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Speaker 1: This is Master's in Business with Barry rid Holds on

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Speaker 1: Bloomberg Radio.

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Speaker 2: This week on the podcasts, What can I say once again?

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Speaker 2: I have an extra special guest. Brad Gersner is a

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Speaker 2: founder and investor in technology startups. His firm, Altimeter Capital

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Speaker 2: runs over ten billion dollars. That's after returning a big

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Speaker 2: chunk of capital and profits to their investors. He's been

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Speaker 2: doing this for about twenty years. They invest primarily in

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Speaker 2: private and public companies. They're no longer very much of

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Speaker 2: a seed investor. Although Brad himself was a very successful entrepreneur.

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Speaker 2: He either started or co founded, or came in to

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Speaker 2: various startups, four of which have had substantial exits, to

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Speaker 2: say nothing of the companies that he's invested in either

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Speaker 2: late stage, private or early IPO and has done exceedingly

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Speaker 2: well with. He has been involved in more than one

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Speaker 2: hundred IPOs. This is really a fascinating conversation, not just

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Speaker 2: because of his acumen as a venture investor, but one

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Speaker 2: of the things that Brad is passionate about is making

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Speaker 2: every child in America feel like they have a stake

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Speaker 2: in the country, they have some skin in the game.

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Speaker 2: And his idea for starting every newborn in the country

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Speaker 2: with a thousand dollars investment in the S and P

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Speaker 2: five hundred, or as he calls it, invest America is

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Speaker 2: gaining traction not just amongst venture capitalists and corporate America,

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Speaker 2: but on both sides of the isle in Washington, DC,

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Speaker 2: and this may actually become a real thing. I thought

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Speaker 2: this conversation was absolutely fascinating. I think you will also

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Speaker 2: with no further ado my discussion without times brad Gersner.

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Speaker 1: It's great to be here, Barry, it's great to have you.

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Speaker 2: I'm kind of fascinated by your backgrounds, and before we

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Speaker 2: get into what you do, we have to talk a

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Speaker 2: little bit about how you got to where you are today,

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Speaker 2: because it's a pretty wild ride. Starting with you're working

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Speaker 2: with Peter Leegal of Forest River that's later sold to

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Speaker 2: Warren Buffett and Berkshire Hathaways, but you're operating as his

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Speaker 2: chief of staff. Tell us what you were doing with

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Speaker 2: Forest River.

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Speaker 1: Well, thanks for having me. I'm a big fan of

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Speaker 1: the show, and you know, I grew up in rural Indiana.

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Speaker 1: I'm fifty two years old, so it is nineteen, you know,

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Speaker 1: early eighties. My dad was first generation college became an

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Speaker 1: entrepreneur started an auto parts manufacturing business. He chose a

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Speaker 1: challenging time. There are double digit interest rates in America,

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Speaker 1: double digit inflation. The Japanese were attacking our auto industry,

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Speaker 1: and as you remember, that part of the world was

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Speaker 1: known as the rust Belt, yep. So it was tough

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Speaker 1: times in America. You know, growing up where I did

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Speaker 1: in Northwest Indiana. But you know, when I got to

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Speaker 1: high school, I realized my dad's business didn't make it.

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Speaker 1: I had to find my way out of this town.

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Speaker 1: I was going to have to pay for college. So

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Speaker 1: I had a job. And you know, Elkhart, Indiana happens

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Speaker 1: to be the r V capital of the world. And

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Speaker 1: there was a gentleman I got introduced to named Pete Legal.

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Speaker 1: Pete Legal had built an RV company called Cobra, sold

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Speaker 1: it or partnered with private equity, had a bad experience,

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Speaker 1: left that and said I'm going to do it over again,

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Speaker 1: and he had a little startup RV company called Forest River.

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Speaker 1: Pete's an absolute force in nature, and you know, so

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Speaker 1: when he asked me if i'd come be his right

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Speaker 1: hand guy, I had no idea what that meant, but

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Speaker 1: I knew i'd learn a lot. So I worked there

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Speaker 1: the summers of my junior and senior year, and then

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Speaker 1: I'd worked throughout the year a bit, and I learned

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Speaker 1: so much from Pete. But Pete was one of these guys.

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Speaker 1: He didn't spend a lot of time analyzing right the

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Speaker 1: way an entrepreneur does. It is the ab test. They're

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Speaker 1: prone to action, right, right, So Pete would say that,

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Speaker 1: you know, jump in the car with me and we

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Speaker 1: go to a competitor's lot and he'd be measuring, you know,

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Speaker 1: the dimensions on the new RVs out from the competitors

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Speaker 1: on their lot. He's like, we don't need to waste

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Speaker 1: money on expensive architects. We can just you know, do

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Speaker 1: this ourselves run a better, lower cost operation. He knew

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Speaker 1: about competitive modes, Like I say, Pete knew about Michael

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Speaker 1: Porter's five forces, all the stuff you learn at Harvard

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Speaker 1: Business School, but he learned it by ab testing it

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Speaker 1: in the real world. And so it was a real

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Speaker 1: pleasure to work for him. And yeah, he went on

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Speaker 1: to that became the biggest RV company in the world,

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Speaker 1: would later sell it to Warren Buffett, and Warren writes

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Speaker 1: about him extensively in his letters.

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Speaker 2: So eventually you get your jd. From the University of Indiana,

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Speaker 2: you don't really strike me as a lawyer. Not only that,

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Speaker 2: two years later you become Deputy Secretary of State for Indiana.

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Speaker 1: So so that happened. So you know, you're you're you're

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Speaker 1: highlighting a random background. And we'll come back to this.

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Speaker 1: I do think when I look for analysts today, I

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Speaker 1: look for interesting backgrounds. You know, all of these things.

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Speaker 1: I would say, the thing that connects them is just

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Speaker 1: voracious curiosity about the world of politics and you know,

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Speaker 1: economies and trying to make sense out of it. But

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Speaker 1: because my dad went broke, I have three siblings, so

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Speaker 1: there were four of us his father, so my grandfather,

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Speaker 1: he came to the grandkids. He sacrificed everything. This was

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Speaker 1: a self taught man. I remember his bookshelf. He had

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Speaker 1: physics textbooks and biology textbooks, you know, and and he

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Speaker 1: would just read them. He couldn't afford to go to college,

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Speaker 1: and so he came to the grandkids and he said,

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Speaker 1: you can't be entrepreneurs. You have to become professionals, law school,

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Speaker 1: medical school, become an architect. But we got to get

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Speaker 1: the family back on track, right, And so really to

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Speaker 1: honor his wish, I went to law school. As it

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Speaker 1: turns out, it's incredible training in just how to think analytically.

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Speaker 2: I love that take. That was my experience as well.

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Speaker 2: But you kept going. You said, Hey, if law school

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Speaker 2: is good, what about business school? And off to Harvard

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Speaker 2: Business School you go. What was the career path? What

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Speaker 2: were you thinking about? From politics to law school to

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Speaker 2: business school.

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Speaker 1: Well, the truth of the matter is I was lucky

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Speaker 1: enough one I came back from studying overseas to work

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Speaker 1: for an incredible statesman, Indiana Senator Dick Lueger. He was

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Speaker 1: nineteen ninety ninety one. He and Sam Nunn were denuclearizing

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Speaker 1: the world with the Sam Nun Bill. This was post

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Speaker 1: the fall of the Wall, the end of the Cold War.

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Speaker 1: It was exciting times. And I just hit it off

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Speaker 1: with this incredible man. And so when I graduated from

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Speaker 1: law school, he pings me one day and he said, hey,

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Speaker 1: I would like for you to accept an appointment as

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Speaker 1: deputy secretary of state. And it was really a launching

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Speaker 1: pad in Indiana for higher political office by who became

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Speaker 1: our governor and a US senator had been deputy secretary

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Speaker 1: of state. So I knew what it meant, but I

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Speaker 1: took the job. I got there. I think it paid

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Speaker 1: sixty thousand dollars a year, and I realized, you know

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Speaker 1: how poor I was, and I had grown up poor,

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Speaker 1: and I didn't want to spend the rest of my

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Speaker 1: life begging for money. So the truth is, I thought

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Speaker 1: to myself, if I could get into one of these

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Speaker 1: fancy business schools, I'll go there, I'll figure out how

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Speaker 1: to make a million bucks, and I'll come back and

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Speaker 1: I'll run for governor. And that was twenty five years ago.

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Speaker 2: Well, if you ever get to the million dollars, you

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Speaker 2: can run for governor.

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Speaker 1: Right So, Harvard Business School, it was a transformative time

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Speaker 1: nineteen ninety nine two thousand. The Internet was blowing up

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Speaker 1: and that would change really everything.

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Speaker 2: So you joined General Catalyst right out at a business school,

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Speaker 2: and then par Capital Management. Tell us a little bit

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Speaker 2: about both of those experiences at fairly established venture funds.

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Speaker 1: Well, at the time, there was no General Catalyst. It

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Speaker 1: was nineteen ninety nine, was going wild. There were some

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Speaker 1: established venture capital firms in Boston, Matrix, Charles River, Graylock,

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Speaker 1: High Highlands, et cetera.

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Speaker 2: Bain.

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Speaker 1: But there are these two enterprising young guys, David Fialco

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Speaker 1: and Joel Cutler, forces of nature, and they wanted to

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Speaker 1: start a venture firm, and so we knew we had

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Speaker 1: to put together a launch deal in order to launch

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Speaker 1: this venture capital firm. I help them put together that

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Speaker 1: launch deal.

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Speaker 2: Wait, so when you joined General Catalyst, it wasn't already established.

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Speaker 1: No, in fact that in fact, I think they were

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Speaker 1: still investing money off their balance sheet called FC Capital.

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Speaker 1: Fialco Cutler Capital gotcha.

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Speaker 2: Okay, all right, now that may that chronology.

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Speaker 1: Makes This was an early online travel company that we started.

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Speaker 1: We would eventually not only eventually. So that was nineteen

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Speaker 1: ninety nine. I was still in business school helping them

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Speaker 1: incubate it. I became co CEO of the business and

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Speaker 1: we sold our stake in the business to Barry Diller

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Speaker 1: in two thousand and one. You know, the Internet had crashed,

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Speaker 1: but our business was working really well. It was fortuitous

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Speaker 1: and on that succes well launch deal they were able

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Speaker 1: then to go raise General Catalyst one. I learned a

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Speaker 1: tremendous amount from them. They're both still dear friends. But

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Speaker 1: one of the things I learned in that first startup,

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Speaker 1: I had two guys on the two investors who were

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Speaker 1: not traditional venture capitalists. One was Seth Clarmen, the founder

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Speaker 1: of bow Post Sure, and the other one was Paul Reader,

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Speaker 1: the founder of Park Capital. So these guys would be

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Speaker 1: bucketed as hedge fund guys.

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Speaker 2: Okay, not traditional vcs.

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Speaker 1: But the truth of the matter is, if you think

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Speaker 1: about Warren Buffett's hedge fund, Seth Klarman's hedge fund, Paul

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Speaker 1: Reader's hedge fund, they never quarantined themselves to just public investments.

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Speaker 1: They just made great investments. Sometimes they were private, sometimes

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Speaker 1: they were public. And so I was really a believer

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Speaker 1: that this was going to be the future of venture capital.

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Speaker 1: The companies were going to scale faster, that there was

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Speaker 1: going to be a lot of information flow that you

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Speaker 1: could extract out a venture into the public markets and

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Speaker 1: vice versa. I had an appetite for both the public

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Speaker 1: markets and the venture markets. So I went to Paul,

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Speaker 1: who was the founder of Park Capital, and I said, Hey,

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Speaker 1: how about if I build your technology practice, I'll run

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Speaker 1: a public sleeve and I'll also run a venture sleeve

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Speaker 1: in technology. Paul was crazy enough to invite me on board.

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Speaker 2: So wait, at this time, you really didn't have a

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Speaker 2: whole lot of investing experience. You were both a lawyer

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Speaker 2: and a business school graduate, so you had a lot

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Speaker 2: of academic knowledge. What was that transition like once you're

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Speaker 2: in the trenches and actually deploying real capital.

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Speaker 1: Yeah, so what I mentioned my grandfather, you know, he

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Speaker 1: sacrificed everything. So it was a surprise when he passed

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Speaker 1: away that we learned that he left one hundred thousand

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Speaker 1: dollars twenty five thousand to each of the four grandchildren.

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Speaker 1: And you know, I vowed on that day that I'm

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Speaker 1: going to I'm going to multiply this money. Like this

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Speaker 1: guy sacrificed everything, I'm not wasting it. And you know,

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Speaker 1: so in law school, I went and got my Series

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Speaker 1: seven and sixty three. I started plotting stocks. I started

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Speaker 1: thinking about public market investing. I started doing some of

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Speaker 1: my own public market investing. And you have to understand

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Speaker 1: what this felt like for a kid on the outside

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Speaker 1: looking in who never had money for the first time

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Speaker 1: to buy a stock, to make some money, to sell

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Speaker 1: a stock. And so I would say I had an

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Speaker 1: appetite for the public markets. When Paul met me, I

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Speaker 1: was modeling companies like Priceline in my spare time and

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Speaker 1: investing out of my you know, probably Fidelity account at

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Speaker 1: the time, and Paul said, hey, I think you would

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Speaker 1: be good in this hedge fund business. And I said, Paul,

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Speaker 1: I don't know anything about managing a public portfolio. But

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Speaker 1: the deal we made with each other, I actually said

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Speaker 1: to him, I'll come work for free. I said, you

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Speaker 1: just have to agree to have lunch with me every day.

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Speaker 1: If I love the business, I'll probably start my own.

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Speaker 1: Because by this time I had started and sold a

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Speaker 1: couple companies and I knew I was an entrepreneur. And

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Speaker 1: you know, I will tell you I still talk to

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Speaker 1: Paul every week. He's an incredible friend, an incredible mentor,

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Speaker 1: and a super investor.

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Speaker 2: You have one of the wildest backgrounds. It's really quite

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Speaker 2: amazing to me seeing how you were driven and you

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Speaker 2: were running pretty fast and you really didn't know in

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Speaker 2: what direction you wanted to go, and once that came

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Speaker 2: into focus, lots of pieces fell in quickly. Now you're

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Speaker 2: running Altimeter Capital. You launched it at a really fortuitous

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Speaker 2: time fifteen years any second thoughts that was this what

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Speaker 2: you were born to do? How does it feel having

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Speaker 2: spent so much time not knowing exactly what direction you

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Speaker 2: want to go, and then suddenly it's all falling into place.

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Speaker 1: You know, you always have to contextualize those moments in

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Speaker 1: your life. So I had started a third company called

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Speaker 1: Room seventy seven that we had end up selling to Google.

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Speaker 1: I had just gotten married in the fall of two

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Speaker 1: thousand and seven. I had my first child in June

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Speaker 1: of two thousand and eight, and I told my you know,

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Speaker 1: you know, very pregnant wife at the time. You know,

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Speaker 1: we didn't have a lot of money, that I was

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Speaker 1: going to leave this secure job. Now the first half

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Speaker 1: of two thousand and eight, I was doing pretty well

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Speaker 1: in the fund. I think I was up twenty or

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Speaker 1: twenty five percent, and so I was feeling pretty good

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Speaker 1: about things. I said, you know, now I've been thinking

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Speaker 1: about starting my own firm. Now's the time I had

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Speaker 1: soft circled a couple hundred million dollars from some endowments.

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Speaker 1: These are folks who said, we'll give you money, we

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Speaker 1: think you're good at this, we want to help you

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Speaker 1: launch your own firm. And explicitly what I was going

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Speaker 1: to do is move to Silicon Valley and start a

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Speaker 1: crossover firm by a founder so I had started three companies.

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Speaker 1: This was somebody who was venture first, public market second,

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Speaker 1: and I thought that was a really unique wedge into

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Speaker 1: the venture community in public investing. But of course I

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Speaker 1: didn't know the world was going to melt down in

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Speaker 1: two thousand and eight. So by August the world started

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Speaker 1: melting down. Remember I've got a three month old child,

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Speaker 1: and September rolls around every morning. Every morning, I'm on

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Speaker 1: you know, watching NBC. It's gapping down five percent, six percent.

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Speaker 1: Advisors were calling me saying, don't leave your firm. The

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Speaker 1: world's ending. This is a terrible time. But what I

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Speaker 1: had was having started three other companies on the back

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Speaker 1: of a napkin. I knew what it felt like to

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Speaker 1: be in a windowless office on the back of a

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Speaker 1: napkin by yourself. And I just said to him, the

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Speaker 1: horse has left the barn, like I'm doing this and

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Speaker 1: I'll have whatever money I'll have. And I knew that

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Speaker 1: Seth Klarman had started with very little money in eighty one.

