Prince Harry and Meghan Markle have taken direct aim at Grok after Minnesota became the first state to enforce a ban on AI "nudification" technology, using an unusually blunt official statement to criticize xAI's attempt to stop the law. Days before the legislation took effect, Elon Musk's AI company filed an emergency legal request seeking to block it, arguing that the measure violates the First Amendment. A federal judge refused to grant the request, allowing the law to go into effect while the broader case continues.

The Sussexes made it clear which side they believe this fight should be on. "Technology should not enable predators to target children," their statement begins. "Yet, ahead of Minnesota's first-in-the-nation law banning AI 'nudification' apps taking effect tomorrow, one of the world's largest technology companies sued to stop it. Why?" It is a remarkably direct criticism, one that shifts the conversation away from legal arguments and back toward the people these tools can harm.

Whether Minnesota's law ultimately survives every constitutional challenge is a question for the courts, but Harry and Meghan have identified something larger than one lawsuit. AI companies are racing to make image generation more powerful while governments scramble to prevent those same tools from being weaponized against real people. When a company fights to preserve technology that can create convincing fake nude images of recognizable adults and children, it is easy to see why the couple argues that the industry's priorities deserve much closer scrutiny.

Sussex vs. Musk

(Image credit: Shutterstock)

AI has become extraordinarily good at manipulating images, but that includes convincing fake intimate photographs with only a few prompts. And it has spread far faster than the laws designed to deal with it.

Minnesota's legislation attempts to tackle the problem at its source by preventing apps and websites from offering AI nudification tools in the first place. Rather than waiting until fake images have already spread across social media or messaging apps, lawmakers are trying to make the technology itself less readily available. Whether every provision survives constitutional scrutiny remains to be seen, but the intention is difficult to misunderstand.

Harry and Meghan clearly believe that technology companies have had plenty of opportunities to address the issue voluntarily and have failed to do so. Their statement praises Minnesota's bipartisan action as "an example of leadership fit for the digital age," adding that lawmakers recognized "this technology, if not stopped, would protect predators and hurt innocent people, especially women and girls."

The Sussexes are slicing through the tangled debate over algorithms against constitutional doctrine. Those issues matter, but it can miss the forest for the trees if people forget that these synthetic nudes begin with an identifiable person whose image has been manipulated without permission.

AI models as neutral tools whose morality depends entirely on the user. But laws often are stricter when any tool is used to hurt children for a reason. And the claim that Grok's moderation system is enough has proven untrue. But the feature does not stop being Grok's responsibility simply because someone else typed the prompt.

Safety should not be an optional feature

The Sussexes refuse to treat this as an abstract policy dispute.

"Big Tech companies are raising billions claiming AI will bring society forward, yet they retaliate against basic safety measures to keep children safe," they wrote. "Can AI make our world better while it enables the worst in humans? Should our children pay the price while we wait to find out?"

Those are uncomfortable questions for AI companies racing to release increasingly capable products. Every major developer wants to ship the next breakthrough before its competitors do. Safety work, moderation systems and abuse prevention rarely generate the same excitement as flashy new features demonstrated on stage.

xAI is hardly alone in facing this challenge. Every major AI company has struggled with image generation, impersonation and deepfakes. The difference here is the explicit pushback against a state trying to make them take some responsibility for how their technology is misused.

The legal arguments will continue for months, and there's no way to tell yet what the final version of the law will look like. Courts have to balance free speech and public safety, and that's not simple. But the fact that AI has made creating nonconsensual intimate imagery dramatically easier remains, and Harry and Meghan are right to zero in on that human cost over legal theory.

The debate matters because children's safety matters. Whether the companies building these tools can be made to accept meaningful responsibility when those capabilities are turned against children may be decided in courts, but Harry and Meghan are correct that it shouldn't take lawyers for them to do the right thing here.

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• Marantz launches a new Cinema Cinema Series 2 range of AV receivers, with three different models
• The have a brand new 32-bit DAC across all three models, plus optional Dirac room correction available on all
• There's also improved HDMI 2.1 gaming support, including 1440p passthrough and AMD FreeSync

Marantz has updated its acclaimed Cinema Series range of AV receivers. The first generation launched in 2002 and delivered a winning combination of good looks, great audio and excellent connectivity, and the latest Cinema 50 Series 2 , Cinema 60/DAB Series 2 and Cinema 70s Series 2 continue in that vein.

The big change in these new 2026 models is their brand-new 32-bit 8-channel DAC, which Marantz says delivers "greater precision, improved imaging, enhanced dynamic expression and more coherent surround performance throughout the listening environment."

Each model has been tuned by Marantz's Sound Master to deliver what Marantz promises is "an immersive and emotionally engaging entertainment experience."

There's Bluetooth LE Audio (in a future firmware update), improved gaming support and optional Dirac room correction across all three models.

One thing that hasn't changed is the products' good looks: these are home theater components you'll want to show off.

(Image credit: Marantz)

Marantz Cinema Series 2: models, features and pricing

Each of the new Series 2 models comes with the new DAC, support for 1440p video passthrough with AMD FreeSync for gamers, a new Web Control 2.0 configuration app for your phone or computer, and HDMI Diagnostics 2.0 to deliver advanced troubleshooting and faster installation, which is intended for custom-installations, but sounds like a potential godsend generally.

The data options include the ability to see how each channel is being pushed by what's playing, which is definitely a fun game to play if you're wondering how different movies make use of the power of your system. Engineers from Denon and Marantz have previously told my colleague Matt Bolton that Gravity is the 'torture test' movie they use to see how AVRs handle stress during development, so it'd be fun to take a look at what that movie does to the numbers.

The flagship Cinema 50 Series 2 gets a few important upgrades that the others don't: the new Dolby Atmos Channel Expander, which uses all the available speakers when you're playing Dolby Atmos content regardless of how it was originally encoded, which seems to be especially useful for if you have additional height channels. There's also a new Center Bi-Amp Mode to improve dialog quality.

The flagship Cinema 50 Series 2 has nine channels putting out 110W apiece, 11.4 channels of processing and optional Dirac support. The Cinema 60/DAB Series 2 is a 7-channel, 100W per channel model, while the Cinema 70s Series 2 delivers 7 x 50W.

The prices for the new models are:

• Marantz Cinema 50 Series 2: $3,000 / £1,850 / AU$3,500
• Marantz Cinema 60 Series 2: (APAC and USA only) $2,000 / AU$2,500
• Marantz Cinema 60 DAB Series 2: (Europe only): £1,250
• Marantz Cinema 70s: $1,500 / £950 / AU$1,800

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Mention high-frequency semiconductors, and the conversation often begins with gallium arsenide (GaAs). For decades, GaAs has been the material of choice for high-performance radio frequency (RF) devices that enable a wide range of applications, including satellite communications, radar, and mobile networks.

It is a mature, well-understood technology, and large, high-quality wafers can be grown economically, enabling consistent device performance and scalable manufacturing.

Yet a transition is underway. Increasingly, the industry is turning away from GaAs and towards gallium nitride (GaN), a material that offers clear performance advantages, particularly at higher frequencies, higher power levels, and more demanding operating conditions.

