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Wealth Capture

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Restructuring AI Ownership

Data Compensation

Data compensation frameworks propose compensating individuals and creators for the use of their personal data and intellectual property in training AI models, effectively treating data as a form of labor or capital.

What it is:

Currently, most AI models are trained on vast datasets scraped from the internet without compensating the original creators. Data compensation schemes seek to formalize the value of this input. Instead of "free" data, these frameworks propose systems where:

  1. Data Dividends: Tech companies pay a tax or direct fee for the user data they monetize (e.g., a "data VAT" redistributed to citizens).

  2. Data Trusts: Intermediaries (unions or fiduciaries) collectively bargain on behalf of data creators to license their data to AI labs.

  3. Direct Royalties: Micropayments are automatically routed to authors, artists, or coders whenever their work is used to generate an AI output.

Who's working on It:
Betsey Stevenson

What’s There to Fear in a World with Transformative AI? With the Right Policy, Nothing.

December 2025

Stevenson proposes a "digital dividend" that treats AI-driven surplus as a collective product built on shared data, public infrastructure, and centuries of accumulated knowledge. Unlike traditional UBI framed as a handout, her dividend is structured as a return on a shared asset; citizens contribute data into the system and receive a dividend from that shared resource. She argues this framing preserves dignity and meaning while decoupling income from employment status, and compares the mechanism to Alaska's Permanent Fund but with data rather than oil as the underlying resource.

Atlantic Council

How data trusts can democratize the AI economy and accelerate innovation

November 2020

The Atlantic Council's GeoTech Center has advocated for data trusts as a mechanism to capture economic value from aggregated personal data — such as patient records, smart city data, or gig worker behavior — and redistribute it as annual dividends to data providers. The Council notes that American Airlines secured a $4.7 billion government loan using its loyalty database as collateral (valued at $18–30 billion), illustrating how a data trust managing similar assets could provide sizable dividends to members, potentially funding universal basic income for workers in an increasingly uncertain labor market.

Jaron Lanier & E. Glen Weyl

A Blueprint for a Better Digital Society

September 2018

Lanier and Weyl coined the term "data dignity" to describe a system where individuals are paid for the data they generate and pay for services that require data from others—replacing the current model of surveillance capitalism. They propose "Mediators of Individual Data" (MIDs), union-like organizations that would negotiate data royalties and engage in collective bargaining on behalf of data creators.

Andrew Yang

Data Dividend Project

June 2020

Former presidential candidate Andrew Yang launched the Data Dividend Project through his nonprofit Humanity Forward, aiming to mobilize one million Americans to "establish and enforce data property rights" under laws like the California Consumer Privacy Act (CCPA). The project asks supporters to submit their email addresses to estimate how many platforms profit from their data, with the goal of building collective bargaining power to demand compensation.

Real-world precedents:
  • In 2025, HarperCollins struck a pioneering deal with a major tech firm (reportedly Microsoft) to pay authors $2,500 per book for permission to use their work in AI training.

  • Adobe and Shutterstock have implemented "creator funds" that pay annual bonuses to artists whose images train their models.

  • Reddit signed licensing deals worth over $200 million with Google and OpenAI to monetize user-generated content, though the proceeds currently flow to the platform rather than individual users.

  • On the technical side, blockchain projects are building the infrastructure for decentralized data monetization. Ocean Protocol has built a marketplace for trading tokenized datasets, while Tim Berners-Lee’s Solid Project is developing "personal data pods" that allow individuals to store their data securely and license it to third parties on their own terms.

Securing humanity's AI future

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