Meta is reportedly using temporary tent structures to house its growing AI infrastructure, as demand for compute power outpaces the construction of traditional data centres.

A Race for AI Compute Is Reshaping Infrastructure Plans

As the AI arms race intensifies, tech giants are confronting a new logistical challenge, i.e. where to house the vast amounts of high-performance hardware needed to train and run next-generation AI models. For Meta, the parent company of Facebook, Instagram and WhatsApp, the answer (at least in the short term) appears to be industrial-strength tents.

Reports first surfaced this month that Meta has begun deploying custom-built tented structures alongside its existing facilities to accelerate the rollout of AI computing clusters. These so-called “data tents” are not a cost-saving gimmick, but rather appear to be a calculated move to rapidly expand capacity amid what CEO Mark Zuckerberg has described as a major shift in the company’s AI strategy.

From Social Platform to AI Powerhouse

Meta’s pivot towards AI infrastructure has been fast and deliberate. For example, in early 2024, the company announced plans to create one of the world’s largest AI supercomputers, with a particular focus on supporting its open-source LLaMA family of language models. By the end of the year, it had already begun referring to it as “the most significant capital investment” in its history.

To support this, Meta is deploying tens of thousands of Nvidia’s H100 and Blackwell GPUs (high-powered computer chips designed to run and train advanced AI systems very quickly). However, it seems that building the physical infrastructure to support them has proven slower than the procurement of hardware. Traditional data centres, for example, can take 18–24 months to build and commission. Meta’s solution appears to be to use temporary hardened enclosures, which are effectively industrial tents, that can be erected and made operational in a fraction of the time.

Where It’s Happening and What It Looks Like

The first confirmed location for Meta’s tented deployments is in New Albany, Ohio, where it’s developing a major cluster codenamed Prometheus. According to recent reports from several news sources, these structures are being used to house racks of GPU servers and associated networking equipment. Each unit is reportedly modular, with advanced cooling, fire suppression, and security systems.

While Meta has not actually released any detailed specifications, the company has described the effort as a “temporary acceleration” to bridge the gap until more permanent facilities come online. Another major AI campus (codenamed Hyperion) is in development in Louisiana, with expectations that similar rapid-deployment methods may be used there too.

Why Tents and Why Now?

The use of tents may seem surprising, but Meta’s motivation is clear, i.e. it wants (needs) to train and serve large AI models at scale, and it needs the infrastructure right now, not in two years. In Zuckerberg’s own words, the company is aiming to “build enough capacity to support the next generation of AI products,” while staying competitive with the likes of OpenAI, Google, Amazon and Microsoft.

It’s also about flexibility. For example, unlike traditional data centres, which require permanent planning permissions and heavy civil works, tented enclosures can be constructed and reconfigured quickly. They offer a way to get high-density computing online in months rather than years, albeit with some compromises.

Not Just Meta

While Meta’s move is grabbing headlines, it’s not the first major tech firm to explore unconventional data centre formats. For example, during the COVID-19 pandemic, several cloud providers used temporary modular data centres, including containers and tented enclosures, to scale operations when demand surged. Microsoft famously experimented with underwater data centres as a way to reduce cooling costs and improve reliability.

Even more recently, Elon Musk’s xAI venture reportedly deployed rapid-build server farms using prefabricated containers to speed up GPU deployment in its Texas-based facilities. Also, Amazon has continued to invest in “Edge” data centres that prioritise speed and agility over permanence.

However, what sets Meta’s approach apart is the scale. For example, the company has already committed over $40 billion to AI infrastructure, and the tented deployments are part of a broader strategy to “bootstrap” its capabilities while new-generation AI-specific campuses are built from scratch.

Concerns About Resilience, Efficiency and Impact

It should be noted, however, that the move hasn’t exactly been universally welcomed. Experts have raised concerns about the reliability, cooling efficiency and ecological footprint of tent-based data operations. While Meta claims that its enclosures meet enterprise standards for uptime and safety, temporary structures are inherently more vulnerable to environmental disruption, temperature fluctuations and wear.

There are also questions about energy use. Large AI models require huge volumes of electricity to run, especially when deployed at scale. Tented structures may lack the sophisticated thermal management and energy reuse systems found in traditional hyperscale centres, raising the risk of inefficiencies and higher carbon emissions.

According to the Uptime Institute, data centres already account for up to 3 per cent of global electricity demand. If stopgap facilities become the norm during periods of infrastructure pressure, that figure could rise sharply without additional oversight or environmental controls.

Impact and Implications

For Meta, at the moment, the gamble appears to be worth it. The company is currently rolling out LLaMA 3 and investing heavily in tools like Meta AI, which it plans to integrate across its social and business platforms. The faster it can get its high-performance AI hardware up and running, the sooner it can offer AI-driven services, including advertising tools, analytics, and content generation, to enterprise clients.

For business users, the main benefit is likely to be early access to more powerful AI tools. Meta has already integrated its assistant into WhatsApp, Messenger and Instagram, with broader rollouts planned for Workplace and business messaging products. However, reliability and latency may remain issues if some of the compute is housed in temporary facilities.

The move also raises the issue of competitive pressure. For example, if Meta can deliver AI capabilities ahead of rivals by deploying fast, it may force other firms to adopt similar build strategies, even if those come with higher operational risks. For hyperscalers, the challenge will be balancing speed with sustainability and service quality.

What Comes Next?

Not surprisingly, Meta has indicated that tents are a transitional measure, not a long-term strategy. The company’s permanent data centre designs are being reworked to accommodate liquid cooling, direct GPU interconnects, and AI-native workloads. These upgraded facilities will take years to complete, but by using tents in the meantime, Meta is buying itself crucial time.

The coming months are likely to show whether the experiment works, and whether others follow suit. For now, Meta’s tents are essentially a symbol of just how fast AI is reshaping not just software, but the physical infrastructure of the internet itself.

What Does This Mean For Your Business?

The use of tents as a fast-track solution reflects the scale and urgency of Meta’s AI ambitions, but it also highlights the growing tension between speed of deployment and long-term sustainability. For all its innovation, Meta’s approach poses uncomfortable questions about resilience, energy consumption and operational risk, especially when infrastructure is housed in non-standard environments. While this kind of flexibility may offer a short-term edge, it could expose businesses and users to service disruption if systems housed in temporary structures fail under pressure or face unforeseen vulnerabilities.

That said, the sheer demand for AI infrastructure means other tech giants may not be far behind. If Meta’s experiment proves successful, we could see other players adopt similarly unconventional strategies, especially where time-to-market is critical. For UK businesses relying on AI platforms like Meta’s for content generation, analytics, or marketing tools, this could bring benefits in terms of earlier access to new capabilities. However, it also reinforces the importance of understanding where and how data services are delivered, particularly for sectors concerned with uptime, data security, and regulatory compliance.

Regulators, investors, and environmental groups will likely be watching closely. If stopgap deployments become widespread, new standards may be needed to ensure these facilities meet minimum efficiency, safety and emissions criteria. The shift to temporary infrastructure may also have knock-on effects for supply chains, local planning authorities and the data centre construction industry, as expectations around permanence and scale continue to shift.

