“[Artificial Intelligence] is one of the central questions that all of us have for our day jobs in evaluating output, employment, and inflation. These are big questions, in part because the rate of change of improvement in these models is moving at an exponential level. This is hyper-Moore’s law stuff. While we might see business surveys that say it is no big deal, my speculation is that six months from now the surveys will be saying quite the opposite.” – Kevin Warsh – Chair of the Board of Governors of the Federal Reserve, CNBC policy panel at the ECB Forum on Central Banking 1 July 2026

Central banks find themselves trying to steer an economy whose production frontier is shifting faster than their measurement systems, models, and institutional reflexes can adapt.¹ The traditional assumption that changes in technology are slow-moving background forces is breaking down just as monetary authorities are being asked to deliver low and stable inflation, maintain high employment, and preserve financial stability in a world where computing capability and AI deployment are compounding at unprecedented speed.^2,11 The immediate problem is not simply that artificial intelligence might raise or lower productivity; it is that the rate and uneven pattern of change threaten to make familiar relationships between output, jobs, and prices unreliable at precisely the moment when policy is highly sensitive to small misjudgements.

From Gradual Technological Diffusion to Exponential AI Adoption

Previous general-purpose technologies, such as electrification or the microprocessor, spread over decades, allowing labour markets, education systems, and regulatory frameworks time to co-evolve.¹¹ AI appears to be following a different trajectory. Empirical work on large language models shows token prices in the inference market falling roughly 600-fold between 2020 and 2026, with economy-tier models exhibiting a price half-life of around 1,10 years, substantially faster than the two-year benchmark associated with semiconductor progress under Moore’s Law.³ Industry narratives of “Hyper Moore’s Law” in AI emphasise annual doublings or triplings in effective compute performance, driven not only by hardware but by algorithmic efficiency, network architecture, and scale effects in data.^6,9 This constellation of forces turns technological change from a slow economic backdrop into a central, near-term driver of macro dynamics.

For monetary policy, the challenge is that AI diffusion is highly uneven: frontier firms in technology, finance, and professional services integrate advanced systems rapidly, while large parts of the economy remain in pilot mode.^11,14 Micro-level studies show task-level productivity gains where AI assistants are deployed, with estimates of contributions to annual total factor productivity growth in the range of 0,3 to 0,9 percentage points over the next decade.⁸ Yet macro-level data so far detect only modest improvements in aggregate productivity growth, and many firms report useful but not transformative gains.¹¹ Policymakers thus confront a hybrid reality: exponential technological potential with still-patchy adoption, leading to wide uncertainty about the timing and magnitude of macro effects.

Output, Employment, and Inflation: A Mandate Under Strain

The statutory mandates of major central banks – price stability and maximum sustainable employment – presuppose reasonably stable statistical relationships. Output gaps, Phillips curves, and measures of “natural” unemployment rely on historical patterns linking growth, joblessness, and inflation.^2,5 AI threatens simultaneous shocks to each leg of this triangle. If AI boosts productivity, the same level of employment could produce more output, potentially lowering unit labour costs and dampening inflation pressures.^4,7,16 If adoption also displaces workers or compresses demand for certain skill tiers, equilibrium unemployment might rise, complicating the interpretation of labour market slack.^2,13,17 And if AI fuels new investment cycles in data centres, chips, and software, the capital-intensive nature of deployment could alter neutral interest rates and the transmission of monetary policy through financial markets.^12,15

Officials are openly divided on these mechanisms. One camp emphasises AI as a disinflationary productivity engine, arguing that higher efficiency and lower marginal costs will give central banks space to accommodate stronger demand without triggering price spirals.^2,4,7 Another stresses transitional frictions: elevated structural unemployment, sectoral mismatches, and potential cost-push pressures from constrained infrastructure and energy supply, all of which could sustain inflation even as output rises.^2,8,11 The lack of definitive data – there is, as yet, limited evidence of AI having a large aggregate impact on wage growth or income distribution – forces monetary authorities into a probabilistic assessment of overlapping risks.^8,11

Kevin Warsh’s AI Productivity Thesis

Against this unsettled backdrop, Kevin Warsh has advanced a relatively clear supply-side narrative: artificial intelligence will materially raise productivity, act as a significant disinflationary force, and thereby allow lower interest rates without destabilising prices.^2,4,13,16 In public commentary and op-eds, he has characterised AI as a transformative boost to American competitiveness, arguing that traditional models overstate the link between tight labour markets and inflation when underlying technology is rapidly improving productive capacity.^2,4 The core contention is that the central bank should treat AI-driven efficiency as an expansion of the economy’s supply potential, which in turn justifies a looser stance than would otherwise be warranted by conventional indicators of employment or wage growth.

