Many of the biggest online platforms have long argued they aren’t publishers of the content their users upload, merely hosts. The likes of Google, YouTube, Meta and TikTok claim they simply store and display information created by others, so should not be seen as legally responsible for it.

But a recent ruling by the Court of Justice of the European Union (CJEU) makes that argument harder to maintain – at least in cases where the platform’s algorithm is deciding which content gets pushed, ranked or amplified.

The case relates to Coyote, a French driving assistance company which stores traffic reports submitted by users and redistributes them to the wider network. The question being tested was whether Coyote merely hosts its users’ information, or also controls (and benefits from) how that information is circulated.

The significance of this case – brought by the Conseil d’État, France’s highest administrative court and the government’s chief legal adviser – extends far beyond driving apps. It clarifies when a provider may lose the legal right of “intermediary immunity” for the user information it stores and circulates.

Traditional book, newspaper and magazine publishers have always been held responsible for their content. A defamatory article generally exposes the publisher to liability, since it chose to make the material available.

In contrast, technology companies have been treated as neutral intermediaries – closer to a corner shop stocking newspapers than a publisher producing content, with clear limits to their liability.

For these companies, the Coyote judgment is significant because it sharpens the boundary between neutral hosting and algorithmic control. A platform that merely stores user content is likely to remain legally protected. But one that decides, in its own interest, how that content is disseminated may no longer be.

One important caveat: as this case is from the CJEU, it does not apply to users in the UK and elsewhere outside the EU. However, it will affect British and international companies that have a significant audience within the EU. So, no technology platform can dismiss the issue simply because it is headquartered outside Europe.

What the court decided

A hosting provider is protected under Article 6 of the EU Digital Services Act (DSA) if it lacks actual knowledge of illegal content – and then acts expeditiously once notified about it.

The question in the Coyote case was whether a service that stores and retransmits user-generated information still counts as a neutral host when its algorithm decides how that information circulates.

The court concluded that a platform can have no knowledge of a specific message’s content, yet can still have legal responsibility for its dissemination. Where a platform’s algorithm determines – in its own interest – the conditions, manner or priority with which information is disseminated, then the “safe harbour” of being a neutral host falls away.

This outcome was anticipated in 2019 by technology law experts Jennifer Cobbe and Jatinder Singh, who argued that algorithmic curation complicated the assumption that platforms are passive, neutral hosts.

The ruling does not doom every algorithm. Simple categorisation, such as showing all posts by one user or from the past 24 hours, remains close to traditional hosting. But ranking or promoting content in the platform’s own interest does not.

None of this makes these platforms automatically liable. Losing the exemption only opens the door to being sued – claimants must still prove the platform’s conduct actually caused harm.

Potential impacts of the Coyote ruling

1. For social media content

A social media company may remain protected when it stores user content or displays it in ways that are largely user-directed or chronological. But algorithmic feeds are different.

“For You” pages, recommendation systems, trending modules and paid amplification systems all decide what content is shown to which users and in what priority. When that decision is made in the platform’s commercial or service interest, Coyote makes it harder to describe the platform as merely hosting the content.

The same reasoning may matter for extreme or graphic material. If a platform simply stores content uploaded by a user, the hosting exemption could still apply. But where a recommender system repeatedly pushes that material to users because it drives engagement, Coyote gives claimants a stronger argument that the platform was not passive.

The content does not always need to be unlawful in itself. But claimants would still need to demonstrate both liability and harm.

2. For paid search

When a platform sells priority, decides how a sponsored result is displayed, and presents that result to users because doing so serves its own commercial interest, it becomes harder to argue that the platform is merely storing or neutrally indexing third-party information.

This matters because online advertising has become a major route for fraud and consumer harm. Following the Coyote ruling, claimants harmed by paid advertisements may have a stronger argument that the harm arose at least in part because the provider sold, ranked and prioritised that information.

But they would still need to prove the advert was unlawful, that the platform’s role fell outside immunity, that the promotion caused their financial loss or other harm.

3. In libel cases

A newspaper that publishes a defamatory allegation can be liable because it has chosen to publish it. In contrast, a hosting platform where a user makes and uploads such an allegation has not been treated in this way.

However, if the platform’s recommender system promotes that post or pushes it into feeds, the CJEU ruling gives claimants a clearer argument that the platform should be held responsible for increasing the audience reach of the allegedly defamatory content.

Changing the terrain

The Coyote judgment does not abolish hosting immunity – but it does change the terrain. It makes clear that the “safe harbour” argument depends not only on where information came from, but what the provider does with it.

The more a platform determines dissemination in its own interest, the harder it is to maintain that it is merely hosting, according to this groundbreaking ruling. And this, remember, holds for international as well as European technology companies.

Iain Nash does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Young people in low- and middle-income countries appear generally more optimistic about how AI can enhance their work prospects and social lives than their western peers, according to our new survey of people in ten countries in Africa and South Asia.

