Jointly authored by Divya Siddarth, Matthew Prewitt,and Glen Weyl, this paper provides a framework for thinking beyond the traditional economic approach of public vs private goods. The paper argues for funding mechanisms that take into account “supermodular” goods, which encompass everything under the familiar umbrella of “public goods”, but also include private or excludable systems that become more effective when provided to more people.

The aim of this paper is to build models of governance for generative foundation models that enable broadly shared benefit. Our initial hypothesis is that data is a high-value lever of governance. Many of these models are trained on publicly available data and use public infrastructure, but 1) may degrade the “digital commons” that they depend on, and 2) do not have processes in place to return value captured to data producers and stakeholders. Separately, we see a need to collect high quality information to understand e.g. the economic impacts of these models.

Existing financialization models have trapped us into only funding certain technologies, with a constrained set of stakeholders, outcomes, and possibilities. We need a better container in which to build the future of technology, be it satellites or space travel or AI research. Other approaches are necessary to build towards a future of collective flourishing: approaches that intentionally incorporate the public good, that factor in exponential returns from advances in AI, that build shared infrastructure, that prioritize steady returns over massive growth.

It is fiendishly difficult to get lots of people to make decisions and work together both non-hierarchically and effectively. Because blockchain technology enables decentralized consensus at scale, the implications for human decision-making and coordination are immensely promising; if we can use it to scale decentralized governance, this could be a paradigm shift in how most of us live and work together.