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.

The post Meta Pulls Facial Recognition Code From Smart Glasses App – IT & Security News appeared first on Quayside Technical Services.

Why It Works

Many businesses focus on protecting their systems but rarely check what information search engines can already see.

Google indexes far more than websites. It can also index PDFs, Word documents, spreadsheets, presentations, and other files that have been made publicly accessible, sometimes intentionally, sometimes accidentally, and sometimes long after they were thought to have been removed.

How To Check

1. Open Google Search.
2. Search for PDF documents containing your company name: “Your Company Name” filetype:pdf
3. Search for Excel spreadsheets containing your company name: “Your Company Name” filetype:xlsx
4. Search for Word documents containing your company name: “Your Company Name” filetype:docx
5. Search for PowerPoint presentations containing your company name: “Your Company Name” filetype:pptx
6. Review any files that appear in the results.

Useful Variations

To focus on files hosted on your own website, search:

PDF files: site:yourcompanywebsite.co.uk filetype:pdf

Excel spreadsheets: site:yourcompanywebsite.co.uk filetype:xlsx

Word documents: site:yourcompanywebsite.co.uk filetype:docx

PowerPoint presentations: site:yourcompanywebsite.co.uk filetype:pptx

This can sometimes uncover files that are no longer linked from your website but are still publicly accessible and indexed by Google.

Why This Is Useful

This quick check can help uncover old proposals, reports, presentations, price lists, spreadsheets, policy documents, or other files that may contain information you would rather not make easily discoverable. It is also a useful way to understand what a potential customer, competitor, journalist, cyber criminal, or AI system might learn about your business simply by searching online.

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What Is The Stratos Project?

The Stratos Project is a planned 40,000-acre energy and technology development in Utah’s Hansel Valley that would support large-scale AI computing, cloud services, and defence-related operations. Project backers say it could create thousands of jobs, strengthen energy resilience, and support national security priorities while generating substantial local revenue.

The development would include both a major data centre campus and dedicated on-site power generation designed to meet its energy needs without drawing power from the wider grid. According to the project itself, Stratos is intended to create “a secure, sustainable and resilient ecosystem” capable of supporting next-generation computing and critical national infrastructure.

Supporters argue this approach will minimise pressure on existing infrastructure while helping establish Utah as a centre for advanced computing and AI development.

Why Environmental Concerns Are Growing

The controversy centres on the sheer amount of energy involved.

For example, according to a preliminary thermal analysis by Utah State University physicist Dr Rob Davies, the completed project could require around 9GW of electricity for the data centre itself. When the associated natural gas power generation is included, the total thermal load could reach approximately 16GW, with all of that energy ultimately ending up as heat released into the local environment.

Davies describes the proposal as “a hyperscale level of thermal dump into this valley”, warning that the concentration of waste heat could create environmental effects extending far beyond the project boundaries.

His analysis estimates that the combined heat output would be equivalent to roughly 23 atomic bombs’ worth of energy being released into the local environment every day, not as an explosion, but as a continuous flow of waste heat.

The Valley Effect

One reason environmental scientists are paying close attention to the proposal is its location.

Hansel Valley is a high-desert basin where air circulation can become restricted, particularly at night when temperature inversions form and limit vertical movement of air. Davies’ analysis suggests this geography could trap heat within the valley rather than allowing it to disperse efficiently.

The preliminary modelling estimates daytime temperatures could rise by between 2°F and 5°F across parts of the valley, while night-time temperatures could increase by 8°F to 12°F. Although the figures remain subject to further study, the projections have raised concerns among environmental groups and local residents.

Potential Ecological Consequences

The concerns here extend beyond simple temperature increases. For example, desert ecosystems rely heavily on nightly cooling cycles that allow dew and frost to form. According to Davies, sustained increases in night-time temperatures could suppress these condensation cycles, reducing an important source of moisture for plants and wildlife. His report warns that “the ecological impact resulting from the combination of elevated temperatures and suppressed nighttime condensation is likely extreme”, while higher temperatures could also accelerate evaporation and increase drying across the region.

Campaigners have also questioned the potential impact on the wider Great Salt Lake watershed, an ecosystem already facing significant environmental pressures. The lake supports important wildlife habitats and migratory bird populations, making any additional environmental stress a sensitive issue.

