Applied Intuition: The $15B Physical AI Company (You Should Know)

Qasar Younis (CEO) & Peter Ludwig (CTO) of Applied Intuition join Sourcery to break down how they’ve quietly built one of the most important AI companies in the world reaching a $15 billion valuation. Founded in 2017, Applied Intuition is building the infrastructure layer for vehicle intelligence and physical AI, powering systems across automotive, defense, trucking, construction, mining, and agriculture. Today, 18 of the top 20 global automakers and major U. S. Department of Defense programs rely on their software. The company has raised $1B+ in total funding, including a $600M Series F at a $15B valuation, co-led by BlackRock and Kleiner Perkins, with participation from global investors such as Franklin Templeton, Qatar Investment Authority, Abu Dhabi Investment Council, Premji Invest, Stripes, Greycroft, BAM Elevate, and 137 Ventures. Existing investors include Fidelity Management & Research Company, General Catalyst, Lux Capital, BOND, Elad Gil, Addition, and Tribe Capital, alongside early backing from Marc Andreessen. What makes the company especially unique: despite raising over $1B, Applied Intuition has largely not relied on that capital to fund operations, instead building a profitable, capital-efficient business with long-term customer contracts and deep integration across industries In this conversation, we cover: Why physical AI is fundamentally different from software AI How breakthroughs like transformers unlocked real-world autonomy The company’s horizontal-first strategy across industries Why they stayed quiet for years—and why that changed How they deploy autonomy in defense and industrial environments What hiring looks like in the AI era Applied Intuition isn’t building another AI app—it’s building the infrastructure for AI in the real world. Qasar Younis: https://x.com/qasar Peter Ludwig: https://www.linkedin.com/in/peterwludwig https://www.linkedin.com/in/peterwludwig/ Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊 YouTube: https://youtu.be/TyLDPNTbkEA 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex https://brex.com/sourcery—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • Turing https://turing.com/sourcery—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance—and enables enterprises to turn those models into powerful, production-ready systems. https://turing.com/sourcery•VCX https://fundrise.com/vcx—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio athttp://GetVCX.com http://getvcx.com • Deel https://www.deel.com/sourcery—Deel is the global people platform that helps startups hire, manage, pay, and equip anyone, anywhere. Trusted by more than 35,000 fast-growing companies, Deel is the people platform that just works, so teams can scale without the chaos. Visit: https://www.deel.com/sourcery • Public http://public.com/sourcery–Investing platform Public just launched Generated Assets https://public.com/generated-asset, which lets you turn any idea into an investable index with AI. With Generated Assets, you can build, backtest, refine, and invest in any thesis with AI. Gone are the days of one-size-fits-all ETFs. https://public.com/sourcery https://public.com/sourcery • Merge https://merge.dev—The leading provider of customer-facing integrations and agentic tools for frontier LLMs, Fortune 500 organizations, and B2B SaaS companies. Visit https://merge.dev Follow Sourcery for the latest updates! https://www.sourcery.vc/ Disclosure Paid Endorsement. Brokerage services by Open to the Public Investing Inc, member FINRA & SIPC. Advisory services by Public Advisors LLC, SEC-registered adviser. Crypto trading provided by Zero Hash LLC, licensed by the NYSDFS. Generated Assets is an interactive analysis tool by Public Advisors. Output is for informational purposes only and is not an investment recommendation or advice. See disclosures at public.com/disclosures/ga http://public.com/disclosures/ga. Matched funds must remain in your account for at least 5 years. Match rate and other terms are subject to change at any time.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Speaker 00

  Mark and Grayson has been with us from day one. Series B was led by Himant at General Catalyst. Series C was Bilal at Lux and D was Elad Gil. Without a doubt, our customers look at the fact that we've been around for nearly 10 years. Our crazy claim to fame that we've never spent the money we've ever raised.

• 0:17Molly O'Shea?

  We have to talk about that.

• 0:18Speaker 00

  Most companies in the Bay Area are more hobby projects than they are earnest, serious businesses.

• 0:25Speaker 02

  This is all happening autonomously and these operators can control a whole fleet of vehicles remotely here.

