Tag: AI

Tech Insight : Supreme Court Weighs Limits On Data Breach Claims

Posted on by Paul Stradling

The Supreme Court is considering whether compensation claims for data protection breaches must meet a minimum level of seriousness, in a case that could affect how businesses handle demands for damages as lawyers warn that AI is making complaints easier to produce.

What Is The Court Deciding?

The court heard arguments in Farley v Paymaster on 7–8 October, and its judgment is awaited. Its central question is whether a “threshold of seriousness” applies to damages claims under the General Data Protection Regulation, or GDPR, and the Data Protection Act 2018.

In practical terms, that means the judges must decide whether someone who suffers genuine but minor harm from a breach can receive compensation, or whether the harm must first reach a minimum level. This is different from deciding that every mistake involving personal information deserves a payout.

How Did The Case Begin?

The dispute concerns pension statements sent to outdated addresses in 2019 by Paymaster, which trades as Equiniti. The 432 respondents are current or former Sussex Police officers whose statements included dates of birth, National Insurance numbers, salary information and pension details.

Sussex Police had supplied updated addresses, but the statements actually went to previous ones. The officers then sought compensation for the emotional effects of fearing their information could be misused, with some also even alleging psychiatric injury.

The High Court initially dismissed most claims because the individuals couldn’t show that their statements had been opened and read. However, the Court of Appeal overturned that decision in August 2025, finding that proof of disclosure was not essential to a data protection claim.

Why Does The Distinction Matter?

Data protection law covers how information is collected, organised, stored and used, so a failure can occur before somebody else reads it. However, establishing that an organisation handled information incorrectly is separate from showing that the mistake caused harm for which compensation is available.

The Court of Appeal found that no minimum seriousness threshold applied, but it didn’t award compensation to everyone. For example, claims still required individual assessment, including whether fears about possible misuse were objectively justified rather than speculative.

That distinction is really central to the appeal because Equiniti wants a threshold excluding trivial harm, while the opposing argument is that requiring proof of actual harm already provides a safeguard against unsupported demands.

What Are The Competing Arguments?

Equiniti’s argument is that minor annoyance shouldn’t lead to compensation, particularly where the cost of pursuing a claim is out of proportion to the harm involved. It also questions how much distress this incident actually caused, pointing out that the statements were sent to a limited number of households and that few officers took up its offer of fraud protection.

It seems the regulator agrees that an upset reaction does not automatically justify a payout, stating that “Not all emotional responses to an infringement of the GDPR will constitute recoverable non-material damage”. However, it argues that people who can show they suffered harm should not also have to prove that it reached a particular level of seriousness.

With that approach, courts would examine the evidence behind each claim and use existing procedures to keep the time and costs appropriate to its value. Equiniti’s objections would therefore be considered when assessing the claims, rather than treated as proof that the officers had no grounds to seek compensation.

The wider concern is that one failure could affect thousands of people while causing only modest harm to each. A minimum seriousness threshold could prevent those individuals from receiving compensation, even where the same mistake had consequences across a large group.

Where Does AI Fit In?

Although the case isn’t about AI, it comes at a time when lawyers are warning that businesses are spending more time dealing with AI-generated complaints. Letters containing incorrect legal references or irrelevant arguments can make it harder to identify what’s actually gone wrong and whether the person has a valid claim.

The regulator received 76,743 data protection complaints in 2025/26, up from 42,315 the previous year. It should be noted that these were actually complaints made directly to the regulator, not compensation claims brought in court, and there’s no breakdown showing exactly how many involved AI.

AI can also help people explain genuine concerns that they might otherwise struggle to put into words. Businesses therefore need to examine what happened and the evidence of harm, rather than dismissing a complaint simply because it appears to have been generated by a chatbot.

What Does This Mean For Your Business?

For businesses, the starting point is to reduce avoidable errors through reliable address updates, clear responsibilities between organisations and their service providers, and checks before sensitive information is sent. This case shows how an ordinary administrative failure can generate years of litigation, even when the extent of any disclosure remains disputed.

When a complaint arrives, staff should preserve relevant records, establish what happened and separate the alleged breach from the harm being claimed. A long, apparently AI-generated letter still needs careful assessment, but its length does not establish its legal strength. Appropriate specialist advice can help businesses respond proportionately without dismissing genuine concerns or accepting unsupported demands.

The eventual judgment may change which compensation claims succeed, but it won’t remove the need to protect personal information. Businesses should follow the ruling while continuing to investigate incidents, support affected people and correct underlying weaknesses. Sound complaint handling depends on evidence and fair treatment, whatever technology someone uses to write their claim.

Tech News : Google Gives AI Agents Their Own Work Email

Posted on by Paul Stradling

Microsoft is replacing individual OneDrive allowances on its Family and Premium subscriptions with a shared 2TB pool, cutting the maximum combined storage by 4TB and leaving some households facing extra costs or decisions about where to keep their files.

What Is Changing?

Previously, Microsoft 365 Family and Premium allowed up to six people to receive 1TB each, giving a fully shared subscription 6TB in total. Under the new arrangement, everyone draws from the same 2TB allowance.

