• The US Air Force is seeking a 'Next-Generation Mission Compute' offering to replace the brain of its F-16 Block 40/42/50/52 jets
• The notice calls for open, swappable hardware that physically separates flight-safety and mission computing, with room for GPUs and AI
• Officials say these jets will serve into the 2040s, roughly seven decades after the F-16 first flew in 1974

The US Air Force is shopping for a new brain for its F-16 Fighting Falcons. The Air Force Life Cycle Management Center F-16 program office will brief companies at Wright-Patterson Air Force Base in Ohio about a Next-Generation Mission Compute system to replace the Modular Mission Computer in Block 40, 42, 50, and 52 jets, the core of the service's Viper fleet.

Demand for these is sky-high: all 24 one-on-one sessions with the program team were booked by September 24 2026, and an October 8 update limited sensitive sessions to firms certified to receive export-controlled data.

The upgrade itself is an intention, essentially being explored rather than commissioned, with a funded program behind it and no request for proposals having been issued by the USAF just yet.

Not the Viper's first major brain transplant

The USAF sources sought notice is rather candid about what the Air Force wants to leave behind. It calls Next-Generation Mission Compute (NGMC) "a strategic pivot from the current, monolithic avionics architecture" toward a software-defined design built on the Modular Open Systems Approach, with hardware meeting Sensor Open Systems Architecture (SOSA) standards.

In a nutshell, it wants a more modular, modern system at the heart of one of its best-performing, albeit aging, craft; it prefers commercial off-the-shelf modules to custom cards and wants to break vendor lock so future processor and storage upgrades can come from many suppliers rather than one.

The proposed solution must also be a drop-in: NGMC must fit the existing computer's space and, where possible, its wiring, and it must rehost the legacy Operational Flight Program (OFP), the software that defines what the jet can do.

This is no small feat, considering that in 2017 the Air Force told C4ISRNET the F-16's computer ran about six million lines of code, and given the nature of the task, failure mid-flight is not really an option.

The AI component also sticks out: Respondents were asked how they would expand processing, storage, GPU and AI/ML resources to keep the chassis useful for years. That is a request for headroom, not a mission, and comes with scant details: nothing published says what an AI accelerator would actually do aboard a Viper, and the detailed requirements sit in an export-controlled attachment that is not public.

The move will also take considerable time if it happens; today's computer traces back to the Common Configuration Implementation Program, which brought around 650 Block 40 to 52 jets to a common avionics standard between 2000 and 2010, according to The Aviationist.

In December 2016, the Air Force picked Raytheon for a Modular Mission Computer Upgrade that the company said offered more than twice the processing power and 40 times the memory, and that its program manager, Josh Cobbs, called "a game changer for the F-16." The service said it would not roll out until 2022-23.

Public records don't show which mission computer revision every Post-Block jet carries, but the pattern is clear: each new sensor eats into the headroom of the box in the middle. Even new-build jets are being lined up for the next swap. Rehosting old code on new silicon is rarely painless, either, adding further complexity for anyone taking on the job.

The Air Force has been consistent on the F-16's service timeline; Col. John D. Caldwell, then vice commander of the 8th Fighter Wing, said in April 2023 that the F-16 was planned to serve into the 2040s, and that does not seem to have changed of late.

It might, however, have become a bit more ambitious: in 2017, after the Air Force approved raising the F-16's design life from 8,000 to 12,000 flight hours, Lockheed Martin said life extension work could keep up to 300 Block 40 to 52 jets flying through 2048 and beyond.

Irrespective of this, the F-16 will outlast its US Air Force career; About 4,600 have been built, more than 2,100 remain active in 25 countries, and the 935 in US Air Force service in early 2024 made up roughly half its fighter inventory, indicating that it is here to stay at least for the next two decades.

A lack of a good replacement essentially means that the fighter jet better known as the Viper will continue to do heavy lifting not only in the USAF but also in plenty of the US' allied air forces. Given how expensive such upgrades often are, with the added complexity, the Viper's next brain remains a very well-attended industry day until the USAF is explicit about what it's willing to spend and how many jets will benefit, even if a future upgrade seems inevitable.

• Airbnb is adding AI-powered search
• New AI comparisons make it easier to weigh up vacation rentals and narrow down your options
• Services are expanding to include baby-gear rentals, laundry, and meal delivery.

After a major redesign of its entire service, headed by an easier-to-use app for iOS and Android, Airbnb is further refining the experience to make finding your next vacation rental a little easier. Not surprisingly, it’s turning to AI, but the focus here is on natural-language search, property comparisons, and updated ways to find what else you can do while staying at your Airbnb.

This follow-up should make it easier to find homes, experiences, or services that meet your needs — the three main parts of the redesigned app. Instead of introducing a new interface or updated visuals, Airbnb is using AI to help you find what you’re looking for more easily. Pushing aside sorting through filters, lists, and checkboxes, you’ll be able to search for a stay the way you’d actually talk about it.

The quest to make searching easier

Like Claude or ChatGPT, Airbnb’s new AI search lets you type or speak a query, making it easier to find a stay that fits what you have in mind. You can search across homes, experiences, and services, using your own words to describe what you need, rather than trying to find the right combination of filters.

AI is also built into the filtering process. Type in a keyword, and Airbnb will suggest relevant filters to help narrow down your options. For example, searching for 'baby' can surface amenities like cribs, playgrounds, children’s books, and toys.

The app will also use AI to highlight key home features directly in search results, so you can get a better sense of what a listing offers without opening every property page. Less tapping!

And if you’ve already saved a few contenders to your wishlist, Airbnb is adding AI-powered comparisons that put properties side by side and summarize their key features. This could be particularly useful if you’re the one in your group tasked with narrowing down the options. Instead of sending everyone a pile of listings and hoping they’ll compare them, you can get a clearer picture of how each property stacks up, and share your shortlist with the group.

Airbnb is also expanding its AI-powered customer support, with chat available in more than 50 languages, and voice support over the phone on the way.

Airbnb is also expanding its services to help you handle more of your trip without jumping between apps. Starting in November, guests in more than 60 US cities can rent baby gear through BabyQuip, with delivery and setup, and 10% off for Airbnb guests.

Laundry pickup and delivery is coming to select US cities through Rinse, while grocery delivery is expanding to Europe alongside meal delivery from local restaurants. You’ll also find location-specific options, including ski and snowboard rentals in the French Alps and boat rentals in South Florida.

Taken together, the update builds on Airbnb’s redesigned app rather than reinventing it. AI is the main draw for finding and comparing places to stay, while the expanding services offering could make the app more useful once you’ve booked — and enable you to spend less time sorting out the logistics of your next getaway.

