DeepSeek 恐慌、美中对决、OpenAI 400 亿美元融资与 DOGE 进展：特拉维斯·卡兰尼克和大卫·萨克斯做客

Jason Calacanis (00:00:00): Alright, everybody. Welcome back to the All In podcast. We've got an incredible crew today. Don't forget to go to our YouTube, blah blah blah. Subscribe. And make sure you check out Freyberg's surprise drop with his hero, Ray Dalio, live on all platforms today. How did that come about? Freeburg, little surprise drop. David Friedberg (00:00:20): Just great. Was talking with Ray about his new book, which he just published on how countries go broke, Obviously, Chamath Palihapitiya (00:00:28): very free topic. Going broke now, favorite. Speaker 4 (00:00:32): America. I David Friedberg (00:00:32): think he talks a lot about the historical context of what's gone on with the debt cycles in different countries. And, basically, at the end of the book, he has a pretty, I think, important recommendation to try and get The US to roughly 3% of GDP as our net deficit, net of all expense including interest expense. So that's the recommendation to the administration. I think it's pretty timely, with the change in administration. Anyway, great topics to talk through and really important book. Jason Calacanis (00:01:03): Awesome. Well done. And we are super delighted to have in the red throne, Travis Cowan. He is the cofounder and CEO of Cloud Kitchens. He also worked in the cab business for a little bit, cofounder and former CEO of Uber. And, yeah, we had a great interview at the Olin Summit last year, and he's back up from his media hiatus. He's been in the lab working on Cloud Kitchens. How you doing, brother? Travis Kalanick (00:01:28): I'm doing really well. I I gotta say, just like at the summit, Jason, I'm I'm Yes. It's an honor to be in the presence of such a prominent Uber investor. Speaker 6 (00:01:39): Absolutely. Absolutely. I mean, finally, somebody has recognized my The great contribution. David Friedberg (00:01:45): The greatness of JKO. Travis Kalanick (00:01:47): Yes. Absolutely. I'll mention it 3 or 4 times. We'll be Appreciate it. I'll give you the props. David Friedberg (00:01:51): You don't have to Travis Kalanick (00:01:51): do it for yourself anymore. Speaker 6 (00:01:52): So thank you. I Speaker 4 (00:01:54): appreciate it. I appreciate it. We'll let your winners ride. Rain man David Sizer. And it said we Speaker 7 (00:02:02): open sourced it to the fans, and they've Jason Calacanis (00:02:11): Give everybody a little overview of Cloud Kitchens and the business and how it's going because people are obviously addicted to ordering food at home, and, it's it's quite a trend. Travis Kalanick (00:02:23): Yeah. I mean, the the high level for it, the way to think about it is it's it's about the future of food. What does the future of food look like? You go, well, in a hundred years, we'll start way out there. In a hundred years, you're gonna have very high quality food, very low cost that's incredibly convenient. And they're gonna be machines that make it. They're gonna be machines that get it to you, and it's gonna be exactly to your dietary preferences, your food preferences, etcetera. And it just comes to you, and it's so inexpensive that it approaches or has surpassed the cost of going to the grocery store. That's more of a, like, a today analogy. So you go a hundred years, of course, that's the thing. Nobody's gonna be making food. What about 20? What about 10? And so the company is real estate, software, and robotics. That's all about the future food. And if you can get the quality there and you can get that cost down to to start approaching the cost of going to the grocery store, you do to the kitchen what Uber did to the car. Mhmm. And that's the thing. David Friedberg (00:03:30): Travis Cueto Travis Kalanick (00:03:31): grind. It's like lot you know, bits and atoms in the Uber world. Mhmm. This is, like, 5 times more atoms per bit. This is, like, heavy duty industrial stuff. Probably more along the lines of, like, you know, where Elon goes in some of his companies. Like, they're super interesting tech, but you gotta grind out those atoms. Chamath Palihapitiya (00:03:52): Do you see people actually cooking in the future, or does it become a centralized service? And is it optimized to people's health? And what do you think the implications to the food supply are if your vision holds? How do you think about all those things? Travis Kalanick (00:04:05): Look. People will cook in the future as a hobby. I sort of I make a joke at the office. I'm like, I like horses. I love horses, but I don't ride a horse to work. And it's gonna be a little bit like that, whereas you can cook. It's it's a soulful thing to do. It's just very human. But, you know, it's late. You know, mom gets home late from the office, needs to get the kids, you know, a nutritious meal, she doesn't have to cook it now, and she doesn't she won't have to cook it, and she won't have to go to McDonald's either. It will be high quality and convenient and low cost all at the same time. And, yes, dietary preference, everything, because it'll be hyper personalized. Like, the way the Internet is in content Speaker 4 (00:04:51): Mhmm. Travis Kalanick (00:04:51): Plus plus plus in terms of your specific preferences for what you want. Jason Calacanis (00:04:57): I mean, you've got these computers rocking oh, these robots rocking, I think, in Philly somewhere, in the lab where they're making bowls. Yeah? Travis Kalanick (00:05:05): I mean, we're out of the lab at this point. We have our machines. So we have a machine called the bowl builder that basically makes different cuisine types with bowls. So, like, think of, like Chamath Palihapitiya (00:05:15): Like sweet greens. Like, what they yeah. Travis Kalanick (00:05:17): We're not working with these brands specifically, but I'll I'll just sort of it's a good analogy. Like, think of Chipotle or Cava or Sweetgreen or You Get The Idea. We created test brands that were like those things and built the machine at the same time as we were building an actual restaurant, and we built that restaurant to prove that the machine works. Then we have our customers now touring, checking out. We're rolling out with 5 customers in April 0. They're using the machine, and the way it will the way it's gonna go down is they will come into and we of course, we have the real estate, so we have kitchens, you know, tens of thousands of kitchens around the world. They will come into 1 of our kitchens in a facility. It's a delivery only restaurant. They'll prep the food in the morning, and then they will leave. And the machine will if you will order online, DoorDash Uber eats, etcetera, they'll order online the way they do. Build your own bowl exactly as you want, and the bowl gets all the ingredients dispensed, hot or cold, sauce, etcetera, gets lidded. The bowl goes into a bag. The utensils go into the bag. The bag is sealed, and then it comes out on a conveyor belt. And machine gets the bag. It goes to the front of the facility, gets put into a locker. That locker then is sitting there. DoorDash reads driver comes, waves their waves their phone with an app in front of a camera, and it pops open the locker that has the food that you're supposed David Friedberg (00:06:53): to get. Chamath Palihapitiya (00:06:54): That's so cool. Travis Kalanick (00:06:55): So, like, if you're if you're a restaurant tour, you're the grind of the on demand meal, which is the restaurant world, goes away. You you basically prep, and that's asynchronous from when people order food. The machine does the final assembly or what's known as plating, essentially. Chamath Palihapitiya (00:07:14): Like, do you think there's a service in the future where my physiology Speaker 4 (00:07:19): Mhmm. Chamath Palihapitiya (00:07:19): I can share that with you with CloudKitchens, and you guys just can always be optimizing my food based on what I know is good or bad for me? Travis Kalanick (00:07:30): So first, what we do is we serve the restaurants. Speaker 4 (00:07:34): Mhmm. Travis Kalanick (00:07:35): So what happened so, Chamath, you'll be sharing your dietary preferences with Uber Eats or DoorDash or Sweetgreen or somebody. We like our our like, our customer promise at our companies, we serve those who serve others. Speaker 4 (00:07:50): Mhmm. Or Travis Kalanick (00:07:50): put in other ways, infrastructure for better food. So we are the either the AWS or the NVIDIA or whatever you wanna call it, but for food, if that makes sense. We're behind the scenes. We're the infrastructure. And so you'll give your preferences. Chamath Palihapitiya (00:08:04): Right. It should be a brand like then Sweet Greens or whomever, Chipotle, that says, hey, guys. Share with me, like, a Yes. An encrypted hash of your dietary restrictions needs, whatever your lipid panel, and I and I'll customize this thing, and then you enable that on the Travis Kalanick (00:08:20): back end. Pretty Jason Calacanis (00:08:21): close to my right? You could do that authenticate your Apple Health. Chamath Palihapitiya (00:08:23): That's really awesome. Jason Calacanis (00:08:24): Just authenticate Apple Health. Travis Kalanick (00:08:25): When these bowls come off the line and see how I talk. It's like an assembly line. When these bowls come off the line, on the label on the bowl is how many grams of every ingredient is in it, plus a picture of what it was before we put the lid in. That can be sent to the person while the bowl's on its way via courier. Right? Chamath Palihapitiya (00:08:45): What do you think, Travis, about this whole Maha movement and just the food supply itself? So then what how does that change? Do restaurants embrace more farm to table stuff? Travis Kalanick (00:08:54): I think look. I think what like, what we see with supply chains in a bunch of different industries, it's just gonna get super wired up. So right now, we're at the point of manufacturing, but what happens so you go, okay. We're doing assembly. Then you go, okay. What about prep? Then you go further upstream, and you're like, what about supply chain, like Cisco, US Foods? And then you go further up, and you're like, well, how does how does the mechanization occur on farms and in agriculture? And then how does that all get wired up to serve the customer and sort of what they're looking for. So, like, you really can know exactly what kind of wheat was put into that food, whether it was organic for real or not. Like, what was the actual field that came from? Things like this. You can imagine, like, really getting tight about supply chain as it relates to dietary stuff. And as it relates to, like, Maha, like, hell to the yes. I mean, I ordered a couple different, I went to the I went to RFK Junior's website, and they have, like, the he has merch. He has Maha merch. I have I have the green Maha merch hat. I should have worn it today. I'm all about it. Jason Calacanis (00:10:02): You got the onesie? Travis Kalanick (00:10:04): Yeah. That's amazing. Chamath Palihapitiya (00:10:05): The onesie was crazy. Your bowl builder, Friedberg, you tried to do this. Right? Your It's a It's an David Friedberg (00:10:11): We had a bowl builder ten years ago or 2015. Yeah. Saw it. He actually Mhmm. Visited it when we built it. And we designed the system around a canister mechanism. So all the food prep was done in a similar sort of, like, commissary model, and then it was loaded in bulk and then put into little canisters. And there are 30 slots in the canister Yeah. Dispenser. And then the canisters would move down the device, open up, and and you could assemble bowls with rice and beans and all sorts of stuff. The whole thing was automated. And we were in the process of building out our first automated store when I actually took a medical leave of absence from IITSA, and, ultimately, the company did not get it into production. But it we had great working demos, and it was a very yeah. I mean, it was just definitely a no brainer that this must have been Chamath Palihapitiya (00:11:03): You love this. David Friedberg (00:11:04): Yeah. And at the time, we were we actually had I'll tell you guys this. We actually had a term sheet with Chipotle. This was nine years ago to actually put this into Chipotle stores, and then we were in the early conversations with Sweetgreen at the time as well. And, obviously, Jonathan and team have gone on to develop their own system. But, you know, basically, you can reduce so much of, like, QSR down to this bowl based system and automate it as Travis is doing. So it's just a no brainer. And it's it's certainly necessary in a time when there's either a labor shortage or labor price inflation that's causing a real issue with the ability. And, yeah, this is the original automats in Mhmm. In New York Yeah. In, in the early twentieth century. Travis Kalanick (00:11:43): I love this. David Friedberg (00:11:44): But they yeah. They had a commissary behind that wall, and they made, like, plates of food. You put in there, you put a quarter in, you turn the the knob, and you get your meal out. It's the classic that's Travis Kalanick (00:11:51): the classic artificial artificial intelligence. Right? This is, like, the mechanical Turk thing. I mean, look. Here's the thing. Here's the a little nuance that's super interesting about automation in QSR restaurants is that they have an existing brick and mortar that's built a certain way. That layout is meant for humans and to for those humans to work in certain processes in exact and very specific way. Every square inch of that kitchen and that space is is dialed. When you go and put a machine like this in, it changes the whole thing. And so just to get going, you've gotta, like, do you've gotta like, if you were to replace the front line of Chipotle, you gotta you gotta take out that front line. You gotta demo it. Yeah. You gotta put in a new machine. David Friedberg (00:12:37): That's the challenge that they all have. Travis Kalanick (00:12:38): And so now it's, like, a huge amount of CapEx. My store's down for two to three months, and the economics start to not work. And by the way, I still have to have humans in that brick and mortar. And so, you know, look. We have a different take. We're in that delivery only model. So these are it's true infrastructure for making food behind the scenes for delivery so you don't have these issues. And, of course, our setup, our infrastructure, these kitchens are designed for these kinds of machines to be in them and vice versa. We've designed the machine to be in them. David Friedberg (00:13:11): When we did this early at Itza, it was, like, food delivery was very early. We built these Itza restaurants that were smaller footprint. We had an 800 square foot restaurant that was doing 3,000,000 a year in revenue, and it had a handful of people working in it. But we were putting about 800 people an hour during the lunch rush through that restaurant ordering custom bowls. Travis Kalanick (00:13:29): This was by 1. Right? That's LAJ. Speaker 4 (00:13:31): One market. David Friedberg (00:13:31): One market. Exactly. And so David Sacks (00:13:33): By the way, did did you