Latest / Giant Ideas / Applied Intuition CEO, Qasar Younis: Why He Raised ~$1B but Never Spent a Dollar

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Applied Intuition CEO, Qasar Younis: Why He Raised ~$1B but Never Spent a Dollar · Giant Ideas

• 0:00
  SPEAKER_01: Again, this takeaway for founders is probably the biggest thing you have to get right is timing and heuristics.
• 0:06
  SPEAKER_01: So, like, if your company could exist two years before you in the same shape and form, then that's probably not right.
• 0:14
  SPEAKER_00: Hello, and welcome to Giant Ideas with me, Cameron McLean, and me, Tommy Stadlin.
• 0:19
  SPEAKER_02: We're co-founders of Giant Ventures, which builds and backs purpose-driven companies.
• 0:23
  SPEAKER_00: At Giant, we're lucky to meet extraordinary people with giant ideas that are changing the world.
• 0:27
  SPEAKER_00: This podcast brings you behind-the-scenes access to those ideas and the inspiring stories of the people behind them.
• 0:33
  SPEAKER_02: We explore how one giant idea can kickstart a billion-dollar company, shape culture, and transform life as we know it.
• 0:41
  SPEAKER_02: Today on Giant Ideas, we're joined by Casa Yunus, the man Mark Andreessen called the best AI CEO no one's ever heard of.
• 0:48
  SPEAKER_02: He is, of course, the CEO of Applied Intuition, a physical AI company on a mission to bring intelligence to every moving machine on the planet.
• 0:56
  SPEAKER_02: They're powering the future of physical AI.
• 0:58
  SPEAKER_02: And compared to some of the other major AI companies, they've kept a much lower profile until now.
• 1:02
  SPEAKER_02: Founded just a decade ago, they're now valued at$15 billion.
• 1:06
  SPEAKER_02: They serve 90% of the world's top car makers, but also other heavy industries, from defense and trucking to construction, mining, and agriculture.
• 1:14
  SPEAKER_02: We're going to talk to Casa about why physical AI is going to be bigger than anyone can possibly imagine.
• 1:19
  SPEAKER_02: We're also going to talk next week about the role he played building Y Combinator into the world's best startup school.
• 1:26
  SPEAKER_02: Casa, thank you for joining us on Giant Ideas.
• 1:28
  SPEAKER_02: Thanks for having me.
• 1:29
  SPEAKER_02: Really appreciate it.
• 1:30
  SPEAKER_02: We've been a giant been investing in physical AI for a while, but very simplistically for us, it's like 85% of the global economy is physical.
• 1:37
  SPEAKER_02: It kind of stands to reason that AI can have a much more profound impact and a bigger outcomes when it's applied to the physical world.
• 1:45
  SPEAKER_02: But it's just way harder.
• 1:46
  SPEAKER_02: We kind of like that because it creates barriers to entry.
• 1:48
  SPEAKER_02: It's way harder, in my opinion, to do what you're doing with AI than building a chatbot or whatever.
• 1:54
  SPEAKER_02: Talk to us before we go deep on apply just about why you think physical AI is going to be so big.
• 2:00
  SPEAKER_02: It's probably less well understood generally.
• 2:02
  SPEAKER_02: But what's your kind of thesis on physical AI?
• 2:05
  SPEAKER_01: Yeah, I think what you said is exactly correct.
• 2:07
  SPEAKER_01: Uh, you know, I've said before that I think when we look back uh 25 years from now and we look back at this time like we look back at the beginning of the internet, uh, which is roughly around that time, um, you know, beginning internet, static websites and the companies that are, you know, interesting, you don't even know them.
• 2:25
  SPEAKER_01: Actually, you don't, you don't think about those companies.
• 2:27
  SPEAKER_01: Uh and even the big ones then, the AOLs uh and the prodigies, I would say most people in college right now would not be able to, they don't know their names.
• 2:37
  SPEAKER_01: No.
• 2:38
  SPEAKER_01: Uh, but so who who do we know?
• 2:40
  SPEAKER_01: Well, we know Google, we know Amazon, we know Apple.
• 2:43
  SPEAKER_01: I think when you look back, it's gonna be, it'll be things that are impacting the average person in their um day-to-day existence.
• 2:51
  SPEAKER_01: But in the heuristic that I use is, you know, you're sitting at a, you know, you're at the airport uh and you're sitting at the gate and you look around.
• 2:59
  SPEAKER_01: And no matter where you're going, even if it's San Francisco, you can look around and say, like, how many of these people, you know, really are like the next, you know, fable is like on top of their mind.
• 3:10
  SPEAKER_01: Because it's you know, it feels like when you're in your world of the internet, in your world of of of uh software development or AI, that everybody knows that Fable is and it's not, you can't use it for these days, and now you can use it.
• 3:22
  SPEAKER_01: It's like people don't even know Claude.
• 3:26
  SPEAKER_01: And I'd see it's and and if that sounds very strange to you as a listener, that just shows you how much of a bubble you're in.
• 3:31
  SPEAKER_01: Like, like if that if that seems really crazy to you, yeah, actually most people don't know.
• 3:37
  SPEAKER_01: Yeah.
• 3:37
  SPEAKER_01: Actually, most people don't know these companies.
• 3:38
  SPEAKER_01: They they they they don't they don't care about the SpaceX IPO.
• 3:41
  SPEAKER_01: Right.
• 3:42
  SPEAKER_01: Um, and so what do those folks do?
• 3:44
  SPEAKER_01: Well, they drive taxicabs and they work at the local convenience store, or then maybe they're a retiree.
• 3:49
  SPEAKER_01: And like, how is AI gonna impact their life?
• 3:52
  SPEAKER_01: It's very different than kind of this, you could say, like, you know, uh unembodied AI, like this, this digital, you know, this large large language models.
• 4:02
  SPEAKER_01: Yeah.
• 4:02
  SPEAKER_01: So I think the embodiment of this, of this intelligence, that's where it's gonna meet most people.
• 4:07
  SPEAKER_02: Do you think that the the big LLM companies, the vibe coding companies, do you do you think we'll see a similar thing that we saw with, for example, search engines back in the day, where you know, nine out of ten of them just won't exist in five to ten years?
• 4:20
  SPEAKER_02: Can you see that?
• 4:21
  SPEAKER_02: Can you see some of these very big LLM companies going to zero?
• 4:23
  SPEAKER_01: I don't I don't like making predictions like that because it's it's such it's you know, it is a little bit of fooled by randomness kind of things where if I can say enough things, some of them will be correct.
• 4:31
  SPEAKER_01: Right.
• 4:31
  SPEAKER_01: Uh, but I think but there are patterns.
• 4:33
  SPEAKER_01: There are patterns in markets.
• 4:35
  SPEAKER_01: And um the reality is um you do tend to coalesce towards a couple of winners, and those folks make so much money that then they get into other things.
• 4:45
  SPEAKER_01: And I thought the question you were gonna ask is do you think some of these LLM companies are gonna ultimately get into physical AI?
• 4:50
  SPEAKER_01: And the answer is yes.
• 4:52
  SPEAKER_01: It's a natural extension.
