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    AI Rebalancing: Insights from Daron Acemoglu and Simon Johnson

    The Drive Toward Automation: Complementing Workers, Not Replacing Them

    Optimistic forecasts regarding the growth implications of AI abound. AI adoption could boost productivity growth by 1.5 percentage points per year over a 10-year period and raise global GDP by 7 percent ($7 trillion in additional output), according to Goldman Sachs. Industry insiders offer even more excited estimates, including a supposed 10 percent chance of an “explosive growth” scenario, with global output rising more than 30 percent a year.

    All this techno-optimism draws on the “productivity bandwagon”: a deep-rooted belief that technological change, including automation, drives higher productivity, which raises net wages and generates shared prosperity.

    However, historical records show that this optimism is at odds with reality, especially in the current path of “just let AI happen,” which focuses primarily on automation. We need to recognize that there is no singular, inevitable path of development for new technology. If the goal is to sustainably improve economic outcomes for more people, we need to consider policies that put AI development on the right path, with a greater focus on enhancing what all workers can do.

    Contrary to popular belief, productivity growth does not necessarily translate into higher demand for workers. While average output per worker may increase with automation, worker marginal productivity, which is the additional contribution brought by one more worker, may remain constant or even decline. Many new technologies, such as industrial robots, expand the set of tasks performed by machines and algorithms, displacing workers. Automation raises average productivity but does not increase, and in fact may reduce, worker marginal productivity. Over the past four decades, automation has raised productivity and multiplied corporate profits, but it has not led to shared prosperity.

    Instead of automating work, there are other options to improve economic efficiency. Some innovations can boost how much individuals contribute to production, for example, new software tools that aid car mechanics and enable greater precision. These innovations increase worker marginal productivity without replacing them with machines.

    The creation of new tasks is even more important for raising worker marginal productivity. When new machines open up new uses for human labor, this expands workers’ contributions to production and increases their marginal productivity. New tasks have been vital in the growth of employment and wages over the past two centuries. Even occupations that have been around for a long time have seen the introduction of new tasks due to technological advances. These new tasks have been integral to productivity growth and have helped launch new products and enabled more efficient production processes.

    Automation can also drive up employment in an industry or the economy broadly if it substantially increases productivity. However, when the productivity gains from automation are small, there is no major boost to stimulate the creation of new jobs elsewhere. Thus, the focus should be on productive automation that creates new tasks and expands employment opportunities.

    Given these considerations, it is crucial to redirect technological change toward a human-complementary path rather than a human-displacing one. To achieve this, governments can take several steps:

    1. Reform business models: Establish clear ownership rights for consumers over their data and tax digital ads to encourage a more diverse range of business models and competition.
    2. Reform the tax system: Create a more symmetric tax structure that equalizes marginal tax rates for hiring labor and investing in automation.
    3. Give workers a voice: Restrict the deployment of untested AI for tasks that could put workers at risk and involve workers in the development of AI.
    4. Fund more human-complementary research: Foster competition and investment in technology that pairs AI tools with human expertise to improve work in vital social sectors.
    5. Develop AI expertise within government: Establish a consultative AI division within government to support more informed decision-making.

    Redirecting AI development toward a human-complementary path is crucial to achieve shared prosperity. If left unchecked, AI automation may lead to increased inequality and hinder job creation. By embracing the potential of AI to complement worker skill and expertise, we can create a future where AI empowers workers and addresses pressing social problems. This transformation requires deliberate choices and the right policy interventions to ensure that AI contributes to the betterment of all humans.

    Note: This article is adapted from the authors’ book, “Power and Progress: Our 1000 Year Struggle over Technology and Prosperity,” and also draws on joint work with David Autor.

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