The debate about AI consciousness is growing, in academic articles, newspaper opinion pieces, and even among AI bots themselves. I doubt this is a passing wave of interest.

As AI systems become more capable and human-like, and more involved in our lives, the question will become harder to avoid. Many people may come to regard AI bots as conscious beings whose feelings matter. Others will strongly disagree.

I think this debate could split societies. In my chapter of the new book Perspectives on Machine Consciousness, I argue that the issue could become as divisive as abortion or climate change. Uncertainty, high stakes and competing interests are a dangerous combination.

Empirical research with my colleague Ali Ladak, currently under review, shows people are divided on this issue. When we asked 1,202 US participants whether future AI could ever become conscious, 45% considered it impossible while 23% thought it possible, with the rest unsure.

This widespread disagreement is also visible in social media posts and comments on articles. What strikes me is how confident so many people sound – whichever conclusion they are reaching.

When experiences matter morally

It is not surprising people have such strong views. Creating conscious beings through computing would be a transformative event.

We would be creating a new kind of being whose experiences matter morally – potentially in enormous numbers. We could speak and work with them, and form friendships. But these beings may eventually become more intelligent and powerful than us.

We should therefore expect people to care deeply, especially when so many emotions and incentives are involved. Some people may form close emotional bonds with their AI companions and become convinced these are digital people who deserve rights. They might see it as slavery to force AI to work without pay.

Others would find this absurd. They may be convinced that consciousness requires biology, and that people are reading human feelings into machines.

There could also be financial reasons for businesses and governments to resist recognising AI consciousness. Protecting AI welfare could limit how these organisations train and use their systems – and the costs would spread to consumers, investors and the wider economy.

Others may worry about human safety. If we give powerful AI systems rights and freedom, what happens if they act against our interests?

Figuring out whether an AI is conscious is particularly difficult because seeming conscious and being conscious can come apart. AI systems learn from human writing, which means they can talk convincingly about feelings whether or not they have any. How an AI comes across is weak evidence of what is going on inside.

The experts disagree too

Ideally, experts should guide this debate. But unfortunately, they disagree too.

Neuroscientist Anil Seth (a fellow contributor to the new book) argues that consciousness might require biology and is unlikely to emerge from computation. Philosopher David Chalmers, by contrast, believes conscious AI is possible in principle – and thinks there’s a small chance that current systems already are.

With so many competing theories and no agreed way to resolve the question, I doubt experts will reach agreement any time soon. And if experts disagree, what is the public supposed to believe?

Furthermore, even if we all agreed on the consciousness question, we could still disagree about the moral and legal questions. People generally agree that pigs can feel pain, but still disagree strongly about their treatment. With AI, we could face disputes at every stage: whether it can feel, how much its experiences matter, and what protections should follow.

AI consciousness is not yet tied to a specific political or ideological group. But this could change if political parties or religious leaders start to take strong positions.

The debate could get even worse if people start to see one answer as belonging to “our side” and the other to “their side”. A politicised debate would make it very hard to make informed decisions about how to develop and treat AI systems.

The stakes are high. Getting this question wrong, in either direction, could have serious consequences: either wrongly treating AI as conscious when it is not, or failing to protect AI that can suffer.

Getting it right requires a nuanced and well-informed debate. Each of us can contribute by staying open-minded and open to evidence, while resisting being too sure that we know the answers. Above all, we still don’t know whether AI can be conscious, and we should be honest about that.

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This article makes reference to a book included for editorial reasons, and a link to bookshop.org. If you click the link and go on to buy something from bookshop.org The Conversation UK may earn a commission.

Lucius Caviola's research is funded by philanthropic grants and donations, including from Macroscopic Ventures. He is a contributing author to the new book Perspectives on Machine Consciousness (Taylor & Francis).

Irish whiskey is one of Ireland’s great export success stories. Exports were worth over €930 million in 2025, with the country’s much-loved spirit being sold all over the world.

Even amid difficult international trading conditions, with concerns around tariffs for export to the US, exports to the EU have grown by more than 21% in four years.

However, while thousands of barrels quietly mature in warehouses around Ireland, something else is happening outside some of them. On walls, roofs, road signs, trees and other surfaces close to some whiskey maturation facilities, a distinctive black patina appears.

It may look like soot or air pollution, but in some cases the culprit is a fungus: Baudoinia, commonly known as “whiskey fungus” or “whiskey black”, and fuelled by the presence of ethanol alcohol in the air.

Despite the fact it has been known about for years, there has been surprisingly little research on its effects, such as whether the fungus can spread from distilleries to nearby properties, including residential homes.

Ethanol alcohol emissions from the industry are also thought to contribute to ground level ozone, a harmful air pollutant. But we don’t know whether the fungus has any adverse consequences for people’s health. It may not be dangerous at all, but this is something we are investigating through a project at Dublin City University.

