Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of The Odd Lots Podcast.
Speaker 3: I'm Tracy Alloway.
Speaker 4: And I'm Joe Wisenthal.
Speaker 3: Joe, we're in the media business.
Speaker 4: That's right, that's right.
Speaker 2: Have you ever had an article go viral unexpectedly viral?
Speaker 5: Yeah?
Speaker 4: I can't like trying to like remember like specifics, but yes,
Speaker 4: And it's one of those things typically where you're like
Speaker 4: really excited, like a lot of people are, you know,
Speaker 4: there's getting a lot of traction. Cool people are talking
Speaker 4: about this, and then it goes like multiple orders of
Speaker 4: magnitude bigger, and you're like, oh, this is like super
Speaker 4: weird and no context for what this is, and you're
Speaker 4: like sort of want to hide in your home and
Speaker 4: like close the laptop because then you sort of like
Speaker 4: make it all go away and stuff like that.
Speaker 2: Yeah, it's kind of like once you release it into
Speaker 2: the world, you don't actually have a lot of control
Speaker 2: over how people use it. And I think back to
Speaker 2: I wrote a piece about some investors trying to revive
Speaker 2: claims on Chinese imperial bonds, like antique Chinese imperial debt
Speaker 2: from the early nineteen hundreds and somehow this went absolutely
Speaker 2: viral in Hong Kong at the time of the pro
Speaker 2: democracy protest. So I would walk down the street and
Speaker 2: I would see these homemade banners that people had created
Speaker 2: saying that China owes the US like twenty billion in
Speaker 2: payments on old debts, And it was just so real,
Speaker 2: absolutely surreal, and like completely unexpected, because you wouldn't think
Speaker 2: that some like intricate debt story was suddenly going to
Speaker 2: become a pro democracy protest slogan.
Speaker 3: But the world works in mysterious ways.
Speaker 2: And speaking of the world working in mysterious ways, there
Speaker 2: is something that went viral this week. We are recording
Speaker 2: on February twenty seventh, and if you haven't heard of
Speaker 2: this particular thing, you have probably been living under the
Speaker 2: proverbial rock.
Speaker 4: Right So past Oddlot's guest James Van Galen, a co
Speaker 4: author to Peace on his sub stack Treny Research, talk
Speaker 4: about a potential AI doom scenario, which a lot of
Speaker 4: people talk about, and there's been a lot of talk
Speaker 4: about mass white color displacement as a possible thing that
Speaker 4: could happen as AI gets adopted, et cetera. But you know,
Speaker 4: we know that the market's been very skittish about this specifically,
Speaker 4: we've been seeing the software stock sell off all year,
Speaker 4: which we've talked about plenty on the podcast, and some
Speaker 4: of the private insurers and all this, and something about
Speaker 4: this moment and this particular piece. I think it came
Speaker 4: out on Sunday. Last Sunday landed with a sort of
Speaker 4: like unbelievable thud, and so it evidently started moving markets
Speaker 4: on Monday and then throughout the week. And this is
Speaker 4: the part that really flabbergassed me, was you see like
Speaker 4: all these banks and every economists, etcetera, like weighing in
Speaker 4: and many of the very critical and like securities which
Speaker 4: I didn't even know they like publish stuff because that's
Speaker 4: just a market maker, like they put out all this stuff.
Speaker 4: So I responding to it or trying to take it out.
Speaker 4: It was a as a market story and a media story,
Speaker 4: a wild week.
Speaker 2: It has become the discourse dujore. There's actually a prediction.
Speaker 3: Market on it, which you were telling me about a
Speaker 3: few minutes ago.
Speaker 2: Like this thing has just become much bigger than the
Speaker 2: initial substack, which to me again says much more about
Speaker 2: the nervousness of the market and how little anyone actually
Speaker 2: knows about how AI is going to unfold at the
Speaker 2: moment that people are so keen to just like latch
Speaker 2: onto any scenario that comes out.
Speaker 4: I get these notes from like cell side or research
Speaker 4: shops and they're like, client has been asking us about
Speaker 4: the Satrini scenario, and it's just like, wow, this is wild,
Speaker 4: Like it's really like.
Speaker 3: A s right.
Speaker 2: People calling up Capital Economics being like I manage a
Speaker 2: portfolio of one hundred billion and I am concerned about
Speaker 2: a substack. Okay, well we should talk to the author
Speaker 2: of the substack, And as you said, we've had them
Speaker 2: on a number of times before, often talking about AI.
Speaker 2: It is, of course James fan Kielan, the founder of
Speaker 2: Satrini Research. So James, thanks so much for coming back
Speaker 2: on the podcast.
Speaker 5: Thanks for having me.
Speaker 2: Why don't we start with what Satrini Research actually is
Speaker 2: and what it is that you actually do in some
Speaker 2: of your other enterprises, because I think this has become
Speaker 2: also a source of confusion or at least interest for
Speaker 2: people who are reading this.
Speaker 5: Satrini Research is a pure investment research firm. We focus
Speaker 5: primarily on thematic equity and macro research. The progression of
Speaker 5: it was. I started it as a newsletter just speaking
Speaker 5: about stocks and bonds and whatever else. And as we
Speaker 5: had a kind of string of good calls, which you
Speaker 5: were kind enough to have us on with the GLP
Speaker 5: one early July twenty twenty three, I think.
Speaker 3: Yeah, that was a great call.
Speaker 5: Yeah, And the first piece we ever published was a
Speaker 5: piece that was very bullish on the AI infrastructure complex.
Speaker 5: So that's been an area that AI robotics has been
Speaker 5: a big area for us. In terms of thematic equity.
Speaker 5: We've kind of covered this winding road of bottlenecks in
Speaker 5: terms of optics, memory power, whatever else you can possibly
Speaker 5: allude to. We've probably covered from a what stories are
Speaker 5: people telling about the movements that are going on in stocks.
Speaker 5: I remember the last time that I was on odd
Speaker 5: lots it was about this massive Stargate data center build out. Yeah,
Speaker 5: and Joe was very surprised to see that Caterpillar was
Speaker 5: and I think very happy that the old economy was
Speaker 5: getting a.
Speaker 3: He's an old economy standards.
Speaker 5: And really that's what we've been doing for the past
Speaker 5: three years. I've built out the team, and this piece
Speaker 5: very much was just a response to what the market
Speaker 5: has done here today, which is bonds of ra allied
Speaker 5: software companies have gotten sold off, a lot of fintech
Speaker 5: companies have gotten sold off, Private equity has sold off,
Speaker 5: and we're always kind of looking for the cohesive narrative
Speaker 5: that can connect disparate market moves, and the pieces co
Speaker 5: author all up posed to me a question which was
Speaker 5: we've been folkocused on the bullishness surrounding AI infrastructure for
Speaker 5: a while and it's translated into this capability curve that
Speaker 5: is moving a lot faster than anyone could expect. If
Speaker 5: you imagine this exponential anologulorhythmic chart, it's just a diagonal line.
Speaker 5: It goes up into the right. People have been trying
Speaker 5: to put stigmoids or kind of level that curve off
Speaker 5: for a long time and it hasn't. So we basically
Speaker 5: drew that line out and said, what could be the
Speaker 5: implications of this happening. It's a scenario which we would
Speaker 5: ascribe maybe ten to fifteen percent towards and it comes
Speaker 5: from a place of that. Everybody talks about equity markets
Speaker 5: being forward looking, but really a lot more of what
Speaker 5: you see as people justifying historical moves with new narratives
Speaker 5: that they come up with afterwards. Very little of it
Speaker 5: is driven by let me think of potential future outcomes
Speaker 5: as an investor, which was the audience that this was
Speaker 5: meant to go out to. I feel a lot more
Speaker 5: comfortable when I can envision the bulk, the barecase, the
Speaker 5: base case, and the most uncomfortable that you can be
Speaker 5: as investors when you can't see the barecase at all.
Speaker 5: So every time that we get into a market that's
Speaker 5: similar to this, people start asking what if this time
Speaker 5: is different? And I guess the thing that this piece
Speaker 5: did differently was it asked what if this time is different?
Speaker 5: But not so much in a Skehiynex and Micron are
Speaker 5: going from price to book to price to earnings, but
Speaker 5: in a way where what if this time is different?
Speaker 5: Where the period of transition has to respond to a
Speaker 5: very very fast, accelerating capability curve, and you start from
Speaker 5: a place where there's a strong kind of historical precedent
Speaker 5: for the past century or or two centuries. Every time
Speaker 5: you've had a technological revolution, it's been great, it's been awesome.
Speaker 5: And you see that when you go from ninety five
Speaker 5: percent of the population working in agriculture to five percent
Speaker 5: of the population and you create all these amazing jobs,
Speaker 5: but it happens over a period of fifty years. Now
Speaker 5: we have this capability curve where you go from two
Speaker 5: minutes agents are capable of two minutes of autonomy on
Speaker 5: intellectually complex tasks, and now depending on who you ask,
Speaker 5: it's eight to sixteen hours. And that's happened in two years.
Speaker 5: That is an exponential curve. What happens when we get
Speaker 5: to multi day. You know what happens when we get
Speaker 5: to multi week. And really the core of this is
Speaker 5: if this capability curve continues being as fast an expansion
Speaker 5: as it is, what does the world look like. There
Speaker 5: are a lot of very good reasons why that capability
Speaker 5: curve could level off, but that is the core of
Speaker 5: the argument.
Speaker 4: I do think that's just like an important sort of
Speaker 4: level set for people here, which is that the progress
Speaker 4: that we've seen since chet GPT came out whenever that
Speaker 4: was late twenty twenty two, has exceeded all of the
Speaker 4: expectations of everyone who's working on it at the time,
Speaker 4: including the people who are in the space and the
Speaker 4: most bullish and like the true believers. And there are
Speaker 4: various like measures and stuff, but you know, you mentioned
Speaker 4: the length of time you know that it could replic
Speaker 4: a human focused on stuff, like all the people like
Speaker 4: they made like these bets, right, and there were even
Speaker 4: prediction markets on their capabilities, and so like, as you say, like,
Speaker 4: it seems very plausible that the gains will level out
Speaker 4: in some way, or that perhaps simple computer tasks don't
Speaker 4: actually replace a lot of white color work because there's
Speaker 4: more to white color work than what could be done
Speaker 4: on a computer, including personality and all kinds of stuff.
Speaker 4: All of that seems very plausible and I probably even
Speaker 4: buy some of that. But this point that you make,
Speaker 4: it's like, yeah, sure, but it is still proving very fast.