• 4:53
  SPEAKER_01: I think, I think that is the next big wave.
• 4:55
  SPEAKER_01: And you see kind of the early remnant or the early, let's say the you know, the the first photons hitting your your eyeballs over the horizon.
• 5:02
  SPEAKER_01: It's uh open AI and this fan, you know, this hardware device that might be coming or not coming.
• 5:07
  SPEAKER_01: And the lawsuit that just got announced, uh, you know, uh uh with Apple suing them.
• 5:11
  SPEAKER_01: These companies are gonna play more and more in the real world.
• 5:13
  SPEAKER_01: I think that'll absolutely happen.
• 5:14
  SPEAKER_01: Anthropic getting into drug design, yeah, for example.
• 5:17
  SPEAKER_01: So uh I I think I think when you so then you have to kind of put a little like, let's say, boundary around what is physical AI.
• 5:25
  SPEAKER_01: And it can be many things.
• 5:27
  SPEAKER_01: It can be, you know, uh designing uh nuclear plants in a more efficient way or something like that.
• 5:32
  SPEAKER_01: In our universe, in the applied intuitions universe, it's pr specifically on making machines more intelligent, putting intelligence in the machines that move around us and move people and move goods.
• 5:43
  SPEAKER_01: The most obvious, easiest one is a car, uh, but maybe the like the non-obvious ones are construction, mining, um drones in defense.
• 5:52
  SPEAKER_01: All these things are physical machines that move.
• 5:54
  SPEAKER_01: And by adding, imagine you just take a cup of intelligence and you just spill them uh on all those machines, those machines are all gonna get way more safe.
• 6:02
  SPEAKER_01: They're gonna get more productive, um, and they're gonna they're gonna drive efficiency for for society.
• 6:08
  SPEAKER_01: And I think unlike the AI revolution that's that's that that's creating so much anxiety in for knowledge workers, specifically uh in in things like accounting, for example, or uh or in finance, putting intelligence into a combine uh on a farm uh is that no one's resisting that.
• 6:30
  SPEAKER_01: The average farmer in America is 58 years old.
• 6:33
  SPEAKER_01: Uh their kids are not coming to take over the farm.
• 6:37
  SPEAKER_01: So there, and the there, the the other kind of crazy stat is something like less than 10% of farmers are under 35.
• 6:44
  SPEAKER_01: So it's like, it's really like there's a there's a there's a big gap opening.
• 6:48
  SPEAKER_01: If you look at Japan, people always talk about the demographic collapse in places like Korea and stuff.
• 6:53
  SPEAKER_01: What they don't really also talk about is the remaining people, what are they working on?
• 6:56
  SPEAKER_01: And in Japan, guess what they're not doing?
• 6:58
  SPEAKER_01: They're not driving trucks.
• 6:59
  SPEAKER_01: Uh they're not they're they're not doing these kind of backbreaking, laborious tasks.
• 7:04
  SPEAKER_01: And so for in the spaces we play in, defense is uh self-explanatory, AI can't get there fast enough, right?
• 7:12
  SPEAKER_01: So you don't have that resistance and that pushback of that.
• 7:16
  SPEAKER_01: Um where you see the most of that is is in something like our taxi uh example, sometimes in trucking, but even in trucking, even in the United States, there's there's a much larger labor shortage than there's anything else.
• 7:27
  SPEAKER_01: Yeah.
• 7:27
  SPEAKER_01: Uh but by and large, our domain in physical AI is one where it's like, uh, you're kind of like a you know, you're cooking a diner and and the people sitting at the table are like, is the food coming?
• 7:38
  SPEAKER_01: Because you know, we're ready, we're ready to ready to buy.
• 7:40
  SPEAKER_02: Well, the the the robotic chef or whatever is one example, but you've got some probably even more life and death scenarios in that, right?
• 7:47
  SPEAKER_02: So let's say if ChatGPT has a lag, if it's a five seconds late, it kind of doesn't really matter.
• 7:51
  SPEAKER_02: If it gets something wrong, if it hallucinates, it doesn't really matter.
• 7:54
  SPEAKER_02: But if your AI makes a mistake, is literally the difference between life and death.
• 7:58
  SPEAKER_02: If it's powering a kind of huge truck that kind of drives off the road, or if it's doing a drone that, you know, shoots the wrong thing or whatever, it is life and death.
• 8:06
  SPEAKER_02: Yeah.
• 8:06
  SPEAKER_02: So it's way harder.
• 8:07
  SPEAKER_02: The kind of the barrier to entry is way harder.
• 8:10
  SPEAKER_02: Um, the stakes are higher, basically.
• 8:11
  SPEAKER_02: Yeah.
• 8:12
  SPEAKER_02: To tell us a little bit about just uh for non-technical listeners, what is the great challenge of latency, um of accuracy that you've had to get right, which has led you to build this huge business where you've got, you know, I guess almost a billion dollars of revenue or whatever serving, I think 90% of the top car makers serving the US military, high-stakes environments.
• 8:31
  SPEAKER_02: What is the technical challenge that you've had to get right?
• 8:34
  SPEAKER_01: So, roughly two big areas that we're uh, you know, we're building models and have intelligence in.
• 8:40
  SPEAKER_01: Uh, one you could simplifying everything.
• 8:43
  SPEAKER_01: One is you could say it's offboard.
• 8:45
  SPEAKER_01: Um, we generally, this is the sometimes people call this a world model.
• 8:50
  SPEAKER_01: So this is the environment where you test and deploy this intelligence.
• 8:54
  SPEAKER_01: You figure out, okay, uh, it's the tools to make the brains, which ultimately go on the machine.
• 9:00
  SPEAKER_01: So those are the two.
• 9:01
  SPEAKER_01: It's off the machine, like the developer, let's say, you know, IDE, yeah, and then on uh on the machine.
• 9:07
  SPEAKER_01: So off machine, that's just uh that's very much like a uh like a cursor or a claude, right?
• 9:13
  SPEAKER_01: If you're you're you're it's a development environment to make sure that uh the models that are gonna be get deployed are being effective, efficient.
• 9:23
  SPEAKER_01: You're testing them.
• 9:24
  SPEAKER_01: Yeah.
• 9:25
  SPEAKER_01: Um this is like everything from neural simulation, uh, this is from synthetic data.
• 9:30
  SPEAKER_01: That's that whole class of problems.
• 9:31
  SPEAKER_02: So that's and it's a neurosimulation, for example, that that is basically not on the actual hardware itself, off of the interesting things and simulating thousands, millions of different scenarios to build the intelligence that you can then later uh put on the machine.
• 9:45
  SPEAKER_01: Yeah, we do tens of millions of simulations a week.
• 9:47
  SPEAKER_01: The difference between the first and the second one, which is an imitation-based or like let's say a learned approach, is in the learned approach, that environment has to be um dynamic in a way, because as the AI moves in the environment, it will bump into agents and and and those agents need to respond to it.
• 10:04
  SPEAKER_01: It's not just testing against a preset set of scenarios.
• 10:08
  SPEAKER_01: That was the old way.
