Why AI Has No ROI with Paul Kedrosky

27:23And it's up something like 200 % 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 has it led to? Because producing more stuff that shows up on GitHub is just a need-immediate 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, they used iOS apps, Android apps, 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.

28:00So 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 sort of slop everything, slop apps, slop content. And so you're flooding and commoditizing 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 is increased, nor does it care. And that's what this paper shows.

28:36And I think the idea that we're doing all of this work and what's increasingly become expensive work, using tokens to produce things and makes coders very happy, having things running in agentic loops. But the broader economy doesn't give a fuck. and that have by any chance did you read semi-analysis is ai dark output i did yes 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 out ai's output is sold at a visible price, only token spend is captured in GDP.

29:21By which they mean, we don't actually measure whether something is good. We just measure whether something. It's actually so... This is the shit a teenager would say when lying about having a girlfriend. Yeah, this is voodoo teen economics. Yeah, it really is. And again, it goes to that National Bureau of Economic Research paper. It's exactly the same thing. I was mentioning at the top, there is this tremendous, and 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. Yeah, yeah, yeah. A couple of days ago. And they argue that essentially AI, quality adjusted AI output is up more than 2 ,000 % per year.

30:05They come up with estimates of like, you know, 250, 300 billion dollars on top of the, but they essentially come to the conclusion that this is all true as long as you accept our redefinition of GDP. And of course, 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 often called motivated reasoning. I need to believe this, therefore I construct an argument to allow me to continue to believe it in the way I get there is by redefining a variable that's already very squishy in the first place. Let's not pretend that measuring GDP is much easier than measuring muons in a cloud chamber or something.

30:51It's still very hard. And you're trying to make it harder to justify something that's just not defensible. And the AI dark output one is great because 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 one and a half trillion dollars in tasks that current AI could substantially augment or automate. To which I say, why hasn't it done it? Right. This 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.

31:30It's doable, but it's not like 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, they 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...

32:25Oh, absolutely. It would be very easy to see.

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34:58I'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 undamed but has a popular newsletter and used to work at a certain venture fund, put out the radiologist paradox, which was the idea that back in 2016, Jeffrey Hinton, computer scientist and Nobelist, early pioneer in image models and deep neural networks, said in a talk that within five years, if not ten years, large language, deep at the time, neural networks learning models would be better than radiologists, and there's really no reason to continue training them.

35:40Now, of course, he said ten years on the outside. Well, it's now ten 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, Kotu or someone else, and said like, well, checkmate, Jeffrey Hinton, look, we have a lot more radiologists. And of course, this is a classic example of a profound misunderstanding of so many things it's hard to keep track. One is that, again, it's not clear that being selectively better than radiologists at certain things like identifying, I don't know, prostate cancers or whatever else, obviously that's not good enough.

36:20Radiologists 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 a significant slice of what radiologists do, it's a misunderstanding of the nature of the markets themselves. So it misunderstands both the technology and the nature of cartelized employment markets, these kinds of arguments.

36:58And yet, it's used as an example of how the inexorable march of these things continues apace and it will always be augmenting. I just think there's so many sort of nested misunderstandings of what pressures AI is having on employment markets and 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 bullshit. No, you can't. We're failing at the simple stuff. well so just a very simple response is okay let's say it can identify them better than a radiologist right now what like the radiologists though it's they don't just look at stuff like they are doctors they've required 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 there's and there's tremendous papers on this and treatment oh absolutely showing how what do we do next right models in general in a medical context and this is writ large applies to models use in all complex environments they tend to over triage 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 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.

38:36And 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 over-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 Now, models do the exact same thing in financial markets where they tend to become over-aggressive when they should be conservative and vice versa, which leads to much more fragility in financial markets. And yet, you know, we march on.

39:10And 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 over-triage. 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 look 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.

39:43Yeah. 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 i keep saying this but everything wouldn't read like the riddler wrote it it would 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 percent 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 we didn't like we didn't look at anything we know we didn't like it was the Same problem with the meter studies, obviously, in terms of the duration of tasks.

40:28The METR, right? Right, right. And 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 benchmark, if that was a human, I would fire those guys. 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 just half the time you get shit wrong. That would be something that would probably lead to review problems at the end of the quarter or year. And so we do, what's the line? Sam Harris' line, this is playing tennis without an ad, right? Yeah. There's no real hurdle here.

41:04And it's also just, we treat these things like they're fucking gifted children. It's like, wow, you could 50 % of the time do this, maybe. and that is it's time for the new york times to write an entire article 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 right 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.

41:51So we made up a new thing, and wow, has it beaten the benchmarks we made up for it. Right, and the problem, of course, is this 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 I need to sell more tokens rather than less because I have to pay the nut on some fixed obligation. Well, I'm going to construct more data centers and construct more larger data centers. And you end up with these massive mega projects like this controversial one that Kevin O 'Leary has been promoting.

42:32I missed the dog shit. Right, north of Salt Lake City. That in the limit might be the size of Manhattan or larger as people point out. This has consequences because the arrow of time only moves in one direction. I defy you to find an example of the old Talking Heads song where 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 sprawling out physically but also sitting on top of water and power, untangling that becomes really, really difficult. as does, for example, 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.

43:22Yeah, behind the meter, yeah. Right, right, right. When we're doing that at the worst possible time because the combination of batteries and alternative sources ranging from 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 they'll still 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.

43:57It's not like an incomplete data center, which I think the vast majority, I don't think any of these things get finished. the vast majority of them don't get fully powered. Like, that's for sure. I think anything that's targeted over a gigawatt doesn't get finished. I fully agree. And the funny thing is with that is people are like, yeah, the dot-com bubble, when at first people had the useful infrastructure. That will cost just as much to finish in the future, except you'll go to a credit fair. You'll 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.

44:30They will shoot you with a gun. They will... But you will get headshotted by the bank manager for saying the words AI. It's just, and it's, these things are going to be everywhere. 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 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'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 giveaways with respect to taxes, and 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.

45:24It'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 front-loaded 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.

46:01And that's going to be the story over the next 25 years. Yeah, it's going to be years of data center collapses, even after the AI bubble burst, in my opinion. 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 open ai has more compute than anyone what are they doing what's different what's the difference what what does what what i keep hearing the term ai factory and i'm like what do you mean what do you mean by that oh are factory full of geniuses that's my favorite oh the data center i oh geez a data center full of geniuses i i really dislike dario amaday 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 open ai another 15 gigawatts of data centers doesn't exist but let's say they did nothing like nothing is gonna 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 gonna make ai profitable because if it isn't this is probably the last generation but that is i think at this point the thing 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 One of the reasons why, and I don't know if you've noticed, but Jensen has gone from being very promotional to extra special very to the third power promotional.

47:47I saw today he was anointing Marvel as the next trillion dollar company. For me, this is really unprecedented, 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 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 peers or quasi-competitors should also be valued at a trillion dollars is really unprecedented.

48:26And 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 among them, which in turn turns into purchasing. And that's the fundamental circularity at the core of all of this. So as we wrap up, I wanted to get, because I've already had emails and texts somehow, I don't know how they got my number, what does this Google thing mean? So Google doing that$80 billion raise at the market.

49:03What does this tell you? Well, a couple of different things. One is that this is the equity raise. Yes, exactly. So$10 billion from Berkshire, and then some other, like$10 billion from Berkshire, and then I think two different at-the-market sales. Yeah. So, I mean, so this tells you that the appetite continues to be incredibly high for equity, which is surprising because for the most part, the funding has been increasingly moving towards credit, obviously, right? Yeah. And because of the saturation of their cash flows with respect to having to sort of inoculate themselves against all of the other commitments they have.

49:43My favorite example being that Microsoft's a good example of this is that their stock-based compensation is so high that they have to, which is obviously only handled through cash flows, that the way 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 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 non-credit equity financing of some of their future obligations because it gives you no call on future cash flows.

50:22So 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? 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. So as of Q1 2026, the hyperscalers are now the largest issuer of investment-grade debt 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 investment grade debt.

51:00In a weird way, they would actually be better if they were issuing junk, 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 most recent credit issuances from the hyperscalers is the usual suspects like European insurance funds, Middle Eastern sovereign wealth. These are the people who famously tend to show up at the end of almost every bubble and so here they are at the door again. So yeah, do you think that this is toward the end?

51:38I'm not asking for a hard one. No, no, no, no, I think very much that. But I think the blow off top is this year's three mega IPOs. And that kind of marks the gonging of the bell with respect to... 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? PaulKadrowski.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 Ed.at? Subscribe to the newsletter, my principal form of income.

52:12I will be back with a monologue on Friday. Thank you all for listening and goodbye.

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53:28We'll see you next time.

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