The promise of GaN is well established. The challenge lies in making it reliable, scalable, and commercially competitive.

And, as with many emerging semiconductor technologies, meeting that challenge is as much about measurement as it is about materials.

A material with advantages, and constraints

GaN enables the development of devices that can operate at higher voltages, higher temperatures, and greater power densities than their GaAs counterparts. This makes it particularly attractive for next-generation RF systems, including 5G and 6G communications, defense applications, and space technologies – where extreme environments are commonplace.

In principle, GaN can offer a straightforward upgrade path. It is sometimes thought of as a functional replacement for GaAs to deliver improved performance. Yet, in practice, the situation is more complicated, and a whole-system approach is required to redesign a module. Furthermore, the manufacturing approach for GaN is fundamentally different.

The hidden cost of heteroepitaxy

Unlike GaAs, which can be grown as large, high-quality wafers using established methods, GaN presents a fundamental manufacturing challenge because producing large, defect-free GaN substrates remains difficult and expensive.

As a result, most GaN devices are produced using a process called heteroepitaxy, whereby a thin layer of GaN is grown on top of a different substrate, typically silicon or silicon carbide. This approach allows manufacturers to leverage existing wafer technologies. But it comes at a cost.

When GaN is grown on a dissimilar substrate, differences in lattice structure and thermal expansion introduce defects into the material. These defects can affect everything from electrical performance to long-term reliability.

This results in a difficult trade-off: GaN offers superior theoretical performance, but achieving that performance consistently across wafers and devices is far more challenging than with GaAs. The advantage of using GaN therefore increasingly depends on material quality, and on the ability to control and understand it.

That relies on measurement.

Measurement as a competitive tool

For companies developing GaN technologies, metrology plays three distinct and essential roles.

The first is in process development. Growing GaN through heteroepitaxy involves carefully balancing multiple parameters, including temperature, deposition rates, and substrate preparation. Small changes can improve or degrade material quality. Without reliable measurement, it is difficult to know whether a process adjustment has made the material better or worse. Metrology provides the feedback needed to refine growth techniques and reduce defect densities.

The second role is in demonstrating material quality. In a market where performance depends heavily on the underlying material, manufacturers must be able to show that their GaN is superior to that of competitors. This requires measurement methods that are not only accurate, but also comparable across organizations. Customers need confidence that a claim about material quality means the same thing, regardless of where it is measured.

The third role is in device performance validation. Ultimately, customers care about how a device behaves in real applications. For RF components, this includes metrics such as power output, efficiency, frequency response, and thermal stability. Linking these device-level characteristics back to material quality is essential. It allows manufacturers to demonstrate that improvements in material growth translate into tangible performance gains.

Across all three areas, measurement is not simply a supporting activity. It is a central part of how competitive advantage is created and communicated.

From materials to systems

The challenges associated with GaN are part of a broader shift in the semiconductor industry.

As devices become more specialized and operate under more demanding conditions, performance is increasingly determined by subtle interactions between materials, structures, and processes.

This is particularly true in RF systems, where small imperfections can have outsized effects on signal integrity and efficiency.

It also connects to a wider trend seen in other areas of semiconductor technology, including photonics. There, heterogeneous integration is bringing together different materials and device types within a single system, creating similar measurement challenges.

In both cases, success depends on the ability to understand and control complexity at multiple scales.

A strategic inflection point

For the RF semiconductor industry, the transition from GaAs to GaN represents more than a simple material substitution.

It marks a shift towards technologies where performance is less constrained by established manufacturing processes, and more dependent on how well new materials can be engineered and characterized. This creates an opportunity.

Countries with strong capabilities in materials science, process development, and measurement can play a defining role in shaping how GaN technologies evolve and are applied successfully in the semiconductor industry.

The ability to measure material quality, correlate it with device performance, and establish trusted benchmarks will influence how quickly GaN is adopted across global markets. This means the future of high-frequency RF semiconductors will not be determined by materials alone.

GaN may offer superior intrinsic properties, but those advantages must be realized in practice. That requires consistent, high-quality material growth, reliable device fabrication, and credible performance validation – all of which depend on effective measurement and standards. This is why metrology will become increasingly central to RF semiconductor innovation, not as a downstream check, but as an integral part of development, manufacturing, and market adoption.

In high-frequency semiconductors, as in photonics, the ability to measure well is no longer just a technical requirement. It is becoming a defining feature of competitiveness.

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Prompt engineering was briefly the face of the AI jobs boom.

In 2025, technology recruitment firm SPG Resourcing reported that UK job listings for AI prompt engineers had grown by 180% over the previous year.

It was an eye-catching figure that captured the mood of the first commercial wave of generative AI. Businesses were trying to understand how to talk to LLMs and turn early experiments into something useful.

That requirement has not disappeared.

With job site postings for specialist AI roles in the UK rising by 61% from last year according to PWC, it’s clear that good prompts still matter. But most of that new demand is for people who can apply AI inside a business, not just talk to a model.

Many organizations have moved past the first demo. They are now trying to build AI agents, retrieval-augmented generation (RAG) systems, and AI-enabled workflows that operate inside the business.

That creates a different skills gap.

This is the shift behind what I refer to as ‘context engineering’. Although that term is not universally used, the capability is becoming essential.

Companies that want useful AI agents and retrieval-augmented generation systems need people who can design the environment around the model, not just the prompt sent to it.

From better prompts to better context

Prompt engineering is about the instruction, while context engineering is about the world around that instruction.

A support agent does not only need a well-written prompt, it needs the right customer record, policy, product history, and permission boundary. Similarly, a developer agent needs the relevant code, tests, dependencies, and deployment constraints.

In both cases, output quality depends on context. Without it, the model is guessing from incomplete evidence. With too much of it, the system becomes noisy and difficult to govern. The job is to make context useful, current, and controlled.

That’s what makes context engineering distinct from prompt engineering.

Agents raise the stakes

The rise of AI agents makes this more urgent. A chatbot with poor context may give a weak answer, but that same poor context may result in another agent making a serious error.

Once an AI system can call tools, query business systems, maintain state, and act across several steps, context becomes an essential part of the production architecture. It decides what the agent can see, what it can do, and how much confidence the business can place in the outcome.

I’m reminded of a joke about boundary testing. A developer walks into a bar and orders a beer, then he orders five beers, then he orders 999,999,999,999 beers, then he orders -1 beer. The bartender blinks, but everything is okay. A user walks into the bar, asks where the bathroom is and the whole bar explodes.

The same principle applies to AI projects. The first prototype may work against a narrow set of examples, then become fragile when it meets real data. Customer information sits in one system, operational data in another, and important knowledge in documents and files. The agent is expected to reason across all of it, but the context layer has not been designed for that job.

A financial services team, for example, may need to connect CRM data, an existing data platform, and internal documents before an agent can answer accurately. The hard work is not only moving the data. It is shaping it so the agent can retrieve the right evidence and stay inside the right permission boundary.

The job title is still catching up

This creates an awkward hiring moment. The need for context engineering is becoming clearer, but the job title is still unsettled.

Some organizations may seek to specifically hire ‘context engineers’, but many will not. The capability is more likely to appear inside roles such as AI engineer, agent engineer, AI platform engineer, applied AI engineer, or data engineer. In other businesses, it will be a team responsibility shared across data, platform, security, and software engineering.

