Author: SpecialistXBT
Original title: Citrini's lingering influence
Excellent articles can make the market confuse "scenario projection" with "real-world prediction".
On February 22, 2026, a report titled "The 2028 Global Intelligence Crisis" went viral on social media and in financial markets, garnering over 27 million views. On the day of its release, IBM's stock plummeted 13%, while shares of companies like DoorDash, American Express, and KKR fell by more than 6%.
This report is authored by James van Geelen, founder of Citrini Research. The 33-year-old researcher boasts over 180,000 followers on X, and his Substack account is ranked number one among financial authors. He specializes in thematic equity investing and global macroeconomic research, known for his cross-asset and lateral analysis style. His real-world portfolio has returned over 200% since 2023. The report presents a hypothetical future set in 2028: AI will massively replace white-collar workers within just two years, leading to a decline in consumption, software asset defaults, and credit tightening, ultimately pushing the economy into a distorted state of "technological boom" and "social recession." Van Geelen notes at the beginning of the article: "This article describes a possible scenario, not a prediction." But the market clearly lacks the patience to distinguish between the two.
However, more noteworthy than the brief market panic is the widespread discussion this article has sparked over the past few days. From academia to the investment community, from Wall Street to the Chinese internet, more than a dozen articles from different perspectives have emerged. Rather than blindly accepting only one extreme conclusion, perhaps we can piece together a clearer picture of the future from the "disagreements and overlaps" of various viewpoints.
What Citrini said
The logic in Citrini's article is not complicated: the leap in AI capabilities leads to the large-scale replacement of white-collar jobs → rising unemployment causes a contraction in consumer spending → structured financial products with SaaS as underlying assets face a wave of defaults → credit crunch spreads to the broader financial system → the economy falls into a distorted state of "technological prosperity" and "social recession" coexisting.
Each link in this causal chain is not without reason. However, to connect them end to end and extrapolate them to a crisis requires a series of rather radical assumptions.
There are many ways to dismantle this chain. Let's explore it step by step along three core sub-arguments: the speed and scale of labor substitution, the transmission mechanism of demand collapse, and the possibility of a financial crisis, to see what the different voices are arguing about around each link.
No destruction, no construction
Citrini's projections begin with the large-scale replacement of white-collar workers by AI. In his narrative, this process accelerates dramatically between 2026 and 2028, with professionals in fields such as law, financial analysis, software development, and customer service bearing the brunt.
Changes in the percentage of enterprise spending on AI model vendors and online workforce platforms, grouped by industry's AI exposure level.
There is indeed evidence to support Citrini's view. 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 (i.e., those that previously had the largest share of spending in the online labor market) significantly increased their spending on AI model providers while reducing their spending in the online labor market by approximately 15%. It's worth noting that this substitution is not an "equal replacement"—for every $1 reduction in labor market spending, companies only increased their AI spending by $0.03 to $0.30. In other words, AI is accomplishing the same amount of work at a much lower cost than human labor.
However, Citrini may have overestimated the speed of change . One counter-argument is based on the US real estate brokerage industry, which, despite the technology's ability to drastically reduce the number of brokers, still employs over 1.5 million people. Institutional inertia, regulatory barriers, and internal power struggles within the industry constitute a far stronger defense than technology. He argues that Citrini severely underestimates the resistance of "institutional momentum."
Other counter-arguments cite a 1998 study by Kimball, Basu, and Fernald, which points out that technological shocks have historically been a positive stimulus to the supply side—they may be accompanied by adjustments in the employment structure in the short term, but in the long term, the output space they create far outweighs the jobs they destroy.
In fact, looking back at each wave of general-purpose technology diffusion in history, the process from laboratory to large-scale penetration has always been much slower than the maturity of the technology itself. It took electricity 30 years to go from 5% household penetration to 50%, telephones took 35 years, and even the fastest-spreading smartphones took 5 years. AI's technological capabilities may already be enough to disrupt many industries, but the gap between technological capabilities and institutional absorption has never been bridged by capabilities alone.
The second key element of the Citrini narrative is the downward spiral of demand: unemployment → reduced income → shrinking consumption → declining corporate profits → further layoffs.
Citrini conflates demand-side deflation with supply-side deflation in this instance. The former signifies a decline in consumer purchasing power, while the latter is driven by technological advancements lowering production costs—AI-driven price reductions are essentially closer to the latter, similar to the price trajectory of electronic products and communication services over the past few decades. Some analysts believe that Jevons' paradox will still hold true: when AI significantly reduces the costs of services like legal advice, medical diagnosis, and software development, demand previously excluded by a large segment of the population due to high prices will be released, resulting not in a contraction but in an explosive growth. Simultaneously, the "Moraviek paradox" will also come into play. For machines, the real challenge often lies not in complex logical deduction or massive data retrieval, but rather in the physical movements, sensory perception, and emotional communication that humans take for granted. This means that manual labor and service industry jobs requiring refined perception may be more resilient than we imagine.