The Other Side Of The Argument

Project supporters strongly dispute suggestions that the development will cause severe environmental harm.

Stratos argues that the project has been designed around “sustainable energy generation and water conservation technologies” and says advanced cooling systems will dramatically reduce water consumption compared with traditional data centre approaches.

Officials involved in the project also point out that the development remains subject to extensive environmental reviews, permitting processes, and regulatory oversight before construction can proceed.

Supporters further argue that much of the 40,000-acre project area will remain undeveloped and that the project could deliver thousands of jobs, major investment, and new infrastructure without placing additional demand on the state’s existing electricity grid.

Why This Matters Beyond Utah

The wider significance of the Stratos debate reaches far beyond a single project. As AI systems become larger and more powerful, the infrastructure needed to support them is expanding rapidly. Data centres already consume vast amounts of electricity, and future AI facilities are expected to demand even more energy, cooling capacity, and physical space.

The discussion surrounding Stratos highlights a growing sustainability challenge for the technology sector. While AI may help solve problems in healthcare, science, transport, and energy management, the infrastructure that powers those systems also carries environmental costs that are becoming increasingly difficult to ignore.

What Does This Mean For Your Organisation?

The Stratos Project illustrates a growing reality of the AI era, which is that digital technologies may appear weightless, but the infrastructure behind them is anything but. As governments and technology companies race to build larger AI facilities, questions about energy consumption, heat generation, water resources, emissions, and ecological impact are likely to become far more prominent.

Whether Stratos ultimately proves to be a model for sustainable AI infrastructure or a warning about the environmental consequences of hyperscale computing will depend on the outcome of future environmental assessments. What is already clear, however, is that sustainability discussions about AI are increasingly moving beyond software and algorithms and towards the physical footprint required to power the technology itself.

For organisations developing AI strategies, procuring cloud services, or pursuing sustainability targets, the debate serves as a reminder that the environmental impact of AI extends far beyond the applications employees see on their screens. As AI infrastructure grows, understanding the energy, resource, and environmental implications behind these technologies is likely to become an increasingly important part of responsible technology planning.

Tagged AI, data centres

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A New Approach To Cyber Defence

The proposed system, which could be operational within five years, would use what GCHQ describes as “cutting-edge agentic AI” to identify and respond to cyber threats at machine speed.

By embedding AI directly into national cyber defence systems protecting sectors such as energy, water, healthcare, transport, telecommunications, and financial services, GCHQ is proposing a fundamentally different approach to cyber security that relies far more heavily on automated threat detection and response.

Speaking during GCHQ’s inaugural Annual Lecture at Bletchley Park, Director Anne Keast-Butler revealed that “in the past few months, GCHQ has developed the blueprint for a new national cyber defence capability that will hardwire cutting-edge agentic AI into machine-speed cyber defence.”

Why GCHQ Believes Change Is Needed

The proposal reflects growing concern that cyber threats are becoming too numerous, too complex, and increasingly too fast for traditional defensive approaches.

According to Keast-Butler, the UK is entering what she described as a “new era of radical uncertainty, contested geopolitics and rapidly changing technology.”

The concern is not limited to criminal hackers. For example, GCHQ has repeatedly warned about increasing cyber activity linked to hostile states, particularly Russia and China, alongside growing threats to critical infrastructure, supply chains, democratic institutions, and public trust.

Keast-Butler warned that “Russia is scaling up its daily hybrid activity against the UK and Europe”, while GCHQ believes the country faces a “narrowing window for the UK and allies to stay ahead.”

At the same time, advances in artificial intelligence are transforming both attack and defence capabilities.

The rapid emergence of AI systems capable of discovering software vulnerabilities, analysing vast quantities of information, generating convincing phishing attacks, and automating complex tasks is creating new challenges for cyber defenders across government and industry.

As Keast-Butler observed, “the latest frontier AI is rapidly unearthing the fault lines in technologies that our society relies on every single day.”

How The Cyber Shield Could Work

Although detailed technical information remains limited, the concept appears to involve AI agents continuously monitoring critical systems, identifying anomalies, assessing threats, and potentially coordinating defensive responses far faster than human teams could achieve alone.

