SPEAKER_00: I have a very funny story to tell you, Jason. Where have you been? I've been trying to text you. You've been offline. What's going on? Where have you been?

SPEAKER_02: I've been working feverishly, but yesterday I had to go to prepare for some meetings that I have on Sunday, which I can't tell you about. But Nat and I went to Pasalaqua, which is in Lake Como, which is stunning. The grounds are stunning. The hotel is stunning. If you have a chance to go to Lake Como. Anyways, this is us at Pasalaqua.

SPEAKER_05: Who's the beautiful woman there? Is that the woman who owns it or something? Is that the queen? No, that's not.

SPEAKER_02: But the best part is we had such a good time. You know how they have like a registry book to leave a message? Sure. So I left a message.

SPEAKER_08: Here we go. What a truly magnificent place, above and beyond any expectation we had. Go below that thought for me. Thank you. We took everything.

SPEAKER_12: We took everything. The Freeburgs. Great. Awesome. Jason, the hangers, the bags, the laundry bags, the robes, the slippers, everything.

SPEAKER_13: Absolutely fantastic.

SPEAKER_15: You're going to have to send a bill to the Freeburgs.

SPEAKER_29: All right, listen, we've got a great panel this week. It's the summer. Things are slow. Some people are busy. I think our prince of panic attacks, our dear sultan of science, is he's at the beep.

SPEAKER_31: Saks is busy. Couldn't make it this week. In his place.

SPEAKER_32: Another brilliant PayPal alumni. And dare I say, a GOP supporter, Heath Raboy. How are you, sir?

SPEAKER_33: Pleasure to be with you again.

SPEAKER_34: Nice to see you. And I'm assuming you're in gorgeous Florida or somewhere.

SPEAKER_31: Yeah, I'm actually in New York. Oh, my hometown. Is it safe? Is it okay? Mom done me chasing you down the street?

SPEAKER_33: Not yet, but it's safe. Did he seize your assets? It's safe right now. We'll see on November 4th. You know, as you probably heard, on July 4th was the first time in recorded history that there were no shootings or no murders in New York on that day. So right now, things are in pretty good shape, but we may be leaving

SPEAKER_26: New York quickly. Yeah. You're going to probably want to sell that place if you got one there, because Momdami is going to seize it and turn it into a drugstore for you. Yes, it's going to be

SPEAKER_43: a Momdami drugstore. Travis Kalanick is back with us. How are you doing, Bestie?

SPEAKER_44: Uh, pretty good. Pretty good.

SPEAKER_43: Yeah. Second appearance here on the round table and, uh, third time on the show. Of course, you spoke at the summit. You've been busy with Cloud Kitchens. Yeah. Lots of exciting things going on.

SPEAKER_47: Lots of stuff. Lots of stuff. The robots, the robots are taking over. We're, we're rolling out.

SPEAKER_50: We're rolling out robots. Yeah. TK, can you tell us what you're doing with this Pony AI? Or not,

SPEAKER_49: that's speculation. Uh, look, you know, obviously is autonomy as we, you know, in the U S we have,

SPEAKER_02: of course, do you want to just frame for people that don't, that may not be up to speed. What was

SPEAKER_43: announced or at least why don't you frame it? So Pony AI is, um, an autonomous company doing self driving. It's one of the few, uh, players that actually have cars on the road. They're based in

SPEAKER_57: China. They've got a lot of operations in the Middle East. They've got a deal with, um, a delivery

SPEAKER_60: company called Uber, which you might be familiar with. Uh, okay. So look, well, the deal was basically

SPEAKER_64: that you could partner with Uber license in the Pony technology and essentially start a competitor,

SPEAKER_65: I guess, to Waymo and Tesla. Let me work on this one. Okay. So, so in the U S we have Waymo,

SPEAKER_66: we see the Waymo's in San Francisco, Los Angeles, Boston, coming soon to Miami, coming soon to Atlanta, coming soon to DC. They're even talking about New York.

SPEAKER_68: Tesla is sort of like the, you know, they're doing it the hard way, you know, classic Elon style. Like,

SPEAKER_66: let's, let's do this sort of in a fundamental, holy shit, let's go all the way kind of, kind of approach. Uh, and it's unclear when it gets over the line. Of course he, he launched sort of a, a semi, semi pilot of sorts in Austin recently, but there's no other alternatives. So what happens is, is some of the folks who are interested in making sure their alternatives have reached out. They they've reached

SPEAKER_68: out to me and they're different discussions that get going because they're like Travis, you did autonomy way back in the day, got the Uber autonomous stuff going in 2014. Uh, maybe there's something to do here to create optionality. Now it may be like, I'm of course, very interested on the

SPEAKER_66: food side. I talk about autonomous burritos being a big deal because if you can automate the kitchen, the production of food, and then you can automate the sort of, uh, logistics around food, you take huge amount of costs out of the food, out of what's going on in food. And that's of course, near and dear to my heart. There's folks that of course, that want to see autonomy and mobility. That's a real thing. It, it may be that, or I would say if you get the autonomy problem, right, you can use it to apply to both problems. So there's a lot of folks interested in moving things, moving food, moving people. And if there is some kind of autonomous technology that maybe I get involved in, it might apply to a bunch of different things. And so I've got some inbound, let's just put it that way. There's no, there's no real deal right now, but there is definitely some inbound. And I think there's some news about some of that inbound that may or may not be occurring. That's probably the best way to put it as long winded. I'll try to tighten that up next time.

SPEAKER_70: No, no. I think it's great to get the overview here first on, uh, all in sharing it with us.

SPEAKER_72: And everybody knows you have been doing a bowl builder, um, lab 37, I think it's called and turn it up on the screen. Not sure what the status of it is. And then I'll let you go to Moth with your followup question. But I think there's a pretty interesting concept here of the bowl getting built and then put into a self-driving car that machine looks huge, but it's actually 60 square

SPEAKER_66: feet. That picture makes it look monstrous. It's a 60 square foot machine. Like, uh, imagine running like a sweet green like brand or a chipotle like brand of making it so it comes to life for people who, who, you know, are like, Hey, what is this thing? Imagine you just order online exactly the kind of bowl you want. And actually this machine could run like many brands at the same time. And, and does you build the bowl you want, whatever ingredients, uh, it's sort of, it, if you look at that bottom, you see those little white bricks at the bottom, that's what carries the bowl underneath dispensers. It fills up the machine puts, uh, uh, it sauces the bowl, then it puts a lid on it. It takes the bowl, puts it in a bag, uh, puts utensils in the bag, seals the bag. And the bag goes down, uh, a conveyor belt where then another machine, uh, what we would call an AGV takes the bowl to the front of house. The bowl gets put into a locker. The courier via door dash who breeds courier will wave their app in front of a camera and it will open up the locker that has the food that they're supposed to pick up. So it just, it takes out a lot of what we would call the, the cost of assembly, um, which is more than reduces mistakes, right? We know exactly how many grams of every ingredient are put in. That's exactly what you're supposed to get. And so you get a higher quality product. It takes a lot of the cost out. You imagine ultimately that's going to be, there are going to be couriers with that as well. That, you know, I like to say autonomous burritos, like, is a Waymo going to carry a burrito or is Tesla going to have, uh, a machine that carries food or, you know, is there another, another company that ends up doing, you know, sort of the, the, the themes that the, the, the autonomous delivery of things. And the point is, is well, where we are right now is we've got customers. And so those customers are starting to deploy this quarter. And it's pretty interesting. I mean, you can see the, the, in our delivery kitchens, the cost of labor is about 30%

SPEAKER_68: of revenue. That's what the successful guy, let's say 30%, 35% of revenue in a, in a brick and mortar,

SPEAKER_66: in a brick and mortar restaurants, it's even higher. Okay. When they're running our machine, it's between seven and 10% of revenue. Amazing. Then you take out the cost of the delivery,

SPEAKER_81: you know, and now it's becoming, everybody can have a private check, which was your original vision for

SPEAKER_83: Uber. It was people don't know the original tagline, but it was your, your pro everybody has a private

SPEAKER_68: driver. Everyone's private driver was the original for Uber. Basically the infrastructure is already

SPEAKER_66: there. And I said this on, you know, one of your recent, I think it was at the all in summit, Jason, but like, um, in the mobility cars, you know, I'm transport, uh, space, the roads were already there. The cars were already built. People weren't using their cars 98% of the day. So the infrastructure is already there to get people around, to do this as a service and do it very efficiently and conveniently with food. The infrastructure is not there. Like, yes, restaurants have excess capacity. That's what Uber eats utilizes, but to go and say, like, let's make 30% of all meals in a, in a city, uh, sort of prepared and delivered by a service. The infrastructure is not there. So you have to build it. So our company, the, the mission is, uh, infrastructure for better food. So that's real estate, that's

SPEAKER_87: software and robotics for the production and delivery of food in a super efficient way.

SPEAKER_90: All right. Uh, Keith, what are your thoughts? Any questions? Well, he's not here, but isn't

SPEAKER_47: this what David Freeberg tried to do a few years ago? Yeah. This came up on the last all in. Yeah.

SPEAKER_32: There's the last one I was at. Yeah. Yeah. It's up. It's the problem was I told Freeberg,

SPEAKER_97: people don't want to eat quinoa. You got to put a little steak in there, maybe a piece of salmon,

SPEAKER_72: but he was kind of really, I think eventually he relented and let people have a little bit of protein.

SPEAKER_08: Uh, but yeah, so it's such a great vision. Wait, he, he died as a vegan martyr.

SPEAKER_104: I think the business died as a vegan martyr. That was the hell he was led to die.

