AI Agents, the Big Undo and the Future of Publishing

The personal web is making a comeback, AI agents are changing how we consume information, and publishers are looking for new ways to get paid. At first, these seem like separate developments. I’m beginning to think they are all part of the same story.

I have been spending quite a lot of time lately exploring what people sometimes call the small web, the personal web or the IndieWeb. These are rather loose terms for an internet where people publish on their own websites, discover one another through independent services and don’t necessarily depend on a handful of enormous platforms.

It started with a few articles that caught my attention. One was Ben Thompson’s The End of the Internet (https://stratechery.com/2026/the-end-of-the-internet/). Another was Dave Winer’s Big Undo (https://bigundo.org/). And then there was Marco Arment’s wonderfully personal essay Community (https://marco.org/2026/10/24/community).

They approached the subject from different directions, but I found myself returning to the same question: what if the internet we have become accustomed to is not the final version of the internet? What if the enormous platforms that dominate so much of our online lives are simply one phase in its development? And what if the next phase consists of thousands, perhaps millions, of much smaller applications and services that communicate through open standards?

But while looking into these projects, I realized that something else is happening at exactly the same time. The way information is consumed is changing. And this second development could have enormous consequences for anyone who creates, owns or publishes content.

I’ve written about several of these developments on Just Me before. Some posts explored the return of the personal web and the ideas behind the Big Undo. Others looked at AI agents, the changing relationship between publishers and technology companies, and emerging ways to license content and get paid for its use.

Each time, I was fascinated by a particular development, project or idea. But the more I explored these subjects, the more I began to realize that they weren’t separate stories at all. They were pieces of a much larger puzzle. And while I had been writing about those pieces individually, I hadn’t really tried to put them all together and see what the complete picture might look like.

So that’s what I wanted to do here: revisit the different ideas I’ve been exploring, explain how they relate to one another, and try to understand what they might mean for the future of the internet, particularly for people and organizations that create and publish information.

Which explains why this has turned into a rather long post. A very, very long post, actually. But I hope you’ll bear with me. I think the connections between all these developments are at least as fascinating as the individual pieces themselves.

First, what exactly is the Big Undo?

Dave Winer is one of the pioneers of blogging, RSS and several other technologies that helped shape the early web. His Big Undo proposal is surprisingly straightforward. Instead of building another gigantic platform that tries to do everything, why not create lots of small applications that work together? A writing application should help you write. A publishing system should publish your work. A discovery service should help people find interesting things. A discussion service should enable conversations. And the connections between those applications should be based on open standards.

Winer has specifically mentioned Markdown and RSS 2.0 as useful foundations. In a follow-up post on October 27 (http://scripting.com/2026/10/27.html), he connected his ideas to Thompson’s observations and Arment’s reflections on online communities.

I particularly like the simplicity of this approach. You don’t need to convince millions of people to move to a new platform. You don’t need to invent an entirely new internet protocol. You can build something useful by connecting existing components.

That sounds almost too obvious. But it is remarkably different from the model that has dominated the technology industry for the past twenty years. Today, a successful internet company is generally expected to attract as many users as possible, keep them within its own environment and continuously expand the services it provides.

Big Undo suggests the opposite. Build something small. Make it useful. Let it communicate with other applications. And make sure people can leave without losing everything they have created.

An internet made of small pieces

The interesting thing is that this isn’t just a theoretical discussion. People are already building parts of this alternative internet. Take Bubbles (https://bubbles.town/), a discovery service developed in Germany. It helps readers find interesting articles published on independent blogs.

The important detail is that Bubbles doesn’t require those bloggers to move their writing to a new platform. They continue publishing on their own websites. Bubbles discovers their articles through RSS and helps other people find them. When you click an article, you visit the original website.

I find that a beautiful idea. Instead of becoming another destination that absorbs everybody’s content, Bubbles acts as a connection between independent publishers and potential readers.

Similar approaches can be found elsewhere. In China, independent blog directories such as Bloghao (https://bloghao.xiaowuleyi.com/) help readers discover personal websites. South Korea has Indieblog (https://indieblog.net/), which brings together independently published blogs. Japan has a wonderful tradition of webrings and small website alliances, including the dōmei communities described at https://do.gt-gt.org/what-is-union/.

In France, the CHATONS network (https://www.chatons.org/) demonstrates that even digital services and hosting infrastructure can be organized through cooperating independent providers rather than a single enormous company.

And around the AT Protocol (https://atproto.com/), developers are experimenting with applications that share identities and data while serving different communities. One particularly interesting development is Standard.site (https://standard.site/), an initiative to make long-form publishing interoperable across applications.

These projects differ enormously in their ambitions and technologies. They are not one coordinated movement, and there is no evidence that they are about to replace the major platforms. But they share an interesting principle. The application is no longer necessarily the place where your information has to live forever. It can simply be one of several tools you use to work with information you control.

AI may be making this possible

Ben Thompson adds another intriguing element to the story. AI is making it easier for individuals and small groups to develop software. A few years ago, building a specialized application required considerable programming expertise, time or money. Today, AI coding assistants can help people create software that might never have been commercially viable. This doesn’t eliminate the need for testing, maintenance or security. AI-generated software can certainly introduce new problems. But it changes the economics of building small tools.

I recognize this from my own experiments. I have been developing applications that help me organize information, explore my bookmarks and work with my own files. These are not products I intend to sell to millions of people. They solve problems that interest me. And I am certainly not the only person experimenting this way.

The result could be a rather different software landscape: instead of adapting everything we do to a relatively small collection of applications, more people may start creating applications around their own needs. If these applications use open standards, they can also become part of a much larger ecosystem. That is one possible future for the personal internet.

I realized I had already built a small piece of this internet

There was something rather amusing about all this. While exploring Bubbles and the ideas behind Big Undo, I suddenly realized that I had already built something that works along surprisingly similar lines. Actually, I had built two of them.

One is Digital Twin News (https://digitaltwinnews.tech/), a website that collects news and articles about digital twins from sources around the world. The other is Around The IT World (https://www.aroundtheitworld.news/), which does something similar for IT news. Both websites run entirely automatically. They use RSS feeds to discover articles on other websites, collect information about those articles and present them in specialized news feeds.

