Why AI Has No ROI with Paul Kedrosky| Better Offline

Greetings. I'm Ed Zitron and this is Better Off Line.

Today we are joined by the mighty economist Paul Kadroski. Paul, thank you for joining me.

Hey Ed, how's it going?

It's going great. Everyone's uh deeply upset because this week and the last week everyone has been saying, "Huh, does AI have a return on investment?" And I've really been enjoying it because it's like watching the dinosaurs look up and see the meteor. They're just like, "What do you mean? What do you mean this costs money?"

Have you, I don't know if you've seen the GitHub co-pilot stuff.

Yeah, I actually put out a thing on it yesterday.

Oh, sorry, Paul. Terribly rude of me that I was, you know me, I've got all sorts of crap on so I haven't read it yet but I'm excited to talk about this. I'm really excited.

It's uh it's really. And not only that, I mean, there's I I'll sort of triangulate with three different things that touch on different aspects of this at the same time. One was obviously the GitHub co-pilot study, which we can get into as deeply as you want. There was also a piece that came out uh in part from the Peterson Institute for International Economics um yesterday or the day before. Jack Cook or Clark at Anthropic said it around who's obviously one of the co-founders there and it's and it's called "Where is the Where is AI and GDP Statistics?" And then of course, there was the debacle which I saw anonymous, someone had anonymously uh disclosed that they had spent almost a half $500 million because they had uncapped token expenses, discovered they sort of blown their credit cards, anyways. Yes, there's a bunch of.

I love that as well. Well, let's start with the GitHub thing. So, for the uninitiated, GitHub Copilot, AI coding tool from Microsoft. Couple weeks ago, I broke the story of course that they were moving their users from a premium request model to a token-based model. So, think of it like this with the listeners. If you, every time you use the cab service, you could just say, "Drive me from the Upper West Side to Red Hook," and that would just that would be one drive and you get a certain amount of drives a month. And then suddenly, the beginning of June, they turn to you and say, "Yeah, you got to pay by the mile." And you suddenly realize you've been taking 95-mile trips. You've been asking to drive from New Jersey to Maryland, which I realize is further than than 95 miles. Don't, I'm not a geographer. All right. But nevertheless, on the GitHub Copilot subreddit, people have just been posting, "What the [ __ ]? What? What do you mean? My whole balance is gone in three prompts? What do you mean by that?"

Uh-huh. It's uh yeah. Uh, and this is part of the problem, right? Is there's been, you can get all econo-wonky about this stuff about the merits of of metered pricing on a per-token basis versus lump-sum pricing, but in a sense, you can think of this was the early pricing in token in in terms of how tokens were metered out had two really important characteristics. One is they were grotesquely subsidized. You weren't actually seeing the real all-in cost with respect to the loaded cost of actually providing you with those tokens. And then as a kind of don't pay a cent event up there with Costco, uh, it was being bundled. So you were had a second layer of masking with respect to what these what tokens were actually costing. And so once it becomes unsubsidized and unbundled, then you see your your your, you know, your ass is dangling in the breeze of real token pricing.

I think it's funny as well because for years people have been saying to me, "That's not happening. They're not subsidizing it. It's different. It's just it's like the the Costco model, for example. People are like, 'Oh yeah, well, they're making money other ways.' It's like, 'No, they're not.' They're just selling in Microsoft's case. They were like, 'We're going to sell you $1,000 for $39.'"

Right.

What do you think? Do you like that?

It's a lovely, it's a lovely come-on. It brings people in. It's like the hot dogs at Costco, except Costco has other things on which they make a boatload of money.

Except the co- the hot dogs cost like $7,000 a packet. It's just.

I think it's quite deceitful. Personally, I think it's because these, on one hand, we can make fun of these people. I will continue to do so. It's funny. But when you look at them, it is also quite depressing because they were intentionally misled. Like these people had no idea. It's not like these subsidized subscriptions were like, "Hey, if you use this many tokens while paying for them, it would cost this much," until they made the change. Microsoft released a calculator that allowed you to see that, but only once they'd announced it. So, you have millions, I would say, the vast majority of people that interact with AI who have no idea what it costs. Literally none.

Right. And ma and which is made worse by some of the early uh over over excitement especially among large corporations that made the mistake of creating leaderboards.

And hell yeah.

Right. So this is we got into this token-maxing phenomenon. If so, if you're inside of which is obviously the idea that the more tokens you use, the better you're doing in your job review because, "Look at you, you're all AI." The problem, of course, is is this is a little bit like um the Saudis handing out Hummers to everyone in America. People saying, "Wow, this is awesome. I love having a Hummer." And then you have to fill it up. And so for a little while, it was like we were subsidizing these grotesquely uh profligate users of tokens, just like profligate users of gasoline. And then all of a sudden, the bill comes due and you say, "Wait a minute, this thing's a pig. It uses a lot of gas. I don't want to drive it for groceries anymore." And the exact same phenomenon is true with respect to being again exposed to the having your ass hanging in the breeze of real token prices.

