Mark Andreessen has been with us from day one. Series B was led by Himant at General Catalyst. Series C was Bal at Lux, and D was Eli 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.
We have to talk about that. Yeah. Most companies in the Bay Area are more hobby projects than they are earnest, serious businesses. This is all happening autonomously, and these operators can control a whole fleet of vehicles remotely here. 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 those problems. 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. At 10 days, we showed them the video of these infantry squad vehicles driving autonomously out in a test environment. This view that everything is going to take a long, long, long time and it requires many years of planning is just false.
Molly, what's the craziest thing I can do to get this thing to be viral?
I don't have a sword. Caster, what is your hottest take?
Uh, 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, you know, 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 co — 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, who's 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 — it's not, I'm not like one of these people like the old days were always great and the new days are terrible. But the new days are definitely popular. Like it is, like I think sometime between like The Social Network and the HBO show Silicon Valley, and then like just us as consumers just adopting smartphones and software becoming a real part of our 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, but you know, a lot of times it's interchangeable because I think the cultures are different.
Yeah. I think the cultures in Sunnyvale and Santa Clara 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 don't know. At least I perceive it. And so the point being, or the punchline being, is that if you look back at finance in the 80s and 90s, 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. 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, I think you probably maybe build a company a different way. Like we wouldn't build a company this way if just making money was the only outcome we wanted.
So I actually wanted to talk to you about that on like a couple different dimensions. One of them being, I feel like you just came out of stealth.
Yeah. Yeah. Well, you know, cult classic, not bestseller. So I think if you know, you know, there's — I mean, Mark Andreessen has been with us from day one. Mark Andreessen is not exactly an unknown person. So I think, yeah, it's — 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 where Series C was Bal at Lux, and you know, D was Eli Gil. These are like, you know, they're the in-folks of Silicon Valley. And I worked at Y Combinator before, we worked at Google before. So we were here. I think we're just — our relationship to the ecosystem has always been different. It's not been antagonistic or anything like that, but I think we're just not — we haven't 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 even a couple years ago, and we had success a couple of years ago. I think there's two practical reasons why we've changed this. One is just recruiting numbers.
Historically, we've gotten all the people that we wanted, but if you look at the mix of outbound versus inbound, you have certain 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. As you roughly — as you get bigger as a startup, your outbound actually drops because people know who you are and you start 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 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, 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 so much attention that it 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 pathy things like the shoes and the name and all of that stuff, but like real things like, hey, we're going to be South Bay and we're not going to be very — we're very loud. And I don't think that's necessarily a bad thing. Maybe it's just perception, but it harkens back to that more of that craftsmanship aspect of building technology and building businesses and building companies.
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.
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 Indian Mission, but like that directly correlates to where we're going to make money, right?
Pretty cool.
Yeah. Yeah. Yeah.
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?
Yeah. So, we started in 2017, in — I call it the early era of self-driving cars. And back then, 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 —
They gave us resources.
Yeah. 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 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 a strategy. One was transformers that were originally successful for large language models like you hear about Anthropic and OpenAI. Those started to have an impact on self-driving technology and robotics, and we thought that was really interesting. 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.
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When are you going to the moon?
Oh, you know it's funny. We actually did work on moon simulators.
Yeah, we did. We did. So we go there in a simulated reality.
Yeah. 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 —
I remember them, but I don't think we're allowed to say who.
Yeah, it was like our vehicle dynamics simulations for simulating like literally the moon rover driving on the moon surface. So we have —
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?
It really depends. I mean, we're in a bunch of the verticals. So 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 you know, both of us collectively know the automotive industry extremely deeply, right? So then you have a pattern, and then you have 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. 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 the ideal is you have 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 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 regulatory environments are different. The machines are different. The cultures are different. And so that 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, a crazy claim to fame that we've, you know, never spent the money we've ever raised, which is —
We have to talk about that. I was just thinking about that. So you've raised over a billion dollars —
Close to that. Yeah. Primary. Yeah. We've also done tenders and secondary. Yeah.
And you're saying you haven't touched any of that money.
Yeah. Yeah. We use some for M&A, but then we make money and it kind of like — that's the, you know, but yeah, roughly that's roughly the truth. I mean, we're not using our capital to like 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's been — they got a thousand engineers, 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, you know, 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 OpenClaw kind of product because that doesn't matter. 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.
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?