• 10:10
  SPEAKER_01: Million scenarios, can you make them through, you know, can you get through them digitally and then we deploy the model on the vehicle?
• 10:16
  SPEAKER_01: Today, it's like actually, you let's just let the model learn how to drive in a virtual environment.
• 10:22
  SPEAKER_01: Specifically, the word you're using was uh uh neural simulation.
• 10:24
  SPEAKER_01: That that's a particular sub uh subcategory of simulation where you can take um images and essentially recreate a world digitally much faster.
• 10:33
  SPEAKER_01: Historically, the way you'd create a simulator world is like slow and painstaking and very much akin to like how Hollywood does CGI.
• 10:41
  SPEAKER_01: You have technical artists that are creating assets, and then you're there's material properties, and there is a heavy emphasis on a kind of a reality to simulation gap.
• 10:50
  SPEAKER_01: Reinforcement learning and neural sim and that this new world, it's it's it's a little different from that.
• 10:55
  SPEAKER_01: You're all in the same broadcast.
• 10:56
  SPEAKER_01: I mean, you're zooming out at all as quote unquote simulation or solid quote-unquote world models or whatever, but um there's a lot of nuance in that.
• 11:03
  SPEAKER_01: That entire category, you can just think of it as like the tools you need to get something to be intelligent, to make intelligent models.
• 11:12
  SPEAKER_01: So that's the one big part of the business.
• 11:14
  SPEAKER_01: Yeah.
• 11:14
  SPEAKER_01: The other part is what you originally talked about, is now you have these models and you're gonna put them on these machines.
• 11:19
  SPEAKER_01: Okay, we're talking about lots of different machines here.
• 11:20
  SPEAKER_01: We're talking about, and when I mean different, I mean not only the form factor like a drone versus a tank.
• 11:26
  SPEAKER_01: Yeah.
• 11:26
  SPEAKER_01: You're talking about that's on the ground versus in the air.
• 11:29
  SPEAKER_01: You're talking about the compute realities.
• 11:31
  SPEAKER_01: Uh, you're talking about the um the mission.
• 11:35
  SPEAKER_01: Uh, you know, are you gonna is this car, is this truck gonna run for 12 hours and long, long haul, or is it gonna move in a factory floor for a short amount of time, but with lot, lots more uh going on around it.
• 11:47
  SPEAKER_01: So the models historically, uh the the or the way that that autonomy was historically built was each of these verticals would have very specific um, you know, models made for it.
• 11:58
  SPEAKER_01: And what's happened in in LLMs is you had this paper, this detention paper, um, and that create, you know, uh introduces transformers, transformers results in Chat GPT and everything like that.
• 12:10
  SPEAKER_01: A very similar thing happened in self-driving.
• 12:12
  SPEAKER_01: Self-driving historically was done differently, and post-transformers is done differently.
• 12:17
  SPEAKER_01: And um, and today, uh, the what's very important for for listeners out there to know is you can have much more generalized models that go on to machines.
• 12:28
  SPEAKER_01: And so what we're doing at Applied Intuition, I think, kind of our are, you know, in the Peter Thiel way of like what do you get that other people didn't get, is that we can actually work across multiple verticals uh using the same models.
• 12:42
  SPEAKER_01: But you're really doing inference on uh on a chip that's not maybe very beefy because the buyer or the drone or whatever, there's constraints there.
• 12:52
  SPEAKER_01: It might be a constraint because the consumer doesn't want to pay a lot for a car.
• 12:56
  SPEAKER_01: So they're not gonna pay for a$10,000,$20,000 computer in the back.
• 13:00
  SPEAKER_01: Yeah.
• 13:00
  SPEAKER_01: And so suddenly you got to do everything on a$500 chip.
• 13:05
  SPEAKER_01: And now this model that works really well with a lot more compute and a lot more sensor data suddenly doesn't work as well.
• 13:12
  SPEAKER_01: Okay.
• 13:13
  SPEAKER_01: And so that's kind of that that's our our our uh other part of the of the world is take model, take a model that works in all these different form factors just as effectively.
• 13:22
  SPEAKER_01: And then you you also said there's so many things we're covering here.
• 13:25
  SPEAKER_01: Yeah, you also said this thing about chips.
• 13:26
  SPEAKER_01: Like what what's the what what is your case?
• 13:28
  SPEAKER_01: So you have latency, you have all those things that are kind of comparable regardless because these are physical machines and they interact with humans.
• 13:34
  SPEAKER_01: And so you you're always gonna have some similarities.
• 13:37
  SPEAKER_01: But on the silicon, you're not gonna have similarities.
• 13:39
  SPEAKER_01: You're gonna have huge, huge, vastly different realities because historically the the intelligence was the humans.
• 13:44
  SPEAKER_01: So uh a human needs to do different things actually in a uh dirt mover in a haul in a haul it system than on flying a drone.
• 13:54
  SPEAKER_01: Yeah.
• 13:54
  SPEAKER_01: And so therefore, the processing power in all these machines is different.
• 13:58
  SPEAKER_01: I think one of the things that we've done, which is particularly special and does we we're hardware agnostic.
• 14:02
  SPEAKER_01: We run on lots of different compute.
• 14:04
  SPEAKER_01: Yeah, way easier said than done.
• 14:07
  SPEAKER_01: And if you're uh a technical in the audience, you're there's another 50 questions that get asked at that point about like how do you abstract hardware away from software in the way that we're talking about.
• 14:16
  SPEAKER_01: And that's another huge part of the business we won't we want to get into.
• 14:20
  SPEAKER_01: But but those are the two big areas where you're we're we're making the environment and we're making the models that actually run the machines.
• 14:25
  SPEAKER_02: Is it kind of lazy way of describing this?
• 14:27
  SPEAKER_02: Um if you bring it back to cars, Waymo may be like a kind of Apple where it's dependent on like their vehicles that they're they're working on.
• 14:33
  SPEAKER_02: And then Yeah, the business tournaments are verticalized.
• 14:35
  SPEAKER_01: They're verticalized.
• 14:36
  SPEAKER_01: Yeah.
• 14:36
  SPEAKER_01: They're doing the chip, they're doing the sensor.
• 14:38
  SPEAKER_01: They're not doing the car, but they're augmenting the car so much.
• 14:41
  SPEAKER_01: And it's it's their consumer brand and it's their app.
• 14:43
  SPEAKER_02: Whereas what you guys are doing, I think of it a bit like kind of Android in the mobile phone world.
• 14:48
  SPEAKER_02: Where it's like it doesn't matter if it's a Samsung phone or a Huawei phone or whatever.
• 14:51
  SPEAKER_02: In your case, it doesn't matter if it's a, you know, a fiat car or a drone or a truck made in Japan.
• 14:57
  SPEAKER_02: It's like all our customers, by the way.
• 14:59
  SPEAKER_02: Yeah.
• 15:00
  SPEAKER_02: Right.
• 15:00
  SPEAKER_02: Your tele intelligence has to work on all of it.
• 15:03
  SPEAKER_02: Exactly.
• 15:03
  SPEAKER_02: And okay.
• 15:04
  SPEAKER_02: And um, how did you do that?