• 0:30Speaker 00

  People don't want to work in mines, in faraway places. These are dangerous jobs. AI really, in these industries, is being pulled out of our hands because technology can help solve the problem. We opened also in India and UK offices last year, about 18 sites globally. Australia could be one of those countries where we're doing driverless trucks and we're doing a bunch of different ports. We're kind of hopscotching like a board game throughout the planet.

• 0:53Speaker 02

  At 10 days, we showed them the video of these infantry squad vehicle driving autonomously out in a test environment.

• 0:58Speaker 00

  This view that everything's going to take a long, long, long time and it requires many years of planning, it's just false.

• 1:15Speaker 00

  Molly, what's the craziest thing I can do to get this thing to be viral?

• 1:20Speaker 00

  I don't have a sword.

• 1:21Molly O'Shea?

  Castor, what is your hottest take?

• 1:24Speaker 00

  My hottest take? I've gotten in trouble for giving hot takes, but you know what? I'll try to be restrained, but maybe I won't. I think most companies in the Bay Area are more hobby projects than they are like earnest, serious businesses. And it's kind of like, we'll take a swing on this. And I don't mean to disrespect my own, you know, I'm in the fraternity of co-founders or founders who are kind of toiling away. And it's very hard to see who's doing it for the right reasons, doing it for the wrong reasons. And I think it's like kind of maybe some of the wrong motivations that exist. I think if you roll back the clock 15 years ago or 20 years ago, right when we were entering the Bay Area, literally around that time.

• 2:07Speaker 00

  It's not, I'm not like one of these people who like the old days are always great and the new days are terrible. But the new days are definitely popular. Like it is like, I think there's some time between like the social network and the HBO show Silicon Valley. It's and then like just us as consumers, just adopting smartphones and software becoming a real part of our day to day to day life. Silicon Valley, like Sunnyvale, went from this cottage industry to suddenly the center of, you know, economic activity for America and for the world. And that attracts a huge group of people like a magnet. And so the valley today, especially, you know, I always make the distinction between South Bay and SF Silicon Valley and San Francisco.

• 2:52Speaker 00

  But you know, a lot of times it's interchange, because I think the cultures are different. Yeah, I think the cultures in Sunnyvale and Santa Clara and and Mountain View are different than the culture in, you know, Mission or Dogpatch. I do, maybe it's a perception and maybe it's not true, but I I don't know, at least I perceive it. And so the, you know, the point being or the punchline being is that the, you know, if you look back at finance in the eighties and nineties, you know, Manhattan attracted everybody and it kind of became a money thing. And I'm not to say that the old Silicon Valley wasn't a money thing. These are businesses to make profits, but it seems like it's more of a money thing.

• 3:37Speaker 00

  And is that good or bad? I mean, who am I to judge? But at least that's not our motivation. I think if that's singularly your motivation, you know, there's the, I think you probably maybe build a company a different way. Like we wouldn't, like we wouldn't build a company this way if just making money was the only only outcome we wanted.

• 3:55Molly O'Shea?

  So I actually wanted to talk to you about that on like a couple of different dimensions. One of them being, I feel like you just came out of stealth.

• 4:04Speaker 00

  Yeah. Well, you know, call it classic, not bestseller.

• 4:09Speaker 00

  So I think if you know, you know, there's, I mean, Marc Andreessen was, has been with us from day one. Marc Andreessen is not exactly an unknown person. So I think, yeah, it's his guy. He created this thing called the web browser, which is, yeah, yeah. But he's been with us from the beginning and, you know, our Series B was led by him at General Catalyst. And, you know, it's like, Series C was Bilal at Lux and, you know, D was Elad Gil. These are like this, you know, they're the in folks of Silicon Valley. And I worked at Y Combinator before, I worked at Google before. So we were here, I think we're just, our relationship to the ecosystem has always been different.