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Speaker 1: Paul Readers started, I think with less than five million dollars.

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Speaker 1: In nineteen ninety one, the SNL crisis, Tiger Chase had started,

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Speaker 1: you know, in the wake of the Internet melting down

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Speaker 1: in two thousand, so a lot of folks who I

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Speaker 1: knew and respected had to stress ERA firms. So I

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Speaker 1: started with less than five million bucks. My first trade

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Speaker 1: I bought price Line on November one, two thousand and eight.

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Speaker 1: Last week we celebrated our fifteenth anniversary, and I would

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Speaker 1: own price Line. I bought it when I was at

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Speaker 1: par We bought it, you know, at I think it

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Speaker 1: was ten bucks a share. We would own it when

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Speaker 1: it was over two thousand dollars a year.

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Speaker 2: Unbelievable. So you start three separate companies. NLG eventually gets

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Speaker 2: sold to Dillar's ic open Lists, which goes to march X,

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Speaker 2: and then Faircast gets sold to Microsoft. Am I missing anything? Well?

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Speaker 1: The third company I starts, Room seventy seven that Google bought, Faircast,

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Speaker 1: was an investment, a Series B investment we made in

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Speaker 1: two thousand and five. I believe, in fact, we backed

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Speaker 1: the head of artificial intelligence. Listen to this very yeah,

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Speaker 1: two thousand and five we backed the head of artificial intelligence.

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Speaker 2: People don't realize AI has been here for.

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Speaker 1: That was my first AI investment. Two thousand and five.

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Speaker 1: We back it. He was out of the University of Washington.

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Speaker 1: He had an idea for being able to build predictive

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Speaker 1: analytics into the future movement of airline ticket pricing using

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Speaker 1: early AI technique, and we would go on to sell

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Speaker 1: that business to Microsoft in two thousand and eight. Now

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Speaker 1: here's the interesting part of the story. The person who

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Speaker 1: bought Faircast in two thousand and eight for Microsoft was

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Speaker 1: Sacha Nadela.

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Speaker 2: Oh really, he.

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Speaker 1: Was running bing at the time, right, and he was

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Speaker 1: buying this as a search business into the search platform.

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Speaker 1: And I said, I had dinner with him the other night,

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Speaker 1: and I said, do you remember the conversation we had

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Speaker 1: when you bought fair Cast And he said, yes. What

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Speaker 1: I said was, we'll never beat Google with ten blue links.

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Speaker 1: We have to get to answers. They think about that.

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Speaker 1: Fifteen years before chat gpt started producing answers, Sacha knew

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Speaker 1: that that's where they had to get to if they

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Speaker 1: were going to leap for all Google. Such a fascinating and.

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Speaker 2: Here we are, and they really put fear gun to

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Speaker 2: Google with chat GPT. It was incredibly disruptive.

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Speaker 1: I think, you know, I've had the true luxury of

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Speaker 1: investing into what we call four super cycles. You know,

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Speaker 1: I think Sacha calls them platform disruptions Internet, mobile, cloud,

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Speaker 1: and now AI. And I've said in several places I

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Speaker 1: think that the platform disruption around augmented intelligence is going

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Speaker 1: to be bigger than the Internet itself. Now follow me

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Speaker 1: on this. If you think about what AI is already

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Speaker 1: doing for the enterprise, we're seeing thirty to fifty percent

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Speaker 1: productivity improvements in engineers. There's never been a technology in

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Speaker 1: the history of technology. There's never been a tool that

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Speaker 1: increases productivity almost instantaneously by thirty to fifty percent. Call

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Speaker 1: centers are now fifty percent more productive, So you're seeing

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Speaker 1: margins explode as people are able to run their call

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Speaker 1: centers more effectively. Sales centers are more productive. So you

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Speaker 1: want to know why Meta doesn't have to cager its

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Speaker 1: employee head count at forty percent anymore. You want to

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Speaker 1: know why Dara reported for Uber that again their number

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Speaker 1: of employees was down quarter over quarter. I wrote this

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Speaker 1: letter a year ago, Time to get Fit. It was

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Speaker 1: an open letter I published ta Meta. You know Mark

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Speaker 1: would go on to write his letter Year of Efficiency.

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Speaker 1: This is what we're seeing. The power of AI is

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Speaker 1: unleashing incredible potential within the enterprise. And then look at consumers.

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Speaker 1: I was speaking at the Javit Center, two thousand people

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Speaker 1: in the audience. I asked, how many of you have

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Speaker 1: used chat GPT in the last two days as a

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Speaker 1: replacement to Google. Half the hands in the room went up.

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Speaker 1: That is the first time in twenty years there's been

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Speaker 1: a challenge to Google search monopoly. And Google search monopoly

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Speaker 1: represents over one hundred percent of the profits of the business.

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Speaker 1: So this is one of the most fascinating times I've

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Speaker 1: seen in my twenty five year career. And we're just

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Speaker 1: getting started. The irony of.

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Speaker 2: What's going on with Google is, and I'll use a

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Speaker 2: dirty word, a lot of the big tech companies having

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Speaker 2: going through a process that Corey doctor calls and notification

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Speaker 2: and Google search results have become worse and worse. It's

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Speaker 2: festudent with ads. Pick a company, Amazon, Apple, Microsoft, any

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Speaker 2: large tech company. At a certain point they kind of

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Speaker 2: lose touch with what made them so successful in the

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Speaker 2: first place. So I don't use Google half as much

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Speaker 2: as I used to. And before I start the research process,

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Speaker 2: I use a little app called Perplexity, which is a

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Speaker 2: pretty decent AI And who is Brad Gersner in upcomes

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Speaker 2: like pages and pages of information organized in such a

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Speaker 2: useful way. It's not different than what I would have

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Speaker 2: found from Google, but it saves me ten steps in between.

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Speaker 1: At the end of the day, you know, a lot

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Speaker 1: of the information on Google you could have also found

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Speaker 1: in a card catalog in a library, but you would

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Speaker 1: have to drive there, you would have to open the

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Speaker 1: card catalog, you would have to go find the periodicals,

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Speaker 1: you would have to find the books, you'd have to

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Speaker 1: read them yourself. So really, when you think about it,

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Speaker 1: I described Google, it's the largest card catalog in the

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Speaker 1: history of the world. Right, it's ten blue links, but

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Speaker 1: it's an infinite number of blue links. But you have

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Speaker 1: to open the blue link, you have to read the

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Speaker 1: information yourself. And you know, So I did this experiment

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Speaker 1: the other day. I was lucky enough to be having

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Speaker 1: dinner in Omaha with Paul Reader, you know, and Paul

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Speaker 1: and I were flying back to New York, and I

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Speaker 1: needed to know the answer to the question what is

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Speaker 1: and we'll talk about invest America in a bit. So

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Speaker 1: as related to that, I wanted to know what was

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Speaker 1: the aggregate amount of corporate matching dollars for four oh

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Speaker 1: one k's on an annual basis. I said to Paul,

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Speaker 1: you use Google and I'll use I'll use chat GPT.

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Speaker 1: I got the answer instantaneously. It took him three or

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Speaker 1: four minutes of hunting and pecking around. It was full

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Speaker 1: of ads about four oh one K providers and everything else.

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Speaker 1: And the reality is this is a was a better

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Speaker 1: tool simply because it made us more productive. In the moment.

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Speaker 1: Google is an extraordinary business. A healthy Google is good

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Speaker 1: for Silicon Valley. The challenge is they are facing a

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Speaker 1: massive innovator's dilemma. The organizing philosophy principle for the Internet

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Speaker 1: for twenty plus years has been Internet search, and it's changing.

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Speaker 1: So I said to somebody the other day. They said, yeah,

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Speaker 1: but Google's got AI. You know, they've got Barre, They've

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Speaker 1: got this and that. I said, I will stipulate. I

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Speaker 1: will stipulate they'll be successful in AI. But do you

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Speaker 1: think they can cross the chasm? And on the other

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Speaker 1: side of this chasm, knowing they have to compete with METAAI,

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Speaker 1: with chat GPT, with Copilot, with claud at Anthropic, with

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Speaker 1: you know whatever the post AI to series going to

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Speaker 1: be at Apple. Do you think they're going to be

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Speaker 1: able to replicate the same dominant monopoly in this new

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Speaker 1: world that they were over here. Now I know your answer.

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Speaker 1: As an investor, you would say, well, Brad, it's possible,

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Speaker 1: but I'd apply a high discount rate to that. And

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Speaker 1: that's the truth. And yet if you look at it today,

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Speaker 1: it shouldn't be like when I say this, it's not

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Speaker 1: attacking Google. It seems to me to be a statement

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Speaker 1: of the that you know, we are ten years from now,

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Speaker 1: We're not going to be using a card catalog called

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Speaker 1: ten blue links to find information. My son came in

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Speaker 1: the other day, fifteen years old. His name's Lincoln. He said, Dad,

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Speaker 1: you know it's funny. All my friends at school they

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Speaker 1: think chat GPT is just good for writing essays. He goes,

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Speaker 1: I now use it for everything. When people say stuff

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Speaker 1: like that. When I go to the Javit Center, they

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Speaker 1: all raise their hands when I'm speaking to groups of

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Speaker 1: founders and they all tell me they're using as a replacement.

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Speaker 1: When I watched the Open AI dev day yesterday and

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Speaker 1: I realize the pace of innovation is faster than any

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Speaker 1: innovation I ever saw with the Internet, Faster than any

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Speaker 1: innovation I ever saw with mobile faster than any innovation

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Speaker 1: I ever saw with the cloud. Okay, so whatever it

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Speaker 1: is today, which already is I poppingly good, whatever it

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Speaker 1: is today, you have no idea how good this is

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Speaker 1: going to be in three to five years.

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Speaker 2: And the amazing thing is markets are mostly kind of

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Speaker 2: sort of efficient, and even if this is obvious now,

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Speaker 2: it's still gonna take a while to work its way

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Speaker 2: into the prices because people just aren't going to believe

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Speaker 2: that a company like Google can be dethroned. I have

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Speaker 2: a vivid recollection of the news coming out about Enron

432
00:23:17,280 --> 00:23:19,840
Speaker 2: being a fraud. The first big article, I think it

433
00:23:19,880 --> 00:23:23,240
Speaker 2: was Fortune magazine Bethany McLain. Took a year for the

434
00:23:23,280 --> 00:23:25,560
Speaker 2: stock to collapse, a full year. No one wanted to

435
00:23:25,560 --> 00:23:29,440
Speaker 2: believe it. People look at at the Magnificent Seven, they

436
00:23:29,440 --> 00:23:34,760
Speaker 2: look at these big, great tech companies. There's a hesitancy

437
00:23:34,800 --> 00:23:38,440
Speaker 2: to believe that the winners are eventually gonna tumble. Thank

438
00:23:38,480 --> 00:23:40,680
Speaker 2: goodness for books like The Innovator's Dilemma.

439
00:23:40,800 --> 00:23:44,240
Speaker 1: Yeah, I mean, and listen, I've also said I think

440
00:23:44,280 --> 00:23:46,679
Speaker 1: what is different? So if you say, well, Brad, are

441
00:23:46,720 --> 00:23:50,040
Speaker 1: you excited about AI and venture capital? You know, given

442
00:23:50,359 --> 00:23:52,800
Speaker 1: you know this new super cycle, and you know, these

443
00:23:52,800 --> 00:23:55,959
Speaker 1: are going to be the disruptors against the incumbents, you know,

444
00:23:56,080 --> 00:23:58,080
Speaker 1: and that was true. If you think back to two thousand,

445
00:23:58,119 --> 00:24:01,760
Speaker 1: Amazon was the disruptor to Walmart, to May Season. If

446
00:24:01,800 --> 00:24:04,520
Speaker 1: you think about it, mobile, the iPhone was the disruptor

447
00:24:04,520 --> 00:24:07,240
Speaker 1: to your black barrier, to Nikia or to you know,

448
00:24:07,320 --> 00:24:10,399
Speaker 1: the palm pilots of the world, or in cloud Snowflake

449
00:24:10,560 --> 00:24:13,560
Speaker 1: was the disruptor to Tarra Data and to Oracle, et cetera.

450
00:24:13,920 --> 00:24:16,960
Speaker 1: But when you think about AI, what are the primitives

451
00:24:17,000 --> 00:24:20,280
Speaker 1: to AI? The first primitive is massive amounts of data.

452
00:24:20,920 --> 00:24:24,800
Speaker 1: The second primitive is massive amounts of compute. Those things

453
00:24:24,840 --> 00:24:29,119
Speaker 1: take scale, and they take money, massive amounts of money,

454
00:24:29,200 --> 00:24:32,159
Speaker 1: tens of billions of dollars, you know, in order to

455
00:24:32,200 --> 00:24:35,160
Speaker 1: build the infrastructure to do what chat GPT is doing.

456
00:24:35,440 --> 00:24:39,320
Speaker 1: So this is not your typical venture domain where you

457
00:24:39,359 --> 00:24:41,359
Speaker 1: put five million bucks in and you wake up in

458
00:24:41,400 --> 00:24:44,359
Speaker 1: ten years and they've disrupted you know, Google or Microsoft.

459
00:24:44,680 --> 00:24:48,120
Speaker 1: The incumbents are not asleep at the switch. Satchya new

460
00:24:48,200 --> 00:24:52,760
Speaker 1: fifteen years ago answers, not ten blue links. He is

461
00:24:52,800 --> 00:24:55,680
Speaker 1: in front of this. He was prescient with open ai

462
00:24:56,280 --> 00:25:00,440
Speaker 1: co pilot in the enterprise is the fastest adopted in

463
00:25:00,480 --> 00:25:03,159
Speaker 1: the history of Microsoft. So I think eighty percent of

464
00:25:03,160 --> 00:25:06,320
Speaker 1: the benefits of AI over the course of the next

465
00:25:06,320 --> 00:25:08,480
Speaker 1: three to four years are going to inure to the

466
00:25:08,520 --> 00:25:12,360
Speaker 1: benefit of the incumbents to the larger platforms that are

467
00:25:12,400 --> 00:25:16,280
Speaker 1: already pro public today, whose businesses will get better, their

468
00:25:16,320 --> 00:25:20,199
Speaker 1: bottom line margins will expand, their top lines will reaccelerate,

469
00:25:21,240 --> 00:25:23,520
Speaker 1: and so it's not just going to be the you know,

470
00:25:23,560 --> 00:25:26,840
Speaker 1: the gold rush for venture, though venture will do just fine.

471
00:25:26,880 --> 00:25:28,440
Speaker 1: I think it will be a great time for venture

472
00:25:28,480 --> 00:25:31,600
Speaker 1: as well, but I do think that the incumbents here

473
00:25:31,720 --> 00:25:33,400
Speaker 1: are going to compete very vigorously.

474
00:25:33,880 --> 00:25:37,560
Speaker 2: So let's stick with the topic of AI. What sort

475
00:25:37,560 --> 00:25:39,680
Speaker 2: of companies are you looking at in that space? Are

476
00:25:39,720 --> 00:25:43,719
Speaker 2: you only focusing on the incumbents that you think are

477
00:25:43,760 --> 00:25:45,280
Speaker 2: going to do really well and find a way to

478
00:25:45,320 --> 00:25:47,800
Speaker 2: integrate this in this business, or are you looking at

479
00:25:47,920 --> 00:25:51,560
Speaker 2: startups or private companies that have been for around for

480
00:25:51,600 --> 00:25:53,800
Speaker 2: a while that are potential disruptive.

481
00:25:53,960 --> 00:25:57,080
Speaker 1: So you know, as you know, twenty twenty two was

482
00:25:57,080 --> 00:25:59,520
Speaker 1: a tech recession. It was a challenging hear for all

483
00:25:59,560 --> 00:26:01,720
Speaker 1: of us who are investing in tech, and yet we

484
00:26:01,800 --> 00:26:03,719
Speaker 1: bounce back in twenty twenty three with one of our

485
00:26:03,760 --> 00:26:06,960
Speaker 1: best years in the history of the firm, and what

486
00:26:07,000 --> 00:26:11,160
Speaker 1: we recognized in the fall of twenty two, In fact,

487
00:26:11,200 --> 00:26:14,440
Speaker 1: we had our investor day for all of our LPs,

488
00:26:14,560 --> 00:26:17,760
Speaker 1: and our investor Day was in October on the age

489
00:26:17,760 --> 00:26:22,320
Speaker 1: of Ai, before chat GPT entered the public consciousness, and

490
00:26:22,520 --> 00:26:26,720
Speaker 1: what we were already hearing throughout twenty twenty two was

491
00:26:26,800 --> 00:26:31,680
Speaker 1: the voracious appetite people had for Nvidia GPUs. We were

492
00:26:31,760 --> 00:26:36,320
Speaker 1: hearing about the moves that Microsoft was making. So we

493
00:26:36,400 --> 00:26:40,080
Speaker 1: repositioned our portfolio at the end of twenty two, recognizing

494
00:26:40,600 --> 00:26:43,680
Speaker 1: that there had been too many dollars that went into

495
00:26:43,840 --> 00:26:47,800
Speaker 1: safety trades, too many dollars that went into bonds right

496
00:26:47,840 --> 00:26:51,920
Speaker 1: before bonds collapse, too many dollars that went into cyclicals

497
00:26:51,920 --> 00:26:54,679
Speaker 1: that were trading at twenty five times earnings despite the

498
00:26:54,680 --> 00:26:57,520
Speaker 1: fact that they had five or seven percent growth and

499
00:26:57,600 --> 00:27:01,960
Speaker 1: everybody had vacated the scene ontoechnology. Remember in December of

500
00:27:02,080 --> 00:27:05,240
Speaker 1: last year, in videos trading at one hundred and twenty

501
00:27:05,240 --> 00:27:09,200
Speaker 1: five dollars a share and the consensus sell side expectation

502
00:27:09,400 --> 00:27:12,560
Speaker 1: for data center growth this year was negative six percent.

