What Wall Street Really Thinks About the AI Trade Now

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Hello and welcome to another episode of the All Thoughts Podcast. I'm Traci Allaway.

And I'm Joe Weisenthal.

Joe, interesting times in markets.

You know what?

What?

I kind of feel like I lost the plot.

I know.

I was like, I had this realization. I don't know what anything's going on anymore. We're just talking markets. And obviously I lost the plot with life in general. Like no one's disputed that. But just as like, what are the consensus trades these days? Like what are people actually worried about with some of these data center stuff? Like what, Corweave, what, et cetera. I have no idea what anyone thinks anymore.

Like I need a reality check badly.

So we're recording this on October 28th. And in terms of market reality checks, we're actually doing better on this one because we often end up recording these right before a Fed decision or right before an S-1 comes out or right before earnings guidance or something. This time, we've timed it pretty well. So we just had the FOMC decision.

Earlier this week, we had the S-1 filing from Anthropic. During the Fed presser, Oracle came out with some additional guidance. There's a lot of stuff going on, even though we're going to be able to talk about some of it. There's more stuff coming up later this week. And so I feel like it's a really good moment in time to kind of stop and figure out what's going on.

What's going on? So you mentioned Oracle had some investor day data coming out. The stock is up 12%. I know. I would say this was a bigger story today than the FOMC.

Yeah.

Right. Like the questions about Capital expenditures, how much debt the big hyperscalers and so forth are going to take on. Clearly, I would say in the minds of many participants, much bigger questions than like, oh, did they are going to hold in October or whatever.

Not a high bar given Kevin Warsh's ability to deflect.

I tuned in for a few minutes of that press conference.

But you're right.

That guy's good at not saying that.

All right. Well, we do in fact have the perfect guest. We're going to be speaking with someone who we've had on the podcast before. Last time we were talking about levered ETFs. This time we're going to get a general sort of market overview. It's Alex Altman, Global Head of Equities Tactical Strategies over at Barclays. So Alex, thank you so much for coming back on OddLots.

Thank you very much for having me again.

So what's going on? No, I mean, is there like a consensus AI outlook at the moment?

Yeah, there is.

The consensus AI outlook is that AI is still going to, this train is in motion. We're not stopping this train. That's still the consensus, to be very clear. And let's say if you were to sort of take a sample of a bunch of investors across different cohorts, whether it's in the US, Europe, Asia, and ask them, gun to their head, do you want to still be bullish or bearish the AI story? You're going to get an overwhelming positive answer.

Yeah. Right.

And we can debate like where people are on the position scale, but like that's the general view. Right. And this is why I think that if you, you know, you've probably seen that chart of where we are in like the bubble kind of thing. And then as it begins to roll over, the first dip down is the denial one.

Yeah.

I'm not going to say we're at kind of this bubbles popped. I know there's commentary out there that actually has said that, but I definitely, if you were to sort of juxtapose those two narratives, we're in the denial phase right now because everyone is still extremely bullish.

Yeah.

You got guys saying like, oh, semiconductors, you got Hynix or whatever, or some of these Korean memory stocks, they're on three times earnings. I'm like, you can look up three times earnings on a Bloomberg terminal. That requires no work at all.

Right.

So everyone is out there.

Thank you for plugging the utility of the Bloomberg terminal, by the way.

What do you mean there are 3X earnings?

There are memory stocks that are.

They're traded with an EPS, not an EPS, a PE of three. Correct. Because people there is. And because the perception is that we're at such a peak and we know how cyclical of an industry it is that when the earnings are this good, you're not going to pay up for them. Exactly.

The old mantra is you don't buy cyclical companies, especially ones as historically cyclical as semiconductors, when they're at peak earnings.

I didn't realize they were that low. Okay. So let's put some pieces together. So we have the fact that the P.E. is actually quite low. People do not expect exponential growth from the memory companies in terms of their EPS anytime soon as implied by the P.E.

Ratios.