And there is one principle I considered essential from the beginning: the original articles stay exactly where they are, on the websites of their original publishers. I am not trying to create another publishing platform where other people’s articles are copied and republished. The purpose is to help readers discover interesting material and then visit the original source.

In that respect, the underlying idea is quite similar to Bubbles, although my websites don’t have its voting and community features. There is, however, another important difference. My systems search for news in roughly 25 language areas, not just English. That means they discover articles published in languages that many English-speaking readers would never normally encounter.

To make those articles accessible, I use locally running AI models to generate short English descriptions of the original publications. These descriptions are intended to give visitors enough information to decide whether an article interests them. The AI-generated text is not supposed to replace the original article. It is a discovery aid, a way of saying: Here’s something interesting happening somewhere in the world. Would you like to know more? And because the AI runs locally, I don’t need to send the articles to an external AI service simply to generate those descriptions.

What I find particularly interesting is the multilingual aspect. The internet contains an enormous amount of valuable information that remains largely invisible to people who don’t speak the language in which it was published. An interesting development in Japanese manufacturing, a new technology project in Brazil or an IT security initiative reported by a publication in Eastern Europe might never appear in the English-language news sources that many of us follow.

But that doesn’t make those stories any less interesting. By combining RSS, local AI and independent publishing, I can create a discovery service that crosses those language barriers while keeping the original publishers at the center of the process.

Of course, these websites are not identical to Bubbles. They are topic-focused news discovery services rather than communities, and the automated summaries introduce questions about attribution, copyright and the boundaries of permitted reuse. Those questions deserve attention, especially as standards for AI-related licensing develop.

Still, the realization made me smile. I had been reading about a movement advocating smaller, independent internet applications that connect existing websites instead of replacing them. And without consciously setting out to follow that philosophy, I had already built two examples of something remarkably similar.

Perhaps that is what makes Big Undo so interesting. You don’t necessarily need to join a movement or adopt a grand technological vision. Sometimes, you simply set out to solve a practical problem using open standards and a handful of small, and maybe self-built tools, only to discover that you’ve arrived at much the same place.

But there is also a more complicated side to this story. My websites use AI to help people discover original publications. Other AI systems increasingly use those same publications to answer questions without necessarily sending readers to the source. And that brings us to the second major development reshaping the internet.

What happens when the reader is an AI agent?

For most of the web’s history, publishers have built their business models around human visitors. Someone searches for information, discovers an article, clicks a link and arrives at a website. The publisher might earn money from advertising, subscriptions, memberships, sponsorships or other services. Even when the article itself is free, the visit has value. Search engines and social networks became enormously influential because they controlled a large part of that journey.

Now imagine a different situation. A data center manager wants to know which liquid-cooling technologies are suitable for a new AI facility. Instead of searching Google and visiting five specialist websites, he or she asks an AI assistant to investigate the subject. The assistant searches the web, retrieves information from technical publications, compares specifications and produces a useful report. The manager gets his answer. But he may never visit the websites that provided the information.

Those publishers have contributed valuable knowledge, yet they might receive no visitor, no advertising revenue and no opportunity to sell a subscription. And if the AI assistant performs the research automatically, the publisher may not even know how its information was used.

This is not merely a new form of search. It changes the relationship between the creator of information and the person or system consuming it. The customer may no longer be the person reading the article. It may be the software agent gathering information on that person’s behalf.

That is where the discussion becomes particularly interesting.

The old deal between publishers and search engines is breaking down

For many years, the relationship between search engines and publishers was based on a relatively simple exchange. Publishers allowed search engines to index their content. In return, search engines helped people discover the original websites. It was never a perfectly balanced relationship, but the principle was understandable.

AI-powered services can work differently. They may use retrieved information to construct answers that satisfy the user without requiring a visit to the original source. There is also an important distinction between different kinds of AI use. Training an AI model on a large collection of documents is not the same as retrieving a current article to answer a specific question. Summarizing a webpage is not the same as reproducing it. And an AI agent purchasing access to a specialized database is different again. These activities have different technical, economic and legal implications. Yet they are often discussed as if they were all the same thing.

Publishers are now trying to establish clearer rules. And an entire collection of initiatives is emerging to help them. What interests me now is how closely these initiatives connect to Big Undo.

RSL: telling AI systems what they may do with your content

One of the most important developments is Really Simple Licensing, or RSL (https://rslstandard.org/). RSL is an open standard designed to let publishers express licensing and usage conditions in a format that software can understand.

Think of it as an additional layer of information attached to published content. A website can indicate that certain uses are free, that attribution is required, or that particular forms of AI processing require a license or payment. RSL 1.0 also includes mechanisms for license discovery, authorization and reporting. It can work alongside familiar web technologies, including RSS and robots.txt.

The connection to RSS is particularly interesting. RSS was designed to make content easier to distribute between independent applications. RSL is attempting to make the conditions for using that content machine-readable as well.

In other words, we are beginning to see a distinction between two questions.

How can software find and retrieve information?

And:

What is that software allowed to do with the information once it has found it?

These are different questions, and they require different answers.

An especially interesting development is that RSL 1.0 can also reference reporting profiles such as SPUR Content Telemetry (https://contenttelemetry.org/). This creates a more concrete connection between machine-readable licensing conditions and the reporting of how AI systems actually use content.

Instead of treating permissions and usage reporting as entirely separate problems, publishers can begin to describe them as parts of the same licensing arrangement. For example, a license could establish the conditions under which content may be retrieved and specify how subsequent usage should be reported.

This doesn’t guarantee compliance or independent verification, but it is an important step toward an interoperable system. RSL doesn’t magically compel every AI company to respect a publisher’s wishes, either. Publishing licensing conditions is not the same as enforcing them. But it provides a common language for communicating those conditions. And common languages are exactly what an internet of independent components needs.

The RSL 1.0 specification is available at https://rslstandard.org/rsl.

x402: what if an AI agent could simply pay?

Now imagine that an AI agent discovers a useful specialist article. The publisher allows access, but only under a paid license. How does the AI agent actually pay? Today, payments often involve creating an account, signing a contract, entering credit card details or subscribing to a service. These are familiar processes for humans, but rather awkward for software agents operating automatically.

