Inside Travis Kalanick’s Wild New AI Company

Summary

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TBPN Podcast Episode Summary

Episode: Inside Travis Kalanick’s Wild New AI Company

Date: July 22, 2026
Hosts: John Coogan & Jordi Hays
Featured Guest: Travis Kalanick

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Episode Overview

In this lively episode, John and Jordi sit down once again with Travis Kalanick, renowned entrepreneur and co-founder of Uber, to discuss his bold new venture in "industrial AI." The conversation covers the recent $1.7 billion fundraise for Kalanick's company, the challenges and opportunities of bringing automation and artificial intelligence into heavy industries like mining and food, the shift from consumer to enterprise GTM (go-to-market) strategies, executive hiring philosophy, regulatory fights, and what the future holds for work in an era of rapid automation.

Kalanick provides a candid, often humorous look into the scale and ambition of his current projects, the realities of deploying robots in the physical world, and reflects on the broader impact of automation on business and jobs.

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Key Discussion Points & Insights

1. Wild Commuting and Tech Therapy

• Travis admits to commuting to work by jet ski at 70 mph and jokes about the uniqueness of such a lifestyle—setting the tone for a wide-ranging, irreverent discussion.
  • Quote: “Who else do you know is jet skiing to work? Nobody. I'm the guy.” (00:46, Travis Kalanick)
• Lighthearted banter covers therapy for retired founders and "office hours on jet skis."

2. Announcing a Massive Fundraise

• Kalanick reveals his company just raised a $1.7 billion round, bringing significant attention (“noise”) to the business.
  • Quote: “I announced earlier today we did a $1.7 billion raise. There’s some noise that’s gonna happen.” (04:15, Travis Kalanick)
• Discussion of the structure: Multiple subsidiaries (mining, food, transportation) merged into a single entity for investor clarity and operational efficiency.

3. From Consumer to B2B: A Different Game

• Kalanick contrasts Uber’s consumer blitz model with the deep, multi-year relationships and technical integration necessary for industries like mining and manufacturing:
  • “That takes years off your lifespan. Doing it, getting good at it, and then owning it.” (09:06, Travis Kalanick)
• GTM now involves flying to remote mines, engaging corporate customers, and selling to the C-suite.
  • Example: Visiting remote operations in the Amazon and on the Saudi border to deploy automation in massive mines.

4. Deploying Physical AI in Mining

• The core product: Adding AI and automation kits (sensors, computation, actuation) to retro-fit existing mining machinery, making even 20-year-old equipment autonomous.
  • “You basically take a kit, ... install it onto a machine and that machine becomes autonomous.” (10:19, Travis)
• Benefits: Improved safety, reduced OPEX, and productivity increases—potentially 30-40% more output per mine.
  • “A gold mine that’s doing 30 or 40% more gold per year is kind of... whoa.” (14:06, Travis)
• Explanation of “no entry mines” (fully autonomous, human-free), challenges in retrofitting, and future plans to expand to more machines and sites worldwide.

5. Building a Muscular Startup

• Kalanick criticizes scrappy, “lean” startup culture as insufficient for enterprise deployments—his goal is to make the company “muscular” to win respect and scale with large industrial partners.
  • “We gotta go from lean to muscular.” (15:21, Travis)

6. Piloting and Scaling: From Proof to Expansion

• Once autonomy beats human productivity, expansion becomes exponential. The proof phase is now yielding momentum and larger orders.
  • “Once it works... it goes big. ... we're sort of in that mode with a bunch of different customers right now.” (13:13, Travis)

7. Partnerships, M&A, and the Uber Mentality

• While open to acquisitions or partnerships, the instinct remains to build rather than buy when possible—an echo of early Uber strategy.

8. Industrial AI: Not Just Humanoids

• Clarifies company focus: Not general-purpose humanoids, but specialized, full-stack automation—“industrial AI”—targeted at transforming legacy industries.
  • “This is a full stack software, robotics, sensors, machinery. Like a full stack solution to automating an industry.” (44:05, Travis)

9. The Pitch to Young Engineers

• For young talent, the appeal is adventure, impact, and working on real-world problems vs. churning out another app.
  • Quote: “You’re not dropping a fucking app in the App Store. You're like automating a 2 million pound machine going 35 miles an hour, carrying gold.” (23:23, Travis)

10. AI Safety & Science Fiction Reflections

• Kalanick cites Isaac Asimov’s iRobot series as formative, and shares a pragmatic view on AI risk:
  • “If you make something that is anti-human, if you make something that doesn’t serve people, I don’t think you’re going to make it.” (25:36, Travis)
  • Discusses the real-world limits and safeguards: Current robots serve paying customers, not themselves.

11. Business Model & Price Dynamics

• Follows enterprise software models: Flat pricing plus outcome-based upcharges, but never revenue share.
  • “You never go to a customer and say, give me a percentage of your stuff.” (29:02, Travis)
• Automation drives prices down, creates surplus, and frees people/capital for new industries and opportunities (Jevons Paradox).

12. Executive Hiring Philosophy

• The key is ambidextrous leadership: Both a “problem solver in chief” and organizer/manager at scale.
  • “There’s two things you want an executive to do... The best executives are the ones that are doing both well... but the problem-solving is the most important thing.” (30:03, Travis)
• Kalanick simulates real work pre-hiring to ensure day one feels like week two.

