The First Annual Reports of the LLM Era
An exploratory analysis of language change in 150 SEC 10-K filings, 2019–2024
Published March 2026 · Revised August 2026
Abstract
This study examines purposively selected narrative passages from 150 SEC Form 10-K filings for a fixed, sector-stratified sample of 50 companies across FY2019, FY2022 and FY2024. A single author scored six directional dimensions of language change on an ordinal scale from zero to two. Five dimensions cover changes in claims, hedging, framing, named specificity and sentence expansion. The sixth concerns newly introduced or reorganised sections and is reported separately.
Across the five editorial dimensions, the score allocation gives annual aggregate rates of 35.3 points during 2019–2022 and 44.0 points during 2022–2024, an increase of 24.5 per cent. The median company difference is zero, a company-resampling bootstrap interval extends from approximately −0.10 to 0.45, and no timing convention gives either interval a majority of companies. The result depends upon purposive passage selection, interpretive scoring and interval allocation. It describes the scored sample and rubric. It does not establish a population trend, identify generative AI use or support causal attribution.
Suggested citation
Foster-Fletcher, R. (2026). The First Annual Reports of the LLM Era: An Exploratory Analysis of Language Change in 150 SEC 10-K Filings, 2019–2024. MKAI Working Paper.
PDF size: 182 KB
Record metadata
- Type
- Working paper
- Theme
- Public reporting and disclosure
- Status
- Published
Methods
- The study uses purposively selected narrative passages from 150 SEC Form 10-K filings across a fixed sector-stratified sample of 50 companies.
- The three observations are labelled FY2019, FY2022 and FY2024.
- A single author scored six directional dimensions on an ordinal scale from zero to two.
- Non-zero scores were allocated between 2019–2022 and 2022–2024 after the company-level scoring.
- The analysis includes aggregate, company, sector, timing, recoding, leave-one-out and allocation sensitivity checks.