Pod Insights All-In with Chamath, Jason, Sacks & Friedberg Grok 4 Wows, The Bitter Lesson, Elon's Third Party, AI Browsers, SCOTUS backs POTUS on RIFs About this episode (0:00) The Besties welcome Travis Kalanick and Keith Rabois!
(3:02) Travis on Pony.ai / Uber rumors and the state of Cloud Kitchens
(18:51) xAI launches Grok 4, learning "The Bitter Lesson" in AI
(40:36) How Grok can catch ChatGPT in usage, OpenAI's product excellence
(46:27) Perplexity and OpenAI building AI-native browsers and taking on Chrome
(58:01) Elon's "America Party": is now the right time for a third party, and could he make an impact in 2026?
(1:13:12) SCOTUS backs Trump over federal government RIF plans
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Referenced in the show:
https://www.nytimes.com/2025/06/26/technology/uber-travis-kalanick-self-driving-car-deal.html
https://www.youtube.com/watch?v=ZW5fJikPmfM
https://grok.com
https://www.youtube.com/watch?v=_wTA90BYo30
https://techcrunch.com/2025/01/08/elon-musk-agrees-that-weve-exhausted-ai-training-data
https://x.com/ArtificialAnlys/status/1943166841150644622
https://x.com/elonmusk/status/1943192643439337753
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
https://x.com/chamath/status/1943177837956968499
https://techcrunch.com/2025/07/09/perplexity-launches-comet-an-ai-powered-web-browser
https://x.com/perplexity_ai/status/1942969263305671143
https://x.com/elonmusk/status/1941584569523732930
https://polymarket.com/event/will-elon-register-the-america-party-by
https://ropercenter.cornell.edu/presidential-approval/highslows
https://news.gallup.com/poll/651278/support-third-political-party-dips.aspx
https://www.whitehouse.gov/presidential-actions/2025/02/implementing-the-presidents-department-of-government-efficiency-workforce-optimization-initiative
https://www.scotusblog.com/2025/07/supreme-court-allows-trump-administration-to-implement-plans-to-significantly-reduce-the-federal-workforce
https://www.afge.org/article/summary-of-afge-lawsuits-against-trump--how-litigation-works
https://cei.org/publication/10kc-2025-numbers-of-rules
https://www.netflix.com/tudum/articles/american-manhunt-osama-bin-laden-release-date-news
Transcript 16,122 words · 6 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Chamath Palihapitiya? I have a very funny story to tell you, Jason.
0:02 Jason Calacanis? Where have you been? I've been trying to text you. You've been offline. What's going on? Where have you been?
0:06 Chamath Palihapitiya? I've been working feverishly, but yesterday I had to
0:12 Chamath Palihapitiya? go to prepare for some meetings that I have on Sunday, which I can't tell you about, but Nat and I, Nat and I went to Paso Laca, which is in Lake Como, which is, I mean, it's stunning. The grounds are stunning. The hotel is stunning. If you have a chance to go to Lake Como. Anyways, this is us at Paso Laca.
0:32 Jason Calacanis? Who's the beautiful woman there? Is that the woman who owns it or something?
0:36 Chamath Palihapitiya? No, that's not. But the best part is, we had such a good time. You know how they have like a registry book to leave a message?
0:43 Jason Calacanis? Sure.
0:43 Chamath Palihapitiya? So I left a message.
0:46 Jason Calacanis? Here we go. What a truly magnificent place, above and beyond any expectation we had.
0:53 Chamath Palihapitiya? Go below, go below that stuff for me.
0:55 Jason Calacanis? Thank you. We took everything.
0:59 Jason Calacanis? We took everything, the free bird.
1:02 Chamath Palihapitiya? Oh my God. Awesome. Jason, the hangers, okay, the bags, the laundry bag, the toothpaste, the robes, the slippers, everything. Absolutely fantastic. They're gonna have to send a bill to the free bird. Absolutely.
1:32 Jason Calacanis? 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. Sax is busy, couldn't make it this week. In his place, another brilliant PayPal alumni and dare I say GOP supporter, Heath Raboy. How are you, sir?
1:56 Keith Rabois? Pleasure to be with you again.
1:58 Jason Calacanis? Nice to see you. And I'm assuming you're in gorgeous Florida or somewhere. Yeah?
2:03 Keith Rabois? I'm actually in New York.
2:04 Jason Calacanis? Oh. Hometown. Is it safe? Is it okay? Mom Dami chasing you down the street?
2:10 Speaker 03 Not yet, but it's safe.
2:12 Jason Calacanis? Did he seize your seize your assets?
2:14 Speaker 03 It's safe. 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 maybe leave in New York quickly.
2:30 Jason Calacanis? Yeah, you're gonna probably want to sell that place if you got one there because Mom Dami is gonna seize it and turn it into a drugstore for you. Yes, it's gonna be a Mom Dami drugstore. Travis Callen is back with us. How are you doing, bestie?
2:43 Speaker 03 Pretty good. Pretty good.
2:45 Jason Calacanis? Yeah, second appearance here on the roundtable and 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.
2:54 Speaker 03 Lots of stuff. Lots of stuff. The robots, the robots are taking over. We're rolling out, we're rolling out robots. Yeah.
3:01 Chamath Palihapitiya? TK, can you tell us what you're doing with this Pony AI? Or not, not speculation?
3:06 Speaker 03 Look, you know, obviously is autonomy as we, you know, in the US, we have, of course.
3:14 Chamath Palihapitiya? Do you want to just frame for people that don't, that may not be up to speed, what was announced, or at least?
3:18 Speaker 03 Why don't you frame it?
3:19 Jason Calacanis? So Pony AI is an autonomous company doing self-driving. It's one of the few players that actually have cars on the road. They're based in China. They've got a lot of operations in the Middle East. They've got to deal with a livery company called Uber, which you might be familiar with.
3:38 Speaker 03 Uh, okay. So, look.
3:41 Jason Calacanis? Well, the deal was basically that you would partner with Uber, license in the Pony technology and essentially start a competitor, I guess, to Waymo and Tesla?
3:53 Speaker 03 Let me work on this one. Okay. So, so in the US, we have Waymo. We see the Waymos in San Francisco, Los Angeles, Boston, coming soon to Miami, coming soon to Atlanta, coming soon to DC, or even talking about New York.
4:11 Speaker 03 Tesla is sort of like, you know, they're doing it the hard way, you know, classic Elon style, like, let's, let's do this sort of in a fundamental, holy shit, let's go all the way kind of, kind of approach. And it's unclear when it gets over the line. Of course, he, he launched sort of 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 out to me and there are different discussions that get going because they're like, Travis, you did autonomy way back in the day.
4:53 Speaker 03 Got the Uber autonomous stuff going in 2014. Maybe there's something to do here to create optionality. Maybe like, I'm of course, very interested on the 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 logistics around food, you take huge amount of cost 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, 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,
5:37 Speaker 03 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 are 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.