That is where x402 (https://www.x402.org/) becomes relevant. The idea is to make payment part of a normal web interaction. A service can indicate that payment is required. A compatible client can then provide the necessary payment authorization and continue its request.

The name “x402” refers to HTTP status code 402, traditionally associated with payment requirements. The interesting possibility is that an AI agent could pay for access to a specific piece of information without requiring a human to manually complete every transaction. Perhaps a specialist publisher charges for a particular technical dataset. Perhaps an AI research service pays to retrieve a verified market report. Or perhaps an agent purchases access to a continuously updated database.

Of course, there are plenty of unresolved questions. How much should an article cost? Who is responsible when an agent makes an unauthorized purchase? How are refunds handled? And what happens when hundreds of small transactions become more expensive to administer than the information itself?

But the principle is significant. If software agents are becoming consumers of digital information, they may need ways to become paying customers as well. Cloudflare has been exploring related ideas with Pay Per Crawl (https://blog.cloudflare.com/introducing-pay-per-crawl/), which allows participating website owners to experiment with charging automated crawlers for access.

These technologies are not identical. Nor do they automatically work together. But they point toward an internet where machine access can be negotiated rather than simply allowed or blocked.

SPUR: how do you know what happened to your content?

Payment for access is one thing. But what if the commercial value of content depends on what happens after it has been retrieved?

Suppose an AI system downloads an article, uses it as evidence in an answer, cites the publisher and presents the resulting information to thousands of users. Those are several different events. Should the publisher receive one payment when the article is downloaded? Another when the content contributes to an answer? Or a share of revenue generated by the service?

Before anyone can make those arrangements, there needs to be a reliable way of describing what happened. That is the problem addressed by Content Telemetry (https://contenttelemetry.org/), stewarded by the SPUR Coalition. The standard provides a structured way to report different stages of content use, including retrieval, grounding, citation, presentation and user engagement.

This distinction matters. An article being downloaded does not necessarily mean it was used to generate an answer. And an article being used as background information does not necessarily mean the user ever saw a reference to its publisher. SPUR is attempting to create a vocabulary and reporting mechanism for those differences.

The connection with RSL 1.0 makes this particularly interesting. RSL can communicate the licensing conditions, while a referenced SPUR reporting profile can describe how relevant usage events should be reported. In principle, that allows a publisher to express both the conditions of access and the expected reporting arrangements through interoperable, machine-readable mechanisms.

It is not yet a complete commercial system, but it moves the discussion beyond simply telling AI crawlers whether they may enter a website – or not.

However, it also raises a difficult question. How can a publisher verify that an AI provider’s usage report is complete? If a platform reports that an article contributed to one hundred answers, how does the publisher know that the actual number wasn’t one thousand? A reporting standard is an important first step. Independent verification is a different problem. And without reliable verification, usage-based licensing becomes difficult to trust.

There is another missing piece: identity

This brings us to a subject that is easy to overlook. How does a publisher know who is requesting access?

A website can receive automated requests from many different sources. Some may come from search engines. Others from AI training crawlers, research tools, monitoring services, malicious scrapers or AI agents acting on behalf of paying customers. They may look surprisingly similar from the publisher’s perspective. But their intentions are very different.

Imagine a specialist publisher that wants to allow an AI agent working for an existing corporate customer to retrieve information. The publisher needs to establish whether the agent really represents that customer, whether it has permission to access the requested material and which licensing conditions apply.

That requires more than simply recognizing a bot. We need to distinguish identity, authorization and accountability. Identity establishes who is making the request, or on whose behalf it is being made. Authorization determines what that requester is allowed to do. Accountability concerns what happened afterward and whether the requester complied with the agreed conditions.

These are separate functions. An agent can have a verifiable identity without having permission to access a particular database. And a valid payment does not automatically grant unlimited rights to redistribute or train models on the retrieved material.

The emerging infrastructure for agent identity, licensing, payments and telemetry therefore needs to work together. This is precisely where open standards become important again.

A Dutch example that made me smile

During my exploration, I came across The Content Exchange, or TCE (https://tce.exchange/), a Dutch company operating a marketplace for licensing and distributing professional content. TCE is interesting because it already deals with a problem that many publishers may face in the AI era: how to make content available to other parties under commercial agreements. The company has experience with content syndication, rights and distribution. That makes it a useful organization to watch as the market for AI-accessible content develops.

But something rather amusing happened when I tried to investigate the company further. I asked an AI assistant to visit its website. The assistant couldn’t get in. The website restricted access from the automated browser. Now, that doesn’t prove TCE deliberately blocks all AI agents. The restriction could be caused by ordinary bot protection, anti-scraping measures or the particular browser environment being used.

Still, I couldn’t help smiling. Here I was, trying to investigate a company involved in licensing content, and the automated tool I was using couldn’t access its website.

It seemed like a perfect illustration of the dilemma. Publishers have good reasons to protect their information against unwanted automated collection. But they may also want to welcome legitimate AI agents that are willing to identify themselves, respect licensing conditions and pay for access. How do you distinguish between the two?

A simple choice between allowing all bots and blocking all bots is unlikely to be sufficient. The future may require something closer to a digital front door: one that lets ordinary visitors enter freely, recognizes authorized customers and can negotiate access with software agents.

Finland is also experimenting with a marketplace for media content

Another project worth following comes from Finland. The Finnish innovation fund Sitra has been exploring the idea of a marketplace where news organizations can license journalistic content and data for AI development and AI-powered services. Sitra published an explanation of the initiative in July 2026 (https://www.sitra.fi/en/articles/ai-can-create-new-business-opportunities-for-media-a-pilot-project-tests-the-marketplace-concept/).

What I find interesting is that the project doesn’t assume that simply putting a price on every article will solve the problem. The participants are investigating which kinds of content AI developers actually need, how they assess its value, what licensing arrangements are practical and how publishers can maintain control over their material.

There is also a collective dimension. A large international publisher may be able to negotiate directly with an AI company. A small independent publisher probably cannot. But what if many publishers could make their content available through a shared marketplace, with common technical and legal arrangements? That might make participation possible for organizations that would otherwise be excluded.

Unfortunately that also introduces a familiar risk. If every publisher becomes dependent on one enormous content marketplace, haven’t we simply created another platform?

