The Reality of AI Economics With Paul Kedrosky

Summary

Better Offline – The Reality of AI Economics With Paul Kedrosky

Podcast: Better Offline (Cool Zone Media & iHeartPodcasts)
Date: April 7, 2026
Host: Ed Zitron
Guest: Paul Kedrosky (Economist)

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Episode Overview

In this episode, host Ed Zitron sits down with economist Paul Kedrosky to dissect the often misunderstood economic reality of the current AI boom. They cut through industry hype, scrutinize actual productivity gains, and examine how AI's growth is primarily reflected in data center investments rather than broad economic benefits. The conversation explores Nvidia’s outsized role, speculative behavior in data centers and land, and the questionable sustainability of the current AI investment frenzy, all while balancing technical insight with skepticism and humor.

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Key Discussion Points & Insights

1. AI’s Elusive Economic Impact

• Productivity Data Gaps:

  • Paul draws a parallel to an old tech trope: “You could find technology everywhere except for in the productivity data.” (03:00)
  • There’s a persistent lack of tangible productivity improvement from AI in broad economic data; the impact is overstated.
  • Two common industry excuses: “It’s too soon to tell” and “It’s there if you cherry pick data.”

• The Real Economic Driver: Data Centers

  • AI’s economic effect shows up almost exclusively in “non-residential fixed investment” figures—i.e., spending on data centers and equipment but not real productivity ([04:01])
  • Other typical drivers (e.g., manufacturing) have been eclipsed in GDP growth contributions by data centers, especially in 2025.
  • “The US would have been in recession...if not for this non-residential fixed investment wonder drug we call data centers.” (05:30)

2. Nvidia’s Critical & Unstable Role

• Nvidia as Mafia Don:

  • Nvidia is at the center of every AI investment—vendor, investor, acquirer— likened to a “mafia don” ([07:38]).
  • “Each dollar that they’re putting out there...is vastly more important because...they are the load bearing beam in the middle of all of this.” – Paul Kedrosky (07:54)

• Unsustainable Growth Projections:

  • For Nvidia's trajectory to continue, sales would have to reach impractical levels (e.g., $120B/year in GPUs).
  • Paul cautions against the "argument from personal incredulity" but concedes the company admits the market dynamics are “changing quickly.” (08:41)

• Shifting from Training to Inference:

  • AI chip margins are high for training models but expected to drop as the focus shifts to inference (using models rather than building them).
  • “The things that gave them a moat in the world of training are far less important in the world of inference.” – Paul (09:38)

3. Speculation, Hype, and “Commoditizing” AI

• The Token Commodity Analogy:

  • Paul analogizes Nvidia to Saudi Arabia and tokens (AI input/output units) to oil or Humvees, framing current industry messaging as industrial-scale self-promotion ([10:42])
  • OpenClaw and rapid AI adoption efforts are described as analogous to “pushing Humvees to consume oil.”

• Speculative Investment & Financing Structures:

  • Rapid AI/data center expansion has outpaced even “profitable” tech companies’ cash flow, so external/private financing (including debt, junk bonds) has stepped in ([20:44]).
  • Tech, once a “no debt” sector, became the largest issuer of investment- and non-investment grade debt in the US in 2025.

• Speculative Land-Buying (“Chinatown” Analogy):

  • Paul describes “powered land companies” buying up strategically located real estate with access to Internet/energy/water for potential future data centers, even if there’s no certain demand yet ([26:22]).
  • This is compared to the speculative water rights plot in the film “Chinatown.”

4. The Demand Mirage and Speculation Feedback Loop

• Long-term Purchase Agreements (LTAs):

  • Many firms are entering LTAs for future GPU supply out of fear of missing out, inflating perceived demand.
  • “A lot of what you see as purchasing is completely speculative by people who are worried…they will never get supply later.” – Paul (27:51)

• Counterparty Risk and Private Credit:

  • The real “counterparties” are tech giants like Microsoft and Google, encouraging reckless lending and real-estate speculation ([29:42]).
  • If AI stalls or falters, leftover data centers could become literal empty shells (“for storage and laser tag,” jokes Paul, 30:29).

