Investing Billions - E160: How a SpaceX Rocket Engineer Became a Top Deep Tech VC

Episode Date: May 2, 2025

Jamie Gull is the GP/Founder of Wave Function Ventures, a deep tech seed fund, and an engineer turned investor who previously worked at SpaceX during its early, intense years of scaling. In this episode, Jamie and I discuss the high-responsibility culture at SpaceX, how it shaped Jamie’s approach to company building and investing, and what makes a deep tech founder stand out. We also explore why fast iteration matters more than perfect planning, how techno-economics drive investment decisions, and why deep tech’s reputation for being overly capital-intensive is becoming outdated. Jamie shares firsthand stories from his time working under Elon Musk, his angel investments in companies like Boom Supersonic and K2 Space, and the founding principles behind Wave Function Ventures. If you're interested in the future of deep tech investing, how to identify category-defining founders, or how hardware startups can scale efficiently, this is a must-listen.

While at SpaceX, you had several one-on-one meetings with Elon. These have been popularized by Marc Andreessen recently. Tell me about these one-on-one meetings, and what did you take from lunch? Generally, they were checking in on a system that I was designing to make sure things were on task. And was there any problems that were happening that he needed to come in and help fix?

And that's kind of what he's best known for. He still retains that chief engineer title. I don't know if it's official, but that's his role. Help solve the problem. Dive in, fix it. Oh, we're fixing this today or this week. And I'm gonna sit here until it's done, which is extremely effective.

Today, I'm thrilled to welcome Jamie Gull, founding partner of Way Function Ventures, a seed fund investing into deep tech and hard science. Jamie began his career as an engineer at SpaceX in 2010 when the company was still in its early days. Today, we'll dive into Jamie's first-hand experience with SpaceX's responsible engineer culture. We'll also share insights from one-on-ones with Elon Musk, lessons learned from building rockets, and how those principles shape

Jamie's investment approach as a venture capitalist. Jamie, welcome to the How I Invest podcast. Thanks for having me. Jamie, you were an engineer at Invest podcast. Thanks for having me. Jamie, you were an engineer at SpaceX in 2010, 15 years ago. What was it like working at SpaceX at this time? Yeah, in 2010 when I started, things were moving super fast. Falcon 9 had launched a handful of times, but we were basically redesigning the rocket in between launches based on what we learned. All the engineers would sit down after a launch, crunch the data, and immediately

go back to work to redesign it for the next time to make it work better, fix any problems. I cut my teeth right before that at Scale Composites doing aircraft design. They're very well known for rapid prototyping and putting a lot of responsibility on young engineers, but when I went to SpaceX a couple years later, it was like that, but it was on steroids. The responsibility level and the excellence level was a large jump up and it reflected it in the culture and the pace of what we were working on. SpaceX pioneered this concept of a responsible engineer.

What is a responsible engineer and how did that apply to how you went about your day to day tasks? What is a responsible engineer and how did that apply to how you went about your day-to-day tasks? So a responsible engineer is the person who's responsible for the design, but more importantly, the delivery of a successful system on the rocket. And what that means in practice is that if they come to you and say, I want you to design this system, traditionally in aerospace, you would sit down, you'd do the requirements, the CAD, and then you would maybe throw it over to an analyst who would tell you, it's not strong enough here, fix it. And then you throw it over to a pre-production team who would build it and you kind of pass it off. At SpaceX, you are responsible all the way through production until the moment the launch button is hit. And so if something goes wrong with the analysis or the pre-production or the testing or the

actual production, you're the person that has to go fix that. So in practice, what that could look like was a vendor promises you they're going to get you a part in eight weeks with some specs. They call you four weeks in and say, Hey, this is going to take 12 weeks. You take that to say the VP of vehicle engineering and say, Oh, this is going to get delayed, which is going to delay the rocket. The answer is more not okay. We got to change our schedule.