6:13 Jason Calacanis? No, no, I think it's great to get the overview here first. All in, sharing it with us. And everybody knows you have been doing a bowl builder.
6:22 Speaker 03 Yeah.
6:23 Jason Calacanis? Lab 37, I think it's called. Turn it up on the screen. Not sure what the status of it is. And then I'll let you go, Chamath, with your follow up question. But I think there's a pretty interesting concept here of the bowl getting built and then put into a self driving car.
6:36 Speaker 03 That machine looks huge, but it's actually 60 square feet. That picture makes it look monstrous. It's a 60 square foot machine. Like, imagine running like a Sweetgreen 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 does. You build the bowl you want, whatever ingredients. It's sort of, 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, it sauces the bowl,
7:18 Speaker 03 then it puts a lid on it. It takes the bowl, puts it in a bag, puts utensils in the bag, seals the bag, and the bag goes down, a conveyor belt where then another machine, what we would call an AGV, takes the bowl to the front of house. The bowl gets put into a locker. The courier via DoorDash, Uber Eats 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, which is more than-
7:54 Jason Calacanis? It reduces mistakes, right?
7:56 Speaker 03 Reduces mistakes.
7:57 Jason Calacanis? It's hard for them to make a mistake, yeah.
7:57 Speaker 03 We know exactly how many grams of every ingredient are put in. That's exactly what you're supposed to get.
8:04 Speaker 03 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 a, a machine that carries food? Or, you know, is there another, another company that ends up doing, you know, sort of the, the things, 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 that in our delivery kitchens, the cost of labor is about thirty percent of revenue.
8:49 Speaker 03 That's what the successful guy, let's say thirty percent, thirty-five percent of revenue. In a, in a brick and mortar, in a brick and mortar restaurants, it's even higher. Okay? When they're running our machine, it's between seven and ten percent of revenue.
9:05 Jason Calacanis? Amazing. Then you take out the cost of the delivery, you know, and now it's becoming, everybody can have a private chef, which was your original vision for Uber. It was, people don't know the original tagline, but it was your, your pri-, everybody has a private driver.
9:16 Speaker 03 Everyone's private driver was the original for Uber. Basically, the infrastructure was already there. And I said this on, you know, one of your recent, I think it was at the All In Summit, Jason, but like,
9:29 Speaker 03 in the mobility cars, you know, I'm transport space, the roads were already there. The cars were already built. People weren't using their cars ninety-eight percent 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 thirty percent of all meals in a, in a city, uh, sort of,
10:05 Speaker 03 prepared and delivered by a service, the infrastructure is not there. You have to build it. So our company, uh, the, the mission is, uh, infrastructure for better food. So that's real estate, that's software and robotics for the production and delivery of food in a super efficient way.
10:24 Jason Calacanis? All right. Uh, Keith, were you, any questions?
10:27 Keith Rabois? Well, he's not here, but isn't this what David Friedberg tried to do a few years ago?
10:32 Speaker 03 This came up on the last All In, yeah, or the last one I was at, yeah.
10:35 Jason Calacanis? Yeah. It's a, it's a, the problem was, I told Friedberg, people don't want to eat quinoa. You gotta put a, a little steak in there, maybe a piece of salmon. But he was kind of really, I think eventually he relented and let people have a little bit of protein. Uh, but yeah, so it's such a great vision. Wait, he, he died as a vegan martyr? I think the business died as a vegan.
10:56 Chamath Palihapitiya? Well.
10:59 Speaker 03 There's a lot of people have died on that hill, but the bottom line is if you're going to get into automation, you have to, it has to be end to end automation. 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 making pizza. And so when you look at these aut- aut- like robotic food production machines
11:43 Speaker 03 or food assembly machines, you have to look at the full stack and say, does it work with the ecosystem that exists in a restaurant? And does it go full stack from, you know, like, like we have this thing, that machine we saw earlier. The staff preps the food, they put the food in the machine and then they leave. They're gone. This restaurant runs itself for many hours without anybody there.
12:11 Chamath Palihapitiya? But this could be McDonald's, Burger King and Taco Bell. Nobody would know.
12:17 Speaker 03 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 Sweetgreen, this is like a, a majority of their labor, right? You go up to a 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 front line where you basically assemble things. I think sometimes I will call it the make line. What will happen over time, you'll have perpendicular lines going into it where you're producing food.
12:57 Speaker 03 Right, so you'll have a production or bake line going into an assembly line here. And then you go, oh, wow, so you have it something that dispenses burgers on buns. That's the dispenser, that's the assembly.
13:13 Chamath Palihapitiya? Right.
13:13 Speaker 03 But.
13:14 Chamath Palihapitiya? It's like factorio on steroids, basically.
13:16 Speaker 03 Yeah, and then it's like, how do you cook that burger? That's what I call, that's what we call state change. So state change is the, is the cooking of the food. Assembly is the like, how do I put it together and plate it?
13:29 Chamath Palihapitiya? Doesn't this collapse, like, for example, if you have a yield of 300 per hour, you said?
13:34 Speaker 03 Yes.
13:35 Chamath Palihapitiya? Out of that one machine?
13:36 Speaker 03 Yes.
13:36 Chamath Palihapitiya? 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?
13:54 Speaker 03 Well, the way to think about it is like 90% of, 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.
14:04 Chamath Palihapitiya? Hmm hmm.
14:05 Speaker 03 And a vast majority of those meals 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, and infrastructure to prepare and deliver meals to people at their homes. And so it's not, restaurants still exist. We're still going to want to go to restaurants. We're still going to want to go outside. We learned that during COVID. We knew it before, we definitely know it after. And so I don't, it's not really like a decimating real estate situation.
14:45 Speaker 03 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.
14:57 Speaker 03 And just infrastructure to get that cost down. 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, in 20 years, I think everybody agrees. You, you will have machines making very high quality, very personalized meals for everybody.
15:22 Jason Calacanis? This would be good for Keith because he measures stuff down to like five calories based on his Instagram.
15:28 Chamath Palihapitiya? Keith, what's your, what's your, what's your body fat like?
15:30 Jason Calacanis? Oh, God.
15:31 Chamath Palihapitiya? Seven percent, eight percent?
15:32 Speaker 03 No, it's like 10.
15:33 Jason Calacanis? Just open his Instagram. He posted four, four times today about his body fat.
15:38 Chamath Palihapitiya? He's like so disgusted with himself at 10%.
15:40 Speaker 03 It's like bad at 10, but um, I actually think the vision of this, actually the natural implication and maybe the home run version of this is everybody has a private chef in their house. A robot in their house actually does this personalized because people do want to cook at home, but they don't have the time.
15:59 Jason Calacanis? Yeah, or space and infrastructure, but man, 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, Shama, about what happens to all this space. When I lived in New York in the eighties and nineties, it was common to, in Tribeca, in West Chelsea, where I lived, to take storefronts, put your little architect's office in the front and live in the back. And many people were hacking real estate. We still need five, 10 million homes in this country and they're already doing this with malls. I, I, I keep seeing malls being turned into colleges and creative spaces.
