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

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Speaker 2: This week on the podcast Another Banger. Mamun Hmid is

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Speaker 2: partner at Clina Perkins, where he's been focusing on early

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Speaker 2: stage AI investments for nine years. He's got a fascinating

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Speaker 2: background early investor in Slack, Figma, Glean, box, et cetera. Previously,

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Speaker 2: he co founded Social Capital with Chamat and worked for

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Speaker 2: a number of other venture firms, including US Venture Partners.

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Speaker 2: I thought this conversation was fascinating and I think you

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Speaker 2: will also with no further ado my conversation with Klina Perkins.

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Speaker 2: Mahmun Kamid Mamun Jamid, Welcome to Bloomberg.

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Speaker 1: Thank you so much for having me.

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Speaker 2: Barry. So, I'm fascinated by your background. You grow up

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Speaker 2: in Frankfurt, Germany. You come to the US to go

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Speaker 2: to college at Purdue bachelors and Electrical and Computer Engineering

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Speaker 2: Masters at Stanford, a MBA from Harvard. What was the

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Speaker 2: original career plan?

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Speaker 1: So let's go back to I think nineteen eighty six.

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Speaker 1: Do you remember the Challenger explosion? Sure, every kid growing

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Speaker 1: up remember that. And one of my teachers was actually

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Speaker 1: supposed to go on the Space Shuttle because there's a teacher.

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

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Speaker 1: Yeah, and every kid got fascinated by especially if you

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Speaker 1: had a teacher going to space. So followed the whole

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Speaker 1: journey of the Challenger space shuttle and the teachers and

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Speaker 1: all that. But with that also came this desire to

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Speaker 1: learn more about space, and I instantly wanted to become

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Speaker 1: an astronaut. Naturally. Think I was seven or eight years old,

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Speaker 1: and as I thought about, you know, high school, liking

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Speaker 1: science and math, thinking about where to go to college.

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Speaker 1: And I was, as you mentioned, I was in Frankfurt,

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Speaker 1: Germany growing up, and one of my uncles he'd given

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Speaker 1: me this list of colleges, the top ten engineering schools,

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Speaker 1: and I just applied to all ten, and one of

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Speaker 1: them happened to Purdue, where actually, to this day, the

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Speaker 1: most number of astronauts have graduated from.

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Speaker 2: Really, oh, that's fascinating.

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Speaker 1: Yeah, So my path was aeronautical engineering and trying to

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Speaker 1: figure out a way to get into space. I've yet

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Speaker 1: to do that, but that is what led me down

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Speaker 1: the path of applying to Purdue in the first place,

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Speaker 1: and the association with Space and NASA and then actually

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Speaker 1: going from something so massive and big space to something

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Speaker 1: so small chips and semiconductors and transistors.

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Speaker 2: Which you're enabling space. So there's definitely a connection. Is

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Speaker 2: it true that when you went to business school you

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Speaker 2: were thinking about already being a venture capitalist?

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Speaker 1: I actually when I applied to business school, so i'd

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Speaker 1: worked for a good six years after undergrad so studied

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Speaker 1: electrical engineering computer engineering, and that naturally made me think

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Speaker 1: about a career in Silicon Valley designing chips, which is

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Speaker 1: what I did for the first six years of my

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Speaker 1: career working in the semiconductor industry. But what really got

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Speaker 1: to pretty interesting for me was the notion of startups

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Speaker 1: and founding companies and how these so called venture capitalists

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Speaker 1: were behind some of the most iconic companies that I

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Speaker 1: was coming across, And so I actually wanted to get

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Speaker 1: into venture capital. And that's actually why I applied the

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Speaker 1: business school, and specifically only applied to one business school, Harvard,

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Speaker 1: because I naively thought that if you wanted to get

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Speaker 1: into venture capital, you had to go to Harvard or Stanford,

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Speaker 1: and I had already gone to Stanford for grad school,

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Speaker 1: so okay, well, it'd be nice to get a change

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Speaker 1: of scenery just round it out and moved to Boston.

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Speaker 2: Yeah, give up the nice weather. Yeah.

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Speaker 1: And so you.

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Speaker 2: Started in between college and grad school, you spent how

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Speaker 2: many years you were at silence?

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Speaker 1: I was at six years. Yeah. So the story actually

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Speaker 1: goes is I was nineteen when I graduated from college,

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Speaker 1: so from Purdue, and I thought, okay, the best thing

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Speaker 1: for a young kid is to continue to go to

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Speaker 1: grad school. So applied to grad school and ended up

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Speaker 1: getting in at Stanford. But I also got a number

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Speaker 1: of job offers. This is nineteen ninety seven, the dot

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Speaker 1: com boom. I think it's a bit like this time

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Speaker 1: where should you opt out of the job market and

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Speaker 1: get like extremely valuable experience or continue on with grad school.

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Speaker 1: So I did the best of both worlds, which is

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Speaker 1: like I went to Stanford, took a few classes every quarter,

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Speaker 1: and then worked full time at Xilenx. And this is

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Speaker 1: back in nineteen ninety seven. We're talking about sort of

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Speaker 1: the middle of the dock com boom.

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Speaker 2: Yeah. Really really interesting. You're known today as someone who

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Speaker 2: thinks about software, generally in enterprise software in particular. That

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Speaker 2: seems like an unusual transition from semiconductors. What led to

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Speaker 2: that shift? What changed your thinking.

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Speaker 1: Great question, Barry. So in two thousand and five, when

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Speaker 1: I got into venture capital, my full intent was I

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Speaker 1: would like to learn how to invest in great semiconductor

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Speaker 1: companies or founders who build semiconductor companies of the future.

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Speaker 1: And it turns out after the dot com bust, there

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Speaker 1: was not a lot of investment in infrastructure, so data

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Speaker 1: centers and networking and switching and semiconductors broadly, and so

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Speaker 1: I realized pretty early on that, hey, like, if I

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Speaker 1: want to build a career in investing, you kind of

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Speaker 1: have to go where the puck is going to, and

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Speaker 1: so skate to where the puck is going, and I

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Speaker 1: sort of skated towards web two point zero and software

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Speaker 1: because I I felt like Also in my own firm

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Speaker 1: at US Venture Partners, where I started my venture capital career,

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Speaker 1: where I was an associate, I was hired to go

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Speaker 1: help the partners there evaluate semi conductor opportunities. And that's

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Speaker 1: actually why I went there, because there were some legendary

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Speaker 1: semi conductor investors who happened to be there, and some

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Speaker 1: of mentors even today were the folks running the firm there.

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Speaker 1: So but I realized that all my friends in two

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Speaker 1: thousand and five were moving to web two point zero internet.

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Speaker 1: This is the beginning of Facebook, which happened to get

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Speaker 1: be started at Harvard when I was there. So you

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Speaker 1: were seeing all these the people my cohort's age group

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Speaker 1: moving into the software and web, and I felt like

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Speaker 1: I had to sort of move along with that. And

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Speaker 1: my day job was evaluating semi conductor businesses. But in

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Speaker 1: the evenings I was in San Francisco, I was going

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Speaker 1: to you know, the web two point zero parties and

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Speaker 1: meeting all the founders starting software businesses, and so I

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Speaker 1: slowly started to just you know, as a side project.

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Speaker 1: The side project became the main project, which was to

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Speaker 1: move from semis to UH to software and internet stuff.

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Speaker 1: And but in the back of my mind, I always

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Speaker 1: remained a semi conductor guy. And UH, you know, semis

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Speaker 1: are back now, as you know.

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Speaker 2: And AI seems to be the application of both semis

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Speaker 2: and software, so you're well prepared. We'll talk about AI

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Speaker 2: in a bit. I want to stay in the two thousands.

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Speaker 2: When you were at us VP, you would early exposure

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Speaker 2: to companies like Box and Yamer. I don't really remember

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Speaker 2: Yam or I remember Box, Big Enterprise software deals. What

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Speaker 2: did you learn from that experience? What have you brought

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Speaker 2: forward with you from from that era?

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Speaker 1: Yeah, sou Box happened to be my first investment at USCP,

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Speaker 1: where I joined the board. It was an early stage company, was,

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Speaker 1: you know, a few hundred k revenue. Two very young

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Speaker 1: founders Dylan and and Aaron twenty and twenty one years old,

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Speaker 1: dropped out of college, sort of like the protypical founder,

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Speaker 1: the archetype of a young founder, and they were going

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Speaker 1: after storing your files in the cloud and sharing them

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Speaker 1: inside your company.

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Speaker 2: Let me stop you for a second, because I think

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Speaker 2: anybody under forty is perplexed by what you just said.

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Speaker 2: I recall like late nineties, early two thousands. If I'm

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Speaker 2: either at home or at work, or on a laptop

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Speaker 2: or at the beach house, whatever I needed was always

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Speaker 2: somewhere else. And the beauty of early blogging software is

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Speaker 2: I could upload files, I could upload charts, I could

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Speaker 2: upload images, and that was the closest thing to the cloud.

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Speaker 2: It just didn't exist then. If you wanted to have

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Speaker 2: something that you can access anywhere you had an Internet connection,

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Speaker 2: literally did not exist.

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Speaker 1: Yeah, So maybe I'll if I go back to exactly.

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Speaker 1: The point I made in my head is that if

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Speaker 1: there is one application that moves into the cloud first,

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Speaker 1: it's going to be file sharing. If you recall, I

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Speaker 1: remember this when I was on my Windows computer in

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Speaker 1: the eighties and nineties. What's one of the applications we

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Speaker 1: all used a lot. Do you remember the Windows File Explorer.

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Speaker 1: We're constantly clicking in and trying to find.

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

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

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Speaker 2: Like the name you used to have to put on

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Speaker 2: a file was important because if you couldn't remember the name,

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Speaker 2: you could find it. It wasn't like, here's a phrase

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Speaker 2: that's somewhere in this document, go find it if you

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Speaker 2: didn't remember exactly where that was nested, what name you put?

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Speaker 2: Good luck, good life?

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Speaker 1: Right? And so as the world moved from the desktop

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Speaker 1: to the browser, and so by two thousand and six seven,

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Speaker 1: we all are using at this point like Firefox, Mozilla,

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Speaker 1: Chromes not even existent yet.

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Speaker 2: It was Internet Explorer until.

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Speaker 1: Chrome came along, exactly right, And so my thesis was, Okay,

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Speaker 1: one of the most one of the applications for business

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Speaker 1: that will move into the browser. So software as a

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Speaker 1: service will be file sharing and collaboration. And so because

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Speaker 1: precisely to your point, like the file that you always

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Speaker 1: needed was somewhere else, and this made so much sense

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Speaker 1: to me. And so at the time in two thousand

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Speaker 1: and seven when I invested in Box, there were probably

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Speaker 1: half a dozen two dozen, I don't know, there's many

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Speaker 1: of these companies doing file sharing, but it was mostly

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Speaker 1: for consumers, mostly for like.

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Speaker 2: Your drop box.

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Speaker 1: Dropbox was in that same era, but there was like

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Speaker 1: x file in Elephant Drive. I mean, I remember these

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Speaker 1: as an associate yours. You know, when you're suggesting an investment,

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Speaker 1: you're going to do a lot of diligence. I remember,

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Speaker 1: like the Laundry list of companies I looked at, there

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Speaker 1: are probably like forty companies that were doing something similar,

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Speaker 1: but most of them were dedicated towards sort of the

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Speaker 1: consumer use cases photos, music, stuff like that.

