Citrini's echo has yet to fade; what is the market still debating?

Author: SpecialistXBT

Original Title: The Echoes of Citrini

Great articles can cause the market to confuse “scenario planning” with “prophecy.”

On February 22, 2026, a report titled “The 2028 Global Intelligence Crisis” exploded across social media and financial markets, with over 27 million views. On the day of its release, IBM plunged 13%, and stocks like DoorDash, American Express, KKR, and others fell more than 6%.

This report was authored by James van Geelen, founder of Citrini Research. The 33-year-old researcher has over 180,000 followers on X, and his Substack ranks first among finance writers, focusing on equity investment and global macro research. His style is known for cross-asset, lateral connections, with a real investment portfolio returning over 200% since 2023. The report uses scenario analysis to imagine a future set in 2028: AI rapidly replacing white-collar workers within two years, leading to consumption contraction, software asset defaults, credit tightening, and ultimately pushing the economy into a distorted state of “technological prosperity” and “social decline” coexisting. Van Geelen notes at the beginning: “This article discusses a possible scenario, not a prophecy.” But the market clearly has little patience to distinguish between the two.

However, beyond the brief market panic, what’s more noteworthy is the widespread discussion this article has sparked over the past few days. From academia to investment circles, from Wall Street to the Chinese internet, responses from various perspectives have emerged one after another. Instead of blindly trusting one extreme conclusion, perhaps we can piece together a clearer future from the “disagreements and overlaps” among different viewpoints.

What Did Citrini Say?

The logic in Citrini’s article isn’t complicated: rapid advances in AI capabilities lead to large-scale replacement of white-collar jobs → rising unemployment causes consumption to shrink → structured financial products based on SaaS assets face defaults → credit tightens across the broader financial system → the economy falls into a distorted state of “technological prosperity” and “social decline.”

Every link in this causal chain is not baseless. But connecting them seamlessly into a crisis requires a series of rather radical assumptions.

There are many ways to dissect this chain. Let’s focus on three core points: the speed and scale of labor replacement, the transmission mechanism of demand collapse, and the potential for a financial crisis. We’ll explore what different voices are debating around each link.

No Ruin, No Rise

Citrini’s scenario begins with AI replacing white-collar labor on a large scale. In his narrative, this acceleration occurs sharply between 2026 and 2028, primarily impacting law, finance analysis, software development, customer service, and similar fields.

Changes in corporate spending on AI model providers and online labor platforms, grouped by industry’s AI exposure level

There is evidence supporting Citrini’s view. An empirical study by Bick, Blandin, and Deming, based on corporate expenditure data, shows that after ChatGPT’s release, companies with the highest AI exposure (those that previously spent most on online labor markets) significantly increased their spending on AI model providers while reducing online labor market expenditures, by about 15%. Notably, this substitution isn’t “one-to-one”—for every dollar cut from labor market spending, companies only increased AI spending by about 0.03 to 0.30 dollars. In other words, AI is doing the same work at a fraction of the human cost.

But Citrini may overestimate the speed of this shift. Critics cite the U.S. real estate agent industry as an example: despite technology capable of drastically reducing the number of agents, the industry still employs over 1.5 million people. Institutional inertia, regulatory barriers, and internal industry interests form a much stronger barrier than technology alone. They argue Citrini underestimates the resistance posed by “institutional momentum.”

Others cite studies by Kimball, Basu, and Fernald (1998), which suggest that technological shocks historically tend to be positive supply-side stimuli—short-term employment adjustments occur, but long-term output gains far outweigh job destruction.

In fact, every wave of general-purpose technology from labs to widespread adoption has historically been slower than the technology’s own maturity. It took 30 years for electricity to go from 5% to 50% household penetration; 35 years for telephones; even the fastest-growing smartphones took 5 years. AI’s capabilities may already threaten many industries, but the gap between technological potential and institutional capacity to absorb it is never bridged solely by technological ability.

The second key link in Citrini’s narrative is demand-side spiral: unemployment → income reduction → consumption contraction → corporate profits decline → further layoffs.

