It’s been reported that Meta has quietly removed facial recognition code from the companion app used by its AI-powered smart glasses, reigniting concerns about how far wearable technology companies may be willing to go in their pursuit of always-on artificial intelligence.

What Was Removed?

The controversy centres on an internal system called NameTag, which was discovered inside the Meta AI smartphone app that works alongside the company’s Ray-Ban smart glasses. According to reporting first published by WIRED, the code appeared to support facial recognition capabilities that had never been publicly released.

The system was reportedly designed to convert faces captured by the glasses into unique biometric identifiers, often referred to as faceprints, and compare them against a database stored on the user’s device. Evidence within the software also suggested that faces the system could not identify would be cropped, indexed, and stored locally for future processing.

Most notably, the code was present inside an application installed on tens of millions of devices despite Meta repeatedly stating that no final decision had been made about introducing facial recognition to its smart glasses platform.

Just one day after the findings became public, Meta released an updated version of the app that removed almost all traces of the NameTag system.

Meta’s Response

Meta says the facial recognition system was an internal exploratory project rather than a planned product feature. However, the speed with which the code was removed has inevitably attracted attention.

Reports indicate that the original software contained multiple AI models dedicated to detecting faces, cropping facial images, and converting them into biometric signatures. The app also reportedly contained a “Person recognised” alert that would have been displayed if someone was successfully identified.

Meta has not publicly explained why the code was removed immediately after the discovery or whether the changes had already been planned before the reporting appeared.

Why Facial Recognition In Glasses Is Different

The debate is not really about facial recognition itself. The technology has existed for many years and is already widely used in smartphones, airports, security systems, and consumer applications.

What makes smart glasses different is that they allow facial recognition to move from fixed locations and deliberate actions into everyday social interactions.

Unlike a phone, which requires someone to consciously point a camera at another person, smart glasses can continuously capture information while being worn. Combined with AI, cameras, microphones, and internet connectivity, they create the possibility of real-time identification in public spaces without the knowledge of the people being observed.

Supporters argue that such technology could have legitimate uses. For example, facial recognition could help visually impaired users identify friends, family members, or colleagues. It could also assist people with memory difficulties or cognitive impairments.

Critics, however, have raised concerns that the same technology could be misused for stalking, harassment, surveillance, or the identification of strangers without consent.

Those concerns become even more significant when combined with generative AI systems capable of searching, analysing, and contextualising information automatically.

Part Of A Bigger Strategy

The discovery also provides an insight into Meta’s longer-term ambitions for wearable AI. For example, chief executive Mark Zuckerberg has repeatedly described smart glasses as a future computing platform where AI assistants become constantly available throughout the day. The company’s recent investments in Ray-Ban and Oakley smart glasses reflect a belief that future digital interactions will increasingly move away from smartphones and towards wearable devices.

Facial recognition could potentially play an important role in that vision. An AI assistant capable of recognising people, understanding context, remembering previous interactions, and providing relevant information could become far more useful than one that simply responds to voice commands.

However, it is precisely that capability which raises difficult questions about privacy, consent, and personal data.

The Wider Privacy Challenge

The incident arrives at a time when regulators in Europe, the UK, and the United States are paying closer attention to biometric technologies.

Unlike passwords or usernames, biometric identifiers are linked directly to an individual’s physical characteristics. If compromised or misused, they cannot simply be changed or reset.

Privacy campaigners have long argued that facial recognition requires stronger safeguards than many other forms of personal data because of its potential to identify individuals at scale and without their active participation.

The rapid removal of the NameTag code suggests that Meta recognises the sensitivity of the issue, even if the company insists the feature was only exploratory.

What Does This Mean For Your Business?

For businesses, the story highlights how quickly AI is beginning to move beyond software and into the physical world.

Many organisations are already evaluating AI tools for productivity, automation, and customer service. The next wave of AI innovation is likely to involve wearable devices that can see, hear, interpret, and respond to the environment around them in real time.

That creates new opportunities, particularly in areas such as accessibility, training, field services, logistics, and hands-free information access. At the same time, it introduces new questions around privacy, data governance, consent, and the collection of biometric information.

