Speaker 1: Media greetums. I'm at Zetron and this is Better Offline. Today.
Speaker 1: We are joined by the mighty economist Paul Keadrowski. Paul,
Speaker 1: thank you for joining me.
Speaker 2: Hey, how's it going.
Speaker 1: It's going gray. Everyone's deeply upset because this week and
Speaker 1: the last week everyone has been saying, huh does AI
Speaker 1: have a return on investment? And it's I've really been
Speaker 1: enjoying it because it's like watching the dinosaurs look up
Speaker 1: and see the meteor. They're just like, what do you
Speaker 1: what do you mean? What do you mean this costs money?
Speaker 2: Have you?
Speaker 1: I don't know if you've seen the GitHub co pilot stuff.
Speaker 2: Yeah, I actually put it a thing on it yesterday.
Speaker 1: Oh sorry, Paul, terribly rude to me. That was you.
Speaker 1: You know me, I've got all sorts of crap on,
Speaker 1: so I haven't read it yet. But I'm excited to
Speaker 1: talk about this. I'm really excited. It's really and not
Speaker 1: only that. I mean there's I'll sort of triangulate with
Speaker 1: three different things that touched on different aspects of this
Speaker 1: at the same time. One was, obviously they get up
Speaker 1: copiled study, which we can get into as deeply as
Speaker 1: you want. There was also a piece that came out
Speaker 1: in part from the Peterson Institute for International Economics.
Speaker 2: Yesterday the day before. Jack Cook at Clark at Anthropics
Speaker 2: set it around who is obviously one of the co
Speaker 2: founders there, and it's and it's called where is the
Speaker 2: where is AI? And GDP statistics. And then of course
Speaker 2: there was the tobacco which I saw anonymous someone that
Speaker 2: had anonymously disclosed that they had spent almost a half
Speaker 2: five hundred million dollars because they had uncapped token expenses,
Speaker 2: and we'll discovered they've sort of blown their credit cards.
Speaker 2: So anyways, yes, there's a bunch of flat as well.
Speaker 1: Well, let's stop with the GitHub things. So for the uninitiated,
Speaker 1: get up Copilot, a coding tool from Microsoft. A couple
Speaker 1: of weeks ago I broke the story, of course that
Speaker 1: they were moving their users from a premium request model
Speaker 1: to a token based model. So think of it like
Speaker 1: this with listeners. If you every time you use the
Speaker 1: cab service, you could just say drive me from the
Speaker 1: Upper West Side to Red Hook and that would just
Speaker 1: that would be one drive, and you get a certain
Speaker 1: amount of drives a month, and then suddenly the beginning
Speaker 1: of June they turned to you and say, yeah, you've
Speaker 1: got to pay by the mile, and you suddenly realize
Speaker 1: you've been taking ninety five mile trips. You've been asking
Speaker 1: to drive from New Jersey to Maryland, which I realized
Speaker 1: is further than ninety five miles. Not a geographer, all right,
Speaker 1: But nevertheless, on the GitHub co pilot subreddit, people have
Speaker 1: just been posting, what the fuck what do you mean?
Speaker 1: My whole balance is gone in three prompts? What do
Speaker 1: you mean by that?
Speaker 2: Uh huh, it's yeah, And this is part of the problem,
Speaker 2: right has this been? You can get all ekon of
Speaker 2: wonky about this stuff about the merits of metered pricing
Speaker 2: on a per token basis versus lump sun pricing, but
Speaker 2: in a sense you can think of this was the
Speaker 2: early pricing and token in terms of how tokens were
Speaker 2: metered out, had two really important characteristics. One is they
Speaker 2: were grotesquely subsidized. You weren't actually seeing the real all
Speaker 2: in cost with respect to the loaded cost of actually
Speaker 2: providing you with those tokens. And then as a kind
Speaker 2: of don't pay a cent event up there with cost
Speaker 2: co it was being bundled, so you we had a
Speaker 2: second layer of masking with respect to what these what
Speaker 2: tokens were actually costing. And so once it becomes unsubsidized
Speaker 2: and unbundled, then you see, you know, your ass is
Speaker 2: dangling in the breeze of real token pricing.
Speaker 1: I think it's funny as well, because for years people
Speaker 1: have been saying to be that's not happening. They're not
Speaker 1: subsidizing it. It's different. It's just it's like the the
Speaker 1: cost Go model, for example. People are like, oh, yeah,
Speaker 1: well they're making money other ways. It's like, no, they're not.
Speaker 1: They're just selling. In Microsoft's case, they were like, we're
Speaker 1: going to sell you one one thousand dollars for thirty
Speaker 1: nine dollars, right, what do you think? You think that's good?
Speaker 1: Do you like that?
Speaker 2: It's lovely, It's a lovely come on. It brings people in.
Speaker 2: It's like the hot dogs and Costco, except Costco has
Speaker 2: other things on which they make a b outload of money.
Speaker 1: Except the hot dogs cost like seven thousand dollars a packet.
Speaker 1: It's just I think it's quite deceitful personally. I think
Speaker 1: it's because these on one hand, we can make fun
Speaker 1: of these people. I will continue to do so. It's funny,
Speaker 1: but when you look at them, it is also quite
Speaker 1: depressing because they were intentionally misled. Like these people had
Speaker 1: no idea. It's not like these subsidize subscriptions, like, hey,
Speaker 1: if you use this many tokens while paying for them,
Speaker 1: it would cost this much. Until they made the change.
Speaker 1: Microsoft release the calculator. It allows you to see that,
Speaker 1: but only once they'd announced it. So you have millions,
Speaker 1: I would say, the vast majority of people that interact
Speaker 1: with AI who have no idea what it costs, literally none.
Speaker 2: Right and may and which which is made worse by
Speaker 2: some of the early over excitement, especially among large corporations
Speaker 2: that made the mistake of creating leader boards and right,
Speaker 2: So this is we got into this token maxing phenomenon.
Speaker 2: If so, if you're inside of which is obviously the
Speaker 2: idea that the more tokens you use, the better you
Speaker 2: do in your job review, because look at you, you're
Speaker 2: all AI. The problem, of course, is that this is
Speaker 2: a little bit like the Saudi's handing out humviies to
Speaker 2: everyone in America. Wow, this is awesome. I love having
Speaker 2: a hum vy. And then you have to fill it
Speaker 2: up and so for a little while it was like,
Speaker 2: we were subsidizing these grotesquely uh profligate users of tokens,
Speaker 2: just like profligate users of gasoline. And then all of
Speaker 2: a sudden, the bill comes to you and you say,
Speaker 2: wait a minute, this thing's a pig. It's a lot
Speaker 2: of gas. I don't want to drive it for growth
Speaker 2: through anywhere. And the exact same phenomena, it's true with
Speaker 2: respect to being again exposed to the having your ass
Speaker 2: hanging in the breeze of real token prices.
