Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Outlass podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Allaway.
Speaker 2: Tracy our colleague here at Bloomberg at Harrison, had interesting
Speaker 2: newsletter today and it's actually something I've been thinking about
Speaker 2: a little bit lately, which is that for all the
Speaker 2: talk of the AI boom or the A bubble driving
Speaker 2: the stock market, there's no AI pure plays really that
Speaker 2: are publicly traded. Like in video is probably the closest,
Speaker 2: but three years ago people were excited because they were
Speaker 2: mining ethereum. Before that, it was like video games. You know,
Speaker 2: this was only an AI company in people's mind. For
Speaker 2: since late twenty twenty two, Google still is you know,
Speaker 2: we know they're all investing in a ton. There's actually
Speaker 2: no like no AI company that people are excited about
Speaker 2: in the public markets.
Speaker 3: I mean, I think it's true. Here you have this
Speaker 3: thing that a lot of people would say is revolutionary technology, right,
Speaker 3: but you kind of have to decide if you're going
Speaker 3: to invest in it, is it going to be like
Speaker 3: upstream or downstream? And there doesn't really seem to be
Speaker 3: that much pure play, as you say.
Speaker 2: As our colleagues said, Verma might like to say, people
Speaker 2: are investing in the picks and the shovels, you know,
Speaker 2: this gold rush. I don't think he actually said that.
Speaker 2: It's just a million other people's but it is.
Speaker 3: It shovels, it's fun, a completely reasonable commentary, That's what
Speaker 3: I say.
Speaker 2: Is there? You know, it turns out instantly picks and
Speaker 2: shovels have been great. You know, you could have bought Caterpillar,
Speaker 2: or you could have bought some old school HVAC company
Speaker 2: that's providing cooling or heating or whatever and made a
Speaker 2: ton of money. So actually, it turns out, at least
Speaker 2: for the last few years, all those awful cliches have
Speaker 2: actually been big money makers and I should not make
Speaker 2: fun of them.
Speaker 3: Well, here's the other thing I would say. It does
Speaker 3: feel like everyone kind of agrees at this moment in
Speaker 3: time that there is froth in the market. Maybe it's
Speaker 3: on a massive bubble, right, but there's some froth, and
Speaker 3: everyone is kind of admitting or saying that you're going
Speaker 3: to have some companies that emerge as big winners, much
Speaker 3: like the dot com era, and then a bunch of
Speaker 3: companies that like actually end up being losers, and I
Speaker 3: think again, like that is consensus at this point, but
Speaker 3: it doesn't really necessarily translate into actual investment, because of
Speaker 3: course the trick is actually picking the winners and losers
Speaker 3: in the market.
Speaker 4: Yeah. I don't know.
Speaker 3: It just seems like a weird point in time where
Speaker 3: people are like, oh, yeah, AI is great, but we
Speaker 3: all know that some of these companies are going to
Speaker 3: be massive losers, right.
Speaker 2: I think now they're all kind of big treated as winners,
Speaker 2: which is the other thee Yeah, anyway, it's a very
Speaker 2: weird time. You got to do more episodes on this
Speaker 2: because it is sort of the central question for on
Speaker 2: whether we're just talking about the market or talk about
Speaker 2: the economy, et cetera. Someone I've wanted to talk to
Speaker 2: for a long time. Earlier this year he wrote a
Speaker 2: essay for Colossus called AI will Not Make You Rich.
Speaker 2: It came out in September. It seems like ages ago.
Speaker 2: It's very disappointing because I think a lot of people
Speaker 2: really are hoping to get rich on AI, so this
Speaker 2: is a very unwelcome message.
Speaker 3: It's also very much a sort of core odd Lots
Speaker 3: thesis because in the essay he compares and contrasts AI
Speaker 3: with containerization, which is another which facing.
Speaker 2: So let's just get to the guest. Someone I've wanted
Speaker 2: to talk to for a very long time, someone who
Speaker 2: literally is the perfect guest longtime VC started a venture
Speaker 2: investing in nineteen ninety seven. Also a professor at Columbia
Speaker 2: Business School. We're going to be talking to investor Jerry Newman,
Speaker 2: also the co author of a recent book, Founder Verse Investor,
Speaker 2: The Honest Truth about Venture Capital from Startup to IPO.
Speaker 2: Maybe he'll tell us one we'll see.
Speaker 3: You the bringer of excess Halloween candy. So he gets
Speaker 3: he gets brownie points for that.
Speaker 2: Literally the perfect guest. Uh, Jerry, thank you so much
Speaker 2: for coming in. Thrilled to finally have you here.
Speaker 4: Thanks, I'm glad to be here.
Speaker 2: What does that mean? A I won't make you rich.
Speaker 2: AI has made people super rich, and it's making people
Speaker 2: rich every single day.
Speaker 4: You know, as an old mentor used to say, money
Speaker 4: is not money till it's cash. Okay, So is anybody
Speaker 4: really rich yet? Oh? Come on?
Speaker 2: I mean Jensen Wong bought an entire bar in Korea.
Speaker 2: He's like bought beer and fried chicken for everyone.
Speaker 4: He's right, I think it's smart to cash out early. Okay,
Speaker 4: that's what he was doing. Okay, let's say you I
Speaker 4: really believed it, would you be selling at stock now?
Speaker 2: I mean, see what you mean talk about this because
Speaker 2: you obviously have a lot of experience. Actually, that brings
Speaker 2: another line of question that I want to get into.
Speaker 2: But what does that mean it's smart to cash out
Speaker 2: early when in your experience? What does that actually mean?
Speaker 4: So, look, I believe that AI is a revolutionary technology. Okay,
Speaker 4: I'm going to put that on the table.
Speaker 2: Which is important to say because not everyone agrees with that.
Speaker 4: So that's yeah, totally. You know, I'm on Blue Sky
Speaker 4: and nobody agrees with that, but I do. I think so.
Speaker 4: But there's a difference between value creation and value capture.
Speaker 4: So even if AI creates a lot of value for society,
Speaker 4: who's going to get that value? Is it going to
Speaker 4: be the early investors? Is it going to be the court,
Speaker 4: you know, the foundation model companies. Is it going to
Speaker 4: be YEA consumers? You know? They think that's the question
Speaker 4: people need to ask.
Speaker 3: I actually broadly agree with this thesis when it comes
Speaker 3: to AI, but maybe just to clarify the idea here,
Speaker 3: compare and contrast this current AI cycle with maybe previous
Speaker 3: technological breakthroughs. And you know, I mentioned containers. I think
Speaker 3: a lot of people aren't used to thinking about boxes
Speaker 3: as this major advancement in technology, but at the time
Speaker 3: they really were.
Speaker 4: Yeah, I mentioned containerization, and most of my peers think
Speaker 4: I'm talking about doctor. So I'm talking about shipping containers. Right,
Speaker 4: the big boxes they put on ships, and then they
Speaker 4: can move from the ships onto the rail cars and
Speaker 4: now to the back of trucks. And this was a
Speaker 4: revolutionary technology. I mean it changed everything about the way
Speaker 4: we live. I don't remember. I'm probably a little older
Speaker 4: than you all, but when I was a kid, my
Speaker 4: grandmother used to send up oranges from Florida at Christmas time, right,
Speaker 4: because they were rare. You couldn't just go into a
Speaker 4: grocery store and buy them. Now you can buy oranges
Speaker 4: anywhere at any time. People don't really realize how much
Speaker 4: our lives have changed because of shipping containerization, because of
Speaker 4: these global logistics and the globalization of shipping. Now this
Speaker 4: is utionary technology. Who got rich from it? I mean generally,
Speaker 4: if you look at the nineteen sixties, say how many
Speaker 4: people became wildly rich from technological innovation? Can you think
Speaker 4: of anyone? Because I've been asking this question for years.
