Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy covering the AI boom is actually reminding me a
Speaker 2: little bit of the tariff boom in April, simply because
Speaker 2: every day they are new headlines, like they're just today
Speaker 2: we're recording this November twelfth, Anthropic commits fifty billion dollars
Speaker 2: to build AI data centers in the US. So the
Speaker 2: advanced model companies are vertically integrating more to build their
Speaker 2: own data centers. Every day some new development.
Speaker 3: Yeah, it's becoming pretty hard to keep up. So I
Speaker 3: think we're probably just going to talk in terms of
Speaker 3: billions and trillions. We're just going to say lots and
Speaker 3: lots of money is going into the space. But the
Speaker 3: way I've been thinking about it is, Okay, at this
Speaker 3: everyone agrees that the AI buildout is super expensive, and
Speaker 3: all these companies are spending massive amounts of capex to
Speaker 3: do this, and I'm starting to think that AI capex
Speaker 3: is kind of like the Schrodinger's Cat of markets in
Speaker 3: the sense that it could either be a massive strength
Speaker 3: for these companies because the capex is so expensive and
Speaker 3: it takes so much money to build out, and so
Speaker 3: anyone who manages to do it kind of builds a
Speaker 3: moat around their business. Or it could be a massive weakness,
Speaker 3: right if you're spending all this money and then that
Speaker 3: doesn't end up generating the revenues that you actually need
Speaker 3: to justify it. And going back to the Schrodinger's analogy,
Speaker 3: it seems like we just don't know what's going to
Speaker 3: come out of the box, right, Like it's simultaneously a
Speaker 3: strength and a weakness, and until we build out AGI
Speaker 3: or whatever, like, we're just not going to know.
Speaker 2: I told her, right, there's so much at stake here,
Speaker 2: and obviously we know the numbers are absolutely enormous. They're staggering,
Speaker 2: and we could talk about them too. The financing structures
Speaker 2: are also very interesting.
Speaker 4: You know.
Speaker 2: It's one thing if you just have Meta or Alphabet
Speaker 2: and they make a ton of money already and they're
Speaker 2: spending money on data centers whatever. That's one thing. It's
Speaker 2: another thing when you start seeing these SPVs where the
Speaker 2: hyperscaler puts in this amount of money and then the
Speaker 2: private credit puts in this equity and then they borrow
Speaker 2: a bunch and then there's all these questions about the payback.
Speaker 2: And we think of tech as from years and years
Speaker 2: as basically being this equity story, and when it becomes
Speaker 2: a credit story. Yeah, and when you know people are
Speaker 2: talking about quoting Oracle CDs, I always forget these companies
Speaker 2: even have CDs because I'm so unused to thinking of
Speaker 2: big tech companies as credits. So when I see people
Speaker 2: starting to tweet Oracle CDs charts or core Weave CDs charts,
Speaker 2: It's like, Okay, we are in a different level of
Speaker 2: capital intensity.
Speaker 3: Right, and some of those swaps have been going up lately.
Speaker 3: I'm going to say one more thing, thinking back to
Speaker 3: the two thousand and eight financial crisis. I remember the
Speaker 3: economist at Raymond James I it is Jeff's out who
Speaker 3: went on to become a very big name. Yeah, we
Speaker 3: should have him on the podcast. But he made the
Speaker 3: point that historically when you had real estate crashes property crashes,
Speaker 3: it was usually because of a problem in the economy.
Speaker 3: But then what happened in the run up to two
Speaker 3: thousand and seven two thousand and eight is the housing
Speaker 3: market crash became the proximate cause of the troubles in
Speaker 3: the economy. And if you think about how much money
Speaker 3: is being spent on AI right now again billions, trillions
Speaker 3: possibly of dollars, it's very easy to see how AI
Speaker 3: could borph into a problem for the wider real economy.
Speaker 2: Totally just on this note, and then we'll get into
Speaker 2: our conversation. The Center for Public Enterprises out with a
Speaker 2: great report today called Bubble or Nothing by Ed vat Aarun,
Speaker 2: pointing out one of the things that makes data centers
Speaker 2: interesting is how they sit at this intersection of essentially
Speaker 2: industrial spending and real estate. It's an interesting ascid class
Speaker 2: for its own right. So much to talk about. We
Speaker 2: could never do a justice in one episode, but that
Speaker 2: means we got to do more. Anyway. I'm very excited
Speaker 2: for today's episode. We really do have the perfect guest.
Speaker 2: Someone who's been writing about this for a long time,
Speaker 2: someone who's just been writing about the Internet and all
Speaker 2: things for longer than any of us, someone who's been
Speaker 2: blogging and investing for far longer than either of us
Speaker 2: or anything like that. Way more knowledgeable about how these
Speaker 2: businesses worked, and most very focused on the data center
Speaker 2: buildout we're going to be speaking with Paul Kadrowski. He
Speaker 2: is a fellow at the MIT Institute for the Digital Economy,
Speaker 2: also a partner at sk Ventures, and longtime internet blogger, writer, newsletter, yapper, etc.
Speaker 2: Someone we've never never had on the podcast before. So Paul,
Speaker 2: thank you so much for joining us.
Speaker 5: Hey, guys, thanks good to be here. Other than the
Speaker 5: blogging part, but.
Speaker 2: No, it's all. It's all. You're a true pioneer in
Speaker 2: that and it's impressive that you still write with the
Speaker 2: output that you do. At some point in the last year,
Speaker 2: I feel like you really got laser focused, maybe in
Speaker 2: the last two years, really got laser focused on the
Speaker 2: data center's story is this is where the action is.
Speaker 6: Yeah, I did, and in part just because I caught
Speaker 6: myself by surprise with it.
Speaker 5: It was weird.
Speaker 6: I was looking at first half GDP day it actually
Speaker 6: first quarter GDP data earlier in the year, and you know,
Speaker 6: this has become a commonplace that people know this, but
Speaker 6: I hadn't realized what a large fraction of GDP growth
Speaker 6: in the first quarter data centers were was on the
Speaker 6: order of fifty percent, much larger if you included all
Speaker 6: sort of externalities all the other things that data center
Speaker 6: spending in turn kind of accelerates. And then obviously the
Speaker 6: same thing was true in the second quarter, and it
Speaker 6: was I got back to thinking about my dog, and
Speaker 6: I was my analogy is that.
Speaker 3: As one does, as one does.
Speaker 6: I got to get like my dog barks when the
Speaker 6: mailman comes to the house and keeps barking, and then
Speaker 6: the mailman goes away. And I'm convinced he thinks he
Speaker 6: makes the mailman go away, right, he has this really
Speaker 6: screw causality, and it's like, dude, if you don't bark,
Speaker 6: it goes away. Anyway, this is part of the job.
Speaker 6: They just go away. And I think about macro policy
Speaker 6: in the same way that if you don't understand and
Speaker 6: the drivers of GDP growth, you're likely to think to
Speaker 6: whatever it is you would most like to be causing
Speaker 6: GDP growth is doing that. So in the case of
Speaker 6: the US in the first half of the year, you know,
Speaker 6: was this puzzle was, well, maybe it's terroifts, maybe tariffs
Speaker 6: are actually contributing to it, maybe consumers are much.
Speaker 5: More resilient than we expected.
