What does Washington say about AI?

We collected public statements about AI from each member of Congress and from each new member elected in 2026. The sources are floor speeches, hearings, posts on X, interviews and press releases. We checked each quote against its source. Two AI models then gave each person a score on five questions. The models used only these quotes.

Last updated … · … people · … verified statements

Each dot is one member. The horizontal axis shows ideology. The vertical axis shows the score. Put the pointer on a dot to see the member. Click a dot to read their quotes.

Ideology is the GovTrack score, from 0 (progressive) to 1 (conservative). GovTrack calculates it from the bills that each member cosponsored in the 119th Congress. New members do not have this score, so the chart does not show them. Each dot moves up or down by a small random amount, so that dots do not cover each other.

Scores by party

Each bar shows the percent of members in one party at each score. "Not enough evidence" means that the member did not make enough public statements to get a score.

Topics

Topic share by party

Percent of each party's AI statements that are about this topic, by quarter.

Members with the most statements

The 15 members with the most statements on this topic. Click a bar to read their quotes.

Member groups

Each button shows a group of members. We select the members by their scores. Click a name to read their quotes.

Scores on AI committees

Each bar shows the members of one committee that writes AI law, by score. Click a bar to see the members. The chart uses the committee seats of today. Congress does not assign the seats for January 2027 until January.

Members

Scores go from 0 to 4. The method section below gives the rules. A dash means that there is not enough evidence. Click a row to see a summary and the quotes for each score.

Method

  1. Who: all 539 current members (senators, representatives and delegates) from the unitedstates/congress-legislators project, plus winners of the November 2026 elections who aren't already in Congress, taken from called races only. The January 2027 view combines continuing members with members-elect. A senator stays unless their seat was up in 2026 and someone else won it; a member who won a 2026 race stays, in the chamber they won.
  2. Official records: AI-related quotes from the Congressional Record and hearings, from CeSIA's Politicians on AI dataset (CC BY 4.0). Only the quote text, date and source are used. CeSIA's own labels are not.
  3. X: each person's posts since January 2023 that match AI keywords (retweets excluded), from X's full-archive search. For the first 132 people we used X's web search instead, which found about 94% as many posts in a side-by-side check.
  4. Web: a language model (Claude Sonnet 5.5) with web search looked for every substantive public statement about AI and was told explicitly that finding nothing is fine. We fetched every source page and kept a quote only if it appears there with a fuzzy match of at least 90%. When a site blocks automated access, we checked against the page text the search engine had retrieved.
  5. Scoring: each question is a claim (for example, "advanced AI could cause a catastrophe for humanity, and this deserves serious policy attention"), scored 0–4 by how strongly the member backs it in their own words. Two models from different companies, Claude Sonnet 5.5 and GPT-6.1 Sol, each read every statement and label the level it shows, using written tests for each level (rubric). Quoting someone else's warning doesn't count unless the member adopts it. A fixed formula then turns the labels into a score. For "how strongly" questions (AGI, x-risk, China), the score is the highest level reached by at least two statements, and members who never discuss the topic get 0. For regulation, the score is the strongest position that the member takes more than one time. For AI overall, the score is the median label. The published score is the average of the two models' scores.
  6. Review: scores where the two models are 2 or more apart, the highest scores on AGI and x-risk, and a random sample are checked by a person.
  7. Committees: current full-committee rosters from the @unitedstates project.

Caveats. What people say in public is not the same as what they believe or how they vote. Scores are produced by language models and only spot-checked. Official records lag by months. X coverage depends on knowing the right accounts, and some members barely post. Members who rarely talk about AI will have little or no evidence. That doesn't mean they hold no views.

Thanks to CeSIA, the @unitedstates project, GovTrack, and Roe's earlier "What Washington Says About AGI", which this project builds on.