Which brings us straight back to the Big Undo.

We could accidentally build the next generation of gatekeepers

This is the part of the story that worries me. The personal web movement wants to reduce dependence on enormous platforms. The emerging AI-content economy wants to create mechanisms for controlling access, negotiating licenses and collecting payments. Those goals are not necessarily incompatible. But they could become incompatible if we aren’t careful.

Imagine a future in which every AI agent must obtain its identity from one dominant provider, purchase licenses through one marketplace and use one payment network. Independent publishers might receive compensation, but they would also become dependent on the companies operating that infrastructure.

We would have solved one problem by creating another.

Or imagine that practically every website begins charging automated systems for access. Large AI companies could afford those fees. Small independent developers, researchers and community projects might not. The result could be an internet that is technically open but economically difficult to explore. That would be a strange outcome for a movement inspired by the freedom of the early web.

I don’t think the solution is to reject licensing or payments. Professional journalism, research and specialist information cost money to produce. Publishers and content owners like research organisations, universities, trade associations and others need sustainable business models. But we should be careful not to confuse open information exchange with unlimited commercial exploitation. It should be possible to publish something freely for human readers while setting different conditions for commercial AI training or large-scale automated reuse. It should also be possible to offer information under open licenses, request attribution, accept voluntary contributions or charge for specialized machine access.

Different publishers have different motivations. The infrastructure should support those differences.

Perhaps publishers will start selling knowledge, not just articles

There is another possibility that I find especially interesting. For most of my professional life, I have thought about information in terms of articles, interviews, reports and other publications. But AI agents often don’t need an entire article. They need particular pieces of reliable information.

Imagine a technical publication that has spent years reporting on data center infrastructure. Its archive contains information about cooling systems, power distribution, rack densities, standards, suppliers and installations. For a human reader, this material exists as hundreds of separate articles. For an AI agent, the archive could become a specialized knowledge resource. A publisher might make certain facts available as structured records, including sources, publication dates, technical context and licensing information.

The idea of breaking journalism down into smaller, structured units of information is not mine. Credit belongs to journalist Sannuta Raghu, who developed the concept of the News Atom. In an excellent article published by the Reuters Institute for the Study of Journalism, Introducing the News Atom: a metadata blueprint for journalism in the age of AI (https://reutersinstitute.politics.ox.ac.uk/introducing-news-atom-metadata-blueprint-journalism-age-ai), she explains how individual statements in a news story can become machine-readable units that retain their meaning, sources, context, provenance and licensing information.

I find her approach fascinating, particularly because it recognizes that the value of journalism lies not simply in the words of an article, but also in the research, verification and interpretation behind those words.

However, I would like to take the idea a little further. Raghu calls these units News Atoms, which makes perfect sense in the context of journalism. But I think the principle could apply to much more than news and reported facts. Research findings, technical specifications, scientific observations, expert knowledge and many other forms of verified information could potentially be structured in similar ways. That is why I prefer the broader term Knowledge Atoms. Not as a replacement for Raghu’s original concept, but as a way of exploring how the same underlying idea might extend beyond journalism into the wider world of knowledge publishing.

Here is how that could work. An article might contain several “atoms”: a product announcement, a verified technical specification, an interview statement, a deployment location or a regulatory change. The atoms would not necessarily replace the article. The article remains valuable because it provides context, interpretation and a coherent story. But structured information could make the underlying knowledge easier for machines to find, verify and license.

This could open the door to entirely new products and business models for publishers. They could continue publishing articles for human readers, either freely available or through subscriptions, while also making the underlying knowledge available in formats designed specifically for AI systems. For example, a specialist publisher could offer access to a structured database containing verified facts, technical specifications, research findings and expert insights extracted from its reporting. AI developers or companies using AI agents could pay to access this information through an API, rather than having their systems search through hundreds of individual articles. Publishers could also offer continuously updated knowledge services, ensuring that AI applications have access to the latest verified information in a particular field. In this model, the publisher is no longer selling access only to articles, but also to the reliable, structured knowledge behind them.

That doesn’t mean every publisher should immediately start building APIs and databases. There is still a great deal we don’t know about actual customer demand, pricing and the costs of maintaining such services. But it suggests that the economic value of publishing may increasingly lie not only in the document or the article itself, but also in the reliable knowledge behind it. And that could be particularly interesting for specialist publishers, research organizations and trade associations.

So how could all these pieces fit together?

I keep imagining a relatively simple arrangement. An independent publisher maintains its own website and content archive. Human readers discover articles through search, RSS, blog directories, newsletters and social communities. Software agents can discover the same publisher’s machine-readable information, but they also encounter clearly defined licensing conditions. When an agent wants access to material requiring authorization, it identifies itself and provides the necessary credentials. If payment is required, a compatible mechanism handles the transaction. And when the agreement calls for usage reporting, a telemetry system records the relevant events.

No single company necessarily has to operate all those functions. The publisher could choose one content management system, another identity provider, a separate payment service and perhaps a collective licensing organization. Ideally, the components would be replaceable. That is the Big Undo applied to the economics of publishing.

The emerging publishing architecture

This is a conceptual architecture, not a complete system that already exists and works seamlessly. The technologies are at different stages of development. Some are established, some are experimental, and interoperability between them remains incomplete. But the pieces are beginning to become visible.

The connection I hadn’t expected

When I started exploring the Big Undo, I was thinking mainly about personal websites, independent developers and the pleasure of discovering unexpected things online. I wasn’t thinking about licensing agreements, automated payments or content telemetry. Those subjects seemed to belong to a completely different discussion.

But now I think they are closely related. The Big Undo asks how people can regain control over their software and information. The emerging content-licensing initiatives ask how creators and publishers can regain control over the economic use of their information. Both depend on a similar principle: the ability to operate independently while communicating through shared standards.

Both also face the same danger. If the solution requires everyone to join one enormous platform, we may end up reproducing the very dependencies we hoped to escape. Perhaps the most important question is therefore not whether AI companies will eventually pay publishers. It is whether the infrastructure for those payments, licenses and permissions will remain open enough for small publishers and independent developers to participate on their own terms.

And what about Just Me?