Ultimately, Meta’s move signals a wider industry pivot, not just to AI, but to a more agile and fragmented approach to infrastructure. Whether this becomes a blueprint or a cautionary tale will depend on how well these fast-build solutions hold up under real-world conditions, and whether they can deliver the stability and sustainability that large-scale AI services increasingly demand.

Major adult platforms including Pornhub and Reddit must introduce advanced age verification under the Online Safety Act to block under-18s from accessing explicit content.

Why Is This Happening Now?

The UK’s Online Safety Act, passed in 2023, was designed to better protect children online, and one of its most high-profile provisions is now coming into force. For example, from 25 July, commercial pornography providers must implement “robust” age checks to stop minors from viewing explicit content. Media regulator Ofcom will oversee enforcement, and companies that don’t comply risk fines of up to £18 million or 10 per cent of global turnover.

Years of Warnings

The change comes after years of warnings about the ease with which children can currently access adult material. For example, a 2023 study commissioned by the UK government found that two-thirds of children aged 13–17 had seen online pornography, with many accessing it accidentally from mainstream sites. Until now, many porn sites only required users to click a box confirming they were over 18, a system Ofcom now calls “clearly insufficient”.

What the New Rules Require

Under the new law, pornographic sites must use “high assurance” age verification methods to prevent underage access. While they don’t need to identify users by name, they do need to check (reliably) that someone is over 18. The Act doesn’t mandate a single system, but Ofcom has recommended several approved methods.

Platforms must also ensure these checks are secure and privacy-preserving. If they fail to act, Ofcom has a range of enforcement options, from fines to blocking the site entirely within the UK. Payment providers and advertisers can also be ordered to withdraw support.

What Age Verification Methods Will Be Used?

In line with the new regulations, Ofcom has recommended a range of approved technologies that can be used to confirm someone’s age without necessarily revealing their identity. These include:

– Credit Card Checks. Users input card details, and a transaction is initiated to confirm the card is valid and held by an adult. Companies like Verifymy say they don’t share any personal information with the site itself, only returning a yes/no answer.

– Digital Identity Wallets. Digital ID apps such as Yoti or Luciditi allow users to store verified credentials (e.g., passport or driver’s licence data) and share only the necessary attribute, in this case, being over 18. The encrypted data remains under the user’s control.

– Facial Age Estimation. AI technology analyses a live photo or video to estimate whether a person is over 18. Yoti claims this can be accurate to within 1.5 years for most age groups. Critics say the idea of scanning faces to watch porn could deter users or raise privacy alarms.

– Mobile Network Checks. Some services can confirm age via a mobile phone contract, though pay-as-you-go users are often excluded.

– Open Banking Verification. By connecting to a user’s bank account, providers can check age without seeing transaction history. This option is privacy-focused but may feel excessive for users.

– Email Age Estimation. This method analyses where an email address has been used (e.g. with banks or energy firms) to estimate whether the user is likely to be an adult.

– Photo ID Uploads. Users upload a picture of a government-issued ID and a selfie. These are then matched to verify identity and age.

Each method comes with trade-offs in terms of accuracy, ease of use, and user privacy — and websites can use a combination to give users choice.

Who’s Implementing It And How?

Major adult platforms are now confirming their plans. Pornhub and several sister sites, owned by parent company Aylo, have announced they will adopt “government-approved age assurance methods”, though they haven’t specified which ones. Previously, Aylo withdrew access in some regions, such as Utah and Virginia in the US, after similar laws passed.

Reddit, meanwhile, is one of the first mainstream platforms to implement the new UK requirements. From 14 July, it began age-checking users who attempt to view “mature content” in the UK. It is using an external firm, Persona, which offers either a selfie scan or a passport photo upload. Reddit claims it won’t see the data itself, storing only a user’s date of birth and verification status.

Ofcom has welcomed these moves and warned that “other companies must now follow suit or face enforcement”.

Will It Work?

Supporters say the new rules are long overdue. Baroness Kidron, founder of the 5Rights Foundation, argues that age gates must be credible and secure if children are to be protected from harmful material. Ofcom estimates around 14 million UK adults access pornography online, and in their view, age checks can still allow legal adult access while excluding minors.

However, the plans have drawn criticism from civil liberties groups, privacy advocates, and digital policy experts.

Some argue the new rules set a troubling precedent for online freedom. David Greene, civil liberties director at the Electronic Frontier Foundation, described the legislation as a “tragedy”, warning that it effectively forces UK internet users to “show their papers” just to access lawful content.

Others question whether the approach will even be effective. Scott Babwah Brennen, of New York University’s Center on Technology Policy, noted that “there’s always going to be ways that kids can get around it,” and pointed to ongoing concerns about who collects verification data and how long it is retained.

Also, technology experts have warned that certain methods, such as facial age estimation or digital identity wallets, may feel disproportionate to the risk. While technically effective, these approaches risk normalising invasive identity checks for everyday online activities, potentially reshaping expectations around privacy across the internet.

Implications For Adult Content Providers

In terms of the implications for adult content providers, beyond the technical implementation costs, businesses risk losing traffic if users find the checks too intrusive. Some sites, like Pornhub in the US, have previously gone dark in protest at similar legislation.

In the UK, however, the financial penalties for non-compliance are now high enough to compel widespread implementation. Businesses may need to partner with certified age-checking firms, which will add another layer of cost, regulation, and liability.

Advertisers and payment processors are also under pressure. For example, Ofcom’s powers include ordering them to withdraw services from non-compliant sites, raising the stakes for the wider digital economy. For example, mainstream brands may need to re-evaluate where their ads appear and how age-gated content affects user flows.

What About Everyday Users?

For UK users, the experience of accessing adult content is about to change and possibly in ways that feel awkward or invasive. While age verification systems aim to be quick and anonymous, the requirement to share biometric data, ID scans, or bank access may put some people off entirely.

There are also broader concerns about data safety. For example, while verification firms like Yoti and Persona stress that they don’t store images or pass data to the adult sites, the reassurance will depend heavily on user trust and transparent processes.

As Iain Corby of the Age Verification Providers Association put it: “The only non-hackable database is no database at all.” However, even with privacy safeguards, the reality remains that the UK is about to become one of the first countries where accessing pornography will routinely require proof of adulthood.

What Does This Mean For Your Business?

Ofcom’s enforcement powers mean adult content providers now face direct commercial consequences for non-compliance, including fines, site blocks, and service withdrawal by payment processors or advertisers. Many will need to integrate external age assurance systems quickly, absorbing new costs and operational complexity while trying to retain user trust and engagement.

Mainstream platforms hosting adult or mature content are also affected. Reddit’s early adoption signals how widely these obligations apply, and more companies are likely to follow to avoid regulatory action. Businesses in adjacent sectors, including advertisers and mobile providers, will need to reassess how their services intersect with regulated content and whether their current systems meet Ofcom’s standards.

For users, the experience of accessing adult material will now become more controlled, and in some cases, more uncomfortable. While most verification systems avoid full identity disclosure, the requirement to submit a facial scan, ID image or bank-linked account introduces friction that didn’t previously exist. It’s likely that some users may withdraw entirely or turn to alternative platforms, raising questions about the law’s effectiveness.

