November 12, 2025 • 48 mins
AI has made a lot of people fabulously wealthy. But sorry, it's probably not going to be the thing that makes you rich. And if history is any guide, we don't even know who the real AI winners are going to be. That's the thesis from longtime Venture Capitalist (now retired) Jerry Neumann. Earlier this year, Neumann published an article, "AI Will Not Make You Rich," putting the AI boom in the context of previous technological revolutions, such as the shipping container. He points out that a lot of the companies that were early to shipping containers didn't make much money, and that the real winners were the new businesses that emerged later and took advantage of the shipping container to build new business models (think about the likes of Walmart or Target). In this conversation, we talk about why it's so hard to invest in technological revolutions, where we are in the cycle, why he's getting out of VC, and when the big opportunities will eventually emerge.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the Outlass podcast.
Speaker 3 (00:22):
I'm Joe Wisenthal and I'm Tracy Allaway.
Speaker 2 (00:24):
Tracy our colleague here at Bloomberg at Harrison, had interesting
newsletter today and it's actually something I've been thinking about
a little bit lately, which is that for all the
talk of the AI boom or the A bubble driving
the stock market, there's no AI pure plays really that
are publicly traded. Like in video is probably the closest,
but three years ago people were excited because they were
mining ethereum. Before that, it was like video games. You know,
(00:47):
this was only an AI company in people's mind. For
since late twenty twenty two, Google still is you know,
we know they're all investing in a ton. There's actually
no like no AI company that people are excited about
in the public markets.
Speaker 3 (01:01):
I mean, I think it's true. 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? And there doesn't really seem to be
that much pure play, as you say.
Speaker 2 (01:18):
As our colleagues said, Verma might like to say, people
are investing in the picks and the shovels, you know,
this gold rush. I don't think he actually said that.
It's just a million other people's but it is.
Speaker 3 (01:27):
It shovels, it's fun, a completely reasonable commentary, That's what
I say.
Speaker 2 (01:32):
Is there? You know, it turns out instantly picks and
shovels have been great. You know, 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. 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 3 (01:51):
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. Maybe it's
on a massive bubble, right, 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, much
like the dot com era, and then a bunch of
(02:12):
companies that like actually end up being losers, and I
think again, like that is consensus at this point, 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 4 (02:26):
Yeah. I don't know.
Speaker 3 (02:27):
It just seems like a weird point in time where
people are like, oh, yeah, AI is great, but we
all know that some of these companies are going to
be massive losers, right.
Speaker 2 (02:34):
I think now they're all kind of big treated as winners,
which is the other thee Yeah, anyway, it's a very
weird time. You 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 talk about
the economy, et cetera. Someone I've wanted to talk to
for a long time. Earlier this year he wrote a
essay for Colossus called AI will Not Make You Rich.
(02:55):
It came out in September. It seems like ages ago.
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 3 (03:03):
It's also very much a sort of core odd Lots
thesis because in the essay he compares and contrasts AI
with containerization, which is another which facing.
Speaker 2 (03:13):
So let's just get to the guest. Someone I've wanted
to talk to for a very long time, someone who
literally is the perfect guest longtime VC started a venture
investing in nineteen ninety seven. Also a professor at Columbia
Business School. We're going to be talking to investor Jerry Newman,
also the co author of a recent book, Founder Verse Investor,
The Honest Truth about Venture Capital from Startup to IPO.
(03:35):
Maybe he'll tell us one we'll see.
Speaker 3 (03:36):
You the bringer of excess Halloween candy. So he gets
he gets brownie points for that.
Speaker 2 (03:41):
Literally the perfect guest. Uh, Jerry, thank you so much
for coming in. Thrilled to finally have you here.
Speaker 4 (03:46):
Thanks, I'm glad to be here.
Speaker 2 (03:47):
What does that mean? A I won't make you rich.
AI has made people super rich, and it's making people
rich every single day.
Speaker 4 (03:53):
You know, as an old mentor used to say, money
is not money till it's cash. Okay, So is anybody
really rich yet? Oh? Come on?
Speaker 2 (04:00):
I mean Jensen Wong bought an entire bar in Korea.
He's like bought beer and fried chicken for everyone.
Speaker 4 (04:05):
He's right, I think it's smart to cash out early. Okay,
that's what he was doing. Okay, let's say you I
really believed it, would you be selling at stock now?
Speaker 2 (04:12):
I mean, see what you mean talk about this because
you obviously have a lot of experience. Actually, that brings
another line of question that I want to get into.
But what does that mean it's smart to cash out
early when in your experience? What does that actually mean?
Speaker 4 (04:24):
So, look, I believe that AI is a revolutionary technology. Okay,
I'm going to put that on the table.
Speaker 2 (04:30):
Which is important to say because not everyone agrees with that.
Speaker 4 (04:32):
So that's yeah, totally. You know, I'm on Blue Sky
and nobody agrees with that, but I do. I think so.
But there's a difference between value creation and value capture.
So even if AI creates a lot of value for society,
who's going to get that value? Is it going to
be the early investors? Is it going to be the court,
you know, the foundation model companies. Is it going to
(04:54):
be YEA consumers? You know? They think that's the question
people need to ask.
Speaker 3 (04:58):
I actually broadly agree with this thesis when it comes
to AI, but maybe just to clarify the idea here,
compare and contrast this current AI cycle with maybe previous
technological breakthroughs. And you know, I mentioned containers. I think
a lot of people aren't used to thinking about boxes
as this major advancement in technology, but at the time
they really were.
Speaker 4 (05:19):
Yeah, I mentioned containerization, and most of my peers think
I'm talking about doctor. 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 rail cars and
now to the back of trucks. And this was a
revolutionary technology. I mean it changed everything about the way
we live. I don't remember. I'm probably a little older
than you all, but when I was a kid, my
(05:39):
grandmother used to send up oranges from Florida at Christmas time, right,
because they were rare. You couldn't just go into a
grocery store and buy them. Now you can buy oranges
anywhere at any time. People don't really realize how much
our lives have changed because of shipping containerization, because of
these global logistics and the globalization of shipping. Now this
is utionary technology. Who got rich from it? I mean generally,
(06:03):
if you look at the nineteen sixties, say how many
people became wildly rich from technological innovation? Can you think
of anyone? Because I've been asking this question for years.
There are people who got rich in media and whatnot,
but there wasn't a lot of technological innovation that made individuals.
Speaker 2 (06:19):
It just start, like twenty years ago, did they have technology.
Speaker 4 (06:21):
Bout the right? I mean, it's I think this is
the thing. Right, So we talk about computer technology, the
information and computer technology revolution as technology, but obviously this
has always been technology. But only at certain times in
this technological cycle do people seem to make money as
investors and as inventors.
Speaker 3 (06:38):
Explain more, though, because I mean I could argue that
Marisk or someone like that got pretty rich off of containerization.
Like maybe it took a while, but even though the
shipping industry is highly cyclical, when they are in the
boom period, they make a lot of money.
Speaker 4 (06:52):
Sure, I mean, the existing shipping companies got very large
and made a lot of money. They got larger and
made more money. Is mayor you know who made money.
Speaker 3 (07:02):
Completely new entrants?
Speaker 4 (07:04):
Yeah? So just to me as background, I'm a venture capitalist,
right or have been a venture capitalist for a long time,
recently retired, And I think about people investing in making
money new companies, inventors or entrepreneurs making money, not the
existing incumbents making money. And I think that people will
make money on AI. Might be Microsoft making a ton
of money on AI. It could be AI. You know,
(07:24):
when containerization shipping containerization came around, Sealand was the instigator
of this, and the founder of Sealand made money primarily
because he sold early, right, he sold Sealand to r
J Nibisco or sorry, it was just r Jr. At
the time, and they thought they were diversifying, which is
the big thing. Then paid them a lot of money
and then they drove it into the ground.
Speaker 2 (07:43):
So what did Sealand do? What was did they? I
actually don't I'm not familiar with this company at all,
which I think kind of speaks to your point. But
what it was Seland?
Speaker 4 (07:49):
Yes, So so it was a truck. It was started
out as a trucking company. And the founder of Sealand
was a trucking entrepreneur. And he said, it's silly you
you go into a port, your truck sits around all day. Well,
you know, the long charman put a cargo net into
a container ship, load everything into it, pull it out,
unload it and then reload it back into your truck.
This is not efficient. And this obviously it's an obvious idea, right,
(08:13):
just put it all in a box and you can
then put that box on a truck.
Speaker 3 (08:15):
The best ideas are always the obvious rights in retrospect.
Speaker 4 (08:18):
But the problem with it was it was a systems problem, right.
The long charman didn't want it, the ports didn't want it,
the port authorities didn't want it, the politicians didn't want it.
Nobody wanted this to happen because this sort of enormous
change would put a lot of people out of jobs.
It would up set the existing order, and it did.
I live in Hoboken, and in Hoboken there's a lot
of peers that nobody uses except to go running on now,
(08:39):
because back in the sixties it was a long sharmantown.
And when I moved there in the early nineties, it
was empty. It started to gentrify, but because all of
those people had lost their jobs and moved out.
Speaker 2 (08:50):
And I suppose, like even in the case of marisk
And I'm sure you know, they obviously have made a
lot of money because the explosion of global trading volumes
and containerization is part of it. Like, it wasn't overnight wealth, right,
it wasn't.
Speaker 4 (09:03):
It was not.
Speaker 2 (09:03):
It was not people got richer, but it was not
like some bubble get rich quick thing where they suddenly
cashed in on a new thing.
Speaker 4 (09:11):
Yeah. I mean, I suppose whoever owns mask may have
gotten richer. Yeah, but it's not like you're going to
look at the forest four hundred and see all these
shipping magnates who became suddenly, you know, enormously wealthy. There
are a few.
Speaker 3 (09:22):
Wait, okay, so if I think about a box, you
know part part of that.
Speaker 2 (09:26):
We just keep this whole conversation on boxes.
Speaker 3 (09:28):
Actually, well one more question then we will be able
to discuss ai. But I think about a box, and
as you say, it's sort of an organizational structural problem,
Like the box itself is not the huge technological advancement necessarily.
So what exactly was it about containerization that prevented it
(09:49):
from being disseminated I guess to new upstarts or new
companies that could actually use that technology.
Speaker 4 (09:56):
Well it was, so I'm not sure I understand the question,
because uanization became widespread very quickly, right, But.
Speaker 3 (10:02):
What I mean is like, why was that value seemingly
captured by incumbents versus startups?
Speaker 4 (10:08):
Right? I think because it disseminated so quickly, Right, it
was an obvious idea. Everybody who saw it said, Okay,
we need to do this. Right. Everybody who's already in
the business said, if this is going to happen, we
have to do it. We can't be left behind. We
will be left behind if we don't do it, which
you know, I think was also obvious. So the reason
nobody else did it first was it was hard to do.
It was hard to make happen. And technology has always
(10:30):
come in these technological systems if they're worthwhile technologies. Right.
So the personal computer didn't change the world on its own, right.
It changed the world alongside the Internet, you know, alongside
a bunch of technologies that formed the system. So the
hard part here was building the system, not the individual technologies.
And this is true I think of computers as well.
The first microprocessors weren't considered revolutionary. Intel didn't consider the
(10:52):
four thousand and four revolutionary. They considered it evolutionary. The
engineers have said this, And it wasn't revolutionary until people
put it to use in ways that they didn't anticipate.
Speaker 2 (11:01):
Actually, can we go. I didn't know that, like, I
hadn't really thought about that with Intel that at the
time it didn't feel to them that it wasn't a
revolutionary technology. That's sort of mind blowing.
Speaker 4 (11:12):
It is, right. This is I think from Michael Malone's
The Intel Trinity of the book. You know, he interviewed
a bunch of Intel engineers and he said, you know,
like they thought they were building a better chip set
to build pocket calculators or desk calculators. I just say so,
they had a client, Busycom, who wanted to build a
better desktop calculator with calculators were big back then in
nineteen seventy ish, and one of the engineers said, well,
(11:33):
why do we keep building custom chip sets for each
different calculator. Why don't we just build a chipset that
we can customize the software and change what it does.