Speaker 4: There are people who got rich in media and whatnot,
Speaker 4: but there wasn't a lot of technological innovation that made individuals.
Speaker 2: It just start, like twenty years ago, did they have technology.
Speaker 4: Bout the right? I mean, it's I think this is
Speaker 4: the thing. Right, So we talk about computer technology, the
Speaker 4: information and computer technology revolution as technology, but obviously this
Speaker 4: has always been technology. But only at certain times in
Speaker 4: this technological cycle do people seem to make money as
Speaker 4: investors and as inventors.
Speaker 3: Explain more, though, because I mean I could argue that
Speaker 3: Marisk or someone like that got pretty rich off of containerization.
Speaker 3: Like maybe it took a while, but even though the
Speaker 3: shipping industry is highly cyclical, when they are in the
Speaker 3: boom period, they make a lot of money.
Speaker 4: Sure, I mean, the existing shipping companies got very large
Speaker 4: and made a lot of money. They got larger and
Speaker 4: made more money. Is mayor you know who made money.
Speaker 3: Completely new entrants?
Speaker 4: Yeah? So just to me as background, I'm a venture capitalist,
Speaker 4: right or have been a venture capitalist for a long time,
Speaker 4: recently retired, And I think about people investing in making
Speaker 4: money new companies, inventors or entrepreneurs making money, not the
Speaker 4: existing incumbents making money. And I think that people will
Speaker 4: make money on AI. Might be Microsoft making a ton
Speaker 4: of money on AI. It could be AI. You know,
Speaker 4: when containerization shipping containerization came around, Sealand was the instigator
Speaker 4: of this, and the founder of Sealand made money primarily
Speaker 4: because he sold early, right, he sold Sealand to r
Speaker 4: J Nibisco or sorry, it was just r Jr. At
Speaker 4: the time, and they thought they were diversifying, which is
Speaker 4: the big thing. Then paid them a lot of money
Speaker 4: and then they drove it into the ground.
Speaker 2: So what did Sealand do? What was did they? I
Speaker 2: actually don't I'm not familiar with this company at all,
Speaker 2: which I think kind of speaks to your point. But
Speaker 2: what it was Seland?
Speaker 4: Yes, So so it was a truck. It was started
Speaker 4: out as a trucking company. And the founder of Sealand
Speaker 4: was a trucking entrepreneur. And he said, it's silly you
Speaker 4: you go into a port, your truck sits around all day. Well,
Speaker 4: you know, the long charman put a cargo net into
Speaker 4: a container ship, load everything into it, pull it out,
Speaker 4: unload it and then reload it back into your truck.
Speaker 4: This is not efficient. And this obviously it's an obvious idea, right,
Speaker 4: just put it all in a box and you can
Speaker 4: then put that box on a truck.
Speaker 3: The best ideas are always the obvious rights in retrospect.
Speaker 4: But the problem with it was it was a systems problem, right.
Speaker 4: The long charman didn't want it, the ports didn't want it,
Speaker 4: the port authorities didn't want it, the politicians didn't want it.
Speaker 4: Nobody wanted this to happen because this sort of enormous
Speaker 4: change would put a lot of people out of jobs.
Speaker 4: It would up set the existing order, and it did.
Speaker 4: I live in Hoboken, and in Hoboken there's a lot
Speaker 4: of peers that nobody uses except to go running on now,
Speaker 4: because back in the sixties it was a long sharmantown.
Speaker 4: And when I moved there in the early nineties, it
Speaker 4: was empty. It started to gentrify, but because all of
Speaker 4: those people had lost their jobs and moved out.
Speaker 2: And I suppose, like even in the case of marisk
Speaker 2: And I'm sure you know, they obviously have made a
Speaker 2: lot of money because the explosion of global trading volumes
Speaker 2: and containerization is part of it. Like, it wasn't overnight wealth, right,
Speaker 2: it wasn't.
Speaker 4: It was not.
Speaker 2: It was not people got richer, but it was not
Speaker 2: like some bubble get rich quick thing where they suddenly
Speaker 2: cashed in on a new thing.
Speaker 4: Yeah. I mean, I suppose whoever owns mask may have
Speaker 4: gotten richer. Yeah, but it's not like you're going to
Speaker 4: look at the forest four hundred and see all these
Speaker 4: shipping magnates who became suddenly, you know, enormously wealthy. There
Speaker 4: are a few.
Speaker 3: Wait, okay, so if I think about a box, you
Speaker 3: know part part of that.
Speaker 2: We just keep this whole conversation on boxes.
Speaker 3: Actually, well one more question then we will be able
Speaker 3: to discuss ai. But I think about a box, and
Speaker 3: as you say, it's sort of an organizational structural problem,
Speaker 3: Like the box itself is not the huge technological advancement necessarily.
Speaker 3: So what exactly was it about containerization that prevented it
Speaker 3: from being disseminated I guess to new upstarts or new
Speaker 3: companies that could actually use that technology.
Speaker 4: Well it was, so I'm not sure I understand the question,
Speaker 4: because uanization became widespread very quickly, right, But.
Speaker 3: What I mean is like, why was that value seemingly
Speaker 3: captured by incumbents versus startups?
Speaker 4: Right? I think because it disseminated so quickly, Right, it
Speaker 4: was an obvious idea. Everybody who saw it said, Okay,
Speaker 4: we need to do this. Right. Everybody who's already in
Speaker 4: the business said, if this is going to happen, we
Speaker 4: have to do it. We can't be left behind. We
Speaker 4: will be left behind if we don't do it, which
Speaker 4: you know, I think was also obvious. So the reason
Speaker 4: nobody else did it first was it was hard to do.
Speaker 4: It was hard to make happen. And technology has always
Speaker 4: come in these technological systems if they're worthwhile technologies. Right.
Speaker 4: So the personal computer didn't change the world on its own, right.
Speaker 4: It changed the world alongside the Internet, you know, alongside
Speaker 4: a bunch of technologies that formed the system. So the
Speaker 4: hard part here was building the system, not the individual technologies.
Speaker 4: And this is true I think of computers as well.
Speaker 4: The first microprocessors weren't considered revolutionary. Intel didn't consider the
Speaker 4: four thousand and four revolutionary. They considered it evolutionary. The
Speaker 4: engineers have said this, And it wasn't revolutionary until people
Speaker 4: put it to use in ways that they didn't anticipate.
Speaker 2: Actually, can we go. I didn't know that, like, I
Speaker 2: hadn't really thought about that with Intel that at the
Speaker 2: time it didn't feel to them that it wasn't a
Speaker 2: revolutionary technology. That's sort of mind blowing.
Speaker 4: It is, right. This is I think from Michael Malone's
Speaker 4: The Intel Trinity of the book. You know, he interviewed
Speaker 4: a bunch of Intel engineers and he said, you know,
Speaker 4: like they thought they were building a better chip set
Speaker 4: to build pocket calculators or desk calculators. I just say so,
Speaker 4: they had a client, Busycom, who wanted to build a
Speaker 4: better desktop calculator with calculators were big back then in
Speaker 4: nineteen seventy ish, and one of the engineers said, well,
Speaker 4: why do we keep building custom chip sets for each
Speaker 4: different calculator. Why don't we just build a chipset that
Speaker 4: we can customize the software and change what it does.
