The widespread belief that artificial intelligence will rescue struggling Western economies from persistent slowdown faces a significant challenge from a leading voice in labour economics. Christopher Pissarides, the London School of Economics professor who won the Nobel Prize in economics in 2010 for research into labour market dynamics, has cast doubt on whether AI will deliver the productivity surge that technology leaders and policymakers are banking on to revive flagging growth rates across developed nations.
Pissarides, whose expertise centres on understanding how automation affects employment and work, presented a sobering assessment in recent remarks, contending that substantial portions of the labour market will prove largely resilient to artificial intelligence's disruption. According to his analysis, approximately four in ten jobs across the United States and United Kingdom sit outside the reach of AI-driven transformation, encompassing sectors such as nursing, elderly care, and hospitality services where human interaction and physical presence remain irreducible requirements.
The economist's intervention carries particular weight given that multinational technology corporations and government officials have increasingly leaned on artificial intelligence as a potential remedy for the anaemic growth that has characterised Western economies since the global financial crisis. For over a decade, productivity gains have remained stubbornly modest across developed markets, creating cascading policy complications for central banks and treasuries while simultaneously constraining wage growth in real terms, a dynamic that has contributed to political turbulence and citizen dissatisfaction in democracies from Brussels to Westminster.
Contesting the bullish narrative promoted by technology chieftains including Nvidia's Jensen Huang and OpenAI's Sam Altman, Pissarides pointed to the absence of compelling evidence that artificial intelligence has meaningfully lifted productivity metrics to date. His scepticism extends to their confident predictions about the technology's capacity to fundamentally reshape labour markets and economic output. The distinction matters significantly because much of the policy optimism surrounding artificial intelligence depends on assumptions about imminent productivity acceleration that empirical data has yet to substantiate.
The structural constraint Pissarides identifies proves particularly revealing about artificial intelligence's genuine economic potential. A substantial cohort of British and American workers, representing at least 40 percent of the employed population, operate in roles that artificial intelligence systems cannot feasibly enhance or automate, meaning these sectors cannot generate the productivity multipliers that growth optimists anticipate. Without transformative improvements across the economy as a whole, even concentrated artificial intelligence deployment in exposed sectors cannot generate the kind of macroeconomic acceleration that governments are hoping will ease fiscal pressures and restore confident consumer spending.
Drawing on historical precedent to temper current expectations, Pissarides expressed profound doubt that artificial intelligence will replicate the sustained productivity boom that characterised the computer revolution spanning the 1980s and 1990s. During that transformative period, information technology deployment across manufacturing, finance, and services generated measurable acceleration in output per worker that fundamentally reshaped developed economies. He cautioned that contemporary observers should resist the assumption that artificial intelligence will achieve equivalent breakthroughs, particularly given what current evidence reveals about the technology's actual deployment and real-world performance.
During a lecture delivered on July 6 at the Royal Economic Society conference held in Newcastle, Pissarides constructed a more detailed argument around the mathematical constraints facing artificial intelligence-driven growth. He contended that even achieving the ambitious productivity scenarios outlined by technology optimists would demand extraordinarily large efficiency gains concentrated in sectors most exposed to artificial intelligence displacement, principally finance and professional services. The notion that such gains could materialise at the scale required to drive substantial overall economic expansion struck him as impractical given technological and market realities.
Pissarides's conclusion carries unsettling implications for policymakers navigating contemporary economic challenges. He argued that acknowledging the likelihood that rapid productivity growth belongs to history, regardless of artificial intelligence's trajectory, represents a necessary adjustment to policy expectations. Rather than constructing economic forecasts and fiscal frameworks predicated on artificial intelligence-enabled acceleration, governments and central banks should instead calibrate their approaches to a future marked by persistent moderate growth and constrained resource availability.
The economist's intervention creates notable tension with the optimistic assessments emerging from monetary policy circles. Andrew Bailey, governor of the Bank of England, has positioned artificial intelligence as a potentially transformative force capable of delivering material improvements in economic growth rates. While Bailey has acknowledged that artificial intelligence's positive effects will require time to manifest in official growth statistics, he suggested the technology "may well ride to the rescue" from current stagnation pressures. This divergence between Bailey's cautious optimism and Pissarides's scepticism reflects broader uncertainty within economic policy institutions about artificial intelligence's actual contribution to productivity.
For Malaysia and Southeast Asia, Pissarides's assessment offers important cautionary lessons as regional policymakers contemplate artificial intelligence investment and integration strategies. The region's growth ambitions increasingly depend on technological advancement and productivity gains rather than demographic expansion or resource extraction. If artificial intelligence proves less transformative than optimists suggest, developing economies relying on artificial intelligence adoption to maintain competitive advantage and growth momentum may face disappointment. The Malaysian context, where manufacturing, financial services, and technology sectors represent growth drivers, necessitates realistic assessment of artificial intelligence's genuine capacity to enhance productivity rather than aspirational thinking that may misdirect capital and policy attention.
The broader economic implication extends beyond artificial intelligence itself to challenge the philosophical foundations underlying contemporary growth expectations. For decades, Western policy establishments have treated technological breakthroughs as primary drivers capable of resolving structural economic challenges. Pissarides's perspective suggests this framework may require fundamental recalibration. If rapid productivity growth truly belongs to history, developed and developing economies alike must reconsider how to construct sustainable prosperity within a slower-growth framework, potentially redirecting focus toward equitable distribution, social resilience, and quality-of-life metrics rather than pursuing growth acceleration as the paramount policy objective.
