Technology

When it comes to getting the right accessories for my home office, I'm usually focused on not only performance, but also comfort. Like many of us, my mouse and keyboard are key parts of my home working set-up, which is spread across a laptop and external monitor, and using them pretty much all day everyday means I need something that is comfortable to use.

So when I saw that Logitech had released a new keyboard and mouse specifically designed around promoting comfort, it seemed like an obvious choice. The company's Signature Comfort Plus line comes with built-in cushioning, which it says can help increase comfort (unsurprisingly) and a better overall user experience.

The MK880 keyboard and M850L mouse are available in a combo bundle, and I've been using both products for a while now - so how did I get on?

Click happy

I've been a fan of Logitech's MX Master mouse range for some time, and the MX Master 4 for Business is still my go-to for everyday use.

The selling point of the new Logitech Signature Comfort Plus M850L wireless mouse is its added cushioning, which the company described as "something we've never done before".

With a declaration like that, you might be expecting some huge extra addition - but the reality is actually quite a bit more low-key.

Like many of you, I'm guessing that when you use a mouse, your hand covers the top of it, with the base of your palm and your wrist laying flat on the tabletop, mouse mat or whatever you choose.

You might think then, that much like its K8880 keyboard, the cushioning would be at the base of the mouse - however this isn't the case, with the cushioned panel actually sitting halfway across the top, just below the scrolling wheel.

Future / Mike MooreFuture / Mike Moore

It's clear to see why Logitech has done this - locating what it calls the "palm cushion" there will help ease RSI or other repetitive finger issues, but for me, where the extra help is needed is further down, at the base, like the keyboard.

The M850L also features a smaller, more sculpted shape than the MX Master series, and has built-in side grips to help improve comfort and again boost usability.

I realize this is a personal view, and it definitely didn't hinder my usage of the mouse, but unlike with the keyboard, I felt myself drifting back towards my usual MX Master 4 mouse, which lifts the arm slightly, coupled with the basic £8 cushioned mouse mat from Amazon I've used for years (available from AU$18 on Amazon AU).

And there's absolutely nothing wrong with the M850L - as you'd expect from a top-line Logitech product, it's fast and responsive, connectivity is excellent, and its light and compact build make it excellent for taking on a work trip or commute. There are four programmable buttons which can be assigned to specific tasks or tools, and there's no need for a dongle to connect - everything is done via Bluetooth.

But at £49.99 / AU$99.99 (which some may still think is a bit much for a mouse) on its own, or £99.99 / AU$169.99 with the MK880 keyboard, maybe a bit more extra cushioning in the future, please?

Just my type

Although many of you might initially think of keyboards as dull, identikit tools that just serve a simple purpose, Logitech has been on somewhat of a crusade to disprove that over the past few years.

This is particularly true when it comes to improving ergonomics - as we all know how uncomfortable it can be slamming away at a basic Microsoft keyboard with clacking keys.

I've been using Logitech's amazing Wave Keys device for some time, with it's eye-catching raised design and built-in palm cushioning really making a difference when it comes to comfort.

(Image credit: Future / Mike Moore)

In some ways, the Signature Comfort Plus MK880 keyboard is a slightly toned-down version of the Wave Keys, offering a more refined look, but including the expected comfort.

It once again features the raised hump in the middle of the keyboard, offering what it says is a more natural typing position, but the keys themselves are far shallower than the Wave Keys, leading to a quieter typing experience.

The built-in palm rest is something we've come to expect from Logitech products over the past few years, but the MK880 really goes above and beyond - rather than just some basic spongey foam like you see in some cheaper options, the cushioning is soft and delicate, with your wrists balancing lightly on top, rather than bouncing off - all again making for a more pleasant typing experience.

Apart from the comfort features, the MK880 is also an excellent all-round keyboard - light and grippy, and easy to connect to your device via Bluetooth, so there's no dongle required.

All in all, the MK880 is a fantastic addition to my workspace, and if you're looking for that extra bit of comfort in your day-to-day work, the £99.99 / AU$169.99 price tag for the keyboard and mouse will pay for itself in the long run.

For all the attention given to sophisticated cyber threats and zero-day vulnerabilities, most organizations are overlooking a far more immediate danger: the risks they already know about – but haven’t fixed.

Recent research paints a sobering picture of enterprise security today. Across more than 800,000 IT assets analyzed, one in three lacks at least one critical security control. Nearly one in five is running end-of-life software and 17% sit entirely outside traditional vulnerability management tools.

In other words, the modern attack surface is not just expanding – it is becoming increasingly fragmented, poorly understood and difficult to control.

Breaches built on the basics

One of the most concerning recent developments is how little attackers need to innovate. Every one of the top 10 most frequently exploited vulnerabilities in recent incident response cases had already been patched by vendors – often months earlier.

At the same time, 65% of incidents involved the abuse of remote access services such as VPNs, RDP and remote management tools. These are not obscure weaknesses; they are foundational components of modern IT environments.

The takeaway is clear: attackers are not outpacing defenders technologically. They are taking advantage of execution gaps. Organizations have become adept at identifying vulnerabilities, but far less effective at prioritizing and remediating them and the gap between knowing, prioritizing and fixing is where risk accumulates. That gap is now where most breaches begin.

The visibility problem

At the heart of the issue lies a more fundamental challenge: visibility. Enterprise IT environments have evolved into highly distributed ecosystems, spanning on-premises infrastructure, cloud services, SaaS applications, remote endpoints and third-party integrations.

Each layer is typically managed by different tools, each maintaining its own inventory. None offers a complete view, and the result is predictable. Assets fall outside patch cycles. Devices are not covered by endpoint protection. Entire systems go unscanned. Almost a fifth (17%) of assets are now not covered by vulnerability management tools at all – effectively rendering them invisible from a security standpoint.

This is not simply a technical oversight. It creates what many security leaders now recognize as a structural weakness: organizations cannot secure what they cannot see.

For UK businesses, this challenge is becoming increasingly consequential. Regulatory frameworks such as the Network and Information Systems (NIS2) Regulations – and the UK’s forthcoming Cyber Security and Resilience Bill – are placing greater emphasis on demonstrable control over assets and risk. Inaccurate inventories and fragmented data make that more and more difficult to prove.

Legacy systems: The risk that won’t go away

Compounding the visibility issue is the persistence of legacy technology. 19% of IT assets are running software or hardware that has reached end-of-life, meaning it no longer receives security updates.

These systems are particularly challenging. In sectors such as healthcare, manufacturing and financial services, they are often embedded in critical processes and cannot be easily replaced. Yet they remain permanently exposed to known vulnerabilities.

The problem is not just their existence, but their opacity. It is not unusual for organizations to believe legacy systems have been decommissioned – only to discover they were still active. This creates a persistent blind spot within the attack surface, one that attackers are quick to exploit.

A growing gap between perception and reality

Perhaps most concerning is the extent to which organizations misunderstand their own security posture. Many rely on a single system of record – such as a configuration management tool as their “source of truth”. However, these systems are often incomplete.

In one example, a company believed it had full endpoint protection coverage based on internal reporting. Independent analysis revealed that a portion of its devices were not included in the system at all. This disconnect between perception and reality creates a false sense of security – one that is increasingly untenable in a climate of rising regulatory scrutiny and board-level accountability.

Across the UK and EU, frameworks such as the Digital Operational Resilience Act in financial services and NIS2 more broadly are raising expectations. Organizations are no longer judged on whether controls are in place, but whether they are effective, verifiable and consistently applied.

From vulnerability management to exposure management

Against this backdrop, there is a broader shift in how organizations approach cybersecurity. Traditional vulnerability management, focused on identifying and scoring flaws, is no longer sufficient. The challenge is not a lack of data, but a lack of clarity about what matters most.

Exposure management, by contrast, adopts a more holistic view. It combines continuous asset discovery with data from multiple sources, applying business and threat context to prioritize risk. Crucially, it also focuses on verification – ensuring that remediation actions are actually completed.

This shift is already delivering measurable results. Organizations that adopt more mature exposure management practices see reductions of more than 40% across key risk categories, including missing controls and end-of-life assets.

A new baseline for cyber resilience

The implications for UK organizations are clear. Cybersecurity is no longer just about defending against the unknown. It is about managing the known – systematically, continuously and at scale.

This is particularly relevant as cyber risk becomes more tightly linked to regulatory compliance, insurance requirements and board-level governance. The ability to demonstrate control over the attack surface – to prove what exists, how it is protected and whether risks have been reduced – is fast becoming a baseline expectation.

Attackers are now able to achieve domain-wide control in minutes once inside an environment. In that context, delays in remediation or gaps in visibility are not just operational issues – they are critical business risks.

The organizations that will succeed are those that close the gap between insight and action. Those that move beyond assumptions and develop a clear, verifiable understanding of their exposure. Ultimately in today’s threat landscape, the greatest risk is not what organizations don’t yet know – it is what they already know, but haven’t fixed.

We've featured the best firewall software.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Insta360 founder and CEO Liu Jingkang, aka 'JK', recently shared a potentially controversial post on Chinese social media platform Weibo, which translated reads:

“Our product vision is to build a ‘Cameraman’ — an intelligent photography robot capable of automatically composing shots and capturing moments like a professional photographer, allowing users to remain fully immersed in the present.

"Today, AI is rapidly evolving, we believe the Cameraman’s 'brain' is becoming increasingly capable, unlocking new possibilities for creative imaging.

"In the long term, the ideal end state for cameras is to progressively become imperceptible, even to ‘disappear,’ until recording no longer consumes effort and becomes a natural part of life.”

As a concerned photographer (and as a concerned human being) I wrote a response to that post, and asked Insta360 for comment.

JK provided me with a statement addressing my concerns and elaborating on his vision, and he and Insta360 also answered a number of specific questions I put to them about the company's potentially game-changing plans for its future cameras.

Below you'll find JK's response, followed by my interview with JK and an Insta360 spokesperson.

Insta360 makes the best tiny videos cameras ideal for POV footage, and new accessories make such inconspicuous cameras easier to operate hands-free. (Image credit: Basil Kronfli)

JK's response: I’ve always believed imaging serves three fundamental human needs: to record, to share, and to create.

AI will undoubtedly reshape parts of this industry, especially in sharing and commercial creation. But when it comes to recording our lives, I believe there is a line AI cannot cross. Technology can make tools easier to use, but it cannot replace the human qualities behind truly meaningful work — judgment, emotion, taste, and the creative energy that come from lived experience.

For me, a photo I take of my daughter’s smile is priceless not because of technical perfection, but because I was there. I felt that moment. AI can generate, enhance, and imitate, but it cannot experience life on our behalf.

Human beings are the measure of all things. You should not hand over experiences that ought to belong to you to algorithms and machines.

So when we talk about the future of the camera, we are not talking about replacing people. We are talking about building tools that help people capture life more freely, more naturally, and with less friction. The future is not about making machines more human — it is about helping humans be more present, more creative, and more themselves.

The interview

TC: What does this 'cameraman' look like? Is it one entity or multiple products?

JK: For us, the future cameraman is a product vision rather than a single device.

Insta360: The core idea is to create a camera that can automatically compose shots, capture key moments, and produce finished content for the user, so people can stay fully immersed in the moment without missing what matters.

When you’re traveling or watching a live event, you often end up holding up a phone the entire time and looking at the screen instead of the experience itself. Our long-term vision is for the camera to work more like a professional photographer: you stay present, and it helps take care of the filming for you.

We’re still exploring what the ideal form factor looks like, so in the near term there won’t be one final ultimate product. But we’re already bringing elements of that vision into existing products. For example, Luna Ultra's POV head tracker enables a more seamless, almost invisible shooting experience, and we see that as an important early form of the future cameraman.

TC: Are we to take it that the 'intelligent photography robot' will be fully automated and independent?

Insta360: First, it’s important to clarify that the future cameraman doesn’t mean a humanoid robot. It’s a product concept and a long-term vision for how cameras should evolve.

The idea is to free users from complicated operation so they can be truly present in the moment while still getting professional-looking results. In that sense, the camera starts to play more of the role of a photographer. Imagine a family outing where the system can automatically frame, capture, edit, and output content end-to-end, without someone constantly needing to manage the device.

That said, this isn’t about a camera acting independently without the user. It’s about highly intelligent operation under user direction, intent, and control. The camera should be able to automate far more of the process, including recording comprehensively when the user wants it to, but always within boundaries set by the user. Our goal is for the experience to feel effortless and natural — even to the point where the camera becomes almost imperceptible in everyday life.

In future, could Insta360 might make cameras such as the Luna Ultra (above) fully autonomous? (Image credit: Future / Tim Coleman)

TC: Can you describe the composing, shooting and editing functionalities?

Insta360: To build the future cameraman, a camera needs to do three things well: see, understand, and act.

First, it needs to see more of the world. That’s one reason 360 imaging matters so much to us. A traditional camera captures only a narrow frame, while a 360 camera captures the full scene. That gives both the user and the AI much more context, reduces the risk of missing important moments, and creates a stronger foundation for tracking, reframing, and scene understanding.

Second, it needs spatial intelligence. The camera has to understand depth, motion, orientation, and what’s happening in the environment — not just record pixels, but actually interpret the scene.

Third, it needs to act in real time. That means tracking subjects, stabilizing footage, composing shots, predicting movement, and eventually making smarter filming decisions automatically based on the user’s intent. If it can’t respond instantly, it doesn’t really behave like a cameraman.

JK: The cameraman sits at the intersection of sensing, on-device AI, and computational imaging. The goal is for the camera to stop being just a recording tool and start becoming an active creative partner.

TC: Could you unpack a specific future feature being explored that leads Insta360's camera to being an 'active creative partner'?

