AI Had Better Work Out - Ed Zitron's Where's Your Ed At

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Soundtrack: Royal Blood — Pull Me Through

At the end of July, Apollo Chief Economist Torsten Slok gave a blunt warning to the Washington Post:

Today, I’m going to quantify exactly how difficult it will be for the “AI thing” to “work out.”

The theoreticals and hypotheticals of this industry are so powerful that they’ve created one of the (if not the) largest equities bubbles in modern history, all heavily-centralized around the valuations of a handful of names related to the so-called AI trade, which translates to “buying or selling stuff to do with AI GPUs.”

This has led to the single-highest inflow of foreign investments into US equities in history — larger than the Dot-Com Bubble, the leadup to the Great Financial Crisis, and even the dying gasps of the Zero Interest Free Era in 2021, per Apollo:

However you might hand-wave away the AI bubble’s economic issues by saying it’s “just like the Dot-Com Bubble” — when the NASDAQ 100 fell 77% from its peak — the consequences are now global, at a scale unseen in history, flowing into both direct stocks and indices.

As of typing this sentence, NVIDIA alone makes up 11.31% of the NASDAQ 100, with the combined weight of NVIDIA, Google, Amazon, Meta, Microsoft, Broadcom, Micron, AMD and CoreWeave representing about 46.5% of the index’s value, or 39.3% of the entire 3000-company NASDAQ composite.

These same names — replacing CoreWeave with Oracle — represent around 28.9% of the value of the S&P 500.

To sustain the valuation of these stocks — let alone continue growing — will require every company involved to either spend or make hundreds of billions of dollars in perpetuity, increasing every single quarter, without fail. This will come from either selling ever-increasing amounts of AI chips and related hardware, or buying and using them to generate revenue either through rentals or building their own software products.

While the centralization was more severe at the top of the Dot-Com era NASDAQ — with Cisco, Intel and Microsoft accounting for roughly 74% — the total value of the index was a mere $1.82 trillion ($3.58 trillion adjusted for inflation).

Today’s top three — NVIDIA, Google and Microsoft — account for around 29% of the NASDAQ 100, for a total market capitalization of $13.5 trillion.

I’m afraid things only get worse.

Per Goldman Sachs (via Reuters), "...equity gains have been the dominant driver of household wealth accumulation ​and the main contributor to a positive wealth ​effect on consumer spending," and per data from the Fed, corporate equities accounted for 37.9% of all household wealth in Q2 2026, the highest number on record since Q4 2000.

Economist Paul Kedrosky told me it might actually be higher:

While the Fed’s data only goes back to Q4 2000, corporate equities as a share of household wealth only sat at 25.9% ($11.5 trillion, or $22.7 trillion adjusted for inflation), back at a time when total household wealth sat at $44.4 trillion, or about $85.2 trillion in today’s money. In Q2 2026, total household wealth sits at about $195.8 trillion, with corporate equities sitting at roughly $74 trillion, or roughly 86.8% of the entire US household wealth 26 years ago.

Retails are also making up an historically-large amount of the volume of trades on the market, per Bloomberg Intelligence, making up 20.3% of trading in 2026 to date, approaching the dangerous heights of the worst excesses of the end of the Zero Interest Rate Policy era.

Retail investors also directly own about 29% of Amazon’s, 26.2% of NVIDIA’s, 22.6% of AMD’s, 21.8% of Microsoft’s, and 18.6% of Meta’s stocks, though these numbers are not inclusive of index funds like SPY or QQQ (a fund tied to the NASDAQ). In totality, retail investors own equity assets (at least as of May) equivalent to about 10% of the market.

In fact, Goldman Sachs had retail volumes significantly higher:

By comparison, Dot-Com era retail trading was much, much lower — less than 2% of the NYSE’s trading volume according to the University of Michigan’s Alicia J. Davis in a paper that begins with the phrase “the American retail investor is dying.”

When all of this is over, I think retail investors might wish they were dead, as they directly own somewhere in the region of $4.65 trillion worth of NVIDIA, Amazon, Microsoft, Google, Meta, Broadcom, AMD, Micron, Oracle, and CoreWeave stocks. The number is likely much smaller than the true exposure retail investors have, as it isn’t inclusive of investments in index funds.

