SPEAKER_00: I first met our next guest, Sam Altman, almost 20 years ago when he was working on a local mobile app called Looped. We were both backed by Sequoia Capital and, in fact, we were both in the first class of Sequoia Scouts. He did investment in a little unknown fintech company called Stripe. I did Uber. And in that tiny experimental fund- You did Uber? I've never heard that before.

SPEAKER_02: Yeah, I think so. It's possible. You've got it starting already.

SPEAKER_03: You should write a book, Jacob.

SPEAKER_00: Maybe. That tiny experimental fund that Sam and I were part of a Scouts is Sequoia's highest multiple returning fund. A couple of low-digit millions turned into over 200 million, I'm told. Really? Yeah, that's what I was talking about, Ruloff. Yeah. And he did a stint at Y Combinator,

SPEAKER_20: where he was president from 2014 to 2019. In 2016, he co-founded OpenAI with the goal of ensuring that artificial general intelligence benefits all of humanity. In 2019, he left YC to join OpenAI

SPEAKER_19: full-time as CEO. Things got really interesting on November 30th of 2022. That's the day OpenAI

SPEAKER_20: launched ChatGPT. In January 2023, Microsoft invested 10 billion. In November 2023, over a crazy five-day span, Sam was fired from OpenAI. Everybody was going to go work at Microsoft. A bunch of heart emojis went viral on X slash Twitter, and people started speculating that the team had reached artificial general intelligence. The world was going to end, and suddenly, a couple days later, he was back to being the CEO of OpenAI. In February, Sam was reportedly looking to raise $7 trillion for an AI chip project. This, after it was reported that Sam was looking to raise a billion from Masayoshi-san to create an iPhone killer with Johnny Ive, the co-creator of the iPhone. All of this while ChatGPT has become better and better, and a household name. It's having a massive impact on how we work and how work is getting done, and it's reportedly the fastest product to hit 100 million users in history in just two months. And check out OpenAI's insane revenue ramp-up. They reportedly hit $2 billion in ARR last year. Welcome to the All-In Podcast, Sam Altman.

SPEAKER_22: Thank you. Thank you, guys.

SPEAKER_20: Sachs, you want to lead us off here?

SPEAKER_22: Okay, sure. I mean, I think the whole industry is waiting with bated breath for the release of

David Sacks: GPT-5. I guess it's been reported that it's launching sometime this summer, but that's a pretty big window. Can you narrow that down? I guess, where are you in the release of GPT-5?

SPEAKER_28: We take our time on releases of major new models, and I don't think we... I think it will be great when we do it, and I think we'll be thoughtful about how we do it. Like, we may release it in a different way than we've released previous models. Also, I don't even know if we'll call it GPT-5. What I will say is, you know, a lot of people have noticed how much better GPT-4 has gotten since we've released it, and particularly over the last few months. I think that's like a better hint of what the world looks like, where it's not the like 1, 2, 3, 4, 5, 6, 7, but you use an AI system, and the whole system just gets better and better fairly continuously. I think that's like both a

SPEAKER_29: better technological direction. I think that's like easier for society to adapt to, but I assume

SPEAKER_31: that's where we'll head. Does that mean that there's not going to be long training cycles,

SPEAKER_33: and it's continuously retraining or training sub-models, Sam? And maybe you could just speak to us about what might change architecturally going forward with respect to large models.

SPEAKER_36: Well, I mean, one thing that you could imagine is just that you keep training a model. That would

SPEAKER_41: seem like a reasonable thing to me. And we talked about releasing it differently this time. Are you thinking maybe releasing it to the paid users first, or, you know, a slower rollout to get the red

SPEAKER_44: teams tight since now there's so much at stake. You have so many customers actually paying, and you've got everybody watching everything you do. You know, is it? You have to be more

SPEAKER_29: thoughtful now. Yeah, still only available to the paid users. But one of the things that we really want to do is figure out how to make more advanced technology available to free users, too. I think

SPEAKER_28: that's a super important part of our mission. And this idea that we build AI tools and make them super widely available, free or, you know, not that expensive, whatever it is, so that people can use them to go kind of invent the future, rather than the magic AGI in the sky, inventing the future and

SPEAKER_50: showing it down upon us. That seems like a much better path. It seems like more inspiring path. I also think it's where things are actually heading. So it makes me sad that we have not figured out how to make GPT-4 level technology available to free users. It's something we really want to do.

SPEAKER_51: It's just very expensive, I take it. It's very expensive. Yeah.

Chamath Palihapitiya: Chamath, your thoughts? I think maybe the two big vectors, Sam, that people always talk about is that underlying cost and sort of the latency that's kind of rate-limited a killer app. And then I think the second is sort of the long-term ability for people to build in an open source world versus a closed source world. And I think the crazy thing about this space is that the open source community is rabid. So one example that I think is incredible is, you know, we had these guys do a pretty crazy demo for Devon. Remember, like even like five or six weeks ago that looked incredible. And then some kid just published it under an open MIT license, like OpenDevon. And it's incredibly good and almost as good as that other thing that was closed source. So maybe we can just start with that, which is tell me about the business decision to keep these models closed source. And where do you see things going in the next couple of years?

SPEAKER_55: So on the first part of your question, speed and cost, those are hugely important to us.

SPEAKER_28: And I don't want to like give a timeline on when we can bring them down a lot because research is hard, but I am confident we'll be able to. We want to like cut the latency super dramatically. We want to cut the cost really, really dramatically. And I believe that will happen. We're still so early in

SPEAKER_50: the development of the science and understanding how this works. Plus, we have all the engineering

SPEAKER_28: tailwinds. So I don't know like when we get to intelligence too cheap to meter and so fast that it

SPEAKER_50: feels instantaneous to us and everything else. But I do believe we can get there for a pretty high level of intelligence. And it's important to us. It's clearly important to users and it'll unlock a lot of stuff. On the sort of open source, closed source thing, I think there's great roles for both. I think you know, we've open sourced some stuff, we'll open source more stuff in the future. But really, like our mission is to build towards AGI and to figure out how to broadly distribute its benefits. We have a strategy for that seems to be resonating with a lot of people. It obviously isn't for everyone. And there's like a big ecosystem. And there'll also be open source models and people who build that way. One area that I'm particularly interested personally in open source for is I want an open source model that is as good as it can be that runs on my phone. And that, I think is gonna, you know, the world doesn't quite have the technology for a good version of that yet. But that seems like a really important thing to go do at some point. Will you do? Will you do that? Will you release it? I don't know if we will or someone will, but someone will.

SPEAKER_59: What about Lama 3? Lama 3 running on a phone? Well, I guess maybe there's like a 7 billion per version.

SPEAKER_63: Yeah, yeah.

SPEAKER_59: I don't know if that will fit on a phone or not.

SPEAKER_26: But that should be fittable on a phone, but I don't, I'm not, I'm not sure if that one was like,

David Sacks: I haven't played with it. I don't know if it's like good enough to kind of do the thing I'm thinking about here. So when, when Lama 3 got released, I think the big takeaway for a lot of people was, oh, wow, they've like caught up to GPT 4. I don't think it's equal in all dimensions, but it's like pretty, pretty close to pretty in the ballpark. I guess the question is, you know, you guys released four a while ago, you're working on five or, you know, more upgrades to four. I mean, I think to Jamal's point about Devon, how do you stay ahead of open source? I mean, it's just, that's just like a very hard thing to do in general, right? I mean, how do you think about that?

SPEAKER_50: What we're trying to do is not make the sort of smartest set of weights that we can, but we're trying to make is like this useful intelligence layer for people to use. And a model is part of that. I think we will stay pretty far ahead of, I hope we'll stay pretty far ahead of the rest of the world on that. But there's a lot of other work around the whole system. That's not just that, you know, the model weights, and we'll have to build up enduring value the old fashioned way, like any other business does. We'll have to figure out a great product and reasons to stick with it

SPEAKER_44: and, you know, deliver it at a great price. When you founded the organization, you, the stated goal or part of what you discussed was, hey, this is too important for any one company to own it. So therefore it needs to be open. Then there was the switch. Hey, it's too dangerous for anybody to be able to see it. And we need to lock this down because you, you had some fear about that, I think. Is that accurate? Because the cynical side is like, well, this is a capitalistic move.

SPEAKER_69: And then the, I think, you know, I'm, I'm curious what the decision was here in terms of going from open. We, the world needs to see this. It's really important to close. Only we can see it.

SPEAKER_46: Well, how did you come to that conclusion? Part of the reason that we released ChatGPT was we want

SPEAKER_50: the world to see this. And we've been trying to tell people that AI is really important. And if you go back to like, October of 2022, not that many people thought AI was going to be that important or that it was really happening. And a huge part of what we try to do is put the technology in the hands of people. Now, again, there's different ways to do that. And I think there really is an important role to just say, like, here's the way to have at it. But the fact that we have so many people using a free version of ChatGPT that we don't, you know, we don't run ads on, we don't try to like make money on, we just put out there because we want people to have these tools, I think has done a lot to provide a lot of value and, you know, teach people how to fish, but also to get the world really thoughtful about what's happening here. Now, we still don't have all the answers. And we're fumbling our way through this, like everybody else, and I assume we'll change strategy many more times as we learn new things. You know, when we started OpenAI, we had really no idea about how things were going to go, that we'd make a language model that we'd ever make a product, we started off just, I remember very clearly that first day where we're like, well, now we're all here, that was, you know, it's difficult to get this set up. But what happens now? Maybe we should write some papers, maybe we should stand around a whiteboard. And we've just been trying to like put one foot in front of the other and figure out what's next and what's next and what's

Chamath Palihapitiya: next. And I think we'll keep doing that. Can I just replay something and just make sure I heard it right? I think what you were saying on the open source, closed source thing is, if I heard it right, all these models, independent of the business decision you make are going to become asymptotically accurate towards some amount of accuracy, like not all, but like, let's just say there's four or five that are well capitalized enough, you guys, meta, Google, Microsoft, whomever, right? So let's just say four or five, maybe one startup, and on the open web, and then quickly, the accuracy or the value of these models will probably shift to these proprietary sources of training data that you could get that others can't or others can get that you can't. Is that how you see this thing evolving, where the open web gets everybody to a certain threshold, and then it's

SPEAKER_28: just an arms race for data beyond that doesn't. So I definitely don't think it'll be an arms race

SPEAKER_29: for data, because when the models get smart enough, at some point, it shouldn't be about more data, at least not for training, it may matter data to make it useful. Look, the one thing that I

SPEAKER_28: have learned most throughout all this is that it's hard to make confident statements a couple of years

SPEAKER_50: in the future about where this is all going to go. And so I don't want to try now. I will say that I expect lots of very capable models in the world. And, you know, like, it feels to me like we just like stumbled on a new fact of nature or science or whatever you want to call it, which is like, we can create, you can like, I mean, I don't believe this literally, but it's like a spiritual

SPEAKER_28: point. You know, intelligence is just this emergent property of matter. And that's like a, that's like a rule of physics or something. So people are going to figure that out. But there will be all these

SPEAKER_50: different ways to design the systems, people will make different choices, figure out new ideas. And I'm sure, like, you know, like any other industry, I would expect there to be multiple approaches and

SPEAKER_78: different people like different ones, you know, some people like iPhone, some people like an Android

SPEAKER_80: phone, I think there will be some effect like that. Let's go back to that first section of just the

Chamath Palihapitiya: the cost and the speed. All of you guys are sort of a little bit rate limited on literally Nvidia's throughput, right. And I think that you and most everybody else have sort of effectively announced how much capacity you can get just because it's as much as they can spin out. What needs to happen at the substrate so that you can actually compute cheaper, compute faster, get access to more energy? How are you helping to frame out the industry solving those problems?

