Speaker A
Bloomberg Audio Studios. Podcasts, Radio, News.
Jerry Neumann discusses the challenges of investing in AI now, comparing it to past tech revolutions and the difference between value creation and capture.
Ask about this video. Answers come from its transcript only — with the timestamp, so you can check them.
Generated from the transcript and can be wrong — check the timestamp.
Key Takeaways
- AI is a revolutionary technology but investing in it now is challenging due to lack of clear pure-play companies.
- Value creation by AI does not guarantee value capture by investors or founders.
- Historical tech breakthroughs like containerization took time to generate wealth for investors.
- Market froth exists, making it difficult to distinguish winners from losers in AI investments.
- Prudent investors might consider cashing out early or focusing on ancillary industries rather than direct AI plays.
What the video covers
- There are few pure-play AI companies publicly traded, with Nvidia being a notable exception.
- Investors often buy 'picks and shovels' stocks rather than direct AI plays, similar to historical gold rush investing.
- The current AI market shows froth, with consensus that some companies will succeed while many will fail.
- Jerry Neumann argues AI is revolutionary technology but cautions about who will capture the created value.
- He compares AI to containerization, a transformative but slow-to-monetize technological breakthrough.
- Containerization revolutionized global trade but did not create many wildly rich individuals directly from the technology.
- Neumann emphasizes the difference between value creation for society and value capture by investors or companies.
- He advises caution and suggests cashing out early may be wise given the uncertainty in picking AI winners.
- The discussion highlights the complexity of investing in emerging technologies and the long timelines involved.
- Neumann brings decades of venture capital experience and academic insight to the conversation.
Full Transcript — Download SRT & Markdown
Speaker A
Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.
Speaker B
And I'm Tracy Away.
Speaker A
Tracy, our colleague here at Bloomberg, Ed Harrison had an interesting newsletter today.
Speaker A
And this is actually something I've been thinking about a little bit lately, which is that for all the talk of the AI boom or the AI bubble driving the stock market, there's no AI pure plays really that are publicly traded. Like Nvidia is probably the closest, but three years ago people were excited because they were mining Ethereum, before that it was like video games.
Speaker A
You know, this was only an AI company in people's mind for since late 2022. Google still is, you know,
Speaker A
We know they're all investing in a ton.
Speaker A
There's actually no like no AI company that people are excited about in the public markets.
Speaker B
I mean, I think it's true.
Speaker B
Here you have this thing that a lot of people would say is revolutionary technology, right? But you kind of have to decide if you're going to invest in it, is it going to be like upstream or downstream?
Speaker B
And there doesn't really seem to be that much pure play, as you say.
Speaker A
As our colleague Sid Verma might like to say, people are investing in the picks and the shovels.
Speaker A
You know, this gold rush.
Speaker A
No, I don't think he actually said that.
Speaker A
It's just a million other people said that.
Speaker A
But it is.
Speaker A
It's picks and shovels, right?
Speaker B
Stop making fun of completely reasonable commentary, that's what I say.
Speaker A
He said.
Speaker A
You know, it turns out incidentally, picks and shovels have been great.
Speaker A
You could have bought Caterpillar or you could have bought some old school Hvac company that's providing cooling or heating or whatever and made a ton of money.
Speaker A
So actually, it turns out at least for the last few years, all those awful cliches have actually been big money makers and I should not make fun of them.
Speaker B
Well, here's the other thing I would say, it does feel like everyone kind of agrees at this moment in time that there is froth in the market.
Speaker B
Maybe it's not a massive bubble, right?
Speaker B
But there's some froth and everyone is kind of admitting or saying that you're going to have some companies that emerge as big winners.
Speaker B
Much like the dotcom era and then a bunch of companies that like actually end up being losers.
Speaker B
And I think again, like that is consensus at this point.
Speaker B
But it doesn't really necessarily translate into actual investment because of course, the trick is actually picking the winners and losers in the market.
Speaker A
Yeah, you know, they're all kind of being treated as winners, which is the other.
Speaker A
Yeah.
Speaker A
Anyway.
Speaker A
It's a very weird time.
Speaker A
We got to do more episodes on this because it is sort of the central question for on whether we're just talking about the market or or talking about the economy, etc.
Speaker A
Someone I've wanted to talk to for a long time, earlier this year he wrote a uh essay for Colossus called AI will not make you rich.
Speaker A
It came out in September.
Speaker A
It seems like ages ago.
Speaker A
It's very disappointing because I think a lot of people really are hoping to get rich on AI, so this is a very unwelcome message.
Speaker B
It's also very much a sort of core odd lots thesis because in the essay he compares and contrasts AI with containerization.
Speaker A
Which is another.
Speaker B
Favorite topic.
Speaker A
Which was fascinating.
Speaker A
So let's just get to the guest.
Speaker A
Someone I've wanted to talk to for very long time, someone who literally is the perfect guest, long time VC, started venture investing in 1997.
Speaker A
Also a professor at Columbia Business School, we're going to be talking to uh investor Jerry Newman.
Speaker A
Also the co-author of a recent book, Founder versus Investor, the honest truth about venture capital from startup to IPO.
