New research suggests that while AI is helping employees work faster, many businesses are creating a new layer of digital busywork that is eroding much of the productivity they hoped to gain.

The Rise Of The Copy And Paste Economy

Artificial intelligence is often presented as a tool that removes repetitive work, eliminates inefficiency and gives employees more time to focus on higher-value tasks.

However, according to new research from Workday, many organisations are discovering that AI can create new forms of work as well as eliminate existing ones. The company’s study of 2,400 UK professionals found that employees are increasingly spending large parts of their day moving information between disconnected systems, checking outputs and acting as the link between AI tools that do not naturally work together.

The result is what Workday calls the “copy/paste economy”, a workplace where workers spend significant amounts of time transferring information between applications rather than focusing on the work those applications are supposed to support.

According to the research, as many as one in four UK employees reported spending more than seven hours each week moving information between systems and reconciling data. More than eight in ten said they spend significant time coordinating work between teams, moving information between platforms or resolving conflicting data from different systems.

Employees Like AI More Than Many Assume

One of the most surprising findings in the report is that employees are not rejecting AI. In fact, the vast majority appear to be positive about both their jobs and the technology itself. Workday found that 97 per cent of UK employees rate their day-to-day work positively, while 81 per cent said AI has improved their work experience. More than half said AI has reduced task completion times, and 45 per cent reported that it has accelerated their work in a productive way.

Those findings seem to challenge the popular narrative that workers are resisting AI adoption. Instead, the research suggests that many employees are eager for AI to help them work more effectively. The problem is not the technology itself, but how organisations are deploying it.

When Faster Tasks Don’t Create Faster Work

Many businesses have introduced AI tools to help employees write documents, summarise information, answer questions or generate content.

Those capabilities can certainly save time on individual tasks, but the challenge is that work rarely consists of isolated tasks.

For example, information often needs to move between departments, applications, approval processes and business systems before a job is complete. If employees still need to manually transfer data between those systems, much of the productivity benefit can disappear.

As one IT director quoted in the report explained: “Dealing with system glitches, chasing approvals and constantly fixing or redoing work because of inconsistent data, it keeps me busy, but doesn’t feel like real progress.”

That distinction between activity and progress is central to the findings. Employees may be working hard, but much of their effort is spent compensating for fragmented systems rather than creating value.

The Human Middleware Problem

Workday’s report uses a particularly revealing phrase to describe what is happening. Many employees have effectively become “the glue” holding disconnected systems together. Rather than technology handling the flow of information automatically, workers are manually transferring data, reconciling inconsistencies and coordinating between applications. This is increasingly becoming one of the hidden costs of AI adoption.

Businesses may deploy multiple AI tools across different departments, but if those systems cannot share information effectively, employees become the human middleware connecting everything together.

One construction industry director quoted in the report described the impact of this fragmentation, saying: “My day often feels busy but not genuinely productive when I’m pulled into constant coordination tasks and system-related issues that interrupt focused, high-value work.”

The irony is that many organisations have invested in AI to reduce administrative work, only to create new administrative burdens elsewhere.

Why Embedded AI Performs Better

The research also points towards a solution. Only 23 per cent of UK organisations have deeply embedded AI into their core business systems and workflows. Most have instead added AI around the edges of existing processes.

According to the report, employees are already showing organisations how they want AI to work: “integrated directly into workflows, proactively surfacing insights and handling coordination in the background.” The difference appears quite significant.

For example, among organisations with AI integrated into core systems, 57 per cent of employees reported task reductions of 25 per cent or more. Where AI was not embedded into core systems, that figure fell to 39 per cent.

Workday argues that the most successful organisations are moving beyond task-oriented AI and towards workflow-oriented AI. Instead of simply drafting content or answering questions, AI becomes part of the process itself by monitoring activity, routing approvals, surfacing insights and coordinating work in the background.

This mirrors a wider trend emerging across the technology industry. Google’s Gemini Spark, Microsoft’s Copilot agents, OpenAI’s growing agent capabilities and Anthropic’s workflow automation initiatives all point towards a future where AI handles increasingly complex coordination tasks rather than individual requests.

What Does This Mean For Your Business?

The Workday research suggests many organisations may really be asking the wrong question about AI. Instead of focusing on whether a particular tool can save five minutes on a specific task, leaders may need to ask whether the overall process requires fewer steps. If employees are still copying information between systems, reconciling conflicting data and manually connecting workflows, the organisation may be automating tasks without truly improving productivity.

