00:00:02
Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.

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Speaker 2: This week on the podcast Another Banger. Mamun Hmid is partner at Clina Perkins, where he's been focusing on early stage AI investments for nine years. He's got a fascinating background early investor in Slack, Figma, Glean, box, et cetera. Previously, he co founded Social Capital with Chamat and worked for a number of other venture firms, including US Venture Partners. I thought this conversation was fascinating and I think you will also with no further ado my conversation with Klina Perkins. Mahmun Kamid Mamun Jamid, Welcome to Bloomberg.

00:00:58
Speaker 1: Thank you so much for having me.

00:00:59
Speaker 2: Barry. So, I'm fascinated by your background. You grow up in Frankfurt, Germany. You come to the US to go to college at Purdue bachelors and Electrical and Computer Engineering Masters at Stanford, a MBA from Harvard. What was the original career plan?

00:01:18
Speaker 1: So let's go back to I think nineteen eighty six. Do you remember the Challenger explosion? Sure, every kid growing up remember that. And one of my teachers was actually supposed to go on the Space Shuttle because there's a teacher.

00:01:34
Speaker 2: That's right, College Christy, that's right.

00:01:36
Speaker 1: Yeah, and every kid got fascinated by especially if you had a teacher going to space. So followed the whole journey of the Challenger space shuttle and the teachers and all that. But with that also came this desire to learn more about space, and I instantly wanted to become an astronaut. Naturally. Think I was seven or eight years old, and as I thought about, you know, high school, liking science and math, thinking about where to go to college. And I was, as you mentioned, I was in Frankfurt, Germany growing up, and one of my uncles he'd given me this list of colleges, the top ten engineering schools, and I just applied to all ten, and one of them happened to Purdue, where actually, to this day, the most number of astronauts have graduated from.

00:02:30
Speaker 2: Really, oh, that's fascinating.

00:02:31
Speaker 1: Yeah, So my path was aeronautical engineering and trying to figure out a way to get into space. I've yet to do that, but that is what led me down the path of applying to Purdue in the first place, and the association with Space and NASA and then actually going from something so massive and big space to something so small chips and semiconductors and transistors.

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Speaker 2: Which you're enabling space. So there's definitely a connection. Is it true that when you went to business school you were thinking about already being a venture capitalist?

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Speaker 1: I actually when I applied to business school, so i'd worked for a good six years after undergrad so studied electrical engineering computer engineering, and that naturally made me think about a career in Silicon Valley designing chips, which is what I did for the first six years of my career working in the semiconductor industry. But what really got to pretty interesting for me was the notion of startups and founding companies and how these so called venture capitalists were behind some of the most iconic companies that I was coming across, And so I actually wanted to get into venture capital. And that's actually why I applied the business school, and specifically only applied to one business school, Harvard, because I naively thought that if you wanted to get into venture capital, you had to go to Harvard or Stanford, and I had already gone to Stanford for grad school, so okay, well, it'd be nice to get a change of scenery just round it out and moved to Boston.

00:03:58
Speaker 2: Yeah, give up the nice weather. Yeah.

00:04:01
Speaker 1: And so you.

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Speaker 2: Started in between college and grad school, you spent how many years you were at silence?

00:04:09
Speaker 1: I was at six years. Yeah. So the story actually goes is I was nineteen when I graduated from college, so from Purdue, and I thought, okay, the best thing for a young kid is to continue to go to grad school. So applied to grad school and ended up getting in at Stanford. But I also got a number of job offers. This is nineteen ninety seven, the dot com boom. I think it's a bit like this time where should you opt out of the job market and get like extremely valuable experience or continue on with grad school. So I did the best of both worlds, which is like I went to Stanford, took a few classes every quarter, and then worked full time at Xilenx. And this is back in nineteen ninety seven. We're talking about sort of the middle of the dock com boom.

00:04:58
Speaker 2: Yeah. Really really interesting. You're known today as someone who thinks about software, generally in enterprise software in particular. That seems like an unusual transition from semiconductors. What led to that shift? What changed your thinking.

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Speaker 1: Great question, Barry. So in two thousand and five, when I got into venture capital, my full intent was I would like to learn how to invest in great semiconductor companies or founders who build semiconductor companies of the future. And it turns out after the dot com bust, there was not a lot of investment in infrastructure, so data centers and networking and switching and semiconductors broadly, and so I realized pretty early on that, hey, like, if I want to build a career in investing, you kind of have to go where the puck is going to, and so skate to where the puck is going, and I sort of skated towards web two point zero and software because I I felt like Also in my own firm at US Venture Partners, where I started my venture capital career, where I was an associate, I was hired to go help the partners there evaluate semi conductor opportunities. And that's actually why I went there, because there were some legendary semi conductor investors who happened to be there, and some of mentors even today were the folks running the firm there. So but I realized that all my friends in two thousand and five were moving to web two point zero internet. This is the beginning of Facebook, which happened to get be started at Harvard when I was there. So you were seeing all these the people my cohort's age group moving into the software and web, and I felt like I had to sort of move along with that. And my day job was evaluating semi conductor businesses. But in the evenings I was in San Francisco, I was going to you know, the web two point zero parties and meeting all the founders starting software businesses, and so I slowly started to just you know, as a side project. The side project became the main project, which was to move from semis to UH to software and internet stuff. And but in the back of my mind, I always remained a semi conductor guy. And UH, you know, semis are back now, as you know.

