The show-me phase of the AI trade: two ways to manage risk
The AI trade has entered a show-me phase: every investor now needs proof that AI spending converts into earnings, not just momentum. With semiconductor volatility rising and leveraged ETFs turbocharging bets, advisors are using buffer ETFs and covered-call strategies to manage downside while staying exposed to the theme.
Key Takeaways
- Markets are demanding evidence that AI capex translates into bottom-line growth before rewarding stocks further.
- Leveraged ETFs now oversee more than $270 billion globally, and daily rebalancing can amplify AI selloffs and rallies.
- Buffer ETFs offer defined downside protection over set periods, trading capped upside for loss guardrails.
- Covered-call strategies generate income by selling call options, a fit when momentum-driven rallies fade.
- Morgan Stanley warns AI bottlenecks keep shifting and the biggest long-term winners may not exist yet.
Why Has the AI Trade Entered a Show-Me Phase?
After years of AI lifting mega-cap tech, investors are no longer paying purely for future promise. Good earnings alone are not enough without proof that heavy AI spending will show up in profits and cash flows.
That shift matters because the trade has grown crowded and complicated. Semiconductor stocks have seen sharp moves in both price and volatility, and traders warn that yesterday's tech-led selloff may not be a one-off event.
For more on how fintech and crypto markets are navigating AI-driven volatility, see our Fintech & Crypto Alerts coverage.
How Are Leveraged ETFs Turbocharging AI Bets?
Retail and institutional investors are increasingly using leveraged ETFs to amplify AI exposure, especially in semiconductor names. These funds use derivatives to deliver multiples of an asset's daily return, then must rebalance as prices move.
Daily rebalancing creates forced buying in rallies and forced selling in downturns. Bloomberg reports that leveraged ETFs oversee more than $270 billion globally, with the U.S. market above $200 billion and Asia exceeding $45 billion.
Barclays estimates U.S. leveraged ETF rebalancing recently averaged about $20 billion a day—roughly four times its one-year average. A rapidly growing leveraged fund tied to SK Hynix, now around $13 billion in assets, has become large enough to influence intraday trading in the AI memory chipmaker.
What Are the Two Risk Strategies Advisors Are Using?
Barron's highlights two options-based approaches for the show-me environment.
Buffer ETFs: Also called defined-outcome or structured-outcome funds, these use options to protect against a preset slice of losses—often the first 10% to 15%—over a fixed period, typically one year. Investors trade away some upside in exchange for a clearer downside floor.
Covered-call strategies: These sell call options against stock holdings to collect premium income. The upside cap hurts less when markets reward proof over hype, making income a practical hedge while maintaining tech exposure.
What Should Every Investor Know About AI Fundamentals?
Even with risk tools in place, the underlying AI cycle remains fast-moving. Morgan Stanley Investment Management outlines 10 truths, noting roughly $2.3 trillion in AI capex since 2017 and token consumption that grew more than 10 times in 2025 alone.
Bottlenecks keep migrating—from chips to power, memory, networking, and cooling—while AI expands into a full-stack capital cycle spanning infrastructure, models, robotics, and power. The firm also cautions that governance is lagging capabilities and that the biggest long-term winners may not have been founded yet.
The playbook for this phase is not to abandon AI, but to demand fundamentals, respect leverage-driven volatility, and use defined-risk tools when conviction outruns proof.