The Exchanges

Q It's a really, I hate kind of broad and generous questions, because they're generally for crap interviewers, but, you know, uh, as I said, I've done 2700, so hopefully I have some skills. But when you think about kind of the AI landscape today, how do you think about where the most value will accrue, and you want to concentrate most of your time and capital?

A We just talked about how everyone's over-investing right now into this cycle, and because none of us can miss, whether it's the large incumbents, Or us as venture investors back in companies. And so your question is like, where do we invest as venture investors? And I can tell you, we're, we've invested in a lot of application layer companies, and that are solving very specific pain points. Uh, and the way we've looked at it pretty simply is, if you think about, we took actually the top 20 jobs in the US, and who makes the most? Simple. And it's doctors, it's lawyers, and it's developers. How do we help supercharge these people who are highly scarce, highly skilled, and we're not producing enough of them? So you try to build software, AI, that helps them do their job better. And, uh, so we backed companies that help doctors, lawyers, and developers with co-pilots. So Harvey, Ambience, And Kodium.

AI assessment note: “we've invested in a lot of application layer companies, and that are solving very specific pain points”

Q When we look at the venture landscape, you have like, in my mind, boutiques, USV benchmark boutique, and then you have like capital accumulators, which is Tiger, KOTU, Andreessen, General Catalyst, Lightspeed, Sequoia now. Respectfully, and I say this with Tony, where does Kleiner sit in that? Because you kind of sat in the middle in my mind. How do you think about that?

A We are primarily early stage focused. Uh, we have a, an eight hundred million dollar fund for that. And then we have a, uh, 1.2000000000 dollar growth fund. And we, this team of seven folks, um, invest Out of both of those funds. I would characterize us as boutique because we're kind of a small team that believes in the craft of venture capital. Uh, it's, we, we think it's a business that doesn't scale, actually. Um, and so, uh, we're not scaling through people, but we have the scale of capital. Because our growth fund, even, half of the dollars are allocated towards, not allocated, but half those dollars are invested in our best companies from our early stage funds. So it doesn't require us to have a, a large, uh, team, so to say, because we're already involved with some of these companies like Rippling and Glean and Figma that we're doubling down into out of our growth fund.

AI assessment note: “I would characterize us as boutique because we're kind of a small team”

Q It, it's fun. It's also challenging from a pricing perspective. I saw three companies my moon last week that raised it over the seven hundred and fifty million pre-product. How do you think about navigating the pricing environment when there is such further pitch excitement for these companies?

A Great question, Harry. And I think we all sort of fall victim to those, uh, every once in a while, but that can't be the core part of the business. That can be the, the one like that got away and you have to get Into this pre-product company because the founder is so exceptional. That can be, you know, one out of the 20 deals you do this year. It can't be every single one of them. Because as you know, Harry, you know, we have to get our ownership at the early stages where you're investing five to ten million dollars for 15 to 20% for the math to work for, for our funds. And it can't be done if you're investing, you know, twenty-five million at 750 post out of an early stage fund.

AI assessment note: “That can be, you know, one out of the 20 deals you do this year.”

Q know, when we look at a lot of potential use cases, a lot could be subsumed by the foundational model companies, if they are big enough. An example could be talking translators, you know, talking avatars that you could talk to in a friendly enough way. How do you, Do you worry about application layer companies being potentially subsumed by foundation model layer if they are such a core competency?

A I don't. It's, it's a bit like, ah, the hyperscalers thinking they can do everything, and they've decided that that's a great business model, is to, ah, own the electricity, and, ah, or the, and the pipes, ah, and then just charge for, by the, by the hour, or the kilowatt hour, and I think that's a pretty darn good business model for the hyperscalers that provide the models, um, in OpenAI, and I was reminded by, you know, I was at OpenAI maybe a month ago, and, you know, we're Going through all these demos of cool products that are coming out, like O-one and Strawberry, and realize that their positioning is, we can't do everything. We're a 600 person company. We can't build the application layer stuff that you guys, we want you guys to build, or your companies to build. And so, I think that there's a great business to be had in LLMs and in providing the compute and the electricity, and there's a great business to be had by being very vertically focused Around applications.

