Fed officials debating AI’s effect on neutral interest rate

The Federal Reserve’s internal debate over artificial intelligence’s (AI) impact on the neutral interest rate continues to shape monetary policy discussions as the March Federal Open Market Committee (FOMC) meeting approaches. While AI’s transformative potential remains a topic of intrigue, Fed officials maintain a cautious, data-driven approach amid persistent uncertainty about the technology’s durable economic effects.

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Fed Officials Remain Divided on AI’s Influence Over the Neutral Interest Rate

The neutral interest rate—the theoretical benchmark consistent with stable inflation and full employment—is broadly estimated by the Fed to lie between 3.5% and 3.75%. The key question animating policymakers is whether AI-driven productivity gains will sustainably push this neutral rate higher, thereby shaping the appropriate monetary policy stance.

• Optimists, led by Fed Governor Philip Jefferson, argue that AI has the capacity to deliver a structural uplift in productivity growth, potentially justifying a higher neutral rate without sparking inflation. Jefferson, however, tempers enthusiasm with pragmatism, emphasizing that any policy recalibration must await clear, sustained economic evidence confirming AI’s long-term benefits.

• Cautious voices within the Fed, including Chair Jerome Powell’s economic advisor Austan Goolsbee, Boston Fed President Susan Collins, and Atlanta Fed President Raphael Bostic, stress that it is still “too soon to bet” on AI’s productivity gains. They highlight the uneven and potentially disruptive labor market effects AI may trigger, such as temporary increases in unemployment, and advocate for a patient, data-dependent stance.

• Collins and Bostic underscore the transitory nature of AI’s labor market disruptions, urging policymakers not to rush into premature interest rate adjustments based on early technological shifts.

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Richmond Fed President Barkin’s “Unicorn” Metaphor Highlights Neutral Rate Uncertainty

Adding a vivid new dimension to the debate, Richmond Fed President Thomas Barkin recently described the neutral interest rate as a “unicorn” alone in the forest, capturing the elusive and uncertain nature of pinning down this key policy benchmark amid rapid technological change.

• Barkin’s metaphor reflects the difficulty in accurately estimating the neutral rate in an environment where AI and other innovations may fundamentally alter productivity, labor dynamics, and inflation trajectories.

• His remarks reinforce a central theme echoed by other Fed officials: the neutral rate remains highly uncertain and fluid, requiring ongoing empirical evaluation rather than fixed assumptions.

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Christopher Waller Signals a “Tight” March Vote and “Coin Flip” Odds on Rate Cuts

Fed Governor Christopher Waller’s recent candid commentary provides a window into the Fed’s internal deliberations:

• Waller characterized the March FOMC vote on interest rates as potentially “tight,” reflecting significant uncertainty within the committee.

• He described the probability of a rate cut at the upcoming meeting as a “coin flip,” underscoring the balanced risks policymakers are grappling with amid mixed economic signals.

• Waller reaffirmed that the labor market remains the Fed’s “immediate compass,” with decisions hinging on distinguishing durable job growth from temporary AI-induced fluctuations.

• He stressed the Fed’s readiness to maintain current rates if labor market data hold strong, prioritizing real-time evidence over speculative optimism regarding AI’s productivity impact.

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January Discount Rate Minutes and Recent Fed Remarks Reinforce Patience and Data Dependence

The recently released minutes from the Federal Reserve’s January 20 and 28 discount rate meetings echo the themes of caution and discipline:

• Policymakers emphasized a patient, data-driven strategy, openly acknowledging the uncertainties, including those associated with AI.

• While recognizing AI’s transformative potential, the consensus remained skeptical about its near-term impact on productivity and inflation.

• Officials agreed on the critical importance of closely monitoring labor market and inflation data before making policy adjustments.

In parallel, recent public remarks from key Fed figures reinforce this prudent stance:

• Boston Fed President Susan Collins reiterated that near-term interest rate decisions will be anchored to upcoming jobs and inflation reports, not speculative AI forecasts.

• Atlanta Fed President Raphael Bostic acknowledged AI’s long-term potential but cautioned about transitory labor market disruptions that may temporarily elevate unemployment.

• Fed Governor Philip Jefferson maintained confidence in AI’s ability to structurally raise the neutral rate but insisted policy changes must be based on clear, sustained economic trends.

• Austan Goolsbee highlighted the Fed’s commitment to a data-dependent approach, warning against premature easing driven by uncertain AI productivity outcomes.

• New York Fed President John Williams advocated a gradualist, flexible policy path, recognizing AI’s potential to shift growth and inflation dynamics while emphasizing the need for adaptation as empirical evidence accumulates.

• Jeffrey Musalem, Vice President at the Fed, described the current policy rate as “near neutral,” suggesting risks are balanced and AI’s theoretical effects alone do not justify immediate rate moves.

• Kansas City Fed President Jeffrey Schmid reiterated that high inflation remains the Fed’s top priority, reinforcing the primacy of inflation control over speculative enthusiasm about AI.

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Fed Economists Tentatively Incorporate AI into Forecasting, Maintaining Prudence

Behind the scenes, Fed economists have begun cautiously integrating AI-related productivity considerations into their models:

• Adjustments to neutral rate estimates and productivity assumptions are underway to better reflect potential structural shifts driven by AI.

• These modeling efforts remain preliminary and fluid, as isolating AI’s distinct economic impact amid numerous other factors is challenging.

• The goal is to deepen understanding of AI’s effects on structural productivity and labor markets without prompting premature policy shifts.

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Market Reaction: Rate-Cut Expectations Shift Further into 2027

Financial markets have responded decisively to the Fed’s cautious messaging and Waller’s “coin flip” characterization:

• The probability of a rate cut in March has declined sharply, with traders pushing significant easing bets further into 2027.

• This market movement reflects growing recognition that, despite AI’s long-term potential, there is insufficient near-term evidence to justify preemptive monetary easing.

• Investors remain wary about the timing and magnitude of AI’s broader economic impact amid persistent inflation challenges.

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Looking Ahead: March FOMC Meeting Hinges on Real-Time Data

As the Fed approaches the March meeting, the path forward is squarely tied to evolving economic data:

• A strong February jobs report would likely bolster the case for holding rates steady, signaling continued labor market tightness.

• Conversely, signs of labor market softening or further inflation moderation could pave the way for measured rate cuts.

• Fed officials uniformly stress that any easing should be grounded in durable, verifiable economic trends—not speculative AI-driven productivity gains.

• The Fed’s dual mandate of maximum employment and price stability remains the guiding framework amid the uncertainties posed by AI and other technological shifts.

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Conclusion

The Federal Reserve’s ongoing deliberations over AI’s influence on the neutral interest rate underscore the complexities of navigating monetary policy in an era of rapid technological innovation. Richmond Fed President Barkin’s evocative “unicorn” metaphor captures the profound uncertainty around estimating the neutral rate amid evolving productivity dynamics.

Governor Waller’s candid “coin flip” remarks, the January discount rate minutes, and recent Fed communications collectively highlight a unified commitment to a patient, data-driven approach that prioritizes concrete economic evidence over speculative forecasts. While divisions persist between officials optimistic about AI’s long-term productivity potential and those urging caution given uneven and uncertain effects, the Fed’s evolving analytical frameworks and prudent communications signal readiness to adapt policy as clearer evidence emerges.

For now, AI remains a potential long-run factor rather than an immediate driver of monetary policy. The Fed’s careful calibration will be critical to sustaining balanced growth and price stability in an increasingly complex economic landscape shaped by technological change.