Speaker A
Maybe he'll tell us when we'll see some of these.
Speaker B
And the bringer of excess Halloween candy, so he gets he gets brownie points for that too.
Speaker A
Literally the perfect guest.
Speaker A
Uh Jerry, thank you so much for coming in.
Speaker A
Thrilled to finally have you here.
Speaker C
Thanks, I'm glad to be here.
Speaker A
What does that mean AI won't make you rich?
Speaker A
AI has made people super rich and it's making people rich every single day.
Speaker C
You know, as an old mentor used to say, money's not money till it's cash.
Speaker A
Okay.
Speaker C
So, is anybody really rich yet?
Speaker A
Oh, come on.
Speaker A
I mean, Jensen Wong bought an entire bar in Korea, he's like bought beer and fried chicken for everyone.
Speaker A
He's rich.
Speaker C
I think it's smart to cash out early.
Speaker A
Okay, it's more.
Speaker C
Is that what he's doing?
Speaker A
Okay, let's say.
Speaker C
But if he really believed it, would he be selling his stock now?
Speaker C
I mean.
Speaker A
Okay, so what do you mean talk about that because you obviously have a lot of experience.
Speaker A
Actually, that brings another line of question that I want to get into.
Speaker A
But what does that mean it's smart to cash out early?
Speaker A
When you're experience, what does that actually mean?
Speaker C
So, look, I I I believe that AI is a revolutionary technology.
Speaker A
Which is important to say.
Speaker A
Because not everyone agrees with that.
Speaker C
Yeah, totally, you know, I'm on Blue Sky and nobody agrees with that.
Speaker C
Um, but I I do.
Speaker C
I think so.
Speaker C
And but there's a difference between value creation and value capture.
Speaker A
Okay.
Speaker C
Uh so, even if AI creates a lot of value for society,
Speaker A
Yeah.
Speaker C
Who's going to get that value? Is it going to be the early investors?
Speaker C
Is it going to be the core, you know, the foundation model companies?
Speaker C
Is it going to be consumers?
Speaker C
You know, I think that's the question people need to ask.
Speaker B
I actually broadly agree with this thesis when it comes to AI, but maybe just to clarify the idea here.
Speaker B
Compare and contrast this current AI cycle with maybe previous technological breakthroughs.
Speaker B
And you know, I mentioned containers.
Speaker B
I think a lot of people aren't used to thinking about boxes as this major advancement in technology.
Speaker A
Yeah.
Speaker B
But at the time, they really were.
Speaker C
Yeah, I I mentioned containerization and most of my peers think I'm talking about Docker.
Speaker A
So.
Speaker C
So I'm talking about shipping containers, right? The big boxes they put on ships and then they can move from the ships onto the, you know, rail cars and onto the truck.
Speaker C
And this was a revolutionary technology.
Speaker C
I mean, it changed everything about the way we live.
Speaker C
I don't remember, I'm probably a little older than you all.
Speaker C
But when I was a kid, my grandmother used to send up oranges from Florida at Christmas time, right?
Speaker C
Because they were rare.
Speaker C
You couldn't just go into a grocery store and buy them.
Speaker C
Now you can buy oranges anywhere and anytime.
Speaker C
People don't really realize how much our lives have changed because of shipping containerization.
Speaker C
Because of these, you know, global logistics and the globalization of shipping.
Speaker C
Now, this is a revolutionary technology.
Speaker C
Who got rich from it?
Speaker C
I mean, generally, if you look at the 1960s, say,
Speaker C
How many people became wildly rich from technological innovation?
Speaker C
Can you think of anyone?
Speaker C
Because I've been asking this question for years.
Speaker C
There are people who got rich in media and whatnot, but there wasn't a lot of technological innovation that made individuals rich.
Speaker A
Did tech just start like 20 years ago?
Speaker A
Did they have technology back then?
Speaker C
Right.
Speaker C
I mean, it's I think but this is the thing, right? So we talk about computer technology, the information and computer technology revolution as technology.
Speaker C
But obviously there's always been technology, but only at certain times in this technological cycle.
Speaker C
Do people seem to make money as investors and as inventors?
Speaker B
Wait, explain more though.
Speaker B
Because I mean, I could argue that Maersk or someone like that got pretty rich off of containerization.
Speaker B
Like maybe it took a while.
Speaker B
But even though the shipping industry is highly cyclical, when they are in the boom period, they make a lot of money.
Speaker C
Sure.
Speaker C
I mean, the existing shipping companies got very large and made a lot of money.
Speaker C
They got larger and made more money.
Speaker C
And is, you know, who made money off?
Speaker B
You're talking about new, completely new entrance.
Speaker C
Yeah, so just I mean as background, I'm a venture capitalist, right?
Speaker C
Or have been a venture capitalist for a long time.
Speaker C
Uh recently retired.
Speaker C
And I think about people investing and making money, new companies, you know, inventors or entrepreneurs making money.
Speaker C
Not the existing incumbents making money.
Speaker C
And I think that people will make money on AI, uh it might be Microsoft making a ton of money on AI.
