对话山姆·奥特曼

Jason Calacanis (00:00:00): I first met our next guest, Sam Altman, almost 20 years ago when he was working on a local mobile app called Loopt. We were both backed by Sequoia Capital. And in fact, we were both in the first class of Sequoia Scouts. He did an investment in a little unknown fintech company called Stripe. I did Uber. And in that tiny experiment you Sam Altman (00:00:19): did Uber? I've never heard that before. David Sacks (00:00:20): Yeah. I think so. It's Jason Calacanis (00:00:22): awesome. You've got it starting already. David Sacks (00:00:24): You should Sam Altman (00:00:24): write a book, David Friedberg (00:00:25): Jason. Maybe David Sacks (00:00:28): the weather winter's wide. And I said, we open source it to the fans and made discoveries. Jason Calacanis (00:00:44): A tiny experimental fund that Sam and I were part of as Scouts is Sequoia's highest multiple returning fund. A couple of low-digit millions turned into over $200 million, I'm told. And then he did yeah. That's what I was talking about, Rudolph. Yeah. And he did a stint at Y Combinator, 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 full time as CEO. Things got really interesting on November 30, 2022. That's the day OpenAI launched In January 2023, Microsoft invested $10 billion. In November 2023, over a crazy five-day span, Sam was fired from OpenAI. Everybody was gonna go work at Microsoft. A bunch of heart emojis went viral on X/Twitter, and people started speculating that the team had reached artificial general intelligence, that the world was gonna end. And suddenly, a couple of days later, he was back to being the CEO of OpenAI. In February, Sam was reportedly looking to raise $7 billion for an AI chip project. This, after it was reported, that Sam was looking to raise $1 billion from Masayoshi Son to create an iPhone killer with Jony 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. Sam Altman (00:02:24): Thank you. Thank you, guys. Jason Calacanis (00:02:26): Sacks, you wanna lead us off here? David Sacks (00:02:27): Okay. Sure. I mean, I think the whole industry is waiting with bated breath for the release of 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? Sam Altman (00:02:44): We take our time on releases of major new models, and 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 a better hint of what the world looks like. Where it's not like 1, 2, 3, 4, 5, 6, 7, but you just use an AI system. And the whole system just gets better and better fairly continuously. I think that's both a better technological direction. I think that's easier for society to adapt to. But I assume that's where we'll head. David Friedberg (00:03:41): Does that mean that there's not gonna be long training cycles 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? Sam Altman (00:03:55): Well, I mean, one thing that you could imagine is just that you keep training. David Sacks (00:04:01): Right. Sam Altman (00:04:01): A model, that would seem like a reasonable thing to me. Jason Calacanis (00:04:06): You 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 teams' input since now there's so much at stake? You have so many customers actually paying, and you've got everybody watching everything you do. Is it, yeah, to be more thoughtful now? Sam Altman (00:04:28): Still only available to the paid users. 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 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 showing it down upon us. That seems like a much better path. It seems like a 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. That's something we really want to do. Jason Calacanis (00:05:10): It's just very expensive. I take expensive. Yeah. Chamath, your thoughts? Chamath Palihapitiya (00:05:15): 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 David. 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? Sam Altman (00:06:15): So on the first part of your question, speed and cost, those are hugely important to us. And I don't wanna, 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 wanna, like, cut the latency super dramatically. We wanna cut the cost really, really dramatically. And I believe that will happen. We're still so early in the development of the science and understanding how this works, plus we have all the engineering tailwinds. So I don't know, like, when we get to intelligence too cheap to meter and so fast that it feels instantaneous to us and everything else, but I do believe we can get there for, you know, 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 this 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 in 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. Jason Calacanis (00:07:55): Will you do? Will you do that? Will you release it? I don't David Sacks (00:07:57): know if Sam Altman (00:07:57): we will or someone will, but David Sacks (00:07:58): someone will. But what about a Lambda 3? Sam Altman (00:08:01): Long with you running on the phone. David Sacks (00:08:02): Well, I guess maybe there's, like, a 7 billion person. Sam Altman (00:08:06): Yeah. Yeah. I David Sacks (00:08:08): don't know if that will fit on our phone or not. But Sam Altman (00:08:10): That should be fit-able on a phone, but I don't know. I'm not sure if that one's, like, 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. David Sacks (00:08:20): So when Llama 3 got released, I think the big takeaway for a lot of people was wow. They've, like, caught up to GPT-4. I don't think it's equal in all dimensions, but it's, like, pretty close or pretty in the ballpark. I guess the question is, you know, you guys released 4 a while ago. You're working on 5 or, you know, more upgrades to 4. I mean, I think to Chamath's point about Devin, how do you stay ahead of open source? I mean, it's just that's like a very hard thing to do in general. Right? I mean, how do you think about that? Sam Altman (00:08:51): What we're trying to do is not make the sort of smartest set of weights that we can, but what 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 and, you know, deliver it at a great price. Jason Calacanis (00:09:32): When you founded the organization, the stated goal or part of what you discussed was, hey. This is too important for any one company to own it. So it definitely 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 had some fear about that, I think. Is that accurate? Because the cynical side is like, well, this is a capitalistic move. And then the, I think, you know, 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. Well, how did you come to that conclusion? What were the discussions like? Sam Altman (00:10:12): Part of the reason that we released ChatGPT was we want 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. 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 David Sacks (00:10:53): a lot Sam Altman (00:10:53): of value and it, you know, teaches 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 gonna go, that we'd make a language model, 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 was 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 next. And I think we'll keep doing that. Chamath Palihapitiya (00:11:38): 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 just an arms race for data beyond that. Sam Altman (00:12:32): I definitely don't think it'll be an arms race 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 for data to make it useful. Look, the one thing that I have learned most throughout all of this is that it's hard to make confident statements a couple of years in the future about where this is all gonna go. So I don't wanna 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 stumbled on a new fact of nature or science or whatever you wanna 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 point. You know, intelligence is just this emergent property of matter, and that's like a rule of physics or something. So what people are gonna figure out, but there'll be all these 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 different people like different ones. You know, some people like iPhone, some people like an Android phone. I think there'll be some effect like that. Chamath Palihapitiya (00:13:47): Let's go back to that first section of just 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? Well, we'll make Sam Altman (00:14:22): huge algorithm that gains for sure. And I don't wanna discount that. I'll, you know, I 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 very complicated. You know, there's logic fab capacity. There's how much HBM the world can make. There's how quickly you can 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 some percentage chance where there is, as you were saying, like, a huge substrate breakthrough. And we have a massively more efficient way to do computing, but I don't bank on that or spend David Sacks (00:15:26): too much time thinking about it. Chamath Palihapitiya (00:15:28): What about the device side and sort of you mentioned sort of the models that can fit on the 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, does it need to be as expensive as an iPhone? Sam Altman (00:15:48): I'm super interested in this. I love, like, great new form factors of computing. And it feels like with every major technological advance, a new thing becomes possible. Phones are unbelievably good. So I think the threshold is, like, very high here. Like, what? Mhmm. Like, I think, like, I personally think an iPhone is, like, the greatest piece of technology humanity has ever made. It's really a wonderful product. Jason Calacanis (00:16:15): What comes after it? Like I don't know. Sam Altman (00:16:17): I mean, I was gonna that was what I was saying. It's so good that to get beyond it. I think the bar is, like, quite high. Jason Calacanis (00:16:23): Well, you've been you've been working with Jony Ive on something. Right? Sam Altman (00:16:26): We've been discussing ideas, but I don't like, if I