Podcast Summary: "What Are the Implications if the AI Boom Turns to Bust?"

The Tech Policy Press Podcast | November 12, 2025
Host: Justin Hendricks
Guests:

• Ryan Cummings, Chief of Staff at the Stanford Institute for Economic Policy Research
• Sarah West, Co-Director of the AI Now Institute
• Brian Merchant, Journalist-in-Residence at the AI Now Institute

Overview

This episode unpacks the question: Is the current AI investment surge a legitimate revolution, or a speculative bubble on the verge of bursting? The panel explores economic fundamentals, historical parallels, the mechanics of investment, policy implications, energy and infrastructure knock-on effects, and how public and political narratives could unravel—or intensify—if the AI boom turns to bust.

Key Discussion Points & Insights

1. Are We in an AI Bubble? Evidence & Economics

• Massive Investments vs. Real Returns
  • Firms like Microsoft, Google, Meta, and Amazon spent ~$400 billion in 2024 on AI-related capital and R&D; revenue from AI products is only ~$20 billion, highlighting a yawning gap.
    • Ryan Cummings: "So unless their revenue catches up to that...20 billion to 1 trillion in 5 years, then we might say it's a bubble because things are just priced in excess of the actual profits that they're going to accumulate." (06:36)
  • OpenAI itself is an outlier with $20B in revenue, but most "AI" revenue is speculative rather than realized.
• Comparisons to Past Tech Booms and Busts
  • The situation echoes both the dot-com bust (2001) and the run-up to the 2008 financial crisis.
  • Key difference: Today's tech giants are spending profits, not just leveraging debt—but that's changing, especially with new players and ‘circular’ financing (see: Oracle and OpenAI deals).
    • Sarah West: "We were starting to see...almost like the conspiracy theory string graph structure becoming more endemic in the market." (09:56)

2. Finance Structures and Risk Exposure

• Shift from Balance Sheet to Debt Financing
  • Early AI infrastructure funded by ‘hyperscaler’ profits (Google, Amazon), but recent contracts (Oracle, CoreWeave) depend on debt and complex financing, raising risks for utilities and taxpayers. (07:44, 09:56)
• Why Financing Source Matters
  • Ryan Cummings: "It's kind of crazy because at the end of the day, the ultimate thing that matters is the profits that accrue back to investors..." (11:18)
  • According to the Modigliani-Miller theorem, what matters is return to shareholders, not if investments are made via profits or credit.

3. Evaluating the Bubble: A Tech Bubble Framework

• Goldfarb & Kirsch’s Four Indicators:
  1. Uncertainty
  2. Pure Play Companies
  3. Novice Investors
  4. Coordinating Narratives
• Brian Merchant: "It was pretty overwhelmingly evident that in each of these cases...the elements necessary for a bubble...are ripe." (14:27)
  • AI ticks all the boxes, especially uncertainty (over business models and future returns) and the power of "everything machine" narratives.
  • Historical analogs: radio and aviation in the 1920s.
  • Brian Merchant: "As Goldfarb said, on a scale of 0 to 8, it's an 8." (19:35)

4. What Happens If The Bubble Bursts?

• Not 2008, More Like 2001—but Still Serious
  • Ryan Cummings: "I think it's closer to [dot-com bust], maybe a little worse." (24:59)
  • Most Americans' wealth is NOT in tech stocks (unlike housing, 2008), but “Mag 7” (AI-exposed) firms account for ~50% of S&P 500 growth since ChatGPT.
  • Wealth effects: Losses mean reduced spending, possible recession—but not as systemic or drawn-out as the housing crash.
• Broader Fallout: Energy, Public Policy, and Society
  • Data center buildout prolongs reliance on fossil fuels, hits local utilities, and raises doubts about government support.
  • Sarah West: "Some of the decision making around energy is pushing back the transition to renewable forms of energy." (25:51)

5. Who Benefits, Who Gets Hurt?

• Winner-Take-All Market Structures
  • Incumbent monopolies (Google, Amazon, Microsoft) are best-positioned.
  • Smaller, less-profitable, or heavily indebted players face greater risk.
  • Ordinary people, local governments, and taxpayers could end up "holding the bag" (10:44).
• Government-Industry Ties
  • Sarah West: "...deepened ties between AI firms and government in ways...are becoming increasingly really worrisome." (26:31)
  • Talk of backstopping AI investments and federal guarantees (OpenAI CFO, Sarah Fryer’s controversial remarks).
  • Tech policy debate heavily influenced by "AI as national mission" narratives (AGI arms race, 'China competition').

6. If Narrative Frays, What Next?

• The Fragility of Coordinating Narratives
  • Governments are invested in "AI is destiny" stories; political consequences if faith cracks.
  • Sarah West: "We may be trading off real moonshots...for all, except honestly, even for Mark Zuckerberg and Sundar Pichai...it's maybe a little bit less risky for them, really risky for the rest of us to be making these trade offs." (30:12)
  • Will politicians double down or recalibrate?
• Risk of "Everything Machine" Disillusionment
  • If AGI promises or job automation fail to materialize, credibility loss and possible rapid public/political backlash.

7. Policy & Regulatory Implications

• What Should Government Do?
  • Main need in a downturn: robust social safety net, not bespoke “AI crisis” policy.
  • Ryan Cummings: "The solution...is to just have a broad social safety net which will catch people as they fall..." (36:57)
  • Concern that current/future administrations (notably Trump) are likely to weaken safety nets, prioritize stock prices over public welfare, and lack whole-of-government crisis coordination (38:38).
• Tech Policy Opportunities If Bubble Bursts
  • Sarah West: "There is an opportunity to lay the groundwork for, you know, a more innovative economy...quit serving the broader public benefit first ahead of protecting the positions of the large incumbent firms." (41:52)
  • Potential openings for antitrust enforcement, redirecting industrial policy, reviving investment in basic research, and recalibrating public interest technology.

8. What to Watch: Signals of Change

• Public Anger & Backlash
  • Brian Merchant: "...people are angry at them at a degree that I...sort of unprecedented for my tenure as a tech journalist..." (44:53)
  • If job losses or lost savings are blamed on AI, anger could become politically explosive—particularly given intensified polarization.
• Investor Sentiment & Earnings Calls
  • Ryan Cummings: "...when is it actually going to accrue to us, the investors? That's when I think things might start to tip a little..." (48:09)
  • Watch for financial analysts becoming skeptical and pressing for profit clarity.
• Voter Pushback & Policy Action
  • Sarah West: "The data center pushback was potentially significant...I think...scrutiny from voters...is...one place where we're most likely to get real, meaningful policy change..." (49:15)

Memorable Quotes & Moments

• Brian Merchant (On Bubble Indicators):
  "As Goldfarb said, on a scale of 0 to 8, it's an 8." (19:35)
• Sarah West (On Tech Narrative):
  "We've been in this moment where there's been sort of this collective blessing of the AI industry as sort of deserving of particular treatment..." (29:20)
• Ryan Cummings (On the Real Economic Risk):
  "If at the end of the day, you spent $400 billion in a year and then you only got back $50 billion, that means there's $350 billion that did not come back to shareholders. So the prices of those shares have to adjust downwards because that money was vaporized." (12:13)
• Sarah West (On Opportunity for Reform):
  "If we're already tipping the scales, let's tip the scales in the ways that are going to have broad benefits to the public at large." (42:52)
• Brian Merchant (Public Backlash):
  "We live in angrier times, more polarized times, more politically activated times...If you do in fact have a bubble burst that is caused by the tech industry...I do think that there is going to be a... difference in the way that the public processes and reacts to that." (46:22)

Key Timestamps (Selected Highlights)

• 03:00 – Ryan Cummings: Bubble evidence, revenue/investment mismatch
• 07:44 – Sarah West: Shift to debt financing, circular deals
• 13:45 – Brian Merchant: Bubble framework, historical comparables
• 21:36 – Ryan Cummings: Why AI bust ≠ 2008, scope of real risk
• 25:36 – Sarah West: Energy/environmental impacts, deepened corporate-government ties
• 29:18 – Sarah West: The fragility of the AI public narrative
• 36:11 – Ryan Cummings: Safety nets vs. bespoke crisis response
• 41:38 – Sarah West: Opportunity for antitrust and public-interest innovation
• 44:50 – Brian Merchant: Watch for public anger and organized backlash
• 48:07 – Ryan Cummings: Analyst skepticism as market signal
• 49:15 – Sarah West: Voter-driven scrutiny as a lever for change

Takeaways

• AI’s investment boom rests on a shaky mismatch between sky-high spending and uncertain, slow-growing revenues.
• Multiple classic bubble indicators are present: uncertainty, hype cycles, pure-play bets, and new, untested investors.
• If/when the bubble bursts, the fallout will likely trigger recession—but, barring a systemic financial crisis, not catastrophic depression.
• Public and political anger at tech—and government’s entangled role—could rapidly escalate, especially if promises don’t materialize.
• This juncture offers a chance for policy reform, renewed antitrust, and public interest tech—if advocates are ready with plans for when the narrative cracks.

Final Signals To Watch

• Scrutiny in financial sector earnings calls (investor patience running out)
• Growing public resentment and electoral pushback
• Infrastructure and energy market stresses, especially around data centers
• Shifts in global policy coordination and narrative coherence

For anyone tuned out of the day-to-day AI policy soap opera, this episode provides a sharp, skeptical look at the "AI revolution"—revealing cracks under the surface hype and raising pointed questions for the future of tech, policy, and democracy.