00:03:20 · Alex Immerman, George Sivulka
[00:03:20] S1: All right. So scaling laws are going to hold. We talked about deep sea briefly there. Is deep sea a nothingburger, or is it a big deal? [00:03:28] S0: I am a believer that, like, it was more on the nothingburger side of things. I think China has shown time and time again that they're able to take technologies that are invented oftentimes in The United States and make them more efficient. And I think China has also shown time and time again that they're willing to obfuscate the truth or omit certain things when talking about, you know, certain technologies or the science behind a variety of different things. You know, regardless of the truth with deep sea, I generally also think it's really an American technology that's cheaper. And Americans can also make it cheaper, and it was invented here in The United States. And China has not shown that they can actually continue to play ball pushing the frontier of AI. This is not an example. [00:04:09] S1: Yeah. I think the one piece on it was also, like, pushing open source in in The US here and making us rethink potentially things on the regulatory side there.
00:04:09 · Alex Immerman, George Sivulka
[00:04:09] S1: Yeah. I think the one piece on it was also, like, pushing open source in in The US here and making us rethink potentially things on the regulatory side there. [00:04:17] S0: It's a good point. I think open source is important, and I think that as software becomes open source, it stops being a geopolitical strength of just like the closed source nation that that has invented it. But at the same time, you know, America, I believe, is so far ahead, and I I think that it will continue to be so far ahead. And these technologies, you know, every single year are so much exponentially better than the last year that we have the head start. And I think that we'll continue to have that start, and we have the world's best scientists, researchers, and minds thinking about this and working on. [00:04:50] S1: Absolutely. And then we also have the best companies building on the AppLayer who are gonna benefit from these models getting cheaper and cheaper. [00:04:56] S0: 100%, especially if they scale at inference. So [00:04:59] S1: Yeah. Exactly. So next one, what's your favorite AI tool that's not named Hebia? [00:05:04] S0: I think that my favorite tool right now is Deep Research, which was launched yesterday or the day before yesterday. [00:05:10] S1: It's pretty new. [00:05:11] S0: Very new. It has changed the amount that I use AI, and I use AI for everything. I try to automate everything. What what have you
00:05:11 · George Sivulka, Alex Immerman
[00:05:11] S0: Very new. It has changed the amount that I use AI, and I use AI for everything. I try to automate everything. What what have you [00:05:16] S1: used it for? What's... What searches? I was looking at microplastics impact on children. That's, like, a hot topic these days. Deep research goes deep. It's it's pretty inconclusive, though. [00:05:26] S0: It's inconclusive. Really? We can get to the difference between deep research and ultimately what other agents over private data will be working on. And I think that this level of agentic deep research is going to be one of the most exciting things that I can't quite disclose now that Hebia has proven out and is working on over private information. I think deep research allows you to experience what an agent can do for the first time. And it uses information retrieval over the web, which is very good. But I... I've used it for things like, you know, looking at a list of names of executives I'm about to meet and understanding everything about them. So just, you know, almost like client prep. I've I've used it for building a competitive strategy around how I should approach a variety of different things strategically with partners in our ecosystem. And, you know, it's not as good. Deep research has been busy for you. It's our number one employee. Don't take that wrong, Hebbe. You know, it's it's not as good as the stuff we'd we'd create internally, but it starts to be more fulsome. It's it's more than just a single step. And that's a really important piece of of the future of AI applications.
00:05:26 · George Sivulka, Alex Immerman
[00:05:26] S0: It's inconclusive. Really? We can get to the difference between deep research and ultimately what other agents over private data will be working on. And I think that this level of agentic deep research is going to be one of the most exciting things that I can't quite disclose now that Hebia has proven out and is working on over private information. I think deep research allows you to experience what an agent can do for the first time. And it uses information retrieval over the web, which is very good. But I... I've used it for things like, you know, looking at a list of names of executives I'm about to meet and understanding everything about them. So just, you know, almost like client prep. I've I've used it for building a competitive strategy around how I should approach a variety of different things strategically with partners in our ecosystem. And, you know, it's not as good. Deep research has been busy for you. It's our number one employee. Don't take that wrong, Hebbe. You know, it's it's not as good as the stuff we'd we'd create internally, but it starts to be more fulsome. It's it's more than just a single step. And that's a really important piece of of the future of AI applications. [00:06:31] S1: Well, you keep alluding to Hebiah, so I think it's time we get there. Let's rewind the clock a little bit. Sure. You're a PhD student. You're studying neuroscience, engineering, applied physics, all the things. And then you decide you're going to build a company that sells into financial services. How'd we get there?
00:06:31 · Alex Immerman, George Sivulka
[00:06:31] S1: Well, you keep alluding to Hebiah, so I think it's time we get there. Let's rewind the clock a little bit. Sure. You're a PhD student. You're studying neuroscience, engineering, applied physics, all the things. And then you decide you're going to build a company that sells into financial services. How'd we get there? [00:06:49] S0: At at Stanford, there's a class, which is CS three thirty, which I think it's probably still around. But it's like an introduction to meta learning and multitask learning. And when I was in grad school, was I was completely captivated by this class. The the idea and the promise of meta learning, and what that means is teaching machines to learn how to learn, was to me going to be the most important technology of all time. It was just the coolest thing in the world. And I was in my PhD, and I think around June 2020, you know, looking at all the research, keeping track on meta learning, multi... Multitask learning, OpenAI releases a paper, and it's a GPT-three, large language models Pretty important. It was very important. But the the title of the GPT-three's paper was actually large language models are multitask and meta learners or or something along those lines. And I remember thinking like, okay. That would have just completely botched this entire field of research that, you know, that is, like, encapsulating all of my academic interest. And I played around with it, and I remember, you know, GPT-three wasn't CHAT GPT. This is actually only, yeah, I think,
00:06:49 · George Sivulka, Alex Immerman
[00:06:49] S0: At at Stanford, there's a class, which is CS three thirty, which I think it's probably still around. But it's like an introduction to meta learning and multitask learning. And when I was in grad school, was I was completely captivated by this class. The the idea and the promise of meta learning, and what that means is teaching machines to learn how to learn, was to me going to be the most important technology of all time. It was just the coolest thing in the world. And I was in my PhD, and I think around June 2020, you know, looking at all the research, keeping track on meta learning, multi... Multitask learning, OpenAI releases a paper, and it's a GPT-three, large language models Pretty important. It was very important. But the the title of the GPT-three's paper was actually large language models are multitask and meta learners or or something along those lines. And I remember thinking like, okay. That would have just completely botched this entire field of research that, you know, that is, like, encapsulating all of my academic interest. And I played around with it, and I remember, you know, GPT-three wasn't CHAT GPT. This is actually only, yeah, I think, [00:07:57] S1: over twelve months before ChatGPT was Yeah. Like, year and a half almost.