Speaker 4: And Intel is kind of like eh, and Busycom was like, okay,
Speaker 4: we'll pay for that. And then Busycom actually tried to
Speaker 4: back out and they gave the rights back to Intel.
Speaker 4: So Intel owned the rights to this four thousand and four,
Speaker 4: and then they started selling it and it wasn't Intel.
Speaker 4: You know, Intel believed at the time it was going
Speaker 4: to be maybe used for dedicated hardware, you know, can
Speaker 4: you hardware controllers that kind of thing, not by consumers.
Speaker 4: So it wasn't until people on the outside said, hey,
Speaker 4: you know, I love these IBM mainframes or these deck
Speaker 4: MANI computers. I'd like to have my own, but obviously
Speaker 4: nobody can afford that. Why don't I just try to
Speaker 4: build my own? Right, So there's these kind of outside inventors,
Speaker 4: this permissionless invention, and then it really the real revolution
Speaker 4: didn't happen until this. You know, everybody's like, oh Intel.
Speaker 4: It was the six five oh two where the price
Speaker 4: came down so dramatically that, you know, Steve Wozniak could
Speaker 4: walk into a computer fair you get some for free
Speaker 4: and go home and build up a personal computer.
Speaker 2: It's crazy. I wonder who made the chips for the
Speaker 2: Commodore sixty four computer that I had.
Speaker 4: They were six five o twos. Those were Intel, that's
Speaker 4: you know, they weren't until there were Moss technologies. Oh
Speaker 4: got it, got it, those are the cheap ones.
Speaker 2: Then yeah, wait, what year was that when you had well,
Speaker 2: I had I think I like learned some basic and
Speaker 2: did some coding on it. I would have said maybe
Speaker 2: nineteen eighty eight, nineteen eighty seven somewhere around there, made
Speaker 2: a few I missed those days. It's crazy that I
Speaker 2: didn't be. Can I just say I sometimes when I
Speaker 2: think about my life path, like how did I not
Speaker 2: end up like a tech guy? Because I was like
Speaker 2: very into math. I was like one of those people
Speaker 2: who had a computer when I was six or seven.
Speaker 3: In nineteen eighty seven, you were coding you were seven
Speaker 3: years old.
Speaker 2: Yeah, I got that. My dad got me this magazine
Speaker 2: that just it was very crazy. There was literally just
Speaker 2: sent you pages of code and then you just typed
Speaker 2: it in and you could like make a video game.
Speaker 2: I was doing that at seven. I could be like
Speaker 2: one of those like who's per s gave of a
Speaker 2: computer when he was seven? It anyway, and it's not
Speaker 2: too late.
Speaker 4: It's not too late.
Speaker 3: Yeah, it isn't too late. Well, maybe it is too
Speaker 3: late because now we have AI doing all the coding, right, Okay,
Speaker 3: just so I understand when it comes to I guess
Speaker 3: the advantages of the incumbents to actually monetizing new technology
Speaker 3: or benefiting from new technology is the moat around their
Speaker 3: business the network and their sort of role in the network,
Speaker 3: or is it the vast amounts of cash they have
Speaker 3: and the ability to sort of roll out massive investment
Speaker 3: to capture that value.
Speaker 4: I think it's the latter, right, So anybody can build
Speaker 4: a foundation model, right if you have the money. I mean,
Speaker 4: the technology is not mysterious. It doesn't feel like the
Speaker 4: technology is really changing very quickly anymore. And of course
Speaker 4: I don't have insight into what's happening inside of open ai,
Speaker 4: but looking at it over the past couple of years,
Speaker 4: it's the same thing, but slightly better. It's evolutionary now, right.
Speaker 4: The first part was revolutionary and now it's evolutionary. So
Speaker 4: if you wanted to build one, you could build one.
Speaker 4: And I have friends who are running them on their
Speaker 4: laptops very slowly, but it's possible. So now the question
Speaker 4: is do you have enough cash to build the data centers,
Speaker 4: to buy all the chips, to build something that is
Speaker 4: large enough that when you train it, it does something useful,
Speaker 4: And it's just a question of having that the authority
Speaker 4: in the market to be able to raise that money.
Speaker 2: By the way, speaking of ideas that were sort of
Speaker 2: really obvious that took a while. I'm always blown away that,
Speaker 2: like how long it took them to put wheels on luggage.
Speaker 2: I don't think anyone like made got super rich on that.
Speaker 2: But when I was a kid, I remember like we
Speaker 2: had these big suitcases, and that's the most obvious thing.
Speaker 2: It took a while. Anyway, I don't think anyone goes.
Speaker 3: Technology is still not perfected, as you know, because you've
Speaker 3: been in airports with me and the wheels on my
Speaker 3: luggage are broken.
Speaker 2: But I don't think anyone got like super rich off
Speaker 2: of wheels. That just seems like an that was just
Speaker 2: sitting there.
Speaker 4: You know.
Speaker 2: I want to jump ahead actually in the conversation a
Speaker 2: little bit because I know to forget this point. But
Speaker 2: this is something I've become a little obsessed with, which
Speaker 2: is I've been meaning to ask a VC about this,
Speaker 2: which is that there seems to be this blurring of
Speaker 2: private and public markets in various ways, retail participation in
Speaker 2: private markets, et cetera. For the VC, in my mind,
Speaker 2: I feel like the exit was the IPO or the acquisition, right,
Speaker 2: so you buy do vcs these days have to think
Speaker 2: a little bit more about market timing and selling early.
Speaker 2: So someone who was an early investor in open ai
Speaker 2: or whatever you know, in the past, they might have
Speaker 2: just held or then sell at the IPO or the acquisition.
Speaker 2: It is probably going to happen in an open AI's case,
Speaker 2: they're too big to be acquired. But to vcs and
Speaker 2: in your experience these days, have to think a little
Speaker 2: bit more about this idea of selling early, timing the exit.
Speaker 4: Well, I think vcs always had to think about timing.
Speaker 4: You know, I've done pretty well in VC, and I
Speaker 4: attributed it entirely to being lucky at starting investing at
Speaker 4: the right time. So the first time around, I was
Speaker 4: starting in ninety seven, which any you could make money in.
Speaker 4: The second time around, I started in two thousand and seven,
Speaker 4: which again it was just two thousand and eight. It
Speaker 4: was just an easy time to buy in.
Speaker 2: But I mean, the timing of the sale is that
Speaker 2: something that where in the past it might have been
Speaker 2: automatic exit, now now is not the case.
Speaker 4: Yeah. I wrote this thing about VC in the nineteen
Speaker 4: eighties a long time ago on the blog. You can
Speaker 4: find it. And the thing because nobody talks about the
Speaker 4: nineteen eighties, right, there's plenty of VC in the eighties,
Speaker 4: but nobody talks about it. People talk about the sixties
Speaker 4: and in the nineties. So I was like, all right,
Speaker 4: what happened. Then. One of the interesting things about it
Speaker 4: is there were the IPO windows then where you know,
Speaker 4: nineteen eighty three the IPO window opened a bunch of
Speaker 4: companies on public and then it closed again, and you
Speaker 4: can see it in the numbers when people in public.
Speaker 4: So people always had to think about timing. The IPO
Speaker 4: is obviously the best exit because you want to sell
Speaker 4: to the greatest fool, and nobody's greater fool than the public, right,
Speaker 4: So you look for the IPO window to open. When
Speaker 4: you can't, you have to sell to somebody else. You know.
Speaker 4: Vcs have this problem of their limited fund life. So
Speaker 4: I look at my portfolio. I'm an really early investor
Speaker 4: or have been a really early investor, so I'll look
Speaker 4: at companies and say, oh, I invested ten years ago.
