General-Purpose Technology and Speculative Bubble Detection

TL;DR

This paper demonstrates that traditional bubble tests are distorted by general-purpose technology adoption and proposes a new decomposition method to better detect speculation, applied to AI and dot-com bubbles.

Contribution

It introduces a fundamental-versus-speculative decomposition that accounts for technology shocks, improving bubble detection accuracy.

Findings

Eliminates false speculation signals during the 2020-2025 AI rally.

Confirms a speculative peak in the dot-com bubble (1999-2000).

Shows that traditional tests are biased during technology adoption periods.

Abstract

We show that the leading bubble test suffers severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, contaminating the test's limit distribution with a non-centrality parameter proportional to the shock's peak. We propose a fundamental-versus-speculative decomposition that projects prices onto observable technology proxies and applies the test to the residual. Empirically, the decomposition eliminates evidence of speculation in the 2020-2025 AI rally while confirming a speculative peak confined to December 1999-March 2000 in the dot-com episode.