There is something slightly amusing about writing all this on a personal blog. I started Just Me because I wanted somewhere to write about things I encounter and find fascinating. Whether thousands of people read those articles has never been my main concern. I enjoy the freedom of having my own little corner of the internet.

I like discovering independent websites through services such as Bubbles. I like following links that lead somewhere unexpected. And I like experimenting with small pieces of software that do exactly what I want them to do.

I have no plans to turn Just Me into a commercial content business. But the principles matter to me. I want my writing to remain mine. I want people to be able to discover it. I want to choose the tools I use. And I don’t want my words to become permanently trapped on a platform simply because it happens to be popular today, when it could disappear as soon as next week.

In my opinion, the same principles matter (or should matter) to professional publishers, although they also need to earn enough money to continue producing reliable information. That is why I think the two developments belong in the same conversation. The personal web is exploring how to make publishing independent again. The emerging AI-content economy is exploring how to make the use of published information economically sustainable.

If both movements embrace open standards, portability and genuine choice, they could reinforce one another. If they don’t, we may simply replace one generation of internet gatekeepers with another.

I don’t know which direction things will take. But I do know that I want to keep watching the people who are building these systems. Not just the large technology companies, but also the independent developers, small publishers, open-standard communities and researchers experimenting at the edges of the internet.

Because perhaps the future of the internet won’t arrive as one enormous new platform. Perhaps it will arrive as thousands of small pieces that gradually learn how to work together. And perhaps, this time, we can also figure out how the people creating the information flowing between those pieces can share fairly in the value they produce. That would be a Big Undo worth getting excited about.

For readers who want to explore further, the key starting points are Dave Winer’s Big Undo (https://bigundo.org/), the RSL standard (https://rslstandard.org/), SPUR-related Content Telemetry (https://contenttelemetry.org/), the x402 payment protocol (https://www.x402.org/) and Sitra’s research into AI content licensing (https://www.sitra.fi/en/articles/ai-can-create-new-business-opportunities-for-media-a-pilot-project-tests-the-marketplace-concept/). On Just Me, you can find several blog posts I’ve written about these and related topics by selecting the “Publishing” and “Content Creation” categories on the left-hand side of the homepage.

AI Agents Are Getting Digital Passports. But Who Checks What They Do?

Yesterday, Bookmark Conversations, the little application I’ve been building to find unexpected connections between my bookmarked articles (see more https://justme.website/content-creation/read-later-more-like-read-never-so-i-built-bookmark-conversations/), came up with something very interesting. It connected a Cloudflare article about the growing amount of automated traffic on the internet with a technical specification from SPUR, an initiative exploring how publishers can track the use of their content by AI systems.

At first, the connection seemed fairly straightforward. If AI agents are becoming increasingly active on the web, publishers need to know who these agents are and what they’re doing with their content. But the more I looked into it, the more fascinating it became. Because there are actually two very different questions here:

How do we know who an AI agent really is?

And how can we verify what it does with the information it collects?

It turns out that quite a few people are working on the first question. The second is considerably more difficult.

Could I see your passport, please?

Imagine someone walking into a library, picking up a book and introducing themselves as a researcher from a well-known university. The librarian might be perfectly happy to help. But how does he or she know the visitor really works for that university?

Something similar happens constantly on the web. Software visiting a website can identify itself through a user-agent string. A crawler might claim to be Google’s crawler, for example, or another well-known automated service. But simply claiming an identity is not the same as proving it. And as AI agents begin doing more than collecting information, this distinction becomes increasingly important. Some agents browse websites, compare products, book services or carry out tasks on behalf of users. Others collect material that may eventually appear in AI-generated answers.

So how do we distinguish a legitimate agent from software pretending to be one? One answer is Web Bot Auth, an emerging approach being developed through the Internet Engineering Task Force (IETF) (https://datatracker.ietf.org/doc/draft-ietf-webbotauth-httpsig-protocol/). Instead of simply announcing its identity, an automated visitor can cryptographically sign its requests. A website can then verify that the request was signed using a particular private key. It’s a little like replacing a handwritten name badge with a digitally verifiable identity document.

Of course, proving that an agent controls a particular cryptographic key doesn’t automatically tell us whether that agent is trustworthy. But it gives us something much more useful than a name that anybody can type into an HTTP request. Cloudflare is among the companies exploring and implementing this approach.

The surprising part: payment companies are interested too

I initially thought this was mainly a problem for website owners and publishers. But then I discovered that payment companies are working on remarkably similar questions. Visa has developed its Trusted Agent Protocol (https://developer.visa.com/docs/trusted-agent-protocol/trusted-agent-protocol-specifications), intended to help merchants recognise AI agents involved in shopping transactions. Mastercard is also exploring ways to establish what a customer has actually authorised an AI agent to do.

Here is what they are trying to do. If an AI assistant buys something on your behalf, a shop needs more than an assurance that the visitor is an AI agent. It needs to know who operates the agent, whether the agent is acting for a genuine customer and whether that customer authorised the purchase. That’s quite a different problem from identifying a web crawler. Yet the underlying questions are more or less the same: Who are you? Who sent you? What are you allowed to do? And can you prove it?

The FIDO Alliance (https://fidoalliance.org/), known for its work on authentication and passkeys, is also involved in discussions about securing interactions involving AI agents.

I find this development rather fascinating. We seem to be watching the early construction of something resembling a digital identity infrastructure for software that acts on its own. Not a single universal passport system, at least not yet. More a collection of standards, protocols and experiments that may eventually fit together.

And that raises another question I hadn’t initially considered: who will issue these identities, and who gets to decide which agents are trusted? I’d much prefer an open system in which a small independent website can verify an agent without depending entirely on a large technology company or government agency. Otherwise, we risk creating another layer of gatekeepers on the internet.

But what happens after the agent leaves?

This brings me back to the second part of the Bookmark Conversations discovery. Let’s imagine that an AI agent visits a news website. It identifies itself cryptographically. The publisher verifies its identity and allows it to retrieve an article.

So far, so good. But what happens next? Does the agent simply read the article to answer a question? Does it store a copy? Does it pass the information to another system? Does the article influence a generated answer without ever being quoted or linked? The publisher can’t necessarily see any of this. And that becomes particularly interesting when publishers want to license their content to AI companies.