Microsoft 365 Pro provides a shared 5TB pool, while Basic and Personal retain their individual allowances of 100GB and 1TB respectively. Additional storage purchased by subscription members is added to the shared pool.

The biggest reduction therefore affects Family and Premium subscriptions with six members making full use of their allowances. Their combined included capacity falls by two-thirds, although the practical impact depends on how much they actually store.

Why Does Microsoft Call This More Flexible?

Microsoft’s explanation is that separate allowances can leave storage unused while another member runs out of space. Its support page states, “Shared storage helps ensure that one person doesn’t run out of space while other members have unused storage available.”

That addresses a limitation of the previous arrangement. For example, someone with 1.2TB of photos and documents couldn’t simply borrow unused capacity from another member’s allocation. However, a shared pool allows that person to exceed 1TB, provided enough space remains.

That said, pooling storage and reducing its total are actually separate decisions. Six people storing 500GB each would previously have been comfortably within their individual limits, but their combined 3TB would exceed the new allowance by 1TB.

For lighter users, the flexibility may be useful. For households with several large photo libraries or document collections, it means less room to grow without paying more.

When Will Subscribers Be Affected?

The shared model applies to new and upgraded subscriptions from 8 October 2026. Existing Family and Premium subscribers generally move across when their subscriptions first renew on or after 2 May 2027, with Microsoft promising advance notification.

The renewal date matters because the change does not take effect for every existing customer on 2 May. An eligible subscription renewing later would normally move at that later renewal.

Upgrading can bring the change forward. For example, Microsoft says moving from Family to Premium before May 2027 triggers the shared model, whereas a downgrade alone does not trigger an early transition. Customers considering a plan change should therefore check its storage consequences before proceeding.

Will Shared Storage Expose Private Files?

It should be noted here that sharing the allowance doesn’t give other members access to someone’s documents or photographs. As Microsoft says, “While storage quotas are shared, files remain private.”

Each person retains their own account and can access only their own files unless someone explicitly shares content. The change concerns available capacity, rather than creating a communal folder.

However, members’ storage habits become more closely connected. One person uploading a large video collection could leave less space for everyone else, making occasional checks of combined usage more important.

What Happens If The New Limit Is Exceeded?

Microsoft says affected customers will receive notifications and can free space, purchase additional capacity or change their subscription. Storage can be expanded to a total of 10TB.

Its transition guidance says customers exceeding the limit can continue accessing their files for up to one year while managing their storage. That shouldn’t be interpreted as a promise that uploading, editing and synchronisation will continue without restrictions throughout that period.

Some existing Family and Premium customers may also receive transitional bonus storage in eligible markets. However, the support page does not specify how much they will receive or provide complete eligibility details, so customers should rely on their own notification rather than assume additional capacity is guaranteed.

Anyone planning to leave should arrange alternative storage before their subscription expires. Microsoft’s cancellation guidance says the standard free cloud allowance is 5GB, meaning cancellation could create a much larger shortfall than the announced reduction.

What Does This Mean For Your Business?

This announcement concerns the named consumer subscriptions, rather than an equivalent reduction across Microsoft 365 business plans. Microsoft’s UK business pricing still lists 1TB of cloud storage per user. However, sole traders and small organisations should check which subscriptions actually hold their working files, particularly where personal accounts have become part of everyday operations.

The immediate task is to establish current usage, renewal dates and likely future requirements. Comparing alternatives should include the cost of moving files, maintaining access and supporting users, as well as the advertised storage price. Before deleting anything to reduce usage, customers should confirm that important files have complete, accessible copies elsewhere and understand whether deletion will also remove synchronised copies.

The key point here is that a cloud subscription’s value can change after customers have built their routines around it. Keeping clear records of where information is stored and maintaining a practical way to move it makes future changes easier to manage. Shared storage may suit some users, but decisions should reflect the capacity and access they need, rather than the flexibility promised in the announcement.

Company Check : Developer Uses AI To Build Free Adobe Alternatives

Posted on by Paul Stradling

Atlanta-based developer Brandon Thomas says he has used AI to build free alternatives to seven Adobe applications, turning frustration with subscription cancellation fees into a project that could widen access to creative software, although the tools remain far from proven professional replacements.

What Has He Built?

The applications cover photo editing, video production, illustration, page design and PDF work. Released through Thomas’s Artcraft company, they include Photocraft, Filmcraft and Vectorcraft, which aim to offer alternatives to Photoshop, Premiere and Illustrator.

Lightcraft, Effectcraft, Designcraft and Printcraft target Lightroom, After Effects, InDesign and Acrobat Pro respectively. Thomas says he used Anthropic’s Claude Opus 5.5 to help develop them in Rust, a programming language used to build native applications.

As well as being free to use, the applications are open source, meaning anyone can inspect their code, make changes or contribute improvements. This gives users with the necessary technical skills more freedom to adapt the software to their needs, although most businesses would still rely on developers to maintain it and fix problems.

Why Take On Adobe?