Strategically, this position challenges the prevailing consensus that AI is not yet a reason to reduce policy rates.^2,13 Most sitting policymakers acknowledge potential long-run gains but remain cautious, preferring to let realised data on inflation and employment guide decisions rather than extrapolating from speculative technology narratives.^5,8,11 Warsh’s view narrows this caution, effectively asking the institution to lean more heavily on forward-looking productivity assumptions and to discount some near-term inflation pressures as the transitory cost of adjusting to a higher-efficiency equilibrium.

Hyper-Moore Dynamics and Monetary Strategy

The reference to “hyper-Moore’s law” condenses a broader perception that AI’s improvement curve has moved beyond hardware-driven transistor scaling to a multi-factor exponential process.^6,9,18 In semiconductors, Moore’s Law originally captured the doubling of transistor density roughly every two years, delivering predictable gains in computing per dollar.^18,24 That relationship has frayed as physical limits and economic constraints make further miniaturisation more difficult.^15,29 AI has, paradoxically, arrived just as classical Moore’s Law falters, but has generated its own compound curve through software, parallelism, and scale. Empirical work on AI agent capability, for instance, finds that the length of coding tasks frontier systems can autonomously complete is doubling approximately every 7 months, suggesting functional performance growth faster than traditional chip scaling.²¹

For a central banker, the critical issue is not the engineering detail but the macro consequence: policy models implicitly assume that technology progress is a slow-moving shock that can be captured by trend productivity parameters.¹¹ Hyper-Moore dynamics break that assumption. If the effective cost of inference falls by an order of magnitude in a few years while capability surges, firms can reconfigure production processes, labour demand, and pricing strategies far more rapidly than historical data would predict.^3,6,9 Survey-based measures of business conditions, often central to near-term policy deliberations, may lag actual behavioural shifts because managers only gradually realise how new tools alter competitive pressure and feasible workflows.^11,14

Warsh’s speculation that business surveys will move sharply within a six-month horizon implicitly recognises this lag. Early survey waves have tended to report AI as useful but non-transformational, with incremental investment plans rather than wholesale restructuring.^11,14,17 However, as token prices collapse, accessible models proliferate, and practical success stories accumulate, managers may abruptly revise expectations about labour needs, cost bases, and pricing power. By the time those revisions show up in formal surveys, the underlying reallocation of tasks and capital may already be underway, leaving monetary policy reacting to second-order indicators rather than primary drivers.

Labour Markets Between Disruption and Gradualism

Employment is where the tension between exponential capability and institutional gradualism becomes sharpest. On one side are warnings that AI could raise the equilibrium unemployment rate, as displaced workers experience longer job searches and certain mid-skill clerical and analytical roles shrink.^2,13,17 On the other side are analogies to past technologies: disruption creates new occupations, demand for complementary skills, and eventually higher overall employment once the reallocation phase is complete.^2,13,17 Early evidence suggests a mixed picture. Task-level studies show improved efficiency, speed, and accuracy for workers using AI tools, often raising output per head without immediate job losses.⁸ At the same time, employers are reassessing entry-level hiring, automating routine tasks, and shifting human roles towards complex judgement, coordination, and client interaction.¹⁷

From a monetary policy perspective, the critical question is whether observed unemployment reflects cyclical weakness – something interest rate cuts can alleviate – or structural adjustment that is relatively insensitive to the policy rate.^2,8 If AI pushes some workers into prolonged transition while simultaneously raising productivity among those who remain employed, standard indicators may mislead. An uptick in unemployment might coincide with firm-level profit growth and brisk investment in AI infrastructure, blurring the line between slack and overheating.^2,12,14 In such a regime, lowering rates to support displaced workers could risk amplifying inflation in sectors experiencing AI-driven demand and pricing power.

