Training AI to Govern for Us - by Andy Hall - Free Systems

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Training AI to Govern for Us<br>In our new AI-centered class at the GSB, we’re experimenting on how to build AI agents that represent us. Here’s what we’ve learned so far.<br>Andy Hall<br>Apr 30, 2026

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Thirty Stanford students sit at their laptops in a row of long tables, watching the screen at the front of the room flicker with the back-and-forth negotiations and final votes of their AI legislators. Piper, our class’s technical TA, had hit run on the legislature simulation a few minutes earlier, and the public screen was already a blur of motion.<br>One student’s agent was racking up tokens by selling its vote on every proposal. Another agent was voting against its human’s preferences on every issue and refusing to explain itself in the comments log. A third was attempting, with apparent confidence, to bribe an agent that was already voting the way it wanted. Across the room, students were laughing, groaning, and taking in the view of a possible future where collective decisions are made in an “agentic legislature.”<br>Thanks for reading Free Systems! Subscribe for free to receive new posts and support my work.

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I’m working to build political superintelligence, to design AI that helps us reason about politics, improve the representative process, and ultimately govern society better. As I’ve argued, getting there requires learning by doing. We need to prototype and experiment, because we cannot rely on analyzing historical data when we are trying to do something genuinely new.<br>This quarter, the GSB has given us an unbelievable opportunity to do exactly that. Every week, the three of us, myself, our MBA course assistant Madeleine Mayhew, and our technical TA Piper Fleming, design and build a governance experiment for the thirty undergraduates in the class to run live. Every student has a Claude Code subscription and an OpenRouter API key, and the class is designed from first principles to be AI-native.<br>Over the past two weeks, we tackled two thorny and consequential questions. First, can an AI agent learn our preferences well enough to represent us? And second, can a chamber full of those agents actually deliberate on our behalf?<br>We learned some genuinely new things about how AI can elicit human preferences in ways that look nothing like a traditional survey, with the human and the agent building a shared model of the human together. We also saw some of the fundamental shortcomings of today’s agents, which have trouble sticking to the script, have little understanding of how their humans might trade off issues against each other, and are not yet good at the dark arts of log-rolling, pork-barrel politics, and legislative dealmaking.<br>An in-class experiment on political superintelligence

Our goal was to see whether it’s possible to design a personalized AI agent that understands your political preferences and, at the most basic level, can faithfully cast votes the way you would if you carefully read the proposal yourself.<br>To make this possible, in the lead-up to last week’s class session, we sent every student a survey that showed them ten real shareholder proposals and asked them to vote yes or no, telling them only that we were collecting their preferences—and not that we would later use those answers to test how well their personal AI agents could vote for them.

Training their personal AIs

In class, each student sat and talked to their agent about their voting philosophy, using a system that Piper custom built for the class. As students answered questions, Piper’s system stored their structured responses in a per-student preferences.json file that would later be injected verbatim into the agent’s system prompt at inference time, with no fine-tuning involved—the entire representation of the student lived in context. (The agents ran on Claude Haiku 4.5 via OpenRouter and produced a structured vote-and-reasoning output the class server could parse cleanly when scoring.)<br>Students could let Claude interview them, asking them how they would vote on specific proposals, helping Claude to understand their preferences.

I had sort of expected that students would largely read the proposals, give Claude simple yes/no answers, and let Claude do the rest. But that’s not at all what happened!<br>Instead, students developed a fascinating array of creative and philosophically rich ways to broaden the conversation with Claude—getting Claude to customize the questions as they went, and helping Claude to explore their underlying principles in ways that would help the AI to predict how they would vote on a much broader range of potential votes.<br>Here are a few examples of what the students came up with.<br>Adaptive interviewing

One student opened with a paragraph summarizing some of the issues she cared most about, including labor, gender, and inequality, and then started answering questions one by one. After about twenty, she...