SPEAKER_68: There's a lot of people have died on that hill, but the bottom line is if you're going to get into

SPEAKER_66: automation, you have to, it has to be end to end automation. And what I mean by that is like, there are pizza, there are pizza companies that have come and gone automated pizza companies where it's like, we have a pizza machine and everybody's like, yeah, this is amazing. And you have a guy, you have a million dollar pizza machine. And then on the left, you have a guy feeding ingredients into the pizza machine. And on the right, you have a guy taking the pizza out and then putting it in a box and doing all this. So instead of one guy making pizzas, I have a million dollar machine and two guys

SPEAKER_68: making pizza. And so when you look at these, uh, uh, like a robotic food production machines or food assembly machines, you have to look at the full stack and say, does it work with the ecosystem that

SPEAKER_66: exists in a restaurant? And does it go full stack from, you know, like, like we have this thing where

SPEAKER_68: that machine we saw earlier, the staff preps the food, they put the food in the machine and then they

SPEAKER_66: leave, they're gone. This restaurant runs itself for many hours without anybody there.

SPEAKER_110: But this could be McDonald's, Burger King and Taco Bell. Nobody would know.

SPEAKER_68: That right there, that machine is a, it's an assembly machine, right? The food is prepped by humans and then assembled by this machine for a Chipotle or a sweet green. This is like a, a majority of their

SPEAKER_66: labor, right? You go up to Chipotle. There's like 10 guys at lunch and you're still in line. That machine right there does 300 bowls an hour, right? And so you go, okay, that's the, this is what's called, um, like the assembly line. It's just that frontline where you basically assemble things. I think sometimes I will call it the make line. What will happen over time is you'll have

SPEAKER_68: perpendicular lines going into it where you're producing food, right? So you'll have a production or make line going into an assembly line here. And then you go, oh, wow. So you have something that dispenses burgers on buns. That's the dispenser. That's the assembly. Right.

SPEAKER_55: But it's like factorio on steroids, basically. Yeah. And then it's like, how do you cook that

SPEAKER_68: burger? That's what I call, that's what we call state change. So state changes that is the cooking of the food. Assembly is like, how do I put it together and plate it? Doesn't this collapse?

Chamath Palihapitiya: Like for example, if you have a yield of 300 per hour, you said out of that one machine, very quickly, you can impute the value of having a smaller footprint store with five of these things in a faceless warehouse with drone delivery or cars, you don't need the physical infrastructure. So then don't you create a wasteland of real estate or how do you repurpose all the real estate? Well,

SPEAKER_87: the way to think about it is like 90%. Well, it's probably a little lower than that right now. Let's say 85% of all meals in the US are at home. They just are. And a vast majority of those meals

SPEAKER_66: are cooked at home. So, you know, like Uber Eats and DoorDash, they represent like 1.8% or 2% of all meals right now. It's very tiny. Right? So what you're doing is you're using real estate to an infrastructure to prepare and deliver meals to people at their homes. And so it's not the restaurants still exist, we're still going to want to go to restaurants, we're still going to want to go outside, we learn that during COVID. We knew it before, we definitely know it after. And so I don't,

SPEAKER_68: it's not really like a decimating real estate situation. It's taking a thing we used to do for ourselves and creating a service that does it higher quality, you know, sort of, I like to say, you don't have to be wealthy to be healthy. And just infrastructure to get that cost down.

SPEAKER_66: And so you're doing something as a service that we used to do at home. I think in the super long run, you're like, what, where's the story on grocery stores? If you go to, like in 20 years, I think

SPEAKER_68: everybody agrees. You will have machines making very high quality, very personalized meals for

SPEAKER_72: everybody. This would be good for Keith, because he measures stuff down to like five calories based

SPEAKER_121: on his Instagram. What's your what's your body fat like? Just open his Instagram.

SPEAKER_124: He's like so disgusted with himself at 10%. It's like about a 10. But I actually think the vision of

SPEAKER_125: this, actually, the natural implication, and maybe the home run version of this is everybody has a

SPEAKER_128: private chef in their house, a robot in their house actually does this personalized because people do

SPEAKER_129: want to cook at home, but they don't have the time. Yeah, or space and infrastructure. But man,

SPEAKER_36: these delivery services are charging, rich people do this all the time, right? They do these crazy meal delivery services for 200 bucks a day. And this is just going to abstract it down to everybody. And man, people get creative when there's that empty space, to your point, Shamath, about what happens to all this space. When I lived in New York in the 80s and 90s, it was common to in Tribeca, in West Chelsea, where I lived to take storefronts, put your little

SPEAKER_28: architect's office in the front and live in the back. And many people were hacking real estate, we still need 5-10 million homes in this country. And they're already doing this with malls.

SPEAKER_36: I keep seeing malls being turned into colleges and creative spaces. One of them in Boston, they turned like the second and third floor into studio apartments for artists. So you know,

SPEAKER_134: where there's a will, there's a way we could use the space.

SPEAKER_133: Yeah, where this goes with Shamath saying, where the real estate goes is we call it the internet food court,

SPEAKER_66: where, you know, you're on Amazon, right? It's the everything store. Now imagine that for food. And then imagine you have an 8,000 square foot facility where basically anything can be made. Anything can be made. Because if you have, that machine you saw has 18 sort of dispensers for food, 10 different sauces, you get the idea. Now what about when it's 50 or 100 dispensers for food? What if you have multiple machines with 100 dispensers for food? That's crazy. You can, the combinatorial math in terms of what's possible, what can be made, sort of, you know, goes exponential. And so, the internet food court is sort of the vision for where this all goes.

SPEAKER_04: Another example of the bitter lesson. The bitter, yeah, we're going to get to that, I guess, today.

SPEAKER_28: In a very full docket before we get to that, just a little bit of housekeeping here.

SPEAKER_43: September 7th, 8th, 9th in Los Angeles, the all in summit again, all in.com slash yada, yada, yada.

SPEAKER_144: Lineup is stacked and we're going to start announcing the speakers. People have been begging us to announce the speakers. Oh, really? I don't know.

SPEAKER_145: Careful, you gotta, you gotta hold some back. Careful, careful.

SPEAKER_43: Hold a couple back, but we got some really nice speakers lined up. That is true. It is going to be extraordinary.

SPEAKER_116: It is the, it is the best one yet.

SPEAKER_43: I mean, every year we have this, yeah, yeah. Every year we have this little bit of panic, like, um, you know,

SPEAKER_28: we're going to get great speakers and man, they started flowing in this week. It's going to be extraordinary. Almost as extraordinary as this delicious tequila behind my head here. Get the all in tequila, tequila.allin.com. Deliveries begin late summer.

SPEAKER_151: He's moving to the side. You can't even tell it's tequila. That's our tequila.

SPEAKER_152: All right. Listen, lots to discuss this week.

SPEAKER_29: Obviously AI is continuing to be the big story in our industry. And for good reason, our bestie, uh, Elon released Grok 4, Wednesday night. Two versions, base model and a heavy model, 30 bucks a month for the base, $300 a month for this heavy model, which has a very unique feature. You can have a multi-agent feature. I got to see this actually when I visited XAI a couple of weeks ago, where multiple agents work

SPEAKER_134: on the same problems and they, and they do that simultaneously, obviously, and then compare each other's work. And it gives you kind of like a study group, the best answer, uh, by consensus.

SPEAKER_29: Really interesting. According to artificial analysis benchmarks, you can pull that up.

SPEAKER_134: Nick rocks for base model has surpassed opening eyes. Oh, three pro Google Gemini's 2.5 pro as the most intelligent model. This includes like seven different industry standard evaluation tests. You can look it up, but reasoning, math, coding, all that kind of stuff. This is, you know, book smarts, not necessarily street smart. So it doesn't mean that these things can reason. And obviously there was a little, um, there was a little kerfluffle on, um, X formerly known as Twitter where X AI got a little frisky and was saying all kinds of crazy stuff and needed to, uh, maybe be red teamed a little bit more decisively. Many of, you know, Grok four was trained on Colossus. That's that giant data center that Elon's been building. And we showed the chart here. You sent us a link to the bitter lesson by, uh, rich Sutton in the group chat. That's the 2019 blog post. We'll pull it up here for people to take a look at and we'll put it in the show notes. Maybe just generally. Yeah.

SPEAKER_29: Your reaction to both how quickly Elon has in that chart showed it, how quickly Elon has caught up and I don't think people expected him to take the lead, but here we are.

SPEAKER_02: Before we start, Nick, can you please show Elon's tweet about how they did on the AGI benchmark? It's absolutely incredible.

