Christopher Pissarides, an economist who won the Nobel Prize for studying automation's effects on employment, has delivered a sobering assessment of artificial intelligence's economic prospects. Speaking to Bloomberg News, the London School of Economics professor contended that AI will not restore Western economies to the sustained high-growth periods they experienced in earlier decades. His intervention strikes directly at the technological hopes that governments and corporate leaders have increasingly embraced as a solution to persistent economic sluggishness across developed nations.
The disappointing trajectory of productivity in recent decades has created genuine policy challenges for Western governments. Unlike the robust expansions of the post-war era, economic growth across Europe and North America has decelerated substantially, constraining the fiscal room available for addressing social priorities and welfare programmes. This stagnation has coincided with stagnant real wage progression, eroding living standards for working populations and contributing to the political instability and populist sentiment that has characterised many Western democracies. Against this difficult backdrop, technology advocates and policymakers have fastened onto artificial intelligence as a potential remedy, imagining that breakthrough innovations might finally unlock the productivity improvements that have remained elusive.
However, Pissarides raises a fundamental objection to this narrative. He emphasises that approximately four in ten jobs across the United States and United Kingdom will experience minimal disruption or transformation from AI technologies. Service-oriented sectors such as nursing, hospitality, and allied healthcare fields represent vast employment categories that operate in inherently human-centred environments. These occupations rely on interpersonal connection, physical proximity, and judgment calls rooted in contextual understanding—precisely the domains where AI's substitution capacity remains strictly bounded. The existence of this substantial insulated workforce segment effectively caps the aggregate productivity gains that economy-wide AI deployment could theoretically generate.
Crucially, despite the considerable hype surrounding artificial intelligence, Pissarides observes that tangible productivity improvements from the technology remain conspicuously absent from economic data. No measurable acceleration in output per worker has yet materialised across major economies. This empirical absence becomes particularly significant when contrasted against the technology sector's exuberant predictions. Industry figures such as Jensen Huang, the chief executive of chip manufacturer Nvidia Corporation, and Sam Altman, who leads OpenAI, have circulated sweeping claims regarding AI's capacity to fundamentally reshape work and economic output. Yet measured economic performance has so far failed to validate these assertions.
Drawing on historical precedent, Pissarides specifically referenced the computing revolution of the 1980s and 1990s as a benchmark against which current AI prospects should be evaluated. During that decades-long period, the widespread adoption of personal computers and networked information systems genuinely did precipitate sustained waves of productivity acceleration across multiple economic sectors. That transformative episode serves as the implicit comparison point for contemporary AI enthusiasm. Pissarides expresses profound scepticism about whether artificial intelligence will replicate that magnitude of impact, suggesting instead that even optimistic scenarios would fall considerably short of matching those historical productivity gains.
The theoretical foundation for achieving the growth levels that AI proponents envision would require extraordinary productivity breakthroughs concentrated in the sectors most exposed to the technology's capabilities. Finance represents perhaps the clearest example of an industry where algorithmic systems and machine learning already operate at significant scale. Yet even if financial services experienced dramatic efficiency improvements, the sector's limited share of total employment means economy-wide effects would remain constrained. Pissarides argues persuasively that such concentrated gains within particular industries cannot compensate for the majority of the workforce operating in domains where AI's applicability remains marginal.
During a presentation at the Royal Economic Society conference held in Newcastle on July 6, Pissarides articulated his core conclusion with particular clarity. He contended that discussing sustained rapid productivity growth has become unrealistic given current technological trajectories and observable economic performance. Rather than maintaining hope for transformative breakthroughs, policymakers and citizens should accept that the extended era of substantial productivity expansion has fundamentally ended. This represents a significant intellectual shift from the consensus narrative that dominated economic policy discourse, which treated AI as a potential silver bullet for stalled growth.
Not all policymakers have abandoned optimism regarding artificial intelligence's economic significance. Andrew Bailey, who serves as Governor of the Bank of England, remains convinced that AI could fundamentally alter economic growth prospects. While acknowledging that integrating the technology throughout economic systems will consume considerable time before measurable statistical impacts emerge, Bailey has suggested that artificial intelligence might ultimately "ride to the rescue" for struggling Western economies. His stance reflects the persistent hope within policy circles that technological solutions can resolve the structural challenges constraining current growth.
The divergence between Pissarides' measured assessment and Bailey's technological optimism encapsulates a broader debate animating economic policymaking. For emerging market economies including Malaysia and other Southeast Asian nations, this discussion carries particular resonance. These regions have historically pursued growth trajectories dependent on labour-intensive manufacturing and services, sectors where AI disruption could prove simultaneously transformative and destabilising. Understanding whether AI will genuinely alter global productivity patterns—or whether developed economies face permanent slower-growth futures—fundamentally shapes investment decisions, human capital development strategies, and policy frameworks across the developing world.
Pissarides' perspective also challenges the underlying assumptions structuring much contemporary economic discourse. If his analysis proves correct, the appropriate policy response would involve adjusting expectations, reforming social safety nets, and reconceptualising national competitiveness around factors beyond labour productivity. For Malaysia's policymakers considering artificial intelligence investments and economic restructuring, embracing Pissarides' more constrained forecast rather than the more exuberant visions circulating in Silicon Valley might yield more durable policy frameworks. The economist's warnings suggest that building strategies around modest, incremental AI-driven improvements rather than transformative productivity leaps would constitute a more prudent foundation for long-term economic planning.
