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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 Kleiner Perkins, Mahmoud Hamid. Mahmoud Hamid, welcome to Bloomberg. Thank you so much for having me, Barry. So I'm fascinated by your background. You grow up in Frankfurt, Germany. You come to the U.S. to go to college at Purdue, bachelor's in electrical and computer engineering, master's at Stanford, MBA from Harvard. What was the original career plan?

So let's go back to, I think, 1986. 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 that's right christy that's right yeah and uh everyone 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. I 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 10 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 really oh that's fascinating yeah so so my path was aeronautical engineering and trying to figure out a way to get into space I've yet to do that but but that is what let 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.

Which are 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?

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 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 to 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'd already gone to Stanford for grad school, so I thought, okay, well, it'd be nice to get a change of scenery. Just round it out a little bit. And move to Boston.

Yeah, give up the nice weather. Yeah. um and and so you started in between college and grad school you spent how many years that you were

at xilinx i was at six years yeah so the story actually goes is i was 19 when i graduated from college so from purdue and uh i thought okay the best thing for a young kid is to continue to go to grad school so i applied to grad school uh and uh ended up getting in uh at stanford but i also got a number of job offers this is 1997 the doc the dot-com boom i'm thinking 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 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 xilinx and this is back in 1997 We're talking about sort of the middle of the dot-com boom.

Yeah, really, really interesting. You're known today as someone who thinks about software generally and enterprise software in particular. That seems like an unusual transition from semiconductors. What led to that shift? What changed your thinking? Great question, Barry.

So in 2005, 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 2.0 and software because I felt like also in my own firm at U.S. Venture Partners, where I started my venture capital career, where I was an associate, I was hired to go help the partners there evaluate semiconductor opportunities. And that's actually why I went there, because there are some legendary semiconductor investors who happen to be there. And some of my mentors, even today, were the folks running the firm there. But I realized that all my friends in 2005 were moving to Web 2.0, Internet. This is the beginning of Facebook, which happened to be started at Harvard when I was there. So you're seeing all these people, my cohorts, age group, moving into software and web. And I felt like I had to sort of move along with that. And my day job was evaluating semiconductor businesses. But in the evenings I was in San Francisco, I was going to, you know, the Web 2.0 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 software and Internet stuff. And but in the back of my mind, I always remained a semiconductor guy. And, you know, semis are back now, as you know.

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 2000s when you were at USVP. You had early exposure to companies like Box and Yammer. I don't really remember Yammer. I remember Box. Yeah. big enterprise software deals what did you learn from that experience what have you brought forward with you from from that era yeah so box happened to be my

first investment at usvp where I joined the board is an early stage company it was you know a few hundred K of revenue two very young founders Dylan and and Aaron 20 and 21 years old dropped out of college sort of like the prototypical So thank you.

founder, the archetype of a young founder. And they were going after storing your files in the

cloud and sharing them inside of your company. Let me stop you for a second, because I think anybody under 40 is perplexed by what you just said. I recall like late 90s, early 2000s, 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.

Yeah. So, 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 80s and 90s. 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 the file or searching for it or placing it in a folder very nicely.

Like the name you used to have to put on a file was important because if you couldn't remember the name, 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 or what name you put, good luck retrieving it.

Good luck, right? And so as the world moved from the desktop to the browser, and so by 2006, 2007, we all are using at this point like Firefox, Mozilla, Chrome's not even existent yet.

Right. It was Internet Explorer until Chrome came along.

Exactly. And so my thesis was, OK, 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 2007, when I invested in Box, there were probably half a dozen, two dozen. And I don't know, there's many of these companies doing file sharing, but it was mostly for consumers, mostly for like your Dropbox. Dropbox was in that same era, but there was like X-File and Elephant Drive. I mean, I remember these as an associate, 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 were probably like 40 companies that were doing something similar. But most of them were dedicated towards sort of consumer use cases, photos, music, stuff like that. Napster era was right around then. Exactly, exactly. And so the 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 a 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 servers in their data centers or wherever in their buildings into files that now reside in the cloud.

And they're willing to pay for it. They're willing to pay for it. Unlike back then anyway, consumers were so reluctant to pay for anything. So it's interesting because you've had a lot of early investment success with a variety of 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 on to something.

