How to Dominate Manufacturing in the US | Chris Power, Hadrian

Today I'm sitting down with Chris Power, the Founder and CEO of Hadrian. Hadrian has opened 6 factories since, like, 2022, latest one being 2.2 million square feet. I want to start this off with: what does it actually require to scale manufacturing in the United States?

Uh, a lot. Um, putting aside the raw horsepower and pain, I think actually it is GPU-style stations that can be modularily configured inside a factory, which gives you standards to automate against, and then a very complex organization that can build real manufacturing autonomy.

Uh, and then the third thing is a very careful balance of physical AI, software, and a new American workforce.

Do you want to go into what does it mean to have, like, a GPU as a factory?

So most, um, production lines are set up linearly, like a CPU. Like, you can do more things, but they're running linearly. So, you know, there's like a 60-minute component that then goes into a 2-hour assembly process, and you're kind of just running things like this, linearly.

The GPU is more like everything is so standardized and automated that you can run a high-mix, low-volume factory, like, as efficiently as a low-mix, high-volume factory, and manage peak capacity like you manage GPUs. It's very complicated to do, but it's the only way you get, like, real high-mix, low-volume performance at a factory scale.

One of the things that you talked about earlier today was this idea that you have to, basically, if you want a contract, you have to kind of build ahead of the contract, and you have to build a factory to show that you're serious enough to actually, you know, own it.

How does that work?

Uh, well, let me give you an analogy. So AWS, uh, you know, developed the largest cloud computing capacity ever, and then, and then second, started selling it to the market. But they already had the largest, you know, data store for Amazon engineering and Amazon retail as the first customer.

So it's pretty believable that, like, hey, you're running AWS internally for Amazon, you can start selling it to, like, Stripe. Contract manufacturing in the United States is this big chicken and the egg problem where it makes sense to anybody that, hey, you are designing a new low-cost interceptor, why would any good startup build their own vertically integrated factories at a worse unit economics position than Hadrian?

It doesn't make sense. What does make sense is that there is no starting point of capacity. Um, so Shenzhen, for example, if you start today a new humanoid robot and you have a circuit board, you're not going to start your own circuit board factory because there's a bunch of capacity, so it's obvious to you that you tap into it and you can trust it because it's at scale.

The United States has no contract manufacturing capacity. So if you want anyone to take a big bet on you, you have to basically build at least 50%, more like 1.2x the percent of your future sales capacity ahead of having those contracts.

Otherwise, you're going to be so far behind the eight ball of both optics and execution, execution being the most important thing because manufacturing, what we sell in manufacturing is trust, that you're never going to scale. Okay, so we're building a data center.

Okay. Um, we've never built a data center before. How, how much easier is it for you to rent GPUs from me in my data center where it's already half-built? As opposed to, like, on a slideshow. So American reindustrialization is largely the former problem, not because people are, uh, bullshitting, just because the capacity doesn't exist.

So you have to build it ahead of demand, uh, which takes a lot of balls and capital.

There's this funny thing that you mentioned earlier today, which was this idea that to actually get some of the contracts that you're going for, it just takes two years from the start line. And I think this is kind of unique where you both have to project out, here's what the contract might be, and build the capacity for it, and also not know necessarily when it's going to come.

How do you kind of, like, math that in your brain?

So, so the math is burn rate and execution risk. Execution risk is the easier one because once you understand that any factory build-out or, you know, I buy a 3D printer or a CNC machine or a robot, there are so many things that can go wrong that if you say it's going to take eight months to get here, I know from experience that it's going to be somewhere between eight and a half weeks to ten and a half weeks.

Point number one. Point number two is, if I told you to build a drone factory and a missile factory, you would think that that is binary risk in the sense that to win either of those contracts, you would have to deploy capex and human capital, but, like, both to have an equal chance of winning those contracts.

If, it's a big if, if you have a flexible workforce and flexible capex via software, the risk mitigation you can do is that 80% of your capex you can switch from program to program. Like data centers. You don't know whether the consumer, uh, ChatGPT is going to take more demand than Anthropic Enterprise Compute, but you do know that they're going to share the same GPU cluster.

You can get manufacturing 80% of the way there to the flexible risk rate if you are detail-oriented enough to break down every product into the manufacturing method and underneath the manufacturing method, the specific capex, which is extremely complicated.

But if you are capable of doing that, then you can make bets that are not risky to you that are extremely risky to everyone else.

Do you want to go through, like, one of those bets that is extremely risky?

If you were to build a, if you were to just look at, um, building a new $300 million casting facility and you had no contracts and the capex takes two years to get here, which means you're in production in year three, best case.

If you are looking at that linearly for, um, castings for turbine blades, for argument's sake, you'd have to be damn sure that the demand is still going to be there in year four and, okay. If you break down the capex that is linked to the process that is a subset of the output, which is a, let's call it a Mannell casting, and you do the same thing for, uh, munitions, 40% of which are castings that are never going to be additive because they're not, it wobbles too much.

And there are some unique bits of capex for sure, but 80% of it can be shared and

you are willing to have a 20% larger capex bill that buys you 100% flexibility. What you're actually doing is deploying 300 million capex against three demand signals, not one linear one. But that is down to the level of detail of, hey, uh, both tonnage of pour by alloy and for, like, a molding line, uh, you know, uh, envelope size of average component by floor space.

All this can be done. It's just stupidly detail-oriented. It's very precise.

Your previous factories were like 20,000 square feet and then 100,000 square feet, and then you jumped to like 2.2 million. How do you kind of think through what risk you're willing to take on what timeline in order to just build the capacity that you see demand for a few years out?

So I, I think there's, um, people think about risk very poorly, and I'll give you a couple of examples. Um, firstly, for large companies, capex risk is binary. What do I mean by that? Um, what I, what I mean is you may think traditionally that if you are building a large circuit board facility to win a large supply chain contract for drones, like for my friends at Saaren, may need to buy a lot of circuit boards, that you would really like to have the contracts before you bought a billion dollars in capex.

And if you don't get the contracts, your company's dead. That is true. What is more real is you have a 0% chance of growing revenue unless you spend a billion in capex because no one's going to give you a production contract unless you've already started executing.

And the second instance is you bought all the capex, and if you don't close the contract, your company dies. But at least now you have a more than 50% chance of executing the revenue. So it is binary in the sense that you need to deploy billions of dollars ahead of contracts, and it doesn't mean that that is not a company-killing event.

It just means that you've increased your probability to more than 50%, and if you don't deploy a billion dollars in capex, you're 100% dead or you're a hundred million dollar acquisition. And until you're at, I think, roughly 50 billion in scale, every capex bet is a company-killing event.

But what is more true is that if you don't do it, you're 100% dead. And that's how, that's how we think about it.

