Why AI is Bigger Than the Industrial Revolution

In this AI wave, we're in the middle of a new industrial revolution. We're seeing companies scale faster than anything I've seen in my lifetime. You're seeing companies investing trillions of dollars in infrastructure. These new companies, many of them are doubling every quarter, and it's adding huge amount of value to the US economy. Productivity is starting to shoot up in a bunch of smaller areas that are going to grow into really big areas over the next few years.

We're going to take you to the edge of the AI wave with some of my very favorite entrepreneurs. We're going to meet Scott and Russell at Cognition. We're going to learn about what's happening in the insurance world and finance with JD Ross and with Coverage. And I love hearing from Joe Ben about what's happening in aerospace and with airplanes and the physical world with AI. And then we're going to break it down with Alice Cole Shitz and Jack, two of my partners at ABC to help you understand how to think about what's happening in the next 5 or 10 years. How are investors and builders approaching it? What's it going to look like as AI spreads throughout our society in terms of business growth and productivity?

Let's just fast forward for fun a few years in the future. When I talk to, yeah, some of the people running the top labs, they're pretty convinced things are going to keep getting better at a pretty high pace for at least two or three years. It's like kind of like with Moore's Law, you can't really see out what's going to get there five years, but but see it feels like things are going to change a lot. Like so, so, so tell us about 2028, 2029. Like, is there certain things that just look very different? Are there certain things that are that are like we have to do now? We don't have to do it all there? Like, what are the unsolved problems for Devon to just be, be like doing massive projects on itself in three years?

Yeah, I think a couple shifts. I mean, one of the obvious ones, which I'll just call out, is just much more widespread usage of all of this and and just good knowledge on how to use these things. I think right now, you know, you have this core group of, we'll call it, like agent-forward engineers, right, or agent-forward companies that understand how to use this, and they are seeing these, you know, 5x, 10x productivity gains as a result. I mean, obviously, you know, all of these big organizations or these, these companies or governments or things like that are are seeing those same results and realizing, wait, we can't just sit here and be five times slower and, you know, we have to go learn how to do this right now. Um, and so that's really happening. I mean, even this year, I would say, um, in terms of the the continued capabilities gains that we're going to see.

Yeah, I mean, I think there's, um, I think I think the models are going to get better and better. You know, one of the stats that people talk about a lot is this MER report, which basically says for, um, each different model that comes out, roughly how much human work can it do, uh, in an automated fashion before you have to go interrupt it and say, "Oh, that was wrong. Let's go do this." Right? And so it's like, you know, just two or three years ago, the answer was like 10 seconds or something. You know, you would have it write one line and it's like, all right, the next line is already wrong. Let's stop here. Right? And at this point, it's already gotten to the point where it's, um, in the scale of, you know, 10, 20 hours, um, is what the latest one, uh, has been. I think Opus 4.6, for example, I think was around 18 hours.

This is always very weird to me because it's implying that models work in human time, though, right? Which is so weird. Why are they faster?

Yeah. So, so, so the answer, they do typically do the tasks in less time than a human would. And then the 18 hours is basically this is how long it would take a human to do that amount of work, okay?

Uh, in, in between each of the interruption points, right? And, and the, the, the AI might be doing that in, in one hour or two hours or something like that, right? Um, and, and the thing that's really crazy about this stat is you just see it very consistently double. And I think for the last few years, it's doubled about four or five times every year, you know, which is insane, which means, you know, you wait two or three months and it's already doing twice as much work.

So the whole world is changing every two or three months in terms of what's possible.

Yeah. And this is, this is what we've, what we've kind of seen as well. And I think we're, we're going to see even more of that. And to, to some of Russell's previous points, the thing that's kind of interesting for us is that the form factor of what you want to deliver or how you want to work with the AI changes a lot as you're going through that, right? And so when we're saying, okay, you do 10 seconds of work, obviously the answer is you as a human need to be staring at your file of code and like shepherding it and handholding it with every single step, right? If you're talking about it's doing days of work or weeks of work, now you're actually giving it whole, you know, output-level tasks of like, hey, you know, we, we really need to go make this app much faster, can you go and run this whole thing and then, you know, do a smoke test and make sure all the changes look right, and it's going to go off and do that entire project, right? Versus, or, or, you know, even, even bigger initiatives of like, yeah, can you auto-respond to all of the, um, you know, the upgrades or the potential vulnerabilities that are coming in with our reporting and just make sure all of that, right? Um, so you're gonna see a lot more, I think, proactive work. Um, you're going to see a lot more basically like event-driven kickoff work where it's, it doesn't have to be human that's that's moderating it every step of the way.

And then, and then in terms of like what does that mean for society or where does it go? I actually think one thing I'll go on the record on is I think there's going to be an explosion in small businesses. Um, I think AI is actually like an extremely small business enabling technology in particular where you think about what's hard about about building or starting a small business. It's, you know, you don't have the resources of a large company. Specialization of labor in each part of the, in each part of the process. And I think AI, it's extraordinarily enabling, right? Think about, you know, the quality of legal gut check you can get from, you know, from a chatbot from a frontier lab, the, the quality of, you know, analysis of your financials, the quality of software that you build, right? It's like, it's all coming together to empower each individual person, again, if you exercise that agency, to just do so much more on their own.