5. Commoditization of Tokens & Efficiency Claims

• Rapid Inference Efficiency Gains (and Hype):

  • Step-change improvements in inference efficiency—new chips, better runtimes—are real, but Paul questions Nvidia-centric, demo-driven narratives ([33:37]).
  • New players (e.g., Thales, Fractile) may further erode Nvidia's dominance.

• But… Usability & Measurement Challenges:

  • Token pricing is unpredictable for customers, making precise cost/performance planning nearly impossible ([37:00-38:08]).
  • “If you can't say how much a task will cost, you don't have a miles per gallon.” – Ed Zitron (37:57)

6. Behavioral, Cultural, and Market Pathologies

• AI Tribalism and Hobby Deficit

  • Ed observes two groups: “pure hatred” and “cult-like” zealotry around AI, nothing in between. “I've never seen anything like this” – Ed Zitron (18:08)
  • Paul theorizes this is partly tribal (signaling “in group” status) and partly a desperate attempt to automate away work forever ([18:49]).

• Unrealistic Hopes for a Too-Big-to-Fail Industry

  • AI’s crash would be different from the Great Financial Crisis but could be messy due to highly-leveraged, speculative bets ([20:29]).

7. The AI Frontier Model “Loser’s Game”

• Frontier Models vs. Harnesses

  • Paul’s provocative theory: The first company to stop building its own “frontier model” wins.
  • “Most of the value's in the harness. The first company to say ‘we don’t need to spend [this much] anymore on training new models’... will be rewarded.” (41:00-42:39)
  • Competitive advantage will shift to wrappers/frameworks (“harnesses”) built on commoditized models rather than the models themselves.

• Practical AI Impact Example: PowerPoint Automation

  • When questioned about real economic value, Paul recounts that investment banks’ main “productivity boost” is automating inconsequential pitch decks using AI ([44:07]).
  • “Junior investment bankers love AI because it lets them do...the completely unproductive, largely inconsequential task of building pitch decks for companies that don't want the pitch deck.” – Paul (44:07)

• The Underwhelming Reality

  • AI’s most prominent use case in finance so far is merely reducing junior analysts’ workweeks from 15 hours to 11 ([45:58])
  • “Are you really... the time they're saving is just lowering their work days from 15 hours to 11.” – Ed Zitron (45:58)

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Notable Quotes & Memorable Moments

• On Economic Visibility:

  • “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...” – Paul Kedrosky (05:07)

• On Nvidia’s Position:

  • “They’re this mafia don... investing in things, playing the role of investor, acquirer, vendor...the load-bearing beam in the middle of all of this.” – Paul (07:38)

• On Token Economics:

  • “Their goal is to get more people purchasing Humvees because it’s good for them, because it consumes the thing they...produce, these things called tokens—not the crypto!” – Paul (10:42)

• On Land Speculation:

  • “This is like Chinatown, right, where I'm buying up real estate with numbered companies in hopes of securing water rights.” – Paul (26:22)

• On Speculator Hype:

  • “A lot of what you're seeing as purchasing is...speculative by people who are worried if they don’t lock in a long-term purchase agreement now, they will never get supply.” – Paul (27:51)

• On AI’s Real World Use in Investment Banking:

  • “Junior investment bankers love AI because it lets them do...the completely unproductive, largely inconsequential task of building pitch decks for companies that don't want the pitch deck.” – Paul (44:07)

• On Commoditization and the Future:

  • “The first frontier model company to abandon frontier models wins.” – Paul (40:46)

• On Silliest Use Cases:

  • “We can do PowerPoints for junk bond raises for microcap companies way better than we used to.” – Paul (45:51)

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Key Timestamps

• 02:37 – Introduction of Paul Kedrosky and opening discussion on AI’s economic impact
• 03:00–05:30 – AI and the myth of economic productivity gains; data center investment as the main story
• 07:38–08:41 – Nvidia’s dominant, precarious, “mafia don” role
• 09:38–10:10 – Shifting AI economics: Training vs. Inference and their profit margins
• 10:42 – Tokens as a commodity; openclaw analogy; hype compared to oil/Humvees
• 18:08–20:29 – AI zealotry, tribalism, and speculative beliefs
• 20:44–23:25 – Data center debt, external financing, and overleveraging
• 26:22–27:51 – Speculative buying of land for future data centers—the “Chinatown” and “powered land company” analogies
• 27:51–28:34 – The role of LTAs and speculative demand fueling Nvidia’s perceived growth
• 29:42–30:35 – Credit risk, what if it crashes? “Laser tag future”
• 33:12–35:34 – The real and hyped efficiency gains in inference
• 37:57–38:42 – The problem with “tokens as a commodity” and the impossibility of cost predictability for customers
• 41:00–42:39 – The case for commoditization and why the first to stop developing models could win
• 44:07–45:58 – Underwhelming but real-world AI impact: automating pitch decks
• 45:58–46:13 – Episode close, skepticism about the industry’s future