And Intel is kind of like eh, and Busycom was like, okay,
we'll pay for that. And then Busycom actually tried to
back out and they gave the rights back to Intel.
So Intel owned the rights to this four thousand and four,
and then they started selling it and it wasn't Intel.
(11:54):
You know, Intel believed at the time it was going
to be maybe used for dedicated hardware, you know, can
you hardware controllers that kind of thing, not by consumers.
So it wasn't until people on the outside said, hey,
you know, I love these IBM mainframes or these deck
MANI computers. I'd like to have my own, but obviously
nobody can afford that. Why don't I just try to
build my own? Right, So there's these kind of outside inventors,
(12:15):
this permissionless invention, and then it really the real revolution
didn't happen until this. You know, everybody's like, oh Intel.
It was the six five oh two where the price
came down so dramatically that, you know, Steve Wozniak could
walk into a computer fair you get some for free
and go home and build up a personal computer.
Speaker 2 (12:31):
It's crazy. I wonder who made the chips for the
Commodore sixty four computer that I had.
Speaker 4 (12:36):
They were six five o twos. Those were Intel, that's
you know, they weren't until there were Moss technologies. Oh
got it, got it, those are the cheap ones.
Speaker 2 (12:43):
Then yeah, wait, what year was that when you had well,
I had I think I like learned some basic and
did some coding on it. I would have said maybe
nineteen eighty eight, nineteen eighty seven somewhere around there, made
a few I missed those days. It's crazy that I
didn't be. Can I just say I sometimes when I
think about my life path, like how did I not
(13:04):
end up like a tech guy? Because I was like
very into math. I was like one of those people
who had a computer when I was six or seven.
Speaker 3 (13:10):
In nineteen eighty seven, you were coding you were seven
years old.
Speaker 2 (13:13):
Yeah, I got that. My dad got me this magazine
that just it was very crazy. There was literally just
sent you pages of code and then you just typed
it in and you could like make a video game.
I was doing that at seven. I could be like
one of those like who's per s gave of a
computer when he was seven? It anyway, and it's not
too late.
Speaker 4 (13:30):
It's not too late.
Speaker 3 (13:31):
Yeah, it isn't too late. Well, maybe it is too
late because now we have AI doing all the coding, right, Okay,
(13:52):
just so I understand when it comes to I guess
the advantages of the incumbents to actually monetizing new technology
or benefiting from new technology 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
and the ability to sort of roll out massive investment
(14:12):
to capture that value.
Speaker 4 (14:14):
I think it's the latter, right, So anybody can build
a foundation model, right if you have the money. I mean,
the technology is not mysterious. It doesn't feel like the
technology is really changing very quickly anymore. And of course
I don't have insight into what's happening inside of open ai,
but looking at it over the past couple of years,
it's the same thing, but slightly better. It's evolutionary now, right.
(14:34):
The first part was revolutionary and now it's evolutionary. So
if you wanted to build one, you could build one.
And I have friends who are running them on their
laptops very slowly, but it's possible. 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,
And it's just a question of having that the authority
(14:57):
in the market to be able to raise that money.
Speaker 2 (14:59):
By the way, speaking of ideas that were sort of
really obvious that took a while. I'm always blown away that,
like how long it took them to put wheels on luggage.
I don't think anyone like made got super rich on that.
But when I was a kid, I remember like we
had these big suitcases, and that's the most obvious thing.
It took a while. Anyway, I don't think anyone goes.
Speaker 3 (15:17):
Technology is still not perfected, as you know, because you've
been in airports with me and the wheels on my
luggage are broken.
Speaker 2 (15:22):
But I don't think anyone got like super rich off
of wheels. That just seems like an that was just
sitting there.
Speaker 4 (15:27):
You know.
Speaker 2 (15:28):
I want to jump ahead actually in the conversation a
little bit because I know to forget this point. But
this is something I've become a little obsessed with, which
is I've been meaning to ask a VC about this,
which is that there seems to be this blurring of
private and public markets in various ways, retail participation in
private markets, et cetera. For the VC, in my mind,
(15:49):
I feel like the exit was the IPO or the acquisition, right,
so you buy do vcs these days have to think
a little bit more about market timing and selling early.
So someone who was an early investor in open ai
or whatever you know, in the past, they might have
just held or then sell at the IPO or the acquisition.
It is probably going to happen in an open AI's case,
(16:10):
they're too big to be acquired. But to vcs and
in your experience these days, have to think a little
bit more about this idea of selling early, timing the exit.
Speaker 4 (16:19):
Well, I think vcs always had to think about timing.
You know, I've done pretty well in VC, and I
attributed it entirely to being lucky at starting investing at
the right time. So the first time around, I was
starting in ninety seven, which any you could make money in.
The second time around, I started in two thousand and seven,
which again it was just two thousand and eight. It
was just an easy time to buy in.
Speaker 2 (16:38):
But I mean, the timing of the sale is that
something that where in the past it might have been
automatic exit, now now is not the case.
Speaker 4 (16:47):
Yeah. I wrote this thing about VC in the nineteen
eighties a long time ago on the blog. You can
find it. And the thing because nobody talks about the
nineteen eighties, right, there's plenty of VC in the eighties,
but nobody talks about it. People talk about the sixties
and in the nineties. So I was like, all right,
what happened. Then. One of the interesting things about it
is there were the IPO windows then where you know,
nineteen eighty three the IPO window opened a bunch of
(17:09):
companies on public and then it closed again, and you
can see it in the numbers when people in public.
So people always had to think about timing. The IPO
is obviously the best exit because you want to sell
to the greatest fool, and nobody's greater fool than the public, right,
So you look for the IPO window to open. When
you can't, you have to sell to somebody else. You know.
Vcs have this problem of their limited fund life. So
(17:30):
I look at my portfolio. I'm an really early investor
or have been a really early investor, so I'll look
at companies and say, oh, I invested ten years ago.
They're going to have to sell, you know, it's the
So people look for the IPO window, but if they
can't find it, they have to sell somewhere else.
Speaker 3 (17:45):
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,
and basically you're going to have consolidation and you will
get that exit.
Speaker 4 (17:58):
I don't think anybody can predict when the IPO opens.
I mean, I wish I could, but I don't think
I've never seen anybody even say they could predict when
the IPO window opens. So I think a smart VC
invests in a company that can become self sustaining to
some degree, and then you wait for the timing to come.
You don't invest and say I'm gonna flip this in
three years now. Yeah. The other problem is vcs don't
(18:20):
invest in all these IA companies saying a bunch of
them are going to become valuable. They invest saying one
of these is going to be come right.
Speaker 3 (18:25):
The lottery ticket theory, yeah, the power law.
Speaker 2 (18:28):
Speaking of the IPO window, I'm never totally satisfied by
a lot of the explanations for the drop off in
IPOs generally. Do I know there was that law passed
in two thousand and one.
Speaker 4 (18:40):
Or what was it, sins ox Yes.
Speaker 2 (18:43):
Surbins ox Ley, and I get that law.
Speaker 3 (18:45):
That law law that everyone hated for a really long time.
Speaker 2 (18:49):
I don't know, it doesn't. But then you see, like
you know, twenty twenty one there's like a billion garbage
companies went public via SPACs et cetera. How much is
it about? Okay, there are some disadvantages to being public
versus there's just so much more private capital out there
such that the imperative to perhaps ever go public and
it's liquid and the rounds and like, what do you
(19:11):
attribute There are these big companies that are a private
stripe but open AI and onanthropic that choose to stay private.
What do you think the main reason for that is?
Speaker 4 (19:19):
Well, it's because they can, right, I mean, being a
public company is no, it's no party, right, I mean
it kind of sucks being a public company.
Speaker 2 (19:25):
What is it about it?
Speaker 4 (19:26):
What sucks? Well, you have to tell everybody what you're
doing every three months. Yeah, that's you know, and then
they come back and complain about it. So sorry, I'm
being a little facetious, but it is. It's hard to
be a public company. 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.
So that's taking away from actually running the company. I
think the flip side is your liquid and that's yeah.
You know, So if you can stay private, why wouldn't
(19:48):
you stay private? Or if you can go public and
retain control of the company, you know, like Henry Ford
or Mark Zuckerberg, then why wouldn't you do that? 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. With all this money being invested in AI,
you may wonder if people are still going to want
(20:09):
to invest late stage stripe or the analogous stripe might
be making money now, I'm not sure.
Speaker 3 (20:15):
I know you brought up previous historic analogies like VC
in the nineteen eighties, but just to focus on the
one that everyone else seems to be focused on at
the moment, which is the dot com bubble in the
early two thousands. What are the key differences you're seeing
in terms of the VC and financing environment now versus
twenty or twenty five years ago.
Speaker 4 (20:35):
I think the key difference is that most of the
money is coming from people who aren't looking for much risk, right,
So I mean open ai is primarily funded by bigger companies. Right,
most of their money is coming from large companies. What
happens if open ai gets hit by a bus? Right,
So Microsoft's at a bunch of money. A whole bunch
of big companies are at a bunch of money. I
(20:56):
don't think much happens to the economy. I think, which
is different than in the bubble, where a lot of
consumers were in it. A lot of consumers were in
it leveraged right, the buying and margin or whatever. A
lot of people had options, right, people employees, and they
were spending the money from their options before they were licking.
You know, it was there was a much I think
it was a different dynamic.
Speaker 3 (21:16):
But the wealth effect, I don't know.
Speaker 2 (21:19):
I think this is a contrarian take on your part,
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, which AI is a
big part of the story. 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 the
(21:41):
data centers and the caterpillar, the turbines for the gas generation,
et cetera. I think many people would say there is
a lot right now riding on the health and the
sustainability of this particular sector.
Speaker 4 (21:54):
So I think we can separate the companies like Microsoft
and Video. Are they over valued because of this? Maybe
doesn't make a huge difference to the economy. Probably not,
I don't think so. And the companies who are spending
money on infrastructure like building data centers, building power generation plants,
those things I think are probably overbuilt or not so
(22:15):
much overbuilt as they are built. And I think in
ten years you're gonna have a lot of extra compute,
a lot of extra power generation, and people will be
able to use that for other things. It'll also drive
down the price of just using AI.
Speaker 2 (22:28):
Probably, yeah, Jevin's paradox bro is gonna be out back
and forth. All right, So AI, where are we? You
talk about cycles? And I think you use word that
I hadn't ereruption? What was the word you use? So well,
I'll tell us how you see cycles in what cycle
we're in?
Speaker 4 (22:43):
Right? Right? So A lot of this is based on KARLOA.
Perez's work, which is pretty familiar to the ventro capitalists
and your audience. I'm sure. She wrote a book called
Technological Revolutions and Financial Capital where she explains the dynamics
behind the Kondrati of waves that the Chumpeter talks about.
So she has this theory about why these happen. And
you look at the Industrial revolution, the Second industry Revolution,
(23:04):
you can see these waves of technology technological systems happening
through the economy where they kind of start out, they
grow really rapidly, and then there's usually some sort of adjustment,
some sort of bubble bursting, and then things kind of
level out and then start to plateau, and then a
new one starts. And this is people have noticed this
since at least Kentdrati nineteen twenty six. She has a
(23:25):
mechanism for explaining it, and her mechanism has these four phases.
The first phase is eruption, which she spells with an I,
which I think is actually in the dictionary as a word.
I don't know what the difference is between that and
the eruption, but it is the part where smart right,
So yeah, keep saying that it is the start. It's
when people have invented something and it is starting to
catch on, but it hasn't caught on yet. There's a
(23:46):
lot of people saying, is this the future? Is it not?