Thomas presents the project as a response to the restrictions and costs of subscription software. For example, in his Reddit announcement, he writes, “No more subscriptions with cancellation fees. These are yours to own forever.”

That frustration has a wider backdrop. Back in June 2024, the US government actually sued Adobe over allegations that it concealed early termination charges and made subscriptions unnecessarily difficult to cancel.

Adobe announced a settlement in March 2026 involving US$75 million in payments and US$75 million in free services for qualifying customers. The proposed order also required clearer disclosure of cancellation fees and simpler cancellation arrangements. Adobe disputed the allegations and denied wrongdoing.

The dispute helps explain the appetite for these alternatives, particularly among occasional users who may struggle to justify recurring payments. Whether they can switch comfortably will depend on the work they need to complete and how reliably the new applications handle it.

How Much Has AI Changed Development?

Thomas argues that AI assistance allows an experienced developer to attempt work that would previously have required substantially more resources. He describes himself as a software industry veteran with 15 years’ experience, including about a decade working with Rust.

He also rejects the suggestion that the applications will be abandoned after attracting attention, stating, “We have a team building popular software for filmmakers, so supporting this is fully funded.”

Although Thomas says the project is fully funded, its long-term costs will depend on how much work is needed to make the applications reliable and keep them running. AI may speed up coding, but developers still need to test the software with different files and computers, fix problems and make sure it can handle the work users depend on it for.

Are The Applications Ready?

Despite Thomas’s ambitions, the project’s own documentation acknowledges that Photocraft isn’t yet ready to replace Photoshop in everyday professional work. It’s currently in early alpha, an initial stage of development, with missing tools and limitations in text editing, professional tasks and support for plug-ins, which add extra features to an application.

Even where familiar menu options are available, they may not work as users expect. For example, a designer could recognise the layout but still struggle to open a client’s file, make the required changes or export the finished work correctly. Recreating the appearance of an established application is only part of making it a useful replacement.

It seems some people commenting beneath Thomas’s announcement on Reddit have also reported crashes and problems handling video files. Those reports don’t establish how every user will find the software, but they seem to suggest businesses should test it with copies of their own files before relying on it for customer work.

Who Will Keep Them Running?

Thomas says Artcraft’s separate business selling access to AI image and video models will help cover development costs. The creative applications themselves are offered without subscription charges, while users are encouraged to report bugs and contribute fixes.

Community contributions could improve the tools, but someone still needs to review changes, maintain releases and resolve problems. The ability to copy and develop the source independently reduces dependence on one supplier, although it does not guarantee that another team will take over if support ends.

What Does This Mean For Your Business?

For businesses interested in reducing software costs, the sensible starting point is perhaps a limited trial using copies of representative files. Staff should check whether documents open correctly, edits survive saving and exports meet customers’ requirements. A free application offers little saving if employees spend longer repairing files or repeating work.

The comparison should also include training, support and collaboration. An alternative may suit occasional image editing while remaining unsuitable for a team exchanging complex projects with external agencies. Keeping existing tools available during testing allows businesses to identify useful opportunities without putting deadlines or original work at risk.

The broader significance is that AI may make it easier to build competing software and challenge established subscription models. However, the work that earns customers’ confidence continues after the first release. Businesses should judge these applications by reliable results, compatibility and sustained support, rather than assuming that rapid development means the difficult parts of software are already solved.

Tech Insight : Bank Of England Warns Of AI Debt Risks

Posted on by Paul Stradling

As the Bank of England warns that growing borrowing is increasing the financial risks surrounding AI, here we look at how the technology’s expansion is being funded, why expected returns could disappoint and what businesses should consider before becoming dependent on its providers.

What Has The Bank Warned About?

The Bank’s Financial Policy Committee, which monitors threats to financial stability, discussed AI financing at its meeting on 25 September. Its published record warns that increasing borrowing and complicated financial relationships could spread losses if the technology delivers less than investors expect.

The concern extends beyond falling technology shares. For example, lenders and other investors are increasingly helping finance the infrastructure behind AI, giving them a stake in whether anticipated demand becomes profitable business. The committee warns that these arrangements could amplify losses if expectations are not met.

Why Does Borrowing Change The Risk?

Building AI services requires substantial spending before the resulting income is certain. For example, data centres need buildings, electricity supplies, cooling and computing equipment, while developers must fund research and the ongoing cost of answering customers’ requests.

When investors buy shares, they accept that their returns depend on the company’s performance. Borrowing creates repayment obligations that remain even if sales grow slowly, customers negotiate lower prices or equipment becomes outdated sooner than expected.

A company can therefore develop useful technology while struggling financially. Strong demand doesn’t automatically produce sufficient profit, particularly when serving more customers also increases operating costs and competition limits what the supplier can charge.

How Large Is The Financing?

The scale of the borrowing is already pretty substantial, with the Bank citing Morgan Stanley’s estimate that AI-related businesses worldwide had raised around US$450 billion through debt by early September, more than double the total for 2025. That borrowing could grow considerably, with JPMorgan forecasting that debt will fund approximately US$4.1 trillion of investment in AI infrastructure and equipment between 2026 and 2030.

