Interviewer (4:46)

Before we dive into what you did with that, you alluded to some fun things that might not have been relevant to the background in that entrepreneur, but what were some of those.

Dave Thornton (4:55)

The first business that I built was a business in which we created bespoke machine learning models in areas that I happened to be a subject matter expert. I grew up playing high school basketball. We worked for some NBA teams doing basically Moneyball. At the beginning of the MBA Data Revolution. I played a lot of poker back in the day, inclusive of a lot of underground poker in Manhattan. And we built Algos to tease apart luck from skill in poker. We even scraped one of two major daily fantasy sports sites and built a small betting operation.

Interviewer (5:30)

You alluded to underground poker games in New York. Any stories to share there?

Dave Thornton (5:36)

Yes, plenty of stories. There was a guy that came and sat at one of the games that I used to play at when I lived in Manhattan. So this is 11 or 12 years ago. Perfectly nice guy, well dressed. He sits down with a lot more money than everybody else was bought in for at the table. And this is not a small game, so I'm trying to say something. He proceeds to make large blind bets before he even checked his cards out over and over and over again. So the folks that are regulars at these poker games know that now is the moment where you just go into the wait for good cards and then see what happens. He blew through all the money that he came with within two hours. And I sat there totally card dead, waiting for my turn and never having had it come. He was a really nice guy. He wasn't being a pain in the butt of the poker table or anything. He didn't have any problem with losing the money that he came with. And afterwards when he left, apparently everybody but me knew that he was part of the mafia. And so I got to play with one of those guys once.

Interviewer (6:45)

What happened with those businesses, the poker.

Dave Thornton (6:48)

Luck and skill algo didn't pan out because we were so focused on marketing and how obvious the business case was for basically matching players by scale at a given table that we forgot that online poker had just gotten kicked out of the United States around the time that we were doing this and that they needed to get their gambling license. The NBA teams that we got to work for, so we worked for the Rockets, the Bucks and the Knicks, we got paid. We built out a proof of concept. The concept was called expected point value. There's a Harvard professor named Kurt Goldsberry who pioneered it and we've instantiated it for these teams. The idea was that you could put the number of points you expect the offense to score at any given moment in a game. The numbers are between 0 and 4. And you could apply it to two different moments and take the difference between those expected point values to put values on things that didn't used to have values that don't belong in the box square. Like what is a pick worth? It's the difference between expected point value before you get around the pick and after you get around the pick, what is the drive worth? It's the difference between what it's expected point value at the top of the key and around the time that you get to the Cup. The problem with that business was not that there wasn't a lot of interesting substance and teams that were willing to pay. The problem with that business was although the teams were willing to pay, they didn't have to. Because it's easy for an MBA team to find a disaffected PhD student that would want nothing more than to work for an MBA team. So once we proved out the concept, they didn't need to pay us again. The real time illiquid asset pricing model that I alluded to had very real legs. And we were going to work with two of the evaluated pricing companies that priced municipal bonds, which was the asset class. One was IDC and one was JJ Kenney. We got a call within a couple weeks from senior people at each shop saying hey, we got to yank the deal. We can't really explain why. Hopefully we can work on this again sometime. At the time I thought it was just the worst executed brush off ever, except for how coincident it was that they were within a couple weeks of each other. We found out later that ICE was in the middle of initiating the purchase of both of those businesses. Usually you don't spend any new money when you're being acquired because every dollar that goes out the door has some revenue multiple attached to it. Then the daily fantasy sports betting operation actually got off to a great start as well. But one of the employees at one of the big daily fantasy sports companies got caught front running a big contest and Elliot Schneiderman, the Attorney General of New York, decided he was going to make a thing of it and he kicked all the daily fantasy sports companies out of New York at the same time. Those two things both had legs and they were taken away from us.

Interviewer (9:24)

So you had this insight that employees don't really understand their stock options. What did you do with that insight?