guys notice that JKEL was plugging his product there in the background even though it has absolutely nothing to do with what Travis was saying? Speaker 6 (00:13:40): Oh, welcome back to the show. Nothing's changed. David Friedberg (00:13:45): Sachs is here for David Sacks (00:13:46): else even noticed that. Travis Kalanick (00:13:47): I just heard this voice from above. It was the czar of AI and crypto. I was like, wow. Descended. Let's all sit back and listen. Jason Calacanis (00:13:55): The czar's back. David Friedberg (00:13:56): Sachs, any anecdotes you wanna share about life in DC? How how exciting it's been in the administration the first week? David Sacks (00:14:04): It's been amazing. I mean, it's hard to believe it's only been a week. Right? So you're Jason Calacanis (00:14:08): in the White House or that building next to it? Do you have an office? Building. David Friedberg (00:14:11): You mean the Treasury Building? Yeah. Because somebody was Jason Calacanis (00:14:14): talking about there's a building next to it or something. I don't know. David Sacks (00:14:17): I have an office in the the, old Executive Office Building, otherwise known as the Eisenhower Building. And then I have a pass where I can just walk over to the West Wing if I wanna walk over to it. There's kind of a whole White House complex behind the gates that the West Wing's part of it and Eisenhower Building, and there's a couple other buildings in that complex. It's really cool. It is really neat to to show up for work at the White House. Jason Calacanis (00:14:41): I'm just saying. It's awesome. It's like being in a movie or something or a TV Speaker 6 (00:14:45): show? David Sacks (00:14:46): It is really cool. You know? David Friedberg (00:14:47): It's awesome. Jason Calacanis (00:14:48): Any interesting meetings you can talk about? And, I mean, I know we are here to tell today to talk about DeepSeek, but any interesting meetings or anecdotes from just the vibes and walking around? What's the coffee like? Is there, like, a commissary? You run into anybody interesting? David Sacks (00:15:03): There there is a commissary actually in the White House called the Navy Mess, and I think they're just opening up for for business now. That is 1 of the cooler things you could do is you could take people to to lunch at the Navy Mess. Jason Calacanis (00:15:16): Oh, look forward to it. Travis Kalanick (00:15:18): Shake out just invited. So Speaker 6 (00:15:20): I look forward to it. David Sacks (00:15:21): I look forward to taking Chamath and Freeburg there. Speaker 6 (00:15:26): I'll wear my MAGA hat. Alright. Jason Calacanis (00:15:27): Well, let's get started. You're here because you, we have Chamath Palihapitiya (00:15:31): a very specific He's here because the world is ending, Jason. The the Western world is ending. Jason Calacanis (00:15:35): Okay. The Western world's ending, and, David Sacks is gonna save it. But we had a little bit of a freak out the last week regarding this DeepSeek. If you don't know, that's a, Chinese AI startup. They released a new language model. It's called R1. And it's on par basically with some of the best models in production in the West, like OpenAI's o 1 model. But they claim and listen, you can trust claims coming out of China, you know, for what it's worth. They claim to have done this all for $6,000,000 on only 2000 GPUs. For comparison, OpenAI spent reportedly 80, a hundred million to train GPT 4, which you're all using now. And, Sam claims they're going to spend a billion dollars training GPT 5. And so that's about 7% of the cost of GPT 4. Obviously there are export restrictions on, NVIDIA H100s to China. So there's a big debate as to if they actually have H1s or not. And, Monday was a bloodbath in the stock market. NVIDIA had the worst worst day in the history of the stock market in terms of total dollar amount of market cap lost. It was down 17%, which is $600,000,000,000 TSMC was down. ARM was down. Broadcom was down. So I guess everybody's asking the question, how did they do this? Did they do it? And then there's a bunch of debate on whether they stole, which is kind of rich coming from OpenAI, which got caught red handed stealing everybody else's content, and now they're crying foul that the Chinese stole or trained did what's called distillation of their model in order to build theirs. Sachs, obviously, you are the czar of AI. I'm curious what your take on all this is, and thanks for coming. David Sacks (00:17:20): Well, I think 1 of the really cool things about this job is just that when something like this happens, I get to kinda talk to everyone, and everyone wants to talk. And I've I feel like I've talked to maybe not everyone in the like, all the top people in AI, but it feels like most of them. And there's definitely a lot of takes all over the map on DeepSeek, but I feel like I've started to put together a a synthesis based on hearing from the top people in the field. It was a bit of a freak out. I mean, it it's rare that a model release is gonna be a global news story or cause a trillion dollars of market cap decline in in 1 day. And so it is interesting to think about, like, why was this such a potent news story? And I think it's because there's 2 things about that company that that are different. 1 is that, obviously, it's a Chinese company rather than an American company, and so you have the whole China versus US competition. And then the other is it's an open source company or at least it open sourced the the r 1 model. And so you've kinda got the whole open source versus closed source debate. And if you take either 1 of those things out, it probably wouldn't have been such a a big story. But I think the synthesis of these things got a lot of people's attention. A huge part of TikTok's audience, for example, is international. Some of them like the idea that that The US may not win the AI race, that The US is kinda getting a comeuppance here, and I think that fueled some of the early attention on TikTok. Similarly, there's a lot of people who are rooting for open source or they're having animosity towards OpenAI. And so they were kinda rooting for this idea that, oh, there's this open source model that's gonna give away what OpenAI has done at one twentieth the cost. So I think all of these things provided fuel for the story. Now I think the question is, okay, what what should we make of this? I mean, I think there are things that are true about the story and then things that are not true or or should be debunked. I think that, let's call it true thing here, is that if you had said to people a few weeks ago that the second company to release a reasoning model along the lines of o 1 would be a Chinese company, I think people would have been surprised by that. David Sacks (00:19:31): So I think there was a surprise. And just to kind of back up for people, you know, there's there's 2 major kinds of AI models now. There's kind of the base LLM model like ChatGPT 4 o or the deep seek equivalent was v 3, which they launched a month ago. And that's basically like a smart PhD. You ask a question, gives you an answer. Then there's the new reasoning models, which are based on reinforcement learning, sort of a separate process as opposed to pretraining. And o 1 was the first model released along those lines. And you can think of a reasoning model as like a smart PhD who doesn't give you a snap answer but actually goes off and does the work. You can give it a much more complicated question, and it'll break that complicated problem into a subset of of smaller problems, and then it'll go step by step to solve the problem. And that's that's called chain of thought. Right? And so the new generation of agents that are coming are based on this type of idea of of chain of thought that that an AI model can sequentially perform tasks, figure out much more complicated problems. So OpenAI was the first to release this type of reasoning model. Google has a similar model they're working on called Gemini 2 o Flash Thinking. They've released kind of an early prototype of this called Deep Research one point five. Anthropic has something, but I don't think they've released it yet. So other companies have similar models to o one either in the works or in some sort of private beta, but DeepSeq was really the next 1 after OpenAI to release, you know, the full public version of it. And moreover, they open sourced it, and so this created a pretty big splash. And I think it was legitimately surprising to people that the next big company to put out a resi model like this would be a a Chinese company. And moreover, that they would open source and give it away for free, and I think the API access is something like one twentieth the the cost. David Sacks (00:21:27): So all of these things really did drive the the news cycle, and I think for good reason because I think that if you had asked most people in the industry a few weeks ago how far behind is China on AI models, they would say six to twelve months. And now I think they might say something more like three to six months. Right? Because o 1 was released about four months ago, and r 1 is comparable to that. So I think it's definitely moved up people's time frames for how close China is on on AI. Now let's take the, we should take the claim that they only did this for $6,000,000. On this 1, I'm with Palmer Luckey and Brad Gerstner and others, and I think this has been pretty much corroborated by everyone I've talked to that that that number should be debunked. So first of all, it's very hard to validate a claim about how much money went into the training of this model. It's not something that we can empirically discover. But even if you accept it at face value, that $6,000,000 was for the final training run. So when the media is hyping up these stories saying that this Chinese company did it for 6,000,000 and and these dumb American companies did it for a billion, it's not an apples to apples comparison. Right? I mean, if you were to make the apples to apples comparison, you would need to compare the final training run cost by DeepSeek to that of OpenAI or Anthropic. And what the founder of Anthropic said and what I think Brad has said, being an investor in OpenAI and having talked to them, is that the final training run cost was more in the tens of millions of dollars about nine or ten months ago. And so, you know, it's not 6,000,000 versus a billion. Okay? It's Jason Calacanis (00:23:17): A billion dollar number might include all the hardware they bought the years of putting into it, a holistic number as opposed to the training number. Yeah. It's not fair to run it is what you're saying. David Sacks (00:23:26): Fair to compare, let's call it a soup to nuts number, a fully loaded number by American AI companies to the final training run by the Chinese company. Travis Kalanick (00:23:36): But real quick, Sacks, you've got you've got an open source model, and they've the the white paper they put out there is very specific about what they did to make it and, sort of the results they got out of it. I don't think they give the training data, but you could start to stress test what they've already put out there and see if you can do it cheap, essentially. David Sacks (00:23:59): Like I said, I think it is hard to validate the number. I think that if let's just assume that we give them credit for the 6,000,000 number. My point is less that they couldn't have done it, but just that we need to be comparing likes to likes. Speaker 6 (00:24:11): Yeah. David Sacks (00:24:12): So if, for example, you're gonna look at the fully loaded cost of what it took DeepSeek to get to this point, then you would need to look at what has been the r and d cost to date of all the models and all the experiments and all the training runs they've done, right, and the compute cluster that they surely have. So Dylan Patel, who's leading semiconductor analyst, has estimated that DeepSeq has about 50000 hoppers. And, specifically, he said they have about 10000 h 1 hundreds, they have 10000 h 8 hundreds, and 30000 h twenties. Now the cost Chamath Palihapitiya (00:24:50): that Saks, sorry. Is they DeepSeek or it's DeepSeek plus the hedge fund? David Sacks (00:24:54): DeepSeek plus the hedge fund. But it's the same founder. Right? And it by the way, that doesn't mean they did anything illegal. Right? Because the h 1 hundreds were banned under export controls in 2022, then they did the h 8 hundreds in 2023, but this founder was very farsighted. He was very ahead of the curve and he was, through his hedge fund, he was using AI to basically do algorithmic trading. So he bought these chips a while ago. In any event, you add up the the cost of a compute cluster with 50000 plus hoppers and it's gonna be over a billion dollars. So this idea that you've got this scrappy company that did it for only 6,000,000, just not true. They have a substantial compute cluster that they use to to train their models. And frankly, that doesn't count any chips that they might have beyond the 50000, you know, that they might have obtained in violation of export restrictions that obviously they're not gonna admit to. And we just don't know. We don't really know the full extent of of what they have. So I just think it's, like, worth pointing that out that I think that part of the story got overhyped. Chamath Palihapitiya (00:26:05): It's hard to know what's fact and what's fiction. Everybody who's on the outside guessing has their own incentive. Right? Like, so if you're a semiconductor analyst that effectively is massively bullish NVIDIA, you want it to be true that it wasn't possible to train on $6,000,000. Obviously, if you're the person that makes an alternative that's that disruptive, you want it to be true that it was trained on $6,000,000. All of that, I think, is all speculation. The thing that struck me was how different their approach was, and TK just mentioned this. But if you dig into not just the original white paper of DeepSeq, but they've also published some subsequent papers that have refined some of the details, I do think that this is a case, and, Saks, you can tell me if you disagree, but this is a case where necessity was the mother of invention. So I'll give you 2 examples where I just read these things and I was like, man, these guys are, like, really clever. The first is, as you said, let's let's put in a pin on whether they distilled o 1, which we can talk about in a second. But at the end of the day, these guys were like, well, how am I gonna do this reinforcement learning thing? They invented a totally different algorithm. There was the the orthodoxy, right, this thing called PPO that everybody used, and they were like, no. We're gonna use something else called I think it's called GRPO or something. It uses a lot less computer memory, and it's highly performant. So maybe they were constrained, Sax practically speaking by some amount of compute that caused them to find this, which you may not have found if you had just a total surplus of compute availability. And then the second thing that was crazy is everybody is used