Policy Studies Technology and Innovation

Can We Predict the Jobs and Skills Needed for the AI Era?


Adam Thierer
Resident Senior Fellow, Technology & Innovation

Key Points

Because human needs and wants are infinite, and because we go on adapting to meet those needs and wants through persistent experimentation, people find creative and often unexpected ways to create new jobs and skills—or reinvent old ones—over time.

When it comes to preparing workers for the future, the great lesson of history is that policymakers cannot plan for every contingency or easily devise policies or programs to address every potential need. A certain degree of humility remains essential because our “epistemic ignorance,” or hubris concerning the limits of our knowledge, remains a chronic problem.

The future remains as uncertain as ever, and the relationship between humans and their machine creations continues to be dynamic and unpredictable. Many will cast this inherent uncertainty in a negative light, but it is why we should be optimistic about the future.

New technological capabilities will give society new business models, new professions and new roles—but likely many that experts are not able to currently envision or plan for.

Executive Summary
To better plan for the economy of the future, many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward. Driving these efforts are fears about how technological automation might disrupt workers, skills, professions, firms and entire industrial sectors. The continued growth of artificial intelligence (AI), robotics and other computational technologies exacerbate these anxieties.

Yet the limits of both our collective knowledge and our individual imaginations constrain well-intentioned efforts to plan for the workforce of the future. Past attempts to assist workers or industries have often failed for various reasons. However, dystopian predictions about mass technological unemployment persist, as do retraining or reskilling programs that typically fail to produce much of value for workers or society. As public efforts to assist or train workers move from general to more specific, the potential for policy missteps grows greater. While transitional-support mechanisms can help alleviate some of the pain associated with fast-moving technological disruption, the most important thing policymakers can do is clear away barriers to economic dynamism and new opportunities for workers.

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