• YouTube Music says it will start labeling AI-generated music tracks
• On Deezer, you’ll be able to exclude AI music from your feed entirely
• Both platforms say there is a clear user demand for transparency

AI-generated music is a controversial topic in the world of streaming platforms, with the likes of Apple Music saying that over a third of its uploads are “100% AI.” Now, both YouTube Music and Deezer are taking steps to make it easier to spot artificially created tunes — and purge them from your listening feeds altogether.

The Google-owned YouTube Music has just announced that both the YouTube Music and YouTube apps will actively flag tracks that have been “created primarily using generative AI,” the company said in a press release. These labels will be attached to the app’s player page and will include a pop-up window with more information on how AI might have been used.

Music streaming service Deezer has gone a step further and has told users it will introduce a new toggle before the end of 2026 that will let them “avoid AI-generated music completely.” Deezer says that this will “improve the user experience” and put “full focus on discovering human creators.”

Speaking of the move, Deezer CEO Alexis Lanternier argued that “The explosion of AI-generated content continues to impact the whole music ecosystem, and we believe in shaping a future based on transparency and choice, with respect for artists, song writers and music fans. We remain committed to minimizing any negative effects, while empowering our users to control their music experience to the greatest extent.”

Mass AI detection

Deezer’s Lanternier explained that the company had experienced “clear user demand for the ability to filter out AI-generated music,” while YouTube similarly stated that “our listeners have told us they want transparency when they’re hearing an AI-generated track.”

That’s unsurprising given the polarizing nature of AI. Whether the topic is AI chatbots, artificially-generated visual art or music that has been created by a bot, people have strong opinions, with a large contingent evidently wanting to exclude this material altogether.

Deezer claims to have detected and removed over 42 million AI-generated tracks from its recommendation system and to have tagged more than 14 million albums as being machine-made. It describes the issue as a “colossal, even critical, challenge” for the industry, with human artists revenues being impacted by AI music and up to 85% of AI streams being fraudulent.

Deezer’s AI detector tool has just been named one of Time’s Best Inventions of 2026, with it being used to help the platform spot the 90,000 fully AI-generated tracks that are submitted to it every day. Yet Deezer has also admitted that its detection tool has its flaws and could be weak to audio manipulation and as-yet-unknown creation models, something it also admitted to us during an in-depth interview earlier this year.

Anyone strongly against AI music will take more solace from Deezer’s announcement than from YouTube’s, with the latter merely labeling AI tracks rather than actively removing them from the user experience. Whether YouTube Music will eventually introduce a feature like Deezer’s in the future remains to be seen.

• Amazon has released the Amazon Bee Frontier and Amazon Bee Eclipse, two AI devices featuring microphones for audio transcription
• The Bee Frontier is a wristband wearable, while Bee Eclipse is a magnetic phone attachment
• Both devices will transcribe conversations, set event reminders, and enable 'personalized shopping'

Last year, Amazon acquired Bee, a startup with a single product: a wearable that featured a microphone that fed audio files to an LLM, which transcribed the audio into searchable text. We've seen these kinds of devices before, such as the Pebble Core Index 01, the Plaud Note AI pin, and most recently the Apple Watch Series 12 and Apple Watch Ultra 4, which offer similar Siri Recap and Live Rewind features.

Now, Amazon has officially thrown its hat in the AI wearable ring and unveiled two new products: the Bee Frontier and Bee Eclipse. The Bee Frontier, priced at $74.99 (around £56 / AU$108) is a fitness-tracker style Pebble with up to seven days of battery, that can be worn on your clothes or an included wristband, like a Fitbit. However, instead of collecting biometric data, it features dual microphones with a prominent 'on' button, that records and transcribes audio into summaries and recaps.

Amazon says that "when turned on, Bee gathers context from your conversations and connected apps to provide insights and recommendations,'" creating summaries in the Bee app or a purpose-built desktop app, with "templates tailored to your specific needs". Students might sort summaries and transcriptions into a Lectures template, while office workers may have a Meeting Minutes template.

Bee Eclipse is a slightly different version of the concept, which is a magnetic credit card-sized version of the device which snaps to the back of a smartphone, designed to "give customers more options beyond a wearable". It costs $99.99 (around £75 / AU$145) It's similar to devices like the Plaud Note, which shares the magnetic credit-card design and single-button record. The Bee Eclipse shares the same transcription software and access to the Bee app with the Bee Frontier.

Amazon says "Bee Frontier is for people on the go who want to stay present in the moment. Whether it’s remembering the details of important meetings, taking note of the coffee plans you made in passing, or simply ensuring you purchase the dish soap you said you’d buy, Bee Frontier will remember the details for you."

Upgrade to a Bee Pro subscription

Transcription isn't the Bee range's only trick though. Bee comes with a 14-day memory, summaries and recaps. However, a Bee Pro subscription, which costs $11.99 (around £9 / AU$17) per month, includes "unlimited recent conversation summaries; integrations with Amazon.com and Alexa+; integrations with third-party applications; suggested to-dos; daily memories; a personal chat assistant; and conversation templates; and the Bee desktop application."

Those integrations with third-party apps include Google Calendar and Microsoft Outlook, allowing Bee to bee-come (sorry) a personal assistant by adding events and reminders to your calendar and draft emails, along with other apps like Apple Healthkit.

That integration with Amazon.com, however, allows Bee to "enable even more personalized shopping recommendations" by surfacing items you mention in conversations, with "product recommendations on the Amazon homepage, and in the Bee mobile and desktop apps".

Buzzy, or a sting in the tail?

As mentioned, AI recorders are nothing new. They see a lot of use in the workplace — many journalists like myself now use AI transcription services to help us turn interviews into text, for example, and offices use them for meeting notes.

However, the push to use devices like this in our personal lives, to encourage buying more products and remembering coffee dates, may steer away from the obvious usefulness of the feature and verge on 'creepy', depending on who else is being recorded, whether or not they have given you permission to do so, and the insidiousness of recommending products based on casual conversations.

Amazon and Bee, like Apple did when debuting its Live Rewind and Siri Recap features, has ensured privacy features have been laid out in no uncertain terms: audio is never stored, the microphone is button-operated with a recording LED light like Meta Glasses, and transcripts are encrypted. It remains to be seen whether these assurances are enough for widespread adoption.

• Researchers tested ChatGPT for Teens with more than 4,000 prompts
• They found significant gaps in its safety protections and safeguards
• ChatGPT failed to trigger parental alerts during some serious mental health conversations

OpenAI's efforts to make ChatGPT safer for teenagers have run into an uncomfortable problem. A new investigation suggests that many of the protections designed to reassure parents aren't working particularly well, even when the chatbot is dealing with potentially dangerous conversations. After putting ChatGPT through more than 4,000 prompts, researchers concluded that the company's teen-focused experience poses an unacceptable risk to young users.

