To lead in AI, the US needs a dedicated workforce policy

It is a critical moment in the race to lead in artificial intelligence (AI) — and by extension AI talent — as we rapidly move toward a world where AI is ubiquitous. Taking and keeping the lead in talent means not only having the best and brightest, but an adequate supply across the entire U.S. AI workforce, along with secure domestic talent pipelines. But this is not guaranteed without dedicated policies.

Winning the competition for AI talent has never been more urgent. It has been said we are in a modern day “Sputnik moment,” where the stakes for inaction are higher than ever. Among others, National Security Commission on Artificial Intelligence (NSCAI) Commissioner Mignon Clyburn called for bold action to grow the AI workforce as quickly as possible. 

However, to date, the discussion surrounding AI education and workforce policy has not adequately focused on AI. Some publications, like Stanford University’s AI Index Report, focus narrowly on top-tier computer research scientists. Others, like the general AI workforce recommendations in NSCAI’s Final Report, focus generically on STEM. But AI workforce policy is broader than computer research scientists and STEM. Neither is an adequate substitute. 

The reality is the United States can’t lead in AI talent without education and workforce policies that target growing and cultivating the U.S. AI workforce. The U.S. needs dedicated policies that consider the entire range of technical and nontechnical talent needed to design, develop and deploy safe and trustworthy AI capabilities. The United States also needs policies that prepare future workers to compete and succeed in a world characterized by widespread AI adoption.

Such policies are evidence-based and data-driven. They are based on a comprehensive definition of AI workforce, rigorous analysis of the associated labor market dynamics, and clearly defined policy goals.

Moreover, such policies do not view this myopically as a purely domestic challenge. As much as this is an education and training issue, it is also a national security issue that must consider the global competition. The most effective policies will involve traditionally siloed policymaking communities working together. 

These policies also act on short-, medium-, and long-term timelines. They level up the workforce now to secure and strengthen U.S. leadership in AI talent and plan ahead to ensure that leadership is sustained. They understand that youth and adults approach career and educational decision-making differently, and both populations must be met where they are.

At the Center for Security and Emerging Technology (CSET), our latest research addresses the need for dedicated AI education and workforce policy. First, we offer three goals to target different segments of the AI workforce: Grow the pool of U.S. AI doctorates, diversify and sustain its non-doctorate technical talent, and provide AI education to make all Americans AI-ready and AI-literate. Second, we provide actionable recommendations that engage all relevant stakeholders to achieve these goals over the short and longer term. 

Our recommendations leverage opportunities within the current U.S. education and training system while attempting to address its challenges. For example, we propose using the fact that the U.S. education system is decentralized with many states and nongovernment organizations actively providing their own AI-related programs as an advantage. This means providing a mechanism to facilitate rigorous program evaluation, share best practices, train and equip educators, and scale what works. The recently established National Artificial Intelligence Initiative Office for Education and Training within the Office of Science and Technology Policy should be leveraged as a key coordinator, compiler and convener, and resourced to do this accordingly.  

Our recommendations also emphasize creating alternative pathways into AI careers. A four-year college degree shouldn’t be a gatekeeper for AI jobs when not all jobs need one. Having alternative pathways will increase diversity in the AI workforce, which is critical for designing, developing and deploying safe and trustworthy AI. It will also promote equity in access and opportunity, which is critical to creating sustainable talent pipelines. 

Perhaps most importantly, they are intentionally specific. Though well-intentioned, many reports on the topic call for general increases in STEM education investment, focus on computer science education, or to solve a declared “AI talent shortage” without a clear definition of AI talent. What is needed are ideas that appreciate the nuances and complexities of the U.S. education system, an understanding of what efforts are already underway to provide AI education in K-12 and beyond, and knowing how initiatives, pilot programs, or real change happens at the district level.

The acceleration of AI adoption has profoundly changed how businesses, individuals, and governments conduct their daily lives. U.S. policies must adapt to this new reality. Through dedicated AI education and workforce policies, we can strategically and effectively design policies that will help America stay ahead. 

Diana Gehlhaus is a research fellow at the Center for Security and Emerging Technology (CSET), focused on AI education and workforce issues as it relates to the United States, China, and U.S. Department of Defense. Follow her on Twitter @dianagcarew.