Reports suggest the system could allow government AI agents to work alongside private-sector security systems, creating a more coordinated national defence capability across multiple sectors.

The idea reflects a growing belief within the cyber security community that future defence systems will need to operate at machine speed if they are to keep pace with increasingly automated attacks.

However, it should be noted here that this does not mean removing humans from decision-making altogether. Rather, it suggests AI may increasingly handle the detection, analysis, prioritisation, and initial response stages, allowing human specialists to focus on higher-level investigation and strategic decisions.

Beyond Cyber Security Alone

The announcement also forms part of a wider transformation taking place inside GCHQ itself.

Keast-Butler revealed that the organisation is embedding frontier AI “responsibly and ethically” into its own operations to “enhance algorithms, translate foreign languages, and find needles in haystacks quicker than ever before.”

The agency is also preparing for emerging challenges associated with quantum computing, space-based technologies, and the protection of critical undersea cables and communications infrastructure.

In her lecture, Keast-Butler highlighted how technological change is accelerating across multiple fronts simultaneously, creating what she described as a “moment of consequence” for the UK and its allies.

Why This Matters

The significance of the proposal extends far beyond government networks.

For example, much of the UK’s critical infrastructure is actually owned and operated by private-sector organisations. Energy providers, telecommunications firms, transport operators, financial institutions, manufacturers, logistics companies, and healthcare suppliers all form part of the broader national infrastructure ecosystem.

If GCHQ succeeds in building an AI-driven cyber shield, it could become one of the first attempts anywhere in the world to create a national-scale cyber defence system built around autonomous AI capabilities.

The announcement also highlights how governments increasingly view cyber security as a national resilience issue rather than simply an IT problem.

Large-scale cyber attacks can disrupt supply chains, affect public services, damage economic activity, undermine confidence, and create wider national security risks. As a result, governments are looking beyond conventional security tools and exploring new approaches capable of operating at much greater speed and scale.

What Does This Mean For Your Business?

For businesses, the proposal is another reminder that cyber security is rapidly becoming more closely connected to national security, critical infrastructure protection, and artificial intelligence.

While a national cyber shield may eventually provide additional layers of protection, GCHQ’s message is not that organisations should wait for government systems to solve the problem. In fact, Keast-Butler urged businesses and citizens alike to make cyber security “ten times more urgent”.

The wider message is that AI is changing the economics of cyber security on both sides of the equation. Attackers are gaining access to increasingly capable tools, while defenders are being forced to adopt new technologies to keep pace.

As governments, businesses, and infrastructure operators increasingly turn to AI-powered defence systems, cyber security may become less about reacting to attacks after they happen and more about identifying and disrupting threats before they have an opportunity to cause harm.

The post GCHQ Turns To AI For National Cyber Defence – IT & Security News appeared first on Quayside Technical Services.

The Research

The findings come from SEO platform Ahrefs, which analysed brand visibility across ChatGPT, Google AI Mode, and Google AI Overviews to identify which factors were most closely associated with appearing in AI-generated responses.

The results suggest that the rules of online visibility may be changing as AI increasingly becomes the way people discover information, products, and services.

According to the study, mentions of a brand on YouTube showed the strongest correlation with AI visibility across all three platforms. These mentions included brand names appearing in video titles, transcripts, and descriptions.

Perhaps even more surprising was the fact that traditional SEO signals performed relatively poorly by comparison. Domain authority, backlinks, referring domains, and website size all showed significantly weaker relationships with whether a brand appeared in AI-generated answers.

Why YouTube Seems To Matter So Much

One reason YouTube may be proving so influential is that it occupies a unique position within the modern AI ecosystem.

Large language models learn from enormous volumes of publicly available text, and video transcripts have become one of the richest sources of conversational content on the internet. Google also regularly cites YouTube content within its own AI-generated responses, creating a feedback loop between the platform and AI search systems.

The study suggests that AI systems increasingly pay attention to where brands are being discussed, not simply what brands publish on their own websites.

“Your brand’s presence across the web—not just your own website—is what AI Overviews draw on when deciding whether to mention you.”

That represents a potentially significant change from traditional SEO thinking, which often focused heavily on optimising pages, building backlinks, and increasing domain authority.