SPEAKER_50: Well, we'll make huge algorithmic gains for sure. And I don't want to discount that, you know, I'm very interested in chips and energy. But if we can make our if we can make a same quality model twice as efficient, that's like we had twice as much compute, right. And I think there's a gigantic amount of work to be done there. And I hope we'll start really seeing those results. Other than that, the whole supply chain is like very complicated, you know, there's, there's logic fab capacity, there's how much HBM the world can make, there's how quickly you can like get permits and pour the concrete, make the data centers, and then have people in there wiring them all up. There's finding the energy, which is a huge bottleneck. But I think when there's this much value to people, the world will do its thing, we'll try to help it happen faster. And there's probably like, I don't know how to give it a number, but there's like some percentage chance, where there is, as you were saying, like a huge substrate breakthrough. And we have like a massively

SPEAKER_84: more efficient way to do computing. But I don't, I don't like bank on that or spend too much time

Chamath Palihapitiya: thinking about it. What about the device side? And sort of, you mentioned sort of the models that can fit on a phone. So obviously, whether that's an LLM or some SLM or something, I'm sure you're thinking about that. But then does the device itself change? I mean, is it does it need to be as

SPEAKER_90: expensive as an iPhone? Oh, I'm super interested in this. I love like great new form factors of

SPEAKER_42: computing. And it feels like with every major technological advance, a new thing becomes possible.

SPEAKER_29: Phones are unbelievably good. So I think the threshold is like very high here. Like, what I think, I think like, I personally think iPhone is like the greatest piece of technology humanity has ever made. It's really a wonderful product. What comes after it? Like, I don't know. I mean, I was gonna, that was what I was saying. It's so good to get beyond it. I think the bar is like, quite high.

SPEAKER_97: Well, you've been working with Johnny Ive on something, right?

SPEAKER_98: We've been discussing ideas. But I don't like, if I knew,

David Friedberg: is it that that it has to be more complicated or actually just much, much cheaper and simpler?

SPEAKER_29: Well, almost everyone's willing to pay for a phone anyway. So if you could like make a way cheaper

SPEAKER_50: device, I think the barrier to carry a second thing or use a second thing is pretty high. So I don't think, given that we're all willing to pay for phones, or most of us are, I don't think cheaper

SPEAKER_106: is the answer. Different is the answer, Ben? Would there be like a specialized chip that would run on the phone that was really good at powering a, you know, a phone size AI model? Probably,

SPEAKER_108: but the phone manufacturers are going to do that for sure. That doesn't that doesn't necessitate

SPEAKER_50: a new device. I think you'd have to like find some really different interaction paradigm that the technology enables. And if I knew what it was, I would be excited to be working on it right now.

SPEAKER_97: But you have, you have voice working right now in the app. In fact, I set my action button on my

SPEAKER_00: phone to go directly to chat GPT voice app, and I use it with my kids and they love it talking,

SPEAKER_115: it's got latency issues, but it's really we'll get we'll get that we'll get that better. And I think

SPEAKER_50: voice is a hint to whatever the next thing is, like, if you can get voice interaction to be really good, it feels. I think that feels like a different way to use a computer. But again,

SPEAKER_44: like with that, by the way, like, why is it not responsive? And, you know, it feels like a CB, you know, like over, over. It's really annoying to use, you know, in that way. But it's also

SPEAKER_28: brilliant when it gives you the right answer. We are working on that. It's, it's so clunky right now. It's slow. It's like, kind of doesn't feel very smooth or authentic or organic, like, we'll get

SPEAKER_26: all that to be much better. What about computer vision? I mean, they have glasses, or maybe you

David Sacks: could wear a pendant, you take the combination of visual or video data, combine it with voice. And

SPEAKER_125: now it's super I knows everything that's happening around you super powerful to be able to like,

SPEAKER_50: the multimodality of saying like, hey, chat GPT, what am I looking at? Or like, what kind of plant is this? I can't quite tell. That's obvious that that's like, that's another I think, like hint, but

SPEAKER_29: whether people want to wear glasses or like hold up something when they want that, like, I there's a bunch of just like, like the, the sort of like, societal interpersonal issues here

SPEAKER_98: are all very complicated about wearing a computer on your face.

SPEAKER_127: Um, we saw that with Google Glass, people got punched in the face in the mission, started a lot about that. I forgot about that. So I think it's like,

SPEAKER_106: what are the apps that could be unlocked if AI was sort of ubiquitous on people's phones?

SPEAKER_132: Do you have a sense of that? Or what would you want to see built?

SPEAKER_50: Uh, I think what I want is just this always on, like super low friction thing where I can either by voice or by text or ideally, like some other, it just kind of knows what I want, have this like constant thing helping me throughout my day that's got like as much context on as possible. It's like the world's greatest assistant. And it's just this like thing working to make me better and better. Uh, there's, there's like a, and when you hear people like talk about the AI future, they're imagine they imagine there's sort of two different approaches and they don't sound

SPEAKER_29: that different, but I think they're like very different for how we'll design the system in practice. There's the, I want to extension of myself. Um, I want like a ghost or an alter ego or this thing that really like is me is acting on my behalf is, um, responding to emails, not even telling me about it is sort of like, it becomes more be and is me. And then there's this other thing, which is like, I want a great senior employee. It may get to know me very well. I may delegate it,

SPEAKER_50: you know, you can like have access to my email and I'll tell you the constraints, but, but I think of it as this like separate entity. And I personally like the separate entity approach better and think that's where we're going to head. Um, and so in that sense, the thing is not you, but it's,

SPEAKER_111: it's like a always available, always great, super capable assistant, executive,

SPEAKER_44: It's an agent in a way like it's out there working on your behalf and understands what you want and anticipates what you want is what I'm reading into what you're saying.

SPEAKER_135: I think there'd be agent like behavior, but there's like a difference between

SPEAKER_72: a senior employee and an agent. Yeah. And like, I want it, you know, I think of like my,

SPEAKER_50: I think like a bit, like one of the things that I like about a senior employee is they'll,

SPEAKER_29: they'll push back on me. They will sometimes not do something I ask, or there sometimes will say like, I can do that thing if you want, but if I do it, here's what I think would happen. And then this, and then that, and are you really sure? Hmm. I definitely want that kind of vibe, which not, not just like this thing that I ask and it blindly does. It can reason. Yeah. Yeah. And it has like the kind of relationship with me that I would expect out of a really competent

SPEAKER_148: person that I worked with, which is different from like a sycophant. Yeah.

Chamath Palihapitiya: Yeah. The thing in that world where if you had this like Jarvis like thing that can reason, what do you think it does to products that you use today where the interface is very valuable? So for example, if you look at an Instacart, or if you look at an Uber, or if you look at a DoorDash, these are not services that are meant to be pipes that are just providing a set of APIs to a smart set of agents that ubiquitously work on behalf of 8 billion people. What do you think has to change in how we think about how apps need to work of how this entire infrastructure of experiences need to work in a world where you're agentically interfacing to the world? I'm actually very interested in designing

SPEAKER_29: a world that is equally usable by humans and by AIs. So I, I, I, I like the interpretability of that. I like the smoothness of the handoffs. I like the ability that we can provide feedback or whatever.

SPEAKER_28: So, you know, DoorDash could just expose some API to my future AI assistant, and they could go put the order in whatever. Or I could say like, I could be holding my phone and I could say, okay, AI assistant,

SPEAKER_50: like you put in this order on DoorDash, please. And I could like watch the app open and see the thing

SPEAKER_29: clicking around and I could say, Hey, no, not this or like, um, there, there's something about designing a world that is usable equally well by humans and AIs that I think is a interesting concept. And I'm more excited about humanoid robots than sort of robots of like very other shapes. The world

SPEAKER_50: is very much designed for humans. I think we should absolutely keep it that way. And a shared interface

SPEAKER_41: is nice. So you see voice chat, that modality kind of gets rid of apps, you just ask it for sushi,

SPEAKER_44: it knows sushi you like before it knows what you don't like and does its best shot at doing it.

SPEAKER_154: I, it's hard for me to imagine that we just go to a world totally where you say like, Hey,

SPEAKER_50: chat GPT, order me sushi. And it says, Okay, do you want it from this restaurant? What kind, what time, whatever, I think user, I think visual user interfaces are super good for a lot of things. Um, and it's hard for me to imagine like a world where you never look at a screen and just use voice mode only, but I, I can't imagine that for a lot of things.

SPEAKER_156: Yeah. I mean, Apple tried with Siri, like you could, supposedly you can order an Uber

SPEAKER_44: automatically with Siri. I don't think anybody's ever done it because why would you take the risk of

Chamath Palihapitiya: not? Well, the quality to your point, the quality is not good, but when the quality is good enough, you're a lot, you'll actually prefer it just because it's just lighter weight. You don't have to take your phone out. You don't have to search for your app and press it. Oh, it automatically logged you out. Oh, hold on. Log back in. Oh, TFA. It's a whole pain in the ass.

SPEAKER_28: You know, it's like setting a timer with Siri. I do every time because it works really well

SPEAKER_50: and it's great and I don't need more information, but ordering an Uber, like I want to see the prices for a few different options. I want to see how far away it is. I want to see like maybe even

SPEAKER_29: where they are on the map. Cause I might walk somewhere. I get a lot more information by, I think in less time by looking at that order the Uber screen than I would if I had to do that

SPEAKER_163: all through the audio channel. I like your idea of watching it happen. That's kind of cool.

SPEAKER_82: I think there will just be like, yeah, different. There are different interfaces we use for different

Chamath Palihapitiya: tasks and I think that'll keep going. Of all the developers that are building apps and experiences on open AI, are there a few that stand out for you where you're like, okay, this is directionally going in a super interesting area, even if it's like a toy app, but are there things that you guys point to and say, this is really important?

SPEAKER_28: Um, I met with a new company this morning, or I'm barely even a company. It's like two people that are going to work on a summer project, trying to actually finally make the AI tutor. Like, and I've always been interested in this space. I, a lot of people have done great stuff

SPEAKER_29: on our platform, but if, if someone can deliver like the way that you actually like,

SPEAKER_50: they used a phrase I love, which is, this is going to be like a Montessori level reinvention for how people, how people learn things. Um, but if you can like find this new way to like, let people explore and learn in new ways on their own, I'm personally super excited about that. Um,

SPEAKER_28: a lot of the coding related stuff you mentioned Devon earlier, I think that's like a super cool vision of the future. The thing that I am health healthcare, I believe should be pretty transformed by this. But the thing I'm personally most excited about is the sort of doing faster

SPEAKER_50: and better scientific discovery. GPT-4 clearly not there in a big way, although maybe it accelerates things a little bit by making scientists more productive, but alpha four, three. Yeah.

