Grok 4 Wows, The Bitter Lesson, Elon’s Third Party, AI Browsers, SCOTUS backs POTUS on RIFs

Summary

All-In Podcast: Grok 4 Wows, The Bitter Lesson, Elon’s Third Party, AI Browsers, SCOTUS Backs POTUS on RIFs

Release Date: July 11, 2025
Hosts: Chamath Palihapitiya, Jason Calacanis, Travis Kalanick, Keith Rabois
Guest: Keith Rabois

---

1. Welcome and Introduction (00:00 - 02:10)

The episode kicks off with the hosts engaging in light-hearted banter, sharing personal anecdotes and catching up with each other's recent activities. The absence of David Sacks and David Friedberg is noted, with Travis Kalanick stepping in as the guest panelist.

---

2. AI Breakthroughs: Grok 4 and The Bitter Lesson (02:11 - 27:07)

Grok 4 Launch and Capabilities
Chamath introduces the primary topic: Elon Musk’s latest AI model, Grok 4, which boasts outperforming competitors like OpenAI’s GPT-4 and Google’s Gemini in several benchmarks.

• Notable Quote:
  Chamath Palihapitiya [04:41]: "Grok 4 base model has surpassed OpenAI's O3 Pro, Google Gemini's 2.5 Pro as the most intelligent model. This includes reasoning, math, coding, and more."

The Bitter Lesson by Rich Sutton
The discussion delves into Rich Sutton's "The Bitter Lesson," emphasizing the superiority of general computational approaches over human-engineered solutions in AI development.

• Notable Quote:
  Jason Calacanis [20:28]: "The Bitter Lesson basically says that you're always better off taking a general learning approach that can scale with computation because it ultimately proves to be the most effective."

Implications for the AI Industry
Travis and Keith debate the impact of Grok 4's capabilities, suggesting that AI’s shift towards scalable, general computation will render human-labeled data and traditional methods obsolete.

• Notable Quote:
  Travis Kalanick [06:54]: "Our mission is infrastructure for better food... taking a general purpose compute approach, which is what we took, waited for these cost curves to come into play, and now you can scale food to every human on Earth."

---

3. Elon Musk’s Political Ambitions: Creating a Third Party (27:08 - 55:55)

Introduction of Elon’s Third Party
Chamath brings up Elon Musk’s announcement to form a new political party aimed at addressing fiscal responsibility, sustainable energy, manufacturing dominance, pronatalism, and technological excellence.

• Notable Quote:
  Chamath Palihapitiya [58:11]: "When it comes to bankrupting our country with waste and graft, we live in a one-party system, not a democracy."

Panel Discussion: Viability and Impact
Keith Rabois critiques the feasibility of Elon’s third-party endeavor, likening it to Michael Jordan’s attempt at baseball—exceptionally difficult given the entrenched two-party system.

• Notable Quote:
  Keith Rabois [63:40]: "Elon Musk is probably a replacement level politician. He's Michael Jordan for entrepreneurial stuff, but the third party stuff is not going to work."

Potential Strategies for Success
Travis suggests that Elon might focus on winning selective House and Senate seats to build a foundational presence, while Keith emphasizes the challenges due to existing party dynamics and voter behavior.

• Notable Quote:
  Travis Kalanick [64:40]: "There's a lot of people that agree with the types of things he's saying. And he knows how to draw. I think it's great... having a few folks and then being levers to get the things you want done."

---

4. AI-Integrated Browsers: The Future of Search (55:56 - 57:36)

Emergence of Agentic Browsers
Chamath discusses the advancements in AI browsers, highlighting features like multi-agent capabilities that perform complex tasks, such as booking flights or managing shopping carts autonomously.

• Notable Quote:
  Chamath Palihapitiya [48:43]: "Having an agentic browser like this available to you to be doing these tasks in real-time... it's a new product category."

Panel Insights on Market Competition
Keith Rabois criticizes current players like Perplexity for their inadequate browser offerings and suggests that integrating AI with robust data sources is crucial for competing with giants like OpenAI.

• Notable Quote:
  Keith Rabois [49:40]: "Perplexity is toast... Whoever's up has uphill ground... Google should be doing this, but they're going to lose the search game."

---

5. Supreme Court Decision on Federal Workforce Reductions (57:37 - 82:20)

Overview of the SCOTUS Ruling
Chamath outlines the Supreme Court’s decision supporting President Trump’s executive order to reduce the federal workforce, a move previously blocked by lower courts citing the need for Congressional approval.

• Notable Quote:
  Chamath Palihapitiya [73:03]: "They have the right to prepare a RIF. They have the right to execute on that RIF."

Implications for Government Efficiency
Jason and Keith discuss the potential outcomes of this ruling, arguing that it could lead to more streamlined government operations by allowing the Executive branch greater flexibility in workforce management.

• Notable Quote:
  Jason Calacanis [75:11]: "If President Trump isn’t allowed to fire people, then all of that stuff just compounds... allowing us to level set how big should the government be."

Constitutional and Practical Challenges
The panel debates the constitutional boundaries of executive power versus Congressional authority, with Keith emphasizing that while the President has significant leeway, certain limitations and ongoing litigations may affect the implementation.

• Notable Quote:
  Keith Rabois [80:55]: "The Constitution clearly says that all executive power resides in the President of the United States, period."

---

6. Personal Anecdotes and Light-Hearted Conversations (82:21 - 90:25)

Backgammon and Personal Interests
Travis shares his newfound passion for backgammon, discussing his involvement in local tournaments and the community's response to his participation.

• Notable Quote:
  Travis Kalanick [85:22]: "I recently purchased the preeminent backgammon engine, XG—Extreme Gamut."

Casual Banter and Future Plans
The hosts engage in humorous exchanges about potential collaborations, such as co-branding ventures, and discuss leisure activities like water skiing and enjoying tequila.

• Notable Quote:
  Chamath Palihapitiya [88:57]: "We got to get some of that money on the table because you don't play poker with us."

---

7. Closing Remarks and Final Thoughts (90:26 - End)

Chamath wraps up the episode by summarizing the key discussions, expressing excitement about future topics, and encouraging listeners to engage with the panel’s diverse insights.

• Notable Quote:
  Chamath Palihapitiya [89:44]: "This has been another amazing episode of the number one podcast in the world..."

---

Key Takeaways

• AI Evolution: Grok 4 represents a significant leap in AI capabilities, emphasizing the power of general computational approaches over human-engineered solutions, aligning with Rich Sutton's "The Bitter Lesson."

• Political Shifts: Elon Musk’s attempt to form a third political party faces substantial challenges within the entrenched two-party system, though selective successes in congressional races might lay foundational support.

• AI in Everyday Tools: The integration of AI into browsers marks a transformative step in how users interact with the internet, potentially phasing out traditional search interfaces in favor of agentic, task-oriented AI assistants.

• Government Efficiency: The Supreme Court's decision to support executive orders for workforce reductions could pave the way for more streamlined and efficient government operations, though constitutional debates remain.

Notable Quotes with Timestamps

• Chamath Palihapitiya [04:41]: "Grok 4 base model has surpassed OpenAI's O3 Pro, Google Gemini's 2.5 Pro as the most intelligent model. This includes reasoning, math, coding, and more."

• Jason Calacanis [20:28]: "The Bitter Lesson basically says that you're always better off taking a general learning approach that can scale with computation because it ultimately proves to be the most effective."

• Keith Rabois [63:40]: "Elon Musk is probably a replacement level politician. He's Michael Jordan for entrepreneurial stuff, but the third party stuff is not going to work."

• Chamath Palihapitiya [58:11]: "When it comes to bankrupting our country with waste and graft, we live in a one-party system, not a democracy."

• Keith Rabois [80:55]: "The Constitution clearly says that all executive power resides in the President of the United States, period."

---

This episode offers a comprehensive exploration of cutting-edge AI developments, the intersection of technology and politics, and significant legal decisions shaping the federal workforce. The panel provides insightful analysis, blending technical expertise with strategic foresight, making it a must-listen for enthusiasts across these domains.

Transcript

[Jason Calacanis]

I have a very funny story to tell you.

[Chamath Palihapitiya]

Jason, where have you been? I've been trying to text you. You've been offline. What's going on? Where have you been?

[Jason Calacanis]

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

[Chamath Palihapitiya]

Who's the beautiful woman there? Is that the woman who owns it or something? Is that the queen?

[Jason Calacanis]

That's not. But the best part is we had such a good time. You know how they have like a registry book to leave a message?

[Chamath Palihapitiya]

Sure.

[Jason Calacanis]

So I left a message.

[Chamath Palihapitiya]

Here we go. What a truly magnificent place. Above and beyond any expectation we had.

[Jason Calacanis]

Go below. Go below that stop for me.

[Chamath Palihapitiya]

Thank you. We took everything. We took everything. The Free Birds.

[Travis Kalanick]

Great.

[Chamath Palihapitiya]

Awesome.

[Jason Calacanis]

Jason, the hangers. Okay, Everything.

[Chamath Palihapitiya]

The laundry bags, did you get the.

[Jason Calacanis]

Base, the robes, the slippers, everything.

[Chamath Palihapitiya]

Absolutely fantastic.

[Jason Calacanis]

They're gonna have to send a bill to the Free Birds.

[Chamath Palihapitiya]

Absolutely.

[Keith Rabois]

Let your winners ride Rain Man.