Speaker 1: Well, the other thing is as well, is I'll just
Speaker 1: put our newsletter about this. You look at how these
Speaker 1: people are freaking out, and you also realize they have
Speaker 1: no idea what AI cost. It's not just like, wow,
Speaker 1: this is a lot of money. It's they're not even
Speaker 1: thinking in terms of cost. It's not like they know,
Speaker 1: I don't know. They're refactoring something, they don't know how
Speaker 1: much that got. They don't know how much anything costs.
Speaker 1: So it's not like they can smoothly transition to token
Speaker 1: based billing because they don't they don't know, they have
Speaker 1: no idea.
Speaker 2: And this is this is the deep this is the
Speaker 2: deep structural problem because they were brought in through the
Speaker 2: side door of bundled pricing, and now that's that's becoming unbundled,
Speaker 2: and of course that also has a is reflected in
Speaker 2: what we're told. And I think the Wall Street Journal
Speaker 2: others have written about this, and it'd be interesting we
Speaker 2: see the final s wyans for some of the upcoming
Speaker 2: I p os that there is this attempt to try
Speaker 2: and even mask it in the financial filings where you
Speaker 2: get into this phenomenon what we used to call earnings
Speaker 2: before bad stuff. And so what they're trying to do
Speaker 2: is hide the costs of training the models and saying
Speaker 2: that's not actually an operating cost, that's a capital cost,
Speaker 2: and we shouldn't have to show that as a function
Speaker 2: of what actually the margins are on producing tokens. And
Speaker 2: that is of course a cheat, right because if that's true,
Speaker 2: then you should be able to capitalize these things and
Speaker 2: expense them over a long period of time. And we
Speaker 2: know full well that these are actually operating costs, because
Speaker 2: they tell us that every eighteen months we're launching a
Speaker 2: new major model. These things are not capitalized. These are
Speaker 2: operating expenses that should be treated accordingly with respect to
Speaker 2: the actual cost of token production. So there's a multifaceted
Speaker 2: game going on here, both in terms of how it's
Speaker 2: being presented to users, but also in terms of how
Speaker 2: they're trying to sell it in the context of the
Speaker 2: upcoming s ones for the anthropic and open a IPO pilots.
Speaker 1: Well, what's really funny as well about the idea of
Speaker 1: capitalizing training costs is they're never going away because it's
Speaker 1: not just free training shoving the stuff in the models.
Speaker 1: They have to constantly tweak them because that's.
Speaker 2: Right and so it's just right, which from a class
Speaker 2: you know, from a classic my years ago accounting, whenever
Speaker 2: you have a regular and predictable cost that you have
Speaker 2: to expense, you have to incur to continue operating your business,
Speaker 2: that is no longer a capital cost, that's an expensable
Speaker 2: item that should be expressed. Such so you get into,
Speaker 2: as I said, like back in the dark days of
Speaker 2: dot com and even the telecom boom, you get into
Speaker 2: this problem of earnings before bad stuff, where they want
Speaker 2: to they want to exclude all of the things that
Speaker 2: make the numbers look bad. And then of course on
Speaker 2: the other side, you have this run rate problem where
Speaker 2: we continually hear about what the run rates are at
Speaker 2: these companies, and the window with respect to the run
Speaker 2: rate could be the last fifteen minutes for all you know, right,
Speaker 2: a run rate is just you extrapolate whatever is most
Speaker 2: convenient for you. So it's a problem on both sides.
Speaker 1: Well yeah, actually that's that's what I love talking about
Speaker 1: run right. Everyone who listens to the show knows I'm
Speaker 1: a real run right pig because because like I've reached
Speaker 1: out to both open ai and Anthropic and said, hey,
Speaker 1: how do you find this number? And they will not respond.
Speaker 1: They will not They're very unfair to me, very nasty.
Speaker 1: They will not respond, probably because from what the information
Speaker 1: has reported. I don't know how open ai does it.
Speaker 1: But Anthropic not only includes the amounts of money that
Speaker 1: Amazon and Google make in their revenues that's like when
Speaker 1: they resell them a but they also they do thirteen
Speaker 1: times the last month's APIs spend and twelve times the
Speaker 1: current days subscribers. So it's just there's so many ways. Also,
Speaker 1: so token spend, so just organizational token spend. That's not
Speaker 1: a recurring costs, that's just you can kind of I
Speaker 1: guess think, well, maybe people are spending this today, but
Speaker 1: that person who spent half a billion dollars. That company
Speaker 1: spent half a billion dollars on AI. Right, that's not
Speaker 1: happening again, that person is that person is You're not
Speaker 1: going to get one half a billion dollar miss the
Speaker 1: bean every single month? Is someone just goofully? I also,
Speaker 1: genuinely I know the report of Madison Mills. She's respective report,
Speaker 1: she's very she's she's good, she is well sourced. It's
Speaker 1: just like I hope Anthropic didn't include that five hundred
Speaker 1: million in their annualized Yeah.
Speaker 2: I'm looking forward to its showing up in a public
Speaker 2: company filing because it almost inevitably will this is going
Speaker 2: to be somebody can time item right.
Speaker 1: And and then you think that that will though.
Speaker 2: Oh absolutely, I mean half a billion, it's material for
Speaker 2: almost anyone. So I my guess is it's going to
Speaker 2: show up somewhere. It'll be really interesting to see. And
Speaker 2: my guess is at that scale, it's a it's a
Speaker 2: public company. So my guess is we will see that,
Speaker 2: we will know where that actually happened, and so it's
Speaker 2: gonna be it's gonna be very entertaining. But that this
Speaker 2: is the deep structural problem, and it gets worse of course,
Speaker 2: because once you unbundled token pricing, and then you're looking
Speaker 2: at the actual year over year decline in quality adjusted
Speaker 2: token pricing, in token pricing, and you see that the
Speaker 2: inherent deflationary curve underneath the hood. Now, let's connect that
Speaker 2: to how all of these data centers that are producing
Speaker 2: these deflating tokens are being being constructed and increasing fractions.
Speaker 1: Can elaborate what you mean by the deflating token, I'm
Speaker 1: not sure I understand.