13. Regulation: Federal Preemption vs. City-by-City

• Kalanick is wary of regulatory capture by federal preemption—prefers open markets, warns against protectionist regulation.
  • “Federal preemption is good when you are pro regulatory capture. When you want to squeeze others out, you should get federal regulatory bigness going for you.” (33:27, Travis)

14. The “Big Bad” in Transport Regulations

• Claims systemic transport issues are products of trial lawyers and insurance companies maximizing for predictable accidents and profit, not safety.
  • “Every bad thing that you see in transport, like systemically… was most likely pushed by the trial lawyers and the insurance companies.” (35:10, Travis)

15. The Vision: Automation, Jobs, and Prosperity

• As physical AI automates both tasks and jobs, prices for goods plummet, freeing up economic surplus. New jobs and industries will be created, and some traditional roles (like plumbers) may ascend in value, just as discussed in prior episodes.

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Memorable Quotes & Moments

• On the uniqueness of his lifestyle:
  “I went 70 miles an hour on my jet ski to work... I'm the guy.” (00:19, Travis)
• On young tech talent:
  “You’re not dropping a fucking app in the App Store. You're like automating a 2 million pound machine going 35 miles an hour, carrying gold.” (23:23, Travis)
• On building new institutions:
  “We gotta go from lean to muscular.” (15:21, Travis)
• On AI risk:
  “If you make something that is anti human... I don’t think you’re going to make it.” (25:36, Travis)
• On executive talent:
  “The only constraint on your imagination is management capacity.” (31:16, Travis)
• On regulation:
  “Federal preemption is good when you are pro regulatory capture... The whole point is to squeeze everybody out.” (33:27, Travis)

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Important Timestamps

• Jet Ski Commute & Founder Therapy: 00:00 – 04:00
• Fundraise Announcement & Structure: 04:13 – 06:13
• B2B vs Consumer GTM: 08:12 – 09:06
• Mining Automation (Deployment & Impact): 09:19 – 15:25
• From Lean to Muscular: 15:21 – 15:52
• Proof and Scaling in Mining: 12:13 – 13:18; 13:45 – 14:47
• Industrial AI Definition: 44:05 – 44:36
• Hiring Executives: 29:35 – 33:01
• Regulation and Systemic Transport Problems: 33:01 – 36:02

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Conclusion

Travis Kalanick’s vision for industrial AI is as ambitious and audacious as his Uber days—but even more grounded in the grit of physical industry. This conversation is a no-holds-barred tour through the frontier of robotics, automation, and physical AI—delivered in the provocative, irreverent Kalanick style—with plenty of actionable insight for founders, investors, engineers, and policy folks.

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Transcript

[A]

I forgot to ask Jason if he has opinions about resto mods for jet skis. I'll ask you. Is there a, is there a jet ski that you'd recommend for the somebody's.

[B]

I saw a wooden jet ski recently. Like a really classic jet ski.

[C]

Don't get me excited. I'm into it. But these things go fast.

[A]

Yeah.

[C]

I think I went 70 miles an hour on my jet ski to work.

[B]

Wow. To work.

[A]

That's faster than most people commute. They're stuck in traffic.

[C]

I mean, I've got a five minute commute to work on a jet ski. Unless it's raining. Yeah.

[B]

Unless it's raining. Yeah. No, not in like.

[C]

And it gets a little weird and you call an Uber.

[A]

Okay. Is it helpful? Do you do your best thinking on the jet ski?

[C]

No. No.

[B]

Does it clear the mind?

[A]

Oh, it's going.

[C]

I think it's a, it's a, it's a, it's a fucking notch on the belt. Like, who else do you know is jet skiing to work? Nobody. I'm the guy. You're the guy. I looked it up. I looked it up on your guys application OpenAI. You know, ChatGPT. On your guys's app. Yeah, on your app.

[A]

You're welcome by the way.

[C]

Yeah, no, I want to thank you for all the great stuff that you guys in chat GPT, but I think there's like one or two other CEOs, but. But nobody at a major. Nobody. Thousand person plus commute is commuting.

[A]

To work on a jet ski.

[C]

On a jet ski.

[A]

Only a Texas resident, right?

[C]

Texas resident. That's right. Primary residence.

[A]

Let's go.

[C]

Yes.

[B]

Before we start, the last time you were on here, that was for me the best moment of making the show ever. John and I, it was totally surreal and we had a. We really enjoyed the conversation. But. But to me, we left that and it was almost depressing because as somebody who, you know, started getting into startups in the 2010s, you were that guy. And then I was realizing with the show, we, we had that conversation with you and it was, you know, a significant day for you, but it was sort of depressing because I realized like a moment like that would never actually come again where I got to basically interview. It will happen, it will happen, it'll happen differently. But you know, a childhood hero having that conversation, that's one of one for me. I don't think it'll happen again. There'll be other. It was peak. It was peak. But anyways, you've been Busy since then?

[C]

I've been busy. Look, I'm super excited. It's my first OpenAI podcast. I'm very excited about it. Also, I want to let you guys know that if you need therapy sessions for what it's like to be a made man in retirement.

[A]

Sure.

[C]

Like, if that's a thing, we'll call you. I can help motivate you guys.

[A]

Is step one of therapy in this situation? Just get a jet ski?

[C]

No, it's just. It's actually denial. You got to get over the denial part.

[A]

Over the denial, then the acceptance.

[C]

Yeah, it's something. I don't know.

[A]

The 12 steps. Yeah, yeah. Everyone just knows denial and acceptance. They don't know any of the other ones. Grieving, bargaining. There's a couple others in there, but you do go through that. It's natural.

[C]

Yeah, it happens.

[A]

But then you start building.

[C]

And if you guys need advice, you need therapy, I'm here for you.

[A]

I love it.

[C]

I mean, all the retard maxing is you're not supposed to do therapy. I'm just saying there are benefits, especially

[B]

with your new partners. They're like. If they're one thing. They wrote into the fundraising round. They wrote into the docs, like, cannot go to therapy.

