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Venture Capital During the AI Revolution with Mamoon Hamid · Masters in Business

0:02
Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
0:08
Speaker 2: This week on the podcast Another Banger. Mamun Hmid is
0:12
Speaker 2: partner at Clina Perkins, where he's been focusing on early
0:15
Speaker 2: stage AI investments for nine years. He's got a fascinating
0:20
Speaker 2: background early investor in Slack, Figma, Glean, box, et cetera. Previously,
0:27
Speaker 2: he co founded Social Capital with Chamat and worked for
0:31
Speaker 2: a number of other venture firms, including US Venture Partners.
0:37
Speaker 2: I thought this conversation was fascinating and I think you
0:40
Speaker 2: will also with no further ado my conversation with Klina Perkins.
0:45
Speaker 2: Mahmun Kamid Mamun Jamid, Welcome to Bloomberg.
0:58
Speaker 1: Thank you so much for having me.
0:59
Speaker 2: Barry. So, I'm fascinated by your background. You grow up
1:03
Speaker 2: in Frankfurt, Germany. You come to the US to go
1:07
Speaker 2: to college at Purdue bachelors and Electrical and Computer Engineering
1:12
Speaker 2: Masters at Stanford, a MBA from Harvard. What was the
1:17
Speaker 2: original career plan?
1:18
Speaker 1: So let's go back to I think nineteen eighty six.
1:23
Speaker 1: Do you remember the Challenger explosion? Sure, every kid growing
1:27
Speaker 1: up remember that. And one of my teachers was actually
1:32
Speaker 1: supposed to go on the Space Shuttle because there's a teacher.
1:34
Speaker 2: That's right, College Christy, that's right.
1:36
Speaker 1: Yeah, and every kid got fascinated by especially if you
1:41
Speaker 1: had a teacher going to space. So followed the whole
1:44
Speaker 1: journey of the Challenger space shuttle and the teachers and
1:50
Speaker 1: all that. But with that also came this desire to
1:53
Speaker 1: learn more about space, and I instantly wanted to become
1:57
Speaker 1: an astronaut. Naturally. Think I was seven or eight years old,
2:01
Speaker 1: and as I thought about, you know, high school, liking
2:07
Speaker 1: science and math, thinking about where to go to college.
2:10
Speaker 1: And I was, as you mentioned, I was in Frankfurt,
2:12
Speaker 1: Germany growing up, and one of my uncles he'd given
2:15
Speaker 1: me this list of colleges, the top ten engineering schools,
2:19
Speaker 1: and I just applied to all ten, and one of
2:23
Speaker 1: them happened to Purdue, where actually, to this day, the
2:27
Speaker 1: most number of astronauts have graduated from.
2:30
Speaker 2: Really, oh, that's fascinating.
2:31
Speaker 1: Yeah, So my path was aeronautical engineering and trying to
2:36
Speaker 1: figure out a way to get into space. I've yet
2:39
Speaker 1: to do that, but that is what led me down
2:41
Speaker 1: the path of applying to Purdue in the first place,
2:44
Speaker 1: and the association with Space and NASA and then actually
2:48
Speaker 1: going from something so massive and big space to something
2:52
Speaker 1: so small chips and semiconductors and transistors.
2:55
Speaker 2: Which you're enabling space. So there's definitely a connection. Is
3:00
Speaker 2: it true that when you went to business school you
3:02
Speaker 2: were thinking about already being a venture capitalist?
3:06
Speaker 1: I actually when I applied to business school, so i'd
3:08
Speaker 1: worked for a good six years after undergrad so studied
3:12
Speaker 1: electrical engineering computer engineering, and that naturally made me think
3:16
Speaker 1: about a career in Silicon Valley designing chips, which is
3:19
Speaker 1: what I did for the first six years of my
3:21
Speaker 1: career working in the semiconductor industry. But what really got
3:25
Speaker 1: to pretty interesting for me was the notion of startups
3:29
Speaker 1: and founding companies and how these so called venture capitalists
3:33
Speaker 1: were behind some of the most iconic companies that I
3:36
Speaker 1: was coming across, And so I actually wanted to get
3:40
Speaker 1: into venture capital. And that's actually why I applied the
3:43
Speaker 1: business school, and specifically only applied to one business school, Harvard,
3:46
Speaker 1: because I naively thought that if you wanted to get
3:48
Speaker 1: into venture capital, you had to go to Harvard or Stanford,
3:52
Speaker 1: and I had already gone to Stanford for grad school,
3:53
Speaker 1: so okay, well, it'd be nice to get a change
3:55
Speaker 1: of scenery just round it out and moved to Boston.
3:58
Speaker 2: Yeah, give up the nice weather. Yeah.
4:01
Speaker 1: And so you.
4:02
Speaker 2: Started in between college and grad school, you spent how
4:07
Speaker 2: many years you were at silence?
4:09
Speaker 1: I was at six years. Yeah. So the story actually
4:11
Speaker 1: goes is I was nineteen when I graduated from college,
4:15
Speaker 1: so from Purdue, and I thought, okay, the best thing
4:18
Speaker 1: for a young kid is to continue to go to
4:21
Speaker 1: grad school. So applied to grad school and ended up
4:26
Speaker 1: getting in at Stanford. But I also got a number
4:29
Speaker 1: of job offers. This is nineteen ninety seven, the dot
4:32
Speaker 1: com boom. I think it's a bit like this time
4:35
Speaker 1: where should you opt out of the job market and
4:38
Speaker 1: get like extremely valuable experience or continue on with grad school.
4:44
Speaker 1: So I did the best of both worlds, which is
4:47
Speaker 1: like I went to Stanford, took a few classes every quarter,
4:51
Speaker 1: and then worked full time at Xilenx. And this is
4:54
Speaker 1: back in nineteen ninety seven. We're talking about sort of
4:56
Speaker 1: the middle of the dock com boom.
4:58
Speaker 2: Yeah. Really really interesting. You're known today as someone who
5:03
Speaker 2: thinks about software, generally in enterprise software in particular. That
5:08
Speaker 2: seems like an unusual transition from semiconductors. What led to
5:13
Speaker 2: that shift? What changed your thinking.
5:16
Speaker 1: Great question, Barry. So in two thousand and five, when
5:19
Speaker 1: I got into venture capital, my full intent was I
5:24
Speaker 1: would like to learn how to invest in great semiconductor
5:26
Speaker 1: companies or founders who build semiconductor companies of the future.
5:30
Speaker 1: And it turns out after the dot com bust, there
5:33
Speaker 1: was not a lot of investment in infrastructure, so data
5:39
Speaker 1: centers and networking and switching and semiconductors broadly, and so
5:45
Speaker 1: I realized pretty early on that, hey, like, if I
5:48
Speaker 1: want to build a career in investing, you kind of
5:51
Speaker 1: have to go where the puck is going to, and
5:53
Speaker 1: so skate to where the puck is going, and I
5:56
Speaker 1: sort of skated towards web two point zero and software
5:59
Speaker 1: because I I felt like Also in my own firm
6:01
Speaker 1: at US Venture Partners, where I started my venture capital career,
6:05
Speaker 1: where I was an associate, I was hired to go
6:08
Speaker 1: help the partners there evaluate semi conductor opportunities. And that's
6:13
Speaker 1: actually why I went there, because there were some legendary
6:15
Speaker 1: semi conductor investors who happened to be there, and some
6:19
Speaker 1: of mentors even today were the folks running the firm there.
6:23
Speaker 1: So but I realized that all my friends in two
6:26
Speaker 1: thousand and five were moving to web two point zero internet.
6:29
Speaker 1: This is the beginning of Facebook, which happened to get
6:34
Speaker 1: be started at Harvard when I was there. So you
6:36
Speaker 1: were seeing all these the people my cohort's age group
6:40
Speaker 1: moving into the software and web, and I felt like
6:43
Speaker 1: I had to sort of move along with that. And
6:46
Speaker 1: my day job was evaluating semi conductor businesses. But in
6:50
Speaker 1: the evenings I was in San Francisco, I was going
6:52
Speaker 1: to you know, the web two point zero parties and
6:54
Speaker 1: meeting all the founders starting software businesses, and so I
6:57
Speaker 1: slowly started to just you know, as a side project.
7:00
Speaker 1: The side project became the main project, which was to
7:03
Speaker 1: move from semis to UH to software and internet stuff.
7:08
Speaker 1: And but in the back of my mind, I always
7:12
Speaker 1: remained a semi conductor guy. And UH, you know, semis
7:16
Speaker 1: are back now, as you know.
7:17
Speaker 2: And AI seems to be the application of both semis
7:22
Speaker 2: and software, so you're well prepared. We'll talk about AI
7:26
Speaker 2: in a bit. I want to stay in the two thousands.