Perhaps the most important finding in the report is that employees appear ready for a different approach. As Workday concludes, employees increasingly expect AI to be “embedded, intelligent and invisible in the flow of work”, adding that “The new work day is not AI assisting with existing work, but work redesigned around what AI and humans each do best.”

That may prove to be one of the most important lessons of the AI era. The biggest gains are unlikely to come from adding more AI tools. They are more likely to come from redesigning how work flows through the organisation in the first place.

Google’s latest wave of AI announcements reveals a company that is moving beyond chatbots and search results, towards a future where AI agents actively perform tasks, manage information and work alongside users throughout the day.

Why Google’s Latest AI Updates Matter

At first glance, Google’s latest announcements appear to be a collection of unrelated AI features. For example, there is:

– Gmail Live, which allows users to search their inbox using natural conversation.

– Google Pics, an AI-powered image generation and editing tool.

– Gemini Omni, a new multimodal model capable of generating and editing video.

There are updates to AI Inbox, along with a new AI agent called Gemini Spark. However, when viewed together, a much clearer picture emerges.

Google is increasingly focused on moving AI beyond answering questions and towards taking action on behalf of users. In other words, the company is attempting to transform AI from a tool that provides information into a digital workforce that helps people get things done.

The Rise Of The AI Agent

Perhaps the clearest example of this strategy is Gemini Spark. Google describes Spark as a “24/7 personal AI agent” that can help users manage their digital lives, take action on their behalf and integrate with Workspace applications such as Gmail and Docs. The system runs on dedicated cloud infrastructure, meaning it can continue working even when a user’s laptop or phone is switched off.

According to Google’s announcement, Spark “helps you navigate your digital life, takes action on your behalf and is under your direction.”

This is significant because it moves beyond the traditional chatbot model. Instead of waiting for a user to ask a question, Spark is designed to complete longer-running tasks and manage activities across multiple applications.

The launch also places Google directly into competition with similar agentic AI offerings from OpenAI and Anthropic, both of which are pursuing the same vision of autonomous digital assistants.

Search Is Becoming Something New

The wider shift towards AI agents is also reshaping Google’s core business. For more than two decades, Google Search has largely revolved around helping users find information through lists of links. Increasingly, that model is changing.

Google’s AI Overviews and AI Mode already provide direct answers to many queries, while upcoming features will introduce information-gathering agents capable of monitoring topics, tracking changes and presenting synthesised updates automatically.

This represents a major change in how information is discovered online. Instead of manually searching for information, users may increasingly rely on AI systems to find, monitor and summarise it for them.

For businesses that depend on website traffic, search visibility or content marketing, this trend is likely to become increasingly important.

Gmail Is Becoming An AI Knowledge Assistant

Another notable development is Gmail Live, which allows users to ask questions about their email using natural language.

Rather than searching for keywords, users can ask questions about flight details, appointment times or information contained within previous conversations. Gmail Live then searches the inbox and presents an answer conversationally.

Google says these new voice capabilities are designed to help users “brainstorm, organise your thoughts and get things done”.

Although this may appear to be a relatively small feature, it reflects a broader shift in how software is being designed. Instead of users learning how applications work, AI increasingly learns how users work.

Google Wants AI To Create As Well As Assist

It seems the company’s ambitions also extend beyond productivity and information management.

For example, Google Pics introduces AI-powered image generation and editing directly into Workspace, allowing users to modify individual objects, translate text within images and collaborate on visual projects without leaving Google’s ecosystem.

Google says the goal is to make image creation feel like “creative direction, not a roll of the dice”.

Alongside this, Gemini Omni expands Google’s generative capabilities into video. The model can combine images, video, audio and text as inputs while allowing users to edit videos through natural conversation. Google describes Omni as a system “where Gemini’s ability to reason meets the ability to create”.

Taken together, all these developments suggest Google increasingly views content creation as a conversational process rather than a collection of separate software tools.

What Does This Mean For Your Business?

The most important takeaway here is that Google’s announcements are not really about email, images or video. Instead, they reveal a company that is systematically embedding AI into every stage of digital work. Search is becoming more agentic, email is becoming more conversational, content creation is becoming more automated, and AI assistants are becoming more capable of taking action independently.

For businesses, this could create significant productivity opportunities. For example, it may mean employees spend less time searching for information, organising documents, drafting communications and creating content. At the same time, organisations that rely heavily on search traffic may need to prepare for a future where AI increasingly intermediates the relationship between users and websites.