Well, the other thing is as well is I just put out a newsletter about this. You look at how these people are freaking out and you also realize they have no idea what AI costs. It's not just like, "Wow, this is a lot of money." It's they're not even thinking in terms of cost. It's not like they know, I don't know, they're refactoring something. They don't know how much that, they don't know how much anything costs. So, it's not like they can smoothly transition to token-based billing because they don't, they don't know. They have no idea.

No. And this is this is the deep, this is the deep structural problem because they were brought in through the side door of bundled pricing and now that's that's becoming unbundled. And of course, that also has a is reflected in what we're told and I think the Wall Street Journal and others have written about this and it'll be interesting. We see the final um S1s for some of the upcoming IPOs that there is this attempt to try and even mask it in the financial filings where you get into this phenomenon of what we used to call earnings before bad stuff. And so what they're trying to do is is hide the costs of training the models and saying that's not actually an operating cost, that's a capital cost and we shouldn't have to show that as a function of what actually the margins are on producing tokens. And that is of course a cheat, right? Because if that's true, then you should be able to capitalize these things and expense them over a long period of time. And we know full well that these are actually operating costs because they tell us that every 18 months we're launching a new a new major model. These things are not capitalized. These are operating expenses that should be treated accordingly with respect to the actual cost of token production. So there's a multifaceted uh game going on here both in terms of how it's being presented to users, but also in terms of how they're trying to sell it in the context of the upcoming S1s for the Anthropic and OpenAI IPO IPO filings.

Well, what's really funny as well about the idea of capitalizing training costs is they're never going away because it's not just pre-training, shoving shoving the stuff in the models. They have to constantly tweak them because.

That's right.

Right. Which from a cl, you know, from a classic, my years ago accounting, whenever you have a regular and predictable cost that you have to expense, you have to incur to continue operating your business, that is no longer a capital cost. That's an expensible item that should be expensed as such. So you you get into, as I said, like back in the dark days of dot and even the telecom boom, you get into this problem of earnings before bad stuff where they want to, they want to exclude all of the things that make the numbers look bad. And then of course, on the other side, you have this run rate problem where we continually hear about what the run rates are at these companies and the window with respect to the run rate could be the last 15 minutes for all you know, right? A run rate is just.

You extrapolate whatever is most convenient for you. So it's a problem on both sides.

Well, yeah, actually that's that's I love talking about run rate. Everyone who listens to this show knows I'm a real run rate pig because because like Anthropic, I've reached out to both OpenAI and Anthropic and said, "Hey, how do you define this number?" And they will not respond. They will not. They're very unfair to me, very nasty. They will not respond. Probably because.

From what the information is reported, I don't know how OpenAI does it. But Anthropic not only includes the amounts of money that Amazon and Google make in their revenues, like when they resell the most, but they also, they do 13 times the last month's API spend and 12 times the current day subscribers. So it's just there's so many ways also AP, so token spend, so just organizational token spend that's not a recurring cost that's.

That's just you can kind of I guess think, well, maybe people are spending this today, but that person who spent half a billion dollars, that company that spent half a billion dollars on AI, right?

That's not happening again.

That person is that person is, you're not going to get one half a billion follow Mr. Bean every single month as someone just goofily. I I also genuinely, I know the reporter Madison Mills. She's a respectable reporter. She's very, she's she's good. She is well-sourced. It's just like.

I hope Anthropic didn't include that $500 million in their annualized revenue because I'm looking forward to it showing up in a public company filing because it almost inevitably will. This is going to be somebody's one-time item, right? And.

You think that they will though?

Oh, absolutely. I mean, half a billion, it's material for almost anyone. So, my my guess is it's going to show up somewhere. It'll be really interesting to see. And my guess is at that scale, uh, it's it's a public company. So, my guess is we will see that we will know where that actually happened. And so, it's going to be it's going to be very entertaining. But that this is the deep structural problem. And it gets worse, of course, because once you unbundle token pricing and then you're looking at um the actual year-over-year decline in quality-adjusted token pricing in token pricing and you see the the inherent deflationary curve underneath the hood. Now, let's connect that to how all of these data centers that are producing these deflating tokens are being uh are being constructed. An increasing fraction of that.

Can you elaborate what you mean by the uh deflating token? I'm not sure I understand.

