Citrini Research has released a report titled “The 2028 Global Intelligence Crisis”, laying out a scenario in which AI-driven productivity gains end up weakening the economic base that supports consumption and credit. The report uses a retrospective 2028 framing to argue that if white-collar work is displaced at scale, stronger AI adoption may coincide with deeper stress in the real economy.
Output rises, but income circulation starts to break
According to the report, by the end of 2026 the headline picture could still look strong, with the S&P 500 and nominal GDP posting solid numbers, while strain builds underneath. Companies cut labor costs and redirect savings into AI compute, boosting efficiency on paper, yet wage growth for white-collar workers stalls or declines. Citrini describes this gap as “Ghost GDP”: reported output increases, but the gains concentrate among a narrow group of compute owners instead of flowing back into broader economic activity.
The report points to a basic imbalance. Human consumption makes up roughly 70% of U.S. GDP, and machines do not consume. If labor income weakens while automation expands, money velocity can fall even as productivity metrics improve.
SaaS, consulting, and intermediary businesses face early pressure
Citrini centers its thesis on what it calls a spiral of human intelligence replacement. Once AI systems can handle coding and routine automation at a high level, demand for software subscriptions, advisory services, and process-heavy middlemen may fall. In its SaaS example, if customers use AI to reduce headcount by 15%, they may also cut software seats and licenses, creating a feedback loop where AI starts eroding the revenue base of the tools and vendors tied to that workforce.
The report extends that logic to sectors built on reducing information asymmetry or managing cumbersome steps, including real estate brokerage, insurance renewals, delivery platforms, and financial advisory services. If AI agents can compare options automatically, find lower-cost choices, and avoid fees, margins in those business models could be pushed down sharply.
Private credit and mortgages become the transmission channel
The report argues that the risk does not stop at layoffs or shrinking corporate margins. Over the past decade, private credit expanded rapidly, and if a meaningful share of that lending is tied to SaaS businesses that lose value under AI disruption, defaults could spread into the insurers holding related debt exposure. The same pattern, in Citrini’s view, could move from industry stress into the financial system.
Its larger warning is aimed at the U.S. mortgage market, which the report sizes at $13 trillion. Even a borrower with a 780 FICO score may become vulnerable if salary income is cut in half or disappears because of AI substitution. In the report’s 2028 scenario, home prices in tech-heavy cities such as San Francisco and Seattle have already posted double-digit declines, marking the start of a broader asset repricing that it compares in scale to the 2008 financial crisis.
Policy ideas focus on taxing compute and redistributing returns
Citrini says policymakers are already confronting the fiscal strain that comes with a shrinking labor base and weaker tax receipts. The report mentions discussion around a “Transition Economy Act”, including possible taxes on AI inference compute and an “AI Common Prosperity Fund” that would distribute returns from intelligence infrastructure directly to households.
The firm stresses that this is a scenario exercise, not a fixed forecast. Its point is narrower and more urgent: rising AI productivity does not automatically guarantee a healthier economy if income, consumption, and credit quality deteriorate at the same time.