Why Physical AI Is the Next Frontier | Applied Intuition with a16z

1. 0:00 – 1:07

   Intro

   1. QYQasar Younis0:00

      Our mission is to put intelligence on a billion machines, and we think that can have a profound impact on society. Applied Intuition is a physical AI company. We put intelligence on machines, cars, trucks, tanks, drones. It's a physical moving thing. We make it intelligent.

   2. PLPeter Ludwig0:16

      Digital AI, of course, is building software and optimizing ads and creating videos. That's all interesting and good, but really where you talk about the global economy, that's physical AI.

   3. QYQasar Younis0:25

      In this intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world.

   4. MAMarc Andreessen0:32

      How many things are there where the idea of physical AI, physical intelligence are going to matter?

   5. QYQasar Younis0:36

      There's no reason autonomy should be this obscure, difficult technology. Our vision for that is a high school kid that can make iPhone apps should be able to make autonomous systems. That platform for designing and developing is what we're launching. It's called Dana.

   6. PLPeter Ludwig0:51

      Everything that we've built and developed over the past nearly a decade, that's available in Dana.

   7. MAMarc Andreessen0:56

      Which will we get first, a perfectly simulated real-world environment for training autonomous devices or Grand Theft Auto VI? [laughing]

2. 1:07 – 3:32

   What Applied Intuition Actually Does

   1. ETErik Torenberg1:07

      Qasar, Peter, welcome to the a16z Podcast.

   2. QYQasar Younis1:10

      Well, thanks for having us. Your name is?

   3. ETErik Torenberg1:12

      [laughing]

   4. MAMarc Andreessen1:13

      Yeah, just one of many.

   5. QYQasar Younis1:15

      I feel like we've, uh-- I think we've all each known each other for-

   6. ETErik Torenberg1:18

      Yeah

   7. QYQasar Younis1:18

      ... too long, more than I'd like to admit. [laughs]

   8. ETErik Torenberg1:21

      Yeah.

   9. MAMarc Andreessen1:22

      A long time.

   10. ETErik Torenberg1:22

       We're lucky to both be the first investors, or among the first investors in the first round. Of course, different check sizes, but-

   11. QYQasar Younis1:27

       And I was an investor-

   12. ETErik Torenberg1:28

       Yes

   13. QYQasar Younis1:28

       ... for you even before then.

   14. ETErik Torenberg1:30

       Exactly. Exactly.

   15. QYQasar Younis1:30

       Yeah.

   16. ETErik Torenberg1:31

       Um, so let's do that as a segue. We have a lot to talk about today. We've, you know, the biggest launch in company history to talk about today. But first of all, we just give an update or a status. What does Applied Intuition do for those who-

   17. QYQasar Younis1:40

       Yeah, for the, for the people who don't know, Applied Intuition is a physical AI company. We put intelligence on machines. That's the, the simple way of, uh, describing it. Um, and all types of machines. So cars, trucks, tanks, drones, you name it, it's a physical moving thing. We, we make it intelligent. And, uh, the history of the company is we originally started by making the tools that would make the int- and then we got into the actual intelligence itself. Um, in a very, like in some ways, like a very, uh, boring AI company in the sense of, you know, eighty-three percent of the company is engineering. We win by making really great products. It's not like a good sales or something like that [chuckles] . I don't think we're good enough for a, for a, for a sales-enabled company. But, uh, yeah, uh, over a thousand engineers, um, and based in Silicon Valley. Uh, but we have offices globally, 18 offices. And, uh, you know, our mission is to, to put intelligence on a billion machines, and, uh, we think that can have a profound impact on society, both in the kind of pithy things everyone talks about, safety. You know, if you really talk to somebody who's been in a car accident or in a mining accident or, you know, uh, in a farming accident, you-- tho-those are real gnarly situations. Beyond just fixing that, you just-- if you can unlock productivity, I think, you know, we've seen the unlock in the digital world, and everyone's super excited about it, and you have trillion-dollar companies emerging. I'm a pretty strong believer that I think when we look back twenty-five years, we look back to the internet now, you know, people actually-- If you look at the original internet companies that are doing, you know, surveying or they're, they're doing some analytics and those are interesting. But really, when you look back twenty-five years from now, the big monolithic companies are Amazon that delivers you stuff, you know, uh, Apple. These are the true kind of companies that come of age. And I think when we look back twenty-five years in this intelligence revolution, the companies that impact the physical world, you know, might actually be bigger than the companies that impact the digital world.