to building models and compiling through CUDA, which is NVIDIA's proprietary language, which I've said for a couple times is their biggest moat, but it's also the biggest threat vector for lock in. And these guys worked totally around CUDA, and they did something called PTX, which goes right to the bare metal. And it's controllable, and it's effectively like writing assembly. Now the only reason I'm bringing these up is we, meaning the West, with all the money that we've had, didn't come up with these ideas. Chamath Palihapitiya (00:28:19): And I think part of why we didn't come up is not that we're not smart enough to do it, but we weren't forced to because the constraints didn't exist. And so I just wonder how we make sure we learn this principle. Meaning, when the AI company wakes up and rolls out of bed and some VC gives them $200,000,000, maybe that's not the right answer for a series a or a seed. And maybe the right answer is 2,000,000 so that they do these deep seek like innovations. Jason Calacanis (00:28:48): And Schrank makes for great art. What do you think, Friedberg, when you're looking at this? David Friedberg (00:28:53): Well, I think it also enables a new class of investment opportunity given the low cost and the speed. It really highlights that maybe the opportunity to create value doesn't really sit at that level in the value chain, but further upstream. Balaji made a comment on Twitter today that was pretty funny or I Chamath Palihapitiya (00:29:12): I think we're The rapper. It's about the rapper. David Friedberg (00:29:14): Yeah. He's like, turns out the rapper may be the The boat. Speaker 6 (00:29:16): The boat. It's the boat. David Friedberg (00:29:18): Which is true Travis Kalanick (00:29:19): at the end of the day. David Friedberg (00:29:20): If model performance continues to improve, get cheaper, and it's so competitive that it commoditizes much faster than anyone even thought, then the value is gonna be created somewhere else in the value chain. Maybe it's not the wrapper. Maybe it's with the user. And maybe, by the way, here's an important point. Maybe it's further in the economy. You know, when electricity production took off in The United States, it's not like the companies are making a lot of money that are making all the electricity. It's the rest of the economy that accrues a lot of the value. Chamath Palihapitiya (00:29:48): Well, you're about to see a big test of this because if OpenAI raises 40,000,000,000 at 340 billion David Friedberg (00:29:54): Hundred percent. Chamath Palihapitiya (00:29:55): That just hit the wire. The underwriting logic at 340,000,000,000, exactly what you just said, Freeburg. It is the wrapper. Meaning, ChatGPT is the next killer app. It's getting to a billion plus MAU, hundreds of millions of DAU. David Friedberg (00:30:09): It's competing for consumer usage. That's that's the model. That's the model is, like, consumer usage. Chamath Palihapitiya (00:30:13): Which puts them on a on a collision course with Meta. It's the only company that could really impact that because the only company right now that has billions of eyeballs of DAUs per day and who and by the way, Zuck said this in his earnings release. He's like, there's only going to be 1 company that brings AI to a billion plus people, and it will be us. Some version of that quote is in its earnings released yesterday. David Friedberg (00:30:39): And then Microsoft showed weakness in the in their cloud. And and then Microsoft's down 6% today. And, you know, I think it's a a window for, OpenAI to say we're gonna go up against Meta. This is it. We're gonna be the players. Chamath Palihapitiya (00:30:52): What do you guys think? David Friedberg (00:30:54): Kind of ignoring Google. Speaker 4 (00:30:55): What do Chamath Palihapitiya (00:30:55): you guys think is happening right now between OpenAI and Microsoft? Because if it's true that this distillation thing actually happened, well, there's only 1 place where you could have distilled the o 1 model. It's on Azure. So what the hell is going on over there? Jason Calacanis (00:31:08): Well, and there r 1 is supported David Friedberg (00:31:10): on Explain distillation real quick. Chamath Palihapitiya (00:31:12): Yeah. So when you have a big large parameter model, the way that you get to a smaller, more usable model along the lines of what Saks mentioned is through this process called distillation where the big model feeds the little model. So the little model is asking questions of the big model, and you take the answers and you refine. And by the way, you can see this. Nick, I sent you a clip. You guys can see this. I mean, it's there's clearly distillation happening. Nick, can you show the clip of the of the deep seek run where it shows the China answer and then deletes it? What was Winston's job in 1984? Right? And it sort of starts to go through this whole summary. And then the person says, are there any actual states that currently do that? Hold on. Here it goes. It says North Korea. Wait. It goes China. And then wait. Watch this. Boom. So the reason why this is happening is, like, you're seeing this chain of thought. You're seeing the several layers, and then it's catching it after the fact. So we know that this is distilled from some other model. And my only point there, it's a little tongue in cheek, is right now when you go and use OpenAI, you're using it sitting in an Azure instance somewhere. Right? So this is Microsoft's cloud infrastructure that runs it. So it begs the question, it's not that it's o one's fault or OpenAI's fault that this distillation happened. And I'm not trying to assign blame. But typically, if this were to happen, you'd look to your cloud provider and say, how are you letting this happen? And I don't think anybody's had a good answer for that. Jason Calacanis (00:32:40): Well and the cloud provider is hosting r 1 now, so they're literally undercutting ChatGPD and OpenAI at the same time. David Sacks (00:32:48): Just to clean that up. They're they're hosting their own copy of it. Right? Yes. Because r one's been able to source they, David Friedberg (00:32:54): who Travis Kalanick (00:32:54): are you who are you referring to, Sachs? Microsoft. Jason Calacanis (00:32:56): Yeah. Microsoft is hosting their version of r 1, which means they are actively subverting their partner, OpenAI, and pushing people to a cheaper model. David Sacks (00:33:07): Well, whatever. I mean, look. Amazon's gonna host their own version of r 1. Grok has a version of r 1. David Friedberg (00:33:12): Yeah. We have 1 of those. Travis Kalanick (00:33:13): Just rolled out. It's open source now. So has r 1 on his laptop. You know? Chamath Palihapitiya (00:33:18): Yeah. Exactly. Yeah. Jason Calacanis (00:33:19): But if it was if it was stolen as, and the IP was stolen as Sam is claiming, that would be, like, you'd think he'd be able to call up Satya and say, hey. Can you not put the stolen IP on your server and promote it to everybody at a lower cost? It just shows Microsoft has no loyalty to OpenAI. Yeah. And they have but you'd think they would have loyalty. Travis Kalanick (00:33:37): They have no loyalty. It would take to distill o 1, like, brute force. It wouldn't be like, oh, jeez. I can't believe it was distilled. It would be like such a massive number of calls against an API or against something Chamath Palihapitiya (00:33:54): Something. Travis Kalanick (00:33:55): That it it wouldn't be unnoticed. And it Oh, Jason Calacanis (00:33:58): they did actually came out and said they blocked some suspicious activity recently. Travis Kalanick (00:34:01): Yeah. No. But they're always doing that. They're always that's that's constant. You're always doing that. That's like, you know, the old school Speaker 4 (00:34:08): Let me Travis Kalanick (00:34:09): You know? Go go ahead, Zach. Speaker 4 (00:34:10): Let me David Sacks (00:34:10): let me address the distillation point. So I I mentioned this a few days ago on on Fox News that I thought it was likely or possible that distillation had occurred, and there was some evidence for this. And it became like a news story, and I I didn't even realize that saying that would be news because it's kind of an open secret in Silicon Valley. Everyone I talked to. Travis Kalanick (00:34:29): They're doing some level of distillation. Yeah. I mean, every single test your model against theirs anyways. David Sacks (00:34:34): Yeah. And every single person I've talked to basically has agreed that there was some distillation here from OpenAI. Now that doesn't mean it was the only thing going on here. I mean, to be sure, the DeepSeq team is very smart, and there were some innovations, but also there was some distillation. And, really, this wasn't even a fresh news story, I think, from the point of view of Silicon Valley because a month ago, we had a press cycle in Silicon Valley when DeepSeek's v 3 model came out that DeepSeek v 3 was self identifying as ChatGPT. When you would ask it, who are you? Like, what model are you? 5 out of 8 times, v 3 would tell you that it was ChatGPT 4. And there's lots of videos and examples of this online that have been posted. Right? The point is that we knew a month ago that v 3 had been trained on a substantial amount of CHAT g p t output, obviously, because v 3 is self identifying as CHAT g p t, and there's 2 ways that that could have happened. So the, let's call it, innocent explanation is that DeepSeek had crawled the web and found lots of published output from ChatGPT and then trained on that, and that wouldn't be a violation of OpenAI's terms of service or their IP. Or the other explanation would be that they used the API from OpenAI and basically, you know Went to town. Yeah. Went to town. And there's no way, I think, based on what we know, to prove that 1 way or another, but I know what most people think happened. And at the end of the day, OpenAI can probably figure it out, and they they've indicated that they think there was some improper distillation here. Travis Kalanick (00:36:18): But but yeah. Jason Calacanis (00:36:18): And then Times, it says, OpenAI says that it's found evidence that Chinese artificial intelligence startup, DC, used The US company's proprietary models to train its own open source competitor. David Sacks (00:36:27): Right. That's what I'm referring to. So they say They've Jason Calacanis (00:36:29): been very clear about this. Chamath Palihapitiya (00:36:30): By the way, you have to be sympathetic, I think, to OpenAI in this because if you're building a start up, you're trying to raise money. We've all gone through this cycle, guys, where it's like there's momentum. We celebrate internally the momentum. That's what gets you the energy to push your team even further and harder. And then all of a sudden, it turns out that some portion of that like, it Travis said it well. Like, your there's probably a chart inside of OpenAI's offices where you're showing how many times these APIs are getting hit. Right? You know, how many times these endpoints are getting hit. It all looks positive. And then you realize that some portion of it was actually bad and trying to undercut your value. It's a hard pill to swallow. And then you have to course correct very quickly. You have to lock down. This is 1 area where Security. Exactly. We have not talked about this. Like, you have to lock these models down. Now you have to lock the endpoints down. Look. In the Biden administration, if this had happened, the first conversation would have been, we need to KYC the people that use these models. And it's like, what are you talking about? We don't KYC the cloud. If you're trying to use, like, an e c 2 endpoint or an s 3 bucket, you don't have to all of a sudden prove who you are. You just use a credit card and go. That's the whole point of why proliferation can happen so quickly. But if we take the wrong takeaways from this period, there's gonna be a bunch of people that'll clamor to, like, lock these folks down and make innovation go much slower. I don't I think that that would be bad. Travis Kalanick (00:37:53): Here's the other side. And totally agree, Chmock, but here's the other side. You go through the white paper, you see what it is they did, what they innovated on Yeah. The science behind it, the thoroughness, and you're like, these guys are bad ass. It doesn't it does not feel or sound like somebody who took something just when you get through it. It could be that it could be that OpenAI wrote the white paper for them, just putting it out there, but it's real innovative. I agree with that. Real innovation, strong tech. You're like, this is legit. Chamath Palihapitiya (00:38:27): I agree with that. David Sacks (00:38:28): But in that paper, they're very hazy about where the data's coming from. And they're they're fairly transparent about everything else they did, but they're not really clear about the data. And, specifically, they say that to get from v 3, which is the base model, to r 1, which is the reasoning model, they had about 800000 samples of reasoning. They were quite unclear about where those reasoning samples came from. By the way, it is remarkable that you can get from a base model to an r 1 with just 800000 samples. Chamath Palihapitiya (00:39:03): But this is a problem. Like, we, meaning, like, the Western AI community, we've been trudging around on this path where we've been very we had a very orthodox approach. The only way you can do reinforcement learning is through PPO. Okay. But is that true? And it turns out that if you're like a really smart team that has no other choice, you move away and you invent your way out of it. And so we have to get that example too. I think it's technically brilliant, some of the things they've done, but they also use constraint as a very much a feature, not a bug. And the the Western AI economy has been the opposite so far. Jason Calacanis (00:39:40): I think the best part of this is the fact that Sam Walton was supposed to be doing open source. He made it a closed source company. He stole everybody's data. Every got caught red handed. He's being sued by the New York Times for all that. And now the Chinese have come and open sourced all the stuff he stole, and he's got a real competitor on the original mission of what OpenAI was supposed to do. So I have 0 sympathy for him or the team over there. There. I'm glad that this is all going open source. It should have been open source, and it's better for humanity. And the fact that the Chinese did it to Sam Weltman has comeuppance for him stealing everybody else's content. That's my point. Chamath Palihapitiya (00:40:13): Okay. There you have it. Jason Calacanis (00:40:15): But I don't have strong opinions on it. It's hilarious. Does nobody see the irony Speaker 4 (00:40:19): in this? Jason Calacanis (00:40:21): He was supposed to be doing open source Well, Speaker 4 (00:40:23): it is. But they Speaker 6 (00:40:23): didn't even think Jake David Friedberg (00:40:24): Allen, I will say, the models are closed. You're right. Yeah. There was the there's the lawsuit with Scarlett Johansson for stealing