a16z Podcast: Capitalizing on an Autonomous Vehicle Future

0:00:06 Hi everyone, welcome to the A6NZ Podcast. I’m Sonal. Today we’re continuing our series
0:00:12 on consumer tech trends with an episode that pulse checks the state of autonomy in 2019.
0:00:17 Where are we with autonomous vehicles right now? We also share some clarity on what levels
0:00:22 of autonomy means there, including touching on regulatory aspects, and also discuss quite
0:00:27 frankly capitalism, what cars mean nationalistically, and what it will take to bridge the worlds
0:00:33 of Silicon Valley and Detroit, which is why our special guests are Kazer Yunus, former
0:00:39 COO at Y Combinator and co-founder and CEO of Applied Intuition, and Peter Ludwig, CTO
0:00:44 at Applied Intuition, which builds software for the autonomous vehicle industry. Throughout
0:00:49 the discussion, we thread the analogy of mobile to autonomous vehicles, where it applies and
0:00:55 where it breaks down. Speaking of, be sure to also check out a6nz.com/autonomy for posts,
0:01:01 decks, and videos from Benedict Evans, Frank Chen, and others. But this conversation begins
0:01:07 by cutting through the hype on whether autonomous vehicles are coming soon or not.
0:01:12 It’s interesting. You can kind of read publications and within a three-month period you’ll hear.
0:01:17 We’re in the early days, the hype site we’re in, the trough of despair, disillusionment,
0:01:20 I think it’s called, right, the Gardner hype cycle.
0:01:22 Yeah, you have pessimism. It’s kind of all over the board.
0:01:24 And then you have people saying it’s here tomorrow.
0:01:29 Yeah, exactly. And I think probably a good analogy to think about where we are specifically
0:01:35 is that I like to use is where mobile was in kind of roughly 2005. What we consider
0:01:38 to be the modern smartphone isn’t really there because you’re like, oh, look at this
0:01:42 Motorola Razer. It’s not that powerful. And I can kind of extrapolate that maybe this
0:01:47 Blackberry will be cheaper, but it’s very hard to really extrapolate. And so being even
0:01:53 more specific, if you look at kind of 2010, 2011, 2012, mobile engineers highly coveted
0:01:58 in Silicon Valley. I had a mobile company. My last startup was a messaging company,
0:02:02 and that was kind of the bleeding heart of Silicon Valley, and that was the next wave.
0:02:05 You kind of have a lot of that right now in autonomy where autonomous vehicle engineers,
0:02:09 roboticists are highly valued, highly coveted.
0:02:14 I mean, didn’t Uber or Waymo suck up the entire CMU robotics department at one point?
0:02:18 Yeah, exactly. And that’s happened multiple times. These companies like May Mobility who
0:02:24 basically are the U of M lab. You have a voyage who came out of Udacity. And so, yeah, that’s
0:02:28 happening left and right. If you take that mobile analogy though, and then you think,
0:02:34 well, 2010, there’s 11, there’s excitement. Just a few years later, 2015, 2016, nobody’s
0:02:40 writing Objective C. These waves go really, really fast. And I think a good kind of adage,
0:02:44 I think it was Bill Gates who said this, “In two years, nothing looks different, but every
0:02:48 10 years, things are dramatically different.” So if we look back at 2017, autonomy doesn’t
0:02:53 look that much different. The players are generally the same. But I think 10 years from
0:02:57 now, autonomy would be very, very different. Insofar as it might even be a commodity.
0:02:59 Interesting. That’s kind of controversial.
0:03:03 I think there will be a lot of parallels as well on the hardware front as well as the
0:03:08 software front, looking back at mobile. A modern mobile phone has GPS and an inertial
0:03:12 measurement unit has all these advanced sensors that, prior to mobile becoming big, were very
0:03:16 expensive electronics that were only present in potentially military systems.
0:03:22 Right. Chris Anderson calls these components the peace dividends of the smartphone wars.
0:03:26 This idea that essentially all the supply chains and all that competition and the commodification
0:03:30 actually created this rich and thriving ecosystem of all these commoditized parts that can now
0:03:32 be recombined and deployed in many ways.
0:03:35 Exactly. And today, we’re just at the beginning of that for automotive sensors and autonomy.
0:03:39 Yeah. The real miracle of, I don’t want to get too philosophical with capitalism, frankly,
0:03:46 philosophy, go for it. This is the, not the engineer in me, this is the MBA in me speaking.
0:03:52 The real miracle of capitalism is, I mean, there are all of these things that are made
0:03:58 much, much better, much, much cheaper, that almost are the rails in which these industries
0:04:04 lie on. And I think, even if you’re sitting in 2012, I don’t think anybody had the, “Aha,
0:04:09 I can’t believe it. The mobile era has arrived,” exclamation mark. And people, when we talk
0:04:15 about autonomy, they almost want a declarative event, a Eureka moment, where autonomy is
0:04:19 suddenly unloaded into the masses. And then you look at mobile, that didn’t ever happen.
0:04:23 You just remember, one day you decided, “I’m going to find and get that iPhone.” In 2007,
0:04:28 I was at Harvard at the time, and I remember one of my buddies getting a phone, and I said,
0:04:31 “Oh, what do you, what do you think of this iPhone thing?” And he goes, “Ah, it’s kind
0:04:34 of like a toy.” Well, you know, Chris Dixon says this at ACC a lot.
0:04:37 The next big thing we’ll start out as a toy, which he’s modeling that off-crate Christians
0:04:41 and disruption theory. Yeah, who’s an HBS professor, bringing a full circle, right?
0:04:45 Exactly, which is essentially that the innovations happen at the lower end, or the underserved
0:04:49 end of the market, before they hit the mainstream, and the kind of tips enabled by some kind
0:04:57 of enabling technology underneath it. Yeah. And so this type of incremental kind of revolution
0:05:02 or incremental changes that one day bring to you a product, you know, even if you take
0:05:07 the iPhone, the iPhone rests on hundreds of companies and thousands and thousands of
0:05:12 innovations, and they’re not just in, you know, the screen. No, it’s an entire ecosystem.
0:05:17 It’s the store, the payment processing, all the way down to, you know, the analytics for
0:05:20 apps. The broadband and the connectivity. I mean, that’s actually the missing piece
0:05:23 for a lot of continuing installation in the mobile phase.
0:05:29 There’s so much, exactly. I think there are these statements made that, you know, the
0:05:36 path to autonomy is far, far away. It is better to start autonomy company today in 2019 than
0:05:37 ever before.
