The conversation around artificial intelligence and the labor market often focuses on job losses, but history shows that new technologies typically create more jobs than they displace. However, today’s discourse is different because we lack clarity about what these ‘new jobs’ will look like.
Over the past year, research for the upcoming book, “The New American Frontier: Job Training for the Next Technological Age,” has revealed that traditional labor market data sources often struggle to capture the extent of the ‘frontier economy’—jobs created by critical and emerging technologies. This analysis highlights several recurring challenges and blind spots for policymakers relying on traditional labor market data and offers strategies to better understand and adapt to these changes.
Challenges in capturing the frontier economy
The first challenge in understanding jobs in frontier industries is definitional. It’s hard to identify the right industry codes to use. For example, data centers can be classified under various codes, leading to different pictures of the jobs available. Similarly, quantum technology jobs lack a single standardized code, making it difficult to visualize the landscape of quantum jobs.
Another challenge is that much of the earliest hiring happens in the supply chain, which is often overlooked. For instance, semiconductor manufacturing depends on specialty chemical manufacturers, industrial equipment manufacturers, and construction firms. Including these supply chain jobs nearly triples the number of postings compared to using only the core manufacturing code.
Emerging technologies also create new jobs that didn’t exist before, known as ‘frontier jobs.’ These roles don’t appear in traditional data sources, making it difficult to understand their distinct needs. For example, the role of a ‘biomechatronics technician’ combines knowledge of biomanufacturing with advanced skills in automation and industrial controls, but it doesn’t have its own classification code.
Even if we could track the mix of occupations perfectly, the content of occupations is changing too. Economists conceive of jobs as a bundle of tasks, and when tasks change, the skills required to do the job change as well. These ‘retooled jobs’ use an existing occupational title but require new skills related to emerging technologies.
Finally, traditional labor market sources can be slow to pick up on real-time shifts. Private sources such as job postings and online worker profiles can show what employers are calling jobs right now, what skills they are asking for, and whether hiring appears to be picking up—before that shift is visible in official forecasts.
Implications for education and training policy
If we undercount the potential in emerging industries, we may lag behind in standing up programs for high-growth opportunities. For example, states and the federal government have increasingly moved to tie workforce funding to ‘high-growth’ or ‘in-demand’ jobs, but if eligibility is defined exclusively from historical data, these funding streams can end up excluding the emerging fields that matter most for national competitiveness.
The opposite problem can also occur. Training programs get built for jobs that don’t materialize. Some institutions recently stood up programs to train electric vehicle fleet technicians in anticipation of coming jobs, but had to shut down or shift focus when local job openings ultimately didn’t appear.
Many job titles are staying the same, but their tasks are changing. If we don’t have good information about those new tasks, the training is likely to fall behind in relevance too. For example, many electrical training programs still focus heavily on traditional topics such as lighting and outlets, but neglect emerging fundamentals needed for solar and batteries.
Strategies for spotting emerging hiring shifts
To better understand and adapt to the evolving job market, education and workforce leaders can take several steps. They can track announced investments, incorporate private data, map the supply chain, build in qualitative inputs from employers, and advocate for alternative metrics or exceptions for emerging fields.
Tracking announced investments can provide a helpful complement to existing information. Incorporating private data such as real-time job postings and worker profile data can show the real titles companies are actually using, the career paths of workers in the industry, and the real-time changes in demand for emerging skills.
Mapping the supply chain can help capture early hiring around a new industry. Practitioners can use tools such as the Bureau of Economic Analysis’ input-output tables as a baseline to map supply chain relationships. Building in qualitative inputs from employers can surface new roles, clarify where skills are changing, and validate the assumptions educators are working from.
Advocating for alternative metrics or exceptions for emerging fields can ensure that emerging industries are not locked out of training funds. For example, Utah builds industry prioritization into its methodology for identifying which occupations to target with workforce funding.
Policymakers need ways to check claims against reality and give learners and job seekers information they can trust.
