JPMorgan Strategist: Investors Don't Have to Exit AI to Hedge the Next Selloff

The Quick Take

• JPMorgan's Gabriela Santos says AI-driven market volatility is likely recurring, but investors can protect without fully exiting the sector
• The strategist identifies defensive positioning strategies that allow investors to stay exposed to the AI boom while reducing drawdown risk
• AI-linked stock concentration risk makes selective hedging more efficient than broad market de-risking in the current environment

As AI-linked equities have driven outsized market returns in 2025–2026, concentration risk has become one of the most discussed structural vulnerabilities among institutional investors. JPMorgan's Gabriela Santos argues that periodic AI-driven selloffs — sharp drawdowns in the most crowded technology trades — are likely to recur as valuations remain elevated and sentiment shifts quickly. Her insight that investors can manage this risk without abandoning AI exposure entirely reflects a more sophisticated market approach than simple sector rotation.

The strategy of staying within AI-related stocks while adding diversification or hedging overlays has broad market implications. It suggests demand for AI-adjacent defensive plays — infrastructure-heavy names with steadier cash flows, dividend-paying technology utilities, or sector-diversified AI beneficiaries rather than pure-play semiconductor and hyperscaler positions. For global equity portfolios, this positions financials, utilities with AI efficiency gains, and healthcare tech as partial hedges that can capture AI's productivity dividend without full exposure to speculative multiple expansion.

The key forward signal is whether AI capex guidance from the major hyperscalers — Microsoft, Alphabet, Meta, Amazon — remains robust through Q3 earnings season. Any capex cut guidance would trigger exactly the selloff Santos describes, validating the hedging thesis. Monitor the NASDAQ-100 concentration ratio and AI chip order books for early signs of demand softening. The macro variable is whether AI monetization timelines — revenue conversion of AI infrastructure spending — accelerate enough to justify current growth multiples into year-end 2026.

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