So, so over the last since 2022 on an annualized basis, on a on a performant basis, so ignoring what the the continual jump to the frontier, if you imagine sort of on a on a comparable token basis per across models across the period, token prices have fallen anywhere from 70 to 90% year-over-year consistently back to 2021.

Right. But they're burning more tokens in the price.

Right. Right. Oh, but let's put that aside for one second. Think about it the other way around. So if I'm now my business is now I'm unbundling and I'm selling tokens and that's the way customers, you're telling my consumers to think about it. So now they're looking out and they start to see what's happening with token prices and if I go back one generation, maybe those prices are cheaper. Now we have this classic financial problem of what's called a duration mismatch. Right? So I have I have debt funding the data centers that's 10 to 15 years duration and longer, which is predicated on fixed payments but being made on the basis of tokens where you're telling the customer to control your costs, you may want to look back in time and use, you know, an older model. So I'm paying for a fixed cost with a deflating commodity. Right? This we know from the over and over and over again. These duration mismatches, especially duration mismatches that are built on top of debt and a deflating commodity are absolutely atomic with respect to causing a blow-up in people's obligations with respect to these these kinds of duration mismatch problems. So there's a there's a deep structural issue that this will expose and people haven't quite realized it yet.

So you're saying that as the token costs get cheaper and and everyone's being encouraged to use this less or more thoughtfully. Um, that's happening, but they're building the data centers as if the number will only ever go up and they'll only ever use.

That's exactly right. And so you've got this wonderful, again, the term of art, you've got a duration mismatch on top of a deflating commodity that can only end very, very badly. And it was masked because for a while, you weren't exposed directly to that. You were just paying a straight-up subscription, almost like Amazon Prime. And of course, that doesn't work because Amazon Prime's costs across the board are declining, whereas um costs are increasing at the frontier, declining in the in the in the back catalog, if you will, of tokens.

And that's the thing with like an Amazon Prime, for example. Yes, they're they have found like Amazon or not like Amazon, people, I know many listeners don't love them, and I agree, but it's like Amazon Prime, they fixed those costs by building their own logistics network and they found ways to, they had, I don't know, ways to make that cheaper. No one has that in AI. No one. Like it's just we are three, four years in and everyone's like, "Oh, we'll do ASICs." No, we won't. That didn't work. Like we have we're like two or three generations of Tranium, Inferentia, TPUs still not profitable.

Still not profitable, Brian.

Yes, we would, we would know. But I think and and and of course the problem is that if you look at, I was just looking at some data yesterday with respect to how small language models are increasingly closing the gap with large language models, which is causing training cycles on large language models to have to accelerate, become more expensive, throw more compute at it, more reinforcement learning. The costs are not are particularly not not declining, they're actually increasing sharply at the frontier because they're essentially being chased like, you know, like the the rabbits and uh like Wile E. Coyote and the Roadrunner. Um, and and so they're they're being chased into this very costly corner as a result. And that's a classic commoditization problem. If you go back to the 19, I don't know, late 19th century, a very similar thing happened in railroads as people were racing desperately to try and, you know, find a way to build a corner and control them so that they could compete with all of these other upstart railroads. And of course, all that really happened was capex exploded, margins went to [ __ ] and uh multiple railroads failed and we led to the crash of what 1873, uh 1893 and arguably was a was a cause in the Great Depression. So, you know, so you're playing out this exact same game because you're sitting in this high capex world that's increasingly funded by debt and built on top of this duration mismatch with token prices being now exposed and raw in front of people. And the other thing is as well is people just literally in my piece today, people make this point about, "Oh, it's like the dot-com bubble." In that we will we will simply just, we'll reuse these things in the future like we will just pick these up. And it's, I re, I hear this from very smart people, are people who are not like.

Beguiled by the AI industry. But it's like, okay, let's talk about what happened in the dot-com bubble. So when it exploded, you had those some Sun Microsystems, the Ultra, whatever it was, I forget, $43, $50,000 a server, but that one server could run an entire company. You could run everything on it. Databases, messy CRM, they had like they have on-prem Lotus. Anyway.

Um, you had those things, but you could run that and it, you could probably run that in a garage. You might need to use the washing machine's plug, but you could do it.

If you. And damn, those things were $50,000, so you probably get them at what, 20, 30 large.

Okay, great. Um, what happens when the AI bubble bursts? You can't just plug in an AI GPU. A B200 GPU is about $50,000. It requires about, I think.

As I looked this up very recently, it's like 1,200, 1,500 watts for a Sun Microsystems server. About the same for a single B200, which will require bespoke cooling, a server, hardware, RAM, all of this other stuff. And then you'll find out that you can't do jack [ __ ] with a single GPU.

