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

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Speaker 2: Hello and welcome to another episode of the Outlass podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Allaway.

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Speaker 2: Tracy our colleague here at Bloomberg at Harrison, had interesting

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Speaker 2: newsletter today and it's actually something I've been thinking about

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Speaker 2: a little bit lately, which is that for all the

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Speaker 2: talk of the AI boom or the A bubble driving

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Speaker 2: the stock market, there's no AI pure plays really that

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Speaker 2: are publicly traded. Like in video is probably the closest,

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Speaker 2: but three years ago people were excited because they were

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Speaker 2: mining ethereum. Before that, it was like video games. You know,

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Speaker 2: this was only an AI company in people's mind. For

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Speaker 2: since late twenty twenty two, Google still is you know,

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Speaker 2: we know they're all investing in a ton. There's actually

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Speaker 2: no like no AI company that people are excited about

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Speaker 2: in the public markets.

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Speaker 3: I mean, I think it's true. Here you have this

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Speaker 3: thing that a lot of people would say is revolutionary technology, right,

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Speaker 3: but you kind of have to decide if you're going

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Speaker 3: to invest in it, is it going to be like

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Speaker 3: upstream or downstream? And there doesn't really seem to be

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Speaker 3: that much pure play, as you say.

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Speaker 2: As our colleagues said, Verma might like to say, people

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Speaker 2: are investing in the picks and the shovels, you know,

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Speaker 2: this gold rush. I don't think he actually said that.

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Speaker 2: It's just a million other people's but it is.

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Speaker 3: It shovels, it's fun, a completely reasonable commentary, That's what

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Speaker 3: I say.

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Speaker 2: Is there? You know, it turns out instantly picks and

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Speaker 2: shovels have been great. You know, you could have bought Caterpillar,

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Speaker 2: or you could have bought some old school HVAC company

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Speaker 2: that's providing cooling or heating or whatever and made a

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Speaker 2: ton of money. So actually, it turns out, at least

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Speaker 2: for the last few years, all those awful cliches have

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Speaker 2: actually been big money makers and I should not make

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Speaker 2: fun of them.

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Speaker 3: Well, here's the other thing I would say. It does

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Speaker 3: feel like everyone kind of agrees at this moment in

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Speaker 3: time that there is froth in the market. Maybe it's

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Speaker 3: on a massive bubble, right, but there's some froth, and

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Speaker 3: everyone is kind of admitting or saying that you're going

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Speaker 3: to have some companies that emerge as big winners, much

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Speaker 3: like the dot com era, and then a bunch of

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Speaker 3: companies that like actually end up being losers, and I

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Speaker 3: think again, like that is consensus at this point, but

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Speaker 3: it doesn't really necessarily translate into actual investment, because of

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Speaker 3: course the trick is actually picking the winners and losers

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Speaker 3: in the market.

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Speaker 4: Yeah. I don't know.

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Speaker 3: It just seems like a weird point in time where

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Speaker 3: people are like, oh, yeah, AI is great, but we

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Speaker 3: all know that some of these companies are going to

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Speaker 3: be massive losers, right.

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Speaker 2: I think now they're all kind of big treated as winners,

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Speaker 2: which is the other thee Yeah, anyway, it's a very

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Speaker 2: weird time. You got to do more episodes on this

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Speaker 2: because it is sort of the central question for on

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Speaker 2: whether we're just talking about the market or talk about

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Speaker 2: the economy, et cetera. Someone I've wanted to talk to

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Speaker 2: for a long time. Earlier this year he wrote a

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Speaker 2: essay for Colossus called AI will Not Make You Rich.

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Speaker 2: It came out in September. It seems like ages ago.

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Speaker 2: It's very disappointing because I think a lot of people

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Speaker 2: really are hoping to get rich on AI, so this

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Speaker 2: is a very unwelcome message.

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Speaker 3: It's also very much a sort of core odd Lots

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Speaker 3: thesis because in the essay he compares and contrasts AI

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Speaker 3: with containerization, which is another which facing.

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Speaker 2: So let's just get to the guest. Someone I've wanted

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Speaker 2: to talk to for a very long time, someone who

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Speaker 2: literally is the perfect guest longtime VC started a venture

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Speaker 2: investing in nineteen ninety seven. Also a professor at Columbia

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Speaker 2: Business School. We're going to be talking to investor Jerry Newman,

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Speaker 2: also the co author of a recent book, Founder Verse Investor,

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Speaker 2: The Honest Truth about Venture Capital from Startup to IPO.

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Speaker 2: Maybe he'll tell us one we'll see.

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Speaker 3: You the bringer of excess Halloween candy. So he gets

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Speaker 3: he gets brownie points for that.

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Speaker 2: Literally the perfect guest. Uh, Jerry, thank you so much

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Speaker 2: for coming in. Thrilled to finally have you here.

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Speaker 4: Thanks, I'm glad to be here.

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Speaker 2: What does that mean? A I won't make you rich.

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Speaker 2: AI has made people super rich, and it's making people

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Speaker 2: rich every single day.

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Speaker 4: You know, as an old mentor used to say, money

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Speaker 4: is not money till it's cash. Okay, So is anybody

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Speaker 4: really rich yet? Oh? Come on?

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Speaker 2: I mean Jensen Wong bought an entire bar in Korea.

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Speaker 2: He's like bought beer and fried chicken for everyone.

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Speaker 4: He's right, I think it's smart to cash out early. Okay,

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Speaker 4: that's what he was doing. Okay, let's say you I

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Speaker 4: really believed it, would you be selling at stock now?

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Speaker 2: I mean, see what you mean talk about this because

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Speaker 2: you obviously have a lot of experience. Actually, that brings

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Speaker 2: another line of question that I want to get into.

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Speaker 2: But what does that mean it's smart to cash out

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Speaker 2: early when in your experience? What does that actually mean?

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Speaker 4: So, look, I believe that AI is a revolutionary technology. Okay,

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Speaker 4: I'm going to put that on the table.

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Speaker 2: Which is important to say because not everyone agrees with that.

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Speaker 4: So that's yeah, totally. You know, I'm on Blue Sky

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Speaker 4: and nobody agrees with that, but I do. I think so.

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Speaker 4: But there's a difference between value creation and value capture.

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Speaker 4: So even if AI creates a lot of value for society,

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Speaker 4: who's going to get that value? Is it going to

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Speaker 4: be the early investors? Is it going to be the court,

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Speaker 4: you know, the foundation model companies. Is it going to

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Speaker 4: be YEA consumers? You know? They think that's the question

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Speaker 4: people need to ask.

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Speaker 3: I actually broadly agree with this thesis when it comes

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Speaker 3: to AI, but maybe just to clarify the idea here,

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Speaker 3: compare and contrast this current AI cycle with maybe previous

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Speaker 3: technological breakthroughs. And you know, I mentioned containers. I think

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Speaker 3: a lot of people aren't used to thinking about boxes

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Speaker 3: as this major advancement in technology, but at the time

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Speaker 3: they really were.

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Speaker 4: Yeah, I mentioned containerization, and most of my peers think

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Speaker 4: I'm talking about doctor. So I'm talking about shipping containers. Right,

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Speaker 4: the big boxes they put on ships, and then they

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Speaker 4: can move from the ships onto the rail cars and

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Speaker 4: now to the back of trucks. And this was a

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Speaker 4: revolutionary technology. I mean it changed everything about the way

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Speaker 4: we live. I don't remember. I'm probably a little older

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Speaker 4: than you all, but when I was a kid, my

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Speaker 4: grandmother used to send up oranges from Florida at Christmas time, right,

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Speaker 4: because they were rare. You couldn't just go into a

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Speaker 4: grocery store and buy them. Now you can buy oranges

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Speaker 4: anywhere at any time. People don't really realize how much

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Speaker 4: our lives have changed because of shipping containerization, because of

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Speaker 4: these global logistics and the globalization of shipping. Now this

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Speaker 4: is utionary technology. Who got rich from it? I mean generally,

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Speaker 4: if you look at the nineteen sixties, say how many

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Speaker 4: people became wildly rich from technological innovation? Can you think

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Speaker 4: of anyone? Because I've been asking this question for years.

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Speaker 4: There are people who got rich in media and whatnot,

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Speaker 4: but there wasn't a lot of technological innovation that made individuals.

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Speaker 2: It just start, like twenty years ago, did they have technology.

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Speaker 4: Bout the right? I mean, it's I think this is

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Speaker 4: the thing. Right, So we talk about computer technology, the

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Speaker 4: information and computer technology revolution as technology, but obviously this

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Speaker 4: has always been technology. But only at certain times in

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Speaker 4: this technological cycle do people seem to make money as

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Speaker 4: investors and as inventors.

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Speaker 3: Explain more, though, because I mean I could argue that

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Speaker 3: Marisk or someone like that got pretty rich off of containerization.

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Speaker 3: Like maybe it took a while, but even though the

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Speaker 3: shipping industry is highly cyclical, when they are in the

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Speaker 3: boom period, they make a lot of money.

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Speaker 4: Sure, I mean, the existing shipping companies got very large

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Speaker 4: and made a lot of money. They got larger and

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Speaker 4: made more money. Is mayor you know who made money.

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Speaker 3: Completely new entrants?

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Speaker 4: Yeah? So just to me as background, I'm a venture capitalist,

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Speaker 4: right or have been a venture capitalist for a long time,

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Speaker 4: recently retired, And I think about people investing in making

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Speaker 4: money new companies, inventors or entrepreneurs making money, not the

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Speaker 4: existing incumbents making money. And I think that people will

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Speaker 4: make money on AI. Might be Microsoft making a ton

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Speaker 4: of money on AI. It could be AI. You know,

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Speaker 4: when containerization shipping containerization came around, Sealand was the instigator

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Speaker 4: of this, and the founder of Sealand made money primarily

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Speaker 4: because he sold early, right, he sold Sealand to r

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Speaker 4: J Nibisco or sorry, it was just r Jr. At

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Speaker 4: the time, and they thought they were diversifying, which is

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Speaker 4: the big thing. Then paid them a lot of money

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Speaker 4: and then they drove it into the ground.

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Speaker 2: So what did Sealand do? What was did they? I

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Speaker 2: actually don't I'm not familiar with this company at all,

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Speaker 2: which I think kind of speaks to your point. But

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Speaker 2: what it was Seland?

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Speaker 4: Yes, So so it was a truck. It was started

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Speaker 4: out as a trucking company. And the founder of Sealand

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Speaker 4: was a trucking entrepreneur. And he said, it's silly you

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Speaker 4: you go into a port, your truck sits around all day. Well,

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Speaker 4: you know, the long charman put a cargo net into

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Speaker 4: a container ship, load everything into it, pull it out,

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Speaker 4: unload it and then reload it back into your truck.

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Speaker 4: This is not efficient. And this obviously it's an obvious idea, right,

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Speaker 4: just put it all in a box and you can

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Speaker 4: then put that box on a truck.

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Speaker 3: The best ideas are always the obvious rights in retrospect.

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Speaker 4: But the problem with it was it was a systems problem, right.

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Speaker 4: The long charman didn't want it, the ports didn't want it,

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Speaker 4: the port authorities didn't want it, the politicians didn't want it.

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Speaker 4: Nobody wanted this to happen because this sort of enormous

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Speaker 4: change would put a lot of people out of jobs.

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Speaker 4: It would up set the existing order, and it did.

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Speaker 4: I live in Hoboken, and in Hoboken there's a lot

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Speaker 4: of peers that nobody uses except to go running on now,

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Speaker 4: because back in the sixties it was a long sharmantown.

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Speaker 4: And when I moved there in the early nineties, it

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Speaker 4: was empty. It started to gentrify, but because all of

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Speaker 4: those people had lost their jobs and moved out.

