Did government writing change after ChatGPT? 55,350 GOV.UK press releases and news stories, 2015 to 2026

SGR-020, conducted 2026-10-15. Analyst: Jack Stovell, founder, ScriptGrain.

Question: Within the same UK government departments, did press releases and news stories published after ChatGPT use more of the words and habits associated with AI writing than those published before it?

Summary

Background. Public bodies write at enormous volume and under pressure to sound clear and consistent, which is exactly where people suspect AI tools have crept in. Whether government writing changed after ChatGPT can be measured, because every GOV.UK press release and news story since 2015 is public, dated and attributed to a department.

What we did. We fixed six hypotheses before the full measurement, copying the design of our study of UK brands (SGR-011). We listed every GOV.UK press release and news story and fetched all 62,399 of them through the public Search and Content APIs; 55,350 pages of 150 words or more, first published between January 2015 and September 2026, were measured in code. Each of 20 departments (renamed and merged departments joined into their lineage) was compared only with itself: 36 department-and-type pairs with at least 10 pages in both 2019 to November 2022 and 2024 onwards.

What we found. AI-associated words rose 22% within the same departments (1.37 to 1.67 per 1,000 words). The words that moved were AI-era marketing words; the quieter LLM-favoured words, the 2024 tells such as "delve" and the publicly mocked ChatGPT words did not move beyond chance. Em dashes did not rise beyond chance (1.77 to 1.92 per 1,000 words). AI-style sentence shapes rose (0.42 to 0.53 per 1,000 words). Average sentence length stayed within 1.5 words (22.9 to 22.8). By calendar year the words peaked in 2025 at 1.96 per 1,000 words, against 1.41 in 2019 to 2022, and stood at 1.77 in 2026. In the Conservative months of 2024 alone the change was within chance, so this study cannot separate the rise from the change of government. For comparison, the same measure rose 39.7% within 37 UK brands' blogs and press releases (SGR-011); in departments the change was +22%.

What it means. Government press writing shifted in vocabulary after ChatGPT, and nothing here shows a break from the trend before 2022. The habits people point to most, such as the em dash, did not increase in these departments' writing. A change in a word list describes style; it does not show who, or what, wrote any release.

Key numbers

• UK government departments' press releases and news stories used 22% more AI-associated words in 2024 to 2026 than the same departments' writing from 2019 to November 2022 (1.37 to 1.67 per 1,000 words; ScriptGrain, 2026; 20 departments, pre-registered).
• Em dashes in GOV.UK press releases and news stories went from 1.77 to 1.92 per 1,000 words between 2019 to 2022 and 2024 to 2026 within the same 20 departments (change +0.15, 95% interval −0.18 to 0.42).
• AI-associated words in GOV.UK press writing peaked in 2025 at 1.96 per 1,000 words, against 1.41 in 2019 to 2022 and 1.12 in 2015.
• The study measured 55,350 GOV.UK press releases and news stories (30,557,892 words) from 20 departments and 293 other bodies, first published between January 2015 and September 2026.
• Average sentence length in GOV.UK press writing went from 22.9 to 22.8 words within the same departments after ChatGPT.
• Within-organisation change in AI-associated words after ChatGPT: +22% in UK government departments (20), +39.7% in UK brands (37).

Sample

Every GOV.UK press release and news story first published from January 2015 to September 2026 that GOV.UK's Search API listed on 11 October 2026: 62,399 items listed, 55,350 measured after exclusions, from 20 departments and 293 other bodies. The confirmatory tests use the departments only; 36 department-and-type pairs qualified. A census, not a sample.

• Document type press release or news story on GOV.UK
• First published between 1 January 2015 and 30 September 2026
• At least 150 words of text after contact details and notes to editors were removed
• At least 10 items before and 10 after in the same department and type, for the confirmatory tests

Method

Corpus: every item of document type press_release or news_story on GOV.UK, listed through the Search API by month and fetched through the Content API, at no more than four requests a second with an identified user agent. 62,399 items were listed; embassy stories (world_news_story) and every other document type are not included. Source: GOV.UK, Open Government Licence v3.0.

Departments: each item's primary publishing organisation. Twenty departments are analysed, with renamed, merged and split departments joined into one lineage (for example the Foreign and Commonwealth Office and the Department for International Development with the FCDO). Agencies, regulators and other bodies are analysed separately as exploratory.

Text measured: the body of each item reduced to plain text, with tables, contact details and notes to editors removed; the title is not measured. Items under 150 words, exact duplicates within a department, withdrawn items, translations and items where more than 40% of the words sit in lines of six words or fewer were excluded.

Measures: computed in code by ScriptGrain's own published instruments, the same as in the brand series. No language model read or scored any item.

