The Reality of AI Economics With Paul Kedrosky

In this week’s Better Offline, Ed Zitron is joined by economist Paul Kedrosky to talk about the large amount of speculative data center land purchases, the brittle NVIDIA GPU economy, and the economic realities of AI. https://paulkedrosky.com/ The Nick, Dick and Paul Show: https://www.youtube.com/channel/UCFbDiETo29GTIjg6Lk4imig Save $10 off a year of my premium newsletter: https://edzitronswheresyouredatghostio.outpost.pub/public/promo-subscription/gzqwkv54e1 - I’d be so grateful! YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more. Buy our new “FUCK DATA CENTERS” shirts today! --- LINKS: https://www.tinyurl.com/betterofflinelinks Newsletter: https://www.wheresyoured.at/ Reddit: https://www.reddit.com/r/BetterOffline/ Discord: chat.wheresyoured.at https://discord.com/invite/QUUQUP9szv Ed's Socials: https://twitter.com/edzitron https://www.instagram.com/edzitron https://bsky.app/profile/edzitron.com https://www.threads.net/@edzitron Email Me: [email protected] mailto:[email protected] See omnystudio.com/listener https://omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 1:07Speaker 02

  Call Zone Media.

• 1:11Ed Zitron?

  Hello and welcome to Better Offline. I'm your host, Ed Zitron.

• 1:26Ed Zitron?

  Download a T-shirt and subscribe to the newsletter. That's where you get your words. But today, you're here for the noises. And joining me today is the wonderful economist, Paul Kudrowski. Paul, good to have you on. Hey, Ed. Good to be here. So your recent work on AI, particularly the data center and economics side, it's been great. And one thing I really want to talk to you about is what actual economic effects have you seen from AI? Because it feels hard to get specifics sometimes, if you know what I mean.

• 1:53Speaker 04

  Yeah, so it's pretty easy to find it. I mean, there's the old joke, which was the early days of the technology industry, that you could find technology everywhere except for in the productivity data. And so that sort of applies right here as well. So the answer you'll get is two answers. One is that it's too soon to tell, which is fine, but it's a little bit of a sop, right? So you'll get that. And then you get the second answer is, which I already see it, it's in, and then someone will ad hoc cherry pick some data and say that's that there goes AI right there. And the answer, of course, is is that it's nowhere to be seen yet in any really meaningful productivity data anywhere. You have some output metrics, like, for example, you know, this incredible number of commits you'll see on GitHub using agentic code. But is that productivity? I think it's largely masturbatory. I don't think it's actually

• 2:38Ed Zitron?

  Yeah.

• 2:39Speaker 04

  I always productivity. And so most of what got me interested was because you see it all on the other side of things. So from an economic standpoint, you actually see it in the economic data because of how dominant it's become in a couple of statistics, like, for example, its share of non-residential fixed investments, which is at levels we last saw with the railroad build out or with rural electrification.

• 3:00Ed Zitron?

  Can you be a little more, can you be specific what non-residential investments mean? Just, just so I get you.

• 3:06Speaker 04

  So atoms that aren't in houses. So that's the short answer. So, so factories, manufacturing, highways, you know, fixed, fixed equipment you're putting in your, in your, in anything that you're building for an economic purpose that isn't residential. Okay. So that usually is a fairly broad and diversified category. So you've got people building out, you know, if the current administration had its way, that statistic would currently be dominated by fill in the blank manufacturing, right? Because the attempt is to onshore manufacturing. So that if, if policy was working in that regard, you'd be seeing the dominant chunk of that being the onshoring of manufacturing as it came back to this, to this blessed

• 3:47Speaker 04

  country. And so it's not the largest chunk of non-residential fixed investment currently, which is, is data centers, which is a basket of things, which is made up predominantly of GPUs, but also the build out of the, of the, of the facilities, HVAC, heating, ventilating, Cooling, all those kinds of things. So what got me interested originally was I couldn't see AI data anywhere except for in these categories of fixed investment. And then even more startling to me, which apparently I was the only one initially startled, then I startled other people, was the idea that it was the largest share of US GDP growth for three of four quarters last year. So that in the absence of this non-residential fixed investment wonder drug we call data centers,

• 4:32Speaker 04

  US would have been in recession in the first quarter and the fourth quarter, neutral in the second and mildly positive in the third. So it was the economic story of 2025. And so the reason why this is important and the analogy I make all the time is, my dog barks when the mailman comes to the house and the dog keeps barking and the mailman goes away, right?

• 4:54Ed Zitron?

  Right.

• 4:55Speaker 04

  The dog thinks he did it. He didn't do it, right? He has a messed up, he has a messed up model of causality is what he has, right? The dog causality implies that my barking made the mailman go away. No, the mailman goes away every time. It's got other things to do, right? So the same thing is true with respect to fixed investment. If you don't realize that the largest share of fixed investment and the thing that's driving US GDP growth is this wonder drug called data centers, you're the dog barking at the mailman and the mailman goes away anyway. You don't actually understand the things that are driving economic growth. So there's, the long answer is that's where you see data centers in economic data and it's in this very strange place that was largely being missed for the longest time.