Speaker C
It could be AI.
Speaker C
You know, when containerization, shipping containerization came around, Sealand was the, you know, instigator of this.
Speaker C
And the founder of Sealand made money, primarily because he sold early, right?
Speaker C
He sold Sealand to RJR Nabisco, or sorry, it was just RJR at the time.
Speaker C
And they thought they were diversifying, which was the big thing then.
Speaker C
Paid him a lot of money.
Speaker C
And then they drove it into the ground.
Speaker A
What did Sealand do?
Speaker A
What was did they I actually don't I'm not familiar with this company at all.
Speaker A
Which I think kind of speaks to your point.
Speaker A
But what was Sealand?
Speaker C
Yeah, so so it was a truck.
Speaker C
It was started out as a trucking company.
Speaker C
Um, and the founder of Sealand was a trucking entrepreneur.
Speaker C
And he said, you know, it's silly, you you you go into a port, your truck sits around all day.
Speaker C
While, you know, the longshoremen put a cargo net into a container ship, load everything into it, pull it out, unload it.
Speaker C
And then reload it back into your truck.
Speaker C
This is not efficient.
Speaker C
And this obviously is a is an obvious idea, right?
Speaker C
Just put it all in a box that you can then put that box on a truck.
Speaker B
The best ideas are always the obvious ones in retrospect.
Speaker C
But but the the problem with it was it was a systems problem, right?
Speaker C
The longshoremen didn't want it, the ports didn't want it, the port authorities didn't want it, the politicians didn't want it.
Speaker C
Nobody wanted this to happen because this sort of enormous change would put a lot of people out of jobs.
Speaker C
It would upset the existing order.
Speaker C
And it did, I live in Hoboken, and in Hoboken,
Speaker C
there's a lot of peers that nobody uses except to go running on now.
Speaker C
Because back in the sixties, it was a longshoreman town.
Speaker C
And when I moved there in, you know, the early nineties, it was empty.
Speaker C
It was starting to gentrify.
Speaker C
But because all of those people had lost their jobs and moved out.
Speaker A
And I suppose like even in the case of Maersk and I'm sure, you know, they obviously have made a lot of money because the explosion of global trading volumes and uh containerization is part of that.
Speaker A
It wasn't overnight wealth, right?
Speaker A
It wasn't like it wasn't not people got richer, but it was not like some bubble get rich quick thing where they suddenly cashed in on the new thing.
Speaker C
Oh, no.
Speaker C
I mean, I suppose whoever owns Maersk may have gotten richer.
Speaker A
Yeah.
Speaker C
But it's not like you're going to look at the Forest 400 and see all these shipping magnates.
Speaker C
Who became suddenly, you know, enormously wealthy.
Speaker C
There are a few.
Speaker B
Wait, okay.
Speaker B
So if I think about a box.
Speaker A
Can we just keep this whole conversation on boxes actually?
Speaker A
That gets to AI at the very end.
Speaker B
On boxes.
Speaker B
Well.
Speaker B
One more question.
Speaker A
No, no, no.
Speaker B
And then we will we will be able to discuss AI.
Speaker B
But I think about a box and as you say, it's sort of an organizational structural problem.
Speaker B
Like the box itself is not the huge technological advancement necessarily.
Speaker C
Well, it was.
Speaker C
So.
Speaker C
I'm not sure I understand the question.
Speaker C
Because containerization became widespread very quickly.
Speaker B
Right.
Speaker B
But what I mean is like, why was that value seemingly captured by incumbents versus startups?
Speaker C
Right.
Speaker C
Uh I think because it disseminated so quickly, right?
Speaker C
It was an obvious idea.
Speaker C
Everybody who saw it said, okay, we need to do this, right?
Speaker C
Everybody who's already in the business said, if this is going to happen, we have to do it.
Speaker C
We we can't be left behind.
Speaker C
We will be left behind if we don't do it.
Speaker C
Which, you know, I think was also obvious.
Speaker C
So, the reason nobody else did it first was it was hard to do.
Speaker C
It was hard to make happen.
Speaker C
And technology's always come in these, you know, technological systems if if they're worthwhile technologies, right?
Speaker C
So the personal computer didn't change the world on its own.
Speaker C
Right? It changed the world alongside the internet, you know, alongside a bunch of technologies that formed a system.
Speaker C
So the hard part here was building the system.
Speaker C
Not the individual technologies.
Speaker C
And this is true, I think of computers as well.
Speaker C
You know, the first microprocessors weren't considered revolutionary.
Speaker C
Intel didn't consider the 4004 revolutionary.
Speaker C
They considered it evolutionary.
Speaker C
The engineers have said this.
Speaker C
And it wasn't revolutionary until people put it to use in ways that they didn't anticipate.
Speaker A
Actually, can we go?
Speaker A
I didn't know that like I hadn't really thought about that with Intel.
Speaker A
That at the time it didn't feel to them that it wasn't a revolutionary technology.
Speaker A
That's sort of mind-blowing.
Speaker C
It is, right?
Speaker C
This is I think from Michael Malone's.
Speaker C
The Intel Trinity, the book.