AI assessment note: “I don't. It's, it's a bit like, ah, the hyperscalers”

Q I cannot wait as long as we bring KD. And then I want to finish today, Mamoun, on your most recent publicly announced investment, and why did you get so excited?

A The most recent investment that was announced, I believe, is Biz.ai. It's a really cool company that provides software to triage stroke patients inside of the ER. As you may know, when you have a stroke, every second that goes by, your brain cells are dying, and what happens with the software is like, When you go to the ER, you get a CT scan. Very quickly, the neurosurgeon on call gets notified that this patient needs to go to the ER and get stented. A process that would typically take three hours takes now five minutes, and we help save lives, and even worse, actually, is people who get paralyzed because they don't get stented quickly enough. We get that time from hours to minutes and get them stented and back to normal life. Founded by a neurosurgeon, Chris Mancy, Englishman, Pretty amazing guy. You should have him on, on the show. He's awesome, and I love working with him.

AI assessment note: “Biz.ai. It's a really cool company that provides software to triage stroke patients”

Q It's a really, I hate kind of broad and generous questions, because they're generally for crap interviewers, but, you know, uh, as I said, I've done 2700, so hopefully I have some skills. But when you think about kind of the AI landscape today, how do you think about where the most value will accrue, and you want to concentrate most of your time and capital?

A We just talked about how everyone's over-investing right now into this cycle, and because none of us can miss, whether it's the large incumbents, Or us as venture investors back in companies. And so your question is like, where do we invest as venture investors? And I can tell you, we're, we've invested in a lot of application layer companies, and that are solving very specific pain points. Uh, and the way we've looked at it pretty simply is, if you think about, we took actually the top 20 jobs in the US, and who makes the most? Simple. And it's doctors, it's lawyers, and it's developers. How do we help supercharge these people who are highly scarce, highly skilled, and we're not producing enough of them? So you try to build software, AI, that helps them do their job better. And, uh, so we backed companies that help doctors, lawyers, and developers with co-pilots. So Harvey, Ambience, And Kodium.

AI assessment note: “we've invested in a lot of application layer companies, and that are solving very specific pain points.”

Q When we look at the venture landscape, you have like, in my mind, boutiques, USV benchmark boutique, and then you have like capital accumulators, which is Tiger, KOTU, Andreessen, General Catalyst, Lightspeed, Sequoia now. Respectfully, and I say this with Tony, where does Kleiner sit in that? Because you kind of sat in the middle in my mind. How do you think about that?

A We are primarily early stage focused. Uh, we have a, an eight hundred million dollar fund for that. And then we have a, uh, 1.2000000000 dollar growth fund. And we, this team of seven folks, um, invest Out of both of those funds. I would characterize us as boutique because we're kind of a small team that believes in the craft of venture capital. Uh, it's, we, we think it's a business that doesn't scale, actually. Um, and so, uh, we're not scaling through people, but we have the scale of capital. Because our growth fund, even, half of the dollars are allocated towards, not allocated, but half those dollars are invested in our best companies from our early stage funds. So it doesn't require us to have a, a large, uh, team, so to say, because we're already involved with some of these companies like Rippling and Glean and Figma that we're doubling down into out of our growth fund.

AI assessment note: “I would characterize us as boutique because we're kind of a small team”

Q It, it's fun. It's also challenging from a pricing perspective. I saw three companies my moon last week that raised it over the seven hundred and fifty million pre-product. How do you think about navigating the pricing environment when there is such further pitch excitement for these companies?

A Great question, Harry. And I think we all sort of fall victim to those, uh, every once in a while, but that can't be the core part of the business. That can be the, the one like that got away and you have to get Into this pre-product company because the founder is so exceptional. That can be, you know, one out of the 20 deals you do this year. It can't be every single one of them. Because as you know, Harry, you know, we have to get our ownership at the early stages where you're investing five to ten million dollars for 15 to 20% for the math to work for, for our funds. And it can't be done if you're investing, you know, twenty-five million at 750 post out of an early stage fund.

AI assessment note: “That can be, you know, one out of the 20 deals you do”

Q Respectfully to Dylan and team, this was pre any revenue scaling, really. This was pre any real inflection point in the company. Cash had been in before, you know, Greylock were in already, Index were in already. He saw something that no one else in the market saw. This was like a real pick. What did you see that no one else saw in this round, and why did you?