Chamath Palihapitiya: Two things. One is how quickly starting in March of 2023. So we're talking about less than two and a half years, what this team has accomplished and how far ahead they are of everybody else as demonstrated by this. But the second is a fundamental architectural decision that Elon made, which I think we didn't fully appreciate until now. And it maps to an architectural decision he made at Tesla as well. And for all we know, we'll figure out that he made an equivalent decision at SpaceX. And that decision is really well encapsulated by this essay, the bitter lesson by Rich Sutton and Nick, you can just throw this up here. But just to summarize what this says, it basically says in a nutshell that you're always better off when you're trying to solve an AI problem, taking a general learning approach that can scale with computation because it ultimately proves to be the most effective. And the alternative would be something that's much more human labored and human involved that requires human knowledge. And so the first method, what it essentially allows you to do is view any problem as an endless scalable search or learning task. And as it's turned out, whether it's chess or Go or speech recognition or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won. And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps in more AI specific language. What it means is that a lot of these systems create these embeddings that are just not understandable by humans at all, but it yields incredible results. So why is this crazy? Well, he made this huge bet on this 100,000 GPU cluster. People thought, wow, that's a lot. Is it going to bear fruit? Then he said, no, actually, I'm scaling it up to 250,000. Then he said, it's going to scale up to a million. And what these results show is a general computational approach that doesn't require as much human labeling, can actually get to the answer and better answers faster. That has huge implications. Because if you think about all these other companies, what is Lama been doing? They just spent 15 billion to buy 49% of scale AI. That's exactly a bet on human knowledge. What is Gemini doing? What is OpenAI doing? What is Anthropic doing? So all these things come into question. And then the last thing I'll say is, if you look back, he made this bet once before, which was Tesla FSD versus Waymo. And Tesla FSD only had cameras, it didn't have LiDAR. But the bet was, I'll just collect billions and billions of driving miles before anybody else does, and apply general compute, and it'll get to autonomy faster than the other more laborious and very expensive approach. So I just think it's an incredible moment in technology where we see so many examples, Travis is another one, what he's just talked about. The bitter lesson is, you could believe that food is this immutable thing that's made meticulously by hand by these individuals, or you can take this general purpose compute approach, which is what he took, waited for these costures to come into play. And now you can scale food to every human on earth. I just think it's so profoundly important.

SPEAKER_87: One thing I'll throw out there, Chamath, is the Tesla approach for autonomy is taking human

SPEAKER_66: knowledge. In fact, the whole idea is to approximate human driving, right? That is the whole damn thing. Now, depending on your approach and the technology, you can do what's called an end-to-end approach, or you can look at, okay, perception, prediction, planning, and control, which are like these four modules that sort of you, you, you sort of engineer, if that makes sense. But it's approximating human driving to do it. The difference is that, you know, I think Elon's taken a almost a more human approach, which is like, I've got two eyes, why can't my car? Why can't my car do it like a human? Like, I don't have any lidar spinning around on my head as a human. Why can't my car? So it's kind of interesting. He's sort of taking what you're saying, Chamath, on the computation side, because hardware five is coming out on Tesla probably next year, which is going to make a big

SPEAKER_68: difference in what FSD can do. That's the compute side you're talking about, but then he is approximating human.

Chamath Palihapitiya: Yeah. I just meant that, you know, other than the first versions of FSD, which I think Andre talked about, Andre Karpathy talked about, you know, they're not really so reliant anymore on human labeling per se, right? So that, yeah, that, that interference. And then the other crazy thing that he said, subsequent versions of Grok are not going to be trained on any traditional data set that exists in the wild.

SPEAKER_167: The cumulative sum of human knowledge has been exhausted in AI training. That happened

SPEAKER_168: basically last, last year. And so the only way to then supplement that is with synthetic data where the AI creates, it'll sort of write an essay or it'll come up with, with a thesis and then it will grade itself, um, and, and, and sort of go through this process of self-learning with synthetic data.

Chamath Palihapitiya: He said that he's going to have agents creating synthetic data from scratch that then drive all the

SPEAKER_15: training, which I just think is, it's crazy.

SPEAKER_171: Just explain this concept one more time with a bitter lesson, hand coding heuristics into the computer and saying, Hey, here's specific openings and just use, yeah, use chess, right?

SPEAKER_173: Yeah. So you're coding specific examples of openings in their end games, et cetera, versus just saying, play every possible game. And here's every game we have.

Chamath Palihapitiya: So here's the, yeah. Yeah. So the two approaches would be, let's say like Travis and I were building competing versions of a chess solver. And Travis's approach would say, I'm just going to define the chess board. I'm going to give the players certain boundaries in which they can move, right? So the Bishop can only move diagonally and there's a couple of boundary conditions. And I'm going to create a reward function. And I'm just going to let the thing self-learn and self-play. That's his version. And then what happens is when you map out every single permutation, when you go and play Keith, who's the best chess player in the world, what you're doing at that point is saying, okay, Keith made this move. So you search for what Keith's move is, and you have a distribution of the best moves that you could make in response or vice versa. That was the cutting edge approach. The different approach, which is more, you know, what people would think is more, quote unquote, elegant and less brute force would be, Jason, for you and I to sit there and say, okay, if Keith moves here, we should do this, we should do this specific variation of the Sicilian defense. And it's too much human knowledge. And I think what it turned out was there was a psychological need for humans to believe we were part of the answer. But what this is showing is because of Moore's law and because of general computation, it's just not necessary. You just have to let go, give up control. And that's very hard for some people. And for others, it's not.

SPEAKER_81: Well, it's also very hard in some circumstances where a car is driving down the road and it's learning in that process, which is why you need a safety driver. And I think Elon made the right decision to put one in there. Keith, your thoughts?

SPEAKER_178: Yeah, a couple of points. It's not quite that binary, Chamath. I generally agree with your arc.

SPEAKER_128: But if you think about LLMs being the most important unlock in AI, LLMs are all trained on human writing. So someone wrote every piece of data that every LLM used, a human wrote at some point in history. So yes, it's true that they've shocked everybody, including OpenAI's original team on the implications, the broad implications, the general applicability to almost every problem. But it's not like there was some tablets floating in space that weren't drafted by humans that we've trained on. As you get in non-LLM-based models, you may be totally right, but almost no one's really using non-LLM-based models at scale. On driving specifically, Travis is totally right, that humans are actually really good drivers except when they get distracted. They get distracted by drugs or alcohol. They get distracted by being tired. They get distracted by turning the radio. They get distracted by chatting with their passenger. So trading against human behavior has actually turned out to be a great decision because for whatever sort of Darwinistic reasons, humans are pretty ideal drivers. And so you don't have to reason from first principles, this is a much better

SPEAKER_182: path. And I think again, there may be a broad sort of lesson there. The most important thing, I think as a VC that you said, as we've been debating for years, should we invest in companies like scale or Mercor or any of these surge? The truth is, I think there's a very short half-life on human-label data. And so everybody who's investing in these companies just to get revenue

SPEAKER_183: traction really didn't understand that there may be a year, two years, three years max when anybody uses human-label data for maybe anything. Because we hit the end of human knowledge,

SPEAKER_66: or just the collection of it is 99% done. Or you train on it so well that you don't need to label anymore. The machines know how to label as good or better than a human. And so we're seeing this in the self-driving space, is labeling was huge, right? You would have a three-dimensional sort of scene that's created by video plus LiDAR, let's say. Okay, I have to label all of these, essentially what become boxes, like I've identified objects. You're some of the players in the autonomous software space, autonomous vehicle software space, are no longer doing any labeling because the machines

SPEAKER_83: are doing it all, just broadly. It'll just be built into the chipset that this is a stop sign,

SPEAKER_190: like it's like, we know what a stop sign is. We don't need the millionth time for somebody to

SPEAKER_66: It's like those captchas, like you're like, find the stop sign, or what's the traffic light? And eventually the machines are just way better than humans at identifying these things.

SPEAKER_182: For you to be very practical, when you see a stop sign, you don't have to identify that it's a stop sign. You just see that every human, when they encounter a stop sign 99.9% of the time, they hit a break. Nobody actually knows it's a stop sign, it's just that hit a break when you see something that looks like this object. It's just a vibe. It's a vibe.

SPEAKER_02: I would just say that that's like intuitive knowledge versus like the expressly labeled

Chamath Palihapitiya: human knowledge. The question for me is, if everybody was so reliant on human labeling initially, if you're an investor now, when you see these Grok 4 results, how do you make an investment decision that's not purely levered to just computation? So if you look at these results, does it mean that there's 300 to 1000 basis points of lag between just letting the computers vibe itself to the answer versus interjecting ourselves? If interjecting ourselves slows us down by 300 to 1000 basis points per successive iteration, then over two or three iterations, you've totally lost. So what does it mean for everybody that's not Grok when they wake up today and they have to decide,

SPEAKER_124: how do I change my strategy or double down?

SPEAKER_66: I think, look, I'm not in the investment game. But if I were, it would be all about scientific breakthrough. So I sometimes get in this place where I'm looking, I'm going down a path. I, you know, I'll be up at four or five in the morning. My day hasn't quite started, but I'm not sleeping anymore. And I'll start go like, I'll be on Quora and see some cool quantum physics question or something else I'm looking into. And I'll go down this thread with GPT or Grok. And I'll start to get to the edge of what's known in quantum physics. And then I'm doing the equivalent of vibe coding except it's vibe physics. And we're approaching what's known and I'm trying to poke and see if there's breakthroughs to be had. And I've gotten pretty damn close to some interesting breakthroughs just doing that. And I, you know, I pinged, uh, I pinged you on at some point, I'm just like, dude, if I'm, if I'm doing this, and I'm super amateur hour physics enthusiast, like, what about all those PhD students and postdocs that are super

SPEAKER_68: legit using this tool? And this is pre Grok four. Now with Grok four, like, like, there's a lot of mistakes I was seeing Grok make that then I would correct. And we would talk about it. Grok

SPEAKER_66: four could be this place where breakthroughs are actually happening new breakthroughs. So if I'm

SPEAKER_68: investing in this space, I would be like, who's got the edge on scientific breakthroughs and, and the application layer on top of these foundational models that orients that direction is your perception

SPEAKER_29: that the LLMs are actually starting to get to the reasoning level that they'll come up with a novel concept theory and have that breakthrough, or that we're kind of reading into it. And it's just

SPEAKER_202: trying random stuff that the at the margins, it's, or maybe it doesn't happen. No, no, no. So what I

SPEAKER_66: what I've seen, and again, I haven't used Grok four, I tried to use it early this morning. But for some reason, I couldn't do it on my on my app. But so let's say we're talking rock three and existing chat GPT as it is, no, it cannot come up with the new idea. These things are so wedded to what is known.

