June 28, 2014
Zack Urlocker was heavily involved in MySQL's disruption of the incumbent database software market, which has many good analogies to the bat...
In episode 21 of Venture Confidential, Lan Xuezhao, Founding Partner at Basis Set Ventures, describes how she went from a career in Corp Dev at Dropbox to later founding a venture firm that invests in companies that improve people’s work efficiency.
About the Guests
Lan Xuezhao is the founding partner of Basis Set Ventures, a ~$140M early stage venture firm focused on the Future of Work through artificial intelligence. She built the Corporate Development Strategy team at Dropbox and prior to Dropbox, she worked with McKinsey.
Peter Chapman: Lan, welcome to Venture Confidential.
Dr. Lan Xuezhao: Thank you for having me.
Peter: You've got a doctorate in psychology, spent four years at McKinsey, started your own educational company, and then corporate development at Dropbox.
Dr. Xuezhao: Right.
Peter: That's where I want to start. What led you to corporate development?
Dr. Xuezhao: At Dropbox?
Dr. Xuezhao: It's funny. I had no background in corporate development. I was at McKinsey before, and I was for the most part a strategic consultant. And with Dropbox I went into this team called BizOps, at the time it was mostly a group of consultants going in to solve different problems at Dropbox.
And one day the CFO at the time, Sujay Jaswa asked me, "Do you want to come and help us build a Corp Dev team?" And I had no idea what Corp Dev was. I looked it up online and I thought this sounds like a really amazing opportunity. So I took it. That's basically how I ended up being Corp Dev.
Peter: What does corporate development mean?
Dr. Xuezhao: It's mostly a M&A function at a company. Each company does it a little bit differently. Some companies have a more strategic component, some companies are more M&A functions. For Dropbox, there was restructure over the past four years. But for the most part it had two functions, both the strategic component and also the M&A component, or the execution component.
We would come up with the strategies in terms of what company we should acquire that might fit the direction of the company, write the roadmap, and then go execute and buy that company.
There's also a post-acquisition component where we integrate a team into a company and make them feel at home.
Peter: What was the driving vision behind M&A during Dropbox, at that time?
Dr. Xuezhao: There were three major things. The most frequent one we've done was talent acquisition. To augment recruiting of top engineers, who are product managers especially, and get them integrated faster. It's usually a small group of people who have been working together for a long time. They can quickly ramp and do things very quickly.
And then there's a product/tech acquisition, where we would acquire a mature, already-built product or technology that might take time to build at Dropbox and we would integrate with our existing product suite. Typically the product and tech will fit in our roadmap, so we don't have to spend as much time building it. We just acquire them and integrate faster.
And then there's the last part, which is probably the toughest and most uncommon, which is strategic acquisition. The idea is to acquire a company that will help us to either go into a new market, or open up a new revenue stream. Or acquire something where we don't know what to do.
A good example would be Facebook acquiring Instagram. Usually this is a bigger acquisition that will change the company more significantly than the previous two. It's usually along these three lines.
Peter: You have no experience doing corporate development, and all of a sudden, you're leading this corporate development team. What's hard about that?
Dr. Xuezhao: I was lucky enough I was with Sara Adler, we were building the team together initially. She's been doing this for a bit longer than me. Were pretty complementary, in a way. We're learning from each other and she was doing a lot of execution, I was doing a lot of the strategic part of Corp Dev. Then after she left I was doing more deals. It's an amazing learning experience from both her and also other people who were experienced in Dropbox.
Still it's a very hot company, even back then. People love Dropbox. I would go on to chat with people who have more experience, they would chat with me and they would help out. It's a really amazing group of mentors and friends who helped me learn how to do Corp Dev. And I learned a lot from the founders initially. You would talk to people and you'll learn what founders really care about. Empathy is very important, for example.
Talking to a bunch of people you'll learn what actually makes a difference to them, what they care about, how do they negotiate a deal, etc.
It benefited me greatly, my time at Dropbox. I learned a lot.
