A (5:49)

Do so for the audience, let's unpack how a PROP desk works versus a typical trading. Yes. Right. So I'm assuming, you know, a typical trading desk, you're working for a hedge fund. There's assets under management and you have portfolio manager.

B (6:04)

Right.

A (6:05)

You know, because of your seniority, you're managing the first book of business that which is. I'm assuming LP is capital. But the process. Tell me Prop says from my understanding is what it sounds. Right. They prop up some capital and they give you this capital to kind of.

B (6:22)

Well, basically, yeah, basically it was, you know, 95, it was pension money and so it was, you know, pretty conservative strategy.

A (6:30)

So it's not like millions, right. You're actually buying like, you know, very conservative, you know.

B (6:36)

Yeah, exactly, exactly. So, exactly. So 95 is like very conservative. Portfolio, you buy three 5 year duration bonds, you know, they give you, you know, three 3, 5% yields trying to optimize where on the yield curve should you position yourself so you can get extra 20 bips return. But 5% of that capital was pretty much like you can do anything. And so as a physics background person, I said let's look at technology. And it was a vibrant sector, you know, one of those industries that you know, have high growth. And so if you look historically in terms of, you know, what has been, you know, building up, building up value, it's been, you know, high growth stocks pretty much. And so because market caps tend to very much correlate with you know, revenue growth and market capture. And so technology companies are the ones to basically, to basically capture it. In the late, you know, 70s and 80s, you know, there were also consumer companies but I think over the last like two, three decades it's been mostly tech companies that really grasped, grasp the, the market. Market value.

A (8:00)

Yeah. So on the, on the trading side, you know, how are you doing the rebalance? So I know you know, essentially typical portfolio managers that are managing money for a buy side equity shop, you know, a large buy side firm, they're, they're managing their weightings, they're managing their holdings. They're also calculating, you know, they're also reading a lot of new.

B (8:27)

Right.

A (8:27)

So would you say kind of your practice is similar to kind of a portfolio manager doing more conservative investments? You know, some of these portfolio managers for large buy side investors, you know, they're not actively trading. They're, they're, they're doing a lot of long term research in a certain sector. After they do that research, they read the news and then you know, they kind of look at what the market is doing to drive their holdings and then they kind of like periodically rebalance. So how often were you rebalancing that? A bond?

B (9:01)

Yeah, on the, on the, on the bond side, I mean you, you, because the position was quite large, it was basically like a few billion dollars. And so basically there was a risk management department that kind of set the frameworks, you know, which sectors you can like go and like up to how much. And then on the prop desk was basically, you know, debt research, you know, to make sure that you know, we select the names that are, that are better. And there were a few traders, you know, executing that. On the equity side, basically there was a little bit more room. It was more, I'd say risk reward driven. And so you basically look for arbitrage. You have two Companies that are relatively operating in the same market and one is valued like 30, 40% cheaper than the other one. And then you have certain factors that they could impact that value unlocking. You basically bet on that. And if you want to hedge that against index, you could do that. And so that was more absolute return driven. And so name specific. But the bond part was kind of sector allocation and trying to bid the index by extra percent and a half. And so if the index generates like five and then you do like six and a half, you've done well. So there you don't expect to outperform the index that substantially by, you know, picking the, by picking the, the right name because everybody kind of correlates with, you know, how the interest rate market moves and. Yeah.

A (10:51)

Yeah, no, that makes sense. And then tell me a little more about, you know, your path into vc. Right, so the prop death was really interesting. You probably did a lot of quantitative management. You learn, you definitely learn a lot of risk management. So what, you know, do you feel like some of that laser physicist was in you to kind of be like, look, you know what, let me, you know, laser. Whenever I think of lasers like Jetsons, Sci Fi and the Future. So you feel like that curiosity kind of drove your interest in technology?

B (11:21)