Insta360: When we talk about an ‘active creative partner,’ we mean an intelligent imaging solution that better understands the scene and helps users capture it more intelligently. In dynamic environments, it can keep up with subjects with less user intervention, so people can stay focused on the moment while the camera handles more of the technical work.

To support that vision, we’ve already built a strong technical foundation, including panoramic generation and a panoramic metric depth model. We’ve already open-sourced these technologies for the industry, and they have potential applications not only in 360 cameras, but in other products and fields as well.

TC: How is the 'cameraman' different from existing cameras and Insta360 models?

Insta360: Insta360 already has some of the foundational capabilities needed for the future cameraman. Features like AI stitching, FlowState stabilization, subject tracking, reframing, auto-editing, and on-device image processing are all part of the camera’s evolution toward more intelligent and autonomous capture. That said, today’s products are still early versions of what that fully realized experience could become.

We’ve spent years building deep expertise in 360 imaging, and that gives us a strong foundation in spatial intelligence. Compared with a traditional camera that records only a single frame, 360 imaging data provides a complete field of view and continuous spatial context. Combined with our work in spatial modeling and generative intelligence, that helps AI move from simply “seeing” the world toward actually understanding it.

That’s a key difference in how we think about the future of imaging. And going forward, we’ll continue investing in areas like panoramic depth estimation, world models, and autonomous follow systems.

TC: What's the timescale for this 'cameraman'?

Insta360: This is a long-term vision, and we expect to realize it through continuous product iteration over time. Rather than arriving all at once in a single product, we see it developing step by step as the underlying technologies mature and become ready for real-world use.

Did you know TechRadar now has membership?

(Image credit: Future)

Become a TechRadar Insider by simply clicking 'Join Now' at the top of this page. Have a question? Please email membership@techradar.com

TC: Does this 'cameraman' record autonomously? If so, couldn't that be a privacy concern?

Insta360: This is one of the most important questions in the age of AI hardware. Our view is that future intelligent imaging devices should be able to automate recording much more deeply — even to the point of capturing everything the user wants preserved — but always under the user’s authorization and control.

The core idea behind cameraman is not uncontrolled, limitless recording. It’s about turning the camera from a manually operated tool into an intelligent companion that understands what the user wants to capture and helps do that more effectively. That may require environmental awareness and autonomous recording when the user explicitly enables them, but those capabilities should always be bounded by clear user intent, permissions, and controls.

In other words, autonomy is important, but user agency is essential. We believe that’s the right balance for the future of intelligent imaging.

TC: Does Insta360 envision the system ultimately replacing professional content creators, or is the vision aimed at consumers?

Insta360: No — this is not about replacing photographers, videographers, or creators. It is mainly about helping consumers capture moments more easily, with less friction and less fear of missing the shot.

For professionals, we see these capabilities as assistive tools, not substitutes for human creativity. Great photography and filmmaking still depend on judgment, storytelling, taste, and human connection. Our goal is to reduce technical friction in capture, not replace the creator behind the camera.

The Luna Ultra now works with a POV Head Tracker (see the device in the man's ear), which synchronizes head and camera movement, truly freeing up the user for hands-free immersive POV shots. (Image credit: Insta360)The right side of the line?

It remains to be seen just how autonomous Insta360 cameras could become, but Insta360's response somewhat allays my fears about where its camera tech could be heading. And I get the logic — the ease of use and streamlined workflow of Insta360's consumer cameras in particular, which include leading 360-degree and vlogging cameras, are integral to their success.

Cameras such as the Insta360 X5, and Insta360's companion app are already packed with smart tools that make 360-degree video content easier to capture and edit. So, thinking ahead, why make the process unnecessarily difficult for the sake of user agency, when an intelligent camera knows which direction to point in?

Why pour over lengthy 360-degree video clips to manually piece together an edit, when AI knows where the best action is happening and can suggest / make the edit for you?

It's this intersection of human creativity and automated AI tools where we might start to see more intelligent functions assisting users in shooting, editing and sharing, freeing them up to "be more present" as JK puts it, rather than being bogged down with camera operation. And if any company is in the position to advance this kind of intelligent camera tech, it's Insta360.

Instead of paying iCloud or Dropbox more every month, get a 20TB cloud storage that stays with you for life.

The post This 20TB Cloud Storage Lifetime Subscription is on Sale for $350 appeared first on TechRepublic.

The new way to switch from an iPhone to Android isn’t perfect, but it’s far smoother.

Samsung Display has reportedly won Apple's exclusive OLED touchscreen contract for future MacBook Pro models, highlighting its lead in large-format display manufacturing.

The post Samsung Wins Exclusive OLED Touchscreen Deal for Apple’s Upcoming MacBook Pro appeared first on TechRepublic.

Both OpenAI and Google have released major new AI models within days of each other. OpenAI launched GPT-5.6, and Google has followed up with Gemini 3.6 Flash. While both companies have talked up the coding and developer features of these models, they also power the consumer versions of ChatGPT and Gemini.

So, rather than measuring them with programming benchmarks, I wanted to find out which is actually better at the kind of messy, everyday problems most people use AI to solve.

For this comparison, I matched Google's Gemini 3.6 Flash against GPT-5.6 Sol using its default Medium reasoning setting, since both are intended to be the standard high-quality models that paid subscribers will use for most tasks.

My digital life

So, I gave them my entire digital life for the week ahead.

I uploaded:

• Bank statements
• My calendar (they had access to my calendar app and I also sent in a screenshot of my calendar from another account I use).
• Grocery receipts
• Emails (both had access to my Gmail)
• WhatsApp screenshots
• Photos of my kitchen cupboards
• An energy bill
• Travel bookings
• Handwritten notes

Then I gave both models exactly the same instruction:

"Tell me everything I should do this week."

It sounds like a simple request, but it forces an AI to combine information from multiple sources, prioritize what's important, spot deadlines, reconcile conflicting information, and produce a practical action plan. In other words, it's exactly the kind of real-world problem people increasingly expect AI assistants to solve.

The results were like night and day.

Two different approachesDid you know TechRadar now has membership?

(Image credit: Future)

Become a TechRadar Insider by simply clicking 'Join Now' at the top of this page. Have a question? Please email membership@techradar.com

When I uploaded the files to Gemini 3.6 Flash and asked what I should do this week, it largely ignored the photos and concentrated on the screenshot of my calendar. Its initial answer mostly repeated the events I already knew were happening. Thanks, Gemini — I had the calendar open in front of me.

I then had to explicitly ask whether it could infer anything useful from the other images. It eventually offered some additional advice and did a good job of dividing the information into categories, including work, shopping, fitness, notes, and receipts. But it failed to flag that I had two clashing events in my calendar that evening. It also offered very little prioritization or practical guidance about what I should do next.

ChatGPT took considerably longer to respond, but its answer was far more useful. From the WhatsApp screenshots, it correctly deduced that attendance at my Friday Tai Chi class was likely to be low and suggested I decide whether it was still worth running. It noticed that yoga had been canceled, and spotted the two conflicting events in my calendar, telling me that I needed to choose between them.

It also totalled the receipts I had uploaded, suggested what I should do with them, and made a decent attempt at deciphering my handwritten notes. More importantly, it organized everything into a day-by-day plan for the coming week, then identified the three most urgent tasks, so I knew exactly where to begin.

The crucial difference

That was the crucial difference. Gemini told me what was in my files. ChatGPT worked out what I should do with the information. In World Cup terms, ChatGPT scored a hat trick while Gemini missed a penalty.

Google says Gemini 3.6 Flash improves coding, knowledge work, and multimodal performance compared with its previous models. That may be true, but in this particular multimodal test, it was comfortably beaten.

When I asked both AIs to make sense of real life rather than pass a benchmark, ChatGPT reasoned about the information in a far better way than Gemini did..

Does innovation happen only under pressure? It is a difficult question to ask, because history does give us a difficult answer.

Wars, shocks and national emergencies have often forced societies to move faster than they would in normal times, and many of the technologies we now take for granted came from moments nobody would wish to repeat.

The Second World War is the obvious example, with its lasting influence on medicine, aviation, computing, communications and manufacturing.

The Covid pandemic gave us a more recent version of the same pattern, when a global health crisis pushed scientists, regulators, governments and pharmaceutical companies to develop and approve vaccines at a speed that would previously have been treated as impossible.

Ukraine’s experience

It is often said that Ukraine’s experience of wartime innovation cannot be replicated in ‘peaceful’ Britain. In one sense, of course it cannot. One country is fighting for its survival against a much larger aggressor, while the other is an island nation that has lived for decades with the habits and assumptions of relative security.

Yet that argument only takes us so far. If urgency drives innovation, then the more useful question is what creates urgency in societies that are not at war. Or, to put it more plainly, what creates urgency in countries that do not really feel themselves to be under existential threat?

Britons continue to go about their daily lives much as they always have. People work, travel, argue about politics, worry about mortgages, watch sport, plan holidays and assume that the basic structures around them will continue to function. This sense of normality is, of course, a privilege. It is also one of the reasons why creating urgency in peacetime can be so difficult.

Britain, though, has repeatedly shown that it can innovate with extraordinary force when the need becomes clear. The Industrial Revolution transformed the country and then the world, with British engineers helping to build the railways, steam power and manufacturing systems that reshaped entire economies. For a small archipelago, Britain has had an unusual ability to turn technical advances into industry, infrastructure and global influence.

During the Second World War, that ability appeared again under extreme pressure. Radar helped defend British skies, then became part of the foundation for aviation, shipping, weather forecasting and modern sensing systems. Penicillin, discovered through British science, became a mass medical revolution because wartime urgency forced production and clinical adoption at scale.

At Bletchley Park, the need to break codes helped push electronic computing from theory into practical machinery, with Colossus showing what computation could achieve when the stakes were national.

More recent experience

More recently, the pandemic showed that this capacity has not disappeared.

Oxford researchers began human trials of their COVID-19 vaccine in April 2020. By December, Oxford and AstraZeneca had published the first peer-reviewed Phase III results for a coronavirus vaccine, and the UK became the first country to authorize the Oxford-AstraZeneca vaccine for public use. That was not an accident of national character. It happened because institutions, expertise, capital, government and industry were all pointed at the same urgent problem.

Innovation is therefore hardly foreign to Britain.. The question is what conditions have historically allowed it to move at speed, and whether those conditions can be created today without waiting for a disaster to create them for us.

That question is now becoming more urgent in defense. The war in Ukraine has shown how quickly military technology can evolve when adaptation becomes a matter of survival. Capabilities that might once have taken years to develop now change in months, and sometimes in weeks. Software updates, autonomous systems, drones, electronic warfare tools and battlefield data are changing how militaries think about capability, procurement and industrial readiness.

This shift has not gone unnoticed in Britain. The Strategic Defence Review repeatedly refers to the need for innovation, agility and "wartime pace" and those phrases now appear across government, military and industry discussions. That is progress, but language is only the beginning - the real test is whether Britain can build systems that make speed practical, funded and repeatable.

For much of its modern history, Ukraine has faced questions of sovereignty, territorial integrity and, at times, national survival. Following Russia's invasion in 2014 and again in 2022, the need to compensate for a larger adversary accelerated the adoption of drones, electronic warfare systems and other asymmetric capabilities. Innovation became part of a broader national effort to preserve statehood.

However, Ukraine also demonstrates why simple comparisons can be misleading. Countries do not all innovate for the same reasons, because countries do not all carry the same history. The pressures that shape Ukrainian urgency are not the same that shape British urgency and pretending otherwise would be lazy.

A very different story

Russia has often mobilized around a very different story, built on encirclement, external enemies and the restoration of great-power status. From Soviet narratives of confrontation with the West to contemporary rhetoric around NATO expansion and historical spheres of influence, the perception of an external challenge has repeatedly been used to justify national consolidation and state-led mobilization.

The United States offers another model again. American technological acceleration has often been driven by the ambition to lead, whether that meant reaching the Moon before the Soviet Union, dominating emerging industries, or maintaining military and economic advantage. Its urgency has often come from competition, scale and the belief that leadership itself is a national objective.

This suggests that urgency itself may not be the defining variable. Different societies move when different ideas become powerful enough to organize institutions, capital, industry and talent. Britain’s challenge is not to recreate Ukraine’s conditions, which are uniquely and painfully Ukrainian. Britain’s challenge is to identify the national objective that can generate serious effort in a British context.

Part of the answer may lie in the way Britain thinks about time. Earlier this year, addressing the United States Congress, King Charles referred to America’s founding 250 years ago and joked that, in British terms, it felt like “just the other day”. It was a light remark, but it captured something real about Britain’s historical outlook. This is a country that often sees itself across long stretches of time.

That can make Britain frustratingly slow at the beginning of a challenge. It can also make the country unusually powerful once it connects immediate pressure to a longer national story. Threat alone has rarely been enough as a British organizing principle. Britain has responded to danger when required, but some of its most important periods of innovation came when immediate pressure was tied to economic transformation, scientific leadership or institution-building.

The changing character of warfare

Seen through that lens, Ukraine is more than a warning about the changing character of warfare. It is an early view of technologies that are likely to shape economies and societies long after this war has ended. To mobilize Britain properly, the opportunity should not be presented as a narrow defense issue. The technologies being tested under the hardest conditions can become the basis of new industries, new standards and new areas of commercial leadership.

Britain has always been good at building industries around serious technologies once the need is understood. Its strength has rarely been invention alone. It has been the ability to create institutions, standards, markets, professional services, capital structures and global relationships around invention.

The country has many of the ingredients needed to lead here. It has excellent universities, serious engineering talent, strong financial markets, a respected legal system, advanced manufacturing capability, deep insurance expertise and global reach. It also has a long relationship with Ukraine and a new political framework through the UK-Ukraine 100 Year Partnership.

