Stanford AI Club: Chamath on How to Win in the AI Era

Tanvir Bhathal 0:00 Thank you guys for coming. Uh, today, we have a very special guest, who I think needs realistically no introduction, but we're gonna introduce him regardless. Um, Chamath is the founder and CEO of 8090, an AI-native company focused on rebuilding and modernizing enterprise solutions. Uh, prior to 8090, Chamath was the founder and CEO of Social Capital, where he backed companies such as Slack, SoFi, Groq, et cetera. The list goes on. Uh, before that, he was an ex-

Chamath Palihapitiya 0:26 SpaceX.

Tanvir Bhathal 0:27 SpaceX, yeah. It's about a, it's about IPO, um. [laughs]

Jason Zhang 0:32 [laughs]

Tanvir Bhathal 0:34 Uh, before that, an early executive at Facebook, where he scaled the company to the size it is today, um, and a co-host of the All-In Podcast. Chamath, it's a pleasure to have you here today. Um, thank you for taking the time to join us.

Chamath Palihapitiya 0:45 Thanks. I went to school with his mom. And I just met his mom.

Jason Zhang 0:52 [laughs]

Chamath Palihapitiya 0:54 It's all full circle.

Tanvir Bhathal 0:55 Small world.

Chamath Palihapitiya 0:56 Collecting them like infinity.

Tanvir Bhathal 0:57 Yeah. [laughs] All right. Uh, so as a brief kind of overview of how the structure's gonna go today, guys, uh, we have some questions that we collected that you guys kind of entered, um, and then we'll open up the floor to some audience questions. Um, so just if you have any questions, note them down, be ready and- later. Uh, so Jason, you wanna kick it off with the first question?

Jason Zhang 1:19 Yeah, sure. I guess, like, the first one, you've had a very unconventional career in tech, I think we'd all say. Obviously, with the beginnings as an immigrant, and then through Facebook, VC, um, and then now to founding. Curious just for the broader audience, but, like, how did your early life shape the way you think, the way you think about risk, ownership, and building?

Chamath Palihapitiya 1:39 Okay, well, f- first of all, thank you for saying it this way because, um, a lot of people sometimes, like, wonder, like, what's the motivation? I think, like, uh, Kobe Bryant actually had the best answer for this, which is the, the winning and the losing is sort of like a by-product of what it feels like to be in the moment, and in the moment, you have the opportunity to, like, practice a craft, get better at something, and mostly prove something to yourself. There are many other ways in which people say that. Some people say that it's how you play an infinite game. Like, if you play a finite game, you and I are competing for the scarce resource of a promotion or for venture capital or for some external validation, some award, some title, um, and that it's finite, right? And that's a proposition where if I'm winning, you're losing, and vice versa. The infinite game is more where you're just like, it's like this constant trajectory. What I've accidentally stumbled into is an infinite game. Where I started was playing finite games, where I was competing with everybody around me. So now if you ask me what my motivation is, it's like I think that I've done a couple of really interesting things. When I was an executive, I was mostly asked by somebody else, in that case Zuck, to take on a task, and I had to be a trusted steward, but fundamentally, it was his show, and I was a supporting actor. And then I left, and then I started an investment business, which was a completely orthogonal thing. And at Facebook, I was really successful, I think, um, and probably could have just stopped. But when I move- when I went to Social Capital, I had to kind of find a way of betting on other people and managing the capital of other people. So, like, and when you take money from other people, I don't know about how you guys would react to it, but it was profoundly stressful for me because the people that gave me money, like Mayo Clinic, the pension fund of XYZ, I felt so bad about this idea that I could lose money for these folks. And so I really took that job seriously, and then I wanted to prove what it meant to take risk and be an investor. I did that job well. The great thing about 8090 is it's my first real shot. I've started many projects. I've gotten them off the ground. I'll be an executive chairman. But it's my first real shot at doing the one thing that I've never done, which is I start something from scratch, the white paper, clean sheet design, this is what I think we should do, and then build it, and I need to see if I can help make it successful. And if I do that, I feel like I've, I will have done everything professionally that is a challenge. How do you build a trillion-dollar company? Check. How do you build a multi-billion dollar investor firm? Check. Can you start and scale and create something that's many billions? I don't know. But the reason I can be motivated is that I'm framing at it as a challenge of who I am as a person. Long-winded answer, but it is such a key to your success. I wish I knew that when I was younger. So many times I blew myself up. So many times I self-sabotaged myself. So many times I just, I fucked up the core essence of what the opportunity was. And the problem with places like this is it can really trick you. I went to an equivalent place like this, not nearly as prestigious, but still quite prestigious in Canada called the University of Waterloo. You're surrounded by the smartest kids. In that case, it's Canadian for the most part, and from, you know, Hong Kong. That's what it was. [laughs] Canadians and Hong Kong. Um. But they're little geniuses. And, uh, when you're surrounded by geniuses, um, it's easy to get very outwardly focused and competitive with one another, and it is a huge distraction.

Jason Zhang 5:35 So you were saying earlier that you shifted from this finite reward system to this infinite reward system where instead of competing with other people for finite resources, you're now in your own lane, if I'm understanding correctly. What were some of the key kind of realizations or practices? Like, how do you, how do you get to that stage? And I feel like a lot of us here grew up in very similar circumstances.

Chamath Palihapitiya 5:56 You have to run into the wall really hard at high speed. Maybe there are people that are more evolved than I am- I think women are more evolved than maybe they can realize, is men are stupid, so you're gonna... You have to, you have to just really run into it face first. And, and the reason is because one of the most shocking things that you can do is not have an org chart. We don't have an org chart. And so, you know, you drop these people into this soup. It's already stressful.

Tanvir Bhathal 6:25 Yeah.

