The System Behind Self-Driving: Waymo’s Dmitri Dolgov

25:08I would say that we've clearly moved past the stage of scientific research and kind of deep core technology development to this new phase of accelerated global scaling and deployment. Yes. We still have work to do, but I don't see today any limitations or any gaps in the core technology. The driving is good enough now. Well, the core technology, I think, is good enough that I can't think of any aspect of driving that is not supported by the fundamental technology. Now, that said, there is a lot of work to do in specialization and in validation before we can deploy responsibly, right? We're not driving everywhere in the world.

26:00We are planning to start operating in London and in Tokyo this year. Do we have a driver that you're using today in San Francisco that we can just plop down in London and go? No, right? But what we're seeing is incredibly encouraging from the perspective of, like, is the core technology there? So now it's a matter of collecting the data, doing some specialization and validation. And you can see the signs are different. In both of those places, people drive on the other side of the road. But that's actually not that hard for computers. And core technology generalizes really well, but you still work that you have to do.

26:37What generalizes least well? Increasingly we're finding, especially now that we're able to kind of hook the Waymo AI to the AI in the digital world, the VLMs, and kind of inherit the general world knowledge from VLMs, we're seeing really strong results from like zero shot or few shot learning because of that general knowledge that we bring in. But there are a few things like, say, cold weather, cold winter weather, where it affects the entire stack. So it's not just the AI, but you actually have to. Hardware, yeah. You need the hardware, you need to have the proper cleaning solution, heating elements in it, and then you think about things that are completely solvable by computers, like motion control and slippery surfaces, right?

27:25So that takes a bunch of work. You don't get that for free from just pulling it. some, you know, VLM decoder.

27:32Dmitri Dolgov:Was it the case, I mean, my impression, not knowing anything, is that in the early days, there was maybe a lot of San Francisco-specific work or Phoenix-specific work in the early markets, whether it be mapping or something else, and that you guys seem to either have solved that in generalizing it, or just scaled up your ability to do the city-specific work. Quash enabled the rapid city expansion? We usually think about it, the capability of the windward driver as well as deployment, not primarily and directly in that space of cities or zip codes. I think about the operating domain. And then that's just the freeways, cold weather, snow, rain, fog, density, et cetera, et cetera.

28:24And then that, that's what we are building, that's where we're evaluating. And then that maps to a city, like a particular city, be within the operating domain or outside of it. So where, if we rewind history a little bit, our initial deployment in where we started offering a fully autonomous commercial service for the first time was in 2020 in Chandler, Arizona. And that was on what we called the fourth generation of the Waymo driver. This was the, if you remember, the Pacifica minivans with different hardware, different software. There, we were super focused on doing the whole thing end-to-end.

29:08Learn how to build the driver, evaluate it, deploy regularly, operate it end-to-end 24-7 with customers, learn from the customers. And they were very focused on that operating domain of mostly Chandler, which is a medium, low-complexity one. Then when we made the jump to the fifth generation of our system, This is what's on my basis today. We really wanted to take a huge bite out of that operating domain. And we collected data all over the United States, all different states, different cities. When we chose to deploy in the hardest parts of San Francisco, hardest parts of Phoenix, we made a big jump on the hardware side and most importantly on the software, the AI side.

29:51And I would say that was the big discontinuous jump. And that's what you're seeing now after we've scaled up and iterated on all of the aspects of building and deploying driver. This is now why you're seeing us go in parallel and scaling in the US.

30:11Dmitri Dolgov:So driver version 5 was just a much more generalizable stack than version 4? And what was it about us? Was it just that it had been trained on a much wider data set? It was when we made this big bet on AI. I think there was a lot more, you know, little AI models and ML models in the fourth generation. Got to make a much bigger bet and jump to kind of AI as the backbone for the fifth generation. AI is the backbone as the core engine, as in you're saying that Gen 4 had lots of small little AI subsystems? Yeah. And that's been, so we kind of made that jump and we've been iterating and improving the model since then.

30:55Can we talk about hardware a second? So, lots of hardware questions,

30:59Dmitri Dolgov:but one is maybe everyone in this space has a very charismatic demo of a vehicle that is custom-made for self-driving. And so, you know, it's often the van with the, you know, no steering wheel, seats facing in both directions. You know, you guys have one, Tesla has the steering wheel-less cyber cab. You know, Cruise had the Cruise Origin. And yet, we're still driving in Jaguars that have a steering wheel in the front and are pretty similar to consumer cars. And it's interesting to me, because, you know, if we were talking about this 10 years ago, we might say, well, yeah, developing a custom car, like, that's relatively straightforward.