This is where the next stage of support for Ukraine should become more ambitious. The UK should continue to support Ukraine militarily, financially and diplomatically, because Ukraine is defending its sovereignty and the security of Europe. Alongside that support, technology partnership should become one of the defining features of the relationship.

The logic is straightforward. Ukraine has some of the most relevant operational learning in the world. Britain has the skills, institutions and history to turn urgent invention into global progress. Put together properly, that combination could help Ukraine rebuild, strengthen Britain’s preparedness and create technologies with value far beyond the current war.

That will require more than warm words. Britain should build a practical UK-Ukraine dual-use technology corridor around real problems and real users. That means identifying Ukrainian technologies and operational lessons with proven relevance, pairing them with British engineers, universities, investors, commercial customers, defense users and regulators, then testing them in real environments.

The focus should be on areas where Britain has a genuine need and Ukraine has hard-won experience. Protecting airports from drone disruption. Inspecting offshore energy assets. Improving emergency communications. Strengthening supply chains. Applying AI to difficult visual data. Detecting interference around critical infrastructure. Monitoring farmland and infrastructure with autonomous systems.

The ethical point matters. Ukraine cannot become a source of raw ideas for others to package, own and sell back to the world. If Britain wants to be trusted by Ukrainian founders, engineers, soldiers and policymakers, it has to treat Ukraine as a partner in value creation. The best arrangements will allow Ukrainian companies to scale internationally through the UK while keeping a proper stake in their intellectual property, commercial future and national recovery.

A hard strategic reason

There is also a hard strategic reason to get this right. Every serious country is now rethinking defense, industrial resilience and technology. The United States, Germany, Japan and others are deepening their relationships with Ukraine because they understand that the war is reshaping the future of security. Britain has been one of Ukraine’s strongest allies, and that support is recognized with real gratitude. The next stage should match that moral commitment with a more ambitious technology partnership.

Procurement will decide whether this becomes real. A start-up cannot wait years for a serious decision. Investors will struggle to back complex national security technologies if successful trials lead nowhere. Defense users need permission to test smaller companies without being trapped by process. Commercial customers need help understanding where battlefield-tested technologies can be used safely, lawfully and usefully in civilian settings.

Without those pathways, Britain will admire Ukrainian innovation in public while failing to absorb its lessons in practice. That would be a serious missed opportunity. The technologies being shaped in Ukraine will influence how we inspect buildings, move goods, respond to disasters, protect power networks, secure public spaces, monitor farmland and manage risk. Some will remain military. Many will move into civilian life so gradually that, in ten years, people may forget where the original lessons came from.

That has always been the way. Radar did not remain a wartime technology. Penicillin did not remain a wartime medicine. Computing did not remain hidden inside codebreaking. Once wartime pressure forced progress, society found wider uses, and the countries that knew how to translate those advances gained economic, scientific and strategic power.

Britain has done this before. It has turned moments of pressure into technologies, industries and institutions that shaped the modern world. Ukraine is now revealing where the next wave of defense and dual-use technology is heading. The UK should help build it, and it should do so with Ukraine.

We've reviewed, rated, and ranked the best online collaboration software.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Artificial intelligence (AI) is constantly reshaping everything we do. Across industries, it is changing the way we do work, but that rapid expansion can’t continue without bumping up against real tangible limitations.

Most notably, planning for hyper scaled data centers across the world and increasingly complex cloud computing infrastructures and AI systems are leading to difficult conversations around energy pricing, generation and availability.

Around the world, electricity consumption is increasing at some of the fastest rates seen in decades, and there are no signs of it slowing down. The International Energy Agency (IEA) projects global electricity demand growth of 3.3% in 2025 and 3.7% in 2026, driven heavily by those same data centers, AI deployment, and other advanced industrial expansion.

The IEA has also warned that electricity demand from data centers is expected to double by 2030, with AI-focused facilities alone projected to triple their power use over the same period.

The financial implications and policy blowbacks are already starting to be felt. With limited expansions of electrical grids, more consumers are fighting for less resources, causing prices to only go up. In fact, according to S&P Global, some regions with AI data centers have seen wholesale electricity prices surge by more than 250% in the past five years.

This growing tension between AI advancement and energy availability is beginning to reshape how the technology sector thinks about the future of innovation. For years, the dominant assumption was that progress in AI would mainly come from scaling model size and centralized compute infrastructure.

But the next wave of value creation will also come from AI embedded in the physical world: machines, devices, buildings, industrial assets, medical wearables, and infrastructure that continuously sense, act, and adapt. In that context, the question is not only how to train larger models, but how to process massive streams of real-world data with minimal latency and minimal energy.

That is why alternative architectures, including low-power and decentralized AI, are becoming strategically important.

What low-power AI systems are

Low-power AI are systems specifically designed to minimize the resources required for inference and online learning, particularly energy consumption, while still delivering on high-performance expectation. Rather than relying entirely on massive cloud-based infrastructure and centralized data centers that guzzle down energy, these systems prioritize resource-efficiency at every level of the technology stack, from semiconductor architecture to data processing and deployment.

Low-power AI is not a single breakthrough at model level. It is a system-design discipline that spans sensing, signal conditioning, embedded processing, semiconductor architecture, algorithm optimization, and deployment. The real challenge is to co-design hardware and software for a specific use case so that intelligence is delivered where it matters, with the lowest possible energy budget.

This is precisely where research-transfer institutions such as CSEM can contribute: by combining expertise in sensors, edge computing, ultra-efficient IC design, and application-driven system integration to translate AI into robust real-world solutions rather than generic demonstrations.

Most of the focus in AI development has been in creating systems that are trained and operated on generalized architecture, handling a wide array of tasks simultaneously. These systems are immensely powerful but rely on the same models that require copious amounts of energy to keep them functioning.

On the contrast, low-power AI systems focus on more highly specialized systems, limited in scope and capabilities to a well-defined set of tasks that allow them to be less reliant on vast infrastructure and energy resources to function.

This includes edge AI, where data is processed directly within devices and systems rather than being sent continuously to remote cloud infrastructure. That shift matters even more in the era of physical AI. When intelligence is embedded into the real world, the volume of potentially relevant data generated by sensors, machines, vehicles, buildings, and other assets becomes enormous.

Sending everything to the cloud is not only inefficient, but often too slow and too costly. Many decisions must be taken locally, in real time, with strong constraints on power, bandwidth, privacy, and reliability.

Low-power AI therefore becomes essential not just to reduce energy use, but to preprocess data close to where it is generated, extract the small fraction of information that is meaningful, and enable the broader system to be monitored and optimized for performance, resources, and health.

Perhaps most importantly, low-power systems expand where AI tools can realistically operate. Wearable medical devices, industrial sensors, remote monitoring systems, transportation infrastructure, and smart manufacturing environments all require AI systems capable of functioning within strict energy constraints.

These contexts show places where sustainability is not only a cost-effective measure, but a functional requirement. At the sub-milliwatt level, some systems can even move beyond battery dependence and become energy-autonomous, harvesting ambient energy from light, heat, or vibration to enable a true fit-and-forget lifecycle.

Why efficiency is becoming an imperative

Power generation capacity, transmission infrastructure, cooling resources, and semiconductor supply chains are all facing mounting, simultaneous pressure. The assumption that future competitiveness depends solely on building larger and more power-intensive systems may no longer hold true, with further expansion likely bringing with it exponentially higher costs.

Organizations capable of delivering efficient, highly targeted distributed AI systems could gain major strategic advantages and offers a pathway toward greater technological resilience, as their design natively makes them more resistant to fluctuations in electricity pricing, supply disruptions and geopolitical instability.

Additionally, a more sustainable option can bring value by reducing environmental impact, while still not sacrificing function. The conversation around responsible AI therefore cannot remain focused solely on software governance and ethical frameworks but needs to be talking about how systems are powered, and how and where they process information.

A strategic opportunity for smaller nations

The rise of low-power AI also bears the opportunity to redefine who can meaningfully participate in the global AI race.

The United States and China have been postured as global tentpoles when it comes to the development of AI, and subsequently massive AI investments that have followed suit.

Both are examples of large nations that have the resources to invest billions into data centers, chip production and other infrastructure. On first glance, this paradigm forces many smaller nations to miss the financial and innovation benefits of the AI movement.

But with low-power and distributed AI systems, smaller countries do not need to compete on scale alone. They can compete through specialization, precision engineering, and the ability to translate research into deployable systems for demanding applications.

My home nation of Switzerland provides a useful framework for what this looks like in practice. Similar to most nations across the world, we cannot outspend the largest economies, but we do possess strong capabilities in microelectronics, embedded intelligence, sensing technologies, and high-value industrial and medical applications.

By recognizing these strong foundations, technology transfer organizations like ours can then play an important role in bridging these unique national strengths with industrial deployment, helping transform AI from a cloud-centric paradigm into efficient intelligence embedded in the physical world.

Even for relatively small nations with limited natural resources, there is an opportunity to be a leader in AI development by embracing low-energy system design. Chip producers with less resources will have to increasingly focus on creating specialized, energy-efficient technologies optimized for targeted applications to let them compete on the global stage.

As energy constraints become more severe, demand will likely grow for AI systems capable of operating efficiently in real-world conditions rather than exclusively within massive, centralized infrastructure environments.

In many ways, low-power AI could democratize portions of the AI boom by rewarding efficiency, precision, and specialization as opposed to simply providing opportunities for regions that can match scale. It can be said that virtually every country on earth has some level of specialized technical expertise that can be bridged to an AI use case.

The next generation of AI will see a shift from chat interfaces and cloud platforms to physical systems that shape daily life and industrial productivity. In that setting, efficient local intelligence is not a secondary optimization; it is a core architectural requirement.

Physical AI will depend on the ability to sense the world continuously, interpret it selectively, and act on relevant information without moving every raw data stream through centralized infrastructure.

The democratization of AI brings with it the need for more democratized solutions and opportunities for all to participate.

Looking ahead

While the early years of the AI boom have been defined by large, multi-purpose models and increasing scale, the next chapter will likely be written by developers and ecosystems that are able to utilize precision engineering to focus on low-power distributed systems that are by nature more resilient and sustainable.

Energy availability is no longer a secondary consideration in AI development and will increasingly be one of the defining variables shaping the future of the industry, and subsequently, how the global economy is built. That reality is making low-power AI an emerging necessity.

The next generation of AI systems must be designed with these restrictions in mind, requiring advances in semiconductor design, edge computing, specialized architectures, and intelligent energy management.

Countries and companies that embrace these more efficient and targeted systems may ultimately be better positioned for long-term competitiveness than those that rely instead on growing as large as possible as quickly as possible.

The European Union’s Joint Chips Undertaking is already bringing together its member nations, as well as some outside partners like Switzerland, to develop pathways to technologies like low-power chips. With that in mind, the future of AI may not belong solely to the biggest players, but to the smartest and most efficient ones.

For the global economy, that may prove to be one of the most important transitions of the AI era which only started to come to prominence recently with the growing controversies regarding hyperscale data centers.

We've featured the best web hosting.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Every few years, enterprise software discovers a future it insists you cannot afford to miss.

Often these claims outrun the product, and amid the hype, organizations commit time and money to innovations that never quite live up to their billing.

Agentic AI is the exception, and that is exactly why it deserves a closer look.

We're not talking about chatbots that draft emails or summarize reports. This is something entirely different: software that acts.

Agentic AI can read a company's own approval hierarchies and permissions, make decisions within finance or supply chain processes, and execute them.

The last wave of enterprise AI offered suggestions. This one does the work. For the organizations that get it right, that is a different order of advantage.

The large vendors know this, which is why they have spent the past year racing to own agentic AI. At its Sapphire conference this spring, SAP unveiled what it calls the autonomous enterprise: more than 200 specialized agents that carry out tasks across the core business functions, orchestrated by some 50 domain-specific assistants and reached through a single interface, Joule.

Oracle has been building the same capability into Fusion, its cloud suite for finance, HR and supply chain.

Unlocking the potential

There has been no shortage of talk about what that could unlock, and for many organizations the excitement is well earned: an agent that can carry out work, not just recommend it, changes what the software is for. But for most of them, already running this software, capability was never going to be the sticking point. Access is.

As the major vendors have built it, agentic AI is native to the system of record, woven into the core platform that runs the business. That is a real achievement: an agent that respects your permissions and executes a live transaction is worth far more than one bolted from outside.

But "native" also carries a second meaning – and this one never makes the keynote speech. With the agent tethered to the vendor's cloud platform, there can be no reaching it without committing to that same platform, regardless of what your systems run on today.

SAP shows how that gate works. Until this spring, Joule reached only customers on its cloud subscriptions: RISE and GROW. At Sapphire, with much of its installed base showing little sign of moving, SAP opened a door, but a narrow one. ECC and S/4HANA on-prem customers are no longer shut out, provided they commit to moving the majority of their SAP estate to Cloud ERP.

Even then, they get only a limited set of the AI capabilities rather than the full portfolio. The on-prem route, in other words, is sold on the condition that you start paying for the cloud. And for the older ECC core, a clock is running regardless: mainstream maintenance ends in December 2027, with a stay of execution to 2030 at a premium, after which the choice narrows to an unsupported system or an upgrade.

So an ECC customer that takes the on-prem route is still paying for the cloud to access AI capabilities, even as support drains out of the system that they rely on (according to a timeline they never chose).

A more direct route

Oracle arrives at the same place by a more direct route: its agents do not exist outside the cloud. The Fusion agentic applications it has rolled out this year run only inside Fusion Cloud, on Oracle's own infrastructure and within its security model. There is no on-prem edition to license. An organization still running E-Business Suite cannot switch these agents on where its systems sit today; to use them at all, that organization will have to re-platform onto Fusion.