SPEAKER_33: That's like, but Sam, that will be a triumph. Those are not like these, these models are trained and built differently than the language models. I mean, to some, obviously there's a lot that's similar, but there's a lot, um, there's kind of a ground up architecture to a lot of these models that are being applied to these specific problem sets, these specific applications like chemistry interaction

SPEAKER_36: modeling, for example. You'll need some of that for sure. But the thing that I think we're missing

SPEAKER_50: across the board for many of these things we've been talking about is models that can do reasoning. And once you have reasoning, you can connect it to chemistry stimulators.

SPEAKER_31: So I guess that, yeah, that's the important question I wanted to kind of talk about today

SPEAKER_33: was this idea of networks of models. People talk a lot about agents as if there's kind of this linear set of call functions that happen. But one of the things that arises in biology is networks of systems that have cross interactions that the aggregation of the system, the aggregation of the network produces an output rather than one thing calling another, that thing calling another. Do we see like an emergence in this architecture of either specialized models or network models that work together to address bigger problem sets use reasoning, there's computational models that do things like chemistry or arithmetic and there's other models that do rather than one model to rule them all that's purely generalized.

SPEAKER_29: I don't know. I don't know how much reasoning is going to turn out to be a super generalizable thing. I suspect it will. But that's more just like an intuition and a hope. And it would be nice

SPEAKER_50: if it worked out that way. I don't know if that's like,

SPEAKER_180: but let's walk through the protein modeling example. There's a bunch of training data, images of proteins,

SPEAKER_33: and then sequence data, and they build a model predictive model, and they have a set of processes and steps for doing that. Do you envision that there's this artificial general intelligence or this great reasoning model that then figures out how to build that sub model that figures out how to solve that problem by acquiring the necessary data, and then resolve it? There's so many ways where that could

SPEAKER_29: go. Like maybe it is it trains a literal model for it, or maybe it just like knows the one big model

SPEAKER_50: what it can like go pick what other training data it needs and ask a question and then update on that.

SPEAKER_33: I guess the real question is, are all these startups going to die? Because so many startups are working in that modality, which is go get special data, and then train a new model on that special data from the ground up. And then it only does that one sort of thing. And it works really well at that one thing. And it works better than anything else at that one. You know, there's like

SPEAKER_50: a version of this, I think you can, like, already see, when you were when you were talking about, like, biology and these complicated networks of systems. The reason I was smiling, I got super sick recently, and I'm mostly better now, but it was just like, body like got beat up, like one system at a time fought like you can really tell like, okay, it's this cascading thing. And, and that reminded me of you like talking about the like biology is just these like, you have no idea how much these systems interact with each other until things start going wrong. And that was sort of like, interesting to see. But I was using, I was like using ChatGPT, to try to like, figure out, like what was happening, whatever, and would say, well, I'm, you know, unsure of this one thing. And then I just like posted a paper on it without even reading the paper, like in the context, and it says, Oh, that was the thing I was unsure of, like, now I think this instead. So there's like a that was like a small version of what you're talking about, where you can like, can say this, I don't, I don't know this thing, and you can put

SPEAKER_90: more information, you don't retrain the model, you're just adding it to the context here. And now you're getting them.

SPEAKER_31: So these models that are predicting protein structure, like, let's say,

SPEAKER_33: right, this is the whole basis, and now now other molecules at alpha fold three. Can they can? Yeah, I mean, is it basically a world where the best generalized model goes in and gets that training data and then figures out on its own? And maybe you could maybe you could use an example for us? Can you tell us about Sora, your video model that generates amazing moving images moving video? And what's different about

SPEAKER_187: the architecture there, whatever you're willing to share on how make how that is different?

SPEAKER_29: Yeah, so my on the general thing first, my, you clearly will need specialized

SPEAKER_28: simulators, connectors, pieces of data, whatever, but my intuition. And again, I don't have this like backed up with science, my intuition would be if we can figure out the core of generalized reasoning, connecting that to new problem domains,

SPEAKER_135: in the same way that humans are generalized reasoners, would I think be doable?

SPEAKER_193: It's like a faster unlock, faster unlock, then I think I think so.

SPEAKER_50: But yeah, you Sora like does not start with a language model. It's that that's a model that is like customized to do video. And so like, we're clearly not at that world yet.

SPEAKER_31: Right? So you guys, so just as an example for you guys to build a good video model,

SPEAKER_33: you built it from scratch using I'm assuming some different architecture and different data. But in the future, the generalized reasoning system, the AGI, whatever system, theoretically could render that by figuring out how to do it.

SPEAKER_50: Yeah, I mean, one example of this is like, okay, you know, as far as I know, all the best text models in the world are still autoregressive models, and the best image and video models are diffusion models. And that's like, sort of strange in some sense.

SPEAKER_196: Yeah. Yeah.

SPEAKER_44: So there's a big debate about training data. You guys have been, I think, the most thoughtful of any company, you've got licensing deals now, FT, etc. And we got to just be gentle here, because you're involved in the New York Times lawsuit, you weren't able to settle, I guess, an arrangement with them for training data. How do you think about fairness in fair use, we've had big debates here on the pod. Obviously, your actions are, you know, speak volumes that you're trying to be fair by doing licensing deals. So what's your personal position on the rights of artists who create beautiful music, lyrics, books, and you taking that and then making

SPEAKER_20: a derivative product out of it and, and then monetizing it? And what's fair here? And how do we get to a world where, you know, artists can make content in the world and then decide what they want other people to do with it? Yeah, and I'm just curious, your personal belief, because I know you to be a thoughtful person on this. And I know a lot of other people in our industry are not very

SPEAKER_202: thoughtful about how they think about content creators.

SPEAKER_204: So I think it's very different for different kinds of I mean, look, on unfair use, I think we

SPEAKER_50: have a very reasonable position under the current law. But I think AI is so different. But for things like art, we'll need to think about them in different ways. But let's say if you go read a

SPEAKER_29: bunch of math on the internet, and learn how to do math, that, I think seems unobjectionable to most

SPEAKER_50: people. And then there's like, you know, another set of people who might have a different opinion.

SPEAKER_68: Well, what if you like, actually, let me not get into that just in the interest of not making this

SPEAKER_50: answer too long. So I think there's like one category people are like, Okay, there's like generalized human knowledge, you can kind of like, go, if you learn that, like, that's, that that's like, open domain or something, if you kind of go learn about the Pythagorean theorem. That's one end of the spectrum. And I think the other extreme end of the spectrum is,

SPEAKER_29: is art, and maybe even like, more than more specifically, I would say it's like doing, it's a system generating art in the style or the likeness of another artist would be kind of the furthest end of that. And then there's many, many cases on the spectrum in between.

SPEAKER_36: I think the conversation has been historically very caught up on training data, but it will

SPEAKER_29: increasingly become more about what happens at inference time, as training data becomes less valuable. And the what the system does, accessing, you know, information in in context in real time, or, you know, taking like, like something like that, what happens at inference time will become more debated and how the what the new economic model is there. So if you say like, create me a song in the style of Taylor Swift, even if the model were never trained on any Taylor Swift songs at all, you can still have a problem, which is that may have read about Taylor Swift, it may know about her themes, Taylor Swift means something. And then the question is like, should that model, even if it were never trained on any Taylor Swift song whatsoever, be allowed to do that? And if so, how should Taylor get paid? Right. So I think there's an opt in opt out in that case, first of all, and then there's an economic model. Staying on the music example, there is something interesting to look at from the historical perspective here, which is sampling and how the economics around that work. This is not quite the same thing, but it's like an interesting place to start looking.

SPEAKER_208: Sam, let me just challenge that. What's the difference in the example you're giving of the model

SPEAKER_33: learning about things like song structure, tempo, melody, harmony, relationships, all the discovering all the underlying structure that makes music successful, and then building new music, using training data. And what a human does that listens to lots of music, learns about and their brain is processing and building all those same sort of predictive models, or those same sort of discoveries or understandings. What's the difference here? And why are you making the case that perhaps artists should be uniquely paid? This is not a sampling situation. You're not, the AI is not outputting, and it's not storing in the model, the actual original song. Yeah, I was learning structure, right? So I wasn't trying to make that that point,

SPEAKER_36: because I agree, like in the same way that humans are inspired by other humans. I was saying, if you if you say generate me a song in the style of Taylor Swift,

SPEAKER_213: I see, right? Okay, where the prompt leverages some artists.

SPEAKER_50: I think personally, that's a different case.

SPEAKER_180: Would you be comfortable asking? Or would you be comfortable letting the model train itself with a

SPEAKER_33: music model being trained on the whole corpus of music that humans have created, without royalties being paid to the artists that that music is being fed in? And then you're not allowed to ask, you know, artists specific prompts, you could just say, hey, pay me, play me a really cool pop song that's fairly modern about heartbreak, you know, with a female voice, you know,

SPEAKER_50: We have currently made the decision not to do music, and partly because exactly these questions of where you draw the lines and you know what, like, even I was meeting with several musicians I really admire recently, I was just trying to like talk about some of these edge cases, but even the world in

SPEAKER_29: which if we went and let's say we paid 10,000 musicians to create a bunch of music just to make a great training set where the music model could learn everything about strong, strong structure.

SPEAKER_50: And what makes a good catchy beat and everything else and only trained on that, let's say we could still make a great music model, which maybe maybe we could. You know, I was kind of like posing that as a thought experiment to musicians and they're like, well, I can't object to that on any principle basis at that point. And yet there's still something I don't like about it. Now, that's not a reason not to do it necessarily, but it is. Did you see that ad that Apple put out? Maybe it was yesterday or

SPEAKER_218: something of like squishing all of human creativity down into one really thin iPad?

SPEAKER_219: What was your take on it? People got really emotional about it. Yeah. Stronger reaction

SPEAKER_29: than you would think. There's something about, I'm obviously hugely positive on AI, but there is something

SPEAKER_28: that I think is beautiful about human creativity and human artistic expression. And, you know, for an AI that just does better science, like, great, bring that on. But an AI that is going to do this like deeply beautiful human creative expression, I think we should like figure out it's going to happen. It's going to be a tool that will lead us to greater creative heights. But I think we should figure out how to do it in a way that like preserves the spirit of what we all care

SPEAKER_44: about here. And I think your actions speak loudly. We were trying to do Star Wars characters in Dolly. And if you ask for Darth Vader, it says, Hey, we can't do that. So you've, I guess,

SPEAKER_20: red teamed or whatever you call it internally. We try. Yeah, you're not allowing people to use other people's IP. So you've taken that decision. Now, if you asked it to make a Jedi bulldog or a Sith Lord

SPEAKER_225: bulldog, which I did, it made my bulldogs as Sith bulldogs. So there's an interesting question about

SPEAKER_135: like spectrum, right? Yeah, you know, we put out this thing yesterday called the spec,

SPEAKER_28: where we're trying to say here are, here's, here's how our model is supposed to behave. And it's very hard. It's a long document. It's very hard to like specify exactly in each case where

SPEAKER_29: the limits should be. And I view this as like a discussion that's going to need a lot more input. Um, but, but these sorts of questions about, okay, maybe it shouldn't generate Darth Vader,

SPEAKER_42: but the idea of a Sith Lord, or a Sith style thing or Jedi at this point is like part of the culture,

SPEAKER_98: like, like, these are, these are all hard decisions.

