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There is an ongoing struggle between the tech world of AI training and the cultural world of content creation. It has led to lots of litigation but also an increasing number of licensing agreements, the obvious market solution. Litigation has helped convince AI companies to share some of the wealth by pursuing licensing. Yet the AI world continues to try to find ways to avoid the basic step of seeking permission from rightsholders for using their valuable content to create their products.
Anyone who has seen the striking graphic “Who is Suing Whom in AI”, created by the design website Information is Beautiful, will be struck by the enormity and breadth of the issue which is so cleverly displayed, with the big AI developers such as Perplexity, Anthropic, Meta, Google, Open AI, Midjourney, Cohere and others at the centre with the creators (every content entity from Conde Nast, Getty Images, Universal Music Group, CNN, Disney and Thomson Reuters to Elsevier, Dow Jones, New York Times and others) ranged around the periphery, a stunning visual encompassing more than 100 lawsuits in the United States. That graphic was up-to-date as of June 26 of this year. Since then, at least one more major lawsuit has been filed, by a group of textbook authors against Meta. The graphic does not include the first such case in Canada where a group of media organizations (Canadian Press, Torstar, The Globe and Mail, Postmedia and CBC/Radio-Canada) is suing OpenAI, or the Getty Images case in the UK, or indeed any cases outside the US. From this graphic, it would seem that to resolve the issue of how copyrighted content is going to be used in AI development and training, litigation is the inevitable route. But is it?
As far as I am aware, Information is Beautiful has not created a similar graphic to display the range of licensing deals that have taken place, many of them between some of the same actors that appear on the litigation chart. If they did it would be similar, but encompassing even more licensing agreements than lawsuits. Licensing deals are being struck so frequently it is just as hard to keep up with them as it is to track all the litigation underway. The University of Glasgow’s CREATe Centre says it has documented 274 licensing deals and has a chart that tracks 109 of them. Whatever the number, it is a lot and it is growing. That is not to say that the AI industry has finally accepted the need to pay for the content they are using to create their products, just as they pay for software engineers or data processing capacity. This is where the link between litigation and licensing becomes interesting.
In a perfect world, AI developers would obtain their inputs through the market on the basis of permission, which would encompass both compensation (in most cases) plus transparency or accountability, i.e. documenting what content was used. But we don’t live in a perfect world, which is why we have the rule of law and courts to enforce those laws. In some cases, AI platforms did begin negotiations with rightsholders but when it was not possible to reach an agreement, the AI industry switched tactics and took the content anyway, arguing it was legal to do so for a variety of reasons. This is precisely the scenario that led to the New York Times suing OpenAI. These cases are even more egregious because there was initially a tacit acknowledgement by the user that the content had value. Then, when the price or conditions did not suit the potential licencee, suddenly it was okay to take the content anyway under the guise of fair use. Various arguments have been deployed ranging from the claim that no copying actually occurs, to the dubious assertion that what is copied is data not content, to the invocation of the US “transformation” doctrine.
On the issue of copying, a study by the Atlantic (AI’s Memorization Crisis: Large language models don’t “learn”—they copy. And that could change everything for the tech industry) convincingly demonstrated the uncomfortable truth that LLMs can reproduce long excerpts from books they have been trained on. The inputs are not just ones and zeros, they are content— someone else’s content that was taken without permission. Whether the use was fair according to US fair use interpretations is still an open question. US courts and other countries are trying to come to grips with this issue. In countries such as Canada or Australia, where there is no statutory copyright exception for Text and Data Mining (TDM) that would permit permissionless AI training on content, the AI industry has been floating various workaround proposals. The “incentives” would include (in Australia) establishing a government-managed fund to compensate rightsholders according to some sort of formula, plus investments in AI data centres. What is missing from proposals such as this is the concept of permission from those who actually own the content, or even discussion of the proposal with them. As Prof. Rod Sims, former Chair of Australian Competition and Consumer Commission, put it in a recent opinion piece in Canada’s National Post, “what other sector refuses to negotiate with suppliers and instead goes to government to bypass such a step?”
Let me use a food industry analogy to make the point even more clearly. When you run a restaurant you have labour costs, rent, taxes, etc. and the cost of ingredients to consider. You don’t get to raid the farmer’s field to obtain your inputs for free, just because you are able to root out crops without the farmer being able to stop you or even know it is happening. Setting up a fund to “compensate” farmers for their stolen crops, on terms set by the government rather than the market, doesn’t even begin to make this right. Legalization of this theft would remove any possibility of litigation or legal protection, for the farmer—or for content owners. Litigation, while protracted, costly and potentially leading to uncertain outcomes, is nonetheless the stick that is needed to facilitate licensing.
The obvious route for the AI industry to take is to license the content they want to use. That may not seem as “efficient” as just taking it for free but with the threat of litigation hanging over the proceedings, licensing suddenly becomes the more efficient alternative. It is also win/win for both AI developers and the content industries. And, it is simply the “right thing to do”.
© Hugh Stephens, 2026. All Rights Reserved
I am pleased to note that this blog was recognized by Feedspot as being among the “40 Best Copyright Blogs to Follow in 2026”. In fact, we hit the middle of the pack at No. 20. I am honoured to be included in such distinguished company.