Speaker 2: So over the last since twenty twenty two, on an
Speaker 2: annualized basis, on a performance basis, so ignoring what this
Speaker 2: continual jump to the frontier, if you imagine sort of
Speaker 2: on a comparable token basis, per cross models, across the period,
Speaker 2: token prices have fallen anywhere from seventy to ninety percent
Speaker 2: year over year consistently back to twenty twenty one.
Speaker 1: Right, but they're burning more tokens in the process.
Speaker 2: So but let's put that aside for one second. Think
Speaker 2: about it the alternative the other way around. So if
Speaker 2: I'm now, my business is now I'm unbundling and I'm
Speaker 2: selling tokens, and that's the way customers. You're telling my
Speaker 2: customers to think about it. So now they're looking out
Speaker 2: and they start to see what happening with token prices,
Speaker 2: and if I go back one generation, maybe those prices
Speaker 2: are cheaper. Now we have this classic financial problem of
Speaker 2: what's called a duration mismatch. Right, So I have debt
Speaker 2: funding the data centers that's ten to fifteen years duration
Speaker 2: and longer, which is predicated on fixed payments but being
Speaker 2: made on the basis of tokens where you're telling the
Speaker 2: customer to control your costs. You may want to look
Speaker 2: back in time and use an older model. So I'm
Speaker 2: paying for a fixed cost with a deflating commodity right
Speaker 2: this WEEKNTE from the over and over and over again.
Speaker 2: These duration mismatches, especially duration mismatches that are built on
Speaker 2: top of debt and a deflating commodity, are absolutely atomic
Speaker 2: with respect to causing a blow up in people's obligations
Speaker 2: with respect to these these kinds of duration mismatch problems.
Speaker 2: So there's a there's a deep structural issue that this
Speaker 2: will expose, and people haven't quite realized it yet.
Speaker 1: So you're saying that as the token costs get cheaper
Speaker 1: and everyone's being encouraged to use this less or more thoughtfully,
Speaker 1: that's happening. But they're building the data centers as if
Speaker 1: number will only ever go up and they'll only ever use.
Speaker 2: That's exactly right, and so you've got this wonderful ye
Speaker 2: again the term of art. You got a duration mismatch
Speaker 2: on top of a deflating commodity that can only end very,
Speaker 2: very badly, and it was masked because for a while
Speaker 2: you weren't exposed directly to that. You were just paying
Speaker 2: a straight up subscription, almost like Amazon Prime. And of
Speaker 2: course that doesn't work because Amazon primes costs across the
Speaker 2: border declining, whereas costs are increasing at the frontier declining
Speaker 2: in the in the in the back catalog, if you
Speaker 2: will have tokens.
Speaker 1: And that's the thing with like an Amazon Prime for example. Yes,
Speaker 1: they have found like Amazon or not like Amazon people.
Speaker 1: I know many listeners don't love them. Then I agree,
Speaker 1: But it's like Amazon Prime. They fixed those costs by
Speaker 1: building their own logistics network and they found ways to
Speaker 1: they they had I don't know, ways to make that cheaper.
Speaker 1: No one has that in AI, No one like it's
Speaker 1: just we have three four years in and that one's like, oh,
Speaker 1: we'll do a six. No, we won't. That didn't work.
Speaker 1: Like we have. We're like two or three generations of
Speaker 1: tranium inferentia TPUs. Still not profitable, still not profitable, right,
Speaker 1: we would know.
Speaker 2: Since we would We wouldn't know. But I think and
Speaker 2: and and of course the problem is that if you
Speaker 2: look at I was just looking at some data yesterday
Speaker 2: with respect to how small language models are increasingly closing
Speaker 2: the gap with large language models, which is causing training
Speaker 2: cycles on large language models to have to accelerate, become
Speaker 2: more expensive, throw more compute, added more reinforcement learning. The
Speaker 2: costs are not are particularly not not not declining, they're
Speaker 2: actually increasing sharply at the frontier because they're essentially being
Speaker 2: chased like you know, like the rabbits and like wiley
Speaker 2: coyote and the road runner, and so they're being chased
Speaker 2: into this very costly corner as a result. And that's
Speaker 2: a classic commoditization problem. If you go back to the
Speaker 2: nineteenth I don't know, late nineteenth century, a very similar
Speaker 2: thing happened in railroads as people were racing desperately to
Speaker 2: try and find a way to build a corner and
Speaker 2: control themselves so that they could compete with all of
Speaker 2: these other upstart railroads, and of course all that really
Speaker 2: happened was Capex exploded, margins went to shit, and multiple
Speaker 2: railroads failed, and we led to the crash of what
Speaker 2: eighteen seventy three, eighteen ninety three and arguably was a
Speaker 2: cause in the Great Depression. So you know, so you're
Speaker 2: playing out this exact same game because you're sitting in
Speaker 2: this high Capex world that's increasingly funded by debt and
Speaker 2: built on top of this duration mismatch, with token prices
Speaker 2: being now exposed and raw in front of people.
Speaker 1: And the other thing is as well, is people are
Speaker 1: just literally in my piece today, people make this point about, oh, well,
Speaker 1: it's like the dot com bubble in the we will
Speaker 1: we will simply just we'll reuse these things in the future,
Speaker 1: like we'll just pick these up. And it's really I
Speaker 1: hear this from very smart people, are people who are
Speaker 1: not like beguiled by AI industry. But it's like, okay,
Speaker 1: let's talk about what happened to the dot com bubble.
Speaker 1: So when it exploded, you had those some microsystems, the
Speaker 1: Ultra whatever it was, I forget forty three fifty grand
Speaker 1: a server, but that one server could run an entire company.
Speaker 1: You could run everything on it, databases, messy CRMs they
Speaker 1: had like did they have on prem lotus notes? Anyway,
Speaker 1: you had those things. But you could run that, and
Speaker 1: you could probably run that in a garage. You might
Speaker 1: need to use the washing machines plug, but you could
Speaker 1: do it if you and that. Yeah, those things were
Speaker 1: fifty grand, so you probably get them at what twenty
Speaker 1: thirty large? Yeah, okay, great, what happens from the AI
Speaker 1: bubble burst? You can't just plug in an AI GPU
Speaker 1: a B two hundred GPUs but fifty grand. It requires
Speaker 1: about I think side look this up for a recently.
Speaker 1: It's like twelve hundred and fifteen hundred watts for a
Speaker 1: Sun Microsystem server, about the same for single B two hundred,
Speaker 1: which will require bespoke cooling, a server, hardware, RAM, all
Speaker 1: of this other stuff. And then you'll find out that
Speaker 1: you can't do jackshit with the single GPU.