[C]

That would be.

[A]

Yes. Are modern therapy for men. This is what men do. They don't go to therapy.

[B]

You should have office hours for founders, but they have to just come out on a jet ski while you're going. And you're going 70 miles an hour. And you'll coach it.

[C]

I am starting to teach many founders and people in tech world how to water ski, how to wake surf. A bunch of my engineers already, so there was one guy who didn't know how to swim, but I got him behind the boat wake surfing. Whoa.

[B]

Whoa.

[A]

Life jacket on.

[C]

Life jacket. It sounds weirder than it is, but it was still very weird.

[A]

It's high risk.

[C]

Yeah, it was good.

[A]

Potentially.

[C]

Okay, cool.

[A]

The business.

[C]

Business, dude. It's business time.

[A]

Yeah.

[C]

Gotta put on the business. Socks.

[B]

Unfinished business.

[C]

Unfinished business. So, yeah, I announced earlier today we did a $1.7 billion raise. There's some. There's some noise that's gonna happen. Wow.

[A]

Now one mallet. This is fucking. You guys have to do another one from downtown.

[C]

We're getting it next time.

[B]

Yeah, you come over the tough.

[C]

So much noise.

[B]

So much noise. All right, but walk us.

[A]

I feel you came in very relaxed.

[B]

Walk us through. I think it's been, what, it been, four months since we talked?

[C]

Three or four months. Something like that. Yeah, I think. Did we talk in April or March?

[B]

I think. March, yeah.

[C]

Oh, that's right. Yes.

[B]

Early March.

[C]

Four months.

[A]

So.

[C]

So, yeah.

[A]

So what happened with the business to unlock the next round?

[C]

I mean, we continue to go up and to the right, but like the announcement of Adams was we're gonna do physical automation, physical AI, what we are calling industrial AI, to transform these industries one at a time. We did food, we moved into mining, we're doing transportation, and it's working. And so that's how you go. Yeah. And then of course, there's like going out of stealth. There's all the things. And it was just the right time.

[B]

Yeah, yeah.

[C]

So. So yeah, we just went to market. We said. When I. When I originally went to market, I was like, these were separate companies. Okay. So our mining and transport was a separate thing, food was a separate thing. And. And we had a bunch of other, you know, a bunch of subsidiaries doing cool stuff. And I said, which one do you guys want to do? Do you want to invest? Do you want to invest in food? Do you want to invest in this? And they're just like, we want to invest in you.

[B]

Yeah, yeah.

[C]

And we heard that, like, we took like the first five folks we talked to all said that.

[B]

Yeah.

[C]

So then what we did is we put the companies together and then sold the equity in a singular entity.

[B]

Yeah, yeah.

[C]

So just put. Put it together and it's much easier for me. I don't know how. How Elon does it with all the different companies. It's wild.

[B]

Well, no, the answer is what's been happening. Right.

[C]

It all comes back together, guys. He did it for 20 years, though.

[A]

Yeah, yeah, yeah. And he's still technically doing it with SpaceX. Like, they are different companies.

[C]

Boring company. Neuralink. Like, he still got a lot of

[B]

lesson in there for investors is like, even with Elon companies, there's such an insane power law where you have a $10 billion company and then you have a, you know, a $2 trillion company. Right. And it's like you just want exposure. You want broad exposure. Ideally, you know, you could just invest in the one that breaks out, but you want broad exposure to the category.

[C]

When things are first getting going, there is a lot of upside of having them separate. Sure. Because if somebody wants to invest in a really cool thing, and this is what happened when we first got the transporter mining thing going. Yeah. If they want to invest in that cool thing, they're like, I don't know anything about food, by the way. Food on its own is Robotics, real estate, like restaurants, like, you know, and so they want. They want to be exposed to that one thing, and they don't want to have to underwrite something going across all things. And they're like, well, if you're losing money over here, I want you to lose money over here. So how much of the money I'm putting in is going to go to that? There has to be a theory of the case of how you put it together, how you allocate capital across. And. And honestly, once you're starting to get to profitability on one or more, then that conversation starts to get easier. And I think that could be why I can't speculate on sort of Elon's World. But certainly I'm super excited to have those pieces put together into a single puzzle.

[A]

What does go to market look like in the mining industry for you?

[C]

It's the fricking best.

[A]

Okay. Because specifically when I think of your go to market magic, okay, it was deploying young people to a new city in Miami, and they're doing a marketing stunt. And it's not like you're calling in favors or leveraging your network to get Uber up and running in a new city. That was something. That was organizational design.

[C]

So hold on. That's consumer.

[A]

Exactly. So how is it different?

[C]

Well, it's just like. Well, all the food stuff we're doing is business. Almost all of it.

[A]

Yep.

[C]

Really? All of it. Mining is all business. So. So look, there is a big thing if you go from doing consumer to doing business. I think we may have talked about this last time. That's a whole other ballgame.

[A]

Yeah.

[C]

I mean, that takes years off your lifespan. Doing it, like, getting good at it and then owning it. But mining go to market is cray. Cray. Yeah. Like, so I'll just give you an example.

[A]

Going to the conference or something?

[C]

Well, yes, I was meeting CEOs. Yes, you do that. But, but, but, you know, I can a lot of times. Look, when you have very efficient transportation, you can go places. So a month ago, I dropped into deep Amazon in Brazil. Okay. Like deep northern Brazil. Like Amazon places.

[B]

You can't even get a jet ski, guys.

[C]

It's the Amazon of the amaz.

[A]

Amazon.

[C]

Okay, okay. And like tiny airports, you just, like, you kind of just dirt. You slide into the DMs. Except as a tarmac. Okay.

[B]

There you go.

