Analyzing the market's reaction to AI narratives in corporate filings
Bibliographic record
Abstract
The recent surge in artificial intelligence (AI) interest and investment, driven by advances in large language models, has led the market to reward adopters and penalize laggards. Yet, AI integration predates this “AI gold rush,” with earlier adopters reaping significant benefits. Drawing on a 2005–2018 sample, a formative period before AI became mainstream, this paper examines how early AI adoption and its disclosure in corporate filings affect U.S. firms. Analyzing 10-K filings, we categorize AI-related mentions as actionable, speculative, or irrelevant. We establish causal links between these disclosures and firm value, with innovation and productivity as likely channels. Our findings indicate that markets distinguish between substantive AI initiatives and opportunistic signaling, swiftly pricing anticipated future gains. Actionable disclosures outlining clear implementation plans yield significant valuation benefits, particularly upon first introduction, whereas speculative or irrelevant disclosures have no impact. Moreover, firms with substantive AI disclosures subsequently increase innovation activities, evidenced by higher R&D spending and patent filings, which are a key step in a pathway to modest, lagged productivity gains and ultimately improved valuation. We further find that these innovation activities act as concurrent signals of strategic reorientation towards AI, reinforcing the market's swift positive valuation. We show that early adopters of actionable disclosures gain competitive advantages, while peers that either remain silent or offer only vague AI disclosures face market penalties. These findings highlight that the strategic communication of genuine technological initiatives can significantly impact a company's perceived value and competitive positioning in the market. • Actionable AI corporate disclosures lead to significant firm value increases. • Early AI adopters gain market advantages over lagging competitors. • AI adoption and disclosures are linked to increased R&D spending and patent filings. • Speculative AI disclosures have little impact on firm valuation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".