You look at personal computers in the nineteen late nineteen seventies,
early nineteen eighties, and maybe even before IBM got involved,
and people didn't think personal computers were Some people thought
they were their future. And then you look back at
computer history. Everybody talks about the people who did think that.
They don't talk about the other ninety nine point nine
percent of smart people who said they weren't. This is
(24:07):
the eruption phase where there's a lot of uncertainty about
where this technology will go. It's starting to build connections
to other technologies, starting to attract money, attract smart people
because it's interesting and it might actually change things. So
this is the beginning. And I think the connection here
to AI is people wonder if we're in the eruption
phase of AI or not. Is this the start of
(24:29):
a new technological revolution phase? I think we're not. I
think we're in. I think this is the end of
the information computer technology wave, the end of the computer wave. Right,
I think it is. This is the culmination of the
computer wave. Right, Why did we build computers. We build
computers to help us think better. Right, this is what
they're for, their knowledge machines. So now we've kind of
(24:50):
reached the natural end stage what they do. They're smart,
machines are smarter. So I think this is not a
new technological revolution. I think it's the end of the
old one. And this is why I compare it to
container is thea because the previous wave was automobiles mass production,
and starting in nineteen fifteen or so up until nineteen
seventy was the previous wave, and containerization was squarely at
(25:12):
the end of that wave, and it was really kind
of pulling together the technologies of that wave into something
that increased productivity, right.
Speaker 3 (25:19):
Like the final step of the global trade and I
guess mobility revolution.
Speaker 4 (25:23):
Yeah exactly.
Speaker 3 (25:24):
Okay, so how do you react as an angel investor?
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,
or how are you actually deploying all these thoughts in
terms of your own investment strategy. I retired, Okay, you know,
(25:46):
I standing it out and I.
Speaker 4 (25:47):
Said, look, how am I going to invest in foundation modes, right,
I don't have a billion dollar fund. I don't think that.
You know, if you look at the big winners from
the early big winners from globalization, the IKEA is right.
I mean it was a Scandinavian company until containerization, and
then they became a global powerhouse, a hugely successful company.
(26:07):
But they didn't need outside money. You know. Comprade, I
think he borrowed like a couple thousand dollars to start
that company or to get that company to buy some inventory.
He never took outside You look at Walmart, which had
been around already. It was an incumbent and used this
kind of globalization to bring a lot more variety of
products to the stores. They didn't need outside money to
do that. Yeah.
Speaker 3 (26:27):
I guess if you're at Kia 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 4 (26:40):
Yeah, exactly, Okay, they were already flat packing.
Speaker 2 (26:43):
Yeah. Your mention of the Walmarts and targets of the world,
it was like in your essay it was like a
sort of very light bulb thing. It's like, yeah, I
don't know. I guess we'd take them for granted. But
they're clear like massive containerization winners. The skill that they
succeeded is impossible to fathom in some prior era.
Speaker 3 (27:01):
Of Walmart is a logistics company and changed my mind.
Speaker 2 (27:04):
Literally and many of you literally literally literally that. But
it doesn't feel like with AI that the equivalent has
emerged yet. Right, We're still at the age where people
are building the container deployed. But the company that exists
and is massive that couldn't exist prior to a like
does not feel does it We haven't seen that yet.
Speaker 4 (27:25):
Well, you gotta be a little patient.
Speaker 2 (27:26):
No, no, but like seriously.
Speaker 4 (27:28):
Iikia so contained. The first container ship sailed in I
think nineteen fifty six. Okay, so when did Akia become
a global powerhouse? It really wasn't until the nineteen seventies
that they started to expand that this is really important.
Speaker 2 (27:39):
They really take a while. Where would you expect it
to show up? Like what industries would you expect because
obviously retail existed for a long time, furniture existed for
a long time. Then you get these Bahamas. Are there
industries that you think are right to produce very torture
analogy the Ikea of the AI way.
Speaker 4 (28:00):
Well, I think they have to be knowledge intensive industries,
right right, I mean this is what AI is doing,
what it is making more efficient. I mean I think
there's people been asking me like, well, then what should
I invest in? And yeah, tell us, well, you know,
I think, well, so give us. I've been thinking that myself.
But the answer is really that as an investor, I
don't decide what to invest in. I evaluate opportunities that
(28:21):
come to me. And so I have built a box
in which you can evaluate opportunities. Right, they have to
look like this. They have to look like an Ikea.
And if I Kia 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. Where nobody else seemed to
see that, right, Certainly the furniture makers in North Carolina
didn't see it. So I think this is the box
(28:43):
that you evaluate things in. And as a long time investor,
I'm used to evaluating and I trying not to come
up with ideas, you know, that said it has to
be a knowledge intensive industry. And I think something that
I said in the essay, which I wish I had
said more about, was the companies that tried to use
shipping containerization to cut costs so that they can increase
margins did poorly. Oh this is key, Whereas the companies
(29:05):
that use the efficiencies and passed the efficiencies onto the
consumers so that they could become larger became larger. Right.
I mean this is you look at these people saying, oh,
we have AI, We're going to fire people. I mean,
that's I think is the exact wrong move. And I
think it's probably just every you know, every new thing
comes along, people like, oh it's we're going to fire people.
You know, it's just an excuse. But but if you're
(29:25):
firing people because of AI, you're doing it wrong. Right.
You should be using AI to say I can use
my people to do more. I can grow my company,
I can vary my products, I can take more market share.
Speaker 3 (29:36):
So the value goes to the consumer. And I guess
you capture the value by selling more right, more knowledge?
Speaker 4 (29:42):
Yeah, I mean I think Walmart never tried to maximize margins.
Speaker 3 (29:46):
They it was it was volume.
Speaker 2 (29:47):
Yeah, Okay, from a consumer standpoint, why do I care
(30:08):
if your workflow internally at your company is become more
efficient things AI, either the product is better or new
or something.
Speaker 4 (30:16):
You know.
Speaker 2 (30:16):
I was thinking about this. I don't mean to like
pick on anyone, but situating like open door meme Stock
they have new CEO. He sent out this memo. He's
like everyone has to do start using A or it's
clearly a press release in the form of an internal
memo because it got it I think waving.
Speaker 3 (30:32):
A flag going us.
Speaker 2 (30:34):
It's like, yeah, but like, does the economics of buying
homes via whatever get better? Like does it actually make
the business better? This strikes me as very interesting, this
idea that like, it's not gonna it's just not that exciting,
especially any sort of customer oriented company, which I guess
is all companies. The fact that AI has become part
of their workflow. It's the electric knife effect, right, So
(30:57):
I don't know that effect we might.
Speaker 4 (30:59):
Just call that, but I mean electric knives are one
of the fastest growing consumer products in the late nineteen
sixties because people are like, oh, we are electrifying things,
like let's electrify the knife and within like three years
they were in some massive percentage of households, like eighty
percent of American households had an electric knife and things
like you know, the blender didn't get adopted that quickly.
But who has an electric knife now? Right? It's I
(31:21):
think people take whatever it is the technology is and
say this is everything. I mean, back in you know,
the early two thousands, late nineteen nineties, every company was
an Internet company. Right. Do people walk around saying that's
an Internet company? Now are you an Internet company? I
mean everybody's an Internet company. Everybody uses it. It's the baseline.
That's I think the what a revolutionary technology does is
it becomes part of everything. So, yeah, you're right. Consumers
(31:43):
don't care that your you know, your lawyer's using AI.
They want to make sure that they have good legal advice. Yeah.
Speaker 3 (31:48):
Our producer just messaged Dash saying that he had an
electric knife. Oh does he have Probably not in the
nineteen sixties.
Speaker 2 (31:55):
There do you have one?
Speaker 4 (31:56):
Now?
Speaker 2 (31:57):
My mom might have one case that didn't get picked.
Speaker 4 (32:01):
Up on the audio.
Speaker 3 (32:01):
Well, his mom might still have one. Dash, will you
bring it in for us and we can we can
use it as a.
Speaker 2 (32:07):
Prop I'm just imagining like going to a restaurant at
the time it's like, oh, we slice your bread with
electric knives. That are like how unexciting that would be
From a consumer stand I don't care, you know.
Speaker 3 (32:19):
Yeah, I guess that's true, but okay, but things are
different from a shareholder standpoint, right. And one of the
reasons we see when companies say that they are using
AI the share price goes up, it's because shareholders expect
all these easy cost cutting gains. Do we have any
indication that investors are actually going to be patient and
wait for I guess the value to spread to the
(32:41):
consumer or are they just going to demand basically these
fast cuts.
Speaker 4 (32:47):
Will And I mean, I'm not a stock market investor.
Yeah I know, I know you're stock marketing, you know.
I think they have a very short attention span. There's
a great quote in that article that you see in
the eighties where the Wall Streetjournal said, you know, there
was the beginning of the eighties, there was a craze
and anything that ended with an onyx rightes right, it
was people were right funny, but it was a short
(33:10):
lived one because it didn't really deliver. And then people
were like, all right, let's move on and do something
else or invest in something else.
Speaker 2 (33:16):
So funny, if I were writing a fiction about the eighties,
I would immediately I would say, like applied fouxtronics or
something that. Yeah, right, yeah, I hadn't thought about that.
That's amazing.
Speaker 4 (33:23):
So I think they want results. But I think it's
two people are impatient. I mean, I was kidding, but
I'm not kidding. People are impatient. They want to see
results today. I don't think AI is going to show
you the real revolutionary results for a decade. Taking AI
and just retrofitting it on to the way you do
things now is only going to add a little bit
to your efficiency. You have to actually re engineer everything
(33:46):
around this, change your processes, hire different people, or get
you know, train people, and then you're going to see
big efficiencies.
Speaker 3 (33:54):
You got to have prompt training in schools, right in
high schools or something. Well, we were talking about this
with Tyler Cowen earlier and he had, you know, a
similar point.
Speaker 2 (34:03):
Are big companies setting aside? Okay, like, yeah, it's not
very exciting that a big company is able to marginally
reduce their workforce. I think a lot of those are fake.
I have a feeling a lot of these companies are
going to have to end up having to hire people
back when these things don't work, or they had nothing
to do with AI and they just wanted to do layoffs.
Can legacy institutions like there's some like inherent roadblock to
(34:27):
the degree to which legacy institutions can incorporate on AI
Or is this the type of thing where it's just
going to be entities that didn't exist before becoming really
big household names.
Speaker 4 (34:40):
No, I think it will be legacy. I mean I
think you know, Walmart was legacy, Ikia was legacy. It
may not be the names you expect, right, Sears and
Woolworths didn't really benefit from shipping containerization. They ended up
going out of business. I think you have to have
the right attitude of how you're going to utilize the
efficiencies it brings within your business again, to grow your business,
(35:00):
not to grow your CEO's salary. Right, you have to
be spreading this to the consumers for your company to
be successful, all Right.
Speaker 3 (35:08):
In the intro, Joe made fun of Sid saying something
completely rational. It Yeah, I know, I know, but this
is your joke, right, Okay, So I'm gonna ask the
cliched question in that theme, are we in a bubble?
Speaker 4 (35:25):
Can you define bubble? No? I should.
Speaker 2 (35:29):
I think the question should be asked liberally.
Speaker 4 (35:32):
It's like, are things overvalued?
Speaker 3 (35:35):
Are things overvalued?
Speaker 4 (35:36):
Yes, but there's a difference between things being overvalued in
the bubble, right, and I think things are overvalued, and
I think there may be an infrastructure bubble. In the
article I wrote for classes as a chart of container
ships being built, of shipping ships being built, and you
can see this huge rise right after containerization started for
a bunch of years, and then it dropped back off
(35:56):
because now people had their ships, but everybody had to
get into the same time. Everybody had to go order ships.
A ton of ships were being built, and then the Capex,
you know, and then they were like, okay, now we
have ships. It's a little different with chips because chips
aren't gonna last you know, yeah, or a long a
container ship last thirty fifty years. But I do think
that you're not going to need as many chips in
the future as you are buying today. Really well, you know,
(36:21):
you're gonna have more use of AI. Probably there's gonna
be more use. But I also would think they would
become more efficient in compute. That's just the history of compute.