[C]

Yes, of course. And massive iron ore mine that we're operating in there. And you see, like, we took. We were there for a couple days because we already have customers There, Sure. Customer is called Valet. It's a massive mining company and they, it's, it's like the world's largest iron ore mine. And you go and you get in a helicopter. Just going over one of the sites takes 30 minutes.

[B]

Wow.

[C]

Okay. And it's fascinating. It's so fascinating. And you're learning how the system works. You're sort of figuring out how do I. You basically take a kit, you apply you, you, you install it onto a machine and that machine becomes autonomous. And some of these machines are like 20 years old, some of them are new. And so there's lots of different kinds of machines as well. And you're making the mine more productive. You are making it way safer. It is super. Like they have lots of safety protocols, but like it is mining dangerous business. It is a dangerous business. And the OPEX goes down all at the same time. It's kind of a beautiful thing. And, and then, you know, I went from Brazil and then straight from there dropped into the border between Iraq and Saudi. On the Saudi side. So we have a phosphate mine that we're doing stuff there. The signals were jammed so we had to like my pilots had to land kind of like old school style, like visual, physical, visual.

[B]

Is that because of the conflict going

[C]

on in the region and just the general, the vibes on the borders there? Yeah, yeah. So, but same story. And so go to market is wild. Just end up in literally go to market crazy places. But it's super needed. And so what's happened is the, the pronto technology has got gotten past human productivity, which means you go to a gold mine CEO, you talk about go to market. You go to a gold mine CEO and you say, would you like to have 20% more gold per year? Mm, absolutely.

[B]

Good stuff.

[C]

We haven't heard. No.

[A]

Yes.

[B]

Okay. Okay.

[C]

But they're. But they say prove it.

[A]

Yeah.

[C]

And that's where the rubber meets the road. Right.

[B]

How long does it take to prove it?

[C]

Used to take a lot longer. Now like once you've proven it enough times, then it sort of gets its own momentum, gets around. And so we're in that, we're in that place on Pronto where that momentum is taking hold because there's enough proof points where it's just working in so many different places where people are like, all right, let's go. We're going to think of mining, autonomous mining, almost like enterprise software where you get a pilot, there's like a 10,000 person company and you got an enterprise startup and they're like, I Got like eight seats. But it's this huge company and if we get it, it's huge. And I've got this other 10 seats over at this other one. It's a pilot, but I swear it's going to work. And they're out there pitching and trying to make it happen. But once it works, and in mining that means human productivity, human level better than human productivity. Once it works, it goes big. And they're like, okay, let's get across all the vehicles. And so we're sort of in that mode with a bunch of different customers right now.

[A]

How big is the opportunity to just increase uptime of mining operations? I imagine that there are mines that are trying to operate 247 but getting a night shift in the middle of the Amazon reliably, everyone showing up and being, you know, healthy and happy and eager. It gets a lot easier when it's like, yeah, we're still going to have a bunch of people on site but they're going to be overseeing robotic work. Sure.

[C]

So yeah, I mean there's two parts to the productivity gain. First is the machine per hour doing more. Yeah, that's part one. Part two is hours and call outs and all of that stuff as well as just, you know, the safety protocols change when you have less risk.

[A]

Yeah.

[C]

So there's a lot of things like this that pile onto each other. My guess is you could even end up 30%, 40% more productive at the end of all of it. And when you do that, the opportunity speaks for itself. A gold mine that's doing 30 or 40% more gold per year is kind of. Whoa. But that's for every mineral. That's lithium. That's like. We also go all the way down to quarries. Quarries are different because quarries are basically. It's about cement. Let's just say that's the main jam. There are others, but let's just go with that. You can't. You don't just go do more rock because you need cement. Customers on the other side, they're only using so much cement.

[B]

Like where to store it.

[C]

Yeah, exactly.

[B]

Yeah.

[C]

And so that's more of an OPEX play and there are thinner margins there. But I'm in the game and it's kind of interesting and it's a lot of fun. And for that company, for pronto, they were super. Anthony Levandowski and the team there, super scrappy, true startup style. Lean as hell. Like so lean. Like that Christian Bale movie, I can't remember the name of it. The machinist dude, he's like super lean. And I'm like, guys, we gotta go from lean to muscular.

[A]

You gotta go to Batman.

[C]

And that's what we're doing. Like, and you think of that this in an.

[B]

We need a muscular. That's a good.

[A]

Yeah, you don't have a good phrase. You just want to be muscular.

[C]

And so you think about enterprise go to market. Part of our go to market is. Is building credibility with our enterprise customers that we're going from lean to muscular because they, the demand is there, it's ready to go. They're like, we need you to be muscular. We need the protein powder and the whatever else.

[B]

What, what holds. What holds you back.

[C]

You go to the gym, whatever.

[B]

What holds you back from scaling. Let's say you do a pilot. It works well. You're attaching hardware to existing systems and hardware that they're using. And they say, okay, we're getting more out. Maybe we want to place orders for more machines. I imagine the lead times on some of this mining equipment could be insane. How much of the stack do you want to own?

[C]

Say the question again. I'm sorry, I just blanked. Go for it one more time.

[B]

Right now you're taking existing mining equipment and you're augmenting it with. You're making it AI enabled, you're making it autonomous, you're making it more efficient. And they say, great, this is working. We want to scale up our operation because maybe we need less or we can do more with the same, you know, human headcount. Yep. But what's the. I imagine there's some things that are out of control for, for you at that point where they're like, okay, we need more of this heavy mining equipment. Let's. Let's add it to the site. But is there like a lag time there?

[C]

The real lag time is getting. So you have to, you're. So let's say we want to get a bunch of machines that are in the Amazon up and running.