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

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Speaker 1: Then, exactly exactly, and so hypothesis was, hey, like this

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Speaker 1: stuff will be relevant to large companies who will want

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Speaker 1: to have file sharing and collaboration for their companies. And

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Speaker 1: Box actually pivoted from being consumer company to being an

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Speaker 1: enterprise company, And that's when I got pretty excited because

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Speaker 1: it lined up with sort of this view that I

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Speaker 1: had that large companies will move from file file servers

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Speaker 1: in their data centers or wherever their and their buildings

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Speaker 1: into files that now reside in the cloud.

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Speaker 2: And they're willing to pay for it. They're willing to

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Speaker 2: pay for it, unlike back then anyway, consumers were so

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Speaker 2: reluctant exact to pay for anything. So it's interesting because

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Speaker 2: you've had a lot of early investment success with a

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Speaker 2: variety companies. Is it easy or difficult to learn from

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Speaker 2: past winners? Is every startup different or do you start

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Speaker 2: a little pattern recognition that gives you some clues, Hey,

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Speaker 2: these guys are onto something.

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Speaker 1: Yeah. I think there's definitely some compounding of learning from

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Speaker 1: early wins and losses. You brought up Box, and so

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Speaker 1: Box first investment, got to spend a lot of time

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Speaker 1: with the founders, got to learn the business with the

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Speaker 1: founders actually because they were young. I was young. I

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Speaker 1: was in my twenties when I joined the board, and

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Speaker 1: from that experience I learned a lot about what it

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Speaker 1: meant to actually bottoms up sell software into large companies,

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Speaker 1: which led me actually to this investment in Yammer, which

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Speaker 1: many folks may not remember, but it was a enterprise

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Speaker 1: social network circa twenty ten, kind of like Twitter meets Facebook,

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Speaker 1: but for your company. And Microsoft ended up acquiring it

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Speaker 1: in twenty twelve, and it's part of the Microsoft Suite

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Speaker 1: now and part of the teams and all that. But

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Speaker 1: the experience of this bottoms up adoption. You looked at Yammer,

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Speaker 1: and a lot of large companies wanted to have a

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Speaker 1: enterprise social network or somewhere where kind of like a

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Speaker 1: town hall, a messaging platform. You share a file, people

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Speaker 1: comment on it like people do it on Twitter or Facebook.

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Speaker 1: And that was taking some of the learnings from the

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Speaker 1: web era and the social era and applying it to

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Speaker 1: the business world. And so at Yammer, I learned a lot.

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Speaker 1: It was a quick journey, but nevertheless, like there was

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Speaker 1: a level of engagement and monetization was good. And a

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Speaker 1: few years later, maybe even like a year later, I

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Speaker 1: came across another company Slack Slack, and it was similar.

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Speaker 1: Now it was true messaging. Actually, on my way over here,

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Speaker 1: I was walking through and I saw a bunch of

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Speaker 1: colleagues on Slack, which makes me really happy. And Slack

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Speaker 1: is used broadly across the globe to this day in

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Speaker 1: twenty twenty six. But the lessons learned in that twenty

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Speaker 1: ten to eleven era led to the investment in Slack

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Speaker 1: two thousand and fourteen for me when it was a

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Speaker 1: ten person company. And so back to the point around

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Speaker 1: like compounding of learning Box led to Yama, Yamer led

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Speaker 1: to Slack, and since Slack there's been others, but there

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Speaker 1: certainly is some pattern recognition around products that are working.

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Speaker 2: And then in twenty eleven you co found social capital

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Speaker 2: with Chamath, very famous model of social capital, which is

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Speaker 2: how can we address many of the social ills that

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Speaker 2: are are hurting the country through the intelligent use of startups, technology,

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Speaker 2: et cetera. Was this an reinvention of venture capital or

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Speaker 2: just a new set of tools within a partnership that

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Speaker 2: you know, it was kind of novel for its z era.

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Speaker 1: It was novel for its era because we decided that

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Speaker 1: we would go after education, healthcare, and finance, you know,

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Speaker 1: three of the largest parts of society, and how do

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Speaker 1: we address inequities in those areas with our investments and

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Speaker 1: believing that technology has the ability to democratize access to healthcare, education,

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Speaker 1: and financial services, and that's you know, largely played out

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Speaker 1: over the last fifteen years. But we just thought that

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Speaker 1: there will be a ton of opportunity. As venture capitalists,

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Speaker 1: we're seeking out opportunity, and we thought that going after

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Speaker 1: large pockets of GDP, you'd identify really exciting opportunities. Turns out,

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Speaker 1: you know, my interests remained in enterprise software, and I

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Speaker 1: did spend a lot of my time in enterprise software

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Speaker 1: even when we started Social Capital.

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Speaker 2: So twenty seventeen, you leave Social Capital for Cline Perkins,

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Speaker 2: a firm that has long been iconic. The laundry list

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Speaker 2: of companies Kliner has backed Google, Cisco, were they early

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Speaker 2: in app also, I.

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Speaker 1: Think they were Google, Amazon, Sun Microsystems, Genentech into it

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Speaker 1: unbelievableandom if you remember, you know, the list of greats

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Speaker 1: is amazing.

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Speaker 2: So they're an iconic company, but they're not the dominant

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Speaker 2: force they once were when you joined. What made that

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Speaker 2: opportunity so attractive?

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Speaker 1: Yeah, So look, I actually long admired Kliner Perkins had

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Speaker 1: an extreme reverence. I would call it for Kliner Perkins.

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Speaker 1: Going back to my days moving to Silicon Valley, I

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Speaker 1: mentioned I moved to Silicon Valley in nineteen ninety seven,

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Speaker 1: I worked for this company's Silenx, And if you think

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Speaker 1: about my first few weeks on the job, I'm in

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Speaker 1: my cubicle. I've got a Sun Microsystems workstation, which is

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Speaker 1: actually a dream because in college we had to share

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Speaker 1: twenty of them amongst like two thousand of us. And

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Speaker 1: now I have my own son Spark I think was

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Speaker 1: a Spark twenty. And guess what, There's a Netscape brows

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Speaker 1: and I'm buying books for grad school on Amazon. And

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Speaker 1: by the way, that there's a couple of guys down

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Speaker 1: the hallway at Stanford who are starting this company called Google,

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Speaker 1: and I'm starting to use that search engine called Google.

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Speaker 1: The one commonality amongst Xilenk's son, Netscape, Google, and Amazon

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Speaker 1: is that Cline Perkins had led the Series A, so

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Speaker 1: the first institutional investor for all five of those companies.

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Speaker 1: So as a young guy, I had this developed this

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Speaker 1: extreme reverence for Clinet Perkins because of the investments they

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Speaker 1: had made in these really like history making companies and so,

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Speaker 1: which is also what led me to think about venture

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Speaker 1: capital as a career as a young engineer. Is like,

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Speaker 1: you know, I want to be like those guys, those

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Speaker 1: guys that are investing all the coolest companies that I'm

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Speaker 1: using as a nineteen year old here, and that's really

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Speaker 1: what got me excited about venture capital. And actually one

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Speaker 1: of the people behind all many of those investments son Netscape, Google,

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Speaker 1: Amazon was my partner, John Dor. And so for me,

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Speaker 1: there was this extreme reverence for John Dor and his

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Speaker 1: career and trying to emulate that, you know, and he

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Speaker 1: was an electrical engineer from Rice, went to Harvard Business School,

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Speaker 1: worked at Intel Corporation, then came to Cline Perkins out

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Speaker 1: of business school, and so it had a deep meaning

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Speaker 1: to me. And actually, truth be told, I actually in

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Speaker 1: my business school essay, which I have still wrote that

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Speaker 1: I wanted to go work at Clina Perkins. This is

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Speaker 1: in two thousand and three when I wrote the essay.

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Speaker 1: Two thousand and two, I wrote the essay. And then

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Speaker 1: you know, when I tried to apply for a job

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Speaker 1: in two thousand and five coming out of business school,

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Speaker 1: I didn't get very far, but I did end up

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Speaker 1: there in twenty seventeen.

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Speaker 2: So you know, eventually, if you keep plugging away, you

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Speaker 2: get to where you want to go. That's great coming up,

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Speaker 2: we continue our conversation with mamu Inhammad clinap Perkins managing member,

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Speaker 2: talking about the reboot of the firm. I'm Barry Ridults.

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Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

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Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.

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Speaker 2: My extra special guest this week is Mamunhamid. He is

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Speaker 2: managing member and general partner at Kleiner Perkins, where he

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Speaker 2: is pivoting the firm towards early investors in software and

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Speaker 2: artificial intelligence and automation. So you co led the refounding

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Speaker 2: of Kleiner Perkins in twenty seventeen twenty eighteen, the firm

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Speaker 2: was refocused on early stage Series A investing. Tell us

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Speaker 2: what was behind the thought process? What made you say

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Speaker 2: we don't want to be bigger, we want to be

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Speaker 2: smaller and more focused.

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Speaker 1: Yeah. If I look back at the decades of being

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Speaker 1: probably the most successful venture capital firm throughout the seventies, eighties, nineties,

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Speaker 1: and even the early two thousands, the one thing that

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Speaker 1: defined Kleiner Perkins was it was a small partnership of

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Speaker 1: seven ish or so partners who sat around a table

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Speaker 1: in Menlo Park, meeting companies and having healthy discourse and

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Speaker 1: debate about what companies to invest in and what the

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Speaker 1: future of technology would bring to the world. And it

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Speaker 1: was defined really by a small group of partners that

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Speaker 1: were in many cases technical, they were operators, They had

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Speaker 1: a passion for technology and its impact on humanity, and

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Speaker 1: that was sort of what I kept coming back to,

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Speaker 1: is that that was what defined Kleiner's decades of success.

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Speaker 1: And we went back to the future in twenty seventeen

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Speaker 1: and eighteen to that model, and today our partnership is

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Speaker 1: six partners and then we have three more investment professionals,

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Speaker 1: so we're a very small, nimble team. So we have

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Speaker 1: two funds, and this team invests from both pools of capital.

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Speaker 2: So early stage does that seed round or is it

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Speaker 2: a little more developed?

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Speaker 1: Yeah, So the early stage fund is Seed Series A

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Speaker 1: mostly and maybe some bees, and then the Growth fund

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Speaker 1: is b c's all the way to. We invested in

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Speaker 1: the last anthropic round at a nine hundred billion our evaluation,

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Speaker 1: which is rare for special companies, but it has the

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Speaker 1: ability to invest across even to the pre IPO.

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Speaker 2: And is it a coincidence that the Growth fund is

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Speaker 2: two and a half times the size of the seed funds.

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Speaker 2: At that point, these companies are bigger, require a bigger

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Speaker 2: check or is that just happenstance.

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Speaker 1: I think our funds are sized based on the opportunity

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Speaker 1: set in front of us. Our early stage funds have

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Speaker 1: been almost exactly thirty five companies for the last fifteen years.

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Speaker 1: So thirty five companies per fund, which we think about

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Speaker 1: is the right number of shots on goal for an

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Speaker 1: early stage fund to return multiples on it.

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Speaker 2: Twenty five to thirty million.

