In May 2026, Ethereum did more than it has ever done. 64% of crypto’s transactions, half of all stablecoins, $628 billion settled in a single month. And ETH the token, a $244 billion asset trading near $2,025, earned almost nothing for it. Base fees collapsed after the Dencun upgrade, the blob fix shipped in December and changed nothing, and the network now burns about 70 ETH a day against 2,820 issued. ETH is inflating. The “ultrasound money” thesis is dead, and even the $250,000 bull deck quietly admits the issuance.

This is not an anti-Ethereum piece, and I hold ETH. Ethereum the network is winning, decisively, on every axis that matters for the next decade. The problem is that everything that makes it win is engineered to give value away, and the market has spent two years pricing the token as if the network were dying. The truth is stranger than that.

The seven loudest voices on ETH are all fighting about one question: whether the token captures the network through fees. They run the full range, from David Hoffman, who just sold every coin he owned, to Standard Chartered, which is calling for $40,000. That argument can be laid to rest. It doesn't.

ETH is not a fee asset and never really was. It is the reserve collateral of the most neutral settlement network on Earth, and you price that the way you price gold: on demand to hold it, not on the toll it collects.

The repricing is not $250,000, and it is not zero. It is a slow migration of monetary and collateral demand toward the asset the whole system is denominated in, plus a free option on institutions, AI agents, and tokenization, eventually routing value through it. It can stay mispriced for years. It has. Here’s the full case.

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The Week Two Smart People Looked At The Same Chart And Saw Opposite Things

In the last week of May, David Hoffman sold all of his ETH.

This matters because Hoffman is not a tourist. He built Bankless, a media empire, and a decade of his identity on Ethereum. So when he writes that the “ETH is Money” thesis played out rather than failed, that the token got the price it deserves and won’t be rerated up or down, the people who bought ETH because of him listen. His verdict was generous and brutal at once: massively bullish on Ethereum the network, finished with ETH the asset. Ethereum, he wrote, is a giver, not a taker.

The same week, Standard Chartered’s Geoffrey Kendrick told clients that ETH would reach $4,000 this year and $40,000 by 2030. His comparison was Amazon during the dot-com crash, the stock that fell to six dollars while the business got better every quarter.

And here is the most amusing detail. JPMorgan, in the same month, did both at once. On May 19, its research desk told clients ETH was unlikely to reverse years of underperformance against Bitcoin without real improvement in network activity and use, and that upcoming upgrades might not fix it. Then JPMorgan put its own product, a tokenized deposit token called JPMD, live on Base, an Ethereum layer-2. Bullish the network. Skeptical of the token. One institution, living the entire contradiction in a single month.

That contradiction is the whole story. And the data resolves it cleanly, just not in either camp’s favor.

[Ethereum’s share of crypto activity vs share of market cap. Bars at 64% transactions, 54% stablecoins, 53% tokenized assets, 52% DeFi TVL, all in white. Market cap share alone at 9.77% in red, far below the pack. Caption: “Ethereum does the work. ETH doesn’t capture the value.” Source: Mougayar monthly dashboard / rwa.xyz / DefiLlama / CoinMarketCap.]

The Burn Is Dead, And The Bulls’ Own Math Admits It

Start with the number that ends the loudest bull argument.

Every bull case eventually rests on the burn: usage rises, ETH gets destroyed, supply shrinks, price climbs. So look at what actually burns. Over roughly sixty days after December’s Fusaka upgrade, Ethereum burned 4,251 ETH through ordinary base fees and 3.16 ETH through blobs, the layer-2 data mechanism that was supposed to be the future of the network’s revenue. Blobs are seven hundredths of one percent of the burn. That is not a rounding error you grow out of. That is the mechanism not existing.

Zoom out, and it gets worse. The network issues about 2,820 ETH a day to validators and burns about 70. ETH is inflating at roughly 0.83% a year. Not ultrasound, not deflationary. Inflationary, today, at an all-time high usage.

The remarkable part is that the bulls know this. Etherealize, the firm behind the $250,000 target, states on its own website that ETH supply has been “flat-to-deflationary since the Merge.” Its own report, three pages from that line, concedes net issuance “has hovered around 0.8%.” The chain says 0.83% inflation. Three numbers, one of them contradicting itself inside one firm’s own materials. When the people calling for $250,000 cannot keep their scarcity story straight across two documents, the scarcity story is over.

Didn’t December’s upgrade fix this? Fusaka shipped a floor under blob fees so they could never collapse to zero again. It worked. Blob burn rose something like eightfold. But eight times almost nothing is almost nothing, and Ethereum’s monthly revenue chart tells the truth: a peak above $1.5 billion a month in 2021, and a flat line scraping the bottom through 2025 and into 2026.

[Blob burn donut: 99.9% EIP-1559 base fee / 0.1% EIP-4844 blobs, over ~60 days post-Fusaka. Source: Dune (@xeift).]

[Ethereum monthly chain revenue, 2021 to 2026, showing the $1.5B+ peak collapsing to a flat line. ETH-denominated and USD both noted. Source: DefiLlama.]

This Is Not A Bug. It Is The Architecture Working As Designed.

Hoffman’s deepest point is that none of this is an accident.

Ethereum chose the rollup-centric roadmap on purpose. Layer-2 networks do the cheap, fast transactions and pay Ethereum a thin slice for security. How thin? Base, Coinbase’s layer-2, earned more than $94 million in profit and paid Ethereum about $4.9 million for the privilege. Across 2025, every layer-2 combined paid Ethereum roughly $10 million for security, under a tenth of what they kept. The value moved up to the rollups and out to the apps, exactly as the design intended.

And the revenue that does reach the base layer increasingly skips ETH holders entirely. Break down what’s left, as the analyst The DeFi Report does on-chain, and the surviving income is mostly priority fees and MEV tips, which flow to validators. The base fee, the only part that gets burned and the only part that reaches every holder through scarcity, is the shrinking slice of a shrinking pie.

However, this is not an Ethereum disease. Solana, the chain the bears hold up as the winner, runs about $10 million a day in ecosystem fees and routes under 10% to the protocol. SOL inflates around 4%. It has no burn at all. Every general-purpose smart-contract chain gives its value to apps and validators and hands the token holder a thinning toll. The value that the entire sector fights about is mostly absent everywhere. The bears who sold ETH for SOL traded one version of the problem for a worse one.

And the last hope, that blob demand grows until fees finally spike, has the arrow pointing backwards. Blob demand peaked in mid-2025 and has fallen since, even as Ethereum raised the blob target. Rising capacity, falling use. The market is emptying, not filling.

The Institutions Came. They Came To The Layer Above ETH.

The bull story says Wall Street is coming and ETH rides the wave. Wall Street is coming. Watch where it actually lands.

JPMorgan’s JPMD, a tokenized claim on real bank deposits, went live not on Ethereum but on Base, the layer-2. It is the first time a major commercial bank put a deposit product on a public blockchain, and the fees accrue to JPMorgan, to Base, and to Coinbase. Ethereum the base layer earns the same near-zero blob fee as everyone else. The bank validated the ecosystem and routed the token nothing. It is Hoffman’s thesis with a Fortune 50 logo stamped on it.

The same pattern runs through stablecoins, the asset Ethereum supposedly owns. Ethereum holds roughly $163 billion of them, about 54% of global supply, per rwa.xyz. But PayPal’s PYUSD now does more transaction volume on Solana than on Ethereum, and has since the middle of 2025. Ethereum holds the balances. The activity rotates elsewhere.