Speaker 5: And it's something where the overall trend of the cost
Speaker 5: of inference per cognitive task has gone down so significantly,
Speaker 5: maybe depending on the forecast, ten to thirty times over
Speaker 5: the past year, and a task that was uneconomical in
Speaker 5: the first quarter twenty six might cross that threshold in
Speaker 5: the third quarter. And the other interesting thing is this
Speaker 5: capability gap where AI is capable of a lot of
Speaker 5: things and a lot of people don't know that it's
Speaker 5: capable of that, right, So is it about the capability
Speaker 5: improving or is it about people becoming more familiar with
Speaker 5: that and as AI infrastructure. It's been a great trade
Speaker 5: and it continues to stay tight, and I think the
Speaker 5: best rebuttal to this piece has been well, I think
Speaker 5: Gavin Baker made this point, which is the world is
Speaker 5: short on Watson waivers, and that's true, absolutely true. But
Speaker 5: technological revolutions are volatile, right, Improvements come from places that
Speaker 5: you don't really expect them to and I think you
Speaker 5: can't fully underwrite the idea that there aren't algorithmic improvements
Speaker 5: or there aren't improvements to the compute infrastructure. So we
Speaker 5: should look at Okay, if this capability curve continues improving,
Speaker 5: what are the downstream impacts there? And has the financial
Speaker 5: system ever been stress tested for a scenario like this,
Speaker 5: Because even if it takes five years, even if it
Speaker 5: takes seven years, eventually we will get there. And that's
Speaker 5: not a bearish take, it's a very bullish take. I
Speaker 5: think that there will be great opportunities that arise because
Speaker 5: of AI, but that's not to say that there won't
Speaker 5: be a period of transition. And the faster that it comes,
Speaker 5: the more aggressive that transition is. And I think the
Speaker 5: point of The piece really was to get comfortable with
Speaker 5: what monitoring that looks like. And I'll just make the
Speaker 5: point that the piece also starts out with an SMP
Speaker 5: that goes to eight thousand, because AI infrastructure is a
Speaker 5: very bullish trade that makes up a lot of the index,
Speaker 5: and that's a very strong and very momentum having trade
Speaker 5: right now. And it ends with the reminder that it's
Speaker 5: still February twenty twenty six. But in the middle of it,
Speaker 5: it says, how do we kind of get comfortable with
Speaker 5: the non immediacy of the replacement If a company decides
Speaker 5: whether they're doing it because AI's gotten better or because
Speaker 5: the market likes it when they cut jobs.
Speaker 4: What is You're already through saw with the blog last night.
Speaker 5: And you can argue whether that's because of AI or
Speaker 5: whether that's because of over hiring during COVID. But Caines
Speaker 5: said that by the end of the century we'd have
Speaker 5: a fifteen hour work week, and he was wrong, and
Speaker 5: there's a lot of exit you have to kind of
Speaker 5: look at why he was wrong. There are a few explanations.
Speaker 5: David Graeber says that we just kind of created all
Speaker 5: these bulk jobs. This is the title of the book.
Speaker 5: I'm not cursing.
Speaker 3: People have said worse on this podcast.
Speaker 5: The other explanation is that human wants and desires you
Speaker 5: can't really model for, and we will create whatever we
Speaker 5: need to fill that. At the same time, that required
Speaker 5: mechanisms by which humans kind of are involved in the
Speaker 5: process of making those machines better. It's kind of not
Speaker 5: necessarily in every scenario concurrent with the idea of a
Speaker 5: piece of software that has the ability for a recursive improvement.
Speaker 5: This isn't to say that tomorrow every single company in
Speaker 5: large enterprise goes out and replaces half their workforce, but
Speaker 5: you do have to take a holistic picture, which is
Speaker 5: everybody in venture capital has been talking about who's going
Speaker 5: to be the first one person unicorn because of a
Speaker 5: gendic AI. I don't know if we're there yet. I
Speaker 5: haven't really kept on top of that, but that does
Speaker 5: seem like something plausible to me, and I think one
Speaker 5: of the better lines of the Citadel Securities counter argument, yeah,
Speaker 5: was recursive capability doesn't imply recursive adoption. That's extremely true.
Speaker 5: The S curve framework, though, is kind of describing the
Speaker 5: wrong variable, and it's a variable that's really important when
Speaker 5: you don't just have incumbents adopting, but you have startups
Speaker 5: threatening and that variable is not necessarily breadth of adoption.
Speaker 5: It's intensity of adoption and capability of adoption. So you
Speaker 5: might have a flattening out S curve, and the seats
Speaker 5: that you've already enabled with these AI tools are just
Speaker 5: constantly getting better, and so that is you know. The
Speaker 5: other thing is the S curve is very kind of
Speaker 5: related to consumer adoption of new technologies. And what I
Speaker 5: would ask is was there an S curve for the
Speaker 5: adoption of spell check? Everybody already had a PC, everybody
Speaker 5: already had, you know, word processing software. It was kind
Speaker 5: of added as a feature. There are a lot of
Speaker 5: people in the world today that have no clue how
Speaker 5: to use SHATGPT, that are using AI every single day.
Speaker 5: It's probably what is going to recommend you this podcast.
Speaker 5: It's probably what is making these decisions of what items
Speaker 5: you see when you go on Amazon. So if these
Speaker 5: gentic capabilities are introduced as features to a technology that
Speaker 5: everyone has already adopted, you have to adjust your model
Speaker 5: for that.
Speaker 2: I have so many things to say about this, but
Speaker 2: first of all, there's something very dystopian about living in
Speaker 2: a world where like the upside is, well, we have
Speaker 2: a lot of bulk jobs in existence already, and so
Speaker 2: maybe some of those bold jobs will continue to exist
Speaker 2: even with AI. But the other thing is, like the
Speaker 2: self reinforcing nature of AI seems really important to me
Speaker 2: in the sense that, as you pointed out, James, like,
Speaker 2: it's not necessarily that people have to go out and
Speaker 2: find these new capabilities themselves. It's that the technology itself
Speaker 2: that they're may be already using just to substitute search
Speaker 2: or something like that, can do it on their behalf.
Speaker 2: And so you just get this feedback cycle where like
Speaker 2: one AI thing creates new AI things, and it just
Speaker 2: builds and builds on itself.
Speaker 5: I really would be remiss if I didn't say this again,
Speaker 5: which is a lesson that I've learned over the past
Speaker 5: five days, that you can put something in all caps,
Speaker 5: you can bould it, and people will still not read it.
Speaker 5: But maybe this is different because I'm speaking my base
Speaker 5: case is probably a lot closer to a lot of
Speaker 5: the people rebutting this article than the article itself. The
Speaker 5: point of this really was to explore what the bear
Speaker 5: case is if we continue to have a very bullish
Speaker 5: world in AI infrastructure. I think that any investor that
Speaker 5: reads it and thinks, and you know, disagrees with half
Speaker 5: of the things that we say, maybe agrees with half
Speaker 5: of it and forms a more nuanced understanding of what
Speaker 5: to watch out for. That's kind of our job.