Citrini confuses demand-side deflation with supply-side deflation here. The former means consumers’ purchasing power shrinks; the latter is driven by technological progress lowering production costs—AI-driven price reductions are more akin to the latter, similar to the trajectory of electronics and communication services over past decades. Some analysts believe the Jevons paradox still applies: as AI drastically reduces costs for legal advice, medical diagnostics, software development, and other services, demand previously excluded by high prices will be unleashed, leading to explosive growth rather than contraction. Meanwhile, the “Moravec paradox” suggests that physical and sensory tasks—like body movements and emotional interactions—are more resilient than we think.

But the Jevons paradox might also fail. Alex Imas, a professor at Chicago Booth, argues that if AI automates most labor and labor’s share of income sharply declines, who will buy all these highly efficient goods and services? This touches on distribution mechanisms. When output capacity approaches infinity and effective demand concentrates, we may face not a recession but an imbalance—material abundance that cannot be accessed.

Looking Through a Keyhole

The most expansive part of Citrini’s scenario is the transmission from employment shocks to a financial crisis. He envisions structured financial products (“Software-Backed Securities”) based on SaaS revenues suffering widespread defaults, triggering a credit crunch similar to 2008.

However, critics point out that compared to 2008, the leverage of U.S. corporations today is much healthier, and the banking system is far more resilient after Dodd-Frank reforms and stress tests.

Compared to the pre-2008 financial crisis, the resilience indicators of the current U.S. financial system have improved significantly: Tier 1 capital ratios rose from 8.1% to 13.7%, household debt-to-disposable income ratio fell from 130% to 97%, and non-performing loan rates dropped from 1.4% to 0.7%.

Even if some SaaS companies face revenue declines, their scale is insufficient to trigger systemic credit crises. Nick Smith, a former Bloomberg finance columnist, argues that Citrini makes a common mistake: extrapolating micro-level industry shocks linearly to macro systemic risks. Regarding demand collapse, Smith’s answer is fiscal policy. If unemployment truly surges, governments have the capacity and willingness to implement large-scale stimulus to support demand.

The system’s response capacity is also underestimated. During COVID-19, for example, after WHO declared a pandemic on March 11, 2020, the U.S. enacted the $2.2 trillion CARES Act just 16 days later. Over the following year, the U.S. deployed a total of $5.68 trillion in fiscal stimulus—about 25% of 2020 GDP.

If AI-driven unemployment occurs at the speed and scale Citrini describes, policy interventions are unlikely to be absent.

Some critics also raise more fundamental questions. Doomsday scenarios in technology often stem from a lack of faith in human institutions. Citrini’s scenario treats markets as a self-operating machine, driven solely by cause and effect until collapse. But in reality, the economy is shaped by laws, institutions, politics, culture, and ideology.

Consensus and Disagreement

We might attempt to identify some common ground and divergences.

Almost no one denies that AI is, and will continue to, change the demand structure for white-collar labor; the debate is only about the speed and scale of change. Also, the pain of transition is real and should not be masked by long-term optimism. Moreover, the quality and speed of policy responses will largely determine the outcome.

Disagreements lie in deeper logic. Some believe this technological shock may surpass historical precedents in speed and scope, limiting the applicability of historical analogy; others trust in the adaptability of institutions and the repeatability of history.

Looking Up

Citrini’s article has several issues: overly tight logical connections, underestimation of institutional responses, and a leap from micro-industry impacts to macro systemic risks lacking sufficient intermediate reasoning. But its fundamental flaw may be a low estimate of human society: it assumes a static institutional environment where technology relentlessly crushes everything at an unstoppable pace. History is full of technological doomsday predictions—technically logical but almost always ignoring the variable “human.” Society’s complexity, friction, redundancy, and seemingly inefficient institutions form a powerful, distributed resilience. We have ample time to avoid the prophesied end, provided we don’t get scared by the scenario itself.

What about optimistic narratives? The “Jevons paradox” is an observation about long-term trends. The “Moravec paradox” tells us physical labor is temporarily safe but doesn’t address what happens to displaced white-collar workers. Historical analogies are instructive but never exact; they rhyme rather than repeat. Optimistic stories need time to prove themselves, and we are at the starting point of that test.

Doomsday narratives produce anxiety, and those who produce them pay the price. Cultivate your judgment, bear the risks, manage your positions—don’t get lost in those “end-of-the-world” articles.