Artificial intelligence emerged as a bigger concern than nuclear weapons during a major strategic stability discussion at the Shangri-La Dialogue in Singapore, as senior military leaders warned that AI is accelerating the speed of conflict beyond the pace of human decision-making.

Why AI Dominated The Discussion

The Shangri-La Dialogue is one of the world’s most important defence and security forums, bringing together defence ministers, military commanders, policymakers, and security experts from across the Indo-Pacific and beyond.

Traditionally, discussions about strategic stability have focused heavily on nuclear deterrence, missile defence, arms control, and the balance of power between major states. This year, however, much of the conversation centred on artificial intelligence and the risks it could introduce into military decision-making.

The concern was not that AI is becoming conscious or uncontrollable. Rather, military leaders repeatedly highlighted the possibility that AI-enabled systems could compress decision-making timelines so dramatically that humans struggle to assess situations properly before responding.

Lieutenant General Nauman Zakria, Commander of 1 Corps and the Army Rocket Force Command of the Pakistan Army, explained the problem through the military concept known as the OODA loop, which stands for Observe, Orient, Decide, and Act.

According to Zakria, AI is compressing that cycle to the point where “a human can’t evaluate the situation fast enough.”

He warned that under such conditions, “People will act irrationally, and the actions will be extreme.”

Why Military Leaders Are So Worried

The concern centres on escalation. For example, for decades, strategic stability has relied on the assumption that leaders have sufficient time to evaluate information, consult advisers, communicate with allies, and consider the consequences of military action.

AI-enabled systems have the potential to analyse data, identify threats, recommend responses, and support operational decisions far faster than human decision-makers can process information themselves.

That speed may offer significant military advantages. However, crucially, it also raises the possibility that misunderstandings, false alarms, technical errors, or incorrect threat assessments could trigger responses before humans have time to intervene.

The faster the decision cycle becomes, the less opportunity there is to question assumptions or prevent mistakes.

Already On The Battlefield

Several speakers stressed that these concerns are no longer theoretical. For example, General Onno Eichelsheim, Chief of Defence of the Netherlands, pointed to the growing use of AI in active conflicts, including Ukrainian efforts to anticipate Russian attacks and coordinate drone operations. The United States has also confirmed using AI tools to support military planning and targeting decisions.

As Eichelsheim put it: “AI is a huge risk in escalation. I think that’s clear.”

At the same time, he acknowledged that military adoption is unlikely to slow, adding: “But I’m not naive. It’ll be used in the domain. It is already being used.”

Those comments reflect a growing reality that military organisations increasingly see AI as a capability they cannot afford to ignore, even while recognising the risks associated with it.

The Pace Is Accelerating

What makes these concerns more pressing is the speed at which military AI capabilities are evolving. Increasingly, AI is being integrated with drones, autonomous systems, and robotics rather than operating solely as a decision-support tool.

For example, US startup Foundation Future Industries, which has Eric Trump as an adviser, recently tested humanoid robots in Ukraine with support from Ukrainian authorities. The company says it hopes to deploy more advanced versions with military forces within the next 12 to 18 months. Although the trials focused on logistics rather than combat, they illustrate how AI, robotics, and military operations are becoming increasingly interconnected.

Taken together, these developments suggest that AI is moving beyond the planning room and becoming a more active part of military operations, raising further questions about human oversight, accountability, and control during conflict.

A Humanitarian Concern

The strongest warning came from Mirjana Spoljaric, President of the International Committee of the Red Cross. While military leaders focused largely on strategic and operational risks, Spoljaric highlighted the humanitarian implications of increasingly automated warfare.

She warned that the growing distance between decision-makers and the battlefield creates new challenges for accountability and civilian protection.

“We don’t know where the trigger is pulled,” she said. “It could be thousands of kilometres away.”

Spoljaric also argued that while AI may eventually offer benefits for civilian protection, current deployments are highlighting more of the risks than the advantages.

Her comments reflect wider concerns among humanitarian organisations that meaningful human control over lethal decisions could become increasingly difficult to maintain as AI capabilities advance.

Nuclear Weapons Have Not Gone Away

It should be noted here, however, that nuclear deterrence remained part of the discussion. Major General Meng Xiangqing of China’s People’s Liberation Army reaffirmed China’s long-standing no-first-use nuclear policy and proposed broader commitments among nuclear powers.