What is going on with the neoclouds? Why is it that despite, as you say, the consensus is bullish, still everyone wants to be long, the AI trade, suddenly there's all this anxiety about, you know, people are quoting Corweave CDS charts.

Sure.

All right.

So let's take a step back for a second, right? Because I want to talk about neoclouds in the context of the broader AI ecosystem. So obviously neoclouds are a group of businesses that are effectively in some way, shape or form mandated to build compute, which they will then sell to predetermined customers, some of which predetermined, some of them will be speculative. Regardless of what you may believe in that business model or not, these are tip of the spear. These businesses don't have operating cash flow today or minimal operating cash flow today in many cases. And so they represent the riskier part of the financing aspect of the AI spectrum.

Right.

And I think that's, that's an irrefutable fact. You know, people can talk about, well, actually they're REITs in disguise and actually they're not, they're nowhere near as risky as people think they are.

Sure.

Let's have that debate. But compared to say the hyperscalers, whether it's, you know, the Googles and the Amazons who have got huge, I mean, they're cash volcano businesses. They are different, different sort of category. And the reason I want to make that distinction is because if you think of neoclouds as periphery and hyperscalers as core.

Yeah.

And I want you to draw a parallel, not directly, cause I'm not trying to make some sensationalist statement here, but draw a parallel to the GFC. Right. The problem doesn't didn't start with GC banks.

Right.

It started with subprime vehicles, 12, 18, 24 months beforehand.

Right.

And so what's I find most interesting is that there is evidently a financing problem within Neo clouds, right? That is evident Cypher try to, they did their convert. nuked their own share price in doing so because they clearly couldn't tap the debt markets directly.

I don't even know who. Cypher?

It's another neocloud. Exactly. A company that you've never heard of. And yet the reason why CoreWeave bonds sold off was not because of CoreWeave. They didn't say anything that week. It was because Cypher, another neocloud, tried to tap financing. And in doing so, materially hurt their own share price.

And then all of a sudden, a bunch of investors, quick off the mark, put two and two together.

Right.

And then it spilled over, but like, quite frankly, if you look at the data center bonds in isolation, whether like the beignets of this.

World, you know, the meta one, exactly.

Yeah.

These had already been widening. For months prior to that.

Right.

And we'd seen a creep in IG hyperscale is one of.

These ones that pivoted from Bitcoin mining to getting into AI.

Yeah. Yeah. All right.

So I guess the point I'm trying to make is, is that there's ripple effects in financing that start in. in the periphery, and we're seeing that. So what I find amazing, back to the original point in terms of you asked what's going on, are people bullish or bearish, and we literally just conducted a survey with a group of investors.

Fantastic.

Yeah. And the only, I mean, you could make this argument, this is a high number, but from the responses that we've got so far, around about 60% of investors, we asked them a question, do you think financing is going to be a problem? AI financing is going to be a problem for the foreseeable future.

Only 60% said yes.

By definition, what's happened over the past couple of weeks means the answer should be 100%, right? And yet, let's just call it just over half think or just under half think there is no financing issue, but there is. So what's happening in AI to me is evidence of like almost a denial of a reality that's already here.

To be fair, one of the reasons CoreWeave started selling off was because Brandon McBee gave an interview to the FT where he was talking literally about unlimited amounts of borrowing, which I think ties into what you're discussing when it comes to capital concerns. So on the financing side, I mean, we've seen some differentiation in terms of lending money to different AI entities out there. Is the overarching point that we've been in a period where it seemed like there was in fact unlimited money and people could tap the IG markets or the loan markets for as much as they needed. And now we're moving to a period where things are more selective and not everyone is going to be able to get the type of funding that they

were tapping before.

That is exactly it.