On April 14, 2000, the NASDAQ composite dropped 9.67% and the S&P 500 5.78%, with a little under 97% of the S&P 500 constituents falling on the day, per TIME and The Washington Post, led by hot inflation numbers and overall anxiety over the tech sector’s valuations:

Yet the Post had one telling quote, emphasis mine:

While it seems unlikely, a drop of around 20% across NVIDIA, Amazon, Google, Meta, Broadcom, AMD, Oracle, Micron, and CoreWeave would have the same effect as the entire market downturn on the worst day of the Dot-Com Bubble crash.

In truth, it wouldn’t even have to be that bad.

Per CNN Moneyline News Hour transcript from April 14, 2000, Qualcomm fell 21%, Yahoo fell 20%, Intel a little over 10%, and Microsoft a mere 5% — and that’s all it took to make the market bottom out.

Brief NVIDIA selloffs have already caused big market declines:

Anyway, remember that quote about momentum investing, overvaluations and growth? Here’s a quote from Bloomberg from last week (October 14), emphasis mine:

And here’s another quote from CNN from a day later, emphasis mine:

History may not repeat, but it rhymes. Every problem that burst the Dot-Com Bubble is here, but it’s worse, bigger and uglier, with global contagion risk that I basically never see discussed.

For example, the ongoing equity price of TSMC on the Taiwanese Stock Exchange makes up about 40% of the entire market, with the ODMs that have seen booming earnings making AI servers for hyperscalers representing another 3% or so. On The Tokyo Stock Exchange, the combined weight of SoftBank, RAM maker Kioxia, and the two major banks (SMBC and MUFG) backing AI data centers make up approximately 11.24% of the TOPIX Index. Samsung and SK Hynix, both of whom have seen remarkable RAM sales (see my Hater’s Guide To The Memory Crisis for more), make up more than 50% of the entire value of Korea’s KOSPI.

Their elevated valuations come from a belief that they’ll grow in perpetuity, much like the American markets.

And let’s be clear about something: all it takes is the vibe souring to the point that people lose faith in the AI trade for everything to start to collapse.

So, what will it take to keep the party going?

The answer is simple: perpetual, parabolic growth in both AI revenues and AI capex.

Per CNBC, well, exactly what I’ve been saying, emphasis mine:

Are…are you sure about that? Isn’t AI the biggest, most hugest thing ever? It’s transformative! It’ll change everything! Aren’t we all excited-

Okay, well, check out Doctor Doom over here, I bet he’s gonna be so surprised when those profitable returns come in-

Well, at least there’s a diverse revenue base for GPU sales-OH MY GOD!

NVIDIA has guided to a 70% increase in its next fiscal year — I assume FY2028, starting February 2027 — or over $670 billion in annual revenue, over three times its FY 2026 sales, with said sales dependent on whether Microsoft, Google, Amazon, Oracle, Meta, SpaceX (who collectively make up 50% to 60% of NVIDIA’s data center revenues), and the various neoclouds like CoreWeave, Nebius, and Nscale keep buying more of them every quarter, or at the very least that a willing vessel can step in to pick up the slack if they stop. If demand wavers, NVIDIA, Broadcom, Micron, AMD, and any associated silicon or hardware counterparties will see their revenue growth stumble, which will have a brutal knock-on effect on their stocks.

NVIDIA already has its own revenue concentration problem — 44% of its first half FY2027 revenues came from three customers, and in Q2 FY2027, 70% of its accounts receivables were from three customers, with 16% of its revenues coming from one. One. How is this not more worrying to people?

Anyway, to buy those GPUs (or TPUs, in Broadcom’s case), everyone outside of Microsoft will have to raise tens of billions of dollars of debt at a time when underlying interest rates are growing and the overall debt markets have chilled around AI data center debt.

Said chill comes from the world finally waking up to what I said in March 2025 — that CoreWeave is a time bomb of expensive debt that’s now selling off as people realize that making any of its supposed $104 billion in backlog (at least 50% of which is OpenAI and Anthropic) will require, per UBS, about $102 billion in debt in the next three years. CoreWeave’s long-dated bonds (by which I mean maturing in 2031) had an effective yield of as much as 16% in the last week.