Dave Thornton (9:32)

The original version of Vested was just a startup equity education platform where there was a website and it had free content and tools. And the free content was basic stuff. An article about the difference between stock and stock options as an example. A tool that would help you calculate the alternative minimum tax associated with your incentive stock option exercise. That's a complicated tool, but everybody needs to know what the tax associated with their exercise is. Just like my old guy did. That was the original version of the business and it wasn't A complicated thing. The idea was going to be that we were going to educate startup employees more than they were, which was not hard. Eventually we'll have 3 million startup employees running around our website. And once they were all here and loved us, we were going to figure out what we could sell them. Mortgages, wealth management referrals, et cetera. That was the original thought. About a year and a half into the business, a lot of the users that were there for the education had started to come back inbound us looking for capital. But they asked questions in these very squishy ways. And so we needed to talk to them to understand what it was they really needed. It was a super interesting experience. We start talking to these startup employees who are really just thinking of us as their sherpa in the world of startup equity. Some of them asked us if we had money. Some of them asked us, do you know people who have money? Some of them just ask, we need money. What do we do? Originally, we hypothesized that we would see five or six different capital use cases. Anybody who's worked at a startup before knows that you tend to be under cash comp. So you get to a certain age, you want to buy a home and you need to make a down payment, you get unlucky, you have unexpected medical bills, maybe you want to buy a car, maybe you want to have walkaround money for the first time in your 20s. All these things are what we expected to see. We ended up seeing a single dominant use case, which was none of those. It was, I just left my job for whatever reason. I now have 90 days within which I have to exercise whatever stock options had vested as of that time, or else I lose them. The capital use case that was the dominant capital use case that came inbound from our user base was, do you have 50 grand? I need to not lose my equity.

Interviewer (11:47)

How did you think about addressing that problem?

Dave Thornton (11:49)

The first reaction that we had was, these are the people that nobody else is going to pick up the phone for. So with my financial services background, I thought this was probably negative Selection, type, deal flow, let's not try to address this problem. But I was intrigued enough that I wanted to start understanding why these people were coming to us in the first place. We canvassed the market that we assumed existed at the time, and it did in fact exist. And it was even reasonably robust with respect to a certain set of startup employees. It was comprised of banks that were getting into the private markets and had wealth management businesses. So JP Morgan, Morgan Stanley, Goldman, Citigroup, Wells, ubs. It was comprised of Silicon Valley bank and First Republic bank and another three or four shops that did stock option exercise specifically as independent investment houses. What we saw was the commonality between all of them. I just described a very crowded market was that they were all focused on senior people that were leaving really late stage private companies. And so if you need $12 million and you just left stripe, everybody falls over you in order to get you your money and you have price competition and you get to choose from different structures with different risk reward profiles. What we realized after having done our market research was that the users that had come inbound to us from our own user base did not represent positive or negative selection. They were just the people that needed 50 grand instead of $12 million. So the very long tail of rank and file startup employees that nobody else would pick up the phone for because of the ticket size, how did you.

Interviewer (13:25)

Think about accessing capital to address that need?

Dave Thornton (13:29)

Let me walk backwards for a second. There are many different transactional structures that have evolved to help people who need funding for their stock option exercise. All of them have one commonality which is that you usually end up with exposure to the underlying common stock that you're helping the person buy. You're getting some access to the equity of a venture backed startup. The first thought that we had was there's probably people that have been locked out of the venture asset class writ large that might think this is an interesting way to get exposure that they otherwise couldn't get exposure. Now that thought did not go too much further at that moment because to us the real trick was figuring out how we could be good investors. The access is interesting, but it's a power lie asset class. A lot of companies go to zero. You got to figure that out first. We ended up with two evolutions in the business that get us to where we are today. The first evolution was we realized that there were some reasonable purchase discounts that were available to us in helping these startup employees. Specifically, we can get exposure to the common stock that they're buying, usually at the independently produced company. Board approved fair market value of that company's common stock, which tends to incorporate a discount for lack of marketability because common stock of private companies never trades anywhere. The first thought that we had was oh well, if we can get stuff at a discount, let's not help the folks that are leaving the clearest dumpster fires that are obviously going to zero and let's see if we can buy the rest and help in a broad way. Diversify it unconcentrated and fairly cheap. The first concept was a VC index at a discount. A couple years in we realized that there was a second source of interesting alpha that we were sitting on, which is that if you help of broad base of startup employees, you will end up with a ton of differentiated data on private companies that may or may not be out there in the world. Otherwise. We got the band back together from that first business where we built the machine learning models and we built a private company selection model powered by a bunch of the stuff that you'd expect us to have as investors and a bunch of the stuff that was coming inbound with and through our startup employee counterparties. Once we had the ability to effectively point at the top 20% of VC backed startups, buying into that portion of the asset class at discounts becomes a really compelling product. Then with that fairly compelling product, it's much easier to point to the investor market and say okay, everybody who's been locked out of this asset class, we have a real thing for you.

