00:16:04 · Ruby Justice Thelot, Angela McNea
[00:16:04] S1: Okay, I see. I think that like my fear or like what would be an intuitive fear is, maybe if you're at a corporation and you're... Know, you have a lot of people who are now working on software. Yeah. And, like, and I guess that's, like, now become a flatter skill that everyone can, you know, partake in to some extent that there would be less intentionality behind systems. And I guess it's different with industries that are very high stakes, but I'm curious, you're saying that it's becoming more intentional with how people are designing the logic of things. [00:16:38] S0: On the big picture process, I think as for an individual's knowledge work, I think that we're still seeing Wild West. Individual's productivity tools. But when you think of, okay, this is an actual process, often it's designed that way historically for a reason, a combination of people, legal, regulatory. Are a lot of factors that went into designing that thing from the beginning. So if you're looking to disrupt it, and if you're looking to automate and inject AI at different components, all of those factors don't go away. You just now have to rethink them in this AI era. So we're seeing, again, a lot of enterprises spend a good amount of their IP focused on that sort of redesign, which has been pretty cool to see. But I do think it is still, again, like Wild West Yeah. For the individual.
00:17:31 · S2, Ruby Justice Thelot, Angela McNea
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00:18:44 · Ruby Justice Thelot, Angela McNea
[00:18:44] S1: Yeah. So I'm I'm curious also, is there anything that you find... Like, what is the hardest to automate? Or even with the building blocks that you were mentioning, Like, what is difficult about designing building blocks? You kind of have to create something that will work across many different teams or whatnot. [00:19:02] S0: I'd say that is mine and Maya's superpower. That, our primitives that we built sort of from the beginning have been able to adapt to all of the sort of technological changes in the past sort of four years. And we were very explicit from the beginning to not throw away, right, like twenty some years, fifty years of distributed systems, security, software best practices, but to kind of like modernise and make them more adaptable. So learn from best practices. And I think the hard part is often the piece, again, that comes after of like, you you can build a triaging workflow, right? But then you as the builder of this workflow, how do you decide who gets these alerts, the volume of those alerts? Are they too noisy? Are they not as important? So the actual workflow design, I think, is the more challenging part. [00:20:00] S1: Yeah. Do you find that you can transfer these across industries as well?
00:20:00 · Ruby Justice Thelot, Angela McNea
[00:20:00] S1: Yeah. Do you find that you can transfer these across industries as well? [00:20:06] S0: So we built our platform to be self-service. So our customers are a lot of our builders, but we do have our applied arm to where we work with our customers and we'll share best practices and knowledge and sort of construct with them. I was thinking about, I saw there was that enterprise AI spend is allegedly rocketing or booming. I think what we're seeing, probably a big piece of that is there's this been huge investment we're seeing in like consulting services, where people are spending a lot of money asking for help. And having those people assemble and build those stacks for them, which to me is a perfect indicator that there is a gap for a platform to fill. Yeah. But people do want help. [00:20:52] S1: Yeah. So tell us more about the platform and how you went about designing it. Did you design this from the get go? Was the vision always we're gonna do a platform? [00:21:02] S0: Yeah. We built horizontal infrastructure past life, wanted to build it now. And it's funny, think Maya and I talk about this, I think we've talked about this publicly, the thing that really inspired us was this paper that came out of Meta. [00:21:16] S1: Sure.
00:21:16 · Ruby Justice Thelot, Angela McNea
[00:21:16] S1: Sure. [00:21:17] S0: And it was the tool former paper where they were like, okay, we have a language model, and we're going to train the last couple layers to speak simple API. And there it's like, I think one was like a calculator or weather or whatever. And if you're a software engineer and you look at that, you're like, well, that's not how an enterprise... Enterprise APIs don't work that way. There are thousands of software providers at an enterprise. Each of these providers might have thousands of APIs with hundreds of different parameters. You have different protocols and different authentication policies and schemes. You have different data types. The ecosystem is so complicated. But the idea that, cool, I could put flexible decision making in software was pretty exciting. And so we're like, okay, how do we take that and then you play it out n years? You're like, what could it look like best case if you have these, again, sort of intelligent systems in and across a company? You're like, well, to do that and to make that a reality, working backwards from that, you're infrastructure. And so that was the vision from the beginning. [00:22:24] S1: How easily do you find you personally to extrapolate all of these trends when it comes to enterprises and their use of technology?
00:22:24 · Ruby Justice Thelot, Angela McNea
[00:22:24] S1: How easily do you find you personally to extrapolate all of these trends when it comes to enterprises and their use of technology? [00:22:33] S0: I think that if you are building in and as like as a product person, if you are building in this space and leading a company in this space that is like table stakes, like that is a skill, a superpower that you have to have. If you're selling also to, like, horizontal, so across these different patterns. And that also, too, is like the fun part of it. At the pattern matching, I get to see, you know, 10 different kinds of workflows, you know, a day across different companies. And again, the fun part is you have all this information. How do you pattern match and how do you pull out the interesting pieces? It's like fun product work. [00:23:14] S1: Yeah, yeah. I'm also curious too, because you seem to think about logic a lot. And so what was your, we talked briefly too about your background in math, computer science, but what is the lens that you view problems in? And maybe, too, at Palantir, what was the lens that they view problems in, and how did that all tie together?