16:41 Jason Calacanis? One of them in Boston, they turned like the second and third floor into studio apartments for artists. So, you know, where there's a will, there's a way. We could use the space.
16:49 Speaker 03 You know, where this goes with Shama saying, where the real estate goes is we call it the internet food court. 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.
17:08 Chamath Palihapitiya? Anything can be made.
17:08 Speaker 03 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?
17:24 Chamath Palihapitiya? That's crazy.
17:24 Speaker 03 You can, the combinatorial math in terms of what's possible, what can be made,
17:30 Speaker 03 sort of, you know, goes exponential. And so the internet food court is sort of the vision for where this all goes.
17:38 Chamath Palihapitiya? Another example of the bitter lesson.
17:41 Jason Calacanis? The bitter, yeah, we're going to get to that, I guess, today.
17:45 Jason Calacanis? In a very full docket, before we get to that, just a little bit of housekeeping here.
17:51 Jason Calacanis? September 7th, 8th, 9th in Los Angeles, the All In Summit again, allin.com slash yada, yada, yada. Lineup is stacked. And we're going to start announcing the speakers. People have been begging us to announce the speakers. I don't know.
18:05 Chamath Palihapitiya? Careful, you gotta, you gotta hold some back, careful, careful.
18:07 Jason Calacanis? Hold a couple back, but we got some really nice speakers lined up.
18:11 Chamath Palihapitiya? That is true.
18:12 Jason Calacanis? Extraordinary.
18:13 Chamath Palihapitiya? It is the best one yet.
18:15 Jason Calacanis? I mean, every year, we have this.
18:17 Chamath Palihapitiya? Well done.
18:18 Jason Calacanis? Yeah, yeah. Every year, we have this little bit of panic, like, um, you know, 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 tequila, tequila. All in.com. Deliveries begin late summer.
18:34 Speaker 03 Moving to the side. You can't even talk tequila.
18:37 Jason Calacanis? Summer back here.
18:40 Jason Calacanis? All right, listen.
18:40 Chamath Palihapitiya? Oh, wow.
18:41 Jason Calacanis? Lots to discuss this week. Obviously, AI is continuing to be the big story in our industry. And for good reason, our bestie Elon released Grok 4 Wednesday night.
18:55 Jason Calacanis? Two versions, base model and a heavy model, thirty bucks a month for the base, three hundred dollars 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 on the same problems 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 by consensus, really interesting. According to artificial analysis benchmarks, you can pull that up, Nick, Grok IV base model has surpassed OpenAI's O3 Pro, Google Gemini's 2.5 Pro as the most intelligent model. This includes like seven different
19:38 Jason Calacanis? 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 smarts. So it doesn't mean that these things can reason. And obviously, there was a little, there was a little kerfluffle on X, formerly known as Twitter, where XAI got a little frisky and was saying all kinds of crazy stuff and needed to maybe be red teamed a little bit more decisively. Many of you know Grok IV was trained on Colossus. That's that giant data center that Elon's been building. And we showed the chart here, Chamath. You sent us a link to The Bitter Lesson by Rich Sutton in the group chat. That's
20:21 Jason Calacanis? 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, your reaction to both how quickly Elon has, and 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.
20:39 Chamath Palihapitiya? Before we start, Nick, can you please show Elon's tweet about how they did on the AGI benchmark? It's absolutely incredible.
20:50 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 that's 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,
21:34 Chamath Palihapitiya? "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,
22:19 Chamath Palihapitiya? whether it's chess, or Go, or speech recognition, or computer vision, whenever there is 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.
22:55 Chamath Palihapitiya? 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 has Llama 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.
23:39 Chamath Palihapitiya? 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. You know, the bitter lesson is you could believe that, you know, food is this immutable thing
24:23 Chamath Palihapitiya? that's made meticulously by hand by these individuals, or you can take this general purpose computer 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 a, it's so profoundly important.
24:38 Speaker 03 One thing I'll throw out there, Chamath, is the Tesla approach for autonomy is taking human knowledge. In fact, the whole idea is to approximate human, human driving. Right? That is the whole damn thing. Now, depending on your approach and the technology, you can do like 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...
25:18 Speaker 03 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 difference in what FSD can do.
25:47 Speaker 03 That's the compute side you're talking about, but then he is approximating human.
25:52 Chamath Palihapitiya? Yeah, I just spent that, you know, other than the first versions of FSD, which I think Andre talked about, Andre Karpov, he talked about, you know, they're not really so reliant anymore on human labeling per se, right? So that yeah, that interference. And then
26:07 Speaker 03 Yeah.
26:08 Chamath Palihapitiya? 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.
26:18 Elon Musk? The cumulative sum of human knowledge has been exhausted in AI training. That happened basically 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 a thesis and then it will grade itself. And, and, and sort of go through this process of self-learning with synthetic data.
26:39 Chamath Palihapitiya? He said that he's going to have agents creating synthetic data from scratch that then drive all the training, which I just think is, it's crazy.
26:48 Jason Calacanis? 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 chess.
26:57 Chamath Palihapitiya? Yeah, use chess, right?
26:58 Jason Calacanis? Yeah, so you're hand 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.
27:07 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 chessboard. 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.
27:38 Chamath Palihapitiya? And then what happens is when you map out every single permutation.
27:45 Chamath Palihapitiya? 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 "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
28:29 Chamath Palihapitiya? 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.
28:39 Jason Calacanis? 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?
28:49 Keith Rabois? Yeah, a couple of points. It's not quite that binary, Chamath. I generally agree with your arc. But like, 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, you know, 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.
29:34 Keith Rabois? 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 training 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 path. And I think, again, there may be a broad lesson there. The most important thing, I think, as a VC that you said, is we've been debating for years, should we invest in companies like Scale or Mercore or any of these surge?
30:15 Keith Rabois? 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 looking at revenue 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.
30:36 Jason Calacanis? Because we hit the end of human knowledge, or just the collection of it, is 99% done.
30:43 Speaker 03 Or you train on, you train on it so well that you don't need to label anymore. Like, the, the machines know how to label as good or better than a human. And so, like, 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, you're, some of the players in the, in the autonomous software space, autonomous vehicle software space, are no longer doing any labeling because the machines are doing it all. Just broadly.
31:24 Jason Calacanis? It'll just be built into the chipset that this is a stop sign. Like, it's like, we know what a stop sign is. We don't need the millionth time for somebody to label.
31:32 Speaker 03 It's like those captures, like, you're like, find the stop sign, or what's the traffic like? And eventually, the machines are just way better than humans at identifying these things.
31:41 Keith Rabois? Or 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 brake. And they never, so nobody actually knows it's a stop sign. It's just that, hit a brake when you see something that looks like this object.
31:59 Speaker 03 It's just a vibe.
32:01 Keith Rabois? Yep.
32:02 Speaker 03 It's a vibe.
32:03 Chamath Palihapitiya? I, I would just say that that's like intuitive knowledge versus like the expressly labeled 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...