Whether Google’s vision ultimately succeeds remains to be seen. What is already clear, however, is that the company believes the next phase of AI will involve systems that do far more than answer questions. Increasingly, they will be expected to carry out work on behalf of the people using them.

A Freedom of Information request has revealed that London’s Metropolitan Police requested communications data from technology companies more than 700,000 times during 2025, highlighting the scale of modern digital surveillance.

The requests covered mobile networks, email providers and online services. Although they generally did not involve message content, they could include metadata such as account details, IP addresses and contact records.

The figures also showed a nearly 500 per cent increase in requests to Lyca Mobile, while data was sought from services including Proton, Signal, Uber and Deliveroo. Some privacy-focused providers disputed aspects of the figures.

For businesses, the findings are a reminder that metadata can reveal a great deal about people, communications and behaviour, making data governance and privacy controls increasingly important.

A new mobile app called Wove is aiming to bring ingredient-style transparency to clothing, allowing shoppers to check garments for potential PFAS chemicals, microplastic risks and other hidden concerns before making a purchase.

Why Wove Has Been Created

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

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

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

The Growing Concern Around Synthetic Fabrics

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

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

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

Regulation Is Adding Momentum

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

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

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

Similar Services

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

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

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

What Does This Mean For Your Organisation?

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

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

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

Microsoft 365 Copilot Search can find information across your emails, documents, chats, meetings and apps from a single search box, helping you locate answers faster, reduce time spent searching, and stay productive without constantly switching between different Microsoft 365 tools.

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Temporary Chat gives you a private, one-off conversation space in ChatGPT that isn’t saved to your chat history or Memory, making it ideal for discussing sensitive projects, client matters, HR issues, business planning, or other topics that you don’t want appearing in your regular chat history.

Why It Works

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

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

How To Use It

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

What To Look For

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

The Business Benefit

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

HMRC is handing a British AI company £175 million to help it spot tax fraud, uncover hidden financial networks, reduce costly mistakes, and improve customer service, as pressure mounts over rising complaints, growing complexity, and a £46.8 billion tax gap.

Deal With Quantexa

The decade-long deal with London-based AI and analytics firm Quantexa marks one of the largest AI deployments ever seen inside the UK public sector. It also signals a major strategic change in how the government wants critical public systems to use artificial intelligence.

Rather than relying on a US technology giant, HMRC is betting heavily on a British-developed “Decision Intelligence” platform designed to connect fragmented data, identify suspicious patterns, and support human investigators and customer service teams.

Why HMRC Wants AI Help

HMRC has been under mounting criticism for years over long waits, processing delays, incorrect tax notices, and declining service standards.

According to figures obtained through Freedom of Information requests by the Contentious Tax Group, complaints against HMRC climbed to more than 93,000 in 2024/25, up sharply from around 70,000 five years earlier.

Also, compensation payments linked to HMRC errors and distress have also risen significantly.

At the same time, the tax authority is handling growing volumes of digital data as initiatives like Making Tax Digital expand across the UK economy.

It seems the problem for HMRC is not a lack of information, but that the information often sits in disconnected systems that can’t easily “see” relationships between people, companies, transactions, and behaviours.

Quantexa specialises in connecting fragmented datasets and using graph analytics and machine learning to identify patterns, relationships, and anomalies that would be extremely difficult for human investigators to spot manually across millions of disconnected records and transactions.

Its technology was originally developed for anti-money laundering work inside banks. Customers already include HSBC and Vodafone.

Now HMRC wants to apply similar techniques to tax compliance, fraud detection, and operational efficiency.

Connecting The Dots

One of the most significant parts of the project involves what Quantexa calls “entity resolution”. In simple terms, the system attempts to identify when multiple records, companies, transactions, or identities may actually be connected.

That matters because complex fraud networks often hide behind layers of shell companies, false references, mismatched addresses, or disconnected records spread across multiple databases.

The technology is designed to create what Quantexa describes as “a clearer, connected view of its data to improve performance, help identify tax at risk, and strengthen control.”

Positive Points

One positive point about the new system is that it should be able to help HMRC track legitimate payments that have been incorrectly referenced, which could potentially reduce some of the administrative headaches faced by businesses and taxpayers.

Also, importantly, Quantexa says the platform is not intended to replace human decision-making. As Quantexa CEO Vishal Marria says: “In government environments, AI cannot operate as a black box,” and that “Decisions need to be transparent, auditable, and explainable, particularly in areas affecting citizens directly.”

In fact, this point matters politically as much as technically. For example, governments worldwide are increasingly nervous about allowing opaque AI systems to make decisions affecting taxes, benefits, healthcare, or policing without clear accountability.

The Digital Sovereignty Angle

There is another layer to this story that goes well beyond tax collection. The Quantexa deal is being viewed inside government as part of a wider push towards so-called “digital sovereignty”.

In recent years, the UK government has awarded huge contracts to American data firms including Palantir Technologies, the US data analytics company co-founded by billionaire Peter Thiel, whose NHS data platform deal generated considerable political controversy.

This time, ministers appear keen to emphasise that the supplier is British, the systems are governed, and the data stays under HMRC control.

Also, Quantexa’s online announcement about the deal with HMRC strongly emphasised sovereignty and governance concerns, with Quantexa highlighting how “Public sector organisations are accelerating digital transformation while needing to maintain sovereignty, auditability and control.”

It added that the platform creates “a trusted, governed foundation for advanced analytics and the safe deployment of AI at scale.”

The language used around the project is deliberate because governments are no longer debating simply whether AI can improve public services, they are increasingly focused on who controls the systems, where sensitive national data is stored, and whether automated decisions can be properly explained, audited, and challenged when citizens are affected.

A Major Test For Government AI

The contract could become a defining test case for how AI is used across British government departments. If successful, similar approaches could spread rapidly into compliance, policing, border control, welfare systems, and other high-data public services.

However, the pressure to deliver will be intense because HMRC’s tax gap currently stands at £46.8 billion, representing money theoretically owed but not collected, and the government is clearly placing significant faith in AI and Quantexa’s ability to help recover far more of it. Quantexa founder and CEO Vishal Marria says governments worldwide are struggling with “how to turn complex, fragmented data into confident, timely decisions”, which goes directly to the heart of HMRC’s long-running problems with disconnected systems, slow processes, and rising operational complexity. The company believes that by “creating context from data and embedding trusted, governed AI”, HMRC will be able to make “confident, informed decisions” more quickly, while improving fraud detection, strengthening oversight, and reducing the kinds of administrative errors that have increasingly damaged public confidence in the tax authority.

What Does This Mean For Your Business?

For businesses, accountants, and taxpayers, this signals a future where HMRC becomes far more data-driven, interconnected, and AI-assisted. That could mean faster identification of fraud and errors, quicker handling of customer queries, and improved detection of suspicious tax activity.

It could also mean increased scrutiny. As AI systems become better at linking records and spotting inconsistencies across datasets, businesses may find it harder to hide mistakes, discrepancies, or unusual financial behaviour inside disconnected systems.

At the same time, the project highlights something much bigger happening across the UK economy. Artificial intelligence is rapidly moving beyond chatbots and productivity tools into core national infrastructure, including taxation, compliance, and public administration.

It now seems that businesses that maintain accurate records, consistent reporting, and well-organised financial systems are likely to face far fewer problems in an environment where AI is increasingly being used to connect data, identify anomalies, and scrutinise tax activity far more efficiently than before.

Google has unveiled a radical new laptop strategy that replaces the Chromebook concept with AI-first “Googlebooks”, devices where Gemini AI is embedded directly into the operating system and even the cursor itself becomes an intelligent assistant.

AI As The Core Layer

The move represents one of the clearest signs yet that major technology companies no longer see AI as simply another app or feature, but increasingly as the core layer through which users interact with computers altogether.

A Big Change For Google’s Laptop Strategy

For more than 15 years, Google’s Chromebook strategy focused on lightweight, low-cost laptops built around the Chrome browser and cloud services.

Now Google says the industry is moving “from an operating system to an intelligence system”, and believes laptops need to be redesigned around AI itself.

The result is Googlebook, a new category of premium laptops built on Android rather than ChromeOS, with Gemini deeply integrated into the entire experience.

According to Google, the devices are “the first laptops designed from the ground up for Gemini Intelligence, to deliver personal and proactive help when and where you need it.”

That wording matters because Google is no longer positioning AI as a separate assistant sitting beside applications. Instead, AI is becoming the interface itself.

The Cursor Becomes The AI

Perhaps the most striking feature is something called “Magic Pointer”, developed with Google DeepMind.