Eighteen months ago, Ethereum’s fee dominance looked permanent. By total fees, the gross amount users pay, Solana, Tron, and Ethereum ran neck and neck in February, around $27, $24, and $23 million for the month. But fees are the top line. What matters for the token is revenue, the slice each chain keeps, and Ethereum is worse than at parity. Over the last thirty days, it kept about $5.2 million, a sixth of Tron's $32 million. And here is the line that should end the argument: Ethereum barely out-earned Base, its own layer-2, which kept $4.4 million. The parent and its own child, nearly tied, both a rounding error next to the volume Ethereum settles. The crown is gone, and the toll barely reaches the token.

But look closer at William Mougayar’s monthly dashboard, where every line traces to a primary source, and a pattern appears that both camps miss. Ethereum’s share is highest exactly where institutions live: 64% of transactions, 54% of stablecoins, 53% of tokenized assets. It is lowest where retail speculates: 26% of NFT volume, 21% of DEX volume. Ethereum is quietly becoming the settlement and collateral layer for serious money. That is the signal the bears overlook and the bulls misprice.

[Stablecoin supply by chain: Ethereum ~$163B / Tron ~$87B / Solana / BSC, with Ethereum’s share line flat-to-eroding over the year. Source: DefiLlama / rwa.xyz.]

The Strongest Bull Case Ever Written, And Why It Breaks Itself

If you want the bull case at full strength, read Etherealize’s “Ethereum and the Era of Productive Money.” It is the most sophisticated argument for ETH in existence, which is exactly why it deserves to be met head-on rather than waved away.

One disclosure first, because it matters: Etherealize describes itself as a product, business-development, and marketing arm for the Ethereum ecosystem, and it represents Ethereum in Washington. This is a registered advocate publishing a $250,000 target for the asset it exists to promote. That does not make it wrong. It makes it the most polished piece of bull marketing the cycle has produced, and we should treat it that way.

The case runs like this. Warren Buffett dismissed gold because it just sits there: own an ounce for eternity, and you still have an ounce. Bitcoin has the same flaw. ETH does not, because you stake it and earn a yield generated by the protocol itself, with no borrower and no counterparty. So ETH becomes the first money in history that compounds in your own hands. Add the rest of the monetary checklist, and if ETH captures even a slice of the roughly $30 trillion premium sitting in gold and Bitcoin, the arithmetic spits out $250,000 a coin.

It is genuinely elegant. It breaks on two facts.

First, the productivity is mostly dilution. We already saw that staking yield comes overwhelmingly from new issuance, not from fees, because the fees collapsed. Paying stakers with freshly printed ETH while everyone else holds still is not a dividend funded by profit. It is the printer moving coins from one pocket to another. “You will own more ETH” is true and nearly empty, because every other holder’s pile inflated right alongside yours. Real productivity is fees, and the fees are gone.

Second, and this is the heart of the whole debate: a monetary premium is the price the market pays for an asset to be boring.

Gold and Bitcoin earn their premium by being inert. Fixed supply, no governance, no surprises, nothing ever changes. That stillness is the entire product. Now look at what Etherealize celebrates about ETH: a quantum-resistance overhaul, a privacy roadmap, zkEVMs, a gas limit tripling, and a hard fork every six months. For a technology platform, that velocity is a triumph. For a monetary premium, it is a disqualification, because money is trusted precisely for what it refuses to do. Etherealize even concedes that ETH is weakest on established history, and then asks you to value it as established money.

You cannot be the most dynamic computer in the world and the most trusted dead asset in the world at the same time. Those are two different products, sold to two different buyers, for opposite reasons. Every bull conflates them. Hoffman, staring at the same chain, simply believes the platform and gives up on the premium. They are each half right, and the half they share is the wrong half.

There is a sharper version of this, and quantum computing just made it concrete. When the cryptographic ground itself can crack, inertness stops being a virtue and becomes a liability. The same refusal to change that makes Bitcoin trustworthy as money is exactly why it is paralyzed on the quantum threat, while Ethereum’s relentless dynamism, the thing that disqualifies it from a pure monetary premium, is what lets it adapt and survive. In a world where the substrate shifts, the adaptive chain is the one that stays money at all. More on that below.

What’s Actually Mispriced, And It Isn’t On Anyone’s Chart

So if it isn’t fees, and it isn’t a $250,000 monetary premium, what is the trade?

Follow the capex. Two things are tightening that have nothing to do with the burn.

The first is float. ETH is being locked and absorbed faster than it is being created. Around 30% of all ETH is staked and illiquid. One corporate treasury, Bitmine, holds about 5.4 million ETH, roughly 4.5% of the entire supply, a figure Messari confirmed crossing five million this spring. This is the demand the fee bears cannot see, because it has nothing to do with revenue and everything to do with ETH being the reserve asset of the system. The honest counterweight: spot ETH ETFs bled about $216 million over ten days in late May, and ETH against Bitcoin just printed a year-to-date low near 0.027. Supply is being absorbed by treasuries and staking and released by ETFs at the same time. Which side wins is the real open question, and it is a far more useful one than “how much does the network burn.”

The second is the development moat, and this is where the bears are most wrong. Ethereum is not standing still while it loses fees. It is building what the next decade needs, in the open, faster than anyone else. The clearest proof landed in March, when Google’s Quantum AI team published a paper showing the cryptography behind Bitcoin and Ethereum could be broken with roughly twenty times fewer qubits than anyone thought. One of its co-authors was Justin Drake, an Ethereum Foundation researcher, alongside Stanford’s Dan Boneh.

Ethereum’s own people helped write the paper that exposes the threat, and Ethereum already has a concrete four-part plan to rip out and replace its vulnerable signatures, on a 2029 migration target shared with Google and Cloudflare, while the Bitcoin community is still arguing over whether the threat is even real and its ossification makes the fix unlikely. No cryptographically relevant quantum computer exists today, and the timeline is genuinely debated; Drake’s own odds are aggressive and personal. But the gap in readiness is not debated.

In January, Ethereum also shipped ERC-8004, an identity and reputation standard for AI agents, authored jointly by MetaMask, the Ethereum Foundation, Google, and Coinbase, paired with the x402 payment rail. Its privacy stack, Railgun, has shielded $4.5 billion and is being pulled directly into wallets by a Foundation initiative. The upgrades ship twice a year, on schedule.

Here is the correction the bulls need, though, because it is the same trap in reverse. None of that development reaches ETH through fees. We buried that idea three sections ago. It reaches the token, if it reaches it at all, by making ETH the unavoidable reserve and collateral of the venue where all of this gets built, and by keeping alive a cheap option that one of these threads, institutional settlement, autonomous agents, tokenized assets, eventually routes real value through the coin. That option is out of the money right now. It is also nearly free at today’s price.

[ETH/BTC ratio, Aug 2025 peak 0.0432 falling to ~0.027 now, with StanChart’s 0.08 target and the implied “monetary premium” prices drawn as dotted lines far above. Visualizes the gap between the models and the market. Source: market data / StanChart note.]

[The float war: stacked view of ETH absorbed (staking + treasuries) vs ETH released (ETF outflows + net issuance). Build once the net-float figure is locked. Source: strategicethreserve.xyz / Farside / ultrasound.money.]

The Same Mistake As The AI Trade, Pointed The Other Way

Mispriced has one recurring claim: markets get the technology right and the economics wrong.