Right. Because, you know, part of the job that my team does is we travel the world. We talk to a lot of different investors and, We ask questions, we collate the kind of feedback that informs our decisions. And there was this almost universal view that, and you guys would have heard it as well, AI was immune to high interest rates. Evidently is not true, right? And so there is a spectrum. And even, I think that's where I think the meta results were pretty instructive in the 14C, in the sense that they highlighted the fact that financing does constrain, financing does have limitations. And we wrote a load of, we did a load of work on this in the summer where we were looking, not specifically at AI,

we were just looking at the holistic net issuance profile of the market. And maybe you guys saw this. And we looked at just the entire corporate market, so IG high yield. We looked at treasuries and we looked at, equity. And we went back to about 1995, collated all the data, and looked at each market, the amount of net issuance in any given year as a percentage of each market's market cap in that given year. And then we plotted it over time. And what we found was there was, unsurprisingly, a very strong relationship. When interest rates went down, overall issuance went up, and vice versa, as you'd expect.

The laws of the economy still exist.

Well, apparently not so much in 2025 and 2026.

Yeah, here's the punchline.

Yeah, this is the... Yeah, exactly. This is the punchline. 2026 has completely shattered what you'd seen in the norms. I think Tracy used You looked at that chart. Yeah.

So you had, you had this great chart plotting the relationship between the two year yield and net issuance. And you can see the relationship really breaks down in 2026. So we are issuing a lot more. We're selling a lot more bonds, a lot more debt related to benchmark rates than pretty much like ever in history. It's, it's kind of biblical.

It's certainly, we have data back to the nineties. So, you know, if you compare it to say.com, And the interesting thing about dot-com is, of course, there was a lot of equity issuance and interest rates were high, but the US government was basically running a surplus and all this kind of stuff. So today is very unique. And the reason why I bring it back to the sort of AI financing is because there is this competition for capital.

It is real. AI is not issuing debt in a vacuum. There are other verticals that need to tap financing too. And there is a finite amount, right? So IG issuance and a high-yield market this year on a net basis, we forecast to be $ 1. 1 trillion, right? And we just throw around trillions as though it's just this casual number that everyone's got, but there is limitations, right?

Reconcile this. If there is this perception that people are still bullish, there is a, the train is still moving at full speed. Why should capital be hesitant? Like, okay. So it's like, you can, you can see on the numbers, like the core weaves and the irons and the cipher and like a bunch of other ones that I had never heard of. Like, yes, clearly there's the capital markets are telling something, telling them something. But why? If the view is that AI is still generally going straight up, what is the fear about these companies that are at the tip of the sphere? Wouldn't the bull say, look, someone is going to pay for these GPUs. The demand is going up. If they buy or rent or whatever, if they have electricity, if they have GPUs, if they

bought them, whatever, they're not going to zero. They're going to be worth a lot of money somewhere or nothing.

Yeah.

So that, that is the bull argument is there's nothing wrong with it in terms of build it and they will come. I would say that, that the challenge will be to the early two things to the earlier point, tip of the spear. So when you start to see cracks or canaries in coal mines or however you want to characterize it, you should probably pay attention. Right.

Um, and, and.

The second point is, is that, thank you. Uh, the second point is we, we need to think about revenues at the end of the day. And this is something which we addressed at the end of August in a note called placing your AI chips more carefully. And the idea was, is that we basically decomposed. It was around the time of the SpaceX TAM. If you remember, that was around about $ 30 trillion.

It was a total addressable market of like the entire universe, not just earth, something like that.

Yeah. $ 30 trillion, which is obviously around about the GDP of the United States. And so we went, again, to the point about people throwing around trillions. Fun fact for you. Since 1990, I can't actually take credit for this stat, by the way. This actually belongs to Cameron Crease at, who also works here?

Our colleague, yeah.

And so give him the necessary plug on this. Looking at the cumulative revenue of the two most successful tech companies in modern history, it was Microsoft and Apple, and it was the cumulative TAM of those two was, I think, inflation-adjusted, not even $ 10 trillion since 1990. But SpaceX's addressable is talking three times that number. So that kind of prompts us, where's all this money going to come from? So punchline again, $ 17 trillion of S & P top line revenue. And they've got around about $ 11 or $ 12 trillion of expenses.

