Why AI Has No ROI with Paul Kedrosky

Summary

Better Offline: "Why AI Has No ROI" with Paul Kedrosky

Podcast: Better Offline
Host: Ed Zitron, Cool Zone Media
Guest: Paul Kedrosky, Economist
Date: June 3, 2026

---

Episode Overview

In this sharp, critical episode, Ed Zitron sits down with economist Paul Kedrosky to dissect what they view as a foundational myth of the current AI boom: that artificial intelligence has a real, measurable return on investment (ROI). Through analysis of AI pricing models, economic incentives, and historic tech bubbles, the pair argue that today's AI infrastructure is largely a product of hype, financial engineering, and misrepresented costs rather than genuine productivity gains. They demystify several prevailing industry narratives, highlighting uncomfortable parallels with previous boom-and-bust cycles.

---

Key Discussion Points & Insights

1. The Real Costs of AI: The GitHub Copilot Controversy

[03:43 – 06:48]

• Ed introduces the recent user backlash to GitHub Copilot's pricing change, going from a subscription model to metered, token-based pricing.
• Ed Zitron: “People have just been posting what the fuck? What do you mean my whole balance is gone in three prompts? What do you mean by that?” (03:28)
• Paul explains that the initial pricing was “grotesquely subsidized,” masking the true cost of AI usage. Once the subsidies ended, users were shocked by actual expenses.
• Paul Kedrosky: “Once it becomes unsubsidized and unbundled, then you see your ass is dangling in the breeze of real token pricing.” (05:18)

2. Hiding Costs, Misleading Metrics, & Revenue "Masking"

[08:21 – 12:22]

• Discussion of “earnings before bad stuff”—a phenomenon where companies try to hide model training expenses as capital costs instead of operational costs, distorting financial reality.
• Paul notes companies like OpenAI and Anthropic obscure how much is truly being spent and the nature of their “run rate” revenue claims.
• Ed Zitron: “Token spend, so just organizational token spend, that's not a recurring cost... that person who spent half a billion dollars on AI. Right, yeah, yeah, that's not happening again.” (11:41)

3. The "Deflating Commodity" Crisis & Data Center Debt

[13:08 – 15:11]

• With token prices deflating 70–90% year-over-year, companies are caught in a “duration mismatch”: long-term, debt-funded data centers versus decreasing unit prices for what they sell.
• Paul Kedrosky: "You've got this... duration mismatch on top of a deflating commodity that can only end very, very badly." (15:11)
• Ed compares this to Amazon Prime, but notes that Amazon actually developed cost-saving logistics—unlike AI providers, whose costs are still high and rising.

4. Capital Investment Parallels: Railroads, Dot-com, & Data Centers

[16:20 – 24:16]

• Paul draws parallels to 19th-century railroad booms and the dot-com bubble: big capital outlays, wild optimism, eventual crashes, and vast overbuilding.
• Data center hardware has characteristics that make reuse unlikely, unlike the servers of past booms.
• Paul Kedrosky: “Almost half of U.S. railroad lines built during the boom years ... were eventually abandoned. Did they find reuse? ... It only took 100 years and now they're mountain biking trails.” (23:07)

5. Faith-Based Arguments & Motivated Reasoning in Tech

[24:54 – 26:15]

• Paul critiques the attitude that "it always works out," suggesting this belief is ahistorical and intellectually lazy.
• Paul Kedrosky: “Now you're acting as if it doesn't matter what I do because ... you've got this idea ... that it always works out. That wasn't true historically.” (25:07)

6. AI Output, GDP, and Productivity: The Measurement Mirage

[29:39 – 42:57]

• AI boosters claim dramatically increased productivity, citing more lines of code or AI-generated content as evidence.
• Researchers find these outputs are mostly “slop”—not meaningful economic value.
• Ed Zitron: “If this did what they said it did, we would have gunfights in the street. We would have the destruction of most knowledge work and it would be happening ... fully today.” (32:12)
• Paul and Ed critique attempts to redefine GDP in AI-friendly terms, dismissing them as "motivated reasoning.”
• Paul Kedrosky: “If you allow me to redefine GDP, I could present you with some tremendous numbers.” (31:06)

7. Misunderstanding Displacement: Radiologists & the Limits of Automation

[35:58 – 41:16]

• Review of the “Radiologist Paradox”: Despite predictions that AI would replace radiologists, their numbers have only grown.
• Paul unpacks how real-world job displacement is complex, affected by regulatory and economic structures that AI projections often ignore.
• Ed Zitron: “If these things were going to replace people, they would just do it… everything wouldn't read like the Riddler wrote it.” (40:43)