The wider lesson is that as AI becomes more deeply embedded into everyday devices, businesses will need to think not only about what these systems can do, but also about what employees, customers, and the public are comfortable allowing them to do. The reaction to Meta’s facial recognition experiment suggests those conversations are only just beginning.

Anthropic has released a public version of the same AI technology that it previously restricted because of concerns about its cyber security capabilities, only for access to be suspended days after an intervention by the US government.

What Is Claude Fable 5?

Claude Fable 5 is a public version of Anthropic’s Mythos-class AI, a highly capable model originally developed for cyber security and vulnerability discovery work.

According to Anthropic, Claude Fable 5 and Claude Mythos 5 are “the same underlying model”, with the main difference being that Fable 5 includes additional safeguards designed to prevent misuse in areas such as cyber security, biology, chemistry, and model extraction. Mythos 5, by contrast, has some of those restrictions removed for approved users.

Anthropic originally developed Mythos-class models as part of Project Glasswing, a programme aimed at helping cyber defenders and critical infrastructure providers identify serious software vulnerabilities before attackers could exploit them.

When the first Mythos model was launched in April, Anthropic limited access to a small group of carefully vetted organisations because it believed the system’s cyber capabilities presented significant risks if made widely available.

Those concerns were not entirely theoretical. According to Anthropic, organisations using Mythos-class models have already identified “more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world”.

Why Anthropic Decided To Release It

Anthropic says it spent several months developing safeguards that would allow Mythos-level capabilities to be released more broadly while reducing the risk of misuse.

The result was Claude Fable 5, which the company described as “a Mythos-class model that we’ve made safe for general use”.

According to Anthropic, Claude Fable 5 delivers capabilities that were previously available only to a small group of approved organisations using Mythos. The company said: “Fable 5’s capabilities exceed those of any model we’ve ever made generally available.”

The model reportedly demonstrates state-of-the-art performance across software engineering, scientific research, vision tasks, analytical reasoning, and long-running autonomous work. Anthropic said it can “work autonomously for longer than any previous Claude models”, enabling it to tackle more complex tasks with less human supervision.

To reduce the risks associated with releasing such a powerful model, Anthropic introduced new safety systems that automatically redirect certain high-risk requests to a less capable model, Claude Opus 4.8.

According to the company, those safeguards were deliberately configured conservatively because “releasing a model this capable comes with risks”.

Suspended

However, just days after launch, Anthropic announced that access to both Fable 5 and Mythos 5 was being suspended.

The company said the US government had issued an export-control directive requiring it to disable access for foreign nationals, whether inside or outside the United States. Anthropic stated that the government believed it had become aware of a method for bypassing, or “jailbreaking”, Fable 5’s safeguards. A jailbreak is a technique designed to trick an AI system into ignoring or circumventing its built-in restrictions.

Challenged By Anthropic

However, Anthropic strongly challenged the significance of the alleged vulnerability. The company said it had reviewed the reported technique and found that it was capable of identifying only “a small number of previously known, minor vulnerabilities”. It also argued that comparable results could already be achieved using other publicly available frontier AI models.

Anthropic further stated: “We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people.”

The company warned that applying the same standard across the industry could effectively prevent the release of future frontier AI models.

A New Kind Of National Security Debate

The dispute highlights a broader change taking place in how governments are approaching advanced AI. For example, historically, software products were largely regulated after release if problems emerged. Frontier AI models are increasingly being treated differently because of concerns that they may create new risks in areas such as cyber security, biotechnology, critical infrastructure, defence, and intelligence.

Anthropic itself appears to recognise that reality, and the company has repeatedly argued that governments should have the ability to intervene when genuinely dangerous models emerge. However, it also insists that such decisions should be transparent and supported by clear technical evidence.

In its response to the suspension order, Anthropic stated that governments should be able to block unsafe deployments “as part of a statutory process that is transparent, fair, clear, and grounded in technical facts”.

The disagreement therefore appears to be less about whether oversight is needed and more about where the threshold for intervention should sit.

