Back to News
Market Impact: 0.35

Billionaire Philippe Laffont Sold CoreWeave and Bought This Artificial Intelligence (AI) Stock Instead

CRWVAMATNVDAINTCTSMMUNFLXNDAQ
Artificial IntelligenceTechnology & InnovationCompany FundamentalsCorporate Guidance & OutlookIPOs & SPACsPrivate Markets & VentureAnalyst InsightsInvestor Sentiment & Positioning
Billionaire Philippe Laffont Sold CoreWeave and Bought This Artificial Intelligence (AI) Stock Instead

Coatue's Phillippe Laffont exited CoreWeave and nearly doubled the fund's stake in Applied Materials in Q4; CoreWeave shares have collapsed ~50% since October. CoreWeave's revenue backlog jumped from $15.1B (end-2024) to $66.8B (end-2025) and it forecasts revenue to more than double in 2026 while remaining highly leveraged and trading at just over 6x trailing sales. Applied Materials is positioned to benefit from semiconductor capex (e.g., TSMC $52–56B budget, Micron >$25B expected), with management guiding >20% equipment growth and analysts forecasting ~25% EPS growth in 2027; the stock trades near 30x forward earnings.

Analysis

CoreWeave’s business model creates acute financing and operational convexity: long-dated customer commitments are functioning as saleable collateral, which accelerates growth but concentrates refinancing and contractor execution risk into discrete windows. That structure makes equity returns highly sensitive to (a) short-term contractor milestones and (b) credit market liquidity — a missed construction milestone or a tightening in rates can cascade into covenant stress far faster than revenue volatility would suggest. The secular winners from higher AI-driven capex are the upstream equipment and materials providers with scale, captive R&D pipelines, and long customer lead times — these firms not only capture order book growth but also widen structural margins as fabs lengthen their delivery calendars. Second-order beneficiaries include specialty chemicals, power/infrastructure contractors for hyperscale cleanrooms, and logistics providers able to schedule long lead-time tool moves; conversely, small/cloud-only GPU rental plays without diversified collateral or sponsor support are exposed to funding shocks. Key near-term catalysts to watch are chipmaker capex guides and explicit GPU allocation/prioritization signals from GPU suppliers; medium-term drivers are interest rate paths and refinancing windows for levered infra players. A tail downside is a rapid improvement in model compute-efficiency or a material GPU oversupply — either would undercut current long-duration demand assumptions. Positioning should therefore be structured: capture exposure to durable equipment demand while explicitly insuring against concentrated execution or credit events at levered infra operators.