[B]

Yeah.

[C]

Okay. How do you do that? So I've got a ship, a bunch of sensors, a bunch of compute, a bunch of equipment and mechanical systems, let's just say. So that a team can then go install it.

[B]

Yeah. So you're basically building a data center on site.

[C]

I wouldn't put it that way. I would say, I mean if you considered a machine with sensors and compute, a data center, I mean you could, but it's really. Think of those. There are servers, but I wouldn't say a data center.

[A]

It's not really some operations Would bring like an armada style like shipping container sized level of. You're just volunteer as one.

[C]

So you bring in the stuff.

[A]

Yeah.

[C]

Okay. You have to install it.

[A]

Yeah.

[C]

You have to like bring it up and make sure. Okay, this is a new place. How does it, does this machine work properly in this new place and calibrate and make sure it's safe and all of this. So there's like a process of getting it up, then there's change management because that, that site's going from. There are people that show up in the morning. There's all this very regimented process to make sure everything's going exactly as planned and people are exactly where they're supposed to be. Because otherwise weird things happen on a mining site. So you have to go from that to okay, we're now running autonomous mining operation. It's just a very different thing. So the installation and the bring up and what we call commissioning are sort of like the things you have to do. And you know, like why does it take a long time to install? Because that machine may not even be dry by wire.

[A]

Yeah.

[C]

So you have a mechanical system. Like if you turn the steering wheel like it's. You know what I mean? It's a mechanical system, hydraulic system. So you're bringing where you have to go.

[A]

Actuator that might push a physical button.

[C]

Yeah. You're trying to make electricity then do a physical thing. So then you need physical actuation to do the things. Because it's not. These machines are not natively dry by wire.

[B]

Yes.

[C]

That's not all that sounds.

[B]

That sounds incredibly difficult but necessary because you're not going to get a mind to rip out tens of millions of dollars of equipment that they already have. But would you eventually go full stack, like build the entire.

[C]

I mean look, we ultimately, I mean if you go in the mining industry there's like this, this term, it's called no entry mine. A no entry mine is a mine where there are no people in the pit.

[B]

Out factory.

[C]

Yeah. Kind of like that version version of it. There might be people in a control center. There might be but like in that pit, no humans.

[A]

And it's a wildly different calculus from a safety perspective I imagine.

[C]

Totally different obviously. And so there's drilling, there's blasting, there's loading, there's haulage, there's crushing. I'm just going through the different parts of the mining operation and what you do is you start somewhere and then you start extending to those other areas to get to that no entry thing. And the no entry thing is you can have an autonomous thing. Like, like our haulage system is autonomous. If you're getting into a new place, you can do remote control and move into autonomous. If you want to go super no entry or lower entry line, if that makes sense. It's super fascinating. And then you're talking about loaded a 2 million pound machine that's moving potentially 35 miles an hour down the road. And it's an off road thing.

[B]

2 million pound machine moving 35 miles an hour off road. Yeah, dude, this is why you have to work.

[C]

This is the ultimate atv. So no, you get in it and you can, you know, you can experience it. Like, I mean it's not like there's like an amusement park for this, but like, I've certainly experienced it where I can get in the, I get in the machines and check out what's going on.

[A]

This is like the dump truck. Are any of these 20 foot tires essentially?

[B]

Are any of these, are any of these companies like acquisition targets where you would, you would be able to come in and say like you're doing a lot of stuff. Well, but here's all the stuff that you're never gonna figure out. Like us.

[C]

And I mean, look, I would say the way we think about it is the, the haulage part of a mine is where most of the vehicles are. And so, and we think of haulage as the cardiovascular system of a mine. So we're obviously very connected to all the other machines, but we don't do all the other machines. So we're like in an ecosystem so we can work with them where like there's APIs. Because like, if you're doing haulage, you need to know where the other machines are and what their status is. As an example, there needs to be orchestration coordination there, which is pretty interesting in terms of like acquisition. Like, you know, I do, I have to sort of admit, like the Uber mentality. My mentality.

[B]

Let's just say. Yeah, yeah, my Dune.

[C]

Yeah, yeah. It's like not, I guess Uber is different today, but in my world we didn't acquire shit, we just built.

[A]

Yeah, that's right.

[C]

I don't know if I have an opinion yet. I'm not like religious about it, but if we feel like we can build something, we do. But that, but sometimes people have differentiated awesome stuff and you're like, let's partner, we're open to it.

[A]

You know, how would you pitch me if I was a young person, Stanford cs, new grad, worried about software engineering not being the easy path where I Can bounce around from Google and maybe Uber had a cushy job for me. Pitch me on going to the Amazon and building.

[C]

I mean, that's awesome. I thought I just did. I mean, that was a good pitch. That was the pitch.

[A]

Do you think. Do you think young people are receptive to this pitch yet? Are we about to be receptive? Why should they be receptive?

[C]

It's really interesting because I only run into the young people that are receptive.

[A]

Sure.

[C]

Like I'm not out there pitching like lame sauce dude who doesn't want to work.

[A]

Sure, sure.

[C]

Like I don't end up. I don't end up in the same

[A]

room as this guy.

[B]

Do you want to. Do you want a job where? Do you want a laptop job? Or do you want to be dropped in to a mine in the Amazon and like build, build, you know, science fiction.

[C]

This is the thing, right? This is why the Adams thing is cool. Because you're not dropping a. You're not dropping a fucking app in the App Store. You're like automating a 2 million pound machine going 35 miles an hour, carrying gold.

[B]

Do you. Do you watch? Do you get. Do you get a.

[C]

There's a lot of profanity happening today. I don't know why it's happening, but it is.

[B]

Oh, it's. Let it flow.

