00:58:36 · Dylan Patel
[00:58:36] S3: Yeah. So one one thing that I think we can sort of point to is there's there's two domains, right, when you think about science. Right? There's discovery and there's engineering. And everyone always talks about the discovery side of things, but actually the bulk of the spend is on the engineering side of things, and you have to get really good at the engineering before you can discover something and bring it to market. And so when you look at, for example, Periodic Labs, they are a company that's specifically focused in this realm. Most of their work is on engineering. Right? Increasing the engineering velocity of these materials. Hey. If I make something, you know, this sort of deposition technique 10% more efficient, is that gonna be... What is the benefit to humanity? And, actually, it's not about inventing a new material. They're, of course, going for that as well. But if I if I if I improve the, you know, the... Let's just say, you know, in semiconductors, the deposition of something, and so then your chip has less resistance, or you can shrink it a little bit further, That's so much economic value that it's immediately delivered. Or, hey. I'm a battery company, and I'm optimizing my, you know, you know, my anode or my cathode or whatever it is, and I've made it have 5% more capacity. That is so much economic value delivered to society. And it's and it's a bunch of small engineering. And the big problem here is when we look at software engineering, everyone's, like, used to pretty advanced software engineering techniques. But if you go look at the broad space of any of these companies in batteries and semiconductors, many... In in in in automobiles and aerospace, all of their tooling sucks, and their research velocity is slow. Their engineering velocity is slow. And so, you know, using AI to create simulations to help, judge all of the data that's coming out. You know, an Intel wafer, you know, they've made this comment. Intel, when they make a wafer, they have about a 100 terabytes of data that gets generated, and they don't do anything with the vast majority of it. Right? There's there's small loops that get closed. Right?
01:00:34 · John Coogan
[01:00:34] S1: Oh, from
01:00:34 · Dylan Patel
[01:00:34] S3: this tool to this tool, there's a small loop. Use the data. Modify it a little bit. But what... How do you possibly analyze all this data when most of your, you know, PhDs in material science and chemistry and all this stuff are, you know, using Excel and MATLAB. Right? And MATLAB is if they're sophisticated. So how about you have AI help this become, you know, something much more advanced in terms of, you know, the data analysis, in terms of extracting information out of this stuff, in terms of building models, building simulations, and closing that sim to real gap, and then actually allowing you to make progress at an... At a faster pace. And and it could mean, hey. Immediately, I could figure out things that improve my yield. You know, if I take, for example... And it's it's quite simple. Right? The chips sit on substrates. Substrates route the wires in and out and power in. Those... The substrates for CPUs, this is stuff that isn't even complicated. You would think it's been around for a while. The yields there are on the range of 40 to 60%. What if those yields become 95%? The economic value that's generated is massive. Or let's take an Intel fab. Their fabs are lower than TSMC's, the yield. If their yields are now all of a sudden higher, than TSMC or equivalent or, you know, both of them just raise yields massively, the economic value that can be delivered is massive. Take NVIDIA. Right? It takes them two and a half years to go from design concept to shipping a chip in volume production. What if it takes them a year? Right? That architecture idea they had two and a half years ago or that change that they're trying to make, you know, a year into the process that's, you know, holding everyone up and and pausing things, all of a sudden, can change it and do it much more quickly. If they wanted to bring optics into the chip, what about all the simulations that they can do? And they can bring optics to, you know, co package optics to reality much faster and get the step function improvement in performance of chips and networking speeds. All of these things are rate limited by the... By intelligence. Right? There's obviously a lot of physical difficulties, but the experiments that these companies are running are not able to close the loop quickly. They're they're they're... You know, the loops are very small.
01:02:34 · Dylan Patel, Jordi Hays
The domains that people operate in. Right? The amount of siloing is just so massive because the complexity here is far beyond that of any software engineering. And so the models need to get really, really good at these things before they can assist these people and and, you know, help close this loop in a full circle, right, and then optimize the heck out of these things, make these experiments that you're running, you know, have have that data actually influence the design and go towards the right process, improve the yields, help you shrink faster, help you do whatever it is, and and then actually generate hundreds of billions, if not trillions of dollars of economic value to the world. [01:03:11] S0: Talk about the incentive for people building in these domains using the models to not wanna talk about what they're doing publicly. Right? Because I feel like part of the current sort of disillusionment with the state of AI is that you have a bunch of new software being created, software engineers are obviously happy. Look, I shipped this new feature. Or or you have, like, really cool... [01:03:35] S3: I don't know, bro. I think I think software engineers are depressed, by the way.
01:03:39 · Jordi Hays
[01:03:39] S0: Yeah. But at the same time, you have, like, the excitement of, like, Karpathy making, like, Hacker Tube. Right? And everyone's like, oh, that's really cool that, like, you can do a two, three, sentence prompt and create this sort of novel product that maybe wouldn't have been worth putting the effort into historically. But I feel like when you talk about these domains like material science, I know from having built businesses in the past, like if I figure out like one simple trick, I assume that like it's only gonna be like, you can only arbitrage it for like a certain amount of time before it becomes sort of common knowledge. And so there's an incentive or or even another example, like if I find an amazing freelancer, like I'm not gonna try to introduce them to like 20 people because I don't want my competitors or other companies in the category to have access to them. I assume over time, they'll probably... That that person will meet a bunch of people. But, like, right now when you talk about, you know, these domains where you can have like a a 20% efficiency gain or things like that, there's so much in... Like, I assume that you guys are calling all these people and like trying to understand the kind of gains that they're getting, but they're not exactly raising their hand and going on LinkedIn or going on X and saying, oh, we're getting this massive speed up or efficiency gain here and we want to tell everyone. Because they want to get as much out of it as they can before it becomes more common knowledge.
01:05:00 · Dylan Patel, John Coogan
[01:05:00] S3: There's there's definitely a big difference in hustler culture, right, in in sort of software and founder land and SF and sort of these things versus, you know, these industries that have... That are manufacturing and making the world go, right, and work and operate. I agree that they're not gonna stick their hand up and scream, what they're doing. They'll they'll do some vague stuff. Hey. We improved our our manufacturing efficiency. Hey, customer. Here's what we did. It'll get out there. Right? You make your product better. Everyone will understand that you made your product better. Your financials get better. Everyone will understand that your financials got better. These are just, like, obvious facts that are public. And so, you know, we're in the very early innings of these things, to be clear, you know, whether it be material science or semiconductor engineering or whatever whatever very hard science thing it is. But our our sort of, like, one of our main, you know, focuses from GDPX looping it back is to focus on these areas where we [01:05:55] S1: think