Conditionality (Mazzucato)

In The You On AI Field Guide

The application of conditionality to AI platforms would produce specific institutional mechanisms. Mandatory interoperability requirements ensure that builders can move between platforms without losing their work, data, or workflows. Data portability standards enable builders to extract their contributions from one platform and import them to another. Transparency requirements obligate platforms to disclose criteria for pricing decisions, feature availability, terms of service changes, and algorithmic ranking. Participatory governance mechanisms — builder advisory councils with formal standing, mandatory consultation before significant changes, independent arbitration for disputes — give builders a structured voice.

Conditionality has substantial precedent in public procurement and industrial policy. Defense contractors face extensive conditions on their procurement contracts. Pharmaceutical companies receiving public research grants face conditions on pricing of resulting products (albeit inadequately enforced). Renewable energy subsidies in many jurisdictions come with domestic content and labor requirements. The principle that public money carries public obligations is well-established; its application to the AI economy is a matter of extending existing precedent rather than inventing novel mechanisms.

The shift to conditionality is fundamental rather than incremental. The current arrangement treats AI companies as recipients of public support whose obligations to the public are minimal — a tax contribution proportional to their (often minimized) profits, compliance with applicable regulations. Conditionality reframes the relationship: the public provides the foundational research, training data, infrastructure, and workforce that make AI capability possible, and these contributions generate legitimate claims on the behavior of firms that commercialize the capability.

The political economy of implementing conditionality is challenging. AI companies have strong incentives to resist requirements that constrain their operational flexibility or reduce their capacity to capture surplus. The counterargument — that conditionality reduces innovation incentives — has been deployed against every conditionality framework in the history of industrial policy and has been empirically refuted by the superior innovation performance of economies with more extensive conditionality frameworks (the Nordic countries, Germany, South Korea).

Origin

Conditionality as a policy principle has roots in classical industrial policy — Hamilton's 1791 Report on Manufactures, the infant industry protection tradition, the Nordic developmental state. Mazzucato's contribution is its systematic application to innovation policy and its development as a framework for the platform and AI economies.

Her most direct applications to AI have come in her critique of the UK AI Action Plan (2024), the Project Syndicate essays with Tommaso Valletti (2025), and the ongoing Algorithmic Rents research program.

Key Ideas

Public money, public obligations. Support flows come with enforceable requirements — the principle is well-established in industrial policy.

Operational mechanisms. Interoperability, portability, transparency, participatory governance — specific requirements rather than vague commitments.

Precedent exists. Defense procurement, pharmaceutical grants, renewable energy — conditionality has been applied in multiple domains without destroying the industries affected.

Redirection of innovation. Conditionality does not reduce innovation; it redirects innovation toward public purposes.

Current absence is design gap. The unconditional support for AI companies is an institutional omission, not a principled choice.