knew. Is Chamath Palihapitiya (00:16:31): that it has to be more complicated or actually just much, much cheaper and simpler? Sam Altman (00:16:36): Well, almost everyone's willing to pay for a phone anyway. So if you could, like, make a way cheaper 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 is the answer. Jason Calacanis (00:16:54): Different is the answer, then? David Sacks (00:16:56): 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? Sam Altman (00:17:03): Probably, but the phone manufacturers are gonna do that for sure. That doesn't necessitate 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. Jason Calacanis (00:17:20): But it's Oh, you have you have voice working right now in the app. In fact, I set my action button on my phone to go directly to ChatGPT's voice app, and I use it with my kids, and they love it talking to it. It's got latency issues, but it's really Sam Altman (00:17:32): We'll get that better. And I think 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, the part of with Jason Calacanis (00:17:47): that. By the way. Like, why 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 it's also brilliant when it gives you the right answer. Sam Altman (00:18:01): We are working on that. It's it's so clunky right now. It's slow. It kinda doesn't feel very smooth or authentic or organic. Like, we'll get all that to be much better. David Sacks (00:18:13): What about computer vision? I mean, they have glasses, or maybe you can wear a pendant. I mean, you take the combination of visual or video data, combine it with voice, and now Super AI knows everything that's happening around you. Sam Altman (00:18:28): Super powerful to be able to, like, the multi-modality of saying, like, hey, ChatGPT, what am I looking at, or, like, what kind of plant is this? I can't quite tell. That's obvious. That's like another, I think, hint. But whether people want to wear glasses or, like, hold up something when they want that, like, there's a bunch of just, like, the sort of, like, societal interpersonal issues here are all very complicated about wearing a computer on your face. We Jason Calacanis (00:18:59): we saw that with Google Glass. People got punched in the face in the Mission. Started a lot Sam Altman (00:19:03): of friction about that. I forgot about that. So I think it's like David Sacks (00:19:08): What are the apps that could be unlocked if AI was sort of ubiquitous on people's phones? Do you have a sense of that, or what would you want to see built? Sam Altman (00:19:22): I 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 kinda 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. There's there there's like a I know when you hear people like talk about the AI future, they imagine they imagine there's sort of 2 different approaches, and they don't sound that different, but I think they're, like, very different for how we'll design the system and practice. There's the I want a extension of myself I want, like, a ghost or an alter ego or this thing that really like is me is acting on my behalf is responding to emails, not even telling me about it, it is sort of like it it becomes more be and is me. And then there's this other thing, which is like, I want a great senior employee. It gets to know me very well. I may delegate it. You know, you can have access to my email, and I'll give you the constraints, but I think of it as this separate entity. And I personally like the separate entity approach better and think that's where we're gonna head. And so in that sense, the thing is not you, but it's like an always available, always great, super capable assistant executive. Jason Calacanis (00:20:57): In a way, it's out there working on your behalf and understands what you want and anticipates what you want. That is what I'm reading into what you're saying. Sam Altman (00:21:05): I think there'd be agent-like behavior, but there's a difference between a senior employee and an agent. David Sacks (00:21:13): Yeah. Sam Altman (00:21:13): And, like, I want it, you know, if I think of, like, my I think like a bit, like, one of the things that I like about a senior employee is they'll push back on me. They will sometimes not do something I ask, or they 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? David Sacks (00:21:39): I Sam Altman (00:21:39): definitely want that kind of vibe, not just like this thing that I rescan it bluntly David Friedberg (00:21:44): does. Yeah. Jason Calacanis (00:21:46): Yeah. And push back. Sam Altman (00:21:47): The reason it has, like, the kind of relationship with me that I would expect out of a really competent person that I worked with, which is different from, like, a sycophant. Jason Calacanis (00:21:56): Yeah. Chamath Palihapitiya (00:21:56): The thing in that world where if you have 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 eight billion people. What do you think has to change in how we think about how apps need to work, how this entire infrastructure of experiences needs to work in a world where you're agentically interfacing with the world? You know? Sam Altman (00:22:36): I'm actually very interested in designing a world that is equally usable by humans and by AIs. So I, 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. So, you know, DoorDash could just expose some API to my future AI assistant, and they could go put the order and whatever. Or I could be holding my phone and I could say, okay, AI assistant, put in this order on DoorDash, please. And I could watch the app open and see the thing clicking around, and I could say, hey, no. Not this. Or there's something about designing a world that is usable equally well by humans and AIs that I think is an interesting concept. Chamath Palihapitiya (00:23:27): And so many are more excited Sam Altman (00:23:29): about humanoid robots than robots of other shapes. The world is very much designed for humans, and I think we should absolutely keep it that way. And a shared interface is nice. Jason Calacanis (00:23:39): So you see voice chat, that modality kind of gets rid of apps. You just ask it for sushi. It knows the sushi you like before. It knows what you don't like and does its best shot at doing it. Sam Altman (00:23:49): It's hard for me to imagine that we just go to a world totally where you say, hey, ChatGPT, order me sushi, and it says, okay. Do you want it from this restaurant? What time, whatever? I think visual user interfaces are super good for a lot of things. And it's hard for me to imagine a world where you'd never look at a screen and just use voice only, but I can imagine that for a lot of things. Jason Calacanis (00:24:19): Yeah. I mean, Apple tried with Siri. Supposedly, you can order an Uber automatically with Siri. I don't think anybody's ever done it because why would you take the risk of not putting the quality Chamath Palihapitiya (00:24:30): To your point? The quality is not good, but when the quality is good enough, you'll actually prefer it just because it's lighter weight. You don't have to take your phone out. You don't have to search for your app and press it. And, oh, it automatically logged you out. Oh, hold on. Log back in. Oh, TFA. So whole pain in the ass. Sam Altman (00:24:45): You know, it's like setting a timer with Siri. I do it every time because it works really well, and it's great. David Sacks (00:24:51): And I Sam Altman (00:24:51): need more information. But ordering an Uber, like, I wanna see the prices for a few different options. I wanna see how far away it is. I wanna see, like, maybe even where they are on the map because I might walk somewhere. I get a lot more information by, I think, in less time, by looking at that order of the Uber screen, than I would if I had to do that all through the audio channel. Jason Calacanis (00:25:12): I like your idea of watching it happen. That's kinda cool. Sam Altman (00:25:15): I think there will just be, like, yeah, different. There are different interfaces we use for different tasks, and I think that'll keep going. Chamath Palihapitiya (00:25:22): Of all the developers that are building apps and experiences on OpenAI, 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? Sam Altman (00:25:41): I met with a new company this morning or and barely even a company. It's like two people that are gonna work on a summer project trying to actually finally make the AI tutor. And I've always been interested in this space. A lot of people have done great stuff on our platform. But if someone can deliver, like, the way that you actually, like, they used a phrase I love, which is this is gonna be like a Montessori level reinvention for how people learn. Jason Calacanis (00:26:09): Yeah. Sam Altman (00:26:10): 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. A lot of the coding-related stuff you mentioned, David, earlier, I think that's like a super cool vision of the future. The thing that I am health care, I believe, should be pretty transformed by this. But the thing I'm personally most excited about is the, sort of, doing faster 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. Chamath Palihapitiya (00:26:46): But AlphaFold 3. Yeah. Sam Altman (00:26:49): That's like But Sam that will be a triumph. Those are not David Friedberg (00:26:55): like 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, there's kind of a ground architecture to a lot of these models that are being applied to these specific problem sets, since these specific applications like chemistry interaction modeling, for example, Does you'll you'll need some Sam Altman (00:27:20): of that for sure, but the thing that I think we're missing 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 simulators David Friedberg (00:27:32): or whatever else. Yeah. That's the important question. I wanted to kinda talk about today 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. Sam Altman (00:28:24): I don't know. I don't know how much reasoning is gonna 