Citrini’s 2028 scenario reframed with market and policy context

Citrini Research’s hypothetical June 2028 memo uses an acute AI productivity shock to illustrate a chained macro stress: measured GDP and headline output climb as automation compresses labor’s share, yet payroll‑backed consumption falls, producing what the memo dubs “ghost GDP.” Firms respond by accelerating automation to defend margins, which further weakens household incomes and consumer demand in a reinforcing loop. Citrini flags a large exposure point in housing: about $13 trillion of U.S. mortgage balances are modelled as sensitive to sustained employment declines.

On markets, Citrini sketches an initial rally followed by a sharp re‑rating; its illustrative S&P peak‑to‑trough range is 40–60%. Other scenario authors and banks produce different magnitudes—published runs cite a >33% aggregate equity drop in central cases, while targeted bank‑stress paths produce regional bank equity writedowns in the low‑double digits—underscoring that loss severity depends heavily on automation speed, policy backstops and credit‑concentration assumptions.

A payment‑rails channel is central to Citrini’s divergence from many macro memos: agentic commerce prioritizes latency, programmability and cost, creating incentives to route transactions to low‑fee settlement layers (stablecoins and high‑throughput chains) rather than legacy card rails that rely on ~2–3% interchange. That migration would compress interchange revenues for processors and networks and shift recurring economics toward protocol owners and cloud compute providers that route inference and settlement workloads.

Multiple independent signals in early 2026 increase this scenario’s plausibility. UBS and market trackers document outsized AI infrastructure commitments—roughly $3 trillion of projects under consideration and as much as $1.5 trillion in hyperscaler procurement—concentrating capex and raising execution risk. UBS stress runs show how concentrated AI buildouts can amplify defaults and liquidity squeezes in private‑credit pools; other analysts, like Arthur Hayes, map early market divergences (Bitcoin vs tech equities) to tightening credit risk and produce illustrative mortgage‑loss and bank‑equity stress figures that differ from Citrini’s but point to overlapping transmission channels.

Yet important frictions remain. Practical constraints—KYC/AML, on‑chain liquidity and finality, privacy, integration and legal liability—limit immediate wholesale migration of everyday consumer spend to public chains. The most likely path is hybrid: pilot integrations, merchant discounts approaching interchange economics, and progressive routing optimization as liquidity tooling, custodial rails and KYC primitives mature.

Policy and risk management responses highlighted across sources converge on two themes: broaden demand support for workers (retraining, stipends or targeted pilots) and mitigate concentrated infrastructure risk (competition policy, portability mandates, clearer procurement timelines). Market actors are already re‑testing covenant terms, shortening holding horizons, and incorporating AI‑driven stress runs into liquidity and credit planning.

In sum, Citrini’s memo functions as a compact stress test that combines credible early signals—data‑center capex concentration, layoffs, merchant stablecoin experiments—with plausible but divergent severity outcomes. The practical takeaway for executives, investors and regulators is not a single predicted path but a menu of contingent risks: faster, concentrated AI adoption raises the odds of more rapid demand compression and payment‑rail reconfiguration, even as the timing and extent of credit and equity losses remain model‑sensitive.