DeepSeek Panic, US vs China, OpenAI $40B?, and Doge Delivers with Travis Kalanick and David Sacks

Jason Calacanis 0:00 All right, 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 Freeberg's surprise drop with his hero, Ray Dalio, live on all platforms today. How did that come about, Freeberg? A little surprise drop.

David Friedberg 0:20 Just great. Was talking with Ray about his new book, which he just published, on how countries go broke. Obviously, very important topic.

Chamath Palihapitiya 0:28 Which country is going broke now, Freeberg? America?

David Friedberg 0:32 Well, I 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, uh, 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, uh, with the change in administration. Anyway, great topics to talk through and really important book.

Jason Calacanis 1:03 Awesome. Well done. And we are super delighted to have, in the red throne, Travis Kalanick. He is the co-founder and CEO of CloudKitchens. He also, uh, worked in the cab business for a little bit.

Travis Kalanick 1:16 Yeah.

Jason Calacanis 1:16 Co-founder and former CEO of Uber. And, uh, yeah, we had a great interview at the All-In summit last year. And he's back up from his media hiatus. He's been in the lab, working on CloudKitchens. How you doing, brother?

Travis Kalanick 1:28 I'm doing really well. I- I got to say, just like at the summit, Jason, I- I'm-

Jason Calacanis 1:32 Yes.

Travis Kalanick 1:33 It's an honor to be in the presence of such a prominent Uber investor.

Chamath Palihapitiya 1:38 (laughs)

Jason Calacanis 1:39 Absolutely, absolutely.

David Friedberg 1:40 (laughs)

Jason Calacanis 1:40 I mean, finally somebody has recognized (laughs) my-

David Friedberg 1:44 The greatness.

Jason Calacanis 1:45 ... contribution.

David Friedberg 1:45 The greatness of J-Cal. Yes.

Jason Calacanis 1:47 Absolutely.

Travis Kalanick 1:48 I'll mention it three or four times.

David Friedberg 1:49 Yeah.

Travis Kalanick 1:49 We'll bo- it- it's all good.

Jason Calacanis 1:50 Appreciate it, brother.

Travis Kalanick 1:50 I'll give you the props. You don't have to do it for yourself anymore.

Chamath Palihapitiya 1:52 (laughs)

Jason Calacanis 1:52 Thank you. Appreciate that. Appreciate that.

Narrator 1:55 Let your winner slide. Rain Man, David Sa- And I said we open source it to the fans and they've just gone crazy with it. Love you, SI. Queen of Kinwa. I'm going all in.

Jason Calacanis 2:11 Give everybody a little overview of CloudKitchens and the business and-

Travis Kalanick 2:15 Yeah.

Jason Calacanis 2:15 ... uh, how it's going because people are obviously addicted to ordering food at home.

Travis Kalanick 2:21 Yeah.

Jason Calacanis 2:21 And, uh, it's- it's quite a trend.

Travis Kalanick 2:23 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 100 years... We'll start way out there. In 100 years you're gonna have very high quality food, very low cost, that's incredibly convenient. And there are gonna be machines that make it, there are gonna be machines that get it to you. And it's gonna be exactly to your dietary preferences, your food preferences, et cetera, 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.

Jason Calacanis 3:00 Hmm.

Travis Kalanick 3:00 So you go, "100 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 of 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.

Jason Calacanis 3:28 Mm-hmm.

Travis Kalanick 3:28 And that's the thing.

Jason Calacanis 3:30 Travis, do you have a-

Travis Kalanick 3:30 And it's like a grind. It's like lot... You know, bits and atoms in the Uber world?

Jason Calacanis 3:35 Mm-hmm.

Travis Kalanick 3:35 This is like five 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've got to grind out those atoms.

Chamath Palihapitiya 3:52 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 for the food supply are, if your vision holds? How do you think about all those things?

Travis Kalanick 4:05 Look, uh, people will cook in the future as a hobby.

Chamath Palihapitiya 4:08 As a hobby.

Travis Kalanick 4:09 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."

Chamath Palihapitiya 4:15 (laughs) Right.

Travis Kalanick 4:15 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, 'cause it'll be hyper-personalized. Like the way the internet is in content-

Chamath Palihapitiya 4:51 Hmm.

Travis Kalanick 4:51 ... plus, plus, plus in terms of your specific preferences for what you want.

Chamath Palihapitiya 4:58 I mean, do you-

Jason Calacanis 4:58 And you've got these computers rocking, or these robots rocking, I think, in Philly somewhere, uh, in the lab. Where they're making bowls, yeah?

Travis Kalanick 5:06 I mean, we're outta the lab at this point. We have our machines that we have, a machine called the Bowl Builder that basically makes different cuisine types with bowls. So like, think of like-

Chamath Palihapitiya 5:16 Like Sweetgreens, like what they... Yeah.

Travis Kalanick 5:18 We're not working with these brands specifically, but I'll- I'll just sort of... It- 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 five customers in April that are 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 one 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, y- uh, people order online, DoorDash, Uber Eats, et cetera, 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, et cetera, 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, Uber Eats driver comes, waves their, waves their phone with the app in front of a camera, and it pops open the locker that has the food that you're supposed to get.

Chamath Palihapitiya 6:53 That's so cool.

Travis Kalanick 6:55 So, like, if you're a, if you're a restaurateur, 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 7:14 Like, do you think there's a service in the future where my physiology ...

Travis Kalanick 7:19 Mm-hmm.

Chamath Palihapitiya 7:19 ... 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 7:31 So first, what we do is we serve the restaurants.

Chamath Palihapitiya 7:34 Mm-hmm.

Travis Kalanick 7:35 So what happen ... so, Chamath, you'll be sharing your dietary preferences with Uber Eats or DoorDash or Sweet Green or somebody. We ... We, like, our m- our, like, our customer promise at our company is we serve those who serve others.

Chamath Palihapitiya 7:50 Mm-hmm.

Travis Kalanick 7:50 Or put another 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.

Chamath Palihapitiya 8:00 Yeah.

Travis Kalanick 8:00 We're behind the scenes-

Chamath Palihapitiya 8:01 Yeah.

Travis Kalanick 8:01 ... we're the infrastructure. And so you'll give your preferences, yeah.

Chamath Palihapitiya 8:04 Right. It should be a brand like then Sweet Greens or whomever, Chipotle, that says, "Hey, guys. Share with me, like, a-"

Travis Kalanick 8:10 Yes.

Chamath Palihapitiya 8:11 "... 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 back end.

David Friedberg 8:20 Yeah, it's pretty close, Chamath, right?

Travis Kalanick 8:22 When these, when th-

David Friedberg 8:22 You could, uh, authenticate your Apple Health, right?

Travis Kalanick 8:23 When these ... When these ... That's really awesome.

David Friedberg 8:24 You just authenticate Apple Health.

Travis Kalanick 8:25 When these bowls come off the line ...

David Friedberg 8:27 Yeah.

Travis Kalanick 8:27 And you see how I talk, it's like an assembly line.

David Friedberg 8:29 Yeah.

Travis Kalanick 8:29 When these bowls come off the line, on th- the label on the bowl is how many grams of every ingredient is in it-

David Friedberg 8:37 Mm-hmm.

Travis Kalanick 8:37 ... plus a picture of what it was before we put the lidin, that can be sent to the person while the bowl's on its way via courier, right?

David Friedberg 8:45 Mm-hmm.

Chamath Palihapitiya 8:45 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 8:54 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 Sysco, 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-

Chamath Palihapitiya 9:23 Mm.

Travis Kalanick 9:23 ... 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 it came from, eh, uh, things like this.

Chamath Palihapitiya 9:39 Right.

Travis Kalanick 9:39 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 ... Uh, I went to the, I went to RFK Jr.'s website and they have, like, the ... He has merch, he has Maha merch.

Chamath Palihapitiya 9:58 Yeah, yeah.

Travis Kalanick 9:58 I have, I have the green Maha merch hat. I shoulda worn it today.

David Friedberg 10:01 (laughs)

Travis Kalanick 10:01 I'm all about it.

David Friedberg 10:02 Did you get the onesie? (laughs)

Travis Kalanick 10:03 (laughs) Yeah, that's amazing.

David Friedberg 10:06 The onesie was crazy.

Chamath Palihapitiya 10:07 Your bowl builder, Friedburg, you tried to do this, right? Your-

David Friedberg 10:10 Eatsa.

Chamath Palihapitiya 10:11 ... Eatsa and ...

David Friedberg 10:12 We had a bowl builder 10 years ago, or 2015, yeah, 2016. Diego saw it. He actually-

Travis Kalanick 10:18 Mm-hmm.

David Friedberg 10:18 ... visited it when we built it.

Travis Kalanick 10:20 Mm-hmm.

David Friedberg 10:20 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 they, there were 30 slots in the canister-

Travis Kalanick 10:36 Yeah.

David Friedberg 10:36 ... 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 Eatsa 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 was gonna happen.

Chamath Palihapitiya 11:02 So you must love this. You love this.

David Friedberg 11:04 Yeah, and at the time, we were, we actually had ... I'll, 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 Sweet Green at the time as well, and obviously Jonathan and team have gone on to develop their own system. But, uh, 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-

Travis Kalanick 11:40 (laughs)

David Friedberg 11:40 ... in New York-

Travis Kalanick 11:41 Yeah.

David Friedberg 11:41 ... in, uh, in the early 20th century.

Travis Kalanick 11:43 It's legit. I love this.

David Friedberg 11:44 But they, yeah, they had a commissary behind that wall-

Travis Kalanick 11:46 Yeah.

David Friedberg 11:46 ... and they made, like, plates of food, you put it in there, you put a quarter in, you turn the, the knob and you get your meal out.

Travis Kalanick 11:50 That's the classic.

David Friedberg 11:51 Super cool, right?

Travis Kalanick 11:51 That's the classic artificial artificial intelligence, right?

David Friedberg 11:53 Totally.

Travis Kalanick 11:53 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 ways.... 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're gonna replace the front line at Chipotle, you've gotta, you've gotta take out that front line. You've gotta demo it.

David Friedberg 12:34 Yeah.

Travis Kalanick 12:34 You've gotta put in a new machine. And they're-

David Friedberg 12:37 That's the challenge that they all have, for sure.

Travis Kalanick 12:38 And so now it's, like, a huge amount of capex. My store is down for two to three months, and the economics start to not work.

David Friedberg 12:46 That's right.

Travis Kalanick 12:46 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.

David Friedberg 12:53 Yeah.

Travis Kalanick 12:53 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 13:11 When we did this early at Eatsa, it was, like, food delivery was very early. We built these Eatsa restaurants that were smaller footprint. We had an 800-square-foot restaurant that was doing $3 million 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-

Jason Calacanis 13:28 Yeah.

David Friedberg 13:28 ... ordering custom bowls.

Jason Calacanis 13:29 This was by OneMarket, right?

Travis Kalanick 13:30 That's so OJ.

Jason Calacanis 13:31 OneMarket, yeah.

David Friedberg 13:31 One, OneMarket, exactly.

Jason Calacanis 13:32 Yeah.

David Friedberg 13:32 And so, yeah, and we-

David Sacks 13:33 By the way, did,

David Sacks 13:34 did you guys notice-

David Friedberg 13:35 Yeah.

David Sacks 13:35 ... that J-Cal was plugging his product there in the background, even though it has absolutely nothing to do with what Travis was saying?

Jason Calacanis 13:40 (laughs) Oh, welcome back to the show.

David Sacks 13:42 (laughs)

Jason Calacanis 13:43 Nothing's changed. (laughs)

David Friedberg 13:45 Sacks is here. (laughs)

David Sacks 13:46 No one else even noticed that.

Travis Kalanick 13:47 I just heard this voice from above. It was the czar of AI and crypto.

Jason Calacanis 13:52 The czar.

Travis Kalanick 13:52 I was like, "Wow."

David Friedberg 13:53 Zoran's descendant.

Travis Kalanick 13:53 "Let's all sit back and listen."

Jason Calacanis 13:55 The czar is back.

David Friedberg 13:57 Sacks, any anecdotes you wanna share, uh, about life in DC? How, how exciting it's been in the administration the first week?

David Sacks 14:04 It's been amazing. I mean, it's hard to believe it's only been a week, right?

Jason Calacanis 14:08 Are you in the White House or that building next to it? Do you have an office?

David Friedberg 14:11 That building. You mean the Treasury building? Or the, uh, Eisenhower?