31:49Dmitri Dolgov:We know how to put a bunch of sensors on a new car. but the software will take a long time. And what's interesting is we've made huge progress in the software, but interestingly, the cars are still derivatives of, you know, cars that people are driving. And so I'm curious why you just think the custom hardware has not happened as of 2026. It's obviously, it's a small improvement compared to, you know, Waymo is the big improvement, but it's just interesting that it still hasn't happened. Well, let's say our sixth generation of the vehicle and the driver is our version of that. It is the O-Hike platform, right?

32:24So that is, you know, it still has the, you know, we can talk about, you know, whether you want to have the seats quite backwards or not. I actually, you know, think it looks nice in a demo, but practically speaking, maybe not the way to go. But that is, it is a custom designed vehicle. And it is, we put a lot of thought into, you know, moving away from a car that's designed around the driver to a car that's designed around passenger. And it's much more spacious, But it's happening. It's not open to the public yet. But I took a ride in it the other day, fully autonomously, and that's coming this year.

33:02Yes. How much better is it as a passenger experience? You'll tell me once you give it a try. I love it. Okay. So it's all about the space and the convenience of ingress and egress and the screens and the interface of the passenger. So we put a lot of thought into every aspect of it. It has sliding doors. It's very easy to get in. It has a flat floor. It is, yeah, if you sit in the back, you can like fully stretch out. There's so much space there. And it looks, you know, from the outside, you know, it looks fairly big. But the actual footprint of that is barely, barely, barely larger than the I-PACE.

33:43So it's kind of amazing that, you know, you walk in and it feels like you're in the living room.

33:47Dmitri Dolgov:Yes. I guess my question is just, you know, Waymo does 25 million rides a year, run-ride-ish, with the Jaguar I-Pace. And it's interesting that so much scaling has happened with self-driving so far on the old retrofit. Maybe that's to be expected. But I think, well, it matches the high. I don't think it's a given. You're right. But if you think about the value proposition, of course there is the safety of it, you don't have to worry about it. There's also the privacy, being in the car by yourself, maybe with other folks, but not having to share that space with another human, right? No, Wayne has great products, yeah.

34:35But I guess this is why we're seeing such consistency in the car, it drives well, very predictable, and you can go beyond that, right? and you specialize even more to make the experience even more magical around the rider. But I guess it would have been disappointing if without the specialized car, and I think I would have been surprised if we leveled off at some other much lower level of customer adoption. Because a car seems like more of an optimization improvement, but the core of the value proposition comes from those other factors.

35:08Dmitri Dolgov:Yes, yes. I guess to just take risk on one thing at a time, we'll start by doing the software layer and then we'll build a specialized car or something like that. That's right. That's right. Yeah. Yeah. Yeah. It's also, I mean, as you said, it's a big investment. Yes. So you have to like, you de-risk the fundamentals. Yes. Um, and you know, throughout our history, we were very focused on setting the most, you know, the biggest goal for the company to de-risk the most important questions, right? We talked about, you know, the third generation where, you know, we wanted to deploy something and go end to end.

35:40We talked about the, what was the goal with the fourth generation and then, oh, sorry, the fifth generation, and then there's the sixth generation, right? So it was the sixth generation where it made sense to go and spend all this effort into the custom.

35:51Dmitri Dolgov:And sixth generation is both a custom vehicle. Is it also a new generation of the driving stack? Yeah, it is the new hardware. Yep. The sensors, the hardware, the soldering hardware they're putting on the Ojai vehicle is the sixth generation. It is very different from the fifth generation. It is simpler. It is more capable. It is much lower cost. It's a fraction of the cost that's comparable to what you would get like a fancy 8S system. Nowadays, the driver assist system. The software is pretty much the same. So when we talk about generalizability of the Wiimote driver, we talk about weather conditions, we talk about cities, but it also generalizes well to different vehicle platforms and different sensor configurations.

36:39Dmitri Dolgov:Okay, so Gen 6 is a new vehicle and a new sensor stack, but it's almost a tick-tock cycle happening here. It's a similar software. That's right. And then we're going to put the sixth-generation Waymo driver on other vehicle platforms, like the Hyundai Ioniq that's coming later in the year. What is different about the sixth-generation hardware stack, and how did you make it cheaper? So it still has the same three sensing modalities, but we've made significant optimizations in all three. Yeah. So unification, simplification, and there's just, you know, the kind of just writing the... Yeah, is it a classic case of, you know, manufacturing scale?