• 4:48Speaker 00

  It's not been antagonistic or anything like that, but I think we're just not, we have actively sought that limelight, I think, before. That's changed in the last year, because now every time people hear me say that, they're like, oh, but you were on this podcast. Yes, we're actively changing that, we're getting our, you know, we're here, we're talking to you. I mean, that's something that we wouldn't have done in a couple of years ago, and we had success a couple of years ago. I think we, there's two practical reasons why we've changed this. One is just recruiting, recruiting numbers. Historically, we've gotten all the people that we've ever wanted. But if you look at the mix of outbound versus inbound, you have certain like benchmarks as a company grows. And as we've gotten bigger, the benchmark moved to where we're doing, we're still doing a disproportionate amount of outbound to the stage we're in.

• 5:30Speaker 00

  As you, roughly as you get bigger as a startup, your outbound actually drops because people know who you are and you're starting getting a lot of inbound. A company like OpenAI or Anthropic, there's other reasons because we're also a technology provider and they're more of a consumer, prosumer product. They're not going to do a lot of outbound, there's enough inbound coming here. And so when we saw that, that was the first time we had that internal debate like, hey, this, and we're, you know, all of our values can be reduced to two words, radical pragmatism. And so it's like, That's maybe not the right strategy. And then the second thing is, and I think, you know, heavily influenced by Mark and some of our investors, who said, "Let's get the word out there. I think this is a great company." And I think earlier on also, you know, we were very, very thoughtful of not creating

• 6:13Speaker 00

  so much attention that would inspire a lot of people to compete with us. Now, it's kind of like, we've grown up a little bit, we got some muscles, you know, we've gotten a few rounds under the belt, and we can go into the ring and fight basically any oncomer. When you're really small, you're embryonic, that's a little dangerous. And I do think, actually, the new crop of Silicon Valley companies, some do take inspiration from us in the sense of, you know, not like the pithy things like the shoes and the name and all of that stuff, but like real things like, "Hey, we're gonna be South Bay based and we're not gonna be very loud." And I don't think that's necessarily a bad thing. Maybe it's just perception, but it hearkens back to that more of that craftsmanship aspect of building technology and building businesses and building companies.

• 6:57Speaker 02

  Yeah, it also anchored back to our company's mission, right? So, our mission is to build physical AI for a safer, more prosperous world. And you sort of hit a point where, to accomplish that mission, we need to be better known, we need to have access to a wider talent pool. And so, that's why we've been more open.

• 7:16Speaker 00

  Yeah, and honestly, our customers do know us. I mean, we are very well-known in our industry. I think we're more well-known in like, you know, construction and mining in Asia than maybe some bar in mission. But like, that directly correlates to where we're gonna make money, right?

• 7:31Molly O'Shea?

  Pretty cool.

• 7:32Speaker 00

  Yeah, yeah.

• 7:33Molly O'Shea?

  So, Peter, you were on stage and you walked through the timeline of the company and some of the biggest milestones. For people who didn't get to see that, maybe they will, but could you share some of those biggest milestones?

• 7:44Speaker 02

  Yeah, so, we started in 2017. In, I'd call it the early era of self-driving cars. And back then...

• 7:55Speaker 02

  For strategy reasons, we didn't think it was going to be smart for us to really go directly into self-driving at that moment. But we started out building tools. We built simulators and data management offerings and large-scale distributed compute offerings. And we sold these out to the industry. And that was sort of our early success as a company. And then

• 8:16Speaker 00

  They gave us resources.

• 8:18Speaker 02

  Yeah, yeah, and we just learned and learned and learned. And we really expanded out and became very horizontal. We started working in a bunch of different industries. And we were very much on top of all of the latest AI breakthroughs at every step. And so we could sort of see when something interesting was happening, should that actually change our strategy? And so there were a few interesting things that happened which evolved the strategy. One was transformers that were originally successful for large language models, like you're about Anthropic and OpenAI. Those started to have an impact on self-driving technology and robotics. And we thought that was really interesting.

• 8:58Speaker 02

  And then on top of that, there was some breakthroughs in what's called end-to-end deep learning, which is where you can now take lots of data and you can train these models to actually control these systems in a way that wasn't possible before. And that was the moment when we actually decided to really enter the self-driving space ourselves, but not so narrowly thinking about self-driving cars, but really more about the foundational physical AI technology needed to control any kind of machine. And that's what we were showing downstairs. That's a bit of the history.