I remember two and a half years ago when I first interviewed you, we were having some conversation. Afterwards, I asked you, like, what do you think the probability is that Hadrian makes it? And you said something along the lines of 20%.

And I, I remember thinking, that's fucking wild. Like, that's crazy that you think that. But since then, you know, you've continued to, if you're saying like a 50 billion in scale, suddenly you're not going to die every time.

Right now you're still on that, like, curve. Um.

Yeah, because each, each year to grow, you have a time constant that your capex build is two years outside of the revenue curve. So every bet you make, you're dead. So the risk doesn't trade until you stop growing.

How do you think the risk has changed over the past two and a half years from, like, has, has the, like, risk of, you know, death gone from, you know, very, very high at 80% to like 30%? Or how does that change?

It's traded at 80%.

Crazy.

And it's not because we're risky. It's because the fundamental construct is that if you want to continue having venture returns and a relatively decent cost of capital, which is required for the reindustrialized mission, therefore you are always growing your risk balance sheet, let's call it capex or R&D, by double the sane amount.

And the only reason the risk retires is if you slow your growth to 20% because then your cash flows catch up to your capex bets. Now, you can get extremely good at, uh, forecasting, tracking, like we're not yellowing a billion dollars of capex.

It's very intentional. But if you, if you deploy 2 billion in capex now, today,

that gives you the opportunity to grow to 5 billion in revenue in 2028. Now, if you don't grow to 5 billion in revenue in 2028, you don't have any venture returns. If we also don't grow 5 billion revenue by 2028, the capex bill is such that, you know, you're turbo fucked.

So the only option is to do both at once. So until such time as you're growing 20% year over year, then the risk doesn't retire.

There's this amazing line that Jeff Bezos once talked about where there's this idea of two-way doors and one-way doors. And two-way doors, you can go in and make a decision and then walk that decision back if it doesn't work.

One-way doors, you basically go and if the thing doesn't work, you've committed.

Yep.

But Isaiah from Valor has this other idea where he's like, in order to move fast, I've had to sprint through some one-way doors.

Yes.

How do you think about sprinting through one-way doors in order to maintain a high pace?

I think about it in two modes. One is, um, and it's mostly about time. And so I'll give you a practical example. And we try to teach the same all the time. So let's say you're building a, um, automated welding line

and you have to deliver a working prototype in month 10, but your capex lead times are nine months.

And to be 100% accurate around what robot is it, what's the station look like, is it theright weld arm? It's going to take you three months. Best team in the world, it's going to take you three months. Okay.

That doesn't work because then you're in month 13. So how do you hit the timeline while minimizing the risk edges of, of what you can't control, which is time? The answer is if you break down that automated welding station, maybe it's a million dollars.

Maybe 800 grand is single skew things like the particular machine and the rest of it is a robot arm, a couple other things. If you're running a big platform like Hadrian, what you can do is say, "Hey guys, you need to spend three weeks out of four weeks being 100% sure that this big block of capex is 100% because that, you can't change that.

But let's hash in a rough idea of what robot arms are going to fit. And if you're wrong, it's going to cost us 50 grand because we have to reuse the robot arm for something else." But if you miss the timeline, the company's dead because the customer cancels the contract.

So you got to think about it as capex, capex and long engineering projects are all one-way doors and you're better off spending 30% more to bake in hedges because the one thing you can do is, "Shit, we ordered all the capex and we got some of the engineering predictions wrong, so it cost us three weeks because we screwed up an API integration."

Or, okay, that happens all the time. But then you're three weeks late. What you can't do is order nine months' worth of capex, it arrives, and on nine months and day one, you realize you're wrong and then you're at 18 months risk.

So you have to balance execution timelines against bets. But if you're dealing with the thing about software or sales or whatever it is, it's bad to miss a month, it's bad to miss a quarter, but you can recover from it because you can observe the problem and then pivot.

And your correction time is maybe, maybe, maybe, maybe you burned 10 million in runway, but you can, you can pivot. Capex is a whole different ball game. And if you don't have 100% certainty that the capex you're buying is correct, then you have to hedge.

Uh, and then you have to financialize that hedge across different trade spaces. So that's, that's the way we think about it. Um, the second most important way is one of my favorite lines of all time is, "Sometimes you just got to roll a hard six."

What does that mean?

It means that sometimes, actually most of the time, you just got to beright. And in businesses where you're dealing with long correction timelines, you just have to be really fucking good. And one of my, this is one of my favorite TV shows of all time is Battle Psycho Lexico.

And you're fighting an asymmetric war and you don't know what's going on. And everyone's like, "Well, you know, we got to, we got to go raid this fuel depot and all this, and the opposing force is 10 times bigger."

And you know, you're going to risk a lot of fighter jets going getting this, you know, fuel for Battlestar. And everyone's like, "Well, we're going to lose, you know, it's, it's very risky. It's super asymmetric. It's never going to work."

But the option space is basically you don't do it, you don't win and you run out of fuel, or you just win and you got to roll a hard six and you got to, you know, like in, in, in, in Dungeons and Dragons, it's a natural 20.

So mostly theright attitude is there's no way to manage this. You just got to roll a bunch of hard sixes. And if you, once you understand that that is the business, that's fine. But there are no one-way or two-way doors.

There, there are only one-way doors. And then you got to get very good at doing that and understanding the difference between something that you can fix later and something that you're not going to be able to fix later.

I'll give you a great example.

So usually when you're constructing your factory, you want to have a very good design and layout. And the layout determines your construction costs because some parts of the layout, you need very expensive foundations and electrical, clean rooms, et cetera.

If you're moving fast, you're not going to have the perfect layout. You want to really think it through because whatever the layout, like once you land the capex, it's stuck. And a lot of factory layout determines your operating efficiency in weird ways.

Travel time, sure, but like where people communicate and stuff like that. What the way, one of the ways we think about it is, okay, the, one of the longest polls in factory bring up is literally where the electrical drops and where the foundations are.

Now, if you want to aim for perfection, maybe you don't have to lay 20-inch concrete vibration isolated foundations everywhere. But if you want to kick off construction tomorrow, you're better off spending $25 million on foundations instead of $15 million so that you're buying your layout team four months' worth of simulation time and they can put the capex wherever they want.

And that is a calculation of terminal efficiency value of that factory.

Um, so we overinvest in things like, uh, we're going to do electrical drops everywhere. And the reason why we do that is you can pay an electrical contractor to do electrical drops everywhere and you're not going to use 30% of them.

That's kind of counterintuitive, but one, they can just do mass electrical drops in two weeks, bang them everywhere. And then a, you know, a manufacturing engineer can connect an electrical drop to a machine. So you want to arbitrage the very expensive lock-in things while not waiting for long lead decisions like foundation buildouts or capex selection.

And you have to think about the calculation of risk in a, in a kind of different way.

