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\section{Conclusion}
\label{sec:conclusion}
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Using a semantic, embedding-based measure of artificial-intelligence language in the SEC filings of
S\&P~500 firms, linked to Yahoo Finance returns and standard factor data, we have shown that the
pricing of AI disclosure is a \emph{conditional, regime-dependent} phenomenon. For most of the 2010s,
how intensively a firm wrote about adopting, building, or being threatened by AI bore only a faint
relation to its subsequent returns, and a risk-adjusted long--short strategy earned no reliable
alpha. After the launch of ChatGPT in late 2022, the same disclosure became strongly priced: the
high-minus-low AI-disclosure return spread jumped from roughly $+2$ to roughly $+15$ percentage points
per year, and the long--short Sharpe ratio rose from 0.15 to 1.33.

The break is robust to signal definition, horizon, dimension reduction, and the exclusion of mega-cap
winners; it is specific to AI-related language (ESG and climate disclosure, tested identically, were
\emph{penalized} in the same window); it is corroborated by a market-model event study in which
high-AI firms earned $+10$ points of additional abnormal return in the year after ChatGPT; and it
operates as a cross-sectional re-rating of persistent firm- and sector-level exposure rather than a
reward for year-to-year wording, fading under firm fixed effects.

Two messages emerge, one economic and one methodological. The economic message is that markets price
the \emph{language} of AI---but only once AI commands their attention; value-relevant information can
sit unpriced in plain sight for years until a salient catalyst turns the market's gaze toward it,
after which the repricing is abrupt rather than gradual. The methodological message is that
embedding-based measurement of disclosure content opens a productive avenue for asset pricing,
capturing economically meaningful, multi-faceted exposures at scale and revealing pricing dynamics
that keyword counts (which cannot read meaning) and firm-differenced designs (which discard the
persistent cross-sectional variation that turns out to matter) would both miss. As generative AI
continues to reshape the corporate landscape, the semantic footprint it leaves in mandatory disclosure
offers a rich, and now demonstrably value-relevant, signal. Several next steps follow naturally:
extending the panel beyond the S\&P~500 to the mid- and small-cap firms where attention frictions are
larger and the effect may be stronger; incorporating earnings-call transcripts and patent text as
complementary, higher-frequency AI-exposure measures; and---perhaps most consequentially for
investors---tracking whether the post-2022 premium persists, decays, or reverses as the AI narrative
matures and the market's attention becomes fully impounded in prices.