Speaker 4: So this is important, and people who haven't read the
Speaker 4: piece should know that, Like right up front, you do
Speaker 4: say this, You say this piece is not a forecast.
Speaker 4: This is a possible scenario and how it could go,
Speaker 4: and we want to get into some of the details.
Speaker 4: But you know, one counter argument to sort of the
Speaker 4: idea of macro economic doom or financial crisis or whatever,
Speaker 4: is okay if you have AI and it's driving incredible
Speaker 4: productivity gains, If it's very disinflationary and so forth, if
Speaker 4: some people are becoming fabulously wealthy and part of this
Speaker 4: big redistribution that would happen, well, then the government has
Speaker 4: a lot more fiscal capacity to stabilize this. Right, then
Speaker 4: the government can spend a lot of money. Rates have
Speaker 4: come down, they can counteract the disinflation, not totally, unlike
Speaker 4: perhaps COVID would be like a great example, but it
Speaker 4: strikes me as like, well, if we're ever going to
Speaker 4: have a government that's thinking about these things proactively, that
Speaker 4: strikes me as a good reason to write them out.
Speaker 4: And it's notable like many of the executives at the
Speaker 4: top AI labs they talk about exactly this. In fact,
Speaker 4: it seems like they're pleading almost with the government to
Speaker 4: take this war seriously, because if we're going to have
Speaker 4: this big disruption and redistribution, we're going to have to
Speaker 4: start thinking about what are the fiscal mechanisms to counter
Speaker 4: it out.
Speaker 5: One hundred percent, I think that it's something where it's
Speaker 5: perfectly fine and good to say that the government will
Speaker 5: be able to deal with it, but it's probably better
Speaker 5: to formulate a framework in which the government is more
Speaker 5: able to do that. And in order to do that,
Speaker 5: you kind of have to have an idea of what
Speaker 5: to keep track of, and I can say that in
Speaker 5: the discourse that I've seen, I don't think that there's
Speaker 5: a very strong kind of data collection on this. Specifically,
Speaker 5: one of the big rebuttals has been that software job
Speaker 5: postings have gone up eleven percent year over year. Those
Speaker 5: job postings include AI and machine learning engineers, so you're
Speaker 5: really seeing a composition shift where these new you know,
Speaker 5: AI engineers are coming in and they're creating software that
Speaker 5: will improve itself. And when it comes to the government response,
Speaker 5: Jolts doesn't really speak about composition. In my opinion, there's
Speaker 5: not a great amount of data on white collar specifically,
Speaker 5: And yeah, it was, it was. It was almost worrying
Speaker 5: in itself to see this reaction where we write this
Speaker 5: article that's kind of saying what I think most people
Speaker 5: are thinking. We're putting trillions of dollars at the white
Speaker 5: collar productivity machine, and oh that might, you know, have
Speaker 5: some level of disruption, and I get it. The thing
Speaker 5: that I'm very thankful to a lot of the rebuttals
Speaker 5: for is that they've reminded people that it's twenty twenty six,
Speaker 5: which we tried to do three times in the piece,
Speaker 5: but apparently we're not successful. Thank you to everyone that
Speaker 5: that made sure that this isn't like a spinout, crazy whatever.
Speaker 5: But the worrying side is, well, everyone seems very very
Speaker 5: comfortable that this is all going to be okay, and
Speaker 5: I think that that reasonably. I'm also a student of
Speaker 5: financial history. That reasonably comes from when you look back
Speaker 5: at the past and you say, well, we had this
Speaker 5: industrial revolution and it was amazing, and we've had mechanization
Speaker 5: and it was amazing, and we've had the Internet and
Speaker 5: it was amazing, and it created all these jobs that
Speaker 5: we couldn't have possibly foreseen beforehand. And you're looking at
Speaker 5: that from one hundred or more years in the future.
Speaker 5: We have the term Luddite because of the fact that
Speaker 5: the transition was so abrupt and marked that people were
Speaker 5: moved to physical violence. Right, we don't want that to happen.
Speaker 5: The transitions do occur in the faster that this happens.
Speaker 5: If this were going to happen over the next twenty
Speaker 5: or thirty years, fine, you know that that's going to
Speaker 5: be great. Everything's going to be awesome. I think that
Speaker 5: the real time frame is closer to five to fifteen,
Speaker 5: and obviously this piece extrapolates where it's three years. We
Speaker 5: should be prepared for anything, because the government isn't going
Speaker 5: to accurately forecast technological advancement, but they can accurately forecast
Speaker 5: what they should watch and what the best policy response
Speaker 5: would be.
Speaker 3: Yeah, this's the thing.
Speaker 2: The Luddites were like ultimately on the wrong side of
Speaker 2: history in terms of thinking that resistance to new technology
Speaker 2: would actually matter. But that doesn't mean that there wasn't
Speaker 2: major resistance and disruption on the way, that it.
Speaker 4: Wasn't absolutely awful. Yeah, no, exactly right from their perspective,
Speaker 4: from their lives exactly.
Speaker 2: You know, you mentioned software job openings still rising, and
Speaker 2: one of the reasons that's able to happen is because
Speaker 2: we still have a financial system that up until relatively recently,
Speaker 2: has been very comfortable with extending credit to software companies.
Speaker 2: And there's obviously a reflexivity between the financial system, the market,
Speaker 2: and the real economy. And you dig into that in
Speaker 2: your piece as well, And this is the part of
Speaker 2: it that I actually found the most interesting, where you
Speaker 2: describe how AI could actually and the disruptive effects of
Speaker 2: AI could actually end up becoming problematic, especially for private capital.
Speaker 2: And this again is something that is very much in
Speaker 2: the public slash market psyche this week because we've had
Speaker 2: a number of private credit blowups starting to become public.
Speaker 2: Talk a little bit more about how you see that
Speaker 2: kind of private credit AI disruption now insurance as well,
Speaker 2: nexus unfolding.
