Planet Money: "So are we in an AI bubble? Here are clues to look for."

Date: January 10, 2026
Hosts: Nick Fountain and Jeff Guo
Featured Guests:

• Robin Greenwood, Professor of Finance, Harvard Business School
• Eugene Fama, Nobel Laureate, University of Chicago (archival audio)
• Gauti Eggertsson (quoted as Gotti Bar Levy), Senior Economist, Chicago Fed

Overview of Main Theme

This episode of Planet Money dives deep into the lingering question dominating financial circles and pop culture alike: Are we currently living through an artificial intelligence (AI) bubble? Hosts Nick Fountain and Jeff Guo examine what defines a bubble, explore the difficulty of spotting one in real time, and discuss whether bubbles are always harmful—or, surprisingly, if some might have hidden benefits for the economy.

Key Discussion Points and Insights

1. Setting the Stage: Is AI a Bubble?

• [00:17] Jeff Guo asks the central question: "Are we in a bubble?"
• The S&P 500 has surged nearly 50% in two years, driven mainly by AI-focused companies – the so-called "Magnificent Seven," including Microsoft, Amazon, Meta, and especially Nvidia, which hosts call "the most valuable company in the world."
• The rapid growth makes people nervous: "If this AI stuff turns out to be a bubble, it's like the biggest bubble our economy has seen in years." (Jeff, 01:06)

2. Defining a Bubble

• [02:34] Robin Greenwood offers his definition:

  "A bubble is something that we can point to as being irrationally valued relative to the value that it delivers." (Robin, 03:26)

• Bubbles are distinct because they're usually linked to new, exciting, and uncertain industries, not mundane goods (“You almost never see bubbles forming in boring or familiar industries. You’re never going to see bubbles in, say, ankle socks.” - Nick, 04:01)

3. Why Spotting Bubbles Is So Hard

• High valuations don’t guarantee a crash—optimism sometimes proves correct.
• Eugene Fama is cited as a skeptic, questioning whether bubbles are even real if you can't reliably predict them beforehand.

  "I believe markets work. And if markets work, those things shouldn't be predictable." (Fama, 06:03)

4. Bubble Detection: The "Bubbles for Fama" Study

• Robin Greenwood and colleagues examined a century of US market history to identify periods where sector prices doubled within two years, then watched what happened next.
• About half the time, prices crashed, but half the time, optimism was justified and prices kept rising.
• Searching for patterns, the study surfaced four key clues that precede most bubbles:
  1. High Valuations: Sky-high price-to-earnings ratios (P/E), indicating optimism for future profits (09:26)
  2. Volatility: Rapid fluctuations in share prices
  3. Issuance: Spikes in companies issuing new stock or going public
  4. Acceleration: Stock prices not just rising, but rising faster and faster (“like an exponential whoosh” - Robin, 10:28)
• Of the four, acceleration was “one of the strongest predictors.” (Robin, 10:40)

5. Applying the Clues to Today’s AI Boom

• Nvidia currently has unusually high P/E ratios (in the 40s), and there’s some volatility.
• However, there is relatively little stock issuance (AI leaders aren’t heavily selling new shares or going public), and prices, while climbing, aren’t accelerating exponentially at the moment.
• Quote:

  “So we have many, but not all of the things that you would look for to call the current AI phenomena a bubble.” (Robin, 11:52)

• Greenwood’s verdict: We could be in an “early bubble” (Robin, 12:13), but the clues only predict bubbles “maybe 60% of the time”—better than a coin flip, but not decisive.

6. Bubble Management: ‘Lean vs. Clean’ Debate

• Policymakers now debate whether to "lean" (intervene to stop or slow a bubble) or just "clean up" after it pops.
• After the dot-com and housing bubbles burst and damaged the broader economy, even macroeconomists started taking bubbles seriously.

7. Economic Impact: How Bubbles Hurt—or Help

• Two main dangers:
  1. Financial Sector Risk: If a bubble pops and is linked to high borrowing, banks and the broader economy can be severely hurt—as in the housing crash of 2008.