8. The Real-World Fallout: Data Centers, Energy, and Economic Development

[42:57 – 47:05]

• Giant data centers lock communities into risky bets, often based on speculative AI demand that may never materialize.
• Paul warns about regional economic development agencies being misled—“if the data center doesn’t get finished, you’re actually in a worse situation than you were previously.” (46:27)

9. The Bubble’s Endgame: Equity Raises, Debt Limits, and the IPO Climax

[49:49 – 52:57]

• Ed and Paul discuss Google’s recent $80 billion equity raise—what it shows about market appetite and the coming limits on debt financing.
• The remaining buyers for “hyperscaler” debt are “the usual suspects ... who famously tend to show up at the end of almost every bubble.”
• Paul Kedrosky: “I think the blow off top is this year's 3 Mega IPOs. And that kind of marks the gonging of the bell.” (52:41)

---

Notable Quotes & Memorable Moments

• On AI Pricing Reality:
  Paul Kedrosky (05:18):
  “Once it becomes unsubsidized and unbundled, then you see your ass is dangling in the breeze of real token pricing.”

• On Run Rates and Revenue Hype:
  Ed Zitron (11:41):
  “That person who spent half a billion dollars on AI... that's not happening again. That person is—You're not going to get one half a billion dollar Mr. Bean every single month.”

• On the Recurring Failure to Learn:
  Paul Kedrosky (23:07):
  “Almost half of U.S. railroad lines built during the boom years ... were eventually abandoned... It only took 100 years and now they're mountain biking trails.”

• On Motivated Reasoning:
  Paul Kedrosky (31:06):
  “If you allow me to redefine GDP, I could present you with some tremendous numbers.”

• On the Circularity of AI Valuations:
  Paul Kedrosky (48:32):
  “The more valuable all of these companies become, the more money is ... flowing around this... captive economy... It's the fundamental circularity at the core of all of this.”

• On the Coming Climax:
  Paul Kedrosky (52:41):
  “I think the blow off top is this year's 3 Mega IPOs. And that kind of marks the gonging of the bell with respect to the seriousness. With respect to you have to take this inability of these companies to make money.”

---

Timestamps for Key Segments

• [03:43] GitHub Copilot pricing shift and uproar
• [08:21] Hiding true operating costs in AI model providers’ financials
• [13:08] The "deflating token" problem
• [16:20] Hardware capital investment parallels: railroad and dot-com bubbles
• [24:54] "Faith-based" narratives in tech discourse
• [29:39] AI productivity measures: All slop, little value
• [35:58] Radiologist Paradox: Why jobs aren't vanishing as predicted
• [42:57] Real-world impact: Data centers and economic development
• [49:49] Google’s $80bn equity raise and the endgame for AI bubble

---

Tone & Style

The conversation is incisive, sarcastic, and blazingly skeptical. Both Ed and Paul bring a mix of sharp economic analysis and irreverent humor, poking holes in industry self-justifications and laying bare the mechanics by which AI hype is constructed and sustained.

---

Summary Takeaway

This episode is a must-listen for anyone wanting to see past the AI industry's buzzwords and self-serving statistics. Zitron and Kedrosky’s dissection reveals an ecosystem built on shaky economic reasoning, with consequences likely to echo historic tech busts—where real costs will ultimately have to be reckoned with, no matter how hot the current story.

Transcript

[iHeart Podcast Announcer]

This is an Iheart podcast. Guaranteed human.

[Ed Zitron]

You can't control what you can't see. JP Morgan brings clarity to your treasury operations. With near real time reporting and forecasting, you get the visibility you need to help optimize your cash flow. J.P. morgan 2026 J.P. morgan Chase Co. All rights reserved. J.P. morgan Chase Bank NA Member FDIC deposits held in non US branches are not FDIC insured. Non deposit products are not FDIC insured. Not all products and services are available in all geographical areas. See J.P. morgan.