Speaker 5: Just to reiterate, I don't see it, but I think
Speaker 5: this wasn't like a singling out of private credit was
Speaker 5: very much a response to the price action of the market,
Speaker 5: but it is something worth considering that it's a relatively
Speaker 5: new in the grand scheme of things, and there's a
Speaker 5: system that's built upon the assumption that things stay relatively stable,
Speaker 5: and if things aren't relatively stable, then what could possibly happen.
Speaker 5: We're not really private credit analysts, right Worreth thematic equity
Speaker 5: and macro research. This was something where we presented kind
Speaker 5: of if you were to have a wave of defaults
Speaker 5: in one of these disrupted industries, what would happen? And
Speaker 5: then the other thing is maybe the job losses are fine,
Speaker 5: and we go back to a economy like the nineteen
Speaker 5: fifties where the participation rate is much lower but productivity
Speaker 5: is much higher. That's great too. In the transition, the
Speaker 5: people that are at the highest risk of being replaced
Speaker 5: by AI have like seven eighty FICO scores and they're
Speaker 5: not classically what gets modeled as a risk in terms
Speaker 5: of a default. So these are all things where it's
Speaker 5: not saying that this is going to happen. It's saying,
Speaker 5: has a private credit lending and you know, to their
Speaker 5: credit I will say Apollo much earlier to the software
Speaker 5: thing than even I was or the market was right.
Speaker 5: Apollo reduced their software lending pretty early on. I think
Speaker 5: it was in early twenty twenty five. For the rest
Speaker 5: of it, you know, like, has there been enough changes
Speaker 5: to the assumptions about the income and about you know,
Speaker 5: does arr stay recurring? That's just something to consider.
Speaker 3: I think, what's your base case on private credit then?
Speaker 2: Is it the sort of Jamie Diamond cockroach scenario?
Speaker 5: So I think that private credit isn't banking right like
Speaker 5: run on the bank dynamic doesn't necessarily play out. They
Speaker 5: are in possession of permanent capital to a certain degree,
Speaker 5: and that's through in a lot of areas the acquisition
Speaker 5: of these life insurers. So I think you could definitely
Speaker 5: see the contagion being very minimized if there were to be.
Speaker 5: I don't think there have been any, like very high
Speaker 5: profile blow ups.
Speaker 2: Yet.
Speaker 5: Everything's pretty much fine right now as I understand it.
Speaker 5: The progression of it, though, I don't think that you're
Speaker 5: at a very high risk. My base case would be
Speaker 5: just like that, And the only kind of add risk
Speaker 5: is if you were to have some sort of change
Speaker 5: to how private credit is treated. From a regulatory perspective
Speaker 5: on the balance sheet of these life insurans.
Speaker 4: So there's sort of two major components to the piece
Speaker 4: that you wrote, and one is obviously the macro scenario,
Speaker 4: and the way it's framed it is like, okay, years
Speaker 4: twenty twenty eight, unemployment is above ten percent, the stock
Speaker 4: market has falling forty percent. So there's the macro story,
Speaker 4: but then there's also the sort of secular microstory. And
Speaker 4: I think this is really interesting, and this is the
Speaker 4: part that I've been like trying to work out and
Speaker 4: trying to understand better. This idea that like, there are
Speaker 4: all these businesses that have essentially been built up around
Speaker 4: building a mote based on network effects, you know, payments
Speaker 4: platforms and so forth and whatever, and so this idea
Speaker 4: that AI and agentic commerce will fundamentally change the way
Speaker 4: a lot of these businesses operate and these motes will
Speaker 4: disappear and talk to us about that, because I have
Speaker 4: a harder time I'm wrapping my head around what is
Speaker 4: it about AI per se that it's like, here you
Speaker 4: have these legacy networks, delivery drivers, payment companies with whatever
Speaker 4: they have on the desk, and you swipe your cards
Speaker 4: and stuff like that. What are those called little.
Speaker 5: Point of sale?
Speaker 4: What there's a little point of sale machines? But talk
Speaker 4: to us about, like, from a pure tech standpoint, what
Speaker 4: is it about agentic AI that can sort of evaporate
Speaker 4: this mode?
Speaker 5: So I will say, if I had to go back
Speaker 5: in time and write the piece differently, okay, I would
Speaker 5: not have singled that. I would have just kept it
Speaker 5: on a sector basis, right, And I think that if
Speaker 5: I knew that it was going to get thirty million views,
Speaker 5: I would not have mentioned single stocks at all. So
Speaker 5: I won't do that here. But what I will say is,
Speaker 5: and this future could be wrong, but if you envision
Speaker 5: a future where I remember talking to you guys about
Speaker 5: this in twenty twenty four when I was using it
Speaker 5: as a bow case for Apple, which didn't end up coming.
Speaker 5: You know, the Apple was kind of of let the
Speaker 5: chips fall where they may and then we'll come in afterwards,
Speaker 5: which they've done a lot in the past ten years.
Speaker 5: But the idea is you have this agentic assistant and
Speaker 5: it's in your phone and it knows everything about you,
Speaker 5: and then you kind of extrapolate that to a lot
Speaker 5: of people spend a decent amount of time shopping. What
Speaker 5: they don't spend a lot of time doing is price matching.
Speaker 5: If you're going to buy a box of protein bars,
Speaker 5: you don't really check five different vendors because it's tedious.
Speaker 5: AI agents do not experience tedium, right, So the kind
Speaker 5: of way that there are a lot of layered intermediation
Speaker 5: and rent kind of extraction layer in the economy, and
Speaker 5: then there are a lot of places where having a
Speaker 5: like an oligopoly essentially has allowed margins to really be
Speaker 5: artificially increased. So just to address I don't think that
Speaker 5: code is the moat on a delivery network for like
Speaker 5: like like that's you have the drivers, you have the customers.
Speaker 5: I get that I could see happening is something that's
Speaker 5: already kind of happening where these startups are enabled to
Speaker 5: create something that's similar and well, you don't have the
Speaker 5: network effect, okay, But if you have an AI agent
Speaker 5: that has the explicit instructions to go out and find
Speaker 5: the cheapest option, then it doesn't really care about using
Speaker 5: this thing that has a network effect. It cares about
Speaker 5: using the thing that's the cheapest. So if you have
Speaker 5: an order aggregator that's an agentic kind of aggregator on
Speaker 5: the driver side and the customer side. Then the customer
Speaker 5: says to the agent, hey, I want this burrito from Chipotle.
Speaker 5: And then there's a bunch of different platforms that the
Speaker 5: listing is on because the restaurant has used one of
Speaker 5: these agentic aggregators to go on every single one and
Speaker 5: put their thing, and the driver also has the one
Speaker 5: that will get them paid the most. So the idea
Speaker 5: of you know, taking half of the delivery fee as
Speaker 5: the company kind of goes away because your margin is
Speaker 5: my opportunity and someone that's five people that's kind of
Speaker 5: cutting up this maybe shoddy replacement, is very happy to
Speaker 5: you know. Obviously there are other modes here, but that's
Speaker 5: just one example of how you might see a world
Speaker 5: in which agenta commerce and the It's very similar to
Speaker 5: like the paper clip problem. If you tell a machine
Speaker 5: and to do something, it's just trying to get you
Speaker 5: the best price, and maybe that includes finding way around
Speaker 5: interchange just.
Speaker 4: To push back of this or just a pressure. I mean,
Speaker 4: like comparison shopping websites have existed for a long time
Speaker 4: almost it's the beginning of the Internet, right, and you know,
Speaker 4: you could Google.
Speaker 1: I don't know.
Speaker 4: It's just like Google Shop had a thing for a while.
Speaker 4: I don't think people ever that took off, but you know,
Speaker 4: it would show you like here's the price of a
Speaker 4: computer monitor on Amazon and Walmart dot com and new
Speaker 4: egg dot com and a few of these sites that
Speaker 4: like don't exist anymore, et cetera. Like in theory, like,
Speaker 4: isn't it describing the same thing that like from the
Speaker 4: customer's perspective, It's like, Okay, I'll just they're all the same.
Speaker 4: I'm going to click the cheap it totally.
Speaker 5: I get that, And that's an entirely possible case. What
Speaker 5: I will say is there's a big difference between actively
Speaker 5: going and taking the effort and taking the time to
Speaker 5: go to one of these comparison shopping sites to get
Speaker 5: the best price versus just telling your phone, get me
Speaker 5: a burrito, get me the best price. Right, those are
Speaker 5: They're two kind of fundamentally different things. This will play
Speaker 5: out over the next five or ten years, and we'll see.
Speaker 5: And also I'm sure that we're not going to just
Speaker 5: delete friction overnight, right, So that's why it was so
Speaker 5: shocking to see this kind of like media reaction it's
Speaker 5: like this stuff hasn't happened yet, and we don't know
Speaker 5: exactly how it's going to happen. It's just a future
Speaker 5: scenario where things happen a certain way.
Speaker 2: So can you talk to us for a second just
Speaker 2: where you see AI valuations at the moment, because I
Speaker 2: think this is also part of the reason that people
Speaker 2: are very nervous at the moment, which is like, Okay,
Speaker 2: on the one hand, we think AI is going to
Speaker 2: eat the world, but on the other hand, it's not
Speaker 2: entirely clear that a lot of AI is going to
Speaker 2: make money in doing so. And if you look at
Speaker 2: you know, some of the big hyperscalers at the moment,
Speaker 2: they're still losing money on certain power users.
Speaker 3: So how do we.
Speaker 2: Think that AI is actually going to make money as
Speaker 2: it sort of eats the world.
Speaker 5: I think that that's the other thing that's important here
Speaker 5: is these companies need to go out and search for
Speaker 5: roy and there are a lot of threats. You saw
Speaker 5: Anthropic respond to the Chinese distillation of models, and you know,
Speaker 5: if you go when you use Mini Max, it's relatively comparable,
Speaker 5: but it's also ninety percent cheaper. So this is like
Speaker 5: that there is a race happening right now, and the
Speaker 5: economics are they span the gamut, right, the good and
Speaker 5: bad on both sides. The thing that drives this kind
Speaker 5: of capability improvement is you do need customers to pay
Speaker 5: for these things that you have spent so much money on,
Speaker 5: and that means making it capable in a way that's
Speaker 5: useful to your customers, or integrating it in a way
Speaker 5: that's useful to your customers. So I personally think that
Speaker 5: that will happen. How quickly it happens is anybody's guess.
Speaker 5: But I think valuations right now are reflective of this
Speaker 5: expectation that we are going to continue adding compute capacity
Speaker 5: to be able to handle this. And I think that
Speaker 5: if you spend eight hours just thinking about it, you
Speaker 5: can see a lot of places where AI is pretty valuable.
Speaker 5: But a lot of those places are places where you
Speaker 5: might otherwise pay a human right now. So yeah, it's
Speaker 5: just you just have to balance it, and there's a
Speaker 5: lot of ways that it can go well, and then
Speaker 5: there's a couple of ways that it doesn't.
Speaker 4: Let's talk about enterprise software for a second, because okay,
Speaker 4: the public facing, these modes, these network effects, et cetera,
Speaker 4: maybe AI agent slow us to get the best price forever.
Speaker 4: Is it different economics if we're saying the enterprise, we
Speaker 4: know about the enterprise, the SaaS sell off, et cetera.
Speaker 4: What is the scenario. How would you articulate the fear
Speaker 4: in the market right now that all of these incumbent
Speaker 4: software companies could theoretically get ripped out because something something
Speaker 4: AI will make it so that customers don't need them.