SPEAKER_68: And they're so like, even when I come up with a new idea, I have to really, it's like pulling a donkey source, you're pulling it, because it doesn't want to break conventional wisdom. It's like really adhering to conventional wisdom, you're pulling it out. And then eventually goes, oh, you got something. But then when it says that, when it says that, then you have to you have to go,

SPEAKER_199: okay, it said that, but I'm not sure like you have to double and triple check to make sure

SPEAKER_140: that you really got something to your point, when these models are fully divorced,

Chamath Palihapitiya: from having to learn on the known world, and instead can just learn synthetically. Yeah, then everything gets flipped upside down to what is the best hypothesis you have,

SPEAKER_208: or what is the best question, you could just give it some problem, and it would just figure it out.

SPEAKER_66: So where I go on this one, guys, is it's all about scientific method. Right? If you get, if you have an LM or foundational model of some kind, that is the best in the world of the scientific method. Game the F over. You basically, you just light up more GPUs, and you just got like 1000 more PhD students working for you. Keith, you're nodding your head here.

SPEAKER_128: I agree with that. I think that's fantastic, because the scientific method also, the faster it is,

SPEAKER_182: the more you, when you have a hypothesis, the faster you get a response, you're more likely to dive in and dive in and dive in recursively and recursively. And every lag, every millisecond lag causes you to like lose your train of thought, sort of, so to speak. So you get the benefits that Travis alluded to plus speed, and you go places you never really guess. This happens all the time when you run a company, and you're doing like analytics, and you have a tool that allows you to constantly query quickly, quickly, quickly, double click, triple click, you get to answers that you never get to,

SPEAKER_128: if there's even a second or two second or three second delay, let alone sending it to a human. Secondly, where you actually see this today, it's already happening. If you look at foundational models that just apply to science, there's lots of things about the human body, let's say health biology, that we humans don't actually understand all the connections. Like why do we do X? Why do some people get cancer? What other people not get cancer? Why does the brain work this way? Models trained solely on science tend to expose connections that no human has ever had before. Hmm. And that's because like the raw materials there, and we only have a conscious awareness of thought 110%. But when you apply it to other human domains, where you're training on human

SPEAKER_182: sort of data, human produced data, human produced output, they're limited to that output. So I think you just take the science and apply it writ large, and you're going to wind up finding things that no

SPEAKER_66: human has ever thought before. And it's the thing about science, though, is that it's the hypothesis that you then have to test in the physical world. So the, you're like, okay, you've got this hive mind, this like, you know, this computation engine, this brain of sorts.

SPEAKER_218: You wanted to say consciousness, but you stopped yourself.

SPEAKER_66: Yeah, there's an idea. I was like, how do I describe this? The big C word, consciousness. Yeah.

SPEAKER_68: But you need to be able to test in the physical world. So you could imagine

SPEAKER_66: a physical lab connected to one of these systems, where then you could say, okay, like, if it was a chemistry experiment, you could do chemistry experiments, or physics, you get the idea.

SPEAKER_222: What could go wrong?

SPEAKER_66: It would be a it's Yeah, no big deal. It's gonna be fine. Okay, so, but this is where it goes. Because if you have a scientific method machine, you still have to be able to test your hypothesis, you have to

SPEAKER_227: go through the science and verification. Yeah, exactly. Yeah. Wow. It's kind of mind blowing.

SPEAKER_81: Reminds really mind blowing. Remember, I don't know if you guys remember dark matter and like the

SPEAKER_43: discovery of it and everything. And as explained to me by Lisa Randall, you know, the discovery was made not by knowing there was dark merit matter there and observing it. But observing, there was

SPEAKER_57: something, you know, gravitational forces around this other matter. And then they said, but wait, what's causing that? And that's why they found dark matter. So these ideas, you know, the idea that LLM could actually do that, come up with something so novel is, it doesn't, it feels like we might be right there, right? Like we're kind of on the cusp of it. One of the seven most difficult problems in

SPEAKER_116: math with the most important problems in math is proving a general solution to this thing called Mavie Stokes, which is basically like viscous fluid dynamics and conservation of mass. We use it every day in the design of everything. You know what, it hasn't been proved. Isn't that the crazy

SPEAKER_02: thing where you're just like, how is this even possible? We use it to design airplanes to design

Chamath Palihapitiya: everything. It hasn't been proved. And so you could just point a computer at this thing and you would unlock all these incredible mysteries of the universe. And we would probably find completely different propulsion systems. We could probably do things that we didn't think were possible. Teleportation. I mean, who knows what's possible?

SPEAKER_187: But remember, remember, you know, how Elon talks about Grok and about AI generally is about why are

SPEAKER_66: we here? What is the purpose? Meaning of the universe? What is the meaning of the universe? How does it work? And a sort of fierce, truth seeking mechanism there. Let me ask you a question,

SPEAKER_237: Keith, Travis, Jason, if you guys were running Grok 4? That'd be so much fun.

Chamath Palihapitiya: How do you judo flip open AI? Because they are marching steadfastly towards a billion Mao, then a billion Dow. It's a juggernaut. So how do you use the better product

SPEAKER_116: in a moment to judo flip the less better product?

SPEAKER_66: Look, yeah. I mean, here's the thing, right? So you do the Elon way. So you have you get a bunch of missionary, like full on missionary engineers that work twice as hard. And you have a culture that is ultra fierce, truth seeking. And you don't, you don't get caught up in politics, bureaucracy, BS. And you just you go for it. And I think, you know,

SPEAKER_68: that's where, you know, and then you go, wow, scientific breakthrough, scientific method,

SPEAKER_66: like you start winning on truth. And that will start, I believe that will start to give the product awesomeness of open AI a run for its money. But like the product of open AI, the product department, those guys are rushing, they're really good. They're not only ahead of the game, but they feel like it just, they're just leading in a lot of different ways. But if you are better at truth,

SPEAKER_49: you will eventually, you'll eventually have an AI product manager.

SPEAKER_134: Yeah, and on a technical basis, too, people forget how good Elon is at factories and physical real world things. What he did standing up Colossus made like, Jensen Wan was like, how is this possible that you did this, right? So pressing that his ability to build factories, and he said many times, like, the factory is the product of Tesla, it's not the cars that come out of the factory or the

SPEAKER_57: batteries, it's the factory itself. So if he can keep solving the energy problem with solar on one side and batteries, and standing up, you know, Colossus two, three, four, fives, he's going to have a massive advantage there. On top of Travis, you know, the missionary individuals, which by the

SPEAKER_34: way, was what he backed, before Sam Altman corrupted the original missionary basis of opening, I made it

SPEAKER_43: closed AI in a, you know, this is nothing derogatory towards him. But he did hoodwink and stab Elon in the back. And it's not nothing personal. I mean, he just screwed him over. And would you say he

SPEAKER_248: bamboozled him, bamboozled him, screwed him, hoodwinked him, you know, pick your term here. But he did it, but he didn't dirty. The original mission was to be a missionary and open source all this content.

SPEAKER_57: That's the other piece I think, is a wild card, and then I'm interested in Keith's position. But open sourcing some of this could have profound ramifications. I think open sourcing the self driving data could have a really profound impact. Elon wanted to do something really disruptive, like he open sources patents for, you know, charging, if he open source the data set and self driving, does anybody have the ability to produce robo taxis at the scale, he can do it?

SPEAKER_116: I don't think so. So this hypothesis is true, then everybody will.

SPEAKER_253: Well, everybody will what? Sorry, everybody will what, Shimon?

SPEAKER_208: If you have access to the money that buys the compute, everyone could solve that problem. But it's a hardware piece I'm talking about. Which problem?

SPEAKER_02: He said, if he published all the FSD data, could somebody build an autonomous vehicle?

SPEAKER_171: Well, yes, but could somebody produce 100 million robo taxis from a factory with batteries in them? Okay, that's a different question. That's the thing I'm saying.

SPEAKER_128: And not really, because last time I was a guest on, you know, and we talked about vertical integration. Products really require vertical integration. So ultimately, you have a self driving something that is custom built for knowing it's going to be self driving, and it interacts differently, the cost structure is different, the controls are different, the seating is

SPEAKER_182: different, everything, you build a product, taking advantage of where the staff you have the most competitive advantage, but then you leverage that and it reinforces. It's still why like Apple, despite missing the AI wave, still a pretty good company from any empirical standpoint. I mean, like, the performance is absolutely miserable on the most important technology for the three of the last 70 years, but the company is still alive and still worth trillions of dollars because it's vertically integrated. OpenAI, at career point, they do have a good product team, and they need to stay ahead on the product level because they can't compete on the factory level. The way to stay ahead of the product level is shipping a device, they've got to ship the device, it's got to be good, it's got to be right, it's got to be the right form factor, it's got to do things for humans that are unexpected. But then if they do that, they're like Apple plus AI.

SPEAKER_66: Shamath, what's the paper you're talking about before? What was the name of it again?

SPEAKER_260: The Bitter Lesson.

SPEAKER_66: That could apply to autonomous driving is right now it's still like, hey, how do I drive like a human? We talked about that. But the leapfrog moment here could be like, hey, drive a car, make sure it's efficient, don't hit anybody. And just simulate that a quadrillion times and it's all good. But right now we're still trying to drive like humans because we don't have

SPEAKER_68: enough data and therefore can't do enough compute. That's the global lesson, by the way. Shamath,

SPEAKER_182: you're totally right. The conceptual blog post is right, but that's only true when you have enough data. And depending on the use case, the level of data you need may not be possible for years,

SPEAKER_198: decades, and you may need to hack your way there through human interactions.