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Speaker 1: Per exactly exactly. So it starts out with maybe in

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Speaker 1: some cases a five million dollar check, and the subsequent

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Speaker 1: checks is another fifteen to twenty, or it could be

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Speaker 1: first check is thirty and then with Parada, you're investing

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Speaker 1: let's say, up to forty million dollars per in a company,

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Speaker 1: and then your growth fund is doubling down investing a

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Speaker 1: lot more in those companies.

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Speaker 2: Huh really really kind of interesting. So the focus is

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Speaker 2: artificial intelligence startups across software, healthcare, transportation, and autonomy industries.

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Speaker 2: So let's unpack that because I'm hearing a little overlap

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Speaker 2: with each of your prior venture experiences. Tell us why

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Speaker 2: those four areas are so attractive.

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Speaker 1: Yeah, So this is a truly once in a lifetime

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Speaker 1: revolution that we're going through with AI, and the number

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Speaker 1: of exciting companies people that we're seeing right now as

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Speaker 1: at an all time high. The whole world in some

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Speaker 1: ways is being refactored with AI, and this is just

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Speaker 1: a very beginning, and so I would say that all

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Speaker 1: parts of the economy, even beyond those four areas, it

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Speaker 1: is like I mentioned earlier, healthcare, financial services, it is

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Speaker 1: all sorts of knowledge work, it is going to be

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Speaker 1: all sorts of physical automation in terms of robotics, We're

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Speaker 1: even space, even defense areas, drug discovery, materials discovery. I

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Speaker 1: think it is all fair game at this point in

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Speaker 1: terms of where the exciting pockets of innovation are, just

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Speaker 1: because there has never been a tailwind like this that

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Speaker 1: really allows all parts of the world to be refactored

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Speaker 1: based on, you know, the biggest technological revolution ever.

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Speaker 2: So I'm glad you described it that way because I

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Speaker 2: keep hearing people compare AI to the Internet, and that

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Speaker 2: seems to contained too timid. I wonder if you agree

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Speaker 2: with the thought that the only thing remotely comparable to

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Speaker 2: this is the Industrial Revolution, which centuries later, we're still

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Speaker 2: dealing with the impact.

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Speaker 1: Of I absolutely agree with you Berry. It is like

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Speaker 1: the industrial revolution, it's like the railroads, it's like the

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Speaker 1: printing US. Is it is that it's not the Internet.

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Speaker 2: That's really interesting because when I think Internet, the first

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Speaker 2: thing you think of is, oh, this is a bubble

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Speaker 2: when this is going to collapse? But you mentioned your

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Speaker 2: an investor in anthropic These are forget not profitable companies.

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Speaker 2: These are companies with giant revenue streams already they're barely

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Speaker 2: a few years old. How big can this sector get?

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Speaker 2: Is this going to take over every corner of the economy.

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Speaker 1: Yeah, let's talk about that. I think that's the I

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Speaker 1: think that's the real conversation. And the more the very

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Speaker 1: exciting conversation that one can have about this topic is

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Speaker 1: and I start at a very high level, which is

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Speaker 1: the GDP of the world today. It's it's about one

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Speaker 1: hundred and twenty trillion dollars, and about half of that

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Speaker 1: is labor, the labor component of it, so roughly sixty

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Speaker 1: trillion dollars. And of that sixty trillion, roughly sixty percent

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Speaker 1: or so is white collar mid fifty sixty somewhere so

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Speaker 1: that's anywhere from thirty thirty five trillion dollars. And if

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Speaker 1: you look at tokens and what the frontier model companies

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Speaker 1: provide is it is units of labor. We're already seeing

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Speaker 1: how those units of labor are being utilized in computer science,

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Speaker 1: so software development, in law, in medicine, in drug discovery.

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Speaker 1: These are little agents and buddies that we as humans

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Speaker 1: have now to help us do more with our intellect.

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Speaker 1: And the way I see it is that you know,

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Speaker 1: we're talking about trillions of dollars that are opening up

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Speaker 1: for these companies that exist. And exhibit A is a

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Speaker 1: company like Anthropic, which is publicly stated has gone from

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Speaker 1: zero to forty five billion revenue run rate in a

436
00:27:02,720 --> 00:27:07,159
Speaker 1: matter of less than three years, okay, and that is

437
00:27:07,480 --> 00:27:11,240
Speaker 1: likely going to double. And you know, and the company

438
00:27:11,280 --> 00:27:15,000
Speaker 1: started this year, I believe at twenty is already doubled

439
00:27:15,640 --> 00:27:17,959
Speaker 1: more than doubled in the short year that we've been

440
00:27:18,000 --> 00:27:21,239
Speaker 1: in so far. And so these numbers are astounding not

441
00:27:21,280 --> 00:27:25,520
Speaker 1: only because these companies are selling software or technology, they're

442
00:27:25,560 --> 00:27:29,399
Speaker 1: selling units of labor. And the labor markets, as we

443
00:27:29,440 --> 00:27:32,840
Speaker 1: all know, are the biggest component. As I just mentioned earlier,

444
00:27:33,480 --> 00:27:36,800
Speaker 1: of the world's GDP. We're talking about trillions of opportunity

445
00:27:36,920 --> 00:27:41,320
Speaker 1: dollars of opportunity. And that's what excites us so much

446
00:27:41,600 --> 00:27:45,560
Speaker 1: about this time, is that it's not just about selling

447
00:27:45,640 --> 00:27:48,040
Speaker 1: tools and software that we've been accustomed to selling to

448
00:27:48,160 --> 00:27:55,600
Speaker 1: IT departments. It's selling like actual labor to companies, to corporations,

449
00:27:56,400 --> 00:27:59,320
Speaker 1: to you know, even to consumers who are using AI

450
00:28:00,520 --> 00:28:01,439
Speaker 1: in their personal lives.

451
00:28:01,680 --> 00:28:03,600
Speaker 2: So let's let's talk a little bit about that. The

452
00:28:04,160 --> 00:28:08,240
Speaker 2: fear I keep hearing is everybody's going to lose their job.

453
00:28:08,680 --> 00:28:13,159
Speaker 2: It's a very Malthusian argument that this technology is going

454
00:28:13,200 --> 00:28:17,880
Speaker 2: to replace labor the way the steam engine did. I'm

455
00:28:17,960 --> 00:28:22,080
Speaker 2: getting a sense from the data and from other analysts

456
00:28:22,119 --> 00:28:26,720
Speaker 2: like Torsten Slock that this isn't a replacement for white

457
00:28:26,720 --> 00:28:30,359
Speaker 2: collar labor. It's an enhancement, or at least that that's

458
00:28:30,400 --> 00:28:33,520
Speaker 2: the argument. Give us, give us your perspective on that.

459
00:28:33,720 --> 00:28:37,439
Speaker 1: I actually fully agree with that point of view that

460
00:28:37,560 --> 00:28:40,320
Speaker 1: you have. Uh it is it's it's like you know,

461
00:28:40,680 --> 00:28:44,360
Speaker 1: uh getting email. I mean I think, uh, the fact

462
00:28:44,400 --> 00:28:47,280
Speaker 1: that we got the computer, didn't you know, the people

463
00:28:47,480 --> 00:28:50,080
Speaker 1: using the typewriters started using computer and started doing other

464
00:28:50,080 --> 00:28:53,360
Speaker 1: types of jobs. Even in the steam engine era or

465
00:28:53,400 --> 00:28:57,480
Speaker 1: the Industrial Revolution, we found ways to repurpose jobs or

466
00:28:57,520 --> 00:29:00,880
Speaker 1: people and their skills. I don't think humans are going

467
00:29:00,880 --> 00:29:04,240
Speaker 1: out of style. I don't think you know, the world

468
00:29:04,280 --> 00:29:07,840
Speaker 1: is going to be largely unemployed and on UBI because

469
00:29:07,880 --> 00:29:11,000
Speaker 1: we are going to displace all this work and all

470
00:29:11,040 --> 00:29:15,800
Speaker 1: these people with AI. I think it's it's the extreme

471
00:29:15,880 --> 00:29:19,040
Speaker 1: where the mind goes to. But that's just not the reality.

472
00:29:19,360 --> 00:29:21,760
Speaker 1: And that sort of bears itself in the numbers that

473
00:29:21,800 --> 00:29:26,320
Speaker 1: we see, you know, in record low unemployment rates, and

474
00:29:26,320 --> 00:29:29,320
Speaker 1: and actually we need more labor, more people than we

475
00:29:29,400 --> 00:29:30,200
Speaker 1: ever have, and so.

476
00:29:30,640 --> 00:29:35,680
Speaker 2: More skilled labor. We are seeing a decrease in job

477
00:29:35,720 --> 00:29:39,760
Speaker 2: availability for kids right out of college for unskilled labor.

478
00:29:40,720 --> 00:29:44,760
Speaker 2: If anything, is this likely to force more people to

479
00:29:44,760 --> 00:29:48,200
Speaker 2: get more technical, to up their skill set.

480
00:29:49,000 --> 00:29:52,720
Speaker 1: I actually to believe that it is not like a, oh,

481
00:29:52,760 --> 00:29:54,520
Speaker 1: you know, you don't need to be a software developer

482
00:29:54,520 --> 00:29:57,600
Speaker 1: and study CS anymore because you know these software jobs

483
00:29:57,600 --> 00:30:01,000
Speaker 1: are going away. It is like now now the job

484
00:30:01,040 --> 00:30:04,240
Speaker 1: of the software engineer is to manage a whole hosts

485
00:30:04,280 --> 00:30:06,600
Speaker 1: to agents and make them do work for them and

486
00:30:06,680 --> 00:30:09,280
Speaker 1: be the brains behind the operation. You think of it

487
00:30:09,320 --> 00:30:11,840
Speaker 1: as like you have all these little agents and came

488
00:30:11,880 --> 00:30:15,400
Speaker 1: little employees who are working on your behalf. And so yeah,

489
00:30:15,640 --> 00:30:18,800
Speaker 1: that is like the higher level thinking and the things

490
00:30:18,800 --> 00:30:20,800
Speaker 1: that actually you know, like when we go to school,

491
00:30:20,800 --> 00:30:22,880
Speaker 1: we think about how to problem solve and you know,

492
00:30:23,040 --> 00:30:25,719
Speaker 1: if we're solving a math problem it's hard, we think

493
00:30:25,760 --> 00:30:28,800
Speaker 1: about many different ways to solve it. And the same

494
00:30:28,840 --> 00:30:30,960
Speaker 1: way it's how am I going to use AI to

495
00:30:31,000 --> 00:30:33,680
Speaker 1: help me solve problems? And that's the way I think

496
00:30:33,720 --> 00:30:35,719
Speaker 1: we all And I would say, you know, we have

497
00:30:35,720 --> 00:30:39,640
Speaker 1: four kids, you know, believe or not, I'm telling them

498
00:30:39,640 --> 00:30:42,960
Speaker 1: going to math science like U and actually art like

499
00:30:43,080 --> 00:30:47,080
Speaker 1: have like spectral diversity in your your learning because all

500
00:30:47,120 --> 00:30:50,320
Speaker 1: the skills that mattered in the past solving you know,

501
00:30:50,400 --> 00:30:54,320
Speaker 1: math problems with pen and paper, will really matter in

502
00:30:54,360 --> 00:30:54,800
Speaker 1: the future.

