May 29, 2025 • 58 mins
Michael Cembalest has been an investment analyst for almost 40 years and his research notes have drawn a cult following on Wall Street. He's known for going super deep into a wide range of topics, like energy and healthcare. And lately he's been writing a lot about AI, with a particular interest in figuring out whether all the investment in data centers and compute will translate into actual profits. On this episode, we talk to the chair of market and investment strategy for JPMorgan Asset Management about why AI is the market "bet of the century," why the dominance of US big tech can't be overstated, and why he's pessimistic about the outlook for small modular nuclear reactors.
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Episode Transcript
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Speaker 1 (00:00):
Hello, Odd Lots listeners. I'm Joe Wisenthal and I'm Tracy Alloway. Tracy,
we're doing another live show and it's right here in
New York City.
Speaker 2 (00:07):
Yeah, this one should be our biggest yet, and we're
going to have a bunch of Odd Lots favorites and
do something maybe a little different to some of our
previous live podcast recordings.
Speaker 1 (00:18):
When the guests are revealed, the show is going to
sell out right away, so you should really just go
get your ticket right now. It's June twenty sixth. It's
at Record NYC, and you can find a ticket link
at Bloomberg dot com slash odd Lots or Bloomberg Events
dot com slash odd Lots Live and why.
Speaker 2 (00:34):
We hope to see you there.
Speaker 3 (00:38):
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:54):
Hello and welcome to another episode of the Odd Loots podcast.
I'm Tracy Alloway.
Speaker 1 (00:58):
And I'm Joe Wisenthal.
Speaker 2 (01:00):
You know, does it feel to you like every day
when you wake up in markets there is a new
sort of dominant narrative, and often it is the exact
opposite of the narrative that was dominant a day or
two ago.
Speaker 1 (01:15):
Go on, say more what you mean, where are you
going with this? I mean, I agree in some sense,
but I think you have something specific in mind.
Speaker 2 (01:21):
Well, no, I actually have a bunch of things in mind.
So for instance, you know, recently there was a lot
of talk about the bond vigilantes. Yeah, back and people
worried about the fiscal deficit.
Speaker 1 (01:30):
Yeah, and then this.
Speaker 2 (01:31):
Week that seems to have suddenly gone away, or at
least it's not the only thing. People are talking about tariffs.
Speaker 4 (01:39):
You know.
Speaker 2 (01:39):
Obviously we had the big market freak out in April,
and then we had subsequent delays and extensions of the deadlines.
I don't hear people talking about tariffs as much as
they were in April.
Speaker 1 (01:51):
So then through it all is you know, on days
when we don't have to talk about tariffs or bond vigilantes,
then we immediately turned to AI. Well this is what
I was saying, you know, So this is the thing.
There's like this default thing that's in the background, which
is AI. And then that of course leads to things
like power and electricity and so forth. So yes, I
(02:12):
now see what you're saying, which is that on any
given day, it's like you spin the roulette wheel or
the wheel of fortune and then it's like, Okay, it's
an AI day. Actually we're recording this May twenty eighth.
By the time people are listening to this and video
earnings are going to be out. Yeah, and so today
is an AI day.
Speaker 4 (02:28):
It might be an AI day.
Speaker 2 (02:29):
What I was going to say is one of our
previous guests, Victor Schwetz over at McCrory. He framed it
really well where he was basically saying, if you know,
if you can't gauge what the dominant narrative is going
to be every day, or if everything that's happening is
so volatile and so dramatic and we're talking about really
big changes, then you kind of have to go back
to doing the normal thing. And the normal thing before
(02:53):
all of this kind of started in the New Year,
was worrying about AI, right, and thinking about AI and
whether or not you know, all of that AI spending
is going to be backed by revenue. So on that note,
that's what we're going to do today. We're going to
go back to normality, although we are going to talk
about some of the current stuff as well. And I
am very pleased to say we have someone who I
(03:13):
have wanted to get on the podcast for a really
long time, and he's finally sort of been let out
into the wild. So we are very excited we're going
to be speaking with Michael semblest He is, of course,
the chairman of market and investment Strategy for JP Morgan
Asset Management. He writes a great, great piece called I
on the Market, which he's been doing for which has a.
Speaker 4 (03:35):
Cult following, cult following.
Speaker 1 (03:36):
Every time a new symbolist piece comes out, everyone talks
about it and reads it.
Speaker 2 (03:40):
Yeah, and I think it's been going on for something
like twenty years now. And he himself has been at
JP Morgan since nineteen eighty seven, so, you know, a
real witness to financial market history. So I'm very excited. Michael.
Thank you so much for finally coming on the show.
Speaker 4 (03:54):
Thank you very much. Good to be here.
Speaker 2 (03:57):
I guess my first question is can you explain what
you do at JP Morgan Asset Management. I on the
Market is a phenomenal publication. I've been a fan for
a long time, but I always wonder like, is there
a difference between writing investment outlooks for asset management clients
versus writing them if you're on the cell side.
Speaker 4 (04:15):
Well, yeah, there's a huge difference. First of all, you know,
thank god for the Chinese wall, Arisa rules and things
like that, because I have the independence, and what our
clients want is for me to share what I'm thinking.
The best example of that is in February twenty twenty one,
the investment bank was making money handover all investment banks
for making money hand over fists in the back market,
(04:38):
and I published forgot about it. I published, you know,
two really scathing pieces on SPACs. The first one was
called Hydraulics Backing, and the next one, because it was
during COVID, was called spacscene hesitancy, where I kind of
pointed out that the sponsors were the only ones making
money and that their break even threshold was something like
an eighty percent decline in the merged stock price. Was
(04:59):
there so anything better than that? They would make money
and everybody else was getting slaughtered. So, you know, being
behind the fiduciary wall allows me to kind of call
it like I see it. And the eye of the
market is the external presentation of our thoughts and views,
and you know, on an internal basis, I'm participating our
investment committees dealing with asset allocation and security selection and
(05:23):
everything else that goes along with managing money for Dowmence foundations,
pension plans, insurance companies, sovereign wealth funds, and individuals.
Speaker 1 (05:31):
It does feel like on the media side we are
at the whims of narratives and all the things that
Tracy talked about at the beginning, Whereas if you talk
to people who are engaged in the art of security selection,
it feels that actually might be liberating because on some level,
whatever is going on, you could still look at an
(05:52):
instrument and say is this a well priced instrument or not,
and not having to get caught up so much in
just you know, you know, whatever the headline of the.
Speaker 4 (06:01):
Day is right, And actually, you know, if you're managing
money other than kind of a fast money macro hedge fund,
you don't have the luxury of changing your asset allocation
every time a new narrative comes along, so you to
some extent you have to kind of choose your poison,
make your asset allocation decisions, and then be either rewarded
or punished based on the outcome.
Speaker 2 (06:24):
So one of the reasons we wanted to get you
on the show was because you've been writing in great
detail about AI. So let me just ask the basic question.
If we're not gonna worry about you know, Trump and
tariffs and bond vigilantes and all the sort of headliney stuff,
if we're going back to considering, I guess the macro
and market importance of AI at the moment. How dominant
(06:45):
is AI when it comes to market valuations at the moment.
I guess how fundamental is AI when it comes to
the wider stock market.
Speaker 4 (06:53):
I don't think you can overstate the importance of this stuff. Wow,
when you dec impose the evolution of S and P
profit margins and you strip off the MAG seven versus
the rest of the market, it's like East and West
Berlin right now, I'm dating myself, right, But that's how different,
(07:14):
that's how divergent those are. If you look at Earning's growth,
I think Earning's growth for the MAG seven over the
last ten years is close to twenty percent. It's six
percent for the S and P four ninety three. So
it's almost like two completely different universes of companies. And
so they are extremely profitable. They are spending somewhere between,
(07:35):
depending on a quarter, twenty five to forty percent of
their revenues on capital spending in R and D. Those
numbers are unprecedented and in the history of the markets,
even going back to the nineteen sixties main frame heer
and things like that, we've never seen anything quite like this.
And what the hyperscalers are doing I would describe. I know,
we're only twenty five years into the century, but this
is the bed of the century that you can spend
(07:57):
this much on AI infrastructure, you know, us your free
cash flow margins for some period of time and wait
for the ultimate payoff.
Speaker 1 (08:04):
Even prior to the AI you know, you mentioned this
extraordinary gap. I mean, there are two things about hearing
you describe the earnings picture within the S and P
five hundred that strike me. One is the absolute gap.
But then there's also the fact that it's the biggest
companies growing really fast. And I in my mind, when
I was younger, assumed that as big companies got bigger,
(08:25):
they grew slower and smaller companies grew faster. When you
look at stock market history, how rare is the type
of earnings growth the year on year, the huge numbers
that they put up earnings growth wise, How unusual is
it for stock market history for the biggest companies to
also just be galloping past everyone else like this, you.
Speaker 4 (08:45):
Can't find it. You can't find it. I mean stock
market history on a security level basis goes back to
around nineteen eighty. There's a variety of databases through facts
that through the University of Chicago. That's about as far back.
Speaker 1 (08:55):
Really, you can't go much.
Speaker 4 (08:57):
It's hard to go back to users in that there's
some Wilsher data, but it's it's difficult. It's very spody.
So if you're looking for things like earnings growth, if
he just wants stock price growth, but if you're looking
for earnings and margins and things like that's hard. So
we can't find We can't find any comparable period where
the bigger companies are growing faster. And even within the
private equity universe, your theory holds true. The generally smaller
(09:20):
and midtized funds of usually you're going to outperform over
the long periods of time the larger funds. This is
negative size bias. So this is pretty unique. And you know,
over the years, I've always thought that the biggest risk
to all of this was going to be some reinterpretation
of the Sherman Act, and you know, anti trust And
while Epic Games has won a lawsuit, here are there
(09:41):
and the Department of Justice is picking away at Amazon,
Apple and Google for different reasons, there really hasn't been
a fundamental rethinking of anti trust law that would get
in the way of this.
Speaker 2 (09:51):
Can you talk a little bit more about that. What
are the drivers or the circumstances that are allowing mag
seven to continue to grow in a phenomenal pace but
also just become the giant cash flow monsters.
Speaker 4 (10:02):
Yeah, well, it has to do with the digitization of
the economy and how much technology is at the core
of how companies run. You know, JP Morgan right now
is a firm. We've got three hundred different language model
and ALI projects going on in and all of that
requires them one way or another, a lot of these
mag seven coming. I think we have to put Tesla
(10:23):
aside for a minute for reasons that we should discuss.
But technology is really at the foundation. And I think
a lot of times when people compare and they say, oh,
US docs are expensive relatives the rest of the world,
they're missing a couple of important things. They're missing the
fact that the US has a massive weight to technology
and in Europe, you know, basically it's it's a value market, right,
(10:45):
It's a lot of energy, financials, and consumer staples, and
so the US is really the dominant player in large
cap tech. There's an amazing chart. Mario Draghi, you know
who he is, obviously wrote a piece at the beginning
of the year what can we possibly I'm poweraphrasing, what
can we possibly do to reinvigorate entrepreneurship in Europe? And
(11:07):
he had a chart in there that we recomputed in
our own way, and it's a bubble chart that shows
the number of new companies created in the US versus
Europe since the year two thousand and Again, it's like
East and West Berlin. They are completely different. Yeah.
Speaker 1 (11:21):
One of the things that struck me recently we did
an episode with the CEO of a women's clothing company,
M M. Lafleur. We're mostly talking about tariffs, but the
thing that I keep thinking about is she said that
when she started the company a little I think when
was it over ten years ago? Tracy, Yeah, her customer
acquisition costs on a platform like Instagram, where she was
(11:43):
like twelve dollars a customer and now it's like over
two hundred dollars. And it just struck me that one
of the things that must be going on in the
economy is everybody's margin becoming Facebook's profits, or everybody's margins
eventually becoming Alphabets profits. That part of this gap that
(12:03):
we're seeing is there is this digital real estate that exists.
It's where everyone is moving, and they could collect massive
rents from owning that real estate from anyone who wants
to do commerce there.
Speaker 4 (12:14):
Yeah, and I think rent is the right word here,
and for reasons both good and bad. You know, Amazon
requires if you want to be if you want to
sell to prime customers, you have to agree to use
Amazon shipping. If you make an in purchase app in
it some kind of a game like Fortnite on an
Android phone, you have to make that purchase through the
(12:35):
Google Play Store. These are some of the issues that
have been litigated in some of the and so I'm
not one hundred percent in favor of some of the
tying that essentially they get. If JP Morgan said if
you want a credit card, you got to do your
mortgage with us, we get slapped down pretty fast, and
for good reason. And so there's a lot of product
tying that takes place in big tech. You know that
(12:55):
a lot of companies are beginning to challenge and the
DJ is looking at as well.
Speaker 2 (13:14):
So we've been talking about the dominance of US tech
and you know, there are a lot of numbers to
support this, and you've been laying some of them out.