Speaker 2: Yeah, you're gonna be a huge source of disappointment once
Speaker 2: you power it up. Your neighborhood lights all blink out
Speaker 2: and you can't still can't do anything. So yeah, I
Speaker 2: think that's exactly right. But that's but that's I call
Speaker 2: this I was just I got into this with someone
Speaker 2: recently who was making a similar I call it faith
Speaker 2: based argumentation. It's this kind of you know, quasi religious
Speaker 2: orthodoxy that requires you to believe the following five things.
Speaker 2: You know, they always create more jobs than they destroy.
Speaker 2: We can always reuse assets after the fact. And I'm
Speaker 2: one of the things I always point to people is
Speaker 2: that almost half of US railroad lines built during the
Speaker 2: Boon years in the late nineteenth century were eventually abandoned.
Speaker 2: And did they find reuse. They absolutely did. It only
Speaker 2: took one hundred years, and now they're mountain biking trails.
Speaker 2: You know, let's let's wait around for that.
Speaker 1: Railroads. They were railroads that didn't require electrification, like.
Speaker 2: That's right, actually, so they were much more stable as assets, right,
Speaker 2: They didn't have the problems that GPUs and data centers do.
Speaker 2: Were not just the huge power and cooling requirements, but
Speaker 2: also they hearent the trajectory of the underlying technology where
Speaker 2: it changes quickly enough that you know, is a twenty
Speaker 2: year old you know, blackwell of any use to anyone
Speaker 2: other than as a paper weight. Of course, the answer
Speaker 2: is probably not. Whereas a railroad pretty heavy, they are
Speaker 2: extremely heavy. I actually was messing around with one recently,
Speaker 2: and so, yeah, and so this is the problem. And
Speaker 2: again it's this sort of naive argumentation, not to mention that,
Speaker 2: you know, the old Canes line that it may be,
Speaker 2: it may be great in the line run, but in
Speaker 2: the long run we're also all dad. So it really
Speaker 2: depends on your time horizon. And I find it honest, honestly,
Speaker 2: in the face of the kinds of consequential changes in
Speaker 2: the US economy, I had a very glib style of argumentation,
Speaker 2: where you're essentially patting people on the head and saying,
Speaker 2: you know, don't worry your pretty little head. This will
Speaker 2: all work out because it always has. And they're arguing
Speaker 2: from a data set sample size of five, which we
Speaker 2: wouldn't launch, we wouldn't lunch a drug on that basis.
Speaker 1: Also, I think it helps them rationalize bad behavior because
Speaker 1: if you say, okay, it worked, the one that actually
Speaker 1: upsets me is, well, the dot com bubble worked out.
Speaker 1: It's like, yeah, the stock market lost eighty percent of
Speaker 1: its value, hundreds of pauses, of people lost their jobs,
Speaker 1: people lost everything in some cases, and at the end,
Speaker 1: it's like, okay, that was also completely different but you're
Speaker 1: being quite glib about the first part. But it's also yeah,
Speaker 1: it's okay that people burn a lot of money for
Speaker 1: basically no reason. This is also allows you to not
Speaker 1: think about bad stuff. It allows you to This is.
Speaker 2: The andrey Sonian argument that there's no point in introspection.
Speaker 2: It's just a really bad idea, right. I think about
Speaker 2: these things that the law sort and stuff out. But
Speaker 2: I also think there's a deeper issue. And we may
Speaker 2: have talked about this before, but the idea a lot
Speaker 2: of people treat as an article of faith. Carlota Perez's
Speaker 2: book Technological Revolutions and Financial Capital, and one of the
Speaker 2: things that they quote take away from that, which I'm
Speaker 2: not convinced they do. I think they only look at
Speaker 2: the pictures. But anyways, one of the things they take
Speaker 2: away from her book and her work and other people's
Speaker 2: work with respect to these violent technological revolutions is the
Speaker 2: idea that it really doesn't matter because it always works out.
Speaker 2: Here's the difference, though in past episodes, we didn't tell
Speaker 2: ourselves that. So there is an element of reflexivity going
Speaker 2: on here, because once you know the plot and you
Speaker 2: act as if the plot is somehow f equals MA,
Speaker 2: it's a law of physics. Then the whole game changes
Speaker 2: because now you're acting as if it doesn't matter what
Speaker 2: I do, because you think it doesn't matter what you do,
Speaker 2: because as you've got this idea in your head as
Speaker 2: an article of faith that it always works out. That
Speaker 2: wasn't true. Historically, no one in building out the railroads,
Speaker 2: rural electrification, the fiber bubble, no one was telling themselves
Speaker 2: in the time, this always works out. That was not
Speaker 2: part of the that was not part of the playbook.
Speaker 2: The idea that we now tell ourselves these things is
Speaker 2: such a deep structural change in terms of the way
Speaker 2: this stuff happens that it amazes me that no one
Speaker 2: understands it.
Speaker 1: Well. I think it's just it's the rationalizing, and it's
Speaker 1: also it gives you a way of avoiding thinking about
Speaker 1: true structural issues.
Speaker 2: The right I'm sucking, I always call it. It's really
Speaker 2: kind of nice. It gives you comforuit.
Speaker 1: It allows you to be like, well, Google isn't stupid
Speaker 1: for raising eighty billion dollars in equity sales. Yeh, Google,
Speaker 1: the largest companies in the world couldn't just destroy their
Speaker 1: companies by wasting all their money. It's like, yeah, go
Speaker 1: and type something into Google. Search anything into Google, and
Speaker 1: tell me if this looks like a company running a
Speaker 1: good business or a good product. Well, just a company
Speaker 1: throwing shit at the wall and being like, this works, right,
Speaker 1: you know.
Speaker 2: And we have we have so many examples of companies
Speaker 2: that were lauded during the run up of prior episodes
Speaker 2: for being really understanding the way the world works and
Speaker 2: being a real path breaker and so on, baiting to
Speaker 2: whether it was the global financial crisis and the banks
Speaker 2: at that time, and my friend Jim Kramer's unfortunately timed
Speaker 2: comments about bear Stearns and all that kind of stuff.
Speaker 2: Because people are so backwards looking and so extrapolative with
Speaker 2: respect of the way they look at things, they just
Speaker 2: can't see the discontinuity, the obvious discontinuities ahead, and so
Speaker 2: they extrapolate and extrapolate, and then they eventually, you know,
Speaker 2: extrapolate their way right off, right off a cliff. And
Speaker 2: I see a lot of that in this going around.