7:28
Speaker 2: When you were at us VP, you would early exposure
7:32
Speaker 2: to companies like Box and Yamer. I don't really remember
7:35
Speaker 2: Yam or I remember Box, Big Enterprise software deals. What
7:41
Speaker 2: did you learn from that experience? What have you brought
7:44
Speaker 2: forward with you from from that era?
7:46
Speaker 1: Yeah, sou Box happened to be my first investment at USCP,
7:52
Speaker 1: where I joined the board. It was an early stage company, was,
7:55
Speaker 1: you know, a few hundred k revenue. Two very young
7:58
Speaker 1: founders Dylan and and Aaron twenty and twenty one years old,
8:03
Speaker 1: dropped out of college, sort of like the protypical founder,
8:07
Speaker 1: the archetype of a young founder, and they were going
8:10
Speaker 1: after storing your files in the cloud and sharing them
8:15
Speaker 1: inside your company.
8:17
Speaker 2: Let me stop you for a second, because I think
8:19
Speaker 2: anybody under forty is perplexed by what you just said.
8:25
Speaker 2: I recall like late nineties, early two thousands. If I'm
8:29
Speaker 2: either at home or at work, or on a laptop
8:32
Speaker 2: or at the beach house, whatever I needed was always
8:36
Speaker 2: somewhere else. And the beauty of early blogging software is
8:41
Speaker 2: I could upload files, I could upload charts, I could
8:44
Speaker 2: upload images, and that was the closest thing to the cloud.
8:49
Speaker 2: It just didn't exist then. If you wanted to have
8:51
Speaker 2: something that you can access anywhere you had an Internet connection,
8:55
Speaker 2: literally did not exist.
8:56
Speaker 1: Yeah, So maybe I'll if I go back to exactly.
9:00
Speaker 1: The point I made in my head is that if
9:02
Speaker 1: there is one application that moves into the cloud first,
9:07
Speaker 1: it's going to be file sharing. If you recall, I
9:10
Speaker 1: remember this when I was on my Windows computer in
9:13
Speaker 1: the eighties and nineties. What's one of the applications we
9:17
Speaker 1: all used a lot. Do you remember the Windows File Explorer.
9:20
Speaker 1: We're constantly clicking in and trying to find.
9:22
Speaker 2: The file in or something or searching for a.
9:24
Speaker 1: Searching for it or placing it in a folder. Very nicely.
9:28
Speaker 2: Like the name you used to have to put on
9:30
Speaker 2: a file was important because if you couldn't remember the name,
9:34
Speaker 2: you could find it. It wasn't like, here's a phrase
9:36
Speaker 2: that's somewhere in this document, go find it if you
9:38
Speaker 2: didn't remember exactly where that was nested, what name you put?
9:42
Speaker 2: Good luck, good life?
9:44
Speaker 1: Right? And so as the world moved from the desktop
9:48
Speaker 1: to the browser, and so by two thousand and six seven,
9:51
Speaker 1: we all are using at this point like Firefox, Mozilla,
9:55
Speaker 1: Chromes not even existent yet.
9:57
Speaker 2: It was Internet Explorer until.
9:58
Speaker 1: Chrome came along, exactly right, And so my thesis was, Okay,
10:02
Speaker 1: one of the most one of the applications for business
10:05
Speaker 1: that will move into the browser. So software as a
10:08
Speaker 1: service will be file sharing and collaboration. And so because
10:14
Speaker 1: precisely to your point, like the file that you always
10:16
Speaker 1: needed was somewhere else, and this made so much sense
10:20
Speaker 1: to me. And so at the time in two thousand
10:22
Speaker 1: and seven when I invested in Box, there were probably
10:25
Speaker 1: half a dozen two dozen, I don't know, there's many
10:27
Speaker 1: of these companies doing file sharing, but it was mostly
10:30
Speaker 1: for consumers, mostly for like.
10:31
Speaker 2: Your drop box.
10:33
Speaker 1: Dropbox was in that same era, but there was like
10:35
Speaker 1: x file in Elephant Drive. I mean, I remember these
10:39
Speaker 1: as an associate yours. You know, when you're suggesting an investment,
10:43
Speaker 1: you're going to do a lot of diligence. I remember,
10:44
Speaker 1: like the Laundry list of companies I looked at, there
10:46
Speaker 1: are probably like forty companies that were doing something similar,
10:49
Speaker 1: but most of them were dedicated towards sort of the
10:52
Speaker 1: consumer use cases photos, music, stuff like that.
10:56
Speaker 2: NAP's the era was right around.
10:58
Speaker 1: Then, exactly exactly, and so hypothesis was, hey, like this
11:01
Speaker 1: stuff will be relevant to large companies who will want
11:05
Speaker 1: to have file sharing and collaboration for their companies. And
11:09
Speaker 1: Box actually pivoted from being consumer company to being an
11:12
Speaker 1: enterprise company, And that's when I got pretty excited because
11:15
Speaker 1: it lined up with sort of this view that I
11:17
Speaker 1: had that large companies will move from file file servers
11:23
Speaker 1: in their data centers or wherever their and their buildings
11:26
Speaker 1: into files that now reside in the cloud.
11:29
Speaker 2: And they're willing to pay for it. They're willing to
11:31
Speaker 2: pay for it, unlike back then anyway, consumers were so
11:34
Speaker 2: reluctant exact to pay for anything. So it's interesting because
11:38
Speaker 2: you've had a lot of early investment success with a
11:42
Speaker 2: variety companies. Is it easy or difficult to learn from
11:48
Speaker 2: past winners? Is every startup different or do you start
11:53
Speaker 2: a little pattern recognition that gives you some clues, Hey,
11:56
Speaker 2: these guys are onto something.
11:58
Speaker 1: Yeah. I think there's definitely some compounding of learning from
12:04
Speaker 1: early wins and losses. You brought up Box, and so
12:08
Speaker 1: Box first investment, got to spend a lot of time
12:12
Speaker 1: with the founders, got to learn the business with the
12:14
Speaker 1: founders actually because they were young. I was young. I
12:16
Speaker 1: was in my twenties when I joined the board, and
12:20
Speaker 1: from that experience I learned a lot about what it
12:24
Speaker 1: meant to actually bottoms up sell software into large companies,
12:28
Speaker 1: which led me actually to this investment in Yammer, which
12:33
Speaker 1: many folks may not remember, but it was a enterprise
12:37
Speaker 1: social network circa twenty ten, kind of like Twitter meets Facebook,
12:42
Speaker 1: but for your company. And Microsoft ended up acquiring it
12:46
Speaker 1: in twenty twelve, and it's part of the Microsoft Suite
12:49
Speaker 1: now and part of the teams and all that. But
12:53
Speaker 1: the experience of this bottoms up adoption. You looked at Yammer,
12:57
Speaker 1: and a lot of large companies wanted to have a
13:00
Speaker 1: enterprise social network or somewhere where kind of like a
13:03
Speaker 1: town hall, a messaging platform. You share a file, people
13:08
Speaker 1: comment on it like people do it on Twitter or Facebook.
13:11
Speaker 1: And that was taking some of the learnings from the
13:14
Speaker 1: web era and the social era and applying it to
13:16
Speaker 1: the business world. And so at Yammer, I learned a lot.
13:21
Speaker 1: It was a quick journey, but nevertheless, like there was
13:24
Speaker 1: a level of engagement and monetization was good. And a
13:32
Speaker 1: few years later, maybe even like a year later, I
13:34
Speaker 1: came across another company Slack Slack, and it was similar.
13:40
Speaker 1: Now it was true messaging. Actually, on my way over here,
13:43
Speaker 1: I was walking through and I saw a bunch of
13:44
Speaker 1: colleagues on Slack, which makes me really happy. And Slack
13:49
Speaker 1: is used broadly across the globe to this day in
13:52
Speaker 1: twenty twenty six. But the lessons learned in that twenty
13:56
Speaker 1: ten to eleven era led to the investment in Slack
14:00
Speaker 1: two thousand and fourteen for me when it was a
14:03
Speaker 1: ten person company. And so back to the point around
14:06
Speaker 1: like compounding of learning Box led to Yama, Yamer led
14:10
Speaker 1: to Slack, and since Slack there's been others, but there
14:13
Speaker 1: certainly is some pattern recognition around products that are working.
14:18
Speaker 2: And then in twenty eleven you co found social capital
14:21
Speaker 2: with Chamath, very famous model of social capital, which is
14:28
Speaker 2: how can we address many of the social ills that
14:31
Speaker 2: are are hurting the country through the intelligent use of startups, technology,
14:36
Speaker 2: et cetera. Was this an reinvention of venture capital or
14:43
Speaker 2: just a new set of tools within a partnership that
14:48
Speaker 2: you know, it was kind of novel for its z era.