• 5:38Ed Zitron?

  So with that number, that includes all of Nvidia's sales all told or just the ones in going to American clients? Or is it just all of their sales?

• 5:50Speaker 04

  No, so you parcel it out, obviously. So by geography, so it matters immensely what's happening in the US. Now, granted, the predominant share of Nvidia sales, just like the largest sales of most of, you know, transformers and everything, are all happening in the US, right? As this build out happens, something like 70 to 80% of the global data center build out is happening in the US and most of that is happening in Northern Virginia or Texas. So it's largely a US phenomenon in the first place, but nevertheless, you have to parcel out the pieces verbally.

• 6:17Ed Zitron?

  Yeah, I just meant it more as when Nvidia makes a dollar, does that count into this? Because

• 6:22Speaker 04

  Yes.

• 6:23Ed Zitron?

  Because, see, that's the thing. That feels like something that is just kind of almost like a a load-bearing chip, a load-bearing GPU. Like, if these sales go down, that's bad for everyone.

• 6:36Speaker 04

  Right. And there has been some tremendous piece, The Wall Street Journal had a piece last week, I think, and I said some Selena and Sanyi things in it. But it was about how Nvidia kind of sits at the center of this, like, you know, Don Carleone and seeing with everything happening and everyone coming to the table. And there this, you know, mafia Don who is investing in things, right? So they play the role of investor, they play the role of acquirer, they play the role of, you know, vendor. So they have this incredible hub role. So each dollar that they're putting out there in a sense is vastly more important. Because in a sense, they are the load-bearing beam in the middle of all of this. They are for now, and it's changing rapidly, but nevertheless, they are for now.

• 7:14Ed Zitron?

  The thing is, that just feels very unstable to me because Nvidia, from what I've worked out, for them to keep growing at their current rate, they're going to be selling $120 billion of GPUs by, in a year. And then in like Q2 FY28, I think it'll be next year. And that's just, that feels impractical, like just on a, on like an economic level for any country or anyone investing.

• 7:39Speaker 04

  I have a tendency to make that argument, and I hate when I do it because, and this is, so we both had fought for this. Is this Paul's argument from personal incredulity? I don't think it can happen, therefore it can't happen. So let's take them at face, so let's take them at face value. Let's say they're right, right? See, this is what's going to happen. You have to look at the dynamics, and they conceded this at GTC, their most recent conference. The dynamics are changing quickly in the marketplace that's driving two things, sales growth and margins. So Nvidia has these anomalous GPU margins in excess of 70% gross margins, which are ridiculous.

• 8:12Ed Zitron?

  And that's to make them, just to be clear.

• 8:14Speaker 04

  That's right, exactly. And so their margins are very high, but they're in the middle of this transition, this admitted transition from what's euphemistically called training, which is kind of a misnomer, to this thing that's called inference, which is probably more accurate. So from training models to answering prompts, right? And the margins on inference are going to change dramatically because the things that gave them, and this is one of these Silicon Valley silly words, but gave them a moat, the things that gave them a moat in the world of training are far less important in the world of inference. And so you're seeing this proliferation of new chip companies coming to market, incumbents with new products. So they're facing much more competition in a world of inference. So even if you grant that the market's going to grow as large as it once did, which is

• 8:59Speaker 04

  whatever, it's not going to all go to them. They're not in the same position to accrue all that benefit that they did, almost accidentally in the world of training, which is really an important point as well.

• 9:09Ed Zitron?

  See, that's the thing. I'm also questioning the demand, and I also question whether they're done with training, because training, you correctly said, is a misnomer, because that can mean everything from a big pre-training run to the post-training that's necessary to make these things work. And it's kind of confusing at the moment, because like last year, they were saying it's all inference all the time. This year, they're kind of talking about open claw. Kind of feels like they're a bit lost, which I mean, is kind of the AI industry at large. It's just so, it's so strange.

• 9:40Speaker 04

  The way I look at it is I always, whenever someone says, um, Nvidia, I say Saudi Arabia. And when they say tokens, I say Humvees, right? So their goal is to get more people, their goal is to get more people purchasing Humvees because it's good for them, because it consumes the thing they, however indirectly produce, which is to say these things called tokens and not the cryptocurrency. So if you think about it in those terms and translate Jensen's, you know, GTC talk and most of the things he says, it doesn't read that differently from a random industrial minister in Saudi Arabia saying, you know, this oil stuff, if you guys just back away, back away from the EVs before it hurts you, get out there in the Humvees, so you're safe on the freeways. It can kind of feel equivalent.

• 10:24Ed Zitron?

  And did that actually, was that something that happened, was that like 2008? I remember there was like the stories about empty, like parking lots full of just unsold cars.

• 10:35Speaker 04

  Yeah, yeah, yeah.

• 10:35Ed Zitron?

  Right?