503
00:27:13,200 --> 00:27:16,280
Speaker 1: So don't give me this craziness about efficient markets. In

504
00:27:16,320 --> 00:27:20,640
Speaker 1: the short run, markets are voting machines, they're not weighing machines.

505
00:27:20,680 --> 00:27:23,720
Speaker 1: And the consensus estimates were radically wrong. At the end

506
00:27:23,720 --> 00:27:26,840
Speaker 1: of last year, we loaded the boat. We shrugged off

507
00:27:26,880 --> 00:27:30,800
Speaker 1: the Mike Wilson hard landing consensus bet that everybody had on.

508
00:27:30,880 --> 00:27:32,920
Speaker 1: At the start of the year. We were ninety three

509
00:27:32,960 --> 00:27:35,600
Speaker 1: percent net long at the start of the year. We're

510
00:27:35,680 --> 00:27:39,159
Speaker 1: sixty percent today. Okay. And if you look at what

511
00:27:39,240 --> 00:27:47,080
Speaker 1: we owned, excuse me, we owned in Nvidia, we owned Meta,

512
00:27:47,880 --> 00:27:50,080
Speaker 1: we owned Uber. Those are three of the top four

513
00:27:50,200 --> 00:27:55,520
Speaker 1: performing Nasdaq one hundred companies I think this year. We

514
00:27:55,560 --> 00:27:59,520
Speaker 1: also owned companies like Snowflake. So remember data and data

515
00:27:59,640 --> 00:28:04,520
Speaker 1: infant structure. That is the number one primitive to AI.

516
00:28:04,760 --> 00:28:08,840
Speaker 1: There is no AI without data and ten thousand or

517
00:28:08,880 --> 00:28:13,080
Speaker 1: so customers. The biggest companies in the world like Apple, JP, Morgan,

518
00:28:13,320 --> 00:28:18,239
Speaker 1: et cetera, have entrusted their mission critical data with Snowflake. Okay, now,

519
00:28:18,280 --> 00:28:20,080
Speaker 1: a lot of people, In fact, I heard Kramer say

520
00:28:20,119 --> 00:28:23,440
Speaker 1: on CNBC yesterday, well, Snowflake's not really an AI company.

521
00:28:23,760 --> 00:28:23,960
Speaker 3: You know.

522
00:28:24,000 --> 00:28:26,480
Speaker 1: I love public commentary like that because that allows me

523
00:28:26,560 --> 00:28:29,960
Speaker 1: to generate alpha. The truth of the matter is that

524
00:28:30,280 --> 00:28:35,520
Speaker 1: Snowflake has seen record increases in their data science and

525
00:28:35,640 --> 00:28:40,040
Speaker 1: AI workloads because no enterprise wants to port all their

526
00:28:40,160 --> 00:28:43,360
Speaker 1: data out of one system and into open AI and

527
00:28:43,440 --> 00:28:46,120
Speaker 1: worry about whether open AI is using this data to

528
00:28:46,200 --> 00:28:48,800
Speaker 1: train models or anything else. You can't do that. You

529
00:28:48,840 --> 00:28:52,760
Speaker 1: have privacy and governance requirements, HIPPA requirements, whatever they are

530
00:28:52,800 --> 00:28:57,560
Speaker 1: if you're these big enterprises. So instead the data has gravity,

531
00:28:57,680 --> 00:29:00,800
Speaker 1: it stays put, and you bring the compute to the data,

532
00:29:00,920 --> 00:29:03,400
Speaker 1: you bring the AI workloads to the data, you bring

533
00:29:03,440 --> 00:29:06,600
Speaker 1: the predictive modeling workloads to the data, the data science

534
00:29:06,640 --> 00:29:09,160
Speaker 1: workloads to them, and that's what Snowflake's doing in space.

535
00:29:09,360 --> 00:29:11,240
Speaker 2: So what I'm hearing from you is there's a lot

536
00:29:11,240 --> 00:29:14,720
Speaker 2: of chatter out there about the market is too top heavy.

537
00:29:14,800 --> 00:29:21,360
Speaker 2: It's just the big legacy companies. But you're big into Meta, Microsoft, Uber,

538
00:29:21,520 --> 00:29:24,440
Speaker 2: go down the list of the companies. You don't think

539
00:29:24,480 --> 00:29:28,080
Speaker 2: this market apparently is too top heavy in the tech space.

540
00:29:28,480 --> 00:29:31,640
Speaker 2: You think these companies are big for a reason. Am

541
00:29:31,680 --> 00:29:32,720
Speaker 2: I putting words in your mouth?

542
00:29:32,800 --> 00:29:35,600
Speaker 1: Or No? I think that's right. I mean there's a

543
00:29:35,640 --> 00:29:39,160
Speaker 1: reason they've gone up Barry right, Remember I said in

544
00:29:39,280 --> 00:29:41,560
Speaker 1: Vidia the expectation was they're going to have negative six

545
00:29:41,560 --> 00:29:43,680
Speaker 1: percent data center growth and that we're going to have

546
00:29:43,720 --> 00:29:47,000
Speaker 1: anemic growth in their earnings for the year. Instead, it's

547
00:29:47,040 --> 00:29:50,800
Speaker 1: exploded higher. The multiple today for in Vidia or at

548
00:29:50,880 --> 00:29:53,240
Speaker 1: least at four hundred bucks. Where the stock was last week,

549
00:29:53,800 --> 00:29:57,600
Speaker 1: the multiple was somewhere around twenty five times next year's

550
00:29:57,600 --> 00:30:00,920
Speaker 1: consensus earnings. We started the year at four times. The

551
00:30:01,080 --> 00:30:05,360
Speaker 1: multiple is compressed. Meta is still trading below twenty times earnings.

552
00:30:05,360 --> 00:30:08,560
Speaker 1: That's below Levi's, that's below Coca Cola companies that are

553
00:30:08,600 --> 00:30:11,160
Speaker 1: growing at a fraction of the rate with nowhere close

554
00:30:11,200 --> 00:30:14,760
Speaker 1: to the incremental EBITDAH margins produced by a company like Meta.

555
00:30:15,080 --> 00:30:17,640
Speaker 1: You know, Meta has is going to have a big

556
00:30:17,720 --> 00:30:20,040
Speaker 1: role to play in AI, so it has all of

557
00:30:20,080 --> 00:30:23,160
Speaker 1: these growth vectors these other companies don't have. So yes,

558
00:30:23,280 --> 00:30:25,480
Speaker 1: I do think those big companies are placed to play.

559
00:30:25,840 --> 00:30:28,280
Speaker 1: But we also in our hedge fund at the start

560
00:30:28,320 --> 00:30:31,280
Speaker 1: of the year were shorting some of the companies that

561
00:30:31,320 --> 00:30:35,320
Speaker 1: we thought were COVID beneficiaries that wouldn't be sustainable, and

562
00:30:35,360 --> 00:30:36,760
Speaker 1: we've seen those companies go down.

563
00:30:36,960 --> 00:30:38,120
Speaker 2: Give us a few examples.

564
00:30:38,320 --> 00:30:40,760
Speaker 1: If you look at the SMP this year, the SMP

565
00:30:40,920 --> 00:30:44,360
Speaker 1: is up fifteen to sixteen percent through through today. If

566
00:30:44,400 --> 00:30:47,080
Speaker 1: you take technology companies out of the SMP, the SMPS

567
00:30:47,160 --> 00:30:50,080
Speaker 1: down on the air. The Russell two thousand is down

568
00:30:50,080 --> 00:30:50,600
Speaker 1: on the year.

569
00:30:50,640 --> 00:30:53,720
Speaker 2: Just down period without even correct taking tech company correct.

570
00:30:53,800 --> 00:30:58,320
Speaker 1: So we were shorting the components of those indexes that

571
00:30:58,360 --> 00:31:01,080
Speaker 1: we thought had gotten too bold up as safety trades

572
00:31:01,120 --> 00:31:03,720
Speaker 1: in twenty twenty two. You had a reversion to the

573
00:31:03,760 --> 00:31:06,760
Speaker 1: mean this year. And so I know, I think in

574
00:31:06,800 --> 00:31:09,120
Speaker 1: this business, I've been doing this for a long time,

575
00:31:09,360 --> 00:31:12,360
Speaker 1: probably you know, twenty years now. In the public markets,

576
00:31:13,240 --> 00:31:17,680
Speaker 1: technology by its very essence is about disruption. If you

577
00:31:17,880 --> 00:31:21,560
Speaker 1: ever think you can go to sleep on a technology company,

578
00:31:21,760 --> 00:31:23,560
Speaker 1: you know it's a sure way to get carried out

579
00:31:23,600 --> 00:31:27,720
Speaker 1: on a stretcher. It requires agility, it requires intensity. That's

580
00:31:27,760 --> 00:31:31,120
Speaker 1: why I think being in Silicon Valley, investing and talking

581
00:31:31,160 --> 00:31:34,480
Speaker 1: every day with venture capital companies, founders, et cetera, is

582
00:31:34,520 --> 00:31:38,120
Speaker 1: a huge competitive advantage test because we see the disruption

583
00:31:38,280 --> 00:31:39,920
Speaker 1: coming years in advance.

584
00:31:40,280 --> 00:31:43,800
Speaker 2: So let's stick with that concept of disruption. You invest

585
00:31:43,800 --> 00:31:47,560
Speaker 2: in startups, you invest in public companies, you invest in privates.

586
00:31:47,880 --> 00:31:51,560
Speaker 2: Where is your sweet spot on the private side early stage,

587
00:31:51,680 --> 00:31:53,160
Speaker 2: later stage? How do you think about that?

588
00:31:53,240 --> 00:31:57,440
Speaker 1: Yeah, great question. You know, so when I started these companies,

589
00:31:57,480 --> 00:31:59,440
Speaker 1: I started literally on the back of a napkin. So

590
00:31:59,480 --> 00:32:01,720
Speaker 1: that would you know, we would call that seed stage

591
00:32:01,720 --> 00:32:03,880
Speaker 1: in the parlance. You know, that's where you're asking your

592
00:32:03,880 --> 00:32:06,440
Speaker 1: friends and your family for money. You're trying to establish

593
00:32:06,440 --> 00:32:10,800
Speaker 1: product market fit. Altimeter generally doesn't invest in seed stage

594
00:32:10,800 --> 00:32:14,719
Speaker 1: companies or early Series A companies because those companies are

595
00:32:14,720 --> 00:32:18,360
Speaker 1: what we call in the business pre product market fit.

596
00:32:18,680 --> 00:32:21,000
Speaker 1: You have an idea, you're trying to build a product.

597
00:32:21,240 --> 00:32:25,640
Speaker 1: There are world class partners of ours in Silicon Valley,

598
00:32:25,800 --> 00:32:27,680
Speaker 1: you know, I think of like a Mike Spiser at

599
00:32:27,720 --> 00:32:31,480
Speaker 1: Suttery Hill, you know a Martine Casado and Andreas and

600
00:32:31,520 --> 00:32:34,200
Speaker 1: you know Gurley when he was at Benchmark but now

601
00:32:34,280 --> 00:32:38,000
Speaker 1: Chathin or Eric Vishria or you know my friends at

602
00:32:38,280 --> 00:32:41,280
Speaker 1: you know Sequoia. That's what they do. The firms are built,

603
00:32:41,640 --> 00:32:46,880
Speaker 1: purpose built, founder's fund to do those early stage gestations

604
00:32:47,280 --> 00:32:51,280
Speaker 1: where we've really carved out our you know, our brand

605
00:32:51,360 --> 00:32:54,000
Speaker 1: and our niche is we we still you know, we

606
00:32:54,000 --> 00:32:56,080
Speaker 1: wear the black T shirts. We're in Silicon Valley. We

607
00:32:56,080 --> 00:32:59,800
Speaker 1: have the sensibilities of venture capitalists, right, I've started the companies,

608
00:32:59,840 --> 00:33:02,120
Speaker 1: I've stood in their shoes, I've hired, i've fired, I've

609
00:33:02,120 --> 00:33:05,719
Speaker 1: done all the challenging stuff. But I also, you know,

610
00:33:06,080 --> 00:33:09,400
Speaker 1: very familiar with New York, with CNBC, with IPOs, with

611
00:33:09,480 --> 00:33:12,280
Speaker 1: scaling to the public markets. So we usually take the

612
00:33:12,320 --> 00:33:16,479
Speaker 1: handoff right when they discover product market fit. Okay, So

613
00:33:16,760 --> 00:33:19,880
Speaker 1: that's usually a couple of years into a business, you know,

614
00:33:20,320 --> 00:33:23,880
Speaker 1: a Series B, you know, a company maybe somewhere between

615
00:33:23,880 --> 00:33:26,280
Speaker 1: fifty and two hundred million dollars. In that first round

616
00:33:26,320 --> 00:33:29,040
Speaker 1: of Funny Snowflake, which was one of our you know, uh,

617
00:33:29,920 --> 00:33:33,640
Speaker 1: you know, Hall of fame, uh, you know moments. We

618
00:33:33,680 --> 00:33:36,920
Speaker 1: invested in that first round. It was pre revenue, they

619
00:33:36,920 --> 00:33:40,400
Speaker 1: had about ten beta customers of the product. It was

620
00:33:40,400 --> 00:33:44,160
Speaker 1: about one hundred and seventy million dollar valuation. And we

621
00:33:44,200 --> 00:33:47,120
Speaker 1: would invest in every subsequent round, and I think owned

622
00:33:47,160 --> 00:33:49,400
Speaker 1: seventeen percent of the company when they went public.

623
00:33:50,200 --> 00:33:53,120
Speaker 2: Seventeen percent. That's a big uh, that's a big chunk

624
00:33:53,120 --> 00:33:56,600
Speaker 2: of a company. Are you still long Snowflake or have

625
00:33:56,720 --> 00:33:57,720
Speaker 2: you paired back a little bit?

626
00:33:57,960 --> 00:33:59,960
Speaker 1: How you make Yeah? So I remember we have different fund,

627
00:34:00,160 --> 00:34:02,560
Speaker 1: so we're investing out of our sixth venture capital fund.

628
00:34:02,560 --> 00:34:04,760
Speaker 1: We just had our first clothes on our seventh venture

629
00:34:04,800 --> 00:34:08,319
Speaker 1: capital fund. So in those funds where we invest in

630
00:34:08,360 --> 00:34:11,680
Speaker 1: those very early rounds in Snowflake, yes, you know, I

631
00:34:11,719 --> 00:34:14,719
Speaker 1: mean our journey with Snowflake started a decade ago. So

632
00:34:14,880 --> 00:34:18,960
Speaker 1: for those investors, we've returned capital and you know, Altimeters

633
00:34:19,000 --> 00:34:21,879
Speaker 1: Fund one will probably be in the top five all

634
00:34:21,960 --> 00:34:25,560
Speaker 1: time returns, you know, for Silicon Valley because of the

635
00:34:25,719 --> 00:34:28,960
Speaker 1: likes of Snowflake and Mango and other great names that

636
00:34:29,000 --> 00:34:32,480
Speaker 1: we had in that portfolio. In our hedge fund, right

637
00:34:32,520 --> 00:34:36,120
Speaker 1: where we're focused on annual returns, just like you, we

638
00:34:36,200 --> 00:34:38,239
Speaker 1: start every year and we say, okay, what's going to

639
00:34:38,280 --> 00:34:41,200
Speaker 1: power a twenty percent you know, risk adjusted return in

640
00:34:41,239 --> 00:34:44,240
Speaker 1: this portfolio? And so yes, we still own Snowflake because

641
00:34:44,239 --> 00:34:47,239
Speaker 1: we see it compounding on the top line, you know,

642
00:34:47,440 --> 00:34:51,320
Speaker 1: in this thirty plus percent range, and it's still expanding margins.