14:50
Speaker 1: It was novel for its era because we decided that
14:53
Speaker 1: we would go after education, healthcare, and finance, you know,
15:00
Speaker 1: three of the largest parts of society, and how do
15:04
Speaker 1: we address inequities in those areas with our investments and
15:10
Speaker 1: believing that technology has the ability to democratize access to healthcare, education,
15:16
Speaker 1: and financial services, and that's you know, largely played out
15:19
Speaker 1: over the last fifteen years. But we just thought that
15:23
Speaker 1: there will be a ton of opportunity. As venture capitalists,
15:25
Speaker 1: we're seeking out opportunity, and we thought that going after
15:29
Speaker 1: large pockets of GDP, you'd identify really exciting opportunities. Turns out,
15:35
Speaker 1: you know, my interests remained in enterprise software, and I
15:38
Speaker 1: did spend a lot of my time in enterprise software
15:44
Speaker 1: even when we started Social Capital.
15:46
Speaker 2: So twenty seventeen, you leave Social Capital for Cline Perkins,
15:51
Speaker 2: a firm that has long been iconic. The laundry list
15:55
Speaker 2: of companies Kliner has backed Google, Cisco, were they early
15:59
Speaker 2: in app also, I.
16:00
Speaker 1: Think they were Google, Amazon, Sun Microsystems, Genentech into it
16:07
Speaker 1: unbelievableandom if you remember, you know, the list of greats
16:11
Speaker 1: is amazing.
16:13
Speaker 2: So they're an iconic company, but they're not the dominant
16:16
Speaker 2: force they once were when you joined. What made that
16:20
Speaker 2: opportunity so attractive?
16:22
Speaker 1: Yeah, So look, I actually long admired Kliner Perkins had
16:27
Speaker 1: an extreme reverence. I would call it for Kliner Perkins.
16:31
Speaker 1: Going back to my days moving to Silicon Valley, I
16:34
Speaker 1: mentioned I moved to Silicon Valley in nineteen ninety seven,
16:38
Speaker 1: I worked for this company's Silenx, And if you think
16:41
Speaker 1: about my first few weeks on the job, I'm in
16:44
Speaker 1: my cubicle. I've got a Sun Microsystems workstation, which is
16:48
Speaker 1: actually a dream because in college we had to share
16:50
Speaker 1: twenty of them amongst like two thousand of us. And
16:53
Speaker 1: now I have my own son Spark I think was
16:55
Speaker 1: a Spark twenty. And guess what, There's a Netscape brows
17:01
Speaker 1: and I'm buying books for grad school on Amazon. And
17:05
Speaker 1: by the way, that there's a couple of guys down
17:07
Speaker 1: the hallway at Stanford who are starting this company called Google,
17:10
Speaker 1: and I'm starting to use that search engine called Google.
17:13
Speaker 1: The one commonality amongst Xilenk's son, Netscape, Google, and Amazon
17:20
Speaker 1: is that Cline Perkins had led the Series A, so
17:22
Speaker 1: the first institutional investor for all five of those companies.
17:26
Speaker 1: So as a young guy, I had this developed this
17:28
Speaker 1: extreme reverence for Clinet Perkins because of the investments they
17:32
Speaker 1: had made in these really like history making companies and so,
17:37
Speaker 1: which is also what led me to think about venture
17:39
Speaker 1: capital as a career as a young engineer. Is like,
17:41
Speaker 1: you know, I want to be like those guys, those
17:43
Speaker 1: guys that are investing all the coolest companies that I'm
17:46
Speaker 1: using as a nineteen year old here, and that's really
17:50
Speaker 1: what got me excited about venture capital. And actually one
17:53
Speaker 1: of the people behind all many of those investments son Netscape, Google,
18:00
Speaker 1: Amazon was my partner, John Dor. And so for me,
18:04
Speaker 1: there was this extreme reverence for John Dor and his
18:07
Speaker 1: career and trying to emulate that, you know, and he
18:10
Speaker 1: was an electrical engineer from Rice, went to Harvard Business School,
18:14
Speaker 1: worked at Intel Corporation, then came to Cline Perkins out
18:18
Speaker 1: of business school, and so it had a deep meaning
18:22
Speaker 1: to me. And actually, truth be told, I actually in
18:25
Speaker 1: my business school essay, which I have still wrote that
18:28
Speaker 1: I wanted to go work at Clina Perkins. This is
18:31
Speaker 1: in two thousand and three when I wrote the essay.
18:34
Speaker 1: Two thousand and two, I wrote the essay. And then
18:37
Speaker 1: you know, when I tried to apply for a job
18:39
Speaker 1: in two thousand and five coming out of business school,
18:41
Speaker 1: I didn't get very far, but I did end up
18:44
Speaker 1: there in twenty seventeen.
18:45
Speaker 2: So you know, eventually, if you keep plugging away, you
18:50
Speaker 2: get to where you want to go. That's great coming up,
18:53
Speaker 2: we continue our conversation with mamu Inhammad clinap Perkins managing member,
18:58
Speaker 2: talking about the reboot of the firm. I'm Barry Ridults.
19:03
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm
19:07
Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.
19:11
Speaker 2: My extra special guest this week is Mamunhamid. He is
19:15
Speaker 2: managing member and general partner at Kleiner Perkins, where he
19:19
Speaker 2: is pivoting the firm towards early investors in software and
19:26
Speaker 2: artificial intelligence and automation. So you co led the refounding
19:32
Speaker 2: of Kleiner Perkins in twenty seventeen twenty eighteen, the firm
19:36
Speaker 2: was refocused on early stage Series A investing. Tell us
19:41
Speaker 2: what was behind the thought process? What made you say
19:44
Speaker 2: we don't want to be bigger, we want to be
19:47
Speaker 2: smaller and more focused.
19:49
Speaker 1: Yeah. If I look back at the decades of being
19:54
Speaker 1: probably the most successful venture capital firm throughout the seventies, eighties, nineties,
20:00
Speaker 1: and even the early two thousands, the one thing that
20:03
Speaker 1: defined Kleiner Perkins was it was a small partnership of
20:10
Speaker 1: seven ish or so partners who sat around a table
20:14
Speaker 1: in Menlo Park, meeting companies and having healthy discourse and
20:21
Speaker 1: debate about what companies to invest in and what the
20:23
Speaker 1: future of technology would bring to the world. And it
20:26
Speaker 1: was defined really by a small group of partners that
20:32
Speaker 1: were in many cases technical, they were operators, They had
20:36
Speaker 1: a passion for technology and its impact on humanity, and
20:41
Speaker 1: that was sort of what I kept coming back to,
20:44
Speaker 1: is that that was what defined Kleiner's decades of success.
20:48
Speaker 1: And we went back to the future in twenty seventeen
20:52
Speaker 1: and eighteen to that model, and today our partnership is
20:58
Speaker 1: six partners and then we have three more investment professionals,
21:01
Speaker 1: so we're a very small, nimble team. So we have
21:05
Speaker 1: two funds, and this team invests from both pools of capital.
21:11
Speaker 2: So early stage does that seed round or is it
21:15
Speaker 2: a little more developed?
21:17
Speaker 1: Yeah, So the early stage fund is Seed Series A
21:20
Speaker 1: mostly and maybe some bees, and then the Growth fund
21:25
Speaker 1: is b c's all the way to. We invested in
21:30
Speaker 1: the last anthropic round at a nine hundred billion our evaluation,
21:34
Speaker 1: which is rare for special companies, but it has the
21:37
Speaker 1: ability to invest across even to the pre IPO.
21:41
Speaker 2: And is it a coincidence that the Growth fund is
21:44
Speaker 2: two and a half times the size of the seed funds.
21:46
Speaker 2: At that point, these companies are bigger, require a bigger
21:51
Speaker 2: check or is that just happenstance.
21:54
Speaker 1: I think our funds are sized based on the opportunity
21:59
Speaker 1: set in front of us. Our early stage funds have
22:02
Speaker 1: been almost exactly thirty five companies for the last fifteen years.
22:09
Speaker 1: So thirty five companies per fund, which we think about
22:12
Speaker 1: is the right number of shots on goal for an
22:15
Speaker 1: early stage fund to return multiples on it.
22:17
Speaker 2: Twenty five to thirty million.
22:19
Speaker 1: Per exactly exactly. So it starts out with maybe in
22:23
Speaker 1: some cases a five million dollar check, and the subsequent
22:26
Speaker 1: checks is another fifteen to twenty, or it could be
22:29
Speaker 1: first check is thirty and then with Parada, you're investing
22:32
Speaker 1: let's say, up to forty million dollars per in a company,
22:35
Speaker 1: and then your growth fund is doubling down investing a
22:38
Speaker 1: lot more in those companies.