[Chamath Palihapitiya]

David sat and I said we open sourced it to the fans and they've just gone crazy with it. Love you, Queen of Quino. All right, listen, we've got a great panel this week. It's the summer, things are slow, some people are busy. I think our prince of panic attacks, our dear sultan of science is. He's at the beep. Sachs is busy. Couldn't make it. This week. In his place, another brilliant PayPal alumni and, dare I say, GOP supporter, Heath Raboy. How are you, sir?

[Keith Rabois]

Pleasure to be with you again.

[Chamath Palihapitiya]

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

[Keith Rabois]

Yeah, I'm actually in New York.

[Chamath Palihapitiya]

Oh, my hometown. Is it safe? Is it okay, mom dummy chasing you down the street?

[Keith Rabois]

Not yet, but it's safe.

[Chamath Palihapitiya]

Did he seize your assets?

[Keith Rabois]

It's safe right now. We'll see on November 4th. You know, as you probably heard, on July 4th was the first time in recorded history that there were no shootings or no murders in. In New York on that day. So right now things are in pretty good shape. But we may be maybe leaving New York quickly.

[Chamath Palihapitiya]

Yeah, you're gonna probably wanna sell that place if you got one there, because Mumdami is gonna seize it and turn it into a drugstore for you. Yes, it's gonna be Dami Drugstores. Travis Kalanick is back with us. How you doing, bestie?

[Travis Kalanick]

Pretty good, Pretty good, yeah.

[Chamath Palihapitiya]

Second appearance here on the Round Table and third time on the show, of course. You spoke at the Simon. You've been busy with cloud kitchens. Yeah. Lots of exciting things going on.

[Travis Kalanick]

Oh, lots of stuff. Lots of stuff. The robots. The robots are taking over. We're rolling out. We're rolling out robots. Yeah.

[Jason Calacanis]

Tk, can you tell us what you're doing with this pony AI thing or not? That's speculation.

[Travis Kalanick]

Look, you know, obviously is autonomy, as we, you know, in the US we have, of course.

[Jason Calacanis]

Do you want to just frame for people that don't. That may not be up to speed, what was announced, or at least why.

[Travis Kalanick]

Don'T you frame it?

[Chamath Palihapitiya]

So is an autonomous company doing self driving? It's one of the few players that actually have cars on the road. They're based in China, They've got a lot of operations in the Middle East. They've got a deal with a delivery company called Uber, which you might be familiar with.

[Travis Kalanick]

Okay, so look.

[Jason Calacanis]

Well, the deal was basically that you partner with Uber, license in the pony technology, and essentially start a competitor, I guess, to Waymo and Tesla.

[Travis Kalanick]

Let me work on this one. Okay, so in the US we have Waymo. We see the Waymos in San Francisco, Los Angeles, Austin, coming soon to Miami, coming soon to Atlanta, coming soon to dc. They're even talking about New York. Tesla's sort of like they're doing it the hard way, classic Elon style. Like, let's do this sort of in a fundamental holy shit, let's go all the way kind of approach. And it's unclear when it gets over the line. Of course, he launched sort of a semi pilot of sorts in Austin recently, but there's no other alternatives. So what happens is some of the folks who are interested in making sure there are alternatives have reached out. They've reached out to me. And there are different discussions that get going because they're like, Travis, you did autonomy way back in the day, got the Uber autonomous stuff going in 2014. Maybe there's something to do here to create optionality. Maybe, like, I'm of course, very interested on the food side. I talk about autonomous burritos being a big deal, because if you can automate the kitchen, the production of food, and then you can automate the sort of logistics around food, you take huge amount of costs out of the food, out of what's going on in food, and that's of course near and dear to my heart. There's folks of course that want to see autonomy and mobility. That's a real thing. It may be that or I would say if you get the autonomy problem right, you can use it to apply to both problems. So there's a lot of folks interested in moving things, moving food, moving people. And if there is some kind of autonomous technology that maybe I get involved in, it might apply to a bunch of different things. And so I've got some inbound, let's just put it that way. There's no real deal right now, but there is definitely some inbound. And I think there are some news about some of that inbound that may or may not be occurring. That's probably the best way to put it as long winded. I'll try to tighten that up next time.

[Chamath Palihapitiya]

No, no, I think it's great to get the overview here first on all in sharing it with us. And everybody knows you have been doing A Bowl Builder Lab 37 I think it's called. You can throw it up on the screen. Not sure what the status of it is and then I'll let you go chamath with your follow up question. But I think there's a pretty interesting concept here of the bowl getting built and then put into a self driving car.

[Travis Kalanick]

Now that machine looks huge, but it's actually 60 square feet. That picture makes it look monstrous. It's a 60 square foot machine. Like imagine running like a sweetgreen like brand or a Chipotle like brand of just making it so it comes to life for people who, who you know are like hey what is this thing? Imagine you just order online exactly the kind of bowl you want. And actually this machine could run like many brands at the same time. And does you build the bowl you want whatever ingredients. It's sort of if you look at that bottom, you see those little white bricks at the bottom. That's what carries the bowl underneath, dispensers. It fills up the machine puts it sauces the bowl, then it puts a lid on it. It takes the bowl, puts it in a bag, puts utensils in the bag, seals the bag and the bag goes down a conveyor belt where then another machine, what we would call an AGV takes the bowl to the front of house. The bowl gets put into a locker. The courier be a doordash Uber eats courier will wave their app in front of a camera and it will open up the locker that has the food that they're supposed to pick up. So it Just it takes out a lot of what we would call the cost of assembly, which is it reduces mistakes.

[Chamath Palihapitiya]

Right, Reduces. Hard for us to make a mistake.

[Travis Kalanick]

Yeah, we know exactly how many grams of every ingredient are put in. That's exactly what you're supposed to get. And so you get a higher quality product. It takes a lot of the cost out. You imagine ultimately that's going to be. They're going to be couriers with that as well. That I like to say autonomous burritos. Like is a Waymo going to carry a burrito or is Tesla going to have a machine that carries food? Or is there another company that ends up doing sort of the things, the autonomous delivery of things? And the point is. Well, where we are right now is we've got customers. And so those customers are starting to deploy this quarter. And it's pretty interesting. I mean, you can see in our delivery kitchens, the cost of labor is about 30% of revenue. That's what the successful guy. Let's say 30%, 35% of revenue. In a brick and mortar. In a brick and mortar restaurants, it's even higher when they're running our machine, it's between 7 and 10% of revenue.

[Chamath Palihapitiya]

Amazing. Then you take out the cost of the delivery, you know, and now it's becoming everybody can have a private chef. Which was your original vision for Uber. It was. People don't know the original tagline, but it was your, your pr. Everybody has a private driver.

[Travis Kalanick]

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

[Chamath Palihapitiya]

All right, Keith, what are your thoughts? Any questions for.

[Keith Rabois]

Well, he's not here, but isn't this what David Freeberg tried to do a Few years ago.

[Travis Kalanick]

Yeah, this came up on the last all in. Yeah. Or the last one I was at. Yeah, yeah.

[Chamath Palihapitiya]

It's a. The problem was I told Freeberg, people don't want to eat quinoa. You got to put a little steak in there, maybe a piece of salmon. But he was kind of. I think eventually he relented and let people have a little bit of protein. But. Yeah, so it's such a great vision.

[Jason Calacanis]

Wait, he. He died as a vegan martyr?

[Chamath Palihapitiya]

I think the business died.

[Travis Kalanick]

Well, that was the hill he was led to. There's a lot of people have died on that hill. But the bottom line is if you're going to get into automation, you have to. It has to be end to end automation. And what I mean by that is like, there are pizza. There are pizza companies that have come and gone, automated pizza companies, where it's like, we have a pizza machine and everybody's like, yeah, this is amazing. And you have a guy, you have a million dollar pizza machine. And then on the left you have a guy feeding ingredients into the pizza machine. And on the right you have a guy taking the pizza out and then putting it in a box and doing all this. So instead of one guy making pizzas, I have a million dollar machine and two guys making pizza. And so when you look at these robotic food production machines or food assembly machines, you have to look at the full stack and say, does it work with the ecosystem that exists in a restaurant? And does it go full stack from, you know, like, like we have this thing, that machine we saw earlier. The staff preps the food, they put the food in the machine and then they leave. They're gone. This restaurant runs itself for many hours without anybody there.

[Jason Calacanis]

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

[Travis Kalanick]

Right there. That machine is a, it's an assembly machine, right? The food is prepped by humans and then assembled by this machine for a Chipotle or a sweet green. This is like a, a majority of their labor, right? You go up to a chipotle, there's like 10 guys at lunch, and you're still in line. That machine right there does 300 bowls an hour, right? And so you go, okay, this is what's called like the assembly line. It's just that front line where you basically assemble things. I think sometimes they all call it the make line. What will happen over time is you'll have perpendicular lines going into it where you're producing food, right? So you'll have a production or make line going into an assembly line here. And then you go, oh, wow. So you have something that dispenses burgers on buns. That's the dispenser. That's the assembly.

[Jason Calacanis]

Right, but it's like Factorio on steroids, basically.

[Travis Kalanick]

Yeah. And then it's like, how do you cook that burger? That's what I call. That's what we call state change. So state change is the. Is the cooking of the food assembly. Is the. Like, how do I put it together and plate it?

[Jason Calacanis]

Doesn't this collapse? Like, for example, if you have a yield of 300 per hour, you said out of that one machine?

[Travis Kalanick]

Yes.