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Speaker 2: And I suppose, like even in the case of marisk

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Speaker 2: And I'm sure you know, they obviously have made a

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Speaker 2: lot of money because the explosion of global trading volumes

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Speaker 2: and containerization is part of it. Like, it wasn't overnight wealth, right,

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Speaker 2: it wasn't.

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Speaker 4: It was not.

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Speaker 2: It was not people got richer, but it was not

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Speaker 2: like some bubble get rich quick thing where they suddenly

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Speaker 2: cashed in on a new thing.

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Speaker 4: Yeah. I mean, I suppose whoever owns mask may have

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Speaker 4: gotten richer. Yeah, but it's not like you're going to

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Speaker 4: look at the forest four hundred and see all these

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Speaker 4: shipping magnates who became suddenly, you know, enormously wealthy. There

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Speaker 4: are a few.

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Speaker 3: Wait, okay, so if I think about a box, you

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Speaker 3: know part part of that.

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Speaker 2: We just keep this whole conversation on boxes.

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Speaker 3: Actually, well one more question then we will be able

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Speaker 3: to discuss ai. But I think about a box, and

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Speaker 3: as you say, it's sort of an organizational structural problem,

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Speaker 3: Like the box itself is not the huge technological advancement necessarily.

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Speaker 3: So what exactly was it about containerization that prevented it

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Speaker 3: from being disseminated I guess to new upstarts or new

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Speaker 3: companies that could actually use that technology.

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Speaker 4: Well it was, so I'm not sure I understand the question,

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Speaker 4: because uanization became widespread very quickly, right, But.

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Speaker 3: What I mean is like, why was that value seemingly

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Speaker 3: captured by incumbents versus startups?

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Speaker 4: Right? I think because it disseminated so quickly, Right, it

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Speaker 4: was an obvious idea. Everybody who saw it said, Okay,

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Speaker 4: we need to do this. Right. Everybody who's already in

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Speaker 4: the business said, if this is going to happen, we

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Speaker 4: have to do it. We can't be left behind. We

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Speaker 4: will be left behind if we don't do it, which

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Speaker 4: you know, I think was also obvious. So the reason

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Speaker 4: nobody else did it first was it was hard to do.

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Speaker 4: It was hard to make happen. And technology has always

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Speaker 4: come in these technological systems if they're worthwhile technologies. Right.

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Speaker 4: So the personal computer didn't change the world on its own, right.

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Speaker 4: It changed the world alongside the Internet, you know, alongside

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Speaker 4: a bunch of technologies that formed the system. So the

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Speaker 4: hard part here was building the system, not the individual technologies.

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Speaker 4: And this is true I think of computers as well.

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Speaker 4: The first microprocessors weren't considered revolutionary. Intel didn't consider the

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Speaker 4: four thousand and four revolutionary. They considered it evolutionary. The

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Speaker 4: engineers have said this, And it wasn't revolutionary until people

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Speaker 4: put it to use in ways that they didn't anticipate.

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Speaker 2: Actually, can we go. I didn't know that, like, I

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Speaker 2: hadn't really thought about that with Intel that at the

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Speaker 2: time it didn't feel to them that it wasn't a

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Speaker 2: revolutionary technology. That's sort of mind blowing.

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Speaker 4: It is, right. This is I think from Michael Malone's

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Speaker 4: The Intel Trinity of the book. You know, he interviewed

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Speaker 4: a bunch of Intel engineers and he said, you know,

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Speaker 4: like they thought they were building a better chip set

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Speaker 4: to build pocket calculators or desk calculators. I just say so,

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Speaker 4: they had a client, Busycom, who wanted to build a

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Speaker 4: better desktop calculator with calculators were big back then in

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Speaker 4: nineteen seventy ish, and one of the engineers said, well,

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Speaker 4: why do we keep building custom chip sets for each

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Speaker 4: different calculator. Why don't we just build a chipset that

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Speaker 4: we can customize the software and change what it does.

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Speaker 4: And Intel is kind of like eh, and Busycom was like, okay,

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Speaker 4: we'll pay for that. And then Busycom actually tried to

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Speaker 4: back out and they gave the rights back to Intel.

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Speaker 4: So Intel owned the rights to this four thousand and four,

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Speaker 4: and then they started selling it and it wasn't Intel.

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Speaker 4: You know, Intel believed at the time it was going

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Speaker 4: to be maybe used for dedicated hardware, you know, can

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Speaker 4: you hardware controllers that kind of thing, not by consumers.

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Speaker 4: So it wasn't until people on the outside said, hey,

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Speaker 4: you know, I love these IBM mainframes or these deck

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Speaker 4: MANI computers. I'd like to have my own, but obviously

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Speaker 4: nobody can afford that. Why don't I just try to

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Speaker 4: build my own? Right, So there's these kind of outside inventors,

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Speaker 4: this permissionless invention, and then it really the real revolution

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Speaker 4: didn't happen until this. You know, everybody's like, oh Intel.

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Speaker 4: It was the six five oh two where the price

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Speaker 4: came down so dramatically that, you know, Steve Wozniak could

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Speaker 4: walk into a computer fair you get some for free

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Speaker 4: and go home and build up a personal computer.

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Speaker 2: It's crazy. I wonder who made the chips for the

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Speaker 2: Commodore sixty four computer that I had.

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Speaker 4: They were six five o twos. Those were Intel, that's

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Speaker 4: you know, they weren't until there were Moss technologies. Oh

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Speaker 4: got it, got it, those are the cheap ones.

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Speaker 2: Then yeah, wait, what year was that when you had well,

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Speaker 2: I had I think I like learned some basic and

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Speaker 2: did some coding on it. I would have said maybe

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Speaker 2: nineteen eighty eight, nineteen eighty seven somewhere around there, made

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Speaker 2: a few I missed those days. It's crazy that I

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Speaker 2: didn't be. Can I just say I sometimes when I

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Speaker 2: think about my life path, like how did I not

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Speaker 2: end up like a tech guy? Because I was like

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Speaker 2: very into math. I was like one of those people

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Speaker 2: who had a computer when I was six or seven.

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Speaker 3: In nineteen eighty seven, you were coding you were seven

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Speaker 3: years old.

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Speaker 2: Yeah, I got that. My dad got me this magazine

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Speaker 2: that just it was very crazy. There was literally just

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Speaker 2: sent you pages of code and then you just typed

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Speaker 2: it in and you could like make a video game.

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Speaker 2: I was doing that at seven. I could be like

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Speaker 2: one of those like who's per s gave of a

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Speaker 2: computer when he was seven? It anyway, and it's not

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Speaker 2: too late.

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Speaker 4: It's not too late.

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Speaker 3: Yeah, it isn't too late. Well, maybe it is too

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Speaker 3: late because now we have AI doing all the coding, right, Okay,

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Speaker 3: just so I understand when it comes to I guess

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Speaker 3: the advantages of the incumbents to actually monetizing new technology

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Speaker 3: or benefiting from new technology is the moat around their

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Speaker 3: business the network and their sort of role in the network,

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Speaker 3: or is it the vast amounts of cash they have

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Speaker 3: and the ability to sort of roll out massive investment

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Speaker 3: to capture that value.

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Speaker 4: I think it's the latter, right, So anybody can build

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Speaker 4: a foundation model, right if you have the money. I mean,

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Speaker 4: the technology is not mysterious. It doesn't feel like the

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Speaker 4: technology is really changing very quickly anymore. And of course

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Speaker 4: I don't have insight into what's happening inside of open ai,

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Speaker 4: but looking at it over the past couple of years,

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Speaker 4: it's the same thing, but slightly better. It's evolutionary now, right.

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Speaker 4: The first part was revolutionary and now it's evolutionary. So

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Speaker 4: if you wanted to build one, you could build one.

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Speaker 4: And I have friends who are running them on their

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Speaker 4: laptops very slowly, but it's possible. So now the question

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Speaker 4: is do you have enough cash to build the data centers,

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Speaker 4: to buy all the chips, to build something that is

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Speaker 4: large enough that when you train it, it does something useful,

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Speaker 4: And it's just a question of having that the authority

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Speaker 4: in the market to be able to raise that money.

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Speaker 2: By the way, speaking of ideas that were sort of

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Speaker 2: really obvious that took a while. I'm always blown away that,

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Speaker 2: like how long it took them to put wheels on luggage.

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Speaker 2: I don't think anyone like made got super rich on that.

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Speaker 2: But when I was a kid, I remember like we

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Speaker 2: had these big suitcases, and that's the most obvious thing.

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Speaker 2: It took a while. Anyway, I don't think anyone goes.

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Speaker 3: Technology is still not perfected, as you know, because you've

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Speaker 3: been in airports with me and the wheels on my

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Speaker 3: luggage are broken.

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Speaker 2: But I don't think anyone got like super rich off

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Speaker 2: of wheels. That just seems like an that was just

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Speaker 2: sitting there.

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Speaker 4: You know.

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Speaker 2: I want to jump ahead actually in the conversation a

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Speaker 2: little bit because I know to forget this point. But

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Speaker 2: this is something I've become a little obsessed with, which

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Speaker 2: is I've been meaning to ask a VC about this,

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Speaker 2: which is that there seems to be this blurring of

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Speaker 2: private and public markets in various ways, retail participation in

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Speaker 2: private markets, et cetera. For the VC, in my mind,

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Speaker 2: I feel like the exit was the IPO or the acquisition, right,

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Speaker 2: so you buy do vcs these days have to think

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Speaker 2: a little bit more about market timing and selling early.

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Speaker 2: So someone who was an early investor in open ai

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Speaker 2: or whatever you know, in the past, they might have

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Speaker 2: just held or then sell at the IPO or the acquisition.

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Speaker 2: It is probably going to happen in an open AI's case,

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Speaker 2: they're too big to be acquired. But to vcs and

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Speaker 2: in your experience these days, have to think a little

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Speaker 2: bit more about this idea of selling early, timing the exit.

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Speaker 4: Well, I think vcs always had to think about timing.

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Speaker 4: You know, I've done pretty well in VC, and I

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Speaker 4: attributed it entirely to being lucky at starting investing at

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Speaker 4: the right time. So the first time around, I was

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Speaker 4: starting in ninety seven, which any you could make money in.

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Speaker 4: The second time around, I started in two thousand and seven,

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Speaker 4: which again it was just two thousand and eight. It

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Speaker 4: was just an easy time to buy in.

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Speaker 2: But I mean, the timing of the sale is that

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Speaker 2: something that where in the past it might have been

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Speaker 2: automatic exit, now now is not the case.

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Speaker 4: Yeah. I wrote this thing about VC in the nineteen

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Speaker 4: eighties a long time ago on the blog. You can

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Speaker 4: find it. And the thing because nobody talks about the

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Speaker 4: nineteen eighties, right, there's plenty of VC in the eighties,

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Speaker 4: but nobody talks about it. People talk about the sixties

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Speaker 4: and in the nineties. So I was like, all right,

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Speaker 4: what happened. Then. One of the interesting things about it

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Speaker 4: is there were the IPO windows then where you know,

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Speaker 4: nineteen eighty three the IPO window opened a bunch of

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Speaker 4: companies on public and then it closed again, and you

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Speaker 4: can see it in the numbers when people in public.

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Speaker 4: So people always had to think about timing. The IPO

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Speaker 4: is obviously the best exit because you want to sell

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Speaker 4: to the greatest fool, and nobody's greater fool than the public, right,

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Speaker 4: So you look for the IPO window to open. When

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Speaker 4: you can't, you have to sell to somebody else. You know.