32:21 Chamath Palihapitiya? 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 the, you know, there's 300 to 1,000 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 1,000 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, how do I change my strategy or double down?
33:01 Speaker 03 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. 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 going, 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.
33:45 Speaker 03 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, I pinged Elon 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 legit using this tool? And this is pre-Grok IV. Now with Grok IV, 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 IV could be this
34:26 Speaker 03 place where breakthroughs are actually happening, new breakthroughs. So if I'm 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?
34:43 Jason Calacanis? Is your perception that the L L Ms 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 trying random stuffs at the at the margins?
34:59 Speaker 03 It's a
35:00 Jason Calacanis? Or maybe it doesn't happen.
35:01 Speaker 03 No, no, no. So what I 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
35:11 Speaker 03 So let's say we're talking Grok three and existing chat G P T as it is. No, it cannot come up with the new idea. These things are so wedded to what is known. And they're so like, even when I come up with a new idea, I have to really, it's like pulling a donkey. Sort of you see 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, shit, you got something. But then when it says that, when it says that, then you have to, you have to go. OK, it said that, but I'm not sure. Like you have to double and triple check to make sure that you really got something.
35:52 Chamath Palihapitiya? To your point, when these models are fully divorced from having to learn on the known world and instead can just learn synthetically.
36:00 Speaker 03 Yeah.
36:01 Chamath Palihapitiya? Then everything gets flipped upside down to what is the best hypothesis you have? Or what is the best question? You could just give it some problem and it would just figure it out.
36:11 Speaker 03 So where I go on this one, guys, is it's all about scientific method. Right? If you get, if you have an L M or foundational model of some kind that is the best in the world at the scientific method, game the F over.
36:28 Speaker 03 You basically, you just light up more GPUs and you just got like a thousand more PhD students working for you.
36:36 Jason Calacanis? Keith, you're nodding your head. Here.
36:39 Keith Rabois? I agree with that. I think that's fantastic because the scientific method also, the faster it is, 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 got. 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, even 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.
37:23 Keith Rabois? There's lots of things about the human body, let's say in 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 is the brain worked this way? Models trained solely on science tend to expose connections that no human has ever had before. And that's because like the raw material's there and we only have a conscious awareness of, call it 110%. But when you apply it to other human domains where you're training on human 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, you're going to wind up finding things that no human has ever thought before.
38:04 Speaker 03 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.
38:18 Speaker 03 This computation engine, this brain of sorts.
38:24 Jason Calacanis? You wanted to say consciousness, but you stopped yourself.
38:26 Speaker 03 Yeah, there's like, I was like, how do I describe?
38:28 Jason Calacanis? The big C word, consciousness.
38:30 Speaker 03 But, but you need to be able to test in the physical world. So you could imagine a, a physical lab connected to one of these systems where then you could say, okay, like if it's a chemistry experiment, you could do chemistry experiments or physics, you, you get the idea.
38:48 Jason Calacanis? What could go wrong?
38:49 Speaker 03 It would be, it's, yeah, no big deal. It's going to be fine. Okay. So, but, 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 go through the scientific.
39:01 Chamath Palihapitiya? And verification. Yeah, exactly.
39:03 Speaker 03 Yeah.
39:04 Jason Calacanis? Wow. It's kind of mind blowing. It reminds me.
39:06 Chamath Palihapitiya? It's really mind blowing.
39:07 Jason Calacanis? If you remember, I don't know if you guys remember dark matter and like the discovery of it and everything. And as explained to me by Lisa Randall, you know, the, the discovery was made not by knowing there was dark matter, matter there and observing it, but observing there was something, you know, gravitational forces around this other matter. And then they said, well, wait, what's causing that? And that's where they found dark matter. So these ideas, you know, the idea that L L M 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.
39:39 Chamath Palihapitiya? One of the seven most difficult problems in math or the most important problems in math is proving a general solution to this thing called Navier-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 craziest thing where you're just like, how is this even possible? We use it to design airplanes, to design 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?
40:18 Speaker 03 But remember, remember, you know, how Elon talks about Grok and, and about AI generally is about why are we here? What is the purpose?
40:29 Jason Calacanis? Meaning of the universe.
40:30 Speaker 03 What is the meaning of the universe? How does it work? And a sort of fierce truth seeking mechanism there.
40:36 Chamath Palihapitiya? Let me ask you a question, Keith, Travis, Jason. If you guys were running Grok 4.
40:43 Speaker 03 That'd be so much fun.
40:46 Jason Calacanis? How do you judo flip OpenAI? 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 in a moment to judo flip the less better product?
41:09 Speaker 03 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, and I think, you know, that's where, you know, and then you go, wow, scientific breakthrough, scientific method. Like you start winning on truth and that will start, I believe, that will start to give the product awesomeness
41:52 Speaker 03 of OpenAI a run for its money. But like the product of OpenAI, the product department, those guys are crushing. 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, you will eventually, you'll eventually have an AI product manager.
42:14 Jason Calacanis? And on a tactical basis too, people forget how good Elon is at factories and physical, real world things. What he did, standing up Colossus, made like Jensen Huang 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 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, five, he's going to have a massive advantage there on top of Travis. You know, the missionary.
42:57 Jason Calacanis? Individuals, which by the way, was what he backed before Sam Altman corrupted the original missionary basis of OpenAI, made it closed AI in a, you know, nothing derogatory towards him, but he did hoodwink and stab Elon in the back. It's not nothing personal. I mean, he just screwed him over.
43:13 Chamath Palihapitiya? Would you say he bamboozled him?
43:15 Jason Calacanis? Bamboozled him, screwed him, hoodwinked him, you know, pick your term here, but he did it. He did it dirty. The original mission was to be missionary, open source all this content. That's the other piece I think is a wild card and then I'll measure certain key'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 sourced his patents for, you know, charging. If he open sourced the data set and self-driving, does anybody have the ability to produce robot taxis at the scale he can do it? I don't think so.
43:56 Chamath Palihapitiya? So Travis's hypothesis is true, then everybody will.
44:00 Speaker 03 Well, sorry, everybody will what, Shimon?
44:03 Chamath Palihapitiya? If you have access to the money that buys the compute, everyone could solve that problem.
44:08 Jason Calacanis? That's a hardware piece I'm talking about.
44:10 Chamath Palihapitiya? He said, he said, if he, if he published all the FSD data, could somebody build an autonomous vehicle?
44:17 Jason Calacanis? Well, yes, but could somebody produce 100 million robot taxis from a factory with batteries in them?
44:22 Chamath Palihapitiya? Okay, that's a, that's a different question.
44:24 Jason Calacanis? That's the thing I'm saying.
44:25 Keith Rabois? And not really, because
44:26 Jason Calacanis? Good.
44:26 Keith Rabois? Last time I was a guest on, you know, all and we talked about vertical integration. Uh, 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 different. Everything, you build a product taking advantage of where in the stack 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 through the last 70 years. But the company is still alive and still worth trillions of dollars because it's vertically
45:10 Keith Rabois? integrated. OpenAI, at your point, they do have a good product team. And they need to stay ahead of 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.