There's this idea in warfare of like asymmetry where if you have, like, take China for example, where they have like 232 to one shipbuilding capacity to the United States. If you tried to just front, you know, gun it and build a bunch of shipbuilding capacity in the United States, you couldn't beat them.

But if you can figure out like how to take out their shipbuilding capacity and stuff, you can kind of like even the playing field.

Yeah, left to value.

Um, is there such a thing in manufacturing as like asymmetric manufacturing?

No.

Fuck.

There's asymmetric products. Like I think you can design a thing that's got a cost to kill of a thousand, you know. There's no asymmetric, asymmetric manufacturing. There's

scale flex, scale flexibility and agility, which we can do. Um, there's no asymmetric manufacturing method where you magically going to make a Tomahawk casting for a dollar. You know, it doesn't exist.

When you built the first factory, uh, in 2022, I got to imagine that there's a whole bunch of things that you did back then that you would not do today if you were to build the same factory. What have been the biggest learnings of how to build factories really efficiently over the past like four years?

I'll break the answer into two, which is factory development and then, um, like factory design. And the first one is really easy and I'll tell you a story. So, um, one of the big things we do at our, one of the parts of Opus is called Mother Brain.

And effectively we take over all the capex software and reprogram it. And you need some decent APIs into the capex or at minimum you need a text file that you can read. But it's brutal engineering. Okay. So you're a small startup and you've got 6 million in capex to spend.

So we buy two types of machines. Makes sense. And you call the president of the German capex vendor because your team sent them an email saying, "Can we please have the API documents?" And they think you're a hacker because no one's ever asked for the API to the machine before.

And you explain it to them and they, they give you the API documents and you read them and it looks all fine. When you get to the factory floor, what ends up happening is both brands of machines, the API that reports off the hardware is completely different.

So it gives stochastic inconsistent results. And one of the capex brands, you hit all the APIs just to test it and 50% of the API calls return like Niche implemented yet in German. And you realize that actually what you're doing and, you know, you try to build software on top of it and one of them takes 12 weeks and the other one takes two weeks.

So what you realize is actually what you're doing is dealing with a bunch of unknown unknowns. So you build every single plan against the fact that everyone's going to fail against you. And you start building capability engineering systems around it.

So you never expect like the PLC of the world robot until you've literally integrated it, you have no idea what reality looks like, which is fine as long as you build boatloads of hedges into your timeline and you plan around the fact that the industry's fucked.

In terms of factory design, the biggest lesson is that, um, people design factories around the success case. And the success case is like 90% yield, smooth flow, et cetera. I'm not talking about like maintenance downtime, just one of the things we do, which I think is you can only do with software-powered factories, but makes complete sense, is you have a ton of slack capacity that's just based around failures.

So the analogy I'll use is like, okay, so you've got a five-lane freeway and a bunch of Waymo's and some of them are going to get screwed up at some point,right? And you've seen traffic jams,right? You hit the brakes, everything backs up.

What you actually want to do is as soon as someone has a blown tire, there's a, there's an off-ramp or there's a slip lane on the freeway where they can just wait, but it doesn't hold everything back. Most factories are designed with the capex, the people, and the, let's call it business processes that if one of the cars blows a tire, there's no off-ramp.

And I'm not saying you trash the car, you need to send it back and fix the tire, but you're, you're sacrificing this like kind of industrial engineering efficiency for like, you know what you would have wished when that car blew a tire?

Man, I really, really wish I had an off-ramp so I could put that to the side and fix it while not holding up five lanes of traffic at rush hour. Now, what it requires is that every bit of every station inside the factory is so standardized that you can switch things out like a GPU and they have high enough efficiency that it doesn't hurt your margins or whatever.

But that is, that is one thing that we've learned very well. Um, the second thing we've learned is that there is a limit to software complexity that doesn't rate match the physical world. And I'll give you an example.

So in, in manufacturing, you've got, yep, production and inspection, and you've got some, let's say they're components and they're components that have stable processes at some yield rate. And the important thing about yield is you can predict it.

Because if you can predict yield and it's 20%, you just make 10, you get eight and it flows through. Um, and then you have new product introduction where you're, you're, you're machining something, you're welding something, you're inspecting it, but you're not 100% sure that that's theright process.

And you, you know, you, you need to make one to, okay. Now you end up with this big queuing system. So something goes from manufacturing to inspection. And what you want to do in new product introduction is have the highest rate of the, the lowest possible impedance between inspection data and manufacturing.

Because the thing that creates cost or late deliveries to the customer is the, not the cycle time of the manufacturing and the inspection, but the, the back and forth. So if you make something, I want to inspect it in an hour.

So the feedback loop happens like this. So in software land, this is pretty easy, like queuing theory. So if you scan an MPI component into the queue, it goes to the top of the inspection queue and there's a, you know, this, this is very easy software.

In reality, it never works like that. So what you do is you have a physical set of capex that's just for the fast lane. And there's this odd property where that's not just because humans are dumb, it's not because the, the, it's, you literally need to separate your fast response part of the factory.

It can be, there can be a rope between it. And the interesting thing about that is software gen, software tends to design itself towards a physical layout of systems. So you need to kind of like bake the factory to the natural software workflows and that you want to match that as close as possible.

So it's interpretable, you know, and the rest of it is like, you know, you just, you just got to fucking go like hell.

On kind of the same thread of sprinting through one-way doors, what's been the best example of something that went absolutely horribly wrong, should have killed the company and somehow you're able to like pull a rabbit out of a hat and stop it.

Okay. So one very clear example is, um, this was a couple of years ago when we had, we had a lot of money for a Series A company, but in aggregate a lot, not a lot of money. Um, and we're landing, we're landing capex two months before customer contracts hit, which is what you do, you know, um, ideally, you know, I, I, I owe you a bit of compute and I switch on the GPU two months before.

You don't want a day before, but yeah. And we, we, we bought all this new equipment, incredibly reputable vendor. And about a couple of weeks in, about four months before we had a bunch of contract deliverables for a very early customer, um, that was very important.

But more importantly, we were coming up to a fundraiser. You couldn't miss a delivery quarter. Cause the whole bet is, you know, obviously the thesis is real, but can you execute against it? And two of my top guys came running up to the office and they said like, hey, and I want one name of the vendor.

And there's, you know, there's some rust. Okay. And we think it's terminal. Okay. And we've called the vendor and they think, you know, it's not a problem. There's not a problem. Now our guys are like the best in the world.

So you got to assume that they're like, yeah, days at least, if not weeks ahead of, you know, the vendor's self-management of. And it turns out that within, within 48 hours, we did assess the fact that a subcomponent on these, in these bits of capex, um, they, they had switched out the subcomponent vendor in this country that produced the capex.