Speaker 4: They're going to have to sell, you know, it's the
Speaker 4: So people look for the IPO window, but if they
Speaker 4: can't find it, they have to sell somewhere else.
Speaker 3: Well, how much of the money flowing into AI startups
Speaker 3: now is just the expectation that a bunch of these
Speaker 3: little companies are eventually going to get bought by larger incumbents,
Speaker 3: and basically you're going to have consolidation and you will
Speaker 3: get that exit.
Speaker 4: I don't think anybody can predict when the IPO opens.
Speaker 4: I mean, I wish I could, but I don't think
Speaker 4: I've never seen anybody even say they could predict when
Speaker 4: the IPO window opens. So I think a smart VC
Speaker 4: invests in a company that can become self sustaining to
Speaker 4: some degree, and then you wait for the timing to come.
Speaker 4: You don't invest and say I'm gonna flip this in
Speaker 4: three years now. Yeah. The other problem is vcs don't
Speaker 4: invest in all these IA companies saying a bunch of
Speaker 4: them are going to become valuable. They invest saying one
Speaker 4: of these is going to be come right.
Speaker 3: The lottery ticket theory, yeah, the power law.
Speaker 2: Speaking of the IPO window, I'm never totally satisfied by
Speaker 2: a lot of the explanations for the drop off in
Speaker 2: IPOs generally. Do I know there was that law passed
Speaker 2: in two thousand and one.
Speaker 4: Or what was it, sins ox Yes.
Speaker 2: Surbins ox Ley, and I get that law.
Speaker 3: That law law that everyone hated for a really long time.
Speaker 2: I don't know, it doesn't. But then you see, like
Speaker 2: you know, twenty twenty one there's like a billion garbage
Speaker 2: companies went public via SPACs et cetera. How much is
Speaker 2: it about? Okay, there are some disadvantages to being public
Speaker 2: versus there's just so much more private capital out there
Speaker 2: such that the imperative to perhaps ever go public and
Speaker 2: it's liquid and the rounds and like, what do you
Speaker 2: attribute There are these big companies that are a private
Speaker 2: stripe but open AI and onanthropic that choose to stay private.
Speaker 2: What do you think the main reason for that is?
Speaker 4: Well, it's because they can, right, I mean, being a
Speaker 4: public company is no, it's no party, right, I mean
Speaker 4: it kind of sucks being a public company.
Speaker 2: What is it about it?
Speaker 4: What sucks? Well, you have to tell everybody what you're
Speaker 4: doing every three months. Yeah, that's you know, and then
Speaker 4: they come back and complain about it. So sorry, I'm
Speaker 4: being a little facetious, but it is. It's hard to
Speaker 4: be a public company. Everybody, you know, anybody who runs
Speaker 4: a public company will tell you they spend a lot
Speaker 4: of time being a public company if they're running the company.
Speaker 4: So that's taking away from actually running the company. I
Speaker 4: think the flip side is your liquid and that's yeah.
Speaker 4: You know, So if you can stay private, why wouldn't
Speaker 4: you stay private? Or if you can go public and
Speaker 4: retain control of the company, you know, like Henry Ford
Speaker 4: or Mark Zuckerberg, then why wouldn't you do that? But
Speaker 4: I think it's because there is so much late stage money.
Speaker 4: This isn't necessarily a good thing. It's because there's so
Speaker 4: much money out there that's not being invested in more
Speaker 4: revolutionary technologies earlier. With all this money being invested in AI,
Speaker 4: you may wonder if people are still going to want
Speaker 4: to invest late stage stripe or the analogous stripe might
Speaker 4: be making money now, I'm not sure.
Speaker 3: I know you brought up previous historic analogies like VC
Speaker 3: in the nineteen eighties, but just to focus on the
Speaker 3: one that everyone else seems to be focused on at
Speaker 3: the moment, which is the dot com bubble in the
Speaker 3: early two thousands. What are the key differences you're seeing
Speaker 3: in terms of the VC and financing environment now versus
Speaker 3: twenty or twenty five years ago.
Speaker 4: I think the key difference is that most of the
Speaker 4: money is coming from people who aren't looking for much risk, right,
Speaker 4: So I mean open ai is primarily funded by bigger companies. Right,
Speaker 4: most of their money is coming from large companies. What
Speaker 4: happens if open ai gets hit by a bus? Right,
Speaker 4: So Microsoft's at a bunch of money. A whole bunch
Speaker 4: of big companies are at a bunch of money. I
Speaker 4: don't think much happens to the economy. I think, which
Speaker 4: is different than in the bubble, where a lot of
Speaker 4: consumers were in it. A lot of consumers were in
Speaker 4: it leveraged right, the buying and margin or whatever. A
Speaker 4: lot of people had options, right, people employees, and they
Speaker 4: were spending the money from their options before they were licking.
Speaker 4: You know, it was there was a much I think
Speaker 4: it was a different dynamic.
Speaker 3: But the wealth effect, I don't know.
Speaker 2: I think this is a contrarian take on your part,
Speaker 2: because you hear a lot about I mean, in two dimensions.
Speaker 2: You hear a lot about the direct wealth effect from
Speaker 2: people's exposure to the stock market, which AI is a
Speaker 2: big part of the story. And then you also hear about,
Speaker 2: of course, the sort of real economy effects through all
Speaker 2: of the spending, which we will get into on the
Speaker 2: data centers and the caterpillar, the turbines for the gas generation,
Speaker 2: et cetera. I think many people would say there is
Speaker 2: a lot right now riding on the health and the
Speaker 2: sustainability of this particular sector.
Speaker 4: So I think we can separate the companies like Microsoft
Speaker 4: and Video. Are they over valued because of this? Maybe
Speaker 4: doesn't make a huge difference to the economy. Probably not,
Speaker 4: I don't think so. And the companies who are spending
Speaker 4: money on infrastructure like building data centers, building power generation plants,
Speaker 4: those things I think are probably overbuilt or not so
Speaker 4: much overbuilt as they are built. And I think in
Speaker 4: ten years you're gonna have a lot of extra compute,
Speaker 4: a lot of extra power generation, and people will be
Speaker 4: able to use that for other things. It'll also drive
Speaker 4: down the price of just using AI.
Speaker 2: Probably, yeah, Jevin's paradox bro is gonna be out back
Speaker 2: and forth. All right, So AI, where are we? You
Speaker 2: talk about cycles? And I think you use word that
Speaker 2: I hadn't ereruption? What was the word you use? So well,
Speaker 2: I'll tell us how you see cycles in what cycle
Speaker 2: we're in?
Speaker 4: Right? Right? So A lot of this is based on KARLOA.
Speaker 4: Perez's work, which is pretty familiar to the ventro capitalists
Speaker 4: and your audience. I'm sure. She wrote a book called
Speaker 4: Technological Revolutions and Financial Capital where she explains the dynamics
Speaker 4: behind the Kondrati of waves that the Chumpeter talks about.
Speaker 4: So she has this theory about why these happen. And
Speaker 4: you look at the Industrial revolution, the Second industry Revolution,
Speaker 4: you can see these waves of technology technological systems happening
Speaker 4: through the economy where they kind of start out, they
Speaker 4: grow really rapidly, and then there's usually some sort of adjustment,
Speaker 4: some sort of bubble bursting, and then things kind of
Speaker 4: level out and then start to plateau, and then a
Speaker 4: new one starts. And this is people have noticed this
Speaker 4: since at least Kentdrati nineteen twenty six. She has a
Speaker 4: mechanism for explaining it, and her mechanism has these four phases.
