The Fed doesn’t know who’s financing the $3 trillion AI boom

The debate over artificial intelligence and monetary policy is already well under way.

Federal Reserve Chair Kevin Warsh and others have rightly emphasized that AI could raise productivity and productive capacity even as the investment boom puts pressure on resources before those benefits arrive.

That timing problem is real.

But it risks obscuring a more immediate challenge: AI is also creating a large and rapidly evolving financing ecosystem whose leverage, exposures and vulnerabilities are much less well understood.

Morgan Stanley projects nearly $3 trillion of global AI-related infrastructure investment through 2028 , with an estimated $1.5 trillion external financing gap.

This investment is already absorbing construction, semiconductors, electricity and skilled labor.

In the near term that can raise resource utilization and prices.

Over time, automation, organizational change and new capital should raise potential output and reduce unit costs.

The mistake would be to treat every sign of pressure from this buildout as an inflation problem requiring higher interest rates.

Monetary policy does not merely restrain demand; it can also affect the investment and innovation that determine future supply.

Patrick Moran and Albert Queralto showed in a 2018 Journal of Monetary Economics paper that when innovation and technology adoption are endogenous, monetary policy changes firms’ incentives to develop and implement new technologies and can therefore affect future productivity.

The 1990s provide the more compelling historical counterfactual.

By the mid-1990s unemployment had fallen below what policymakers then regarded as its natural rate, and pressure was building inside the Fed to tighten.

Chairman Alan Greenspan instead entertained the possibility that the models were wrong—that faster productivity growth had raised the economy’s speed limit—and largely resisted further rate increases.