“It is called the lump-of-labor fallacy for a reason. Who knew when the internet was born that the internet was going to create a million and a half jobs as Uber drivers? We are in the first or second inning of [the AI] revolution. This is a big paradigm shift, both for the conduct of our policy and for our economies. I think the jobs will be greater and prosperity will be stronger.” – 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

Anxiety about artificial intelligence and jobs reflects a deeper confusion about how modern economies generate work, absorb technological shocks, and turn productivity gains into living standards. In public debate, the prospect of widespread automation is often treated as a zero-sum contest in which every task performed by a machine implies a human job lost forever. That framing misdiagnoses both the nature of labour demand and the historical record of past technological upheavals, from electrification and mass manufacturing to the internet and platform economies.^1,6 It also risks steering policy towards defensive attempts to freeze existing job structures rather than towards the institutional and macroeconomic adjustments needed to harness a new wave of productivity growth.^5,11

The lump-of-labour intuition and why it keeps returning

The underlying belief that fuels much contemporary fear is the notion that there is a fixed quantity of work to be done in an economy. If technology allows one group of workers, or machines, to perform more tasks, the remaining workers are assumed to be surplus to requirements.^6,12 Economists label this the lump-of-labour fallacy: the mistaken idea that the aggregate demand for labour is constant, so any addition of workers or machines simply redistributes a finite stock of jobs.^6,12

The doctrine has intuitive appeal. Individuals experience work as slots: a firm has a certain number of positions; if a robot takes one of those positions, a person is displaced. At the level of a single factory or office, that description may be accurate in the short term. But labour demand at the macroeconomic level is a derived demand: it is determined by the demand for goods and services that labour helps produce.⁶ When productivity increases, unit costs tend to fall, prices are reduced or quality improves, and new customers enter the market. The resulting expansion of output often raises, rather than reduces, total labour demand, even if the mix of occupations changes.

Historically, each major general-purpose technology has triggered a similar wave of fear. Mechanisation provoked nineteenth-century worries about permanent unemployment among textile workers; electrification reshaped manufacturing employment; computers were forecast to end clerical work. Yet in advanced economies, employment as a share of the adult population rose over long periods, especially for women, while working hours declined.^6,15 The fallacy persists because transitional pain is real: job losses in specific sectors occur before new roles are visible, and those losses are concentrated in communities and occupations with limited mobility.

Internet platforms and the emergence of unforeseen occupations

The experience of the commercial internet illustrates how technological change can generate new forms of work that would have been difficult to anticipate during its early development. In the 1990s, policymakers discussed the web primarily as an information medium and a communication tool. Few foresaw a global ecosystem of search engine optimisation consultants, social media managers, app developers, and content creators. Ride-hailing services provide a vivid example: by combining smartphones, mapping, digital payments and algorithmic matching, platforms such as Uber enabled millions of people to earn income through quasi-formal driving work, often on a flexible basis that blurred the boundary between employment and self-employment.^1,4

From an aggregate labour-market perspective, this was not a straightforward net addition of jobs. Platform driving substituted for some traditional taxi work and for other forms of casual labour. But the key analytical point is that the technology did not merely redistribute a pre-existing lump of tasks. It lowered transaction costs in urban transport, brought new riders into the market, and turned underutilised time and assets into productive inputs. In standard economic terms, a fall in the effective price of rides expanded demand; as demand rose, so did the derived demand for driving services, even if the nature and stability of those roles raised separate regulatory questions.⁶

For central banks and macroeconomic policymakers, the internet boom posed an early version of today’s challenge: how to interpret a technology-driven surge in productivity that coexisted with structural labour-market change. Alan Greenspan’s decision to allow the US economy to “run hot” in the late 1990s rested on a belief that higher measured and unmeasured productivity growth would allow faster output expansion without igniting inflation.¹⁹ That judgement reflected a willingness to treat technological progress as a disinflationary force, at least while labour supply and institutional frameworks could accommodate workers moving into new sectors.

AI as a new general-purpose technology

Artificial intelligence now occupies a similar, but more pervasive, role in macroeconomic debate. Advances in generative models, pattern recognition and optimisation promise simultaneous shocks to productivity across services, manufacturing, logistics, finance and creative industries. Senior policymakers increasingly frame AI as a general-purpose technology with the potential to reshape both the structure of the economy and the transmission of monetary policy.^5,11,23

Kevin Warsh has argued in speeches and testimony that AI is rapidly reshaping the US economy and that the Federal Reserve will need substantial changes in its models to account for the technology’s impact.^5,11 His view emphasises AI as a significant disinflationary force: by enabling workers and firms to produce more output with the same or fewer inputs, AI should lower the cost of goods and services and relieve some of the upward pressure on prices.^11,23 That perspective connects directly to the historical experience of the internet and earlier waves of digitisation, in which higher productivity helped central banks maintain or lower interest rates even as economies expanded.^11,19

Conceptually, an AI-driven productivity shock can be represented in standard growth accounting. Output Y depends on capital K, labour L, and total factor productivity A. A simple formulation is Y = A \times F(K,L). AI, in this framing, operates primarily through A: better algorithms, decision-making and automation raise A, allowing higher Y for given K and L. If monetary policy holds nominal demand roughly stable, higher A can lead to downward pressure on prices rather than reduced employment, provided that labour and capital reallocate towards expanding sectors.