22:39
Speaker 2: Huh really really kind of interesting. So the focus is
22:43
Speaker 2: artificial intelligence startups across software, healthcare, transportation, and autonomy industries.
22:51
Speaker 2: So let's unpack that because I'm hearing a little overlap
22:56
Speaker 2: with each of your prior venture experiences. Tell us why
23:02
Speaker 2: those four areas are so attractive.
23:04
Speaker 1: Yeah, So this is a truly once in a lifetime
23:13
Speaker 1: revolution that we're going through with AI, and the number
23:19
Speaker 1: of exciting companies people that we're seeing right now as
23:26
Speaker 1: at an all time high. The whole world in some
23:30
Speaker 1: ways is being refactored with AI, and this is just
23:34
Speaker 1: a very beginning, and so I would say that all
23:38
Speaker 1: parts of the economy, even beyond those four areas, it
23:42
Speaker 1: is like I mentioned earlier, healthcare, financial services, it is
23:48
Speaker 1: all sorts of knowledge work, it is going to be
23:51
Speaker 1: all sorts of physical automation in terms of robotics, We're
23:58
Speaker 1: even space, even defense areas, drug discovery, materials discovery. I
24:07
Speaker 1: think it is all fair game at this point in
24:09
Speaker 1: terms of where the exciting pockets of innovation are, just
24:14
Speaker 1: because there has never been a tailwind like this that
24:19
Speaker 1: really allows all parts of the world to be refactored
24:24
Speaker 1: based on, you know, the biggest technological revolution ever.
24:30
Speaker 2: So I'm glad you described it that way because I
24:33
Speaker 2: keep hearing people compare AI to the Internet, and that
24:38
Speaker 2: seems to contained too timid. I wonder if you agree
24:43
Speaker 2: with the thought that the only thing remotely comparable to
24:47
Speaker 2: this is the Industrial Revolution, which centuries later, we're still
24:51
Speaker 2: dealing with the impact.
24:54
Speaker 1: Of I absolutely agree with you Berry. It is like
24:56
Speaker 1: the industrial revolution, it's like the railroads, it's like the
24:59
Speaker 1: printing US. Is it is that it's not the Internet.
25:04
Speaker 2: That's really interesting because when I think Internet, the first
25:08
Speaker 2: thing you think of is, oh, this is a bubble
25:10
Speaker 2: when this is going to collapse? But you mentioned your
25:12
Speaker 2: an investor in anthropic These are forget not profitable companies.
25:18
Speaker 2: These are companies with giant revenue streams already they're barely
25:22
Speaker 2: a few years old. How big can this sector get?
25:26
Speaker 2: Is this going to take over every corner of the economy.
25:31
Speaker 1: Yeah, let's talk about that. I think that's the I
25:33
Speaker 1: think that's the real conversation. And the more the very
25:37
Speaker 1: exciting conversation that one can have about this topic is
25:40
Speaker 1: and I start at a very high level, which is
25:44
Speaker 1: the GDP of the world today. It's it's about one
25:48
Speaker 1: hundred and twenty trillion dollars, and about half of that
25:53
Speaker 1: is labor, the labor component of it, so roughly sixty
25:58
Speaker 1: trillion dollars. And of that sixty trillion, roughly sixty percent
26:05
Speaker 1: or so is white collar mid fifty sixty somewhere so
26:09
Speaker 1: that's anywhere from thirty thirty five trillion dollars. And if
26:15
Speaker 1: you look at tokens and what the frontier model companies
26:21
Speaker 1: provide is it is units of labor. We're already seeing
26:25
Speaker 1: how those units of labor are being utilized in computer science,
26:30
Speaker 1: so software development, in law, in medicine, in drug discovery.
26:36
Speaker 1: These are little agents and buddies that we as humans
26:39
Speaker 1: have now to help us do more with our intellect.
26:43
Speaker 1: And the way I see it is that you know,
26:45
Speaker 1: we're talking about trillions of dollars that are opening up
26:50
Speaker 1: for these companies that exist. And exhibit A is a
26:53
Speaker 1: company like Anthropic, which is publicly stated has gone from
26:57
Speaker 1: zero to forty five billion revenue run rate in a
27:02
Speaker 1: matter of less than three years, okay, and that is
27:07
Speaker 1: likely going to double. And you know, and the company
27:11
Speaker 1: started this year, I believe at twenty is already doubled
27:15
Speaker 1: more than doubled in the short year that we've been
27:18
Speaker 1: in so far. And so these numbers are astounding not
27:21
Speaker 1: only because these companies are selling software or technology, they're
27:25
Speaker 1: selling units of labor. And the labor markets, as we
27:29
Speaker 1: all know, are the biggest component. As I just mentioned earlier,
27:33
Speaker 1: of the world's GDP. We're talking about trillions of opportunity
27:36
Speaker 1: dollars of opportunity. And that's what excites us so much
27:41
Speaker 1: about this time, is that it's not just about selling
27:45
Speaker 1: tools and software that we've been accustomed to selling to
27:48
Speaker 1: IT departments. It's selling like actual labor to companies, to corporations,
27:56
Speaker 1: to you know, even to consumers who are using AI
28:00
Speaker 1: in their personal lives.
28:01
Speaker 2: So let's let's talk a little bit about that. The
28:04
Speaker 2: fear I keep hearing is everybody's going to lose their job.
28:08
Speaker 2: It's a very Malthusian argument that this technology is going
28:13
Speaker 2: to replace labor the way the steam engine did. I'm
28:17
Speaker 2: getting a sense from the data and from other analysts
28:22
Speaker 2: like Torsten Slock that this isn't a replacement for white
28:26
Speaker 2: collar labor. It's an enhancement, or at least that that's
28:30
Speaker 2: the argument. Give us, give us your perspective on that.
28:33
Speaker 1: I actually fully agree with that point of view that
28:37
Speaker 1: you have. Uh it is it's it's like you know,
28:40
Speaker 1: uh getting email. I mean I think, uh, the fact
28:44
Speaker 1: that we got the computer, didn't you know, the people
28:47
Speaker 1: using the typewriters started using computer and started doing other
28:50
Speaker 1: types of jobs. Even in the steam engine era or
28:53
Speaker 1: the Industrial Revolution, we found ways to repurpose jobs or
28:57
Speaker 1: people and their skills. I don't think humans are going
29:00
Speaker 1: out of style. I don't think you know, the world
29:04
Speaker 1: is going to be largely unemployed and on UBI because
29:07
Speaker 1: we are going to displace all this work and all
29:11
Speaker 1: these people with AI. I think it's it's the extreme
29:15
Speaker 1: where the mind goes to. But that's just not the reality.
29:19
Speaker 1: And that sort of bears itself in the numbers that
29:21
Speaker 1: we see, you know, in record low unemployment rates, and
29:26
Speaker 1: and actually we need more labor, more people than we
29:29
Speaker 1: ever have, and so.
29:30
Speaker 2: More skilled labor. We are seeing a decrease in job
29:35
Speaker 2: availability for kids right out of college for unskilled labor.
29:40
Speaker 2: If anything, is this likely to force more people to
29:44
Speaker 2: get more technical, to up their skill set.
29:49
Speaker 1: I actually to believe that it is not like a, oh,
29:52
Speaker 1: you know, you don't need to be a software developer
29:54
Speaker 1: and study CS anymore because you know these software jobs
29:57
Speaker 1: are going away. It is like now now the job
30:01
Speaker 1: of the software engineer is to manage a whole hosts
30:04
Speaker 1: to agents and make them do work for them and
30:06
Speaker 1: be the brains behind the operation. You think of it
30:09
Speaker 1: as like you have all these little agents and came
30:11
Speaker 1: little employees who are working on your behalf. And so yeah,
30:15
Speaker 1: that is like the higher level thinking and the things
30:18
Speaker 1: that actually you know, like when we go to school,
30:20
Speaker 1: we think about how to problem solve and you know,
30:23
Speaker 1: if we're solving a math problem it's hard, we think
30:25
Speaker 1: about many different ways to solve it. And the same
30:28
Speaker 1: way it's how am I going to use AI to
30:31
Speaker 1: help me solve problems? And that's the way I think
30:33
Speaker 1: we all And I would say, you know, we have
30:35
Speaker 1: four kids, you know, believe or not, I'm telling them
30:39
Speaker 1: going to math science like U and actually art like
30:43
Speaker 1: have like spectral diversity in your your learning because all
30:47
Speaker 1: the skills that mattered in the past solving you know,
30:50
Speaker 1: math problems with pen and paper, will really matter in
30:54
Speaker 1: the future.
