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