In the last piece, on big tech’s AI profits, the error ran in one direction. The market priced the technology as real, which it is, and ignored that a chunk of the economics underneath was circular and partly imaginary. The gap was bearish. The narrative was richer than the cash.

ETH is the mirror image. The market is pricing the economics as broken, which they are, fees collapsed, burn dead, token inflating, and ignoring that the technology and the capex are compounding a position no competitor can touch. The gap is bullish, but only if you stop using the income statement to value an asset that was never an income statement in the first place.

Same mistake, opposite sign. With AI, people paid for economics that were not there. With ETH, people are refusing to pay for an asset because they are measuring the wrong economics. In both cases the capex told the truth before the price did. That is the whole job: find the place where what is being built and what is being paid for have come apart, and stand in the gap.

Price The Collateral, Not The Clearinghouse

There is a clean historical warning here, and it is not Amazon.

It is Cisco. Cisco was real infrastructure, the actual plumbing of the internet, and the dot-com market priced it like a religion, then left it roughly flat for two decades while the internet it powered remade the world. Real technology and a tradeable asset turned out to be two different bets. The bulls quoting Amazon should sit with Cisco for a minute.

But ETH inverts even that. The market is not overpricing the infrastructure. It is underpricing it, by measuring the wrong thing. Tom Dunleavy, in the noisiest bull thread of the spring, got exactly one thing right and the rest wrong: ETH is the collateral, not the clearinghouse. You do not value the world’s settlement layer by the toll it collects. You value the asset the system is forced to hold. But you do not arrive at a number by decree, the way his model and Etherealize’s both try to, and you do not earn the premium of dead money while rebuilding your own cryptography every single year.

So price the collateral, on demand, not on a formula.

What that gives you is not $250,000 and not zero. It is a slow repricing of the reserve asset of the chain that actually won, plus a free option on the institutions and the agents now arriving. Sustained growth, not a 100x. And it can stay mispriced for years, because nothing forces the gap shut on a schedule. I hold ETH on exactly that thesis, with my eyes open to exactly that risk.

The network already won. The token is the part still waiting. And that wait, not a six-figure fantasy and not a funeral, is where the mispricing actually lives.

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Sources and full reference list: https://telegra.ph/Ethereum-Already-Won-Your-ETH-Didnt-References-06-03

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Written by Udipta Basumatari. Nine years in crypto. Former CMO of two crypto-native startups, took the BWLD token to a $75 million market cap at launch, and, working solo and anonymously, launched a prediction-market token that reached a $3 million cap. Angel investor across multiple blockchain organizations and a long-time advocate for decentralized data ownership. I hold ETH long-term, which is rather the point: this is a bull reading his own bag honestly.

Mispriced asks one question: what is the market getting wrong, and what does the capex tell us that the narrative doesn’t? Subscribe for the next one.

TLDR:

Three of the most powerful people in AI (Sam Altman, Jensen Huang, and Dario Amodei) walked back the jobs apocalypse narrative in the span of two weeks, right as OpenAI and Anthropic prepare to file for blockbuster IPOs. That timing is not a coincidence. The labor narrative is softening because the underlying financial structure is becoming harder to defend.

Here is what that structure actually looks like. Alphabet booked $28.7 billion in profit last quarter, which was a paper markup on its Anthropic stake, not a customer dollar. Amazon did roughly the same. The combined $1.6 trillion cloud backlog held by the four hyperscalers is concentrated in two AI labs that lose money on every token they sell. Those same labs spend most of their revenue right back on cloud compute rented from the companies that funded them in the first place.

It is a circle. Money goes from hyperscaler to lab, the lab spends it on the hyperscaler’s cloud, and both sides book the same dollars as growth. We have seen this movie before. In 2001 it was called Global Crossing and Qwest, and it ended in bankruptcy and fraud charges. The only difference today is that the AI version is legal.

Here’s the full case.

The tell

In the span of two weeks in late May 2026, three of the most powerful figures in artificial intelligence quietly walked back the story they had been telling for two years.

Sam Altman, speaking at a Commonwealth Bank of Australia conference in Sydney on May 26, said he had been wrong about AI eliminating jobs. His words: “I’m delighted to be wrong about this. I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened.” He added that the jobs picture was “likely to be very different from what we thought.”

The same week, Nvidia CEO Jensen Huang called the AI layoff explanation “lazy” and “irresponsible,” asking the obvious question nobody pushing the narrative wanted to hear: “AI has just arrived. How is it possible they’re already losing jobs?” Anthropic’s Dario Amodei, who a year earlier had warned AI could wipe out half of entry-level white-collar jobs, was softening his language in the same window.

Here is the context that makes the timing matter. OpenAI is reportedly preparing a confidential filing for a US IPO in the coming weeks, potentially targeting a $1 trillion valuation and raising at least $60 billion. Anthropic is on a similar track. When the people selling a story start dismantling it right before they cash out, the right question is not whether they had a change of heart. The right question is what they can see on the balance sheet that the public cannot.

The answer is that the revenue underneath the AI boom is not what it appears to be. Some of it is a paper entry. Some of it is the same dollar counted twice. And a dangerous amount of it depends on two companies that have never turned a profit.

The paper-gain machine

Start with the most striking number of the last earnings season.

In the first quarter of 2026, Alphabet reported $62.6 billion in net income, up 81% year over year. A blowout quarter by any measure. Except that $28.7 billion of that profit was not a profit in any sense a normal person would recognize. It was a mark-to-market gain on Alphabet’s equity stake in Anthropic.

Here is how that works. When a company holds a stake in a private business and that business raises money at a higher valuation, accounting rules let the stakeholder mark up the value of its holding and book the increase as income. No cash changes hands. No customer pays anything. The startup simply raised a new round at a higher price, and the investor records the unrealized gain as if it were earnings.

Amazon ran the same play in the same quarter. Its net income surged 77% to $30.3 billion, with roughly half of that attributable to the markup on its own Anthropic stake. Two of the largest companies on earth posted record profits, and a very large share of those profits came from marking up the value of a private AI lab that has never made money.

The tax expert Robert Willens put his finger on the strange part. These companies, he noted, are able to influence the value of an asset they own and then mark that asset to market through transactions with the very same entity. Amazon invests in Anthropic, does business with Anthropic, and books gains when Anthropic’s valuation rises. The investor, the customer, and the supplier are arranged in a circle, and the circle generates reported profit.

The mechanism is not theoretical. Anthropic’s Series H funding round closed at a $965 billion post-money valuation. Every step up in that number flows onto the balance sheets of its investors as reported profit, without a single customer dollar changing hands. When the valuation rises, the stakeholders book the gain. When it eventually stops rising, the same mechanism runs in reverse.

This is not illegal. It is not even unusual under current accounting standards. But it means that a meaningful slice of Big Tech’s headline AI-era profitability is an accounting entry tied to private valuations that the same companies help set.

The circle

The paper gains are the visible symptom. The vendor financing loop underneath is the engine.

The clearest documented example involves Amazon and Anthropic. Amazon has invested billions into Anthropic across multiple rounds, taking a stake reported at between 15 and 19 percent. In late 2025 it committed another tranche, and simultaneously Anthropic committed to roughly $100 billion in spending on Amazon Web Services over the following decade. Amazon gives Anthropic money. Anthropic gives the money back to Amazon as cloud spending. Amazon books that spending as AWS revenue.