643
00:34:51,520 --> 00:34:53,840
Speaker 1: So this is one of those unique and rare software

644
00:34:53,880 --> 00:34:56,840
Speaker 1: businesses that's going to have over thirty percent free cash

645
00:34:56,840 --> 00:35:00,880
Speaker 1: flow margins. Has a massive market all this AI stuff

646
00:35:00,880 --> 00:35:03,680
Speaker 1: that we're talking about, the entire database market. Do you

647
00:35:03,800 --> 00:35:06,719
Speaker 1: know in the year two thousand, the database market was

648
00:35:06,719 --> 00:35:10,960
Speaker 1: worth a trillion dollars, right, It's the single largest market

649
00:35:11,000 --> 00:35:13,520
Speaker 1: in all of software, and it should be. Think what

650
00:35:13,600 --> 00:35:16,400
Speaker 1: is the primitive to every you know, everything we do

651
00:35:16,480 --> 00:35:21,080
Speaker 1: in our lives. It's data. Data isn't oil, Data is oxygen.

652
00:35:21,440 --> 00:35:25,839
Speaker 2: Huh, that's really interesting. You mentioned meta. I know you're

653
00:35:25,880 --> 00:35:28,360
Speaker 2: a fan of Twitter, or at least the way Twitter

654
00:35:28,480 --> 00:35:31,000
Speaker 2: used to be. What's going on over there? Is this

655
00:35:31,080 --> 00:35:34,600
Speaker 2: doing the full friends to MySpace circling the drain? Is

656
00:35:34,600 --> 00:35:37,640
Speaker 2: there any hope for Twitter becoming what it once was

657
00:35:37,800 --> 00:35:40,759
Speaker 2: or is it mutating into something unrecognized?

658
00:35:40,800 --> 00:35:43,480
Speaker 1: Oh well, call me outside of consensus on this. Yea.

659
00:35:44,200 --> 00:35:46,399
Speaker 1: You know, I know it's not popular today, but I'm

660
00:35:46,440 --> 00:35:50,080
Speaker 1: in camp Elon. I think Twitter was pretty broken before.

661
00:35:50,200 --> 00:35:52,920
Speaker 1: I think, you know, what we what we've learned about

662
00:35:52,960 --> 00:35:55,400
Speaker 1: Twitter was they may have had a lot of advertisers,

663
00:35:55,600 --> 00:35:58,120
Speaker 1: but I'm not sure how well it was actually working

664
00:35:58,160 --> 00:35:58,800
Speaker 1: for users.

665
00:35:59,760 --> 00:35:59,960
Speaker 2: You know.

666
00:36:00,000 --> 00:36:03,520
Speaker 1: So I'm on Twitter. I find it probably my most

667
00:36:03,760 --> 00:36:07,120
Speaker 1: most valuable source of information. Bloomberg's right up there, Barry,

668
00:36:07,440 --> 00:36:11,720
Speaker 1: but I would say that my newsfeed in Twitter is extraordinary.

669
00:36:12,239 --> 00:36:16,279
Speaker 1: And Elon came in there. Think about this, He let

670
00:36:16,280 --> 00:36:19,640
Speaker 1: go eighty percent of the people at Twitter, and I

671
00:36:19,719 --> 00:36:22,759
Speaker 1: know everybody on the East coast flip their lids. How

672
00:36:22,760 --> 00:36:25,880
Speaker 1: can he do this? You know, mean spirited, you know,

673
00:36:25,960 --> 00:36:28,799
Speaker 1: et cetera. But you know, the cultures at a lot

674
00:36:28,840 --> 00:36:32,680
Speaker 1: of these places in Silicon Valley were broken, they were bloated,

675
00:36:32,960 --> 00:36:36,120
Speaker 1: and they were entitled. What I've seen out of Twitter

676
00:36:36,280 --> 00:36:40,600
Speaker 1: is a ten x increase in product velocity. Okay, I'm

677
00:36:40,640 --> 00:36:40,960
Speaker 1: talking you.

678
00:36:41,000 --> 00:36:42,720
Speaker 2: How do you define product velocity?

679
00:36:42,880 --> 00:36:44,840
Speaker 1: Think about the things that they're now doing on Twitter,

680
00:36:45,280 --> 00:36:50,920
Speaker 1: everything from virtual and fully encrypted private messaging calls, fully

681
00:36:51,000 --> 00:36:56,840
Speaker 1: hosted videos, short form videos, long form videos, subscription business

682
00:36:57,680 --> 00:37:01,239
Speaker 1: that empowers content creators to share in the upside of

683
00:37:01,239 --> 00:37:04,799
Speaker 1: those subscriptions. There's payments coming. I mean like this is.

684
00:37:04,880 --> 00:37:08,200
Speaker 1: And now we have x dot ai where you know,

685
00:37:08,280 --> 00:37:11,440
Speaker 1: he launched groc last week. It's rumored that, you know,

686
00:37:11,520 --> 00:37:14,880
Speaker 1: twenty people working for about three months have produced a

687
00:37:15,120 --> 00:37:19,680
Speaker 1: very credible AI. Remember Elon was also the founder of

688
00:37:19,800 --> 00:37:24,680
Speaker 1: open ai. Okay, so to underestimate him. He's the first

689
00:37:24,719 --> 00:37:26,880
Speaker 1: to raise his hand. He said, I overpaid for the asset.

690
00:37:27,120 --> 00:37:29,120
Speaker 1: I did it because I wanted to protect free speech.

691
00:37:29,160 --> 00:37:31,000
Speaker 1: You can quibble about that one way or the other.

692
00:37:31,160 --> 00:37:32,160
Speaker 1: It's a longer debate.

693
00:37:32,200 --> 00:37:35,120
Speaker 2: But what I would say, free speech his definition of

694
00:37:35,120 --> 00:37:39,120
Speaker 2: free speech is kind of off kilter. Free speech is

695
00:37:39,160 --> 00:37:42,120
Speaker 2: about not having the government coming in and telling people

696
00:37:42,200 --> 00:37:44,439
Speaker 2: what they can and can't say. But if you own

697
00:37:44,480 --> 00:37:47,839
Speaker 2: a company, you're free to say, Hey, Nazis are bad

698
00:37:47,840 --> 00:37:50,799
Speaker 2: for my business. So I'm going to keep Nazis out

699
00:37:50,880 --> 00:37:55,640
Speaker 2: of my out of my public business because I like

700
00:37:55,800 --> 00:37:58,920
Speaker 2: Procter and Gamble. I like these big advertisers, but they

701
00:37:58,960 --> 00:38:01,800
Speaker 2: don't want to be associated with that. So his definition

702
00:38:01,800 --> 00:38:05,919
Speaker 2: of free speech kind of perplexes those of us who

703
00:38:06,080 --> 00:38:10,080
Speaker 2: have been to law school or who like to open

704
00:38:10,120 --> 00:38:14,320
Speaker 2: our feed and not see a stream of anti Semitic vitriol.

705
00:38:14,600 --> 00:38:17,120
Speaker 2: And that's the perplexing part. And I don't think that's

706
00:38:17,120 --> 00:38:19,600
Speaker 2: an East Coast or a West Coast thing. I think

707
00:38:19,640 --> 00:38:24,880
Speaker 2: it's a dude. It was toxic. Social media in general

708
00:38:25,440 --> 00:38:30,840
Speaker 2: has this toxicity problem, but opening the floodgates. So I

709
00:38:30,880 --> 00:38:33,000
Speaker 2: really want to hear your thoughts on this.

710
00:38:33,080 --> 00:38:35,279
Speaker 1: Well. I mean, I want to take two angles on

711
00:38:35,320 --> 00:38:37,880
Speaker 1: I want to talk about respond to that, and then

712
00:38:37,920 --> 00:38:39,000
Speaker 1: give you the business angle.

713
00:38:39,719 --> 00:38:39,920
Speaker 3: You know.

714
00:38:40,080 --> 00:38:43,200
Speaker 1: The truth of the matter is, the public square has

715
00:38:43,239 --> 00:38:45,879
Speaker 1: been a pretty brutal place for two hundred and fifty

716
00:38:45,960 --> 00:38:49,000
Speaker 1: years of American history. Whether you're standing in the halls

717
00:38:49,040 --> 00:38:52,000
Speaker 1: of Congress or you're in the public square. People always

718
00:38:52,000 --> 00:38:54,960
Speaker 1: said things that people disagreed with, and that's the beauty

719
00:38:55,000 --> 00:38:58,640
Speaker 1: of this country. Right. So if you do work on Twitter,

720
00:38:58,800 --> 00:39:01,560
Speaker 1: you can tailor your feet in a way that will

721
00:39:01,600 --> 00:39:05,000
Speaker 1: be more productive for you. Right. I imagine with AI,

722
00:39:05,360 --> 00:39:07,239
Speaker 1: they'll tailor the feed in a way that it can

723
00:39:07,280 --> 00:39:10,280
Speaker 1: be more productive to you. They don't want to alienate users.

724
00:39:10,360 --> 00:39:13,320
Speaker 1: In fact, the user volume or the user and engagement

725
00:39:13,360 --> 00:39:17,000
Speaker 1: studies that I'm seeing are higher, not lower, over the

726
00:39:17,040 --> 00:39:20,319
Speaker 1: course of the past four or five months. But I

727
00:39:20,360 --> 00:39:22,440
Speaker 1: agree with you and stipulate that a lot of the

728
00:39:22,480 --> 00:39:26,080
Speaker 1: advertisers that were there have left. I think that Elon

729
00:39:26,160 --> 00:39:29,560
Speaker 1: is trying to get them back. But more importantly, Elon

730
00:39:29,840 --> 00:39:33,200
Speaker 1: wants this platform to be something totally different. He thinks

731
00:39:33,239 --> 00:39:36,880
Speaker 1: there's an opportunity for this to really live at the intersection,

732
00:39:37,080 --> 00:39:40,600
Speaker 1: you know, to harness the power of our collective brain

733
00:39:41,120 --> 00:39:44,160
Speaker 1: and provide a great AI build on the back of Twitter,

734
00:39:45,040 --> 00:39:47,839
Speaker 1: to open up the public square to discussion. So when

735
00:39:47,880 --> 00:39:50,400
Speaker 1: you think about it as a business, I you know,

736
00:39:50,640 --> 00:39:54,880
Speaker 1: like when I look at Elon building reusable rockets, electric cars,

737
00:39:55,160 --> 00:39:58,080
Speaker 1: changing the world, you might you might say this was

738
00:39:58,160 --> 00:40:00,800
Speaker 1: not the highest and best use of his time or energy,

739
00:40:01,040 --> 00:40:05,000
Speaker 1: But having had that conversation with him, he's deeply passionate

740
00:40:05,040 --> 00:40:07,560
Speaker 1: about it. He believes in it. I would never bet

741
00:40:07,600 --> 00:40:08,160
Speaker 1: against the guy.

742
00:40:08,280 --> 00:40:10,080
Speaker 2: Yeah, No, he's a tough guy to bet against. I

743
00:40:10,120 --> 00:40:14,120
Speaker 2: will say if anybody thinks the public square, as we've

744
00:40:14,160 --> 00:40:18,839
Speaker 2: traditionally known it is what people go to social media for, well,

745
00:40:18,960 --> 00:40:22,520
Speaker 2: they completely don't understand what that technology is about. It's

746
00:40:22,560 --> 00:40:26,399
Speaker 2: not where you're can have long form, intelligent discussions. It's

747
00:40:26,400 --> 00:40:28,799
Speaker 2: where people throw tomatoes at each other. Yeah, and it's

748
00:40:28,840 --> 00:40:31,040
Speaker 2: fun and it's amusing, and we listen.

749
00:40:30,920 --> 00:40:34,640
Speaker 1: I Twitter spaces. I think is some of the most

750
00:40:34,680 --> 00:40:38,920
Speaker 1: interesting dialogue that's happening anywhere in media today. Okay, and

751
00:40:39,000 --> 00:40:43,120
Speaker 1: so again, I think there's a spot moment in time

752
00:40:43,440 --> 00:40:46,680
Speaker 1: where there's some fair criticisms, and I think Elon himself

753
00:40:46,719 --> 00:40:49,560
Speaker 1: has said, we've made some mistakes. But I'm just saying

754
00:40:49,600 --> 00:40:53,480
Speaker 1: the consensus view that you know, and I've heard it

755
00:40:53,560 --> 00:40:55,920
Speaker 1: every time this guy starts a business. I love the

756
00:40:57,880 --> 00:41:01,840
Speaker 1: consensus view that a guy who's created trillions of dollars

757
00:41:01,840 --> 00:41:06,279
Speaker 1: in value, okay, dating back twenty five years across I

758
00:41:06,280 --> 00:41:08,920
Speaker 1: don't know ten companies. He's probably never done a down

759
00:41:09,000 --> 00:41:12,520
Speaker 1: round of financing in the last ten years. And yet

760
00:41:12,560 --> 00:41:14,600
Speaker 1: we have all these people who've never started a single

761
00:41:14,640 --> 00:41:18,480
Speaker 1: business who like to, you know, lecture him and say,

762
00:41:18,680 --> 00:41:20,480
Speaker 1: you know that he has no idea what he's doing.

763
00:41:20,520 --> 00:41:22,479
Speaker 1: He's running the business into the ground. All I would

764
00:41:22,520 --> 00:41:25,720
Speaker 1: say is weight watch and see, I don't know. Business

765
00:41:25,760 --> 00:41:29,160
Speaker 1: is hard, right that changing the culture at Twitter is hard.

766
00:41:29,560 --> 00:41:32,440
Speaker 1: Rebuilding the business is hard. But if anybody can do it,

767
00:41:32,440 --> 00:41:34,640
Speaker 1: if anybody's gonna work twenty hour days to pull it off,

768
00:41:34,680 --> 00:41:35,480
Speaker 1: it's Elon Musk.

769
00:41:35,760 --> 00:41:38,920
Speaker 2: So Brad tell us about invest America.

770
00:41:38,960 --> 00:41:42,000
Speaker 1: Well, you know, Barry, we talked at the start of

771
00:41:42,040 --> 00:41:45,360
Speaker 1: the pod about my childhood. I was on the outside

772
00:41:45,360 --> 00:41:48,000
Speaker 1: looking in, didn't have any money, like a lot of

773
00:41:48,320 --> 00:41:53,759
Speaker 1: rural kids in America. And you know, it seems to me,

774
00:41:53,880 --> 00:41:56,279
Speaker 1: over the course of the last twenty years, we've experienced

775
00:41:56,719 --> 00:42:02,120
Speaker 1: perhaps the greatest explosion in the history of modern capitalism.

776
00:42:02,600 --> 00:42:06,640
Speaker 1: Massive amounts of wealth have been created, We've seen extraordinary

777
00:42:06,840 --> 00:42:10,080
Speaker 1: leaps forward in human progress. But the problem with it

778
00:42:10,120 --> 00:42:12,759
Speaker 1: is seventy percent of the people in this country will

779
00:42:12,800 --> 00:42:15,600
Speaker 1: never have a savings or investment account. They feel left

780
00:42:15,600 --> 00:42:17,480
Speaker 1: out of the system, and it's part of the reason

781
00:42:17,880 --> 00:42:20,239
Speaker 1: that under the age of forty less than half of

782
00:42:20,239 --> 00:42:23,799
Speaker 1: the people believe in capitalism. Ray Dalio tells us that

783
00:42:23,840 --> 00:42:26,799
Speaker 1: it's a threat to democracy. In the Rise and Fall

784
00:42:26,840 --> 00:42:30,319
Speaker 1: of Nations, he says, this is where it starts. Wealth inequality.

785
00:42:30,600 --> 00:42:34,360
Speaker 1: People lose confidence in the organizing principles of society, and

786
00:42:34,400 --> 00:42:37,040
Speaker 1: it leads to strife. We see this in some of

787
00:42:37,080 --> 00:42:41,120
Speaker 1: the populist movements. We have right that you know, whether

788
00:42:41,160 --> 00:42:44,840
Speaker 1: it's occupy Wall Street or whether it's charging the White House,

789
00:42:45,080 --> 00:42:48,320
Speaker 1: and so to me, we have agency and there's a fix.

