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Is A.I. A Massive Lie? | Ed Zitron

Adam Taggart | Thoughtful Money® Aug 11, 2026

Summary

Ed Zitron frames the AI boom as a speculative bubble — roughly 70%+ of hyperscaler AI revenue is tied to OpenAI and Anthropic's circular compute spending.

He argues LLMs are fundamentally probabilistic and can't reliably catch their own errors, so trillion-dollar productivity gains won't materialize.

A reckoning could cut AI-exposed names 50%+ with cascading damage to venture capital, private credit, and Japanese lenders exposed to OpenAI bets.

The dot-com analogy misleads: AI data centers are unfinished assets needing continuous capital, not already-paid 1990s-style fiber.

Job-displacement claims are overstated; AI mostly erodes work that was already being automated, like transcription and contract roles.

Adam Taggart's takeaways

  • Top takeaway Adam Taggart frames the AI trade as the central driver of the S&P 500 and NASDAQ 100, noting AI-linked stocks now account for roughly 45% of the S&P and nearly 70% of the NASDAQ 100.
  • He argues that if the AI capex narrative fails, the resulting correction could reach 50% or more, given current price-to-sales ratios in the teens for many of the names involved.
  • Taggart draws a parallel to the COVID-era loss of trust in authority, suggesting the post-AI bust fallout could include a deeper credibility crisis for financial media and analysts.
  • He positions the interview as responding to heavy audience demand, signaling that retail investors are increasingly anxious about an AI bubble.

Fred Hickey's takeaways

  • Top takeaway Taggart notes that Fred Hickey expressed the same doubts about AI unit economics in a previous Thoughtful Money interview a few weeks earlier.

Ed Zitron's takeaways

  • Top takeaway Ed Zitron argues LLMs are not actually intelligent, pointing to instances like Bloomberg's AskB platform confidently returning an obviously wrong Microsoft share price as proof that subtle errors make them unfit for financial work.
  • He maintains the AI investment cycle is sustained by circular financing, with hyperscalers, Nvidia, SoftBank, and venture capital repeatedly recycling money into the same two loss-making compute buyers (Anthropic and OpenAI).
  • Zitron predicts a 50% or larger revaluation of AI-exposed equities and warns the aftermath could be worse than the dot-com bust because unfinished data centers still carry ongoing electricity and completion costs.
  • He argues that any productivity benefit from AI is hard to verify, that coding metrics like commits are essentially meaningless, and that engineer-hours spent babysitting LLMs often exceed the value produced.
  • Zitron frames the cultural pressure to adopt AI as a kind of corporate religion that CEOs embrace to appear forward-looking, drawing a sharp contrast with the begrudging adoption of prior mandates like DEI.