Yeah, I think, I think it's, there's a lot of similarities, you know, in terms of, you know, being inside kind of a lab and being inside of a startup and managing a VC fund. Because at the end of the day, what's a business? Business is a set of experiments. I mean some people like run that business and that's how we like run around hours. Basically. Any idea or any startup ultimately is a set of hypotheses. Whether like it realizes or not depends on whether whether you're right or not and whether the team that was able to run those experiment is good or not. But there's a lot of similarities. You set certain assumptions and then you verify certain assumptions against that. And in the physics world that assumption could be can you construct a certain laser system? Will it be powerful enough to power the next system? And in business is can you bring the churn to a certain level. That makes interesting to basically put more money into sales and marketing, to basically scale that business. And so running a business is a lot like running multiple physics experiments. And running a VC fund is like running few labs which if they tend to perform, you put more money in them and then they tend to scale hopefully, hopefully to a big size. So the way that we've quantified our Visa business is basically taking that approach is risk reward basis in terms of how we allocate dollars. So it's very IRR driven and DPI as a result driven because we look for how much return can we generate from any specific private market investing. So it's very much similar to what the micro cap stock investing is. You get an illiquid stock initially and then if you're right and the company gets to 10, 20, 30, 50, $100 million of revenue, that illiquidity disappears. Most companies, especially if you tend to focus on B2B software companies, upon reaching about $10 million of revenue, you get a lot of interest from private equity buyers and then also strategic, tend to be more active around them because you've built or captured a portion of the market and so the liquidity discount starts to disappear. So your job as a fund manager, and we're investing predominantly at series A stage when there is a million or two or three million dollars of revenue is to predict in a sense what's the likelihood of that experiment to reach like a $10 million mark with our capital. And so, and if we write that becomes liquid, you know, how far will it scale? It's a matter of kind of another, another topic and some scale, some businesses scale, you know, to $100 million million dollars of revenue. Some businesses, how quickly are you looking.

A (14:35)

To get to 10 million? Because you're doing series A, right? So a couple years.

B (14:40)

Yeah, I mean in a sense like the, the, the better performing portfolio companies, you know, tend to triple, triple, you know, companies that have challenges, you know, either because of, you know, churn levels or regulation or you know, long sale cycles where it might be like, you know, very dependent on how the, you know, whether the market is buying or not. You know, in 2022 wasn't like 2023 wasn't an easy year, you know, especially for enterprise customers, you know, you, you might take like three or four years, if it takes four years to get from one to 10, likelihood that this company will have challenges scaling beyond 10. But you know, we, we've been in shareholders in like Chess.com you know, since 2012 and you know, sometimes you get surprises, you know, by the market, you know, first pandemic hit, then Queen's gambit hit and the business went quite viral during that period of time. And so it took a while to get to that double digit mark, but then once it was the market leader in that segment and then the certain factors hit, the business almost unfolded in the course of a couple of years. From a $10 million mark. So. So you might get sometimes surprises by the market. Even though you quantify the decisions, you get an externality that is a positive externality.

A (16:11)

Yeah, no, I agree. I mean, so tell me a little more about the private equity side. What were some of the things that you observed, just notable things that you saw when you were working in private equity. And then how has that evolved? Maybe at the revenue level, the business type level, maybe funds that are investing in private equity. Just what's changed?

B (16:36)

Well, that was, you know, more than a decade ago. And I think, you know, historically, private equities have been, you know, much more, you know, offline businesses focused. And so. And however, the challenges is that you don't really have substantial growth rates. So all of the real return drivers have been either around consolidating other businesses. And so you're unlocking this.

A (17:06)

But they're already, most of them are probably profitable, right?

B (17:09)

Yeah, and they're profitable. And so you might have some operational improvement driving ebitda margin from 20% to 25%. You might have some multiple expansion. You all this like we're making money by taking, by taking leverage. And so there were, I'd say those were the factors how people, like, mostly return money, I think, I think 10 years forward, I think today. And that's partly why private equities has really moved into the space is that technology companies, they tend to still have inherent growth rates in them. And so that's why you see PSG and firms like PSG and Thoma Bravo basically taking a big piece of the market because it's not just being operationally prudent and making that EBITDA margin improvement and not just taking the, the debt and doing the right transaction structuring, but because software companies, they actually have embedded growth in them, predominantly because there is a net dollar retention component. And so if you'll decompose where I think private equities today also make money, a lot of that. I don't have the data to back it up, but my gut feel is that a lot of private equities really make money by just internal growth of their core platform business. Yes, you get a lot of the, you know, let's acquire some, some companies. But predominant also portion of it really comes from just the business growing by itself.

A (18:48)

Yeah, I mean, so I think my question to you was, you know, what industries were you in? Because I appreciate there's a lot of software now, right, where private equity is playing. But how involved was software in the CP space? Where were you playing if. Were you doing software back then?

B (19:06)

No, it was, it was more, it was more traditional economies. So they were. Yeah. So we were looking at you know, doing like some agricultural farms and you know like how to you know, acquire a couple of them and the merge and do operational improvements. So it's like more financial case so. But it was nothing related to technology to be honest. And so that's why I actually went from a private equity and like build a startup because it felt a bit slow and a little bit boring and technology definitely like brought more excitement to the table.

A (19:43)

Walk us through the origin story. How did you start the startup? What motivated you to do it? What was the problem?