Speaker C
You know, he interviewed a bunch of Intel engineers.
Speaker C
And he said, you know, like they thought they were building a better chipset to build pocket calculators.
Speaker C
Or desktop calculators, I should say.
Speaker C
So they had a client Busycom.
Speaker C
Who wanted to build a better desktop calculator.
Speaker C
Calculators were big back then in 1970-ish.
Speaker C
And one of the engineers said, well, why do we keep building custom chipsets for each different calculator?
Speaker C
Why don't we just build a chipset that we can customize the software and change what it does?
Speaker C
And Intel's kind of like, eh.
Speaker C
Um, and Busycom was like, okay, we'll pay for that.
Speaker C
And then Busycom actually tried to back out and they gave the rights back to Intel.
Speaker C
So Intel owned the rights to this 4004.
Speaker C
And then they started selling it.
Speaker C
And it wasn't Intel, you know, Intel believed at the time it was going to be maybe used for dedicated hardware.
Speaker C
You know, hardware controllers, that kind of thing, not by consumers.
Speaker C
So it wasn't until people on the outside said, hey, you know, I love these IBM mainframes or these deck mini computers.
Speaker C
I'd like to have my own, but obviously nobody can afford that.
Speaker C
Why don't I just try to build my own?
Speaker C
Right? So these kind of outside inventors, these this permissionless invention.
Speaker C
And then it really the real revolution didn't happen until this, you know, everybody's like, oh, Intel.
Speaker C
It was the 6502 where the price came down so dramatically that, you know, Steve Wozniak could walk into a computer fair.
Speaker C
Get get some for free and go home and build a personal computer.
Speaker A
That's crazy.
Speaker A
I wonder who made the chips for the Commodore 64 computer that I had.
Speaker C
They were 6502s.
Speaker A
Those were Intel.
Speaker C
No, they weren't Intel.
Speaker C
They were MOS technologies.
Speaker A
Oh, got it.
Speaker A
Got it.
Speaker A
I wonder.
Speaker C
Those were the cheap ones.
Speaker B
Wait, what year was that?
Speaker B
When you had that?
Speaker A
Well, I had I think I like learned some basic and did some coding on the I would have said,
Speaker A
maybe 1988, 1987, somewhere around there.
Speaker A
Made a few.
Speaker A
I missed those days.
Speaker A
It's crazy that I didn't can I just say?
Speaker A
I sometimes when I think about my life path, like how did I not end up like a tech guy?
Speaker A
Because I was like very into math.
Speaker B
Wait, in 1987?
Speaker B
You were coding?
Speaker A
You were seven years old.
Speaker A
Yeah, yeah.
Speaker A
I had I got that my dad got me this magazine that just it was very crazy.
Speaker A
It literally just sent you pages of code and then you just typed it in and you could like make a video game.
Speaker A
I was doing that at seven.
Speaker A
I could be like one of those like who's parents gave him a computer when he was seven.
Speaker A
And anyway.
Speaker C
It's not too late, Joe.
Speaker C
It's not too late.
Speaker B
Yeah, it isn't too late.
Speaker B
Well, maybe it is too late because now we have AI doing all the coding, right?
Speaker A
Okay, just so I understand, when it comes to, I guess, the advantages of the incumbents.
Speaker A
To actually monetizing new technology or benefiting from new technology.
Speaker A
Is the moat around their business, the network and their sort of role in the network or is it the vast amounts of cash they have?
Speaker A
And the ability to sort of roll out massive investment to capture that value.
Speaker C
I I think it's the latter, right?
Speaker C
So anybody can build a foundation model, right?
Speaker C
If you have the money.
Speaker C
I mean, the the technology is not mysterious.
Speaker C
It doesn't feel like the technology is really changing very quickly anymore.
Speaker C
And of course, I don't have insight into what's happening inside of Open AI.
Speaker C
But looking at it over the past couple of years, it's the same thing, but slightly better.
Speaker C
It's evolutionary now, right?
Speaker C
The the first part was revolutionary.
Speaker C
And now it's evolutionary.
Speaker C
So if you wanted to build one, you could build one.
Speaker C
And I have friends who are running them on their laptops.
Speaker C
Very slowly.
Speaker C
But it's possible.
Speaker C
So now the question is, do you have enough cash to build the data centers, to buy all the chips, to build something that is large enough that when you train it, it does something useful?
Speaker C
And it's just a question of having that, you know, the the authority in the market to be able to raise that money.
Speaker A
By the way, speaking of ideas that were sort of really obvious that took a while.
Speaker A
I'm always blown away that like how long it took them to put wheels on luggage.
Speaker A
I don't think anyone like made super got super rich on that.
Speaker A
But when I was a kid, I remember like we had these big suitcases.
Speaker A
And that's the most obvious thing, it took a while.
Speaker A
Anyway.
Speaker A
I know I don't think anyone got.
Speaker B
Technology's still not perfected, as you know, because you've been in airports with me.
Speaker B
And the wheels on my luggage are broken.
Speaker A
But I also I don't think anyone got like super rich.
Speaker A
Off of wheels, that just seems like an obvious.