• 15:06
  SPEAKER_01: Yeah, I mean, it's uh it's that's the story of the company.
• 15:11
  SPEAKER_01: We started originally in automotive.
• 15:12
  SPEAKER_01: Yeah, it's the uh uh bread and butter that I went to the General Motors Institute in the grad.
• 15:16
  SPEAKER_01: Yeah.
• 15:16
  SPEAKER_01: And I worked as an engineer in the automotive industry.
• 15:18
  SPEAKER_01: And I think that's has a huge impact, influence, and impact on our company.
• 15:22
  SPEAKER_01: Um, and fairly early in the company's history, like literally in the first year, we realized, oh, actually the problems are the same.
• 15:29
  SPEAKER_01: But it's the self-driving kind of the tech, the the you know, the technology didn't exist at the time.
• 15:34
  SPEAKER_01: Um and uh we entered the self-driving arena when the technology was, frankly, more ready.
• 15:41
  SPEAKER_01: And uh I think that was a huge plus for us because again, this takeaway for founders is um probably the biggest thing you have to get right is timing.
• 15:52
  SPEAKER_01: I mean, almost everything else, maybe your co-founder or or can can you can kind of change.
• 15:58
  SPEAKER_01: It's like maybe it's the other thing you can't change to your co-founder.
• 16:00
  SPEAKER_01: Like once co-founder relationships break, the company's in pretty dire situation.
• 16:05
  SPEAKER_01: Um, but outside of those things, you can almost change everything.
• 16:07
  SPEAKER_01: You'd like, you know, you have some not so good investors, not a big deal.
• 16:10
  SPEAKER_01: You can ignore them more than than others.
• 16:12
  SPEAKER_01: If uh you need to change the products, you need to change the market, you need to change the company's name, you need to change the cover, all those things are two to two-way doors.
• 16:20
  SPEAKER_01: You can come in and out of them.
• 16:21
  SPEAKER_01: But um, but the timing aspect is one of these things that you have to really, really focus on and say, and uh heuristics.
• 16:29
  SPEAKER_01: Yeah.
• 16:30
  SPEAKER_01: So like if your company could exist two years before you in the same shape and form, then that's probably not right.
• 16:38
  SPEAKER_01: And if you can exist two years later, then we're what that's probably not right.
• 16:42
  SPEAKER_01: So if you it was too early, it's like my the technology doesn't exist to do what we wanted to do.
• 16:46
  SPEAKER_01: If it's too late, there's too many entrants, there's too many players.
• 16:49
  SPEAKER_01: So you have to get in at the right spot where you know it detect it's like the technology's just flipped over and you're you're maximizing its use for for for for the thing.
• 16:59
  SPEAKER_01: And um, so not to be too early, not to be too late.
• 17:01
  SPEAKER_01: Yeah.
• 17:02
  SPEAKER_02: Because you, as I understand it, you you're not actually that big on pivots, are you?
• 17:06
  SPEAKER_02: You you feel, am I right in thinking you think pivots can be quite dangerous once you've raised a bunch of money and hired a bunch of people.
• 17:11
  SPEAKER_01: Well, uh, so yeah, a nuance there in the classic YC, and I still like pitch the YC book because you know that's kind of the university that I went to.
• 17:18
  SPEAKER_01: Uh, YC is very pivot-friendly.
• 17:21
  SPEAKER_01: The belief is you, and that's because you're looking at the big bang of the startup.
• 17:25
  SPEAKER_01: You're looking at the first milliseconds of the company emerging.
• 17:27
  SPEAKER_01: There's lots of, you know, things happening.
• 17:29
  SPEAKER_01: And I think in that point, you should always, you should be completely open.
• 17:33
  SPEAKER_01: Um the moment you raise the a dollar, the moment you hire the first employee, suddenly the universe is now becoming the laws of physics are becoming more defined.
• 17:43
  SPEAKER_01: And those are much more difficult to change.
• 17:46
  SPEAKER_01: And uh, and then you fast forward to you have 50 employees, you have 200 employees.
• 17:50
  SPEAKER_01: There are companies that famously pivoted and done great.
• 17:52
  SPEAKER_01: Uh uh, Twitter was one of them.
• 17:55
  SPEAKER_01: Um, but it's more difficult.
• 17:57
  SPEAKER_01: It's more difficult just because, like, you know, you evangelize to employ 34.
• 18:04
  SPEAKER_01: This is a great company, and you're still early enough, and we're gonna do these great things.
• 18:09
  SPEAKER_01: And then, you know, six months later, they're like, by the way, that hill that we're gonna take.
• 18:13
  SPEAKER_01: That's not the hill that we're gonna take, we're gonna go the other way.
• 18:15
  SPEAKER_01: Yeah.
• 18:15
  SPEAKER_01: And uh, you know, as a general in a battlefield, you might lose your troops in the process.
• 18:20
  SPEAKER_02: Yeah, yeah.
• 18:20
  SPEAKER_02: You, in fact, are backed by three of our most prominent best guests we've had on giant ideas Ray Dalio, Mustafa Sullivan, and Reed Hoffman.
• 18:28
  SPEAKER_02: Yes, yes.
• 18:29
  SPEAKER_01: And some other I don't want to also discount my other investors.
• 18:32
  SPEAKER_01: I think that's a good idea.
• 18:32
  SPEAKER_01: No, imagine they're a small piece of the pipe.
• 18:34
  SPEAKER_01: You raise around a billion dollars, is that right?
• 18:36
  SPEAKER_01: Yeah, yeah.
• 18:36
  SPEAKER_01: We've uh we've uh we've been around for about 10 years.
• 18:39
  SPEAKER_01: Yeah.
• 18:39
  SPEAKER_01: So, you know, and we do kind of uh regular drum beats of fundraises.
• 18:43
  SPEAKER_01: But we, you know, um, you told me to the audience's founders and stuff like that.
• 18:47
  SPEAKER_01: A lot of times founders ask me, well, uh, why do you guys raise money?
• 18:50
  SPEAKER_01: You know, we've been uh famously uh, you know, we have a business which is like sustainable, which is uh another way of saying like it makes more money than it consumes.
• 19:00
  SPEAKER_01: Right.
• 19:01
  SPEAKER_01: And uh and that's been the history of the company.
• 19:03
  SPEAKER_01: So in the entire history of the company, we've never spent any money we've ever raised.
• 19:06
  SPEAKER_01: What?
• 19:06
  SPEAKER_01: So Ray's money and Mustafa's money and whoever, you know, whoever, uh Mark Andreessen from the beginning, or yeah, or Himanth from General Catalyst or whoever it is, uh Mamoon from Kleiner, all their money's in some bank account, uh, you know, earning some amount of interest.
• 19:21
  SPEAKER_01: How did treasury these?
• 19:24
  SPEAKER_01: Yeah, it actually they you know, once you get to big numbers, that actually also kicks out money.
• 19:27
  SPEAKER_01: But the but the punchline is um, you know, uh the the uh the the investors actually, whether they're famous, like you know, the folks that you just mentioned or or or or or people that are not, I think for founders, it's important that you don't you're you're kind of assembling the cap table almost like like if you are building, like you're having a dinner and you can invite anybody on the planet.