SPEAKER_66: Yeah. Physical world AI is lacking in data. And so you just try to approximate humans.

SPEAKER_82: I don't know if you guys have seen this. In related news, OpenAI and Perplexity are going after the

SPEAKER_29: browser perplexity launch comment for their $200 a month tier. I actually downloaded it, I'll show it to you in a second. But this is a really interesting category. It's something developers can do already and they do it all the time, you know, but having your browser connected to agents lets you do really

SPEAKER_134: interesting things. I'll show you an example here that I just fired off while we're talking. So I just

SPEAKER_72: asked it, Hey, give me the best flights from United Airlines and business class from New York City, from San Francisco to New York City, it does some searches. But what you see here is it's popped up

SPEAKER_29: a browser window, and it's actually doing that work. And you can see the steps it's using. And then I can actually open that browser window and watch it do that. This is just a screenshot of it. And it will

SPEAKER_134: open multiple of these. So you could, I was doing a search the other day saying like, Hey, tell me all the autobiographies I haven't bought on Amazon, put them into my, you know, shopping cart and summarize each of them because I like biographies like doing here. And when it did this last time, it put my flight into like, and I was logged in under my account, and it basically put it into my account in

SPEAKER_28: the checkout. So again, this isn't like if you're a developer, you do this all day long. But this really

SPEAKER_57: seems to be a new product category. I'm curious if you guys have played with it yet. And then what your thoughts are on having an agentic browser like this available to you to be doing these tasks. In real time, you can also connect obviously your Gmail, your calendar to it. So I did a search, tell me every restaurant I've been to and then put it by city. And then I was going to open my open table and then pull that data as well. What's interesting about this, Keith, and I know you're a product guy and you've done a lot of product work. I'm curious your thoughts on it is you don't have to do this in the cloud, you're authenticated already into a lot of your accounts, nor do you have to worry about being blocked by these services because it doesn't look like a scraper or a bot. It just it's your browser doing the work. Your thoughts on this? Have you played with it at all?

SPEAKER_178: Yeah, I think it's a great Hail Mary attempt by perplexity. I think up since something like this,

SPEAKER_128: perplexity is toast. Like for the stat about ChatGPT is going to a billion users, like it's becoming the verb, you know, that the way you describe using AI for a normal consumer, there's nothing left of perplexity if they can't pull this off. So it's a great idea because like the history of like consumer technology companies is whoever's up has uphill ground, like in a military sense, whoever's first has a lot of control. This is actually what Google should be doing, truthfully. Like I think Google is also Google search cross search is toast. And since they have Chrome, and they theoretically have a quality team in Gemini, they should be putting these two things together and hoping to compete with ChatGPT. They're going to lose the search game, like the assets

SPEAKER_182: that are best at Google right now have nothing to do with search. It's every other product is the only thing that's going to save that company if they can figure out how to use them.

SPEAKER_32: Travis, your thoughts on this category? Anything come to mind for you in terms of,

SPEAKER_43: you know, feature sets that would be extraordinary here? I know you like to think about products and

SPEAKER_270: the consumer experience. It's really interesting. So, you know, I've been spending years, you guys know,

SPEAKER_66: I've been spending my time on real estate and construction and robotics. And so I've been out of this kind of consumer software game for a long time, but super interesting over the last six months. There have been a number of consumer software CEOs. Like when I hang out them or whatever, they're like, you know, how are we gonna? How are we gonna keep doing what we do when the agents take over?

SPEAKER_274: Yeah, the paradigm shift is so profound, that the idea that you would visit a web page goes away,

SPEAKER_87: and you're just in a chat. You have an agent that's just taking care of your flights for you.

SPEAKER_66: So I, I kind of, I think there's a leapfrog over that. I think it's just like you tell something, yo, I want to go to New York. Can you, you know, I'm sort of looking at this time range. Can you just go find something I'm probably going to like and give me a couple options? Yeah. And it's just a whole, you have an interface. And then, you know, is perplex is this thing that you just showed on perplexly? Is that the interface? Or do I just have an agent that just goes and does everything for me? And is this the start of that? You know, I just haven't spent enough time. I do know that every consumer software CEO that has an app in the app store is tripping. They're tripping right now. And I mean, big boys, I mean, guys with real stuff. And sometimes I, I'm doing like, almost like therapy sessions with them. I'm like, it's going to be fine. You actually, you actually have stuff. You have a moat, you have

SPEAKER_68: real stuff. That's a value. They can't replace it with an agent. And so you're lying to them.

SPEAKER_279: You're doing hospice care and you're telling them everything's going to be okay. But the patient's not making options on Robinhood while he's like, yeah, yeah. Tell me more. Tell me more.

SPEAKER_283: There's certain things that are protected and there's certain things that aren't. That's all.

SPEAKER_72: Well, let's talk about that because the, you and I are old enough to remember, uh, general magic. This vision was out there a long time ago with personal digital assistance. And you would just

SPEAKER_36: talk to an agent. It would go do this for you. This feels like a step to that where it does all

SPEAKER_144: the work for you, presents you the final moment and says approve. So look, it's like a concierge or a

Chamath Palihapitiya: butler. Yeah. I think what you're describing is what we want, but I think more specifically for today, Keith and Travis totally nail it. Look, I think building a browser is an absolutely stupid capital allocation decision, just totally stupid and unjustifiable in 2025, specifically for perplexity. I think their path to building a legacy business is to replace Bloomberg. Everything that they've done in financial information and financial data in going beyond the model has been excellent. As somebody who's paid $25,000 to Bloomberg for many years, the terminal is atrocious. It's terrible. It's not very good. It's very limited. And anybody that could build a better product would take over a $100 billion enterprise because I think it's there for the taking. I wish that perplexity would double and triple down on that. And so when

SPEAKER_47: you see this kind of random sprawl- Let's do it. Let's just go do it.

Chamath Palihapitiya: When you do the random sprawl, I think it doesn't work, but I just want to say like a browser is like the dumbest thing to build in 2025. Because in a world of agents, what is a browser? It's a glorified markup reader. It's like handling HTML. It's handling CSS and JavaScript. It's doing some networking. It's doing some security. It's doing some rendering. But it's like, this is all under the water type stuff. I get it that we had to deal with all that nonsense in 1998 to try Lycos or Google for the first time. But in 2025, there's something that you just speak to. And eventually there's probably something that's in your brain, which you just think, and it just doesn't. You're thinking, I need a flight to JFK. Or at the maximum today,

SPEAKER_15: in a very elegant, beautiful search bar, you type in, get me a flight. And it already knows what to do.

SPEAKER_171: Keith, in some ways, this is a step towards that ultimate vision. So you'd think it's worth it to,

SPEAKER_72: you know, sort of perplexity to make this waypoint, perhaps if you look at it as a waypoint between the ultimate vision, which is a command line, and earpiece.

SPEAKER_146: How do you get distribution, Jason, for the 19th web browser in 2025?

SPEAKER_43: Well, yeah, that is a challenge. And I think most people are speculating Apple, which has a lot of users might buy perplexity or do a deal with perplexity and give them that distribution because of the Justice Department case against Google. So there's been a lot of speculation about

SPEAKER_183: that. But Keith, what do you think? Well, I don't think they'd buy anything worth it. Like, what is Apple going to get? I mean, we continue this failed strategy of Apple.

SPEAKER_182: Apple has missed every possible window on AI and continues to miss it. And it has cultural, I think the CEO has challenges. I think culturally, they have challenges and they have infrastructure challenges. So it's not an easy fix. But buying perplexity is not going to help. Like, Chabot strategy is actually a pretty coherent one for perplexity, co-op perplexity. So I think that's not about it.

SPEAKER_297: The pick of vertical and owning strategy.

SPEAKER_182: Not a bad idea, especially because you need unique data sources. Some of those data sources may or may not license their data to open AI. So you can do some clever things there,

SPEAKER_128: but I don't think there's any residual value that Apple would get out of perplexity, except there's some product taste. But what are you going to spend, like a billion dollars for product taste? I mean, Mark's spending hundreds of billions of dollars or whatever he's spending these days. And Grok, if anything, Grok 4 shows that Mark really does need to spend money to build

SPEAKER_299: a whole new team because everything they've done in AI has also missed the boat.

SPEAKER_29: Well, I mean, Keith, the way you phrase it there almost makes it worth it for Apple to throw a Hail Mary, have a team with some taste, because that's how they tend to do things, is something

SPEAKER_72: that is elegant. And why not just throw your search to it, throw 10 billion at perplexity

SPEAKER_124: team and see what happens? What's elegant would be if there's a bunch of agents in just a chat box.

SPEAKER_116: Seeing a bunch of visual diarrhea is not elegant. It's lazy.

SPEAKER_49: On our little Bloomberg clone, I'll give you naming rights.

SPEAKER_87: So you can call it the polyhapatea. So, hey, can somebody bring up the polyhapatea? You know what's so funny? It just rolls right off your tongue.

SPEAKER_124: TK, listen, we were trying to do a screen of companies and it maxes out at five companies on a specific type of screen where you're trying to compare stock price to EBIT and you're like, okay, I can only choose five, I guess. So which five should I choose?

SPEAKER_306: LaFont was on, right? Like two episodes ago, he was like, I can't pull this up. It's limited to six

Chamath Palihapitiya: companies. Dude, so what do people use Bloomberg for? They use it for the messaging. Now, like my team has traded huge positions via text message on Bloomberg. So there is something very valuable

SPEAKER_15: there. But the core usability and the core UI of that company has not evolved. I have my contribution. And perplexity is very good at that, by the way. They do a very good job. I got a new domain name, Travis. Let this one just sink in here. This is my way to weasel my way

SPEAKER_34: into the deal. Begin.com. Begin.com.