[Jason Calacanis]

Very quickly you can impute the value of having a smaller footprint store with five of these things in a faceless warehouse with drone delivery or cars, you don't need the physical infrastructure. So then don't you create a wasteland of real estate? Or how do you repurpose all the real estate?

[Travis Kalanick]

Well, the way to think about it is like 90%. Well, it's probably a little lower than that right now. Let's say 85% of all meals in the US are at home. They just are. And a vast majority of those meals are cooked at home. So, you know, like Uber eats and doordash, they represent like 1.8% or 2% of all meals right now. It's very tiny. Right. So what you're doing is you're using real estate to. And infrastructure to prepare and deliver meals to people at their homes. And so it's not. The restaurants still exist. We're still going to want to go to restaurants, we're still going to want to go outside. We learned that during COVID We knew it before, we definitely know it after. And so I don't. It's not really like a decimating real estate situation. It's taking a thing we used to do for ourselves and creating a service that does it higher quality, sort of. I like to say you don't have to be wealthy to be healthy and just infrastructure to get that cost down. And so you're doing something as a service that do at home. I think in the super long run, you're like, what? Where's the story on grocery stores? If you go to like in. In 20 years, I think everybody agrees you will have machines making very high quality, very personalized meals for everybody.

[Chamath Palihapitiya]

This would be good for Keith because he measures stuff down to like 5 calories based on his era.

[Jason Calacanis]

What's your. What's your body fat? Like 7%, 8%?

[Keith Rabois]

No, it's like 10.

[Chamath Palihapitiya]

Just open his Instagram. He posted four times today about his body.

[Jason Calacanis]

He's like so disgusted with himself.

[Keith Rabois]

At 10%, it's like about a 10. But I actually think the vision of this, actually the natural implication and maybe the home run version of this is everybody has a private chef in their house, a robot in their house that actually does this personalized. Because people do want to cook at home, but they don't have the time.

[Chamath Palihapitiya]

Yeah. Or space and infrastructure. But man, these delivery services are charging. Rich people do this all the time, Right. They do these crazy meal Delivery services for 200 bucks a day. And this is just going to abstract it down to everybody. And man, people get creative when there's that empty space. To your point, Chamath, about what happens to all this space. When I lived in New York in the 80s and 90s, it was common to in Tribeca in West Chelsea, where I lived, to take storefronts, put your little architect's office in the front and live in the back. And many people were hacking real estate. We still need 5, 10 million homes in this country. And they're already doing this with malls. I keep seeing malls being turned into colleges and creative spaces. One of them in Boston, they turned like the second and third floor into studio apartments for artists. So where there's a will, there's a way we could use the space.

[Travis Kalanick]

I mean, where this goes with Chamas saying, and where the real estate goes is we call it the Internet food court, where you're on Amazon, Right. It's the everything store. Now imagine that for food. And then imagine you have an 8,000 square foot facility where basically anything can be made.

[Jason Calacanis]

Anything can be made.

[Travis Kalanick]

Because if you have that machine you saw has 18 sort of dispensers for food, 10 different sauces. You get the idea. Now what about when it's 50 or 100 dispensers for food? What if you have multiple machines with 100 dispensers for food?

[Jason Calacanis]

That's crazy.

[Travis Kalanick]

The combinatorial math in terms of what, what's possible, what can be made, sort of goes exponential. And so the Internet food court is sort of the vision for where this all goes.

[Jason Calacanis]

Another example of the bitter lesson.

[Chamath Palihapitiya]

The bitter. Yeah, we're going to get to that, I guess today in a very full docket. Before we get to that, just a little bit of housekeeping here. September 7th, 8th, 9th in Los Angeles, the All in Summit again, all in dot com. Yada, yada yada. Lineup is stacked and we're going to start announcing the speakers People have been begging us to announce the speakers. I don't know.

[Jason Calacanis]

You got to hold some back.

[Chamath Palihapitiya]

Careful, careful. Hold a couple back. But we got some really nice speakers lined up. It is going to be extraordinary.

[Jason Calacanis]

It is the best one yet. I mean, well done.

[Chamath Palihapitiya]

Every year we have this. Well done. Yeah, yeah. Every year we have this little bit of panic, like, you know, we're going to get great speakers and man, they started flowing in. This week. It's going to be extraordinary. Almost as extraordinary as this delicious tequila behind my head here. Get the all in tequila. Tequila.allin.com Deliveries begin late summer.

[Travis Kalanick]

Moving to the side. You can't even tell. Tequila.

[Chamath Palihapitiya]

All right, listen.

[Jason Calacanis]

Oh, wow.

[Chamath Palihapitiya]

Lots to discuss this week. Obviously, AI is continuing to be the big story in our industry and for good reason. Our bestie Elon released Grok 4 Wednesday night. Two versions, base model and a heavy model. 30 bucks a month for the base, $300 a month for this heavy model, which has a very unique feature. You can have a multi agent feature. I got to see this actually when I visited XAI a couple of weeks ago, where multiple agents work on the same problems and they do that simultaneously, obviously, and then compare each other's work and it gives you kind of like a study group the best answer by consensus. Really interesting. According to artificial analysis benchmarks, you can pull that up, Nick. Grox 4 base model has surpassed OpenAI's O3 Pro, Google Gemini's 2.5 Pro as the most intelligent model. This includes like seven different industry standard evaluation tests. You can look it up, but reasoning, math, coding, all that kind of stuff. This is, you know, book smarts, not necessarily street smart. So it doesn't mean that these things can reason. And obviously there was a little, there was a little kerfuffle on X, formerly known as Twitter, where XAI got a little frisky and was saying all kinds of crazy stuff and needed to maybe be red teamed a little bit more decisively. Many of you know Grok4 was trained on Colossus. That's that giant data center that Elon's been building. And we showed the chart here. Chamath, you sent us a link to the Bitter Lesson by Rich Sutton in the group chat. That's the 2019 blog post. We'll pull it up here for people to take a look at and we'll put it in the show notes. Maybe just generally.

[Jason Calacanis]

Yeah.

[Chamath Palihapitiya]

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

[Jason Calacanis]

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

[Travis Kalanick]

One thing I'll throw out there, Chamath, is the Tesla approach for autonomy is taking human knowledge. In fact, the whole idea is to approximate human, human driving. Right. Is the whole damn thing. Now, depending on your approach and the technology, you can do like what's called an end to end approach, or you can look at, okay, perception, prediction, planning and control, which are like these four modules that sort of, you sort of engineer, if that makes sense. But it's approximating human driving to do it. The difference is that I think Elon's taken almost a more human approach, which is like, I've got two eyes. Why can't my car, why can't my car do it like a human? I don't have any lidar spinning around on my head as a human. Why can't my car? So it's kind of interesting he's sort of taking what you're saying, Chamath, on the computation side, because Hardware 5 is coming out on Tesla probably next year, which is going to make a big difference in what FSD can do. That's the compute side you're talking about. But then he is approximating human.

[Jason Calacanis]

Yeah, I just meant that other than the first versions of fsd, which I think Andre talked about, Andre Karpathy talked about, they're not really so reliant anymore on human labeling per se. Right. So that, that interference and then the other crazy thing that he said, subsequent versions of GROK are not going to be trained on any traditional data set that exists in the wild.

[Travis Kalanick]

The cumulative Sum of human knowledge has been exhausted in AI training. That happened basically last year. And so the only way to then supplement that is with synthetic data where the AI creates. It'll sort of write an essay or it'll come up with, with a thesis and then it will grade itself and sort of go through this process of self learning with synthetic data.

[Jason Calacanis]

He said that he's going to have agents creating synthetic data from scratch that then drive all the training, which I just think is. It's crazy.

[Chamath Palihapitiya]

Just explain this concept one more time with a bitter lesson. Hand coding heuristics into the computer and saying, hey, here's specific openings in chess.

[Jason Calacanis]

Yeah, use chess, right? Yes, chess.

[Chamath Palihapitiya]

You're hand coding specific examples of openings in there, endgames, et cetera, versus just saying, saying play every possible game and here's every game we have.

[Jason Calacanis]

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

[Chamath Palihapitiya]

In some circumstances where a car is driving down the road and it's learning in that process. Which is why you need a safety driver and I think Elon made the right decision to put one in there. Keith, your thoughts?

[Keith Rabois]

Yeah, a couple of points. It's not quite that binary. Chamath, I generally agree with your arc, but like if you think about LLMs being the most important unlock in AI, LLMs are all trained on human writing. So someone wrote every piece of data that every LLM use a human wrote at some point in history. So yes, it's true that they've shocked everybody, including OpenAI's original team on the implications, the broad implications and general applicability to almost every problem. But it's not like there was some tablets floating in space that weren't drafted by humans that we've trained on. As you get in non LLM based models, you may be totally right, but almost no one's really using non LLM based models at scale. On driving specifically, Travis is totally right that humans are actually really good drivers except when they get distracted. They get distracted by drugs or alcohol, they get distracted by being tired, they get distracted by turning the radio, they get distracted by chatting with their passenger. So training against human behaviors actually turned out to be a great decision because for whatever sort of darwinistic reasons, humans are pretty ideal drivers. And so you don't have to reason from first principles. This is a much better path. And I think again there may be a broad sort of lesson there. The most important thing I think as a VC that you said is we've been debating for years should we invest in companies like Skale or Mercur or any of these surgeons. The truth is I think there's a very short half life on human label data. And so everybody who's investing in these companies just looking at revenue traction really didn't understand that there may be a year, two years, three years max when anybody uses human label data for maybe.