A Yeah, I mean, Credit goes first and foremost to Dylan and Evan who'd built an incredible product. It was just, it took a while to build. Uh, we've heard sort of famously the story around like WebGL, um, advancing, and finally by 2017 Figma had a product that could work multi-player inside the browser as, as, you know, a design tool, and that just wasn't the case in It just didn't, wasn't, didn't have the latency for people, like designers are very high end in terms of like their needs for product and, uh, naturally, right? And so lucky for us that we got to see the company when the product started to work and actually in the metrics for the product, even though the numbers, number of users was small, the amount of use, the, you look at something like, uh, you know, Dao Mao or look at an L-Twenty-Eight. You just saw that designers were using the product 1516, 1718 days out of a month. So effectively, every workday, a designer was going in and collaborating inside of Figma, or using it to design inside of Figma. And so you saw early indications that the product that was just, had just launched, and it was just in, you know, a few hundred K of revenue, it was working. And, ah, yeah, lucky for us that we, we got to Catch it before it, uh, went into hyperscale.

AI assessment note: “designers were using the product 1516, 1718 days out of a month”

Q What have been your biggest lessons? I suck at doing reserves. I think it's a very hard thing to get good at. Please give me your wisdom. What have been your biggest lessons in how to do reserves management and concentration of capital well?

A Yeah, so reserves is one. It's like when you have a, uh, an early stage fund, and we typically invest In about 35 companies per fund. Uh, and so, so do you, how much do you reserve for each one of those investments? Uh, it, typically we, we try to invest about, and we've looked at the math, uh, more than half in that first check. So let's say you're doing, over the life of the company, you're investing twenty-five million dollars in a, in that early stage company, let's, but you're starting out with a fifteen million dollar check, and then you're reserving another 10 for The series B and beyond. And I would say it's, it's generally worked out pretty well. Like, you know, we're now.

AI assessment note: “typically we try to invest about, and we've looked at the math, more than half”

Q Respectfully to Dylan and team, this was pre any revenue scaling, really. This was pre any real inflection point in the company. Cash had been in before, you know, Greylock were in already, Index were in already. He saw something that no one else in the market saw. This was like a real pick. What did you see that no one else saw in this round, and why did you?

A Yeah, I mean, Credit goes first and foremost to Dylan and Evan who'd built an incredible product. It was just, it took a while to build. Uh, we've heard sort of famously the story around like WebGL, um, advancing, and finally by 2017 Figma had a product that could work multi-player inside the browser as, as, you know, a design tool, and that just wasn't the case in It just didn't, wasn't, didn't have the latency for people, like designers are very high end in terms of like their needs for product and, uh, naturally, right? And so lucky for us that we got to see the company when the product started to work and actually in the metrics for the product, even though the numbers, number of users was small, the amount of use, the, you look at something like, uh, you know, Dao Mao or look at an L-Twenty-Eight. You just saw that designers were using the product 1516, 1718 days out of a month. So effectively, every workday, a designer was going in and collaborating inside of Figma, or using it to design inside of Figma. And so you saw early indications that the product that was just, had just launched, and it was just in, you know, a few hundred K of revenue, it was working. And, ah, yeah, lucky for us that we, we got to Catch it before it, uh, went into hyperscale.

AI assessment note: “You just saw that designers were using the product 1516, 1718 days out of a month.”

Q In terms of another founder of yours that provided a fantastic question with regards to team building, it was Jason at And he said that you have a great analogy when it comes to actually on the flip side, building a top performing VC team. So tell me, Mamoun, I love an analogy. So what are the takeaways?

A Yeah, I view venture capital as a bit like a sports league, and it's highly competitive. People have like their time, you know, where they're in their prime, and it's 10 years, it's 20 years, but there's definitely like, you know, when your network is at its peak, your performance is big, and so I've likened it over the last few years more like a professional sports league, and And if you want to build a championship winning team, you have to, I liken it actually to a basketball team because basketball teams are fairly small. They have five players on the court and then they have a bench and many players from the bench play. And so the way I view it is like you have five positions in basketball and each position is very different. Point guard, shooting guard, small forward, power forward, center. And if you want to win championships, you want to have a top one, two, three player in every one of those roles. And that's how I like in our own team is like, if we're going to win championships, we want to have a top one, two, three person doing enterprise and consumer and hard tech and fintech and et cetera. It's important to have the players on the field be top at what they do. And then also to make sure that you have a bench of amazing players, either you're drafting them from college that you're grooming on the team. And every once in a while, you know, if you're the Warriors, yo…

AI assessment note: “I liken it actually to a basketball team because basketball teams are fairly small.”

Q Now, talk to me, uh, I want to hear about your way into venture. We, we see Mamoon now at Social, but how did you come into the venture industry, and how did you come to co-found Social?

A It's a kind of a long story, actually, so, um, but I'll start with, I, I moved to Silicon Valley in 1997 to join a semiconductor company called Xilinx, uh, as an engineer, uh, Right after college, and this is right in the middle of the first dot-com wave. I had no idea what VCs were or what they did, so I was totally green to what made Silicon Valley Silicon Valley, but all I knew was how to design chips. So, uh, but just like most companies at that time, my company was involved in selling to a lot of these dot-com, um, double companies, and, uh, actually our chips were used in a lot of the networking gear that was, uh, driving the first internet wave. Then I'd come across this company that I was helping out, With, uh, some chip designs, and they'd, next day got bought for six billion dollars with Cisco, and I was, didn't make a lot of sense to me, but this is all happening around the time of when I got started here in Silicon Valley, and like a lot of companies at that time, actually, uh, my company, Xilinx, started a corporate venture fund, and, uh, somehow, actually, at one point, I was asked to help evaluate a company in an area that I knew something about, and, um, I did that for those guys, and, uh, I kind of liked it, actually, and I, and I sort of set out to Explore what this VC thing or investing thing was about. Uh, I always liked stocks, but I never really had a chan…

AI assessment note: “making the transition from an engineer or product person to more see if I could”

Q That is absolutely amazing. Where do you think a lot of people are spending time today in the investing world that you don't understand or don't think they should be?

A There's a lot of time being spent on a lot of the middle layer between the foundation models and the applications. And I think in the middle layers, middleware, things that allow you to use those models better, faster, cheaper, uh, and build applications on top. So if you think the application at the top of the pyramid, the foundation model at the bottom of the pyramid, Pyramid. In the middle, you've got middle layer. There's a lot of new technologies emerging that allowed it, for example, to capture vector databases or, you know, uh, weights for, for fine tuning of models inside of vector databases and things of that ilk. Uh, and there's just a lot going on there. At the same time, some of the value seems fleeting in nature. And, and so I think in early innings, when things are In such high degree of change, so the rate of change is so high, there's, there is a lot of that investing that happens, and I feel like perhaps it's all over invested.

AI assessment note: “perhaps it's all over invested”

Q How do you think about differentiation in this world when there are 10 transcribers note-taking apps for doctors?

A Yeah, I think it's like any other space, any other traditional linear software space I call them. It's, it's about teams, That will out hustle and will out work and have, in this case, actually, the technology really does matter. The quality of the output of their models really does matter. The tuning of what they've done to the frontier model does matter. You can't have a medical transcriber that's 87% good, ok? It has to be close to like 99% good. And that actually requires real Technical depth and adeptness. And I, I would say all three of these examples I cited are started by founders who are extremely technical. And, uh, they've been at it. This is not just like some tourist AI engineer. It is like sort of deep ML experts have been doing this before they started these companies and paired up with a, a very, a domain expert co-founder who understood the market that they're going after.

AI assessment note: “the quality of the output of their models really does matter.”

Q know, when we look at a lot of potential use cases, a lot could be subsumed by the foundational model companies, if they are big enough. An example could be talking translators, you know, talking avatars that you could talk to in a friendly enough way. How do you, Do you worry about application layer companies being potentially subsumed by foundation model layer if they are such a core competency?