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Speaker 2: And AI seems to be the application of both semis and software, so you're well prepared. We'll talk about AI in a bit. I want to stay in the two thousands. When you were at us VP, you would early exposure to companies like Box and Yamer. I don't really remember Yam or I remember Box, Big Enterprise software deals. What did you learn from that experience? What have you brought forward with you from from that era?

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Speaker 1: Yeah, sou Box happened to be my first investment at USCP, where I joined the board. It was an early stage company, was, you know, a few hundred k revenue. Two very young founders Dylan and and Aaron twenty and twenty one years old, dropped out of college, sort of like the protypical founder, the archetype of a young founder, and they were going after storing your files in the cloud and sharing them inside your company.

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Speaker 2: Let me stop you for a second, because I think anybody under forty is perplexed by what you just said. I recall like late nineties, early two thousands. If I'm either at home or at work, or on a laptop or at the beach house, whatever I needed was always somewhere else. And the beauty of early blogging software is I could upload files, I could upload charts, I could upload images, and that was the closest thing to the cloud. It just didn't exist then. If you wanted to have something that you can access anywhere you had an Internet connection, literally did not exist.

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Speaker 1: Yeah, So maybe I'll if I go back to exactly. The point I made in my head is that if there is one application that moves into the cloud first, it's going to be file sharing. If you recall, I remember this when I was on my Windows computer in the eighties and nineties. What's one of the applications we all used a lot. Do you remember the Windows File Explorer. We're constantly clicking in and trying to find.

00:09:22
Speaker 2: The file in or something or searching for a.

00:09:24
Speaker 1: Searching for it or placing it in a folder. Very nicely.

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Speaker 2: Like the name you used to have to put on a file was important because if you couldn't remember the name, you could find it. It wasn't like, here's a phrase that's somewhere in this document, go find it if you didn't remember exactly where that was nested, what name you put? Good luck, good life?

00:09:44
Speaker 1: Right? And so as the world moved from the desktop to the browser, and so by two thousand and six seven, we all are using at this point like Firefox, Mozilla, Chromes not even existent yet.

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Speaker 2: It was Internet Explorer until.

00:09:58
Speaker 1: Chrome came along, exactly right, And so my thesis was, Okay, one of the most one of the applications for business that will move into the browser. So software as a service will be file sharing and collaboration. And so because precisely to your point, like the file that you always needed was somewhere else, and this made so much sense to me. And so at the time in two thousand and seven when I invested in Box, there were probably half a dozen two dozen, I don't know, there's many of these companies doing file sharing, but it was mostly for consumers, mostly for like.

00:10:31
Speaker 2: Your drop box.

00:10:33
Speaker 1: Dropbox was in that same era, but there was like x file in Elephant Drive. I mean, I remember these as an associate yours. You know, when you're suggesting an investment, you're going to do a lot of diligence. I remember, like the Laundry list of companies I looked at, there are probably like forty companies that were doing something similar, but most of them were dedicated towards sort of the consumer use cases photos, music, stuff like that.

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Speaker 2: NAP's the era was right around.

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Speaker 1: Then, exactly exactly, and so hypothesis was, hey, like this stuff will be relevant to large companies who will want to have file sharing and collaboration for their companies. And Box actually pivoted from being consumer company to being an enterprise company, And that's when I got pretty excited because it lined up with sort of this view that I had that large companies will move from file file servers in their data centers or wherever their and their buildings into files that now reside in the cloud.

00:11:29
Speaker 2: And they're willing to pay for it. They're willing to pay for it, unlike back then anyway, consumers were so reluctant exact to pay for anything. So it's interesting because you've had a lot of early investment success with a variety companies. Is it easy or difficult to learn from past winners? Is every startup different or do you start a little pattern recognition that gives you some clues, Hey, these guys are onto something.

00:11:58
Speaker 1: Yeah. I think there's definitely some compounding of learning from early wins and losses. You brought up Box, and so Box first investment, got to spend a lot of time with the founders, got to learn the business with the founders actually because they were young. I was young. I was in my twenties when I joined the board, and from that experience I learned a lot about what it meant to actually bottoms up sell software into large companies, which led me actually to this investment in Yammer, which many folks may not remember, but it was a enterprise social network circa twenty ten, kind of like Twitter meets Facebook, but for your company. And Microsoft ended up acquiring it in twenty twelve, and it's part of the Microsoft Suite now and part of the teams and all that. But the experience of this bottoms up adoption. You looked at Yammer, and a lot of large companies wanted to have a enterprise social network or somewhere where kind of like a town hall, a messaging platform. You share a file, people comment on it like people do it on Twitter or Facebook. And that was taking some of the learnings from the web era and the social era and applying it to the business world. And so at Yammer, I learned a lot. It was a quick journey, but nevertheless, like there was a level of engagement and monetization was good. And a few years later, maybe even like a year later, I came across another company Slack Slack, and it was similar. Now it was true messaging. Actually, on my way over here, I was walking through and I saw a bunch of colleagues on Slack, which makes me really happy. And Slack is used broadly across the globe to this day in twenty twenty six. But the lessons learned in that twenty ten to eleven era led to the investment in Slack two thousand and fourteen for me when it was a ten person company. And so back to the point around like compounding of learning Box led to Yama, Yamer led to Slack, and since Slack there's been others, but there certainly is some pattern recognition around products that are working.

00:14:18
Speaker 2: And then in twenty eleven you co found social capital with Chamath, very famous model of social capital, which is how can we address many of the social ills that are are hurting the country through the intelligent use of startups, technology, et cetera. Was this an reinvention of venture capital or just a new set of tools within a partnership that you know, it was kind of novel for its z era.