Jason Calacanis 14:14 I don't know. Somebody was talking about there's a building next to it or something. I don't know.

David Sacks 14:17 Uh, I have an office in the, uh, 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 is kind of a whole White House complex behind the gates that with the West Wing is part of it, and the Eisenhower Building, and there's a couple other-

David Friedberg 14:34 Mm-hmm.

David Sacks 14:34 ... buildings in that complex. It's really cool.

David Friedberg 14:37 Awesome.

David Sacks 14:37 It is really neat to, to show up for work at the White House, I have to say.

David Friedberg 14:42 (laughs) It's awesome. (laughs)

Travis Kalanick 14:43 It's awesome.

Jason Calacanis 14:43 It's like being in a movie or something or a TV show? (laughs)

David Sacks 14:46 It is really cool, you know?

David Friedberg 14:47 It's awesome.

Jason Calacanis 14:48 Any interesting meetings you can talk about? And I mean, I know we are here to t- 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 15:03 Uh, there is a commissary actually in the White House called the Navy Mess.

Jason Calacanis 15:07 Hmm.

David Sacks 15:07 And I think they're just opening up for, for business now. That is one of the cooler things you could do is you can take people to, to lunch at the Navy Mess.

Jason Calacanis 15:16 Oh, look forward to it. (laughs)

Travis Kalanick 15:19 (laughs) J-Cal just invited himself.

David Sacks 15:20 We'll see.

Jason Calacanis 15:20 Oh, look forward to it.

David Friedberg 15:21 (laughs)

David Sacks 15:21 I look forward to taking Chamath and Friedberg there. (laughs)

Travis Kalanick 15:24 (laughs)

David Friedberg 15:24 (laughs)

Jason Calacanis 15:24 (laughs) I'll wear my MAGA hat. All right, well, let's get started. You're here because you, uh... We have a very specific-

Travis Kalanick 15:32 He's here because the world is ending, Jason. The, the Western world-

Jason Calacanis 15:35 Is it?

Travis Kalanick 15:35 ... is ending.

Jason Calacanis 15:36 Okay, the Western world's ending, and, uh, David Sacks is

Jason Calacanis 15:38 gonna save it, but we had a little bit of a freakout the last week regarding this DeepSeek. If you don't know, that's a, uh, 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, uh, in production in the West, like OpenAI's o1 model. But they claim... And listen, you can trust claims coming out of China, you know, for what it's worth. Uh, they claim to have done this all for $6 million on only 2,000 GPUs. For, uh, comparison, OpenAI spent reportedly $80, $100 million to train GPT-4, which you're all using now. And, uh, Sam claims they're gonna 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, uh, NVIDIA H100s to China, so there's a big debate as to if they actually have H1s or not. And, uh, Monday was a bloodbath in the stock market. NVIDIA had the 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 billion. 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, uh, which is kinda 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. Sacks, obviously, you are the czar of AI. I'm curious what your take on all this is, and thanks for coming.

David Sacks 17:20 Well, I think one 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, uh, 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 freakout. 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 one 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 two things about that company that are, that are different. One 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 o- an open source company, or at least it opened source to the R1 model, and so you've kinda got the whole open source versus closed source debate.And if you take either one 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 have 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, well, wha- 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 the, 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 o1 would be a Chinese company, I think people would've been surprised by that. So I think there was a surprise. And just to kinda back up for people, you know, there's, there's two major kinds of AI models now. There's kind of the base LLM model, like ChatGPT-4o or the DeepSeek equivalent was v3, which they launched a month ago, and that's basically like a smart PhD. You ask a question, it 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 pre-training, and o1 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 2.0 FlashThinking. They've released kind of an early prototype of this called Deep Research 1.5. Anthropic has something but I don't think they've released it yet. So other companies have similar models to o1 either in the works or in some sort of private beta, but DeepSeek was really the next one 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 reasoning model like this would be a, a Chinese company, and moreover, that they would open source it, give it away for free, and I think the API access is something like one-twentieth the, the cost. 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 12 months." And now I think they might say something more like three to six months, right? Because o1 was released about four months ago and r1 is comparable to that. So I think it's definitely moved up people's timeframes for how close China is on, on AI. Now let's take the, um... We should take the claim that they only did this for $6 million. On this one, 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 accepted it at face value, that $6 million was for the final training run. So when the media is hyping up these stories saying that this Chinese company did it for $6 million and, and these dumb American companies did it for a billion, th- 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 10 months ago. And so, you know, it's not $6 million versus a billion, okay? It's-

Jason Calacanis 23:17 The billion-dollar number might include all the hardware they've bought, the years of putting into it, a holistic number, as opposed to the training number.

David Sacks 23:24 Yeah, it's not fair to, to-

Jason Calacanis 23:25 Like, just running it is what you're saying.

David Sacks 23:26 It's not fair to compare, let's call it a soup to nuts number, a fully loaded number-

Jason Calacanis 23:30 Right. Comprehensive.

David Sacks 23:31 ... by American AI companies to the final training run by the Chinese company.

Travis Kalanick 23:37 But real quick, Sax, 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, uh, sort of the results they got out of it. I don't think they give the training data, but you could start to t- stress test what they've already put out there and see if you can do it cheap, essentially.

David Sacks 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 six million number. My point is less th- that they couldn't have done it, but just that we need to be comparing likes to likes.

Travis Kalanick 24:12 Yeah.

David Sacks 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&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 a leading semiconductor analyst, has estimated that DeepSeek has about 50,000 hoppers. And specifically, he said they have about 10,000 H100s, they have 10,000 H800s, and 30,000 H20s. Now, the cost of that-

Chamath Palihapitiya 24:50 Is they... Sax, sorry. Is they DeepSeek, or it's DeepSeek plus the hedge fund?

David Sacks 24:54 DeepSeek plus the hedge fund, but it's the same founder, right? And by the way, that doesn't mean they did anything illegal, right, because the H100s were banned under export controls in 2022. Then they did the H800s 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 50,000-plus hoppers, and it's going to be over a billion dollars. So, this idea that you've got this scrappy company that did it for only six million, 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 50,000, you know, that they might have obtained in violation of export restrictions that obviously they're not going to admit to. I mean, 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 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 million. 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 million. 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 DeepSeek, but they've also published some subsequent papers that have refined some of the details, I do think that this is a case, and Sax, you can tell me if you disagree, but this is a case where necessity was the mother of invention. So, I'll give you two 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 O1, which we can talk about in a second, but at the end of the day, these guys were like, "Well, how am I going to 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 going to 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. 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 f- rolls out of bed and some VC gives them $200 million, maybe that's not the right answer for a series A or a seed, and maybe the right (laughs) answer is two million so that they do these DeepSeek-like innovations.