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 if it worked out that way. I don't know if that's, like, David Friedberg (00:28:46): But let's walk through the protein modeling example. There's a bunch of training data, images of proteins, and then sequence data, and they build a model, a 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 resolving there's Sam Altman (00:29:13): so many ways where that could go. Like, maybe it trains a literal model for it, or maybe it just, like, knows the one big model, what, it can, like, go pick what other training data needs and ask a question and then update on that. David Friedberg (00:29:27): I guess the real question is, are all these startups gonna 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 at that one. Sam Altman (00:29:43): You know, there's like a version of this. I think you can, like, already see. When you were talking about, like, biology in these complicated networks of systems. The reason I was smiling, I got super sick recently. And, mostly better now, but it was just like body, like, got beat up, like, one system at a time. Fuck. Like, you can really tell, like, okay. It's this cascading thing. And that reminded me of you, like, talking about, 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 interesting to see. But I was using, I was, like, using ChatGPT, to try to, like, figure out 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 that was like a small version of what you're talking about. Where you can say this. I don't know this thing. You can put more information. You don't retrain the model, just add it into the context here. Jason Calacanis (00:30:50): And now you're Sam Altman (00:30:51): getting them. David Friedberg (00:30:52): So these models that are predicting protein structure, like, let's say, right, this is the whole basis. And now other molecules at AlphaFold 3. 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 use an example for us. Can you tell us about Sauron, your video model that generates amazing moving images, moving video, and what's different about the architecture there, whatever you're willing to share. On how that is different. Sam Altman (00:31:28): Yeah. So on the general thing first, you clearly will need specialized 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 in the same way that humans are generalized reasoners, would I think be doable. David Friedberg (00:32:02): Take a faster unlock, faster unlock than I think I Sam Altman (00:32:05): think so. But, yeah, Soura does not start with a language model. It's a model that is customized to do video, and so we're clearly not at that world yet. David Friedberg (00:32:24): Right. So just as an example, for you guys to build a good video model, 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. Sam Altman (00:32:46): 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. Jason Calacanis (00:32:59): Yeah. Yeah. 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, etcetera. And, we gotta just be gentle here because you're involved in a 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, your own 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 a derivative product out of it 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 about your personal belief because I know you to be thoughtful on this. And I know a lot of other people in our industry are not very thoughtful about how they think about content creators. Sam Altman (00:34:11): So I think it's very different for different kinds of I mean, look, on fair use, I think we 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 bunch of math on the internet and learn how to do math. That I think seems unobjectionable to most people. And then there's, like, you know, another set of people who might have a different opinion. Well, what if you like? Actually, let me not get into that just in the interest of not making this 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 Euclidean theorem. That's one end of the spectrum. And then I think the other extreme end of the spectrum is art. And maybe even, like, 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. I think the conversation has been historically very caught up on training data, but it will increasingly become more about what happens at inference time as training data becomes less valuable and what the system does accessing, you know, information 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, 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 it may have read about Taylor Swift that may know about her themes, Taylor means something. And then, and then the question is, like, should that model even if it would never train on any Taylor Swift song whatsoever be allowed to do that. Sam Altman (00:36:33): And if so, how should Taylor get paid? Jason Calacanis (00:36:37): Right. Sam Altman (00:36:38): 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 an interesting place to start looking. David Friedberg (00:36:56): Sam, let me just challenge that. What's the difference in the example you're giving of the model 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 say 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. The AI is not outputting, and it's not storing in the model the actual original song, Jason Calacanis (00:37:42): Yeah. David Friedberg (00:37:42): It is learning structure. Right? So I Sam Altman (00:37:44): wasn't trying to make that point because I agree. Like, in the same way that humans are inspired by other humans. David Friedberg (00:37:50): I was Sam Altman (00:37:50): saying if you say generate me a song in the style of Taylor Swift. David Friedberg (00:37:54): I see. Right. Okay. We think that's like it. Where the prompt leverages some artists. Sam Altman (00:37:58): I think personally that's a different case. David Friedberg (00:38:00): Would you be comfortable asking, or would you be comfortable letting the model train itself, or a music model being trained on the whole corpus of music that humans have created without royalties being paid to the artists that music is being fed in? And then you're not allowed to ask, you know, artist-specific prompts. You could just say, hey, play me a plenty of a really cool pop song that's fairly modern about heartbreak, you know, with a female voice. You know? Sam Altman (00:38:27): We have currently made the decision not to do music, and partly because exactly these questions of where you draw the lines and David Sacks (00:38:34): Mhmm. Sam Altman (00:38:34): 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 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 song structure, 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. 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 something, of, like, squishing all of human creativity down into one really thin iPad? Jason Calacanis (00:39:35): What was your take on it? People got really emotional about it. Yeah. It’s a stronger reaction than you would think. Sam Altman (00:39:43): Yeah. There's something about—I'm obviously hugely positive on AI, but there is something 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 gonna do this, like, deeply beautiful human creative expression, I think we should, like, figure out—it's gonna happen. 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 about here. Jason Calacanis (00:40:18): Alright. And I think your actions speak loudly. We were trying to do Star Wars characters in DALL-E. And if you ask for Darth Vader, it says, hey, we can't do that. So you've, I guess, red teamed or whatever you call it internally. Sam Altman (00:40:34): We try. Jason Calacanis (00:40:35): 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 Bulldog, which I did, it made my bulldogs as Sith bulldogs. So there's Sam Altman (00:40:47): an interesting question. Right? Yeah. You know, we put out this thing yesterday called the spec. Where we're trying to say, here are—here's how our model's 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 the limit should be. And I view this as, like, a discussion that's gonna need a lot more input. But these sorts of questions about—okay. Maybe it shouldn't generate Darth Vader, but the idea of a Sith Lord or a Sith-style thing or Jedi at this point is, like, part of the culture. Like, these are all hard decisions. Jason Calacanis (00:41:25): Yeah. And I think you're right. The music industry is going to consider this opportunity to make covers or songs their opportunity. It's part of the four-part fair use test is, you know, who gets to capitalize on new innovations for existing art. And Disney has an argument that, hey, you know, if you're gonna make AI versions of Ahsoka or whatever Obi-Wan Kenobi, that's Disney's opportunity, and that's a great partnership for you. You know, to pursue. Chamath Palihapitiya (00:41:55): So I think this section I would label as AI and the law. So let me ask maybe a higher-level question. What does it mean when people say regulate AI? Sam, what does that even mean? David Friedberg (00:42:09): And comment on California's new proposed regulations as well if you're up for it. Sam Altman (00:42:15): I'm concerned. I mean, there's so many proposed. But most of the ones I've seen on the California state things I'm concerned about. I also have a general fear of the states all doing this 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, require it to be open source. The thing that I am personally most interested in is 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 from now, where AI systems are capable of causing significant global harm. And for those kinds of systems, in the same way we have global oversight of nuclear weapons or synthetic bio or things that can really have a very negative impact way beyond the realm of one country. I would like to see some sort of international agency that is looking at the most powerful systems and ensuring reasonable safety testing. Know these things are not going to