Interviewer (16:09)

Now I'd love to walk through the different aspects of what you just gave at a high level the model, teasing out what you would have exposure to and then this other data that comes from the employees. What are those two streams that feed into the model?

Dave Thornton (16:24)

I would characterize it as three streams. I munged two of them into the non proprietary stuff. There's what I view as the table stakes data, which is financing trajectory for a private company. Just is it going up into the right or not? It's very important that it is because in the venture asset class you don't know how the company's going to do until the music stops. But before the music stopped, it better be going up and to the right. Financing terms. The terms at which investors buy preferred stock in a private company matter a lot for how much risk the common stock is at. Investor quality, by investor quality. I'm not saying that we need to know that the name of the investors backing this company are Sequoia and Andreessen, but we need to know that they have historically produced good cash on cash returns, investor behavior. Are they continuing to do their pro rata in subsequent rounds and stay involved with the company and put more capital into it and double down on their winners or are they running away and does every round look like it's a new set of people that I view as the table stakes stuff? There's a set of differentiated data which lives in the middle, which is not ours, is not proprietary. But to the extent that somebody wanted to build this stuff on their own over the next few years. It's doable. One is a set of financial performance estimates that we have for private companies that are based on state and local tax and labor filings. For idiosyncratic VC backed startups, they're never particularly accurate, but they also have a lot of signal in the trend. They're always consistently inaccurate for the same company and so if they go up, that means something. There's another differentiated data set which is built around employee flows to and from companies. So if a company just hired its first cfo, that's an incredible signal. If a company just hired its first non founding sales team, that means they found product market fit and it's on repeat and they can get the founder out of their sales job. If a company just fired 50% of its people quietly, it's going to zero. Those are the moment in time differentiated data sets. And then we've got the stuff that we collect from and through the employees. And some of these I'm not going to be able to talk in too much detail about, but just to give you an example, we reach out to them typically on LinkedIn, they will respond to us sometimes with a thanks, I'll check out the website. Sometimes they will just dump their brain out in an unsolicited way. You see things that are as high signal as I've been early exercising my options at every available opportunity with my own money. So thanks but I don't need your help which is as good as it gets. I've also seen things as crazy as I wouldn't exercise my options if you paid me. As in thanks for the free option, it's not good enough. But even if you put money on top of that and gave me some walk around money, I'm still not doing this. So you see the full gamut and some of the unsolicited reactions that we get from the employees. You will also see their behavior on our website which is where they come to structure and submit their deal and we give them the option to counter on price. You lose some of the purchase discounts when there is a price counter, but it's also a great signal and evidences belief in the company. We also currently do a transaction where we're buying the minimum number of the shares that they're about to exercise their way into in order to get them all the money they need to do all of their exercise. We view that as somewhere between neutral and positive selection because they're only parting with the shares that they have to in order to buy as much stock and own as much stock as they can. But we also give them the opportunity to sell more than the minimum. It's one thing if they sell $5,000 worth more than the minimum, that's fine. It's another thing if they try to sell all of their shares and look like they're running away from the company as fast as they can. It's an example of the types of employee signals that are available with your.

Interviewer (20:08)

Modeling and comp sci background. How do you think about hypothesis testing on all those would sound like very common sense potential signals.

Dave Thornton (20:18)

It's hard because real hypothesis testing requires that you live long enough to see your investments do well or poorly. And the private markets have been super quiet for the last three years. The liquidity events that are the final arbiter of whether you've done something right or wrong and can update yourself. That feedback loop has been pretty slow in the last bit. Instead, the way that we do it is with really robust out of sample.

Interviewer (20:39)

Back testing as you turn this into investment strategies. A bunch of pieces that you talked about. So one is the website brings in these employees. Is there another way that you're sourcing these deals?