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Speaker 1: It was novel for its era because we decided that we would go after education, healthcare, and finance, you know, three of the largest parts of society, and how do we address inequities in those areas with our investments and believing that technology has the ability to democratize access to healthcare, education, and financial services, and that's you know, largely played out over the last fifteen years. But we just thought that there will be a ton of opportunity. As venture capitalists, we're seeking out opportunity, and we thought that going after large pockets of GDP, you'd identify really exciting opportunities. Turns out, you know, my interests remained in enterprise software, and I did spend a lot of my time in enterprise software even when we started Social Capital.

00:15:46
Speaker 2: So twenty seventeen, you leave Social Capital for Cline Perkins, a firm that has long been iconic. The laundry list of companies Kliner has backed Google, Cisco, were they early in app also, I.

00:16:00
Speaker 1: Think they were Google, Amazon, Sun Microsystems, Genentech into it unbelievableandom if you remember, you know, the list of greats is amazing.

00:16:13
Speaker 2: So they're an iconic company, but they're not the dominant force they once were when you joined. What made that opportunity so attractive?

00:16:22
Speaker 1: Yeah, So look, I actually long admired Kliner Perkins had an extreme reverence. I would call it for Kliner Perkins. Going back to my days moving to Silicon Valley, I mentioned I moved to Silicon Valley in nineteen ninety seven, I worked for this company's Silenx, And if you think about my first few weeks on the job, I'm in my cubicle. I've got a Sun Microsystems workstation, which is actually a dream because in college we had to share twenty of them amongst like two thousand of us. And now I have my own son Spark I think was a Spark twenty. And guess what, There's a Netscape brows and I'm buying books for grad school on Amazon. And by the way, that there's a couple of guys down the hallway at Stanford who are starting this company called Google, and I'm starting to use that search engine called Google. The one commonality amongst Xilenk's son, Netscape, Google, and Amazon is that Cline Perkins had led the Series A, so the first institutional investor for all five of those companies. So as a young guy, I had this developed this extreme reverence for Clinet Perkins because of the investments they had made in these really like history making companies and so, which is also what led me to think about venture capital as a career as a young engineer. Is like, you know, I want to be like those guys, those guys that are investing all the coolest companies that I'm using as a nineteen year old here, and that's really what got me excited about venture capital. And actually one of the people behind all many of those investments son Netscape, Google, Amazon was my partner, John Dor. And so for me, there was this extreme reverence for John Dor and his career and trying to emulate that, you know, and he was an electrical engineer from Rice, went to Harvard Business School, worked at Intel Corporation, then came to Cline Perkins out of business school, and so it had a deep meaning to me. And actually, truth be told, I actually in my business school essay, which I have still wrote that I wanted to go work at Clina Perkins. This is in two thousand and three when I wrote the essay. Two thousand and two, I wrote the essay. And then you know, when I tried to apply for a job in two thousand and five coming out of business school, I didn't get very far, but I did end up there in twenty seventeen.

00:18:45
Speaker 2: So you know, eventually, if you keep plugging away, you get to where you want to go. That's great coming up, we continue our conversation with mamu Inhammad clinap Perkins managing member, talking about the reboot of the firm. I'm Barry Ridults. You're listening to Masters in Business on Bloomberg Radio. I'm Barry Ridults. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Mamunhamid. He is managing member and general partner at Kleiner Perkins, where he is pivoting the firm towards early investors in software and artificial intelligence and automation. So you co led the refounding of Kleiner Perkins in twenty seventeen twenty eighteen, the firm was refocused on early stage Series A investing. Tell us what was behind the thought process? What made you say we don't want to be bigger, we want to be smaller and more focused.

00:19:49
Speaker 1: Yeah. If I look back at the decades of being probably the most successful venture capital firm throughout the seventies, eighties, nineties, and even the early two thousands, the one thing that defined Kleiner Perkins was it was a small partnership of seven ish or so partners who sat around a table in Menlo Park, meeting companies and having healthy discourse and debate about what companies to invest in and what the future of technology would bring to the world. And it was defined really by a small group of partners that were in many cases technical, they were operators, They had a passion for technology and its impact on humanity, and that was sort of what I kept coming back to, is that that was what defined Kleiner's decades of success. And we went back to the future in twenty seventeen and eighteen to that model, and today our partnership is six partners and then we have three more investment professionals, so we're a very small, nimble team. So we have two funds, and this team invests from both pools of capital.

00:21:11
Speaker 2: So early stage does that seed round or is it a little more developed?

00:21:17
Speaker 1: Yeah, So the early stage fund is Seed Series A mostly and maybe some bees, and then the Growth fund is b c's all the way to. We invested in the last anthropic round at a nine hundred billion our evaluation, which is rare for special companies, but it has the ability to invest across even to the pre IPO.

00:21:41
Speaker 2: And is it a coincidence that the Growth fund is two and a half times the size of the seed funds. At that point, these companies are bigger, require a bigger check or is that just happenstance.

00:21:54
Speaker 1: I think our funds are sized based on the opportunity set in front of us. Our early stage funds have been almost exactly thirty five companies for the last fifteen years. So thirty five companies per fund, which we think about is the right number of shots on goal for an early stage fund to return multiples on it.

00:22:17
Speaker 2: Twenty five to thirty million.