Strip away the packaging and the structure is the same in both cases: the on-ramp to agentic AI is the migration these vendors have been trying to sell all this time. What is new is the leverage. Cost, risk and disruption have held many organizations on-prem for years – despite all the pressure to move. Could the prospect of being shut out of agentic AI be the argument that finally overcomes their objections?

The budget is already committed

The migration and the AI draw on the same budget, and the migration has first claim on it. In the Americas' SAP Users' Group, 61% of members reported that budget was the biggest challenge they faced this year, and the group's research director was blunt about the cause: the cloud ERP projects are themselves creating the pressure, with AI expected to land on the same budgets next.

The sequence is unforgiving. Pay to reach the platform, then pay again to use the AI once you are there, because the headline subscription covers only a limited band of embedded features, and the rest is metered by consumption. The capital a CIO would want to invest in building an advantage is spoken twice before a single agent has delivered a measurable outcome.

And that is before the program meets its harder test. Boards have grown tired of pilots that never reach production and spending that generates activity no one can tie to an outcome. In some cases, these will be genuine execution failures, but a program that starts short of capital, on a timetable set by someone else, is not starting from a position of control.

Who decides the order of operations?

A CIO should separate the two decisions the vendor has deliberately combined. Whether to modernize is one question. When to do it, in what order, and against which budget is another. Nothing requires that the second be dictated by an end-of-support date printed on someone else's roadmap.

The critical issue here is who holds the authority to decide how the organization's most critical systems change and when. When a vendor sets the timetable, the sequence and the price of innovation, the executive accountable for that estate is not really the one running it anymore. Restoring that authority does not mean refusing to modernize. It means refusing to let the vendor selling the upgrade also decide when and how you buy it.

This is the case for third-party software support. An independent provider maintains the existing ERP estate, keeping it secure and preserving interoperability in place of the vendor's maintenance contract – and typically at a fraction of the cost. That does two things. It removes the end-of-support date as a forcing function, because now the system will stay supported whether or not the organization moves.

And it frees the maintenance budget that would otherwise fund the vendor's marketing roadmapping, so the money can go towards AI investments the organization actually wants to make. The stable core stops being a liability to escape on the vendor's schedule and becomes what it always was: productive capital, running the business while leadership decides on its own terms where agentic AI earns its place.

From there the real options open up. An organization can hold that stable core and run agentic AI as an orchestration layer above it, reaching into the system of record without re-platforming the whole estate first.

Or it can modernize selectively – moving from proprietary databases to open-source alternatives that eliminate vendor license fees, where there’s a business case for it – on a timetable set by business value rather than a maintenance deadline. What matters is that the sequence belongs to the organization.

The case for authority

The case for holding that authority is stronger with agents than it was with assistants. Tying the behavior and the governance of a system that acts on your business to a single vendor's platform roadmap is a heavier dependency than the previous era ever required.

Governed autonomy is the right ambition, but governance an organization sets for itself is a wholly different thing from governance inherited from the platform it happens to sit on.

The vendors are right about one thing: agentic AI is a capital decision, not a software purchase. But a capital decision means choosing where the money goes and what you expect back from it. When the vendor sets the timing, the sequence and the price, that is not allocating capital, it is settling an invoice.

The autonomous enterprise may well be worth building. What a board should refuse is to let its timetable and its budget be set by the company with the most to gain from the move.

We've featured the best business cloud storage services.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

The global AI in IoT market has grown rapidly within the last year, with its value reaching $4.08 billion in 2025, and forecast to reach $6.45 billion by 2035. This progress reflects a wider industry shift, as with the rise of AI organizations are increasingly looking towards a more intelligent and connected future to achieve more from their IoT deployments.

Building additional defenses with AI-powered IoT security

The nature of the IoT can be complex, with devices often operating outside traditional IT security perimeters and in globally distributed environments. Within this landscape, connected devices now form the backbone of daily operations, from energy and healthcare to retail and manufacturing. As digitalization increases, so too will the critical functions that depend on connected devices.

Importantly, as deployments accelerate, so too does the potential IoT attack surface area. Due to the interconnected nature of the IoT, each device acts as a potential entry point for cybercriminals. With the UK government reporting the annual cost of cyberattacks as £14.7 billion annually, it is crucial that the entire IoT deployment is secure.

While private IP addressing and private APN approaches are useful in limiting exposure, they offer limited visibility into device behavior once traffic is flowing, particularly as deployments grow more complex.

AI, however, can see what may have previously been missed. Security has become a key area that can be elevated by AI-powered anomaly and threat detection, which detects behavior such as suspicious IPs, remote code execution, device backdoors or an abnormal port connection. This threat detection enables organizations to quickly identify the first sign of a cyberattack.

AI can even help identify the nature of attacks, including distributed denial-of-service (DDoS) and man-in-the-middle (MiTM) attacks, and device takeovers. By flagging irregularities in real time and enabling rapid corrective action, AI strengthens IoT security and supports more effective risk management, helping organizations avoid operational disruption, financial loss and reputational damage.

AI unlocks the value of IoT data

Beyond security concerns emanating from the IoT’s fragmented landscape, organizations may also struggle with data management and deriving meaningful analysis across the IoT’s web of networks, devices, cloud environments and enterprise processes.

The IoT constantly collects and transmits vast quantities of information, with a single internet-connected security camera generating around 300 GB of data per month. When these connected devices are multiplied and deployed globally on a much larger scale, organizations can be left with an overwhelming amount of data that can be difficult to utilize effectively.

Additionally, the role of the IoT is evolving. Whilst historically the IoT has purely had the role of connecting devices and gathering and transmitting data, there is growing industry pressure for intelligent solutions to inform decision-making and drive outcomes. Enterprises are now seeking more effective solutions to extract meaningful insights from IoT information, and without this, they will fail to unlock the true value of their data.

AI offers a promising solution to these challenges. AI-ready infrastructure is being increasingly prioritized by enterprises to support the collection, transmission, processing and integration of data into systems to enable AI-driven insights. The intelligent analysis AI offers enables patterns and trends in device fleets to be identified and analyzed more efficiently to help reduce manual intervention and lower costs.

Through advanced analytics and machine learning, AIoT can help organizations make better use of their data across the entire fleet, transforming data into an increasingly valuable asset that drives efficiency and long-term value.

Automation supports efficiency

Vast quantities of data across the IoT also make manual intervention expensive, ineffective and time consuming. These challenges can result in performance issues escalating unnoticed, or key device faults being missed.

The analytical and automation capabilities of AI can also support through real-time monitoring, automated fault resolution and predictive maintenance. By analyzing data on device usage, lifecycle maturity and performance, AI can identify potential issues before they arise. This enables a shift from reactive to proactive operations, reduced downtime and lower operating costs.

The benefits of AI-driven analytics are industry-wide. In manufacturing, for example, AI systems can use equipment performance data to estimate maintenance costs, consequently minimizing later expenses and downtime. In industries like healthcare, predictive maintenance also helps ensure the resilience of critical services like remote patient monitoring, where it is crucial that devices remain connected.

Rules-based automation can also support policy-driven fleet management and eSIM orchestration, with many capabilities gradually moving towards greater automation. In practice, adoption is iterative, starting with defined rules and oversight, before layering in AI-driven insights such as real-time usage, performance monitoring and anomaly detection to address issues more quickly.

As automation scales further, IoT environments will be able to respond flexibly to changing conditions with reduced manual intervention.

However, it must also be noted that as this progress continues, transparency and explainability remain crucial. Organizations must understand how decisions are made, and where human oversight remains critical as they scale.

Looking ahead to intelligent connectivity

AIoT’s influence will only grow moving forward. In fact, Transforma Insights’ forecast suggests a more than six-fold increase in connections over 10 years. Managing fleets intelligently and securely at scale, with transparency at the center of operations, must become an industry priority.

Those that can successfully integrate AI with scalable connectivity will be best positioned to move beyond simply managing devices towards truly intelligent operations. In doing so, they will unlock new efficiencies, strengthen resilience and create differentiated value in the IoT.

We've featured the best AI tool.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Artificial intelligence has moved from experimentation to expectation at a remarkable speed. What began not that long ago as isolated pilots is now being embedded across every industry, from highly regulated sectors like financial services to those closest to the human experience, such as healthcare and the arts.

In just four years of widespread business use, these technologies have already moved from operational tools to business infrastructure, underpinning resilience and requiring the same meticulous planning and protection as any other critical system.

This shift is changing the way AI investment decisions must be made. As organizations rush to deploy AI tools, the challenge is no longer whether to invest but how to ensure investments create long-term value, while mitigating growing operational, security, and compliance risks.

Moreover, AI is not a purely digital investment. Behind every model, application, and workflow sits a physical technology estate: servers, storage, networking equipment, energy-intensive infrastructure, devices, and the supply chains that support them.

As AI adoption accelerates, enterprises risk expanding this estate without fully understanding the lifecycle consequences, from rising energy use and infrastructure refresh cycles to underutilized assets, electronic waste, and lost residual value.

Translating investment into impact

Investment in AI continues to rise exponentially, seemingly unimpeded by rising market prices or economic instability. Today, 71% of CEOs rank AI as a top investment priority, yet many still struggle to translate capital expenditure into operational value.

According to Gartner, at least 50% of AI projects are abandoned after proof of concept. Projects that do become operational often fail to deliver a return on investment, with 56% of CEOs saying they have not realized any revenue or cost benefits from AI projects.

This points to a deeper issue: AI success depends less on experimentation alone and more on the quality of the investment, governance, and capability-building decisions that follow.

The AI impact gap

As complexity increases, an investment impact gap is emerging. Leaders expect AI and the tech that supports it to deliver strategic value, improve performance, and reduce risk, but when making investment decisions, they often continue to prioritize near-term costs over the lifecycle factors that determine whether those outcomes can actually be achieved.

Without a view of the lifecycle consequences of their tech investments, as AI adoption accelerates, businesses tend to prioritize factors that are easier to quantify and act on in the short term. Our own research shows that 64% of organizations have rejected a superior technology solution because of its upfront price.

This may reduce immediate financial strain, but it can also introduce operational friction, scalability issues, and erode performance over time. As EY's Americas CTO, Dan Diasio, recently noted, "There's a very clear limit to the amount of value you can create by just focusing on productivity and cost reduction."

AI introduces entirely new cost dynamics. Enterprises must account not only for acquisition and implementation, but also for ongoing expenditure tied to usage, energy consumption, governance, compliance, infrastructure, and model evolution. These lifecycle impacts are often invisible in traditional business cases, yet they increasingly determine whether AI investments create durable value.

Good AI governance starts with accountability and visibility

Similarly, AI does not respect organizational boundaries. Its costs, risks, and value are distributed across the business, making cross-functional accountability essential. And yet, investment decisions are often assessed in silos, making it harder to build a complete view of the risks and value that emerge across the lifecycle.

Likewise, organizations need to understand the scope and reach of the systems they have in production, as well as the financial, operational, security, and environmental impacts they will have throughout their lifecycle.

Without this end-to-end view, major financial and operational blind spots can emerge at critical moments, many of which are not anticipated or planned for. While security, privacy, and compliance consistently rank among the top AI concerns, our research found fewer than half rate data protection (49%) or compliance capabilities (46%) as a high priority when making technology investment decisions.

From cost to AI-driven impact

To close this gap, businesses need to move beyond narrow assessments of upfront price and near-term ROI. These measures still matter, but they do not capture the full lifecycle consequences of AI investments, particularly as systems become embedded in critical operations and begin influencing performance, resilience, compliance, risk, reputation, and long-term value creation.

This is the thinking behind Total Cost of Impact (TCI). TCI is a new model that helps organizations evaluate technology investments through a broader lifecycle lens, assessing not only what a solution costs to buy and implement, but what it will require, enable, constrain, and expose the business to over time.

TCI assesses four core areas of technology impact - financial, operational, security and compliance, and environmental and social - encouraging businesses to understand how investment decisions made today influence outcomes over time.

When applied at the point of investment, TCI makes the trade-offs, risks, and downstream consequences that conventional procurement models often overlook visible. In doing so, it creates a common language across business functions, reducing friction, improving collaboration, and helping ensure technology investments are aligned with strategic priorities from the outset.

Importantly, TCI is an agnostic model: it can be used not only to evaluate whether AI-enabled technologies deliver business value, but also to assess how the infrastructure that supports them is procured, used, scaled, maintained, reused, and eventually retired.

Looking ahead

As AI lifecycles shorten, infrastructure demands grow, and resource constraints intensify, a lifecycle approach to technology investment is becoming a strategic necessity.

AI rollout cannot be separated from the physical infrastructure that enables it. Organizations need to understand not only what AI systems can deliver, but what they will require in energy, data, and asset governance, maintenance, refresh cycles, and end-of-life management over time.

This is why circularity must be part of the AI investment conversation. By taking an end-to-end view of technology assets, businesses can identify opportunities to extend lifespans, increase utilization, recover residual value, reduce waste, and manage end-of-life risk. Circularity is not a separate sustainability agenda; it is a practical way to reduce hidden costs, strengthen resilience, and improve the long-term impact of AI investment.

Ultimately, success with AI will depend not only on the capabilities organizations deploy, but on the quality of the decisions that support them. Those that assess the full impact of their technology decisions from the start will be better positioned to capture value, manage risk, and build resilient, future-ready digital infrastructure.

We've featured the best AI website builder.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

HP Omen 16 Slim: Two-minute review

In these strange times where PC memory prices seemingly double by the day, an affordable gaming laptop is a tantalising prospect. But is it actually realistic? Enter the HP Omen 16 Slim, HP's latest mainstream gaming portable.

There are two core variants of the Omen 16, a standard-sized model and this so-called "Slim". Long story short, this Slim model is a little skinnier but still isn't exactly super compact for a 16-inch laptop, thanks to not only a fairly hefty lower screen bezel, but also a chassis that sticks out beyond the base of the display. At least the slightly tanky proportions translate into a tanky chassis with a solid feeling keyboard bed.