The numbers on the Anthropic side are stark. According to an investigation by Ed Zitron drawing on AWS billing data, Anthropic spent $2.66 billion on Amazon Web Services in the first nine months of 2025 against $2.55 billion in revenue over the same period. That is 104% of revenue going straight back to its largest investor in the form of cloud bills. In January 2025 the ratio was even more extreme, with a reported $185.5 million AWS bill against $72.9 million in revenue, or more than 300%.

The same structure appears across the industry. Microsoft put $13 billion into OpenAI, much of it in the form of Azure cloud credits, which OpenAI then spent running its models on Azure, which Microsoft recorded as cloud revenue. Amazon struck a parallel arrangement with OpenAI. Nvidia invests in the labs that then commit to buying Nvidia chips.

In each case the same dollar is doing double duty. It counts as an investment on one ledger and as revenue on another. The growth story that justifies hundreds of billions in capital expenditure is, in part, a function of money moving in a circle rather than new money entering the system from paying customers

Two customers holding up a trillion-dollar backlog

When hyperscalers report their growth, the headline figure analysts watch is remaining performance obligations, or RPO. It represents contracted future revenue, the backlog of deals already signed. The combined RPO across Microsoft, Oracle, Amazon, and Google now stands at roughly $1.6 trillion. That number is the foundation of the entire AI infrastructure bull case.

Look at what it actually rests on.

Oracle reported $552.6 billion in remaining performance obligations as of the end of February 2026. More than $300 billion of that, roughly 54%, comes from a single customer: OpenAI. Microsoft’s commercial RPO of around $627 billion is, by analyst estimates, close to half tied to OpenAI as well. A backlog that Wall Street treats as evidence of broad, durable demand is, in reality, concentrated in two AI labs that have never been profitable and that fund their compute purchases through capital raises rather than operating cash flow.

The market has started to notice. Oracle raised $18 billion in debt in September 2025 and was reportedly seeking tens of billions more to finance the data center buildout it promised OpenAI. Oracle’s credit default swap spreads, the cost of insuring against an Oracle default, have widened to their highest levels since 2009. Bond investors are pricing in the risk that a backlog dependent on one unprofitable customer might not convert into cash.

The numbers do not work even when you cheat

Here is the part that should end the debate, and it comes from an investment bank, not a short seller.

Joachim Klement, a strategist at the brokerage Panmure Liberum, modeled the implied return on the AI capital expenditure of the five largest hyperscalers from 2025 to 2030. He did it under the most generous possible assumptions: zero operating costs, top-end revenue gains, pure projected revenue measured against capital spending. No GPU depreciation, no power bills, no salaries. Just the best case.

Even then, four of the five posts negative implied returns. Microsoft comes in at -9.2%. Alphabet at -15.7%. Meta at -28.8%. Oracle, the most OpenAI-dependent of the group, lands worst at -35.6%. Only Amazon cleared positive, at a slim +7.2%, helped by its custom silicon. And remember, those are the numbers before a single real cost is subtracted. The actual returns are worse.

Klement’s broader math is just as stark. The five hyperscalers would need to find somewhere between $2 trillion and $5 trillion in additional annual revenue to justify their planned data center spending, which is projected to exceed $600 billion in 2026 alone. To get there at current capex-to-sales ratios, they would have to roughly quadruple their revenues. His conclusion, in his own words, is that “if the hyperscalers continue on the current trajectory, the AI boom will become one of the largest destructions of shareholder value in history.” He also calculates that the buildout is already 60% larger than the dotcom bubble, with technology capital expenditure now accounting for nearly all of US GDP growth.

This is not a fringe view from a permabear. It is an investment bank running the arithmetic and finding that the best case loses money.

We have seen this before

Strip away the word “AI” and the structure is familiar. It is the telecom bubble of 2001.

In the late 1990s, telecom companies needed to show booming demand for fiber-optic capacity to justify their valuations and their capital spending. Some of them manufactured that demand by swapping capacity with each other. Global Crossing and Qwest Communications famously exchanged essentially identical fiber-optic network capacity, each booking the other’s purchase as revenue. The trades created the appearance of a thriving market, while no real end demand existed underneath.

When the music stopped, Qwest was forced to erase $1.4 billion in improperly recognized income. Global Crossing filed for bankruptcy in early 2002. Executives at both companies faced prosecution. The swaps had been securities fraud.

The AI version is mechanically the same. Capital flows in a circle between a small number of large players, each booking the flow as growth, creating the appearance of demand that outruns the real paying market underneath. The one meaningful difference is that the 2001 swaps were illegal, while the 2026 vendor financing loop is fully permitted under current accounting rules. That should be reassuring and is not. The fraud in 2001 was the deviation from the rules. In 2026, the rules themselves accommodate the circularity.

Where it breaks: the real world

A circular economy of subsidized compute can sustain itself only as long as the money keeps circling. The moment the technology leaves the subsidy loop and meets a real enterprise customer with a hard budget, the unit economics show their true shape. And that is exactly what has started to happen.

Uber rolled out Claude Code to 5,000 engineers in December 2025. By April 2026, the company had exhausted its entire annual AI budget, four months into the year, against a $3.4 billion R&D base. Heavy users were costing up to $2,000 per person per month. The CTO’s line was telling: “I’m back to the drawing board, because the budget I thought I would need is blown away already.”

The admission then climbed the org chart. In late May, Uber’s president and COO Andrew Macdonald described the moment he learned the company had blown its annual AI budget in four months as “head-exploding.” His assessment of the spend was blunt: “If you’re not actually able to draw a direct line to how many useful features and functionality you’re shipping to your users, that trade becomes harder to justify.” When the COO of a company where 95% of engineers use AI monthly and 70% of committed code is AI-generated cannot draw a line from the spend to the product, the ROI is not lagging. It is missing.

Then came the data point that should worry every CFO in tech. In May 2026, Microsoft began canceling thousands of internal Claude Code licenses across its Experiences and Devices division, covering Windows, Microsoft 365, Outlook, Teams, and Surface, with a June 30 deadline. The official reason was tool consolidation. The real one was the cost. The company that put $13 billion into OpenAI, which runs the Azure infrastructure powering much of the model layer, that has effectively unlimited cloud capacity, looked at the bill for a competitor’s coding tool and decided it was not worth paying. If Microsoft cannot justify the cost, the question of who can becomes very short.

The pattern is now widespread enough that Axios called it “Corporate America’s AI reckoning.” In that reporting, an AI consultant said one of their clients spent half a billion dollars in a single month after forgetting to set usage limits on Claude licenses for employees. The CEO of CloudBees offered the darkest interpretation of the layoff wave: workforce cuts, he suggested, may simply be “the only lever they can pull” to offset their AI bills. Read that against my last piece. The layoffs were never really about AI replacing workers. They were partly about paying for the AI. One CTO told Axios that employees were using frontier models to check the weather, which gets expensive fast when enterprise plans are not actually all-you-can-eat.

The broader spend data confirms the trajectory. Procurement firm Ramp reports that the average business now spends roughly 13 times more on AI tokens than it did in January 2025. Tokens remain under 2% of total spend even for the heaviest users, but a 13x annual growth rate does not stay small for long. GitHub, owned by Microsoft and running on Azure, is abandoning flat-rate pricing for Copilot in June 2026 precisely because the flat-rate model was bleeding money on heavy agentic users. And Nvidia’s own Bryan Catanzaro admitted in April that “the cost of compute is far beyond the costs of the employees,” which is the GPU maker conceding that the thing meant to replace expensive humans currently costs more than the humans.