SPEAKER_00: Yeah. And I think you're right, the music industry is going to consider this opportunity to make Taylor

SPEAKER_20: Swift songs their opportunity. It's part of the four part fair use test is, you know, these who gets to capitalize on new innovations for existing art. And Disney has an argument that, hey, you know,

SPEAKER_228: if you're going to make Sora versions of Ashoka or whatever, Obi-Wan Kenobi, that's Disney's opportunity. And that's a great partnership for you, you know, to pursue.

Chamath Palihapitiya: So we're, I think this section I would label as AI and the law. So let me ask maybe a higher level

SPEAKER_88: question. What does it mean when people say regulate AI? Sam, what does it, what does that even mean?

SPEAKER_180: And comment on California's new proposed regulations as well, if you, if you're up for it.

SPEAKER_161: I'm concerned. I mean, there's so many proposed regulations, but most of the ones I've seen on

SPEAKER_50: the California state things I'm concerned about. I also have a general fear of the states all doing this them themselves. When people say regulate AI, I don't think they mean one thing. I think there's like, some people are like, ban the whole thing. Some people like don't allow it to be open source,

SPEAKER_29: require it to be open source. The thing that I am personally most interested in is,

SPEAKER_50: I think there will come. Look, I may be wrong about this, I will acknowledge that this is a forward looking statement. And those are always dangerous to make. But I think there will come a time in the not super distant future, like, you know, we're not talking like decades and decades from now,

SPEAKER_29: where AI says the frontier AI systems are capable of causing significant global harm. And for those

SPEAKER_50: kinds of systems, in the same way we have like, global oversight of nuclear weapons or synthetic bio or things that can really like, have a very negative impact way beyond the realm of one country.

SPEAKER_29: I would like to see some sort of international agency that is looking at the most powerful systems

SPEAKER_50: and ensuring like reasonable safety testing, you know, these things are not going to escape and

SPEAKER_00: recursively self improve or whatever. The criticism of this is that your you have the resources

SPEAKER_20: to cozy up to lobby to be involved in you've been very involved with politicians and then startups, which are also passionate about and invest in are not going to have the ability to resource and deal with this and that this regulatory capture as per our friend, you know, Bill Gurley did a great talk

SPEAKER_240: last year about it. So maybe you could address that head on. Do you feel like if the line were

SPEAKER_36: we're only going to look at models that are trained on computers that cost more than 10 billion or more

SPEAKER_50: than 100 billion or whatever dollars? I'd be fine with that. There'd be some line that'd be fine. And I don't think that puts any regulatory burden on startups. So if you have like, the nuclear raw

SPEAKER_44: material to make a nuclear bomb, like there's a small subset of people who have that therefore you use the analogy of like a nuclear inspectors kind of situation. Yeah, I think that's interesting. Sachs, you have a question? Well, Thomas, go ahead. You had a follow up.

SPEAKER_26: Can I say one more thing about that? Of course, I'd be super nervous about regulatory overreach

SPEAKER_50: here. I think we can get this wrong by doing way too much or even a little too much. I think we can get this wrong by doing not enough. But but I do think part of and I and now I mean, you know, we have seen regulatory overstepping or capture just get super bad in other areas. And, you know, also maybe nothing will happen. But but I think it is part of our duty and our mission to like, talk about what we believe is likely to happen and what it takes to get that right.

SPEAKER_31: The challenge, Sam, is that we have statute that is meant to protect people protect society at large.

SPEAKER_33: What we're creating, however, is statute that gives the government rights to go in and audit code, to audit business trade secrets. We've never seen that to this degree before. Basically, the California legislation that's proposed and some of the federal legislation that's been proposed basically requires the federal government to audit a model, to audit software, to audit and review the parameters and the weightings of the model. And then you need their checkmark in order to deploy it for commercial or public use. And for me, it just feels like we're trying to rein in the government agencies for fear. And and because folks have a hard time understanding this and are scared about the implications of it, they want to control it. And because they want and the only way to control it is

SPEAKER_180: to say, give me a right to audit before you can release it. Yeah. And they're clueless.

SPEAKER_33: These people are clueless. I mean, the way that the stuff is written, you read it, you're like going to pull your hair out because, as you know better than anyone, in 12 months, none of this stuff is going to make sense anyway.

SPEAKER_154: Totally. Right.

SPEAKER_28: Look, the reason I have pushed for an agency-based approach for kind of like the big picture stuff

SPEAKER_50: and not a like write it in laws, I don't in 12 months, it will all be written wrong. And I don't think even if these people were like true world experts, I don't think they could get it right looking out in 12 or 24 months. And I don't these policies, which is like, we're going to look at,

SPEAKER_28: you know, we're going to audit all of your source code and like, look at all of your weights one by

SPEAKER_98: one. Like, yeah, I think there's a lot of crazy proposals out there.

SPEAKER_180: By the way, especially if the models are always being retrained all the time, if they become more dynamic.

SPEAKER_50: Again, this is why I think it's yeah. But but like, when before an airplane gets certified, there's like a set of safety tests, we put the airplane through it. And totally, it's different than reading all of your code.

SPEAKER_180: That's reviewing the output of the model, not reviewing the insights of the model.

SPEAKER_72: And so what I was gonna say is, that is the kind of thing that I think as safety testing makes sense.

SPEAKER_31: How are we going to get that to happen, Sam? And I'm not just speaking for open AI,

SPEAKER_33: I speak for the industry for for humanity, because I am concerned that we draw ourselves into almost like a dark ages type of era, by restricting the growth of these incredible technologies that can prosper human that humanity can prosper from so significantly. How do we change the sentiment and get that to happen? Because this is all moving so quickly at the government levels. And folks seem to be getting it wrong. And I'm just

Chamath Palihapitiya: just to build on that, Sam, the architectural decision, for example, that llama took is pretty interesting in that it's like, we're going to let llama grow and be as unfettered as possible. And we have this other kind of thing that we call llama guard that's meant to be these protective guardrails. Is that how you see the problem being solved correctly? Or do you see that

SPEAKER_28: the current at the current strength of models, definitely some things are going to go wrong. And I

SPEAKER_50: don't want to like, make light of those or not take those seriously. But I'm not like, I don't have

SPEAKER_29: any like catastrophic risk worries with a GPT-4 level model. And I think there's many safe ways to choose to deploy this. Maybe we'd find more common ground if we said that, and I think like, you know, the specific example of models that are capable, that are technically capable, not even if they're not

SPEAKER_50: going to be used this way, of recursive self-improvement, or of, you know, autonomously

SPEAKER_29: designing and deploying a bioweapon, or something like that. Or a new model. That was the recursive self-improvement point. You know, we should have safety testing on the outputs at an international level for models that, you know, have a reasonable chance of posing a threat there.

SPEAKER_50: I don't think like GPT-4, of course, does not pose any sort of, well, I don't want to say any sort, because we don't, yeah, I don't think that GPT-4 poses a material threat on those kinds of things. And I think there's many safe ways to release a model like this. But, you know, when like,

SPEAKER_29: significant loss of human life is a serious possibility like airplanes or any number of other examples where I think we're happy to have some sort of testing framework. Like, I don't think about an airplane when I get on it. I just assume it's going to be safe.

SPEAKER_171: Right. Right.

SPEAKER_97: There's a lot of hand-wringing right now, Sam, about jobs. And you had a lot of, I think you did

SPEAKER_267: like some sort of a test when you were at YC about UBI, and you've been-

SPEAKER_154: Our results in that come out very soon. I just, it was a five-year study that wrapped up,

SPEAKER_50: or started five years ago. Well, there was like a beta study first, and then it was like a long one that ran.

SPEAKER_193: Well, Mark, what did you learn about that? Yeah, why'd you start, why'd you start it? Maybe just explain UBI and why you started it.

SPEAKER_28: So we started thinking about this in 2016, kind of about the same time, started taking AI really seriously. And the theory was that the magnitude of the change that may come

SPEAKER_29: to society and jobs and the economy, and sort of in some deeper sense than that, like what the social contract looks like, meant that we should have many studies to study many ideas about new ways to arrange that. I also think that, you know, I'm not like a super fan of how the government has handled most policies designed to help poor people. And I kind of believe that if you could just give people money, they would make good decisions and the market would do its thing. And, you know, I'm very much in favor of lifting up the floor and reducing, eliminating poverty. But I'm interested in better ways to do that than what we have tried for the existing social safety net and kind of the way things have been handled. And I think giving people money is not going to go solve all problems. It's certainly not going to make people happy. But it might solve some problems and it might give people a better horizon with which to help themselves. And I'm interested in that. I think that now that we see some of the ways, so 2016 was a very long time ago. You know, now that we see some of the ways that AI is developing, I wonder if there's better things to do than the traditional conceptualization of UBI. Like, I wonder, I wonder if the future

SPEAKER_50: looks something like more like universal basic compute than universal basic income. And everybody gets like a slice of GPT-7's compute and they can use it, they can resell it, they can donate it to

SPEAKER_29: somebody to use for cancer research. But but what you get is not dollars, but this like productivity slice. Yeah, you own like part of the productivity, right? I would like to shift to

SPEAKER_278: the gossip part of this. Okay, gossip. Let's go back to November. What the flying happened?

SPEAKER_281: Um, you know, I if you have specific questions, I'm happy to talk about it at some point. So here's the

SPEAKER_69: point. What happened? You were fired, you came back, it was palace intrigue. Did somebody stab you in the back? Did you find AGI? What's going on? This is a safe space. Yeah. Um, I was fired. I was

SPEAKER_28: I talked about coming back, I kind of was a little bit unsure at the moment about what I wanted to do, because I was very upset. Um, and I realized that I really loved OpenAI and the people and that I would come back and I kind of, I knew it was going to be hard. It was even harder than I thought. But I kind of was like, Alright, fine. Um, I agreed to come back. Um, the board like took a while to figure things out. And then, uh, you know, we were kind of like trying to keep the team together and keep doing things for our customers and, uh, you know, sort of started making other plans. Then the board decided to hire a

SPEAKER_98: different interim CEO. Um, and then everybody, there are many people. Oh, my gosh. What was, what was that

SPEAKER_88: guy's name? He was there for like a Scaramucci, right? Like, uh, Emmett's great. And I have nothing but good things to say about

SPEAKER_296: it. Um, and then where were you in the, um, when you found the news that you've been fired? Like,

SPEAKER_154: well, take me to that moment. I was in a hotel room in Vegas for F1 weekend. I think that's

SPEAKER_298: happened to you before J. Cal. So you get a text and they're like, what'd you say? I said, I think

SPEAKER_297: that's happened to you before J. Cal. I'm trying to think if I ever got fired. I don't think I've gotten

SPEAKER_299: fired. Um, yeah, I got a text. No, it's just a weird thing. Like it's a text from who? Actually,

SPEAKER_300: no, I got a text the night before and then I got on a phone call with the board,

SPEAKER_98: uh, and then that was that. And then I kind of like, I mean, then everything went crazy. I was

SPEAKER_42: like, uh, it was like, I mean, I have, my phone was like unusable. It was just a nonstop vibrating

SPEAKER_125: thing of like text messages. You got fired by tweet that happened a few times that during the Trump administration, a few, uh, they didn't call me first before tweeting nice of them. Um, and then

SPEAKER_98: like, you know, I kind of did like a few hours of just this like absolute fugue state, um, in the hotel room, trying to like, I was just confused beyond belief, trying to figure out what to do. And, uh, so weird. And then like flew home. It may be like, I don't know, 3 PM or something like that. Um, still just like, you know, crazy nonstop phone blowing up, uh, met up with some people in person by that evening. I was like, okay, you know, I'll just like, go do AGI research. And I was feeling pretty happy about the future. And yeah, you have options. And then, and then the next morning, uh, had this call with a couple of board members about coming back and that led to a few more days of craziness. And then, uh, and then it kind of, I think it got resolved. Well, it was like a lot of

SPEAKER_105: insanity in between, but what percent, what percent of it was because of these nonprofit board members?