It's why aren't you on a plane to that vendor right now? Why are you talking to me? Go fix this. So you would have to fly out, sit down with the vendor and pull the schedule back in and get it done right. talking to me, go fix this. So you would have to fly out, sit down with the vendor and pull the schedule back in and get it done right. Like, so the responsibility doesn't end when you release a drawing and then throw it over the fence.

Uh, you go out to the production floor and you damn well better be out there a couple of times a day to make sure your stuff is being built properly. And there's no problems. Uh, and if there is a problem, you can literally pull out a red pen, red lining, fix the drawing, tell them how to do it differently. And that is a release drawing at that stage of the company. It's changed a little bit now.

But that again is going back to you being the responsible engineer, you're making the call live and it just flows down from there. So it's a very high level of responsibility and essentially a zero excuse environment. You don't get to make an excuse that some other team didn't do it fast enough or drop the ball. It comes down to you. And if it's not delivered on time on the rocket with a successful launch, it's your fault. It's essentially getting every single engineer in the company, the same

level of responsibility that a startup CEO might have. And you put this on every single person within the organization. That's a great analogy. Like you don't get to make an excuse and you don't get to say, Hey, that team changed some requirement. Like you have to sit down and hash it out live with the other responsible engineer and come to an agreement that's best for the company in the program. What are the downsides of this responsible engineer framework?

And why don't more companies do that? The biggest reason people don't do that is that a lot of people can't perform to that level. And so if you don't have the systems and checks in place, it falls apart. Like all of a sudden something doesn't get done, falls through the cracks, and nobody catches it because there's not enough safety nets in place. And so you have to have the entire company bought into that culture and idea, and you have to have everybody who can perform in that level.

And that's really hard to do. Let's be honest, like a lot of people can't perform at that level, uh, in a high pressure environment. And so if you don't have that, you gotta have different systems in place. When we last chatted, you mentioned that there was three different types of people at SpaceX. What are these three different types of people? Yeah.

I mean, it's kind of a broad generalization that I've come up with and it kind of goes back to the responsible engineer, ethos and culture, and I've come up with and it kind of goes back to the responsible engineer ethos and culture. And I've kind of bucket three people, people into three buckets, which is those who are there for three months to a year and they have to depart because they're let go or because they decide that they can't keep up, uh, either pace wise or responsibility wise and leave on their own accord. Then there's folks kind of like myself.

I loved it there. I was there five and a half years. So put in a serious stint, but wanted to do other things with my life and try other things. And then there's folks who've kind of been there 10 or 15 years. And when you ask them, do you want to do something else? They're like, what else could I possibly work on that's cooler than this? Why would I leave? There's no way to outframe the mission of going to Mars. For some people that is the ultimate mission for humanity.

And there are other very important missions out there that in my eyes are just as important, but it's hard to beat that straight up. Like more than, you know, an order of magnitude more interesting or harder. It's not possible. You were in this very intense culture at SpaceX of the responsible engineer of everything having to be done, everything being mission critical, looking back, that was now 15 years ago and you stayed till 2015, roughly 10 years ago. How did that evolve you as

a person and do you bring anything from that period into what you're doing today? Oh yeah, absolutely. The upsides is once you're in that environment, that's what your expectations are for yourself and people around you. And it means you can perform at a really high level, get other people to perform at a really high level and do really big interesting things. And that's the upside. The downside is the exact same thing. Once you're exposed to that, and that's your expectation, it's really hard to reproduce. Or you go to another company, and it's not at that level. And you get to maybe sit back, which is nice, take things a little bit slower, not be as stressed out. But it also gets super frustrating when somebody will come to you.

I hear this all the time from former colleagues. Like, oh yeah, I had to buy something today. It was $300. I think I got six signatures. It took two weeks. And at SpaceX, you would have had it that afternoon. You make your own decision on something that costs that little. And then, you know, your colleagues making excuses, like I was talking about with the

responsible engineer framework of, oh, that's somebody else's problem. And that's really frustrating to hear when you're not used to that. But you do have a more relaxed environment. And in some ways, that can make people happy. While at SpaceX, you had several one-on-one meetings with Elon. These have been popularized by Mark Andreessen recently. Tell me about these one-on-one meetings and what did you take from them? My personal experience was maybe less interesting from a Mark Andreessen or public sphere approach.