“If we can do so, we can reduce the risk and we can further enhance strategic stability,” he said. However, the fact that AI repeatedly returned to the centre of the discussion was itself notable.

A panel that would traditionally be dominated by nuclear strategy spent much of its time examining how artificial intelligence could reshape military escalation, crisis management, and conflict decision-making.

What Does This Mean For Your Business?

For businesses, the discussion highlights how quickly AI is moving from a productivity and automation tool into an issue of national security, geopolitics, and strategic risk.

Many organisations currently view AI primarily through the lens of efficiency, customer service, software development, or data analysis. Governments and defence planners are increasingly focused on a different question, namely what happens when AI systems begin influencing decisions where mistakes carry serious consequences.

The wider significance is that AI’s impact may ultimately extend far beyond individual applications or business processes. The technology is beginning to affect how countries think about defence, deterrence, crisis management, and international stability.

The message emerging from the Shangri-La Dialogue was not that AI should be stopped. Rather, it was that societies may need to think much more carefully about how much decision-making authority is delegated to systems that can operate faster than humans can fully understand the situations they are responding to.

Microsoft has drawn criticism from parts of the cyber security community after publicly condemning a researcher who disclosed several unpatched Windows vulnerabilities and warning that its Digital Crimes Unit would continue pursuing those who enable criminal activity.

What Happened?

The dispute centres on a researcher known online as “Nightmare Eclipse”, who recently published proof-of-concept exploit code for a series of vulnerabilities affecting Microsoft Defender and BitLocker.

The flaws, which became known as BlueHammer, RedSun, UnDefend, YellowKey, GreenPlasma, and MiniPlasma, were disclosed publicly before Microsoft had released patches for all of them. Some have since been assigned CVE identifiers, while Microsoft and the US Cybersecurity and Infrastructure Security Agency (CISA) have confirmed that certain vulnerabilities have been exploited in real-world attacks.

Microsoft argues that the disclosures put customers at unnecessary risk because the company was not given sufficient opportunity to investigate and fix the flaws before exploit code became publicly available.

In a post published by the Microsoft Security Response Center, the company said: “The details of these vulnerabilities were not shared with Microsoft prior to release, and the disclosures put our customers at unnecessary risk.”

The company also stated: “Uncoordinated disclosures that put proof-of-concept code for unpatched vulnerabilities into the hands of bad actors are never justifiable and have real-world consequences.”

Why The Response Has Proved Controversial

The strongest reaction did not come from Microsoft’s criticism of the disclosures themselves, but from language used elsewhere in the company’s statement.

Microsoft wrote that its Digital Crimes Unit “will continue bringing cases against these actors and those that enable their criminal activity – coordinating as needed with law enforcement around the world.”

Many researchers interpreted that wording as a threat directed at vulnerability researchers, particularly given the public nature of the dispute.

The controversy intensified because Nightmare Eclipse claims to have previously attempted to engage with Microsoft through its Microsoft Security Response Center process before later having their account revoked. Microsoft has not publicly addressed those specific claims.

The researcher subsequently published the vulnerabilities through GitHub and GitLab, and their accounts on both platforms have since been removed.

A Debate That Has Been Running For Decades

The dispute touches on one of cyber security’s longest-running arguments about how vulnerabilities should be disclosed.

For example, most of the industry now follows what is known as Coordinated Vulnerability Disclosure, or CVD. Under this model, researchers privately notify software vendors about security flaws, give them time to investigate and develop fixes, and then publish technical details once patches become available.

Microsoft strongly supports that approach. In its statement, the company described CVD as “the industry standard” and said it works with “hundreds of security researchers” each year through the process.

However, critics argue that disclosure relationships only work when vendors respond quickly, communicate effectively, and treat researchers fairly. When researchers believe their concerns are being ignored, disputes can arise over whether public disclosure becomes justified.

The disagreement is significant because independent researchers play a major role in identifying vulnerabilities that software vendors might otherwise miss.