Yeah, I think there's definitely some compounding of learning from early wins and losses. You brought up Box, and so Box, sort of 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 20s when I joined the board, and from that experience, I learned a lot about, like, 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 2010, kind of like Twitter meets Facebook, but for your company. And Microsoft ended up acquiring it in 2012 and is part of the Microsoft suite now and part of 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, there was a level of engagement and monetization that was good. And a few years later, maybe even like a year later, I came across another company, Slack. And it was sort of 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 2026. But the lessons learned in that 2010-11 era led to the investment in Slack in 2014 for me when it was a 10-person company. And so back to the point around like compounding of learning, Box led to Yammer, Yammer led to Slack, and since Slack there's been others. But there certainly is some pattern recognition around products that are working.

And then in 2011, 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 hurting the country through the intelligent use of startups, technology, et cetera. um what was this a reinvention of of venture capital or just a new set of tools within a partnership that um you know was kind of novel for its era it was novel for its era because we

decided that we would go after uh education healthcare and uh finance um 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 largely played out over the last 15 years, but we just thought that there would 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. So 2017, you leave social capital for Klarna Perkins, a firm that has long been iconic, The laundry list of companies, Kleiner, has backed Google, Cisco. Were they early in Apple also, I think?

They were Google, Amazon, Sun Microsystems, Genentech, Intuit. Unbelievable. Tandem, if you remember. The list of greats is amazing.

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?

Yeah, so look, I actually long admired Kleiner Perkins, had an extreme reverence, I would call it, for Kleiner Perkins, going back to my days moving to Silicon Valley. I mentioned I moved to Silicon Valley in 1997. I worked for this company, Xilinx. 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 20 of them amongst like 2,000 of us. And now I have my own SunSpark. I think it was a Spark 20. And guess what? There's a Netscape browser and I'm buying books for grad school on Amazon. And by the way, 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 Xilinx, Sun, Netscape, Google, and Amazon is that Kleiner Perkins had led the Series A, so the first institutional investor for all five of those companies. So as a young guy, I developed this extreme reverence for Kleiner Perkins because of the investments they had made in these really history-making companies, which is also what led me to think about venture capital as a career as a young engineer. I want to be like those guys, those guys that are investing in all the coolest companies that I'm using as a 19-year-old here. And that's really what got me excited about venture capital. And actually, one of the people behind many of those investments, Sun, Netscape, Google, and Amazon, was my partner, John Doerr. And so for me, there was this extreme reverence for John Doerr.

door 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 Kleiner 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 Kleiner Perkins. This is in 2003 when I wrote the essay. 2002, I wrote the essay. And then, you know, when I tried to apply for a job in 2005, coming out of business school, I didn't get very far. But I did end up there in 2017.

So, you know, eventually, if you keep plugging away, you get you get to where you want to go. That's great. Coming up, we continue our conversation with Mamoun Hamid, Kleiner Perkins managing member, talking about the reboot of the firm. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio.

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I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Mamoun Hamid. 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 2017, 2018. 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?

Yeah, if I look back at the decades of being probably the most successful venture capital firm throughout the 70s, 80s, 90s, and even the early 2000s, 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 2017 and 18 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.

So early stage, is that seed round, or is it a little more developed?

Yeah. So the early stage fund is seed series A mostly, and maybe some Bs. And then the growth fund is BCs all the way to, we invested in the last anthropic round at a $900 billion valuation, which is rare for special companies, but it has the ability to invest across even to the pre-IPO.

And is it a coincidence that the growth fund is two and a half times the size of the seed fund? At that point, these companies are bigger, require a bigger check, or is that just happenstance?

I think our funds are sized based on the opportunity set in front of us. Our early stage funds have been almost exactly 35 companies for the last 15 years. So 35 companies per fund, which we think about as the right number of shots on goal for an early stage fund to return multiples on it. Like 25 to 30 million per. Exactly. Exactly. So it starts out with maybe, in some cases, a $5 million check, and the subsequent check is another $15, $20, or it could be first check is $30, and then with ProRata, you're investing, let's say, up to $40 million in a company, and then your growth fund is doubling down and investing a lot more in those companies.

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.

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 is at an all-time high. The whole world in some ways is being refactored with AI. And this is just the very beginning. And so I would say that all parts of the economy, even beyond those four areas, it is, like I mentioned earlier, health care, financial services. It is all sorts of knowledge work. It is going to be all sorts of physical automation in terms of robotics, 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 the biggest technological revolution ever.