It should be noted here, though, that both figures cover the wider AI sector, rather than a single company, and the longer-term estimate depends on how investment develops over the next few years.

What Are Circular Financing Arrangements?

The financial picture becomes a bit more complicated when a company invests in an AI developer that then uses the money to buy cloud services or computing equipment from that same investor. These arrangements aren’t necessarily improper, but they can make it harder to tell how much of the reported growth reflects demand from independent customers and how much relies on investment flowing back to the companies providing it.

For example, an infrastructure supplier may benefit from a developer’s spending while also depending on that developer becoming successful enough to keep paying. If funding dries up, both sides could face pressure, with consequences for other companies relying on them.

What Does Anthropic’s Reported Spending Show?

Details reported from Anthropic’s IPO prospectus illustrate how rapidly an AI company’s costs can grow even as sales increase. The document sets out its plans for an initial public offering, through which it would sell shares to public investors.

The reported figures show that Anthropic generated nearly US$4.6 billion in revenue in 2025 but recorded an operating loss exceeding US$8 billion. Its overall net loss was much larger at around US$42 billion, although roughly US$34 billion of that came from accounting charges linked to financing arrangements, rather than money spent running the business.

The prospectus also reportedly sets out US$518 billion in future cloud and infrastructure commitments, showing the scale of spending planned over the coming years rather than a bill due in a single year.

Could Competition Make Repayment Harder?

As competing AI models improve, customers may be able to get the results they need at lower prices, putting pressure on the profits providers are relying on to recover their investment. That competition includes open-weight models, which make the settings learned during training available for others to use or adapt, allowing businesses to run them themselves or choose a company to host them.

However, running these models still involves costs for hardware, technical support and security, so a cheaper alternative is not automatically cheaper overall. Businesses need to compare the full cost of getting a task done reliably, including connecting the model to existing systems, supervising its work and correcting mistakes, before deciding whether a lower price represents a worthwhile saving.

What Does This Mean For Your Business?

For businesses buying AI services, the warning highlights how supplier resilience should be part of the purchasing decision. A convincing demonstration should be followed by questions about service continuity, pricing commitments and what happens if the provider changes ownership or withdraws a product. Where an intermediary supplies the service, businesses should also understand which underlying model and cloud providers it depends on.

Contracts and implementation choices should preserve a practical way to move elsewhere. Keeping accessible copies of business information, documenting integrations and testing alternatives can reduce disruption if prices rise or service quality falls. That doesn’t require avoiding newer suppliers, but the importance of the task should determine how much dependence is acceptable.

Businesses should really base their own AI spending on what the technology can do for them, using a limited trial to test whether improvements in speed and accuracy justify the cost of subscriptions, setup and staff time spent checking the work. Even if some AI investors lose money, customers could still benefit from useful services, provided the savings or improvements hold up in everyday use and do not depend on prices staying artificially low.

Company Check : McDonald’s Faces Questions Over AI Pricing

Posted on by Paul Stradling

McDonald’s is facing scrutiny over its reported use of AI to recommend menu prices, raising questions about how algorithms influence what customers pay even as the company denies using personalised prices or changing charges throughout the day.

What’s Been Reported?

There have been recent allegations of McDonald’s using a pricing system that analyses transactions and local market information to recommend prices at individual restaurants. The reporting has focused on the US and some international markets, rather than establishing that the same arrangements apply throughout the UK.

The system reportedly considers how customers in an area respond to prices and includes publicly available competitor pricing. It’s also been reported that some franchisees, the independent operators running restaurants under the McDonald’s brand, say they feel pressure to follow its recommendations, although the company maintains that pricing decisions remain theirs.

What Does McDonald’s Say?

In a statement published on 1 October, McDonald’s rejected claims that (AI) software automatically determines what customers are charged, saying, “The tool provides information. People make the final pricing decisions.”

Its explanation describes recommendations based on conditions affecting particular restaurants, including geography, local costs, competition and demand. The company says operators decide whether to use those suggestions and are not required to accept them.

McDonald’s says restaurant operators make the final decision, but some franchisees reportedly feel under pressure to follow the suggested prices, raising questions about how much freedom they have to reject recommendations they believe are wrong for their customers or business.

Is This Really Dynamic Pricing?

Describing McDonald’s approach as ‘dynamic pricing’ could suggest that the price of a meal rises when a restaurant gets busy, much as fares can increase on ride-hailing apps during periods of high demand. That’s actually known as surge pricing, while personalised pricing is different, i.e., using information about an individual customer to decide what they are charged.

However, McDonald’s says it does neither, explaining that its tool “does not change prices in real-time or at different hours during the day” and does not recommend prices for individual customers. Instead, it says the recommendations reflect conditions at each restaurant, where rent, wages and competition can vary even between nearby branches.

A price difference between two restaurants therefore doesn’t establish that either is using surge or personalised pricing. The question raised by the reporting is how much influence AI recommendations have on those local prices and whether operators feel able to choose a different price when they believe it would better suit their customers.