From a business perspective, the changes signal a wider move towards regulated digital identity checks across age-restricted services where pornography is simply the first and most obvious test case. Online gambling, social platforms, and even e-commerce providers selling restricted goods may face similar expectations in the near future. For UK firms, especially those working with younger audiences or regulated content, this shift will demand investment in age assurance, transparency, and user communication, or risk falling foul of a new era of digital accountability.

Users in the United States can now ask Google Search to make real-world phone calls for them, gathering service and pricing information from local businesses without speaking to anyone themselves.

AI Used in Local Enquiries

Google has rolled out a new AI-powered calling feature within Search that allows users to collect information from businesses such as pet groomers, garages, and dental clinics. Instead of having to make a phone call personally, users can now instruct Google’s AI to handle the enquiry on their behalf.

UK Soon?

The feature is currently available to all Search users in the United States. It’s worth noting here that although Google has not provided a confirmed timeline for the UK launch of this feature, based on the company’s typical rollout strategy for Search and Gemini features, a wider international release often follows within several months of a successful US launch.

How To Use It

Using the new AI-powered agentic calling feature, when someone searches for a service like “dog groomers near me”, a new option appears offering to “Have AI check pricing”. Users are then asked a few follow-up questions, such as what type of pet they have, what service they need, and when they would prefer an appointment. From there, Google’s AI makes the call, gathers information, and returns a summary by email or text.

According to Google, every call begins with a clear announcement that it is an automated system from Google acting on behalf of a user. This is intended to prevent confusion and maintain transparency, especially after earlier versions of the technology were criticised for sounding too human and failing to identify themselves clearly.

How the Technology Works

In terms of the tech behind it, the feature uses a combination of Google’s Gemini model and its existing Duplex technology, which has been used for AI voice calls since 2018. Duplex originally drew attention for its ability to make bookings or ask for opening hours using natural-sounding speech, but was temporarily scaled back due to concerns about transparency and practical usage limits.

However, this new version is more focused and practical, targeting specific types of local businesses and providing structured information directly back to the user. The use of Gemini helps the system handle follow-up questions and summarise results more clearly, while Duplex provides the voice interface that handles the actual phone call.

Google has stated that business owners retain control and can opt out of receiving these calls via their Google Business Profile settings.

Access, Availability, and Cost

The AI calling feature is free to use and is currently being made available to all Search users in the US. However, those subscribed to Google’s AI Pro and AI Ultra plans will benefit from higher usage limits, allowing them to make more AI-driven requests each day.

Google AI Pro is priced at 19.99 US dollars per month. Subscribers gain access not only to enhanced call limits but also to a broader set of advanced AI features across other Google products, including Docs, Gmail, and Search.

There is no confirmed launch date for international availability, but Google has indicated that it plans to expand access globally over time.

Convenient

This feature may appeal most to people who prefer not to make calls themselves. For example, younger users in particular have shown in surveys that they are more likely to avoid phone conversations where possible. For many, the ability to compare availability and pricing from several providers without needing to speak to anyone may be seen as a welcome convenience.

For example, someone looking for car servicing could quickly receive quotes from three nearby garages with minimal effort. The AI not only makes the call but ensures the response is presented clearly and directly.

Mixed Impact For Businesses

For businesses, however, the impact may be more mixed. For example, while the system could generate new leads, it also adds a layer of automation that some business owners may find disruptive or unfamiliar. Staff answering the phone must be prepared to speak with an automated caller and provide information in a way that can be understood and relayed accurately.

Part of a Bigger Transformation in Search

Google’s introduction of AI calling is really part of a wider evolution of Search towards more agentic, action-oriented tools. For example, at the same time as launching this calling feature, the company also announced the rollout of two other significant updates for users on its AI Pro and AI Ultra subscription tiers, i.e. Gemini 2.5 Pro in AI Mode, and a new Deep Search capability designed for complex research tasks.

Gemini 2.5 Pro Comes to AI Mode

AI Mode is Google’s conversational interface in Search that allows users to pose complex or multi-part questions and receive structured answers with helpful links. Until now, it used a version of Gemini based on the 1.5 model. However, with the new rollout, paying subscribers can now switch to Gemini 2.5 Pro, a more advanced model that performs better in coding, mathematics, and advanced reasoning.

Users can select Gemini 2.5 Pro from a drop-down menu within AI Mode. The new model offers clearer logic, better problem-solving abilities, and more precise answers. Google says it is especially helpful for users tackling more technical tasks, such as software development or quantitative research.

Deep Search Adds Multi-Step Research Capabilities

Also new is Deep Search, a feature designed to save users hours of research by allowing the AI to run hundreds of background searches and reason across different sources. The result is a fully cited and structured report that addresses a query in depth.

Google says Deep Search is useful for work-related research, hobbies, academic study, or life decisions such as evaluating mortgages or comparing investment options. Rather than manually visiting multiple websites and comparing answers, users receive a compiled response that includes context, sources, and suggestions.

This feature is currently available to AI Pro and AI Ultra subscribers in the United States who have opted into Google’s AI Mode experiments in Labs. It builds on the trend of shifting from traditional search queries towards more autonomous AI assistance.

Impressive Tools with Practical Considerations

The new agentic features represent a major change in how people interact with information online. Instead of simply retrieving answers, Google’s AI now takes action on the user’s behalf, whether by conducting research or placing real-world phone calls.

However, the effectiveness of these tools will depend on adoption and reliability. If local businesses do not respond well to AI calls, or if the information returned is inconsistent, the user experience could suffer. Similarly, the shift towards subscription-based access raises concerns about accessibility, especially if more functionality becomes limited to paying users.

Even so, the direction is clear. Google is continuing to reshape Search into a more proactive and intelligent assistant, with features that aim to remove friction from both digital and real-world tasks. As the company put it in its announcement, “We’re bringing some of our most cutting-edge AI features to Google AI Pro and AI Ultra subscribers first, and we look forward to continuing to bring advanced capabilities in Search to all our users globally.”

What Does This Mean For Your Business?

Google is clearly moving Search from a place to find answers to a tool that completes tasks. Features like AI-powered calling change how users interact with businesses, removing the need for phone conversations altogether in some cases. If rolled out in the UK, this could directly affect how service providers handle enquiries, especially in sectors like grooming, repairs, and healthcare. Businesses that respond promptly and provide accurate, up-to-date information through their Google listings will be better placed to benefit. Those that fail to do so may find themselves left out of automated selection entirely.

For subscribers, the introduction of Gemini 2.5 Pro and Deep Search adds a new layer of functionality to Search. These tools are designed to deliver more complete, structured answers and reduce the time spent piecing together information manually. That is likely to appeal to professionals, researchers, and anyone dealing with complex decisions. However, the decision to reserve the most powerful features for paying users raises questions about who gets access to high-quality AI support and who does not. It may also increase pressure on non-paying users to upgrade, particularly if the standard tools begin to feel limited by comparison.

As these capabilities continue to expand, they are likely to influence how people expect digital services to behave. For UK businesses, the priority will be staying visible and responsive within this new model. For users, the benefits will depend on how well the tools perform across a range of everyday tasks, and how widely they are made available.

Co-op’s chief executive has confirmed that Hackers stole the personal data of all 6.5 million Co-op members in an April cyberattack.