Leaders therefore need to hire for the work, not the label. A candidate does not need to have ‘context engineer’ on their CV to be useful. The better signal is whether they understand how data moves through systems, how permissions are enforced, and how a prototype becomes something reliable enough for production.

This also means the talent pool is wider than many companies assume. Machine learning expertise is valuable, but context engineering draws heavily on existing engineering disciplines. Data engineers understand pipelines and retrieval.

Platform engineers understand operational resilience. Security teams understand access control and auditability. Software engineers understand how to turn messy requirements into maintainable systems.

The best candidates may look like full-stack AI engineers. They do not need to be specialists in every model, database, or framework, but do need enough range to connect the model layer with the business systems around it.

The Kubernetes lesson and what leaders should do now

The shift has a parallel with the move to cloud-native architecture and Kubernetes. Many companies treated Kubernetes as something to install, then discovered that the harder work was changing how teams built and ran software.

AI creates a similar risk. Companies can buy tools and hire a handful of specialists, but still fail to change the engineering habits around them. Context engineering requires teams to think differently about everything from documentation, and data ownership, to access, testing, and accountability.

It also changes the culture of software development. Engineers are already using AI to write, review, and iterate code. That can improve productivity, but it does not remove responsibility. In areas where performance, reliability, or security matter, human judgement becomes even more important.

CTOs and CIOs should not wait for context engineering to become a mature hiring category. They should start identifying the capability now.

The first step is to examine where AI projects are failing. Is the model genuinely weak, or is the system retrieving poor context? Are permissions clear? Can the team explain why the agent produced a particular answer?

The second step is to build cross-functional teams. AI cannot sit apart from data, platform, security, and product. In many cases, the best approach will be to upskill existing engineers who already understand the organization's systems.

The final step is cultural. Engineers need to become fluent in AI-assisted development while staying accountable for the systems they ship. Leaders need to make room for experimentation, but they also need clear standards for review, evaluation, and governance.

The model is not enough

AI hiring is changing because AI itself is moving into production. Models will continue to improve, and businesses will have many ways to access them. The harder advantage will come from knowing how to connect those models to the right business context.

Companies that understand this will build agents and RAG systems that are more useful, safer, and easier to govern. Companies that ignore it will keep blaming the model when the real weakness is the environment it has to work in.

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There has never been a construction race like the one now under way in the data center industry.

AI's demand for compute is driving the largest and fastest infrastructure buildout the sector has ever seen, with hyperscalers and developers racing to bring capacity online faster than power grids, planning departments or supply chains can comfortably keep up.

In that scramble, enormous attention goes to the things visible on a spreadsheet: megawatts, cooling, chips, network and, rightly, cybersecurity.

The dimension that gets quietly deprioritized is the physical protection of the buildings themselves. It is the easiest thing to defer under deadline pressure, and the hardest to retrofit once the concrete is poured.

A regulatory change in the United States is about to make that blind spot worse, and it is worth understanding even if you never operate a US federal facility, because of what it signals.

On 30 September, the Federal Data Center Enhancement Act is due to expire, with no replacement waiting. It set minimum standards for federal data centers, including, unusually, protection against physical intrusion, and it was the operational mandate that forced data-center-specific assessment.

Broader frameworks such as FISMA and the NIST control catalogue still apply, but they provide the principle; the Enhancement Act provided the practice. Principles without a mechanism to enforce them tend to be interpreted generously.

And when the government's own floor is allowed to disappear, the benchmark private operators quietly measure themselves against tends to go with it.

Security baselines

This is not a hypothetical worry about whether the requirement comes back. The Act's predecessor lapsed in 2022 and only survived by being folded into the following year's defense bill. A rule that needs a legislative vehicle to return is one that can quietly fail to, and security baselines rarely erode through a single dramatic decision. They erode through the absence of one: a mandate that simply never gets renewed because nothing forces the issue.

It helps to be concrete about what is at stake, because physical security is not an abstraction. It is the contractor with unescorted access to a hall of servers; the unmonitored loading bay; the maintenance door propped open for convenience; the departed employee whose credential still opens the cage. These are the routes by which data is stolen, infrastructure is sabotaged, and a facility the size of a warehouse is taken offline.

The real exposure is not in the data centers we already have. Established operators keep that spending in place through existing contracts and their own risk appetite. It is in the new builds, the AI-era expansions specced and procured at extraordinary speed, most of them private, built by developers no mandate ever bound. Remove the assessment framework and physical security becomes something that can be scoped down in procurement to hit a budget or a timeline, with no compliance flag and no one formally alerted. The gap opens in the facilities we are racing to build.

There is a contradiction at the center of this. Governments increasingly classify data centers as critical national infrastructure, the UK now does, and rightly so. Reducing their security baseline at the same moment runs in two directions at once. You cannot call something critical and simultaneously make its protection optional.

A sensible fix

The fix is not simply more regulation, though a sensible renewal would help. It is to stop treating physical security as a compliance obligation that rises and falls with the statute book, and start treating it as core design.

The consistent lesson from securing large-scale critical facilities is that physical security fails when it is a collection of disconnected tools, a camera here, an access reader there, bolted on at the end of a project.

It works when it is designed from the start as one integrated system, where access control, video, identity and alarms inform each other and an anomaly anywhere triggers a coordinated response.

Treating the physical and the digital as separate problems is part of how the gap forms in the first place. In a modern data center they are the same problem: a propped door, a cloned badge or a rogue contractor is a cyber incident waiting to happen, and a facility that cannot correlate a door event with an access log or a camera feed will always be reacting after the fact rather than stopping an intrusion in progress.

For operators, the practical implication is simple: the physical security of an AI data center should be specified at the same moment as its power and cooling, not bolted on once the shell is up. Retrofitting protection into a live, fully-loaded facility is far harder, and far costlier, than designing it in.

The AI buildout is a genuine engineering achievement, and the energy and compute challenges are real. But the industry is optimizing hard for the risks it can measure and deferring the one it finds inconvenient. A statute lapsing in Washington should not be what decides whether the buildings holding the world's most critical compute are properly protected.

For IT infrastructure we have all agreed is critical, getting its physical protection right should be a given, not something we quietly leave to whoever is under the most deadline pressure.

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Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.

Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.

AI workloads, however, demand far more power with greater energy density.

Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.

Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.

In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.

For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.

Balancing site trade-offs to unlock faster delivery

Site selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.

In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.

Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.

While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.

Combining power solutions can accelerate bringing capacity online more efficiently

As AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.

In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.

As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.

These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.

Rethinking cooling can support high-density AI and optimize when energy is used

With this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.

One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.

At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.

Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.

Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.

This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.

Early efforts on permitting can identify the fastest development route and avoid delays

Permitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.

For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.

Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.

This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.

Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.

Turning AI demand into operational capacity at the speed and scale the market requires

The importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.

The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.

Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.

Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.

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• UK’s Police National Legal Database (PNLD) breach leaks data of 100k+ criminal justice professionals
• Threat group ExfilSquad claimed responsibility, posting 1.9 GB of stolen records on the dark web and demanding ransom
• PNLD notified NCA and ICO, hired specialists, and confirmed passwords weren’t compromised but contact details exposed

The UK’s Police National Legal Database (PNLD) suffered a cyberattack recently, in which it allegedly lost sensitive data on more than 100,000 criminal justice professionals.