[Chamath Palihapitiya]

Anything because we hit the end of human knowledge or just the collection of it.

[Travis Kalanick]

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

[Chamath Palihapitiya]

It'll just be built into the chipset that this is a stop sign. Like, it's like, we know what a stop sign is. We don't need the millionth time for.

[Travis Kalanick]

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

[Keith Rabois]

For you to be very practical, when you see a stop sign, you don't have to identify that it's a stop sign. You just see that every human, when they encounter a stop sign, 99.9% of the time they hit a break and they never. So nobody actually knows it's a stop sign. It's just that hit a break when you see something that looks like this object.

[Travis Kalanick]

It's just a vibe. Yeah, it's a vibe.

[Jason Calacanis]

I would just say that that's like intuited knowledge versus like the expressly labeled human knowledge. The question for me is if everybody was so reliant on human labeling initially, if you're an investor now, when you see these Grok4 results, how do you make an investment decision that's not purely levered to just computation? So if you look at these results, does it mean that there's 300 to 1,000 basis points of lag between just letting the computers vibe itself to the answer versus interjecting ourselves? If interjecting ourselves slows us down by 300 to 1000 basis points per successive iteration, then over two or three iterations, you've totally lost. So what does it mean for everybody that's not Grok when they wake up today and they have to decide how do I change my strategy or double down?

[Travis Kalanick]

I think, look, I'm not in the investment game, but if I were, it would be all about scientific breakthrough. So I sometimes get in this place where I'm going down a path. I'll be up at 4 or 5 in the morning, my day hasn't quite started, but I'm not sleeping anymore. And I'll start, go like I'll be on Quora and see some cool quantum physics question or something else I'm looking into. And I'll go down this thread with GPT or GROK and I'll start to get to the edge of what's known in quantum physics. And then I'm doing the equivalent of Vibe coding, except it's vibe physics and we're approaching what's known. And I'm trying to poke and see if there's breakthroughs to be had and I've gotten pretty damn close to some interesting breakthroughs just doing that.

[Keith Rabois]

And.

[Travis Kalanick]

I pinged you on at some point. I'm just like, dude, if I'm doing this and I'm super amateur hour physics enthusiast, what about all those PhD students and postdocs that are super legit using this tool? And this is pre Grok 4. Now with Grok 4, like, like there's a lot of mistakes I was seeing Grok make that then I would correct and we would talk about it. Grok4 could be this place where breakthroughs are actually happening, new breakthroughs. So if I'm investing in this space, I would be like, who's got the edge on scientific breakthroughs? And the application layer on top of these foundational models that orients that direction.

[Chamath Palihapitiya]

Is your perception that the LLMs are actually starting to get to the reasoning level, that they'll come up with a novel concept theory and have that breakthrough, or that we're kind of reading into it and it's just trying random stuff at the, at the margins.

[Travis Kalanick]

It's.

[Chamath Palihapitiya]

Or maybe it doesn't happen.

[Travis Kalanick]

No, no, no. So what I, what I've seen, and again, I haven't used Grok for, I tried to use it early this morning, but for some reason I couldn't do it on my, on my app. But. So let's say we're talking Grok 3 and existing ChatGPT as it is. No, it cannot come up with the new idea. These things are so wedded to what is known and they're so like, even when I come up with a new idea, I have to really. It's like pulling a donkey, sort of. You see, you're pulling it because it doesn't want to break conventional wisdom. It's like really adhering to conventional wisdom. You're pulling it out and then eventually goes, oh, you got something. But then when it says that, when it says that, then you have to go, okay, it said that, but I'm not sure. Like you have to double and triple check to make sure that you really got something.

[Jason Calacanis]

To your point, when these models are fully divorced from having to learn on the known world and instead can just learn synthetically.

[Travis Kalanick]

Yeah.

[Jason Calacanis]

Then everything gets flips upside down to what is the best hypothesis you have or what is the best question. You could just give it some problem and it would just figure it out.

[Travis Kalanick]

So where I go on this one, guys, is it's all about scientific method, right? If you get, if you have an LM or Foundational model of some kind that is the best in the world at the scientific method game. The S over you, basically you just light up more GPUs and you just got like a thousand more PhD students working for you.

[Chamath Palihapitiya]

Keith, you're nodding your head here.

[Keith Rabois]

I agree with that. I think that's fantastic. Because the scientific method also, the faster it is, the more when you have a hypothesis, the faster you get a response. You're more likely to dive in and dive in and dive in recursively and recursively. And every lag, every millisecond lag causes you to like lose your train of thought, sort of, so to speak. So, so you get the benefits that Travis alluding to plus speed. And you go places you never really get. This happens all the time when you run a company and you're doing like analytics and you have a tool that allows you to constantly query quickly, quickly, quickly, double click, triple click, you get to answers that you never get to if there's even a second or two second or three second delay, let alone sending it to a human. Secondly, where you actually see this today, it's already happening. If you look at foundational models that just apply to science, there's lots of things about the human body, let's say in health biology, that we humans don't actually understand all the connections. Like why do we do X? Why do some people get cancer? Why do other people not get cancer? Why does the brain work this way? Models trained solely on science tend to expose connections that no human has ever had before. And that's because the raw materials there, and we only have a conscious awareness of, call it one 10%. But when you apply it to other human domains where you're training on human sort of data, human produced data, human produced output, they're limited to that output. So I think you just take the science and apply it writ large and you're going to wind up finding things that no human has ever thought before.

[Travis Kalanick]

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

[Chamath Palihapitiya]

You wanted to say consciousness, but you stopped yourself.

[Travis Kalanick]

I was like, how do I describe.

[Chamath Palihapitiya]

The big C word consciousness?

[Travis Kalanick]

But you need to be able to test in the physical world. So you could imagine a physical lab connected to one of these systems where then you could say, okay, if it's a chemistry experiment, you could do chemistry experiments or physics you, you get the idea.

[Chamath Palihapitiya]

What could go wrong?

[Travis Kalanick]

It would be, it's, yeah, no big deal. It's going to be fine. Okay, so, but, but this is where it goes because if you have a scientific method machine, you still have to be able to test your hypothesis. You have to go through the scientific method and verification.

[Jason Calacanis]

Yeah, exactly.

[Travis Kalanick]

Yeah. Wow.

[Chamath Palihapitiya]

It's kind of mind blowing. Reminds me, it's really mind blowing if you remember, I don't know if you guys remember dark matter and like the discovery of it and everything. And as explained to me by Lisa Randall, you know, the discovery was made not by knowing there was dark matter there and observing it, but observing there was something, you know, gravitational forces around this other matter. And then they said, well wait, what's causing that? And that's why they found dark matter. So these ideas, you know, the idea that an LLM could actually do that, come up with something so novel is it doesn't, it feels like we might be right there, right? Like we're kind of on the cusp of it.

[Jason Calacanis]

One of the seven most difficult problems in math, or the most important problems in math is proving a general solution to this thing called Mavier Stokes, which is basically like viscous fluid dynamics and conservation of mass. We use it every day in the design of everything. You know what? It hasn't been proved. Isn't that the craziest thing where you're just like, how is this even possible? We use it to design airplanes, to design everything. It hasn't been proved. And so you could just point a computer at this thing and you would unlock all these incredible mysteries of the universe and we would probably find completely different propulsion systems. We could probably do things that we didn't think were possible. Teleportation. I mean, who knows what's possible?

[Travis Kalanick]

But remember, remember, you know how Elon talks about Grok and about AI generally is about why are we here? What is the purpose?

[Chamath Palihapitiya]

Meaning of the universe.

[Travis Kalanick]

What is the meaning of the universe? How does it work? And a sort of fierce truth seeking mechanism there.

[Jason Calacanis]

Let me ask you a question, Keith, Travis, Jason. If you guys were running Grok 4.

[Travis Kalanick]

To be so much fun, how do.

[Jason Calacanis]

You judo flip OpenAI? Because they are marching steadfastly towards a billion ma then a billion dao. It's a juggernaut. So how do you use the better product in a moment to judo flip the less better product?

[Travis Kalanick]

Look, yeah, I mean, here's the thing, right? So you do the Elon way. So you get a bunch of missionary, like full on missionary engineers that Work twice as hard and you have a culture that is ultra fierce truth seeking and you don't get caught up in politics, bureaucracy, B.S. and you just, you go for it and I think you know, that's where you know and then you go, wow, scientific breakthrough, scientific method, like you start winning on truth and that will start, I believe that will start to give the product awesomeness of OpenAI a run for its money. But like the product of OpenAI, the product department, those guys are rushing, they're cracking, they're really good. They're not only ahead of the game, but they feel like it just, they're just leading in a lot of different ways. But if you are better at truth, you will eventually, you'll eventually have an AI product manager and on a tactical basis too.

[Chamath Palihapitiya]

People forget how good Elon is at factories and physical real world things. What he did standing up, Colossus made like Jensen. Juan was like how is this possible that you did this right? So pressing that his ability to build factories. And he said many times like the factory is the product of Tesla. It's not the cars that come out of the factory or the batteries, it's the factory itself. So if he can keep solving the energy problem with solar on one side and batteries and standing up, you know, Colossus 2, 3, 4, 5, he's going to have a massive advantage there. On top of Travis, you know the missionary individuals, which by the way was what he backed before Sam Altman corrupted the original missionary basis of OpenAI, made it closed AI and A, you know, nothing derogatory towards him, but he did hoodwink and stab Elon in the back. It's not nothing personal. I mean he just screwed him over.

