Vibe Coding Hardware

the way that I'm judging you as an engineer is like, are you producing the factory that will produce multiplicative outputs B through Z? It's not even ten X, it's hundred X or a thousand X, and it always has been. Claude or Chat GPT is basically as good as you are in a domain.

I would say just waste tokens, save time. Don't look at the tokens either as inputs or outputs. Just look at your time and look at the final output. No matter how expensive these models might seem, they're still way cheaper than a human. The models at some point graduated. They used to be junior engineers. Now they're principal engineers. And now with the agents, you just don't get stuck anymore, which is pretty amazing. Cure software dead.

Blake, how are you applying all this stuff at uh Boom Supersonic? Yeah. What I found is it completely changes the role of software and hardware developers. The thing that we did from day one was uh try to take a lot of traditional engineering workflows, and I mean hardware engineering workflows.

and turn them into software. And so for if you haven't been around hardware engineering, let me see if I can make this more clear. There uh there's a lot of engineering, hardware engineering that happens in Excel spreadsheets on engineers' laptops in a silo. And it's your very complex uh spreadsheets. sometimes like VB script code and this all of this is actually software, but it's it's treated as if it's not software. It's there's no there's no source control. There's no automated testing.

If you want to hand something off from like an aerodynamicist to a structures engineer that's done manually with like a spreadsheet over email, like it's the 1990s, it's terrible. And so we we started building these kind of like software frameworks. they can automate and make repeatable hardware engineering flows with the idea we could reduce the cost of iteration. But it was it was slow going because we could never get enough, we could never like afford enough software engineering.

And what we've gotten into is this uh mind-blowingly different model where the software engineers actually create the architectures because they understand systems, they understand the algorithms, they uh they under understand, you know, division of concerns. Uh, and then the hardware engineers can vibe code their pieces because what they know about hardware engineering.

And the result is just like mind-blowingly different productivity for small teams. Like give an example. Like like if you're designing a turbine blade, like classically, so a turbine blade starts like sm uh cold, but when it runs, it's hot. So it gets bigger.

And so you have to design both the aerodynamics and the structural design of the thing to work with its cold shape and this hot shape. And so you have to convert between cold and hot and you have to convert between structures and aerodynamics. And this takes like one engineer one day for one blade for one piece of the analysis. And there are like a thousand blades in a jet engine.

And and so you can't do much. And we literally now with a combination of software and hardware people created the solution. You can change blade geometry. You can see in real time the structures and the aerodynamics results. And so it allows two engineers to design an entire jet engine. Which is just wildly different.

One of the things you mentioned is that you have software engineers creating the tools and architectures for the rest of the engineers. That to me is the biggest um the cataclysm of enterprise software. is that there is no like startup that builds hardware collaboration tools that can sell you anything anymore. Because in internally you're just coding the right things that you need at any given time. Even spreadsheets are kinda cooked, right? Because

The reason spreadsheets were successful is that no one could build custom software. So the thing that approximates custom software the most is a spreadsheet with a bunch of V V script functions. I personally have moved almost entirely from uh Excel to Python models, uh, where I can actually like get like believable simulations of things.

Yeah. I mean the thing that that AI hasn't come to yet that I think it it will within the next year, like probably within twenty six, that will be very, very exciting, is right now it can generate software, but soon it'll be uh it will generate step files and PCB layout. And when it comes for mechanical and electrical engineering, that will be a whole other thing that we haven't seen yet. That'll be very, very cool.

Yeah, on the hardware side, I think it's really a boon for like all these little gadget companies and part companies that write really bad software. 'Cause they can't make great software and now they're gonna be able to make good enough software. Or it may not even software that is a human front end. It might just be completely agentic for an agent to access and you just talk to it through voice and control hardware.