Speaker 2: And I don't know if you saw it, but there
Speaker 2: was a paper came out as a good example of this.
Speaker 2: There was a paper came out yesterday and this goes
Speaker 2: to the heart of the token pricing problem. And it
Speaker 2: came out I think it was on SSR and or
Speaker 2: Amberg or yeah, National Bureaueconomic Research, and so the paper
Speaker 2: basically was about how there's been, as you and I
Speaker 2: both know, there's been this explosion in the number of
Speaker 2: GitHub commits and repositories or repositories and commits within them,
Speaker 2: and it's up something like two hundred percent. Of it's
Speaker 2: just over the last eighteen months, largely driven by harnesses
Speaker 2: and everything and all of these coding tools. And of
Speaker 2: course then they looked at the other side of it
Speaker 2: was this is this profligate use of tokens, what does
Speaker 2: it led to? Because producing more stuff that shows up
Speaker 2: on GitHub is just their need immediate variable, and nobody
Speaker 2: in the real economy cares other than maybe Microsoft, and
Speaker 2: even they probably wish there was probably a little less
Speaker 2: activity on GitHub. And so they showed that this was
Speaker 2: just they used iOS apps, Android apps and one other
Speaker 2: category anyways, and they showed that the number of reviews
Speaker 2: per app has declined sharply as the number of repositories
Speaker 2: and commits has gone up. So essentially what we're seeing
Speaker 2: and This is the thing that I think is really
Speaker 2: important is these can be very effective, if wildly subsidized,
Speaker 2: productivity tools for coders, but the end economic result is
Speaker 2: mostly the production of sort of slop everything, slap apps,
Speaker 2: slap content, slop And so you're you're you're flooding and
Speaker 2: commoditizing these these markets that are becoming both saturated and
Speaker 2: declining margins. And this is an incredibly important distinction that
Speaker 2: just because it's helping you produce more stuff, it doesn't
Speaker 2: mean that in the broader economy its ability to absorb
Speaker 2: it as increase, nor does it care. And that's what
Speaker 2: this paper shows. And I think the idea that we're
Speaker 2: doing all of this work and what's increasingly become expensive
Speaker 2: work using tokens to produce things and makes coders very
Speaker 2: happy having, you know, things running in agentic loops, but
Speaker 2: the broader economy, you know, doesn't give a fuck.
Speaker 1: And that have By any chance, did you read semi
Speaker 1: analysis is AI doc output? I think, which is is
Speaker 1: one of the funniest things I have read in my life.
Speaker 1: So for the listeners, you'll have a link to this,
Speaker 1: but it's basically, yeah, AI is, so AI output will
Speaker 1: be real before it is measurable. We can capture token spend,
Speaker 1: we can capture jobs lost. But unless AI's output is
Speaker 1: sold at a visible price, only token spend is captured
Speaker 1: in GDP. But which they mean, we don't actually measure
Speaker 1: whether something is good. We just measure whether something like
Speaker 1: we just it's actually so that this is the ship
Speaker 1: a teenager would say when lying about having a girlfriend,
Speaker 1: like this.
Speaker 2: Is voodoo teen economics. Yeah, it really is. And uh
Speaker 2: and it and again it goes to that that that
Speaker 2: National Buer of Economic Research paper, it's exactly the same
Speaker 2: thing I was mentioning at the at the top, there
Speaker 2: is this tremendous and I can. I'll send you the
Speaker 2: link if you haven't seen it. And it's called where
Speaker 2: is AI and GDP statistics Filling the measurement gap? Yeah, yeah, yeah,
Speaker 2: a couple of days or a couple of days ago.
Speaker 2: And they argue that essentially AI quality adjusted air output
Speaker 2: is up more than two thousand percent per year. They
Speaker 2: with the estimates of like, you know, two hundred and
Speaker 2: fifty three hundred billion dollars on top of the and
Speaker 2: then but they could then essentially come to the conclusion
Speaker 2: that this is all true as long as you accept
Speaker 2: our readition of redefinition of GDP, and of course.
Speaker 1: Ah, right, right, okay, if.
Speaker 2: You allow me to redefine GDP, I could present you
Speaker 2: with some tremendous numbers, and the entire paper is absolutely
Speaker 2: fascinating as an example of what's what's often called motivated reasoning.
Speaker 2: I need to believe this. Therefore I construct an argument
Speaker 2: to allow me to continue to believe it. And the
Speaker 2: way I get there is by redefining a variable that's
Speaker 2: already very squishy in the first place. Let's not pretend
Speaker 2: that you know, measuring GDP is much easier than, like,
Speaker 2: I don't know, measuring muons in a cloud chamber or something.
Speaker 2: It's still very hard, and we're about to You're trying
Speaker 2: to make it harder to justify something that's just not defensible.
Speaker 1: And the AI dalk output one is great because they
Speaker 1: substitution dalk output is work that was previously done by
Speaker 1: humans and he's not done by AI. In our dark
Speaker 1: output monitor, we have identified roughly one and a half
Speaker 1: trillion dollars in tasks that current AI could substantially augment
Speaker 1: or automate, to which I say, why hasn't it done
Speaker 1: it right. This is the AI thing though, because specifically
Speaker 1: with AI, with other things, productivity is hard to measure.
Speaker 1: It's hard to measure outputs with workers in knowledge work,
Speaker 1: especially like it's doable, but it's not like a linear path,
Speaker 1: except you're selling a tool that can theoretically do anything.
Speaker 1: If this did what they said it did, we would
Speaker 1: have gunfights in the stream. We would have the destruction
Speaker 1: of most knowledge work, and it would be happening a
Speaker 1: year ago. It would be half happening year ago, happening
Speaker 1: fully today. We would have the destruction of law firms.
Speaker 1: We'd have the destruction of hyperscalers, because anyone would just
Speaker 1: be like, build me a Microsoft word, and it would
Speaker 1: build them a Microsoft word and they would use it
Speaker 1: and it would be functional, bug free, all of these things.
Speaker 1: They would be well. I mean, we've already seen a
Speaker 1: spike in litigation from pro se people representing themselves. But nevertheless,
Speaker 1: we would see law firms turning into two or three
Speaker 1: person shops that would beat the leading litigators because they would.
Speaker 3: Have Oh absolutely, it would be very easy to see.
Speaker 3: I'll give you a.