A I don't. It's, it's a bit like, ah, the hyperscalers thinking they can do everything, and they've decided that that's a great business model, is to, ah, own the electricity, and, ah, or the, and the pipes, ah, and then just charge for, by the, by the hour, or the kilowatt hour, and I think that's a pretty darn good business model for the hyperscalers that provide the models, um, in OpenAI, and I was reminded by, you know, I was at OpenAI maybe a month ago, and, you know, we're Going through all these demos of cool products that are coming out, like O-one and Strawberry, and realize that their positioning is, we can't do everything. We're a 600 person company. We can't build the application layer stuff that you guys, we want you guys to build, or your companies to build. And so, I think that there's a great business to be had in LLMs and in providing the compute and the electricity, and there's a great business to be had by being very vertically focused Around applications.

AI assessment note: “I don't. It's, it's a bit like, ah, the hyperscalers”

Q How do the best CEOs run a board?

A They start off with a high level overview of how the company's doing, and then they give a chance for their leaders. They're very capable leaders to go dive in deep and, uh, share and be Ask questions, and there's a fair bit of cheerleading, but a fair bit of, like, asking the hard questions. Uh, and I like board meetings where there's one or two things that are talked about in detail. Uh, meaning, like, you go deep dive into one or two things, because at any given point in time in a company's juncture, that moment in time, one or two things that really matter that can, we can help change a trajectory on. And so, if we're talking about seven different things that matter, we're probably missing the point. And so I, I love board meetings where that's sort of the structure.

AI assessment note: “They start off with a high level overview of how the company's doing”

Q How do you think about differentiation in this world when there are 10 transcribers note-taking apps for doctors?

A Yeah, I think it's like any other space, any other traditional linear software space I call them. It's, it's about teams, That will out hustle and will out work and have, in this case, actually, the technology really does matter. The quality of the output of their models really does matter. The tuning of what they've done to the frontier model does matter. You can't have a medical transcriber that's 87% good, ok? It has to be close to like 99% good. And that actually requires real Technical depth and adeptness. And I, I would say all three of these examples I cited are started by founders who are extremely technical. And, uh, they've been at it. This is not just like some tourist AI engineer. It is like sort of deep ML experts have been doing this before they started these companies and paired up with a, a very, a domain expert co-founder who understood the market that they're going after.

AI assessment note: “The quality of the output of their models really does matter.”

Q That is absolutely amazing. Where do you think a lot of people are spending time today in the investing world that you don't understand or don't think they should be?

A There's a lot of time being spent on a lot of the middle layer between the foundation models and the applications. And I think in the middle layers, middleware, things that allow you to use those models better, faster, cheaper, uh, and build applications on top. So if you think the application at the top of the pyramid, the foundation model at the bottom of the pyramid, Pyramid. In the middle, you've got middle layer. There's a lot of new technologies emerging that allowed it, for example, to capture vector databases or, you know, uh, weights for, for fine tuning of models inside of vector databases and things of that ilk. Uh, and there's just a lot going on there. At the same time, some of the value seems fleeting in nature. And, and so I think in early innings, when things are In such high degree of change, so the rate of change is so high, there's, there is a lot of that investing that happens, and I feel like perhaps it's all over invested.

AI assessment note: “There's a lot of time being spent on a lot of the middle layer”

Q You mentioned Box there. Box obviously IPOs. A liquidity event is, is always, you know, welcomed by LPs and investors. How do you think about when's the right time to sell? It's the age old thing. You look at all of, you know, Bessemer's memos, you always underestimate the size of your winners. How do you think about when to sell?

A I wish there was a bit more selling happening right now, uh, or opportunities to sell. Uh, as you know, the M&A markets have been pretty, pretty slow, uh, recently. So, uh, but to answer your question, Harry, there's a, Local maxima that I think about sometimes with companies where this is sort of the local maximum in terms of perceived value by the market for a company, and that's a great time to sell, and so now you have to figure out when is the local maximum for a company, and I would say it's like, it's sort of, you know, the markets are riding high, but it's also like where people believe that this company is the leader, but there's questions around whether there's a standalone company to be built, and it's way better off being acquired by a strategic who can do Even better things with the company. Uh, but that is, again, doesn't happen as much anymore.

