Fed Governor Waller Discusses in New Delhi How Central Banks Should Respond to AI

At the Bloomberg New Economy Forum 2026 in New Delhi, Christopher Waller, one of the seven members of the US Federal Reserve Board of Governors, spoke with Bloomberg correspondent Saleha Mohsin about how central banks can respond to the economic and social changes driven by artificial intelligence. The announcement of the conversation contains no quotes, forecasts, or figures. So below we lay out the framing of the discussion and the questions it raises for regulators. This is not a recap of Waller's words.

Why AI has reached the central banker's office

The Fed has a dual mandate: maximum employment and stable prices, with an inflation target of 2%. AI touches both halves of the mandate at once. If generative models and agents really do raise labor productivity, the economy could grow faster without stoking prices. If they quickly cut demand for certain professions, the labor market will start to shift before aggregate statistics show it.

There is also an investment side. Hyperscalers spend hundreds of billions of dollars a year on data centers, chips, and power supply. That spending affects credit demand, construction, electricity, and asset valuations. For a central bank, this is already a macroeconomic factor, not industry news.

What questions lie behind the topic

The New Delhi forum took place in a country where AI is seen both as an opportunity for services exports and as a risk to mass employment. At a venue like this, a US regulator's remarks on AI almost inevitably reach beyond the American market.

The main areas in which central banks typically assess the impact of AI:

• Productivity. How quickly AI adoption turns into higher output per worker, rather than just higher infrastructure spending.
• Neutral rate. Productivity growth and an investment boom could raise the interest rate level that neither restrains nor stimulates the economy.
• Labor market. The pace at which tasks are replaced, especially in entry-level office and software jobs.
• Inflation. Cheaper services through automation, against price pressure from demand for electricity, land, and equipment.
• Financial stability. Capital concentrated in a narrow group of AI companies, and debt financing of data centers.

What to do about it in practice

For tech executives and investors, the main takeaway is that AI strategy now depends on monetary signals too. The cost of capital determines how long you can fund model training and capacity buildouts without a quick payoff. If you are planning large capital expenditures or fundraising for AI projects, build several interest-rate scenarios into your model rather than a single baseline.

Teams that deploy AI inside a business should record its effect in measurable terms: time per task, cost of processing a request, revenue per employee. This is exactly the data regulators and analysts will look for when deciding whether productivity growth is sustainable.

Readers who want Waller's specific assessments should wait for the recording or transcript of the talk on Bloomberg's site. Take interest rates, forecasts, and wording on risks from the primary source, not from secondhand accounts.