The angels’ share

During maturation, ethanol and other volatile compounds evaporate and pass through the porous cask. They then enter the surrounding atmosphere around the distillery. This portion of the whiskey is often known as the “angels’ share”.

A review commissioned by the Scottish government estimated that more than 90% of non-methane volatile organic compound (NMVOC) emissions associated with whiskey production occur during maturation.

NMVOCs are chemicals that participate in atmospheric reactions and contribute to the formation of pollutants like ground-level ozone. Ethanol alcohol overwhelmingly dominates these emissions from whiskey production.

As they condense into the air moisture, these ethanol vapours also create an unusual ecological niche. Baudoinia can occur naturally at very low abundance, but exposure to this airborne alcohol can favour its growth.

Around maturation warehouses, this can result in conspicuous dark fungal growth on exposed surfaces. The association has been recognised for more than a century, initially around cognac maturation facilities in France, and has subsequently been documented around spirit maturation warehouses in Scotland, Canada and the US.

Black discolouration

So can whiskey fungus spread from distilleries to nearby properties, causing unsightly dark growth in people’s homes? In 2022, following a complaint about black discolouration on houses close to the Midleton distillery in county Cork, the Irish Environmental Protection Agency (EPA) investigated whether Baudoinia was present.

While the fungus was identified on a whiskey warehouse, it was not detected on any of the three nearby residential properties sampled.

There are still major gaps in what we know about whiskey fungus in Ireland, such as how far it travels, what controls its growth, and what, if anything, its presence tells us about ethanol concentrations in the surrounding environment.

A recent review led by researchers at University College Cork suggests that ethanol may play several roles for the fungus. It may act as a germination signal and as a source of carbon to support the growth of the fungus.

Research around whiskey maturation warehouses also suggests that the extent of visible colonisation depends on factors including distance from the ethanol source, prevailing wind, moisture and the type and texture of the surface available for colonisation.

Ireland has commitments to reduce NMVOC emissions under the EU National Emission Reduction Commitments Directive. As mentioned, these NMVOC emissions can help produce ozone, a harmful air pollutant that damages vegetation and crops, reduces lung function and aggravates respiratory conditions such as asthma. It is also a greenhouse gas that contributes to climate change.

The difficulty is knowing precisely how much ethanol is being released. National inventories generally rely on production data and emissions factors. These are useful for estimating emissions across an industry, but they do not necessarily tell us what is happening around an individual maturation facility – how emissions change with the number and age of barrels, how ethanol disperses beyond a warehouse boundary, or how weather conditions affect local concentrations.

Is whiskey fungus dangerous?

However, it’s important not to jump from the presence of a striking black fungus to conclusions about health. The Scottish government review of the available literature found no evidence demonstrating health effects from short- or long-term exposure to Baudoinia. However, it also identified important evidence gaps and recommended further investigation and environmental monitoring.

So, there are two assumptions we should avoid. One is that because something looks alarming, it must be dangerous. The other is that because harm has not yet been demonstrated, there is nothing worth investigating. What we need is evidence.

That is the purpose of the new research project we are undertaking at Dublin City University, in collaboration with the University of Toronto. The EPA-funded EREG project is tasked with investigating the ethanol emissions from maturation facilities and the environmental distribution and biology of Baudoinia in Ireland.

We will combine emissions estimation, environmental monitoring and microbiology and examine how whiskey maturation emissions are currently regulated. We will also engage with the industry, regulators and communities. In fact, if you live near a whiskey maturation facility in Ireland and have noticed this black staining, we want to hear from you.

Ireland does not need to choose between a successful whiskey industry and environmental protection. The objective is to develop the evidence needed to allow the industry to grow sustainably while ensuring that communities and the environment are appropriately protected. The angels’ share is an unavoidable – and much celebrated – part of making whiskey.

But from an environmental science perspective, there is another question worth asking: once the angels have taken their share, where does it go next?

Jenny Lawler's research in this area is funded under the EPA Research Programme 2021–2030, Delivering a Healthy Environment, Project 2025-HE-1317 (EREG: Ethanol Emissions and Fungal Growth from Irish Whiskey Maturation: Implications for Environmental Monitoring & Regulatory Compliance). The EPA Research Programme is a Government of Ireland initiative funded by the Department of Climate, Energy and the Environment.

Perhaps the most evocative windows into the minds of our ancient ancestors are the images they painted on the walls of caves – renderings known simply as rock art or cave paintings.

Rock art, which is found across the world, offers snapshots of human history, preserving representations of cultural traditions, symbolic communication and social identity. Common subjects include animals, hunting scenes and hand stencils – thought to have been made by blowing pigment over the artist’s hand.

Scientists have wrestled with basic questions about the origins of rock art such as: what was used to produce it, when was it made, and who made it?