Chamath Palihapitiya 6:26 That's one way. There's nobody... So, you know, we have, like, nice, kind people that will- that are also struggling, and so there's camaraderie in the struggle, and they're willing to help you, but they have just as little of a fucking clue as you do. But that's- there's something beautiful in that because now you're, again, you have to just dismantle all of this traditional way of thinking. Two, you under hire massively. If you have 80 humans worth of work, having 40 is an incredibly interesting observational experiment. What happens? And now you combine it with this other pressure of there is no hierarchy to help you. What happens? And eventually what you see is all of these traditional things melt away. Traditional answers to questions melt away. If this, then that all melts away, and all of a sudden, people are forced to just think from first principles. That is a beautiful place to be, especially early in your career.

Tanvir Bhathal 7:27 That's where the magic happens.

Chamath Palihapitiya 7:28 That's where the magic happens. You become a wholly different person. So I tell people, "If you find one of these places," Anthropic is such a place. OpenAI is such a place. Facebook was such a place. Google was such a place. Maybe there's places, pockets. No, no, no, but maybe there are pockets of that that are like that now. SpaceX is such a place. And you get into those places because it takes you into that flow state. There's so much work, there's so much pressure, but it strips away the traditional thinking. You have no time for arrogance. Like, if you talk to, like, Mark Giancola at SpaceX, one of the most humble human beings, and you'd be like, "How the fuck is this guy?" And he's like, "What can we do better? What can we do?" I was with somebody this weekend, and we were just commenting on Mark. It's always the question is, what can I do better? What is SpaceX not doing well enough? When you're a $2 trillion company and the CTO asks those kinds of questions, it's a living embodiment of, like, what it means to be in that flow state. But could you imagine the stuff that he's juggling, the pressure that he must be under? [laughs] And so he has no time to be arrogant. That's what you want. You wanna find places like that, and you wanna run to those places. They're incredibly, um, challenging in some other ways, but they're incredibly unique.

Tanvir Bhathal 8:42 I just wanna keep us on track. Um, you mentioned 8090. This is obviously, like, a newer company of yours. What was kind of the rationale? Like, what was the, the catalyst of why you started 8090? What was something that you saw, some inefficiency-

Chamath Palihapitiya 8:55 So-

Tanvir Bhathal 8:55 ... that made you realize that I'm not gonna start another SaaS app or something?

Chamath Palihapitiya 8:59 Look, the way, the way that my mind works is I always think in terms of is there a framework and a set of rules that allow me to abstract what is happening and give it a context? The most important context in AI is all the way back to how the internet initially developed, and the most important context of that is this thing called the OSI reference model. And if you just Google that very quickly on your phone, what you're gonna see is this image of a seven-layer cake. And at every layer, what is the most important thing to acknowledge is at every layer of that stack, you have seen hundreds of billions to trillions of dollars of value created. So even back then, when those people were imagining what the layers of abstraction would be, they got it so right. That thing was so valuable to me because I used to refer to that all the time. When I would meet a new company and I was thinking, "Is this an interesting company?" I would go back to this OSI model and I would say, "Does this logically fit in any one of these things? Where does it fit? What will it do?" It wasn't the f- you know, the ultimate question, but it was a very important starting point. So I have a framework of that for AI, and in that framework, I spent the first number of years purely in the silicon. Helped this thing called Groq get off the ground, and initially, how 8090 started was I thought that what we needed to do was essentially a transpiler, where you could take essentially CUDA and allow it to get to Tranium, Inferentia, AMD, all of this stuff. That's how it initially started. But it turned out to be wrong. I'd spent, like, a bunch of years, like, kind of working and helping the team at Groq. Then I spent a handful of years completely pivoting and doing what I call the fulcrum assets of AI. What are those? It's Prismatic LFP and it's actuation. Why? Because if you look inside of a digital AI stack, the most important thing that it lacks is power. The most critical thing for power is both the power generation and the power storage. The power storage, fundamentally iron. Where's the OpenAI guy? I mean, where's the Anthropic guy? He was here. Uh, iron, and then you'll s- Look, it'll be Prismatic LFP. So I went and I helped start a bunch of things there. If you look at physical AI, the biggest Achilles' heel that people will have is the actual actuation. You know, how do you get the sensors to work? How do you kind of like pass the num-match test, as they call it? How do you do that? The only answer for that is rare earths, and

Chamath Palihapitiya 11:31 so I spend a bunch of time in that. Then when I came back, back to my reference model, I was looking around, started 8090, thought it was a transpilation problem, realized very quickly it was not, and then it kind of occurred. We have done so much, and I, and I hate to offend anybody here when I say this. We have done so much in AI, okay? We have. But we are training models to understand patterns that we have seen. We do not think from first principles. It's token, and then the prediction of the next token is prediction of the next token. And I think that that's f- it's, it's still an incredible leap that we've created, or that, you know, OpenAI, Anthropic, xAI, that they have created. But what is missing? What's missing is being able to translate that in two dimensions. Dimension number one is long horizon tasks are fundamentally still a joke. It doesn't work. And I don't care what anybody says, don't show me a stupid eval. Don't tell me about some dumb script you ran for, you know, 48 hours. That's all bullshit. Long horizon tasks are not well-handled. They just don't work. And second is complex problems also don't work. They are not well-addressed and they're not well-handled. Why is this important? If AI looks like any other technology, we're gonna go through the rise, right? The initial hype cycle. We're gonna see a natural contraction because somehow, somewhere, something is going to fail. We are all gonna see this. And then that's what's called the trough of disillusionment, I think, the business... The MBA folks will confirm if that's true.

Speaker 13:05 Mm-hmm.

Chamath Palihapitiya 13:06 And then you see the slow gradual uptake of the real final solution. Happened in the internet, happened before. It's happened in many, many, many cases. The problem with this is that we, though, are spending hundreds and hundreds of billions to trillions of dollars to try to figure out how to cross this chasm. So what do we do? If we don't figure this out, people will hit the, hit this trough of disillusionment and say, "This was a joke." I think we need to be able to take I- AI into very complicated environments and make it work. So what's my solution to that? I think at a very basic level, you have to have a symbolic space that guides the embedded space, so maybe a little less kind of jar- jargony. What is a secret of a company? Like, what's the secret inside of Google? What's the secret inside of Uber? What's the secret inside of TikTok? All of that. But, so, but what is that? What that is, is a symbolic representation. It's when you're in a meeting and somebody says, "Well, you know, here's how we should do the, you know, page rank this way. Here's how we should build GFS in big T." It's like it's all in the symbolic space of English. It's all these, like, secrets that a company has that describes what they should do. So I was thinking to myself, like, the most valuable thing we could do is build a massive honeypot for the symbolic space, for English, like, for the requirements, for all the secrets, for all of the... Like, if you go, go... When you guys go recognize inside a company, at some point, if your company does poorly, they'll bring in McKinsey.