37:23Well, scale hasn't fully come in place, but all of those, if you think about the supply chains, the industries, cameras is pretty mature. radars way many years ago used to be bulky, complex, very expensive when we were putting them on planes but then we started putting them on cars. Now you can get a decent automotive radar for tens of dollars. There is a variant of the automotive radar it's called imaging radar and gives you a richer something. So that is also has come down and caused drastically but it's a little bit behind your standard automotive radars. LIDARs are following the same very predictable, very well-known trend.

38:09So we're writing that and we're also learning from the previous generation to just make improvements and simplifications and optimizations.

38:16Dmitri Dolgov:I have a very silly question. What are LIDARs versus radars better at in a self-driving company? LIDAR... Are they complementary? They're very complementary. It's all blasting. you know, uh, uh, effectively, like, you know, blasting, you know, photons out there and then, uh, they bounce off of something, they come back, you know, you measure what comes back. The frequencies are very different. So laser, uh, gives you, it's, uh, very, very high resolution. So you can, you know, think of it as like a laser beam that goes out, you know, spins around, it, you know, shoots out millions of these laser pulses, you know, per second.

38:59And then each one comes back and you're kind of sampling the 3D structure of the world with very high resolution. So LIDAR for very fine-grained mapping. That's right. Radar has much lower resolution, but because of the physics of it, it degrades much better in adverse weather conditions. So fog, snow, heavy rain. So it's not going to be occluded by verticals between it and the target. So imagine driving in super dense fog. Yes. We're close to San Francisco, so we probably don't have to think that hard. It can be really hard to see. So cameras degrade. Yes. Laser, depending on kind of the size of the particulates, can degrade better or worse than camera.

39:46Radar is not well affected. So you can imagine driving on a freeway, then radar will give you really good returns for cars that are absolutely invisible in the camera space.

39:56Dmitri Dolgov:That's interesting. So does that mean there are some environments where you'll be relying significantly more on radar? Well, it's a combination of the sensors, right? So we rely on, you know, each one is noisy, right? How the noise characteristics show up in different environments is different, but it is, I mean, it's not like we switch from one to another. It's not like we know we estimate you know what's happening with the world through cameras and through radars and through lighters and then we compare no they're like there's an encoder for camera there's an encoder for lighter there's a corner and they all go into the you know the system uh that gives you jointly the best view of what's happening uh in the world so if you were you know if it's a nice bright sunny day cameras are you know very valuable if you know it's pitch dark or you have like sun in your face or you're blinded by the headlights from you know oncoming car then camera will degrade.

40:52There's still some, you know, noisy signal, but it will degrade. And radar, LiDAR is completely unaffected.

40:59Dmitri Dolgov:Are there technical problems that are your white whale, or you're just, you're still chasing, or you are particularly interested in solving, even if they're kind of niche for the, you know, we just, we really want to have, you know, driving when it's actually snowing nailed, or steep hills in San Francisco, or, you know, are there problems you've been very interested in historically, or still are? I'm super excited right now about the accelerating global expansion. More cities in the United States and going internationally. So being, I understand I'm not answering your question about the knowledge.

41:37I'll come back to that. But really, that's the thing that I'm today most excited about. Just getting to a place where any major metropolitan area, you can fly into the airport and then take away more and go anywhere you want to go. Like that is insanely exciting to me right now. So then, you know, technically what I'm most excited about is all of the rapid progress in AI. And the world models, the foundational model work. and it is just such a massive boost to how much we can simplify the system, how much we can bring down the cost and how we can scale globally. And there's some magic that happens that I don't think I would have anticipated a few years ago.

42:31So that I find from the technical perspective just insanely thrilling.

42:36Dmitri Dolgov:Yes. When you talk about kind of the progress in AI, what are the most fun parts of it for you these days? I think it's seeing the capability and the scaling laws from this approach of starting, you know, with that cornerstone of the foundational model and then specializing to teachers and then, you know, distilling. It just, you get such big wins in performance across the board. I just, you know, you invest something into the architecture or get better data or training recipes. And then, you know, you invest in that early stage and then it just has massive amplification and ripple effects. So that is, in some ways, is kind of magical.

43:25And then you, I guess, then you see it on the car. and I've had some moments where, you know, a card does something and you look at a log and I've been surprised. Like it does things that I didn't think it was capable of doing. So it's that, you know, it's... When you see emergent behavior, that's kind of a proud moment. One example, yeah. You know, it's, you know, when you build a system and then, you know, you think you understand, you know, how it works and you understand fully, you know, the limits of its capability and performance, and then it does something, you know, kind of almost magical, it's exhilarating.