Generally, they were checking in on a system that I was designing to make sure things were on task. There weren't any decisions that had to be made to change course. And was there any problems that were happening that he needed to come in and help fix? And that's kind of what he's best known for. He still retains that chief engineer title. I don't know if it's official, but that's his role. And so he from a high level looks at things, but then also deep dives into all the subsystems once in a while, checks on things and makes decisions. And when there are problems, generally schedule wise, but also performance wise, he'll go sit down with the responsible engineer that's at very bottom of the engineering org,

and help solve the problem live. Dive in, fix it, or we're fixing this today or this week, and I'm going to sit here until it's done, which is extremely effective. It also can be nerve wracking if you're in that seat from the R&D side. Is there some higher level strategy there to show that he's in the pit with the responsible engineers or just that he sees this as unlocking the most important bottleneck? It's both for sure. Like it's, I mean, as CEO's biggest job is outside of being the chief storyteller is on, you know, unblocking bottlenecks. And so when you dive in like that, everybody knows that you have the

resources there, the backing, but it's also a spotlight on the problem. And you don't get to hide anything, right? Like you'd have to fix it. Now the boss is in the seat with you. So let's fast forward to today. You run a seed fund called Way Function Ventures, a $10 million fund focused on deep tech, tell me about million fund focused on deep tech. Tell me about your fund and tell me about what you look for when it comes to founders.

Wave Function is a deep tech VC fund that I studied last summer. My definition of deep tech for this fund is hardware, hard problems. I'm not looking at biotech and I'm not doing software for hardware. So I'm focused on actual atoms. I do that due to my background and expertise, but also my strong belief that that's what makes a large difference, a positive difference in this world is the actual physical structures. Like we're in a post-software change everything world now outside of AI. So that's what I'm focused on. I bring to the table a pretty interesting background as a fairly

hardcore engineer, and then I was a two-time deep tech founder myself. I started a space deployables company and then an electric vertical takeoff and landing aircraft company. It went through Y accommodator, raised money. I got eight government contracts with the Air Force, and then we got acquired by a company in LA called AmpAir in 2023. So I've been through that ringer on the founder side, been through that ringer on the engineer side. So I can bring a pretty unique lens to the investing landscape, both through assessing companies, but then

also once I make an investment, actually helping them. You have a pretty strong view on business founders solving technical problems or MBA going after technical problems. Why is it such an issue for an MBA to go after a technical problem, assuming that they could partner with the right chief technical officer? I don't love seeing like an MBA run a deep tech company without some, a very high bar and some other things being in place, not even, not just a technical co-founder.

And one of the reasons there is in deep tech, it's, you know, if you look at software, you're like, okay, I'm solving this problem for a customer that I can understand really well, and somebody can build that software. I know they can do it. It's just a matter of how fast, how efficiently, and how good will the software be? In deep tech, there's a bigger question of can it be built? Can it work? And can you do it with the right economic impact for your customers?

And not understanding that deeply from a technical perspective makes it much harder to navigate the business side, the pitching side of a company as a CEO. So it's, you really got to have that understanding because especially in the early days, you know, you're changing things on the fly. You're talking to customers. If you don't understand that from a deep technical perspective, how can you talk to a customer and say, here's what I can actually deliver for you. Now that I've heard what your pain points and needs are, if you're an MBA, you got to go back to your CTO and say, here's what they need.

