Adam Michael Bauer & Gernot Wagner
The AI race is already generating forces that are transforming the global economy. That makes it surprisingly similar to the green transition, given the potential of both to upend traditional industries, labour markets, and geopolitical balances. Both call for trillions of dollars in upfront investments in exchange for significant benefits over the medium and long term. The promise of AI is that it will cut unnecessary costs, boost labour productivity, and help humans solve previously intractable problems. Equally, the green transition would do nothing less than contain climate change, the mother of all global externalities. It would eliminate the risk of both “climateflation” (higher prices caused by climate-driven supply shocks) and “fossilflation” (when hydrocarbon supply shocks, like the one caused by the current closure of the Strait of Hormuz, reverberate through the world economy). It would also improve public health, increase economic resilience, create jobs, preserve fragile ecosystems, and deliver many additional benefits. But while the long-run gains in each case are clear, the shorter-term effects of a mis-or unmanaged transition could be wildly disruptive. Consider the implications of a near-term spending burst. The Black Rock Investment Institute estimates that the AI buildout could increase inflation by up to half a percentage point over the next ten years, before finally mitigating inflationary pressures through productivity gains. Whether the green transition will cause near-term upward pressure on inflation is an open topic of debate. But what is not in doubt is the need for significant upfront investments to tackle major challenges down the road, combined with policy responses to manage the concurrent transition risks. One major risk associated with both the AI buildout and the green transition is worker displacement. For AI, the most direct effect may be on early-career positions in sectors like customer service and software development, where relative employment has already declined by 16 per cent in three years.
Moreover, An thropic, one of the leading AI labs, estimates that this observed displace ment refl ects only a fraction of the effect that AI could have. Among the job categories situated most squarely in AI’s automation crosshairs are white-collar occupations ranging from programming to financial and legal services. In the case of the green transition, the impacts on labour markets are potentially as large, but blue-collar workers, beginning in the fossil-energy sector, will be the hardest hit. Although few will shed tears for the laid-off investment banker, entire political campaigns, including those mounted by US President Donald Trump over the past decade, have successfully tapped into working-class voters’ frustrations with economic changes that lie outside their control.
The geopolitics of AI and the green transition are no less significant. While the United States holds the upper hand in chip design and utilisation, China has a substantial lead in green technologies—including solar, wind, and electric vehicles—and in the critical minerals that go into them. Each transition features one superpower with a large incumbency advantage, leading the other to pursue protectionist policies to aid its domestic industries. China has been pursuing its own national semiconductor industrial policy since 2014, with the goal of creating a “manufacturing ecosystem with self-sufficiency” that “[disrupts] the fabric of the global semiconductor value chain.” And former US President Joe Biden launched a green industrial policy to promote more domestic clean-energy manufacturing and supply chains. But neither superpower has achieved parity yet (owing to various green policy reversals and setbacks in the case of the US).
The similarities between the AI buildout and the green transition present opportunities for policymakers to help guide each transition. The operative word here is indeed “guide.” Since both changes are all but inevitable, it makes no sense to try to stand in their way, as the Trump administration is doing by trying to block economically advantageous renewables projects in the US. Instead, policies should aim to channel technological and market forces in the right direction, while giving due attention to their all-important distributive effects.
Among the top policy priorities is to help reskill workers and ensure that communities share in the benefits generated by renewables and data centers. In each case, the role of policy is to advance the public good. With those boxes checked, policymakers can focus on supporting the buildout itself, such as by promoting sensible permitting reform that can help overcome the sometimes-understandable NIMBY (“not in my backyard”) resistance that many projects will face. Markets will inevitably find the least costly, most immediately profitable uses for each new technology. But it is up to policymakers to seek shared, longer-term benefits and to identify potential synergies across both transitions. There are many ways that AI could accelerate the green transition, but without the right incentives in place, it could also become yet another massive source of planet-warming emissions. The time to start thinking about those incentives was yesterday.
Adam Michael Bauer is a postdoctoral research scholar at the University of Chicago Institute for Climate and Sustainable Growth and Climate Systems Engineering Initiative. Gernot Wagner is a climate economist at Columbia Business School.
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