[C]

I want to acknowledge it.

[B]

Do you. Do you watch science? Do you get inspired by science fiction at all? I can, I can imagine, like watching Dune for you. You're just like texting pictures to the team. Of course.

[C]

I'm like, my fav is Asimov. He's my fave. You know, the iRobot series is like, just so epic.

[A]

What is your takeaway from the iRobot series with regard to AI safety doom generally? Have you ever had moments of maybe we won't figure it out?

[C]

Won't figure what out?

[A]

The alignment problem, broadly, like the. The iRobot, the three laws of robotics. Elegant solution tested, obviously.

[C]

But I love to come back to

[A]

a world where everyone, both the Doomers and the AI builders agree that, yep, the three laws of robotics will be.

[C]

But I mean, in some ways. Well, in some ways in the series, the three laws don't always work out. Yeah. So I think there's a lot of. I thought there was a lot of nuance to those three laws, even though the laws are sort of so simple. I love the intention of those laws. I sort of think of it a little bit differently, which is, I have been entrepreneuring for a long time, like a long time And I have failed. And when I think about why I failed, it's usually because I was building something that nobody liked. So if you build something that people don't like, I don't think you're going to succeed. So how does that relate to your question? He's like, please tell me because I'm not connecting the dots at all.

[A]

What are you talking about?

[C]

Well, if you make something that is anti human, if you make something that doesn't serve people, I don't think you're going to make it. I don't think you're going to make it. And by the way, like yes, we're using AI to help us make decisions, et cetera, but what do those AIs really, really want to do? Almost too much. They want to please us. So I just think if you're not making stuff that humans want, it's not going to work out. And that's kind of obvious obviously, but I think it keeps going. And yes, there's the dangers and the things and then this. But that's my starting point for how I think about these things. And we can't control all the things. And I do think, of course you have to have safety situations and there's collisions of like what do I prioritize first and how do I do it? Which is I think where Asimov's laws go. But instead of writing sci fi books, I'm just doing the thing and I'm making sure that the machine stays on the road.

[A]

Yes. And related to that idea of like doing the thing, making the machine stay on the road. I imagine that you're, your world view is somewhat informed by your contact with reality. The fact that you can see the progress of diffusion, how long drive by wire systems take took to roll out and the need for AI to be deployed in like tactile ways that, that, that you just see it as more positive. Some more. There's more.

[C]

I feel like you're deploying robots that people want right now. Robot. I mean look, there's some point where robots have their own bank accounts and their citizens and all this. We're just not there yet.

[A]

Sure, sure.

[C]

And before we, until we get there, that robot is owned by somebody and that somebody has a bank account they are paying based on the value you're bringing them because they like your stuff. So if you are doing things that humans don't like, you're done. And trust me, I've done it. I've built things that nobody liked and it sucked. I don't recommend anybody do It. If you can avoid it, you totally should.

[A]

Yeah.

[B]

On the business model side, what are you doing now in mining and where do you think it could go over time? Because if you're able to bring in a system that helps someone increase their yield 30 to 40%, I imagine eventually you just do some type of JV. So that you guys have.

[C]

Look, there's, there's, you know, and the instinct should be, how do enterprise software companies do it? Start there and you guys will know that you know that. What's the answer? Let's just say your enterprise software company, you're making a company more productive. What do you do?

[A]

Raise prices. Subscription.

[C]

Or the price goes up when you prove that productivity. So there's baseline and then based on outcomes, you get a little extra juice. Sure. And you can.

[B]

Or you're always trying to make sure that, like you want to be producing, creating more value than you're capturing. But there's this sort of cat and mouse game where you're always trying to. You don't want to give away maybe too much value.

[C]

Totally. But here's the thing. You never go to a customer. I don't care what you're selling. Okay. I don't care if it's enterprise software. I don't care if it's widgets. I don't care what it is. You never go to a customer and say, give me a percentage of your stuff.

[B]

Yeah.

[C]

You go to a customer and say, here's the price of our stuff. And if it does really well for you, we think we should get a little more scratch cachesh stuff, you know, Whatever. You know what I mean? And it's that simple. Don't be crass about it. And partner with folks and they're down. They want to win too. It's literally an enterprise. It's an enterprise software style negotiation or approach to the whole thing. And the more differentiated your value is, the more you're going to get.

[A]

Yeah.

[B]

What is your process for hiring executives today?

[C]

Pray?

[B]

I was hoping. I was hoping you had the Kalanic system to achieve a 99.

[C]

No, but why would I tell you? Why would I gain, period? No, I mean.

[B]

No, but I think you can. This is one of those things. You can tell people exactly what you do and they're not. They're not Kalanick. So they're not. It doesn't. That doesn't mean they can compete with you.

[C]

You know, they could. They. Yeah. Okay, so how would I put it? Look, I think no matter who you go, nobody's nailed executives all the Way it's weird because what will happen is executives talk a fucking awesome game. And there's two things you want an executive to do. You want them to be able to organize at scale. Organize and manage at scale, lead at scale. You also want them to be epic problem solvers. The most strategic, badass problem solvers alive. This is like being left handed or right handed. And there's very few people that are ambidextrous. But you need that now. Somebody, they're always leaning a little bit, one side or the other. The best executives are the ones that are doing both well. But I have come to the conclusion over my years, doing the stuff is the problem solving is the most important thing. If you get somebody who organizes and manages well but cannot solve a problem, they're going to be doing ridiculous stuff in a super organized way. And so that's. And sort of my theory, maybe there's a couple theories on how I manage or how I lead. Is that the only constraint on your imagination is management capacity. But what is management capacity? It's really problem solving at scale. Because if you are doing super well over there, guess what? They're problem solving there. I can create other awesome problems. Like I love creating problems. Sure, go solve those too. But if I don't have the management capacity, then I'm effed.