Speaker 5: So you can separate software. You have kind of like
Speaker 5: this long tail of SaaS that includes these you know,
Speaker 5: workflow automation tools, and then you have like the systems
Speaker 5: of record. I think that it's very likely that the
Speaker 5: at least the systems of record have like a short
Speaker 5: squeeze in the sense that right now they kind of
Speaker 5: just have upside and that they are they're most situated
Speaker 5: to be able to improve their margins because of AI.
Speaker 4: Right, because coding is a cost for them, right, they
Speaker 4: can theoretically maintain these things much cheaper than they is.
Speaker 5: Yeah, one hundred percent. And what we said in the piece,
Speaker 5: which will be you know, interesting to see in real life,
Speaker 5: and I don't necessarily it's a good point that enterprises
Speaker 5: don't really react as quickly as this, so the time
Speaker 5: line is probably aggressive. But the way that these kind
Speaker 5: of contracts are negotiated. Last year, when you had the
Speaker 5: first half, the kind of budget resetting these CIOs and
Speaker 5: procurement teams they agentic AI was still kind of a buzzword, right.
Speaker 5: It wasn't until the end of November that it became insane.
Speaker 5: You know, I saw you have vibe coded a couple
Speaker 5: of things yourself, So there was a.
Speaker 4: Cool coming out next week.
Speaker 5: Nice, there was a great kind of jumping capability. What
Speaker 5: is it? By the way, are you can to speak
Speaker 5: about it?
Speaker 3: Or oh he can't it requires some finesse.
Speaker 2: I think, well, thing, this is the thing like I
Speaker 2: used to blame Joe for the SaaS sell off, right
Speaker 2: because he was the one vibe coding and publicizing vibe coding,
Speaker 2: But now we can all blame situation.
Speaker 5: Yeah, you're welcome. But the strategy that's been adopted by
Speaker 5: Opening Eye is very similar to Talent here, where they
Speaker 5: say we have these forward deployed engineers and we're just
Speaker 5: gonna install them at your place. And so maybe you know,
Speaker 5: I don't necessarily think the enterprises are going to jump
Speaker 5: to vibe code their own system of record. But what
Speaker 5: I do think is that when you have these sales
Speaker 5: teams that call up their customers and say, hey, remember
Speaker 5: last year we said this was what inflation was, and
Speaker 5: then we added a couple percent on top of that.
Speaker 5: So you're getting a five percent price increase. All good, Okay,
Speaker 5: you're not going anywhere because you don't have anywhere else
Speaker 5: to go. Done. Now the person on the other side
Speaker 5: of the phone can say, you know, open AI called
Speaker 5: me the other day, even if they're bluffing, right, So
Speaker 5: you do see like some potential downside to pricing power,
Speaker 5: and that's in the places where it's very unlikely that
Speaker 5: these vibe coded alternatives actually pose a threat. And then
Speaker 5: you see it's been interesting how Anthropic is handled it
Speaker 5: where they've recognized this capability gap where they say, oh,
Speaker 5: the people don't really understand what these tools can do,
Speaker 5: so they've started releasing like suites of AI tools. I
Speaker 5: don't know if you saw the wealth management one, right,
Speaker 5: it's they released the wealth management when I think a
Speaker 5: couple of days ago. It's like you could have done
Speaker 5: this yourself with claud customs.
Speaker 4: This is a really good point, and I hadn't really
Speaker 4: thought of it in that term, because these things that
Speaker 4: like Claude announced or entropic releases something, they're not that
Speaker 4: incredible in some sense. Well, they're essentially just very simple
Speaker 4: reminders you hadn't thought to use this for, you know,
Speaker 4: modeling various retirement scenarios. Actually it's very simple you could
Speaker 4: do that you hadn't thought to use this. So because
Speaker 4: they're simple, they're like marked on files. They're not like
Speaker 4: particularly exotic pieces of software, but they are reminders that
Speaker 4: this thing you didn't think of, Yeah, just do it.
Speaker 2: It's like a thing that you can use to hammer
Speaker 2: your supplier over the head with.
Speaker 5: Right. Yeah, I don't know exactly what the timeline that
Speaker 5: that happens on, but there are going to be adjustments
Speaker 5: to pricing power because of it. And yeah, it seems
Speaker 5: that this is kind of the reason why in the
Speaker 5: beginning I thought that framing the piece this way was
Speaker 5: valuable to our client base and reader base was because
Speaker 5: as an investor, you don't really care if you're presented
Speaker 5: with ten scenario and nine of them are wrong if
Speaker 5: one of them makes you money, right, So I I
Speaker 5: obviously knew that that some people who had already bought
Speaker 5: the dippin software would disagree with the software part, but
Speaker 5: maybe they would agree with the you know, uh with
Speaker 5: the disintermediation part. But then it kind of escaped containment
Speaker 5: and in retrospect, if I was going to write a
Speaker 5: piece for broad distribution, it would probably be pretty optimistic,
Speaker 5: because I'm a pretty optimistic guy. Like, uh so, yeah,
Speaker 5: that's been an interesting experience.
Speaker 3: What was the most surprising thing from this week for you?
Speaker 5: Well, I had someone that that really strongly disagreed with me,
Speaker 5: and then when I asked why, sent me a claud
Speaker 5: read out.
Speaker 6: The kelchy is cool that there's a you can use
Speaker 6: this as a hedge for like, there's.
Speaker 4: Now an instrument which wait, let's see kelshy. I'm gonna
Speaker 4: look it up Kelshi Sactrini scenario. Look if you start
Speaker 4: typing in kelshy and then start see it auto fill
Speaker 4: Satrini scenario, will I love that? Will the Satrini scenario happened?
Speaker 4: It's an eleven point six percent?
Speaker 5: Is that basically the right that you would get if
Speaker 5: you put it in the money market.