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Speaker 4: Vcs have this problem of their limited fund life. So

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Speaker 4: I look at my portfolio. I'm an really early investor

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Speaker 4: or have been a really early investor, so I'll look

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Speaker 4: at companies and say, oh, I invested ten years ago.

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Speaker 4: They're going to have to sell, you know, it's the

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Speaker 4: So people look for the IPO window, but if they

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Speaker 4: can't find it, they have to sell somewhere else.

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Speaker 3: Well, how much of the money flowing into AI startups

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Speaker 3: now is just the expectation that a bunch of these

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Speaker 3: little companies are eventually going to get bought by larger incumbents,

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Speaker 3: and basically you're going to have consolidation and you will

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Speaker 3: get that exit.

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Speaker 4: I don't think anybody can predict when the IPO opens.

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Speaker 4: I mean, I wish I could, but I don't think

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Speaker 4: I've never seen anybody even say they could predict when

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Speaker 4: the IPO window opens. So I think a smart VC

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Speaker 4: invests in a company that can become self sustaining to

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Speaker 4: some degree, and then you wait for the timing to come.

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Speaker 4: You don't invest and say I'm gonna flip this in

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Speaker 4: three years now. Yeah. The other problem is vcs don't

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Speaker 4: invest in all these IA companies saying a bunch of

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Speaker 4: them are going to become valuable. They invest saying one

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Speaker 4: of these is going to be come right.

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Speaker 3: The lottery ticket theory, yeah, the power law.

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Speaker 2: Speaking of the IPO window, I'm never totally satisfied by

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Speaker 2: a lot of the explanations for the drop off in

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Speaker 2: IPOs generally. Do I know there was that law passed

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Speaker 2: in two thousand and one.

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Speaker 4: Or what was it, sins ox Yes.

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Speaker 2: Surbins ox Ley, and I get that law.

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Speaker 3: That law law that everyone hated for a really long time.

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Speaker 2: I don't know, it doesn't. But then you see, like

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Speaker 2: you know, twenty twenty one there's like a billion garbage

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Speaker 2: companies went public via SPACs et cetera. How much is

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Speaker 2: it about? Okay, there are some disadvantages to being public

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Speaker 2: versus there's just so much more private capital out there

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Speaker 2: such that the imperative to perhaps ever go public and

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Speaker 2: it's liquid and the rounds and like, what do you

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Speaker 2: attribute There are these big companies that are a private

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Speaker 2: stripe but open AI and onanthropic that choose to stay private.

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Speaker 2: What do you think the main reason for that is?

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Speaker 4: Well, it's because they can, right, I mean, being a

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Speaker 4: public company is no, it's no party, right, I mean

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Speaker 4: it kind of sucks being a public company.

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Speaker 2: What is it about it?

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Speaker 4: What sucks? Well, you have to tell everybody what you're

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Speaker 4: doing every three months. Yeah, that's you know, and then

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Speaker 4: they come back and complain about it. So sorry, I'm

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Speaker 4: being a little facetious, but it is. It's hard to

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Speaker 4: be a public company. Everybody, you know, anybody who runs

434
00:19:37,119 --> 00:19:38,479
Speaker 4: a public company will tell you they spend a lot

435
00:19:38,520 --> 00:19:40,840
Speaker 4: of time being a public company if they're running the company.

436
00:19:41,040 --> 00:19:43,439
Speaker 4: So that's taking away from actually running the company. I

437
00:19:43,440 --> 00:19:45,720
Speaker 4: think the flip side is your liquid and that's yeah.

438
00:19:45,840 --> 00:19:48,080
Speaker 4: You know, So if you can stay private, why wouldn't

439
00:19:48,119 --> 00:19:50,720
Speaker 4: you stay private? Or if you can go public and

440
00:19:50,760 --> 00:19:53,400
Speaker 4: retain control of the company, you know, like Henry Ford

441
00:19:53,440 --> 00:19:55,560
Speaker 4: or Mark Zuckerberg, then why wouldn't you do that? But

442
00:19:56,000 --> 00:19:58,240
Speaker 4: I think it's because there is so much late stage money.

443
00:19:58,520 --> 00:20:00,720
Speaker 4: This isn't necessarily a good thing. It's because there's so

444
00:20:00,800 --> 00:20:03,719
Speaker 4: much money out there that's not being invested in more

445
00:20:03,760 --> 00:20:07,520
Speaker 4: revolutionary technologies earlier. With all this money being invested in AI,

446
00:20:07,640 --> 00:20:09,159
Speaker 4: you may wonder if people are still going to want

447
00:20:09,200 --> 00:20:13,080
Speaker 4: to invest late stage stripe or the analogous stripe might

448
00:20:13,080 --> 00:20:14,200
Speaker 4: be making money now, I'm not sure.

449
00:20:15,800 --> 00:20:19,120
Speaker 3: I know you brought up previous historic analogies like VC

450
00:20:19,400 --> 00:20:21,840
Speaker 3: in the nineteen eighties, but just to focus on the

451
00:20:21,840 --> 00:20:24,040
Speaker 3: one that everyone else seems to be focused on at

452
00:20:24,040 --> 00:20:25,840
Speaker 3: the moment, which is the dot com bubble in the

453
00:20:25,880 --> 00:20:28,879
Speaker 3: early two thousands. What are the key differences you're seeing

454
00:20:28,880 --> 00:20:33,320
Speaker 3: in terms of the VC and financing environment now versus

455
00:20:33,560 --> 00:20:35,000
Speaker 3: twenty or twenty five years ago.

456
00:20:35,520 --> 00:20:37,320
Speaker 4: I think the key difference is that most of the

457
00:20:37,359 --> 00:20:41,280
Speaker 4: money is coming from people who aren't looking for much risk, right,

458
00:20:41,320 --> 00:20:44,960
Speaker 4: So I mean open ai is primarily funded by bigger companies. Right,

459
00:20:45,080 --> 00:20:47,439
Speaker 4: most of their money is coming from large companies. What

460
00:20:47,560 --> 00:20:50,720
Speaker 4: happens if open ai gets hit by a bus? Right,

461
00:20:51,400 --> 00:20:53,720
Speaker 4: So Microsoft's at a bunch of money. A whole bunch

462
00:20:53,760 --> 00:20:56,119
Speaker 4: of big companies are at a bunch of money. I

463
00:20:56,160 --> 00:20:58,680
Speaker 4: don't think much happens to the economy. I think, which

464
00:20:58,720 --> 00:21:01,159
Speaker 4: is different than in the bubble, where a lot of

465
00:21:01,160 --> 00:21:03,199
Speaker 4: consumers were in it. A lot of consumers were in

466
00:21:03,240 --> 00:21:06,320
Speaker 4: it leveraged right, the buying and margin or whatever. A

467
00:21:06,320 --> 00:21:09,320
Speaker 4: lot of people had options, right, people employees, and they

468
00:21:09,320 --> 00:21:14,080
Speaker 4: were spending the money from their options before they were licking.

469
00:21:14,400 --> 00:21:15,920
Speaker 4: You know, it was there was a much I think

470
00:21:15,920 --> 00:21:16,760
Speaker 4: it was a different dynamic.

471
00:21:16,800 --> 00:21:18,840
Speaker 3: But the wealth effect, I don't know.

472
00:21:19,359 --> 00:21:21,680
Speaker 2: I think this is a contrarian take on your part,

473
00:21:21,680 --> 00:21:25,040
Speaker 2: because you hear a lot about I mean, in two dimensions.

474
00:21:25,160 --> 00:21:28,280
Speaker 2: You hear a lot about the direct wealth effect from

475
00:21:28,560 --> 00:21:31,720
Speaker 2: people's exposure to the stock market, which AI is a

476
00:21:31,720 --> 00:21:35,359
Speaker 2: big part of the story. And then you also hear about,

477
00:21:35,480 --> 00:21:38,480
Speaker 2: of course, the sort of real economy effects through all

478
00:21:38,520 --> 00:21:41,200
Speaker 2: of the spending, which we will get into on the

479
00:21:41,280 --> 00:21:45,640
Speaker 2: data centers and the caterpillar, the turbines for the gas generation,

480
00:21:45,920 --> 00:21:48,560
Speaker 2: et cetera. I think many people would say there is

481
00:21:48,600 --> 00:21:51,960
Speaker 2: a lot right now riding on the health and the

482
00:21:52,000 --> 00:21:54,119
Speaker 2: sustainability of this particular sector.

483
00:21:54,840 --> 00:21:58,119
Speaker 4: So I think we can separate the companies like Microsoft

484
00:21:58,240 --> 00:22:01,840
Speaker 4: and Video. Are they over valued because of this? Maybe

485
00:22:02,000 --> 00:22:05,000
Speaker 4: doesn't make a huge difference to the economy. Probably not,

486
00:22:05,080 --> 00:22:08,840
Speaker 4: I don't think so. And the companies who are spending

487
00:22:08,880 --> 00:22:12,840
Speaker 4: money on infrastructure like building data centers, building power generation plants,

488
00:22:13,119 --> 00:22:15,760
Speaker 4: those things I think are probably overbuilt or not so

489
00:22:15,840 --> 00:22:19,399
Speaker 4: much overbuilt as they are built. And I think in

490
00:22:19,400 --> 00:22:21,000
Speaker 4: ten years you're gonna have a lot of extra compute,

491
00:22:21,040 --> 00:22:24,000
Speaker 4: a lot of extra power generation, and people will be

492
00:22:24,040 --> 00:22:26,720
Speaker 4: able to use that for other things. It'll also drive

493
00:22:26,760 --> 00:22:28,480
Speaker 4: down the price of just using AI.

494
00:22:28,560 --> 00:22:31,960
Speaker 2: Probably, yeah, Jevin's paradox bro is gonna be out back

495
00:22:32,000 --> 00:22:35,119
Speaker 2: and forth. All right, So AI, where are we? You

496
00:22:35,160 --> 00:22:37,600
Speaker 2: talk about cycles? And I think you use word that

497
00:22:37,640 --> 00:22:40,080
Speaker 2: I hadn't ereruption? What was the word you use? So well,

498
00:22:40,119 --> 00:22:42,920
Speaker 2: I'll tell us how you see cycles in what cycle

499
00:22:42,920 --> 00:22:43,119
Speaker 2: we're in?

500
00:22:43,200 --> 00:22:44,879
Speaker 4: Right? Right? So A lot of this is based on KARLOA.

501
00:22:45,000 --> 00:22:47,680
Speaker 4: Perez's work, which is pretty familiar to the ventro capitalists

502
00:22:47,680 --> 00:22:49,760
Speaker 4: and your audience. I'm sure. She wrote a book called

503
00:22:49,800 --> 00:22:54,760
Speaker 4: Technological Revolutions and Financial Capital where she explains the dynamics

504
00:22:54,800 --> 00:22:57,639
Speaker 4: behind the Kondrati of waves that the Chumpeter talks about.

505
00:22:58,000 --> 00:23:00,280
Speaker 4: So she has this theory about why these happen. And

506
00:23:00,760 --> 00:23:04,400
Speaker 4: you look at the Industrial revolution, the Second industry Revolution,

507
00:23:04,800 --> 00:23:08,320
Speaker 4: you can see these waves of technology technological systems happening

508
00:23:08,320 --> 00:23:11,160
Speaker 4: through the economy where they kind of start out, they

509
00:23:11,160 --> 00:23:14,640
Speaker 4: grow really rapidly, and then there's usually some sort of adjustment,

510
00:23:14,720 --> 00:23:16,879
Speaker 4: some sort of bubble bursting, and then things kind of

511
00:23:16,960 --> 00:23:18,879
Speaker 4: level out and then start to plateau, and then a

512
00:23:18,880 --> 00:23:21,600
Speaker 4: new one starts. And this is people have noticed this

513
00:23:21,920 --> 00:23:24,920
Speaker 4: since at least Kentdrati nineteen twenty six. She has a

514
00:23:25,320 --> 00:23:28,120
Speaker 4: mechanism for explaining it, and her mechanism has these four phases.