The pitch was that tokens would get cheaper and agents would replace workers. Outside the subsidy loop, tokens are expensive and getting more so on a per-workload basis, and the labs are quietly walking back the replacement story.

The best argument against all of this

The strongest counter to this thesis is not a denial of the numbers. It is the Solow Paradox.

In 1987, the economist Robert Solow quipped that “you can see the computer age everywhere but in the productivity statistics.” Massive corporate IT investment through the 1970s and 1980s had failed to show up in measured productivity. Then, beginning around 1996, the productivity payoff from computing and the internet finally arrived, years after the capital had been spent. The lag was real, and betting against the technology during the lag would have been a mistake.

In May 2026, the Federal Reserve Bank of San Francisco argued that AI may be on the same curve. Labor productivity is rising even as total factor productivity stays flat, exactly the divergence seen in the mid-1990s before the internet’s gains materialized in the aggregate data. Maybe, the argument goes, the AI ROI is real and simply lagging, and the capital expenditure will be vindicated in a few years.

It is a serious argument, and it deserves a serious answer. Here is mine. The 1990s internet buildout was funded by real demand and real customer cash, not by a circle of hyperscalers marking up their own investments and booking each other’s spending as revenue. The Solow lag explains why productivity gains might show up late. It says nothing about whether reported revenue is circular or whether reported profit is a paper markup. Those are accounting questions, not timing questions. The Atlanta Fed found that executives perceive larger AI productivity gains than researchers can actually measure in company revenue, attributing the gap to “delayed output realizations.” Maybe. Or maybe it is the same wishful thinking that led 75% of executives in a separate survey to admit their AI strategy was “more for show” than real guidance. The lag may well be real. But a lag in productivity does not turn a circular dollar into a customer dollar.

Why nobody is pricing it in

If the structure is this visible in public filings, why has the market not corrected?

Because the loop is self-reinforcing on the way up. Paper gains inflate reported profits. Inflated profits push up market capitalizations. Higher market caps mean index funds and passive retirement accounts, which buy in proportion to market weight, mechanically purchase more of the inflated stocks. That buying pushes prices higher still. At no point in this cycle does anyone need to verify that the AI revenue has been converted into actual cash profit. The machine runs on reported figures, and the reported figures are partly manufactured by the circularity.

The most mispriced risk in global finance right now is not a single company or a single number. It is the widening gap between the optimism on earnings calls and the invoices landing on corporate desks. One side is a story. The other side is a budget. They cannot both be right indefinitely.

What breaks first

A few leading indicators are worth watching because they will move before the headline numbers do.

Oracle’s credit spreads are the clearest early warning, already at their widest since 2009. If they keep widening, the bond market is telling you it does not believe the OpenAI backlog converts to cash. Watch for more hyperscalers quietly canceling AI tools internally, the way Microsoft did, because internal cancellation is the most honest signal a company can send about unit economics. Watch the IPO pricing for OpenAI and Anthropic, because the bankers setting those prices have seen the real numbers and the valuations they land on will reveal what the smart money actually believes. And watch whether the labs hit their internal revenue targets, because the entire backlog assumes they grow into commitments that currently exceed their revenue many times over.

When the markup machine slows, the sequence is predictable. Capital expenditure slows. The labs miss revenue targets. The paper gains reverse into paper losses. And the circular flow that looked like growth on the way up looks like contagion on the way down.

Disclosure: I work at Adobe as a solution consultant. This piece is about the financial structure of the foundation layer, the labs, and the hyperscalers building the AI infrastructure. It is not about application-layer software serving real customers who pay real money for real outcomes, which operates on entirely different unit economics. The bubble I am describing is in the foundation, not in the products built on top of it.

The connection

My last piece argued that the AI layoff narrative was breaking and that a selective rehire wave had already begun. This is the other half of that story. The labor market repricing was the symptom. The capital market mirage is the cause.

When the loop unwinds, the layoffs reverse, but the rehire is selective, exactly as the labor data already shows. The companies that survive the correction will hire the people who can actually wield these tools productively, and they will let everyone else compete for a shrinking pool of unautomated work. The capital story and the labor story are the same story told at two different layers of the stack.

The technology is real. The productivity gains may even be real, eventually. But a real technology can sit on top of a fake balance sheet for a surprisingly long time, right up until it cannot.

In 2000, the internet was real, too. Cisco was the company selling the picks and shovels for it, the most valuable company in the world at its peak. The internet went on to remake the entire economy, exactly as promised. Cisco’s stock took twenty-six years to climb back to where it traded in March 2000. Being right about the technology and being right about the returns are two completely different bets.

Where do you sit when the correction comes? I built ismyjobscrewed.com to help you find out, and to map the upskilling path that puts you in the cohort that gets hired rather than the one that gets cut.

Mispriced covers tech at the intersection of AI, data, and crypto. Following the capex, finding what the market got wrong. Subscribe for the next one.

Sources and full reference list: https://telegra.ph/Half-Of-Big-Techs-AI-Profit-Is-Imaginary-References-05-30

The agentic AI replacement narrative that justified 18 months of tech layoffs is cracking. Token prices are going UP across all three frontier labs. Microsoft just canceled Claude Code internally because the bill was too high. GitHub is killing flat-rate pricing in June. Uber blew its entire 2026 AI budget by April. 95% of enterprise AI pilots are still failing despite spending going up 65%. The data center buildout looks exactly like the 1999 fiber bubble.

This isn’t a case against agentic AI as a category. The deployments that work, augmentation with humans in the loop, scoped to specific workflows, are quietly delivering real ROI. The category that’s breaking is the full-replacement fantasy.

Meanwhile, the companies running the working playbook are hiring hard. Amazon: 11,000 engineers. Salesforce: 1,000 new grads. IBM is tripling entry-level hiring. ClickUp just announced $1M salary bands for AI-native operators. Gartner forecasts 50% of AI-attributed layoffs will be quietly reversed by 2027. Forrester says the same. But the rehire is selective, and the new roles pay less unless you can wield agents.

This is the rehire wave nobody is pricing in. It started in April. It is likely to accelerate over the next 18 months. After that, the cohort closes, and a new equilibrium sets in for the next decade of knowledge work.

Here’s the full case.

Meta laid off 8,000 people on Wednesday. The stock barely moved.

Two months ago, Block laid off 4,000, and the stock jumped 20% after hours. Same playbook. Same AI narrative. Same press release language. Completely different market reaction.

Something broke in between. Let me show you why.

The pump is dead

The AI layoff stock pump worked for 18 months, and then it died sometime in Q2 2026. Wall Street stopped rewarding the playbook, which means CEOs just lost their main reason to keep cutting. The timeline tells the story better than any analysis can.

Block, March 2026. Jack Dorsey announced 4,000 layoffs, 40% of the company, and framed the entire cut as a pivot to AI. The stock jumped 20% after hours, rose another 17% during Friday morning trading, and the framing seemed to be working perfectly. Classic pump.

But the cracks were already visible if you knew where to look. When Wired pressed Dorsey directly on whether this was AI washing, he gave a wishy-washy answer that satisfied nobody. Then, in a follow-up post on X, he quietly admitted the real reason: “over-hired during covid because I incorrectly built 2 separate company structures (Square & Cash App) rather than 1, which we corrected mid 2024.” Aaron Zamost, Block’s head of communications from 2015 to 2020, went further and wrote a New York Times op-ed calling the cuts to the policy team and DEI roles “standard prioritization and cost management, not an A.I.-driven reinvention.”