3. 3:32 – 5:33

   Beyond Automotive: How Big Is Physical AI?

   1. MAMarc Andreessen3:32

      I would love for you to talk about the following, which is when you first started the company, you know, the knock on the company I think was, oh, well, it's, it's like, you know, car-- it's making, making cars autonomous, right?

   2. QYQasar Younis3:39

      Yeah.

   3. MAMarc Andreessen3:39

      Self-driving cars. But it's kind of like, okay, there's like whatever, you know, there's, there's Tesla and Waymo building their own self-driving cars, and then, and then there's like six or eight other car companies that matter, and then the company just could never get that big 'cause there are just not that many customers.

   4. QYQasar Younis3:51

      Yeah.

   5. MAMarc Andreessen3:51

      So what's the, like-- how should people think about, like, how many things are there that are things that move where the idea of physical AI, physical intelligence are going to matter?

   6. QYQasar Younis4:00

      Yeah, I mean, even today, even if you put that, you know, uh, let's say, uh, uh, view on us, automotive is like thirty percent of our business, so seventy percent already is non-automotive.

   7. MAMarc Andreessen4:10

      Right.

   8. QYQasar Younis4:10

      And I think if you fast-forward another ten, twenty years, uh, even the manufacturers themselves as a customer base will be a small amount. I think our-- that mission, just keep thinking a thou- uh, you know, a billion machines becoming intelligent, and you think about all the types of machines that exist. Automotive is just an easy one. I think it sticks in people's heads because we all drive cars and it's a big market. Um, but I think it's-- it'll be a minority of, of the business. I mean, already is a minority business. I think it'll be increasingly a minority of the business. But that doesn't necessarily mean it'll be small.

   9. MAMarc Andreessen4:38

      Right.

   10. QYQasar Younis4:39

       Automotive is still huge. Just as a part of the globe's GDP, automotive is something like three percent-

   11. MAMarc Andreessen4:44

       Right

   12. QYQasar Younis4:44

       ... of all GDP. Um, I think the way we always think about it, like as you try to get to your mission, initially the manufacturers were the distribution to that intelligence to consumers. But then you start working in defense, and you start working in construction and mining and agriculture, and suddenly the manufacturers are important, but maybe the mining operator is actually really important, or the Department of War is really important, and suddenly they become customers, and all of those are customers of ours as well.

   13. MAMarc Andreessen5:10

       Right.

   14. PLPeter Ludwig5:10

       Yeah, I think if you split AI into digital AI and physical AI, right?

   15. QYQasar Younis5:14

       Right.

   16. PLPeter Ludwig5:14

       Digital AI, of course, is building software and optimizing ads and creating videos, that sort of thing.

   17. QYQasar Younis5:19

       Right.

   18. PLPeter Ludwig5:19

       Uh, that's all interesting and good, but really where you talk about the global economy-

   19. QYQasar Younis5:24

       Right

   20. PLPeter Ludwig5:24

       ... that's physical AI.

   21. QYQasar Younis5:25

       Right.

   22. PLPeter Ludwig5:25

       And then we're talking about manufacturing and, uh, and mining and logistics and transportation, all of these things that-

   23. QYQasar Younis5:31

       Supply chains.

   24. PLPeter Ludwig5:32

       Yeah, supply chains, exactly.

4. 5:33 – 19:20

   Why Autonomy Changes What Machines Look Like

   1. MAMarc Andreessen5:33

      Well, I mean, l-let's build on that though for a second, which is like, so things that move today are, you know, historically things that move are things that have human beings at the wheel or at the controls in some form, right?

   2. QYQasar Younis5:41

      Yeah.