Google says the feature “brings Gemini’s helpfulness right to your fingertips” by turning the cursor into a context-aware AI agent capable of understanding what is on screen and proactively suggesting actions.

For example, hovering over a date inside an email could trigger an option to create a meeting automatically. Pointing at two images could allow Gemini to combine them instantly, and highlighting text could trigger summarisation, rewriting, or translation suggestions.

Importantly, the system is designed to work proactively rather than waiting for typed prompts.

Google says users can “wiggle your cursor and watch it come alive with Gemini, offering quick, contextual suggestions every time you point at something on your screen.”

That may sound like a relatively small interface change, but strategically it is extremely significant.

For decades, the cursor has simply been a pointing mechanism, but now it seems Google is effectively turning it into an AI interaction layer that constantly interprets context and anticipates actions.

Strategically Different To Competitors

This also represents a noticeably different strategy from rivals like Microsoft and Apple. For example, Microsoft largely places Copilot alongside applications, while Apple has focused heavily on embedding intelligence into individual apps and workflows. Google, by contrast, appears to be positioning Gemini as the layer sitting between the user and the entire operating system.

Unifying Android And AI

The launch also attempts to solve a long-running Google problem. Traditional Chromebooks could run Android apps, but often through compatibility layers and container systems that created limitations around multitasking, file access, and desktop integration.

Googlebook removes that separation entirely because the laptops themselves now run Android-based software natively.

Google says this allows users to move more seamlessly between phones and laptops while sharing apps, files, AI services, and workflows across devices.

Features such as “Quick Access” will reportedly allow users to browse and use phone files directly from their laptop without transfers, while “Cast my Apps” will let Android phone apps appear directly on the laptop screen.

Google describes the overall goal as “keeping you in the flow”, especially as people increasingly move between multiple connected devices throughout the day.

The company is also introducing “Create your Widget”, where users describe a dashboard or widget in natural language and Gemini builds it automatically using information pulled from services like Gmail, Calendar, and web search.

In practical terms, users are increasingly being asked not to choose software from menus, but instead describe what they want AI to create for them dynamically.

A Premium AI Device

One of the most surprising aspects of the announcement is Google’s decision to move away from the Chromebook market’s traditional low-cost positioning.

Googlebook devices are being described as premium products with “premium craftsmanship and materials”, launching through partners including Acer, ASUS, Dell, HP, and Lenovo. This could create important questions for education markets where Chromebooks became dominant largely because they were cheap, simple, and easy to manage.

Chromebooks currently hold a huge share of the global education laptop market, particularly in the US, and Google says existing devices will continue receiving support for now.

However, the long-term direction does seem to be becoming clearer, with Google now appearing to see Gemini itself as the core product, with the laptop becoming just the delivery mechanism for AI-powered experiences across Google’s wider ecosystem.

What Does This Mean For Your Business?

For businesses, Googlebook is another strong signal that the next phase of computing may revolve less around applications and more around AI-mediated workflows and interfaces.

The bigger story here is not simply a new laptop category. It is that major technology firms are redesigning operating systems, interfaces, and entire ecosystems around context-aware AI systems that attempt to anticipate user intent in real time.

That could eventually change how employees interact with software altogether, particularly in areas like administration, scheduling, document handling, collaboration, and workflow automation.

This also raises important questions around privacy, regulation, AI dependency, cloud processing costs, and how much contextual access businesses are comfortable giving AI systems embedded deeply inside everyday devices.

Google’s original Chromebook strategy argued that the browser was becoming the operating system. Googlebook now suggests the company believes AI itself may become the operating system instead.

Fresh controversy has erupted around the NHS Federated Data Platform after reports claimed Palantir contractors and other external staff could be granted much broader access to identifiable patient data inside one of the NHS’s most sensitive systems.

What’s Happening To Our Health Data?

According to a recent report in the Financial Times, NHS England has approved the creation of a new administrative access role inside its National Data Integration Tenant, or NDIT, which sits at the heart of the NHS Federated Data Platform (FDP).

The NDIT is effectively a controlled environment where identifiable patient data is held before information is pseudonymised and distributed into other operational systems connected to the FDP.

Until now, external personnel working on the platform reportedly had to apply for access to specific datasets individually through what NHS England calls Controlled Data Access requests.

However, it’s been reported that leaked internal briefing documents argued that the process had become operationally difficult and time-consuming, particularly given the scale and complexity of the FDP programme.