45:32 Speaker 03 Chamath, what's the paper you were talking about before? What was the name of it again?
45:36 Jason Calacanis? The bitter lesson.
45:37 Speaker 03 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. Right? But right now, we're still trying to drive like humans because we don't have enough data. And therefore can't do enough compute.
46:04 Keith Rabois? That's the global lesson, by the way. Chamath, you're totally right. Conceptual, you know, the 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, decades, and you may need to hack your way there through human interactions.
46:20 Speaker 03 Physical world AI is lacking in data. And so you just try to approximate humans.
46:27 Jason Calacanis? I don't know if you guys have seen this. In related news, OpenAI and Perplexity are going after the browser. Perplexity launched Comet 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 interesting things. I'll show you an example here that I just fired off while we're talking. So I just asked it, hey, give me the best flights from United Airlines and business class from New York City.
47:05 Jason Calacanis? From San Francisco to New York City, it does some searches, but what you see here is it's popped up 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 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 and I like doing it 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 the checkout.
47:47 Jason Calacanis? So again, this isn't like, if you're a developer, you do this all day long. But this really 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
48:06 Jason Calacanis? in real time. You can also connect obviously your Gmail, your calendar to it. So I did a 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's just that your browser doing the work. Your thoughts on this? Have you played with it at all?
48:43 Keith Rabois? Yep. I think it's a great hail Mary attempt by perplexity. I think absent something like this, perplexity is toast. Like for the stat about chat GPT 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's 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 chat GPT.
49:28 Keith Rabois? They are going to lose the search game, like the assets 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.
49:40 Jason Calacanis? Travis, your thoughts on this category? Anything come to mind for you in terms of,
49:46 Jason Calacanis? you know, feature sets that would be extraordinary here? I know you like to think about products and the consumer experience.
49:54 Speaker 03 It's really interesting. So, you know, I've been spending, as you guys know, 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 with them or whatever, they're like, yo, how are we gonna, how are we gonna keep doing what we do when the agents take over?
50:24 Jason Calacanis? Yeah. The paradigm shift is so profound that the idea that you would visit a webpage goes away and you're just in a chat.
50:31 Speaker 03 You have an agent that's just taking care of your flights for you. So I, I kinda, 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 of options?
50:52 Jason Calacanis? Yeah.
50:52 Speaker 03 And it's just a whole, you have an interface and then, you know, is perplex, is this thing that you just showed 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? Yeah, I just haven't spent enough time. I, I do know that every consumer software CEO.
51:16 Speaker 03 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'm doing like, almost like therapy sessions with them. I'm like, it's gonna be fine. You actually, you actually have stuff, you have a mo, you have real stuff that's of value. They can't replace it with an agent.
51:36 Jason Calacanis? So you're lying to them. You're doing hospice care and you're telling them everything's gonna be okay? But the patient's not making a lot of options on Robin Hood while he's like, yeah, yeah, tell me more, tell me more.
51:48 Speaker 03 There's certain things that are protected and there's certain not things that aren't. That's all.
51:52 Jason Calacanis? 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 talk to an agent, it would go do this for you. This feels like a step to that where it does all the work for you, presents you the final moment and says approve. So look, I think you're a butler. Yeah.
52:15 Chamath Palihapitiya? 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.
52:57 Chamath Palihapitiya? It's terrible. It's not very good. It's very limited. And anybody that could build a better product would take over a hundred billion dollar enterprise. Because I think it's there for the taking. I wish that perplexity would double and triple down on that. And so when you see this kind of random sprawl.
53:17 Speaker 03 Let's do it, Chamath. Let's just go do it.
53:20 Jason Calacanis? When you do the random sprawl, I think it doesn't work, but I just want to say like.
53:24 Chamath Palihapitiya? 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 is something that you just speak to and eventually there's probably something that's in your brain which you just think and it just does it. You're thinking,
54:06 Chamath Palihapitiya? "I need a flight to JFK." Or at the maximum today, in a very elegant, beautiful search bar, you type in "get me a flight" and it already knows what to do.
54:16 Jason Calacanis? Keith, in some ways, this is a step towards that ultimate vision. So you'd think it's worth it to, 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, an earpiece.
54:30 Chamath Palihapitiya? How do you get distribution, Jason, for the 19th web browser in 2025?
54:35 Jason Calacanis? 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 that. But Keith, what do you think?
54:53 Keith Rabois? I don't think they'd buy anything worth it. Like, what is Apple going to get? If you continue this failed strategy of Apple. Apple's 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. I think they have infrastructure challenges. So it's not an easy fix, but buying perplexity is not going to help. Like, Chav's strategy is actually a pretty coherent one for perplexity, co-op perplexity. So I think that's not a bad idea.
55:21 Jason Calacanis? Pick a vertical and own a strategy.
55:22 Keith Rabois? Not a bad idea, especially because you need unique data sources. Some of those data sources are May or may not license their data to OpenAI. So you can do some clever things there. But I don't think there's any residual value that Apple will 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 millions of dollars, hundreds of billions of dollars or whatever he's spending these days. And you know, Grok, if anything, Grok four shows that Mark really doesn't need to spend money to build a whole new team because everything they've done in AI has also missed the boat.
55:55 Jason Calacanis? 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 that is elegant. And why not just throw your search to it? Throw 10 billion at perplexity and see what happens.
56:11 Chamath Palihapitiya? What's elegant would be if there's a bunch of agents and just a chat box. Seeing a bunch of visual diarrhea is not elegant. It's lazy.
56:20 Keith Rabois? Shemoff on our, on our little Bloomberg clone, I'll give you naming rights. So you can call it the polyapatia. So hey, can somebody, can somebody bring up the polyapatia?
56:32 Jason Calacanis? You know what's so funny?
56:33 Keith Rabois? It just rolls right off your tongue.
56:36 Chamath Palihapitiya? 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 like, 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?
56:52 Jason Calacanis? Lafont was on, right? Like two episodes ago, he was like, I can't pull this up. It's limited to six companies.
56:57 Chamath Palihapitiya? Dude, you, it's 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 there. But the core usability and the core UI of that company has not evolved.
57:13 Jason Calacanis? I have my contribution.
57:15 Chamath Palihapitiya? And perplexity is very good at that, by the way. It is, they, they do a very good job.
57:19 Jason Calacanis? I got a new domain name, Travis. Let this one just sink in here. This is my way to weasel my way into the deal. Begin.com. Begin.com.
57:27 Chamath Palihapitiya? You own that, don't you?
57:29 Jason Calacanis? 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 domain names.
57:36 Chamath Palihapitiya? You're like, you're like one of these old people that show up at those flea markets.
57:39 Jason Calacanis? And then, yeah, they almost suddenly got a Picasso.
57:42 Chamath Palihapitiya? And you're like, oh, I have this thing that I bought 1845.
57:46 Keith Rabois? Guys, Jason, Jason is, Jason is the daddy in Go Daddy. Okay?