0:05:40 So I want to ask a few questions on this. So the first thing is the kind of theme of
0:05:46 what you’re saying, and I buy this, is that, you know, innovations, they seem incremental
0:05:51 at the time, and then they kind of tip to where they accelerate very fast. And there’s
0:05:57 some kind of combo of the two, where the iPhone was like 20, 30 years in the making. But then
0:06:02 I would also argue that there is, well, there may not be a discrete specific single event.
0:06:07 It is an accumulation. There is still a quote, iPhone moment in every industry where that
0:06:13 industry sort of mainstreams, and you really then begin to see and experience the potential
0:06:18 even if it does start off as a toy. So my first question is for autonomy, how far away
0:06:22 do you think we are? Not just in terms of time, but steps towards that quote, iPhone
0:06:23 moment.
0:06:28 So let’s define the iPhone moment first. You know, I think it was Steve Jobs who said,
0:06:32 you know, the ’60s really happened in the ’70s, and the iPhone moment really happened
0:06:40 in like 2012, 2013. Right? The iPhone moment in my mind is when you have, you know, frankly,
0:06:47 the 12 or 24-month period where Instagram, Snapchat, Uber, and WhatsApp are all created,
0:06:51 and they all are created in a roughly pretty tight time bound. That’s the iPhone moment
0:06:52 in my mind.
0:06:56 So you’re really saying the app layer where people are really using things.
0:07:01 I think that’s what generally the public thinks about it. Now, I think probably the more specific
0:07:06 moment is the announcement of the iPhone. But if you look back, the announcement of
0:07:09 the iPhone is met with skepticism.
0:07:10 We forget that now in hindsight.
0:07:14 There’s that famous video, I think, of Steve Ballmer talking about how great Windows Mobile
0:07:17 is in comparison to iPhone because it has so many more features.
0:07:18 Oh, I forgot that.
0:07:23 Blackberry was like, well, no way, this is going to be actually a real thing. Again,
0:07:28 the gimmick, the toy. So if you take the iPhone moment as a 2007, we’ve already had that.
0:07:32 That’s the Waymo shuttles. People are like, wow, these things can only go during the day.
0:07:36 That’s not very useful. I don’t live in Arizona. So what the general public, though, will consider
0:07:41 the autonomy moment is when you meet somebody who doesn’t live in Silicon Valley, doesn’t
0:07:46 work in technology, and they’ve had an autonomous ride. Maybe it’s on college campuses. Maybe
0:07:50 it’s an airport shuttle. Maybe some goods appear at their house with an autonomous robot.
0:07:54 That’s when you’re seeing the penetration of the market into areas which are far beyond
0:07:56 the early adopters or something like that.
0:07:59 I mean, one could argue that that, to your point, in terms of defining what the iPhone
0:08:06 moment is, is not the moment. It’s actually the experience of the iPhone. It’s the applications,
0:08:11 the iPhone phenomenon even. So that’s really what we’re talking about here. So then on
0:08:16 that front, where do you guys think we are? How far are we from there?
0:08:23 So we have shuttles already. This is another, I think, mischaracterization or classification
0:08:28 of autonomy. It’s almost always, excuse me, thought as robo-taxis. And autonomy is actually
0:08:34 much more the adage that anything that moves will one day be autonomous. We blew that very
0:08:40 deeply. And so the point being is they’ll come in these little waves. And each of those
0:08:46 are different. The robo-taxi wave is kind of a bit orthogonal to the shuttles wave,
0:08:52 which is a real thing, which is campus shuttles, retirement communities. So those are different,
0:08:56 which is orthogonal to the self-driving truck wave, which is orthogonal to the, I would
0:08:59 say, the warehouse robots.
0:09:01 Why do you think all of these are orthogonal to each other? One would argue that they’re
0:09:07 the same underlying kind of roboticization, automation. So why are they orthogonal in your
0:09:08 taxonomy and worldview?
0:09:12 There are many similar technologies that are shared across the different verticals, but
0:09:16 there is a lot of domain-specific work that’s still done to make the system actually production
0:09:23 worthy. For example, John Deere has had a production semi-autonomous tractor/trailer system for harvesting
0:09:27 crops for more than a decade. As these systems become more and more sophisticated and more
0:09:31 autonomous to the point where there’s no human in the loop, there is a lot of engineering
0:09:34 effort that sort of goes in that last 10% to get to that production quality.
0:09:38 Yeah, that’s what people always talk about is that sort of last 10%, that last mile, that
0:09:43 you get the 80%, the 99%, but then you have this percentage left, which is quote all the
0:09:47 edge cases and all the things that people are trying to tackle. There are levels out
0:09:51 there for how people describe these things. And so Elon Musk will make a claim about Teslas
0:09:55 and people will say, “Well, they can’t handle all these edge cases,” etc. So in this state
0:10:01 of autonomy 2019, where are we on the levels of autonomy? Can you quickly break down that
0:10:02 taxonomy for our listeners?
0:10:06 Sure, so going through the levels just one by one. So level zero is where most production
0:10:11 vehicles are today. And so this would be a car that perhaps has anti-lock brakes and
0:10:16 traction control, some version of electronic stability control. But the systems are all
0:10:22 fairly done in a sense that they’re not necessarily seeing the world in any way. Level one system
0:10:27 will mean that there’s some level of automation. So adaptive cruise control is an example of
0:10:31 a level one system where typically there’s a radar that’s seeing the vehicles in front
0:10:35 of you on the road and then the vehicle is able to accelerate and apply the brakes automatically.
0:10:39 Level two is where things get pretty interesting. That’s where you typically have a combination
0:10:44 of a lane-keep system with an adaptive cruise system. So for example, the Tesla autopilot
0:10:49 system is a level two system. It’s able to maintain its own lane safely on the highway.
0:10:54 And right now the trend in production systems is automakers are trying to go to what they’re
0:10:59 calling level two plus, which is taking these level two sort of lane-keep plus adaptive
0:11:05 cruise systems, and they’re adding on functionality for automatically taking freeway interchanges.
0:11:09 And so if you can automatically take an exit and then perhaps automatically merge into freeway,
0:11:12 while the human is still behind the wheel and paying attention, that’s called a level
0:11:18 two plus system. That’s a level two plus. Exactly. And so major vendors, for example,
0:11:23 Mobileye, they are now marketing their level two plus systems to OEMs. Level three is sort
0:11:30 of a bit of a dubious classification where it’s essentially saying that the user should
0:11:36 be able to not pay attention, and the system should be able to alert them when they need
0:11:39 to take over. So it’s kind of like a passive driver, a passive human in the loop, not an
0:11:44 active human in the loop. Exactly. The problem with that classification though is it sort
0:11:49 of breaks down at the technical detail level. There are lots of situations where dangerous
0:11:53 things can occur where the system wouldn’t necessarily be able to warn the driver ahead
0:11:58 of time. Within the industry, there’s been hesitation to use that actual classification
0:12:02 of level three. And that’s where really the level two plus classification comes from.