SPEAKER_98: Um, well, we only have a nonprofit board. So it was all the nonprofit board members. Uh, there, the board had gotten down to six people. Um, they, and then they removed Greg from the board and then fired me. Um, so, but it was like, you know,

David Sacks: but I mean, like, was there a culture clash between the people on the board who had only nonprofit experience versus the people who had started experience?

SPEAKER_33: And maybe you can share a little bit about if you're willing to the motivation behind the

SPEAKER_161: action, anything you can, I think there's always been culture clashes at look, obviously

SPEAKER_29: not all of those board members are my favorite people in the world, but I have serious respect for

SPEAKER_28: the gravity with which they treat AGI and the importance of getting AI safety, right. And even if I stringently disagree with their decision-making and actions, which I do, um, I have never once doubted

SPEAKER_50: their integrity or commitment to, um, the sort of shared mission of safe and beneficial AGI. Um, you know, do I think they like made good decisions in the process of that or kind of know how to balance all the things opening. I has to get right. No, but, but I think the, like the intent,

SPEAKER_29: the intent of the magnitude of yeah, AGI and getting that right.

SPEAKER_125: I actually, let me ask you about that. So the mission of open AI is explicitly to create AGI,

David Sacks: which I think is really interesting. A lot of people would say that if we create AGI, that would be like an unintended consequence of something gone horribly wrong. And they're very afraid of that outcome, but open AI makes that the actual mission. Does that create like more fear about what you're doing? I mean, I understand it can create motivation too, but how do you reconcile that? I guess why

SPEAKER_318: I think a lot of, I think a lot of the, well, I mean, first I'll say that I'll answer the first

SPEAKER_28: question in the second one. I think it does create a great deal of fear. Uh, I think a lot of the world

SPEAKER_29: is understandably very afraid of AGI or very afraid of even current AI and, and very excited about it and even more afraid and even more excited about where it's going. Um, and we, we wrestle with that, but like, I think it is unavoidable that this is going to happen. I also think it's going to be

SPEAKER_28: tremendously beneficial, but we do have to navigate how to get there in a reasonable way. And like a lot

SPEAKER_29: of stuff is going to change and changes, you know, pretty, pretty uncomfortable for people. So there's a lot of pieces that we got to get right. Can I ask a, can I ask a different question? You, you have

Chamath Palihapitiya: created, I mean, it's the hottest company and you are literally at the center of the center of the center, but then it's so unique in the sense that all of this value you eschewed economically. Can you just

SPEAKER_324: like walk us through like, yeah, I wish I had taken, I wish I had taken equity. So I never had to answer this question. If I could go back in time, why don't they give you a grant now? Why doesn't the board

SPEAKER_328: just give you a big option grant? Like you deserve. Yeah. Give you five points. What was the decision back

SPEAKER_50: then? Like, why was that so important? The decision back then, the re the original reason was just like the structure of our nonprofit. It was like, there was something about, yeah, okay, this is like nice from a motivations perspective, but mostly it was that our board needed to be a majority of disinterested directors. And I was like, that's fine. I don't need equity right now. I kind of but like,

SPEAKER_332: but now that you're running a company, yeah, it creates these weird questions of like,

SPEAKER_29: well, what's your real motivation for us? Yeah, that's that it is so deeply on it. One thing I have noticed it is it's so deeply unimaginable to people to say, I don't really need

SPEAKER_337: more money. Like, and I think, I think people think it's a little bit of an ulterior motive.

SPEAKER_340: Well, yeah, yeah, yeah. No, it's so it assumes it's doing on the side to make money.

SPEAKER_98: If I were just trying to say like, I'm going to try to make a trillion dollars with open AI, I think everybody would have an easier time and it wouldn't save me. Well, it would save a lot

SPEAKER_69: of conspiracy theories. Sam, this is totally the back channel. You are a great deal maker. I've watched your whole career. I mean, you're just great at it. You got all these connections. You're really good at raising money. You're fantastic at it. And you got this Johnny Ive thing going, you're inhumane, you're investing in companies, you got the orb, you're raising seven trillion dollars to build fabs, all this stuff. All of that put together. J.K.L loves fake news. Well, I'm kind of being a little facetious here. You know, obviously, you're not raising seven trillion dollars, but maybe that's the market cap or something. Putting all that aside, the T was, you're doing all these deals. They don't trust you because what's your motivation, your end running and what opportunities belong inside of open AI, what opportunities should be Sam's,

SPEAKER_347: and this group of nonprofit people didn't trust you. Is that what happened?

SPEAKER_189: So the things like, you know, device companies or if we were doing some chip fab company, it's like,

SPEAKER_28: those are not Sam project. Those would be like opening. I would get that equity.

SPEAKER_351: They would. Okay, that's not the public's perception.

SPEAKER_29: Well, that's not like kind of the people like you who have to like commentate on this stuff all day's perception, which is fair because we haven't announced the stuff because it's not done. I don't think most people in the world like are thinking about this. But I agree, it spins up a lot of conspiracies, conspiracy theories in like tech commentators. Yeah. And if I could go back, yeah, I would just say like, let me take equity and make that super clear. And then I'd be like,

SPEAKER_50: all right, like, I'd still be doing it because I really care about AGI and think this is like the most interesting work in the world. But it would at least type check to everybody. What's the chip

SPEAKER_357: project? That's a $7 trillion? And where does 7 trillion number come from? It makes no sense.

SPEAKER_28: I don't know where that came from. Actually, I genuinely don't. I think, I think the world needs a lot more AI infrastructure, a lot more than it's currently planning to build and with a different

SPEAKER_29: cost structure. The exact way for us to play there is, we're still trying to figure that out.

David Friedberg: What's your preferred model of organizing OpenAI? Is it sort of like the

Chamath Palihapitiya: move fast, break things, highly distributed small teams? Or is it more of this organized effort where you need to plan because you want to prevent some of these edge cases?

David Friedberg: Oh, I have to go in a minute. It's not because it's not to prevent the edge case that we need

SPEAKER_28: to be more organized. But it is that these systems are so complicated and concentrating bets are so important. Like one, you know, at the time, before it was like obvious to do this, you have like DeepMind or whatever has all these different teams doing all these different things, and they're spreading their bets out. And you had OpenAI say, we're going to like basically put the

SPEAKER_50: whole company and work together to make GPT-4. And that was like unimaginable for how to run an AI research lab. But it is, I think what works at a minimum, it's what works for us. So not because

SPEAKER_28: we're trying to prevent edge cases, but because we want to concentrate resources and do these

SPEAKER_50: like big, hard, complicated things. We do have a lot of coordination on what we work on.

SPEAKER_97: All right, Sam, I know you got to go. You've been great on the hour. Come back anytime.

SPEAKER_359: Great talking to you guys. Yeah. Thanks for being so open about it. We've been talking about it for

SPEAKER_28: like a year plus. I'm really happy it finally happened. Yeah, it's awesome. I really appreciate it. I would love to come back on after our next like major launch and I'll be able to talk more

SPEAKER_362: directly about something. You got the Zoom link, same Zoom link every week, just same time,

SPEAKER_363: same Zoom link, just drop in any time. Just drop in. Just put it on your calendar. Come back to the

SPEAKER_364: game. Come back to the game. Yeah, come back to the game. I, you know, I would love to play poker.

SPEAKER_366: It has been forever. That would be a lot of fun. Yeah. That famous hand where Chamath, when you

SPEAKER_69: and I were heads up and you, you had, you and I were heads up and you went all in, I had a set, but there was a straight and a flush on the board and I'm in the tank trying to figure out if I want to

SPEAKER_228: lose because back when we were playing small stakes, it might have been like 5k pot or something.

SPEAKER_347: And then Chamath can't stay out of the pot and he starts taunting the two of us. You should call, you shouldn't call. He's bluffing. And I'm like, Chamath, I'm going, I'm trying to figure out if I

SPEAKER_20: make the call here. I make the call. And, uh, it was like, uh, you had a really good hand and I just happened to have a set. I think you had like top pair, top kicker or something, but you made a great move because the board was so textured, almost like, oh, bottom set. Sam has a great style of playing,

SPEAKER_293: which I would call random jam. Totally. You gotta just get out of the way.

SPEAKER_372: Chamath, I don't really know if you, I don't, I don't know if you can say that about anybody.

SPEAKER_374: I don't, I don't, I'm not gonna. You haven't seen Chamath play in the last 18 months. It's a lot different. I've taken down the game. I'm much more snug.

SPEAKER_375: So much fun now. Have you played Bomb Pots before? Have you played Bomb Pots before?

SPEAKER_377: Have you played Bomb Pots? I don't know what that is. Okay. You'll love it. All right. We'll see you next time.

SPEAKER_383: Yeah. He's nuts. It's PLO. Sam, thank you. Two awards. And congrats on everything, honestly. Thank you, Chamath.

SPEAKER_387: Thanks for coming on and see you guys. Love to have you back when the next F the Big launch. Sounds good. Please do. Cool. Bye.

SPEAKER_69: Gentlemen, some breaking news here. All those projects, he said, are part of OpenAI. That's something people didn't know before this and a lot of confusion there. Chamath, what was your major

SPEAKER_389: takeaway from our hour with Sam?

SPEAKER_75: I think that these guys are going to be one of the four major companies that matter in this whole

Chamath Palihapitiya: space. I think that that's clear. I think what's still unclear is where is the economics going to be? He said something very discreet, but I thought was important, which is, I think he basically, my interpretation is these models will roughly all be the same, but there's going to be a lot of scaffolding around these models that actually allow you to build these apps. So in many ways, that is like the open source movement. So even if the model itself is never open source, it doesn't much matter because you have to pay for the infrastructure, right? There's a lot of open source software that runs on Amazon. You still pay AWS something. So I think the right way to think about this now is the models will basically be all really good. And then it's all this other stuff that you'll have to pay for the interface. Whoever builds all this other stuff is going to be in a position to build a

SPEAKER_69: really good business. Freberg, he talked a lot about reasoning. It seemed like that he kept going to reasoning and away from the language model. Did you note that and anything else that you noted in

SPEAKER_33: our hour with Sam? Yeah, I mean, that's a longer conversation because there is a lot of talk about language models eventually evolving to be so generalizable that they can resolve pretty much like all intelligent function. And so the language model is the foundational model that that yields AGI. But that's all I think there's a lot of people that are different schools of thought on this,

Chamath Palihapitiya: and how much my other takeaway I think is that the I think what he also seemed to indicate is, there's like so many like we're also enraptured by LLMs. But there's so many things other than LLMs that are being baked and rolled by him and by other groups. And I think we have to pay some amount of attention to all those because that's probably where and I think Freberg you tried to go there in your question. That's where reasoning will really come from is this mixture of experts approach. And so you're going to have to think multi dimensionally to reason, right? We do that, right? Do I cross the street or not? In this point in time, you reason based on all these multi inputs. And so there's, there's all these little systems that go into making that decision in your brain. And if you if you use that as a simple example, there's all this stuff that has to go into making some experience being able to reason intelligently.