Speaker 3 (36:30):
Does your definition of a bubble, does that have to
include a buildup of leverage of some sort.
Speaker 4 (36:35):
Well, I lived through the dot com bubble, so yeah,
I would say so, right, because you're not seeing that right? Well,
because I think a bubble popping has to hurt. And
if Microsoft loses a billion dollars, that doesn't hurt. Doesn't
hurt any I mean, sure it hurts whoever's invested that
money at Microsoft, but it.
Speaker 2 (36:49):
Doesn't who has which is everyone who is a four
one K?
Speaker 4 (36:53):
Well, I guess, but I mean, really, Microsoft loses a
billion dollars, how much does that affect their stock price?
And even if the stock win down ten percent, that's
not a It's not like the dot com bubble. Right
When that popped, people were laid off, the economy went
to a recession. I mean it was pretty deep recession.
Took a while to kind of pull ourselves back out
of that.
Speaker 2 (37:10):
Talk to us more about the dot com bubble. I
tried to bring it up in every conversation because that's
when I got interested in markets, did a little day
trading in those days when I was in college, et cetera,
and I just remember that period very fondly because I
was young. What do people get wrong in their memories
of the dot com bubble?
Speaker 4 (37:29):
So here's the thing I remember most about the bubble.
I was a corporate VC. I worked for a big
company here in New York for to my front of company.
And one of the companies I had invested in was
a public company and they were raising more money. This
was in January, right for the bubble popped right.
Speaker 2 (37:45):
Was the peak was in market in March two thousand.
Speaker 4 (37:47):
Then it was January two thousand, so the company was
selling stock. They said, hey, you know, if you want
to buy some more stock in our company, we'll sell
to you without the underwrited discount or before the underwted discounts.
We'd get it a seven percent below the market price.
And I went to CFO of this giant company and
I said, hey, we can get a good deal on
this stock. We can get seven percent off, right, who
doesn't love a bargain? And he said, well, do you
(38:08):
think the company is worth that price? I said, no,
nowhere near. He's like, so why are you buying it?
I'm like, oh, I guess that's a good point. He's like,
so why would we still own it? And it's like,
that's also a good point, right, which why aren't we
selling this if it's overvalued? I mean the thing that
people forget is everybody knew it was overvalued. They were
all just waiting for it to go up more before
(38:29):
they sold. And he said this, and I'm like, yeah,
I guess better to sell early than late. And we
ended up selling that entire position, which luckily paid for
the whole portfolio before the bubble popped.
Speaker 2 (38:39):
This is a I mean, I think this is there's
sort of the difference between John Authors had a really
good newsletter I think about a year ago, which is.
Speaker 3 (38:48):
There, you're just awsletters I'm doing.
Speaker 2 (38:50):
I'm doing my job to Bloomberg. I like, but it
was basically like, you know, you get these situations like
dot Com where everyone said this is massively overvalued. We
all know, we all It's then you have bubbles that
are more like the housing bubble, in which I don't
think on any sort of traditional metrics the banks were overvalued.
I think probably the pees are probably normal. It's just
(39:11):
that the earning extreme was entirely unsustainable and I guess
that's the question with AI, Like I don't know, videos
prebaby expensive, but like I don't think people think it's
like crazy stretched on pees. It's more the question of,
like is this chip demand at this pace sustainable? Like
are the earnings estimates realistic?
Speaker 4 (39:30):
Right? Well, but I mean the economist said the housing
housing was overpriced in two thousand and.
Speaker 2 (39:34):
Five, right, the homes were, yeah, the banks, you know,
but the banks got obliterated. And it was not because
the ratios of the banks were completely out of way,
because just the profits could not be sustained by any
stretch at that point. Anyway, I think it's an interesting distinction.
Speaker 3 (39:51):
Well, I think there's still an open question with AI
about the network of relationships that are sort of driving
a lot of this business. Like that's where I would
see some of the maybe two thousand and seven two
thousand and eight analogy actually being true. This idea that
like you have this whole system of funding with banks
that's keeping the whole machine going, but when the collateral
(40:13):
that's underpinning that system suddenly loses value, the whole thing
falls apart. You could maybe make a sort of similar
argument for AI, where you have this network of companies
that are sort of investing and selling to each other.
If the value of the underlying asset, which I guess
would be compute in this case, starts to fall really precipitously,
the whole thing kind of collapses. But I'm stretching that.
Speaker 4 (40:37):
It's amazing to me how much of the lessons we
learned in the nineties people just don't know or remember.
I mean, the whole blank check company thing. We did
that in nineteen ninety nine, right, and it'll fill all
the SPACs, right, and it's totally failed, and then people
did it again. It was crazy to me. And it's
kind of circular. Yeah, I know, right, Yeah, I mean
it's still a bad idea. And I think the circular
(40:57):
revenue was also happened in the nineties and that was
all also a bad idea. You'd think that people could
see that and factor that out.
Speaker 2 (41:03):
Gotta have so many questions. Why are specs a bad idea?
On paper? It seems like a totally fine way to
go public. In practice, it only seems like total garbage
companies take that route.
Speaker 3 (41:14):
It feels self selecting.
Speaker 4 (41:15):
Yeah, well, of course, because the way they're structured to
get people to invest in a company you don't even
know what it is yet, it means that it's not
great for the companies, right, you get a ton of yeah,
oh right, that's just leakage.
Speaker 2 (41:29):
Well, just explained the mechanism again.
Speaker 4 (41:31):
So people, you know, if you put money into a spack,
when they decide they're going to do a deal, you're
allowed to take your money back out of the spack.
I can't remember all the details, all right, Right, So
if you're like, well it's a good deal, I'll leave
my money in. But if you're the company being acquired,
you don't know if you're going to be acquired or not,
because people could take their money out. So just why
wouldn't if you could go public on your own, you
would prefer to do that. So by definition, the spacks
(41:52):
are buying companies that couldn't go public on their own, right, right?
Speaker 3 (41:55):
Is VC investing fun again? I got the impression like
two years ago everyone was pretty depressed, and I'm quite
sure we did a few episodes on it at the time.
But are people having fun again?
Speaker 4 (42:07):
Well again, I retired, so no, nobody's having fun. I
wasn't having fun. I think if you have a billion dollars.
It's probably fun if you're doing the big deals. I
think if you're in AI, it's fun. If you're doing
anything else, it should be fun if you're doing something
besides AI. Right, because now everybody's distracted by the shiny
new thing, you can go find companies that are interesting
(42:28):
to you and invest. The problem is you have to
worry about what happens a year from now when they
need the next round. Is anybody going to be paying attention?
I think it's probably pretty hard to be an early
stage investor unless you're investing in AI. And if you're
investing in AI, you're probably not writing small checks, low valuations,
and you can't control the outcome at the end. You
(42:49):
can only control what you do at the beginning, so
you probably won't be making money.
Speaker 2 (42:54):
I have this theory that actually nobody likes bubbles or
booms even but let's say bubbles in part like, if
I missed it, I'm upset because someone else is getting rich.
If I'm in it, I'm like really anxious. Am I
gonna like I'm anxious about two things. I'm anxious. Am
I going to sell it at the right time? I'm
also kicking myself for not investing more. No matter how
much I invested, I'm upset with myself for not having
(43:16):
invested more.
Speaker 4 (43:17):
Is that right?
Speaker 2 (43:17):
This is just my impression when I read history, which
is that everyone, even in the boom times, there's like
this like din of like stress underneath. Is that true?
Or am I just fantasy projecting how my own neuroses
from birth onto.
Speaker 3 (43:31):
Everyone thinks they're gonna time it.
Speaker 4 (43:32):
Right, But it's fun. The bubbles are fun, especially if
you're young and stupid. Right, there's the New Yorker cartoon.
I want my bubble back. Yeah. Yeah. The flip side
is it is stressful. I remember, well, its stressfull both
after obviously I still have my Razor of His stock certificates.
I had a certificated because I couldn't sell it for anything,
you know, for any real money, and that at one
point had been worth quite a bit of a month
(43:53):
more than.
Speaker 2 (43:54):
Again, I remember, what did that company do? It is
like an ad network or something.
Speaker 4 (43:57):
No, No, they built websites. Oh yeah, yeah, I remember.
They were great. I mean they were a great company
when nobody else knew how to build websites.
Speaker 2 (44:04):
Friends who worked for Razor fish even as like recently.
It is like twenty ten.
Speaker 4 (44:08):
They sort of hung on.
Speaker 2 (44:09):
For a little while. Jerry Newman, thank you so much
for coming on odd lot. It's been wanting to chat
with you for a long time, and I really appreciate
your joining us. Yeah, thank you both, Tracy. I love
(44:32):
that conversation. There's a lot there. I mean, obviously, I'd
like anytime we can talk about the nineties bubble, but
I had never really thought about or any come anywhere
close to thinking about the AI analogy with containerization. It's
a little embarrassing because that's such a core topic for us.
We've talked about boxes so many times, but it's sort
of re orient my thinking of AI and thes term
(44:54):
very helpful.
Speaker 3 (44:55):
Yeah. Well, I mean this just kind of proves the
point that no one thinks of containerization. I mean, I
said before, it is a technology story. And one of
the reasons I do think of it that way is
because I read that book The Box, Yeah, which is
really good, but no one thinks about it as an
investment story. No, because of the reasons that Jerry just
laid out.
Speaker 2 (45:13):
Yeah, No, that's really interesting, and you know, again like
it does feel at some point that non tech businesses
non AI businesses. Eventually someone hopefully for the industry, like
makes a lot of money actually using these tools because
we've been in the picture shovels phase or whatever. But
(45:34):
at some point maybe it's an existing healthcare company or etc.
Or maybe it's a new kind of law firm or
an incumbent law firm where it's like, Okay, we have
found a way to use this technology in a manner
that is very profitable, productive, and market expanses.
Speaker 3 (45:52):
So to me, that's the key thing. So the key
thing is it's not that we're going to use this
technology necessarily to cut costs and boost profit margins. It's
that we will actually expand our customer base and make
it up in volume by selling more knowledge.
Speaker 2 (46:08):
You know, it's interesting thing that Jerry said, and you
put into words something that I hadn't really thought about before,
but this idea about being at the end of this
sort of computer revolution, and there is something about AI
specifically where people it's like and it can't really literally
be this where it's like, well, this is the last technology,
(46:29):
right because.
Speaker 3 (46:30):
Because we're gonna get robots, yeah, right.
Speaker 2 (46:32):
And I don't know if like other booms or technological
revolutions had this feeling where it's like, this is the
last one. Theoretically, if you get Agi or whatever robots,
you don't need any further technological innovation, et cetera. It creates,
I think, a very weird, uncomfortable dynamic. But the idea
of AI is the end of what we do with
computers rather than the start of like something genuinely new
(46:55):
like that actually like snaps into place a lot of
thoughts for me.
Speaker 3 (46:59):
Once we invent God, we're done.
Speaker 4 (47:02):
We're done.
Speaker 2 (47:02):
Everything else takes care of it.
Speaker 3 (47:04):
So yeah, all right, shall we leave it there?
Speaker 2 (47:06):
Let's leave it there?
Speaker 4 (47:06):
All right?
Speaker 3 (47:07):
This has been another episode of the oud Lots podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2 (47:12):
And I'm joll Wisenthal. You can follow me at the Stalwart.
Follow our guest Jerry Newman, He's at g A Newman.
Follow our producers Carmen Rodriguez at Carman armand dash Ol
Bennett at Dashbod and Keil Brooks at Kilbrooks. More odd
Lots content go to Bloomberg dot com slash odd Lots.
We've been daily newsletter and all of our episodes and
you can chat about all of these topics with fellow
(47:32):
listeners in our discord Discord dot gg slash od Lots and.
