TeX 69.3%
Python 30.6%
1\begin{table}[H]\centering2\caption{The Post-ChatGPT Structural Break}\label{tab:break}3\small4\begin{tabular}{lccc}\toprule5& \multicolumn{2}{c}{Q5$-$Q1 spread} & Interaction \\6\cmidrule(lr){2-3}7Signal & Pre (2010--21) & Post (2022--24) & AI$\times$Post \\\midrule8AI adoption & +2.2 \tstat{2.04} & +14.3 \tstat{2.15} & +0.0199 \tstat{1.17} \\9AI infrastructure & +1.4 \tstat{0.91} & +13.6 \tstat{2.44} & +0.0277 \tstat{2.63} \\10AI composite & +2.5 \tstat{1.58} & +14.6 \tstat{2.07} & +0.0270 \tstat{1.78} \\11\bottomrule12\end{tabular}13\tablelegend{This table quantifies the change in the pricing of AI disclosure around the November 2022 launch of ChatGPT. Columns~2--3 report the equal-weighted Q5$-$Q1 forward-return spread (in percent per year, with Newey--West $t$-statistics in parentheses) separately for the pre-period (formation years 2010--2021) and the post-period (2022--2024). Column~4 reports the coefficient on the interaction of the within-year standardized AI signal with the post-2022 indicator from a pooled OLS regression that includes size and momentum controls and year-clustered standard errors. The spread rises roughly sixfold across the break for every AI signal, and the interaction term is positive throughout---most strongly for AI infrastructure, the facet tied to the compute build-out.}14\end{table}15