On Monday, the S&P 500 fell over 1%. Uber, Mastercard, American Express and DoorDash each dropped between 4% and 6%. The software sector hit its lowest point since the tariff shock of April 2025. The cause was not an earnings miss, a central bank surprise or a geopolitical event. It was a speculative thought experiment, written as a fictional memo from the year 2028, by a relatively small finance-focused research outfit called Citrini Research.
The report, titled “The 2028 Global Intelligence Crisis,” imagines a world where AI agents become so capable and so widely deployed that they systematically dismantle the friction-based business models underpinning large parts of the economy. SaaS companies are replicated in-house by agentic coding tools. Consumer AI agents bypass intermediaries like DoorDash, Uber and travel platforms. White-collar displacement accelerates in a self-reinforcing loop: companies lay off staff, reinvest the savings in AI, the AI gets better, more layoffs follow. The scenario’s timeline is aggressive, with the median American consuming 400,000 tokens per day by early 2027 and the “long tail of SaaS” collapsing within months.
The rapid jobs and displacement argument at the heart of the Citrini scenario is familiar territory. At ExoBrain, we’ve written extensively about the productivity J-curve, the dangerous window where massive infrastructure investment has yet to yield widespread returns while the displacement effects are already building. Our view remains that a majority of knowledge work is potentially automatable with the current generation of models, but that compute constraints and a vast unfinished adoption effort create a natural brake on the pace. The implementation lag, the slow work of dismantling and rebuilding organisational structures, has so far converted what might have been mass layoffs into a quieter pattern of frozen hiring and severed entry-level career paths.
Critics of the Citrini report largely agree on this point. Zvi Mowshowitz, writing a detailed response, called the adoption speed described “Can’t Happen levels of fast,” noting that physical compute constraints would throttle deployment long before the scenario’s most extreme predictions played out. Citadel Securities published a blunt rebuttal arguing that technological diffusion follows an S-curve, not an exponential, and that displacing white-collar work at the pace described would require “orders of magnitude more compute intensity than the current level of utilisation.”
Beneath the headline-grabbing jobs scenario sits a model of how AI-driven disruption could propagate through the financial system. This is the part that rattled the market, and it deserves closer scrutiny than it has received.
The chain goes like this. Private credit has grown from under $1 trillion in 2015 to over $2.5 trillion today, with a meaningful share deployed into leveraged buyouts of SaaS companies. Those deals were struck at valuations assuming mid-teens revenue growth stretching into the future. The debt was underwritten against Annual Recurring Revenue, the defining metric of the SaaS era. Citrini asks: what happens when recurring revenue stops recurring? The report uses Zendesk as its case study, taken private in 2022 for $10.2 billion with $5 billion in direct lending structured around ARR assumptions. If AI agents can handle customer service autonomously, the category Zendesk built simply contracts. “The ARR the loan was underwritten against was no longer recurring,” Citrini writes. “It was just revenue that hadn’t left yet.”
The next link in the chain is less obvious. Over the past decade, the large alternative asset managers acquired life insurance companies. Apollo bought Athene. Brookfield bought American Equity. KKR took Global Atlantic. They used annuity deposits as a stable funding base, invested those deposits into the private credit they themselves originated, and earned fees on both sides. Citrini describes this as a “fee-on-fee perpetual motion machine” that works under one condition: the private credit has to be money good. When it isn’t, the “permanent capital” that theoretically cannot run turns out to be household savings structured as annuities. The report then extends the logic further into prime mortgages, arguing that $13 trillion in residential mortgage debt is underwritten against income assumptions that AI could structurally impair. These are not subprime borrowers. They are 780+ FICO scores with 20% deposits. “The loans were good on day one,” Citrini argues. “The world just changed after they were written.”
Citrini notes the cycle is self-reinforcing: because AI spending replaces headcount rather than requiring new capital investment, companies can accelerate automation even as total costs fall.
Is any of this grounded in reality? In early February, before Citrini published, UBS analyst Matthew Mish warned that tens of billions in corporate loans could default in the coming year, particularly among software firms under private equity ownership. Under an aggressive disruption scenario, Mish projected default rates in US private credit could reach 13%, well above stress levels for leveraged loans and high-yield bonds. CNBC reported that software accounts for roughly 17% of Business Development Company loans by deal count, the second-largest sector exposure. Moody’s Analytics flagged the rapid rise in AI-related borrowing, increasing leverage and lack of transparency as raising “significant yellow flags.” Credit rating agency KBRA published its own assessment of AI and software risk in private credit, conceding it is “too early to assess the durability” of current borrower performance.
Takeaways: The Citrini report’s job displacement timeline is almost certainly too fast, and the major technical and economic critiques are well founded. But the financial contagion model deserves serious attention because every structure it describes, PE-backed software debt, insurance-funded private credit, income-dependent prime mortgages, exists today. At ExoBrain we have been warning since late 2025 that we are walking a tightrope between a rapid unwinding that triggers a displacement crisis and a persistent adoption lag that exposes the debt-fuelled infrastructure bubble. The Citrini report models one way the first scenario could unfold. Watch for stress in private credit marks on software portfolios, particularly BDC exposure to SaaS. Watch for regulatory attention to the insurance-to-private-credit pipeline. And note the gap between what central banks are studying, AI operating inside financial markets, and what may actually matter: AI disrupting the real economy and feeding back into credit markets.