Jason Calacanis 28:48 Constraint makes for great art. What do you think, uh, Friedberg, when you're looking at this?

David Friedberg 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 think-

Chamath Palihapitiya 29:12 The wrapper?

David Friedberg 29:13 ... reflects this.

Chamath Palihapitiya 29:13 About the wrapper?

David Friedberg 29:13 Yeah, he's like-

Chamath Palihapitiya 29:14 Yeah.

David Friedberg 29:14 "Turns out the wrapper may be the-"

Chamath Palihapitiya 29:16 (laughs)

David Friedberg 29:16 ... the moat-

Chamath Palihapitiya 29:16 The moat. (laughs)

David Friedberg 29:18 (laughs) ... is the moat.

Chamath Palihapitiya 29:18 (laughs)

David Friedberg 29:18 Which is true.

Chamath Palihapitiya 29:19 Yeah.

David Friedberg 29:19 At the end of the day, 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 going to be created somewhere else in the value chain. Maybe it's not the wrapper. Maybe it's-

Chamath Palihapitiya 29:35 Well, by the way-

David Friedberg 29:35 ... with the user. And maybe-

Chamath Palihapitiya 29:36 ... sure.

David Friedberg 29:36 ... 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 29:48 Well, you're about to see a big test of this because if OpenAI raises 40 billion at 340 billion, that just hit-

David Friedberg 29:54 100%.

Chamath Palihapitiya 29:55 That just hit the wire. The underwriting logic at 340 billion, exactly what you just said, Friedberg, it is the wrapper, meaning ChatGPT is the next killer app. It's getting to a billion-plus MAU, hundreds of millions of DAO.

David Friedberg 30:09 It's competing for consumer usage. That's, that's the mod- that's the model, is, like, consumer usage, yeah.

Chamath Palihapitiya 30:14 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 one company..."... that brings AI to a billion plus people and it will be us. Some version of that quote is (laughs) -

David Sacks 30:38 (laughs)

Chamath Palihapitiya 30:38 ... in its earnings release yesterday. (laughs)

David Sacks 30:39 That's so Zach. That's so Zach.

David Friedberg 30:39 And then Microsoft showed weakness in the- in their cloud-

David Sacks 30:42 It's so strong. (laughs)

David Friedberg 30:42 ... and, and then Microsoft's down 6% today. And, you know, I think it's a, a window for, uh, OpenAI to say, "We're gonna go up against Meta. This is it. We're gonna be the players."

Chamath Palihapitiya 30:53 What do you guys think-

David Friedberg 30:53 And everyone's kind of ignoring Google at this point, by the way.

Chamath Palihapitiya 30:55 What do you guys think is happening right now between OpenAI and Microsoft? 'Cause if it's true that this distillation thing actually happened, well, there's only one place where you could have distilled the o1 model (laughs) and it's on Azure. So what the hell is going on over there?

Jason Calacanis 31:08 Well, and there, o1 is supported on-

David Friedberg 31:10 Explain distillation real quick.

Chamath Palihapitiya 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 Sacks 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 DeepSeek 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-

Jason Calacanis 31:57 Uh-oh.

Chamath Palihapitiya 31:57 ... North Korea. Wait, it goes China. And then wait, watch this.

Jason Calacanis 31:59 Ooh.

Chamath Palihapitiya 31:59 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's sitting in an Azure instance somewhere, right? So this is Microsoft's cloud infrastructure that runs this. So it begs the question, it's not that it's o1'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?" (laughs) And I don't think anybody's had a good answer for that.

Jason Calacanis 32:40 Well, and the cloud provider is hosting o1 now. (laughs) So they're-

Chamath Palihapitiya 32:44 Yeah.

Jason Calacanis 32:44 ... literally undercutting ChatGPT and OpenAI at the same time.

David Sacks 32:48 Just to clean that up, they're, they're hosting-

Jason Calacanis 32:50 Yeah.

David Sacks 32:50 ... their own copy of it, right?

Chamath Palihapitiya 32:52 Yes.

David Sacks 32:52 Because o1's been open source. When you say they, who are you, who, who are you referring to, Sacks? Microsoft.

Jason Calacanis 32:56 Yeah, Microsoft is hosting their version of o1.

Chamath Palihapitiya 32:59 Oh, okay. Got it.

Jason Calacanis 33:00 Which means they are actively (laughs) subverting their partner, OpenAI, and pushing people to a cheaper model.

David Sacks 33:07 Well, whatever. I mean, look, Amazon's gonna host their own version of o1. Groq has a version of o1 that they're hosting.

Jason Calacanis 33:12 Yeah, we have one. Cerebras just rolled out ... exactly.

David Sacks 33:15 I mean, it's open source now, so everyone's gonna use it. I have a buddy who has o1 on his laptop, you know?

Chamath Palihapitiya 33:18 Yeah, exactly.

David Sacks 33:19 Yeah.

Jason Calacanis 33:19 Yeah, but if it was, if it was stolen and as, um, and the IP was stolen, as Sam is claiming, that would be like, you'd think he'd be able to call up SoftAI and say, "Hey, can you not put the stolen IP on your server and promote it to everybody (laughs) at a lower cost?" It just shows Microsoft has no loyalty-

David Sacks 33:34 The horse is out of the barn.

Jason Calacanis 33:34 ... to OpenAI. Yeah. And they have n- But you'd think they would have loyalty. They have no loyalty.

David Sacks 33:37 But guys, what it would take to distill o1, like brute force, it wouldn't be like, "Oh, geez, I can't believe it was distilled." It would be such a massive number of calls against an API or against something...

Chamath Palihapitiya 33:54 Something.

David Sacks 33:55 ... that you, it wouldn't be unnoticed in, in a-

Jason Calacanis 33:58 Well, they did, actually came out and said they blocked some suspicious activity recently.

David Sacks 34:01 Yeah, no-

Jason Calacanis 34:02 So as you can imagine.