44:06So one example I can give you, I think I've shared some videos of that publicly in some talks, was this example where the situation that happened in San Francisco, a fairly benign situation, where at an intersection, our light is red, There's new cross traffic. A bus goes by and, you know, it stops partially blocking. Our light turns green. So we start to go. We're nudging around the bus. And then you see a pedestrian being detected on the other side of the bus. And then your car responds appropriately. It slows down, goes a little bit wider. And, you know, then a pedestrian actually emerges from the bus and, you know, we go on our own way.

44:54So the first time I looked at that log, what's going on here? I know we have pretty darn good sensors, and the software is very capable. We don't see through stuff, right? That's not how cameras or lighters and radars work, right? It saw the pedestrian through the bus. It saw the pedestrian on the other side of the bus. And it's not like, you know, you look at the windows, you're like, okay, you know, radars shouldn't do this massive metal box. Yeah. Yeah, you know, look at the sensor data. Yes. And like it just shouldn't, radar shouldn't be able to go through it, right? You know, camera, like you can't see in the camera because, you know, there's reflections and there's people on the bus.

45:33So it's not like you can see through the windows. Right. So like, what is going on? Maybe it's, you know, noise or some coincidence. And I, you know, first time I saw it, I couldn't actually believe it. I was like, no, no, there's something. It doesn't spell right. So what actually turned out was happening is that our peripheral lighters bounce under the bus. And there was just a little bit of very, very noisy reflection of the movement of the person's feet. That was enough for the AI models. Hey, likely there's a pedestrian there. And I'm going to, you know, I'll detect it as such. And moreover, there's enough data there to predict what they're going to do.

46:09It just kind of blew my mind.

46:11Dmitri Dolgov:Is this the perfect example to explain what we were talking about earlier? The value of one, fusion across a sensor suite, but then secondly, building, I mean, relatedly, building an intermediate representation of what's going on, where if you're just dealing with pixels, I mean, the person behind the bus does not exist in pixel space, and so you need to have some representation of the world that exists to be able to reason about the person behind the bus? I think it's an example where giving it using that intermediate representation to boost the level of performance of all parts of the model is what's happening here.

46:58Just imagine solving this problem with a black box, purely open loop, imitative system. be, is it, you know, impossible? No. In practice, what would it take to achieve that level of performance? Yes. Very, very difficult.

47:15Dmitri Dolgov:What metrics can you share on just where the business is at today in terms of rides, revenues, cars on the roads? We have about 3 ,000 cars on the roads. We're doing about half a million rides per week. that transits about over 4 million fully autonomous miles per week. We are operating in a fully autonomous mode in 11 cities in the US. In 10 of those, we have riders. What's the ghost city? The ghost city is Nashville. We just started there. So we just opened it up to riders in four new cities in one day. That was one of those little but super exciting moments where I thought back to the history, like how long did it take us from the first time we started fully autonomous rider-only operation to the first time we had external riders in four cities.

48:23It was about eight years. And then the other week, we just launched four in one day. Yes, yes.

48:29Dmitri Dolgov:It seems now clear that in... 15 years, most miles that are driven will be autonomous. Like there'll be some burn in Puritan, there's lots of old cars in the road, I think it'll actually take a little while. And some of that will be by level four, level five systems expanding in new cities and that expansion continuing. Some of it will be, you referenced existing driver assist systems and kind of getting up to level two and level three and existing systems across current car brands getting more and more capable. What do you think that working your way up from the lower levels versus working your way expanding from existing products like Waymo, what will that convergence look like?

49:17Dmitri Dolgov:Because we're going to eat it from both sides. I don't believe we will. And I actually think this... That's a great answer. Cars will get smarter. There's going to be advances in driver-assist systems. And there is, at the same time, from level four autonomy, there is simplification. And the sensors of today are not going to be the sensors of tomorrow. So they'll be much more integrated. They'll be simpler. There'll be much lower cost. So from that perspective, there is a path of convergence. And there's also a path of convergence from the product lines. Right hailing and what, you know, you can take, you know, a ride through the Waymo app today.

50:02Eventually, they'll be on your personal car. So that I see. And talk about the technology. And I see it just as fundamentally two different problems. There's driver assist systems. And then there is full autonomy. And I think it's deceptive to think of them as kind of incremental, you know, on one spectrum of complexity.