• 10:36Speaker 04

  Yeah, no, no, exactly. So, so, so you, I think you have to translate a lot of Jensen's speak into this kind of idea that there is this new commodity emerging, no different than oil, no different than, I don't know, copper or whatever else, and this new token is this, or this new commodity is this thing called tokens? And he's doing what he can as a, as a diligent ambassador for the, for this commodity called tokens to make sure people use as much as possible, like for example, endorsing this completely half-assed, wild-eyed thing called OpenClaw, which is a ridiculous idea to suggest that people should be using this in their house. It's like I should have, you know, my own sort of, you know, home nuclear reactor. It's, it's, it's a ridiculously dangerous technology for most normals to be using.

• 11:18Ed Zitron?

  Yeah, I just, I, I also feel like it's a sign they're a little washed when it's, you've got a 3D, a AI-generated picture of Jensen Huang, the CEO of a company with a multi-trillion-dollar market cap with crab claws. Like the indignity of it, he wears $7,000 jackets. My man, my man should have a little more swag than claws. It's just-

• 11:39Speaker 04

  Those are tremendous jackets, though.

• 11:41Ed Zitron?

  Oh, tremen-, they are tremen-, I had the menswear guy on a few years, amazing jackets. The man does have good, he has a good tailor as well. But anyhow.

• 11:49Speaker 04

  So, yeah, so I think that's, that's a really important point you make, though, that I think you have to, when you, the read-through on OpenClaw isn't just some confusion about where the market's going, but the read-through is the promotion in almost an industrial affairs level of this commodity called tokens. And how can I get people to use more of this? So that's why you hear, you know, song and dance acts about OpenClaw, these incredible hyperbole about Claude Code and how Claude Code is going to rapidly migrate from the world of software into all of white-collar work, which is again an error. It's not to say Claude Code isn't a really interesting and important piece of technology, but people are very misguided about how these technologies are going to move or can move or will move from a world of software, which is wildly anomalous in terms of both the amount of tokens it produces,

• 12:35Speaker 04

  but also in terms of whether you can leave it alone. So think about it, the way I sometimes think about it is in terms of the idea of like a ground truth. I can look at my software and I create some code and change it and it breaks. In AI terms, that's a really tight gradient descent, meaning that obviously I've just learned something really quickly. Changing this to this doesn't work. In most of white collar work, that's not true. What matters is I create a PowerPoint presentation, does my boss like it? Tell me the gradient descent there. There's no gradient descent, it's very subjective, it's almost an aesthetic answer. There are parts of white collar work where that's not true, but much of white collar work doesn't have the same characteristics as software. So if you want to project the kind of growth that people like Jensen are projecting, you

• 13:18Speaker 04

  need to believe that these harnesses, Claude Code, Codex, blah, blah, blah.

• 13:23Ed Zitron?

  Yeah, the things that you use with the models.

• 13:25Speaker 04

  Yeah, yeah, yeah. You have to believe that like the velociraptors in Jurassic Park, that they can escape containment, that they're going to escape containment and they're going to get out of this corral that we call software and they're going to be everywhere. And not only are they going to be everywhere, which is happening to a degree, they will act in the same way, which is to say they will produce huge amounts of code for tiny or huge amounts of output for tiny input and they can be left alone because there is this tight gradient descent that tells them whether what they're doing is working or not.

• 13:53Ed Zitron?

  The thing is, they don't know if that, like, they don't know anything, so you can't guarantee that.

• 13:57Speaker 04

  Right, right, because there's no ground truth, right? So this gradient descent doesn't work. It doesn't work in almost any domain outside of software. So the weird thing is, is we've actually started off in the nearly perfect domain to give a completely unrepresentative example of what the future looks like. We start off in coding, which is why coding or the coders and developers are some of the biggest sort of flag-waving ambassadors for what's going on. It's like, just wait till this shows up in, you know, I don't know, pick it, pick your domain in white collar work. And the reality is those domains are very different.

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• 17:44Ed Zitron?

  It is strange as well, like the you get very few people who are just like, yeah, I kind of like this. It's either the pure hatred, which I name my listeners are definitely, or there's just this cult like thing around it. I've never, I've been around tech for a while. You've probably been around it longer. Yeah, I've never seen anything like this. And I've been on web forums for games consoles. I've been on, I've been on, I've used to, because I'm a strange person, read bodybuilding forums and BMW forums. And the arguments on there were the same. Like if you don't drive an M3, I will run you over with mine and that kind of thing. I've never seen anything like this happen. Like maybe in stocks.

• 18:25Speaker 04

  Yeah, in stocks to a limited degree, right? So there's these two overlapping phenomena going on, I'm convinced. One is this is a tribal signifier. I show what tribe I'm in by, by, by being as, you know, wild eyed as possible about my support for this shows I'm in the in group or the out group. And you see that, right? This sort of tribal signifier stuff. And the other one is, and this is the one that I find most fascinating, is this kind of frantic quest to believe that if we work hard enough and fast enough, that I'll never have to work again. And you hear this from the people in the acceleration, on the acceleration camp, the singularity camp, that when you penetrate deeply enough, it's really just, I think if I'm kind of creating, think of me as a bomber and I've

• 19:08Speaker 04

  got a vest, I've got a vest attached with all these explosives and I'm threatened to Walk into your economy and blow it all up, right? And just try and stop me because there's hundreds of us doing this. We're all going to come and we're all going to blow up your economy. And what are you going to do about it, right? And so that's what I think a lot of this is because they feel like if I do that, then the government has no option but to institute some kind of broad policy of support where I can then go out and, you know, make figurines all day or whatever it is I want to do.