I actually, I love this. I actually have a, a small thing on the side where I'm trying to help create tens of thousands of small business owners. So, so I'm totally in line with this. This is a really good theme right now for us for for us to do. What one last thing I want to ask you guys about the business that I'm just so impressed by. So, I, I hired a lot of the first 200 people at Palantir. I spent a lot of time on talent. Obviously, I even hired Scott at one point a long time ago with one of my companies with with Vlad's help.

How did he do? How did he do it? He's very, very, very impressive, but I did not, I did not see him at the time as someone who was like a CEO person. And so he really grew a lot, which is good. I mean, you know, he's like, he's learning and growing as he goes and definitely see him as a CEO and founder now. Um, but one thing you guys have done is like a significant percent of the Cognition hires are actually former founders. So not only are you hiring the best people in the world, you're hiring a ton of former founders. Like, like, why are you doing that? How are you doing that? Tell us a little bit about the talent stuff here.

Yeah. Yeah, for sure. No, I mean, I, I think the, um, I, I think the reality is we, we just have such a massive problem that we're going after. We're just, you know, solving all of code. And I think the way we even started this company is, you know, Russell was a founder before this. I was a founder before this. All of us were, you know, I think of our, of our kind of like initial crew. And, and, and the idea for us, I think, was let's make this one the big one. You know, we're going to go for it all. We're going to go for the most ambitious. And even, I mean, the, the, the play of solving software engineering feels like a big enough one, you know, that that we can all do that together. And, and I think that's a lot of what it comes down to, honestly, is just like, um, yeah, are we working on something and doing something that's that's that's exciting for for folks who, to your point, are, are, you know, I think a ton of the folks at Cognition are could, could very, very easily go off and start their own companies and get funded and build their teams and so on. And, and, and the question I think for us always has been about, um, how, how do we make this the place that that that makes more sense for them to do that?

And it's, it's honestly easier to do that now than ever before. Also, as a, as a company, like I'm thinking, you know, we have one team at the company. Uh, they're called Special Projects Engineers, and basically every person on that team is a former founder. Like, every single one. And, and they, they do, they do, uh, you know, a really interesting mix of engineering work, of product work, of talking to customers, of driving commercial outcomes. You know, the problem space, to to your point, is so big that actually if you're, again, a high-agency person, going to take initiative, and you're working at a small, fast-growing company with, with a, you know, with the problem space so big that, you know, your only constraint is like your own ambition. Well, it does seem like there's this renaissance or revolution going on in the world where the capabilities are doubling every two or three months, and like this is a place you can come and be around some of the other smartest people in the world who are part of growing something with that, which I guess is pretty fun for people.

We had a good time.

One, one thing about the the interview process or the selection process, to your point, which I think is kind of interesting to call out, is I think, um, you know, one or two years ago, I think a lot of people had this mentality in terms of how you interview of like, okay, there's all these AI tools. How do we interview people in a way that, um, you know, how do we make sure that people aren't using AI while we're interviewing them because we're, and I think that has totally flipped, honestly. I think that was wrong, right? And, and I think if, if you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that's kind of the wrong. And, and, and so, so, so, you know, I think for, for us, and this has always been the case for us, you know, our interview process has always been, you can use as much AI as you want to use, you know, it's just like, we're going to give you a few hours, just build, build your whole own product surface, right? And build, build your own, um, a lot of these are projects that frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So, you kind of have to use AI for this, right? But, but in reality, what we actually want to test is, in addition to to kind of how familiar you are with these tools, what we actually want to test is, is yeah, what, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing? Um, and, and, and so we found that that's that's helped us a lot.

Not a single one of our full-time engineers right now writes code anymore. They do everything through Codex, Cloud Code, or Cursor.

It's all, it's all agentic.

And I, a lot of them are like managing lots of agents at once, right? So they're working.

Yeah. Cole, who runs our engineering team, has like six agents that are running at once at all times, just doing different kind of projects. When we create a new ticket in Linear, it automatically kicks off this thing that basically creates a dev environment, which then tries to one-shot the solution and then creates a PR that we review. Like the whole way you build software is changing so quickly. And I, I think right now, like the cost of software is just asymptotically dropping to zero of building new software, which means that the way you build a company completely changes. Like we can now have an enterprise customer come up who says, "Oh yeah, this is so close. What we actually need it for is X. We can build X in one day, ship it live, and close that deal." We did this last week.

That's amazing.

It's like a completely different way of even thinking.

