Better Offline - The Reality of AI Economics With Paul Kedrosky

Episode Date: April 7, 2026

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

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AllZone Media. Hello and welcome to Better Offline. I'm your host, Ed Zittron. 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 Kodroski. Paul, good to have you on. Hey, Ed, good to be here. 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. Yeah. So it's pretty easy to find it. I mean, there's the old joke, which was 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, Terry pick some data and say that there goes AI right there. And the answer, of course, 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 comments 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, I don't 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 example, its share of non-residential fixed investments, which is at levels we last saw with the railroad buildout or with rural electrification. Can you be specific at what non-residential investments mean, just so I get you?

So atoms that aren't in houses. So that's the short answer. So factories, manufacturing, highways, you know, fixed equipment you're putting in your, in anything that you're building for an economic purpose. It isn't resident. residential. Okay. So that usually is a fairly broad and diversified category. So you've got people building out, you know, if the current administration had its way, that statistic would currently be dominated by fill in the blank manufacturing, right? Because the attempt is to onshore manufacturing. So that, if policy was working in that regard, you'd be seeing the dominant chunk of that being the onshoreing 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 data centers, which is a basket of things, which is made up predominantly of GPUs, but also the build-out of the facilities, HVAC, 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 than I startled other people was the idea that it was the largest share of U.S. GDP growth for three of four quarters last year.

Right. So that in the absence of this non-residential fixed investment wonder drug we call data centers, U.S. would have been in recession in the first quarter and the fourth quarter, neutral in the second and mildly positive in the third. So it was the economic story of 2025. And so the reason why this is important, and the analogy, I make all the time is my dog barks when the mailman comes to the house and the dog keeps barking and the mailman goes away, right? The dog thinks he did it. He didn't do it, right? He has a messed up,

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

ones in going to American clients, or is it just all of their sales? No, so you parcel it out, obviously, so by geography, so it matters immensely what's happening in the U.S., no, granted, the predominant share of invidious sales, just like the largest sales of most of, you know, transformers and everything, are all happening in the U.S., right? As this buildout happens, something like 70 to 80 percent of the global data center buildout is happening in the U.S., and most of that is happening in Northern Virginia or Texas. So it's largely a U.S. phenomenon in the first place, but nevertheless, you have to parcel out the pieces appropriately. 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. Right. And there has been some tremendous piece. The Wall Street Journal had 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, you know, Don Carleone and sitting with everything happening and everyone coming to the table. And they're this, you know, Mafia Don who is investing in things, right? So they play the role of investor. They play the role of acquirer. They play the role of, you know, vendor. So they have this incredible hub role. So each dollar that they're putting out there in a sense is vastly more important because in a sense they are the load bearing beam in the middle of all of this. They are for now.

and it's changing rapidly, but nevertheless, they are for now. The thing is, that just feels very unstable to me, because in VDIA, 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 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, on a, on like an economic level for any country or anyone investing. So I have a tendency to make that argument, and I hate when I'm,

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't happen. So let's take them at face value. Let's say they're right, right? Say 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, uh, invidia has these anomalous GPU margins in excess of 70% gross margins, which are ridiculous. And that's to make them just to be clear. That's right. Exactly. And so, so their margins are very high, but they're in the middle of this transition, this admitted transition from what's euphemistically called training, which is kind of a misnomer, to this thing that's called inference, which is probably more accurate. So from training models to answering prompts, right? And the margins on inference are going to change dramatically

because the things that gave them, and this is one of these Silicon Valley silly words, but gave them a moat, the things that gave them a moat, the things that gave them a moat in the world of training are far less important in the world of inference. And so you're seeing this proliferation of new chip companies coming to market, incumbents with new products. So they're facing much more competition in a world of inference. So even if you grant that the market's going to grow as large as it once did, which is 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. 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. I, it's just so, it's so strange. The way I look at at it is I always, whenever someone says, um,

invidia, I say Saudi Arabia. And when they say, um, tokens, I say Humvees, right? So their goal is to get more people,

their goal is to get more purchased people purchasing humvees because it's good for them because it, right. It consumes the thing they, however indirectly produce, which is to say these things called tokens and not the crypto. Right. So,

so if you think about it in those terms and translate Jensen's, you know, GTC talk and most of the things he says, it doesn't read that. differently from a random industrial minister in Saudi Arabia saying, you know, this oil stuff, if you guys just back away 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. 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. Yeah, yeah, yeah. No, no, exactly. So, so 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 OpenClaught, which is a ridiculous idea to suggest that people should be using this in their house. It's like I should have you know, my own sort of, you know, home nuclear reactor. It's, it's a ridiculously dangerous

technology for most normals to be using. Yeah, I just, I also feel like it's a sign they're a little washed when it's, you've got a three, 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 dollar jackets. My man, my man should have a little more swag than claws. It's just, Those are tremendous jackets, though. Oh, they are tremendous. I had the menswear guy on a few years. Amazing jackets.