Why Use AI To Recommend Prices?

For a large restaurant network, pricing involves many decisions across products, locations and changing costs. Analysing sales patterns can help operators estimate whether an increase would reduce purchases or whether a discount might attract enough extra customers to be worthwhile.

Used well, that could help a restaurant preserve affordable options while covering higher operating costs. There’s no reason a recommendation must always involve an increase, because lowering a price can sometimes generate more business.

However, the result depends on what the system is asked to achieve. Increasing revenue, improving profit and encouraging repeat visits are related goals, but a price that helps one may undermine another.

What Can Sales Figures Miss?

A completed purchase shows that someone accepted a price on that occasion, but it doesn’t really establish that they considered it good value or that they intend to return. In fact, someone buying food during a rushed journey may pay more than they would when choosing where to eat near home.

That creates a potential weakness if recommendations focus too heavily on existing transactions. Customers who have stopped visiting are no longer generating purchases, even though understanding why they left could be essential to maintaining the business.

Operators therefore really need to assess prices against repeat visits, customer feedback and profit after costs, rather than treating an immediate increase in takings as proof that a decision worked.

Who Has The Final Say?

The reports (allegations) of pressure on franchisees raise questions about how freely they can set prices, although McDonald’s says it assesses the overall customer experience rather than price alone and states, “Franchisees are not required to accept a pricing recommendation.”

For that freedom to actually work in practice, operators need to understand why a price has been suggested and be able to reject it based on their knowledge of local customers and costs. The same applies to any business using automated advice, where human approval only provides useful oversight if the person involved can question the recommendation and act on their own judgement.

What Does This Mean For Your Business?

Businesses considering pricing software should first define the result they want, whether that is sustainable profit, more repeat customers or better use of spare capacity. A limited trial should measure those outcomes over time, including whether an apparent improvement simply reflects customers buying sooner or switching to cheaper products.

Suppliers should explain which information feeds their recommendations and how uncertainty is handled. Managers also need clear authority to reject or reverse changes, with records showing why decisions were made. Where software uses information from other businesses, its sources and permitted uses deserve scrutiny before recommendations become routine.

The key point here is really that customers judge the price they encounter, not the sophistication of the system behind it. AI can support better decisions, but businesses should remain able to explain their approach and recognise when a short-term gain risks weakening the trust that brings customers back.

Tech Insight: ChatGPT Dominates AI Website Referrals

Posted on by Paul Stradling

With ChatGPT accounting for 95.1 per cent of AI referrals measured by BrightEdge in August, in this Tech Insight we examine why its lead is growing, how AI is influencing purchasing decisions and what businesses should do to make themselves easier to find.

What Does The 95.1 Per Cent Mean?

BrightEdge, which sells search marketing and AI visibility tools, recently published figures showing visits referred to websites by AI platforms, rather than all website traffic, chatbot usage or online searches.

Within that category, ChatGPT’s August share far exceeded Gemini’s 2.4 per cent, Perplexity’s 1.1 per cent and Claude’s 0.8 per cent. ChatGPT therefore generated roughly 22 times the combined referrals of those three competitors!

However, BrightEdge doesn’t give a detailed breakdown of the websites included in its research, so the figure reflects the traffic it measured and may not represent the wider web.

Why Is ChatGPT’s Lead Growing?

ChatGPT’s referral traffic increased 101 per cent between January and August, including a 36 per cent rise in August alone. Over that final month, Gemini grew 1 per cent, while Claude and Perplexity declined by 15 and 17 per cent respectively.

The distinction between traffic growth and market share matters because a platform could gain share simply through competitors declining. Here, BrightEdge reports that ChatGPT also sent more visitors to websites, with chief executive Jim Yu explaining, “It is also sending substantially more users to the web than it was at the beginning of the year.”

BrightEdge links the recovery to OpenAI’s renewed focus on improving ChatGPT, although the figures don’t actually isolate which changes caused people to click. More users, different questions and clearer links could all influence referral volumes.

How The Interface Can Influence Clicks

Separate research from website analytics company Similarweb provides an example of how presentation can affect traffic. For example, its desktop panel recorded a sharp increase in ChatGPT referrals after more prominent website links appeared in May.

The proportion landing on company homepages rose from roughly 26–32 per cent to around 60 per cent during the period studied. This suggests an assistant can help someone identify a business before sending them to its website to investigate further.

Those findings cover a different period and dataset, so they don’t explain BrightEdge’s August results directly. They do, however, show why businesses should pay attention to where AI visitors arrive, rather than assuming they will land on the page containing the information that first attracted them.

AI Shopping Is Becoming More Common

The referral findings arrive as research from NielsenIQ, known as NIQ, points to wider use of AI during shopping. Its September announcement says 51 per cent of US consumers reported using at least one AI-powered shopping tool in the previous month.

Product recommendations were the most common application, used by 20 per cent, followed by personal shopping assistants at 16 per cent. The tracker uses a monthly sample of approximately 500 US consumers, so these are survey findings about reported behaviour, rather than a count of transactions.

