The most important thing to note about the study is that AI bots personalized arguments by analyzing users' post histories to infer demographic details like age, gender, political orientation, and ethnicity. The bots then tailored arguments for the specific person based on this information, and used this information to successfully persuade real people to change their minds on controversial topics.
Think about what AI had access to in that Reddit experiment: just public comment history. Now imagine what's possible with the data ecosystem surrounding legislative advocacy.
Legislatures Are A Data Goldmine
Consider everything that's already public about a member of Congress or any state legislator. Here are just a few of the countless examples:
🔱 Every floor speech going back to their first term—searchable, transcribed, ready for AI analysis of language patterns and priority signals
🔱 Committee hearing questions that reveal specific concerns, knowledge gaps, and the issues that genuinely activate them versus the ones they're just checking boxes on
🔱 Influencer profiles showing career trajectories, alma maters, previous employers, professional networks, and countless other forms of information about the people who have the decision-maker's ear
🔱 Voting records showing not just yes/no, but co-sponsorships, amendments offered, and procedural votes that telegraph real priorities
🔱 Media appearances including podcast transcripts where they're more candid about reasoning and personal values than they are in official statements
🔱 District event coverage revealing how they talk to constituents versus colleagues—and which arguments they lean on when they need to persuade
🔱 Campaign finance data mapping which industries, regions, and issue coalitions they're closest to
AI can not only read this data—it can find the patterns in the data. It can learn that Senator X frames certain economic issues through small business impact, and other economic issues through the lens of consumer costs. It can learn that Representative Y's healthcare positions consistently filter through a rural access lens. That Congressman Z's language measurably shifts when discussing his military-heavy district.
Then it can generate advocacy messages that feel like they were written by someone (or something) who truly gets that member—because the AI does get the member.