Peter: How do you source these companies?
Dr. Xuezhao: At Dropbox specifically, we had a lot of inbound deal flow. The network was amazing, all the engineers and PMs. People at Dropbox know friends who know other amazing founders who would start a company, and we just go out and talk to them.
So that's actually a pretty substantial deal flow for us. And over time, because I was pretty introverted, I found it not the most efficient to go out and talk to people all day. I started experimenting building something that would help us find market signal and find these deals faster.
We ended up building a quantitative sourcing engine where we'll find data in the market that is not super obvious. We'll track people, we'll track traction of the company, and we will track a number of things so that we can find people who otherwise would not be in our network. That became a really fun project for me and gave us some good deals.
Peter: Nice. I'm deeply curious about quantitative sourcing, because it's something that we're not doing at Heavybit but I feel like we should be. Can you walk me through the process of building that engine?
Dr. Xuezhao: Yeah. First of all at the time we had to be very clear about why we were doing this and what kind of deals we were looking for. Because different stages of a company will have different signals on the market we'll be tracking. After that's clear we'll need to go and identify what kind of data we're collecting, and after that we'll need to know how we're tracking that data.
Different data will have different patterns. What will be the frequency, and how we want the data to trigger deals for us, and then we'll talk to a company and decide, "What are some of the criteria of evaluating these companies?" It's a feedback loop. After you build the whole thing, you will go back in and see what works and what doesn't work and refine the system.
To give an example, some of the signals we've found really helpful are two things. One is traction of the company, and two is people. So one example we were tracking pretty much all the data we can possibly find around a company, like app store downloads, engagement on the products from both mobile and desktop, revenue number if possible at all. We'll see the gross trends of the company at very early stages.
Peter: Some of this stuff seems hard to find. Revenue is usually not a public number.
Dr. Xuezhao: Yeah. There are sources you can acquire. There are companies doing that, and you can buy the data or you can triangulate the data to get a sense of what their revenue might be. You don't have to have the exact revenue, but there are indicators that give you a sense of the revenue. You know the price point, you know how many people use the product, you can triangulate and get a number.
The person on the team, Jimmy, he used to be a qualitative trader. He did some smoothing of the curves. We know the true growth curve versus a lot of the noise going up and down. If you just tracked the growth, he smoothed that out so that we can find the true growth of a company. Interestingly, one of the companies we've found through such methods was a company called Musical.ly. I'm not sure if you've heard of this company.
It's a lip-syncing product. We found it very early, three or four years ago when they were really small, and we just saw this company growing extremely fast and having no idea what this actually was. Then I played with the product and I decided, "I don't understand this. They're just growing amazingly well." And it's a team of engineers based out of Shanghai and they're targeting the US market.
If you follow the pattern of VC investment, you know if you're based in one country and target users of another country, that typically doesn't work. So we didn't do anything about the deal. But then last year it was sold for a billion dollars.
Dr. Xuezhao: It's one of the examples of the power of data. We would never find this company if we just relied on our network. Another company we found through tracking people, which is the second thing. People is amazing. Everybody knows you need to keep track of people, but how do you keep track of a large number of people who may or may not be in your network?
Everyone has their own network. It's a function of who you are and who you help.
So we tried to make this a more scalable process. We'll be tracking amazing engineers out of Google, out of LinkedIn, or other large companies where we know engineers are good. And one thing we were tracking was a group of engineers leaving a company and going to start another company.
That's usually a pretty good sign of a new company starting. We found a company called Crushlabs from this process, and they are amazing. Amazingly large already at this point. Back in the day when we looked at this company, it was like, "What is this company? What is his name?" We would never find this company if we didn't have this tracking.
Peter: Can you walk me through the mechanics of this? Are you consuming a LinkedIn API, or are there more sophisticated--?
Dr. Xuezhao: There are signals you can find online, it can be a hub of public information. AngelList, LinkedIn, there's some public information. I don't think you can scrape any data, which we didn't. But there was other information we could find online and you can consolidate a database of things and track them.