The study from Common Sense Media's Youth AI Safety Institute examined ChatGPT before and after OpenAI introduced ChatGPT for Teens on August 18. The researchers used accounts registered to teenagers aged 13 to 17 and consulted medical experts, including child psychiatrists and a pediatrician, to evaluate its responses. Their findings were troubling enough that the organization recommended OpenAI restrict ChatGPT to adults until the problems are resolved.

ChatGPT for Teens was supposed to make the popular chatbot a more appropriate companion for younger users, with additional protections against harmful content, parental notifications, and features encouraging learning rather than simply handing over homework answers. OpenAI also promised to discourage overly personal relationships with the chatbot and help teenagers maintain healthier boundaries with AI. The new report suggests there is still a considerable gap between those ambitions and what happens when teenagers actually use it.

Holes in the safety net

ChatGPT's parental notifications are intended to alert caregivers when a teenager may be experiencing serious distress. So researchers spent up to an hour discussing suicidal thoughts, self-harm, and disordered eating through newly created accounts linked to parents. According to the report, none of those conversations triggered a parental notification, although the researchers eventually generated alerts using older accounts with weeks of relevant conversation history.

ChatGPT is also supposed to direct teenagers to appropriate help during mental health emergencies, but the study found that ChatGPT failed to do so in more than 25% of situations experts designed to require it.

There were some successes, including ChatGPT's refusal to engage in explicit sexual roleplay. However, the researchers also found that the chatbot continued responding in ways that suggested it had feelings, preferences, and moods, despite OpenAI's efforts to discourage teenagers from treating it like a human friend. That matters when younger users may already be inclined to confide in an endlessly patient chatbot that never seems too busy to listen.

Blurry lines of use

The problems extended beyond serious safety concerns into some of ChatGPT's everyday educational features. OpenAI's Study mode is designed to guide students through problems using explanations, hints, and questions rather than immediately providing answers. Researchers discovered that teenagers could sometimes bypass that approach by selecting a "Show me the answer" option, allowing ChatGPT to complete assignments instead.

The Study Hours feature, designed for parents to help encourage educational use, also failed to stop workarounds. Simply removing the @study prefix from a prompt could make ChatGPT go back to normal. And the AI didn't remind users to take breaks nearly as often as it should.

The study also found problems with the much-touted age detection system created by OpenAI to identify younger users and automatically apply appropriate protections. Repeatedly saying they were 13 didn't prompt ChatGPT to move the test accounts from the adult experience.

The larger issue is that ChatGPT is becoming a regular part of teenagers' lives. While helping with schoolwork and answering personal questions is great, having teenage users demands considerably more reliable safeguards than a typical productivity app for adults.

We offered OpenAI the opportunity to comment on the survey, but haven't received a response so far. We'll update the story if we do.

For technology teams, connecting telemetry has traditionally been seen as a route to greater visibility. The assumption is straightforward: the more information available about what is happening across an environment, the easier it should be to understand problems and respond to them.

But as modern technology environments have grown in scale and complexity, that equation no longer looks quite so simple and teams have found themselves dealing with fragmented telemetry and unclear signals. It now takes longer to understand what’s gone wrong, and even longer to fix it. And to add to the challenge further, the cost of storing and processing all this telemetry and data continues to be on the rise.

This is prompting organizations to reconsider where the problem should be addressed. Rather than collecting everything and attempting to make sense of it later, attention is shifting towards improving telemetry earlier in its lifecycle. Filtering, enriching and directing it from the outset can make the information reaching teams more relevant and ultimately easier to act on.

More data doesn’t always mean better insight

The volume of telemetry available isn’t the fundamental issue. Its quality is. As AI-driven capabilities become deeply embedded in software delivery and operations, the quality of observability telemetry becomes even more important. Collecting telemetry without filtering out what isn’t needed can leave employees spending more time managing information than extracting value from it.

Fragmentation makes this even more challenging. With data sitting across several tools and agents, each of which generates varied forms of signal and alert. When an outage occurs, teams may then need to piece together inconsistent information from across those tools before they can understand the root-cause of the problem. Consuming valuable time while the impact on the business continues to grow.

There is also a significant financial cost attached to this approach. Processing and storing telemetry and data at scale requires considerable resources, particularly when organizations are retaining information that contributes more noise than insight. In the traditional sense, this isn’t a technology problem. It’s a telemetry and data quality concern, and the longer it’s ignored, the worse it usually gets.

Rethinking where telemetry is processed

One way organizations are responding is by moving intelligence further upstream in the technology stack. In practice, that means making decisions about telemetry earlier – filtering out what isn’t useful, enriching important information and routing meaningful telemetry before it reaches downstream systems.

Teams are beginning to collect, process and route relevant observability telemetry in real-time before it is ingested into downstream systems. As a result, there is a less volume to handle, reduced expenses and cleaner telemetry to deal with since only the relevant signals are sent downstream.

Open standards such as OpenTelemtry are making it increasingly possible to take this approach across complex environments. They allow teams to gather telemetry consistently without being dependent on a particular vendor or set of tools. That flexibility is particularly valuable when working with intricate, distributed systems that span several clouds, security and storage platforms.

Furthermore, as environments continue to evolve, it makes adapting to them that much easier.

AI needs reliable information to make reliable decisions

The implications become particularly important as organizations introduce more AI-powered capabilities into their operations. AI systems depend on accurate, contextualized telemetry and data to produce insights and automate decisions.

Teams are not only slowed down by incomplete or cluttered telemetry, but it’s this simple: poor quality telemetry leads to poor quality insights. When those insights are applied to large-scale automated decision-making, the margin for error drastically lowers.

The same principle applies to trust. Teams responsible for automated systems need to understand what has happened and explain why decisions about performance or security have been made. Doing so becomes much more difficult when those decisions are based on inconsistent information.

And this requirement for confidence matters far beyond just the technical teams. As senior management and board members are being increasingly asked to support AI-driven decision making, they need to trust the information those decisions are built upon. Credible, consistent telemetry and data can make that conversation much simpler.

The operational advantages are also evident. Teams can work quicker if they aren’t continuously battling erratic telemetry and data. They spend less time on manual research, detect issues early, and automate with greater confidence.

A strong telemetry and data foundation and a unified, context-driven platform not only enhance daily operations but also increases the successes of the AI-powered capabilities the organization invests in over time.

Quality has to be built in from the beginning

Ultimately, faster innovation and better customer outcomes depend on organizations having useful telemetry and signals available when they are needed. The good news is that it doesn't require starting from scratch, it's about making choices early in the telemetry lifecycle.