No Fortune 500 company right now, I don't think, works that way. They, they always have these like three-month and six-month plans, and we'll get back to you, and we can't sell you because you don't have it yet. And like in the old days, you were told, I was told a lot, "Cause I'm too expensive. Like, Joe, stop trying to, yeah, you can't do that. Stop trying to like make promises that are going to get in the way of our roadmap in the next 12 months." But, and they still probably don't want to do some promises because they probably still are pretty hard. But certain promises, you just go around and just do. You can just make it happen. I think even the, the job historically, the job of product, engineering, and design, the sort of three-legged stool, is that product was responsible for like the why and the prioritization. Um, design was responsible for the what, like what does this actually do and how does it work? And engineering owned the how, like how do we actually implement it? What's possible? And today, like that's still kind of true, but the loop doesn't go from, let's come up with a hypothesis, let's build it, let's learn. It becomes more like we're a group of wizards who are creating spells that the entire organization can use to make things better. And that is, we're at the very early stages of this transition where now most companies, including us, are just like accelerating that loop of, hypothesis, learn, deliver. But it's quickly becoming, hey everyone in the company, tell us what you need, and like, and we're going to ingest that into a self-learning loop of AI, and that's going to tell us what we should build, and it's going to try and prototype it, and we're going to use that to determine what to ship.

It's interesting. It's almost like there should be like some kind of AI intelligence guided by your top product mind just to listen to everything going on and help you organize it, right? Because that was always a hard part of the product job for me. Like I love the product job. It's like the vision of what I think everyone wants. But then there's like this 14-hour a day job of prioritizing everything you're hearing from everyone in the company. And I, I feel like AI could be much better at that. Is that, is that literally what you're saying? Like, just everything you hear, you should be prioritizing that to prioritize and just constantly trying to build stuff?

Yeah. If the job, like in the past, was writing, now it's editing. And shifting from being a writer to an editor is a very, uh, I think for some people, they're thriving in it. And some people are really shocked by it.

I mean, what's the output difference, right? Because it's not, it's not some people are saying, Joe, it's 100x. It doesn't, like, seem to me like it's 100x yet. Like, as an example, like Adapar is doing a lot with AI now, and there's a lot of people there, each managing tons of agents, and there's, you know, several hundred engineers around the world. Um, I mentioned this because we built this together a long time ago. And, you know, you've probably invested almost two billion dollars now into the product, right? Because it's like 16 years old, and this is what happens. And, and, and it's definitely the case that like, it wouldn't take that much money to build it now. And like, but the question is, is it like 2x? Is it 10x? I don't think it's 100x. I don't think it's like $20 million to build out Adapar, but, but it might only be like two or three hundred million. I, I don't know. Like, how should, and, and, and I think out of Adapar, because it's using AI to build more now, it's like harder to catch, right? But it's like, how should we think about this? Like, all these SAS companies, the low-end ones got crushed last week, right? And like, a lot of the, the firms you probably saw that invest in low-end SAS companies got crushed because in Anthropic's new stuff came out, and everyone's scared they can rebuild it. Like, like, how are you thinking about how this is evolving?

There, there's so much in that. I think one, like Anthropic, OpenAI, Google, they're all going to, in the same way that you probably don't want to build an email company while Google's on its, cuz they're going to come and eat that. Like, every one of these companies is looking at where are our tokens being used? Where's a lot of tokens being used? Great. Let's go after and like attack that surface.

You think so? You think those companies are themselves are going to try to go build a bunch of the very most important SAS?

For the most important areas. 100%. In the same way that Google would never let, you know, um, you know, Meitheloma, uh, become like its own. They're going to capture as much of that value as possible because it's the highest value keyword. That's actually different than what I've heard because I'm worried about the low-end SAS companies, but maybe, maybe I should be worried about both, like the bottom half and like the top 1% as well, or something.

I think it's a barbell thing. Like the, the large AI companies are going to eat the top end of value, and everyone in the middle is going to just commoditize. Or people who aren't even in the software business. Like, in the next two or three years, this isn't true, right? But if you take the five-year view, kids who are in college today who come out and start working in a construction company or something are going to look at every problem they're paying for for software and say, "This doesn't do exactly what I need, and I can one-shot a much better version of this right now." All of those SAS companies are worthless.

And then they're going to go after the top, too. I mean, why don't, why don't they go after you? Like, how do you think about that?

I, I think it's really important to understand what business you're in. And our business is, we are in many ways in the business of answering, uh, unknown unknowns for people who know it's really important. Right? We're selling to CFOs, GCs, people who know that insurance is there for when something really bad happens, it covers them. They know that risk management is really important, right? They know that when someone, you know, falls in a factory or cuts their hand, that they need to deal with that, and they want it to not happen in the first place, but that's not their full job. That's not their expertise. So you're, so you're going to be the trusted expert partner in the area of understanding their insurance and what works for them and how to get a better deal than they are now.

Exactly. So we help them manage risk and we help them save money.

And so when you think, maybe stepping back, maybe this is more for me than for you, because I'm a venture capitalist and I'm investing in lots of things, but I know, I know you angel invest, you think about it too. Like, what are the categories of things that the model companies are going to eat versus not, as it relates to this? Like, is, like, obviously the model companies are pretty good at being experts. Yeah. Like, how do you think about the moats here?

