
The Fed launched an AI-focused task force to assess how AI could affect growth and productivity, led by Marc Andreessen, Charles I. Jones, and Asha Sharma. While Andreessen/Jones are sharply optimistic that AI could lift growth materially (Jones citing potential growth >5%/yr if weak links are automated), several FOMC participants still see timing and magnitude of productivity gains as uncertain. Separately, New York Fed President John Williams warned AI-related demand may push up electricity and semiconductor prices—some components rising 2-3x—raising inflation risk; the Fed is expected to hold rates steady at its late-July meeting as the work runs through year-end.
This is less a clean AI-positive event than a potential shift in the Fed’s reaction function. If policymakers start treating AI as a durable productivity accelerator, the medium-term implications are lower terminal rates and a higher fair multiple for long-duration assets, especially mega-cap software where cash flows are far out the curve. The near-term market mechanism is messier: AI is still a capex-and-power demand shock before it is a measurable productivity shock. That favors the picks-and-shovels around compute, semis, and grid capex, but it also keeps inflation stickier than the consensus narrative implies, which is negative for TLT, small caps, and highly levered balance sheets that need an imminent easing cycle. Contrarian take: the market is likely overconfident that “AI = disinflation.” The more realistic sequence is 1) earnings outlays and electricity costs rise first, 2) measured productivity improves later, 3) the Fed stays cautious until the data proves the second step. If core services and power prices keep re-accelerating into Q3, the productivity story loses policy relevance and the valuation tailwind for growth can reverse quickly.
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