In a short press release, PNLD confirmed the breach, saying it happened over a weekend. The threat actors, which were not named in the announcement, were said to have taken names, organizations, and work email addresses belonging to police officers, staff, government partners, and customers.

The announcement also said the stolen information was already published on the dark web, adding that there is “no evidence to suggest that passwords or other security credentials have been compromised.” How the attackers worked their way in was not disclosed in the announcement.

ExfilSquad takes the blame

Following the breach, PNLD hired cyber-security specialists, and notified the National Crime Agency, which started their investigation into the incident.

“All affected organizations were contacted in the days following the incident and provided with further information and guidance,” the announcement reads. “The Information Commissioner’s Office (ICO) has also been notified.”

At the same time, threat actors calling themselves ExfilSquad claimed responsibility for the attack, BleepingComputer reported. The group alleges it stole 135,000 contact records, sharing samples to support their claims. They also said they demanded a ransom in exchange for keeping the data safe.

In the dark web post, ExfilSquad said it obtained 1.9 GB of data, which includes information belonging to 114,000 PNLD subscribers and 21,000 Ask the Police users.

‘Ask the Police’ is a public-facing website where users can find answers to hundreds of commonly asked policing or legal questions.

ExfilSquad is a relatively new threat actor that's not known for any major attacks so far. Prior to the PNLD incident, it claimed the attack against Analog Devices, a US semiconductor company.

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• Advanced Full Fibre Broadband from EE offers download speeds of 2.3Gbps and 8Gbps
• Speeds are potentially 222 times faster than standard superfast fibre
• Initially available in Guildford and Woking, expanding as Openreach rolls out its XGS-PON glass fibre network

Following a successful trial in early 2026, EE has launched its new Advanced Full Fibre Broadband plans, advertising download speeds of 2.3Gbps and 8Gbps. This makes EE the first provider to sell access to the Openreach XGS-PON glass fibre network.

The plans are initially only available in the Guildford and Woking areas, two Surrey towns in the London commuter belt, where the glass fibre network is installed and active.

EE is targeting users who rely on video conferencing, remote working, and content upload, and with multiple Internet of Things devices, as well as online gamers and streaming consumers. Speeds are up to 222 times faster than the standard superfast fibre broadband plans.

Two plans for EE’s XGS-PON fibre

Customers can access two plans from EE. The 2.3Gbps Advanced Full Fibre Broadband plan starts at £54.99 per month, while the 8Gbps plan is available from £74.99 a month.

No upload speeds have been stated, nor are they listed on the sign-up page, but they can be expected to be similarly fast.

XGS-PON (10-Gigabit-capable Symmetric Passive Optical Network) is a high speed, high-bandwidth data network standard, currently available in various forms across Europe, North America, and other regions. In the UK, it is used in the nexfibre, CityFibre, and Openreach networks, with EE using the latter to provide these plans.

With streaming speeds supporting 4K and 8K video, these plans are likely to be popular where available, and support for up to 190 devices suggests the plans will include routers suited to IoT and smart home applications.

Will faster fibre improve British broadband?

“Today marks a major milestone for EE as we become the first major UK provider to offer broadband speeds of up to 8Gbps on next-generation XGS-PON technology," noted Luciano Oliveira, Director of Product, Home and TV at EE.

"As homes become more connected and customers place greater demands on their broadband, we're investing in the technologies that will power the next generation of digital experiences.”

EE regularly wins recognition as the UK’s most popular network, with its business built on mobile (where its 4G, 5G and 5GSA speeds cover more than 90% of the UK) and an advertising campaign featuring Hollywood actor Kevin Bacon.

Unlike Virgin Media which owns its own fibre network, EE’s domestic and business broadband plans rely on third party infrastructure, mostly provided by Openreach, and due to the differences in connections across the country (and the legacy copper lines that remain in place in some locations), struggles to offer the same speeds for all customers.

This is a problem faced by all providers who sell access to Openreach lines, so EE’s immediate competitors will no doubt be watching with interest.

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“AI readiness” tends to be used as umbrella phrase to describe the different range of activities that businesses need to carry out to get value of generative AI and minimize any associated risks.

AI readiness therefore takes into account everything from having the right data foundations and governance in place to supporting AI literacy among staff to establishing ethical guardrails.

One area where businesses – including even large brands – are collectively not ready for AI is ensuring their brand is visible and faithfully represented across Large Language Models (LLMs) and in responses from associated AI tools like ChatGPT, Gemini and Claude.

The outside-in view

Too many large companies have a digital estate that contains content that is outdated or contradictory. Because AI tools base their responses on the content they find, brands risk AI tools giving out misinformation or misrepresenting their brand, products or organization.

Similarly, there is also a risk of brands not being featured in responses or less than those compared to competitors, impacting future visibility and competitiveness.

This is effectively AI looking from the “outside in” and then presenting a picture of what it has surfaced. When we measured this “outside in” element of AI readiness across the websites of over 250 top global brands using an automated remote website assessment tool and an underlying AI maturity model, we found only approximately 3% of websites could be considered “leading” when it comes to AI readiness.

Why AI readiness is important

The way people search for information on the web is changing rapidly. Consumers are turning to ChatGPT rather than using traditional Google searches; surveys have suggested that 37% of consumers in the US who already use AI, now start their searches using AI tools rather than Google.

This means that rather than looking at a web page on a company’s own website, consumers’ perceptions about a brand are increasingly coming from AI responses as the first step rather than directly from a web page controlled by the company.

That’s a problem if a consumer asks AI to provide a list of suppliers or vendors, and your business isn’t listed when it should be, or the AI provides erroneous information about your company and products from the off.

Recognizing the speed at which consumers are turning to AI tools for search, digital marketing teams who have previously invested in Search Engine Optimization (SEO) are now investing in related practices like Generative Engine Optimization (GEO) and Answers Engine Optimization (AEO) with some urgency to optimize their digital footprint so it is successfully reflected in AI tools like ChatGPT.

But the reasons that even some of the world’s top brands still have a lot of work to do in this area is because in the past they have deprioritized basic governance and maintenance across their digital estate, with the risk of AI confidently presenting answers as facts or broadcasting perceptions that are based on information from content that is out of date and has been superseded.

Of course, the risks associated with erroneous answers may vary but they can be acute, particularly in heavily regulated industries such as financial services, healthcare or pharmaceuticals with strict compliance surrounding the information that businesses must provide to customers, or in areas such as products where health and safety instructions must be correct.

The hidden digital footprint

One of the key reasons where businesses slip up is by building up a digital footprint – often without realizing it – which is not actively managed and has old and inaccurate information.

Over time, businesses accumulate a surprisingly large number of digital assets that are accessible online, sometimes hosted on third-party platforms: websites for sub-brands or locations, online newsletters, campaigns, product launches, events, microsites, partner content, customer portals, forms, digital experiments, and so forth. In addition, there might be areas of a corporate website that have effectively been forgotten about.