[A]

Sure.

[C]

So the management style that I do is sort of problem solver in chief, which is I take the most impactful problems that are not being solved and that's on my desk or desk or room or whatever you want to call it. That's where I'm spending my time. So people go, oh, what do you spend your time on? Like, it depends what the fricking problems are that matter. And it can change. And that's how I roll. But it means once you have a problem solver in chief mentality that flows downward. That means any direct report of mine must be the deputized problem solver in chief. And they've got their. Because there's only 24 hours in a day, I can only solve so many myself. They have to then take that for their world and do the same thing and then do the same thing to their people. So the bottom line is you gotta prove that these folks can solve actual problems and aren't just talking the talk. That's the number one. And then on the interview process, simulate what it's like working together so that day one really feels like week two and day one, you better be excited. So if you're excited in day one, after simulating what it's like working together in the interview process. Then day one is really week two, and you're still excited. You took a lot of risk out of the system. That's all I got for you.

[A]

I have a question about regulation. Uber famously went city by city.

[C]

Yeah.

[A]

The AI labs are duking it out over federal preemption. Did you ever have develop a theory around when federal preemption is better than state by state regulation? Do you have a philosophy around this? I. It seems like the labs go back and forth on what they want. It's hard to see where the chips are falling.

[C]

Federal preemption is good when you are pro regulatory capture.

[A]

Okay.

[C]

When you want to squeeze others out, you should get federal regulatory bigness going for you.

[A]

Yeah. Because then you don't have to do the ground game that you win.

[C]

Well, no, you're squeezing others out. Okay. It's just the whole point is to squeeze everybody out. I never did that. Like, we never did that Uber. We basically never, ever proposed or pushed any rule that would be beneficial to us versus somebody else. We always were trying to open up the market, and we said, let the best man win and we just went for it. But I think we got to be careful of some of these close weight things that are creating situations where they need to be regulated and they want it. I'd be very. I'd keep an eye on that.

[A]

Yeah. Well, you got to have customers that love your product and are willing to.

[C]

So when you guys. When you guys, you know, decide to tell your own hacker to hack the thing and then go to somebody, then go to the federal government, say. Then go to the federal government and say, dude, we saved the day. Like, you know, you don't have to. You guys don't have to do this. You guys don't have to do it.

[B]

On regulation, I'm sure you saw the trial lawyers that are fighting back against autonomous vehicles because they're worried they're gonna be too safe.

[C]

Yes.

[B]

I'm sure that's not surprising to you.

[C]

No. So look, every bad thing that you see in transport, like, systemically, anything in transport that you view as systemically bad was most likely pushed by the trial lawyers and the insurance companies.

[A]

Wow.

[C]

Every single bad rule. That's weird and dumb.

[A]

Yeah.

[C]

The insurance companies and the trial lawyers were in the game big time.

[B]

Where. Where do they align?

[C]

What do you mean?

[B]

Well, because trial lawyers, I imagine, want more accidents.

[C]

Yeah.

[A]

Insurance companies are the ones that pay for it. Wait, yes.

[C]

No, no. Remember, insurance companies make margin on accidents.

[A]

Okay.

[C]

If there's no accidents, there's no insurance company. They in a weird way they love accidents go up. As long as it's in their actuarial table, they're pumped.

[A]

Wow.

[C]

Yeah, right. They don't like is accidents they didn't plan for sure, but accidents that they planned for business, big insurance outcomes they love. Like I remember we went to DC and the taxi system, the, the liability on a ride, if you took a taxi, it might still be this way to this day. Was like $25,000 in a taxi. But we went to, we being Uber at the time went to D.C. and they pushed a one and a half million dollar policy per ride. Okay, so what does that mean? That means, well this, you know, accidents are going to happen. We're probably like Uber's probably safer. But it just. Do you think the trial lawyers weren't pumped about that? You think the insurance companies weren't also

[A]

pumped about that they can go get up to.

[C]

Because by the way the insurance company might be on the other side and they're like, oh, there's a, there's a one and a half million dollar bank account here that I can get access to on a random accident.

[B]

Right. What can you share on the transportation side of the business right now? How much are you, how much is that business in service of mining or

[C]

food versus it's number one. So number one is it's, it's so I call it wheelbase for robots. Which is if you're going to do specialized robots that move and act in the physical world, they're either humanoids, which we're not, I'm not anti humanoid, I'm just non humanoid. Specialized industrial robots. Right. So that's, it's high scale industrial scale tasks. Which means you would not have a humanoid ever do that. That means you got to be on wheels. So we got to build wheels. So that means. Okay, well when food, when supply chain is going into our facilities, that's a freight vehicle. We probably should just turn that into a robot that moves stuff and actually interfaces with our facility in a really cool way. When the food is coming out of our facilities, there's probably like a machine that holds food at temperature that's like a box on wheels. I call them autonomous burritos. And it brings it to your home and it costs 75 cents instead of like the $12 per drop that it costs like an Uber Eats or doordash today. So it's serving. Remember I'm taking, I'm sort of going through an industry and saying how do we transform it full stack. How do we automate full stack? That entire industry? So okay, that's the food thing. Obviously mining's pretty obvious, but you can imagine there's a lot of other machines that move. Like I talked about haulage. But what about like what about grading the roads? The dirt roads. You got to grade them. That's a machine. What about the, you spray water so there's not a lot of dust all over the place. That's a frickin machine. Like what about the material that ultimately goes somewhere beyond the mine? Well, that's a freight machine. Like there's lots of things moving. You know, I saw something was like, think about just forklifts. I know a company remain unnamed that's spending three and a half, this is on the supply chain side. Three and a half billion dollars a year on forklift labor in their facilities.