Speaker A
You know, I wanted to jump ahead actually in the conversation a little bit.
Speaker A
Because I don't want to forget this point.
Speaker A
But this is something I've become a little obsessed with, which is that.
Speaker A
I've been meaning to ask a VC about this.
Speaker A
Which is that there seems to be this blurring of private and public markets in various ways.
Speaker A
You've retail participation, private markets, etc.
Speaker A
For the VC, in my mind, I feel like the exit was the IPO or the acquisition, right?
Speaker A
So you buy.
Speaker A
Do VCs these days have to think a little bit more about market timing and selling early?
Speaker A
So someone who was an early investor in Open AI or whatever.
Speaker A
You know, in the past they might have just held and then sell at the IPO or the acquisition.
Speaker A
Probably not going to happen in Open AI's case, they're too big to be acquired.
Speaker A
But do VCs and in your experience these days have to think a little bit more about this idea of selling early, timing the exit?
Speaker C
Well, I think VCs always had to think about timing.
Speaker A
They did.
Speaker C
I you know, I I've done pretty well in VC and I attribute it entirely to being lucky at starting investing at the right time.
Speaker C
So the first time around I was starting in 97, which any.
Speaker C
You could make money in the second time around I started in 2007.
Speaker C
Which again, it was just or 2008.
Speaker C
It was just an easy time to buy in.
Speaker A
But I mean the timing of the sale.
Speaker A
Like does that something that.
Speaker C
No.
Speaker A
Where in the past it might have been automatic.
Speaker C
No, I wrote this thing about VC in the 1980s.
Speaker A
The exit.
Speaker C
A long time ago.
Speaker C
It's on the on the blog.
Speaker C
You can find it.
Speaker A
Now, that was not the case.
Speaker C
Right.
Speaker C
Um, and and the thing because nobody talks about the 1980s, right?
Speaker C
It was there was plenty of VC in the eighties, but nobody talks about it.
Speaker C
People talk about the sixties and the seventies and the nineties.
Speaker C
So I was like, all right.
Speaker C
What happened then?
Speaker C
One of the interesting things about it is there were IPO windows then where, you know, 1983, the IPO window opened.
Speaker C
A bunch of companies went public and then it closed again.
Speaker C
And you can see it in the numbers, when people went public.
Speaker C
So people always had to think about timing.
Speaker C
The IPO is obviously the best exit because you want to sell to the greatest fool.
Speaker C
And nobody's greater fool than the public, right?
Speaker C
So you look for the the um IPO window to open.
Speaker C
When you can't, you have to sell to somebody else.
Speaker C
You know, VCs have this problem of their limited fund life.
Speaker C
So I look at my portfolio and I'm a really early investor.
Speaker C
Or have been a really early investor, so I look at companies and say, oh, it's I invested 10 years ago.
Speaker C
They're going to have to sell.
Speaker C
You know, it's the.
Speaker C
So people look for the IPO window, but if they can't find it, they have to sell somewhere else.
Speaker B
Well, how much of the money flowing into AI startups now is just the expectation that a bunch of these little companies are eventually going to get bought by larger incumbents?
Speaker B
And basically, you're going to have consolidation and you will get that exit.
Speaker C
I don't think anybody can predict when the IPO window opens.
Speaker C
I mean, I wish I could.
Speaker C
But I don't think I've never seen anybody even say they could predict when the IPO window opens.
Speaker C
So I think a smart VC invests in a company that can become self-sustaining to some degree.
Speaker C
And then you wait for the timing to come.
Speaker C
You don't invest and say, I'm going to flip this in three years.
Speaker C
No.
Speaker C
Yeah, the other problem is VCs don't invest in all these AI companies.
Speaker C
Saying a bunch of them are going to become valuable.
Speaker C
They invest saying one of these is going to become valuable.
Speaker B
Right, the lottery ticket theory.
Speaker C
Yeah.
Speaker C
The power law.
Speaker A
Speaking of the IPO window, I'm never totally satisfied by a lot of the explanations.
Speaker A
For the drop off in IPOs, generally, although I know there was that law passed in 2001.
Speaker A
Or.
Speaker C
Sarbanes Oxley.
Speaker A
Sarbanes Oxley.
Speaker A
And I get.
Speaker B
That law that everyone hated for a really long time.
Speaker A
That law.
Speaker A
That.
Speaker A
But then you see like, you know, 2021, there's like a billion garbage companies went public via SPACs, etc.
Speaker A
How much is it about, okay, there are some disadvantages to being public versus there's just so much more private capital out there.
Speaker A
Such that the imperative to perhaps ever go public and it's liquid and they're around and like, what do you attribute?
Speaker A
There are these big companies that are private, Stripe and Open AI and Anthropic that choose to stay private.
Speaker A
What do you think the main reason for that is?
Speaker C
Well, it's because they can, right?
Speaker C
I mean, being a public company is no, it's no party, right?
Speaker C
I mean, it it kind of sucks being a public company.
Speaker A
What is it about it?
Speaker A
What sucks?
Speaker C
Well, you have to tell everybody what you're doing every three months.
Speaker A
Yeah.
Speaker C
That's you know.
Speaker C
And then they come back and complain about it.
Speaker C
So, sorry, I'm being a little facetious.
Speaker C
But it is.
Speaker C
It's hard to be a public company.
Speaker C
Everybody, you know, anybody who runs a public company will tell you they spend a lot of time being a public company if they're running the company.
Speaker C
So that's taking away from actually running the company.
Speaker C
I think the flip side is you're liquid and that's.
Speaker C
Yeah, you know.
Speaker C
So if you can stay private, why wouldn't you stay private?
Speaker C
Or if you can go public and retain control of the company, you know, like Henry Ford or Mark Zuckerberg.
Speaker A
Yeah.
Speaker C
Then why wouldn't you do that?
Speaker C
But I think it's because there is so much late stage money, this isn't necessarily a good thing, it's because there's so much money out there that's not being invested in more revolutionary technologies earlier.
Speaker C
With all this money being invested in AI, you may wonder if people are still going to want to invest late stage Stripe.
Speaker C
Or the analog is Stripe might be making money now, not sure.
Speaker B
I know you brought up previous historic analogies like VC in the 1980s.
Speaker B
But just to focus on the one that everyone else seems to be focused on at the moment, which is the dotcom bubble in the early 2000s.
Speaker B
What are the key differences you're seeing in terms of the VC and financing environment now versus 20 or 25 years ago?
Speaker C
I think the key difference is that most of the money is coming from people who aren't looking for much risk.
Speaker C
Right? So I mean Open AI is primarily funded by bigger companies, right?
Speaker C
Most of their money is coming from large companies.
Speaker C
What happens if Open AI, you know, gets hit by a bus, right?
Speaker C
So Microsoft's had a bunch of money.
Speaker C
A whole bunch of big companies are out a bunch of money.
Speaker C
I don't think much happens to the economy.
Speaker C
I think which is different than in the dotcom bubble.
Speaker C
Where a lot of consumers were in it, a lot of consumers were in it leveraged, right?
Speaker C
Buying on margin or whatever.
Speaker C
A lot of people had options, right? People employees and and they were spending the money from their options.
Speaker C
Before they were liquid, you know.
Speaker C
It was there was much I think it was a different dynamic with the economy.
Speaker B
The wealth effect.
Speaker A
I don't know.
Speaker A
This is I think this is a contrarian take on your part.
Speaker A
Because you hear a lot about, I mean, in two dimensions, you hear a lot about the direct wealth effect from people's exposure to the stock market.
Speaker A
Which AI is a big part of the story.
Speaker A
And then you also hear about, of course, the sort of real economy effects through all of the spending, which we will get into on, you know, the data centers.
Speaker A
And the caterpillar, the the turbines for the uh gas generation, etc.
Speaker A
I think many people would say there is a lot right now riding on the health and the sustainability of this particular sector.
Speaker C
So I I think we can separate the companies like Microsoft and Nvidia.
Speaker C
Are they overvalued because of this?
Speaker C
Maybe.
Speaker C
Doesn't make a huge difference to the economy.
Speaker C
Probably not.
Speaker C
I don't think so.
Speaker C
And the companies who are spending money on infrastructure, like building data centers, building power generation plants.
Speaker C
Those things, I think, are probably overbuilt.
Speaker C
Or not so much overbuilt as they are built.
Speaker C
And I think in 10 years, you're going to have a lot of extra compute, a lot of extra power generation.
Speaker C
And people will be able to use that for other things.
Speaker C
It'll also drive down the price of just using AI, probably.
Speaker A
Jevon's paradox, bro.
Speaker A
It's going to be out back and forth.
Speaker A
All right.
Speaker A
So.
Speaker A
AI.
Speaker A
Where are we?
Speaker A
You talk about cycles and I think you use a word that I hadn't.
Speaker A
Eruption.
Speaker A
What was the word you use?
Speaker C
So I'll.
Speaker A
Tell us your how you see cycles and what cycle we're in right now.
Speaker C
Right, so a lot of this is based on Carlotta Perez's work.
Speaker C
Which is pretty familiar to the venture capitalist in your audience.
Speaker C
I'm sure she wrote a book called Technological Revolutions and Financial Capital.
Speaker C
Where she explains the dynamics behind the Contradia waves that the Schumperter talks about.
Speaker C
So she has this theory about why these happen.
Speaker C
And you look at the Industrial Revolution, the second Industrial Revolution.
Speaker C
You can see these waves of technology, technological systems happening through the economy where they kind of start out.
Speaker C
They grow really rapidly.
Speaker C
And then there's usually some sort of adjustment, some sort of bubble bursting.
Speaker C
And then things kind of level out and then start to plateau and then a new one starts.
Speaker C
And this is people have noticed this since at least Contradia in 1926.
Speaker C
She has a rhythm mechanism for explaining it.
Speaker C
And in her mechanism has these four phases.
Speaker C
The first phase is eruption, which she spells with an I.
Speaker C
Which I think is actually in the dictionary as a word.
Speaker C
I don't know what the difference is between that and eruption.