The man does have good. He has a good tailor as well. But anywho. So I think that's a really important point you make, though. That I think you have to, when you, the read through on OpenClaw isn't just some confusion about where the market's going, but the read through is the promotion in almost an industrial affairs level of this commodity called tokens. And how can I get people to use more of this?

So that's why you hear, you know, song and dance acts about OpenClaw. these incredible hyperbole about Claude Code and how cloud 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 cloud 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 like a ground truth. I can look at my software and I create some code and change it and it breaks. In AI terms, that's a really tight gradient descent, meaning that obviously I've just learned something really quickly. Changing this to this doesn't work. In most of white-collar work, that's not true. What matters is I create a PowerPoint presentation. Does my boss like it? Tell me the gradient descent there.

There's no gradient descent. It's very subjective. It's almost an aesthetic answer. There are parts of white-collar work where that's not true, but much of white-collar work doesn't have the same characteristics as software. So if you want to project the kind of growth that people like Jensen are projecting, you need to believe that these harnesses, Claude, code, codex, blah, blah, blah. Yeah, the things that you've used with the models. 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. That's not true. But the thing is they don't know anything, so you can't guarantee that. Right, right. Because there's no ground truth, right? So this gradient descent doesn't work. It doesn't work in almost any domain outside of software. So the weird thing is, is we've actually started off in the nearly perfect domain. to give a completely unrepresentative example of what the future looks like. But I start off in coding, which is why coding are the coders and developers are some of the biggest sort of flag-waving ambassadors for what's going on. It's like, just wait till this shows up in, you know, I don't know, pick it, pick your domain in the white-collar work.

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It is strange as well. Like the, you get very few people who are just like, yeah, I kind of like this. It's either the pure hatred, which I name my little. This is 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 that anything like this happened.

Like maybe in stocks. 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, you know, wild-eyed as possible about my support for this. Shows I'm in the in-group or the out-group. And you see that, right, this sort of tribal signifier stuff.

And the other one is, and this is the one that I find most fascinating, is this kind of frantic quest to believe that if, we work hard enough and fast enough that I'll never have to work again. And you hear this from the people 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. I've got a vest attached with all these explosives. And I'm threatened to walk into your economy and blow it all up, right? And just try and stop me because there's hundreds of us doing this. We're all going to come and we're all going to blow up your economy. And what are you going to do

about it, right? And so that's what? That's what. I think a lot of this is because they feel like if I do that, then the government has no option but to institute some kind of broad policy of support where I can then go out and, you know, make figurines all day or whatever it is I want to do. Yeah, but I will also say that the people who are most excited about AI don't seem to have other hobbies. It's not, that's always the thing of point. Yeah, it's totally true. What are you planning to do with your spare time? I'm learning piano right now. I'm learning to code for fun and it's like, I look at these people and they're like, what do you do? I run 17 subagents an hour.

If these things don't create my special project that I'll never show you, I will die. I like to gray out the power in my neighborhood by running as many sub-processes as possible. Yeah, great hobbies, get on that. Just, 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.

Yeah, no, the financial system. 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 your pretty little head about this, because these are big boys and girls who are spending all this money, the hyperscalers, the Microsoft's, Amazon's, Google, and everything else, what they spend their money on should be no concern to you because these are big and profitable companies, well, leaving a site open iron 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, end of the year of 2025, a data center related debt was the largest chunk of investment grade debt issued in the U.S. in 2025. Tech used to be the no debt sector. They went from no debt to the

largest issuer of investment grade and non-investment grade debt in the United States last year. And of course, all the way along, people normalize it and say, you know, it's fine. They're very profitable. And then when it turned out the profits weren't enough to pay for all the data centers, that's also fine, right? So they'll did these things end up being fine. And then, of course, it all came home to Ruth's to a degree at the end of the year as private credit started sort of got attacked from through the side door because of this problems with this, you know, very large positions they held in software as 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 over exposure of private credit to data center related debt. Because if you think about it, it's not the consequentiality of it. It is something like the global financial crisis. the more entertaining part of this is they're treating data centers as real estate.