[iHeart Podcast Announcer]

We believe in starting with your financial goals, not a formula. At Oppenheimer, we put the full strength of our long standing expertise to work understanding your life and your ambitions and designing the precise strategies that build and protect your wealth with confidence across this generation and the next. Put the power of Oppenheimer thinking to work for you. Wealth management, capital markets, investment banking. Run a business and not thinking about podcasting, Think again. More Americans listen to podcasts than ad supported streaming music from Spotify and Pandora. And as the number one podcaster, iHeart's twice as large as the next two combined. Learn how podcasting can help your business. Call 844-844-IHeart Premier Protein it's for getting after life, not just Fitness. With 30 grams of protein, 160 calories and no sugar added, helping people fuel their joy. With Premier Protein, you can say yes to more. Whether it's crushing a big presentation at work, building an epic fort with the kids, or hitting the hiking trail with friends. Premier Protein offers delicious flavors like cafe latte, chocolate, caramel, vanilla, strawberry and cake batter, to name a few. Find your favorite [e-mail]

[Ed Zitron]

cool zone media greetums. I'm Ed Zitron and this is Better offline. Today we are joined by the mighty economist Paul Kudrowski. Paul, thank you for joining me.

[Paul Kudrowski]

Hey Ed, how's it going?

[Ed Zitron]

It's going great. Everyone's deeply upset because this week and the last week everyone has been saying, huh, does AI have a return on investment? And it's. I've really been enjoying it because it's like watching the dinosaurs look up and see the meteor. They're just like, what do you mean? What do you mean? This costs money. I don't know if you've seen the GitHub copilot stuff.

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

So anyways, yes, there's a bunch of that as well. Well, let's start with the GitHub thing. So for the uninitiated GitHub Copilot AI coding tool from Microsoft couple weeks ago, I broke the story of course that they were moving their users from a premium request model to a token based model. So think of it like this for the listeners. If you, every time you use the cab service you could just say drive me from the Upper west side to Red Hook. And that would just, that would be one drive. You get a certain amount of drives a month and then suddenly the beginning of June they turn to you and say, yeah, you got to pay by the mile. And you suddenly realize You've been taking 95 mile trips. You've been asking to drive from New Jersey to Maryland, which I realize is further than 95 miles. Not a geographer. All right, but nevertheless, on the GitHub Copilot subreddit people have just been posting what the fuck? What do you mean my whole balance is gone in three prompts? What do you mean by that?

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

Right. What do you think?

[Ed Zitron]

Do you think that's good? Do you like that?

[Paul Kudrowski]

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

[Ed Zitron]

Except the hot dogs cost like $7,000 a packet. It's just. I think it's quite deceitful, personally. I think it's because these, on one hand, we can make fun of these people. I will continue to do so. It's funny. But when you look at them, it is also quite depressing because they were intentionally misled. Like, these people had no idea. It's not like these subsidized subscriptions were like, hey, if you use this many tokens while paying for them, it would cost this much. Until they made the change. Microso released a calculator that allowed you to see that, but only once they'd announced it. So you have millions. I would say the vast majority of people that interact with AI who have no idea what it costs. Literally none.

[Paul Kudrowski]

Right. And which is made worse by some of the early overexcitement, especially among large corporations that made the mistake of creating leaderboards and oh, my God, hell yeah. Right. So this is. We got into this token maxing phenomenon. So if you're inside of. Which is obviously the idea that the more tokens you use, the better you do in your job review. Because look at you. You're all AI. The problem, of course, is this is a little bit like the Saudis handing out Humvees to everyone in America. People saying, wow, this is awesome. I love having a Humvee. And then you have to fill it up. And so for a little while, it was like we were subsidizing these grotesquely profligate users of tokens just like profligate users of gasoline. And then all of a sudden the bill comes due and you say, wait a minute, this thing's a pig. It's a lot of I don't want to drive it for groceries anymore. And the exact same phenomenon is true with respect to being again exposed to the having your ass hanging in the breeze of real token prices.

[Ed Zitron]

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

[Paul Kudrowski]

And this is the deep structural problem because they were brought in through the side door of bundled pricing and now that's becoming unbundled. And of course that also is reflected in what we're told. And I think the Wall Street Journal and others have written about this and it'll be interesting. We see the, the final S1s for some of the upcoming IPOs that there is this attempt to try and even mask it in the financial filings where you get into this phenomenon of what we used to call earnings before bad stuff. And so what they're trying to do is hide the costs of training the models and saying that's not actually an operating cost, that's a capital cost. And we shouldn't have to show that as a function of what actually the margins are on producing tokens. And that is of course a cheat. Right? Because if that's true, then you should be able to capitalize these things and expense them for a long period of time. And we know full well that these are actually operating costs because they tell us that every 18 months we're launching a new major model. These things are not capitalized. These are operating expenses that should be treated accordingly with respect to the actual cost of token production. So there's a multifaceted game going on here, both in terms of how it's being presented to users, but also in terms of how they're trying to sell it in the context of the upcoming S1s for the Anthropic and OpenAI IPO filings.