503
00:30:55,480 --> 00:30:58,600
Speaker 2: So let's bring this back to how you think about

504
00:30:59,080 --> 00:31:03,200
Speaker 2: the opportunity the set that's out there. At Clina Perkins,

505
00:31:03,280 --> 00:31:07,680
Speaker 2: you do structured reviews of every interesting deal that was

506
00:31:07,920 --> 00:31:12,560
Speaker 2: passed on. I'm kind of fascinated by by that. I

507
00:31:12,600 --> 00:31:16,560
Speaker 2: know a lot of vcs kind of hold the their

508
00:31:16,640 --> 00:31:20,160
Speaker 2: misses as a badge of honor. Some some firms posted

509
00:31:20,240 --> 00:31:26,280
Speaker 2: on their website, what do you think of what's driving

510
00:31:26,360 --> 00:31:31,200
Speaker 2: the thought process around revisiting either missed deals or mistakes

511
00:31:31,280 --> 00:31:34,719
Speaker 2: or what have you. What what does the process teach you?

512
00:31:35,160 --> 00:31:37,560
Speaker 1: Yeah. So, actually, one of the things that I did

513
00:31:37,560 --> 00:31:40,400
Speaker 1: when I got to Cline Perkins in twenty seventeen was

514
00:31:40,440 --> 00:31:45,920
Speaker 1: that we would look every week at the that week's

515
00:31:45,960 --> 00:31:48,640
Speaker 1: series as that got done by our peer firms about

516
00:31:48,680 --> 00:31:53,280
Speaker 1: thirty forty firms, and whether we had seen the company

517
00:31:53,320 --> 00:31:56,640
Speaker 1: that was invested in or not, and as simple heuristic

518
00:31:56,680 --> 00:31:59,040
Speaker 1: of like, are we seeing the things that matter because

519
00:31:59,560 --> 00:32:01,680
Speaker 1: they seem to matter because our peer firms invested in

520
00:32:01,720 --> 00:32:04,640
Speaker 1: those companies. And so we've been doing this now for

521
00:32:04,680 --> 00:32:10,040
Speaker 1: the last nine years. And initially our goal was like

522
00:32:10,200 --> 00:32:12,680
Speaker 1: we should see sixty percent, and seeing means you met

523
00:32:12,720 --> 00:32:17,160
Speaker 1: the company, and for us that hovers around now seventy

524
00:32:17,160 --> 00:32:18,520
Speaker 1: percent or so. You don't want it to be one

525
00:32:18,560 --> 00:32:21,360
Speaker 1: hundred percent because then you're just that's the game you're playing.

526
00:32:21,440 --> 00:32:23,400
Speaker 1: We just see everything, and you don't want to be

527
00:32:23,440 --> 00:32:25,840
Speaker 1: twenty percent because you're not seeing enough. But we think

528
00:32:25,880 --> 00:32:29,240
Speaker 1: seventy percent is a good number, and we look at

529
00:32:29,280 --> 00:32:31,840
Speaker 1: the ones, Okay we saw if we saw seventy percent

530
00:32:32,520 --> 00:32:36,040
Speaker 1: of the good stuff let's say, let's call it, or

531
00:32:36,600 --> 00:32:39,080
Speaker 1: was it thirty percent that we didn't see even better?

532
00:32:40,360 --> 00:32:43,880
Speaker 1: Or if we saw the seventy percent, do we pass

533
00:32:43,920 --> 00:32:46,320
Speaker 1: on the good stuff and do the bad stuff? And

534
00:32:46,400 --> 00:32:50,960
Speaker 1: so we go through that exercise quite frequently. We just

535
00:32:50,960 --> 00:32:53,040
Speaker 1: had an off site a few weeks ago, and we

536
00:32:53,160 --> 00:32:57,600
Speaker 1: go through again. We pour salt on the wounds and say, okay,

537
00:32:57,920 --> 00:33:01,720
Speaker 1: we saw these companies and we passed. Why do you

538
00:33:01,760 --> 00:33:04,600
Speaker 1: pass at that round? That early stage round? And just

539
00:33:04,640 --> 00:33:09,320
Speaker 1: to remind ourselves why we need to adjust the way

540
00:33:09,360 --> 00:33:14,320
Speaker 1: we do things, I'll give you an example. The Anthropic,

541
00:33:14,320 --> 00:33:17,360
Speaker 1: which is an incredible company. Its Series A was not

542
00:33:17,440 --> 00:33:24,040
Speaker 1: a traditional one. SBF from FTX led the Series A famously.

543
00:33:24,960 --> 00:33:26,479
Speaker 1: We do know how that worked out. It was an

544
00:33:26,520 --> 00:33:30,720
Speaker 1: amazing investment and very precient on his behalf. I forget

545
00:33:30,720 --> 00:33:32,320
Speaker 1: how much it were worth today, but it would be

546
00:33:32,360 --> 00:33:36,960
Speaker 1: worth a lot. But the Series B was a sort

547
00:33:36,960 --> 00:33:41,280
Speaker 1: of non consensus, non obvious round, and we actually met

548
00:33:41,320 --> 00:33:45,360
Speaker 1: with the founders and but we met them over zoom

549
00:33:46,000 --> 00:33:49,040
Speaker 1: and we played with the product. But you don't get

550
00:33:49,040 --> 00:33:54,480
Speaker 1: the same visceral feeling about a company and the founders

551
00:33:54,560 --> 00:33:57,880
Speaker 1: and their ambitions and aspirations and what they're trying to

552
00:33:57,880 --> 00:34:00,280
Speaker 1: do with with their with their with their company, and

553
00:34:00,320 --> 00:34:03,680
Speaker 1: so what a miss right, And I think that round

554
00:34:03,680 --> 00:34:06,040
Speaker 1: got done in that four billion dollar valuation or something,

555
00:34:06,040 --> 00:34:09,360
Speaker 1: which not a small valuation but still compared to today,

556
00:34:09,560 --> 00:34:13,800
Speaker 1: compared today, and so and we looked a lot of

557
00:34:13,840 --> 00:34:19,120
Speaker 1: our passes that we were good companies, and many times

558
00:34:19,120 --> 00:34:21,600
Speaker 1: we just didn't meet them in person. So the pandemic

559
00:34:21,680 --> 00:34:24,799
Speaker 1: era bred some really bad habits of you know, you

560
00:34:24,840 --> 00:34:28,759
Speaker 1: did as first meeting over zoom and so and I

561
00:34:28,800 --> 00:34:32,000
Speaker 1: looked at my own investments and the last twenty investments

562
00:34:32,040 --> 00:34:36,160
Speaker 1: that I've done, the first meeting was always in person,

563
00:34:37,200 --> 00:34:40,480
Speaker 1: the first meeting. So I've driven my whole calendar to like,

564
00:34:40,640 --> 00:34:44,360
Speaker 1: I just don't want to do any meetings on Zoom anymore.

565
00:34:44,920 --> 00:34:47,239
Speaker 1: I want to meet people in person, and if it's

566
00:34:47,239 --> 00:34:50,640
Speaker 1: worth a thirty minute Zoom, it should be worth a

567
00:34:50,719 --> 00:34:53,920
Speaker 1: thirty minute in person meeting. And I'm glad we get

568
00:34:53,920 --> 00:34:57,080
Speaker 1: to do this in person, because you know, it wouldn't

569
00:34:57,080 --> 00:34:58,439
Speaker 1: be the same thing if I was on a Zoom

570
00:34:58,480 --> 00:34:59,440
Speaker 1: screen doing this thing with you.

571
00:34:59,760 --> 00:35:03,200
Speaker 2: That's exactly right. What about the reverse of that. If

572
00:35:03,200 --> 00:35:07,680
Speaker 2: you're analyzing your missus, do you ever review your hits,

573
00:35:07,760 --> 00:35:10,640
Speaker 2: your wins, and saying why did we get this right?

574
00:35:10,880 --> 00:35:12,440
Speaker 2: What can we take forward from this?

575
00:35:14,880 --> 00:35:19,959
Speaker 1: That's a great question. I think we just assume what

576
00:35:20,000 --> 00:35:23,040
Speaker 1: we do look at, okay, is are the ones that

577
00:35:23,080 --> 00:35:26,440
Speaker 1: we did the portfolio that we built, is it better

578
00:35:26,520 --> 00:35:31,160
Speaker 1: than the portfolio that we missed? And it's actually a

579
00:35:31,160 --> 00:35:31,640
Speaker 1: toss up.

580
00:35:31,880 --> 00:35:34,480
Speaker 2: It's that you know, huh, that's really interesting.

581
00:35:34,320 --> 00:35:37,279
Speaker 1: In the sense that we didn't even see it, and

582
00:35:38,760 --> 00:35:43,760
Speaker 1: because it'd be bad if we didn't see companies, and

583
00:35:43,000 --> 00:35:46,760
Speaker 1: that basket of companies was way better than the companies

584
00:35:46,800 --> 00:35:51,120
Speaker 1: we saw. So and we've try to be very intellectually

585
00:35:51,160 --> 00:35:53,960
Speaker 1: honest about like are we seeing the right stuff or

586
00:35:54,000 --> 00:35:56,320
Speaker 1: the wrong stuff? And it turns out we're seeing it's

587
00:35:56,320 --> 00:35:56,839
Speaker 1: a toss up.

588
00:35:56,760 --> 00:36:01,160
Speaker 2: Actually, right. The reason I asked that question is you

589
00:36:01,280 --> 00:36:04,000
Speaker 2: learn in the public markets, you learn more from your

590
00:36:04,040 --> 00:36:07,120
Speaker 2: missus than your wins, because it's very hard to tell

591
00:36:07,160 --> 00:36:11,200
Speaker 2: the difference between skill and luck in the public markets.

592
00:36:11,560 --> 00:36:15,000
Speaker 2: I'm curious if the same sort of thing applies to

593
00:36:15,120 --> 00:36:16,359
Speaker 2: venture I think it does.

594
00:36:16,640 --> 00:36:19,440
Speaker 1: I think you you really beat yourself up on the

595
00:36:19,520 --> 00:36:22,440
Speaker 1: missus and the ones that you do. You're just like, okay,

596
00:36:22,520 --> 00:36:27,040
Speaker 1: check you know it happened and you don't. Uh, maybe

597
00:36:27,040 --> 00:36:28,839
Speaker 1: you don't think about it as much as the ones

598
00:36:28,880 --> 00:36:29,320
Speaker 1: you missed.