Several initiatives are trying to create the infrastructure for such arrangements. Really Simple Licensing (https://rslstandard.org/) focuses on machine-readable licensing conditions. Cloudflare has been experimenting with Pay Per Crawl, allowing website owners to charge automated visitors for access (https://developers.cloudflare.com/ai-crawl-control/features/pay-per-crawl/what-is-pay-per-crawl/).

SPUR (https://github.com/SPUR-Coalition/telemetry) takes another approach. Its Content Telemetry specification describes ways to report how content moves through AI systems and how it is used. When I examined SPUR more closely, I discovered that its specification already considers verified bot identities. So the original Bookmark Conversations discovery wasn’t entirely correct in suggesting that identity verification had simply been overlooked.

But it had uncovered a much more interesting issue: Knowing who collected an article is not the same as knowing what happened to that piece of content afterwards. An AI company might report that an article was used in a particular way. But can the publisher independently verify that report? That’s where things become difficult.

A receipt isn’t necessarily proof

Suppose a publisher receives a report saying that an article was retrieved 500 times and used in 30 AI-generated answers. That’s useful information. But where did those numbers come from? If they were reported by the AI provider itself, the publisher still has to trust that provider’s reporting system. Even a cryptographically signed report only proves who signed it and that it hasn’t subsequently been altered. It doesn’t automatically prove that every relevant use was recorded.

This reminds me a little of advertising measurement. For years, advertisers and publishers have wrestled with questions about impressions, clicks, attribution and independent verification. AI introduces an additional complication. An article might influence an answer without appearing in the final response. Information might pass through several services. And the distinction between retrieving content, processing it and actually using it becomes surprisingly difficult to define.

Perhaps some of these events can eventually be recorded in independently auditable systems. Perhaps trusted execution environments, cryptographic proofs or third-party measurement services will play a role. But none of these approaches should be mistaken for a complete solution. Proving what happened inside a complex AI system is very different from proving that a particular computer sent a particular HTTP request. And I suspect this distinction will become increasingly important as the market for licensing content to AI companies develops.

Two pieces of a much bigger puzzle

Interestingly, these developments are coming from different corners of the internet. Security researchers are thinking about cryptographic identities. Payment companies are working on agent authorisation. Publishers are experimenting with licensing and usage reporting. Internet standards organisations are trying to make sure the underlying technologies can work together. They aren’t necessarily building one coordinated system. But they are encountering different parts of the same problem.

The internet is gradually filling with software that can act on behalf of people and organisations. We need ways to recognise that software, establish what it is allowed to do and hold it accountable for its actions. A digital passport may help with the first part. It doesn’t automatically solve the last. And there is a rather uncomfortable possibility here: we might eventually become very good at identifying the AI agents that visit our websites while remaining surprisingly dependent on their operators to tell us what happens to our content.

One discovery, quite a few new questions

I like that this whole line of thought began with two bookmarks that decided were related. The application didn’t provide a definitive answer. In fact, one part of its original interpretation needed correcting after I checked the sources. But that’s fine. I didn’t build it to deliver unquestionable conclusions. I built it to notice connections I might otherwise miss and give me something worth investigating.

And this one certainly did. I started with a question about a technical specification for AI content telemetry. I ended up wondering whether we’re witnessing the birth of an identity system for autonomous software, and whether the people creating the content these systems consume will ever be able to verify how that content is used. And get paid for it. Not bad for a couple of bookmarks that might otherwise have remained in my read later collection forever.

What If AI Paid for Information Instead of Articles?

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.

What If AI Had to Pay for the Web It Reads?

This is a long one, so bear with me. But I’ve really wanted to write about this for a while now, because it’s been on my mind for quite some time. So, here we go!

I love AI. I use it almost every day, experiment with local language models, and build little tools with it. Some of those experiments are useful. Others are simply fun to build. And quite a few are related to journalism, which is how I make my living.

But there is something about the AI industry that has bothered me for quite some time. AI companies have built incredibly powerful products using enormous amounts of information from the web. Articles, blogs, tutorials, research papers, discussions, documentation. Much of that material was written by people who never received a penny from the companies using it.

Whether all that training was legally permitted is still being debated in courts and elsewhere. But regardless of the legal arguments, there is an economic question here. If AI becomes one of the main ways we consume information, shouldn’t some of the money flow back to the people who create that information? Especially when those people are independent writers, bloggers, journalists and small publishers who don’t have the resources to negotiate licensing agreements with companies like Google or OpenAI.

Recently, I came across something called x402. And it made me wonder whether we might finally be seeing the beginnings of a different model. The interesting thing is that x402 isn’t the only initiative moving in this direction. There are experiments involving machine-readable journalism, new licensing standards and even marketplaces where publishers could sell their information to AI companies and others. Individually, these developments are interesting. Put them together, and something rather fascinating begins to emerge.

Two Cents for an Article? Why Not?

The idea behind x402 (https://x402.org/) is surprisingly simple. When your browser requests a webpage, the website normally sends the requested information back. Sometimes it refuses access, perhaps because you need to log in or don’t have permission. There is also an HTTP status code called 402, which stands for Payment Required. It has existed for decades but has never become a widely used way of paying for things on the web.

x402 builds on that idea. Imagine an AI agent looking for information about a particular cybersecurity vulnerability. It finds a relevant article on an independent technology website and requests access. Instead of simply handing over the article, the website responds: this will cost you two cents. The agent pays, the website verifies the payment, and the article is delivered. No subscription. No registration form. No credit card details to enter. No human intervention. The payment becomes part of the normal communication between two pieces of software.

Technically, x402 can use digital currencies such as stablecoins, which are designed to maintain a relatively stable value. That doesn’t mean a publisher has to become a cryptocurrency enthusiast. It does mean that wallets, payment providers and conversion into ordinary money are practical issues that need to be solved.

And two cents is just an example. A publisher could charge a fraction of a cent for a short piece of information or considerably more for access to a specialist report. For humans, paying separately for every article would probably be rather annoying. For software that automatically collects and processes information, however, such payments could be perfectly manageable.

This is what makes x402 interesting. It was originally developed by Coinbase and is now being developed as an open protocol under the Linux Foundation (https://www.linuxfoundation.org/press/linux-foundation-is-launching-the-x402-foundation-and-welcoming-the-contribution-of-the-x402-protocol). That matters because the web doesn’t particularly need another proprietary payment system controlled by a single company.

