Entrepreneurial Thought Leaders (ETL)
00:19:10 · Ravi Belani, Carina Hong
[00:19:10] S1: So you're you're you're open... You're you're putting a lot of bets, sounds like. You're open to being wrong on certain ones, but you have enough bets where you have confidence that you'll still be relevant and and need [00:19:19] S2: Yes, and you also have focus. So there are things that you're like, wow, like data, for example, that's my big focus. We think that the self improvement aspect comes from the ability to generate data synthetically. And so these are things that we're very sort of... We hold it tight to our chest. [00:19:35] S1: And do you think you need proprietary data to sustain as an AI company? [00:19:40] S2: I think so. I think it's kind of one of data and benchmarks are the definition of model, I think. [00:19:45] S1: Okay. Data and benchmarks. And do you think most people right now are building for the wrong AI stack?
00:19:45 · Ravi Belani, Carina Hong
[00:19:45] S1: Okay. Data and benchmarks. And do you think most people right now are building for the wrong AI stack? [00:19:51] S2: That's a question that we touched on. There's this old word where it's a hard coded harness calling a bunch of specialized models. Yeah. We think that the word is going to be... At least right now, we see it as a de minimis harness. It was an agenda or orchestrator model. Mhmm. It can call itself recursively. It can call, like, other hard coded tools that look more like even the hard coded harness is actor. So that's the word we see. And a lot of people are building infrastructure for the old stack and we're like, we probably should always be two steps ahead because look, even when you lose on the capability layer, you earn the right to play as infrastructure layer. But if you're not like two or three steps ahead, then you're kind of in a zombie position. [00:20:36] S1: Okay. So you're in the stratosphere. I just want people to really feel this of rarified group of companies. You raised a lot of capital. You're growing now [00:20:43] S2: New lab. [00:20:44] S1: With high ambitions. [00:20:44] S2: New lab. [00:20:45] S1: New lab. [00:20:46] S2: Yeah. This is what they call it. I think there is a... DD DAS has this like 50 companies, new lab list, and we're definitely one of them.
00:20:46 · Carina Hong, Ravi Belani
[00:20:46] S2: Yeah. This is what they call it. I think there is a... DD DAS has this like 50 companies, new lab list, and we're definitely one of them. [00:20:51] S1: You're one of the new lab companies. So for that species of companies, can you give color on the nature of the problems that you have to deal with which other startups might not? For example, when you're growing so rapidly, even when you're expanding the team, does that look different than a typical startup in terms of how you're expanding? [00:21:08] S2: Yeah. I think as CEO, a lot of times like are inevitably in people management. And I think for a new lab, this is especially true because like talent density, right? Like that that defines you as business. And if you don't have the talent mode, then I think it's getting into dangerous now where you need to not be in your lab to start like do revenue crunching. So a lot of, I think my day to day are all sorting out the technical management. And this is a shift since the beginning of time. [00:21:42] S1: And this is something that I think is... I wanna make sure that people really grok this because as a as a 24 year old at a Stanford, you may manage to retain and attract talent [00:21:52] S2: Yeah. [00:21:52] S1: That is world class that others have spent, you know, generations trying to cultivate. Can you talk about who... The CTO that you first brought on board, their background, and how you've managed or any hacks that you've managed to get exceptional talent to join you.
00:22:06 · Carina Hong
[00:22:06] S2: Yeah. So Shubo is a he's a former formerly at Facebook AI Research. He's an IC director there. He's this visionary thinker that I always enjoy just hearing him talk about anything. And I think at the beginning it was a time where also Facebook was kind of crumbling. So it was sort of, I think, opportunistic timing. But to align each person to fit into this team that we have cultivated here at Axiom, sometimes I had to be like understanding of what motivates them, especially it's probably the ideal case if what motivates them is the technical problem that you're working on. And in that case, he has worked on testing AI generated unit tests. He believes in scaling, he believes in like just infinite data and big scale. He was one of the authors, Deep Speech and Deep Voice in 2014. So it's very aligned. And that made, I think, a really good foundation. Of course, we have our like ups and downs during like, the normal course of startup when stress is high, but very proactive, very sort of real communication too. I think later, like when you talk about retention, that is I think what I learned. I think the most difficult thing honestly, as a founder is that there are a lot of days where you just kind of are like, everything is going down. You're like, you wanna take care of yourself, but you also have the team out there and you really want to make sure that people are doing well. And I think to the ability to feel empathy and balance that is something that not necessarily in my academia training was like the emphasis, right? It's always about higher, faster, stronger. Like how can you get that higher score in this like Olympia? Necessarily about like just connecting with people, heart to heart. And I think that's very important in the entrepreneurship journey. So that's one of the unique challenges of I think a new lab. The other aspect of a new lab is you're basically dealing with, I mean technical management, not just people management. So there are a lot of priorities that might be conflicting. A constant dynamic I'm dealing with is you can ambitiously set goals, but you cannot afford to also overlook the risk. So it turns out that the people who set up like very ambitious goals and the people who are best at like catching the risk in execution, very, very few times there's like overlap.
00:24:30 · Carina Hong, Ravi Belani
Mean, if you define someone as one or the other, it's very much a disjoint set. Now, of course, the people in the company who are setting ambitious goals are very solid technically. But that kind of comes as like an afterthought, right? Like the risk flagging people, right? Their whole like focus is on this might not work, this might not work. So it sounds like naysayers, but when they are naysayers, they're actually not naysayers. They're just flagging like actual things you need to hear. So sometimes I'll be like, well, I can tell him to talk about it more politely, but I don't... But he's right. And so I think sort of like juggling between these actual technical priorities, doing more versus doing less, sometimes less is more. I talked about the full stack solution, the focus when we are like focused on things like classification finance and both groups, we're just focused on it. And it's like everyone focused on it. [00:25:28] S1: And are there any unintuitive hacks or that you act... Or tactics that you really treasure when it comes to managing people or attracting people? I heard just as context from from the... Some of the venture capitalists that funded you. They said you had the superpower around attracting amazing talent. And I know that all the things that you said are part of that, but I'm wondering if there's any specific tactics that the future founders should adopt. [00:25:51] S2: There are three very, very important things I didn't realize before. I mean, and by the way, each of these, like, you know, behind this tip, are, like, a lot of days of...
00:25:51 · Carina Hong, Ravi Belani
[00:25:51] S2: There are three very, very important things I didn't realize before. I mean, and by the way, each of these, like, you know, behind this tip, are, like, a lot of days of... [00:26:02] S1: Yeah.