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

Ti Morse (0:00)

Today, I'm sitting down with Chris Power, the founder and CEO of Hadrian. Hadrian has opened six 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?

Chris Power (0:16)

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

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

Ti Morse (0:48)

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

Chris Power (0:54)

So, most 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 two-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.

Ti Morse (1:42)

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?

Chris Power (1:58)

Well, let me give you an analogy. So, AWS, you know, developed the largest cloud computing capacity ever, and then second started selling it to the market. But they already had the largest 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 upgraded factories at a worse economic position than Hadrian? It doesn't make sense. What does make sense is that there is no starting point of capacity.

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 capacities, 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 percent, 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 scare. Okay, so we're building a data center. Okay.

We've never built a data center before. How much easier is it for you to rent GPUs from me and 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 bullshitting, just because the capacity doesn't exist. So you have to build it ahead of demand, which takes a lot of balls and capital.

Ti Morse (4:21)

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?

Chris Power (4:46)

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 I buy a 3D printer or CNC machine or 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 10 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.