SPEAKER_394: Saks, you went right there with the corporate structure, the board. And he gave us a lot more

SPEAKER_20: information here. What are your thoughts on the, hey, you know, the chip stuff and the other stuff I'm working on, that's all part of open AI, people just don't realize it in that moment. And then, you know, your questions to him about equity, your thoughts on?

SPEAKER_397: Saks, I'm sure I was like the main guy who asked that question, J. Cal, but

SPEAKER_347: um, well, no, you did talk about the nonprofit, the difference between the nonprofit question

David Sacks: about the clearly was some sort of culture clash on the board between the people who originated from the nonprofit world, the people who came from the startup and the tech side, we don't really know more than that. But there clearly was some sort of culture clash. I thought one of the a couple of the other areas that he drew attention to that were kind of interesting is he clearly thinks there's a big opportunity on mobile that goes beyond just like having, you know, a chat GPT app on your phone, or maybe even having like a Siri on your phone. There's clearly something bigger there. He doesn't know exactly what it is, but it's going to require more inputs. It's that, you know, personal assistant that's seeing everything around you and helping you.

SPEAKER_44: I think that's a great insight, David, because he was talking about, hey, I'm looking for a senior team member who can push back on me and understands all context. I thought that was like a very interesting to think about.

SPEAKER_401: Yeah. He's talking about an executive assistant or an assistant that has executive function as

David Sacks: opposed to being like just an alter ego for you or what he called a sycophant. That's kind of interesting. I thought that was interesting. Yeah. Yeah. And clearly he thinks there's a big opportunity in biology and scientific discovery.

SPEAKER_33: After the break, I think we should talk about AlphaFold 3. It was just announced.

SPEAKER_44: Yeah, let's do that. And we can talk about the, the Apple ad in depth. I just want to also make sure people understand when people come on the pod, we don't show them questions. They don't edit the transcript. Nothing is out of bounds. If you were wondering why I didn't ask, or we didn't ask about the Elon lawsuit, he's just not going to be able to comment on that. So it'd be a no comment. So, you

David Sacks: know, and we're not like our time was limited and there's a lot of questions that we could ask him that would have just been a waste of time. And frankly, he's already been asked.

SPEAKER_408: So I just want to make sure people understand that.

David Sacks: Yeah, of course, he's going to no comment on any lawsuit. And he's already been asked about that 500 times. All right. Yeah.

SPEAKER_408: Should we take a quick break before the next, before we come back? Yeah, I'll take a bio break and then we'll come back with some news for you and some more banter

SPEAKER_20: with your favorite besties on the number one podcast in the world, the Elon podcast. All right. Welcome back, everybody. Second half of the show. Great guest, Sam Altman. Thanks for coming on the pod. We've got a bunch of news on the docket. So let's get started. Freiberg, you told me I could give some

SPEAKER_413: names of the guests that we booked for the All In Summit. I did not. You did. You've said each week, every week that I get to say some names.

SPEAKER_180: I did not say that. I did not. I appreciate your interest in the All In Summit's lineup,

SPEAKER_209: but we do not yet have enough critical mass to feel like we should go out there.

SPEAKER_19: Well, I am a loose cannon. So I will announce my two guests. And I created the summit, and you took

SPEAKER_20: it from me. So I've done a great job. I will announce my guests. I don't care what your opinion is. I have booked two guests for the summit, and it's going to be sold out. Look at these two guests I booked for the third time coming back to the summit. Our guy Elon Musk will be there, hopefully in person, if not, you know, from 40,000 feet on Starling Connection, wherever he is in the world. And for the first time, our friend Mark Cuban will be coming. And so two great guests for you to look forward to. But Freiberg's got like 1,000 guests coming. He'll tell you when

SPEAKER_240: it's like 48 hours before the conference. But yeah, two great guests coming.

SPEAKER_401: Speaking of billionaires who are coming, isn't coming too? Yes, coming. Yes, he's booked. So we have three billionaires. Three billionaires. Yes. Okay.

SPEAKER_424: Hasn't fully confirmed, so don't. Okay. Well, we're going to say it anyway. Has penciled in. Don't say it. That's it. Don't back up. We'll say penciled. Yeah, don't back out.

SPEAKER_332: This is going to be catnip for all these protest organizers. Like if you have to go to one place.

SPEAKER_427: Do not poke the bear. Well, by the way, speaking of updates, what did you guys think of the bottle for the all in tequila?

SPEAKER_428: Ooh. Oh, beautiful. Honestly, I will just say, I think you are doing a marvelous job.

Chamath Palihapitiya: That, I was shocked at the design. Shocked meaning it is so unique and high quality. I think it's amazing.

SPEAKER_332: It would make me drink tequila.

SPEAKER_431: You're going to. You're going to want to.

SPEAKER_432: Gotcha. It is stunning.

SPEAKER_44: Just congratulations. And yeah, it was just, when we went through the deck at the monthly meeting,

SPEAKER_20: it was like, oh, that's nice. Oh, that's nice. We're going through the concept bottles.

SPEAKER_434: And then that bottle came up and everybody went like crazy. It was like somebody hitting like a, Steph Curry hitting a half court shot. It was like, oh my God.

SPEAKER_424: It was just so clear that you've made an iconic bottle that if we can produce it. Oh Lord. It is going to be.

SPEAKER_439: Looks like we can.

SPEAKER_438: The. Oh, is it going to make it?

David Sacks: Yeah.

SPEAKER_439: It's going to be amazing. I'm excited. I'm excited for it. You know, it's like.

David Sacks: I mean, the bottle design is so complicated that we had to do a feasibility analysis on whether it was actually manufacturable, but it is. So, or at least the early reports are good. So we're going to. Hopefully we'll have some made for the, in time for the all in summit.

SPEAKER_442: I mean, why not? I mean, it's great.

SPEAKER_337: When we get barricaded in by all these protesters, we can drink the tequila.

SPEAKER_447: Did you guys see, did you see Peter Thiel? Peter Thiel got barricaded by these ding-dongs at Cambridge. My God.

SPEAKER_20: Listen, people have the right to protest. I think it's great people are protesting, but surrounding people and threatening them is a little bit over the top and dangerous.

David Sacks: I think you're exaggerating what happened.

SPEAKER_44: Well, I don't know exactly what happened because all we see is these videos.

David Sacks: Look, they're not threatening anybody. And I don't even think they tried to barricade him in. They were just outside the building. And because they were blocking the driveway, his car couldn't leave. But he wasn't physically locked in the building or something.

SPEAKER_451: Yeah, that's what the headlines say, but that could be fake news, fake social. Yeah. This was not on my bingo card.

Chamath Palihapitiya: This pro-protester support by Sachs was not on the bingo card, I got to say.

SPEAKER_401: I didn't see it coming. The Constitution of the United States in the First Amendment provides for the right of assembly,

SPEAKER_327: which includes protests and sit-ins, as long as they're peaceable. Now, obviously, if they go too far and they vandalize or break into buildings or use violence, then that's not peaceable. However, expressing sentiments with which you disagree does not make it violent.

David Sacks: And there's all these people out there now making the argument that if you hear something from a protester that you don't like and you subjectively experience that as a threat to your safety,

SPEAKER_327: then that somehow should be treated as valid, like that's basically violent. Well, that's not what the Constitution says. And these people understood well just a few months ago that that was basically snowflakery. That, you know, just because somebody, you know what I'm saying? Snowflakery, peaceable. We have the rise of the woke right now, where they're buying into this idea of safetyism, which is being exposed to ideas you don't like, to protests you don't like, is a threat to your safety. No, it's not. So now we have snowflakes on what side? We absolutely have snowflakery on both sides now.

SPEAKER_44: It's ridiculous. The only thing I will say that I've seen and is this surrounding individuals who you don't want

SPEAKER_69: there and locking them in a circle and then moving them out of the protest area, that's not cool.

David Sacks: Yeah, obviously you can't do that. But look, I think that most of the protests on most of the campuses have not crossed the line. They've just occupied the lawns of these campuses.

SPEAKER_327: And look, I've seen some troublemakers try to barge through the encampments and claim

David Sacks: that because they can't go through there, that somehow they're being prevented from going to class. Look, you just walk around the lawn and you can get to class, okay? And you know, some of these videos are showing that these are effectively right-wing provocateurs who are engaging in left-wing tactics. And I don't support it either way. Okay.

Chamath Palihapitiya: By the way, some of these camps are some of the funniest things you've ever seen.

SPEAKER_465: It's like there are like one tent that's dedicated to like a reading room and you go in there and there's like these like-

SPEAKER_467: Mindfulness center. Oh my God. It's unbelievably hilarious.

David Sacks: Look, there's no question that because the protests are originating on the left, that there's some goofy views. Like, you know, you're dealing with like a left-wing idea complex, right? But, and you know, it's easy to make fun of them doing different things. But the fact of the matter is that most of the protests on most of these campuses are, even though they can be annoying because they're occupying part of the lawn, they're not violent. Yeah. And you know, the way they're being cracked down on, they're sending the police in at 5am to crack down on these encampments with batons and riot gear.

SPEAKER_327: And I find that part to be completely excessive.

SPEAKER_00: Well, it's also dangerous because, you know, things can escalate when you have mobs of people and large groups of people.

SPEAKER_20: So I just want to make sure people understand that. Large group of people, you have a diffusion of responsibility that occurs when there's large groups of people who are passionate about things and people can get hurt. People have gotten killed at these things. So just, you know, keep it calm, everybody. I agree with you. What's the harm of these folks protesting on a lawn? It's not a big deal when they break into buildings, of course.

SPEAKER_471: Yeah, that crosses the line, obviously.

SPEAKER_20: Yeah, but I mean, let them sit out there and then they'll run out their food cars, their campus food cart, and they'll run out of waffles.

Chamath Palihapitiya: Did you guys see the clip? I think it was on the University of Washington campus where one kid challenged this Antifa guy to a push-up contest. Oh, fantastic.

SPEAKER_465: I mean, it is some of the funniest stuff. Some content is coming out that's just hilarious.

SPEAKER_219: My favorite was the woman who came out and said that the Columbia students needed humanitarian aid. Oh, my God.

SPEAKER_477: The overdubs on her were hilarious.

SPEAKER_478: I was like, humanitarian aid from the coast. I was like, we need our door dash right now. We double dash some boba and we can't get it through the police. We need our boba. Low sugar boba with the popping boba.