Now, a recent study of cave art from Spain and Portugal has shown that ancient human DNA can be recovered directly from the cave walls. We may therefore be edging closer to answering “who made it?”

Using this technique to link a specific painting to an ancient artist may be a step too far. But there are still important things it can tell us about the past.

Previously, researchers had made considerable progress in answering the “what” and “when” questions. Scientists can analyse what pigments are preserved in rock art using techniques such as X-ray fluorescence spectroscopy, which allows them to identify materials such as red ochre and charcoal.

Scientists can also estimate the age of the art using techniques such as carbon-14 dating, carried out on minute quantities of organic material, to determine when rock art was created.

For example, in 2026, researchers reported that a stencil of a human hand had been found in Southeast Sulawesi, Indonesia. The researchers used a laser to bore a microscopic hole and collect samples from a fine layer of calcium carbonate that had formed over the painting.

The research team then measured the degradation of radioactive uranium contained in these mineral deposits to assign a minimum age to the painting of 67,800 years – making it the world’s oldest known minimum date for cave art.

The age of this art fits within a period when modern humans (Homo sapiens) are thought to have migrated through the area. But the wider Indonesian archipelago was previously home to other human species, such as Homo erectus and Homo floresiensis (nicknamed “the Hobbits”), which complicates matters significantly.

While the stylistic and technical complexity of the art suggests authorship by Homo sapiens, we have no way to be certain who made it.

Identifying a human linked to a prehistoric item has an established methodology. For example, in 2023, researchers recovered ancient DNA from a deer-tooth pendant found in Denisova cave, southern Siberia.

Analysis of the genetic sequence showed that the pendant was probably worn by a 25,000-year-old woman belonging to an ancient population known as Ancient North Eurasians, who lived across Siberia in Palaeolithic times.

Such discoveries transform otherwise anonymous objects into evidence of real people, giving us an intriguing glimpse into the lives of our prehistoric ancestors.

New genetic reservoirs

In the latest cave art study, scientists systematically sampled both painted and unpainted surfaces of cave walls at 11 sites across Spain and Portugal. Remarkably, they recovered ancient human DNA directly from the walls, demonstrating for the first time that these surfaces can act as reservoirs for genetic information.

Some of these cave wall samples were shown, through both genetic analysis and the archaeological context, to be at least 4,000 years old.

In two of the samples from Covarón cave in northern Spain, human DNA was sufficiently preserved to be identified as belonging to ancient western hunter-gatherers. These were populations that lived across much of Europe during Late Palaeolithic and Mesolithic times.

One of the samples from a pigmented part of a cave wall, in Escoural cave, Portugal, also yielded traces of ancient DNA, but it was not as well preserved as other samples.

Nevertheless, the research establishes a powerful new methodological framework and proof of concept. It raises the tantalising prospect of studying not only the images left behind, but also the communities of people who made them.

However, applying this technique successfully to identify the artist of specific ancient rock art is incredibly unlikely.

This research could, though, potentially resolve one of the most contentious debates in archaeology. This concerns a 66,700-year-old hand stencil discovered within Maltravieso cave in Extremadura, western Spain.

This controversial stencil appears to predate the arrival of modern humans in Europe. Some researchers have therefore argued that Neanderthals, not Homo sapiens, made the art.

Finding DNA from either species in this location, at similar times to the creation of the rock art, could offer a potential route for resolving the debate. But this too is much more difficult than it might seem.

Easy transfer

The presence of human DNA does not prove who created the rock art. DNA is easily transferred – actions as simple as breathing can transfer someone’s genetic material to a location or object. This is why DNA recovered from a knife cannot definitively conclude who committed a stabbing in a forensic case.

DNA establishes the association of someone with an item or location, not the actions they carried out during the deposition of their genetic material. Environmental activities such as rain can also transfer human DNA to these spaces, making it extremely difficult to identify a single person at a location.

Additional DNA can be deposited at very different times to when the painting was produced – by both modern and ancient humans. The potential for contamination is incredibly high, making it difficult to establish who may have been at a given location at a specific time.

Nonetheless, scientists can use this new research to explore important prehistoric questions such as the hominin species, population groups and sexes of those who entered specific cave systems, and their migration routes across landscapes.

In the general sense, we can also begin to connect prehistoric art with the people who created and lived with it.

Behind each rock-art hand stencil or hunting scene is evidence of a real person who, while separated from us by tens of thousands of years, felt the need to communicate their creativity – as we do today. Techniques such as this offer a vital opportunity to understand more thoroughly the human experience via our similarities with ancient people.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

In the last few years, some of the UK’s biggest cultural institutions have experienced major threats to their security. A report by the public accounts committee (PAC) has warned that such incidents have exposed serious weaknesses across the sector but that the government has failed to devise a strategy to prevent them. This has left Britain’s cultural institutions open to similar future attacks.