00:22:19
Speaker 1: Per exactly exactly. So it starts out with maybe in some cases a five million dollar check, and the subsequent checks is another fifteen to twenty, or it could be first check is thirty and then with Parada, you're investing let's say, up to forty million dollars per in a company, and then your growth fund is doubling down investing a lot more in those companies.

00:22:39
Speaker 2: Huh really really kind of interesting. So the focus is artificial intelligence startups across software, healthcare, transportation, and autonomy industries. So let's unpack that because I'm hearing a little overlap with each of your prior venture experiences. Tell us why those four areas are so attractive.

00:23:04
Speaker 1: Yeah, So this is a truly once in a lifetime revolution that we're going through with AI, and the number of exciting companies people that we're seeing right now as at an all time high. The whole world in some ways is being refactored with AI, and this is just a very beginning, and so I would say that all parts of the economy, even beyond those four areas, it is like I mentioned earlier, healthcare, financial services, it is all sorts of knowledge work, it is going to be all sorts of physical automation in terms of robotics, We're even space, even defense areas, drug discovery, materials discovery. I think it is all fair game at this point in terms of where the exciting pockets of innovation are, just because there has never been a tailwind like this that really allows all parts of the world to be refactored based on, you know, the biggest technological revolution ever.

00:24:30
Speaker 2: So I'm glad you described it that way because I keep hearing people compare AI to the Internet, and that seems to contained too timid. I wonder if you agree with the thought that the only thing remotely comparable to this is the Industrial Revolution, which centuries later, we're still dealing with the impact.

00:24:54
Speaker 1: Of I absolutely agree with you Berry. It is like the industrial revolution, it's like the railroads, it's like the printing US. Is it is that it's not the Internet.

00:25:04
Speaker 2: That's really interesting because when I think Internet, the first thing you think of is, oh, this is a bubble when this is going to collapse? But you mentioned your an investor in anthropic These are forget not profitable companies. These are companies with giant revenue streams already they're barely a few years old. How big can this sector get? Is this going to take over every corner of the economy.

00:25:31
Speaker 1: Yeah, let's talk about that. I think that's the I think that's the real conversation. And the more the very exciting conversation that one can have about this topic is and I start at a very high level, which is the GDP of the world today. It's it's about one hundred and twenty trillion dollars, and about half of that is labor, the labor component of it, so roughly sixty trillion dollars. And of that sixty trillion, roughly sixty percent or so is white collar mid fifty sixty somewhere so that's anywhere from thirty thirty five trillion dollars. And if you look at tokens and what the frontier model companies provide is it is units of labor. We're already seeing how those units of labor are being utilized in computer science, so software development, in law, in medicine, in drug discovery. These are little agents and buddies that we as humans have now to help us do more with our intellect. And the way I see it is that you know, we're talking about trillions of dollars that are opening up for these companies that exist. And exhibit A is a company like Anthropic, which is publicly stated has gone from zero to forty five billion revenue run rate in a matter of less than three years, okay, and that is likely going to double. And you know, and the company started this year, I believe at twenty is already doubled more than doubled in the short year that we've been in so far. And so these numbers are astounding not only because these companies are selling software or technology, they're selling units of labor. And the labor markets, as we all know, are the biggest component. As I just mentioned earlier, of the world's GDP. We're talking about trillions of opportunity dollars of opportunity. And that's what excites us so much about this time, is that it's not just about selling tools and software that we've been accustomed to selling to IT departments. It's selling like actual labor to companies, to corporations, to you know, even to consumers who are using AI in their personal lives.

00:28:01
Speaker 2: So let's let's talk a little bit about that. The fear I keep hearing is everybody's going to lose their job. It's a very Malthusian argument that this technology is going to replace labor the way the steam engine did. I'm getting a sense from the data and from other analysts like Torsten Slock that this isn't a replacement for white collar labor. It's an enhancement, or at least that that's the argument. Give us, give us your perspective on that.

00:28:33
Speaker 1: I actually fully agree with that point of view that you have. Uh it is it's it's like you know, uh getting email. I mean I think, uh, the fact that we got the computer, didn't you know, the people using the typewriters started using computer and started doing other types of jobs. Even in the steam engine era or the Industrial Revolution, we found ways to repurpose jobs or people and their skills. I don't think humans are going out of style. I don't think you know, the world is going to be largely unemployed and on UBI because we are going to displace all this work and all these people with AI. I think it's it's the extreme where the mind goes to. But that's just not the reality. And that sort of bears itself in the numbers that we see, you know, in record low unemployment rates, and and actually we need more labor, more people than we ever have, and so.

00:29:30
Speaker 2: More skilled labor. We are seeing a decrease in job availability for kids right out of college for unskilled labor. If anything, is this likely to force more people to get more technical, to up their skill set.

00:29:49
Speaker 1: I actually to believe that it is not like a, oh, you know, you don't need to be a software developer and study CS anymore because you know these software jobs are going away. It is like now now the job of the software engineer is to manage a whole hosts to agents and make them do work for them and be the brains behind the operation. You think of it as like you have all these little agents and came little employees who are working on your behalf. And so yeah, that is like the higher level thinking and the things that actually you know, like when we go to school, we think about how to problem solve and you know, if we're solving a math problem it's hard, we think about many different ways to solve it. And the same way it's how am I going to use AI to help me solve problems? And that's the way I think we all And I would say, you know, we have four kids, you know, believe or not, I'm telling them going to math science like U and actually art like have like spectral diversity in your your learning because all the skills that mattered in the past solving you know, math problems with pen and paper, will really matter in the future.