SPEAKER_479: Bubbles wasn't getting in. But, you know, people have the right to protest.

SPEAKER_69: And peaceable, by the way. There's a word I've never heard. Very good sex. Peaceable, inclined to avoid argument or violent conflict. Very nice.

SPEAKER_480: Well, it's in the Constitution. It's in the First Amendment.

SPEAKER_223: Is it really? I've never, yeah, I haven't heard the word peaceable before. I mean, you and I are simpatico on this.

SPEAKER_69: Like, I don't, we used to have the ACLU, like, backing up the KKK, going down Main Street and really fighting for- Yeah, the Skokie decision.

SPEAKER_20: Yeah, they were really fighting for, and I have to say the Overton window is opened back up. And I think it's great. All right. We got some things on the docket here. I don't know if you guys saw the Apple new iPad ad. It's getting a bunch of criticism. They use like some giant hydraulic press to crush a bunch of creative tools. EJ turntable, trumpet, piano. People really care about Apple's ads and what they represent. We talked about that Mother Earth little vignette they created here. What do you think, Freeberg? Did you see the ad? What was your reaction to it?

SPEAKER_37: It made me sad. It did not make me want to buy an iPad.

SPEAKER_337: So it did not seem like it made you sad. It actually elicited an emotion, meaning like commercials. It's very rare that commercials can actually do that. Most people just zone out.

SPEAKER_290: Yeah, they took all this beautiful stuff and heard it. It didn't feel good.

SPEAKER_482: I don't know. It just didn't seem like a good ad. I don't know why they did that. I don't get it.

David Sacks: I think maybe what they're trying to do is the selling point of this new iPad is that it's the thinnest one. I mean, there's no innovation left, so they're just making the devices thinner. Yeah.

SPEAKER_327: So I think the idea was that they're going to take this hydraulic press to represent how ridiculously thin the new iPad is. Now, I don't know if the point there was to smush all of that good stuff into the iPad. I don't know if that's what they were trying to convey. But yeah, I think that by destroying all those creative tools that Apple is supposed to represent, it definitely seemed very off brand for them. And I think people were reacting to the fact that it was so different than what they would have done in the past. And of course, everyone was saying, well, Steve would never have done this. I do think it did land wrong. I mean, I didn't care that much, but I was kind of asking the question, like, why are they destroying all these creator tools that they're renowned for creating or for turning into the digital version?

SPEAKER_20: Yeah, it just didn't land. I mean, Chamath, how are you doing emotionally after seeing that? Are you okay, buddy?

Chamath Palihapitiya: Yeah, I think this is, you guys see that in the Berkshire annual meeting last weekend, Tim Cook was in the audience and Buffett was very laudatory. This is an incredible company. But he's so clever with words. He's like, you know, this is an incredible business that we will hold forever.

SPEAKER_80: Most likely. And then it turns out that he sold $20 billion worth of Apple shares. In the corner. Caveat.

Chamath Palihapitiya: We're going to hold it forever. Which, by the way, if you guys remember, we put that little chart up, which shows when he doesn't mention it in the annual letter, it's basically like, it's foreshadowing the fact that he is just pounding the sell. And he sold $20 billion.

SPEAKER_386: Well, also holding it forever could mean one share.

SPEAKER_480: Yeah, exactly. We kind of need to know, like, how much are we talking about?

Chamath Palihapitiya: I mean, it's an incredible business that has so much money with nothing to do. They're probably just going to buy back the stock. Just a total waste.

SPEAKER_69: They were floating this rumor of buying Rivian, you know, after they shut down Titan project, the internal project to make a car. It seems like a car is the only thing people can think of that would

SPEAKER_99: move the needle in terms of earnings. I think the problem is, J. Cal, like, you kind of become afraid of your own shadow.

Chamath Palihapitiya: Meaning, the folks that are really good at M&A, like you look at Benioff. The thing with Benioff's M&A strategy is that he's been doing it for 20 years. Yeah. And so he's cut his teeth on small acquisitions and the market learns to give him trust so that when he proposes like the $27 billion Slack acquisition, he's allowed to do that. Another guy, you know, Nikesh Arora at PanW, these last five years, people were very skeptical that he could actually roll up security because it was a super fragmented market. He's gotten permission. Then there are companies like Danaher that buy hundreds of companies. So all of these folks are examples of you start small and you earn the right to do more. Apple hasn't bought anything more than 50 or $100 million. And so the idea that all of a sudden they come out of the blue and buy a 10, 20 billion dollar company, I think is just totally doesn't stand logic.

SPEAKER_447: It's just not possible for them because they'll be so afraid of their own shadow. That's the big problem. It's themselves.

David Sacks: Well, if you're running out of in-house innovation and you can't do M&A, then your options are kind of limited. I mean, I do think that the fact that the big news out of Apple is the iPad is getting thinner does represent kind of the end of the road in terms of innovation. It's kind of like when they added the third camera to the iPhone. Yeah. It reminds me of those. Remember when the Gillette Mach 3 came out and then they did the 5.

SPEAKER_493: It was the best onion thing. It was like, we're doing 5, eff it.

SPEAKER_327: But then Gillette actually came out with the Mach 5. So the parody became the reality. What are they going to do? Add two more cameras to the iPhone? You have five cameras on it?

SPEAKER_399: No, it makes no sense. And then I don't know anybody who wants to... Remember, the Apple Vision was going to change everything.

SPEAKER_262: Plus, why are they body shaming the fat iPads?

SPEAKER_44: That's a fair point. That's a fair point. Actually, you know what? Actually, this didn't come out yet, but it turns out the iPad is on Ozempic.

SPEAKER_20: It's actually dropped. That would have been a funnier ad.

SPEAKER_500: Yeah. Yeah, exactly.

SPEAKER_478: Oh, oh, oh, Ozempic. We could just workshop that right here. But there was another funny one, which was making the iPhone smaller and smaller and smaller, and the iPod smaller and smaller to the point it was like, you know, like a thumb size iPhone.

SPEAKER_451: Like the Ben Stiller phone in Zoolander. Yes.

SPEAKER_505: Or in Zoolander. Correct. Yeah. That was a great scene.

SPEAKER_507: Is there a category that you can think of that you would love an Apple product for?

SPEAKER_424: There's a product in your life that you would love to have Apple's version of it.

SPEAKER_337: They killed it. I think a lot of people would be very open-minded to an Apple car.

Chamath Palihapitiya: Okay. They just would. It's a connected internet device, increasingly so. Yeah. And they managed to flub it. They had a chance to buy Tesla. They managed to flub it.

SPEAKER_513: Yeah.

Chamath Palihapitiya: Right? There are just too many examples here where these guys have so much money and not enough ideas. That's a shame.

SPEAKER_515: It's a bummer, yeah. The one I always wanted to see them do, Zach, was- TV?

SPEAKER_20: The one I always wanted to see them do was the TV, and they were supposedly working on it, like the actual TV, not the little Apple TV box in the back, and that would have been extraordinary to

SPEAKER_228: actually have a gorgeous, you know, big television.

SPEAKER_465: What about a gaming console? They could have done that, you know?

SPEAKER_337: There's just all these things that they could have done. It's not a lack of imagination, because these aren't exactly incredibly world-beating ideas. They're sitting right in front of your face. It's just the will to do it. Yeah.

SPEAKER_397: Yeah, the all-in-one TV would have been good.

SPEAKER_180: If you think back on Apple's product lineup over the years,

SPEAKER_33: where they've really created value is on how unique the products are. They almost create new categories. Sure, there may have been a, quote, tablet computer prior to the iPad, but the iPad really defined the tablet computer era. Sure, there was a smartphone or two before the iPhone came along, but it really defined the smartphone. And sure, there was a computer before the Apple II, and then it came along and it defined the personal computer. In all these cases, I think Apple strives to define the category. So it's very hard to define a television, if you think about it, or a gaming console in a way that you take a step up and you say, this is the new thing, this is the new platform. So I don't know, that's the lens I would look at if I'm Apple in terms of like, can I redefine a car? Can I make, you know, we're all trying to fit them into an existing product bucket. But I think what they've always been so good at is identifying consumer needs and then creating an entirely new way of addressing that need in a real step change function. From the iPod, it was so different from any MP3 player ever.

SPEAKER_75: I think the reason why the car could have been completely reimagined by Apple is that

SPEAKER_337: they have a level of credibility and trust that I think probably no other company has, and absolutely no other tech company has. And we talked about this, but I think this was the third Steve Jobs story that, that I left out.

Chamath Palihapitiya: But in 2000 and I don't know, was it one? I launched a 99 cent download store. Right. I think I've told you this story in Winamp and Steve Jobs just ran total circles around us. But the reason he was able to, is he had all the credibility to go to the labels and get deals done for licensing music that nobody could get done before. I think that's an example of what Apple's able to do, which is to use their political capital to change the rules. So if the thing that we would all want is safer roads and autonomous vehicles, there are regions in every town and city that could be completely converted to level five autonomous zones. If I had to pick one company that had the credibility to go and change those rules, it's them because they could demonstrate that there was a methodical safe approach to doing something. And so the point is that even in these categories that could be totally reimagined, it's not for a lack of imagination. Again, it just goes back to a complete lack of will. And I understand because if they had, if you, if you had $200 billion of capital on your balance sheet, I think it's probably pretty easy to get fat and lazy.

SPEAKER_69: Yeah, it is. And they want to have everything built there. People don't remember, but they actually built one of the first digital cameras. You must've owned this, right?

SPEAKER_20: Freedberg? You're like, I remember this. Yeah, totally. It was beautiful. What did they call it? Was it the eye camera or something?

SPEAKER_520: Quick take. Quick take.

SPEAKER_20: Quick take. Yeah. Um, the thing I would like to see Apple build, and I'm surprised they didn't, was a smart home system, the way Apple has Nest.

SPEAKER_228: A drop cam, a door lock, you know, uh, AV system, go after Questron or whatever, and just have your whole home automated thermostat nest. All of that would be brilliant by Apple. And right now I'm an Apple family that has our, all of our home automation through Google.

SPEAKER_240: So it's just, it kind of sucks. I would, I would like that all to be integrated.

David Sacks: Actually, that would be pretty amazing. Like if they did a Crestron or Savant, because then when you just go to your Apple TV, all your cameras just work, you don't need to. Yes.

SPEAKER_424: That's the, that, I mean, and everybody has a home and everybody automates their home. So just think.

SPEAKER_106: Well, everyone has Apple TV at this point.

David Sacks: So you just make Apple TV the brain for the home system.

SPEAKER_394: Right.

David Sacks: That would be your hub. And you can connect your phone to it. And then yes, that would be very nice.

SPEAKER_44: Yeah. Like, can you imagine like the ring cameras, all that stuff being integrated?

SPEAKER_20: I don't know why they didn't go after that. That seems like the easy layout.

SPEAKER_69: Hey, you know, everybody's been talking Freiburg about this, uh, alpha fold this folding proteins.

SPEAKER_00: And there's some new version out from Google and, uh, also Google reportedly.

SPEAKER_44: And we talked about this before is also advancing talks to acquire HubSpot. So that rumor for the $30 billion market cap HubSpot is out there as well. Freiburg you're as our resident science.

