No Mercy / No Malice: 1999.AI

Summary

The Prof G Pod with Scott Galloway

Episode: No Mercy / No Malice: 1999.AI
Date: July 18, 2026
Host: Scott Galloway (essay read by George Hahn)

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Overview:

This episode explores the parallels between the late-1990s dot-com bubble and the current cycle of hype and speculative investment in artificial intelligence. Scott Galloway draws on historical lessons from the implosion of Web 1.0 to assess the early warning signs of a potential AI bubble. He analyzes the sustainability of AI business models, recent financial revelations, and the tendency for technological revolutions to create value—often for customers more than shareholders.

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Key Discussion Points & Insights

1. Financial Bubbles: Echoes from 1999

• Historical Cycles: Galloway references Jamie Dimon’s view that financial crises recur every five to seven years and notes that visible cycles become more apparent with age.

  “As you age, cycles become more visible. You’ve seen this movie before and begin to recognize the moment as a point on a curved line. Slowly, then suddenly, the line changes direction.” (03:00)

• Dot-Com Bubble Revisited: The late ‘90s tech boom prioritized scale (“get big fast”) over profitability. 39% of venture capital went to Internet companies in 1999.

  • Galloway’s own company, Red Envelope, raised $30M at high valuations despite heavy losses—“the markets did eventually show up and inform me this made no sense.” (05:15)

• Poster Children of Boom and Bust:

  • Pets.com: Great thesis, but a decade too early. Famously went bankrupt shortly after its IPO, despite its cultural prominence (Macy’s Thanksgiving balloon, Super Bowl ads). (06:15)
  • Webvan, eToys.com: Other notable collapse stories.
  • The collapse spread: first B2C firms, then B2B, and finally infrastructure.

2. The Domino Effect & Infrastructure Fallout

• Dot-com collapse led to cascading effects in B2C, then B2B (e.g., Sun Microsystems), then infrastructure (Nortel, Lucent)—all eventually went under or were acquired for fractions of once-lofty valuations.

  • “From peak to trough, [Sun Microsystems] shed 96% of its market cap.” (08:10)

• Investor Delusion: At the peak, 74% of stocks had buy recommendations (up from 60% four years prior)—evidence of “consensual hallucination.” (09:40)

3. AI: The Next Bubble?

• Warning Signs at OpenAI:

  • OpenAI lost $21 billion in 2025, faces executive turnover, lawsuits, and may delay its IPO.
  • “The company’s financials aren’t sustainable. For every dollar subscribers spend on ChatGPT, OpenAI spends nearly 3.” (10:35)
  • Projected $100 billion in ad revenue by 2030 is wildly ambitious—ad business is falling short by 90% (per eMarketer).

• Sovereign Bailout Appeal:

  • Sam Altman pushing for a taxpayer bailout—a “5% stake in OpenAI”—but “AI has no profits.”
  • Bipartisan proposals (like a “wealth fund” financed by a one-time 50% AI equity tax) are deemed bad ideas.

  “Forcing the American taxpayer to invest in a well-connected private firm isn’t socialism, it’s cronyism in the form of an SOS signal.” (11:40)

• Circular Financing:

  • Warning from dot-com days: circular, mutually dependent financing between B2C, B2B, and infrastructure players. Current AI companies are seeing similar patterns, exacerbated by heroic enterprise spending.

• Enterprise AI Spending “Sobriety”:

  • Noted examples: “Uber blew through its entire AI budget for 2026 in just four months.” (13:18)
  • Companies pivoting from open-ended AI use (“token maxing”) to limiting usage to proven cases.
  • Token usage explained: ~4 characters per token; “the best use case for AI is coding.” (14:10)

• Cracks in the Hype:

  • Enthusiasm for “sell software, buy chips” faltering as adoption and ROI plateau.
  • Nikesh Arora (Palo Alto Networks CEO): Widespread adoption depends on driving down token costs by 90%.

4. Sober Assessment and Value Creation

• Shift from Hype to Measurement:

  • Emphasis on actual productivity gains—not just usage/load—guides responsible adoption.
  • Meta CTO Andrew Bosworth:

    “Nobody should be using AI tools just for the sake of using them. All motion is not progress, and token usage alone is not a measure of impact of any kind.” (12:59)

• Open Source Advantage:

  • Open-source AI models are cost-effective (“80% of the Frontier models bang for 20% of the bucks.”).
  • The “grim reaper is knocking” for VCs overly exposed to 2025-2026 AI speculative bets. (15:10)

• Productivity Paradox:

  • Analogy: if AI is like electricity, most value accrues over time and often to customers, not immediate shareholders.
  • Real leaps in productivity/displacement come from new companies and products, not incumbents bolting AI onto existing workflows.
  • “I’m as bullish on AI as I was on the Internet in 1999, but... I know not to conflate valuations with value.” (15:45)

• Bubble Risk Concentration:

  • “The danger with the AI bubble isn’t that the technology is overhyped... it’s that the speculation is so concentrated that the 10 most valuable companies in the S&P 500 account for 43% of the index’s total market cap.” (16:10)
  • When a bubble deflates, economic risk is systemic.

5. Who Will Benefit?

• Historical pattern: technologies generate vast wealth, but ultimate beneficiaries are often customers who use them (e.g., jet travel, vaccines, the PC), not the VCs/shareholders who hyped them initially.
  • “The biggest winners won’t be shareholders in AI companies, but the people who use the technology. In sum, we may have passed a wealth tax, just not the one AOC envisioned.” (16:30)

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Notable Quotes & Memorable Moments:

• On Cycles and Narratives:

  “Echoes of 1999, i.e. peak dotcom, have been growing louder. I believe we’re witnessing the initial stages of the unraveling of the AI bubble. But unlike in 1999, we could be in for a twist ending.” (02:50)

• On OpenAI’s Risky Model:

  “OpenAI is projecting $100 billion in advertising revenue by 2030, but the company’s ad business is on pace to fall short of its own forecast by 90%...” (10:56)

• On Political Risk of Bailouts:

  “Forcing the American taxpayer to invest in a well-connected private firm isn’t socialism, it’s cronyism in the form of an SOS signal.” (11:40)

• Meta’s Internal Pivot:

  Andrew Bosworth, Meta CTO: “Nobody should be using AI tools just for the sake of using them. All motion is not progress, and token usage alone is not a measure of impact of any kind.” (12:59)

• On Bubble Risk:

  “When AI sneezes, the US economy’s lungs may begin to fill with fluid.” (16:20)

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Timestamps for Key Segments

| Time | Segment | |--------|----------------------------------------------| | 01:27 | Opening theme and episode setup | | 03:00 | Financial cycles, dotcom & investment boom | | 06:15 | Pets.com, Webvan, etoys.com collapse | | 08:10 | B2B & infrastructure domino cascade | | 09:40 | Consensual hallucination in investment | | 10:35 | OpenAI’s financials & sustainability | | 11:40 | AI sovereign wealth fund, Altman’s bailout | | 12:59 | Meta CTO Bosworth on AI usage | | 13:18 | Enterprise AI spending “sobriety” | | 15:10 | Open-source models & venture capital risks | | 15:45 | AI vs. electricity & long-term value creation| | 16:10 | AI bubble risk concentration | | 16:30 | Who actually benefits from AI? | | 16:40 | “Life is so rich.” (Closing) |

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Tone & Style

Scott Galloway brings a mix of historical perspective, self-awareness, market skepticism, and caustic humor. His language is blunt and sometimes sardonic, especially regarding industry hype, political risk, and pervasive market delusions.

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For Listeners Who Haven’t Tuned In

This episode is a sharp, engaging, and cautionary history lesson. Galloway uses the collapse of the dot-com era as a lens to interpret current events around AI investment, unflinchingly critiques both business models and political proposals, and leaves listeners with the message that technological revolutions take longer than optimists claim — but eventually, value flows to users, not just to those who build or fund the platforms.

Transcript

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[C]

I'm Scott Galloway and this is no mercy, no malice. When the dot com bubble burst, the contagion began with B2C, then spread to B2B and ultimately hit infrastructure. A similar pattern is forming in AI, with cracks emerging at OpenAI 1999 AI as read by George Hahn.

[D]

Jamie Dimon once defined a financial crisis as something that happens every five to seven years. Well, it's been 18 years since the last crisis. As you age, cycles become more visible. You've seen this movie before and begin to recognize the moment as a point on a curved line. Slowly, then suddenly, the line changes direction. For better or worse. Recently, echoes of 1999, I.e. peak.com have been growing louder. I believe we're witnessing the initial stages of the unraveling of the AI bubble. But unlike in 1999, we could be in for a twist ending if you were raising capital. In 1999, the hero wasn't a profitable business model, but a suffix dot com. The defining philosophy of the era was get big fast. Entrepreneurs and investors believed the Internet represented a once in a generation opportunity to capture margin and market share. By 1999, 39% of all venture capital investments were being deployed into Internet companies. My firm, red envelope, raised $30 million at a valuation of $120 million on revenues of $30 million, losing $20 million. Most profitable specialty retailers were trading between 0.8x and 1.2x. Spoiler alert. The markets did eventually show up and inform me this made no sense. That same year, 80% of US IPOs were related to Internet companies. Pets.com, the poster child of the dot com bubble, had the correct thesis. Consumers would buy pet food and supplies online. But the company was a decade early. See Chewy. Founded in 2011. Like many B2C Internet startups, Pets.com incurred net operating losses but spent heavily on advertising. In the run up to its IPO in 1999, the Pets.com sock puppet mascot was so popular it was a balloon in the Macy's Thanksgiving Day Parade. A few months later, Pets.com was one of 17 Internet companies to buy Super bowl ads, up from two in 1998. The following month, the company went public, raising $82.5 million. In less than a year, however, Pets.com declared bankruptcy and shuttered operations. Similar fates befell Webvan, an early iteration of online grocery delivery eToys.com once considered a brick and mortar toy store killer, and hundreds of other B2C startups. The first dominoes to fall were B2C firms, as their business models relied on consumers ready to buy dog food via dial up modem. The fallout took longer to reach B2B's as enterprise companies have longer sales cycles and stickier customers. Sun Microsystems, whose tagline was we're the.in.com powered B2C startups. At its 2000 peak, sun was valued at $205 billion, nearly as much as General Electric at the time. But as its Internet clients went bankrupt, the business collapsed. Sun reported net income of $1.8 billion in 2000, but that number halved to $927 million in 2001. Sun lost $628 million in 2002 and $2.4 billion the following year. From peak to trough, the company shed 96% of its market cap. It was eventually acquired by Oracle for $7.4 billion in 2009. Along similar lines, Double Click was the advertising company of the era, with a $12 billion valuation. But as dot com startups stopped advertising, its valuation dropped to $800 million, and it was soon taken private. In 2007, Google acquired DoubleClick for $3 billion, demonstrating that some technology developed during Web 1.0 was sound even if the dot com business models weren't eventually, the falling dominoes hit the infrastructure layer, causing a separate but related telecom crash in 2001. At its peak, Nortel Networks carried 75% of North America's Internet traffic. In the summer of 2000, just as the dot com bubble was bursting, Nortel was valued at $230 billion. A year later, more than 90% of its value had been erased. Along with Global Crossing and Lucent Technologies, Nortel had extended vendor financing to the same dot coms that were now bankrupt. None of the three survived the crash. In retrospect, their downfalls seem obvious. But at the market peak, just as the falling dominoes were moving from B2C's to B2B's, 74% of stocks had buy recommendations, up from 60% four years earlier. Hype Cycles aren't just entrepreneurs, I.e. storytellers getting out over their skis. They're business models that incentivize consensual hallucination. In unrelated news, Goldman Sachs and Morgan Stanley, lead underwriters for SpaceX, have buy recommendations on the company, with price targets of $200 and $300, respectively. The echoes of the dot com and telecom implosions are deafening. OpenAI's leaked financials reveal the company lost $21 billion in 2025, a C Suite Exodus, the lawsuit from Apple, and reports that OpenAI is considering delaying its IPO until 2027 or all feel very 1999. The company's financials aren't sustainable. For every dollar subscribers spend on ChatGPT, OpenAI spends nearly 3. Its business model resembles an LLM hallucination. Case in point, OpenAI is projecting $100 billion in advertising revenue by 2030, but the company's ad business is on pace to fall short of its own forecast by 90%, according to eMarketer. The biggest red flag, however, is Sam Altman's request for a bailout, cosplaying and investment opportunity, offering US taxpayers a 5% stake in OpenAI. As my markets co host Ed Elson wrote last week, the idea is to provide every citizen a share in the profits of AI. But here's the AI has no profits. A key component of capitalism versus Socialism is citizens get to make up their own minds regarding which stocks they buy. Or don't. Even Senator Bernie Sanders is floating a sovereign wealth fund financed by a one time 50% tax on AI equities. When the far left and far right agree on something, it's almost always a terrible idea. Like anti vaccine sentiment, isolationism antisemitism, etc. Forcing the American taxpayer to invest in a well connected private firm isn't socialism, it's cronyism in the form of an SOS signal. It's also a testament to the power of marketing, as OpenAI's advertising spend in 2025 alone would have been enough to buy every super bowl ad spot for the past seven years. Last year I observed that circular financing deals were common toward the end of the dot com bubble and in retrospect, a strong signal of fragility as one falling domino triggers a chain reaction. The circular financing deals connecting B2C and B2B AI companies with companies building AI infrastructure are easy to look past as long as customers, especially enterprise users, continue spending. According to the Economist, corporate spending on AI increased 13x from 2025 to 2026, but recently that narrative has hit a speed bump. In May, Axios reported that an anonymous company spent $500 million in a single month after failing to put usage limits on clawed licenses for employees. Uber blew through its entire AI budget for 2026 in just four months. DoorDash, Meta, Microsoft and Salesforce are now pivoting from token maxing to sobriety, I.e. limiting it to proven use cases. FYI, tokens are what the LLMs call chunks of data. A token equals about four characters. To date, the best use case for AI is coding. But the dominant tech trade of 2026 sell software stocks to buy chips is showing signs of falling apart, suggesting that investors overestimated the scale and timeline of the AI disruption. Meanwhile, Palo Alto Network's CEO Nikesh Arora told CNBC that widespread adoption depends on token costs coming down 20% this year and 90% this year.

[E]

Next year.

[D]

Meta CTO Andrew Bosworth summed up the about face in an April memo to employees. Nobody should be using AI tools just for the sake of using them. All motion is not progress, and token usage alone is not a measure of impact of any kind. Good point, except that runaway enterprise spending is what's driving Anthropic's $47 billion in annual recurring reven and justifying the company's $965 billion valuation. The pivot to measuring productivity is a good thing, but in the short term it benefits cheaper open source models like China that deliver 80% of the Frontier models bang for 20% of the bucks. The grim reaper is knocking at the door of every VC who has gone all in in 2025 and 2026 on AI. Zooming out AI could end up being similar to electricity, a foundational technology that distributes value to end users. In that scenario, the real jumps in productivity and job displacement come from new companies and processes rather than incumbents grafting new technology onto existing workflows, John Byrne Murdoch wrote in the Financial Times. The fact that incumbent software and knowledge work companies are finding only modest productivity gains by incorporating AI into existing workflows and organizational structures, while usage, revenue and productivity explode at anthropic and OpenAI companies built around AI with products written and reviewed by it is perhaps early evidence of the same dynamic playing out here, only much faster. I'm as bullish on AI as I was on the Internet in 1999, but with hindsight and scars I know not to conflate valuations with value, as transformative technologies take longer to deploy than the carnival barkers claim. The danger with the AI bubble isn't that the technology is overhyped and still in search of use cases. It's that the speculation is so concentrated that the 10 most valuable companies in the S&P 500 account for 43% of the index's total market cap. In other words, when AI sneezes, the US economy's lungs may begin to fill with fluid. Every bubble creates extraordinary wealth. The question is, who keeps it? I suspect AI will create enormous value, but unlike search, social or e commerce, much of that value will leak past shareholders and into the hands of customers. Think jet, transportation, vaccines and the PC. The biggest winners won't be shareholders in AI companies, but the people who use the technology. In sum, we may have passed a wealth tax, just not the one AOC envisioned.

[C]

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