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

Well, yeah, actually that's, that's, I love talking about run rate. Everyone who listens to this show knows I'm a real run rate pig because, because like Anthropic, I've reached out to both OpenAI and Anthropic and said, hey, how do you define this number? And they will not respond. They will not, they're very unfair to me, very nasty. They will not respond probably because from what the information is reported, I don't know how OpenAI does it, but Anthropic not only includes the amounts of money that Amazon and Google make in their revenues, like when they resell the most, but they Also, they do 13 times the last month's API spend and 12 times the current day's subscribers. So it's just, there's so many ways also. So token spend, so just organizational token spend, that's not a recurring cost. That's, that's just, you can kind of, I guess think, well maybe people are spending this today, but that person who spent half a billion dollars, that company that spent half a billion dollars on AI. Right, yeah, yeah, that's not happening again. That person is, that person is. You're not going to get one half a billion dollar Mr. Bean every single month. As someone just goofily. I also genuinely, I, I know the reporter Madison Mills, she's respectful reporter. She's very, she's, she's good, she is well sourced. It's just like, I hope Anthropic didn't include that 500 million in their annualized revenue.

[Paul Kudrowski]

Because I'm looking forward to it showing up in a public company filing because it almost inevitably will. This is going to be somebody's one time item, right?

[Ed Zitron]

You think that that will though?

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

And that's the thing with like an Amazon prime, for example. Yes, they have found, like Amazon or not like Amazon people I know many listeners don't love them, I agree, but it's like Amazon prime. They fix those costs by building their own logistics network and they found ways to, they had, I don't know, ways to make that cheaper. No one has that in AI. No one. Like it's just three, four years in and everyone's like, oh, we'll do Asics. No we won't. That didn't work. We're like two or three generations of Trainium inferentia tpus. Still not profitable.

[Paul Kudrowski]

Still not profitable. Right.

[Ed Zitron]

We would know.

[Paul Kudrowski]

We would know. But I think, and of course the problem is that if you look at, I was just looking at some data yesterday with respect to how small language models are increasingly closing the gap with large language models, which is causing training cycles on large language models to have to accelerate, become more expensive, throw more compute, added more reinforcement learning. The costs are particularly not declining. They're actually increasing sharply at the frontier because they're essentially being chased like the rabbits, like Wile E. Coyote and the Roadrunner. They're being chased into this very costly corner as a result. And that's a classic commoditization problem. If you go back to, I don't know, late 19th century, a very similar thing happened in railroads as people were racing desperately to try and find a way to build a corner and control themselves so that they could compete with all of these other upstart railroads. And of course all that really happened was capex exploded, margins went to shit and multiple railroads failed and we led to the crash of what, 1873, 1893, and arguably was a cause in the great Depression. So you're playing out this exact same game because you're sitting in this high capex world that's increasingly funded by debt and built on top of this duration mismatch. With token prices being now exposed and raw in front of people,

[Ed Zitron]

The world is transforming faster than ever and standing still isn't an option. At Oppenheimer, we're working at the forefront of the innovation economy to invest where progress begins, finding opportunities that build and protect wealth for individuals and institutions that want a seat at the edge of tomorrow. Put the power of Oppenheimer thinking to work for you. Wealth management, capital markets, investment banking.

[iHeart Podcast Announcer]

Run a business and not thinking about podcasting, think again. More Americans listen to podcasts than ad supported streaming music from Spotify and Pandora. And as the number one podcast for iHeart's twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus, only iHeart can extend your message to audiences across broadcast radio. Think podcasting can help your business. Think iHeart streaming radio and podcasting. Let us show [e-mail] that's iheartadvertising.com.

[Ed Zitron]

hey, it's us, the Jonas Brothers. And guess what? We have some big news.

[Paul Kudrowski]

What's the news? Huge news.

[Ed Zitron]

We created our own podcast called hey Jonas.

[Paul Kudrowski]

We invented a podcast.

[Ed Zitron]

Well, we didn't invent it. We, we just contributed to it. First people to do podcasts. Pretty. Yeah, pretty wide range of podcasts, but this one's extra special.

[Paul Kudrowski]

So how did we. How do we actually come up with the name hey Jonas? Guys, I honestly don't remember. I think it was on a call about what we should call it and. Well, we were thinking.

[Ed Zitron]

I'm originally calling it one of the

[Paul Kudrowski]

early names of our band before Jonas Brothers was.