escape and recursively self-improve or whatever. Jason Calacanis (00:43:31): The criticism of this is that you have the resources to cozy up, to lobby, to be involved, and you've been very involved with politicians, and then startups, which you are also passionate about and invest in, are not going to have the ability to resource and deal with this. And that this is regulatory capture as per our friend, you know, Bill Gurley, did a great talk last year about it. So maybe you could address that head-on. Do you feel like Sam Altman (00:43:57): if the line where we're only gonna look at models that are trained on computers that cost more than $10 billion or more than $100 billion or whatever dollars, I'd be fine with that. There'd be some line that'd be fine. And, don't think that puts any regulatory burden on startups. Jason Calacanis (00:44:12): So if you have, like, the nuclear raw material to make a nuclear bomb, like there's a small subset of people who have that, therefore, use the analogy of, like, a nuclear inspectors kind of situation. Yep. I think that that's interesting. Sacks, you have a question? David Sacks (00:44:26): Well, Chamath, go ahead. You had a follow-up. Sam Altman (00:44:29): Can I say one more thing about that? Jason Calacanis (00:44:31): Of course. Sam Altman (00:44:31): I'd be super nervous about regulatory overreach 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 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 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. David Friedberg (00:45:08): The challenge, Sam, is that we have statutes that are meant to protect people, protect society at large. What we're creating, however, is a 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 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 check mark 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 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 to say, give me a right to audit before you can release it. And we're clueless. These people are clueless. I mean, the way that the stuff is written, you read it. You're like, gonna pull your hair out because as you know better than anyone, in 12 months, none of this stuff's gonna make sense anyway. Totally. Right. Sam Altman (00:46:22): Look. The reason I have pushed for an agency-based approach for kind of like the big picture stuff and not, like, write it in laws, is that 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 at 12 or 24 months. And I don't support these policies, which is like, we're gonna look at, you know, we're gonna audit all of your source code and, like, look at all of your weights one by one. Like, yeah, I think there's a lot of crazy proposals out there. David Friedberg (00:46:55): By the way, especially if the models are always being retrained all the time if they become more dynamic. Sam Altman (00:46:58): Yeah. Again, this is why David Friedberg (00:46:59): I think Sam Altman (00:47:00): 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 David Friedberg (00:47:11): your code. That's reviewing the output of the model, not reviewing the insides of the model. Sam Altman (00:47:17): And so what I was gonna say is that is the kind of thing that I think as safety testing makes sense. David Friedberg (00:47:24): We gonna get that to happen, Sam? And I'm not just speaking for OpenAI. 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 that humanity can prosper from. So significantly, how do we change the the sentiment and get that to happen? Cause this is all moving so quickly at the government levels. And folks seem to be getting it wrong. And I I'm I'm perfectly concerned. Jason Calacanis (00:47:51): Just to just to Chamath Palihapitiya (00:47:51): build on that, Sam, the architectural decision, for example, that LLaMA took is pretty interesting in that it's like we're gonna let LLaMA grow and be as unfettered as possible, and we have this other kind of thing that we call LLaMAguard that's meant to be these protective guardrails. Is that how you see the problem being solved correctly, or do you Jason Calacanis (00:48:11): see that? The current Sam Altman (00:48:12): At the current strength of models, definitely some things are gonna go wrong. And I don't wanna, like, make light of those or not take those seriously, but I'm not, like, I don't have 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 going to be used this way, of recursive self-improvement, or of, you know, autonomously designing and deploying a bioweapon or something like that. David Sacks (00:49:01): Or a David Friedberg (00:49:01): new model. That was the recursive self- Sam Altman (00:49:03): 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. I don't think like GPT-4, of course, does not pose it in any sort of well, I don't wanna say any sort because we don't yeah. I don't think that GPT-4 poses a material threat on those kinds of things. I think there's many safe ways to release a model like this. But, you know, when, like, significant loss of human life is a serious possibility, like airplanes or any number of other examples where I think we're happy to some sort of testing framework. Like, I don't think about an airplane when I get on it. I just assume it's gonna be safe. David Friedberg (00:49:54): Right. David Sacks (00:49:55): Right. David Friedberg (00:49:55): There's a Jason Calacanis (00:49:55): lot of hand-wringing right now, Sam, about jobs. And you had a lot of, I think you did like some sort of a test when you were at YC about UBI, and you've been our Sam Altman (00:50:04): results in that come out very soon. It just it was a five-year study that wrapped up, or started five years ago. Well, there was like a beta study first, and there was like a long one that ran. But Mark, David Friedberg (00:50:15): what what did you explain? Yeah. Why do you start why do you start it? Maybe just explain UBI and why you started it. Sam Altman (00:50:21): So we started thinking about this in 2016. Kind of about the same time, started thinking AI really seriously. And the theory was that the magnitude of the change that may come to society and jobs in the economy and 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 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 if 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 and kind of the way things have been handled. And I think giving people money is not gonna go solve all problems. It's certainly not gonna make people happy, but it might 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 I think that now that we see some of the ways so 2016 was a very long time ago. 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 if the future looks something 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 somebody to use for cancer research. But what you get is not dollars, but this. You get David Friedberg (00:52:20): like productivity. Sam Altman (00:52:21): Slice. Yeah. You own like part of the productivity. Chamath Palihapitiya (00:52:24): Right. I would like to shift to the gossip part of this. Jason Calacanis (00:52:27): Okay. Awesome. David Sacks (00:52:28): Awesome. Sam, let's go back. Chamath Palihapitiya (00:52:30): Let's go back to November. What the flying Sam Altman (00:52:38): you know, if you have specific questions, I'm happy to but maybe I said maybe talk Jason Calacanis (00:52:43): about it at some point. So here's the point. What the hell 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? David Sacks (00:52:54): Tell me. Jason Calacanis (00:52:55): This is a safe space now. Sam Altman (00:52:59): I was fired. I was 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. 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 gonna be hard. It was even harder than I thought, but I, I kind of was like, alright. Fine. I agreed to come back. The board, like, took a while to figure things out. And then, you know, we were kind of like trying to keep the team together and keep doing things to our customers and, you know, sort of started making out plans, then the board decided to hire a different interim CEO. And then everybody There are many people. Oh my Chamath Palihapitiya (00:53:48): gosh. What was what was that guy's name? He was there for, like, a Scaramucci. Right? Like David Sacks (00:53:53): and and Sam Altman (00:53:54): and it's great. And I have nothing but good to scare about you David Friedberg (00:53:57): in our process. Sam Altman (00:54:01): And then Jason Calacanis (00:54:01): Where were you when they when you found the news that you'd been fired. Like, well, taking that moment. Sam Altman (00:54:06): I was at a hotel room in Vegas for F1 weekend. David Sacks (00:54:09): I think that's what I was hearing before too. Yeah. Jason Calacanis (00:54:11): Text and the, like, What Chamath Palihapitiya (00:54:12): did you say? Fire? Jason Calacanis (00:54:12): Pickup is that? David Sacks (00:54:13): I think that's happened to you before Jason. I I I'm trying to think if I ever got fired. I Jason Calacanis (00:54:17): don't think I've gotten time. David Sacks (00:54:19): Yeah. I got a text. Know Jason Calacanis (00:54:20): it's just what we were thinking. Like, it's a text from who? Chamath Palihapitiya (00:54:22): Actually, no. Sam Altman (00:54:23): I got a text the night before, and then I got on a phone call with the board. And then that was that. And then I kind of like I mean, then everything went crazy. I was like, it was like, I mean, my phone was, like, unusable. It was just a nonstop vibrating thing. If, like, text messages call. Basically, you David Sacks (00:54:42): got fired by tweet. That happened a few times during the Trump administration. A few Capitol Sam Altman (00:54:48): points in the call got David Sacks (00:54:49): tweeted out. Before tweet. Sam Altman (00:54:50): Nice of them. And then, like, you know, I kind of did, like, a few hours of just this, like, absolute fugue state, in the hotel room trying to, like, I was just confused beyond