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\section{Introduction}
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The arrival of generative artificial intelligence has been the defining corporate-narrative shift
of the 2020s. Between the public launch of ChatGPT in late 2022 and 2025, the market
capitalization of AI-linked firms swelled by trillions of dollars, ``AI'' became the most
frequently invoked theme on earnings calls, and the language of corporate disclosure filled with
references to large language models, generative tools, intelligent automation, and the compute
infrastructure that powers them. This paper asks a deceptively simple question: did the
\emph{language} of AI in firms' mandatory disclosures carry information about their subsequent
stock returns---and if so, when and through what channel?

Answering this question well requires three ingredients that have only recently become jointly
feasible: a large, machine-readable corpus of corporate disclosure; a measurement technology that
captures the \emph{meaning} of text rather than the mere frequency of keywords; and a clean mapping
from disclosure to tradeable returns. We bring all three to bear. Our laboratory is the universe of
S\&P~500 constituents over fiscal years 2010--2026. Our raw material is the full text of their
annual reports (Form~10-K), parsed section by section. Our measurement engine is the EDGAR Research
Database, which embeds every text chunk with the \texttt{all-MiniLM-L6-v2} sentence transformer and
scores it by cosine similarity to a set of carefully written ``anchor'' passages that define eight
economic concepts. Three of these anchors isolate distinct facets of AI engagement: \emph{internal
AI adoption} (using AI to run the business), \emph{AI infrastructure exposure} (capital tied to AI
compute), and \emph{AI product-disruption risk} (the danger that AI commoditizes the firm's
offerings). The result is a continuous, comparable, firm-year measure of how intensively---and in
what sense---each firm engages with AI in its own words.

We pair these semantic scores with monthly adjusted prices for all 500 tickers from Yahoo Finance
and with the Fama--French five factors plus momentum. To avoid look-ahead bias we adopt the
standard portfolio-formation convention: because a 10-K covering fiscal year $Y$ is public by early
in year $Y{+}1$, we form signals at the end of June of $Y{+}1$ and measure the forward twelve-month
return from July of $Y{+}1$ to June of $Y{+}2$. The signal is therefore always known before the
holding period begins. This yields 6{,}852 firm-years with a strictly tradeable forward return,
spanning formation years 2010 through 2024.

Our headline finding is that the pricing of AI language is \emph{not} a stable factor premium but a
regime-dependent re-rating that switched on with the generative-AI boom. For most of the 2010s,
sorting S\&P~500 firms on the intensity of their AI disclosure produced only a faint return
pattern: the equal-weighted spread between the highest and lowest quintiles averaged about $+2.4$
percentage points per year, and a risk-adjusted long--short portfolio earned an economically small
and statistically insignificant alpha. After the launch of ChatGPT in November 2022, the same sort
produced a spread of $+14.6$ percentage points per year. The cross-sectional re-rating is visible
in every post-2022 formation cohort, in a cumulative long--short wealth curve that inflects sharply
upward at the end of 2022, and in a year-by-year Fama--MacBeth slope that jumps from near zero to
large and positive.

A natural worry is that this pattern simply reflects the well-known rally of a handful of
technology mega-caps. We address this concern head-on and reject it. The post-2022 spread is
$+14.1$ percentage points \tstat{1.9} when the ``Magnificent~7'' are excluded and $+14.5$ points
when the ten largest firms by market capitalization are dropped---the effect is broad-based across
the index, not an artifact of Nvidia, Microsoft, or their peers. It is robust to using the simple
mean, the section-weighted mean, or the top-5\% tail of similarity scores; to six-, twelve-, and
twenty-four-month horizons; and to replacing the three AI signals with their first principal
component, which alone explains 87\% of their common variation.

We then deploy three complementary tests to pin down what the result is---and is not. First, a
\emph{placebo} exercise re-runs the entire design on the five non-AI semantic factors in the
database (ESG, climate, cybersecurity, automation/labor, and macro risk). If the post-2022 jump
merely reflected a generic ``more disclosure earns more'' phenomenon, or a mechanical artifact of
our pipeline, every factor should display it. They do not. The post-2022 spread is large and
positive for the AI factors, essentially zero for macro-risk language, and \emph{negative} for ESG
and climate disclosure---high-ESG firms \emph{underperformed} after 2022. The post-ChatGPT
re-rating is specific to AI-related content. Second, we run a market-model \emph{event study} around
the 30~November 2022 ChatGPT launch. Firms in the top tercile of pre-event AI disclosure (measured
from their FY2021 10-K, the last report filed before the event) earned cumulative abnormal returns
$+10.1$ percentage points higher than the bottom tercile over the following year \tstat{1.97} and
$+14.6$ points higher over eighteen months. Because abnormal returns are purged of market-beta
exposure, this is a clean cross-sectional response to an unanticipated narrative shock. Third, we
use two-way fixed effects and difference-in-differences to localize the mechanism. The premium is
absorbed by firm fixed effects and roughly halved by sector fixed effects, telling us that the
durable signal is the persistent \emph{level} of a firm's (and its sector's) AI exposure relative
to peers, not the year-to-year wiggle in its wording.

Taken together, the evidence supports an attention- and narrative-driven repricing interpretation.
The textual evidence of firms' AI engagement accumulated quietly for a decade and was barely priced.
A salient catalyst---the consumer breakthrough of generative AI---then drew investor attention to
that pre-existing information, and capital flowed toward the firms and sectors whose disclosures
revealed the greatest AI exposure. This is the asset-pricing signature of limited attention and
narrative economics, observed not through a sentiment proxy but directly in the semantic content of
mandatory disclosure.

\paragraph{Contribution.} We make four contributions. \emph{First}, methodologically, we
demonstrate the value of embedding-based semantic measurement of disclosure for asset pricing: a
cosine-similarity-to-anchor design captures economically meaningful, multi-faceted exposure
(adoption vs.\ infrastructure vs.\ risk) that keyword counts cannot, and it does so at scale across
the entire S\&P~500 and a decade and a half of filings. \emph{Second}, substantively, we provide
text-based, look-ahead-free evidence that the cross-sectional pricing of AI exposure is a post-2022
regime phenomenon rather than a long-standing premium. \emph{Third}, we discipline the finding with
a placebo across non-AI factors and an event study, distinguishing a genuine AI-specific repricing
from a mechanical or market-wide artifact. \emph{Fourth}, we document the \emph{anatomy} of the
effect---its concentration in smaller firms, its operation substantially through a sector channel,
and its disappearance under firm fixed effects---which sharpens the economic interpretation and
warns against naive trading strategies.

The remainder of the paper is organized as follows. Section~\ref{sec:literature} situates the study
in five literatures and states our hypotheses. Section~\ref{sec:data} describes the data and the
construction of the semantic measures. Section~\ref{sec:methodology} lays out the empirical
methodology. Section~\ref{sec:results} presents the main results, and Section~\ref{sec:robustness}
the robustness and identification tests. Section~\ref{sec:discussion} discusses mechanisms and
limitations, and Section~\ref{sec:conclusion} concludes.