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 with a goal of making recommendations to strengthen 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.
Among the key findings, AI is distinct 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. This rapid advancement is leading to an unpredictable transition, where AI has already shown the potential to simultaneously expand human work and eliminate or transform other jobs, with disruption and complications expected during transitions.
The Task Force also highlighted the need for restructured data collection to assess changing skill sets and hiring patterns. Workers need to understand how AI may automate or augment key tasks at a task level, and how roles may be reconfigured even without changes in job titles or employment levels. The increased use of AI for entry-level labor may affect the traditional pipeline of talent and established pathways for career advancement in certain industries.
Variable adoption across sectors and organizations will depend on factors including incentives, workflows, and institutional constraints. 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 enduring human traits such as critical thinking, leadership, and adaptability. Additionally, skilled labor is needed for the construction of data centers and other facilities to support AI infrastructure.
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. 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.
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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