50:23Dmitri Dolgov:Okay, but you think one cannot work one's way up from driver assist systems to full self-driving? You think you have to start building a full self-driving system? I think you have to tackle, if I think about the hardest parts of building a fully autonomous rider-only system, they are very different from what you do for a driver assist system. And of course, some work in this space helps you. I don't want to say you can't make the jump, but it is a qualitative jump. Yes. When can I buy a Waymo so that I don't need to wait for it when I want to go? I can just like, when I'm ready, I can walk out the door and it's there.

51:07I'm not going to give you a date today, but you're not the first person to bring this up as a product request. Duly noted. Okay.

51:16Dmitri Dolgov:I'll add it to the list. Just, you know, that waiting for the car, it should be nice just sitting in the garage there and keep your stuff in it and everything. It's not the first time you've heard that request.

51:27Dmitri Dolgov:So how, it seems to me operationally very intensive and very hard. Like a self-driving car is actually not self-driving. It takes a village. You have all of the human operator ready to step in. And, you know, there was that thundering herd incident that you guys talked about in San Francisco that kind of highlighted that for people. And then there's just like keeping the cars clean and, you know, keeping everything running in that regard. And so can you describe just what the operational infrastructure that sits behind Waymo looks like? Sure. I will say that we are overall, you know, in all of those areas on a path of increasing efficiency and automation.

52:15Yep. So the number of manual steps that one had to do five years ago to launch a Waymo versus where we are today is drastically different. But nowadays, if you look at one of our depots, it's like a fully automatically orchestrated dance of autonomous vehicles. So the way it looks, what it looks like today is cars will automatically go on there to pick up their riders, serve their trips. If for some reason they need to come back, maybe they're low on energy, maybe somebody left a mess in the car, they will automatically come to the depot. right if it is so cleaning today is a manual process right so i'll get flagged in the car you know we have fleet management systems say hey you know car you know number you know 378 needs cleaning and we'll actually uh on the sensor dome we're able to you know display icon so we'll you know show you like a little you know emoji yeah yeah and you know there's you know people whose job it is to clean cars they'll come you know clean up if that's you know cleaning is not required and it's just charging, we'll automatically pull into a charging stall and we'll say, hey, I need charging.

53:43We don't yet have automated charging. In the future you can imagine that being fully automated, but a person will come in and plug in a cable and the car will charge and say, hey, now I'm ready to go. And it will get unplugged and the car will pull out of its parking stall and then go

53:58Dmitri Dolgov:on its merry way. One of the new Porsches, I think it is, has inductive charging, just like your iPhone where you just drive over the charging mat. I was amazed that that works at car scale, but presumably in the future they'll just be able to drive on the charging mat. Or do you think just robotic plug-in will be easier? We'll see. We'll see. I don't know. I think there's some questions about, you know, efficiency and, you know, how that plays into the overall cost and which one will be, you know, most cost beneficial remains to be seen, I think. How well behaved are the Waymo riding population in terms of not living a mess in the car?

54:34We have wonderful riders. We have the most amazing customers in the world. Generally, I would say they are very good. I think there is something about, I talked about not having a person in the car that's not somebody else's car. In some ways, you kind of want to preserve the, I think generally people want to preserve the nice aspects of it.

54:59Dmitri Dolgov:It's a broken window thing where it's so clean to begin with. I know, yeah. It's kind of like, you know, I think that that's the general trend that we see. Right. And it's like, because there's not somebody else's space, you know, you're in it. It feels like it's your own. So you don't like want to mess up, you know, your own space. I think I mean, I don't want to speculate too much on the psychology of things. However, I will say that it varies. And you can imagine, you know, a college town on a Saturday night. And yeah, that's a different distribution. Yes. Yes. Will I be able to get Waymo at any address that has USPS service in the US?

55:36Or will there be some head-tail dynamic where Ketchkin Alaska is just never worth it? Eventually it will, absolutely. There's no doubt in my mind. I think it's just a matter of when and what modality would make the most commercial sense. Is it for you? Right share versus privately owned? For right, it's not a technical problem. I mean, technology is solved. But then if you're in the middle of nowhere and there's just not enough density of the trips, does it make sense for the right hailing service that Waymo is running to have cars on standby? Probably not, right? They can be deployed somewhere else and you probably don't want a horribly bad ETA.

56:17And this is where a personally-owned vehicle that is equipped with the Waymo driver is maybe how you will see it materialized.