790
00:42:49,000 --> 00:42:54,000
Speaker 1: This idea is a bipartisan piece of legislation where the

791
00:42:54,040 --> 00:42:58,760
Speaker 1: federal government will seed a private investment account for every

792
00:42:58,840 --> 00:43:01,799
Speaker 1: child born in America. So think about this, three point

793
00:43:01,880 --> 00:43:04,719
Speaker 1: seven million children born a year. When you're born, you're

794
00:43:04,760 --> 00:43:07,080
Speaker 1: issued a Social Security number and you're going to be

795
00:43:07,160 --> 00:43:10,520
Speaker 1: issued an invest America account. The invest America account will

796
00:43:10,520 --> 00:43:12,480
Speaker 1: have a one thousand dollars in it invested in the

797
00:43:12,560 --> 00:43:14,920
Speaker 1: S and P five hundred. You own it. You'll be

798
00:43:14,960 --> 00:43:18,160
Speaker 1: able to open it up on your mobile device. At eighteen.

799
00:43:18,239 --> 00:43:20,160
Speaker 1: You'll be able to take out up to twenty percent

800
00:43:20,239 --> 00:43:26,000
Speaker 1: for a qualified purchase college, first time home. Otherwise it's

801
00:43:26,000 --> 00:43:28,360
Speaker 1: got to compound until you're fifty. Now here's where it

802
00:43:28,360 --> 00:43:29,040
Speaker 1: gets interesting.

803
00:43:29,360 --> 00:43:32,800
Speaker 2: Hold on, let me just stop right there, fifty years

804
00:43:32,840 --> 00:43:37,040
Speaker 2: of compounding. People are very bad at recognizing what that

805
00:43:37,160 --> 00:43:41,160
Speaker 2: looks like. The rule of seventy two isn't very intuitive.

806
00:43:41,640 --> 00:43:44,760
Speaker 2: If on average markets gained let's use a conservative number

807
00:43:45,440 --> 00:43:49,280
Speaker 2: eight percent. That means that pile of money is doubling

808
00:43:49,400 --> 00:43:53,600
Speaker 2: every nine years. So by the time you're fifty four,

809
00:43:54,320 --> 00:43:56,000
Speaker 2: that's doubled quite a few times.

810
00:43:56,160 --> 00:43:58,840
Speaker 1: Right, Yeah, So what we envision is think of a

811
00:43:58,840 --> 00:44:01,360
Speaker 1: four to h one k from birth. So the federal

812
00:44:01,360 --> 00:44:03,960
Speaker 1: government seeds it. It's a tiny investment by the federal

813
00:44:04,000 --> 00:44:06,879
Speaker 1: government three point seven billion a year. That's less than

814
00:44:06,960 --> 00:44:10,360
Speaker 1: one one hundredth of one percent of the annual budget

815
00:44:10,400 --> 00:44:13,200
Speaker 1: around Okay, so they seed it and then get out

816
00:44:13,239 --> 00:44:17,000
Speaker 1: of the way. Now private industry comes in and like

817
00:44:17,040 --> 00:44:20,480
Speaker 1: four oh one K matches. Dara at Uber will say, hey,

818
00:44:20,880 --> 00:44:23,920
Speaker 1: I'll give this to the kids of my of my employees.

819
00:44:24,400 --> 00:44:26,600
Speaker 1: Rich Barton at Zillo says, I'll give this to the

820
00:44:26,680 --> 00:44:30,000
Speaker 1: kids of my employees. United Airlines, Microsoft. Go down the list.

821
00:44:30,000 --> 00:44:33,480
Speaker 1: You've gotten commitments from various commitments already for various So

822
00:44:33,560 --> 00:44:34,440
Speaker 1: if you see.

823
00:44:34,280 --> 00:44:38,600
Speaker 2: This account will match our employees' children on a certain.

824
00:44:38,400 --> 00:44:40,080
Speaker 1: Yeah, we'll give them a thousand bucks an account. So

825
00:44:40,120 --> 00:44:42,800
Speaker 1: here's the math, Barry. If you start with one thousand,

826
00:44:43,360 --> 00:44:45,960
Speaker 1: and you only have an addition of seven hundred and

827
00:44:46,000 --> 00:44:49,840
Speaker 1: fifty dollars a year, okay, families can contribute to that.

828
00:44:50,480 --> 00:44:54,400
Speaker 1: Your quacks free, you're correctation can contribute to it. If

829
00:44:54,440 --> 00:44:56,760
Speaker 1: you have seve hundred fifty dollars incremental year. Then every

830
00:44:56,840 --> 00:44:59,680
Speaker 1: ten year old in America when they enter into the

831
00:44:59,680 --> 00:45:02,120
Speaker 1: fifth they're sixth grade, and the teacher says, hey, today

832
00:45:02,160 --> 00:45:05,440
Speaker 1: we're going to talk about math, or compounding, or stocks

833
00:45:05,560 --> 00:45:08,360
Speaker 1: or capitalism. They'll say open up. You all have phones,

834
00:45:08,640 --> 00:45:11,080
Speaker 1: get out your phone and open up your invest America account.

835
00:45:11,280 --> 00:45:13,800
Speaker 1: Those kids are going to have over ten thousand dollars

836
00:45:14,680 --> 00:45:18,239
Speaker 1: their ownership in America, their slice of America. They'll see

837
00:45:18,239 --> 00:45:21,200
Speaker 1: their the amount at the top, twelve thousand, five hundred dollars,

838
00:45:21,320 --> 00:45:24,840
Speaker 1: and they'll see their top five holdings, their slice of Apple,

839
00:45:25,080 --> 00:45:28,800
Speaker 1: their slice of Microsoft, their slice of United Health. Okay,

840
00:45:29,120 --> 00:45:31,240
Speaker 1: by the age of thirty, they'll have over one hundred

841
00:45:31,280 --> 00:45:33,799
Speaker 1: and fifty thousand dollars in that account. And if you

842
00:45:33,840 --> 00:45:36,120
Speaker 1: take one thousand dollars at seven hundred and fifty bucks

843
00:45:36,160 --> 00:45:38,560
Speaker 1: a year, and it compounds at the exact same rate

844
00:45:38,600 --> 00:45:40,520
Speaker 1: as the S and P five hundred for the last

845
00:45:40,600 --> 00:45:44,160
Speaker 1: fifty years of a million dollars in their account. This

846
00:45:44,400 --> 00:45:47,520
Speaker 1: is a way we get people to believe in capitalism again,

847
00:45:47,840 --> 00:45:50,600
Speaker 1: this is a way we teach financial literacy. This is

848
00:45:50,640 --> 00:45:53,120
Speaker 1: a way we give people hope, the people who feel

849
00:45:53,160 --> 00:45:55,799
Speaker 1: like they're left out of the system. You give everybody's

850
00:45:55,840 --> 00:45:58,560
Speaker 1: skin in the game. And if we can afford to

851
00:45:58,680 --> 00:46:02,759
Speaker 1: send five billion year in foreign aid to our top

852
00:46:02,840 --> 00:46:06,200
Speaker 1: fifteen countries that we send foreign aid to, then we

853
00:46:06,280 --> 00:46:09,279
Speaker 1: can spend three point seven billion dollars a year to

854
00:46:09,400 --> 00:46:14,200
Speaker 1: seed every American child with upside in American enterprise and capitalism.

855
00:46:14,239 --> 00:46:18,239
Speaker 2: What's the appetite on the hill on amongst the Democrats

856
00:46:18,239 --> 00:46:22,000
Speaker 2: and Republicans for a program like this.

857
00:46:21,880 --> 00:46:24,239
Speaker 1: Well, I can tell you you know. I mentioned I've

858
00:46:24,239 --> 00:46:26,520
Speaker 1: been a four time founder and the number one thing

859
00:46:26,560 --> 00:46:28,160
Speaker 1: you got to do when you're starting a company is

860
00:46:28,200 --> 00:46:31,040
Speaker 1: called product market fit. Do the dogs want to eat

861
00:46:31,040 --> 00:46:31,760
Speaker 1: the dog food?

862
00:46:32,200 --> 00:46:32,439
Speaker 2: Right?

863
00:46:32,760 --> 00:46:35,759
Speaker 1: And so for two years now I've been funding this

864
00:46:35,880 --> 00:46:38,960
Speaker 1: and bootstrapping and going out and talking to everybody from

865
00:46:39,000 --> 00:46:43,239
Speaker 1: the Clintons to the Cookes. A giant tent, okay, And

866
00:46:43,560 --> 00:46:46,600
Speaker 1: I'm happy to say that now we have bipartisan authors

867
00:46:46,640 --> 00:46:49,360
Speaker 1: and co sponsors lining up in the House and the Senate.

868
00:46:49,600 --> 00:46:52,160
Speaker 1: We hope to introduce it in the spring. I think

869
00:46:52,160 --> 00:46:55,000
Speaker 1: there's going to be massive support for this. This is

870
00:46:55,080 --> 00:46:58,280
Speaker 1: one thing that all both political parties in a highly

871
00:46:58,320 --> 00:47:02,360
Speaker 1: divisive country can agree on that every kid not to

872
00:47:02,560 --> 00:47:06,440
Speaker 1: This is not a big entitlement program to guarantee anybody anything.

873
00:47:06,480 --> 00:47:09,320
Speaker 1: What this is is a seed program by the federal

874
00:47:09,360 --> 00:47:12,440
Speaker 1: government that partners with the power of the private sector

875
00:47:12,760 --> 00:47:16,080
Speaker 1: to give everybody a shot, to give everybody hope, to

876
00:47:16,160 --> 00:47:20,640
Speaker 1: teach everybody what it means to participate in American capitalists.

877
00:47:20,680 --> 00:47:23,080
Speaker 2: So we've heard a couple of variations of this over

878
00:47:23,080 --> 00:47:27,040
Speaker 2: the years, Baby bonds. Senator Booker had had a couple

879
00:47:27,040 --> 00:47:31,080
Speaker 2: of programs. How does this compare to various you know,

880
00:47:31,200 --> 00:47:34,640
Speaker 2: opportunity accounts type things that are out there, so.

881
00:47:35,480 --> 00:47:37,440
Speaker 1: You know, and and by the way, I think a

882
00:47:37,440 --> 00:47:41,680
Speaker 1: lot of them have great motivations and so there's no

883
00:47:41,719 --> 00:47:44,480
Speaker 1: pride of authorship here. The more people who want to

884
00:47:44,520 --> 00:47:47,120
Speaker 1: work on this issue together. And I have some of

885
00:47:47,160 --> 00:47:50,640
Speaker 1: the greats, the investing greats, who are excited to participate,

886
00:47:50,680 --> 00:47:53,520
Speaker 1: who've offered to write the financial literacy, give us a

887
00:47:53,600 --> 00:47:57,840
Speaker 1: namel components like this. You know, I'm not going to

888
00:47:57,920 --> 00:48:00,239
Speaker 1: drop all those names right now because I want to

889
00:48:00,239 --> 00:48:02,640
Speaker 1: have a big splash when we do, Barry, I'll come

890
00:48:02,640 --> 00:48:05,239
Speaker 1: to you, I promise. But all the people who are

891
00:48:05,280 --> 00:48:08,440
Speaker 1: my friends, who I talk to, who run these big firms,

892
00:48:08,640 --> 00:48:12,719
Speaker 1: they believe in this, right they because they it is

893
00:48:12,760 --> 00:48:15,400
Speaker 1: their life experience. So think about this versus a baby bond.

894
00:48:15,760 --> 00:48:18,440
Speaker 1: You know, bonds don't appreciate very much. They don't compound

895
00:48:18,520 --> 00:48:20,680
Speaker 1: very much. It's not very exciting for a kid. They

896
00:48:20,719 --> 00:48:23,520
Speaker 1: don't own part of a company, you know, and and

897
00:48:23,680 --> 00:48:27,399
Speaker 1: so you don't achieve the same things. On top of that,

898
00:48:27,960 --> 00:48:31,160
Speaker 1: this program needs to be universal. If you're a poor

899
00:48:31,200 --> 00:48:34,480
Speaker 1: white kid in Indiana, right, or you're a black kid

900
00:48:34,520 --> 00:48:39,280
Speaker 1: in Trenton, or you're you know, if you are a

901
00:48:39,400 --> 00:48:43,440
Speaker 1: Latino kid living in East pal Aato. This is an

902
00:48:43,480 --> 00:48:48,480
Speaker 1: opportunity to unite America. We are all on team America, okay.

903
00:48:48,719 --> 00:48:51,319
Speaker 1: And so that's the other thing. We're not gonna pick

904
00:48:51,360 --> 00:48:53,960
Speaker 1: and choose right, And we're not gonna pick and choose companies.

905
00:48:54,000 --> 00:48:56,040
Speaker 1: All goes into the S and P five hundred, five

906
00:48:56,120 --> 00:48:58,200
Speaker 1: hundred of the best companies in America. If it's good

907
00:48:58,320 --> 00:49:00,799
Speaker 1: enough for everybody else to index off of, it's good

908
00:49:00,880 --> 00:49:03,840
Speaker 1: enough for these kids. And so I would say in

909
00:49:03,880 --> 00:49:07,279
Speaker 1: addition to that, there have been other attempts previously. I

910
00:49:07,280 --> 00:49:09,400
Speaker 1: think a lot of those attempts were also tied to

911
00:49:09,400 --> 00:49:11,320
Speaker 1: Social Security, and I think it's a big mistake.

912
00:49:11,680 --> 00:49:16,480
Speaker 2: You anticipated my next question, because does this eventually set

913
00:49:16,560 --> 00:49:19,560
Speaker 2: up a method for people to say, all right, now

914
00:49:19,560 --> 00:49:21,880
Speaker 2: everybody has an invest in American account, we could stop

915
00:49:21,880 --> 00:49:22,960
Speaker 2: funding socials now.

916
00:49:23,480 --> 00:49:26,920
Speaker 1: In fact, we need to fully fund social security. Right.

917
00:49:26,960 --> 00:49:29,200
Speaker 1: We made a commitment to seniors. We need to keep

918
00:49:29,239 --> 00:49:31,560
Speaker 1: our commitment. We need to do the things we need

919
00:49:31,560 --> 00:49:35,080
Speaker 1: to do around financial discipline in this country. We have

920
00:49:35,080 --> 00:49:37,960
Speaker 1: a two trillion dollar deficit. We spend a trillion dollars

921
00:49:37,960 --> 00:49:41,000
Speaker 1: more today than we did in twenty nineteen. COVID's over

922
00:49:41,280 --> 00:49:46,040
Speaker 1: the extenuating circumstances that COVID are over, So the extenuated

923
00:49:46,719 --> 00:49:50,560
Speaker 1: circumstances of our spending increase should also come back to normal.

924
00:49:50,880 --> 00:49:52,520
Speaker 1: So we got to deal with our debt. We got

925
00:49:52,560 --> 00:49:55,959
Speaker 1: to deal with our deficit, and we can solve social

926
00:49:55,960 --> 00:49:59,240
Speaker 1: security in the context of that. But we all know Barry,

927
00:50:00,000 --> 00:50:02,560
Speaker 1: sacrifics are going to have to be made. But that

928
00:50:02,680 --> 00:50:06,160
Speaker 1: is a separate and a distinct problem. This is much

929
00:50:06,239 --> 00:50:09,719
Speaker 1: more fundamental. I want to give every child hope for

930
00:50:09,800 --> 00:50:14,439
Speaker 1: their entire life about my opportunity to participate in the game,

931
00:50:14,560 --> 00:50:16,839
Speaker 1: in the system, to know what it feels like to

932
00:50:16,880 --> 00:50:20,080
Speaker 1: own something. Most of these families and kids will never

933
00:50:20,239 --> 00:50:22,040
Speaker 1: know what it feels like to own something. And we

934
00:50:22,480 --> 00:50:26,160
Speaker 1: talked a lot today about AI. My prediction is that

935
00:50:26,280 --> 00:50:29,040
Speaker 1: artificial intelligence will lead to some of the biggest labor

936
00:50:29,080 --> 00:50:33,560
Speaker 1: dislocations in the history of capitalism. Okay, so if we

937
00:50:33,640 --> 00:50:36,400
Speaker 1: already have people who disbelieve in capitalism, they feel like

938
00:50:36,440 --> 00:50:39,600
Speaker 1: the system's rigged against them. Now, imagine you're one of

939
00:50:39,600 --> 00:50:44,080
Speaker 1: those people right in a sales center or a call center. Right,

940
00:50:44,200 --> 00:50:46,799
Speaker 1: You've done everything that was asked of you, Right, you

941
00:50:46,880 --> 00:50:49,680
Speaker 1: worked really hard, and now you lose your job. It's

942
00:50:49,719 --> 00:50:52,560
Speaker 1: going to take a while to integrate integrate those folks

943
00:50:52,600 --> 00:50:55,319
Speaker 1: back into other parts of the economy. And so we

944
00:50:55,400 --> 00:50:59,240
Speaker 1: need people to understand that human progress is not always

945
00:50:59,280 --> 00:51:02,799
Speaker 1: distributed equally. But one of the dividends, one of the

946
00:51:02,840 --> 00:51:06,040
Speaker 1: birthrights of being part of this great system is you

947
00:51:06,040 --> 00:51:08,080
Speaker 1: ought to have a little skin in the game. And

948
00:51:08,120 --> 00:51:11,719
Speaker 1: it's you know, Warren Buffett said it best. Compounding is

949
00:51:11,760 --> 00:51:14,759
Speaker 1: the eighth wonder of the world. He said, give me

950
00:51:14,920 --> 00:51:19,040
Speaker 1: a really small snowball and a really long hill, Barry.