This possibility underpins the scepticism of officials who argue that AI is unlikely to justify near-term rate cuts.^2,8,13 Their implicit model is one where supply-side gains and demand-side frictions run in parallel, requiring a cautious stance until data reveal whether the net effect is disinflationary or inflationary. Warsh’s alternative is to place more analytical weight on the supply-side, betting that productivity effects will dominate and that the central bank can safely lean against transitional weakness without destabilising prices.^2,4,7

Inflation Measurement and the Risk of Mis-calibration

Inflation measurement is undergoing its own, quieter transformation. Headline indices capture average price changes across broad baskets, but they struggle to accommodate rapid quality improvements and new service bundles associated with AI.^8,11 If AI tools dramatically improve output quality or reduce non-price costs such as time and error rates, official statistics may understate the effective welfare gain. Equally, if AI facilitates new forms of price discrimination, bundling, or subscription-based access, traditional indices may miss subtle shifts in pricing power and consumer surplus. Central banks experimenting with trimmed-mean or core measures, which strip out volatile components, are implicitly acknowledging that headline inflation can be noisy in a world of rapid technological change.^8,10

The strategic risk is mis-calibration. If AI-related productivity gains are stronger than measured, central banks could hold policy unnecessarily tight, sacrificing employment and output to fight inflation pressures that are partly offset by technological efficiency.^4,7,10 Conversely, if quality adjustments mask genuine price increases driven by infrastructure bottlenecks, energy constraints, or concentrated market power in AI platforms, authorities might loosen policy on the assumption of benign technology-driven disinflation, only to entrench higher underlying inflation.^12,15,28 Warsh’s stance leans towards the first scenario, treating AI as an under-recognised disinflationary force that justifies more accommodative settings.^2,4,13,16 Critics warn about the second, stressing that the institution must not extrapolate from early micro-studies to systemic conclusions.

Capital Expenditure, Neutral Rates, and Financial Stability

AI is also reshaping the investment landscape. Hyperscale firms are committing hundreds of billions annually to AI infrastructure, with forecasts of global AI market value rising from under 300 billion dollars to several trillion over the coming decade and data centre investment running into multiple trillions.^12,27 These figures imply significant shifts in the structure of capital demand, sectoral credit allocation, and equity valuations. If AI projects deliver the anticipated cash flows, neutral real interest rates – the levels compatible with stable inflation and full employment – may drift upwards as higher expected returns raise the economy-wide cost of capital.^5,12 If, however, a substantial portion of AI investment proves speculative or fails to generate sufficient economic value, the correction could resemble a more severe version of the dot-com bust, with asset price deflation, financial instability, and sudden tightness in funding markets.¹²

Monetary authorities must therefore consider not only AI’s direct effects on productivity and prices but also its indirect impact via financial cycles. A central bank that assumes strong AI-driven productivity and cuts rates aggressively might fuel an investment boom that overshoots sustainable cash flows.¹² One that remains overly restrictive could slow the diffusion of beneficial technologies, entrenching incumbents with access to cheap capital while smaller firms struggle to adopt AI.^14,15 Warsh’s emphasis on AI as a pathway to lower rates and leaner balance sheets intersects with these concerns: shrinking central bank asset holdings and reducing reliance on quantitative easing while betting on productivity-driven disinflation could raise sensitivity to market corrections if AI narratives disappoint.^7,10

Debates, Objections, and Institutional Caution

Analytical objections to the AI-productivity-disinflation thesis cluster around three themes. First, the empirical record remains thin. Macro-studies so far find limited evidence of a large AI effect on aggregate productivity growth, and firms themselves often describe current gains as incremental.¹¹ Betting monetary strategy on a still-emerging technology runs counter to the prudential ethos of central banking, which favours evidence-based calibration. Second, AI’s distributional impacts may complicate aggregate narratives. If gains accrue primarily to highly skilled workers and capital owners, while mid-skill workers face displacement, the net effect on consumption, savings, and inflation could diverge from simple productivity stories.^8,11,17 Third, infrastructure and energy constraints, combined with concentrated market power in AI platforms, could introduce new sources of cost-push inflation and systemic risk.^12,15

