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