Christopher Pissarides, the 2010 Nobel Prize winner in economics, has issued a sobering assessment of artificial intelligence's capacity to reinvigorate Western economies, directly contradicting the bullish predictions emanating from major technology companies and their leaders. Speaking with Bloomberg News, the London School of Economics professor argued that hopes pinned on AI as a solution to decades of sluggish growth are likely misplaced, and that policymakers and business leaders must accept a new reality of permanently slower expansion.
Pissarides, whose Nobel recognition stemmed from his groundbreaking research into labour market frictions and the mechanics of employment, identifies a fundamental limitation in AI's transformative potential: approximately four in ten jobs across the United States and United Kingdom remain largely immune to artificial intelligence's disruptive influence. Healthcare professions such as nursing and the broader hospitality sector exemplify industries where human contact and judgment remain irreplaceable and where productivity gains from automation will remain minimal. This structural constraint on AI adoption means that a substantial portion of the economy simply cannot benefit from the efficiency improvements that technology evangelists envision.
The broader context for Pissarides's scepticism reflects a decade-long malaise affecting advanced Western economies. Productivity growth has decelerated markedly since the early 2000s, creating profound implications for policymaking and public sentiment. Sluggish wage growth in real terms has corroded living standards for many workers, while reduced economic expansion has narrowed the fiscal space available for governments confronting competing demands. This stagnation has coincided with increasingly volatile political environments across Europe and North America, where frustration with economic stagnation has fuelled populist movements and social discord. In this context, technology firms and policymakers have seized on AI as a potential circuit-breaker, a transformative force that might restore the robust growth rates characteristic of earlier decades.
Yet despite these elevated expectations, Pissarides observes a conspicuous absence of empirical evidence supporting the promised productivity revolution. To date, observable productivity improvements from AI deployment remain minimal and largely confined to specific applications and sectors. This gap between rhetoric and reality has prompted Pissarides to question the claims advanced by prominent industry figures including Jensen Huang, chief executive of Nvidia, and Sam Altman of OpenAI, both of whom have articulated sweeping predictions about AI's capacity to reshape labour markets and economic output.
During a lecture delivered on July 6 at the Royal Economic Society conference in Newcastle, Pissarides elaborated on his pessimistic outlook by examining the mathematical and practical constraints facing productivity growth even under optimistic AI adoption scenarios. He contended that achieving the elevated growth rates predicted by technology optimists would require extraordinarily large productivity improvements concentrated in sectors most exposed to artificial intelligence disruption, particularly financial services. Yet even within finance—perhaps the sector most amenable to algorithmic automation—such dramatic efficiency gains appear unrealistic given existing capital constraints, regulatory frameworks, and the skill requirements embedded in sophisticated financial decision-making.
Drawing on his expertise regarding technological disruption in labour markets, Pissarides cautioned against the assumption that productivity gains will necessarily materialise and compound across the economy. The historical precedent frequently invoked by AI enthusiasts—the computing revolution of the 1980s and 1990s—provides an imperfect template for contemporary expectations. That earlier technological transformation occurred within specific economic and organisational contexts that facilitated rapid adoption and integration. The current technological landscape presents different constraints, and Pissarides explicitly stated his doubt that AI would generate productivity benefits approaching the magnitude of the computer era.
For Southeast Asian policymakers and business leaders monitoring these debates, Pissarides's analysis carries substantial relevance. Many governments across the region have articulated strategies positioning their economies as AI innovation hubs or AI-adopting nations, betting that technological advancement will accelerate growth and create employment opportunities. The Malaysian government, alongside peers in Thailand, Indonesia, and Singapore, has invested in digital infrastructure and education initiatives with implicit assumptions about AI's transformative potential. Pissarides's cautionary perspective suggests that such bets should be hedged with realistic expectations about productivity gains and employment disruption.
Moreover, the sectoral composition of Southeast Asian economies introduces specific vulnerabilities and opportunities within this framework. Many regional economies remain heavily dependent on labour-intensive manufacturing, agriculture, and service sectors—precisely the domains where AI's disruptive capacity may prove most severe even as productivity gains remain modest. The contradiction between job displacement and productivity improvement could generate acute social pressures across the region, particularly if retraining and social protection systems prove inadequate.
Pissarides's position represents a minority view within elite economic circles, where optimism about AI persists despite the absence of compelling productivity data. Bank of England Governor Andrew Bailey exemplifies the prevailing institutional perspective, suggesting that AI constitutes a potentially transformative technology capable of rescuing Western economies from their current trajectory. Bailey has cautioned that measurable economic impacts will require time to materialise, but he maintains confidence in AI's ultimate significance. This divergence between Pissarides and establishment figures like Bailey reflects genuine intellectual disagreement about technological trajectories and economic potential.
Yet Pissarides's realism about productivity growth prospects offers a counterweight to potentially excessive expectations. By acknowledging that rapid productivity growth may represent a historical aberration rather than an achievable baseline, he implicitly suggests that policymakers should prepare for a world of persistent modest growth, structural employment challenges, and difficult distributional questions about how gains are shared. Accepting this premise need not imply resignation to stagnation, but rather requires recalibrating expectations and redirecting policy focus toward managing transitions and protecting vulnerable populations rather than betting everything on technological salvation. For policymakers across Southeast Asia and the world, that reorientation may ultimately prove more prudent than the current widespread enthusiasm for AI as a growth panacea.