Speaker 2: Related example which made the rounds yesterday, and it kind
Speaker 2: of gets to the heart of this misunderstanding is there
Speaker 2: was someone who shall remain unnamed but has a popular
Speaker 2: newsletter and used to work at a certain venture fund
Speaker 2: put out something about put out the radiologists the radiologist paradox,
Speaker 2: which was the idea that back in twenty sixteen, Jeffrey
Speaker 2: Hinton computer sciences, a really pioneer in image models and
Speaker 2: deep neural networks, said in a talk that probably within
Speaker 2: five years, if not ten years, a large language deep
Speaker 2: deep at the time neural networks learning models would be
Speaker 2: better than radiologists and there's really no reason to continue
Speaker 2: training them now. Of course, he said ten years on
Speaker 2: the outside, well it's now ten years later, and if
Speaker 2: you look at the data, we're continuing to produce more radiologists.
Speaker 2: And that analyst then put out a note yesterday and
Speaker 2: so did I think KO two or someone else and said, like, well, checkmate,
Speaker 2: Jeffrey hinted, Look, we have a lot more radiologists. And
Speaker 2: of course this is a classic example of a profound
Speaker 2: misunderstanding of so many things it's once it's hard to
Speaker 2: keep track. One is that again it's not clear that
Speaker 2: being a selectively better than radiologists at certain things like
Speaker 2: identifying I don't know, prostate cancers or whatever else. If
Speaker 2: that's obviously that's not good enough. Radiologists do more than that.
Speaker 1: But it also.
Speaker 2: Misunderstands the nature of the employment market because radiologists, like
Speaker 2: most of medicine, has created a very comfortable little cartel
Speaker 2: for themselves. So even if there was gale force winds
Speaker 2: blowing at radiologists because of AI, the likelihood of you
Speaker 2: seeing it in such a short time, even if Hinton
Speaker 2: was right that they could in theory replace you know,
Speaker 2: a significant slice of what radiologists do, it's a misunderstanding
Speaker 2: of the nature of the markets themselves. So it misunderstands
Speaker 2: both the technology and the nature of cartelized employment markets.
Speaker 2: Whenever you get these kinds of arguments, and yet it's
Speaker 2: used as an example of how the inexorable march of
Speaker 2: these things, you know, continues apace and it will always
Speaker 2: be augmenting. And I just think there's so many sort
Speaker 2: of nested misunderstandings of how, how what pressures AI is
Speaker 2: having on employment markets and how we might see it
Speaker 2: where it might show up that then to take it
Speaker 2: up a level, so then do these calculations and say, oh, look,
Speaker 2: I can now come up with a defensible measure of
Speaker 2: how the augmenting function is working and then incorporate that ENERGDP.
Speaker 2: I kind of have to say bullshit, no, you can't
Speaker 2: because we're failing at the simple stuff.
Speaker 1: We also just a very simple response is okay, let's
Speaker 1: say it can identify them better than radiologist right now?
Speaker 1: What like the radiologists, it's they don't just look at
Speaker 1: stuff like they are doctors. They've required like there's more
Speaker 1: to the process than just like yes or no. And
Speaker 1: also you are buying the experience. You're buying their experience
Speaker 1: and their connections and their ability to work within a
Speaker 1: hospital system.
Speaker 2: And actually there's and there's tremendous papers on this treatment,
Speaker 2: oh absolutely showing how models do we do next?
Speaker 1: Right?
Speaker 2: Models in general in a medical context, and this is
Speaker 2: writ large applies to models the use in all complex environments.
Speaker 2: They tend to over triage trivial cases, meaning that if
Speaker 2: you come in with like a cut, like dude, this
Speaker 2: could be sepsis, Let's take you in and start doing
Speaker 2: tissue biopsies, and it's like, no, no, it's just a cot,
Speaker 2: leave me alone. And at the other end of extreme,
Speaker 2: a woman comes in with chess well with pain in
Speaker 2: her back, which sometimes is indicative of some kind of
Speaker 2: cardiac event. They're like, yeah, it's probably just a strain.
Speaker 2: And so this idea of marching straight through and saying
Speaker 2: that the only thing that matters is the input data,
Speaker 2: and therefore I can use these things in these complex environments.
Speaker 2: We know these tendencies towards over triaging trivial cases and
Speaker 2: under triag in critical ones. That's also true. Just as
Speaker 2: a side note, I gave a talk about this recently
Speaker 2: to the FED where I was showing how models do
Speaker 2: the exact same thing in financial markets, where they tend
Speaker 2: to become over aggressive when they should be conservative and
Speaker 2: vice versa, which leads to much more fragility and financial markets.
Speaker 2: And yet you know, we march on. And this is
Speaker 2: the deep problem, is this kind of complete misunderstanding of
Speaker 2: the nature of how these things respond in these complex environments,
Speaker 2: and then the systemic consequences of doing it. Like, for example,
Speaker 2: you replace idiologists with something with a tendency to overtrioge,
Speaker 2: you guess what you're gonna get. Far more testing, much
Speaker 2: more testing getting done, which may or may not be
Speaker 2: profitable for hospitals. But We'll have cascading consequences for people
Speaker 2: who have to have follow up biopsies because of things
Speaker 2: that looked like possibly malignant season turns out they weren't.
Speaker 2: And what we know from medicine is that for the
Speaker 2: most part, most things should be left alone.
Speaker 1: Yeah, and again I keep coming back to the really
Speaker 1: simple thing, which is if these things were going to
Speaker 1: replace people, they would just do it. They wouldn't be
Speaker 1: everything wouldn't. I keep saying this, but everything wouldn't read
Speaker 1: like the riddler rod tip. They wouldn't just every every
Speaker 1: single AI jobs thing is like, well, it's AI affected
Speaker 1: careers that might be doing this in this time in
Speaker 1: this way. There was a CNBC headline last year it
Speaker 1: was like eleven percent of jobs can already be done
Speaker 1: by AI. But when you looked, it was like, yeah,
Speaker 1: it was the labor simulates that we made, right, We
Speaker 1: didn't We just like we'll look at anything like it's
Speaker 1: the same.
Speaker 2: Problem with the with the meter studies. Obviously, in terms
Speaker 2: of the duration, right, the duration of tasks met right
Speaker 2: right right those ones and were the duration of tasks
Speaker 2: where you can get to fifty percent likelihood of completion.
Speaker 2: And of course if that was a human using that
Speaker 2: as your as your as your benchmark. If that was
Speaker 2: a human, I would fire those guys, right. I mean,
Speaker 2: that's not a useful measurement in terms of how a
Speaker 2: human might think about a productive co worker. I don't
Speaker 2: think about you just half the time you get shit wrong. Right,
Speaker 2: That would be kind of that would be something that
Speaker 2: would probably lead to review problems in the at the
Speaker 2: end of the quarter or a year. And so we
Speaker 2: do what's the line, Sam Harris's line, This is playing
Speaker 2: tennis without an ad right.
