The David Lin Report
The David Lin Report

Nov 3, 2026 · 40 min

Horwitz warns AI debt could deepen the next stock sell-off

Trader Called Tech Crash--Now Predicts ‘Black Christmas’ For Stocks | Todd Horwitz

The conversation connects election volatility to deeper risks in technology valuations, household weakness, inflation, debt, and Treasury yields.

3 key takeaways
  1. 1Election-driven market volatility may fade quickly, but stretched technology valuations and AI borrowing could sustain pressure on stocks.
  2. 2Horwitz sees a recession already affecting lower-income households while wealthier consumers keep the economy appearing more resilient.
  3. 3He remains cautious on gold and stocks, separating long-term accumulation from a defensive, short-term trading book.

Don't miss

Horwitz explains how his long-term model bought technology shares during the sell-off even as his short-term trading book remained short.

The brief

Todd Horwitz argues that a midterm-election market move could be brief because investors may have already priced in the result, leaving broader risks unresolved.

The discussion shifts to technology’s sell-off, where disappointing reactions from Apple and Palantir raise questions about whether AI expectations and infrastructure spending have outrun earnings.

Horwitz questions whether data-center construction can sustainably support growth when companies rely on government support and borrowing that may exceed their earnings.

He describes a K-shaped economy: wealthier consumers remain resilient, while lower-income households are already experiencing recessionary conditions despite official data.

The standout tension is his split positioning: a long-term portfolio accumulates quality shares during declines, while his short-term trading book stays defensive and short.

Horwitz expects persistent inflation, consequential Treasury yields, and lower oil prices, while remaining a leveraged-free buyer of gold despite possible downside.

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Horwitz warns AI debt could deepen the next stock sell-off · PodLume