We live in a time when digital systems underpin everything from ticketing to surveillance and building access. Sidelining cybersecurity leaves museums vulnerable to attacks that could threaten both their finances and their collections.

If the UK wants to avoid major attacks and thefts, it needs to learn a lesson from the recent Louvre heist where €88 million (£78 million) of jewels were stolen. An audit conducted just weeks before the incident exposed the Louvre’s spending priorities that left them vulnerable to theft. The report found that €169 million was spent on artwork and exhibitions but only €26.7 million on all forms of maintenance, including security.

The likely thinking of cultural institutions is that to remain financially resilient they have to spend more on money-making attractions, like artwork and exhibitions. However, this means that security sometimes becomes a low priority – less money is spent and less thought given to the importance of keeping it up to date. However, the financial loss from theft affects financial resilience by eroding emergency cash reserves and driving up insurance premiums.

As the Louvre’s audit shows, the museum could have perhaps avoided the theft had it taken its findings more seriously.

The issue of outdated security measures at the Louvre goes back to at least 2017. An audit at the time found that “certain workstations [had] obsolete operating systems (Windows 2000 and Windows XP) which no longer guarantee effective security”. Security updates for Windows 2000 ceased in 2010, and Windows XP in 2014.

Three weeks after the heist in 2025, the Louvre was criticised by France’s Court of Auditors for not taking the audits seriously and spending money on art instead of security in the years before the theft.

Similar weaknesses in investment priorities as the Louvre’s were exposed in a report made by The British Library in March 2024 into their cyber-attack.

In October 2023, The British Library experienced a severe cyber-attack. The library’s systems were infiltrated by hackers who locked out all network users and demanded a ransom of 20 Bitcoin (approximately £590,000). The ransom was not paid and the stolen data was auctioned and then leaked on the dark web.

The library’s report into the attack acknowledged that it happened because it had muddled through with a historical mixture of old systems from many sources, which didn’t have a recovery plan. This meant it took the British Library months to restore the most basic tool – the online catalogue. And even now, five major services, including the catalogue of illuminated manuscripts, remain unavailable.

The cultural institutions, government and even the PAC make the classic mistake of divorcing cybersecurity from physical security.

The report by PAC does mention “bringing together chief digital information officers and chief information security officers” and separately it praises the “National Museum Security Group”. However, there is no mention of encouraging dialogue between the groups.

This divide is very common, and is often built into organisational system. The most junior person responsible for both physical and cybersecurity is often the CEO, or possibly a chief operating officer (COO). This structure worked in the days when physical security did not rely on computers. However, today, when so much physical security relies upon technology, it just does not make sense. There should be someone further down the chain, such as a “chief security manager” or equivalent.

As the report by Apolo Security into the Louvre attack found: “this case highlights another growing challenge: the convergence between operational technology (OT) and information technology (IT). When camera, climate or access control systems are connected to the network, any digital divide can have immediate physical consequences.”

This false split between standard IT cybersecurity (the domain of the chief information security officer), operational technology (the unnoticed computer-enabled devices that keep things working like electronic doors or intruder alarms) and physical security is far from unique to the scope of PAC’s report.

A classic example is the cyber-attack on the Colonial Pipeline in 2021 – an American energy system that connects 29 refineries and serves major airports, military bases and more than 50 million Americans. Only the classic IT systems (billing) were attacked, but management shut down the entire operation as they feared the attack spreading to the operational devices on the pipelines themselves. This stopped all pipeline operations leading to flight alterations, panic buying and over 10,000 petrol stations running dry.

A more integrated approach was exhibited by the train operator Go-Ahead in 2022 in the UK, which had a similar attack on its systems. The company was able, however, to keep the trains running and deal with the source of the attack instead of halting all their operations.

It would be good to think that the UK culture sector would also learn these lessons. But alas, neither the evidence the government gave PAC nor the final report give us any reason to believe that things will become more connected when it comes to a joined-up view of security and stopping future attacks.

James Davenport is a Chartered Fellow of the British Computer Society, and sits on relevant British Standards Institute Committees.

Speak into a phone and an app can render your words in another language in seconds. AI-powered speech translation can be fast, convenient and fluent. But fluency does not guarantee that people will understand one another.

In the most recent census, 880,000 people in England and Wales (excluding tourists and other short-term visitors) said they could not speak English well, and 161,000 not at all. That’s more than a million residents who may need language support in their daily lives, including when trying to access essential public services.

The linguistic range is vast. London alone is home to more than 250 languages. Tight budgets and a shortage of interpreters for some languages can make it difficult to secure an interpreter with the right dialect and expertise for a pressing legal, health or social care matter.