Speaker A
You make us out smarter.
Speaker C
Right.
Speaker C
So.
Speaker A
Yeah, yeah.
Speaker C
Keep saying that.
Speaker C
It it is the start, it's when people have invented something and it is starting to catch on.
Speaker C
But it hasn't caught on yet.
Speaker C
There's a lot of people saying, is this the future, is it not?
Speaker C
You look at personal computers in the 19 late seventies.
Speaker C
Early 1980s.
Speaker C
You know, maybe even before IBM got involved.
Speaker C
And people didn't think personal computers were some people thought they were the future.
Speaker C
And you know, if you look back at computer history, everybody talks about the people who did think that.
Speaker C
They don't talk about the other 99.9% of smart people who said they weren't.
Speaker C
This is the eruption phase where there's a lot of uncertainty about where this technology will go.
Speaker C
It's starting to build connections to other technologies.
Speaker C
Starting to attract money, attract smart people because it's interesting.
Speaker C
And it it might actually change things.
Speaker C
So this is the beginning.
Speaker C
And and I think the the connection here to AI is people wonder if we're in the eruption phase of AI.
Speaker C
Or not.
Speaker C
Is this the start of a new technological revolution?
Speaker A
So which phase are we in?
Speaker C
I think we're not.
Speaker C
I think we're in the I think this is the end of the information computer technology wave.
Speaker C
The the end of the computer wave.
Speaker C
Right?
Speaker C
I mean, this is this is the culmination of the computer wave.
Speaker C
Right? I mean, why did we build computers?
Speaker C
We we built computers to help us think better, right?
Speaker C
This is what they're for.
Speaker C
They're they're knowledge machines.
Speaker C
So now we've kind of reached the natural end stage of what they do.
Speaker C
They're they're smart machines or smarter.
Speaker C
So I think this is not a new technological revolution.
Speaker C
I think it's the end of the old one.
Speaker C
And this is why I compared it to containerization.
Speaker C
Because the previous wave was.
Speaker C
Automobiles, mass production, and starting in 1915 or so.
Speaker C
Up until 1970 was the previous wave.
Speaker C
And containerization was squarely at the end of that wave.
Speaker C
And it was really kind of pulling together the technologies of that wave into something that increased productivity.
Speaker B
Right, like the final step of the global trade and I guess mobility revolution.
Speaker C
Yeah, exactly.
Speaker B
Okay.
Speaker B
So, how do you react as a angel investor?
Speaker B
Are you at the stage where you're looking for, I guess, the downstream winners, like the companies that are going to be able to apply or use AI most effectively?
Speaker B
Or how are you actually deploying all these thoughts in terms of your own investment strategy?
Speaker C
I retired.
Speaker B
Okay.
Speaker C
That's.
Speaker A
So you're standing it out.
Speaker C
That's, you know, I looked around and I said, look, how am I going to invest in foundation models?
Speaker C
Right?
Speaker C
I I'm I don't have a billion dollar fund.
Speaker C
I don't think that, you know, if you look at the big winners from the early winners from globalization, the IKEAs, right?
Speaker C
I mean, IKEA was a Scandinavian company until containerization.
Speaker C
And then then they became a global powerhouse.
Speaker C
A hugely successful company.
Speaker C
But they didn't need outside money.
Speaker C
I you know, Ingvar Kamprad, I think he he borrowed like a couple thousand dollars to start that company.
Speaker C
Or to to get that company to buy some inventory.
Speaker C
He never took outside money.
Speaker C
You look at, you know, Walmart, which had been around already, was an incumbent and used this kind of globalization to bring a lot more variety of products to the stores.
Speaker C
They didn't need outside money to do that.
Speaker B
Yeah, I guess if you're IKEA and suddenly you're flat packing everything and shipping it in containers and that's your big innovation, it's a money saving technology, right? So you don't actually have to raise new capital in order to flat pack everything.
Speaker C
Yeah, exactly.
Speaker C
They they were already flat packing.
Speaker A
Yeah, your mention of the Walmarts and Targets of the world is like in your essay.
Speaker A
It's like a sort of very light bulb thing.
Speaker A
It's like, yeah, we I don't know.
Speaker A
I guess we take them for granted.
Speaker A
But they're clear like massive containerization winners.
Speaker A
The scale that they succeed at is impossible to fathom in some prior era of.
Speaker B
Walmart is a logistics company, change my mind.
Speaker A
Literally, and many people literally, literally, literally that.
Speaker A
But it doesn't feel like with AI, the the equivalent has emerged yet, right?
Speaker A
We're still at the age where people are building the container deployment.
Speaker A
But the the company that exists and is massive that couldn't exist prior to AI, like does not feel doesn't we haven't seen that yet.
Speaker C
Well, you got to be a little patient.
Speaker A
No, no, I get.
Speaker A
No, I get it.
Speaker C
But like.
Speaker A
But like seriously.
Speaker C
I IKEA, so container.
Speaker C
The first container ship sailed in 1956.
Speaker A
Okay.
Speaker C
So when did IKEA become a global powerhouse, it really wasn't until the 1970s that they started to expand that way.