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 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, the, the, these notes that have been issued with respect to the debt to finance data centers, with a thing that's falling 70 or 80% year over you're in price, just try and run an auto company that way with a significant doubt.

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? Yeah, yeah. So there's a double whammy. There's both sides of it, right? So to a degree, if you're working with a model directly through the API, which the largest issuers are, or 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 there are a capital investment around which the investment's predicated, the depreciation of those items is relatively rapid to it. 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, 24-7, huge workload, it's kind of like you know, you had a car that was only driven to church on Sundays and a car that was raced at Le Mans one weekend. They have the same number of miles.

I know which car I want. The same thing applies to GPUs. Yeah. And the other thing as well is, I've really. been looking at this. So you wrote up the Wood Mac 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. 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? Like,

where are we putting these things? I mean, Taiwan warehouses, I guess. 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 what you told in the Northeast, just recently put out some data on this, showing that something like the data you suggest, which is like 25% of the total commit was actually ever produced and likely will ever be built out. And that's in part because a lot of these things are speculative projects, naming no names. There's a very large Texas company that's doing this directly. 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, this is a game we've seen back to, I mean, the analogy I make all the time is back to Chinatown. This is like Chinatown, right? Where I'm

I'm buying up real estate with numbered companies in hopes of securing water rights. I saw this. I saw this when Jack Nicholson was wandering around. Jake Gitties was wandering around in the deserts of California. I don't know what you mean. Tell me. What do you mean the Chinatown example? So what's happening is increasingly a lot of what's going on under the hood here that's

creating the impression of a buildout that doesn't exist are these things called powered land companies. So powered land companies are these speculators, they don't call themselves that, who look for strategic locations where using numbered companies, they can purchase real estate that has access to peering points, so high-speed interconnection to the... What is a numbered company as well? I'm really sorry?

So a company that doesn't make it obvious to who the actual direct owners are. So it's not clear what their purpose is. So I think of it as like Cayman Islands, but it doesn'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, 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 hypers. Look at me. You've got nowhere else to go. I've locked all this up already on, kind of like locking up the water for the orange groves. Right. Is that widespread? Is that widespread?

That's so bad because I've been, This whole time I've been looking for the speculative part. I will fully admit that's been it. If it's the land, that's not great for anyone involved. But even then, though, GPUs are still being sold. That's the real thing that's getting me. Right, but part of the problem is, and there's, was a great piece from Trend Force, 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 LTAs, 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 a 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.

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 these sorts of long-term purchase agreements, which have nothing to do with actual units being shipped. The mob boss thing again. Yeah, yeah, yeah. But that's, but that's, I was joking me up to, I told someone this the other day, who was I talking to? I was the Wall Street Journal was talking to them and saying like it's a, it's kind of like the old mafia threat and like really nice AI market you have there.

I would be ashamed of something happened to it. Oh, or you want you don't, I guess you don't want Vera Rubin anymore. I guess we'll have to give the Vera Rubin. It's exactly like that. So this is the thing. So those two pieces are really important because it creates the impression of unit growth where unit growth doesn't actually. exists because it's predicated on locking supply in the hopes of something later. But at the other side of it, you've also got this speculative land component. There was a company the other day,

it was a great Bloomberg story about it, who raised, I think it was $3 billion in junk bonds for exactly this purpose. So this is highly speculative stuff that's actually seen. Terrible. No, no, not terrible. It was just literally last week. It was called Tract. I think it was called TR-C-T. Yes, tracked. I remember this one. This is the thing. This is the insane thing. They're still able to raise those bonds, though. They're still able to get the money. Well, that's because, again, this is the insidious problem here.

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

you know, who knows. But if it's Microsoft, Google, and the other hyperskills, the other side, I'm like, you know what? If this works, they're good for it. But what if it doesn't work? 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. I've been, I'm excited about the laser tag arena.