But before we had all these teriff related market freakouts,
the freakout that everyone was focused on was deep seek,
of course, and the idea that suddenly China has invented
a low cost AI model that is very competitive with
(13:37):
what we've seen from the giant companies like the Microsoft's
and the Googles of the world. Looking back at that,
was that freakout overstated or just was okay?
Speaker 3 (13:47):
Right?
Speaker 4 (13:47):
A couple of reasons. One, I think it's become clear
and I wrote a piece at the time. They were
not completely transparent about how they accomplished some of their
goals imitations. This is serious form of flattery. They actually
used i think open AI models to train their models,
and so they did a lot of piggybacking. That isn't
(14:09):
necessarily a sign of some kind of productivity breakthrough. Also,
the price at which they are willing to sell something
is not necessarily reflective of its true cost, and so
there's some loss leading that's going on in there. That
again is different that said, cheaper AI models, if that's
where we're going, is probably going to make adoption even easier.
(14:33):
So if this can all be done with less energy
at lower token cost, that's a bullish story for AI,
not a negative one.
Speaker 1 (14:41):
It also occurred to me when that happened that if
there is this existential battle between the US and China
for AI supremacy, probably spend more on it. You know
it you know, it becomes a nuclear bomb type race,
then the impulse is going to be to spend more
on it. But actually, you mentioned things like energy cost
per token and so forth, and I'm curious, from your
(15:03):
perspective or you know, even doing this for a long time,
how did you learn about that? How did you educate
yourself on understanding the sort of the key economic variables
that you have to get wrap your head around in
order to say informed things about this new topic.
Speaker 4 (15:21):
Well, there's a bunch of things.
Speaker 1 (15:23):
We're trying to figure that out ourselves. You know, we
probably should be doing more about AI and talking so
we need to learn about how you tell yourself about it.
Speaker 4 (15:30):
I think you have to budget your time. And my
most important thing is reading about twentusand to twenty five
hundred pages of research each week, So that comes first,
other than spending time with my wife and my dog.
Those that's the most important thing I do because that's
where whether it's on energy topics or politics, or healthcare
(15:50):
or biotech like, it's an information laid in world. And
the great thing about JP Morgan is I can call
just about anybody say Hi, I'm the chief strategist on
the asthmatters, Miss JP Morgan. I'd like to talk to
you about something. Would you talk to me? I can
count on one hand the number of times that somebody
(16:10):
had just flat out said no. And one of those
people was the bundler for Bernie Madoff. And that's the
great thing about the halo effect that Jamie has brought
to JP Morgan. And remember I worked at JP Morgan
for many years when there was no halo, and you
know that halo effect is very powerful and benefits me
(16:31):
and a whole bunch of other people to company. And
I recognize that every day. So I spent a lot
of time working you know, deeply on some of these topics,
and energy as an example, I needed to learn about
energy from somebody that understood energy better than anybody else.
So fifteen years ago I made a pilgrimage to Manitoba
in February, right, which was can you prove yourself because
(16:51):
it was cold, and I established a relationship with vaclub Smill,
who for over a decade was my personal technical shirpa
on standing and learning about energy.
Speaker 1 (17:01):
So it's someone we haven't had on the podcast.
Speaker 4 (17:04):
He is a hermit, he's he's check, he's over eighty oh okay,
and he's a hermit, and he he hates the political
dynamics in the United States. So he will never get
it him here.
Speaker 1 (17:16):
We'll go to Manitoba, we'll try.
Speaker 2 (17:19):
I'm still stuck on the idea of the made off guy,
like answering the phone and being like, oh, we would
prefer not to talk in depth about why our line
just keeps going up in a very smooth way.
Speaker 4 (17:30):
It was an eleven compound, yeah turn with two percent vault.
It was an unprecedented return history, like in the history
of edge funds.
Speaker 1 (17:39):
So well, also, Tracy, I don't expect you to comment
on this, Michael. But Tracy, if I recall, one of
the people who was initially raised alarms about Madoff was
actually a guy man Zames at JP Morgan. Oh yeah,
so perhaps there was a reason I don't we don't
want to talk to these JP Morgan fellows.
Speaker 4 (17:54):
Actually, if you go back, if you go back even further,
in either two thousand and one or two thousand and two,
there was a guy named Harry Markoppolos, Yeah, in Boston,
and he wrote a piece basically saying Bernie Madoff as
a fraud as upon his scheme, and he sent it
to the SEC and they kind of poked around and
did nothing.
Speaker 2 (18:12):
Did nothing. Yeah, Okay, So when it comes to I
guess learning about news sectors like AI, I think one
of the things that everyone us included is struggling with
at the moment is to understand like how much actual
corporate spend there is going to be on this. And
for obvious reasons, companies, you know, they're not necessarily that
(18:33):
vocal about how much they're spending on this technology, although
they do like to talk about the importance of AI
in a sort of very nebulous theoretical manner, but what
are you doing to understand the actual spend on this tech?
Speaker 4 (18:46):
Right? So there's this multiple stages to this. The first
stage was understanding how much the hyperscalers were spending. And
once we looked at those numbers, we were astonished. And
for the reasons you're mentioning concerned, right, I mean, there's
not a lot of examples. If you look back at
cable and airlines and casinos. When you start seeing R
(19:09):
and D and CAPEX as a presera of revenue go
to twenty to thirty percent for a sustained period, you know,
usually that's turned out bad. So we said, here, okay,
what's going on here? The next thing to look at
was signs of corporate adoption. Now there's nobody that hates
McKinsey surveys more than me, but you know, we had
(19:30):
mckensey surveys, you have Baine surveys, you've got Census surveys,
a lot of which we're pointing in the same direction,
which is that corporate adoption has been accelerating, particularly over
the last six months. And CEO surveys, I mean, there's
too many of them that are all telling you the
same thing. For them to be wildly off the mark.
(19:53):
But again, that doesn't tell you how much they're willing
to spend on it, right, You know, everybody might enjoy lollipops,
but that doesn't mean they're going to spend a lot
of their personal income on them. Then the question is
how much are the big hyperscalers starting to earn on AI?
And so far Microsoft is really the only company that's
(20:14):
disclosing it very discreetly, and we had a chart. I
published a piece on this on May thirteenth, And Microsoft's
the only company so far that's really explicitly telling you
how much they're earning. And those numbers are growing at
a pretty rapid base from a low base. But they're
earning a lot.
Speaker 2 (20:31):
One hundred and fifty percent over the past year is
what I see in the right.
Speaker 4 (20:35):
Right, the other companies are still burying it in cloud revenue.
Speaker 1 (20:38):
Yeah, there's a lot of questions just on this, so
get to them. It's one thing for a company to
spend on AI. You know a lot of pilot projects
on there. When you ask companies in terms of yes,
but is this actually saving money? Is this actually profitable investment?
It starts to get Cagier. And sometimes when you hear
(20:59):
people talk about the returns that they've got from AI spending,
it actually sounds like they're talking about more traditional machine
learning algorithm stuff that's been going on for a while.
But for this spend to continue, it's going to have
to flow through to the bottom line in some way.
And I'm curious what you're seeing on that in terms of, like,
here is a company that's not a tech company, maybe
(21:19):
it's an airline company, maybe it's a chemicals company, whatever,
where it's like, you know what they can point to
something that's not just algorithms, that is actually generative AI
that is making money for them or saving money for
them in some way. And what are you seeing on that?
Speaker 4 (21:33):
Yeah, we're seeing anecdotes, anecdotes, We're seeing anecdotes, anacdata. Yeah,
and usually it has to do with faster throughput and
call centers with less people, Okay, stuff like that, faster
customer acquisition, fraud reduction. Right for big banks like JP
Morgan JENERAI, one of the huge potential payoffs is identifying
(21:55):
serial defaulters and mortgages and credit cards and things like that.
So what is interesting is that among the best surveys
that are done. The surveys are asking them explicitly for
generative AI, how much money are you saving? And most
of the answers are still in the lowest category zero
to ten percent okay by far, and then it's the above.
(22:18):
Those levels have fewer responses. We're still in the exploratory
phase here, and I don't think we're out of the woods,
and there may be a day of reckoning at some point.
I think, because of how absurdly profitable these companies are,
the markets are going to give them another eighteen months
at least for the proof statement to bear out.
Speaker 2 (22:35):
This was going to be my next question was, like,
how much of a leash are investors allowing some of
these company As.
Speaker 4 (22:40):
Long as as long as the cloud revenue and the
AI revenue specifically are growing at fifty percent plus and
as long as the free cash flow margins don't fall
below some critical level, I think, you know, the markets
will still feel okay about it.
Speaker 2 (22:57):
I wanted to also ask a question about your overall career.
As we said in the intro, you've been at JP
Morgan since nineteen eighty seven, which is a phenomenal, phenomenal
amount of time. Looking back, what was the hardest or
most challenging sector to wrap your head around over that
period of time, Because, as we've been discussing, you jump
from thing to thing and you basically become an expert
(23:20):
on whatever is important to investors or the market at
that particular moment in time.
Speaker 4 (23:26):
Yeah, and I usually get airlifted into things that nobody
you know that are pain like I became for the
asset manager business and even for the broader firm. I
ended up being our COVID point person, and I put
together a medical advisory committee of biophysicians and mathematical epidemiologists
and people like that from La Joya Institute of Imminology,
and so I get airlifted into things. I would say,
(23:49):
oddly enough, of all the sectors and subtectors, I can
think of, the one that drives me nuts the most
is health care and managed care because you know how
they snuck this provision into the energy bill three years
ago where the government can negotiate drug prices. Finally I
can be able to do that. On the face of it,
that should be negative for the big farmer companies, but
(24:13):
because they can play around with all sorts of discounting
mechanisms and distribution arrangements, a lot of which aren't super public.
The actual impact on their margins so far has been
somewhat negligible, So there's a there's an impenetrability to parts
of the way the healthcare system function that drives me
a little bananas. That said, large cap pharma and biotech
(24:37):
right now are among the cheapest sectors if you compare
price to book to projected ROE or something like that.
We have this giant map of all those sectors and
subsectors and industries and where they trade relative to price
to book and ROA and ROE, and you know, healthcare
is cheaper. But it would normally make me want to
(24:59):
dive in and kind of come up with an asset
allocation recommendation for our investment process, and it's just very
difficult to do.
Speaker 2 (25:06):
This is how I feel about US health insurance. By
the way, as someone who did not grow up that
much in the US, I still struggle to wrap my
head around how all of this works. It is truly
impenetrable to me.
Speaker 1 (25:18):
I think many people would agree with that assessment. I
want to go back to actually AI for a second,
because you're talking about the hyperscalers and this booming business
for them. It's still actually a little bit hard for
me to understand, because for some companies, their AI revenue
is very easy to understand. Right, So Microsoft sells various
(25:41):
things that go along with its sweet that whatever, whether
it's coding tools or so forth. Okay, they're selling an
AI service. Maybe Google one day figure out how to
do that too. For a company like Meta, their model
is an open source model. They don't sell enterprise LAMA
as far as I know, yet, they spend like crazy
on AI and they talk about how much they spend.
(26:03):
Can you articulate for me what Meta's AI business model
is because all I hear is about their spending and
I don't even know what revenue they're deriving.
Speaker 4 (26:13):
From the yeh. When you when you try to understand that,
what you learn is that Meta basically considers itself a
generative AI company and that everything they do and the
success of all of its algorithms and social media tools
maintain their market dominance because of how well they function
and the way that they function is because they're driven
(26:35):
by AI. And I and I agree. I think they
thought that the open source LAM model would become an
indirect profit center as people build tools and applications on
top of it. And it seems like they're stepping away
from that, huh right now like they a year ago.
That was the kind of message you were hearing in
not so much right now, but they you know that,
(26:57):
I think they would say.
Speaker 1 (26:58):
Are somehow all of this spending which they talk as
AI spending specifically, even though.
Speaker 4 (27:05):
They're selling AI.
Speaker 1 (27:07):
Here's how, somehow it's accruing to their fundamental business of
selling ads or selling content selling.
Speaker 4 (27:12):
Here, And here's how I would I would articulate that
if you go back three four years, there were these
really interesting cell side presentations that talked about, well, Google's
going to try to compete with Apple here, Apple's going
to try to compete with Nvidia here, and video is
going to try and compete with Opening out here. And
guess what, a lot of those motes are still in place,
(27:32):
and a lot of the encroachment that people were looking
for hasn't happened. So Meta's AI spend is primarily focused
on maintaining an impenetrable mode for other companies that might
otherwise want to compete with their core business. And that's
how they see it, which means that you basically will
(27:53):
want to look at top line, free cash flow and
other profitability metrics for the company as a whole to
understand the benefits of all that spent. I think investors
are right to be skeptical. It's only three years ago
that they took a ton of money, put it in
a pile, and set it on fire. Yeah, with the metaverse, right,
so they they have had a history of misreading where
(28:18):
things are going.
