A coalition of more than 200 economists, researchers and industry experts—including 15 Nobel Prize laureates and senior scientists from major technology firms—has issued an urgent plea for policymakers and business leaders to develop comprehensive economic frameworks for artificial intelligence. Released on Monday, the jointly signed statement represents a rare consensus among leading academics and technologists that governments cannot afford to delay action on how AI will reshape labour markets, corporate structures and public institutions across the global economy.
The signatories argue that the economic upheaval triggered by AI could dwarf previous technological revolutions, yet unfold with unprecedented speed. Where the steam engine, electricity and computer revolutions each granted societies several decades to reorganise their workforces and institutions, artificial intelligence may compress equivalent economic dislocation into just a handful of years. This compressed timeline creates an acute policy emergency that cannot be resolved through incremental responses or wait-and-see approaches that have historically guided technology regulation.
Antonis Korinek, an economics professor at the University of Virginia who organised the initiative alongside Erik Brynjolfsson, Ajay Agrawal and Tom Cunningham, articulated the core concern with stark clarity. "Steam, electricity, and computers each gave societies decades to adapt. AI may give us only a few years. We cannot improvise our strategy and institutions in the middle of the transformation; waiting for certainty means arriving too late," he stated. The warning underscores a fundamental challenge facing policymakers: traditional cost-benefit analysis and regulatory timelines may prove inadequate when technological change accelerates beyond the pace institutions can accommodate.
The statement calls for three interconnected policy priorities. First, governments and researchers must dramatically expand investigation into how AI will affect employment patterns, income distribution and economic productivity across different sectors and demographic groups. Current understanding of these impacts remains fragmentary, with most analysis focusing narrowly on specific occupations rather than systemic economic restructuring. Second, policymakers must begin designing and building the institutional architecture needed to ensure AI technologies generate broadly shared prosperity rather than concentrating gains among technology owners and a narrow skilled class. Third, societies must proactively develop risk mitigation strategies for large-scale job displacement, including social safety nets and workforce transition programmes.
The coalition's composition lends particular weight to these arguments. The signatories include financial leadership from OpenAI in the form of chief financial officer Sarah Friar, demonstrating that technology companies themselves recognise the need for external scrutiny and policy development. Jeff Dean, the chief scientist at Google DeepMind, and Jack Clark, a co-founder of Anthropic, similarly underline that major AI firms acknowledge the legitimacy of economic concerns. Notably, researchers from Anthropic—the Claude chatbot developer—joined the statement, suggesting that even companies most directly shaping AI advancement recognise the urgency of economic policy discussion.
The involvement of distinguished Nobel laureates including Michael Spence, Daron Acemoglu and Simon Johnson brings credibility to claims that this represents genuine expert consensus rather than a narrow activist perspective. These economists have shaped macroeconomic and institutional thinking over decades, and their participation signals that mainstream economics recognises AI as a phenomenon requiring fundamental rethinking of labour markets, capital allocation and social organisation. Their backing counters dismissals of AI concerns as mere technophobia or Luddite regression.
For Malaysia and Southeast Asian nations, this statement carries particular significance. The region's economies have benefited substantially from positioning themselves within global manufacturing and business process outsourcing supply chains, sectors potentially vulnerable to AI-driven automation. While technology leaders in Kuala Lumpur, Bangkok and Singapore emphasise AI's productivity potential, policymakers in the region largely lack comprehensive strategies for managing transition risks. The call from global experts for proactive institutional development suggests that waiting for AI impacts to crystallise before designing responses would be strategically unwise.
The statement also highlights a growing fault line between technological capability and policy capacity. Major AI labs now possess resources and talent that rival many government agencies, yet they operate within regulatory frameworks designed decades ago. The fact that leading technologists are themselves calling for stronger external governance structures suggests that industry self-regulation has reached its limits. This creates an opening for regional governments to shape AI governance before entrenched corporate interests and geopolitical competition calcify less-flexible arrangements.
The emphasis on research into AI's economic impacts reflects genuine uncertainty about specific outcomes. Economists remain divided on whether AI will prove massively disruptive to employment or whether historical patterns of technology creating new job categories will recur. This uncertainty, however, should not paralyse policy action. Rather, it argues for adaptive frameworks that can incorporate new evidence and adjust course as AI's actual economic effects become clearer. The statement essentially advocates for precautionary institutional development—building policy infrastructure now based on plausible risks, rather than waiting for definitive proof before responding.
The speed dimension remains central to the coalition's argument. Unlike past technological transitions where institutions evolved gradually alongside technological deployment, AI advancement is occurring so rapidly that institutional adaptation is being outpaced. Companies announce major capabilities monthly; regulation typically moves in years or decades. This structural timing mismatch means that by the time most governments complete legislative processes for AI governance, the technology may already have reshaped labour markets in ways that become difficult to reverse.
For developing economies in Southeast Asia, the statement carries an implicit warning about policy sequencing. Richer nations possess more fiscal resources and institutional flexibility to absorb and redistribute AI-driven productivity gains. Poorer economies with more vulnerable workforces and less developed social protection systems require earlier, more deliberate policy intervention to prevent AI-driven disruption from exacerbating inequality. The coalition's call for urgent action particularly applies to nations with limited capacity to manage rapid labour market transitions.
