Economic and Labor Force Implications of Artificial Intelligence
History, logic, and economic analysis all strongly point to the conclusion that the next technology wave, powered by artificial intelligence and robotics, will not lead to above average unemployment levels and that we will not run out of work. What it could do however, is significantly improve labor productivity growth rates, making society better off and boost per-capita incomes for virtually all Americans.
As such, policymakers should not give in the rising techno-panic over AI or take steps to slow down AI progress. Rather, they should take steps to support AI, including by using AI much more extensively within state government operations. Finally, while the next wave of innovation won’t create mass unemployment, it will likely increase labor market churn, making it much more essential that states and the federal government do a better job equipping workers with the support, tools, and skills they need to navigate a more turbulent labor market. This testimony lays out specific steps California and other states might do in this regard.
First, states should support AI development and adoption, particularly in state government functions, while at the same time avoiding policies, such as regulation and taxes, that would hinder AI adoption. This is important, because as ITIF has written, AI has the potential to usher in the next stage of e-government, bringing new efficiencies and improved services. By embracing AI in California’s government, the state will not only be helping to spur overall AI progress, it will also be improving the quality of California government.
At the same time, California should avoid imposing regulations on AI. As an inherently cross-border technology, any regulatory frameworks should be federal in scope. Moreover, in most cases regulation should not focus on AI or robotics themselves, but on the areas they are applied, such as credit reporting, e-commerce and privacy, financial transactions, health care, etc. We don’t and shouldn’t say that regulations regarding credit reporting should be different depending on the type of computer system a credit reporting firm uses. Therefore, we shouldn’t regulate AI itself.
Second, states should focus on improving their systems for helping workers make transitions between jobs and occupations. As noted above, in the last two decades the U.S. labor market has actually been remarkably calm, at least in historical terms. But the pace of disruption and productivity is likely to increase somewhat, and states and the federal government need to do a better job of helping affected workers.
One step states should not take is to consider universal basic income. Under this widely touted scheme, government would somehow take money from somewhere and write monthly checks to all adults, whether they are working or not, poor or rich. This allegedly would establish a stable floor upon which everyone would build their own brighter future. Universal basic income is one idea policymakers should categorically reject. UBI would lead to the very thing its advocates warn us technology will bring: large-scale unemployment as the government incentivizes workers to be idle instead of helping pave pathways for those at risk of displacement to prepare for and to find success in new jobs.
A forthcoming ITIF report will focus in greater detail on what a comprehensive agenda for easing worker transitions should entail. But in general, states can and should do a better job of enabling workers to get “better” skills, not necessarily more. In this case, better skills would entail not only higher levels of education, but also education and skills more attuned to the needs of employers. When worker skills are more developed, worker adjustment from dislocation becomes easier. Moreover, having a stronger base of general skills provides an important foundation if demand for a worker’s specific skills dries up.
States can partner with non-profit organizations to establish better online portals for access to skill assessments, training resources, and job search. For example, the Council for Adult and Experiential Learning (CAEL) has established sites to help workers understand jobs and competencies needed for jobs in the petrochemical and financial services industries and find specific jobs and training related to occupations in these industries.
States should also work to better enable workers to receive unemployment insurance while they are in training. An ideal time for a worker to obtain new skills to enter a new occupation is when they are unemployed. However, for that to work effectively, the worker should be able to collect unemployment insurance while unemployed. While federal law requires states to allow workers enrolled in certified training programs to collect unemployment insurance, few states adequately inform unemployed workers of this option and many actively limit the number of qualifying courses. They do this, of course, because state unemployment insurance offices are motivated principally by one goal: getting workers back to work as quickly as possible, in part to keep unemployment costs, and taxes, as low as possible.
One place to start changing this would be for California to actively and clearly notify workers once they apply for unemployment insurance that they qualify for unemployment insurance benefits if they are in approved training. One study found that dislocated workers who collect UI and are sent information regarding training (its potential benefits, how to enroll, and information on financial assistance) are 40 percent more likely to enroll in training. At the same time, California should use a profiling system to predict who is likely be unemployed long-term and then quickly encourage them to enroll, including advising them to meet with staff at regional “One-Stops” for counseling about training opportunities.
Finally, it is important that all of us—policymakers, journalists, experts, and citizens—take a deep breath and calm down: Labor market disruption is not abnormally high, and the doomsday scenarios of massive job loss from AI are just that: scenarios that are unlikely to happen. Claims that we are all one fast-growing tech “unicorn” away from redundancy only raise fears and lead policymakers to, at minimum, ignore policies that would spur automation and technological innovation, and at worse support policies that would limit them.
