Dave (3:06)

They're almost able to take this long term feedback cycle and turn it into kind of short term rewards, which is the gamification of these kind of more worthwhile pursuits than the seven deadly sins.

Brian (3:17)

100%.

Dave (3:18)

There's a view that Instagram, Facebook, all these companies were kind of part of this deterministic wave of, you know, somebody was going to create a photo sharing app, somebody was going to create a social network. To what degree do you believe that's true?

Brian (3:32)

I definitely believe that's, that's true. A lot of these companies started with some intrinsic need. They were usually not the first ones to solve, but ultimately came up the right solution at the right time. And so if you think about Facebook, they were solving this natural human desire to connect. And they were able to do so in a way that once you had cloud servers and once you had more Internet connectivity was just possible, you know, better than if you had to pick up the phone and call someone long distance before. If they hadn't done it, I believe someone else would have come along and solved the problem with the scale that they did. But you got to remember they were not the first mover in that category. There were companies like Friendster, there were companies like MySpace, and each of those had their own, you know, degrees of success.

Dave (4:15)

When you invest in consumer, is there any way to invest free traction? Is there a way to say, well, this entrepreneur is going to build something special in the space before they've actually gotten traction.

Brian (4:25)

So these days there is less interest in some of these consumer companies. And so that gives me the ability to look at seed investments or even pre seed investments that actually have a live product. It might be a smaller sample set, but you can see that level of user engagement before you invest. But I've also been the first investor in several companies before they even launched. And in that Case what you're really looking at is what problems are there and do they have a really unique understanding of how to solve those problems. So I'll give you an example from the 4Runner portfolio. We were early investors in himss and invested, you know, pre product there. And that was a category where you had Andrew who really understood some of the changes going on around how people were circumventing their doctors or their existing medical professionals to try to solve their challenges on their own. They had a unique legal structure behind the scenes where they could both have a medical office as well as be a technology company. And so that unlocked capabilities that previously weren't there. And then you combine that with a really special founder. And so that was one where we ultimately had comfort getting in early, before they were launched because we had confidence that their solution a they were going to be able to deliver it and B, once they delivered it, it was going to be pretty special.

Dave (5:36)

In a lot of other verticals in tech, a lot of early stage investors are focused on the team and the problem, not necessarily the current solution. The idea being that they'll iterate their way into a good solution. Does that work for consumer and what are the nuances when investing to consumer?

Brian (5:53)

Consumer is a little bit different in that you need to look at the alternative that people already have available to them. I think about a company like Uber and when they got launched it was both this really magical experience that they were able to offer where you pushed a button and then that kicked off these set of chain reaction events behind the scenes in order for a car to ultimately show up. But Uber would have not remotely been successful if they started out in New York where the yellow cab system was pretty darn good. You need to remember what the taxi system was like in San Francisco. You would call, you would sit on hold for maybe 20, 30 minutes, they would send a car and then 50, 50 of the time that car would pick someone else along the way and never show up. And so I look at the delta between what is available today and what can this new company offer as being important as opposed to just looking at it in a vacuum where you're solely looking at that new solution.

Dave (6:45)

Facebook's the same, famously started in Harvard. If it just started as a generalist software, if it a generalist social network, if it hadn't used real names. You know, we imagine these companies as they are today, but we forget that they were at some point kind of these two year old companies. And if that, you know, taken on a 10 year old competitor 10 year old market. They may have never lived to kind of be these, these large conglomerates.

Brian (7:13)

People forget about, I would say two things related to Facebook, the first of which is that yes, they were live originally at, at Harvard, but so many students at Harvard had friends at these other universities that they wanted to connect with. And so when they launched the other Ivy League schools, they, they would get to over 90% penetration within those schools within a couple weeks. And that was largely because the pent up demand, because the friend networks weren't local just to the college you went to. You had high school friends that were now at all of these different schools and they were very deliberate about how they expanded from one university to the next. People also forget that for a long time you needed a Edu email address to even access the site. When I was tracking the company down in 2004, unsuccessfully, unfortunately, I had to get a. Edu email address from my old college in order to even be able to access the product. And that meant that the company actually tapped out at around 4 million users for some period of time. The growth, the growth slowed and flatlined. And so there were some questions about it. But what people didn't understand is there was still so much pent up demand for people outside of colleges that it was really about Zuck being very deliberate about when he moved into new areas and the way that he ultimately moved into those areas areas.

Dave (8:27)

When we last chatted, you said that we were in the toy phase of AI. What did you mean by that?

