's how we would hope AI progresses. And so how do you tie yourself to that mission?

Speaker 1

28:25

How do you make sure you do not deviate from that mission? That other incentives that are profit-driven don't interfere with the mission?

Speaker 2

28:36

So this was actually a really core question for us for the past couple of years, because I'd say that the way that our history went was that for the first year, we were getting off the ground. We had this high-level picture, but we didn't know exactly how we wanted to accomplish it. And really 2 years ago is when we first started realizing in order to build AGI, we're just going to need to raise way more money than we can as a nonprofit.

Speaker 2

29:00

And you know, we're talking many billions of dollars. And so the first question is, how are you supposed to do that and stay true to this mission? And we looked at every legal structure out there and concluded none of them are quite right for what we wanted to do. And I guess it shouldn't be too surprising if you're going to do some like crazy unprecedented technology that you're going to have to come with some crazy unprecedented structure to do it in.

Speaker 2

29:20

And a lot of our conversation was with people at OpenAI, the people who really joined because they believe so much in this mission, and thinking about how do we actually raise the resources to do it and also stay true to what we stand for. And the place you've got to start is to really align on what is it that we stand for, right? What are those values? What's really important to us?

Speaker 2

29:41

And so I'd say that we spent about a year really compiling the opening charter and that determines and if you even look at the first line item in there, it says that, look, we expect we're gonna have to marshal huge amounts of resources, but we're going to make sure that we minimize conflict of interest with the mission, and that kind of aligning on all of those pieces was the most important step towards figuring out how do we structure a company that can actually raise the resources to do what we need to do. I imagine OpenAI, the decision to create OpenAILP was a really difficult 1 and there was

Speaker 1

30:16

a lot of discussions as you mentioned for a year and there was different ideas perhaps detractors within OpenAI, sort of different paths that you could have taken. What were those concerns? What were the different paths considered?

Speaker 1

30:31

What was that process of making that decision like?

Speaker 2

30:34

Yep, so if you look actually at the OPAI charter, that there's almost 2 paths embedded within it. There is, we are primarily trying to build AGI ourselves, but we're also okay if someone else does it. And this is a weird thing for a company.

Speaker 1

30:48

It's really interesting actually. Yeah. There is an element of competition that you do wanna be the 1 that does it, but at the same time you're okay if somebody else doesn't.

Speaker 1

30:58

We'll talk about that a little bit, that trade off, that dance, that's really interesting.

Speaker 2

31:02

And I think this was the core tension as we were designing OpenAI LP and really the OpenAI strategy, is how do you make sure that both you have a shot at being a primary actor, which really requires building an organization, raising massive resources, and really having the will to go and execute on some really, really hard vision. You need to really sign up for a long period to go and take on a lot of pain and a lot of risk. To do that, normally you just import the startup mindset.

Speaker 2

31:31

You think about, okay, how do we out-execute everyone? You have this very competitive angle. But you also have the second angle of saying that, well, the true mission isn't for OpenAI to build AGI. The true mission is for AGI to go well for humanity.

Speaker 2

31:45

And so how do you take all of those first actions and make sure you don't close the door on outcomes that would actually be positive in fulfilled mission? And so I think it's a very delicate balance, right? I think that going 100% 1 direction or the other is clearly not the correct answer. And so I think that even in terms of just how we talk about open AI and think about it, there's just like 1 thing that's always in the back of my mind is to make sure that we're not just saying open AI's goal is to build AGI, right?

Speaker 2

32:13

That it's actually much broader than that, right? That first of all, you know, it's not just AGI, it's safe AGI that's very important. But secondly, our goal isn't to be the ones to build it, our goal is to make sure it goes well for the world. And so I think that figuring out how do you balance all of those and to get people to really come to the table and compile the like a single document that that encompasses all that wasn't trivial.

Speaker 1

32:37

So part of the challenge here is your mission is, I would say, beautiful, empowering, and a beacon of hope for people in the research community and just people thinking about AI. So your decisions are scrutinized more than, I think, a regular profit-driven company. Do you feel the burden of this in the creation of the charter and just in the way you operate?

Speaker 1

33:00

Yes. So why do you lean into the burden by creating such a charter? Why not to keep it quiet?

Speaker 2

33:10

I mean, it just boils down to the, to the mission, right? Like, like I'm here and everyone else is here because we think this is the most important mission. Dare to dream.

Speaker 1

33:18

All right, so do you think you can be good for the world or create an AGI system that's good when you're a for-profit company? From my perspective, I don't understand why profit interferes with positive impact on society. I don't understand why Google, that makes most of its money from ads, can't also do good for the world, or other companies, Facebook, anything.

Speaker 1

33:47

I don't understand why those have to interfere. Profit isn't the thing, in my view, that affects the impact of a company. What affects the impact of the company is the charter, is the culture, is the people inside, and profit is the thing that just fuels those people. So what are your views there?

Speaker 2

34:08

Yeah, so I think that's a really good question, and there's some real longstanding debates in human society that are wrapped up in it. The way that I think about it is just think about what are the most impactful non-profits in the world? What are the most impactful for-profits in the world?

Speaker 1

34:26

Right. It's much easier to list the for-profits.

Speaker 2

34:29

That's right. And I think that there's some real truth here that the system that we set up the system for kind of how you know today's world is organized is 1 that that really allows for huge impact and that that you know kind of part of that is that you need to be the the you know for for profits are are self-sustaining and able to to kind of, you know, build on their own momentum. I think that's a really powerful thing.

Speaker 2

34:52

It's something that when it turns out that we haven't set the guardrails correctly causes problems right think about logging companies that go into forest, you know, the rainforest that's really bad. We don't want that. And it's actually really interesting to me that kind of this, this question of how do you get positive benefits out of a for-profit company? It's actually very similar to how do you get positive benefits out of an AGI, right?

Speaker 2

35:15

That you have this like very powerful system It's more powerful than any human and is kind of autonomous in some ways You know It's superhuman in a lot of axes and somehow you have to set the guardrails to get good things to happen But when you do the benefits are massive And so I think that that when when I think about nonprofit versus for-profit, I think just not enough happens in non-profits. They're very pure, but it's just hard to do things there. In for-profits in some ways, too much happens. But If kind of shaped in the right way, it can actually be very positive.

Speaker 2

35:47

And so with open ILP, we're picking a road in between. Now the thing that I think is really important to recognize is that the way that we think about open ILP is that in the world where AGI actually happens, right? In a world where we are successful, we build the most transformative technology ever, the amount of value we're going to create will be astronomical. Then in that case, the cap that we have will be a small fraction of the value we create.

Speaker 2

36:15

The amount of value that goes back to investors and employees looks pretty similar to what would happen in a pretty successful startup. And that's really the case that we're optimizing for, right? That we're thinking about in the success case, making sure that the value we create doesn't get locked up. And I expect that in other, you know, for-profit companies that it's possible to do something like that.

Speaker 2

36:37

I think it's not obvious how to do it, right? And I think that as a for-profit company, you have a lot of fiduciary duty to your shareholders and that there are certain decisions that you just cannot make. In our structure, we've set it up so that we have a fiduciary duty to the charter. That we always get to make the decision that is right for the charter, rather than, even if it comes at the expense of our own stakeholders.