So, you know, the car analogy would be we kept buying Toyota Camrys, but they'd switched out the tire vendor, which is fine. People, people do that all the time. And we worked, and because of the serial numbers, like we had 40 machines and the serial numbers were like 600 to 800, which means there's a batch problem,right?

It was propagating really fast. So we realized, and you know, the response plan from the vendor and the warranty was basically like, well, we'll get it done by Thanksgiving. And Thanksgiving would have killed the company. Cause their, their fix it plan was, we're going to deconstruct machines one by one.

We're going to replace this stuff. They didn't really understand that you, you had to replace it completely because it's rust, you know? And the guys wereright. I spent 48 hours on it myself and I, I agreed with them.

Um, and then I spent a week straight yelling at the president of this company

and told them that I would, if they didn't fix it in the next four weeks, the company would die, which was true. That's not a bullshit statement. Cause the first thing is they didn't realize how bad the problem was and what the impact would be.

Um, you know, there's like eight weeks of downtime and then there's eight weeks of downtime kills the revenue curve of the company because it, it's like missing a sales quarter in SaaS. Like suddenly your burn rate quadruples because you've got all these salespeople in no air are.

Um, and then force them to fly in 30 engineers from this foreign country and get the whole thing done in four weeks through pure just like, you know, I was going to headlock these guys. So that's a real operating problem, uh, that would have killed the company.

Uh, there are, there are many others. It happens about once a quarter, but it's manufacturing. This is the game.

What's the most recent company killing event?

Nothing, nothing, nothing in the last three months, but there's one every month. But I think this is the point is you're making, you're hard rolling sixes every quarter and there's always a company killing event once a month because manufacturing.

I recently interviewed Keller from Zipline.

Great.

And yeah, he's great.

But by the way, I, I, I think Keller is probably the best Deep Tech CEO, top five.

I was recently interviewing him and his entire timeline was basically, you know, like five years from now or something. He wanted to have a million deliveries a day on his network. And then Uber came in like a month before the interview and basically pulled in the entire timeline by two and a half years and said, I'm going to take all of your, all your capacity.

And this completely radically changed like how they are thinking about scaling their business. Cause that's just one customer and there's going to be a hell of a lot more in two and a half years. When you're thinking about not only like pulling in timelines, but also resetting your expectations upwards.

So you go from like your projections were, I'm going to make a billion dollars in revenue three years from now. And suddenly you're like, actually I'm going to make like five billion three years from now and I need to make a billion next year with these like long lead times on not only being able to like buy capex, but also get contracts for that.

How do you like manage that?

So, so for us, this actually happened. So the founders fund led out Series C, I think it was a year ago, and then Tierra priced it to C2, and we just raised a Series D. And the main reason was because every month we were raising the forecast.

And it wasn't like a sales thing. It was just like, yeah, we, we barely have a federal sales team. They're great, but there's eight of them. And yet there's the, yeah, every quarter there's two billion more in pipeline and we're not asking for it.

So you've got two problems there. You've got a burn rate of talent problem, more Opus engineering to make things more scalable, more for deployed engineering to build more contracts, but your rate keeps going up like this. So you can't, you can't plan.

And you've got the capex problem. So the way we think about it is in aggregate, you know, are we sure that we're going to close this contract? That if we close it, we're going to need 50 more robotics engineers?

No, no one does. Highly competitive bidding processes, competitors of all sides, but we do know that we have a 95% statistical chance across 10 contracts that we're going to need at least a hundred robotics engineers. So it kind of doesn't matter.

So you aggregate everything and then you, then you always go like, then you bet into the slope because in a fast growing business, you can always stop hiring for a quarter. You can never, uh, restart hiring on the capex side.

Once you've got multiple programs, at least for our business, which is factories as a service, we can start aggregating capex and robotics and stuff to shared Lego bricks and start forward buying them. So you're kind of pulling capex from inventory.

Now, 20% of the capex, you can't do this because if there's one weird 3D printer that this one contract is going to use, it's not an aggregate, but look, we standardize around FANUC arms for everything: small, medium, large, big, big, big.

In, in specific use cases, that is inefficient at the company level. What it means is that we're buying 300, 200, and a hundred, and then our programs and capability engineering teams can pull them and plug them into whatever they need.

And you can massively compress the risk and timeline. That is actually very hard to execute. Um, cause you need to take a program to a contract forecast to a simulated, which, which arm are you using? That's not, not trivial, but the math is really easy.

And scale has this interesting property of making that easy. But the challenging thing is the lead times to adjust to that. So, you know, what is recruiting? Recruiting is an incredibly strong firewall to make sure that under pressure, no one's going to hire a C plus engineer.

Okay. Let's say we have that. Then it is marketing and outbound sales.

If one recruiter can produce four great hires in a highly competitive environment and it takes 90 days to hire a great recruiter, by the time you understand that you need to double recruiting for next year, your slack time is six months.

Okay. Hire a recruiter, 90 days. They, so you have to kind of just bet on scale. And it has this interesting property where it looks like a risk, but it's actually, it's actually not. You're never going to fire people.

You're just going to like pause hiring for a month at max and let it catch up. But that was intentional because the properties of all these complicated capex and long lead time businesses is everything gets easier. The bigger you get, operating the company gets harder every week because it's complicated, but the risk decisions get much more stomachable.

The bigger you get.

Sam, I want to talk about this idea of like a merchant properties of scale.

Yes. And that's real. Yes. And they're not predictable until you're in it. And then you understand what it actually means.

I remember this story where this guy wanted to start a like machine shop or something, and he wanted to buy it like a laser, and he wasn't able to get any financing for this laser. So he had to buy it outright with his own cash.

The gym.

Um, actually, yeah. And then suddenly, as you kind of scale and you're generating a lot of revenue, lots of people line up to like fund it with debt and other mechanisms. How do things kind of shift from zero million in revenue to like 1,000 million in revenue on the, your ability to like scale things?

The operation of the company gets harder, but the fundamentals of the market get far easier. Um, material suddenly gets cheaper. Debt is easier. Being at the front of the capex queue is easier because you're everyone's biggest customer. Cause the market adjusts every business wants to grow.

And if you're the fastest growth engine, the market will correct to

you. It's just getting there is very hard.

What have been the biggest things in this business where at the beginning they were just impossibly difficult? And then over the past couple of years, they've gotten ridiculously simple.

Well, like, um, it used to be setting up a new line of machines was like a fucking

hell on wheels. Um, and it is hell on wheels for some of the teams now, especially when we're doing new things, but like, we've got a playbook. You know, it's going to roll out on some cadence, you know.

Right now you're at something like 725 ish employees. And over the next like four months, you're going to almost double that. I think hire something like 600 people. How do you design the company where it can actually absorb that new influx of hires?

So, um, there, there's a couple of things. Um, the first thing is that our org design is, is built around modular capabilities and programs with extremely strong engineering leadership that roughly have a maximum headcount of 30 to 40.