Dave Thornton (20:51)

Yes. It's an interesting full circle by the way. We started off with a website that brought people in that ended up asking us questions. Then as a relatively early stage company moving into the world of asset management, it was important that we stay focused. And so we stopped working on the equity education piece and we started working on the let's make sure that we're good at deploying and making sure that the employees are having a good experience as they come in. There was a period where we were entirely focused on that, which that period ends roughly right now. I'll explain sourcing during that period and then I'll talk about how we want to source going forward. The sourcing right now is we've got a set of companies that we like based on our selection model. We are sitting on top of job website Data. Think of LinkedIn and we are paying attention to the employees of the companies that we like. And as soon as we see they update their bio in some relevant way, specifically by putting an end date next to their tenure with a company that we like, we will know about them immediately and we will proactively and automatedly reach out typically also on LinkedIn when they raise their hand, which they do at very high rates because we're solving a super acute problem that most of them usually have given up on by the time we Find them. We'll direct them to our website. They'll provide the relevant information for us to make a quote. They will take a look at the quote and then submit it back to us as a deal for our approval. That's the sourcing motion right now. It is entirely proactive and entirely predicated on finding employees that have recently left companies that are currently on our list. There's still a little bit of general brand awareness that's out there in the world, so people find us anyway, but that's mostly what we do. How I think about this long term though is we need to be valuable to people at all stages of their startup equity lifecycle from when they are first considering joining a startup all the way through when they might need our money. In that 90 day window of total distress. We're about to start refocusing on equity education to bring people in early so that they think of us when it's time.

Interviewer (22:52)

So once you found these people or they found you and you're offering them a deal, what are the important pieces of the deal from their perspective?

Dave Thornton (23:00)

The single most important piece is how much money they need all in to do their full exercise. The pieces of the money are what is your total exercise cost, which is just a strike price times however many options you have across however many grants you have. Then there is the tax cost, which is the tax that is actually associated with the act of exercising. That is typically applied to the paper gain between the current board approved fair market value of the stock as of the moment you're exercising and whatever your strike price is, which is a disaster. There's no reason that that should be taxed. You can't turn your stock into money when you buy it. It's kind of messed up, but it is what it is and it's hard to change laws. Let's just assume that that's going to continue forward for a while. If you have incentive stock options, that gain will be applied to the alternative minimum tax regime. If you have non qualified stock options, it'll be applied to the ordinary income tax. And then there is the tax related to actually doing a transaction with us. So our specific transaction is that we're buying some of your shares, which means there's a capital gain event potentially. Those are the three components. If you've been at a company for a long time, the biggest component is usually the second, which is the tax associated with your exercise. If you've been at a company a shorter time, it's usually the exercise cost. I'll give you a Numeric example, if you have 100,000 options at a $1 strike price and tax doesn't exist. So you need 100,000. Let's say the current board approved fair market value of the stock is $3 a share. We will typically be buying at $3 a share until you have 100 grand. In that case, we would buy 33,333 of your shares. Our money will go out the door today. You will now have $100,000 that you will immediately turn around and exercise all 100,000 options with which is the capital use case. And then you will be titled to 100,000 shares and you will owe us delivery of the 33,000 that we bought. Whenever it is that the transfer restrictions on those shares lapse or are nullified.

Interviewer (25:03)

How do you ensure that that delivery occurs?

Dave Thornton (25:07)

We totally obsessed about this as a problem at the beginning and we've now become so comfortable with it, we don't think about it twice. Originally we were like, okay, delivery risk is going to be the main risk in this business. We need to get our head around this type of contract that we're using and how it's historically been used and what it's deliver and non delivery rates have been. We found only two examples in the entirety of all the canvassing that we did where non delivery became a thing. One was an example where somebody didn't actually own the options in the first place. Whoever bought from them didn't validate, they were the proper option holder, which is a super easy thing to get around. You just need people to show you their option grant management account and go to the source, which we do. The second was instead of doing option exercise, somebody forward sold all of their stock in a particular private company and then it had a monster liquidity event and there was a $50 million delivery that was owed and it was just economically rational for that person to fight it tooth and nail and disappear. Those were the two things that we saw as potential issues. But knowing that those were not issues for us, we proceeded forward and lived our lives. We have had thus far a 100% delivery rate on enough liquidity events that the sample size is. You can trust the number we're over 60 and change liquidity events that we've collected on with no hair whatsoever, inclusive of reasonably large ones. We typically get thank you notes from our counterparties. I'll give you my two or three cents about why the delivery rates have not been an issue for us. One is that the moment that the transfer restrictions on private shares become not A thing. It's almost always around some sort of a liquidity event, which is to say at the moment you owe us something. You have it. This isn't a credit risk type problem. The second is we're doing small deals. We're helping the rank and file startup employees out. $100,000 doesn't become $50 million. People don't have the opportunity to think to themselves this is enough money that it's fine, if I nuke my career, I could live in Aruba for the rest of my life. The third is that we're doing stock option funding. So it is way more often the case than not that our counterparty owns more shares than we do and therefore they meaningfully participate in the liquidity event. And that's the reason we get the thank yous. Now they have a million dollars and they just shipped us 700,000 and that's a good deal for them. Now they have a million dollars they wouldn't have.