• 19:33Ed Zitron?

  Yeah, but I will also say that the people who are most excited about AI don't seem to have other hobbies.

• 19:39Speaker 04

  That's always, that's always the thing. It's totally true. What do you plan to do with your spare time?

• 19:45Ed Zitron?

  I'm learning piano right now. I'm learning to code for fun. And it's like, I look at these people and they're like, what do you do? I run 17 subagents an hour. If these things, if these things don't create my special project that I'll never show you, I will die.

• 19:58Speaker 04

  I like to gray out, gray out the power in my neighborhood by running as many sub-processes as possible. Yeah, great hobbies. Get on that.

• 20:05Ed Zitron?

  Just, I also think that people, I just did a, an episode about this, actually. I think people also assume some too big to fail thing will happen, even though that's just too big to fail. And the great financial crisis was so much bigger and so much worse. And also very different.

• 20:21Speaker 04

  Yeah, no, the, the financial scale was very different, which is funny because, you know, early on in this, one of the things that I found most striking was how quickly. So initially people were arguing to me that Paul, don't worry, you're pretty little had about this because these are big boys and girls who are spending all this money, the hyperscalers, the Microsofts, Amazons, Google and everything else. What they spend on their money on should be no concern to you because these are big and profitable companies, well, leaving aside OpenAI and Anthropic, but that are big and profitable companies. How are you to tell them what to do? And then I was like, so I said, you know, fine, it's, it's a somewhat self-serving argument. It's my job, but fine. But then it rapidly changed, right? Because halfway through last year, maybe in the second quarter of last year, suddenly the cash needs of building out these data centers exceeded the cash flow.

• 21:05Speaker 04

  I should say the unencumbered cash flow of these large and profitable companies because they have other uses for cash flow. So you started moving more and more towards external sources of capital. Private credit most notoriously, but also a host of other special purpose vehicles and other off balance sheet financing structures to the point that by the end End of the year of 2025, a data center related debt was the largest chunk of investment grade debt issued in the US in 2025. Tech used to be the no debt sector. They went from no debt to the largest issuer of investment grade and non-investment grade debt in the United States last year. And of course, all the way along, people normalize it and say, you know, it's fine. They're very profitable.

• 21:46Speaker 04

  And then when it turned out the profits weren't enough to pay for all the data centers, that's also fine, right? So those these things end up being fine. And then, of course, it all came home to roost to a degree at the end of the year as private credit started sort of got attacked from through the side door because of this problems with this, you know, very large positions they held in software as a service companies, which it turned out were at least seen as threatened by AI. So that's the first shoe to drop on this stuff. But there's still another shoe to drop, which is the overexposure of private credit to data center related debt. Because if you think about it, it's not the consequentiality of it. It is something like the global financial crisis. The more entertaining part of this is they're treating data centers as real estate.

• 22:25Speaker 04

  They look at data centers as being like apartment buildings with who the hell knows what's going on inside, but they're good for the rent, right? So this is the way they look at, at this way they look at data centers. But the problem is, the thing that's generating the income is inherently deflationary, hyper-deflationary, falling 70 to 80% year over year. These are tokens. So the idea that you can, you're having to pay a fixed obligation, the, the, these notes that have been issued with respect to the debt to finance data centers with a thing that's falling 70 or 80% year over year in price, just try and run an auto company that way with a significant down.

• 23:01Ed Zitron?

  But the price of tokens coming down wouldn't affect GPU compute in that way, though. Also, that price coming down isn't necessarily a result of cost savings, but it's a result of the companies cutting the prices. Isn't the problem that they're also full of these depreciating GPUs as well?

• 23:18Speaker 04

  Yeah, yeah. So it's, there's a double whammy. There's both sides of it, right? So to, to a degree, if you're, if you're working with a model directly through the API, which the largest issuers are, were, or at least if you're in a production position, you're actually paying an API price which tend, which is. Metered at the token level. So you do see those token prices directly. And then secondarily, you have the problem that the GPUs themselves, insofar as their, you know, capital investment around which the investment is predicated, the depreciation of those items is relatively rapid to it. And the analogy I always make to people is that it really depends on what they were used for historically. So if they're just being used for inference, to a degree, they have a longer lifespan. But if they were ever used for training,

• 23:58Speaker 04

  which is like flat out pedal to the floor, 24/7, huge workload, it's kind of like, you know, you had a car that was only driven to church on Sundays and a car that was raced at Le Mans one weekend. They have the same number of miles. I know which car I want. The same thing applies to GPUs.

• 24:15Ed Zitron?

  Yeah. And the other thing as well is, I've really been looking at this. So you've you brought up the WoodMac study, which was awesome. I don't know if you saw the Sightline Climate one where it was like, of the 16 gigawatts that were meant to come online this year, only five are actually under construction. I think that there's a big problem with just the speed of the rollout and the upgrade cycle, because we are still going to be installing Blackwell GPUs into 2027, if not 2028. That's insane. That like that it I worked it out as it's like, it takes six months to install a single quarter's worth of GPUs. But it's like, how at some point, Nvidia has to slow, not even because of me wanting it to or not,

• 24:58Ed Zitron?

  but because where are they going? Like, where are we putting these things? I mean, Taiwan warehouses, I guess.