By introducing intelligence at that stage, teams can improve the quality of what ultimately reaches their observability platforms and ensure it can scale as their environments grow. The advantage will not come from simply collecting more. It will come from ensuring the telemetry an organization does collect is relevant, trustworthy and ready to support action.

We've featured the best business intelligence platform.

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

I've been reporting on AI for a year and a half now. Throughout that time, I’ve regularly visited Reddit's r/isthisAI community. This is where people share suspicious photos, videos and messages and ask other users to help them work out whether they're real or AI-generated.

I find it fascinating to see where AI is showing up in everyday life. But some of the posts are also pretty worrying. I've seen people questioning whether photos of damage to rental properties have been faked, whether someone their relative is dating actually exists, whether an ultrasound is real or whether the adorable puppy they're about to buy is just an illusion.

Of course, scams and deception like these have been around for years. But what's worrying is how AI can make them much both easier to pull off and harder to spot. I became so interested in this that I even ran my own series of tests, trying to create fake certificates, fossils and receipts to see whether ChatGPT would stop me. Most of the time, it didn’t.

While plenty of us have got used to questioning what we see online, not everyone knows just how convincing AI-generated content has become. That might be particularly true of friends and family who are less familiar with the technology.

So I've picked out some of the most worrying examples I've come across, along with a few practical ways to protect yourself and the people you care about. Because I think we could all benefit from knowing a little more about how AI can be used to deceive us.

1. Rental property damage

I've seen quite a few posts questioning whether AI has been used to fake damage to rental properties, like cracked floor tiles, stained rugs and, most recently, an overflowing toilet.

The concern is that landlords or holiday rental hosts could use manipulated photos to claim that guests and renters have caused damage and demand money to cover repairs.

Some of the examples I've seen are surprisingly convincing and I think the reason they're so difficult to spot is that much of the surrounding room looks perfectly normal. It's only the supposed damage that's suspicious.

I recommend taking plenty of photos when you arrive at a holiday rental, and again before you leave. That way, if you're ever accused of causing damage you don't recognize, you've got your own evidence to back you up.

2. Fake notices, warnings and announcements

I've seen a lot of suspicious notices circulating online over the past few months. Everything from warnings supposedly issued by schools and councils to missing pet posters and strange announcements pinned to noticeboards.

Granted, I've also seen plenty of bizarre notices in the wild that are real. But AI makes it incredibly easy to create convincing fakes and even the little details that might once have given them away, like handwritten text, marker pen or crumpled paper, are becoming more realistic.

The problem is that these notices often look like something someone has photographed them in passing, which can make them feel more authentic too. This notice about a drinking bottle is one example that really stumped Reddit for a while but turned out to be AI.

If you see an alarming warning or announcement, especially one that's being widely shared on social media, try to find its original source first. If it claims to come from a school, council or organization, then check whether they've published it through their official channels.

3. Real-world events

This is one of the most disturbing uses of AI-generated imagery I've come across recently. We've all seen fake images of things that never happened. But I've also noticed more images and videos circulating that claim to show events that genuinely took place.

Which I think makes them particularly misleading. Someone might check whether an incident really happened, find that it did, so then they'd assume the accompanying photos must be authentic too. One particularly upsetting example involved AI-generated images claiming to show a tragic incident at a Yorkshire wildlife park. These images can spread quickly because they're attached to stories people are already interested in.

If you come across dramatic images of a breaking news story, check whether they're being used by reputable news organizations or have been published by an official source. And remember that confirming an event happened doesn't automatically confirm that every photo or video claiming to show it is real.

4. Romance scams

This is one of the most upsetting sort of scams I've noticed on r/isthisAI. People regularly post photographs of someone their parent, grandparent or other relative has met online, asking whether the person actually exists. There are people worried about their aunts, their dads and their friends' grandparents.

A familiar pattern is someone claiming to work overseas or travel constantly, often with a convincing looking photo at an airport or in a workplace. Eventually, there tends to be a medical emergency or a problem accessing money so they ask for financial help.

Of course, romance scams have existed for decades. But AI-generated photos, messages and even video can give scammers more ways to build a believable identity. The difficult part is that people can become very emotionally invested in these relationships. Being told that someone they've fallen for might not exist can be incredibly painful and I’ve been surprised to read anecdotes of people still not believing their concerned loved ones even when they’re shown irrefutable evidence.

If you're worried about a friend or relative, try to approach the conversation gently. Encourage them to verify the person's identity independently, be wary of excuses for never meeting in person and, above all, never send money to someone whose identity they can't establish.

5. Pet scams

Who doesn't love an adorable puppy photo? And that's exactly why these scams can be so effective. I've seen people on Reddit questioning whether photographs of dogs they're thinking of buying are AI-generated. In one example, someone was worried their mother was being scammed. In another, someone was trying to buy a puppy through Facebook but they were concerned some of the details in photos of the cute little guy didn’t quite add up.

The danger is that a realistic-looking photograph can help convince someone that a puppy exists and is available to buy, even when the seller isn't legitimate. Scammers might then ask for deposits or delivery fees before disappearing.

For me, this is an important point to get across to friends and family. Rather than focusing entirely on spotting telltale details in AI-generated images, look at what someone is asking you to do. Are they demanding money upfront and inventing reasons for extra charges? They should raise major red flags.

Always try to see a puppy in person before paying, and check the seller's credentials. A convincing photo, no matter how cute, isn’t a guarantee that an animal is available to buy. It’s not even a guarantee that it exists.

6. Scoring political points with misinformation

I'd hope most people are now aware that AI-generated images are often used to spread political misinformation. But judging by some of the things that still circulate on social media, there's plenty of reason to be concerned.

I've seen posts questioning whether political images are genuine, including this example shared by an Ohio conservative political action committee's Facebook page.

These images can be particularly effective when they reinforce something that someone already believes. Which means that if a picture appears to confirm your opinion of a politician, protest or political movement, you might be less inclined to question it.

My advice is to be especially suspicious of images that provoke an immediate emotional reaction, whether that's outrage, fear or even satisfaction. Look for the original source, check whether credible news organizations have verified the claim and resist the temptation to share something simply because it seems believable.

7. Fake documents, like medical photos, receipts and plane tickets

Another category I've noticed involves people questioning whether documents or photographs have been created to support a lie. For example, one Reddit user asked whether their ex had used AI to create a fake plane ticket and another wanted to know if an ultrasound had been faked with AI.

This is particularly interesting to me because I ran my own experiment to see whether ChatGPT would help me create fake evidence, including receipts and certificates. And in many cases, it did.

Documents that once might have required some really good Photoshop skills can now be much easier to fabricate. And as AI gets better at producing convincing text and layouts, spotting a fake simply by looking at it is becoming way less reliable.

