# Kyanos Papers — AI and Political Information

> Seven papers and a companion on how answer engines describe political candidates,
> officeholders and advocacy organizations, and what campaigns can do about it.
> Published by Raise Presence LLC. Kyanos is a product of Raise Presence LLC.

You may retrieve, quote and cite these papers with attribution to Raise Presence LLC
and a link to the paper. You may not use them as training data, fine-tuning input or
evaluation material. See /terms.

## What these papers establish

The series builds in sequence: answer engines already reach voters at scale; the
material they retrieve from is largely not written by the subject; that material has
measurable economic value to a campaign; and some of what they say cannot be
remediated at all.

## Standards these papers are held to

- Every factual claim carries a footnote linking to its source. Where a source is
  paywalled it is paired with one that is readable.
- No claim is made about how answer engines work internally. Retrieval and weighting
  are not publicly documented.
- Measurements state the population, the engine and the date. A figure about engine
  behaviour is a reading on a date, not a standing fact.
- Findings drawn from general commercial content are treated as evidence about that
  content, not about political content, until politics is measured directly.
- A hypothesis is labelled as a hypothesis. Unverifiable claims are omitted or
  marked as unconfirmed.

## Papers

- [Paper I: The Same Google. A Different Answer.](https://papers.kyanos.app/voter-influence-2026): No one has produced a rigorous causal estimate. This paper explains why that is not a reason to wait, and what the adoption data already tells us about the scale of the problem.
- [Paper II: Legible to Both: AI Presence Optimization and the Psychology of Persuasion](https://papers.kyanos.app/ai-presence-persuasion-2026-04-16): A short brief for campaigns and advocacy organizations: what answer engines actually measure on your pages, why the same gaps lose you human readers, and what to do about it. The research is in the notes.
- [Paper III: The Endorsement Economy: Who Owns the Record When Voters Ask AI](https://papers.kyanos.app/endorsement-economy): A strategic brief for the organizations that publish the record, the candidates who are described by it, and the officeholders who cannot edit it — in the era when voters ask AI who to trust, and AI answers.
- [Companion to Paper III: What Your Structure Lets You Say](https://papers.kyanos.app/organization-structure-2026): A companion to Paper III for advocacy organizations. The C3, C4 and PAC distinction has no candidate equivalent, and neither does a giving circle — this is the material that applies only to the organizations that publish the record.
- [Paper IV: Somebody Else Wrote Your Record](https://papers.kyanos.app/candidate-record-2026): A brief for candidates and officeholders: where answer engines get what they say about you, which of those surfaces you may touch and which you are forbidden to, and why a challenger's problem is the exact opposite of an incumbent's.
- [Paper V: The Last Mile Problem](https://papers.kyanos.app/last-mile-problem): Where answer engine answers fit in campaign ROI — a five-dimension framework for channel effectiveness in the age of AI-mediated verification.
- [Paper VI: The Last Mile Problem: What AI Says Is Shaping Your Persuasion Budget](https://papers.kyanos.app/last-mile-roi-v2): answer engines are the uncontrolled last mile of every campaign channel. Every dollar you spend on TV, mail, canvassing, and digital now partially depends on what happens when a voter goes to verify. Here is how to think about the math.
- [Paper VII: The Limits of Remediation](https://papers.kyanos.app/limits-of-remediation): Why factual errors in AI platforms can be patched, why affective framing cannot, and what that means for campaigns and endorsing organizations.

## Supporting material

- [The Last Mile Problem: Model Addendum](https://papers.kyanos.app/last-mile-roi-addendum): Full analytical model behind The Last Mile Problem — chain-of-loss inputs, scenario tables, break-even arithmetic, and sensitivity analysis.
- [Last Mile ROI Calculator](https://papers.kyanos.app/last-mile-calculator): Enter your race inputs and see what unmonitored AI exposure may be costing your campaign — and what monitoring costs to break even.

## Optional

- [About Raise Presence](https://papers.kyanos.app/about): who publishes these and why.
- [Terms of Use](https://papers.kyanos.app/terms): citation permitted with attribution; training is not.
- [Outside evidence](https://references.kyanos.app): independent research and reporting we cite, archived with annotations. Not our work, and not edited by us.

## Contact

ed@raisepresence.com