And so that means that the net profit approximately of the S & P is somewhere between five and five and a half trillion.

Dollars, right?

So it's like, all right, we can make some bold claims about the amount of money that's going to get allocated to AI expenditures and all the rest of it. But last time I checked, like we all look at the ramp data or the open router data, and we can clearly see that that token expenditure has kind of begun to level off a bit, right? At least near term.

So it's like, all right, our company is going to spend this much money. And that's where the other point to answer your question, Joe, is.

Well, you know, your company Barclays is, I know, managing the anthropic IPO. So, or as, you know, involved in this, we're not going to ask any anthropic questions to you, but I will say to Tracy.

Okay.

No, I won't make you comment. You know, we understand the rules here, but I will say to Tracy that anthropic numbers on the top line look great. And I do not think that nobody, and I don't think we learned that much about open route, open router data. because that captures a specific slice of people who are usually looking for cheap Chinese models, which they're doing well, but the top line numbers for Anthropic look unbelievably good. That's the interesting.

Thank you. There's earnings progress there in the S1. You can see the earnings progression. However, billions in earnings versus valuations at trillions, I think that is the gap that people are debating.

I'm just pointing out that There's lots of charts. There's ramp data that's floating around. Lots of people look at the ramp data and lots of people look at the statistics on OpenRouter. And they start to say, oh, the American Frontier Labs, I guess, are they in trouble? And they might be. But I'm just saying, if you look at the data that's filed with the SEC, as of pretty recently, at least one of the companies, the one that is filed in S1, that number is going straight up still.

Why don't we talk about the frontier models more generally? So, you know, the argument has been that they can come up with a better model, a better agent, a better technology, and that people are ultimately going to pay for it. And the persistent, I guess, doubt in the market has been, well, you know, open source models are kind of catching up very quickly. They're worse on their benchmark test, but not by that much anymore. And they're a lot cheaper to operate.

What are people saying about the open source versus frontier debate?

I would say here, this is weirdly, is a bit of a contradiction versus that bullish narrative. Because the data is the data you can evidently see on a whole bunch of different sources that open weight has kind of gone from a 20% market share to anything up to about an 80% market share. And then you'll also see that the token cost is a fraction, one one hundredth.

Versus- Incredible token deflation that's happened every year.

It might be one of the fastest deflating assets in history. It makes those NFTs back in 2021 too.

But GPU rental rates have been going up. They have.

Yes, absolutely. And so there is clearly a two-way debate. And I think it again goes back to the evolution of this broader AI narrative. It's not just necessarily a one-way train, rising tide lifts all boats. There's going to be degrees of differentiation. But you asked about open weight models. And I think where one of the leading edge questions right now is not even just open weight, but combine that with locally run open weight. And this is something which I like to call being long or short human creativity.

Right.

In so far that if, when the iPhone came out in 2007, stay with me here, uh, that if I had, if someone from the future had said 20 years from now, you're going to have whatever the orders of magnitude compute differential And the battery is going to be the same size. That 2007 person would have said impossible. You'd need an iPhone the size of this table, right? So clearly we got there amongst many other things. And so the idea that we necessarily need to ping a bunch of hoppers or Vera Rubins in an Ohio data center just so that we can sort of get our.

Agent to- Manage my email for me.

Yeah, or to send an angry response letter to the hotel.

You're going to be able to do that with the chip on your phone by next year.

Right, if not already. So we've all kind of got supercomputers in our pocket. And the ability to run, say, multi-billion parameter models locally using whether an open-weight model or a closed-weight model, but that's provided by NVIDIA's Nematron or whatever Meta's putting out, which Meta's already put out a- multi-billion parameter model that's run locally on your laptop.

It's crazy. Right.

But it's not because that technology, right? And so I think that's the other point of the debate is it's not even so much about open weight. It's about the fact that you're moving along this efficiency curve And that was also bringing in the revenue question as well, which ties in with those frontier models as well. Frontier models, we're not going to be producing, you and I, with the greatest respect to everyone in this room, we're probably not going to be discovering the new biotechnological drugs.