Yeah, it's going to be a huge source of disappointment once you power it up. Your neighborhood lights all blink out and you can't still can't do anything. So, yeah.

I just think you.

That's exactly right. But that's but that's I call this, I was just I got into this with someone recently who was making a similar, I call it faith-based argumentation. It's this kind of >> you know, quasi-religious uh orthodoxy that requires you to believe the following five things. You know, they always create more jobs than they destroy. We can always reuse assets after the fact. And I'm one of the things I always point to people is that almost half of US railroad lines built during the boom years in the late 19th century were eventually abandoned. And did they find reuse? They absolutely did. It only took a hundred years and now they're mountain biking trails.

So, you know, let's let's wait around for that. So.

Those were railroads.

They were railroads that didn't require >> electrification. I guess so. So, they were much more stable as assets, right? They didn't have the problems that GPUs and data centers do where not just uh the huge power and cooling requirements, but also the inherent the trajectory of the underlying technology where it changes quickly enough that, you know, is a 20-year-old you know, Blackwell of any use to anyone other than as a paperweight? Of course, the answer is probably not. Whereas a railroad.

Pretty heavy.

Yeah, they are extremely heavy. I actually was messing around with one recently. And so yeah. And so this is the problem. And again, it's this sort of naive argumentation, not to mention the, you know, the old Keynesian line that it may be it may be great in the long run, but in the long run, we're also all dead. So it really depends on your time horizon. And I find it, honestly, in the face of the kinds of consequential changes in the US economy, I find it a very glib style of argumentation where you're essentially patting people on the head and saying, "Yeah, don't worry your pretty little head, this will all work out because it it always has." And they're arguing from a data set sample size of five, which we wouldn't launch, we wouldn't launch a drug on that basis. Also, I think it helps them rationalize bad behavior.

Because of course.

If you say, "Okay, it worked."

The one that actually upsets me is, "Well, the dot-com bubble worked out." It's like, "Yeah, the stock market lost 80% of its value. Hundreds of thousands of people lost their jobs. People lost everything in some cases." And at the end, it's like, okay, that was also completely different, but you're being quite glib about the first part, but it's also, yeah, it's okay that people burn a lot of money for basically no reason.

This is.

And it also allows you to not think about bad stuff. It allows you to. This is the Andresonian argument that that there's no point in introspection. It's just a really bad idea.

Right. Why think about these things? It'll all sort itself out. But I also think there's a deeper issue and we may have talked about this before, but the idea a lot of people treat as an article of faith Carlota Perez's book "Technological Revolutions and Financial Capital." And one of the things that they quote take away from that, which I'm not convinced they do. I think they only look at the pictures. But anyways, one of the things they take away from her book and her work and other people's work with respect to these violent technological uh uh revolutions is the idea that it really doesn't matter because it always works out. Here's the difference though. In past episodes, we didn't tell ourselves that. So, there's an element of reflexivity going on here because once you know the plot and you act as if the plot is somehow F=MA, it's a law of physics. Then the whole game changes because now you're acting as if it doesn't matter what I do because you think it doesn't matter what you do because you've got this idea in your head as an article of faith that it always works out. That wasn't true historically. No one in building out the railroads, rural electrification, the fiber bubble. No one was telling themselves in the time, "This always works out." That was not part of the that was not part of the playbook. The idea that we now tell ourselves these things is such a deep structural change in terms of the way this stuff happens that it it amazes me that no one understands it.

Well, I think it's just it's the rationalizing and it's also it gives you a way of avoiding thinking about true structural issues. The right kind of thumb. It's thumb-sucking, I always call it. It's really a kind of thumb-sucking gives you comfort.

It allows you to be like, well.