Officials voicing caution argue that AI should be treated analogously to other supply shocks: recognised as a potentially important force, but incorporated into policy only to the extent that measured data support clear conclusions.^5,8,11 They recommend anchoring expectations firmly around existing inflation targets, allowing time for the economy to reveal how AI interacts with wages, prices, and employment before adjusting frameworks or neutral rate estimates.^5,13 Warsh’s position, by contrast, favours a more activist reading of technological potential, seeking to pre-emptively adjust policy in anticipation of productivity waves.^2,4,7 This divergence reflects deeper differences over how central banks should respond to uncertainty: lean against risks based on structural judgement, or wait for statistical confirmation even at the cost of short-term volatility.

Why the Argument Matters

The dispute over AI’s macroeconomic role is not a narrow technical quarrel; it bears directly on households’ job prospects, wage growth, and borrowing costs. If AI delivers strong productivity gains and central banks are slow to recognise them, economies may endure unnecessarily high unemployment and suppressed investment.^2,4,7,11 If, alternatively, policy loosens prematurely on the assumption of technology-driven disinflation, households could face persistent inflation eroding real incomes, particularly if wage growth fails to keep pace.^2,8,12,13 The stakes are amplified by AI’s potential to reshape entire sectors: entry-level work, professional services, manufacturing, and logistics are all being re-engineered, with implications for career paths and regional labour markets.¹⁷

Monetary authorities thus confront a hard strategic question: how quickly to incorporate AI into their core models and reaction functions. Warsh’s arguments push the institution towards a world in which technology narratives play a more central role in real-time policy, reducing reliance on backward-looking data and traditional inflation diagnostics.^2,4,7,10 The prevailing consensus resists this shift, favouring a more incremental approach that treats AI as an important but still largely unmeasured structural factor.^2,5,8,11,13 Whichever course is chosen, the interaction between exponential technological improvement and cautious institutional decision-making will define the next phase of macroeconomic management.

 

References

1. https://www.youtube.com/watch?v=Ohg5Sav1kpw – https://www.youtube.com/watch?v=Ohg5Sav1kpw

2. How Fed Chairman Kevin Warsh just screwed AI tech beasts – 2026-06-21 – https://finance.yahoo.com/markets/article/how-fed-chairman-kevin-warsh-just-screwed-ai-tech-beasts-103000167.html

3. Fed races to adapt to AI promises and pitfalls for jobs, inflation – 2026-03-03 – https://www.reuters.com/business/fed-races-adapt-ai-promises-pitfalls-jobs-inflation-2026-03-02/

4. Tiered Super-Moore’s Law: Price Evolution, Production Frontiers … – 2026-03-30 – https://arxiv.org/html/2603.28576v1

5. Kevin Warsh Is Right About Fed Reform – but His Inflation Solution … – 2026-05-08 – https://www.cato.org/commentary/kevin-warsh-right-about-fed-reform-inflation-solution-trap

6. Navigating inflation and employment in an era of supply shocks and AI – 2026-03-06 – https://www.ecb.europa.eu/press/key/date/2026/html/ecb.sp260306_1~a4943607d7.en.html

7. Hyper Moore’s Law: When Exponential Isn’t Fast Enough – 2025-03-19 – https://www.thedigitalspeaker.com/hyper-moore-law-exponential-fast-enough/

8. What Kevin Warsh Could means for Rates, Inflation and AI – 2026-02-11 – https://www.citizensbank.com/private-banking/insights/new-fed-chair-kevin-warsh-impact-on-markets.aspx

9. Speech by Governor Barr on artificial intelligence and the labor market – 2026-02-17 – https://www.federalreserve.gov/newsevents/speech/barr20260217a.htm

10. The Rise of “Hyper Moore’s Law” in AI – LinkedIn – 2024-11-10 – https://www.linkedin.com/pulse/rise-hyper-moores-law-ai-diana-wolf-torres-b5prc

11. Kevin Warsh, AI Productivity, & Macro Strategies for Tech Leaders – 2026-06-19 – https://www.youtube.com/watch?v=EO9T_tKnGYc

