Public AI White Paper – A Public Alternative to Private AI Dominance
Citation: Felix Sieker, Alek Tarkowski, Lea Gimpel, Cailean Osborne (2025/05) Public AI White Paper – A Public Alternative to Private AI Dominance.
Internet Archive Scholar (search for fulltext): Public AI White Paper – A Public Alternative to Private AI Dominance
Wikidata (metadata): Q135644012
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Summary
This white paper defines Public AI and turns it into a policy framework with three characteristics—public attributes, public functions, and public control—that together determine a system’s “degree of publicness.” It proposes a gradient of publicness spanning commercial systems with some public attributes, through public provision of individual components (e.g., datasets, tooling), up to full-stack public AI infrastructure that integrates compute, data, and models with high public attributes/functions/control.
To operationalize the vision, it lays out three pathways (compute, data, model) under an orchestrating public institution:
(i) public compute access—especially for fully open-source projects—to ensure at least one fully open model with capabilities near state-of-the-art;
(ii) data commons and stewardship models that treat key inputs as digital public goods; and
(iii) support for open-source model development ecosystems.
The paper also articulates governance principles (commons-based governance; open release of models and components; conditional compute tying public resources to openness; protection of digital rights; environmental sustainability; and reciprocity to prevent privatization of public value).
Theoretical and Practical Relevance
The framework gives policymakers a map and a metric: use the gradient to situate any AI initiative, then move it “up” by boosting public attributes/functions/control—preferably via full-stack strategies rather than isolated components. It reframes public investment away from racing private labs and toward reducing dependencies, building independent capacity, and setting openness-linked conditions on compute and other inputs. For practitioners (research institutions, platforms, funders), it points to actionable levers: allocate compute to truly open projects, stand up data commons under commons-based governance, and publish models/components openly with strong documentation and oversight.