Brian (8:32)

Been a couple of weeks since we chatted and I would even argue that we're, we're starting to move out of that so much changing so quickly. But if you think about the early cases when ChatGPT first came out, I remember one of the first thing I did would, you know, be write like a silly song and have it sung in a pirate voice or something like that. There was a lot of this playing around because it was so different than what you would experience with any other product online. But from there people started moving into more serious queries. There's obviously a lot of usage in and around education, maybe less monetizable, but people using AI for more serious endeavors. You now have more people asking AI questions about more monetizable events, which you could see from an advertising platform would have real intrinsic value going back to these platform model companies. But at the end of the day, it takes a couple things for AI to really move out of this toy phase. And I look at it in two ways, the first of which is trust from the user to be able to share more about both their existing preferences as well as their existing third party profiles. So how does AI not just give me a recommendation, but actually go ahead and execute that recommendation with my dollars? So I'm trusting the system with my hard earned, hard earned cash. But also the second thing is that 85% of purchases still still take place in the offline environment. And so when you think about an Uber, when you think about a doordash, an Airbnb, those tools are ultimately connecting the digital and the physical world in a clever way. And a lot of the companies that I'm most excited about are the ones that can do that. Leveraging AI, where you're ultimately not just taking budget from other digital spend, but you're helping people in their offline world. And that's when I think it'll really emerge from this toy phase into something that is a serious contender.

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Dave (11:39)

You reference these AI agents that basically go on and do these, these complex tasks on your behalf. What are some early use cases for AI agents? How do you see them being used today?

Brian (11:49)

I think that final step is still a little bit lacking. Like we see a fair amount of AI agents in and around the travel space where they can help you come up with where you want to go on your honeymoon or your family reunion. They might be able to help you with particular recommendations on what to do there. But that final mile is still largely on the consumer in terms of being able to actually go ahead and execute that. So if I look at the two investments that I've made in around travel, Fora, for example, at the end of the day is a modern travel agency. So they help people who want to build a business and selling travel to their friends and family be able to do that with AI powered tools and a fundamental technology driven booking platform. And so they're able to close out that loop because they have the integrations with the hotels to actually facilitate the bookings. Another example of a company that I invested in around that category is called Boom Pop. And what they do is they help corporations who are planning off sites, sales kickoffs, any sort of group event, be able to plan those more easily as well as actually facilitate them. And so in that case they have AI that goes back and forth and coordinates with vendors. Um, but ultimately there's a person involved that's going to make sure that when you sign up the IT vendor or you book that banquet room, that, that is going to live up to the expectations of, of the end consumer. So when you start talking about really important events, you only get to take a vacation a couple times a year. You only have one major customer summit a year. People are still at the point where they want a human in the loop in order to validate that the AI is doing the right thing. Because hallucination, hallucinations are still a real element. And you can prompt engineer, you can have the system double check itself, but the intrinsic value of still having a human, you know, 5% in the loop, ultimately makes a lot more people comfortable spending real dollars on these platforms.

Dave (13:42)

So another way having AI do 95% of the work is also extremely valuable. And Instead of taking 20 units of energy, you're doing one unit of energy. So you're basically bringing it down by 95%.

Brian (13:55)

Exactly. And it's, it's interesting I mentioned Atticus earlier. They've still erred on the side of onboarding new clients over the phone. Um, the process used to take about five hours, it's down to about 45 minutes. They think they can get down to about 15 minutes, but they never want to bring it down to zero. Because if you're going through a life event where you need Social Security disability, you want to feel like there's a human helping you on the other side. And so it's really important to have people pick up that phone initially. But when it comes to pulling medical records, when it comes to filing paperwork with the local court system, a lot of that can be automated behind the scenes. And even subsequent conversations, those can be pushed to chat, where it's a lot easier to use AI systems than AI voice, which is still a little bit clunky right now.

Dave (14:39)

There's a belief out there that AI and these large LLM models will take the route of autonomous and self driving. And the last 1% is going to take 5, 10, 20 years to perfect. Do you subscribe to that? And if so, why do you think that is?

Brian (14:58)

I think it depends on what you're, what you're asking it to do. There are certain tasks with AI where 95% right is really a hundred percent right. Like you think about writing a blog post or writing an email to someone, it might not be the exact words, but it's close enough that it solves the goal of what you're trying to accomplish. Whereas something that's more quantitative, like filing someone's taxes on their behalf, if there's an error deep in there, that's ultimately going to get me audited, that's going to cause a bigger problem. So in that case, 95% accurate really is not a hundred percent. And so I think there's some truth to the fact that it will take a little while to get there. When I think about Waymo in San Francisco, it took them longer to deliver the product than expected, but once it was out there, it's both satisfied customer needs, the extent that they can charge a premium, and also the safety incidents are way down. Now what I don't think people are fully looking at with AI yet, which is also true with Waymo, is just what are the intrinsic costs of training in New City? And from a pure economic standpoint, what's the break even time period to get back on those initial investments? And how do the economics ultimately work? Because from what I understand, the cost to train San Francisco for Waymo was very expensive to the extent where you couldn't just automatically do that across the rest of the country unless capital was seemingly free for a very long, long period of time. I think we're in that same place with AI where that last 5% of training there will ultimately be a question once gross margins matter, once these companies need to show a path to profitability, whether that cost actually lines up with the benefit and that's where right now I think we're seeing that having a human in the loop for that part, not only does it complete the needs of the end customer, but but it also might be a more cost effective solution than trying to get a hundred percent of the way there anytime soon.