57:50 Jason Calacanis? I am. I'm your dad. I'm your daddy. Hey, speaking of daddy.
57:56 Jason Calacanis? Let's go to our next story.
57:58 Speaker 03 Come on.
57:59 Jason Calacanis? Oh, 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.
58:14 Jason Calacanis? He said, quote, "When it comes to bankrupting our country with waste and graft, we live in a 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 seemed to get a good reaction on X. Fiscal responsibility slash Doge, sustainable energy and dominance in that. Manufacturing in the U.S., which Elon has done, uh, 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.
58:55 Jason Calacanis? And, you know, one thing I was trying to figure out is just how unpopular are these candidates? And, uh, these political parties? This is a very interesting chart that I think we can have a great conversation around. It turns out, we used to love our presidents. If you look here from Kennedy at 83% is the highest approval rating. His lowest was 56%. That was his lowest approval rating. So he operated in a very high band. Look at Bush two during after nine 11. 92% was his peak. His lowest was 19, right? Wartime president. But then you get to Trump one, Biden and Trump two. Historically low, high approval.
59:34 Jason Calacanis? Their high watermark, 49 for Trump one, 63 for Biden, one of one, and then 47 for Trump two. And their lowest 29, 31, 40. So maybe it is time. For a third party candidate. Let's discuss it, boys.
59:51 Speaker 03 I have no idea how to read this graph.
59:54 Jason Calacanis? It is the worst.
59:56 Speaker 03 I'm like, what is happening here?
59:58 Jason Calacanis? This is the worst formatted chart. This is a confusing chart, but well, the reason I'm putting it up is for debate. So you should be saying, thank you. Yeah, we're debating that it's creating great debate.
1:00:08 Chamath Palihapitiya? Why did you put it up?
1:00:08 Jason Calacanis? Here's another one. Gallup poll Americans desire for a viable third party 63% in 2023. So it's bumping along an all time high.
1:00:17 Speaker 03 Okay, I'm really concentrating on this one.
1:00:19 Jason Calacanis? Okay, anyway, I'm gonna stop there. What's the gray? I'm gonna let you. Okay, God, I got it. Different sense during that time period and how popular the party was. Let's stop here. This is a good, this is a good place to stop.
1:00:33 Speaker 03 I just blew a GPU.
1:00:34 Keith Rabois? Yeah, look, a couple of points.
1:00:36 Jason Calacanis? Yes.
1:00:36 Keith Rabois? 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 he became a replacement level baseball player, which is actually really hard to do, by the way. Elon is probably a replacement level politician. Um, he's Michael Jordan for entrepreneurial stuff, but the third party stuff is not going to work. First of all, um, there, that chart is misleading. It's a flaw of average. It was badly designed and it's a flaw of average.
1:01:13 Jason Calacanis? Great.
1:01:13 Keith Rabois? 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, which is perfectly fine. Being polarizing is, is an ingredient to being successful, including with people on the show. Like 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
1:01:56 Keith Rabois? 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 think you can have like this kind of transformation on one party within a two-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 CS, and you know, 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,
1:02:36 Keith Rabois? 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 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 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
1:03:17 Keith Rabois? on the ballot, but he is not specifically on the ballot. So unless, because 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 Reagan. Without that personality, specific ideas just are not going to galvanize the American people.
1:03:40 Jason Calacanis? 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. 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 he could build a caucus, a platform.
1:04:17 Jason Calacanis? 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 off a couple of congressional seats?
1:04:30 Speaker 03 Okay, so first I have this axiom that I'm making up right now.
1:04:34 Jason Calacanis? Okay.
1:04:35 Speaker 03 Okay, it's called Elon is almost always right.
1:04:38 Jason Calacanis? Okay.
1:04:38 Speaker 03 Okay, right.
1:04:40 Jason Calacanis? Elon was right about everything.
1:04:41 Speaker 03 Seriously, let's just be real and like,
1:04:45 Speaker 03 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.
1:04:58 Speaker 03 We've never had somebody with this kind of capital that can be a quote unquote, party boss outside of the system. Right? And
1:05:12 Speaker 03 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 in his own right, I 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 happen separately, even if it doesn't go all the way. I just love it. I'm on, I'm on the Elon train.
1:05:54 Jason Calacanis? Yeah, I'm, I'm in love with this role for Elon more than picking a party because he's picking a very 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 in America.
1:06:10 Speaker 03 The problem with that is like, he's actually wrong about the reason why we have a deficit or debt. It's not because we're under taxed. It's we're massively overspending. If we just.
1:06:21 Jason Calacanis? I think he believes we're overspending.
1:06:23 Speaker 03 They should have been supporting the last, you know, beautiful bill. Because if you just held federal spending to 2019 levels, so 2019 is not like, you know, decades ago. Literally with our current tax revenues, we would be in a surplus.
1:06:38 Keith Rabois? 500 billion.
1:06:40 Speaker 03 Yeah. So there, all we need to do is cut spending. Now I admit that there's.
1:06:43 Jason Calacanis? Why didn't that happen with the big beautiful bill?
1:06:45 Speaker 03 So.
1:06:47 Keith Rabois? This is where details do matter. I think there is a willingness and 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 votes. And then, in 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, 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,
1:07:29 Keith Rabois? 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, and at some point, it's going to go away. So maybe the time is now, maybe we should just fix everything now.
1:07:43 Chamath Palihapitiya? I think you're exactly right. I think that the filibuster, it's just a matter 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 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.
1:08:27 Chamath Palihapitiya? 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.
1:09:09 Chamath Palihapitiya? 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 changed FEC rules, Jason, I think what you said is the only path. But I just, 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, the arcaneness of these rules, having to do a reconciliation bill and, you know, needing a super majority, veto proof, super majority, and the other case, it just means that nothing gets done. And I think somebody will eventually get impatient and just steamroll this thing.
1:09:51 Jason Calacanis? 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 just the idea that Elon could create a platform that people could opt into and support, just the existence of that would make the other two parties get their act together.
1:10:13 Chamath Palihapitiya? By the way, the other thing that-
1:10:14 Jason Calacanis? That's what we need is a little bit of a stick there and a carrot.
1:10:16 Chamath Palihapitiya? Yeah.
1:10:16 Jason Calacanis? Hey, if you 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 Chamath, you know, you're probably set where you're, where you want to be right now. But I can tell you, we go through our top 10, 20 list. Out of those, 50% will join Elon's party.
1:10:35 Chamath Palihapitiya? Well, the other, the other thing, Jason, that, that Keith said, which I think is, 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 Gray Davis recall. He didn't run on a platform.
1:11:08 Jason Calacanis? I don't think any of us could mention- J D Vance, J D Vance had this great book, Capture People's Imagination. He's an incredible speaker. He pisses off a third or two thirds of the country, depending on where you are in the country. But you can't ignore him. I think Elon can find 10 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 already to say, I'm thinking of running. Can you put me in touch with Elon?