her voice even when she said no. There's a real question and and people have asked New York Times, and then there's now the question about YouTube data being used to train the video models. So there's a lot of being on their on their heels a little bit. So I definitely I definitely hear a point. Jason Calacanis (00:40:45): Stealing. David Friedberg (00:40:45): I think Yeah. I think Chamath Palihapitiya (00:40:47): the the really all the pressure right now, I think, is on Meta because I think Meta has to show up with the next iteration of llama that beats and exceeds Gemini, that exceeds r 1. And I think that that is going to be crucial for us to have a counterweight to whatever China's gonna put out after this. And But, David Friedberg (00:41:09): I mean, Chamath, it's open source. Like, does it not Speaker 4 (00:41:11): kind of Travis Kalanick (00:41:12): So this is so this is my point. Embrace and extend. Embrace and extend. Chamath Palihapitiya (00:41:15): Meta has to embrace and extend everything that these guys have shown. Meaning, like, Meta's buying tens of thousands of NVIDIA GPUs. Great. But what did this show? This shows that actually CUDA high level languages in general. I think we've all known that they suck. Okay? And so we've all been going through it thinking that it's, like, the right thing to do. DeepSeek throws it out the window. They use something called PTX. What does Meta do is critical not to understand. They need to embrace this stuff. And this is where I think again, apologies to the invidiables, but it's gonna create a more heterogeneous environment. And the reason is because there's too much money and risk on the line to go through a single point of failure. A chip, a high level framework to get to that chip, that's nuts. So I think, like, that kind of, like, emperor has no close moment is upon us. David Friedberg (00:42:08): Well, let me ask you another question. Let's assume that we start the world of AI today, so there's no legacy of the last three years. And you wake up today, and there's this open source model that's 670000000000 parameters. You can run it on your desktop computer. It's completely available. Everything's completely transparent. And I ask you the question, forget about all the big companies that are involved in everyone's strategy historically. What's the model today to build value here? Where do you build equity value as a business? If you're gonna start a company, if you're gonna invest as, like, a as an investor, where do you go? Chamath Palihapitiya (00:42:42): The first is you have to build a shim. And I think the reason why a shim is really critical is that there's so much entropy at the model level. What this should show you is you can't pick any model. And the problem is that the people that manipulate these models, the machine learning engineers and whatnot, they become too oriented to understanding how to get output of high quality using 1 thing. Meaning, it shouldn't have been the case that we have engineers that can only use Sonnet. Right? That's the anthropic model. Right? It shouldn't be the case that people can only use OpenAI or people can only use LAMA. Right now, that is kinda what we have. You don't have the flexibility to hot swap as models change. So if you were starting a company today, the first technical problem I would wanna solve for is that. Because tomorrow, if it's r 2 or Alibaba's model or llama, I would wanna be able to rip it out and put it back in and have everything work. And right now, we can't do that. Jason Calacanis (00:43:37): The answer to your question is Yeah. Yeah. You're starting a company. Yeah. The the answer to your question is the application layer because this is all going to become storage. It's like YouTube being built on top of storage or Uber being built on top of GPS. All these innovations are being commoditized, and this 1 is happening faster than all the rest. Do you want to be in the storage business or you want to be in the YouTube business? Do you want to be in the Uber business or do you wanna be in the GPS chip business? I mean, they're both decent businesses, but Gavin Baker came on this podcast and said it's the fastest deprecating asset in the world was a large language model. He's been proven right. They're not worth anything. They're all all gonna be open source. They're all gonna be commoditized, and that's for the best of humanity. And now we're gonna be on the application level, the hardware level with robots, and I think that's where the opportunity is. David Friedberg (00:44:18): Travis, what do you do what what company do you start today? If you start a company today, given where the world is at, given the open source models, like, what do you do? David Sacks (00:44:27): I'm Travis Kalanick (00:44:28): getting so excited. Look. I I think the first the first degree out is it's what you got is there a rapper company? Okay. So, of course, maybe those companies already exist. And then is there a tools company? Right? So in a funny way, even though Facebook could be the rapper, they have a tools business that these, you know, that the that DeepSeek is basically challenging going full open source and, like, putting something out there that's really good. And what has to happen is, Meta has to decide, like, we are gonna embrace and extend this. We're gonna make sure that all the developers come to us, that all the cool applications get built here. So I think it's like there's a tools business, and then there's the wrapper business. And then, you know, look, when AI here's the 1 thing on the NVIDIA thing that I would counter with a little bit of what's been said here. Is, like, when AI gets cheap, you know what's gonna happen, guys? There's gonna be a lot more AI. Right? I don't think I think the price elasticity on this 1 is actually positive. So as the price goes down David Friedberg (00:45:34): That's right. Travis Kalanick (00:45:34): The revenue usage, everything's gonna go up. Go through David Friedberg (00:45:37): the roof. Travis Kalanick (00:45:38): This is the history of tech forever since, like, Bill Gates said, I don't know what to do with more than 64 kilobytes of memory. Like Yeah. You know, the question is, did we Cheap oil David Friedberg (00:45:48): cheap oil in The United States drove the industrial revolution. Right? And, like, when we started discovering oil, suddenly, we were able to build factories and make stuff that we never imagined possible. Travis Kalanick (00:45:58): And so then you're like, okay. AI is like you know, it it's gonna get cheap. It's gonna be oil, but it's also gonna be specialized for different tasks. Speaker 4 (00:46:07): Right. Travis Kalanick (00:46:08): Like, you're gonna start getting into nuances of, like, what is the invest the investor AI look like? What does the, autonomous car AI look like? What is the, Google search? I'm trying to figure some Lawyer AI look like. Yeah. Yeah. Pilot. So you could go vertical and siloed siloed air quotes. Do you Jason Calacanis (00:46:26): guys see Travis Kalanick (00:46:27): what I'm saying? David Sacks (00:46:28): Yeah. So there's a a thing called, Jevons paradox, which kinda speaks to this concept. Satya actually tweeted about it, which is the it's a economic concept where as the the cost of a particular use goes down, the aggregate demand for all consumption of that thing goes up. So the basic idea is that as the price of AI gets cheaper and cheaper, we're gonna wanna use more and more of it. So you might actually get more spending on it in the aggregate. David Friedberg (00:46:55): That's right. David Sacks (00:46:56): Because more and more applications will become cost efficient. Feasible. Economically feasible. Exactly. Yeah. That is, I think, a powerful argument for why companies are gonna wanna continue to innovate on frontier models. You guys are taking a very strong point of view that open source is definitely gonna win, that the leading model companies are all gonna get commoditized, and therefore there'll be no return on capital and basically continue to innovate on on the frontier. I'm I'm not sure that's true. You know, for 1 thing, the the r 1 model is is basically comparable to o 1, which which OpenAI released four months ago and was training on internally, call it nine or ten months ago. So OpenAI is on o 3 now. Its frontier is ahead of where r one is. Anthropic and Google, I think, have things in the work, and even Meta, that may be ahead of where r one is. So I think r one or DeepSeek's done a good job being a fast follower here. It's not clear that this is the frontier, and those frontier model companies now having seen what might have happened with distillation have a pretty strong incentive to make sure that doesn't happen again, and they're gonna be taking countermeasures. I mean, there's a question of, like, how much you can do to stop it, but I think it's a little premature to conclude that there's no reward for being at the frontier. Jason Calacanis (00:48:19): Anybody, have any other questions for Sachs before we drop him off to go back to serving the American people, before we drop him off? David Sacks (00:48:27): 1 final point on on the whole open source versus closed source. Look, I I'm not gonna take sides in that, but I I think that it's a mistake to just view what happened here as, oh, it's this, like, plucky upstart that's, like, doing the community a huge service out of the goodness of its heart. You know, it's basically open sourcing all of Chamath Palihapitiya (00:48:47): this stuff. They stole it. Jason Calacanis (00:48:49): It's a Chinese. Come on. David Sacks (00:48:50): You still have this huge geopolitical aspect to it, right? And DeepSeek is a Chinese company and they're trying to catch up, and so if you're if you're behind and you're trying to catch up, then open source is a strategy that actually really makes sense for you. Absolutely. And, you know, they're trying to basically undercut the leading American companies, and I don't think they did it with $6,000,000 I mean, they have massive resources behind them. So I think some of the pro deep seek vibes, I think, are they're a little bit naive, you know, in Silicon Valley. It's like Jason Calacanis (00:49:23): That's only the, people who worked for Sam previously and Quint who feel that way. David Friedberg (00:49:28): Or to catch up with all these I think there's David Sacks (00:49:29): a lot of, like, support for DeepSeq Jason Calacanis (00:49:32): Yeah. There's a David Sacks (00:49:32): Silicon Valley, because, again, people think that they're doing this huge service for the community, and I think it's a little bit more self interested than that than that. Jason Calacanis (00:49:39): It could be both. Right? I mean, there there is a theory that they're trying to undercut and neuter the lead, and at the same time, there's a bunch of people who believe in open source and nobody should control this and certainly not Sam Altman should be the person who controls it. So 2 things could be true at the same time. David, thank you so much for coming on. We appreciate it. And, thank you for all the hard part of our podcast, David. David Sacks (00:49:59): We appreciate it. Jason Calacanis (00:50:00): Thank you, David. I know that this is Speaker 4 (00:50:02): and now Jason Calacanis (00:50:02): we're gonna talk about a bunch of other crazy stuff. Travis Kalanick (00:50:04): You're a scholar Speaker 6 (00:50:04): and a gentleman, David. Yes. Jason Calacanis (00:50:06): Thank you. Alright. Thanks to David Sacks for coming in. And, you know, I I guess let's open up the aperture here and talk a little bit about relations with China. We're obviously in a bit of a cold war with them. We have tariffs. We have Taiwan. And then we have, the sort of trade war going on here with, exports of h 1 hundreds. Where do we want to start, gentlemen? And, you know, Travis, you've got some deep you're 1 of probably 5 American entrepreneurs who ran an at scale business with Uber and the Didi relationship in China. So you have a unique position of understanding business in this along with maybe Tim Cook and Elon are the only other 2 people who've really had an upscale business there. Maybe Disney. They have Disneyland there. What was your take on the relationship and what's going on here geopolitically? David Friedberg (00:50:55): China how's China gonna operate differently than The US, Travis, from your experience, your point of view? Tell us a little bit about the culture and business ethics in China, particularly as it relates to AI. Travis Kalanick (00:51:05): Okay. So okay. So look. We I had this thing. This is I'm I'm going back almost ten years here. Uber day. We're running Uber China. And, I mean, I cannot there's no way I could express the frenetic intensity of copying that they would do on everything that we would roll out in China. David Friedberg (00:51:31): Mhmm. Travis Kalanick (00:51:31): And it was so epically intense that I basically had a a massive amount of respect for their ability to copy what we did. I I just couldn't believe it. We would do real hard work, make it. We dial it, and it would be epic, and it would be awesome. We'd roll it out. And then, like, two weeks later, boom. They've got it. A week later, boom. They've got it. And, of course, I use that to drive our team. And there's so many great stories. I mean, we had we had, like, 400 Chinese nationals in Silicon Valley. At our offices in San Francisco, we had a whole floor for the China growth team, and it was primarily Chinese nationals. We had billboards on the one zero one in Silicon Valley in Chinese, Uber billboards to join our team in Chinese to to serve the homeland. Right? It was like an all out war. It was really epic. It was epic. And by the way, when you went to that floor in our office, you were in China. Like, they rolled China style. Like, the desks were literally smaller. Like, the density of the space was it was China. Okay? So but what happens is when you get really, really good at copying and that time gets tighter and tighter and tighter and tighter and tighter, you eventually run out of things to copy. Mhmm. And then it flips Chamath Palihapitiya (00:53:06): To creativity. Travis Kalanick (00:53:07): To creativity and innovation. Now at the beginning, you you know, it's sort of all over the place. Like, the kinda innovation when it was new, it was like, what? You know, you're like, really? But as they exercise that muscle, it gets better and better and better. So if you wanna know about the future of food, like online food delivery, you don't go to New York City. You go to Shanghai. Right? Jason Calacanis (00:53:32): What's an example, like, of, like, something really innovative there? David Friedberg (00:53:35): Doesn't doesn't Meituan do drone delivery and stuff? Travis Kalanick (00:53:38): Like, here's an example. If you went to offices, like, let's say Shanghai, Beijing, any of the major cities, Hangzhou, etcetera, the office buildings have hundreds of lockers around their perimeter so that everything that you get, whether it be food or anything else, but especially