Excellent articles can confuse the market into mixing "scenario analysis" with "real prophecy."

On February 22, 2026, a report titled "The 2028 Global Intelligence Crisis" ignited social media and the financial markets, with views surpassing 27 million. On the day of the report's release, IBM plunged 13%, and numerous companies such as DoorDash, American Express, KKR, and others saw their stock prices drop by over 6%.

This report was authored by James van Geelen, the founder of Citrini Research. The 33-year-old researcher has over 180,000 followers on X, and his Substack ranks first among financial authors, focusing on themes of equity investment and global macro research, known for its cross-asset and horizontal associations, with a real investment portfolio that has returned over 200% since 2023. The report takes the form of scenario analysis, fabricating a future set in 2028 where AI massively replaces white-collar labor within just two years, leading to a decline in consumption, defaults on software assets, and credit tightening, ultimately pushing the economy into a grotesque state of "technical prosperity" and "social recession" coexisting. Van Geelen noted at the beginning, "This article discusses a possible scenario, not a prophecy." However, the market clearly lacked the patience to differentiate between the two.

However, what warrants more attention than the brief market panic is the extensive discussion this article has sparked over the past few days. From academia to investment circles, from Wall Street to the Chinese internet, dozens of response articles from different perspectives have emerged. Instead of only believing a single extreme conclusion, we might piece together a clearer future from the "divergences and overlaps" of various viewpoints.

What Citrini Said

The logical clues in the Citrini article are not complex: The leap in AI capabilities leads to the massive replacement of white-collar positions → The rise in unemployment triggers a decline in consumer spending → Structured financial products with SaaS as underlying assets encounter a wave of defaults → Credit tightening spreads to the broader financial system → The economy falls into a grotesque state where "technical prosperity" and "social recession" coexist.

Each link in this causal chain is not without basis. However, connecting them coherently to deduce a crisis requires a series of quite radical underlying assumptions.

There are many ways to dissect this chain. We might follow three core sub-arguments, namely the speed and scale of labor replacement, the transmission mechanism of demand collapse, and the possibility of a financial crisis, to explore what different voices are debating around each link.

Break the Old to Establish the New

The starting point of Citrini's analysis is the large-scale replacement of white-collar labor by AI. In his narrative, this process suddenly accelerates between 2026 and 2028, with professionals in fields such as law, financial analysis, software development, and customer service being the first to be affected.

Changes in corporate spending ratios on AI model suppliers and online labor platforms, categorized by industry’s AI exposure

There is evidence to support Citrini's viewpoint. An empirical study by Bick, Blandin, and Deming based on corporate spending data shows that after the release of ChatGPT, companies with the highest AI exposure (those that previously had the largest share of spending on online labor markets) significantly increased their spending on AI model suppliers while reducing their spending on online labor markets by about 15%. Notably, this replacement is not an "equal substitution"—for every $1 reduction in spending on the labor market, companies only increased AI spending by $0.03 to $0.30. In other words, AI is completing the same amount of work at a far lower cost than human labor.

However, Citrini may have overestimated the speed of the transition. Critics point to the U.S. real estate agent industry as an example; despite the existing technology that could significantly reduce the number of agents, this industry still employs over 1.5 million people. The inertia of institutions, regulatory barriers, and the internal interests of the industry form a much sturdier defense than technology itself. He believes that Citrini severely underestimates the resistance of "institutional potential."

Other critics cite the 1998 research by Kimball, Basu, and Fernald, pointing out that technological shocks historically have often provided positive stimulation to the supply side—there may be short-term adjustments to the employment structure, but in the long run, the output space it creates far exceeds the jobs it destroys.

In fact, looking back at every cycle of general-purpose technology diffusion, the process from laboratory to large-scale penetration is always much slower than the speed of technological maturity itself. It took electricity 30 years to reach a household penetration rate of 50% from 5%, the telephone took 35 years, and even the fastest-diffusing smartphones took 5 years. The technological capabilities of AI may be sufficient to disrupt many industries, but the gap between technological capability and institutional absorption has never been bridged solely by capability itself.

The second critical link in Citrini's narrative is the downward spiral on the demand side: unemployment → reduction in income → decline in consumption → drop in corporate profits → further layoffs.

Citrini confuses demand-side deflation with supply-side deflation at this juncture. The former means a shrinkage of consumers' purchasing power, while the latter indicates that technological advancements have lowered production costs—in essence, AI-driven price reductions are more aligned with the latter, resembling the price trajectory of electronic products and communication services over the past few decades. Some analysts believe that the Jevons Paradox will still hold: when AI drastically reduces the costs of services like legal consulting, medical diagnosis, and software development, the demand previously excluded due to high prices will be released, leading to explosive growth rather than a decline in total. Meanwhile, the "Moravec Paradox" will also come into play. For machines, the truly challenging tasks often aren't advanced logical reasoning or massive data retrieval, but rather the routine physical movements, sensory cognition, and emotional communication that humans take for granted. This implies that physical labor and service sector jobs requiring fine perception may be more resilient than we imagine.