0:12:06 Right. It’s a funny little distinction, but I get it. It’s almost like it’s like one,
0:12:11 two, three, four, five, and then you have like three in the middle of this weird blurry pivot
0:12:16 to quote true autonomy. Exactly. And I think I’ve seen some demos of systems that were
0:12:22 purported to be level three, but actually then in the demos, there were events that
0:12:25 required the driver to take over immediately. So that’s not really a level three. That’s
0:12:29 really a level two system. And then when you get to level four, that’s really where we’re
0:12:35 talking about these fully autonomous robotaxies that have some geographic fence. So for example,
0:12:39 the Waymo pilot in Arizona, that’s a level four system where there’s fully autonomous
0:12:43 vehicles, but only within a certain geographic region. So the geofencing is just like the
0:12:48 physical location of how far it can operate in. So generally, there’s what’s called an
0:12:54 ODD and operational design domain. And that’s the set of capabilities that the car has.
0:13:02 And so as long as the car is within the region of the world where it knows based on the engineers
0:13:05 who worked on the system, where they have good confidence that it’s able to handle all the
0:13:09 situations that can occur, that’s considered within the ODD. And oftentimes that also has
0:13:14 to do with the mapping system that’s on the vehicle. And the weather and time of day.
0:13:18 Is this by the way also where like a lot of these cart robots and delivery robots sit
0:13:22 because they’re only delivering on campuses and constrained spaces? Does that count as
0:13:23 level four?
0:13:28 That’s absolutely a level four system. Because for those, there’s no human operator typically.
0:13:31 And so it is a level four system within the ODD of the robot.
0:13:34 And so level four is fully autonomous in that there is no human in the loop. Or at least
0:13:39 is a human offsite, like not in the car, but maybe monitoring feeds.
0:13:46 So technically, you can have a human in a loop, but the system needs to be able to safely
0:13:50 handle any situation that it can be in for it to be considered level four. And so that
0:13:54 might entail the vehicle pulling off to the side of the road, waiting for a human to do
0:13:57 something. But typically, most of the systems that are considered level four operate the
0:14:02 vast majority of the time fully autonomously. And then the very last level is level five,
0:14:08 which is more of an idea than a reality. It’s the notion that there could be a vehicle that
0:14:12 is able to drive autonomously in all conditions where a human would be able to operate that
0:14:18 vehicle. And the truth is in the industry, no one is even close to that particular goal.
0:14:19 So that’s further off.
0:14:20 That’s quite a bit further off.
0:14:21 Yeah.
0:14:23 Okay. So what we’re talking about here when we’re talking about autonomy in this context
0:14:27 of this podcast, you guys are actually focusing more on level four.
0:14:31 We fundamentally believe that the tools that you’re using to develop your level two systems
0:14:35 should be actually the same tools that you use for three and four. And if you look at
0:14:40 kind of the tooling used to develop these systems, historically the tooling for level
0:14:46 two system, what Peter mentioned earlier, LCC and ACC lane keep and adaptive cruise control,
0:14:51 those were more hardware focused tools. And so they would, the quote unquote simulators
0:14:56 were trying to spoof the hardware that is actually controlling the system. So the radar
0:15:03 or the camera system, they’re literally tools where you actually point the camera that would
0:15:08 be sitting in a car in front of like a monitor. That’s the quote unquote simulation. Now the
0:15:13 fundamental differences you go up the levels is there’s a proliferation of scenarios. There’s
0:15:16 a finite number of scenarios when you’re just going down the highway trying to keep a lane
0:15:21 and a certain distance when you’re in an intersection with, you know, four lane intersection with
0:15:25 multiple agents, all those agents can behave in many, many different ways. And the vehicle
0:15:31 needs to be able to understand and then navigate in that environment. And so we build tools
0:15:35 that not only start with a level two but then take, you know, take development all the way
0:15:41 to level four. Interesting. So you drew the analogy earlier about the mobile analogy,
0:15:43 but where does that apply? And where does that fall apart? Because the one, a couple
0:15:48 of differences, all right, I would argue here are one that with mobile, we knew there would
0:15:52 be some application, but there’s been a lot of second and third order, you know, applications
0:15:56 that no one could have predicted or maybe would not have known that selfies would be
0:16:00 such a big deal or social would be so, you know, powerful. They might have thought it
0:16:06 might, transactions, commerce, I think people predicted. So that’s one thing. So with autonomy,
0:16:09 it feels like it’s the other way around where actually I think people do know what a lot
0:16:12 of these things could be. Of course, there’ll be second and third order effects. One of
0:16:16 our partners Frank Chen did a whole series on the, you know, second order effects of
0:16:17 autonomy.
0:16:18 Real estate. Yeah, exactly.
0:16:22 Insurance. How does it change? You know, infrastructure. I didn’t op-ed when I was at Wired on folks
0:16:25 from Autodesk that we’re thinking about the future of infrastructure because you have
0:16:26 cars out there today.
0:16:28 Exactly. So we have an analog.
0:16:32 You have an analog, which mobile, you had computers, but they’re kind of fundamentally
0:16:33 different.
0:16:35 People didn’t even believe that they could even handle the constraints in this way because
0:16:39 that completely changes the design. And here we are talking about cars still look like
0:16:44 cars for the most part. And yeah, Google’s cars could be a little Kauai-like and cutesy
0:16:47 and Waymo’s and all the other ones have different looks and feels, but overall, they look like
0:16:48 cars.
0:16:50 I think that’s where we’re getting to the edge. The interesting stuff happens beyond
0:16:58 that we can draw the Instagram analogy now because we’re in 2019 and not 2005. In 2029
0:17:02 or 2039, we’ll be able to say, well, it was actually in hindsight, it was so obvious that
0:17:07 there’d be people who are living maybe in autonomy or some more unique and crazy things or implications
0:17:11 that we just don’t have right now. I think sci-fi, I’m a big fan of sci-fi. And I think
0:17:17 our imagination only goes so far and there will without a doubt be autonomy applications,
0:17:18 which we’re just not thinking of.
0:17:19 I agree.
0:17:22 I think it was in the sci-fi world, I think it was William Gibson, I can’t remember who
0:17:25 said that quote about, you know, don’t predict the car of the future, predict the traffic
0:17:27 jam of the future or whatever that is.
0:17:32 Or the iRobots scene, right, where I think Will Smith jumps in and he says, I want to
0:17:35 drive this manually. So are you crazy? Are you going to drive this manually? Like that
0:17:44 will become a norm. We always get caught up of will that be 2025, 2029, 2035, 2045? I’m
0:17:48 less concerned about the preciseness of when that date will come, but that will happen.