Dave (16:45)

To use the autonomous example, once every couple of months media would get up in arms. You know, Tesla car gets in an accident, of course it didn't report on the 10 times, 20 times more accidents that happen in the non Tesla vehicles. So it's kind of this like impossible strawman where you're comparing something that causes accidents every 10,000 rides with something that causes accidents every 5,000 rides. And there's no relative kind of comparison there. And I think you brought up a good example like email and how many errors do human beings do on emails? When I put emails into AI, that AI at least today does a much better job than I do in terms of drafting and responding to emails. So there's not only the error rate but it's also the relative error rate that I think gets lost on, on people, especially in consumer products.

Brian (17:39)

At the end of the day these products need to be adopted by people in society. And I think we've all been trained our entire lives that people make mistakes and that that's just a part of living. And so if you get in a little fender bender from someone else, it's frustrating but there's some understanding that people are going to screw things up. When computers screw things up, people don't really know how to process that. I think there's a similar example. When my flight's been canceled, I call United and I kind of yell agent until they finally put me through to someone human. And in that case that person, I've had them screw up and book me in the wrong flight. But like you're more understanding that people are going to make mistakes. When the computer systems make mistakes, I think people ultimately get a lot more frustrating. And so that's why for some of these businesses even having a human front end to provide some level of empathy and some level of connection, if you screw it up or if you get it wrong, there's going to be a greater level of forgiveness than there currently is for these systems. And so I think you talked about Tesla earlier, this is something that we're seeing it in self driving, but it's going to move itself all the way through as people have more challenges. And the reality is we're sitting at a point right now where there's a complete lack of Trust in big tech, I would say this is kind of the lowest that it's been in a long period of time. So people don't fully trust big, big tech to have their best interests at heart. And so when it screws up, not only are you frustrated, but you're also wondering, is this one of these examples where I'm not the customer, I'm the product and there's someone else who's ultimately determining how this plays out?

Dave (19:10)

There's a study that recently pitted AI versus cardiologists in 2023, has got an echocardiogram LVEF assessment, and what it found was quite interesting. It found that cardiologists, they were off by roughly 27.2% on the first time, and AI was actually off 16.8%. So AI was on the first time off more. And then when they got to the final gold standard answer, the cardiologists were off on average by 3.77%. The AI was off by 2.79%. Also, other cardiologists couldn't tell whether the analysis was a cardiologist or whether an AI. So again, to this point, yes. Is AI perfect? Is it? It should be at, you know, hopefully 1% error if it's something as important as somebody's heart. But the reality is today the standard is actually more errors. So how many lives are being hurt by not applying more AI?

Brian (20:14)

It's, it's a real problem. It comes back to human conditioning and it might ultimately be generational in terms of who the first adopters are to not just use this technology, but really trust it. If I look at my kids, they are much more comfortable trusting AI because they've grown up around it than maybe I am. Where, you know, I've looked at a lot of these pre AI systems and you think about how many people went to WebMD and got completely the wrong information when they went to ultimately chat with their, with their doctor. So some of this is conditioning. And I look to solutions that are ultimately helping Gen Z or younger as being a great place to start because they don't have some of those intrinsic negative beliefs. And then they can expand from there, going back to social networking. There is a company called Eons that was founded by the guy who founded Monster Jobs and that was targeting older people. And you can argue that older people have all the challenges around loneliness, meeting friends, arguably even more so than people who are in a dorm together on a daily basis. But that just is a harder audience to drive new adoption of new technologies. So as Much as different demographics might have all these challenges, when you're thinking about something new and different, it's easier to start with the younger crowd. It's your initial audience as a human being.

Dave (21:27)

It's hard not to anthropomorphize the AI. So if ChatGPT 4.0 gave you a wrong answer, you kind of, you kind of think ChatGPT wronged you and you're kind of like, you know, I can no longer trust him or her. Where of course like the models get better and you know, you kind of get, have to get out of that evolutionary wiring of, you know, it's like a person on the other side purposely helping or hurting you. And think of it more kind of as another AI would do it, which is based on the updated probabilities of errors versus kind of first generation.

Brian (21:59)

Absolutely.

Dave (22:00)

You spent time as a partner at Battery at Accel, at Forerunner. Today those firms look very different than they were when you were there. How do you look at the large multi stage VC platforms today and what game are they playing?