30:55
Speaker 2: So let's bring this back to how you think about
30:59
Speaker 2: the opportunity the set that's out there. At Clina Perkins,
31:03
Speaker 2: you do structured reviews of every interesting deal that was
31:07
Speaker 2: passed on. I'm kind of fascinated by by that. I
31:12
Speaker 2: know a lot of vcs kind of hold the their
31:16
Speaker 2: misses as a badge of honor. Some some firms posted
31:20
Speaker 2: on their website, what do you think of what's driving
31:26
Speaker 2: the thought process around revisiting either missed deals or mistakes
31:31
Speaker 2: or what have you. What what does the process teach you?
31:35
Speaker 1: Yeah. So, actually, one of the things that I did
31:37
Speaker 1: when I got to Cline Perkins in twenty seventeen was
31:40
Speaker 1: that we would look every week at the that week's
31:45
Speaker 1: series as that got done by our peer firms about
31:48
Speaker 1: thirty forty firms, and whether we had seen the company
31:53
Speaker 1: that was invested in or not, and as simple heuristic
31:56
Speaker 1: of like, are we seeing the things that matter because
31:59
Speaker 1: they seem to matter because our peer firms invested in
32:01
Speaker 1: those companies. And so we've been doing this now for
32:04
Speaker 1: the last nine years. And initially our goal was like
32:10
Speaker 1: we should see sixty percent, and seeing means you met
32:12
Speaker 1: the company, and for us that hovers around now seventy
32:17
Speaker 1: percent or so. You don't want it to be one
32:18
Speaker 1: hundred percent because then you're just that's the game you're playing.
32:21
Speaker 1: We just see everything, and you don't want to be
32:23
Speaker 1: twenty percent because you're not seeing enough. But we think
32:25
Speaker 1: seventy percent is a good number, and we look at
32:29
Speaker 1: the ones, Okay we saw if we saw seventy percent
32:32
Speaker 1: of the good stuff let's say, let's call it, or
32:36
Speaker 1: was it thirty percent that we didn't see even better?
32:40
Speaker 1: Or if we saw the seventy percent, do we pass
32:43
Speaker 1: on the good stuff and do the bad stuff? And
32:46
Speaker 1: so we go through that exercise quite frequently. We just
32:50
Speaker 1: had an off site a few weeks ago, and we
32:53
Speaker 1: go through again. We pour salt on the wounds and say, okay,
32:57
Speaker 1: we saw these companies and we passed. Why do you
33:01
Speaker 1: pass at that round? That early stage round? And just
33:04
Speaker 1: to remind ourselves why we need to adjust the way
33:09
Speaker 1: we do things, I'll give you an example. The Anthropic,
33:14
Speaker 1: which is an incredible company. Its Series A was not
33:17
Speaker 1: a traditional one. SBF from FTX led the Series A famously.
33:24
Speaker 1: We do know how that worked out. It was an
33:26
Speaker 1: amazing investment and very precient on his behalf. I forget
33:30
Speaker 1: how much it were worth today, but it would be
33:32
Speaker 1: worth a lot. But the Series B was a sort
33:36
Speaker 1: of non consensus, non obvious round, and we actually met
33:41
Speaker 1: with the founders and but we met them over zoom
33:46
Speaker 1: and we played with the product. But you don't get
33:49
Speaker 1: the same visceral feeling about a company and the founders
33:54
Speaker 1: and their ambitions and aspirations and what they're trying to
33:57
Speaker 1: do with with their with their with their company, and
34:00
Speaker 1: so what a miss right, And I think that round
34:03
Speaker 1: got done in that four billion dollar valuation or something,
34:06
Speaker 1: which not a small valuation but still compared to today,
34:09
Speaker 1: compared today, and so and we looked a lot of
34:13
Speaker 1: our passes that we were good companies, and many times
34:19
Speaker 1: we just didn't meet them in person. So the pandemic
34:21
Speaker 1: era bred some really bad habits of you know, you
34:24
Speaker 1: did as first meeting over zoom and so and I
34:28
Speaker 1: looked at my own investments and the last twenty investments
34:32
Speaker 1: that I've done, the first meeting was always in person,
34:37
Speaker 1: the first meeting. So I've driven my whole calendar to like,
34:40
Speaker 1: I just don't want to do any meetings on Zoom anymore.
34:44
Speaker 1: I want to meet people in person, and if it's
34:47
Speaker 1: worth a thirty minute Zoom, it should be worth a
34:50
Speaker 1: thirty minute in person meeting. And I'm glad we get
34:53
Speaker 1: to do this in person, because you know, it wouldn't
34:57
Speaker 1: be the same thing if I was on a Zoom
34:58
Speaker 1: screen doing this thing with you.
34:59
Speaker 2: That's exactly right. What about the reverse of that. If
35:03
Speaker 2: you're analyzing your missus, do you ever review your hits,
35:07
Speaker 2: your wins, and saying why did we get this right?
35:10
Speaker 2: What can we take forward from this?
35:14
Speaker 1: That's a great question. I think we just assume what
35:20
Speaker 1: we do look at, okay, is are the ones that
35:23
Speaker 1: we did the portfolio that we built, is it better
35:26
Speaker 1: than the portfolio that we missed? And it's actually a
35:31
Speaker 1: toss up.
35:31
Speaker 2: It's that you know, huh, that's really interesting.
35:34
Speaker 1: In the sense that we didn't even see it, and
35:38
Speaker 1: because it'd be bad if we didn't see companies, and
35:43
Speaker 1: that basket of companies was way better than the companies
35:46
Speaker 1: we saw. So and we've try to be very intellectually
35:51
Speaker 1: honest about like are we seeing the right stuff or
35:54
Speaker 1: the wrong stuff? And it turns out we're seeing it's
35:56
Speaker 1: a toss up.
35:56
Speaker 2: Actually, right. The reason I asked that question is you
36:01
Speaker 2: learn in the public markets, you learn more from your
36:04
Speaker 2: missus than your wins, because it's very hard to tell
36:07
Speaker 2: the difference between skill and luck in the public markets.
36:11
Speaker 2: I'm curious if the same sort of thing applies to
36:15
Speaker 2: venture I think it does.
36:16
Speaker 1: I think you you really beat yourself up on the
36:19
Speaker 1: missus and the ones that you do. You're just like, okay,
36:22
Speaker 1: check you know it happened and you don't. Uh, maybe
36:27
Speaker 1: you don't think about it as much as the ones
36:28
Speaker 1: you missed.
36:29
Speaker 2: Huh, really really interesting. Coming up, we continue our conversation
36:33
Speaker 2: with Mamunhahmed, partner at Kleiner Perkins, discussing the state of
36:39
Speaker 2: venture investing today. I'm Barry Ridults. You're listening to Masters
36:43
Speaker 2: in Business on Bloomberg Radio. I'm Bury rid Halts. You're
36:48
Speaker 2: listening to Masters in Business on Bloomberg Radio. Mamun Hmed
36:52
Speaker 2: is my extra special guest. He is a partner at
36:56
Speaker 2: Kleiner Perkins, where he is driving the firms focus on
37:00
Speaker 2: early stage investments in artificial intelligence and related technology. So
37:07
Speaker 2: let's talk a little bit about the state of venture
37:09
Speaker 2: investing today. When you meet a founder for the first time,
37:15
Speaker 2: preferably in person, what are you looking for? What are
37:18
Speaker 2: you trying to spot that isn't in the pitch deck
37:23
Speaker 2: they sent earlier? How do you separate intensity from delusion? Like?
37:28
Speaker 2: What are you trying to identify in that first meeting?
37:32
Speaker 1: Yeah, it is the most interesting time in my venture career,
37:37
Speaker 1: and for obvious reasons. This AI revolution, the tailwinds that
37:41
Speaker 1: AI brings to companies that are being started today is
37:45
Speaker 1: unlike anything we've seen before. So the quality of ideas
37:51
Speaker 1: is at an all time high, and I would say
37:54
Speaker 1: the quality of the people is also at an all
37:56
Speaker 1: time high. And those two forces combined suggests that there's
38:02
Speaker 1: a lot of really high quality ideas and founders that
38:05
Speaker 1: we're seeing. And so I would say it's the volume
38:10
Speaker 1: is because of that, at an all time high. And
38:12
Speaker 1: so to your question of like what do we what
38:15
Speaker 1: are we looking for? I think obviously everything today is
38:22
Speaker 1: AI enabled AI tailwinds, and venture capital isn't venture capital
38:29
Speaker 1: unless there's like a strong tailwind of something. And so
38:33
Speaker 1: this is a makes it such an exciting time because
38:35
Speaker 1: there's such a strong tailwind and the pace at which
38:41
Speaker 1: the tailwinds are like the winds are so strong, and
38:46
Speaker 1: in some ways because the frontier model, companies are providing
38:50
Speaker 1: better and better models, which allow companies that build on
38:54
Speaker 1: these models to provide strong value proposition. Take for example,
39:00
Speaker 1: a company like Harvey, which is AI for legal is
39:04
Speaker 1: in most of the am law one hundreds sells to
39:07
Speaker 1: large enterprises and Fortune one hundred type companies and becoming
39:13
Speaker 1: sort of the de facto way that legal law firms
39:16
Speaker 1: and legal departments are using AI inside of their companies
39:19
Speaker 1: and inside of law firms. And yes, they have a
39:22
Speaker 1: lot of secret sauce on top of what the model companies.
39:25
Speaker 1: But at the same time they get better and better
39:28
Speaker 1: as these models get better and better. And this is
39:30
Speaker 1: a category that just got created in the last few years,
39:34
Speaker 1: and there are numerous examples like it in every sector
39:37
Speaker 1: of society. And just to take a step back when
39:41
Speaker 1: we first saw I don't know what were your reaction
39:45
Speaker 1: was to chat GPT when you first saw it, but
39:47
Speaker 1: for me, it was the same reaction I had when
39:50
Speaker 1: I first got to use a browser Internet browser. To me,
39:54
Speaker 1: that Netscape moment was equivalent to the chat GPT moment.
40:00
Speaker 1: And we started to think about what are the second
40:03
Speaker 1: order effect what are the things that come from here,
40:06
Speaker 1: from chat GPT, from LMS, and it was we thought
40:10
Speaker 1: about like the labor pyramid, think about knowledge work and
40:14
Speaker 1: we went straight to okay, what is what are the
40:16
Speaker 1: most highly skilled, highly paid jobs in the in the country.
40:21
Speaker 1: And if you actually look at the top twenty list
40:24
Speaker 1: of jobs dollars made by job, it's some form of engineer, doctor,
40:31
Speaker 1: or lawyer. And so what we did at Kindel Perkins
40:34
Speaker 1: was to invest in companies that do AI for legal,
40:38
Speaker 1: AI for software development, AI for medicine, or and so
40:41
Speaker 1: we invested in companies like Harvey Windsurf, which got acquired
40:45
Speaker 1: by Google. We invested in a company called Ambience Open
40:50
Speaker 1: Evidence to go after that that labor pyramid, top of
40:55
Speaker 1: the pyramid, top of the pyramid, and back to your point,
40:58
Speaker 1: it's like an enhancement to those people. It was supposed
41:00
Speaker 1: to be like a co pilot, in which it is.
41:03
Speaker 1: It's and in something.
41:04
Speaker 2: Register trademark Microsoft Corporation.
41:06
Speaker 1: Yeah, yeah, exactly, copilot exactly, and in some cases it's
41:12
Speaker 1: become an autonomous agent for those people. So software developers
41:16
Speaker 1: run agents to new work for them now. And so
41:18
Speaker 1: we worked worked our way down the labor pyramid a bit,
41:21
Speaker 1: and we went down what's the next level? So think
41:23
Speaker 1: of it like if doctors, lawyers, engineers are in the
41:27
Speaker 1: two hundred k plus a year pay, then what's one
41:32
Speaker 1: below that, you know, like you've got financial analysts, salespeople,
41:38
Speaker 1: nurses and so then we're invested in the next class
41:42
Speaker 1: of companies, so Rogo, which is AI for finance, Hippocratic,
41:46
Speaker 1: which is an agentic nursing platform, Nooks, and revo which
41:51
Speaker 1: are helping salespeople PI copilots for their work. And so
41:55
Speaker 1: we've sort of worked our way down the labor pyramid.
41:58
Speaker 1: And if you think about the pyramid, guess what's at
42:01
Speaker 1: the bottom of the pyramid. It's physical labor. It is
42:05
Speaker 1: the lowest paid, low scale in some and eventually robotics
42:11
Speaker 1: will get there and hopefully do a lot of the
42:13
Speaker 1: backbreaking work that nobody should be doing, right, and I
42:17
Speaker 1: think we eventually that is sort of the way also
42:20
Speaker 1: goes along with the timeline of the way we're thinking
42:22
Speaker 1: about these things, is that you start with the highly scaled,
42:25
Speaker 1: highly paid, and eventually, over the next decade, we get
42:28
Speaker 1: down to the sort of the lowest scale, lowest paid work.
42:31
Speaker 2: So you mentioned the tailwind behind AI. What does that
42:36
Speaker 2: do to valuations? How do you underwrite startups in a
42:41
Speaker 2: market where even the half decent companies look kind of expensive?
42:46
Speaker 1: Great question. So when you have companies have gone from
42:54
Speaker 1: zero to trillion in value, you know, in the last
42:58
Speaker 1: three to four years, and when you're a founder, what
43:03
Speaker 1: do you do you point to that that's what I
43:04
Speaker 1: can be. I can get to trillion dollars because I'm
43:09
Speaker 1: going after really large problems and we love that that ambition.
43:18
Speaker 1: And what do people do then is that, well, you know,
43:20
Speaker 1: if the probability of a of a one trillion dollar
43:23
Speaker 1: outcome is one percent, and you do all the other
43:28
Speaker 1: ninety nine percent of probabilities for the rest of the numbers,
43:31
Speaker 1: you've got yourself to a expected value of ten billion
43:36
Speaker 1: dollars or more. At least that's the minimum, right, you
43:39
Speaker 1: do the one percent of ten billion, and you add
43:41
Speaker 1: up all the other probabilities, it's probably like thirty forty
43:45
Speaker 1: fifty billion. And so if you're looking at a series
43:48
Speaker 1: A company where the ambition is tremendous, you're the valuation
43:55
Speaker 1: may just be you know, you're raising one hundred million
43:56
Speaker 1: or a billion dollar valuation for two guys out the gate.
44:00
Speaker 1: And those are real examples. And I think we've we've
44:03
Speaker 1: got a bit of this, you know, pointing at the
44:06
Speaker 1: large outcome and if it's paired with smart people, ambitious idea,
44:10
Speaker 1: it has that kind of potential. But the reality is
44:13
Speaker 1: that there's usually one or two of those, the real
44:16
Speaker 1: outliers that are mag seven scale and just the you know,
44:22
Speaker 1: just gravity in itself doesn't allow for you know, the
44:25
Speaker 1: world can't have you know, one hundred trillion dollar companies.
44:29
Speaker 2: Plus if it's a trillion dollar total addressable market, it's
44:32
Speaker 2: going to attract a lot of competition, a lot of
44:34
Speaker 2: other startups, perhaps more than if you're focusing on a
44:39
Speaker 2: smaller niche. Although even the cloud storage was you mentioned
44:45
Speaker 2: forty companies early days, there's got to be tens of
44:49
Speaker 2: thousands of companies going for each of these segments in AI.
44:53
Speaker 1: Yeah, and there are. And I think the hard part
44:56
Speaker 1: of the work that we have is is identifying what
44:59
Speaker 1: we think with the leading company because in technology, generally speaking,
45:04
Speaker 1: the leading company gets most of the market cap, which
45:08
Speaker 1: which is if you look at the mag seven winner
45:11
Speaker 1: take all, winner take most, right, right, like take ninety
45:14
Speaker 1: percent of the market you know in Vidia of all
45:17
Speaker 1: GPU spend. You know, take Google alphabet you know, multiple businesses,
45:22
Speaker 1: or take a Tesla, take a meta own social right,
45:27
Speaker 1: so winner takes most in technology.
45:30
Speaker 2: Huh really interesting. You've described the AI moment as one
45:35
Speaker 2: of the most important company building opportunities in our lifetime.
45:40
Speaker 2: What's the risk of venture funding too many of these
45:43
Speaker 2: startups or is this just a fat head of winners
45:48
Speaker 2: and a long tail of Well, we gave it a shot.
45:51
Speaker 2: Is this just the nature of this business where it's
45:55
Speaker 2: a couple of winners are driving all the returns for
45:58
Speaker 2: for your funds.
46:00
Speaker 1: Yeah, venture is a power law business. There's no question
46:03
Speaker 1: about it. And if you already look at like there's
46:07
Speaker 1: this article I read the other day where ninety percent
46:11
Speaker 1: the AI revenue is in the hands of two companies.
46:13
Speaker 1: That's crazy opening and anthropic.
46:16
Speaker 2: Right.