• 19:52
  SPEAKER_01: You don't want just your friends.
• 19:54
  SPEAKER_01: You also don't want a bunch of people who are just famous and you don't know and are useless.
• 19:58
  SPEAKER_01: It's kind of like a, you know, a like an intentional way.
• 20:01
  SPEAKER_01: And the theme of our company's always been intentionality.
• 20:04
  SPEAKER_01: But the more tactical root question that founders always ask is well, why do you keep raising if you don't need it?
• 20:10
  SPEAKER_01: And uh actually it is for their ideas.
• 20:13
  SPEAKER_01: The reason the venture ecosystem exists and the reason that it there isn't venture capital for laundromats is because it scales really effectively.
• 20:22
  SPEAKER_01: So that means the prize is bigger.
• 20:24
  SPEAKER_01: That means the number one to number two distance is is is is more important.
• 20:30
  SPEAKER_01: Like being number one is really important.
• 20:32
  SPEAKER_01: Uh you can be the fifth best laundromat in town and still be okay.
• 20:35
  SPEAKER_01: You can't be the fifth best uh, you know, whatever, like a credit card company.
• 20:41
  SPEAKER_01: You gotta be ramp, you gotta be Brex, and then it's like dot, dot, dot.
• 20:44
  SPEAKER_01: There's, you know, it's it's it you're not gonna get the spoils.
• 20:48
  SPEAKER_01: And in order to be number one, you're just maybe a couple of percentage points and decision making better, obviously abstractly speaking.
• 20:56
  SPEAKER_01: And then so having the right people around you is important who are financially incentivized to see you succeed.
• 21:02
  SPEAKER_02: We really try to curate the cap tables of the companies that we back a giant.
• 21:05
  SPEAKER_02: When we lead rounds, we really try and think about not just who are the kind of big uh co-investor venture firms, but also who are the angels, how do we surround the people to basically give it the best shot at succeeding?
• 21:15
  SPEAKER_02: And often we found that, you know, yes, if you can bring in a mega cap US venture firm, clearly that has a halo effect around the company that helps them attract more capital.
• 21:24
  SPEAKER_02: And I think that's probably even more true today that if you get one of those sort of top five mega cap venture firms, it's a kind of self-fulfilling prophecy.
• 21:31
  SPEAKER_02: But actually, often we found it's the angels, it's the former CEOs who can provide the biggest value.
• 21:36
  SPEAKER_02: Like we've got a physical AI company here in the UK called Cusp AI.
• 21:40
  SPEAKER_02: Yeah.
• 21:40
  SPEAKER_02: And NEA coming into that kind of top US megacat firm was was transformational because it really put the halo effect around it.
• 21:47
  SPEAKER_02: But then we also tried to get people like John Brown, the former CEO of BP, who was on the Intel board for many years, one of one of our uh advisory board members, to go in as an angel and an advisory board member.
• 21:56
  SPEAKER_02: And he's had a massive impact and making intros and helping the CEO.
• 22:00
  SPEAKER_02: Or come some of the corporate LPs, people at Henkel as well, kind of industrial partners who can then become customers.
• 22:05
  SPEAKER_02: How how do you think I have a bunch of thoughts on a couple of those things too?
• 22:08
  SPEAKER_01: Tell us.
• 22:08
  SPEAKER_01: Yeah, go for it before you even ask me the question.
• 22:12
  SPEAKER_01: The first thing I would say is, you know, this is my third company, and I'm I came out of YC.
• 22:18
  SPEAKER_01: So I I could pick Mark and Dreeson to be on the board, right?
• 22:21
  SPEAKER_01: It's it's it's a weird asset class where the you know the asset picks the manager.
• 22:24
  SPEAKER_01: Yes.
• 22:25
  SPEAKER_01: Whereas most other asset classes, the manager picks the asset, right?
• 22:28
  SPEAKER_01: And uh, or all maybe.
• 22:30
  SPEAKER_01: The the but my first companies, you know, we I couldn't raise money if we tried.
• 22:36
  SPEAKER_01: No, we tried definitely, we tried.
• 22:37
  SPEAKER_01: Like we tried for years.
• 22:39
  SPEAKER_01: So the macro point I would say to founders is you could probably change some people on a cap table and doesn't mean that applied is fundamentally like a bankrupt company, then.
• 22:48
  SPEAKER_01: Um, but at the same time, it, you know, you hold that into contradiction with what I just said earlier, is you want people who are giving you the right ideas because that one or two percent or five, 10% abstractly speaking, kind of go in the right direction can have an impact in you being the in the poll position versus being number three.
• 23:06
  SPEAKER_01: And that can have a huge impact.
• 23:08
  SPEAKER_01: In terms of um assembling the cap table between operators and and VCs, I think the multi-stage funds are more interchangeable if you're not getting the Himonts, you know, the Mamoons, you know, maybe arguably top two or three investors in Silicon Valley, because they've also like they're just like a like a language model.
• 23:31
  SPEAKER_01: They've absorbed so they have so much experience and seen so many deals in this very obscure edge case world of private venture financing that they can actually incrementally help you.
• 23:46
  SPEAKER_01: And then there's the old adage of like, does a company make money or does money make a company?
• 23:50
  SPEAKER_01: Like if you and and do what you alluded to earlier is today, there's more of the money making the company than than maybe five or 10 years ago.
• 23:58
  SPEAKER_01: It goes in ebbs and uh ebbs and flows.
• 24:01
  SPEAKER_01: Whereas if there's a consensus bet on a specific company and all the major VCs are in it, they're more likely to win.
• 24:08
  SPEAKER_01: Right.
• 24:09
  SPEAKER_01: Like then the best employees and the best engineers.
• 24:12
  SPEAKER_01: Uh, they join, the best researchers join.
• 24:14
  SPEAKER_01: And then because there's a bet, they make the best product, and the best product is the best customers.
• 24:18
  SPEAKER_01: The customers then tell the investors, and then you're suddenly before you know it, you're like the chosen winner.
• 24:23
  SPEAKER_01: Yeah.
• 24:23
  SPEAKER_01: So that's another strategy as a founder have to like kind of keep in mind to try to get after you know that you can just get money.
• 24:29
  SPEAKER_01: Yeah versus just get any money.
• 24:31
  SPEAKER_01: Sure.
• 24:31
  SPEAKER_01: Then try to get good money.
• 24:32
  SPEAKER_01: Then we talk about corporates.
• 24:33
  SPEAKER_01: This is a very very particular uh word of use.
• 24:36
  SPEAKER_01: Uh my YC experience, you know, uh CVCs were like uh like a profanity.
• 24:44
  SPEAKER_01: Yes.
• 24:44
  SPEAKER_01: And uh I think that's outdated.
• 24:47
  SPEAKER_01: Yeah, right.
• 24:48
  SPEAKER_01: I don't think so.
• 24:49
  SPEAKER_01: You know, that's that was my my what I was gonna push back on.
• 24:52
  SPEAKER_01: I think, I think uh it's kind of like the multi-stage funds.