Interviewer (27:34)

How have you used AI and seen it both as an opportunity to improve what you're doing and a potential risk?

Dave Thornton (27:42)

For the purposes that we're putting all of our interesting data to, which is helping us understand which companies are good, we need to continue doing proper regression, not proper machine learning models and not bring in the LLMs and try to have them do work for us. I do think that one, the use of unstructured data, getting it ready for the machine learning model. If we wanted to take the tens of thousands of message conversations we've ever had with people on LinkedIn and we wanted to turn that into Signal, LLMs would be excellent for that. We've currently got an activity internally going on that is doing just that. Two, I think that external facing messaging, especially as we start spinning up the equity education part of our business again, we're going to have the ability to write a lot more content and spend our expertise and time editing it as opposed to writing it from scratch, which can take 95% out of 100% of the time.

Interviewer (28:33)

When you put this all together, how do you think about what a portfolio looks like?

Dave Thornton (28:38)

It's a really interesting question. At the beginning we said to ourselves in our pilot fund, let's see what happens. What we ended up seeing as the natural shape of our portfolio is we saw that the stage weights so private companies raise funding rounds and usually angel precedes seed series A, B, C, D, E, F, G until forever. We saw that the natural weight and distribution by stage was roughly uniform from A through call it F with a little bit Underweight in the earlier stages and a little bit underweight in the later stages. Just to fix an intuition on why that is. Series A is usually your first scaling round. You don't have a lot of employees, but also nobody's leaving Series F plus those are the companies that are more likely to have proper secondary markets, OpenAI, Stripe and SpaceX and they will do tenders for their employees. And so it really is few and far between that those deals make their way into our portfolio. So we're mostly B, C, D and E is where a lot of the weight is. We're actually comfortable with that. The thing that was scary to us from a stage perspective when we were Starting this was SoftBank Tiger CO2D1 had bid up the late stage companies significantly for a while, especially in 2020 and 2021. And we were nervous about the companies whose liquidation preference stack was so high that the common stock of the company should be at risk. So we have a natural bent towards wanting the early and mid stage companies in the portfolio. We also have done a ton of reading on whatever empirical literature has been built on how good people are at picking winners. And what we've determined is winner picking is very hard, especially in the early and mid stages of the asset class, which is where the dominant amount of the companies in our portfolio are. And so you really do want to be diversified because it's so hard. That's the commentary on portfolio construction for stage. The commentary on sector was interestingly, we end up getting the sectors that are the VC backed companies in exactly the proportions that you'd expect to get them. It's mostly tech companies and it's meaningful. Sleeves of healthcare, B2B, B2C, small sleeves of energy and financial services.

Interviewer (30:55)

How do you think about who the VC backers were?

Dave Thornton (30:58)

As a signal, we went back and forth on this when we were building the model in the first place. Originally we were like, let's just take the Tier 1 VCs and then there's a pause. It's like okay, who are they? I know some names, but really who are the Tier 1 VCs and are there results and are there performance out there? First you have to actually agree on who top quartile VCs are, which is not that obvious and easy a thing to do. Second, around the time that we were doing this, the markets were heating up and then crashing and we heard whispers that particular funds within even some of the top tier firms that everybody would agree with the top tier firms were battling pretty significant go to zero risk. And we were just like, I don't know if we should be incorporating brand names as a major predictor in our algorithm. Instead what we did was we split the baby and we said we do have a sense for what their cash on cash returns have been historically. Let's analyze whether cash on cash returns were historically. Let's sort them by that and let's let the algorithm figure out where the natural cutoff is.

Interviewer (32:01)

So what does that look like at the end of that process?

Dave Thornton (32:04)

I can say that in one of our more recent funds we had 236 positions. That's 236 employees that we helped across 167 companies. I think it was in the next funds that we run, which are going to be a little bit bigger, my goal would be to have closer to a thousand positions and maybe six or seven hundred names.

Interviewer (32:23)

How do you think about the biggest risks to the strategy?