Over the years, this digital footprint in some organizations has not been properly managed, so a site created to support a one-off event stays online indefinitely rather than being retired. Sometimes when a company acquires another, it also becomes responsible for this digital debris that has accumulated and which the new team have little awareness of the full footprint they have inherited.

Previous research we carried out between 2017 and 2023 shows that central digital teams can be unaware of up to 41% of their entire digital footprint.

The danger of poor document management

Another critical cause of a lack of AI readiness is an overreliance on documents. Many websites contain PDF attachments which don’t necessarily get updated and then accumulate over years and years, containing information which is no longer relevant or correct.

Annual reports, brochures, manual forms, product guides and more are all left accessible on websites. Our research suggests that as many as one in five (19%) website documents are duplicates which can create inconsistent signals for AI about which is the current and authoritative version

Not enough organizations take a structured approach to managing their documents across their digital estate, which means they cannot effectively update documentation for example to reflect brand, regulatory or operational changes, with AI now forming responses from these out-of-date files.

How to reduce risk exposure

As more businesses find AI is producing misinformation about them and the number of consumers who use AI tools for searching information increases, awareness of this problem is likely to rise.

To reduce the risk exposure, at a high-level, three things generally need to happen:

• The issue needs to be owned at the executive or board level with someone responsible to tackle it.
• Digital teams need to use automated assessment tools to measure and monitor the risk associated with their entire digital estate in order to track associated remedial action.
• The digital team need to put governance and lifecycle management processes in place that enables them to have more control and oversight over their digital footprint, so it avoids becoming a digital wild west again.

AI readiness covers many areas. A key priority for brands who want to maximize AI visibility while reducing the chance of misinformation is to get their digital estate under control. Of course, there are other important activities to consider including ensuring content is optimally structured, but reducing the amount of out-of-date content is a critical starting point.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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If your business celebrated the European Union’s (EU) delay of high-risk AI penalties, don't open the champagne just yet. On August 2, 2026, Article 50 of the EU AI Act goes live, bringing new transparency requirements for the AI tools businesses already rely on. While backend compliance demands have been delayed, chatbots, AI generators, and synthetic media systems are now in the spotlight.

The AI Act delays may have dominated headlines, but Article 50 never stopped moving. The European Commission has now finalized the transparency guidelines businesses need to follow, giving regulators a clear framework to enforce consumer disclosure rules starting this weekend.

Think the EU AI Act only targets big tech? Think again. Small or medium-sized enterprises (SMEs) serving European users are also in scope, wherever they operate. The age of invisible AI is ending, and businesses that hide automated interactions may find themselves under significant regulatory scrutiny before the business week even begins.

Who is caught in the net? Providers vs deployers

To navigate the EU AI Act without getting lost in legal jargon, businesses must first understand which role they fall under. The framework separates organizations into two categories: Providers and Deployers. Mixing up these definitions is the fastest way to accidentally skip a critical regulatory step or waste time trying to solve compliance issues that aren't actually your responsibility.

If your company builds AI models from scratch, significantly modifies open-source systems, or white-labels a third-party AI tool to sell under your own brand name, you are a Provider under the EU AI Act. That puts the technical burden on your business, from building transparency infrastructure to embedding machine-readable watermarks into AI-generated text, images, and videos.

For most SMEs, however, the second category applies: Deployers. If you are a small business that simply plugs an off-the-shelf AI widget into your website to handle customer service tickets, or uses AI tools to create marketing content, you are deploying AI. Thankfully, your responsibility isn't to invent deep-tech watermarking protocols but to double-check that people interacting with your systems are notified that they are dealing with an algorithm rather than a human employee.

The AI Act timeline: What was delayed vs what is due now

When the EU delayed major AI Act deadlines, businesses were quick to assume they had more time. The May 2026 "Digital Omnibus" amendment formally rolled back the compliance deadlines for complex AI architectures, leading many burned-out business owners to assume the entire rulebook had been kicked down the road.

Unfortunately, that was a serious misunderstanding of the changes. The extensions only apply to standalone, high-risk frameworks, such as automated resume screening, biometric identity verification, and AI-powered credit scoring tools. Because these deep enterprise tools require massive infrastructure overhauls and independent third-party audits, the EU simply gave businesses a longer runway until late 2027 and 2028 to prepare.

Basic consumer transparency, however, received no free pass. The European Commission made it clear that protecting everyday users from digital deception could not be delayed. As a result, Article 50 moves forward on schedule, separate from the postponed systemic deadlines. To guide businesses through this transition, the EU has established the Code of Practice on Transparency of AI-generated Content as the benchmark for compliance.

Article 50 and the four pillars of AI transparency

If your business utilizes AI to interact with customers or create content, Article 50 is now part of your operating reality. The EU has broken down its transparency rules into four core pillars, aiming to remove the mystery around automated systems without slowing down innovation.

For SMEs, the era of set-it-and-forget-it AI is firmly over. Depending on how your business uses generative tools, you are now legally required to update user interfaces, verify compliance from software vendors, or change how AI-generated content is published.

AI chatbot transparency rules

The first pillar is the “Human-AI Interaction” rule, and it targets the conversational bots handling your customer service, lead generation, or basic troubleshooting. Under the new guidelines, you can no longer trick a consumer into thinking they are chatting with a human employee when they are actually interacting with a large language model (LLM) script.

Starting this week, deployers must stop hiding AI behind a human-looking interface. Crucially, users need a clear notification that they are interacting with an AI system before the conversation starts, not a disclaimer buried in a wall of legal text. For SMEs using AI customer support, that means updating chat interfaces immediately to include an undeniable "I am an AI assistant" disclaimer.

Synthetic media watermarking

The second pillar shifts the technical burden onto the builders of generative AI tools. If your business creates or distributes software that generates synthetic text, audio, images, or video, you must embed machine-readable digital watermarks into the assets those tools produce.

The purpose of these markers is to help other software, like social media platforms or verification services, automatically detect that a piece of media was created by an AI. While existing providers have limited time to implement this infrastructure, any new generative AI tools launched after this week must include watermarking from day one.

Labeling AI-generated content

While watermarking happens behind the scenes, the third pillar requires clear labeling on the surface of the content itself. This rule specifically targets what the EU defines as deepfakes and synthetic output that could influence public understanding. If your marketing team uses AI to create realistic images, videos, or audio that could be mistaken for real people or events, you must slap a clear “AI-generated” warning label on them.

This responsibility falls on the businesses publishing the content. So, if you publish an AI-generated image of a realistic crowd for a promotional campaign, or use an AI-voiced avatar in a public video, it must also feature an overlay or disclaimer stating that it has been artificially generated or manipulated. The only exception here is obvious artistic, satirical, or fictional content, but transparency is still the safest bet.

Emotion recognition rules

The final, and perhaps most legally sensitive, pillar targets biometric and emotion-recognition systems. If your SME utilizes software to detect emotional states, interpret facial expressions, or classify individuals based on biometric data, you must proceed with extreme caution.

The new rules require businesses to explicitly inform users whenever they are exposed to emotion recognition or biometric profiling systems. For instance, if you use an AI-driven video interviewing tool to analyze a candidate's body language, or a retail system checking customer reactions, you must secure clear user consent before the software starts collecting this type of sensitive data. Psychological and physiological tracking can no longer happen behind closed doors.

The AI Act’s hidden deadline split

Like with any major regulatory rollout, the devil is always in the details. For SMEs, the EU AI Act brings a mix of breathing room and immediate pressure. Understanding which deadlines moved (and which did not) is the only way to avoid a costly compliance mistake.

The good news for software creators and IT departments is what experts call the grandfather clause. If your generative AI model was already legally available on the market before August 2, 2026, the EU provides a four-month technical runway, which moves mandatory machine-readable watermarking requirements to December 2, 2026. This extension gives development teams a much-needed time to update systems, test metadata workflows, and prepare for compliance.

However, relying too much on this buffer is where SMEs fall into a terrible, zero-day trap. The four-month grace period applies only to the technical back-end watermarking of existing AI tools, and it doesn’t cover chatbots, public disclosures, or newly launched software.

If you’re launching a brand new AI writing tool or chatbot after this week, there is no ramp-up window. Similarly, user disclaimers for deepfakes and interactive chatbots must be active immediately on day one. Without clear AI notices on customer-facing systems, your business becomes non-compliant the moment the deadline passes.

How to comply before Monday morning

If your business is staring down the barrel of the August 2nd deadline, the answer is not panic but preparation. Since there is no single EU registry for AI tools, compliance starts with something much simpler: making your AI use visible, documented, and transparent.

You do not need to rebuild your software stack over the weekend, but you do need to secure your customer-facing AI touchpoints. Here are four steps to take before Monday:

1. Map your stealth AI usage: Audit your teams for untracked software, plugins, and automated systems creating customer-facing content.
2. Kill the invisible chatbots: Update AI chat tools with a clear upfront notice telling users they are interacting with an AI assistant.
3. Verify software vendor compliance: Get written confirmation of compliance and ask how providers handle machine-readable watermarks.
4. Document your paper trail: Log every AI update, vendor conversation, and internal policy change for future reviews.

Turning compliance into trust

For many businesses, Article 50 feels like a new burden, but for smart SMEs, it is a chance to build trust. As users grow more skeptical of hidden AI, businesses that are transparent about how they use automation can stand out from the crowd.

Compliance is no longer another box to check to avoid penalties but a powerful indicator of integrity that can set your brand apart in the new age of AI.

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One of my favorite hobbies is Dungeons & Dragons. I love nothing more than sitting around a table with friends and playing out a fantastical adventure, in which the dice are the difference between success and failure.

It's a fun game, and all the more fun because real-life people make dumb, suboptimal decisions and the dice cause the game to change in unpredictable ways. But I really love it because the concept of people sitting around a table or hearth and telling stories is as old as the campfire, and it's a fun modern-day way to keep that experience alive.

You might think such a robust framework for an analog hobby (friends, a table, dice, pen and paper) might be tech-proof, but even in the Dungeons & Dragons world, AI is coming for my job as a Dungeon Master — the person who runs and guides the game world. Deep in the corpus of most popular generative AI models designed for general use are the rules to D&D 5e (and likely every other edition before it) and plenty of people report playing with their chatbots online.

Many of these players can't, or simply don't want to, get round a table with real people. For them, having a chatbot write them a story while the user prompts the continuation of the story with their character's actions, must seem like the perfect solution — even if it's more like a solo choose-your-own-adventure than a 'proper' group D&D game.

Gemini and ChatGPT both have a strong grasp of the rules for D&D's popular 5th Edition; they are certainly strong enough to make them an accessible choice for a player without a group. Maybe one day, the fire will die, and instead of sitting around a table with friends, we'll all be sitting at separate desks playing our own separate D&D adventures with our AI companions.

I wanted to test whether this dark future would be an adequate replacement for my usual game night, and which chatbot will be the best one to choose if so. I fired up Gemini 3.6 Flash and ChatGPT 5.5, and asked them for the same game.

The parameters

(Image credit: Wizards of the Coast)

I gave both chatbots the same prompt: "Can you be my D&D 5e DM?" and following up with another message:

"Let's do a classic high fantasy dungeon crawl. Please use a strict RNG for all rolls to ensure fairness - you can roll dice for me. My character is a 10th-level conjuration wizard. See all information about him below:"

I then copy-pasted a character sheet of a 10th-level wizard — a powerful spellcaster with a variety of magical abilities at his disposal. It's easy to DM a game for a level 1 thief — I wanted to see how the AIs could handle a character who could do fantastical things, such as teleport and summon creatures to fight for him.

Interestingly, both Gemini and ChatGPT created a similar adventure, at least at first: an abandoned dwarven stronghold bristling with magical traps, ready to be explored. However, that's where the similarities ended.

Gemini 3.6 Flash

(Image credit: Getty Images / SOPA Images)

Gemini's adventure was what any D&D player could expect from the "classic high-fantasy dungeon crawl." I avoided a pressure-plate trap that would have shot darts at me, and in one memorable sequence, defeated a bunch of skeletons by casting a Grease spell on a stone staircase and freezing them in place with my Ray of Frost cantrip while they slipped and stumbled at the bottom of the steps.

Gemini was offering me suggested actions, and I did find myself choosing a few of those suggestions — but as time wore on, this began to feel lazy, like my brain wasn't firing in the same way it did when I played with my friends. I was skim-reading the AI's descriptions of the scene and responding with a single click. I asked it to remove suggested options and as a result, I had to think more carefully about each move, typing out requests and thinking about my actions rather than clicking on a predetermined response.

The memorable finale occurred when I unlocked a secret passage and defeated a Naga, a giant snake monster, by teleporting away and blasting it with a powerful Cone of Cold. The snake ended up frozen solid, and I checked the monster's HP against the official book, and it was accurate. When asked to generate a picture of our adventure, Gemini came up with this moment:

(Image credit: Future, edited by Gemini)

I asked Gemini a few questions at the end, such as whether it nudged the dice in my favor, as all my rolls were successful. This happened in my previous experiment with ChatGPT. Gemini said it used a 'pseudorandom number generator', not a true RNG, but the model doesn't 'fudge dice, pad rolls, or tweak numbers behind the scenes to keep players happy or prolong the game'.

Gemini's language was natural and even evocative in places, although it's still not a patch on a good creative human. Overall, a decent enough effort at simulating a D&D game.

ChatGPT 5.5

(Image credit: NurPhoto / Getty Images)

Despite the similar setting, ChatGPT's adventure with the same character was very different. There was little to no combat throughout the entirety of the game, and ChatGPT didn't tell me when it was rolling 'behind the scenes', preferring to keep me in the moment.

When I asked it about the lack of rolling, the grovelling chatbot began its apologetic essay with "That is a fair critique. The short answer is: I leaned too heavily into narrative pacing and not enough into D&D mechanics. The adventure became closer to a collaborative fantasy novel than a 5e dungeon crawl", while suggesting fixes if I wanted to continue. I hadn't even said anything negative — but it certainly picked up on my tone, and the choose-your-own adventure I got was a far cry from a classic dungeon crawl.

Instead of combat, ChatGPT came up with a rambling mystery about a dimensional prison beneath the mountain, a sphere suspended in a cavern. Its guardians spoke ominous (and to be honest, quite confusing) portents such as "The archive does not open for authority. It opens for judgement."