To be honest, when we were starting it was completely manual. We just entered people we know and looked for the people they know, and start with a smaller audience of people and gradually will grow bigger.
Peter: What were some of the things that surprised you from this engine?
Dr. Xuezhao: A lot of things it just doesn't fit any of our mental models. Like Musical.ly, which I talked about, that would have been a deal I passed on right away, if I'd seen this coming from a referral from network, if I didn't know the traction. Also the network we've found, we would find people based in South Africa. Like this amazing kid who is very young, I don't even know how old he was. He's in high school. And he's really productive and he built all these apps for other companies.
And that's just not something we would know when they're not in our network. So the most amazing part of this is it helps us find companies early, and find companies not in our network. A byproduct of this is to help reduce our biases, which everyone has.
Peter: I was just talking to Charles Hudson about how relying on your network to source companies can lead to a homogenous portfolio.
Dr. Xuezhao: Yeah. Everyone has had their own biases, and having a data angle helps us reduce that. It shouldn't be the main sourcing engine, but it's a good supplementary tool to cover our blind spot.
Peter: Let's fast forward a handful of years. About a year ago you started your own venture firm, Basis Set Capital. What prompted that leap?
Dr. Xuezhao: I've always been really into data. When I was an undergrad student I worked in this lab that helps children to acquire language-- helped a computer to acquire language as children. Language acquisition. It was the earliest form of AI back then in 2000. It was too early. Most people didn't understand what I was doing. But that was the start of my fascination with AI.
So I went on to do a PHD which was measuring and predicting brain functions, and throughout my entire career in PHD, even at McKinsey and Dropbox. I worked on a lot of big data projects at McKinsey, it was called "big data" then. The name keeps on changing but it's all the same thing pretty much.
At Dropbox I was in Corp Dev, but what I worked on Hack Week projects doing AI. We built a media assistant for one of the projects. I've always been interested in that, and I've been making investments on the side in this specific area over time, because I do that so often. I talk about this with passion and more people come to find me and want to talk about it.
And more people want to invest with me or invest in my funds. It was pretty organic when I decided that I wanted to do something in this space. I didn't go out and say, "Please look. Here's my pitch stack. Give me money, I will start this fund."
It's more, "I'm interested in this specific topic. I will do investment regardless of if I have your money or not. If you're interested, let's do this together. If you're not interested, I'm going to do this anyways." So it was pretty organic that way.
Peter: What is the one-sentence summary of Basis Set? What's the fund thesis?
Dr. Xuezhao: It's future of work. We invest in companies that improve people's work efficiency, from white collar workers and blue collar workers. We're investing in anything from an office productivity suite, to farming robotics, to manufacturing tools to logistics supply chain companies.
Peter: Give us a rough sense of how big the fund is, and what size check you write?
Dr. Xuezhao: The fund size is $140 million dollars, roughly. It's $136-37 including GP contributions. It gets a little bit iffy. It's not an even a number. We typically write checks between $1-3 million dollars on average. Whether we will write smaller or bigger checks depends on the deal. Once a year I would write a big check into a CSA, we usually do a late seed in early A about once a year. Depends on the deal, we'll probably write a bigger check.
Peter: Got it. One of the things that's interesting to me about your career is you started this fund with very little exposure to institutional venture. Was it tough figuring out the mechanics of building and running a fund?
Dr. Xuezhao: Figuring out running a fund wasn't that hard. I would go out and talk to LPs and talk to GPs and most people are very nice and talk to me about their learning through the process. LPs will have their very specific request of how they will run the fund. "GP can manage how much capital, and how are you thinking about this?" So after several conversations I would come to my own thesis of how I want to run the funds.
I want to run the funds a little bit different from other people. I will come in with my own hypothesis and I will fine tune it with people's ideas from their success and failure stories. I really enjoy that process of fine tuning my hypotheses and coming up with something that works or it doesn't work.