Speaker 3 (47:37):
If you enjoy odd Lots, if you like it when
we talk to you about why you're not going to
get rich from AI, then please leave us a positive
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is find the Bloomberg channel on Apple Podcasts and follow
the instructions there. Thanks for listening in
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the Outlass podcast.
Speaker 3 (00:22):
I'm Joe Wisenthal and I'm Tracy Allaway.
Speaker 2 (00:24):
Tracy our colleague here at Bloomberg at Harrison, had interesting
newsletter today and it's actually something I've been thinking about
a little bit lately, which is that for all the
talk of the AI boom or the A bubble driving
the stock market, there's no AI pure plays really that
are publicly traded. Like in video is probably the closest,
but three years ago people were excited because they were
mining ethereum. Before that, it was like video games. You know,
(00:47):
this was only an AI company in people's mind. For
since late twenty twenty two, Google still is you know,
we know they're all investing in a ton. There's actually
no like no AI company that people are excited about
in the public markets.
Speaker 3 (01:01):
I mean, I think it's true. 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? And there doesn't really seem to be
that much pure play, as you say.
Speaker 2 (01:18):
As our colleagues said, Verma might like to say, people
are investing in the picks and the shovels, you know,
this gold rush. I don't think he actually said that.
It's just a million other people's but it is.
Speaker 3 (01:27):
It shovels, it's fun, a completely reasonable commentary, That's what
I say.
Speaker 2 (01:32):
Is there? You know, it turns out instantly picks and
shovels have been great. You know, 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. 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 3 (01:51):
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. Maybe it's
on a massive bubble, right, 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, much
like the dot com era, and then a bunch of
(02:12):
companies that like actually end up being losers, and I
think again, like that is consensus at this point, 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 4 (02:26):
Yeah. I don't know.
Speaker 3 (02:27):
It just seems like a weird point in time where
people are like, oh, yeah, AI is great, but we
all know that some of these companies are going to
be massive losers, right.
Speaker 2 (02:34):
I think now they're all kind of big treated as winners,
which is the other thee Yeah, anyway, it's a very
weird time. You 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 talk about
the economy, et cetera. Someone I've wanted to talk to
for a long time. Earlier this year he wrote a
essay for Colossus called AI will Not Make You Rich.
(02:55):
It came out in September. It seems like ages ago.
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 3 (03:03):
It's also very much a sort of core odd Lots
thesis because in the essay he compares and contrasts AI
with containerization, which is another which facing.
Speaker 2 (03:13):
So let's just get to the guest. Someone I've wanted
to talk to for a very long time, someone who
literally is the perfect guest longtime VC started a venture
investing in nineteen ninety seven. Also a professor at Columbia
Business School. We're going to be talking to investor Jerry Newman,
also the co author of a recent book, Founder Verse Investor,
The Honest Truth about Venture Capital from Startup to IPO.
(03:35):
Maybe he'll tell us one we'll see.
Speaker 3 (03:36):
You the bringer of excess Halloween candy. So he gets
he gets brownie points for that.
Speaker 2 (03:41):
Literally the perfect guest. Uh, Jerry, thank you so much
for coming in. Thrilled to finally have you here.
Speaker 4 (03:46):
Thanks, I'm glad to be here.
Speaker 2 (03:47):
What does that mean? A I won't make you rich.
AI has made people super rich, and it's making people
rich every single day.
Speaker 4 (03:53):
You know, as an old mentor used to say, money
is not money till it's cash. Okay, So is anybody
really rich yet? Oh? Come on?
Speaker 2 (04:00):
I mean Jensen Wong bought an entire bar in Korea.
He's like bought beer and fried chicken for everyone.
Speaker 4 (04:05):
He's right, I think it's smart to cash out early. Okay,
that's what he was doing. Okay, let's say you I
really believed it, would you be selling at stock now?
Speaker 2 (04:12):
I mean, see what you mean talk about this because
you obviously have a lot of experience. Actually, that brings
another line of question that I want to get into.
But what does that mean it's smart to cash out
early when in your experience? What does that actually mean?
Speaker 4 (04:24):
So, look, I believe that AI is a revolutionary technology. Okay,
I'm going to put that on the table.
Speaker 2 (04:30):
Which is important to say because not everyone agrees with that.
Speaker 4 (04:32):
So that's yeah, totally. You know, I'm on Blue Sky
and nobody agrees with that, but I do. I think so.
But there's a difference between value creation and value capture.
So even if AI creates a lot of value for society,
who's going to get that value? Is it going to
be the early investors? Is it going to be the court,
you know, the foundation model companies. Is it going to
(04:54):
be YEA consumers? You know? They think that's the question
people need to ask.
Speaker 3 (04:58):
I actually broadly agree with this thesis when it comes
to AI, but maybe just to clarify the idea here,
compare and contrast this current AI cycle with maybe previous
technological breakthroughs. And you know, I mentioned containers. I think
a lot of people aren't used to thinking about boxes
as this major advancement in technology, but at the time
they really were.
Speaker 4 (05:19):
Yeah, I mentioned containerization, and most of my peers think
I'm talking about doctor. 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 rail cars and
now to the back of trucks. And this was a
revolutionary technology. I mean it changed everything about the way
we live. I don't remember. I'm probably a little older
than you all, but when I was a kid, my
(05:39):
grandmother used to send up oranges from Florida at Christmas time, right,
because they were rare. You couldn't just go into a
grocery store and buy them. Now you can buy oranges
anywhere at any time. People don't really realize how much
our lives have changed because of shipping containerization, because of
these global logistics and the globalization of shipping. Now this
is utionary technology. Who got rich from it? I mean generally,
(06:03):
if you look at the nineteen sixties, say how many
people became wildly rich from technological innovation? Can you think
of anyone? Because I've been asking this question for years.
There are people who got rich in media and whatnot,
but there wasn't a lot of technological innovation that made individuals.
Speaker 2 (06:19):
It just start, like twenty years ago, did they have technology.
Speaker 4 (06:21):
Bout the right? I mean, it's I think this is
the thing. Right, So we talk about computer technology, the
information and computer technology revolution as technology, but obviously this
has always been technology. But only at certain times in
this technological cycle do people seem to make money as
investors and as inventors.
Speaker 3 (06:38):
Explain more, though, because I mean I could argue that
Marisk or someone like that got pretty rich off of containerization.
Like maybe it took a while, but even though the
shipping industry is highly cyclical, when they are in the
boom period, they make a lot of money.
Speaker 4 (06:52):
Sure, I mean, the existing shipping companies got very large
and made a lot of money. They got larger and
made more money. Is mayor you know who made money.
Speaker 3 (07:02):
Completely new entrants?
Speaker 4 (07:04):
Yeah? So just to me as background, I'm a venture capitalist,
right or have been a venture capitalist for a long time,
recently retired, And I think about people investing in making
money new companies, inventors or entrepreneurs making money, not the
existing incumbents making money. And I think that people will
make money on AI. Might be Microsoft making a ton
of money on AI. It could be AI. You know,
(07:24):
when containerization shipping containerization came around, Sealand was the instigator
of this, and the founder of Sealand made money primarily
because he sold early, right, he sold Sealand to r
J Nibisco or sorry, it was just r Jr. At
the time, and they thought they were diversifying, which is
the big thing. Then paid them a lot of money
and then they drove it into the ground.
Speaker 2 (07:43):
So what did Sealand do? What was did they? I
actually don't I'm not familiar with this company at all,
which I think kind of speaks to your point. But
what it was Seland?
Speaker 4 (07:49):
Yes, So so it was a truck. It was started
out as a trucking company. And the founder of Sealand
was a trucking entrepreneur. And he said, it's silly you
you go into a port, your truck sits around all day. Well,
you know, the long charman put a cargo net into
a container ship, load everything into it, pull it out,
unload it and then reload it back into your truck.
This is not efficient. And this obviously it's an obvious idea, right,
(08:13):
just put it all in a box and you can
then put that box on a truck.
Speaker 3 (08:15):
The best ideas are always the obvious rights in retrospect.
Speaker 4 (08:18):
But the problem with it was it was a systems problem, right.
The long charman didn't want it, the ports didn't want it,
the port authorities didn't want it, the politicians didn't want it.
Nobody wanted this to happen because this sort of enormous
change would put a lot of people out of jobs.
It would up set the existing order, and it did.
I live in Hoboken, and in Hoboken there's a lot
of peers that nobody uses except to go running on now,
(08:39):
because back in the sixties it was a long sharmantown.
And when I moved there in the early nineties, it
was empty. It started to gentrify, but because all of
those people had lost their jobs and moved out.
Speaker 2 (08:50):
And I suppose, like even in the case of marisk
And I'm sure you know, they obviously have made a
lot of money because the explosion of global trading volumes
and containerization is part of it. Like, it wasn't overnight wealth, right,
it wasn't.
Speaker 4 (09:03):
It was not.
Speaker 2 (09:03):
It was not people got richer, but it was not
like some bubble get rich quick thing where they suddenly
cashed in on a new thing.
Speaker 4 (09:11):
Yeah. I mean, I suppose whoever owns mask may have
gotten richer. Yeah, but it's not like you're going to
look at the forest four hundred and see all these
shipping magnates who became suddenly, you know, enormously wealthy. There
are a few.
Speaker 3 (09:22):
Wait, okay, so if I think about a box, you
know part part of that.
Speaker 2 (09:26):
We just keep this whole conversation on boxes.
Speaker 3 (09:28):
Actually, well one more question then we will be able
to discuss ai. But I think about a box, and
as you say, it's sort of an organizational structural problem,
Like the box itself is not the huge technological advancement necessarily.
So what exactly was it about containerization that prevented it
(09:49):
from being disseminated I guess to new upstarts or new
companies that could actually use that technology.
Speaker 4 (09:56):
Well it was, so I'm not sure I understand the question,
because uanization became widespread very quickly, right, But.
Speaker 3 (10:02):
What I mean is like, why was that value seemingly
captured by incumbents versus startups?
Speaker 4 (10:08):
Right? I think because it disseminated so quickly, Right, it
was an obvious idea. Everybody who saw it said, Okay,
we need to do this. Right. Everybody who's already in
the business said, if this is going to happen, we
have to do it. We can't be left behind. We
will be left behind if we don't do it, which
you know, I think was also obvious. So the reason
nobody else did it first was it was hard to do.
It was hard to make happen. And technology has always
(10:30):
come in these technological systems if they're worthwhile technologies. Right.
So the personal computer didn't change the world on its own, right.
It changed the world alongside the Internet, you know, alongside
a bunch of technologies that formed the system. So the
hard part here was building the system, not the individual technologies.
And this is true I think of computers as well.
The first microprocessors weren't considered revolutionary. Intel didn't consider the
(10:52):
four thousand and four revolutionary. They considered it evolutionary. The
engineers have said this, And it wasn't revolutionary until people
put it to use in ways that they didn't anticipate.
Speaker 2 (11:01):
Actually, can we go. I didn't know that, like, I
hadn't really thought about that with Intel that at the
time it didn't feel to them that it wasn't a
revolutionary technology. That's sort of mind blowing.
Speaker 4 (11:12):
It is, right. This is I think from Michael Malone's
The Intel Trinity of the book. You know, he interviewed
a bunch of Intel engineers and he said, you know,
like they thought they were building a better chip set
to build pocket calculators or desk calculators. I just say so,
they had a client, Busycom, who wanted to build a
better desktop calculator with calculators were big back then in
nineteen seventy ish, and one of the engineers said, well,
(11:33):
why do we keep building custom chip sets for each
different calculator. Why don't we just build a chipset that
we can customize the software and change what it does.
And Intel is kind of like eh, and Busycom was like, okay,
we'll pay for that. And then Busycom actually tried to
back out and they gave the rights back to Intel.
So Intel owned the rights to this four thousand and four,
and then they started selling it and it wasn't Intel.