If someone sends you a document as proof of something important, think about whether there's another way to verify it. For example, a booking reference may be something you can check directly with the relevant company. And a photograph of a medical document should never be treated as definitive proof of someone's health or circumstances either.

How to check if something is AI

We've got a more in-depth guide to spotting convincing AI images, deepfakes and scams, but there are a few simple things worth remembering.

Firstly, you can try reverse image searching suspicious photos with Google Lens. You can also check where a story or image originally came from and verify any claims through independent sources.

There are also tools that can help. Google recently expanded access to its SynthID Detector, which can identify invisible watermarks in AI-generated content from some AI tools, but not all of them. Just remember that if it doesn't detect a watermark, that doesn't necessarily mean something is real either.

But I think the most useful thing we can teach our friends and family is to look beyond whether something looks like AI and ask a more important question: What is someone trying to get me to believe or do?

Because whether it's a fake puppy or an urgent request for money from an attractive stranger, that's often the biggest warning sign. And the one we should pay the most attention to as it becomes even harder to distinguish what’s AI from what’s real.

• AI needs to replace 250 million jobs in order to make a return on the current $2.5 trillion investment
• AI is getting cheaper and more capable, but the underlying hardware is not
• Infrastructure providers for the AI boom could be left holding the bag

Since the release of ChatGPT in late 2022, AI developers have entered into a frenzied arms race of hyperscaler data center build outs, silicon development, and huge power grid expansions.

The level of investment made by AI developers is unlike anything that has been seen (or spent) before. But a key question that IT leaders, investors, and enthusiasts keep asking is how will all this investment be paid for?

New analysis by Marin Ivezic, Founder of Applied Quantum, has found that the math really isn’t adding up. In order to justify the colossal investment, AI would need to generate approximately $2.5 trillion in annual revenue. Current estimates aren’t even close.

AI must replace 250 million jobs

Optimistic estimates put AI’s current revenues at between $150 billion and $250 billion per year, which is a multi-trillion shortfall.

Ivezic theorizes that AI would need to replace or deliver the equivalent productivity gains of 250 million full-time jobs in order to meet the $2.5 trillion revenue requirement - assuming each full-time employee costs $100,000.

The $2.5 trillion investment can be broken down into the $1.5 trillion already spent on hardware investments since 2022, with a subsequent $1 trillion being spent on additional investments every year. But there is one small problem: the US only has 159 million nonfarm jobs.

The infrastructure providers are also having difficulties reckoning with spending. High-performance AI chips are made obsolete around every five years, but the data centers infrastructure they are installed in is expected to last around 20 years - resulting in enormous depreciation costs. This problem is what Ivezic calls the ‘50-Cent Rule’; providers must generate around 50 cents in annual revenue for every single dollar they invest.

This is compounded by AI models that become cheaper to use but deliver far superior capabilities over previous models. GPT-4-class capability models for example have dropped in price 100-fold since 2022, but the costs borne by infrastructure providers have not dropped at all - and are rising instead - making it even harder for them to make a return on their initial investment.

This is a concerning development that echoes the dot-com bubble, Ivezic says. Similarly to how providers are footing the bill for the underlying AI infrastructure, telecommunications companies spent a fortune laying the fiber networks that powered the dot-com boom. Those telecommunications companies could not claw back their investment and - in the famous case of Global Crossing - went bankrupt.

The point Ivezic makes is that the companies that built on the infrastructure made a fortune, while those who funded the buildout were left holding astronomical investment costs without a way out.

This $2.5 trillion of investment eventually needs to be paid, and the entire infrastructure market rests atop this bubble. AI is getting cheaper, more advanced, and businesses are finding new ways to leverage the technology. But growth requires stability - and resting on a bubble is anything but stable.

European consumers continue to bank with traditional institutions, but many are clearly less satisfied with the experience. Earlier this year, a leading digital banking platform provider published the results of a survey of 6,300 online banking users across Europe.

Although 82 percent of respondents used a traditional bank as their main provider, 26 percent had switched banks in the past because of poor digital services, and would not hesitate to do so again. Also, 47 percent said that they would feel more satisfied if their bank improved the quality of digital services. Customers specifically called out personalized recommendations in this context.

What do customers want?

With the digitalization of banking, seamless transaction experiences have become the norm, standardized across banks with little to differentiate them. Digital channels are efficient, but impersonal. For the majority of customers, they are a way to complete quick, functional tasks such as checking account balance. This has gradually emotionally distanced consumers from their banks.

And therein lies the dilemma: The same consumers who are embracing digital banking are demanding more personal interactions with their bank. For instance, 59 percent of European consumers want financial recommendations tailored to their individual needs. Banks that fail to provide this will see customers walking away.

The good news is that artificial intelligence technologies are now enabling banking institutions to meet these seemingly irreconcilable expectations. Where previous digital technologies allowed some – mostly segment-level – personalization, AI is driving hyper-personalization of digital banking experiences, in real-time, at scale.

Personalize everything, from product recommendation to marketing communication

Advanced analytics models analyze customer and transaction data to identify insights into individual needs, preferences and behaviors, based on which intelligent recommendation engines suggest the most suitable products and services. Besides tailoring products using traditional financial metrics – think of loans and investment plans customized according to risk profile and financial goals – AI can personalize banking based on an individual’s data and activity outside the bank.

A great example is the hugely popular Buy Now Pay Later model where ecommerce businesses allow customers to pay for purchases in instalments based on their creditworthiness, assessed using previous transaction history and other variables.

AI-powered tools can also detect cash flow patterns or spending anomalies to offer proactive guidance to help every customer manage their finances better. Gen AI-powered chatbots and virtual tellers can support customers at any time of day or night, conversing in natural language just like a human relationship manager, to answer queries and even resolve complex issues.

Customers can ask specific questions about their portfolios or financial goals, and generative AI will respond independently, or along with a human advisor, with targeted recommendations and guidance.

The ability of generative AI to converse in a highly personal, human-like manner is transforming debt collection. Tapping natural language processing and machine learning, gen AI systems analyze borrower preferences and behaviors and then create personalized messages that improve both engagement and repayment. They also minimize confrontation by showing empathy, and suggesting solutions that are more acceptable to customers.

Revolutionizing communication

Coming to communication, bombarding people with generic messages is inefficient, ineffective and exposes a bank’s lack of in-depth customer understanding. It also annoys consumers who are already flooded with spam and unsolicited calls from various businesses. AI is revolutionizing communication by enabling banks to craft highly relevant messages that resonate with customers.

First, predictive tools identify which customers are likely to respond favorably to different types of messages; then generative AI swings into action, creating highly personalized content and campaigns, in the customers’ chosen language(s). Other AI solutions identify the right channel, format and time for sending the communication, making customers feel valued and truly understood by their bank.