Speaker 4: The first phase is eruption, which she spells with an I,
Speaker 4: which I think is actually in the dictionary as a word.
Speaker 4: I don't know what the difference is between that and
Speaker 4: the eruption, but it is the part where smart right,
Speaker 4: So yeah, keep saying that it is the start. It's
Speaker 4: when people have invented something and it is starting to
Speaker 4: catch on, but it hasn't caught on yet. There's a
Speaker 4: lot of people saying, is this the future? Is it not?
Speaker 4: You look at personal computers in the nineteen late nineteen seventies,
Speaker 4: early nineteen eighties, and maybe even before IBM got involved,
Speaker 4: and people didn't think personal computers were Some people thought
Speaker 4: they were their future. And then you look back at
Speaker 4: computer history. Everybody talks about the people who did think that.
Speaker 4: They don't talk about the other ninety nine point nine
Speaker 4: percent of smart people who said they weren't. This is
Speaker 4: the eruption phase where there's a lot of uncertainty about
Speaker 4: where this technology will go. It's starting to build connections
Speaker 4: to other technologies, starting to attract money, attract smart people
Speaker 4: because it's interesting and it might actually change things. So
Speaker 4: this is the beginning. And I think the connection here
Speaker 4: to AI is people wonder if we're in the eruption
Speaker 4: phase of AI or not. Is this the start of
Speaker 4: a new technological revolution phase? I think we're not. I
Speaker 4: think we're in. I think this is the end of
Speaker 4: the information computer technology wave, the end of the computer wave. Right,
Speaker 4: I think it is. This is the culmination of the
Speaker 4: computer wave. Right, Why did we build computers. We build
Speaker 4: computers to help us think better. Right, this is what
Speaker 4: they're for, their knowledge machines. So now we've kind of
Speaker 4: reached the natural end stage what they do. They're smart,
Speaker 4: machines are smarter. So I think this is not a
Speaker 4: new technological revolution. I think it's the end of the
Speaker 4: old one. And this is why I compare it to
Speaker 4: container is thea because the previous wave was automobiles mass production,
Speaker 4: and starting in nineteen fifteen or so up until nineteen
Speaker 4: seventy was the previous wave, and containerization was squarely at
Speaker 4: the end of that wave, and it was really kind
Speaker 4: of pulling together the technologies of that wave into something
Speaker 4: that increased productivity, right.
Speaker 3: Like the final step of the global trade and I
Speaker 3: guess mobility revolution.
Speaker 4: Yeah exactly.
Speaker 3: Okay, so how do you react as an angel investor?
Speaker 3: Are you at the stage where you're looking for I
Speaker 3: guess the downstream winners, like the companies that are going
Speaker 3: to be able to apply or use AI most effectively,
Speaker 3: or how are you actually deploying all these thoughts in
Speaker 3: terms of your own investment strategy. I retired, Okay, you know,
Speaker 3: I standing it out and I.
Speaker 4: Said, look, how am I going to invest in foundation modes, right,
Speaker 4: I don't have a billion dollar fund. I don't think that.
Speaker 4: You know, if you look at the big winners from
Speaker 4: the early big winners from globalization, the IKEA is right.
Speaker 4: I mean it was a Scandinavian company until containerization, and
Speaker 4: then they became a global powerhouse, a hugely successful company.
Speaker 4: But they didn't need outside money. You know. Comprade, I
Speaker 4: think he borrowed like a couple thousand dollars to start
Speaker 4: that company or to get that company to buy some inventory.
Speaker 4: He never took outside You look at Walmart, which had
Speaker 4: been around already. It was an incumbent and used this
Speaker 4: kind of globalization to bring a lot more variety of
Speaker 4: products to the stores. They didn't need outside money to
Speaker 4: do that. Yeah.
Speaker 3: I guess if you're at Kia and suddenly you're flat
Speaker 3: packing everything and shipping it in containers, and that's your
Speaker 3: big innovation. It's a money saving technology, right, So you
Speaker 3: don't actually have to raise new capital in order to
Speaker 3: flat pack everything.
Speaker 4: Yeah, exactly, Okay, they were already flat packing.
Speaker 2: Yeah. Your mention of the Walmarts and targets of the world,
Speaker 2: it was like in your essay it was like a
Speaker 2: sort of very light bulb thing. It's like, yeah, I
Speaker 2: don't know. I guess we'd take them for granted. But
Speaker 2: they're clear like massive containerization winners. The skill that they
Speaker 2: succeeded is impossible to fathom in some prior era.
Speaker 3: Of Walmart is a logistics company and changed my mind.
Speaker 2: Literally and many of you literally literally literally that. But
Speaker 2: it doesn't feel like with AI that the equivalent has
Speaker 2: emerged yet. Right, We're still at the age where people
Speaker 2: are building the container deployed. But the company that exists
Speaker 2: and is massive that couldn't exist prior to a like
Speaker 2: does not feel does it We haven't seen that yet.
Speaker 4: Well, you gotta be a little patient.
Speaker 2: No, no, but like seriously.
Speaker 4: Iikia so contained. The first container ship sailed in I
Speaker 4: think nineteen fifty six. Okay, so when did Akia become
Speaker 4: a global powerhouse? It really wasn't until the nineteen seventies
Speaker 4: that they started to expand that this is really important.
Speaker 2: They really take a while. Where would you expect it
Speaker 2: to show up? Like what industries would you expect because
Speaker 2: obviously retail existed for a long time, furniture existed for
Speaker 2: a long time. Then you get these Bahamas. Are there
Speaker 2: industries that you think are right to produce very torture
Speaker 2: analogy the Ikea of the AI way.
Speaker 4: Well, I think they have to be knowledge intensive industries,
Speaker 4: right right, I mean this is what AI is doing,
Speaker 4: what it is making more efficient. I mean I think
Speaker 4: there's people been asking me like, well, then what should
Speaker 4: I invest in? And yeah, tell us, well, you know,
Speaker 4: I think, well, so give us. I've been thinking that myself.
Speaker 4: But the answer is really that as an investor, I
Speaker 4: don't decide what to invest in. I evaluate opportunities that
Speaker 4: come to me. And so I have built a box
Speaker 4: in which you can evaluate opportunities. Right, they have to
Speaker 4: look like this. They have to look like an Ikea.
Speaker 4: And if I Kia came to you and said I
Speaker 4: needed money at that time, you should have said, okay,
Speaker 4: I can see how shipping containerization is going to make
Speaker 4: you a much larger company. Where nobody else seemed to
Speaker 4: see that, right, Certainly the furniture makers in North Carolina
Speaker 4: didn't see it. So I think this is the box
Speaker 4: that you evaluate things in. And as a long time investor,
Speaker 4: I'm used to evaluating and I trying not to come
Speaker 4: up with ideas, you know, that said it has to
Speaker 4: be a knowledge intensive industry. And I think something that
Speaker 4: I said in the essay, which I wish I had
Speaker 4: said more about, was the companies that tried to use
Speaker 4: shipping containerization to cut costs so that they can increase
Speaker 4: margins did poorly. Oh this is key, Whereas the companies
Speaker 4: that use the efficiencies and passed the efficiencies onto the
Speaker 4: consumers so that they could become larger became larger. Right.
Speaker 4: I mean this is you look at these people saying, oh,
Speaker 4: we have AI, We're going to fire people. I mean,
Speaker 4: that's I think is the exact wrong move. And I
Speaker 4: think it's probably just every you know, every new thing
Speaker 4: comes along, people like, oh it's we're going to fire people.
