A 2028 AI-Driven Left-Tail Scenario: Ghost GDP and Intermediation Erosion
- A speculative macro scenario imagines a 2028 crisis born from a rapid, pervasive advance in AI that makes intelligence cheap and abundant. Productivity surges, but the economy’s real, human-centered engine weakens as white-collar work is displaced, consumer demand falters, and financial intermediation becomes fragile. - The trigger sequence: in 2025–2026 agentic AI tools enable in-house replication of mid-market SaaS capabilities; procurement shifts to buy-or-build decisions, with large firms pursuing aggressive AI adoption. By 2026–2027, widespread white-collar layoffs, margin expansion from automation, and a collapse in consumer velocity create a paradox: strong headline productivity alongside stagnating or deteriorating real demand (a “Ghost GDP” environment). - The reflexive feedback loop intensifies: as firms cut payroll, they reinvest savings into AI, sustaining margins while reducing employment and consumer purchasing power. By early 2027, AI-driven commerce and agents remove friction across many sectors, turning transactions into continuous, automated optimization rather than human-led decisions. Intermediation pricing collapses as agents shop around, bypassing traditional platforms, subscriptions, and advisory services. - Intermediation erosion and sectoral disruption: consumer agents begin replacing human-led processes in travel, insurance, financial planning, tax work, and even real estate. Platforms like delivery and gig-economy services face intense competition, with agents routing around traditional intermediaries and lowering or eliminating many fees. Payment rails come under pressure as transactions migrate toward near-free, fast settlement via stablecoins or new rails, undermining card networks’ moats. - Financial system and credit dynamics: private credit balloons to trillions, backed by PE-backed software assets and ARR-like structures. The Zendesk case becomes emblematic: customer-service automation erodes ARR, turning large ARR-backed loans into stressed, potentially defaulting positions. Insurance capital and offshore SPV structures add opacity and interconnected risk, amplifying systemic worry as regulators tighten capital treatment for such assets. The mortgage market faces stress as income growth collapses for the upper end of the distribution, raising delinquencies and threatening prime mortgages in ways not seen before. - Macroeconomic outcomes and policy tension: unemployment concentrates among high earners, provoking a sharp consumption drag even as overall AI investment remains strong. Government deficits widen as automatic stabilizers kick in, but traditional tools (rate cuts, QE) address financial stress more easily than the real-economy distortions caused by AI-driven labor displacement. Proposals surface for a “Transition Economy Act” to direct transfers to displaced workers and a “Shared AI Prosperity Act” to claim some returns from AI infrastructure for household support, but political gridlock and social tensions (including protest movements) complicate timely action. - Core takeaway and uncertainties: this is a thought exercise about potential left-tail risks from accelerating AI, not a forecast. It emphasizes how the premium on human intelligence could compress as machine intelligence scales, creating a cascade of financial and real-economy disruptions that outpace policy and institutions. There is time to reassess portfolios and prepare, but substantial uncertainties remain about timing, policy responses, and which sectors or regions adapt best. The scenario warns that the canary remains alive and that proactive planning is essential.
Source: citriniresearch.com