I mean, the first one, obviously, is anything that's adjacent to software development. They're like, they've decided that they're going to war to win that. And I think it's a really hard place to, you have to be,

You could be the very best in the world.

The very best in the world.

I, I think I'm pretty bullish on Cognition because I think Scott and like these, like 20 other like gold medal like Olympiad winners, like probably could do something that's very, very valuable despite everyone going after it. But if you don't have 20 other global gold medal physics and math leaders around, you probably shouldn't be bothering to compete in that area.

You have to know that you're competing. You have to like, at least be aware that we are directly going to be at war with these companies for a long time, and we're going to win.

Or, or, or maybe, yeah. And there's probably different territories they can each capture or something like this, but, but they're going to try to win as much as they can.

Yeah. And there, you know, there's companies like Harvey who are going in saying, "We're going to be the ones who win on professional service, you know, AI for legal or for accounting and things like that." That's a big, big, big area for to conquer. Yeah.

Yeah. And I, I think over time, I would be surprised if more of that doesn't get attacked by the large AI foundational companies.

It is very interesting because some of these foundational companies are really good at doing the foundational research, but not at enterprise stuff, right? But I guess the question is, can they get good at enterprise stuff, which you know?

I, I think the, the crazy thing is how quickly this is all changing, right? And so in many ways, we're all forecasting. But if you look at Claude co-work, which came out, you know, two weeks ago, and the types of spreadsheets and models that they're creating, and you're like, oh, this is actually like valuable, good work. Like, we use this internally for simple things like logging into carrier portals to download documents and ingest them and do a whole thing. Like that didn't exist three weeks ago.

Yeah. It's crazy.

It's an entire, entire area of our business that now is automated that zero people work on. It feels like most of the world is just not aware of like how fast everything is going to change. Like, like, what, what does this mean for business a year from now?

I, I think right now the ability to absorb and adopt the tools is going to be the limiting factor for years to come. And I think that the capabilities of these models is already so far ahead of our ability in most enterprises to just absorb and adopt change that that is like the wave behind us. Right now we have a system that, let's say we're doing like email drafting, for example, right? All of our, all the emails that are that we send are drafted by AI before we send them, and a person reviews them and sends them out.

If you switch that model from, let's say, you know, Sonnet to Opus, you know, Anthropic's middle, get better. If it does, don't do anything, stop working on it, just let the model, let the hundreds of billions of dollars of investment behind you push you forward. If it doesn't get better, that's actually a good sign that you can invest more in getting everything ready. Because right now, I think like the models are already so good that the constraining factor is actually your ability to provide them the context information to win.

Let's talk a little bit about about AI and and autonomy. So, you're saying like at scale, if you have thousands or tens of thousands of these, at some point, enough pilots would become a blocker, and you're going to want to like legalize autonomy. That's probably not a focus, I assume, the next couple years, but, but how are you thinking about AI in general in your operations and, you know, how, how does it scale?

Yeah. So, let, let's take the, uh, autonomous aircraft and, and the autonomy systems. That is an absolute game-changer, uh, whether that's on the safety side, whether that's on the productivity and efficiency side. Really, really excited. And again, the, the work that's happening on airspace from the administration, uh, from the DOT, uh, and from the FAA is absolutely phenomenal. That's laying the foundation for a really, really exciting new future. With the EIP, you're going to see a lot of that start getting tested.

So, they're going to start testing autonomy in addition to the pilots flying, for, uh, certain of these use cases. That's why having all those 12 states is absolutely fantastic. So, we're going to see, uh, this is fast-forwarding this really fantastic future all across the country. So that's, that's on, on autonomy. And, and in fact, over the last two days, I was at NVIDIA's GTC conference. NVIDIA is an incredible partner. Uh, they've, uh, put invested, uh, vast sums of, uh, both, uh, people hours into building this really safe autonomy stack. They're using that for autonomous driving, using that for robotics, and we're able to layer on top of that for, uh, all the work we're doing, um, for autonomous aircraft. So, this is really fantastic, incredible partner, very grateful for all the work. And, and they've been, uh, working on this for for more than a decade.

So, and you think they'll actually end up there for being NVIDIA chips potentially in the, in, in the Joby Aviation?

Oh, absolutely. We've been, we've been partnered with them for many years and we're using, uh, the, the latest generation of, um, of their autonomous chipsets.

Very cool. So each, each of these things will have these chipsets, and I, I guess you want that for safety anyway. If your pilot somehow has a problem, you're going to want the autonomy backup. Either, even if you did have a pilot, I'd assume.

The perfect initial use case.

Yeah. Then eventually, it's like, maybe you don't actually need the pilot. Right now, by law, you do. We got, we got to figure that out. Uh, I guess you're going to have to train a bunch of pilots to start, though. And I guess maybe there's some things you're going to want to hopefully have a bunch of pilots for. So, so, so how's that work? And is it, is this awkward? Like, we're going to have autonomy, but also, please come learn to be a pilot for us for thousands of pilots. How, how are you thinking about that?

There, there is an insatiable demand for pilots, uh, across the aviation ecosystem today.

Just everywhere.

Uh, and so we have built, uh, something called the Joby Academy, where, uh, we are training, training pilots. We've taken delivery of, uh, our first simulator, which we developed in, uh, partnership with CAE. CAE is the leader in flight simulators.

This is an incredible experience where you get in and it feels like you're flying. You're in a cockpit that has the exactly the same avionics.

Oh, wow. So you actually feel like you're sitting in, in a Joby.

Absolutely. And you've, you've got the displays, and it looks like just like a flight simulator that you'd learn to fly, uh, a Boeing or Airbus, um, aircraft in. So these are the same, same pedigree of.

You practice crashing it and see what happens.

Not allowed. The, uh, not allowed.

Flight controls. Uh.

This is what I always did as a kid on these simulators. Is that weird?

No. The one of the really cool things about our aircraft is because of the fly-by-wire, you can take your hands off the the controls and the aircraft, uh, maintains, maintains the attitude. You can get into the simulator and you can fly the aircraft flawlessly the first flight. Hundreds of people or thousands of people have done it now.

So, it's pretty easy to do.

It's super easy to fly. So, if, if people are interested in learning to fly, they should go to Joby, uh, jobyacademy.com. We would love to have them. This is going to be a, a fantastic opportunity to, uh, get to be part of of this next stage of aviation. And.

And these things can go like, like what, almost how fast are you letting them fly right now? When they, when they go up to 200 miles an hour?

Really? It's got to be up to 200 miles an hour. I love it. That's pretty fast.

It is. It, uh, and it's point-to-point, right? Yeah. So.

No, of course, you're not, you're not going over the road system. You're going directly as the, as the crow flies, as they say. It's, uh, you know, being able to take a, a trip that, that, you know, takes you half an hour or an hour on the ground and turn it into a five or 10-minute trip. You know, sometimes, sometimes you're replacing a two-hour trip because you've got traffic.

Of course, you can fly over. You see the traffic jam, you get a little shot in Freud, right? And just go right by it. It's pretty awesome. So, I mean, if you were to train yourself, like, could you actually then just like fly yourself, I guess, places or how would that work? I mean, you know, because they still need to go.

That's what I'm getting excited about. I, I, you know, I came over from, uh, from Santa Cruz, and if I, if I could have been here in 10 minutes, the, what is it, the 237? Is it what is it, 680? What, what's that road there? I forget.

The 17.

The 17. That's right. It's been a while. I'm in Texas now. Uh, so, so, yeah, the 17 is probably a lot faster to hop over the hill. It's a pretty hill.

10, 10, 10-minute trip.

Uh.

There's probably plenty of room by my pool next time. We have to do one of these episodes where we get you landing somehow. I don't know. The, uh, Woodside might not allow it. They're like, the, like, it's always like the last places to allow things is like these little communist like strongholds of California. I don't know. We'll see. We'll see. We'll, uh, we can hope.

My bias was always that even though it's really quiet, you could still sort of hear it if you're a neighbor. So, if you'd have like a tall building, you couldn't hear it on top of a tall building. Shouldn't we just build one tall building per nice neighborhood? Just land there. Wouldn't that make sense?

Uh, landing on rooftops is a fantastic opportunity, uh, especially if it's high. You can have like one building where you have like housing for all the staff in the neighborhood for a nice neighborhood. And then they can land on the top of it.

Tall buildings have got nice views.

Exactly.

So some people pay premium prices for penthouses.

That's true. And Woodside, you have Woodside penthouses. That's, that's you never know. You know, there's a few other exciting developments I want to ask about. I think like it leaked there's like hybrid aircraft for defense purposes. You, you guys announced the fact that it's hybrid aircraft. Something about hydrogen is is leaking out in public. That, that, what can you tell us?

So, uh, the work that we've, we've been doing on both the, uh, hybrid aircraft, uh, we flew a hydrogen-electric version of our VTOL aircraft, uh, more than 500 miles. And, uh, and then, you know, we have, uh, some really exciting, exciting advanced research programs that have, uh, incredible applications for, for both commercial and defense applications. Um, and, and we think that that hydrogen is a game-changer across the, the aviation ecosystem. It's three times the specific energy of jet fuel, and with fuel cells, we can convert that into propulsion twice as efficiently.

So this is fuel cells. It's not Hindenburg hydrogen.

Correct.

It's liquid hydrogen.

It's liquid. So it's not, not as explosive.

It's cryogenic.

It's, you keep it really cold.

Yeah.

So if, like, you ran out of power, then it'd be bad.

Well, no. So it's, it's in a, you can think about this like, you know, what's, what's on Starship.

Got it, got it. And, and, and so it's there. There's there haven't really been major accidents with this. This is a safe, this is a very safe technology.

Yeah. It's been deployed across, uh, um, lots of applications for ground transportation and sea transportation. Um, we have matured that technology and we're using it for aviation. Aviation is the best use because hydrogen is so light. So hydrocarbons are like chains of carbon and hydrogen on them, and they're pretty efficient and pretty, pretty light for the power they have. Like, how much more efficient per, per weight is, is your hydrogen fuel cells than, than, than fuel?