Speaker 4: You know, it's just an excuse. But but if you're
Speaker 4: firing people because of AI, you're doing it wrong. Right.
Speaker 4: You should be using AI to say I can use
Speaker 4: my people to do more. I can grow my company,
Speaker 4: I can vary my products, I can take more market share.
Speaker 3: So the value goes to the consumer. And I guess
Speaker 3: you capture the value by selling more right, more knowledge?
Speaker 4: Yeah, I mean I think Walmart never tried to maximize margins.
Speaker 3: They it was it was volume.
Speaker 2: Yeah, Okay, from a consumer standpoint, why do I care
Speaker 2: if your workflow internally at your company is become more
Speaker 2: efficient things AI, either the product is better or new
Speaker 2: or something.
Speaker 4: You know.
Speaker 2: I was thinking about this. I don't mean to like
Speaker 2: pick on anyone, but situating like open door meme Stock
Speaker 2: they have new CEO. He sent out this memo. He's
Speaker 2: like everyone has to do start using A or it's
Speaker 2: clearly a press release in the form of an internal
Speaker 2: memo because it got it I think waving.
Speaker 3: A flag going us.
Speaker 2: It's like, yeah, but like, does the economics of buying
Speaker 2: homes via whatever get better? Like does it actually make
Speaker 2: the business better? This strikes me as very interesting, this
Speaker 2: idea that like, it's not gonna it's just not that exciting,
Speaker 2: especially any sort of customer oriented company, which I guess
Speaker 2: is all companies. The fact that AI has become part
Speaker 2: of their workflow. It's the electric knife effect, right, So
Speaker 2: I don't know that effect we might.
Speaker 4: Just call that, but I mean electric knives are one
Speaker 4: of the fastest growing consumer products in the late nineteen
Speaker 4: sixties because people are like, oh, we are electrifying things,
Speaker 4: like let's electrify the knife and within like three years
Speaker 4: they were in some massive percentage of households, like eighty
Speaker 4: percent of American households had an electric knife and things
Speaker 4: like you know, the blender didn't get adopted that quickly.
Speaker 4: But who has an electric knife now? Right? It's I
Speaker 4: think people take whatever it is the technology is and
Speaker 4: say this is everything. I mean, back in you know,
Speaker 4: the early two thousands, late nineteen nineties, every company was
Speaker 4: an Internet company. Right. Do people walk around saying that's
Speaker 4: an Internet company? Now are you an Internet company? I
Speaker 4: mean everybody's an Internet company. Everybody uses it. It's the baseline.
Speaker 4: That's I think the what a revolutionary technology does is
Speaker 4: it becomes part of everything. So, yeah, you're right. Consumers
Speaker 4: don't care that your you know, your lawyer's using AI.
Speaker 4: They want to make sure that they have good legal advice. Yeah.
Speaker 3: Our producer just messaged Dash saying that he had an
Speaker 3: electric knife. Oh does he have Probably not in the
Speaker 3: nineteen sixties.
Speaker 2: There do you have one?
Speaker 4: Now?
Speaker 2: My mom might have one case that didn't get picked.
Speaker 4: Up on the audio.
Speaker 3: Well, his mom might still have one. Dash, will you
Speaker 3: bring it in for us and we can we can
Speaker 3: use it as a.
Speaker 2: Prop I'm just imagining like going to a restaurant at
Speaker 2: the time it's like, oh, we slice your bread with
Speaker 2: electric knives. That are like how unexciting that would be
Speaker 2: From a consumer stand I don't care, you know.
Speaker 3: Yeah, I guess that's true, but okay, but things are
Speaker 3: different from a shareholder standpoint, right. And one of the
Speaker 3: reasons we see when companies say that they are using
Speaker 3: AI the share price goes up, it's because shareholders expect
Speaker 3: all these easy cost cutting gains. Do we have any
Speaker 3: indication that investors are actually going to be patient and
Speaker 3: wait for I guess the value to spread to the
Speaker 3: consumer or are they just going to demand basically these
Speaker 3: fast cuts.
Speaker 4: Will And I mean, I'm not a stock market investor.
Speaker 4: Yeah I know, I know you're stock marketing, you know.
Speaker 4: I think they have a very short attention span. There's
Speaker 4: a great quote in that article that you see in
Speaker 4: the eighties where the Wall Streetjournal said, you know, there
Speaker 4: was the beginning of the eighties, there was a craze
Speaker 4: and anything that ended with an onyx rightes right, it
Speaker 4: was people were right funny, but it was a short
Speaker 4: lived one because it didn't really deliver. And then people
Speaker 4: were like, all right, let's move on and do something
Speaker 4: else or invest in something else.
Speaker 2: So funny, if I were writing a fiction about the eighties,
Speaker 2: I would immediately I would say, like applied fouxtronics or
Speaker 2: something that. Yeah, right, yeah, I hadn't thought about that.
Speaker 2: That's amazing.
Speaker 4: So I think they want results. But I think it's
Speaker 4: two people are impatient. I mean, I was kidding, but
Speaker 4: I'm not kidding. People are impatient. They want to see
Speaker 4: results today. I don't think AI is going to show
Speaker 4: you the real revolutionary results for a decade. Taking AI
Speaker 4: and just retrofitting it on to the way you do
Speaker 4: things now is only going to add a little bit
Speaker 4: to your efficiency. You have to actually re engineer everything
Speaker 4: around this, change your processes, hire different people, or get
Speaker 4: you know, train people, and then you're going to see
Speaker 4: big efficiencies.
Speaker 3: You got to have prompt training in schools, right in
Speaker 3: high schools or something. Well, we were talking about this
Speaker 3: with Tyler Cowen earlier and he had, you know, a
Speaker 3: similar point.
Speaker 2: Are big companies setting aside? Okay, like, yeah, it's not
Speaker 2: very exciting that a big company is able to marginally
Speaker 2: reduce their workforce. I think a lot of those are fake.
Speaker 2: I have a feeling a lot of these companies are
Speaker 2: going to have to end up having to hire people
Speaker 2: back when these things don't work, or they had nothing
Speaker 2: to do with AI and they just wanted to do layoffs.
Speaker 2: Can legacy institutions like there's some like inherent roadblock to
Speaker 2: the degree to which legacy institutions can incorporate on AI
Speaker 2: Or is this the type of thing where it's just
Speaker 2: going to be entities that didn't exist before becoming really
Speaker 2: big household names.
Speaker 4: No, I think it will be legacy. I mean I
Speaker 4: think you know, Walmart was legacy, Ikia was legacy. It
Speaker 4: may not be the names you expect, right, Sears and
Speaker 4: Woolworths didn't really benefit from shipping containerization. They ended up
Speaker 4: going out of business. I think you have to have
Speaker 4: the right attitude of how you're going to utilize the
Speaker 4: efficiencies it brings within your business again, to grow your business,
Speaker 4: not to grow your CEO's salary. Right, you have to
Speaker 4: be spreading this to the consumers for your company to
Speaker 4: be successful, all Right.
Speaker 3: In the intro, Joe made fun of Sid saying something
Speaker 3: completely rational. It Yeah, I know, I know, but this
Speaker 3: is your joke, right, Okay, So I'm gonna ask the
Speaker 3: cliched question in that theme, are we in a bubble?
Speaker 4: Can you define bubble? No? I should.
Speaker 2: I think the question should be asked liberally.
Speaker 4: It's like, are things overvalued?
Speaker 3: Are things overvalued?