Speaker 1: Yeah, it's it's just as and it's also just we
Speaker 1: treat these things like the fucking like gifted children. It's like, Wow,
Speaker 1: you could of the time do this maybe, And that
Speaker 1: is it's time for the New York Times to write
Speaker 1: it in hire article of it. We need an odd
Speaker 1: Lots episode that covers that fifty percent of the time
Speaker 1: this could do this. And it's just because you can't
Speaker 1: do the thing that every other obvious innovation is done.
Speaker 1: You can't do it where you just go, wow, this
Speaker 1: does this. We could do this. Now, it's if this happens,
Speaker 1: and that is a load bearing if we might be
Speaker 1: able to possibly do this, we can't measure it in
Speaker 1: the way you measure other things, which is how we
Speaker 1: would otherwise distinguish whether something was good or not. So
Speaker 1: we made up a new thing, and wow, has it
Speaker 1: beaten the benchmarks were made up for it right.
Speaker 2: And the problem, of course is is this is all
Speaker 2: becomes a bit facile and glib but everything else in
Speaker 2: terms of the arguments being made, But it has spillover
Speaker 2: consequences in the real world, which is the unfortunate thing
Speaker 2: is that let's follow the logic forward. If my job
Speaker 2: is I'm selling tokens, and tokens, I need to sell
Speaker 2: more tokens rather than less because I have to pay
Speaker 2: the not on some fixed obligation. I'm going to construct
Speaker 2: more data centers and construct more larger data centers, and
Speaker 2: you end up with these massive mega projects like this
Speaker 2: controversial one that Kevin O'Leary has been lting right north
Speaker 2: of Salt Lake City. You know this that in its
Speaker 2: in the limit might be the size of Manhattan or larger.
Speaker 2: As people pointing out, this has consequences because the arrow
Speaker 2: of time only moves in one direction. I defy you
Speaker 2: to find an example of you know the old Talking
Speaker 2: Head song where it. You know, this used to be
Speaker 2: a parking lot, now it's covered with flowers. The data
Speaker 2: center is not going to reverse. Once you build these
Speaker 2: giant things in the real world with real consequences in
Speaker 2: terms of you know, sprawling out physically, but also sitting
Speaker 2: on top of water and power untangling, that becomes really,
Speaker 2: really difficult, as does, for example, having to spin up
Speaker 2: all of these new natural gas plants to power these things,
Speaker 2: because we're increasingly asking the hyperscalers come with their own
Speaker 2: power behind the meat. Yeah probably, yeah, right right right
Speaker 2: when we're doing that at the worst possible time, because
Speaker 2: the combination of batteries and alternative sources ranging from wind
Speaker 2: and solar for example, are becoming much more effective and
Speaker 2: able to be more persistent with battery backup. And yet
Speaker 2: we're installing these co two intensive things with thirty and
Speaker 2: forty year life spans funded by debt that are almost
Speaker 2: all likely to end up being stranded. Assets like the
Speaker 2: still be like the statues at Easter Island eventually, except
Speaker 2: natural gas plans.
Speaker 1: Well, that's and this is what I've been saying. It
Speaker 1: goes back to the dot com thing. I was saying.
Speaker 1: It's not like an incomplete data center, which I think
Speaker 1: the vast majority I don't think any of these things
Speaker 1: get finished. The vast majority of them don't get fully
Speaker 1: powered like that.
Speaker 2: Yeah, I think anything that's targeted over a giggle why
Speaker 2: doesn't get finished it fully agree.
Speaker 1: And the funny thing is with that is people are like, yeah,
Speaker 1: the dot com bubble, when at burst people had the
Speaker 1: useful infrastructure that will cost just as much to finish
Speaker 1: in the future, except that you'll go to a credit
Speaker 1: for you go to a well probably not private credit
Speaker 1: in the end of this, but go to a bank like, yeah,
Speaker 1: I want to finish this data center. They will shoot
Speaker 1: you with a gun. Will they will. You will get
Speaker 1: headshotted by the bank manager for saying the words AI.
Speaker 2: It's just right and it's going to be. I'll give
Speaker 2: you an even more. It's even more insidious than that.
Speaker 2: And I spend a lot of time talking trying to
Speaker 2: talk off the ledge, if you will, various regional economic
Speaker 2: development people. I was just talking to some people in
Speaker 2: New Mexico about this, and the problem they have is,
Speaker 2: you know, they've been trying to land some large employer
Speaker 2: for twenty five years in these high unemployment regions, and
Speaker 2: so I'm entirely sympathetic to the problem that a data
Speaker 2: center hyperscaler shows up and says, listen, let me install this,
Speaker 2: give me the following, you know, giveaways with respect to taxes,
Speaker 2: and this will eventually, after construction, will have this many
Speaker 2: jobs and so on, and you don't have to keep
Speaker 2: fighting for the Hundai battery factory or the FOD assembly
Speaker 2: plan or whatever else. It'll just be here spinning off
Speaker 2: tax revenues. And so what happens is a that looks
Speaker 2: like a pretty good bet because it's a fixed obligation
Speaker 2: in terms of what will be flowing back into your
Speaker 2: county for years to come.
Speaker 1: And what do they do?
Speaker 2: Then they start pre budgeting that and saying, okay, we'll
Speaker 2: start building new playgrounds, we'll start fixing the water supply,
Speaker 2: we'll be able to fund schools.
Speaker 1: Great.
Speaker 2: Now, okay, you've front loaded all of that stuff. What
Speaker 2: happens whenever the data center doesn't get finished, you're actually
Speaker 2: in a worse situation than you were previously. So it
Speaker 2: has real world consequences in terms of these annuity streams
Speaker 2: that are being dangled in front of people whose regions
Speaker 2: have suffered economically for decades, and that's going to be
Speaker 2: the story over the next twenty five years.
Speaker 1: Yeah, it's going to be years of data center collapses.
Speaker 1: Even after the AI bubble bursts. In my opinion, there's
Speaker 1: going to just be years of this because you're already
Speaker 1: seeing a lot of this stuff is speculative. And even then,
Speaker 1: even if these things get turned on, as you said
Speaker 1: at the beginning, we are in an era where people
Speaker 1: are going to be trying to cut back on costs.
Speaker 1: But then there's the really basic answer, what do more
Speaker 1: data centers do? What do we get out of these
Speaker 1: Because open ai has more compute than anyone. What are
Speaker 1: they doing? What's different? What's the difference? What does what? What?