0:17:49 Yeah. You’re just saying it’s inevitable.
0:17:54 It’s inevitable because of the kind of the three prongs of, you know, of new products,
0:17:58 which is cost convenience and safety. And guess what autonomy gives you all three of
0:18:02 those things. It’s cheaper, it’s safer, and it’s more convenient.
0:18:06 And safer in the sense of accidents overall, right now, focus on the outlier incidents,
0:18:09 which are real and we have to worry about them, but we’re not there yet.
0:18:14 Exactly. I mean, again, the mobile analogy is relevant here. In 2005, you’d see those
0:18:18 bumper stickers, you know, get off your phone. And now if you get in the car and somebody
0:18:22 is in your phone, there’s like, what are you crazy? How are you? It’s like the opposite
0:18:26 because it’s mapping and all these other things that you wouldn’t have thought of when you
0:18:27 had the Motorola Razer.
0:18:30 Well, since we’re talking about right now, and I agree that we don’t know what we don’t
0:18:35 know, who are the players in the ecosystem right now? Like I can guess some of the obvious
0:18:41 ones like the manufacturers of cars, the mapping companies, the mobilize that, you know, supply
0:18:45 components and sensors, like how would you break down the taxonomy of the players?
0:18:50 I think the automotive industry is a good analog to some degree of what I think the
0:18:55 autonomy industry will be. You’ll have end consumer facing companies who will have brands
0:19:00 that interface with the consumer. Now, whether those are ride sharing companies, AV providers
0:19:05 or the continue to be the BMW or the Tesla’s, I think that’s up for debate. Then you’ll
0:19:10 have folks who are supplying services. Right now in the automotive business services
0:19:15 quote unquote are the dealer services. But in the autonomy world, we always talk about
0:19:19 and is the emergence of the software car. And so in the software car, those services
0:19:24 are much more, they look like kind of your phone. I think that seems fairly obvious because
0:19:29 you see some of those already. CarPlay and Android Auto are early indications of that.
0:19:33 And then you have the thing that you can call the infrastructure companies. Just like you
0:19:38 have in phones and in the web, there’s this, you know, every time you go to San Jose, you
0:19:43 see these office parks of companies you’ve never heard of. And you wonder, why do they
0:19:48 have 10 glass buildings? Yeah, totally. And they’ll be those companies and they’ll exist
0:19:53 in automotive as they exist right now. They’re, I mean, people, you know, Forci and Magna,
0:19:57 these are becoming more known in the valley. When I worked at Bosch before, Bosch was unknown
0:20:02 just a few years ago. And it’s finally now because of autonomy, Bosch is like a relevant
0:20:07 name. And so I think the ecosystem will be like that. Each of the things that you have
0:20:12 in mobile and web or more accurately automotive will continue to exist just in different shapes
0:20:16 and shapes and forms because the change is pretty significant.
0:20:19 What we’re talking about autonomy, the human driver becoming a software product, but you
0:20:23 also have the internal combustion engine becoming an electric drivetrain.
0:20:27 And so an electric drivetrain, for example, that doesn’t just impact the propulsion system,
0:20:31 but it actually impacts every other component in the vehicle. For example, the cooling system,
0:20:35 an air conditioner, that’s on a typical gas car is going to be different from an air conditioner
0:20:37 system that’s on, it’s on an electric car.
0:20:43 Yeah. I mean, I have a Prius, which is nowhere near autonomous, but it is electronic partially.
0:20:47 And I have to say, it was like a huge like mindset chef for me to even realize like,
0:20:51 oh, all those tips about how to check your coolant and open your hood in case of an emergency
0:20:56 before triple A comes like, they don’t apply anymore. The mere fact of pushing a button
0:20:59 to turn it on instead of using a key, like, I mean, those are really mundane examples,
0:21:03 but it’s an example of what you’re talking about, which is like a changes everything.
0:21:04 Things you don’t even think about.
0:21:06 All these great revolutions are very mundane.
0:21:09 Yes. I like that concept, actually, because I think about that even in terms of self-improvement
0:21:13 in your life. Like it’s always like at the mundane level that the real shit happens.
0:21:18 Yeah. Day to day, nothing looks different, but when you reflect, you know, five, 10 years,
0:21:20 it’s pretty significant.
0:21:26 I think in front of the old and new and zooming into just autonomy, you have this rich universe
0:21:31 of companies now that are either form forming or are quite mature, that are doing individual
0:21:35 components. So you have sensor companies that Peter mentioned earlier, you have mapping
0:21:39 companies, you have companies like us, infrastructure companies.
0:21:42 Do you guys would categorize yourself as infrastructure?
0:21:46 Yeah. I think that’s probably the most accurate term. What’s different about simulation in
0:21:51 the past versus simulation today, is simulation in the past was usually used to build hardware
0:21:55 products and we’re using simulation to build a software product.
0:21:57 That’s actually really interesting. Let’s pause on that for a moment. I love talking
0:22:00 about simulation on this podcast, actually, in general, because to me, to your earlier
0:22:06 point about virtual worlds, it reminds me of one of my old edities concepts of mirror
0:22:10 worlds, David Galerinter. And this idea that you can essentially turn everything into something
0:22:15 that can be in a virtual system. And that is, I think, what you mean by virtual world
0:22:19 as opposed to quote, you know, VR virtual world like only immersive. And so this idea
0:22:25 that you can essentially softwareify everything, that’s pretty significant. So that swap that
0:22:28 you’re talking about that before we would use simulation to build hardware, now we’re
0:22:32 using simulation to build software. Let’s talk a little bit more about that.
0:22:37 Yeah. Simulation is not new to automotive or aerospace. These methodologies that existed
0:22:44 for decades and even longer than that, you would develop a product, let’s say a turbine,
0:22:49 and then you would manufacture, you develop a bridge, and then you would build it. Using
0:22:53 software simulation is different because you have these products that are out there in
0:22:57 the real world and they’re going to continue to inform the thing that you’re developing
0:23:01 in the simulator world. And so this connection of, it’s almost like reality in the loop.
0:23:04 It’s a little feedback loop, but you’re right. Reality in the loop is a more significant
0:23:10 wave. It’s less linear. X creates Y, Y creates Z, Z goes and influences X and it’s a nonlinear
0:23:16 circle. And because of that, we’re more infrastructure than purely simulation because, like for instance,
0:23:19 if you’re managing large amounts of data, is that really simulation? Technically, it’s
0:23:25 not. But you need to do that in order to make your simulations useful. Connecting to the
0:23:29 car, or is that part of simulation? No. So that’s why I think where the larger umbrella
0:23:35 is infrastructure. You could say HD mapping is really an infrastructure play. Those are
0:23:40 the rails of which the train rides. It’s also infrastructure in the sense that it’s used
0:23:45 continuously on an ongoing basis, whereas the traditional forms of simulation were typically
0:23:51 used sort of for this big, big moment, which is the creation of this final hardware specification
0:23:55 which is then going to be made. It’s shipped. It is delivered. It is done. This is never
0:23:58 done. You guys, I mean, it’s a terrible analogy, but it’s a little bit like a Kanye album.