• 25:06Speaker 04

  Yeah, Taiwan warehouses. Yeah. And that's a big problem is the build out. So this this problem of and and one of the Northeast, I've forgotten which utility in the Northeast just recently put out some data on this, showing that something like the data you suggest, which is like 25% of the total commit was actually ever produced and likely will ever be built out. And that's in part because a lot of these things are speculative projects, naming no names. There's a very large Texas company that's doing this directly. The that is a very speculative position. We're gonna, we're gonna power it all behind the meter with nuclear reactors and all these kinds of things. But this is a, this is, this is a game we've seen back to, I mean, the analogy I make all the time is back to Chinatown. This is like Chinatown, right? Where I'm, I'm buying up real estate with numbered companies in hopes

• 25:48Speaker 04

  of securing water rights. I saw this. I saw this when Jack Nicholson was wandering around, Jake Giddes was wandering around in the deserts of California.

• 25:57Ed Zitron?

  What do you mean the Chinatown example?

• 25:59Speaker 04

  So what's happening is increasingly a lot of what's going on under the hood here that's creating the impression of a build out that doesn't exist are these things called powered land companies. So powered land companies are these speculators, they don't call themselves that, who look for strategic locations where using numbered companies, they can purchase real estate that has access to peering points. So high speed interconnection to the

• 26:20Ed Zitron?

  What is a numbered company as well? I'm really sorry.

• 26:23Speaker 04

  So a company that doesn't make it obvious to who the actual direct owners are. So it's not clear what their purpose is. So like, you know, think of it as like Cayman Islands, but it doesn't. So the idea that you're trying, you're trying to at least loosely obfuscate who the, what the ownership and purpose are. But even that's less important. The idea, though, that they're buying on a speculative basis, this tracts of land could be hundreds of acres in some location that has access to power, access to water, potentially access to a peering point, a high speed interconnection point to the broader internet. And then they, then they lock that up. And then they go out and say, okay, I've got this position. You guys need to build out more data center capacity. I'm talking to the hyperscalers. Look at me. You got nowhere else to go. I've locked all this up early on, kind of like locking up the water for the orange groves. Right.

• 27:08Ed Zitron?

  Is that, is that widespread? Is that?

• 27:10Speaker 04

  Widespread.

• 27:11Ed Zitron?

  You think? That's so bad because I've been, this whole time I've been looking for the speculative part. I will fully admit that's been it. If it's the land, that's not great for anyone involved. But even then, though, GPUs are still being sold. Like that, that's the real thing that's getting me.

• 27:28Speaker 04

  Right. But part of the problem is, and there's, it was a great piece from Trend Force, I think it was the other day, one of the market research firms in this, they're pointing out how, how, how many firms are now buying LTA's, long-term purchase agreements? Because they, they're being told that if they don't lock in demand now, lock in now, they won't get product in two years. So this, this is the other layer, that a lot of what you're seeing as purchasing is completely speculative by people who are worried that they don't lock in a long-term purchase agreement now, they will never get supply later. So that their approach is, I'll worry, I'll worry about that other stuff later on, but for now I need to sign up. So that's the other piece that you, a lot of people miss here is a lot of the demand now is increasingly tied into these sorts of long-term purchase agreements, which have nothing to do with actual units being shipped.

• 28:11Ed Zitron?

  The mob boss thing again.

• 28:13Speaker 04

  Yeah, yeah, yeah.

• 28:14Ed Zitron?

  But that's.

• 28:15Speaker 04

  I was joking, I told someone this the other day. Who was I talking to? It was the Wall Street Journal. I was talking to them, saying like, it's kind of like the old mafia threat, like really nice AI market you have. There'll be a show if something happened to it.

• 28:27Ed Zitron?

  Or you don't, I guess you don't want Vera Rubin anymore. I guess we'll have to give the Vera Rubin.

• 28:34Speaker 04

  It's exactly like that. So this is the thing. So those two pieces are really important because it creates the impression of unit growth, where unit growth doesn't actually exist because it's predicated on lacking supply in the hopes of something later. But at the other side of it, you've also got this speculative land component. There was a company the other day, it was a great Bloomberg story about it, who raised, I think it was $3 billion in junk bonds for exactly this purpose. This is highly speculative stuff that's actually seeing.

• 29:02Ed Zitron?

  Maybe?

• 29:03Speaker 04

  No, no, not terrible. It was just literally last week. It was called Tract, I think it was called T R A C T.

• 29:09Ed Zitron?

  Yes, Tract. I remember this one. This is the insane thing. They're still able to set, like, raise those bonds, though. Like, they're still able to get the money.