belief trying to figure out what to do. And, Jason Calacanis (00:55:05): So weird. Sam Altman (00:55:06): And then, like, flew home. It may be, like, maybe got on a plane like, I don't know, 3 p.m. or something like that. Still just like, you know, crazy nonstop phone blowing up, met up with some people in person. By that evening, I was like, okay. You know, I'll just, like, go do AI research and just feeling pretty happy about the future and Jason Calacanis (00:55:31): You have options. Sam Altman (00:55:32): And then and then the next morning, had this call with a couple of board members about coming back and that led to a few more days of craziness. And then, and then it kind of I think it got resolved. Well, it was like a lot of insanity in between. What what percent of David Sacks (00:55:53): what percent of it was because of these nonprofit board members? Sam Altman (00:55:58): Well, we only have a nonprofit board. So it was all the nonprofit board members. The board had gotten down to six people. They and then they removed Greg from the board and then fired me. So but it was like, you know. David Sacks (00:56:15): 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 experience. David Friedberg (00:56:22): And maybe you can share a little bit about, if you're willing, the motivation behind the action. Anything you can. Sam Altman (00:56:29): I think there's always been culture clashes. Look, obviously, not all of those board members are my favorite people in the world, but I have serious respect for 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, I have never once doubted their integrity or commitment to the sort of shared mission of safe and beneficial AGI. 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 OpenAI has to get right? No. But but I think that, like, Jason Calacanis (00:57:26): The intent. Sam Altman (00:57:27): The intent of the magnitude of Jason Calacanis (00:57:31): Yeah. Sam Altman (00:57:32): AGI. And getting that right. I actually, David Sacks (00:57:36): Let me ask you about that. So the mission of OpenAI is explicitly to create AGI, 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 OpenAI 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 is that? Sam Altman (00:58:07): I think a lot of the well, I mean, first, I'll say that I'll answer the first question in the second. I think it does create a great deal of fear. David Sacks (00:58:16): I Sam Altman (00:58:16): think a lot of the world is understandably very afraid of AGI or very afraid of even current AI. And very excited about it and even more afraid and even more excited about where it's going. And 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 tremendously beneficial, but we do have to navigate how to get there in a reasonable way. And, like, a lot of stuff is gonna change. It changes, you know, pretty uncomfortable for people. So there's a lot of pieces that we gotta get right. Chamath Palihapitiya (00:58:54): And let's go. Can I ask a different question? You have 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 issued economically. Can you just, like, walk us through, like Yeah. Sam Altman (00:59:18): 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 David Sacks (00:59:23): time, why don't they give you a grant now? Why doesn't the board just give you a big option grant like you deserve? Jason Calacanis (00:59:28): Yeah. Give you a 5 point What Chamath Palihapitiya (00:59:29): was the decision back then? Like, why was that so important? Sam Altman (00:59:32): The decision back then, 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 But David Friedberg (00:59:56): in a weird way Chamath Palihapitiya (00:59:57): now that you're running a company, yeah, it creates these weird questions of like, well, what's your real motivation for us? David Sacks (01:00:02): It told told Yeah. Sam Altman (01:00:03): That's that. It is so deeply un... One thing I have noticed, it is just so deeply unimaginable to people. To say, I don't really need more money. Like and I both know what to think of. Chamath Palihapitiya (01:00:16): I think people think it's a little bit of an ulterior motive. David Sacks (01:00:19): Think it. Well. Yeah. Yeah. Yeah. No. It's so it Sam Altman (01:00:21): it assumes you've seen what else David Friedberg (01:00:22): is he doing on the side to make money? Sam Altman (01:00:24): Something's not. If I were just trying to say, like, I'm gonna try to make $1 trillion with OpenAI, I think everybody would have an easier time, and it would save me. Jason Calacanis (01:00:31): It would save a Sam Altman (01:00:32): lot of conspiracy theories. Jason Calacanis (01:00:33): Sam, this is totally the back channel. You are a great dealmaker. 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. Raising $7 trillion to build, fads, all this stuff. All of that put together. David Sacks (01:01:00): Chamath loves fake news. David Friedberg (01:01:01): Chamath loves fake news. But I'm Jason Calacanis (01:01:02): I'm kind of being a little facetious. Obviously, it's not, you know, raising $7 trillion, but maybe that's the market cap of something. Putting all that aside, the tea was you're doing all these deals. They don't trust you because what's your motivation? You're end running and and what opportunities belong inside of OpenAI. What opportunity should be Sam's, and this group of nonprofit people didn't trust you. Is that what happens? Sam Altman (01:01:26): So the things like, you know, device companies or we were doing some chip fab company, it's like, those are not Sam projects. Those would be like, OpenAI. I would get that equity. Jason Calacanis (01:01:36): They would. Okay. That's not the Sam Altman (01:01:37): public's perception. 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 this stuff because it's not done. Don't think most people in the world, like, are thinking about this, but I agree it spins up a lot of conspiracy theories in, like, tech commentators. Jason Calacanis (01:01:57): Yeah. No. That means. Sam Altman (01:01:58): And if I could go back, yeah, I would just say, like, let me take equity and make that super clear. And then if it would be like, alright. 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. Jason Calacanis (01:02:12): What's the chip project? That's a $7 trillion and when does $7 trillion number come from? Because I don't know Sam Altman (01:02:17): 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 cost structure. The exact way for us to play there is we're still trying to figure that out. Jason Calacanis (01:02:36): Got it. Chamath Palihapitiya (01:02:36): What's your preferred model of organizing OpenAI? Is it sort of like the move fast break things, highly distributed small teams, or is it more of this organized effort where you need planned because you want to prevent some of these edge cases? Sam Altman (01:02:53): Oh, I have to go in a minute. It's not to prevent the edge cases that we need to be more organized, but it is that these systems are so complicated in concentrating bets or so important. Like, one, you know, at the time, for it was, like, obvious to do this, you have, like, Demis, whatever, has all these different teams doing all these different things, and they're spreading their bets out. And right. David Sacks (01:03:18): You Sam Altman (01:03:19): had OpenAI say, we're gonna, like, basically, put the 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, and at a minimum, it's what works for us. So not because we're trying to prevent edge cases, but because we want to concentrate resources and do these, like, big, hard, complicated things, we do have a lot of coordination on what we work on. Jason Calacanis (01:03:41): Alright, Sam. I know you gotta go. You've been great on the hour. Come back anytime. Sam Altman (01:03:46): Great talking to you guys. David Friedberg (01:03:51): A year plus. Yeah. Sam Altman (01:03:52): I'm really happy it finally happened. David Friedberg (01:03:53): Yeah. It's awesome. I really appreciate it. Sam Altman (01:03:55): I would love to come back on after our next, like, major launch, and I'll be able to talk more directly about— Jason Calacanis (01:03:59): Definitely. You got the Zoom link. Same Zoom link every week. Just same time, same Zoom link. Drop the wrong time. David Friedberg (01:04:05): Please drop it. Jason Calacanis (01:04:05): You put on your account. Chamath Palihapitiya (01:04:07): Come back to the game. Come back to the game. Come back to the game. David Friedberg (01:04:09): In a while. Sam Altman (01:04:11): I, you know, I would love to play poker. It has been forever. That would be a lot of fun. Jason Calacanis (01:04:17): When you and I were heads up and you had to remind me? 