515
00:23:28,480 --> 00:23:30,680
Speaker 4: The first phase is eruption, which she spells with an I,

516
00:23:31,160 --> 00:23:33,160
Speaker 4: which I think is actually in the dictionary as a word.

517
00:23:33,440 --> 00:23:34,960
Speaker 4: I don't know what the difference is between that and

518
00:23:35,000 --> 00:23:38,520
Speaker 4: the eruption, but it is the part where smart right,

519
00:23:38,680 --> 00:23:41,560
Speaker 4: So yeah, keep saying that it is the start. It's

520
00:23:41,600 --> 00:23:44,119
Speaker 4: when people have invented something and it is starting to

521
00:23:44,160 --> 00:23:46,240
Speaker 4: catch on, but it hasn't caught on yet. There's a

522
00:23:46,280 --> 00:23:48,399
Speaker 4: lot of people saying, is this the future? Is it not?

523
00:23:48,920 --> 00:23:51,880
Speaker 4: You look at personal computers in the nineteen late nineteen seventies,

524
00:23:51,920 --> 00:23:55,200
Speaker 4: early nineteen eighties, and maybe even before IBM got involved,

525
00:23:55,480 --> 00:23:58,400
Speaker 4: and people didn't think personal computers were Some people thought

526
00:23:58,400 --> 00:24:00,760
Speaker 4: they were their future. And then you look back at

527
00:24:01,000 --> 00:24:03,400
Speaker 4: computer history. Everybody talks about the people who did think that.

528
00:24:03,600 --> 00:24:05,399
Speaker 4: They don't talk about the other ninety nine point nine

529
00:24:05,400 --> 00:24:07,879
Speaker 4: percent of smart people who said they weren't. This is

530
00:24:07,880 --> 00:24:09,720
Speaker 4: the eruption phase where there's a lot of uncertainty about

531
00:24:09,720 --> 00:24:12,320
Speaker 4: where this technology will go. It's starting to build connections

532
00:24:12,320 --> 00:24:16,560
Speaker 4: to other technologies, starting to attract money, attract smart people

533
00:24:16,800 --> 00:24:20,119
Speaker 4: because it's interesting and it might actually change things. So

534
00:24:20,200 --> 00:24:23,200
Speaker 4: this is the beginning. And I think the connection here

535
00:24:23,280 --> 00:24:25,600
Speaker 4: to AI is people wonder if we're in the eruption

536
00:24:25,680 --> 00:24:29,119
Speaker 4: phase of AI or not. Is this the start of

537
00:24:29,160 --> 00:24:32,919
Speaker 4: a new technological revolution phase? I think we're not. I

538
00:24:32,920 --> 00:24:34,439
Speaker 4: think we're in. I think this is the end of

539
00:24:34,480 --> 00:24:38,480
Speaker 4: the information computer technology wave, the end of the computer wave. Right,

540
00:24:38,600 --> 00:24:41,240
Speaker 4: I think it is. This is the culmination of the

541
00:24:41,240 --> 00:24:44,640
Speaker 4: computer wave. Right, Why did we build computers. We build

542
00:24:44,640 --> 00:24:47,320
Speaker 4: computers to help us think better. Right, this is what

543
00:24:47,359 --> 00:24:50,080
Speaker 4: they're for, their knowledge machines. So now we've kind of

544
00:24:50,080 --> 00:24:53,720
Speaker 4: reached the natural end stage what they do. They're smart,

545
00:24:53,760 --> 00:24:56,159
Speaker 4: machines are smarter. So I think this is not a

546
00:24:56,200 --> 00:24:58,119
Speaker 4: new technological revolution. I think it's the end of the

547
00:24:58,160 --> 00:24:59,399
Speaker 4: old one. And this is why I compare it to

548
00:24:59,400 --> 00:25:04,240
Speaker 4: container is thea because the previous wave was automobiles mass production,

549
00:25:04,920 --> 00:25:09,040
Speaker 4: and starting in nineteen fifteen or so up until nineteen

550
00:25:09,080 --> 00:25:12,879
Speaker 4: seventy was the previous wave, and containerization was squarely at

551
00:25:12,880 --> 00:25:15,120
Speaker 4: the end of that wave, and it was really kind

552
00:25:15,119 --> 00:25:17,880
Speaker 4: of pulling together the technologies of that wave into something

553
00:25:17,920 --> 00:25:20,040
Speaker 4: that increased productivity, right.

554
00:25:19,920 --> 00:25:22,080
Speaker 3: Like the final step of the global trade and I

555
00:25:22,080 --> 00:25:23,440
Speaker 3: guess mobility revolution.

556
00:25:23,600 --> 00:25:24,240
Speaker 4: Yeah exactly.

557
00:25:24,359 --> 00:25:28,920
Speaker 3: Okay, so how do you react as an angel investor?

558
00:25:29,480 --> 00:25:32,600
Speaker 3: Are you at the stage where you're looking for I

559
00:25:32,600 --> 00:25:35,400
Speaker 3: guess the downstream winners, like the companies that are going

560
00:25:35,440 --> 00:25:38,639
Speaker 3: to be able to apply or use AI most effectively,

561
00:25:38,760 --> 00:25:41,680
Speaker 3: or how are you actually deploying all these thoughts in

562
00:25:41,800 --> 00:25:46,119
Speaker 3: terms of your own investment strategy. I retired, Okay, you know,

563
00:25:46,240 --> 00:25:47,680
Speaker 3: I standing it out and I.

564
00:25:47,600 --> 00:25:50,240
Speaker 4: Said, look, how am I going to invest in foundation modes, right,

565
00:25:50,480 --> 00:25:54,199
Speaker 4: I don't have a billion dollar fund. I don't think that.

566
00:25:54,640 --> 00:25:56,800
Speaker 4: You know, if you look at the big winners from

567
00:25:57,000 --> 00:25:59,479
Speaker 4: the early big winners from globalization, the IKEA is right.

568
00:25:59,520 --> 00:26:03,720
Speaker 4: I mean it was a Scandinavian company until containerization, and

569
00:26:03,760 --> 00:26:07,280
Speaker 4: then they became a global powerhouse, a hugely successful company.

570
00:26:07,640 --> 00:26:11,159
Speaker 4: But they didn't need outside money. You know. Comprade, I

571
00:26:11,200 --> 00:26:14,160
Speaker 4: think he borrowed like a couple thousand dollars to start

572
00:26:14,160 --> 00:26:16,320
Speaker 4: that company or to get that company to buy some inventory.

573
00:26:16,560 --> 00:26:19,560
Speaker 4: He never took outside You look at Walmart, which had

574
00:26:19,560 --> 00:26:22,080
Speaker 4: been around already. It was an incumbent and used this

575
00:26:22,160 --> 00:26:24,119
Speaker 4: kind of globalization to bring a lot more variety of

576
00:26:24,160 --> 00:26:26,600
Speaker 4: products to the stores. They didn't need outside money to

577
00:26:26,640 --> 00:26:27,680
Speaker 4: do that. Yeah.

578
00:26:27,720 --> 00:26:30,399
Speaker 3: I guess if you're at Kia and suddenly you're flat

579
00:26:30,400 --> 00:26:33,040
Speaker 3: packing everything and shipping it in containers, and that's your

580
00:26:33,040 --> 00:26:36,600
Speaker 3: big innovation. It's a money saving technology, right, So you

581
00:26:36,600 --> 00:26:39,560
Speaker 3: don't actually have to raise new capital in order to

582
00:26:39,600 --> 00:26:40,600
Speaker 3: flat pack everything.

583
00:26:40,720 --> 00:26:43,520
Speaker 4: Yeah, exactly, Okay, they were already flat packing.

584
00:26:43,680 --> 00:26:47,320
Speaker 2: Yeah. Your mention of the Walmarts and targets of the world,

585
00:26:47,359 --> 00:26:48,639
Speaker 2: it was like in your essay it was like a

586
00:26:48,760 --> 00:26:52,119
Speaker 2: sort of very light bulb thing. It's like, yeah, I

587
00:26:52,119 --> 00:26:53,879
Speaker 2: don't know. I guess we'd take them for granted. But

588
00:26:53,920 --> 00:26:57,800
Speaker 2: they're clear like massive containerization winners. The skill that they

589
00:26:57,960 --> 00:27:01,560
Speaker 2: succeeded is impossible to fathom in some prior era.

590
00:27:01,440 --> 00:27:04,480
Speaker 3: Of Walmart is a logistics company and changed my mind.

591
00:27:04,320 --> 00:27:08,520
Speaker 2: Literally and many of you literally literally literally that. But

592
00:27:08,600 --> 00:27:11,880
Speaker 2: it doesn't feel like with AI that the equivalent has

593
00:27:11,920 --> 00:27:15,160
Speaker 2: emerged yet. Right, We're still at the age where people

594
00:27:15,160 --> 00:27:18,840
Speaker 2: are building the container deployed. But the company that exists

595
00:27:18,920 --> 00:27:22,359
Speaker 2: and is massive that couldn't exist prior to a like

596
00:27:22,680 --> 00:27:25,440
Speaker 2: does not feel does it We haven't seen that yet.

597
00:27:25,560 --> 00:27:26,720
Speaker 4: Well, you gotta be a little patient.

598
00:27:26,960 --> 00:27:29,000
Speaker 2: No, no, but like seriously.

599
00:27:28,720 --> 00:27:31,920
Speaker 4: Iikia so contained. The first container ship sailed in I

600
00:27:31,920 --> 00:27:34,840
Speaker 4: think nineteen fifty six. Okay, so when did Akia become

601
00:27:34,840 --> 00:27:37,320
Speaker 4: a global powerhouse? It really wasn't until the nineteen seventies

602
00:27:37,359 --> 00:27:39,360
Speaker 4: that they started to expand that this is really important.

603
00:27:39,440 --> 00:27:42,960
Speaker 2: They really take a while. Where would you expect it

604
00:27:43,040 --> 00:27:46,640
Speaker 2: to show up? Like what industries would you expect because

605
00:27:46,680 --> 00:27:50,119
Speaker 2: obviously retail existed for a long time, furniture existed for

606
00:27:50,160 --> 00:27:52,640
Speaker 2: a long time. Then you get these Bahamas. Are there

607
00:27:52,680 --> 00:27:57,399
Speaker 2: industries that you think are right to produce very torture

608
00:27:57,440 --> 00:28:00,040
Speaker 2: analogy the Ikea of the AI way.

609
00:28:00,640 --> 00:28:02,880
Speaker 4: Well, I think they have to be knowledge intensive industries,

610
00:28:03,040 --> 00:28:04,680
Speaker 4: right right, I mean this is what AI is doing,

611
00:28:04,760 --> 00:28:07,120
Speaker 4: what it is making more efficient. I mean I think

612
00:28:07,119 --> 00:28:09,200
Speaker 4: there's people been asking me like, well, then what should

613
00:28:09,200 --> 00:28:12,240
Speaker 4: I invest in? And yeah, tell us, well, you know,

614
00:28:12,480 --> 00:28:16,520
Speaker 4: I think, well, so give us. I've been thinking that myself.

615
00:28:16,800 --> 00:28:18,960
Speaker 4: But the answer is really that as an investor, I

616
00:28:18,960 --> 00:28:21,639
Speaker 4: don't decide what to invest in. I evaluate opportunities that

617
00:28:21,640 --> 00:28:23,760
Speaker 4: come to me. And so I have built a box

618
00:28:23,800 --> 00:28:26,000
Speaker 4: in which you can evaluate opportunities. Right, they have to

619
00:28:26,000 --> 00:28:28,280
Speaker 4: look like this. They have to look like an Ikea.