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Tone & Language

• Tone: Wry, skeptical, sharp, humorous at times; deeply critical of hype and wishful thinking in the AI industry.
• Language: Direct, plain-spoken with technical explanations as needed. Paul excels at vivid analogies (dog and mailman, mafia don, Chinatown) to make economic points accessible and memorable.

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For New Listeners

This episode is a must-listen (or read) for anyone confused about where and how the “AI boom” actually shows up in real economic activity. Rather than regurgitating Silicon Valley optimism, Ed Zitron and Paul Kedrosky break down how the tech world’s AI obsession currently generates more speculative real estate deals, junk bond issues, and PowerPoint slide decks than genuine productivity or widespread wealth. With accessible analogies and unflinching skepticism, they pull the curtain back on who’s benefiting and what’s actually at risk in the current AI gold rush.

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Guest contact: paulkedrosky.com
Host/newsletter: betteroffline.com

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Transcript

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[Paul Kudrowski]

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[Ed Zitron]

hello and welcome to Better Offline. I'm your host Ed Zit.

[Paul Kudrowski]

Better Offline

[Ed 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.

[Paul Kudrowski]

Hey Ed, good to be here.

[Ed Zitron]

So your recent work on AI, particularly the data center and economic 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 specific sometimes, if you know what I mean.

[Paul Kudrowski]

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, there's 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 there go 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. Yeah, I think it's 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 examp, its share of non residential fixed investments, which is at levels we last saw with the railroad build out or with rural electrification.

[Ed Zitron]

And be a little more. Can you be specific what non residential investments mean? Just so I get you.

[Paul Kudrowski]

So atoms that aren't in houses. So that's the short answer. Factories, manufacturing, highways, fixed equipment, you're putting 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. If the current administration had its way, that statistic would currently be dominated by, fill in the blank, manufacturing. Right. Because the intent is to onshore manufacturing. So that 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 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 it's made up predominantly of GPUs, but also the build out of the facilities. H vac heating, ventilating, air conditioning, 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, and I startled other people, was the idea that it was the largest share of US GDP growth for three or four quarters last year. So that in the absence of this non residential fixed investment wonder drug we call data centers, 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. The dog thinks he did it, he didn't do it. He has a messed up model of causality is what he has the dog. Causality implies that my barking made the mailman go away. No, the mailman goes away every time he's got other things to do. 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.

[Ed Zitron]

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

[Paul Kudrowski]

No. So you parcel it out obviously, so by geography. So it matters immensely what's happening in the U.S. now, granted, the predominant share of Nvidia sales, just like the largest sales of most of transformers and everything, are all happening in the U.S. 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 appropriately.

[Ed Zitron]

Yeah, I just meant it more as when Nvidia makes a dollar, does that count into this? Because yes. See, that's the thing that feels like something that is just kind of almost like a load bearing chip, a load bearing gpu. Like if these sales go down, that's bad for everyone.

[Paul Kudrowski]

Right. And there's been some tremendous piece the Wall Street Journal did a piece last week, I think, and I said some saline incendiary things in it, but it was about how Nvidia kind of sits at the center of this, like Don Carleoni and seeing with everything happening and everyone coming to the table and they're this 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 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. This. They are for now and it's changing rapidly, but nevertheless they are for now.

[Ed Zitron]

The thing is it 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, in their next. 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.

[Paul Kudrowski]

I have a tendency to make that argument and I hate when I do it because, and this is so we both at fault for this is this is Paul's argument from personal incredulity. I don't think it can happen, therefore it can 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.

[Ed Zitron]

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

[Paul Kudrowski]

That's right, exactly. 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 a misnomer, to this thing that's called inference, which is probably more accurate. From training models to answering prompts, the margins on inference are going to change dramatically because the things that gave them, 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 saying 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 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.