Speaker 14:47 [laughs]

Chamath Palihapitiya 14:48 And then-

Speaker 14:48 [laughs]

Chamath Palihapitiya 14:49 And you'll spend tens of millions of dollars, and what will you get at the end? You'll get a deck.

Speaker 14:55 [laughs]

Chamath Palihapitiya 14:57 A nice slide deck. What do you, what do you do with the deck? M- most of the time, nothing.

Speaker 15:03 [laughs]

Chamath Palihapitiya 15:05 And that's the business model. You-

Speaker 15:08 [laughs]

Chamath Palihapitiya 15:09 The executives feel better. You, uh... I mean, I, I don't even. You'll buy some off-the-shelf piece of software. It will be sold to you as a solution. You'll try to implement it. It really won't work. Then you'll go to a consulting firm. They'll hire hundreds and hundreds of time and materials people that look like me.

Speaker 15:27 [laughs]

Chamath Palihapitiya 15:29 Will try to implement it. It won't work, obviously. [laughs] And this cruft builds up. So my idea is I wanna build basically a control plane for AI. I wanna be able to really focus on the things that is the symbolic golden source, the true source, the English language understanding of what should be done, what is done, what works, what doesn't work. PRDs, requirements, those are the hard-fought secrets that allows companies to be successful. Code is a mechanistically deterministic thing that either works or it doesn't work, so you will get that to perfection well before you get requirements and understanding to perfection. So back to this original problem. When you put trillions of dollars in the ground and someone finally says, "What is the ROI?" You're going to have to point to the economy and say, "This, these companies are 50% more productive." Unless you completely rebuild how those companies operate, it will not pay that off, and there will be blood in the streets. The stock market will just totally turn over on itself. So we have a responsibility to do that. So we have this thing that we call a software factory. You get all these humans that can collaborate with a bunch of very powerful models, and you can take all these requirements, and you can get them into a very amazing sense of what the scope of what you wanna do is, and then we bind it to an engineering plan. You bind that to, you know, work orders. And now all of a sudden, it- things can work in the forward pass, the reverse pass. We can take tens of billions of lines of, or tens of millions of lines of code bases, dump them in, and all of a sudden it just propagates backwards, and now all of a sudden you have English language understanding this is exactly what this does. And you, and these companies, their eyes are like, "We didn't even know." We have this one customer who's got such an old legacy code base, they have to go and get retirees to c- And I swear to God, dude, this is $100 billion a year company. Out of retirement to come into the office to explain what this COBOL code does.

Speaker 17:33 [laughs]

Chamath Palihapitiya 17:35 And none of you geniuses were able to figure that out, and nobody before you was able to figure it out, and it turns out that these kinds of problems are the thing that's stopping ROI on trillions of dollars of investment.

Speaker 17:49 It's all the hidden context, right? The-

Chamath Palihapitiya 17:51 Hidden context, tribal knowledge.

Speaker 17:53 Information assets.

Chamath Palihapitiya 17:55 Information. So Peter Thiel calls it, like, you know, like, like all great companies are hidden around secrets. And I think a, an interesting thought challenge is what happens if all these secrets get documented?

Speaker 18:07 Yeah.

Chamath Palihapitiya 18:07 What happens if there's like... So you guys live in a world where, like, you know, GitHub is, like, the ground truth. Well, instead of GitHub, what if there was an actual set of wiki documents in plain language, English that the CEO could actually read? That were the actual secrets. You know, what if there was a document that Uber had that actually described surge pricing in English, not in code? And when you manipulate the English language understanding of the rule, the actual surge pricing algorithm then changes downstream to hundreds of millions of people. What if that were possible? You start to unlock a level of optimization and improvement because you don't have to be technical anymore. You just have to have good judgment and be smart. There's way more people that have good judgment and are smart than are technical. So this is the whole point. Like, when technology works, you're opening the aperture. Like when I was, when I was in college, I had to learn how to code in C and C++. And you're manipulating memory at this ultra-low level, and then at some point, you know, PHP shows up, and then Python shows up, and JavaScript shows up, and you start to abstract up, and what happened? More of you knew how to do it. Many m- many more of you... Like, P- Python, raise your hand. Okay, most of the room. C++, raise your hand. Oh, bullshit. Come on, that-- [laughing] stop that. Come on, stop. I'm trying to make a point here, guys.

Jason Zhang 19:23 But unfortunately-

Chamath Palihapitiya 19:24 Stay with me here.

Jason Zhang 19:26 Unfortunately, your-

Chamath Palihapitiya 19:27 Yeah, you're a Stanford-

Jason Zhang 19:27 ... intro Stanford class is C++.

Chamath Palihapitiya 19:29 Okay, well, that's smart 'cause you need to understand memory and memory structures.

Jason Zhang 19:32 Yeah.

Chamath Palihapitiya 19:32 And so I, I get that. But for most people-

Jason Zhang 19:33 Yeah

Chamath Palihapitiya 19:34 ... the point I'm trying to make-

Jason Zhang 19:35 Yeah. You might, you might get more people with C or may- maybe Assembly.

Chamath Palihapitiya 19:39 Okay. How many of you know Assembly?

Jason Zhang 19:40 There's, there's a few classes.

Chamath Palihapitiya 19:41 Oh my God, Jesus Christ. But the-

Jason Zhang 19:44 There's a few classes

Chamath Palihapitiya 19:44 ... but, but, but the point I'm trying to make is as the languages get abstracted-

Jason Zhang 19:49 Mm-hmm

Chamath Palihapitiya 19:49 ... the N, the number of people that can participate in that ecosystem goes up. And so the ultimate hack for AI is when things are in English. That is the unifying language for eight billion human beings. It is not C, it's not Python, it's none of these languages here. It's this language here. And the space of innovation are not algorithms anymore. It's judgment. It's really good ideas, and that many, many, many more people have.

