James van Geelen on His Viral AI Doom Scenario

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You can find new episodes of the Bloomberg Daybreak Europe podcast by 7am in Dublin or 8am in Brussels, Berlin and Paris. on Apple, Spotify, YouTube, or wherever you get your podcasts. Bloomberg Audio Studios. Podcasts. Radio. News. Hello and welcome to another episode of the Odd Lots podcast. I'm Tracey Alloway. And I'm Joe Weisenthal. 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. I'm trying to remember specifics, but yes. And it's one of those things typically where you're really excited. It's like, oh, a lot of people are getting a lot of traction. Cool. People are talking about this. And then it goes multiple orders of magnitude bigger. And you're like, oh, this is super weird and no context for what this is. And you're sort of wanting 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 1900s. 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 U.S. like 20 billion in payments on old debt. And it was just surreal. 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 27th. And if you haven't heard of this particular thing, you have probably been living under the proverbial rock. Right. So past Odd Lots guest, James Van Geelen, co-authored a piece on his Substack, Citrini 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, etc. 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 of 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 flabbergasted me, was you see like all these banks and economists, et cetera, like weighing in. And many of them are very critical. And like Citadel 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'll be responding to it and trying to take it out. It was a as a market story and a media story, a wild week. It has become the discourse du jour. 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 on to any scenario that comes out. I get these notes from like sell side or research shops and they're like, clients have been asking us about the Citrini scenario. And it's just like, wow, this is wild. Like, really like. That's right. People calling up capital economics being like, I manage a portfolio of 100 billion and I am concerned about a substack. OK, well, we should talk to the author of this substack. And as you said, we've had him on a number of times before, often talking about AI. It is, of course, James Van Geelen, the founder of Citrini Research. So, James, thanks so much for coming back on the podcast. Thanks for having me. Why don't we start with what Citrini 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. Citrini 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-1 early July 2023, I think it was. 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 OddLots, it was about this massive Stargate data center build out. And Joe was very surprised to see that Caterpillar was, and I think very happy that the old economy was getting a boost. He's an old economy stand. That's right. 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 year to date, which is bonds have rallied. 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 piece's co-author, Ala, posed to me a question, which was, we've been focused 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 analogal rhythmic chart, it's just a diagonal line. It goes up and to the right. People have been trying to put sigmoids 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 10, 15 percent towards. And it comes from a place of everybody talks about equity markets being forward looking. But really, a lot more of what you see is 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 bull case, the bear case, the base case. And the most uncomfortable that you can be as an investor is when you can't see the bear case 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 SK Hynix 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 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 95% of the population working in agriculture to 5% of the population and you create all these amazing jobs. But it happens over a period of 50 years. And 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 16 hours. And that's happened in two years. That is an exponential curve. What happens when we get to multi-day? What happens when we get to multi-week? And really the core of this is if this capability curve continues being as fast and exponential 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 ChatGPT came out whenever that was late 2022 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 replicate 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 collar work because there's more to white collar 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 improving 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 10 to 30 times over the past year. and a task that was uneconomical in the first quarter of 26 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 S&P that goes to 8,000, 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 2026. 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 has gotten better or because the market likes it when they cut jobs, what is that? Which we're seeing already. We saw with Block last night. And you can argue whether that's because of AI or whether that's because of overhiring during COVID. But Keynes said that by the end of the century, we'd have a 15-hour work week, and he was wrong. And there's a lot of, 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 bullsh** jobs. This is the title of the book. I'm not cursing. People have said worse on this podcast. The other explanation is that, you know, 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 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 agentic 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, 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 spellcheck? Everybody already had a PC. Everybody already had 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 ChatGPT 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 agentic capabilities are introduced as features to a technology that everyone has already adopted, you have to adjust your model for that. The news doesn't stop on the weekends. Context changes constantly. And now Bloomberg is the place to stay on top of it all. Hi, I'm David Gurra. 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Make us part of your weekend routine on Bloomberg Television, radio, and wherever you get your podcasts. 