[Ed Zitron]

This is how you guys remember it going down? Yes, I have a very different memory of this. We were talking about a thing a bit for the podcast. People could call in and say, hey Jonas. And then I wrote down on my little notepad, hey Jonas. And offered it up as a potential title for the podcast. But thanks for remembering that. Guys, listen to hey Jonas on the iHeartRadio app, Apple Podcasts, or wherever you get your podcast.

[Paul Kudrowski]

Just listen. We don't care where you hear it.

[iHeart Podcast Announcer]

Here's something that should not be as complicated as it is getting a racist statue removed. And here's something that should be a whole lot easier than it getting a new one put up in its place. As long as there's a politics of race in America, there's going to be

[Paul Kudrowski]

a politics of remembering the Civil War. To get to school, I had to go down Robert E. Lee Boulevard. To get to the grocery store, I had to go down Jefferson Davis Parkway.

[iHeart Podcast Announcer]

If you're a historian and you leave out half of what the history is, you're not doing your job. I'm Akilah Hughes and Rebel Spirit Season 2 goes deep on blue. Both of those things, the fights, the politics, the people who won, and my personal campaign to add something to the Kentucky State House that's actually worth the wall space. We are more than our bodies. We contain essence. We contain spirit. How do you represent that? They are just fueling a fire that is really catching. You'll see what I mean. Listen to Rebel Spirit Season 2 on the iHeartRadio app, Apple Podcast, or wherever you get your podcasts.

[Ed Zitron]

And the other thing is as well is people just literally in my piece today, people make this point about, oh, it's like the dot com bubble in the. We will, we will simply just, we'll reuse these things in the future. Like, we'll just pick these up and it's. I re. I, I hear this from very smart people, people who are not like beguiled by the AI industry. But it's like, okay, let's talk about what happened to the dot com bubble. So when it exploded, you had those. Some microsystems, the ultra, whatever it was, I forget, 43, 50 grand a server. But that one server could run an entire company. You could run everything on it. Databases, messy CRMs they had. Like, did they have on Prem Lotus Notes. Anyway, you had those things, but you could run that and it. You could probably run that in a garage. You might need to use the washing machines plug, but you could do it. Those things were 50 grand, so you probably get them at what, 20, 30 large. Okay, great. What happens when the AI bubble bursts? You can't just plug in an AI GPU. A B200 GPU is about 50 grand. It requires about. I think I looked this up very recently. It's like 1200, 1500 watts for a Sun microsystem server, about the same for a single B200, which will require bespoke cooling, a server, hardware, ram, all of this other stuff. And then you'll find out that you can't do Jack shit. With a single gpu,

[Paul Kudrowski]

it's gonna be a huge source of disappointment once you power it up, your neighborhood lights all blink out and you still can't do anything. So. Yeah, I just don't think you can actually. That's exactly right. But that's, but that's. I call this, I was just, I got into this with someone recently who was making a similar. I call it faith based argumentation. It's this kind of quasi religious orthodoxy that requires you to believe the following five things. They always create more jobs and they decide we can always reuse assets after the fact. And one of the things I always point to people is that Almost half of U.S. railroad lines built during the boom years in the late 19th century were eventually abandoned. And did they find reuse? They absolutely did. It only took 100 years and now they're mountain biking trails. Let's wait around for that.

[Ed Zitron]

Those were railroads. They were railroads that didn't require electrification, I guess.

[Paul Kudrowski]

So they were much more stable as assets. Right. They didn't have the problems that GPUs and data centers do where not just the huge power and cooling requirements, but also the inherent, the trajectory of the underlying technology where it changes quickly enough that, you know, is, is a 20 year old, you know, Blackwell of any use to anyone other than as a paperweight. And of course the answer is probably not. Whereas a railroad pretty heavily. They are extremely heavy. I actually was messing around with one recently. And so, yeah, and so this is the problem. And again, it's this sort of naive argumentation, not to mention the old Keynes line that it may be great in the long run, but in the long run we're also all dead. So it really depends on your time horizon. And I find it honestly, in the face of the kinds of consequential changes in the US economy, I find it a very glib style of argumentation where you're essentially patting people on the head and saying, don't worry your predator head, this will all work out, because it always has. And they're arguing from a data set sample size of 5, which we wouldn't launch a drug on that basis.

