Inside Active: Alger’s Crawford on Finding Durable Growth

The article highlights that markets have been driven by a narrow group of AI-linked winners, and the key question is what remains attractive versus already priced in. It is commentary from Bloomberg Intelligence’s David Cohne and Alger portfolio manager Dr. Ankur Crawford, with no new earnings, guidance, or policy data. The piece is mainly a framing discussion on AI leadership, positioning, and opportunity selection rather than a market-moving event.

Analysis

The key setup is not whether AI remains secularly important, but whether the market is still paying a growth multiple for scarcity that is already becoming less scarce. Once a theme becomes the default explanation for outperformance, capital intensity rises, supplier bargaining power shifts upward, and the easy alpha migrates from the obvious “pick-and-shovel” beneficiaries to the less crowded second-order enablers and laggards. In practice, that means the next leg is more likely to come from infrastructure bottlenecks, power, networking, and software monetization than from the handful of headline mega-caps that already dominate positioning.

The bigger risk is that investors are conflating AI demand growth with AI profit growth. Over the next 3-12 months, the market may continue to reward companies that can translate AI capex into visible bookings or margin expansion, but many beneficiaries will face a lag between revenue acceleration and free-cash-flow conversion as they reinvest aggressively. That creates a setup where the underlying demand narrative can stay intact while the stocks de-rate if estimates move too far ahead of monetization.

A contrarian angle is that consensus is probably underpricing mean reversion within the AI trade itself. Crowded longs tend to underperform first on any disappointment in guide or commentary, even if fundamentals remain strong, because positioning—not fundamentals—drives the first air pocket. The cleaner opportunity may be to fade the most universally owned winners on strength and express bullish AI exposure through relative value in less saturated names with clearer operating leverage and lower embedded expectations.

Key Decisions for Investors

• Trim outright exposure to the most crowded AI mega-cap basket over the next 2-6 weeks; keep only core positions where earnings revision momentum is still positive. Risk/reward favors reducing exposure into strength rather than chasing another leg higher.
• Go long AI infrastructure and power-adjacent beneficiaries vs. short the most extended platform names via a pair trade such as XLK long / QQQ equal-weighted short overlay, or a targeted basket long in utilities/power equipment, for 3-6 months. Thesis: capex and power demand are still underappreciated relative to software multiple risk.
• Sell upside calls against existing AI winners 1-2 quarters out to monetize elevated implied volatility. This captures premium while limiting participation in a potential crowded-name correction if sentiment cools.
• Look for a long/short basket: long under-owned networking, memory, or semiconductor capital-equipment names with visible order books; short the most consensus AI beneficiaries with the highest forward EV/sales. Aim for 6-9 months and require at least 1.5:1 downside/upside asymmetry.
• Set a tactical trigger to add to AI exposure only on a 10-15% drawdown in the crowded leaders or after an earnings reset. Absent a pullback, the risk/reward is inferior to waiting for better entry.