Speaker 4: Yes, but there's a difference between things being overvalued in
Speaker 4: the bubble, right, and I think things are overvalued, and
Speaker 4: I think there may be an infrastructure bubble. In the
Speaker 4: article I wrote for classes as a chart of container
Speaker 4: ships being built, of shipping ships being built, and you
Speaker 4: can see this huge rise right after containerization started for
Speaker 4: a bunch of years, and then it dropped back off
Speaker 4: because now people had their ships, but everybody had to
Speaker 4: get into the same time. Everybody had to go order ships.
Speaker 4: A ton of ships were being built, and then the Capex,
Speaker 4: you know, and then they were like, okay, now we
Speaker 4: have ships. It's a little different with chips because chips
Speaker 4: aren't gonna last you know, yeah, or a long a
Speaker 4: container ship last thirty fifty years. But I do think
Speaker 4: that you're not going to need as many chips in
Speaker 4: the future as you are buying today. Really well, you know,
Speaker 4: you're gonna have more use of AI. Probably there's gonna
Speaker 4: be more use. But I also would think they would
Speaker 4: become more efficient in compute. That's just the history of compute.
Speaker 3: Does your definition of a bubble, does that have to
Speaker 3: include a buildup of leverage of some sort.
Speaker 4: Well, I lived through the dot com bubble, so yeah,
Speaker 4: I would say so, right, because you're not seeing that right? Well,
Speaker 4: because I think a bubble popping has to hurt. And
Speaker 4: if Microsoft loses a billion dollars, that doesn't hurt. Doesn't
Speaker 4: hurt any I mean, sure it hurts whoever's invested that
Speaker 4: money at Microsoft, but it.
Speaker 2: Doesn't who has which is everyone who is a four
Speaker 2: one K?
Speaker 4: Well, I guess, but I mean, really, Microsoft loses a
Speaker 4: billion dollars, how much does that affect their stock price?
Speaker 4: And even if the stock win down ten percent, that's
Speaker 4: not a It's not like the dot com bubble. Right
Speaker 4: When that popped, people were laid off, the economy went
Speaker 4: to a recession. I mean it was pretty deep recession.
Speaker 4: Took a while to kind of pull ourselves back out
Speaker 4: of that.
Speaker 2: Talk to us more about the dot com bubble. I
Speaker 2: tried to bring it up in every conversation because that's
Speaker 2: when I got interested in markets, did a little day
Speaker 2: trading in those days when I was in college, et cetera,
Speaker 2: and I just remember that period very fondly because I
Speaker 2: was young. What do people get wrong in their memories
Speaker 2: of the dot com bubble?
Speaker 4: So here's the thing I remember most about the bubble.
Speaker 4: I was a corporate VC. I worked for a big
Speaker 4: company here in New York for to my front of company.
Speaker 4: And one of the companies I had invested in was
Speaker 4: a public company and they were raising more money. This
Speaker 4: was in January, right for the bubble popped right.
Speaker 2: Was the peak was in market in March two thousand.
Speaker 4: Then it was January two thousand, so the company was
Speaker 4: selling stock. They said, hey, you know, if you want
Speaker 4: to buy some more stock in our company, we'll sell
Speaker 4: to you without the underwrited discount or before the underwted discounts.
Speaker 4: We'd get it a seven percent below the market price.
Speaker 4: And I went to CFO of this giant company and
Speaker 4: I said, hey, we can get a good deal on
Speaker 4: this stock. We can get seven percent off, right, who
Speaker 4: doesn't love a bargain? And he said, well, do you
Speaker 4: think the company is worth that price? I said, no,
Speaker 4: nowhere near. He's like, so why are you buying it?
Speaker 4: I'm like, oh, I guess that's a good point. He's like,
Speaker 4: so why would we still own it? And it's like,
Speaker 4: that's also a good point, right, which why aren't we
Speaker 4: selling this if it's overvalued? I mean the thing that
Speaker 4: people forget is everybody knew it was overvalued. They were
Speaker 4: all just waiting for it to go up more before
Speaker 4: they sold. And he said this, and I'm like, yeah,
Speaker 4: I guess better to sell early than late. And we
Speaker 4: ended up selling that entire position, which luckily paid for
Speaker 4: the whole portfolio before the bubble popped.
Speaker 2: This is a I mean, I think this is there's
Speaker 2: sort of the difference between John Authors had a really
Speaker 2: good newsletter I think about a year ago, which is.
Speaker 3: There, you're just awsletters I'm doing.
Speaker 2: I'm doing my job to Bloomberg. I like, but it
Speaker 2: was basically like, you know, you get these situations like
Speaker 2: dot Com where everyone said this is massively overvalued. We
Speaker 2: all know, we all It's then you have bubbles that
Speaker 2: are more like the housing bubble, in which I don't
Speaker 2: think on any sort of traditional metrics the banks were overvalued.
Speaker 2: I think probably the pees are probably normal. It's just
Speaker 2: that the earning extreme was entirely unsustainable and I guess
Speaker 2: that's the question with AI, Like I don't know, videos
Speaker 2: prebaby expensive, but like I don't think people think it's
Speaker 2: like crazy stretched on pees. It's more the question of,
Speaker 2: like is this chip demand at this pace sustainable? Like
Speaker 2: are the earnings estimates realistic?
Speaker 4: Right? Well, but I mean the economist said the housing
Speaker 4: housing was overpriced in two thousand and.
Speaker 2: Five, right, the homes were, yeah, the banks, you know,
Speaker 2: but the banks got obliterated. And it was not because
Speaker 2: the ratios of the banks were completely out of way,
Speaker 2: because just the profits could not be sustained by any
Speaker 2: stretch at that point. Anyway, I think it's an interesting distinction.
Speaker 3: Well, I think there's still an open question with AI
Speaker 3: about the network of relationships that are sort of driving
Speaker 3: a lot of this business. Like that's where I would
Speaker 3: see some of the maybe two thousand and seven two
Speaker 3: thousand and eight analogy actually being true. This idea that
Speaker 3: like you have this whole system of funding with banks
Speaker 3: that's keeping the whole machine going, but when the collateral
Speaker 3: that's underpinning that system suddenly loses value, the whole thing
Speaker 3: falls apart. You could maybe make a sort of similar
Speaker 3: argument for AI, where you have this network of companies
Speaker 3: that are sort of investing and selling to each other.
Speaker 3: If the value of the underlying asset, which I guess
Speaker 3: would be compute in this case, starts to fall really precipitously,
Speaker 3: the whole thing kind of collapses. But I'm stretching that.
Speaker 4: It's amazing to me how much of the lessons we
Speaker 4: learned in the nineties people just don't know or remember.
Speaker 4: I mean, the whole blank check company thing. We did
Speaker 4: that in nineteen ninety nine, right, and it'll fill all
Speaker 4: the SPACs, right, and it's totally failed, and then people
Speaker 4: did it again. It was crazy to me. And it's
Speaker 4: kind of circular. Yeah, I know, right, Yeah, I mean
Speaker 4: it's still a bad idea. And I think the circular
Speaker 4: revenue was also happened in the nineties and that was
Speaker 4: all also a bad idea. You'd think that people could
Speaker 4: see that and factor that out.
Speaker 2: Gotta have so many questions. Why are specs a bad idea?
Speaker 2: On paper? It seems like a totally fine way to
Speaker 2: go public. In practice, it only seems like total garbage
Speaker 2: companies take that route.
Speaker 3: It feels self selecting.
Speaker 4: Yeah, well, of course, because the way they're structured to
Speaker 4: get people to invest in a company you don't even
Speaker 4: know what it is yet, it means that it's not
Speaker 4: great for the companies, right, you get a ton of yeah,
Speaker 4: oh right, that's just leakage.