And I this is why uh one of the reasons why I think for example China is big into open source models, right? They're basically going all in on it because they have hardware superiority. They have these very complex supply chains and component chains. And they're basically saying, Hey, if I can just generate software on demand, then I don't have this disadvantage anymore against Silicon Valley. So that's not the only reason why they're doing open source. I think they're also

behind their distilling models, they're catching, you know, they're collaborating resources. But I think the Chinese government has a history of funding efforts that then sort of help their entire ecosystem along, especially in network effect businesses. And so I think they want to like Yeah.

pool all their resources, catch up on AI and use it to give their hardware stuff an advantage. And ironically, they're doing all the open source stuff because open AI is not open. You know, Grox publishes models, but I think they're a model or two behind. Uh Google has some local models but nothing really that competitive.

anthropic to my knowledge. I don't even know of any open source models from them. So all the open source heft is coming from China. It helps all our hardware founders, but it helps their hardware founders and factories and so on that much more. But all all the crappy little software that goes with all the little random n knickknacks and thingamajigs that you buy off of Amazon and l for to tinker with a lazy Saturday afternoon, that software's getting a lot better very quickly.

I think everyone's had the wake up call that without great frontier coding models, you don't have self improvement. And so imagine China as a whole not having the ability to produce Frontier everything, right? It's not just producing software is in any piece of this hardware pipeline, like Blake was saying, like you need to generate software. If you fall behind on your ability to generate software, you fall behind on the ability to generate everything.

One thing I'm curious about from you guys is like'cause everyone loves to talk about Chinese models, like Do you use Chinese models? Do you know anybody that uses Chinese models? This is an argument I had yesterday actually, which is uh one person at the table uh dinner was claiming that uh you know you just use Deep Seek for ninety seven percent of things'cause it's so cheap.

And if you need more intelligence, you'll just run it over and over again, the same problem. And you'll only use the open AI, Anthropic, et cetera, models for the most advanced tasks. And I was kinda like, I don't know, I think intelligence is an unalloyed good. You always want more intelligence. And when these models make a mistake, you don't know it.

And it's always cheaper than a real person and real time. So you just use the most intelligent model available, which isn't great news necessarily,'cause it means that, you know, y you're gonna end up creating a monopoly or oligopoly kind of situation in AI. But uh I always want the most intelligent programmer. I always want the most correct answer. I always want the best judgment. And given the amount of leverage that I'm gonna pour into it.

through capital and code and people and, you know, marketing, I want to make the right decision every time. And often when between two models, let let's say like I had one model that I know is a little smarter than the next one, and they both give me answers, often I actually don't know which is the correct answer.

Right. So if I know one model's a little smarter, I'm gonna go with that answer and eventually I'm gonna stop asking the model that I think is less intelligent. But I don't know, have you guys found a use for the these, you know, so called less intelligent models? We see uses. So that uh so we have the AI gateways uh data that basically like every application agent setter goes through and so there's definitely usage of op open models.

But the top is like heavily dominated by the frontier intelligence. And there's a subcategory, or there's like a caveat to that, which is that. Frontier intelligence at reasonable cost and performance like slaps at scale. So like people don't get really excited about Gemini, but they put out these models that are like super smart. at the right performance cost c combination. And for a lot of tasks

Other than cot coding, actually, interestingly enough, uh they're the best models. They're like the best like industrial production models. Uh you can throw them at like support tasks or browser automation. Like I would always put a Gemini model there. Uh and I would look to Chinese models for those kinds of things. But anytime I'm working to push the frontier, you need the best possible coding model.

And that's basically now like two or three models. And uh and the Chinese are not uh certainly not in it.

Hey Max, you're pushing pretty hard into vertical integration and extreme urgency. Do you want to talk about that? Yeah, I mean for many things we Um you can't buy it. So you gotta make it somehow. Our preference would always be to buy something. Um like if there's a vendor that offers a service at a great price, if like for example, like P C Bs, like we don't make P C Bs, like those are they're basically free. You can buy them in unlimited quantity from Asia.

