Technology routinely creates more jobs than it eliminates. The diffusion of computers in the United States since 1970 has led to a loss of 3.5 million jobs in such fields as typewriter manufacturing but has created more than 19 million jobs in areas ranging from computer manufacturing to e-commerce. And, according to one prominent study, the technology-driven creation of new occupations accounted for 85 percent of employment growth over a period of 78 years.

Building the infrastructure for new technologies such as AI is itself a source of job creation. For example, management consulting firm McKinsey estimates that the United States will need 130,000 additional electricians in the coming years, largely because of the build-out of data centers and manufacturing facilities. This demand surge will also affect a range of other occupations, from welders to HVAC engineers. And we can only begin to imagine the other jobs that will flow from AI’s integration into our economy, from those focused on building autonomous vehicles to positions specialising in robotics or the future of gaming.

However, technology’s impacts are distributed unevenly, so certain groups are likely to bear more of the transition costs. For example, when AT&T introduced automated switching in the 1920s, it was a major shock for telephone operators. Though overall employment was not reduced, some operators saw their wages go down or exited the labour force.

This is where the key challenge lies now – and here the lessons of history are less encouraging. In many prior waves of technology, such as the automation that took place across manufacturing in the 1980s and 90s, support for workers needing to change how they worked or to find other occupations was inadequate. Many who were laid off were unable to find new jobs, and their communities still bear the scars. Policymakers should be thinking now about how to manage the transition better this time.

The key is training workers to make the most of AI. Since most of our 2030 workforce is already employed, we should meet people where they are and provide opportunities to acquire new skills mid-career. There is economic evidence that appropriately designed reskilling support can be highly effective.

The private sector has a major role to play in this area. Employer-led retraining efforts – including apprenticeships and on-the-job training – have scored successes in the past, as they tend to involve training in transferrable skills most valued by employers and can reduce employment barriers for nontraditional workers in high-wage sectors. Workers’ ability to demonstrate and document the acquisition of new AI skills will also be important. Initiatives such as the Career Certificates offered by Google’s training programme, Grow with Google, aim to help them do this.

AI represents an extraordinary economic opportunity, and most signs point to labour markets continuing to thrive, especially in the face of the demographic reality that advanced economies are running out of workers rather than running out of work.

Ensuring that this promise of economic advancement is shared by all requires deliberate action, not wishful thinking or defeatism. The future is not a forecasting exercise – it’s a design challenge.

Fabien Curto Millet is Google’s chief economist. Diane Coyle is Bennett professor of public policy at the University of Cambridge.

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