A few new developments suggest that the idea of paying journalists and publishers for the information AI uses is becoming a little less theoretical.
Last week, I published a rather long post called What If AI Had to Pay for the Web It Reads? (https://justme.website/content-creation/what-if-ai-had-to-pay-for-the-web-it-reads/) In it, I explored several initiatives that might eventually help journalists, publishers and other content creators get paid when AI systems use their work. We looked at x402, a protocol that allows software to make payments automatically, and News Atom, which proposes making individual pieces of journalistic information understandable to machines. I also discussed Sitra’s experiment with a marketplace for licensing journalistic content to AI developers, along with initiatives such as RSL, Cloudflare, TollBit and SPUR.
Although these projects approach the problem from different directions, they all touch on the same question: if AI companies increasingly depend on information produced by others, shouldn’t some of the money they make find its way back to the people who created that information?
A few interesting developments have emerged since I wrote that post. One new research paper in particular caught my attention because it takes the discussion beyond simply paying for access to articles. Instead, it asks how we might determine the economic value of the information contained in those articles, and how that value should be distributed among the people and organisations responsible for producing it.
What exactly are we paying for?
On October 21, a group of researchers published a paper titled Paying for Information, Not for Clicks: A Block-Level Publisher Content Marketplace for AI Answers (https://arxiv.org/abs/2610.30324). The title almost says it all. Rather than paying publishers for website visits or even for access to complete articles, the researchers propose a system in which publishers receive compensation for the actual information that contributes to AI-generated answers.
They introduce the idea of lego blocks: individual pieces of information, such as facts, figures, observations or statements. An article might contain dozens of these blocks, and several publications might contain the same information. The challenge is determining who should receive compensation when an AI system uses one or more of those blocks.
Imagine a journalist discovering that a major technology company is planning to build a new data center in the Netherlands. The journalist investigates the project, verifies the information and publishes an article containing details about its location, investment, power capacity and expected completion date. A few hours later, other publications report the same news. Some add useful information of their own, while others largely repeat the original reporting.
Now imagine someone asking an AI assistant how much capacity the planned data center will have. The assistant retrieves several articles, combines the available information and produces an answer. Who should get paid? The publication that originally discovered the project? Every publisher whose article was consulted? Or only those publications that contributed information the AI could not have obtained elsewhere?
These are not merely philosophical questions. They determine how money might be distributed in a future marketplace for journalistic information.
Three ways to divide the money
The researchers developed a framework for comparing three approaches to distributing payments. The first divides the payment equally among publishers that provide the same information. The second rewards the publisher that reported the information first. The third considers how indispensable a particular contribution was. If removing one source would make certain information unavailable, that source receives more credit.
I find the second and third approaches particularly interesting because journalism is about much more than publishing information. A substantial part of its value comes from discovering things that weren’t previously known, checking whether claims are accurate and adding context that others have missed. If an original investigation costs thousands of euros while repeating its findings takes ten minutes, should both activities receive the same compensation?
There is, however, an important complication. The publication that reports something first is not necessarily the one that reports it most accurately. Another journalist might later discover that the original information was incomplete or even incorrect. Rewarding originality makes sense, but accuracy, verification and corrections should remain part of the equation.
The researchers tested their framework using AI-generated answers to 1974 search queries concerning a heavily covered news event. Their results suggest that the method used to distribute payments can make a measurable difference. Under an equal-sharing model, established publishers with substantial news-gathering operations received 42% of the allocated value. When the model rewarded first publication, their share increased to between 46% and 50%, depending on how publication times were interpreted.
Another finding may be even more significant. When researchers examined only the articles retrieved by an AI system for a particular question, many pieces of information appeared unique. Determining who originally published something required a much broader record of the available journalism. A fair payment system might therefore need a shared information index that knows not only what information exists, but also where it first appeared and how it subsequently developed.
Remember News Atom?
That sounds like quite an undertaking. But it also reminded me of something from my previous post. In that post, I discussed News Atom (https://reutersinstitute.politics.ox.ac.uk/introducing-news-atom-metadata-blueprint-journalism-age-ai), a proposal developed by journalist Sannuta Raghu. The idea is to make journalism understandable to machines without losing the information that gives it credibility. An individual piece of information could carry metadata describing its source, publication date, verification status, context and licensing conditions.