As a result, NHS England has reportedly approved a broader “admin” role allowing a small number of approved non-NHS personnel to access data inside the NDIT without repeated case-by-case approvals.

Some critics are even describing the arrangement as effectively creating “unlimited access” for contractors inside part of the NHS’s flagship data infrastructure project.

NHS England has strongly pushed back against suggestions that controls are being weakened, saying the organisation maintains “strict policies in place for managing access to patient data” and carries out “regular audits to ensure compliance”, while also stressing that any external access requires government security clearance and director-level approval.

What Is The Federated Data Platform?

The FDP is one of the NHS’s largest digital transformation projects. The £330 million contract was awarded in 2023 to a consortium led by Palantir Technologies, a US data analytics company best known for its work in defence, intelligence, security, and large-scale data integration.

The platform is designed to connect fragmented NHS operational datasets into a unified system intended to improve waiting list management, resource allocation, planning, and operational efficiency.

NHS England argues the FDP will help modernise healthcare operations and improve patient outcomes by allowing NHS organisations to use data more effectively across trusts and services.

The NHS also insists that patient data remains under NHS control at all times, with Palantir legally acting only as a “data processor” operating under NHS instructions.

Who Are Palantir And Peter Thiel?

Much of the controversy surrounding the FDP stems not simply from the technology itself, but from Palantir’s wider reputation and affiliations.

Palantir Technologies was co-founded in 2003 by billionaire investor Peter Thiel alongside executives linked to PayPal and US intelligence circles.

Thiel is one of Silicon Valley’s most influential and controversial figures. He was an early Facebook investor, co-founder of PayPal, and has longstanding links to conservative US political movements and defence technology investment.

Palantir itself originally built software for US intelligence and military agencies following the September 11 attacks and has since expanded heavily into defence, immigration enforcement, policing, and government analytics worldwide.

The company has worked with organisations including the CIA, FBI, Pentagon, US Immigration and Customs Enforcement (ICE), NATO, and multiple Western defence agencies.

Critics argue that background makes Palantir an uncomfortable fit for handling sensitive NHS infrastructure and patient data, particularly given growing public concern about AI, surveillance, and data concentration inside critical public services.

Supporters, however, argue that Palantir specialises precisely in the kind of large-scale data integration and operational analytics the NHS badly needs.

Why This Matters Politically

The latest reports have reignited long-running concerns from privacy campaigners, MPs, and patient rights groups who argue the NHS risks eroding public confidence if governance boundaries become unclear.

The leaked NHS briefing itself reportedly acknowledged “considerable public interest and concern” around how much access Palantir staff may have to NHS patient data.

Labour MP Rachael Maskell has described the latest development as “dangerous”, while patient advocacy groups questioned why patients had not been more directly consulted.

At the centre of the debate is a broader tension facing governments worldwide.

Modern AI systems and advanced analytics often work best when large datasets can be integrated, connected, and analysed centrally. However, the more powerful and interconnected those systems become, the greater the concerns around access control, oversight, accountability, and misuse.

The NHS insists safeguards remain in place, including role-based access controls, UK-only data storage, security clearances, auditing, and contractual restrictions preventing Palantir from commercialising NHS data or training AI models on it. However, critics argue the issue is increasingly about trust as much as technical controls.

What Does This Mean For Your Business?

For businesses and organisations, the controversy highlights how rapidly debates around AI, analytics, and data governance are moving from technical discussions into questions of trust, transparency, and public legitimacy.

The NHS FDP project also demonstrates how AI and large-scale analytics are increasingly becoming embedded inside critical national infrastructure rather than remaining standalone software tools.

Many organisations are now facing similar tensions themselves, i.e., balancing operational efficiency, automation, and AI capability against privacy concerns, governance expectations, supplier concentration risks, and reputational exposure.

The Palantir row may ultimately become less about one NHS contract and more about how comfortable people are with huge global technology corporations having access to highly sensitive personal health information, particularly as AI-driven systems become more deeply embedded inside essential public services and everyday decision-making.

Researchers at the University of Southern California have developed a memristor memory device capable of operating at 700°C, a temperature hotter than molten lava and beyond the surface conditions found on Venus.

Why This Matters

The breakthrough is important not simply because of the extreme temperatures involved, but because it points towards a new generation of AI hardware designed to operate in environments where conventional computing systems quickly fail.