• 29:19Speaker 04

  Well, that's because, again, this is the insidious problem here, is there's this idea that if I'm successful, my counterparty, the counterparty in the data centers, they're good for it because Microsoft, Google, so instead of having a bunch of dodgy, you know, Florida strippers or something on the other side of this, like in the financial crisis, what I've got on the other side, my counterparty, has a high credit, very focused, very small group. It's the Microsofts and Googles and others. So people are willing to take much crazier risks because the counterparty looks so good from a credit standpoint, which is very different from what happened in the financial crisis, where I wouldn't have issued junk to create something that was going to be purchased by, you know, who knows? But if it's Microsoft, Google, and the other hyperscales on the other side, I'm like, you know what? If this works, they're good for it.

• 30:04Ed Zitron?

  But what if it doesn't work?

• 30:05Speaker 04

  Well, then, of course, you end up with a lot of room, a lot of extra buildings for, you know, laser tag or something like this.

• 30:10Ed Zitron?

  I've been, I'm excited about the laser tag arena, arena future we have. Just America's the laser tag capital of the world.

• 30:19Speaker 04

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  Because the only thing we know for sure is that the efficiency of inference, if you buy the argument that we're transitioning rapidly to inference, the efficiency of inference is going on is rising rapidly because of things like distillation, models are shrinking, chips are becoming more efficient, there's less memory required.

• 34:10Ed Zitron?

  Because that's the thing, the people that have told me inference is getting more efficient are usually referring to Nvidia demos based entirely on deep-seated GPT-LLS.

• 34:19Speaker 04

  Oh, yeah. No, no, that's a ridiculous position. Right, right, right. Look at companies like Fractal out of the UK, an interesting company with, I think, some really groundbreaking inference technology that we'll be shipping this year. Look at a company like Talus in Toronto, T A L, T A A, L A S. So Talos is a good example. They're doing some innovative stuff showing, so a high-speed inference chip today might do a few hundred tokens per second. That's considered a relative, leaving aside the wattage required. That's a relatively high-speed token producing chip. So Talos is demoing 16,000 tokens per second. So we're seeing step function increases at lower power from some of these next-generation silicon vendors. Granted, they're not going to dominate the marketplace, but the idea that we're not going to see step function

• 35:04Speaker 04

  changes that we can project into the future based on what we've seen in the recent past is just dead wrong.

• 35:10Ed Zitron?

  But the thing is, what models are they able to do that with? Because again, a lot of, I mean, all of the benchmarks we see are based on open-source models because open-source models are open-source, those are the ones they can test on. I feel like everything, every time I get any kind of leak out of Azure or AWS about these models, it's, I have a Microsoft personally, it's like four to six, sorry, four to 12 GPUs for one generation of the smaller reasoning model, 04, I think it was?

• 35:43Speaker 04

  Yeah.

• 35:43Ed Zitron?

  And that's just for one generation over several minutes that someone's doing a particularly different coding task. It doesn't feel like these inference chain things will trickle down there. So maybe it will be the future of LLMs is this small industry run on these smaller chips or something like that.

• 36:00Speaker 04

  I think you're going to see all of the above thing. Well, let's say, for example, we're seeing inference happening inside of EVs where it's, we're doing, I'm doing rapid ingestion of video tokens for the purposes of deciding whether I'm going to back into my neighbor's trash can. And these kinds of things will-

• 36:15Ed Zitron?

  Those large language models?

• 36:16Speaker 04

  Oh, absolutely. Because you can, you can imagine, I'm ingesting all of that, the video, I'm going to have a lot more granularity with respect to the tokens flowing back to me, and I can do more with it and know more about what's happening in my environment. I think most of video ingestion is going to move towards edge tokens, but done on small language models, very, very small stuff.

• 36:34Ed Zitron?

  We've already had edge compute AI

• 36:37Speaker 04

  Yeah, yeah.

• 36:37Ed Zitron?

  Vision stuff for like a decade or more. Like I was working on stuff like in like 2014. It just feels like a lot of this is trying to make tokens do stuff that we already kind of do.

• 36:50Speaker 04

  So it is, but the thing I, and again, you know, I'm a, I'm deeply in the skeptical camp here. The thing I will say in favor of tokens in terms of absorbing a lot of this stuff is that it makes it all less ad hoc. Because you now have this sort of universal commodity for ingestion and production of information. That's, that makes things a little more interesting because I can now abstract away some of the hard problems of video processing. I can abstract away some of the hard problems of speech synthesis because they all kind of disappear and become, in a sense, this universal token. And, you know, to a degree, I think that's true. And I think it will lower the barriers to more people playing in the worlds of, you know, video and speech and other places.

• 37:30Speaker 04

  But this doesn't create the kind of marketplace that people who are pushing huge numbers of tokens run on frontier models want. This is just edge stuff, cheap and dirty.

• 37:38Ed Zitron?

  Also, the problem with tokens as a commodity as well is it's very hard to know, like one, like a million tokens, but per million tokens. It's impossible to actually measure how long a task, how, how many tokens a task will use because of the inherent unreliability of large language models. And it's like, so it's hard to even, like, right now, you're seeing with this, have you seen this, uh, people complaining about anthropics rate limits? For example, I'm not sure.

• 38:05Speaker 04

  All the time. I see it constantly.

• 38:07Ed Zitron?