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 wanna lose because back when we played small stakes, they might have been, like, 5k pot or something. And then Chamath can't stay out of the pot, and he starts pausing the two of us. You should call. You shouldn't call. He's blocking. You think about it. And I'm like, I'm going I'm trying to figure out if I make the call here. I make the call. And, it was like, you had a really good hand, and I just happen to have a set. I think you had top pair, top kicker, or something. But you made a great move because the board was so textured, almost like old bottom set. Chamath Palihapitiya (01:04:57): Sam has a great style of playing, which I would call random jam. David Sacks (01:05:00): Totally. You gotta just get it. Sam Altman (01:05:01): I don't even know if you I don't I don't know if you could say it by anybody. I don't I don't I'm not gonna David Friedberg (01:05:07): You haven't seen them play in the last 18 months. It's a lot different. Chamath Palihapitiya (01:05:10): I've never David Sacks (01:05:10): got a game. Yeah. Chamath Palihapitiya (01:05:11): Much more Jason Calacanis (01:05:12): so much fun now. Sam Altman (01:05:14): Find out Jason Calacanis (01:05:14): you find that David Sacks (01:05:15): really hard before? Jason Calacanis (01:05:16): Have you played bomb pots? Sam Altman (01:05:17): Yeah. I don't know what that is. David Friedberg (01:05:18): Okay. You'll love it. We'll see. David Sacks (01:05:20): Yeah. It's nuts. Jason Calacanis (01:05:21): You it's big yellow. David Friedberg (01:05:23): Sam, thank you. Chamath Palihapitiya (01:05:23): 2 boards. And congrats on everything, honestly. Sam Altman (01:05:26): Thank you so much. David Sacks (01:05:26): Thanks for coming on. Jason Calacanis (01:05:27): And thanks, Sam. David Sacks (01:05:28): Let's have you back after the big launch. David Friedberg (01:05:30): Sounds good. Please do. Sam Altman (01:05:31): Cool. Bye. Jason Calacanis (01:05:33): Gentlemen, some breaking news here. All those projects he said are part of OpenAI. And that's something people didn't know before this and a lot of confusion there. Chamath, what was your major takeaway from our hour with Sam? Chamath Palihapitiya (01:05:46): I think that these guys are going David Sacks (01:05:48): to be one of the four major Chamath Palihapitiya (01:05:51): companies Jason Calacanis (01:05:52): Okay. Chamath Palihapitiya (01:05:52): that matter in this whole space. I think that that's clear. I think what's still unclear is where the economics are 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 interface. Whoever builds all this other stuff is going to be in a position to build a really good business. Jason Calacanis (01:06:48): Friedberg, 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 our hour with him? David Friedberg (01:06:57): 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 yields AGI, but that's all. I think there's a lot of people that have different schools of thought on this and how much Chamath Palihapitiya (01:07:19): Oh, my other takeaway, I think, is that 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, Friedberg, 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 gonna have to think multidimensionally to reason. Right? We do that. Right? Do I cross the street or not at this point in time? You reason based on all these multi inputs. And so there's all these little systems that go into making that decision in your brain. And 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. Jason Calacanis (01:08:11): Sacks, you went right there with the corporate structure, the board. And, he gave us a lot more 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 OpenAI. People just don't realize it. And in that moment, and then you know, your questions to him about equity. Your thoughts on David Sacks (01:08:34): I'm not sure. I was like the main guy who asked that question, Jason, but Jason Calacanis (01:08:38): Well, no. You did talk about the nonprofit. The difference between nonprofit. Yeah. David Sacks (01:08:43): That's what I'm saying. Clearly, there was some sort of culture clash on the board between the people who originated from the nonprofit world and the people who came from the startup world. Chamath Palihapitiya (01:08:51): The tech idea. David Sacks (01:08:52): We don't really know more than that, but there clearly was some sort of culture clash. I thought a couple of the other areas that he drew attention to that were kinda interesting is he clearly thinks there's a big opportunity on mobile. That goes beyond just like having, you know, a chatty bot 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 gonna require more inputs. It's that, you know, personal assistant that's seen everything Jason Calacanis (01:09:21): out to you and helping you. 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 on me and understands all context. I thought that was very interesting to David Sacks (01:09:32): think about. Jason Calacanis (01:09:32): You talked David Sacks (01:09:33): about an executive assistant or an assistant that has executive function as opposed to being, like, just an alter ego. Yes. For you or what he called a second brain. That's kinda interesting. Jason Calacanis (01:09:44): I thought that was interesting. Yeah. David Sacks (01:09:45): Yeah. And clearly, he thinks there's a big opportunity in biology and scientific discovery. David Friedberg (01:09:50): After the break, I think we should talk about AlphaFold. It was just announced. Yeah. Jason Calacanis (01:09:53): Let's do that. And we can talk about the app 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 all 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 gonna be able to comment on that. So it'd be no comment. So, you know, David Friedberg (01:10:11): And we're Jason Calacanis (01:10:12): not gonna David Sacks (01:10:12): do that. Time was limited, and there's a lot of questions that we could ask them that would have just been a waste of time. And But they would have Jason Calacanis (01:10:17): got no cost. So I just want to make sure people understand. David Sacks (01:10:20): Of course, he's gonna know movie lawsuit, and he's already been asked about that 500 times. Alright. Jason Calacanis (01:10:24): Yes. Should we take David Friedberg (01:10:25): a quick break before we come back? Jason Calacanis (01:10:27): Yeah. Take a bio break, and then we'll come back with some news for you and some more banter. With your favorite besties on the number one podcast in the world. The only podcast. Alright. 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. Friedberg, you told me I could give some names of the guests that we booked for the All-In Summit. David Friedberg (01:10:53): I did not. Jason Calacanis (01:10:54): You did. You've said each week every week that I get to David Friedberg (01:10:57): see some news. I did not. I appreciate your interest in the All-In Summit lineup, but we do not yet have enough critical mass to feel like we should go out there. Jason Calacanis (01:11:09): Well, I am a loose candidate, so I will announce my two guests. And, I created the summit and you took 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 gonna 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 Starlink 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 Friedberg got, like, 1,000 guests coming. He'll tell you when it's, like, 48 hours before the conference. But, yeah, two great guests coming. David Sacks (01:11:51): Billionaires who are coming isn't coming too? Jason Calacanis (01:11:54): Yes. Coming. Yes. He's booked. David Sacks (01:11:56): So we have three billionaires. Jason Calacanis (01:11:58): Three billionaires. Yes. David Friedberg (01:11:59): Okay. Hasn't fully confirmed. So don't okay. Jason Calacanis (01:12:01): Well, we're gonna say it anyway. As penciled in. Don't say it. David Sacks (01:12:04): Don't back out. Jason Calacanis (01:12:05): I'll say penciled. Yeah. Don't back out. Chamath Palihapitiya (01:12:07): This is gonna be catnip for all these protest organizers. Like, if you haven't got one place David Sacks (01:12:12): The bear. Sam Altman (01:12:13): Well, by David Sacks (01:12:13): the way, speaking of updates, what do you guys think of the bottle for the All-In tequila? Sam Altman (01:12:19): Oh, beautiful. Chamath Palihapitiya (01:12:20): Honestly, honestly, I will just say, I think you are doing a marvelous job. That I was shocked at the design, shock, meaning it is so unique and high quality. I think it's amazing. It would make me drink tequila. David Sacks (01:12:38): You're going to. You're gonna want to. Jason Calacanis (01:12:39): Gotcha. It is stunning. Just congratulations and, yeah. It was just when we went through the deck at the monthly meeting, it was like, oh, that's nice. Oh, that's nice. We're gonna do the concept bottles. 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. 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 David Sacks (01:13:09): It looks like we can. The, oh, is it good? Make it. Yeah. David Friedberg (01:13:12): Can be amazing. I'm excited. Jason Calacanis (01:13:13): I'm excited for it. You know, it's like for the things. David Sacks (01:13:15): So complicated that we had to do a feasibility analysis on whether it was actually manufacturable, but it is. So at least the early reports are good. So we're gonna hopefully, we'll have some made in time for the All-In Summit. Jason Calacanis (01:13:28): I mean, why not? I mean, it's great. Chamath Palihapitiya (01:13:30): When we get barricaded in by all these protesters, David Sacks (01:13:33): they can drink it to kill time. Jason Calacanis (01:13:35): Did you wanna see did Chamath Palihapitiya (01:13:36): you see Peter Thiel got barricaded by these ding dongs at Cambridge. Jason Calacanis (01:13:40): I'm like, 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 (01:13:49): Exaggerating what happened. Jason Calacanis (01:13:51): Well, I don't know exactly what happens. All we see is these videos David Sacks (01:13:54): Look. They're not threatening anybody, and I don't even think they tried to barricade them in. They were just outside the building. And because they were blocking the driveway, his car couldn't leave, but he wasn't physically, like, locked in the building or something. Jason Calacanis (01:14:09): Yeah. That's what the headlines say, but that could be fake news, fake social. Yeah. Chamath Palihapitiya (01:14:13): This was not on my bingo card. This protester support by Sacks was not on the thing to cover, I gotta say. I didn't see that. David Sacks (01:14:21): The Constitution, the Constitution of the United States, in the First Amendment, provides for the right of assembly, 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. 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, then that somehow should be, you know, 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?—of the woke right now where they're buying into, yeah, the woke right. 