Speaker 6: And as it turns out, a huge factor, probably the
Speaker 6: largest factor, was this sort of unintentional private sector stimulus
Speaker 6: program otherwise known as data centers, and for me that
Speaker 6: I'll start it. So that started this puzzle of understanding
Speaker 6: this sort of disconmisserate size, the consequences of that size,
Speaker 6: and the acceleration's consequences in terms of where where the
Speaker 6: money is coming from, and all.
Speaker 5: Sorts of other things.
Speaker 6: But just to reframe in terms of something you guys
Speaker 6: were already talking about, and this I think is super important,
Speaker 6: and understanding why this particular episode is likely to turn
Speaker 6: out to be historically really important.
Speaker 2: Wait, when you say you're referred to this podcast episode,
Speaker 2: you're not referring to the broader episode of AI data Center.
Speaker 5: Entirely, just the podcast.
Speaker 6: Who Cares about data centers at the ten year anniversary
Speaker 6: of bad Law. So the reason why sort of it's
Speaker 6: going to be historically important is because, for the first time,
Speaker 6: we combine all the major ingredients of every historical bubbles
Speaker 6: in a single bubble. We have a metabubble no pun
Speaker 6: intended for meta. We have real estate. You guys just
Speaker 6: talked about this, right, Some of the largest bubbles in
Speaker 6: US history had some relationship to real estate. We have
Speaker 6: a great technology story. Almost all the large modern bubbles
Speaker 6: has something to do with technology.
Speaker 5: We have loose credit.
Speaker 6: Most of the major bubbles in some sense have a
Speaker 6: loose credit aspect. And one of the other exacerbating pieces
Speaker 6: that some of the largest bubbles, thinking about even the
Speaker 6: financial crisis, is some kind of notional government backstop. You know,
Speaker 6: think about the role in terms of broadening home ownership
Speaker 6: in the context of the real estate bubble, and the
Speaker 6: role that Fanny and Freddie played and loosening credit standards
Speaker 6: and all of those things. This is the first bubble
Speaker 6: that has all of that. It's like, we said, you
Speaker 6: know what would be great, Let's create a bubble that
Speaker 6: takes everything that ever worked and put it.
Speaker 5: All in one. And this is what we've done.
Speaker 6: Got a speculative real estate component is probably one of
Speaker 6: the strongest technology stories we ever had back to rural electrification.
Speaker 6: In terms of a technology story, we have loose credit.
Speaker 6: You guys talked about what's happening with respect to not
Speaker 6: just the role of private credit, but how private credit
Speaker 6: is largely supplanted commercial banks with respect to being lenders here.
Speaker 6: So we have all of these pieces that have all
Speaker 6: come together at once, and I think in terms of
Speaker 6: framing what's going on right now. It's really important to
Speaker 6: understand that it brings together all of these components and
Speaker 6: ways we've never seen before, which is one of the
Speaker 6: reasons why the notion that we can land this thing
Speaker 6: on the runway gently is nonsense.
Speaker 3: I love that framing the metal babble is perfect. Also,
Speaker 3: I had an epiphany earlier. I already told Joe, so
Speaker 3: you can attest to this, but I realized private credit
Speaker 3: kind of supplanted shadow banking as the term. Right like
Speaker 3: after two thousand and eight, we called it shadow banking,
Speaker 3: and then at some point it flipped to I guess
Speaker 3: the couplier private credit.
Speaker 2: Shadow bank always owned it sinister right away that private
Speaker 2: credit is.
Speaker 3: Well, someone figured that out and they're like, well, now
Speaker 3: it's private credit.
Speaker 5: I like to think of it as a kind of
Speaker 5: financial witness protection program. It was like, oh, you're those guys.
Speaker 5: That's great, now who you are?
Speaker 6: Yeah, it's kind of like that, And it's now like
Speaker 6: one point whatever. It is one point seven trillion dollars
Speaker 6: is the size of which is larger than many components
Speaker 6: of the orthodox lending market combined. In terms of the
Speaker 6: private credit industry itself, so that's a huge new piece
Speaker 6: of this that sometimes escapes notice how big it is
Speaker 6: and why it emerged, So all of those pieces.
Speaker 3: Yeah, it's stunning the growth that we've seen. Let me
Speaker 3: ask a very basic question before we go further. But
Speaker 3: one thing I've been wondering is Joe mentioned that anthropic
Speaker 3: headline that we heard before. We've seen Meta raising financing
Speaker 3: for data center builds, all that stuff. Why do these
Speaker 3: massively profitable and cash rich companies have to raise financing
Speaker 3: at all?
Speaker 6: Well, they don't, but there's these irritating shareholders out there
Speaker 6: get all pissy whenever you start diluting earnings pre shared
Speaker 6: too much and diverting it towards a single source. Now
Speaker 6: that's not the case with private companies obviously, but by
Speaker 6: the same token, open ai doesn't have the luxury of
Speaker 6: having cash flows via which they can do any of
Speaker 6: the things we're describing, so anthropic open Ai and everyone
Speaker 6: else they have no option other than to do exactly
Speaker 6: what we're describing. It's a different story with respect to
Speaker 6: how what percentage of Google's free cash flow or Amazon
Speaker 6: free cash flow that they want to continue to divert
Speaker 6: towards data centers. So in terms of the privates, this
Speaker 6: is the only option that they have. The public's obviously
Speaker 6: increasing the hyperscalers increasingly. We've got up to the point
Speaker 6: where around five hundred billion dollars or fifty percent of
Speaker 6: their free cash flow is going directly towards spending on
Speaker 6: data centers, and that's obviously a point at which you know,
Speaker 6: we have other things we have to do with free
Speaker 6: cash flow, and including having some of it be earnings
Speaker 6: per share, and so we increasingly it's become the option.
Speaker 5: You see what METT is doing recently with respect it
Speaker 5: is SPVs.
Speaker 6: We bring in other participants, create new financing vehicles, and
Speaker 6: then we play this entertaining game of it's not really
Speaker 6: our debt.
Speaker 5: It's in an SPV. I don't have to roll it
Speaker 5: back onto my own.
Speaker 6: Balance sheet and then bring in new lenders, new private
Speaker 6: credit firms and others.
Speaker 5: So that's the reason. Obviously it's partly because of the scale.
Speaker 6: It's probably because the privates who have no other option,
Speaker 6: and it's probably we've kind of tapped out the public
Speaker 6: companies in terms of the fraction of free cash flow
Speaker 6: that they.
Speaker 5: Feel as if they can spend with impunity on these projects.
Speaker 2: Explain to us for those who don't know. You know, again,
Speaker 2: SPV one of these terms that we really haven't heard
Speaker 2: in a while. And there's nothing inherently bad about an
Speaker 2: SPV except that you only hear about them typically after
Speaker 2: there's something, you know, some sort of crazy.
Speaker 5: Ride, which is weird obviously, But yes, tell.
Speaker 2: How would you U say in the broad strokes, how
Speaker 2: would you characterize what these financing vehicles are?
Speaker 6: So Mechanically, it's just a way of making sure that
Speaker 6: I don't have to roll data onto my balance sheet.
Speaker 6: But legally it's a structure into which I and my
Speaker 6: partners contribute capital that in exchange for which they retain
Speaker 6: legal title to the project that we've created, which allows
Speaker 6: us to all contribute capitalists but not have to put
Speaker 6: it back on my balance sheet and therefore not to
Speaker 6: have that debt rated.