• 24:55
  SPEAKER_01: Actually, the more important thing is the person that's that's there.
• 24:57
  SPEAKER_01: Um, in multi-stage fund, who the partners, you could have this three founders have the same firm and have wildly different experiences because of the of the partners.
• 25:07
  SPEAKER_01: Because the the firms are actually, there's more diversity among partners within a firm than between the firms.
• 25:13
  SPEAKER_01: Absolutely.
• 25:14
  SPEAKER_01: So, like you could you could let's take, let's say, the top firms you put in Dreeson, you put GC, you put you know, Kleiner.
• 25:20
  SPEAKER_01: Um, a number of those partners could work in other firms and do completely successful.
• 25:25
  SPEAKER_01: But um, even within an Andreessen, there's gonna be such a huge, huge diversity of people.
• 25:30
  SPEAKER_01: Now, if you talk about CVCs, um, they're consistent in the sense of the corporate mandates are actually more aligned.
• 25:38
  SPEAKER_01: They tend to be let's invest in something that accelerates the business.
• 25:41
  SPEAKER_01: They tend not to be purely financial.
• 25:42
  SPEAKER_01: They're they're but they, you know, maybe they're they can be a distribution for for you, and that's why they want to get involved.
• 25:48
  SPEAKER_01: Um the partners themselves are different, but the thing that I see a lot from the YCDs, and I haven't been proven differently here, uh, is the it's one thing is the who's the VC, and then there's a corporation.
• 26:03
  SPEAKER_01: The corporation acts very differently from who the CVC is.
• 26:06
  SPEAKER_01: So the person who's in the CVC, they could have the best intentions and they're happy and everything is great.
• 26:11
  SPEAKER_01: And then the corporate is like decides to get in the product line that you're in, or the corporate is running out of money and they need they need you, like there's all 50 other things that can happen.
• 26:21
  SPEAKER_01: Or the corporate decides that they don't want a CVC anymore.
• 26:24
  SPEAKER_01: And suddenly you're gonna get a call and you're like, hey, our our you know, our fund is shutting down, and like we'd like to sell our position.
• 26:30
  SPEAKER_01: And you're like, wait, what?
• 26:31
  SPEAKER_01: That doesn't, I'm not I'm in the series A company.
• 26:33
  SPEAKER_01: What are you talking about?
• 26:35
  SPEAKER_01: You're not selling the position.
• 26:36
  SPEAKER_01: Yeah.
• 26:36
  SPEAKER_01: And um, and I've seen at least CVCs have a much higher turnover.
• 26:40
  SPEAKER_01: Yeah, yeah.
• 26:40
  SPEAKER_01: Like just it's so then you're exposed to the corporation more.
• 26:44
  SPEAKER_01: Sure.
• 26:44
  SPEAKER_01: So I think you have to be of all those categories, the operators, the multi-stage funds, the CVC you want to be the most particular on.
• 26:52
  SPEAKER_01: And like, you know, I I I I can never be anything but what my my on I and sometimes I get in trouble because I'm just so direct.
• 26:59
  SPEAKER_01: No, that was great.
• 27:00
  SPEAKER_01: I generally wouldn't take CVC more.
• 27:01
  SPEAKER_01: You wouldn't, and you haven't we we have we've taken some.
• 27:04
  SPEAKER_01: You have we've taken some, and I but it's like extreme.
• 27:07
  SPEAKER_01: I think it's I mean it's it's it's small slices.
• 27:10
  SPEAKER_01: Okay.
• 27:10
  SPEAKER_01: You want to be thoughtful, and and we entered those conversations with that with that view.
• 27:13
  SPEAKER_01: Yeah.
• 27:14
  SPEAKER_01: I think generally speaking, um the and again, also for your audience, you know, all of my advice and all of my thinking is in a very particular, let's say, lens.
• 27:26
  SPEAKER_01: Yeah.
• 27:26
  SPEAKER_01: It's for like Bay Area, uh deep tech AI companies, uh, and I don't even say South Bay companies, um, that are venture-backed and uh, you know, are are predominantly software.
• 27:40
  SPEAKER_01: They're not, we're not a hardware company.
• 27:41
  SPEAKER_01: Yeah.
• 27:41
  SPEAKER_01: They would dabble in hardware.
• 27:42
  SPEAKER_01: We're not fundamentally a hardware company.
• 27:44
  SPEAKER_01: And so everything's hyper-focused on totally get it.
• 27:47
  SPEAKER_01: Yeah.
• 27:47
  SPEAKER_01: Yeah.
• 27:47
  SPEAKER_01: Because it's the answer changes very quickly if you're in London.
• 27:50
  SPEAKER_01: For sure.
• 27:50
  SPEAKER_01: The answer changes very quickly if you're in Tokyo.
• 27:52
  SPEAKER_01: For sure.
• 27:52
  SPEAKER_02: Yeah.
• 27:53
  SPEAKER_02: No, I I I hear you on the on the corporates.
• 27:55
  SPEAKER_02: I I think definitely we've seen it get better.
• 27:57
  SPEAKER_02: You know, it used to be terrible where they'd have these horrific terms where they had the right-of-first refusal to buy companies.
• 28:02
  SPEAKER_02: We we've seen it work a bit better recently, but but uh but I hear you.
• 28:05
  SPEAKER_02: Who who's been But if you don't have if you don't have any choices, I would take all the corporate money on the planet.
• 28:10
  SPEAKER_02: Sure, sure, sure.
• 28:10
  SPEAKER_02: You gotta survive.
• 28:11
  SPEAKER_02: We're talking about the best who you can pick.
• 28:12
  SPEAKER_02: Yeah, yeah.
• 28:13
  SPEAKER_02: Who's been have you found that um the big venture firms have been more helpful, or has it been more some of the the kind of uh operators that we've talked about?
• 28:20
  SPEAKER_02: Who's been the most helpful to you?
• 28:22
  SPEAKER_02: Again, the context is important.
• 28:24
  SPEAKER_01: I have a CO at YC.
• 28:25
  SPEAKER_01: Right.
• 28:26
  SPEAKER_01: So I'm walking out, you know, I'm I'm I'm one of them in that way.
• 28:29
  SPEAKER_01: And then I've you know, before that I'd done multiple companies.
• 28:32
  SPEAKER_01: I'm an engineer.
• 28:33
  SPEAKER_01: So uh my context is different.
• 28:35
  SPEAKER_01: So what I think is valuable is different.
• 28:37
  SPEAKER_01: All of them, um, you you look at you, they're they're all technical.
• 28:42
  SPEAKER_01: Uh, they've all started things themselves.
• 28:45
  SPEAKER_01: Um, these are folks who are, you know, they're they're the mix of the operator, um, uh operator investor.
• 28:52
  SPEAKER_01: So they you kind of get a little bit of the best of the both worlds.
• 28:55
  SPEAKER_01: You get the the angel investor CEO, but you get somebody who's like managing 70 billion in capital.
• 29:00
  SPEAKER_01: Right.
• 29:00
  SPEAKER_01: Um, and so I think the takeaway, if you're a founder, is generally speaking, at the top firms, the top people are really, really good.