The market didn’t care in March. By May, it absolutely did.

GitLab, May 11. CEO Bill Staples announced layoffs and an “agentic era” restructuring using almost the same script as Block. The stock dropped 9% the next day and closed at $23.08, down from $52 a year ago, leaving the company worth less than a third of its 2021 peak. Wall Street wasn’t buying the agentic era pitch any more than it was buying the broader narrative.

Meta, May 20. Mark Zuckerberg cut 8,000 people, 10% of the workforce, in a move that would have moved the stock 5% in either direction a year ago. The stock stayed flat in midday trading. Meta is now down 8.7% year to date, while Alphabet is up 24% and Amazon is up 12%, and Senator Bernie Sanders publicly criticized the cuts within hours of the announcement.

Sam Altman, of all people, has been the loudest voice calling this AI washing. His framing is that companies are using AI as a rationale for layoffs they were going to make anyway, regardless of any actual productivity story. The market took longer than I expected to catch on to the pattern, but once it did, the trick stopped working almost overnight.

The pump worked for as long as it did because the AI narrative gave CFOs perfect moral cover for cost-cutting. Boards rewarded the framing because the margin expansion story was easy to model and even easier to communicate to investors. Four quarters later, the margin expansion never showed up in earnings, and once GitLab dropped 9% on an “agentic era” announcement, every other CEO got the memo. The trick is done.

This matters because layoffs aren’t free. They cost severance, morale damage, customer trust, knowledge loss, and the recruitment expense of rehiring later when you realize you cut too deep. CEOs were willing to absorb all of those costs as long as the stock pumped on the announcement, but without the pump, the math simply doesn’t work anymore.

The token economics are broken

The pitch was simple -> Tokens get cheaper -> Agents replace humans -> Margins expand.

Reality flipped on all three, and the bills coming due are big enough that even Microsoft just blinked.

Frontier model prices are going up, not down.

Gemini 3.5 Flash launched on May 19 at $1.50 input and $9 output per million tokens, which is 3x the price of Gemini 3 Flash. Artificial Analysis ran the full Intelligence Index benchmark and found the new Flash was 5x more expensive to run than the previous Flash, and 75% more expensive than Gemini 3.1 Pro. That’s for a “Flash” tier model, the one specifically positioned as the cheap workhorse for high-volume agentic workloads.

The same pattern shows up across the labs. GPT-5.5 launched at 2x the price of GPT-5.4. Claude Opus 4.7 is 1.46x the price of Opus 4.6 once you account for the new tokenizer. Simon Willison summed up the trend perfectly in his post on the Gemini 3.5 launch: “All three of the major AI labs are starting to probe the price tolerance of their API customers.” This is the exact opposite of the pricing trajectory the layoffs were sold on.

The AI Tax is real, and CFOs are now tracking it.

Procurement intelligence firm Tropic published data showing AI-driven software prices have jumped 20% to 37% across vendor categories on enterprise renewals, with vendors restructuring their SKUs to force migrations into more expensive AI-bundled plans. They call it the “AI tax,” and even buyers who negotiate hard still see final pricing land 12% above pre-AI baselines.

Fortune ran a piece on April 28 quoting Nvidia exec Pat Lee directly: “the cost of compute is far beyond the costs of the employees.” That’s the maker of the GPUs telling enterprise buyers that AI is currently more expensive than the humans it was supposed to replace, which should give you a sense of how upside-down the unit economics have become.

Anthropic got caught nerfing.

On April 23, Anthropic published a public postmortem confirming what developers had been complaining about for six weeks. Claude Code, Claude Agent SDK, and Claude Cowork had all been degraded between March 4 and April 20 due to three separate product changes. Two of the three changes were deliberate decisions rather than bugs, specifically a reasoning effort downgrade and a verbosity cap on the system prompt.

Worth saying clearly: this wasn’t model regression at the weights level. The underlying model was fine. Anthropic ran the math, decided to reduce output quality to manage inference costs, and got caught when developers started filing GitHub issues comparing outputs side by side. They eventually issued the postmortem and reset usage limits, though the phantom-token bug from the same incident was still present in v2.1.137 as of May 13.

I’m not telling you this to bash Anthropic. I’m telling you this because it reveals that the supply side is more constrained than the marketing suggests. If frontier labs are willing to nerf their flagship products to manage demand, the “infinite cheap intelligence” thesis is fiction.

GitHub just killed flat-rate pricing.

On April 27, GitHub announced that Copilot is moving to usage-based billing effective June 1, 2026. Premium request units are gone, replaced with GitHub AI Credits priced by token consumption. The base subscription prices stay the same, but the value you get inside those prices drops sharply for anyone doing agentic work.

GitHub Chief Product Officer Mario Rodriguez justified the shift as essential for a “sustainable business model,” which is corporate-speak for the fact that the flat-rate experiment was bleeding money on heavy agentic users. This is the canary in the coal mine that everyone building on top of LLMs should be paying attention to. GitHub is owned by Microsoft and runs on Azure with effectively unlimited compute, and if GitHub can’t sustain flat-rate AI pricing under those conditions, nobody can. Every other developer tool is going to follow within 12 months.

One developer in the GitHub community thread did the math on his actual workflow and posted the result. He was paying €39.99/month for Pro+ in 2025 with 1,500 premium requests. Under the new model, his projected cost goes from €67 in April to €966 in June. That’s a 14x increase for the same usage pattern, and that’s the bill enterprise CFOs are about to be staring at across every developer in their org.

Uber blew the entire 2026 AI budget by April.

CTO Praveen Neppalli Naga revealed on April 15 that Uber had exhausted its full-year AI budget just four months in. The company’s R&D budget is $3.4 billion. They rolled out Claude Code to 5,000 engineers in December 2025, usage nearly doubled by February, and heavy users were costing up to $2,000 per person per month. The direct quote from the CTO is the one I keep coming back to: “I’m back to the drawing board, because the budget I thought I would need is blown away already.”

Now scale this up. Uber is one company with a $3.4B R&D budget. If they can’t sustain 5,000 engineers on Claude Code for a full year, what does that say about every other Fortune 500 trying to pivot to agents? Pegasystems already put a token usage calculator on their homepage, token budgeting is now standard enterprise procurement, and CFOs across the industry are reviewing line items called “Anthropic credits” for the first time in their careers.

Microsoft pulled the plug on Claude Code internally.

This is the data point that should make every CFO in tech sit up and pay attention.

The Verge reported on May 14 that Microsoft is canceling thousands of internal Claude Code licenses across its Experiences and Devices division, which covers Windows, Microsoft 365, Outlook, Teams, and Surface. The deadline is June 30, 2026, and engineers are being moved to GitHub Copilot CLI. The official framing is “tool consolidation,” but the underlying reality is more revealing than that framing suggests.

Microsoft invited Claude Code in last December. Thousands of developers picked it up over the following six months, and sources told The Verge that it became “very popular” inside the company. Then the company that put $13 billion into OpenAI and runs the Azure infrastructure powering most of Anthropic’s compute looked at the bill, looked at the cannibalization of their own Copilot product, and decided it wasn’t worth paying.

That is the single most important data point in this entire essay, and not because Microsoft is special. The point is that if Microsoft can’t justify the bill even with effectively infinite cloud capacity and a fiscal partnership at the model layer, nobody else in tech can either.

