Is AI Writing American Law?

· Effort ·

5 min read Original article ↗

We scanned every bill in the US Congress since 2023 to find out

5 minute read

AI models are writing our laws. An Effort investigation1 found the fraction of bills in the US Congress written by AI has tripled in the past three years.

AI in Congressional Bills2

Findings & Preambles: 2023Q1 1.8168%; 2023Q2 2.3257%; 2023Q3 2.3375%; 2023Q4 2.4957%; 2024Q1 3.2889%; 2024Q2 2.5563%; 2024Q3 2.8957%; 2024Q4 3.4740%; 2025Q1 3.3655%; 2025Q2 4.1422%; 2025Q3 4.9592%; 2025Q4 5.9067%; 2026Q1 5.1022%; 2026Q2 6.3574%. Statutory text only: 2023Q1 0.3776%; 2023Q2 0.3705%; 2023Q3 0.4505%; 2023Q4 0.4487%; 2024Q1 0.3547%; 2024Q2 0.4533%; 2024Q3 0.3619%; 2024Q4 0.4671%; 2025Q1 0.3492%; 2025Q2 0.4973%; 2025Q3 0.5331%; 2025Q4 0.5196%; 2026Q1 0.4731%; 2026Q2 0.6468%.Congress AggregateFindings & PreamblesStatutory text only0123456789Expected AI %2023Q12023Q32024Q12024Q32025Q12025Q32026Q16.4%0.6%

Democratic Staff: 2023Q1 1.9110%; 2023Q2 2.1018%; 2023Q3 2.4087%; 2023Q4 2.4289%; 2024Q1 3.0206%; 2024Q2 2.7617%; 2024Q3 2.3004%; 2024Q4 3.1625%; 2025Q1 3.0618%; 2025Q2 3.2786%; 2025Q3 5.0153%; 2025Q4 4.5959%; 2026Q1 4.3112%; 2026Q2 6.1408%. Republican Staff: 2023Q1 1.6925%; 2023Q2 2.7148%; 2023Q3 2.2325%; 2023Q4 2.5741%; 2024Q1 3.6671%; 2024Q2 2.2586%; 2024Q3 3.9264%; 2024Q4 4.3203%; 2025Q1 3.6496%; 2025Q2 5.2354%; 2025Q3 4.9054%; 2025Q4 8.0079%; 2026Q1 6.5201%; 2026Q2 6.7534%.Party ComparisonsDemocratic StaffRepublican Staff0123456789Expected AI %2023Q12023Q32024Q12024Q32025Q12025Q32026Q1D: 6.1%R: 6.8%

Among congressional bills with preambles and findings sections, AI generated text rose from just under 2% to 6.4% likely AI written from Q1 2023 to in the last quarter, Q2 20262. The rate among bills with no preambles and findings is significantly lower. A large number of these bills are renewals, extensions and other copies of pre-AI legislation.

The Epstein Files Transparency Act is the only majority AI-written bill in our sample that became law. It is one of the most widely covered and politically divisive bills in the past year.

We confirmed that it was majority-written by AI using the state of the art AI detection model, Pangram 4. "Pangram's false positive rate is below 1 in 10,000: it is highly likely that text flagged as AI-generated by pangram was at least significantly if not fully generated by AI," Pangram's cofounder and CEO Max Spero told Effort.

Pangram 4.0 report dated August 31, 2026 for BILLS-119hr4405ih.pdf: 1,121 words; 74.4% AI Generated and 25.6% Human Written.

The Epstein Files Transparency Act may violate the House AI use policy, according to a POPVOX summary. Neither the House or Senate AI policy is public, but the POPVOX foundation summarised the House policy.

POPVOX House Approved Use Cases. The prohibited category includes finalizing legislation, with a note that its practical meaning is unclear.

There is no public disclosure of AI use on any bill. The Epstein Transparency Act would be a clear case of 'finalizing legislation' with AI, which the policy prohibits. Staff are likely unaware of the House AI policy. As of publication, there have been no public consequences for undisclosed AI use in congress.