Jason Zhang 20:22 I think touching on this, there is an interesting analogy, um, that said, like, when the first compiler was invented, there was a lot of kind of uncertainty around with this new tool, right? Like, you would have this code, you'd write it, and then pass it through the compiler, but you weren't sure that, like, the assembly that it generated was actually correct. And I think we're in, like, this kind of similar stage with AI, right? Like, we have this grand vision. We want to reason on the symbolic level. But in order to do that, we have to pass it through kind of this software factory, as you're describing it, and there are all these kind of like lossy conversions that can happen. Um, and so you mentioned earlier as well, like, these agents, like, there's so much hype around like, "Oh, like, OpenClau was running for 48 hours and solved my life," or something like crazy like that. Like, it's clearly not true. It's clearly an exaggeration, and one of the big reasons of this is, as you were saying, there's a lot of secrets, right? There's landmines. You have to bring in the retired developer from 15 years ago to explain why something happened and explain why, like, ChatGPT deleted this line, and now the whole, like, code base is broken. So in this path towards this ultimate goal, right, we wanna reason in English, what are some of the biggest ch- challenges that you guys are facing that you think more people need to be thinking about and-

Chamath Palihapitiya 21:35 So one is a technical challenge, and the other one is one of business. The tech- the technical challenge is we're struggling with the same things that everybody else struggles with. How-- W- what is the interplay and the interface between where the agent starts and ends and where the humans get involved? How do you actually create the right evals and the right guardrails for the agents to stay on task? How do you actually create a knowledge graph that these, these agents can traverse so that they can stay at the level of embeddings and actually be able to act over reasonably long-horizon tasks? So those are a set of technical challenges, and that creates really interesting questions of business strategy. So, you know, we had a very important conversation today, which is like what is the value of a control plane, and won't we just be ceding ourselves to Cursor, as an example, which is a phenomenal product. And what you're having to navigate there is just, you know, in, in my language and my understanding, I would rather build MS-DOS and Windows-

Jason Zhang 22:34 Mm-hmm

Chamath Palihapitiya 22:34 ... not necessarily, you know, Adobe Photoshop. And in, in this context, I think that for AI, we don't have that control plane. That's what I would like to build. I wanna have something that basically says the ground source golden truth for all of these agents downstream will always be this hardware-independent, database-independent, language-independent symbolic representation of what you wanna do. So go and convince business people of that. They get that. So we can sell the shit out of that. But then translating that into a set of agents that can act on target and on task is very, very difficult. It's a very complicated problem. The business challenge is then to convince them why those innovations should stay with us. What do I mean by that? So now I go back to how do you build a trillion-dollar company? What was the term? Network effect. What is a network effect that has never been built before? There's never been a network effect built around these kinds of secrets, around code, meaning if I was a hospital and I'm trying to diagnose a cancer patient, or I'm an airplane company designing a new wing, immediately where your brain goes to is those are two totally different sets of challenges, two totally different sets of problems, and what I would say is wrong. I challenge you to say the following: That may be a different ontology, but it's probably the same problem. And at the level of code and at the level of assembly, it all looks the same. You agree?

Jason Zhang 24:02 Yeah.

Chamath Palihapitiya 24:03 So what if it were actually possible for this company to be able to leverage that code because it's just recompiled with a different ontology so that you understand it? Now, all of a sudden, you have a network effect. Now, the N plus first company gets to leverage all the secrets of the N companies before it. So when Elon talks about abundance, that's how I translate it. That's how you create an al- like, just a logarithmic expansion of, of abundance. You can do anything quickly, cheaply. So we are navigating this very complicated business path to convince these businesses to let us do that with them, to convince them why this is important. Now that's a unique task, but if we accomplish it, we are giving all of these technical people that work at 8090 a chance to do something very profound. If we don't have that, it's a wonderful business. We'll do the same thing as everybody else does. We'll raise money, it'll be a unicorn, then it'll be a decacorn, blah, blah, blah, whatever. But that's not what I care about. I want to prove this other thing. There's a network effect in the code that businesses can have a shared cooperative approach to this thing, that there is no reason why Boeing needs to be afraid of Memorial Sloan Kettering. In fact, it's the opposite. Boeing and Memorial Sloan Kettering can sort of cooperate and both can thrive. Their costs go down, their value goes up, their downstream impact on their customers and patients go up. That is a positive sum view of AI.

Tanvir Bhathal 25:28 There has to be some sort of initial apprehension, though, with these companies-

Chamath Palihapitiya 25:31 Huge

Tanvir Bhathal 25:31 ... sharing my secrets, right?

Chamath Palihapitiya 25:32 Huge. It's enormous.

Tanvir Bhathal 25:34 How do you overcome that?

Chamath Palihapitiya 25:35 That's about trust and reputation. The thing that we do is we methodically... First of all, so this is more about business than technology.

Tanvir Bhathal 25:42 Sure.

Chamath Palihapitiya 25:43 In business, it's very important to sort of fish where the fish are, which is to say that in every technology adoption curve, you'll always go through the same transition. You have the early adopters, then you have this sort of like mass middle, and then you have laggards. And historically, there was a very pejorative or negative way in which you viewed those three classes, particularly the last two classes. And I've challenged these assumptions to say something different, which is everybody has a different risk posture. It's not-- I, I, I think like it's very unfair to call folks that wait till the end laggards. If you're a regulated pharmaceutical company, I was reminded of this this weekend, and you do something wrong, you can go to jail. So it's not like they're like, "I wanna poo-poo this thing." It's like they have a responsibility [laughs] to all of you that when you land a drug inside of your body, that it doesn't, you know, grow an arm out of your forehead. Do you know what I mean?