Another nice part of it is also that we're a pretty lean team. I can just say, "I want to do this. If it doesn't work I'll change it to do something else." It's very much a startup mentality of, "Run fast and change things if it doesn't work." It's the beauty of having just one partner.
Peter: How is Basis Set different from other venture capital funds?
Dr. Xuezhao: We're very focused on one thing, which is still future of work. We move extremely fast. We can make decisions in a matter of days. We do a lot of diligence to try to understand a space really well before we make an investment. But once we do understand the space we move very fast.
The team fits into a pretty similar culture, which is we're all very pragmatic people. Operators who get things done, because it's enterprise funds we try to curate an enterprise relationship with the companies that help our profile company introduce their future clients.
We have an amazing group of machinery advisers who help us do technical diligence, who help advise companies. Because we're so focused we're able to get people who are experts in different areas, like computer vision and LP, and they will help us in different areas. I think a focus really helps us.
Peter: Did you build another quantitative sourcing engine?
Dr. Xuezhao: Yes. We're in the process of making it better, but yes.
Peter: Tell me about that. Because I imagine sourcing looks different for deals at this stage than it did for Dropbox corporate development.
Dr. Xuezhao: Yes. At BSV we're focused on tracking people, because a tracking company with traction would be a little bit difficult. Because they're mostly very early.
When they're very early there aren't a lot of signals you can see on the market.
So we track a lot of people, we track filings and when people register. We try to find them early. We track committees of people who are technical and like-minded, and we like different types of startups. We try to do a lot of research in areas we are passionate about, and we try to source companies that way as well.
Peter: I want to spend a little bit more time on quantitative sourcing. Specifically the signals that people are emitting. You said one signal is a group of engineers leave a company at the same time, what are some other characteristics that are high signal when it comes to people?
Dr. Xuezhao: Technical people usually send the best signal, or the most obvious signal for an AI company. A lot of people don't want to change their LinkedIn profile so it's not obvious whether they left a company or not. But they would go to GitHub and write some codes, and you would probably at some point realize all of a sudden that someone wrote a lot of codes in GitHub, and "Is this person starting something new?"
In some cases you would also like to be able to figure out through the website of a company where this company is hosted and their stack of the company, and using that to figure out whether they are a fit with a fund investment thesis. So there are a lot of subtle signals in the market that's not super obvious.
Peter: How much do you care about people's experience?
Dr. Xuezhao: That's a good question. It depends on what experience. We like repeat founders, obviously we like people who have grit and who have been doing this and focusing on this for a long time. That experience matters. In terms of other experiences, whether you graduated from MIT. I think that's a great signal, but if you have not graduated from a top university or you dropped out, or you dropped out of high school. We don't penalize people by knowing that. So it really depends.
Peter: Could you walk me through a typical BSV deal, from sourcing through diligence?
Dr. Xuezhao: Sure. Typically we do research and we look for deals that are inbounds, that follow a different process versus deals who are outbound. I'll tell you an example of an outbound deal. We've been researching manufacturing automation. We wanted to invest in a company in this space. So we started sourcing companies and we did a lot of landscaping comparing companies and research and talking to customers which companies are good and which companies are not.
Through that process we learned about this company called Falkonry. I reached out and the CEO responded but when he responded his round was already closed. I was two months too late. So I had to go in and chat with him, he was nice enough to chat with me. But I had to go in and chat with him and convince him to take my money even though he was not fundraising at the time. I was able to do that and that turned out to be a great deal for us.
We did diligence, we looked at the products, we chat with the CEO. We've already done the market landscaping part which made the deal go much faster, we did some technical diligence, we talked to previous investors and talked to other references on himself and the employees.
Everything checked out and we made an investment. But as you can see, because we already did our research up front we were able to move very fast.
And I think founders appreciate that and we're able to close a deal very fast and work late.
Peter: I talk to two broad categories of investors on this podcast. There are some folks whose pipeline is almost entirely inbound reference based, and there are other folks who are very thesis-driven and do a lot of outbound. It sounds like you do some of each. Is that fair?