00:30:55
Speaker 2: So let's bring this back to how you think about the opportunity the set that's out there. At Clina Perkins, you do structured reviews of every interesting deal that was passed on. I'm kind of fascinated by by that. I know a lot of vcs kind of hold the their misses as a badge of honor. Some some firms posted on their website, what do you think of what's driving the thought process around revisiting either missed deals or mistakes or what have you. What what does the process teach you?

00:31:35
Speaker 1: Yeah. So, actually, one of the things that I did when I got to Cline Perkins in twenty seventeen was that we would look every week at the that week's series as that got done by our peer firms about thirty forty firms, and whether we had seen the company that was invested in or not, and as simple heuristic of like, are we seeing the things that matter because they seem to matter because our peer firms invested in those companies. And so we've been doing this now for the last nine years. And initially our goal was like we should see sixty percent, and seeing means you met the company, and for us that hovers around now seventy percent or so. You don't want it to be one hundred percent because then you're just that's the game you're playing. We just see everything, and you don't want to be twenty percent because you're not seeing enough. But we think seventy percent is a good number, and we look at the ones, Okay we saw if we saw seventy percent of the good stuff let's say, let's call it, or was it thirty percent that we didn't see even better? Or if we saw the seventy percent, do we pass on the good stuff and do the bad stuff? And so we go through that exercise quite frequently. We just had an off site a few weeks ago, and we go through again. We pour salt on the wounds and say, okay, we saw these companies and we passed. Why do you pass at that round? That early stage round? And just to remind ourselves why we need to adjust the way we do things, I'll give you an example. The Anthropic, which is an incredible company. Its Series A was not a traditional one. SBF from FTX led the Series A famously. We do know how that worked out. It was an amazing investment and very precient on his behalf. I forget how much it were worth today, but it would be worth a lot. But the Series B was a sort of non consensus, non obvious round, and we actually met with the founders and but we met them over zoom and we played with the product. But you don't get the same visceral feeling about a company and the founders and their ambitions and aspirations and what they're trying to do with with their with their with their company, and so what a miss right, And I think that round got done in that four billion dollar valuation or something, which not a small valuation but still compared to today, compared today, and so and we looked a lot of our passes that we were good companies, and many times we just didn't meet them in person. So the pandemic era bred some really bad habits of you know, you did as first meeting over zoom and so and I looked at my own investments and the last twenty investments that I've done, the first meeting was always in person, the first meeting. So I've driven my whole calendar to like, I just don't want to do any meetings on Zoom anymore. I want to meet people in person, and if it's worth a thirty minute Zoom, it should be worth a thirty minute in person meeting. And I'm glad we get to do this in person, because you know, it wouldn't be the same thing if I was on a Zoom screen doing this thing with you.

00:34:59
Speaker 2: That's exactly right. What about the reverse of that. If you're analyzing your missus, do you ever review your hits, your wins, and saying why did we get this right? What can we take forward from this?

00:35:14
Speaker 1: That's a great question. I think we just assume what we do look at, okay, is are the ones that we did the portfolio that we built, is it better than the portfolio that we missed? And it's actually a toss up.

00:35:31
Speaker 2: It's that you know, huh, that's really interesting.

00:35:34
Speaker 1: In the sense that we didn't even see it, and because it'd be bad if we didn't see companies, and that basket of companies was way better than the companies we saw. So and we've try to be very intellectually honest about like are we seeing the right stuff or the wrong stuff? And it turns out we're seeing it's a toss up.

00:35:56
Speaker 2: Actually, right. The reason I asked that question is you learn in the public markets, you learn more from your missus than your wins, because it's very hard to tell the difference between skill and luck in the public markets. I'm curious if the same sort of thing applies to venture I think it does.

00:36:16
Speaker 1: I think you you really beat yourself up on the missus and the ones that you do. You're just like, okay, check you know it happened and you don't. Uh, maybe you don't think about it as much as the ones you missed.

00:36:29
Speaker 2: Huh, really really interesting. Coming up, we continue our conversation with Mamunhahmed, partner at Kleiner Perkins, discussing the state of venture investing today. I'm Barry Ridults. You're listening to Masters in Business on Bloomberg Radio. I'm Bury rid Halts. You're listening to Masters in Business on Bloomberg Radio. Mamun Hmed is my extra special guest. He is a partner at Kleiner Perkins, where he is driving the firms focus on early stage investments in artificial intelligence and related technology. So let's talk a little bit about the state of venture investing today. When you meet a founder for the first time, preferably in person, what are you looking for? What are you trying to spot that isn't in the pitch deck they sent earlier? How do you separate intensity from delusion? Like? What are you trying to identify in that first meeting?