  It, well, right now, people are mad that they can only spend a thousand dollars on a $200 a month plan. But the thing with that is people very clearly do not know how much a, how far a token goes or a million tokens goes. Like they, it's, it's difficult to evaluate and measure that, which feels like kind of economic poison at some point. Because if you can't say how much a task will cost, you don't have a miles per gallon, like 16,000 tokens per second. Well, you could do inference fast, but if a customer can't afford it, if a customer can't actually reliably say, I'll be able to use this in this way. What uses tokens as a measure? But I mean, I know what they're used for as a measurement, but it's like, you can't say what even a million tokens might do.

• 38:49Speaker 04

  No, you can't, but that's kind of innate to the world of like a true, a true commodity. I don't know how much you're going to put in your car. I don't know. It really, that becomes an engineering decision for you, not a production decision for me. Right?

• 39:02Ed Zitron?

  Yeah, that makes that.

• 39:03Speaker 04

  You have to separate those two, those two pieces. And so that, that I don't, can't tell you, here's how many tokens it will take for you to do X. That's no more my job than it is for me to tell us, you know, a copper mine, how many, how much copper is going to take for GM in a particular car?

• 39:16Ed Zitron?

  So know how much? Yeah, but they know how mu- I get what you mean. It's like they, they know how much copper they need to use, but.

• 39:23Speaker 04

  Right. And then, and then they'll make constrained, you know, constraining decisions where they'll say, I only want to use this much copper because copper is really frigging expensive. And so, you know, we're going to like, I just saw, I think it was Riven, Riven the other day said they'd cut out like, I don't know, like 10 miles of electric cable inside their cars, which seemed ridiculous to me, but it was again, because of the price of copper. Um, so there's, yeah, so they turn into, and these things turn into engineering decisions. Right now, we're in this kind of subsidized wild, wild west where everyone thinks it's a land grab and they're, so they're subsidizing it to a degree. And people are overusing tokens. Oh, I've had a model running for three days and it's doing all these agentic things. And it's like, well, what are you trying to create out of it? And it's like, I don't know, it's some nonsensical thing. So it's, people are being subsidized to do non-economic behaviors to an incredible degree right now.

• 40:06Speaker 04

  And, and I find that remarkable, which is a statement about this kind of land grab mentality among the frontier model vendors. And, and Dario has been real, Dario Amadeo at Anthropic has been very upfront about this as he believes that we are in a land grab mode. There's only going to be a couple of frontier model vendors left standing. And so we need to make sure that, you know, we're the dominant provider, if you will, of, you know, coding harnesses and frontier models. No, I think that's a misnomer too, but that's another.

• 40:30Ed Zitron?

  Yeah. I think, but my core economic, I mean, one of the many core economic things such as it's totally unprofitable. My thing right now is these rate limit changes are more severe on an economic level than people give credit for. Not just because of the economics, but because of the habits. If you believe, like, the way I analogize it is like, if your car can drive 15 miles and then one day it can only drive three, can you get to work?

• 40:55Speaker 04

  Yeah.

• 40:55Ed Zitron?

  And is it because?

• 40:56Speaker 04

  Or even worse, if you, if you bought a house predicated on being able to be, drive the 15 miles to work. Right? So it has, it has externalities, it has outside consequences.

• 41:04Ed Zitron?

  But the thing is you're paying, they've trained everyone in this land grab to act in a way that doesn't make sense long-term. And I'm not, I don't think they can become profitable. I, I actually truly don't think that there's an economic way for it to happen. But they've trained people to use the product in a way that doesn't make sense. Like, it's not even a, "Oh, they can charge more." Your habits are not built for this.

• 41:26Speaker 04

  That's, that's exactly right. So I have a wild-eyed theory. Are you ready?

• 41:30Ed Zitron?

  Go, go, go.

• 41:31Speaker 04

  Hell, yeah. So my theory is the first, the first frontier model company to abandon frontier models wins.

• 41:38Ed Zitron?

  How do you mean?

• 41:40Speaker 04

  So my theory is that all of this stuff, most people in a Pepsi-Cola challenge kind of way, can't tell the difference. They claim they can, but they can't actually tell the difference between most frontier models for a typical test. Certainly, normals can't. Coders claim they can, but if you actually do it in a blind way, most of them can't tell. This is just ego.

• 41:56Ed Zitron?

  Right.

• 41:56Speaker 04

  And so increasingly, what people see instead is these coding harnesses, these tools like Claude Code and Codex and Open Code and all these sorts of things. So most of the value they see and most of what they actually think of as the model is just the harness. And that's where most of the innovation is happening. So my argument is, and that's why it was so dangerous for Anthropic this week, when they accidentally did a whoops and it released Claude Code, most of the values in the harness, and so the first company to say, "You know what? We don't need to spend this kind of money anymore on training new models, because we're going to just sit on top of models from all of these loons who are out there spending crazily on new frontier models that aren't improving very much anymore. Not, certainly not like they were four or five years ago." And that, they will be rewarded for that, no different than saying, you know, "I've let go all of my employees," or "I've decided to stop spending money on hydroelectric dams."