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 — as 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 — yeah, like surprisingly small, right? And so, yeah, I think like the — so then why would an engineer choose us versus, you know, working at like a cool company in San Francisco? And I kind of — I'm implying we're not cool. 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 to you because that's your family's, you know, upbringing, or construction or mining — those are the people that fit that Venn diagram, that overlap of they love AI and they want to make, you know, 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 — 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. 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 —
And you have plenty of runway.
And yeah. Yes. Exactly. Yeah. Exactly.
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 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 a 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 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.
Is it true there's over 40 CTOs?
Yeah, we have — yeah, ex-CTOs or co-founders of companies.
It's a very entrepreneurial culture.
Yeah. Yeah. Yeah. Which sounds so like flat and dry, but it's absolutely true. Like 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 to 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 the founders 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 and, you know, to have it go out into the world in 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/careers — or you can just email me directly. 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?
Yeah, that's right. Yes. So, Secretary of the Army Dan Driscoll visited our headquarters, this campus, about a year ago, and —
Oh, it was the old office.
Oh. Oh, you're right. Oh, yeah. Yes. Yeah. 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 10 days we could 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 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. And then yeah, at 10 days we showed them a video of these infantry squad vehicles driving autonomously 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. Yeah, there's a lesson there, which is I think this view that everything is 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.
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Okay, so this is a plug for Brex, but Sourcery is 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 that you use to measure the business, and for success, what are the milestones for this year?
Yeah, I mean, we're an at-scale business, so you know, all the classics — top line, bottom line, gross margins, margin numbers. 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 somebody who's just visiting the office, what's actually good and what's not good? And I say even deeper: how do we know — how are we not biased towards our own 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 — with 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, we're 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 gamed in ways that are, you know, not actually making the technology better.
On the engineering side, we care a lot about productivity. And right, productivity is not just about intensity, although that's certainly an important factor, but there's all these like 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.
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.
I've known Mark for many years. Yeah. Yeah.
And his retweet of your first tweet ever did go viral.
Oh yes. Yeah. Yeah. Yeah. Yeah. Yeah. So on your website you have a couple different sections. You have books, you have photos and writing and more. The photos, the photography was actually really interesting.
Thank you. Thank you. Not enough people give me credit for that. In some parallel universe I would have been a photographer. Yeah.
And then on the book side, I've heard some hot takes you've had on books. People that read books and don't read books.
Yeah. Yeah.
So what's your hot take and what are your favorite books?
I don't know if it's necessarily a hot take. I mean, we did a kind of a Q&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. 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 going to read a very small amount, but even if you're a kind of an avid reader, a thousand books — I mean, there's not many, 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 want to read diverse books that are not just kind of what your friends are telling you to read. So they 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 that view. But yeah, I'm a huge, huge fan of reading. I think there's also something again a little old school, but this like long-form, you know, deep content — I think you just — there's some — your brain somehow processes things differently. And again, as a founder, when you read things, I also don't recommend reading tech books. 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 really good is My Years with General Motors by Alfred P. Sloan. The book that I have on my list, which I think is even a better book, is Adventures of a White Collar Man. But My Years with General Motors — 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-World War II. And that's like a very obvious one, you know, for us. But, you know, reading the 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 these things. So 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 know, you can try out. Yeah.
Peter, what are you reading as CTO in the AI era? What are you reading? Is it just research papers or —
I read all kinds of things. I actually — the most recent book I read was actually Apple in China.
Oh, so good.
Yeah. Yeah. So good.
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.
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 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 a16z 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 No Rules Rules. So I recommend — I mean, as a founder again, I think you don't have to take all of Reed Hastings' 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 — 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 re-tailoring 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 — phenomenal book, excellent, excellent book. Just makes you think like, 'Oh, that's how Walmart was being built, you know, in the 70s and 80s, in the late 80s. 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.
Books are good.
Yes. Yes. Reading good.
Reading good.
Yeah. Yeah.
All right. Thank you guys so much. I really appreciate the time and the tour and seeing everything today. It was —
Yeah. Yeah. Sorry we can't give you any, you know, viral moments where, you know, I —
Well, next time maybe bring the mining equipment and I'll get in the really, really big truck and we can drive that around.
There you go. Awesome. It'll drive itself.
It'll drive itself.
Yeah, exactly.
Thank you very much.
Thank you. Appreciate it.
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