AI assessment note: “there's a, Local maxima that I think about sometimes with companies”

Q Absolutely, they do, but for those that missed our first episode, let's kick off, Mamoun, with how did you make your foray into what I definitely call the wonderful world of venture, and how did you come to be GP at Kleiner today?

A Yeah, so I found my way to Silicon Valley in 1997 as a nineteen-year-old Kid. We just graduated college as an engineer, and I'd taken my first job at a company called Xilinx, working as a FPGA designer, and I looked around my office or my cubicle, and I had a Sun workstation, and I was using this thing called Netscape for the browser, and fairly early on, got exposed to all these products that were backed by, well, founded by great technical founders, but really had these great venture capitalists behind them, and I really took an interest in what These folks called venture capitalists did in this tech ecosystem. So, and actually, as a sidebar, my company's iLinks, Sun, my, the workstation I had in front of me, and the Netscape browser were all companies that were series of investments by Kleiner Perkins, which is how Kleiner Perkins showed up on my radar as a, as a kid. And so, really, it was about how do these technical founders, engineers, really, take venture capital money and create these really amazing companies. And so, that was my First foray into knowing about venture capital, and over the course of the years, in the first dot-com bubble, and then the bust, you saw all these companies, high-flying companies, and again, the commonality was venture capital, and it's, I just took a liking towards this new business creation that were technology companies, so at some point,…

AI assessment note: “I found my way to Silicon Valley in 1997 as a nineteen-year-old Kid.”

Q Yeah, no, I do get you. The final element before I give you a break and take off my LP hat, this has been so unfair of me, but Six hundred million is fantastic, but quite a large early stage fund with a good amount of reserve bait in, I'm sure. How do you think about optimizing reserve allocation with the new fund, and what's the thinking around that?

A Yeah, reserves is, um, somewhat of a art and a science in itself, uh, so for those of the folks who haven't heard the term reserves, but as venture capitalists, we invest in a company, but we reserve capital to invest in follow-on rounds of these companies, so typically our First check may be 50% of the overall amount of dollars that we allocate to an investment in a company over a period of time, and the reserves typically varies based on the capital intensity of a project, and so if it's a hardware business, you typically reserve more, so maybe the first check is ten million dollars, and you reserve another fifteen million over the course of time for that company, because it's more capital intensive, and you want to protect your ownership, whereas in a software company, you reserve less because you think you'll get the profitability sooner, and you have to Need to provide less capital to that company. Usually not the case, but what does happen is that some of these software companies, if they take off, you can, you know, limit the check you write in later stage rounds, because that's not what our business is. Our business is investing these companies early stage. So we end up reserving less. But yeah, in that six hundred million, there is a substantial amount that is reserved for following rounds. But I would say more than half of the dollars are allocated to the first check …

AI assessment note: “more than half of the dollars are allocated to the first check that we write.”

Q times to Establish great, huge companies. We had Matt Oko from Data Collective on the show recently, and he said that fund cycles were inherently inefficient in the way that they're too short to generate those large, uh, established companies in those sectors. Do you, what are your thoughts on current venture fund life cycles? Do you think they are potentially too short at the 10 to maximum 12 years?

A Yeah, so, it's a great point. There are 10 to 12 years, typically. The average, uh, Successful company takes about eight years. Massive successful company have taken sort of eight years to get to that point of exit or liquidity or IPO. And in many cases, you don't even want to distribute in those eight years because think about how much you would have made if you would have stayed in, in, in Amazon or Facebook or Google over the course of time, uh, it probably would have been much better, uh, for those VC funds that invested to stick around with those great companies and Over the next two decades, then to invest in the, the N plus one startup. So fund cycles are short and they are limiting in, in what the investors, the VCs end up doing. And we have sort of an approach, which is we have a typical fund cycle, but we invest in these long gestation cycle businesses that are in healthcare and in financial services that take much, much longer. And the, the sales cycles are longer. The adoption cycles are longer. The feedback cycles are way longer, as you pointed out. How do we make it work in the The fund cycles that we do have, and so what we have a very deliberate approach of sort of half of our investments are in those longer gestation cycle businesses, and the other half are in more predictable enterprise software and consumer businesses, where it's, it's more apparent much fast…

AI assessment note: “fund cycles are short and they are limiting in, in what the investors, the VCs end up doing”

Q What has been your best performing investment on a pure multiples basis?

