very citizen's interaction with AI systems generates valuable training data currently locked within corporate silos. This represents both a market failure and a democratic deficit that demands both technical innovation and immediate regulatory intervention.
Technical Implementation Standards
Stakeholders must move beyond traditional data protection and toward data liberation. An example would include requiring all LLM providers to offer downloads of complete conversation histories in standardized formats, while also opening public APIs for automated retrieval and enabling real–time streaming so users can cache interactions locally as they occur.
Critically, these exports must include user control and opt–out mechanisms with rich appended metadata—timestamps, model parameters, and context attributes. This transforms simple chat logs into actionable datasets capable of training personal AI assistants, agentic digital twins, or contributing to collective public intelligence, ensuring that users don't just retrieve their data but can actively build upon it and monetize it, to participate in the emerging AI economy.
The Policy Case for User Data Access
Just as GDPR established data portability rights, regulators and tech stakeholders must explore "AI Conversation Portability"—requiring platforms to provide users complete, machine-readable exports of their LLM interactions, behaviors, and algorithms. This isn't radical; it's the logical extension of existing data rights into the AI era.
Current platforms generate billions in value from user interactions while providing no mechanism for users to access their own conversational histories and metadata. This creates a more expansive asymmetry between user terms and conditions and the aggregate value of their data sold in predictive futures markets via ads and query responses. It diminishes competition, concentrates market power, and deprives citizens of economic participation in the AI economy they're building.
Policy Framework Architecture
The transformation begins immediately by extending existing data protection laws to explicitly cover AI interactions. For instance, the EU's Data Act as a template enables IoT data provisions to translate seamlessly to LLM conversations, providing a ready–made legal framework. Implementation demands major providers with over one million users get six months to comply, smaller platforms twelve months, with non-compliance triggering GDPR–style percentage–of–revenue fines that make resistance economically irrational.
To prevent companies from technically complying while practically obstructing—dumping unusable data in proprietary formats—regulators must establish a multi–stakeholder technical committee bringing together technologists, civil society, and industry to define truly interoperable export and new AI training standards.
The Economic Argument
Ideally these opportunities realign incentive structures that transmute information dilution into collective wisdom and data integrity measures.
Beyond mere compliance, governments should reward excellence through tax reduction for platforms that exceed requirements or meet upskilling goals especially for those impacted by automation. Mandatory data access will catalyze a new economy of personal AI services, sovereign knowledge models, data cooperatives, and novel AI training approaches. Conservative estimates suggest this could create a $50B market within five years while reducing platform monopoly power.
Leaders, technologists, and policymakers must act now. Every day of delay means millions more valuable interactions locked away from their creators. Instead of regulating innovation with compliance and restriction to slow its pace, we should align incentives with the ultimate commodity of the future–our data.
a global affairs media network
Reimagining AI data ownership models for prosperity
October 30, 2025
Every time a user interacts with AI systems, it generates valuable training data that remains locked within corporate silos. Nikos Acuña explores policy levers for addressing this apparent market failure and democratic deficit.
E
very citizen's interaction with AI systems generates valuable training data currently locked within corporate silos. This represents both a market failure and a democratic deficit that demands both technical innovation and immediate regulatory intervention.
Technical Implementation Standards
Stakeholders must move beyond traditional data protection and toward data liberation. An example would include requiring all LLM providers to offer downloads of complete conversation histories in standardized formats, while also opening public APIs for automated retrieval and enabling real–time streaming so users can cache interactions locally as they occur.
Critically, these exports must include user control and opt–out mechanisms with rich appended metadata—timestamps, model parameters, and context attributes. This transforms simple chat logs into actionable datasets capable of training personal AI assistants, agentic digital twins, or contributing to collective public intelligence, ensuring that users don't just retrieve their data but can actively build upon it and monetize it, to participate in the emerging AI economy.
The Policy Case for User Data Access
Just as GDPR established data portability rights, regulators and tech stakeholders must explore "AI Conversation Portability"—requiring platforms to provide users complete, machine-readable exports of their LLM interactions, behaviors, and algorithms. This isn't radical; it's the logical extension of existing data rights into the AI era.
Current platforms generate billions in value from user interactions while providing no mechanism for users to access their own conversational histories and metadata. This creates a more expansive asymmetry between user terms and conditions and the aggregate value of their data sold in predictive futures markets via ads and query responses. It diminishes competition, concentrates market power, and deprives citizens of economic participation in the AI economy they're building.
Policy Framework Architecture
The transformation begins immediately by extending existing data protection laws to explicitly cover AI interactions. For instance, the EU's Data Act as a template enables IoT data provisions to translate seamlessly to LLM conversations, providing a ready–made legal framework. Implementation demands major providers with over one million users get six months to comply, smaller platforms twelve months, with non-compliance triggering GDPR–style percentage–of–revenue fines that make resistance economically irrational.
To prevent companies from technically complying while practically obstructing—dumping unusable data in proprietary formats—regulators must establish a multi–stakeholder technical committee bringing together technologists, civil society, and industry to define truly interoperable export and new AI training standards.
The Economic Argument
Ideally these opportunities realign incentive structures that transmute information dilution into collective wisdom and data integrity measures.
Beyond mere compliance, governments should reward excellence through tax reduction for platforms that exceed requirements or meet upskilling goals especially for those impacted by automation. Mandatory data access will catalyze a new economy of personal AI services, sovereign knowledge models, data cooperatives, and novel AI training approaches. Conservative estimates suggest this could create a $50B market within five years while reducing platform monopoly power.
Leaders, technologists, and policymakers must act now. Every day of delay means millions more valuable interactions locked away from their creators. Instead of regulating innovation with compliance and restriction to slow its pace, we should align incentives with the ultimate commodity of the future–our data.