Tanvir Bhathal 26:41 Yeah. [laughs]

Chamath Palihapitiya 26:42 So that's not being a laggard. That's being responsible for their customers, one. Two, I said this earlier, right now we have a very complicated moment in AI, which is that there is doomerism on one side and full-throttle capitalism for me on the other. Those are the two choices. Like think of the compact of the internet up until AI. The compact of the internet was we would make products where you would participate and contribute, and the quid pro quo was you would get a value that you would assess as being greater than what you are giving me. You use Facebook and Instagram. You take photos. Well, thank you very much. It allows us to create a network effect. We're able to build a trillion-dollar company. You still find value. Now think of what AI says. AI says, "I'm gonna learn. I'm gonna tokenize that knowledge, and then I'm gonna sell subscriptions. Thank you very much. See you later." That is not a positive sum view, nor is the one that says, "I'm gonna go and then use that to replace you and fire you." That is deeply and fundamentally irresponsible. The positive sum view is we're gonna work together. We're gonna take our time and methodically navigate this complexity. When you need help with regulators or the government, we will go beside you and help you figure that out together. We're gonna get to the finish line in a constructive, positive sum view. We're gonna actually show how you're hiring more people, you're paying them more. That's how we do it. So it's slow, it's methodical, but it's really working. Um, 'cause it turns out humans, again, are amazing, resilient, positive, not zero-sum, you know? Many people just wanna play an infinite game. They wanna take care of their family. They don't wanna fucking get screwed over. And so why are we here?

Tanvir Bhathal 28:35 Yeah.

Chamath Palihapitiya 28:35 It's just terrible leadership. Terrible fucking leadership. So I go talk to the Fortune 1000, and I tell them my view of the world, and they can vote with their feet. They vote with their dollars. And so, so far, so good.

Tanvir Bhathal 28:47 I don't have a billion-dollar company, but I would sign up. I wanna ask you your opinion on all the hype around like these local AI agents. You have OpenClaw, OpenJarvis, et cetera. Um, as the models keep on improving with like quantization, efficient architectures, better on-device GPUs, we're gonna see a lot of the AI workload shifting to like local compute and local devices, right? Where do you kind of see this sh- like split between like local and cloud go? Does this kind of affect like business creation or like the enterprise side of the world? Um, do you think there is actually any merit in these, or it's just like all hype and you don't really-

Chamath Palihapitiya 29:23 I think it's fantastic. I think the most important thing that is going to happen in AI which is going to be ginormously disruptive is, uh, fundamentally open source models. We don't-- First of all, we don't have open source models, okay? We have closed source in America. We have open weight in China. That's what we have. We do not have open source models. But number one is we need an ensemble of open source models, and then number two is we need a fundamentally, completely unregulated, totally distributed form of compute, initially training and then inference. The combination of those two things is profound. How OpenClaw and all of these local agents work in those realms, I don't know well enough to say. But man, that is a huge, huge trend. Subnet 3 of BitTensor, um, Folding@home, uh, Pluralys, um, there's a couple of others. I, I have, I have no stake in any of these, but I would recommend you to learn about all of them. It is incredibly important, I think, what's happening. That is a... It's a Cambrian explosion that's just gonna go crazy because then you are totally de-levered- From government oversight and government infrastructure, there is no kill switch. And why that's important is that if these things do become able to symbolically reason, you cannot have that gate kept by five or six entities. Because what will happen if five or six entities control it, is you get immediately this distribution of you have the people that are aligned to the model maker, you know, sort of like think of it as like a planet with a handful of moons rotating around it. And then you have everybody else which is forced to become effectively a vassal state of that model maker, and I think that's a very complicated outcome and a, and not a good one. But there are these projects out there, I encourage you to learn about them. I am deeply fascinated. I spend a lot of time tinkering and learning. Uh, it's really cool. So I, I would say like instead of OpenClaw, OpenAges, I would think about more, you know, how do you pr- like how do, how does local training work when you're participating as part of a training pool? How will l- inference work? I- that's hugely technically complicated scale. 8B, 10B, 50B models are happening right now kind of don't matter, but when you get to 100B, you're like, you're in the game. Like you can really compete commercially. Um.

Speaker 31:47 Okay. Uh, Chamath, Mr. Chamath Palihaphi- Palihapitiya, is it right?

Chamath Palihapitiya 31:53 No, but-

Speaker 31:53 Uh, I've been a-

Chamath Palihapitiya 31:54 Go, go to your question. Bro, go to your question

Speaker 31:56 I've been a big fan of you, and you're one of my biggest inspirations. It's, it feels like seeing a mythical ch- character out in real life. [laughs] Before, before, before, before I ask you the question, I'll tell you a bit about me. You gotta try something. I'm J- [laughs] Okay, I'm, uh, uh-

Chamath Palihapitiya 32:12 He knows kung fu, bro. I'm telling you

Speaker 32:14 ... I'm Jayshree Vasanth.

Chamath Palihapitiya 32:14 Be very careful

Speaker 32:15 And I was born in Hayward, California, and I grew up in South India in poverty. Education is very important to me, and I run a ed tech startup right now called Educately.ai under J- Jayshree LLC. Now I go to George Washington University with $250,000 scholarship, a university alumni award, and I'm very grateful for that. And that changed my life, education changed my life, and I'm also a Buddhist and a feminist. I'm very empathetic. [laughs] Uh, if you cry, I'll cry. Where are you going with this? Wait, I'm almost there. Almost there. Al- I feel like I'm reading a Tinder profile. [laughs] [clapping] I'm almost there.

Chamath Palihapitiya 32:50 Bro, the girls are behind you. I mean, like-

Speaker 32:51 [laughs]

Chamath Palihapitiya 32:52 ... I don't know what... What do, what do you want from me?

Speaker 32:54 Okay, I'm very empathetic. If you cry, I'll cry. If you're happy, I'll be 10 times happy for you. Now the question. [laughs] Question, question. I'm almost there. Question. I'm an information system student. Uh, I'm taught to sit right be- between business logic and technical implementation. If AI is moving toward an AI first co- uh, English first control plane, should we focus less on mastering specific tools like Power BI or Python, and more on, more on the art of requirements engineering and first prin- principles thinking?

Chamath Palihapitiya 33:25 Yes.