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. Jason Calacanis (01:15:29): So you know, we have snowflakery on both sides. Everybody's seen David Sacks (01:15:32): snowflakery on both sides now. Jason Calacanis (01:15:34): It's ridiculous. The only thing I will say that I've seen is this surrounding individuals who you don't want there, and locking them in a circle and then moving them out of, David Sacks (01:15:46): like, processing. That's not cool. 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. And look, I've seen some troublemakers try to barge through the encampments and claim 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. Chamath Palihapitiya (01:16:26): I see. Some of these camps are some of the funniest things you've ever seen. It's like, there are, like, a tent that's dedicated to, like, a reading room. And you go in there, and there's, like, these, Jason Calacanis (01:16:36): like, frontline focused center. Chamath Palihapitiya (01:16:37): Oh my god. It's unbelievably hilarious. David Sacks (01:16:40): Let's just say 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? And then, 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. Yep. And, you know, the way they're being cracked down on, they're sending the police in at 5 a.m. to crack down on these encampments with batons and riot gear, and I find that part to be completely excessive. Jason Calacanis (01:17:16): Well, it's also dangerous because, you know, things can escalate when you have mobs of people large groups of people. So I just wanna make sure people understand that. Large groups of people are you have a diffusion of responsibility that occurs when there's large groups of people who are passionate about things. And and people could get hurt. People have gotten killed at these things. So just, you know, keep it calm, everybody. I agree with you. Like, what's the harm of these folks protesting on a lawn. It's not a big deal. When they break into buildings, of course. Yeah. David Sacks (01:17:42): That crosses the line, obviously. Jason Calacanis (01:17:43): Yeah. But, I mean, let them sit out there and then they'll run out their food cars. Sam Altman (01:17:47): The the their Jason Calacanis (01:17:48): I, and they run out of rations. Did you guys see Chamath Palihapitiya (01:17:51): the clip? I think it was on the University of Washington campus where one kid challenged this Antifa guy to shut Jason Calacanis (01:17:58): contest. Oh, fantastic. Chamath Palihapitiya (01:18:01): I mean, it is some of the funniest stuff. Some some content that's coming up. That's just the way. Jason Calacanis (01:18:06): My favorite was the woman who came out and said that the Columbia students need humanitarian aid. Chamath Palihapitiya (01:18:12): Oh my god. The overdubs on her were hilarious. Jason Calacanis (01:18:15): I was like, humanitarian on the go. David Sacks (01:18:18): I was like, we need our DoorDash right now, we need to double dash some boba, and we can't get it through the police. We need our boba. Jason Calacanis (01:18:26): Low sugar boba with the popping boba bubbles wasn't getting in. But, you know, people have the right to protest. And, peaceable, by the way, there's a word I've never heard. Very good, Sacks, peaceable, inclined to avoid argument or violent conflict. Very nice. David Sacks (01:18:41): Well, it's in the Constitution. It's in the First Amendment. Jason Calacanis (01:18:43): Is it right? I've never yeah. I haven't heard the word peaceable before. I mean, you and I are simpatico on like, I don't we used to have the ACLU, like, backing up the KKK going down Main Street and really fighting for. Yeah. They were really fighting for and I have to say the Overton window is opened back up, and I think it's great. Alright. 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, DJ 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. Do you think, Friedberg? Did you see the ad? What was your reaction to it? David Friedberg (01:19:31): It made me sad. It did not make me wanna buy an iPad. Chamath Palihapitiya (01:19:35): 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. David Friedberg (01:19:44): Yeah. They took all this beautiful stuff and hurt it. It didn't feel good. 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. I don't know. I don't know. David Sacks (01:19:53): 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. So I think the idea was that they're gonna 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 kinda 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. Jason Calacanis (01:20:56): Yeah. It just didn't land. I mean, Chamath, how are you doing emotionally after seeing that? Are you okay, buddy? Chamath Palihapitiya (01:21:04): 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, most likely. And then it turns out he sold $20 billion worth of Apple shares. David Sacks (01:21:33): We're gonna hold it forever. Which by the way? Jason Calacanis (01:21:37): Sell. Sell. If you guys remember, we Chamath Palihapitiya (01:21:39): we put that little chart up, which shows when he doesn't mention it in the in the annual letter, David Friedberg (01:21:44): but Chamath Palihapitiya (01:21:44): it's basically like it's foreshadowing the fact that he is just pounding the sell. And he sold David Sacks (01:21:51): Well, also holding it forever could mean one share. Yeah. Exactly. We kinda need to know, like, how much are we talking about? Chamath Palihapitiya (01:21:59): I mean, it's an incredible business that has so much money with nothing to do. They're probably just gonna buy back the stock just a total waste Jason Calacanis (01:22:06): there were floating rumors of buying Rivian, you know, after they shut down the Titan project, the internal project to make a car. It seems like a car is the only thing people could think of that would move the needle in terms of earnings. Chamath Palihapitiya (01:22:17): I think the problem is J. Cal, like, you you kind of become afraid of your own shadow. Meaning, the 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. 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 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 billion company, I think, just totally doesn't stand logic. 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 (01:23:19): 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's 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, like, when the Gillette David Friedberg (01:23:41): Yeah. They did David Sacks (01:23:42): the 5 came out, and Sam Altman (01:23:43): then they did Jason Calacanis (01:23:43): It was the best second thing. It was like, we're doing effort. David Sacks (01:23:47): But then Gillette actually came out with the Mach Five. So yeah. Like, the parody became the reality. What are they gonna do? Add two more cameras to the iPhone? Have five cameras on it? Sam Altman (01:23:56): Right. No. Jason Calacanis (01:23:56): It makes no sense. And then I don't know anybody who wants to remember the Apple Vision's, like, in a Chamath Palihapitiya (01:24:01): Plus, why are they changing the fat iPads? Jason Calacanis (01:24:06): That's fair point. Fair point. Actually, you know what? It's actually this didn't come out yet, but it turns out the iPad is on Ozempic. It's actually dropped a lot. David Sacks (01:24:15): That would've been a fun year at. Jason Calacanis (01:24:17): Yeah. Exactly. Oh, oh, oh, we can 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 and smaller and smaller. To the point, it was like, you know, like a thumb-size iPhone. Chamath Palihapitiya (01:24:31): Like the Ben Stiller phone in Zoolander or in David Friedberg (01:24:33): Zoolander. Correct. Jason Calacanis (01:24:37): Yeah. That was a great scene. David Friedberg (01:24:39): Is there Jason Calacanis (01:24:39): a category that you can think of that you would love an Apple product for? There's a product in your life that you would love to have Apple's version Chamath Palihapitiya (01:24:50): of it. They killed it. I think a lot of people would be very open-minded to an Apple car. Jason Calacanis (01:24:56): Okay. Chamath Palihapitiya (01:24:56): 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 Jason Calacanis (01:25:07): it. Yeah. Chamath Palihapitiya (01:25:08): Right? There are just too many examples here where these guys have so much money and not enough ideas. That's a shame. Jason Calacanis (01:25:15): It's a bummer. Yeah. The one I always wanted to see them do, Sacks, was David Sacks (01:25:18): TV? Jason Calacanis (01:25:19): 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 actually have a gorgeous you know, big television. Chamath Palihapitiya (01:25:32): What about a gaming console? They could have done that. You know, 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. David Sacks (01:25:49): What do you call one TV would have been good? David Friedberg (01:25:51): If you think back on Apple's product lineup over the years, where they've really created value, it's on how unique the products are. They almost create new categories. Sure, there may have been a "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, like, the iPod. It was so different from any MP3 player ever. Chamath Palihapitiya (01:26:52): I think the reason why the car could have been completely reimagined by Apple is that 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 I left out, but in 2000 and I don't know. Was it 2001? I launched a 99-cent download store. David Sacks (01:27:21): Right. Chamath Palihapitiya (01:27:21): I think I've told you this story in Winham. 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 5 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 goes back to a complete lack of will. And I understand because if you had $200 billion of capital on your balance sheet, I think it's probably pretty easy to get fat and lazy. Jason Calacanis (01:28:23): Yeah. It is. And they and they wanna have everything built there. People don't remember, but they actually built one of the first digital cameras. You must have owned this, right, Friedberg? David Sacks (01:28:31): You're you're like, I don't Chamath Palihapitiya (01:28:32): remember this. David Friedberg (01:28:33): Yeah. Totally. Jason Calacanis (01:28:33): It's beautiful. What did they call it? Was it the iCamera or something? QuickTake? David Friedberg (01:28:37): Tape. Quick tape. Jason Calacanis (01:28:38): Quick take. Yeah. 