Google isn't stupid for raising $80 billion in equity sales. Yeah, Google, the largest companies in the world couldn't just destroy their companies by wasting all their money. It's like, yeah, go and type something into Google search. Go and type anything into Google and tell me if this looks like a company running a good business or a good product or just a company throwing [ __ ] at the wall and being like, "This works, right?" You know, [ __ ] it. And and we have we have so many examples of companies that were lauded during the run-up of prior episodes for being really understanding the way the world works and being a real uh pathbreaker and so on, baiting to whether it was the global financial crisis and the banks at that time and my friend Jim Cramer's unfortunately timed comments about Bear Stearns and all that kind of stuff because people are so backwards looking and so extrapolative with respect to the way they look at things. They just can't see the discontinuity, the obvious discontinuities ahead. And so they extrapolate and extrapolate and then they eventually, you know, extrapolate their way right off a right off a cliff. And I I see a lot of that in this going around. And I don't know if you saw it, but there was a paper came out as a good example of this. There was a paper came out yesterday and this goes to the heart of the token pricing problem. And it came out, I think it was on SSRN or Ember um or yeah, National Bureau of Economic Research. And so the paper basically was about how um there's. And as you and I both know, there's been this explosion in the number of GitHub commits and repositories or repositories and commits within them. And it's up something like 200% over just over the last 18 months, largely driven by harnesses and everything and all of these coding tools. And of course, then they looked at the other side of it was this is this profligate use of tokens. What is it? What has it led to? Because producing more stuff that shows up on GitHub is just an intermediate variable. Nobody in the real economy cares other than maybe Microsoft. And even they probably wish there was probably a little less activity on GitHub. And so they showed that, and this was just, they used iOS apps, Android apps um and one other category, anyways. And they showed that the number of reviews per app has declined sharply as the number of repositories and commits has gone up. So essentially what we're seeing, and this is the thing that I think is really important, is these can be very effective if wildly subsidized productivity tools for coders, but the end economic result is mostly the production of of sort of slop, everything slop apps, slop content, slop. And so you're you're you're flooding and commoditizing these these markets that are becoming both saturated and declining margins. And this is an incredibly important distinction that just because it's helping you produce more stuff, it doesn't mean that in the broader economy, its ability to absorb it has increased, nor does it care. And that's what this paper shows. And I think the idea that we're doing all of this um work and what's increasingly become expensive work using tokens to produce things and makes coders very happy having, you know, things running in agentic loops. Um, but the broader economy, you know, doesn't give a [ __ ]. And have by any chance did you read SemiAnalysis's AI dark output?

I did, which is it is one of the funniest things I have read in my life. So for the for the listeners, you'll have a link to this, but it's basically, yeah, AI is so AI output will be real before it is measurable. We can capture token spend. We can capture jobs lost, but unless AI AI's output is sold at a visible price, only token spend is captured in GDP. By which they mean we don't actually measure whether something is good.

We just measure whether something.

Like we just, it's actually so this is the [ __ ] a teenager would say when lying about having a girlfriend, voodoo teen economics. Yeah, it really is. And uh, and it and again, it goes to that that that National Bureau of Economic Research paper. It's exactly the same thing. I I was mentioning at the at the top there is this tremendous and I can I'll send you the link if you haven't seen it and it's called "Where is AI and GDP Statistics? Filling the Measurement Gap." Came up a couple of days or a couple of days ago.

And they argue that um essentially AI quality-adjusted AI output is up more than two 2,000% per year. They come up with estimates of like, you know, $250, $300 billion on top of the. And but they could then essentially come to the conclusion that this is all true as long as you accept our redefinition of GDP. And of course.

Right. That. Okay.

Right. If you allow me to redefine GDP, I could present you with some tremendous numbers. And the entire paper is absolutely fascinating as an example of what's, you know, what's often called motivated reasoning. I need to believe this, therefore I construct an argument to allow me to continue to believe it. And the way I get there, um, is by redefining a variable that's already very squishy in the first place. Let's not pretend that, you know, measuring GDP is much easier than like, I don't know, measuring muons in a cloud chamber or something. It's still very hard. And we're about to, you're trying to make it harder to justify something that's just not defensible. And the the AI dark output one is great because they, substitution dark output is work that was previously done by humans and is now done by AI. In our dark output monitor, we have identified roughly $1.5 trillion in tasks that current AI could substantially augment or automate. To which I say, why hasn't it done it?

Right?

It's that the is the AI thing though? Because specifically with AI, with other things, productivity is hard to measure. It's hard to measure outputs with workers in knowledge work, especially. Like it's it's doable, but it's not like a a linear path. Except you're selling a tool that can theoretically do anything.

If this did what they said it did, we would have gunfights in the street. We would have the destruction of most knowledge work. And it would be happening a year ago. It would be half happening a year ago, happening fully today. We would have the destruction of law firms. We'd have the destruction of hyperscalers because anyone would just be like, "Build me a Microsoft Word." And it would build them a Microsoft Word and they would use it and it would be functional, bug-free, all of these things. Uh, there would be, well, I mean, we've already seen a spike in litigation from pro se people representing themselves, but nevertheless, we would see law firms turning into two or three-person shops that would beat the leading litigators because they would have.

Oh, absolutely.