Speaker 5: Which is really the key.
Speaker 6: Now, if you look at the actual intrinstics, say, for example,
Speaker 6: the reason that a project that they did in conjunction
Speaker 6: with blue Out, it's wild and byzantine. It looks like
Speaker 6: something you might have seen and what was that in
Speaker 6: Harry Potter or the forest with all the spider webs.
Speaker 5: It looks a little like that, right where.
Speaker 6: Everything's connected to everything and all I know is something
Speaker 6: and here's going to get me. So there's incredible complexity,
Speaker 6: but at the core, it's a mechanism via which I
Speaker 6: can raise more capital and keep it off my balance
Speaker 6: sheet by creating a legal entity that controls the actual
Speaker 6: data center and I don't. Therefore I have to put
Speaker 6: it back, roll it all back onto my balance sheet, navierated.
Speaker 6: Now there's weird intricacies obviously, So for example, what happens
Speaker 6: if at some period in the future this thing isn't
Speaker 6: performing the way we expect who owns it at that point?
Speaker 5: Is there a payment exchange, does.
Speaker 6: It become metas, does it become blue ouls, does it
Speaker 6: become someone else? And these things will turn out to matter.
Speaker 6: Right now, no one cares. If you go through some
Speaker 6: of the documents on these things, it's not entirely clear
Speaker 6: what the recourse payment will be when it ever, if
Speaker 6: and when it ever has to revert back to another owner,
Speaker 6: and it's not going to be held on to by
Speaker 6: the SPV. And I think this will turn out to
Speaker 6: be really important four or five years down the road,
Speaker 6: but right now nobody cares.
Speaker 3: So Number one, the lifespan of data centers is actually
Speaker 3: not that long. I can't remember the exact estimate, but
Speaker 3: maybe like three or four years something like that. And
Speaker 3: then also you have this risk that tenants are sort
Speaker 3: of rolling through and no one knows what that actually
Speaker 3: means for the structure of the debt, and you kind
Speaker 3: of get this asset liability mismatch.
Speaker 6: Yeah, so I'll start with the first one first. So
Speaker 6: this gets into something Michael Berry was tweeting about the
Speaker 6: other day, which was sort of entertaining that back about
Speaker 6: four years ago, tech companies changed the appreciation schedule or
Speaker 6: the assets inside of data centers.
Speaker 5: They extended them somewhat. Now, that wasn't an error.
Speaker 6: The reality is that data centers used for the purposes
Speaker 6: like at aws, where You've got a big S three
Speaker 6: bucket and I'm storing data inside of it. Those things
Speaker 6: generally speaking, the assets are long lived. I'm not running
Speaker 6: them flat out, it's not. These are not streetcar racers
Speaker 6: that I'm running around inside of a data center. These
Speaker 6: are relatively inexpensive chips that I'm using for really mundane
Speaker 6: purposes like storing large amounts terabytes exhibites of data inside
Speaker 6: of s three buckets, so it's not unreasonable to say
Speaker 6: their lifespans fairly long. They're not being taxed that heavily,
Speaker 6: so pushing out the depreciation schedule makes a lot of sense.
Speaker 6: But that was coincident with the emergence of GPU driven
Speaker 6: data centers using products like the chips from Nvidia, and
Speaker 6: those have much shorter lifespans, so depending on the usage.
Speaker 5: So there's two.
Speaker 6: Different reasons why the lifespan and therefore the depreciation schedule
Speaker 6: of a GPU inside of a data center is very different.
Speaker 6: So the reason most people think about is, oh, well,
Speaker 6: technology changes really quickly and I want to have the
Speaker 6: latest and greatest, and therefore I'm going to have to
Speaker 6: upgrade all the time. That's important, but it's probably about equal,
Speaker 6: if not maybe slightly less important the nature of how
Speaker 6: the chip is used inside the data center. So when
Speaker 6: you run using like the latest, say a Nvidia chip
Speaker 6: for training a model, those things are being run flat
Speaker 6: out twenty four hours a day, seven days a week,
Speaker 6: which is why they're liquid cool. They're inside of these
Speaker 6: giant centers where one of your primary problems is keeping
Speaker 6: them all cool.
Speaker 5: It's like saying I bought a used car and.
Speaker 6: I don't care what it was used for. Well, if
Speaker 6: it turns out it was used by someone who was
Speaker 6: doing like Laman's twenty four hours of endurance with it,
Speaker 6: that's very different. Even if the mileage is the same
Speaker 6: as someone who only drove to.
Speaker 5: Church on Sundays.
Speaker 6: Right, these are very different consequences with respect to what's
Speaker 6: called the thermal degradation of the chip. The chip's been
Speaker 6: run hot and flat out, so it probably it's useful.
Speaker 6: Lifespan might be on the order of two years, maybe
Speaker 6: even eighteen months. So there's a huge difference in terms
Speaker 6: of how the chip was used, leaving aside whether or
Speaker 6: not there's a new generation of what's come along. So
Speaker 6: that takes us back to these depreciation schedules. So these
Speaker 6: depreciation schedules change, just as the nature of how the
Speaker 6: lifespan of the chips changed dramatically, because I can use
Speaker 6: something for you know, storing things in s three buckets
Speaker 6: for a long time, six to eight years isn't unreasonable.
Speaker 6: But if I'm doing the the Laman's endurance equivalent with
Speaker 6: a GPU, it might be eighteen months. That's a huge
Speaker 6: difference in terms of the likely lifespan of a product
Speaker 6: that I'm depreciating over a very different period. And so
Speaker 6: that's a huge part of the problem here with respect
Speaker 6: to understanding the intrinsics in terms of how data centers
Speaker 6: can and can't make money. How you have to think
Speaker 6: about the likely capex requirements because of this much shorter
Speaker 6: life span of the underlying technology, and then.
Speaker 3: Talk about the tenancy rollover risk. I guess we might
Speaker 3: call it.
Speaker 6: Yeah, it's really interesting. So one way to think about
Speaker 6: data centers is as giant apartment buildings. Right, They're essentially
Speaker 6: gigantic commercial pieces of commercial real estate with a bunch
Speaker 6: of tenants. Sometimes there's a lot of tenants, sometimes there's
Speaker 6: only one. Sometimes Google bought the whole apartment building and
Speaker 6: just moved in, Or it's a giant office building they
Speaker 6: just moved in. It's all theirs, right, So think about
Speaker 6: it in those sorts of terms. And the reason why
Speaker 6: as a sponsor of a data center I might take
Speaker 6: a different view on how many tenants I want is
Speaker 6: again you think about it in terms of what can
Speaker 6: I get Google to pay? But whereasus what can I
Speaker 6: get someone who's a much flightier tenant to pay? Well,
Speaker 6: I can get the flightier tenants, more of them and
Speaker 6: diversified as all leasing inside the data center, paying higher
Speaker 6: lease rates for GPUs over the period of tendency than
Speaker 6: I can get a Google to pay. Why because Google's
Speaker 6: got great credit, they don't have to pay very much
Speaker 6: and they know they don't.
Speaker 5: So if you look at the commercial real estate.
Speaker 6: Data, the cap rate, the blended cap rate for these
Speaker 6: for the largest data centers that are tenanted by hyperscalers
Speaker 6: is horrible. It's like four point eight five point three percent.
Speaker 6: It's like, why don't you just buy a treasure you're doing.
Speaker 6: So what happens then is people start blending in more
Speaker 6: different kinds of tenants to Tracy's point, as an effort
Speaker 6: to try and improve the yield the cap rate on
Speaker 6: the underlying instrument, which is the data center. So you
Speaker 6: could do all of this should start to sound familiar
Speaker 6: because it's this idea of a blend together all of
Speaker 6: these different tendencies. I can increase the yield of the
Speaker 6: securitized instrument, but that also changes the risk profile of
Speaker 6: what comes out at the other end, which just takes
Speaker 6: us to things like the increasing usage of these things
Speaker 6: in asset backed securities, which are these trench securities that
Speaker 6: have all the different pieces, We have different layers associated
Speaker 6: with it, and that's a reflection of well, there's different
Speaker 6: tenants inside these data centers, and people want different exposures
Speaker 6: to risks. So I may only want to buy the
Speaker 6: senior tranch. You may want to buy the mezzanine and trace.
Speaker 6: He may want to buy the equity charge.
Speaker 3: Can I just say, I know we already said this,
Speaker 3: but Paul is truly, truly the perfect guest. I remember
Speaker 3: reading his coverage of subprime and securitization in like two
Speaker 3: thousand and eight, and so having someone who's able to
Speaker 3: synthesize that experience with what's going on now is just fantastic.
Speaker 2: I kind of can't believe we're doing this again. I know,
Speaker 2: I mean, look, I mean again, there's nothing inherently wrong
Speaker 2: with SPVs. There's nothing inherently wrong tranching, right, Like a
Speaker 2: lot of these things are very intuitive, etc. But it
Speaker 2: is still a little weird how central this is and
Speaker 2: how it's the same old There's nothing I mean, on
Speaker 2: some financial level, it feels very familiar.
Speaker 5: No, there's nothing new un to the sun.
Speaker 6: But I think that point is really important It's not
Speaker 6: that tranches are evil. It's not the securitization is evil,
Speaker 6: or that asset backed security your project finance is evil.
Speaker 5: No, all of these things are terrific pieces of the arsenal.
Speaker 6: Whenever you're actually raising money for projects, the issues start
Speaker 6: to arise at the scale, which is what you guys
Speaker 6: have already alluded to. But the secondary piece, which again
Speaker 6: will sound painfully familiar to the financial crisis, is there's
Speaker 6: a flywheel that gets created at the back end of this.
Speaker 6: So once you start securitizing the yield producing assets in
Speaker 6: the form of these tranch securities, the people who are
Speaker 6: purchasing those things don't give a rats ask what's going
Speaker 6: on inside this AI. I joke all the time that
Speaker 6: a lot of these people can't spell AI. They don't
Speaker 6: care what's going on inside the.
Speaker 5: Data center, right.
Speaker 6: It could be you know, the world Hide and Go
Speaker 6: Seek Championships had going on in there. I don't care
Speaker 6: as long as it generates heels and I.
Speaker 5: Can securitize it.
Speaker 3: Well.
Speaker 6: It's very much an analogous to what's happened in prior
Speaker 6: periods like this, where again you get this secondary flywheel
Speaker 6: effect of let's just create more of these things because
Speaker 6: our customers want more and they're really easy to securitize
Speaker 6: and look gets backst up by Meta and Google or
Speaker 6: whoever else.
Speaker 2: Well, so this actually brings important point. I mentioned this
Speaker 2: great report out from the Center for Public Enterprise. One
Speaker 2: of the things that they pointed out is in this
Speaker 2: market environment where everyone is just you know, there's this
Speaker 2: sort of AI pixi us, but also just the reality
Speaker 2: if your revenues are surging, the market probably loves you,
Speaker 2: like talk to us about the unit economics. Here is
Speaker 2: the incentive for all the players essentially to just grow
Speaker 2: the top line as much as possible, even if these
Speaker 2: aren't whether we're talking about inference on a per token basis,
Speaker 2: even if these aren't particularly profitable, how do you think
Speaker 2: about the union economics of some of these businesses and
Speaker 2: how that could eventually perhaps sort of you know, come
Speaker 2: home to Ruster to speak.
Speaker 5: Yeah, So.
Speaker 6: The term of art obviously is these things have negative
Speaker 6: unit economics, which is a fancy way of saying that
Speaker 6: we lose money on every sale and try to make
Speaker 6: it up on volume. Right, I mean, that's the that's
Speaker 6: the problem here. So but that's okay, I mean, we've
Speaker 6: had lots of Amazon.
Speaker 5: In its early days that negative unit economics. You can
Speaker 5: get past that.
Speaker 6: And as an aside, I'll say right here, all of
Speaker 6: the things that I'm saying is and to say that
Speaker 6: you know AI is some kind of you know, free
Speaker 6: tamagatche thing, that's just a fad as an incredibly important technology.
Speaker 6: What we're talking about is how it's funded and the
Speaker 6: consequences of doing that in terms of what's going to
Speaker 6: happen with respect to the businesses and the return on
Speaker 6: those businesses. Right, So, the unit economics are dire for
Speaker 6: a bunch of reasons, have mostly having to do with
Speaker 6: the more tokens you have to produce. The costs rise
Speaker 6: more or less linearly with the demand on the system.
Speaker 6: As opposed to an orthodox software business where the more
Speaker 6: people use my service, the more people across which I
Speaker 6: can spread my relatively fixed costs. That's not the way
Speaker 6: that for the most part, current generation large language models
Speaker 6: were costs rise linearly or sublinearly with the number of users,
Speaker 6: which makes for really crappy unit.
Speaker 5: Economics, and that's a big part of the problem.
Speaker 6: So from there you get to the question of Okay,
Speaker 6: so what does it have to look like in terms
Speaker 6: of making it look profitable. There's lots of ways to
Speaker 6: back into this. You can do bottoms up models. It
Speaker 6: would suggest that like if every iPhone newsrun earth paid
Speaker 6: fifty bucks do at work, we could have around a
Speaker 6: four hundred billion dollar, five hundred billion dollar annual stream
Speaker 6: of revenue flowing. And well, that's not going to happen,
Speaker 6: but it's worth pointing out like that would do it.
Speaker 6: But it gives you a sense of the kind of
Speaker 6: scale of what at a consumer level, for example, it
Speaker 6: might have to look like.
Speaker 5: People come out it from the other end.
Speaker 6: One of my favorite ways that people come out is
Speaker 6: to say, well, we could create a viable model here.
Speaker 6: If you think this was in the JPM call last week.
Speaker 6: I don't know if you guys saw the summary of it,
Speaker 6: but it was huge fun for the whole family listening.
Speaker 6: And so one of the ways they backed into it
Speaker 6: was a top model where they said, well, the global
Speaker 6: TAM for human labor.
Speaker 5: I love the five trillion dollars. I love the global TAM.
Speaker 5: I said.
Speaker 6: That was right up there with saying like if I
Speaker 6: reduce humans to their chemical components, here's what.
Speaker 5: I can get for you.
Speaker 3: Well, this was this was Steve Eisman's line, which was like,
Speaker 3: beware of anyone that mentions tam right.
Speaker 6: Right, right, no exactly, and so then and then they play.
Speaker 6: The next step is of course to say, well, imagine
Speaker 6: we can get ten percent of that, right, which is
Speaker 6: which is obviously one of the oldest cliches. It's like saying,
Speaker 6: you know, I'm going to get five percent of the
Speaker 6: Chinese market. No one ever gets five percent of the
Speaker 6: Chinese market.
Speaker 5: This doesn't happen.
Speaker 6: So the same thing won't happen with global labor. But
Speaker 6: if you were to do that, you do the math
Speaker 6: on that that call those kinds of numbers gets you
Speaker 6: to a weighted average cost of capital basis to a
Speaker 6: reasonable return on current and planned expenditures with respect to
Speaker 6: AI data centers. If you assume we're heading to about
Speaker 6: a three or four trillion dollar a number, which is
Speaker 6: kind of the I think it's around the number that
Speaker 6: most people put out there, which I think is a
Speaker 6: completely wrong number, but nevertheles that's the kind of number
Speaker 6: and what you'd have to do to get there.
Speaker 5: So you can get there from.
Speaker 6: A bottoms up model by making some really unreasonable assumptions
Speaker 6: about the total numbers of subscribers and what they pay.
Speaker 6: You can get there from a top down model. You
Speaker 6: can also get there by thinking about it purely in
Speaker 6: terms of industrial users. I think about purely API users
Speaker 6: just for end retail users of AI don't exist. And say,
Speaker 6: you know, Andthropics projecting seventy billion dollars in revenue in
Speaker 6: twenty twenty eight, something like thirty five percent of their
Speaker 6: current revenues. Most of their revenues today are from their API.
Speaker 6: Thirty five percent of that is from software developers that
Speaker 6: split between two large users, Copilot and Cursor. And so
Speaker 6: you know, we can model that out. Everybody has to
Speaker 6: become a software developer.
Speaker 5: And we can make the math work.
Speaker 6: The problem is it's got huge fragility right in customer
Speaker 6: concentration risk. So a Cursor disappears as a user of
Speaker 6: Entropics API, and you just blew out fifteen percent of
Speaker 6: your revenues because they're gone and they've done something else.
Speaker 6: And as it turns out, Cursor a two weeks ago
Speaker 6: announced that they were trading their own internal model that
Speaker 6: you could use for software developed and you wouldn't have
Speaker 6: to call the Anthropic API so you can think about
Speaker 6: all these different ways to get there, but they all
Speaker 6: have a lot of built in fragility with respect to
Speaker 6: so we all become software developers and we all subscribe
Speaker 6: to Cursor.
Speaker 3: Just going back to the used car analogy that you
Speaker 3: mentioned before, when we're thinking about all this financing of
Speaker 3: the AI capex spen, is it useful to think of
Speaker 3: GPUs essentially as the collateral the problem?
Speaker 5: Yes, or what would you.
Speaker 3: Call the collateral in this case?
Speaker 5: So what ends up happening.
Speaker 6: The collateral in this case is the gp There's no
Speaker 6: question it is the GPA. The issue is this disconnect,
Speaker 6: this temporal mismatch that you alluded to earlier with respect
Speaker 6: to the duration of the underlying debt and the assets
Speaker 6: that are producing.
Speaker 5: The income that allows me to pay for the debt.
Speaker 6: Right, so we've got this probably unprecedented temporal messmatch with
Speaker 6: thirty year loans and two year depreciation on the underlying collateral,
Speaker 6: which is essentially the GPUs that are the income producing assets.
Speaker 6: And so that creates this constant refinancing risk because I'm
Speaker 6: going to can you only have to turn over the
Speaker 6: base And we've seen this many many times right now,
Speaker 6: it's easy to turn it over, but in two years
Speaker 6: it may not be possible. There's a wave of refinancings
Speaker 6: coming in twenty twenty eight in many of the more.
Speaker 5: Speculative data centers.
Speaker 6: Will they be able to turn over their debt and
Speaker 6: refinance all the GPUs today?
Speaker 5: They could? This today is in twenty twenty eight.
Speaker 6: So that's the inherent problem, is this structural temporal mismatch
Speaker 6: between the income producing assets and the duration of the lungs.
Speaker 6: And it gets worse if you think about it in
Speaker 6: more realistic terms, think about it in terms of one
Speaker 6: of the other gating factors here that's driving all.
Speaker 5: Of this is the scarcity of energy supply. It's really difficult.
Speaker 6: You can hook them up to the well. It's actually
Speaker 6: kind of turned into a bit of a joke. I
Speaker 6: can hook you up to the grid, but I can't
Speaker 6: give you power. I don't know if you saw the
Speaker 6: recent episode with the Oregon Public Utilities Commission, Amazon had
Speaker 6: three data centers that they connected to the grid, and
Speaker 6: it was kind of like the Oregon PUC said, Oh,
Speaker 6: you want power too, Oh, I can't help you with that.
Speaker 5: We can't help you with that.
Speaker 6: So now there's a complaint in it the Oregon PUC
Speaker 6: from ADS, Amazon's the digital services group that runs aws,
Speaker 6: complaining we now have data centers, but.
Speaker 5: We have no power.
Speaker 6: Right it sounds a little bit like like a winter
Speaker 6: storm hazard or something, but it's the structural problem with
Speaker 6: respect to the inability. We can connect people, but we
Speaker 6: can't provide them with power. So the next stage is
Speaker 6: and this takes bets back to the collateral problem in
Speaker 6: the temporal mismatch, is that people are doing behind the
Speaker 6: meter power. They're building natural gas or if you're fair me,
Speaker 6: you're saying wild things about nuclear power and you're saying, Okay,
Speaker 6: I'm coming with my own power. You don't need to
Speaker 6: connect me to the grid. I'm going to power this myself.
Speaker 6: That creates two or three different issues, but among the
Speaker 6: more important is think about how long lived an asset
Speaker 6: a natural gas plant is. This is not something that's
Speaker 6: got a five year lifespan and we just truly wave goodbye.
Speaker 6: This is going to be running probably twenty five to
Speaker 6: thirty years. And the only thing your ability to forecast.
Speaker 6: We know the cost of the natural gas plant, but
Speaker 6: in terms of the cost of the center, and it's
Speaker 6: incompability to generate enough income to pay off the loan
Speaker 6: associated with the natural gas plant. God help you if
Speaker 6: you think you can sort that out, because what you've
Speaker 6: really got is a huge likelihood of a stranded asset
Speaker 6: of their natural gas plants that are longer useful for
Speaker 6: powering these things that they were built for.
Speaker 2: The good news is that Daniel Jurgen said this on
Speaker 2: the show. You know the back orders for natural gas turbines,
Speaker 2: like you probably if you ordered one today, you would
Speaker 2: probably get it in twenty thirty. So the good news
Speaker 2: that I suppose ten years is that at least you
Speaker 2: don't have to have the turbines sitting there for years.
Speaker 2: Like I don't know, Maybe I don't know if that's
Speaker 2: good news at all, but there are se I may
Speaker 2: never get it in, You may never get the gas
Speaker 2: plant built. Anyway, someone will be stuck with the book.
Speaker 6: It kind of raises this goes back to Tracy's question earlier.
Speaker 6: This raises a really interesting thing. So like, honestly, what
Speaker 6: the f are all these people doing who are announcing
Speaker 6: the giant unding translation. I think of it like people
Speaker 6: all showing up with the OK Corral at once and
Speaker 6: It's like, dude over there has one gun, I got two.
Speaker 3: Yeah, I got Oh that's not a nice this is anie.
Speaker 5: Yeah.
Speaker 6: But it's this deterrence. It's this deterrence program that's going on.
Speaker 6: Don't even imagine spending fifty because I'm spending one hundred.
Speaker 5: No point in you doing any of those. That's very
Speaker 5: game theoretic.
Speaker 3: Well, this also worries me because you hear so many
Speaker 3: people framing this as like an existential competition. Right, and
Speaker 3: once you start calling something existential, the limit on spend,
Speaker 3: well it becomes unlimited.
Speaker 5: Right.
Speaker 3: It's about survival, so you'll spend anything.
Speaker 2: That's why the conversation has turned in recent weeks to
Speaker 2: the one entity that actually, at least in theory, can
Speaker 2: print as much money as possible.
Speaker 6: Right, that's the you know, the Sarah Friar's accidental foot
Speaker 6: in mouth the thing earlier in the week.
Speaker 5: But that's right.
Speaker 6: But that's again goes back to my original point about
Speaker 6: what makes this bubble unusual. It's this element that not
Speaker 6: only is there a kind of bagstock, but there's actually
Speaker 6: a notion of wrapping in the flag. We have to
Speaker 6: win this competition, we have to do what it takes.
Speaker 6: This is existential. It's US versus China, and it's not
Speaker 6: just the US doing this. I was talking to some
Speaker 6: Canadian policymakers just earlier this morning, exact same thing going
Speaker 6: on there. We have to build a domestic in the
Speaker 6: same thing in the UK, same thing in Germany. And
Speaker 6: so there's this idea around the world that sovereign ai
Speaker 6: is something that's incredibly important. So this this government backstop
Speaker 6: isn't just mythic, it's it's global. It's this idea that
Speaker 6: we all have to win, we all have to win,
Speaker 6: which obviously can't happen, but that the government's playing a
Speaker 6: role and that that be can trace this kind of
Speaker 6: limitless course of capital.
Speaker 2: You know. So one of the things that's going on,
Speaker 2: and maybe it's part of the same the sort of
Speaker 2: maximalist strategy mentioned Anthropic wants to get into data centers,
Speaker 2: so everyone's sort of looking at how they can expand vertically.
Speaker 2: Can I own the data centers? I think? You know,
Speaker 2: Sam Altman has talked about owning chips or owning a
Speaker 2: semiconductor fab at some point, like maybe that'll be part
Speaker 2: of the story. Who knows. There's one thing that I don't.
Speaker 2: I'm sort of curious. I'd love to have your take
Speaker 2: on there was. At the end of September, Meta announced
Speaker 2: the deal to buy Compute from core Weave, one of
Speaker 2: these neo clouds. I don't totally get that because Meta
Speaker 2: has its own data centers, et cetera. Do you have
Speaker 2: some intuitive sense about what an established hyperscaler needs a
Speaker 2: neo cloud for in this arrangement, what core Weave can
Speaker 2: supply that Meta can't build on its own or buy
Speaker 2: on its own.
Speaker 5: Nothing.
Speaker 6: So here's what's going on. This is what's going on
Speaker 6: is that there's this form of hoarding going on. So
Speaker 6: what's happening is is people saying, you have capacity, I
Speaker 6: can lock that up.
Speaker 5: I'll lock that up.
Speaker 6: And because I can't lock it up yet by building
Speaker 6: a data center quickly enough, I'll lock it up in
Speaker 6: the marketplace. So once you start thinking of compute as
Speaker 6: a hordable commodity, and what people are doing is trying
Speaker 6: to hoard it, control it before someone else can do it,
Speaker 6: because until they bring on their own access capacity. That's
Speaker 6: really what's going on in a lot of these transactions.
Speaker 6: This is a way of making sure that I may
Speaker 6: not need this but you sure can have it. And
Speaker 6: so there's there's an element of compute hoarding going on
Speaker 6: across the map because of you know, this backlog and building.
Speaker 5: Data centers that may or may not ever get built.
Speaker 5: So that's the answer.
Speaker 6: The answer isn't that they care at all about whether
Speaker 6: or not they can run giant workloads on any particular
Speaker 6: neo clouds provider. It's the idea of hoarding capacity and
Speaker 6: making sure that no one else can have it, like
Speaker 6: trying to have like the Hunt Brothers and the getting
Speaker 6: a corner on the silver market.
Speaker 3: You know, I want to go back to China because
Speaker 3: it is true that the US and China seem locked
Speaker 3: in this existential race for AI supremacy, but they seem
Speaker 3: to be taking very different approaches to it. And in
Speaker 3: the US, it's all about spending as much money as
Speaker 3: you can developing these you know, state of the art,
Speaker 3: mostly closed source models, whereas in China it seems to
Speaker 3: be much more about rapid adoption and creating open source
Speaker 3: models that just get out into the market much faster
Speaker 3: and much more cheaply. And so I'm curious, like, which
Speaker 3: of those approaches do you think it's going to win?
Speaker 5: Here.
Speaker 6: Yeah, so that's a really good question. So I think
Speaker 6: it's going to be something closer to.
Speaker 5: The Chinese approach, but not for the reasons they expect.
Speaker 6: So the reason is because, so what, let's I'll reframe
Speaker 6: what the Chinese are doing slightly, so I'll say that
Speaker 6: instead of it just being a sort of an example
Speaker 6: of open source, I don't think that's right. The right
Speaker 6: way to think about it is they're using this kind
Speaker 6: of distillation approach increasingly where there's kind of a you
Speaker 6: think about it like, Okay, I'm a sales manager. I
Speaker 6: don't want to train all my salespeople. I'm going to
Speaker 6: train this dude.
Speaker 5: And they're going to train all the sales But that's distillation, right.
Speaker 6: You train the trainer, I train somebody who trains something else,
Speaker 6: and something else in this case are these smaller models.
Speaker 6: So that approach of kind of training the trainer really
Speaker 6: speeds up the process of creating new models because I
Speaker 6: distill them, I train them out of out of other
Speaker 6: models that are really compute intensive, like anthropics or opening
Speaker 6: eyes or whoever else is right. So the notion is,
Speaker 6: so is there are huge efficiency gains to be had
Speaker 6: in training and the Chinese are showing the huge efficiency
Speaker 6: gains to be had, and the one way to think
Speaker 6: about it is that the transformer models that underlie large
Speaker 6: language models that are so computationally intensive, went from the
Speaker 6: lab to the market faster than any product in technology history.
Speaker 6: So they're absolutely bloated and full of crap. Right, So
Speaker 6: these things are wildly inefficient. There's all kinds of other
Speaker 6: ways to do the same sorts of things, one of
Speaker 6: which is distillations. So what you're really seeing is a
Speaker 6: kind of an accident of history that we've came down.
Speaker 6: The US came down this path that led directly out
Speaker 6: of the original transformer paper in twenty seventeen, and the
Speaker 6: Chinese have said, yeah, we're not going to be able
Speaker 6: to do that for a bunch.
Speaker 5: Of different reasons. But we don't have to do.
Speaker 6: That because I can take this approach of distillation, which
Speaker 6: lets us get you.
Speaker 5: If you look at Kimmy, this sort.
Speaker 6: Of relatively recent open source these things are actually really
Speaker 6: effective in benchmark very well, and it's not surprising because
Speaker 6: they've been trained by really good trainers, which.
Speaker 5: Is to say some of the other models that are
Speaker 5: out there.
Speaker 6: But these are about efficiency games, which should then ask
Speaker 6: the question is whoa wait a minute, if there's all
Speaker 6: these efficiency gains ahead from training, and training is seventy
Speaker 6: percent of the workload on data centers? Hang on a second,
Speaker 6: aren't we completely misforecasting the likely future the arc of
Speaker 6: demand for compute And the answer is yes. And this
Speaker 6: is rather than looking at it as an example of
Speaker 6: why China is doing something better for worse, another way
Speaker 6: of looking at is saying, just just refuted the approach
Speaker 6: that we're taking to training altogether, because it shows how
Speaker 6: blowdd and inefficient the approach we're taking is, and yet
Speaker 6: we're projecting on that basis what future data center needs are.
Speaker 2: Part of the question, it seems to me, and this
Speaker 2: is where it gets a little bit philosophical, is what
Speaker 2: do these AI companies think they're building? Because one theory
Speaker 2: is like, well, maybe they're building business tools, right, maybe
Speaker 2: they're building business tools of various sorts. And if they're
Speaker 2: building business tools of various sorts, that implies the possibility
Speaker 2: that eventually they get good enough. This does the job right,
Speaker 2: This makes it easier for this website. You can use
Speaker 2: an agent to book your travel, and the technology works,
Speaker 2: and we don't have to keep building it because we
Speaker 2: got to the point where it works. And then there
Speaker 2: is this other question of like, well, maybe they want
Speaker 2: to build something called AGI or ASI that's like so
Speaker 2: sci fi et cetera, in which case you could never
Speaker 2: get enough, or simply having built the thing that allows
Speaker 2: you to book your travel or book a dinner reservation
Speaker 2: or translated text or whatever, that's not nearly enough. You
Speaker 2: you hear different things. But what do you think the
Speaker 2: builders at the cutting edge of these labs are going for?
Speaker 2: Is it really the sort of sci fi building god
Speaker 2: cliche or do they want to build profitable business tools?
Speaker 5: So it's the first.
Speaker 6: Thing until you challenge them, and then it's the second.
Speaker 6: So what happens is if you have the conversation internally,
Speaker 6: they'll say, yeah, no, no, no, we're building this really effective
Speaker 6: productivity enhancing tools that'll be used across a host of businesses,
Speaker 6: and these all sounds really good.
Speaker 5: But then when you walk through some of the math.
Speaker 6: In terms of justifying the ROI on the spend, all
Speaker 6: of a sudden, then it turns into what I call faith.
Speaker 5: Argumentation about AGI, and they.
Speaker 6: Say it's like the greatest call option ever, Like what
Speaker 6: would you pay for a call option that could get
Speaker 6: you anything, and it's like, well, wait a minute, this
Speaker 6: isn't a way of justifying any particular expenditure.
Speaker 5: This is just faith based argumentation.
Speaker 6: We're saying, you know, with the uber call option for anything,
Speaker 6: you should be willing to pay anything for it. And
Speaker 6: obviously that that kind of justification doesn't get you anywhere.
Speaker 6: So in house they'll arm wave a lot about these
Speaker 6: different models that will emerge.
Speaker 5: Who knows.
Speaker 6: I had someone at inn Vidia tell me the other
Speaker 6: day that we really are just waiting for the uber
Speaker 6: of ai to come along and show.
Speaker 5: Us the future. And I'm like, okay, so that's it's
Speaker 5: not an answer, right.
Speaker 2: So because in theory, if you're building a business productivity tool,
Speaker 2: then eventually you could solve your unit economics problem. Right,
Speaker 2: If you're just trying to build a really great business opportunity,
Speaker 2: then as simply you know what, we don't have to
Speaker 2: build anymore. It works, and then the cash flow just
Speaker 2: starts pouring in and the cost per token goes down can.
Speaker 6: And there's a bunch of that already happening. It's really interesting.
Speaker 6: But what's incre thing happening is the problems they're solving
Speaker 6: are really mundane, and so it's things like I'm trying
Speaker 6: to onboard a bunch of new suppliers right now that
Speaker 6: people have weird zip codes and they sometimes don't match up.
Speaker 6: I have a dude in the back who fixes that.
Speaker 6: I'd rather have someone who could do it faster so
Speaker 6: they could onboard a lot more suppliers. Oh, it turns
Speaker 6: out these small language models are really good at that.
Speaker 6: These micro models like IBM's granted and whatever else, But
Speaker 6: those things require a fraction of the training, are very cheap,
Speaker 6: are not going to justify anywhere near the economics needed
Speaker 6: to pay for the current spend. And yet those things
Speaker 6: are almost likely very likely the future because it'll be
Speaker 6: profitably get token used from micro models often hosted internally
Speaker 6: to do really mundane background tasks, not very glamorous onboarding
Speaker 6: new suppliers, matching records, great stuff, just not really very exciting.
Speaker 6: But large language models are amazing at it, and small
Speaker 6: language models are amazing at it, and almost.
Speaker 3: Free and writing songs, right, Joe, I'm actually I'm still
Speaker 3: annoyed that AI is like getting into art and music
Speaker 3: writing and all the fun stuff versus the stuff that
Speaker 3: I don't want to do like folding launchy to your classic.
Speaker 5: Example or matching customer records. Are that?
Speaker 3: So, going back to the beginning of this conversation when
Speaker 3: we were just talking about the scale of AI investment
Speaker 3: and its impact on the US economy, I'm pretty sure
Speaker 3: you are one of the ones who's described AI capex
Speaker 3: as like a private sector stimulus program for the US economy.
Speaker 3: What are the actual consequences, either positive or negative, of
Speaker 3: having this massive private sector spend in the economy versus
Speaker 3: something I guess more typical, which would be a government
Speaker 3: stimulus or maybe growth driven by consumer spending or something
Speaker 3: like that.
Speaker 6: Yeah, So to an orthodox economist, the old line is like,
Speaker 6: it really doesn't matter what we pay people to do as.
Speaker 5: Long as we pay them, right. It's the idea of I.
Speaker 6: Should be, I should be you should be willing to
Speaker 6: pay people to dig holes in the ground and people.
Speaker 5: Over there to fill the holes back in.
Speaker 6: Again, it really doesn't matter as long as the money
Speaker 6: he's out there circulation, right, It's just it's all just stimulus.
Speaker 5: Right. So, to that way of thinking, it doesn't matter
Speaker 5: because the money's all finding its way back into the economy.
Speaker 6: But I think that's obviously hugely misleading, because in this context,
Speaker 6: these are investments created with an expectation of a return.
Speaker 6: If they can't, then that flows backwards into all the
Speaker 6: entities that are built on that basis, whether it's private
Speaker 6: credit firms and their returns, the S and P five hundred,
Speaker 6: what is it like? Thirty five percent now is AI
Speaker 6: related mag seven meg ten whatever? Fifty percent now the
Speaker 6: last two years return. So this is a massive negative
Speaker 6: wealth effect when you unwind it, not just in terms
Speaker 6: of the direct spending, but in terms of the wealth
Speaker 6: effect with respect to what people's holdings are. So this
Speaker 6: is not as simple as saying this has just been
Speaker 6: a wonderful stimulus program.
Speaker 5: We're paying people to dig holes and filling them back in. Again,
Speaker 5: this is.
Speaker 6: A wasting asset on something that's likely to be produced
Speaker 6: in quantities that we can never earn an economic return from,
Speaker 6: in part because of wildly flawed assumptions and projections about
Speaker 6: the future of demand for those units. And so that's
Speaker 6: that's the deep structural problem, and can get into this
Speaker 6: whole question of like, well it was just private equity
Speaker 6: guys get hurt, you know, cares Screw those guys, right,
Speaker 6: And it's not, of course, because as we just talked
Speaker 6: about it, it's it's in equity funds.
Speaker 2: It's firefighters and teachers money.
Speaker 6: Yeah, and it's in reeds now look at the larger
Speaker 6: holdings and reads now increasingly our data centers.
Speaker 5: Yeah. And it's even in.
Speaker 6: Sort of sneaky backdoor ways like we're seeing increasing I
Speaker 6: don't if you guys are familiar with these new interval funds.
Speaker 5: They're appearing there all over.
Speaker 2: Now, Paul Kadrowski, we could I have a million more
Speaker 2: questions you could ask you, But much like the race
Speaker 2: towards a GI itself, that would imply that we'll ever
Speaker 2: actually get to the end of this conversation. So how
Speaker 2: about we wrap here and then just plan on, you know,
Speaker 2: revisiting the com six months, maybe three years. We just
Speaker 2: keep revisiting down the line where we are in the cycle.
Speaker 5: As long as we haven't been turned into paper clips.
Speaker 1: I'm good.
Speaker 2: Yeah, that's the no one talks about the nightmare. I
Speaker 2: feel like that was a no one talks about the
Speaker 2: old school paper clip maximizer stuff. Everyone's onto more esoteric fears.
Speaker 5: I know people have moved on. We need to worry.
Speaker 3: Does anyone wait, did anyone ever try to securitize Clippy?
Speaker 5: They didn't, right, I don't think so.
Speaker 2: No, thanks Paul.
Speaker 6: Hey, thanks guys.
Speaker 2: Paul's so good. That's a lot of fun. He's so good.
Speaker 3: Here's my highest form of praise for an odd thought's guest.
Speaker 3: I am going to go back and read that transcript
Speaker 3: from beginning to end.
Speaker 2: It is a very good that is a very good
Speaker 2: practice to do. You're not going to listen to it.
Speaker 5: I'm going to read it.
Speaker 2: Yeah, I can read it. I can't listen to it.
Speaker 3: I just listened to it.
Speaker 2: I can need to read it. I can't listen to
Speaker 2: our episodes. No, I just you know, I think there's
Speaker 2: a lot, there's a lot more to do on all
Speaker 2: this topic, but the financing in particular and some of
Speaker 2: these arrangements. It's just incredible how the speed with which
Speaker 2: I guess I would say the financing has gotten interesting.
Speaker 2: Do you know what I'm saying that? I think like
Speaker 2: a data center project ten years ago, Microsoft AWS thing
Speaker 2: just seemed like a fairly straightforward is probably more complicated
Speaker 2: than I appreciate at the time, but basically straightforward. We
Speaker 2: make this money and part of it is going to
Speaker 2: go to building more data centers to you know, serve
Speaker 2: you know, Amazon Prime Streaming or whatever it is, or
Speaker 2: some client thing or whatever. And then the degree of
Speaker 2: complexity with these SPVs and rollover risk and depreciation schedules
Speaker 2: and changing of who it's gotten very interesting, very fast.
Speaker 3: Life Uh finds a way life finds. Yeah, that was
Speaker 3: my terrible, terrible impression. I think that's absolutely right. One
Speaker 3: thing I would say is the fact that a lot
Speaker 3: of these big, supposedly cash rich companies are doing this
Speaker 3: through SPVs that effectively preserve their balance sheet and their
Speaker 3: cash flow so they can do something else with it.
Speaker 3: I mean a lot of companies use SPVs. Sure, yeah,
Speaker 3: But I do think it says something about the scale, yes, right,
Speaker 3: Like there's a scale problem here where if all you're
Speaker 3: spending was appearing on balance sheet investment might think very
Speaker 3: very differently about your company. And then the other thing
Speaker 3: I would say is I still think the comparing contrast
Speaker 3: between the US and China and their approaches to AI.
Speaker 3: You know, both of them, I think would agree that
Speaker 3: this is an existential problem of some sort or an
Speaker 3: existential competition. But they're following very different paths, and it
Speaker 3: does seem to me like the arc of history kind
Speaker 3: of leans towards stuff becoming cheaper.
Speaker 2: The artifactory bend towards China.
Speaker 3: Well that's that too, but it bends towards you know,
Speaker 3: people generally want the cheaper thing, and they want the
Speaker 3: thing that's like available now, and China seems to be
Speaker 3: going for that.
Speaker 2: The counter argument is that if you're going to use
Speaker 2: an open source model for some purposes, you have to
Speaker 2: supply your own electricity, right, you have to supply your
Speaker 2: own inference. You've got to host on your service, like,
Speaker 2: you still run into some constraints, and so rather than
Speaker 2: having it beyond whatever whoever else is data center, you
Speaker 2: gotta find a way to run it yourself.
Speaker 3: Yeah, okay, but China has a leg an electricity.
Speaker 2: Which was the point that Jensen Wong made. I mean,
Speaker 2: part of the reason, like there's so much talk about
Speaker 2: this these days right now, is that the industry insiders
Speaker 2: are saying a bunch of weird things. Paul mentioned the
Speaker 2: Sarah Friar comment yea, and she she sort of had
Speaker 2: to walk back, but then she said there was the
Speaker 2: Sam Altman thing where he was asked how are you
Speaker 2: going to pay for all this? And he said, look,
Speaker 2: you want to sell your shares or not, which is
Speaker 2: like the interviewer probably thought he.
Speaker 3: Was little defensive.
Speaker 2: Obviously, Jensen Wong talking at a recently about how China
Speaker 2: was going to win. Maybe he was saying that because
Speaker 2: he wanted to catalyze more action on solving some of
Speaker 2: the electricity problems in the US. But you know, the
Speaker 2: very people at the center of this are saying things
Speaker 2: right now that you know. What's interesting too, is you
Speaker 2: know this bullwhip phenomenon everyone as Paul described it, he
Speaker 2: didn't use the word bullwhip, but when everyone is trying
Speaker 2: to get their hands on the same gear, you gotta
Speaker 2: wonder how sustaint what's the other side of a bullwep
Speaker 2: could look like? We just got to do more episodes
Speaker 2: on this.
Speaker 3: Yeah, we have to. Shall we leave it there for now?
Speaker 2: Let's leave it there all right?
Speaker 3: This has been another episode of the Audthots podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway and.
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