food, is just the the couriers drop them off in these lockers in the at the office buildings, and then there are a whole other set of people that are sort of, like, interoffice Chamath Palihapitiya (00:54:11): Runners. Travis Kalanick (00:54:12): Runners that then bring it to your office. As an example, like and when you see it, you're like and it's epically efficient. And it's you know, they're taking advantage of their economics on labor and things like this. It wouldn't exactly work that way here. But a lot of the innovation you will see coming out on Uber Eats or DoorDash, Like, the stuff that's coming out now is stuff that existed three years ago, four years ago in China, maybe longer. So, like, eventually, you cross that threshold of copying and you are innovating, and then you're leading. And I think we see that in a in a in a whole bunch of different places. Jason Calacanis (00:54:51): Yeah. Here's a look at these smart lockers that you can see. They're just available for sale when you when you go online. But, yeah, these things are crazy. And you've experimented with those as well. Didn't you have, like, a commissary concept in DTLA? Travis Kalanick (00:55:03): Well, look. We okay. So we had a couple things. So we have in every one of our facilities, and we've got, you know, hundreds of them. We'll have lockers there so the the courier then waves their phone in front of a camera. The right locker pops open. They get the food from there and they go. The courier pickup is asynchronous from production of food. You know, you don't have lines anymore. There's no more lines, which then speeds up delivery, shortens the amount of time, shortens it reduces how much money you spend on couriers. And we've got a whole other thing. This doesn't work in it probably wouldn't work in China because well, for a lot of reasons. But let me explain what it is. It's called picnic, where if you are in an office building, you order food, you go to a website, you order whatever it is from a hundred different restaurants. Those restaurants happen to be in David Friedberg (00:55:55): my Travis Kalanick (00:55:55): facilities. And there'll be 1 courier that goes to 1 of our facilities and picks up 50 orders at a time, brings it to an office, puts it there's a shelf on every floor. You get notified when your food arrives and arrives the same time time every day, and you just go to the shelf, get it on your floor, and dip that right back into your meeting. Saving people time at the office, giving them selection on food, especially in food deserts, but even going like, there's a sweet green right down there, in my current in my office right now. I could say twenty minutes by just using our own service versus doing that. And you get at the same price because the courier economics, the courier is bring is delivering 50 orders at a time. So courier costs go basically to 0. Jason Calacanis (00:56:38): What do we think of the of the export controls here? Should Wechimov be maybe banning more h 1 hundreds or other chipsets going there, or is that futile? Chamath Palihapitiya (00:56:49): I don't know the answer to that. And I think that's I think Sachs and president Trump will make a good decision. But here's the curious case of the export controls. Nick, I sent you a couple of tweets if you wanna just bring this up. So the first thing that people are claiming is that DeepSeek is getting access to a bunch of NVIDIA GPUs using Singapore as a backdoor. So, essentially, you create a Singaporean shell company. You place an order with NVIDIA. NVIDIA fulfills that into Singapore, and then the chips go someplace. Jason Calacanis (00:57:25): Uh-oh. Chamath Palihapitiya (00:57:25): And so there's a bunch of examples where people are saying that you're talking about up to a quarter of all NVIDIA revenue goes into Singapore. And the speculation right now is that a % of those then go into China, which is an enormous claim because that's a huge amount of of NVIDIA's revenue. Now the interesting thing is if you actually try to understand, well, maybe that's not true and maybe it's sitting inside of Singapore, this is where that kind of unravels. So just to be clear, like, Singapore is about 250 or 260 square miles. Like, this is like a small, small place. Also the TikTok headquarters. And I tried to find out how many data centers are in Singapore, and it's about a hundred. And so you would think that, okay, well, what does that mean? Hundred could mean anything. But then you look at the energy and they publish that. And all of those hundred data centers consume about 876 megawatts. So these are small data centers. Right? And the entire industry is like a one and a half, two billion dollar revenue business. So I do think that Sachs and the administration are gonna have to dig into this and figure out what their opinion should be. But there is clearly a ton of of these chips going into Singapore. I don't think anybody knows where they end up. And the question is, what does America think about that? And why did we implement these export controls in the first place? And if there's a simple backdoor, how do you wanna react? David Friedberg (00:58:58): If The US finds a path I mean, let's talk about, like, what happened with sanctions in Russia and other prior kind of sanctioning efforts around the world. But as you kind of close the the floodgates and close access, the buyer or the receiver of those goods or that capital are gonna look elsewhere. They're gonna look to create a market somewhere else. And so if we do cut off access to NVIDIA chips, we do cut off access to US exports, Are we not kind of recognizing that the second order effect of that is that China will take IP that they've stolen, copies that they've made to Travis's point, and develop and build out their own fabs, and they'll find ways to copy the ASML technology. And, you know, at the end of the day, there's a lot to put together. I know it's deeply technically complex, but if ever there were a group of people in the history of human civilization to pull it off, it's probably the modern Chinese to be able to say, let's go build our own It's worse than that. Our own infrastructure. Chamath Palihapitiya (00:59:59): This is a great point, but it's worse than that. The models today are capable of designing chips for you that don't rely on the most complicated technologies that ASML creates. I mean, look, 1 of the luckiest things that happened to Grok was we designed our chip at 14 nanometer, which is effectively in the spectrum of technology like VHS and beta. So we chose a simple, simple technology stack to build towards. The latest cutting edge chips at like 2 nanometer that use these complicated ASML machines. It's not clear that the yield is actually that good. So why would you spend all that money? And if China is forced to engineer its way around it? Yeah, Freeburg. The answer to your question is they'll use these models to design chips that can be manufactured in simple ways, and they'll make simple stuff. So this is David Friedberg (01:00:51): why not sure it solves the problem is my point. Chamath Palihapitiya (01:00:53): Well, it doesn't. And this is why I think, like, it doesn't solve the real problem, which is how do we incentivize people in America to really out engineer and out innovate? Competition. David Friedberg (01:01:03): Or AI. Usher was in an era of extraordinary abundance, and that abundance ultimately reduces the the drive for conflict and things are better off. Chamath Palihapitiya (01:01:11): Or the other version as well is that China could just bear the cost as a central authority of building an incredibly great model. Right? And they will spend all the money and then they will tell the Chinese companies, you can distill from this model for free because we have a golden vote and a seat on your board anyways, which is effectively de facto what happens if you get big enough in China. So there's that possibility as well where 1 central authority bears the CapEx of creating something that then everybody else can can draft off of. Jason Calacanis (01:01:43): And let's talk a little bit about OpenAI's, there in Washington asking for money now. Is that the, is that the concept now? Is that the our government should back? David Friedberg (01:01:52): The rumor today was they're raising 40,000,000,000 at a $340,000,000,000 pre money with Masa potentially being the lead. I would love Travis's read on this because Travis has taken large money from from Masa in the past and has been through this. But how does he think about and make this decision? Obviously, we all know and I mentioned you guys the meeting I had with him last summer where he basically kicked me out of the room because my company is not generative AI. Like, someone said you should go meet with Masa. So I'm like, sure. I'll sit down with him and start talking. And he just, like, looked at me and he's like, this is not generative AI. I, only do generative AI. I think your company will be very successful. You will be very successful. Goodbye. And he always walked out, and that was, like, the end of the meeting. So great. Yeah. Well, that's all he's doing now. So this is the big bet. Right? Travis Kalanick (01:02:35): So okay. So I need to I need to bust a myth. I did not take money for Masa. So he begged me to take money for years, and we did not take it because he is a he's, what's the word I'm looking for? He's a he's a promiscuous investor. So once he once he invests in you, you should probably count on him in using your information and investing in all of your competitors. At least that's historically what he's done. So I didn't go there. But then he just kept investing in all my competitors, and they kept subsidizing these markets. And then I'm like, maybe I should've just saturated, soaked up the money that was there. So the 1 of the things you should think about, like, when you look at, like, oh, is OpenAI taking a lot of money from a Masa type situation? Is it's a little bit of, like, a double edged sword. Is if you don't take that money, it goes somewhere else. But if you do take that money, just know that whatever intelligence they get when they go through the process of giving you the money and maybe hanging around the board or who knows what is going to be used to do other things. And that is the nature of the MASA machine. Chamath Palihapitiya (01:03:42): So you're damned if you do, damned if you do. Travis Kalanick (01:03:44): But you gotta pick. And if the money's going and it's flowing and and access to capital is a strategic competitive weapon or advantage, you must you must play ball. Now we were able to we we we did stuff with the Saudis before even Vision Fund existed. They stroked a 3 check when that was, like, the biggest thing that ever happened. So we were okay with not having the Masa money, but that Masa money then went to all of our competitors. Jason Calacanis (01:04:12): Mhmm. DoorDash. David Friedberg (01:04:13): And so in this OpenAI context, Travis, I mean, like, just knowing what you know about AI, is this gonna be a competitive advantage for Sam to raise 40,000,000,000? Where does it go when he's up against, we don't know what in China, Microsoft, Alphabet, and Meta? Travis Kalanick (01:04:31): Well, look. I think this goes to some of the things that Shamath is saying, which is, like, if if constraint is the mother of invention or or whatever that that euphemism is, the the the aphorism is, if if that's the case, you get into a real weird spot when you get over Capitalized. Overcapitalized. In the in the Uber model, like, the war was subsidizing rides for market share, essentially being the wrapper for transportation and using the parlance we were using earlier to, in the in in this discussion. So it was necessary. You're screwed if you don't. The the question is is do you get to this place of over capitalized, too big, you know, too bureaucratic, too loose, too weak, too soft? And with when you have an open source model that's very smart and it's a thousand flowers blooming, lots of innovation happening everywhere, could be an overwhelming force. Now I think there's gonna be different sectors treated different ways where, like, going full stack in certain industry sectors is gonna matter. And then in other places, having, like, a very sort of chaotic, everybody does a little slice, is gonna be okay in other places. And I think we could probably spend days or hundreds of dozens of hours just talking about the nuances there. You know? David Friedberg (01:05:54): Well, it seems like there's some degree of relationship between the Stargate announcement with Masa and Sam standing up there with Larry and then Satya showing up in the conversation as well, and this raise and the idea that more hardware, more infrastructure, faster creates a moat. And I guess that's the real thing you have to believe, which becomes harder to believe in the context of what happened in the last week. I personally think that these models are and I've said this for a while. It doesn't make sense to have 1 large do everything model. This mixture of experts architecture Speaker 4 (01:06:31): Mhmm. David Friedberg (01:06:32): Ultimately, you can kind of think about taking a large model, making 2 copies of it, and then trying to shrink each model down to whatever the necessary so that you run 2 models in less frequently, meaning that that combination of 2 models uses less power and takes less time. And then you do the same thing again, and you shrink it down to 4 and then 12, and eventually, you have lots of smaller models, some of which, in some cases, are experts at 1 thing, like doing mathematics or reading or writing. But the reality is we don't know how whether humans have kind of thought about the world the right way, that the AI may resolve to having smaller expert models that we don't really understand why that's the expert on something. But you have a network of very small kind of things that work together. And that ultimately leads to a, like, commoditization, not just in kind of model cost and and development and run time, but also in, like, what's needed? Like, do you really create much of an advantage by having all these data centers? Centers? Yeah. That's what I mean. Jason Calacanis (01:07:27): This is the key point I think, Friedberg, is you're not gonna get an advantage by having more h 1 hundreds at a certain point. And the actual advantage is gonna be in the IP and owning content. And the really smart thing to do Data. Be for somebody to go buy Reddit, Quora, The New York Times, The Washington Post, and Disney, and take all that IP, and then not allow other people to use it, sue the hell out of them every time they try. Travis Kalanick (01:07:48): Washington Post off that list. But yes. David Friedberg (01:07:51): But I'll say Travis Kalanick (01:07:51): The New York Times comes off the list too. Jason Calacanis (01:07:54): Well, whatever. I mean, all those archives are definitely going to be what would be great about those is you could then, like a patent troll, then tell anybody else who's absorbed New