AI Disruption Debate Roils Markets and Refocuses Risk

While everyone is worried about AI’s ability to write everything from term papers to publication-ready journalism, it was a human writing about AI that rocked the markets. But as with many things surrounding artificial intelligence, a moderate approach is emerging.

Citrini Research’s Sunday (Feb. 22) Substack essay, “The 2028 Global Intelligence Crisis,” ricocheted through markets this week, contributing to an estimated $300 billion sell-off as investors reacted to a stark hypothetical: a near-term future in which agentic AI sharply reduces the value of white-collar labor, pushes unemployment above 10% and drives a 38% drawdown in the S&P 500 from 2026 highs.

Written as a fictional macro memo set in June 2028, the piece argued that AI-driven productivity would generate what it calls “ghost GDP,” economic output that appears in statistics but fails to circulate as wages or consumer spending. Enterprise software firms, delivery marketplaces such as DoorDash and Uber, and payment networks including Visa and Mastercard were cited as business models vulnerable to AI agents that comparison-shop, write code autonomously or reroute transactions around fee-based networks.

Bloomberg reported that Citrini founder James van Geelen described the post as “a hypothetical economic plunge in which mass white-collar layoffs create a deflationary cascade that pushes the unemployment rate above 10% while stock prices are wiped out.” Shares of companies named in the report fell on Monday (Feb. 23).

A Scenario That Hit a Nerve

Van Geelen said he did not anticipate the scale of the reaction. “If I thought that stocks were gonna move on this, I wouldn’t have made it free,” he told Bloomberg. He added that “the market is clearly jumpy about this” and that the article “served as a focus point for investors who were already concerned about the second-order disruption to incumbents by AI.”

Alap Shah, co-author of the report, framed the memo as a warning exercise rather than a forecast. According to Bloomberg, the report was meant “to prevent anything like what’s described in the article from coming to pass.”

The authors acknowledged uncertainty, saying they “couldn’t get to the point where we were comfortable saying there’s a 0% chance that something like this happens.”

The Wall Street Journal described the essay as a “fictional memo from the future,” underscoring its narrative structure. The Financial Times wrote that the sell-off reflected growing “anxiety about AI disruption,” rather than new company-specific deterioration.

“The argument leans heavily on narrative and emotion rather than hard evidence,” Jim Reid, a strategist at Deutsche Bank, said of the report. “That doesn’t mean it will ultimately be wrong.” But he added that the “vibes-to-substance ratio is undeniably high.”

The Economic Assumptions

The memo rests on two central assumptions: that agentic AI will sharply reduce white-collar employment, and that this displacement will occur fast enough to weaken consumption before new jobs or policy responses stabilize demand.

Noah Smith, writing in Noahpinion, called the piece “just a scary bedtime story,” arguing that it strings together worst-case outcomes without a clear macro model for how income, demand and policy response would interact. A jump in unemployment above 10% would historically prompt fiscal intervention or monetary easing. The memo gives limited attention to those counterweights.

The corporate examples similarly assume rapid friction removal. Visa and Mastercard operate regulated global settlement systems tied to fraud management and compliance. DoorDash and Uber rely on physical logistics networks. Enterprise software contracts often involve multi-year commitments and procurement cycles. Institutional and regulatory frictions can slow technological substitution.

Joshua Brown, chief executive of Ritholtz Wealth Management, in a podcast argued that its chain of outcomes was overly linear. “Every single negative piling on top of each other without an offset in sight- that is very rarely how these things end up,” he said.

Friction, Work and Adaptation

Brown framed his critique around how economies evolve. “If you think of businesses as just solutions to problems,” he said, “then what this piece is saying is that we’re going to run out of problems.” He added, “In 100,000 years of the evolution of human society, do we ever actually run out of problems to solve?”

He pointed to historical job displacement as context. “There were people who used to sit on an elevator all day,” he said, describing elevator operators who manually ran lifts. “Is anyone like, ‘What happened to all the good elevator operator jobs?’” Entire job categories disappeared, but new industries emerged in their place.

Brown’s point is less about denying disruption and more about how it unfolds. Investors are pricing a synchronized shock, where job losses, margin pressure and demand collapse hit simultaneously. History rarely works that way.Technological change tends to move unevenly, industry by industry, with losses in one area creating capacity and opportunity in another. Removing friction does not eliminate work; it changes where it happens. Economies do not run out of problems to solve; they reorganize around new ones.

None of this negates Citrini’s central insight. AI can erode friction-based moats and redistribute economic surplus. If agentic systems reduce paid seats in enterprise software, compress payment fees or intensify competition in marketplaces, margins will feel pressure. Markets are rational to examine those risks.

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