599
00:36:29,719 --> 00:36:33,560
Speaker 2: Huh, really really interesting. Coming up, we continue our conversation

600
00:36:33,680 --> 00:36:39,279
Speaker 2: with Mamunhahmed, partner at Kleiner Perkins, discussing the state of

601
00:36:39,400 --> 00:36:43,880
Speaker 2: venture investing today. I'm Barry Ridults. You're listening to Masters

602
00:36:43,960 --> 00:36:48,080
Speaker 2: in Business on Bloomberg Radio. I'm Bury rid Halts. You're

603
00:36:48,160 --> 00:36:52,720
Speaker 2: listening to Masters in Business on Bloomberg Radio. Mamun Hmed

604
00:36:52,880 --> 00:36:56,560
Speaker 2: is my extra special guest. He is a partner at

605
00:36:56,600 --> 00:37:00,480
Speaker 2: Kleiner Perkins, where he is driving the firms focus on

606
00:37:00,600 --> 00:37:06,799
Speaker 2: early stage investments in artificial intelligence and related technology. So

607
00:37:07,000 --> 00:37:09,880
Speaker 2: let's talk a little bit about the state of venture

608
00:37:09,920 --> 00:37:14,600
Speaker 2: investing today. When you meet a founder for the first time,

609
00:37:15,320 --> 00:37:18,880
Speaker 2: preferably in person, what are you looking for? What are

610
00:37:18,880 --> 00:37:23,319
Speaker 2: you trying to spot that isn't in the pitch deck

611
00:37:23,440 --> 00:37:28,839
Speaker 2: they sent earlier? How do you separate intensity from delusion? Like?

612
00:37:28,920 --> 00:37:31,680
Speaker 2: What are you trying to identify in that first meeting?

613
00:37:32,320 --> 00:37:36,880
Speaker 1: Yeah, it is the most interesting time in my venture career,

614
00:37:37,640 --> 00:37:41,359
Speaker 1: and for obvious reasons. This AI revolution, the tailwinds that

615
00:37:41,400 --> 00:37:45,319
Speaker 1: AI brings to companies that are being started today is

616
00:37:45,440 --> 00:37:51,080
Speaker 1: unlike anything we've seen before. So the quality of ideas

617
00:37:51,600 --> 00:37:54,160
Speaker 1: is at an all time high, and I would say

618
00:37:54,160 --> 00:37:56,640
Speaker 1: the quality of the people is also at an all

619
00:37:56,680 --> 00:38:02,040
Speaker 1: time high. And those two forces combined suggests that there's

620
00:38:02,239 --> 00:38:05,640
Speaker 1: a lot of really high quality ideas and founders that

621
00:38:05,680 --> 00:38:10,320
Speaker 1: we're seeing. And so I would say it's the volume

622
00:38:10,400 --> 00:38:12,799
Speaker 1: is because of that, at an all time high. And

623
00:38:12,840 --> 00:38:15,600
Speaker 1: so to your question of like what do we what

624
00:38:15,640 --> 00:38:22,279
Speaker 1: are we looking for? I think obviously everything today is

625
00:38:22,520 --> 00:38:29,240
Speaker 1: AI enabled AI tailwinds, and venture capital isn't venture capital

626
00:38:29,360 --> 00:38:33,120
Speaker 1: unless there's like a strong tailwind of something. And so

627
00:38:33,239 --> 00:38:35,839
Speaker 1: this is a makes it such an exciting time because

628
00:38:35,880 --> 00:38:40,200
Speaker 1: there's such a strong tailwind and the pace at which

629
00:38:41,520 --> 00:38:46,759
Speaker 1: the tailwinds are like the winds are so strong, and

630
00:38:46,800 --> 00:38:50,640
Speaker 1: in some ways because the frontier model, companies are providing

631
00:38:50,640 --> 00:38:54,880
Speaker 1: better and better models, which allow companies that build on

632
00:38:54,920 --> 00:38:59,840
Speaker 1: these models to provide strong value proposition. Take for example,

633
00:39:00,760 --> 00:39:04,279
Speaker 1: a company like Harvey, which is AI for legal is

634
00:39:04,560 --> 00:39:07,480
Speaker 1: in most of the am law one hundreds sells to

635
00:39:07,600 --> 00:39:13,400
Speaker 1: large enterprises and Fortune one hundred type companies and becoming

636
00:39:13,400 --> 00:39:16,640
Speaker 1: sort of the de facto way that legal law firms

637
00:39:16,680 --> 00:39:19,160
Speaker 1: and legal departments are using AI inside of their companies

638
00:39:19,200 --> 00:39:22,400
Speaker 1: and inside of law firms. And yes, they have a

639
00:39:22,440 --> 00:39:25,520
Speaker 1: lot of secret sauce on top of what the model companies.

640
00:39:25,560 --> 00:39:28,279
Speaker 1: But at the same time they get better and better

641
00:39:28,280 --> 00:39:30,680
Speaker 1: as these models get better and better. And this is

642
00:39:30,719 --> 00:39:33,760
Speaker 1: a category that just got created in the last few years,

643
00:39:34,520 --> 00:39:37,240
Speaker 1: and there are numerous examples like it in every sector

644
00:39:37,400 --> 00:39:41,640
Speaker 1: of society. And just to take a step back when

645
00:39:41,680 --> 00:39:45,080
Speaker 1: we first saw I don't know what were your reaction

646
00:39:45,239 --> 00:39:47,319
Speaker 1: was to chat GPT when you first saw it, but

647
00:39:47,360 --> 00:39:50,200
Speaker 1: for me, it was the same reaction I had when

648
00:39:50,200 --> 00:39:54,759
Speaker 1: I first got to use a browser Internet browser. To me,

649
00:39:54,880 --> 00:39:59,480
Speaker 1: that Netscape moment was equivalent to the chat GPT moment.

650
00:40:00,200 --> 00:40:03,720
Speaker 1: And we started to think about what are the second

651
00:40:03,840 --> 00:40:06,120
Speaker 1: order effect what are the things that come from here,

652
00:40:06,320 --> 00:40:10,719
Speaker 1: from chat GPT, from LMS, and it was we thought

653
00:40:10,760 --> 00:40:14,800
Speaker 1: about like the labor pyramid, think about knowledge work and

654
00:40:14,880 --> 00:40:16,680
Speaker 1: we went straight to okay, what is what are the

655
00:40:16,719 --> 00:40:20,760
Speaker 1: most highly skilled, highly paid jobs in the in the country.

656
00:40:21,360 --> 00:40:23,680
Speaker 1: And if you actually look at the top twenty list

657
00:40:24,200 --> 00:40:31,040
Speaker 1: of jobs dollars made by job, it's some form of engineer, doctor,

658
00:40:31,160 --> 00:40:34,480
Speaker 1: or lawyer. And so what we did at Kindel Perkins

659
00:40:34,560 --> 00:40:38,320
Speaker 1: was to invest in companies that do AI for legal,

660
00:40:38,440 --> 00:40:41,759
Speaker 1: AI for software development, AI for medicine, or and so

661
00:40:41,840 --> 00:40:45,280
Speaker 1: we invested in companies like Harvey Windsurf, which got acquired

662
00:40:45,280 --> 00:40:50,880
Speaker 1: by Google. We invested in a company called Ambience Open

663
00:40:50,920 --> 00:40:55,480
Speaker 1: Evidence to go after that that labor pyramid, top of

664
00:40:55,520 --> 00:40:58,000
Speaker 1: the pyramid, top of the pyramid, and back to your point,

665
00:40:58,080 --> 00:41:00,880
Speaker 1: it's like an enhancement to those people. It was supposed

666
00:41:00,880 --> 00:41:02,680
Speaker 1: to be like a co pilot, in which it is.

667
00:41:03,040 --> 00:41:04,360
Speaker 1: It's and in something.

668
00:41:04,200 --> 00:41:06,200
Speaker 2: Register trademark Microsoft Corporation.

669
00:41:06,640 --> 00:41:12,319
Speaker 1: Yeah, yeah, exactly, copilot exactly, and in some cases it's

670
00:41:12,360 --> 00:41:16,120
Speaker 1: become an autonomous agent for those people. So software developers

671
00:41:16,200 --> 00:41:18,680
Speaker 1: run agents to new work for them now. And so

672
00:41:18,719 --> 00:41:21,239
Speaker 1: we worked worked our way down the labor pyramid a bit,

673
00:41:21,600 --> 00:41:23,680
Speaker 1: and we went down what's the next level? So think

674
00:41:23,719 --> 00:41:26,279
Speaker 1: of it like if doctors, lawyers, engineers are in the

675
00:41:27,120 --> 00:41:32,040
Speaker 1: two hundred k plus a year pay, then what's one

676
00:41:32,120 --> 00:41:37,560
Speaker 1: below that, you know, like you've got financial analysts, salespeople,

677
00:41:38,760 --> 00:41:42,600
Speaker 1: nurses and so then we're invested in the next class

678
00:41:42,600 --> 00:41:46,759
Speaker 1: of companies, so Rogo, which is AI for finance, Hippocratic,

679
00:41:46,800 --> 00:41:51,719
Speaker 1: which is an agentic nursing platform, Nooks, and revo which

680
00:41:51,760 --> 00:41:55,680
Speaker 1: are helping salespeople PI copilots for their work. And so

681
00:41:55,840 --> 00:41:58,280
Speaker 1: we've sort of worked our way down the labor pyramid.

682
00:41:58,800 --> 00:42:01,040
Speaker 1: And if you think about the pyramid, guess what's at

683
00:42:01,080 --> 00:42:04,440
Speaker 1: the bottom of the pyramid. It's physical labor. It is

684
00:42:05,080 --> 00:42:11,400
Speaker 1: the lowest paid, low scale in some and eventually robotics

685
00:42:11,440 --> 00:42:13,920
Speaker 1: will get there and hopefully do a lot of the

686
00:42:13,960 --> 00:42:17,560
Speaker 1: backbreaking work that nobody should be doing, right, and I

687
00:42:17,600 --> 00:42:20,600
Speaker 1: think we eventually that is sort of the way also

688
00:42:20,760 --> 00:42:22,839
Speaker 1: goes along with the timeline of the way we're thinking

689
00:42:22,840 --> 00:42:25,120
Speaker 1: about these things, is that you start with the highly scaled,

690
00:42:25,160 --> 00:42:28,040
Speaker 1: highly paid, and eventually, over the next decade, we get

691
00:42:28,080 --> 00:42:31,080
Speaker 1: down to the sort of the lowest scale, lowest paid work.

692
00:42:31,280 --> 00:42:36,719
Speaker 2: So you mentioned the tailwind behind AI. What does that

693
00:42:36,800 --> 00:42:40,960
Speaker 2: do to valuations? How do you underwrite startups in a

694
00:42:41,080 --> 00:42:45,799
Speaker 2: market where even the half decent companies look kind of expensive?

695
00:42:46,640 --> 00:42:53,960
Speaker 1: Great question. So when you have companies have gone from

696
00:42:54,200 --> 00:42:58,160
Speaker 1: zero to trillion in value, you know, in the last

697
00:42:58,760 --> 00:43:03,319
Speaker 1: three to four years, and when you're a founder, what

698
00:43:03,320 --> 00:43:04,880
Speaker 1: do you do you point to that that's what I

699
00:43:04,920 --> 00:43:09,000
Speaker 1: can be. I can get to trillion dollars because I'm

700
00:43:09,000 --> 00:43:15,320
Speaker 1: going after really large problems and we love that that ambition.