951
00:51:19,080 --> 00:51:22,440
Speaker 1: There's no longer hill than starting at birth.

952
00:51:22,880 --> 00:51:25,840
Speaker 2: All right, So I have a couple of questions around

953
00:51:25,880 --> 00:51:28,520
Speaker 2: what you've described. I just want to make sure I

954
00:51:28,600 --> 00:51:32,560
Speaker 2: understand this money's locked up for fifty years. You can

955
00:51:32,600 --> 00:51:35,399
Speaker 2: either borrow against it or draw against it to pay

956
00:51:35,400 --> 00:51:37,960
Speaker 2: for college or the first purchase of a house. But

957
00:51:38,160 --> 00:51:40,600
Speaker 2: other than that, it's there. It can't be a touch

958
00:51:40,800 --> 00:51:44,480
Speaker 2: by creditors. You can be forced to sign it away.

959
00:51:45,680 --> 00:51:47,400
Speaker 2: It's locked up and nothing can happen.

960
00:51:47,480 --> 00:51:50,279
Speaker 1: Correct, and you own personal title to it. Now, of

961
00:51:50,320 --> 00:51:53,320
Speaker 1: course we know that saviors save. What do I expect's

962
00:51:53,320 --> 00:51:56,680
Speaker 1: going to happen? Right, They're gonna open adjacent accounts. They're gonna,

963
00:51:57,280 --> 00:51:59,560
Speaker 1: you know, they're gonna learn the power of compounding.

964
00:51:59,680 --> 00:52:07,040
Speaker 3: Who can contribute to one babies two thousand dollars is

965
00:52:07,080 --> 00:52:10,120
Speaker 3: the proposal can be contributed to those accounts.

966
00:52:09,719 --> 00:52:13,760
Speaker 1: Annually like like an ira, and that can come from anyone,

967
00:52:14,400 --> 00:52:15,600
Speaker 1: can come from family, can.

968
00:52:15,520 --> 00:52:19,040
Speaker 2: Come frozen and appreciate. Correct, you take it out text

969
00:52:19,040 --> 00:52:23,359
Speaker 2: free as well. That's right the thinking, And why the

970
00:52:23,440 --> 00:52:26,160
Speaker 2: S and P five hundred? Why not a broader index?

971
00:52:27,280 --> 00:52:29,600
Speaker 2: Off the top of my head, the Vanguard total market

972
00:52:29,640 --> 00:52:33,880
Speaker 2: is two thousand stocks. It's a little less volatile, and

973
00:52:33,920 --> 00:52:35,560
Speaker 2: it has the S and P five hundred in it.

974
00:52:35,760 --> 00:52:38,120
Speaker 1: So you know, I'm sure there'll be plenty of debate

975
00:52:38,160 --> 00:52:41,160
Speaker 1: about this, you know, when when you know the rubber

976
00:52:41,160 --> 00:52:45,240
Speaker 1: meets the road, five hundred of the biggest, best, safest

977
00:52:45,280 --> 00:52:50,720
Speaker 1: companies constantly rebalanced, that represent the best of America benchmark

978
00:52:50,760 --> 00:52:52,440
Speaker 1: to be right. And then you just look at the

979
00:52:52,440 --> 00:52:55,080
Speaker 1: fifty hundred year history of that index. We've got so

980
00:52:55,239 --> 00:52:58,080
Speaker 1: much data over that period of time. It's compounded at

981
00:52:58,080 --> 00:53:01,839
Speaker 1: ten point two percent. Right, And so to me, let's

982
00:53:01,840 --> 00:53:04,400
Speaker 1: not let perfection be the enemy of the good. Let's

983
00:53:04,440 --> 00:53:05,120
Speaker 1: get started.

984
00:53:05,200 --> 00:53:06,799
Speaker 2: I love it. I love it all right, So put

985
00:53:06,800 --> 00:53:10,520
Speaker 2: on your AI hat a second. You've been working on

986
00:53:10,520 --> 00:53:13,200
Speaker 2: this for two years. In the beginning, even though it

987
00:53:13,200 --> 00:53:16,160
Speaker 2: was a great idea, not a lot of traction from

988
00:53:16,400 --> 00:53:19,480
Speaker 2: my sense of doing a little homework on this. It

989
00:53:19,600 --> 00:53:22,480
Speaker 2: seems to be getting a little bit of attraction. There's

990
00:53:22,520 --> 00:53:26,400
Speaker 2: a little more lift to how broadly both sides of

991
00:53:26,440 --> 00:53:28,920
Speaker 2: the aisle looking at it. What are the odds that

992
00:53:28,960 --> 00:53:30,040
Speaker 2: this gets done next year?

993
00:53:30,880 --> 00:53:35,160
Speaker 1: You know, I've recruited a great guy, Matt Lera, to

994
00:53:35,239 --> 00:53:38,160
Speaker 1: run this day to day in Washington. He helped push through

995
00:53:38,280 --> 00:53:43,359
Speaker 1: opportunity zones, another one, another great bipartisan you know, piece

996
00:53:43,400 --> 00:53:48,080
Speaker 1: of legislation. Washington is tricky. It's a long putt to

997
00:53:48,120 --> 00:53:50,440
Speaker 1: get anything done right. But I will tell you we're

998
00:53:50,480 --> 00:53:53,680
Speaker 1: gonna spend the money we got to. Nobody's gonna work

999
00:53:53,680 --> 00:53:57,040
Speaker 1: harder to make this happen. And the goodwill of people

1000
00:53:57,160 --> 00:54:01,919
Speaker 1: on both sides. It's this has made me so optimistic

1001
00:54:01,960 --> 00:54:06,680
Speaker 1: on America because you know, if you read Twitter like

1002
00:54:06,719 --> 00:54:08,920
Speaker 1: you were saying, you may think that people on the

1003
00:54:09,000 --> 00:54:11,920
Speaker 1: left are people on the right, you know you radically

1004
00:54:11,960 --> 00:54:14,960
Speaker 1: disagree with them. They must be bad people. You know,

1005
00:54:15,239 --> 00:54:19,120
Speaker 1: this has given me the opportunity to canvass everybody right

1006
00:54:19,320 --> 00:54:22,520
Speaker 1: across the political spectrum. And I'll tell you, they all

1007
00:54:22,560 --> 00:54:25,200
Speaker 1: believe in America. They all believe it can be improved.

1008
00:54:25,520 --> 00:54:27,799
Speaker 1: And you know, people are climbing on board, So I

1009
00:54:27,840 --> 00:54:29,840
Speaker 1: think this can be one of those big tent movements.

1010
00:54:30,080 --> 00:54:32,600
Speaker 1: I hope that, you know, it enters the fray of

1011
00:54:32,680 --> 00:54:35,960
Speaker 1: presidential politics. I would love to see both presidential candidates

1012
00:54:36,239 --> 00:54:38,960
Speaker 1: endorse it. You know, it's tough to get anything done

1013
00:54:38,960 --> 00:54:41,799
Speaker 1: in a presidential year, Barry, but I'm not stopping. You know,

1014
00:54:41,880 --> 00:54:44,319
Speaker 1: I'm not stopping next year. I think we're gonna put

1015
00:54:44,320 --> 00:54:47,640
Speaker 1: it on the agenda next year. Hopefully magically we can

1016
00:54:47,640 --> 00:54:49,919
Speaker 1: get it through. But if we don't, we're coming back

1017
00:54:49,960 --> 00:54:51,600
Speaker 1: the next year, and we're gonna come back the year

1018
00:54:51,600 --> 00:54:52,040
Speaker 1: after that.

1019
00:54:52,280 --> 00:54:54,600
Speaker 2: Well, Brad, really good on you for this. This is

1020
00:54:54,640 --> 00:55:00,840
Speaker 2: so fascinating and just such an obvious and hindsight way

1021
00:55:01,040 --> 00:55:05,480
Speaker 2: to bring everybody in. It's always nice to see somebody

1022
00:55:06,680 --> 00:55:08,520
Speaker 2: do more than just write a check, come up with

1023
00:55:08,560 --> 00:55:13,040
Speaker 2: a great idea that's disruptive and innovative and just makes

1024
00:55:13,080 --> 00:55:16,839
Speaker 2: so much sense. Congrats to you to getting it this far.

1025
00:55:17,160 --> 00:55:19,920
Speaker 2: I hope you get over the goal line. But even

1026
00:55:20,040 --> 00:55:22,480
Speaker 2: taking it as far as you have, you should really

1027
00:55:22,560 --> 00:55:23,120
Speaker 2: be proud.

1028
00:55:22,880 --> 00:55:25,000
Speaker 1: Of what I can. I tell you one quick Stu,

1029
00:55:25,120 --> 00:55:25,680
Speaker 1: and before I.

1030
00:55:25,600 --> 00:55:29,560
Speaker 2: Forget at invest America twenty four is the Twitter handle right?

1031
00:55:29,640 --> 00:55:33,320
Speaker 1: Yes? At invest America twenty four follow it. We need followers.

1032
00:55:33,360 --> 00:55:35,160
Speaker 1: We need you to build the movement. We need you

1033
00:55:35,239 --> 00:55:38,600
Speaker 1: to tell your friends, talk to your congress people about it. Right, really,

1034
00:55:38,800 --> 00:55:40,400
Speaker 1: let me tell you a quick story about go ahead.

1035
00:55:40,600 --> 00:55:42,840
Speaker 1: You know, I'm a four time founder. I live in

1036
00:55:42,840 --> 00:55:43,640
Speaker 1: Silicon Valley.

1037
00:55:44,400 --> 00:55:44,560
Speaker 2: Right.

1038
00:55:44,719 --> 00:55:48,040
Speaker 1: Human progress is people who take risk to push the

1039
00:55:48,080 --> 00:55:52,279
Speaker 1: whole system forward. That's not just starting a company. Right.

1040
00:55:52,600 --> 00:55:54,880
Speaker 1: My son and I were on Capitol Hill this summer.

1041
00:55:55,000 --> 00:55:57,640
Speaker 1: My son, Lincoln's fifteen years old. I've taken him into

1042
00:55:57,719 --> 00:56:00,560
Speaker 1: all these meetings with me to meet the to meet

1043
00:56:00,600 --> 00:56:03,440
Speaker 1: Congress people, and to have this conversation. And we're walking

1044
00:56:03,480 --> 00:56:05,960
Speaker 1: out of McCarthy's office and we had been in there

1045
00:56:05,960 --> 00:56:10,239
Speaker 1: for sixty minutes. It was a great meeting. And we're

1046
00:56:10,239 --> 00:56:13,279
Speaker 1: walking down the hallway. The capital, you know, just has

1047
00:56:13,320 --> 00:56:15,879
Speaker 1: the power of the Capitol. And I look at him

1048
00:56:15,920 --> 00:56:18,960
Speaker 1: and he just said, Wow, that was amazing. I said, dude,

1049
00:56:19,320 --> 00:56:23,000
Speaker 1: For two hundred and fifty years, there's not a single

1050
00:56:23,239 --> 00:56:26,760
Speaker 1: thing that got done in this country without it starting

1051
00:56:26,800 --> 00:56:30,879
Speaker 1: with a conversation between two people like that. Every piece

1052
00:56:30,880 --> 00:56:34,360
Speaker 1: of legislation, every idea is the back of a napkin.

1053
00:56:34,600 --> 00:56:37,720
Speaker 1: And somebody who's tenacious enough not to give up.

1054
00:56:37,840 --> 00:56:41,480
Speaker 2: Really fascinating stuff. Let's talk a little bit about the

1055
00:56:41,560 --> 00:56:46,320
Speaker 2: state of the IPO environment currently. For a while it

1056
00:56:46,360 --> 00:56:49,640
Speaker 2: looked like the IPO window reopened a bit, then it

1057
00:56:49,680 --> 00:56:53,160
Speaker 2: seemed to have closed. What does the IPO market look

1058
00:56:53,239 --> 00:56:53,520
Speaker 2: like to you?

1059
00:56:54,200 --> 00:56:56,359
Speaker 1: Well, I tweeted something the other day and I said,

1060
00:56:56,400 --> 00:57:00,319
Speaker 1: the IPO market is always open, right, It's just question

1061
00:57:00,400 --> 00:57:01,960
Speaker 1: of whether or not the sellers want to sell at

1062
00:57:02,000 --> 00:57:05,480
Speaker 1: the price the buyers are willing to buy. And it's

1063
00:57:05,520 --> 00:57:07,960
Speaker 1: really true. What I mean by this is, so we

1064
00:57:08,000 --> 00:57:11,279
Speaker 1: had three IPOs, right, we had Armclavyo, and Instacart. We

1065
00:57:11,360 --> 00:57:15,719
Speaker 1: participated in each of those IPOs, Okay, you know, and

1066
00:57:16,840 --> 00:57:19,680
Speaker 1: if you think about the opening that we had that

1067
00:57:19,840 --> 00:57:22,280
Speaker 1: was in June of this year, the ten year was

1068
00:57:22,280 --> 00:57:26,560
Speaker 1: closer to four, right, the Nasdaq was doing well. What

1069
00:57:26,680 --> 00:57:30,920
Speaker 1: happened between July and October of this year, the ten

1070
00:57:31,000 --> 00:57:34,200
Speaker 1: year bond moved twenty five percent, Okay, so went down

1071
00:57:34,240 --> 00:57:37,600
Speaker 1: twenty five percent, and you had the nasdak off twelve percent.

1072
00:57:37,680 --> 00:57:40,160
Speaker 1: So of course, when you have that level of volatility

1073
00:57:40,200 --> 00:57:41,920
Speaker 1: in the market, it's going to put a little chill

1074
00:57:42,200 --> 00:57:43,280
Speaker 1: into people's plans.

1075
00:57:43,280 --> 00:57:44,680
Speaker 2: But look at that that was last month.

1076
00:57:45,120 --> 00:57:46,920
Speaker 1: Now let me tell you this. Let me tell you this.

1077
00:57:47,440 --> 00:57:52,439
Speaker 1: We have a thousand unicorns just in technology that need

1078
00:57:52,480 --> 00:57:55,920
Speaker 1: to get public. These are companies that are valued in

1079
00:57:55,920 --> 00:57:59,360
Speaker 1: the private market. Last valued at over a billion dollars.

1080
00:57:59,520 --> 00:58:01,680
Speaker 1: And what I say said in that tweet is if

1081
00:58:01,680 --> 00:58:03,960
Speaker 1: you were valued at over two to three billion bucks,

1082
00:58:04,120 --> 00:58:06,840
Speaker 1: if you have a decent amount of revenue, it's time

1083
00:58:06,880 --> 00:58:10,880
Speaker 1: to get public. You cannot anchor yourself to some delusional

1084
00:58:10,960 --> 00:58:13,800
Speaker 1: price you got in twenty twenty or twenty twenty one.

1085
00:58:14,080 --> 00:58:17,040
Speaker 1: Anybody who's in the stock market will tell you gross

1086
00:58:17,040 --> 00:58:19,720
Speaker 1: stocks are down fifty to seventy percent during that period

1087
00:58:19,760 --> 00:58:23,160
Speaker 1: of time. So it's reasonable to assume that these late

1088
00:58:23,240 --> 00:58:26,880
Speaker 1: stage venture capital companies also lost that value. We see

1089
00:58:26,920 --> 00:58:29,960
Speaker 1: it with great coming to think about Stripe. You know,

1090
00:58:30,120 --> 00:58:33,080
Speaker 1: Patrick and John Collison have built one of the premier

1091
00:58:33,160 --> 00:58:36,440
Speaker 1: companies in Silicon Valley. At its peak, I think they

1092
00:58:36,480 --> 00:58:38,800
Speaker 1: did around at one hundred and twenty billion. Its most

1093
00:58:38,840 --> 00:58:41,880
Speaker 1: recent round was at sixty billion. Right, you see Odden

1094
00:58:41,920 --> 00:58:44,640
Speaker 1: in some of these companies getting you know, really getting

1095
00:58:44,680 --> 00:58:48,560
Speaker 1: crushed in the public markets. The clearing price will be

1096
00:58:48,640 --> 00:58:52,479
Speaker 1: the clearing price. If you're a great business Okay, think

1097
00:58:52,560 --> 00:58:54,920
Speaker 1: back to the day when you and I watched the

1098
00:58:55,000 --> 00:58:58,320
Speaker 1: Sales Force ipo, the Google IPO, the Apple I go

1099
00:58:58,440 --> 00:59:01,920
Speaker 1: through the list. You get into the public markets. You

1100
00:59:01,960 --> 00:59:05,400
Speaker 1: can innovate plenty in the public markets. Public markets exact

1101
00:59:05,400 --> 00:59:08,640
Speaker 1: a discipline which is good for companies. They keep companies fit.