• 9:31Molly O'Shea?

  Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture-backed startups in the US. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, shipping away at it with fees, Brex's designs help you spend smarter and move faster. Their all-in-one solution combines checking, treasury, and FDIC protection into one powerful account. You can send and receive money globally at lightning speeds, get 20 times the standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime.

• 10:16Molly O'Shea?

  Companies like Scale AI, DoorDash, ServiceTitan, Hims, Anthropic, Flexport, Robinhood, and Plaid, trust and use Brex.

• 10:25Molly O'Shea?

  Start today at brex.com/sorcery. That's B-R-E-X dot com slash sorcery.

• 10:33Molly O'Shea?

  Turing is training the next generation of AI with tasks that require real expertise and real-world judgment. That's why companies like Nvidia, Anthropic, Salesforce, and Gemini partner with Turing. Turing builds realistic reinforcement learning environments and data systems based on real operational traces, the kind of infrastructure Frontier Labs need to train superintelligence. Visit turing.com/sourcery. When are you going to the moon?

• 11:02Speaker 00

  Oh, you know, it's funny.

• 11:05Speaker 02

  We actually did work on moon simulators.

• 11:07Speaker 00

  Yeah, we did. We did. So we go there in a simulated reality.

• 11:13Speaker 02

  Yeah, I can't remember exactly the customer at the time, but there was somebody that was developing a moon rover, and they used our...

• 11:22Speaker 00

  I remember them, but I don't think we're allowed to say who.

• 11:24Speaker 02

  Yeah, it was like our vehicle dynamics simulations for simulating, like, literally the moon rover driving on the moon's surface. So we have.

• 11:34Speaker 00

  Yeah.

• 11:35Molly O'Shea?

  Wow. Oh, my gosh. So when you work with a new customer or a new industry or vertical, how do you start to get integrated and onboarded within them?

• 11:44Speaker 00

  It really depends. I mean, we're in a bunch of the verticals. Maybe it was better to look back than look forward because we're in a bunch of them already. It's a mix. And this is more for like education for founders who are trying to do this. So I'll put it within that context. What's best is first if you know some vertical that establishes a pattern. So I'm, you know, both of us collectively know the automotive industry extremely deeply, right? So then you have a pattern. And then you have to, defense being the next one that we went into. We saw some inclinations of like, okay, well, we can sell to like a General Dynamics as an example, and they'll use the tools kind of like General Motors. Once you have your foothold in there, then you start very quickly understanding, okay, these are the types of people with, these are the types of backgrounds that are going to be successful in this environment.

• 12:28Speaker 00

  And then we hired our, which became our Gov team. First with just one person and then ultimately all around them. I think founders sometimes make this mistake where they want the perfect lead for the Tokyo office. They want the perfect lead for their, you know, whatever, construction or mining business. And perfect is often the enemy of done. And so it's, the ideal is you had, the first person ends up becoming the lead and is the lead for the rest of the years. But it's better just to start. And so we started, I mean, all the things that you're seeing now, we're seeing, these are compounding results from many, many years. There's no fast way. One of the things that I think is underreported in physical AI is, you know, how this technology

• 13:11Speaker 00

  will kind of diffuse to the real world. Whether you're, you know, if you're working in kind of anything from consumer AI to code complete, you know, products, all that diffusion happens, all that distribution happens through browsers and phones. And everyone has one of them and they're all fairly consistent environments. Physical AI is very different. The companies are different, the geographies and the regulatory environments are different, the machines are different, the cultures are different. And so the diffusion is actually way, way slower. So time becomes a much more important thing. Without a doubt, our customers look at the fact that we've been around for nearly 10 years, our crazy claim to fame that we've, you know, never spent the money we've ever

• 13:56Speaker 00

  raised, which is-

• 13:57Molly O'Shea?

  We have to talk about that.

• 13:58Speaker 00

  Yeah.

• 13:58Molly O'Shea?

  I was just thinking about that. So you've raised over a billion dollars.

• 14:02Speaker 00

  Close to that, yeah. In primary, yeah.

