00:34:34 · Ryan Deng, JC Lozano
[00:34:34] S0: So there's still like an abundance? [00:34:35] S1: They're... What's getting more challenging is like actually getting the metals. Right? So ore deposits are getting deeper and something called the mineralogies, which are basically like how the ores are formed into solids, how the metals are intercalated with other other metals and rocks and mineralogy. It's a blanket term that refer... It's not a blanket term. Geologists have a very precise definition of it. But Yeah. For the point of this, it's just like the the way that you... It's getting more challenging to to mine metals. Like that's that's the other part that's getting hard is that basically like the deposits are getting much much deeper. Right. As they get deeper, it's more expensive to get to them. [00:35:11] S0: Because you have to [00:35:11] S1: dig a more. Yeah. Exactly. [00:35:13] S0: I can imagine, like, most of the surface level stuff has been probably like mined. Right? [00:35:18] S1: I would say most of the easy surface level Easy stuff [00:35:21] S0: deposits have already, been identified and they've looked at that space. Right? [00:35:25] S1: Well, I mean, we've been mining for, like, fifteen thousand years, probably. Ten thousand years at least. [00:35:29] S0: Yeah. [00:35:29] S1: Eight thousand. Something like that. Mhmm. For a while. Okay. [00:35:33] S0: That that puts into perspective.
00:35:33 · Ryan Deng, JC Lozano
[00:35:33] S0: That that puts into perspective. [00:35:34] S1: Yeah. But the the challenge you have is that, like, the the... Like, one way to think about it is, like, all the easy stuff's been been found. And so what you're left with is more challenging resources. So what makes them more challenging is either they're deeper underground, so it takes a lot more money to get to them Yeah. Or they're lower grade, which basically means the pound of metal. Oh. Sorry. The percentage of metal and like a pound of material is lower. [00:36:00] S0: So the just degrades, essentially. Correct. [00:36:02] S1: And then the third thing is what I was gesturing at earlier, [00:36:06] S0: which is just [00:36:06] S1: like, mineralogy is getting more And so that just means that like, it's more expensive to process it and actually extract the metal Uh-huh. From like the gang material, which is like all the material that it's like embedded in. [00:36:18] S0: That's it. Yeah. And like, you know, aside from America, are there are there regions of the world that are pretty like unexplored that you could go and and look at
00:36:26 · JC Lozano
[00:36:26] S1: or reach out the world? There's plenty of metals in The US. Okay. Like, that's the crazy part. Right? There's actually like a bunch of these things. The problem that we have in The US specifically is that we're not very good at this anymore. We need to get better at it. And so, like, lot of the mines that are operational in The US tend to be one of... Fit, like, a couple of criteria. Right. So either one, the grade is really, really high, which basically going back to, like, if I mine a pound of material from the earth, like, what percentage of it is, like, actual valuable ore. Mhmm. Right? So the grade can be really high, and the deposit can be small. That's like economical and we know how to do that. Right. There's like relatively few of those. The second is it can be like a massive, massive deposit. Mhmm. And then there, we also know how to do that because you get these incredible economies of scale where you can just throw a bunch of, like, money and really large equipment and really, you know, large groups of people at the problem. And because, like, the resource is so large and because you get economies of scale, you know, your your cost of operation goes down and you're able to do it economically. Sure. The problem is that, like, there aren't that many of those assets left. There's a bunch of assets which are, like, medium sized Mhmm. Low grade that we don't know how to go after economically. So that's where autonomy and kind of like doing it in a different way becomes really, really important. Right. Because you're like unlocking these resources that other folks can't economically mine.
00:37:47 · Ryan Deng, JC Lozano
[00:37:47] S0: So you wanna get it as cheap as possible, as fast as possible so that way you can deploy it to these mines that aren't... Were not worth going after in the beginning Mhmm. Now because you can do it more cheaply. Because you have a differentiated Yeah. Let's dive a little bit more about your early undergrad days. Okay. I wanna dive into the person that you are, you know. You've heard about the company, exciting stuff. Yeah. What what was that feeling like when you when you got into Stanford University? Oh, super exciting. [00:38:13] S1: What was that feeling like? Man, it feels... It was ages ago, actually. [00:38:18] S0: What was that like? [00:38:21] S1: Let's see. Really exciting. So I'm from Wisconsin originally. Yeah. So I was like very excited to go someplace warm. Uh-huh. As simple as that sounds. [00:38:30] S0: Was that like your number one target score? Did you have any other safeties? [00:38:33] S1: I had a couple other like schools in the East Coast that I was excited to go to as well. [00:38:36] S0: The Harvard, MIT maybe? [00:38:37] S1: Those are, yeah. [00:38:38] S0: Okay. Alright. So it's safe to say that you were you were cracked. You're very smart. [00:38:46] S1: I was very excited to go to Stanford. Yeah. And then, you know, studied computer science and physics. I got super lucky, actually. Like, didn't really know what computer science was until I got to Stanford. [00:38:56] S0: Oh, so you weren't technical beforehand? Like, you weren't dabbling in, like, computers and things like that?
00:38:56 · Ryan Deng, JC Lozano
[00:38:56] S0: Oh, so you weren't technical beforehand? Like, you weren't dabbling in, like, computers and things like that? [00:39:01] S1: During high school? No. I like no. Yeah. Like, you know, played sports. Studied a lot, you know, like all the the usual stuff. [00:39:15] S0: Fair enough, though. [00:39:16] S1: It was pretty Yeah. I mean, Stanford's an amazing place. So, no, got super lucky. Got to go there, which is an amazing experience. Studied physics and computer science. Took a computer science course. Think it was because it was, like, required as, like, a intro course. Mhmm. Got really excited about that. Like, was just like, oh, this makes a lot of sense. I really enjoy this. So I started studying computer science, got really into machine learning, and then was also like, you know, always very passionate about physics. Mhmm. So I got to study both of those, which is amazing. Wow. And then had a couple of internships at like larger companies, and was like, okay, this is definitely not what I wanna do. Right. So I wanted to go into startups. So actually met the two co founders. So I was like the third co founder of a three d mapping company. Mhmm. I met them act... I actually had an internship. [00:40:00] S0: Oh, so you didn't meet them? I mean, they were at Stanford. Right?
00:40:00 · Ryan Deng, JC Lozano
[00:40:00] S0: Oh, so you didn't meet them? I mean, they were at Stanford. Right? [00:40:02] S1: But you didn't meet them up No. No. No. These were these were folks... These are actually great. So they're much more senior than me. So they were like in their early forties. So it's actually amazing to learn from them. So I was kind of... Were they the two primary founders and I was like a junior co founder basically. Wow. So I like joined them super early and then, you know, we went through StartX together. We raised a bunch of seed funding together for this three d mapping company. But I got to learn from them, is awesome. Like they'd... Like one of them... They're both from Sony. Yeah. And so I got to learn a bunch about, like, how to run a company and how to fundraise and just, like, was, you know, in the pitches with them and just basically got to, like, learn the ropes. And exchange, like, know, built a bunch of really cool stuff. Like, we were doing three d mapping of indoor spaces. So taking pictures with your iPhone and then reconstructing a three d model. Oh, wow. Which back when we started, like, now it's, like, it's easy to do. There's, a bunch of different machine learning models and Right. Large language So one do that. [00:40:50] S0: One shot with.