SPEAKER_36: You own that, don't you? I do. I'm just a little, I sniped some good ones once in a while. I got begin.com and I got annotated.com. Those are my two little domains. Bro, you're like, you're like one of these

Chamath Palihapitiya: old people that show up at those flea markets. Oh, like the road show? The antique road show. And you're like, oh, I have this thing that I bought 1845.

SPEAKER_66: Guys, Jason, Jason is, Jason is the daddy in GoDaddy. Okay. That's just what it is. I am. I am your dad. That's what it is.

SPEAKER_317: Who's your daddy? Hey, speaking of daddy, let's go on to our next story. Come on.

SPEAKER_57: Is now the right time for a third party? Elon seems to think so. Last week, he announced that X, he would be creating a new political party. I'll let you decide who daddy is in this one. He said, quote, when it comes to bankrupting our country with waste and graft, we live in a

SPEAKER_36: one party system, not a democracy. He's not yet outlined a platform for the American party. We talked about it here last week. I listed four core values, which seem to get a good reaction on X. Fiscal responsibility slash doge, sustainable energy and dominance in that manufacturing in the US which Elon has done single-handedly here. Pronatalism, which I think is a passion project for him. And Shabbat, you punched it up with the fifth technological excellence. According to Polymarket, 55% chance that Elon registers the American party by the end of the year.

SPEAKER_134: And, you know, one thing I was trying to figure out is just how unpopular are these candidates

SPEAKER_32: and these political parties. This is a very interesting chart that I think we can have a

SPEAKER_323: great conversation around. It turns out we used to love our presidents. If you look here from Kennedy, at 83%, his highest approval rating, his lowest was 56%. That was his lowest approval rating. So he operated in a very high band. Look at Bush 2, during, after 9-11, 92% was his peak, his lowest was 19,

SPEAKER_57: right? Wartime president. But then you get to Trump 1, Biden, and Trump 2, historically low, high approval. Their high watermark, 49 for Trump 1, 63 for Biden, 1 of 1, and then 47 for Trump 2. And their lowest, 29, 31, 40. So maybe it is time for a third party candidate. Let's discuss it,

SPEAKER_325: boys. I have no idea how to read this graph. I have zero idea. It is the worst. I'm like,

SPEAKER_00: what is happening here? This is the worst formatted chart. This is a confusing chart, but the reason I'm putting it up is for debate. So you should be saying thank you for debating that

SPEAKER_08: it's creating great debate. Why did you put it up? Here's another one. Gallup all Americans desire

SPEAKER_28: for a viable third party 63% in 2023. So it's, it's bumping along at all time. Hi. Okay. I'm

SPEAKER_327: really concentrating on this one. Okay. Anyway, I'm going to stop there. What's the gray? I'm going to let you. Okay. Okay. Got it. I got it. I got it. I got it. I got it.

SPEAKER_333: During that time period and how popular parties were. Okay. I got it. I got it. Let's stop here. This is a good, this is a good place to stop. I just blew a GPU.

SPEAKER_182: Yeah. A couple of points. Yes. The idea of Elon creating a third party is for any other human being like absolutely absurd and ridiculous. Elon has obviously done incredible things. So dismissing anything he's touching is a bad idea. However, I think the best metaphor I've seen is it's a little bit like Michael Jordan tried to play baseball and became a replacement level baseball player, which actually really hard to do, by the way. Elon is probably a replacement level politician. He's Michael Jordan for entrepreneurial stuff, but the third party stuff is not going to work. First of all, that chart is misleading. It's a flaw of average. It's badly designed and it's a flaw of average. Trump is incredibly popular among Republicans. He actually has the highest approval rate of any Republican ever measured in recorded history. It's 95%. Reagan was peaked out at 93%. It's just Democrats don't like them,

SPEAKER_128: which is perfectly fine. Being polarizing is an ingredient to being successful,

SPEAKER_182: including with people on the show. The point of accomplishing things in the world is you don't really care what half the world thinks. You need to make sure that there's a lot of people who like you and really approve and are enthusiastic about what you do. And Trump is about as popular with his party as anybody's ever been ever, period. No exceptions. Secondly, MAGA has kind of already changed the Republican Party. Trump is sort of like a third party takeover of the Republican Party. And so it's kind of already happened. And maybe you can do this every 20 years or 30 years. I don't

SPEAKER_128: think you can have like this kind of transformation on one party within a too compressed period of time for a lot of reasons. Third is really smart parties absorb the lesson of political science. Unfortunately, I studied political science. I wasted kind of my college years. And instead of saying, see us and maybe then I'd be coding stuff and doing physics like Travis. But one thing I did learn is smart parties absorb the best ideas of third parties. So the oxygen is usually not there because there's a Darwinistic evolution. If you get traction on an idea, it's really easy to conscript some of those ideas and take away the momentum. No third party candidate. That's a true like third party has won a Senate seat since 1970. And that's actually Bill Buckley's brother. And so he has some name ID. The other thing Elon I think is missing and the proponents of what he's doing is people vote

SPEAKER_182: not just for ideas, they vote for people. It's a combination. The product is, what do you believe and who are you? And you can't divorce the two. Trump is a person and that generates a lot of

SPEAKER_128: enthusiasm. And it's one of the reasons why he has challenges in midterms because he's not on the ballot. His ideas may be on the ballot, but he is not specifically on the ballot. So unless, because

SPEAKER_182: Elon can't be the figurehead of the party, he literally can't constitutionally, you need a face that's a person, Obama, a Clinton, like there's reasons why people resonate. Sure. Reagan.

SPEAKER_183: Without that personality, specific ideas just are not going to galvanize the American people.

SPEAKER_72: Okay. So the counter to that and what people believe he's going to try to do is win a couple of seats in the house, Travis, win maybe one or two Senate seats. If you were to do that, those things are pretty affordable to back a couple of million dollars for a house race, Senate, maybe 25 million. If Elon puts, I don't know, 250 million to work every two years, which he, I think he put 280 million to work on the last one, he could kind of create the Joe Manchin moment. And, uh, he could

SPEAKER_57: build a caucus, a platform, Grover Norquist kind of pledge along these lines. So what do you think of that? If he's not going to create a viable third party presidential candidate, could he, Travis, pick off a couple of Senate seats, pick up a couple of congressional seats?

SPEAKER_66: Okay. So first I have this axiom that I'm making up right now. Okay. Okay. It's called Elon is almost always right. Okay. All right.

SPEAKER_344: And I was right about everything.

SPEAKER_66: Seriously, let's just be real. And like, honestly, the things he's upset about, and that he's riled up about, especially when you look at the deficit, like, man, I am right on board that train. Part one, part two, we've never had somebody with this kind of capital that can be a quote, unquote, party boss outside of the system. Right. And there's a lot of people that agree with the types of things he's saying. And he knows how to draw, you know, he, Elon, his own right kind of has a populist vibe, like he does his thing. And he's turned x into what it is. And he's, he's a big part of x. And so I think it's the, I think it's great. And honestly, there's, there's the moves you can make on Senate and House and just having a few folks, and then being you being levers, then to get the things you want done, that's part one. And then part two of that is the threat of that happening can make good things

SPEAKER_68: happen separately, even if it doesn't go all the way. I just love it. I'm on the train.

SPEAKER_82: Yeah, I'm in love with this role for Elon more than picking a party, because he's picking a very

SPEAKER_72: specific platform that I think resonates with folks, which is just balance the budget. Don't put us in so much debt. And let's have some sustainable energy. You know, job done, great jobs.

SPEAKER_178: Yeah, the problem with that is like, he's actually wrong about the reason why we have

SPEAKER_182: a deficit or debt. It's not because we're under taxed, it's we're massively overspending.

SPEAKER_354: If we just I think he believes we're overspending, but they should have been supporting the last,

SPEAKER_128: you know, beautiful bill. Because if you just held federal spending to 2019 levels to 2019 is not

SPEAKER_182: like, you know, decades ago, literally, with our current tax revenues, we would be in a surplus. Five hundred billion. Yeah. So all we need to do is cut spending. Now,

SPEAKER_355: I admit that that happened with the big beautiful bill. So this is where details do matter.

SPEAKER_128: I think there is a willingness in a, you know, discipline problem on both parties. And I think maybe he can help fix that. The second thing is that we have these arcane rules, particularly in the Senate, and you need 60 votes in many ways to cut things, except through very hacky methods. And that's a reality. So the best thing truthfully you could do is help get a Republican party to 60

SPEAKER_182: votes. And then, in theory, he could be absolutely furious if you didn't cut back to 2019 levels. But it's very tricky. Or you can just overrule like this. The filibuster is an artifact of history. And at some point, some majority leader is just going to say, we're done with the filibuster and just steamroll through all the cuts at 50 or 51 votes, which you can do. There's no constitutional right to a filibuster. It is an artifact of centuries of American history.