It would be very easy to. I'll give you a related example which made the rounds yesterday and it kind of gets to the heart of this misunderstanding. There was someone who shall remain unnamed but has a popular newsletter and used to work at a certain venture fund um put out something. We put out the radiologist paradox, which was the idea that back in 2016, Jeffrey Hinton, computer scientist uh and Nobelist, early pioneer in image models and deep neural networks, said in a talk that probably within five years, if not 10 years, um large language deep learn, deep at the time, neural networks learning models would be better than radiologists. And there's really no reason to continue training them. Now, of course, he said 10 years on the outside. Well, it's now 10 years later and if you look at the data, we're continuing to produce more radiologists. And that analyst then put out a note yesterday and so did I think CO2 or someone else and said, like, "Well, checkmate Jeffrey Hinton. We have a lot more radiologists." And of course, this is a classic example of a profound misunderstanding of of so many things. That's once it's it's hard to keep track. One is that again, it's not clear that being a selectively better than radiologists at certain things like identifying, I don't know, prostate cancers or whatever else, if that obviously that's not good enough. Radiologists do more than that. But it also misunderstands the nature of the employment market because radiologists, like most of medicine, has created a very comfortable little cartel for themselves. So even if there was gale-force winds blowing at radiologists because of AI, the likelihood of you seeing it in such a short time, even if Hinton was right that they could in theory replace, you know, a significant slice of what radiologists do, the it's a misunderstanding of the nature of the markets themselves. So it misunderstands both the technology and the nature of cartelized employment markets whenever you these kinds of arguments. And yet, it's used as an example of how the inexorable march of these things, you know, continues apace and it will always be augmenting. And I just think there's so many sort of nested misunderstandings of how how what pressures AI is having on employment markets and how we how we might see it, where it might show up that then to take it up a level to then do these calculations and say, "Oh, look, I can now come up with a defensible measure of how the augmenting function is working and then incorporate that in GDP." I kind of have to say [ __ ] No, you can't because we're failing at the simple stuff.

Well, also just a very simple response is, okay, let's say it can identify them better than a radiologist, right? Now what?

Like the radiologists don't, it's they don't just look at stuff like they are doctors. They've acquired like there's more to the process than just like yes or no.

And also you are buying the experience. You're buying their experience and their connections and their ability to work within a hospital system.

And actually.

And there's and there's tremendous papers on this.

And treatment.

Oh, absolutely. Showing how models.

What do we do next?

Right. Models in general in a medical context, and this is writ large applies to models used in all complex environments. They tend to overtriage trivial cases, meaning that if you come in with like a cut, they're like, "Dude, this could be sepsis. Let's take you in and start doing tissue biopsies." And it's like, "No, no, it's just a cut. Leave me alone." And um, and at the other end of the extreme, a a woman comes in with chest well with pain in her back, which sometimes is indicative of some kind of cardiac event. They're like, "Yeah, it's probably just a strain." And so this idea of marching straight through and saying that the only thing that matters is the input data and therefore I can use these things in these complex environments. We know these tendencies towards overt triaging trivial cases and under triaging critical ones. That's also true. Just as a side note, I gave a talk about this recently to the Fed where I was showing how models do the exact same thing in financial markets where they tend to uh become overaggressive when they should be conservative and vice versa, which leads to much more fragility in financial markets. And yet, you know, we we march on. And this is the deep problem is this kind of complete misunderstanding of the nature of how these things respond in these complex environments and then the systemic consequences of doing it. Like, for example, you replace radiologists with something with a tendency to overtriage. Guess what? You're going to get far more testing, much more testing getting done, which may or may not be profitable for hospitals, but will have cascading consequences for people who have to have follow-up biopsies because of things that looked like possibly malignancies and turns out they weren't. And what we know from medicine is that for the most part, most things should be left alone.

Yeah. I and again, I keep coming back to the really simple thing which is if these things were going to replace people, they would just do it. They wouldn't be everything wouldn't read like the Riddler wrote it. It wouldn't just every every single AI jobs thing is like, "Well, it's AI-affected careers that might be doing this in this time in this way." There was a CNBC headline last year. It was like, "11% of jobs can already be done by AI." But when you looked, it was like, yeah, it was a labor simulator we made.

Right?

We didn't, like we didn't look at anything. We weren't like.

The same problem with the with the meter studies obviously in terms of the duration, right? The duration of tasks, right? That one.

Right. Right. Those ones and where the duration of tasks where you can get to a 50% likelihood of completion. And of course, if that was a human, using that as your as your as your benchmark, if that was a human, I would fire those guys, right? I mean, that's not a useful measurement in terms of how a human might think about a productive co-worker. I don't think about you half the time you get [ __ ] wrong, right? That would be kind of that would be something that would probably lead to review problems in the at the end of the quarter or year. And so, we we do what's the line? Sam Harris's line, this is playing tennis without an ad, right? Uh.

Yeah, it's it's just as.

And it's also just we treat these things like they're [ __ ] they're like gifted children. It's like, "Wow, you could 50% of the time do this maybe."