   3. MAMarc Andreessen5:42

      Um, and so, and, you know, airplanes have had to get designed around a human in the cockpit. Uh, boats have had to, you know, get designed around a human, you know, steering things. Um, uh, like in a world where-- in a world of autonomy, like d-d-do-- Uh, do we already know what the things are that move or are we gonna discover that there are a lot of new things that are gonna get built, uh, when you don't need a, a human in the, in the driver's seat?

   4. QYQasar Younis6:02

      I think both. Uh, be-- Well, the, the, the, the thing that you have to remember is like you take like a, a haulage system that's on a, in a, in a port. Um- Like a caterp- a Komatsu, a dirt mover, uh, in a mine, those are made for twenty, twenty-five years.

   5. MAMarc Andreessen6:17

      Mm-hmm.

   6. QYQasar Younis6:18

      So the buyers of that, of those products, they're-- they might not have gotten their full cycle, you know, uh, uh, ROI on them. So they're not immediately gonna buy something new, no matter how much better it is. So one part of our strategy is you gotta make those things intelligent 'cause they're not going anywhere. The second is what you're talking about, which is, well, that depends on a human in a cab. If you don't have a human in a cab, you can run... The, the machine can be smaller. It can be shaped in very different ways. When you talk about mining underground-

   7. MAMarc Andreessen6:46

      Right

   8. QYQasar Younis6:46

      ... the constraint actually is the human, because the human needs to breathe, and it needs-- it's very dangerous, and so you can build a very, very different machine. We're doing both of those things.

   9. MAMarc Andreessen6:54

      Right.

   10. QYQasar Younis6:54

       And, um, and then the thing that we're not talking about is we're all talking about intelligence almost, like, within a system, but the system-level intelligence is where the unlock is. And we're already doing work like that, where you say, "Hey, let's take an entire port. Let's take an entire mine. Let's take an entire query," and this heterogeneous mix of machines, they're-- all can talk to each other, and they can optimize and be efficient when one machine goes down or one machine has an issue. The rest of the mine doesn't have to stop. When it's human-driven, we don't even know the machine's gonna go down because there's no analysis. The human is not plugged into the core, uh, systems of the machine.

   11. MAMarc Andreessen7:31

       Right.

   12. QYQasar Younis7:31

       So, like, a simple thing like knowing when a brake system is gonna break-

   13. MAMarc Andreessen7:37

       Mm-hmm

   14. QYQasar Younis7:37

       ... is actually huge because you can start preparing for it in advance. You're like, "Oh, this wear and tear is higher than in other mines."

   15. MAMarc Andreessen7:43

       Yeah.

   16. QYQasar Younis7:43

       Just using an example. But, like, the, the other macro point is if you look at agriculture as an example, you know, average American farmer is fifty-eight years old. The, there's-- The, the number is something like under thirty-five, it's, like, less than ten percent of farmers are that young. So what's gonna happen?

   17. MAMarc Andreessen7:58

       Right.

   18. QYQasar Younis7:58

       The f- need for food growth is continuing to grow. The need for, uh, rare earth materials is continuing. So th- these, these demands are only growing, but the humans who are the bottleneck are decreasing.

   19. MAMarc Andreessen8:09

       Right.

   20. QYQasar Younis8:09

       Trucking is the same way. Um, and so you can, you can really just unlock a lot more efficiency. So I mean, one way to think, maybe think about this is, like, imagine if the cost for food decreases-

   21. MAMarc Andreessen8:22

       Right

   22. QYQasar Younis8:23

       ... because it's way, way more efficient. What, what's the downstream impact? Then imagine for goods being transported. Let's say, you know, instead of a few dollars a mile, it's twenty cents a mile.

   23. MAMarc Andreessen8:32

       Right.

   24. QYQasar Younis8:32

       And suddenly it's, it's-- I, I think the, the unlock is very, very, very big. Um, and that doesn't ne- I think doesn't necessarily need for all the machines to be redesigned from the ground up.

   25. MAMarc Andreessen8:41

       Right. Right. Got it. Makes sense. And then maybe just one, one more question would be just give, give, give us a sense of parameterize, like, the scope and scale of the company today.