Then they have to do a bunch of research and then you got to go back and forth. So it just rapidly slows you down as you go through the idea maze and you're finding product market fit. So it's a, it's a tough sell for me to have a non-technical CEO. You can't disintermediate the selling from the technical consultation. It becomes something that one person needs to be handling and one person leading the company. At later stages, I think it's totally reasonable to decouple that. But at early stages,

it's a huge hindrance. It can be done, but it's going to slow you down. It's going to cost more. And early stage startups, it's all about speed of execution. And so you're basically handicapping yourself. Talk to me about techno economics. What are techno economics and how does that inform your decision making process? Techno economics is just a machine of the economics on the business side and the technical design and build. I give you an example. I've looked at a number of companies and say, hydrogen generation space. That is a product you have to deliver to a customer at a certain price point, certain

volume. And you've got all these assumptions through your tech stack of how you can actually produce hydrogen. So you can do that analysis and then do a rough sensitivity analysis at the early stage. It says, what happens if this input doubles in cost to geopolitical concerns? What does that do to how much I can sell it to my customer for? Or what does that do to my margins? And in deep tech, they're just so deeply entwined compared to software where your

technical economics is basically how many engineers do I need to build this and how much does it cost to sell to a customer? You're not really dealing with the fact that your software itself is going to change in price due to some external factor. So even though something could be technically feasible, it becomes infeasible because of the economics and that nobody's going to buy it. If you were to create it. Exactly.

And if you get that wrong by a large factor upfront, what you could find is that you've built something successfully, you've delivered, but you can't sell it. And you can back yourself into a corner where it's not possible to get that price down to a point where it makes sense for people. Click on this framework on how you figure out whether a hard tech company is investable. Thank you for listening. To join our community and to make sure you do not miss any future episodes, please click the follow button above to subscribe.

I mean, there's a bunch of things I look at, some of which are very similar to all venture capital. The top one being founders, founders, founders. This can go back to my requirement or near requirement, I should say, about the CEO being a technical founder and also their ability to execute at an excellent level in the build process, which looks like a responsible engineer from SpaceX. You have to iterate quickly.

And if you're coming from a background of slow waterfall design processes where you are choosing your requirements incredibly carefully upfront and then doing this very careful design and build to this perfect end product, you're going to find out that your assumptions were wrong in some way via your customers or your technical assumptions. And now you're stuck and you've wasted all this time. So the ability to execute crazy fast on the engineering side, but also do it on the fly and adjust as you go is incredibly important on the founder side. One of my super powers is because of my engineering and founder background, it's relatively easy for me to assess is this tech possible?

Does it make sense or is it pie in the sky? And kind of skip through that process incredibly quickly and dive into the tech economics and the founders at a deeper level, rather than having to spend a bunch of time researching the technology to see if it's even feasible. Um, so those are the kind of kind of the top things I look for. There is a somewhat of a myth out there around deep tech that it's much more capital intensive than software and you can't get good returns. And that's the myth is getting busted.

It doesn't mean that it's not more capital intensive and time intensive upfront. It is, it's hardware. It takes longer and it's more expensive to develop. But where the myth is getting busted is once you're in market, you can scale differently. So you can scale with things like project financing or government contracts that you're getting paid for and not by continuing to have the venture capital cannon fired over and over as you eat through capital. And we've seen software, the early days, it didn't require a lot to get into market. And then once you were there, you could scale rapidly.

Now it's such a crowded space. And we've seen this so much right now in AI. It becomes a race. It's easy to replicate. So it becomes a race and how much money can you throw at the problem to scale your team and scale your customers? There's some interesting charts floating around the Twitter sphere right now about comparing valuations and amount of capital required for some very well known companies.

And when you put them side by side, a lot of the deep tech stuff is about the same as the software for capital required and valuation of the company. So that myth is being busted. And the reason is the second half or second two thirds of company growth in deep tech can look more like an industrial process where you scale with project financing, you scale with government help, and you're bringing in tons of revenue from your customers. And you've built this large moat because the hardware is hard to develop because there's a somewhat limited customer set and you've locked

them in, it's much harder for somebody to chase you once you're there. And so you don't have to keep throwing dollars at that problem. You can scale more again. Somebody might counter-argue that by saying, well, that's only in the software companies that have scaled really big or the ride share companies that have raised billions and billions of dollars. But venture capital fundamentally is about those companies. What happens if you succeed and all the returns actually go to those power

law outcomes to those huge winners. So the real question is if you're going to be successful in this company, will it take more or less money? And if it takes the same amount of money to go public, that's really what investors should be looking at versus what does it take the average company or the median performing company to get to scale? That's exactly right. It's still a power law driven business.