I was actually, I was talking to someone when I was out in San Francisco recently, and I actually want to ask you about East and West Coast views on the technology, maybe. But I was talking to someone in San Francisco who had this term, he called it the frontier sink. And it's like, what are the industries that actually need PhD level and beyond technology? And there's a few that we could come up with pretty easily. One of them is probably biotech, right? You can probably never have too much intelligence when it comes to medicine. Another one actually is probably semiconductor design itself. You'll probably never be too smart, et cetera, for that. Maybe another one of them has to do with quant trading where you

can never be too smart. I'm not sure. Who knows? But it actually does get to be hard to think about. There might just be a lot of industry for which there is a certain level of intelligence that is enough.

I mean, for the moment, again, Do you remember when the, I like going back to the iPhone as an analogy, because it really was a technological revolution at the time. And do you remember when we thought like peak iPhone in 07, 08 was when you had that app, which had like the pint of beer in it.

And you could drink it.

I didn't, but no, everyone is like, Oh, this is so cool. You can pretend. I know exactly.

Joe, you're allowed to take joy in small things. No, no.

This looks like whimsy to me. I don't get this. Oh, yeah.

Joe hates whimsy.

I hate fun. But keep going. But I remember how, yo, why is everyone going crazy about the fact that you could, like, this is a very good point. Keep going.

And so I just don't think we know what the future of AI really looks like. So, yes, you're right. Today, we probably say we just don't need more than 100 IQ persons sitting in our pocket, managing our emails, sending angry messages to the car rental company because they charged me $ 2, 000 for a ding. I do think the consumer layer AI is going to be, people ask me all the time, like, what will get you really constructive on AI again? I said, when we figure out that killer consumer application layer. Because at the end of the day, the consumer is everything. It's the economy. It's where, to the point about revenues, like enterprise revenues, I just don't think is going to plug that gap. Really? I just

don't see how.

Yeah, no, you're right. The numbers. I don't see.

It can probably get there eventually, but it can't get there in the time frame that the market wants to see the ROI. And you need to bust into the consumer layer. And so back to the point about frontier, the consumer probably doesn't need frontier for the time being to do all of these things. And so my big mantra is that one of the best technological changes that we experience as humans is actually not necessarily some new advanced tech. It's changing the application of existing tech. And so it's very possible that we take the 100 IQ agentic workflow that still kind of becomes almost ubiquitous within the next 12 to 18 months. And we find really cool ways to use it. And I think that's probably

where you're really going to get the next upswing in AI.

Can I just say, by the way, Tracy, last several days, my wife was traveling out of town. My kids are at a very easy age. It's not a big challenge or anything to solo parent for a few days. But it happened to be a very busy schedule for them, birthday parties and a few other things. And it was very nice having ChatGPT being able to say, where should I be in the next hour? Where should I be in the hour? Because there were a lot of things going on. And it was very easy. It was very nice.

I saw your tweet on this, Joe.

No, you saw the tweet on the right.

This is part of the problem. You tweet everything before you tell it to me, and so I can't react to it in real time.

And now I'm really upset that you said that because you saw the tweet from my private alt. I did not tweet that from my main account. And now a bunch of people are going to know that I have an alt and they're going to ask me for it and you're not going to get it. So I wish you hadn't said that. Sorry.

Sorry. I can't, I can't differentiate the two.

Can I, can I, can I jump in?

Yeah, of course.

So what, what's that worth to you a month? What? Well, just to have that ability, let's just say almost like an account executive or something or like an administrator, you know, Because to answer that question, it matters. It matters for the revenue profile, right?

I mean, also, if you just had the right user interface on a calendar, this would be solved too, wouldn't it? If you know you have to be somewhere at 3 p.m., 4 p.m., 5 p.m. There's a lot.