• 42:40Speaker 04

  You've cut CapEx, you've cut, you've made your business more financially appealing by taking away the single biggest piece of cost, because you're recognizing that the world has changed, that I'm not getting incrementally as much value for dollar on training a model as I was five years ago. And that's very clear in the data. If you look at any of the composite benchmark models, getting away from like, can they solve math problems? But literally, real-world composite models, it's been a sharp decline from like, you know, 18% year-over-year improvements in models to like 4% or 5% at vastly higher costs and more dollar, and more time. And so that really matters. And so my completely will never happen theory is that the winner here is the first one to stop doing it.

• 43:20Ed Zitron?

  Isn't that just describing cursor?

• 43:23Speaker 04

  So Cursor is an interesting example. So Cursor doesn't actually embrace the full idea of being a harness across all of these white collar applications. You're still kind of trapped in a coding world. And so the idea, so if you, right. So I just think people get trapped in coding because it was the first place this stuff emerged. So you have to think about like, cowork is a good example of at least an attempt to break, again, a skip containment and get out of the world of coding and say, okay, this is actually for all white collar workers. Like I watched people struggle with Claude, pardon me.

• 43:51Ed Zitron?

  But it didn't work.

• 43:53Speaker 04

  Doesn't work, but it's at least it's directionally the right idea. If you buy my theory that all of this stuff is commoditizing so fast and is a loser's game financially, that maybe the right sort of game theoretic strategy is to be the first frontier model company to stop making frontier models. And in a weird way, Apple kind of showed the way, right? Because early on, they were getting pilloried for, why isn't Apple spending more on AI? Why does Apple not have a model? And now, of course, it's reverse where it's like, look at Apple, they're so smart.

• 44:20Ed Zitron?

  Look how smart they are without their CapEx. I don't know. I just feel like, I feel like what you were describing is just AI model wrapper companies.

• 44:28Speaker 04

  Yes.

• 44:29Ed Zitron?

  But I, I sort of, I, my whole thing is, unless someone is able to break out of coding, there isn't really a hope for any of this. Because to this point, every time I read about an integration with like a Goldman Sachs or somewhere.

• 44:43Speaker 04

  Yeah.

• 44:43Ed Zitron?

  I can never actually find out what it does. I can never, and the further you get into the read.

• 44:48Speaker 04

  So I have a good one for you. So I was, I was talking to a very large investment bank the other day about how, about their, you know, prodigious AI integration efforts. And so they built it out, they told me across equity, research, sales, trading, and investment banking. And they asked me, which one do you think has seen the greatest benefit? I said, oh God, I said, I used to work on the sell side. I said, my first instinct is none, that they're all lying to you. But my second instinct is, I'll say equity research because, you know, no one likes to build spreadsheets and maybe it helps them build spreadsheets. And they said, no. So the answer, of course, was investment banking. And I said, why investment banking? These are like knuckle dragging dinosaur, arcade men. What, what, what are they doing? And he said, the answer, of course, is, The main thing junior investment bankers do is build, well, they get shouted at. That's the main thing they do.

• 45:33Speaker 04

  But the second thing they do after being shouted at is they build pitch decks for companies that don't want them. So they build a pitch deck because a partner wants a pitch deck built for some rando company somewhere. And that's a pain in the ass. And so now that used to cause all these sleepless nights and blah, blah, blah. They're doing them all with AI. So junior investment bankers love AI because it lets them do this completely unproductive, largely inconsequential task of building pitch decks for companies that don't want the pitch deck. And so that's, these are the kinds of applications that have really minimal economic value and yet sort of superficially appear really exciting because if I'm someone who otherwise had to stay up all weekend building a PowerPoint deck to pitch to some random small cap company, I'm like, yeah, this is terrific. So that's, that was the answer. It was really interesting.

• 46:17Ed Zitron?

  But that's also kind of worrying because like the best example we have for this thing that has taken over everything, at least optically, even though there hasn't in economic terms, is like we can do PowerPoints, kind of.

• 46:32Speaker 04

  For we can do PowerPoints for junk bond raises for micro cap companies way better than we used to.

• 46:38Ed Zitron?

  Wow. And it's like helping junior analysts. So it's like, are you really, the time they're saving is just lowering their workdays from 15 hours to 11?

• 46:47Speaker 04

  Well, and they're still being shouted at, unfortunately for them, but you know, that's the way it goes.

• 46:50Ed Zitron?

  But that's part of the job.

• 46:52Speaker 04

  That's part of the job.

• 46:54Ed Zitron?

  Paul, it's been such a pleasure having you. Where can people find you?

• 46:57Speaker 04

  PaulKudrowski.com.

• 46:59Ed Zitron?

  Thank you for joining us. And yes, we'll be back with a monologue this week. I'm of course Ed Zitron. Thank you, everyone, for listening.

• 47:13Ed Zitron?

  Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski. You can check out more of his music and audio projects at mattosowski.com. M A T T O S O W S K I dot com. You can email me at [email protected] or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat dot where's your ed dot at to visit the Discord and go to r slash better offline to check out our Reddit. Thank you so much for listening.

• 47:46Speaker 02

  Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit our website, coolzonemedia.com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

• 48:18Speaker 01

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