With endless delays and hundreds of billions of dollars of chips sitting in storage, AI data centers have dramatically turned from the panacea for all infrastructure investment to fixed-income nightmare fuel. For whatever reason, nobody thought that lending hundreds of billions of dollars to a selection of different dodgy SPVs and neoclouds to build the world’s most ambitious infrastructure projects was a problem until, well, everybody, per the WSJ:

Per the Journal, this debt heavily relies on both the continued value of the data center and critical IT gear and whether the bloody thing actually gets built:

In other words, the value of that data center debt is heavily contingent on whether the underlying assets retain their value, which in turn is contingent on whether there are customers that exist. For whatever reason, nobody wants to consider the most obvious problem: that if one customer can’t pay, other customers can’t pay, and if other customers can’t pay, there’s going to be less demand for AI data centers in general, which means there will be a glut of data center infrastructure, which will mean those assets you intend to resell if everything goes poorly won’t be worth as much.

I sure hope that financial institutions have a plan…

Oh okay, the problem with overly-expensive AI data center debt is simply that the markets will feel better when the underlying data centers (which are overwhelmingly delayed) get built? Awesome! It’s just that simple! I’m sure somebody out there will deal with the massive delays for the talent, materials and power connections necessary to complete them!

As of writing this sentence, CoreWeave’s bonds are trading somewhere between 14.1% and 15.1%, and the only way it can calm investor anxiety will be building AI data centers at a rate that has proven next to impossible.

Part of the problem is that recent pushes by smaller and somehow-dodgier neoclouds like Firmus (which just pulled its IPO) and Nscale (whose investors are pushing for it to nearly halve its IPO float to $16 billion and just lost Microsoft as a client due to power delays) have made investors intimately aware of the overpromises of AI data center companies.

Both Nscale and Firmus have the same customer concentration problems as CoreWeave, with 85% of Nscale’s backlog coming from Anthropic and Microsoft, and 90% of Firmus’ coming from Meta and OpenAI. Again, these are problems that CoreWeave, IREN and Nebius have had for years, but for whatever reason everybody has decided to wake up. Everybody has the same backlog customers — Microsoft (for OpenAI), Meta, OpenAI, Google (for Anthropic and OpenAI), Amazon (for Anthropic), and, of course, Anthropic.

All this ire towards AI data centers means that the price of borrowing for them is skyrocketing on top of ten-year-dated Treasuries stubbornly sitting above 5%, which makes the overall economics of AI data centers increasingly problematic, as nobody can seem to measure A) how much they make and B) how much they cost.

Or, indeed, how any of this will pay off.

Congratulations to Eugene Kim of Business Insider for getting one of the biggest scoops of the AI bubble, confirming my estimate that billions of dollars of NVIDIA GPUs were being warehoused in this incredible story, emphasis mine:

If you’re wondering exactly how much it’s warehousing…

One Amazon email projected the company would take delivery of about $5 billion of Nvidia GB300 racks in August, followed by another roughly $3 billion each month "for the foreseeable future."

That’s right folks — Amazon, which spent $54 billion in capex last quarter — is taking $3 billion worth of GPU deliveries every month “for the foreseeable future,” despite having such a supply glut that it cannot install them, and in some cases lacks an idea of when it may do so.

As I discussed a few weeks ago, neoclouds and hyperscalers have over $374 billion in “construction in progress” assets, and I estimate that $200 billion or more of them are GPUs, primarily from NVIDIA. One quote from the piece stood out to me, emphasis mine:

It appears that NVIDIA is channel stuffing. The phrase “compete more for scarce resources” doesn’t make sense in the context of GPUs that you don’t have enough power or space to install…unless, of course, NVIDIA is saying that if you don’t order and take delivery right now, the GPUs won’t be available by the time you actually build that data center.