Pre-registration: the hypotheses, eras, exclusions and tests were committed to the public repository before the full measurement (docs/research/sgr-020-preregistration.md). The run was done once, on the day before publication.

Tests: within-department change in department-weighted means, with 95% intervals from 2,000 bootstrap resamples of departments (fixed seed). H2 compares the per-year rate of change from 2015-18 to 2019-22 with that from 2019-22 to 2024-26, for cells with all three eras. H6 repeats H1 using only 1 January to 4 July 2024. Analyses beyond H1 to H6 are labelled exploratory.

Variables measured

• AI-associated words per 1,000 words (and each word list)
• AI sentence shapes per 1,000 words
• Em dashes per 1,000 words
• Average sentence length
• Contractions per 1,000 words
• "You" share

What each measure means

Before and after

Before: items first published from January 2019 to November 2022 (ChatGPT launched on 30 November 2022). After: January 2024 to September 2026. December 2022 to December 2023 is left out of the main test as a transition year and reported separately. The 2015 to 2018 items are used only to test whether the change is a continuing trend.

Within-department change

For each department and type of item (press release, or news story), the average after an item minus the average before; then averaged over departments, each counting once. A department is compared only with itself, so a shift in which departments publish cannot create a trend. Renamed and merged departments are joined into one lineage.

AI-associated words

Uses per 1,000 words of three published word lists, each occurrence counted once: the 2024 words studies of scientific writing found language models over-use ("innovative", "utilized", "advancements", "pivotal", "facilitates", "firstly", "tackle", "showcasing"…), words publicly mocked as ChatGPT tells, and AI-era marketing words ("seamless", "elevate", "unlock", "empower", "streamline", "supercharge", "unleash", "effortless"…). The same measure as SGR-007 and SGR-011. A separate list of quieter words ("crucial", "comprehensive", "insights", "additionally", "potential", "significant"…) is reported on its own.

AI sentence shapes

A fixed scan in code for 'not X but Y' balancing, reflexive lists of three, chains of semicolons and stock phrases, per 1,000 words.

Dates

The date GOV.UK shows as first published, in London time. The date of the last update is not used in the main test, because updating an old release would move it; a check using it is reported.

95% interval

From 2,000 bootstrap resamples of departments. A hypothesis counts as supported only if the interval excludes zero in the predicted direction (for H5, if the 90% interval sits inside ±1.5 words).

Findings

• H1 (supported): AI-associated words rose within the same departments (+0.30 (95% interval 0.13 to 0.49) per 1,000 words, +22%): From 1.37 to 1.67 per 1,000 words across 20 departments. By word list: AI-era marketing words +54% (interval clears zero); the quieter LLM-favoured words +10% (within chance); the 2024 tells such as "delve" −4% (within chance); the publicly mocked ChatGPT words +28% (within chance). Leaving out any one department, the change stays between +0.26 and +0.34.
• H2 (not supported): the change after ChatGPT was not clearly different from the trend before (0.067 per 1,000 words a year after, against 0.018 a year from 2015-18 to 2019-22): For the 20 departments with pages in all three eras, the difference in yearly change (+0.049, 95% interval −0.007 to 0.096) includes zero. These words were already moving in books and the web before ChatGPT, as SGR-007 found.
• H3 (not supported): em dashes did not rise (1.77 to 1.92 per 1,000 words; change +0.15 (95% interval −0.18 to 0.42)): Em dashes and spaced en dashes are counted together. Despite the 'ChatGPT dash' reputation, these departments used dashes at a rate within chance of the earlier one.
• H4 (supported): AI sentence shapes rose (0.42 to 0.53 per 1,000 words; change +0.11 (95% interval 0.05 to 0.19)): 'Not X but Y' constructions, reflexive lists of three, chains of semicolons and stock phrases, per 1,000 words.
• H5 (supported): sentence length stayed within 1.5 words (22.9 to 22.8 words; change −0.12 (95% interval −0.52 to 0.32)): The 90% interval (−0.47 to 0.25) sits inside ±1.5 words.
• H6 (not supported): the Conservative months of 2024 alone do not show a clear rise (+0.13 (95% interval −0.13 to 0.36) per 1,000 words (1 January to 4 July 2024 against 2019 to 2022)): 20 departments qualified. 1 January to 4 July 2024 is six months and includes the election campaign, so this study cannot say whether the change of government or the time since ChatGPT explains the shift.
• Robustness and where the change sits (H1 with TRANSITION counted as after +0.22 (95% interval 0.06 to 0.40); items of 300+ words +0.31 (95% interval 0.10 to 0.52)): Using the date of last update instead of first publication: +0.30, 95% interval 0.11 to 0.50. Press releases +0.42, 95% interval 0.21 to 0.64; news stories +0.13, 95% interval −0.15 to 0.36. Labour months (from 5 July 2024) against 2019 to 2022: +0.32, 95% interval 0.13 to 0.52.