Brian (22:18)

The game has changed and it's changed for each of those firms as well. So it's hard for me to even bucket the three of them together. If you look, they all come from very strong research backgrounds. Battery was originally founded out of the Yankee Group, which is like a Gartner for people who don't, don't know it. Excel started with this Arthur Patterson prepared mind thesis type work. And then Kirsten who started 4Runner, had a background as an equity analyst. And so all three firms, the similarity between them was really that there was this underlying thesis driven investing that evolved over time. But if you look, if you fast forward to now and look at what's going on at those different entities. Battery, for example, has leaned much more heavily into the tech buyout space and been a very lucrative part of their business. I think they're one of the few Boston powerhouse firms that didn't ultimately relocate to the West Coast. And one of the reasons why is that they found this strategy, which has both been very successful for them as a firm. It's highly repeatable and it's also driven great results for LPs. So battery, in some ways a lot of that business is playing a different game than the rest of Silicon Valley is playing. Accel, on the other hand, is very much leaned into this multi stage capital approach where they operate internationally, they do the earliest of early seed deals, they have a growth fund, they have this leaders fund which is really about piling into some of the best Companies both inside and outside their portfolio. And they're really looking at access to capital as an offensive weapon. I remember talking to founders and saying, hey, you can get a hundred million dollars just from us and never have to worry about going to talk to another venture person again if you don't want to. And that was a, that was a powerful message for. On the other hand, started much more doing seed investments in early days and now is investing out of a $500 million really core early stage, early stage platform. And so they're writing larger checks, they're very ambitious with the kinds of checks that they're making, but they're still ultimately playing a very fundamentally different game from the Excels of the world, where it's less about putting a lot of people in front of founders and really more about having a concentrated portfolio with a relatively small investment team. At the end of the day, as.

Dave (24:34)

You get larger in these platforms, what do the incentives start to direct you to? Is it really towards management fees? Are there firms that are able to maintain their alpha, and if so, in what way? And talk to me about the incentives as you start to grow these platforms.

Brian (24:51)

Well, the first thing I'll say is that if you are able to grow at that level, you've obviously done something very well historically. And that means that you've driven capital returns back to LPs. And it's been interesting watching in this new wave a lot of the best firms investing around AI, they are the best firms that have been investing over the last 20, 30 years plus. That said, more and more capital to deploy does create challenges. When I was at Excel in the US alone, we had both the early stage team as well as the growth team, and we were deploying two very different playbooks. The growth team loved businesses like a Qualtrics or like an Atlassian that had grown to some level, that were profitable, were outside of the Bay Area and where they were able to get real dollars to work when the company had already achieved many milestones. Whereas on the venture side, we were very much focused on which founders were going to be able to ultimately impact these massive market and outflank the large incumbents that they were playing against. And so we ran into a challenge at one point where I was personally looking at DoorDash and the growth team was looking at Postmates. And if you looked at the lens that they were looking at the world through, Postmates was a really phenomenal investment candidate. The lens I was looking at DoorDash through DoorDash was a really phenomenal investment candidate. But ultimately that challenge of trying to look at both those deals at the same time, we ultimately invested in neither of them and both of them would have, would have been real successful for the firm. So those sorts of challenges can come up and you constantly try to navigate them and make sure that the pros outweigh the cons. But additional dollars are, you know, are difficult to manage when you think about the incentives along the way. Ultimately depends based on who you are in the organization and what you're really trying to accomplish. If you think about someone who's been there for a long time and might be retiring soon, they might care more about the fees going into the management company which are a direct proxy to the fund size and maybe have less concerns about how that's going to play out. 10, 15 years down the road. If you have someone else who's been a successful investor investor, they are in the management company already, but they still have a good 10 plus years that they're looking to be investing at that firm. They might care more about whether they're swinging for the fences and ultimately owning enough of important companies to be able to justify the fund size and the management fees that they've been getting. And then you have other people who are coming up within the organization where they might have carrying the fund, but they're not going to see a dime of carry until those management fees are paid back to the partner. They might be more focused on really just getting promoted to the next level. And in a lot of organizations the best way to get promoted to the next level is to drive actual DPI returns. Especially in an environment where we're not seeing a lot of that. So those individuals might be more focused on hey, what are some 5 to 6x return type deals where the timeframe's a little bit sooner. I can get involved, I can get out of that and that that's ultimately going to help me get promoted. Because additional promotions means that now I'm in a both more secure position in the firm as well as my economic situation maybe has changed. And so I'm a big believer that incentives drive outcomes. That will be a recurring theme if you listen to me over time. And you just got to think different people within these organizations based on their seniority, based on how their success is measured, they're going to have very different behaviors along the way.

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Dave (29:11)

It's not just organizations have different incentives. It's different levels inside the organization. Different individuals probably on the same level have different incentives. If you had a magic wand, you could create the perfect incentives in a large firm that played into the benefit of both everybody in the firm as well as LPs. How would you do that? How would you align everyone's incentives?

Brian (29:31)

The first thing that I would do is really to agree on what are our objectives here. Right. Like some firms are really much more focused on let's just get into the best logos we can. If you think about an Andreessen playbook, they'd care about what are the 25 companies any given year that really matter. If that's their North Star, then you want to make sure your incentives are lined up to get into those type of companies. At Excel, we cared a lot more about which individual founders were we backing and were they special individuals that were going to be able to drive these outlier outcomes. And so that might be more of a named account approach where you're really thinking about did we ultimately see the deals that were the most important ones and did we win our fair share of those? Whereas battery was much more straightforward. We were very much DPI driven at.

Dave (30:14)

The end of the day.

Brian (30:15)

So when you think about how you got promoted after you'd been there for a while and after you made a fair amount of investments, it was really driven by your numbers and your returns. So I think step one is deciding as a firm what is your North Star and what are you ultimately trying to solve for and then make sure that people all up and down the food chain have that sort of focus. I'll give you an example. If you're at a firm that is a little bit more risk adverse, you might have a junior person who makes all the right decisions invest in something that's really interesting. Like for example, I invested in a company called Vidi. They were the Instagram for video. Before Instagram had video. We got to the point where we were doing a million downloads a day. We're the top app on the app store for over a month. Ultimately, Facebook launched video. It didn't work out. We still made a little bit of money on the deal, but it didn't materialize in a fashion that was a real winner for the firm. But if you look, it felt like we did the right diligence work. We got involved early, we bought material ownership, the inputs were right even though the output wasn't what we ultimately wanted. And I think to be really intellectually honest with yourself as a firm, if you're trying to create the right behavior incentives, you need some of those to be driven by the inputs. And then you also want to align people economically based on outputs. A lot of these firms, for example, have carry that is weighed equally across everyone's deals within the organization. That is seen as fair. You're now all fighting equally for each company within the portfolio. And I think there's a lot of merit to that. But I also think there's value in having some people's carry be determined not just by the deals that they lead, but really by the deals that they put their weight behind internally. Some of my best companies, coupa, for example, is a. It was an early investment of mine, wasn't really liked within the partnership, but it was something that a few people were supporters and we ultimately made that investment and ended up being really impactful to the battery funds. And so I think thinking about how can you tie compensation to the people who go out on a limb a little bit and actually make the investments that drive the returns. To summarize, it's. It's twofold. One, it's really deciding what are the north stars across the entire organization and making sure that everyone is aligned.

Dave (32:27)

There's.

Brian (32:28)

And then two, having the rewards be set up in a way where if you have a junior person who did a great deal, they're not going to be looking to leave because sticking around, they're actually going to get paid on that as well as if you have a senior person who maybe didn't lead the deal but spent a lot of time shepherding that junior person and helping them win something that was ultimately important. I love the idea of the incentives being team oriented and team aligned because that's going to create a better culture naturally along the way.

Dave (32:54)

Such a good answer. So one is you have to just assume, just have to get out of your mind that people will do anything that they're not incentivized towards. So start with this kind of ground truth. You have to go incentives. And the next part is, before you start going into incentives, what do you want to do and what do you want to incentivize? It sounds like a simple question, but it's contextual to your market position. And said another way, if everybody had the exact same incentive incentives, then every firm would have beta, not alpha. There's a game theory to what is your North Star. And then you build around that kind of North Star that's differentiated from other venture firms. And then you incentivize around that. So it kind of starts with the strategy and then the incentive base. And then you have to look for things like the inputs versus outputs. In other words, are we going to incentivize the process is like doing the right thing. Are we going to incentivize the right outcome? And to what proportion are we going to incentivize both things? And then there's like team versus individuals. It's a pretty complex system.

Brian (33:57)

Oh, I'll. I'll throw in one more thing to make it even more complex is you actually really want to understand the incentives of the firms you're competing with as well, because that's going to determine how they show up in competitive situations. And you mentioned if more people have the same incentives, you ultimately lose alpha with that. And a lot of firms have incentives right now internally for people just to go out and do deals, to really deploy capital. And so if you're competing with a firm where you ultimately know that that individual there is really more focused on deploying capital than they are about getting a certain ownership percentage or really thinking about the risks associated with it, you're going to compete differently. There's another firm that we would compete with frequently where it was really all about your win rate competitively against the best other firms on the way world. And so they wouldn't necessarily do a ton of diligence. They knew that if the right other firms were looking at it, they probably did the diligence. And now it was really about winning. And so you had to know that they were going to show up competitively a little bit differently, where they might not take as much time to try to answer the diligence questions. They were really going to be 100% focused on selling those founders and getting them to want to work with them. So you need to understand the incentives at different organizations and Then have that really inform the North Star you have and then what incentives you have. And I mentioned some that were economic. But a lot of these incentives are also cultural in terms of what sort of behavior do you recognize and do you reward internally just based on people doing the right actions that ultimately lead to better outcomes.

Dave (35:23)

It almost makes startup incentives extremely easy because especially at the early stage, everybody's equity is worth so much more than their incremental status or their incremental salary. That makes actually very easy for everybody to be on the same boat, rowing in one direction.

Brian (35:37)

Yeah, yeah. The big, the biggest challenge right now with startup incentives, it's just that these companies are becoming more capital efficient and a lot of the dollars showing up in later stages, a lot of that money is going out the door in secondaries. And so you have this weird dynamic there where certain people are getting access to much faster liquidity than the rank and file people who are actually building the product. And that can create some complexity internally along the way. So I would say as the market has gotten more sophisticated, as it's gotten more competitive, you need to be more nuanced with the incentives as well as thinking about the downfield ramifications of those two.

Dave (36:15)

Also want to hit on LP incentives. What are their incentives and is there a principal agent issue with LPs themselves and kind of their capital pools?

Brian (36:25)

Going back to what we were just talking about, venture firms, you really need to first understand what is the North Star of that LP that is driving their behaviors. So some organizations might really care about access to some of these large storied firms because they're ultimately a fund to fund. And so they're selling access to that firm, at least the most of the fund to funds that I understand. The more capital they're managing, you know, the more revenue that they're ultimately getting from that. So some of them might really like the idea of getting access to these bigger and bigger funds. Now another group might have, for example, a really deliberate direct investing strategy. This is something that I'm seeing from more and more LPs, whether it be that they feel like they have some unique knowledge, whether it be that they're trying to blend the fees and carry down along the way. But direct investing has become much more prominent topic when I'm catching up with folks. So they might actually like having more of an emerging manager program because they know that those funds aren't going to have the necessary follow on dollars to participate in all the pro rata and their best companies, whereas the larger firms are already have that capital in the house. And so part of my job when I'm having LP conversations is really to try to put myself in their shoes, understand what it is that they're trying to build. A lot of LPs are challenged on their front because of the lack of liquidity that the venture ecosystem has created. And so a lot of LPs have challenges as well right now. And so part of my job is instead of sitting on the other side of the table, really trying to understand what is motivating them, what their challenges are, what are the things that they need to accomplish this year before getting into the next year. And if I can be complimentary to that, I'm going to have a lot more success in those conversations.

Dave (38:09)

I think one of the reasons why these incentive structures are so complex or so complicated is there's very large stakes and there's a lag between rewarded behavior and desired behavior. In other words, you might make an investment today, it doesn't go into carry for, you know, seven, eight, sometimes early stage, 14 years. So you have this lag of behavior, these principal agent problems, these North Star differentiations, and it's, it's something that's underreported to say the least and under thought about. And every, every GP to your point, is going and pushing and talking about their returns. This is what I did last fund, this is what I did two funds ago. And sure that's important, but there's so much more to it than just returns.

Brian (38:53)

I don't remember if it was a podcast, but I think douglione went and was talking about how hard the environment is right now and that some of that is the strategic value of trying to suck capital away from competitors because having capital is a real advantage. I'm having a lot of LP conversations that are moving a little slower than I would like to because funds that were supposed to show up next year have gone through their capital a little more quickly and now they're having to make re up decisions now based on those funds. And so there is the strategic element at the, at the end of the day, this capital is scarce. And that, you know, that is going to be a factor for folks.

Dave (39:30)

It's kind of like budgeting. Somebody has a spend and whether you come to them in Q1, Q2, Q3, Q4, LD doesn't just have this money in the bank account and sitting in cash. There's a budgeting aspect to it. There's tpi, there's other funds.

Brian (39:44)

Yeah, it's budgeting, it's Bandwidth and it's also political capital. All, all three of those, those add up. And then one other thing I want to say earlier about your point about just all this complexity. You also have on the backdrop a relatively imperfect mark to market process for how to actually value these end assets. So not only do you have you know, 10 plus years waiting for liquidity, in a lot of cases the value of some of these underlying companies is somewhat ambiguous. At the end of the day you'll take the write up if one person agreed to evaluation. But that might not have actually been the market clearing price if this was a, this was ultimately a public company. On the flip side, I have companies that have been profitable for years. They're now, you know, doing almost 200 million in revenue, but they're still marked at the last round was at like 150 or something like that. How one firm versus another actually values their companies and the incentives behind that. That is another tricky element that the savviest LPs understand and really dig under the covers to understand the intrinsic value of these portfolios as opposed to just the reported value of these portfolios.

Dave (40:50)

There's incentive on marking as well. I had Professor Steve Kaplan, University of Chicago researcher, arguably the best researcher in space and he mentioned that one of his studies looked at how are funds marking their positions and those with, with more vintages, the more established managers were actually slightly under marking their TVPI and emerging managers were over marking. Why? Because if you're on your Fund 6 Fund 7, the way that you lose an LP's check is actually to lose credibility or confidence. By default LPs have incentive to re opt, which is another incentive thing we won't go into. But when you're an emerging manager, you're trying to kind of get new LPs you need to show top, top quartile, ideally top decile performance. Their incentive again is to overmark. And some of these biases are unconscious and some of them aren't conscious and it's just brutal market dynamics lead to kind of very interesting behaviors to say the least.

Brian (41:52)

Definitely.

Dave (41:53)

Tell me about Tactile, your new fund.

Brian (41:54)

We're a early stage fund focused on solving these problems for everyday Americans. So we want to link arms with founders who have an intrinsic understanding of not just what challenges people have across America, but what new technologies, most especially AI right now, what new business models, what new regulatory or cultural changes open the door to be able to address those. And so we're focused on this emerging category. There's been a trillion dollars invested in the underlying AI infrastructure. And we're now at a point where we're going to see a lot more innovation happening at the application layer. This is something that we've seen time and again. If you think about the Netscape browser coming out in 94, there was a couple years before you saw the Amazons, the Googles, the pricelines of the world. Same thing with the iPhone. The App Store coming out in 2008. The initial apps were like popping bubbles, were very touchscreen centric. Took a little while for people to understand that the real magic of the phone was the intersection of the GPS chip as well as the camera. And that's when you had things like Instagram, Uber, Instacart come out. And we're now at the similar point with AI where people are playing around and understanding what the real magic of this is and how that actually solves people's problems. And we want to be in a position where we can be the first institutional investor to work with those founders and to work with them over many years.

Dave (43:17)

I know you're limited to the startups that invest in the founders that you talk to, but I'm sure you have your views on what should exist in kind of the second wave of AI and consumer. What are some categories that you're actively looking for companies to invest in and why?

Brian (43:34)

Yeah, absolutely. So I kind of slice it both vertically as well as horizontally across the business models. And so the main verticals we're looking at falls into categories like simplicity. People's lives are complicated. Three out of four Americans, our dual income families where both parents are working, and so anything that can simplify people's lives on a daily basis, those are solutions that we're excited to look at. The second one is prosperity. I think a lot of people are worried about what AI is going to do to their jobs, but there are ways of harnessing these new technologies. So we spend a lot of time around small business software businesses where their main owner or their main proprietor is in the field all day, but the AI can handle a lot of the back office requirements. Um, we'll even invest in things that help people in their corporate job because we think there's this greater level of agency people can take today to help make themselves successful in an increasingly ambiguous world. And then the third piece is around this longevity trend. I'm on the board of a company called Prenuvo, which does preventative MRI screens. It's all self pay. But one of the really interesting stats about that business is that 26% of their customers make less than $50,000 a year. So these solutions are not just for the rich or the wealthy, but really for anyone who wants to take control of their health. Because at the end of the day, a lot of solutions that go to the insurance companies are not the best in the market and they don't react quite quickly enough. And so if you add up those three sectors, it adds up to about 48% of the US GDP. So these are, these are meaty categories. And then the other slice we'll take at it is really looking from a business model perspective. And I've been enamored with two models. One is called the digitally native franchise. So this is really a fancy word for talking about business in a box where you're providing the toolset and the capabilities for people to build their own companies as well as this AI powered services model, which is something that my colleague Alex and I put a piece out on last summer, which is really how do you leverage some of these AI technologies but ultimately deliver it in a traditionally human service category, but do so with high technology level gross margins versus much lower service margins. And so if you look at the intersection of those areas, that's very much what I'm excited about today. But one of the real pleasures of this job is the ability to sit down with other seed investors, other angels and just say, hey, which founder did you guys back that's been most inspiring to you guys, where you're just really excited about what they're building. And a lot of times that will open the door for us to look at things that we never really even thought of as being exciting. Exciting. But we're able to get inspired by that founder's vision. And it's a lot of fun to be able to go explore things both that I know a lot about as well as get exposure to things that I have very limited knowledge and I need to come up, come up to.

Dave (46:20)

Feed quite quickly on one of the themes in the market is that the large multi stage firms that we talked about, the Sequoias and Andreessens, are going down and competing in the, sometimes even the pre seed round, but usually the seed round, let's say douglione from Sequoia is pitching against you. Not an enviable position. How do you differentiate yourself and how do you get into that round versus Sequoia?

Brian (46:41)

Doug's gonna be a tough one to beat. So I don't, I don't know if I want to go, go head up against that one. But I, I am happy competing with the best firms in the world because that means I'm, I'm barking up the right trees. So a lot of it really just comes back to first principles where I want to show for that conversation more prepared, more informed than whoever I'm competing against. And some of that involves really understanding what is happening recently in terms of new capabilities. And some of that is having a much more longitudinal background where I know what happened with companies that tried similar things five, ten plus years ago. You think about early investors in Instacart, you had an intimate knowledge around what happened with webvan, you're going to be much more prepared to be helpful to get guidance to those new companies. And so a lot of it is just back to basics and then we'll throw in a few unique things along the way. One of the things we can do as a smaller firm, that's harder to do if you're larger, where we don't need to own quite as much of the business, we don't need to write quite as big of a check. We'll do something that I call Flash Syndicate, which is this network of angels, other seed funds, groups that can add strategic value along the way. Where I can come into a five, six million round, say, hey, I'm going to do two and a half to three million or historically at a larger firm I would have been doing four and a half million dollars, taking the vast majority of that round. And I can now give the founders, you know, almost this like smorgasbord of alternatives of saying, hey, we can work with, you know, both phenomenal investors who I used to compete with, who are now angels investing in companies. We can bring in executives from people who have taken their companies public, built 10 plus billion of economic value. Let's say you have a challenge of taking a bottoms up product and then trying to think about how that evolves into an enterprise sales scenario. We can go grab the guy who, you know, ran sales at Dropbox, Slack and then, and then Atlassian. And so I'm able to be a lot more strategic about what value do I bring to the table, which is really very simple. It's, I want to make sure the founder's vision is crisp, I want to make sure that they're hiring the right people around the table and I want to make sure that they never run out of money. Like that's what I'm bringing to the table. But along the way we can go work with other people who don't just used to have an operating background, but are still very much the tip of the spear, the best people in market and we have capital available and we have a history of investing with those folks to make it really easy on the front founders. So at the end of the day, like, we're probably not going to win every deal we get after, but we can win the vast majority by showing up more informed about the business, showing up a little bit earlier than everyone else, and then having this roster of other individuals we can bring to bear to make sure that in a lot of ways we're punching above our weight class as a relatively small firm.

Dave (49:16)

The best answer that I've gotten to this question so far, junior from industry and when he said that the best or early stage investors, they essentially act as the second and a half co founder. So there might be two co founders, they'll be the second and a half, or there might be three, they'll be the third and a half. And they do the one thing that the multi stage firms can't do, which is give their time. It's, it's the unscalable asset, give senior partner time early on, essentially help build the company, which is quite a value add. In fact, in the example you gave with Paravc, they have a partnership, they, they have a close relationship with Sequoia where oftentimes they'll do the deal and then Sequoia will mark them up later on as well. So you have these kind of synergistic relationships as well between the larger firms and the early stage investors.

Brian (50:01)

Absolutely. And that was one of the reasons why I wanted to go start this smaller firm because that's, that's the part of the job that I really love doing, which for better or worse, doesn't scale incredibly well. There's, there's probably easier ways to make money in venture by managing lots of assets, but the area where I think I'm special and the area where I've had the most personal fulfillment historically is by getting involved with people before the rest of the market believes them. Being able to develop trust and rapport to a level where they feel comfortable sharing not just the good news with me, but also the bad news. And where you can look a couple years later and see that this thing that was an idea without a whole lot of meat on the bones is now not only solving, you know, hundreds of thousands or millions of people's problems, but also now the livelihood for 50 and 100, 200 people around the gate. And that's ultimately, that's ultimately not very scalable, but, but really fulfilling for me.

Dave (50:55)

And I Think it's an area where.

Brian (50:56)

You know, if I get to the point where founders are doing references with other people I've worked with, usually those end up well because they can speak to me, filling that role for them because it's not economically driven. At the end of the day, it's what I love doing.

Dave (51:09)

What's one thing that you want our listeners to know about Tactile vc?

Brian (51:13)

One thing that I think is really important to know about Tactile that's a little bit different from some other folks spinning out these large, larger organizations is that many people will leave their firm because they got tired of the bigger partnership and they really just love doing deals individually. Personally, I'm the byproduct of some really phenomenal organizations. Organizations that took me in when I was younger, trained me, and then gave me upward mobility to be able to grow into the investor that I am today. These firms still have great training programs in terms of being able to bring in junior people and teach them the business, but they don't have necessarily the same level of bandwidth to mentor as much as I got when I was coming up the ranks. And the upward mobility within these organizations has gotten harder as they're just bigger companies by now. So one of the things that's really exciting to me about the opportunity in front of us at Tactile is to think about how you can take the best of these heritage firms, map it with new technology, and map it with a plan to really hire some of the best emerging investors, people who have been trained by these great firms, but where they have ambition to look outside of those organizations for where the future will be. And ultimately, like I want to be measured, certainly I'll be measured on the investments I make. That's going to be the first. First bar we got to cross. But eventually I'll be measured based on the organization that we can build and based on the success of the other people that come on where they've gotten trained elsewhere. But where Tactile is where they want to ultimately ping their flag. And that's where we're going to be successful over time.

Dave (52:42)

As Elon says, the product is the factory. It's not the car. It's actually the factory making the car. It's the culture and the ability to recruit and retain the very best talent.

Brian (52:52)

Absolutely. I haven't heard that one. I'm going to steal that one. So I appreciate you bringing it up, Brian.

Dave (52:56)

This has been an absolute masterclass. Thank you. Thanks a lot for jumping on. Look forward to continuing this conversation. Live.

Brian (53:02)

Appreciate it, Dave. Thank you for having me.

Podcast Host (53:03)

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