• 29:12
  SPEAKER_01: So it that's so I think you can be pretty like if you're if you're deciding between general catalysts and lightspeed, like Ruby is like a you know, really smart, amazing.
• 29:21
  SPEAKER_01: You can't really go wrong both the ways.
• 29:24
  SPEAKER_01: Um, there are a few investors who are both well known and are also terrible.
• 29:29
  SPEAKER_01: Yep.
• 29:29
  SPEAKER_01: But we won't talk about that.
• 29:32
  SPEAKER_02: There's just been this wonderful thing on X of finally revealing, you know, the atrocious behavior of each and there are some bad ones.
• 29:38
  SPEAKER_01: Yeah, yeah.
• 29:38
  SPEAKER_01: There the at YC there's uh investor database.
• 29:41
  SPEAKER_01: Yeah.
• 29:41
  SPEAKER_01: And it's an anonymous database.
• 29:43
  SPEAKER_01: So it's like it's it's not anonymous to YC to the partners, but it's anonymous to everybody else who's in the YC universe.
• 29:48
  SPEAKER_01: And you'll see scathing reviews on people and they're generally accurate.
• 29:51
  SPEAKER_01: Yeah.
• 29:52
  SPEAKER_01: I don't I think I think the founders are more correct than like holding a vendetta.
• 29:55
  SPEAKER_01: Yeah.
• 29:56
  SPEAKER_02: I would say that the YC kind of internal database on venture investors is is probably the most important thing for founders in a way that, and I wish there was more of that.
• 30:05
  SPEAKER_02: Where, you know, we we were very conscious of that always at Giant.
• 30:08
  SPEAKER_02: Is like if you mess with a YC founder, it's going on that database and you'll be blacklisted and you'll never get into any YC company again.
• 30:15
  SPEAKER_02: And it's like the Uber driver versus the taxi.
• 30:17
  SPEAKER_02: Yeah, it's the same thing.
• 30:18
  SPEAKER_01: There's not enough of that, I think, for for founders.
• 30:20
  SPEAKER_01: But I uh the you the there's there, you know, in the in an industry like this, you have to be super, super careful.
• 30:25
  SPEAKER_01: And we're really talking about like this is a real inside baseball.
• 30:28
  SPEAKER_01: Like reputations are a tricky thing because um if I say, hey, uh, you know, uh whatever investor is really great, uh, and they treat one of your friends really poorly, both things can actually be true.
• 30:42
  SPEAKER_01: Sure.
• 30:43
  SPEAKER_01: And then you're now you have a term sheet from this situation where you have an investor who's maybe famous, but also is like, you know, your friend had a bad experience with him.
• 30:50
  SPEAKER_01: And what's what's the truth?
• 30:52
  SPEAKER_01: And and that's really, really that's that's the that's a difficult, that's the fog of war, right?
• 30:57
  SPEAKER_02: Totally.
• 30:58
  SPEAKER_02: Yes.
• 30:58
  SPEAKER_02: Let's round out the this first uh episode with just a bit of future gazing on physical AI and on applied intuition.
• 31:05
  SPEAKER_02: So what what do you think this is gonna do physical AI to change how the listeners to this podcast are gonna live their lives?
• 31:12
  SPEAKER_02: How how is this gonna fundamentally change things, perhaps in ways that people don't quite grasp yet?
• 31:16
  SPEAKER_01: I think uh, you know, if we were having this conversation five years ago, there'd still be this debate about is is self-driving gonna happen?
• 31:23
  SPEAKER_01: Is it gonna be safe enough?
• 31:24
  SPEAKER_01: And uh uh will the regulators be able to keep it.
• 31:27
  SPEAKER_01: You notice that we didn't ask any of those questions in in you know third 30 minutes because is we just assume it's gonna happen.
• 31:32
  SPEAKER_01: And that's a big thing.
• 31:34
  SPEAKER_01: Um, for a listener, you want to extrapolate that onto okay, so I kind of get the robo taxi thing.
• 31:41
  SPEAKER_01: Look, what else is gonna happen?
• 31:42
  SPEAKER_01: What about the car that I own?
• 31:43
  SPEAKER_01: What about the construction site that I drive by?
• 31:47
  SPEAKER_01: What about the truck that's on the highway with me?
• 31:50
  SPEAKER_01: Now I go to the airport.
• 31:51
  SPEAKER_01: Why is my luggage moved by humans?
• 31:54
  SPEAKER_01: And then, you know, then you get on a plane.
• 31:56
  SPEAKER_01: And so all of the everywhere there's a machine, just think about like there's gonna be intelligence being put onto it.
• 32:01
  SPEAKER_01: And uh, and I think that's gonna have pretty, pretty profound impacts.
• 32:05
  SPEAKER_01: And um, as a, let's say, a corporate executive or as a banker or consultant, I think there's knock-on effects on all those things.
• 32:12
  SPEAKER_01: Yeah.
• 32:13
  SPEAKER_01: The mistake that people tend to make though is they expect the same the issue in the physical AI world is diffusion.
• 32:20
  SPEAKER_01: It's like getting, you know, getting intelligence onto an airliner is not the same as getting, you know, um, something through a browser because the whole infrastructure already exists.
• 32:30
  SPEAKER_01: There's Apple Pay and Google Pay.
• 32:32
  SPEAKER_01: There is these operating systems, which are quite mature.
• 32:35
  SPEAKER_01: There's the devices that physically are in everybody's pockets and in their homes.
• 32:39
  SPEAKER_01: And so the distribution is very, very fast.
• 32:42
  SPEAKER_01: To get intelligence onto a plane or an F-16, that's extremely that's extremely difficult.
• 32:47
  SPEAKER_01: There's lots of gates to get there.
• 32:49
  SPEAKER_01: But the opposite is also true.
• 32:51
  SPEAKER_01: Once you're in, as a company providing that intelligence like applied, then you're really in because you uh went through not only the how difficult it is to make models that are efficient and and uh and and effective and performant, but you also have to, you know, build the relationship with the company and the company has to trust you.
• 33:12
  SPEAKER_01: And and so so that ends up becoming its own moat, frankly speaking.
• 33:16
  SPEAKER_02: You've just done a talk at Goodwood, the Festival of Speed, which is one of uh, you know, conference or event for for car enthusiasts, basically old car enthusiasts.
• 33:24
  SPEAKER_02: People love, you know, old cars and love driving.
• 33:26
  SPEAKER_02: Yeah.
• 33:26
  SPEAKER_02: Do you think in 10 years, 20 years, anyone will drive at all?
• 33:29
  SPEAKER_02: Will it will it just be a few tinkerers who love old cars?
• 33:31
  SPEAKER_02: Do you think regular people will be driving?
• 33:33
  SPEAKER_01: It won't be much different than it is today.
• 33:35
  SPEAKER_01: That's that's that's a shocking thing.
• 33:37
  SPEAKER_01: I mean, in the sense of like uh, you know, I'll speak from a personal experience.
• 33:40
  SPEAKER_01: Like, what do I drive?