Speaker 1 (28:19):
Tracy, by the way, I actually really admire how quick
they were willing to pivot off that, because you think,
you know, you make this big bed. It's embarrassing you
rename your company meta off of this, and then you're like,
we're an a company. I actually that raised my admiration
for Zuckerberg as a CEO, that he didn't like succumb
to some big sunk cost fallacy there.
Speaker 4 (28:38):
Anyway, Well, look, I would.
Speaker 2 (28:40):
Change my business model. That's my version of the Canes question.
Speaker 4 (28:43):
He has demonstrated a mental agility to pivot as well
earlier this year, and I'm not going to make a
value judgment on this, but I was struck by him
saying that he needed to reinject the dose of masculinity
and oh yeah, yeah, I think that's a sign of
somebody that yeah, this is audio, so you can't see
my eyes rolling, but that's but you know, that's a
(29:05):
sign of somebody that's that is willing to kind of
change horses when yeah.
Speaker 2 (29:10):
Oh, dear, I have thoughts on AI as a masculine endeavor.
But anyway, one thing I wanted to ask is Okay,
so at the beginning of this conversation, you described AI
as the bet of the century.
Speaker 4 (29:22):
It is.
Speaker 2 (29:23):
And when I hear you know, when we're talking about
the dominance of the big US tech players and the
moats and the billions, if not trillions of dollars that
people are spending on this now, it feels like almost
certainly it's the big players that are going to emerge
as the winners from this. It seems very very hard
for smaller entrance to get into this. Is that right?
(29:44):
Is it just you know, a fate a'll complete. I
guess the big guys are.
Speaker 4 (29:48):
Going to be hyperscalar level, yes, but in a whole
bunch of other industries not necessarily plenty of room. And
but look, you're seeing these unicorns all the time that
are chipping away at different aspects of you know, machine
learning and generative AI. And one of the most fascinating
ones is look at Palenteer, right, I mean, Palenteer is
taking a run at the large defense contractors primarily through
(30:12):
its nimbleness and better application and understanding of the intersection
between generative AI and defense weaponry. And you know, for
everybody that thinks about data centers, the massive concentration of
data centers in Virginia and in the pgm isoregion more
broadly is very clear indication of just how the national
(30:33):
security and defense stuff is tied into generative AI and
who their big users are. Right, if that didn't matter,
there would be a lot more data centers built in
the Midwest where twenty to thirty percent of the time
electricity prices are actually negative. Right. Think about this. You
have certain applications that are so reliant on not there
(30:53):
being a millisecond of lag that they'd be willing to
pay more for power and proximity than to have cheaper
power that has just a tiny bit of lag.
Speaker 1 (31:03):
Built one company where there's some question about the moat
(31:23):
is alphabet and you know, people are like, well, could
open Ai just become Peopil's default homepage or the first
pages that people go to when they want to learn something.
And you've seen Alphabet shares underperformed into some of the rebound.
I think from a multiple standpoint, it's trading at some
of its cheapest levels in a very long time. There's
(31:46):
a lot of questions about the degree to which it
can productize its own models. Historically it's not so good
at that. It makes great tech. The models are very good.
I'm still discovering new alphabet models that I never knew
existed because they're very under some URL. Is there a
risk to their business model, specifically that perhaps their distribution
(32:07):
their ownership of digital real estate risk to the rent
that they can extract from it.
Speaker 4 (32:12):
Yeah, but this is the kind of thing where I
would continue to just look at their market share. You know,
for a while Bing was taking a run at.
Speaker 1 (32:21):
Them, and then.
Speaker 4 (32:22):
Nothing really happened. And okay, So in Europe, when you
buy a device, the Apple is no longer able to
make Google the default engine, so you have to set
it up yourself. People pay Google anyway, so you could
actually argue that Google is overpaying Apple for the default
status on their devices because people would be willing to
pick it anyway. I don't know. I think obviously the
(32:45):
tools open a I personally love perplexity, but I use
them when necessary. And I wouldn't bet against Google's generic
market share for search until you actually saw them beginning
to lose market share. It reminds me for the last
fifteen years people have been talking and screaming about the
end of the dollar as the world's reserve currency. And
(33:07):
every year now we look at the We look at
the data on the share of farm exchange reserves investment
in dollars, the share of corporate debt and equity debt
issuance in dollars, particularly offshore, the share of swift transaction payments,
and the dollar share of all these things BIS intercompany
bank loans across border. The dollar share is kind of unchanged.
(33:28):
So it's you know, well, let's you have to wait
until you see stuff.
Speaker 2 (33:32):
Network effects are definitely a thing. One thing I wanted
to ask you, or one of the reasons we wanted
to have you on is, as you say, you read
a lot, you talk to a lot of people, a
lot of clients, and I'm curious, what's the sort of
time split or the question split between I guess Trump
and policy versus you know, other market questions like AI.
Speaker 4 (33:57):
It's hard. I mean, it's hard to say, right. I
mean the tariff, the whole tariff drama was took an
enormous amount of time because I would say that a
lot of CEOs, and I've described this recently, as I
went to college in the Boston area, and every time
it snowed, some of the local kids would come out
and when there's a lot of snow and ice on
the road, they would hold onto the back of trucks
(34:17):
and go skitching. And I had never heard of skitching
until I saw people doing it. I never did it because,
like after you stop skitching, you tend to slam face
first into the ice. A lot of CEOs are sketching
right now on this administration because they thought that what
they were going to get was deregulation oil and gas
pipelines and lower corporate tax rates. That's what they were
(34:38):
supporting this guy for. And they come out of the
gate with deportations and tariffs, and the productivity shock supply
side agenda got put in the back seat for a while.
They appeared to be wanting to get started on some
of that stuff, and I think it's instant we can
talk about it. It'll be interesting to see if they
can get the Constitution pipeline reinvigorated, which would bring Marcella's
(34:59):
shale gas to the northeast. But in the beginning, because
so much of the first stuff was tariffs and deportation,
there's a lot of focus on that, and tariffs in
particular require a lot of work. There's twelve thousand HTS
codes across two hundred something countries, and they came up
with a patchwork of legislation that differentiate and so there's
(35:20):
just a lot of math involved in really understanding what
the impacts are.
Speaker 1 (35:25):
By the way, I see you into Tufts and so
I think you're the first person to ever say you
went to a Boston area college and that not being
code word for Harvard, it actually you actually did go
to a Boston area college. Excellent university. There's lots of them, No,
I know there's lots, but at all you know, you
weren't being kloy when you said Boston area college. As
you mentioned. You know, another huge theme over the years,
(35:48):
and you write a lot about it, is energy and
learned a lot from Folkloff's mill. And you know, we
could talk hours just about energy. I would love to
talk hours about energy at some point, but in the
interest of time. How much has the AI story over
the last few years changed how you thought about the
future of North American energy needs and you know, energy
(36:12):
production and just the energy story in general.
Speaker 4 (36:15):
You know, not until very recently, because it was a
negligible part of over electricity consumption, you know, sub one
percent until a couple of years ago, and has been
growing slowly. There are three big components to this electricity story.
Ones AI, Another one's electrification of transportation, you know ease yeah.
And the third one that people should focus on because
(36:37):
it's just as big, is the potential for electrification of
winter heating in homes, office buildings, and industrial locations. One
of the hardest things when you when you break down
energy consumption, an enormous amount of it, enormous found of
fossil fuel consumption is used for heat. Some of the heat,
(36:58):
around around a third of all the heat. Heat that's
used is used at temperatures below two hundred degrees integrade,
and that's where heat pumps are extremely efficient and measured
by something called the coefficient to performance, which is the
units of heat you get per unit of electricity you
put in. And so there's three components roughly equal, right,
So data centers, electrification of heating, electrication, transport, the wide
(37:22):
eyed you know, kind of fairy tail projections of the
Rocky Mountain Institute and green tech media people, Yeah, are
looking at twenty to twenty five percent growth in electricity
demand over the next ten years. I think that's way
too high adding up those three components. The lower end
is closer of like five to seven percent. BNF, which
(37:46):
I think does very good work on these topics. Is
somewhere in.
Speaker 2 (37:48):
Between, thank you, thank you for the plug.
Speaker 4 (37:50):
Yeah. Again, I know all of the sources, yeah, rigor
of their work, and so so I think we will need,
you know, seven or eight percent additional electricity capacity for
generation over the next decade or so. Electricity conception of
the United States has been roughly flat for twenty years,
but before that it went up a lot. The issue
(38:10):
back then was we were meeting it with nuclear and
large natural gas bets. It's a lot harder today because
most of the country's going to try to do it
with renewals, which is just very difficult. And I'm also
for reasons I wrote about in our annual energy paper,
I'm a skeptic of small modular reactors. Great idea on paper,
very complicated in terms of execution.
Speaker 1 (38:32):
By the way, Tracy, it's interesting the heat as the
one of the big consumption drivers of energy. You know,
there's a lot of Northeastern chauvinism where they say people
shouldn't be living out in the out in Arizona and
all these like air conditioned places, but it really is heat, Like, really,
if we want to be more energy efficient, my understanding
is we should all leave the Northeast and go to
the desert and live in air conditioning instead of heating
(38:55):
our home.
Speaker 2 (38:55):
No, for real, As someone with an old house in Connecticut,
I will push back on that narrative. You know, I
have a personal bias on this, but I was very
excited about heat pumps. But then we looked into it
for a home that was built in eighteen fifty, and
it turns out you would have to like reinsulate the
entire house for this thing to have any effect.
Speaker 4 (39:13):
Oh yeah, No. The biggest problem with heat pumps is
the fact that while it's more efficient you're buying electricity
instead of natural gas, and per megadule of energy it
can be depending on the state you live in, three
or four times more expensive. So it's great for the climate,
it's just economically, you know, challenging. And that's why I
think some of those Rocky Mountain Institute and Green Tech
(39:35):
media forecasts are way too aggressive, because I think these
transitions are going to take place over a much longer
period of time.
Speaker 2 (39:57):
Since we have you, since you've been in the business
for almost four decades now. If I was reading one
of your notes or one of your research pieces in
let's say nineteen eighty seven or nineteen eighty eight, I'm
not sure what exactly you were doing.
Speaker 4 (40:09):
It was an analyst, oh okay, and I was sitting
someplace behind a pile of pizza boxes and nobody was
talking to me. I don't know.
Speaker 2 (40:16):
So how different would your research then be versus your
research now.
Speaker 4 (40:21):
I'm definitely somebody that has taken advantage of massive quantities
of information and research right, it was a lot harder.
I used to have to go to the mid Manhattan
Research Library to find things, and I used to go
and you pull out microfiche and then you would go
and you would try to you know, you put in
a quarter and they would let you print it. I mean,
(40:42):
it was hard to get historical data back then, and
I'm a data hound, and so the way the world
works today is much easier for me to draw parallels.
I've also learned a lot of lessons in investing for
all these years. I was the chief investment officer just
in the private bank for a long time, and so
I've learned a lot of less And we're in the
process of putting together a twenty year on the Market retrospective,
(41:03):
and it's amazing to go back and publish some of
those pieces and see what we learned at the time.
Speaker 2 (41:09):
Oh well, okay, give us a preview. What were the
big lessons?
Speaker 4 (41:12):
The number one thing more than anything else. And we
have thirty topics we're going to show, and some of
them are in there kind of for humor or sarcasm,
it would or But the number one investment lesson was
equity's bottom before everything else. And so does credit, and
so does high yield, and so does real estate. In
other words, asset prices bottom so far in advance of
(41:36):
the related fundamentals. For instance, during the financial crisis in
two thousand and nine, bank stocks bottomed when only ten
percent of the ultimate bank failures had taken place. So
if you were listening to the really bariess voices at
the time, they would say, too soon to invest. There's
a giant title wave of bank failures coming. But the
(42:00):
KBW or whatever index you want to look at, bottomed
in advance of all of that. And the same thing
happened during the SNL crisis. Stocks bottomed way before all
the devaults eventually took place. And if you then take
the seven post war recessions, in six out of the
seven of them, equity is bottomed before you even saw
(42:20):
an upturn in payrolls, industrial production, housing, credit card delinquencies.
And so as an investments person, I have to acclimate
our clients and our investment committee to be willing to
take risk. When you look out the window and everything
looks terrible.
Speaker 1 (42:36):
Yeah, oh this is a great This is a great
lesson because you know, people say, right now, oh, the
shortages from the tariffs haven't even hit. Maybe we won't
have them because the tariffs have been dialed back so much.
But the idea that maybe the market already bottomed for
this cycle on everything related to tariffs. I sometimes I
like to say hope is the only strategy, because if
you wait until all of the data confirms that things
(42:58):
are back, it's clear really going to be priced in
at that point. But I actually wanted to ask something else.
I am an SMR skeptic. Two, But I form my
opinions based on seeing five tweets that I like. Okay, yeah,
those sound good to me. I'm an SMR skeptic.
Speaker 4 (43:13):
You don't.
Speaker 1 (43:14):
You have a much more rigorous process. Can you explain
to us why you are a skeptic of small modular reactors?
Speaker 4 (43:20):
Okay, So for the easiest one, is nobody's built one?