   26. QYQasar Younis8:48

       Yeah. Uh, uh, uh, north of a thousand engineers, and those engineers are, you know, obviously the classic, you know, uh, uh, uh, software and AI, uh, engineering teams. But we also have engineers who really know safety systems.

   27. MAMarc Andreessen9:01

       Right.

   28. QYQasar Younis9:01

       Uh, we also have engineers who really know hardware.

   29. MAMarc Andreessen9:03

       Right.

   30. QYQasar Younis9:03

       Because the i-important thing that we-we're kind of just, uh, tipping around, stepping around is all this stuff is hard because it ultimately has to meet the real world, and the real world is, has way more complexity and, and, and has a lot more issues, and we have engineering teams that can... I mean, we've deployed our, our, our models onto, like, fifty-some platforms.
5. 19:20 – 33:10

   Selling to Legacy Automakers & the GM Culture

   1. MAMarc Andreessen19:20

      by General Motors and they-

   2. QYQasar Younis19:21

      One of my first distributions personally, so I, I enjoyed that [laughs] .

   3. MAMarc Andreessen19:23

      There, there, there we go. A, Y Combinator, Y Combinator-

   4. QYQasar Younis19:25

      Yes, yes, yeah

   5. MAMarc Andreessen19:25

      ... Y Com- Y Combinator company. Um, and, um, and, you know, to a top, top-end team, and they, they were, you know, by all accounts making excellent progress. They got bought by General, General Motors. They became the GM Autonomy program. GM got a lot of praise, at least, you know, at least in the, in the, in the te- in tech circles for being like, okay, being like the-

   6. QYQasar Younis19:41

      Yeah

   7. MAMarc Andreessen19:41

      ... o- the legacy automaker with the biggest investment.

   8. QYQasar Younis19:43

      I, I called Peter when, uh, before, before it was announced on that, and I said, "Hey, Cruise just got bought." You know, he's also GM family. We're both GM families.

   9. MAMarc Andreessen19:50

      Okay.

   10. QYQasar Younis19:50

       And, uh, uh, Peter guessed it was, he said, "Nvidia?" I said, "No." He said... I said, "Go fish." He says, "Apple?" I said, "No." I said, "General," explicit "ive Motors." [laughs]

   11. MAMarc Andreessen19:58

       [laughs] Yeah. That's right.

   12. QYQasar Younis19:59

       So that's surprising to people who are from GM.

   13. MAMarc Andreessen20:02

       That they were willing to buy, that they, they were w-

   14. QYQasar Younis20:03

       Yeah, that they did it

   15. MAMarc Andreessen20:04

       ... okay, that, that they did it.

   16. QYQasar Younis20:05

       Yeah.

   17. MAMarc Andreessen20:05

       And then by all accounts, they were, I mean, as far as I, as far as I ever heard, like they were making excellent progress.

   18. QYQasar Younis20:09

       Yeah.

   19. MAMarc Andreessen20:09

       Uh, and then they had this, there was a, there was an accident. There was a-

   20. QYQasar Younis20:12

       Yeah

   21. MAMarc Andreessen20:12

       ... was that, it was a fa- an injury or fatality or-

   22. QYQasar Younis20:15

       Uh, it wasn't a fatality, but it was a serious injury.

   23. MAMarc Andreessen20:16

       Serious, serious injury.

   24. QYQasar Younis20:16

       Somebody was dragged for 20 feet.

   25. MAMarc Andreessen20:18

       Yeah, ser- serious injury, bad press, and then, and then they put a bullet, they, the GM CEO on board put a bullet in the Cruise project, and-

   26. QYQasar Younis20:25

       Yeah

   27. MAMarc Andreessen20:25

       ... I know the cr- that at least some of the senior Cruise people were extremely upset, um-

   28. QYQasar Younis20:28

       Yeah

   29. MAMarc Andreessen20:28

       ... you know, the, the, by, by the aftermath of that. Was it surprising that they reacted the way that they did?