The Web Is Getting Some New Customers

There is another reason I find this interesting. For years, websites have largely been designed around human visitors. We write articles, people find them through search engines or social media, and hopefully they visit our websites. Those visitors might see advertisements, subscribe to a newsletter or pay for content.

AI is changing that relationship. Increasingly, people ask an AI assistant a question and receive an answer without visiting the websites where the underlying information originated. And AI agents are taking this further. They can search, compare sources, retrieve documents and process information without a human opening a browser at all.

For publishers, that creates a rather strange situation. Their information might become more useful than ever, while the number of people visiting their websites declines. A small specialist publication could produce an excellent article that is consulted by dozens of AI agents. Yet the publisher might receive no traffic, no advertising revenue and no indication that the article was used. That doesn’t sound like a particularly sustainable future for independent journalism.

But what if AI agents became paying customers? A specialist publication might offer its articles free to human readers while charging commercial AI services for automated access. Or it could offer a paid feed containing structured information specifically intended for machines. The website would no longer depend entirely on persuading humans to visit it. It could also earn money when software uses its work.

x402 Is Only One Piece of the Puzzle

Of course, the problem is more complicated than simply asking an AI agent to pay two cents. And x402 isn’t the only initiative trying to address it. Cloudflare, for example, has been developing several approaches to monetizing content used by AI. One is Pay Per Crawl (https://blog.cloudflare.com/introducing-pay-per-crawl/), which allows website owners to charge AI crawlers for accessing their pages. Another is Pay Per Use (https://blog.cloudflare.com/pay-per-use/), which takes a different approach. Rather than charging when information is collected, it aims to charge when that information is actually used by an AI service.

That distinction is important. An AI company might collect a thousand articles but use only a handful to produce an answer. Should it pay for collecting all thousand? Or only for the information it actually uses? There are arguments for both approaches, and they might eventually coexist.

Cloudflare has also introduced a Monetization Gateway (https://blog.cloudflare.com/monetization-gateway/) that supports x402, bringing automated payments closer to the infrastructure websites already use. Some of these services are still experimental or geographically restricted, so they are not yet ready-made solutions for every European blogger.

Then there is Tollbit (https://tollbit.com/), which focuses on licensed access to publishers’ content. Its approach includes different ways of charging for different uses, such as summarizing an article or reproducing more of its contents.

Another initiative worth watching is Really Simple Licensing (https://rslstandard.org/), or RSL. I particularly like the thinking behind this one. RSL is about making licensing conditions understandable to machines. A website could specify whether its content may be used for AI training, whether commercial AI services must pay, and which conditions apply. Think of it as a way for a website to tell automated visitors what they are allowed to do with its content. RSL could describe the rules, while x402 could handle a payment.

Another interesting development is SPUR, the Standards for Publisher Usage Rights coalition (https://www.spurcoalition.org/). It brings together more than 80 publishers and media organisations, including the BBC, Financial Times, The New York Times and AFP, to develop common standards for how AI companies use journalistic content.

One of its first initiatives is the Content Telemetry Standard, which aims to make AI systems record and report how they use publishers’ information. That addresses another important question: how can publishers charge for the use of their content if they don’t even know when or how it is being used? SPUR doesn’t solve that problem overnight. AI companies would still need to adopt these standards, and publishers would need ways to verify the information they receive. But it could provide another important piece of the puzzle: transparency. After all, before you can expect someone to pay for using your work, you need to know what they’re doing with it.

These initiatives don’t all solve the same problem. Some focus on payments, others on licensing, access control or measuring usage. But together they suggest something interesting: we may be moving towards a web where machines can negotiate access to information instead of simply taking whatever they can retrieve.

What If We Stopped Selling Articles and Started Selling Knowledge?

There is another idea that I think fits beautifully into this discussion: News Atom (https://reutersinstitute.politics.ox.ac.uk/introducing-news-atom-metadata-blueprint-journalism-age-ai). Developed by journalist Sannuta Raghu, News Atom starts with a rather interesting observation. Journalism isn’t just a collection of articles. Every article contains smaller pieces of information: facts, statements, figures, events, sources and context.

Those pieces of information have value of their own. Imagine a journalist writing about a new data center in Europe. The article mentions the investment, the planned power capacity, the location, the companies involved and the expected opening date. Now imagine an AI agent that only needs to know the power capacity of that data center. Why should it retrieve and process an entire article when it could obtain that particular piece of information directly?

More importantly, why shouldn’t the journalist or publisher who researched and verified that information receive something in return? News Atom proposes a way of making such information machine-readable while preserving the details that make journalism valuable in the first place. Where did the information come from? Who verified it? When was it published? Has it been corrected? What licensing conditions apply?

That last point is particularly interesting. Combine the thinking behind News Atom with machine-readable licensing and a payment protocol such as x402, and something new becomes possible. Not just for publishers, large and small, but also for the many other organisations that produce valuable content, from research institutes and industry bodies to all kinds of specialist organisations. An AI agent could potentially find a specific piece of verified information, check where it came from, understand the conditions for using it, pay for access and retrieve it. All without human intervention. Of course, News Atom itself is not a payment system, and this combination is still more of a possible future architecture than an established business model.

There is also an important distinction between facts and the journalism that produces them. A bare fact may not be protected by copyright, but the reporting, verification, context and structured data services surrounding it can still have commercial value. And that is precisely why I find this idea so interesting. Perhaps journalism’s future isn’t just about finding new ways to sell articles. Perhaps it is also about recognising that the knowledge journalists produce can have value independently of the articles in which it originally appeared.

For someone like me, who spends quite a bit of time experimenting with AI, journalism and automated news websites, that opens up some fascinating possibilities.

And What If Publishers Joined Forces?

There is another experiment worth mentioning, this time from Finland. Sitra, the Finnish Innovation Fund (https://staging.sitra.fi/en/articles/ai-can-create-new-business-opportunities-for-media-a-pilot-project-tests-the-marketplace-concept/), has been exploring something that sounds remarkably logical: a marketplace where news organisations can license their content to AI developers. And this isn’t just a theoretical exercise. Several Finnish media organisations, including Sanoma, Alma Media and public broadcaster Yle, are participating in a pilot that tests how journalistic content can be licensed, what AI developers actually need, and how much they might be willing to pay.