• 14:03Molly O'Shea?

  In primary.

• 14:04Speaker 00

  Because we've also done tenders and secondary, yeah.

• 14:05Molly O'Shea?

  And you're saying you haven't touched any of that money?

• 14:08Speaker 00

  Yeah, yeah. We use some for M and A, but then we make money and it kind of, like, that's the, you know, but, but yeah, roughly, that's, that's roughly the truth. And we're not using our, our capital to like pay payroll and things like that. Yeah, which is kind of crazy. But for a customer who wants to have this long-term relationship- That's really confidence inspiring. They're like, hey, this company has been, they got a thousand years, been in business nearly 10 years. And they have a functioning business. Like, we can do a five-year relationship with them. We can do a 10-year relationship with them. And that makes it, frankly, difficult to compete with us because, you know, we've been in the business. But like I said, that's very relevant. It's not relevant if you're building, you know, Anthropic, you're building some open claw kind of product. Because that doesn't matter.

• 14:51Speaker 00

  But in physical AI, where the diffusion is way more complex and multivariate, or variegated, I should say, then suddenly time becomes an important thing. Staying power becomes important.

• 15:03Molly O'Shea?

  So what is it like, we talked about this a little bit, but what is it like hiring out in this AI era versus before? And what are you looking for in those hires?

• 15:13Speaker 00

  The funny thing is, I mean, the people who come to Applied are frankly different than people who go to foundation lab companies. Though we, I don't know if you saw our research talk, like, we are as technical as any AI company in the business. And a lot of people, I think, because they see our commercial success and they somehow think we're like a really commercially oriented company. It's like, this is as technical of a company as you can imagine, all the way to the big labs. Our commercial teams are actually really small. The commercial teams are surprisingly small, right? And so, yeah, I think like, so then why would an engineer choose us versus, you know, working at like, a cool company in San Francisco? And I kind of am implying we're not cool.

• 15:58Speaker 00

  Really what I'm implying is, we tend not to attract those types of people. Particularly because the type of technology you need to know and be interested in is different. If you like sports cars and you like fighter jets, Applied is a great place. Because those companies aren't doing sports cars and fighter jets. Or if agriculture resonates with you because that's your family's upbringing, or construction or mining, those are the people that, they fit that Venn diagram, that overlap of they love AI and they want to make the farmer's life in America easier. That's a great candidate for us. And so I think in that way, we've done quite well. And also, just like, you know, what I was saying earlier about us being more public, it's not necessarily that we've had...

• 16:43Speaker 00

  Everyone has a talent, you know, everyone's fighting for talent. The real point is, I think, the more we get our message out and how distinct the company is, the more it resonates to those specialists, who are maybe working at Google right down the street from us and don't even know that we exist. And they're like, "I love the car business. I grew up working at Ford and then I came to the Bay Area and worked at Google for the last 10 years." That's a great candidate for us. That candidate is not as relevant for like Harvey, you know, and we could both really, all of us could really love AI.

• 17:13Molly O'Shea?

  And you have plenty of runway.

• 17:14Speaker 00

  Yeah, yes, exactly, yeah, exactly.

• 17:16Speaker 02

  But yeah, at a high level, though, there's honestly two types of people that we're really looking for. One is, it's the enthusiastic engineer who loves AI tools and they can use them super effectively. I think that it's becoming a little bit less important now to be, let's say, a specialist software engineer in a given field because the AI tools, they can turn you into specialist much more quickly. But you need to be an expert in the AI tool use itself, and that is a real skill. And so we test for this hard now and we're looking for people who are just really, really good at using the AI tools. And then the second type of person relating more to the research is

• 17:58Speaker 02

  individuals who so deeply understand how the AI systems work that they can actually push forward the state of the art. And that's really where you get into the deeper research topics. But it's that combination of like deep research plus just experts in the application of these tools. And that's what makes us a strong engineering organization.

• 18:17Molly O'Shea?

  Is it true there's over 40 CTOs?

• 18:20Speaker 00

  Yeah, we have, yeah, ex-CTOs or co-founders of the company.

• 18:24Speaker 02

  It's a very entrepreneurial culture.