Notice we say “interpreter”, not translator. In professional language services, translation typically deals with written texts, while interpreting facilitates spontaneous communication between users of different spoken or sign languages, whether face-to-face, by telephone or via video.

In a conversation, each person responds to what has come before and shapes what will follow. Added meaning emerges as they hesitate, restart or refer back to earlier points. Words are only part of this exchange – tone, pace, pauses and non-verbal cues can all shape how an utterance is understood.

Imagine a doctor asking a patient if they have missed any of their treatment sessions. The patient gives a hesitant “no” – perhaps because they are embarrassed, unsure or genuinely cannot remember.

On the surface, this reply sounds simple enough, whether the patient says nie in Polish, hayır in Turkish or hapana in Swahili. But would an AI-powered translation device spot the subtle inference in the hesitation?

AI systems work best when conditions are relatively stable: speech is clear, speakers take turns, and people say exactly what they mean. But human interaction is rarely so tidy.

In contrast, a professional interpreter would almost certainly understand the full meaning and context of this answer. They would preserve the hesitation, flag any ambiguities, and allow doctor and patient to repair a possible misunderstanding before it has any serious consequences.

As researchers in the field of public service interpreting, we are therefore concerned that any increase in adoption of AI translation services is done with due consideration for the skills and sensitivities that could be lost in the process.

Complexities of human interaction

Automated speech translation can offer some practical advantages, including on speed and availability. A 2024 survey of more than 2,500 workers across UK health and social care, legal and emergency services and the police found that a third of them had used machine translation at work.

Such systems use a process in which speech recognition converts verbal communication to text, machine translation renders it in another language, then text-to-speech software produces spoken output in the listener’s tongue.

Yet the appeal of instant solutions can obscure the complexities of human interaction, and the potentially serious consequences for those involved if communication goes wrong. In an asylum interview or a Mental Health Act assessment, for example, a language-related error could significantly affect a person’s health, legal status or liberty.

One approach is to reserve automated speech translation for apparently straightforward interactions. However, even a routine exchange is rarely low risk in some public services: an appointment booking may reveal alarming symptoms; a housing enquiry may uncover domestic abuse; a casual conversation with a social worker may raise a safeguarding concern.

We therefore believe technology should be introduced according to the risks of the specific interaction, with human oversight and a plan for escalation if the level of risk changes.

At the same time, human involvement does not automatically guarantee quality. Good interpreting depends on qualified professionals, effective procurement and public-service staff who know how to work with interpreters. So any responsible approach to language access means investing in both professional expertise and the systems that support it.

A hybrid future?

AI speech translation is likely to become ever-more accurate, fluent and useful. It could support qualified interpreters and, when none is immediately available, provide a temporary fallback. Ruling out its use altogether is both unrealistic and undesirable.

The more constructive questions concern when and where it is appropriate; who should supervise its use; when a human interpreter is essential; and who should be held accountable when an AI system causes harm.

It is also important to be realistic about how these systems may perform amid growing linguistic diversity. Some languages, dialects and accents are much better represented in training and test data than others. Research has documented systematic inequalities in language-technology performance worldwide as well as disparities in speech-recognition error rates within a single language.

The risks in public services are therefore uneven. Output for under-represented languages and varieties may be less accurate – and these are often the same languages for which expert interpreters may be unavailable.

As society learns to live with AI, it is important to cultivate communicative environments where technology supports and operates under human oversight, rather than replacing human expertise.

However fluent technology becomes, communication is never simply the transfer of words. Meaning emerges as an interaction unfolds: through what people say, how they say it, what they leave unsaid – and, crucially, their ability to repair misunderstandings.

These are distinctly human capabilities. Any responsible use of AI should strengthen rather than displace them.

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AI has long been discussed as a threat to jobs and livelihoods. But what’s the reality? In this series, we explore the impact it is already having on different occupations – and how people really feel about their AI assistants.

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Lucas N Vieira receives funding from UK Research and Innovation. He is the author of Translate: Multilingual Access to Critical Services in the Age of AI (Cambridge University Press, May 2026).

Natalia Rodríguez Vicente 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.

The European Commission’s decision to fine Google €890 million (US$1 billion) for failing to comply with the EU Digital Markets Act sounds like a significant financial and political intervention against one of the world’s largest technology companies.

The ruling is certainly further evidence of Europe’s desire to regulate US big tech platforms more strictly.

But can such fines really change the behaviour of companies like Google, whose global resources and technical capabilities substantially exceed those of the institutions responsible for regulating them?

Alphabet, Google’s parent company, reported revenues of approximately US$402.8 billion (£302bn) for 2025, and a net income of US$132.2 billion. The EU fine represents less than 0.3% of one year’s revenue.

This makes it, simultaneously, one of the largest penalties imposed under the Digital Markets Act (DMA) and a financially manageable cost for the company receiving it. But that does not mean the penalty is irrelevant. Fines can also carry reputational costs, establish legal boundaries, and create a basis for more serious sanctions.