NIQ describes the change clearly, saying, “Rather than replacing shopping, AI is compressing decision-making.” People are using it to compare options, assess value and narrow their choices, which means a business could be considered or excluded before a potential customer reaches its website.

This doesn’t mean half of consumers have authorised autonomous purchases, nor should the US figure be treated as a UK adoption rate. It does, however, provide context for why appearances in AI recommendations could matter commercially.

Being Mentioned Is Not The Same As Winning Business

It’s worth noting here that an AI answer can mention a company without supplying a link, while a linked recommendation may never actually receive a click. Even when somebody visits, the business still needs to establish whether that visit produces an enquiry, booking or sale.

That makes visibility, referrals and commercial results quite different measures. Treating them as interchangeable could encourage spending on improving an impressive-looking mention count without establishing whether customers are finding useful information or taking action.

What Does This Mean For Your Business?

For businesses, the starting point is their own website data. Marketing teams should identify visits from AI platforms, check which pages receive them and compare the resulting enquiries or sales with other channels. A growing referral source deserves attention, but the appropriate investment depends on its contribution to the business, rather than ChatGPT’s share of somebody else’s dataset.

Website content should also help people make a decision once they arrive. Clear service descriptions, current product details, transparent terms and evidence supporting important claims can help visitors assess suitability. If AI sends them to the homepage, that page needs an obvious route to relevant information and a straightforward way to contact the business or buy.

The wider risk is becoming dependent on another platform’s decisions about what to recommend and where to send users. Businesses should develop AI visibility while continuing to support conventional search, customer relationships and other sources of demand, judging progress by useful customer contact rather than traffic alone.

Sustainability-In-Tech: AI Boom Could Leave An E-Waste Trail Around Earth

Posted on by Paul Stradling

The expansion of AI could leave enough retired electronic equipment to fill shipping containers stretching six times around Earth, according to a new report warning that the technology’s environmental footprint extends far beyond electricity and water consumption.

What Has The Report Found?

The Basel Action Network (BAN), a non-profit organisation campaigning against toxic waste and irresponsible waste exports, estimates that AI-driven equipment retirements could total between 395 million and 617 million tonnes from 2025 to 2050.

To put that volume of waste into perspective, The Coming AI Waste Wave estimates that it would fill between 15 million and 23 million shipping containers, with a line of 20 million stretching roughly 244,000 kilometres, enough to circle Earth about six times!

However, how much of that equipment eventually becomes waste will depend on how quickly AI infrastructure expands, how often devices are replaced and whether older hardware is reused. The figures therefore show the potential scale of the problem if those assumptions hold, rather than the amount certain to end up in landfill.

Why Count More Than AI Chips?

Much of the discussion about AI hardware focuses on graphics processing units, or GPUs, the specialised chips used to train and run models. However, those chips need servers, networking equipment, electricity distribution, backup power and cooling systems to function.

BAN estimates that servers, accelerators and racks account for only 13 per cent of the equipment mass in its reference data centre. Across all five equipment categories, it calculates approximately 7,000 tonnes for a facility with 100 megawatts of capacity.

The report argues that overlooking this supporting infrastructure understates the eventual disposal challenge, explaining, “Counting servers alone is like estimating the scrap value of an automobile factory by weighing only the engines.”

This wider view matters because an upgrade can affect more than the computing hardware. For example, installing more powerful systems may also require changes to electrical connections and cooling, potentially displacing equipment that still works but no longer meets the facility’s requirements.

Why Might Equipment Be Replaced So Quickly?

It seems that BAN’s calculations assume that servers, accelerators and racks are replaced every 2.5 years on average, with networking equipment lasting 3.5 years, power distribution equipment eight years, and backup power and cooling systems five years. It’s worth noting that some of these timings come from industry sources and others are BAN’s own estimates, so they actually describe the replacement patterns used in the model rather than a schedule followed by every operator.

The concern is that pressure to keep up with newer AI systems could lead businesses to replace equipment while it still has some useful life left. That creates demand for more materials and energy to manufacture replacements, making it important to find another use for the outgoing hardware wherever possible and recycle it responsibly when reuse is no longer practical.

The Report Also Counts Phones And PCs

BAN also says it expects AI adoption to encourage people and businesses to replace computers, phones and telecommunications equipment sooner, adding to the waste generated outside data centres. For example, some new AI features need more powerful processors or extra memory, while others may simply tempt customers to upgrade even though their existing devices still work.

However, missing out on a new feature doesn’t make a device unusable, and many cloud-based AI services can still run through older equipment. How much extra replacement AI will cause is therefore difficult to predict, as BAN acknowledges in its report, saying “very little data exists to quantify this segment.” Its higher estimate actually includes upgrades driven by the appeal of new features, making consumer choices part of the projected waste total.

How Certain Is The Forecast?

The scale of BAN’s forecast also really depends on data-centre capacity continuing to grow by approximately 8.8 per cent a year through 2050. Sustaining that pace for decades would mean considerably more equipment being installed and eventually replaced, while the report’s own calculations show that slower growth would produce much lower waste estimates.