SPEAKER_20: Sultan, uh, our resident Sultan of science.

SPEAKER_389: And as a Google alumni pick either story and let's go for it. Yeah.

SPEAKER_31: I mean, I'm not sure there's much more to add on the HubSpot acquisition rumors. They are still just rumors.

SPEAKER_33: And I think we covered the topic a couple of weeks ago, but I will say that alpha fold three that was just announced today and demonstrated by Google, um, is a real, uh, I would say breathtaking moment, um, for biology, for bioengineering, for human health, for medicine. And maybe I'll just take 30 seconds to kind of explain it. Um, you remember when they introduced alpha fold, alpha fold two, we talked about DNA codes for proteins. So every three letters of DNA codes for an amino acid. So a string of DNA codes for a string of amino acids. And that's called a gene that produces a protein. And that protein is basically a long, like think about beads. There's 20 different types of beads, 20 different amino acids that can be strung together. And what happens is that necklace that bead necklace basically collapses on itself. And all those little beads stick together with each other in some complicated way that we can't deterministically model. And that creates a three dimensional structure, which is called a protein, that molecule, and that molecule does something interesting, it can break apart other molecules, it can bind molecules, it can move molecules around. So it's basically the machinery of chemistry, of biochemistry. And so proteins are what is encoded in our DNA. And then the proteins do all the work of making living organisms. So Google's alpha fold project took three dimensional images of proteins, and the DNA sequence that codes for those proteins, and then they built a predictive model that predicted the three dimensional structure of a protein from the DNA that codes for it. And that was a huge breakthrough years ago.

SPEAKER_180: What they just announced with alpha fold three today, is that they're now including all small molecules. So all the other little molecules that go into chemistry and biology that drive the function of everything we see around us. And the way that all those molecules actually bind and fit together is part of the predictive model. Why is that important? Well, let's see that you're designing a new drug. And it's a protein based drug, which biologic drugs, which most drugs are today, you could find a biologic drug that binds to a cancer cell. And then you'll spend 10 years going to clinical trials. And billions of dollars later, you find out that that protein accidentally binds to other stuff and hurts other stuff in the body. And that's an off target effect or a side effect. And that drug is pulled from the clinical trials and it never goes to market. Most drugs go through that process. They are actually tested in animals and then in humans. And we find all these side effects that arise from those drugs, because we don't know how those drugs are going to bind or interact with other things in our biochemistry. And we only discovered after we put it in. But now we can actually model that with software. We can take that drug, we can create a three dimensional representation of it using the software, and we can model how that drug might interact with all the other cells, all the other proteins, all the other small molecules in the body to find all the off target effects that may arise and decide whether or not that presents a good drug candidate. That is one example of how this capability can be used. And there are many, many others, including creating new proteins that could be used to bind molecules or stick molecules together or new proteins that could be designed to rip molecules apart. We can now predict the function of three dimensional molecules using this capability, which opens up all of the software based design of chemistry, of biology, of drugs. And it really is an incredible breakthrough moment. The interesting thing that happened, though, is Google Alphabet has a subsidiary called Isomorphic Labs. It is a drug development subsidiary of Alphabet. And they've basically kept all the IP for Alpha Fold 3 in isomorphic. So Google is going to monetize the heck out of this capability. And what they made available was not open source code, but a web based viewer that scientists for quote, non commercial purposes can use to do some fundamental research in a web based viewer, and make some experiments and try stuff out and how interactions might occur. But no one can use it for commercial use, only Google's Isomorphic Labs can. So number one, it's an incredible demonstration of what AI outside of LLMs, which we just talked about with Sam today. And obviously, we talked about other models, but LLMs being kind of this consumer text predictive model capability. But outside of that, there's this capability in things like chemistry, with these new AI models that can be trained and built to predict things like three dimensional chemical interactions, that is going to open up an entirely new era for human progress. And I think that's what's so exciting. I think the other side of this is Google is hugely advantaged. And they just showed the world a little bit about some of these jewels that they have in the treasure chest. And they're like, Look at what we got, we're gonna make all these drugs. And they've got partnerships with all these pharma companies and isomorphic labs that they've talked about. And it's gonna usher in a new era of drug development design for human health. So all in all, I'd say it's a pretty like astounding day, a lot of people are going crazy over the capability that

SPEAKER_33: they just demonstrated. And then it begs all this really interesting question around like, you know, what's Google going to do with it? And how much value is going to be created here? So anyway, I thought it was a great story. And I just rambled on for a couple minutes. But

SPEAKER_19: I don't know. Super interesting. Is this AI capable of making a science corner that David

Chamath Palihapitiya: Sachs pays attention to? Well, it will, it will predict the cure, I think, for the common cold and for herpes. So he should pay attention. Folding cells is the app that

SPEAKER_538: casual game Sachs just download is playing how many chess moves did you make during that segment?

SPEAKER_180: Sorry, let me just say one more thing. Do you guys remember we talked about Yamanaka factors? And how challenging it is to basically we can reverse aging if we can get the right proteins into cells to tune the expression of certain genes to make those cells youthful? Right now, it's a shotgun approach to trying millions of compounds and combinations of compounds to do them. There's a lot of companies actually trying to do this right now to come up with a fountain of youth type product. We can now simulate that. So with this system, one of the things that this alpha fold three can do is predict what molecules will bind and promote certain sequences of DNA, which is exactly what we try and do with the Yamanaka factor based expression systems, and find ones that won't trigger off target expression. So meaning we can now go through the search space and software of creating a combination of molecules that theoretically could unlock this fountain of youth to de age all the cells in the body, and introduce an extraordinary kind of health benefit. And that's just again, one example of the many things that are possible with this sort of platform. And I'm really I gotta be honest, I'm really just sort of skimming the surface here, of what this can do. The capabilities and the impact are going to be like, I don't know, I know I say this sort of stuff a lot, but it's gonna be pretty profound. There's a on the blog post, they have

Chamath Palihapitiya: this incredible video that they show of the Coronavirus that creates a common cold, I think the seven pnm protein, and not only did they literally, like predicted accurately, they also predicted how it interacts with an antibody with a sugar. It's nuts. So you could see a world where like, I don't know, you just get a vaccine for the cold, and it's kind of like you never have colds again. Amazing.

SPEAKER_180: I mean, simple stuff, but so powerful. And you can filter out stuff that has off target effects. So so much of drug discovery and all the side effects stuff can start to be solved for in silico. And you could think about running extraordinarily large, use a model like this, run extraordinarily large simulations in a search space of chemistry to find stuff that does things in the body, that can unlock, you know, all these benefits can do all sorts of amazing things to destroy cancer, to destroy viruses, to repair cells to DH cells.

SPEAKER_156: And this is $100 billion business, they say.

SPEAKER_272: Oh, my God, I mean, this alone, I feel like, this is where I, I've said this before, I think Google's got this like portfolio of like, quiet, you know, extraordinarily high bets.

SPEAKER_546: Yeah, yeah, this one, what if they hit?

SPEAKER_180: And the fact, and I think the fact that they didn't open source everything in this

SPEAKER_44: says a lot of their intentions. Yeah, yeah, open source when you're behind closed source, lock it up when you're ahead. But Yamanaka, actually, interestingly, Yamanaka is the Japanese

SPEAKER_00: whiskey that Saks serves on his plane as well. It's delicious. I love that Hokkaido, Yakanama.

SPEAKER_272: Jason, I feel like if you didn't find your way to Silicon Valley, you could be like a Vegas lounge comedy guy. Absolutely, yeah, for sure.

SPEAKER_549: Yeah, I was actually yeah, somebody said I should do like those 1950s talk shows where

SPEAKER_272: the guys would do like the stage show. The shtick. Yeah, the shtick.

SPEAKER_44: Somebody told me I should do like Spauld and Gray, Eric Boghossian style stuff. I don't know if you guys remember like the, the monologue is from the 80s in New York. It's like, oh, that's interesting. Maybe. All right, everybody.

SPEAKER_20: Thanks for tuning in to the world's number one podcast. Can you believe we did it, Chamath? The number one podcast in the world. And the All In Summit, the Ted Killer. If you are going to Ted, congratulations for genuflecting. If you want to talk about real issues, come to the All In Summit. And if you are protesting at the All In Summit, let us know what mock meat you would like to have. Freeburg is setting up mock meat stations for all of our protesters and what milk you would like. Vegan food? Yeah, all vegan. If you want, if you're oat milk, soy nut milk, just please, when you come to protest.

SPEAKER_556: We have five different kinds of xanthan gum you can choose from, right, Chamath?

SPEAKER_557: All of the nut milks you could want and then they'll be mindful of yoga with gums. Can we have some soy like this?

SPEAKER_46: Yes, on the South Lawn, we'll have the goat yoga going on. So just please note that the goat

SPEAKER_561: yoga going on for all of you. It's very thoughtful for you to make sure that our protesters are going to be well fed, well taken care of. Yes, we're actually, Freiburg is working on the protester

SPEAKER_535: gift bags, the protester gift bags. They're made of Yakama folding proteins, so you're good. Folding proteins, I think I saw them open for the Smashing Pumpkins in 2003.

SPEAKER_571: All right, enough. Enough. I'll be here for three more nights.

SPEAKER_574: Love you, boys. Bye-bye. Love you, besties. Is this the All In Pod or open mic night? What's going on? It's basically it. I'm just bored.

SPEAKER_13: Brain man, David Sacks. Open source it to them.

SPEAKER_584: Love you, besties. Queen of your feet.

SPEAKER_592: We need to get merch. Besties are back.

SPEAKER_598: That's episode 178, and now the plugs. The All In Summit is taking place in Los Angeles on September 8th

SPEAKER_20: through the 10th. You can apply for a ticket at summit.allinpodcast.co. Scholarships will be coming soon. If you want to see the four of us interview Sam Altman, you can actually see the video of this podcast on YouTube, youtube.com slash at All In, or just search All In Podcasts, and hit the alert bell, and you'll get updates when we post. We're doing a Q&A episode live when the YouTube channel hits 500,000, and we're going to do a party in Vegas, my understanding, when we hit a million subscribers. So look for that as well. You can follow us on x, x.com slash theallinpod. TikTok is all underscore in underscore talk. Instagram, theallinpod. And on LinkedIn, just search for theallinpodcast. You can follow Chamath at x.com slash Chamath. And you can sign up for a substack at chamath.substack.com. I do. Freeberg can be followed at x.com slash Freeberg. And Ohalo is hiring. Click on the careers page at ohalogenetics.com. And you can follow Saks at x.com slash David Saks. Saks recently spoke at the American Moment conference, and people are going crazy for it. It's pinned to his tweet on his X profile. I'm Jason Calacanis. I am x.com slash Jason. And if you want to see pictures of my bulldogs

SPEAKER_19: and the food I'm eating, go to instagram.com slash Jason in the first name club. You can listen to my other podcasts, This Week in Startups. Just search for it on YouTube. We're your favorite podcast player. We are hiring a researcher. Apply to be a researcher, doing primary research and working

SPEAKER_20: with me and producer Nick, working in data and science and being able to do great research, finance, et cetera. allinpodcast.co slash research. It's a full-time job working with us, the besties. We'll see you all next time on the All In Podcast.