Speaker 33:26 [laughs] Thank you.

Chamath Palihapitiya 33:28 Yes.

Speaker 33:29 One over there.

Chamath Palihapitiya 33:29 Yes. Yeah. [laughs] Let's go over right here.

Speaker 33:32 Okay, he's-

Speaker 33:33 Oh. Yeah, so when I think about like early Facebook product and growth leaders like Stan or Naomi, I think one way that maybe you differ from them too is just how you've been able to connect with people and sell, and I think about like the story of how you taught, um, like Sheryl Sandberg's kids to play poker after Dave passed away, and I just feel like you have this like incredible like empathy and just ability to connect. So I was wondering like what experiences in your journey helped you develop that skill?

Chamath Palihapitiya 34:06 Um, well, look, I think it's kind of a little... It's, it's relatively well-known, but I felt very deeply, uh, less than growing up, um, because I would always think to myself, like my dad was a wonderful... Like to now I've, I've totally dispelled all this anger, but my dad used to, I mean, truly beat the shit out of me. And, uh, you know, I said this on Lex once, like it's a crazy thing, and I'd remembered it recently. He would allow me to pick the thing that he would beat me with, was the belt or stick. And I remember like learning like how to like judge the tensile strength of various sticks because I was like, "Well, this one I can get hit

Chamath Palihapitiya 34:45 by 10 times and it'll break, and the other one will be..." You know what I mean? But when, when that is done, you're like, "Why it... This must mean that I am just so worthless," right? 'Cause why would you do that to somebody else, especially somebody that you gave birth to, essentially? So that, in the back of my mind, was there for a very long time. And so I would find ways where I could like, not, not ex- like purposely, but like I was like it would make me feel better, and so I did it in a weird way for myself. And then over time, I've just learned to like just let it all go, and it's like actually it just happened. It was a thing. And so what's left over though is this desire. It's fun to be around people, to make them laugh, you know, to tell the truth, just to be who you are, and it's all just normal. Um, and it's been, it's just been a process of learning that I am just like everybody else, that I'm not worthless. That's what got me there. But it started from a very negative place, which I don't wish upon you, but I think there are other ways to get there. I just don't know what they are [laughs] because I didn't live that journey.

Speaker 35:59 Hi. Um, I wanna ask a couple questions that should be related. One is, uh, now that you see what's happening with AI, uh, and how it's abstracting away processes and businesses, what should universities be teaching, especially in computer science? And then secondly, what kind of people are you looking for at 8090?

Chamath Palihapitiya 36:20 Uh, I want people that are extremely, extremely interested in navigating the journey to their own resilience. That's all it is. Um, I want them to hear the story, and I want them to have the courage to either self-select out or double down, and, uh, that's what I want In terms of technology, I do think that we are still at a place where technological capability is still paramount. And I think for probably the next ten or twenty years, it's going to be increasingly critical that people can really refine their understanding. Meaning, I actually think you're gonna have to get more low level than stay at the high level. Um, but in success, there, there will be this fifty or a hundred million artisans that will be building something for billions of people. And for them, they'll be able to live in the symbolic language of English and be able to be productive human beings. Um, so yeah, they'll be both, but, you know.

Speaker 37:22 I really appreciate the initial statement that you kinda made coming into the podca- uh, coming into the talk today. Uh, you talked about something that, like, kinda parallels, like, death of the ego and, like, really, like, living in the moment. I think it's, like, emotional intelligence is something that I've also learned being... is, is one of the most important things in building a business. How do you continue to maintain emotional intelligence over time and, like, maintain that death of the ego and the lack of need for external validation? Because I feel like you, you get there, and it's great to get there, but then keeping that up, especially, like, being someone who, like, seeks validation or something like that, historically speaking-

Chamath Palihapitiya 38:01 Superpower, which is I have an incredible partner, my wife. Um, she is very orthogonal to me. She's not motivated this way. My wife runs a pharma company. She makes drugs for rare diseases. You know, she's getting her head bashed in every day. Everything fails. Everything fails all the time. She's always on the knife's edge. And so when she has a blockbuster drug, she only has it for six or seven years, so all that money has to go back into an R&D program. So she lives this very practical day-to-day life, and she's incredibly humble. She's not motivated by ego. So when I am, she basically slaps me upside the head. [laughs] She's also Italian, so she's not afraid to throw things.

Speaker 38:41 [laughs]

Chamath Palihapitiya 38:42 And I'm like, "Oh, shit." I have friends who see me change. They will tell me. I have one in particular that's really good. So I have my wife, I have one friend. I have my poker game. This may sound crazy, but poker is an incredible insight into your mind and your current mental state, and I'm a very good poker player, to be totally honest with you. But-

Speaker 39:05 Mm-hmm

Chamath Palihapitiya 39:05 ... like, at the highest of highest levels. Like, the people that come into my game are murderer's row of the best professionals. But when I lose four or five weeks in a row, it's because of my ego. It's because something has happened, and then I go and I dissect. And then if all else fails, I fly commercial and I kite a jet.

Speaker 39:23 [laughs]

Speaker 39:30 Yeah, it's a really amazing conversation. Thank you very much. And, uh, uh, let me do a little bit introduction. So we are the AI foundational model company behind the model GeoM, so we are the open source model. And thank you for clarification. It's only open weight, not open source. [laughs] But why we want to do open weight? Because we want to booming the whole ecosystem and to get everyone benefits. So my question is, uh, we actually want to achieve the sort of AGI, so I wanna know your definition of AGI. And do you think we can achieve the final intelligence through the language model, not the other models, like physical models or the work models or the others?

Chamath Palihapitiya 40:18 I, I don't, I don't think AGI is practical with what we know today. I think we're just scratching the surface. There will be some forms of super intelligence, and I think that those will be some narrow verticals of capabilities. But for, for, for most part, I don't think we're dealing with an A- AGI on the realm of anything. I think when you strip away the, um, the requirements for fundraising, it's mostly bluster.