So the problem that most people face is accidentally hiring bad people fast or hiring great people that can't integrate into the company. And in, in very large software teams, like you have a thousand backend engineers, objectively true. The way we run is that like, um, the weld engineering team and the backend software team for weld engineering together are going from 30 to 70, which for two competent people, the chief weld engineer and the chief software engineer, not that bad.

So we have 80 of those. And if you get the leadershipright and you have the first five engineers have very high standards and you just tell them like, don't hire anyone that is not up to your bar, uh, you can scale the output and capability of the company without running into these weird bottlenecks where like all of a sudden you have a hundred backend engineers and now there needs to be eight managers because you're fragmenting it across the board.

What you have to do before that is fragment the APIs of the organization such that the weld team doesn't even know who the scheduling team lead is because it's just APIs and you need to. So that's, those are two things.

Um, the second thing is that you need to pick, you really need to pick what you don't care about.

What does that mean?

So you need to care about, most people start with like 10 cultural things that are really important that you, you know, and you need to get down to two. And mostly because in their onboarding, you're going to say like, look, we're going to fire you if you don't do these two things.

Um, and for us, they're very clear. The second thing you need to do is have extremely scalable testing where you're not reliant on a new engineering manager designing a great hiring process for a weld engineer. The company has spent a lot of time on the weld engineering interview test and anyone who passes it, unless they're trying to be fraudulent, is going to be default good.

But anything other than that is kind of false. Like you're not going to like everybody. You're not going to, um, have the same execution culture. As long as you have the same pace and the same methodology and the same core product development principles, it's going to be fine.

And then, you know, the company looks very different every month and that's, that's fine. You kind of got to really, really, really pick the sacred cows and most companies have far too many of them, or they're not specific enough.

How do you decide which cows to accent in your case?

Judgment. It's very nuanced. Some of it's observing, like, hey, we, we hired 20 people and they had these six rules and this kind of, it wasn't, the clarity of the writing wasn't quite there. Or it actually doesn't matter.

You know, it doesn't matter. Like, um, you know, there's like luxury beliefs. Okay. Like a lot of cultural values and a lot of execution values are luxury beliefs. So like, what are the two or three that you're really going to care about?

And are they your most, the most important thing? Or is that a luxury of being small? Um, and, and then you, you know, it, but it's, it's, it's, it's much more art than science for sure.

Let's say you're like a couple hundred million in revenueright now and you're going to scale very quickly into like the billions, but your pipeline is growing even faster and your like opportunities are growing even faster. How do you decide which things to go after versus which things to just say no?

Mission first. Um, we have a ton of commercial opportunity that we can't tackle because we don't have the resources. We care a lot about re-industrialization. We care a lot about defense. Point number one. Point number two is

where are the, where are the doors that are open and are going to shut and never reopen again? So there are some programs that we work with the Department of War or the Primes where it's the first time a customer has ever let a contractor into their division in the last 50 years and they're probably never going to do it again.

You know, and there's 20 billion behind it. There's a lot of impact behind it. So those are, cause you're never going to be able to like get in there again, as opposed to we're turning down a lot of, um, factories as a service business for low-cost interceptors.

Makes perfect sense. There's a ton of people who've got great designs. Um, they haven't scaled manufacturing. These things are not hard to produce. You can very imagine in your head a low-cost interceptor factory with a bunch of different designs and it's very efficient for the market.

It's efficient for us. We print money. It's much more efficient for the Neo Primes because it's literally cheaper for them to go with us. So we can load balance it than trying to, okay. You know, but, um, there are going to be many low-cost interceptor programs.

So while I hate not being able to partner with those companiesright now, it's, it's below the line of mission or it's never going to happen again. And then the fourth thing, which is obvious, is to deploy a factory,

maximum Lego bricks inside that factory are 50. Um, we have a long R&D roadmap to do everything. Some programs, our product team will say to deliver this factory, it requires, um, 20 Lego bricks, 19 of which we already have.

Okay. So we, we might be five engineers worth of R&D risk. Some programs, we need 20 Lego bricks and we have one of them. Okay. But it's not R&D, but it's more like company-level execution risk. But those are the easy decisions to make because it's like, how good is your HR software versus the requirements?

Mostly it's mission and then, uh, like open shut doors.

If you have like way more demand than you possibly like service today, what is the like bottleneck for deciding to grow faster?

A very careful sense of how over the skis the entire company is.

Does that kind of shift day to day or week to week?

Yeah.

If you had to like think through the past year, what have been moments where you felt like you were over your skis versus like completely off on the brakes?

I think that's slightly the wrong question.

What's theright one?

I think as an individual, you have a near perfect understanding of where you're at and your own technical capability. What I find is difficult is that, um,

my judgment is based off my capability to rapidly hire engineers and build something never been seen before or my 10 best people's capability. But what you have to realize is that the 10 people running the 10th program, you've never met before, and you have to put aside your, put aside your priors and let the organization show you where reality is at and then judge off the worst case scenario.

So like, imagine if you were recording 20 podcasts and you never wanted to make less than a B plus podcast, but you're, and you're doing seven a week by yourself and you know when you get to eight, the eight one is like a little bit wobbly because you're tired.

Well, the 10th podcast interview you are is not going to be you. Um, but you're growing so fast that you're kind of like, you're not going to show the results of that bad podcast until you see the YouTube numbers in month six.

So how much, how much, how fucked are you before you can see the results? That, that is, that is, uh, very intuitive and counterintuitive in your own judgment.

Has there ever been a moment where you felt like you were doing 10 podcasts a week?

Oh yeah. I mean, uh, the first 18 months of the company for sure, we raised 1.3 million dollars and decided to build 200 million dollars worth of software and, you know, okay. And then definitely in the last nine months,

uh, but I think I'm much better at it now and I can self-judge like, wow, you know, that team is actually doing really good and I just fucking blew them up. Like I'm pretty sure I haven't slept, you know, it's like drinking too many beers,right?

Um, by the time you had the sixth beer, you're not going to be able to judge whether it's a great idea to like leave your keys at home or not. The only way to do it is, um, write down a rule that is once I have my third beer, no matter how confident I'm feeling, the keys are staying, you know, put aside.

Because when you're tired or when you're manic or when you're winning or losing or whatever, you actually degrade your judgment so much. So you have to write down a series of what I call Ulysses contracts with yourself, because when you're in this state of sleep deprivation or, or hype, and actually, actually I think it's more hype is, is what kills people.

A great phrase a big hedge fund investor told me is, and this is how I think about it is, why do real estate cycles exist? Real estate cycles exist because, um, all new real estate investors are 25 years old and have not felt the physical pain of losing their shirt on real estate bets.

And therefore the cycle continues because they, they literally don't emotionally feel like they're in a real estate bubble. So, which, which is obvious because why is the market growing? Because the market is a poor proxy for a mania or in depression.