46:16
Speaker 1: So in the remaining ten percent is in a bunch
46:19
Speaker 1: of companies that are excellent companies. But the scale of
46:22
Speaker 1: those two other companies is so massive, right, that it
46:25
Speaker 1: dwarfs all the other good, great work that's happening in
46:28
Speaker 1: tons of other companies that we've backed. And by the way,
46:31
Speaker 1: like you know, those will be great outcomes and be
46:33
Speaker 1: great companies. There's no question about it. It's just that
46:37
Speaker 1: it dwarfs and the power law really does. You can
46:40
Speaker 1: see it when you look at the numbers.
46:43
Speaker 2: And.
46:45
Speaker 1: It's yes, our job is to be in the companies
46:48
Speaker 1: that make history and are in the power are following
46:52
Speaker 1: the power law. What we find is that they're actually
46:57
Speaker 1: you would think there's dozens of companies, there's actually tens,
47:01
Speaker 1: maybe like less than ten, and there's like two to
47:04
Speaker 1: three that are converging to become winners in a category.
47:07
Speaker 1: And I think everyone's trying to identify those two to
47:10
Speaker 1: three companies and be in those two to three companies.
47:13
Speaker 1: And so one of the challenges we face is that
47:15
Speaker 1: we try to invest in what we think is the
47:17
Speaker 1: winner in the category early. Sometimes you don't know early
47:22
Speaker 1: that this is the winner, and if you invest too early,
47:25
Speaker 1: you conflict yourself out of the eventual winner.
47:27
Speaker 2: Right, you only invest in one company per silo, so
47:31
Speaker 2: to speak. Yes, right, So just to avoid those sort
47:34
Speaker 2: of conflicts.
47:35
Speaker 1: So the conflict typically we're joining the boards of these
47:37
Speaker 1: companies and if you have, you know, confidential board level information,
47:41
Speaker 1: and you don't want that, Like now you're invest in
47:44
Speaker 1: a competitor, all of a sudden, there's a chance of conflict,
47:47
Speaker 1: conflict of interest, and so we definitely try to avoid
47:50
Speaker 1: that conflict of interest.
47:51
Speaker 2: So I'm kind of fascinated by as venture investors. You
47:56
Speaker 2: obviously see the promise of AI A care so ald
48:00
Speaker 2: these different economic sectors. I'm curious how you're using AI internally,
48:07
Speaker 2: Clina Perkins are using it to source deals or do
48:10
Speaker 2: due diligence or predict specific outcomes. How does AI fit
48:15
Speaker 2: into your operations.
48:17
Speaker 1: Yeah, we have definitely been maxing out on AI internally,
48:22
Speaker 1: not only because we invest in these companies whether we use.
48:28
Speaker 1: Gleen is a company we incubated inside of Clina Perkins
48:30
Speaker 1: actually and it sits in year seven now is pre
48:33
Speaker 1: AI company started by an incredible engineer, Arvin Jane. And
48:39
Speaker 1: that's like our knowledge management. Every single piece of knowledge
48:43
Speaker 1: inside of Clina Perkins resides in Glean and you can
48:45
Speaker 1: go to it, query it, chat chat with it. Like
48:48
Speaker 1: if I want to find out your phone number and email,
48:51
Speaker 1: like and then I've never met you before, but I
48:53
Speaker 1: know someone at Kleiner probably knows you. I'll go to Glean.
48:56
Speaker 1: Or if I want to ask about like an HR policy,
48:59
Speaker 1: I'll go to Lean quickly asked. Because it just knows
49:01
Speaker 1: everything about Cliine Perkins. It knows investment memos, it can
49:04
Speaker 1: it knows cap tables, it knows like the really like
49:08
Speaker 1: confidential stuff. It's permission in a way where the people
49:11
Speaker 1: who are supposed to know will can know and it's
49:15
Speaker 1: very That is part of the magic is that it's
49:17
Speaker 1: very safe, secure. You trust it to really know the
49:22
Speaker 1: things they're supposed to know and not know the supposed
49:24
Speaker 1: to things is not supposed to know so and so
49:26
Speaker 1: that's an example. But we are. We get so much information,
49:31
Speaker 1: and whether it's board decks and their long board memos,
49:36
Speaker 1: financials and uh so when I'm going to board meeting
49:40
Speaker 1: after this, I got the board memo, and the first
49:43
Speaker 1: thing I do, it's it's sent to an email ALIAS
49:48
Speaker 1: and it and that runs it through AI and it
49:50
Speaker 1: produces a summary for me of the of the board
49:53
Speaker 1: meeting and questions I should be thinking about as an
49:56
Speaker 1: instant thing that I do once I get the board materials,
49:59
Speaker 1: just so that I start thinking about it before I
50:02
Speaker 1: actually go read the board memo. I sort of have
50:04
Speaker 1: a preview of it in my mind. We do a
50:09
Speaker 1: quarterly or every four month portfolio of view, and we
50:12
Speaker 1: have a couple hundred companies. It used to be a
50:15
Speaker 1: very manual process that we had a dedicated person working
50:19
Speaker 1: on it, and now our technology team has built the
50:21
Speaker 1: system where we take these summaries. They get piped into
50:24
Speaker 1: this portfolio book that we create and with all the
50:28
Speaker 1: financials and all the metrics and everything, and it's something
50:31
Speaker 1: that we are heavily leveraging AI. In this case, it's
50:35
Speaker 1: Glean's and clawed. So the underlying models obviously, and we
50:42
Speaker 1: have done a bunch of other things. Actually love to
50:47
Speaker 1: rate my meetings just so I know, like so I remember,
50:50
Speaker 1: like what are the tens and the nines that I
50:53
Speaker 1: should have paid attention to but I forgot because I didn't.
50:56
Speaker 1: So I want to have an exhaust of all the
50:57
Speaker 1: things that I am encountering my real life. And AI
51:00
Speaker 1: is an amazing capture of the exhaust and to provide
51:04
Speaker 1: intelligence and signals to our team and it can pipe
51:07
Speaker 1: it into our CRM. And so there's all these cool
51:10
Speaker 1: things that we've done. We have an internal tech team
51:12
Speaker 1: which is an amazing team of four folks who build
51:15
Speaker 1: a lot of these tools, and we are definitely maxing
51:18
Speaker 1: out on using everything that's out there.
51:20
Speaker 2: Huh really really quite fascinating. So final question before we
51:24
Speaker 2: get to our favorites that we ask all our guests
51:27
Speaker 2: what are investors not thinking about when it comes to
51:31
Speaker 2: AI or anything else? But perhaps should be what sort
51:36
Speaker 2: of topics policy, data, geography, what's getting overlooked? But shouldn't
51:42
Speaker 2: I think right.
51:44
Speaker 1: Now we're going through a time of where software is
51:47
Speaker 1: considered to be dead and or there's called the saaspocalypse and.
51:53
Speaker 2: Although they're just coming offish clothes.
51:55
Speaker 1: Yeah, and there's always an overreaction to, oh my god,
51:59
Speaker 1: it's going to be a Capex world and only chips
52:01
Speaker 1: will matter, and only like you know, like fiber and
52:05
Speaker 1: data centers and power will matter. The reality is like
52:08
Speaker 1: the way we as humans interact with software, with with
52:11
Speaker 1: the technologies through software and through things you know we
52:14
Speaker 1: have known to be called software and tools and so.
52:17
Speaker 1: And by the way CIOs and large companies buy buy
52:21
Speaker 1: from companies that sell to them, and they don't just buy,
52:24
Speaker 1: you know, a smart agent. And so I think something
52:28
Speaker 1: that's been certainly just the pendulum swung a little too
52:32
Speaker 1: far is that we underappreciate what software still does and
52:36
Speaker 1: will continue to do forever, for for our enterprises, for governments,
52:40
Speaker 1: et cetera.
52:40
Speaker 2: Software not going away.
52:42
Speaker 1: Yeah.
52:42
Speaker 2: Really interesting. All right, let's jump to our favorite questions,
52:46
Speaker 2: starting with who are your mentors who helped shape your career?
52:52
Speaker 1: Yeah. One of my mentors, uh is Urban Fetterman. I
52:58
Speaker 1: dearly love he's he's ninety years old. Now he's a
53:01
Speaker 1: New Yorker. Wow, he sold peanuts. I believe that a
53:04
Speaker 1: Dodger stadium when there's still the Dodgers fifties. Yeah, but
53:08
Speaker 1: he was my mentor at us VP, and he's a
53:12
Speaker 1: legendary semiconductor investor, believe it or not. He was one
53:16
Speaker 1: of the co founders of sand Disk Corporation. Before that,
53:20
Speaker 1: he was the CEO of am D.
53:22
Speaker 2: I hope he still has a few shares of.