York Times stories historically or Disney ideas or and you could just sue the hell out of them, and then you've got the best, most proprietary 1. And and red keeps giving David Friedberg (01:08:13): You're describing text. So you're describing text content, which is a fraction of where well, and this is important. So video, I think, you can recognize that Google's YouTube content library is probably a 200 times larger than the rest of the Internet combined. But they don't have Jason Calacanis (01:08:28): the right to do it. David Friedberg (01:08:29): Well, they do actually. Travis Kalanick (01:08:30): So Jason, you're such an old school copyright guy. You're such an old school media guy, by the way. Jason Calacanis (01:08:35): Sorry. I believe in artists and their right to content. Yeah. David Friedberg (01:08:38): We've had a series of conversations that I I feel very confident to tell you that they do have the right in in a good chunk of that content, not in a lot of the copyrighted content that the big media companies have given them, but a lot of user generated content. They do have the right, and they are using it, and they're legally doing it. And then there's the separate kind of body of content, which I think comes, for example, from Tesla. Tesla has an extraordinary advantage that they were really pressured to put cameras on everything years ago, and that gives them this ability to build models that do self driving. So I think that there's a lot more data advantage that arises in certain industry segments than others, and that's where the moat will lie. And that moat will allow you to actually build better products that get you a more persistent advantage in gathering more data. That's ultimately where I think this resolves to. It may not necessarily be about who's got the biggest data center network. Yeah. Travis Kalanick (01:09:24): I mean, here's the thing, guys. At some point, the amount of data becomes the long pole in the tent. At some point, the quality of the algorithms becomes a long pole in the tent, and more compute is not going to change that. We I don't think we're there yet. That's the 1 thing that counters the cheap AI means more AI is is there enough data and or algorithms to make the more AI, to make it work? And I do agree with the the siloing it and getting expert and getting better in these ways. But I think this is a interesting sort of trade off between some of these these variables. Jason Calacanis (01:10:02): I got you got offered $2,500 to put Angel, my book, into because Harper Collins did a a deal with, Microsoft. And so Chamath Palihapitiya (01:10:10): I'm thinking hundred per year? Jason Calacanis (01:10:12): I think it's for three years is the license, and they just did this blanket license for every book. They didn't look at your sales. They didn't look at how desirable it was. It was just like a blanket deal. Everybody gets $2,500 per book for three years. And I think I'm going to just do it just to support proper licensing so that people can start going down this path. But let's get into DOGE. It's been a, I think we're in ten days into this administration and, Trump formally established DOGE, the Department of Government Efficiency, in an executive order. Apparently, Elon's been spending a lot of time at the offices. Bunch of wins. Doge is claiming on the interwebs to be saving American taxpayers around a billion dollars a day. That's $3 for every American every day, about a thousand dollars a year in savings for each US citizen, and they claim they can triple this. And so for a family 5, that'd be about $15,000 a year, maybe $60,000 during Trump's, second term. We got $36,000,000,000,000 in debt. Have fun with some numbers there if you like. But the key announcement was a very similar to the Twitter execution, the ability for people to resign done in a very kind way. Eight months of severance ish is being offered to federal workers. They expect 5 to 10% of federal workers to take this buyout. And it's, I mean, this could be something like a hundred billion dollars in savings. Eight months of severance, is not actually a legal concept that you can do. So these are some sort of buyouts. And there's obviously some hand wringing about it, but I think they're off to a good start. They've also been canceling leases, as we talked about, you know, pre election. There is so much space not being used that, the federal government is terminating a ton of stuff they own and going to sell it and consolidating folks. And at the same time all of this is happening, everybody has to return to office. Jason Calacanis (01:12:17): Who wants to go first here with, you know, the sort of first ten days of Doge? I see some eggplant emojis in the group chat. First ten days of Doge. How exciting Travis Kalanick (01:12:27): group chat. What's that about? Chamath Palihapitiya (01:12:29): I'm adding you you Speaker 6 (01:12:30): not in the group chat? Jason Calacanis (01:12:30): Get it out of the group chat. David Friedberg (01:12:32): Right now. I'm adding Speaker 6 (01:12:32): you Literally every time Jason Calacanis (01:12:34): 1 of these hits Speaker 6 (01:12:34): the group chat, it's just hilarious. True? Eggplants. People are like, oh my god. We're we're not burning tax credit dollars. Jason Calacanis (01:12:43): And and the eggplant always comes from Freeberg first. David Friedberg (01:12:45): Oh, brilliant. I'm outing him as an eggplant. I'm a I'm a I'm a big Doge eggplant guy. Jason Calacanis (01:12:51): Oh, so much eggplant. So, Freebark, tell us about how much eggplant you love this. David Friedberg (01:12:57): There's nothing that I would say is particularly surprising in the first week. A lot of this was kind of talked about leading up to the inauguration. Vivek and Elon published their piece in the Wall Street Journal a couple weeks ago. They talked about the mechanisms of action that they could utilize to kind of drive reduction in cost, 1 of which was come back to the office, another 1 of which is, you know, giving people a buyout offer. And by the way, the buyout offer is not new. Bill Clinton did the same thing during his presidency. Yeah. If you guys remember when he tried to balance the budget, get to a surplus, which he did successfully, and his intention was to actually reduce US debt to 0 by the year 2013. And he had a very specific economic and fiscal plan for doing that, which he put into place. Incredible era. I think we're seeing them take the actions that they said they would do. They said they would demand to employees, federal employees, come back to the office, and they assume some degree of attrition from that and now the buyout offer. And we'll see how far things go with the courts with respect to their ability to stop a legislative or statutorily mandated spending. There's a big question mark here on how much authority the executive branch has in stopping spending and how much they're not allowed to stop because it's demanded by law. It's demanded by Congress in acts or laws that have passed. And so that's gonna be the big test here. Over the next couple of months, a lot of lawsuits will fly. The courts will ultimately adjudicate, and we'll see how far the Doge intention can take things. And then there's a separate set of efforts around legislative action here. There's about a $2,000,000,000,000 annual deficit right now in The United States, federal government. David Friedberg (01:14:39): 2,000,000,000,000 a year. And if you look at the the Dalio book on why countries go broke, you know, there's a pretty simple kind of arithmetic in there, which is not complicated. It's just at the end of the day, The US needs to get our federal deficit down below 3% of GDP, which means we've gotta cut about a trillion trillion 1 of spending. If we can do that, then we're in kind of a more economically sound place. By the way, really important point, which is in the Dalio interview. As you cut spending, interest rates will come down. Because right now, there's a pretty significant sell off in treasuries and a lot of risk associated with The US's ability to deliver, its debt obligations over the next thirty years, which is why thirty year treasuries are at 5% right now. Even though the Federal Reserve is cutting rates, the rate on treasuries is going up. People are still selling off treasuries. That was inflationary. Travis Kalanick (01:15:28): It's also inflationary, Dave. Yeah. For sure. David Friedberg (01:15:31): And so as we cut spending, we also will see that the intent that the there'll be less inflation, and The US ability to pay back their debt obligations over the next thirty years goes up, so the rates will come down. And so there's actually a really nice kind of cyclical effect as these cuts start to come into play. The rate at which you can make the cuts actually affects the numb the amount of cuts you have to make. The faster you make the cuts, the less you have to cut. And that's a really key kind of principle going into this, which I think we should expect a big whirlwind of cutting in the next couple of months or an attempt to. The courts will adjudicate what needs to be legislated, and then they're gonna go to Congress and start to try and get some of these cuts in. But I will tell you once again after our visit in DC last week, there was not a single member of congress that I spoke with who views cutting to be a mandate for them in the laws that they're trying to pass. They all have a very different kind of agenda than those. Travis Kalanick (01:16:19): Right. Look. This is this is really 1 of those interesting things where it's like the difference between legislature and executive branch is like doge is really bringing it to life. It's like, what powers and controls does the executive branch have to spend and not to spend, and especially to not spend when it's been legislated to spend. This is where the action is. Like, there's no law that says, you know, you can give a bunch of folks eight months of severance and they're gone and you don't replace them. There's no law that says that. The executive branch and, again, I don't know the all the the laws the sort of the rules or laws about whether, you know, how they go about doing it. But let's say, presumably, they're doing this and there's some legal backing behind it. Like, they just go and do it, and now they're not spending money. If it was really hard to hire people and they could even make it harder to hire people, do they do they fight bureaucracy with bureaucracy that it's harder to spend? Hard to hire people, harder to procure certain things that you're supposed to spend money on. You can reduce this spend through a lot of very interesting nuanced Friction. Rules that they're in control of. Jason Calacanis (01:17:33): Yeah. Some friction could slow things down. They're talking about putting competency tests in. They're talking about giving people reviews, and maybe they have to hit some standards. And the gentleman's riff. I mean, when you force people to come back to the office, you're gonna lose 10% of people, and 10% take the buyout. And now all of a sudden, we're saving things. Travis Kalanick (01:17:49): I mean, it'll be interesting to see if it's 5 or 10% on RTO. I mean, that Yeah. Speaker 4 (01:17:54): It could Travis Kalanick (01:17:54): be a lot more. I mean, what I'm hearing about these buildings is that they are super, super empty, like, next level empty. And let's just say I'm really glad I don't hold it. Like, I'm an owner that has a bunch of leases Yeah. To the to the the federal government right now. Speaker 4 (01:18:14): Yeah. Oh, the Jason Calacanis (01:18:14): gov and then, you know what the interesting thing about those leases? I was talking to the team at Density, which does people counting in buildings, so they obviously, you know, are very interested in that. The government is such a reliable Yeah. Client that they're all on one year leases. So people don't, you know, do what they do with startups, which is force them to do five or ten years because they know, hey, this company could go out of business. They're just like, yeah. Yeah. We're just on a rolling year over year lease. So you can actually just cut these. It's gonna flood the market. Shimonth, your thoughts on also the stopping of, because they're obviously going for it. They stopped all payments, which is a a part of the playbook I saw on Twitter up close and personal, which is, hey, let's let's turn off subscriptions and see, you know, if anybody's using these subscriptions, basically. Obviously, a judge got involved in that, but aid going to other countries. You know, we're just starting to look at what are we actually sending to other countries and for what purpose? And then there's a naming and shaming and maybe appealing to the public through social media and saying, hey, do you want this money going here when, hey, we have tragedies in our own country that need to be solved? We have health care. We have houses burned down. We have infrastructure. And so maybe you could talk a little bit about hearts and minds and winning those and what your general take is so far. Chamath Palihapitiya (01:19:27): I think that we have to remember that we're only nine or ten days into Doge. So the fact that we're already at a billion dollars a day is really incredible, and there has really been no discernible impact. There has been a lot of fissures of fake news and misinformation, but the real impacts have been negligible to none since they started making those cuts. I think that Doge is a 3 layer onion. So layer 1 is the people. We have now given a pretty generous offer to folks. And I think Elon said it. It was, like, basically the maximum allowed by these contracts, but they tried to do a very good thing there. The second, as you guys just said, the second layer of the onion is going to be the infrastructure, all the buildings, all the physical plants that the government owns and operates that may be empty, that may be idle, and getting them back into private hands so that they be they can be repurposed. That's gonna save a ton of money. But both of them will pale in comparison to the third layer of this onion, which is the IT and the services and the spend. And what I mean by that is when you read how the department is set up, at the center and nucleus of every single 1 of these Doge teams is an engineer. And I think the reason is that they can get into these systems of record and start to trace where the money is going. And I think when you start to uncover through forensic analysis, where these dollars are going and how it's spent, that's probably how you're gonna close the gap from a a trillion to and I suspect, to be honest, it could be more than $2,000,000,000,000 when it's all said and done. That is an enormous amount of waste, and it's unproductive. So I'm very excited for what happens over this next little while. Just the the transparency is gonna be incredible. Travis Kalanick (01:21:23): Guys, just for kicks, check this out. Right? 