Why This Matters Beyond Microsoft

The row has happened at a time when vulnerability discovery is accelerating across the industry. For example, recent advances in AI-assisted security research are enabling researchers and organisations to identify flaws at unprecedented speed. At the same time, software suppliers are facing growing backlogs of vulnerabilities to investigate, validate, and patch.

That creates tension on both sides. Vendors want time to protect customers before details become public. Researchers want assurance that their findings will be taken seriously and addressed promptly.

The result is growing pressure on disclosure processes that were designed for a slower era of software development and vulnerability discovery.

The wider concern expressed by many researchers is that aggressive responses to disclosure disputes could discourage future reporting.

Microsoft itself acknowledged the importance of the research community, stating: “Our team will continue to support responsible research as we do everything we can to quickly investigate, address, and release updates for vulnerabilities that impact our customers.”

The company also said: “We always have and will continue to welcome vulnerability submissions from anyone through our public researcher portal, regardless of past interactions or reputation.”

What Does This Mean For Your Business?

For businesses, the most important issue here is not really the disagreement itself but the vulnerabilities at the centre of it.

The Defender and BitLocker flaws highlight how even widely trusted security tools can contain weaknesses that attackers may seek to exploit. Organisations should therefore continue prioritising patch management, endpoint monitoring, vulnerability scanning, and defence-in-depth controls rather than assuming any single security product provides complete protection.

The wider lesson is that the relationship between software vendors and independent researchers remains an essential part of cyber security. Vulnerabilities are often discovered by external researchers long before vendors become aware of them, making cooperation between the two groups critical to keeping systems secure.

Whether Microsoft handled this particular dispute correctly will continue to be debated. However, most security professionals would at least agree that a disclosure process that encourages researchers to report vulnerabilities and vendors to fix them quickly remains one of the most important defences the industry has.

Literary agents are increasingly being impersonated by scammers as AI makes it easier to create convincing fake identities, websites, emails, and publishing credentials.

Mark Gottlieb of Trident Media Group says artificial intelligence has not simply accelerated publishing fraud but has “industrialised it”. Scammers can now quickly create realistic agency websites, correspondence, and professional profiles.

The scams exploit the fact that many aspiring authors have limited experience working with literary agents and often communicate remotely. Fraudsters can therefore pose as legitimate agents and offer representation or publishing opportunities.

The threat is also evolving beyond upfront fees. Stolen manuscripts can be turned into counterfeit ebooks, fake audiobooks, unauthorised translations, or AI-generated derivative works.

Businesses and individuals should verify identities through official channels, check websites and email domains carefully, and be cautious of unexpected approaches. As AI makes impersonation more convincing, confirming who you are dealing with is becoming an increasingly important security measure.

The European Commission is encouraging households to move electricity consumption away from peak periods as rising demand from AI data centres, electrification, and digital infrastructure places growing pressure on Europe’s power grids.

What The EU Has Announced

As part of its new Strategic Roadmap for Digitalisation and Artificial Intelligence in Energy, the European Commission has outlined plans to accelerate the rollout of smart meters and other digital technologies designed to help consumers use electricity when demand is lower and prices are cheaper.

The initiative forms part of a broader effort to modernise Europe’s energy system while managing rapidly growing electricity demand.

Alongside the roadmap, the Commission has also introduced a Data Centre Energy Efficiency Package that includes a new rating scheme for data centres and lays the groundwork for future minimum energy performance standards.

According to the Commission, digital solutions can help consumers “shift consumption to hours when electricity is cheaper and thereby lower their energy bills.”

The Commission believes that greater demand-side flexibility could reduce electricity costs for EU consumers by more than €71 billion per year.

Why Data Centres Are Becoming Part Of The Energy Debate

The growing focus on electricity demand is closely linked to the rapid expansion of AI infrastructure.

Training and operating advanced AI models requires vast computing resources, much of which is housed in large-scale data centres. As AI adoption accelerates, so does the amount of electricity needed to power and cool those facilities.

According to the Commission, data centres already account for around 2.5 per cent of EU electricity consumption, and demand is expected to more than double over the next four years.

At the same time, electricity demand is also increasing from electric vehicles, heat pumps, hydrogen production, and the wider electrification of the economy.

The result is a growing challenge for policymakers attempting to balance economic growth, climate goals, energy security, and affordability.