Anyway, it's the value proposition that matters and that means getting into the specs. CPU-wise, we're talking Intel Core Ultra 7 255H, which is a pretty modern 16-core chip from Intel's Arrow Lake generation. Here it's configured with 24GB of DDR5 in dual 12GB SoDIMMs. But various configs are available, including 16GB and 24GB.

There's also 1TB Gen 4 SSD, but with any gaming laptop surely the most important single component is the GPU and it's no surprise to find an Nvidia GeForce RTX 5060 8GB doing the pixel pumping duties.

(Image credit: Future)

It's one up from the bottom of Nvidia's RTX 50 Series laptop GPUs, but it gets the full feature set, including all the latest DLSS upscaling bells and whistles. It outputs to a 165Hz 16-inch LCD display with a native resolution of 1,920 by 1,200 pixels, making it a 16:10 aspect panel rather than the usual 16:9 ratio.

The battery is a pretty generous 70Wh item, meanwhile, and networking includes WiFi 6E, Bluetooth 5.4 and Ethernet. Slightly less impressive is the physical connectivity. Not only are you limited to just one USB-C port (plus three USB-A), it can only do a measly 10Gbps. So, the only video out is the single HDMI 2.1 port.

Of course, gaming performance is the key metric here, and the results are pretty good, albeit with one major caveat. This skinnier Omen 16 Slim makes do with 110W of Total Platform Power (TPP) and 80W maximum for the GPU, where the full-sized Omen 16 cranks that up significantly to 170W TPP. Given premium gaming laptops can supply as much as 175W and more just for the GPU, you can see how 110W for everything, including CPU, RAM and storage, can be a limiting factor.

That said, the RTX 5060 GPU is pretty efficient and well matched with the 1200p (near-enough 1080p) 165Hz display. You'll need to use DLSS upscaling to get slick frame rates in the most demanding games. But, broadly, you'll get fairly smooth results at very high detail settings. What's more, the chassis doesn't get too hot and the fan noise is moderate.

All of which means HP has done a nice job with the HP Omen 16 Slim. It's well built, mostly well specified and delivers a pretty sweet gaming experience. Whether it makes sense as a buy comes down to pricing. As configured here with 24GB of RAM, it's a little spendy. But there's an otherwise identical 16GB model that we just spotted for a whisker under $1,200. Right here in 2026, that's a very decent deal.

HP Omen 16 Slim review: Price & availability

(Image credit: Future)

• Available for £1,499 in the UK with 24GB of DDR5
• The closest US config rocks 16GB of DDR5 for $1,599
• Looks out for deals on sites like Newegg

In this precise configuration with 24GB of DDR5 RAM, the HP Omen 16 Slim weighs in at £1,399 in the UK. That's actually very good given current memory prices, right now. Indeed, you could argue it would make sense to knock that back to 16GB, keep everything else the same, and save some money.

Indeed, in the US, just that configuration is available, while the 24GB option is not. With 16GB, you're currently looking at $1,599, which is a fair bit less appealing. Currently, an otherwise identical version but with an RTX 5050 instead of the RTX 5060 can be had for $1,198 on Newegg, which implies you might be able to bag one with the 5060 and 16GB for around $1,400 at some point if you hang around and wait for a deal. At that price point, the HP Omen 16 Slim would start making a lot of sense.

HP Omen 16 Slim review: Specs

Base spec

Review config

Price

$1,189 / £1,100 (estimated UK price)

£1,399 / $1,799 (estimated US price)

CPU

Intel Core Ultra 7 255H

Intel Core Ultra 7 255H

Graphics

Nvidia GeForce RTX 5060 Laptop GPU

Nvidia GeForce RTX 5060 Laptop GPU

RAM

16GB DDR5

24GB DDR5

Display

16-inch 1200p (1,920 x 1,200), 144Hz, IPS, 300 nits

16-inch 1200p (1,920 x 1,200), 165Hz, IPS, 400 nits

Storage

1TB NVMe SSD PCIe Gen4

1TB NVMe SSD PCIe Gen4

Ports and Connectivity

1x USB Type-C 10Gbps (USB Power Delivery, DisplayPort 1.4), 3x USB Type-A 5Gbps, 1x ethernet, 1x headphone/mic combo jack, 1x HDMI 2.1, Wi-Fi 76E Bluetooth 5.4

1x USB Type-C 10Gbps (USB Power Delivery, DisplayPort 1.4), 2x USB Type-A 5Gbps, 1x ethernet, 1x headphone/mic combo jack, 1x HDMI 2.1, Wi-Fi 76E Bluetooth 5.4

Battery

70Whr

70Whr

Weight

5.35lbs / 2.425kg)

5.35lbs / 2.425kg

Dimensions

14.1 x 10.6 x 0.9 inches / 35.75 x 26.9 x 2.27 cm

14.1 x 10.6 x 0.9 inches / 35.75 x 26.9 x 2.27 cm

HP Omen 16 Slim review: Design

(Image credit: Future)

• Not actually that "Slim"
• Solid, well-built chassis
• Connectivity is only OK
• Cooling is well executed

The HP Omen 16 Slim is mostly plastic, just the bottom cover is metal. But it still feels very solid and durable. The Keyboard is nice and stable, with only a touch of flex when pushed hard. The keyboard, while we're on the subject, has four configurable RGB lighting zones, but at this price point, you're not going to get per-key lighting.

HP has done a nice job with the cooling, too. The twin fans draw air from the bottom cover and push it out the back and do so without making too much noise. However, despite the "Slim" branding, this is not a super compact laptop.

(Image credit: Future)

Coming in at 1.99cm to 2.27cm thick, where the standard Omen 16 measures between 2.39cm and 2.54cm, the 16 Slim is thinner than its sibling. But it's still not that compact.

Thanks to a rather large chine bezel and the way the rear of the chassis sticks out beyond the screen hinge, the Omen 16 Slim actually has a larger footprint than many laptops that trade on pure performance rather than portability and also tips the scales at 2.42kg. So, it's no featherweight.

In that context, the somewhat limited connectivity is a touch disappointing. You get three USB-A ports, but just one USB-C and even the latter is limited to 10Gbps, though it does at least support video out and charging the laptop itself.

HP Omen 16 Slim review: Performance

(Image credit: Future)

• Decent frame rates at 1200p with DLSS
• Seriously quick SSD
• Fan noise is reasonable

HP Omen 16 Slim benchmarks

3DMark: Night Raid: 57,311; Fire Strike: 25,198; Time Spy: 10,454; Port Royal: 6,372
Geekbench 6: Multicore: 14,468; Single-core: 3,114
Cinebench R24: Single Core: 134; Multi Core: 2,713
Crossmark: Overall: 1,992; Productivity: 1,888; Creativity: 2,182; Responsiveness: 1,783
Passmark Overall: 10,817; CPU: 38,631; 2D Graphics: 819; 3D Graphics: 17,174; Memory: 3,094; Disk: 36,232
CrystalDiskMark: Read: 7,025MB/s; Write: 5,895MB/s
Shadow of the Tomb Raider: (1200p, High): 132fps; (1200p, High, DLSS Quality): 139fps
Total War: Warhammer III: (1200p, Ultra): 83fps; (1200p, Low): 205fps
Cyberpunk 2077: (1200p, Ultra, no RT): 70fps; (1200p, RT Ultra, DLSS Balanced): 51fps
Doom: Dark Ages: (1200p, Ultra): 57fps; (1200p, Ultra, DLSS Quality): 83fps
Battery Life (TechRadar movie test): 12 hours and 32 minutes

Intel's Core Ultra 7 255H is very much a known quantity, what with its eight Performance and eight Efficient CPU cores. It's all you need for gaming in CPU terms, so the HP Omen 16 Slim's critical gaming component compared with the competition among the best gaming laptops is that Nvidia RTX 5060 GPU and how well it's been implemented.

This thinner HP Omen 16 Slim model does suffer slightly in the wattage department, with the Total Platform Power shared across the GPU, CPU, memory and more capped at 110W. But that doesn't absolutely hobble gaming performance.

For instance, running at the 16-inch display's native 1,920 by 1,200 resolution, you can crank Cyberpunk 2077 right up, with Ultra RT settings and Quality DLSS scaling and still get 44fps average frame rate.

(Image credit: Future)

OK, that's not spectacular. But it is acceptable for a single-player title. Switch off the ray tracing and you'll be looking at frame rates nearer 100. Moreover, in less demanding games, let's say something a little older like Shadow of the Tomb Raider, you can hit over 130fps even without DLSS upscaling and really make the most of the 165Hz refresh rate of the 16-inch display.

Speaking of the screen, the 16:10 aspect is a little odd and adds a little GPU load versus a vanilla 1080p panel. But it's reasonably vibrant, with nice-ish colours and contrast and decent 3ms response times. It's nothing special, but probably what you'd expect at this price point.

Just note the panel reviewed here is the upgrade 400 nit option. The base display is only 300 nits, runs at 144Hz and has much less colour fidelity. Avoid that if at all possible.

(Image credit: Future)

The 16 Slim's thermal performance is pretty impressive, too. It doesn't get too hot under load and the fan noise is reasonable. Well, it is unless you enable "Unleashed Mode" in the Omen Gaming Hub app. That cranks up the fans, but doesn't do much for frame rates.

Elsewhere, the SSD is particularly nippy, clocking over 7GB/s for reads and nearly 6GB/s for writes. For what is pitched as a value-orientated system, that's impressive. As for other elements, the sound quality from the speakers is tolerable at this price point, but the volume could be a little higher, and the webcam quality is OK.

In terms of physical performance, the chassis feels robust, even though only the bottom cover is made of metal, and the keyboard base is nice and solid with minimal flex. As for the trackpad, it's mechanical and doesn't have niceties like a glass cover. But it's large and works well enough. It's also worth noting that the memory, storage and Wi-Fi cards are all upgradeable, which is welcome in an age where too many laptops have everything soldered on.

HP Omen 16 Slim review: Battery life

(Image credit: Future)

• Surprisingly good for a budget gaming laptop
• USB-C charging is supported
• Not actually all that portable

Undoubtedly one of the HP Omen 16 Slim's more impressive aspects is battery life. OK, you're not going to get more than a couple of hours of light gaming off the mains, but for watching video, this laptop has some real legs.

Our local video playback test netted over 12 and a half hours with the screen at half brightness. That's a realistic setting for use on, say, a plane, albeit you might want to wind the screen up a little in bright daylight.

Whatever, that's a very impressive result for a gaming laptop and makes this portable much more plausible as an all-round computing device to take with you on trips and outings. The caveat to that is that the fairly large proportions and relatively hefty 2.4kg kerb weight mean this isn't a laptop you're going to sling into a small shoulder bag and forget about.

Should I buy the HP Omen 16 Slim?HP Omen 16 Slim: Scorecard

Attributes

Notes

Rating

Value

Prices are pretty variable depending on spec and location. But certain Omen 16 Slim configurations do look appealing. You may need to keep your scanners peeled for a good deal, however.

4 / 5

Design

The "Slim" branding suggests something very compact, but this is actually a fairly beefy 16-inch laptop. That said, it is solidly built with a decent keyboard. It's just a pity the connectivity isn't a touch more comprehensive.

4 / 5

Performance

This is a value-orientated gaming laptop, so expectations should be kept in check. But the RTX 5060 GPU matches well with the 16-inch display and you can run pretty much any title at high detail settings with the aid of DLSS upscaling.

3.5 / 5

Battery life

If there's one metric where the HP Omen 16 Slim delivers beyond expectations, it's battery life. You can expect over 12 hours of video playback, which is well beyond what most affordable gaming laptops can manage.

5 / 5

Total Score

Much depends on the price you can bag on the HP Omen 16 Slim. But if you can find a good deal, you'll get good performance, solid build and a nice all-round package.

4 / 5

Buy the HP Omen 16 Slim if…

You want decent value in today's weird market
Nothing's cheap when it comes to gaming PCs, these days. But the HP Omen 16 Slim is definitely decent value in the current context of spiralling memory prices.

You want good battery life
Most gaming laptops struggle away from the mains. But the HP Omen 16 Slim will do 12 hours-plus of video playback on the battery.

Don't buy it if…

You're looking for great portability
Despite the "Slim" branding, this is not a particularly compact or light 16-inch laptop. But it is solid and feels built to last.

You need cutting-edge performance
The HP Omen 16 Slim puts out decent frame rates for a mainstream laptop. But its RTX 5060 GPU is limited to 80W and this is not a high-end machine.View Deal

HP Omen 16 Slim review: Also consider

Asus V16
If you're looking for something even cheaper, try the Asus V16. It uses older Nvidia RTX 30 and 40 Series GPUs to achieve a few savings. But it's surprisingly well built and very portable for a gaming laptop. Read our full Asus V16 review.

Medion Erazer Major 16 X1
If you want more performance without breaking the bank, try the Medion Erazer Major 16 X1 and its RTX 5070 Ti GPU. It's heavy, feels and bit cheap and the battery life is poor. But it'll kick out some very impressive frame rates. Read our full Medion Erazer Major 16 X1 review.

How I tested the HP Omen 16 Slim HX

(Image credit: Future)

• Used as my daily rig for two weeks
• Threw my Steam library at
• Got forensic with the screen and build quality

I've been round the block when it comes to gaming laptops. My first portable ran a GeForce 2 Go, if you can remember back that far. My current lappie runs an RTX 4080, and there have been plenty more in between. ANd those are just my personal machines. I've long since lost count of how many gaming laptops I've reviewed. There are always a few knocking around.

Over the years, I've learned a lot about what makes a good gaming laptop and I applied that to the HP Omen 16 Slim over a couple of weeks. That means throwing everything in my Steam Library at it, from classic strategy titles like Total War to graphics fests including Cyberpunk 2077. All round performance, including the SSD, is also important, as is battery life for what is, ultimately, meant to be a portable machine.