While debates in higher-income countries often focus on job losses, misinformation and mental health risks, many of the 1,864 young adults we surveyed (all aged 18-35) placed more emphasis on the opportunities artificial intelligence tools present for them. In particular, nearly 80% of our participants expect AI to deliver improved education and learning for them.

Until now, there has been little evidence of how young people in middle- and low-income countries use AI, and how they feel about this technology – for good and bad. The 2026 youth AI survey – run by global charity Restless Development and academics from the universities of Makerere in Uganda and Cambridge in the UK – seeks to correct this imbalance.

It comes immediately after the UN Independent International Scientific Panel released its preliminary report on AI. Commenting on the need for more science about AI’s use around the world, the UN’s secretary general, António Guterres, admitted: “The world cannot govern what it cannot understand.”

Our responses were collected in Angola, Kenya, India, Nepal, Nigeria, Sierra Leone, Tanzania, Uganda, Zambia and Zimbabwe – with the UK included for comparison. Two-thirds of respondents said they use AI every day, with Meta AI the most common tool, followed by Claude.

The extent of AI use is striking given the obstacles many young people encounter. In every African country surveyed except Nigeria, more than half said high data costs limit their access. Weak internet connections and a lack of digital skills were also raised, with many relying on basic-feature phones. By contrast, UK participants said ethical concerns played a stronger role in limiting their AI usage.

AI work opportunities

While few respondents (14%) said they earn income directly from AI tools yet, half expected AI to improve their employment and economic opportunities within the next five years.

Currently, AI’s main work-related uses among our respondents are learning new skills and job searches. In almost every lower-income country, young people held broadly positive attitudes about AI’s future role in their working lives – although many (32%-50% per country) expressed concerned about its impact on their future income.

How will your income be affected by AI?

Among those from lower-income countries, more than seven in ten thought AI would bring them better education and learning opportunities in the near future. A 28-year-old self-employed man in Nepal commented: “AI has the potential to empower young people in developing countries like Nepal by improving access to knowledge, digital skills, and economic opportunities.”

Our sentiment analysis found respondents to be most positive in Tanzania and Zambia, with young women there even more upbeat than young men. In the UK, young men diverged from their international peers in feeling more negative towards AI and work.

Biggest AI concerns

Some respondents described AI tools as “Americanised”, built around western norms and unable to grasp local nuance. They worried this could lead to their languages being further marginalised.

“These AI tools are fed major languages that are spoken worldwide, unlike Shona,” said a 25-year-old student in urban Zimbabwe. Shona is spoken by around 14 million people mainly in southern Africa, but is severely underserved by AI and other technology tools.

A Ugandan respondent was similarly critical about provision for her native language, Luganda – the country’s most widely spoken Indigenous language. “AI barely knows the spellings, grammar and how to interpret sentences. It’s not even familiar with our culture at all, which increases mistakes when we get to things concerning our own world.”

What most concerns you about AI?

Despite the widespread belief that AI would improve education and training, some held strong reservations. A 21-year-old female student in rural Uganda said: “Slowly by slowly, AI is making us dumb. We can no longer think for ourselves.”

The more often these young people use AI, the more worried they appear to be about it. The biggest concerns highlighted by daily users were around who owns their data and the power of AI companies.

Concern about AI’s environmental impact and the welfare of AI workers followed a similar pattern of higher usage co-existing with higher concern. Mental health is the only domain where non-users worry slightly more than daily users.

Gender differences

Significantly more young men (38%) than women (23%) reported having “a lot” of AI knowledge. This probably reflects a combination of diverging gender self-confidence, which tends to emerge from school age, and different levels of AI use. “AI is scary but helpful too,” said one female homemaker in urban Nepal.

Almost twice as many men than women use AI to search for work (33% vs. 17%). This difference is probably rooted in gendered access to data and technology, as well as differing social roles – such as the tendency for women to do more care work.

What have you used AI for in the past month?

In most of the ten lower-income countries surveyed, young women expressed greater concern about AI than men – with their biggest concern being over who owns their data. In the UK, 68% of young women were strongly concerned about data ownership, compared with 29% of men.

What young people want from AI

Many of our participants stressed the need for cheaper data and internet connections that work well. But they also want AI tools that fit their lives and culture – and speak their language. Until then, most recognise that talk of AI “leapfrogging” traditional stages of economic and social development is just that – talk.

Some efforts to make AI more inclusive and relevant for lower-income countries are underway. These include initiatives for “frugal AI”, as well as regional collaborations such as Masakhane for African languages and QuechuaBase in Peru.

How can AI be made safer?

Most of our participants framed AI safety as much more of a governance problem than a technical one. Many countries are establishing new AI policies, shaped in part by global forums such as the UN-led AI for Good summits. Overwhelmingly, our respondents agreed that governments, not tech companies, should be setting the rules when it comes to AI regulation and privacy protection.