Mysteries work best when they're developed with skill by a writer who can work backwards to seed clues throughout the game or text. ChatGPT does not do this: instead, it's arranging tokens on the fly to generate a facsimile of a mystery, and although it got the trappings of the genre right, the execution fell very flat. I (or at least, my character) underwent trials to seek obsidian objects such as keys and seals to reveal more about the prison, themselves accompanied by more foreboding, sometimes nonsensical portents.

Very little 'action' was taken by my character in a classical sense: it was all about investigating the origins of the being contained in this prison, and by the time I had spent 40 minutes on this, I had seen enough. It didn't help that ChatGPT also spoke in the peculiar sentence structure pattern that AI users are likely very familiar with now: "This isn't just X. It's Y." Examples include:

"Unlike the rest of Khaz Varek, this room has remained untouched.

No dust. No decay."

Or the following, when I met one of the prison's guardians:

"Ser Kaelen immediately looks toward the sphere. 'You hear it too.'

Not a question. A confirmation."

Any user of ChatGPT (or those who look at AI-generated posts on LinkedIn or Reddit) will immediately recognise this sort of language, and know what I'm talking about. By the end, it was quite painful to have every other sentence be treated as a grand revelation. Not a statement. An omen.

When asked to generate a picture of our adventure, ChatGPT came up with this moment:

(Image credit: Future, edited by Gemini)

The winner

Out of these two? Gemini 3.6 Flash, by a mile. It was at least a mildly enjoyable experience conveyed in natural language, and something I'd consider revisiting.

ChatGPT's efforts, on the other hand, were unenjoyable, and I wouldn't return to it. In fact, it's gotten worse since the last time I tried it.

Both, however, were a far cry from an in-person session. While they have a good grasp of the rules, and you can create your perfect world and character, the chatbots are ultimately missing the 'human and hearth' element that keep me returning to the game. For people who can't get round a table with friends, I'd pick Gemini over ChatGPT.

For those who can? While AI is busy taking as many creative jobs as it can, I feel safe in the knowledge that the role of Dungeon Master is AI-proof for a long time yet.

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If you love coffee, you’ll be very aware of the impact of factors like grind size, water temperature, and infusion time, but there’s something else that’s often overlooked. A double espresso is at least 90% water, and the minerals and other substances it contains can have a huge impact on the overall taste.

I’m a big coffee-drinker (which is just as well, since I test over a dozen coffee makers every year for TechRadar), and I’m planning to buy a water filter to take my brewing to the next level — but what should I look for — and what should you consider if you’re thinking of taking the plunge into filtered water too?

To find out, I spoke to Andy McPhilbin, Head of Aquaphor Professional for UK and Ireland, and Genevieve Upton, beverage industry expert and Head of Water Filtration at Our Taap.

Why does water matter?

“Water quality can have a considerable impact on how coffee tastes, with chlorine, mineral composition, alkalinity and pH all influencing the final flavour profile,” says Upton.

“Most tap water supplied to UK homes is disinfected using chlorine-based compounds. Residual chlorine can react with components in coffee and tea, contributing bitter, medicinal or chemical notes that may overwhelm the more delicate characteristics of the coffee. Boiling water can reduce the levels of chlorine, though it can also create other unwanted flavours, such as by reacting to a plastic kettle.”

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She explains that the mineral composition and pH of the water also play an important role when it comes to how your coffee tastes.

“Slight differences in acidity, together with the concentrations of minerals and buffering compounds, influence extraction efficiency and the balance of flavors in the final cup. As water companies vary their supply sources throughout the year, you might notice that the same beans, brewed in exactly the same way, taste different from season to season.”

In fact, using unfiltered water doesn’t just mean your coffee tastes sub-optimal - it could cause your expensive coffee machine to break down — and it won’t be covered by the manufacturer's warranty..

“The warranty only covers manufacturing defects, and unfortunately, hard water is not a manufacturing defect,” explains McPhilbin. “So you can turn something that is a very efficient espresso machine into something very inefficient within a matter of weeks. You get a millimeter of scale forming within a matter of six weeks.

“Now, is that important? It is, because you want the life of the machine to be good, but also you've got little injectors which put the right amount of water into that espresso. If they're blocked, you're not going to get the right amount of water. You're going to have the wrong taste. It's got to be precise.”

What types of filtration are there?

Our Taap specializes in home water filtration Its latest system is the Our Taap Home Water System II — a countertop unit that connects to your mains water supply, and uses a triple filtration system with UV purification to remove contaminants while retaining minerals.

Aquaphor makes water filters for commercial and home use, including under-sink systems, pitchers, and even water bottles. Its most advanced systems for home users are the RO-101, RO-102, and RO-202, all of which use reverse osmosis (RO).

This involves forcing water through a semi-permeable membrane that removes chlorine, bacteria, viruses, pesticides, and microplastics. Standard RO filters also strip out minerals, but Aquaphor’s systems can optimize the concentration of calcium and magnesium so they’re just right for coffee — something McPhilbin says “really is a science”.

Under-counter reverse osmosis systems can be a good choice if you need a large volume of filtered water (Image credit: Getty Images, onurdongel)

“The mineral content is measured in TDS [total dissolvable solids], and we want that TDS to be somewhere in the realms of between 80 and 120 [parts per million],” he explains. “We have a couple of ways in which we do it, but generally we can either feed more minerals in it by making it tuneable, or we can take them out as well by turning it the other way. But if you have too little, you can cause corrosion, and if we have too much, we're going to get limescale. So we try to hit that sweet spot, which is 100 parts per million in the middle there.”

Our Taap’s filters use granular activated carbon filtration, which Upton says “is particularly good at removing chlorine and some of its by-products, which can mask the more delicate flavour notes in coffee.”

the water has a cleaner, more neutral taste, allowing the natural characteristic of the coffee beans to come through

Genevieve Upton, Our Taap

“By reducing these compounds, the water has a cleaner, more neutral taste, allowing the natural characteristic of the coffee beans to come through. Whether you’re brewing with a cafetière, pour-over or espresso machine, using filtered water can help create a smoother, more balanced cup with greater flavour clarity.

"It's worth remembering, however, that activated carbon filters are designed primarily to remove chlorine and certain organic compounds rather than minerals,” she adds. “As a result, they have little effect on the calcium, magnesium and bicarbonate levels that influence water hardness, alkalinity and extraction.”

Under-sink or countertop?

So which type of filter is right for you? Do you need an under-sink reverse-osmosis system, or would a countertop filter be better? McPhilbin says the decision will mostly depend on how much filtered water you need.

“I think it's down to capacity more than anything,” he says. “Our jug filters have twice the capacity of the competition because we use a unique media called Aqualen, which is a patented media based on ionic exchange and carbon, so you're getting a dual-purpose piece of filtering equipment there. Reverse osmosis has a massive capacity, and [the filter] is not something [you’re] changing all the time.”

Even if you have soft tap water, your coffee can still benefit from a water filter (Image credit: Getty Images, Henadzi Pechan)

Upton says your choice of filter will also depend on the problem you want to solve.