I'm also very picky when it comes to screen quality and keyboard feel and have something of a forensic obsession with laptop build quality. You have to be realistic at this price point, but build quality is particularly important to gaming laptop longevity. Bendy chassis usually end up in flakey performance, eventually.

• First reviewed: July 2026
• Read more about how we test

The nominations are in - and the finalists for the 2026 edition of the Power 50 can now be revealed.

Part of the Mobile Industry Awards 2026, the Power 50 highlights the most important and influential figures in the UK mobile industry during the past year.

The finalists for the Power 50 2026 have now been decided, with the final standings set to be revealed at the awards.

(Please note: these names are ranked A- Z by first name and not the final rankings.)

View the 2026 Power 50 here!

The winner, recognised with the Power 50 award at the Mobile Industry Awards on September 17 2026, is selected following in-depth interviews with key senior figures across the industry, from operators and retailers to manufacturers and distributors.

It rewards those individuals who inspire their businesses with their values, but also have influence beyond existing roles, serving as an inspiration to the rest of the trade.

To make the Power 50 list, an executive needs to be bold in their leadership and vision, and have exceeded expectations in the different categories we have chosen.

Power 50 Previous Winners 

• 2025: Max Taylor, CEO, VodafoneThree
• 2024: Marc Allera, CEO - EE and Consumer Division at BT Group
• 2023: Ahmed Essam, CEO, Vodafone UK
• 2022: Lutz Schüler, CEO, Virgin Media O2
• 2021: Marc Allera, CEO, EE and BT Consumer
• 2020: Nick Jeffrey, CEO, Vodafone
• 2019: Marc Allera, CEO, EE and BT Consumer
• 2018: Marc Allera, CEO, EE and BT Consumer
• 2017: David Dyson, CEO Three UK
• 2016: Sebastian James, Group CEO, Dixons Carphone
• 2015: Sebastian James, Group CEO, Dixons Carphone
• 2014: Andrew Harrison, CEO, Carphone Warehouse
• 2013: Olaf Swantee, CEO, EE
• 2012: Simon Stanford, VP of UK & Ireland telecommunications and networks division, Samsung UK
• 2011: Guy Laurence, CEO, Vodafone UK
• 2010: Tom Alexander, CEO, Everything Everywhere
• 2009: Andrew Harrison, CEO, Carphone Warehouse
• 2008: Kevin Russell, CEO, Three UK

• For more information on the Power 50, or to get more involved in the Mobile Industry Awards 2026, visit the awards site here

Most fraud strategies fail before a fraudster is ever detected, because they evaluate documents, rather than people.

Consider the operational reality: a customer signs up for a fintech service. Their government-issued ID clears every document check. Their selfie matches. Their name passes the database lookup. Onboarding completes.

What no one detected was that the identity didn’t belong to a person. It was assembled by generative AI in minutes — a synthetic construction engineered to pass exactly the checks that were run.

The account sits dormant for weeks, builds behavioral credibility, then executes a scheme that costs the platform tens of thousands of pounds. Deepfake-enabled fraud alone caused more than $200 million in losses in the first quarter of last year.

This is not a stress-test scenario. Synthetic identity fraud is the operating condition for every business that onboards customers digitally. And the industry’s default response — collect an ID, verify it, move on — was not designed for it.

The document-first blind spot

The document-first model has a structural flaw in that it only responds to fraud after a document arrives. Intent, behavioral context and the coherence of a user’s digital existence sit outside its scope entirely. Identity verification becomes a single event rather than a continuous assessment.

That architecture now has a measurable cost. GenAI-enabled fraud losses in the U.S. will reach $40 billion by 2027, up from $12.3 billion in 2023 — a compound annual growth rate of 32%. The dark web has already industrialized the supply side: scam kits that produce deepfake videos and synthetic documents sell for as little as $20, placing sophisticated fraud tools within reach of anyone willing to pay.

The damage shows up at the portfolio level. Synthetic identity fraud, fabricated identities assembled from combinations of real and invented data, accounts for 10 to 15% of charge-offs in a typical unsecured lending portfolio. That is not a tail risk. It is built into the cost structure of credit.

From KYC to proof of personhood

The problem is not that document verification is imperfect. The problem is that document verification asks the wrong question. Proof of personhood reframes the objective entirely. Rather than asking "Is this ID real?" it asks "Is there a real, unique human being behind this transaction?" And it maintains that inquiry continuously across the customer lifecycle rather than resolving it once at onboarding.

The distinction matters operationally. Fraudsters engineer synthetic identities specifically to clear document checks. A convincing fake ID passes the same OCR and liveness detection as a legitimate one. What it cannot reproduce — not convincingly, not at scale — is the full context of a real person’s digital existence. That context is where proof of personhood lives and where effective verification has to operate.

A real person leaves years of accumulated traces across the digital economy: email addresses registered to real services, phone numbers tied to consistent carriers, devices with histories spanning multiple sessions and networks, and behavioral patterns shaped by how actual humans navigate products.

None of these traces is individually decisive. But their presence or their absence tells a story that a fabricated identity, however polished its documents, has not had time to build.

What a multi-signal assessment looks like

No single signal tells the full story. Effective identity assessment requires multiple independent signals that together reveal a coherent identity or expose its absence.

Explicit signals form the foundation. eKYC and eID database verification cross-references user-provided information against authoritative government records, confirming that the identity exists and belongs to the person claiming it. Document authentication validates submitted IDs. Biometric liveness checks confirm a real person in real time.

These explicit signals are necessary but insufficient. Implicit signals provide the surrounding context and a fraudster finds them far harder to fabricate. Digital footprint analysis examines whether an email address carries a real history, whether social media accounts show genuine activity and whether a phone number maps to real-world patterns. Device intelligence surfaces anomalies in hardware and software environments. Behavioral signals flag interaction patterns that deviate from how humans actually behave.

The value of layering these signals is combinatorial. A fraudster can fabricate a convincing document. Simultaneously manufacturing a years-old email address, a coherent social presence, a clean device fingerprint and natural behavioral rhythms is a different order of problem. Each additional signal compounds the cost and complexity of fabrication until spoofing the full picture becomes economically unviable.

Operating across borders

For businesses with multinational customer bases, multi-layered verification is a structural requirement. The regulatory landscape is moving fast. The European Union’s Digital Identity Wallet, which all member states must support by December 2026, will reshape how consumer identity functions across the bloc. National eID programs are expanding across Asia, Africa and Latin America on varying timelines and to varying technical standards. The World Bank estimates 850 million people still lack official government identification — a reality that makes digital coverage and inclusion inseparable priorities.

Businesses that operate effectively in this environment will need verification coverage broad enough to match their customer base and adaptable enough to keep pace with shifting regulatory requirements. eKYC and eID checks across national and regional schemes are the baseline. The operational advantage lies in what surrounds them; the implicit signals that distinguish a genuine applicant from a synthetic one, regardless of which jurisdiction’s ID scheme they present.

Verifying the person, not the ID

Risk assessment should be well underway before a document is ever presented. When digital footprint, device intelligence and behavioral signals work at first contact, synthetic identities are filtered before they reach your most expensive verification steps — and legitimate users clear faster because the system already has context.

That architecture only works when fraud, IDV and AML share a unified data environment instead of operating in parallel. The organizations building that capability now set themselves up to absorb fewer losses and onboard the customers that their competitors cannot confidently clear.

Best Identity Theft Protection: tried and tested protection from Aura, Norton, Experian, and more.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

You all know what a guitar amplifier is. Big, intimidating stacks of speakers and dials, designed for some nice loud guitar playing. But do they work as well for other people's guitars; i.e., can a big powerful block of music-playing kit double as a home speaker for recorded music?

It may seem like a stupid question, but the more I thought about it, the more I realized that speakers and amps are the same thing... kinda. And for people who don't have the money to buy a guitar amp and a home speaker, could buying the former kill two birds with one stone? I had to find out.

Now, I’m not talking about a guitar amp-esque speaker made by a brand traditionally known for its amps (and before these new Bluetooth speaker things became so jolly well lucrative), such as the Marshall Stanmore IV or the Orange Box. No, I’m talking about a real power-speaker designed for guitars, with jacks and dials galore and no Bluetooth in sight.

Specifically, I’m talking about my Line 6 Spider IV amp, which I have owned for at least half of my life, thus far.

Now, I know Line 6 has a mixed reputation, making it an odd choice to incorporate into a home sound system. Its speakers aren't even for serious guitarists — they're for edgy 14-year-olds without much cash who just want goofy levels of distortion. In my defense, when I bought it, I was an edge 14-year-old without much cash, who just wanted goofy levels of distortion. And it still works fine for what I need.

For the tech-heads among you, my Spider IV offers 75W of power. For the purposes of this article, you should know it has a 3.5mm and a 6.35mm input, and I played music from my turntable as well as some Edifier speakers (the same set-up as in my recent article about setting up a new turntable, so an Edifier M90 and Sony PS-LX310BT). The amp was getting incorporated into an existing hi-fi system, not replacing it.

The other important point of context before we get started is that I had to make a conscious effort to turn off my brain before undergoing these experiments. In many parts of my rational brain, I could tell that a guitar amp wouldn't function smoothly as a speaker; they're not designed to hit a wide range of frequencies, just the ones guitars play at, and they're not intended to be connected to a chain of other players. If I switched off the scientific part of my cranium, I could just mess around with wires, have some fun, and see what happened.

Turning my amp into a subwoofer

(Image credit: Future)

With my first experiment, I decided to start small: I’d see if my amp could function as a subwoofer for my Edifier M90s. I wanted to use its wattage just to give me some extra 'oomph', even though it’s not a bass amp.

First, I used a 3.5mm to 3.5mm cable to connect the M90s, from their Sub Out port, to the amp’s smaller input… and nothing happened. I couldn’t hear a peep from the Line 6, though oddly, the amp’s volume control adjusted the output from the speakers.

Next, I took the risky move of using a 3.5mm to 6.35mm to connect the Sub Out port to the amp’s primary input — risky because I was using an unbalanced stereo cable across devices really not meant for this use, and I wasn’t sure what was going to happen.

I turned the Line 6’s dials all the way down, plugged the cable in, and popped on some Morgan Wallen before slowly turning the volume up.

The resulting speaker sound is best described as ‘rhythmic, distorted pulses’: I was getting something, but it sounded more like asthmatic coughs than music. The amp was clearly connected, and outputting something, but it was just rhythmic fuzz. I suppose that's the Line 6 effect.

I spent quite a while fiddling with the dials, but it was impossible to reduce the drive, while also retaining enough volume for the amp to be audible when I wasn’t putting my ear to it. So it wasn't good for most music, but turn the volume up while listening to a rock track, and the pulses sounded like the strums of a really keen rhythm guitarist trying to play along. This was pretty nostalgic for me, because when I bought the Line 6, that's basically exactly what I was doing.

I tried the same experiment with my turntable, which was connected to the Edifiers via Bluetooth, and found the same result, albeit watered-down.

How about as a giant aux speaker?

(Image credit: Future)

Perhaps I was trying to run before I could walk? For my next experiment, I decided to make things a bit more simple. I connected the iPad I was using as a music player, to a USB-C to 3.5mm converter, and then plugged in the aforementioned 3.5mm to 6.35mm cable which led to the guitar amp’s primary input.

I could hear music! I mean, if you could call it that. The Line 6 not being a bass amp became patently clear here, because I was basically just getting treble. It was surreal; I've tested speakers and earbuds with barely any bass, but never any with literally no low frequencies to speak of.

Plus, a weird phase effect was causing noticeable oscillations in the music, providing a powerful attack but fast fade. It was most audible in Post Malone’s voice (I’d moved on to Post Malone at this point) – it sounded like he’d learnt Mongolian Throat Singing.

The resulting sound was odd, and didn't match even the affordable Tribit Stormbox Micro 3 I use in the shower, but at least the guitar amp gave me some control over it: I could use the dials to add a bit of warmth, and some fuzz to mask the lack of bass.

Then I had a brainwave: I returned to the 3.5mm to 3.5mm cable from the first section, and music sounded much better. There was finally some bass (not much), and Malone was back to his regular singing voice. It was the closest to a speaker I'd managed to bring the amp.

It wasn’t perfect, though: Line 6 amps can sound quite tinny, and I felt like I was listening to music on some cheap earbuds you get free with a flight… just 75W ones that'd annoy the downstairs neighbor.

Let's add some drive

(Image credit: Future)

Obviously, the thing missing from my set-up was more things, so I whacked out a Pigtronix Fat Drive to add to the mix. You can probably tell what this does; it’s obviously a vital addition to an amp which is slated for offering nothing but drive.

Here’s how the set-up looked now: a USB-C to 3.5mm converter plugged into my iPad, leading to a 3.5mm to 6.35mm cable, which was plugged into the Fat Drive pedal, which was connected to another 6.35mm to 6.35mm cable, which led into my amp. That music was getting quite a few air miles before being played.

You can probably imagine what more drive did to already-too-distorted music; it sounded like it was playing from behind a waterfall. Add to that the phase effect that playing music from the 6.35mm port provided, and it was even more of a write-off than before.

I wasn't expecting sudden audio clarity, though. As with the rest of this piece, it was just a fun experiment to try.

Unfortunately, there was a casualty to this experiment: my Fat Drive pedal conked out the day after the test, and I’ve not been able to revive it. I don’t blame my tests, as much as the several years of misuse and poor management prior to them, but I’d still like to pour one out for the Pigtronix.

Lessons learnt

(Image credit: Future)

Obviously, I didn’t go into this experiment thinking that a guitar amplifier would make for a genuine home speaker alternative. That’s like thinking a pottery kiln is a good microwave replacement; it technically does the same thing, but things are going to get a little crunchy.