(11:54):
You know, Intel believed at the time it was going
to be maybe used for dedicated hardware, you know, can
you hardware controllers that kind of thing, not by consumers.
So it wasn't until people on the outside said, hey,
you know, I love these IBM mainframes or these deck
MANI computers. I'd like to have my own, but obviously
nobody can afford that. Why don't I just try to
build my own? Right, So there's these kind of outside inventors,
(12:15):
this permissionless invention, and then it really the real revolution
didn't happen until this. You know, everybody's like, oh Intel.
It was the six five oh two where the price
came down so dramatically that, you know, Steve Wozniak could
walk into a computer fair you get some for free
and go home and build up a personal computer.
Speaker 2 (12:31):
It's crazy. I wonder who made the chips for the
Commodore sixty four computer that I had.
Speaker 4 (12:36):
They were six five o twos. Those were Intel, that's
you know, they weren't until there were Moss technologies. Oh
got it, got it, those are the cheap ones.
Speaker 2 (12:43):
Then yeah, wait, what year was that when you had well,
I had I think I like learned some basic and
did some coding on it. I would have said maybe
nineteen eighty eight, nineteen eighty seven somewhere around there, made
a few I missed those days. It's crazy that I
didn't be. Can I just say I sometimes when I
think about my life path, like how did I not
(13:04):
end up like a tech guy? Because I was like
very into math. I was like one of those people
who had a computer when I was six or seven.
Speaker 3 (13:10):
In nineteen eighty seven, you were coding you were seven
years old.
Speaker 2 (13:13):
Yeah, I got that. My dad got me this magazine
that just it was very crazy. There was literally just
sent you pages of code and then you just typed
it in and you could like make a video game.
I was doing that at seven. I could be like
one of those like who's per s gave of a
computer when he was seven? It anyway, and it's not
too late.
Speaker 4 (13:30):
It's not too late.
Speaker 3 (13:31):
Yeah, it isn't too late. Well, maybe it is too
late because now we have AI doing all the coding, right, Okay,
(13:52):
just so I understand when it comes to I guess
the advantages of the incumbents to actually monetizing new technology
or benefiting from new technology 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
and the ability to sort of roll out massive investment
(14:12):
to capture that value.
Speaker 4 (14:14):
I think it's the latter, right, So anybody can build
a foundation model, right if you have the money. I mean,
the technology is not mysterious. It doesn't feel like the
technology is really changing very quickly anymore. And of course
I don't have insight into what's happening inside of open ai,
but looking at it over the past couple of years,
it's the same thing, but slightly better. It's evolutionary now, right.
(14:34):
The first part was revolutionary and now it's evolutionary. So
if you wanted to build one, you could build one.
And I have friends who are running them on their
laptops very slowly, but it's possible. 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,
And it's just a question of having that the authority
(14:57):
in the market to be able to raise that money.
Speaker 2 (14:59):
By the way, speaking of ideas that were sort of
really obvious that took a while. I'm always blown away that,
like how long it took them to put wheels on luggage.
I don't think anyone like made got super rich on that.
But when I was a kid, I remember like we
had these big suitcases, and that's the most obvious thing.
It took a while. Anyway, I don't think anyone goes.
Speaker 3 (15:17):
Technology is still not perfected, as you know, because you've
been in airports with me and the wheels on my
luggage are broken.
Speaker 2 (15:22):
But I don't think anyone got like super rich off
of wheels. That just seems like an that was just
sitting there.
Speaker 4 (15:27):
You know.
Speaker 2 (15:28):
I want to jump ahead actually in the conversation a
little bit because I know to forget this point. But
this is something I've become a little obsessed with, which
is I've been meaning to ask a VC about this,
which is that there seems to be this blurring of
private and public markets in various ways, retail participation in
private markets, et cetera. For the VC, in my mind,
(15:49):
I feel like the exit was the IPO or the acquisition, right,
so you buy do vcs these days have to think
a little bit more about market timing and selling early.
So someone who was an early investor in open ai
or whatever you know, in the past, they might have
just held or then sell at the IPO or the acquisition.
It is probably going to happen in an open AI's case,
(16:10):
they're too big to be acquired. But to vcs and
in your experience these days, have to think a little
bit more about this idea of selling early, timing the exit.
Speaker 4 (16:19):
Well, I think vcs always had to think about timing.
You know, I've done pretty well in VC, and I
attributed it entirely to being lucky at starting investing at
the right time. So the first time around, I was
starting in ninety seven, which any you could make money in.
The second time around, I started in two thousand and seven,
which again it was just two thousand and eight. It
was just an easy time to buy in.
Speaker 2 (16:38):
But I mean, the timing of the sale is that
something that where in the past it might have been
automatic exit, now now is not the case.
Speaker 4 (16:47):
Yeah. I wrote this thing about VC in the nineteen
eighties a long time ago on the blog. You can
find it. And the thing because nobody talks about the
nineteen eighties, right, there's plenty of VC in the eighties,
but nobody talks about it. People talk about the sixties
and in the nineties. So I was like, all right,
what happened. Then. One of the interesting things about it
is there were the IPO windows then where you know,
nineteen eighty three the IPO window opened a bunch of
(17:09):
companies on public and then it closed again, and you
can see it in the numbers when people in public.
So people always had to think about timing. The IPO
is obviously the best exit because you want to sell
to the greatest fool, and nobody's greater fool than the public, right,
So you look for the IPO window to open. When
you can't, you have to sell to somebody else. You know.
Vcs have this problem of their limited fund life. So
(17:30):
I look at my portfolio. I'm an really early investor
or have been a really early investor, so I'll look
at companies and say, oh, I invested ten years ago.
They're going to have to sell, you know, it's the
So people look for the IPO window, but if they
can't find it, they have to sell somewhere else.
Speaker 3 (17:45):
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,
and basically you're going to have consolidation and you will
get that exit.
Speaker 4 (17:58):
I don't think anybody can predict when the IPO opens.
I mean, I wish I could, but I don't think
I've never seen anybody even say they could predict when
the IPO window opens. So I think a smart VC
invests in a company that can become self sustaining to
some degree, and then you wait for the timing to come.
You don't invest and say I'm gonna flip this in
three years now. Yeah. The other problem is vcs don't
(18:20):
invest in all these IA companies saying a bunch of
them are going to become valuable. They invest saying one
of these is going to be come right.
Speaker 3 (18:25):
The lottery ticket theory, yeah, the power law.
Speaker 2 (18:28):
Speaking of the IPO window, I'm never totally satisfied by
a lot of the explanations for the drop off in
IPOs generally. Do I know there was that law passed
in two thousand and one.
Speaker 4 (18:40):
Or what was it, sins ox Yes.
Speaker 2 (18:43):
Surbins ox Ley, and I get that law.
Speaker 3 (18:45):
That law law that everyone hated for a really long time.
Speaker 2 (18:49):
I don't know, it doesn't. But then you see, like
you know, twenty twenty one there's like a billion garbage
companies went public via SPACs et cetera. How much is
it about? Okay, there are some disadvantages to being public
versus there's just so much more private capital out there
such that the imperative to perhaps ever go public and
it's liquid and the rounds and like, what do you
(19:11):
attribute There are these big companies that are a private
stripe but open AI and onanthropic that choose to stay private.
What do you think the main reason for that is?
Speaker 4 (19:19):
Well, it's because they can, right, I mean, being a
public company is no, it's no party, right, I mean
it kind of sucks being a public company.
Speaker 2 (19:25):
What is it about it?
Speaker 4 (19:26):
What sucks? Well, you have to tell everybody what you're
doing every three months. Yeah, that's you know, and then
they come back and complain about it. So sorry, I'm
being a little facetious, but it is. It's hard to
be a public company. 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.
So that's taking away from actually running the company. I
think the flip side is your liquid and that's yeah.
You know, So if you can stay private, why wouldn't
(19:48):
you stay private? Or if you can go public and
retain control of the company, you know, like Henry Ford
or Mark Zuckerberg, then why wouldn't you do that? 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. With all this money being invested in AI,
you may wonder if people are still going to want
(20:09):
to invest late stage stripe or the analogous stripe might
be making money now, I'm not sure.
Speaker 3 (20:15):
I know you brought up previous historic analogies like VC
in the nineteen eighties, but just to focus on the
one that everyone else seems to be focused on at
the moment, which is the dot com bubble in the
early two thousands. What are the key differences you're seeing
in terms of the VC and financing environment now versus
twenty or twenty five years ago.
Speaker 4 (20:35):
I think the key difference is that most of the
money is coming from people who aren't looking for much risk, right,
So I mean open ai is primarily funded by bigger companies. Right,
most of their money is coming from large companies. What
happens if open ai gets hit by a bus? Right,
So Microsoft's at a bunch of money. A whole bunch
of big companies are at a bunch of money. I
(20:56):
don't think much happens to the economy. I think, which
is different than in the bubble, where a lot of
consumers were in it. A lot of consumers were in
it leveraged right, the buying and margin or whatever. A
lot of people had options, right, people employees, and they
were spending the money from their options before they were licking.
You know, it was there was a much I think
it was a different dynamic.
Speaker 3 (21:16):
But the wealth effect, I don't know.
Speaker 2 (21:19):
I think this is a contrarian take on your part,
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, which AI is a
big part of the story. 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 the
(21:41):
data centers and the caterpillar, the turbines for the gas generation,
et cetera. I think many people would say there is
a lot right now riding on the health and the
sustainability of this particular sector.
Speaker 4 (21:54):
So I think we can separate the companies like Microsoft
and Video. Are they over valued because of this? Maybe
doesn't make a huge difference to the economy. Probably not,
I don't think so. And the companies who are spending
money on infrastructure like building data centers, building power generation plants,
those things I think are probably overbuilt or not so
(22:15):
much overbuilt as they are built. And I think in
ten years you're gonna have a lot of extra compute,
a lot of extra power generation, and people will be
able to use that for other things. It'll also drive
down the price of just using AI.
Speaker 2 (22:28):
Probably, yeah, Jevin's paradox bro is gonna be out back
and forth. All right, So AI, where are we? You
talk about cycles? And I think you use word that
I hadn't ereruption? What was the word you use? So well,
I'll tell us how you see cycles in what cycle
we're in?
Speaker 4 (22:43):
Right? Right? So A lot of this is based on KARLOA.
Perez's work, which is pretty familiar to the ventro capitalists
and your audience. I'm sure. She wrote a book called
Technological Revolutions and Financial Capital where she explains the dynamics
behind the Kondrati of waves that the Chumpeter talks about.
So she has this theory about why these happen. And
you look at the Industrial revolution, the Second industry Revolution,
(23:04):
you can see these waves of technology technological systems happening
through the economy where they kind of start out, they
grow really rapidly, and then there's usually some sort of adjustment,
some sort of bubble bursting, and then things kind of
level out and then start to plateau, and then a
new one starts. And this is people have noticed this
since at least Kentdrati nineteen twenty six. She has a
(23:25):
mechanism for explaining it, and her mechanism has these four phases.
The first phase is eruption, which she spells with an I,
which I think is actually in the dictionary as a word.
I don't know what the difference is between that and
the eruption, but it is the part where smart right,
So yeah, keep saying that it is the start. It's
when people have invented something and it is starting to
catch on, but it hasn't caught on yet. There's a
(23:46):
lot of people saying, is this the future? Is it not?
You look at personal computers in the nineteen late nineteen seventies,
early nineteen eighties, and maybe even before IBM got involved,
and people didn't think personal computers were Some people thought
they were their future. And then you look back at
computer history. Everybody talks about the people who did think that.