That's a good answer in the United States. Yeah, one
hasn't been built. Point number two, there are three or
four of them, depending on how you want to define
what small is that have been built in China and Russia,
and the costs have been multiples of the original projections,
(43:42):
and the time frames have been much longer, So failure
even in places that have success building regular.
Speaker 1 (43:49):
He's what I figured China would have built one hundred
by the right.
Speaker 4 (43:52):
Third point, there's no industrial project on Earth that is
more capital intentsive the nuclear power and so when nuclear
power for started to become popular in the late sixties early seventies,
the goal was how big can we make them, because
we want to spread some of the sunk costs over
the most megawats we can build. The notion that you
want to shrink a capital project to make it more
(44:14):
evisient makes no sense to me unless you can completely
commoditize them. The mistake I think people are making is
that there has been an incredible learning curve in polysilicon panels,
in to lesser extent in wind turbines, both on shore
and offshore. And the best example is lithium ion batteries,
best example of a learning curve you've ever seen, but
(44:36):
that's because millions and tens of millions of units are
being produced. I don't believe that you can from going
from one reactor to four reactors or six reactors develop
the kind of learning curve that would result in exponential
cost decliinents and so you know, I'll wait until I
see it, and I don't think we will. But I
(44:57):
would love to eat my hat if Aklan or some
of the other public companies or private companies are working
on this can deliver a small module or reactor in
anywhere of the zip code of the original projections. And
just as just one last thing as a reminder, the
new scale, you know, originally said three million dollars a megawatt.
(45:20):
When it hit twenty the plug was pulled. So the
most recent example of such efforts in the United States
failed when the budget hit six x.
Speaker 1 (45:29):
That was a fantastic answer, very intuitive answer, too real quickly,
what about a geothermal do you think that could be
a meaningful contributor to our portfolio?
Speaker 4 (45:37):
And I do? You know it takes effort. Chris Wright,
who's a second Advantagy's a big fan. Yeah, And you know,
there's different kinds of geothermal. The most interesting kind is
where you're going really deep and you're accessing like supercritical
fluids whose heat is comparable to nuclear power. But you
know that instrumentation has to be able to withstand very
(45:57):
high heat. You have to use plasma drilling bets. I
think there's opportunity for expansion of geothermal, but their long
term industrial projects, it'll take a long time to do,
not a quick fix. Yeah, you know. And the other
thing too is and this is amazing. I've been very
focused on the rising costs of wind and solar power,
and I should have been paying more attention to it.
(46:18):
I turned my head the other day. The cost of
a national gas combined cycle turbine has gone from twelve
hundred per kilowatt to twenty five hundred, like more than
double just in the last two years. Supply chains and
mostly a reflection of the fact that g E Vvernova
and Siemens and the other companies involved don't believe the
(46:39):
long term demand story enough to expand their production facilities.
So that's why classic energy phenomenon, right. So that's why
you go to Gefernova today. We're happy to deliver you
a plant. It's going to cost two times more than
it did a couple of years ago, and we'll give
it to you in five years. That's how AI bottleneck.
Speaker 1 (46:57):
This is how falling demand can result in higher prices falling.
Speaker 4 (47:01):
Right, because the two to three entities Mitsubishi as well,
that are involved in this sector don't feel comfortable enough
to go out and do a mega capital raise to
double and triple the size of their production facilities for
these kind of turbines, and that I think is going
to end up being a binding constraint for AI at
some point.
Speaker 2 (47:20):
I just don't know western So one of the reasons
I'm a big fan of your research is because it
is so wide ranging, and again, you have a very
diverse diet when it comes to what you read and
the information and data that you consume. Last question for me,
but give us one under the radar thing that you
are watching at the moment. Hmm, what should basically, what
(47:44):
should we do an episode on?
Speaker 1 (47:46):
Hah, yeah, what should we do an episode on?
Speaker 4 (47:50):
Let's see, I mean, not in any kind of order importance.
I'm just going to get a stream of consciousness sometimes.
I heard you in the introduction talk about narratives that
come and go, and I agree with that totally. And
one of the narratives that came and went but might
come back is there are still five to seven hundred
billion dollars of unrealized interest rate related losses on bank
(48:13):
balance sheets that has not gone away. Now it only
goes away if you go back to you know, three
percent or some three percent on the tenure. But we
still have a banking system where many of the participants
went hog wild extending duration on treasuries and agencies at
(48:33):
a time of you know, historically low interest rates because
you had this deposit surge from all the FED Bazuka money,
and that hasn't gone away now the Fed, the FED
has proven itself willing to provide whatever emergency facilities. I
actually was, you know, personally against bailing out the VC
depositors in Silicon Valley Bank. You know, the average deposit
(48:56):
balance in that bank was two and a half million dollars.
I mean, it wasn't really a bank, it was something else,
but they decided for systemic reasons to do it. They'd
probably do it again. But that issue hasn't gone away. Yeah,
and the higher ten year rates go, the more those
losses get pronounced. So that's an issue that's kind of
still out.
Speaker 1 (49:10):
I'm glad you brought that up because I remember we
traced and I did a bunch of episodes in like
twenty twenty one, twenty two. It's like, why isn't the
rate rise having more of an effect that everyone's like, oh,
you know, they got turned out. Everyone locked in low rates,
et cetera. And then I was like, hey, but now
rates are highering at some point, like they only locked
them in for so long.
Speaker 4 (49:26):
I'm only I'm almost tempted to believe that the FED
essentially created this problem through financial repression, tempted some bad
asset liability managers at these regional banks to load up
at low rates and feel some sense of actual responsibility
for this mess because it was Fed monetary and congressional
fiscal policy that led to this mess. So yeah, so
(49:50):
I think they feel somewhat, you know, beholden to try
to manage around some of the risks. Another thing I
would say is, I don't think the tariff story is done. Okay,
you know, the average tariff rate at the end of
last year was something like two and a half percent.
I'm using round numbers. Yeah, if you did all the
math and you don't assume any import substitution. At the
peak of the kind of you know Lutnick cardboard cut out,
(50:13):
you know, presentation, we were looking at a twenty five
percent all in rate, again assuming no import substitution and
things like that. Now we're back down at like thirteen percent.
So the market's like, wow, we dodged a bullet. It
went from twenty something to fourteen, But fourteen is still
very high compared to two.
Speaker 2 (50:33):
Isn't it like the highest since the nineteen thirties or
forties or something.
Speaker 4 (50:36):
Yeah, now he may backtrack again, you know, we'll see.
But historically, whenever there's been a disconnect between hard data
and soft data, it takes about ninety days for that
to resolve itself. So some of the shocks that are
seeping through the system are going to take until July
or August to play out. One of the things I
(50:56):
wrote earlier this year that resonated with a lot of
people is the markets are the one that you can't
deport them. You can't intimidate them, you can't arrest them.
You know, at the end of the day, the markets
are going to be a binding constraint on what this
administration can do and can't do. And that's actually a
plus for investors because it means at some point they're
(51:18):
going to have to adhere to some generally acceptable level
of policy making.
Speaker 1 (51:23):
I just have one last quick question going back to AI.
You hear a lot about inference versus training and people
wondering could there be some alternative to in Nvidia for
some of these services, because right now on the chip side,
all the profits seem to be accruing to in VideA.
Is that mode you know people talk about the Kuda ecosystem.
Is that an impenetrable moat? Or are there opportunities for
(51:43):
other chip companies to gain some real profits in the
AI world?
Speaker 4 (51:47):
There are, you know, in the piece that we did
last year, we listed all the companies that are trying
to pick away in video mode. But in the asset
management business or when companies hire underwriters, if an underwriting
goes bad and you picked JP Morgan Goldman and Morgan Stanley,
nobody said to question you. If you pick a ranked
(52:10):
eight or eleven, you know underwriter and something goes wrong,
people will question you. So to some extent, the network
effect and the modes that you guys are referring to,
nobody's going to get blamed for spending extra money to
buy in video products and wait for a long time
until these other products have proven themselves. And so I
think it's going to take a long time for that
to play out. But yeah, eventually, if this AI story works,
(52:36):
we should be looking at eighty percent inference and twenty
percent training in two to three years. Right at that point,
corporate adoption, which drives the training models are supposed to
be the overwhelming amount of what's going on. But again,
Amazon claims to have been working on a foundational model.
Haven't seen it yet. Microsoft has actually kind of hinted that, oh,
(52:59):
we've created our own foundational model. There was this great
story recently, if you like AI. The head of the
Microsoft AI group was basically told to pound sand by
the Open Eye people when he was asking them to
fully disclose how some of the new reasoning models worked.
So Microsoft is working on their own foundational model. Hasn't
been released yet either, even internally within Microsoft. So again,
(53:22):
so some of these motes, I wouldn't underestimate the amount
of time that video is spending defending that moat and
improving its products and its energy efficiency so that they
don't give up market share prematurely.
Speaker 2 (53:34):
All right, Well, by the time this episode comes out
in video, earnings will have been published, so maybe we'll
get a little bit more insight. But Michael Symbolists, thank
you so much for coming on. Yeah, that was a
real pleasure.
Speaker 1 (53:45):
There's a crime that we hadn't had you before, but
I guess you couldn't.
Speaker 4 (53:49):
Use I think I'm being put out of the back
in the cage after this time.
Speaker 2 (54:05):
Joe. That was a real treat to get Michael finally
finally on the podcast.
Speaker 1 (54:10):
It was so good, and it's like, I've always liked
his notes, Yeah, I mean a I think everyone who
hasn't read them will now understand why he has sort
of this cult following on Wall Street where everyone devours
his notes. But then even beyond that, hearing him, like
in my mind, it's like, Okay, these notes are great,
but there's a difference between comfort at like being able
(54:31):
to put together this big research product or there's a
big note product, but also just being able to talk
about all these things extemporaneously. Absolutely, it's really impressive.
Speaker 2 (54:40):
There's so much interesting stuff to pick out there. But
one of the things that struck me was, I guess
the importance of geographical location for data centers. Yeah, because
I hadn't thought about that. But he's absolutely right. You know,
you could locate a bunch of data centers in the
middle of nowhere where maybe there's cheaper energy costs, but
people so far have not to do that. Yeah.
Speaker 1 (55:01):
I hadn't really thought about that either, because it's like,
you know, we all know that Northern Virginia is this
huge data center cluster, But why the data centers really
need to be that close to the government and the
defence industry and so forth. It's like, come on, It's like,
isn't really that big of a deal, right to like
you're an extra millisecond of or something. But I mean,
(55:24):
you know, they're obviously there for a reason. I also
just thought, like this idea of the moats you know
obviously that the big tech companies have built up, and
the amount of effort they have to sustain those moats
and distribute through those modes and so forth, and like
how powerful that is and how much that is the
story of the fact that the biggest keep getting bigger
(55:44):
at literally at the expense of anything else. It's such
an important perspective have on all kinds of dimensions, whether
you're thinking US versus international, the price of US equities
and so forth. You just have to remember, like how
extraordinarily unusual these companies.
Speaker 2 (55:58):
Are, absolutely and the fact that they continued to just
throw off cash even while they're spending billions and billions
of dollars on AI tech which were not entirely sure,
like the degree to which it's actually going to be
able to be monetized, but they still managed to do
it while generating cash.
Speaker 1 (56:15):
The clarity of the way he spoke about energy is
really impressive, and I really liked what he said about
small modular reactors.
Speaker 2 (56:21):
You know, no one's done it yet in the US.
Speaker 1 (56:23):
Yeah, And when we talked to Jiggershaw, a body, he
used an analogy which I thought was fantastic. Know he's
more optimistic about them. He used an analogy that I
thought was great, which is that the nuclear industry has
to be in the business of building airplanes instead of airports,
because airports are all one offs. But airplanes are obviously
you know, they're built down a very complex assembly line,
(56:44):
but you know, after you've built several the cost gets cheaper.
And this idea that there's so much capital intensivity in
building nuclear and the idea that there is no insight
prospect for that learning curve where you do it over
and over and over again for the cost to follow dramatically,
that they haven't even done that in China. Maybe you're
(57:05):
not going to ever really get the sort of learning
curves that you get in solar or lithium ion batteries.
Super very clear, intuitive explanation of why the opportunity may
not be there.
Speaker 2 (57:17):
Yeah, we have to have them back on.
Speaker 1 (57:19):
Yeah, oh so much more we could talk about.
Speaker 2 (57:21):
All right, shall we leave it there?
Speaker 1 (57:22):
Let's leave it there.
Speaker 2 (57:23):
This has been another episode of the Outhoughts podcast. I'm
Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 1 (57:28):
I'm joll Wisenthal. You can follow me at the Stalwart.
Check out all of Michael Symbolist's notes. They're all online.
Search for them. All of them are a must read.
I'm gonna go back and read a bunch of them.
Follow our producers Kerman Rodriguez at Kerman Arman, dash Ob
Bennett at dashbod and cal Brooks at Kelbrooks. For more
Oddlots content, go to Bloomberg dot com slash Odlocks. We
(57:49):
have a daily newsletter and all of our episodes, and
you can chat about all of these topics twenty four
seven in our discord Discord dot gg slash odd.