00:37:32
Speaker 1: Yeah, it is the most interesting time in my venture career, and for obvious reasons. This AI revolution, the tailwinds that AI brings to companies that are being started today is unlike anything we've seen before. So the quality of ideas is at an all time high, and I would say the quality of the people is also at an all time high. And those two forces combined suggests that there's a lot of really high quality ideas and founders that we're seeing. And so I would say it's the volume is because of that, at an all time high. And so to your question of like what do we what are we looking for? I think obviously everything today is AI enabled AI tailwinds, and venture capital isn't venture capital unless there's like a strong tailwind of something. And so this is a makes it such an exciting time because there's such a strong tailwind and the pace at which the tailwinds are like the winds are so strong, and in some ways because the frontier model, companies are providing better and better models, which allow companies that build on these models to provide strong value proposition. Take for example, a company like Harvey, which is AI for legal is in most of the am law one hundreds sells to large enterprises and Fortune one hundred type companies and becoming sort of the de facto way that legal law firms and legal departments are using AI inside of their companies and inside of law firms. And yes, they have a lot of secret sauce on top of what the model companies. But at the same time they get better and better as these models get better and better. And this is a category that just got created in the last few years, and there are numerous examples like it in every sector of society. And just to take a step back when we first saw I don't know what were your reaction was to chat GPT when you first saw it, but for me, it was the same reaction I had when I first got to use a browser Internet browser. To me, that Netscape moment was equivalent to the chat GPT moment. And we started to think about what are the second order effect what are the things that come from here, from chat GPT, from LMS, and it was we thought about like the labor pyramid, think about knowledge work and we went straight to okay, what is what are the most highly skilled, highly paid jobs in the in the country. And if you actually look at the top twenty list of jobs dollars made by job, it's some form of engineer, doctor, or lawyer. And so what we did at Kindel Perkins was to invest in companies that do AI for legal, AI for software development, AI for medicine, or and so we invested in companies like Harvey Windsurf, which got acquired by Google. We invested in a company called Ambience Open Evidence to go after that that labor pyramid, top of the pyramid, top of the pyramid, and back to your point, it's like an enhancement to those people. It was supposed to be like a co pilot, in which it is. It's and in something.

00:41:04
Speaker 2: Register trademark Microsoft Corporation.

00:41:06
Speaker 1: Yeah, yeah, exactly, copilot exactly, and in some cases it's become an autonomous agent for those people. So software developers run agents to new work for them now. And so we worked worked our way down the labor pyramid a bit, and we went down what's the next level? So think of it like if doctors, lawyers, engineers are in the two hundred k plus a year pay, then what's one below that, you know, like you've got financial analysts, salespeople, nurses and so then we're invested in the next class of companies, so Rogo, which is AI for finance, Hippocratic, which is an agentic nursing platform, Nooks, and revo which are helping salespeople PI copilots for their work. And so we've sort of worked our way down the labor pyramid. And if you think about the pyramid, guess what's at the bottom of the pyramid. It's physical labor. It is the lowest paid, low scale in some and eventually robotics will get there and hopefully do a lot of the backbreaking work that nobody should be doing, right, and I think we eventually that is sort of the way also goes along with the timeline of the way we're thinking about these things, is that you start with the highly scaled, highly paid, and eventually, over the next decade, we get down to the sort of the lowest scale, lowest paid work.

00:42:31
Speaker 2: So you mentioned the tailwind behind AI. What does that do to valuations? How do you underwrite startups in a market where even the half decent companies look kind of expensive?

00:42:46
Speaker 1: Great question. So when you have companies have gone from zero to trillion in value, you know, in the last three to four years, and when you're a founder, what do you do you point to that that's what I can be. I can get to trillion dollars because I'm going after really large problems and we love that that ambition. And what do people do then is that, well, you know, if the probability of a of a one trillion dollar outcome is one percent, and you do all the other ninety nine percent of probabilities for the rest of the numbers, you've got yourself to a expected value of ten billion dollars or more. At least that's the minimum, right, you do the one percent of ten billion, and you add up all the other probabilities, it's probably like thirty forty fifty billion. And so if you're looking at a series A company where the ambition is tremendous, you're the valuation may just be you know, you're raising one hundred million or a billion dollar valuation for two guys out the gate. And those are real examples. And I think we've we've got a bit of this, you know, pointing at the large outcome and if it's paired with smart people, ambitious idea, it has that kind of potential. But the reality is that there's usually one or two of those, the real outliers that are mag seven scale and just the you know, just gravity in itself doesn't allow for you know, the world can't have you know, one hundred trillion dollar companies.

00:44:29
Speaker 2: Plus if it's a trillion dollar total addressable market, it's going to attract a lot of competition, a lot of other startups, perhaps more than if you're focusing on a smaller niche. Although even the cloud storage was you mentioned forty companies early days, there's got to be tens of thousands of companies going for each of these segments in AI.

00:44:53
Speaker 1: Yeah, and there are. And I think the hard part of the work that we have is is identifying what we think with the leading company because in technology, generally speaking, the leading company gets most of the market cap, which which is if you look at the mag seven winner take all, winner take most, right, right, like take ninety percent of the market you know in Vidia of all GPU spend. You know, take Google alphabet you know, multiple businesses, or take a Tesla, take a meta own social right, so winner takes most in technology.

00:45:30
Speaker 2: Huh really interesting. You've described the AI moment as one of the most important company building opportunities in our lifetime. What's the risk of venture funding too many of these startups or is this just a fat head of winners and a long tail of Well, we gave it a shot. Is this just the nature of this business where it's a couple of winners are driving all the returns for for your funds.

00:46:00
Speaker 1: Yeah, venture is a power law business. There's no question about it. And if you already look at like there's this article I read the other day where ninety percent the AI revenue is in the hands of two companies. That's crazy opening and anthropic.

00:46:16
Speaker 2: Right.

00:46:16
Speaker 1: So in the remaining ten percent is in a bunch of companies that are excellent companies. But the scale of those two other companies is so massive, right, that it dwarfs all the other good, great work that's happening in tons of other companies that we've backed. And by the way, like you know, those will be great outcomes and be great companies. There's no question about it. It's just that it dwarfs and the power law really does. You can see it when you look at the numbers.

00:46:43
Speaker 2: And.