B (19:50)

I think the fact that I could like do it is you know, you don't really need to do a lot of. I knew how to code from school and so in most fields laser physicists do or just most physicists know how to code because you have to run experiments, you have to write some scripts. So I educated myself like Ruby on Rails, built the startup and basically pushed the team and it felt like cool, let's just build something and see what yields. We didn't really put too much thought about like what will like the monetization strategy will look like and what would be like the online conversion funnels. So it was more entrepreneurial urge to just do something. It felt like after and it was at around the time when Facebook got public. So as you can imagine it's like you read like this is a hundred billion dollar company, you know you could do it like okay, how it's like you're not different from Mark and that's the underlying, you know, I think for a lot of the people like starting something is about that. It's just like going and building something and like maybe like as a repeat time entrepreneur you kind of like really self impose a lot of restrictions and you really think about okay, is that a really big time and that really big idea that you know like passionate about it. But like when you do the first, the first gig it's, it's a lot, it's a lot like you know, why don't you just go and do it?

A (21:27)

Yeah, absolutely. So tell me a little more about Flashpoint. You know your guys, you know, sourcing, screening process, kind of what your business is. What are you guys excited about?

B (21:42)

Sure.

A (21:42)

What, what is fame? You know, I mean I, I would say one more thing, I would say before you jump into that, you know on the private equity side there's a lot of innovation and revenue based Financing. I think there'll be a lot of software platforms that can use the financial data. Like you know, these fintech companies are out right now. You can plug your financial data in and then all of that data can tell you, you know, your ebitda, you know, what your, what's your revenue multiple. If you got graphs and analytics from like last year and you can, you know, can be kind of really calculated and possibly be automated if you want funding, just plug in your, your CRM and your, your payment system and then it could almost be like this AI, private, private equity. You're at this multiple. The business that you're in is a, you know, two trillion dollar market. Here's your competitors because they pull in all the data from Facebook or whatever and you know, a lot of things that we use our mind to do. I feel like there's now nodes and structures that, that way to possibly like offer a transaction. Right. And I don't know what your thoughts are on how far away are you. I mean there are softwares that people use to, on that process. And then you know, on the founder side there are softwares now that like founders can go in and you know, kind of explore funding, you know. But where do you see that in itself?

B (23:11)

Where? Yeah, I think, I mean we, the VC like organization, you know that we, that we invest from is basically it focuses on kind of series A stage when it's still like relatively early. And so there are still a lot of unknowns and they're all high growth businesses. So when we come in they tend to grow like 100%, 200%, 300%. And so there is a lot of still uncertainty about what's to come and how long will that persist and how scalable the user acquisition channels are. And so when we started Flashpoint like 12 years ago, the first fund was more consumer centric. A lot of the markets weren't really, weren't really still taken. And so we backed like company like just.com and it was like basically a play on mobile and they were just getting into the App Store and Google Store. And so that played out quite well. Eventually General Atlantic acquired that business for about $650 million. And so we backed a company like Shazam, which got acquired by Apple. So so, and so, you know, in fund two we went into B2B motion and marketplaces in fund three and backed companies like yes, which just announced like a series F from kkr. And so in fund three we went, you know, predominantly into continue to do more like B2B software so 95% of what we do today is B2B software. And then we also started to on focus, focus more on AI and machine learning as those technologies really got mainstream adoption. And now companies like Test Trigger, which were backed a few years ago, really started to generative AI has really been at the forefront. So I think in terms of as a generalist VC fund, I think you really need to be, you know, anticipating where the pluck is going and you know, not where it is today. And as we, you know, start to raise our fund for, you know, we're also thinking about the climate technologies. I think over the last like couple years that's there's been a lot of talk. I think the regulation is coming or it's increasing and there is a lot of like net zero, net zero talk by public companies that it's important. I think the social movement also propelled that path forward and that it's. You'll not just need to make money but also be socially responsible. So all of that impacts corporates to start paying attention. So that's also one of the sectors where we're spending more and more time. But Today, you know, 95% of what we do is B2B software. This is the most, I'd say, predictable and scalable way to make money. I mean if you got the product right and at a series A stage you kind of get that just, you know, is there net dollar retention, you know, how good Usually the company by series A stage has, you know, 20, 25, 30 people. So you can already get a feel of, you know, whom can the founders hire and how good are they at hiring the first level of execs or middle managers. Maybe it's still not that VP level person, but it's head of somebody. And so you can get that first understanding how good the founder is able to transition out of a founder role into kind of a CEO role. It's still not like to the fullest extent but because that typically happens by kind of their, by 10 to 20 million dollars when they really phase out and really like kind of grow into their professional CEO roles. But you can get the gist like how the founder thinks, what is his action steps have been to really grow into kind of ex founder to a proper CEO role. But VC has definitely changed quite a bit over the last decade. But if you're not curious, you know, where you know the markets are heading and you know you're not always like researching and you know, grinding. I think it's, it's a very, it's A very competitive market in general. So.

