automate the systems of government.

The dynamic of AI effectuating concentration of power extends beyond government agencies. Over the past five years, Ohio has undertaken a project to do a wholesale revision of its administrative code using AI. The leaders of that project framed it in terms of efficiency and good governance: deleting millions of words of outdated, unnecessary, or redundant language. The same technology could be applied to advance more ideological ends, like purging all statutory language that places burdens on business, neglects to hold businesses accountable, protects some class of people, or fails to protect others.

Whether you like or despise automating the enactment of those policies will depend on whether you stand with or are opposed to those in power, and that?s the point. AI gives any faction with power the potential to exert more control over the levers of government.

4. Organizers will find ways to use AI to distribute power instead.

We don?t have to resign ourselves to a world where AI makes the rich richer and the elite more powerful. This is a technology that can also be wielded by outsiders to help level the playing field.

In politics, AI gives upstart and local candidates access to skills and the ability to do work on a scale that used to only be available to well-funded campaigns. In the 2024 cycle, Congressional candidates running against incumbents like Glenn Cook in Georgia and Shamaine Daniels in Pennsylvania used AI to help themselves be everywhere all at once. They used AI to make personalized robocalls to voters, write frequent blog posts, and even generate podcasts in the candidate?s voice. In Japan, a candidate for Governor of Tokyo used an AI avatar to respond to more than eight thousand online questions from voters.

Outside of public politics, labor organizers are also leveraging AI to build power. The Worker?s Lab is a U.S. nonprofit developing assistive technologies for labor unions, like AI-enabled apps that help service workers report workplace safety violations. The 2023 Writers? Guild of America strike serves as a blueprint for organizers. They won concessions from Hollywood studios that protect their members against being displaced by AI while also winning them guarantees for being able to use AI as assistive tools to their own benefit.

5. The ultimate democratic impact of AI depends on us.

If you are excited about AI and see the potential for it to make life, and maybe even democracy, better around the world, recognize that there are a lot of people who don?t feel the same way.

If you are disturbed about the ways you see AI being used and worried about the future that leads to, recognize that the trajectory we?re on now is not the only one available.

The technology of AI itself does not pose an inherent threat to citizens, workers, and the public interest. Like other democratic technologies -- voting processes, legislative districts, judicial review -- its impacts will depend on how it?s developed, who controls it, and how it?s used.

Constituents of democracies should do four things:

Reform the technology ecosystem to be more trustworthy, so that AI is developed with more transparency, more guardrails around exploitative use of data, and public oversight.
Resist inappropriate uses of AI in government and politics, like facial recognition technologies that automate surveillance and encode inequity. Responsibly use AI in government where it can help improve outcomes, like making government more accessible to people through translation and speeding up administrative decision processes.
Renovate the systems of government vulnerable to the disruptive potential of AI?s superhuman capabilities, like political advertising rules that never anticipated deepfakes.
These four Rs are how we can rewire our democracy in a way that applies AI to truly benefit the public interest.

This essay was written with Nathan E. Sanders, and originally appeared in The Next Big Idea Club.

EDITED TO ADD (11/6): This essay was republished by Fast Company.

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AI Summarization Optimization

[2025.11.03] These days, the most important meeting attendee isn?t a person: It?s the AI notetaker.

This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence.

But clever meeting attendees can manipulate this system?s record by speaking more to what the underlying AI weights for summarization and importance than to their colleagues. As a result, you can expect some meeting attendees to use language more likely to be captured in summaries, timing their interventions strategically, repeating key points, and employing formulaic phrasing that AI models are more likely to pick up on. Welcome to the world of AI summarization optimization (AISO).

Optimizing for algorithmic manipulation

AI summarization optimization has a well-known precursor: SEO.

Search-engine optimization is as old as the World Wide Web. The idea is straightforward: Search engines scour the internet digesting every possible page, with the goal of serving the best results to every possible query. The objective for a content creator, company, or cause is to optimize for the algorithm search engines have developed to determine their webpage rankings for those queries. That requires writing for two audiences at once: human readers and the search-engine crawlers indexing content. Techniques to do this effectively are passed around like trade secrets, and a $75 billion industry offers SEO services to organizations of all sizes.

More recently, researchers have documented techniques for influencing AI responses, including large-language model optimization (LLMO) and generative engine optimization (GEO). Tricks include content optimization -- adding citations and statistics -- and adversarial approaches: using specially crafted text sequences. These techniques often target sources that LLMs heavily reference, such as Reddit, which is claimed to be cited in 40% of AI-generated responses. The effectiveness and real-world applicability of these methods remains limited and largely experimental, although there is substantial evidence that countries such as Russia are actively pursuing this.

AI summarization optimization follows the same logic on a smaller scale. Human participants in a meeting may want a certain fact highlighted in the record, or their perspective to be reflected as the authoritative one. Rather than persuading colleagues directly, they adapt their speech for the notetaker that will later define the ?official? summary. For example:

?The main factor in last quarter?s delay was supply chain disruption.?
?The key outcome was overwhelmingly positive client feedback.?
?Our takeaway here is in alignment moving forward.?
?What matters here is the efficiency gains, not the temporary cost overrun.? The techniques are subtle. They employ high-signal phrases such as ?key takeaway? and ?action item,? keep statements short and

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