1102
00:59:09,000 --> 00:59:11,040
Speaker 1: And the reality is your price is your price. It

1103
00:59:11,040 --> 00:59:14,080
Speaker 1: will go up and down depending upon factors in your

1104
00:59:14,120 --> 00:59:17,560
Speaker 1: control and many factors beyond your control. But hiding in

1105
00:59:17,680 --> 00:59:20,320
Speaker 1: the private markets, like you know, Bill Gurley uses this,

1106
00:59:20,360 --> 00:59:22,640
Speaker 1: it's you know, this is like a college a great

1107
00:59:22,680 --> 00:59:25,880
Speaker 1: college player who's too afraid to play in the NFL. Right, No,

1108
00:59:26,280 --> 00:59:28,400
Speaker 1: you got to enter the draft. You're gonna get picked.

1109
00:59:28,520 --> 00:59:30,680
Speaker 1: You may get picked lower than you think you deserve.

1110
00:59:30,960 --> 00:59:32,640
Speaker 1: Then you step on the field and you prove. It

1111
00:59:33,160 --> 00:59:34,600
Speaker 1: shows the power of the public market.

1112
00:59:34,920 --> 00:59:39,000
Speaker 2: So in late twenty one twenty twenty two, valuations had

1113
00:59:39,040 --> 00:59:43,760
Speaker 2: gotten a touch frothy in both the public and the

1114
00:59:43,800 --> 00:59:47,080
Speaker 2: private markets. When you see that sort of reset that

1115
00:59:47,160 --> 00:59:50,640
Speaker 2: takes place in the public markets, how long does it

1116
00:59:50,680 --> 00:59:54,280
Speaker 2: take for the private markets to similarly adjust? What's the

1117
00:59:54,440 --> 00:59:58,120
Speaker 2: lag from when the NAZAC is down thirty percent? How

1118
00:59:58,160 --> 01:00:00,040
Speaker 2: long does it take for private stocks to get that

1119
01:00:00,160 --> 01:00:00,800
Speaker 2: big of a whist.

1120
01:00:00,880 --> 01:00:02,680
Speaker 1: You know, we've been doing some study on this. It's

1121
01:00:02,680 --> 01:00:05,600
Speaker 1: pretty fascinating. So I would say over most of my

1122
01:00:05,680 --> 01:00:08,160
Speaker 1: venture career, which is now over two decades, I would

1123
01:00:08,200 --> 01:00:10,680
Speaker 1: say that was like nine to twelve months, right. And

1124
01:00:10,720 --> 01:00:14,200
Speaker 1: the reason that they adjusted that timeframe is most companies

1125
01:00:14,240 --> 01:00:17,120
Speaker 1: needed to raise money in nine to twelve months. So

1126
01:00:17,200 --> 01:00:19,480
Speaker 1: you know, when you discover what you're worth when you

1127
01:00:19,560 --> 01:00:22,800
Speaker 1: have to go raise more money. Okay, but what happened

1128
01:00:22,800 --> 01:00:25,800
Speaker 1: in twenty and twenty one was so unique. We were

1129
01:00:25,920 --> 01:00:29,440
Speaker 1: so aw wash in money because of the ZERP environment

1130
01:00:29,840 --> 01:00:33,600
Speaker 1: that startups were raising gobs of money way more and

1131
01:00:33,680 --> 01:00:36,040
Speaker 1: putting it on their balance sheet than they normally raise.

1132
01:00:36,440 --> 01:00:39,280
Speaker 1: So they have not needed to go raise money in

1133
01:00:39,320 --> 01:00:42,080
Speaker 1: that nine to twelve month window. Instead, they need to

1134
01:00:42,160 --> 01:00:44,360
Speaker 1: raise money more like in a twenty four or a

1135
01:00:44,400 --> 01:00:47,320
Speaker 1: thirty month window. Well, now we're starting to come up

1136
01:00:47,360 --> 01:00:50,800
Speaker 1: on that period. So twenty twenty four, particularly the back

1137
01:00:50,840 --> 01:00:54,160
Speaker 1: half of twenty four, that's when real price discovery is

1138
01:00:54,200 --> 01:00:56,320
Speaker 1: going to occur. I will tell you, though we've been

1139
01:00:56,320 --> 01:00:59,640
Speaker 1: talking about late stage venture in the IPO market, early

1140
01:00:59,720 --> 01:01:03,360
Speaker 1: stage market has already reset pricing. Okay, we have a

1141
01:01:03,360 --> 01:01:05,240
Speaker 1: little bit of an AI bubble and some things, and

1142
01:01:05,240 --> 01:01:08,000
Speaker 1: we can chat about that. But normal venture these are

1143
01:01:08,040 --> 01:01:10,160
Speaker 1: companies that raise the series A now have to go

1144
01:01:10,280 --> 01:01:12,560
Speaker 1: raise the series B or they're coming to market for

1145
01:01:12,600 --> 01:01:15,280
Speaker 1: the first time. Prices are already back to what I

1146
01:01:15,280 --> 01:01:17,240
Speaker 1: would call like twenty thirteen levels.

1147
01:01:17,240 --> 01:01:20,240
Speaker 2: All right, So you mentioned the AI bubble. Is it

1148
01:01:20,320 --> 01:01:22,600
Speaker 2: a bubble? Is there just a lot of interest because

1149
01:01:22,680 --> 01:01:25,800
Speaker 2: everybody's figured out, hey, this is a game changer, this

1150
01:01:25,880 --> 01:01:31,200
Speaker 2: is another one of those platform or supercycles. Or is

1151
01:01:31,240 --> 01:01:33,800
Speaker 2: it just too much money chasing too few ideas and

1152
01:01:33,840 --> 01:01:34,400
Speaker 2: it's a bubble?

1153
01:01:34,520 --> 01:01:37,520
Speaker 1: Yeah? No, I think I think there's a moment that

1154
01:01:38,080 --> 01:01:43,880
Speaker 1: you have to hold two simultaneous truths. Intention it is true,

1155
01:01:44,280 --> 01:01:46,240
Speaker 1: all right, believe it to be true that this is

1156
01:01:46,240 --> 01:01:48,400
Speaker 1: going to be bigger than the Internet itself. It's having

1157
01:01:48,520 --> 01:01:53,600
Speaker 1: massive impact already on every enterprise and you know, most consumers, right,

1158
01:01:53,680 --> 01:01:56,360
Speaker 1: And so I don't think in fact, if anything, I

1159
01:01:56,360 --> 01:01:59,240
Speaker 1: think we're likely overestimating it a little bit in the

1160
01:01:59,280 --> 01:02:02,360
Speaker 1: short term and underestimating it in the two to five

1161
01:02:02,440 --> 01:02:03,280
Speaker 1: year range.

1162
01:02:03,360 --> 01:02:04,840
Speaker 2: The famous Bill Gates, Right.

1163
01:02:04,840 --> 01:02:09,200
Speaker 1: But with that said, remember nineteen ninety eight, vividly we

1164
01:02:09,240 --> 01:02:11,720
Speaker 1: all knew. We all knew the Internet was going to

1165
01:02:11,840 --> 01:02:14,720
Speaker 1: change the world forever, and a lot of us knew

1166
01:02:14,880 --> 01:02:17,040
Speaker 1: that internet search was going to be one of the

1167
01:02:17,040 --> 01:02:19,200
Speaker 1: biggest businesses to come out of it because it was

1168
01:02:19,240 --> 01:02:22,600
Speaker 1: the tollkeeper, it was organizing all the world's information. In

1169
01:02:22,640 --> 01:02:25,720
Speaker 1: nineteen ninety eight, there's mad scramble was on among venture capitalists.

1170
01:02:25,720 --> 01:02:27,840
Speaker 1: They got to get a search logo, so they went out.

1171
01:02:27,880 --> 01:02:31,960
Speaker 1: They invested in Alta Vista, in Infoseek, do in Licos,

1172
01:02:32,200 --> 01:02:37,160
Speaker 1: in Dogpile, you know, go through the list, Yes, Jeeves, ash, Jeeves,

1173
01:02:37,200 --> 01:02:42,320
Speaker 1: et cetera. All of those companies right a few years

1174
01:02:42,400 --> 01:02:45,960
Speaker 1: later would basically be where zero. You could have waited

1175
01:02:45,960 --> 01:02:47,680
Speaker 1: to invest in Google in two thousand and four in

1176
01:02:47,720 --> 01:02:50,480
Speaker 1: their IPO, and you would have captured ninety plus percent

1177
01:02:50,520 --> 01:02:54,080
Speaker 1: of all the profits ever generated in Internet search. Okay,

1178
01:02:54,320 --> 01:02:57,680
Speaker 1: So my point there is sometimes in venture capital, right,

1179
01:02:57,720 --> 01:02:59,440
Speaker 1: you've got to be in the trenches. You've got to

1180
01:02:59,480 --> 01:03:01,320
Speaker 1: be studying, you've got to be researching, you've got to

1181
01:03:01,320 --> 01:03:05,000
Speaker 1: be preparing your mind, developing conviction. But as Warren Buffett

1182
01:03:05,000 --> 01:03:07,640
Speaker 1: has said, the hardest thing investing is to do all

1183
01:03:07,680 --> 01:03:11,520
Speaker 1: the work and then do nothing right right and right now,

1184
01:03:11,640 --> 01:03:13,480
Speaker 1: I think this has been one of those times where

1185
01:03:13,480 --> 01:03:16,480
Speaker 1: we've passed on over fifty AI companies. We've made some

1186
01:03:16,520 --> 01:03:19,720
Speaker 1: select investments, but it's been easier to invest in AI

1187
01:03:19,840 --> 01:03:23,120
Speaker 1: in the public markets because we already see the fruits

1188
01:03:23,120 --> 01:03:27,760
Speaker 1: of Microsoft's labors of snowflakes, labors of Nvidia's labors, et cetera.

1189
01:03:28,440 --> 01:03:31,680
Speaker 1: The private markets, it's hard to know which assets are

1190
01:03:31,720 --> 01:03:33,760
Speaker 1: going to have durable value. I mean, think about it.

1191
01:03:33,960 --> 01:03:38,400
Speaker 1: Open Ai just dropped its prices ninety percent again. Okay,

1192
01:03:38,600 --> 01:03:40,600
Speaker 1: So if you're trying to build a model barry like

1193
01:03:40,640 --> 01:03:42,760
Speaker 1: you and I do, for one of these you know,

1194
01:03:42,800 --> 01:03:46,680
Speaker 1: for Mistral or Anthropic or cohere, any one of these

1195
01:03:46,680 --> 01:03:50,280
Speaker 1: companies that are in the model space, it's very, very difficult.

1196
01:03:50,320 --> 01:03:53,160
Speaker 1: So I think you know, study a lot, but tread lightly.

1197
01:03:53,560 --> 01:03:57,360
Speaker 2: Huh really interesting. Before I ask you all of our

1198
01:03:57,400 --> 01:03:59,640
Speaker 2: favorite questions we ask all of our guests, I have

1199
01:03:59,680 --> 01:04:02,360
Speaker 2: to throw a little curveball at you. You have what

1200
01:04:02,680 --> 01:04:05,959
Speaker 2: has to be the best job title I've ever seen

1201
01:04:06,080 --> 01:04:11,080
Speaker 2: from anyone in tech. Founder and chief boat washer. Tell

1202
01:04:11,160 --> 01:04:15,280
Speaker 2: us a little bit about your first entrepreneurial gig where

1203
01:04:15,320 --> 01:04:16,840
Speaker 2: you were washing boats.

1204
01:04:16,960 --> 01:04:21,040
Speaker 1: Well, you know, my dad moved us to a small

1205
01:04:21,080 --> 01:04:23,480
Speaker 1: town in northwest Indiana because he thought it would provide

1206
01:04:23,680 --> 01:04:25,040
Speaker 1: He didn't have a lot of money, but he thought

1207
01:04:25,080 --> 01:04:27,080
Speaker 1: it was a nice town, provide a better life for

1208
01:04:27,120 --> 01:04:29,960
Speaker 1: his kids. What this town had was a really beautiful,

1209
01:04:30,000 --> 01:04:33,360
Speaker 1: big lake in it. It's called Lake Wawasee, and you know,

1210
01:04:33,360 --> 01:04:36,920
Speaker 1: about twelve miles long, a couple miles wide. Everybody had

1211
01:04:36,920 --> 01:04:39,080
Speaker 1: a dock out front and a boat. All the boats

1212
01:04:39,120 --> 01:04:40,840
Speaker 1: had to be cleaned, and I had to make money,

1213
01:04:41,240 --> 01:04:45,080
Speaker 1: and so I partner with my friend Jimmy Koshnik, and

1214
01:04:45,160 --> 01:04:48,880
Speaker 1: we co founded Wawasee Boat Care and we ran the

1215
01:04:48,920 --> 01:04:51,520
Speaker 1: table on boat cleaning on the lake. We had a

1216
01:04:51,600 --> 01:04:54,240
Speaker 1: lot of fun. We probably spent all the money before

1217
01:04:54,280 --> 01:04:55,080
Speaker 1: the summer was over.

1218
01:04:55,160 --> 01:04:59,280
Speaker 2: But how much did that affect that experience effect how

1219
01:04:59,320 --> 01:05:02,720
Speaker 2: you approach being an entrepreneur? How seminal was that for you?

1220
01:05:02,720 --> 01:05:05,680
Speaker 1: You know, I think those moments working for guys like

1221
01:05:05,760 --> 01:05:10,000
Speaker 1: Pete legal omer Krupp at Supreme Industries, I was always working.

1222
01:05:10,360 --> 01:05:12,000
Speaker 1: I was always a student of the game. I was

1223
01:05:12,040 --> 01:05:15,120
Speaker 1: always studying. I was always curious, like how does businesses

1224
01:05:15,120 --> 01:05:17,160
Speaker 1: get built? And you know the reason, Barry, I think,

1225
01:05:17,160 --> 01:05:19,040
Speaker 1: if you really boil it all down, my dad went.

1226
01:05:19,600 --> 01:05:21,880
Speaker 1: My dad was kind of my hero mm hmm, you know,

1227
01:05:22,240 --> 01:05:27,480
Speaker 1: and I saw him give everything and his business go

1228
01:05:27,600 --> 01:05:31,200
Speaker 1: under right, and it affected his health, his marriage, lost

1229
01:05:31,240 --> 01:05:33,439
Speaker 1: the house. And so I think, if you're the son,

1230
01:05:33,560 --> 01:05:36,360
Speaker 1: the youngest son of a guy who's like your hero,

1231
01:05:36,480 --> 01:05:38,680
Speaker 1: you see him go through that trauma, you kind of

1232
01:05:38,680 --> 01:05:41,760
Speaker 1: spend the rest of your life trying to figure it out,

1233
01:05:41,960 --> 01:05:44,880
Speaker 1: like why didn't it work out? And really, you know,

1234
01:05:45,200 --> 01:05:48,240
Speaker 1: doing it for him, avenging what the dream you know

1235
01:05:48,320 --> 01:05:50,600
Speaker 1: that he didn't have. And so from an early age,

1236
01:05:50,600 --> 01:05:51,919
Speaker 1: I knew I had to put it on my own

1237
01:05:51,960 --> 01:05:53,720
Speaker 1: back and get it done. And here we.

1238
01:05:53,680 --> 01:05:56,520
Speaker 2: Are, Here we are, So we have you for four

1239
01:05:56,520 --> 01:05:58,520
Speaker 2: and a half minutes. Let's let's turn this into a

1240
01:05:58,560 --> 01:06:01,960
Speaker 2: speed round and I'm going to ask you the questions

1241
01:06:01,960 --> 01:06:04,120
Speaker 2: that we ask all of our guests, starting with are

1242
01:06:04,160 --> 01:06:06,880
Speaker 2: you streaming anything, listen to anything? What's keeping you entertained

1243
01:06:06,920 --> 01:06:07,440
Speaker 2: these days?

1244
01:06:08,080 --> 01:06:12,040
Speaker 1: The Diplomat I was watching so good, so good, so terrific.

