Christopher Pissarides, the 2010 Nobel laureate in economics, has cast significant doubt on the prevailing narrative that artificial intelligence will resurrect the era of robust economic growth that has eluded developed nations for over two decades. Speaking to Bloomberg News, the London School of Economics professor specialising in labour market dynamics and automation offered a sobering assessment that contradicts the enthusiasm of technology executives and policymakers banking on AI to revitalise flagging productivity.
The stakes for this debate are substantial, particularly for Malaysia and other Southeast Asian economies watching Western policy responses. Developed nations have struggled with persistently sluggish productivity gains since the 2000s, a phenomenon that has constrained wage growth, complicated fiscal policy decisions, and contributed to political volatility across Europe and North America. Governments and technology firms have increasingly viewed artificial intelligence as a potential solution to reverse this stagnation, yet Pissarides argues such hopes rest on shaky foundations.
At the Royal Economic Society conference in Newcastle on July 6, Pissarides presented detailed analysis suggesting that fundamental constraints limit AI's transformative potential. He highlighted a critical vulnerability in the optimists' case: approximately four in ten jobs in both the United States and United Kingdom would remain largely insulated from AI disruption. Sectors such as nursing, hospitality, and personal care services—areas requiring human touch, emotional intelligence, and physical presence—cannot easily benefit from algorithmic efficiency. This structural reality means that productivity gains in AI-exposed industries would need to be extraordinarily large to compensate for the stagnation in these resistant sectors and produce the headline growth rates that enthusiasts predict.
The professor's scepticism gains weight from the absence of measurable productivity acceleration since AI systems like ChatGPT captured public imagination in late 2022. Despite months of deployment across businesses and the enormous capital investment flowing into the sector, conventional productivity metrics show little sign of the revolutionary gains that technology leaders such as Nvidia's Jensen Huang and OpenAI's Sam Altman have confidently forecast. This gap between hype and empirical evidence troubles Pissarides, who emphasises the importance of grounding economic policy discussions in observable data rather than speculative scenarios.
Drawing parallels with the personal computing revolution of the 1980s and 1990s, Pissarides argues that AI will likely produce modest improvements but cannot replicate that transformative period. During the earlier computing wave, productivity surged across multiple sectors simultaneously, creating a broadly-based economic acceleration that lifted living standards across demographic groups. The conditions enabling such comprehensive transformation appear absent today. AI deployment is concentrated in specific domains, and many sectors face inherent limitations on how much automation can meaningfully enhance output or quality.
The implications for policymakers extend beyond growth forecasts. The sluggish productivity environment across Western economies has complicated political calculations and constrained options for addressing public demands. Low growth reduces fiscal resources available for redistribution or investment without raising tax rates, while stagnant real wages fuel resentment that fuels populist movements and political polarisation. Governments desperate for an economic fix have projected onto AI the role that previous generations assigned to globalisation and technological advancement. Pissarides's warning suggests this bet may prove misplaced.
For Southeast Asian observers, the debate carries particular relevance. Malaysia and its regional neighbours have positioned themselves as beneficiaries of a potential AI-driven global economic rebound through increased foreign investment and technology transfer. However, if Western economies prove unable to generate strong growth from AI, the spillover effects that Asian policymakers anticipate could prove disappointing. Simultaneously, if AI does concentrate wealth and productivity gains in specific sectors and jurisdictions—as Pissarides implies—competition for these gains could intensify, potentially disadvantaging emerging economies.
The economist emphasised the profound uncertainty clouding any AI forecast, a caveat that tempers his pessimism but does not eliminate it. Technology rarely unfolds exactly as anticipated; genuine breakthroughs occasionally emerge to transform industries and societies. Yet the burden of proof has shifted. Rather than assuming AI will deliver revolutionary change unless evidence proves otherwise, Pissarides suggests the analytical default should recognise that even powerful technologies operate within structural constraints. Banking on AI to reverse decades of productivity stagnation requires not merely faith in technological potential but concrete evidence that such gains are materialising.
Bank of England Governor Andrew Bailey represents the counterargument, maintaining faith in AI's potential to catalyse significant economic expansion. While acknowledging that benefits would require time to materialise in official statistics, Bailey has suggested the technology could prove a decisive factor for growth. This divergence of expert opinion reflects genuine analytical disagreement about both technical possibilities and the timescale for economic integration of new capabilities. The outcome will shape macroeconomic policy, investment strategies, and labour market dynamics across developed and developing nations throughout the coming decade.