So, if I can dump $20 million into a software company and they can sell for 500, that's awesome for the founders. Venture capitalists aren't chasing that. They need those 50, 100 or 1000 X returns to have their fund perform. And so it's about the big winners, just the same in deep tech. And given that it's deep tech versus traditional software, are you still looking for the same in deep tech. And given that it's deep tech versus traditional software, are you still looking for the same level of power law outcomes? And do you still have the same economics as an investor or do they differ slightly? Definitely still looking for the power law outcomes, almost, almost exactly in the

same way as more traditional venture capital. You know, outcome wise, I think in deep tech, you might be able to expect a few more smaller winners instead of failures because if they can get to market, they can make some good money and be a decent company versus maybe you're a consumer where you just never found consumer product fit and it's going to go to zero. I think at Deep Tech you'll see more small middle outcomes as opposed to going to zero. But again, because you're power law driven, those don't really matter. You still need the big winners. And at Deep Tech, you typically have the issue of technical risk, not market risk.

Yeah, the stuff that I'm focused on, that is the case with the caveat. So I don't want to back something where it's unclear if you can, if you are able to build it, that there's no market for it. There's obviously market risk in the sense that you're not totally sure the customers will choose you. Um, or, you know, that somebody else might not come along and eat your lunch. But it's clear to me when I'm investing that like there is demand for that product. Um, the technical risk is there except for, I would, I would copy out that

with, I'm not looking for science, technical risk, I'm not looking for, can this be done or not? I don't want that risk. I want it. Can the team execute well enough with this idea, that kind of technical risk from an engineering risk perspective. So there's definitely cases where they can't execute well enough and they can't build what they're promising, but it's not, is it even possible? And it's going to take three years to find out and you get a binary yes or

no after three years, which you could compare it more like biotech not, is it even possible? And it's going to take three years to find out and you get a binary yes or no after three years, which you could compare it more like biotech is commonly in that realm where you can have a early stage that they, after three years, they just forgot in the lab like, oh, this doesn't actually work. So I'm not looking for that type of technical risk at all. How do you gauge whether something's a scientific risk or an engineering or execution risk before something's actually developed? The stuff I focus on is right idea at the right time, bringing the right things together.

And I love to give the example of K2Space, which is one of my angel portfolio companies. K2Space is building large satellites for a Falcon 9 and Starship world where launch costs have come down. They're competing against what used to be a half billion dollar communications bus that took five to 10 years to develop and build with a much cheaper thing where they can throw mass at the problem or throw non-exquisite engineering at the problem because it's so cheap to launch. So there's technical risks in the sense of can they engineer that?

But it's all known processes. Satellites have been flown for a long time. They're putting components together in a new way with a larger burst satellite, but they're not inventing something from scratch. And so the timing there is key because if they had done that pre Falcon 9, it would have been a non-starter. If they had done that pre Falcon 9, reusability probably wouldn't have been viable. Once Falcon 9 is launching multiple times per week and costs have come down a bit, also to make sense, and they know when Starship comes online, costs come down even more and

they can throw even more mass at the problem. So that's a right time, you know, bringing together the right things without a lot of science risk involved and a killer team that executes like crazy. So that's kind of a canonical example of what I would say. So there that company is taking advantage of the second order effects of something that is highly likely to happen. Just a lot of people might not be thinking about what that kind of world looks like and who will be the natural buyers of the product. Another company you invested in, in the seed round was Boom Supersonic. We had the CEO, Blake Scholl, Episode 153, who talked us through the story of meeting

Richard Branson, raising hundreds of millions of dollars and getting to launch. What did you see in Blake's show when you invested over a decade ago? The way I met Blake was pretty funny. That was, I want to say in 2014, it was before he had gone to Y Combinator, before he built out a team. He started asking friends and telling them this idea and then who do I talk to? And so I was actually the first person in aerospace Blake talked to.