Speaker 2 (57:58):
Locks and if you and joy all thoughts. If you
like it when we talk to market veterans like Michael Symbolus,
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.
Hello, Odd Lots listeners. I'm Joe Wisenthal and I'm Tracy Alloway. Tracy,
we're doing another live show and it's right here in
New York City.
Speaker 2 (00:07):
Yeah, this one should be our biggest yet, and we're
going to have a bunch of Odd Lots favorites and
do something maybe a little different to some of our
previous live podcast recordings.
Speaker 1 (00:18):
When the guests are revealed, the show is going to
sell out right away, so you should really just go
get your ticket right now. It's June twenty sixth. It's
at Record NYC, and you can find a ticket link
at Bloomberg dot com slash odd Lots or Bloomberg Events
dot com slash odd Lots Live and why.
Speaker 2 (00:34):
We hope to see you there.
Speaker 3 (00:38):
Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2 (00:54):
Hello and welcome to another episode of the Odd Loots podcast.
I'm Tracy Alloway.
Speaker 1 (00:58):
And I'm Joe Wisenthal.
Speaker 2 (01:00):
You know, does it feel to you like every day
when you wake up in markets there is a new
sort of dominant narrative, and often it is the exact
opposite of the narrative that was dominant a day or
two ago.
Speaker 1 (01:15):
Go on, say more what you mean, where are you
going with this? I mean, I agree in some sense,
but I think you have something specific in mind.
Speaker 2 (01:21):
Well, no, I actually have a bunch of things in mind.
So for instance, you know, recently there was a lot
of talk about the bond vigilantes. Yeah, back and people
worried about the fiscal deficit.
Speaker 1 (01:30):
Yeah, and then this.
Speaker 2 (01:31):
Week that seems to have suddenly gone away, or at
least it's not the only thing. People are talking about tariffs.
Speaker 4 (01:39):
You know.
Speaker 2 (01:39):
Obviously we had the big market freak out in April,
and then we had subsequent delays and extensions of the deadlines.
I don't hear people talking about tariffs as much as
they were in April.
Speaker 1 (01:51):
So then through it all is you know, on days
when we don't have to talk about tariffs or bond vigilantes,
then we immediately turned to AI. Well this is what
I was saying, you know, So this is the thing.
There's like this default thing that's in the background, which
is AI. And then that of course leads to things
like power and electricity and so forth. So yes, I
(02:12):
now see what you're saying, which is that on any
given day, it's like you spin the roulette wheel or
the wheel of fortune and then it's like, Okay, it's
an AI day. Actually we're recording this May twenty eighth.
By the time people are listening to this and video
earnings are going to be out. Yeah, and so today
is an AI day.
Speaker 4 (02:28):
It might be an AI day.
Speaker 2 (02:29):
What I was going to say is one of our
previous guests, Victor Schwetz over at McCrory. He framed it
really well where he was basically saying, if you know,
if you can't gauge what the dominant narrative is going
to be every day, or if everything that's happening is
so volatile and so dramatic and we're talking about really
big changes, then you kind of have to go back
to doing the normal thing. And the normal thing before
(02:53):
all of this kind of started in the New Year,
was worrying about AI, right, and thinking about AI and
whether or not you know, all of that AI spending
is going to be backed by revenue. So on that note,
that's what we're going to do today. We're going to
go back to normality, although we are going to talk
about some of the current stuff as well. And I
am very pleased to say we have someone who I
(03:13):
have wanted to get on the podcast for a really
long time, and he's finally sort of been let out
into the wild. So we are very excited we're going
to be speaking with Michael semblest He is, of course,
the chairman of market and investment Strategy for JP Morgan
Asset Management. He writes a great, great piece called I
on the Market, which he's been doing for which has a.
Speaker 4 (03:35):
Cult following, cult following.
Speaker 1 (03:36):
Every time a new symbolist piece comes out, everyone talks
about it and reads it.
Speaker 2 (03:40):
Yeah, and I think it's been going on for something
like twenty years now. And he himself has been at
JP Morgan since nineteen eighty seven, so, you know, a
real witness to financial market history. So I'm very excited. Michael.
Thank you so much for finally coming on the show.
Speaker 4 (03:54):
Thank you very much. Good to be here.
Speaker 2 (03:57):
I guess my first question is can you explain what
you do at JP Morgan Asset Management. I on the
Market is a phenomenal publication. I've been a fan for
a long time, but I always wonder like, is there
a difference between writing investment outlooks for asset management clients
versus writing them if you're on the cell side.
Speaker 4 (04:15):
Well, yeah, there's a huge difference. First of all, you know,
thank god for the Chinese wall, Arisa rules and things
like that, because I have the independence, and what our
clients want is for me to share what I'm thinking.
The best example of that is in February twenty twenty one,
the investment bank was making money handover all investment banks
for making money hand over fists in the back market,
(04:38):
and I published forgot about it. I published, you know,
two really scathing pieces on SPACs. The first one was
called Hydraulics Backing, and the next one, because it was
during COVID, was called spacscene hesitancy, where I kind of
pointed out that the sponsors were the only ones making
money and that their break even threshold was something like
an eighty percent decline in the merged stock price. Was
(04:59):
there so anything better than that? They would make money
and everybody else was getting slaughtered. So, you know, being
behind the fiduciary wall allows me to kind of call
it like I see it. And the eye of the
market is the external presentation of our thoughts and views,
and you know, on an internal basis, I'm participating our
investment committees dealing with asset allocation and security selection and
(05:23):
everything else that goes along with managing money for Dowmence foundations,
pension plans, insurance companies, sovereign wealth funds, and individuals.
Speaker 1 (05:31):
It does feel like on the media side we are
at the whims of narratives and all the things that
Tracy talked about at the beginning, Whereas if you talk
to people who are engaged in the art of security selection,
it feels that actually might be liberating because on some level,
whatever is going on, you could still look at an
(05:52):
instrument and say is this a well priced instrument or not,
and not having to get caught up so much in
just you know, you know, whatever the headline of the.
Speaker 4 (06:01):
Day is right, And actually, you know, if you're managing
money other than kind of a fast money macro hedge fund,
you don't have the luxury of changing your asset allocation
every time a new narrative comes along, so you to
some extent you have to kind of choose your poison,
make your asset allocation decisions, and then be either rewarded
or punished based on the outcome.
Speaker 2 (06:24):
So one of the reasons we wanted to get you
on the show was because you've been writing in great
detail about AI. So let me just ask the basic question.
If we're not gonna worry about you know, Trump and
tariffs and bond vigilantes and all the sort of headliney stuff,
if we're going back to considering, I guess the macro
and market importance of AI at the moment. How dominant
(06:45):
is AI when it comes to market valuations at the moment.
I guess how fundamental is AI when it comes to
the wider stock market.
Speaker 4 (06:53):
I don't think you can overstate the importance of this stuff. Wow,
when you dec impose the evolution of S and P
profit margins and you strip off the MAG seven versus
the rest of the market, it's like East and West
Berlin right now, I'm dating myself, right, But that's how different,
(07:14):
that's how divergent those are. If you look at Earning's growth,
I think Earning's growth for the MAG seven over the
last ten years is close to twenty percent. It's six
percent for the S and P four ninety three. So
it's almost like two completely different universes of companies. And
so they are extremely profitable. They are spending somewhere between,
(07:35):
depending on a quarter, twenty five to forty percent of
their revenues on capital spending in R and D. Those
numbers are unprecedented and in the history of the markets,
even going back to the nineteen sixties main frame heer
and things like that, we've never seen anything quite like this.
And what the hyperscalers are doing I would describe. I know,
we're only twenty five years into the century, but this
is the bed of the century that you can spend
(07:57):
this much on AI infrastructure, you know, us your free
cash flow margins for some period of time and wait
for the ultimate payoff.
Speaker 1 (08:04):
Even prior to the AI you know, you mentioned this
extraordinary gap. I mean, there are two things about hearing
you describe the earnings picture within the S and P
five hundred that strike me. One is the absolute gap.
But then there's also the fact that it's the biggest
companies growing really fast. And I in my mind, when
I was younger, assumed that as big companies got bigger,
(08:25):
they grew slower and smaller companies grew faster. When you
look at stock market history, how rare is the type
of earnings growth the year on year, the huge numbers
that they put up earnings growth wise, How unusual is
it for stock market history for the biggest companies to
also just be galloping past everyone else like this, you.
Speaker 4 (08:45):
Can't find it. You can't find it. I mean stock
market history on a security level basis goes back to
around nineteen eighty. There's a variety of databases through facts
that through the University of Chicago. That's about as far back.
Speaker 1 (08:55):
Really, you can't go much.
Speaker 4 (08:57):
It's hard to go back to users in that there's
some Wilsher data, but it's it's difficult. It's very spody.
So if you're looking for things like earnings growth, if
he just wants stock price growth, but if you're looking
for earnings and margins and things like that's hard. So
we can't find We can't find any comparable period where
the bigger companies are growing faster. And even within the
private equity universe, your theory holds true. The generally smaller
(09:20):
and midtized funds of usually you're going to outperform over
the long periods of time the larger funds. This is
negative size bias. So this is pretty unique. And you know,
over the years, I've always thought that the biggest risk
to all of this was going to be some reinterpretation
of the Sherman Act, and you know, anti trust And
while Epic Games has won a lawsuit, here are there
(09:41):
and the Department of Justice is picking away at Amazon,
Apple and Google for different reasons, there really hasn't been
a fundamental rethinking of anti trust law that would get
in the way of this.
Speaker 2 (09:51):
Can you talk a little bit more about that. What
are the drivers or the circumstances that are allowing mag
seven to continue to grow in a phenomenal pace but
also just become the giant cash flow monsters.
Speaker 4 (10:02):
Yeah, well, it has to do with the digitization of
the economy and how much technology is at the core
of how companies run. You know, JP Morgan right now
is a firm. We've got three hundred different language model
and ALI projects going on in and all of that
requires them one way or another, a lot of these
mag seven coming. I think we have to put Tesla
(10:23):
aside for a minute for reasons that we should discuss.
But technology is really at the foundation. And I think
a lot of times when people compare and they say, oh,
US docs are expensive relatives the rest of the world,
they're missing a couple of important things. They're missing the
fact that the US has a massive weight to technology
and in Europe, you know, basically it's it's a value market, right,
(10:45):
It's a lot of energy, financials, and consumer staples, and
so the US is really the dominant player in large
cap tech. There's an amazing chart. Mario Draghi, you know
who he is, obviously wrote a piece at the beginning
of the year what can we possibly I'm poweraphrasing, what
can we possibly do to reinvigorate entrepreneurship in Europe? And
(11:07):
he had a chart in there that we recomputed in
our own way, and it's a bubble chart that shows
the number of new companies created in the US versus
Europe since the year two thousand and Again, it's like
East and West Berlin. They are completely different. Yeah.
Speaker 1 (11:21):
One of the things that struck me recently we did
an episode with the CEO of a women's clothing company,
M M. Lafleur. We're mostly talking about tariffs, but the
thing that I keep thinking about is she said that
when she started the company a little I think when
was it over ten years ago? Tracy, Yeah, her customer
acquisition costs on a platform like Instagram, where she was
(11:43):
like twelve dollars a customer and now it's like over
two hundred dollars. And it just struck me that one
of the things that must be going on in the
economy is everybody's margin becoming Facebook's profits, or everybody's margins
eventually becoming Alphabets profits. That part of this gap that
(12:03):
we're seeing is there is this digital real estate that exists.
It's where everyone is moving, and they could collect massive
rents from owning that real estate from anyone who wants
to do commerce there.
Speaker 4 (12:14):
Yeah, and I think rent is the right word here,
and for reasons both good and bad. You know, Amazon
requires if you want to be if you want to
sell to prime customers, you have to agree to use
Amazon shipping. If you make an in purchase app in
it some kind of a game like Fortnite on an
Android phone, you have to make that purchase through the
(12:35):
Google Play Store. These are some of the issues that
have been litigated in some of the and so I'm
not one hundred percent in favor of some of the
tying that essentially they get. If JP Morgan said if
you want a credit card, you got to do your
mortgage with us, we get slapped down pretty fast, and
for good reason. And so there's a lot of product
tying that takes place in big tech. You know that
(12:55):
a lot of companies are beginning to challenge and the
DJ is looking at as well.
Speaker 2 (13:14):
So we've been talking about the dominance of US tech
and you know, there are a lot of numbers to
support this, and you've been laying some of them out.
But before we had all these teriff related market freakouts,
the freakout that everyone was focused on was deep seek,
of course, and the idea that suddenly China has invented
a low cost AI model that is very competitive with
(13:37):
what we've seen from the giant companies like the Microsoft's
and the Googles of the world. Looking back at that,
was that freakout overstated or just was okay?
Speaker 3 (13:47):
Right?
