On Monday a hypothetical stress case portrayed an abrupt, disorderly economic response to rapid artificial intelligence deployment, suggesting a wide-ranging shakeout in labor markets, credit and equity valuations by mid-2028. That scenario - explicitly described as an exercise rather than a forecast - painted a picture of the S&P 500 plunging 38%, unemployment rising above 10% and strains in private credit markets as AI-driven productivity shocks exceed the economy's capacity to adapt.
The hypothetical drew attention among investors already sensitive to disruption risks across payments, software and cybersecurity. It also prompted a prompt alternative from Wolfe Research's chief economist, who urged a more balanced reading of potential macro paths as AI adoption accelerates.
Wolfe's response does not dismiss the risks highlighted in the exercise. Instead, the research team characterizes the original argument as a legitimate concern about timing - specifically, what occurs if AI adoption advances faster than the economy's ability to reallocate labor and capital. But the firm also outlined a different trajectory in which the same forces underpinning the bearish case ultimately support growth rather than trigger a deep demand shock.
In Wolfe's baseline scenario, the initial phase of rapid AI roll-out across 2025 and 2026 looks disruptive. Employers in white-collar sectors impose hiring freezes, productivity steps up materially and profit margins widen for a time - creating the impression that returns accrue heavily to capital. Technology companies scale back recruiting plans, consulting firms automate junior-level tasks and financial firms rely more on AI-driven research tools, producing a near-term picture of concentrated gains.
Crucially, Wolfe expects those early margin windfalls to be short-lived as competitive forces take hold through 2026 into 2027. What first appears as excess profitability increasingly translates into lower prices, faster service delivery and new product features. Premium AI functionality becomes a standard element across enterprise software and customer-facing workflows, eroding the one-off margin boost seen in the earliest adopters.
Under Wolfe's projection, the economy avoids the severe outcomes imagined in the hypothetical stress case. Unemployment in this alternative path rises only modestly to about 4.5%, while inflation gradually cools to roughly 1.8% year over year by May 2028. Rather than provoking a demand collapse, the productivity surge behaves like a positive supply shock that eases price pressures while supporting output.
The labor market transition in Wolfe's view is more gradual and sectorally uneven than the doomsday sketch. Job losses occur in waves across technology, finance and business services, but offsetting hiring appears in construction, manufacturing and logistics. Firms find that AI complements experienced employees who can direct and validate machine outputs, reducing the case for wholesale replacement of teams.
Demographics and labor supply constraints are central to this buffering effect. With aging workforces and tighter immigration, automation often addresses persistent labor shortfalls rather than displacing surplus workers. Health systems apply AI for diagnostics and scheduling, manufacturers use automation to compensate for skilled labor scarcity and large infrastructure endeavours adopt AI-enabled project management tools.
Significantly, Wolfe's scenario envisions the productivity gains broadening into a tangible investment cycle. Capital expenditure picks up across data centers, power grids, semiconductor capacity and automation equipment, supporting activity in construction, engineering and industrial sectors. Over time this physical investment helps sustain demand for labor and materials in areas outside the early-hit white-collar industries.
By 2028, the alternative path leads to a fresh equilibrium in which inflation pressures subside as AI improves supply chains and service efficiency, and real wages begin to recover as price growth slows faster than nominal pay. Wolfe notes the economy still experiences disruption and inequality through this transition, but the negative spiral depicted in the stress exercise does not materialize.
The research note situates AI alongside prior general-purpose technologies - disruptive at first, disinflationary over time and ultimately supportive of growth once adoption widens. The analysis thus complements the original exercise: one narrative highlights the tail-risk of a fast, disorderly transition; the other maps how the same technological shock could evolve into a net-positive structural shift.
Clear summary
- Citrini Research presented a hypothetical "Global Intelligence Crisis" that envisions a 38% fall in the S&P 500, unemployment topping 10% and private credit strains by mid-2028 if AI adoption outpaces economic adjustment.
- Wolfe Research offered a complementary scenario in which initial disruption during 2025-2026 gives way to competitive pricing, broader adoption and a supply-side productivity boost, resulting in only modest unemployment increases and lower inflation by May 2028.
- The Wolfe path emphasizes sectoral reallocation, demographic buffers and a follow-on physical investment cycle into data centers, power grids, semiconductors and automation equipment.
Key points
- The hypothetical stress case frames a severe downside risk: deep equity losses, high unemployment and private credit market strain.
- Wolfe Research's alternative projects a more benign outcome in which productivity gains act as a supply shock, reducing inflation to roughly 1.8% year over year by May 2028 and holding unemployment near 4.5%.
- Sectors likely to feel pronounced effects include technology and financial services in the near term, with construction, manufacturing, logistics, data centers and semiconductors benefiting from the subsequent investment cycle.
Risks and uncertainties
- The speed of AI adoption relative to the economy's adjustment capacity - a faster-than-manageable deployment could still produce severe labor disruption and credit stress, notably in payments, software and cybersecurity.
- Private credit market vulnerability if rapid productivity shocks translate into widespread borrower distress before reallocation and investment can absorb displaced labor.
- Uneven sectoral impacts and rising inequality during the transition - even in the Wolfe alternative, disruption persists across technology, finance and business services.