He asked a buddy, he said, Hey, I played hockey with this guy at Stanford. He's a SpaceX now. You should talk to him. Blake flew his aircraft down to Hawthorne and we met there. And his kind of question was like, from a technical perspective, is this a dumb idea? It's like, well, no, actually, I think you've kind of hit the nail on the head of this has all been done, but things have progressed technically so you can do it better. You can do it cheaper. And his insight around going around the right market and not focusing on business jets or

massive passenger jets like the Concorde, massive in the number of passengers, I think is the right way to go about it. And so she's kind of like, they think this is a good idea. And so as soon as he formed it up, I asked him to put some money in and then helped him with his first non-founder hire with a friend of mine. So that's how I met Blake and got involved. And watching him in those early days as this kind of goes against my grain of non-technical founder, you know, he was a software guy before, so I was skeptical, but I watched him knock down barriers of getting

people on board or getting into YC. And I don't know if he told the whole story about Branson right before demo day, but like it's crazy. Like somebody who can run through those walls that early as a non-technical founder just gave me a lot of confidence. Like if anybody's going to do this, it's Blake. So I want to back him. And so been a supporter throughout and very excited. They just flew supersonic very recently.

I got to go watch that flight myself and Blake and I remain close. He was an advisor to my prior company and he's advisor to my fund now. So we're still heavily in touch. Blake's one of these geniuses in asking questions. You must have been on the receiving end of the question he used to ask back in the day,

which was who is the best engineer that you know, regardless of whether you think I could recruit him or her? In your case, he actually wasn't able to recruit you and you still invested. But it's one of his great questions. The other question they asked Richard Branson is, when we do accomplish the first flight, do you want a Virgin logo on it? So these powerful questions really helped shape the trajectory of that company. Yeah. And Blake's question around hiring, like who's the best person, you know, whether or not I could get them obviously led to him getting some of them. So it's a great question.

And then all, and it lets people not filter other folks out, right? Like you might be like, oh, you should be so and so, but there's no way they're gonna leave wherever they are. And then that and his investor updates and format and cadence, both that question and his updates have become basically standard wide combinator advice. So he kind of pioneered those. And now a lot of people think of that as like, here's how you do an investor update.

YC teaches you this, but like Blake was kind of the OG around both those things. It's been disseminated down to a lot of founders through YC. When it comes to weighing the probability of a success of a deep tech founder, how much do you weigh the actual founder and the founder's conviction versus the problem that they're going after or the market? So I think the problem, the market, the techno economics are boxes that have to be checked, but they're not enough. Their requirement, but they won't tip me over.

So the founder thing weighs incredibly heavy in that process. So it's almost like you can use those other things to eliminate companies from consideration, but not to make the investment. They're table stakes. As human beings, I think we systematically undervalue the compounding benefit of fast iterations. Give me an example of the most extreme outcome where you saw somebody start with a problem maybe in completely wrong area and how they were able to iterate into success.

Iteration remains critically important in deep tech startups. You do not want to see a founding team. I mentioned this earlier, set up these like perfect requirements. Here's exactly what they want from us. Here's exactly I'm going to build it. I'm going to do this very carefully. You want to see them build something and start testing it, both with customers, but also internally as fast as possible. And it's a little bit different from the startup perspective, but when I was designing the thermal shield on F9, for example, so it

enabled the re-entry and landing of F9 for the first time. Nobody had done that. So knowing what material or what shape or how to interface with the engines was a completely open problem. And I think I ripped through five to 10 designs and materials that took about a week each in the early days. We threw them all away. This was all on computer, but then we started prototyping parts. I saw this play out over and over where you could design a key joint in a system that has to take all