0:24:03 It’s continuing to evolve in the wild. And after it’s dropped, it’s going to keep getting
0:24:10 modified. It’s a real life goal I had of comparing my simulation company to… Well, we’re all
0:24:15 fans of music. So using your analogy of trains, because you mentioned the train tracks. So
0:24:19 this is interesting because what we’re really describing here is laying down the tracks while
0:24:22 also inventing the train itself. And the two things are kind of like moving targets against
0:24:27 each other, et cetera. So what does that mean for the evolution of the ecosystem? I think
0:24:31 overall, there is a co-evolution of sorts that happens between each of the different
0:24:36 components involved. And so, for example, sensor companies and mapping companies, ensuring
0:24:40 that the latest advancements that they have in their own products are then accurately
0:24:44 represented inside of simulation. It’s like the phone supply chain we’re talking about
0:24:50 earlier. So all these little revolutions and miracles happening. And the untold story that
0:24:57 hasn’t been discussed is if you’re building autonomy yourself, what’s the right path?
0:25:01 Is the path to go vertical and build everything yourself? Or is the path to buy things off
0:25:08 the shelf? Where in this ecosystem, where do you draw that line of what is critical for
0:25:17 autonomy, quote-unquote, and what is not? And so my rough view, and I would say largely,
0:25:22 what is not differentiated between the companies, the mapping companies included, you should
0:25:27 basically buy off the shelf. That’s commoditized. And you should be differentiating elsewhere.
0:25:30 Because mapping companies are sensor companies, which are all kind of have the same role in
0:25:37 different ways. We’re spreading our R&D costs across 10, 20, 30, 50 players. And therefore,
0:25:42 each individual player gets a more advanced product for a cheaper cost. And that’s capitalism,
0:25:47 right? You’re driving market efficiency. When people talk about a new industry, we’ll drive
0:25:51 market efficiencies like tactically. How does it happen? It happens where you have individual
0:25:57 players who are now unbundling the cost onto a bunch of people, a bunch of different companies,
0:26:02 and then those people who are participating almost in that consortium are getting the benefit
0:26:05 of it. Now, that doesn’t mean you can go ahead and you can definitely go and do that
0:26:08 at a vertical company. There will always be an apple in every ecosystem.
0:26:15 Yeah, exactly. But you better be Steve Jobs. Right, exactly. And what I love about what
0:26:18 you’re describing, and this is capitalism, it’s funny because we might as well say cloud
0:26:24 is capitalism at this point to make that syllogism. But it is the AWS moment in this ecosystem.
0:26:29 And it’s talking about the fact that you can actually then free a whole new wave of companies
0:26:34 to do things. I do find that very fascinating because until now, I would have thought that
0:26:38 autonomy is only for like the big, the big, big five car companies.
0:26:42 So the AWS example is exactly right. You can roll your own server. Some people have pride
0:26:47 in running their website off their local, but guess what? Your consumers actually don’t
0:26:53 care if you’re running on-prem, AWS, GCP, Azure or whatever.
0:26:57 They just want the service. They just want the service. And so by the way, this happened
0:27:01 in automotive. Automotive started, you know, Alfred P. Sloan.
0:27:03 Wait, who’s Alfred P. Sloan? I don’t even know who that is.
0:27:05 Oh, okay. So they are early automotive pioneers.
0:27:07 Oh, I would have thought it was Henry Ford.
0:27:12 Sloan was, so he’s not technically the founder of General Motors, but Sloan and Kettering
0:27:17 were essentially the leaders of General Motors. GM was founded by a person named William Durant,
0:27:21 who had started another car company. The amazing thing about the AV industry today is it’s
0:27:25 almost copy and paste of the automotive industry a hundred years ago because you have these
0:27:30 individual personalities who are shaping companies in their own way. Some get fired, they start
0:27:34 competing companies. There’s all this drama between the existing players who are coming
0:27:38 in. There’s a lot of M&A activity happening.
0:27:42 Oh, this is one of my favorite things when we talk about how software and tech evolution
0:27:46 is taking you back to an earlier era. That’s one of my favorite themes ever.
0:27:52 The Sloans of the world and the Henry Fords of the world, they wrote and they tried to
0:27:58 build vertical companies. I mean, Ford used to do everything. They used to get rubber
0:28:04 from plants. They would forge steel. I think they even owned the farms where things were
0:28:09 grown. So guess what? We don’t do that. Why? Because it’s actually more efficient to have
0:28:12 a supplier ecosystem. Well, that’s like capitalism to the T. I mean,
0:28:17 that’s like the classic, you don’t want to, someone did an experiment where they tried
0:28:21 making their own sandwich from scratch. If they grew the vegetables, I think they had
0:28:26 to outsource the cheat. They take the cows and the cheese and I think they estimated it
0:28:31 to be like over almost $2,000 and capitalism makes that sandwich $7.
0:28:37 So try doing that for like a computer, something that’s more manageable. You can go on YouTube
0:28:40 and watch videos about people trying to build their own phones. They end up just going to
0:28:43 China and buying for a bunch of suppliers because that’s actually the faster way to
0:28:49 do it. And the ecosystem conversation that’s happening every single day in these autonomy
0:28:55 teams is, oh, wow, we don’t have that many engineers. Oh, wow, there’s another huge pilot
0:29:01 that somebody has announced and how can we move faster? One of the easy rules of thumbs
0:29:07 of you can see how sophisticated and AV leadership is just asking them, where’s that line? And
0:29:12 that line, that circle of competence should be as small as possible. That small circle
0:29:16 in autonomy is algorithms. That’s the coveted golden nugget.
0:29:19 Takes confidence to focus, narrow laser focus like that.
0:29:24 So you can go to like a completely different industry, go to consumer CPG or you can go
0:29:30 to consulting. If McKinsey or a, you know, Unilever or whoever it is will very clearly
0:29:35 say, hey, you know what, this is the thing, this is the hill we die on. This hill, we
0:29:38 have to be better than everybody else. The only way we win this hill is we abandon every
0:29:39 other hill.
0:29:43 Right. Well, this begs the question and Benedict often asked a similar question in his post
0:29:48 on autonomy a lot, which is, you know, will Tesla become more like Detroit? Is Detroit
0:29:53 more likely to acquire the Silicon Valley mindset faster or is Silicon Valley going
0:29:56 to move faster in sort of learning the skills of Detroit?