[Ed Zitron]

Also, I think it helps them rationalize bad behavior because if you say, okay, it worked, the one that actually upsets me is, well, the dot com bubble worked out. It's like, yeah, the stock market lost 80% of its value, hundreds of thousands of people lost their jobs, people lost everything in some cases. And at the end it's like, okay, that was also completely different. But you're being Quite glib about the first part, but it's also, yeah, it's okay that people burn a lot of money for basically no reason. This is also allows you to not think about bad stuff. It allows you to rest as the

[Paul Kudrowski]

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

[Ed Zitron]

Well, I think it's just. It's the rationalizing and it's also. It gives you a way of avoiding thinking about true structural issues. Right?

[Paul Kudrowski]

It's kind of. It's thumb sucking, I always call it. It's really kind of thumb sucking. It gives you comfort.

[Ed Zitron]

It allows you to be like, well, Google isn't stupid for raising $80 billion in equity sales. Yeah, Google, the largest companies in the world couldn't just destroy their companies by wasting all their money. It's like, yeah, go and type something into Google Search. Go and type anything into Google and tell me if this looks like a company running a good business or a good product or just a company throwing shit at the Wall and being like, this works, right? You know, fuck it.

[Paul Kudrowski]

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

[Ed Zitron]

By any chance, did you read Semianalysis's AI Dark Outbreak?

[Paul Kudrowski]

I did, yes. Which is.

[Ed Zitron]

It is one of the funniest things I have read in my life. So for the listeners, you'll have a link to this. But it's basically, yeah, AI is. So AI output will be real before it is measurable. We can capture token spend and we can capture jobs lost, but unless AI's output is sold at a visible price, only token spend is captured in gdp. By which they mean we don't actually measure whether something is good, we just measure whether something like we just. It's actually. So this is the shit a teenager would say when lying about having a girlfriend.

[Paul Kudrowski]

This is voodoo teen economics. Yeah, it really is. And again, it goes to that National Bureau of Economic Research paper. It's exactly the same thing I was mentioning. At the top there is this tremendous. And I'll send you the link if you haven't seen it. And it's called where is AI? And GDP statistics Filling the Measurement Gap came out.

[Ed Zitron]

Yeah, yeah, yeah.

[Paul Kudrowski]

Or a couple of days ago. And they argue that essentially AI quality adjusted AI output is up more than 2,000% per year. They come up with estimates of like 250, $300 billion on top of the. But they could then essentially come to the conclusion that this is all true as long as you accept our redefinition of gdp. And of course.

[Ed Zitron]

Right, right. Okay.

[Paul Kudrowski]

If you allow me to redefine gdp, I could present you with some tremendous numbers. And the entire paper is absolutely fasc. As an example of what's often called motivated reasoning. I need to believe this. Therefore I construct an argument to allow me to continue to believe it. And the way I get there is by redefining a variable that's already very squishy in the first place. Let's not pretend that measuring GDP is much easier than, I don't know, measuring muons in a cloud chamber or something. It's still very hard. And you're trying to make it harder to justify something that's just not defensible.

[Ed Zitron]

And the AI dark output one is great because substitution dark output is work that was previously done by humans and is now done by AI. In our dark output monitor, we have identified roughly one and a half trillion dollars in tasks that current AI could substantially augment or automate. To which I say, why hasn't it done it right? This is the AI thing though, because. Because specifically with AI, with other things, productivity is hard to measure. It's hard to measure outputs with workers in knowledge work, especially, like, it's doable, but it's not like a linear path, except you're selling a tool that can theoretically do anything. If this did what they said it did, we would have gunfights in the street. We would have the destruction of most knowledge work and it would be happening, happening a year ago, it would be half happening a year ago, happening fully today. We would have the destruction of law firms, we'd have the destruction of hyperscalers. Because anyone would just be like, build me a Microsoft Word and it would build them a Microsoft Word and they would use it and it would be functional, bug free. All of these things. They would be. Well, I mean, we've already seen a spike in litigation from pro se people representing themselves, but nevertheless, we would see, see law firms turning into two or three person shops that would beat the leading litigators because they would have.

[Paul Kudrowski]

Oh, absolutely.

[Ed Zitron]

It would be very easy to see.