Mentions Matter More Than Links

One of the most interesting findings was the strength of unlinked brand mentions.

Historically, SEO professionals have often focused on backlinks because search engines used them as a signal of authority. However, AI systems appear to place greater value on simply seeing brands discussed across a wide range of sources.

Ahrefs found that branded web mentions showed a much stronger relationship with AI visibility than backlinks. The company concluded: “Web mentions (0.664) correlate much more strongly than backlinks (0.218).”

The study also found that the strongest factors were all external signals rather than metrics controlled directly by a company’s own website. Branded web mentions, branded anchor text, and branded search volume all outperformed traditional technical SEO measurements.

Importantly, the findings do not suggest that backlinks or domain authority have become irrelevant. Rather, they suggest those signals may now form only part of a much wider picture that AI systems use when evaluating brands.

A Different Way Of Thinking About Visibility

The findings arrive at a time when AI-generated answers are increasingly replacing traditional search results.

Many users now receive answers directly from ChatGPT, Google AI Mode, or AI Overviews without ever clicking through to a website. This has led marketers to focus increasingly on how often AI systems mention their brands rather than where they rank on a search results page.

Ahrefs argues that visibility in AI search increasingly resembles reputation management rather than traditional SEO.

“The strongest correlations with AI Overview mentions are all off-site factors.”

The implication is that businesses may need to think less about producing ever larger volumes of content and more about encouraging genuine discussion across the wider web.

What Does This Mean For Your Business?

For businesses, the research suggests that visibility in AI search is becoming increasingly tied to brand awareness, reputation, and third-party discussion rather than purely technical SEO factors.

A strong YouTube presence may help, but the wider lesson is that AI systems appear to be paying close attention to what other people say about your business. Reviews, interviews, podcasts, media coverage, industry commentary, customer discussions, and creator content may therefore become increasingly important sources of visibility.

The study also challenges the idea that publishing more content automatically improves discoverability. Ahrefs found only a weak relationship between the number of pages on a website and AI visibility, suggesting that simply expanding content volume may deliver limited returns.

As AI search continues to evolve, businesses may need to think beyond their own websites and focus more heavily on building a presence across the wider digital ecosystem. The companies most likely to be recommended by AI increasingly appear to be those that people are already talking about, discussing, reviewing, and mentioning across the internet.

The post Why YouTube Mentions Matter More Than Backlinks In AI Search – IT & Security News appeared first on Quayside Technical Services.

A New Copilot Removal Policy

The change arrived as part of Microsoft’s April 2026 Windows 11 update and introduces a policy called “Remove Microsoft Copilot app”.

According to Microsoft’s own documentation, “This policy setting allows you to uninstall Microsoft Copilot from devices in a targeted way.” The company explains that the policy applies only under specific circumstances, including where Microsoft Copilot was not installed directly by the user and has not been used recently.

Microsoft also states: “The Microsoft Copilot app will be uninstalled. Users can still re-install if they choose to.”

Although that may sound like a relatively minor technical change, it represents quite a notable change in Microsoft’s approach to AI deployment.

Why Microsoft Is Making The Change

For the past two years, Microsoft has invested heavily in positioning Copilot as a central part of the Windows and Microsoft 365 experience.

The company has integrated AI into Windows, Office applications, security products, development tools, search functions, and business workflows. Microsoft’s long-term strategy clearly assumes that AI assistants will become a standard part of everyday computing.

However, adoption has not always matched the enthusiasm coming from technology vendors.

Many organisations remain cautious about introducing AI assistants into business environments due to concerns around governance, licensing costs, staff training, compliance obligations, data protection, and uncertainty about where AI genuinely improves productivity.

The introduction of an official removal policy seems to suggest Microsoft now recognises that many organisations still want the ability to decide for themselves when, where, and how AI tools should be deployed.

Importantly, Microsoft has not positioned the policy as a rejection of AI. Instead, it is being presented as a management and governance tool that gives administrators greater control over managed devices.

What The Policy Actually Does

The policy is aimed primarily at Enterprise, Education, and other managed environments where IT teams oversee large numbers of devices.

Rather than preventing Copilot from ever being installed, the policy allows administrators to remove inactive installations that meet Microsoft’s criteria.

