Snappy performance, long battery life, great keyboard and excellent new haptic touchpad make the best of Windows 11
Microsoft’s Surface laptop for consumers is back, faster and with longer battery life and a hefty price increase because of the high cost of memory and chips.
The Surface Laptop 8 is a straight replacement for the seventh edition from 2024, which was the first of Microsoft’s new generation of ARM-based, Qualcomm-powered PCs designed to better rival Apple’s MacBook Air and other thin and light machines.
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.
A Coyote user explains how the app works. Video: Loic Le Mur.
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.
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?
Dark blue to light green reflects large to small proportions of respondents expressing ‘high levels of concern’ about the issues listed on x-axis.Sonal Gupta/Youth AI Survey 2026, CC BY-SA
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.
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?
Note: the bars do not sum to 100% because survey participants selected multiple responses.Sonal Gupta, CC BY-SA
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.
The two photos started out as typical teenage selfies: looking into the mirror, fully clothed. But once online predators had got hold of those pictures and ran them through an AI imaging tool, they had become the basis for extreme pornography videos.
These examples come from the Report Remove service, which allows children who have had explicit pictures of themselves distributed without their consent to flag the image confidentially and have it blocked or taken down from social media. Due to breakthroughs in AI, and the wide availability of AI models and nudification apps, some under-18s are becoming victims without even being in contact with criminals.
AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.
To understand this challenge, consider the foundation of the web itself. The web was not designed for the automated discovery and retrieval that new AI applications demand. Overcoming this inherent design constraint requires infrastructure.
The next frontier in AI may depend on a new web data infrastructure layer that can enable models to discover and map this ever-expanding digital realm. This layer must be able to navigate hundreds of millions of existing web domains and billions of new URLs created each week, delivering real-time information and overcoming technical barriers.
“The data suggests there’s far more data out there,” says Or Lenchner, CEO of Bright Data, a web data collection platform. “Think of the universe: It’s out there, but you don’t know what you don’t know.”
Enabling access to fresh, relevant, and trustworthy data
While early AI breakthroughs were driven by scaling training data and model size, organizations are now encountering a fundamental bottleneck: They need to keep pace with the dynamic, unstructured, and constantly evolving nature of web data in order to ground outputs in current and verifiable information. AI performance increasingly depends not just on model architecture but on a system’s compute, networking, retrieval, and data engineering capabilities—that is, the system’s ability to quickly and reliably retrieve data that is fresh, relevant, and trustworthy.
Traditional model training relies on snapshots of information collected at a particular point in time. Training AI on such static data is no longer sufficient. To track fluctuations such as competitor pricing, consumer sentiment, and market trends, companies need a constant feed of new information, pulling data in real time along with relevant context. Their infrastructure must therefore be able to handle millions of simultaneous interactions across websites that vary by geography, language, format, and access rules.
“If it can’t retrieve real-time information, it lacks context,” Lenchner says. “In a business setting, that’s not acceptable anymore. Stale answers lead to bad decisions and disappointed consumers.”
Speed is not merely a matter of convenience; it’s a matter of necessity. Today’s organizations operate in environments where prices, inventory, markets, security threats, and customer behavior change continuously. Delayed data retrieval can reduce the usefulness of an otherwise sophisticated model.
Using live, high-quality web data can also reduce AI hallucinations because the model has a more relevant knowledge base. This builds user trust. In fact, one survey found that 56% of AI practitioners said businesses need access to real-time web data to improve trust in AI outputs. To ensure the model runs efficiently and effectively, the information must also be pared down to the appropriate essentials.
Despite the introduction of retrieval-augmented generation (RAG), where models pull in external data at the moment of a query, many AI systems still struggle to deliver outputs that are current, contextually relevant, and trustworthy in operational settings. According to Gartner, 60% of AI projects that are not supported by AI-ready data—accurate, structured, organized, and contextualized—will be abandoned by the end of the year.
This is because large-scale retrieval alone does not solve the problem. As Lenchner puts it, “You need to retrieve data at scale, but also in real time. Latency becomes an issue because of the end user who is waiting for the output.”
Accessing fresh, AI-ready data at scale introduces technical and structural challenges. In practice, many enterprise systems combine public web retrieval with APIs, licensed datasets, and proprietary internal data in their AI applications. Integrating these fragmented sources into a timely and usable knowledge layer requires specialized capabilities. Some research has found that 97% of AI organizations depend on real-time web data infrastructure, but 90% feel boxed in by various restrictions. Companies are increasingly developing technical approaches to navigate these constraints.