Dave Thornton (32:28)

All the ones that we thought were the big risks were not. So delivery risk being an example of that. The biggest risk is that it is a relatively attractive strategy and competition is going to show up. As soon as the IPO window stays open for long enough that the non distressed M and A markets and the secondary markets start also producing their own versions of liquidity events, we are going to have competition. The biggest risk for the strategy is that we don't put a moat around it before some very well heeled asset management firm decides it's also a good idea.

Interviewer (33:03)

How do you think about putting that moat around it?

Dave Thornton (33:05)

Oh, it's a data moat, there's no question about it. It's interesting. Our selection model is fundamentally a pricing model that we use for selection. The model predicts the exit price of a private company's common stock as a ratio to the price at which it most recently sold preferred stock to investors in its last round. One way to fix intuitions on what that thing is capturing is it's capturing how much growth is left in a company before its exit. It is fundamentally a pricing model. At the moment, what we are doing with respect to all of the employees that walk in the door is we're saying it's not the easiest thing to have deep price opinions on up to tens of thousands of VC backed startups. But the one price that we know exists and has been produced independently and has been approved by the company's board and even has a little bit of a discount baked into it is this fair market value. If anyone were to rip off our trade, they would probably do the same thing and offer the same price we need to be ready at the point that we get meaningful competition to move up on price, which is a function of how much we believe our pricing model. The more employees we serve, the more data exhaust we collect, especially the stuff that is truly proprietary to this business and you can't get anywhere else, the more comfortable we're going to be moving up on price 10% to win deals. Once we live in a world where there's competition and that's a pretty good asset management moat because that means that we're going to be moving up on price to win the best deals and our portfolio is going to have the best stuff in it. And whoever copies us, they'll raise a first fund successfully and then see what happens. We'll see what happens.

Interviewer (34:38)

In a power law business, you wouldn't think from the outside you could put a pricing model together and it would have any accuracy, because what you really care about are those Power Law winners. So how does it work?

Dave Thornton (34:51)

That's right, and we don't believe you can either. One question that follows that theme that you might ask is why don't you just take your five favorite companies? Why does it need to be the top 20%? And the answer is because I wouldn't believe that the top five was really the top five at a decently high level of zoom. All of the work that we've done to date, both on benchmarking our actual portfolio and on robust backtesting, has suggested that we can believe that the top 20% of companies, as determined by our selection model, are really the top 20% of companies. The way that we think about the power law is one, it's real. Most of the returns are going to be driven by the big generational companies. Two, picking winners is very hard. Combining those two points you need to be in every credible deal. We think our selection model does a really good job helping us to identify the pond to fish in and it's our job to go try to get one unit of everything.

Interviewer (35:47)

How do you go about building the vested brand so that you're more likely to be the person that an employee.

Dave Thornton (35:55)

Reaches out to to start with? It's terrible news for startup employees, but good news for the purposes of your question. This 90 day period of total distress happens to every startup employee and we already have perfect visibility into who those startup employees are. So we don't yet need people to come to us. We can reach out to them and we're actually quite good at it over the course of time. We need to build the brand and make sure that the original concept of vested, which is 3 million startup employees are already here, does manifest and we just have to be valuable to employees over the course of time. We need to make sure that there is educational content and tools that is available to them long before they need our money. We've got a tool that is going to help us a lot with the overall life cycle that we're just putting out there now. It's called the vestimate. The vestimate estimates the fair market value of your company's common stock and it allows you to therefore track it over time. The tracking period is the longest period in which we can add value. And showing an employee of a startup how his or her company's vestimate is moving relative to say, a basket of competitors is a very interesting thing that will keep them engaged and paying attention to their equity.

Interviewer (37:04)

When you have an approach that came from such a different insight and different methodology than might think of a secondaries firm that's looking for those larger businesses. What have you heard from the LP community when you've gone out to talk to people about the strategy?

Dave Thornton (37:19)

Oh, you get all kinds of reactions. The folks who think that you need to be in the winter picking business if you are in vc, which almost definitionally means you need to be a primary VC doing rounds for companies they were never going to like the strategy and one day the cash on cash returns will change their mind and until then, probably not. For the folks that can't even get access to the asset class, this is a godsend. And then there's probably a middle layer of LPs where the most interesting thing is less the access and the return profile and more that they know they're supposed to be in VC but they're not sufficiently staffed to properly run a VC fund manager sourcing diligence, relationship maintenance program. So we see all kinds of reactions both substantively to the strategy and also to the practicalities of the strategy and what it means for them. As an investor, how do you think.

