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Big technological breakthroughs always bring major economic upheaval. From the Industrial Revolution onwards, economies that are nimble and able to adapt to productivity shocks are the ones that enjoy the most growth during periods of rapid technological change.
Managing the transition is never easy and requires a delicate balance between facilitating innovation, mitigating job displacement when it happens and ensuring that the macroeconomic settings are right so that countries can capture the long-term benefits of a technological change. The artificial intelligence (AI) revolution that is now hard upon us will be no different.
Predicting how new technologies affect structural change is always difficult, and their impacts have historically been both overestimated and underestimated: many everyday items like the microwave were on their introduction heralded as capable of revolutionising our lives. On the other hand, as eminent economist Paul Krugman famously suggested, the internet might have the same productivity effect as the fax machine.
Itās too early to tell whether AI is more microwave or Internet, but the rapid pace of improvement suggests that even if its full impact is limited to a segment of the economy, the increase it brings in the productivity of labour will not be minor. AI also promises to push productivity upward in the services sector, which typically lags behind productivity growth in the goods sector. This could revive economies like Japanās, or the eurozoneās, that are stagnating in the face of major demographic headwinds.
In this weekās lead article, Amit Singh and Adam Triggs argue that AIās disruptive potential is fundamentally a macroeconomic challenge rather than a purely technological one. Their simulation of an AI-driven productivity boom highlights some major policy imperatives ā increasing the investment capacity of national economies, reforms that make financial markets more efficient and making sure that labour can adapt and talented workers can easily move to areas where the return to their skills are most needed. Itās a compelling roadmap and one that should help policymakers in the Asia Pacific region plan for what will likely be a turbulent decade.
āUpskilling the workforce will be keyā, say Singh and Triggs. āThe AI boom creates new jobs and higher wages, particularly in STEM fields, which will encourage more people to train in those areas. But the lag-time in this upskilling means that it is crucial to get ahead of the curve and use direct subsidies to start upskilling quickly in STEM capabilities. And developed economies need to increase skilled migration, not reduce it. If businesses canāt expand due to a lack of skilled workers, the economy wonāt get the full benefits of the AI boomā. Just as the Huguenot refugees in England helped provide the skilled labour necessary for the first Industrial Revolution, skilled migrants will play a major role in building capacity in AI. The growing global backlash against immigration will only hold economies back from building the workforce they need.
Singh and Triggs emphasise the importance of access to savings, arguing that countries with deeper capital markets and sound fiscal policies will be best positioned to make the most of AI-driven growth. Doing so requires policymakers to swim against a strong protectionist and regulatory current, made more dangerous with the re-election of US President Donald Trump. The mood in Europe as well as Washington is darkening and the world economy is at risk of fracturing on geopolitical as well as economic lines.
Technological revolutions like AI do not respect national boundaries, though. Policymakers in Asia will need to be bold in rejecting the inward turn of the North Atlantic economies.
If the global economy fractures into competing blocs, access to AI-related technology, talent and capital may be determined more by geopolitical alliances than by market forces. Responding to the deteriorating institutional environment and the AI boom both ought to point in the same policy direction ā a buttressing of the liberal economic order among countries for which it is an economic necessity and the pursuit of sound macroeconomic policy.
On the domestic front, labour mobility will be key. Removing barriers to that mobility ā such as non-compete clauses and occupational licensing restrictions ā are tangible reforms that can help, but the real challenge will be in managing the social and political dimensions of AI-driven structural change.
Itās too soon to tell what shape this labour market disruption might take, but the experience of the United States during the āChina shockā, where poorly functioning tax and transfer systems did little to cushion the economic blow for workers in manufacturing, suggests that a failure to manage the politics of economic change could have big long-run political consequences.
For policymakers this is a marathon, as Singh and Triggs suggest, not a sprint. Policy needs to be responsive to new developments but also offer stability for investors. Far from an easy challenge at the best of times, this is a challenge that will prove extremely daunting for a world that seems to be daily retreating into isolation, driven by baffling policy decisions in Washington. But for those leaders who value economic growth and social cohesion, Singh and Triggs offer as good a blueprint as is out there for getting policy settings right for the next major technological revolution.
The EAF Editorial Board is located in the Crawford School of Public Policy, College of Law, Policy and Governance, The Australian National University.