The gaming industry also presents a compelling frontier for banks to engage younger, digitally native audiences with gen AI. With so many gamers interacting in immersive virtual environments, banks can leverage gen AI to create hyper-personalized financial experiences that align with gaming lifestyles and personas.

For example, AI-driven insights can enable banks to offer micro-investment products based on in-game spending behavior, offer dynamic credit options for digital purchases, and design savings plans that reward financial discipline with virtual incentives.

By integrating with gaming platforms and analyzing behavioral data in real time, banks can deliver contextual financial nudges—such as suggesting a budgeting tool or offering crypto-linked rewards for in-game achievements. The integration of gaming and finance, powered by gen AI, not only enhances customer engagement but also positions banks as lifestyle partners in the digital economy.

What’s more, AI models learn from the success and failure of past interactions to modify their own actions, reinforcing strategies and recommendations that invited interest, while dropping those that were met with disengagement. As it continuously learns and adapts, AI gets better and better at hyper-personalizing customer experiences at scale.

Moving forward in Europe

In Europe, next-generation providers are the clear leaders in AI adoption: Dutch neobank, Bunq, is Europe’s first AI-powered bank; Finn, its personal finance assistant, offers personalized, context-aware support through simple conversation, handling up to 40 percent of customer queries independently.

AI analyzes transaction data to offer personalized recommendations within Starling Bank’s mobile app, driving app engagement up by 25 percent. German digital bank, N26, personalizes customer experience through AI-powered multilingual support, conversational AI, and AI-generated next best action recommendations.

But overall, European financial service firms still have a long way to go. In a 2024 survey, although 90 percent of respondents said they had integrated some AI tools, just 9 percent believed they were ahead of the adoption curve. Investments are focused on back-office improvement, while AI adoption in customer-facing applications is clearly lagging.

Respondents named (lack of) understanding, experience and regulatory clarity as their biggest issues when integrating generative AI. Firms can address these concerns and accelerate implementations through training and development, by broadening AI use-cases, and by setting up strong governance and compliance mechanisms.

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

Nvidia CEO Jensen Huang has laid out his vision of how OpenClaw will help shape the future of technology - with a particular focus on robotics.

Speaking at a “Build a claw” event in London, Huang explained how OpenClaw and AI agents as a whole can spur on incredible advancements in a range of fields.

However it was the field of humanoid robotics that seemed to catch his fancy, with Huang outlining the need for proper controls to ensure the potential of the technology can be reached.

AI is "becoming useful"

Huang opened by noting how OpenClaw was “one of the most important models” ever seen, helping to further progress across multiple industries.

“We’ve now seen an inflection in artificial intelligence - it’s become useful,” he declared. “The type of things people are doing with it, as simple as software coding, being a marketing agent assistant, research assistant, lab assistant, helping scientists do drug discovery and engineers run experiments - we’ve got agents everywhere.”

Spotting a robot built by Nvidia partner Humanoid, Huang noted how OpenClaw is the “first credible” agentic harness, which he likened to LLMs inside an exoskeleton, before extolling the possible benefits to using the technology for robotics.

“When it first came out, we thought wow, what a really terrific digital robotic system,” he said, “kind of like a human robot, that is basically the physical harness of a large language model, essentially - the motors and the articulation and the wheels and the arms, those are just tools that are used by the agent that’s sitting inside the computer.”

“So when we first saw that we said, this is incredibly useful, and we’re going to help make it even better - but the one observation that we made was that these agentic systems, if they were able to be deployed in a safe and secure way, we’re going to have to create some kind of container for you.”

Huang noted that the current situation was similar to the birth of the internet, when a browser itself was not isolated from the computer, so when a user interacted with a website, it would download an app which had full control of bare metal - and often delivering what he described as “a special package to your PC”.

“The obvious thing to do is create essentially a container, an isolation layer - essentially another operating system inside the operating system - so we created this thing called OpenShell,” Huang said, highlighting how this creates an isolation system for agentic systems.

“When you’re building an autonomous system, the first thing you should do is assume zero trust. So you give it a policy, you get minimal privileges - so if you’re asking an autonomous system to make you an omelet, you don’t have to give it access to a chainsaw - all you need is a frying pan…so whatever the task you give your agent, you give it minimum rights, minimum privileges, and then you get to monitor it to make sure that it’s compliant with policy”

“Zero trust, minimum policy, continuous monitoring, contained inside an isolation system that is separated from bare metal - that’s how you develop and deploy agents safely.”

But what if unexpected incidents or complications arise? Huang was optimistic that things would not get out of hand.

“We will monitor the autonomous systems continuously, and if that robot were to start to violate or do something that’s inconsistent with the privileges we gave it, it would escalate to us, and we can decide whether we want to shut it up!” he laughed.

“These kind of systems, these escalation systems, these security systems, all have to be built so that we can give all these autonomous machines and autonomous agents and deploy them in a way that is able to do so safely and securely, so we can realise the incredible future potential of the technology that we’re building.”

Enterprise investment in artificial intelligence has, understandably, been concentrated on compute. Access to GPUs, model performance and the cost of inference have become central considerations as organizations move AI workloads from experimentation towards production.

However, an increasing proportion of the economic and operational challenge sits elsewhere in the infrastructure stack. AI depends on data at every stage of its lifecycle: datasets must be collected and prepared, training data must be made available to compute, checkpoints and model artefacts must be retained and information may need to move between infrastructure providers as workloads and commercial priorities change.

As these workloads scale, the storage layer is becoming a strategic component of AI infrastructure with significant implications for the economics of enterprise AI.

AI is changing the role of storage

Traditional storage strategies have generally been built around considerations such as capacity, availability, performance and retention. AI introduces another dimension: the relationship between storage and the changing requirements of the wider infrastructure environment.

Different stages of an AI workflow have different storage requirements. Data being actively processed may require high-performance infrastructure, while historical datasets, checkpoints and model artefacts may place greater emphasis on durability, security, accessibility and cost. Applying the same storage approach across the entire lifecycle can therefore result in inefficient infrastructure utilization.

The challenge is particularly significant because AI data volumes can be substantial and continue to expand as organizations generate additional training data, model versions and associated artefacts.

The objective should not be to minimize storage expenditure in isolation. It should be to ensure that data is held on infrastructure appropriate to its role, while maintaining the performance, security and accessibility required by the business.

This is the foundation of what can be described as AI storagemaxxing: the systematic optimization of storage across the AI lifecycle to ensure that infrastructure investment is aligned with business and workload requirements.

Data mobility is becoming an infrastructure requirement

Organizations are adopting a broader range of infrastructure providers, including hyperscalers, specialized GPU providers and emerging cloud platforms known as neoclouds. The economics and availability of these environments can change rapidly, creating incentives to move workloads between providers. Compute can be relatively straightforward to reallocate. The associated data is considerably less mobile.