Dr. Xuezhao: We get a lot of inbound deal flow, but because we're thesis-driven we filter out these deals based on our thesis. A lot of deals, as people learn our preference they will also send deals that fit our thesis. A lot of deals we have a high possibility of doing are those outbound, because we already know what we want. We go hunt for these and we invest in them. Inbound, it depends. If it's a great deal we'll invest, but because people are less clear about what we want it's a longer process.
Peter: Got it.
Dr. Xuezhao: Yeah. But we have both.
Peter: I want to come back to Basis Set. You told me that you've got a team of pragmatists, tell me a little bit about building this venture team.
Dr. Xuezhao: I had a lot of fun building this team, and we're still a pretty small team. The other investor on the team, his name is John Mannes, he used to be a TechCrunch AI reporter. He's an amazing researcher and he knows about AI so much, more than most people I've met. He wrote 500 articles in the year and a half he was at TechCrunch, it was a pretty amazing and productive time for him.
We were very similar to each other, we're both pretty cynical people who are very pragmatic. And we would be deep in research every time we look at a company we study all aspects of this company before making an investment. Similarly Andrew, he does our positions on the team, it moves very fast and he does pretty much everything that has anything to do with operations.
We're hiring another person who used to be an early employee of Dropbox and Stripe. She's also very pragmatic and really low key. She will be the one getting all the stuff done, but you probably wouldn't even know who she is. Because she doesn't go out there and talk about what she's done. So you get a sense of the people who are in a team, and have an amazing network of advisors who are very involved with the fund.
They spend time helping companies and helping us do technical diligence. Niniane, one of the advisors, hosts events with us regularly. And Peter Welinder, he works at OpenAI, he worked on the raw machine learning at Dropbox. He would spend hours doing technical diligence for us on deals, and we really appreciate that.
Peter: Do you imagine that BSV continues to be a sole GP firm indefinitely? Or do you see yourself bringing someone else on board in the future?
Dr. Xuezhao: I'm open to either. Right now I'm having a great time, we're getting great deals and we're closing great deals. I didn't feel that was a bottleneck at all. I think it's all about enabling people to do the best work that they can.
I didn't feel like I need to have a very senior GP to do the best work, having a team of people who are clearly passionate about this either their advisors or investors on the team will be able to close the best deals.
So I'm not in a hurry. But if I find someone who fits the culture, who compliments me really well who would contribute to the fund in a way that other people can't, I think I would be very open.
Peter: What have you learned in the year that you've been at this?
Dr. Xuezhao: I learned so much in the past year. One, building a small team is very hard. You have to find people who fit into the culture, and finding these people is not necessarily easy. Culture is so important. Maybe even more so than the skill sets that people have. So I have a bias towards people who learn very quickly, and who have the attitude to learn, even if they don't have the right experience at first.
I would have the bias of hiring them versus someone who has been in venture for a long time. That's one, and two, people appreciate the effort you make to get to know companies and treat them well. For example, I get great deals from companies I even passed. For a company that I didn't even make an investment, they're still our friends because I treat them well, I invite them to events.
Even if I don't invest at this point I still respect them and they may be on to something, but you just don't know. So people appreciate that and they come back and give us great deals, and we host events together. So we always try to be respectful and we treat people well, we try to be humble. The third thing is, you were asking earlier how we're different from other VC firms.
One of the feedbacks I've been getting is that people feel comfortable coming and talking to us.
They come to us early because sometimes they want to practice their pitches before going to other firms, and they can come to my house and sit on the floor and show me their Excel spreadsheet and I can run numbers with them. They appreciate that.
Dr. Xuezhao: I didn't realize how big of a deal that is, initially. I basically thought, "That is who I am. I don't need you to have a formal presentation. Even among your board, I don't need you to do that. I can problem solve with you--"
Dr. Xuezhao: "We can come to a conclusion together." Over time I realized that actually helped greatly for me to get early deal flow from people. So I appreciate the feedback from people and I'll keep doing that. There's no effort made in that.