But the the closer that our products get to being like a single block of covalently bonded matter, the better they'll be. Lower power, smaller, higher performance, last longer. And um There's just like there are like the components aren't available.

And in order to do that type of integration, be able to actually innovate beyond things just piecing together things that you can buy off the shelf, which really is is very, very limiting. I guess you have to like learn it to do it yourself. And that shows up as vertical integration.

So we own a captive mems foundry on the East Coast, which we bought because there was really no other way to do the type of packaging and assembly stuff that we wanted to do. And I think that all of this is going to be affected heavily by AI over the next few years. It's not quite there yet.

In fact, ironically, one of the biggest impacts that we've seen of AI inside the companies in regulatory interactions, because if we can do things like generate documentation or if we can ask like we want to change, we want to evolve this product, like

There's thousands of ISO standards that might apply. Which ones do we have to comply with and like trace this through? This used to be like you're like you're following a whole regulatory and quality team for several months as they trace this and now the AI just kinda knows. Um But when I think about stuff like the the surgical program or the mems fab, I think ultimately the software still needs hands. Like it's gonna be smarter than us. But if it can't make things, then

Like those are real, real boundaries. And so we've instrumented our foundry as well as many other parts of the company in in in ways where um as these models get better, uh that should show up pretty immediately in in things like the the cell engineering that we're doing and the material science that we're that we're developing. It sort of makes me realize that like it's been a while since I've generated a basic legal document using a lawyer. Right. I stopped asking lawyers for

NDAs and, you know, agreement for this and sign that and research this and like all the basic legal tasks are gone too. Because Uh you know, there's the old joke that law is like spaghetti code. You know, they have this very complicated code that they try to put in English and it contradicts this code over here and has to fit into that code over here and there are no real APIs for it.

Um, but for just like junior engineers and junior engineering I should say. Junior engineers basically got a promotion to senior engineers and junior engineering got taken over by agents and So the same way I think in a way the downside is you can look at law and say, you know, paralegal just got fired or you could say paralegals just got promoted to senior lawyers and now they can spend their time thinking about

It's actually kind of interesting to think about the parallels of how s software engineering is evolving with lawyers, because lawyers You never know what they put into this documents exactly. You just trust them. Like, hey lawyer, can you look at this document and can you tell me if it's legit? Can you do red lines? Whatever. Like at the end of the day you're

what you're valuing in the relationship with a lawyer is that is that they're a trusted authority. They went to law school and they're putting their reputation on the line. I think there's a parallel parallel with like the biggest problem in software engineering today is this mountains of slob that end up as a BR.

And and then people are say like there's all these memes on Twitter but like way back in the day we used to read every line of code of a PR. Well, in my world, infrastructure I want engineers to be able to say, I understand doesn't necessarily mean that you've read every line of the of the PR, you need to be able to say I am signing off on understanding the consequences of this PR or I wrote the test harness

the simulations, the proofs, the type checkers, et cetera, to be able to say, even without reading this, I have confidence I can sign off on it's gonna be safe in production. And so it's it's kinda interesting'cause uh there's a world in which we embrace that everything's gonna be a spaghetti code and that we don't fully understand it, but we write the basically evaluators that give us confidence and then we rely on like people.

uh like the infrastructure production engineers to say, okay, I'm fine uh sending this into prod. You know, at the end of the day, like someone is gonna get paged if your systems go down. I think another thing that people are underestimating is that creating software is really easy, zero to one. But think about a thousand days from now.

What does what does your software look like? Is it secure? Is it tested? Is it production grade? Uh yeah, is it performant? And are you still motivated to invest all of those tokens in maintaining it in product?

I mean humans are becoming verifiers, right? And and that's kinda how we train these models with good verification data and now we need human verifiers. So yeah, I think a lot of the a a lot of the old function of people, lawyers, engineers, Operations people move to verifying the stack and saying, Yeah, this is roughly correct, and I I'll roughly stand behind it and I'll support you if it goes wrong.