In a sweeping and provocative macro-analysis titled “The 2028 Global Intelligence Crisis,” Citrini Research has outlined a sobering vision of a financial future where the very success of Artificial Intelligence becomes the primary engine of economic instability.
The report, co-authored by Alap Shah, suggests that by June 2028, the global economy may be caught in an “intelligence displacement spiral”—a negative feedback loop where abundant machine intelligence renders human labor obsolete.
In the scenario laid out by Citrini of June 30, 1928, the unemployment rate has been reported as 10.2%, a 0.3% upside surprise, which has triggered a 38% drawdown in the S&P 500 from its October 2026 highs.
The central thesis of the report is that human intelligence, historically the scarcest and most valuable input in the global economy, is undergoing a rapid and violent “repricing.”
As machines become competent substitutes for complex white-collar tasks, the “intelligence premium” that once anchored the global middle class is evaporating, leaving a wake of structural deflation and what the authors call “Ghost GDP.”
The mechanics of the displacement spiral and ‘Ghost GDP’
The crisis described by Citrini Research did not begin with a sudden crash, but with an era of unparalleled corporate euphoria.
It envisioned a late 2026 scenario wherein the S&P 500 was flirting with the 8,000 mark, and the Nasdaq broke above 30,000 as corporations realized massive margin expansions, triggering stock rallies while the profits were ploughed back into AI compute.
On paper, the economy looked stronger than ever.
Nominal GDP printed mid-to-high single-digit growth, and productivity reached levels not seen since the 1950s.
However, this productivity surge masked a deteriorating foundation.
Those who owned computes saw their wealth surge even as labour costs fell drastically, dragging down real wage growth, and while-collar jobs were lost to machines and replaced by lower-paying roles.
The report identifies a phenomenon called “Ghost GDP”: economic output that appears in national accounts and corporate balance sheets but never actually circulates through the human consumer economy.
Because machines do not spend money on discretionary goods, vacations, or mortgages, the velocity of money has flatlined.
As companies use the savings from payroll reductions to buy more AI compute, they inadvertently accelerate the destruction of their own customer base.
This cycle—AI capability improves, payroll shrinks, consumer spending softens, and companies buy more AI to protect margins—is described as a feedback loop with no natural brake.
By June 2028, this spiral resulted in a 10.2% unemployment rate, a figure that caught the market by surprise but had been building in the data for years.
Source: CitriniResearch
Friction goes to zero as business moats crumble
The report argues that the US economy has spent fifty years building a massive “rent-extraction layer” on top of human limitations.
Trillions of dollars in enterprise value currently depend on the fact that humans take time to make decisions, find price-matching tedious, and rely on brand familiarity to avoid the effort of due diligence.
AI agents, which find nothing tedious and possess perfect information, are now dismantling these moats.
The report identifies the “long tail” of Software-as-a-Service (SaaS) as the first casualty.
Coding agents now allow procurement teams to replicate the core functionality of expensive platforms like Monday.com or Zapier in-house for a fraction of the cost.
This has turned SaaS pricing negotiations into a “race to the bottom,” as incumbents fight for survival against upstart challengers that have no legacy cost structures to protect.
The disruption quickly escaped the software sector and moved into the broader service economy.
Travel booking platforms, insurance renewals, and routine legal work have disintegrated.
In the real estate sector, commission structures have collapsed as AI agents equipped with decades of transaction data replicate the knowledge base of human brokers instantly.
The report describes a world of “agent on agent violence,” where the median buy-side commission in major metros has compressed from 3% to under 1%.
Even “habitual” moats like DoorDash and Uber Eats are under fire.
The DoorDash moat was predicated on a hungry human being too lazy to look past the app on their home screen.
An AI agent, however, has no home screen.
It checks every available delivery site and restaurant portal simultaneously to find the lowest fee and fastest delivery, destroying the concept of brand loyalty overnight.
The plumbing of finance and the end of interchange
The plumbing of the financial system is also at risk.
The 2% to 3% card interchange fees that fund the banking sector and rewards programs are being routed around by agents using stablecoins on Solana or Ethereum L2s.
In machine-to-machine commerce, these near-instant, fraction-of-a-penny transactions have made traditional credit card rails obsolete.
A hypothetical April 2027 report from Mastercard serves as the turning point in the Citrini model with management reporting “agent-led price optimisation” and “pressure in discretionary categories”.
As purchase volume growth slowed, the market realized that the “toll booths” of finance were being bypassed.
This struck a devastating blow to card-focused banks and issuers like American Express and Capital One, whose business models were made of the very “friction” that AI was busy eliminating.