What happens to that equipment after replacement will also make a difference, because a server removed from one facility may still be suitable for less demanding work elsewhere. BAN says it plans to examine reuse, refurbishment and repurposing in a later instalment, leaving an important question about how much retired hardware could remain useful rather than immediately becoming waste.

That said, some companies are already working on this. For example, Microsoft has reported reusing or recycling 90.9 per cent of its servers and components in 2024, although combining those activities in one figure does not show how much equipment continued to be used. The wider picture is somewhat less encouraging, with the UN’s Global E-waste Monitor finding that only 22.3 per cent of e-waste generated worldwide in 2022 was documented as formally collected and recycled, highlighting how much work is needed even before AI adds to the volume.

What Does This Mean For Your Business?

For businesses, the practical starting point is to question whether an AI project genuinely requires new equipment. Purchasing decisions should really follow a demonstrated operational need, with repair, memory upgrades and reassignment considered before replacement. Keeping suitable devices in productive use can reduce both costs and demand for newly manufactured hardware.

When buying equipment or selecting cloud services, organisations should also ask about expected lifetimes, repairability, spare parts and what happens when systems are retired. A recycling claim is more useful when it explains how much equipment is reused, which materials are recovered and how downstream contractors are checked, rather than simply promising that nothing reaches landfill.

The report’s strongest message is really that sustainability planning must begin before purchasing and construction. Cleaner electricity can’t remove the material footprint of repeated replacement, while recycling alone can’t preserve all the value invested in making equipment. Businesses can act now by extending useful life and making end-of-use arrangements part of the original investment decision.

Featured Article : AI Chatbot Blunder Almost Sparked US–China Clash

Posted on by Paul Stradling

An AI chatbot’s false assessment of a Chinese ship’s cargo reportedly prompted US military preparations to intercept the vessel, exposing how unchecked AI output can influence dangerous decisions when it is presented as trusted intelligence.

What Happened?

According to a CNN investigation, the incident occurred during the spring of 2026, amid the war with Iran, when an intelligence report claimed that a Chinese vessel in the Middle East was transporting components for a nuclear weapons programme.

It’s been reported that four sources familiar with the episode described plans to intercept the ship, with two saying armed personnel were preparing to board it! Also, scarily, it seems military aircraft were already airborne (according to sources cited in the report).

Officials examined the underlying information shortly before the planned operation and discovered that an analyst had used AI to produce the assessment, with the chatbot incorrectly identifying the cargo! CNN reports that it couldn’t establish what the vessel was actually carrying.

How Did An AI Error Become Intelligence?

It seems the analyst had asked a chatbot about reporting on the ship’s manifest originating from US Special Operations Command Pacific, based in Hawaii. The system then combined publicly available information with classified signals intelligence, which involves information obtained from intercepted communications or electronic signals.

After the chatbot reached its incorrect conclusion, the analyst used AI again to turn the findings into a standard intelligence report and circulated it. That second step matters because the information reached decision-makers in a familiar format normally associated with professional intelligence work.

This seems to explain how an unsupported answer could gain credibility without gaining evidence. This meant that rather than seeing an uncertain chatbot response, recipients were presented with a document that appeared ready to inform an operational decision.

Did It Really Nearly Start A War?

The suggestion that the incident almost started a war comes from an anonymous source quoted by CNN, rather than a published military assessment. Boarding a Chinese vessel could have created a serious confrontation, but the available reporting cannot establish how either government would have responded.

The chatbot and its provider have not been identified, and it remains unclear whether the analyst used a commercial service or a government system. The Pentagon and US Special Operations Command Pacific had not responded to CNN’s requests for comment when its investigation was published.

What the account does describe is people preparing to act on inaccurate AI-generated information, rather than an autonomous system ordering an attack. That distinction helps explain why keeping people involved is necessary but doesn’t, by itself, prevent (potentially very serious) mistakes.

Why Human Oversight Needs More Than Approval

An AI hallucination occurs when an AI system produces incorrect or invented information as though it were factual. When that output is fluent, detailed and presented confidently, checking it can require more effort than accepting it.

In this case, the reported safeguard was deeper examination before the operation proceeded. The real concern is how far preparations had apparently advanced before that examination exposed the error.

For organisations adopting AI, this raises a question about where verification happens. If checks take place only after a report has circulated and decisions are under way, an error may already have shaped expectations, committed resources or narrowed the options people consider.

Calls For Military AI Safeguards

The incident also gives some context to proposals from US and Chinese security experts involved in a dialogue convened by the Brookings Institution and Tsinghua University, who have called for safeguards intended to reduce the risk of AI-related military escalation.

Their recommendations include (thankfully) keeping nuclear decisions under human control and establishing a dedicated channel for communicating about military AI incidents. That said, neither government has formally adopted the proposals, so they represent expert recommendations rather than agreed international rules.

What Does This Mean For Your Business?

For businesses, the lesson is that an AI-generated error can become harder to spot as it passes through familiar processes. A supplier assessment, financial briefing or security report may look authoritative because it follows the company’s usual template, even when its central claim has not been checked. Employees reviewing important recommendations should be able to trace them back to supporting evidence and distinguish what the source actually says from what the AI has inferred.