So, and the new wave of real estate investors are going like this. And it's, it's not the fact that there's a risk book that says you're over leveraged. It's that like you have not felt the gut punch of losing your shirt.

Okay. So if you're really serious about it, then there's a couple of things, there's a couple of rules you don't break. And the point of it is you have to write them when you're clear-headed so that when you're tired or over your skis or whatever, you don't, you don't suddenly decide that the rules are no longer valid because you're incapable of, nurses do this very well, you know?

Earlier today, you mentioned that when you started the company or even three years ago, you just didn't know almost anything, especially on like the government sales side and how that worked. But then you also had this thing where you were like, but you just fucking do it.

And how do you kind of, in your mind, decide what is worth just fucking doing versus like, oh, I don't know that and I'm probably not going to know that.

Well, I think if you are starting the company

and let's say that I think a common pattern is you're, you're great, you're a great engineer, but you're terrible at recruiting. And the number one thing you need to do as an engineering CEO is recruit, but great people.

And let's say you've never recruited anyone in your life. Well, most people are wrong. Um, you can ask people for advice, but the best way to get good at weightlifting is just lift weights. Okay. So, so what I did literally in the first 12 weeks of the company, and I've never really recruited before, is, you know, I read a couple of blog posts and then I was just on 20 screening calls a day.

I was doing outbound, I was screening people, I was interviewing people, blah, blah, blah, blah. And very quickly after eight weeks, you pretty much get it. Um, what most people prevent themselves from doing is being completely okay with, it's not failure.

You're just learning really fast, but it's like bodybuilding. Like if you've never lifted, if you've never done a bench press before and you can't lift 200 pounds, that's fine. You just need to start with 20 and ramp up.

But for some reason in business, whether it's product development or engineering or manufacturing or go to market or recruiting or finance, all this stuff is super learnable, but like these people get very afraid that like, I'm not great at this in two weeks and suddenly their whole construct of are they a good human being or not collapses.

That's point number one. Point number two is if you're a really good CEO, apart from the basics, which I struggle at every day, I think if you're running a complicated company, you have to be the second best person in the company at every function.

The reason why I say second best is you want your CFO to be the best accounting leader, capital markets. You want your VP of talent to be the best recruiting leader, et cetera. But if you want to hire the best people, the best people report to the best CEOs, which means you've got to have empathy for the function and you've got to be able to partner with them on being world-class.

I'm not the best engineer of the company by a long shot, but I am good enough that I'm probably one of the best engineering CEOs that people have worked for, which means I can make real trade-offs with staff engineers in real time and make decisions instead of being like, oh, write me a spreadsheet.

Same thing with finance, same thing with M&A. That doesn't mean you're doing it yourself, but you have to be good enough to be able to judge what is a good executive or IC or not. And the only way I've found to do that is, you know, you don't have to be the best engineer, but you better fucking be able to write some level of code to be able to judge what good or bad looks like.

It's like, who's the best jazz musician? Like, I don't know, like not me, but I know enough to be like, that's world-class versus that's like a poor Rembrandt, you know?

If you think through like the competitive advantages of a business like this, I think one of them is the finance side and being able to just raise unimaginable amounts of money and on good terms. How did you kind of think through building out the finance function so that you guys can raise huge amounts of debt and all these weird wacky structures in order to pay for all this build out?

So, um, first principles and the first principles of capex heavy businesses is there are only two successful capex heavy businesses. Um, number one is you happen to be, uh, in a market like early SpaceX where the customer set is already prepaying for your build out.

So NASA prepaid all their contracts. The Pentagon doesn't do this. Um, retail customers of base power or Solar City don't do this. Um, it's very rare. Data centers don't do this. If that's not true, then you have to architect your business model such that you can access long duration infrastructure credit at some point, at some point.

And you have to be able to be smart enough at predicting that over a four-year time horizons that you can tell your investors, where's my cost of capital and duration curve coming down? Because one of the things you have to subsidize.

So, allright, what is engineering? Engineering is a subsidy of gross margin because you're saying like, hey, I'm inventing this new, um, I'm not picking on Jordan, wire harness business, and at some point we're going to be faster and cheaper.

There is at least a year, probably a two-year period, but that's a lie. So, but everyone's used to the fact that, hey, hire software engineers and at some point it's going to be more efficient because you're building more software.

Okay. That is a subsidy of your gross margin. Now, as a good CEO, you don't want to be more than six months off that. Because if you say it's two years and ends up being six, then you know, you're there and us.

It's the same with capex, capex duration and capex cost. Um, so we spent a lot of time saying what do the business metrics have to be and what is the business stability, contract duration, capex, useful life, capex uptime such that we would be able to scale factories using low cost of real estate or infrastructure credit.

And every round, you know, we started off with, uh, venture debt and then equipment leasing and then, you know, a couple of rungs down, but we were underwater on our financing costs until this late series B. And the only reason why people let us do that is because we were good enough financial engineers to be able to paint that, paint that picture and paint it accurately.

So if you called someone that may fund you in year four and they said, hey, if there's a sort of business that looks like this and they're doing a hundred million in revenue and the durations look like this, you know, is this financeable at this rate?

People that we never met before. And a venture investor calls, you know, someone in a big credit fund and they say, yeah, then there's trust that you'll get down that curve, but you have to run the curve like you're running a software engineering curve or a cost of customer acquisition curve, you know, but it's first principles because it has to happen.

Otherwise, it's not a venture scale business and eventually the game doesn't work.

Was there some process, uh, that you had to go through where you were like educating venture investors?

Yeah. Every round.

What has that like story or narrative been like throughout the different rounds?

Well, there's like big demand signal, what's changed, et cetera, the normal venture stuff. The financial side was mostly, um, I would argue until us, there is no, I think every venture capitalist that understands manufacturing or capex heavy business is invested in Hadrian.

And every round we have to teach people that our way of cost accounting is correct.

What is your way of cost accounting?

It's nothing fancy. It's just that like, um, very few growth funds or venture capital funds can accurately understand a manufacturing balance sheet and how we think about it, which is very different. Not like we're making up our own accounting rules, but we do, we do think about it very ac it's a first principles way of thinking about where costs are in manufacturing, where they aren't, and it's correct.

But, you know, you're tapping a venture market that's used to investing in product companies like Andor or SaaS companies like Databricks, both of which carry almost no capex. And manufacturing accounting and gap accounting and revenue recognition is extremely different than any other business model.

And it's not that invent venture investors are dumb, it's just that they've never seen it before. So you have to be better than their LPs at being so accurate about your numbers and how you present them and how you think it through, they can trust you enough to over time outsource, outsource their critical thinking on that domain to you, which is a big leap.