Why This Matters

The release and subsequent suspension of Fable 5 suggests that AI developers are now reaching capability levels where some models may be viewed as strategic assets rather than ordinary software products.

That raises some difficult questions for regulators, governments, technology companies, and investors alike. If advanced AI models can genuinely accelerate vulnerability discovery, scientific research, software development, and other high-value activities, restricting access could slow innovation. However, if those same capabilities can be misused, governments may feel increasing pressure to intervene.

Anthropic appears to believe that tension will become increasingly common as frontier AI systems become more capable. The company has argued that governments should have powers to intervene where genuine risks exist, while also warning that overly broad restrictions could hinder beneficial uses of the technology.

The dispute over Fable 5 therefore highlights a growing challenge facing policymakers: deciding when an AI model should be treated as a normal commercial product and when it should be treated as a potential national security concern.

What Does This Mean For Your Business?

For businesses, the story highlights how rapidly the AI landscape is evolving beyond questions of productivity and automation.

Many organisations are still deciding which AI tools to adopt, yet policymakers are already debating whether some frontier models should be treated as potential national security concerns. That represents a notable change in how AI is viewed by governments.

The wider lesson is that future AI adoption may be influenced not only by technological progress but also by regulation, export controls, safety requirements, and geopolitical considerations. As AI systems become more capable, businesses may find that access to certain models, features, or services depends as much on policy decisions as on technical innovation.

The dispute over Fable 5 may ultimately be remembered as an early example of a much larger challenge: how to make increasingly powerful AI systems broadly available while still managing the risks that come with them.

Aviva says fraudsters are increasingly using AI-generated evidence to support fake or exaggerated insurance claims, particularly in motor insurance.

The insurer says it detected more than 18,400 fraudulent claims during 2025, worth an estimated £233 million if paid out.

According to Aviva, fraudsters are using altered accident photos, fabricated documents, inflated repair costs, and exaggerated damage reports. The value of fraudulent motor claims rose by 39 per cent during the year.

Pete Ward, head of claims counter fraud at Aviva, said: “We’re seeing fraud become more sophisticated, from exaggerated claims to the use of AI-generated documents.”

Businesses should be aware that AI can now create highly convincing fake images and documents, making independent verification of evidence increasingly important when assessing claims, transactions, or other high-value requests.

A UK startup claims it can reduce the power consumed by AI data centre networks by 81 per cent by replacing conventional electronic switching equipment with technology that routes data using light.

Why AI’s Energy Problem Is Growing

The rapid growth of artificial intelligence is creating a major sustainability challenge. As AI models become larger and more widely used, the data centres that power them are consuming increasing amounts of electricity. Industry forecasts suggest global data centre energy demand could rise significantly over the coming decade, driven largely by AI training and inference workloads.

Much of the attention has focused on the energy consumed by powerful processors such as GPUs. However, another important source of energy consumption sits in the networks that connect those processors together.

Modern AI systems rely on thousands of chips constantly exchanging data. Every time information moves through conventional networking equipment, energy is consumed and heat is generated. As AI clusters grow larger, those networking systems are becoming increasingly expensive to power and cool.

That has prompted researchers and technology companies to look for ways of making AI infrastructure more efficient.

What Oriole Networks Has Developed

London-based startup Oriole Networks believes it has found one possible solution.

The company has developed a networking platform called PRISM that replaces traditional electronic switches in data centre networks with optical circuits that route information as photons rather than electrical signals.

For decades, data centre networks have depended on electrical switching technology. While highly effective, these systems consume significant amounts of energy and generate large quantities of heat.

Oriole argues that by allowing data to travel directly as light, much of that inefficiency can be removed.

According to the company, PRISM “removes the need for electronic switches entirely” within the network core and replaces them with “nanosecond-switched optical circuits”.

The company claims this can reduce core network power consumption by 81 per cent. It also says GPU idle time can fall from around 60 per cent to less than 1 per cent because processors spend less time waiting for information to move through the network.

Why Energy Savings Matter

The sustainability implications extend beyond electricity consumption alone. For example, networking equipment generates heat, and removing that heat requires cooling systems. Cooling can account for a substantial proportion of overall data centre energy consumption and often involves significant water usage as well.