Arena future we have. Just America's the laser tag capital of the world. That's right. We got a lot of extra space for you store it and laser tag. Another podcast from some SNL late night comedy guy, not quite. Unhumor me with Robert Smygel and friends. Me and hilarious guests from Bob Odenkirk to David Letterman help make you funnier. This week, my guest, SNL's Mikey Day and head writer, Streeter Seidel, help an

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to become whole. This podcast is for you to hear more. Listen to deeply well with Debbie Brown from the Black Effect Podcast Network on the Iheart Radio app, Apple Podcasts, or wherever you get your podcast. 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 the inference is rising rapidly because of things like distillation, models are shrinking, chips are becoming more efficient.

there's less memory required. 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 DEC. Oh yeah, yeah, no, no, that's a ridiculous position. Right, right, right, right. Look at companies like 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 Tallis in Toronto, TAA. I'll look into them. So Talas 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, leaving aside the wattage required. That's a relatively high-speed token producing chip.

So Talas 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 rock.

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 four to six, sorry, four to 12 GPUs for one generation of the smaller reasoning model, 04, I think it was. Yeah. And that's just for one generation over several minutes that someone's doing a particularly different coding task.

It doesn't feel like these inference chain things will trickle down there. So maybe it will be the future of LLMs, is this small industry run on these smaller chips or something like that. I think you're going to see all of the above thing. Let's say, for example, we're seeing inference happening inside. of EVs where it's you we're doing, I'm doing rapid ingestion of video tokens for the purposes of deciding whether I'm going to back into my neighbor's trash can. And, um, these kinds of things will have, which models. Oh, absolutely. Because you can, you can imagine I'm ingesting all of that.

The video, I'm going to have a lot more granularity with respect to the tokens flowing back to me and I can do more with it and know more about what's happening in my environment. I think most of video ingestion is going to move towards edge tokens, but done on small language models, very, very small stuff. we've already had edge compute AI, television stuff for like a decade or more. Like I was working on stuff like in 2014. It just feels like a lot of this is trying to make tokens do stuff that we already kind of do.

So it is. But the thing I, and again, you know, 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. Also, the problem with tokens as a commodity as well is it's very hard to know, like, one, like a million tokens per million tokens. It's impossible to actually measure how long a task, how many tokens are tasks 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 of you see now. I see it constantly. Well, right now people are mad that they can only spend $1,000 on a $200 a month plan. But the thing with that is people very clearly do not know how much a token goes or a million tokens goes. Like, 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. Wow, 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. 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, right? So, yeah, that makes that.

You have to separate 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 us, you know, a copper mine, how many, how much copper is going to take for GM in a particular car. So. You know how much. 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. Right. And then they'll make constrained, you know, constraining decisions where they'll say, I only want to use this much copper because copper is really friggin' expensive. And so, you know, we're going to, like, I just saw, I think it was Riven, Riving in 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 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 things. 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 real, Dario Amade Anthropics, been very upfront about this, as he believes that we are in a land grab mode. There's only going to be a couple of frontier model vendors left standing. And so we need to make sure that, you know, we're the dominant provider, if you will, of, you know, coating harnesses and frontier models. No, I think that's a misnomer too, but that's another problem. Yeah.

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

It is outside consequences. But the thing is they've trained everyone in this land grab to act in a way that doesn't make sense long term. And I'm not sure. I don't think they can become profitable. 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.

That's exactly right. So I have a wild-eyed theory. Are you ready? Go, go, go. So my theory is the first, the first frontier model company to abandon frontier models wins. How'd you mean? 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 test.

Certainly normals can't. Coters claim they can, but if you actually do it in a blind way, most of them can't tell. This is just ego. Right. 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 values in

the harness. And so the first company to say, you know what, we don't need to spend this kind of money anymore on training new models because we're going to just sit on top of models from all of these loons who are out there spending crazily on new frontier models that aren't improving very much anymore. Now, certainly not like they were four or five years ago. And they will be rewarded for that, no different than saying, you know, I've let go all of my employees or I've decided to stop spending money on hydroelectric dams. You've cut CAPX. You've made your business more financially appealing by taking away the single biggest piece of cost because you're recognizing that the world has changed, that I'm not getting incrementally as much

value for dollar on training a model as I was five years ago. And that's very clear in the data. If you look at any of the composite benchmark models, getting away from like, can they solve math problems, but literally real world composite models, it's been a sharp decline from like, you know, 18% year over year improvements in models to like four or five percent at vastly higher costs and more dollar or more time. And so that really matters. And so my completely, will never happen theory is that the winner here is the first one to stop doing it. Isn't that just describing cursor? 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. And so I just think people get trapped in coding because it was the first place this stuff emerged. So you have to think about like, co-work 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. Like, I watch people struggle with Claude... But it didn't work. It doesn't work, but it's at least... It's directionally the right idea.

