Venture with Grace
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.
00:29:14 · Jay Zhao
[00:29:14] S1: Yeah, it's definitely talent acquisition. It's at the lab level. It's the talent acquisition, it's definitely a talent density play. So I think in that lens it makes sense. But for application stuff and for software infrastructure stuff, algorithm suggests that basically the three or four star founders, they tend to have the best shot in this delivery mature scale return, both for them and for investors. So to answer your question, the reason that matters is because oftentimes we got invited into hot deals at high valuation. It's the same lens that we use when it comes to growth stage companies. We actually have a multi stage fund that we launched last year, and the goal is to translate our early stage research and role view into selectively pick a few companies at growth stage to invest and back. And the idea is that if you invest at high valuation or at a growth stage, then you really have to believe two things. One is even though the valuation is high, which in and by itself is not a bad thing, but you really have to believe that there is a 3x or 10x potential growth from there, right? And then that's one component we have to get to a conclusion. And then the second part, which is the engine behind that is the moat question matters a lot. In the hype environment, will get concentrated to pattern recognized. Founders will get concentrated to narrative driven companies, but nobody really digs underneath in terms of... I think part of the reason is that AI is very new, so a lot of business models are emerging as we see it. But I think there are some cases where we can't get to a conviction level that the moat actually will compound over time, therefore we can still get to 10x return. So that's how we evaluate the high valuation deals and those type of companies. Obviously it's a very high level abstract answer, but because the fund is being stealth, we have some positions in some growth stage companies we can talk about maybe offline later. When
00:31:29 · Grace Gong
[00:31:29] S0: you're thinking about going back to the portfolio construction piece, so obviously the fund is significantly bigger. I guess do you guys just make the check size bigger when you're doing the investment? How do you think about follow on as For
00:31:47 · Jay Zhao
[00:31:47] S1: fund two, we reserve about 15% out of the fund, and in fund three, we only dial up to 20%. I think the logic is quite simple, right? If we are research driven, concentrated play of a VC firm, then most of the ownership acquisition should happen at the first check stage, right? And then the other part is that a lot of our investors, they do have co investment mandate, they do want to double down on companies, so we want to give our LP the opportunity to do that as well. So for example, if Victor is raising a new round, we know the company for two years and we'll definitely double down. In Fund one, we didn't have any reserve. So when a company like MaintainX, Spectro Cloud, or the breakout companies raising a round, Even though we have that location, it's hard to form SPV and do all that stuff. So in Fund three, I think when we think about the journey of our scaling up 10 to 40, 40 to 75, makes sense for us and is at a level that we feel confident to deliver the same result to LPs. Now there is multiple ways where we can increase the font size heading to font four and font five. One way is that we're recruiting good partners. So I think the way to look at this is capital per partner on the platform. And then the other part is that you can grow your AUM if we establish the practice of the growth stage side, which I think we have an interesting methodology as well, but that part is only on the early end. Over time, you know, we just want to partner with the best company in the most meaningful way, whether it is the first check or double down stage. We don't want to... One thing that we don't want to do is that, like, if a company is already really hard with high valuation and we have to make a decision by this Friday, like in two days, that's kind of hard for us. I know it's tempting, but it's kind of hard for us. So we have to miss out on deals that way as well.
00:33:56 · Grace Gong, Jay Zhao
[00:33:56] S0: When you were thinking about the hot deals, since you guys are doing the research, let's say like a Victor or MaintainX, would assume they were not necessarily the most competitive deals at [00:34:11] S1: a very [00:34:11] S0: early stage. How do you kind of like in your research, what is in the I guess like the quote unquote report? Which part would make it to the research that like contributing to you guys investing in the company? Then, what does the diligence process look like for MaintainX? You have to chat with our customers? How much time do you need to get comfortable since you make sure you won't make the two day
00:34:43 · Jay Zhao
[00:34:43] S1: kind of decisions. Yeah, yeah. So I think the older VC firm, I forgot what the other is, a sales of SMR, they have this term called Prepare Mind. I think it's a very old term, it sounds very old type of term, but I think the essence of it does stay, right? So what that means is that, for example, everybody has been talking about fiscal We have not placed bets in physical AI space yet for some concern about the whole subspace story, some part of that stack is very hyped with capital excitement, and the fact that the cap investors, most investors are not technical, they are attracted to a very impressive demo. And I think physical AI is one of the spaces that leave a lot of imagination for investors. But what we did as an example of that, right, internally we have a private research that we don't put up on the website and we don't share publicly, but it's like a 90 page of deck where we actually break down the whole value creation chain of physical AI. I think some of the opinions that we share internally, we share with some LPs and stuff, it's quite, I would say, should be quite censored. Because we have opinions about some public companies, we think it's very not deserved what they are right now. But to answer your question, so stuff like that, we have the form of a big understanding about the space. And in Fund III, we do want to play some bets in the physical AI space, but not in the traditional companies like the interactive world model layer. That part we actually feel pretty bearish about right now. But there is some other pocket opportunity. We think it's actually good timing to make some place bets on bets in 2017. And I think in that sense, kind of like Vipshop, kind of like MaintainX, in the early round, there's always something that the market does not appreciate or understand. In Fund one, one thing that we form as a thesis of the fund is that when everybody was talking about the early days of LM, we actually looked for more edge play, especially debt play. So MaintainX is a good example of that. They were actually rolling out into manufacturers like property maintenance and stuff. We think that can actually fit into LMs much easier and much more unique high value way because nobody was able pay attention to that. And now, like MaintainX, after acquisition by Autodesk, now it becomes Autodesk Industrial AI solution, right?