[A]

That probably shows up in an SEC filing if we want to get creative and figure that out. What company you're talking about.

[C]

Just saying.

[A]

But you said, yeah, big opportunity if

[C]

you just solve the forklift problem.

[A]

Yes, but on solving the problem, what do you think about this distinction between jobs versus tasks? Like a lot of people would have assumed that there would be no more marketing people because the job is just writing marketing copy. But the job is actually much more. Writing copy is one task. I was looking at automated trucking and I found some stat. Like I think 30% of truck drivers are armed, they carry weapons. And so driving the vehicle is one task. But in that job you are also providing security for that payload. And you are also doing other things, refueling the vehicle, maybe some minor maintenance. And so just the, just the steering and gas and brake pressure is just one task that you're doing. How do you think about that in the context of all this?

[C]

This really gets to the jobs question, I think.

[A]

Yes.

[C]

Which is basically like okay, well if I do everything that we are imagining on food, which is I have industrial real estate, which is manufacturing and logistics. I automate the manufacturing, which is production. Robotic food. Robotic food machines. Robots. And I have robotic couriers. What happens?

[A]

Food.

[C]

The price of food goes down. Yeah. Okay. When the price of food goes down. Remember robots don't have bank accounts. When the price of food goes down, what happens? More people have more money.

[A]

Yeah.

[B]

Jevons paradox.

[C]

What do they do?

[A]

You start eating more? They just start having 10. Everyone's gonna eat fat. Because this is hilarious.

[C]

That's not what I'm saying. That's not what I'm saying. No, what I'm saying Is what I'm saying is when once

[B]

I'll take. I was gonna get three pizzas,

[C]

like one tenth the price. No, no, no. So what happens? You have more money to do other things, but remember that money is only ultimately going to humans.

[A]

Yes.

[C]

So it's. It's the things that get automated go down in price.

[A]

Yes.

[C]

Which then creates surplus. Yes to do what? Yes, to do other things.

[A]

Yeah, this is the bomb.

[C]

So it doesn't always have to be, oh, marketing's automated, but sort of. And there's still people doing it. It's like, what? Whatever. There's going to be a hundred other new things that come out because there's this excess of capital and progress continues. And as long as humans still have things that we do that robots cannot, it's go, go time, man. It's going to be super prosperity. We talked about the plumber that is paid like LeBron last time. It's going to be across a thousand categories, and some categories we don't even know. Yeah, like, we don't even know what they are today. Yeah, yeah.

[B]

You raised 1.7 billion. Why didn't you raise more?

[C]

That's a good question.

[B]

I mean, because last time we were here, you talked about, like, oh, well, if you were doing something and it was easy, you weren't going hard enough.

[A]

Seems pretty. Going pretty hard.

[C]

But look, you have to stop somewhere.

[A]

No, even I have my limits now.

[C]

It's like. But like, look, I. As you can imagine, today, my phone's blowing up. I mean, I'm pumped like a 16. These guys. We should have done business at Uber.

[A]

That's right.

[C]

If we did it business at Uber, my 2017 would have been a different year. Yeah.

[B]

Yeah.

[C]

Okay. So that's why I called it unfinished business. Yeah. And so. But, yeah, like, my phone's blowing up. Like, we're probably just gonna do a second. We'll do a second close.

[A]

Yeah, I figured we'll come back for the second close.

[C]

Run it back. No, I mean, we're not gonna do a big announcement on the same clothes, but, like, you know those people who are. Who are texting me and hitting me hard right now? Got room, you know? Well, we'll see.

[A]

We'll see. Depends on what? The previous text message.

[C]

If you're a homie, we definitely have room. If we're not a homie, you should talk to one of my homies.

[B]

Yeah, I did come away from the last conversation thinking, all right, there's a lot of exciting companies in physical AI and you could spend years and years and years trying to find all the best teams. Or you could just give TK a big pile of cash and just say, go cook. And sometimes the easier route is better.

[C]

Yeah. And I think there's this thing, physical AI. People are like, well, is that a humanoid? Is that a world model? Is it? And so on this one I sort of dialed the language a little bit and am calling it industrial AI. Yeah, it's like, okay, this is a full stack software, robotics, sensors, machinery. Like a full stack solution to automating an industry. And that's kind of how we think about it. And it's industrial. Yep. So it's like heavy atom stuff. Yeah.

[A]

Well, thank you so much.

[B]

It was incredible.

[A]

You want to get a signature? Can we get an autograph?

[C]

Sure, why not?

[A]

We get something. Which way?

[B]

They can figure it out back there. Oh, we got a gong. We'll hang it in the raft.

[A]

We want to hang it in the rafters.

[B]

We're trying to build our, our museum of business.

[A]

The museum of business grows one gong stronger today.

[B]

And we will. We'll see you in Austin.

[A]

Yeah.

[B]

Next time you're on your commute, if you see two jet skis moving out of, out of your, you know, out of out of sight coming in, it's probably us.

[A]

That's us.

[B]

If it's not you're guys, let me know.

[C]

If you want to learn how to slalom ski.

[A]

Oh yeah.

[C]

If you want to learn how to wake surf like, well, I've only been

[A]

water skiing once or twice in, in 20 years ago.

[C]

I go into 7:30 in the morning every morning and I'd say half the time I'm out there at 8:30 when I leave the office.

[A]

That's amazing.

[C]

So I love. That's what we do.

[B]

Beauty of summer.

[A]

Thank you so much for coming on the show. Always a pleasure. Have a great rest of your day.

[C]

For sure. Good to see you guys.