1:11:36 Chamath Palihapitiya? I was thinking more like actors and sports stars, meaning where they just come with their own inbuilt distribute. Like, I think you almost have to to rank X followers and Instagram followers and do a join and say, okay, these are, do you know what I mean? Like, I think it's like totally different.
1:11:53 Speaker 03 It's painful, guys. It's painful. Like, let's not get more celebrities as politicians. Like, let's get like people who've led large, large efforts, large initiatives, complex things.
1:12:05 Jason Calacanis? Ideally, but they still have to communicate, right, Keith? They have to be able to communicate on a podcast. That's the new platform. If they can't spend two hours, three hours chopping 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, you're out in today's political arena.
1:12:28 Keith Rabois? It'll be 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 doable. I don't think you can win a Senate race.
1:12:46 Jason Calacanis? Well, there it is, Elon. Keith doesn't think you can win a Senate race, but he thinks you'll win a couple of congressional ones. Thanks for giving him the motivation, Keith. I appreciate it.
1:12:53 Speaker 03 I'm sure he's gonna love that.
1:12:54 Jason Calacanis? Biggest mistake you've ever made. He's not gonna win two. People in the Republican Party right now are going, oh, no, don't poke the tiger. Uh, listen.
1:13:03 Keith Rabois? But that's how Trump got into politics, so I don't want to be Obama here.
1:13:06 Jason Calacanis? You just don't, Elon, right? Yeah. Congratulations. All right, listen. SCOTUS made a big decision here. This is a really important decision. Uh, they've sided with Trump. Four plans for federal workforce, RIFs, reductions in workforce, for those of you who don't know.
1:13:26 Jason Calacanis? As you know, Elon, Trump, they wanted to downsize the 3 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. 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 RIF 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.
1:14:09 Jason Calacanis? This is a key debate because the Congress, as you know, has power in 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 accuse 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.
1:14:54 Jason Calacanis? They have the right to prepare a RIF. The question is, can they actually execute on that RIF? And who has that power, Chuma? Does the power reside with the president to make large-scale RIFs? Or do they have to consult Congress first? Your thoughts on this issue?
1:15:11 Chamath Palihapitiya? 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, Jason, there are more than 2,000 federal agencies.
1:15:32 Chamath Palihapitiya? Employees plus contractors, I think, number almost 3 million people. If you put 3 million people into 2,000 agencies, and then you give them very poor and outdated technology, which unfortunately, most of the government operates on, what are you gonna get? You're gonna 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.
1:16:15 Chamath Palihapitiya? 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.
1:16:59 Chamath Palihapitiya? 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.
1:17:23 Jason Calacanis? Travis.
1:17:23 Chamath Palihapitiya? So I think it's a big deal.
1:17:24 Keith Rabois? Except that ruling doesn't happen without Doge. That Doge caused that ruling to occur.
1:17:29 Chamath Palihapitiya? True.
1:17:29 Jason Calacanis? Well, the EO did. You could have passed.
1:17:32 Keith Rabois? That was all Doge style, though. You know what I'm saying?
1:17:34 Chamath Palihapitiya? Yeah.
1:17:35 Jason Calacanis? If they wasn't firing people, yeah, they probably wouldn't felt the need, to your point, Travis, to actually file this. But Travis, if you are living in the age of AI 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 coach.
1:18:00 Chamath Palihapitiya? Well, you can grow it. You just can't shrink it.
1:18:03 Keith Rabois? It's like running a unionized company, which actually does exist. Our large companies where you can't do any of these things.
1:18:10 Jason Calacanis? Right. Do they still exist or are they all gone?
1:18:12 Keith Rabois? I think.
1:18:13 Jason Calacanis? They're going quickly.
1:18:14 Keith Rabois? Yeah, probably.
1:18:16 Speaker 03 I think this just gets back to 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 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 are the ways in which to spend it, but it's not specific about how many people you hire, then that is different.
1:18:55 Jason Calacanis? Yeah, it should be outcome based. Hey, here's the goal. Here's the, the key objectives, right?
1:19:00 Keith Rabois? Travis is totally right. Like there are, there's a variety of different laws, some with incredible specificity, some with very broad management. The Constitution clearly says that all executive power resides in the President of the United States, period. There's no exceptions there. However, Congress does appropriate money and post Watergate.
1:19:19 Keith Rabois? 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 little bit easier. It'll get more complicated again, like this EO is only approved to allow for 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 has to navigate its way through the courts again.
1:20:00 Jason Calacanis? Yeah, it's super fascinating. Yeah, I wonder if they're going to get to the point where they're going to say in every bill, you need to hire this number of people to hit this goal.
1:20:09 Keith Rabois? 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 constitutional duties, has to hire a certain number of people. That feels pretty precarious.
1:20:24 Speaker 03 Well, I, I, I'm not sure, Keith. It's just like they prescribe a whole bunch of other things.
1:20:29 Keith Rabois? I know, but like.
1:20:30 Speaker 03 You must, you must appropriate money for, to this specific institution to do this specific work.
1:20:38 Keith Rabois? But that's not an executive function. Like if you said, like the Secretary of State has to have 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 that the president should, I mean, yes, Congress does set, you know, which people are subject to Senate confirmation, 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 and it's never going to, I don't think it'll be constitutional for Congress to mandate and put all kinds of handcuffs on the president.
1:21:17 Jason Calacanis? Well, then you also have performance that comes in here. What if you look at the Department of Education, you 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 them for cause and I'm going to hire new people. How are you going to stop the executive from doing that?
1:21:35 Keith Rabois? There's been a bunch of litigation, you know, in parallel to this litigation about the president's ability to fire people. And for the most part, the Supreme Court's basically with maybe the exception of the Federal Reserve Chair, said that the president can fire pretty much anybody he wants.
1:21:52 Jason Calacanis? I mean, that's the way to go is like, I mean, I hate to be cutthroat about it, but if the results aren't there, then you fire people.
1:21:58 Keith Rabois? Yeah, if they're a presidential appointee, the president should be able to fire you at will. Just like if you were a VP at one of our companies, the CEO should be able to fire you at will.
1:22:07 Jason Calacanis? 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 starting over. Like, you should be allowed to do that. How are we going to have an efficient government?
1:22:20 Keith Rabois? 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 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.
1:22:41 Jason Calacanis? All right, listen, we're at an hour and a half, gentlemen. Do you want to do the FICO story or should we just wrap, Chuma? And we got plenty of show here. It's a great episode. Anything else you want to hit?
1:22:51 Chamath Palihapitiya? I don't really have much to say on the FICO story. I thought these other topics were really good, though.
1:22:54 Jason Calacanis? We did great today. This is a great panel. I'm so 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?
1:23:08 Jason Calacanis? Movies you watch, books you read. 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?
1:23:19 Keith Rabois? Well, I don't want to share any good restaurants or hotels because
1:23:22 Jason Calacanis? Oh, you're gatekeeping. You're gatekeeping? Come on, man. Give us your favorite New York place.
1:23:29 Keith Rabois? It's like if you had a babysitter.