[Ed Zitron]

See, that's the thing. I'm also questioning the demand and I also question whether they're done with training, because training, and you correctly said it's 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.

[Paul Kudrowski]

It's just so.

[Ed Zitron]

It's so the way I look, the

[Paul Kudrowski]

way I look at it is I always, whenever someone says 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 purchase 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 crypto. So if you think about it in those terms and translate Jensen's 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 from the EVs before it hurts, you get out there in the Humvee so you're safe on the freeways, it can kind of feel equivalent.

[Ed 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, right?

[Paul Kudrowski]

Yeah, no, exactly. I think you have to translate a lot of Jensen 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 diligent ambassador 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 my own sort of home nuclear reactor. It's a ridiculously dangerous technology for most normals to be using.

[Ed Zitron]

Yeah. I also feel like it's a sign they're a little washed when you've got 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.

[Paul Kudrowski]

It's just a tremendous jackets though.

[Ed Zitron]

Oh they are at the menswear guy on a few years. Amazing jackets the man does. He has a good tailor as well. But anyhow.

[Paul Kudrowski]

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

[Ed Zitron]

Yeah, the things that you use with the models.

[Paul Kudrowski]

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.

[Ed Zitron]

But the thing is they don't know if that. They don't know anything. So you can't guarantee that.

[Paul Kudrowski]

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 we've actually started off in the nearly perfect domain to give a completely unrepresentative example of what the future looks like by start off in coding, which is why 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, I don't know, pick your domain and the way color work. And the reality is those domains are very different.

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[Ed 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 me and 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 I maybe in stocks.

[Paul Kudrowski]

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 being as 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, people 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 got a vest attached with all these explosives and I'm threatened to walk into your economy and blow it all up and just try and stop me because there's hundreds of us doing this and 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? 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 make figurines all day or whatever it is I want to do.

[Ed Zitron]

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

[Paul Kudrowski]

That's always the thing of fun. It's totally true. What do you plan to do in your spare time?

[Ed 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 sub agents an hour. If these things don't create my special project that I'll never show, I will die.

[Paul Kudrowski]

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

[Ed Zitron]

I also think that people. I just did 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.

[Paul Kudrowski]

Yeah, no, 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 ahead about this because these are big boys and girls who are spending all this money. The hyperscalers, the Microsoft's, 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 anthropic, but that are big and profitable companies. How are you to tell them what to do? And then I was. So I said, you know, fine, it's a somewhat, 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, 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 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. Of course, all the way along people normalize it and say it's fine, they're very profitable. Then when it turned out the profits weren't enough to pay for all of the data centers, that's also fine. So both of these things end up being fine. Then of course it all came home to roost to a degree at the end of the year as private credit got attacked from through the side door because of this problems with these 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 that is something like the global financial crisis. The more entertaining part of this is they're treating data centers as real estate. 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 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're having to pay a fixed obligation, 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.

[Ed 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?

[Paul Kudrowski]

Yeah, yeah. So there's a double whammy. There's both sides of it. Right. So to, to a degree, if you're working with a model directly through the API, which the largest issuers are, at least if you're in a production position, you're actually paying an API price 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 capital investment, around which the investment's predicated, the depreciation of those items is relatively rapid too. 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, which is like flat out, pedal to the floor, 24, seven, huge workload. It's kind of like 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.

[Ed Zitron]

Yeah. And the other thing as well is I've really been looking at this. So you wrote 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 at some point Nvidia has to slow. Not even because of me wanting it to or not, but because where are they going? Where are we putting these things? I mean, Taiwan Warehouses, I guess.

[Paul Kudrowski]

Yeah, Taiwan Warehouses, yeah. And that's a big problem is the build out. So this problem of, and one of the Northeastern, I've forgotten which utilities in the Northeast just recently put out some data on this showing that something like the data usage 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. That is a very speculative position. We're going to power it all behind the meter with nuclear reactors and all these kinds of things. But 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 buying up real estate with numbered companies in hopes, hopes of securing water rights. I saw this, I saw this when Jack Nicholson was wandering around, Jake Giddies was wandering around in the deserts of California.

[Ed Zitron]

I don't know what you mean. Tell me, what do you mean? The Chinatown example.

