The independent research publication Citrini, led by CEO James van Geelen, has gained recognition for its insightful analysis of emerging trends, particularly in the realms of GLP-1 drugs and artificial intelligence (AI). In a recent collaboration with Alap Shah, managing partner at Lotus Technology Management, Citrini published a thought-provoking article envisioning a future scenario set in 2028, where unexpected advancements in AI have caused a significant market collapse.
This particular piece garnered substantial attention, racking up 28 million views on social media platform X by early March. Although Citrini characterized the article as a scenario rather than a straightforward prediction, its implications resonated deeply within the investment community, seemingly contributing to market declines observed on February 23.
At the heart of Citrini and Shah’s analysis lies the argument that AI could trigger a severe downturn in the white-collar job market. They contend that advancements in AI will create a challenging “negative feedback loop.” Enhanced AI capabilities could lead to the automation of numerous software development tasks that currently require extensive human resources. As a result, the publication anticipates an increase in the unemployment rate from the current 4.3% to potentially over 10% by 2028.
The article highlights how AI will integrate seamlessly into personal devices, introducing what the authors termed “agentic commerce.” This form of commerce is expected to eliminate inefficiencies traditionally found in business operations, such as subscription services that consumers overlook. For instance, AI agents could automatically re-evaluate consumers’ insurance policies each year, potentially displacing those who currently rely on passive renewals.
Similarly, in the real estate market, AI’s capabilities could eliminate the need for human agents who profit from commission-based sales, thus disrupting traditional revenue streams. Furthermore, AI’s ability to identify cheaper payment options, such as stablecoins, may erode the profit margins of established payment networks like Visa and Mastercard.
The central concern raised by Citrini and Shah is that the automation of white-collar tasks will not yield new job opportunities, contrasting with past technological revolutions that led to job creation. The authors illustrate a cyclic dilemma in which companies, in an effort to protect their margins, will resort to further layoffs, particularly targeting high-paid workers whose spending drives approximately 70% of U.S. gross domestic product (GDP). Consequently, rising unemployment could curtail consumer spending, potentially plunging the economy into a prolonged recession and increasing defaults across various credit categories, including those involving high-income borrowers.
Citrini predicts that the S&P 500 could see a decline of 38% between late 2026 and mid-2028. However, the effects on economic data may be obscured by enhanced productivity levels, leading to what the authors term “Ghost GDP,” a reflection of economic activity that fails to manifest in observable consumer behavior.
While the publication emphasizes that its article is meant to provoke thought rather than serve as concrete forecasting, it raises crucial points about the transformation of labor dynamics and the economy as AI technology becomes more pervasive. The nuances of productivity growth observed in the current labor market, characterized by stagnant hiring and firing practices alongside relatively low unemployment rates, also suggest that AI is already influencing economic factors in measurable ways.
Moreover, the authors question whether the existing power and resource infrastructure can support the exponential computing demands associated with widespread AI usage. With consumer spending being a driving force of the economy, any notable decline in this area could adversely affect companies at both the top and bottom lines, potentially leading to a deflationary spiral.
As advancements in AI continue to evolve, the potential consequences outlined by Citrini and Shah serve as a critical reminder of the technology’s transformative power and the challenges that may lie ahead. Their article prompts industry stakeholders and the public to consider the broader implications of AI, which remain extremely hard to predict amidst the rapidly shifting landscape.