[Jason Calacanis]

Would you say he bamboozled him?

[Chamath Palihapitiya]

Bamboozled him, Screwed him, hoodwinked him. You know, pick your term here. But he did it, he did him dirty. The original mission was to open source all this content. That's the other piece I think is a wildcard and then I'll measure it in Keith's position. But open sourcing some of this could have profound ramifications. I think open sourcing the self driving data could have a really profound impact. Elon wanted to do something really disruptive like he open sourced his patents for charging if he open sourced the data set and self driving. Does anybody have the ability to produce robo taxis at the scale he can do it? I don't think so.

[Jason Calacanis]

Well, if Travis's hypothesis is true, then everybody will.

[Travis Kalanick]

What? Sorry, everybody will what?

[Jason Calacanis]

Chamath, if you have access to the money that buys the compute. Everyone could solve that problem.

[Chamath Palihapitiya]

What's the hardware piece?

[Travis Kalanick]

I'm talking which problem?

[Jason Calacanis]

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

[Chamath Palihapitiya]

Well, yes, but could somebody produce 100 million robo taxis from a factory with batteries in them?

[Jason Calacanis]

Okay, that's a different question.

[Keith Rabois]

I'm saying not really because last time I was a guest on all end we talked about vertical integration. Products really require vertical integration. So ultimately you have a self driving something that is custom built for knowing it's going to be self driving and it interacts differently. The cost structure is different, the controls are different, the seating's different, everything. You build a product taking advantage of where in the stock you have the most competitive advantage, but then you leverage that and it reinforces it's still why like Apple, despite missing the AI wave, still a pretty good company from any empirical standpoint. I mean like the performance is absolutely miserable on the most important technology through over the last 70 years, but the company's still alive and still worth trillions of dollars because it's vertically integrated. OpenAI per your point, they do have a good product team and they need to stay ahead of the product level because they can't compete on the factory level. The way to stay ahead of the product level is shipping a device. Got to ship the device. It's got to be good, it's got to be right, it's got to be the right form factor. It's got to do things for humans that are unexpected but then if they do that, they're like Apple plus AI.

[Travis Kalanick]

Chamath, what's the paper you were talking about before? What was the name of it again? The bitter lesson it could apply to autonomous driving is right now it's still like hey, how do I drive like a human? We talked about that. But the leapfrog moment here could be like hey, drive a car, make sure it's efficient, don't hit anybody and just simulate that quadrillion times and it's all good, Right? But right now we're still trying to drive like humans because we don't have enough data and therefore can't do enough compute.

[Keith Rabois]

That's the global lesson. By the way Chamath, you're totally right. Conceptual. The blog post is right. But that's only true when you have enough data. And depending upon the use case, the level of data you need may not be possible for years, decades. And you may need to hack your way there through human interactions.

[Travis Kalanick]

Physical world AI is lacking in data and so you just try to approximate humans.

[Chamath Palihapitiya]

I don't know if you guys have seen this. In related news, OpenAI and perplexity are going after the browser Perplexity launch comet for their 200amonth tier. I actually downloaded. I'll show it to you in a second. But this is a really interesting category. It's something developers can do already and they do it all the time, you know. But having your browser connected to agents lets you do really interesting things. I'll show you an example here that I just fired off while we're talking. So I just asked it, hey, give me the best flights from United Airlines and business class from New York City, from San Francisco to New York City. It does some searches, but what you see here is it's popped up a browser window and it's actually doing that work and you can see the steps it's using. And then I can actually open that browser window and watch it do that. This is just a screenshot of it and it will open multiple of these. So you could. I was doing a search the other day saying like, hey, tell me all the autobiographies I haven't bought on Amazon. Put them into my, you know, shopping cart and summarize each of them because I like biographies and I like doing it here. And when it did this last time, it put my flight into like, and I was logged in under my account and it basically put it into my account in the checkout. So again, this isn't like if you're a developer, you do this all day long. But this really seems to be a new product category. I'm curious if you guys have played with it yet and then what your thoughts are on having an agentic browser like this available to you to be doing these tasks in real time. You can also connect obviously your Gmail, your calendar to it. So I did a search, tell me every restaurant I've been to and then put it by city. And then I was going to open my Open Table and then pull that data as well. What's interesting about this, Keith, and I know you're a product guy and done a lot of product work. I'm curious your thoughts on it is you don't have to do this in the cloud. You're authenticated already into a lot of your accounts. Nor do you have to worry about being blocked by these services because it doesn't look like a scraper or a bot. It just it's your browser doing the work. Your thoughts on this? Have you played with it at all?

[Keith Rabois]

Yeah, I Think it's a great Hail Mary attempt by perplexity. I think absence, something like this, perplexity is toast. Like, for the stat about ChatGPT going to a billion users, like, it's becoming the verb. You know that the way you describe using AI for a normal consumer. There's nothing left of perplexity if they can't pull this off. So it's a great idea because, like, the history of, like, consumer technology companies is whoever's up has uphill ground, like in a military sense, whoever's first has a lot of control. This is actually what Google should be doing. Truthfully. Like, I think Google's also Google search, cross search is toast. And since they have Chrome and they theoretically have a quality team in Gemini, they should be putting these two things together and hopefully to compete with ChatGPT, they're going to lose the search game. Like, the assets that are best at Google right now have nothing to do with search. It's every other product is the only thing that's going to save that company if they can figure out how to use them.

[Chamath Palihapitiya]

Travis, your thoughts on this category? Anything come to mind for you in terms of feature sets? That would be extraordinary here. I know you like to think about products and the consumer experience.

[Travis Kalanick]

Well, it's really interesting. So, you know, I've been spending, as you guys know, I've been spending my time on real estate and construction and robotics. And so I've been out of this kind of consumer software game for a long time. But it's super interesting. Over the last six months, there have been a number of consumer software CEOs. Like, when I hang out with them or whatever, they're like, yo, how are we gonna. How are we gonna keep doing what we do when the agents take over?

[Chamath Palihapitiya]

Yeah, the paradigm shift is so profound that the idea that you would visit a web page goes away and you're just in a chat dialogue.

[Travis Kalanick]

You have an agent that's just taking care of your flights for you. So I think there's a leapfrog over that. I think it's just like, you tell something, yo, I want to go to New York. Can you. I'm sort of looking at this time range. Can you just go find something I'm probably going to like and give me a couple options?

[Chamath Palihapitiya]

Yeah.

[Travis Kalanick]

And it's just a whole. You have an interface and then, you know, is perplex. Is this thing that you just showed on Perplexity, is that the interface? Or do I just have an agent that just goes and does everything for me and Is this the start of that? You know, I just haven't spent enough time. I do know that every consumer software CEO that has an app in the App Store is tripping. They're tripping right now. And I mean big boys, I mean guys with real stuff. And sometimes I'm doing like, almost like therapy sessions with them. I'm like, it's going to be fine. You actually have stuff. You have a moat. You have real stuff that's of value. They can't replace it with an agent.

[Chamath Palihapitiya]

And they're like, so you're lying to them. You're doing hospice care and you're telling them everything's going to be okay. But the patient stopping options on Robinhood.

[Jason Calacanis]

While he's like, yeah, yeah, tell me.

[Travis Kalanick]

More, tell me more about all these things. There's certain things that are protected and there's certain things that aren't. That's all.

[Chamath Palihapitiya]

Well, let's talk about that because the. You and I are old enough to remember general magic. This vision was out there a long time ago with personal digital assistance. And you would just talk to an agent, it would go do this for you. This feels like a step to that where it does all the work for you, presents you the final moment and says, approve. So almost like a concierge or a butler. Yeah.

[Jason Calacanis]

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

[Travis Kalanick]

Of random sprawl, let's do it, let's just go do it.

[Jason Calacanis]

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

[Chamath Palihapitiya]

Keith, in some ways this is a step towards that ultimate vision. So you'd think it's worth it to, you know, sort of perplexity to make this waypoint perhaps if you look at it as a waypoint between the ultimate vision, which is a command line, an earpiece.

[Jason Calacanis]

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

[Chamath Palihapitiya]

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

[Keith Rabois]

Well, I don't think they'd buy anything worth it. Like what is Apple going to get? And if you continue this failed strategy of Apple, Apple has missed every possible window on AI and continues to miss it. And it has cultural. I think the CEO has challenges. I think culturally they have challenges, I think they have infrastructure challenges. So it's not an easy fix. But buying Perplexity is not going to help. Chamath strategy is actually a pretty coherent one for perplexity qua Perplexity. So I think that pick a vertical.

[Chamath Palihapitiya]

And own it strategy in this case.

[Keith Rabois]

Not a bad idea, especially because you need unique data sources. Some of those data sources may or may not license their data to OpenAI. So you can do some clever things. There's but I don't think there's any residual value that Apple would get out of Perplexity, except there's some product taste. But what are you going to spend like $1 billion for product taste? I mean Mark spending hundreds of millions of dollars, hundreds of billions of dollars or whatever he's spending these days and you know, Grok, if anything, Grok 4 shows that Mark really doesn't need to Spend money to build a whole new team because everything they've done in AI is also missed the boat.