Speaker A
This is really important.
Speaker C
It takes a little time.
Speaker A
That it really take a while.
Speaker C
Yeah.
Speaker A
Where would you expect it to show up?
Speaker A
Like what industries?
Speaker A
Would you expect because obviously retail existed for a long time, furniture existed for a long time, then you get these behemoths.
Speaker A
Are there industries that you think are ripe to produce very tortured analogies, the IKEA of the AI wave?
Speaker C
Well, I I think they have to be knowledge intensive industries.
Speaker C
Right?
Speaker C
I mean, that's that's what AI is doing.
Speaker C
What it is making more efficient.
Speaker C
I mean, I think there's people that ask me like, well, then what should I invest in?
Speaker A
Tell us.
Speaker B
Yeah, this is the natural.
Speaker C
Well, this is.
Speaker A
Give us the answers.
Speaker C
I I've been thinking that myself.
Speaker C
But but the answer is really that as an investor, I don't decide what to invest in, I evaluate opportunities that come to me.
Speaker C
Um, and so I I have built a box in which you can evaluate opportunities, right?
Speaker C
They have to look like this, they have to look like an IKEA.
Speaker C
And if IKEA came to you and said I needed money at that time, you should have said, okay, I can see how shipping containerization is going to make you a much larger company.
Speaker C
Where nobody else seemed to see that, right?
Speaker C
Certainly the furniture makers in North Carolina didn't see it.
Speaker C
So I think this is the box that you evaluate things in.
Speaker C
And and as a a long time investor, I'm used to evaluating and I'm I try not to come up with ideas.
Speaker C
You know, that said, it has to be a knowledge intensive industry.
Speaker C
And I think something that I.
Speaker C
I said in the essay, which I wish I had said more about was.
Speaker C
The companies that tried to use shipping containerization to cut costs.
Speaker C
So they can increase margins, did poorly.
Speaker A
Oh, this is key.
Speaker A
Say more.
Speaker C
Yeah, whereas the companies that that use the efficiencies and pass the efficiencies on to the consumers.
Speaker C
So that they could become larger, became larger, right?
Speaker C
I mean, this is you look at these people saying, oh, we have AI, we're going to fire people.
Speaker C
I mean, that's I think it's the exact wrong move.
Speaker C
And I I think it's probably just every.
Speaker C
You know, every new thing comes along, people are like, oh, it's we're going to fire people.
Speaker C
You know, it's just an excuse.
Speaker C
But but if you're firing people because of AI.
Speaker C
You're doing it wrong.
Speaker C
Right? You should be using AI to say, I can use my people to do more.
Speaker C
I can grow my company.
Speaker C
I can vary my products.
Speaker C
I can take more market share.
Speaker B
So the value goes to the consumer and I guess you capture the value by selling more, right? More knowledge.
Speaker C
Yeah, I mean, I think Walmart never tried to maximize margins.
Speaker B
Right.
Speaker C
They.
Speaker B
It was it was volume.
Speaker C
Yeah.
Speaker B
Yeah.
Speaker B
Okay.
Speaker A
You know, it's a.
Speaker A
Interesting thing that Jerry said and he put into words.
Speaker A
Something that I hadn't really or thought about before.
Speaker A
But this idea about being at the end of the sort of computer revolution.
Speaker A
And it there is something about AI specifically.
Speaker A
Where people, it's like, it can't really literally be this.
Speaker A
Or it's like, well, this is the last technology, right?
Speaker B
Oh, no.
Speaker B
Because we're going to get robots.
Speaker A
Yeah, right.
Speaker A
Like and I don't know if like other booms or technological revolutions had this feeling where it's like, this is the last one.
Speaker A
Theoretically, if you get AGI or whatever, maybe robots.
Speaker A
You don't need any further technological innovation, etc.
Speaker A
It creates, I think, a very weird, uncomfortable dynamic, but the idea of AI is the end of what we do with computers.
Speaker A
Rather than the start of like something genuinely new, like that actually like snaps into place a lot of thoughts for me.
Speaker B
Once we invent God, we're done, because we can't get bigger.
Speaker A
Yeah, we can't get bigger.
Speaker A
We're done.
Speaker A
Everything else takes care of itself.
Speaker B
Yeah.
Speaker B
All right.
Speaker B
Shall we leave it there?
Speaker A
Let's leave it there.
Speaker B
All right, this has been another episode of the Odd Lots podcast.
Speaker B
I'm Tracy Away.
Speaker B
You can follow me at Tracy Away.
Speaker A
And I'm Joe Weisenthal, you can follow me at the Swart.
Speaker A
Follow our guest Jerry Newman.
Speaker A
He's at GA Newman.
Speaker A
Follow our producers Carmen Rodriguez at Carmen Arman, Dashal Bennett at Dashbot and Kyle Brooks at Kyle Brooks.
Speaker A
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Speaker A
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Speaker B
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Speaker B
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Speaker B
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Topics:AI investingventure capitaltechnology revolutionvalue creationvalue capturecontainerizationmarket frothNvidiastartup investingJerry Neumann