Interviewer (38:17)

About scaling this over time? Typically a strategy where you can take advantage of the fact that other people aren't going after the small deals and you can do that at some scale gets hard to scale as a business.

Dave Thornton (38:29)

The good news on the scaling side is this is just a monster market. At the moment, around 70% of employees abandon their stock options and employees tend to own 10 to 15 points of the cap table of a given startup. All the US headquartered venture backed startups might be worth a couple trillion dollars right now. Which is to say that 2 to 300 billion is what's on the table over the course of a market cycle to be abandoned and 70% of it goes abandoned. So this market goes on for days. It still has a natural end, but we could scale significantly just helping employees that were leaving if there are no other macro structural change to the world for quite a while. We had some interesting feedback from the companies, the VC backed startups themselves early on, which lays out our scaling path pretty clearly. Rather than me just hand waving at how big the direct to employee market is. When we first started doing this fund strategy, in our pilot fund, we would do deals directly with the employees exactly the way I described to you. We'll give you the money now, you exercise and you will just owe us delivery. Whenever the transfer restrictions on the shares we bought lift afterwards we would go back to the company and we would say, hey, we just helped Ted for $54,000. Would you mind retitling just the small set of Ted's shares that we bought? And we got feedback that should have been obvious to us, but wasn't, which is, on the one hand, we have no problem with Ted exercising the options that he earned over the course of his tenure here. Two, mostly transfer restrictions exist to prevent the disincentivization of current employees. Ted doesn't work here anymore, so that's not a big deal. On the other hand, three, if you ask us to retitle this small set of shares, we're going to have to bring an external counsel to do board consents and pay them. We're going to have to explain to our board this small line item on our cap table called vested. If you transact at any other price than the current board approved fair market value, we might have to reset the fair market value of our company's common stock because you told us about this little $54,000 transaction. So all these things considered, please just go and work directly with the employees. The point at which that feedback flips on its head is when we are scaling and doing $5 million of a company's stock. As soon as you're doing a meaningful amount of a company's stock, then for sure they want to be involved and for sure we want to be involved with them. We're only operating in this lane because the companies put us there based on the scale and the capital use case. So the most likely specific scaling path for us is there are some companies that are providing liquidity to their employees in a great way. For example, Stripe and OpenAI and Gusto and SpaceX but most of the earlier stage companies, that's just not their first priority. So they haven't figured out how to do it. What we do as we're getting ready to start scaling within a given company, which might be in the next couple years, is we go to the founders or the management teams of these company and we get on our soapbox and preach the recruiting and retention benefits of doing anything useful with respect to liquidity for your employees, because they currently do nothing and employees tend to treat their stock options as like paper lottery tickets that they mostly forget about during their tenure. There is a huge advantage for companies that do basic liquidity programs. And so if we can show up with a liquidity program in a box that doesn't put them out, that's how we start scaling.

Interviewer (41:52)

So the employees love this, the companies love this. You get to buy assets that are attractive cheaply. Where along the way have you seen pressure points of people pushing back?

Dave Thornton (42:04)

I would say that the companies are tolerant of it, but loving it is, I think, a thing that they'll do once they realize that programmatic liquidity that they can provide to their employees is a huge benefit. Otherwise, I'd say they're neutral on it. If we hugely scale, then at some point primary venture funds are going to care because they're probably counting on some of this employee's stock not getting exercise and coming back to the cap table. But I don't think we're going to be at a place where they care in any meaningful way for a long time.

Interviewer (42:35)

Where do you hope this business goes over the next couple of years?

Dave Thornton (42:38)

I really want to put a moat around it because it's so interesting and it gives us a chance to stretch our brains almost daily. There's probably two stages for it. One is the scaling up of the asset management business, putting as much capital as we reasonably can to work without reaching diminishing marginal returns. While there's no competition, eventually competition will come. It'll erode our purchase discounts. And at that point we have a lot of optionality in the business. We will be sitting on one of the best private company data sets that exists, given the breadth at which we serve the startup employee base. One of the pet ideas I have for a second stage of the business, which is once the purchase discounts have been competed away and there's lots of competition, is that the secondary markets for private shares right now are totally anemic. Maybe a hundred late stage companies, stock trades. The primary reason that's the case is because the buy side on These markets just don't know anything about the other 30,000 venture backed startups. Using our data to help characterize those startups at some minimal level to the buy side of the secondary markets should be a profitable thing to do and should provide a lot of value in the world. There's one world in which we run a proper index business on one hand that's at steady state and then start trying to make the secondary markets work.