This distinction matters because Microsoft is not abandoning its AI strategy. The company is simply providing organisations with more flexibility around deployment.

The documentation makes clear that users retain the ability to reinstall Copilot if they choose to do so later.

It is also important to understand that removing the Copilot app does not remove artificial intelligence from Windows entirely.

AI-powered capabilities remain embedded across numerous Microsoft products and services, including Microsoft 365, security tools, developer platforms, cloud services, and various operating system features.

In practical terms, the policy removes one specific application rather than reversing Microsoft’s broader AI integration strategy.

A Wider Industry Trend

The change actually reflects a broader trend emerging across the technology industry.

Many software providers initially approached AI as a feature that should be added everywhere as quickly as possible. Increasingly, vendors are discovering that customers want flexibility, transparency, and control alongside innovation.

Businesses are often willing to adopt AI where there is a clear business case, measurable productivity gains, or operational benefits. Resistance tends to emerge when tools appear to be imposed without clear governance frameworks or obvious value.

This is particularly true in regulated sectors where organisations must consider compliance, security, auditability, and data handling requirements before introducing new technologies.

Microsoft’s decision to introduce a removal policy can therefore be viewed as a recognition that successful AI adoption depends as much on customer trust and organisational readiness as it does on technical capability.

What Does This Mean For Your Business?

For businesses, the announcement is less about uninstalling one application and more about the growing importance of AI governance.

Many organisations are still working out which AI tools genuinely improve productivity, which require additional oversight, and which may create unnecessary complexity or cost. The ability to manage deployment more precisely gives IT teams greater flexibility while those decisions are being made.

The wider significance is that the AI market appears to be entering a more mature phase. Rather than simply asking how quickly AI can be rolled out, businesses are increasingly asking where it delivers value, how it should be governed, and whether users actually want it.

Microsoft’s new policy suggests the company understands that customer choice will remain an important part of AI adoption, even as artificial intelligence becomes more deeply embedded throughout Windows and the wider software ecosystem.

The post Microsoft Makes Copilot Optional In Windows 11 – IT & Security News appeared first on Quayside Technical Services.

Why?

For most businesses, AI is still thought of primarily as chatbots, virtual assistants, or productivity tools.

Behind the scenes, however, every AI request consumes computing resources that carry real costs. For example, every prompt submitted to ChatGPT, Claude, Gemini, or other large language models requires processing power, memory, storage, networking, and electricity.

Increasingly, those costs are being measured and priced using tokens, which are the units that represent how much information an AI system processes.

In practical terms, this means that tokens are becoming one of the fundamental economic building blocks of the AI industry. Every question asked, every document analysed, every image generated, and every AI agent action consumes them.

As AI adoption accelerates, token consumption is growing rapidly across governments, businesses, software providers, and consumers.

The Emergence Of AI Markets

It seems that growth is now attracting the attention of financial markets. For example, reports indicate that China’s Shanghai Futures Exchange is now exploring the development of futures contracts linked to AI tokens, while major US exchanges are examining futures products linked to AI computing power.

Although these developments remain at an early stage, they point towards a significant change in how AI infrastructure may eventually be bought and sold.

Traditionally, futures contracts allow organisations to manage uncertainty by locking in future prices for important resources. Airlines hedge jet fuel prices, manufacturers hedge metal costs, and energy companies hedge electricity and gas prices.

As AI becomes a core operational expense, similar financial mechanisms may begin emerging around AI infrastructure itself.

What Are Businesses Actually Buying?

One reason this development may seem unusual is that many organisations never directly see the underlying economics of AI. Most users simply pay a monthly subscription or software licence.

Behind those subscriptions, however, AI providers are already pricing services based on token usage, processing volumes, and computing consumption. The more powerful the model, the larger the context window, and the greater the volume of activity, the higher the underlying costs become.

Large organisations running AI-powered customer support, software development, research, analytics, and automation systems can consume vast quantities of tokens every day.

As AI becomes embedded across more business processes, controlling those costs becomes increasingly important.

The Infrastructure Race

This also helps explain why technology companies, cloud providers, semiconductor manufacturers, and investors are spending hundreds of billions of pounds on AI infrastructure.

