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?
00:23:34 · Angela McNea
[00:23:34] S0: I think, you know, I grew up, you know, I'm the child of two climate scientists, grew up in a household where you're always deconstructing and talking about things at dinner tables. So there's a lot of the scientific method being applied throughout my life. And then spent I was at Columbia where I studied computer science and math. And so there is an engineering element, a scientific element to how I approach problems. So how do you deconstruct problems? How do you determine what the base case is? And how do you hypothesis test? So there is that element to that. But I think there has to be we don't live in a vacuum. We don't live in a simulation with all the hot Hot take. Yes. With perfect actors applying perfect behavior, right? We live in a deeply messy and human world. And so the way that you approach problems, you have to approach them with empathy and understanding and try to solve the problem. That's where, again, the scientific method comes of, like, approaching problems, being data driven, forming hypotheses, like, actually challenging those hypotheses. But the big picture has to be, like, so deeply human. And I think that was a great lesson I learned as an FDE. And I think that is one of the most important things for me as a founder. You need to have a balance of confidence and conviction and humility. And I think that as, again, you learn that as an FTE of like you are confident in yourself and the things and you're helping this customer and you're solving problems together, but you need to be you are talking they're the expert in whatever the thing is, not you, and so how do you work with them to solve problems together is I think if you're a good FD, that is the skill that you have.
00:25:32 · Ruby Justice Thelot, Angela McNea
[00:25:32] S1: Yeah, And I guess last question before we wrap. What have you found has been maybe notoriously difficult to automate in building like thread AI, but even just insights that you may have seen into industries? [00:25:46] S0: Yeah. I think there are a lot of interesting ways. Guess decision making. Yeah, yeah. Where I'm not in the again this may be also a hot take I'm not in the AGI camp. [00:26:14] S1: You're on the AGI show.
00:26:14 · Ruby Justice Thelot, Angela McNea
[00:26:14] S1: You're on the AGI show. [00:26:15] S0: I believe in intelligence systems and that we're building intelligence systems and using intelligence systems. We can talk definitions all day, but I think a lot of the decision making that I've seen attempted to be automated across the stack, across the process be kind of unsuccessful, except when the feature space is so constrained, where you have a finite number of paths, a finite number of opportunities, whether it's like cool route to this person, or hey, do this thing, or maybe we try this, be successful. But when you like open up the aperture, I've seen things struggle still. But I think part of that is, and I also used to say this also too, past life of the death to innovation is also too, this like inability to get started. And AI workflows are always iterative, right? So like, you're not going to have the perfect automated system, the perfect agent out of the get go from the beginning. You need to start and put in the building blocks and make sure capturing the right information, you're capturing that human AI exchange, and then you're building this data asset that then your system can learn from and then get more intelligent and evolve over time. And so I think it's the expectation often that like, I can automate every decision, everything from the beginning. [00:27:44] S1: Yeah. [00:27:44] S0: When in reality, it's gonna take a little bit. [00:27:47] S1: Yeah. Yeah. Well, think that's a great place to wrap. Thanks so much, Angela, for talking to us about Thread AI. [00:27:52] S0: For having
00:27:52 · Angela McNea, Ruby Justice Thelot, S2
[00:27:52] S0: For having [00:27:52] S1: Glad [00:27:53] S0: to have you. Pleasure. Ad[00:27:54] S1: A huge thanks to MTS sponsors. Blitzy, autonomous software development for enterprise code bases. Ship five x faster. Blitzy.com. Adquick, make your brand a billboard out of home advertising as easy to scale as digital. Adquick.com. Arena, measuring AI performance in the real world. Arena.ai. [00:28:15] S2: Support for the show comes from VCX, the public ticker for private tech. The US stock market started history's greatest wave of wealth creation. From factory workers in Detroit to farmers in Omaha, anyone could own a piece of the great American companies. But today, our most innovative companies are staying private longer, which means everyday Americans are missing out. Until now. Introducing VCX, a public ticker for private tech. Visit getvcx.com for more info. That's getvcx.com. Carefully consider the investment material before investing, including risks, charges, and expenses. This and other information can be found in the innovations fund prospectus at getvcx.com. This is a paid sponsorship.