[Chamath Palihapitiya]

Well, I mean, Keith, the way you phrase it there almost makes it worth it for Apple to throw a Hail Mary, have a team with some taste, because that's how they tend to do things is something that is elegant. And why not just throw your search to it? Throw 10 billion at what's elegant.

[Jason Calacanis]

What's elegant would be if there's a bunch of agents and just a chat box. Seeing a bunch of visual diarrhea is not elegant. It's lazy.

[Travis Kalanick]

On our little Bloomberg clone, I'll give you naming rights so you can call it Polyhapatia. So, hey, can somebody. Can somebody bring up the Polyhapatia?

[Jason Calacanis]

You know what's so funny?

[Travis Kalanick]

Rolls right off your tongue.

[Jason Calacanis]

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

[Chamath Palihapitiya]

Lafont was on, right, like two episodes ago. He was like, I can't pull this off. It's limited to six companies, dude.

[Jason Calacanis]

So what do people use? Bloomberg. They use it for the messaging now. Like, my team has traded huge positions via text message on Bloomberg. So there is something very valuable there. But the core usability and the core UI of that company has not evolved.

[Chamath Palihapitiya]

I have my contribution and Perplexity is.

[Jason Calacanis]

Very good at that, by the way. They do a very good job.

[Chamath Palihapitiya]

I got a new domain named Travis. Let this one just sink in here. This is my way to weasel my way into the deal. Begin.com. begin.com.

[Jason Calacanis]

You own that, don't you?

[Chamath Palihapitiya]

I do. I'm just a little. I sniped some good ones once in a while. I got begin.com and it got annotated.com. those are my two little domains.

[Jason Calacanis]

You're like. You're like one of these old people that show up at those flea market.

[Chamath Palihapitiya]

Show and then they almost show and.

[Jason Calacanis]

You'Re like, oh, I have this thing that I bought 1845.

[Travis Kalanick]

Guys. Jason. Jason is. Jason is the daddy and Godaddy.

[Chamath Palihapitiya]

Okay, I am your dad.

[Travis Kalanick]

That's what it is.

[Chamath Palihapitiya]

Who's your daddy? Hey, speaking of daddy, let's go to our next story. Is now the right time for a third party? Elon seems to think so. Last week he announced that actually would be creating a new political party. I'll let you decide who daddy is in this one. He said, quote, when it comes to bankrupting our country with waste and graft, we live in a one party system, not a democracy. He's not yet outlined a platform for the American party. We talked about it here last week. I listed four core values which seem to get a good reaction on X Fiscal responsibility, Doge, sustainable energy and dominance in that manufacturing in the US which Elon has done single handedly here. Pronatalism, which I think is a passion project for him. And Shabbat, you punched it out with the fifth technological excellence according to Polymarket, 55% chance that Elon registers the American party by the end of the year. And you know, one thing I was trying to figure out is just how unpopular are these candidates and these political parties. This is a very interesting chart that I think we can have a great conversation around. It turns out we used to love our presidents. If you look here From Kennedy at 83%, his highest approval rating. His lowest was 56%. That was his lowest approval rating. So he operated in a very high band. Look at Bush 2 during after 9, 11, 92% was his peak. His lowest was 19. Right. Wartime president. But then you get to Trump 1. Biden and Trump 2. Historically low, high approval. Their high watermark, 49 for Trump 1, 63 for Biden 1 of 1 and then 47 for Trump 2 and their lowest, 29, 31, 40. So maybe it is time for a third party candidate. Let's discuss it, boys.

[Travis Kalanick]

I have no idea how to read this graph. It is the worst. I'm like, what is happening here?

[Jason Calacanis]

This is the worst formatted chart this.

[Chamath Palihapitiya]

Is a confusing chart but. Well, the reason I'm putting it up is for debate. So you should be saying thank you.

[Jason Calacanis]

We're debating that.

[Chamath Palihapitiya]

It's creating great debating.

[Jason Calacanis]

Why did you put it up?

[Chamath Palihapitiya]

Here's another one. Gallup poll. Americans desire for a viable third party. 63% in 2023. So it's, it's bumping along an all time high.

[Travis Kalanick]

Okay, I'm really concentrating on this one.

[Jason Calacanis]

Okay.

[Chamath Palihapitiya]

Anyway, I'm going to stop there.

[Jason Calacanis]

What's the gray.

[Chamath Palihapitiya]

I'm going to let you.

[Jason Calacanis]

Okay, got it.

[Chamath Palihapitiya]

These are the different sense during that time period and how popular parties are. Let's stop here. This is a good, this is a good place to stop.

[Travis Kalanick]

I just blew a gpu.

[Keith Rabois]

Yeah, a couple points.

[Chamath Palihapitiya]

Yes.

[Keith Rabois]

The idea of Elon creating a third party is for any other human being, like absolutely absurd and ridiculous. Elon has obviously done incredible things so dismissing anything he's touching is a bad idea. However, I think the best metaphor I've seen is it's a little bit like Michael Jordan tried to play baseball and became a replacement level baseball player, which is actually really hard to do, by the way. Elon Musk is probably a replacement level politician. He's Michael Jordan for entrepreneurial stuff, but the third party stuff is not going to work. First of all, that chart is misleading. It's a flaw of average. It was badly designed and it's a flaw of average. Trump is incredibly popular among Republicans. He actually has the highest approval rate of any Republican ever measured in recorded history. It's 95%. Reagan was peaked out at 93%. It's just Democrats don't like them, which is perfectly fine. Being polarizing is an ingredient to being successful, including with people on the show. The point of accomplishing things in the world is you don't really care what half the world thinks. You need to make sure that there's a lot of people who like you and really approve and are enthusiastic about what you do. And Trump is about as popular with his party as anybody's ever been, ever, Period. No exceptions. Secondly, MAGA has kind of already change the Republican Party. Trump is sort of like a third party takeover of the Republican Party. And so it's kind of already happened. And maybe you can do this every 20 years or 30 years. I don't think you can have like this kind of transformation on one party within a too compressed period of time for a lot of reasons. Third is really. Smart parties absorb the lesson of political science. Unfortunately, I studied political science. I wasted kind of my college years and instead of saying CS and, you know, maybe then I'd be coding stuff and doing physics like Travis. But one thing I did learn is smart parties absorb the best ideas of third parties. So the oxygen is usually not there. Because there's a Darwinistic evolution. If you get traction on an idea, it's really easy to conscript some of those ideas and take away the momentum. No third party candidate that's a true, like third party has won a Senate seat since 1970. And that's actually Bill Buckley's brother. And so he had some name id. The other thing Elon, I think is missing and the proponents of what he's doing is people vote not just for ideas, they vote for people. It's a combination. The product is what do you believe? And who are you? And you can't divorce the two. Trump is a person, and that generates a lot of enthusiasm. And it's one of the reasons why he has challenges in midterms, because he's not on the ballot. His ideas may be on the ballot, but he is not specifically on the ballot. So unless, because Elon can't be the figurehead of the party, he literally can't. Constitutionally, you need a face that's a person. Obama, a Clinton. Like, there's reasons why people resonate Reagan without that personality, specific ideas just are not going to galvanize the American people.

[Chamath Palihapitiya]

Okay. So the counter to that and what people believe he's going to try to do is win a couple of seats in the House. Travis, win maybe one, one or two Senate seats, if you were to do that. Those things are pretty affordable to back a couple of million dollars for a House race. Senate, maybe 25 million. If Elon puts, I don't know, 250 million to work every two years, which he, I think, put 280 million to work on the last one, he could kind of create the Joe Manchin moment, and he could build a caucus, a platform, Grover Norquist kind of pledge along these lines. So what do you think of that? If he's not going to create a viable third party presidential candidate, could he, Travis, pick off a couple of Senate seats, pick up a couple of congressional seats?

[Travis Kalanick]

Okay, so first, I have this axiom that I'm making up right now.

[Chamath Palihapitiya]

Okay?

[Travis Kalanick]

Okay. It's called Elon is almost always right. Okay. All right.

[Chamath Palihapitiya]

Elon was right about everything.

[Travis Kalanick]

Seriously, let's just be real. And Mike, honestly, the things he's upset about and that he's riled up about, especially when you look at the deficit, like, man, I am right on board that train. Part one, part two, we've never had somebody with this kind of capital that can be a party boss outside of the system. Right. And there's a lot of people that agree with the types of things he's saying. And he knows how to draw. You know, he. Elon, in his own right, kind of has a populist vibe. Like, he does his thing and he's turned X into what it is. And he's a big part of X. And so I think it's the. I think it's great. And honestly, there's. There's the moves you can make on Senate and House and just having a few folks and then being you being levers then to get the things you want done, that's part one. And then part two of that is the threat of that happening can make good things happen separately, even if it doesn't go all the way. I just love. I'm on the train.

[Chamath Palihapitiya]

Yeah. I'm in love with this role for Elon more than picking a party, because he's picking a very specific platform that I think resonates with folks, which is just balance the budget, don't put us in so much debt, and let's have some sustainable energy. You know, job done. Great job.

[Travis Kalanick]

Yeah.

[Keith Rabois]

The problem with that is, like, he's actually wrong about the reason why we have a deficit or debt. It's not because we're under taxed. It's we're massively overspending. If we just.

[Chamath Palihapitiya]

No, I think he believes we're overspending.

[Keith Rabois]

They should have been supporting the last, you know, beautiful bill, because if you just held federal spending to 2019 levels, so 2019 is not like, you know, decades ago, literally, with our current tax revenues, we would be in a surplus 500 billion. Yeah. So all we need to do is cut spending. Now. I admit that.