     "If there is a lot of borrowing, a lot of the people who borrowed end up defaulting on their loans … you will have a more severe recession." (Gotti, 17:47)

  2. Misallocated Resources: Bubbles can encourage wasteful investment in fads (like the hypothetical “Labubu” doll bubble).
• But if a bubble centers on assets with real economic use—like fiber optic cable during the dot-com boom—society might benefit after the bubble pops. The unused “dark fiber” eventually enabled the broadband revolution.

  “Not all bubbles are entirely bad. Maybe some have silver linings. Maybe bubbles can even boost the economy.” (Nick, 22:16)

8. Provocative Idea: Bubbles Can Fix Market Failures

• Some economists theorize that bubbles might counteract society’s chronic underinvestment in valuable but risky endeavors like R&D (externalities problem).

  “If somehow there’s a bubble in some high tech industry, investors will start throwing money at those companies. And the argument is maybe society will end up with more of the R and D that we need.” (Jeff, 23:37)

• Robin Greenwood and Gotti both express caution, with Gotti summarizing:

  "By encouraging what looks like excessive creation of an asset that as far as society is concerned, is under provided to begin with, that might actually be a great thing." (Gotti, 23:52)

Memorable Quotes & Moments with Timestamps

• "Are we in a bubble?" – The enduring central question (Jeff, 00:17)
• On AI speculation:
  "People are calling them the Magnificent Seven." (Jeff, 00:43)
• Robin Greenwood’s tongue-in-cheek origin story:
  "Maybe it's — that quote [Fama’s skepticism] originally motivated us, because we wanted to rise to the Fama challenge." (Robin, 06:50)
• On why bubbles are fascinating:
  “Bubbles usually involve something new, something exciting or unknown... You’re never going to see bubbles in, say, ankle socks.” (Nick, 04:01)
• Bubble detection challenge:
  "What we did find was that there's a constellation of things happening around bubbles that does make them somewhat predictable." (Robin, 08:31)
• On the predictive power of clues:
  "These clues could help you pick out the bubbles, like maybe 60% of the time." (Robin, 12:13)
• Policymakers’ dilemma:
  "Do you lean into a bubble or do you wait until it collapses and then you clean up after the fact? That was the nature of the lean versus clean debate." (Gotti, 14:55)
• On the accidental benefits of bubbles:
  “After the dot com bubble popped, a lot of that fiber just sat there unused. It was called dark fiber. But that didn’t turn out to be a total waste of money.” (Jeff, 21:39)
• The punchline:
  “A bubble, through the power of its delusion, can fix some of these problems. So it’s like two wrongs making a right.” (Jeff, 24:03)
• Playful banter at the end:
  “You know what I always thought was a bubble? Podcasts. It hasn’t popped yet, though.” (Nick, 24:54)

Timestamps for Key Segments

• 00:17 – 02:34 | Framing the "AI bubble" question
• 02:34 – 05:52 | What is a bubble? Definitions and context
• 05:52 – 07:24 | Eugene Fama and skepticism about bubble spotting
• 07:24 – 13:25 | Robin Greenwood's research: history and the "Bubbles for Fama" criteria
• 13:25 – 14:20 | Can you spot bubbles before they pop? What about now?
• 14:20 – 18:20 | Policymakers’ approaches: Lean vs. Clean
• 18:20 – 21:39 | How bubbles cause economic harm (and the “Labubu” example)
• 21:39 – 24:18 | Unexpected upsides: bubbles creating useful assets for the future
• 24:18 – end | Final reflections, playful banter, and sign-off

Takeaways

• It’s complicated: While there are markers of bubbles, predicting them with certainty is elusive.
• Is AI a bubble? There are signs of froth—high valuations, some volatility—but not all the classic warning signs.
• Should we worry? Perhaps, but not all bubbles are equally dangerous or destructive; sometimes, even bubbles leave legacies society can leverage.
• The bubble paradox: Bubbles can occasionally help fix real-world market failures by overinvesting in areas society often neglects (like R&D).
• Final word: The AI boom may or may not be a bubble, but as Jeff puts it, if it is, we might not yet be at the center—and history shows it’s rarely possible to know in real time.

For anyone curious about the intersection of markets, technology, and the occasional madness of crowds, this episode offers a nuanced, practical, and at times even optimistic view of bubbles—and why we may never really escape their seductive power.