SPEAKER_183: And at some point, it's going to go away. So maybe the time is now. Maybe we should just fix

SPEAKER_110: everything now. I think you're exactly right. I think that the filibuster, it's just a matter

SPEAKER_116: of time. I think it's on borrowed time. And I think in a world where it is on borrowed time, Jason, I think your path is probably the one that gives the American party, if it does come into existence, the most leverage, which is if you control three to five independent candidates, you gain substantial

Chamath Palihapitiya: leverage. I just want to take a step back and just note something. I don't know if you guys know this, but the only reason we're even having this conversation, or this is even possible, is because in 2023, the FEC, Federal Elections Commission, they actually released guidance, and they changed a bunch of rules. And the big change that they made then was it allowed super PACs to do a lot more than just run ads. Up until that point, all you could do if you were a super PAC is just basically run advertising, television and radio, I guess online as well. But what they were allowed to do starting in 23 was they were allowed to fund ground operations, they were allowed to do things like door knocking, phone banking, you know, get out the vote. So in other words, what happened was a super PAC became more like a full campaign machine. And Trump showed the blueprint of using a super PAC specifically his to win the presidential election. So he was able to fund this massive ground game, he built infrastructure across the swing states, he was obviously incredibly effective. And now that playbook can actually be used by other folks. And so to the extent that Elon decides to use those change FEC rules, Jason, I think what you said is the only path but I just I thought I just wanted to double click on Keith's point because it's so important. I do think the filibuster is going to go away. And it is because the arcaneness of these rules, having to do a reconciliation bill then, you know, needing a supermajority, a veto proof supermajority and the other case, it just means that nothing gets done. And I think somebody will eventually get impatient and just steamroll this thing.

SPEAKER_134: We've never had so many people say they feel politically homeless, as we did the last two cycles. And that includes many people on this podcast, people in our friend circle. And I think

SPEAKER_29: just the idea that Elon could create a platform that people could opt into and support, just the

SPEAKER_362: existence of that would make the other two parties get their act together. By the way,

SPEAKER_29: the other thing that we need is a little bit of a stick there and a carrot. Yeah, hey, if you

SPEAKER_36: don't control spending, there's this third option. And if Travis and I are in it, and Keith, I know you'll never leave the Republican Party, but you know, you're probably set where you're where you want

SPEAKER_72: to be right now. But I can tell you, we go to our top 1020 friend list. Out of those 50% will join

Chamath Palihapitiya: Elon's party, day one. The other thing, Jason, that Keith said, which I think is really important is, if he were to run people, I think they have to transcend politics and policy. And I think they need to be straight up bosses, people that have enormous name recognition, so that effectively, what you're voting is a name and not an agenda. Equivalent to I think what happened to Schwarzenegger when he ran, he ran on an enormous amount of name recognition in the Great Davis recall,

SPEAKER_15: he didn't run on the platform. Which is JD Vance. JD Vance had this great book,

SPEAKER_171: captured people's imagination. He's an incredible speaker. He pisses off a third or two thirds of the

SPEAKER_72: country, depending on where you are in the country. But you can't ignore him. I think Elon can find 10

SPEAKER_29: JD Vance type characters and back them fairly easily. He is a magnet for talent. People will line up, I have been contacted by high profile people. I'm thinking of running. Can you put me in touch

SPEAKER_116: with Elon? I was thinking more like actors and sports stars, meaning where they just come with

Chamath Palihapitiya: their own inbuilt distribution. Like I think you almost have to rank x followers and Instagram followers and do a join and say, Okay, these do you know what I mean? Like, I think it's like

SPEAKER_371: totally different. Guys, it's painful. Like, let's not get more celebrities as politicians,

SPEAKER_53: like, let's get like people who've led large, large efforts, large initiatives,

SPEAKER_250: complex things. Ideally, but they still have to communicate, right? Keith, they have to be able

SPEAKER_36: to share it on a podcast. That's the new platform. If they can't spend two hours, three hours chopping

SPEAKER_57: it up on a podcast like this, or Joe Rogan, you know, that's Kamala's. The reason she couldn't even contend was because she couldn't hang for two hours in an intellectual discussion. If you can't hang,

SPEAKER_182: you're out. It's interesting to see if he can tune his algorithm for talent, which is epic to tune for politics because it's a slightly different audience. But if you can tune the algorithm and quality, that might work. I think you can win a few house races. I think that's

SPEAKER_128: doable. I don't think you can win a Senate race. Well, there it is, Elon. Keith doesn't think you

SPEAKER_09: can win a Senate race, but he thinks you win a couple of congressional ones. Thanks for giving him the motivation, Keith. I appreciate it. I'm sure he's going to love that. That is the biggest

SPEAKER_381: mistake you've ever made. He's not going to win, too. People in the Republican Party right now are going, oh, no, don't poke the tiger. Listen. But that's how Trump got into politics, so I don't want to be Obama here. You just don't bomb it. Elon, right. Yeah. Congratulations. All right,

SPEAKER_72: listen. SCOTUS made a big decision here. This is a really important decision. They've sided with

SPEAKER_134: Trump for plans for federal workforce rifts, reductions in workforce, for those of you don't

SPEAKER_36: know. As you know, Elon, Trump, they wanted to downsize the three million people who are federal employees. This is just federal employees we're talking about. We're not talking about military, and we're not talking about state and city. That's tens of millions of additional people.

SPEAKER_29: If you remember, Trump issued this executive order back in February, we got an office implementing the president's doge workforce optimization initiative. And he asked all the federal agencies, hey, just prepare a rift for their departments consistent with applicable laws, was part of this EO. Okay. In April, the American Federation of Government Employees, AFGE sued the Trump administration, saying the president must consult Congress on large scale workforce changes. This is a key debate because the Congress, as you know, has power of the purse. They set up the money. But the president and the executive branch, they have to execute on that. And that's what the key is here. So they accused Trump of violating the separation of powers under the Constitution Act. AFGE has 820,000 members. In May, a San Francisco-based federal judge sided with the unions blocking the executive order. The judge, who was appointed by Clinton, said any reduction in the federal workforce must be authorized by Congress. This is a key issue. And the White House submitted an emergency appeal, yada yada. Eight of nine Supreme Court justices sided with the White House in overturning this block. And so the reasoning, it's very likely the White House will win the argument of the executive order. They have the right to prepare a rift. The question is, can they actually execute on that rift? And who has that power, Chamath? Does the power reside with the president to make large-scale rifts, or do they have to consult Congress first, your thoughts on this issue?

SPEAKER_116: It's an incredibly important ruling, incredibly right. I think President Trump should have absolute leeway to decide how the people that report to him act and do their job. If you take a step back,

Chamath Palihapitiya: Jason, there are more than 2000 federal agencies. Employees plus contractors, I think number almost 3 million people. If you put 3 million people into 2000 agencies, and then you give them very poor and outdated technology, which unfortunately, most of the government operates on, what are you going to get? You're going to get incredibly slow processes. You're going to get a lot of checking and double checking. And you're going to ultimately just get a lot of regulations because they're trying to do what they think is the right job. So since 1993, what have we seen? Regulations have gotten out of control. It's like 100,000 new rules per some number of months. It's just crazy. So eventually, we all succumb to an infinite number of rules that we all end up violating and not even know it. So if the CEO of the United States, President Trump, isn't allowed to fire people, then all of that stuff just compounds. So I think that this is a really important thing that just happened. It allows us to now level set how big should the government be. But more importantly, the number of people in the government are also the ones that then direct downstream spend that make net new rules. And if you can slow the growth of that down, you're actually doing a lot. In many ways, I wish Elon had come in and created Doge now. Could you imagine if Doge was created the day after this Supreme Court ruling? It would have been a totally different outcome, I think, because with that Supreme Court ruling in hand, these guys probably would have been like a hot knife through butter.

SPEAKER_02: Travis, so I think it's a big deal.

SPEAKER_66: Except that ruling doesn't happen without Doge. That Doge caused that ruling to occur.

SPEAKER_00: True. Well, the EO did. You could have passed.

SPEAKER_53: Right, right. That was all Doge style though. You know what I'm saying?

SPEAKER_72: Yeah. If he wasn't firing people, yeah, they probably wouldn't have felt the need, to your point, Travis, to actually file this. But Travis, if you are living in the age of AI

SPEAKER_29: efficiency right now, operations of companies is changing dramatically. Can you imagine telling somebody you can be CEO, but you can't change personnel? That's the job. You get to be CEO, but you just can't change the players on the team. You can buy the Knicks, but you can't change the

SPEAKER_391: coach. You know, you can grow it, you just can't shrink it.

SPEAKER_182: It's like running a unionized company, which actually does exist. Our large unionized companies

SPEAKER_394: where you can't do any of these things. Right. Do they still exist or are they all gone? I think they're going quickly. Yeah, probably.

SPEAKER_66: I think this just gets back to what, what is actually Congress authorizing when a bill occurs. And there's certain things that are specific and certain things that aren't. And I don't, I'm not sure that in a lot of these bills, it's not very specific about exactly how many people people must be hired. And so if it's, I'm just doing the common man's sort of approach to this, which is like, if, if the law says you have to hire X number of people, then that is what it is. If the law says you, here's some money to spend here, the ways in which to spend it, but it's not specific about how many people you hire, then that is different.

SPEAKER_353: Yeah. It should be outcome based. Hey, here's the goal. Here's the, the key objectives, right?

SPEAKER_398: Well, Travis, Travis is totally right. Like there are, there's a variety of different laws,

SPEAKER_182: some with incredible specificities, some with very broad man age. The constitution clearly says that all executive power resides in the president of the United States, period. There's no exceptions

SPEAKER_128: there. However, Congress does appropriate money and post Watergate, many people think Congress has the power to force the president to spend the money. And you can debate that and you can debate it on a per statute basis. And that will be more nuanced. And that's going to get litigated, whether the president can refuse to spend money that Congress explicitly instructed him to spend, sometimes called empowerment. That's a very interesting intellectual debate. This one's a

SPEAKER_182: little bit easier. It'll get more complicated again. Like this EO is only approved to allow for

SPEAKER_128: the planning. I think the vote might be closer. I think there's still a majority on the Supreme Court for the actual implementation, but it may not be eight one when there's a specific plan that

SPEAKER_400: has to navigate its way through the courts again. Yeah. It's super fascinating. Yeah. I wonder if

SPEAKER_29: they're going to get to the point where they're going to say in every bill, you need to hire this

SPEAKER_403: number of people to hit this goal. I don't know if they can, like that's where it gets borderline unconstitutional. Like where you actually prescribe that the president in the exercise of his

SPEAKER_182: constitutional duties has to hire a certain number of people. That feels pretty precarious.