620
00:28:28,480 --> 00:28:29,840
Speaker 4: And if I Kia came to you and said I

621
00:28:29,920 --> 00:28:32,040
Speaker 4: needed money at that time, you should have said, okay,

622
00:28:32,080 --> 00:28:34,640
Speaker 4: I can see how shipping containerization is going to make

623
00:28:34,640 --> 00:28:37,720
Speaker 4: you a much larger company. Where nobody else seemed to

624
00:28:37,880 --> 00:28:40,720
Speaker 4: see that, right, Certainly the furniture makers in North Carolina

625
00:28:40,760 --> 00:28:43,080
Speaker 4: didn't see it. So I think this is the box

626
00:28:43,120 --> 00:28:45,840
Speaker 4: that you evaluate things in. And as a long time investor,

627
00:28:45,880 --> 00:28:48,120
Speaker 4: I'm used to evaluating and I trying not to come

628
00:28:48,200 --> 00:28:50,240
Speaker 4: up with ideas, you know, that said it has to

629
00:28:50,240 --> 00:28:52,680
Speaker 4: be a knowledge intensive industry. And I think something that

630
00:28:52,720 --> 00:28:54,760
Speaker 4: I said in the essay, which I wish I had

631
00:28:54,800 --> 00:28:57,960
Speaker 4: said more about, was the companies that tried to use

632
00:28:58,360 --> 00:29:01,520
Speaker 4: shipping containerization to cut costs so that they can increase

633
00:29:01,600 --> 00:29:05,800
Speaker 4: margins did poorly. Oh this is key, Whereas the companies

634
00:29:05,800 --> 00:29:09,320
Speaker 4: that use the efficiencies and passed the efficiencies onto the

635
00:29:09,320 --> 00:29:13,200
Speaker 4: consumers so that they could become larger became larger. Right.

636
00:29:13,240 --> 00:29:15,080
Speaker 4: I mean this is you look at these people saying, oh,

637
00:29:15,080 --> 00:29:17,240
Speaker 4: we have AI, We're going to fire people. I mean,

638
00:29:17,240 --> 00:29:18,960
Speaker 4: that's I think is the exact wrong move. And I

639
00:29:19,000 --> 00:29:21,880
Speaker 4: think it's probably just every you know, every new thing

640
00:29:21,920 --> 00:29:23,960
Speaker 4: comes along, people like, oh it's we're going to fire people.

641
00:29:24,160 --> 00:29:25,880
Speaker 4: You know, it's just an excuse. But but if you're

642
00:29:25,880 --> 00:29:29,040
Speaker 4: firing people because of AI, you're doing it wrong. Right.

643
00:29:29,120 --> 00:29:31,200
Speaker 4: You should be using AI to say I can use

644
00:29:31,240 --> 00:29:33,320
Speaker 4: my people to do more. I can grow my company,

645
00:29:33,360 --> 00:29:36,200
Speaker 4: I can vary my products, I can take more market share.

646
00:29:36,480 --> 00:29:38,600
Speaker 3: So the value goes to the consumer. And I guess

647
00:29:38,680 --> 00:29:42,280
Speaker 3: you capture the value by selling more right, more knowledge?

648
00:29:42,360 --> 00:29:45,640
Speaker 4: Yeah, I mean I think Walmart never tried to maximize margins.

649
00:29:46,240 --> 00:29:47,760
Speaker 3: They it was it was volume.

650
00:29:47,840 --> 00:30:08,240
Speaker 2: Yeah, Okay, from a consumer standpoint, why do I care

651
00:30:08,360 --> 00:30:13,120
Speaker 2: if your workflow internally at your company is become more

652
00:30:13,120 --> 00:30:15,920
Speaker 2: efficient things AI, either the product is better or new

653
00:30:15,960 --> 00:30:16,360
Speaker 2: or something.

654
00:30:16,520 --> 00:30:16,680
Speaker 4: You know.

655
00:30:16,680 --> 00:30:18,240
Speaker 2: I was thinking about this. I don't mean to like

656
00:30:18,240 --> 00:30:21,960
Speaker 2: pick on anyone, but situating like open door meme Stock

657
00:30:22,040 --> 00:30:24,160
Speaker 2: they have new CEO. He sent out this memo. He's

658
00:30:24,160 --> 00:30:27,239
Speaker 2: like everyone has to do start using A or it's

659
00:30:27,320 --> 00:30:30,160
Speaker 2: clearly a press release in the form of an internal

660
00:30:30,200 --> 00:30:33,080
Speaker 2: memo because it got it I think waving.

661
00:30:32,840 --> 00:30:34,120
Speaker 3: A flag going us.

662
00:30:34,960 --> 00:30:38,360
Speaker 2: It's like, yeah, but like, does the economics of buying

663
00:30:38,400 --> 00:30:41,480
Speaker 2: homes via whatever get better? Like does it actually make

664
00:30:41,520 --> 00:30:44,800
Speaker 2: the business better? This strikes me as very interesting, this

665
00:30:44,960 --> 00:30:48,400
Speaker 2: idea that like, it's not gonna it's just not that exciting,

666
00:30:48,560 --> 00:30:52,000
Speaker 2: especially any sort of customer oriented company, which I guess

667
00:30:52,080 --> 00:30:54,920
Speaker 2: is all companies. The fact that AI has become part

668
00:30:54,920 --> 00:30:57,440
Speaker 2: of their workflow. It's the electric knife effect, right, So

669
00:30:57,880 --> 00:30:59,760
Speaker 2: I don't know that effect we might.

670
00:30:59,640 --> 00:31:01,760
Speaker 4: Just call that, but I mean electric knives are one

671
00:31:01,800 --> 00:31:03,800
Speaker 4: of the fastest growing consumer products in the late nineteen

672
00:31:03,840 --> 00:31:07,400
Speaker 4: sixties because people are like, oh, we are electrifying things,

673
00:31:07,240 --> 00:31:10,640
Speaker 4: like let's electrify the knife and within like three years

674
00:31:10,640 --> 00:31:12,680
Speaker 4: they were in some massive percentage of households, like eighty

675
00:31:12,720 --> 00:31:15,840
Speaker 4: percent of American households had an electric knife and things

676
00:31:15,880 --> 00:31:18,640
Speaker 4: like you know, the blender didn't get adopted that quickly.

677
00:31:18,680 --> 00:31:21,840
Speaker 4: But who has an electric knife now? Right? It's I

678
00:31:21,840 --> 00:31:24,440
Speaker 4: think people take whatever it is the technology is and

679
00:31:24,480 --> 00:31:27,000
Speaker 4: say this is everything. I mean, back in you know,

680
00:31:27,120 --> 00:31:29,960
Speaker 4: the early two thousands, late nineteen nineties, every company was

681
00:31:29,960 --> 00:31:32,520
Speaker 4: an Internet company. Right. Do people walk around saying that's

682
00:31:32,520 --> 00:31:34,480
Speaker 4: an Internet company? Now are you an Internet company? I

683
00:31:34,520 --> 00:31:37,720
Speaker 4: mean everybody's an Internet company. Everybody uses it. It's the baseline.

684
00:31:37,760 --> 00:31:41,200
Speaker 4: That's I think the what a revolutionary technology does is

685
00:31:41,320 --> 00:31:43,880
Speaker 4: it becomes part of everything. So, yeah, you're right. Consumers

686
00:31:43,920 --> 00:31:46,360
Speaker 4: don't care that your you know, your lawyer's using AI.

687
00:31:46,440 --> 00:31:48,560
Speaker 4: They want to make sure that they have good legal advice. Yeah.

688
00:31:48,720 --> 00:31:51,959
Speaker 3: Our producer just messaged Dash saying that he had an

689
00:31:51,960 --> 00:31:54,320
Speaker 3: electric knife. Oh does he have Probably not in the

690
00:31:54,360 --> 00:31:55,080
Speaker 3: nineteen sixties.

691
00:31:55,080 --> 00:31:56,240
Speaker 2: There do you have one?

692
00:31:56,240 --> 00:31:56,440
Speaker 4: Now?

693
00:31:57,600 --> 00:32:01,240
Speaker 2: My mom might have one case that didn't get picked.

694
00:32:01,120 --> 00:32:01,680
Speaker 4: Up on the audio.

695
00:32:01,840 --> 00:32:04,680
Speaker 3: Well, his mom might still have one. Dash, will you

696
00:32:04,680 --> 00:32:06,360
Speaker 3: bring it in for us and we can we can

697
00:32:06,560 --> 00:32:07,160
Speaker 3: use it as a.

698
00:32:07,120 --> 00:32:11,080
Speaker 2: Prop I'm just imagining like going to a restaurant at

699
00:32:11,080 --> 00:32:13,000
Speaker 2: the time it's like, oh, we slice your bread with

700
00:32:13,040 --> 00:32:15,840
Speaker 2: electric knives. That are like how unexciting that would be

701
00:32:15,880 --> 00:32:19,160
Speaker 2: From a consumer stand I don't care, you know.

702
00:32:19,360 --> 00:32:21,880
Speaker 3: Yeah, I guess that's true, but okay, but things are

703
00:32:21,880 --> 00:32:24,640
Speaker 3: different from a shareholder standpoint, right. And one of the

704
00:32:24,680 --> 00:32:28,200
Speaker 3: reasons we see when companies say that they are using

705
00:32:28,280 --> 00:32:31,680
Speaker 3: AI the share price goes up, it's because shareholders expect

706
00:32:31,720 --> 00:32:35,520
Speaker 3: all these easy cost cutting gains. Do we have any

707
00:32:35,560 --> 00:32:38,840
Speaker 3: indication that investors are actually going to be patient and

708
00:32:38,920 --> 00:32:41,719
Speaker 3: wait for I guess the value to spread to the

709
00:32:41,760 --> 00:32:45,280
Speaker 3: consumer or are they just going to demand basically these

710
00:32:45,520 --> 00:32:46,880
Speaker 3: fast cuts.

711
00:32:47,120 --> 00:32:50,320
Speaker 4: Will And I mean, I'm not a stock market investor.

712
00:32:50,400 --> 00:32:53,480
Speaker 4: Yeah I know, I know you're stock marketing, you know.

713
00:32:53,560 --> 00:32:57,280
Speaker 4: I think they have a very short attention span. There's

714
00:32:57,280 --> 00:32:59,000
Speaker 4: a great quote in that article that you see in

715
00:32:59,000 --> 00:33:01,160
Speaker 4: the eighties where the Wall Streetjournal said, you know, there

716
00:33:01,200 --> 00:33:03,240
Speaker 4: was the beginning of the eighties, there was a craze

717
00:33:03,240 --> 00:33:06,719
Speaker 4: and anything that ended with an onyx rightes right, it

718
00:33:06,760 --> 00:33:10,280
Speaker 4: was people were right funny, but it was a short

719
00:33:10,280 --> 00:33:13,000
Speaker 4: lived one because it didn't really deliver. And then people

720
00:33:13,000 --> 00:33:14,320
Speaker 4: were like, all right, let's move on and do something

721
00:33:14,320 --> 00:33:15,920
Speaker 4: else or invest in something else.

722
00:33:16,000 --> 00:33:18,440
Speaker 2: So funny, if I were writing a fiction about the eighties,

723
00:33:18,520 --> 00:33:21,040
Speaker 2: I would immediately I would say, like applied fouxtronics or

724
00:33:21,080 --> 00:33:23,120
Speaker 2: something that. Yeah, right, yeah, I hadn't thought about that.