• 18:25Speaker 00

  Yeah, yeah, which sounds so like flat and dry, but it's absolutely true. We attract a lot of founders. I think, like I said, cult classic, not bestseller. I think among founders, and I'm really proud of this because I want us to be founders' founders, you know. I think in some degree, I like the respect of other founders. Is that I think, you know, my YC background and us... Building this very intricate business in all these different verticals globally. I think as a founder, you know how difficult that is. And they know that that's a special thing we've accomplished. So I think the magnet has been like, this company has somehow figured out how to take really complex technology.

• 19:11Speaker 00

  And you know, to have it go out into the world and all these different places and maybe I can learn from that. And so if you're a CTO whose company isn't working, you know, we're always hiring. Apply.co slash careers or you can just email me directly.

• 19:25Molly O'Shea?

  Oh my God. So today you launched the Applied Edge product. I think, again, I think your defense, well, I think mostly all of Applied Intuition is a bit underrated. But I think in the defense space, at least for the public, it is. So there's a stat that you launched an autonomous infantry unit in less than 10 days. Is that correct?

• 19:52Speaker 02

  Yeah, that's right. Yes. So Secretary of the Army Dan Driscoll visited our headquarters, this campus about a year ago. And

• 20:01Speaker 00

  Oh, it's the old office.

• 20:02Speaker 02

  Oh, you're right. Oh, yeah. Yes. Yeah. And he, sort of a throwaway comment, he sort of said like, hey, what if I gave you like an ISV and a Humvee? Like, could you guys do something with it? And I said like, yeah, I think in like in 10 days, we can do something pretty interesting. And to be honest, I wasn't expecting much, but then after he left, we got a phone call. And it was like somebody in the army saying like, I need like an address and delivery instructions. And they just dropped off these machines in our, in our office, these military vehicles. And so yeah, in 10 days, we had a very small team retrofit our vehicle operating system and our SDS or self-driving system onto the vehicles.

• 20:46Speaker 02

  And then yeah, at 10 days, we showed them a video of these infantry squad vehicle driving autonomously out, out in a test environment. And then they shipped it off to an army test facility and soldiers got to use it for a few weeks. So it was just a great example of adapting commercial technology into defense. And I think Dan Driscoll has spoken about this event, actually on many occasions now, because it's a pretty interesting story.

• 21:09Speaker 00

  Yeah, there's a lesson there, which is I think this view that everything's going to take a long, long, long time and it requires, you know, many, many years of planning is just false. And I think especially within the reality of today, this technology needs to get out there faster and faster and faster. We don't have the luxury of waiting three or five or 10 years.

• 21:30Molly O'Shea?

  V C X by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View the portfolio at getvcx.com. That's getvcx.com. Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cash flow, or defense tech companies growing revenue over 25% year over year. Public's AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis.

• 22:13Molly O'Shea?

  What really stands out is how clearly it explains why each stock is included. And before you invest, you can even backtest your idea against the S and P 500, so you're making decisions with real context, not just guessing. And beyond Generated Assets, Public lets you invest in stocks, bonds, options, crypto, all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com/sorcery. Paid for by Public Investing. Full disclosures in the description. Enterprise AI runs on Merge, the AI infra platform for integrations, agent tooling, and model orchestration, so your teams ship product, not plumbing. Mistral, Dropbox, and Drata already trust Merge in production.

• 22:58Molly O'Shea?

  Start building at merge.dev. Founders scale faster on Deal. Set up payroll for any country in minutes, hire anyone, anywhere, get visas handled fast, and get back to building. Visit deal.com/sorcery. That's D E E L dot com slash sorcery.

• 23:17Molly O'Shea?

  Okay, so this is a plug for Brex, but sorcery sponsored by Brex. They're all about performance, spending smarter and moving faster. So with that, I'd like to ask, what are the metrics they use to measure the business and for success? What are the milestones for this year?

• 23:32Speaker 00

  Yeah, I mean, we're an at-scale business. So, you know, all the classics, top line, bottom line, gross margins.

• 23:41Molly O'Shea?

  What are those numbers?