Three times lighter. Uh, the hydrogen's three times lighter than jet fuel.

Yep.

And then we can convert it into propulsion twice as efficiently. So you get a 6x gain.

6x.

And so if you want to build an aircraft that flies long distances, seems like a much better deal.

Why, why aren't others doing this already?

Uh, we've cracked the secret sauce of it. We've been leaned into this for a number of years.

Scientists in the background on the on the hydrogen as well as on Joby itself.

Well, I mean, this is, this is one of the pillars of of the incredible future that we're building, and this has a, this is a, is a game-changer on both the commercial applications and for the Department of War.

And so tell us a little bit more about this. We've been hearing rumors leaking out of the Pentagon. People are very excited. Like, why is the Department of War potentially excited about this? Why would they be engaging with you?

If you can build aircraft that are, uh, dramatically lower, uh, cost to make and to operate, and, uh, that have unprecedented performance. That's, uh, really, really important.

So, like, what would this potentially compete with that they're using right now? How, how would someone think about that? Is this like instead of a Black Hawk helicopter? Like, those are very popular. Is this, like, what kind of missions? Just, I know you can't tell us everything, but like, how would you think about this? Just, just from a high level.

I think that over time, uh, hydrogen propulsion will, uh, redefine every class of aircraft that's flown today, both on, uh, you know, on the commercial side and, uh, and for defense applications.

Versus a jet aircraft as well, because it makes more sense.

Because it's so much higher specific power, specific energy, um, and specific power.

When you're talking right now, I think a lot of people are picturing a Joby that has a fuel cell attached to it, but you're actually talking also about other types of aircraft that that that are potentially even longer distance.

What we built is, is what I believe to be the world's most vertically integrated aviation company. So this is from design to testing to manufacturing to operations across the full stack, and also the full width. So we're building the everything from the autonomy computers to, uh, the battery systems, to the motors, uh, to the airframe, uh, the propellers, the propulsion systems, like everything on the aircraft, um, or the vast, you know, more than 90% of the components on the aircraft are, uh, designed and manufactured in-house.

But this is not just like an EVTOL aircraft necessarily. It could also be some other kind of airplane that that can go really far with hydrogen.

Exactly. But we're not announcing that yet. We're just teasing people.

Um, you had also brought up earlier, uh, AI and, and you, we, we touched on the autonomy piece of it, but I think it's really important to talk about what a game-changer AI is, and, you know, this is the most profound, uh, technology I think in the history of humanity.

Does it make you go faster in everything you do?

Absolutely.

Tell us, what does that look like internally? Like, is there something that would take you a few weeks that you get back in a day now? Or explain, like, what does it mean faster?

Uh, our chief aerodynamicist was absolutely giddy. I was talking to him, uh, just, just a few hours ago, and he's like, he can't sleep because of how excited he is about how performant these new models are. Where there have been, uh, project on for project after project that he, even though he runs a huge team, um, that he hasn't had resources to go and conquer those projects for years. So he's got this whole stack of ideas and opportunities.

He's, he's now able to build, uh, in, uh, an afternoon what would have previously taken him weeks or months. Um, he's like, I'm 10x, 10x as productive as I was just a few months ago. And so that kind of breakthrough, when you take one of the greatest minds, aerodynamic minds on the planet, and you enable him with something that makes him 10x as productive, the, the benefits compound in a crazy way. Uh, one of the folks on his team, one of the, uh, PhDs who's one of the greatest aerodynamics, uh, optimization, uh, people on the planet, he said, "Joby Ben, thank you so much. I haven't been this excited in years. Thank you for giving us the opport, the, uh, access to the very best models and unlimited tokens. This allows me to be superhuman." And he was so excited about it. Just again and again, across, and, you know, that's, that's on the, like, the ultra-high-end research. Yeah. Yeah.

These are the folks that have a book of a dozen new aircraft designs that not just are like 10% better than aircraft that we have today, but you know, five and 10 times better. For them to be able to unlock that productivity is incredible. Then you layer this across every single portion of the company, HR, finance, the, um, the engineering teams, the certification teams, the manufacturing teams, just everything is faster. If we're more productive, and this is like a force multiplier that takes our vertical integration, and allows us to build a true, you know, game-changing aviation company and bring really exciting, uh, engineering innovation to people's daily lives.

It feels like you're taking like the 2030s and 2040s and you're compacting them into the next few years, right? Some of these things.

That is our ambition, that like, just drive as much innovation. This is, what I feel like our nation's greatest advantage right now is the incredible compute that we're able to bring to bear. If we can make every one of our engineers a 10x engineer, that is a a paradigm shift. Like we can, uh, create so much prosperity that like we can deliver on the American optimist vision. So it's, it's like this is so fantastic.

I love it. I mean, and I hear these models are only getting better. Like, when you talk to.

They're getting better by the, like, every few days. Like, this is the first time I've coded in in a decade, in more than a decade, where like I'm up all night coding because it is so exciting.

