AI Policy Shifts From Innovation to Economic Payoff

Watch more: TechReg Talks With Progressive Policy Institute’s Dr. Michael Mandel

Governments are reexamining technology as industrial policy increasingly confronts an era defined by artificial intelligence and uneven economic gains.

Dr. Michael Mandel, chief economist and vice president at the Progressive Policy Institute, told Competition Policy International (CPI), a PYMNTS company, in an interview that the core issue is not whether innovation is occurring, but where its benefits are accumulating.

“We’ve got rapid productivity growth in the information sector,” he said, “but productivity growth in the physical sector has slowed down to close to zero.” That divergence has left entire industries and regions lagging, creating what he described as both an economic and political problem that now sits at the center of industrial policy.

Defining the Modern Industrial Policy Challenge

At its foundation, tech industrial policy is an effort to correct that imbalance. Over the past two decades, software and digital services have delivered measurable gains, while sectors such as construction, agriculture and manufacturing have failed to keep pace. Mandel emphasized that this outcome was not anticipated during the early years of the information revolution, when expectations were that digital tools would raise productivity across the entire economy.

“Everybody needs to be focused on this as the main problem to be solved by industrial policy,” he said, pointing to the need to restore momentum in physical industries that underpin employment, affordability and regional economic stability.

Why the US Cannot Rely on Markets Alone

This challenge is taking shape as governments expand their role in shaping technology markets, particularly in artificial intelligence, semiconductors and advanced manufacturing. Mandel argued that the United States cannot rely solely on private capital to address these structural gaps. Public policy must guide priorities while still allowing for decentralized execution and competition. In practice, this means setting national goals for AI infrastructure, semiconductor capacity and advanced manufacturing while creating incentives for private and regional actors to compete in achieving them.

China’s Decentralized Model Offers a Strategic Lesson

China provides a reference point for how such a strategy can operate at scale. Its system combines central direction with local competition, enabling provinces and municipalities to pursue national objectives through their own funding and development efforts.

This has created what Mandel described as a form of state-supported venture capital, in which regions compete to build industries, absorb risk and accelerate innovation. Although this approach has produced inefficiencies, including overcapacity and uneven returns, it has also driven measurable gains in productivity and global competitiveness.

For the United States, the implication is not to replicate China’s institutional model, but to draw from its emphasis on decentralized investment and competition. Mandel suggested that federal policy should define strategic priorities, while state and local governments play a more active role in financing, experimentation and implementation.

Rethinking Economic Performance in a Software-Driven Economy

A second area where U.S. policy must evolve, he said, is in measuring economic performance. The tools used to assess economic performance were designed for a different era, when industrial output and physical production were the primary indicators of capacity.

“If you go back to the origins of the national income accounts, they were designed … to identify what the capacity of the U.S. was to produce to wage war … and maintain consumer spending at the same time,” Mandel said. In the current environment, where software, data and computational power shape both economic and strategic outcomes, those metrics no longer capture what matters most.

Mandel argued that policymakers need to develop new frameworks that reflect the realities of a digital economy. “We do not have any way of tracking our software generating capabilities in a way that makes sense,” he said. Without these tools, it becomes difficult to assess whether investments in AI are translating into meaningful gains or simply increasing activity within already productive sectors.

Spreading AI Gains Beyond the Digital Core

The broader objective, in Mandel’s view, is to ensure that technological progress extends beyond the digital core of the economy. The past two decades, he said, were marked by an unusual pattern in which innovation failed to raise productivity in areas where it was most needed.

Closing that gap will require a more deliberate focus on diffusion, rather than on innovation alone. Mandel pointed to the need for policies that help translate advances in AI into practical applications across industries.

The experience of Europe offers an additional lesson for U.S. policymakers. Over the past decade, European authorities have pursued extensive regulatory interventions aimed at large technology firms, with the expectation that these measures would stimulate competition and productivity. Mandel said the data do not support that outcome. “There’s no sign in the data at this point that multiple layers of regulation have done anything to accelerate European productivity growth,” he said.

What the US Must Do Next

Looking ahead, Mandel outlined a set of actions that he believes should guide U.S. policy over the next decade. The first is sustained investment in data centers and energy resources, which underpin both economic activity and national security. “We need to fund the AI infrastructure … that’s essential,” he said.

Beyond infrastructure, he emphasized the importance of execution at the state and local level. Governors and regional leaders, he suggested, should take a more active role in deploying AI across industries through training programs, extension initiatives and targeted support for businesses. Such efforts would help ensure that productivity gains are not confined to large technology firms but are distributed across sectors and regions.

Equally important is the need to align investment with outcomes. Policies should be evaluated not only on the volume of spending but on their ability to raise productivity in lagging industries, improve wages and expand economic participation, he said.

Mandel returned to a lesson drawn from recent history, when expectations about the spread of digital innovation proved overly optimistic. “We can’t do that again,” he said, referring to the failure to extend productivity gains beyond the information sector. And as for AI and the necessity of investment in technology-focused infrastructure, “we cannot increase the productivity of the lagging industries without it.”