However, the Jevons Paradox may also fail. University of Chicago economics professor Alex Imas suggests that if AI automates the vast majority of labor while the share of labor income in total income sharply declines, then who will buy these efficiently produced goods and services? This touches on the distribution mechanism itself. When output capacity approaches infinity while effective demand tends to concentrate, what we face may not be a recession, but an imbalance not sufficiently discussed in economic textbooks—material abundance that cannot be accessed.

A Glimpse of Reality

The most expansive aspect of Citrini's projection is the transmission from employment shock to financial crisis. In his narrative, structured financial products backed by SaaS revenue (which he refers to as "Software-Backed Securities") encounter massive defaults amidst the AI transformation wave, triggering a credit tightening similar to that of 2008.

However, commentators point out that, compared to 2008, the leverage ratio of the current U.S. corporate sector is far healthier, and the banking system, having endured the Dodd-Frank reforms and multiple rounds of stress tests, is also much more robust than it was then.

Regarding resilience indicators, the current U.S. financial system has seen significant improvements compared to just before the 2008 economic crisis: the bank's tier one capital adequacy ratio rose from 8.1% to 13.7%, the ratio of household debt to disposable income decreased from 130% to 97%, and the non-performing loan rate dropped from 1.4% to 0.7%.

Even if some SaaS companies do face declining revenues, their scale is insufficient to trigger a systemic credit crisis. Former Bloomberg financial columnist Nick Smith believes that Citrini made a common error here: linearly extrapolating micro-level industry shocks into macro-level systemic risks. In response to demand collapse, Smith suggests fiscal policy. If unemployment truly rises significantly, the government has both the capability and willingness to stabilize demand through large-scale fiscal stimulus.

The responsiveness of institutions also seems to be underestimated; for example, looking at the policy response during the COVID period, when WHO declared the pandemic on March 11, 2020, only 16 days later, the $2.2 trillion CARES Act was signed into law. Within the following year, the U.S. implemented a total of $5.68 trillion in fiscal stimulus, amounting to approximately 25% of 2020's GDP.

If AI-driven unemployment truly emerges at the speed and scale described by Citrini, policy interventions are unlikely to be absent.

Other commentators raise basic questions. The technological doomsday narrative often stems from a lack of faith in the humanities. Citrini's projection views the market as an unattended machine, allowing "causality" to unfold until collapse. But in reality, economic systems do not operate this way. Laws, institutions, politics, culture, and ideology profoundly determine how the real world absorbs technological shocks.

Consensus and Divergence

We might attempt to label some consensus and divergences.

AI is changing and will continue to change the demand structure of white-collar labor; this point is almost universally acknowledged, with divergence only regarding the speed and scale of the change. Additionally, the pain during the transition period is real and should not be obscured by long-term optimism. Moreover, the quality and speed of policy responses will largely determine the outcomes.

Divergence exists at a more fundamental level of logic. Some believe that this technological shock may surpass historical precedents regarding speed and breadth, and thus the applicability of historical analogies is limited; others have more faith in institutional adaptability and the repeatability of history.

Look Up

Citrini's article contains numerous issues; the logical connections are too tight, institutional reactions are systematically underestimated, and the leap from micro-industry shocks to macro systemic risks lacks sufficient intermediate reasoning. However, its fundamental problem may lie in an underestimation of human society: it assumes a static institutional environment where technology crushes everything at an almost unstoppable speed. Throughout history, technological doomsday narratives have emerged endlessly; they are often impeccable in technological logic but almost universally overlook the variable of "human." The complexity of human society, its friction, its redundancy, and its seemingly inefficient institutional arrangements constitute a powerful, distributed shock-resistance capability. We have ample time to avoid those projected doomsdays, as long as we are not intimidated by the projection itself.

What about the optimistic narratives? The "Jevons Paradox" is an observation about long-term trends. The "Moravec Paradox" tells us that physical labor is temporarily safe, but does not tell us where the displaced white-collar workers should go. Historical analogies are enlightening, but history never repeats itself exactly; it merely follows a cadence. Optimistic narratives require time to be tested, and we are at the starting point of that test.

Doomsday narratives generate anxiety, and anxious individuals pay the price. Forge your judgments, accept risks, manage your positions, and do not indulge in those "short-sighted" articles.

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