The macroeconomic policy tension: productivity vs unemployment risk

The debate among central bankers and economists centres on how quickly and smoothly that reallocation occurs, and whether AI’s productivity effects could be so extreme that they overwhelm traditional adjustment mechanisms. At the European Central Bank’s annual forum, participants highlighted both the opportunity for efficiency gains and the risk that AI could accelerate bubbles, transmit shocks through financial markets, and destabilise labour demand in ways that conventional models may not capture.¹⁰

One scenario discussed in policy circles imagines AI-driven automation replacing large segments of routine and cognitive work, from call centres and paralegal tasks to driving and warehousing. If the resulting productivity gains are huge, the cost of these services might plummet. Consumers would have more disposable income, which they could spend on new goods and experiences, creating fresh jobs elsewhere.^9,21 This is the canonical anti-lump-of-labour mechanism. However, if automation outpaces the creation of new high-demand sectors, or if capital owners capture the bulk of the gains without translating them into broader spending, the adjustment could be slower, leading to pockets of long-term unemployment and downward pressure on wages.²¹

Critics of an overly optimistic view argue that standard economic reasoning relies on assumptions about demand elasticity and redistribution that may not hold in extreme AI scenarios.^21,8 If machines perform an ever-widening set of tasks, including creative and managerial functions, the share of income going to labour could fall significantly. In that case, the circular flow described in textbooks-workers earn wages, spend them on goods and services, businesses hire more workers-may be disrupted. The lump-of-labour fallacy, they contend, is not a fallacy in every conceivable technological regime; rather, it is an empirical claim about how far substitution can proceed before the structure of income and demand fundamentally changes.²¹

Warsh’s stance within the emerging policy landscape

Warsh’s position sits within a broader spectrum of central bank thinking. He acknowledges labour concerns but insists that the labour market will evolve over time, with new roles emerging even as some existing jobs are displaced.^1,5,20 In his public remarks, he stresses that AI does not repeal the basic logic of competitive markets or the derived nature of labour demand.^9,24 Instead, he views AI as an accelerant of trends already familiar from earlier technological shifts, such as the internet: greater productivity, lower costs, and new categories of employment that are difficult to forecast in advance.^1,5

For the Federal Reserve, such a stance has concrete policy implications. If AI is expected to be a disinflationary force, the central bank may judge that the neutral interest rate-consistent with stable inflation and full employment-is lower than previously assumed.¹¹ That could justify a more accommodative stance than headline inflation data might suggest, especially if AI-related productivity gains are initially undercounted. However, Warsh has also emphasised that near-term inflation risks have only recently declined and that the Fed must still work to manage elevated prices.^2,13 AI therefore becomes part of a medium-term story about potential growth and the equilibrium real interest rate, rather than a justification for immediate aggressive easing.

Furthermore, Warsh’s comments about updating the Fed’s models indicate an institutional recognition that standard forecasting tools-such as Phillips curve-based relationships between unemployment and inflation-may not fully capture AI-induced shifts in labour-market behaviour.⁵ If workers can augment their output dramatically using AI tools, measures of slack and productivity may become more volatile. Monetary policy would need to incorporate richer data on technology adoption, sectoral reallocation, and wage dispersion to avoid misreading the signals.

Why the lump-of-labour dispute matters for AI governance

The argument over whether fears of job loss are a fallacious carryover from earlier eras, or a rational response to a uniquely powerful technology, shapes not just interest-rate decisions but the broader governance of AI. If policymakers are persuaded that labour demand is fundamentally elastic and that new jobs will arise to absorb displaced workers, they are more likely to focus on transitional support: retraining schemes, portable benefits, and regional adjustment policies.^6,24 If, by contrast, they believe that AI could induce a structural decline in labour’s share of income and a persistent shortage of suitable jobs, they may consider more radical measures such as universal basic income, public job guarantees, or aggressive regulation of automation.

Economic history offers both reassurance and caution. On the reassuring side, the simple circular-flow reasoning supported by empirical studies suggests that as workers gain new jobs and income, the additional spending increases demand for goods and services and thereby for labour.⁶ Labour is not a fixed resource; workers move from declining sectors to expanding ones, and the “economic pie” grows over time.⁶ On the cautionary side, globalisation and digitalisation have already produced regions and cohorts experiencing years of stagnation or decline, even while aggregate indicators improved.^12,15 Institutional context-education systems, social insurance, labour-market flexibility, competition policy-determines how effectively economies reallocate workers.