Mhm. And that is it's time for the New York Times to write an entire article about we need an Odd Lots episode that covers that 50% of the time this could do this. And it's just because you can't do the thing that every other obvious innovation has done.

You can't do it where you just go, "Wow, this does this. We could do this now." It's if this happens and that is a like load-bearing if we might be able to possibly do this. We can't measure it in the way you measure other things, which is how we would otherwise distinguish whether something was good or not. So we made up a new thing and wow has it beaten the the benchmarks we made up for it.

Right. And and the problem, of course, is is this is all becomes a bit facile and glib and everything else in terms of the arguments being made, but it has spillover consequences in the real world, which is the unfortunate thing. Is that let's follow the logic forward. If my job is I'm selling tokens and tokens, I need to sell more tokens rather than less because I have to pay the the nut on some fixed obligation. Well, I'm going to construct more data centers. I'm going to construct more larger data centers. And you end up with these massive mega projects like this controversial one that Kevin Olri has been promoting.

Mr. Dog Ship.

Right, north of uh north of Salt Lake City, you know, this that in it in the limit might be the size of Manhattan or larger as people are pointing out. This has consequences because the arrow of time only moves in one direction. I don't I defy you to find an example of, you know, the old Talking Heads song where, you know, "This used to be a parking lot," and now it's covered with flowers. The data center is not going to reverse once you build these giant things in the real world with real consequences in terms of, you know, sprawling out physically, but also sitting on top of water and power. Untangling that becomes really, really difficult. As does, for example, uh having to spin up all of these new natural gas plants to power these things because we're increasingly asking that hyperscalers come with their own power behind the meter.

Yeah.

Right. Right. Right. When we're doing that at the worst possible time because b the combination of batteries and um alternative sources ranging from uh wind and solar, for example, are becoming much more effective and able to be more persistent with battery backup. And yet we're installing these CO2 intensive things with 30 and 40-year lifespans funded by debt that are almost all likely to end up being stranded assets. Like this, they'll be like the statues at Easter Island eventually, except natural gas plants.

Well, that's and this is what I've been saying. It goes back to the dot-com thing I was saying. It's not like like an incomplete data center, which I think the vast majority, I don't think any of these things get the mo, vast majority of them don't get fully powered. Like that's.

I think anything that's targeted over a gigawatt doesn't get finished.

It fully agree. And the funny thing is with that is people like, yeah, the.com bubble when it burst, people had the useful infrastructure that will cost just as much to finish in the future. Except the de, you'll go to a credit for, you go to a well, probably not private credit in the end of this, but go to a bank like, "Yeah, I want to finish this data center." They will shoot you with a gun. They will, you will get headshotted by the bank manager for saying the words AI. It's just. And it's very. I'll give you an even more it's an even more insidious than that. And I spend a lot of time talking trying to talk off the ledge, if you will, various uh regional economic development people. I was just talking to some people in New Mexico about this. And the problem they have is, you know, they they've been trying to land some large employer for 25 years in these high unemployment regions. And so I'm entirely sympathetic to the problem that a data center hyperscaler shows up and says, "Listen, let me install this. Give me the following, you know, giveaways with respect to taxes and I'll you this will eventually after construction, we'll have this many jobs and so on." And you don't have to keep fighting for the Hyundai battery factory or the Ford assembly plant or whatever else. It'll just be here spinning off tax revenues. And so what happens is a that looks like a pretty good bet because it's a fixed obligation in terms of what will be flowing back into your county for years to come. And what do they do then? They start pre-budgeting that and saying, "Okay, we'll start building new playgrounds. We'll start fixing the water supply. We'll be able to fund schools." Great. Now, okay, you've frontloaded all of that stuff. What happens whenever the data center doesn't get finished? You're actually in a worse situation than you were previously. So, it has real-world consequences in terms of these annuity streams that are being dangled in front of people whose regions have suffered economically for decades. And that's going to be the the story over the next 25 years.

Yeah, it's going to be years of data center collapses. Even after the AI bubble bursts, in my opinion, there there's going to just be years of this because you're already seeing a lot of this stuff is speculative. And even then, even if these things get turned on, as you said at the beginning, we are in an era where people are going to be trying to cut back on costs. But then there's the really basic answer. What do more data centers do? What do we get out of these? Because OpenAI has more compute than anyone. What are they doing? What's different? What's the difference? What what do what I keep hearing the term AI factory and I'm like, "What do you mean? What do you mean by that?"

Or a factory full of geniuses. That's my favorite.

Oh, the data center. A Oh, jeez. A data center full of geniuses. I I really dislike Dario Amodei. I hate how he sounds. I hate how he speaks. Just like, "No. What are you [ __ ] talking about?" Because more data centers so far has not actually improved these products. It's not like there's not I if you gave OpenAI another 15 gig of data centers, doesn't exist, but let's say they did, nothing like nothing is going to change about this.