Speaker 4 (13:47):
A couple of reasons. One, I think it's become clear
and I wrote a piece at the time. They were
not completely transparent about how they accomplished some of their
goals imitations. This is serious form of flattery. They actually
used i think open AI models to train their models,
and so they did a lot of piggybacking. That isn't
(14:09):
necessarily a sign of some kind of productivity breakthrough. Also,
the price at which they are willing to sell something
is not necessarily reflective of its true cost, and so
there's some loss leading that's going on in there. That
again is different that said, cheaper AI models, if that's
where we're going, is probably going to make adoption even easier.
(14:33):
So if this can all be done with less energy
at lower token cost, that's a bullish story for AI,
not a negative one.
Speaker 1 (14:41):
It also occurred to me when that happened that if
there is this existential battle between the US and China
for AI supremacy, probably spend more on it. You know
it you know, it becomes a nuclear bomb type race,
then the impulse is going to be to spend more
on it. But actually, you mentioned things like energy cost
per token and so forth, and I'm curious, from your
(15:03):
perspective or you know, even doing this for a long time,
how did you learn about that? How did you educate
yourself on understanding the sort of the key economic variables
that you have to get wrap your head around in
order to say informed things about this new topic.
Speaker 4 (15:21):
Well, there's a bunch of things.
Speaker 1 (15:23):
We're trying to figure that out ourselves. You know, we
probably should be doing more about AI and talking so
we need to learn about how you tell yourself about it.
Speaker 4 (15:30):
I think you have to budget your time. And my
most important thing is reading about twentusand to twenty five
hundred pages of research each week, So that comes first,
other than spending time with my wife and my dog.
Those that's the most important thing I do because that's
where whether it's on energy topics or politics, or healthcare
(15:50):
or biotech like, it's an information laid in world. And
the great thing about JP Morgan is I can call
just about anybody say Hi, I'm the chief strategist on
the asthmatters, Miss JP Morgan. I'd like to talk to
you about something. Would you talk to me? I can
count on one hand the number of times that somebody
(16:10):
had just flat out said no. And one of those
people was the bundler for Bernie Madoff. And that's the
great thing about the halo effect that Jamie has brought
to JP Morgan. And remember I worked at JP Morgan
for many years when there was no halo, and you
know that halo effect is very powerful and benefits me
(16:31):
and a whole bunch of other people to company. And
I recognize that every day. So I spent a lot
of time working you know, deeply on some of these topics,
and energy as an example, I needed to learn about
energy from somebody that understood energy better than anybody else.
So fifteen years ago I made a pilgrimage to Manitoba
in February, right, which was can you prove yourself because
(16:51):
it was cold, and I established a relationship with vaclub Smill,
who for over a decade was my personal technical shirpa
on standing and learning about energy.
Speaker 1 (17:01):
So it's someone we haven't had on the podcast.
Speaker 4 (17:04):
He is a hermit, he's he's check, he's over eighty oh okay,
and he's a hermit, and he he hates the political
dynamics in the United States. So he will never get
it him here.
Speaker 1 (17:16):
We'll go to Manitoba, we'll try.
Speaker 2 (17:19):
I'm still stuck on the idea of the made off guy,
like answering the phone and being like, oh, we would
prefer not to talk in depth about why our line
just keeps going up in a very smooth way.
Speaker 4 (17:30):
It was an eleven compound, yeah turn with two percent vault.
It was an unprecedented return history, like in the history
of edge funds.
Speaker 1 (17:39):
So well, also, Tracy, I don't expect you to comment
on this, Michael. But Tracy, if I recall, one of
the people who was initially raised alarms about Madoff was
actually a guy man Zames at JP Morgan. Oh yeah,
so perhaps there was a reason I don't we don't
want to talk to these JP Morgan fellows.
Speaker 4 (17:54):
Actually, if you go back, if you go back even further,
in either two thousand and one or two thousand and two,
there was a guy named Harry Markoppolos, Yeah, in Boston,
and he wrote a piece basically saying Bernie Madoff as
a fraud as upon his scheme, and he sent it
to the SEC and they kind of poked around and
did nothing.
Speaker 2 (18:12):
Did nothing. Yeah, Okay, So when it comes to I
guess learning about news sectors like AI, I think one
of the things that everyone us included is struggling with
at the moment is to understand like how much actual
corporate spend there is going to be on this. And
for obvious reasons, companies, you know, they're not necessarily that
(18:33):
vocal about how much they're spending on this technology, although
they do like to talk about the importance of AI
in a sort of very nebulous theoretical manner, but what
are you doing to understand the actual spend on this tech?
Speaker 4 (18:46):
Right? So there's this multiple stages to this. The first
stage was understanding how much the hyperscalers were spending. And
once we looked at those numbers, we were astonished. And
for the reasons you're mentioning concerned, right, I mean, there's
not a lot of examples. If you look back at
cable and airlines and casinos. When you start seeing R
(19:09):
and D and CAPEX as a presera of revenue go
to twenty to thirty percent for a sustained period, you know,
usually that's turned out bad. So we said, here, okay,
what's going on here? The next thing to look at
was signs of corporate adoption. Now there's nobody that hates
McKinsey surveys more than me, but you know, we had
(19:30):
mckensey surveys, you have Baine surveys, you've got Census surveys,
a lot of which we're pointing in the same direction,
which is that corporate adoption has been accelerating, particularly over
the last six months. And CEO surveys, I mean, there's
too many of them that are all telling you the
same thing. For them to be wildly off the mark.
(19:53):
But again, that doesn't tell you how much they're willing
to spend on it, right, You know, everybody might enjoy lollipops,
but that doesn't mean they're going to spend a lot
of their personal income on them. Then the question is
how much are the big hyperscalers starting to earn on AI?
And so far Microsoft is really the only company that's
(20:14):
disclosing it very discreetly, and we had a chart. I
published a piece on this on May thirteenth, And Microsoft's
the only company so far that's really explicitly telling you
how much they're earning. And those numbers are growing at
a pretty rapid base from a low base. But they're
earning a lot.
Speaker 2 (20:31):
One hundred and fifty percent over the past year is
what I see in the right.
Speaker 4 (20:35):
Right, the other companies are still burying it in cloud revenue.
Speaker 1 (20:38):
Yeah, there's a lot of questions just on this, so
get to them. It's one thing for a company to
spend on AI. You know a lot of pilot projects
on there. When you ask companies in terms of yes,
but is this actually saving money? Is this actually profitable investment?
It starts to get Cagier. And sometimes when you hear
(20:59):
people talk about the returns that they've got from AI spending,
it actually sounds like they're talking about more traditional machine
learning algorithm stuff that's been going on for a while.
But for this spend to continue, it's going to have
to flow through to the bottom line in some way.
And I'm curious what you're seeing on that in terms of, like,
here is a company that's not a tech company, maybe
(21:19):
it's an airline company, maybe it's a chemicals company, whatever,
where it's like, you know what they can point to
something that's not just algorithms, that is actually generative AI
that is making money for them or saving money for
them in some way. And what are you seeing on that?
Speaker 4 (21:33):
Yeah, we're seeing anecdotes, anecdotes, We're seeing anecdotes, anacdata. Yeah,
and usually it has to do with faster throughput and
call centers with less people, Okay, stuff like that, faster
customer acquisition, fraud reduction. Right for big banks like JP
Morgan JENERAI, one of the huge potential payoffs is identifying
(21:55):
serial defaulters and mortgages and credit cards and things like that.
So what is interesting is that among the best surveys
that are done. The surveys are asking them explicitly for
generative AI, how much money are you saving? And most
of the answers are still in the lowest category zero
to ten percent okay by far, and then it's the above.
(22:18):
Those levels have fewer responses. We're still in the exploratory
phase here, and I don't think we're out of the woods,
and there may be a day of reckoning at some point.
I think, because of how absurdly profitable these companies are,
the markets are going to give them another eighteen months
at least for the proof statement to bear out.
Speaker 2 (22:35):
This was going to be my next question was, like,
how much of a leash are investors allowing some of
these company As.
Speaker 4 (22:40):
Long as as long as the cloud revenue and the
AI revenue specifically are growing at fifty percent plus and
as long as the free cash flow margins don't fall
below some critical level, I think, you know, the markets
will still feel okay about it.
Speaker 2 (22:57):
I wanted to also ask a question about your overall career.
As we said in the intro, you've been at JP
Morgan since nineteen eighty seven, which is a phenomenal, phenomenal
amount of time. Looking back, what was the hardest or
most challenging sector to wrap your head around over that
period of time, Because, as we've been discussing, you jump
from thing to thing and you basically become an expert
(23:20):
on whatever is important to investors or the market at
that particular moment in time.
Speaker 4 (23:26):
Yeah, and I usually get airlifted into things that nobody
you know that are pain like I became for the
asset manager business and even for the broader firm. I
ended up being our COVID point person, and I put
together a medical advisory committee of biophysicians and mathematical epidemiologists
and people like that from La Joya Institute of Imminology,
and so I get airlifted into things. I would say,
(23:49):
oddly enough, of all the sectors and subtectors, I can
think of, the one that drives me nuts the most
is health care and managed care because you know how
they snuck this provision into the energy bill three years
ago where the government can negotiate drug prices. Finally I
can be able to do that. On the face of it,
that should be negative for the big farmer companies, but
(24:13):
because they can play around with all sorts of discounting
mechanisms and distribution arrangements, a lot of which aren't super public.
The actual impact on their margins so far has been
somewhat negligible, So there's a there's an impenetrability to parts
of the way the healthcare system function that drives me
a little bananas. That said, large cap pharma and biotech
(24:37):
right now are among the cheapest sectors if you compare
price to book to projected ROE or something like that.
We have this giant map of all those sectors and
subsectors and industries and where they trade relative to price
to book and ROA and ROE, and you know, healthcare
is cheaper. But it would normally make me want to
(24:59):
dive in and kind of come up with an asset
allocation recommendation for our investment process, and it's just very
difficult to do.
Speaker 2 (25:06):
This is how I feel about US health insurance. By
the way, as someone who did not grow up that
much in the US, I still struggle to wrap my
head around how all of this works. It is truly
impenetrable to me.
Speaker 1 (25:18):
I think many people would agree with that assessment. I
want to go back to actually AI for a second,
because you're talking about the hyperscalers and this booming business
for them. It's still actually a little bit hard for
me to understand, because for some companies, their AI revenue
is very easy to understand. Right, So Microsoft sells various
(25:41):
things that go along with its sweet that whatever, whether
it's coding tools or so forth. Okay, they're selling an
AI service. Maybe Google one day figure out how to
do that too. For a company like Meta, their model
is an open source model. They don't sell enterprise LAMA
as far as I know, yet, they spend like crazy
on AI and they talk about how much they spend.
(26:03):
Can you articulate for me what Meta's AI business model
is because all I hear is about their spending and
I don't even know what revenue they're deriving.
Speaker 4 (26:13):
From the yeh. When you when you try to understand that,
what you learn is that Meta basically considers itself a
generative AI company and that everything they do and the
success of all of its algorithms and social media tools
maintain their market dominance because of how well they function
and the way that they function is because they're driven
(26:35):
by AI. And I and I agree. I think they
thought that the open source LAM model would become an
indirect profit center as people build tools and applications on
top of it. And it seems like they're stepping away
from that, huh right now like they a year ago.
That was the kind of message you were hearing in
not so much right now, but they you know that,
(26:57):
I think they would say.
Speaker 1 (26:58):
Are somehow all of this spending which they talk as
AI spending specifically, even though.
Speaker 4 (27:05):
They're selling AI.
Speaker 1 (27:07):
Here's how, somehow it's accruing to their fundamental business of
selling ads or selling content selling.
Speaker 4 (27:12):
Here, And here's how I would I would articulate that
if you go back three four years, there were these
really interesting cell side presentations that talked about, well, Google's
going to try to compete with Apple here, Apple's going
to try to compete with Nvidia here, and video is
going to try and compete with Opening out here. And
guess what, a lot of those motes are still in place,
(27:32):
and a lot of the encroachment that people were looking
for hasn't happened. So Meta's AI spend is primarily focused
on maintaining an impenetrable mode for other companies that might
otherwise want to compete with their core business. And that's
how they see it, which means that you basically will
(27:53):
want to look at top line, free cash flow and
other profitability metrics for the company as a whole to
understand the benefits of all that spent. I think investors
are right to be skeptical. It's only three years ago
that they took a ton of money, put it in
a pile, and set it on fire. Yeah, with the metaverse, right,
so they they have had a history of misreading where
(28:18):
things are going.
Speaker 1 (28:19):
Tracy, by the way, I actually really admire how quick
they were willing to pivot off that, because you think,
you know, you make this big bed. It's embarrassing you
rename your company meta off of this, and then you're like,
we're an a company. I actually that raised my admiration
for Zuckerberg as a CEO, that he didn't like succumb
to some big sunk cost fallacy there.
Speaker 4 (28:38):
Anyway, Well, look, I would.
Speaker 2 (28:40):
Change my business model. That's my version of the Canes question.