The level of checking should reflect the consequences of getting something wrong. Summarising routine notes is different from recommending a payment, alleging misconduct or making a decision affecting someone’s safety. For higher-risk work, an appropriate specialist needs time and authority to challenge the output before action follows, with clear arrangements for pausing the process when evidence is missing or contradictory.

There is also a management question about what happens to the time AI saves. If faster drafting simply creates pressure to approve more reports, organisations may increase the volume of decisions without improving their quality. Using some of that saved time to examine sources, test assumptions and consider alternatives would make automation more useful, while reducing the chance that a convincing answer becomes a costly mistake.

Tech Insight : Huawei Predicts A 100,000-Fold Surge In AI Use

Posted on by Paul Stradling

Huawei predicts that global AI token consumption will grow 100,000-fold by 2035, with autonomous agents generating over 90 per cent of the associated traffic, raising questions about the infrastructure, energy and oversight needed to turn that activity into useful work.

What Has Huawei Announced?

The Chinese technology company published two reports on 16 September, ahead of its Huawei Connect conference, setting out how it expects AI to develop and what would need to be built to support it.

Intelligent World 2035 focuses on the technology, while the Global Digitalization and Intelligence Index 2026, developed with Tsinghua University’s Institute of Economics, examines the economic potential. The latter forecasts more than US$27 trillion in cumulative AI economic value over five years, with annual investment in digital and intelligent infrastructure exceeding US$4 trillion by 2030.

It should be noted here that these are just projections, rather than measured outcomes, and Huawei has a commercial interest in their underlying argument because it supplies computing, networking, storage and power infrastructure. Its forecasts therefore offer insight into the future it is preparing to serve.

What Does Token Consumption Mean?

Tokens are the units into which AI systems divide information for processing, including words, parts of words and representations of other content. They cover what a model receives and generates, so they are not simply a count of the words someone sees in an answer.

For example, an AI assistant summarising a report processes the document and produces a response. An agent investigating a business problem might repeatedly read information, plan its next step, search databases and review results before delivering anything to its user.

That longer chain of activity explains Huawei’s expectation of much heavier demand. As David Wang, Huawei’s deputy chairman and rotating chairman, puts it, “Agentic AI is a key variable in this transformation.”

Why Would Agents Generate So Much Traffic?

Unlike a chatbot that mainly responds to individual questions, an agent can work through a task using software tools and information from different sources. Several agents may also divide the work between them, creating further exchanges as they compare findings or pass instructions.

There is already evidence that this approach requires more processing. For example, in a June 2025 account of its research system, Anthropic reported that agents typically used about four times as many tokens as chat interactions, while systems using multiple agents consumed about fifteen times as many.

Those figures describe Anthropic’s experience, rather than proving Huawei’s global forecast, but they still illustrate why widespread agent use could increase demand considerably. They also show why businesses need to assess whether the work completed justifies the resources consumed.

What Would Need To Change?

Huawei identifies ten areas requiring development, including computing clusters, memory, connectivity, chip design, autonomous systems and security. Its proposals include increasing computing cluster scale 100-fold and reducing the cost of completing an agent task 1,000-fold.

The connection between those ambitions matters because widespread automation becomes more affordable if each task costs substantially less. However, cheaper processing could also encourage businesses to run many more tasks, so falling unit costs would not necessarily mean lower overall spending.

Huawei also highlights the need for reliable memory and traceable information, recognising that agents need more than processing power. As Wang states, “The direction is clear, but bringing our vision to life demands concrete action.”

Can Energy Supplies Keep Pace?

More AI activity creates demands on electricity supplies and cooling, but a 100,000-fold increase in tokens would not automatically mean the same increase in energy consumption. The relationship depends on the models used, hardware efficiency and how effectively systems share resources.

The International Energy Agency’s 2025 Energy and AI report projected that global data centre electricity consumption would more than double to around 945 terawatt-hours by 2030. It also warned that grid constraints could delay projects, showing why infrastructure availability will influence how quickly AI expands.

What Does This Mean For Your Business?

For businesses, the useful starting point is the cost of completing a job successfully, including checking and correcting the result. An agent that processes thousands of documents may deliver considerable value, but one that repeatedly searches the same material or pursues irrelevant tasks can generate costs without improving the outcome. Trials should therefore measure accuracy, completion time and staff effort as well as usage.

Spending controls also become more important when software can keep working without another instruction. Businesses should establish limits on budgets, running time and access to paid services, with clear arrangements for stopping unsuccessful tasks or seeking human approval. Suppliers should explain how customers can see what agents have done and identify unexpected increases in consumption.

The US$27 trillion forecast describes a potential global opportunity, but individual organisations will benefit only where AI improves their own operations. Better data, suitable tasks and employees who can judge the results remain essential investments, whatever happens to token volumes. More automated activity becomes valuable when it produces dependable services, useful decisions or work that would otherwise be difficult to complete.