701
00:43:18,239 --> 00:43:20,400
Speaker 1: And what do people do then is that, well, you know,

702
00:43:20,560 --> 00:43:23,399
Speaker 1: if the probability of a of a one trillion dollar

703
00:43:23,480 --> 00:43:27,920
Speaker 1: outcome is one percent, and you do all the other

704
00:43:28,000 --> 00:43:30,560
Speaker 1: ninety nine percent of probabilities for the rest of the numbers,

705
00:43:31,520 --> 00:43:36,640
Speaker 1: you've got yourself to a expected value of ten billion

706
00:43:36,680 --> 00:43:39,040
Speaker 1: dollars or more. At least that's the minimum, right, you

707
00:43:39,080 --> 00:43:41,839
Speaker 1: do the one percent of ten billion, and you add

708
00:43:41,920 --> 00:43:45,120
Speaker 1: up all the other probabilities, it's probably like thirty forty

709
00:43:45,160 --> 00:43:48,280
Speaker 1: fifty billion. And so if you're looking at a series

710
00:43:48,320 --> 00:43:54,880
Speaker 1: A company where the ambition is tremendous, you're the valuation

711
00:43:55,000 --> 00:43:56,799
Speaker 1: may just be you know, you're raising one hundred million

712
00:43:56,840 --> 00:43:59,399
Speaker 1: or a billion dollar valuation for two guys out the gate.

713
00:44:00,360 --> 00:44:03,520
Speaker 1: And those are real examples. And I think we've we've

714
00:44:03,520 --> 00:44:05,440
Speaker 1: got a bit of this, you know, pointing at the

715
00:44:06,000 --> 00:44:09,960
Speaker 1: large outcome and if it's paired with smart people, ambitious idea,

716
00:44:10,920 --> 00:44:13,719
Speaker 1: it has that kind of potential. But the reality is

717
00:44:13,760 --> 00:44:16,239
Speaker 1: that there's usually one or two of those, the real

718
00:44:16,280 --> 00:44:22,160
Speaker 1: outliers that are mag seven scale and just the you know,

719
00:44:22,239 --> 00:44:25,160
Speaker 1: just gravity in itself doesn't allow for you know, the

720
00:44:25,440 --> 00:44:28,800
Speaker 1: world can't have you know, one hundred trillion dollar companies.

721
00:44:29,160 --> 00:44:32,759
Speaker 2: Plus if it's a trillion dollar total addressable market, it's

722
00:44:32,800 --> 00:44:34,759
Speaker 2: going to attract a lot of competition, a lot of

723
00:44:34,800 --> 00:44:39,480
Speaker 2: other startups, perhaps more than if you're focusing on a

724
00:44:39,640 --> 00:44:45,440
Speaker 2: smaller niche. Although even the cloud storage was you mentioned

725
00:44:45,440 --> 00:44:49,120
Speaker 2: forty companies early days, there's got to be tens of

726
00:44:49,160 --> 00:44:53,280
Speaker 2: thousands of companies going for each of these segments in AI.

727
00:44:53,800 --> 00:44:56,600
Speaker 1: Yeah, and there are. And I think the hard part

728
00:44:56,640 --> 00:44:59,640
Speaker 1: of the work that we have is is identifying what

729
00:44:59,640 --> 00:45:03,960
Speaker 1: we think with the leading company because in technology, generally speaking,

730
00:45:04,600 --> 00:45:08,799
Speaker 1: the leading company gets most of the market cap, which

731
00:45:08,800 --> 00:45:11,040
Speaker 1: which is if you look at the mag seven winner

732
00:45:11,120 --> 00:45:14,200
Speaker 1: take all, winner take most, right, right, like take ninety

733
00:45:14,200 --> 00:45:16,839
Speaker 1: percent of the market you know in Vidia of all

734
00:45:17,160 --> 00:45:22,360
Speaker 1: GPU spend. You know, take Google alphabet you know, multiple businesses,

735
00:45:22,520 --> 00:45:27,799
Speaker 1: or take a Tesla, take a meta own social right,

736
00:45:27,880 --> 00:45:30,480
Speaker 1: so winner takes most in technology.

737
00:45:30,640 --> 00:45:35,000
Speaker 2: Huh really interesting. You've described the AI moment as one

738
00:45:35,040 --> 00:45:39,520
Speaker 2: of the most important company building opportunities in our lifetime.

739
00:45:40,880 --> 00:45:43,839
Speaker 2: What's the risk of venture funding too many of these

740
00:45:43,880 --> 00:45:47,960
Speaker 2: startups or is this just a fat head of winners

741
00:45:48,000 --> 00:45:51,000
Speaker 2: and a long tail of Well, we gave it a shot.

742
00:45:51,719 --> 00:45:55,080
Speaker 2: Is this just the nature of this business where it's

743
00:45:55,160 --> 00:45:58,640
Speaker 2: a couple of winners are driving all the returns for

744
00:45:58,640 --> 00:45:59,480
Speaker 2: for your funds.

745
00:46:00,280 --> 00:46:03,640
Speaker 1: Yeah, venture is a power law business. There's no question

746
00:46:03,680 --> 00:46:07,879
Speaker 1: about it. And if you already look at like there's

747
00:46:07,920 --> 00:46:10,959
Speaker 1: this article I read the other day where ninety percent

748
00:46:11,040 --> 00:46:13,600
Speaker 1: the AI revenue is in the hands of two companies.

749
00:46:13,719 --> 00:46:16,040
Speaker 1: That's crazy opening and anthropic.

750
00:46:16,239 --> 00:46:16,359
Speaker 2: Right.

751
00:46:16,640 --> 00:46:19,879
Speaker 1: So in the remaining ten percent is in a bunch

752
00:46:19,880 --> 00:46:22,560
Speaker 1: of companies that are excellent companies. But the scale of

753
00:46:22,600 --> 00:46:25,400
Speaker 1: those two other companies is so massive, right, that it

754
00:46:25,520 --> 00:46:28,279
Speaker 1: dwarfs all the other good, great work that's happening in

755
00:46:28,480 --> 00:46:31,120
Speaker 1: tons of other companies that we've backed. And by the way,

756
00:46:31,160 --> 00:46:33,360
Speaker 1: like you know, those will be great outcomes and be

757
00:46:33,440 --> 00:46:36,600
Speaker 1: great companies. There's no question about it. It's just that

758
00:46:37,120 --> 00:46:40,600
Speaker 1: it dwarfs and the power law really does. You can

759
00:46:40,640 --> 00:46:42,880
Speaker 1: see it when you look at the numbers.

760
00:46:43,760 --> 00:46:44,000
Speaker 2: And.

761
00:46:45,520 --> 00:46:48,160
Speaker 1: It's yes, our job is to be in the companies

762
00:46:48,200 --> 00:46:52,200
Speaker 1: that make history and are in the power are following

763
00:46:52,200 --> 00:46:56,520
Speaker 1: the power law. What we find is that they're actually

764
00:46:57,360 --> 00:47:01,200
Speaker 1: you would think there's dozens of companies, there's actually tens,

765
00:47:01,320 --> 00:47:04,000
Speaker 1: maybe like less than ten, and there's like two to

766
00:47:04,080 --> 00:47:07,360
Speaker 1: three that are converging to become winners in a category.

767
00:47:07,719 --> 00:47:10,080
Speaker 1: And I think everyone's trying to identify those two to

768
00:47:10,120 --> 00:47:12,600
Speaker 1: three companies and be in those two to three companies.

769
00:47:13,120 --> 00:47:15,480
Speaker 1: And so one of the challenges we face is that

770
00:47:15,520 --> 00:47:17,920
Speaker 1: we try to invest in what we think is the

771
00:47:17,920 --> 00:47:22,240
Speaker 1: winner in the category early. Sometimes you don't know early

772
00:47:22,560 --> 00:47:24,960
Speaker 1: that this is the winner, and if you invest too early,

773
00:47:25,080 --> 00:47:27,640
Speaker 1: you conflict yourself out of the eventual winner.

774
00:47:27,760 --> 00:47:31,640
Speaker 2: Right, you only invest in one company per silo, so

775
00:47:31,719 --> 00:47:34,680
Speaker 2: to speak. Yes, right, So just to avoid those sort

776
00:47:34,680 --> 00:47:35,360
Speaker 2: of conflicts.

777
00:47:35,360 --> 00:47:37,560
Speaker 1: So the conflict typically we're joining the boards of these

778
00:47:37,560 --> 00:47:41,520
Speaker 1: companies and if you have, you know, confidential board level information,

779
00:47:41,640 --> 00:47:44,520
Speaker 1: and you don't want that, Like now you're invest in

780
00:47:44,560 --> 00:47:47,400
Speaker 1: a competitor, all of a sudden, there's a chance of conflict,

781
00:47:47,600 --> 00:47:50,480
Speaker 1: conflict of interest, and so we definitely try to avoid

782
00:47:50,480 --> 00:47:51,440
Speaker 1: that conflict of interest.

783
00:47:51,600 --> 00:47:56,200
Speaker 2: So I'm kind of fascinated by as venture investors. You

784
00:47:56,360 --> 00:48:00,359
Speaker 2: obviously see the promise of AI A care so ald

785
00:48:00,400 --> 00:48:07,120
Speaker 2: these different economic sectors. I'm curious how you're using AI internally,

786
00:48:07,280 --> 00:48:10,440
Speaker 2: Clina Perkins are using it to source deals or do

787
00:48:10,680 --> 00:48:15,560
Speaker 2: due diligence or predict specific outcomes. How does AI fit

788
00:48:15,600 --> 00:48:17,280
Speaker 2: into your operations.

789
00:48:17,640 --> 00:48:22,239
Speaker 1: Yeah, we have definitely been maxing out on AI internally,

790
00:48:22,400 --> 00:48:26,839
Speaker 1: not only because we invest in these companies whether we use.

791
00:48:28,239 --> 00:48:30,640
Speaker 1: Gleen is a company we incubated inside of Clina Perkins

792
00:48:30,640 --> 00:48:33,040
Speaker 1: actually and it sits in year seven now is pre

793
00:48:33,120 --> 00:48:39,000
Speaker 1: AI company started by an incredible engineer, Arvin Jane. And

794
00:48:39,400 --> 00:48:43,000
Speaker 1: that's like our knowledge management. Every single piece of knowledge

795
00:48:43,000 --> 00:48:45,520
Speaker 1: inside of Clina Perkins resides in Glean and you can

796
00:48:45,560 --> 00:48:48,000
Speaker 1: go to it, query it, chat chat with it. Like

797
00:48:48,239 --> 00:48:50,760
Speaker 1: if I want to find out your phone number and email,

798
00:48:51,120 --> 00:48:53,840
Speaker 1: like and then I've never met you before, but I

799
00:48:53,920 --> 00:48:56,040
Speaker 1: know someone at Kleiner probably knows you. I'll go to Glean.