56:25Dmitri Dolgov:Relatedly, what will the second order effects of, say, majority autonomous traffic be? Like, it feels like a lot of things will work better where, as you say, you know, when someone merges into a lane very poorly and everyone all the way back has to, you know, slam on the brakes. That's kind of anti-social behavior. And so it feels like higher quality and more pro-social driving will just, I mean, basically reduce traffic a little bit, even for the same number of cars on the road. but presumably there'll be other second order effects like we'll want higher throughput traffic lights and yeah, how else will things change?

56:58So the first thing I think, you know, that you mentioned is, I think that's a huge deal. I just need to think about traffic jams. Yep. What's that saying? The Navy SEALs? Slow is smooth and smooth is fast. That's what like traffic jams are. You accelerate abruptly, then you come to a stop and sometimes you have the traffic like what happened? Well, you know, an old lady crossed the road three hours ago and we still have the standing wave. Right. So if everybody, you know, was a kind of a smooth, predictable driver and a consistent driver and you would still have those traffic jams at the time of, but then the time constant to clean it out, I think would be very different.

57:43But longer term, and you know, things like parking lots, right? Right now, if you look at, you know, what is our most interesting, you know, pieces of land allocated to, you know, it's parking lots, it's garages. And why is that? Well, because again, you know, your car is just sitting there 90 % of the time, right? If, you know, more cars become fully autonomous, then there's no, you know, right. And like, then imagine, just imagine what you can do with, you know, your favorite city in the world if you don't have to spend that money, that huge fraction of it on, you know, just keeping your, these chunks of metal sitting around.

58:22Dmitri Dolgov:Yeah, I don't think people often realize how big a deal parking minimums are for the layout of the urban landscape. The coffee shop here where I am would like to have outdoor seating, but can't because it would reclaim parking spots. Yeah, wouldn't that be wonderful? Yeah. I have a few more questions, but I'm curious to talk about Google's relationship with self-driving, where, again, And it feels like right now, Waymo is, aside from everything else AI-related, kind of the most exciting thing happening at Google. But it was a very long journey to get here. I mean, I feel like you could say that Google almost started working on it too early, because you were saying there's been a bunch of recent enabling technologies.

59:09Dmitri Dolgov:And so did it require Google starting when it did so early? or could one have spun up this project in 2015, 2020? And then how did Google keep the faith when it always felt like it was perennially two years away? Yeah, no, on the latter part, I just have to give credit and huge kudos and gratitude to Larry and Sergey and Alphabet Leadership Center company. Uh, it's part of the culture and the DNA of the company is to have that vision and have the stamina and conviction to go the distance.

1:00:00So to the other part of the question, you know, was it too early? I don't know. I think what we've been seeing, you know, clearly all of the breakthroughs that we've seen over the years have changed, you know, how we're building the system. But the complexity of the problem is such that like you need to go through these eater of cycles, right? It's not, you know, still, and we've seen many waves of technology, right? There's, you know, breakthroughs in, you know, 2013, ImageNet came around. Okay, like, that is the right time to start BSL driving company. And, you know, transformers came around, and, you know, VLMs, and that, and all of those are super powerful, and you have applications in other spaces.

1:00:50In the digital world, they certainly have an impact on, you know, our AI, in the physical world, there are no silver bullets. They drastically reshape that early part of the curve. It's always been the nature of this problem. It's very easy to get started. It's deceptively easy to get started. But it is super hard to go the full distance and get the number of nines, right? There's the standard engineering rule of thumb that every next nine takes 10x more. So I, yeah, maybe there is a more optimal path, but I don't see there's some magical moment where the true complexity of the problem goes away and then you can just take some off-the-shelf components and you're a business.

1:01:36If that were the case, then I think the industry would look very different today.

1:01:41Dmitri Dolgov:Last question I have. You've been promoted a lot at Google. It feels like Google really recognized your talents. Just what do you think Google does? Like Google is famously one of the very best in the world. at technical talent and say, you know, the current AI wave more broadly happening, you know, is either stuff happening at Google or generally Google alumni. But just what have you observed firsthand from how Google does this so well? Yeah, I would say Google, you know, that culture of Google of not accepting the status quo, having a big vision

1:02:28and investing in technical talent and the people who can go the distance and realize the vision, that is part of the culture. I think this is what you're seeing. And with the breakthroughs in AI in the digital world and all of the early investments in transformers and other fundamental technologies, quantum computing, And I guess we're not unlike those efforts as well.

1:02:54Dmitri Dolgov:To be sure. Thank you. Yeah. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our sub stack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

1:03:48Dmitri Dolgov:but A16Z does not guarantee its accuracy.