Venture with Grace
00:22:44 · Jay Zhao
[00:22:44] S1: Yeah, they want to concentrate and focus on what's good for them for the past decade also. I think it kind makes sense, Once you get to $1,000,000,000 and above AUM, you really have to focus on the core strengths to generate return at that high AUM level. And which is why it's important for us to stay at the earlier stage because our fund size supports that strategy and frankly that's our strengths and our passion lies at the earlier stage. The other thing I would say, there is art and science in early stage investing, right? The science, the art part is really just about understanding founder psychology. I think one of the, even for good and bad, I've been in this field for fourteen years and then it's just like, I studied founder psychology really well because I used to be a founder. I know what struggle means and what it takes for that person to actually persist and have that resilience. And we actually look for some sort of life experience that highly impact the person's journey and thinking, some sort of adversity along the way. And I think that's really, you know, not everybody can be a founder, but founder is for long jobs and it's quite suffering a lot of times. So I think we look for that as our piece. That part I actually don't think AI can replace yet. I can generate a bunch of text, right, and guide you with different directions and different thoughts, but I just don't think AI understands the real world and real human interaction the way that we actually do a So person, that part is one component of the peeking piece. The science piece is actually well suited for AI stuff. So for example, we built our own AI agent internally. We've been doing that for the past two years now. But now with open call from our early this year, it's actually introduced a really good framework for the agent to be more proactive and actually in the self improving path as well. One example is that we analyze all the companies, including YC, including all the accelerator companies, and understand founder trade and market, and we come up with an algorithm, what we call the Founder Shine Score, and we actually retrofit, retro analyze our own founder case studies on what works and what doesn't work. And it turned out that founder shine score is a five star score that we have.
00:25:21 · Jay Zhao, Grace Gong
It turned out that the most high valuation company founders is that ranked at five star because they are the typical pattern recognized founders, break out from big labs or big companies and have certain contracts. So that's like the gut founders? Well actually, counterintuitively it's not. I agree. I [00:25:44] S0: so I was also introduced by one of the top one lab person, one of the very early employee of the top one lab person and introduced me to two companies like and then one of them is like it's seemingly like coming out of like the right background, tier one firm, one like you know experiences, blah blah blah and then on the other hand there was another company that's like in YC and it's like very scrappy person come from high school I guess, not high school but like I'm sure they went to college at some point but like drop out or something and then like on paper like one would outperform the the other one a lot, but the other one kind of sunset their company recently, the big firm people, and then I guess they went back to big firm. And then the other person is like, their company got acquired. And I would say that part, I don't think that part was a huge success of your IPO ing, but on the other hand, they didn't really sense of the company and they have some sort of access. So it's a really interesting lesson for myself. I felt you can never really judge people [00:26:53] S1: by That's the off the true. I think the part that's super interesting is that out of the five scores, it's actually two and three historically produced the best venture outcome.
00:26:53 · Jay Zhao, Grace Gong
[00:26:53] S1: by That's the off the true. I think the part that's super interesting is that out of the five scores, it's actually two and three historically produced the best venture outcome. [00:27:07] S0: What is two and three?
00:27:07 · Grace Gong, Jay Zhao
[00:27:07] S0: What is two and three? [00:27:10] S1: Like two or Oh, three and four is actually the best. One and two is tough. One and two meaning that from our AI score ranking, One and two meetings that it's not like it's impossible for them to become successful, it's just that the headwind is too strong. We would still take the meeting, we have a lot of pitch meetings right now, so we actually just use AI, what we call alpha, alpha rank the meeting priorities, so we're just kind of trying to figure out where we can spend the time most effectively. So three and four star founders are actually the best founders. So maybe translating into some qualitative characteristics, These are the founders, either repeat founders, past startup experience, but not a huge asset, but they learn the lesson. They learn what it takes to start companies. Maybe it's a second round, third round of doing that. I think that's additive to the startup component. Then two or three founders are, you know, so one thing that's kind of interesting is that within the AI era there's a lot of maybe this is a hot take, but there's a lot of professors who start companies. According to alpha analysis, actually it's tough. It's tough to have a professor as a founder. I don't know why. I think probably if I have to break it down, it's like you just need the full time commitment into the creation phase of the company. And I think professors are oftentimes engaged with a lot of activities on their own.
00:27:10 · Jay Zhao, Grace Gong
[00:27:10] S1: Like two or Oh, three and four is actually the best. One and two is tough. One and two meaning that from our AI score ranking, One and two meetings that it's not like it's impossible for them to become successful, it's just that the headwind is too strong. We would still take the meeting, we have a lot of pitch meetings right now, so we actually just use AI, what we call alpha, alpha rank the meeting priorities, so we're just kind of trying to figure out where we can spend the time most effectively. So three and four star founders are actually the best founders. So maybe translating into some qualitative characteristics, These are the founders, either repeat founders, past startup experience, but not a huge asset, but they learn the lesson. They learn what it takes to start companies. Maybe it's a second round, third round of doing that. I think that's additive to the startup component. Then two or three founders are, you know, so one thing that's kind of interesting is that within the AI era there's a lot of maybe this is a hot take, but there's a lot of professors who start companies. According to alpha analysis, actually it's tough. It's tough to have a professor as a founder. I don't know why. I think probably if I have to break it down, it's like you just need the full time commitment into the creation phase of the company. And I think professors are oftentimes engaged with a lot of activities on their own. [00:28:43] S0: I would say they're kind of not really in touch with current the realistic commercialized equipment.
00:28:43 · Grace Gong, Jay Zhao
[00:28:43] S0: I would say they're kind of not really in touch with current the realistic commercialized equipment. [00:28:51] S1: Yeah, that's right. [00:28:52] S0: I would assume they are probably perfect if they are doing... Like, who does a lab that's fit into her [00:29:01] S1: strength. [00:29:01] S0: And I got to acquire because it's kind of like, talent acquisition for these companies. Yeah. I would like to finish.