The a16z Show
00:09:35 · Erin Price-Wright, Connor Love, Elena Burger
[00:09:35] S1: Well well, I mean, SpaceX and Tesla were doing first of their kind. Like, there didn't exist industrial base in The US to support them, and so they had to build it themselves. I think where there are high quality suppliers, you should use For sure. Suppliers. [00:09:46] S2: Yeah. Yeah. I mean, again, I think the learning though is, number one, if you don't have an industrial base that you can rely on quickly, reliably, etcetera, then you have to do it yourself. And inherently, that is a hard journey. That's really hard to do. This concept of saying, okay, I'm gonna focus on the end system. I'm gonna focus on building the most cost effective system that I can. And oh, by the way, I gotta go figure out the ball bearing. I gotta go figure out the solenoid valve. That's a lot to do. And in the case of, you know, Tesla and SpaceX, I I would argue the the auto industry was a little bit different because there was a... But there wasn't a battery supply chain. You know, Elon had to recreate that. [00:10:21] S1: Well, the whole the whole power electronics... The whole drivetrain, you know, they had to really build completely from scratch based off papers that were published in the seventies that, you know, had been collecting dust for for forty years. [00:10:32] S3: In terms of companies that we talk about in the a b portfolio, there's Mariana Minerals, which bought a copper mine and is kind of reinventing things. And and would they be a good example to to talk about here?
00:10:32 · Elena Burger, Erin Price-Wright
[00:10:32] S3: In terms of companies that we talk about in the a b portfolio, there's Mariana Minerals, which bought a copper mine and is kind of reinventing things. And and would they be a good example to to talk about here? [00:10:46] S1: I think that they they recognize that the, you know, the existing players in the mining industry just were not gonna pick pick up technology fast enough for it to really make a difference and really move the needle. That... And they also recognize that they wanted to actually own the entire value chain to be able to really put their money where their mouth was and capture the end value of the new technology they were bringing. So, yeah, they do everything from, you know, acquiring the asset, developing the asset, mining the asset, operating the asset, and refining the material themselves across multiple different multiple different minerals, and then they sell a final commodity product in the market and have really leaned in. That's an... Another trend I'd point to is how much founders and entrepreneurs building in these spaces are leaning into AI and software as a huge, huge component of their build.
00:11:37 · Connor Love, Erin Price-Wright
[00:11:37] S2: Yeah. I mean, I I think when you look at the physical world, applying software and AI will look different than what has happened in say legal or customer service or any other category where you see AI taking off in crazy way today. But to Erin's point, like many times what you have to do is you have to own the asset in order really to change that asset. Not every time, but a lot of times, you know, kind of showing is believing in the physical world. Mhmm. Whereas, you know, when you're when you're purely a digital AI product, you can kinda just see it, and it's very easily transferable and kind of understanding. What Mariana is doing with this copper mine is really impressive in a lot of ways. It's not just saying, hey, we're gonna be better at extracting copper. It's rethinking everything. [00:12:17] S1: It's It's rethinking entire end to end process of how you plan, operate, mine, and refine materials. And, you know, what's cool is once you once you own that process end to end and are using software at every stage of that journey, you actually capture that data, and then it feeds back Yeah.
00:12:35 · Connor Love
[00:12:35] S2: This is this is an important point. Mean, this is not just Mariana, but if you look at another business like Hadrian, I mean this is a perfect example of how one, you can utilize software, more importantly, it's not one silver bullet that all of a sudden makes a factory or a mine perfect. It's actually 15 or 20 different things, and when you look at what Chris has done at Hadrian, yes, it's kinda upfront what it takes to quote and even take an order of a part. It is the actual physical making of the part. It's how you inspect it and transfer it, and there's all these different interim steps that exist within a factory, and the beautiful thing when you kind of either start from a fresh slate like Chris has in building his factories, or in Mariana's case, when you kind of own the thing and take it over, you can look at every single node from a first principles perspective and say, hey, was this process that's been done the last hundred years the right process? And that's what's happening across every layer of the physical world right now. It's not just mines, it's not just factories. Mean, again, it exists in many different permutations. And and, you know, my personal belief, and we see this in a lot of our portfolio companies is, look, AI will be a phenomenal tool that can be used in the physical world. The reality is AI cannot turn a wrench yet. It will at some point, but we're far off that, again, can maybe have discussion about the robotic models and autonomy that we see in We're this getting closer, I just think the nuance here, and the portfolio companies, many that are on this list, or many non portfolio companies on this list, that they understand that the physical world is not about just putting a silver bullet forward, like I said, that some shiny AI product, It's deep understanding of what a process looks like, physically how to manipulate the process or how to change something that's happening in the world. It's just gonna look different based off.
00:14:26 · Erin Price-Wright
[00:14:26] S1: Well, well, maybe like a specific example or... To talk through is like like low mix high volume versus high mix low volume. Yeah. And how how AI can kind of change change the game there. Historically, like, you're spending a lot and setting up a line with a lot of automation, you really need to produce... Be producing, like, a lot of one single thing. Mhmm. And that's how you get the cost down, and that's how you justify the cost of investing in, you know, a pretty bespoke manufacturing line. In The US, we do a lot of low mix, high volume manufacturing, which is very hard to automate because it's very expensive when you have to change the line all the time. But what we're seeing across our portfolio is that manufacturing technicians themselves are... Have this... Have the intuition and now have the software tools to be able to to to change the economics of what it looks like to produce low volume, high mix on the line. So you can have a manufacturing technician. This is not someone with a college degree. This is not someone... Maybe they are, but not necessarily, who's studied software engineering, is now able to use these tools to reprogram and reconfigure their manufacturing lines on the fly using the fact that they understand the manufacturing process and they know how to do what they're doing in the physical world. They now suddenly have all these tools so they can... You know, you can retool a line instead of having to call a system integrator. You know? And it takes three months and hundreds of thousands of dollars to kind of reconfigure a line for a new setup. You can have a manufacturing engineer use Claude and do that in, you know, twenty minutes. And so that's been a real game changer across lots of companies that we've seen
00:16:02 · Connor Love, Erin Price-Wright
[00:16:02] S2: in And this why that's important is when you look back at how we've articulated AD 50 this year, it's kind of three categories. And a lot of times what we focus on, at least, you know, I have and and many people in the in in the sector are focused on, is just the systems companies. And look, there's a lot of really interesting systems companies. I mean, Anduril, Serrano, Castelion, mean, you know, the list goes on and on of who's building really good end systems, But the problem is, is if you can't iterate on the design and iterate on that prototype, you don't not only not get to an end system that is different, better, but you also just move at a slower pace. So then what happens is by the time you finally fix a production level, missile or plane or whatever, then all of a sudden, then you gotta go back and rejigger and build up all these subsystems in order to meet the scale. But if you have a more transferable line, a more adjustable line, number one, you can also build a prototype faster, but then two, you can then, you know, set up each one of these different components in a way where it actually can get to rate production quicker. [00:17:05] S1: Yeah. You're you're you're designing for you're designing for manufacture from the outset.