David Sacks 34:02 ... but they're always doing that. They're alway-

Jason Calacanis 34:04 Yeah.

David Sacks 34:04 That's, that's constant. You're always doing that. That's like, you know, the old school- Well, let me- ... you know. Go, go ahead, Sacks. Let me, 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 talk to... They're doing some level of distillation. Yeah, I mean, 'cause everyone- Because you need to test your model against theirs anyways. 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 DeepSeek 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 V3 model came out, that DeepSeek V3 was self-identifying as ChatGPT. When you would ask it, "Who are you? Like what model are you?" Five out of eight times, V3 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 V3 had been trained on a substantial amount of ChatGPT output, obviously, because V3 was self-identifying as ChatGPT. And there's two 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-

Chamath Palihapitiya 36:00 Went to town.

David Sacks 36:01 Yeah, went to town. And there's no way, I think, based on what we know, to prove that one 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. But, but another-

Jason Calacanis 36:19 Yeah, in the Financial Times it says, "OpenAI says that it's found evidence that Chinese artificial intelligence startup DeepSeek used the US company's proprietary models to train its own open source competitor."

David Sacks 36:28 Right, that's what I'm referring to.

Jason Calacanis 36:29 So, yeah.

David Sacks 36:29 So they say-

Jason Calacanis 36:29 Yeah, they've been very clear about this.

Chamath Palihapitiya 36:31 By the way, you have to be sympathetic, I think, to OpenAI in this because if you're building a startup...... 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, it, Travis said it well, like, you're pro- 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 (laughs) 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 one area where-

Jason Calacanis 37:14 Security.

Chamath Palihapitiya 37:15 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 EC2 endpoint or an S3 bucket, (laughs) 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.

David Friedberg 37:53 Here's the other side, and totally agree with Chamath, but here's the other side. You go through the white paper, you see what it is they did, what they innovated on-

Chamath Palihapitiya 38:03 Yeah.

David Friedberg 38:03 ... the science behind it, the thoroughness, and you're like, "These guys are badass." It doesn't-

Chamath Palihapitiya 38:10 They're badass, yeah.

David Friedberg 38:10 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-

Chamath Palihapitiya 38:23 I agree with that.

David Friedberg 38:23 ... real innovation, strong tech. You're like, "This is legit."

Chamath Palihapitiya 38:28 I agree with that.

David Sacks 38:28 But in that paper, they're very hazy about where the data's coming from and there, 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 V3, which is the base model, to R1, which is the reasoning model, they had about 800,000 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 R1 with just 800,000 samples.

Chamath Palihapitiya 39:03 But this is the 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 orthodoxical 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 39:40 I think the best part of this is the fact (laughs) that Sam Altman was supposed to be doing open source. He made it a closed source company, he stole everybody's data, every... He 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 zero sympathy for him or the team over 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 Altman's comeuppance for him stealing everybody else's content. That's my thing.

Chamath Palihapitiya 40:13 (laughs) Okay, there you have it. (laughs)

Jason Calacanis 40:15 But I don't have strong opinions on it. It's hilarious. Does nobody see the irony in this? (laughs) He was supposed to be doing open source for the good of humanity.

David Friedberg 40:24 Well, and, and it is interesting because, J-Cal, I will say, the models are closed. You're right.

Jason Calacanis 40:27 Yeah.

David Friedberg 40:27 There was the, there's the lawsuit with Scarlett Johansson for stealing her voice even when she said no.

Jason Calacanis 40:32 Yes. New York Times.

David Friedberg 40:32 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.

Jason Calacanis 40:39 Yes.

David Friedberg 40:39 So, there's a lot of being on their, on their heels a little bit, so I definitely, I definitely see your point.

Jason Calacanis 40:45 Stealing.

Chamath Palihapitiya 40:45 I think-

David Friedberg 40:46 Yeah.

Chamath Palihapitiya 40:47 ... I think the re- 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 R1, and I think that that is going to be crucial for us to have a counterweight to-

David Friedberg 41:06 Right.

Chamath Palihapitiya 41:06 ... whatever China's going to put out after this, and-

David Friedberg 41:08 But, I mean, Chamath, it's open source. Like, does it not kind of-

Chamath Palihapitiya 41:11 Well, so this i- so this is my point, like, Meta-

David Friedberg 41:14 No, embrace and extend. Embrace and extend.

Chamath Palihapitiya 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... H- 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 NVIDIA bulls, 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 clothes moment is upon us.

David Friedberg 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 670 billion 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- as an investor, where do you go?

Chamath Palihapitiya 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 one 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 LLaMA. Right now, that is kind of 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 R2 or Alibaba's model or LLaMA, I would want to be able to rip it out and put it back in and have everything work. And right now-

David Friedberg 43:36 The answer to your-

Chamath Palihapitiya 43:36 ... we can't do that.

Jason Calacanis 43:37 The answer to your question, Freeberg, is-

David Friedberg 43:39 Yeah, if you were starting a company today-

Chamath Palihapitiya 43:40 Yeah, the answer to your question is, hold on, yeah, the answer to your question is the application layer, because this is all gonna 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 one is happening faster than all the rest. Do you wanna be in the storage business or do you wanna be in the YouTube business? Do you want to be in the Uber business or do you want to 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 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 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?

Travis Kalanick 44:27 Oh, I'm getting so excited. Um, look, I- I think the first, the first degree out is, it's what you got... Is there a wrapper 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 wrapper, 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 fa- uh, 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. Um, and then, you know, look, when AI... Here's the one 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 one is actually positive. So as the price goes down-

David Friedberg 45:34 That's right.

Travis Kalanick 45:34 ... the revenue usage, everything's gonna go up. We've seen this-

David Friedberg 45:37 It goes through the roof, through the roof.

Travis Kalanick 45:38 This is the his- history of tech forever since like Bill Gates said, "I don't know what to do with more than 64 kilobytes of memory." Like-

Jason Calacanis 45:45 Yeah.

Travis Kalanick 45:46 ... you know, the question is, did we, you know-

David Friedberg 45:48 Cheap oil, 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 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.

David Friedberg 46:07 Right. Yeah.

Travis Kalanick 46:08 Like you're gonna start getting into nuances of like, what does the invest- the investor AI look like? What does the, uh, autonomous car AI look like? What does the, uh, Google search, I'm trying to figure some shit out AI look like?