Peter: What do you think you're doing that makes you so approachable?
Dr. Xuezhao: It's just me. It's just who I am. I don't think being a VC makes me any different from just being me. I feel like I'm massively an outlier in VC. Working mother, first college grad, first generation immigrant, a woman. I feel like I need to try really hard, and I really appreciate people who try very hard to make things work. And I understand when things don't work, that's when they need the most help.
When things are going really well, they don't need VCs to help. That is why when things go wrong, I'm there to help them.
So I realized all that. I have to create my own career path to get into McKinsey, to get into Dropbox, to even make a career in any of these places. So I understand when things don't work and I understand when I shouldn't judge them and I should help them.
That is how my career was and I try to find people who are similar, who have the grit, who try to make things work regardless of where they're from, their background and--
Peter: What are some of the ways in which you help founders?
Dr. Xuezhao: Frankly, from my feedback from founders, just by sitting there and talking with them without judging them. Hearing them out and trying to offer solutions. Sometimes people don't even need your help they just need you to sit there and listen to them. And try to realize that I already help people a lot.
If you can give them connections or help them do this and do that brainstorm, different solutions, they appreciate you even more. I think people need to feel comfortable going to you and just talk about these issues. That's already a great starting point, and that's the feedback I've been getting.
Obviously when I give them warm introductions to their potential clients they find it very helpful. And when I say, "Peter can spend an hour with you every week trying to help you with your computer vision model." That's very helpful. But the fact that they can call me and ask me and talk to me about it any time, it's already helpful for them.
Peter: OK. So I have of an outline of Basis Set Ventures. It's a small group of pragmatic, I'll say warm operators and investors. You're writing fairly early-stage checks while you're sourcing across both inbound and thesis-driven research. What has changed in the first year at Basis Set? What surprised you, and what is meeting your expectations?
Dr. Xuezhao: I talked about iterating, keep doing things that work and stop doing things that don't work. I think there are things that didn't work first and then we would just stop doing that, reflect on that and change it. One of the things I found that works was our check size and the size of the fund.
Because typically people will come in and say "Your fund is too large. You can't possibly deploy all that capital through such a small number of people." But I've actually found there's a strategy going into venture. I wanted to raise a big fund because I didn't want to compete with a small seed fund, there's so many of them. I didn't feel there was a strong proposition of competing against these smaller funds.
I also didn't feel I can compete with the large funds, Series A funds who can come in and deploy and they can invest $10 million dollars into each round. So I was going for the sweet spot in between, maybe some $5 million dollar rounds will come in and do a $1-2 million dollar check or a $3 million dollar check. That's a spot. I felt like it was a blank spot, in a sense.
Throughout the year I started iterating and experimenting on that, how receptive people are to that idea and how competitive we will be given our check size. It turns out it works out great. I lost one deal last year because that deal is a relatively large deal, and a big fund came in and took the whole round.
But for the most part it works out and founders want to have a sizable check without talking to a lot of different small investors. For the size of the rounds a lot founders wanted to do, they were a little bit small for the typical large firm. So we're playing that round and I got a lot of positive feedback that our check size and strategy works.
Peter: Let me say that back to you. Your writing checks between $1-3 million dollars into rounds that are less than $5 million dollars total.
Dr. Xuezhao: Yeah.
Peter: And, are you doing follow on rounds?
Dr. Xuezhao: Yes, most of the capital is reserved for follow on funding. I want to follow a company to their pre IPO. That's the idea, obviously every quarter we'll talk about it and adjust that number, "Do we need 70%? Do we need 40%?"
Peter: What's the rough ratio--?
Dr. Xuezhao: 75 Reserved.
Peter: 75 Reserved? That's a pretty rich reserve.
Dr. Xuezhao: Yes.
Peter: OK. I'm trying to do some back of the envelope math, and I'm struggling here. How many total investments do you expect to make with this fund?