the load. Rather than do everything carefully, you just build a prototype of the joint and break it and see what happened. And that's your like key, your key linchpin to that system, a prototype and break it and see what happens and then go from there. Don't, you know, spend a long time doing incredibly careful analysis. So, you know, we used to do some analysis that was both incredibly sophisticated but also very rough around the edges and then just go test it and correlate it to the analysis and move rather than spending, you know, months trying to get your analysis perfect. So that's what I want to see in founders too is the best is when they come in even

an early stage idea and they've like built something in their garage and tested it in some way and adjusted course. Like that's a very good sign that they're going to do this in the right way. Some of these things that I'm investing in are too big, too complex to do that at scale, but immediately what you'll see is the same process applied to subsystems. So what can we build small now and test and start testing our major assumptions as fast as possible? Again, rather than spending a year doing,

you know, fantastic design work. I've heard the story of Idea Lab when they would do rapid prototyping. So if they were to create an iPad, he would be walking around with a block of wood and pressing buttons on a block of wood before they even created the mainframe for the product. It's very difficult to actually overdo rapid prototyping. Yeah, I totally agree. That's a good example and that's hardware.

It's different than the hardware and stuff that I'm looking at. But yeah, it would have been really easy to sit there and design a really nice iPad on your computer and then get it sent off to a manufacturing facility to come back with working buttons and screens and then realize that you put the button in the wrong place or whatever. Why did you just spend months doing that when you could have just gone down to the shop and shaved it out of a block of wood in a couple of hours? The stuff that I'm looking at, you can't do it as easily, but how can you apply that

mindset to do that as much as possible is incredibly important. When you look at your portfolio, you look at the biggest winners, the power law outcomes. Are these cultures that embraced idiosyncratic attributes like doing something very odd or were these best in class engineers hacking away in an existing paradigm? It's more the latter. There's some idiosyncrasies that usually show up in organizational design or the way the culture goes, but I think the key thing comes more down to the rapid iteration and engineering excellence as being the key drivers there. You have to know the rules before you break them.

And Elon's famous for reducing and eliminating requirements as much as possible or questioning them. And that's also key. And you see that again in a lot of big aerospace where they sit down with a bunch of committees and create all those requirements and something that will be designed and built for the next five years based on those requirements. And they never actually had the engineers on either team sit down and talk to each other and say, Hey, is this a dumb requirement? Like, do you actually need this?

Do I have to make this design to go operate at a hundred degrees Celsius? Or can you give me back 20 degrees or 40 degrees and have the other team say, you know what? Yeah, we can give you that. It's only going to cost us a pound here and it's going to save you 20. Let's do that. That doesn't happen in other organizations. You all get the system that you design the requirements around on day one and that's that and nobody's ever questioned it ever. There's almost this redesigning within a corporation you have to do in order to account for the

evolutionary need to be consistent. So everybody's been doing this process. You have to opt out of that process versus opt out of the process of using first principles. Exactly. And it goes right back to the classic innovators dilemma, which is like, you know how many people ask Blake, why isn't Boeing do this? And still, yeah, still one of the answers, you know, is that like, they don't have the culture in place that's able to do it. Um,

and the same thing happened with SpaceX. Like everybody laughed at us for a long time as being cowboys. What they didn't see is that every week, the design and the team was getting so much better that if you extrapolate that curve out, they were going to get their luncheating by the time they figured that out, it was too late. Um, it's almost impossible to revamp an organization to embrace that. That's large. And so that's why startups have an advantage and they come in and disrupt it.

So they get to build that culture and from day one. I think Zuckerberg realized this, which is why he was so focused on M&A and finding the company that had, you know, just grown very fast that might still have a small user base like in Instagram when it was starting out, he was so paranoid about this because he understood that even though at the top of the organization, he embraced innovators to develop, he talked to everybody about it. There was no way that he could really evolve the ossified nature of Facebook, that he was doomed to being disrupted if he didn't buy the next disruptor.

