Summary
- Greenspan vs. Warsh: Greenspan waited for productivity gains to show in the data before easing. Warsh wants to cut rates in anticipation of AI productivity gains — a regime change in Fed doctrine.
- Monetary Policy as Subsidy: By framing AI as disinflationary, Warsh effectively subsidizes massive corporate capex — Google’s $185B build‑out and Microsoft’s $100B Stargate projects.
- Policy Shock: Lower rates would fuel equity markets and reduce borrowing costs for AI‑heavy industries, making the Fed a silent partner in the infrastructure war for compute sovereignty.
- Integrity Risk: If AI productivity gains lag, inflation could resurface, creating a legitimacy breach. Warsh’s pre‑emptive bet puts Fed credibility on the line.
The End of the Greenspan Era
In the 1990s, Fed chair Alan Greenspan saw the rise of computing power but waited for proof in the numbers — like falling unit labor costs — before easing policy. Greenspan’s caution meant the Fed acted only once productivity gains were visible, preserving its credibility.
Warsh signals a break from that tradition. He isn’t waiting to see productivity gains in the rear‑view mirror. Instead, he wants to cut rates now to fund their construction — a regime change in how monetary policy is used.
How We Decoded Warsh’s Stance
- Nomination Coverage (Jan 2026): When Donald Trump announced Kevin Warsh as his choice for Fed chair, reports highlighted his belief that AI‑driven productivity gains could justify faster rate cuts.
- Warsh’s Prior Commentary: He has long argued for a “regime change” at the Fed, criticizing reliance on backward‑looking data and pushing for forward‑looking policy.
- Analytical Reports: Investor notes described Warsh’s philosophy as productivity‑anchored, suggesting he would align monetary policy with AI‑driven growth expectations.
This is the stance we decoded: Warsh wants the Fed to act ahead of the data, betting that AI will deliver a productivity boom.
Monetary Policy as Infrastructure Subsidy
Warsh argues that AI is a disinflationary force — meaning it will lower costs and tame inflation. That belief gives him cover to cut rates sooner.
Why does this matter? Because building AI infrastructure is enormously expensive. Google is planning $185 billion in spending, while Microsoft is chasing $100 billion “Stargate” projects. Lower interest rates make it easier for these companies to borrow and build. In this way, Warsh is positioning the Fed as a silent partner in the AI infrastructure war. Cheap money becomes the rails on which corporate nations construct their Data Cathedral — vast networks of chips and data centers.
The Policy Shock
If Warsh is right, rate cuts could arrive faster than markets expect. That would:
- Boost equity markets.
- Lower borrowing costs for AI‑heavy industries like semiconductors and cloud platforms.
- Align Fed policy with corporate capex shocks, effectively underwriting the next layer of the global economy.
The Integrity Risk: What if the Gains Don’t Arrive?
Greenspan’s caution meant the Fed only acted once productivity gains were visible. Warsh’s pre‑emptive bet puts credibility at risk.
If AI productivity takes years to show up, but rate cuts happen immediately, inflation could resurface. That would create a legitimacy breach: the Fed would be seen as gambling on a productivity miracle that turned out to be a mirage.
Investor Takeaway
The contrast is stark: Greenspan observed the productivity miracle before cutting. Warsh wants to cut in anticipation of one. The former was cautious empiricism; the latter is speculative sovereignty.
For investors, this means:
- Upside: Equity markets and AI infrastructure could surge if productivity gains arrive quickly.
- Risk: If gains lag, inflation could return, forcing a painful reversal.
- Strategic lens: Monetary policy is no longer just about inflation. It is becoming a structural bet on AI as the next utility layer of the global economy.
Subscribe to Truth Cartographer — because here we map the borders of power, the engines of capital, and the infrastructures of the future.
Further reading: