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When an OpenAI researcher told company president Greg Brockman about \u201ca hack to get around nytimes paywall,\u201d Brockman replied, \u201cah nice,\u201d according to newly unsealed court filings in The New York Times\u2019<\/em> copyright case against OpenAI and Microsoft.<\/p>\n The exchange is jarring. But it also reveals something about the AI economy to come: machines are becoming some of the largest consumers of human knowledge, and we have no working system for them to pay for it.<\/p>\n OpenAI and Microsoft argue their use is protected by fair use. Publishers disagree. Courts will decide. But the AI economy needs more than a judicial answer. It needs an architecture that preserves three things the internet is pulling apart: rights, credit and compensation.<\/p>\n One of the most practical pieces of that architecture may be an old idea that has finally found its use case: micropayments, powered by agentic payments.<\/p>\n An AI agent may need one paragraph from a newspaper, one data point from a research service, one photograph or one court opinion. It may need it once, for seconds. A monthly subscription is the wrong instrument. Free access is not a sustainable answer either.<\/p>\n Micropayments have existed for decades but saw little practical use. Transaction costs mattered, but the bigger obstacle was human: people do not want to make hundreds of tiny purchasing decisions a day, and the operational overhead rarely justified the revenue.<\/p>\n AI agents do not have that problem. They can make thousands of such decisions programmatically.<\/p>\n There is still considerable debate over whether agentic payments will become a major economic layer. In my work on agentic payments and the trust infrastructure they require, I have come to believe they will.<\/p>\n I have worked on both sides of the payments system: as a financial regulator and international standard setter, and as a C-level executive in the payments industry. I am now writing a book on what the economy looks like when AI agents are authorized to act and pay on our behalf, and on the trust and guardrails needed to make that ecosystem work.<\/p>\n From that vantage point, the pieces are converging. Agents will not only discover products. They will increasingly be authorized to complete transactions under a consumer\u2019s instructions: buying a news article for a few cents, paying for computing power according to actual usage, or executing smart contracts that settle automatically once agreed conditions are met.<\/p>\n Increasingly, agents will also transact with other agents. An algorithm acting for a buyer may evaluate price, timing and terms against another algorithm acting for a seller. The transaction can be completed automatically within parameters set by the humans or businesses behind them.<\/p>\n This does not mean giving agents unlimited access to our money. More likely, we will delegate narrowly: defined mandates, spending limits, approved categories and explicit conditions. Buy this if it costs less than $20. Purchase this article if it costs less than 10 cents. Renew this service only if the price remains within a specified range.<\/p>\n That is where micropayments become especially powerful.<\/p>\n Agentic payments can operate across several financial rails, depending on the transaction: cards, account-to-account payments supported by open banking, bank transfers, stablecoins, prepaid balances and new machine-native protocols. For some cross-border micropayments, stablecoins and other programmable rails may make very small transactions faster and cheaper than traditional methods.<\/p>\n The important development is not one particular rail. It is the creation of an ecosystem in which software can identify a transaction, verify authority, select an appropriate payment method and execute it within defined parameters.<\/p>\n That makes previously impractical economic models commercially viable: paying fractions of a cent for data, a few cents for an article, charging by token or API call, or automatically splitting revenue among multiple rights holders.<\/p>\n But moving money is only part of the challenge.<\/p>\n Counterparties need to know which agent they are dealing with, who authorized it, the scope of its mandate, the user\u2019s intent and its spending limits. \u201cKnow your agent,\u201d verifiable intent, permissioning, fraud prevention, audit trails and dispute mechanisms are becoming part of a new trust architecture for delegated machine action.<\/p>\n Those same capabilities fit the copyright problem remarkably well.<\/p>\n Copyright is not merely a payment claim. It recognizes that creative work has an author, and that principle should not disappear because the reader is now a machine.<\/p>\n Imagine content carrying machine-readable information identifying its creator, rights holder, permitted uses, attribution requirements and price. An agent requests access and checks those terms against its mandate. If the use is permitted, it pays automatically and carries the provenance forward. If attribution is required, it stays attached. If the use is prohibited, payment does not make it permissible. Smart contracts could distribute proceeds automatically among relevant rights holders.<\/p>\n The building blocks are already appearing. Mastercard\u2019s Agent Pay for Machines is designed for continuous machine-to-machine transactions, including microtransactions worth fractions of a cent. Visa\u2019s Trusted Agent Protocol focuses on verifying agent identity and authorization. Stripe and Tempo\u2019s Machine Payments Protocol provides an open standard for programmatic, machine-native transactions.<\/p>\n This will not resolve disputes over historical training data, nor should it replace copyright law. It would give copyright something it has never had at internet scale: an operational layer built for machines.<\/p>\n The stakes go beyond publishers\u2019 revenues. If AI systems increasingly substitute for visits to original sources while returning little value or recognition to the people who create them, the incentive to produce expensive, original human knowledge weakens. Meanwhile, the web is filling with synthetic material, and future models increasingly learn from earlier ones.<\/p>\n The answer cannot be to lock knowledge behind walls and hope increasingly capable machines never cross them. Nor should it be to declare human knowledge free raw material for AI.<\/p>\n Copyright can establish the rights. Provenance can preserve authorship and credit. Agentic payments can make compensation granular, automatic and economically viable at machine scale.<\/p>\n If AI agents are going to become some of the world\u2019s largest consumers of human knowledge, they should also become paying customers. They should recognize who created what they use, respect the rights attached to it, and pay for the value they consume.<\/p>\n The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of <\/em>Fortune.<\/em><\/p>\n This story was originally featured on Fortune.com<\/p>\n #consuming #human #knowledge #learn #pay<\/p>\n","protected":false},"excerpt":{"rendered":" When an OpenAI researcher told… <\/p>\n","protected":false},"author":1,"featured_media":5766,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[8856,1263,7373,96,430,6454],"class_list":["post-5765","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-consuming","tag-human","tag-knowledge","tag-learn","tag-pay","tag-publishing"],"yoast_head":"\n