- The divergence between US equity performance and global economic stress signals is unusually sharp. AI earnings are pulling prices higher even as inflation, trade disruption, and geopolitical uncertainty weigh on the broader economy.
- Morgan Stanley Research recommends overweight equities, equal-weight fixed income, and underweight commodities — a positioning that bets on AI growth continuing to outpace macroeconomic headwinds.
- The risk is that supply chain disruptions are slow-moving: their full effect on corporate margins may not appear in earnings until Q2 or Q3, meaning current valuations could be outpacing the fundamental picture.
US equities have traded at or near record highs through the first months of 2026, led by large-cap technology companies whose AI-driven revenue growth has compounded across multiple quarters. The S&P 500’s performance has been disproportionately explained by a small cluster of firms — Nvidia, Microsoft, Google parent Alphabet, Amazon, and Meta — all of which have reported earnings driven by AI infrastructure investment that has, so far, proved insensitive to the macroeconomic headwinds that have slowed growth elsewhere.
That divergence is the central puzzle of the current market. Brent crude is below $65; gold has pulled back 16 percent from its January high; credit spreads in European and emerging market debt have widened; global goods trade contracted in Q1 for the second consecutive quarter. Yet the Morgan Stanley overweight on US equities reflects a view that AI capital expenditure — which hyperscale operators have publicly committed to sustaining “almost irrespective of whether stocks keep performing” — is a structural floor under a specific set of large-cap earnings that doesn’t require a healthy global economy to continue.
What could change it
The equity market’s insulation from global stress is not guaranteed. Three scenarios could break the divergence. First, supply chain disruption from Hormuz rerouting eventually reaches semiconductor and electronics supply chains, raising input costs for the hyperscale operators whose capital spending drives AI revenue. Second, the Fed’s extended hold — potentially strengthened by Kevin Warsh’s hawkish reputation — compresses valuations on high-multiple growth stocks as the discount rate rises. Third, a genuine demand slowdown in China reduces revenue from enterprise software and consumer electronics sold by US tech companies across one of their largest markets.
None of these scenarios is yet visible in earnings data. The pattern of recent quarters — AI-linked companies beating estimates by significant margins — has reinforced the market’s willingness to overlook near-term risks. The BlackRock Investment Institute’s Q2 2026 outlook described US equities as the “least bad” asset class in an environment where bonds face inflation risk and commodities face geopolitical volatility. That framing captures the current market logic: stocks are not cheap, but they remain the most defensible position available.
Sector rotation within equities
Within the US market, defensive sectors have underperformed significantly. Utilities and consumer staples, which attract capital when investors are risk-averse, have lagged the broader index by 8 to 12 percent year-to-date. Energy stocks present a more complicated picture: higher oil prices help upstream producers but refiners and petrochemical companies face margin pressure from elevated feedstock costs. The cleanest expression of the AI-versus-macro divergence is in the performance of semiconductor equipment companies — ASML, Applied Materials, Lam Research — which are priced for a multi-year capital expenditure cycle regardless of what happens in the Middle East.