725
00:33:23,120 --> 00:33:23,680
Speaker 2: That's amazing.

726
00:33:23,920 --> 00:33:27,240
Speaker 4: So I think they want results. But I think it's

727
00:33:27,320 --> 00:33:30,000
Speaker 4: two people are impatient. I mean, I was kidding, but

728
00:33:30,080 --> 00:33:32,080
Speaker 4: I'm not kidding. People are impatient. They want to see

729
00:33:32,120 --> 00:33:34,280
Speaker 4: results today. I don't think AI is going to show

730
00:33:34,320 --> 00:33:38,600
Speaker 4: you the real revolutionary results for a decade. Taking AI

731
00:33:38,720 --> 00:33:40,520
Speaker 4: and just retrofitting it on to the way you do

732
00:33:40,560 --> 00:33:43,040
Speaker 4: things now is only going to add a little bit

733
00:33:43,040 --> 00:33:46,720
Speaker 4: to your efficiency. You have to actually re engineer everything

734
00:33:46,760 --> 00:33:50,880
Speaker 4: around this, change your processes, hire different people, or get

735
00:33:50,920 --> 00:33:53,320
Speaker 4: you know, train people, and then you're going to see

736
00:33:53,360 --> 00:33:54,040
Speaker 4: big efficiencies.

737
00:33:54,120 --> 00:33:56,680
Speaker 3: You got to have prompt training in schools, right in

738
00:33:56,760 --> 00:33:59,000
Speaker 3: high schools or something. Well, we were talking about this

739
00:33:59,080 --> 00:34:01,800
Speaker 3: with Tyler Cowen earlier and he had, you know, a

740
00:34:02,000 --> 00:34:02,680
Speaker 3: similar point.

741
00:34:03,200 --> 00:34:06,960
Speaker 2: Are big companies setting aside? Okay, like, yeah, it's not

742
00:34:07,080 --> 00:34:10,560
Speaker 2: very exciting that a big company is able to marginally

743
00:34:10,760 --> 00:34:12,919
Speaker 2: reduce their workforce. I think a lot of those are fake.

744
00:34:12,960 --> 00:34:14,400
Speaker 2: I have a feeling a lot of these companies are

745
00:34:14,440 --> 00:34:16,160
Speaker 2: going to have to end up having to hire people

746
00:34:16,200 --> 00:34:19,319
Speaker 2: back when these things don't work, or they had nothing

747
00:34:19,360 --> 00:34:22,319
Speaker 2: to do with AI and they just wanted to do layoffs.

748
00:34:22,760 --> 00:34:27,520
Speaker 2: Can legacy institutions like there's some like inherent roadblock to

749
00:34:27,600 --> 00:34:32,440
Speaker 2: the degree to which legacy institutions can incorporate on AI

750
00:34:32,640 --> 00:34:34,160
Speaker 2: Or is this the type of thing where it's just

751
00:34:34,200 --> 00:34:38,880
Speaker 2: going to be entities that didn't exist before becoming really

752
00:34:38,880 --> 00:34:40,080
Speaker 2: big household names.

753
00:34:40,280 --> 00:34:42,319
Speaker 4: No, I think it will be legacy. I mean I

754
00:34:42,320 --> 00:34:45,279
Speaker 4: think you know, Walmart was legacy, Ikia was legacy. It

755
00:34:45,360 --> 00:34:47,680
Speaker 4: may not be the names you expect, right, Sears and

756
00:34:47,680 --> 00:34:50,920
Speaker 4: Woolworths didn't really benefit from shipping containerization. They ended up

757
00:34:50,960 --> 00:34:52,960
Speaker 4: going out of business. I think you have to have

758
00:34:53,040 --> 00:34:56,680
Speaker 4: the right attitude of how you're going to utilize the

759
00:34:56,680 --> 00:35:00,560
Speaker 4: efficiencies it brings within your business again, to grow your business,

760
00:35:00,560 --> 00:35:04,759
Speaker 4: not to grow your CEO's salary. Right, you have to

761
00:35:04,760 --> 00:35:07,239
Speaker 4: be spreading this to the consumers for your company to

762
00:35:07,280 --> 00:35:08,680
Speaker 4: be successful, all Right.

763
00:35:08,760 --> 00:35:12,000
Speaker 3: In the intro, Joe made fun of Sid saying something

764
00:35:12,040 --> 00:35:15,560
Speaker 3: completely rational. It Yeah, I know, I know, but this

765
00:35:15,680 --> 00:35:19,440
Speaker 3: is your joke, right, Okay, So I'm gonna ask the

766
00:35:19,480 --> 00:35:22,680
Speaker 3: cliched question in that theme, are we in a bubble?

767
00:35:25,239 --> 00:35:29,560
Speaker 4: Can you define bubble? No? I should.

768
00:35:29,880 --> 00:35:32,480
Speaker 2: I think the question should be asked liberally.

769
00:35:32,600 --> 00:35:35,239
Speaker 4: It's like, are things overvalued?

770
00:35:35,520 --> 00:35:36,400
Speaker 3: Are things overvalued?

771
00:35:36,640 --> 00:35:38,960
Speaker 4: Yes, but there's a difference between things being overvalued in

772
00:35:39,000 --> 00:35:41,200
Speaker 4: the bubble, right, and I think things are overvalued, and

773
00:35:41,320 --> 00:35:44,719
Speaker 4: I think there may be an infrastructure bubble. In the

774
00:35:44,800 --> 00:35:48,240
Speaker 4: article I wrote for classes as a chart of container

775
00:35:48,320 --> 00:35:50,960
Speaker 4: ships being built, of shipping ships being built, and you

776
00:35:51,000 --> 00:35:54,880
Speaker 4: can see this huge rise right after containerization started for

777
00:35:55,120 --> 00:35:56,919
Speaker 4: a bunch of years, and then it dropped back off

778
00:35:56,960 --> 00:35:59,400
Speaker 4: because now people had their ships, but everybody had to

779
00:35:59,400 --> 00:36:02,000
Speaker 4: get into the same time. Everybody had to go order ships.

780
00:36:02,120 --> 00:36:04,440
Speaker 4: A ton of ships were being built, and then the Capex,

781
00:36:04,440 --> 00:36:05,640
Speaker 4: you know, and then they were like, okay, now we

782
00:36:05,680 --> 00:36:09,160
Speaker 4: have ships. It's a little different with chips because chips

783
00:36:09,200 --> 00:36:11,239
Speaker 4: aren't gonna last you know, yeah, or a long a

784
00:36:11,239 --> 00:36:14,560
Speaker 4: container ship last thirty fifty years. But I do think

785
00:36:14,640 --> 00:36:16,799
Speaker 4: that you're not going to need as many chips in

786
00:36:16,840 --> 00:36:20,560
Speaker 4: the future as you are buying today. Really well, you know,

787
00:36:21,120 --> 00:36:23,359
Speaker 4: you're gonna have more use of AI. Probably there's gonna

788
00:36:23,360 --> 00:36:26,040
Speaker 4: be more use. But I also would think they would

789
00:36:26,040 --> 00:36:30,280
Speaker 4: become more efficient in compute. That's just the history of compute.

790
00:36:30,320 --> 00:36:32,440
Speaker 3: Does your definition of a bubble, does that have to

791
00:36:32,480 --> 00:36:34,719
Speaker 3: include a buildup of leverage of some sort.

792
00:36:35,280 --> 00:36:38,000
Speaker 4: Well, I lived through the dot com bubble, so yeah,

793
00:36:38,040 --> 00:36:40,560
Speaker 4: I would say so, right, because you're not seeing that right? Well,

794
00:36:40,560 --> 00:36:42,799
Speaker 4: because I think a bubble popping has to hurt. And

795
00:36:42,880 --> 00:36:46,000
Speaker 4: if Microsoft loses a billion dollars, that doesn't hurt. Doesn't

796
00:36:46,040 --> 00:36:48,960
Speaker 4: hurt any I mean, sure it hurts whoever's invested that

797
00:36:49,000 --> 00:36:49,880
Speaker 4: money at Microsoft, but it.

798
00:36:49,880 --> 00:36:53,040
Speaker 2: Doesn't who has which is everyone who is a four

799
00:36:53,120 --> 00:36:53,440
Speaker 2: one K?

800
00:36:53,719 --> 00:36:55,920
Speaker 4: Well, I guess, but I mean, really, Microsoft loses a

801
00:36:55,920 --> 00:36:58,560
Speaker 4: billion dollars, how much does that affect their stock price?

802
00:36:59,080 --> 00:37:01,480
Speaker 4: And even if the stock win down ten percent, that's

803
00:37:01,160 --> 00:37:03,480
Speaker 4: not a It's not like the dot com bubble. Right

804
00:37:03,480 --> 00:37:06,080
Speaker 4: When that popped, people were laid off, the economy went

805
00:37:06,080 --> 00:37:08,040
Speaker 4: to a recession. I mean it was pretty deep recession.

806
00:37:08,360 --> 00:37:10,120
Speaker 4: Took a while to kind of pull ourselves back out

807
00:37:10,160 --> 00:37:10,359
Speaker 4: of that.

808
00:37:10,680 --> 00:37:13,239
Speaker 2: Talk to us more about the dot com bubble. I

809
00:37:13,560 --> 00:37:16,160
Speaker 2: tried to bring it up in every conversation because that's

810
00:37:16,160 --> 00:37:19,279
Speaker 2: when I got interested in markets, did a little day

811
00:37:19,320 --> 00:37:22,280
Speaker 2: trading in those days when I was in college, et cetera,

812
00:37:22,560 --> 00:37:26,080
Speaker 2: and I just remember that period very fondly because I

813
00:37:26,120 --> 00:37:28,640
Speaker 2: was young. What do people get wrong in their memories

814
00:37:28,640 --> 00:37:29,640
Speaker 2: of the dot com bubble?

815
00:37:29,960 --> 00:37:32,640
Speaker 4: So here's the thing I remember most about the bubble.

816
00:37:33,000 --> 00:37:35,160
Speaker 4: I was a corporate VC. I worked for a big

817
00:37:35,200 --> 00:37:36,880
Speaker 4: company here in New York for to my front of company.

818
00:37:37,200 --> 00:37:40,040
Speaker 4: And one of the companies I had invested in was

819
00:37:40,040 --> 00:37:42,600
Speaker 4: a public company and they were raising more money. This

820
00:37:42,719 --> 00:37:44,960
Speaker 4: was in January, right for the bubble popped right.

821
00:37:45,000 --> 00:37:47,880
Speaker 2: Was the peak was in market in March two thousand.

822
00:37:47,960 --> 00:37:51,200
Speaker 4: Then it was January two thousand, so the company was

823
00:37:51,200 --> 00:37:52,680
Speaker 4: selling stock. They said, hey, you know, if you want

824
00:37:52,680 --> 00:37:54,399
Speaker 4: to buy some more stock in our company, we'll sell

825
00:37:54,440 --> 00:37:56,840
Speaker 4: to you without the underwrited discount or before the underwted discounts.

826
00:37:56,880 --> 00:37:58,600
Speaker 4: We'd get it a seven percent below the market price.

827
00:37:59,080 --> 00:38:01,840
Speaker 4: And I went to CFO of this giant company and

828
00:38:01,880 --> 00:38:03,840
Speaker 4: I said, hey, we can get a good deal on

829
00:38:03,880 --> 00:38:06,040
Speaker 4: this stock. We can get seven percent off, right, who

830
00:38:06,080 --> 00:38:08,560
Speaker 4: doesn't love a bargain? And he said, well, do you

831
00:38:08,600 --> 00:38:11,360
Speaker 4: think the company is worth that price? I said, no,

832
00:38:11,360 --> 00:38:14,880
Speaker 4: nowhere near. He's like, so why are you buying it?

833
00:38:14,960 --> 00:38:17,440
Speaker 4: I'm like, oh, I guess that's a good point. He's like,

834
00:38:17,480 --> 00:38:20,719
Speaker 4: so why would we still own it? And it's like,

835
00:38:20,800 --> 00:38:22,840
Speaker 4: that's also a good point, right, which why aren't we

836
00:38:22,880 --> 00:38:25,200
Speaker 4: selling this if it's overvalued? I mean the thing that

837
00:38:25,200 --> 00:38:28,160
Speaker 4: people forget is everybody knew it was overvalued. They were

838
00:38:28,160 --> 00:38:29,719
Speaker 4: all just waiting for it to go up more before

839
00:38:29,760 --> 00:38:32,200
Speaker 4: they sold. And he said this, and I'm like, yeah,

840
00:38:32,200 --> 00:38:34,200
Speaker 4: I guess better to sell early than late. And we

841
00:38:34,280 --> 00:38:36,719
Speaker 4: ended up selling that entire position, which luckily paid for

842
00:38:36,719 --> 00:38:39,400
Speaker 4: the whole portfolio before the bubble popped.

843
00:38:39,840 --> 00:38:42,560
Speaker 2: This is a I mean, I think this is there's

844
00:38:42,600 --> 00:38:45,799
Speaker 2: sort of the difference between John Authors had a really

845
00:38:45,800 --> 00:38:48,200
Speaker 2: good newsletter I think about a year ago, which is.

846
00:38:48,160 --> 00:38:50,799
Speaker 3: There, you're just awsletters I'm doing.

847
00:38:50,920 --> 00:38:53,560
Speaker 2: I'm doing my job to Bloomberg. I like, but it

848
00:38:53,640 --> 00:38:55,920
Speaker 2: was basically like, you know, you get these situations like

849
00:38:55,960 --> 00:38:58,640
Speaker 2: dot Com where everyone said this is massively overvalued. We

850
00:38:58,680 --> 00:39:02,840
Speaker 2: all know, we all It's then you have bubbles that

851
00:39:02,920 --> 00:39:05,839
Speaker 2: are more like the housing bubble, in which I don't

852
00:39:05,880 --> 00:39:09,320
Speaker 2: think on any sort of traditional metrics the banks were overvalued.

853
00:39:09,360 --> 00:39:11,239
Speaker 2: I think probably the pees are probably normal. It's just

854
00:39:11,239 --> 00:39:15,399
Speaker 2: that the earning extreme was entirely unsustainable and I guess

855
00:39:15,480 --> 00:39:17,919
Speaker 2: that's the question with AI, Like I don't know, videos

856
00:39:17,920 --> 00:39:19,719
Speaker 2: prebaby expensive, but like I don't think people think it's

857
00:39:19,760 --> 00:39:23,799
Speaker 2: like crazy stretched on pees. It's more the question of,

858
00:39:23,840 --> 00:39:28,279
Speaker 2: like is this chip demand at this pace sustainable? Like

859
00:39:28,320 --> 00:39:30,000
Speaker 2: are the earnings estimates realistic?

860
00:39:30,239 --> 00:39:32,480
Speaker 4: Right? Well, but I mean the economist said the housing

861
00:39:32,760 --> 00:39:34,520
Speaker 4: housing was overpriced in two thousand and.

862
00:39:34,400 --> 00:39:38,319
Speaker 2: Five, right, the homes were, yeah, the banks, you know,

863
00:39:38,400 --> 00:39:41,680
Speaker 2: but the banks got obliterated. And it was not because

864
00:39:41,840 --> 00:39:45,080
Speaker 2: the ratios of the banks were completely out of way,

865
00:39:45,120 --> 00:39:48,160
Speaker 2: because just the profits could not be sustained by any

866
00:39:48,160 --> 00:39:51,560
Speaker 2: stretch at that point. Anyway, I think it's an interesting distinction.

867
00:39:51,840 --> 00:39:53,759
Speaker 3: Well, I think there's still an open question with AI

868
00:39:54,000 --> 00:39:57,760
Speaker 3: about the network of relationships that are sort of driving

869
00:39:57,920 --> 00:40:00,440
Speaker 3: a lot of this business. Like that's where I would

870
00:40:00,440 --> 00:40:03,160
Speaker 3: see some of the maybe two thousand and seven two

871
00:40:03,200 --> 00:40:06,440
Speaker 3: thousand and eight analogy actually being true. This idea that

872
00:40:06,520 --> 00:40:10,000
Speaker 3: like you have this whole system of funding with banks

873
00:40:10,239 --> 00:40:13,480
Speaker 3: that's keeping the whole machine going, but when the collateral

874
00:40:13,800 --> 00:40:16,960
Speaker 3: that's underpinning that system suddenly loses value, the whole thing

875
00:40:17,000 --> 00:40:20,480
Speaker 3: falls apart. You could maybe make a sort of similar

876
00:40:20,560 --> 00:40:22,960
Speaker 3: argument for AI, where you have this network of companies

877
00:40:23,000 --> 00:40:25,200
Speaker 3: that are sort of investing and selling to each other.

878
00:40:25,560 --> 00:40:29,520
Speaker 3: If the value of the underlying asset, which I guess

879
00:40:29,520 --> 00:40:33,359
Speaker 3: would be compute in this case, starts to fall really precipitously,

880
00:40:33,400 --> 00:40:36,879
Speaker 3: the whole thing kind of collapses. But I'm stretching that.

881
00:40:37,120 --> 00:40:39,759
Speaker 4: It's amazing to me how much of the lessons we

882
00:40:39,840 --> 00:40:42,200
Speaker 4: learned in the nineties people just don't know or remember.

883
00:40:42,200 --> 00:40:44,440
Speaker 4: I mean, the whole blank check company thing. We did

884
00:40:44,480 --> 00:40:47,319
Speaker 4: that in nineteen ninety nine, right, and it'll fill all

885
00:40:47,360 --> 00:40:49,680
Speaker 4: the SPACs, right, and it's totally failed, and then people

886
00:40:49,680 --> 00:40:52,279
Speaker 4: did it again. It was crazy to me. And it's

887
00:40:52,360 --> 00:40:54,719
Speaker 4: kind of circular. Yeah, I know, right, Yeah, I mean

888
00:40:54,760 --> 00:40:57,520
Speaker 4: it's still a bad idea. And I think the circular

889
00:40:57,520 --> 00:40:59,800
Speaker 4: revenue was also happened in the nineties and that was

890
00:40:59,840 --> 00:41:01,600
Speaker 4: all also a bad idea. You'd think that people could

891
00:41:01,680 --> 00:41:03,640
Speaker 4: see that and factor that out.

892
00:41:03,920 --> 00:41:06,320
Speaker 2: Gotta have so many questions. Why are specs a bad idea?

893
00:41:06,360 --> 00:41:09,319
Speaker 2: On paper? It seems like a totally fine way to

894
00:41:09,360 --> 00:41:13,040
Speaker 2: go public. In practice, it only seems like total garbage

895
00:41:13,080 --> 00:41:14,160
Speaker 2: companies take that route.

896
00:41:14,320 --> 00:41:15,480
Speaker 3: It feels self selecting.

897
00:41:15,840 --> 00:41:19,279
Speaker 4: Yeah, well, of course, because the way they're structured to

898
00:41:19,280 --> 00:41:20,879
Speaker 4: get people to invest in a company you don't even

899
00:41:20,880 --> 00:41:23,440
Speaker 4: know what it is yet, it means that it's not

900
00:41:23,560 --> 00:41:27,120
Speaker 4: great for the companies, right, you get a ton of yeah,

901
00:41:27,320 --> 00:41:29,080
Speaker 4: oh right, that's just leakage.

902
00:41:29,280 --> 00:41:31,000
Speaker 2: Well, just explained the mechanism again.

903
00:41:31,160 --> 00:41:32,799
Speaker 4: So people, you know, if you put money into a spack,

904
00:41:32,960 --> 00:41:34,200
Speaker 4: when they decide they're going to do a deal, you're

905
00:41:34,200 --> 00:41:35,520
Speaker 4: allowed to take your money back out of the spack.

906
00:41:35,560 --> 00:41:37,719
Speaker 4: I can't remember all the details, all right, Right, So

907
00:41:38,120 --> 00:41:39,759
Speaker 4: if you're like, well it's a good deal, I'll leave

908
00:41:39,760 --> 00:41:42,520
Speaker 4: my money in. But if you're the company being acquired,

909
00:41:42,960 --> 00:41:44,400
Speaker 4: you don't know if you're going to be acquired or not,

910
00:41:44,440 --> 00:41:46,960
Speaker 4: because people could take their money out. So just why

911
00:41:47,000 --> 00:41:49,080
Speaker 4: wouldn't if you could go public on your own, you

912
00:41:49,080 --> 00:41:52,280
Speaker 4: would prefer to do that. So by definition, the spacks

913
00:41:52,280 --> 00:41:55,240
Speaker 4: are buying companies that couldn't go public on their own, right, right?

914
00:41:55,520 --> 00:41:58,560
Speaker 3: Is VC investing fun again? I got the impression like

915
00:41:58,600 --> 00:42:03,000
Speaker 3: two years ago everyone was pretty depressed, and I'm quite

916
00:42:03,040 --> 00:42:04,959
Speaker 3: sure we did a few episodes on it at the time.

917
00:42:05,040 --> 00:42:06,600
Speaker 3: But are people having fun again?

918
00:42:07,040 --> 00:42:10,040
Speaker 4: Well again, I retired, so no, nobody's having fun. I

919
00:42:10,080 --> 00:42:12,080
Speaker 4: wasn't having fun. I think if you have a billion dollars.

920
00:42:12,080 --> 00:42:14,759
Speaker 4: It's probably fun if you're doing the big deals. I

921
00:42:14,800 --> 00:42:19,000
Speaker 4: think if you're in AI, it's fun. If you're doing

922
00:42:19,080 --> 00:42:21,959
Speaker 4: anything else, it should be fun if you're doing something

923
00:42:22,000 --> 00:42:25,960
Speaker 4: besides AI. Right, because now everybody's distracted by the shiny

924
00:42:26,000 --> 00:42:28,360
Speaker 4: new thing, you can go find companies that are interesting

925
00:42:28,360 --> 00:42:30,239
Speaker 4: to you and invest. The problem is you have to

926
00:42:30,280 --> 00:42:32,560
Speaker 4: worry about what happens a year from now when they

927
00:42:32,600 --> 00:42:35,480
Speaker 4: need the next round. Is anybody going to be paying attention?

928
00:42:36,320 --> 00:42:38,359
Speaker 4: I think it's probably pretty hard to be an early

929
00:42:38,400 --> 00:42:41,399
Speaker 4: stage investor unless you're investing in AI. And if you're

930
00:42:41,400 --> 00:42:45,719
Speaker 4: investing in AI, you're probably not writing small checks, low valuations,

931
00:42:46,400 --> 00:42:49,360
Speaker 4: and you can't control the outcome at the end. You

932
00:42:49,360 --> 00:42:51,640
Speaker 4: can only control what you do at the beginning, so

933
00:42:52,400 --> 00:42:54,240
Speaker 4: you probably won't be making money.

934
00:42:54,440 --> 00:42:57,400
Speaker 2: I have this theory that actually nobody likes bubbles or

935
00:42:57,440 --> 00:43:00,879
Speaker 2: booms even but let's say bubbles in part like, if

936
00:43:00,880 --> 00:43:03,839
Speaker 2: I missed it, I'm upset because someone else is getting rich.