02/9 if we took if we took 02/19 spend, right, the year before co and put it up against twenty twenty four revenues, $500,000,000,000 surplus. Chamath Palihapitiya (01:21:38): Wow. Bingo. Travis Kalanick (01:21:39): That's crazy. Versus versus the 1 and a half trillion dollar deficit. So I took $2,000,000,000,000 swing on, like, forty years. Yeah. On a $4,000,000,000,000 budget. That's all waste. David Friedberg (01:21:52): Well, a lot of it's in there. Remember, we've got a trillion dollars a year of interest payments now. Travis Kalanick (01:21:57): I mean, this is guys this is the thing. Like, there's 2 deflationary things that we need. 1 is Doge, and 2 is where AI is gonna take us if it really does its thing. And that will keep us in an okay spot economically. But, like, we gotta this spend has to go or we're in we're in sorta, like, we're in Greek Greek territory, if that makes sense. Jason Calacanis (01:22:21): Yeah. And I think this is, the popular support for this is pretty incredible. I'll just go through a couple of numbers with you. You know, looking at what people agree with that Trump's doing early on and what they disagree with. You know, obviously we talked about it last week, Chamath, pardoning the Jan. 6 and, you know, ending requirements for government employees to report gifts. That's sort of like the Supreme Court thing. These are tremendously unpopular. And then if you go and you look at downsizing the federal government and imposing a hiring freeze and requiring all federal employees to return to an office, these are incredibly popular. And Elon tweeted these graphs out as well. So right now, you have Trump at the apex of his political popularity, and you have these issues specifically in a very polarized time as incredibly popular. He's also done an incredible job with the border. That's another consensus based issue. So Trump now has downsizing the government and controlling immigration and getting rid of violent immigrants has incredibly popular parts of his mandate. And that's the big win for him. If you look at his popularity, Trump is massively more popular than he was the first time around. He's at 49% compared to last time, 44. He's still the historically least popular president ever. So my point in all of this is, when you see Trump doing things like his meme coin or, you know, taking on Pete Buttigieg today, all that kind of Trump one point o negativity, grifting, that's the stuff that's gonna derail this. But the stuff that's not gonna derail it is focusing on the Trump two point o agenda. And that is as somebody who was a never Trumper, as you all know in the audience, and now somebody who is supporting him relentlessly, that margin, that extra 10% of people who support him right now is me and other folks who are looking at the people we put around him. He has to stay with the 2 o agenda as hard as it is and stay away from the Steve Bannon agenda and the grifting. Those are the things that will take this all apart. So that's my appeal to them. I told everybody I'd give a letter grade. Jason Calacanis (01:24:30): I give him a b so far. Could do better, but pretty good. Less of the meme coin, less of the you know, we have to make sure that we're not dragging dishwashers and teachers and and people who've been here twenty years out of the country. And it's gonna be a very deft, important approach here if this is going to be sustained. And I think it's a coin toss if he will be able to maintain his popularity. And what he did today with this like, I don't know if you saw the Pete Buttigieg. He was attacking him over this tragedy. That's the kind of stuff people don't want. Less of that, please, more of the Doge. That's my little rant. David Friedberg (01:25:06): Can we talk to Travis about Waymo now? Travis, can I ask, have you taken a production Waymo? Yes. What do you think about it? And do you think that's the future of transportation? And how does Uber play into the, self driving car business now? Travis Kalanick (01:25:20): I mean, look. It it's funny because, as you guys know, back in the back in the day, 02/1617, we had our own autonomous vehicles out there. And I remember the first one of ours that I took, And I got in the back, and all I had was a stop button, a big red stop button that I could push if things got weird. David Friedberg (01:25:43): Yeah. Travis Kalanick (01:25:43): And, I remember this is in Pittsburgh where we had our robotics division and autonomy division at Uber. And I got out of that car, and literally, it's like I got off a roller coaster ride. Like, my legs were I could not stand straight. Like, I was, like, a little wobbly because I was so freaked out, and the adrenaline was pumping. You get in a way more today, and it's like, you're you're not even thinking twice. You're just like, it's all good. You just get in. You get out. Now part of it's just the normalization. It's like, it's just working, and it that that normalizing matters in terms of the psychology around it is we're just there. So it just works. Now is it a optimized experience for ride sharing? No. Like, the cyber cab is sort of the ultra sort of destination for what it means to get transported across the city in a vehicle that is not meant for a human to drive. No steering wheel. You know, folks potentially been facing each other, you know, just a whole bunch of different formats. The The the technology works. We know that there are different ways to get to the technology. I think that the probably the most interesting thing that we should be or 1 of the most interesting things to be thinking about, maybe there's a few. First is a cheap AI makes cheap autonomy. Chamath Palihapitiya (01:27:04): Mhmm. Travis Kalanick (01:27:04): Okay? So if as as cheap AI gets out there and proliferates and gets broadly distributed, we should expect autonomy gets easier and easier and easier. And you see some of the stuff that's happening with Tesla and FSD. Their new models are like I think in a three month period, he, they went up, like, 10 x in terms of performance, meaning in number of miles per per human intervention. Like, they're seeing you know, that's the thing that Elon's seeing right now because cheap AI, cheap good AI makes cheap good autonomy, and that's a thing we need to connect the dots on. I think the thing then you go 1 level past that you're like, okay. There's the possibility literally that autonomy just gets easy and commoditized similar to what's happening to AI. The next part is, okay. You get the hardware. You're like, okay. Manufacturing's hard. That's interesting. That could be a long pole in the tent. I think that that could be a place where Tesla, of course, has huge advantage. You then look at who are Waymo's partners. Are they getting set up to do the right kind of manufacturing and get scale of of cars out there? But then there's, like, this dark horse that nobody's talking about, which is it's called electricity. It's called power. And all these vehicles are electric vehicles. And if you said, you know, I just did some, like, quick back of the envelope calcs. If all of the miles in California went EV ride sharing, you would need to double the energy capacity of California. David Friedberg (01:28:36): Right. Travis Kalanick (01:28:36): Let's not even talk about what it would take to double the energy capacity and the grid and things like that in California. Like, let's not even go there. Even getting 20% more, 10% more is going to be a gargantuan five to ten year exercise. You know? Look. I live in, you know, I live in LA. It's a nice area in LA, and we have power outages all the freaking time because the grid is effed up, and they're sort of upgrading it as things break. That's literally where we're at in LA, One of the most affluent neighborhoods in LA. That's just where we are. So I think the the sort of the dark horse kinda hot take is combustion engine AVs because I I don't know how you can go fast getting AV out there really, really, really massive with the electric grid as it Jason Calacanis (01:29:33): is. What do you think about regulation in this regard? Because, obviously, there was the cruise. You know, a person got hit by a regular car. They dragged it. The whole thing imploded. We had, at Uber, the, the tragedy in Arizona where somebody was playing candy crush when they were a safety driver. You know, what what is what is your outlook on this stuff rolls out and somebody gets hurt? And then that, you know, tens of thousands of cities that you brought Uber to, how receptive are they going to be towards this? And what do you think the regulatory framework will be like? Travis Kalanick (01:30:09): You know, I think similar to how you get normalized, it's like you're used to getting in a car. It's normalized psychologically in in the sort of public sphere, the public mindset. You get used to it. So, like, we're getting to a place where these vehicles are provably safer than human driven vehicles. So, yes, there are mistakes, but they're just provably safer, and people are just getting used to it. And that's a big part of the cycle. So I think we're getting out of the hysteria, and we're getting into, like, yeah, it's just great. Like, talk to people who are using it, and they feel safer from, of course, like, I I I feel like we're gonna get in less accidents, but also I feel safer because there's, like, there's less chance of, like, an interpersonal problem that does happen especially, you know, late at night, you know, when people are out partying and things like this. There's just, like, there's a level of safety on many different aspects to Yeah. For the dry notes, for the you yeah. There's, like, there's there's safety aspects across the board. Sure. Right? Jason Calacanis (01:31:14): What do you think about BYD and, like, you you sort of mentioned everybody getting to autonomy at the same time? Obviously, Waymo's got the biggest lead. Tesla's behind them. BYD and about 10 other providers are out there doing this. Does does, you know, do 10 players get there at the same time? And then it's just who can incorporate these into their network? And what do you think of the strategy that Uber is doing of, hey. We've got these 8 partners. We'll take everybody into the network, and we'll manage people vomiting in the back of cars, cleaning them, and charging them. Travis Kalanick (01:31:43): So look. I think the big issue you have with anything Chinese is will you be allowed to bring it in The US? Just period. Like, you maybe kinda can now. What happens with tariffs? Will there be blocks in bringing this kind of technology into The US? What happens there? I think that's a whole thing. The bet that Uber makes is that whether consciously or subconsciously, that it's like, will AI will cheap democratized AI happen? And if so, does that make cheap democratized autonomy? Then you've gotta line up your physical sort of hardware partners, the car manufacturers. Then you've gotta say, okay. Is the electricity where it's at? And are there other bets to make to make sure that I can charge my car? So, like, there is a huge real estate play here and fleet management play of, like, how do I electrify these plots of land known as parking lots and also set them up so that robots can clean cars in sort of a very, very efficient way. There's, like, a whole when you we talk about David Friedberg (01:32:48): That's super interesting, Travis. That's like it's almost like the idea that we all talk talk about today is data centers, and data centers need their own power substation Yeah. In order to meet the power demands. But if if we do see a world of robotics, automation generally, and we've got these kind of moving robotic systems in our world, they need to have a similar sort of, like, power demand met that probably looks like, hey. They all go into their their recharge building, and they get recharged whether they're a car or a humanoid robot or a food delivery robot on the sidewalk or whatever they or Speaker 4 (01:33:19): or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or or David Friedberg (01:33:20): or or You know what I'm talking about? Get recharged. Chamath Palihapitiya (01:33:22): Robots need actuators. Do you know what you need for an actuator or permanent magnet? You know what you need for a permanent magnet? Rare earths. Who's the rare earth king ex China? Speaker 6 (01:33:34): Greenland. Greenland. Greenland. Let's Travis Kalanick (01:33:36): go. So so, guys, I think there is a there is a re there's a couple interesting things. 1 of them is gonna be how are these companies thinking about real estate, electrifying that real estate in urban environments, and roboticizing that real estate so that they can do the servicing, maintenance, etcetera. Look. I guess it could be manual for Speaker 4 (01:33:56): a while. Chamath Palihapitiya (01:33:57): Can I can I put you on the spot? Just go 1 level above it because merge the last 2 concepts together. You talked about we talked about the federal government, Doge, etcetera. Isn't there the potential for just a complete surplus of physical inventory that exists in America? Travis Kalanick (01:34:13): Oh, yeah. Okay. So Big time. Chamath Palihapitiya (01:34:15): So what does that mean for commercial real like, how do you, like, navigate around that? Because you gotta evade the falling knives first. Travis Kalanick (01:34:21): So okay. So let's just go down ride sharing lane. So autonomous ride sharing lane. You go down that lane, car ownership, which is already dropping, drops like a knife all the way down. And there's this thing in cities which takes up 20 to 30% of all the land. It's called parking. It's no longer necessary because cars are getting utilized. The cars that exist on the roads are getting you utilized 15 x more than they were before Speaker 6 (01:34:47): Mhmm. Travis Kalanick (01:34:47): Per car. So you need, hypothetically, one fifteenth number of cars. Maybe you could say one fifth or one tenth because you wanna be able to surge to, like, rush hour and things like that. It depends on what kind of carpooling and things like this are going on. But let's just call it 10 x fewer cars, 1 the land necessary for parking. At least one tenth. Like, maybe it's less than that. K? So now you're opening up you're opening up 20% of the land in a city that just goes fallow. Chamath Palihapitiya (01:35:20): But what what should we do with that? And is there a demand for that land? Travis Kalanick (01:35:24): Well, look. I mean, maybe it's these Should it Chamath Palihapitiya (01:35:26): be housing? You know, like and then don't we have to reevaluate all of the city planning today? Because city planning today, to your point, works backwards from all these Yeah. Constraints that are 1 o constraints. Like Yeah. Here's the traffic flow. Here are the traffic patterns. Those don't exist theoretically anymore or they would exist in a totally different way. Right? Travis Kalanick (01:35:45): Yeah. I mean, we've got a there's, like, a massive amount of creativity to say, what can I do with that land with a high ROI? Right? Like, some people are like, you're gonna have farms, you know, hydroponic farms in urban environments. I'm like, Jason