Speaker 40:39 Uh, hi, Chamath. Thank

Speaker 40:40 you so much for the talk. Uh, around network effects, right, you said it about enterprises, but what if normal people could also contribute to it? And, uh, so we were building a, uh, uh, especially around, uh, the knowledge base, uh, work that Karpathy recently announced, and so we are building in a very similar space. And what if I told you I could give you access today to something that you were just talking about thirty minutes ago?

Speaker 41:05 [laughs]

Chamath Palihapitiya 41:08 Well, great. No. I've just spent the last f- twenty-five years trying to get there, but great. Um, one thing that I think is really interesting is this idea that, you know, like, you know how we have open source projects and people contribute to repos. It would be, uh, really interesting to think about that in the context of business and symbolic language and understanding. Hospital administrators that can work on the way to build the best hospital, and what it is is a set of foundational documents that, that can guide all the AI and all the agents. I think that idea is so wonderful. It's deeply collaborative. It's genuinely, you know, again, positive sum. I, I just think it's a beautiful, beautiful idea, and I ho- and I hope there's a moment where we can play a small part in that.

Speaker 41:51 Chamath, you talked a lot about first principles as well as address the problem of creating guardrails and evals for agents to stay on track for complex tasks. Um, how much of this do you think is an issue of mechanistic interpretability and being able to study these models as physical systems?

Chamath Palihapitiya 42:09 Huh?

Speaker 42:10 [laughs]

Chamath Palihapitiya 42:16 I don't totally know the question, but try it again. W- maybe are you asking, like, why do agents go off track?

Speaker 42:24 Or I guess, yeah, and how much of it is related to being able to study these systems as physical systems?

Chamath Palihapitiya 42:30 I, I, I think, like, as far as I can tell, I think the reason that it goes off task is, is that it's really what we have is, like, a very fancy autocomplete, and it's, it's, it's not trained on first principles thinking because it's not possible. It just kind of says, "Well, I've seen this pattern so many times, so it must be this thing on this distribution." And so sometimes you'll get the right token. Sometimes I'll get the wrong token. I'll go boo. You know, there'll be some reinforcement learning, and then, then both of us will get the right next token. That's what it is right now. It's a, it's a deeply powerful but also deeply primitive- ... software system. Doesn't mean we should stop. In fact, it means we should double, triple, quadruple down, 'cause there's so much to do. It just means we're super early. Uh, that's why I don't like, like, the overselling of what this is and what this moment is. 'Cause we're almost, like, pulling forward the reality of this moment, you know?

Speaker 43:19 Hi, Chamath. Uh, great talk. Thank you. I'm wondering, given that, uh, mammals have a fraction of compute to the frontiers model have, models have, why do you think it is time to look at that symbolic stuff instead of look into more effective learning algorithms?

Chamath Palihapitiya 43:36 Okay, so this is a g- this is a great question. Because then you're leveraging this extremely power-efficient thing called the human brain, and you're right, maybe what we find out is that the way that we translate the power of the human brain through symbolic language into th- into this embedding space doesn't actually make sense. But that would mean that there's a completely different compute modality that we should be inventing. But my point is, this is perfected over billions of years. It is power efficient, it's extremely information dense, and it just works. And because there's eight billion of these things, the compute power we have collectively as a species, I think, is unrivaled. You can't measure what we are able to do in FLOPS. I just think it's an extremely sad approximation of how the human brain actually works. So instead of saying that we need s- this to change, I think it's in fact, we should map everything from this forward.

Speaker 44:27 Uh, Chamath, you talked a lot about flow state during the, during the talk, and I think it's very important. But, uh, you shared some ingredients like flat hierarchy and more work than people. Do you have any other secrets or ingredients that you can share that helps get a group in flow state?

Chamath Palihapitiya 44:44 Um, meaning like what else do we do at 8090?

Speaker 44:47 For example, or something that you've seen work. [laughs]

Chamath Palihapitiya 44:50 Well, I mean, our entire company's extremely young. Um, I'm not a huge fan of the, the recency bias that comes with experience. I think new things should, are largely proven and pioneered by new, new ways of thinking, which essentially comes with no priors and no bias. Um, so we have a very young company. We have a very inexperienced in the classical sense. Um, they're very experienced in the sense that they're good at taking risk and taking shots on goal. Um, they don't question the lack of hierarchy. They don't question the lack of resources. Um, I don't know, man, it's a very, it's a very, very, very special place. Um, it's a very special... It's small, you know, it's 40 people or so, but it's very special. Um, it's chaos. It's truly k- I've never done anything like this, meaning like just the structural underpinnings, the organization. I have like a Claude chat that I, that I have an ongoing dialogue with, that's called 8090 Org Design, and I s- and I just keep feeding it stuff and just kind of trying to figure out, like, why is this not working? Why did this work? It's just chaos, and I come in with some of the stupidest ideas, and sometimes these guys are like, "This is a bridge too far. [laughs] We, we..." But I think you're, but I think when you look back, like said differently, if you, if you go inside of a normal company, you look at an org chart, the reason why that org chart is like that is I suspect somewhere in the '80s and '90s, somebody sold them some shitty piece of enterprise software and convinced them that that title needs to exist. Oh, you're a CXO? Well, here's this piece of software perfectly suited for the CXO. Oh, you're the CYO? Here's a perfect piece of software for the CYO. And you do this on and on down the line, and you have a multi-trillion dollar complex. But then it turns out that it doesn't work, so then you take that multi-trillion dollar complex, multiply it by four, and you create a services complex to make the multi-trillion dollar complex actually try to do something. But then you realize that most companies have a 15 to 20% profit margin, and you're like, "Wait a minute." But then you look at the few companies that have built everything from scratch, like Facebook and Google, and they have almost triple. Has anybody connected these dots at any point and said, "Wait a minute. Is Facebook and Google literally the employees like three times smarter than the employees at, I'm just making it up, Merck? Why is this 60% EBITDA and this is 20?" Is it industry? I don't know, because when you look at all of the ways you can strip a company down to its studs, you end up realizing that, wait a minute, there's all this inefficiency that sometimes gets trapped by poor software. And so we should just rip that all out. Should rewrite it from scratch.