[Chamath Palihapitiya]

Why didn't that happen with the big, beautiful bill?

[Keith Rabois]

So this is where details do matter. I think there is a willingness and a, you know, discipline problem on both parties. And I think maybe, maybe he can help fix that. The second thing is that we have these arcane rules, particularly in the Senate, that you need 60 votes in many ways to cut things, except through very hacky methods. And that's a reality. So the best thing, truthfully, you could do is help get a Republican Party to 60 votes. And then in theory, he could be absolutely furious if you didn't cut back to 2019 levels. But it's very tricky. Or you can just overrule. The filibuster is an artifact of history, and at some point, some majority leader is just going to say, we're done with the filibuster and just steamroll through all the cuts at 50 or 51 votes, which you can do. There's no constitutional right to a filibuster. It is an artifact of centuries of American history, and at some point it's going to go away. So maybe the time is now. Maybe we should just fix everything now.

[Jason Calacanis]

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

[Chamath Palihapitiya]

We've never had so many people say they feel politically homeless, as we did the last two cycles, and that includes many people on this podcast, people in our friend circle. And I think just the idea that Elon could create a platform that people could opt into and support, just the existence of that would make the other two parties get their act together.

[Jason Calacanis]

By the way, the other thing we.

[Chamath Palihapitiya]

Need is a little bit of a stick there and a carrot. Hey, if you don't control spending, there's this third option. And if Travis and I are in it, and Keith, I know you'll never leave the Republican Party, but Chamath, you know, you're probably set where you're where you want to be right now, but I can tell you, we go to our top 1020 friend list. Out of those, 50% will join Elon's party day one.

[Jason Calacanis]

The other, the other thing, Jason, that Keith said, which I think is really important, is if he were to run People, I think they have to transcend politics and policy and I think they need to be straight up bosses. People that have enormous name recognition so that effectively what you're voting is a name and not an agenda. Equivalent to, I think, what happened to Schwarzenegger when he ran. He ran on an enormous amount of name recognition. In the great Davis recall, he didn't run on a platform I don't think any of us could mention.

[Chamath Palihapitiya]

JD Vance had this great book, captured people's imagination. He's an incredible speaker. He pisses off a third or two thirds of the country, depending on where you are in the country. But you can't ignore him. I think elon can find 10 JD Vance type characters and back them fairly easily. He is a magnet for talent. People will line up. I have been contacted by high profile people already thinking of running. Can you put me in touch with Elon?

[Jason Calacanis]

I was thinking more like actors and sports stars, meaning where they just come with their own inbuilt distribution. Like, I think you almost have to rank X followers and Instagram followers and do a join and say, okay, these are. Do you know what I mean? Like, I think it's like totally different, guys.

[Travis Kalanick]

It's painful. Like, let's not get more celebrities as politicians. Like, let's get like people who've led large, large efforts, large initiatives, complex things, ideally.

[Chamath Palihapitiya]

But they still have to communicate, right, Keith? They have to be able to communicate on a podcast. That's the new platform. If they can't spend 2 hours, 3 hours chopping it up on a podcast like this, or Joe Rogan, you know, that's Kamala's. The reason she couldn't even contend was because she couldn't hang for two hours. In an intellectual discussion, if you can't hang, you're out in today's political arena.

[Keith Rabois]

It'll be interesting to see if he can tune his algorithm for talent, which is epic to tune for politics because it's a slightly different audience. But if you can tune the algorithm and quality, that might work. I think you can win a few House races. I think that's doable. I don't think you can win a Senate race.

[Chamath Palihapitiya]

Well, there it is, Elon. Keith doesn't think you can win a Senate race, but he thinks you win a couple of congressional ones. Thanks for giving him the motivation, Keith. I appreciate it.

[Keith Rabois]

I'm sure he's gonna love that.

[Chamath Palihapitiya]

That's the biggest mistake you've ever made. He's now gonna win too. People in the Republican Party right now are going no don't poke the tiger. Listen, speaking.

[Keith Rabois]

That's how Trump got into politics. So I don't wanna be Obama here.

[Chamath Palihapitiya]

You just Obama Elog. Right? Yeah. Congratulations. All right, listen, SCOTUS made a big decision here. This is a really important decision. They've sided with Trump for plans for federal workforce rifts, reductions in workforce. For those of you who don't know, as you know, Elon Trump, they wanted to downsize the 3 million people who are federal employees. This is just federal employees we're talking about. We're not talking about military and we're not talking about state and city. That's tens of millions of additional people. If you remember, Trump issued this executive order back in February when we got in office implementing the president's DOGE workforce optimization initiative. And he asked all the federal agencies, hey, just prepare a RIF for their departments consistent with applicable laws. Was part of this eo.

[Jason Calacanis]

Okay.

[Chamath Palihapitiya]

In April, the American Federation of Government Employees, afge sued the Trump administration, saying the President must consult Congress on large scale workforce changes. This is a key debate because the Congress, as you know, has power of the purse. They set up the money, but the president and the executive branch, they have to execute on that. And that's what the key is here. So they accuse Trump of violating the separation of powers under the Constitution Act. AFGE has 820,000 members. In May, a San Francisco based federal judge sided with the unions, blocking the executive order. The judge who was appointed by Clinton, said any reduction in the federal workforce must be authorized by Congress. This is a key issue. And the White House submitted an emergency appeal, yada yada. Eight of nine Supreme Court justices sided with the White House in overturning this block. And so the reasoning, it's very likely the White House will win the argument of the executive order. They have the right to prepare a rif. The question is, can they actually execute on that rif and who has that power? Chamath, does the power reside with the president to make large scale or, you know, RIFs, or do they have to consult Congress first? Your thoughts on this issue?

[Jason Calacanis]

It's an incredibly important ruling. Incredibly. Right. I think President Trump should have absolute leeway to decide how the people that report to him act and do their job. If you take a step back, Jason, there are more than 2,000 federal agencies. Employees plus contractors, I think number almost 3 million people. If you put 3 million people into 2,000 agencies and then you give them very poor and outdated technology, which unfortunately most of the government operates on, what are you going to get you're going to get incredibly slow processes. You're going to get a lot of checking and double checking and you're going to ultimately just get a lot of regulations because they're trying to do what they think is the right job. So since 1993, what have we seen? Regulations have gotten out of control. It's like 100,000 new rules per some number of months. It's just crazy. So eventually we all succumb to an infinite number of rules that we all end up violating and not even know it. So if the CEO of the United States, President Trump, isn't allowed to fire people, then all of that stuff just compounds. So I think that this is a really important thing that just happened. It allows us to now level set how big should the government be. But more importantly, the number of people in the government are also the ones that then direct downstream spend that make net new rules. And if you can slow the growth of that down, you're actually doing a lot. In many ways, I wish Elon had come in and created doge. Now, could you imagine if DOGE was created the day after this Supreme Court court ruling? It would have been a totally different outcome, I think, because with that Supreme Court ruling in hand, these guys probably would have been like a hot knife through butter.

[Chamath Palihapitiya]

Travis.

[Jason Calacanis]

So I, I think it's a big deal.

[Travis Kalanick]

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

[Jason Calacanis]

Well, the EO did. You could have passed the eo.

[Travis Kalanick]

That was all Doge style though. You know what I'm saying?

[Jason Calacanis]

Yes.

[Chamath Palihapitiya]

If he wasn't firing people. Yeah. They probably wouldn't felt the need to your point, Travis, to actually file this. But Travis, if you are living in the age of AI efficiency right now, operations of companies is changing dramatically. Can you imagine telling somebody you, you can be CEO, but you can't change personnel? That's the job. You get to be CEO, but you just can't change the players on the team. You can buy the Knicks, but you can't change the coach.

[Jason Calacanis]

So you can grow it, you just can't shrink it.

[Keith Rabois]

It's like running a unionized company which actually does exist. Are large unionized companies where you can't do any of these things?

[Chamath Palihapitiya]

Right. Do they still exist or are they all gone? I think they're going quickly.

[Keith Rabois]

Yeah, probably.

[Travis Kalanick]

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

[Chamath Palihapitiya]

Yeah, it should be outcome based. Hey, here's the goal. Here's the key objectives. Right.

[Keith Rabois]

Travis is totally right. There's a variety of different laws, some with incredible specificities, some with very broad man age. The Constitution clearly says that all executive power resides in the President of the United States, period. There's no exceptions there. However, Congress does appropriate money. And post Watergate, many people think Congress has the power to force the President to spend the money. And you can debate that. You can debate it on a per statute basis and that will be more nuanced and that's going to get litigated. Whether the President can refuse to spend money that Congress explicitly instructed him to spend, sometimes called impoundment. That's a very interesting intellectual debate. This one's a little bit easier. It'll get more complicated. Again, like this EO is only approved to allow for the planning. I think the vote might be closer. I think there's still a majority on the Supreme Court for the actual implementation, but, but it may not be 8 and 1 when there's a specific plan that has to navigate its way through the courts. Again.

[Chamath Palihapitiya]

Yeah, it's super fascinating. Yeah. I wonder if they're going to get to the point where they're going to say in every bill you need to hire this number of people to hear.

[Keith Rabois]

I don't know if they can. Like that's where it gets borderline unconstitutional. Like where you actually prescribe that the President in the exercise of his constitutional duties has to hire certain number of people. That feels pretty precarious.