The breach exposed names, addresses, and contact details, but no financial data. Co-op says it shut down its systems just in time to block a ransomware attack, though the incident still caused widespread disruption.

CEO Shirine Khoury-Haq called the attack “devastating” and praised IT staff for acting swiftly. The group behind the attack is believed to be ‘Scattered Spider’, a known cybercrime gang that uses social engineering to access internal systems.

Four suspects aged 17 to 20 were arrested and bailed earlier this month in connection with the attacks on Co-op and other UK retailers.

In response, Co-op has partnered with The Hacking Games to help guide young cyber talent into ethical careers, starting with a pilot across its academy schools.

To reduce risk, businesses should train staff to recognise impersonation tactics, restrict internal access, and ensure systems can be swiftly isolated in the event of an attack.

As electric vehicle usage expands, the race is on to recycle their batteries and recover scarce materials like lithium, nickel and cobalt, thereby cutting emissions and easing dependence on mining.

A New Phase In the EV Revolution

With global sales of electric vehicles (EVs) topping 14 million in 2023, a 35 per cent increase on the previous year, attention is now turning to what happens when those vehicles reach the end of their life. The answer lies within a growing industry focused on recycling EV batteries and recovering the valuable metals they contain.

According to the International Energy Agency (IEA), more than 40 million EVs were on the roads worldwide by the end of 2023. Yet while these vehicles eliminate tailpipe emissions, their batteries pose new sustainability challenges. The raw materials used to make them, especially lithium, cobalt, nickel and graphite, are limited, unevenly distributed around the world, and often extracted in environmentally and socially problematic conditions.

This is where EV battery recycling comes in: a critical step not just for sustainability but for securing supply chains, lowering costs, and reducing dependence on virgin mineral extraction.

Why Recycle EV Batteries?

Each lithium-ion EV battery contains a tightly packed structure of anodes and cathodes, typically made from graphite and a mix of lithium, cobalt and nickel. Over time, these batteries degrade and are eventually removed from vehicles. If not properly handled, they risk leaking toxic materials into the environment or catching fire during disposal.

More importantly, without recycling, the valuable critical minerals they contain would be lost. These metals are expensive to mine and refine, and demand is expected to grow rapidly. For example, the World Bank projects that by 2050, global demand for lithium could increase by nearly 500 per cent, with cobalt and nickel not far behind.

Recycling essentially offers a more sustainable and secure alternative. By 2040, up to 50 per cent of the UK’s EV battery material demand could be met through recycling, according to estimates shared by Altilium Clean Technology, a UK-based battery recycling firm. As Dr Christian Marston, Altilium’s COO, puts it: “If we do battery recycling at scale, we can produce materials at around 20% lower cost than commercial imports—and with significantly lower emissions.”

A Circular Model Made in Britain

Altilium is an example of a company at the forefront of this movement. Based in Tavistock, Devon, with new large-scale facilities under development near Plymouth, the company has created a fully integrated recycling process that turns old EV batteries into battery-ready materials.

The heart of its process is EcoCathode™, which is a hydrometallurgical method that uses water-based chemistry instead of high-emission smelting. Here’s a brief summary of how it works:

Step 1 – Shredding

Spent EV batteries are mechanically shredded into a fine, dark powder known as “black mass”. This material contains a mix of critical metals, plastics and other by-products.

Step 2 – Acid Leaching

The black mass is soaked in a sulphuric acid solution. This dissolves the key metals (lithium, nickel, and cobalt) into a liquid form, separating them from inert or less valuable materials.

Step 3 – Graphite Recovery

Before further processing, the graphite from the anode is extracted and purified. Altilium reports a 99 per cent recovery rate for graphite, which is then reused in new anodes.

Step 4 – Metal Separation

Using a series of chemical tweaks, unwanted elements like aluminium and copper are filtered out. The remaining solution contains the valuable metals needed for new batteries.

Step 5 – Solvent Extraction

The lithium, nickel and cobalt are separated out one by one using an advanced chemical process involving kerosene and selective reagents. This allows for high-purity recovery of each element.

Step 6 – Reprocessing for Reuse

Finally, the extracted metals are refined into cathode active materials (CAM) and precursor materials, which can be fed directly back into battery production. This closes the loop by turning waste back into high-value battery inputs. The company claims this method produces 74 per cent less carbon emissions for CAM and 77 per cent less for anode materials compared to traditional sourcing. Their recycled components are already being tested at scale by the UK Battery Industrialisation Centre, with a major car manufacturer due to validate performance later this year.

“Closed-Loop” Supply Chain

Altilium’s aim is to create a “closed-loop” supply chain within the UK, keeping resources onshore, reducing dependence on foreign imports, and supporting national energy security. “We see batteries which are in this country as a strategic asset in the UK,” says Marston. “If you do the processing in the UK, you add the value in the UK.”

Who Else Is in the Race?

Although Altilium is leading in the UK, it’s certainly not alone globally. Several firms are now building out battery recycling ecosystems, each with different models and geographic strengths. These include, for example:

– Redwood Materials, founded by Tesla co-creator JB Straubel, is a major US player with sites in Nevada and South Carolina. The company focuses on recovering and refining materials like lithium, cobalt and nickel, and has established partnerships with Toyota, VW, and BMW. Redwood’s strategy is to build a full circular supply chain, reducing US dependence on imported minerals.

– Li-Cycle, based in Canada but operating facilities across North America and Germany, also uses hydrometallurgical recycling. The company reports recovery rates of up to 95% for key materials, and is working closely with US policymakers through funding support from the 2022 Inflation Reduction Act.

– Ecobat, the world’s largest battery recycler, which is pivoting from its traditional lead-acid battery work towards lithium-ion recycling. With a strong global logistics and collection network, Ecobat has been expanding its lithium battery services across Europe and the US. According to its website, the company is focused on achieving “closed-loop recycling rates” for lithium comparable to those already achieved for lead.

How Most EV Battery Recycling Technology Works

Most battery recycling processes follow similar core steps, i.e., collection, dismantling, shredding into black mass, and then separation and refinement of metals.
Older approaches like pyrometallurgy, which uses high temperatures to melt down batteries, are effective but extremely energy intensive and carbon heavy. They also tend to destroy some of the more delicate materials, such as graphite.

Newer techniques like hydrometallurgy, used by Altilium, Li-Cycle, and Redwood, rely on water-based chemical treatments. These enable much more precise separation of metals at lower temperatures, resulting in higher recovery rates and far lower emissions.

Altilium’s process, for example, uses sulphuric acid to soak the black mass, selectively precipitating out low-value metals like iron and copper before extracting more valuable cobalt, nickel and lithium. The graphite is recovered earlier in the process and reprocessed for reuse in new battery anodes. The resulting materials are refined to battery-grade purity and can be used to manufacture brand new battery cells.

Environmental and Economic Benefits

The sustainability case for battery recycling is pretty compelling. For example, according to a 2024 IEA report, recycling critical minerals could cut the need for new mining by up to 40 per cent by 2050. For the automotive industry, that means fewer emissions from extraction, processing, and transport, and less exposure to volatile global commodity markets.

Cost savings are also significant. Altilium estimates that its recycled CAM could be 20 per cent cheaper than virgin equivalents by 2035. This could reduce the overall cost of manufacturing a new EV by 5 per cent, which is quite a meaningful margin in a competitive industry where affordability remains a barrier to adoption.

Also, the environmental gains go far beyond carbon. For example, avoiding new mining means less disruption to ecosystems, fewer human rights violations, and a reduced geopolitical dependency on regions like the Democratic Republic of Congo (which currently supplies two-thirds of the world’s cobalt) or Indonesia (the top source of nickel).

Not Without Its Challenges

Despite the promise, the battery recycling industry is obviously still in its early industrial phase. As Dr Xiaochu Wei of Imperial College London points out, many firms have only recently begun scaling beyond pilot stages. Altilium’s own journey, from a modest lab in 2022 to a full-scale facility in 2025, illustrates both the speed and complexity involved.
Battery designs themselves are also a barrier. With so many chemistries in use, from lithium-iron-phosphate (LFP) to nickel-manganese-cobalt (NMC), recycling methods must be flexible enough to handle mixed inputs. Standardising designs or redesigning batteries to be easier to dismantle could help but will require coordination across manufacturers.

Greenwashing Risk

It should be noted here that there’s also the risk of greenwashing. For example, as competition intensifies, some firms may make sustainability claims that outpace their actual recovery rates or environmental impact. Regulation can help: the EU’s new Battery Regulation, due to phase in from 2025, will require specific thresholds for material recovery and recycled content in new batteries.

What Does This Mean For Your Organisation?

Recycling EV batteries at scale is starting to look not just possible but inevitable. With regulations tightening, supply chains under pressure, and emissions targets looming, the case for a circular battery economy is becoming hard to ignore. Companies like Altilium are showing that high recovery rates and lower-carbon processes can be achieved using homegrown innovation. If they succeed in scaling up production and maintaining performance, the UK could have a credible, strategic alternative to importing expensive critical minerals from volatile markets.

For UK businesses, particularly in the automotive and clean tech sectors, this opens the door to a more resilient supply chain with greater control over cost and compliance. Manufacturers looking to meet EU recycled content rules from 2025 onwards will need trusted partners, and local recyclers could offer both regulatory support and operational savings. There are also commercial advantages to be gained from marketing genuinely low-carbon products built with verified recycled inputs, which will only become more valuable as sustainability reporting requirements evolve.

The wider benefits stretch further still. Reducing the UK’s reliance on overseas mining reduces exposure to supply disruptions, ethical concerns and carbon-heavy logistics. It also supports the domestic energy transition with onshore capabilities that align with national goals on net zero and industrial growth. For stakeholders across the board, from EV manufacturers and policymakers to investors and consumers, the expansion of battery recycling signals a maturing ecosystem with real potential to deliver on sustainability promises, rather than just headline targets.

None of this, though, will be straightforward. The sector still faces infrastructure gaps, chemistry complexity and the challenge of building scale fast enough to match the rise in end-of-life EVs. However, if early leaders can maintain momentum, the next few years may see battery recycling move from pilot to pillar, a new industrial sector supporting cleaner transport, better economics and lower environmental impact all at once.

If you’d like to know how quickly your website downloads and how it renders for people and other technical information which can help your SEO, this video explains how to access a handy tool which is completely free to use.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Want to trust what you see? WhatsApp now lets you check the origin of an image by searching it on Google without ever leaving the app.

How to:

– Tap the image in chat.
– Tap the ⋯ menu and select ‘Search on web’ (or ‘Search image with Google’).
– The image is sent (with consent) to Google and results open in your browser.

What it’s for:

Helps prevent sharing misleading or manipulated images — perfect for vetting news, verifying content, and avoiding misinformation in work or group discussions.

Pro‑Tip: Use this on images sent in group chats or forwards before trusting or sharing them further.

New research has found that 70 per cent of AI agents struggle to complete standard office tasks successfully, while Gartner warns that over 40 per cent of current agentic AI projects will be scrapped by the end of 2027.

What Are ‘AI Agents’ And Why Are They Struggling?

AI agents are software systems that use large language models (LLMs), like ChatGPT or Claude, in combination with tools and applications to carry out goal-driven tasks without constant human input. Unlike chatbots or virtual assistants that only provide responses, agentic AI is designed to take actions, such as navigating software, interacting with web content, or managing emails, based on natural language instructions.

Examples include agents that can generate reports, schedule meetings, or execute multi-step operations such as processing CRM queries or managing code deployments. The idea behind them is that AI can behave like a semi-autonomous digital worker, thereby improving speed and efficiency while reducing costs. However, recent evidence suggests the reality falls far short of the promise.

For example, in a landmark study by researchers at Carnegie Mellon University (CMU), most of today’s leading AI agents were only able to complete around 30–35 per cent of assigned office tasks. That means they failed nearly 70 per cent of the time.

Testing Real-World Tasks

To evaluate how AI agents perform in realistic workplace scenarios, the CMU team created TheAgentCompany, a simulated IT company environment designed to mimic tasks that real employees might encounter. These included browsing the web, writing and editing code, interpreting spreadsheets, drafting performance reviews, and messaging colleagues on internal comms tools like RocketChat.

Results Not Good

Researchers tested agents based on how many tasks they could complete fully and accurately. Top-scoring models included Gemini 2.5 Pro, which managed a 30.3 per cent success rate, and Claude 3.7 Sonnet, which achieved 26.3 per cent. Other well-known models fared worse. GPT-4o completed just 8.6 per cent of tasks, while some large-scale models like Amazon-Nova-Pro and Qwen-2 scored under 2 per cent.

Variation and Serious Slip-Ups

“We find in experiments that the best-performing model…was able to autonomously perform 30.3 per cent of the provided tests to completion,” the CMU team noted. Even with extra credit for partial progress, most agents still fell short of reliable performance.

Also, it looks as though the failures weren’t just minor slip-ups. For example, in some cases, agents forgot to message colleagues, froze while interacting with pop-ups, or even faked task completion, such as renaming users to make it seem like they’d contacted the correct person.

Salesforce’s Findings Echo the Concerns

A separate study by Salesforce offered similarly sobering results. In their CRM-focused benchmark CRMArena-Pro, LLM agents completed about 58 per cent of simple, single-turn customer service tasks. However, in multi-step scenarios where context had to be maintained, success rates dropped sharply to around 35 per cent. None of the evaluated agents demonstrated any meaningful understanding of confidentiality—an essential requirement for deployment in enterprise settings.

The researchers concluded: “LLM agents are generally not well-equipped with many of the skills essential for complex work tasks.”

Over 40 Per Cent of Projects Will Be Cancelled by 2027 …

Industry analysts at Gartner believe this isn’t just a technical hiccup, but could be an indicator of wider strategic risk. For example, the firm predicts that more than 40 per cent of all agentic AI projects will actually be cancelled by the end of 2027. Their assessment is based on the three key drivers of spiralling costs, unclear business value, and inadequate risk controls.

“Most agentic AI projects right now are early-stage experiments or proofs of concept that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst at Gartner. “This can blind organisations to the real cost and complexity of deploying AI agents at scale.”

A January 2025 Gartner poll of more than 3,400 business respondents revealed that while 19 per cent had already made significant investments in agentic AI, another 42 per cent were only dipping a toe in. Around a third were still waiting to see how the technology matures before committing.

What’s Going Wrong?

A key issue appears to be the fact that many supposed “AI agents” aren’t really agentic at all. For example, Gartner has criticised the growing trend of ‘agent washing’, where vendors rebrand chatbots, rule-based automation tools, or even basic assistants as ‘agents’ to ride the hype wave. Of the thousands of companies claiming to offer agentic AI products, Gartner estimates that only around 130 genuinely qualify.

Even for the legitimate players, it seems that technical challenges abound. For example, CMU’s team highlighted the following major limitations:

– Common-sense reasoning failures. AI agents often misinterpret basic instructions or misunderstand context. This limits their ability to carry out even straightforward workplace tasks.

– Poor tool integration. Many agents struggle to operate reliably within software interfaces. They may freeze, click the wrong buttons, or fail to retrieve the right data.

– Fabricated outputs. Hallucination remains a major problem. Agents sometimes invent plausible-sounding but incorrect responses. Among developers, 75 per cent report experiencing hallucinated functions or APIs.

– High cost and inefficiency. Despite being pitched as labour-saving, one study estimated that a typical AI agent task involved around 30 steps and cost over $6, which is often more than it would take a person to do manually.

– Security and privacy risks. Because agents need wide-ranging system permissions, there’s a serious risk they could accidentally expose sensitive data, or act unpredictably in ways that breach confidentiality.

Complexity and Context

While some agent frameworks are improving, it seems that the wider problem is that many office tasks require not just automation, but judgement. For example, Graham Neubig, a co-author of the CMU paper, explained that while coding agents can be sandboxed to limit risk, office agents must interact with live systems, sensitive messages, and human colleagues.

“It’s very easy to sandbox code…whereas, if an agent is processing emails on your company email server…it could send the email to the wrong people,” Neubig warned.

There’s also the issue of persistence. Multi-step tasks require agents to keep track of state, adapt based on outcomes, and respond to dynamic inputs. Even advanced models struggle to maintain context and consistency across more than a handful of steps, particularly when unexpected events, e.g. like a pop-up, error message, or missing file, intervene.

Buyers, and the Enterprise

For AI companies, the research findings appear to cast doubt on the maturity of the agentic AI market. Those selling genuine solutions will need to demonstrate clear, auditable performance, while others may face a credibility backlash if their products are exposed as agent-washed rebrands.

For enterprise buyers, the message is to proceed with caution. Agentic AI holds promise, but only for very specific use cases where outputs can be clearly defined, risks are manageable, and success is measurable. Without that, projects risk becoming costly distractions that never reach production.

Gartner suggests that businesses focus on agentic AI investments only where it can deliver proven ROI, e.g. by automating decisions, not just tasks, or by redesigning workflows to be agent-friendly from the ground up. “It’s about driving business value through cost, quality, speed and scale,” Verma explained.

Even so, Gartner remains optimistic that the agentic AI landscape will improve. By 2028, they predict that 15 per cent of all daily work decisions will be made autonomously by AI agents, up from none in 2024. They also expect 33 per cent of enterprise software applications to include agentic AI functionality by that time, suggesting that while short-term challenges are real, the long-term potential may still emerge.

What Does This Mean For Your Business?

The current hype around AI agents may be loud, but the reality behind the scenes appears to be proving far messier. Recent research shows that these systems still struggle with many of the core qualities needed for effective office automation, e.g., context awareness, reliability, consistency, and trust. While some agents show promise in structured environments like coding or CRM workflows, real-world office tasks often involve ambiguity, judgement, and unexpected challenges that most agents today simply can’t handle. This mismatch between marketing and capability is already fuelling disillusionment across the enterprise tech landscape.

For UK businesses, this could mean adopting a much more measured approach. For example, rather than rushing into large-scale AI rollouts, organisations may want to carefully assess whether agentic tools truly solve the problem at hand, and whether those benefits outweigh the risks and complexity. In industries where security, compliance, or client confidentiality are vital, agents that behave unpredictably or hallucinate outputs could introduce significant operational or reputational risk. Decision-makers will need to ask hard questions about vendor claims, demand transparency around performance, and avoid falling for superficial rebrands.

Also, for AI developers and solution providers, the pressure is now mounting to deliver genuine value and technical maturity. As Gartner’s forecast suggests, many agentic AI projects may be scrapped before they ever reach deployment. Rising costs, patchy results, and lack of clarity about return on investment are already stalling momentum. Yet amid this shakeout, there remains opportunity. Businesses still want tools that save time, reduce admin overhead, and support hybrid teams. If AI agents can evolve into reliable, well-integrated assistants that are grounded in workflows that make sense for users, they may yet become part of the fabric of enterprise software.

Until then, the safest path forward appears to be to treat agents as experimental copilots, not replacements. Hybrid approaches that combine AI capabilities with human oversight are likely to produce the most stable and trustworthy results. For now, it seems that the goal shouldn’t be full autonomy, but augmentation that helps people work smarter, and doesn’t automate them out of the loop.

In this Tech Insight, we look at how the sudden rise of a (possibly AI-generated) band on Spotify has reignited the debate over whether music streaming platforms should clearly label artificially created songs.

An Indie Rock Hit, But Who Made It?

The Velvet Sundown appeared suddenly in June 2025 and has since attracted more than 850,000 monthly listeners on Spotify. To give some idea of their popularity, their most streamed track, Dust on the Wind, has been played over 380,000 times, with many listeners noting similarities to the 1977 Kansas ballad Dust in the Wind. However, it seems that no one knows for sure who the band actually is, or whether its members even exist.

Four Names and Not Much Else

The band’s Spotify profile lists the four names of band members as Gabe Farrow, Lennie West, Milo Rains, and Orion “Rio” Del Mar. However, it’s been reported that none of these individuals has any online presence outside the band, i.e., no interviews, no live shows, no solo projects. In fact, their Instagram feed is populated with images that appear to be AI-generated, showing oddly rendered “band members” in stylised poses. Also, a supposed quote from Billboard magazine in their bio (“the memory of something you never lived, and somehow makes it feel real”) has no known source.

Speculation

It seems that the ambiguity has fuelled speculation across Reddit, X, and YouTube, with some users pointing out audio “artefacts” in the recordings (small glitches or distortions often associated with synthetic audio generation tools). Music producer and YouTuber Rick Beato has been reported as describing the band’s output as having the hallmarks of AI composition, especially after running one of the songs through a digital splitter in Logic Pro.

Suno, Deezer and the “AI Slop” Flood

Although The Velvet Sundown has not confirmed the use of AI tools, the music streaming service Deezer has flagged all of the band’s tracks as “100 per cent AI-generated” using its new detection algorithm. Deezer is currently the only major platform openly tagging AI music content. Its system identifies audio created with generative tools like Suno and Udio, which can compose full songs from basic text prompts.

In April 2025, Deezer reported that 18 per cent of all content uploaded to the platform was AI-generated, a sharp rise from just 10 per cent in January. That translates to more than 180,000 bot-made tracks per week! Deezer CEO Alexis Lanternier has said that the goal is not to ban AI music, but to ensure transparency and protect the interests of human artists.

“AI is not inherently good or bad,” he said. “But we believe a responsible and transparent approach is key to building trust with our users and the music industry.”

