Forecasting the 2028 Global Intelligence Crisis: When AI Productivity Explodes, Why Does It Instead Drag Down the Stock Market, Employment, and Mortgages?
Citrini Research Simulation of 2028: Why AI Productivity Boom Could Trigger Unemployment, Chain Risks in Private Credit and Housing Market, and Systemic Concerns Under Bullish Narratives
When market optimism about AI continues to “bet right,” companies use AI to cut labor costs and boost profit margins, causing stock prices to soar — this sounds like a perfect bullish story. But Citrini Research, in “THE 2028 GLOBAL INTELLIGENCE CRISIS,” presents an counterintuitive thought experiment: if AI exceeds expectations significantly, it could actually trigger deeper systemic risks.
This is not a prediction or dystopian fiction, but a macro memo looking back from 2028 to 2026–2028, analyzing how “overabundance of intelligence” could simultaneously stall employment, consumption, credit, and financial markets in a left-tail scenario.
High Unemployment Becomes the New Normal Within Two Years
In the June 2028 scenario, the U.S. unemployment rate hits 10.2%, 0.3% higher than expected; markets fall 2%, erasing 38% from the S&P 500 since its October 2026 peak. The authors describe traders as increasingly numb: six months earlier, such data might have triggered circuit breakers, but now only fatigue-driven sell pressure remains.
This memo asks not whether “AI will improve,” but what happens when AI advances too rapidly and cheaply, fundamentally altering the economy centered on human income and consumption cycles.
Stock Market First Rages, But “Market is AI, Economy is Not”
Rewinding to October 2026: the S&P 500 approaches 8,000, Nasdaq surpasses 30,000. White-collar layoffs driven by AI had already begun in early 2026, and the short-term effects “look promising” — layoffs reduce costs, expand profit margins, beat earnings estimates, and push stock prices higher; companies reinvest record profits into computing power, further enhancing AI capabilities.
The problem is, financial prosperity on paper does not equate to felt prosperity. The authors introduce the concept of “Ghost GDP”: output grows on national accounts but does not effectively flow into households, preventing the formation of new consumption cycles. A more straightforward analogy is that a GPU cluster replacing 10,000 Manhattan white-collar workers resembles an “economic pandemic,” because machines do not buy homes, travel, or impulse shop.
The Stronger AI Gets, the Weaker White-Collar, the Cooler Consumption Becomes
The core mechanism is a negative feedback loop with no natural bottom: AI capability improves → companies lay off workers → displaced workers’ income drops, spending declines → demand weakens, corporate gross margins shrink → companies pour more into AI to cut costs → AI becomes even stronger → next round of layoffs accelerates.
What makes this spiral most frightening is that it does not resemble traditional economic cycles (inventory, interest rates, investment) that “self-correct” after falling to a certain point. Instead, the driving factor is not credit tightening but AI becoming continuously cheaper and more capable. The authors even state bluntly: “Claude costs $200/month to replace a product manager earning $180,000 annually.”
Agentic E-commerce Reshapes Intermediary Industries, Stablecoins Bypass 2–3% Card Processing Fees
By 2027, LLMs become everyday tools, leading to spillover effects of “agentic” e-commerce: AI no longer waits for commands but autonomously compares prices, cancels subscriptions, negotiates, and renews in the background 24/7, systematically eroding the “consumer inertia” that sustains subscription economies. The article notes that by March 2027, the median American consumes about 400,000 tokens daily, ten times more than at the end of 2026.
More critically, “channels” matter. When transactions are dominated by agents, the 2–3% interchange fee becomes the most visible cost. The simulation describes agents shifting to settle payments via Solana or Ethereum Layer 2 stablecoins, enabling near-instant, ultra-low-cost transactions—“less than a cent.”
Citing Mastercard Q1 2027: revenue grew 6% annually, but consumer spending slowed to 3.4% (from 5.9% last quarter). Management cited “agent-led price optimization” and “pressure on non-essential spending,” leading to a 9% stock drop the next day; meanwhile, Visa’s stronger positioning in stablecoin infrastructure resulted in a smaller decline.
From “Controllable Industry Risks” to “Unseen Systemic Exposure”
The financial shock point is placed in private credit: from under $1 trillion in 2015 to over $2.5 trillion in 2026, with much capital flowing into PE-backed software and SaaS deals, betting on “recurring revenue” (ARR).
In this scenario, Moody’s downgrades 14 issuers totaling $18 billion of PE software debt in April 2027; by September 2027, Zendesk breaches debt covenants, and a $5 billion direct lending instrument drops to 58 cents, marking one of the largest private credit software defaults in history.
Even more troubling is the “permanent capital” myth. The simulation suggests large asset managers acquire life insurers, channeling annuity deposits into private credit. When regulators tighten capital rules for certain private assets (as indicated by state and NAIC guidelines in November 2027), they may force capital injections or asset sales, pushing originally “non-forced” structures into liquidity stress.
The Next Concern: Housing Market—$13 Trillion Based on “Stable White-Collar Income”
The simulation finally shifts focus to the housing market: Zillow index shows San Francisco home prices down 11% YoY in June 2028, Seattle 9%, Austin 8%; Fannie Mae warns of early delinquencies in ZIP codes with high tech/finance employment.
The key is not poor borrower credit—quite the opposite, these are mostly high-quality groups aged 70s and 80s—but that “the loans were good at origination, but the world has changed.” As white-collar income capacity is structurally weakened, the market must reconsider: are prime mortgages still “money good”?
The authors even estimate that if mortgage defaults truly spike in late 2028, stock market declines could approach 57%, similar to a financial crisis, with the S&P 500 falling toward 3,500 (close to November 2022 levels before the “ChatGPT moment”).
The value of this thought experiment is not in predicting “it will definitely happen,” but in revealing a often-overlooked contradiction: when intelligence is no longer scarce, how will the entire financial system based on human wages, consumption, and credit be re-priced?
In the conclusion, the authors note that the canary is still alive—but perhaps it’s time to start re-evaluating the assumptions.
- This article is reprinted with permission from: 《Chain News》
- Original title: “2028 ‘Global Intelligence Crisis’ Scenario Simulation: Why AI Productivity Explosion Might Weaken Stocks, Employment, and Mortgages?”
- Original author: Elponcho