A (27:52)

Absolutely. Ways that you're able to parse out AI. A lot of these AI companies, there's companies that are not AI companies but now they're branding themselves, having some type of automation or AI. So what when you're diligent into these companies maybe in the first or second path, what are some telling signs that maybe this could be a winning. Especially in the early.

B (28:20)

Yeah, I mean not all A is created equal in that sense. I think if you look at, you know, how the like value capturing is taking place, you see that a lot of the horizontal SaaS companies, the ones that have been built out over the last decade from Workday to Salesforce to HubSpot to Servicenow to I don't know, UiPath, you name it, there's a lot of horizontal SaaS companies. And so a lot of the value that AI has really created with you know, GPT models specifically and like with the LLMs being like the core, the core capturing mechanism is this is where like those companies will more likely to capture value rather than the new companies. And so when we look at you know, certain industries, this is like where we believe like, like with AI that there's going to be value created. Yeah. So like for instance like in hospitality and travel, you know, we've built like a solid track record and you know it's a, it's industry that is not very keen to innovate very quickly. And so people who know the industry, who've got relationships in it when applying AI and LLM specifically to set of problems there, there is long term modes created because there is a business mode, it's not an easy one to get in. Construction is one of other niches. It's very hard to actually build something if you're not coming from a construction industry. You really have to love what you're doing and customers need to understand that you breathe and leave their industry. Also healthcare, it's one of those niches where it's not everybody can go and just build product. So we look for founder product fit and founder market fit. And so we want to understand that the person has really lived through, you know, the problem and if he hasn't, you know, she hasn't, you know that that's a really red flag because it's very hard to like build sustainable value in those industries if you haven't really lived through the problem like yourself. But you know, also, you know in, in the, in the world of AI is like how much data do you, do you have like a Company like Test Trigger is an example. You know, it processes like a million tests a month, right? So, so they've got, you know, internal data done by the QA tests and so they can train their model to actually generate new value. And so if you're trained, because if you look at all of the LLMs now that Apple has trained their own model and their last model is much more smaller parameters wise versus OpenAI's model, and it's already on par, if not better. So it kind of tells you that if you're taking the same exact data set, whether that's Quora or open Internet scraping or some other data sets, Wikipedia or others, basically there is no ultimately value. Everything just gets cheaper. And the question is, who's got the faster model in that sense. But if you've got like proprietary data set, this is what's valuable. And so if the company doesn't have it, it's very hard to create like sustainable value and mode, technological mode or product mode in the era of AI. So this is like some of the things that when we kind of look at series A stage, whether there is just besides production is how much of a founder market fit is there and how much data mode is there really, how much.

A (32:18)

Well, let me. What advice would you give to someone who's evolving from maybe from fund one to fun two and now you guys are on front three, right? So like, because you got it sounds like you guys have a little bit of activity, right? So how should people think about that as they're, you know, building their. And you know, how should they tell that story?

B (32:45)

I think just like any business, like, you cannot stop selling. So if you're a fund manager that kind of like raised its first fund and like kind of thought that you could stop fundraising, you can't really stop fundraising. You're always fundraising. And just like any entrepreneur that is actually running like the company, that's the companies that you invest in. So it's a, you know, the venture capital business really operates on constantly raising money. It takes time to build trust with limited partners. And so they understand your strategy, how unique you are versus the rest of the market and how you can build systemic alpha. And so if you're not constantly raising, it's like you're going to have challenges because not every, it's not always up, up and away. Sometimes I'd rather say that VC is a chainsaw business. So everything is like great until. Until it's not. Then it's like, you know, you drop dead. And so it's a very complicated model from funding perspective, because everybody's kind of. It's a game of musical chairs. So if you're not constantly raising, you might have challenges. And so you need to keep on doing that. I think you need to love what you do and always be curious if you're not specific in. I mean, in general, this is a business about curiosity and how to. How to structure that, how to farm that insight out of the market and, you know, provide capital to people who are also curious enough and builders. But just keep on learning and, you know, keep on reading and keep on talking to people. And this is an industry where you will make mistakes. Everybody makes mistakes.

A (34:34)

Yeah.

B (34:34)

And so I think it's a question of how can you quickly. Can you quickly reflect on your mistakes and learn from it and move on?

A (34:45)

Absolutely. Well, this was really great. Thanks for making time for us and excited to. To stay in touch and bring out in New York at the next cocktail.

B (35:00)

Awesome.

A (35:01)

All right, man. Take care.

B (35:03)

Take care.

A (35:03)

Bye.