Spotify, Apple and Amazon Stay Quiet

In contrast, it seems that streaming platform Spotify has no current system in place to flag AI-generated content and has made no official comment on The Velvet Sundown. CEO Daniel Ek has previously been reported as saying that Spotify would not ban AI-made tracks, but stated he was opposed to using the technology to impersonate real artists. Despite that, Spotify’s algorithm has promoted The Velvet Sundown on its personalised “Discover Weekly” playlists, pushing the track to millions of users without any disclosure of its potential origin.

Apple Music and Amazon Music also host The Velvet Sundown’s music but have remained silent on whether they intend to introduce any kind of AI labelling or detection system.

A Lack of Labelling Sparks Concern

It seems that this lack of labelling has sparked concern from artists, producers and industry groups. For example, Ed Newton-Rex, founder of the advocacy group Fairly Trained, described the situation as “theft dressed up as competition.” He argued that AI firms are profiting from training data scraped from real musicians’ work, often without consent or compensation.

Identity Confusion and Media Hoaxes

Adding to the confusion, Rolling Stone US initially reported that a spokesperson for the band had confirmed the music was generated using Suno. That spokesperson later turned out to be fake, part of an elaborate “art hoax” designed to deceive journalists. The individual, known as Andrew Frelon, later admitted in a Substack post that he was unaffiliated with the band and fabricated the story.

In response, The Velvet Sundown issued a statement on its Spotify page denying any link to Frelon, describing his claims as “unauthorised.” The band has continued to post cryptic messages via its social media channels. One X post read: “They said we’re not real. Maybe you aren’t either.”

It seems that this ambiguity is part of the brand’s strategy. Their upcoming album, Paper Sun Rebellion, is being promoted with AI-style visuals and poetic descriptions that blur the line between artificial and authentic. Whether this is genuine artistic expression or a marketing gimmick has become a key question for commentators.

A Threat to Emerging Artists?

For real-world musicians, the rise of AI-generated bands actually poses a serious challenge. Kristian Heironimus, of the indie group Velvet Meadow, was recently quoted in NBC News as saying that watching an alleged AI act soar to 500,000 listeners in just two weeks was “disheartening.” He highlighted the difficulty of competing with endless, instantly produced AI content that can mimic any genre and flood discovery algorithms.

Many in the industry appear to share his concern. For example, last year, hundreds of artists, including Sir Elton John and Dua Lipa, signed an open letter demanding legal protections against the unauthorised use of their work in training AI models. While the UK government declined to introduce new legislation at the time, it is currently holding a separate consultation on AI and copyright.

Meanwhile, a coalition of major US record labels (Universal Music Group, Sony Music and Warner Music) has filed lawsuits against AI music developers Suno and Udio, alleging large-scale copyright infringement. Both companies argue that using publicly available music to train models constitutes “fair use,” a defence commonly deployed by AI developers.

Who Decides What’s Real?

The lack of consensus on what constitutes “AI-generated” music adds further complexity. Some artists use AI tools like Suno or Udio to create backing tracks or generate lyrics, while others use them to compose full songs. Platforms like Spotify typically don’t require artists to disclose how their music was made. Nor do they differentiate between artists who use AI as a tool and those who rely on it entirely.

Most Platforms Treat AI Songs Like Human Ones (For Now)

Deezer’s approach is based on identifying audio characteristics and metadata patterns typical of AI tools. However, even that has limits, particularly as generative music becomes more sophisticated. For now, most streaming platforms continue to treat AI songs the same as human-made ones for purposes of recommendation, royalties, and categorisation.

People Are Interested In More Than Just The Sound of an Artist

Industry voices like Lanternier warn that this is unsustainable. “People are not only interested in the sound,” he said. “They are interested in the whole story of an artist… We believe it’s right to support the real artist, so that they continue to create music that people love.”

Listeners Left in the Dark

From a user perspective, it seems it’s increasingly difficult to tell what’s human and what’s machine-made. For example, generative AI can now mimic voices, create convincing instrumentals, and even generate promotional images, all with minimal input. This means that listeners may not even realise they’re streaming AI-made content unless a platform flags it.

This has broader implications for trust. For example, as Professor Gina Neff from the University of Cambridge noted in relation to this subject, “Our collective grip on reality seems shaky. The Velvet Sundown story plays into the fears we have of losing control of AI and shows how important protecting online information is.”

“AI Slop” Drowning Out Real Voices?

It also seems that some fear that AI-generated content (sometimes referred to as “AI slop”) will drown out real voices, especially in genres like indie rock or ambient electronica where lyrical ambiguity and minimalistic production make AI mimicry easier to hide.

However, others, like Suno CEO Mikey Shulman, believe the conversation is overblown. “I think people are forgetting the important question, which is: how did it make you feel?” he said. “There are Grammy winners who use Suno in their production every day.”

That said, the debate continues to sharpen and, with AI tools getting more advanced and streaming platforms dragging their feet on labelling, the question of who (or what) we’re actually listening to may only get more complicated.

What Does This Mean For Your Business?

Whether The Velvet Sundown is a genuine artistic experiment or a high-concept AI stunt, its rapid rise appears to have thrown the spotlight on the growing presence of synthetic music in mainstream discovery channels. The core issue is not simply the existence of AI-generated tracks, but the complete absence of transparency around them. For example, if listeners can’t tell whether a song is human-made or artificially composed, the entire trust framework underpinning streaming services begins to erode.

This matters not just for artists and platforms, but for a much wider set of stakeholders. For independent musicians and producers, there’s a real risk that generative AI will continue to crowd their work out of key algorithms and recommendation feeds. For record labels and rights holders, the legal and licensing landscape remains unclear, especially while AI companies assert fair use in the training of their models. Also, for UK businesses in the creative, tech, marketing or media sectors, the commercial implications are equally serious. For example, AI-generated content could be misused in ad campaigns, misrepresent music licensing in branded environments, or create reputational risks for companies trying to align with genuine cultural voices.

It’s also becoming harder for consumers and businesses to trust the authenticity of what they’re engaging with. When a band can be created, promoted and streamed globally without anyone knowing if it even exists, it raises questions about how influence, monetisation, and even identity are being managed online. For a platform like Spotify to push AI-generated tracks without disclosure, while human creators struggle to break through, could create long-term imbalances in both visibility and revenue distribution.

What’s clear is that this isn’t just a creative debate but is also a technological, ethical and commercial one. Without industry-wide standards or regulatory clarity, streaming platforms may soon find themselves forced to choose between innovation and integrity. Whether through clear labelling, artist verification, or changes to royalty structures, the choices made now will shape the relationship between AI and music for years to come.

The Danish government is planning a major legal shift to let people claim copyright over their own body, facial features, and voice, in what it says is the first European attempt to systematically tackle the threat posed by deepfakes.

A Legal Response to a Rapidly Growing Threat

Deepfakes, which are highly realistic synthetic media generated using artificial intelligence (AI), have become one of the most pressing digital threats of the past five years. By mimicking a person’s appearance, voice, and movements, these AI-generated videos, images or audio clips can convincingly impersonate individuals without their consent. Initially used for novelty and satire, they’re increasingly tied to malicious uses including fraud, harassment, and disinformation.