They don't talk about the other ninety nine point nine
percent of smart people who said they weren't. This is
(24:07):
the eruption phase where there's a lot of uncertainty about
where this technology will go. It's starting to build connections
to other technologies, starting to attract money, attract smart people
because it's interesting and it might actually change things. So
this is the beginning. And I think the connection here
to AI is people wonder if we're in the eruption
phase of AI or not. Is this the start of
(24:29):
a new technological revolution phase? I think we're not. I
think we're in. I think this is the end of
the information computer technology wave, the end of the computer wave. Right,
I think it is. This is the culmination of the
computer wave. Right, Why did we build computers. We build
computers to help us think better. Right, this is what
they're for, their knowledge machines. So now we've kind of
(24:50):
reached the natural end stage what they do. They're smart,
machines are smarter. So I think this is not a
new technological revolution. I think it's the end of the
old one. And this is why I compare it to
container is thea because the previous wave was automobiles mass production,
and starting in nineteen fifteen or so up until nineteen
seventy was the previous wave, and containerization was squarely at
(25:12):
the end of that wave, and it was really kind
of pulling together the technologies of that wave into something
that increased productivity, right.
Speaker 3 (25:19):
Like the final step of the global trade and I
guess mobility revolution.
Speaker 4 (25:23):
Yeah exactly.
Speaker 3 (25:24):
Okay, so how do you react as an angel investor?
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,
or how are you actually deploying all these thoughts in
terms of your own investment strategy. I retired, Okay, you know,
(25:46):
I standing it out and I.
Speaker 4 (25:47):
Said, look, how am I going to invest in foundation modes, right,
I don't have a billion dollar fund. I don't think that.
You know, if you look at the big winners from
the early big winners from globalization, the IKEA is right.
I mean it was a Scandinavian company until containerization, and
then they became a global powerhouse, a hugely successful company.
(26:07):
But they didn't need outside money. You know. Comprade, I
think he borrowed like a couple thousand dollars to start
that company or to get that company to buy some inventory.
He never took outside You look at Walmart, which had
been around already. It was an incumbent and used this
kind of globalization to bring a lot more variety of
products to the stores. They didn't need outside money to
do that. Yeah.
Speaker 3 (26:27):
I guess if you're at Kia 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 4 (26:40):
Yeah, exactly, Okay, they were already flat packing.
Speaker 2 (26:43):
Yeah. Your mention of the Walmarts and targets of the world,
it was like in your essay it was like a
sort of very light bulb thing. It's like, yeah, I
don't know. I guess we'd take them for granted. But
they're clear like massive containerization winners. The skill that they
succeeded is impossible to fathom in some prior era.
Speaker 3 (27:01):
Of Walmart is a logistics company and changed my mind.
Speaker 2 (27:04):
Literally and many of you literally literally literally that. But
it doesn't feel like with AI that the equivalent has
emerged yet. Right, We're still at the age where people
are building the container deployed. But the company that exists
and is massive that couldn't exist prior to a like
does not feel does it We haven't seen that yet.
Speaker 4 (27:25):
Well, you gotta be a little patient.
Speaker 2 (27:26):
No, no, but like seriously.
Speaker 4 (27:28):
Iikia so contained. The first container ship sailed in I
think nineteen fifty six. Okay, so when did Akia become
a global powerhouse? It really wasn't until the nineteen seventies
that they started to expand that this is really important.
Speaker 2 (27:39):
They really take a while. Where would you expect it
to show up? Like what industries would you expect because
obviously retail existed for a long time, furniture existed for
a long time. Then you get these Bahamas. Are there
industries that you think are right to produce very torture
analogy the Ikea of the AI way.
Speaker 4 (28:00):
Well, I think they have to be knowledge intensive industries,
right right, I mean this is what AI is doing,
what it is making more efficient. I mean I think
there's people been asking me like, well, then what should
I invest in? And yeah, tell us, well, you know,
I think, well, so give us. I've been thinking that myself.
But the answer is really that as an investor, I
don't decide what to invest in. I evaluate opportunities that
(28:21):
come to me. And so I have built a box
in which you can evaluate opportunities. Right, they have to
look like this. They have to look like an Ikea.
And if I Kia 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. Where nobody else seemed to
see that, right, Certainly the furniture makers in North Carolina
didn't see it. So I think this is the box
(28:43):
that you evaluate things in. And as a long time investor,
I'm used to evaluating and I trying not to come
up with ideas, you know, that said it has to
be a knowledge intensive industry. And I think something that
I said in the essay, which I wish I had
said more about, was the companies that tried to use
shipping containerization to cut costs so that they can increase
margins did poorly. Oh this is key, Whereas the companies
(29:05):
that use the efficiencies and passed the efficiencies onto the
consumers so that they could become larger became larger. Right.
I mean this is you look at these people saying, oh,
we have AI, We're going to fire people. I mean,
that's I think is the exact wrong move. And I
think it's probably just every you know, every new thing
comes along, people like, oh it's we're going to fire people.
You know, it's just an excuse. But but if you're
(29:25):
firing people because of AI, you're doing it wrong. Right.
You should be using AI to say I can use
my people to do more. I can grow my company,
I can vary my products, I can take more market share.
Speaker 3 (29:36):
So the value goes to the consumer. And I guess
you capture the value by selling more right, more knowledge?
Speaker 4 (29:42):
Yeah, I mean I think Walmart never tried to maximize margins.
Speaker 3 (29:46):
They it was it was volume.
Speaker 2 (29:47):
Yeah, Okay, from a consumer standpoint, why do I care
(30:08):
if your workflow internally at your company is become more
efficient things AI, either the product is better or new
or something.
Speaker 4 (30:16):
You know.
Speaker 2 (30:16):
I was thinking about this. I don't mean to like
pick on anyone, but situating like open door meme Stock
they have new CEO. He sent out this memo. He's
like everyone has to do start using A or it's
clearly a press release in the form of an internal
memo because it got it I think waving.
Speaker 3 (30:32):
A flag going us.
Speaker 2 (30:34):
It's like, yeah, but like, does the economics of buying
homes via whatever get better? Like does it actually make
the business better? This strikes me as very interesting, this
idea that like, it's not gonna it's just not that exciting,
especially any sort of customer oriented company, which I guess
is all companies. The fact that AI has become part
of their workflow. It's the electric knife effect, right, So
(30:57):
I don't know that effect we might.
Speaker 4 (30:59):
Just call that, but I mean electric knives are one
of the fastest growing consumer products in the late nineteen
sixties because people are like, oh, we are electrifying things,
like let's electrify the knife and within like three years
they were in some massive percentage of households, like eighty
percent of American households had an electric knife and things
like you know, the blender didn't get adopted that quickly.
But who has an electric knife now? Right? It's I
(31:21):
think people take whatever it is the technology is and
say this is everything. I mean, back in you know,
the early two thousands, late nineteen nineties, every company was
an Internet company. Right. Do people walk around saying that's
an Internet company? Now are you an Internet company? I
mean everybody's an Internet company. Everybody uses it. It's the baseline.
That's I think the what a revolutionary technology does is
it becomes part of everything. So, yeah, you're right. Consumers
(31:43):
don't care that your you know, your lawyer's using AI.
They want to make sure that they have good legal advice. Yeah.
Speaker 3 (31:48):
Our producer just messaged Dash saying that he had an
electric knife. Oh does he have Probably not in the
nineteen sixties.
Speaker 2 (31:55):
There do you have one?
Speaker 4 (31:56):
Now?
Speaker 2 (31:57):
My mom might have one case that didn't get picked.
Speaker 4 (32:01):
Up on the audio.
Speaker 3 (32:01):
Well, his mom might still have one. Dash, will you
bring it in for us and we can we can
use it as a.
Speaker 2 (32:07):
Prop I'm just imagining like going to a restaurant at
the time it's like, oh, we slice your bread with
electric knives. That are like how unexciting that would be
From a consumer stand I don't care, you know.
Speaker 3 (32:19):
Yeah, I guess that's true, but okay, but things are
different from a shareholder standpoint, right. And one of the
reasons we see when companies say that they are using
AI the share price goes up, it's because shareholders expect
all these easy cost cutting gains. Do we have any
indication that investors are actually going to be patient and
wait for I guess the value to spread to the
(32:41):
consumer or are they just going to demand basically these
fast cuts.
Speaker 4 (32:47):
Will And I mean, I'm not a stock market investor.
Yeah I know, I know you're stock marketing, you know.
I think they have a very short attention span. There's
a great quote in that article that you see in
the eighties where the Wall Streetjournal said, you know, there
was the beginning of the eighties, there was a craze
and anything that ended with an onyx rightes right, it
was people were right funny, but it was a short
(33:10):
lived one because it didn't really deliver. And then people
were like, all right, let's move on and do something
else or invest in something else.
Speaker 2 (33:16):
So funny, if I were writing a fiction about the eighties,
I would immediately I would say, like applied fouxtronics or
something that. Yeah, right, yeah, I hadn't thought about that.
That's amazing.
Speaker 4 (33:23):
So I think they want results. But I think it's
two people are impatient. I mean, I was kidding, but
I'm not kidding. People are impatient. They want to see
results today. I don't think AI is going to show
you the real revolutionary results for a decade. Taking AI
and just retrofitting it on to the way you do
things now is only going to add a little bit
to your efficiency. You have to actually re engineer everything
(33:46):
around this, change your processes, hire different people, or get
you know, train people, and then you're going to see
big efficiencies.
Speaker 3 (33:54):
You got to have prompt training in schools, right in
high schools or something. Well, we were talking about this
with Tyler Cowen earlier and he had, you know, a
similar point.
Speaker 2 (34:03):
Are big companies setting aside? Okay, like, yeah, it's not
very exciting that a big company is able to marginally
reduce their workforce. I think a lot of those are fake.
I have a feeling a lot of these companies are
going to have to end up having to hire people
back when these things don't work, or they had nothing
to do with AI and they just wanted to do layoffs.
Can legacy institutions like there's some like inherent roadblock to
(34:27):
the degree to which legacy institutions can incorporate on AI
Or is this the type of thing where it's just
going to be entities that didn't exist before becoming really
big household names.
Speaker 4 (34:40):
No, I think it will be legacy. I mean I
think you know, Walmart was legacy, Ikia was legacy. It
may not be the names you expect, right, Sears and
Woolworths didn't really benefit from shipping containerization. They ended up
going out of business. I think you have to have
the right attitude of how you're going to utilize the
efficiencies it brings within your business again, to grow your business,
(35:00):
not to grow your CEO's salary. Right, you have to
be spreading this to the consumers for your company to
be successful, all Right.
Speaker 3 (35:08):
In the intro, Joe made fun of Sid saying something
completely rational. It Yeah, I know, I know, but this
is your joke, right, Okay, So I'm gonna ask the
cliched question in that theme, are we in a bubble?
Speaker 4 (35:25):
Can you define bubble? No? I should.
Speaker 2 (35:29):
I think the question should be asked liberally.
Speaker 4 (35:32):
It's like, are things overvalued?
Speaker 3 (35:35):
Are things overvalued?
Speaker 4 (35:36):
Yes, but there's a difference between things being overvalued in
the bubble, right, and I think things are overvalued, and
I think there may be an infrastructure bubble. In the
article I wrote for classes as a chart of container
ships being built, of shipping ships being built, and you
can see this huge rise right after containerization started for
a bunch of years, and then it dropped back off
(35:56):
because now people had their ships, but everybody had to
get into the same time. Everybody had to go order ships.
A ton of ships were being built, and then the Capex,
you know, and then they were like, okay, now we
have ships. It's a little different with chips because chips
aren't gonna last you know, yeah, or a long a
container ship last thirty fifty years. But I do think
that you're not going to need as many chips in
the future as you are buying today. Really well, you know,
(36:21):
you're gonna have more use of AI. Probably there's gonna
be more use. But I also would think they would
become more efficient in compute. That's just the history of compute.
Speaker 3 (36:30):
Does your definition of a bubble, does that have to
include a buildup of leverage of some sort.
Speaker 4 (36:35):
Well, I lived through the dot com bubble, so yeah,
I would say so, right, because you're not seeing that right? Well,
because I think a bubble popping has to hurt. And
if Microsoft loses a billion dollars, that doesn't hurt. Doesn't
hurt any I mean, sure it hurts whoever's invested that
money at Microsoft, but it.