Yeah.

I I don't. And I don't think I but the other thing is as well, hey uh is Vera Rubin going to make AI profitable? Because if it isn't, this is probably the last generation. That is. I think at this point, the thing.

I I think very much so. And I think that's one of the other consequences here that's going on. And I think it's part of the one of the reasons why, and I don't know if you've noticed, but that Jensen has be has gone from being very promotional to extra special, very to the third power promotional in terms of I saw today he was anointing Marvel as the next trillion-dollar company. And for me, this is really unprecedented. And but it only works if you start thinking about it in terms of the ecosystem of buyers and sellers in the context of AI capex and realizing that the more valuable all of these companies become, the more money is sort of flowing around this what used to be called like a a captive economy. And then it just recirculates amongst all the players as they become increasingly wealthy because their stocks get bid up. And so this notion of having people suggesting that one of their sort of peers or quasi competitors should also be valued at a trillion dollars is really unprecedented. And it's only un you can only really understand it once you understand it that they are all essentially running printing presses in their basement. And the printing press is their stock. And they're hoping that the value of the printing press and the currency keeps going up. And that way they can circulate more script script among them, which in turn turns into purchasing. And that's the that's the.

fundamental circularity at the core of all of this. So as we wrap up, I wanted to get like, because I've already had emails and texts, somehow I don't know how they got my number. Um, what do you think of this goo? What does this Google thing mean? So Google doing their $80 billion raise at the market. What does this tell you?

>> Well, that they uh, a couple of different things. One is that this is, this is the equity raise.

>> Yes, exactly. So 10 billion from Berkshire.

>> And then some other like 10 billion for Berkshire and then I think two different at the market sales.

>> Yeah. So I mean, so this, it tells you that the appetite continues to be incredibly high for their pay for for non for equity, which is surprising because for the most part, the the funding has been increasingly moving towards credit, obviously, right?

>> And because the saturation of their cash cash flows with respect to having to sort of uh inoculate themselves against all of the other commitments they have. My favorite example being that you, Microsoft's a good example of this is that their stock-based compensation is so high that they have to, which is obviously uh only handled through cash flows that the way they they inoculate themselves against it is they have to do stock buybacks. And once you start doing that, you've got a much larger commitment of cash, which forces you after you pay for hyperscaler data centers, you then have to start doing raises off balance sheet using SPVs and uh other kinds of funding vehicles. So that they're able to do this is sort of surprising to me to a degree that there's still this much appetite for, you know, non-credit equity financing of of some of their future obligations because it gives you no call on future cash flows. So what's in it for you as a provider of equity here? It's not clear.

>> Yeah. Is it also a sign that the debt is running out?

>> Like, why would they do this instead of raising debt?

>> So there's no question about that as well. So that's the other side of this is that as of Q1 2020, what year are we in? 2026. I have to look around the room. That's bad. Um, so as of Q1 2026, we're the hyperscalers are now the largest issuer of investment grade debt uh on in investment grade markets worldwide. They just passed the banks. So yes, the other answer to this question is is there is a capacity issue with respect to the further issuance of of investment grade debt. In a weird way, they would actually be better if they were issuing junk uh high yield because there's a higher appetite for high yield, but they just so happen to be currently anyways prime credits. So they're issuing investment grade and the appetite for that stuff is finite, which is why increasingly the marginal buyer for the the most recent credit issuances from the hyperscalers is the usual suspects like, you know, European insurance funds, uh Middle Eastern sovereign wealth. These are the people who, you know, famously tend to show up at the end of almost every bubble and so there here they are at the door again.

>> So yeah, this is you think do you think that this is toward the end?

>> I'm not asking for a harden.

>> No, no, no, no. I I think very much that I think the the blowoff top is the is the these year's three mega IPOs and and that kind of marks the the gonging of the bell with respect to take the seriousness with respect. You have to take this inability of these companies to make money.

>> Paul, it's always such a pleasure to have you. Where can people find you?

>> Uh polcadoski.com is the best place. Hell yeah. Everyone, thank you so much for listening. I'm of course Ed Zitron. You can catch me on this podcast, Better Offline. Where's your Edith? Subscribe to newsletter, my principal form of income. I will be back with a monologue on Friday. Thank you all for listening and goodbye.

Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Oski. You can check out more of his music and audio projects at matasowski.com. m a tt oso wski.com. You can email me at easy@betoffline.com or visit better offline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat.w's.app to visit the Discord and go to r/bettoffline to check out our Reddit. Thank you so much for listening.

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