800
00:48:56,560 --> 00:48:59,240
Speaker 1: Or if I want to ask about like an HR policy,

801
00:48:59,520 --> 00:49:01,719
Speaker 1: I'll go to Lean quickly asked. Because it just knows

802
00:49:01,719 --> 00:49:04,560
Speaker 1: everything about Cliine Perkins. It knows investment memos, it can

803
00:49:04,920 --> 00:49:07,719
Speaker 1: it knows cap tables, it knows like the really like

804
00:49:08,000 --> 00:49:11,000
Speaker 1: confidential stuff. It's permission in a way where the people

805
00:49:11,040 --> 00:49:15,640
Speaker 1: who are supposed to know will can know and it's

806
00:49:15,719 --> 00:49:17,560
Speaker 1: very That is part of the magic is that it's

807
00:49:17,800 --> 00:49:22,560
Speaker 1: very safe, secure. You trust it to really know the

808
00:49:22,560 --> 00:49:24,480
Speaker 1: things they're supposed to know and not know the supposed

809
00:49:24,480 --> 00:49:26,759
Speaker 1: to things is not supposed to know so and so

810
00:49:26,880 --> 00:49:30,840
Speaker 1: that's an example. But we are. We get so much information,

811
00:49:31,560 --> 00:49:35,120
Speaker 1: and whether it's board decks and their long board memos,

812
00:49:36,000 --> 00:49:40,200
Speaker 1: financials and uh so when I'm going to board meeting

813
00:49:40,200 --> 00:49:43,879
Speaker 1: after this, I got the board memo, and the first

814
00:49:43,920 --> 00:49:47,880
Speaker 1: thing I do, it's it's sent to an email ALIAS

815
00:49:48,040 --> 00:49:50,480
Speaker 1: and it and that runs it through AI and it

816
00:49:50,520 --> 00:49:53,200
Speaker 1: produces a summary for me of the of the board

817
00:49:53,280 --> 00:49:55,920
Speaker 1: meeting and questions I should be thinking about as an

818
00:49:56,000 --> 00:49:59,520
Speaker 1: instant thing that I do once I get the board materials,

819
00:49:59,840 --> 00:50:02,239
Speaker 1: just so that I start thinking about it before I

820
00:50:02,360 --> 00:50:04,920
Speaker 1: actually go read the board memo. I sort of have

821
00:50:04,960 --> 00:50:08,960
Speaker 1: a preview of it in my mind. We do a

822
00:50:09,040 --> 00:50:12,759
Speaker 1: quarterly or every four month portfolio of view, and we

823
00:50:12,840 --> 00:50:15,520
Speaker 1: have a couple hundred companies. It used to be a

824
00:50:15,600 --> 00:50:19,280
Speaker 1: very manual process that we had a dedicated person working

825
00:50:19,280 --> 00:50:21,799
Speaker 1: on it, and now our technology team has built the

826
00:50:21,800 --> 00:50:24,799
Speaker 1: system where we take these summaries. They get piped into

827
00:50:24,840 --> 00:50:28,279
Speaker 1: this portfolio book that we create and with all the

828
00:50:28,280 --> 00:50:31,120
Speaker 1: financials and all the metrics and everything, and it's something

829
00:50:31,120 --> 00:50:35,759
Speaker 1: that we are heavily leveraging AI. In this case, it's

830
00:50:35,840 --> 00:50:42,239
Speaker 1: Glean's and clawed. So the underlying models obviously, and we

831
00:50:42,320 --> 00:50:47,080
Speaker 1: have done a bunch of other things. Actually love to

832
00:50:47,200 --> 00:50:50,160
Speaker 1: rate my meetings just so I know, like so I remember,

833
00:50:50,239 --> 00:50:53,080
Speaker 1: like what are the tens and the nines that I

834
00:50:53,080 --> 00:50:55,480
Speaker 1: should have paid attention to but I forgot because I didn't.

835
00:50:56,040 --> 00:50:57,680
Speaker 1: So I want to have an exhaust of all the

836
00:50:57,680 --> 00:51:00,520
Speaker 1: things that I am encountering my real life. And AI

837
00:51:00,600 --> 00:51:04,160
Speaker 1: is an amazing capture of the exhaust and to provide

838
00:51:04,200 --> 00:51:07,560
Speaker 1: intelligence and signals to our team and it can pipe

839
00:51:07,600 --> 00:51:10,359
Speaker 1: it into our CRM. And so there's all these cool

840
00:51:10,400 --> 00:51:12,480
Speaker 1: things that we've done. We have an internal tech team

841
00:51:12,480 --> 00:51:15,959
Speaker 1: which is an amazing team of four folks who build

842
00:51:15,960 --> 00:51:17,960
Speaker 1: a lot of these tools, and we are definitely maxing

843
00:51:18,160 --> 00:51:20,040
Speaker 1: out on using everything that's out there.

844
00:51:20,239 --> 00:51:24,040
Speaker 2: Huh really really quite fascinating. So final question before we

845
00:51:24,080 --> 00:51:26,160
Speaker 2: get to our favorites that we ask all our guests

846
00:51:27,040 --> 00:51:31,279
Speaker 2: what are investors not thinking about when it comes to

847
00:51:31,360 --> 00:51:36,600
Speaker 2: AI or anything else? But perhaps should be what sort

848
00:51:36,600 --> 00:51:41,640
Speaker 2: of topics policy, data, geography, what's getting overlooked? But shouldn't

849
00:51:42,960 --> 00:51:44,200
Speaker 2: I think right.

850
00:51:44,040 --> 00:51:47,160
Speaker 1: Now we're going through a time of where software is

851
00:51:47,840 --> 00:51:52,560
Speaker 1: considered to be dead and or there's called the saaspocalypse and.

852
00:51:53,120 --> 00:51:55,160
Speaker 2: Although they're just coming offish clothes.

853
00:51:55,320 --> 00:51:59,439
Speaker 1: Yeah, and there's always an overreaction to, oh my god,

854
00:51:59,480 --> 00:52:01,960
Speaker 1: it's going to be a Capex world and only chips

855
00:52:01,960 --> 00:52:04,960
Speaker 1: will matter, and only like you know, like fiber and

856
00:52:05,040 --> 00:52:07,840
Speaker 1: data centers and power will matter. The reality is like

857
00:52:08,160 --> 00:52:11,319
Speaker 1: the way we as humans interact with software, with with

858
00:52:11,400 --> 00:52:14,600
Speaker 1: the technologies through software and through things you know we

859
00:52:14,680 --> 00:52:16,520
Speaker 1: have known to be called software and tools and so.

860
00:52:17,000 --> 00:52:21,680
Speaker 1: And by the way CIOs and large companies buy buy

861
00:52:21,760 --> 00:52:24,759
Speaker 1: from companies that sell to them, and they don't just buy,

862
00:52:24,960 --> 00:52:27,960
Speaker 1: you know, a smart agent. And so I think something

863
00:52:28,000 --> 00:52:32,239
Speaker 1: that's been certainly just the pendulum swung a little too

864
00:52:32,280 --> 00:52:36,040
Speaker 1: far is that we underappreciate what software still does and

865
00:52:36,080 --> 00:52:40,200
Speaker 1: will continue to do forever, for for our enterprises, for governments,

866
00:52:40,200 --> 00:52:40,600
Speaker 1: et cetera.

867
00:52:40,800 --> 00:52:41,959
Speaker 2: Software not going away.

868
00:52:42,040 --> 00:52:42,800
Speaker 1: Yeah.

869
00:52:42,920 --> 00:52:46,239
Speaker 2: Really interesting. All right, let's jump to our favorite questions,

870
00:52:46,480 --> 00:52:50,720
Speaker 2: starting with who are your mentors who helped shape your career?

871
00:52:52,040 --> 00:52:58,200
Speaker 1: Yeah. One of my mentors, uh is Urban Fetterman. I

872
00:52:58,320 --> 00:53:01,120
Speaker 1: dearly love he's he's ninety years old. Now he's a

873
00:53:01,120 --> 00:53:04,000
Speaker 1: New Yorker. Wow, he sold peanuts. I believe that a

874
00:53:04,080 --> 00:53:08,560
Speaker 1: Dodger stadium when there's still the Dodgers fifties. Yeah, but

875
00:53:08,719 --> 00:53:12,600
Speaker 1: he was my mentor at us VP, and he's a

876
00:53:12,680 --> 00:53:16,719
Speaker 1: legendary semiconductor investor, believe it or not. He was one

877
00:53:16,760 --> 00:53:20,239
Speaker 1: of the co founders of sand Disk Corporation. Before that,

878
00:53:20,280 --> 00:53:21,440
Speaker 1: he was the CEO of am D.

879
00:53:22,280 --> 00:53:23,799
Speaker 2: I hope he still has a few shares of.

880
00:53:23,760 --> 00:53:27,359
Speaker 1: Those, Yeah, and he probably does. But sand Disc, which

881
00:53:27,400 --> 00:53:30,440
Speaker 1: is in this memory hype cycle that we're going through,

882
00:53:31,120 --> 00:53:33,680
Speaker 1: hype or not, but like there's a real need for memory,

883
00:53:34,400 --> 00:53:36,320
Speaker 1: I believe now is maybe like a half a trillion

884
00:53:36,320 --> 00:53:39,440
Speaker 1: dollar market cap company something crazy. Don't quote me on that,

885
00:53:39,480 --> 00:53:43,280
Speaker 1: but memories having its day right now. But in any case,

886
00:53:43,840 --> 00:53:48,720
Speaker 1: he was a mentor because not only I worked for him,

887
00:53:49,080 --> 00:53:51,400
Speaker 1: but I saw through his lens how to be a

888
00:53:51,440 --> 00:53:55,280
Speaker 1: great board member, how to make investments, how to back people,

889
00:53:55,600 --> 00:53:59,919
Speaker 1: how to build relationships with people. But he also gave

890
00:54:00,200 --> 00:54:03,600
Speaker 1: feedback that no one else in life would because I

891
00:54:03,600 --> 00:54:07,440
Speaker 1: think he just wanted He loved me. I really felt

892
00:54:07,440 --> 00:54:09,920
Speaker 1: the love because the kind of feedback he gave me

893
00:54:10,520 --> 00:54:13,440
Speaker 1: was feedback that I don't think people would generally have

894
00:54:13,480 --> 00:54:15,400
Speaker 1: the courage to give you. And he sort of you know,

895
00:54:15,440 --> 00:54:20,040
Speaker 1: it's it's like pretty direct. It's a tough love. Yeah,

896
00:54:20,080 --> 00:54:23,520
Speaker 1: And I love that about him, and you know, makes

897
00:54:23,719 --> 00:54:25,200
Speaker 1: reminds me that I need to go pay him a visit.

898
00:54:25,719 --> 00:54:27,200
Speaker 2: Well, you're in New York, amun as well.

899
00:54:27,480 --> 00:54:28,960
Speaker 1: He's actually, you know, he lives in the BA area.

900
00:54:29,040 --> 00:54:32,239
Speaker 1: Oh yeah, so's he's a New Yorker relocated to the

901
00:54:32,280 --> 00:54:33,919
Speaker 1: BA area I think like fifty years ago.

902
00:54:34,360 --> 00:54:36,840
Speaker 2: So let's talk about books. What are some of your favorites.

903
00:54:36,880 --> 00:54:37,920
Speaker 2: What are you reading currently?

904
00:54:38,480 --> 00:54:42,640
Speaker 1: So I'm just starting on this book. It's called Believe

905
00:54:43,600 --> 00:54:47,680
Speaker 1: Why You Should Believe especially this like Era of AI,

906
00:54:48,680 --> 00:54:54,600
Speaker 1: And it's my I'm on the board of a company

907
00:54:54,600 --> 00:54:57,320
Speaker 1: called Thrive Global and the CEO founder is Ariana Huffington.