A Probably Slack, I would say, you know, even, um, at the 250 post, uh, that's, you know, with all the dilution over time and, If you take the twenty-seven billion or some other number, um, that's a, obviously, a good, great multiple. Um, Figma, you know, the initial investment was done at about a hundred post. Um, multiple-wise, that's, um, you know, Rippling was also in the sort of 250 post when we did it, and, um, you know, currently valued. There's one investment I remember doing, uh, early days of USVP, Where we did it at, I think, TenPost. And, uh, it was a three million dollar check for like 30% of the company. And that company, about a year and a half ago, the founder CEO, Steve Flagg, still a friend, amazing guy, uh, they sold it to Siemens for seven hundred million dollars. And that ended up being, you know, that's, uh, seven, 70 x. Um, crazy multiple, right? Some ridiculous multiple. Uh, but you know, When you hold on to something for 15 years, uh, guess what the IRR on that investment was? If you took, if you still owned, let's say, 25% of that company at seven hundred million, so like a hundred and something, a hundred 70,000,003 million becomes a hundred seventy million, or something like that, or.

AI assessment note: “Probably Slack, I would say... sold it to Siemens for seven hundred million dollars.”

Q What have been your biggest lessons? I suck at doing reserves. I think it's a very hard thing to get good at. Please give me your wisdom. What have been your biggest lessons in how to do reserves management and concentration of capital well?

A Yeah, so reserves is one. It's like when you have a, uh, an early stage fund, and we typically invest In about 35 companies per fund. Uh, and so, so do you, how much do you reserve for each one of those investments? Uh, it, typically we, we try to invest about, and we've looked at the math, uh, more than half in that first check. So let's say you're doing, over the life of the company, you're investing twenty-five million dollars in a, in that early stage company, let's, but you're starting out with a fifteen million dollar check, and then you're reserving another 10 for The series B and beyond. And I would say it's, it's generally worked out pretty well. Like, you know, we're now.

AI assessment note: “we try to invest about, and we've looked at the math, uh, more than half in that first check”

Q What has been your best performing investment on a pure multiples basis?

A Probably Slack, I would say, you know, even, um, at the 250 post, uh, that's, you know, with all the dilution over time and, If you take the twenty-seven billion or some other number, um, that's a, obviously, a good, great multiple. Um, Figma, you know, the initial investment was done at about a hundred post. Um, multiple-wise, that's, um, you know, Rippling was also in the sort of 250 post when we did it, and, um, you know, currently valued. There's one investment I remember doing, uh, early days of USVP, Where we did it at, I think, TenPost. And, uh, it was a three million dollar check for like 30% of the company. And that company, about a year and a half ago, the founder CEO, Steve Flagg, still a friend, amazing guy, uh, they sold it to Siemens for seven hundred million dollars. And that ended up being, you know, that's, uh, seven, 70 x. Um, crazy multiple, right? Some ridiculous multiple. Uh, but you know, When you hold on to something for 15 years, uh, guess what the IRR on that investment was? If you took, if you still owned, let's say, 25% of that company at seven hundred million, so like a hundred and something, a hundred 70,000,003 million becomes a hundred seventy million, or something like that, or.

AI assessment note: “sold it to Siemens for seven hundred million dollars. And that ended up being, you know, 70 x”

Q layer? When you look at the price dumping that's occurring right now, it relatively, and the commoditization that we're seeing occurring, you know, you get people like Sarah Taville, who we love, is like the fastest depreciating asset in history. You know, every week is like, Anthropics better than OpenAI. Now, yeah, OpenAI is better than Anthropic, and bluntly, the price dumps are real. Is it actually a good business?