   30. QYQasar Younis20:33

       Uh, so f- you know, full disclosure, General Motors [laughs] is a customer, and I went to the General Motors Institute, so we have a lot of, uh, love for the company. Uh, but incidentally and ironically, I'm reading, uh, a coincidentally I should say, I'm reading this, uh, very famous book, which I had actually never read before, called On a Clear Day You Can See General Motors.
6. 33:10 – 42:22

   The State of Self-Driving Cars in 2025

   1. MAMarc Andreessen33:10

      shortly after that.

   2. QYQasar Younis33:11

      Yeah, late 00s. Yeah.

   3. MAMarc Andreessen33:12

      Late 00s. Um, uh, so almost 20, basically around 20, a little less than 20 years maybe. Um, and there have been lots of predictions over the last 20 years of like self-driving cars are imminent at any moment. Um, t- so I guess the, the, the bad news is we're sitting here today and most cars are not self-driving. The good news is there are now self-driving cars.

   4. QYQasar Younis33:26

      Yeah.

   5. MAMarc Andreessen33:27

      And so the Waymo cars are driving all over, you know, in the places they're deployed. It's become a, you know, like people in San Francisco are-

   6. QYQasar Younis33:32

      Yeah

   7. MAMarc Andreessen33:32

      ... I think treat it now as routine that they get into self-driving cars.

   8. QYQasar Younis33:34

      And I think you can call, I think Tesla-

   9. MAMarc Andreessen33:35

      Tesla

   10. QYQasar Younis33:36

       ... it's kind of like the AGI thing.

   11. MAMarc Andreessen33:37

       Right.

   12. QYQasar Younis33:37

       It's like-

   13. MAMarc Andreessen33:37

       Right

   14. QYQasar Younis33:38

       ... you know, if we're talking 20 years ago, everything we're seeing right now is like a mind-blowingly AGI.

   15. MAMarc Andreessen33:43

       Right. [laughs]

   16. QYQasar Younis33:43

       The, the post keeps moving.

   17. MAMarc Andreessen33:44

       Right.

   18. QYQasar Younis33:44

       The Tesla stuff's amazing. You can look a bunch of manufacturers, Blue Cruise, Super Cruise, BMW, Volvo's Pilot, they're all quite impressive systems. They're not full self-driving.

   19. MAMarc Andreessen33:53

       Right.

   20. QYQasar Younis33:54

       But yeah.

   21. MAMarc Andreessen33:54

       Well, it's, it's full self-driving X, whatever remote monitoring is happening.

   22. QYQasar Younis33:58

       Yeah. [laughs]

   23. MAMarc Andreessen33:58

       Um, uh, the, the Tesla, uh, we have, we have a home in, in Los Angeles, and you, you guys may recall there was a, there was a large fire in Los Angeles.

   24. QYQasar Younis34:05

       Yeah, yeah.

   25. MAMarc Andreessen34:05

       And then the power, and then the, and then the California power grid was buckling even before that. And so, um, it actually turns out among the things Cybertrucks are good at is they're, they're very good, uh, batteries-

   26. QYQasar Younis34:13

       Yeah

   27. MAMarc Andreessen34:14

       ... uh, for powering your house.

   28. QYQasar Younis34:15

       Yeah.

   29. MAMarc Andreessen34:15

       Um, and so literally we have Cybertrucks as our backup battery for the house. And as of last year, whatever the FSD release, I forget the exact one-

   30. QYQasar Younis34:21

       Yeah
7. 42:22 – 49:53

   Long-Haul Trucking, Mining & Why Nobody Wants Those Jobs

   1. QYQasar Younis42:22

      cheap.

   2. MAMarc Andreessen42:22

      Good. And what about long-haul trucking?

   3. QYQasar Younis42:24

      So long that, so that's, so that's what we, that's, so that's the pa- passenger side. The, the long-haul trucking, completely different economics, completely different, um, uh, a business model. Um, there are many companies right now, I would say probably north of five, that are running long-haul trucks with drivers carrying loads between America and China. Actually yeah, China's probably getting into double digits.

   4. MAMarc Andreessen42:44

      Mm-hmm.

   5. QYQasar Younis42:44

      So it's there, but the reason, uh, you don't know it and the reason it's not top of mind is it's not a consumer product.