Jason Calacanis 46:20 The lawyer, the accountant, pilot.

Travis Kalanick 46:23 Yeah, yeah. So it could go vertical in "siloed," siloed, air quotes, if you understand what I'm saying.

David Friedberg 46:28 Absolutely. Absolutely.

David Sacks 46:28 Yeah, so there's a- a thing called, uh, Jevons paradox, which kind of speaks to this concept. Satya actually tweeted about it, which is the, it's an 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 want to use more and more of it. So you might actually get more spending on it in the aggregate.

David Friedberg 46:55 That's right.

David Sacks 46:56 Because more and more applications will become cost-efficient.

David Friedberg 46:59 Economically feasible, yeah.

David Sacks 47:00 Economically feasible, exactly.

David Friedberg 47:02 Yeah.

David Sacks 47:02 That is, I think, a powerful argument for why companies are gonna want to 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 one thing, the- the r1 model is- is basically comparable to o1, which- which OpenAI released four months ago and was training on internally, call it nine or ten months ago. So, OpenAI is on o3 now. Its frontier is ahead of where r1 is. Anthropic and Google, I think, have things in the work, and even Meta, that may be ahead of where r1 is. So, I think r1 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 48:19 Anybody, uh, have any other questions for Sacks before we drop him off to go back to serving the American people, before we drop him off?

David Sacks 48:27 One 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 this stuff.

Jason Calacanis 48:47 Oh, they stole it. They stole it. It's the Chinese, come on.

David Sacks 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.

Jason Calacanis 49:04 Absolutely.

David Sacks 49:05 And, you know, they're trying to basically undercut the leading American companies. And I, I don't think they did it with $6 million. I mean, they have massive resources behind them.

Jason Calacanis 49:15 Absolutely.

David Sacks 49:15 So I think some of the, the pro-DeepSeek vibes, I think, are ... They're a little bit naive, you know, in Silicon Valley. It's like-

Jason Calacanis 49:23 That's only the, uh, people who worked for Sam previously and quit who feel that way. (laughs) In order to cash out their options.

David Sacks 49:29 No, I think there's a lot, I think there's a lot of, like, support for DeepSeek-

Jason Calacanis 49:32 Yeah, there is.

David Sacks 49:32 ... in 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.

Jason Calacanis 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 bu- 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 two things could be true at the same time. David, thank you so much for coming on. We appreciate it. And, uh, thank you for all your hard work and service.

Travis Kalanick 49:57 Thank you for coming on your own podcast, David. We appreciate it. (laughs)

Jason Calacanis 49:59 Thank you, David. I know that this is, uh ...

Travis Kalanick 50:02 (laughs)

Jason Calacanis 50:02 And now we're gonna talk about a bunch of other crazy stuff.

Travis Kalanick 50:04 You're a scholar. You're a scholar and a gentleman, David.

Jason Calacanis 50:05 Yes. Thank you. All right. Thanks to David Sacks for coming in. And, uh, you know, I, I guess let's open up the aperture here and talk a little bit

Jason Calacanis 50:14 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, uh, the sort of trade war going on here with, uh, exports of H-100s. Where do we wanna start, gentlemen? And, you know, Travis, you've got some, uh, deep ... You're one of probably five American entrepreneurs who ran an at-scale business with Uber and the DiDi relationship-

Travis Kalanick 50:40 Yeah.

Jason Calacanis 50:40 ... in China, so you have a unique position of understanding business in this, along with-

Travis Kalanick 50:44 Yes.

Jason Calacanis 50:44 ... maybe Tim Cook and Elon are the only other two people who've really had an at-scale business there.

Travis Kalanick 50:49 Yeah.

Jason Calacanis 50:49 Maybe Disney. They have Disneyland there, yeah.

Travis Kalanick 50:51 Do they? I don't know.

Jason Calacanis 50:52 What's your take on the relationship and what's going on here geopolitically?

Travis Kalanick 50:55 Well, how's 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 development. Okay, so, okay, so look. We, I had this thing ... This is, I'm going back almost 10 years here. Uber Day, we're running Uber China, and w- I mean, I cannot ... There's no way I could express the frenetic intensity of copying that they would do on every thing that we would roll out in China. Mm.

Jason Calacanis 51:31 Mm.

Travis Kalanick 51:32 And it was so epically intense that I basically had a h- 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'd dial it, and it would be epic and it would be awesome. We'd roll it out and then, like, two weeks later, boom. (laughs) They've got it. A week later, boom. They've got it. And of course, I used 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 101 in Silicon Valley in Chinese. Uber billboards to join our team in Chinese, to, to serve the homeland, right?

Jason Calacanis 52:33 Mm-hmm.

Travis Kalanick 52:34 Um, 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.

Jason Calacanis 52:42 (laughs)

Travis Kalanick 52:43 Like, they ruled China style. That's awesome. Like, the desks were literally smaller. Like, the density-

Jason Calacanis 52:49 Elbow to elbow.

Travis Kalanick 52:49 ... of the space was, it was China, okay? So but what happens is when you get really, really good at copying and that sh- time gets tighter and tighter and tighter and tighter and tighter, you eventually run out of things to copy. Mm. And then it flips ... To creativity and innovation. Mm-hmm. Now, at the beginning, you, you know, it's sort of all over the place. Like, the kind of innovation when it was new was, like, "Wh- 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. Mm. Right?

Jason Calacanis 53:32 What's an example? Like, of, uh, like something really innovative they're doing.

Travis Kalanick 53:35 Well, doesn't, doesn't Meituan do drone delivery and stuff? Like, here's an example. If you went to offices, like, let's say Shanghai, um, Beijing, any of the major cities, Hangzhou, et cetera, 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 inter-office ... Runners. ... runners that then bring it to your office, as an example. Like, and when you see it, you're like th- and it's epically efficient. And it's, you know, they're taking a- 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, er, 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 54:51 Yeah. Here's a look at (laughs) these smart lockers that you can see. You know, just available for sale when you, when you go online but yeah, these things are crazy. And y- you've experimented with those as well, didn't you?

Travis Kalanick 55:00 Yeah.

Jason Calacanis 55:00 Haven't you had like a commissary concept in DTLA?