Lenchner draws this metaphor: “Think of the trained model as intelligence and relevant data as knowledge. A powerful intelligence layer sitting on top of a hollow knowledge layer is like a genius who knows nothing—useless in practice. Intelligence and knowledge have to come together.”
The promise of new infrastructure
A new layer of web data infrastructure can address this developing need for stronger AI inputs by enabling discovery of data, real-time access, and tailoring to a specific context. As Lechner describes it, “It’s all about collecting data at scale, super-low latency, without being blocked.”
Rather than relying on increased computing power, this type of platform emulates human browsing behavior to access available content and transform raw code into structured data feeds. It can work with websites that might not interact with traditional scraping tools, such as those heavy in JavaScript, or with aggressive antibot software.
As Lenchner explains, “It’s basically having infrastructure that can mimic a web user with identifying information—IP address, location, and 1,000 more parameters. And at scale. Think of doing that 80 billion times a day for millions of websites. And every single time, you are looking exactly as the website expects you to look.”
Of course, continuous retrieval introduces new data governance challenges. To address them, platforms can enforce strict compliance protocols aligned with global privacy frameworks, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). They can also be limited to openly accessible, public information, avoiding paywalls or private logins. Any networks used can be vetted and consent-based, and incentives can be provided to owners of IP addresses. In this way, systems can be designed to comply with tightening regulation.
Such complex capabilities do not come easy. “When this is critical infrastructure for a company,” Lenchner says, “doing it in-house becomes a full-time engineering problem that competes with the actual AI work.” Addressing this complexity requires organizations to commit significant resources, leading many to seek specialized platforms designed specifically for data retrieval, orchestration, and observability.
Infrastructure for the real world
Real-time data retrieval is changing what AI systems can do inside organizations. For example, a retail company can use public information to enable a dynamic pricing engine, and global brands can track trademark infringements.
As the ecosystem matures, organizations that invest in this emerging data infrastructure layer will be better positioned to build AI systems that are more responsive, reliable, and aligned with real-world conditions—AI systems that can continuously adapt using current web data. Over time, the distinction between AI models and the infrastructure that feeds them may even begin to disappear.
As Lenchner says, “The world is changing. And everything that is happening in the world is being uploaded to the public web. The amount of new data that is being generated is growing and accelerating.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce.
Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across an organization. In early applications that center on customer service, HR, and sales, adoption of agentic AI has led to productivity gains of 30-50%.
Their autonomy positions agents more as collaborators than tools, working side-by-side with human employees in blended teams that look poised to upend traditional workplace dynamics.
More than three-quarters of HR leaders believe that the deployment of AI agents will transform existing workplace norms, driving a complete reappraisal of how roles and responsibilities are distributed, how skills are prioritized, and how workplace culture is shaped.
Though many admit they’re in the early or preparatory phase of this shift, 86% of chief HR officers predict that navigating digital labor shaped by agentic AI will be a central component of their role in the years ahead.
Fluency in the change management aspect of agentic AI adoption will be a crucial differentiator when it comes to unlocking the full potential of the technology going forward, believes Ateet Jayaswal, chief culture and employee experience officer at Wipro, a leading technology services and consulting company. This moment is one that he says, “calls for a mindset shift in how HR leaders would enable their organizations.”
Redeploying roles to enable higher-value work
As AI agents assume ownership of more complex and integral tasks, the distribution of roles and responsibilities within an organization will undergo significant change. It’s estimated that three-quarters of current roles will require redesign, reskilling, or redeployment by 2030 as a result of agentic AI.
For leadership, this shift should be about reskilling employees toward higher-value work in order to optimize the potential of an agent-human hybrid workforce, says Jayaswal.
For example, Wipro is a complex organization of 240,000 employees across 65 countries. It previously had multiple policies, documents, and knowledge fragmented across different systems, which delayed response to employee queries.
But the company has recently integrated a custom agentic AI assistant—an agent co-created in partnership with enterprise agentic AI platform Ema Unlimited—that can swiftly navigate this complex system, assuming responsibility for 50 HR tasks that had previously fallen to human employees. With the help of an AI agent, average response time to queries has lowered from 48 hours to five seconds.
Human employees have more time to focus on work “that requires a creative and imaginative mind and cross-functional collaboration, leveraging diverse ideas and thoughts to problem-solve,” says Jayaswal. The AI agent, meanwhile, handles rote administrative tasks like sorting timesheets or helping employees navigate policies and take actions in the flow of work.