• 23:43Speaker 00

  Those numbers are not public. But they're, you know, I mean, they're healthy. This is a mature business. So we look at all those things from like a traditional finance perspective. The things that are more difficult to measure, which might be even more important because, you know, those financial figures are lagging indicators of your technology and your products. We don't have good financials because we focus on the financials. We have good financials because we focus on the products. And the products are really world-class. And measuring those things are hard. Like, there's many companies that will say, "We do self-driving for X, or we do autonomy, or we do intelligence in this place, or that place." And how do you know objectively, either as a customer or as somebody who's just visiting the office, what's actually good and what's not good? And say even deeper,

• 24:28Speaker 00

  how do we know? You know, how are we not biased towards our own kind of proclivities? And those are way more nuanced. And, you know, how we do benchmarking and how we think about how good the autonomous system is and is it progressing at the right rate? And that requires a bunch of things. It requires truly subject matter experts. When we say we're a technical company, the root word of technical is technique. We have people who know these technologies extremely well. It's truly the reason the company is good is because of very high talent density. And those people can individually say, "Hey, I think this is..." "We're moving forward or not moving forward." It's not like a metric that always is up and to the right. These metrics themselves, especially when you're talking about frontier technology, can be gained in ways that are,

• 25:12Speaker 00

  you know, not actually making the technology better.

• 25:14Speaker 02

  On the engineering side, we care a lot about productivity. And productivity is not just about intensity, although that's certainly an important factor. But there's all these technical measures that you can get very deep into that indicate, is the engineering team actually functioning well? Like, how quickly can we actually build and launch new things? And we do a ton of work in model training and AI. And there's a big metric there is like, how fast can you actually get new data and train a new model and deploy it? And you want that to be as fast as possible.

• 25:41Molly O'Shea?

  Okay. Well, earlier today, you did have Mark Andreessen on the stage. You two definitely have spoken before. You're both incredibly well-read and it seems like you could talk for hours.

• 25:53Speaker 00

  I've known Mark for many years. Yeah, yeah.

• 25:55Molly O'Shea?

  And his retweet of your first tweet ever did go viral.

• 25:59Speaker 00

  Oh, yes. Yeah, yeah, yeah, yeah.

• 26:02Molly O'Shea?

  So on your website, you have a couple of different sections. You have books, you have photos, and writing, and more. The photos, the photography was actually really interesting.

• 26:11Speaker 00

  Thank you. Thank you. Not enough people give me credit for that. In some parallel universe, I would have been a photographer. Yeah.

• 26:20Molly O'Shea?

  And then on the book side, I've heard some hot takes you've had on books.

• 26:23Speaker 00

  Yes.

• 26:24Molly O'Shea?

  People that read books and don't read books.

• 26:26Speaker 00

  Yeah, yeah.

• 26:26Molly O'Shea?

  So what's your hot take? And what are your favorite books?

• 26:28Speaker 00

  I don't know if it's necessarily a hot take. I mean, we did a kind of a Q and A like Berkshire Hathaway, and the person I'm quoting is Charlie Munger, rest in peace. But I don't think there's, you'll meet, you know, very, very successful people that don't read a lot, that don't read a lot. And specifically, and I think Mark and I share this view independently, but the best books to read are the old books. So over 25 years, over 50 years, because time has filtered all the kind of noise, and you really get a lot of signal. And frankly speaking, you only get, I mean, in your life, how many books are you going to read? You're going to read maybe Most people are gonna read very small amount, but even if you're a kind of an avid reader, a thousand books, I mean, there's not many.

• 27:12Speaker 00

  And there are lots to pick from, so just pick the really good ones because what you read does impact your view of the world and you know, you wanna read diverse books that are not just kind of what you know, your friends are telling you to read. So they're really kind of challenge your existing views and even if you come back on the other side and you still have that view, then either maybe you have a better argument against against that view. But yeah, I'm I'm a huge, huge fan of reading. I think there's also something, again, a little old school, but this is like long-form, you know, deep content. I think you just, there's something, your brain somehow processes things differently. And again, as a founder, when you read things, I also don't recommend reading tech books.