So I'm glad you described it that way, because I keep hearing people compare AI to the internet, and that seems too 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 of.

i absolutely agree with you barry it is like the industrial revolution it's like the railroads it's like the printing press it is it is that it's not the internet um that's really interesting because

when i think internet the first thing you think of is oh this is a bubble and this is going to collapse but you mentioned you're 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 yeah let's let's talk about that i

think that's the i think that's the real conversation and the more uh the very exciting conversation that one can have about this topic is uh and i and i start at a very high level which is uh the gdp of the world today uh it's it's about 120 trillion dollars and uh about half of that is labor, the labor component of it. So roughly $60 trillion. And of that $60 trillion, roughly 60% or so is white collar, 50, 60, somewhere. So that's anywhere from $30, $35 trillion. And if you look at tokens and what the frontier model companies provide, 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 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 uh publicly stated uh has gone from zero to 45 billion revenue run rate that's unbelievable in a matter of less than three years okay and that is likely going to double and you know and and uh the company started this year i believe at 20 it 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 actual labor to companies, to corporations, even to consumers who are using AI in their personal lives.

So 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. And 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's the argument. Give us your perspective on that.

I actually fully agree with that point of view that you have. It's like getting email. I mean, I think the fact that we got the computer didn't, you know, the people using the typewriter started using the 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 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 actually we need more labor, more people than we ever have.

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?

I actually do 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 the job of a software engineer is to manage a whole host of 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 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 like, 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 it or not, I'm telling them going to math, science, like, and actually art, like have like spectral diversity in 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.

So let's bring this back to how you think about the opportunity set that's out there. At Kleiner Perkins, you do structured reviews of every interesting deal that was passed on. I'm kind of fascinated by that. by that. I know a lot of VCs kind of hold their misses as a badge of honor. Some firms post it 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 does the process teach you?

Yeah. So actually, one of the things that I did when I got to Kleiner Perkins in 2017 was that We would look every week at that week's Series A's that got done by our peer firms, about 30, 40 firms, and whether we had seen the company that it was invested in or not. And a 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. um and our initially our goal was like we should see 60 and seeing means you met the company and for us that hovers around now 70 or so you don't want it to be 100 because then you're just that's the that's the game you're playing we just see everything and you don't want to be 20 because you're not seeing enough but we think 70 is a good number um and we look at the ones okay we saw if we saw 70 percent of the good stuff let's say let's call it uh or was the 30 percent that we didn't see even better uh or if we saw the 70 percent do we pass on the good stuff and do the bad stuff and and so we we go through that exercise um quite frequently we just had an off-site a few weeks ago and we go through again we we pour salt on the wounds and 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 uh we need to adjust the way we do things i'll give you an example um the the anthropic which is an incredible company um it's series a was not a traditional one um uh sbf from ftx led the series a famously we know how that worked we we do know how that worked out it was an amazing investment and very prescient on his behalf. I forget how much it would be 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, 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 what their with their with their companies and so uh what a miss right uh and i think that round got done in that four billion dollar valuation or something which not a small valuation but still um compared to today compared to today and so uh and we we looked a lot of our passes that we were good companies um and in many times we just didn't meet them in person so the pandemic era bred some really bad habits of you know you did a first meeting over zoom and so and i looked at my own investments and the last

20 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 uh on zoom anymore i want to meet people in person and if it's worth a 30 minute zoom it should be worth a 30 30 minute in-person meeting and i'm glad we get to do this in person because you know big difference it

wouldn't be the same thing if i was on a zoom screen doing this thing with you that's exactly right what about the reverse of that um if you're analyzing your misses do you ever review your your hits your wins and saying why did we get this right what can we take forward from this

that's a great question uh i think we just assume uh 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. It's that, you know... That's really interesting. In the sense that we didn't even see it, because it would be bad if we didn't see companies and that basket of companies was way better than the companies we saw. And we try to be very intellectually honest about, are we seeing the right stuff or the wrong stuff? And it turns out we're seeing it's a it's a toss up.

Right. The reason I ask that question is you learn in the public market, you learn more from your misses 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. I think you you you really beat yourself up on the misses and the ones that you do, you're just like, OK, check. You know, it happened. And, you know, maybe you don't think about it as much as the ones you missed.

Really, really interesting. Coming up, we continue our conversation with Mamoun Hamid, partner at Kleiner Perkins, discussing the state of venture investing today. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio.

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I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. Mamoun Hamid is my extra special guest. He is a partner at Kleiner Perkins, where he is driving the firm's 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?