1:23:32 Jason Calacanis? Yes. Can I get your nanny's email?
1:23:36 Keith Rabois? There are, there are things that are, what do you call it? No marginal cost consumption like Netflix. So for example, you know, this documentary on Osama bin Laden is phenomenal. Like, I don't know if any of you have seen it.
1:23:48 Jason Calacanis? I haven't seen it.
1:23:49 Keith Rabois? And, you know, I, I'm a student of this stuff and I, I thought, you know, 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 highly, highly recommend it.
1:24:04 Jason Calacanis? What, what was the big takeaway for you so far?
1:24:06 Keith Rabois? I don't know if there's any like specific takeaway, but just like so many parts of the story are misunderstood and not really understood. And how the various confluences of somewhat random things lead to a very catastrophic result. But it, it's, it's as dramatic as the best movie. But it's a full documentary and you will learn things and absorb things. I, I just, I've had friends who I've been recommending it to friends and for a story you think you know, it's incredible, incredibly revealing.
1:24:37 Jason Calacanis? Okay. Travis, anything you got on your plate there that you're enjoying? A restaurant, a dish?
1:24:42 Speaker 03 I mean, look, you know, I mean, Jason, you know, I go to Austin a lot.
1:24:47 Jason Calacanis? Yes.
1:24:48 Speaker 03 Like basically from March till October, I do about 15 weekends in Austin. I have a lake house. Jason's hung out a couple of times.
1:25:00 Speaker 03 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.
1:25:07 Jason Calacanis? Very sad, very sad.
1:25:07 Speaker 03 And it's like, 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.
1:25:22 Jason Calacanis? XG.
1:25:23 Speaker 03 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
1:25:48 Jason Calacanis? savant?
1:25:49 Speaker 03 Like a savant essentially. But hasn't worked on it for many years. So I'm getting back into it and
1:25:56 Jason Calacanis? Love it.
1:25:57 Speaker 03 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.
1:26:19 Jason Calacanis? No, wait. Yeah. Okay.
1:26:21 Speaker 03 Yeah.
1:26:22 Jason Calacanis? All due respect, you're the founder of Uber, you're very high profile, you go to this back and is this like held at the Motel 8? In like a conference room in the back?
1:26:30 Speaker 03 It was amazing. It's set, it was at the, it was like a month ago or so. There's like a big tournament and it was, uh, so the United States Backgammon Federation had this big tournament. I guess it was, um, at the Los Angeles LAX, at the LAX Hilton. And it was in the, it was in the basement of the Hilton.
1:26:53 Jason Calacanis? Great.
1:26:53 Speaker 03 And it was like,
1:26:55 Jason Calacanis? Next to the Dungeons and Dragons convention.
1:26:58 Speaker 03 It had those kinds of legit vibes.
1:27:01 Jason Calacanis? I love it.
1:27:02 Speaker 03 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.
1:27:14 Jason Calacanis? Ooh, the owner of XG.
1:27:15 Speaker 03 And then there was like a full on melee that basically occurred. They're like, oh, the owner of XG, Travis is here.
1:27:22 Jason Calacanis? Chamath, I feel like we've got a window here to do the All In Backgammon high-end tournament. We gotta lock this down now. We gotta lock down the All In Backgammon set.
1:27:33 Speaker 03 I get the co-branding rights on this, okay?
1:27:35 Jason Calacanis? Absolutely.
1:27:36 Speaker 03 XG.
1:27:37 Jason Calacanis? Well, no, the All In XG, you know, like I said, I love a great 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, Free Bird's trying to protect. We could murder them and then make.
1:27:52 Chamath Palihapitiya? That would be so great.
1:27:54 Jason Calacanis? 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 and make the, the Backgammon set for you.
1:28:06 Chamath Palihapitiya? I love Backgammon. And honestly, like, if I wasn't 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.
1:28:19 Speaker 03 Chamath, let's go, dude.
1:28:21 Chamath Palihapitiya? No, it's, uh, Backgammon is, Backgammon is a beautiful, beautiful, beautiful game.
1:28:25 Jason Calacanis? I love the vibes of sitting, Travis and I sat, I got some cigars out, you know, we pour a little of the All In tequila, tequila.olin.com, uh, we get that going, a couple of, uh, the All In cigars, and then we have the All In Backgammon. It's a wonderful hang.
1:28:41 Speaker 03 Yeah.
1:28:41 Jason Calacanis? Keith, would you consider giving us some of your money playing Backgammon?
1:28:44 Speaker 03 Absolutely, absolutely, absolutely.
1:28:46 Jason Calacanis? We gotta, we gotta get some of that Keith Roy money on the table, because you don't play poker with us.
1:28:51 Speaker 03 I don't play poker, but Backgammon, yeah, that sounds great. And I'll bring, I'll bring better tequila. I'll have better tequila, well, like, we're gonna upgrade.
1:28:57 Jason Calacanis? We'll do a little taste off. Yeah, so you've insulted now Elon with the Senate seats and facts with his, uh, tequila.
1:29:03 Speaker 03 My tequila is much better, trust me.
1:29:05 Chamath Palihapitiya? Wait, who, who is left in the PayPal mafia you'd like to insult before this episode?
1:29:09 Jason Calacanis? Yeah, and he can talk about Reed Hoffman.
1:29:12 Jason Calacanis? Or Peter, anything about Peter?
1:29:15 Keith Rabois? Ree could join Elon's party. He's collecting a bunch of misfits. So he might as well take Ree too.
1:29:19 Jason Calacanis? All right, listen, this has been another amazing episode of the number one podcast in the world, the all-in podcast for your Sultan of Science who couldn't make it today. He's at the beep conference that we don't mention. And David Sacks, who is out making America safe in AI and crypto. Chamath Palihapitiya, world's greatest moderator, Travis, Keith, TK.
1:29:44 Chamath Palihapitiya? Thanks for pinshitting.
1:29:45 Jason Calacanis? You guys were great today. What a panel. See y'all next time. Bye bye.
1:29:51 Speaker 05 We'll let your winners ride.
1:29:54 Speaker 05 Rain Man David Sacks.
1:29:58 Speaker 05 And it said, we open source it to the fans and they've just gone crazy with it. Love you, Sis. I'm the queen of Kinoah.
1:30:11 Speaker 05 Besties are gone.
1:30:14 Speaker 05 That's my dog taking a hit in your driveway.
1:30:18 Speaker 05 Oh, man.
1:30:21 Chamath Palihapitiya? We should all just get a room and just have one big huge orgy because they're all just it's like this like sexual tension that we just need to release somehow.
1:30:29 Speaker 05 What your B B? What your B? What your B? B? What? We need to get merch. Besties are gone.
Previous Big Beautiful Bill, Elon/Trump, Dollar Down Big, Harvard's Money Problems, Figma IPO Next Trump vs Powell, Solving the Debt Crisis, The $10T AGI Prize, GENIUS Act Becomes Law Page updated October 26, 2026 · transcript and summary are machine-generated