Speaker 2: Well, just explained the mechanism again.
Speaker 4: So people, you know, if you put money into a spack,
Speaker 4: when they decide they're going to do a deal, you're
Speaker 4: allowed to take your money back out of the spack.
Speaker 4: I can't remember all the details, all right, Right, So
Speaker 4: if you're like, well it's a good deal, I'll leave
Speaker 4: my money in. But if you're the company being acquired,
Speaker 4: you don't know if you're going to be acquired or not,
Speaker 4: because people could take their money out. So just why
Speaker 4: wouldn't if you could go public on your own, you
Speaker 4: would prefer to do that. So by definition, the spacks
Speaker 4: are buying companies that couldn't go public on their own, right, right?
Speaker 3: Is VC investing fun again? I got the impression like
Speaker 3: two years ago everyone was pretty depressed, and I'm quite
Speaker 3: sure we did a few episodes on it at the time.
Speaker 3: But are people having fun again?
Speaker 4: Well again, I retired, so no, nobody's having fun. I
Speaker 4: wasn't having fun. I think if you have a billion dollars.
Speaker 4: It's probably fun if you're doing the big deals. I
Speaker 4: think if you're in AI, it's fun. If you're doing
Speaker 4: anything else, it should be fun if you're doing something
Speaker 4: besides AI. Right, because now everybody's distracted by the shiny
Speaker 4: new thing, you can go find companies that are interesting
Speaker 4: to you and invest. The problem is you have to
Speaker 4: worry about what happens a year from now when they
Speaker 4: need the next round. Is anybody going to be paying attention?
Speaker 4: I think it's probably pretty hard to be an early
Speaker 4: stage investor unless you're investing in AI. And if you're
Speaker 4: investing in AI, you're probably not writing small checks, low valuations,
Speaker 4: and you can't control the outcome at the end. You
Speaker 4: can only control what you do at the beginning, so
Speaker 4: you probably won't be making money.
Speaker 2: I have this theory that actually nobody likes bubbles or
Speaker 2: booms even but let's say bubbles in part like, if
Speaker 2: I missed it, I'm upset because someone else is getting rich.
Speaker 2: If I'm in it, I'm like really anxious. Am I
Speaker 2: gonna like I'm anxious about two things. I'm anxious. Am
Speaker 2: I going to sell it at the right time? I'm
Speaker 2: also kicking myself for not investing more. No matter how
Speaker 2: much I invested, I'm upset with myself for not having
Speaker 2: invested more.
Speaker 4: Is that right?
Speaker 2: This is just my impression when I read history, which
Speaker 2: is that everyone, even in the boom times, there's like
Speaker 2: this like din of like stress underneath. Is that true?
Speaker 2: Or am I just fantasy projecting how my own neuroses
Speaker 2: from birth onto.
Speaker 3: Everyone thinks they're gonna time it.
Speaker 4: Right, But it's fun. The bubbles are fun, especially if
Speaker 4: you're young and stupid. Right, there's the New Yorker cartoon.
Speaker 4: I want my bubble back. Yeah. Yeah. The flip side
Speaker 4: is it is stressful. I remember, well, its stressfull both
Speaker 4: after obviously I still have my Razor of His stock certificates.
Speaker 4: I had a certificated because I couldn't sell it for anything,
Speaker 4: you know, for any real money, and that at one
Speaker 4: point had been worth quite a bit of a month
Speaker 4: more than.
Speaker 2: Again, I remember, what did that company do? It is
Speaker 2: like an ad network or something.
Speaker 4: No, No, they built websites. Oh yeah, yeah, I remember.
Speaker 4: They were great. I mean they were a great company
Speaker 4: when nobody else knew how to build websites.
Speaker 2: Friends who worked for Razor fish even as like recently.
Speaker 2: It is like twenty ten.
Speaker 4: They sort of hung on.
Speaker 2: For a little while. Jerry Newman, thank you so much
Speaker 2: for coming on odd lot. It's been wanting to chat
Speaker 2: with you for a long time, and I really appreciate
Speaker 2: your joining us. Yeah, thank you both, Tracy. I love
Speaker 2: that conversation. There's a lot there. I mean, obviously, I'd
Speaker 2: like anytime we can talk about the nineties bubble, but
Speaker 2: I had never really thought about or any come anywhere
Speaker 2: close to thinking about the AI analogy with containerization. It's
Speaker 2: a little embarrassing because that's such a core topic for us.
Speaker 2: We've talked about boxes so many times, but it's sort
Speaker 2: of re orient my thinking of AI and thes term
Speaker 2: very helpful.
Speaker 3: Yeah. Well, I mean this just kind of proves the
Speaker 3: point that no one thinks of containerization. I mean, I
Speaker 3: said before, it is a technology story. And one of
Speaker 3: the reasons I do think of it that way is
Speaker 3: because I read that book The Box, Yeah, which is
Speaker 3: really good, but no one thinks about it as an
Speaker 3: investment story. No, because of the reasons that Jerry just
Speaker 3: laid out.
Speaker 2: Yeah, No, that's really interesting, and you know, again like
Speaker 2: it does feel at some point that non tech businesses
Speaker 2: non AI businesses. Eventually someone hopefully for the industry, like
Speaker 2: makes a lot of money actually using these tools because
Speaker 2: we've been in the picture shovels phase or whatever. But
Speaker 2: at some point maybe it's an existing healthcare company or etc.
Speaker 2: Or maybe it's a new kind of law firm or
Speaker 2: an incumbent law firm where it's like, Okay, we have
Speaker 2: found a way to use this technology in a manner
Speaker 2: that is very profitable, productive, and market expanses.
Speaker 3: So to me, that's the key thing. So the key
Speaker 3: thing is it's not that we're going to use this
Speaker 3: technology necessarily to cut costs and boost profit margins. It's
Speaker 3: that we will actually expand our customer base and make
Speaker 3: it up in volume by selling more knowledge.
Speaker 2: You know, it's interesting thing that Jerry said, and you
Speaker 2: put into words something that I hadn't really thought about before,
Speaker 2: but this idea about being at the end of this
Speaker 2: sort of computer revolution, and there is something about AI
Speaker 2: specifically where people it's like and it can't really literally
Speaker 2: be this where it's like, well, this is the last technology,
Speaker 2: right because.
Speaker 3: Because we're gonna get robots, yeah, right.
Speaker 2: And I don't know if like other booms or technological
Speaker 2: revolutions had this feeling where it's like, this is the
Speaker 2: last one. Theoretically, if you get Agi or whatever robots,
Speaker 2: you don't need any further technological innovation, et cetera. It creates,
Speaker 2: I think, a very weird, uncomfortable dynamic. But the idea
Speaker 2: of AI is the end of what we do with
Speaker 2: computers rather than the start of like something genuinely new
Speaker 2: like that actually like snaps into place a lot of
Speaker 2: thoughts for me.
Speaker 3: Once we invent God, we're done.
Speaker 4: We're done.
Speaker 2: Everything else takes care of it.
Speaker 3: So yeah, all right, shall we leave it there?
Speaker 2: Let's leave it there?
Speaker 4: All right?
Speaker 3: This has been another episode of the oud Lots podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm joll Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Jerry Newman, He's at g A Newman.
Speaker 2: Follow our producers Carmen Rodriguez at Carman armand dash Ol
Speaker 2: Bennett at Dashbod and Keil Brooks at Kilbrooks. More odd
Speaker 2: Lots content go to Bloomberg dot com slash odd Lots.
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