[iHeart Podcast Announcer]

Financial growth begins long before the first investment. It comes from understanding what you're building toward, what's at stake, and what success looks like for you. At Oppenheimer, we bring bold thinking, guided by the full strength of our expertise to put capital to work building and protecting wealth that lasts generations. Put the power of Oppenheimer thinking to work for you. Wealth management, capital markets, investment banking. Run a business and not thinking about podcasting, Think again. More Americans listen to podcasts than ad supported streaming music from Spotify and Pandora. And as the number one podcaster, iHeart's twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus, only iHeart can extend your message to audiences across broadcast radio. Think podcasting can help your business? Think iHeart streaming radio and podcast. Let us show [e-mail] that's iheartadvertising.com number one hits, millions of records sold awards, sold out tours. You think the Jonas Brothers are satisfied? Nope. It's podcast time.

[Paul Kudrowski]

We get to ask other people questions because we're sick and tired of being asked questions.

[iHeart Podcast Announcer]

A Jonas is available now and their first guest is a big one, Paul Rudd.

[Paul Kudrowski]

You know, Steve Carell is a great singer.

[Ed Zitron]

Can he tell you not to audition the office or something.

[Paul Kudrowski]

I told him, whoa, we were filming Anchorman. Clearly, I was the idiot. Thank God he. He didn't listen to me, right.

[iHeart Podcast Announcer]

Listen to hey, Jonas on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

[Ed Zitron]

June is Black Music Month, and on the Drink Champs podcast, we're speaking with the hottest names in the culture, like Swae Lee. Do you realize how legendary you are?

[Paul Kudrowski]

I appreciate that I be seeing it,

[iHeart Podcast Announcer]

but I'm like, man, I still got,

[Ed Zitron]

like, so much more to do.

[iHeart Podcast Announcer]

Like Prince, he dropped like, 30 albums.

[Paul Kudrowski]

Albums.

[Ed Zitron]

We dropped like five right now. That's the rate we gotta be going. Yep, that's a good attitude. You also hear stories from industry legends and hip hop pioneers like Fab five Freddy.

[Paul Kudrowski]

I directed one of Nas's early videos.

[Ed Zitron]

Which one? One Love. Wow. Yes. I literally filmed in his apartment in Queensbridge. His moms were still up in that apartment.

[Paul Kudrowski]

Nas was just beginning to take off.

[Ed Zitron]

His pops used to live near me in Harlem. His dad. Dad introduced him to a whole lot

[Paul Kudrowski]

of, you know, conscious stuff, and he

[Ed Zitron]

made a young prodigy. No matter the era, Drink Champs brings you the biggest names and the most unfiltered conversations. Listen to Drink Champs from the Black Effect podcast Network on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

[Paul Kudrowski]

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

[Ed Zitron]

Well, so just a very simple response is, okay, let's say it can identify them better than radiology right now. What radiologists, they don't just look at stuff like they are doctors, they require like there's more to the process than just like yes or no. And also you are buying the experience. You're buying their experience and their connections and their ability to work within a hospital system. And actually.

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

Right.

[Ed Zitron]

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

[Paul Kudrowski]

It was the same problem with the meter studies obviously in terms of the duration. Right. The duration of tasks, the metr.

[Ed Zitron]

Right, right, right.

[Paul Kudrowski]

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

[Ed Zitron]

Yeah, there's no real way. And it's also just we treat these things like they're fucking gifted children. It's like, wow, you could 50% of the time do this. This may be. And that is it's time for the New York Times to write an entire article about. We need an odd Lots episode that covers that 50% of the time this could do this. And it's just because you can't do the thing that every other obvious innovation has done. You can't do it where you just go, wow, this does this. We could do this. Now it's. If this happens, and that is a like load bearing if we might be able to possibly do this, we can't measure it in the way you measure other things, which is how we would otherwise distinguish whether something was good or not. So we made up a new thing and wow, has it beaten the benchmarks. We made up for it.

[Paul Kudrowski]

Right. And the problem of course is this all becomes a bit facile and glib and everything else in terms of the arguments being made. But it has spillover consequences in the real world, which is the unfortunate thing is that. But let's follow the logic forward. If my job is I'm selling tokens and tokens, I need to sell more tokens rather than less because I have to pay the nut on some fixed obligation. Well, I'm going to construct more data centers and construct more larger data centers and you end up with these massive megaprojects like this controversial one that Kevin O' Leary has been promoting, Mr. Dog Shit, right north of Salt Lake City that in the limit might be the size of Manhattan or larger. As people point out, this has consequences because the arrow of time only moves in one direction. I defy you to find an example of the old talking head song where this used to be a parking lot and now it's covered with flowers. The data center is not gonna reverse. Once you build these giant things in the real world with real consequences in terms of sprawling out physically, but also sitting on top of water and power, untangling that becomes really, really difficult. As does, for example, having to spend spin up all of these new natural gas plants to power these things. Because we're increasingly asking that hyperscalers come with their own power behind the meter. Yeah, right, right, right. When we're doing that at the worst possible time. Because the combination of batteries and alternative sources ranging from wind and solar, for example, are becoming much more effective and able to be more persistent with battery pack backup. And yet we're installing these CO2 intensive things with 30 and 40 year lifespans, funded by debt that are almost all likely to end up being stranded assets. They'll Be like the statues at Easter island eventually. Except natural gas plants.