The industry is currently experiencing one of the largest technology infrastructure buildouts in history.

New data centres are being constructed across the world. GPU manufacturers are expanding production. Cloud providers are investing heavily in additional capacity. Entire businesses are emerging to rent computing power to AI developers.

The underlying assumption is that demand for AI processing will continue growing for years. If that happens, the resources needed to power AI systems could become increasingly valuable in their own right.

A New Asset Class?

Some industry figures believe AI computing resources could eventually develop into an entirely new financial asset class, the logic being that businesses already trade commodities that are essential to economic activity, i.e., electricity powers factories, oil fuels transport networks, and natural gas supports manufacturing and heating.

AI is increasingly becoming part of the infrastructure that powers knowledge work, decision-making, automation, software development, customer service, and business operations.

As organisations become more dependent on AI, the costs associated with computing power and token consumption may become significant enough to justify dedicated financial markets.

This would allow companies to manage future price volatility and provide investors with new ways to gain exposure to the growth of AI infrastructure.

Why This Matters

The bigger story is not really about financial derivatives. The more important issue is that AI is increasingly being treated as a utility rather than simply a software product.

Most technology platforms charge for access to applications. Increasingly, AI providers are charging for consumption of intelligence itself. That distinction may sound subtle, but it represents quite a change.

Historically, businesses bought software licences but increasingly, they are purchasing access to processing capability, model capacity, and AI-generated outputs. It now seems that the financial markets are beginning to recognise that change.

What Does This Mean For Your Business?

For businesses, the emergence of AI-related futures markets is another indication that AI is rapidly becoming part of the world’s economic infrastructure rather than simply another technology trend.

Of course, most organisations won’t be trading AI token futures any time soon. However, they are likely to feel the effects indirectly as AI costs become more visible, more measurable, and more closely linked to underlying computing resources.

As AI becomes embedded into customer service, marketing, software development, administration, research, and operational workflows, understanding how AI usage translates into business costs will become increasingly important.

The wider significance is that the technology industry appears to be moving towards a future where intelligence itself becomes a metered resource. If financial markets are already beginning to build mechanisms for trading and hedging AI consumption, it suggests many investors and infrastructure providers believe AI will become as economically important as the utilities and commodities that underpin the modern economy today.

Posted in Artificial intelligence (AI)

The post Why Financial Markets Are Starting To Trade AI – IT & Security News appeared first on Quayside Technical Services.

Project Glasswing

The findings come from Project Glasswing, a restricted cybersecurity initiative launched by Anthropic to help protect critical software infrastructure before increasingly capable AI systems can be used by attackers.

At the heart of the programme is Claude Mythos Preview, a specialised version of Anthropic’s AI designed specifically for vulnerability discovery, software analysis, and cyber defence tasks.

Unlike publicly available AI models, Mythos Preview has only been made available to around 50 carefully selected partners, including organisations responsible for maintaining and defending some of the world’s most important digital infrastructure.

According to Anthropic, those partners have collectively used the system to find “more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world” in just one month.

The Scale Of What Was Found

Anthropic says its partners have identified more than 10,000 high- or critical-severity vulnerability candidates. Of those, over 1,700 have already been verified as genuine security flaws, while more than 1,000 have been confirmed as high- or critical-severity vulnerabilities.

The company says it’s also been using Mythos Preview internally to scan more than 1,000 open-source software projects that underpin large parts of the internet.

So far, Anthropic says the model has identified 6,202 potential high- or critical-severity vulnerabilities within those projects alone. After detailed assessment by independent security researchers, 1,094 have already been confirmed as genuine high- or critical-severity flaws.

One example involved a serious vulnerability in wolfSSL, a widely used cryptographic library deployed across billions of devices. Anthropic says Mythos Preview discovered a flaw that could have allowed attackers to forge digital certificates and impersonate legitimate online services. The vulnerability has since been patched.

Finding Bugs Is No Longer The Bottleneck

Perhaps the most important aspect of the announcement is that Anthropic believes the economics of cybersecurity may now be changing thanks to AI.

Historically, security teams struggled to find vulnerabilities quickly enough, but now the company believes the opposite problem is emerging.

“Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it’s limited by how quickly we can verify, disclose, and patch the large numbers of vulnerabilities found by AI.”

In other words, AI may be becoming so effective at discovering software flaws that human security teams cannot process, investigate, and fix them quickly enough.

Industry-Wide

That concern appears to be reflected across the industry. For example, Anthropic points to reports from Microsoft that patch volumes are expected to continue rising, while Oracle has already accelerated its patching schedules. The company also says Cloudflare found 2,000 bugs across critical systems while using Mythos Preview, including 400 classified as high- or critical-severity. Mozilla reportedly found more than ten times as many vulnerabilities in one Firefox testing cycle compared with earlier testing using conventional methods.

More Than Just Vulnerability Hunting

Anthropic says Mythos Preview has also shown value beyond traditional vulnerability discovery.

For example, one banking partner reportedly used the system to identify and prevent a fraudulent $1.5 million wire transfer after attackers compromised a customer email account and used spoofed phone calls to support the fraud attempt.

The company argues this demonstrates how advanced AI could increasingly act as a defensive force multiplier, helping cyber defenders analyse vast quantities of information far more quickly than human analysts alone.

However, Anthropic is also being careful about how widely it releases these capabilities. The company has not made Mythos Preview publicly available because it believes safeguards remain insufficient to prevent misuse.

“At present, no company, including Anthropic, has developed safeguards strong enough to prevent such models from being misused and potentially causing severe harm.”

Why This Matters

The announcement seems to highlight a broader change taking place across cybersecurity. For years, security professionals worried about attackers using AI to create phishing campaigns, malware, and social engineering attacks. Increasingly, attention is turning towards AI-assisted vulnerability discovery, where software flaws can be found at unprecedented speed and scale.

Anthropic itself acknowledges the challenge directly, saying: “The relative ease of finding vulnerabilities compared with the difficulty of fixing them amounts to a major challenge for cybersecurity.”

That challenge becomes even more significant if similar capabilities become widely available across the industry. Although Anthropic has restricted access to Mythos Preview, the company openly states that models with comparable capabilities are likely to emerge elsewhere and eventually become more broadly accessible.

What Does This Mean For Your Business?

For businesses, the most important takeaway here is that vulnerability discovery is accelerating rapidly, which means the value of slow patching cycles is diminishing just as quickly.

Many organisations still spend weeks or months testing and deploying updates, particularly in operational technology, manufacturing, healthcare, and other environments where change control is complex. As AI systems become better at uncovering vulnerabilities, those delays could create increasingly attractive opportunities for attackers.

Anthropic is urging organisations to focus on fundamentals such as:

• Faster patch deployment
• Stronger network configurations
• Multi-factor authentication
• Comprehensive security logging

Those recommendations are not new, but the urgency behind them is growing because AI is dramatically reducing the effort required to find weaknesses in software.

The wider message is that AI is changing the balance between attackers and defenders. For now tools such as Mythos Preview may provide what Anthropic describes as an “asymmetric advantage” for defenders. The question facing the cyber security industry is how long that advantage will last once similar capabilities become widely available.

The post AI Finds Bugs Faster Than They Can Be Patched – IT & Security News appeared first on Quayside Technical Services.

Why It Works

Most people use ChatGPT in the same chat environment all the time. Temporary Chat creates a separate space for conversations that you don’t want appearing in your normal chat history and don’t want influencing future conversations through Memory.

It’s particularly useful when you need help thinking through a sensitive business issue, drafting a confidential document, reviewing a commercial situation, or brainstorming ideas that are only relevant to a specific project.

How To Use It

1. Open ChatGPT.
2. Click the Temporary Chat icon, the dotted or broken speech bubble, near the top right of the screen.
3. A new Temporary Chat window will open.
4. Start your conversation as normal.
5. When you have finished, simply close the chat.
6. Return to your normal chats whenever you want to continue using your saved chat history and Memory.

What To Look For

You’ll see a Temporary Chat indicator within the conversation, confirming that the chat won’t appear in your history and won’t be used to update Memory.

The Business Benefit

Temporary Chat helps keep sensitive discussions separate from your everyday work, reduces clutter in your chat history, and provides a convenient way to discuss one-off projects, client matters, HR issues, and other sensitive topics without them becoming part of your ongoing ChatGPT record.