Speaker 2 (36:49):
Doesn't who has which is everyone who is a four
one K?
Speaker 4 (36:53):
Well, I guess, but I mean, really, Microsoft loses a
billion dollars, how much does that affect their stock price?
And even if the stock win down ten percent, that's
not a It's not like the dot com bubble. Right
When that popped, people were laid off, the economy went
to a recession. I mean it was pretty deep recession.
Took a while to kind of pull ourselves back out
of that.
Speaker 2 (37:10):
Talk to us more about the dot com bubble. I
tried to bring it up in every conversation because that's
when I got interested in markets, did a little day
trading in those days when I was in college, et cetera,
and I just remember that period very fondly because I
was young. What do people get wrong in their memories
of the dot com bubble?
Speaker 4 (37:29):
So here's the thing I remember most about the bubble.
I was a corporate VC. I worked for a big
company here in New York for to my front of company.
And one of the companies I had invested in was
a public company and they were raising more money. This
was in January, right for the bubble popped right.
Speaker 2 (37:45):
Was the peak was in market in March two thousand.
Speaker 4 (37:47):
Then it was January two thousand, so the company was
selling stock. They said, hey, you know, if you want
to buy some more stock in our company, we'll sell
to you without the underwrited discount or before the underwted discounts.
We'd get it a seven percent below the market price.
And I went to CFO of this giant company and
I said, hey, we can get a good deal on
this stock. We can get seven percent off, right, who
doesn't love a bargain? And he said, well, do you
(38:08):
think the company is worth that price? I said, no,
nowhere near. He's like, so why are you buying it?
I'm like, oh, I guess that's a good point. He's like,
so why would we still own it? And it's like,
that's also a good point, right, which why aren't we
selling this if it's overvalued? I mean the thing that
people forget is everybody knew it was overvalued. They were
all just waiting for it to go up more before
(38:29):
they sold. And he said this, and I'm like, yeah,
I guess better to sell early than late. And we
ended up selling that entire position, which luckily paid for
the whole portfolio before the bubble popped.
Speaker 2 (38:39):
This is a I mean, I think this is there's
sort of the difference between John Authors had a really
good newsletter I think about a year ago, which is.
Speaker 3 (38:48):
There, you're just awsletters I'm doing.
Speaker 2 (38:50):
I'm doing my job to Bloomberg. I like, but it
was basically like, you know, you get these situations like
dot Com where everyone said this is massively overvalued. We
all know, we all It's then you have bubbles that
are more like the housing bubble, in which I don't
think on any sort of traditional metrics the banks were overvalued.
I think probably the pees are probably normal. It's just
(39:11):
that the earning extreme was entirely unsustainable and I guess
that's the question with AI, Like I don't know, videos
prebaby expensive, but like I don't think people think it's
like crazy stretched on pees. It's more the question of,
like is this chip demand at this pace sustainable? Like
are the earnings estimates realistic?
Speaker 4 (39:30):
Right? Well, but I mean the economist said the housing
housing was overpriced in two thousand and.
Speaker 2 (39:34):
Five, right, the homes were, yeah, the banks, you know,
but the banks got obliterated. And it was not because
the ratios of the banks were completely out of way,
because just the profits could not be sustained by any
stretch at that point. Anyway, I think it's an interesting distinction.
Speaker 3 (39:51):
Well, I think there's still an open question with AI
about the network of relationships that are sort of driving
a lot of this business. Like that's where I would
see some of the maybe two thousand and seven two
thousand and eight analogy actually being true. This idea that
like you have this whole system of funding with banks
that's keeping the whole machine going, but when the collateral
(40:13):
that's underpinning that system suddenly loses value, the whole thing
falls apart. You could maybe make a sort of similar
argument for AI, where you have this network of companies
that are sort of investing and selling to each other.
If the value of the underlying asset, which I guess
would be compute in this case, starts to fall really precipitously,
the whole thing kind of collapses. But I'm stretching that.
Speaker 4 (40:37):
It's amazing to me how much of the lessons we
learned in the nineties people just don't know or remember.
I mean, the whole blank check company thing. We did
that in nineteen ninety nine, right, and it'll fill all
the SPACs, right, and it's totally failed, and then people
did it again. It was crazy to me. And it's
kind of circular. Yeah, I know, right, Yeah, I mean
it's still a bad idea. And I think the circular
(40:57):
revenue was also happened in the nineties and that was
all also a bad idea. You'd think that people could
see that and factor that out.
Speaker 2 (41:03):
Gotta have so many questions. Why are specs a bad idea?
On paper? It seems like a totally fine way to
go public. In practice, it only seems like total garbage
companies take that route.
Speaker 3 (41:14):
It feels self selecting.
Speaker 4 (41:15):
Yeah, well, of course, because the way they're structured to
get people to invest in a company you don't even
know what it is yet, it means that it's not
great for the companies, right, you get a ton of yeah,
oh right, that's just leakage.
Speaker 2 (41:29):
Well, just explained the mechanism again.
Speaker 4 (41:31):
So people, you know, if you put money into a spack,
when they decide they're going to do a deal, you're
allowed to take your money back out of the spack.
I can't remember all the details, all right, Right, So
if you're like, well it's a good deal, I'll leave
my money in. But if you're the company being acquired,
you don't know if you're going to be acquired or not,
because people could take their money out. So just why
wouldn't if you could go public on your own, you
would prefer to do that. So by definition, the spacks
(41:52):
are buying companies that couldn't go public on their own, right, right?
Speaker 3 (41:55):
Is VC investing fun again? I got the impression like
two years ago everyone was pretty depressed, and I'm quite
sure we did a few episodes on it at the time.
But are people having fun again?
Speaker 4 (42:07):
Well again, I retired, so no, nobody's having fun. I
wasn't having fun. I think if you have a billion dollars.
It's probably fun if you're doing the big deals. I
think if you're in AI, it's fun. If you're doing
anything else, it should be fun if you're doing something
besides AI. Right, because now everybody's distracted by the shiny
new thing, you can go find companies that are interesting
(42:28):
to you and invest. The problem is you have to
worry about what happens a year from now when they
need the next round. Is anybody going to be paying attention?
I think it's probably pretty hard to be an early
stage investor unless you're investing in AI. And if you're
investing in AI, you're probably not writing small checks, low valuations,
and you can't control the outcome at the end. You
(42:49):
can only control what you do at the beginning, so
you probably won't be making money.
Speaker 2 (42:54):
I have this theory that actually nobody likes bubbles or
booms even but let's say bubbles in part like, if
I missed it, I'm upset because someone else is getting rich.
If I'm in it, I'm like really anxious. Am I
gonna like I'm anxious about two things. I'm anxious. Am
I going to sell it at the right time? I'm
also kicking myself for not investing more. No matter how
much I invested, I'm upset with myself for not having
(43:16):
invested more.
Speaker 4 (43:17):
Is that right?
Speaker 2 (43:17):
This is just my impression when I read history, which
is that everyone, even in the boom times, there's like
this like din of like stress underneath. Is that true?
Or am I just fantasy projecting how my own neuroses
from birth onto.
Speaker 3 (43:31):
Everyone thinks they're gonna time it.
Speaker 4 (43:32):
Right, But it's fun. The bubbles are fun, especially if
you're young and stupid. Right, there's the New Yorker cartoon.
I want my bubble back. Yeah. Yeah. The flip side
is it is stressful. I remember, well, its stressfull both
after obviously I still have my Razor of His stock certificates.
I had a certificated because I couldn't sell it for anything,
you know, for any real money, and that at one
point had been worth quite a bit of a month
(43:53):
more than.
Speaker 2 (43:54):
Again, I remember, what did that company do? It is
like an ad network or something.
Speaker 4 (43:57):
No, No, they built websites. Oh yeah, yeah, I remember.
They were great. I mean they were a great company
when nobody else knew how to build websites.
Speaker 2 (44:04):
Friends who worked for Razor fish even as like recently.
It is like twenty ten.
Speaker 4 (44:08):
They sort of hung on.
Speaker 2 (44:09):
For a little while. Jerry Newman, thank you so much
for coming on odd lot. It's been wanting to chat
with you for a long time, and I really appreciate
your joining us. Yeah, thank you both, Tracy. I love
(44:32):
that conversation. There's a lot there. I mean, obviously, I'd
like anytime we can talk about the nineties bubble, but
I had never really thought about or any come anywhere
close to thinking about the AI analogy with containerization. It's
a little embarrassing because that's such a core topic for us.
We've talked about boxes so many times, but it's sort
of re orient my thinking of AI and thes term
(44:54):
very helpful.
Speaker 3 (44:55):
Yeah. Well, I mean this just kind of proves the
point that no one thinks of containerization. I mean, I
said before, it is a technology story. And one of
the reasons I do think of it that way is
because I read that book The Box, Yeah, which is
really good, but no one thinks about it as an
investment story. No, because of the reasons that Jerry just
laid out.
Speaker 2 (45:13):
Yeah, No, that's really interesting, and you know, again like
it does feel at some point that non tech businesses
non AI businesses. Eventually someone hopefully for the industry, like
makes a lot of money actually using these tools because
we've been in the picture shovels phase or whatever. But
(45:34):
at some point maybe it's an existing healthcare company or etc.
Or maybe it's a new kind of law firm or
an incumbent law firm where it's like, Okay, we have
found a way to use this technology in a manner
that is very profitable, productive, and market expanses.
Speaker 3 (45:52):
So to me, that's the key thing. So the key
thing is it's not that we're going to use this
technology necessarily to cut costs and boost profit margins. It's
that we will actually expand our customer base and make
it up in volume by selling more knowledge.
Speaker 2 (46:08):
You know, it's interesting thing that Jerry said, and you
put into words something that I hadn't really thought about before,
but this idea about being at the end of this
sort of computer revolution, and there is something about AI
specifically where people it's like and it can't really literally
be this where it's like, well, this is the last technology,
(46:29):
right because.
Speaker 3 (46:30):
Because we're gonna get robots, yeah, right.
Speaker 2 (46:32):
And I don't know if like other booms or technological
revolutions had this feeling where it's like, this is the
last one. Theoretically, if you get Agi or whatever robots,
you don't need any further technological innovation, et cetera. It creates,
I think, a very weird, uncomfortable dynamic. But the idea
of AI is the end of what we do with
computers rather than the start of like something genuinely new
(46:55):
like that actually like snaps into place a lot of
thoughts for me.
Speaker 3 (46:59):
Once we invent God, we're done.
Speaker 4 (47:02):
We're done.
Speaker 2 (47:02):
Everything else takes care of it.
Speaker 3 (47:04):
So yeah, all right, shall we leave it there?
Speaker 2 (47:06):
Let's leave it there?
Speaker 4 (47:06):
All right?
Speaker 3 (47:07):
This has been another episode of the oud Lots podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2 (47:12):
And I'm joll Wisenthal. You can follow me at the Stalwart.
Follow our guest Jerry Newman, He's at g A Newman.
Follow our producers Carmen Rodriguez at Carman armand dash Ol
Bennett at Dashbod and Keil Brooks at Kilbrooks. More odd
Lots content go to Bloomberg dot com slash odd Lots.
We've been daily newsletter and all of our episodes and
you can chat about all of these topics with fellow
(47:32):
listeners in our discord Discord dot gg slash od Lots and.
Speaker 3 (47:37):
If you enjoy odd Lots, if you like it when
we talk to you about why you're not going to
get rich from AI, then please leave us a positive
review on your favorite podcast platform. And remember, if you
are a Bloomberg subscriber, you can listen to all of
our episodes absolutely ad free. All you need to do
is find the Bloomberg channel on Apple Podcasts and follow
the instructions there. Thanks for listening in
Hosts And Creators
Joe Weisenthal
Tracy Alloway
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