SPEAKER_49: Well, I, I, I'm not sure Keith, that's just like they prescribe a whole bunch of other things.

SPEAKER_68: I know, but you must, you must appropriate money for, to this specific institution to do this specific

SPEAKER_403: work. But that's not an executive function. Like if you said like the secretary of state has to have

SPEAKER_128: X number of employees doing something, the secretary of state is your personal representative to conduct foreign affairs on behalf of the president of the United States. It gets a little bit more messy as you translate it to people, um, that the president should, I mean, yes, Congress does set, you know, which people are subject to Senate confirmation and what their salaries and compensation bands are. So it's, it's never going to be fully binary where the president can do whatever he wants.

SPEAKER_183: And it's never going to, I don't think it'll be constitutional for Congress to mandate and put all

SPEAKER_29: kinds of handcuffs on the president. Well, then you also have performance that comes in here. What if you look at the department of education and say, scores have gone down. We've spent this money. We're not getting the results. Therefore, these people are incompetent. Therefore I'm firing

SPEAKER_408: them for cause and I'm going to hire new people. How are you going to stop the executive from doing that?

SPEAKER_410: There's been a bunch of litigation, you know, in parallel to this litigation about the president's

SPEAKER_128: ability to fire people. And for the most part, the Supreme courts, basically with maybe the exception of the federal reserve chair said that the president can fire pretty much anybody he wants.

SPEAKER_288: I mean, that's the way to go is like, I mean, I hate to be cutthroat about it,

SPEAKER_410: but if the results aren't there, then you fire people. Yeah. If they're a presidential appointee, the president should be able to fire you at will.

SPEAKER_182: Just like if you were a VP at one of our companies, the CEO should be able to fire you at will.

SPEAKER_36: But what about Keith if the whole department sucks? Hey, you guys were responsible for early education. You had to put together a plan. The plan failed. Everybody's fired. We're

SPEAKER_323: starting over. Like you should be allowed to do that. How are we going to have an efficient government?

SPEAKER_128: Some of these departments were created by congressional statute, like the Department of Education in 1979. And you're right. Every single educational stat has

SPEAKER_182: got worse in the United States since the department was created. But there is a law on the books that says there shall be a Department of Education. So you may have to repeal that.

SPEAKER_120: Hmm. All right. Listen, we're at an hour and a half, gentlemen. Do you want to do the FICO story?

SPEAKER_34: Or should we just wrap, Jamal? And we've got plenty of show here. It's a great episode.

SPEAKER_116: Anything else you want to hit? I don't really have much to say on the FICO story. I thought

SPEAKER_146: these other topics were really good, though. We did great today. This is a great panel. I'm so

SPEAKER_57: excited you guys are here. Let me just ask you guys, any off duty stuff that you can share with us, with the audience, any recommendations, restaurants, hotels, trips, movies you watch, books you read?

SPEAKER_323: Keith, I know that you are an active guy. What's on your agenda this summer? Anything interesting you can share with the audience that you're consuming, conspicuous or otherwise?

SPEAKER_425: Well, I don't want to share any good restaurants or hotels because... Oh, you're gatekeeping. You're gatekeeping?

SPEAKER_429: Come on, man. Absolutely. Give us your favorite New York City. It's amazing. It's like if you had a babysitter, you're not going to tell everybody who you're babysitting.

SPEAKER_432: Yes. Can I get your nanny's email?

SPEAKER_436: There are things that are, what do you call it, no marginal cost consumption, like Netflix. So,

SPEAKER_128: for example, this documentary on Osama bin Laden is phenomenal. I don't know if any of you have seen it. It's brand new. I haven't seen it. And I'm a student of this stuff, and I thought I knew the whole story and et cetera. Watch episode one. Just start with episode one. And it just blew me away with new information, new footage, just absolutely incredible stuff. So, I highly, highly recommend it.

SPEAKER_135: What was the big takeaway for you so far?

SPEAKER_128: I don't know if there's any specific takeaway, but just so many parts of the story are misunderstood and not really understood, and how various confidences of somewhat random things lead to a very catastrophic result. But it's as dramatic as the best movie, but it's a full documentary, and you will learn things and absorb things. I've had friends, I've been recommending it to friends, and for a story you think you know, it's incredibly revealing.

SPEAKER_134: Okay. Travis, anything you got on your plate there that you're enjoying, a restaurant, a dish?

SPEAKER_66: I mean, look, you know, I mean, Jason, you know, I go to Austin a lot. Yes. Like, basically, from March till October, I do about 15 weekends in Austin. I have a lake house. Jason's hung out a couple times. So, I love water skiing. That's my whole thing. That's my, like, that's, I just love it. It's just my thing since I was a kid. Very zen. Very zen. And it's lake, it's, I call it lake life. So, that's a thing. And then I recently, this little bit of like a side quest, I recently purchased the preeminent backgammon engine.

SPEAKER_445: XG.

SPEAKER_66: XG, that's right. It's acronym is, it's extreme gammon. And so the preeminent engine, so all the pros rate themselves based on this. It was done, it was built by this amazing entrepreneur, this guy Xavier, who is just a full on sort of ultra, ultra, I mean, just, what's the word I'm looking for? It's not like a savant, essentially. But hasn't worked on it for many years. So I'm getting back into it and love it. And making it like taking modern machine learning, sort of deep learning techniques, and like, big compute and saying, Can we push the game of backgammon forward? So super exciting, and ultra training apps to get people up to speed quickly. I played in my first backgammon tournament in cashed. So that was pretty cool. No, wait. Yeah. Okay. Yeah. All due respect,

SPEAKER_36: you found an Uber, you're very high profile, you go to this back, and it's just like held at the motel eight in like a conference room in the back. It was amazing. It was like a month ago or so.

SPEAKER_66: There's like a big tournament and it was, uh, so the United States backend federation had this big tournament. It was, I guess it was, um, at the Los Angeles LAX at the LAX Hilton. And it was in the, yes, it was in the basement of the Hilton. Great. And it was like next to the Dungeons and Dragons convention. It w it had those kinds of legit vibes. I love it. And like people. So, so I went in super low pro just did my thing, but eventually was recognized, but I was not recognized as the founder of Uber. I was recognized as the owner of XG. Ooh, the owner. And then there was like a full

SPEAKER_220: on melee that basically occurred. They're like, Oh, the owner XG Travis is here. Chamath. I feel like

SPEAKER_463: we've got a window here to do the all in backgammon high end tournament. We got to lock this down. Now we've got to lock down the all in backgammon set. I get the co-branding rights on

SPEAKER_248: this. Okay. Absolutely. XG. Well, no, the all in XG, you know, like, cause I love a great

SPEAKER_323: backgammon set. If we could make like a $10,000 one Chamath, we could kill turtles or white rhinos, all the animals that, you know, um, free birds trying to protect, we could murder them and then

SPEAKER_248: make, that would be so great. Yes. Like maybe the white could be, you know, rhinos, and then you could take something else, elephant skin, something, you know, just really tragic and then eat the meat

SPEAKER_04: and make the, the, the backgammon set for you. I love backgammon. And honestly, like if I wasn't

SPEAKER_116: attempting to be like expert poker player, that is the game. I mean, if you're talking about a Pandora's box where once you open it, oh my God, you can go down the rabbit.

SPEAKER_472: Chamath, let's go dude, let's do that.

SPEAKER_358: Backgammon is, backgammon is a beautiful, beautiful, beautiful game.

SPEAKER_32: I love the vibes of sitting. Travis and I sat, I got some cigars out, you know,

SPEAKER_72: we pour a little of the all in tequila, tequila.allin.com. Uh, we get that going, a couple of, uh, the all in cigars, and then we have the all in back. It's a wonderful hang. Yeah. Keith, would you consider giving us some of your money playing backgammon, Keith?

SPEAKER_477: Absolutely. Absolutely. Absolutely.

SPEAKER_480: We gotta, we gotta get some of that money on the table. Cause you don't play poker with us.

SPEAKER_128: I don't play poker, but backgammon, yeah, that sounds great. And I'll bring, I'll bring better tequila. I have better tequila. Well, like we're gonna upgrade.

SPEAKER_144: We'll do a little taste off. Yeah. So you've insulted now Elon with the Senate seats and facts

SPEAKER_481: with his, uh, tequila. My tequila is much better, trust me.

SPEAKER_02: Wait, what? Okay. Who is left in the PayPal mafia you'd like to insult before this episode is left? Yeah, I saw Reed Hoffman.

SPEAKER_485: Or Peter. Anything about Peter? Reed could join Elon's party. He's collecting a bunch of misfits, so he might as well take Reed too.

SPEAKER_385: All right, listen, this has been another amazing episode of the number one podcast in the world,

SPEAKER_72: the all in podcast for your Sultan of science who couldn't make it today. He's at the beep conference, so we don't mention.

SPEAKER_09: And, uh, David Sachs, who is out, uh, making America safe and AI and crypto. Chamath Palihapitiya, world's greatest moderator, Travis. Keith, thank you for coming.

SPEAKER_493: Thanks for pinch hitting.

SPEAKER_494: You guys were great today. What a pal. See y'all next time.

SPEAKER_493: Bye-bye.

SPEAKER_20: Rain man, David Sachs.

SPEAKER_503: We need to get merch.

SPEAKER_508: I'm going on.