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. A Dropcam, a door lock, you know, an AV system, go after Crestron 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 all of our home automation through Google. So it just kinda sucks. I would like that also. David Sacks (01:29:08): 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. Jason Calacanis (01:29:16): Yes. That's the that I mean, and everybody has a home. And everybody automates their home. David Sacks (01:29:22): Everyone has Apple TV at this point. So you just make Apple TV the brain for the home system. Jason Calacanis (01:29:28): Right. That would be your home. David Sacks (01:29:30): And you can connect your phone to it. And then, yes, that would be very nice. Jason Calacanis (01:29:34): Yeah. Like, can you imagine, like, the Ring cameras, all that stuff being integrated? I don't know why they didn't go after that. That seems like the easy layup. Hey. You know, everybody's been talking Friedberg about this, AlphaFold, this folding proteins. And there's some new version out from Google and, also Google reportedly, we talked about this before, is also advancing talks to acquire HubSpot. So that rumor for the $30,000,000,000 market cap, HubSpot is out there as well. Friedberg, as our resident science sultan, or resident sultan of science, and as a Google alum, pick either story and let's go for David Friedberg (01:30:14): I mean, I'm not sure there's much more to add on the HubSpot acquisition rumors. They are still just rumors, and I think we covered the topic a couple of weeks ago. But I'd say that AlphaFold 3 that was just announced today and demonstrated by Google, is a real, I would say, breathtaking moment. For biology, for bioengineering, for human health, for medicine. And maybe I'll just take 30 seconds to kind of explain it. You remember when they introduced AlphaFold, at AlphaFold 2, 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 AlphaFold 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. What they just announced with AlphaFold 3 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 say that you're designing a new drug. And it's a protein-based drug, which biologic drugs, which most drugs are today. David Friedberg (01:32:17): You could find a biologic drug that binds to a cancer cell, and then you'll spend 10 years going to clinical trials. And $1 billion 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 gonna bind or interact with other things in our biochemistry. And we only discover it 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 AlphaFold 3 in Isomorphic. So Google is gonna 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. David Friedberg (01:34:28): 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 gonna open up an entirely new era for, you know, 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 at 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 they just demonstrated. And then it begs all this really interesting question around, like, you know, what's Google gonna do with it and how much value is gonna be created here? Anyway, I thought it was a great story. I just rambled on for a couple minutes, but I don't know. It's pretty cool. Jason Calacanis (01:35:19): Super interesting. Is this AI capable of making a science corner that David Sacks pays attention to? Chamath Palihapitiya (01:35:27): It will predict the cure, I think, for the common cold and for herpes. So he should pay attention. Jason Calacanis (01:35:32): Yeah. So folding cells is the app that Sacks, on a casual game, Sacks just downloaded and is playing. How many, how many moves did you make during that second text? David Friedberg (01:35:41): Sorry. Let me just say one more thing. Do you guys remember we talked about Yamanaka factors and how challenging it is to basically 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 AlphaFold 3 can do is predict what molecules will bind and promote certain sequences of DNA, 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 AI. 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 gonna be, like, don't know. I know I say this sort of stuff a lot, but it's gonna be pretty dramatic. There's, Chamath Palihapitiya (01:36:53): on the blog post, they have this incredible video that they show of the coronavirus that creates a common cold. I think the 7PNM protein. And not only did they literally, like, predict it 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. Jason Calacanis (01:37:20): Amazing. Chamath Palihapitiya (01:37:21): I mean, seminal stuff, but so powerful. David Friedberg (01:37:23): And you can filter out stuff that has off-target effects. 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 de-age cells. Jason Calacanis (01:37:51): This is a $100 billion business, they say. David Friedberg (01:37:53): Oh my god. I mean, this alone is getting a huge, like, this is where I’ve said this before. I think Google's got this, like, portfolio of, like, quiet, you know, the other. Yeah. Yeah. What if they hit? And I think the fact that they didn't open source everything in this says a lot about their intentions. Yeah. Jason Calacanis (01:38:10): Yeah. Open source when you're behind, closed source lock it up when you're ahead. David Friedberg (01:38:14): But shout out Yamanaka. Jason Calacanis (01:38:16): Actually, interestingly, Yamanaka is the Japanese whiskey that Sacks serves on his plane as well. It's delicious. I love that Hokkaido. Nikka. David Friedberg (01:38:24): Jason, I feel like Jason Calacanis (01:38:24): you have that. David Friedberg (01:38:25): If you didn't find your way to Silicon Valley, you could be like a Vegas lounge comedy guy. David Sacks (01:38:29): Absolutely. Yeah. For sure. Yeah. Jason Calacanis (01:38:31): Actually, yeah. Somebody said I should do, like, the David Friedberg (01:38:33): 1950s, those 1950s talk shows where the guys would do, like, the stage Jason Calacanis (01:38:37): just shtick. Yeah. Somebody told me I should do, like, Spalding Gray, Eric Bogosian style stuff. I don't know if you guys remember, like, the monologues from the eighties in New York. And I was like, oh, that's interesting. Maybe. Alright, everybody. 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 wanna 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. Friedberg is setting up mock meat stations for all of our protesters and what mock meat you like. Yeah. All vegan, if you want, if you're old male. Chamath Palihapitiya (01:39:21): To soy your gut reference of milk, please, when you come. David Friedberg (01:39:23): To it, you have different kinds of xanthan gum you can choose from. Right? Jason Calacanis (01:39:27): Two of the nut milks you could want, and then there'll be mindful. Chamath Palihapitiya (01:39:30): Soy like that. With some soy like the include. Jason Calacanis (01:39:33): Yes. On the south lawn, we'll have the goat yoga going on. So just please look at the goat yoga money for all of you. David Sacks (01:39:40): It's very thoughtful for you to make sure that our protesters are gonna be, well, well, fine. We'll take care of it. Jason Calacanis (01:39:46): Yes. Actually, Friedberg is working on the protester gift. The protests would give access. They're made of young, folding proteins. So they're good. A folding I think I saw them open for the Smashing Pumpkins in 2003. David Sacks (01:40:03): Unfire. Unfire. Enough. Jason Calacanis (01:40:05): I'll be here for three more nights. Lovely boys. But, love you besties. David Sacks (01:40:08): Is this the All-In Pod or open mic night? What's going on? Jason Calacanis (01:40:11): It's basic. I'm just bored. David Sacks (01:40:15): We'll let your winners ride. Jason Calacanis (01:40:18): Rainman, David Sacks. David Sacks (01:40:21): I'm going home. And it said we open store sits in the fans, and they've just gone crazy with it. On air when I try. I'm gonna bake you, George, because they're all just useless. It's like this, like, sexual tension, but they just need to release it out. Jason Calacanis (01:40:56): Flirt. That's episode 178, and now the bugs, the All-In Summit is taking place in Los Angeles on September 8th through 10th. You can apply for a ticket at summit.allinpodcast.co. Scholarships will be coming soon. If you wanna see the four of us interview Sam Altman, you can actually see the video of this podcast on YouTube, youtube.com/@allin, or just search All-In Podcast 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 gonna do a party in Vegas, my understanding, when we hit 1 million subscribers, so look for that as well. You can follow us on xxx.com/theall in pod ChikTok is paul_in_talk, Instagram, the all in pod, and on LinkedIn, just search for the all in podcast You can follow chamath@x.com/chamath, and you can sign up for a substack at chamath.substack.com. I do. Freeburn can be followed at dotcom/freebergandohaloishiring. Click on the careers page atohalogenetics.com, and you can follow sacks@x.com/david sacks Sachs recently spoke at the American moment conference and people are going crazy for it. It's pinned to his tweet on his ex profile. I'm Jason Calacanis. Imx.com/jason. And if you wanna see pictures of my bulldogs and the food I'm eating, go to instagram.com/jason. In the first name club, you can listen to my other podcast, This Week in Startups. Just search for it on YouTube or your favorite podcast player. We are hiring a researcher. Apply to be a researcher doing primary research and working with me and Producer Nick, working in data and science and being able to do great research, finance, etc. allinpodcast.co/research. It's a full-time job working with us, the besties. We'll see you all next time on the All-In Podcast.