In the AI context, the distributional question looms particularly large. If AI makes low-wage jobs more productive, allowing the same output with fewer workers, the proportion of middle- and high-wage roles could rise, potentially reducing income inequality by expanding access to high-value services for lower-income households.⁹ Yet that benign scenario depends on complementary policies that foster broad AI access, prevent excessive concentration of market power, and mitigate the risks of algorithmic discrimination or exclusion.

Debates, objections and political constraints

Warsh’s optimism about jobs and prosperity attracts several lines of criticism. First, some economists argue that general-purpose technologies may differ in their labour impact depending on which tasks they primarily automate.^21,8 The internet and earlier computing waves largely enhanced information access and communications, creating new industries around digital advertising, e-commerce and user-generated content. AI systems that can handle complex reasoning, code generation and professional services may threaten a wider range of occupations, including many that were previously insulated.

Second, sceptics question the pace at which new roles appear. Historically, job-creating industries have sometimes lagged behind job-destroying innovations, leaving multi-decade stretches of adjustment. If AI accelerates both destruction and creation, but institutional mechanisms for retraining and relocation remain slow, communities dependent on vulnerable sectors could face prolonged stress. The lump-of-labour fallacy, they suggest, may be less a logical error and more a reflection of practical bottlenecks in matching displaced workers to emerging opportunities.^12,21

Third, political economy considerations complicate the picture. Large firms investing heavily in AI may have incentives to lobby for regulatory frameworks that favour capital-intensive solutions over labour-intensive ones. If those firms capture outsize productivity gains and return them primarily to shareholders, the macroeconomic feedback loop from wages to demand could weaken.²¹ Central banks might then confront a world in which headline productivity is strong, inflation is subdued, but labour participation and wage growth stagnate, challenging traditional mandates focused on maximum employment and price stability.

Finally, there is an epistemic objection: the claim that we simply do not yet know enough about AI’s trajectory to make confident statements about its long-run labour impact.^5,10,14 Warsh himself acknowledges that key effects remain uncertain and that the Fed must update its frameworks as evidence accumulates.⁵ This humility contrasts with stronger pronouncements from both techno-optimists and AI pessimists, underscoring that the argument about the lump-of-labour fallacy is partly a dispute about the reliability of historical analogies in unprecedented circumstances.

Why the argument matters for future prosperity

Despite disagreement on specifics, the discussion reveals a common thread: the prosperity of advanced economies over the coming decades will hinge on how effectively they translate AI-driven productivity into broad-based gains in employment, income and welfare. A policy stance that overestimates the risk of permanent job loss may discourage innovation and lead to defensive regulation that freezes outdated production structures. Conversely, an overly sanguine belief that jobs will always appear could justify neglect of transitional support and structural reforms.

Warsh’s framing places central banks in an active role. By recognising AI as both an opportunity and a source of model uncertainty, monetary authorities are encouraged to reconsider assumptions about equilibrium interest rates, inflation dynamics and labour-market slack.^5,11 At the same time, their mandate does not extend to redistributive policy, education or industrial strategy. That division of responsibilities means that even if AI turns out to be strongly disinflationary and job-creating at the aggregate level, failings in other parts of the policy system could still leave many workers behind.

The unresolved tension between lump-of-labour intuitions and dynamic labour-demand models therefore matters far beyond academic economics. It shapes public expectations, electoral debates and the legitimacy of institutions tasked with managing technological transitions. If societies accept that the amount of work is not fixed but remain sceptical that new work will be accessible and decent, they may demand more explicit guarantees of inclusion. The way central bankers, including Warsh, articulate the relationship between AI, jobs and policy will influence whether those demands are channelled into constructive reforms or into resistance to technological change itself.^1,5,10

Ultimately, the question is not whether AI will eliminate a certain share of current occupations, but whether economic systems can be steered so that higher productivity expands the range of meaningful, well-paid work rather than narrowing it. Contesting the lump-of-labour fallacy is one part of that steering: it insists that labour demand can grow with innovation. The harder task lies in building the institutional scaffolding-skills, mobility, safety nets, competition frameworks-that ensures the expansion of jobs and prosperity that optimistic policymakers anticipate becomes a lived reality rather than a theoretical promise.