But now when we explain that we're going to scorecard a business unit like X, it's largely understood that we've thought it through in the level of detail that if it was a public company, anyone would look at it and go like, actually, that's not inventive, but that is the correct way to outside in think about this company.

Very high bar, but that's the only way to do it.

When you think about the, imagine competitive advantages or like vectors and each one, you know, it could be like you get a smaller lead time or like better financing options and stuff for customers, or you might be able to get raised capital cheaper or on a different timeline than other competitors.

If you think about like your competitive advantages in this business and the other ones that could exist, but you've decided like these are not the vectors that we're going to optimize for, what are those?

So I'll say two things is lower cost of capital or large quantum capital are a derivative of being better at executing than anybody else. I don't think they're actually good fundraisers or bad fundraisers. They're better storytellers, but I think people that actually end up with large amounts of capital have better businesses.

In terms of the things to not optimize for, and one of the things that I was wrong about is speed. So we started off with the theory that, so there, there are, um, there are two markets in American manufacturing very broadly, at least on the technical side.

And one is print, no print. Um, and our good friend Jim Belozic runs an incredible no print business and there's nothing wrong with it. It's amazing. We were always in the print business. Technology is harder, it's higher compliance.

Um, and we picked our first customer, which is a very large rocket company with the theory that they're the toughest customer. And if we built a thing that they were vaguely happy with, we could sell to the primes, which is a pretty standard designed venture pattern.

Anyway, it turns out that, um, when we expanded to other customers, you know, the theory being that there were many other growth companies and they would pay for speed at this complexity, high tolerance print level. Anyway, it turns out that two things were wrong.

Thing number one that was wrong was there were no other companies in that rocket company of any sort of scale, at least at that time. So there were no other startup customers and that everyone else's business model, um, they didn't care about speed at all.

And this particular rocket company, I think is best summed out as they're the only space or defense prime that can convert speed to value.

So, uh, now there's, there's an outer limit to this statement, which is like you can't be slow, but the engineering or manufacturing cost and complexity of being everyone else is at 12 weeks and you're at eight and you're cheaper versus you're at two and everyone's at 12 weeks is orders of magnitude.

So most of the market just doesn't value speed. The market very much values flexibility, scale, total program cost, um, a lot of other things, but that, that was one thing I got wrong in the early days that we pivoted into.

When you think about the government side, uh, one of the main inputs to having the contract in two years is having people that trust you today.

Yes.

How did you learn to build the relationships where the government wants you to win and is like incentivized for you to win?

I will tell you how we build trust, but it is an inverse of what you just said. Because what you just said was we want government contract, therefore I would want to build trust.

And I have found in my personal relationships, venture investors, um, certainly the government, certainly large enterprise customers, that if you are serious about the mission and you are serious about creating value, um, the first thing you should do is create value and then

be so trusted that people come to you with your hardest problems that if you design a solution that can credibly execute them, you will receive a contract. Instead of trying to

fake trust or credibility in order to win a contract, the tactical answer is we spent two and a half years telling the government in Biden one we're not ready to tackle any of these problems, but here is our advice on how we think you should think about policy and manufacturing to the point of near frustration.

And it was real because we could have probably won contracts earlier, but there was nowhere even close to the line of execution or scale. There's a lot of stuff that had to be worked out,right? And I think when you do that honestly, not as a sales tactic, because people do this as, oh, shucks, we're not really ready for this.

You know, if you do it honestly, it builds so much credibility over time that when you do say, we are ready for this, people do trust you. And equally, we have built a reputation of saying no to many contracts because we don't think they're going to create value to the customer.

And that is, I think, a slow way to build a billion-dollar company, but it is a very fast way to build a hundred billion-dollar company. Like, do you want to go on a date and kiss the girl by like acting like you're rich and taking photos with your posture on TikTok, or are you looking for a wife?

And we are just the type of people that we're not happy to wait, but we are honest actors. And by being honest and execution-driven, you build credibility versus I want credibility and therefore I'm like marketing to credibility.

One of the things I really like about you is I think you have like a slightly different worldview than most people that I've met. And I'd almost like to use a metaphor, which is like, imagine someone starts a company in defense and they're like, we are going to have contracts next year.

And you, knowing what you know, are like, well, there's just no way that you're not going to have contracts before 24 months or like 36 months. But people kind of believe that because they just don't know.

Sure.

What are the biggest areas where conventional wisdom in your experience is just completely wrong and you've figured out some unknown truth?

In any industry, there are many false doors. And what I mean by that is, um, I'll use an analogy. Let's say you go into a party and it's a really great party full of really great people and you really, you know, you're young and 22, you want to make friends and you really want to have cigars with the people at the, you know, the balcony because that's where the cool people are.

Many people, I think, manipulate the social sphere or you can stand next to a certain person and signal some friendship or insider basis and, and then you get invited to have drinks with the host, for example, and you're fully convinced that you're like in the club now.

In reality, you've taken door number B. Because most long value families, the government, enterprise customers, people that you want to hire, people that you want to be friends with, at a certain point of scale or success or credibility, um, you are forced to build these trapdoor immune systems such that you're catching disingenuous people.

And I think an unspoken reality is that most people who are disingenuous and are trying to seek power genuinely believe they are in the room and they have simply just fallen into the abstract door number B trap. And once you really understand that and come to terms with the fact that the only people who should be in the room are the genuine people who are not seeking power, they just want to do the thing, and you just do that, you will accidentally find theright people who you want to be with or theright customers.

So when anything is going on, I think if you're a truth-seeking organization, if you're an honest broker and your job, your job in enterprise sales is to advise and provide solutions to the customer as an expert and only bid on contracts that you genuinely believe that you are the best to deliver to and you act like it, then the market will adopt your vision for the world.

Versus, hey, we're going to throw up all the marketing copy like we're the thing, but we're not really the thing. Again, I think it's a very fast way to build a billion-dollar company or a fake marriage. I think it's a very slow way to build something real.

Because I do think that honesty and seriousness compounds in strange ways that are not, you can't write down on paper.

Do you have any examples of an experience that occurred due to this compounding?

The entire company. Every round gets easier for us, not because we're good at fundraising, because in every round we have provided more objective data and more honesty around what is going to happen the next time around that people pass on rounds and then they come back around.

It's not because of the hype cycle. It's because they realize like what you said you would do in the last 12 months seemed like absolute horseshit and 80% of it came true.

So because what you're ascribing to in capital allocation or large government contracts or talent is trust that gets built over time. What people mispredict is how fast that compounds. And like there are no hacks. You just got to do theright thing

and generally the market will work that out.

Ideally, you spend almost all your time on like the highest leverage activity that you possibly can be working on. But at least in my own experience, I've only been able to really understand what the high leverage activities were by doing a bunch of stuff that wasn't high leverage and then realizing in hindsight that is not what I should be focused on.

How has your kind of like time and focus shifted over the past couple of years to the things that are actually high leverage versus the things that you thought were high leverage?

Uh, it changes every day.

And there are different, there are different, uh, I think inflection points. And I'll give you a tactical example. So I've always thought our onboarding process was like, okay, at best, no one's fault, you know, whatever. Leverage point number one is you say like, hey guys, I think this is shit.

We need to fix it, but not that fast. Like, okay. Now I think it's one of the most important things because we're in the process of hiring 2000 people. So spending an extra hour on onboarding means that the productivity or mission orientation or vector of 2000 people is massively impacted by that hour.

So that's a, it just depends. Um, sometimes the business surprises you with like things are really stable. Um, things are not stable and you need to massively intervene. It's mostly about what you can pay, what you can fix in three months versus what you got to fix tomorrow and where the asymmetry of that plays.

And that's, that's not obvious. Um, you know, sometimes it is writing a better performance management document so the whole company does it better. And sometimes it's performance managing an individual contributor that reports four layers deep. It depends. It's, it's a hard, it's a hard call every single time.

And I know when you're good enough at this.

I thinkright now you're probably kind of experiencing like one of the best bottlenecks or problems that a startup can ever face, which is like insane demand and it just keeps on ramping faster than you think it will. Same thing with Keller's business.

When you start to see the curve go like this consistently, you need to stop planning for a linear growth curve. And you need to figure out like, okay, here is where our actual projections are going to go or our actual business is going to go and then project even further out and really shift, uh, the way that you think about things.

How have you kind of done that? How are you, how are you predicting an unpredictable future better than you used to?

It's all vibes. Um,

I think that what you want to do is not predict things at all, is that you want to

massively invest in velocity

and let the market and the company show you how fast you can grow. So I, I guess a way of saying this is how many engineers can we hire because our current productivity rate is X and they, you know, therefore we, okay, when you've got infinite demand, the number one thing you should work on is

where are we not at max velocity today and let everything accelerate past the business. You know, I remember seeing this interview with Sam and, you know, he's running the org,right? The org is this whole other thing. And he was always fundraising ahead of the org.

And I think this is a great phrase because you can always find, you can always slow things down, you can always find more customers, you can always say like, hey, 200 engineers going to work on this. What you can't change is that what you can't, when you want to speed up, you can't is the velocity of the org.

So when you've got infinite demand, I think the thing is just like work on the, like maximize the velocity and orientation of the org and let it go as fast as humanly possible. But like, don't plan what you're, I think Ramp does this brilliantly.

There's like, we need to ship this product by this date because we've got some like hairbrained VP of product scheme. No, no, no.

Just have the highest product and engineering velocity and the highest taste for what products to ship next. Ship the products as fast as possible and then announce them as soon as they're ready.

Versus like this kind of, we need to do this Salesforce release by December Q4 and therefore just go as fast as fucking possible and focus on the cadence of how fast you're shipping stuff and then let, and then announce it when it's done.

And then see this clip rate go up, up, up, up, up, up, up, up, up, up, and then try and build a lot of, like try and build a lot of leverage. Uh, that's kind of how I got to this.

Imagine that as the CEO of an organization that's moving like this, you are effectively, when you start it, you're on this treadmill and it's set to one. And then as time goes on, it gets reset to two and then three and then three and a half and four and it just keeps on moving up and up and up.

What does it actually feel like when the organization and the company starts moving at a pace where you're like running at five miles or six miles or seven miles an hour and then you're like, ah, fuck, I wasn't, I haven't trained for, you know, eight or nine or ten.

Yeah, I think the other funny example is the treadmill's going at 10.

Right.

And you were running at 12 and then you got to be at eight for a little bit. Again, I think it's down to like what algorithms that the company are running that you're very happy to let run for eight to 12 weeks and what has to be fixed tomorrow because it's going to trail in a weird way.

Um, and then the meta game I found honestly is energy management. And what I mean by that is like everyone is, so what I find is I can basically work as hard as possible. And I think that's a wrong way to think about it.

I think it is through all the problems of the business, what are you, what gives you more energy or neutral energy to solve so you can keep going as fast as possible versus what like ties you out. And I think it's honestly this is a very human thing.

Like let's talk about the gym. You talked about a treadmill. Some people, they get on the treadmill and they can run 20, 30, 40, 60 minutes and they feel great. Some people can lift weights for 60 minutes and they want to, they want to do more.

Some people, it's the opposite, you know, yeah, I hate cardio, but if you're sitting in front of a deadlift, I can go all day. And that is a bit like business. Like I can, um, do complex product management every single day of the week.

I've got about three days of high switching cost in me and then I'm cooked. So I really do structure the things that I'm working on based on some things I work on for an hour and it burns me the hell out and I'm like done with the day.

Um, or at least I'm at 50% speed and that's unique. And I think it's the same with your people. I think it's the same with the organization. Like what are you doing? Like where are you kind of at energetically?

Not to be woo woo, but I do think this is like a real thing. Um, and honestly, like there's a hundred difficult problems to solve in the day. I think you got to get really, really good at like not taking it personally.

So if you're going to do the hard thing, like what I found, actually Sam taught me this, which was my number one thing is

something goes wrong, I got to fire someone. You know, I got to do something hard, empathetically difficult, let's call it that. And I think your gut instinct is write it down, schedule it for Thursday, and you blow the whole week.

The kind of, it seems flippant at the time, but like flicking someone an email being like, hey, we got to talk tomorrow. It's about this topic, no big deal. And it's in the calendar. You remove, like as soon as you find a problem, you got to start actioning it.

And I think your life gets a lot easier. So my main, my main thing on this velocity thing is, is effectively like, um, if you're the type of person that puts things to aside because they're emotionally painful or they're difficult, and for some people this is engineering, some people this is HR stuff, some people this is, you know, they don't want to tell the CFO they spent too much money, whatever it is, kind of just do it in two seconds as soon as you see it and don't think about it.

I will go to extensive like working until midnight, drafting everything and leaving it all in drafts. So the only thing I have to do in the morning is send all the hard stuff out of drafts. It works. Um, so that's, that's, that's some of the things I've found of like keeping up velocity where like how much pain can you bear in a day?

You kind of got to trick yourself into like, well, you know, Ty, you're, you're, you're a lovely guy, but I don't think we're going to give you an internship,right? Um, if I stood on that and like I tell you on Friday, like I'm probably going to reschedule the meeting.

If I flick you a quick note saying, hey man, like not sure, not sure if we're going to give you a return offer, let's chat about it tomorrow. And I CC someone to be like, let's make sure it's on the calendar at 9 p.m.

I kind of avoid it. So there's, there's a lot of stuff like that where you, you got to really know where you're at, you know?

Just pulling the hard decisions.

Just do it.

Yeah.

And do it kindly, but just do it.