• 27:55Speaker 00

  Like I'm just like one of those people as well, like no airport books and things like that. And so if you read something that's really old and outside in another industry, I'll use an example, a book that's not on the book list, but is is really good is My Years with General Motors by Alfred P. Sloan. The book that I have on my on my list is, which I think is even a better book is Adventures of a White Collar Man. But My Years with General Motors is this, General Motors is the company that establishes the modern corporation. He writes a book about establishing the modern corporation. You will learn a lot about why organizations look this way from the person who really kind of, you know, made it a reality post, post-World War II.

• 28:39Speaker 00

  And that's like a very obvious one, you know, for us, but but, you know, reading the, you know, autobiography of Malcolm X. I think that like that opens your mind a little bit of like, what does it mean to be American and all of these things? So I, yeah, I highly recommend reading. You don't have to read my, you know, you don't have to read Krishnamurti's The First and Last Freedom, but I do recommend it and there's many others on that list that you can try out.

• 29:04Molly O'Shea?

  Peter, what are you reading? As CTO in the AI era, what are you reading? Is it just research papers or are we?

• 29:12Speaker 02

  I read all kinds of things. Actually, the most recent book I read was actually Apple in China.

• 29:18Molly O'Shea?

  Oh, so good.

• 29:19Speaker 02

  Yeah.

• 29:20Molly O'Shea?

  So good.

• 29:20Speaker 02

  Fascinating read because of the combination of geopolitics and technology and yeah, just how much investment has flowed into developing the Chinese electronics ecosystem. And it's actually quite relevant to a lot of stuff.

• 29:35Speaker 00

  We also read books as a company, as like a leadership team, and then we invite the whole company to do it. So we've read probably had a half a dozen books. They're work related. They're used to reflect on our company. So we will then chunk out the book into three or four parts and then talk about those parts. And then record it and distribute it to the other company. So we've done, you know, the hard thing about hard things, just plugging a 16Z as much as I can. You know, we did House of Huawei, really fascinating book, super good to read. Breaks the rule of very old, but it is definitely very, very good. We've done a bunch of this, you know, the Netflix.

• 30:11Speaker 02

  No Rules Rules.

• 30:11Speaker 00

  No Rules Rules. So I recommend, I mean, as a founder again, I think you don't have to take all of Reed Hastings, you know, things about Netflix, but if you talk to your leadership team about they did this for compensation and they did this for recruiting, it forces you to reflect on what we're doing. And are we doing the right thing or should we now change it? Because your company, it's like, think about your company, and again, I'm talking to founders here. Think about your company as like this bespoke piece of clothing that you get made. And then you grow and then before you know it, it's kind of, and you have to constantly be retailing your company for the scale, position and type of company it is and type of products you're. And I think these books just help you reflect on like, you know, Made in America by Sam Walton,

• 30:55Speaker 00

  phenomenal book, excellent, excellent book. This makes you think like, oh, that's how Walmart was being built, you know, in the seventies and eighties, in the late eighties. Are we doing it the right way? And you will get lessons there. But macro point, read outside of like business and technology. You'll get even more value there.

• 31:13Molly O'Shea?

  Books are good.

• 31:15Speaker 00

  Yes, yes. Reading good.

• 31:17Molly O'Shea?

  Reading good.

• 31:18Speaker 00

  Yeah, yeah.

• 31:19Molly O'Shea?

  All right. Thank you guys so much.

• 31:21Speaker 00

  Yeah, thanks for having us.

• 31:22Molly O'Shea?

  The time and the tour and seeing everything today.

• 31:24Speaker 00

  Yeah, yeah. Sorry we can't give you any, you know, viral moments where, you know.

• 31:29Molly O'Shea?

  Well, next time, maybe bring the mining equipment and I'll get in the really, really big truck. And you can drive that around.

• 31:35Speaker 00

  There you go. It'll drive itself. It'll drive itself.

• 31:38Molly O'Shea?

  Yeah, exactly.

• 31:39Speaker 00

  Thank you very much.

• 31:40Molly O'Shea?

  Thank you. Appreciate it.

• 31:42Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.