[Paul Kudrowski]

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 a high speed interconnection to the

[Ed Zitron]

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

[Paul Kudrowski]

So a company that doesn't make it obvious who the actual direct owners are. So it's not clear what their purpose is. So I think of it as like Cayman Islands, but it isn't. So the idea that you're trying to at least loosely obfuscate what the ownership and purpose are, but even that's less important. The idea though that they're buying on a speculative basis, this tracks of land could be hundreds of acres in some location that has access to power, access to water and potentially access to a peering point, a high speed interconnection point to the broader Internet. And 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. 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?

[Ed Zitron]

Is that widespread? Is that widespread? That's so bad. Because 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's the real thing that's getting me.

[Paul Kudrowski]

Right. But part of the problem is, and it was a great piece from Trendforce, I think it was the other day, one of the market research firms in this, they were pointing out how many firms are now buying LTA's long term purchase agreements because 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 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 their approach is I'll worry about that other stuff later on, but for now I need to sign up. So that's the other piece that a lot of people miss here is a lot of the demand now is increasingly tied into this, these sorts of long term purchase agreements which have nothing to do with actual units being shipped.

[Ed Zitron]

The mob boss thing again.

[Paul Kudrowski]

Yeah, yeah, yeah. But that's. I was joking. I told someone this the other day. Who was I talking to? 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. Be ashamed if something happened to it.

[Ed Zitron]

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

[Paul Kudrowski]

It's exactly like that. 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 locking supply in the hopes of something later. But at the other side of it you've also got the speculative land component. There was a company the other day, there 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 maybe, no, no, not terrible, but just literally last week it was called Tractorist. I think it was called T R A C T. Yes, Tracked.

[Ed Zitron]

I remember this one. This is the insane thing. They're still able to raise those bonds though. They're still able to get the money.

[Paul Kudrowski]

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 Florida strippers or something on the other side of this 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 who knows? But if it's Microsoft, Google and the other hyperscalers, I'm like, you know, what if this works, they're good for it.

[Ed Zitron]

But what if it doesn't work?

[Paul Kudrowski]

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.

[Ed Zitron]

I'm excited about the laser tag arena, arena future. We have just America's the laser tag capital of the world.

[Paul Kudrowski]

That's right. We got a lot of extra space for use, storage and laser tag.

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[Paul Kudrowski]

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 rising rapidly because of things like distillation. Models are shrinking, chips are becoming more efficient, there's less memory required.

[Ed 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 these.

[Paul Kudrowski]

Oh yeah, yeah. No, no, that's a ridiculous position. Right, right, right, right. Look, Fractile 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 Thales in Toronto taa. So Thales 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 thales 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 changes that we can project into the future based on what we've seen in the recent past is just dead wrong.

[Ed 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 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 topic. It's like 4 to 6, sorry, 4 to 12 GPUs for one generation of the smaller reasoning model 04 I think it was.

[Paul Kudrowski]

Yeah.

[Ed Zitron]

And that's just for one generation over several minutes that 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.

[Paul Kudrowski]

I think you're going to see all of the above thing where, let's say for example, we're seeing inference happening inside of EVs where 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.

[Ed Zitron]

Language models.

[Paul Kudrowski]

Oh absolutely, because 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.

[Ed Zitron]

We've already had edge compute, AI, computer vision stuff for like a decade or more. Like I was working on stuff like that in 2014. It just feels like a lot of this is trying to make tokens do stuff that we already kind of do.

[Paul Kudrowski]

It is. But the thing I. And again, 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 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 to a degree I think that's true. And I think it will lower the barriers to more people playing in the worlds of video and speech and other places. But that 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 stuff.

[Ed Zitron]

Also, the problem with tokens as a commodity as well is it's very hard to know like one to like a million tokens per million tokens. It's impossible to actually measure how long a task, 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, people complaining about anthropics, rate limits for Example, I'm not sure I see it constantly. Well, right now people are mad that they can only spend $1,000 on a 200 dol a month plan. But the thing with that is people very clearly do not know how much, 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 measurement? 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.

[Paul Kudrowski]

No, you can't. But that's kind of innate to the world of like 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.

[Ed Zitron]

Right, yeah, that makes sense.

[Paul Kudrowski]

You have to separate those two, those two pieces. And so 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 as a, you know, a copper mine, how many, how much copper it's going to take for GM in a particular car. So know how much.