And he was very good at extrapolating those curves out. Why would you mess with Instagram? It's like tiny user base and nobody cares. And he looks at the curve and he's like, well, in five years, this is bad. Let's just take it down now and bring it to house. He was very good at doing that very early. As somebody that worked in the earlier days of SpaceX and was around Elon, what would you say is his one biggest superpower,

especially one that most people wouldn't recognize? Elon's superpower is organizational design and culture. He's very good at his first principles thinking and how he came up with the idea of SpaceX in the first place. He's a good chief engineer, although I've seen him make decisions in that regard that a lot

of us thought were really bad decisions and like kind of ended up being right about that. But so his superpowers that he builds the org that can out execute everybody through that culture. And once he figured that out, you know, he's replicated it what five or six times now. That's crazy. Like, so it's not that he's sitting down and doing this amazing engineering. He's like a brilliant physicist and engineer. And that's his suit. A lot of people think it's that it's this is Billy to like build the org and replicate that and unleash a team and then build them into a mission, um, that

motivates them to work that hard and take on that much responsibility. So picking the right ideas. Tell me more about his superpower around organizational design. Yeah, so it really harks back to the responsible engineer culture, but other things that are interesting is like a pretty flat organization. The ability to move up and down the org rapidly with decisions is built in. I'll give you an example back to requirements. I'm a responsible engineer.

I look at a requirement. I'm butting up against another team. I want them to give me some space here so that my system can be better. And I can make an assumption that it will barely affect them, but they come back and they disagree with me and this happens all the time. So in a lot of words, what happens is like, you might, you basically like give it to your boss and like your hands off at that point. SpaceX, the culture is you email your managers, both managers.

Here's the problems. As we see them, you sit down, you hash it out. Then the managers can hash it out. If they can't come to a mutual agreement, that just gets run straight up the tree. And this happens like basically on a day by day basis. And sometimes it just goes straight to Elon. And that you're encouraged to do that. And you're still on that email chain as a responsible engineer.

You're like, ended up, you're part of that decision process. If people can't agree on something that's mutually beneficial to the company, you run it straight up to Elon if you have to. He can be the final arbiter or maybe the VP of vehicle engineering can make the decision. That's pretty rare. You generally just pass that decision off to somebody with, you know, more decision making power and then they come back to you and say, this is what we decided. So that's like a good example of keeping it relatively flat, but the ability to go up rapidly.

So you go up to your two higher ups, they can't decide, so it's a stalemate. So it goes up to another two people. Essentially the top people are only focused on decisions that are non-obvious, non-consensus. The question is like, if you're on a stalemate, it's always like, why have you escalated it yet? And you just, you can just write up. And I've tried to talk to friends who would be like at other companies, you're like, I'm having this big problem.

And I'm like, have you escalated it yet? And you're like, you can't do that here. We're both investors in a company Varda started by Deleon from Founders Fund. Tell me about Varda. What was your thesis when you invested and tell me about the company today. Varda is an interesting one for me. I met Brewe, the CEO, when the company was an idea. I helped him with his first pitch deck and then wasn't really deploying. So I didn't, didn't invest. I didn't ask to invest, but I've stayed in touch.

And then, you know, around or two later, I was like, I know I'm a small check, but can I get in now? Because like, I love what you guys, like how fast you're moving, executing, and the capability that you're bringing online. So it's definitely exciting. I mean, that's an excellent team. And there's a lot of former SpaceXers there. It's the same culture. It's hard charging, high responsibility. What would you like the audience to know about you, about Wave Function Ventures, or anything else you'd like to share? My favorite time to get involved is before the pitch deck is even polished, before the idea is polished. Because I've been through that process.

I've helped other founders through that process. It lets me help the founder shape the story and kind of dig into the company as a potential investor in a different way, rather than just seeing, you know, what is then a polished pitch deck. So like, I'd love to talk to founders when it's just an idea and help them through that. And then I can decide from there if it's an investment or not. And they may love what you're building. Thanks for jumping on the podcast.

Look forward to seeing you down soon. Thanks for having me, David. Thanks for listening to my conversation with Jamie. If you enjoy this episode, please share with a friend. This helps us grow and also provides the best feedback when we review the episodes analytics. Thank you for your support.