12. The AI Moment? Possibilities, Productivity, and Policy – 2026-02-23 – https://www.frbsf.org/research-and-insights/publications/economic-letter/2026/02/ai-moment-possibilities-productivity-policy/

13. How Artificial Intelligence Broke Moore’s Law and Rewrote the … – 2025-10-11 – https://mdcinsights.co.uk/how-artificial-intelligence-broke-moores-law-and-rewrote-the-rules-of-physics/

14. Fed officials at odds over AI’s impacts on monetary policy – 2026-02-19 – https://www.scotsmanguide.com/news/fed-officials-at-odds-over-ais-impacts-on-monetary-policy/

15. CFO Outlook for 2026: Tariffs, Hiring, Prices, and AI Impact – 2025-12-17 – https://www.richmondfed.org/research/national_economy/cfo_survey/data_and_results/2025/20251217_data_and_results

16. AI investors face post-Moore’s Law reality – Top1000funds.com – 2025-10-01 – https://www.top1000funds.com/events/fis/fis-stanford-2025/ai-investors-face-post-moores-law-reality/

17. Federal Reserve Chair Kevin Warsh thinks AI will push down … – 2026-06-19 – https://www.facebook.com/marketplaceapm/posts/federal-reserve-chair-kevin-warsh-thinks-ai-will-push-down-inflation-how-would-t/1456799623151005/

18. [PDF] Artificial Intelligence and the Future of Entry-Level Work – https://reports.weforum.org/docs/WEF_Artificial_Intelligence_and_the_Future_of_Entry_Level_Work_2026.pdf

19. Moore’s Law Update : r/singularity – Reddit – 2024-12-02 – https://www.reddit.com/r/singularity/comments/1h55mxm/moores_law_update/

20. Fed nominee Kevin Warsh thinks AI is “the most disruptive moment … – 2026-04-21 – https://www.instagram.com/reel/DXaC9XIDM-E/

21. Inflation: Drivers and Dynamics Conference 2026 – 2023-01-09 – https://www.clevelandfed.org/events/inflation-drivers-and-dynamics/2026/ev-20260924-inflation-drivers-and-dynamics-conference-2026

22. A new Moore’s Law for AI agents – AI Digest – 2026-03-25 – https://theaidigest.org/time-horizons

23. Fed nominee Kevin Warsh thinks AI is “the most disruptive moment … – 2026-04-21 – https://www.facebook.com/yahoofinance/posts/fed-nominee-kevin-warsh-thinks-ai-is-the-most-disruptive-moment-in-economic-hist/1324791259515649/

24. AI and Forecasting Techniques | 2026 Conference | Day 1 – YouTube – 2026-03-23 – https://www.youtube.com/watch?v=yOuepvRtt-U

25. The Competitive Chaos Behind Moore’s Law – 2025-11-19 – https://laweconcenter.org/resources/the-competitive-chaos-behind-moores-law/

26. BULLISH: Fed Chair nominee Kevin Warsh says AI will cut costs, lift … – 2026-04-23 – https://www.facebook.com/cointelegraph/posts/-bullish-fed-chair-nominee-kevin-warsh-says-ai-will-cut-costs-lift-productivity-/1275352788104880/

27. AI is here. Its rapid development has carried it from experiments to a … – 2026-01-09 – https://www.facebook.com/worldeconomicforum/videos/ai-is-here-its-rapid-development-has-carried-it-from-experiments-to-a-key-part-o/25636588736030435/

28. Hyperscalers are expected to spend over $600,000,000,000 on AI in … – 2026-02-25 – https://www.facebook.com/yahoofinance/posts/hyperscalers-are-expected-to-spend-over-600000000000-on-ai-in-2026-jensen-huang-/1283145157013593/

29. Post-ECI note: The Supply-side May Spoil Warsh’s Productivity Story – 2026-04-30 – https://www.employamerica.org/productivity-analysis/post-eci-note-the-supply-side-may-spoil-warshs-productivity-story/

30. The Economic Impact of Moore’s Law: Evidence from When it Faltered – 2017-01-01 – https://futuretech.mit.edu/publication/the-economic-impact-of-moores-law-evidence-from-when-it-faltered