53:23
Speaker 1: Those, Yeah, and he probably does. But sand Disc, which
53:27
Speaker 1: is in this memory hype cycle that we're going through,
53:31
Speaker 1: hype or not, but like there's a real need for memory,
53:34
Speaker 1: I believe now is maybe like a half a trillion
53:36
Speaker 1: dollar market cap company something crazy. Don't quote me on that,
53:39
Speaker 1: but memories having its day right now. But in any case,
53:43
Speaker 1: he was a mentor because not only I worked for him,
53:49
Speaker 1: but I saw through his lens how to be a
53:51
Speaker 1: great board member, how to make investments, how to back people,
53:55
Speaker 1: how to build relationships with people. But he also gave
54:00
Speaker 1: feedback that no one else in life would because I
54:03
Speaker 1: think he just wanted He loved me. I really felt
54:07
Speaker 1: the love because the kind of feedback he gave me
54:10
Speaker 1: was feedback that I don't think people would generally have
54:13
Speaker 1: the courage to give you. And he sort of you know,
54:15
Speaker 1: it's it's like pretty direct. It's a tough love. Yeah,
54:20
Speaker 1: And I love that about him, and you know, makes
54:23
Speaker 1: reminds me that I need to go pay him a visit.
54:25
Speaker 2: Well, you're in New York, amun as well.
54:27
Speaker 1: He's actually, you know, he lives in the BA area.
54:29
Speaker 1: Oh yeah, so's he's a New Yorker relocated to the
54:32
Speaker 1: BA area I think like fifty years ago.
54:34
Speaker 2: So let's talk about books. What are some of your favorites.
54:36
Speaker 2: What are you reading currently?
54:38
Speaker 1: So I'm just starting on this book. It's called Believe
54:43
Speaker 1: Why You Should Believe especially this like Era of AI,
54:48
Speaker 1: And it's my I'm on the board of a company
54:54
Speaker 1: called Thrive Global and the CEO founder is Ariana Huffington.
54:57
Speaker 1: And Ariana gave me the book and she and I
55:00
Speaker 1: have very aligned views on faith and spirituality. And she
55:08
Speaker 1: gave me this book and said, hey, you know, like,
55:09
Speaker 1: so Aria and I believe Greek Orthodox I'm Muslim, and
55:15
Speaker 1: we talk about how faith guides our lives and we
55:20
Speaker 1: talk about how in this age of AI. This is
55:22
Speaker 1: actually the conversation she and I have and She's like,
55:24
Speaker 1: I have the perfect book for you, and it's about
55:27
Speaker 1: how we should believe it even more in this age
55:29
Speaker 1: of AI because it helps us actually understand the world
55:34
Speaker 1: because we're trying to make sense of it all the time,
55:37
Speaker 1: all the time. And actually religion can give us a
55:42
Speaker 1: bit more constrained view of like what the world actually is,
55:45
Speaker 1: because otherwise it's just like a black hole, black box.
55:49
Speaker 1: You won't be able to comprehend the vastness of what
55:54
Speaker 1: we're trying to comprehend as human beings. And especially with AI,
55:57
Speaker 1: now we're pushing boundaries of what's possible. And I think
56:01
Speaker 1: there's actually more more in the in the scriptures that
56:04
Speaker 1: you think is sort of the view that she and
56:07
Speaker 1: I share. And this book actually sort of hits home
56:09
Speaker 1: with it. It's a New York Times columnist Roy Dehut,
56:13
Speaker 1: But yeah, unbelieve check it.
56:15
Speaker 2: Out on my list. Now, what about streaming? I know
56:19
Speaker 2: you host a podcast. What do you either watch or
56:22
Speaker 2: listen these days?
56:25
Speaker 1: So my wife and I we love to watch Dateline
56:28
Speaker 1: forty eight hours, these all the crime shows, all the
56:32
Speaker 1: crime shows. It's like in some ways it's it just
56:35
Speaker 1: kind of takes it down a notch. But it also
56:39
Speaker 1: is so instructive around human psychology, what motivates people to
56:44
Speaker 1: do not so great things and there's generally like a
56:50
Speaker 1: theme to it at this point. It's a very repetitive theme.
56:52
Speaker 2: But Junning Krueger, it's all they have no idea, that
56:55
Speaker 2: just trail of DNA evidence they leave everywhere. Anytime I've
56:59
Speaker 2: watched that show is it's like, what are you doing?
57:01
Speaker 1: And it's you know, it's it's it should be that
57:03
Speaker 1: it's harder and harder to commit crimes. It should be
57:07
Speaker 1: harder and harder, and yet there are still crimes.
57:09
Speaker 2: There's still and just as many as ever, only people
57:12
Speaker 2: are getting caught more easily.
57:14
Speaker 1: Yeah, and we know the cell phones they're pining, those towers.
57:17
Speaker 2: You know, what do you mean you weren't in the house.
57:20
Speaker 2: We could tell you within one hundred feet of this
57:22
Speaker 2: person the exact time.
57:23
Speaker 1: Yeah, So it is like, it's not probably a very
57:28
Speaker 1: full answer, and you're probably looking for some cool shows.
57:30
Speaker 2: No, not at all. I'm fascinated by that. My my.
57:34
Speaker 2: It's funny because there was used to be this giant
57:37
Speaker 2: gap between the CSIS and and and what actually was
57:42
Speaker 2: going on. But if you watch the two of them,
57:44
Speaker 2: it's really closed. Because maybe there's a little selection bias here,
57:49
Speaker 2: but all those shows, it's all these people they got
57:52
Speaker 2: that got caught. So you're seeing where the technology worked,
57:55
Speaker 2: where the forensic science was that guy. Yeah, it's really
57:59
Speaker 2: really very funny.
58:00
Speaker 1: All right.
58:01
Speaker 2: Our final two questions, what sort of advice would you
58:04
Speaker 2: give to a recent college grad interest in the career
58:08
Speaker 2: in either venture investing or technology.
58:12
Speaker 1: I would It's probably the same advice I would have
58:15
Speaker 1: given twenty years ago or given myself when I came
58:18
Speaker 1: out of college, is go work at a fast growing
58:21
Speaker 1: company where you can learn from the growth that it's encountering,
58:26
Speaker 1: but also the people and the probably the network that
58:30
Speaker 1: you build for the rest of your life. It is
58:31
Speaker 1: probably the best time in your life coming out of
58:34
Speaker 1: college to go learn from others around you and to
58:37
Speaker 1: experience high growth because from high growth, lots of lessons
58:42
Speaker 1: are learned, and so if you can find a way
58:45
Speaker 1: to get into a high growth technology startup AI startup,
58:51
Speaker 1: it is the best way to develop the skills. But
58:55
Speaker 1: also like the empathy of what it means to have
58:58
Speaker 1: carried a bag and sold some thing and built something
59:02
Speaker 1: and shipped something. I always tell folks like, there shouldn't
59:07
Speaker 1: be a direct path in a venture capital a. It's
59:11
Speaker 1: a second thing. It's not the first thing you do
59:13
Speaker 1: coming out of college or coming out of grad school.
59:15
Speaker 1: It's like you have to have built and shipped and
59:19
Speaker 1: sold and developed that empathy for high growth and the
59:23
Speaker 1: lessons learned before you get into our career.
59:27
Speaker 2: We really really interesting answer, and our final question, what
59:30
Speaker 2: do you know about the world of venture investing or
59:33
Speaker 2: technology today, might have been useful twenty five thirty years
59:36
Speaker 2: ago when you were first ramping up.
59:40
Speaker 1: It's all about the people. It sounds so trite, but
59:44
Speaker 1: it is about especially it's i say, ordinary looking people
59:51
Speaker 1: doing extraordinary things. And how do you assess those ordinary
59:55
Speaker 1: people who are doing extraordinary things is by really understanding
59:59
Speaker 1: the people their intentionality, their desires, their ambition, what drives them,
1:00:04
Speaker 1: their motivation, which goes back to the why do you
1:00:07
Speaker 1: meet them in person because you're trying to figure out,
1:00:09
Speaker 1: like why are they doing this? Building a startup company
1:00:12
Speaker 1: is hard work, it's a sacrifice on life, so there
1:00:16
Speaker 1: better be a good reason why you're doing this.
1:00:18
Speaker 2: Huh, really really interesting answer. Thank you Mamun for being
1:00:22
Speaker 2: so generous with your time. We have been speaking with
1:00:25
Speaker 2: Mamun Ahmed. He is partner at Kleiner Perkins. If you
1:00:29
Speaker 2: enjoy this conversation, Well, check out any of the six
1:00:33
Speaker 2: hundred and forty seven we've done over the past twelve years.
1:00:37
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1:00:41
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1:00:45
Speaker 2: I did not think the crack staff that helps put
1:00:48
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1:00:54
Speaker 2: Joan Russo is my researcher. Anna Luke is my podcast producer.
1:00:59
Speaker 2: I'm Barry Results. You've been listening to Masters in Business
1:01:03
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