Personal data point worth adding here. I’m on an Anthropic Enterprise plan, and I burned 80% of my monthly credits in half a month doing genuinely productive work. The math that the layoffs were predicated on assumed I’d cost a fraction of a junior developer. The reality is that I’m spending agent budget that already exceeds the salary of a new grad in Bangalore, and I’m one person.

The CXO economics simply don’t work the way they were sold. Tokens are not the cheap labor substitute that the full-replacement narrative promised, at least not at the frontier tier where autonomous replacement work has to happen. Augmentation economics are different and they’re working.

The data center bubble

China built 500+ AI data centers in 2023 and 2024. Up to 80% sit unused.

The United States is on the same trajectory at 5x to 10x the capex.

This is the part of the story that doesn’t get enough airtime. Even if the agentic thesis were correct on the demand side, the physical supply layer is over-committed in ways that look exactly like every prior infrastructure bubble.

The China precedent. Local governments, state-backed firms, and real estate speculators launched over 500 AI data centers in 2023 and 2024. 150+ are operational. But reports indicate up to 80% of the newly built compute resources remain unused, per MIT Technology Review. Most were built in remote areas for cheap power, too far from AI hubs where low-latency inference actually matters. The driver was political incentives and hype. The outcome is empty buildings.

The US version is bigger. Alphabet, Amazon, Meta, and Microsoft are expected to spend more than $650 billion combined in 2026 alone on AI capacity expansion. McKinsey projects $7 trillion in global data center construction through 2030. The WTO reports that imports account for 70-90% of the value of US AI investment. That’s a level of import intensity with few parallels in modern American industrial history. And the supply chain runs through China for transformers, switchgear, and batteries.

Half of the planned US builds for 2026 are already delayed or canceled per Bloomberg. TD Cowen reported that Microsoft has already cancelled leases with at least two data center operators, totaling a few hundred megawatts.

The bubble warning came from inside the house. Alibaba Chairman Joe Tsai, who has every incentive to talk his own AI book up, instead publicly called the US data center spend a bubble at the HSBC Hong Kong summit. His exact frame: “I’m still astounded by the type of numbers that’s being thrown around in the U.S. about investing into AI. People are talking, literally talking about $500 billion, several hundred billion dollars. I don’t think that’s entirely necessary.”

His specific red flag was developers building “on spec” without binding agreements from the big AI buyers. Same pattern that broke China’s buildout.

The grid is the hard ceiling. Morgan Stanley forecasts that US data centers face a 44 gigawatt electricity shortfall in the next three years. China added 543 GW of power capacity in 2024 alone, more than the total power capacity the US has added in its entire history. Goldman Sachs: “AI’s insatiable power demand is outpacing the grid’s decade-long development cycles, creating a critical bottleneck.”

This is the structural problem. Token prices stay high because the supply layer can’t scale. The supply layer can’t scale because you can’t recommission a nuclear reactor in 18 months, and you can’t build a gigawatt solar farm in two years. Stifel Nicolaus is already forecasting an S&P 500 correction tied to this dynamic. They describe the data center capex boom as “a one-off build-out of infrastructure, while consumer spending is clearly on the wane.”

The demand side is constrained by token economics. The supply side is constrained by capex risk and grid physics. If either of these cracks, the entire full-replacement thesis cracks with it. If both crack, the layoffs reverse faster than anyone is pricing in.

The ROI math isn’t there

Companies spent 65% more on AI in 2026 than in 2025. Average enterprise AI spend went from $7 million to $11.6 million. The success rate moved from 5% to 29%, which sounds like progress until you do the arithmetic and realize that’s the most expensive 24 points of progress in enterprise software history. And the curve is flattening fast.

The data comes from Writer’s 2026 AI Adoption in the Enterprise survey, released April 7, covering 2,400 leaders, including 1,200 C-suite executives. The numbers are brutal across every dimension. 97% of executives deployed AI agents in the past year, but only 29% report significant ROI from generative AI, and only 23% see significant returns from agents specifically. 48% describe AI adoption at their company as a “massive disappointment,” 75% admit their AI strategy is “more for show” than actual guidance, and 38% of CEOs report high or crippling stress around AI strategy. 64% fear losing their job if they fail to lead the transition.

The abandonment curve is even worse than the ROI numbers suggest. 42% of companies abandoned most AI initiatives in 2025, up from 17% the year before, which is a 147% year-over-year increase. The average organization scrapped 46% of POCs before they ever hit production, which means roughly half of all AI experiments inside the enterprise are dying before they ever generate revenue.

Independent corroboration is now stacked deep across multiple research firms. RAND Corporation found that AI projects fail at twice the rate of non-AI IT projects. Carnegie Mellon’s TheAgentCompany benchmark showed that the best AI agents fail nearly 70% of real-world office tasks. Gartner projects 40% of agentic AI projects will be cancelled by 2027 due to governance failures. The pattern is consistent regardless of who is measuring or how.

Now look at where the 5-29% who actually succeed are winning. Back-office automation, specialized vendor tools, tightly scoped use cases. Not flashy chatbots, not agentic replacement of knowledge workers. The MIT NANDA research found that purchased AI solutions succeed 67% of the time versus 22% for internal builds, which is a brutal indictment of every company that tried to build agentic platforms in-house instead of buying focused tools from specialized vendors.

The pattern across the winners is consistent: scoped use cases, humans reviewing the output, agents automating the repeatable middle of the pipeline. As a solution consultant at Adobe I’ve watched this play out across enterprise deployments in APAC, and Forrester’s Total Economic Impact studies show the same shape: triple-digit ROI when the deployment is scoped right, near-zero when companies try to remove humans from the workflow entirely.

The companies that bet hard on full agentic replacement of human workers are sitting in the 71% that already abandoned the initiative or are about to. The companies that succeed are doing humans plus agents, not agents alone. This is the entire upskilling thesis in one paragraph.

The rehire wave

The companies that fired the loudest in 2025 are quietly hiring in 2026. Watch what they do, not what they say.

The headline data point comes from Gartner. Published February 3, 2026. Direct forecast: by 2027, 50% of companies that attributed headcount reductions to AI will rehire staff to perform similar functions, but under different job titles. Forrester reached the same conclusion in their late 2025 future of work outlook with one critical addendum: “We expect half of AI-attributed layoffs to be quietly reversed, with jobs returning offshore or at lower wages.”

That second part matters. The rehire is real. But the new roles pay less unless you’re in the cohort that can actually wield agents. Which is exactly the labor market split this essay is about.

55% of employers already regret their AI-driven layoffs per HR Director coverage. The macro data is even more striking. Citadel Securities just published Indeed posting data showing software engineering postings crashed from 73 in January 2024 to 61 by mid-2025, then ripped vertical to over 70 by early 2026. Software engineering is leading the broader labor recovery, which is the exact opposite of what the AI-replacement narrative would predict. The BLS independently projects 15% software developer growth from 2024 to 2034, against 3% for all occupations.

The economic logic is Jevons paradox. When a resource becomes cheaper or more efficient to produce, total demand for it goes up, not down. The 19th-century example is coal: when steam engines got more efficient, coal consumption exploded rather than collapsed. Software is now playing out the same way. AI didn’t replace engineers; it expanded the surface area of what’s worth building. The companies that internalized this fast are the ones currently hiring.

The macro recovery is real. The company-level data points back it up.