But Jevons' paradox might also fail. Alex Imas, an economics professor at the University of Chicago, argues that if AI automates the vast majority of labor, and labor income's share of total income declines sharply, then who will buy these efficiently produced goods and services? This touches upon the very distribution mechanism. When output capacity approaches infinity while effective demand becomes concentrated, we may not be facing a recession, but rather an imbalance not yet fully discussed in economics textbooks—material abundance without access to tangible benefits.
A glimpse of the whole picture
The most significant part of Citrini's projection is the transmission from the employment shock to the financial crisis. In his narrative, structured financial products with SaaS revenue as the underlying asset (which he calls "Software-Backed Securities") experienced widespread defaults during the AI transformation wave, triggering a credit crunch similar to that of 2008.
However, commentators point out that compared to 2008, the current leverage ratio of the US corporate sector is far healthier, and the banking system is far more robust than it was then, after undergoing Dodd-Frank reforms and multiple rounds of stress tests.
Compared to the eve of the 2008 financial crisis, the current resilience indicators of the U.S. financial system have improved significantly: the Tier 1 capital adequacy ratio of banks has increased from 8.1% to 13.7%, the ratio of household debt to disposable income has decreased from 130% to 97%, and the non-performing loan ratio has decreased from 1.4% to 0.7%.
Even if some SaaS companies do face revenue declines, their scale is insufficient to trigger a systemic credit crisis. Former Bloomberg financial columnist Nick Smith believes Citrini made a common mistake at this stage: linearly extrapolating micro-level industry shocks to macro-level systemic risks. Smith's answer to the demand collapse is fiscal policy. If unemployment does indeed rise sharply, the government is fully capable and willing to prop up demand through large-scale fiscal stimulus.
The responsiveness of the system also appears to have been underestimated. Take the policy response during the COVID-19 pandemic as an example: on March 11, 2020, the WHO declared the outbreak a pandemic, and just 16 days later, the $2.2 trillion CARES Act was signed into law. In the following year, the United States rolled out a total of $5.68 trillion in fiscal stimulus, equivalent to approximately 25% of its 2020 GDP.
If AI-driven unemployment does indeed occur at the speed and scale described by Citrini, policy intervention is unlikely to be absent.
Other commentators have raised more fundamental questions . Technological doomsday theories generally stem from a lack of faith in humanity. Citrini's projections treat the market as an unattended machine, allowing "cause and effect" to unfold on its own until it collapses. But the real economic system does not operate this way. Laws, institutions, politics, culture, and ideology profoundly determine how the real world absorbs technological shocks.
Consensus and Disagreement
We might try to mark some points of consensus and disagreement.
AI is changing, and will continue to change, the demand structure of the white-collar workforce—this is almost universally acknowledged; the only disagreement lies in the speed and scale of this change. Furthermore, the pain of the transition period is real and should not be obscured by long-term optimism. And the quality and speed of policy responses will largely determine the outcome.
The disagreement lies in a deeper, more fundamental logic. Some argue that this technological shock may surpass historical precedents in both speed and scope, thus limiting the applicability of historical analogies; others place greater trust in the adaptability of institutions and the repeatability of history.
look up
Citrini's article has several problems: the logical connections are too tight, the institutional response is systematically underestimated, and the leap from micro-industry shocks to macro-systemic risks lacks sufficient intermediate evidence. However, its most fundamental problem may lie in an underestimation of human society: it assumes a static institutional environment in which technology crushes everything at an almost unstoppable pace. Historically, technological doomsday theories have abound, often flawlessly logically, but almost without exception, they ignore the variable of "human beings." The complexity of human society, its friction, its redundancy, and its seemingly inefficient institutional arrangements actually constitute a powerful, distributed resilience. We have ample time to avoid those predicted doomsday scenarios, provided we are not intimidated by the predictions themselves.
What about those optimistic narratives? The Jevons Paradox is an observation about long-term trends. The Moravec Paradox tells us that manual labor is temporarily safe, but it doesn't tell us what will happen to the white-collar workers who are being replaced. Historical analogies are enlightening, but history never repeats itself exactly; it just rhymes. Optimistic narratives need time to be tested, and we are at the starting point of that test.
Doomsday prophecies lead to production losses, with the anxious paying the price. Develop your own judgment, take risks, and manage your positions carefully, rather than getting lost in articles that offer a predictable conclusion .
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