The booster counter-argument will be that “Amazon is just planning ahead,” but riddle me this — if Amazon, Google, Microsoft (CEO Satya Nadella said he had chips “sitting in inventory he couldn’t plug in) and many other companies do not have the power to plug in the chips…why are they scarce? What’s making them scarce? If companies cannot install the chips (or, put another way, do not need them yet) what is creating the scarcity, other than something that NVIDIA itself is demanding its partners do?

Then there’s that curious matter of the whole “Amazon doesn't pay for racks until the gear is put into a data center.” How long has that been the case?

Let’s get specific about the verbiage — “...suppliers…carry the cost of the equipment in transit…” heavily suggests that these racks (which had GPUs in them, by the way!) were not actually shipped to customers until the customers needed them…until some indeterminate point at which the “Early Rack Receipt” program took place.

This, if anything, proves that the “demand for AI chips” is far more a result of NVIDIA’s deliberate channel stuffing and antagonistic approach to doing business than anything about how many chips the world needs right now.

Anyway, even if hyperscalers installed all the fucking things, it’s unclear who would actually pay for them at a volume that would match analyst expectations.

Per Slok at Apollo, financial analysts covering the tech sector seem to be pricing in cash flows that do not match with reality, emphasis mine:

So, in case you didn’t know, every time a company reports earnings, financial analysts at big financial institutions set expectations — sometimes years in advance — of their expected revenues and cashflows. Right now, the analysts covering tech companies are guiding towards them adding $1.2 trillion in operating cash flows, all as the rest of the S&P 500 (ostensibly its customer base) doesn’t add anywhere near that much money.

This is one of those very simple points that nobody seems to be able to answer about AI: where will all the money come from to pay for those revenues?

As Torsten said, tech analysts are expecting a parabolic surge of revenues across the entire tech stack, but the analysts covering them are saying the cash won’t be there.

The answer, it appears, is “Anthropic and OpenAI,” and maybe “open source” sales of indeterminate value.

Sadly, there’s another wrinkle in the plan.

Japanese financial services firm Nomura put out a worrying take a few days ago, emphases mine and from the poster I found it from:

To simmer it down, Nomura is saying based on how much a data center costs both to build and operate, you shouldn’t spend more than $60 billion given the amount of revenue you could hope to generate.

It’s hard to get a precise measurement of how much one costs. The CEO of IBM said $80 billion a gigawatt at the end of 2025. Foxconn said $47 billion per gigawatt in September 2026. Back in June, Jensen Huang said future data centers could cost $80 billion to $100 billion a gigawatt. It is completely insane we do not have a consensus here.

Nomura also throws cold water on the many people excitedly suggesting that Anthropic could make $50 billion to $75 billion a gigawatt, suggesting instead that the number is around $20 billion. In other words, if Anthropic is paying anything more than $20 million a megawatt, it’s losing money hand over fist just on the compute alone.

Anyway, I sure hope that the people building these data centers aren’t dependent on Anthropic’s revenues…

More-importantly, outside of the companies selling the hardware, we know very little about the actual revenue generated from renting AI GPUs. Amazon, Google, Microsoft, and Meta refuse to disclose their actual AI revenues — but analysts estimate that more than 70% of Amazon, Google and Microsoft’s AI revenues come from Anthropic and OpenAI’s compute spend, which is contingent on their continued ability to raise tens of billions of dollars.

As I’ve discussed in the past, I see around $22 billion in non-OpenAI/Anthropic compute spend globally, with an indeterminate amount of that coming from AI startups that, well…

Said billions are contingent on the AI labs’ continued revenue growth, and considering that 80% of Anthropic and OpenAI’s enterprise revenues come from loss-making AI startups allowing their customers to burn many times their subscription price in AI tokens (as much as $1,300 on Cognition’s $200-a-month plan, for example), meaning that Anthropic and OpenAI’s revenue growth is contingent on venture capital’s continued ability to fund their customers.

This is one of the core issues with measuring AI demand — that the vast majority of it comes from selling services that do not make any economic sense.

And their spend is contingent on their backers funding them in perpetuity.