908
00:54:57,840 --> 00:55:00,560
Speaker 1: And Ariana gave me the book and she and I

909
00:55:00,600 --> 00:55:08,080
Speaker 1: have very aligned views on faith and spirituality. And she

910
00:55:08,160 --> 00:55:09,520
Speaker 1: gave me this book and said, hey, you know, like,

911
00:55:09,680 --> 00:55:15,600
Speaker 1: so Aria and I believe Greek Orthodox I'm Muslim, and

912
00:55:15,719 --> 00:55:19,719
Speaker 1: we talk about how faith guides our lives and we

913
00:55:20,120 --> 00:55:22,680
Speaker 1: talk about how in this age of AI. This is

914
00:55:22,719 --> 00:55:24,480
Speaker 1: actually the conversation she and I have and She's like,

915
00:55:24,520 --> 00:55:26,960
Speaker 1: I have the perfect book for you, and it's about

916
00:55:27,160 --> 00:55:29,839
Speaker 1: how we should believe it even more in this age

917
00:55:29,840 --> 00:55:34,640
Speaker 1: of AI because it helps us actually understand the world

918
00:55:34,719 --> 00:55:37,239
Speaker 1: because we're trying to make sense of it all the time,

919
00:55:37,719 --> 00:55:42,920
Speaker 1: all the time. And actually religion can give us a

920
00:55:42,920 --> 00:55:45,600
Speaker 1: bit more constrained view of like what the world actually is,

921
00:55:45,600 --> 00:55:48,880
Speaker 1: because otherwise it's just like a black hole, black box.

922
00:55:49,000 --> 00:55:54,400
Speaker 1: You won't be able to comprehend the vastness of what

923
00:55:54,440 --> 00:55:57,879
Speaker 1: we're trying to comprehend as human beings. And especially with AI,

924
00:55:57,920 --> 00:56:01,040
Speaker 1: now we're pushing boundaries of what's possible. And I think

925
00:56:01,040 --> 00:56:04,279
Speaker 1: there's actually more more in the in the scriptures that

926
00:56:04,360 --> 00:56:07,600
Speaker 1: you think is sort of the view that she and

927
00:56:07,600 --> 00:56:09,560
Speaker 1: I share. And this book actually sort of hits home

928
00:56:09,560 --> 00:56:13,520
Speaker 1: with it. It's a New York Times columnist Roy Dehut,

929
00:56:13,960 --> 00:56:15,920
Speaker 1: But yeah, unbelieve check it.

930
00:56:15,840 --> 00:56:19,279
Speaker 2: Out on my list. Now, what about streaming? I know

931
00:56:19,400 --> 00:56:22,319
Speaker 2: you host a podcast. What do you either watch or

932
00:56:22,360 --> 00:56:23,240
Speaker 2: listen these days?

933
00:56:25,320 --> 00:56:28,279
Speaker 1: So my wife and I we love to watch Dateline

934
00:56:28,760 --> 00:56:32,239
Speaker 1: forty eight hours, these all the crime shows, all the

935
00:56:32,280 --> 00:56:35,600
Speaker 1: crime shows. It's like in some ways it's it just

936
00:56:35,680 --> 00:56:38,880
Speaker 1: kind of takes it down a notch. But it also

937
00:56:39,719 --> 00:56:44,360
Speaker 1: is so instructive around human psychology, what motivates people to

938
00:56:44,440 --> 00:56:49,960
Speaker 1: do not so great things and there's generally like a

939
00:56:50,040 --> 00:56:52,439
Speaker 1: theme to it at this point. It's a very repetitive theme.

940
00:56:52,719 --> 00:56:55,719
Speaker 2: But Junning Krueger, it's all they have no idea, that

941
00:56:55,920 --> 00:56:59,719
Speaker 2: just trail of DNA evidence they leave everywhere. Anytime I've

942
00:56:59,719 --> 00:57:01,800
Speaker 2: watched that show is it's like, what are you doing?

943
00:57:01,960 --> 00:57:03,880
Speaker 1: And it's you know, it's it's it should be that

944
00:57:03,960 --> 00:57:07,200
Speaker 1: it's harder and harder to commit crimes. It should be

945
00:57:07,280 --> 00:57:09,600
Speaker 1: harder and harder, and yet there are still crimes.

946
00:57:09,760 --> 00:57:12,680
Speaker 2: There's still and just as many as ever, only people

947
00:57:12,719 --> 00:57:14,120
Speaker 2: are getting caught more easily.

948
00:57:14,239 --> 00:57:17,040
Speaker 1: Yeah, and we know the cell phones they're pining, those towers.

949
00:57:17,480 --> 00:57:20,040
Speaker 2: You know, what do you mean you weren't in the house.

950
00:57:20,120 --> 00:57:22,120
Speaker 2: We could tell you within one hundred feet of this

951
00:57:22,200 --> 00:57:23,240
Speaker 2: person the exact time.

952
00:57:23,480 --> 00:57:27,560
Speaker 1: Yeah, So it is like, it's not probably a very

953
00:57:28,360 --> 00:57:30,640
Speaker 1: full answer, and you're probably looking for some cool shows.

954
00:57:30,720 --> 00:57:34,240
Speaker 2: No, not at all. I'm fascinated by that. My my.

955
00:57:34,680 --> 00:57:36,960
Speaker 2: It's funny because there was used to be this giant

956
00:57:37,000 --> 00:57:42,400
Speaker 2: gap between the CSIS and and and what actually was

957
00:57:42,440 --> 00:57:44,720
Speaker 2: going on. But if you watch the two of them,

958
00:57:44,760 --> 00:57:49,240
Speaker 2: it's really closed. Because maybe there's a little selection bias here,

959
00:57:49,280 --> 00:57:52,360
Speaker 2: but all those shows, it's all these people they got

960
00:57:52,560 --> 00:57:55,480
Speaker 2: that got caught. So you're seeing where the technology worked,

961
00:57:55,680 --> 00:57:59,760
Speaker 2: where the forensic science was that guy. Yeah, it's really

962
00:57:59,760 --> 00:58:00,720
Speaker 2: really very funny.

963
00:58:00,800 --> 00:58:01,160
Speaker 1: All right.

964
00:58:01,400 --> 00:58:04,680
Speaker 2: Our final two questions, what sort of advice would you

965
00:58:04,720 --> 00:58:07,640
Speaker 2: give to a recent college grad interest in the career

966
00:58:08,280 --> 00:58:11,360
Speaker 2: in either venture investing or technology.

967
00:58:12,760 --> 00:58:15,080
Speaker 1: I would It's probably the same advice I would have

968
00:58:15,080 --> 00:58:18,200
Speaker 1: given twenty years ago or given myself when I came

969
00:58:18,200 --> 00:58:21,880
Speaker 1: out of college, is go work at a fast growing

970
00:58:21,960 --> 00:58:26,480
Speaker 1: company where you can learn from the growth that it's encountering,

971
00:58:26,520 --> 00:58:30,000
Speaker 1: but also the people and the probably the network that

972
00:58:30,040 --> 00:58:31,880
Speaker 1: you build for the rest of your life. It is

973
00:58:31,920 --> 00:58:34,200
Speaker 1: probably the best time in your life coming out of

974
00:58:34,200 --> 00:58:37,840
Speaker 1: college to go learn from others around you and to

975
00:58:37,960 --> 00:58:42,280
Speaker 1: experience high growth because from high growth, lots of lessons

976
00:58:42,280 --> 00:58:45,320
Speaker 1: are learned, and so if you can find a way

977
00:58:45,360 --> 00:58:49,320
Speaker 1: to get into a high growth technology startup AI startup,

978
00:58:51,160 --> 00:58:55,240
Speaker 1: it is the best way to develop the skills. But

979
00:58:55,280 --> 00:58:58,040
Speaker 1: also like the empathy of what it means to have

980
00:58:58,640 --> 00:59:01,840
Speaker 1: carried a bag and sold some thing and built something

981
00:59:02,000 --> 00:59:07,600
Speaker 1: and shipped something. I always tell folks like, there shouldn't

982
00:59:07,600 --> 00:59:11,280
Speaker 1: be a direct path in a venture capital a. It's

983
00:59:11,280 --> 00:59:12,960
Speaker 1: a second thing. It's not the first thing you do

984
00:59:13,320 --> 00:59:15,920
Speaker 1: coming out of college or coming out of grad school.

985
00:59:15,960 --> 00:59:19,520
Speaker 1: It's like you have to have built and shipped and

986
00:59:19,600 --> 00:59:23,360
Speaker 1: sold and developed that empathy for high growth and the

987
00:59:23,960 --> 00:59:26,840
Speaker 1: lessons learned before you get into our career.

988
00:59:27,000 --> 00:59:30,440
Speaker 2: We really really interesting answer, and our final question, what

989
00:59:30,480 --> 00:59:32,920
Speaker 2: do you know about the world of venture investing or

990
00:59:33,040 --> 00:59:36,960
Speaker 2: technology today, might have been useful twenty five thirty years

991
00:59:36,960 --> 00:59:39,040
Speaker 2: ago when you were first ramping up.

992
00:59:40,960 --> 00:59:44,160
Speaker 1: It's all about the people. It sounds so trite, but

993
00:59:44,480 --> 00:59:50,760
Speaker 1: it is about especially it's i say, ordinary looking people

994
00:59:51,240 --> 00:59:55,760
Speaker 1: doing extraordinary things. And how do you assess those ordinary

995
00:59:55,760 --> 00:59:59,680
Speaker 1: people who are doing extraordinary things is by really understanding

996
00:59:59,680 --> 01:00:04,600
Speaker 1: the people their intentionality, their desires, their ambition, what drives them,

997
01:00:04,680 --> 01:00:07,360
Speaker 1: their motivation, which goes back to the why do you

998
01:00:07,360 --> 01:00:09,080
Speaker 1: meet them in person because you're trying to figure out,

999
01:00:09,080 --> 01:00:12,520
Speaker 1: like why are they doing this? Building a startup company

1000
01:00:12,800 --> 01:00:16,240
Speaker 1: is hard work, it's a sacrifice on life, so there

1001
01:00:16,280 --> 01:00:17,880
Speaker 1: better be a good reason why you're doing this.

1002
01:00:18,480 --> 01:00:22,680
Speaker 2: Huh, really really interesting answer. Thank you Mamun for being

1003
01:00:22,720 --> 01:00:25,280
Speaker 2: so generous with your time. We have been speaking with

1004
01:00:25,400 --> 01:00:29,720
Speaker 2: Mamun Ahmed. He is partner at Kleiner Perkins. If you

1005
01:00:29,880 --> 01:00:33,120
Speaker 2: enjoy this conversation, Well, check out any of the six

1006
01:00:33,200 --> 01:00:36,680
Speaker 2: hundred and forty seven we've done over the past twelve years.

1007
01:00:37,000 --> 01:00:41,600
Speaker 2: You can find those at Apple, Spotify, YouTube, Bloomberg, wherever

1008
01:00:41,680 --> 01:00:45,240
Speaker 2: you get your favorite podcasts. I would be remiss if

1009
01:00:45,240 --> 01:00:48,240
Speaker 2: I did not think the crack staff that helps put

1010
01:00:48,280 --> 01:00:54,240
Speaker 2: these conversations together each week. Alexis Noriega is my video producer.

1011
01:00:54,640 --> 01:00:58,840
Speaker 2: Joan Russo is my researcher. Anna Luke is my podcast producer.

1012
01:00:59,400 --> 01:01:03,040
Speaker 2: I'm Barry Results. You've been listening to Masters in Business

1013
01:01:03,480 --> 01:01:04,640
Speaker 2: on Bloomberg Radio