On the business side, like, like, I mean, are the markets going up a ton the next decade because productivity is shooting upwards, and this is just like a, a golden age, as long as we don't ban AI? Where are things going?

Yeah, I think, you know, it was a good week because this week was the JP Morgan Healthcare Conference. I'll do 20% of my investments in healthcare. So I had a chance of talking to people kind of outside of the AI bubble ecosystem, and the number one question was, is AI a bubble? Right? Is it overvalued? And I think it's an emphatic no, it's not a bubble. Probably, it's still a little underhyped. Like, there's always questions of, are the models going to get better? And I think the reality today is, if you look at the capabilities of the model, you could stop all development. You know, we could tax the billionaires and everyone can leave, and even if we do that, you'll still have a revolution in the economy. You'll still have a revolution in the way things happen because the limiting factors now are not the technology, it's diffusion of the technology into the economy. That means knowing how to build product and the models, knowing how to deliver product with the models, like new product. And it's even to the point of like, you have to reform some institutions. So imagine law, right? Law fits into four of those core skills that LLMs are good at today. It's like reading documents, searching, writing documents, some reasoning. Um, that's going to be, law is gonna be fully transformed, um, from really the bottom up. Like their whole business model will have to shift. So you'll have to move from today, law is sold per hour. You're going to have, hours don't make any sense when it's agents doing work or when it's LLM doing work. You have to shift now to fee-like pricing outcomes. That's a whole business model transition of a huge part of the economy. That's another bottleneck, right? And so these are the challenges, these are the things that we're going to overcome. Every year, 4% growth in, in sort of productivity in these spaces, and there will be a slow revolution that we'll see.

It's about people and institutions adapting to the new possibilities. So basically, the possibilities are already there, and now we have to like roll it out.

Exactly. And it's the rolling out where there's friction. The tech is way ahead of the roll out today.

Yeah. And this, and we didn't talk much about healthcare and AI, but of course, like healthcare itself could be, I think we believe, like a tiny fraction of the cost and better at the same time for many areas of healthcare. Maybe not certain surgeries. Yeah. All of those are going to be impacted too, but like so many areas. And, uh, but you're right, this is going to be special interest going to war. I guess, I guess the people who are trying to tax the billionaires here in California are the healthcare workers unions, which is like ironic because it's like the area we need to make efficient. You know?

Just start the counter. I think that's mentioned number four during the podcast.

I think the key point here is technology is no longer the bottleneck. It's the process and organizational structure. And these things are really hard to predict and really hard to measure, which is why I think with all of these cycles, the direction is correct, but the timing is off. And so this stuff is going to happen. It's just a question of if. Sorry, not if, but when. And people can disagree on that.

I think it's just probably slowly over 10 years. And so if you look at a lot of these public companies, no earnings multiples are dislocated or anything like that, really. If you look at them, NVIDIA is like 25 times forward earnings. It's not crazy. And so I, I'm actually surprised how little of a bubble there is, to be honest with you. Um.

These things are just making so much money, but the multiples are not that high, you're saying, basically.

Absolutely. And it's going to continue, and it'll probably get more crazy, to be honest with you, because.

We're going to every year there's going to be more inference and these companies are growing quickly which means more inference.

I think his productivity clearly grows because we've already seen examples in trillions of dollars of the economy where productivity can double or triple. So, as it clearly grows, I think the bubble does at some point you actually do get a bubble because people get so excited they put a lot more money.

But we're not there yet. And this is what I mean it's not a macro podcast, but this is what a lot of the macro financial commentators don't get is you could look at like money supply, you could look at interest rates, but everyone's missing like actually productivity is getting better in the economy and it's going to keep getting better. You know, one of the reasons why I think that's the case, by the way, is the macro people are so far away from the practical reality on the ground. They're not talking to 5, 10, 15 person teams who are actually doing this stuff. Like, it's actually really difficult to internalize how impactful that this stuff is unless you're literally using cloud code and seeing what it can actually do for you. Like, it's really hard because of how drastic of a change it is. It is something where I think you have to be doing it yourself. And it's like I think young people who are kind of native to this and learning and and and like focused on it like they're they're adapting really really quickly. Whereas I think most of the macro people that I know anyway tend to be on the older side and they and they and most of them it's just like it's just it's like you there's a famous book called this time is different in macro and you're supposed to like make fun of the fact and and I think by the way there are parallels to the past when productivity grows. We can probably learn a lot from the late 19th century by the way. It's a really good thing to study. But but it but it but it is probably very different than the last few generations of their their lifetime because we haven't had something like this in their lifetime.

Totally. Look, not to nerd out about public stock investing, but to Alex's point, you know, Micron, which is obviously a key participator in the AI wave so far. It produces high bandwidth memory. The stock is up 250% over the last 12 months and it was a nine times P forward multiple stock 12 months ago and it's still a nine times forward PE stock today. Exactly. Like where's the bubble in that? And to give you a sense of capex like what are we spending now on AI capex like 500 or trillion trillion dollars. If you look at the industrial revolution in Russia in the early 20th century I think investment as a percent of GDP got up to 20 or 25%. And then China was investing 40 or 50% in their industrial revolution of GDP and now in AI we're investing like 3% or 4%. It's like it's not even yet on the scale of the industrial revolution.