What this study does not show

• That any release, or any department, used AI to write. Word counts show style; people used these words before ChatGPT and keep using them.
• That voice measurement, ScriptGrain's or anyone's, can tell whether a text was written by AI. Nothing in this study tests that.
• Why the writing changed. A new government and new ministers (July 2024), new staff and agencies, new GOV.UK guidance, the end of pandemic communications and more writing about AI itself could each move these numbers; this study measures the change, not its cause.
• Anything about speeches, written answers, local government or the output of agencies and regulators beyond the exploratory table.

AI-associated words, year by year

Exploratory. Each year is the mean over departments with at least 5 pages that year; the pre-registered tests compare each department only with itself.

What changed, within the same departments

Pre-registered hypotheses and results

Department by department

Exploratory. Mean AI-associated words per 1,000 words in each period, all item types pooled; a figure appears only where the department has at least 10 items in the period.

By word list and habit

Within-department change from 2019-22 to 2024-26, same departments as H1.

Press releases and news stories separately

Checks on the dating and the government

Exploratory robustness checks, each repeating the main comparison with one change. Conservative months: 1 January to 4 July 2024. Labour months: from 5 July 2024.

Agencies, regulators and other bodies

Exploratory. The same comparison for the 293 organisations outside the twenty departments (20,928 items), each organisation counting once.

Year by year

Exploratory; department-weighted means over the departments with at least 5 items each year.

Limitations

• The Content API serves each item as it reads now. Corrected or edited old releases are measured as edited; the dating check using the last-update date shows how much that matters.
• Ministers' quotations, shared boilerplate and lists of facts sit inside many releases and are measured with the rest; the press office's own prose cannot be separated from them.
• Word lists count spellings. These words rose in human writing for decades before ChatGPT, and topics shifted too: government now writes more about AI itself.
• Renamed and merged departments are joined into lineages by judgement. The business, energy, trade and science lineage mixes departments that did different work, and some work moved between departments in 2023.
• The 2020 and 2021 pandemic communications are inside the before period and changed what departments wrote; departments, not items, are weighted equally so the volume does not dominate.
• The Conservative-months check covers six months including an election campaign, so it is a weak test of whether the change of government explains the shift.
• UK central government only, in English, and only press releases and news stories.

Competing interests

ScriptGrain sells writing-voice measurement and generation, and the analyst builds it. The hypotheses, eras, measures and tests were committed to the public repository before the full measurement, every result is reported whether it passed or failed, and the data and scripts are public.

Reproducing this

• Pre-registration and scripts: docs/research/sgr-020-preregistration.md and scripts/experiments/sgr-020/ in the ScriptGrain repository (collect.mjs fetches the corpus, run.mjs measures and analyses it, write-entry.mjs builds this page from the summary).
• Every item is named by its GOV.UK path in the data, so any release can be re-read on GOV.UK and measured again.

Data

• Every item measured: path, department, type, date, era and every measure (CSV) (csv)
• Department by period: item counts and mean of every measure (CSV) (csv)
• Year by year: department-weighted means (CSV) (csv)
• Summary: hypotheses, intervals, robustness checks, exclusions (JSON) (json)

Released under CC BY 4.0: free to reuse, including commercially, with credit to ScriptGrain and a link to this page.

Citations

• GOV.UK Search API and Content API. Contains public sector information licensed under the Open Government Licence v3.0
• ScriptGrain (2026). Did brand writing change after ChatGPT? (SGR-011)
• ScriptGrain (2026). Where did AI-isms come from? (SGR-007)
• Kobak, D. et al. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances

How to cite

ScriptGrain (2026). Did government writing change after ChatGPT? 55,350 GOV.UK press releases and news stories, 2015 to 2026 (Study SGR-020, conducted 15 October 2026). Dataset licensed CC BY 4.0. https://scriptgrain.com/research/did-government-writing-change-after-chatgpt

About the analyst

Jack Stovell has worked in finance and data for more than twelve years, building management reporting, forecasting and profitability models for advertising agencies and tech scale-ups, from SQL and Power BI reporting to board-level analysis. Since 2016 he has run Adapt Progress Evolve, an applied AI studio, where he builds and operates AI systems and data products: ScriptGrain's measurement of writing voice across 45 attributes, UK Spend, which brings 16.7 million rows of UK council spending into one queryable dataset, and more than thirty AI agents running in production. He designs ScriptGrain's studies and is accountable for every number in them.

• jstov.uk
• LinkedIn
• GitHub

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