   6. MAMarc Andreessen42:49

      Right.

   7. QYQasar Younis42:49

      And unlike, uh, on the Waymo and Tesla side where investors are willing to essentially give you, uh, you know, some, um, market cap, uh, you know, uh, adjustment for the potential of, i- the, the, t- they say the cal- the trucking business is like, you know, made, uh, what's the-

   8. MAMarc Andreessen43:08

      Uh, you, you buy a car with your heartstrings-

   9. QYQasar Younis43:10

      Yeah

   10. MAMarc Andreessen43:10

       ... you buy a truck with a calculator.

   11. QYQasar Younis43:11

       Yeah.

   12. MAMarc Andreessen43:12

       [laughs]

   13. QYQasar Younis43:12

       It's a calculator business.

   14. MAMarc Andreessen43:13

       Right.

   15. QYQasar Younis43:13

       And they, the, and so it's like pure dollars and cents.

   16. MAMarc Andreessen43:16

       Right.

   17. QYQasar Younis43:16

       And so I think, um, you as the provider of self-driving trucks, if you're doing the whole thing like some of the companies are, which we're not-

   18. MAMarc Andreessen43:24

       Right

   19. QYQasar Younis43:25

       ... you have to show every mile I'm gonna, I'm gonna save you this many dollars.

   20. MAMarc Andreessen43:28

       Right.

   21. QYQasar Younis43:28

       And it's like for sure, for sure, for sure. 'Cause the buyer's unsophisticated-

   22. MAMarc Andreessen43:32

       Right

   23. QYQasar Younis43:32

       ... and they're just like, "Well, I, I already got a staff that can drive," and it's like, uh, and they're like, they're just not m- uh, inclined. Now, where we're playing in Japan, it's not random that we're doing trucking in Japan. There's a massive labor shortage today, and there's an imploding demographic, uh, uh, you know, a situation. And so there's a demand from almost every sector, and that's why we've, we've picked that market to, to, to, to really grow. But, uh, I think, like, you can take like even more, uh, obscure, like, when will all, uh, quarries, you know, l- literally like, uh, where you d- you know, you, you're, you're moving cement, you're moving-

   24. MAMarc Andreessen44:06

       Mm

   25. QYQasar Younis44:06

       ... you're moving dirt, uh, w- not queries, Q-U-E-A-R-S, Q-U-A-R-R-Y.

   26. MAMarc Andreessen44:11

       Yeah, yeah. [laughs]

   27. QYQasar Younis44:11

       Quarries, quarries.

   28. MAMarc Andreessen44:12

       The rock, rock stone.

   29. QYQasar Younis44:13

       Rock, stone, cement.

   30. MAMarc Andreessen44:15

       Right, right, right, right, right, right.
8. 49:53 – 56:47

   Introducing Dana: Autonomy for High Schoolers

   1. MAMarc Andreessen49:53

      Exactly.

   2. QYQasar Younis49:53

      Yeah.

   3. MAMarc Andreessen49:54

      Do, do you guys... Do, do you like, it's, the, you know, these little delivery robots? Like, is that... Do, do you see a world where there's a billion of those running around?

   4. QYQasar Younis49:59

      Yeah, I think so. I mean, the, uh, the, the, the product that we're announcing, uh, I think has probably come out around with this time. It's called, uh, Dana. So there's... You can just simplify everything that Applied Intuition does into two buckets, which is the... We've been talking mostly about the models that go on the machines.

   5. MAMarc Andreessen50:15

      Mm-hmm.

   6. QYQasar Younis50:15

      Then there's, as we say, onboard software or onboard AI. Then there's offboard AI. This is the tools to de-design and develop these same systems, the, the models that actually go on the machines. Our, uh, you know, vision for that is, and the, the delivery robot is a great example, is, like a high school kid or a middle schooler, they can make iPhone apps. They should be able to make autonomous systems.

   7. MAMarc Andreessen50:37

      Mm-hmm.

   8. QYQasar Younis50:37

      So why can't they? Just ask that very simple question. Why can't a ninth grader make a delivery robot in their, in their home? Well, they don't have the, the actual environment that they would first develop the scenarios in.

   9. MAMarc Andreessen50:50

      Mm-hmm.

   10. QYQasar Younis50:50

       They would define the requirements. Say I want this robot to go on my high school campus around these, let's say, four buildings. Uh, then how... Okay, now that you de-define the, the requirements, then you have the scenarios get made. Where are all the scenarios that I can, that can, uh, that can be made by using, let's say, a satellite image of the high school? Uh, then now you have to train the robot, so you need some data. Where do you get that data? There's maybe enough publicly available data that can actually train a fairly s- uh, uh, rudimentary robot. Okay, now you got that data from online, maybe YouTube videos, a couple of other places. Suddenly the robot's not doing... Now you need to deploy it onto the actual machine. So then you deploy it onto the machine, and then the robot runs into the wall. Okay, what happened there? The loop closes. That platform for designing and developing is what we're launching. It's called Dana, uh, which is the street that Applied Intuition [laughs] is, uh, headquartered on. Uh, and, uh, and our, our, our... You know, this comes from our tooling background.

   11. MAMarc Andreessen51:47

       Mm-hmm.

   12. QYQasar Younis51:47

       And if you look at, like, how tooling has changed in the digital AI world, if you look at, like, what Claude did to all... You, you also remember like, you know, from Mixpanel to, you know, uh, GitLab, GitHub, all these... Now everything has moved into a very different, almost IDE, frankly speaking. We think the same thing's gonna happen in the physical world. And so, uh, th-that, that, that's, yeah, that's what we're, that's what we're building. That's what, uh, that's what we've built, and that's what we're launching. And we already use it in-house, uh, to develop our autonomy system, which is, you know... And we're working on the most kind of scaled, complex systems, uh, on the planet in all these different verticals, so we're pretty confident that it's actually quite useful.

   13. MAMarc Andreessen52:23

       Right.

   14. QYQasar Younis52:23

       And we've seen massive productivity gains. Uh, but also, uh, you know, we think, like, uh, other companies will use this to, to, to build their own systems 'cause it, it gets to that mission, that, uh, a billion intelli-intelligent machines.

   15. MAMarc Andreessen52:35

       Yeah. F-fundamentally, right, Dana is our agentic platform-

   16. QYQasar Younis52:38

       Right

   17. MAMarc Andreessen52:38

       ... for Physical AI.

   18. QYQasar Younis52:38

       Right.

   19. MAMarc Andreessen52:39

       And, and everything that we've built and developed over the past nearly a decade-

   20. QYQasar Younis52:43

       Right

   21. MAMarc Andreessen52:43

       ... every, every tool, e-every technique, that's available in Dana.

   22. QYQasar Younis52:47

       Right.

   23. MAMarc Andreessen52:47

       And it's very actually easy to use, uh, with the agentic interface.

   24. QYQasar Younis52:51

       Right.

   25. MAMarc Andreessen52:51

       And so workflows that used to maybe take days or weeks to run-

   26. QYQasar Younis52:55

       Right

   27. MAMarc Andreessen52:56

       ... you can now run those in, in minutes-

   28. QYQasar Younis52:57

       Right

   29. MAMarc Andreessen52:58

       ... uh, in many cases.

   30. QYQasar Younis52:58

       Right.
9. 56:47 – 1:20:00

   What Comes Next: Humanoids, Home Bots & the Long Tail

   1. QYQasar Younis56:47

      [laughs]

   2. MAMarc Andreessen56:47

      [laughs]

   3. ETErik Torenberg56:47

      So you were talking earlier about how when, you know, um, the technology got, got so good in mobile that there was a wave of these companies, you know, Uber, WhatsApp-

   4. QYQasar Younis56:56

      Yeah

   5. ETErik Torenberg56:57

      ... Snap, you know, Air-Airbnb, e-et cetera, that emerged in quick succession. And so now that technology is getting there, or the infrastructure for physical AI, what are some use cases or companies that you could... I mean, obviously it's hard to predict the future, but where, where are you most excited for? Like, what, what could we be talking about the equivalent here of in quick succession?