AI text is even more prevalent in Extensions of Remarks in the Congressional Record — AI wrote ~15% of them last quarter, more than twice the rate for bills3. We found no statistically significant difference between Democratic and Republican use of AI.

Extensions of Remarks: 2023Q1 0.6199%; 2023Q2 1.3677%; 2023Q3 2.1519%; 2023Q4 2.0369%; 2024Q1 3.0842%; 2024Q2 4.1033%; 2024Q3 4.5445%; 2024Q4 8.7531%; 2025Q1 9.9620%; 2025Q2 11.6668%; 2025Q3 11.7734%; 2025Q4 13.7883%; 2026Q1 13.9144%; 2026Q2 15.2513%.AI in Congressional Record Extensions of Remarks02.557.51012.51517.520Expected AI %2023Q12023Q22023Q32023Q42024Q12024Q22024Q32024Q42025Q12025Q22025Q32025Q42026Q12026Q215.3%

CSV means · Data

Across all policy4 areas we investigated, law enforcement and finance bills are the likeliest to be written by AI.5 Other factors have small, but statistically significant effects on likely AI usage.

Correlates of AI Likelihood5

Workbook coefficient estimates with 90% intervals calculated from the supplied standard errors. Six predictors are available. The workbook contains no estimate for bill length. Finance & Financial Sector: +7.87 percentage points, interval 1.75 to 13.99. Crime & Law Enforcement: +7.92 percentage points, interval 3.75 to 12.09. Labor & Employment: -2.89 percentage points, interval -3.57 to -2.20. 10y younger sponsor: +1.15 percentage points, interval 0.63 to 1.67. Solo sponsor: +2.27 percentage points, interval 0.49 to 4.06. House sponsor: +2.03 percentage points, interval 0.91 to 3.16.-16-12-8-40+4+8+12+16Finance & Financial Sector+7.87 pp[1.7, 14.0]p=0.034Crime & Law Enforcement+7.92 pp[3.7, 12.1]p=0.002Labor & Employment-2.89 pp[-3.6, -2.2]p<0.00110y younger sponsor+1.15 pp[0.6, 1.7]p<0.001Solo sponsor+2.27 pp[0.5, 4.1]p=0.036House sponsor+2.03 pp[0.9, 3.2]p=0.003Change in probability of AI text (pp)

Topic coefficients point to institutional mechanisms. Financial bills flagged as AI are sponsored by committees where a majority of members come from corporate backgrounds6. They tend to adopt AI faster than members from traditional government careers. At the other end, Labor bills are usually drafted with large unions, which are slower adopters, and are reviewed more carefully given their implications for the voter base.

Crime bills stood out because the mechanism was less obvious. One hypothesis is that many are responses to recent events requiring fast turnarounds. For example, in response to the Los Angeles ICE raids (6th June 2025), a bill was introduced in the House (10th June 2025) demanding condemnation of acts of violence against law enforcement officers, similarly in response to the assasination of Charlie Kirk (10th Sep 2025), a bill was introduced in the Senate (5th Nov 2025) demanding the death penalty for ideological motivated crime.

As AI becomes an ordinary part of an increasing number of Americans' lives, it is likely to become a more important part of the lawmaking process. The ability to detect AI use will shape norms around using and disclosing AI. "Pangram is a useful tool for enforcing AI policies, especially in high stakes situations such as legislation," Spero told Effort.

Parth Goyal is a quantitative analyst and the author of Parth's Datastack. Find him on X at PashaG1206

  1. We use editlens_Llama-3.2-3B, an open-source version of Pangram, calibrated via Pangram 3.3.2 to improve accuracy.

  2. Bills from the 118th and 119th Congress are analysed. (bills_full_analysis_data.csv) Data

  3. Daily records from 118th and 119th Congress are analysed. (extensions_full_analysis_data.csv)

  4. To understand the institutional mechanisms that drive AI use, we built a linear probability model built to predict whether a bill is flagged AI or not. Graph shows 90% CIs. (table at the end of the article) chart_data_for_recreation.xlsx · Data

  5. Majority of the AI bills come from Banking and Financial Service committee