The Federal Reserve just made AI officially part of how it thinks about the economy.

On July 9, Fed Chair Kevin Warsh announced five external task forces as part of a sweeping review of U.S. monetary policy. The one getting the most attention: the Productivity and Jobs panel, co-led by Marc Andreessen of a16z, Charles I. Jones (a Stanford economist currently on leave at Anthropic), and Asha Sharma (Microsoft’s EVP and Xbox CEO).

Their mandate is to evaluate how artificial intelligence and other emerging technologies are reshaping productivity, employment, and economic growth, then report back to the Fed before the end of 2026.

This is not a think tank exercise. What this panel concludes will directly inform how the Fed sets interest rates.

The Greenspan Bet, Version 2.0

Warsh has been explicit about the intellectual framework here. He’s drawing parallels to Alan Greenspan’s strategy in the 1990s, when unmeasured productivity gains from the internet allowed the Fed to hold rates lower for longer without triggering inflation. Greenspan detected a productivity surge in corporate reports before official statistics caught it, and he bet on it.

Warsh is running the same play on AI.

If AI is genuinely driving productivity through the enterprise economy, the Fed can afford a different posture on rates than it could otherwise justify. If AI’s productivity impact turns out to be slower to materialise than the hype suggests, the policy math changes.

The Productivity and Jobs task force is designed to answer that question faster than official government statistics will.

Who’s Involved and Why It Matters

The co-leadership lineup is notable. Andreessen’s venture firm a16z reportedly has around $90 billion invested in AI companies, which has raised conflict-of-interest questions in some quarters. But from a signal-reading perspective, it also means this panel has the deepest possible access to what’s actually happening inside AI deployments right now, not six months from now when the data shows up in GDP reports.

Charles I. Jones brings the academic rigour. He’s one of the leading economists studying long-run productivity and growth, and his current leave of absence at Anthropic means he’s watching frontier AI development from the inside.

Asha Sharma represents the enterprise deployment side. Microsoft has rolled out AI tools across hundreds of thousands of business customers and has some of the clearest data on what AI is actually doing (and not doing) to worker output.

This is not a group assembled to rubber-stamp a predetermined answer. These are people who have serious financial, professional, and intellectual skin in getting the answer right.

What This Means for Business

For business owners and executive teams, this development signals something important: AI is no longer just a technology decision. It’s becoming a macroeconomic variable.

Warsh’s framing of this as a monetary policy question means that enterprise AI adoption, productivity gains, and workforce reshaping will be studied at the highest levels of the U.S. financial system. The conclusions could influence borrowing costs, capital allocation, and the broader investment environment for years.

More practically, businesses that are already building genuine AI-driven productivity gains are the ones the panel will be studying as proof points. Companies that are still in pilot mode, waiting to see what happens, are the ones that will be left out of the dataset.

The task force is expected to deliver recommendations by the end of 2026. Whether or not rates move as a result, the report will almost certainly become a reference document for enterprise AI strategy across the board.

What to Watch

A few things worth tracking as this plays out:

The task force methodology will matter. If they rely on official productivity statistics, they’ll see a lagged picture. If they build proprietary data collection from corporate partners, the signal will be much faster and more actionable.

Andreessen’s credibility here cuts both ways. His optimism about AI’s economic impact is well-documented and public. If the panel’s findings land on the optimistic end, sceptics will question the objectivity. If they come in cautious, it will carry real weight precisely because the expectation was otherwise.

The interaction between this panel’s work and the EU AI Act’s rollout will be worth watching too. Europe is adding governance requirements; the U.S. is asking whether AI justifies looser monetary conditions. The two approaches reflect genuinely different bets on where the technology lands.

For Enterprise DNA customers building AI systems and upskilling their teams right now, the direction is clear regardless of how the Fed eventually moves: AI productivity gains are real, measurable, and now interesting enough to set monetary policy around.

That’s not hype. That’s the Federal Reserve treating AI as a serious economic force.