Calacanis (01:36:01): you know That's Chamath Palihapitiya (01:36:01): not a bad idea if you wanna have farm to table, healthy food. It's literally farm to table. It's like a mile away from you. Travis Kalanick (01:36:07): Yeah. So there's some interesting ideas. The land price has to really come crashing down, and there's interesting ramifications. If it were to do that, you'd imagine. That's what Chamath Palihapitiya (01:36:16): that's what I wanted you to say, not to try to get you there, but Travis Kalanick (01:36:18): that seems Chamath Palihapitiya (01:36:21): like the witness. Well, that seems like the crazy thing that nobody is thinking about, which is in this push, this physical built inventory has so much value built up in Mhmm. The four zero one k's of of individuals to the balance sheets of huge pension funds, but that value is could be very different. Travis Kalanick (01:36:37): Right. But the crazy part is is it could just be electricity production and electric capacity on the grid could be the gating factor that makes it a slow burn, potentially. I'm just riffing here, guys. Chamath Palihapitiya (01:36:50): Right. Jason Calacanis (01:36:50): Yeah. Right. Right. Right. Makes total sense. And if you wanna see what happens when you have, like, unlimited land, if you live in Austin and you see the distance between San Antonio, Houston, and Dallas and Austin in that triangle, you know, you get thirty minutes outside of the city centers. There's just unlimited land and there's less regulation. And you know what's happened? Housing prices and rents have come down two or three years in a row. So this could happen in other major cities. And if Doge has less regulation, you can build more. It could be amazing for Americans to actually be able to afford homes again and maybe convert some of this space. Travis Kalanick (01:37:23): You go energy storage, electric grid upgrades, sort of modular energy capacity upgrades, like, in in production. These are this is going to be very, very important. Right now, if you wanna when we do this all the time, we have, of course, facilities all over every major city in The US and really around the world. Utility upgrades is the long pole in the tent in in construction development in a lot of our cities, not all cities, but in a lot of our cities. Jason Calacanis (01:37:57): The Fed, held rates. They're getting close to the goal of 2%. I guess we're at 2.4, 2 point 9 percent in terms of inflation. Any thoughts on, where we're at with the Fed deciding to not cut and just, you put it on the docket here, Jamont. Any any wider thoughts there? Chamath Palihapitiya (01:38:14): I would just say that the long end of the yield curve is basically telling us that there's still a chance for inflation. So I think that the the question is these next thirty or sixty days from the administration, I think, are basically they're they're critical. And I think if if DOGE gets to the 3,000,000,000 a day number quicker than people thought, there's going to be a lot of room for, I think, the president to make a very valid argument that rates are too high for where they are and that we're going to be able to have a lot more cost control in the expenses, which means that there'll be less need to spend. It doesn't solve the problem that Yellen created. Yellen and Biden on the way out the door, the biggest problem was that they put America in this very difficult position because they issued so much short term paper that is extremely expensive. And as all of that rolls off, we have to go and finance a ton of this debt at now 5%. So it's still David Friedberg (01:39:16): Nearly 30% of of, the debt is gonna get refinanced us here. Travis Kalanick (01:39:20): And then it's like, what are these auctions gonna look like, guys? This is the thing we we all gotta believe. David Friedberg (01:39:25): Last auction barely had 2 x coverage. Chamath Palihapitiya (01:39:27): And I think that that could take a lot of the energy out of the market. David Friedberg (01:39:29): Watch the Dalio interview because this this is exactly the topic he covers. You know, as we end up needing to refinance this debt, the rates climb, the appetite isn't there, and it becomes a spiral. That's why we have to cut fast in terms of the deficit to basically attract the market. Now, you know, the market's moved a little bit. Right? So on Jan. 13, the thirty year treasury peaked at exactly 5%, and it's come down today. It's at 4,.77. So a little bit of relief, since that that peak as as kind of the administration's gone into office and actually taken action. But as more of this action is realized, if people do appreciate and doge it successful and the court's adjudication does allow reduction in spending, which I think is the intention, I think we could see this rate drop from four seven eight much more significantly than where it is. And and that'll create a great deal of relief. Travis Kalanick (01:40:22): And, David, it's like, it either does that or it really, really doesn't. Yeah. Exactly. Or it does, like, the exact that Chamath Palihapitiya (01:40:30): or think tick 1 Travis Kalanick (01:40:30): is super nasty, really bad. Chamath Palihapitiya (01:40:32): That's right. David Friedberg (01:40:33): I got a text from someone who is pretty senior in capital markets, so thinks this is gonna go to 5 and a half percent before it goes down. So they think that there's gonna be a little bit more of a turbulent run ahead. Travis Kalanick (01:40:45): But it's like but the thing is it's like that whole thing of, like, it's gonna get to 5 and a half before it comes down. It's like By David Friedberg (01:40:50): the way Travis Kalanick (01:40:50): it spirals on itself. Speaker 4 (01:40:52): It's Travis Kalanick (01:40:52): like you gotta print money to then get to that place, and then the printing drives it for you know, you get to that spiral. Chamath Palihapitiya (01:40:59): The problem is if we go to 5 and a half percent, that's not 80 basis points. What you really need to think about is the total tonnage of actual dollars that need to get repaid back. And if you look backwards, that's effectively like 10% rates from 02/. Could you imagine what the economy David Friedberg (01:41:14): That's right. Chamath Palihapitiya (01:41:15): Would have done if you had brought rates to ten, eleven percent twenty years ago? It would have crippled the economy. So we don't have a lot of room here where you can walk rates up to 5 and a half, 6% without a lot of things starting to break. This is why I actually think Doge will be successful because as people internalize all of these things where every single congressperson, Freeburg, that may have wanted their own benefit for their community, they'll have to take a step back because the broader optimization for America just needs to take priority. Travis Kalanick (01:41:44): But, Shamath, it just doesn't work like that, man. Like, my thing is, like, I, like, I agree with the notion, but I just don't believe that any individual congressperson will take responsibility in this way. Chamath Palihapitiya (01:41:53): They won't. They won't. But the question is, can they block it? Travis Kalanick (01:41:56): Yeah. But or put another way, again, the executive branch can slow roll spend in a lot of different ways. David Friedberg (01:42:07): Except you cannot with Medicare and Social Security. Discretionary spending is, like, 20%. The mandatory spending, Social Security, Medicare, Medicaid, these are the the larger outlay. And, this where where we come back to the fact that this will never I hear you. Get addressed until it has to be I I hear you. Political suicide that arises. Chamath Palihapitiya (01:42:31): I just think there's this is where I think Elon's fame can be helpful, and I mean very specifically this following idea. You know, that famous Sputnik comment where NASA spent millions of dollars trying to engineer a pen that could write upside down, and it turned out that in Sputnik, the Russians just took a pencil. That is what we need to do to the US government because I suspect even though there's a lot of mandated spend, the real question that nobody knows the answer to is, is that spend useful? So even though it's appropriated by congress, there has to be a feedback loop that says you can just use a pencil. You don't need the upside down writing pen. And I think that if there's anybody that can broadcast that to the world, it's him. And this is where I think Trump gets enormous leverage by having Elon in the West Wing. But nobody else could give him. The rest of us would just be chirping into the darkness. Yeah. Jason Calacanis (01:43:22): This is the naming and shaming of government waste that's actually going to work, and the Doge account on Twitter is doing it. They're basically saying, hey, we're giving foreign aid for this project, for that project. Is it going to be perfect every time? No. But you show an empty office space, you show people not coming to work, you show people wasting money. Well, yeah, if that's even real, you know, there's going to be a bunch of, you know, back and forth here. But overall, if you keep naming and shaming each of these projects and then, you know, they were talking about blockchain or whatever, and supposed to report Elon is at, like, the government building working on leases at the moment. Like this stuff is going to be extraordinary popular because you can just take the number of 330000000 Americans and whatever you just saved, you can just divide it by that number and tell every American how much they just paid less in taxes or how much they just saved individually. The naming, shaming and doing the back of the envelope math for every American is going to work. Do we want to wrap maybe a little bit on this tragedy in DC? Okay. What are your thoughts? We were talking with our friend Sky Dayton, who is very involved in aviation, and, he's got a lot of blog posts he's done recently, and he's got a company he invested in to do, pilot training. Chamath Palihapitiya (01:44:33): I'll share 2 things. 1 is anonymous. It's from a friend of mine gave it to me and said I could share it. He's a commercial pilot, and he and I and I posted this. I'll just read it. Honestly, DCA is the sketchiest airport we fly into. I feel like the controllers there play fast and loose, hence the periodic runway incursions. I've said to every first officer in my threat briefings that we both need to be on red alert at all times there. DCA calls out helo traffic, helicopter traffic, and vice versa all the time, but it's borderline impossible to see them when you're bombing along at a 50 miles per hour. I mean, that's from a pilot that is I don't think he has any incentive to sugarcoat things. And then I just wanted to read a message from Brian Yutko, who's the CEO of Wisk, who's building a lot of these autonomous systems. He said, first, auto traffic collision avoidance systems do exist. Right now, these aircraft will not take control from the pilot to save the aircraft even if software and systems on the aircraft know that it's going to collide. That's the big flip that needs to happen in aviation. Automation can actually kick in and take over even in piloted aircraft to prevent a crash. That's the minimum of where we need to go. Some fighter jets have something called automatic ground collision avoidance systems that do exactly this when fighter pilots pass out, and it's possible for commercial. And then the second, he said, is we need to have better ATC, air traffic control, software and automation. Right now, we use VHF radio communications for safety and for critical instructions, and that's kind of insane. We should be using data links, etcetera. The whole ATC system runs on technology. They deserve better software and automation in the control towers. It's totally ripe for change. The problem is that attempts at reform have failed. So I just wanted you guys to have that. Chamath Palihapitiya (01:46:34): 1 from this commercial pilot and then 2 from Brian Yudko, who I I think understands this issue really well. There's so much opportunity here to make this better. This should have never happened. Our other friend, Sky Dayton, has been pushing really hard for the US government to do advanced pilot training. 1 of the things that he says constantly is just that a lot of the pushback is just union rhetoric around what they perceive the right thing for their constituency is. And, hopefully, this starts this conversation because I think guys like Skye, guys like Brian are working on this next level of autonomous solution that can just make flying totally, totally safe beyond what it was. The crazy stat is that we haven't had a commercial airline disaster in The United States in almost twenty five years. Isn't that incredible? Jason Calacanis (01:47:19): Fifteen. Yeah. It's looking like pilot error here, and it's there also seems to be some question of why these Apaches are flying around this really crowded airspace, and it seems like they're shuttling, politicians around. And maybe that's not the best idea in this really dense area, as your pilot friend was referring to Jama. So God, thoughts and prayers and all that stuff for for the families of the people who died. It's just terrible tragedy. Terrible tragedy. Yeah. This is an area to invest money and use the private sector and all this incredible innovation that's available to upgrade these systems and infrastructure. This has been another amazing episode of the All In Podcast. Thanks, Travis, for joining us. Thank you, TKZR, for coming. Travis Kalanick (01:48:04): Fun, guys. First time. Speaker 4 (01:48:05): It's my Travis Kalanick (01:48:05): first time on a podcast ever. Jason Calacanis (01:48:07): Yes. You guys Speaker 4 (01:48:08): are all good. Jason Calacanis (01:48:08): Right in. You were very compact. David Friedberg (01:48:09): Anytime. You were great, man. Travis Kalanick (01:48:10): You were great. Great time. David Friedberg (01:48:12): You were great. Time. You were Jason Calacanis (01:48:12): great, man. Speaker 4 (01:48:13): You were great. Appreciate it, guys. Very based. It's just Jason Calacanis (01:48:13): gonna like it. Speaker 4 (01:48:14): Tell us Speaker 6 (01:48:14): what you think. Jason Calacanis (01:48:15): Boy. And we'll see you all next time. Chamath Palihapitiya (01:48:17): Love you, boys. Bye bye. Speaker 4 (01:48:20): We'll let your winners ride. Rain man David Cyrus. Speaker 7 (01:48:27): And it said, we open sourced it to the fans, and they've just gone crazy with it. Speaker 4 (01:48:31): Love you, s I. Sweet. A kid who wants. Besties are gone. Chamath Palihapitiya (01:48:50): We should all just get a room and just have 1 big huge orgy because they're all just useless. It's like this, like, sexual tension that they just need to release somehow. Let David Friedberg (01:48:58): your feet. Let your feet. Let your feet. Speaker 6 (01:49:02): Feet. Feet. Feet. Speaker 4 (01:49:02): Feet. Feet. Feet. Feet. Feet. What? Look at you. We need did you get Jason Calacanis (01:49:04): Mercies Arback? I'm doing Speaker 4 (01:49:14): all in.