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Why Wove Has Been Created

Consumers have become increasingly accustomed to checking ingredients in food, cosmetics and household products. Clothing, however, remains one of the least transparent consumer categories despite spending most of the day in direct contact with the body.

Wove, a North Carolina-based consumer technology startup focused on clothing transparency, allows users to upload a product photo, screenshot, clothing label, shopping URL or product description. The Wove app then analyses the garment and produces a score based on factors including fibre composition, microplastic shedding potential, PFAS concerns and overall sustainability. According to the company, its goal is to reveal “the information brands don’t put on the label” and provide “transparency you can trust”.

The company is also keen to stress its independence, stating: “No manufacturer or brand can influence the scores or recommendations Wove provides. Every grade is earned, never purchased.” That positioning reflects growing consumer scepticism around sustainability claims and greenwashing across the fashion industry.

The Growing Concern Around Synthetic Fabrics

Growing awareness of microplastics and chemical exposure is helping to drive interest in tools such as Wove. According to figures cited by the company, synthetic fibres represented around 73 per cent of global fibre production in 2023, up from approximately 45 per cent in 1996. Polyester alone now accounts for more than half of all fibre production worldwide.

Microplastics are tiny plastic particles released as synthetic fabrics wear and are washed. These particles can enter waterways, oceans and ecosystems, contributing to a growing environmental challenge. PFAS chemicals, often used to provide water and stain resistance, have attracted increasing regulatory attention because they persist in the environment for extremely long periods and have been linked by researchers to a range of potential health concerns.

Despite growing awareness of microplastic pollution, research cited by Wove suggests many consumers still do not associate their clothing with the issue. A 2025 survey found that only 42 per cent of consumers who were aware of microplastics connected them directly to clothing, highlighting the knowledge gap the app is designed to address.

Regulation Is Adding Momentum

The launch also comes at a time when regulators are paying closer attention to chemicals used in textiles.

France introduced a ban on PFAS in textiles from January 2026, California already restricts intentionally added PFAS in clothing, and the European Union is tightening controls on related substances under its REACH chemicals framework. At the same time, public awareness has increased following documentaries such as Netflix’s The Plastic Detox, which highlighted concerns around microplastics, synthetic materials and chemical exposure.

Wove founder Emily Hemphill says the platform was inspired by the fact that many people have already made changes to areas such as food, drinking water and skincare products, while “clothing is often the last blind spot”.

Similar Services

Wove is not the only company attempting to improve transparency within the fashion industry, although its focus on chemical and microplastic exposure appears relatively distinctive.

Apps such as Good On You allow consumers to assess fashion brands based on environmental, labour and animal welfare criteria, helping shoppers compare thousands of brands using independent sustainability ratings. Other platforms such as Renoon focus on helping consumers discover products with stronger sustainability credentials, while services such as Save Your Wardrobe aim to extend garment life through repair, care and circular economy initiatives.

What sets Wove apart is its focus on the composition of individual garments rather than the sustainability performance of brands as a whole. By analysing specific products, it attempts to provide consumers with a more detailed understanding of what they are actually wearing and what environmental or health concerns may be associated with those materials.

What Does This Mean For Your Organisation?

The emergence of tools such as Wove reflects growing demand for transparency across supply chains. Consumers, regulators and investors increasingly want clearer information about the materials used in products and their environmental impact, and technology is making it easier than ever for people to access that information for themselves.

For clothing brands, retailers and manufacturers, this trend could increase pressure to provide more detailed information about fibre composition, chemical treatments and sustainability performance. Products that once relied on broad claims such as “eco-friendly” or “sustainable” may increasingly face scrutiny from consumers who expect more specific evidence about what materials are being used and how those materials affect health and the environment.

UK businesses should also view developments such as Wove in the context of wider sustainability and regulatory trends. As governments continue to tighten rules around chemicals, environmental reporting and product transparency, organisations that can clearly explain the contents and environmental impact of their products are likely to be better placed than those that cannot. Whether Wove itself becomes widely adopted remains to be seen, but the direction of travel is becoming increasingly clear: consumers are beginning to expect the same level of transparency from clothing that they already expect from food, cosmetics and other everyday products.

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