Yeah. And so if it is an industrial revolution, this thing just gets a lot crazier the next few years.

That's the analog people are missing is it could be that like sure it is digital, right? It's more digital work than physical work, but it could be on that scale. And if you think about in those historical parallels and obviously in the US investment's always been less than in the Soviet Union and China, of course, but um it shows you things can get a lot crazier, right? if you really pushed it and in the US you'd still have investment of percentage GP in the 20s.

Before I ask you at the end, we always try to see what makes people optimistic. What what worries you the most because we're being pretty optimistic about this giant important wave and how great it is. Like what what what's a big concern over the next 10 years?

I think it's the social dynamics. I mean, you have people who are always worried about job displacement. And when I went home for um the holidays, you know, I come from more of a working-class area. So, I mentioned a few of our companies, some of the autonomy companies were invested in and they were like, "Oh, so who's going to drive the taxis, right? If you're thinking about a Whimo, like where are those jobs going to go?" And I said, "Okay, look, why aren't we all tilling wheat in the field, right?" Like, I could have said, "Well, who's going to, you know, sighthe the wheat or do the harvest this year?" And that's cuz we have machines to do that. And that's the reality of it is that it's better. It's positive some. We'll have more productivity in the economy, but there is always friction. And when that happens in the, you know, industrial revolution, you had the lites who in some sense have a point where they're like, I like the way my life is. This is going to we got we got rid of Coopers. The people have last name of Cooper because they made barrels, right? And we got we got rid of the Boers. We got rid of like all there's all these jobs that you go back and it's tied to people's names, you know, and it's like we don't have the Smiths anymore on the local town. So, it's I think it's very unintuitive to people that that that was like a really good thing and it should happen again because it's scary.

And I think you need to manage it though, right? Because I do think there's some right people have to the way they culture.

You say you need to manage it though. That's a really tough thing is who should manage it because it's it's what's going to happen is some crazy populists both on the right and left. This is like non-political comment. Crazy populists want to manage it because they want the power and and then and then they're just going to screw it up, right? And they're going to steal.

Well, that's the risk you're going to have, but you'd want to make sure people have access to um you know, retraining, things like that.

So, you got to have competent training. By the way, we have $40 billion a year of training programs and they're just completely incompetent. We should make them accountable. So, stuff like that that we're working on. So, no, I I agree. There's there's policy that makes it work better.

Exactly.

What do you think?

I think the cital stuff is totally right and I agree with everything that was said. I think on the more financial side, two things are true. One is as with any cycle I have no doubt that at some point we're going to overshoot what the economy can actually efficiently absorb and it's going to be a correction like there's a reason semis manufacturers are cyclical businesses like the psychology takes over at some point and then two I do think it's extremely reasonable to say that there are pockets of the market that have very bubbly behavior you know I was working with you as during the sort of po peak zerp 2122 cycle and one of the big behavior patterns back then was a company would go out to raise money they would get a really good round done and then be three or four investors around the table who unfortunately didn't get into the round two right away and two months later they would be investing in the same business with two months of progress at 5x the valuation so we're starting to see that in Silicon Valley and these are just like inherently really difficult things to uh sort of balance. You can be long the trend and you can be rationally optimistic about both the timeline and the magnitude of the change that's coming and you can still not make any money doing it if you're off on some of these things.

No. And and the infrastructure is hard historically to invest in. I think the American railroads, we famously have all these bonds and certificates of like all the British guys who like lost everything betting on various crazy railroads around the country. This tends to happen in every cycle where the infrastructure gets overdone at some point. So that but I think this this is again speaking to our book is I think some of the apps and services layer companies are a lot safer when that happens. But but I I don't know. So I think that's the question. You know, one of the interesting Zerp lessons that I think we learned is technology in this as an industry is actually more cyclical than people gave it credit to be and a lot of the startups in that cohort were indexed to serving other technology startups. And when inevitably the venture industry went through a bit of a correction and there was less money going on those companies balance sheets and they had less money to actually spend on other software products. The companies serving the customer segment actually really struggled like there were lots of companies selling HR tools to other SAS companies like

you have to be careful what you index to.

Yeah. It makes it more up and down. I mean the very the famous quote from Eugene Kleiner who obviously was a famous semiconductor leader who started Kleiner Perkins was like the time they take the vignettes or when they're being passed he was Austrian so I guess they're beignettes I think that's what he said but I guess the point is like there are times when there's money available and there's times when it's not uh it's definitely available for great companies right now is it's a great time for builders uh we still if you look at the aggregates like we are well below 2021 in terms of like raised money in venture capital and deployed in venture capital. Oh yeah, I think the funds overall raised like even a lot less money last year. I think only like the funds doing really well are crushing it and then all these small funds that probably shouldn't have been around anyway in our view like

exactly are struggling.