 

References

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

2. Kevin Warsh Downplays Concerns Of Job Losses Due To AI, Says … – 2026-07-01 – https://stocktwits.com/news-articles/markets/equity/kevin-warsh-downplays-ai-job-loss-concerns-not-a-doomer/cZm3LPeR7Kx

3. Fed Chairman Warsh: Inflation risks declining, AI to create jobs – 2026-07-01 – https://www.nbcnews.com/business/economy/fed-chairman-warsh-inflation-ai-jobs-rcna352550

4. Lump of Labor Fallacy: AI’s True Impact on Work – Draup – 2023-07-03 – https://draup.com/talent/ceo-newsletter/ai-and-the-evolution-of-work-unraveling-the-lump-of-labor-fallacy

5. Uber sues New York City over ‘reckless’ driver protection law – Reuters – 2026-06-10 – https://www.reuters.com/business/uber-sues-new-york-city-over-reckless-driver-protection-law-2026-06-10/

6. Federal Reserve Nominee Warsh Says AI is Reshaping the Economy – 2026-04-21 – https://www.meritalk.com/articles/federal-reserve-nominee-warsh-says-ai-is-reshaping-the-economy/

7. Examining the ‘Lump of Labor’ Fallacy Using a Simple Economic … – 2020-11-02 – https://www.stlouisfed.org/publications/page-one-economics/2020/11/02/examining-the-lump-of-labor-fallacy-using-a-simple-economic-model

8. ‘Grandma Rule’ culture: Local movers hiring team-focused workers – 2026-06-30 – https://www.aol.com/news/grandma-rule-culture-local-movers-214148047.html

9. Kevin Warsh Is Already Getting It Wrong – The New York Times – 2026-05-12 – https://www.nytimes.com/2026/05/12/opinion/kevin-warsh-fed-ai.html

10. Studies Show AI Triggers Delirium in Leading Experts – In the Arena – 2024-10-15 – https://www.policyarena.org/p/studies-show-ai-triggers-delirium

11. Analysis-AI hopes and fears dominate global central bank meet – 2026-07-01 – https://kfgo.com/2026/07/01/analysis-ai-hopes-and-fears-dominate-global-central-bank-meet/

12. Breakingviews – Kevin Warsh has a point on AI and inflation | Reuters – 2026-03-05 – https://www.reuters.com/commentary/breakingviews/kevin-warsh-has-point-ai-inflation-2026-03-04/

13. Automation and the Lump of Labor fallacy – Notre Dame Sites – 2025-06-12 – https://sites.nd.edu/lawrence-c-marsh/2025/06/12/automation-and-the-lump-of-labor-fallacy/

14. All eyes are on Fed chair Kevin Warsh’s first moves on interest rates – 2026-06-16 – https://www.latimes.com/business/story/2026-06-16/all-eyes-turn-to-fed-chair-kevin-warsh-his-first-moves-on-interest-rates

15. Warsh on AI: Filled with opportunity and risks – YouTube – 2026-06-17 – https://www.youtube.com/shorts/GQcDpfY6dSo

16. Lump of labor fallacy in regards to AI? : r/AskEconomics – Reddit – 2023-09-17 – https://www.reddit.com/r/AskEconomics/comments/16kqz6k/lump_of_labor_fallacy_in_regards_to_ai/

17. Kevin Warsh | Hoover Institution – https://www.hoover.org/profiles/kevin-warsh

18. Fed Chairman Kevin Warsh discussed how he thinks about AI when … – 2026-06-17 – https://www.instagram.com/reel/DZsvL8HAjRJ/

19. AI Automation Won’t Replace Humans | Jason Calacanis posted on … – 2026-02-25 – https://www.linkedin.com/posts/jasoncalacanis_the-lump-of-labor-fallacy-is-the-biggest-activity-7432564237287464960-r8l2

20. Federal Reserve Chair Kevin Warsh thinks AI will push … – Instagram – 2026-06-19 – https://www.instagram.com/reel/DZxk8v3CQ4W/

21. 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/

22. Four Scenarios of Job-Reducing AI – LessWrong – 2026-03-10 – https://www.lesswrong.com/posts/qHTEoHcmYzvDrQ7gW/four-scenarios-of-job-reducing-ai

23. Kevin Warsh is comparing AI to the 1990s Internet boom … – Facebook – 2026-04-20 – https://www.facebook.com/labcoatagents/posts/kevin-warsh-is-comparing-ai-to-the-1990s-internet-boom-and-pushing-the-fed-to-cu/10236653842580774/

24. Warsh’s AI task force could reshape Fed economic models – Facebook – 2026-06-23 – https://www.facebook.com/TheStreet/posts/warshs-ai-task-force-could-reshape-fed-economic-modelswarshs-quiet-ai-move-could/1550754776645901/

25. The lump-of-labor fallacy: AI, jobs, and economic growth – LinkedIn – 2025-08-04 – https://www.linkedin.com/posts/kannan-raghavan-251695179_will-ai-take-away-jobs-lump-of-labor-fallacy-activity-7358122759236829185-8rEV

26. 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/

27. Kevin Warsh – Wikipedia – 2006-04-15 – https://en.wikipedia.org/wiki/Kevin_Warsh