There’s a growing idea in some AI-governance circles that advanced AI agents could reduce transaction costs enough that many political and coordination problems could be handled through continuous bargaining. In this vision, personal agents negotiate externalities on our behalf — noise, zoning, pollution, traffic, development conflicts, etc. If bargaining becomes cheap, the argument goes, many regulatory functions become unnecessary.

I think this is an interesting direction, but also one with deep structural problems.

The first issue is epistemic: it assumes political preferences are fixed inputs that can be inferred or aggregated. But most preferences — especially about public goods, long-term risks, and ethical trade-offs — are formed through deliberation, exposure to other perspectives, and reasoning about values. If agents act on inferred or “revealed” preferences before people have had the chance to reflect, we risk degrading the underlying process by which political judgment is developed at all.

The second issue concerns distributional failure. If models infer that someone will accept less because they are poor, conflict-averse, or have historically acquiesced, then inequality becomes embedded directly into the negotiation process. What looks like a voluntary agreement can collapse into a technocratic simulation determined more by model architecture and training data than by actual consent.

There are other concerns — legitimacy, preference endogeneity, strategic non-participation — but in the piece I try to move beyond critique and sketch a constructive alternative. If AI can reduce the cost of bargaining, it can also reduce the cost of deliberation. Instead of automating political judgment, agents could strengthen it — which seems especially important for high-stakes domains like biotech, AI safety, and genetic engineering.

Very roughly, I outline three roles:

* **Agents as guides** for individual reasoning (value clarification, forecasting, identifying cruxes)
* **Agents as scaffolds** for collective deliberation (argument mapping, structured disagreement, preference evolution tracking)
* **Agents as executors** of democratically or collectively chosen aims

I’m working on a Part 2 exploring what institutions built around deliberation-supportive AI might look like. Would be very interested in critiques from this community!

Should AI agents automate politics? The dangers and the alternative