% ============================================================================ \section{Discussion: Mechanism and Interpretation} \label{sec:discussion} % ============================================================================ What economic mechanism best fits this constellation of facts? We find an attention- and narrative-driven repricing the most parsimonious explanation, and we read the evidence as follows. \paragraph{The information pre-existed; the attention did not.} Firms disclosed their AI engagement for years before it was priced. The semantic content of those disclosures was, in cross-section, only faintly related to returns through 2021. What changed in late 2022 was not the information but its \emph{salience}: a consumer breakthrough (ChatGPT) made AI the dominant market narrative \citep{shiller2017narrative}, drawing investor attention \citep{da2011,barberodean2008} to a body of disclosure that had been hiding in plain sight. The abrupt, near-simultaneous repricing across the cross-section---visible in the event study within months of the launch---is the signature of an attention shock rather than the gradual accrual of a risk premium. \paragraph{It is a re-rating of levels, not of changes.} The disappearance of the effect under firm fixed effects, the muted difference-in-differences estimates, and the event-time profile all point the same way: the market repriced the persistent \emph{level} of a firm's AI exposure, not the year-to-year change in its wording. This has a clear practical corollary---firms cannot mechanically earn the premium by adding AI boilerplate to their filings (and our top-5\% and simple-mean robustness suggests the measure is not easily gamed)---and a clear methodological one: studies that difference out firm effects will understate the cross-sectional importance of disclosure content. \paragraph{It operates substantially, but not entirely, through sectors.} The sector-neutral and sector-fixed-effects results show that much of the AI premium reflects the re-rating of AI-exposed \emph{sectors}---technology, communication services, and the industrial firms supplying the AI build-out---relative to the rest of the index. This is economically sensible: the generative-AI boom was, to first order, a sectoral reallocation of expected cash flows and valuations. Yet a positive within-sector residual remains in the event study, indicating that the firm's own disclosure carried incremental information even among industry peers. \paragraph{The thematic asymmetry is telling.} Perhaps the most revealing single fact is the placebo: in the very same post-2022 window, high-AI firms were rewarded while high-ESG and high-climate firms were penalized. Markets did not simply reward verbose or virtuous-sounding disclosure; they rotated toward one theme (AI) and away from another (sustainability). This is difficult to reconcile with a purely mechanical or risk-based account and natural under a narrative-rotation view of investor attention. \paragraph{Relation to the AI-asset-pricing literature.} Our findings complement \citet{eisfeldt2023generative}, who show that an occupation-task measure of generative-AI exposure was priced around ChatGPT. We reach a convergent conclusion from an entirely different measurement---firms' own disclosure language---and extend it along the time dimension, showing that the same exposure was \emph{un}priced for a decade beforehand. The agreement of two independent measures on the timing and sign of the effect strengthens confidence that a real AI repricing occurred. \subsection{Implications} Our findings carry distinct implications for three audiences. \emph{For investors and quantitative strategists}, the results are a caution as much as an opportunity. The AI-disclosure signal delivered a striking post-2022 Sharpe ratio, but the same signal was nearly inert for the preceding decade, and the effect is absorbed by firm fixed effects---so a strategy that traded on year-to-year changes in AI verbosity would have captured little. The tradeable content lies in the persistent cross-sectional \emph{level} of AI exposure, and even that is partly a sector bet whose durability is unproven. The honest reading is that disclosure-based AI signals are best viewed as a lens on a regime-dependent theme rather than as a stable alpha source. \emph{For corporate managers and disclosure policy}, the asymmetry between AI (rewarded) and ESG/climate (penalized) after 2022 illustrates how powerfully the prevailing market narrative conditions the valuation consequences of what firms choose to emphasize. Because the premium attaches to persistent, hard-to-fake exposure rather than to boilerplate, the result does not invite cheap disclosure manipulation; but it does suggest that the market reads filings for genuine strategic content and reprices it when attention turns. \emph{For researchers}, the study demonstrates that embedding-based semantic measurement uncovers pricing dynamics invisible to both keyword counts (which cannot distinguish meaning) and firm-differenced panels (which discard the persistent cross-sectional variation that turns out to matter). \subsection{Summary of the evidence across designs} It is worth stepping back to see how the designs corroborate one another, since each has different weaknesses. The portfolio sorts establish the raw magnitude and its post-2022 timing but are model-free and could in principle reflect risk; the factor-model spanning regressions show the unconditional spread is not explained by standard factors yet is statistically weak over the full sample; the sub-period and interaction tests localize the effect to the post-ChatGPT regime; the placebo rules out a mechanical or generic-disclosure explanation by showing the opposite sign for ESG and climate; the event study, using daily abnormal returns purged of beta, provides cleanly-timed, cross-sectional confirmation within months of the catalyst; and the fixed-effects and difference-in-differences estimates pin down that the priced object is persistent firm- and sector-level exposure, not incremental wording. No single design is decisive, but their convergence---on the same sign, the same timing, and a consistent economic story---is what gives us confidence in the conclusion. \subsection{Limitations} \label{subsec:limits} Several caveats temper our conclusions. \emph{First}, the post-2022 window contains only three formation cohorts, and the size-controlled premium is concentrated in the most recent (2024) cohort, which captures the 2024--2025 AI-infrastructure capital-expenditure boom. The regime result is economically large and consistent across designs, but its post-period rests on a short sample and could in principle reflect a single extraordinary episode rather than a durable new regime; only future data can settle this. \emph{Second}, our universe is current S\&P~500 membership, which introduces survivorship and look-ahead-at-the-index-level bias. This biases the level of returns upward but is less likely to drive the \emph{cross-sectional} AI--return relation that is our focus. \emph{Third}, the market-capitalization proxy holds shares outstanding at their latest value, approximating historical size with error. \emph{Fourth}, the semantic anchors, though carefully written, embed the authors' definitional choices; different anchor wording would yield somewhat different scores, although the robustness to mean/tail aggregations and to PCA mitigates this concern. \emph{Fifth}, disclosure intensity is correlated with sector, and while our within-year, size-, momentum-, and volatility-controlled tests and the sector-FE event study address this, we cannot fully separate ``AI exposure'' from ``technology sector'' as the priced characteristic. \emph{Finally}, our designs establish a robust association and an event-study-based timing argument, but not strict causality; the attention-repricing interpretation is the most consistent with the evidence rather than the only logically possible one.