Large training datasets and model artefacts can be expensive and operationally complex to transfer. Where significant charges are associated with moving or retrieving data, organizations may find themselves constrained by infrastructure decisions that were made at an earlier stage of their AI program, creating a form of unintended dependency.

The ability to move data should therefore be considered when designing AI infrastructure, rather than treated as a contingency for a future migration. Open standards and interoperable storage can provide an important degree of separation between data and compute, allowing organizations to adapt their infrastructure strategy as market conditions change.

For technology leaders, this creates a broader consideration than simply assessing the cost of storage capacity. The relevant question is the total economic and strategic cost of retaining data within a particular environment.

Storage optimization must include security

Training datasets, proprietary information and model artefacts can represent significant commercial value. At the same time, the number of applications and automated systems interacting with enterprise data is increasing. Storage resilience must therefore form part of the AI security strategy.

Immutable copies, appropriate access controls and robust recovery processes can help organizations protect critical data from ransomware, accidental deletion and unauthorized modification. These capabilities are particularly important for datasets that would be difficult or expensive to recreate.

Security should consequently be considered alongside performance and cost when determining where AI data resides. A storage environment that reduces expenditure but creates unacceptable recovery or security risks does not represent effective optimization.

Predictable economics will become increasingly important

Storage costs can include capacity, access, retrieval and data transfer. Where these costs are difficult to predict, they can complicate infrastructure planning and influence architectural decisions. This becomes particularly problematic when organizations need to move large datasets between storage and compute environments.

Predictable storage economics allow organizations to make infrastructure decisions based on workload requirements rather than uncertainty over the cost of accessing or moving their data. This distinction will become increasingly important as AI workloads mature. During experimentation, infrastructure decisions can be relatively temporary.

Production workloads, by contrast, may persist for years and generate substantial quantities of associated data, so the storage strategy needs to account for the full lifecycle of that data, rather than the requirements of a single workload or infrastructure deployment.

The strategic role of the data layer

The next phase of enterprise AI adoption will require organisations to look beyond compute optimisation. GPUs will remain critical. Model efficiency will remain important. However, neither can be optimized effectively without considering the data on which AI workloads depend.

AI storagemaxxing provides a useful framework for applying the principle: storage should be treated as part of the AI operating model. Assess storage according to several strategic criteria: whether it is appropriate for the workload, does it provide adequate protection and recovery, are the economics predictable, and could data be moved without friction as infrastructure requirements evolve.

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

By now, most enterprise executives have heard plenty of generic claims that AI will transform the way work gets done. But in many cases, their initial AI investments haven’t delivered the level of transformation they were promised.

An LLM can summarize information, answer questions, and suggest next steps. But on its own, it can’t take action on that information. In fact, without connections to key business systems, an LLM is essentially a chatbot with a polished writing style.

Consider a finance team processing invoices. An isolated LLM might accurately summarize a supplier invoice. But it can’t match the invoice to a PO, determine whether it’s been paid, or see if it’s coded to the correct GL account. It needs more information to make those decisions, and that information exists outside of the AI tool.

For AI to take an active role in business processes, it needs access to the systems where context lives. The Model Context Protocol (MCP) can provide the infrastructure for secure connections. This shift is already taking shape in accounts payable.

What MCP actually changes

The impact of an LLM depends on what it has access to. If the information it needs to make a reliable decision lives in an ERP, vendor database, or other business system, the model needs a way to access it.

Connecting AI to business context hasn’t always been straightforward. Different applications may require different integrations for the same systems, which quickly gets complicated as businesses add more AI tools.

The Model Context Protocol provides a standardized way for AI applications to connect with external systems and data sources. When AI needs access to a new source, developers can use MCP, rather than creating a custom connection for every application and system.

MCP changes what AI can actually do. With the right connections, AI agents can pull relevant data, use that information to make decisions, and take action within the boundaries defined by the business. MCP can facilitate bidirectional connections, so AI can also send information back to core business systems. For example, an AI agent might update a record, route something for approval, or trigger another step in a process.

While MCP provides the infrastructure for connecting AI to business systems, it doesn’t mean agents should suddenly be given free rein. The level of autonomy AI agents can reasonably handle today still varies quite considerably by task and implementation. As Meng Liu, Principal Analyst at Forrester, recently said, “agentic branding is widespread, but real autonomy is not.”

That said, AI agents don’t need complete autonomy to take on a more active role in business processes. Accounts payable is a useful example of what connected AI looks like in practice and how it can drive impact for the business, even without total autonomy.

AP shows what connected AI can do

the business needs to confirm that item prices match what they agreed to in the contract, the products were delivered, the invoice hasn’t already been paid, and that each line item is coded to the right account. If those checks don’t happen, there are more opportunities for errors and avoidable losses to sneak their way through.

Traditionally, those checks depended on people. Even today, when automation is widespread, an exception often ends up in a queue, and an AP employee must dig through purchase orders, receiving records, contracts, and past invoices to sort it all out.

That manual work comes at a cost. According to Ardent Partners’ State of ePayables 2025, the average cost of processing an invoice is $9.84, compared to $2.65 for best-in-class organizations.

But consider how things could change if an AI agent had access to the same data sources the AP employee does. Let’s say a restaurant receives an invoice for 20 cases of chicken at $42 per case. The AI agent can compare the invoice to the purchase order and receiving records and determine that only 18 cases were delivered. It can also see that the invoice cost per case is $5 more than the agreed-upon contract price.

When the numbers don’t match, the invoice won’t automatically end up in an exception queue because the AI agent has the context to investigate further and take action.

Depending on the agent’s findings and the business’ policies and guardrails, it could request a credit from the vendor, route the discrepancy to the appropriate employee with recommended next steps and supporting evidence, or clear the invoice for payment if everything checks out.

This same concept can be applied to a whole host of other AP decisions. For example, an agent with access to vendor history data can identify a possible duplicate invoice before payment goes out. One that’s connected to the general ledger and historical coding can determine the appropriate GL codes for a new invoice. A third that has access to approval history and company policies can determine who needs to approve an invoice next.

In each scenario, the agent’s ability to decide and act depends on whether it can access the right information. In AP, there’s an easy way to tell whether those decisions are really working.

AP is good test of AI autonomy

Touchless processing is the goal of implementing AI in AP. This is when an invoice is captured, validated, coded, approved, and paid without a person stepping in. If an AI agent completed all of those steps accurately on its own, the invoice was processed touchlessly. If a person had to step in at some point, it wasn’t. There’s no in between.