But I was hoping it'd turn into a fun, albeit messy, party speaker in a pinch, in case I had people over and suddenly wanted room-filling sound. Sure, I could buy such a device, but given how expensive tech is (and how much space that'd take if I already own an amp), who needs that?

I always knew, too, that some useful features would be missing. My amp doesn't allow for a wireless connection, for example, and it's far from being portable.

The solution is already out there: recent Bluetooth speakers released under the Fender name (though made by by a different company called Riffsound) had an XLR port that doubles as a 6.35mm jack, so you can plug a guitar and use it as an amp, even though it's technically a speaker.

Given how many guitar companies now make consumer tech (Marshall, Fender, Orange; Yamaha does too but it's an everything-music company really), it'd be great to see this kind of feature again. Or, at the very least, some way of blurring the borders between amp and speaker. Not all of us have the space or money for both.

In an era increasingly dominated by AI and automation, it’s still incredible just how much construction work remains manual. The contrast is most stark in data centers – facilities built to power cutting-edge technology, but whose delivery is generally still slowed down by major fragmentation and time-consuming manual tasks.

One of the industry’s biggest challenges seems to be capturing and understanding what’s actually happening on a project. Project managers can spend countless hours walking sites, checking completed work and coordinating contractors operating to different schedules. When documentation falls behind, small issues can go unnoticed and eventually develop into costly delays.

While the industry is finally starting to get to grips with this technology, construction is presenting own on challenges. Autonomy works best within fixed parameters and with a limited number of variables, but live sites offer the opposite – changing plans, moving materials, new structures being built and multiple trades working alongside each other.

Construction is where automation fails – is now the turning point?

These are the exact conditions that have made automation ineffective in construction. But automation isn’t impractical or impossible, it just means vendors will need to focus on the tasks where machines can deliver the best results.

Progress capturing, side documentation and routine inspections are some of the areas where automation could work best, and better still, companies like OpenSpace argue much of this work can actually be done outside of normal operating hours to both reduce disruption, and to reduce exposure to a live and dynamic environment.

Data centers could be a good proving ground for this, with large floor plans, repeatable layouts and intense schedule pressures. With up-to-date data from overnight checks, for example, leaders could assess progress and identify areas that need extra attention.

But collecting that data is only the first step, because the AI running behind the scenes need to be able to interpret site imagery to cross-reference it with site plans, drawings, schedules and other information held across other systems.

In this Q&A, OpenSpace CEO Jeevan Kalanithi explains why construction automation is starting to gain momentum, why ‘good enough’ may indeed be good enough without needing immediate perfection, and how automated monitoring could actually help soften the blow of ongoing labor shortages.

• Construction has traditionally been one of the least automated industries. Why is that beginning to change now?

I actually think the idea that construction is slow to adopt technology is a bit of a misconception. Builders adopt tools that genuinely make their jobs easier – they've just been waiting for technology that understands how they actually work.

Construction is fundamentally different from industries like manufacturing. Every project is unique. Jobsites change every day. Teams are working in environments that are constantly evolving, with dozens of trades operating simultaneously. That's a much harder environment to automate than a factory or warehouse where conditions are highly controlled.

For a long time, most construction software focused on documents, schedules and reports because that's what computers could understand. But construction isn't really a document problem – it's a physical-world problem. The most important information lives on the jobsite itself: what's been built, what's changed, where work is progressing and where risks are emerging.

What's changing now is that AI is becoming capable of understanding the physical world. Instead of asking people to manually document what's happening, AI can interpret images and other real-world data to understand the state of a project. That makes robotics and automation much more practical because they fit naturally into how builders already work instead of forcing them to adopt entirely new processes.

• Robotics in construction has been discussed for years but has seen relatively limited adoption. What factors are contributing to the increased interest in autonomous and semi-autonomous systems on jobsites today?

The conversation has become much more practical.

Ten years ago, people asked whether robots would replace construction workers or build entire buildings autonomously. Today the question is much simpler: How can robots help experienced builders work more efficiently? That's an important shift because construction has always adopted tools that solve real problems.

The biggest opportunities today are around repetitive, time-consuming tasks like documenting progress, capturing site conditions or performing routine inspections. Those activities are incredibly valuable, but they're not necessarily the best use of a superintendent's or project engineer's time.

I think of robotics a bit like the introduction of nail guns. Nail guns didn't replace carpenters – they helped carpenters work faster and more consistently. Robotics is following a similar path. The goal isn't to automate construction. It's to automate specific tasks that allow skilled professionals to spend more time coordinating work, solving problems and making decisions.

We're already seeing that with the robotics companies that we integrate with, each approaching different aspects of autonomy. Rather than trying to solve every problem, they're proving that robots can reliably perform specific tasks that create immediate value on today's jobsites.

• Data centers and other large-scale projects have been cited as promising early use cases for construction robotics. What characteristics make those environments more suitable for autonomous technologies?

Data centers are actually a great example of where robotics and AI can demonstrate value today because they combine three characteristics that work well for autonomous systems.

First, they're relatively structured environments. Compared to a renovation project or an occupied hospital, data centers typically have large floor plates, repeatable layouts and fewer unexpected obstacles, making them easier for robots to navigate.

Second, they're incredibly schedule-sensitive. AI infrastructure is expanding at an unprecedented pace, and every day matters. Owners and contractors need continuous visibility into progress because hundreds of activities are happening simultaneously.

Finally, they're highly repetitive. The same systems and construction sequences occur over and over, which allows robotics to operate more consistently and makes it easier to measure progress over time.

Autonomous data capture is particularly valuable in these environments because it creates a consistent visual record without disrupting work during the day. That gives project teams objective information about what's actually happening on site, helping them identify issues earlier, coordinate more effectively and keep stakeholders aligned.

• Construction sites are highly dynamic environments. What are some of the biggest technical and operational challenges robots face when deployed on active projects?

Construction sites are probably one of the hardest environments you could ask an autonomous system to operate in.

Unlike a warehouse, where everything is designed to be predictable, construction sites change constantly. Materials move. Equipment gets relocated. Walls appear. Doors that were open yesterday might be closed today. You also have dozens or sometimes hundreds of people working alongside autonomous systems, so safety has to remain the highest priority.

There are practical challenges as well. Connectivity isn't always reliable. GPS often doesn't work indoors. Navigation has to account for changing layouts and temporary obstacles that don't exist in more controlled environments.

That's why I think we'll continue seeing supervised autonomy for quite some time. Humans are still remarkably good at adapting to unexpected situations, and construction has plenty of them.

One lesson we've learned is that "good enough" often beats "technically perfect." Builders don't need the most sophisticated robot, they need one that's reliable enough to show up every day, operate safely and consistently, and fit into the way projects already run.

• As robotics adoption grows, what role does visual intelligence play in helping machines understand and navigate the physical world?

Whether imagery comes from a person carrying a 360° camera, a robot, a drone or another autonomous system, collecting the data is really only the first step. The real challenge is understanding what that data means.

AI has become remarkably good at understanding language, but if it's going to operate in the real-world economy, it also needs to understand physical places, physical assets and physical work.

Construction has always had information about what was supposed to happen – drawings, BIM models, schedules and specifications. What's historically been much harder is understanding what actually happened in the field.

That's where visual intelligence comes in. AI can compare what's been built against what was intended to be built, measure progress over time, identify potential issues and surface insights that help project teams make better decisions.

Our philosophy is simple: we don't really care how the data gets collected. Whether it comes from a phone, a 360° camera, a drone, a laser scanner or a robotics platform from one of our robotics partners, our goal is to bring that information together into a common understanding of the jobsite.

Today, our customers have captured imagery across more than 70 billion square feet of construction. That scale creates an opportunity not only to help builders today, but also to help train and validate the next generation of AI systems that need to understand the physical world.

• There's often concern that robotics could replace workers. How are builders currently thinking about the relationship between automation and the existing construction workforce?

I think that's a very understandable concern, but it doesn't reflect what we're seeing on jobsites.

The builders we work with aren't trying to replace experienced people. If anything, they're trying to figure out how to help those people do more.

Construction has faced labor shortages for decades, and demand continues to outpace the available workforce. There simply aren't enough skilled people entering the trades to meet the amount of work that needs to get done. That's especially true as we build more data centers, manufacturing facilities and infrastructure.

Robotics and AI help address that challenge by taking on repetitive work like routine documentation, progress capture or inspections, allowing experienced professionals to spend more time coordinating work, solving problems and applying their expertise.

The best technology doesn't replace people – it amplifies what people are already good at. That's how I think construction will continue to adopt AI-powered tools and robotics. They become another set of tools that help experienced teams make better decisions, work more efficiently and get more done with the resources they already have.

• The concept of "human-in-the-loop" robotics is gaining attention across industries. How does that approach apply within construction environments?

I think human-in-the-loop is going to be the dominant model for construction for quite some time.

Construction is simply too dynamic to expect fully autonomous systems to handle every situation. Something unexpected happens every day – a blocked corridor, a relocated piece of equipment, a new safety barrier, an area that's suddenly inaccessible. Humans are still exceptionally good at recognizing those situations and adapting in real time.

What we're seeing today is a very practical division of responsibilities. Robots handle routine, repetitive tasks consistently, while people provide judgment, context and intervention whenever it's needed.

Some of our robotics partners are already operating this way, where autonomous systems perform most of the work but remain remotely supervised so a person can step in if something unexpected occurs.

I don't think that's a compromise. I think it's actually the right model. The goal isn't autonomy for its own sake. The goal is giving project teams better information while maintaining the flexibility and judgment that complex construction projects require.

• Some contractors are experimenting with autonomous data capture during off-hours, such as overnight or before crews arrive. What lessons are emerging from those early deployments?

The biggest lesson has been consistency.

One of the challenges with manual documentation is that it depends on people having the time to do it. On a busy project, it's easy for documentation to become a lower priority because everyone's focused on solving immediate problems.

Autonomous capture changes that. Robots can document the site on a predictable schedule, often overnight or before crews arrive, creating a consistent visual record every day without interrupting construction activities.

That consistency gives teams a much clearer understanding of how a project is progressing over time. It also makes documentation much more resilient. If a superintendent is tied up or a project engineer is out that day, the visual record doesn't stop. Everyone still has access to current information about what's happening on site.

Capturing a visual record has always been a natural byproduct of walking a site with a 360° camera. It's also a very natural task to hand to a robot. That allows experienced people to spend more of their time interpreting information and making decisions instead of simply collecting data.

• Looking ahead, what indicators should the industry watch to better understand whether construction robotics is moving beyond pilot programs and toward broader adoption?

I don't think the biggest indicator will be the number of robots on jobsites. It will be whether contractors keep using them after the pilot is over.

Construction is a very practical industry. Builders don't adopt technology because it's exciting, they adopt it because it saves time, reduces risk or helps projects run more smoothly. If a technology creates more work than it eliminates, it won't last.

We'll also know the industry has reached the next stage when robotics becomes just another way of collecting project information. Builders shouldn't have to think about whether data came from a person, a robot, a drone or another autonomous system. They should simply have access to an accurate, up-to-date understanding of what's happening on their projects.

Ultimately, I think the story is bigger than robotics. The real-world economy doesn't run on documents alone. It runs on physical places, physical assets and physical work. If AI is going to create value there, it has to understand reality, not just language.

That's why we say agents need eyes. When AI can reliably see, understand and reason about what's happening in the physical world, robotics becomes much more than automation. It becomes a new way for people to interact with the built environment and make better decisions. I think that's the transition we'll be talking about over the next decade.

Apple is reportedly preparing a 7-inch AI home hub, new Apple TV, and updated HomePod mini as Siri becomes central to its smart home strategy.

The post Apple Readies AI Home Hub, New Apple TV, and HomePod Mini appeared first on TechRepublic.

A relative newcomer in the industry, DataImpulse has grabbed a slice of the proxy pie thanks to its wallet-friendly, high-performing service. From more than 90 million ethically-sourced residential IPs across 195 locations to mobile and datacenter proxies, users gain access to a speedy and reliable global network.

The company specializes in creating custom proxy services for businesses, though individual users who aim to access data from anywhere and collect the information they need will have no problem finding something for themselves.

Arguably, the biggest selling point is a highly competitive, pay-as-you-go pricing model with traffic that never expires. DataImpulse throws in free country-level targeting for every plan (more granular targeting options are also available), and full support for HTTP, HTTPS, and SOCKS5 proxy protocols across all of its proxy networks.

Plans and Pricing

DataImpulse operates on a transparent pay-as-you-go model where traffic never expires. In case you’re unfamiliar with the concept, the bandwidth you buy here is yours until your scrapers actually consume the bytes, whether that takes days or months.

The price structure is based on the type of proxy and the amount of traffic.

For instance, residential proxies start at a flat $1 per GB for a 5GB starter block, and go up to 50GB at the same dollar-to-gigabyte ratio. If you ramp up your scraping to the Advanced tier, the cost drops down to $0.80 per GB for an 800GB package, with custom enterprise pricing scaling down even further for terabyte-scale deployments - though you’ll have to pony up at least $4000 for the option.

Datacenter proxies start at a mere $0.50 per GB for new users, slipping down to $0.45 per GB when upgrading to a larger $450 volume pool. Once again, you can get a custom price per gigabyte.

Gaining access to mobile proxies kicks off at $2 per GB for the entry-level 5GB option. The price goes down to $1.6 per GB for bulk operations, with custom pricing available for truly massive projects.

Finally, premium residential proxies will set you back $5 per GB via 1GB and 10GB plans. And yes, there is a custom option too with a $20k starting point.