When reallocating employee responsibilities, though, it is imperative that humans remain in the loop, Jayaswal caveats. When agentic AI is incorporated into enterprise technology, it must work with sensitive and personal data and therefore needs even more stringent guardrails and constraints than consumer applications. “When you expose an AI agent to organizational data, when you integrate it into multiple enterprise systems, then pathways around the AI agent become extremely important,” he says. “It’s an evolving space that leadership needs to have front-of-mind.” Governance should include robust data privacy rules and the establishment of governance layers, such as an AI council, he suggests.
At a fundamental level, the adoption of AI agents will force a re-evaluation of human roles, believes Jayaswal. Rather than employees primarily performing repetitive tasks or troubleshooting, a significant proportion of their time will shift to designing, teaching, and optimizing an AI agent that can do this work for them with far greater speed and predictability and without the agent getting bored.
“The nature of your job changes from being the hero who comes in to solve the problem to designing the hero who can solve the problem,” he summarizes. “The individuals who I have seen thrive in this environment are the ones who make this shift.”
An evolving employee skillset
Just as roles and responsibilities will be reconfigured to reflect the input of AI agents, the core skills of human employees will be reprioritized. More than four in five HR leaders say they’re planning to reskill workers to become more competitive in a market shaped by AI agents.
Technical skills will be increasingly important. Leading employers such as Salesforce, Danone, and Walmart are already rolling out dedicated AI and digital skills programs that aim to equip everyone from frontline workers to C-suite executives with a baseline level of AI literacy in response to the pervasiveness of the technology.
But desirable soft skills will also evolve, Jayaswal points out. Employees who assign tasks to an AI agent need to plainly articulate what modular steps may be needed to accomplish a task, what the desired outcome should be, and what parameters or guardrails need to be in place to ensure the agent doesn’t access or share confidential data.
As HR executives adapt to a blended workforce, three skills are emerging as top priorities during recruitment, according to a recent survey: relationship building, like forging constructive partnerships and account management; collaboration; and adaptability.
Maintaining a healthy workplace culture
In freeing up human employees to focus on higher-value tasks, the hope is that AI agents can elevate the employee experience, deepening fulfilment and satisfaction in the workplace.
“At Wipro, our vision is to improve the life of Wiproites,” says Jayaswal. “We are taking away non-value added work by embracing modern ways of collaborating, engaging, and transacting, leaving associates with higher order work content.”
But leadership teams embracing agentic AI will also need to plan for the new pressures and stressors that the technology can place on a workforce.
There is already confusion and knowledge gaps, with 73% of HR leaders reporting their employees don’t yet understand how digital labor will impact their work. Many organizations have opted to define AI agents as teammates or colleagues on org charts, but new research says this could erode trust and a sense of professional identity. It also raises new questions around accountability and ownership.
The role of management in addressing these concerns is critical, says Jayaswal. To maintain healthy dynamics, managers need to become skilled at orchestrating blended systems, splitting their focus between supervising AI agents and motivating human employees as they also build and supervise AI agents.
Upgrading employee well-being programs will be a core part of maintaining a robust workplace culture. “As there are more interactions with AI agents, you are losing some of the human touch that was provided by service delivery partners or leaders, or often even by colleagues and peers,” Jayaswal says. Employee services that encourage social connection and empathetic communication may help teams navigate this.
A breakneck transformation
Agentic AI looks set to scale at breakneck speed across many enterprises, and it will significantly transform how these organizations operate.
Carefully considering and deciding how to adapt to this newly blended workforce is now a top priority for leadership teams. Reviewing and refining organizational strategies is essential for optimizing both technological gains and the employee experience.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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.
A new study suggests that subtle changes in the brain’s immune cells could help explain why some people remain mentally sharp despite Alzheimer’s pathology. For decades, Alzheimer’s research has focused on the buildup of amyloid plaques and tau tangles, the two hallmark features of the disease. Yet a puzzling reality has continued to challenge scientists: [...]
Scientists caught a famous “sandwich” molecule in a rare hidden state, uncovering a long-missing step in its formation. For more than 70 years, metallocenes have been among the most important molecules in organometallic chemistry. These compounds feature a metal atom positioned between two carbon-based rings, creating a structure often described as a molecular sandwich. They [...]
A mysterious low-frequency sound known as The Hum has been reported around the world for decades, leaving researchers searching for its source. Imagine lying in bed late at night and hearing a faint engine-like drone that seems to come from somewhere outside. You check the windows, listen for traffic, and search for a nearby machine. [...]
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