Speaker 1: I keep hearing the term AI factory, and I'm like,
Speaker 1: what do you mean, what do you mean?
Speaker 2: Oh, a factory full of geniuses? That's my favorite.
Speaker 1: Oh the data center. Oh Jesus, a data center full
Speaker 1: of geniuses. I really dislike Dario ama Day. I hate
Speaker 1: how he sounds, I hate how he speaks, Just like, no,
Speaker 1: what are you fucking talking about? Because more data centers
Speaker 1: so far has not actually improved these products. It's not
Speaker 1: like there's not if you gave open ai another fifteen
Speaker 1: gigawats of data centers doesn't exist. But let's say they did.
Speaker 1: Nothing Like, nothing is going to change about this. Yeah,
Speaker 1: I don't, and I don't think I But the other
Speaker 1: thing is as well, Hey, h is Vera Rubin gonna
Speaker 1: make AI profitable? Because if it isn't, this is probably
Speaker 1: the last generation. But that is I think at this
Speaker 1: point the thing.
Speaker 2: I think very much so. And I think that's one
Speaker 2: of the other consequences here that's going on, and I
Speaker 2: think it's part of the one of the reasons why.
Speaker 2: And I don't know if you've noticed, but the Jensen
Speaker 2: has has gone from being very promotional the extra special
Speaker 2: vary to the third power promotional in terms of I
Speaker 2: saw today he was anointing Marvel as the next trillion
Speaker 2: dollar company. And for me, this is really unprecedented. But
Speaker 2: it only works if you start thinking about it in
Speaker 2: terms of the ecosystem of buyers and sellers in the
Speaker 2: context of AI capex, and realizing that the more valuable
Speaker 2: all of these companies become, the more money is sort
Speaker 2: of flowing around this what we used to be called
Speaker 2: like a captive economy, and then it just recirculates amongst
Speaker 2: all the players as they become increasingly wealthy because their
Speaker 2: stocks get bit up. And so this notion of having
Speaker 2: people suggesting that one of their sort of peer or
Speaker 2: quasi competitors should also be valued at a trillion dollars
Speaker 2: is really unprecedented, and it's only under You can only
Speaker 2: really understand it once you understand it that they are
Speaker 2: all essentially running printing presses in their basement, and the
Speaker 2: printing press is their stock, and they're hoping that the
Speaker 2: value of the printing press and the currency keeps going
Speaker 2: up and that way they can circulate more script among them,
Speaker 2: which in turn turns into purchasing. And that's the that's
Speaker 2: the fundamental circularity at the core of all of this.
Speaker 1: So as we wrap up, I wanted to get like,
Speaker 1: because I've already had emails and texts somehow, I don't
Speaker 1: know how they got my number. What do you think
Speaker 1: of this? Good? What does this Google thing mean? So
Speaker 1: Google doing that eighty billion dollar raise at the market,
Speaker 1: what does this tell you?
Speaker 2: Well that they a couple of different things. One is
Speaker 2: that this is this is the equity raise.
Speaker 1: Yes exactly, so ten billion from Berkshire and then some
Speaker 1: other like ten billion from Berkshire and then I think
Speaker 1: two different at the market sales.
Speaker 2: Yeah, so, I mean, so this tells you that the
Speaker 2: appetite continues to be incredibly high for their pay for equity,
Speaker 2: which is surprising because for the most part the funding
Speaker 2: has been increasingly moving towards credit obviously right, and because
Speaker 2: the saturation of their cash flows with respect to having
Speaker 2: to sort of inoculate themselves against all of the other
Speaker 2: commitments they have, my favorite example being that Microsoft's a
Speaker 2: good example of this is that their stock based compensation
Speaker 2: is so high that they have to which is obviously
Speaker 2: only handled through cash flows, that the way they inoculate
Speaker 2: themselves against it is they have to do stock buybacks.
Speaker 2: And once you start doing that, you've got a much
Speaker 2: larger commitment of cash, which forces you have to you
Speaker 2: pay for hyperscalar data centers. You then have to start
Speaker 2: doing raises off balance sheet using SPVs and other kinds
Speaker 2: of funding vehicles so that they're able to do this
Speaker 2: is sort of surprising to me to a degree that
Speaker 2: there's still this much appetite for, you know, non credit
Speaker 2: equity financing of some of their future obligations, because it
Speaker 2: gives you no call on future cash flows. So what's
Speaker 2: in it for you as a provider of equity here?
Speaker 2: It's not clear.
Speaker 1: Yeah, is it also assigned that the debt is running out?
Speaker 1: Like why would they do this instead of raising debt?
Speaker 2: So there's no question about that as well. So that's
Speaker 2: the other side of this is that as of Q
Speaker 2: one twenty, what a year are we in? Twenty twenty six?
Speaker 2: I have to look around the room. That's bad. So
Speaker 2: as of six where the hyperscalers are now the largest
Speaker 2: issue of investment grade debt on in investment grade markets worldwide?
Speaker 2: They just passed the banks. So yes. The other answer
Speaker 2: to this question is is there is a capacity issue
Speaker 2: with respect to the further issuance of investment grade debt.
Speaker 2: In a weird way, they would actually be better if
Speaker 2: they were issuing junk high yield because there's a higher
Speaker 2: appetite for high yield, but they just so happen to
Speaker 2: be currently anyways prime credits, so they're issuing investment grade
Speaker 2: and the appetite for that stuff is finite, which is
Speaker 2: why increasingly the marginal buyer for the most recent credit
Speaker 2: issuances from the hyperscalers is the usual suspects like European
Speaker 2: Insurance finds Middle Eastern sovereign wealth. These are the people
Speaker 2: who famously tend to show up at the end of
Speaker 2: almost every bubble, and so here they are at the
Speaker 2: door again.
Speaker 1: So yeah, you think, do you think that this is
Speaker 1: toward the end? I'm just looking for a hard.
Speaker 2: No, no, no, no, I think very much that I
Speaker 2: think the blowoff top is the is these this year's
Speaker 2: three meg I p O s and and that kind
Speaker 2: of marks the the gonging of the bell with respect.
Speaker 2: To take the seriousness with respect, you have to take
Speaker 2: this inability of these companies to make money.
Speaker 1: Paul. It's always such a pleasure to have you. Welcome.
Speaker 1: People find you.
Speaker 2: Paulkadarski dot com is the best place.
Speaker 1: Hell yeah, everyone, thank you so much for listening. I'm
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