Last week I sat down with the Drive Time crew to explain our AI Implosion thesis. The response was explosive. Here’s what I said, what got cut, and the three questions listeners kept calling in about.

The most dangerous number in AI right now isn’t a valuation multiple — it’s the ratio of experimental-to-production deployments.”

Last Thursday I walked into the WJNO studio knowing I’d get pushback. That was the point. When a Drive Time producer called me the week before, they wanted the standard segment: two minutes on markets, a prediction, out. I told them I needed six minutes minimum and I’d probably make their audience angry. They gave me eight.

The segment was about our AI Implosion thesis — the idea that the current AI investment cycle is structurally similar to the dot-com bubble, not in degree but in kind. Same pattern of capital flowing faster than revenue can justify. Same gap between what’s promised in pitch decks and what’s delivered in production.

Before I get into what happened after, let me be clear about what I’m not saying. I’m not saying AI is a fad. I’m not saying the technology doesn’t work. I’m saying the investment thesis surrounding it — the valuations, the hiring, the infrastructure spend — is disconnected from the actual rate of enterprise adoption. And that gap has consequences.

What I Actually Said

The core of my argument comes down to three data points our sentiment model has been tracking since Q3 2025. These aren’t proprietary numbers — they’re public if you know where to look. But nobody’s connecting them.

  1. The capex-to-revenue ratio is historically unprecedented. The amount being spent on AI infrastructure versus the revenue it’s generating makes the 1999 telecom buildout look conservative. That’s not hyperbole — run the numbers.
  2. Enterprise deployment rates have plateaued. After a surge in 2024, the percentage of Fortune 500 companies moving AI projects from pilot to production has flatlined. They’re still experimenting. They’re not operationalizing.
  3. Developer sentiment has shifted. This is the qualitative signal our model picks up earliest. The tone on internal forums, in conference hallway conversations, in hiring patterns — it’s moved from “this changes everything” to “this is useful for specific things.” That’s a meaningful shift.

“Every major correction in the last 30 years was preceded by the same four words from analysts: ‘This time is different.'”

— From the Pulse Index Sentiment Model, Q1 2026 Report

What Got Cut

I had a whole section prepared on the historical pattern matching — how the current moment maps to specific weeks in 1999 and 2007. Time ran out. I’ll publish that as a standalone analysis piece next week.

The Questions That Kept Coming

Three questions dominated the call-in segment afterward. First: “Should I sell my NVIDIA stock?” (I don’t give stock advice — I give sentiment readings.) Second: “When does it pop?” (Q2-Q3 2026 is our window of maximum vulnerability.) Third: “If AI is a bubble, what’s real?” (The technology is real. The valuations aren’t. Those are different things.)

The full analysis is published on the site. The white paper goes deeper. And I’ll be back on air in two weeks to cover the Iran scenarios.