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 suggest that there's a lot of really high-quality ideas and founders that we're seeing. And so I would say the volume is because of that at an all-time high. And so to your question of what do we what are we looking for um i think obviously uh everything today is ai enabled ai tailwinds and um venture capital uh 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 100s, sells to large enterprises and Fortune 100 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 your reaction was to ChatGPT 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 ChatGPT moment. And we started to think about what are the second order effect? What are the things that come from here, from ChatGPT, from LLMs? And it was, we thought about like the labor pyramid, think about knowledge work. And we went straight to, okay, what are the most highly skilled, highly paid jobs in the country? And if you actually look at the top 20 list of jobs, dollars made by job, it's some form of engineer, doctor, or lawyer. And so what we did at Kleiner Perkins was to invest in companies that do AI for legal, AI for software development, AI for medicine. 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 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, which it is. Registered Trademark Microsoft Corporation. Yeah, exactly. Co-pilot, exactly. Exactly. And in some cases, it's become an autonomous agent for those people. So software developers run agents to do work for them now. And so we 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 200K plus a year pay, then what's one below that? You've got financial analysts, salespeople, nurses. And so then we've 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 be co-pilots 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 is the you know lowest paid low scale in some some and eventually robotics will get there right and hopefully do a lot of the back-breaking work that nobody should be doing right um and i think we eventually that is sort of the the way also goes along with the timeline of the way we're thinking about these things is that you start with the highly skilled highly paid and eventually over the next decade we get down to the sort of the lowest scale, lowest paid work.

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?

great question uh so when you have uh companies i've gone from zero to a trillion in value you know in the last three to four years and uh when you're a founder what do you do you point to that that's what i can be i can get to a trillion dollars uh because i'm going after really large problems. And we love that, that ambition. And what do people do then? Is it, well, you know, if the probability of a $1 trillion outcome is 1% and you do all the other 99% of probabilities for the rest of the numbers, you've got yourself to a expected value of $10 billion dollars or more at least that's the minimum right you do the one percent of one trillion is 10 billion and you add up all the other probabilities it's probably like 30 40 50 billion and so if you're looking at a series a company where the ambition is is tremendous your uh the valuation may just be you know you're raising 100 million at a billion dollar valuation for two guys out the gate and those that those are real examples and i think we've we've got a of this you know pointing at the the large outcome and if it's paired with smart people ambitious idea it has that kind of potential but the reality is like there's usually one or two of those the real outliers that that are mag 7 scale right um and just uh the you know just gravity in itself doesn't allow for you know the world can't have you know 100 trillion dollar companies plus if

if it's a trillion dollar total addressable market, it's gonna 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 40 companies early days. There's gotta be tens of thousands of companies going for each of these segments in AI.

Yeah, and there are. And I think the hard part of the work that we have is identifying what we think will be the leading company because in technology, generally speaking, the leading company gets most of the market cap, which is if you look at the Mag 7. Winner take all. Winner take most, right? Right. Like take 90% of the market. NVIDIA, 95% of all GPUs spent. Take Google, Alphabet, multiple businesses, or take a Tesla, take a Meta, own social.

right? So winner takes most in technology. 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 fathead 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 your funds.

Yeah, venture is a power law business. There's no question about it. And if you already look at, there's this article I read the other day where 90% of the AI revenue is in the hands of two companies. That's crazy. OpenAI and Anthropic. Right. And the remaining 10% is in a bunch of companies. They're excellent companies. But the scale of those two other companies is so massive that it dwarfs all the other great work that's happening in tons of other companies that we've backed. And by the way, 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. And 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 10 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.

Right. You only invest in one company per silo, so to speak?

Yes.

Right. Just to avoid those sort of conflicts.

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

interest. So I'm kind of fascinated by, as venture investors, you obviously see the promise of AI across all these different economic sectors. I'm curious how you're using AI internally at Kleiner Perkins. Are you using it to source deals or do due diligence or predict specific outcomes. How does AI fit into your operations? Yeah, we have definitely been maxing

out on AI internally, not only because we invest in these companies, whether we use Glean is a company we incubated inside of Kleiner 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 Kleiner 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 a HR policy, I'll go to Glean quickly ask because it just knows everything about Kleiner Perkins. It knows investment memos. It knows cap tables. It knows like the really like confidential stuff. It's permission in a where the people who are supposed to know will can know and it's very like that is part of the magic is that it's very safe secure um and you trust it to to really know the things they're supposed to know and not know the supposed things they're not supposed to know so um and so that's an example but uh we are we get so much information and whether it's board decks and they're long board memos, financials. And so when I'm going to a board meeting after this, I got the board memo. And the first thing I do, it's sent to an email alias and that runs it through AI and it produces a summary for me 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 i sort of have a preview of it in my mind um we do uh a quarterly or every four month portfolio review and we have a couple hundred companies it used to be a very manual process that are we had a dedicated person working on it and now our technology team has built a system where we take these summaries they get piped into this this this portfolio book that we create and with all the financials all the metrics and everything and it's something that we are heavily leveraging AI. In this case, it's Glean, it's Claude, so the underlying models, obviously. And we have done a bunch of other things. I actually love to rate my meetings just so I know, like, so I remember, like, what are the 10s and the 9s 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'm encountering in 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 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. 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 now we're going through a time of where software is considered to be dead and or there's called the SaaSpocalypse. Although they're just coming off their Yeah. And there's always an overreaction to, oh, my God, it's going to be an AI 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 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 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 a pendulum swung a little too far is that we underappreciate what software still does and will continue to do forever for our enterprises, for governments, etc.

Software not going away. Yeah. Really interesting. All right, let's jump to our favorite question. starting with who are your mentors who helped shape your career yeah one of my

mentors is urban Fetterman I dearly love he's he's 90 years old now he's a New Yorker Wow he sold peanuts I believe at a Dodger Stadium when there's still the Dodgers 50s yeah yeah that's a man but he was my mentor at USVP and he's a legendary semiconductor investor. Believe it or not, he was one of the co-founders of Sandisk Corporation. Before that, he was a CEO of AMD. I hope he still has a few shares of those. Yeah, and he probably does. But Sandisk, which is in this memory hype cycle that we're going through, and I'm hype or not, but 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 memory's 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 me 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 generally have the courage to give you. And it's sort of, you know, it's like pretty direct. Tough love? Tough love.

Yeah.

And I love that about him. And, you know, it reminds me that I need to go pay him a visit. Well, you're in New York. You might as well. Oh, he's actually, you know, he lives in the Bay Area. Oh, okay. Yeah. So he's a New Yorker relocated to the Bay Area, I think like 50 years ago. So let's talk about books. What are some of your favorites?

What are you reading currently?

So I'm just starting on this book. It's called Believe. Why you should believe, especially in this era of AI. 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 Ariana, 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 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 and actually religion can give us a bit more constrained view of 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 in the scriptures than 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 DeHood. But yeah, I believe. Check it out.

On my list now. What about streaming? I know you host a podcast. What do you either watch or listen these days?

uh so my wife and i we love to watch dateline 48 hours these all the crime shows all the crime shows it's like in some ways it's uh 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 uh and there's generally like a theme to it at this point it's a very repetitive theme but gunning crew

It's all, they have no idea, they're just trail of DNA evidence they leave everywhere. Anytime I've watched that show, it's like, what are you doing?

And it's, you know, it should be that it's harder and harder to commit crimes. It should be harder and harder. And yet? There are still crimes.

And just as many as ever, only people are getting caught more easily.

Yeah, and we know the cell phones, they're pinging those towers, you know.

What do you mean you weren't in the house? We could tell you were within 100 feet of this person at that time.

Yeah. So it is like it's not probably a very full answer and you're probably looking for some cool show.

No, not at all. I'm fascinated by that. It's funny because there used to be this giant gap between the CSIs and what actually was going on. But if you watch the two of them, it's really close.

because maybe there's a little selection bias here, but all those shows, it's all these people that got caught. So you're seeing where the technology worked, where the forensic science was that guy. It's really, really very funny. All right, our final two questions. What sort of advice would you give to a recent college grad interested in a career in either venture investing or technology?

I would it's it's probably the same advice I would have given 20 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 then probably the network that you will 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 something and built something and shipped something. um i i always tell folks like there shouldn't be a direct path into venture capital this is not 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 the lessons learned before you get into our career 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 25 30 years ago when you were first ramping up

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, a company is hard work. It's a sacrifice on life. So there better be a good reason why you're doing

this. Really, really interesting answer. Thank you, Mamoun, for being so generous with your time. we have been speaking with Mamoun Hamid. He is partner at Kleiner Perkins. If you enjoy this conversation, well, check out any of the 647 we've done over the past 12 years. You can find those at Apple, Spotify, YouTube, Bloomberg, wherever you get your favorite podcasts. I would be remiss if I did not thank the crack staff that helps put these conversations together each week. Alexis Noriega is my video producer. Sean Russo is my researcher. Anna Luke is my podcast producer. I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.

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