Reducing the amount of heat produced inside a facility can therefore create multiple environmental benefits simultaneously.

Oriole argues that its technology could help reduce cooling requirements while making better use of existing AI hardware. Rather than building more data centres or adding more processors to achieve higher performance, operators may be able to extract more useful work from the infrastructure they already have.

The company also believes its approach could reduce dependence on some of the complex supply chains associated with today’s networking equipment.

Moving Into Real-World Testing

The technology is now moving beyond the laboratory. Oriole has announced that its system will be deployed as part of the UK’s £50 million ARIA Scaling Inference Lab, a government-backed initiative designed to address performance and efficiency bottlenecks in large-scale AI infrastructure.

The deployment combines Oriole’s networking technology with AMD Instinct GPUs and AMD EPYC processors.

Madhu Rangarajan, corporate vice president of Compute and Enterprise AI at AMD, described the technology as “a fundamentally different way to connect accelerators at scale” and said the collaboration is helping validate how photonic networking can provide the connectivity needed for AI inference workloads.

For Oriole, the deployment represents a significant milestone. Chief executive James Regan said: “A year ago, we were proving the physics; today, we’re proving the business.” He added that the project demonstrates how “photonic networking stops being a research curiosity and starts being the foundation of how serious AI infrastructure gets built.”

The Important Caveat

The headline figures remain company claims rather than independently verified industry benchmarks.

The ARIA deployment will provide the first large-scale commercial test of whether the technology can deliver the same benefits under real-world conditions that it has demonstrated during development.

That distinction matters because many promising hardware technologies perform well in controlled environments but struggle when deployed at the enormous scale used by major cloud and AI providers.

The wider rollout planned for 2027 will provide a clearer indication of whether photonic networking can become a practical alternative to conventional data centre infrastructure.

What Does This Mean For Your Organisation?

For organisations concerned about the environmental impact of AI, the story highlights the increasingly important reality that making AI more sustainable is not simply about building better processors.

Attention is increasingly turning towards the wider infrastructure that supports AI, including networking, cooling, power delivery, and resource utilisation.

If technologies such as Oriole’s can genuinely reduce network power consumption while improving hardware efficiency, they could help address some of the environmental pressures associated with AI’s rapid growth. Lower electricity demand, reduced cooling requirements, and better utilisation of existing hardware would all contribute towards more sustainable AI infrastructure.

Whether Oriole’s specific approach succeeds remains to be seen. However, the broader message is clear. As AI energy consumption continues to grow, innovations that reduce waste inside data centres may become just as important as advances in the AI models themselves.

Microsoft’s new Copilot Word Agent can turn a brief instruction into a fully structured business document, helping you create reports, proposals, and other paperwork far faster than starting from a blank page.

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One of the easiest ways to avoid accidentally revealing emails, Teams chats, confidential documents, browser tabs, or other sensitive information during a presentation is to create a separate desktop in Windows just for screen sharing. Here’s how to do it.

Why It Works

Windows includes a built-in Virtual Desktop feature that lets you create multiple separate workspaces on the same PC.

Instead of sharing your normal desktop, where emails, notifications, and work files may be visible, you can create a clean desktop containing only the applications you want your audience to see.

This is particularly useful for Teams meetings, Zoom calls, webinars, training sessions, customer demonstrations, and presentations.

How To Create A Presentation Desktop

Press:

Windows + Tab

Click:

New Desktop

A new, empty desktop will be created.

Open your presentation, browser window, application, or demonstration materials on this new desktop.

You can switch between desktops at any time using:

Windows + Ctrl + Left Arrow

or

Windows + Ctrl + Right Arrow

Why This Matters

Accidentally exposing confidential information during screen sharing is surprisingly common. A separate presentation desktop creates a cleaner, more professional environment and reduces the risk of unintentionally revealing emails, Teams messages, customer information, internal documents, or other sensitive business data.

It only takes a few seconds to set up and can help prevent an embarrassing and potentially costly mistake.