A The beautiful thing about just, uh, uh, Um, GPU's getting better, and, uh, the infrastructure just layer just being more performant. Model's getting better. Sure, they're getting, the models are getting bigger, too, at the same time. So there's, let's maybe, like, draw the difference between there's all the folks who are providing, there's GPUs, there's the people providing data centers, there's people who've now built LLMs on top of, uh, all this compute infrastructure that's there. Uh, so what's a, clearly, NVIDIA's a great business. So, hyperscalers are investing today for the future, and I think ultimately the margins, just like if you look at 20 years later of AWS, how great of a business that is, that standalone basis would be a top four enterprise software company, right? Same with Google Cloud. So, that's a great business over time, and then you look at the LLMs, so if you're just providing Tokens, or if you're selling tokens, is that a great business? Uh, and right now, uh, given the public profile of OpenAI and financials that we've all seen, today it's, it's not a great business, but, you know, I think they're smart enough to figure out how they can get to a gross margin that will allow them to be a highly profitable business over time.

AI assessment note: “today it's, it's not a great business, but, you know, I think they're smart”

Q Now, talk to me, uh, I want to hear about your way into venture. We, we see Mamoon now at Social, but how did you come into the venture industry, and how did you come to co-found Social?

A It's a kind of a long story, actually, so, um, but I'll start with, I, I moved to Silicon Valley in 1997 to join a semiconductor company called Xilinx, uh, as an engineer, uh, Right after college, and this is right in the middle of the first dot-com wave. I had no idea what VCs were or what they did, so I was totally green to what made Silicon Valley Silicon Valley, but all I knew was how to design chips. So, uh, but just like most companies at that time, my company was involved in selling to a lot of these dot-com, um, double companies, and, uh, actually our chips were used in a lot of the networking gear that was, uh, driving the first internet wave. Then I'd come across this company that I was helping out, With, uh, some chip designs, and they'd, next day got bought for six billion dollars with Cisco, and I was, didn't make a lot of sense to me, but this is all happening around the time of when I got started here in Silicon Valley, and like a lot of companies at that time, actually, uh, my company, Xilinx, started a corporate venture fund, and, uh, somehow, actually, at one point, I was asked to help evaluate a company in an area that I knew something about, and, um, I did that for those guys, and, uh, I kind of liked it, actually, and I, and I sort of set out to Explore what this VC thing or investing thing was about. Uh, I always liked stocks, but I never really had a chan…

AI assessment note: “Xilinx started a corporate venture fund, and, uh, somehow, actually, at one point, I was asked to help evaluate”

Q No, I'm intrigued because you said about the explosive growth in the last 18 months of the team. Is that a problem in terms of maintaining the same internal VC culture then that you had when you were three, four, five man social capital?

A Yeah, certainly, and I think we've put in place a lot of mechanisms that allow us to see each other often enough, uh, have ways to give feedback to each other, slack bots to give, uh, run sort of Weekly pulse reports of how things are going, how people have any feedback, uh, anybody in the, in all of social capital has the ability to give, uh, anonymous feedback around things they'd like to see change, uh, what they're happy about, what they're not happy about. And so, uh, we kind of dog food all of our software, whether it's like Slack or bots that we've created to other companies that we're evaluating. We actually use a lot of that software internally to To, to make social capital more transparent, open, collaborative, a well-run organization. Um, I think there's always room to improve, and it's not for a lack of trying, at least. Yeah, and it's great. You know, we have people who are super talented, who are, who bring original thought to how we could do better. So, uh, as a partnership, I think we are a dozen partners, and those partners are not just on the investment team. You know, we have a, uh, a growth in data science team, which is, You know, run by my partner, Ray, who came from Facebook, and, uh, on that team, you have Jonathan and Pink Cash, who are from Facebook and Instagram, and, and Andy was from Zynga. So people who came from companies where they had substantia…

AI assessment note: “Yeah, certainly, and I think we've put in place a lot of mechanisms”

Q How do the best CEOs run a board?

A They start off with a high level overview of how the company's doing, and then they give a chance for their leaders. They're very capable leaders to go dive in deep and, uh, share and be Ask questions, and there's a fair bit of cheerleading, but a fair bit of, like, asking the hard questions. Uh, and I like board meetings where there's one or two things that are talked about in detail. Uh, meaning, like, you go deep dive into one or two things, because at any given point in time in a company's juncture, that moment in time, one or two things that really matter that can, we can help change a trajectory on. And so, if we're talking about seven different things that matter, we're probably missing the point. And so I, I love board meetings where that's sort of the structure.

AI assessment note: “They start off with a high level overview of how the company's doing”