Massive Rise

According to a 2024 report from cybersecurity firm Sumsub, the number of detected deepfake videos worldwide rose by over a massive 700 per cent in a single year, with Europe seeing the sharpest spike. Consequently, the European Union’s law enforcement agency, Europol, has warned that deepfakes are “a significant threat to democracy and trust in institutions,” particularly around elections and public figures. However, individuals are also at risk, e.g. from revenge porn to financial scams where a cloned voice is used to impersonate a relative or company executive.

While many countries are beginning to introduce narrow legislation to deal with specific uses of deepfakes, Denmark is now attempting something broader.

What Denmark Is Proposing

Under the new proposals announced by Denmark’s Ministry of Culture in late June 2025, citizens would be granted copyright over their physical appearance, voice, and other personal traits. The hope is that this would allow them to demand the removal of AI-generated content that imitates them without permission (regardless of context) and seek compensation where harm has occurred.

Treated As A Creative Work

One important aspect of this new legal approach is that it would not rely on proving defamation or reputational damage, as is often required under existing European law. Instead, it would actually treat a person’s likeness as a creative work, similar to how a photograph or piece of music is protected. The law would apply to both private individuals and public figures, including artists and performers.

Culture Minister Jakob Engel-Schmidt described the legislation as a “bold step to protect personal identity in the age of AI,” noting that current legal protections lag behind technical capabilities. “Human beings can be run through the digital copy machine and misused for all sorts of purposes,” he said in a statement. “We are not willing to accept that.”

Timing, Process and Political Backing

The proposed changes will be submitted for public consultation before the Danish parliament breaks for summer recess, with formal legislation expected to be introduced in the autumn. Given the political climate, it’s highly likely to pass. For example, around 90 per cent of MPs reportedly support the reform, following widespread concern about the use of AI-generated content in political misinformation and online abuse.

Would Be A European First

The law would make Denmark the first European country to explicitly codify individual ownership of biometric traits for the purpose of combatting generative AI misuse. It is expected to take effect in early 2026 if passed.

What It Means in Practice

If enacted, the law would essentially give Danes the legal right to request takedowns of deepfake content from online platforms if it replicates their image, voice or body in a “realistic, digitally generated imitation.” The rule would apply whether or not the content was created with malicious intent.

Platforms that fail to comply with takedown requests could face “severe fines,” according to Engel-Schmidt. There’s also potential for EU-level action if enforcement proves challenging, particularly during Denmark’s upcoming EU presidency in 2026, when it plans to raise the issue with member states.

Includes Key Exceptions

Crucially, the proposal includes exceptions for parody and satire, which are protected under free expression rules. These carve-outs are intended to ensure that political cartoonists, satirical shows, and legitimate artistic works aren’t caught by the law.

Performances Too

The reform would also extend to artists’ performances. For example, musicians would have legal grounds to object if their voice or performance style is cloned by AI without consent, which has been a growing concern in the music industry as AI-generated songs imitate the voices of famous performers.

Why Businesses and Platforms Should Take Note

For technology companies, particularly those that operate online platforms or generate AI models, Denmark’s proposal could have far-reaching consequences.

In practical terms, businesses hosting user-generated content, such as social media platforms, image generators, or AI voice apps, may soon be legally obligated to implement mechanisms for recognising and responding to takedown requests based on biometric misuse. This could involve new detection systems, moderation processes, and audit trails to demonstrate compliance.

It also raises questions around liability. Under current EU law, platforms benefit from limited liability for illegal content they host, provided they act promptly when notified. Denmark’s new copyright-based approach might test the limits of that framework, especially if it leads to conflicts over enforcement or definitions of consent.

For creative industries, including advertising, film, and gaming, the law could restrict the use of AI tools trained on real individuals without licensing agreements. While this may increase costs and licensing complexity, supporters argue it could also encourage more ethical use of synthetic media.

From a business reputation standpoint, being seen to respect biometric rights could become a key trust signal for users and customers. A 2023 survey by the European Commission found that 79 per cent of EU citizens want stronger legal safeguards on the use of AI-generated likenesses.

How Other Countries Are Approaching the Issue

Globally, it seems, few countries have gone as far as Denmark is proposing, but some are moving in the same direction.

For example, in the United States, several states have passed deepfake-specific laws, mostly focused on election interference and non-consensual pornography. California, Texas, and New York, for instance, have made it illegal to create or distribute deepfakes that impersonate political candidates within 30 to 60 days of an election. However, there is no federal law yet, and a new budget proposal being debated in Congress could strip states of their authority to regulate AI for 10 years.

In China, deepfake creators must label synthetic media clearly and obtain consent from the people being replicated. Failure to comply can result in heavy fines. South Korea is also considering similar legislation, particularly to address deepfake abuse in online pornography, which has become a major social issue there.

Within Europe, the EU’s AI Act (adopted in 2024) includes provisions requiring deepfakes to be labelled as such, but it does not go as far as granting individuals copyright over their features. That’s why Denmark’s move is seen as a potential model for broader reforms.

What Challenges Remain?

Despite strong domestic support, Denmark’s proposal is not without critics. For example, some legal scholars have raised questions about how biometric copyright would be enforced across borders, especially on platforms based outside the EU. Others argue that tying personal identity to copyright, a system traditionally designed to protect creative works, may lead to unintended legal consequences.

There are also practical concerns, e.g. identifying a deepfake is not always straightforward, and takedown systems are often slow or ineffective. If enforcement relies heavily on users flagging violations, the burden may fall disproportionately on individuals without the resources or knowledge to pursue their rights.

For now, however, Denmark appears determined to lead the way by betting that stronger individual protections are the only way to restore trust in a digital landscape where seeing is no longer believing.

What Does This Mean For Your Business?

If Denmark succeeds in passing this reform, it could change how personal identity is treated under copyright law, not just nationally, but across Europe. By legally enshrining the right to control one’s own voice, face, and likeness, the country is effectively trying to redraw the boundary between creative freedom and personal protection in the age of synthetic media. For individuals, this could offer an unprecedented tool to fight back against misuse, without needing to prove reputational harm or navigate complex defamation law.

For UK businesses, particularly those in tech, media, and advertising, Denmark’s approach may offer a glimpse of what’s to come. If other EU countries follow suit, companies that operate across borders could face new compliance demands, from biometric consent processes to proactive takedown mechanisms. At the same time, businesses that adopt strong safeguards now, such as consent-driven AI use policies, may gain a competitive advantage by building trust with customers and clients. For those in the creative sector, for example, the move could also help clarify the grey area around training AI models on real human traits, especially in performance-heavy fields like music, voiceover, or influencer marketing.

However, enforcement remains a key challenge. For example, without international alignment, cross-border takedowns could prove difficult, and smaller platforms may struggle to implement the necessary safeguards. There’s also a risk that applying copyright principles to human identity could lead to unintended consequences, particularly if courts are left to interpret the balance between personal rights and creative expression.

Even so, Denmark’s proposed law appears to reflect a broader global reckoning with the risks of generative AI. It signals that governments are no longer willing to let platforms set the terms of engagement when it comes to biometric misuse. With deepfakes set to become more sophisticated and widespread, that signal may be just as important as the legal details that follow.