Artificial intelligence (AI) has the potential to raise the standard of living, expand economic opportunities, and make America more competitive globally, but an honest assessment of how AI is currently reshaping labor and the economy is needed, according to experts at the Special Competitive Studies Project (SCSP), a nonprofit and nonpartisan initiative focused on strengthening America's long-term competitiveness in AI.
At the SCSP's annual AI+Expo, a Task Force on AI and the Future of Work unveiled preliminary findings that can serve as a foundation for further development and inform discussions. The Task Force, a joint effort between SCSP and Nvidia, identified ten aspects of AI's current and potential impact on work and the economy.
First, rapid advancement distinguishes AI from most prior general-purpose technologies because it extends into certain types of cognitive work by impacting the foundational skills by which tasks are performed. Second, the transition is unpredictable; although AI has already shown the potential to simultaneously expand human work and eliminate or transform other jobs, disruption and complications are to be expected during transitions.
Third, restructured data collection is necessary. New data collection and analytics will be needed to assess the changing skill sets and hiring patterns in the workforce now and in the future. Fourth, at the task level, workers need to understand how AI may automate or augment key tasks, and how roles may be reconfigured even without changes in job titles or employment levels.
Fifth, entry-level labor is affected. Although the long-term impact on entry-level jobs remains unclear, the increased use of AI for these jobs may affect the traditional pipeline of talent and established pathways for career advancement in certain industries. Sixth, variable adoption means the degree to which AI is adopted by different sectors and organizations will vary based on factors including incentives, workflows, and institutional constraints. Therefore, use of AI in certain areas is not inevitable and may not be consistent.
Seventh, shifting skills are required. The types of training and roles that will have enduring value in an AI-driven economy include not only new digital and technical skills, but also the enduring human traits of critical thinking, leadership, and adaptability. Additionally, skilled labor is needed for the construction of data centers and other facilities to support AI infrastructure. Eighth, training shortfalls must be addressed: education must be reimagined from a front-loaded learning system to a more flexible model that uses AI as a training tool to hone foundational capabilities and adaptable domain-specific skills.
Ninth, expanding access is crucial. AI can be deployed widely to improve worker performance by expanding access to high-productivity tools. The outcomes depend on how AI is used, who benefits from the increased productivity, and how quickly workers can adapt their skill sets and jobs. Tenth, unifying national action is essential. Ultimately, the speed, scale, and complexity of AI require coordination across institutions—government, industry, and educational—to manage the transition by aligning incentives and investing in workforce development to ensure that the benefits of AI can be broadly shared. “The window for proactive intervention is open,” the authors concluded.
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