[Ed Zitron]

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

[Paul Kudrowski]

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

[Ed Zitron]

Fully agree. And the funny thing is with that is people like yeah, the dot com bubble, when that burst, people had the useful infrastructure that will cost just as much to finish in the future. Except the debt. You'll go to a credit for. You go to a, well probably not private credit in the end of this, but go to a bank like yeah, I want to finish this data center. They will shoot you with a gun. They will, they will. You will get headshotted by the bank manager for saying the words AI it's just. And it's these things.

[Paul Kudrowski]

I'll give you an even more. It's an even more insidious than that. And I spend a lot of time talking, trying to talk off the ledge if you will, various regional economic development people. I was just talking to some people in New Mexico about this and the problem they have is, you know, they've been trying to land some large employer for 25 years in these high unemployment regions. And so I'm entirely sympathetic to the problem that a data center hyperscaler shows up and says listen, let me install this. Give me the following giveaways with respect to taxes. And this will eventually, after construction will have this many jobs and so on. And you don't have to keep fighting for the Hyundai battery factory or the Ford assembly plant or whatever else. It'll just be here spinning off tax revenues. And so what happens? A. That looks like a pretty good bet because it's a fixed obligation in terms of what will be flowing back into your county for years to come. And what do they do then? They start pre budgeting that and saying okay, we'll start building new playgrounds, we'll start fixing the water supply, we'll be able to fund schools. Great. Now okay, you front loaded all of that stuff. What happens whenever the data center doesn't get finished? You're actually in a worse situation than you were previously. So it has real world consequences in terms of these annuity streams that are being dangled in front of people whose regions have suffered economically for decades. And that's going to be the story over the next 25 years.

[Ed Zitron]

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

[Paul Kudrowski]

Oh, or a factory full of geniuses. That's my favorite.

[Ed Zitron]

Oh, the data center. Oh, geez. A data center full of geniuses. I. I really dislike Dario Amadai. I hate how he sounds, I hate how he speaks. Just like, what are you fucking talking about? Because more data centers so far has not actually improved these products. It's not like there's not. If you gave OpenAI another 15 gigawatts of data centers doesn't exist, but let's say they did nothing. Like nothing is going to change about this. Yeah, I don't. And I don't think I. But the other thing is as well, hey, is Vera Rubin going to make AI profitable? Because if it isn't, this is probably the last generation. But that is, I think at this point the thing.

[Paul Kudrowski]

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

[Ed Zitron]

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

[Paul Kudrowski]

Well that they. A couple of different things. One is that this is, this is the equity raise.

[Ed Zitron]

Yes, exactly. So 10 billion from Berkshire and then some other like 10 billion from Berkshire and then I think two different at the market sales.

[Paul Kudrowski]

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

[Ed Zitron]

Yeah. Is it also a sign that the debt is running out? Why would they do this instead of raising debt?

[Paul Kudrowski]

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

[Ed Zitron]

So yeah, do you think that this is toward the end? I'm not asking for a hug, no, no, no, no.

[Paul Kudrowski]

I think very much that I think the blow off top is this year's 3 Mega IPOs. And that kind of marks the gonging of the bell with respect to the seriousness. With respect to you have to take this inability of these companies to make money.

[Ed Zitron]

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

[Paul Kudrowski]

Paul? Kadraski.com is the best place.

[Ed Zitron]

Hell yeah. Everyone, thank you so much for listening. I'm of course Ed Zitron. You can catch me on this podcast, Better Offline. Where's your Ed? Subscribe to the newsletter My principal form of income. I will be back with a monologue on Friday. Thank you all for listening and goodbye. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski. 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 me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat. Where's your ED? 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.

[iHeart Podcast Announcer]

For more from Cool Zone Media, visit our website coolzone media.com or check us

[Paul Kudrowski]

out on the iHeartRadio app, Apple Podcasts

[iHeart Podcast Announcer]

or wherever you get your podcast. This is an iHeart podcast. Guaranteed Human.