Travis Kalanick 55:04 Well look, we... Okay, so we got a couple of things. So w- 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 never, you don't have lines anymore. There's no more lines, which then speeds up delivery, shortens the amount of time, shortens, it, 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 100 different restaurants, those restaurants happen to be in my facilities, and there'll be one courier that goes to one 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 it arrives the same tame- time every day, and you just go to the shelf, get it on your floor and dip 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 Sweetgreen right down there, uh, in my current, in my office right now, uh, I can save 20 minutes by just d- using our own service versus doing that, and you get it 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 zero.

Jason Calacanis 56:39 What do we think of the, um, of the export controls here? Should we, Chamath, be maybe banning more H100s or other chipsets going there or is that futile?

Chamath Palihapitiya 56:49 I don't know the answer to that and I think that's... I think Sachs and President Trump-

Jason Calacanis 56:54 DJT.

Chamath Palihapitiya 56:54 ... 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 57:25 Uh-oh.

Chamath Palihapitiya 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 100% 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 it's (laughs) like a small, small place.

Jason Calacanis 58:05 Also the TikTok headquarters. (laughs)

Chamath Palihapitiya 58:07 And I tried to find out how many data centers are in Singapore, and it's about 100. And so you would think that okay, well what does that mean? 100 could mean anything. But then you look at the energy and they publish that, and all of those 100 data centers consume about 876 megawatts. So these are small data centers, right?

Jason Calacanis 58:29 Mm-mm.

Chamath Palihapitiya 58:29 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-

Jason Calacanis 58:39 I know.

Chamath Palihapitiya 58:39 ... 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 want to react?

David Friedberg 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 and 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-

Chamath Palihapitiya 59:57 It's worse than that."

David Friedberg 59:58 ... our own infrastructure."

Chamath Palihapitiya 59:59 And 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, one of the luckiest things that happened to Groq 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 two 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, Friedberg, 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 1:00:51 I'm just not sure it solves the problem, is my point.

Chamath Palihapitiya 1:00:53 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?

Jason Calacanis 1:01:02 Competition.

David Friedberg 1:01:03 Or AI ushers in an era of extraordinary abundance, and that abundance ultimately reduces the, the drive for conflict and things are better off.

Chamath Palihapitiya 1:01:11 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 one central authority bears the capex of creating something that then everybody else can, can draft off of.

Jason Calacanis 1:01:43 And let's talk a little bit about OpenAI's, uh, they're in Washington asking for money now. Is that the, uh, is that the concept now is that th- our

Jason Calacanis 1:01:51 government-

David Friedberg 1:01:51 The-

Jason Calacanis 1:01:51 ... should back?

David Friedberg 1:01:52 The rumor today was they're raising 40 billion at a $340 billion pre-money, with Masa potentially being the lead. I would love Travis' read on this 'cause-

Chamath Palihapitiya 1:02:02 Yeah.

David Friedberg 1:02:02 ... 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 to you guys the meeting I had with him last summer where he basically kicked me out of the room 'cause my company's not generative AI.

Jason Calacanis 1:02:16 (laughs)

Chamath Palihapitiya 1:02:16 (laughs)

David Friedberg 1:02:16 He was 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, "Uh, this is not generative AI. I, uh, only do generative AI. I think your company will be very successful. You will be very successful. Goodbye." And he just walked out.

Jason Calacanis 1:02:29 (laughs)

David Friedberg 1:02:29 And that was like the end of everything.

Jason Calacanis 1:02:31 So grim.

David Friedberg 1:02:31 Uh-huh.

Chamath Palihapitiya 1:02:32 Yeah.

David Friedberg 1:02:32 But that's all he's doing now. So this is the big bet, right?

Travis Kalanick 1:02:35 So, okay, so I need to, I need to bust a myth. I did not take money from Masa. So he begged me to take money for years, and we did not take it because he is a, he's a, what's the word I'm looking for? He's a pro-

Jason Calacanis 1:02:51 I don't know.

Travis Kalanick 1:02:51 He's a promiscuous investor. So-

Jason Calacanis 1:02:53 (laughs)

Travis Kalanick 1:02:54 ... once he in-

Jason Calacanis 1:02:54 Sharp eyehole?

David Friedberg 1:02:54 Mm-hmm.

Travis Kalanick 1:02:54 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 have just saturated, soaked up the money that was there." So th- one of the things you should think about w- like when you look at like, oh, is OpenII 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.

Chamath Palihapitiya 1:03:39 Mm-hmm.

Travis Kalanick 1:03:39 And that is the nature of the Masa machine.

Chamath Palihapitiya 1:03:42 So you're damned if you do, damned if you don't.

Travis Kalanick 1:03:44 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.

Chamath Palihapitiya 1:03:54 Hmm.

Travis Kalanick 1:03:55 Now, we were able to, we, we, we did stuff with the Saudis before even Vision Fund existed. They stroked a three and a half billion dollar 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.

Chamath Palihapitiya 1:04:12 Mm.

Jason Calacanis 1:04:12 DoorDash.

David Friedberg 1:04:13 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 billion, where does it go when he's up against, we don't know what in China, Microsoft, Alphabet, and Meta?

Travis Kalanick 1:04:31 Well, look, I think this goes to some of the things that Chamath 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, i- if that's the case, you get into a real weird spot when you get over-

Chamath Palihapitiya 1:04:49 Capitalized.

Travis Kalanick 1:04:50 ... overcapitalized.

Chamath Palihapitiya 1:04:51 Yeah.

Travis Kalanick 1:04:51 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, uh, in the, in, in this discussion. So it was necessary. You're screwed if you don't. The, the question is, do you get to this place of overcapitalized, too big f- you know, too bureaucratic, too loose, too weak, too soft? Uh, 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. Uh, 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, uh, is gonna be okay in other places. And, uh, I think we could probably spend days or m- hundreds of, dozens of hours just talking about the nuances there, you know?

David Friedberg 1:05:54 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-

Travis Kalanick 1:06:03 Yeah.

David Friedberg 1:06:03 ... 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 one large do-everything model. This mixture of experts architecture-

Travis Kalanick 1:06:31 Mm-hmm.

David Friedberg 1:06:32 ... ultimately you can kind of think about taking a large model, making two copies of it, and then trying to shrink each model down to whatever the necessary so that you run two models in, less frequently. Meaning that that combination of two models uses less power and takes less time. And then you do the same thing again and you shrink it down to four and then t- twelve. And eventually you have lots of smaller models, some of which in some cases are experts at one 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-

Chamath Palihapitiya 1:07:11 Yeah.

