The AI race is already creating forces that are transforming the global economy. As such, they are strikingly similar to the green transition, given that both have the potential to upend traditional industrial, labor market, and geopolitical balances. Both require trillions of dollars of upfront investment in exchange for significant gains over the medium to long term.
The promise of AI is to reduce unnecessary costs, increase labor productivity, and help humans solve problems that were previously unsolvable. Similarly, the green transition is all about curbing climate change, the mother of all global externalities. This would eliminate the risks of both “climateflation” (price increases caused by climate change-induced supply shocks) and “fossilflation” (when hydrocarbon supply shocks, such as those caused by the current closure of the Strait of Hormuz, spill over into the global economy). It will also improve public health, increase economic resilience, create jobs, protect fragile ecosystems, and deliver many more benefits.
However, while the long-term benefits in each case are clear, the short-term effects of a misguided or mismanaged transition can be devastating. Consider the impact of short-term spending spikes. BlackRock Investment Institute estimates that increased AI could increase inflation by up to 0.5 percentage points over the next decade. Ultimately, it eases inflationary pressures through increased productivity. It is debatable whether the green transition will cause upward pressure on inflation in the short term. However, there is no question that significant upfront investment will be required to tackle the big challenges ahead, along with policy responses to manage concurrent transition risks.
One of the big risks associated with both AI ramp-up and environmental migration is the displacement of workers. In the case of AI, the most direct impact will likely be on early career jobs in areas such as customer service and software development, where relative employment has already declined by 16% in three years. Furthermore, Anthropic, one of the leading AI research institutes, estimates that this observed displacement reflects only a fraction of the impact that AI can have. White-collar occupations are among the most targeted for AI automation, from programming to financial and legal services.
In the case of a green transition, the impact on the labor market is potentially equally large, but the impact on blue-collar workers is greater. Workers, especially those in the fossil energy sector, will be hit hardest. Few would shed a tear for a fired investment banker, but entire political movements, including those run by President Donald Trump over the past decade, have successfully capitalized on working-class voters’ frustration with economic changes outside of their control.
The geopolitics of AI and the green transition are equally important. While the United States has an advantage in chip design and use, China has a significant lead in green technologies such as solar power, wind power, and electric vehicles, as well as the critical minerals they contain.
In each transition, one superpower will have a significant incumbency advantage and the other will pursue protectionist policies to support domestic industry. Since 2014, China has been promoting its own national semiconductor industry policy with the goal of building a “self-sufficient manufacturing ecosystem.”[disrupts] The structure of the global semiconductor value chain. ” and former US President Joe Biden launched a green industrial policy to further promote domestic clean energy manufacturing and supply chains. However, neither superpower has yet reached parity (in the case of the United States, due to various environmental policy reversals and rollbacks).
The similarities between building AI and green transitions provide an opportunity for policymakers to guide each transition. The operative word here is just “guide.” Both changes are almost inevitable, so it makes no sense to try to thwart them, as the Trump administration is trying to do to block economically advantageous renewable energy projects in the United States. Instead, policy should aim to channel technological and market forces in the right direction, while paying due attention to their most important distributional effects.
One of the top policy priorities is to help reskill workers and ensure that communities can share in the benefits of renewable energy and data centers. In both cases, the role of policy is to promote the public interest. Checking these boxes allows policymakers to focus on supporting the construction itself, including promoting smart permitting reforms that help overcome the sometimes understandable NIMBY (“not in my backyard”) resistance that many projects face.
The market will inevitably find the lowest cost, most immediately profitable use for each new technology. However, it is up to policymakers to pursue shared long-term benefits and identify potential synergies across both transitions. There are many ways AI can accelerate the green transition. But without the right incentives, they could become a new source of massive emissions that contribute to global warming. It was only yesterday that I started thinking about these incentives.
Adam Michael Bauer is a postdoctoral fellow at the University of Chicago Institute for Climate and Sustainable Growth and the Climate Systems Engineering Initiative.
Garnaut Wagner is a climate economist at Columbia Business School.
Copyright: Project Syndicate, 2026.
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