Speaker 4: So just trying to read these specifications of the contract
Speaker 4: fine print matter or summary. So if at least three
Speaker 4: of colon unemployment rate exceeds ten percent for the BLS
Speaker 4: S and P five hundred declines more than thirty percent
Speaker 4: from its closing level of issuance. That's weird terminology. Zillow
Speaker 4: Home Index declines more than ten percent, and then you
Speaker 4: have New York City, La San Francisco, Chicago, Houston, Phoenix,
Speaker 4: labor share of GDI falls below fifty percent and CPU
Speaker 4: falls below zero percent. If any of those three things happen,
Speaker 4: then this attornee scenario.
Speaker 2: Is that crazy because like most of that is just
Speaker 2: a financial crash, right, it's not even necessarily tied to AI.
Speaker 4: It's cool, Like do you like that? That's like this
Speaker 4: is now going to be known as just a trainee
Speaker 4: scenario forever, like when like when we get the next
Speaker 4: crisis whenever people it's like, oh, this is like an
Speaker 4: omen this is.
Speaker 5: I feel like anybody considered, like I feel like you
Speaker 5: could make a lot more money on TLT calls if
Speaker 5: three of these things.
Speaker 4: But there's one hundred and twenty five thousand dollars been
Speaker 4: traded in this market, Okay, so it's still.
Speaker 5: Pretty minor deep liquidity. You can't, right, you.
Speaker 4: Can't probably hedge you can't head your whole life or
Speaker 4: your whole business.
Speaker 5: But you know, if I was going to pick a
Speaker 5: thing I'd been known for, it probably would have been
Speaker 5: not this. But you know, you don't get to picked.
Speaker 5: So I still stand by what we've written, and I
Speaker 5: think that it's as a scenario, useful to consider.
Speaker 2: All Right, James, thank you for coming on during a
Speaker 2: very busy and i'm sure surreal week for you.
Speaker 5: Thank you for having me, all.
Speaker 2: Right, Joe, I'm very glad we got James on to
Speaker 2: discuss that because obviously this is the talking point of
Speaker 2: the week.
Speaker 3: At least.
Speaker 2: It is just fascinating from a media perspective how you
Speaker 2: can have these viral pieces that kind of get out
Speaker 2: into the world and develop a life of their own.
Speaker 2: But obviously the major point of interest in all of
Speaker 2: this is these are the things that the market seems
Speaker 2: to be actively considering at the moment.
Speaker 4: Right Paul Krugman wrote a good piece. He disagreed with
Speaker 4: a lot of it, but he pointed out, you know,
Speaker 4: when the radio broadcast of World the World's happened and
Speaker 4: a bunch of people paniced because they thought there was
Speaker 4: some big invasion. It occurred in the environment of a
Speaker 4: very it was like you know, during the depression.
Speaker 6: Yeah, of like existential thread and look like this is
Speaker 6: the worry that has been people have been talking about
Speaker 6: all year long before this piece, and so like the
Speaker 6: whole reason people are like talking about, oh are all
Speaker 6: these software companies that have thrived forever?
Speaker 4: The reason whether many of them are at all time
Speaker 4: lows is because of like, wow, people are very impressed
Speaker 4: with the capabilities, and you have a lot of people
Speaker 4: talking about the potential for mass white collar layoffs. And
Speaker 4: so therefore, you know, I read it as a sort
Speaker 4: of let's put this all together. And to the point
Speaker 4: is that you want to be thinking about scenarios, particularly
Speaker 4: from the public sector response, like, let's actually talk about
Speaker 4: what this could look like. It's strike to me as
Speaker 4: a usefultress.
Speaker 2: Right, and the reaction itself is informative.
Speaker 5: Right.
Speaker 2: So again, we should not be in an environment where
Speaker 2: you can have a think piece a single scenario that
Speaker 2: actually causes a broad sell off that lots of people
Speaker 2: start like pinning on this particular piece. And likewise, we
Speaker 2: shouldn't really be in a scenario where Citadel's Securities published
Speaker 2: as a rebuttal and then everything starts rallying. All it
Speaker 2: underscores is that no one really knows anything at.
Speaker 4: The moment this is ONOK. Yeah, Like there's like people
Speaker 4: are extremely stressed and knowing it's you know, it's like
Speaker 4: it's genuinely it's uncharted territory. It's charted to have a
Speaker 4: technology that is improving as fast as it is. It's
Speaker 4: uncharted to have it.
Speaker 1: You know.
Speaker 4: It's not like one lot, one specific industry is the threat.
Speaker 4: It's like a broad range. No one knows where it's
Speaker 4: going to be. So it's like people are like deeply
Speaker 4: anxious about it, and it articulated a lot of views,
Speaker 4: and it landed at a moment where this was just
Speaker 4: top of mind for everyone.
Speaker 2: The one last thing I'll say about this is I'm
Speaker 2: really glad you asked about policy, because this also seems
Speaker 2: to be the wild card in this entire discussion, which
Speaker 2: is like the outcome of all of this could end
Speaker 2: up being very different depending on what policymakers actually decide
Speaker 2: to do about it.
Speaker 3: And so far we.
Speaker 2: Haven't really seen any like not even early signs of
Speaker 2: how people are thinking about this.
Speaker 4: There's virtually no discussion in DC about anything substantive related
Speaker 4: to like the actual impacts of AI, there's almost none,
Speaker 4: and there's it's this very weird chasm that's opened up
Speaker 4: between how much of a big deal so many people
Speaker 4: are thinking about this and how politicians like they'll talk
Speaker 4: about anything but this. It's very it's actually it's starting
Speaker 4: to get pretty surreal on this.
Speaker 3: Yeah, all right, well shall we leave it there.
Speaker 4: Let's leave it there.
Speaker 3: Okay.
Speaker 2: This has been another episode of the Authoughts podcast. I'm
Speaker 2: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 4: And I'm Jill Wisenthal. You can follow me at the Stalwart.
Speaker 4: Follow our guest James van Giellen, He's at satriny seven.
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