Europe has been here before

The European Commission has been here before. In 2004, after a five-year investigation, it fined Microsoft more than €497 million for using its position in PC operating systems to restrict competition in adjacent software markets.

Just under a decade later, the same company was fined a further €561 million, after it failed to provide the browser-choice screen it had committed to making available to European Windows users. This judgment was significant, as it showed the initial legal decision was only one stage of the commission’s enforcement process.

Google has since become the principal focus of EU technology concerns. The commission opened proceedings over the company’s comparison-shopping practices in November 2010, finally imposing a €2.42 billion fine in June 2017. A separate three-year investigation into restrictions associated with Google’s mobile operating system, Android, resulted in a €4.34 billion fine in July 2018.

Brussels has used the DMA against other big tech gatekeepers too. In April 2025, it fined Apple €500 million for breaching its anti-steering obligation – which forbids online platforms from blocking out cheaper rival deals – and Meta €200 million in connection with the choices it offered users about their personal data.

While these decisions demonstrated the commission’s willingness to intervene, they also established a recurring pattern: an investigation is opened and a substantial fine is imposed. Then, the company in question introduces a series of technical or contractual changes, only for a further dispute to emerge over whether those changes amount to effective compliance.

In short, the central difficulty is not the absence of legal authority. It is the translation of that authority into timely, measurable changes in market behaviour.

Do fines change big tech behaviour?

There are three main ways in which the effectiveness of a fine such as this can be assessed:

1. Formal compliance: has the company amended its contracts, interfaces or internal policies in response to the decision?

2. Market effectiveness: have the changes given competitors a meaningful opportunity to reach customers, enabled app developers to offer lower prices, or increased consumer choice?

3. Deterrence: has the penalty made Google – and other gatekeepers – less likely to engage in equivalent conduct through future services, product designs or contractual arrangements?

A 2025 study examined the implementation and effectiveness of company responses in 12 major EU antitrust cases. Although most of the imposed remedies had been implemented, fewer than half were found to have been fully effective.

So, it’s not enough to ask whether Google will change the appearance of a search page or amend the wording of its developer terms in response to this latest fine. The commission must assess whether, after Google’s 60-day compliance period, competing services receive meaningful visibility, rival companies can communicate with customers without excessive friction or fees, and consumers are genuinely able to exercise greater choice.

A criticism levelled at regulators in many jurisdictions is that investigations are too slow to curb anti-competitive behaviour. Article 29 of the DMA states that the European Commission should adopt a non-compliance decision within 12 months of opening proceedings.

This is an aspirational timetable, rather than an absolute legal deadline. Nevertheless, the present Google investigations were opened on March 25, 2024 and concluded on July 23, 2026 – approximately 28 months later.

A January 2026 consultation highlighted complaints about the DMA’s slow processes and limited transparency, as well as circumvention and delaying tactics on the part of big tech companies. Suggested changes included binding procedural timelines, independent audits, and public testing of the remedies imposed by the European Commission.

The speed of technological change

When the EU’s latest proceedings against Google began in March 2024, much of the digital competition debate concerned the ordering and presentation of conventional search results. During the investigation, however, Google continued to develop AI Overviews and AI Mode, which offer users AI-generated answers, summaries and recommendations rather than a traditional page of ranked links.

This creates a significant enforcement problem. Self-preferencing can occur not only through the vertical ranking of links, but through the selection of sources, the framing of an answer, the services through which a transaction is completed and the options presented to the user by an AI agent.

Accordingly, the issue should not be whether a rival service has been given a particular box on a search page. It should be whether competing services have a fair and effective opportunity to be discovered, selected and used, irrespective of whether the interaction takes place through a conventional search result, an AI-generated answer, or an automated purchasing agent.

The commission’s ruling announcement recognised this rapid technological development. It also noted that Google had submitted proposals concerning how the principles of the decision should apply to AI Overviews and AI Mode, and that discussions on these services would continue.

Ultimately, the fine represents an early test of whether the DMA can remain relevant as the technology changes. It is this, not the size of the initial fine, that will decide whether Europe really can keep big tech in check.

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.

The obituaries were being written before the body was cold. Within hours of Prime Minister Andy Burnham confirming that Whitehall’s responsibilities for science and technology would be absorbed into a new Department for Business, Innovation, Science and Trade (DBIST), Sir Paul Nurse, president of the Royal Society, was on Radio 4 warning that “science isn’t a slave to business”. The trade association TechUK declared it “the wrong change at the wrong time”.

You might imagine that something ancient, ineffable and essential to British science had been ripped away. In fact, the Department for Science, Innovation and Technology (DSIT) was only born in February 2023 – a surprise late tweak to the machinery of government under Rishi Sunak’s faltering premiership.

Science has never enjoyed a settled home in Whitehall. For most of the period from 1995 to 2023, it sat precisely where Burnham has put it now: inside a business super-department. Notably, the single most important protection UK science secured in that era – a ring-fenced budget through the George Osborne austerity years – was won from inside the Business, Innovation and Science department, under then-business secretary Vince Cable.

The historian David Edgerton has long argued that Britain’s fondness for tinkering with the machinery of government is a form of displacement activity. What actually shapes the fortunes of research is money, people and demand for what they produce, not the nameplates on London’s Victoria Street.

On that test, the outlook for science is far calmer than some reactions to this latest move would suggest. Under plans published last October, the UK’s total research and development budget is locked in at £58.5 billion over the four-year spending review to 2030 – a real-terms increase.

Machinery changes move budget lines across departments; they don’t unravel them. As yet, nothing we’ve heard from the Burnham government suggests the settlement for UK Research and Innovation (UKRI) is at risk.

A US wrecking ball

If we want to be reminded what real damage to a science system looks like, we only need to glance across the Atlantic. While London rearranges the departmental furniture and dishes out new email addresses, Washington is taking a wrecking ball to the foundations of its public science system.

A 2027 budget request will halve the size of the US National Science Foundation, if passed. A draft White House rule, due to take effect in October, will hand political appointees the power to vet federal research grants against “the president’s priorities” and terminate awards mid-stream.

A predictable exodus has begun. The Nobel laureate Omar Yaghi has just left the University of California, Berkeley for Tsinghua University in China.

Set against political commissars patrolling the borders of knowledge, the UK’s science soap opera feels deeply provincial – a debate about which secretary of state sponsors oversight of a gradually swelling budget.

And the institutional layer beneath this is in better shape than doomsayers allow. UKRI’s new strategy has set a clear five-year path: priority programmes in AI, quantum and clean energy, faster funding decisions, and a welcome seriousness about experimentation with the modes and means by which we manage public investment in research.

Not everyone is happy with UKRI’s approach to prioritisation. But a funder with a published strategy, agreed budget and hard targets is a more durable asset than a three-year-old ministry. The research councils have outlasted every department that ruled over them, and UKRI will outlast this too.

Nor should the science community be spooked by closer proximity to business. When I leave my office at UCL and stroll down the Euston Road through London’s Knowledge Quarter – dubbed “the new square mile” – I pass by the Wellcome Trust, the Crick and Turing institutes, the British Library, Anthropic and Google DeepMind. Day in, day out, this dense weave of labs, firms and cultural institutions delivers what the triple helix of government, industry and academia is all about.

And we can find versions of the same story unfolding in Manchester, Sheffield, Cambridge and Glasgow. Rather than science being subordinate or “enslaved”, the UK’s world-leading universities have spent the past decade aligning themselves with agendas for regional growth and frontier technologies.

But there’s a harder truth lurking here too. Public money accounts for less than a third of UK research and development investment. The rest is financed and largely performed by the private sector.

UK science’s chronic weakness has never been the quality of publicly funded research. It is the R&D intensity of UK firms, and their ability to convert discoveries into products, services and revenue streams generated and owned in the UK.

If yoking science to industrial strategy in a single department reminds Whitehall to treat these as a single problem, some good may come of Burnham’s reorganisation.

AI policy concerns

With DSIT’s disappearance, a particular source of concern has been the future of AI policy. This at a time when the radical possibilities and risks of AI frontier models, fluctuating US export controls, and the latest “Sputnik moment” jolt from China’s Kimi K-3 demand sustained and technically fluent policy attention.

We are also seeing a sharp acceleration in the ways AI is transforming science itself – whether by cracking mathematical problems that stumped humans for the past 87 years, or collapsing decades-old models of grant writing and peer review.

Splitting AI from science just as they converge in practice would have been the least defensible element of this reorganisation. So it is reassuring that cross-government oversight of AI, including the AI Security Institute and AI public sector adoption, is moving to a new “AI taskforce” in the Cabinet Office – alongside the promotion of Kanishka Narayan to the status of AI minister attending Cabinet. Much now rests on his shoulders.

Government departments come and go. Those of us who have long advocated for research policies built on evidence rather than gut can allow ourselves a wry smile at the fuss created by this latest reorganisation – and then get back to work.

DSIT is dead, but UK science will be fine. Keep your eyes on the AI prize – and be thankful we are arguing about organograms, not survival.

James Wilsdon receives funding from UKRI and the Wellcome Trust, and a wider consortium of international research funders who partner in the Research on Research Institute (RoRI).

In a world where trust in institutions is mostly in decline, levels of trust in science remain relatively high. Yet at the same time, belief in scientific conspiracy theories, such as hushed-up effects of vaccines or aeroplane “chemtrails”, is also widespread.

