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Participatory, decentralized AI markets built for human participation, reward, and oversight

Mission
We need new market institutions, technical standards, and economic mechanisms to ensure that value circulates broadly rather than becoming trapped inside a handful of gatekeepers.

We identify and advance the key protocols needed to make Human+AI markets participatory and decentralized by design.

We prototype and convene for public-interest protocols and market design efforts that turn this vision into a functioning reality.

What We Do

Research

Peer-reviewed work on AI market structure, open protocols, and architectures of participation.

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Convenings

Gatherings that bring economists, builders, and public-interest technologists into the same room.

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Prototypes

Live architectures that let data producers and owners share in the benefits from AI.

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Market Design

Commentary and mechanisms for building open, decentralized, and participatory AI markets.

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Convenings
Past Convening

Rockefeller Foundation, Bellagio Center, Italy

27 April – 1 May 2026
Rockefeller Foundation · 20 Leaders
June 26–28

FOO Camp

Lighthaven · Berkeley, CA
TBC 2026

Economists Convening

With Microsoft Research
Featured
Press
The Regulatory Review "Regulating AI Washing" Mar 7, 2026 Partnership on AI "2026 Transparency Report on Foundation Model Impacts" 2026 Financial Times "AI hallucinations haunt users more than job losses" Mar 21, 2026 TechCrunch "Researchers suggest OpenAI trained AI models on paywalled O'Reilly books" Apr 1, 2025 Fast Company "An AI watchdog accused OpenAI of using copyrighted books" Apr 2025 The Register "OpenAI wants to bend copyright rules" Apr 3, 2025 World Economic Forum "The Policy Case for Open AI Protocols" 2025 The Batch "Study shows GPT-4o can identify verbatim excerpts from paywalled books" 2025 AI Frontiers Podcast "Open Protocols Can Prevent AI Monopolies" 2025 Up Next Podcast "Ilan Strauss on AI Markets, Not Models" May 28, 2026 The Regulatory Review "Regulating AI Washing" Mar 7, 2026 Partnership on AI "2026 Transparency Report on Foundation Model Impacts" 2026 Financial Times "AI hallucinations haunt users more than job losses" Mar 21, 2026 TechCrunch "Researchers suggest OpenAI trained AI models on paywalled O'Reilly books" Apr 1, 2025 Fast Company "An AI watchdog accused OpenAI of using copyrighted books" Apr 2025 The Register "OpenAI wants to bend copyright rules" Apr 3, 2025 World Economic Forum "The Policy Case for Open AI Protocols" 2025 The Batch "Study shows GPT-4o can identify verbatim excerpts from paywalled books" 2025 AI Frontiers Podcast "Open Protocols Can Prevent AI Monopolies" 2025 Up Next Podcast "Ilan Strauss on AI Markets, Not Models" May 28, 2026
Big Picture Questions

Why Protocols?

We see disclosures through the lens of networking protocols and standards. Every networking protocol can also be thought of as a system of disclosures; these are far more than warning labels or mandated reports.

They are a form of structured communication that enables independent, decentralized action. The race for first-mover advantage by large centralized AI providers suggests a hub-and-spoke railroad design, while a world of open-weight AI models connected by new modes of standardized communication could look more like a road system, or today's World Wide Web.

If we want a world where everyone — not just AI model developers and those building on top of their centralized networks — is able to innovate and offer their work to others without paying a tax to access centralized networks, we need a system of disclosures that enables interoperability and discovery.

In this approach, protocols, as a type of disclosure, can architect healthier AI markets — not after things are already too far gone, but through operating as foundational "rules of the road" that enable interoperability.

"We need to stop thinking of disclosures as some kind of mandated transparency that acts as an inhibition to innovation. Instead, we should understand them as an enabler. The more control rests with systems whose ownership is limited, and whose behavior is self-interested and opaque, the more permission is required to innovate. The more we have built 'the rule of law' (i.e. standards) into our systems, the more distributed innovation can flourish."

— Tim O'Reilly

Further reading
Disclosures. I do not think that word means what you think it means Protocols and Power

Why Disclosures?

You can't regulate what you don't understand. And right now, critical information about how AI systems work, what data they use, and how they make decisions remains hidden inside corporate black boxes.

Guard against AI's enshittification. Cory Doctorow's term captures how platforms start out serving users, then shift to serving business customers, and finally optimize for extracting value from both. Without transparency about operating metrics, we won't know when AI systems begin this transition until it's too late.

Disclosures as a language of benchmarks. Just as accounting standards created a shared language for understanding business performance, we need disclosure frameworks that let us compare AI systems against meaningful benchmarks: not just capability metrics, but measures of fairness, safety, and alignment with user interests.

Disclosures shape market structure. The choice between open and closed disclosure regimes determines whether AI markets evolve like the open internet (where anyone can participate) or like railroads (controlled by a few gatekeepers). Disclosure standards, when designed well, become the protocols that enable competitive, innovative markets.

Further reading
The Alignment Problem Is Not New Algorithms Start Out as Magic But... What Can AI Learn from Accounting?