Speaker 4 (28:43):
He has demonstrated a mental agility to pivot as well
earlier this year, and I'm not going to make a
value judgment on this, but I was struck by him
saying that he needed to reinject the dose of masculinity
and oh yeah, yeah, I think that's a sign of
somebody that yeah, this is audio, so you can't see
my eyes rolling, but that's but you know, that's a
(29:05):
sign of somebody that's that is willing to kind of
change horses when yeah.
Speaker 2 (29:10):
Oh, dear, I have thoughts on AI as a masculine endeavor.
But anyway, one thing I wanted to ask is Okay,
so at the beginning of this conversation, you described AI
as the bet of the century.
Speaker 4 (29:22):
It is.
Speaker 2 (29:23):
And when I hear you know, when we're talking about
the dominance of the big US tech players and the
moats and the billions, if not trillions of dollars that
people are spending on this now, it feels like almost
certainly it's the big players that are going to emerge
as the winners from this. It seems very very hard
for smaller entrance to get into this. Is that right?
(29:44):
Is it just you know, a fate a'll complete. I
guess the big guys are.
Speaker 4 (29:48):
Going to be hyperscalar level, yes, but in a whole
bunch of other industries not necessarily plenty of room. And
but look, you're seeing these unicorns all the time that
are chipping away at different aspects of you know, machine
learning and generative AI. And one of the most fascinating
ones is look at Palenteer, right, I mean, Palenteer is
taking a run at the large defense contractors primarily through
(30:12):
its nimbleness and better application and understanding of the intersection
between generative AI and defense weaponry. And you know, for
everybody that thinks about data centers, the massive concentration of
data centers in Virginia and in the pgm isoregion more
broadly is very clear indication of just how the national
(30:33):
security and defense stuff is tied into generative AI and
who their big users are. Right, if that didn't matter,
there would be a lot more data centers built in
the Midwest where twenty to thirty percent of the time
electricity prices are actually negative. Right. Think about this. You
have certain applications that are so reliant on not there
(30:53):
being a millisecond of lag that they'd be willing to
pay more for power and proximity than to have cheaper
power that has just a tiny bit of lag.
Speaker 1 (31:03):
Built one company where there's some question about the moat
(31:23):
is alphabet and you know, people are like, well, could
open Ai just become Peopil's default homepage or the first
pages that people go to when they want to learn something.
And you've seen Alphabet shares underperformed into some of the rebound.
I think from a multiple standpoint, it's trading at some
of its cheapest levels in a very long time. There's
(31:46):
a lot of questions about the degree to which it
can productize its own models. Historically it's not so good
at that. It makes great tech. The models are very good.
I'm still discovering new alphabet models that I never knew
existed because they're very under some URL. Is there a
risk to their business model, specifically that perhaps their distribution
(32:07):
their ownership of digital real estate risk to the rent
that they can extract from it.
Speaker 4 (32:12):
Yeah, but this is the kind of thing where I
would continue to just look at their market share. You know,
for a while Bing was taking a run at.
Speaker 1 (32:21):
Them, and then.
Speaker 4 (32:22):
Nothing really happened. And okay, So in Europe, when you
buy a device, the Apple is no longer able to
make Google the default engine, so you have to set
it up yourself. People pay Google anyway, so you could
actually argue that Google is overpaying Apple for the default
status on their devices because people would be willing to
pick it anyway. I don't know. I think obviously the
(32:45):
tools open a I personally love perplexity, but I use
them when necessary. And I wouldn't bet against Google's generic
market share for search until you actually saw them beginning
to lose market share. It reminds me for the last
fifteen years people have been talking and screaming about the
end of the dollar as the world's reserve currency. And
(33:07):
every year now we look at the We look at
the data on the share of farm exchange reserves investment
in dollars, the share of corporate debt and equity debt
issuance in dollars, particularly offshore, the share of swift transaction payments,
and the dollar share of all these things BIS intercompany
bank loans across border. The dollar share is kind of unchanged.
(33:28):
So it's you know, well, let's you have to wait
until you see stuff.
Speaker 2 (33:32):
Network effects are definitely a thing. One thing I wanted
to ask you, or one of the reasons we wanted
to have you on is, as you say, you read
a lot, you talk to a lot of people, a
lot of clients, and I'm curious, what's the sort of
time split or the question split between I guess Trump
and policy versus you know, other market questions like AI.
Speaker 4 (33:57):
It's hard. I mean, it's hard to say, right. I
mean the tariff, the whole tariff drama was took an
enormous amount of time because I would say that a
lot of CEOs, and I've described this recently, as I
went to college in the Boston area, and every time
it snowed, some of the local kids would come out
and when there's a lot of snow and ice on
the road, they would hold onto the back of trucks
(34:17):
and go skitching. And I had never heard of skitching
until I saw people doing it. I never did it because,
like after you stop skitching, you tend to slam face
first into the ice. A lot of CEOs are sketching
right now on this administration because they thought that what
they were going to get was deregulation oil and gas
pipelines and lower corporate tax rates. That's what they were
(34:38):
supporting this guy for. And they come out of the
gate with deportations and tariffs, and the productivity shock supply
side agenda got put in the back seat for a while.
They appeared to be wanting to get started on some
of that stuff, and I think it's instant we can
talk about it. It'll be interesting to see if they
can get the Constitution pipeline reinvigorated, which would bring Marcella's
(34:59):
shale gas to the northeast. But in the beginning, because
so much of the first stuff was tariffs and deportation,
there's a lot of focus on that, and tariffs in
particular require a lot of work. There's twelve thousand HTS
codes across two hundred something countries, and they came up
with a patchwork of legislation that differentiate and so there's
(35:20):
just a lot of math involved in really understanding what
the impacts are.
Speaker 1 (35:25):
By the way, I see you into Tufts and so
I think you're the first person to ever say you
went to a Boston area college and that not being
code word for Harvard, it actually you actually did go
to a Boston area college. Excellent university. There's lots of them, No,
I know there's lots, but at all you know, you
weren't being kloy when you said Boston area college. As
you mentioned. You know, another huge theme over the years,
(35:48):
and you write a lot about it, is energy and
learned a lot from Folkloff's mill. And you know, we
could talk hours just about energy. I would love to
talk hours about energy at some point, but in the
interest of time. How much has the AI story over
the last few years changed how you thought about the
future of North American energy needs and you know, energy
(36:12):
production and just the energy story in general.
Speaker 4 (36:15):
You know, not until very recently, because it was a
negligible part of over electricity consumption, you know, sub one
percent until a couple of years ago, and has been
growing slowly. There are three big components to this electricity story.
Ones AI, Another one's electrification of transportation, you know ease yeah.
And the third one that people should focus on because
(36:37):
it's just as big, is the potential for electrification of
winter heating in homes, office buildings, and industrial locations. One
of the hardest things when you when you break down
energy consumption, an enormous amount of it, enormous found of
fossil fuel consumption is used for heat. Some of the heat,
(36:58):
around around a third of all the heat. Heat that's
used is used at temperatures below two hundred degrees integrade,
and that's where heat pumps are extremely efficient and measured
by something called the coefficient to performance, which is the
units of heat you get per unit of electricity you
put in. And so there's three components roughly equal, right,
So data centers, electrification of heating, electrication, transport, the wide
(37:22):
eyed you know, kind of fairy tail projections of the
Rocky Mountain Institute and green tech media people, Yeah, are
looking at twenty to twenty five percent growth in electricity
demand over the next ten years. I think that's way
too high adding up those three components. The lower end
is closer of like five to seven percent. BNF, which
(37:46):
I think does very good work on these topics. Is
somewhere in.
Speaker 2 (37:48):
Between, thank you, thank you for the plug.
Speaker 4 (37:50):
Yeah. Again, I know all of the sources, yeah, rigor
of their work, and so so I think we will need,
you know, seven or eight percent additional electricity capacity for
generation over the next decade or so. Electricity conception of
the United States has been roughly flat for twenty years,
but before that it went up a lot. The issue
(38:10):
back then was we were meeting it with nuclear and
large natural gas bets. It's a lot harder today because
most of the country's going to try to do it
with renewals, which is just very difficult. And I'm also
for reasons I wrote about in our annual energy paper,
I'm a skeptic of small modular reactors. Great idea on paper,
very complicated in terms of execution.
Speaker 1 (38:32):
By the way, Tracy, it's interesting the heat as the
one of the big consumption drivers of energy. You know,
there's a lot of Northeastern chauvinism where they say people
shouldn't be living out in the out in Arizona and
all these like air conditioned places, but it really is heat, Like, really,
if we want to be more energy efficient, my understanding
is we should all leave the Northeast and go to
the desert and live in air conditioning instead of heating
(38:55):
our home.
Speaker 2 (38:55):
No, for real, As someone with an old house in Connecticut,
I will push back on that narrative. You know, I
have a personal bias on this, but I was very
excited about heat pumps. But then we looked into it
for a home that was built in eighteen fifty, and
it turns out you would have to like reinsulate the
entire house for this thing to have any effect.
Speaker 4 (39:13):
Oh yeah, No. The biggest problem with heat pumps is
the fact that while it's more efficient you're buying electricity
instead of natural gas, and per megadule of energy it
can be depending on the state you live in, three
or four times more expensive. So it's great for the climate,
it's just economically, you know, challenging. And that's why I
think some of those Rocky Mountain Institute and Green Tech
(39:35):
media forecasts are way too aggressive, because I think these
transitions are going to take place over a much longer
period of time.
Speaker 2 (39:57):
Since we have you, since you've been in the business
for almost four decades now. If I was reading one
of your notes or one of your research pieces in
let's say nineteen eighty seven or nineteen eighty eight, I'm
not sure what exactly you were doing.
Speaker 4 (40:09):
It was an analyst, oh okay, and I was sitting
someplace behind a pile of pizza boxes and nobody was
talking to me. I don't know.
Speaker 2 (40:16):
So how different would your research then be versus your
research now.
Speaker 4 (40:21):
I'm definitely somebody that has taken advantage of massive quantities
of information and research right, it was a lot harder.
I used to have to go to the mid Manhattan
Research Library to find things, and I used to go
and you pull out microfiche and then you would go
and you would try to you know, you put in
a quarter and they would let you print it. I mean,
(40:42):
it was hard to get historical data back then, and
I'm a data hound, and so the way the world
works today is much easier for me to draw parallels.
I've also learned a lot of lessons in investing for
all these years. I was the chief investment officer just
in the private bank for a long time, and so
I've learned a lot of less And we're in the
process of putting together a twenty year on the Market retrospective,
(41:03):
and it's amazing to go back and publish some of
those pieces and see what we learned at the time.
Speaker 2 (41:09):
Oh well, okay, give us a preview. What were the
big lessons?
Speaker 4 (41:12):
The number one thing more than anything else. And we
have thirty topics we're going to show, and some of
them are in there kind of for humor or sarcasm,
it would or But the number one investment lesson was
equity's bottom before everything else. And so does credit, and
so does high yield, and so does real estate. In
other words, asset prices bottom so far in advance of
(41:36):
the related fundamentals. For instance, during the financial crisis in
two thousand and nine, bank stocks bottomed when only ten
percent of the ultimate bank failures had taken place. So
if you were listening to the really bariess voices at
the time, they would say, too soon to invest. There's
a giant title wave of bank failures coming. But the
(42:00):
KBW or whatever index you want to look at, bottomed
in advance of all of that. And the same thing
happened during the SNL crisis. Stocks bottomed way before all
the devaults eventually took place. And if you then take
the seven post war recessions, in six out of the
seven of them, equity is bottomed before you even saw
(42:20):
an upturn in payrolls, industrial production, housing, credit card delinquencies.
And so as an investments person, I have to acclimate
our clients and our investment committee to be willing to
take risk. When you look out the window and everything
looks terrible.
Speaker 1 (42:36):
Yeah, oh this is a great This is a great
lesson because you know, people say, right now, oh, the
shortages from the tariffs haven't even hit. Maybe we won't
have them because the tariffs have been dialed back so much.
But the idea that maybe the market already bottomed for
this cycle on everything related to tariffs. I sometimes I
like to say hope is the only strategy, because if
you wait until all of the data confirms that things
(42:58):
are back, it's clear really going to be priced in
at that point. But I actually wanted to ask something else.
I am an SMR skeptic. Two, But I form my
opinions based on seeing five tweets that I like. Okay, yeah,
those sound good to me. I'm an SMR skeptic.
Speaker 4 (43:13):
You don't.
Speaker 1 (43:14):
You have a much more rigorous process. Can you explain
to us why you are a skeptic of small modular reactors?
Speaker 4 (43:20):
Okay, So for the easiest one, is nobody's built one?
That's a good answer in the United States. Yeah, one
hasn't been built. Point number two, there are three or
four of them, depending on how you want to define
what small is that have been built in China and Russia,
and the costs have been multiples of the original projections,
(43:42):
and the time frames have been much longer, So failure
even in places that have success building regular.
Speaker 1 (43:49):
He's what I figured China would have built one hundred
by the right.