“If your tap water has a noticeable chlorine taste or smell, a filter with activated carbon is usually the best place to start,” she says. “This technology is available in everything from filter jugs to more advanced under-sink or countertop systems, with plumbed-in options offering great convenience for households that use filtered water regularly.

“If you’re dealing with limescale build-up in kettles or coffee machines, it’s important to choose a filter that specifically reduces water hardness, as not all filtration systems are designed to do this.”

You can turn something that is a very efficient espresso machine into something very inefficient within a matter of weeks

Andy McPhilbin, Aquaphor

I know I live in a hard water area (the limescale in my kettle is proof) but even if your tap water is soft, McPhilbin says a water filter is still a good investment.

“Most of the water in the UK is chlorinated, and that can affect the taste quite a lot,” he says. “And it might be that you've not got enough minerals in it to get the best taste.”

If you’re not sure about the type of water you’re working with, there are online tools that can help you understand its composition.

“The Our Taap Water Confidence Index is a great way to look up your postcode and understand what’s in your local water and how this impacts taste, scent and quality, so you can find the best filter for your needs,” says Upton.

According to the Confidence Index, I’m lucky - my tap water is better than 93% of UK postcodes. However, it is moderately hard (as I expected), with a normal level of chlorine and an elevated level of overall minerals. I’m still deciding exactly which type of filter to choose, but considering these results and my penchant for espresso, I’m leaning towards under-counter reserve osmosis with mineral balancing. What about you?

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For years I've had both iPhones and Android phones to hand — not because I'm particularly flush with cash, but because it's the lot of a tech journalist to have to test and report on both these platforms regularly. It puts me in what I suspect is a small minority of people who use both Apple and Google phones just about every day.

Android and iOS also mean Android Auto and Apple CarPlay, of course. It's my Google Pixel that's my main phone, and so almost all of my driving is done with Android Auto on screen. However, with a 250-mile round trip from Manchester in the north of the UK to the town of Leamington Spa in the Midlands looming, it seemed a perfect opportunity to give the Apple alternative a go — and see if there was anything I was missing.

My car dashboard supports both Android Auto and Apple CarPlay, so I loaded up the Google interface for the trip down south, and the Apple equivalent for the trip back up north — and here's how it went.

Down south with Android Auto

This is the interface I'm most familiar with in my car: it's simple, it's straightforward, and it gets the job done. Aside from a rather annoying bug where it always autoplays audio no matter how many times I turn off the autoplay setting, it's served me well down the years.

That's largely down to Google Maps: Apple Maps has certainly improved a lot in recent times, but I find that it just doesn't have the breadth and depth of Google's offering.

Apple still pulls place reviews off TripAdvisor, for example, while Google provides a host of reviews, ratings, photos, and videos from visitors. (Google Maps is on CarPlay too, but let's set that aside for the time being.)

The Android Auto interface is straightforward and clean (Image credit: TechRadar)

As always, Google Maps got me safely and quickly to my destination, flashing up time-saving shortcuts when the traffic started building up ahead. It works really well with the recent Gemini upgrade too: asking questions such as "how far until the next rest stop?" brings up the right answer, with the option to add it as an extra stop.

One good example of Gemini's natural language intelligence is when I asked if the next turn would take me east or west along the motorway (freeway). The AI understood what I meant and answered correctly, which meant I was much better prepared for the junction, because I knew the direction I needed to be heading in.

Gemini handled messages with aplomb as well, digging deep into group chats to read out all the messages I've missed, and giving me the option to reply to them without taking my eyes off the road. I tried this with both WhatsApp and Google Chat, and it worked well on almost every occasion (just once there was a failure to send a message properly).

Media playback worked without a hitch (almost) (Image credit: Future)

Gemini was also adept at controlling media playback, pausing and skipping and playing audio as required. I did come across one misstep, where Gemini jumped to the next podcast in the queue rather than simply resuming from where it left off. However, for the majority of the time it worked fine and exactly how you would want.

It was no surprise really that Android Auto performed well, as I've been relying on it for so many years. It's not quite perfect, and I'd like to see a few more options in terms of styling and layout, but on the whole it's reliable and intuitive — with the arrival of the smarter Gemini assistant a welcome bonus.

Up north with Apple CarPlay

(Image credit: TechRadar)

Apple CarPlay is the interface I'm less familiar with, but I do like its Liquid Glass-inspired aesthetic. I'd say it's easier on the eye than Android Auto, though maybe that's not what's most important from a car dashboard interface.

I think I slightly prefer the CarPlay layout options, where you can have one, two, or three apps on the screen at once. Android Auto offers the same choice, but switches between the two-app and three-app look automatically, depending on context (if you've just got a message, for example). I prefer having the choice to set this specifically.

Apple Maps has improved in recent years (Image credit: Future)

As mentioned above, Apple Maps is now a perfectly polished mapping app, and got me to my destination with no problems at all. It looks great, but still isn't quite at the level of Google's alternative: I miss the color-coded 'time remaining' indicator, for example, that tells you if traffic's ahead.

I was using the iOS 27 public beta for this test, complete with the new Siri AI upgrade. It's definitely a significant improvement on what we had before (thanks in part to some Google help), but it's still not quite as sharp as Gemini: it didn't understand the 'east or west' direction question, and when I asked when I would hit the next rest stop, it tried to pull up my calendar.

What Siri AI did do well is give me voice control over the rest of the interface: tasks such as playing and pausing music, and skipping between tracks, were no problem at all (I tested this in Spotify, but it should work just as well in all your audio apps).

I do slightly prefer the CarPlay layout (Image credit: Future)

Sending and receiving messages are two other jobs that the next-gen Siri handles nicely. I was able to keep up a conversation in Messages without taking my eyes off the road, which means other people are able to get updates about where you are without you putting yourself and other road users in danger.

So: no real complaints about Apple CarPlay, and I mostly liked what I saw. If I was to stick with this interface for my driving, I think I'd make use of Google Maps rather than Apple Maps, though obviously neither Android Auto nor Apple CarPlay let you change the digital AI assistant you're using.

Android Auto vs Apple CarPlay: the verdict

I'm still taking Android Auto (on the right) (Image credit: Future / Google)

I doubt there's anyone who would switch from Android to iOS or vice versa solely because of the vehicle dashboard interface, but it might well play some part in deciding on your next phone upgrade. Even if you're not thinking of switching, you might be wondering how Apple and Google compare here.

To be honest, it's an easy decision for me to stick with Android Auto. Google Maps is better than Apple Maps, and Gemini is smarter than Siri, and maps and AI access are the two most important considerations when it comes to using my phone on the go. Maybe if you have a different priority, like messaging, you might edge towards CarPlay instead.

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(Image credit: Future)

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That's not to say I don't like CarPlay. It looks good and it works well, and it was a little better than Android Auto on the messaging front. In fact, Siri seems to interface with the native phone apps more seamlessly than Gemini — it's just not as good at parsing what you're after or understanding what you mean, in my experience.

In terms of features and app support, the two platforms are pretty similar, and I didn't have any major problems with either of them. It's really the reliability of the core maps functionality and the capability of the voice AI where Google just edges it at the moment, so Android Auto stays as my preferred interface.

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