This, in turn, is contingent on venture capital’s continued ability to fund raise, and as I discussed in last week’s premium, VC has been on an historic losing streak since 2017, with the median net TVPI (total value put in, as how much you get in return on a per-dollar basis) sitting between 92 cents and a dollar, with 2025 fundraising dropping by 35%, and 2026 fundraising for first-time funds at its lowest rate in over a decade, per Murph Capital, which adds that despite representing only 6% of funds closed, funds of $500 million or more took 78.1% of all capital raised in 2026 to date.

AI startups, which are predominantly unprofitable, unsustainable, and require near-constant flow of venture capital, represented 64% of all venture capital investment in Q3 2026, with 50% of all global venture flowing into AI in 2025.

As discussed both last year in the Enshittifinancial Crisis and last week’s premium, AI startups represent a devil’s deal for venture — by virtue of being buzzy “AI” companies, their valuations are naturally high, and by virtue of their capital-intensive businesses, they require more rounds of venture capital, with each round increasing valuations to unsustainable heights that make their IP-light, EBITDA-negative businesses impossible for either an IPO or potential acquisition.

In fact, both venture capital and private equity are having real problems selling their investments, with IPOs becoming effectively non-existent as a share of exits for PE, per S&P Global.

Meanwhile, per DealRoom, venture-backed from 2026 seem high, but are inflated by SpaceX’s IPO ($75 billion), the PE-backed take-private of gaming company Electronic Arts ($55 billion), SpaceX’s acquisition of Cursor (all stock, $60 billion), NVIDIA’s acquisition of Hugging Face ($13 billion), and Google’s acquisition of Wiz ($29 billion).

And since 2015, the $2.7 trillion in venture capital invested has only managed to increase its value by about 1.6x — to around $4.4 trillion in value (not returns), or around a third of what you’d get if you’d have invested in the S&P 500.

In other words, venture capital is not particularly good at creating value, and has, in a period of poor value creation, centralized the majority of its investments in a single industry, with the majority of those investments going to companies related to or powered by Large Language Models that have yet to prove their economic worth.

And in this historically-low period of fundraising and exits, venture capital has invested a historically large amount of money, with the vast majority of it going into one bet: AI.

What’s that phrase? “You should always keep your eggs in one basket, so that you know where they all are”? I think that’s it, right?

At this point, the sheer scale of the AI bubble is too large and unwieldy for things, to quote Mr. Slok, “work out,” and yes, I believe we “have a problem.” I say this with no sarcasm, as I think Torsten has done fantastic work posing reasonable, logical questions that nobody seems to want to answer, like “how does this work out?” and “where does the money come from?” even as most media outlets and analysts nod and smile at basically any statement said by an AI lab, hyperscaler, or neocloud.

For everything to “work out,” the current equities surge will have to continue in perpetuity, which requires both hyperscaler AI revenues — which are already depressingly small — and hyperscaler capex to expand infinitely, along with neoclouds, sovereign clouds and AI labs’ vague infrastructure plans to also expand infinitely.

All the while, there can be no perceptual shift against generative AI, because any further souring of the debt markets will have neoclouds and would-be data center operators paying 12% to 15% in interest to build data centers sometime three years in the future for a customer base that’s pretty much entirely made up of Anthropic and OpenAI.

As we’ve seen in the last few weeks, AI debt markets can quickly sour even without any bad news, and conditions will only improve in connection to an incredible earnings season for CoreWeave and other AI infrastructure players.

Said “incredible” earnings would require A) data centers to be built and B) said customers to actually pay for them, which roughly translates to “OpenAI and Anthropic’s continued ability to spend more and more money.” Anthropic’s 47% quarter-over-quarter growth rattled the markets because this company has to grow fast enough to cover $413 billion of non-cancellable compute contracts over the next few years, and everybody kind of knows deep down that the bubble can only continue with parabolic growth, which is why everybody freaked out when OpenAI reported a lower annualized run rate (a made up number) than expected, because growth is the only thing keeping this going.

And said growth rate is predominantly based on whether AI startups continue to let their customers spend anywhere from $2 to $10 in tokens per dollar of subscription, which in turn is dependent on venture capitalists’ continued status as the world’s most braggadocious paypigs, and at some point that venture money will run out, much like it did during the Dot Com Bubble.