Speaker 4 (43:52):
Third point, there's no industrial project on Earth that is
more capital intentsive the nuclear power and so when nuclear
power for started to become popular in the late sixties early seventies,
the goal was how big can we make them, because
we want to spread some of the sunk costs over
the most megawats we can build. The notion that you
want to shrink a capital project to make it more
(44:14):
evisient makes no sense to me unless you can completely
commoditize them. The mistake I think people are making is
that there has been an incredible learning curve in polysilicon panels,
in to lesser extent in wind turbines, both on shore
and offshore. And the best example is lithium ion batteries,
best example of a learning curve you've ever seen, but
(44:36):
that's because millions and tens of millions of units are
being produced. I don't believe that you can from going
from one reactor to four reactors or six reactors develop
the kind of learning curve that would result in exponential
cost decliinents and so you know, I'll wait until I
see it, and I don't think we will. But I
(44:57):
would love to eat my hat if Aklan or some
of the other public companies or private companies are working
on this can deliver a small module or reactor in
anywhere of the zip code of the original projections. And
just as just one last thing as a reminder, the
new scale, you know, originally said three million dollars a megawatt.
(45:20):
When it hit twenty the plug was pulled. So the
most recent example of such efforts in the United States
failed when the budget hit six x.
Speaker 1 (45:29):
That was a fantastic answer, very intuitive answer, too real quickly,
what about a geothermal do you think that could be
a meaningful contributor to our portfolio?
Speaker 4 (45:37):
And I do? You know it takes effort. Chris Wright,
who's a second Advantagy's a big fan. Yeah, And you know,
there's different kinds of geothermal. The most interesting kind is
where you're going really deep and you're accessing like supercritical
fluids whose heat is comparable to nuclear power. But you
know that instrumentation has to be able to withstand very
(45:57):
high heat. You have to use plasma drilling bets. I
think there's opportunity for expansion of geothermal, but their long
term industrial projects, it'll take a long time to do,
not a quick fix. Yeah, you know. And the other
thing too is and this is amazing. I've been very
focused on the rising costs of wind and solar power,
and I should have been paying more attention to it.
(46:18):
I turned my head the other day. The cost of
a national gas combined cycle turbine has gone from twelve
hundred per kilowatt to twenty five hundred, like more than
double just in the last two years. Supply chains and
mostly a reflection of the fact that g E Vvernova
and Siemens and the other companies involved don't believe the
(46:39):
long term demand story enough to expand their production facilities.
So that's why classic energy phenomenon, right. So that's why
you go to Gefernova today. We're happy to deliver you
a plant. It's going to cost two times more than
it did a couple of years ago, and we'll give
it to you in five years. That's how AI bottleneck.
Speaker 1 (46:57):
This is how falling demand can result in higher prices falling.
Speaker 4 (47:01):
Right, because the two to three entities Mitsubishi as well,
that are involved in this sector don't feel comfortable enough
to go out and do a mega capital raise to
double and triple the size of their production facilities for
these kind of turbines, and that I think is going
to end up being a binding constraint for AI at
some point.
Speaker 2 (47:20):
I just don't know western So one of the reasons
I'm a big fan of your research is because it
is so wide ranging, and again, you have a very
diverse diet when it comes to what you read and
the information and data that you consume. Last question for me,
but give us one under the radar thing that you
are watching at the moment. Hmm, what should basically, what
(47:44):
should we do an episode on?
Speaker 1 (47:46):
Hah, yeah, what should we do an episode on?
Speaker 4 (47:50):
Let's see, I mean, not in any kind of order importance.
I'm just going to get a stream of consciousness sometimes.
I heard you in the introduction talk about narratives that
come and go, and I agree with that totally. And
one of the narratives that came and went but might
come back is there are still five to seven hundred
billion dollars of unrealized interest rate related losses on bank
(48:13):
balance sheets that has not gone away. Now it only
goes away if you go back to you know, three
percent or some three percent on the tenure. But we
still have a banking system where many of the participants
went hog wild extending duration on treasuries and agencies at
(48:33):
a time of you know, historically low interest rates because
you had this deposit surge from all the FED Bazuka money,
and that hasn't gone away now the Fed, the FED
has proven itself willing to provide whatever emergency facilities. I
actually was, you know, personally against bailing out the VC
depositors in Silicon Valley Bank. You know, the average deposit
(48:56):
balance in that bank was two and a half million dollars.
I mean, it wasn't really a bank, it was something else,
but they decided for systemic reasons to do it. They'd
probably do it again. But that issue hasn't gone away. Yeah,
and the higher ten year rates go, the more those
losses get pronounced. So that's an issue that's kind of
still out.
Speaker 1 (49:10):
I'm glad you brought that up because I remember we
traced and I did a bunch of episodes in like
twenty twenty one, twenty two. It's like, why isn't the
rate rise having more of an effect that everyone's like, oh,
you know, they got turned out. Everyone locked in low rates,
et cetera. And then I was like, hey, but now
rates are highering at some point, like they only locked
them in for so long.
Speaker 4 (49:26):
I'm only I'm almost tempted to believe that the FED
essentially created this problem through financial repression, tempted some bad
asset liability managers at these regional banks to load up
at low rates and feel some sense of actual responsibility
for this mess because it was Fed monetary and congressional
fiscal policy that led to this mess. So yeah, so
(49:50):
I think they feel somewhat, you know, beholden to try
to manage around some of the risks. Another thing I
would say is, I don't think the tariff story is done. Okay,
you know, the average tariff rate at the end of
last year was something like two and a half percent.
I'm using round numbers. Yeah, if you did all the
math and you don't assume any import substitution. At the
peak of the kind of you know Lutnick cardboard cut out,
(50:13):
you know, presentation, we were looking at a twenty five
percent all in rate, again assuming no import substitution and
things like that. Now we're back down at like thirteen percent.
So the market's like, wow, we dodged a bullet. It
went from twenty something to fourteen, But fourteen is still
very high compared to two.
Speaker 2 (50:33):
Isn't it like the highest since the nineteen thirties or
forties or something.
Speaker 4 (50:36):
Yeah, now he may backtrack again, you know, we'll see.
But historically, whenever there's been a disconnect between hard data
and soft data, it takes about ninety days for that
to resolve itself. So some of the shocks that are
seeping through the system are going to take until July
or August to play out. One of the things I
(50:56):
wrote earlier this year that resonated with a lot of
people is the markets are the one that you can't
deport them. You can't intimidate them, you can't arrest them.
You know, at the end of the day, the markets
are going to be a binding constraint on what this
administration can do and can't do. And that's actually a
plus for investors because it means at some point they're
(51:18):
going to have to adhere to some generally acceptable level
of policy making.
Speaker 1 (51:23):
I just have one last quick question going back to AI.
You hear a lot about inference versus training and people
wondering could there be some alternative to in Nvidia for
some of these services, because right now on the chip side,
all the profits seem to be accruing to in VideA.
Is that mode you know people talk about the Kuda ecosystem.
Is that an impenetrable moat? Or are there opportunities for
(51:43):
other chip companies to gain some real profits in the
AI world?
Speaker 4 (51:47):
There are, you know, in the piece that we did
last year, we listed all the companies that are trying
to pick away in video mode. But in the asset
management business or when companies hire underwriters, if an underwriting
goes bad and you picked JP Morgan Goldman and Morgan Stanley,
nobody said to question you. If you pick a ranked
(52:10):
eight or eleven, you know underwriter and something goes wrong,
people will question you. So to some extent, the network
effect and the modes that you guys are referring to,
nobody's going to get blamed for spending extra money to
buy in video products and wait for a long time
until these other products have proven themselves. And so I
think it's going to take a long time for that
to play out. But yeah, eventually, if this AI story works,
(52:36):
we should be looking at eighty percent inference and twenty
percent training in two to three years. Right at that point,
corporate adoption, which drives the training models are supposed to
be the overwhelming amount of what's going on. But again,
Amazon claims to have been working on a foundational model.
Haven't seen it yet. Microsoft has actually kind of hinted that, oh,
(52:59):
we've created our own foundational model. There was this great
story recently, if you like AI. The head of the
Microsoft AI group was basically told to pound sand by
the Open Eye people when he was asking them to
fully disclose how some of the new reasoning models worked.
So Microsoft is working on their own foundational model. Hasn't
been released yet either, even internally within Microsoft. So again,
(53:22):
so some of these motes, I wouldn't underestimate the amount
of time that video is spending defending that moat and
improving its products and its energy efficiency so that they
don't give up market share prematurely.
Speaker 2 (53:34):
All right, Well, by the time this episode comes out
in video, earnings will have been published, so maybe we'll
get a little bit more insight. But Michael Symbolists, thank
you so much for coming on. Yeah, that was a
real pleasure.
Speaker 1 (53:45):
There's a crime that we hadn't had you before, but
I guess you couldn't.
Speaker 4 (53:49):
Use I think I'm being put out of the back
in the cage after this time.
Speaker 2 (54:05):
Joe. That was a real treat to get Michael finally
finally on the podcast.
Speaker 1 (54:10):
It was so good, and it's like, I've always liked
his notes, Yeah, I mean a I think everyone who
hasn't read them will now understand why he has sort
of this cult following on Wall Street where everyone devours
his notes. But then even beyond that, hearing him, like
in my mind, it's like, Okay, these notes are great,
but there's a difference between comfort at like being able
(54:31):
to put together this big research product or there's a
big note product, but also just being able to talk
about all these things extemporaneously. Absolutely, it's really impressive.
Speaker 2 (54:40):
There's so much interesting stuff to pick out there. But
one of the things that struck me was, I guess
the importance of geographical location for data centers. Yeah, because
I hadn't thought about that. But he's absolutely right. You know,
you could locate a bunch of data centers in the
middle of nowhere where maybe there's cheaper energy costs, but
people so far have not to do that. Yeah.
Speaker 1 (55:01):
I hadn't really thought about that either, because it's like,
you know, we all know that Northern Virginia is this
huge data center cluster, But why the data centers really
need to be that close to the government and the
defence industry and so forth. It's like, come on, It's like,
isn't really that big of a deal, right to like
you're an extra millisecond of or something. But I mean,
(55:24):
you know, they're obviously there for a reason. I also
just thought, like this idea of the moats you know
obviously that the big tech companies have built up, and
the amount of effort they have to sustain those moats
and distribute through those modes and so forth, and like
how powerful that is and how much that is the
story of the fact that the biggest keep getting bigger
(55:44):
at literally at the expense of anything else. It's such
an important perspective have on all kinds of dimensions, whether
you're thinking US versus international, the price of US equities
and so forth. You just have to remember, like how
extraordinarily unusual these companies.
Speaker 2 (55:58):
Are, absolutely and the fact that they continued to just
throw off cash even while they're spending billions and billions
of dollars on AI tech which were not entirely sure,
like the degree to which it's actually going to be
able to be monetized, but they still managed to do
it while generating cash.
Speaker 1 (56:15):
The clarity of the way he spoke about energy is
really impressive, and I really liked what he said about
small modular reactors.
Speaker 2 (56:21):
You know, no one's done it yet in the US.
Speaker 1 (56:23):
Yeah, And when we talked to Jiggershaw, a body, he
used an analogy which I thought was fantastic. Know he's
more optimistic about them. He used an analogy that I
thought was great, which is that the nuclear industry has
to be in the business of building airplanes instead of airports,
because airports are all one offs. But airplanes are obviously
you know, they're built down a very complex assembly line,
(56:44):
but you know, after you've built several the cost gets cheaper.
And this idea that there's so much capital intensivity in
building nuclear and the idea that there is no insight
prospect for that learning curve where you do it over
and over and over again for the cost to follow dramatically,
that they haven't even done that in China. Maybe you're
(57:05):
not going to ever really get the sort of learning
curves that you get in solar or lithium ion batteries.
Super very clear, intuitive explanation of why the opportunity may
not be there.
Speaker 2 (57:17):
Yeah, we have to have them back on.
Speaker 1 (57:19):
Yeah, oh so much more we could talk about.
Speaker 2 (57:21):
All right, shall we leave it there?
Speaker 1 (57:22):
Let's leave it there.
Speaker 2 (57:23):
This has been another episode of the Outhoughts podcast. I'm
Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 1 (57:28):
I'm joll Wisenthal. You can follow me at the Stalwart.
Check out all of Michael Symbolist's notes. They're all online.
Search for them. All of them are a must read.
I'm gonna go back and read a bunch of them.
Follow our producers Kerman Rodriguez at Kerman Arman, dash Ob
Bennett at dashbod and cal Brooks at Kelbrooks. For more
Oddlots content, go to Bloomberg dot com slash Odlocks. We
(57:49):
have a daily newsletter and all of our episodes, and
you can chat about all of these topics twenty four
seven in our discord Discord dot gg slash odd.
Speaker 2 (57:58):
Locks and if you and joy all thoughts. If you
like it when we talk to market veterans like Michael Symbolus,
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.
Hosts And Creators
Joe Weisenthal
Tracy Alloway
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