0:30:00 I think there’s no path to autonomy that doesn’t go through Silicon Valley and Detroit.
0:30:01 So it’s an and not a word.
0:30:04 And when you say Detroit, we mean roughly the automotive centers. Jit guard included.
0:30:08 I mean, the Japan and Korea and China included in that.
0:30:12 Right. You don’t mean Detroit geographically, you mean the entire category of automotive.
0:30:13 Detroit is the concept of automotive.
0:30:14 Right.
0:30:20 Yeah. As a second hand for the automotive industry because Detroit has the delivery mechanisms,
0:30:27 which are the brands and the factories which build these vehicles and the channel for lack
0:30:28 of better words.
0:30:33 This is not an internet product. The channel is not a website. The channel is the traditional
0:30:40 OEM business. But the thing that you’re distributing through this channel is almost ideally built
0:30:43 in Silicon Valley. Again, we’re talking about that circle of competence and how small you
0:30:44 can make it.
0:30:50 So we’re Silicon Valley, I think strays is when we start doing things, which frankly
0:30:56 speaking, are outside this very small circle of software. And I get a little nervous when
0:31:01 companies are doing a lot of hardware because there are other hardware centers in the world
0:31:06 which are arguably better or when even broadly like on podcast, people start talking about
0:31:08 like these other things.
0:31:13 And it’s like, if we went to some group of factory owners who I don’t know are specialists
0:31:20 and they don’t get on podcasts and then start advocating about things outside of their little
0:31:26 circle of competence, they talk about a leather price and how are you getting cheaper electricity?
0:31:30 I hear you. It’s both. It’s both arrogant and charming at the same time.
0:31:31 Exactly.
0:31:37 But it’s good because it pushes you to go into trying new things, agree. And so the magic
0:31:42 happens where you’re pushing trying new things in your area of competence. I can go and try
0:31:47 to be an NBA basketball player, but guess what? It’s probably not going to work no matter
0:31:52 how much effort and, you know, I try, I put into, but so I think there’s a similar, you
0:31:58 know, relationship between Detroit and Silicon Valley. There is a real merger. And my background,
0:32:01 you know, both Peter and I, we grew up in a group in Detroit area.
0:32:02 I had no idea. You guys grew up in Detroit?
0:32:06 Yeah. Of all the random coincidence, not only we grew up in the same town, we grew up in
0:32:11 the same subdivision. We’re literally at the same crossroads for people who are in Detroit
0:32:16 as 22 and Shayner and Shelby Township. I went to GMI or now Kettering University, which
0:32:18 is the General Motors Institute. Peter went to U of M.
0:32:22 So I actually started my career at a small engineering tool company in Michigan, but
0:32:23 really my entire family works on a motive.
0:32:25 So you guys are like Detroit, born and bred?
0:32:29 Yeah. I worked five years at General Motors, two years at Bosch. And then we’re in the
0:32:33 same team on Google Maps. This is five, eight years, seven years ago, a long time ago. And
0:32:39 we saw chauffeur, which was, which became Waymo. And I remember saying to Peter, man,
0:32:42 this is going to hit Detroit like a ton of bricks.
0:32:46 Kettering is located in Flint, Flint, Michigan. So I spent five years at Flint. And when you
0:32:52 look at places like Flint, you really start thinking long and hard about like, well, where
0:32:56 do people get in these new jobs? That was the theme in the 90s and the early 2000s when
0:33:00 I was growing up was, oh, there’s going to be this revolution and all these people in
0:33:03 Michigan are so suddenly going to have these great new jobs. And guess what? My family
0:33:09 included those jobs didn’t come. My dad never became a software engineer in his late 50s.
0:33:13 That doesn’t happen. And then also I think any business in the human experience is emotional
0:33:18 to some degree. I mean, we very much like practice that belief that there is this connection
0:33:19 between these two.
0:33:23 You guys are really, we’re really long on the Detroit Silicon Valley and not the ore.
0:33:28 So what do you think then that the winning company, maybe it’s not a winning company,
0:33:32 there’s plenty of room for many, but where is it going to sit? And how is it going to
0:33:33 look?
0:33:39 Well, it’s like, you know, where does the winning automotive player today sit? I think
0:33:40 it’s very hard to answer that question.
0:33:43 There’s at least a few in every major geography.
0:33:48 And the supply chain, which is really what the auto business is, is everywhere. These
0:33:53 are such massive industries. They have epicenters. So I think the autonomy software stack will
0:33:59 probably for a long time be in Silicon Valley, but even you can look at like TRIAD, the Toyota
0:34:05 Research Institute’s Autonomous Division based in Tokyo. You have other companies, BMW,
0:34:10 even TRIAD actually has presence here in the valleys as well in the Detroit area.
0:34:15 So I think this concept of like, there’s a company that wins it for a town, I think
0:34:19 that’s different. I think we sometimes get that analog because of the internet where
0:34:22 you have Google, which is basically home home, home team, which is Mountain View.
0:34:23 Yeah.
0:34:27 A lot of the companies are like Silicon Valley and Seattle and there’s like a few centers
0:34:28 that are very focused.
0:34:33 I think these large industries that are very intertwined with each other, it’s a lot less
0:34:39 concentrated like that. I think the real fundamental issue we have, and this is getting more philosophical
0:34:44 again is what the internet has done and what software has done is it’s concentrated wealth.
0:34:48 We talk about wealth concentration as like somehow blaming sometimes, you know, a certain
0:34:53 political viewpoint, but really they’re so efficient software companies that does bring
0:35:00 a disproportionate amount of money to where the epicenter is. And so how can we make sure
0:35:09 that concentration, you know, that the next wave, which is autonomy, doesn’t keep just
0:35:12 kind of underlying that. One of the other things that is not talked a lot about autonomy
0:35:18 but should be talked about autonomy is that these are national questions. The German government
0:35:24 won’t just let Waymo come take over Germany and let Daimler and BMW go under business.
0:35:29 And the same thing is true for Hyundai in Korea, Hyundai and Toyota in Japan and the Chinese
0:35:32 companies because there’s a recognition that if all of these cash flows end up going to
0:35:36 these little neighborhoods in the suburbs of San Francisco, maybe that’s not good for
0:35:43 our national interest. In the internet, because it was a new market, it wasn’t very visceral.
0:35:48 Daimler is a visceral, Bosch is a German thing. Peugeot is a French thing.
0:35:53 It’s like the classic discussions around manufacturing, like, you know, this idea that like it’s a
0:35:58 physical product that is made in India, made in China, made in Japan, made in Italy, made
0:36:04 in Italy. You know, it’s very specific and you’re right, there is a very national sentiment.