Ireland Offers A Glimpse Of The Challenge

Ireland provides one of the clearest examples of the pressures that can emerge when data centre growth outpaces energy infrastructure investment.

Data centres now consume more than 22 per cent of Ireland’s national electricity supply, making it one of the most concentrated data centre markets in the world.

The issue has become significant enough that some proposed developments have faced planning and grid-capacity challenges. Concerns have also been raised about the potential impact on electricity prices in regions with large concentrations of digital infrastructure.

While AI data centres are not the sole cause of rising energy demand, they are becoming an increasingly visible contributor to a broader capacity challenge affecting many countries.

A Difficult Balancing Act

The situation highlights a growing tension within European policy. For example, on one hand, the EU wants to accelerate AI development and reduce dependence on foreign technology providers. On the other, the infrastructure required to support those ambitions consumes large amounts of electricity at a time when Europe is simultaneously trying to decarbonise its economy and keep energy affordable.

The Commission argues that digitalisation can help address part of the problem. The roadmap notes that AI-based optimisation of energy systems could improve efficiency, reduce waste, and make better use of existing infrastructure.

As the Commission states, “Tech sovereignty in the energy sector is therefore more urgent than ever” while digital technologies can help create “a clean, competitive and secure EU energy system.”

However, efficiency improvements alone may not solve the underlying challenge if electricity demand continues to grow faster than generation and grid capacity.

What Does This Mean For Your Organisation?

For organisations, the announcement highlights a sustainability issue that is likely to become increasingly important over the next decade.

AI offers significant opportunities for innovation, productivity, and economic growth. However, the infrastructure required to support those benefits has real environmental and energy consequences that governments, businesses, and consumers will need to manage.

The Commission’s response suggests that future energy policy may focus not only on generating more electricity but also on using existing capacity more intelligently through smart meters, AI-enabled grid management, demand flexibility, and stricter efficiency standards.

The wider lesson is that the sustainability debate around AI is moving beyond questions about individual technologies and towards a much larger discussion about how societies generate, distribute, and consume energy in an increasingly digital world.

The new Copilot PowerPoint agent can turn a simple prompt into a fully structured presentation, helping you create polished slides in minutes while reducing manual effort and saving time.

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

Google Lens in Chrome can extract text directly from paused videos, webinars, Teams recordings, training videos, and online presentations, making it easy to copy URLs, settings, serial numbers, product codes, or notes without having to type them manually.

Why It Works

Most people associate Google Lens with photographs, but it can also analyse any image displayed in your browser, including individual frames from videos.

This means you can quickly capture information displayed on screen, even if the video creator hasn’t provided it elsewhere.

It’s particularly useful for copying technical settings, website addresses, software licence information, presentation content, contact details, or troubleshooting instructions from training videos and webinars.

How To Use It

1. Open the video in Google Chrome.

2. Pause the video on the frame containing the text you want to copy.

3. Right-click the video frame.

4. Select ‘Search Image With Google Lens’.

5. When the Lens panel opens, select the ‘Text’ option.

6. Highlight the text and click ‘Copy Text’.

A Few Things To Remember

Google Lens works best when the text is clear and reasonably large on screen.

It can be used with YouTube videos, online training platforms, recorded Teams meetings, webinars, presentations, screenshots, and many other types of visual content.

For business users, it’s a simple way to save time and avoid mistakes when copying information from video-based training or support materials.

Anthropic says its experimental cybersecurity AI has already uncovered more than 10,000 high- or critical-severity vulnerabilities across some of the world’s most important software systems, highlighting what could become one of the biggest challenges facing cyber security in the AI era.

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.

As Anthropic explains: “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.

As Anthropic notes: “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, and 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.

In this Tech Insight, we look at how artificial intelligence is becoming so economically important that financial markets are starting to treat it like a tradable commodity.

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.

Microsoft has introduced a new Windows 11 policy that allows organisations to remove the Microsoft Copilot app from managed devices, giving IT teams greater control over how AI is deployed.

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.

A study of 75,000 brands has found that being talked about on YouTube may now be a stronger predictor of AI visibility than backlinks, domain authority, or even the size of a company’s website.

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.

As Ahrefs notes: “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.

As the company explains: “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.