937
00:43:04,280 --> 00:43:06,600
Speaker 2: If I'm in it, I'm like really anxious. Am I

938
00:43:06,600 --> 00:43:08,880
Speaker 2: gonna like I'm anxious about two things. I'm anxious. Am

939
00:43:08,920 --> 00:43:10,399
Speaker 2: I going to sell it at the right time? I'm

940
00:43:10,400 --> 00:43:12,960
Speaker 2: also kicking myself for not investing more. No matter how

941
00:43:13,000 --> 00:43:16,400
Speaker 2: much I invested, I'm upset with myself for not having

942
00:43:16,440 --> 00:43:17,040
Speaker 2: invested more.

943
00:43:17,280 --> 00:43:17,680
Speaker 4: Is that right?

944
00:43:17,760 --> 00:43:20,439
Speaker 2: This is just my impression when I read history, which

945
00:43:20,440 --> 00:43:22,480
Speaker 2: is that everyone, even in the boom times, there's like

946
00:43:22,520 --> 00:43:25,319
Speaker 2: this like din of like stress underneath. Is that true?

947
00:43:25,360 --> 00:43:29,800
Speaker 2: Or am I just fantasy projecting how my own neuroses

948
00:43:29,880 --> 00:43:31,480
Speaker 2: from birth onto.

949
00:43:31,560 --> 00:43:32,960
Speaker 3: Everyone thinks they're gonna time it.

950
00:43:32,960 --> 00:43:35,480
Speaker 4: Right, But it's fun. The bubbles are fun, especially if

951
00:43:35,520 --> 00:43:38,040
Speaker 4: you're young and stupid. Right, there's the New Yorker cartoon.

952
00:43:38,120 --> 00:43:40,919
Speaker 4: I want my bubble back. Yeah. Yeah. The flip side

953
00:43:41,000 --> 00:43:43,480
Speaker 4: is it is stressful. I remember, well, its stressfull both

954
00:43:43,640 --> 00:43:47,320
Speaker 4: after obviously I still have my Razor of His stock certificates.

955
00:43:47,400 --> 00:43:50,120
Speaker 4: I had a certificated because I couldn't sell it for anything,

956
00:43:50,280 --> 00:43:52,319
Speaker 4: you know, for any real money, and that at one

957
00:43:52,320 --> 00:43:53,520
Speaker 4: point had been worth quite a bit of a month

958
00:43:53,760 --> 00:43:54,040
Speaker 4: more than.

959
00:43:54,239 --> 00:43:56,520
Speaker 2: Again, I remember, what did that company do? It is

960
00:43:56,560 --> 00:43:57,719
Speaker 2: like an ad network or something.

961
00:43:57,800 --> 00:44:01,240
Speaker 4: No, No, they built websites. Oh yeah, yeah, I remember.

962
00:44:01,600 --> 00:44:03,200
Speaker 4: They were great. I mean they were a great company

963
00:44:03,239 --> 00:44:04,760
Speaker 4: when nobody else knew how to build websites.

964
00:44:04,880 --> 00:44:07,520
Speaker 2: Friends who worked for Razor fish even as like recently.

965
00:44:07,520 --> 00:44:08,719
Speaker 2: It is like twenty ten.

966
00:44:08,800 --> 00:44:09,640
Speaker 4: They sort of hung on.

967
00:44:09,719 --> 00:44:12,400
Speaker 2: For a little while. Jerry Newman, thank you so much

968
00:44:12,440 --> 00:44:14,439
Speaker 2: for coming on odd lot. It's been wanting to chat

969
00:44:14,480 --> 00:44:17,240
Speaker 2: with you for a long time, and I really appreciate

970
00:44:17,280 --> 00:44:32,120
Speaker 2: your joining us. Yeah, thank you both, Tracy. I love

971
00:44:32,200 --> 00:44:35,759
Speaker 2: that conversation. There's a lot there. I mean, obviously, I'd

972
00:44:35,800 --> 00:44:38,880
Speaker 2: like anytime we can talk about the nineties bubble, but

973
00:44:38,920 --> 00:44:42,520
Speaker 2: I had never really thought about or any come anywhere

974
00:44:42,560 --> 00:44:46,319
Speaker 2: close to thinking about the AI analogy with containerization. It's

975
00:44:46,320 --> 00:44:48,799
Speaker 2: a little embarrassing because that's such a core topic for us.

976
00:44:48,840 --> 00:44:51,359
Speaker 2: We've talked about boxes so many times, but it's sort

977
00:44:51,360 --> 00:44:54,040
Speaker 2: of re orient my thinking of AI and thes term

978
00:44:54,320 --> 00:44:54,880
Speaker 2: very helpful.

979
00:44:55,080 --> 00:44:57,080
Speaker 3: Yeah. Well, I mean this just kind of proves the

980
00:44:57,120 --> 00:45:00,239
Speaker 3: point that no one thinks of containerization. I mean, I

981
00:45:00,280 --> 00:45:02,640
Speaker 3: said before, it is a technology story. And one of

982
00:45:02,719 --> 00:45:04,480
Speaker 3: the reasons I do think of it that way is

983
00:45:04,520 --> 00:45:06,840
Speaker 3: because I read that book The Box, Yeah, which is

984
00:45:06,920 --> 00:45:08,960
Speaker 3: really good, but no one thinks about it as an

985
00:45:09,040 --> 00:45:12,759
Speaker 3: investment story. No, because of the reasons that Jerry just

986
00:45:12,840 --> 00:45:13,239
Speaker 3: laid out.

987
00:45:13,440 --> 00:45:16,120
Speaker 2: Yeah, No, that's really interesting, and you know, again like

988
00:45:16,200 --> 00:45:21,480
Speaker 2: it does feel at some point that non tech businesses

989
00:45:21,719 --> 00:45:28,040
Speaker 2: non AI businesses. Eventually someone hopefully for the industry, like

990
00:45:28,160 --> 00:45:31,600
Speaker 2: makes a lot of money actually using these tools because

991
00:45:31,600 --> 00:45:34,719
Speaker 2: we've been in the picture shovels phase or whatever. But

992
00:45:34,840 --> 00:45:39,439
Speaker 2: at some point maybe it's an existing healthcare company or etc.

993
00:45:39,920 --> 00:45:42,880
Speaker 2: Or maybe it's a new kind of law firm or

994
00:45:42,920 --> 00:45:46,080
Speaker 2: an incumbent law firm where it's like, Okay, we have

995
00:45:46,200 --> 00:45:49,800
Speaker 2: found a way to use this technology in a manner

996
00:45:50,040 --> 00:45:52,640
Speaker 2: that is very profitable, productive, and market expanses.

997
00:45:52,680 --> 00:45:55,000
Speaker 3: So to me, that's the key thing. So the key

998
00:45:55,000 --> 00:45:57,040
Speaker 3: thing is it's not that we're going to use this

999
00:45:57,120 --> 00:46:01,520
Speaker 3: technology necessarily to cut costs and boost profit margins. It's

1000
00:46:01,560 --> 00:46:04,440
Speaker 3: that we will actually expand our customer base and make

1001
00:46:04,480 --> 00:46:07,880
Speaker 3: it up in volume by selling more knowledge.

1002
00:46:08,040 --> 00:46:11,960
Speaker 2: You know, it's interesting thing that Jerry said, and you

1003
00:46:12,040 --> 00:46:16,000
Speaker 2: put into words something that I hadn't really thought about before,

1004
00:46:16,320 --> 00:46:19,480
Speaker 2: but this idea about being at the end of this

1005
00:46:19,600 --> 00:46:23,319
Speaker 2: sort of computer revolution, and there is something about AI

1006
00:46:23,520 --> 00:46:27,160
Speaker 2: specifically where people it's like and it can't really literally

1007
00:46:27,200 --> 00:46:29,480
Speaker 2: be this where it's like, well, this is the last technology,

1008
00:46:29,560 --> 00:46:30,320
Speaker 2: right because.

1009
00:46:30,520 --> 00:46:32,399
Speaker 3: Because we're gonna get robots, yeah, right.

1010
00:46:32,719 --> 00:46:35,239
Speaker 2: And I don't know if like other booms or technological

1011
00:46:35,280 --> 00:46:37,640
Speaker 2: revolutions had this feeling where it's like, this is the

1012
00:46:37,719 --> 00:46:41,680
Speaker 2: last one. Theoretically, if you get Agi or whatever robots,

1013
00:46:41,920 --> 00:46:45,760
Speaker 2: you don't need any further technological innovation, et cetera. It creates,

1014
00:46:45,800 --> 00:46:49,959
Speaker 2: I think, a very weird, uncomfortable dynamic. But the idea

1015
00:46:50,000 --> 00:46:51,920
Speaker 2: of AI is the end of what we do with

1016
00:46:51,960 --> 00:46:55,600
Speaker 2: computers rather than the start of like something genuinely new

1017
00:46:55,680 --> 00:46:58,960
Speaker 2: like that actually like snaps into place a lot of

1018
00:46:58,960 --> 00:46:59,480
Speaker 2: thoughts for me.

1019
00:46:59,600 --> 00:47:01,360
Speaker 3: Once we invent God, we're done.

1020
00:47:02,520 --> 00:47:02,879
Speaker 4: We're done.

1021
00:47:02,880 --> 00:47:04,000
Speaker 2: Everything else takes care of it.

1022
00:47:04,040 --> 00:47:06,080
Speaker 3: So yeah, all right, shall we leave it there?

1023
00:47:06,160 --> 00:47:06,799
Speaker 2: Let's leave it there?

1024
00:47:06,840 --> 00:47:07,160
Speaker 4: All right?

1025
00:47:07,200 --> 00:47:09,600
Speaker 3: This has been another episode of the oud Lots podcast.

1026
00:47:09,680 --> 00:47:12,880
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

1027
00:47:12,520 --> 00:47:15,160
Speaker 2: And I'm joll Wisenthal. You can follow me at the Stalwart.

1028
00:47:15,320 --> 00:47:18,160
Speaker 2: Follow our guest Jerry Newman, He's at g A Newman.

1029
00:47:18,400 --> 00:47:21,640
Speaker 2: Follow our producers Carmen Rodriguez at Carman armand dash Ol

1030
00:47:21,640 --> 00:47:25,000
Speaker 2: Bennett at Dashbod and Keil Brooks at Kilbrooks. More odd

1031
00:47:25,040 --> 00:47:27,680
Speaker 2: Lots content go to Bloomberg dot com slash odd Lots.

1032
00:47:27,680 --> 00:47:30,520
Speaker 2: We've been daily newsletter and all of our episodes and

1033
00:47:30,520 --> 00:47:32,720
Speaker 2: you can chat about all of these topics with fellow

1034
00:47:32,760 --> 00:47:37,040
Speaker 2: listeners in our discord Discord dot gg slash od Lots and.

1035
00:47:37,040 --> 00:47:39,160
Speaker 3: If you enjoy odd Lots, if you like it when

1036
00:47:39,200 --> 00:47:40,960
Speaker 3: we talk to you about why you're not going to

1037
00:47:41,040 --> 00:47:43,600
Speaker 3: get rich from AI, then please leave us a positive

1038
00:47:43,600 --> 00:47:46,520
Speaker 3: review on your favorite podcast platform. And remember, if you

1039
00:47:46,600 --> 00:47:49,120
Speaker 3: are a Bloomberg subscriber, you can listen to all of

1040
00:47:49,120 --> 00:47:51,600
Speaker 3: our episodes absolutely ad free. All you need to do

1041
00:47:51,680 --> 00:47:54,280
Speaker 3: is find the Bloomberg channel on Apple Podcasts and follow

1042
00:47:54,320 --> 00:48:01,680
Speaker 3: the instructions there. Thanks for listening in