Source: CitriniResearch
The daisy chain of correlated bets and private credit
The crisis is now moving from the real economy into the financial sector, specifically through the $2.5 trillion private credit market.
During the AI boom of 2025, vast amounts of capital were deployed into software and technology leveraged buyouts (LBOs) at valuations that assumed perpetual revenue growth.
As revenue models for SaaS companies eroded, these private marks became wildly disconnected from public market reality.
The report cites a hypothetical September 2027 default by Zendesk as the “smoking gun.”
The $10.2 billion company had been taken private with a $5 billion direct lending facility—the largest “ARR-backed” loan in history.
When its Annual Recurring Revenue proved not to be recurring, but simply “revenue that hadn’t left yet,” the loan defaulted.
This has triggered a crisis in what the industry calls “permanent capital.”
Over the prior decade, large asset managers like Apollo, Blackstone, and KKR acquired life insurance companies to fund their private credit lending.
The logic was that annuity deposits provided a stable, long-duration liability base.
However, when the underlying software loans began defaulting, the savings of “Main Street” households were suddenly at risk.
The “permanent capital” that was supposed to make the system resilient is proving to be a spider web of opaque, offshore regulatory arbitrage.
As regulators began tightening capital requirements for these insurers in late 2027, the market seized up, unable to find buyers for impaired, illiquid assets.
Are prime mortgages money good?
Perhaps the most alarming section of the report concerns the $13 trillion US residential mortgage market.
Unlike the 2008 crisis, where the loans were “bad on day one” due to subprime lending, the potential 2028 crisis involves loans that were “good on day one” but were written for an economic reality that has ceased to exist.
Mortgage underwriting is built on the fundamental assumption that a 780-FICO borrower will maintain their current income level for thirty years.
However, the AI-driven displacement of high-earning professionals—product managers, lawyers, finance executives, and coders—has structurally impaired their debt-to-income ratios.
In “tech-heavy” hubs like San Francisco, Seattle, and Austin, home values are beginning to fall as the “marginal buyer” faces chronic income impairment.
The report notes that these high earners represent only 10% of employment but drive over 50% of all discretionary consumer spending.
When they “downshift” into lower-paying gig economy roles, the impact on the housing market and the broader economy is disproportionately large.
If the income assumptions underlying $13 trillion in debt fail, Citrini warns that the resulting equity drawdown could rival the Great Financial Crisis.
The battle against time and the fiscal breakdown
The US government is facing a structural deficit as its revenue base—which is essentially a tax on human time—erodes.
Payroll and income tax receipts are falling as productivity gains flow to the owners of compute and capital rather than labor.
Labor’s share of GDP, which stood at 56% in 2024, has reportedly dropped to 46% in the report’s 2028 scenario—the sharpest decline in modern history.
While output remains high, it is no longer routing through households on the way back to firms, which means it is no longer routing through the IRS either.
The circular flow of the economy is breaking.
The government is expected to step in, but it is doing so with a revenue base that is shrinking just as the need for outlays for social support is exploding.
The policy response and the social fabric
The legislative response remains mired in ideological gridlock and partisan grandstanding.
Proposals like the “Transition Economy Act” and the “Shared AI Prosperity Act” seek to redistribute the gains from AI through direct transfers or a “royalty on compute.”
However, the “Occupy Silicon Valley” movement, which recently blockaded AI labs in San Francisco, suggests that the social fabric is fraying faster than the legislative process can move.
The public’s dissatisfaction is fueled by the unprecedented pace of wealth accumulation among AI founders and early investors.
From the perspective of the masses, the gains of the productivity boom are accruing to a tiny elite, while the middle class is left to navigate a world where their skills are being commoditized by a technology that continues to improve every quarter.
A call for proactive adjustment
Citrini Research concludes that while the “repricing” of human intelligence is painful, disorderly, and far from complete, it is not necessarily a signal of total collapse.
The economy can find a new equilibrium, but only if institutions and investors adapt to a world where the most productive asset produces fewer, not more, jobs.
The report serves as a warning that traditional monetary policy tools—like interest rate cuts and quantitative easing—may be ineffective in this crisis.
Those tools can address financial conditions, but they cannot address the fundamental problem of technological substitution.
“Repricing is not the same as collapse,” the report notes.
“But getting there is one of the few tasks left that only humans can do.”
As of February 2026, the S&P is still near all-time highs and the displacement spiral has not yet begun.
However, the report warns that the window to assess which assumptions will survive the decade is narrowing.
Investors are urged to look at their portfolios through the lens of “atoms vs. bits” and determine what can’t be prompted out of existence.
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