Anna Barford receives funding from UKRI. She has previously been funded by the British Academy, Leverhulme Trust, ESRC, the Asian Development Bank, the Mastercard Foundation, and a philanthropic gift to the University from Unilever. She is a co-director of the Business Fights Poverty Institute and does consultancy work for the International Labour Organization.

Anthony Mugeere works as a Research Fellow for the Advocates Coalition for Development, Uganda's leading thinktank specialising in advocacy and analysis on climate change. He has received funding from the British Academy and the Mastercard Foundation to conduct research on climate change and young people.

Giulia Occhini does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

When I talk to my son, an engineering student, and we have a question or disagreement, he immediately turns to ChatGPT as his primary source of information and confirmation.

He is not alone in this. The use of generative AI tools has exploded across different demographic groups. For many people, these tools can be entertaining, informative and beneficial. However, they also have a dark side.

Generative AI is not formally recognised as addictive right now – the medical evidence is still being gathered. But there is a significant amount of data showing heavy use of chatbots and other systems that produce text, images and video leads to neural patterns and behaviour that are associated with addiction.

In light of Meta’s and YouTube’s recent legal defeat in a landmark social media addiction trial, I believe it’s time to ask whether a similar logic applies to generative AI – and how it could be addressed. The starting point would be to identify who carries responsibility for overuse of generative AI.

The science on this is not settled, and there are some who counsel caution when using the term addiction. They propose the use of other expressions such as “problematic use”. However, in a recent paper, our team of researchers suggest there is strong evidence to suggest that generative AI has addictive properties.

Much-discussed examples include emotional dependency on chatbot companions, compulsive engagement with them, and the loss of real-world acquaintances and friends.

A key factor here is that, as in all cases of addiction, the behaviour has negative consequences for the user which may affect both their personal and professional lives.

If we follow the argument that generative AI is a candidate for addictive behaviour, then we also need to look at responsibility. Societies tend to find ways to deal with harm by holding people or groups responsible for fixing it. Those who could be accountable include legislators, regulators, industry and health systems.

Historical examples

Historical precedents such as smoking might offer insights into how the area of generative AI addiction could evolve.

Older readers may remember when the Marlboro Man would appear before any feature movie in their local cinemas. It eventually transpired that not only was smoking addictive and bad for your health, but that tobacco companies knew this. Nevertheless, it was publicly denied.

This led to lengthy and high-profile litigation, eventually resulting in large-scale financial payouts and changes to the industry. These changes included the plain packaging of tobacco products and gruesome warning labels on them.

Gambling could be following a similar trajectory – and now social media companies may be taking their first steps into a similar process.

A key question is whether the makers of a product – be it tobacco, gambling or social media – are aware of its addictive properties. Another important factor being considered is whether certain companies may even use the allegedly addictive properties of their products for corporate advantage.

AI is not tobacco, of course, but there may be parallels to be studied.

In our research, we have identified four groups of stakeholders that are now being called upon to address the challenges linked to the possibility of addiction to generative AI.

The first is governments and regulators. These have a key role to play in highlighting the problems, setting the rules of engagement, and creating incentives for other parties to engage with the topic.

They can do this by requiring labelling, restricting advertising, applying liability law and providing research funding – along with many other mechanisms.

But the most important role in addressing potential addictive behaviour associated with generative AI would be held by big tech companies that develop and own these technologies – and stand to benefit financially from them.

These companies own and have access to user data, which would be needed to ascertain the features that support or alleviate addiction. They are also the parties that would benefit financially from addiction by increasing user numbers and engagement, the main currency of the digital age.

In addition to these two groups, academic researchers have an important role in collecting and interpreting data, and providing the evidence needed to recognise addiction and addictive features – in ways that allow for evidence-based political or legal debate.

Finally, civil society organisations such as user or patient groups can help by providing support, advocating for members’ interests, and establishing early-warning structures.

The point is that none of these interested parties can address the problem on their own. They need to collaborate.

Someone else’s problem

A key problem at the moment is the lack of structured debate about responsibilities – everybody assumes it is someone else’s problem. But there is ample precedent showing how greater engagement from those involved with the issue may be achieved.

With tobacco, the World Health Organization (WHO) formed the Framework Convention on Tobacco Control – a treaty-based mechanism that brought together governments, public health bodies, researchers and civil society to evaluate evidence and draw up common rules. The International AI Safety Report shows comparable international consensus-building activities are already happening in other aspects of AI.

Some responsibility also falls on the users of AI, who should try to avoid or control their own potentially harmful behaviour. But appeals to individual moderation or mindfulness have been shown with other addictions to be insufficient.

While the harms associated with smoking or alcohol misuse are well known, society still relies on age limits, packaging rules and advertising restrictions. Generative AI is being integrated into the everyday fabric of our society. The choices we now make will determine what acceptable use looks like for years to come.

Bernd Stahl does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.