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 maybe 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 bold 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 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 say this. You say this piece is not a forecast. This is a possible scenario and how it could go. And we're going to get into some of the details. But, you know, one counter argument to sort of the idea of macroeconomic doom or financial crisis or whatever is, OK, if you have AI and it's driving incredible productivity gains, it's very disinflationary and so forth. If some people are becoming fabulously wealthy in 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 more 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? A 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 11% year over year. Those job postings include AI and machine learning engineers. So you're really seeing a composition shift where these new 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 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 2026, which we tried to do three times in the piece, but apparently we're not successful. Thank you to everyone that made sure that this isn't like a spin out 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 a 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. And the faster that this happens, if this were going to happen over the next 20 or 30 years, fine. That's going to be great. Everything's going to be awesome. I think that the real time frame is closer to 5 to 15. 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, that'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 there. And that it wasn't absolutely awful. Yeah. No, exactly. They're like 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 had a number of private credit blow ups 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 unfolding. But I think this wasn't like a singling out of private credit. It 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? We're 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 1950s, 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 780 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 2025. For the rest of it, Has there been enough changes to the assumptions about the income and about 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 Dimon cockroach scenario? So I think that private credit isn't banking, right? The 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 blowups 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 added 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 insurers. 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 is like, OK, the year is 2028. Unemployment is above 10 percent. The stock market has fallen 40 percent. So there's the macro story, but then there's also the sort of secular micro story. 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 moat 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 moats will disappear. And talk to us about that, because I have a harder time 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 card and stuff like that. What are those called? point of sale what yeah the little point of sale machines but talk to us about like just 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 uh i think that if i knew that it was going to get 30 million views i would not have mentioned single stocks at all so i won't do that here uh 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 2024, when I was using it as a bull case for Apple, which didn't end up coming. You know, Apple was kind of let the chips fall where they may, and then will come in afterwards, which they've done a lot in the past 10 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 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. You have the drivers, you have the customers. I get that. What 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 if someone that's five people that's kind of coding up this maybe shoddy replacement is very happy to, you know, obviously there are other remotes here. But that's just one example of how you might see a world in which agendic commerce and it's very similar to like the paperclip problem. If you tell a machine to do something, it's just trying to get you the best price. And maybe that includes finding a way around interchange. Just to push back on this or just to pressure, I mean, like comparison shopping websites have existed for a long time, almost since the beginning of the internet, right? And And, you know, in theory, you can Google, I don't know, it's just like Google Shop had a thing for a while. I don't think people ever that ever took off. But, you know, it'd show you like, here's the price of a computer monitor on Amazon and Walmart.com and Newegg.com and a few of these sites that like don't exist anymore, et cetera. Like, in theory, like, isn't that describing the same thing that like from the customer's perspective? It's like, OK, I just they're all the same. I'm going to click the cheapest. 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 there. They're two kind of fundamentally different things. This will play out over the next five or 10 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. I'm Carol Masser. And I'm Tim Stenevec, inviting you to join us for the Bloomberg Business Week daily podcast. Now, every day we are bringing you reporting from the magazine that helps global leaders stay ahead. We've got insight on the people, the companies and trends that are shaping today's complex economy. That's right, Tim. We're all over global business, finance, tech news, all as it is happening in real time. And we've got complete coverage of the U.S. market close. Gotta say, basically, if it impacts financial markets, if it impacts companies, if it's impacting trends and narratives that are out there, we are on it. We also have a lot of fun doing it. Bloomberg Businessweek also brings you the analysis behind the headlines through conversations with our expert guests. And we are doing this all live each weekday. And then we bring you the best analysis in our daily podcast. Search for Bloomberg Business Week on YouTube, Apple, Spotify, or anywhere else you listen. Check it out on your way home from work to catch up on the conversations that you miss during the business day. And on the weekend, check it out for a complete wrap up of your business week. That's the Bloomberg Business Week Daily Podcast. I'm Carol Masser. And I'm Tim Stanovic. Subscribe today wherever you get your podcasts. 