00:46:45
Speaker 1: It's yes, our job is to be in the companies that make history and are in the power are following the power law. What we find is that they're actually you would think there's dozens of companies, there's actually tens, maybe like less than ten, and there's like two to three that are converging to become winners in a category. And I think everyone's trying to identify those two to three companies and be in those two to three companies. And so one of the challenges we face is that we try to invest in what we think is the winner in the category early. Sometimes you don't know early that this is the winner, and if you invest too early, you conflict yourself out of the eventual winner.

00:47:27
Speaker 2: Right, you only invest in one company per silo, so to speak. Yes, right, So just to avoid those sort of conflicts.

00:47:35
Speaker 1: So the conflict typically we're joining the boards of these companies and if you have, you know, confidential board level information, and you don't want that, Like now you're invest in a competitor, all of a sudden, there's a chance of conflict, conflict of interest, and so we definitely try to avoid that conflict of interest.

00:47:51
Speaker 2: So I'm kind of fascinated by as venture investors. You obviously see the promise of AI A care so ald these different economic sectors. I'm curious how you're using AI internally, Clina Perkins are using it to source deals or do due diligence or predict specific outcomes. How does AI fit into your operations.

00:48:17
Speaker 1: Yeah, we have definitely been maxing out on AI internally, not only because we invest in these companies whether we use. Gleen is a company we incubated inside of Clina Perkins actually and it sits in year seven now is pre AI company started by an incredible engineer, Arvin Jane. And that's like our knowledge management. Every single piece of knowledge inside of Clina Perkins resides in Glean and you can go to it, query it, chat chat with it. Like if I want to find out your phone number and email, like and then I've never met you before, but I know someone at Kleiner probably knows you. I'll go to Glean. Or if I want to ask about like an HR policy, I'll go to Lean quickly asked. Because it just knows everything about Cliine Perkins. It knows investment memos, it can it knows cap tables, it knows like the really like confidential stuff. It's permission in a way where the people who are supposed to know will can know and it's very That is part of the magic is that it's very safe, secure. You trust it to really know the things they're supposed to know and not know the supposed to things is not supposed to know so and so that's an example. But we are. We get so much information, and whether it's board decks and their long board memos, financials and uh so when I'm going to board meeting after this, I got the board memo, and the first thing I do, it's it's sent to an email ALIAS and it and that runs it through AI and it produces a summary for me of the of the board meeting and questions I should be thinking about as an instant thing that I do once I get the board materials, just so that I start thinking about it before I actually go read the board memo. I sort of have a preview of it in my mind. We do a quarterly or every four month portfolio of view, and we have a couple hundred companies. It used to be a very manual process that we had a dedicated person working on it, and now our technology team has built the system where we take these summaries. They get piped into this portfolio book that we create and with all the financials and all the metrics and everything, and it's something that we are heavily leveraging AI. In this case, it's Glean's and clawed. So the underlying models obviously, and we have done a bunch of other things. Actually love to rate my meetings just so I know, like so I remember, like what are the tens and the nines that I should have paid attention to but I forgot because I didn't. So I want to have an exhaust of all the things that I am encountering my real life. And AI is an amazing capture of the exhaust and to provide intelligence and signals to our team and it can pipe it into our CRM. And so there's all these cool things that we've done. We have an internal tech team which is an amazing team of four folks who build a lot of these tools, and we are definitely maxing out on using everything that's out there.

00:51:20
Speaker 2: Huh really really quite fascinating. So final question before we get to our favorites that we ask all our guests what are investors not thinking about when it comes to AI or anything else? But perhaps should be what sort of topics policy, data, geography, what's getting overlooked? But shouldn't I think right.

00:51:44
Speaker 1: Now we're going through a time of where software is considered to be dead and or there's called the saaspocalypse and.

00:51:53
Speaker 2: Although they're just coming offish clothes.

00:51:55
Speaker 1: Yeah, and there's always an overreaction to, oh my god, it's going to be a Capex world and only chips will matter, and only like you know, like fiber and data centers and power will matter. The reality is like the way we as humans interact with software, with with the technologies through software and through things you know we have known to be called software and tools and so. And by the way CIOs and large companies buy buy from companies that sell to them, and they don't just buy, you know, a smart agent. And so I think something that's been certainly just the pendulum swung a little too far is that we underappreciate what software still does and will continue to do forever, for for our enterprises, for governments, et cetera.

00:52:40
Speaker 2: Software not going away.

00:52:42
Speaker 1: Yeah.

00:52:42
Speaker 2: Really interesting. All right, let's jump to our favorite questions, starting with who are your mentors who helped shape your career?

00:52:52
Speaker 1: Yeah. One of my mentors, uh is Urban Fetterman. I dearly love he's he's ninety years old. Now he's a New Yorker. Wow, he sold peanuts. I believe that a Dodger stadium when there's still the Dodgers fifties. Yeah, but he was my mentor at us VP, and he's a legendary semiconductor investor, believe it or not. He was one of the co founders of sand Disk Corporation. Before that, he was the CEO of am D.

00:53:22
Speaker 2: I hope he still has a few shares of.

00:53:23
Speaker 1: Those, Yeah, and he probably does. But sand Disc, which is in this memory hype cycle that we're going through, hype or not, but like there's a real need for memory, I believe now is maybe like a half a trillion dollar market cap company something crazy. Don't quote me on that, but memories having its day right now. But in any case, he was a mentor because not only I worked for him, but I saw through his lens how to be a great board member, how to make investments, how to back people, how to build relationships with people. But he also gave feedback that no one else in life would because I think he just wanted He loved me. I really felt the love because the kind of feedback he gave me was feedback that I don't think people would generally have the courage to give you. And he sort of you know, it's it's like pretty direct. It's a tough love. Yeah, And I love that about him, and you know, makes reminds me that I need to go pay him a visit.