[Ed Zitron]

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

[Paul Kudrowski]

Right. And then they'll make constraint, you know, constraining decisions where they'll say I only want to use this much copper because copper, copper is really freaking expensive. And so, you know, we're gonna, like I just saw, I think it was Riven Rivian 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. 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 people are being subsidized to do non economic behaviors to an incredible degree right now. And I find that remarkable, which is a statement about this kind of land grab mentality among the frontier model vendors. And Dario has been. Dario Amade 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 models vendors left standing. And so we need to make sure that we're the dominant provider, if you will, will of coding harnesses and frontier models. Now I think that's a misnomer too, but that's another problem.

[Ed 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.

[Paul Kudrowski]

Yes.

[Ed Zitron]

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 one work?

[Paul Kudrowski]

Yeah.

[Ed Zitron]

And is it because I.

[Paul Kudrowski]

If you bought a house predicated on being able to drive the 15 miles to work. Right. So it has, it has externalities, it has outside consequences.

[Ed Zitron]

Yeah. 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.

[Paul Kudrowski]

That's exactly right. So I have a wild eyed theory. Are you ready? Go, go, go. Hell yeah. So the EYE theory is the first frontier model company to abandon frontier models wins.

[Ed Zitron]

How do you mean?

[Paul Kudrowski]

So my theory is that all of this stuff, most people in a Pepsi Coke 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 task. Certainly normals can't. Yeah. Coders claim they can, but if you actually do it in a blind way. Most of them can't tell. This is just ego. 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 quad code, most of the value's in the harness. And so the first company to say, you know what, but we don't need to spend this kind of money anymore on training new models, because we are 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 now, certainly not like they were four or five years ago. And they will be rewarded for that. No different than saying, I've let go all of my employees or I've decided to stop spending money on hydroelectric dams. You've cut capex. 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 can they solve math problems? But literally real world composite models, it's been a sharp decline from 18% year over year improvements in models to 4 or 5% at vastly higher costs and more time. That really matters. My completely will never happen theory is that the winner here is the first one to stop doing it.

[Ed Zitron]

Isn't that just describing cursor?

[Paul Kudrowski]

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. They're still kind of trapped in a coding world. 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 Cowork is a good example of at least an attempt to break again, escape containment, and get out of the world of coding and say, okay, this is actually for all white collar workers. I watch people struggle with Claude, pardon

[Ed Zitron]

me, but it didn't work.

[Paul Kudrowski]

It doesn't work, but it's the 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.

[Ed Zitron]

Right?

[Paul Kudrowski]

Because early on they were getting pilloried for why isn't Apple spending more on AI? Why does Apple don't have a model? And now of course it's reverse where it's like look at Apple, they're so

[Ed Zitron]

smart, 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. Yes, but I sort of. My whole thing is unless someone is able to break out of coding, there isn't really a hope for any of this. Because. Because to this point, every time I read about an integration with a Goldman Sachs or somewhere, I can never actually find out what it does. The further you get into the reason,

[Paul Kudrowski]

I have a good one for you. I was talking to a very large investment bank the other day about their 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 of? 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 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 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. 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. 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. But that was the answer. It was really interesting.

[Ed Zitron]

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

[Paul Kudrowski]

We can do PowerPoints for junk bond raises for microcap companies way better than we used to.

[Ed 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 work days from 15 hours to 11.

[Paul Kudrowski]

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

[Ed Zitron]

That's part of the job.

[Paul Kudrowski]

That's part of the job.

[Ed Zitron]

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

[Paul Kudrowski]

Paulkadrosky.com thank you for joining us.

[Ed Zitron]

And yes, we'll be back with a monologue this week. I'm of course, Said Zitron. Thank you everyone for listening. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Mattasowski. You can check out more of his music and audio [e-mail] m a t t o s o w s k I.com you can email [e-mail] or visit betteroffline.com to find more podcasts, links and of course, my newsletter. I also really recommend you go to Chat wheresyoured app to visit the Discord and go to R betteroffline to check out our Reddit. Thank you so much for listening. Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit

[Paul Kudrowski]

our website coolzone media.com or check us

[Ed Zitron]

out on the iHeartRadio app, Apple Podcasts

[Podcast Host / Advertiser]

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