If you buy my theory that all of this stuff is commoditizing so fast and is a loser's game financially, that maybe the right sort of game theoretic strategy is to be the first frontier model company to stop making frontier models. And in a weird way, Apple kind of showed the way, right? Because early on, they were getting pilloried for, why isn't Apple spending more on AI?

Why does Apple not have a model? And now, of course, it's reversed. where it's like, look at Apple, they're so smart. They're going to continue. Look how smart they are without their CAPEX. I don't know. I just feel like what you were describing is just AI model rapper 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 to this point, every time I read about an integration with like a Goldman Sachs or somewhere, I can never actually find out what it does. I can never, and the further you get into the reason. 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? I said, oh, God. I said, I used to work on the cell site. I said, my first instinct is none that they're all lying to you. But my second instinct is, I'll say equity research because, you know, no one likes

to build spreadsheets and maybe it helps them build spreadsheets. And they said, no. So the answer, of course, was investment banking. And I said, why investment banking? These are like knuckle-dragging dinosaur, Arcade Man. 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. And so 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. But that's also kind of worrying because like the best example we have for this thing that has taken over everything, at least optically, even though it hasn't in economic terms, is like we can do PowerPoints, kind of. We can do powerpoints for junk bond raises for microcap companies way better than we used to. Wow. And it's like helping junior analysts. So it's like, are you really, the time they're saving is just lowering their workdays from 15 hours to 11. Well, and they're still being shouted at, unfortunately, for them.

But, you know, that's the way it goes. That's part of the job. That's part of the job. Paul, it's been such a pleasure having you. Where can people find you? Paul Kandroski.com. Thank you for joining us. And yes, we'll be back with the monologue this week.

I'm, of course, Ed Zitron. Thank you everyone for listening. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Mattersowski. You can check out more of his music and audio projects at Mattersowski.com. I-T-T-O-S-K-I-com. You can email me at E-Z at Better Offline.com or visit Better Offline.com to find more podcast links and, of course, my newsletter. I also really recommend you go to chat. Where's Your Ed dot at to visit the Discord and go to

R-S-Better Offline 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 our website, CoolzoneMedia.com, or check us out on the IHeart Radio app, Apple Podcasts, or wherever you get your podcast. Another podcast from some SNL, late-night comedy guy, not quite. Unhumor me with Robert Smygel and Friends. Me and hilarious guests from Bob Odenkirk to David Letterman help make you funnier.

This week, my guest, SNL's Mikey Day and head writer, Streeter Seidel, help an a cappella band with their Between Songs banter. Where does your group perform? We do some retirement homes. Those people are starving for banter. Listen to humor me with Robert Smigel and Friends on the heart radio app, Apple Podcasts, or wherever you get your podcasts. Hey, everyone.

It's Ryder Strong and Wilfredel from PodMeets World. And now the PodMeets Twirled podcast. We're two men who were completely clueless to reality TV, and we're gearing up for the season finale of Survivor. I know we annoyed a lot of our listeners by our severe lack of survivor knowledge. That is the point of the show. I'm just going to remind you. Ah, ha, ooh, ah, who.

Again, we are experts. Listen to Podmeets Twirl on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. There are times when the mind becomes a difficult place to live. This is David Eagleman with the Inner Cosmos podcast, and for Mental Health Awareness Month, we'll talk with singer-songwriter Jewel about anxiety. I started living in my car, and then my car got stolen. I was having panic attacks. I was agoraphobic.

This is a month of deeply personal and honest conversations about what happens when the brain goes off-course. Listen to Intercosmos on the IHart Radio app, Apple Podcasts, or wherever you get your podcasts. Most people out here think that taking care of one another is important. And most people would step up for a neighbor going through a tough time. Most people around here help out friends and family when they need it. But the funny thing is, most of us won't look for help when we need it. Talk to someone if you're struggling with mental health. Because most people out here really care.

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