Speaker 47:32 Hey, Chamath. On your point on the knowledge sharing, uh, for scaling the, the knowledge and AI between companies. So in 2017, Element AI from Canada raised like 150 million to, like, tackle this issue between sh- making a promise that we can take the data from each companies, like banks for example, and use that knowledge to help other banks grow. But that idea quickly has proved like it's not gonna scale. So what do you think is different right now, and especially with 8090, that companies are okay with sharing their secrets?

Chamath Palihapitiya 48:07 Look, y- I knew it, it didn't fail the minute you told me you led with the funding round. Banks don't care how much money you raise. Banks care that what you said is actually true and it works. They're not stupid. They run banks. So the reason it failed is because whoever was able to convince an investor to give them $150 is not the same person that can sit with the CEO of a half a trillion dollar bank and convince them why it's the right thing to do. Okay, well, well one last question. Like a hard one. No, hard. It must be... Who... Don't raise your hand unless it's hard, guys. [laughs] No, all of you guys think it's hard. You're all graded on a curve-

Tanvir Bhathal 48:43 I think, Chamath-

Chamath Palihapitiya 48:43 ... so you're not used to hard

Tanvir Bhathal 48:44 ... Chamath, you need to pick on the person. You need to pick on the hard person.

Chamath Palihapitiya 48:48 Why didn't you- You just gotta see some judgment.

Tanvir Bhathal 48:51 Yeah. You, it's your judgment here.

Chamath Palihapitiya 48:53 Who looks like they're the hardest worker? Put your fucking hand down. You've, you're blocking my view. [laughs] Is it a hard question? Is it a hard question? I have one. I have one. Okay. Okay. Okay. How can you be yourself after it be fi- That was not me. That was-

Tanvir Bhathal 49:09 How can you be yourself

Chamath Palihapitiya 49:12 How do you what?

Tanvir Bhathal 49:13 Pick yourself up.

Speaker 49:14 Too complicated.

Chamath Palihapitiya 49:15 Bro, because, okay. Bro, there is no failure. That is what other people think of you to keep you down. There is no failure. If you were left on a, on an island, and it happened, you would just brush it off and move on. It's everybody else that you think is judging you. But then here's a secret: they don't give a fuck about you. They are living their own lives. So it's your perception of what they think of you. There is no failure. There's do and learn. Do and learn. Do and learn. [cheering] Okay, last question. Go. Last question, we gotta go.

Speaker 49:59 Uh, thank you, Chamath. My name is Carl. I'm from Dartmouth College. Uh, I wanted to ask that you have a very special engineering, um, uh, screening pipeline where it's very rigorous, and in the end, you're trying to not, you know, convince them to join.

Chamath Palihapitiya 50:13 So far, this is not going to a hard question.

Speaker 50:15 But, but, and you want to, uh, take out people who can survive not four minutes swimming, but 60 minutes.

Chamath Palihapitiya 50:22 No, not survive. I want people who want to see if they can survive. It's totally different.

Speaker 50:30 And that leads to my question: How do you actually measure it? For example, for an engineer, is it the lines of code? Is it the number of parallel agents running? What is it? How do you measure it?

Chamath Palihapitiya 50:40 You see it, you see it in their character. It's not the person that codes the fastest. It's not the person that codes the best. It's this ongoing ability to process your moment in time, internalize the pressure, learn from it, thrive in it, see that you are growing and stretching as a human being. You help the colleagues around you, and you go to the next challenge. The best example of this, I, I would just leave you with a parting thought. So if you wanna learn business, go look at the OSI stack. Take a few seconds to try to build the AI stack for yourself. That's lesson one. Lesson two is go and Google Raptor 1, 2, and 3. And when you look at that, there's a beautiful picture where Elon took a picture of Raptor 1 beside Raptor 2 beside Raptor 3. And what the first one was is a gnarly mess, and Raptor 2 was good, and it was an incredible engineering feat that made it much more refined, but still very beastly and rough. And then you look at Raptor 3, it is so beautiful. And when I look at that, what I see is not a technical innovation. I see a process of just constantly figuring out, try, do, learn. Do, learn. Do, learn. And the, from getting from there to here, that's what I want, the person that is like, "I'm just gonna keep chiseling and shaping and trying, and I'm gonna try things. And oh, I fucked this up, and oh, I was really bad yesterday." And, but you did the best of what you could, and you don't beat yourself up, and you're like, "All right." And then you have people around you that are not trying to pin it on you, right? And if you have a, a lot fewer people, there's a lot less politics because there's, there's, you're not fighting over scarce resources. There's too much shit to do. And so you go from Raptor 1 to Raptor 2 almost accidentally, and you take a step back, you're like, "Wow, we did that." We're in the middle of this thing I can't really say, for an entity that I can't really talk about. And I guarantee you that if we get this right, you or your parents will immediately and directly see a benefit from it. And that is something that the guys that did it... I, I shouldn't even say this. It's two fucking people. And it is, it's an, it's, they sit beside Will, actually. It's an in, it's an incred- it's an incredible thing. No?

Speaker 53:09 There's a correlation.

Chamath Palihapitiya 53:10 Don't say what it is. Yeah. It's an incredible, incredible, incredible thing. So my point is, find a place where you can go from Raptor 1 to Raptor 2 to Raptor 3 of yourself, and it is not about the superficial things of PRs. It's not, that's not what it is. It's hard to describe what it is. It's just about you. Look, we have huge attrition. Don't get me wrong. [laughs] We, a lot of people don't fit. Uh, 'cause the problem is we have very orthogonal ways of hiring. Like, you go through the screens, you go through the coding reviews, but then when you're in it, it's very clear, and it's, it's a, it's a mindset. It's a desire about figuring out for yourself what you're all about. No, you're not listening to me. [laughs] No, I c- this is my point. If you want a binary deterministic answer, I can't give that to you. Life will never give that to you. There is no outcome. There's only do and...

Speaker 54:10 Failure.

Tanvir Bhathal 54:11 Create.

Chamath Palihapitiya 54:12 What? [cheering] Do not let this guy back into Stanford ever. Guys, thank you very much. [cheering]