[Travis Kalanick]

Well, I, I, I'm not sure, Keith. It's just like they prescribe a whole bunch of other things like must you must appropriate money for to this specific institution to do this specific work.

[Keith Rabois]

But that's not an executive function. Like if you said like the Secretary of State has to have X number of employees doing something, the Secretary of State is your personal representative to conduct foreign affairs on behalf of the President states, it gets a little bit more messy as you translate it to people that the President should. I mean, yes, Congress does set, you know, which people are subject to Senate confirmation, what their salaries and compensation bans are. So it's, it's never going to be fully binary where the President can do whatever he wants and it's never going to. I don't think it'll be constitutional for Congress to mandate and put all kinds of handcuffs on the President.

[Chamath Palihapitiya]

Well, then you also have performance that comes in here. What if you look at the Department of Education, you say, scores have gone down. We've spent this money. We're not getting the results, therefore these people are incompetent, therefore I'm firing them for cause and I'm going to hire new people. How are you going to stop the executive from doing that?

[Keith Rabois]

There's been a bunch of litigation in parallel to this litigation about the President's ability to fire people. And for the most part, the Supreme Court's basically, with maybe the exception of the Federal Reserve chair, said that the President can fire pretty much anybody he wants.

[Chamath Palihapitiya]

I mean, that's the way to go is like. I mean, I hate to be cutthroat about if the results aren't there.

[Keith Rabois]

I think if they're presidential. Yeah. If they're a presidential appointee, the President should be able to fire you at will. Just like if you were a VP at one of our companies, the CEO should be able to fire you at will.

[Chamath Palihapitiya]

But what about Keith if the whole department sucks? Hey, you guys were responsible for early education. You had to put together a plan. The plan failed, everybody's fired. We're starting over. Like you should be allowed to do that. How are we going to have an efficient government?

[Keith Rabois]

Some of these departments were created by congressional statute, like the Department of education in 1979. And you're right, every single educational stat has got worse in the United States since the department was created. But there is a law on the books that says there shall be a Department of Education. So you may have to repeal that.

[Chamath Palihapitiya]

All right, listen, we're at an hour and a half, gentlemen. Do you want to do the FICO story or should we just wrap Chama and we got plenty of show here. It's a great episode. Anything else you want to hear?

[Jason Calacanis]

I don't really have much to say on the FICO story. I thought these other topics were really good, though.

[Chamath Palihapitiya]

We did great today. This is a great panel. I'm so excited you guys are here. Let me just ask you guys, any off duty stuff that you can share with us, with the audience, any Recommendations, restaurants, hotels, trips, movies you watch, books you read. Keith, I know that you are an active guy. What's on your agenda this summer? Anything interesting you can share with the audience that you're consuming, conspicuous or otherwise?

[Keith Rabois]

Well, I don't want to share any good restaurants or hotels because.

[Chamath Palihapitiya]

Oh, you're gatekeeping. You're gatekeeping.

[Keith Rabois]

Come on, man, give us your babysitter. It's like if you had a babysitter. You're not going to tell everybody when you hear your babysitter.

[Chamath Palihapitiya]

Yes, can I get your nanny's email?

[Keith Rabois]

But there are, there are things that are, what do you call, no marginal cost consumption. Like Netflix. So, for example, you know, this documentary on Osama bin Laden is phenomenal. Like, I don't know if any of you have seen it. It's brand new. And, you know, I, I, I'm a student of this stuff and I, I thought, you know, I knew the whole story and et cetera. Watch episode one. Just start with episode one. And it just blew me away with new information, new footage. Just absolutely incredible stuff. So highly, highly recommend it.

[Chamath Palihapitiya]

What was the big takeaway for you so far?

[Keith Rabois]

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

[Chamath Palihapitiya]

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

[Travis Kalanick]

A dish, I mean, look, you know, I mean, Jason, you know, I go to Austin a lot.

[Chamath Palihapitiya]

Yes.

[Travis Kalanick]

Like, basically from March till October, I do about 15 weekends in Austin. I have a lake house. Jason's hung out a couple times. So I love water skiing. That's my whole thing. That's my, like, that's, I just love it. It's just my thing since I was very Zen.

[Chamath Palihapitiya]

Very Zen.

[Travis Kalanick]

It's lake, It's, I call it Lake Life. So that's a thing. And then I recently, this little bit of, like, a side quest. I recently purchased the preeminent backgammon engine.

[Chamath Palihapitiya]

Xg.

[Travis Kalanick]

Xg, that's right. This acronym is, It's Extreme Gamut. And so the preeminent engine. So all the pros rate themselves based on this. It was done, it was built by this amazing entrepreneur. This Guy Xavier, who is just a full on sort of ultra. Ultra. I mean just. What's the word I'm looking for? It's not savant, like a savant essentially, but hasn't worked on it for many years. So I'm getting back into it and love it and making it like taking modern machine learning sort of deep learning techniques and like big compute and saying can we push the game of backgammon forward? So super exciting and ultra training apps to get people up to speed quickly. I played in my first backgammon tournament and cashed. So that was pretty cool.

[Chamath Palihapitiya]

No, wait. Yeah. Okay.

[Travis Kalanick]

Yeah.

[Chamath Palihapitiya]

All due respect, you're the founder of Uber, you're very high profile. You go to this back and is this like held at the Motel 8 in like a conference room in the back?

[Travis Kalanick]

It was amazing. It was at the, it was like a month ago or so. There's like a big tournament and it was. So the, the United States Backhand Federation had this big tournament. It was, I guess it was at the Los Angeles lax. At the LAX Hilton. And it was in the, it was in the basement of the Hilton.

[Chamath Palihapitiya]

Great.

[Travis Kalanick]

And it was like next to the.

[Chamath Palihapitiya]

Dungeons and Dragons convention.

[Travis Kalanick]

It was, it had those kinds of legit vibes. I love and like people. So. So I went in super low pro, just did my thing but eventually was recognized. But I was not recognized as the founder of Uber. I was recognized as the owner of xg.

[Chamath Palihapitiya]

Oh, the owner.

[Travis Kalanick]

And then there was like a full on melee that basically occurred. They're like, oh, the owner XG Travis is here.

[Chamath Palihapitiya]

Chamath. I feel like we've got a window here to do the all in back image high end tournament. We gotta lock this down now. We gotta lock down the all in backgammon set.

[Travis Kalanick]

I get the co branding rights on this. Okay.

[Chamath Palihapitiya]

Absolutely.

[Travis Kalanick]

Xg.

[Chamath Palihapitiya]

Well no, the all in xg, you know, like, because I love a great backgammon set. If we could make like a $10,000 one Chamath, we could kill turtles or white rhinos. All the animals that, you know, Freeburg's trying to protect, we could murder them and then make.

[Jason Calacanis]

That would be so great.

[Chamath Palihapitiya]

Yes. Like maybe the white could be, you know, rhinos and then you could take something else, elephant skin, something, you know, just really tragic and then eat the meat and make the, the backgammon set for you.

[Jason Calacanis]

I love backgammon and honestly like if I wasn't attempting to be like expert poker player, that is the game. I mean if you're talking about a Pandora's box where once you open it. Oh, my God. You can go down the rabbit.

[Travis Kalanick]

Let's.

[Jason Calacanis]

Dude, backgammon is a beautiful, beautiful, beautiful game.

[Chamath Palihapitiya]

I love the vibes of sitting. Travis and I sat. I got some cigars out. You know, we pour a little of the all in tequila. Tequila.all in.com. we get that going. A couple of the all in cigars, and then we have the all in back. It's a wonderful hang.

[Travis Kalanick]

Yeah.

[Chamath Palihapitiya]

Keith, would you consider giving us some of your money playing back? Keith?

[Keith Rabois]

Absolutely.

[Chamath Palihapitiya]

We got to get some of that money on the table because you don't play poker with us.

[Keith Rabois]

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

[Chamath Palihapitiya]

We'll do a little taste off. Yeah. So you've insulted now Elon with the senate seats and facts with his tequila.

[Keith Rabois]

My tequila is much better, trust me.

[Jason Calacanis]

Who is Left in the PayPal mafia you'd like to insult before this episode or Peter? Anything about Peter re can join Elon's party.

[Keith Rabois]

He's collecting a bunch of misfits, so he might as well take Reed too.

[Chamath Palihapitiya]

All right, listen. This has been another amazing episode of the number one podcast in the world, the all in podcast for your sultan of science who couldn't make it today. He's at the BEEP conference, so we don't mention and David Sachs, who is out making America safe in AI and crypto. World's greatest moderator, Travis. Keith, thank you for coming.

[Jason Calacanis]

Thanks for pin shitting.

[Chamath Palihapitiya]

You guys were great today. What a panel. See you all next time. Bye.

[Jason Calacanis]

Bye.

[Chamath Palihapitiya]

Let your winners ride Rain Man David Sachet. And instead we open source it to the fans and they've just gone crazy with it. Love you, West. Queen of Kin one. Besties are gone. That is my dog taking a notice in your driveway.

[Keith Rabois]

Oh, man, my app.

[Jason Calacanis]

We should all just get a room and just have one big, huge orgy.

[Chamath Palihapitiya]

Because they're all just useless.

[Jason Calacanis]

It's like this, like, sexual tension, but they just need to release them out. Wet your feet.

[Chamath Palihapitiya]

Be wet your feet. We need to get Murphy's our back.