1245
01:06:13,160 --> 01:06:15,640
Speaker 1: You know, watch a lot of docs, listen to a

1246
01:06:15,680 --> 01:06:18,760
Speaker 1: lot of pods, and listen to a lot of books

1247
01:06:18,800 --> 01:06:19,120
Speaker 1: on tape.

1248
01:06:19,160 --> 01:06:20,920
Speaker 2: By the way, I enjoy you on the oil in

1249
01:06:21,080 --> 01:06:24,480
Speaker 2: pod with a Chamatha and company. That that's a hell

1250
01:06:24,520 --> 01:06:25,240
Speaker 2: of a podcast.

1251
01:06:25,600 --> 01:06:29,560
Speaker 1: You know, what I'll tell you about that group is

1252
01:06:29,800 --> 01:06:33,360
Speaker 1: authentic friends. Right. You could tell they've all known each

1253
01:06:33,400 --> 01:06:36,760
Speaker 1: other in some capacity. You could tell a long time.

1254
01:06:36,800 --> 01:06:39,320
Speaker 1: We do play poker, uh, you know pretty much every

1255
01:06:39,320 --> 01:06:42,040
Speaker 1: week we're in town on Thursday nights. We like to

1256
01:06:42,080 --> 01:06:44,880
Speaker 1: give it to each other. But importantly, you know, we

1257
01:06:44,920 --> 01:06:47,000
Speaker 1: have a we have a thread with one another throughout

1258
01:06:47,000 --> 01:06:50,680
Speaker 1: the week. We are all analysts, We're all I mean,

1259
01:06:50,880 --> 01:06:57,240
Speaker 1: it's like a Bloomberg stream constantly sharing news, analysis, politics, economics,

1260
01:06:57,480 --> 01:07:01,320
Speaker 1: company specific venture capital because we care. And so to

1261
01:07:01,400 --> 01:07:03,520
Speaker 1: have friends that you get to play a game with,

1262
01:07:03,840 --> 01:07:06,440
Speaker 1: you get to debate with. You don't always agree on everything,

1263
01:07:07,080 --> 01:07:09,120
Speaker 1: but at the end of the day, you know, you

1264
01:07:09,240 --> 01:07:11,480
Speaker 1: learn a lot with you know, it's a real privilege.

1265
01:07:11,640 --> 01:07:14,560
Speaker 2: You've already mentioned some of your early mentors. Who else

1266
01:07:14,720 --> 01:07:16,000
Speaker 2: helped shape your career?

1267
01:07:16,800 --> 01:07:20,040
Speaker 1: You know, I think you know Dick Luger clearly as

1268
01:07:20,040 --> 01:07:23,240
Speaker 1: a statesman. I think in life, you know, we talk

1269
01:07:23,280 --> 01:07:27,280
Speaker 1: about compounding, right, and in compounding, you know, you got

1270
01:07:27,320 --> 01:07:31,560
Speaker 1: your principal p times your rate are you know for

1271
01:07:31,720 --> 01:07:34,800
Speaker 1: duration T right, And I think you can apply that

1272
01:07:34,880 --> 01:07:38,400
Speaker 1: same formula to life. But your principle is your purpose,

1273
01:07:38,720 --> 01:07:41,960
Speaker 1: it's your passion, right, And I was lucky enough to

1274
01:07:42,000 --> 01:07:44,160
Speaker 1: find that. I just like to be a student and

1275
01:07:44,200 --> 01:07:46,680
Speaker 1: study the world, be an anthropologist and try to make

1276
01:07:46,720 --> 01:07:48,280
Speaker 1: sense out of it. You know, if I was a

1277
01:07:48,320 --> 01:07:50,480
Speaker 1: college professor, I'd do all the same studying, but then

1278
01:07:50,520 --> 01:07:52,800
Speaker 1: i'd teach a class. Right. If I was a host,

1279
01:07:52,840 --> 01:07:54,400
Speaker 1: I would do all the same studying, and then I'd

1280
01:07:54,440 --> 01:07:56,880
Speaker 1: do a TV show. I happen to be an investor,

1281
01:07:57,080 --> 01:07:58,680
Speaker 1: so I do all the studying and then I get

1282
01:07:58,680 --> 01:08:01,160
Speaker 1: to place bets. You know, the r I think of

1283
01:08:01,200 --> 01:08:04,560
Speaker 1: as the Raid accelerators. Who are the people in your life?

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01:08:04,640 --> 01:08:07,640
Speaker 1: The Paul Readers, you know, the the David Fialco and

1285
01:08:07,760 --> 01:08:10,960
Speaker 1: Joel Cutler's, the Pete legals of the world, right, who

1286
01:08:11,400 --> 01:08:14,280
Speaker 1: Rich Barton, Bill Gurley. You know, when you find those people,

1287
01:08:14,360 --> 01:08:18,920
Speaker 1: collect them, and you need to make yourself collectible. And

1288
01:08:18,960 --> 01:08:21,840
Speaker 1: the way you make yourself collectible, right is you have

1289
01:08:21,920 --> 01:08:24,360
Speaker 1: to invest in them. You have to you have to

1290
01:08:24,400 --> 01:08:28,160
Speaker 1: be you know, insightful to them, right. And I tell

1291
01:08:28,160 --> 01:08:30,960
Speaker 1: my kids all the time, you got to invest in friendships, right,

1292
01:08:31,479 --> 01:08:35,040
Speaker 1: And you know, so that's been really the the byproduct

1293
01:08:35,080 --> 01:08:37,240
Speaker 1: of that has been a lot of fun, a lot

1294
01:08:37,280 --> 01:08:39,160
Speaker 1: of learning, and a few good investments.

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01:08:39,200 --> 01:08:40,679
Speaker 2: H really good answer.

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01:08:41,479 --> 01:08:42,200
Speaker 1: What about books?

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01:08:42,200 --> 01:08:43,840
Speaker 2: What are some of your favorites? What are you reading

1298
01:08:44,040 --> 01:08:44,639
Speaker 2: right now?

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01:08:44,840 --> 01:08:48,320
Speaker 1: Well, I'm going to tell you one. You know, there's

1300
01:08:49,160 --> 01:08:52,120
Speaker 1: a lot of stuff I'm listening to on audible. You know,

1301
01:08:52,280 --> 01:08:54,760
Speaker 1: Chama said something the other day. Somebody was talking about

1302
01:08:54,800 --> 01:08:56,320
Speaker 1: a book in our thread that he needs to read,

1303
01:08:56,760 --> 01:08:59,519
Speaker 1: and you know, within fifteen seconds he snapp called it.

1304
01:08:59,600 --> 01:09:02,840
Speaker 1: He put the GPT summary in there. He's like, I'm done,

1305
01:09:03,720 --> 01:09:06,880
Speaker 1: which I thought was was pretty funny but actually quite true.

1306
01:09:07,120 --> 01:09:11,360
Speaker 1: Essentialism by Greg mchun the art of doing less better.

1307
01:09:12,080 --> 01:09:16,479
Speaker 1: So for me, my design philosophy is minimalism and my

1308
01:09:16,800 --> 01:09:21,200
Speaker 1: life philosophy is essentialism. And that's also really how we

1309
01:09:21,280 --> 01:09:23,479
Speaker 1: run the business. We don't have to do everything, we

1310
01:09:23,479 --> 01:09:27,200
Speaker 1: don't have a lot of fomo. We're happy being students.

1311
01:09:27,680 --> 01:09:30,720
Speaker 1: But when we develop deep conviction, we put a lot

1312
01:09:30,720 --> 01:09:36,720
Speaker 1: of wood behind the bat, and that leads to enduring relationships,

1313
01:09:37,080 --> 01:09:40,320
Speaker 1: enduring investments, and the ability to compound over a really

1314
01:09:40,320 --> 01:09:41,200
Speaker 1: long period of time.

1315
01:09:41,439 --> 01:09:45,559
Speaker 2: Really again, really good answer. Our final two questions, what

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01:09:45,640 --> 01:09:47,960
Speaker 2: sort of advice would you give to a recent college

1317
01:09:48,000 --> 01:09:55,640
Speaker 2: grad who is interested in either startups, entrepreneurism or venture capitalism.

1318
01:09:56,080 --> 01:09:58,599
Speaker 1: You know, I'll tell you where I go to look

1319
01:09:58,600 --> 01:10:02,720
Speaker 1: for analysts in my firm. I go to Twitter. I

1320
01:10:02,760 --> 01:10:06,240
Speaker 1: want to see what they're saying. Right. And so if

1321
01:10:06,280 --> 01:10:09,719
Speaker 1: you want to break through, if you want to get noticed, right,

1322
01:10:10,040 --> 01:10:13,080
Speaker 1: then do research study something. If you come in and

1323
01:10:13,080 --> 01:10:15,479
Speaker 1: tell me you're super passionate about something, but then I

1324
01:10:15,520 --> 01:10:17,439
Speaker 1: asked you a couple questions, it's pretty clear to me

1325
01:10:17,479 --> 01:10:20,000
Speaker 1: you've never really done any work on it, right, It's

1326
01:10:20,040 --> 01:10:23,040
Speaker 1: not very convincing. But you know, jam And Ball one

1327
01:10:23,080 --> 01:10:25,439
Speaker 1: of my partners in the firm, he was a young

1328
01:10:26,320 --> 01:10:29,920
Speaker 1: a young partner at Redpoint venture capital firm in Silicon Valley.

1329
01:10:30,320 --> 01:10:33,080
Speaker 1: He had seventy five thousand followers on his substack called

1330
01:10:33,120 --> 01:10:35,880
Speaker 1: Clouded Judgment. This guy's twenty nine thirty years old.

1331
01:10:36,160 --> 01:10:36,320
Speaker 2: Right.

1332
01:10:36,360 --> 01:10:38,519
Speaker 1: How did Bill Gurley break through in Silicon Valley? He

1333
01:10:38,600 --> 01:10:40,680
Speaker 1: started I think he was twenty eight years old. He

1334
01:10:40,760 --> 01:10:43,759
Speaker 1: started sending out a facts called above the Crowd every

1335
01:10:43,800 --> 01:10:46,720
Speaker 1: Thursday night at five o'clock, telling people what he had

1336
01:10:46,800 --> 01:10:49,800
Speaker 1: learned that week in Silicon Valley. That is the type

1337
01:10:49,840 --> 01:10:52,840
Speaker 1: of hack that shows people your passion. It shows them

1338
01:10:52,880 --> 01:10:56,240
Speaker 1: your insights, your capability to process information. So I mean

1339
01:10:56,240 --> 01:10:58,120
Speaker 1: you got to earn it, nobody's handing it to you.

1340
01:10:58,439 --> 01:11:01,240
Speaker 2: Love that. And finally, what do you know about the

1341
01:11:01,240 --> 01:11:05,000
Speaker 2: world of venture investing today? You wish you knew twenty

1342
01:11:05,040 --> 01:11:07,759
Speaker 2: plus years or so ago when you were first getting started.

1343
01:11:08,280 --> 01:11:10,519
Speaker 1: Power law, power law, power law.

1344
01:11:10,520 --> 01:11:11,439
Speaker 2: It's all about company.

1345
01:11:11,520 --> 01:11:14,839
Speaker 1: The first ten years of my of my venture career,

1346
01:11:15,160 --> 01:11:18,120
Speaker 1: I would see something as a founder, I'd say, oh, yeah,

1347
01:11:18,160 --> 01:11:20,599
Speaker 1: I can help make that work if we just change

1348
01:11:20,640 --> 01:11:22,840
Speaker 1: this and this and this, and you know, I could

1349
01:11:22,920 --> 01:11:24,479
Speaker 1: earn a three X on this, a four x, a

1350
01:11:24,520 --> 01:11:27,680
Speaker 1: five x. Right. The truth is in venture you have

1351
01:11:27,760 --> 01:11:31,200
Speaker 1: to play for spectacular outcomes. So we don't invest in

1352
01:11:31,200 --> 01:11:32,960
Speaker 1: a software company if we think it can be a

1353
01:11:33,040 --> 01:11:35,960
Speaker 1: nice little company with thirty or forty million dollars in revenue.

1354
01:11:36,240 --> 01:11:38,519
Speaker 1: We want to go after the biggest market with the

1355
01:11:38,520 --> 01:11:42,200
Speaker 1: most audacious founders, Founders that want to change the world,

1356
01:11:42,400 --> 01:11:45,599
Speaker 1: that want to do things. Palmer Lucky, what he's doing

1357
01:11:45,640 --> 01:11:50,360
Speaker 1: at Anderill Right, Mark Zuckerberg, what do he wanted to

1358
01:11:50,360 --> 01:11:53,479
Speaker 1: do in social you know, Sam what he wants to

1359
01:11:53,520 --> 01:11:56,840
Speaker 1: do you know at open ai, Elon in electrification. Those

1360
01:11:56,920 --> 01:12:00,240
Speaker 1: are the people, right, who do in fact change, you know,

1361
01:12:01,080 --> 01:12:04,320
Speaker 1: bend the universe, and you know you want to back them.

1362
01:12:05,560 --> 01:12:07,800
Speaker 1: It's not gonna happen fast. There won't be a lot

1363
01:12:07,800 --> 01:12:09,320
Speaker 1: of people who agree with you at the start. You know,

1364
01:12:09,360 --> 01:12:12,400
Speaker 1: when we when we invest in Snowflake, that first round,

1365
01:12:12,800 --> 01:12:15,320
Speaker 1: it was backing the founders out of Oracle that we're

1366
01:12:15,320 --> 01:12:18,280
Speaker 1: going to rebuild the Oracle database in the cloud, separate

1367
01:12:18,400 --> 01:12:22,559
Speaker 1: storage and compute. Most of the smart enterprise software investors

1368
01:12:22,560 --> 01:12:26,800
Speaker 1: in Silicon Valley past. Okay, really and so like, you know,

1369
01:12:27,160 --> 01:12:31,200
Speaker 1: because the cloud in twenty twelve, twenty thirteen was out

1370
01:12:31,280 --> 01:12:33,600
Speaker 1: of favor, it was out of consensus. People were like,

1371
01:12:33,640 --> 01:12:36,920
Speaker 1: it's not as secure, it's not as fast, it costs more.

1372
01:12:37,280 --> 01:12:39,240
Speaker 1: So you had to see a future where it was

1373
01:12:39,280 --> 01:12:41,000
Speaker 1: going to cost less, it was going to be faster,

1374
01:12:41,080 --> 01:12:42,880
Speaker 1: and there's going to be more secure, which we did.

1375
01:12:43,200 --> 01:12:46,040
Speaker 1: And so that only comes from study, only comes from study.

1376
01:12:46,080 --> 01:12:50,880
Speaker 1: So study hard, look for the really big outcomes, find

1377
01:12:51,360 --> 01:12:55,640
Speaker 1: people who are undeniable outliers working in those areas, and

1378
01:12:55,680 --> 01:12:56,320
Speaker 1: back them.

1379
01:12:56,680 --> 01:12:58,920
Speaker 2: Brad, thank you for being so generous with your time.

1380
01:12:59,280 --> 01:13:03,320
Speaker 2: This was just we have been speaking with Brad Gersner.

1381
01:13:03,400 --> 01:13:08,200
Speaker 2: He is the founder and CEO of altiminer Capital. If

1382
01:13:08,240 --> 01:13:11,479
Speaker 2: you enjoy this conversation, well, be sure and check out

1383
01:13:11,479 --> 01:13:14,920
Speaker 2: any of the previous five hundred such discussions we've had

1384
01:13:14,960 --> 01:13:18,120
Speaker 2: over the past nine and a half years. You can

1385
01:13:18,160 --> 01:13:24,879
Speaker 2: find those on Apple Podcast, Bloomberg, YouTube, Spotify, or wherever

1386
01:13:24,960 --> 01:13:29,559
Speaker 2: you get your favorite podcasts. Follow my daily reads at

1387
01:13:29,640 --> 01:13:33,759
Speaker 2: Ridolts dot com. Follow me on Twitter at Ridholts. Follow

1388
01:13:34,080 --> 01:13:39,000
Speaker 2: all of the Bloomberg family of podcasts at podcast I

1389
01:13:39,040 --> 01:13:41,120
Speaker 2: would be remiss if I did not thank the crack

1390
01:13:41,240 --> 01:13:45,960
Speaker 2: staff that helps put these conversations together each week. Sarah

1391
01:13:46,000 --> 01:13:50,879
Speaker 2: Livesey is my audio engineer. Attika Valbroun is my project manager.

1392
01:13:51,680 --> 01:13:55,120
Speaker 2: Anna Luke is my producer. Sean Russo is my researcher.

1393
01:13:55,840 --> 01:13:59,639
Speaker 2: I'm Barry Ridholts. You've been listening to Masters in Business

1394
01:14:00,200 --> 01:14:01,920
Speaker 2: on Bloomberg Radio.