It’s worth mentioning that DataImpulse doesn’t play favorites with its plans. Each includes free country-level geo-targeting, rotating and sticky sessions, HTTP(S) and SOCKS5 support, full programmatic API access, 24/7 support, and unrestricted concurrent threads. There are no overage penalties or hidden connection fees, which means your infrastructure bill will match your actual code execution down to a T.

Features

If you decide to give DataImpulse a go, your first step will be to register, either the old-fashioned way or through your Google, GitHub, or LinkedIn profile. A classic dashboard layout will greet you, where all the important stuff is located in the left sidebar.

(Image credit: DataImpulse )

The usage chart and details will be your starting point. Once you settle on a plan, you can begin with the proxy configuration by setting the targeting options down to the ASN (Autonomous System Number) and IP rotation interval, which you can save altogether as a dedicated configuration.

You have to choose between a sticky or rotating proxy, as well as the protocol in charge. Sticky proxies are port-based (IP addresses are bound to a specific port for a specific timeframe), and the rotation interval can range from 1 to 120 minutes. Rotating proxies change the IP address automatically with each new request.

From here, it’s also possible to top up your balance by choosing the number of GBs you want to add in the event you need more traffic.

Here’s a more detailed look at DataImpulse’s offering:

Residential proxies

Arguably the workhorse of the platform, the standard residential proxy pool provides on-demand access to a sizable footprint of over 90 million active IP addresses across 195 countries. DataImpulse proudly markets its pool as entirely ethically sourced, which means all nodes join the network legally and transparently.

As a result, the IPs maintain a pristine reputation score and are drastically less likely to be blacklisted by major CDN platforms. In other words, when your script routes data through these nodes, target web servers view the connection as an ordinary home user browsing a site, rather than an automated bot. In our tests, residential proxies delivered a consistently high scraping success rate.

Premium residential proxies

DataImpulse labels these as ultra-responsive IP addresses with minimal latency that deliver better reliability and security compared to regular residential proxies. So, you should see faster connection speeds and more precise geo-targeting, especially considering you get full customization options to tweak your scripts.

Moreover, upgrading to the premium residential line significantly reduces the risk of encountering unexpected access blocks or sudden CAPTCHAs. Since this is a premium offering, it includes additional operational perks, such as a dedicated account manager to help optimize your efforts and resolve any doubts or issues.

Datacenter proxies

When targeting open public directories or basic landing pages (basically, anything that isn’t buffed up with sophisticated anti-bot frameworks), residential IPs can be an expensive overkill. To that extent, DataImpulse offers 20 million datacenter IPs in 195+ countries to tide you over in a cost-efficient manner.

With a response time of less than 100 milliseconds and the fact that these IPs connect directly to high-speed enterprise data systems, this tier is built for pure speed and instances where you need to process thousands of requests per second. You can also configure your IP address to be active for up to 30 minutes.

Mobile proxies

For the most challenging anti-bot systems, like social networks or review platforms, DataImpulse provides rotating and sticky mobile proxies. Pulling from a pool of over 16 million real 3G, 4G, 5G, and LTE cellular towers across 195 locations, these carry a distinct security advantage due to the same public-facing IP to thousands of mobile devices simultaneously.

Hence, web firewalls seldom block a mobile IP address outright since doing so would mean blocking hundreds of legitimate human users on that cell tower network. The largest pool of DataImpulse’s mobile IPs is in India (1.57 million), Saudi Arabia (444k), Italy (257k), Morocco (239k), while the United States is covered with 129k IPs. You can have a single address remain active for up to two hours.

Traffic that never expires

Perhaps the defining USP separating DataImpulse from a chunk of its competitors is the fact that your purchased pay-as-you-go traffic never expires.

In most cases, you’ll wind up with monthly billing subscriptions or restrictions on how long your traffic is valid for. If you have unused bandwidth, it’s gone for good. With DataImpulse, it remains completely active until your crawlers actively consume the bytes. Having peace of mind that you can use data as long as you need without worrying about losing access makes the model a great fit for small businesses and dev teams with limited budgetary and operational width.

Scraping

Unlike some of its peers, DataImpulse leans heavily into a developer-first, DIY network architecture. This means there is no web scraping API, as the vendor’s focus is solely on providing raw proxy connections, rather than managed scraping tools. You must write your own code to manage requests, parse the HTML, handle retries, and bypass CAPTCHAs.

On the plus side, such a model brings financial and structural flexibility for teams writing custom data collectors. By routing scripts through DataImpulse’s proxy layer, they can plug the proxies directly into any automation framework or headless browser engine using basic credentials or token structures.

(Image credit: DataImpulse)

In that regard, DataImpulse provides a Gateway API to programmatically manage, authenticate, and automate the provisioning of their global proxy pools. From there, you can dynamically generate proxy lists, rotate IP addresses, track bandwidth consumption and request volumes, programmatically set advanced targeting filters, and more.

There are also comprehensive integration blueprints for popular developer frameworks and third-party apps. Some of these include Scrapy, Puppeteer, Selenium, Shadowrocket, Multilogin, AdsPower, Playwright, and Zapier, to name a few. The platform also provides short code snippets for Python, Node.js, PHP, C#, Go, Ruby, and cURL to set up proxies. All of the above is neatly documented with built-in support and code examples.

Advanced geo-targeting and session controls

Country-level geo-targeting across all 195 covered regions is built natively into the base $1/GB price floor with zero add-on activation fees. For more accurate data parsing, there are State, City, ZIP code, and ASN filter toggles.

When it comes to rotating sessions, the DataImpulse gateway dynamically inserts a randomized IP address on every network request your scraper submits. For standard HTTP/HTTPS traffic, you simply point your scripts to gw.dataimpulse.com on port 823. In case your stack relies on the more robust SOCKS5 protocol to handle raw TCP tunnels or high-performance scraping, you use port 824.

For situations where your code requires session continuity (like managing a social profile or handling account-tied logins), you can bind your traffic to a dedicated port range anywhere from port 10000 through 20000. Doing so locks a single IP address to your active thread for up to 120 minutes, though the system gracefully defaults to a 30-minute window if no interval is set.

Ease of Use

There’s not much to say here. Most importantly, the dashboard allows you to move effortlessly around via a single-page command center tailored for fast deployment. That said, the arrangement of certain sections could be better.

For starters, the section ‘Resources’ that features the onboarding guide is tucked away in the upper right corner of your respective plan, instead of being the first thing you see. The same place holds the links for documentation and tutorials.

Then, the ‘Proxy Locations’ section provides a table overview of supported countries for each of the four types of proxies available on DataImpulse. There’s no additional functionality or details to it, which feels like a missed opportunity.

(Image credit: DataImpulse)

Acting as a de facto homepage, there is a filterable real-time line chart that plots your request volume across custom date ranges and breaks down traffic metrics by total requests, charged traffic, and total spend.

The dashboard also logs individual session activity in an audit table showing the timestamp, targeted host, consumed traffic, and error/success states, with the option to export this data directly to a CSV report.

In terms of generating your target connection parameters, the dashboard provides a straightforward copy-paste system where you can grab your master username and password strings.

You can also manage whitelisted IPs via a simple menu. Just save your scraping server's public IP address within your dashboard profile, and the gateway will automatically recognize your incoming traffic tunnels, thus shaving valuable milliseconds off your script's execution times.

Customer Support

One of the things DataImpulse singles out is its 24/7 personalized, human support. The company proudly mentions that you’ll get an answer in under 3 minutes, courtesy of an ever-present chat icon in the lower right corner. Our experience was no different - we got a tidy reply in less than a minute.

If you prefer self-service support, the company provides a thorough Documentation page. Here, you’ll find various technical reference materials, API endpoints, authentication methods, and basic proxy setup instructions.

(Image credit: DataImpulse)

Covering the more practical elements is the Tutorials page. It acts as an extensive step-by-step engineering handbook, packed with detailed screenshots, videos, configuration blueprints, and copy-pasteable code snippets for integrating proxies across dozens of operating systems, scraping frameworks, and headless browsers.

For customers deploying huge data operations at the custom enterprise level, DataImpulse boosts its support to include dedicated account managers and custom feature engineering tailored specifically to the customer’s unique data requirements.

The Competition

Truth be told, proxy services are tough competition. DataImpulse has robust providers like Bright Data, Oxylabs, and Decodo playing in the same field, though its unexpiring pay-as-you-go traffic and a highly disruptive $1 per GB residential proxy baseline certainly make it stand out. Nonetheless, the main problem is a lack of out-of-the-box software tools or any sort of target templates, leaving all the coding execution to your team.

Final Verdict

If anything, DataImpulse proves you don’t have to pay a premium to get access to a reliable and ethically sourced proxy network. In no small part thanks to its price tag and data that never expires, the company has created an accessible, developer-first platform that is finely tuned to the needs of individual developers, startups, dev squads, and growing small businesses.

So, if you have the know-how and are looking to drastically slash data collection costs, DataImpulse will more than suffice. But, if you’re not much of a technical user and require a fully managed, hands-off scraping solution, it will likely be too bare-bones for your liking.

• Take a look at our guide to the best proxy services

AI has sparked a race to build ever more powerful infrastructure, with headlines dominated by GPUs, networking and energy demand.

But behind the scenes, another part of the technology stack is undergoing an equally significant transformation: storage.

As AI models grow larger and enterprises retain more data for training, inference and retrieval, storage is no longer simply where information resides.

It has become an active part of AI infrastructure, responsible for feeding compute efficiently, supporting vast datasets and helping organizations balance performance with cost.

That shift has fundamentally changed what primary storage looks like in modern cloud environments.

Historically, primary storage meant tightly coupled block or file systems sitting close to compute.

Today's hyperscale cloud providers have taken a different approach, treating object storage as the persistent system of record while software coordinates how data is stored, protected and accessed across distributed infrastructure.

The move to cloud computing

The move to cloud computing didn't simply increase the amount of storage organizations needed. It fundamentally changed how storage had to work.

Traditional enterprise storage was built around tightly coupled systems where applications interacted directly with file systems. That model worked well when infrastructure was relatively contained. At hyperscale, however, coordinating millions or billions of files across distributed environments introduces complexity that limits scalability.

Cloud providers responded by redesigning storage around software-defined architectures that separate how data is managed from where it is physically stored. Rather than treating storage devices as isolated resources, modern cloud platforms orchestrate entire fleets of storage through software, allowing them to scale far beyond the limits of traditional enterprise architectures.

AI has only accelerated this transition. Training large models, serving inference and retaining ever-growing datasets all place sustained demands on shared infrastructure, making software-defined storage more important than ever.

Although every hyperscale platform has evolved differently, the same architectural ideas appear time and again. Four principles in particular have become fundamental to supporting AI at scale.

1. Object storage is designed around sequential data movement

Traditional cloud storage systems frequently modify files in place. At hyperscale, continually updating data across distributed environments becomes increasingly difficult to manage.

Object storage takes a different approach. Rather than overwriting existing data, new versions are typically written as separate objects. That naturally favors large, sequential data flows, allowing storage systems to operate more efficiently as datasets continue to grow.

As AI workloads generate larger datasets, frequent checkpointing and continuous data movement, designing storage around sequential throughput becomes increasingly important.

2. Metadata has become a software challenge

As organizations store billions or even trillions of objects, tracking where everything lives becomes just as important as storing the data itself.

Rather than asking storage devices to manage both data and metadata, cloud platforms increasingly separate these responsibilities. Dedicated software layers handle object locations, namespaces and system coordination while storage media focuses on delivering scalable capacity.

Separating these functions makes infrastructure easier to scale while allowing storage systems to concentrate on what they do best: storing vast amounts of data efficiently.

3. Resilience comes from architecture, not individual devices

Cloud providers assume that, somewhere across thousands of servers and storage devices, failures will happen. Rather than relying on individual hardware to eliminate every fault, modern architectures distribute data intelligently across large storage pools.

Techniques such as erasure coding allow platforms to recover data efficiently while maintaining availability and reducing the overhead associated with traditional replication strategies.

The result is infrastructure that remains resilient even as AI workloads continue to grow in size and complexity.

4. Intelligent data staging protects performance

AI rarely produces smooth, predictable storage workloads. Training jobs, inference requests and application traffic often arrive in bursts, placing sudden pressure on infrastructure.

Rather than writing every request directly to capacity storage, cloud platforms increasingly use flash and memory as staging layers. These absorb incoming traffic before organizing data into larger, more efficient writes.

This allows organizations to maintain responsive applications while making better use of high-capacity storage as AI workloads become increasingly demanding.

Software has become the defining layer

Taken together, these four principles point to a broader industry shift. Storage performance is no longer determined solely by faster hardware. Increasingly, it is software that decides how efficiently infrastructure performs by orchestrating data movement, coordinating metadata and balancing workloads across distributed systems.

That evolution has fundamentally changed the role of primary storage. Rather than serving individual applications or servers, storage increasingly underpins shared object platforms that support analytics, cloud services and AI workloads simultaneously.

In other words, primary storage is no longer defined simply by where data resides. It is defined by how effectively software enables that data to move, scale and remain available across distributed infrastructure.

A new definition of primary storage

The rise of AI has accelerated changes that cloud providers have been making for years. Primary storage is no longer defined by proximity to compute or by storage hardware alone. Increasingly, it is software-managed, globally distributed and designed to balance performance, resilience and economics at enormous scale.

For organizations investing in AI, storage decisions can no longer focus purely on capacity or latency. Understanding how software, data movement and storage media work together has become just as important.

The organizations that succeed won't necessarily be those deploying the most hardware. They'll be those that build storage architectures capable of feeding AI workloads efficiently while keeping infrastructure scalable, resilient and economically sustainable.

In modern cloud environments, primary storage is no longer simply where data lives. It has become one of the technologies that determines how effectively AI can scale.

We've reviewed, rated, and ranked the best business cloud storage.

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

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit