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\section{Results}
\label{sec:results}
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\subsection{Portfolio sorts: a weak unconditional relation}
Table~\ref{tab:sorts} reports the quintile sorts over the full sample. The relation between AI
disclosure and future returns is positive and broadly monotone but economically modest. The
equal-weighted Q5$-$Q1 spread is $+4.6$ percentage points per year for AI adoption \tstat{1.83},
$+3.9$ for AI infrastructure \tstat{1.58}, $+5.3$ for AI disruption risk \tstat{2.27}, and $+4.9$ for
the composite \tstat{1.88}. Value-weighting attenuates the adoption and composite spreads, indicating
the effect is stronger among smaller index members. The left panel of Figure~\ref{fig:quint} displays
the full-sample pattern: returns rise gently from the lowest to the highest AI quintile. On its own,
this is suggestive but far from decisive evidence---and, as we now show, it averages over two very
different regimes.

\input{tables/tab_sorts}

\subsection{Does AI disclosure earn risk-adjusted alpha unconditionally? (H1)}
Table~\ref{tab:alpha} reports CAPM and FF6 alphas of the long--short portfolios. The AI-opportunity
composite earns a raw spread of about $+2.3\%$ per year, of which the FF6 alpha is $+1.9\%$---but
statistically insignificant \tstat{1.24}. The AI-adoption and AI-infrastructure long--shorts are
similarly insignificant after risk adjustment. The one exception is the AI-disruption-risk spread,
whose FF6 alpha of $+2.8\%$ per year is marginally significant \tstat{2.10}, hinting at a small risk
premium for firms that flag AI as a competitive threat. The factor loadings (not the focus here) show
the AI long--short is roughly market-neutral with modest negative profitability (RMW) and investment
(CMA) tilts, consistent with a growth-oriented long leg. The verdict on H1 is clear:
\textbf{unconditionally, AI disclosure is at best weakly priced.} This is not the end of the story but
the beginning of it.

\input{tables/tab_alpha}

\subsection{Panel and Fama--MacBeth estimates}
Table~\ref{tab:panel} juxtaposes the two-way fixed-effects panel with the Fama--MacBeth estimator. The
contrast is instructive. In the Fama--MacBeth (cross-sectional) regressions, the AI-adoption and
AI-infrastructure premia are positive---$+1.3$ and $+1.6$ percentage points per standard
deviation---with \textit{t}-statistics near 1.6--1.7, indicating a weak but positive cross-sectional
premium on average. In the two-way fixed-effects panel, however, every AI coefficient collapses to
essentially zero. Because firm fixed effects remove all permanent cross-firm differences, this tells
us the signal lives \emph{across} firms, not \emph{within} them: a given firm writing relatively more
about AI from one year to the next does not predict its own subsequent return (anticipating H4).
Throughout, size carries the familiar negative premium ($t\approx-8$) and volatility a positive one,
consistent with the broader cross-sectional literature in this large-cap universe.

\input{tables/tab_panel}

\subsection{The structural break: AI pricing switched on after ChatGPT (H2)}
The full-sample averages are not so much wrong as misleading: they blend two regimes that look
nothing alike, and in doing so they understate the post-2022 effect as badly as they overstate the
pre-2022 one. This regime change is the paper's central result. Splitting the sample at the November
2022 ChatGPT launch, the center and right panels of Figure~\ref{fig:quint} reveal a striking
transformation. Before 2022 the relationship between AI quintile and forward return is nearly flat---a
firm in the top AI quintile earned little more than one in the bottom quintile. After 2022 the same
schedule tilts steeply upward, rising monotonically from the lowest to the highest quintile. The
high-minus-low composite spread is $+2.4$ percentage points per year pre-ChatGPT but $+14.6$ points
per year post-ChatGPT---a more than sixfold increase in the price of an essentially unchanged
characteristic.

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig1_quintile_returns.png}
\caption{\textbf{Forward returns by AI-disclosure quintile, before and after ChatGPT.} Each panel
plots the mean forward twelve-month return across quintiles of the AI-disclosure composite: the left
panel pools the full sample, the center panel covers the pre-ChatGPT period (2010--2021), and the
right panel the post-ChatGPT period (2022--2024). The quintile schedule is nearly flat before 2022 and
steeply increasing afterward; the high-minus-low (Q5$-$Q1) spread rises from $+2.4$ percentage points
per year before ChatGPT to $+14.6$ points after.}\label{fig:quint}
\end{figure}

Table~\ref{tab:break} quantifies the break two ways. In sub-period sorts, the AI-adoption Q5$-$Q1
spread rises from $+2.2\%$ per year \tstat{2.04} pre-2022 to $+14.3\%$ \tstat{2.15} post-2022; the
composite rises from $+2.5\%$ to $+14.6\%$; AI infrastructure from $+1.4\%$ to $+13.6\%$. In the
pooled interaction regressions, the post-2022 incremental slope is positive for all three signals and
statistically significant for AI infrastructure \tstat{2.63}---the facet most directly tied to the
chip, cloud, data-center, and power firms that led the 2023--2025 rally. Figure~\ref{fig:spread} shows
the annual spreads: small and mixed-sign through 2021, uniformly large and positive for every
post-2022 cohort.

\input{tables/tab_break}

\begin{figure}[htbp]\centering
\includegraphics[width=0.9\textwidth]{fig3_annual_spread.png}
\caption{\textbf{Annual AI-disclosure return spreads by cohort.} The figure plots the annual
high-minus-low (Q5$-$Q1) forward-return spread on the AI-disclosure composite, by 10-K fiscal (formation)
year. Spreads are small and of mixed sign through 2021, whereas the post-ChatGPT cohorts (shown in red)
are uniformly and substantially positive---showing that the regime change appears in every post-2022
cohort taken on its own, not only in the period average.}
\label{fig:spread}
\end{figure}

The same regime change is visible in two further lenses. Figure~\ref{fig:cum} plots the cumulative
value of one dollar invested in the equal-weighted, annually rebalanced, self-financing
high-minus-low portfolio: it drifts sideways-to-up for a decade and then inflects upward after the
dashed ChatGPT line. Figure~\ref{fig:fmyear} plots the year-by-year Fama--MacBeth slope on the AI
composite (with controls): it hovers near zero for the entire 2010s and then turns sharply positive,
peaking in the 2024 cohort. The latter figure also delivers an important nuance: the size-controlled
premium is largest in the most recent cohort, consistent with the AI \emph{infrastructure}
super-cycle of 2024--2025 (the build-out of data centers and accelerated compute). We return to this
concentration when discussing limitations.

\begin{figure}[htbp]\centering
\includegraphics[width=0.86\textwidth]{fig2_cumulative_ls.png}
\caption{\textbf{Cumulative performance of the AI long--short portfolio.} The figure plots the
cumulative value of \$1 invested in the equal-weighted, annually rebalanced, self-financing
high-minus-low AI-disclosure portfolio, shown for both the AI-opportunity composite and AI
disruption-risk signals. Both curves drift sideways-to-up for a decade and then inflect sharply upward
after the dashed ChatGPT line, visualizing the regime change as a kink in accumulated wealth.}
\label{fig:cum}
\end{figure}

\begin{figure}[htbp]\centering
\includegraphics[width=0.9\textwidth]{fig6_fm_byyear.png}
\caption{\textbf{The AI premium over time: year-by-year Fama--MacBeth slopes.} The figure plots the
annual Fama--MacBeth slope on the AI-disclosure composite---the forward return earned per
cross-sectional standard deviation of AI disclosure---controlling for size, momentum, and volatility,
with error bars of $\pm1$ standard error. The slope hovers near zero throughout the 2010s and turns
sharply positive after ChatGPT, peaking in the 2024 cohort, consistent with the 2024--2025
AI-infrastructure super-cycle.}\label{fig:fmyear}
\end{figure}

Finally, Figure~\ref{fig:bin} confirms the pattern is not an artifact of a few influential
observations: the binned relation between the AI composite and forward returns is essentially flat in
the pre-ChatGPT sub-period and distinctly upward-sloping afterward, across the entire distribution of
AI disclosure rather than only at the extreme quintiles.

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig5_binscatter.png}
\caption{\textbf{Binned relation between AI disclosure and returns, by sub-period.} The figure shows a
binned scatter of the forward twelve-month return against the AI-disclosure composite, plotted
separately for the pre- and post-ChatGPT periods. Each point is a portfolio of firms within a bin of
the AI signal and the line is an OLS fit. The conditional relation is essentially flat before ChatGPT
and distinctly upward-sloping afterward, across the entire distribution of AI disclosure rather than
only at the extreme quintiles---confirming that the effect is not driven by a few influential
observations.}\label{fig:bin}
\end{figure}

\subsection{The anatomy of the premium: size and sector}
Where in the cross-section does the post-2022 premium concentrate? Table~\ref{tab:doublesort} and
Figure~\ref{fig:double} report an AI~$\times$~size double sort. Before 2022 the AI gradient is flat
within both size halves. After 2022 it is steep, and dramatically so among smaller firms: small,
high-AI firms earned a mean forward return of 34.6\%, versus 19.5\% for small, low-AI firms---a
15-point gap---while the corresponding gap among large firms is 7 points. The premium is thus
\emph{not} merely a mega-cap phenomenon (a point we reinforce in Section~\ref{sec:robustness}); if
anything it is strongest among the smaller, less-followed names, consistent with an attention-based
mechanism in which information diffuses more slowly into smaller stocks.

\input{tables/tab_doublesort}

\begin{figure}[htbp]\centering
\includegraphics[width=\textwidth]{fig9_double_sort.png}
\caption{\textbf{AI disclosure $\times$ firm-size double sort.} The figure plots the mean forward
twelve-month return by AI tercile within small and large firms, separately for the pre- and
post-ChatGPT periods. The post-2022 AI gradient is steep in both size groups but dramatically steeper
among small firms, where high-AI firms outperform low-AI firms by roughly 15 percentage points,
consistent with an attention-based mechanism that operates most strongly in less-followed
stocks.}\label{fig:double}
\end{figure}

\subsection{Economic significance}
The statistical break is matched by an economic one, and it is worth dwelling on the magnitudes
because they are unusual. The Sharpe ratio of the equal-weighted AI-composite long--short
portfolio---a direct gauge of return earned per unit of risk borne---rises from 0.15 in the
pre-ChatGPT decade to 1.33 afterward; for the AI-disruption-risk spread it rises from 0.33 to 1.15. To
put the post-2022 figure in perspective, the broad U.S.\ equity market has historically delivered a
Sharpe ratio in the neighborhood of 0.4, so a value above one for a single-characteristic,
self-financing strategy is exceptional, and its emergence precisely at the ChatGPT boundary---rather
than gradually, as a slowly learned risk premium would---is hard to reconcile with a constant risk
premium. The point estimates in the quintile sorts are equally vivid: a 14-point annual spread
between the top and bottom AI quintiles is roughly three times the unconditional equity risk premium
and an order of magnitude larger than the pre-2022 spread. Even acknowledging that a portion of this
reflects the higher realized volatility of AI-exposed firms (documented in Section~\ref{sec:robustness})
and the broad technology rally, the magnitude of the post-2022 re-rating is among the largest
single-theme cross-sectional effects in the recent asset-pricing record.