• 33:41
  SPEAKER_01: I just got a 1987 Land Cruiser, uh, you know, manual diesel.
• 33:47
  SPEAKER_01: In in in Europe, that's like a very passe, but in America, there's you can't even buy those cars.
• 33:52
  SPEAKER_01: The Land Cruisers are not those versions of Land Cruiser are not sold.
• 33:54
  SPEAKER_01: So I had to import it.
• 33:55
  SPEAKER_01: Why am I not driving uh a Tesla plaid, you know, or whatever?
• 33:59
  SPEAKER_01: And it's like consumer behavior is not just, you know, Porsche tried to get a get get out of the you know manual game.
• 34:08
  SPEAKER_01: GT3s, they stopped making it with manuals, and then there's like an uproar.
• 34:11
  SPEAKER_01: It's a worse technology.
• 34:13
  SPEAKER_01: PDK is a better technology.
• 34:14
  SPEAKER_01: It shifts gears faster, it makes you faster, but that's not you know the the re the reason that it's like why do people play vinyl records?
• 34:21
  SPEAKER_01: Right.
• 34:22
  SPEAKER_01: Right.
• 34:22
  SPEAKER_01: And so I think it's too simple to reduce everyone's speaking, consumers, desires to what is the fastest, most automated thing.
• 34:34
  SPEAKER_01: So that's a broad, broad point.
• 34:36
  SPEAKER_01: More specifically, uh, or more tactical pragmatically, a car's shelf life is about 15 years.
• 34:41
  SPEAKER_01: At least in America, half of Americans live on paycheck to paycheck or like uh limited savings uh more accurately.
• 34:48
  SPEAKER_01: And so when they buy a car, it's a real, it's not a luxury purchase.
• 34:52
  SPEAKER_01: It is a functional purchase to live their life.
• 34:55
  SPEAKER_01: And so you can come with a new product that has more automation.
• 34:58
  SPEAKER_01: Doesn't mean that they're gonna sell their Honda Core because maybe they don't want to sell the Honda Core, maybe they can't buy the new thing.
• 35:06
  SPEAKER_01: So you're talking about it takes a decade for just the cars that are bought today in 2026 to make it out of the cycle.
• 35:12
  SPEAKER_01: And realistically, more like 15 years, 20 years, cars are really, really good products right now, extremely cheap for what you get.
• 35:19
  SPEAKER_01: Um and so I I I think but what you but at the same time, as somebody who's like lives in the self-driving universe, yeah.
• 35:27
  SPEAKER_01: I think when we're here in 10 years from now and you're walking around London, there's no way you won't see many robotaxis, many brands, uh personally owned, uh shared systems that are not completely autonomous.
• 35:41
  SPEAKER_01: I I think that'll happen.
• 35:42
  SPEAKER_01: Absolutely.
• 35:43
  SPEAKER_01: It'll be mixed fleets.
• 35:44
  SPEAKER_02: And last question for part one what is the the vision for the impact supply is gonna have on the world?
• 35:49
  SPEAKER_01: I mean, I think we want to put you know our intelligence on a billion machines.
• 35:55
  SPEAKER_01: And um, and why do we want to do that?
• 35:58
  SPEAKER_01: Because it makes the world a better place.
• 36:00
  SPEAKER_01: It's so pithy and almost reductive.
• 36:04
  SPEAKER_01: But these are these are dangerous machines.
• 36:07
  SPEAKER_01: So we're gonna make the world a safer place.
• 36:09
  SPEAKER_01: I think you don't need to have a long conversation with somebody who's had somebody in a workplace accident or in a car accident or a truck, you know, and it's it's horrible.
• 36:21
  SPEAKER_01: It's so bad that you don't think about it.
• 36:23
  SPEAKER_01: Actually crosses your mind and your brain just doesn't want to like actually live with a feeling because it's so, so atrocious.
• 36:30
  SPEAKER_01: Um that's a pretty, that's a pretty good way to live, live a life.
• 36:34
  SPEAKER_01: If we can do that, I think that that that's a that's very, very positive.
• 36:37
  SPEAKER_01: Um, and we also do believe, I think beyond the raw safety aspect of it, I think um as efficiency is brought in into people's lives, uh, they live better lives.
• 36:48
  SPEAKER_01: They live just literally happier lives.
• 36:49
  SPEAKER_01: So not only for the the bad stuff of you won't be, you know, injured or maimed, uh, but you just have a happier life.
• 36:54
  SPEAKER_01: And uh for all the, you know, let's say criticisms we have of modern technology, let's talk, you know, that made something very like uh, let's say divisive, like social media.
• 37:06
  SPEAKER_01: Well, you know, the nice thing is you can get a hold of anybody for free around the world instantly.
• 37:12
  SPEAKER_01: Like we forget about that.
• 37:13
  SPEAKER_01: You know, when my family moved from Pakistan, which was from Small Vision, Pakistan to America, like we communicated to Pakistan with handwritten letters that would take 30 days to circumnavigate the globe.
• 37:27
  SPEAKER_01: And today it's like WhatsApp is free.
• 37:30
  SPEAKER_01: Yeah.
• 37:31
  SPEAKER_01: Like that is a miracle.
• 37:33
  SPEAKER_01: Yeah.
• 37:33
  SPEAKER_01: And I think we don't like appreciate that enough because I think it's just, and it's not, I'm not saying like, you know, everyone should just shut up and eat it, and technology is great.
• 37:42
  SPEAKER_01: I'm also not like simplifying it that much.
• 37:46
  SPEAKER_01: But yeah, we live in a pretty amazing time.
• 37:49
  SPEAKER_01: Like, if you have some sort of pain in your body, you can immediately open up a supercomputer and say, these are all the things I'm feeling.
• 38:00
  SPEAKER_01: Should I go to the doctor?
• 38:01
  SPEAKER_01: That's that's unbelievable.
• 38:02
  SPEAKER_01: You know, the uh for vast majority of humanity, five years ago, 10 years ago, that was like not even comprehendable.
• 38:11
  SPEAKER_01: And now we have these cheap phones that exist everywhere, and those cheap phones can access these really uh really significant models.
• 38:16
  SPEAKER_01: So I think hopefully, yeah, you know, near the near the end of uh hopefully we can live a long life.
• 38:21
  SPEAKER_01: And at the near the end of life, we can look back and say, like, hey, we made the world a safer place, we made it more efficient, and um and you know that that's that's a that's a great, great existence.
• 38:34
  SPEAKER_01: I mean, that's a that's a pretty great way to live.
• 38:35
  SPEAKER_02: You mentioned your upbringing uh was a masterful broadcaster teaser for part two, which is gonna be coming out next week.
• 38:41
  SPEAKER_02: And we're gonna because you've had this incredible life journey, we're gonna talk about that.
• 38:44
  SPEAKER_02: We're gonna talk about running YC and all of the lessons from YC.
• 38:48
  SPEAKER_02: Uh, but but most importantly, I think you've probably got the best career advice for our listeners of all of our guests.
• 38:53
  SPEAKER_02: So we're gonna talk about that next week on part two.
• 38:55
  SPEAKER_02: But thank you so much for joining us for part one.