0:36:07 But what I love about what you’re describing too, though, is it is true capitalism because
0:36:12 I think capitalism gets a bad rap for the inequality, which is a fair complaint and
0:36:17 a fair criticism. But to me, true capitalism is something that raises the all boats in
0:36:18 the ocean.
0:36:23 Yeah. It’s on Pakistani, by birth on Pakistan. My family were from a small farming village
0:36:27 for the first, you know, seven, rough seven years of my life. I was in this, you know,
0:36:31 in this remote farming village in the, roughly in this valley. And you know what’s a real
0:36:35 luxury? Hot showers. Yeah. And so, you know, when I, when I had that hot shower in the
0:36:37 morning and I drank that cold water.
0:36:38 You don’t take it for granted.
0:36:39 Capitalism.
0:36:43 I know. I go the same way about electricity. I mean, my dad, I was born and raised here,
0:36:47 but my dad’s from India, small village, and he grew up without electricity. And then he
0:36:51 later got electricity and I was just marveling just very recently in our families at the
0:36:58 valley that we had electric BIAS. That’s insane. Like before electricity was not even available
0:37:03 to people. And now you have mass produced little tiny LED lights and like little bases
0:37:04 as candles.
0:37:05 That’s fricking amazing.
0:37:06 Exactly.
0:37:07 No, I agree.
0:37:08 So on that front.
0:37:13 I think where autonomy is different is I think it has the potential. And I think whether
0:37:17 we like it or not, there is a regulatory aspect to this entire conversation.
0:37:20 So I have a question about this because you brought up the point about there being a national
0:37:25 interest. There’s also a local city and state level of interest. Mark wrote an op-ed a few
0:37:30 years ago in Politico, arguing that you can use it for a regulatory arbitrage where like
0:37:34 say, Detroit could actually loosen some of the barriers. Just like, you know, I think
0:37:38 Governor Ducey is doing in Arizona where you have different cities offering different
0:37:42 incentives and doing more experiments so that they can ensure the ecosystem kind of grows
0:37:47 up locally. How is that really happening? Given your thesis, it sounds like you’re saying
0:37:52 that everything can happen everywhere and there’s room for all kinds of players and
0:37:57 B, what do you see as sort of the regulatory and policy issues in the autonomy ecosystem?
0:38:00 Yeah, I think everything can happen everywhere is more of this concept of there are so many
0:38:04 components and these components will come from everywhere. One of my friends who’s Indian
0:38:09 who was wished that states themselves in India would have more of a control over their own
0:38:14 laws because he believes that within the US, the states creating their own regulatory, many
0:38:20 regulatory environments is almost like a mini form of capitalism. It’s a grand laboratory
0:38:25 of capitalism actually. States are laboratories of innovation. There’s cities are to that
0:38:31 sort of federalist style. I think it was an enabling condition for success, not a bug.
0:38:36 And it’s a great feature because the state like Indiana says, Hey, listen, maybe this
0:38:40 is in our best interest because we’re a state that trucks go through and want to make sure
0:38:44 we make that toll, toll income. But if you’re a state like Arizona, and maybe you don’t
0:38:49 have that and you have this great testing ground, historically Arizona belong before
0:38:54 autonomy has been approving grounds for the auto business, that, Hey, we see that, you
0:38:58 know, the Arizona approving grounds for General Motors brought all of this, you know, business
0:38:59 over the last 20 years.
0:39:02 Right. Why can’t we do the same for this? It takes a lot of courage, by the way, because
0:39:06 they did have, I think, the first instance of a fatality through autonomy. So when we
0:39:11 do talk about these states sort of taking the leap, there is sort of a cost you pay.
0:39:15 Because in the case of Arizona, I think they were the first to have the first fatality
0:39:19 related to autonomy. And of course that’s going to happen. I’m not trying to minimize
0:39:24 it. That’s a really big deal. But that is, I think one of the trade offs is that cost.
0:39:29 I think the states that are making those decisions are opening some of them and their citizens
0:39:34 to that risk. And so the citizens then elect those, you know, those representatives who
0:39:39 then say, Hey, this is or is not the trade off that I want to have.
0:39:44 That matches their needs. And I think, I think though, probably what again doesn’t get covered
0:39:49 is I think that night, there were also 10 other pedestrian accidents in America where
0:39:50 people died.
0:39:54 You’re right. There is a statistical thing, which is hard to think about when you’re
0:39:56 talking at a personal level.
0:40:01 It is tough because that’s a real family. And if you’re that person, you don’t care.
0:40:06 There’s 10 other 11 other people that died that night. Also, these are the guardrails
0:40:13 we roughly think are ones that can be employed. You don’t want to have complete laissez-faire
0:40:16 open. Everybody does whatever and
0:40:21 Pure permissionless innovation. We’re talking about moving, killing robots.
0:40:25 This is like a human being. Like they’re like one of the things we don’t think about in
0:40:30 Silicon Valley. A lot of the, a lot of times engineers in the auto business over the last
0:40:32 years have gone to prison.
0:40:33 I had no idea.
0:40:39 Just a Volkswagen diesel scandal that put employees of Volkswagen in prison. And so there is real
0:40:45 consequences when you’re dealing with a product, which an automotive product, which can harm
0:40:46 the public.
0:40:51 The other end though, is if you put in regulations and, and they’re onerous and they’re significant,
0:40:52 guess what?
0:40:53 Squelch is innovation.
0:40:59 100%. And we are, we’re talking this, this, this conversation has been very US centric.
0:41:03 In 1980 capitalism was a very, you know, regional thing. The real revolution that’s happened
0:41:07 in the, in the last, you know, 30, 40 years is a capitalist revolution. It’s just the
0:41:10 shade of capitalism. And so when you think about China, which is a different shade of
0:41:13 capitalism, you think about Europe, which is a different shade of capitalism, generally
0:41:18 different approaches. But, you know, some, some, some regulatory environments are very
0:41:24 open and we have to be aware of that for not only the Silicon Valley and Detroit companies,
0:41:29 but just in general as Americans of being in an economy which is healthy and productive
0:41:34 and at the cutting edge. But at the same time, you as citizens, you don’t want to be a laboratory
0:41:42 for private entities to make a profit. And so there is a very nuanced approach there.
0:41:47 At the end of the day, we’re really advocates of best practices for safe development. And
0:41:53 so that means really taking the steps necessary to ensure that the systems, the software are
0:41:58 safe before they actually go to the public. Yeah. You’re talking about simulation here.
