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Yes—but not by sending a micropayment to everyone whenever an AI agent answers. When a system makes material use of identifiable human work or judgment, its developers should build permission, provenance, attribution, and fair compensation into that relationship. That means distinguishing pay for labor from licenses for rights-controlled material and from proposals to compensate people collectively. They solve related problems, but they are not interchangeable.
Who helps an AI agent?
Human contributions enter AI systems at different stages, and the kind of contribution matters when deciding who should be paid and why.
Creators and rightsholders
Books, articles, images, music, code, and other works may be part of training data. The U.S. Federal Trade Commission describes pretraining data as potentially scraped, licensed, or obtained from existing services. Whether a particular work was used, who controls the relevant rights, and what permission is required are separate questions; simply being a creator does not establish a particular payment claim. The FTC’s 2025 report describes these different data sources.
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People may label examples, rank model outputs, correct answers, or supply expert judgments. The FTC describes human output-ranking for reinforcement learning from human feedback as labor-intensive work that is often outsourced. Paying someone for that defined task addresses compensation for labor; it does not, by itself, settle the rights in any underlying work used to create the examples.
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People who evaluate deployed agents
Human involvement does not end when training does. A 2026 study of 86 practitioners working with deployed systems across 26 domains reports that 74% of the production agents in its survey depended primarily on human evaluation. The paper also reports that 68% executed at most 10 steps before human intervention, and that 70% relied on prompting off-the-shelf models instead of weight tuning. These are findings about the study’s surveyed systems, not a census of all deployed agents. The study, “Measuring Agents in Production,” shows why ongoing evaluation and operational feedback belong in the compensation discussion.
What does the cost of human contribution look like?
A notable estimate concerns the labor that would be needed to recreate training datasets—not an invoice for existing models or a calculation of what any particular person is owed. Nikhil Kandpal and Colin Raffel studied 64 LLMs released between 2016 and 2024. Under the wage assumptions in their 2025 paper, they estimate that paying people to produce the training datasets from scratch would cost 10–1,000 times the cost of training the models. The range is the authors’ modeled estimate, not a universal observed amount owed to data producers. Their paper argues that data producers’ work should be treated as a major cost of producing an LLM.
The estimate makes the scale of possible human effort harder to ignore, but it does not tell us how to divide value among creators, data suppliers, annotators, or evaluators. Nor does it show that every model uses the same data, that every contribution has equal value, or that payment is legally due in every case.
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How could compensation work?
No single payment mechanism fits every contribution. The right approach depends on who contributed what, what permission or rights apply, and whether the contribution can be reliably identified.
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| Approach | Who may qualify and what triggers payment | What it addresses—and what it does not |
|---|---|---|
| Direct pay for work | Annotators, raters, or experts are paid for a defined task, such as labeling or ranking outputs. | Compensates labor performed for the task. It does not automatically license the source material used in examples. The FTC describes human ranking work as labor-intensive and often outsourced. |
| License specific material | A rightsholder or contributor authorizes a defined use of identifiable material in return for a fee or other agreed terms. | Can document permission and terms for particular material. Whether material is available to license, who can grant permission, and what rights apply vary. The FTC identifies licensing as one possible route for obtaining training data; the UK government’s 2025 copyright report discusses licensing among the approaches raised in consultation responses. |
| Collective levy or pooled compensation | A policy could collect funds across a broader class of uses and distribute them to a defined group. | May address cases where tracing each use and paying each contributor individually is impractical. Levies appear in consultation responses recorded in the UK government’s report; they are proposals, not a universal payment system already in force. |
| Opt-in contribution marketplace | Contributors choose material to share, and payment follows licensing under the platform’s stated terms. | Could make consent and provenance part of the transaction. The company benchturn describes its approach this way; that description is the company’s own account, not independent evidence of broad adoption or outcomes. |
The UK government’s 2025 report on copyright and AI records stakeholder views and proposals about rights reservation, licensing, and levy systems. Those positions should not be mistaken for one settled rule that applies everywhere.
These approaches can also be combined. A company might pay evaluators for their time while separately licensing material used to build or test a system. A collective fund might complement individual licensing where usage is difficult to trace. The key is to state which contribution each payment covers instead of treating all human involvement as one undifferentiated “data” claim.
Why is it hard to decide who gets paid?
Turning a general principle into a fair system raises practical questions that payment technology alone cannot answer:
- Identification and provenance: Can a system connect a contribution to a person and show how, where, and under what terms it was used?
- Rights versus labor: Is the claim about copyright or another right in material, pay for a task, or both? The answer may differ for a creator, an annotator, and an evaluator.
- Privacy and confidentiality: Can a contribution be documented and compensated without exposing private or confidential material?
- Duplicate or fraudulent claims: How would a system handle disputed ownership, multiple claimants, or attempts to claim material without authority?
- Proportionate payments: Would the amount reaching each contributor justify the costs of tracking, verification, administration, and payment?
- Auditability: Can contributors check whether their work was used and understand how any earnings were calculated?
These are design challenges, not proof that every contribution can be traced or assigned a precise monetary value. A credible scheme needs clear eligibility rules, a record of permission and use, a way to resolve disputes, and terms contributors can understand.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can an AI agent make those payments?
Technically, an agent can be built to execute actions such as making payments on a user’s behalf. That capability does not show that agents routinely pay contributors, that a payment system is trustworthy, or that payment is legally required. The UK Department for Business and Trade’s 2026 report on agentic AI and consumers describes consumer applications as early and bounded, with risks involving errors, manipulation, transparency, incentives, and accountability.
In the UK context, the report states: “If an AI agent steers, pressures or misleads consumers in ways that harm their economic interests this is likely to be unlawful.” That is the UK government report’s statement about consumer-facing agents in the UK, not a global rule about compensation for AI training data.
A payment-capable agent is therefore only one possible tool. A trusted compensation arrangement still needs authorization for the transaction, clear and auditable records, understandable incentives, and accountability when something goes wrong. The harder questions are who qualifies, what use triggers payment, and how the parties can verify the amount.
What should a fair system aim to do?
The most defensible starting point is not to promise a payment for every appearance of a person’s work in training or for every AI-generated answer. It is to make material contributions visible and governable. Developers and data suppliers should be able to explain what was contributed, how it may be used, what permission or labor terms apply, and how a payment can be checked or disputed. Where a person’s work is identifiable and materially used, compensation should be designed into the relationship rather than left invisible behind the system.
That approach recognizes different kinds of value without pretending that one formula can settle every rights question. Direct wages, material-specific licenses, and collective policy mechanisms each have a role to consider; none alone answers every case.
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