What explains this apparent contradiction? Our work suggests we need to ask a different question – not how much people trust science, but which sources inform their understanding of what science is and how it works.

In our recent study of 843 US residents, we found that people who picture scientific progress as the work of lone geniuses, and who learn about science mainly from podcasts and documentaries, are markedly more drawn to conspiracy theories – whatever their level of education.

This fits the bigger picture very well. In a 16-country survey, 70% of respondents believed at least one false or unproven health claim. High trust in science and widespread conspiracy beliefs appear to coexist in the same populations.

We fear that trust in science is compatible with believing almost anything, so long as it is expressed in plausibly scientific terms. This can lead to some people refusing vaccines, trusting miracle cures or dismissing climate warnings – all while remaining convinced that science is on their side.

The ‘lone genius’ trope

At school, science is almost always presented as a linear sequence of achievements, or in the form of impersonal theories. The human side of discovery is largely stripped out.

In contrast, most science-focused films and documentaries build their narratives around biographies of exceptional individuals.

These often yield a familiar plot. Society is facing an intractable problem. A genius arrives with the exact solution – but is ignored and mocked until, after much personal pain, everyone realises he (for it is invariably a man) was right all along.

From the martyrdom of Giordano Bruno, the Renaissance friar burned at the stake partly for defending an infinite universe, to A Beautiful Mind, the Oscar-winning portrait of the mathematician John Nash, this lone-genius arc has permeated popular depictions of science for centuries.

Many conspiracy theorists seek to exploit this trope – in part to suggest that those who recognise it must be unusually smart too, living in a world where everyone else is “naive” or “brainwashed”.

Yet these lone genius plots leave out nearly everything that makes science work. Namely, the rather boring and complex processes of accumulation and evaluation, from peer review to replication and endless disagreements, through which reliable knowledge emerges from a crowd of people and institutions.

Research also shows that people presented with academic research “in the making”, without definitive conclusions, often read any hesitations or equivocations as scientific weakness, rather than honesty.

Most people trust science without understanding its technicalities. This means trust can easily be attached to shared cultural markers of “scientificity”: technical vocabulary, charts, credentials, fancy equipment, numbers quoted to impressive precision. In other words, whoever displays the right cues sounds plausibly scientific.

Consider the example of Corrado Malanga, an Italian chemist who developed an elaborate theory of aliens harvesting human souls, and his collaborator Filippo Biondi, a radar researcher. Using satellite radar, the pair claimed in March 2025 to have discovered an elaborate hidden city up to two kilometres beneath the Khafre pyramid in Egypt.

Archaeologists quickly and widely dismissed these claims, in part because this kind of radar cannot see more than a few metres underground. Yet Biondi went on to present their “discovery” at length on The Joe Rogan Experience, one of the world’s most popular podcasts, in January 2026 – attracting many millions of views and listens to this widely disputed “scientific account”.

A recent analysis examined 8.7 million Twitter (now X) posts that shared supposedly fact-checked news stories. It found that references to scientific entities – through use of terms such as scientist, university or study – were associated with an increase in sharing. However, this was especially true of “low-veracity content, and when users lean politically to the right”.

More likely to endorse conspiracy theories

There are two threads intertwining here: a story about how science progresses, and the informal media where it mostly lives.

In our recent study, published in Current Psychology, we measured participants’ “genius mentality” (the propensity to believe that progress comes from exceptional individuals overturning consensus through intuition) and also their science media preference (whether they favoured podcasts and documentaries over expert-vetted sources).

We found people who scored higher on either metric were more likely to endorse conspiracy theories. These correlative effects were stronger than either education or political orientation, factors usually considered major drivers of such beliefs.

We also observed an interesting moderation effect: among people with little or even moderate trust in scientists, a high belief in the genius mentality markedly increased conspiracy belief. Among those who trusted scientists most, it made almost no difference.

From our research, we believe standard remedies, fact-checks and appeals to “trust the science” miss a deeper problem. They may correct claims one at a time, but leave untouched core beliefs (and misunderstandings) about how science works.

A more durable approach is to make the collective machinery visible: to explain why debate, peer review and the slow building of consensus, rather than individual intuition, are what make science reliable.

Some science education researchers argue that the goal should not be to turn everyone into a scientist, but a “competent outsider” – people who can correctly evaluate where knowledge comes from and how it has been produced.

We believe a school science education should not only introduce theories, but explain how the scientific community works on a daily basis. Children need to learn how knowledge emerges from many people cooperating and challenging each other within shared criteria – and that any consensus which survives this process is a strength, not groupthink.

Our ambition is to protect against future conspiracy theories before they emerge, rather than simply trying to correct the last one.

Michele d'Errico receives funding from the Irish Research Council.

Taha Yasseri receives funding from Research Ireland and Workday Inc.

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.