David Friedberg 1:07:11 ... 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 runtime, but also in, like what's needed. Like do you really create much of an advantage by having all these data centers? Do you create much of an advantage-

Jason Calacanis 1:07:26 But that's the key, this is the key point, I think, Freeberg, is that you're not gonna get an advantage by having more H100s at a certain point. And the actual advantage is gonna be in the IP and owning content, and the really smart thing to do-

David Friedberg 1:07:37 Data.

Jason Calacanis 1:07:37 ... would 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 1:07:48 Well, I take Washington Post off that list, but yes.

David Friedberg 1:07:50 (laughs) But I'll say ... (laughs)

Travis Kalanick 1:07:52 And New York Times comes off the list too.

David Friedberg 1:07:54 (laughs)

Jason Calacanis 1:07:54 Well, whatever. I mean, all those archives-

David Friedberg 1:07:56 Post 1990, uh, post-2010.

Jason Calacanis 1:07:56 ... 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 IP historically-

David Friedberg 1:08:07 But Jason, a lot of what-

Jason Calacanis 1:08:07 ... and you could just sue the hell-

David Friedberg 1:08:08 Yeah.

Jason Calacanis 1:08:08 ... out of them, and then you've got the best, most proprietary one. And, and Reddit keeps giving-

David Friedberg 1:08:13 You're describing, you're, you're, you're describing text. So you're describing text content, which is a fraction of-

Jason Calacanis 1:08:17 Also video. Also video.

David Friedberg 1:08:17 Where ... Oh, and this is important.

Travis Kalanick 1:08:18 Yeah, yeah.

David Friedberg 1:08:18 So video, I think you can recognize that Google's YouTube content library is probably 100 to 200 times larger than the rest of the internet combined. So YouTube is a s-

Jason Calacanis 1:08:27 But they don't have the right to do it in their terms of service.

David Friedberg 1:08:30 Well, they, they do actually. So, so if you-

Travis Kalanick 1:08:31 Jason, you're such an old school copyright guy.

David Friedberg 1:08:33 Yeah.

Travis Kalanick 1:08:33 You're such an old school media guy, by the way.

Jason Calacanis 1:08:35 A lot of it, believe in, sorry, I believe in artists and their right to content, yeah.

David Friedberg 1:08:38 We've had a series of conversations that I, I feel very confident to tell you that they do have the right in s- 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 prescient 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.

Travis Kalanick 1:09:24 Yeah. 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 the 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 one 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. Um, but I think this is an interesting sort of trade-off between some of these, these variables.

Jason Calacanis 1:10:02 I got just about offered 2,500 bucks to put Angel, my book into, because HarperCollins did a, a deal with, uh, Microsoft. And so I'm thinking-

David Friedberg 1:10:11 2,500 per year?

Jason Calacanis 1:10:13 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 bucks per book for three years, and I think I'm gonna just do it just to support proper licensing so that people can start going down, uh, this path. But let's get into DOGE. It's been a, uh, I think we're in, 10 days into this administration and, um,

Jason Calacanis 1:10:37 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 $1,000 a year in savings for each US citizen. And they claim they can triple this, and so for a family five, that'd be about, what, $15,000 a year, maybe $60,000 during Trump's, uh, second term. We got $36 trillion 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, um, s- I mean, this could be something like $100 billion in savings. Eight months of severance, um, 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, uh, the federal government is terminating a ton of stuff they own and gonna sell it and consolidating folks and at the same time all of this is happening, everybody has to return to office. Who wants to go first here with, uh, you know, the sort of first 10 days of DOGE? I see some eggplant emojis in the group chat. First 10 days of DOGE, how excited are you?

Chamath Palihapitiya 1:12:27 How do I get in on this group chat? What's that about?

David Friedberg 1:12:28 (laughs)

Chamath Palihapitiya 1:12:29 Uh, I'm adding you right now.

Jason Calacanis 1:12:29 Yeah, how are you not in the group chat? Get in the group chat.

David Friedberg 1:12:31 I'm adding, I'm adding you right now. I'm adding you right now.

Jason Calacanis 1:12:32 Literally every time one of these hits the group chat, it's just hilarious-

David Friedberg 1:12:36 Polka crew.

Jason Calacanis 1:12:37 ... eggplants.

David Friedberg 1:12:37 (laughs)

Jason Calacanis 1:12:37 People are like, "Oh my God. We're, we're not burning tax payer dollars."

David Friedberg 1:12:42 I'm, I'm adding you right, I'm adding you right now.

Jason Calacanis 1:12:43 (laughs) And I, and, and the eggplant always comes from Freeberg first.

David Friedberg 1:12:46 Oh, okay.

Jason Calacanis 1:12:46 I'm outing him-

David Friedberg 1:12:47 (laughs)

Jason Calacanis 1:12:47 ... as an eggplanter.

David Friedberg 1:12:48 I'm a, I'm a, I'm a, I'm a big DOGE eggplant guy. Yeah.

Jason Calacanis 1:12:51 Oh, so much eggplant.

Travis Kalanick 1:12:53 Oh, man.

Jason Calacanis 1:12:53 So Freeberg, tell us about how much eggplant you love this.

David Friedberg 1:12:57 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 publish their piece in the Wall Street Journal a couple of weeks ago. They talked about the mechanisms of action that they could utilize to kind of drive reduction in cost, one of which was come back to the office, another one 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-

Travis Kalanick 1:13:28 Yeah.

David Friedberg 1:13:29 ... 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 zero 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 assumed 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 trillion annual deficit right now in the United States, um, federal government. 2 trillion 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 got to cut about a trillion, trillion one of spending. If we can do that, then we're in kind of a more economically sound place. By the way, a 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, um, its debt obligations over the next 30 years, which is why 30-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 will stop-

Travis Kalanick 1:15:27 But it's also inflationary. It's also inflationary, Dave.

David Friedberg 1:15:30 And it's impulsive. That's right.

Jason Calacanis 1:15:30 Yeah, yeah. For sure.

David Friedberg 1:15:31 And so as we cut spending, we also will see that the intent... th- the, there'll be less inflation and the US ability to pay back their debt obligations over the next 30 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 num- 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 going to go to Congress and start to try and get some