HalfFull Daisy: A bug bounty for doing good with AI

Context & Motivation

The current AI safety discourse is largely focused on what could go wrong and how to fix it. Labs pay bug bounties for flaws. Yet in the AI revolution, only a handful of people's job is simply to build AI-enabled products that do good. In-house beneficial-deployment teams (within company goals and guardrails), nonprofits, volunteers and governments do great work, but with limited resources, while material uses of AI outpace those for social good. Shouldn't one of the main goals of a general-purpose socio-technical system like AI be to bring society the most benefit? Yet half of U.S. adults still say the increased use of AI in daily life makes them more concerned than excited (Ref A). I found no programme committing a share of lab revenue to public ideas for good.

There is precedent for deliberate choices like this. Before the telegraph, the postal network was the general-purpose information system of its day. The Postal Service Act of 1792 charged newspapers far less than letters so an electorate could stay informed, and extended post roads to lead settlement into the frontier, helping bind a young nation together (Ref B). In 1935, fewer than 11% of U.S. farms had electricity because utilities saw little profit in rural lines. The Rural Electrification Act of 1936 lent to member-owned cooperatives, and by 1963 about 99% of farms had power (Ref C). Those choices came from governments. With AI, the labs building it can make a similar one.

Target Audience

Frontier AI labs that want a public, bounty-style way to direct a share of revenue to society's biggest problems, earning trust beyond their products. Anyone with an idea for good, from frontline workers to researchers and students, who wants paid time and frontier resources to build tools that serve people at scale. Members of the public who want a say in what AI is built for, and governments, foundations and other funders who want a public record of vetted ideas to back.

Customer Problems

An agronomist sees small farmers lose harvests to crop disease a phone photo could catch early. Her idea: a voice-first AI advisor in local languages, built once and free to every farming community. Open innovation challenges seek answers to sponsor-set problems and lack frontier compute, models and engineers, and she has no budget or claim on the idea. The problem then is that those closest to society's biggest problems have no paid, well-resourced route from an idea for good to a tool that serves everyone. Until AI's capabilities become everyday tools that visibly improve well-being, fear of the unknown may continue to overshadow its potential.

Hypothesis

1. Sponsor-set problems: open innovation challenges invite anyone, but only on problems sponsors define and funding is limited. (Ref D, E).
2. No frontier resources: compute, models and engineers are expensive.
3. Scattered ideas: public ideas sit across separate challenges, unlinked to frontier-lab resources.
4. Business filters: in-house beneficial-deployment teams work within company goals, so ideas must also fit the business.
5. No public voice: the public has little say in what frontier AI is built to do.

Solutions

1. Public rounds: time-boxed online rounds by track, on problems proposers choose, open in many languages and via libraries, post every idea meeting published guidelines into one public record, anonymously if preferred.
2. Paid proposals: labs shortlist and pay shortlisted proposers for detailed write-ups.
3. Two paths: winners build with lab funding, compute and engineers, or take the bounty while the lab owns and runs it free, not-for-profit.
4. Funding: a published share of lab revenue funds the programme, never safety budgets, much as DaisyGrove gives a share of a data centre's compute to communities.
5. Judging: public benefit is the only test with multiple tracks (environment, food, poverty etc.) ; a framework favouring scale asks if AI is the right tool, and the public votes on plain summaries.
6. Accountability: each deployment has an owner, impact assessment and public incident log, counting success in terms of impact and the number of people served.

Outcome

1. Doing good with AI becomes part of the discourse, and not just a task left for someone else.
2. The public, not only markets, gets a say in what frontier AI is built to do.
3. Labs pursue what could go right with the rigour they bring to material metrics and finding and fixing what could go wrong, and compete on the good they do.
4. Ideas for good become a public commons that governments, funders and researchers can build on.
5. People see AI improve lives around them, and the conversation shifts from zero-sum fear to shared progress.
6. Labs learn how their models behave in real settings, and deployments serve as evidence in policy.

Appendix

1. Pew Research Center, "Key findings about how Americans view artificial intelligence," March 2026, drawing on a June 2025 survey of 5,023 U.S. adults: read the findings.
2. Smithsonian National Postal Museum, on the Post Office Act of 1792's low newspaper rates and post roads that led expansion: read the history; U.S. Postal Service, on 1792 newspaper rates: read the history.
3. Minnesota Historical Society (MNopedia), on farm electrification in 1935, the 1936 Act and coverage by 1963: read the entry.
4. A university-run open innovation platform that seeks tech-based solutions from anyone, anywhere: see its challenges.
5. An open innovation platform whose challenges require financial sponsorship to launch and cannot take topic requests from the public: see its FAQ.