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, OK, 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 ROI. And there are a lot of threats. You saw Anthropic respond to the Chinese distillation of models. And, you know, if you go and you use Minimax, it's relatively comparable, but it's also 90% cheaper. so this is like a there is a race happening right now and the economics are they span the gamut right 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 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, OK, the public facing these moats, these network effects, et cetera, maybe AI agents allow us to get the best price, whatever. It's a 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 need them so you can separate software you have kind of like this long tail of sass that includes these uh 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 in that they are most situated to be able to improve their margins because of AI. Right, because coding is a cost for them, right? Exactly. They can theoretically maintain these things much cheaper than they are. Yeah, 100%. And what we said in the piece, which will be 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 timeline 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, the 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 five coded a couple of things yourself. So there was something cool coming out next week. Nice. There was a great kind of jump in capability. What is it, by the way? Are you kidding you to speak about it? No, he can't. It requires some finesse, I think. Well, this is the thing. 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 Citrine. Oh, yeah. I'm off the hook, too. Yeah, you're off the hook, too. You're welcome. But the strategy that's been adopted by OpenAI is very similar to Palantir, where they say, we have these forward-deployed engineers, and we're just going to 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 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 5% price increase. All good. OK, 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, OpenAI called me the other day. even if they're bluffing right so so you do see like some potential downside to pricing power and that's that's in the places where it's very unlikely that these vibe coded alternatives actually pose a threat and then you see uh it's been interesting how anthropic has handled it where they've recognized this capability gap where they say oh these people the people don't really understand what the these tools can do so they've started releasing like suites of ai tools yeah i I don't know if you saw the wealth management one, right? It's they released the wealth management one, I think, a couple of days ago. It's like you could have done this yourself with Claude Custom Skills. This is a really good point. And I hadn't really thought of it in that terms because these things that like Claude announced or Anthropic releases something, they're not that incredible in some sense. But 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 Markdown 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 10 scenarios and nine of them are wrong if one of them makes you money. Right. So I obviously knew that that some people who had already bought the dip in software would disagree with the software part, but maybe they would agree with the, you know, 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. So, yeah, that's been an interesting experience. What was the most surprising thing from this week for you? Well, I had someone that really strongly disagreed with me. And then when I asked why, sent me a Claude readout. the uh the kelsey is cool that uh there's a you can use this as a hedge for like your own there's now an instrument at which let's see kelsey i'm gonna look it up kelsey citrini scenario like if you start typing in kelsey and then start the word c it autofill citrini scenario will this i love that will the citrini scenario happen it's at 11.6 percent is that basically the rate that you would get if you put it in the money market it probably is so So this is interesting. Can I read the specifications of the contract? Fine print matters. The rule summary. So if at least three of, colon, unemployment rate exceeds 10% for the BLS. S&P 500 declines more than 30% from its closing level of issuance. That's weird terminology. Zillow Home Index declines more than 10%. Then you have New York City, LA, San Francisco, Chicago, Houston, Phoenix. Labor share of GDI falls below 50%, and CPU falls below 0%. If any of those three things happen, then the Citrini scenario. That's crazy because like most of that is just a financial crash, right? Like 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 Trini scenario forever. Like when we get the next crisis, whenever people are like, oh, this is like an omen. I feel like anybody consider – like I feel like you could make a lot more money on TLT calls if three of these things hit. There's $125,000 been traded in this market. So it's still pretty minor. Deep liquidity. You can't probably hedge your whole life or your whole portfolio. The whole business. But, you know. If I was going to pick a thing I'd be known for, it probably would have been not this. But, you know, you don't get to pick. 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 of Worlds happened and a bunch of people panicked 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. Like existential dread. 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 why many of them are at all time lows is because it's 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 like 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 strikes me as a useful exercise. 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. Lots of people start like pinning on this particular piece. And likewise, we shouldn't really be in a scenario where Citadel Securities publishes a rebuttal and then everything starts rallying. All it underscores is that no one really knows anything at the moment. This is on tenterhooks, right? Like there's like people are extremely stressed and no one – it's – you know, it's like genuinely – it's uncharted territory. It's uncharted 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 – 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 D.C. about anything substantive related to like the actual impacts of AI. There's almost none. And there'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 wild. It's actually it's starting to get pretty surreal. Yeah. All right. Well, shall we leave it there? Let's leave it there. OK, this has been another episode of the All Thoughts podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Jill Weisenthal. 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