  Democratization of AI means not only that people can freely use AI, but also
that people can collectively decide how AI is to be used. In particular,
collective decision-making power is required to redress the negative
externalities from the development of increasingly advanced AI systems,
including degradation of the digital commons and unemployment from automation.
The rapid pace of AI development and deployment currently leaves little room
for this power. Monopolized in the hands of private corporations, the
development of the most capable foundation models has proceeded largely without
public input. There is currently no implemented mechanism for ensuring that the
economic value generated by such models is redistributed to account for their
negative externalities. The citizens that have generated the data necessary to
train models do not have input on how their data are to be used. In this work,
we propose that a public data trust assert control over training data for
foundation models. In particular, this trust should scrape the internet as a
digital commons, to license to commercial model developers for a percentage cut
of revenues from deployment. First, we argue in detail for the existence of
such a trust. We also discuss feasibility and potential risks. Second, we
detail a number of ways for a data trust to incentivize model developers to use
training data only from the trust. We propose a mix of verification mechanisms,
potential regulatory action, and positive incentives. We conclude by
highlighting other potential benefits of our proposed data trust and connecting
our work to ongoing efforts in data and compute governance.