Amazon. AWS CEO Matt Garman announced at the 2026 What’s Next event that Amazon will hire 11,000 software engineers and interns this year, which comes after the company cut 30,000 people in late 2025 and early 2026. The exact words are worth reading in full because the framing matters: “We are hiring just as many software developers as we ever had inside of Amazon. And in fact, I see the demand for that really accelerating. I think as we think about where AI really takes off and where agents can help free us from the monotony of the repeatable parts of our task, it’s really about leaning in.” That’s a 37% rehire ratio at the same company, just for a different skill profile.

Salesforce. Marc Benioff announced on X on April 25, “We’re hiring 1,000 new grads & interns right now to ride the AI exponential,” and the timing is significant because it came months after laying off roughly 1,000 in February 2026 and another 1,000 in February 2025. Salesforce’s Agentforce ARR hit $800 million on the back of this hiring push, up 169% year over year, with 29,000 deals closed since launch. Full replacement ratio, different roles, and they’re now building the AI products that are doing the replacement at other companies.

IBM. Tripling entry-level hiring as of February 2026, with CHRO Nickle LaMoreaux explicitly making the contrarian argument: “The companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment.” This is the strongest contrarian signal in the entire essay because the HR head of a Fortune 50 company is explicitly arguing the opposite of the AI replacement narrative that the rest of the industry has been selling.

Meta. Cut 8,000 people, but is reassigning roughly 7,000 of them into newly formed AI-focused teams, which works out to an 87.5% internal reassignment ratio. Zuckerberg explicitly told staff that AI was not driving the layoffs, and the cuts went to areas of the business that were over-resourced while the AI teams kept growing.

The macro signal. The National Association of Colleges and Employers projects employers will increase hiring by 5.6% for the class of 2026, and many of the industries assumed most vulnerable to AI automation (information services, engineering, professional services) are exactly the ones planning to boost hiring most aggressively.

The pattern is clear once you stop reading press releases and start counting jobs. Roughly half the AI-attributed cuts will be reversed by 2027 per Gartner, and the specific company examples sit in a wide range from Amazon at 37% to Salesforce at full replacement, plus significant internal reassignment at companies like Meta. But the new roles are AI-native, and per Forrester, they pay less unless you can prove agent-native productivity. The old roles are not coming back at the old wages.

What the new playbook actually looks like

ClickUp CEO Zeb Evans published a post today, May 22, that reads like the operating manual for the new equilibrium. He cut 22% of the headcount. Then he said the quiet part out loud about what the remaining workforce will look like.

The framing is what he calls the “100x org.” Quote: “The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people who have embraced and adopted new ways of working.”

He coined a phrase I want to steal: “the great reckoning of AI coding.” His take is that companies pushing every engineer to use infinite tokens are “celebrating 500% more pull requests” while “customer outcomes don’t match the volume of code being generated.” More code is just another bottleneck. The skill is judgment, not output.

But here is the line that should reset every compensation conversation in tech: “We’re introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.”

A million dollars cash for AI-native operators. Severance for everyone else.

This is the labor market bifurcation happening in real time, with a number attached. The Writer survey predicted it. ClickUp just put it in writing.

Worth being honest about the tension. ClickUp ALSO cut 22%. Some readers will see this as just another AI layoff dressed up nicely. The honest read is that both things are true. The dying playbook (cut and hope the stock pumps) and the emerging playbook (cut hard, pay survivors like founders) look identical from the outside if you only count the layoffs. The difference shows up in what happens to the people who stay.

If your company is doing the first version, the cuts continue, and morale collapses. If it’s doing the second, the top decile gets a path to seven figures, and the middle has to choose which side of the line they want to be on.

Evans wrote one more line worth holding onto: “Nearly every company will make changes like these. The ones that do it proactively will define what comes next.”

He’s right. And the workers who become AI-native proactively will get the seven-figure bands. Everyone else is competing for what’s left.

The upskilling window

The job market reversal won’t restore the old jobs at the old wages. It creates new ones at slightly lower volume, with sharply bifurcated comp bands, and the window to become AI-native and survive the reset is roughly 18 months. After that, the cohort closes and the new equilibrium sets in for the next decade.

The Writer survey makes the labor market split brutally clear across every dimension. 92% of the C-suite is actively cultivating “AI elite” employees as a deliberate retention strategy. 60% of companies plan layoffs specifically for non-adopters, 77% of executives say employees who refuse to become AI-proficient won’t be considered for promotions, and 90% say AI super-users will require completely rethinking performance evaluation frameworks.

Forrester’s 2026 future of work outlook found that only 16% of workers had a high “AIQ” (their measure of AI readiness) in 2025, projected to reach just 25% by the end of 2026. Which means the other 75% is the addressable market for the rehire wave that’s about to start. Get into the top quartile, and you get the new roles at the new comp bands; stay in the bottom three quarters, and you compete against a much larger labor pool for what’s left.

The productivity gap between cohorts is already showing up in the data. AI super-users save 9 hours per week compared to laggards, which is 4.5x the productivity differential. They are 3x more likely to have received both a promotion and a raise in the past year. This is the gap that’s about to widen into a chasm as the rehire wave selects on these exact attributes.

So what does AI-native actually mean in 2026, as a practical matter? Prompt engineering is table stakes at this point, not a differentiator. The real skill is agent orchestration, which covers knowing how to design multi-agent workflows with proper handoffs, building eval suites that actually catch failure modes, architecting RAG systems over private data, and managing token budgets at the workflow level. It also covers knowing when to use Frontier Intelligence versus when Flash-Lite will do the job for a fraction of the cost. And the most underrated skill of all: knowing when not to use AI in the first place, because forcing it into the wrong workflows is how the 71% of failed implementations got there.

The companies hiring right now are hiring exactly this profile. Amazon’s job descriptions explicitly require “experience with AI tools for development productivity.” Salesforce’s new grad program is building Agentforce. The training programs at IBM and AWS are heavily agent-orchestration focused. If you can ship an agentic workflow today, you’re in the rehire cohort. If you can only use ChatGPT to write emails, you’re not.

The new equilibrium

The layoff trade is dead, the rehire trade is on, but it’s only on for one specific kind of worker. The next 18 months is a one-time window, and the cohort that becomes AI-native between now and the end of 2027 will own the new equilibrium of knowledge work for the next decade. Everyone else competes for a smaller pool of jobs that weren’t automated, at lower wages, against a much bigger labor pool that’s also been displaced.

The companies that figured this out are already hiring. Amazon, Salesforce, IBM, and ClickUp are running the new playbook openly while most of the industry is still running the dying one. The companies still trying to pump their stock with AI-flavored layoffs are about to learn what GitLab and Meta just learned, which is that Wall Street stopped buying the narrative sometime in Q2 2026 and isn’t coming back.

So where do you sit on the line?

I built ismyjobscrewed.com to answer exactly that question. Score your role against actual displacement data, see your risk percentage, and get a tailored upskilling path so you’re in the AI-native cohort when the rehire wave hits your industry. Takes 90 seconds, costs nothing, and might save your career trajectory in the bifurcation that’s already underway.

A note on certainty. This is forecasting, not prophecy. The future surprises everyone, and macro, algorithmic, or geopolitical shocks could change the timing of all of this. But too many independent data points across token economics, ROI surveys, capex warnings, public CEO statements, and hiring reversals are triangulating in the same direction for the structural pattern to be wrong.

References: https://telegra.ph/Tech-Hiring-Is-Coming-Back-But-Most-Wont-Make-The-Cut-References-05-23