Interviewer (43:56)

David, I want to make sure I get a chance to ask a couple fun closing questions.

Dave Thornton (43:59)

Sure.

Interviewer (44:00)

What is your favorite hobby or activity outside of work and family?

Dave Thornton (44:03)

Basketball is the only physical activity that I like. I will work out because I've read that it's a good thing to work out. It's good for your health. You should do it. But I miss basketball like crazy and haven't played as much, especially since COVID I used to play a ton of poker and I really miss it. I miss being able to sit for four or five hours and play cards.

Interviewer (44:23)

What was your first paid job and what'd you learn from it?

Dave Thornton (44:26)

My first paid job, which I should air quote the paid part. I was 16. I was working for one of the dot com boom nonsense companies in the 90s. It was called jodriver.com and my best memory of what they were doing was they were trying to just aggregate all the information you need to pass your driver's test in a given state and put it in one place for some reason. And this should have been a harbinger of things to come. I was like, oh, I love the idea of stock options. I was getting paid $6 an hour and I was given the opportunity to have half my pay be in stock options. And I was like, yes, sign me up. Needless to say, I got paid $3 an hour that summer.

Interviewer (45:07)

Which two people have had the biggest impact on your professional life?

Dave Thornton (45:11)

One is Randy Nguyen and the other is Emilio Seijo. Randy was one of the founders and longtime CEO of Capital iq, but relevant to me. He was my first backer and angel investor and mentor in the first business and he's been with me all the way through and he's taught me what it actually means to be somebody's backer. I've lived also in the venture world and I've seen a lot of venture firms say what they were going to do for you and not develop any relationship with the people that they invested in and also frequently fall down in terms of delivering any value after their capital. I hope I have the opportunity one day to do for somebody else. What Randy's done for me, Emilio is the other person. Emilio and Randy and I all met each other around the same time. Emilio was my first partner in that business in which we were building the machine learning models. He is one of the smartest people that I've ever met, but in a way that really opened my eyes about what kind of things you should look for in a person that you work with. He is A mathematical statistics PhD from Columbia. It's easy to put him in a bucket on the basis of that. But when he was rebuilding our illiquid asset pricing model inside of the brand name bank, he put on every hat possible and knocked down roadblocks until that thing was algo trading. He got it through legal and compliance, which you have to imagine is a near impossibility. That's like 50% of what's hard about doing anything inside of a bank. He was able to convince all the retail traders that this was not the end of their job, to the point that they became supporters and users of the model. One of the most fundamental things that I've learned from working alongside of and near Emilio for the last 15 years is that we're all just people solving problems. I have a computer science background and a business background, and I guess I went to law school. You could put me in one or two or maximum three categories and say these are the three things you should be doing. No, we're all just solving problems in a business and you need to figure out what you need to figure out in order to get it done. And I learned that from Emilio.

Interviewer (47:15)

What's the best advice you ever received?

Dave Thornton (47:17)

It was from my dad and I've never done a great job implementing it, but it was so clearly true the second he said it that I've held onto it aspirationally for the longest time, which was don't let your highs be too high and don't let your lows be too low. Which, having lived in the startup world for 15 years, if I could have implemented it, my life would have been better for the last 15 years.

Interviewer (47:35)

All right, David, last one. If the next five years are a chapter in your life, what's that chapter about?

Dave Thornton (47:40)

It's easier to think of somebody else's book. I love Michael Lewis. I am convinced that the thing that we're doing right now belongs in some chapter in the indexing of the private markets. There's a book called Trillions by Robin Wigglesworth. It was my education on the advent of the public markets indices and how Even in the 50s, everybody knew that you should probably be indexing, and then the technology to actually do the thing didn't exist until the 80s, and then it didn't become dominant until now. I feel like we're living that right now in the private markets. I mean, BlackRock just bought Prequin and they're going to be putting out private equity index products. First and foremost. We're playing a meaningful role in that, I hope. I would love Michael Lewis to write some book on that and I'd love us to have one chapter reserved.

Interviewer (48:31)

Well, David, thanks so much for sharing this early window of what's to come.

Dave Thornton (48:35)

Thank you for having me.

Ted Seides (48:36)

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Podcast Disclaimer Voice (49:01)

All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of Capital Allocators or their firms. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Capital Allocators or podcast guests may maintain positions in securities discussed on this podcast.