Of things that are in so many different emails. So, for example, my daughter goes to– I take her to play rehearsals. And some days, it's from 1 to 6 in the afternoon on Sundays. But some days, they don't do the full cast for the five hours. So somewhere in the email, it says, oh, these students or these participants, they can leave at 3 and stuff. And it's scattered across so many emails. And I don't know which emails to read, which ones they're in. And so being able to say, Chad, it's Sunday, October 25th or whatever. What time am I supposed to pick up my daughter at 3 or 6? It's very easy and nice to have.

Fair enough. I just want to go back to the beginning of the conversation. You talked about neoclouds as the sort of tip of the spear and maybe being in the beginning of the denial phase. What do you think would spook investors? What should we look out for next in terms of a degradation in the AI bull belief?

That's a great question.

Off the top of my head, I would say it would be some level of capex being reined in by one of the hyperscalers. In fact, on the walk over here, I was asked by one of my clients who was questioning whether that would actually even come up potentially in the results this week. And famous last words, it's probably still premature. Joe, to answer your question about West Coast, East Coast, if you think about West Coast, the further you are closer to the epicenter of this technology, I'm sorry, the closer you are to the epicenter, the more I would say that both the companies and investors are bought in.

On that narrative.

And there's definitely kind of almost like an echo chamber within that certain ecosystem. You think about the rooms that people are sitting in. So my suspicion is that it's not going to be these set of results. And that may be we have to wait till the new year that we get some kind of potential temperament of the enthusiasm.

But Google kind of set this all off again for the next wave because on the CapEx side, it was actually the last set of results where they said it's going to be, I forget the exact wording they used, but they were talking about CapEx being significantly higher. And that got everyone enthused once again and got them all G'd up.

Well, Oracle just said at its investor day that it was going to limit its additional fundraising to, I think, $ 20 billion through May. So we might be getting close to that. But again, the reaction so far is this is a good thing and the build-out can continue as long as people are raising a reasonable amount of capital, I guess.

Yeah. And that kind of goes back to maybe just a slightly more holistic market view, which is This almost S & P does nothing because what's good for hyperscalers will be bad for memory. So you just get this constant violent rotation. And I call it like a knife fight in a phone booth, you know, for all these funds that have to run long, short portfolios.

It's a nightmare. I'm glad you asked this because this is a question that I wanted to ask you. So it's like we look at the core weaves and there's this anxiety and we see all the volatility. We're seeing volatility everywhere except our measures of volatility. So if we look at realized volatility, it's pretty low. If we look at the VIX, it's not that high. Can you explain why it is that there's all this anxiety about certain parts of the most important trade in the entire world?

And yet, when we actually look at the way we measure volatility, it looks very benign.

Yeah, I've been asked, this is probably the most asked question that I get in some way, shape, or form. And there's two parts to the answer, right? There's a structural part. And then there's a really technical part.

Yeah, let's hear it.

All right.

So the structural part has got to do with the fact with the way that money is deployed these days. So obviously, equities are meant to be a long-only product, really. But in this day and age, they're not. We've got 2020, the AUM deployed in multi-manager funds was about $ 300 billion. And today, it's in excess of a trillion dollars. And if you think about the leverage- This is the pod shop, seriously.

Yeah.

I mean, not, not just pods, but obviously let's just think about like that sort of ilk.

Yeah.

And it makes perfect sense, right? Because if you, especially in a higher interest rate environment, high interest rate environment is perfect if you're running a long short strategy, because I deploy money on my gross long side and on my growth short side. And actually I'm not really deploying. I'm deploying units of risk, but the actual capital being deployed is really only what I'm actually posting as margin on my long and my short member. I can leverage that too, through the prime prime businesses. So actually what I'm doing is I'm taking a large chunk of capital and I can put that into say a high interest bearing, a high interest instrument. Mm-hmm clip a very nice coupon.

And then I try and generate alpha on that gross capital deployed. That's the business model. And it's for the most part, a very good business model, right? The problem is, is that you've effectively tripled more than tripled the AUM plus obviously leverage. And the number of securities you can trade has stayed roughly the same or actually shrunk over time.

So that creates crowding out. So how do I how do I organize or how do I mitigate that crowding out effect? Well, I've got all kinds of cool risk parameters. So, you know, Barra, I'm sure you've heard, or Axioma, just, you know, all these different style factors, quality, value, size, growth, you know, all these different things. You would have heard us talk about them quite a lot. They've become ubiquitous and obviously funds spend an inordinate amount of time and computing power trying to neutralize those factors. So that's cool.

And you try and isolate the idiosyncratic risk of whatever you're trying to get. Now, The problem is, is that at the end of the day, everyone's still doing this, which means all you're creating is different kinds of crowding. And so that's why the volatility isn't showing up so much at an index level, right? Because ultimately a lot of this AUM is kind of index agnostic, so to speak.

And you're seeing the violent factor rotations happening because ultimately you've got a huge amount of crowding sitting there. The one thing that leads me into sort of the technical aspect, which is the one thing that I think a lot of funds have not yet been able to solve is the AI factor, right?

Because you could be.

Everything either is or isn't AI now.

Right, exactly.

And this is a problem, right? It's dawned on me recently because I've been asked a series of questions by investors specifically along the lines of, have you managed to map the AI factor? And the answer is no. Because the AI factor is changing all of the time. The betas are changing all of the time. Remember the beginning of the year, you had a lot of people that wanted to be short software. Now software is trading close to 12 month highs. And so the idea of being short software, it was part of the AI factor. And now is it so much? Well, not necessarily unclear. So because the narrative is shifting, the factor of AI is shifting too. And that in turn complicates the ability to funds to actually try and hedge that. And the bottom

line is, is you're either long AI or you're short AI, right? And let's face it, the past three years, being short AI probably hasn't been a particularly lucrative trade.

By the way, Tracy, in the time that Alex was giving us, that Alty was giving us this great answer.

You sorted out your entire weekend schedule using ChatGPT.

No, in that time, we just got earnings from Alphabet and Microsoft. And it's very interesting. Alphabet down 7.9%, revenue X-TAC misses estimates. Microsoft jumped 6.4% after Q1 cloud revenue tops estimate. So you get two gigantic stocks. They both have very violent reactions. But if you're the indexed investor on net, looks like it all kind of washes out.

Yeah, it does indeed.

But man, some of these, like these numbers are incredible. Microsoft Q1 Azure Revenue X, Azure. This is their cloud platform. Azure. What's your guess? You're not looking at a terminal the way I am. What do you think it got? This is fun.

The bokeh was, I think, 47% growth.

Yeah, man, you're really good at your job. So the official estimate that we had collated at Bloomberg was 44.8%. They hit 48%. 48% growth for these gigantic companies. That's a good number.

That's unbelievable.

Cloud revenue at Google was $ 30 billion. I'm not seeing what the growth number was.

But these are unreal.

Yeah. I mean, they are. And this goes back to the point about kind of AI winners and losers. If you've got a fantastic legacy business and you're a cash volcano from your legacy operating business, then if you want to go and embark upon the largest CapEx experiment really in the history of America, total non sequitur, by the way, I was trying to work out because we're spending, if you believe the CapEx numbers and the terms of the forward, and I'm sure that they're going to change from what the consensus looks like now, but on face value, it's about 12% of.

GDP, right?

It's like two Netherlands, right? Um, and I was thinking you have not in modern history, like the Louisiana purchase, the railroads, it's blown all that out of the water. What have we done as a human society where we've spent that much money? Certainly in a short period of time. And I had to start getting really creative. I'm sure, Joe, you'll be awake at night thinking about this and trying to find some historical work on it. Pyramids, maybe? But they took hundreds of years, if not thousands. Great Wall of China, maybe? I can't think of a period where we've spent.

All right, wait here. What do you think? How fast do you think Google Cloud revenue grew? Again, I'm turning my laptop away from me. I actually don't even... I can't even believe this number is real. I'm giving you a hint. 30-something percent? I don't really understand. I don't, I, I almost.

Yeah.

103% Google cloud revenue grew 103%. It seems great. I don't know. All these numbers are insane. Anyway, that's what it says.

I mean, these are impressive businesses and this is, this is why, you know, a lot of people say S and P undefeated.

Right.

I mean, by the way, funny, the concentration of the S and P it basically given Tracy, to your point, everything's kind of AI. Concentration of S & P, next year, 75% of your earnings growth in the S & P is going to come from memory, from semiconductors.

What percent?

Sorry, a bigger part. It's actually, it's about two-thirds from semiconductors, and then the broader AI ecosystem gets you to around about 75%. Wow. So you basically, S & P has become Cosby.

Oh, cospification.

Cospification, yeah. All right, well, Alex, we're going to leave it there because we want to get this episode out pretty quickly. But thank you so much for coming back on All Thoughts.

Really appreciate it.

Thank you.

See you next week. Thank you so much.

Joe, it was great catching up with Alex. I think he does a great job of sort of synthesizing a lot of different things going on in the market right now. But I do think the key thing that's emerging is this differentiation idea and the idea that we're moving from this sort of capital abundant environment into an area where people are perhaps a little bit more selective and what that means for the business models.

I do think, completely agree. I also think, you know, every... The classic is different this time is, oh, interest rates don't matter anymore, right? And this is a classic thing. Oh, this is not going to be a rate-sensitive industry because the numbers are so unbelievably high. If you're growing 44% in a year, what do you care about when the rates are 5% or 5.25? It's completely irrelevant. You're just going to keep building, et cetera. But that is a lie that investors tell themselves. Rates, at least at the margin, at least at the tip of the sphere– sphere– at least for some of these leveraged companies like the Neoclouds or whatever, for one thing, it looks like a rates matter.

Yeah. And also, I mean, just the costification idea of the market, it is really quite stunning because a lot of the earnings power of the tech companies, the hyperscalers, is predicated on the fact that, well, other companies are going to buy the services so that they can boost their own productivity and get the earnings up.

But like.

we're not actually seeing that materialize in their earnings numbers just yet. And in the meantime, everything, the whole index just becomes AI more and more on the expectation that eventually it won't just be about AI.

This is the thing that like, you know, when you talked about the pod shops, for example, the whole premise of a pod shop is that like everyone is supposed to find their own undifferentiated alpha.

Yeah. Where is that right now?

So I think like, basically, if you're an investor, And you made money in the last year at scale. I mean, of course, like you might be able to like, oh, I opened, you know, a new like pasta restaurant in the East Village. That's a separate question. But if you're at scale and you made money in the last year, in some way it was connected to AI. You have all these different pods and some of them are chips and some of them are industrials, et cetera. But it's hard for me to believe that the ones who are making money aren't just in some way finding a way to belong to AI fans.

Levered long.

Yeah, levered long.

The AI factor, I would imagine.

The AI factor. You know, I was talking to... It's so funny, you know, we try to... Alex has such a good read on the industry, but also it's great to talk to him because he talks to clients to get the Wall Street view and so forth. I was talking to this computer science student at Stanford recently, and they were like, oh, Wall Street is an AI pill.

And I was like, what are you talking about, Wall Street is an AI pill? Look at all these stocks that are going to the moon.

The numbers on my screen tell a different story.

What are you talking about?

But.

That person's view was that if you're even having a discussion about the possibility of overabundance of GPUs, you just don't get it. like what do you well you're like oh you you don't you don't get ai sufficiently it's so funny it's.

Um yeah definitely an interesting time in markets shall we leave it there.

Alphabet they do 32 billion tokens per minute that was up 40 percent q of recovery i don't even know what these words mean let's leave.

It there All right. This has been another episode of the All Thoughts Podcast. I'm Traci Allaway. You can follow me at Traci Allaway.

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What Wall Street Really Thinks About the AI Trade Now

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