Glen Weyl
Generative AI is being deployed faster than the governance, voting, and ownership systems around it can adapt. Boards now have to decide which AI systems get a seat at the decision table, who is accountable when those systems shape public opinion, and what legitimacy looks like when a model can speak with more authority than an executive. The hard question is no longer whether to use AI. It is how to keep human institutions credible while doing so.
Glen Weyl is a political economist at Microsoft Research who helps governments, AI labs, and boards design the rules that keep democratic institutions credible as generative AI scales.
Full Profile
Why organisations work with Glen Weyl
- He is the working bridge between three of the people who matter most in this debate: Vitalik Buterin on Ethereum and Web3, Jaron Lanier on AI and data, and Audrey Tang on civic technology. Senior teams get one room with the network, not three separate conversations.
- He co-invented Quadratic Voting and Quadratic Funding, mechanisms now used inside crypto ecosystems, civic budgets, and corporate innovation funds. Boards looking at participatory capital allocation are looking at his work, often without knowing it.
- He led Microsoft’s Office of the CTO work on the OpenAI relationship and now runs Microsoft Research’s Plural Technology Collaboratory. He has seen the inside of how the largest AI partnership in the world is governed, and can talk about it candidly.
- His book Radical Markets, written with University of Chicago law professor Eric Posner, was named an Economist Book of the Year in 2018 and changed how serious economists discuss property, voting, and antitrust in a digital economy.
- He works in the same language as policymakers in Taiwan, the EU, and the US Treasury, which makes him unusually useful to organisations whose AI exposure is also a regulatory exposure.
Biography highlights
- Founder and Research Lead, Plural Technology Collaboratory, Microsoft Research.
- Former Political Economist and Social Technologist, Microsoft Office of the CTO, including the leadership team for the OpenAI relationship.
- Co-author, Radical Markets, Princeton University Press, 2018; an Economist Book of the Year.
- Co-author with Audrey Tang of Plurality: The Future of Collaborative Technology and Democracy, 2024.
- Founder, RadicalxChange Foundation; co-founder and chair, Plurality Institute; co-founder, Harvard GETTING-Plurality Research Network.
- Named by CoinDesk, WIRED, and Bloomberg Businessweek on their respective lists of most influential people in technology, 2018.
Biography
Most institutions designed for the industrial economy do not have a good answer to generative AI. Voting systems, ownership rules, antitrust law, and corporate governance were all written for a world where the most powerful entities in a room were human. Glen Weyl’s career has been a sustained attempt to redesign those institutions for a world where they are not.
His 2018 book with Eric Posner, Radical Markets, was an Economist Book of the Year and forced a new conversation about property, voting, and antitrust inside serious policy circles. Out of that work came Quadratic Voting and, with Vitalik Buterin, Quadratic Funding. Both are now in operational use in civic budgets and Web3 ecosystems, including projects funded by Ethereum.
At Microsoft, he sat inside the Office of the CTO during the period when the company’s relationship with OpenAI became one of the most consequential commercial arrangements in technology. He now runs the Plural Technology Collaboratory, Microsoft Research’s special project on how democratic societies function under generative AI, working with Taiwan’s Audrey Tang, with Harvard, and with civil society groups including the Collective Intelligence Project.
His 2024 book with Tang, Plurality, was written openly on GitHub and translated by volunteers into more than a dozen languages, an unusual proof point that the governance ideas in it actually work in practice. For a board trying to decide what AI legitimacy means inside its own organisation, that record makes him one of the few people in the world worth bringing into the room.
Key speaking topics
- Generative AI and democratic institutions
- Plurality and collaborative technology
- Quadratic Voting and Quadratic Funding
- Web3, decentralised identity, and digital ownership
- Antitrust and platform competition
- Data as labour and the economics of AI training
Ideal for
- Boards and CEOs setting AI governance and partnership strategy
- Government, regulator, and central bank audiences on AI and digital policy
- CTOs and Chief AI Officers working on legitimacy and deployment frameworks
- Foundations, philanthropic capital allocators, and corporate venture funds rethinking how they fund collective goods
Audience outcomes
- A clearer view of how AI partnerships at the scale of Microsoft and OpenAI are actually governed.
- A working vocabulary for Quadratic Voting, Quadratic Funding, and Plurality, with concrete examples of where each is operating.
- A sharper sense of where current antitrust, data, and AI regulation is heading in the US, EU, and Asia.
- Specific design ideas for participatory decision-making inside large organisations, not generic engagement theory.
Talks
A direct argument about how generative AI concentrates power and shapes public opinion, and what democratic societies can do about it.
Key takeaways:
- The specific risks of deception and capture in AI-mediated communication.
- How Taiwan and other civic-tech jurisdictions are responding in practice.
- Design principles for AI deployment that protect institutional legitimacy.
An applied talk on cryptographic identity, provenance, and trust infrastructure as the defence against AI-driven manipulation.
Key takeaways:
- Why existing identity systems fail against generative AI.
- Lessons from Web3 that translate into corporate and civic settings.
- What boards and CISOs should be asking of their identity stack now.
The co-inventor’s account of how Quadratic Funding works and where it is being used, from Ethereum’s Gitcoin to civic budgets.
Key takeaways:
- The mathematical intuition behind optimal public goods funding.
- Live examples from crypto, philanthropy, and government.
- How corporates can apply the model to internal innovation funds.
A policy-grade argument on data, platforms, and the next generation of competition regulation.
Key takeaways:
- Why current antitrust frameworks struggle with platform economics.
- The data-as-labour proposition and its commercial implications.
- Where US, EU, and Asian regulators are converging and diverging.