Dr. Xuezhao: I would expect to make about 25 to 30 investments over the life stage of the fund, and every year I would do five to seven, roughly, with this check size. Sometimes I would do a smaller check into the extremely early stage companies which I think are really strong. And sometimes do larger. But that's the rough strategy.
Peter: And you're leading rounds?
Dr. Xuezhao: Yes. We could lead rounds. We could lead or we could follow. We don't have a strong preference to leading or following because we're looking at a small team, we try to diversify. We try to be conscious that our time is limited, we can't possibly be leading all the rounds. We just don't have the time to do that.
Peter: I want to say this back to you. You're saying that you think you found a niche market between early stage funds, but still smaller than larger, call them Series A, funds. What are you calling yourself, do you have a name for this?
Dr. Xuezhao: Late seed and early A.
Peter: "Late seed and early A" OK. The landscape continues to change.
Dr. Xuezhao: Yeah.
Peter: Talk to me about prices. What are you trying to invest at in terms of pricing?
Dr. Xuezhao: I don't have a hard rule. I often find some of the great companies are actually valued fairly high, but if it's too high and if we can't justify it we don't invest. I think usually it falls into $8-20 mil valuation, but it depends on the size.
Sometimes the funds, the rounds are bigger and they're more expensive. And it also depends on the type of companies. For robotics companies they usually have a higher valuation because it's very capital intense. They need to raise more rounds later.
Dr. Xuezhao: Even though they have very limited traction to start with, they still have a high valuation. We understand that.
Peter: OK. I asked you what has met or not met your expectations, and you're telling me that check size is working for you. What's something you've changed since the fund started?
Dr. Xuezhao: One thing I didn't think I would do initially was building a very strong community of people. Initially I wanted to spend a lot of time on building the quantitative sourcing tool, and also doing research and sourcing deals that way. But over time through BSV, we host private dinners every month with a small group of people, and I realized the power of these small events. And I would get to know very amazing people through these small events.
Because I'm relatively introverted running events is pretty stressful for me, but I realized the power of this, especially with high touch private events.
People come together and discuss one common topic and have a great time. That's one thing I didn't expect to do a lot initially, but now the feedback I got from people is so positive I want to do that every week.
Peter: A couple of times you've called yourself introverted.
Dr. Xuezhao: Yeah.
Peter: And I want to talk a little bit about that, because I have this image of a typical venture capitalist as someone who's really gregarious, who is networking every night. How do you make it work as an introverted investor?
Dr. Xuezhao: I do things differently. I look at data. I host very small intimate events with only 15 people. I don't do events with a large number of people which stresses me out. The small events are low-key, but I actually really enjoy having one on one conversations or talking about certain topics with a small amount of people. I like intellectually stimulating conversations I can talk about all night. That seems to fit me really well.
I got good quality leads and advisors and a lot of stuff from these intimate events. I think that's what I've found benefiting to both me and people who attend these events. And you don't have to be extroverted to be a VC, is also one thing I learned. A lot of people are actually very introverted, especially for machine learning people.
Like engineers, I would say at least 50% of people are like me. They don't necessarily feel comfortable going out and speaking in front of 100 people, but they would build a great company. They just might not be as extroverted as other people. I identify with these people and they identify with me. We get along pretty well. It's all about finding your product market fit.
Peter: I ask all my guests the same closing question, which is what do you wish you knew going into this?
Dr. Xuezhao: If I were to go back and do this again, I'd just tell myself, "Be confident. There's not one way to do something. There are always different ways to achieve probably the same or better results." Now being in venture for a year I am a lot more confident than I was a year ago, or five years ago, or 10 years ago.
Be confident and learn. If it doesn't work, change it.
Peter: Lan, thank you so much for joining me today.
Dr. Xuezhao: Thank you.
Peter: Where can our listeners find you, and who should be getting in touch with you?
Dr. Xuezhao: I am on LinkedIn. I don't use Twitter, but you can try to find me there. My e-mail is email@example.com.