00:54:25
Speaker 2: Well, you're in New York, amun as well.

00:54:27
Speaker 1: He's actually, you know, he lives in the BA area. Oh yeah, so's he's a New Yorker relocated to the BA area I think like fifty years ago.

00:54:34
Speaker 2: So let's talk about books. What are some of your favorites. What are you reading currently?

00:54:38
Speaker 1: So I'm just starting on this book. It's called Believe Why You Should Believe especially this like Era of AI, And it's my I'm on the board of a company called Thrive Global and the CEO founder is Ariana Huffington. And Ariana gave me the book and she and I have very aligned views on faith and spirituality. And she gave me this book and said, hey, you know, like, so Aria and I believe Greek Orthodox I'm Muslim, and we talk about how faith guides our lives and we talk about how in this age of AI. This is actually the conversation she and I have and She's like, I have the perfect book for you, and it's about how we should believe it even more in this age of AI because it helps us actually understand the world because we're trying to make sense of it all the time, all the time. And actually religion can give us a bit more constrained view of like what the world actually is, because otherwise it's just like a black hole, black box. You won't be able to comprehend the vastness of what we're trying to comprehend as human beings. And especially with AI, now we're pushing boundaries of what's possible. And I think there's actually more more in the in the scriptures that you think is sort of the view that she and I share. And this book actually sort of hits home with it. It's a New York Times columnist Roy Dehut, But yeah, unbelieve check it.

00:56:15
Speaker 2: Out on my list. Now, what about streaming? I know you host a podcast. What do you either watch or listen these days?

00:56:25
Speaker 1: So my wife and I we love to watch Dateline forty eight hours, these all the crime shows, all the crime shows. It's like in some ways it's it just kind of takes it down a notch. But it also is so instructive around human psychology, what motivates people to do not so great things and there's generally like a theme to it at this point. It's a very repetitive theme.

00:56:52
Speaker 2: But Junning Krueger, it's all they have no idea, that just trail of DNA evidence they leave everywhere. Anytime I've watched that show is it's like, what are you doing?

00:57:01
Speaker 1: And it's you know, it's it's it should be that it's harder and harder to commit crimes. It should be harder and harder, and yet there are still crimes.

00:57:09
Speaker 2: There's still and just as many as ever, only people are getting caught more easily.

00:57:14
Speaker 1: Yeah, and we know the cell phones they're pining, those towers.

00:57:17
Speaker 2: You know, what do you mean you weren't in the house. We could tell you within one hundred feet of this person the exact time.

00:57:23
Speaker 1: Yeah, So it is like, it's not probably a very full answer, and you're probably looking for some cool shows.

00:57:30
Speaker 2: No, not at all. I'm fascinated by that. My my. It's funny because there was used to be this giant gap between the CSIS and and and what actually was going on. But if you watch the two of them, it's really closed. Because maybe there's a little selection bias here, but all those shows, it's all these people they got that got caught. So you're seeing where the technology worked, where the forensic science was that guy. Yeah, it's really really very funny.

00:58:00
Speaker 1: All right.

00:58:01
Speaker 2: Our final two questions, what sort of advice would you give to a recent college grad interest in the career in either venture investing or technology.

00:58:12
Speaker 1: I would It's probably the same advice I would have given twenty years ago or given myself when I came out of college, is go work at a fast growing company where you can learn from the growth that it's encountering, but also the people and the probably the network that you build for the rest of your life. It is probably the best time in your life coming out of college to go learn from others around you and to experience high growth because from high growth, lots of lessons are learned, and so if you can find a way to get into a high growth technology startup AI startup, it is the best way to develop the skills. But also like the empathy of what it means to have carried a bag and sold some thing and built something and shipped something. I always tell folks like, there shouldn't be a direct path in a venture capital a. It's a second thing. It's not the first thing you do coming out of college or coming out of grad school. It's like you have to have built and shipped and sold and developed that empathy for high growth and the lessons learned before you get into our career.

00:59:27
Speaker 2: We really really interesting answer, and our final question, what do you know about the world of venture investing or technology today, might have been useful twenty five thirty years ago when you were first ramping up.

00:59:40
Speaker 1: It's all about the people. It sounds so trite, but it is about especially it's i say, ordinary looking people doing extraordinary things. And how do you assess those ordinary people who are doing extraordinary things is by really understanding the people their intentionality, their desires, their ambition, what drives them, their motivation, which goes back to the why do you meet them in person because you're trying to figure out, like why are they doing this? Building a startup company is hard work, it's a sacrifice on life, so there better be a good reason why you're doing this.

01:00:18
Speaker 2: Huh, really really interesting answer. Thank you Mamun for being so generous with your time. We have been speaking with Mamun Ahmed. He is partner at Kleiner Perkins. If you enjoy this conversation, Well, check out any of the six hundred and forty seven we've done over the past twelve years. You can find those at Apple, Spotify, YouTube, Bloomberg, wherever you get your favorite podcasts. I would be remiss if I did not think the crack staff that helps put these conversations together each week. Alexis Noriega is my video producer. Joan Russo is my researcher. Anna Luke is my podcast producer. I'm Barry Results. You've been listening to Masters in Business on Bloomberg Radio