I find this particularly interesting because it addresses a problem that protocols such as x402 cannot solve on their own. A small publisher might have valuable information, but how does an AI company discover it? How does it know what rights are available? And how does the publisher find potential customers without employing a team of lawyers and salespeople? A shared marketplace could help solve those problems.

There is also a fascinating observation in Sitra’s research. AI developers training models often care more about having large quantities of usable information than about obtaining one particularly well-written article. That is not necessarily encouraging for an individual journalist or a small publisher. But it suggests that publishers might have more opportunities if they work together. Imagine hundreds of independent European publications making their journalism available through a shared marketplace. Each would retain control over its content and licensing conditions, while AI developers could discover and purchase information from many different sources. Combine that with ideas such as News Atom, machine-readable licensing and automatic payments through x402, and you can begin to imagine a rather different information economy.

Of course, we aren’t there yet. Sitra’s initiative is still testing the market, and nobody has demonstrated that this approach will generate substantial revenue for small publishers. But I like the direction. Instead of every publisher individually trying to negotiate with a handful of enormous AI companies, perhaps we could create an infrastructure where even the smallest independent publication can participate.

That sounds like a much more interesting future for journalism than simply putting another paywall in front of another article.

What About All the Content AI Has Already Consumed?

There is, however, an uncomfortable issue. Much of the content used to train today’s AI models has already been collected. And in many cases, the people who produced it weren’t asked for permission or offered compensation. I don’t think x402 solves that problem. A payment protocol cannot retroactively compensate millions of writers for material used in previous training runs. Nor can it decide whether a particular use of copyrighted material was legal. Those questions involve copyright law, licensing, transparency and, probably, a fair number of court cases.

But x402 and the other initiatives could help establish a different arrangement for the future. And I think that matters. I am not against AI companies making money. I use their products myself, and some of the technology is genuinely remarkable. But journalism costs money. Research costs money. Maintaining specialist knowledge costs money. Even writing a personal blog requires time, effort and usually a few hosting bills.

If AI services increasingly depend on information created by others, there should at least be practical ways for those creators to receive something in return. Not necessarily for every sentence an AI system reads. And not necessarily through one universal payment model. But the current situation, in which much of the economic value accumulates around the companies operating AI systems, deserves to be questioned.

Could This Work for a Tiny Independent Website?

This is where my curiosity really kicks in. Imagine a small independent technology publication that produces original reporting about European data centers, industrial automation or cybersecurity. It doesn’t have millions of readers. It doesn’t employ a sales team. And it certainly doesn’t have the negotiating power of The New York Times or the Financial Times. But it does produce information that might be valuable to AI services.

With an open payment protocol, the publisher could theoretically make that information available to automated customers without negotiating individual contracts. An AI agent requests an article. It receives a price. It pays. It gets access. That sounds almost too simple.

And perhaps it is. There are still plenty of unanswered questions. How does a website distinguish a legitimate paying AI agent from a crawler that ignores the rules? How do publishers know what their information is worth? Who pays the transaction fees? How do they prevent someone from buying an article once and redistributing it indefinitely? And perhaps most importantly: will AI companies actually be willing to pay? A technically elegant payment system is not much use if nobody wants to use it.

The economics are also uncertain. Ten thousand paid requests at one cent each would generate €100 before costs. That might be a nice contribution towards hosting expenses, but it isn’t going to finance a newsroom. A specialist publisher would need either substantial demand, higher prices, or other sources of revenue.

Still, I wouldn’t dismiss the idea just because the first numbers might be small. The important development is that a small publisher could potentially participate in a market that previously required direct commercial agreements with large technology companies.

Please Don’t Turn the Entire Web Into a Paywall

There is one thing I would hate to see happen. Imagine an internet where every article, blog post, tutorial and personal website demands a payment before anyone can read it. That would be awful. One of the things I love about the independent web is that people publish information simply because they want to share it. Someone writes about an obscure music genre. Someone else documents a strange Linux experiment. Another person explains how they built a little tool that nobody else thought of. There is no business plan. No marketing funnel. No expectation that every page must generate revenue.

I certainly don’t want to lose that. But I don’t think we have to choose between an open web and a web where creators can get paid. A personal blog could remain completely free for human visitors. The owner could allow non-commercial crawlers to access it, while requiring payment from commercial AI services that use the material in their products. Or the owner could decide not to charge anyone.

The important thing is that the website owner gets to decide.

That fits rather nicely with the whole idea of the IndieWeb. Own your website. Own your content. Choose your tools. Decide how your work may be used. And, perhaps, decide whether someone should pay for using it commercially.

Maybe This Is a New Opportunity for Journalism

I have been experimenting with AI and journalism for quite some time. One of my projects is an automated technology news website that collects information from sources in more than 23 languages, using two local language models running on a server in my own workroom. Experiments like this have made me increasingly aware of how much modern information systems depend on the work of other people. Without those sources, there is very little for the software to work with.

That is one reason I find this whole development so fascinating. I can imagine independent publishers offering machine-readable news feeds, specialist databases, research archives or individual articles through systems like x402. I can also imagine AI agents with small budgets that decide which information is worth buying. Some information might be free. Some might cost a fraction of a cent. Some highly specialised material might cost considerably more. And perhaps we could finally develop business models that don’t depend entirely on advertising, subscriptions or the goodwill of readers.

Will that happen? I have no idea. x402 might become an important part of the web’s infrastructure. Or another standard might take its place. Perhaps licensing systems and payment services will develop in completely different directions.

But something is clearly changing. For years, the conversation about AI and publishers has largely revolved around whether AI companies should be allowed to collect and use content. Now we are beginning to discuss how they might pay for it. And thanks to initiatives such as News Atom and Sitra’s marketplace experiment, we are also beginning to think differently about what exactly journalism could sell. Not just articles, but verified information, context and knowledge.

That is a much more interesting conversation. And for someone who loves both AI and the independent web, it is one I intend to follow closely. Because I don’t want a future without AI. But I certainly don’t want a future where AI has consumed the very web that made it possible.