0:42:03 We’ve talked about simulation earlier in terms of the industry evolution, but simulation
0:42:06 itself got a bad rap for a while. You know, there’s a lot of companies that sort of felt
0:42:10 like, oh my God, simulation, it had a bad rap for a while. It’s like a, you know, I think
0:42:16 trying to do VR and AR to some degree in the 1980s. And so I think simulation, which has
0:42:21 been different than AR VR is there are no complex systems that are being developed without
0:42:28 simulation aircraft, military systems, automotive, internal combustion engines, microprocessors,
0:42:36 simulation is everywhere. And so I think that’s because the underlying kind of software industry
0:42:43 has become so much more advanced. It’s computationally more efficient. You can apply, you have things
0:42:48 like the cloud revolution, the ability to point lots and lots of resources at the problems.
0:42:52 What to say is shifted from constrained to abundant. And that essentially creates abundant
0:42:58 sensors, abundant data. You can waste bits. You can essentially simulate complex things
0:43:03 unbounded in a way that humans can’t even remotely conceive of. That does answer the
0:43:08 why now question. What are the limits of simulation? I mean, we are talking about complex systems
0:43:13 on a ton of edge cases here. Yeah. I mean, at the more technical level,
0:43:18 simulations are never perfect. There’s always going to be some difference between a simulation
0:43:22 and a real physical system. We like to get our simulations to the point where they are
0:43:26 plenty good enough for useful development. Good enough for development. Good enough for
0:43:30 development. Good enough for pushing things forward and to give a very high confidence
0:43:35 that the behaviors and simulation are representative of the real behaviors. But with that said,
0:43:40 there will always be situations and scenarios where there are differences in behavior between
0:43:43 the simulation environment and the real environment. Of course. Right. Okay. So what’s also interesting
0:43:47 about this is that it essentially lets you get the three C’s that you described earlier,
0:43:51 cost, convenience and safety in one system. And to the regulatory point that you brought
0:43:56 up, Casar, it is unless you kind of strike that just right balance in there. But the
0:44:00 big thing now, because you’ve been talking in this podcast about this importance of differentiation,
0:44:05 if this is a tool that everyone has, it sounds like they would differentiate on data. So
0:44:08 how do you in this ecosystem where you’re making this argument that there’s this horizontal
0:44:12 versus vertical layer, are all these players willing to share in the ecosystem, the mapping
0:44:17 companies, the sensor companies, the big vehicle companies? How do you navigate the data side?
0:44:22 Data means a lot of different things. It’s not like scenarios and, you know, the data
0:44:28 that you have for autonomy, but it is the autonomy engineer who themselves are understanding
0:44:34 how are the methodologies to best develop an autonomy system. There is some, what we
0:44:38 call light network effects there between companies. Well, I mean, if you go to Stanford, they
0:44:43 teach classes that help you learn ANSYS’s simulation tools. So there’s literally this
0:44:49 public company called ANSYS that does simulation tools and you can learn how to use it by taking
0:44:53 classes at Stanford. And that’s the same thing with AutoCAD. If you look back, if you look
0:44:58 there, there are many tools that kind of fall into the, into this group. I mean, when you
0:45:01 learn how to program, you’re actually just learning tools. Now, what’s happened with
0:45:06 software development is those tools, it becomes a really just a commodity and there, and there’s
0:45:11 many different ways. And so we’re still in a quite a nascent niche field with autonomy.
0:45:15 So the tools are not a commodity. These tools are so hard to build. These two simulations,
0:45:17 it’s not a trivial thing to build.
0:45:22 At the end of the day, the lowest cost solution will win, but of course it has to be a real
0:45:23 solution.
0:45:24 Yeah, it has to solve something.
0:45:25 And that’s what the industry is still working on.
0:45:29 Right. It’s actually kind of funny because the conundrum here is that software is bits
0:45:34 and it’s abundant and therefore it’s accessible to everybody. But the specialties and the algorithms
0:45:39 I’m clearly hearing and like the nuance of the art, and we used to call it know-how when
0:45:44 I used to be at park. It’s kind of the idea of the know-how and the differentiation. But
0:45:48 the point is, it’s basically going the way of mobile and you’ve been drawing the analogy
0:45:51 and we’ve talked a little bit about where the analogy breaks down and where it applies.
0:45:55 How do you think this plays out given that you are a horizontal player? There aren’t
0:46:01 really big horizontal huge like apples and Googles. There are vertical companies.
0:46:07 Well, they are, but actually, you know, each of the sub components, the phone manufacturers
0:46:12 themselves, companies that do analytics for mobile, companies that do ads for mobile,
0:46:19 a lot of horizontal players, anything that exists both on Android and iOS is in some
0:46:25 way a cross-platform horizontal play. And so I think where the commoditization has happened,
0:46:30 quote unquote, is in the apps themselves. I’m reading this book, The Five Ages of the
0:46:32 Universe, which is The Physics of Eternity. Fascinating.
0:46:40 So what happens at the end of the universe, right? All the stars have now died.
0:46:45 Oh my God, I really need to read this book. This is totally my jam. I’m like really obsessed
0:46:48 with space and evolution right now. Yeah, it can be dry. I find it very interesting.
0:46:55 But the point is, once you get into these outer edges, the strange things start happening.
0:47:02 And so we’re now in that mobile age where there are applications that are gaining users
0:47:07 very, very, very quickly and still not being valuable or some applications that might not
0:47:11 have as many users but can become super, super valuable because they’re catered towards
0:47:18 a very specific audience that needs that thing. And so I think with autonomy, I think the
0:47:23 arc here, you’ll see all these individual modules will be run by individual players
0:47:27 because there is this natural arc in capitalism, which says the independent providers can do
0:47:32 cheaper, better and faster than anybody doing it vertically. But the question is, will the
0:47:36 algorithms themselves ultimately commoditize? And I think that’s when you get into this
0:47:40 far edge of the universe. It’s like, could we be in a situation in 10 or 15 years that
0:47:46 like today, starting a mobile app is very easy. That starting an autonomy company is very
0:47:47 trivial. It’s just that easy.
0:47:52 I think it would be very hard in 2005 to think that Kim Kardashian or whoever would have
0:47:56 their own app and it would make millions and tens of millions of dollars. But that’s the
0:48:01 reality today because that’s so niche. It’s not just, hey, it’s a phone app. It’s a phone
0:48:05 app on a specific platform for a specific celebrity and just their fans because everybody
0:48:10 else can just consume Instagram or something else. And so that real edge, I think that
0:48:15 far off the world of autonomy, 10 or 15 years from now, imagine if you could build an autonomous
0:48:20 vehicle very quickly and very easily. If that could happen, how does that, what does that
0:48:21 make the industry?
0:48:24 Yeah, no, it’s like a theme we talk about actually is that the edge is where it’s at.
0:48:28 I mean, in computing and innovation, I mean, it’s basically the democratization of autonomy.
0:48:31 Well, you guys, thank you for joining the A6NZ podcast.
0:48:32 Thanks for having us.