I didn’t expect this piece to be so sobering, but when I sat down to write it it just kinda flowed out of me how profoundly difficult the AI industry’s future has become. The fundamental disconnection between the perception of AI growth inflating the value of hyperscaler stocks and the worrying fact that we don’t really know exactly how much AI revenue they make means that the S&P 500 and NASDAQ are held up by a kind of childish excitement that will, at some point, turn to existential fear.

Those reflexively responding with “they’ll get bailed out!” neither understand the markets nor the situation at hand. The AI bubble was inflated by hyperscaler revenue growth (without any disclosure of AI revenue) and NVIDIA’s GPU sales, meaning that the moment one of those slows, everybody freaks out. The US government cannot bail out NVIDIA, Microsoft, Google, Amazon, CoreWeave or Meta’s future misses of analyst expectations, nor can it cover every single bankrupt data center, nor can it bail out investor sentiment when it realizes the AI “boom” was yet another tech-based bust. I went into significantly more detail about it in my premium piece about how AI Isn’t Too Big To Fail.

Remember: even with the bank bailout in 2008, the market still crumbled, and that was before we had over $40 trillion in debt, stubborn inflation, and a stock market that has fundamentally disconnected from reality.

The underlying weakness of the AI trade is that it isn’t really related to anything other than NVIDIA and the hyperscalers’ earnings.

We don’t know how many GPUs are in operation, we don’t know how much money the hyperscalers make from using them or renting them, we don’t know how many active gigawatts of AI capacity they have, we don’t know their actual customer concentration outside of analyst estimates, we don’t really know how much people pay to rent GPUs, and we don’t even know how much it costs to run a GPU on an hourly basis.

OpenAI and Anthropic are horrifyingly unprofitable, deeply unstable, and as I’ve discussed, 80% of their revenues come from companies that literally can’t afford to pay them, and yet the markets never react, because this information doesn’t really matter as long as the right numbers keep going up.

This is why Anthropic’s IPO travails are so profoundly dangerous for the AI trade. Investors are now suggesting that it prices its IPO at $1.5 trillion, meaning a significantly lower payoff for investors and much less capital raised on listing, meaning that it will have to take on more and more debt — like its 10% interest chip financing deal with Broadcom and Blackstone — that will provide a brutal education in what a high-burn-rate, negative-EBITDA company really looks like when you remove the layers of annualized run rates and hype.

And, per Irrational Analysis, Anthropic’s investor day didn’t go particularly well:

Any further delays to Anthropic’s IPO will be somewhere between catastrophic and fatal, and at this point I think it’s fair to question whether it ever makes it to the public markets.

If it does, a $1.5 trillion valuation would spell disaster for OpenAI, which has (as mentioned above) been trying to raise $30 billion at a $1.4 trillion valuation. Closing that round will be difficult to impossible at that price even without Anthropic’s drooping numbers, and if it somehow IPOs at something approaching $1 trillion, I have no idea how Clammy Sammy and OpenAI make it out of this one alive.

And really, does any of this look like the future? Does any of this feel like the next industrial revolution?

Or does it feel like the run-up to another burst bubble and resulting financial crisis?

The difference here is that AI has, for the most part, been sold as a panacea for the rich and powerful — a way to control, to steal, to acquire and to expand, to take and take and take as they always have, occasionally vaguely muttering something about “abundance” as both the media and financial institutions help force through yet another doomed cycle of “innovation.”

The people to blame are the tech CEOs — Sam Altman, Dario Amodei, Sundar Pichai, Andy Jassy, Elon Musk, Hock Tan, Jensen Huang, Sanjay Mehrotra to name a few — and the financiers that have helped them pump this bubble, with the worst of them being that rat fuck Stephen A. Schwarzman, CEO of Blackstone, a private equity (or “alternative asset”) firm that has funded basically every name I just wrote down on some level, funded by everyone from the Saudis to the California state pension system.

This week’s premium will be the Hater’s Guide To Blackstone, a company that vibrates with evil, and has staked its entire future on whether AI works out.

It’s a stupid fucking bet from an evil fucking company, and I can’t wait to tell you all about it on Friday.

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If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.