   6. QYQasar Younis57:13

      I mean, I think l- uh, you know, midterm we want Dana, if not the, the short term, to really, you know, make humanoids way more real. Uh, there's... I mean, how many? It's like 1,000 core tasks in a home from, uh, from humanoids. And these companies, it's like such... I mean, I'm-- If you, you talk to people who work in these companies, it's everything is difficult. Every step of the way is difficult. Collecting data is difficult. Uh, you know, cleaning that data is difficult. Training those models or deploying the model is difficult. And the bar being, "I want a high school kid to make a humanoid," so that, that's our, our, our path. And we think there, there could be a lot there. But that's, like, these, the obvious stuff. I think the true non-obvious stuff is gonna be, we'll look back, will be, will be way, way more interesting.

   7. MAMarc Andreessen57:55

      And, and there's, there's some core ingredients that we're bringing together in Dana, right? We're, we're making it way easier to, to actually get imitation learning to work, way easier to make reinforcement learning work in combination with that, um, where we, we have pre-trained models that can be used as a baseline for a lot of things. Um, world models, advanced simulation tech, like all, all of these things come together, and then you're sort of limited by your cre-creativity. Like, well, what, what do I wanna do? And if you think about any kind of physical AI task as it's, it's a, uh, you are un-understanding the world and you're manipulating something and, and we can build that. That can be built now much more easily in this, in this tool.

   8. QYQasar Younis58:30

      And I think sometimes people ask, like us being a tooling company, and like you take self-driving trucks, we deploy self-driving trucks, and many of the self-driving trucking companies use our tools. I, I, I think, uh, so sometimes people ask, "Well, look, you know, with Dana, aren't you gonna like enable all these competitors?" That's great.

   9. MAMarc Andreessen58:46

      Right.

   10. QYQasar Younis58:46

       That's absolutely completely fine. If you look at Google and what Google did to web applications, there was a massive internet. Uh, Google still succeeded through, you know, Search and YouTube and, and, and other web apps, and other folks learned and used open source products and then ultimately closed source products and ultimately venture-backed products. So we think, we think this, the, the, the, the, the same thing can happen here.

   11. MAMarc Andreessen59:06

       I was at a, uh, robotics startup, uh, a while back that you, you guys know well, um, and they had, they were training, you know, they were doing, go through a training process, training their, one of their arms to do the, particularly a killer app that I thought was very appealing, which was picking up dog poop.

   12. QYQasar Younis59:19

       [laughs]

   13. MAMarc Andreessen59:20

       Literally, you know, training over and over again the difference-

   14. QYQasar Younis59:22

       Yeah, yeah

   15. MAMarc Andreessen59:23

       ... it was for the... And so, you know, I don't know. Why not, right?

   16. QYQasar Younis59:25

       Yeah. [laughs]

   17. MAMarc Andreessen59:26

       Why not have the little, why not have the little robot follow you around when you walk the dog-

   18. QYQasar Younis59:29

       Yes, yes

   19. MAMarc Andreessen59:29

       ... and it just go pick up the poop.

   20. QYQasar Younis59:30

       Yeah. And I think, like, like you-

   21. MAMarc Andreessen59:32

       I, I know somebody who built a-

   22. QYQasar Younis59:33

       It's a great idea

   23. MAMarc Andreessen59:33

       ... I forget who it was, but somebody built a, uh, a, a little lawn robot that would go around and individ- pick up individual leaves.

   24. QYQasar Younis59:38

       Yeah. [laughs] Yeah.

   25. MAMarc Andreessen59:39

       'Cause you got that problem, right? Okay, you rake-

   26. QYQasar Younis59:40

       Yeah

   27. MAMarc Andreessen59:40

       ... you, y-you, you rake your yard, it's completely clean, and then like two hours later there's like 14 leaves and you're like...

   28. QYQasar Younis59:46

       Yeah, yeah. The development costs-

   29. MAMarc Andreessen59:47

       So it's like send out the little bot to pick up the leaves.

   30. QYQasar Younis59:48

       It's like if development costs are zero, then people-

Episode duration: 1:20:21