It also highlights how memristors, a type of electronic component that can both store data and process information in the same location, have long been viewed as an experimental technology but may finally be moving towards real-world commercial deployment inside AI infrastructure, industrial systems, defence platforms, and autonomous machines.

What The Researchers Built

The research, published in ‘Science’, focused on a type of electronic component called a memristor, a device capable of storing memory and performing computation in the same location.

This matters because conventional computing systems separate processing and memory physically, forcing data to move constantly between processors and storage. This creates major energy, speed, and heat limitations, particularly for AI workloads.

Memristors attempt to solve that problem by combining storage and processing together, making them particularly attractive for AI inference and neuromorphic computing systems designed to mimic aspects of the human brain.

The USC team demonstrated that their graphene-based memristor continued operating reliably at temperatures up to 700°C. The devices also survived more than one billion switching cycles at those temperatures while maintaining stable resistance states.

Professor J. Joshua Yang from USC said in the university’s announcement: “This work establishes a pathway toward electronics capable of operating in extreme environments previously inaccessible to conventional semiconductor systems.”

How They Solved The Heat Problem

One of the biggest technical challenges involved preventing tungsten atoms from diffusing through the device structure at high temperatures. Traditional memristors often fail in this area because heat causes conductive materials to migrate uncontrollably inside the memory layer, eventually destroying the device.

The USC researchers solved much of this problem using multilayer graphene electrodes that dramatically slowed tungsten diffusion. As their supplementary paper explains: “W atoms diffuse more easily on the Pt (111) surface compared to Gra surface”, referring to graphene.

The researchers also concluded that “regardless of graphene thickness, W adatom adsorption remains weak and surface diffusion is intrinsically slow on graphene.”

In simple terms, the graphene acted as an ultra-stable barrier layer that prevented the internal structure from degrading under extreme heat.

The paper also noted that “solving W diffusion issue is the key for HT memristors”, referring to high-temperature operation.

Why TetraMem Matters

The commercial significance of the story comes from TetraMem, the startup helping commercialise the underlying technology. TetraMem is developing analogue AI inference chips based on memristor architectures designed to process AI workloads far more efficiently than conventional digital processors.

Unlike many experimental semiconductor breakthroughs that remain trapped inside laboratories, TetraMem says it has already moved room-temperature versions of its inference chips onto 300mm semiconductor production wafers in partnership with SK hynix and NY CREATES, with support linked to the US CHIPS Act.

That matters because 300mm wafers are the standard used in advanced commercial semiconductor manufacturing.

In a company statement, TetraMem CEO Guangyu Xu said: “This breakthrough validates the robustness of our memristor technology platform and opens the door to AI computing in some of the harshest environments imaginable.”

The company believes memristor systems could dramatically reduce the energy demands of AI inference while enabling far smaller and more efficient edge AI devices.

An Important Change In AI Hardware

The timing of this announcement is important because AI infrastructure is becoming increasingly constrained by energy consumption, heat generation, memory bottlenecks, and scaling limitations. Large language models and AI agents require enormous quantities of data movement between processors and memory, which consumes huge amounts of electricity.

Memristor-based systems could potentially reduce those inefficiencies significantly by processing information directly where it is stored. That could become particularly valuable for edge AI systems operating in remote or hostile environments where power, cooling, and maintenance are severely limited.

Possible future applications could include spacecraft, geothermal drilling systems, industrial robotics, autonomous military platforms, high-temperature manufacturing, nuclear facilities, and even future Venus exploration missions.

Importantly, this also reflects a broader change taking place across the semiconductor industry.

For years, AI progress largely depended on scaling conventional GPUs and cloud infrastructure. Increasingly, researchers are now looking towards entirely new memory architectures, analogue computing approaches, and neuromorphic hardware designs to overcome the physical and economic limits of traditional systems.

What Does This Mean For Your Business?

For businesses, the breakthrough is another sign that the next wave of AI competition may depend as much on hardware innovation as software models.

The wider significance here is not simply a chip surviving extreme temperatures. It is that memristor computing, long viewed as an experimental concept, is now beginning to move closer towards industrial-scale manufacturing and commercial AI deployment.

That could eventually reshape sectors ranging from industrial automation and aerospace to defence, logistics, infrastructure monitoring, and autonomous systems.

It also reinforces how AI infrastructure itself is rapidly becoming a major strategic battleground, with governments, semiconductor firms, and startups all racing to develop hardware that is faster, more energy efficient, and capable of operating in environments where conventional computing struggles or fails entirely.