Now consider that alongside the new research. News Atom addresses how journalistic information might be represented and described, while the researchers are asking how its economic value could be attributed and distributed. These are different problems, but they fit together rather nicely.
Imagine a future system in which an AI agent can identify a particular piece of information, determine where it originated, establish whether it has been verified, understand the licensing conditions and pay for using it. If several publishers contributed to that information, the payment could be distributed according to an agreed set of rules. That would be very different from today’s situation, where AI services can produce answers based on numerous sources while the original creators may receive little more than a citation, if they receive one at all.
Of course, there are still enormous practical difficulties. Who maintains the shared information index? How do we identify two statements that express the same fact in different words? How do we distinguish original reporting from repetition? And who decides which publisher made the most valuable contribution?
There is also a legal distinction worth remembering. Facts themselves are not necessarily protected by copyright. The commercial value may instead lie in the reporting, verification, structured datasets and services that make those facts available. The research doesn’t solve all these problems, but it does provide a framework for examining them.
What interests me most is that we are beginning to look beyond the traditional article as the basic unit of journalism. Perhaps the information gathered and verified during the reporting process has an economic value of its own, even when it is used outside the article in which it originally appeared.
Mastercard is teaching AI Agents to pay
While researchers are investigating how to value information, payment companies are developing the technology that might eventually allow AI systems to purchase it.
On October 20, Mastercard demonstrated Agent Pay for Machines (https://www.mastercard.com/global/en/news-and-trends/press/2026/october/Mastercard-Agent-Pay-for-Machines-in-action.html), a payment system that builds on x402. The demonstration took place during Cloudflare Connect, an event organised by Cloudflare. It showed how an AI agent could discover digital services, pay for them and continue working without requiring human approval for every transaction.
In the demonstration, a marketing agency asked an AI agent to help prepare a campaign for a protein bar. The agent purchased social media research from one provider and generated eight campaign images through another. In total, it completed nine payments involving two suppliers, all within an approved budget.
The system uses digitally signed payment vouchers, allowing transactions to be grouped and settled later rather than processing every tiny payment individually. That is important because handling thousands of micropayments separately could become expensive and inefficient. Mastercard has also opened an Early Access programme for developers interested in experimenting with the technology.
The demonstration had nothing to do with journalism, of course. But imagine replacing the social media research provider with a specialist news database, an independent research publication or a service offering verified information about industrial technologies. An AI agent researching a particular subject could purchase exactly the information it needs, without requiring a subscription or a separate commercial agreement with every provider. That is essentially the idea behind x402, but the Mastercard demonstration shows how the concept might become part of a broader payment infrastructure.
It doesn’t prove that AI companies will start paying journalists, nor does it mean the technology is already widely available. But it does make machine-to-machine payments considerably more tangible. And when you combine that with research into information-level compensation, you can begin to see how the different pieces might eventually fit together.
But who actually controls the content?
There is another side to this story, and it is considerably less encouraging. While researchers and technology companies are developing ways to pay for information, publishers are still arguing with major platforms about whether their content can be collected and used without permission.
The latest example involves Meta. In October, the company introduced revised commercial terms that raised concerns among publishers and legal experts. The original wording appeared to give Meta broad permission to collect content from websites linked to business accounts using Meta’s services and to use that content to develop or train its products.
Meta subsequently changed the language, removing explicit references to training. Critics argued, however, that the revised terms still left too much room for the company to collect and use publishers’ content. Publishers and advertisers in Europe and the United Kingdom asked competition authorities to intervene, and on October 23 Meta postponed the introduction of the new terms from October 30 to November 20.
Meta disputes the criticism. The company says the changes do not grant new rights to scrape websites and that website owners can continue using robots.txt to communicate their preferences. The dispute is described in more detail by Nieman Journalism Lab(https://www.niemanlab.org/2026/10/meta-delays-rollout-of-terms-that-would-allow-broad-scraping-of-news-sites-legal-experts-are-still-skeptical/).
I find this development interesting because it illustrates how far we still have to go. On the one hand, we are discussing sophisticated systems that could identify individual pieces of information and compensate the people who produced them. On the other, publishers are still trying to establish something much more basic: who gets to decide how their content is used? Without clear agreements about permissions and licensing, payment technology alone won’t solve much.
A marketplace needs more than a payment button
In my previous post, I also discussed the Finnish innovation fund Sitra (https://www.sitra.fi/en/articles/ai-can-create-new-business-opportunities-for-media-a-pilot-project-tests-the-marketplace-concept/), which is experimenting with a marketplace for journalistic content. The idea is that publishers could collectively make their information available to AI developers under agreed licensing conditions.
That could be especially interesting for smaller publishers. A large newspaper might be able to negotiate directly with an AI company, but a small specialist publication probably cannot. A shared marketplace could make its information discoverable and commercially accessible alongside content from hundreds of other publishers.
The new research suggests that such a marketplace might eventually need to do more than simply sell access to articles. It could also require information about where particular facts originated, whether reporting was original or repeated, and how payments should be distributed when several publishers contribute to an AI-generated answer.
That brings us back to the initiatives discussed in my earlier post. News Atom could help describe the information, while RSL (https://rslstandard.org/) could communicate licensing conditions. SPUR (https://www.spurcoalition.org/) is working on standards for tracking how publishers’ content is used, and x402 and related payment systems could handle transactions. Marketplaces such as the one Sitra is exploring could connect buyers and sellers.
None of these initiatives currently forms a complete, integrated solution, and there is no guarantee that they will eventually work together. Nevertheless, I find it fascinating that different organisations are beginning to address the separate components of what could become a new economic infrastructure for journalism.
There is also a question of scale. A marketplace might make sense for large news organisations with extensive archives, but could it also work for independent journalists, small specialist publishers, research institutes and other organisations that produce valuable information? That is where I think the real opportunity lies. A market that only benefits the largest publishers would do little to change the existing balance of power. A system that allows smaller content creators to participate on reasonable terms could be considerably more interesting.
And what about the Independent Web?
There is one concern I keep coming back to. I don’t want the web to become a place where every piece of information has a price tag. One of the reasons I love the IndieWeb is that people share things simply because they want to. They write about their hobbies, document experiments, publish tutorials and tell stories that might interest only a handful of readers. There is no business plan behind every post, and nobody expects every page to generate revenue. I certainly don’t want to lose that.
But I also don’t see why sharing something freely with human readers should automatically mean that commercial AI services can use it without any conditions. Those are two different things, and I think we should be able to treat them differently.
A personal website could remain completely open to human readers while offering paid access to commercial AI agents. A specialist publisher could sell verified information through an API while continuing to publish ordinary articles. A research institute could make certain findings publicly available while licensing structured datasets for commercial applications. And perhaps a journalist could eventually receive compensation not just when someone reads an article, but also when an AI service uses a piece of original reporting.
Whether any of this becomes economically significant remains to be seen. The research is still experimental, the payment systems are developing and commercial demand for paid journalistic information has yet to be demonstrated at scale.
There is also a risk that large publishers, payment providers and technology platforms end up controlling this new market, leaving independent creators with little more influence than they have today. That would be rather disappointing (to say the least), especially when the underlying technologies offer the possibility of a more open and accessible system.
Maybe we are finally asking the right questions
When I wrote my previous post, I was mainly interested in whether AI companies could be made to pay for the web content they use. Maybe there is an even more interesting question: what exactly is the value of journalistic information, and how do we make sure that value reaches the people who created it?
A payment protocol such as x402 can help move money between machines, but it cannot determine who deserves that money. That requires information about sources, originality, verification, licensing and actual usage. It will probably also require considerable cooperation between publishers, researchers, technology companies and standards organisations.
The latest developments certainly don’t mean we have solved the problem. There are still technical, legal and commercial obstacles, and we have very little evidence about how much money such systems might eventually generate.
But the discussion is becoming more interesting. Instead of arguing only about whether AI companies should pay publishers, we are beginning to examine what they should pay for, how those payments could work and who should receive them.
And that is something I intend to keep following. Because I still believe AI can create wonderful opportunities for journalism and the independent web. I’d just like the people who make that web worth reading to benefit from those opportunities too.