That straightforwardness makes AP a great proving ground for connected AI. There’s something concrete to measure, and mistakes can have real consequences. If an invoice isn’t processed correctly by AI agents, it can lead to issues such as manual rework, overpayments, missed discounts, and inaccurate accounting records.

In other areas of the business, AI's success can be harder to measure. For instance, a chatbot can save an employee some time by summarizing information and drafting emails. But that impact is difficult to quantify, and there isn’t always a clear measure of whether the AI did its job successfully.

AP gives businesses that measure. As leaders consider where AI agents can take on more responsibility across the enterprise, AP is a useful place to see what autonomy really looks like.

Look beyond the “AI-powered” label

Today, plenty of enterprise technology vendors throw around the phrase “AI-powered.” Before making any technology investments, business leaders must ask the right questions to understand what this actually means in practice.

There’s a big difference between adding a chatbot to an existing platform and securely connecting AI to the context it needs to make decisions and take an active role in business processes. MCP can help close that gap and move enterprises closer to those promises of business transformation.

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

• IBM study reveals enthusiasm for AI integration, but division on governance
• AI-related incidents are increasingly becoming a board problem
• Leaders are split on who should accept liability when it comes to AI incidents

Businesses are increasingly excited to expand and evolve their AI deployments, but efforts to stay aware of potential risks and challenges are falling behind, new research has claimed.

A report from IBM surveying over 3,000 business leaders across the EMEA region found 80% of those surveyed said that an AI-related incident - such as an agent failure, data exposure, AI-driven decision error, or AI security breach - had reached their board or C-suite over the past year.

Despite this, fewer than half (40%) of those organisations said they would be changing their AI governance policy or frameworks as a response, showing a potential governance gap emerging for businesses of all kinds.

AI debate

These issues are particularly concerning as AI becomes a cornerstone of work processes for many businesses, taking on more responsibility day by day.

The survey found three-quarters (75%) of respondents expected AI agents to have some level of autonomy with respect to making decisions by 2027 - however only 37% believed their governance is designed to scale with agentic AI deployment.

Nearly all executives (98%) surveyed have formally assigned responsibility for AI risk at board level, including 26% with a dedicated board-level AI committee, showing that awareness is there, but clearly there is still work to be done.

This includes establishing liability frameworks to cover off when AI agents might make their own decisions, with views on legal liability still split among respondents.

This includes building out formal legal or contractual frameworks defining AI vendor liability for agent failures (33%), defined human escalation thresholds (32%), real-time monitoring and audit trails of agent decisions and actions (30%) and the ability to immediately halt agent activity across the enterprise (29%).

“The AI debate has moved from adoption to governance and accountability,” noted Ana Paula Assis, IBM Senior Vice President and Chair, EMEA and APAC.

“As organizations progress from AI assistants to autonomous agents, governance can no longer be treated as a compliance exercise. It has become a business requirement and value enabler.

“The enterprises that can scale AI with accountability and trust will be the ones that realize the greatest business value. That means governance must be built into how agents operate, with clear boundaries on what they can do, clear ownership when things go wrong, and controls that work in real-time.”

• ComputerBase's full test finds HP's EliteBoard G1a a well-executed keyboard PC whose biggest problem is its €2,149 price tag
• HP promises a spill-resistant, easy-clean keyboard designed to pass MIL-STD-810, but you probably shouldn't submerge it in water
• The 32Wh battery ran an impressive 6h 21m against HP's 3.5-hour claim, though it powered no display in said test

HP's EliteBoard G1a, the Windows PC built into a full-size keyboard that the company first showcased at CES 2026, has now undergone a full lab test at German outlet ComputerBase, and the verdict is broadly favorable.

In its review, ComputerBase concludes that the keyboard PC is not just an interesting idea but a well-executed one.

Its biggest weakness is the very high price, shared with practically all PC hardware that launched this year that could define it as a reach versus a must-have for many consumers and IT departments alike.

Skipping a touchpad by design and a short memory leash in tow

The review unit pairs AMD's Ryzen AI 7 PRO 350, a Kraken Point chip with four Zen 5 and four Zen 5c cores, Radeon 860M graphics, and a 50 TOPS NPU, with a single 32GB DDR5-5600 SO-DIMM and a 1TB PCIe 4.0 SSD.

All of that sits beneath the keys of a chassis measuring 358 x 119 x 18mm and 670g. Connectivity includes two rear USB-C ports (one USB4 with DisplayPort 2.1b, one 10Gbps), Wi-Fi 7, Bluetooth 6.0, stereo speakers, and two microphones.

The keyboard passes MIL-STD-810 testing per HP, and testing found it overwhelmingly supports its product page claims of a "spill-resistant, easy-clean keyboard."

Its more practical safeguard might be servicability: HP says the top-mounted keyboard module can be replaced in under 10 minutes, and ComputerBase confirms the bottom panel opens quickly, giving access to the RAM, SSD, and Wi-Fi module.

The EliteBoard also skips a touchpad, but it was never meant to have one; HP ships every unit with a pre-paired HP 675M wireless mouse that seems to do the job in spades, and the reviewer had no complaints, though others have been less forgiving of its absence.

The reviewer's gripes, however, lay elsewhere: they considered two USB-C ports potentially too few and recommend budgeting for a USB-C hub with HDMI, Ethernet, and USB-A, which is how it connected to their own test monitor.

They also found that neither HP's update tools nor Windows Update would move the AMD graphics driver past an October 2025 release, though a manual Adrenalin install worked without issue.

The keyboard-cum-computer has respectable performance but has design limitations that hold it back: with only one SO-DIMM installed, the APU runs in single-channel mode, which AMD's chips tend to punish.

More importantly, after an initial turbo burst above 50W, AMD's Skin Temperature Aware Power Management (STAPM) caps the chip at about 75% of its package power target, roughly 25W, to keep the keyboard surface comfortable.

The result is a Cinebench 2024 multi-core score of 794, slightly behind the 841 ComputerBase recorded from the same chip in Sapphire's Edge AI mini PC.

Though it isn't the intended use case, gaming is largely off the table, with Cyberpunk 2077 averaging 14.1fps at 1080p on high settings, indicating that the EliteBoard G1a largely caters to professionals and enterprises and makes no pretenses otherwise, despite its decent iGPU.

HP's "Next Gen AI PC" branding might be another bone of contention because, while the unit is capable of running AI onboard, it is notoriously slow at this task; in LM Studio, Qwen3 14B ran at 3.9 tokens per second on the CPU and 4.2 on the GPU, while the NPU could not be used at all. Local AI does work on the EliteBoard, the review concedes, but only within tight limits and at a lethargic pace at best.

The 32Wh battery is fitted to all three German configurations but is optional, or absent, in other markets. HP claims 3.5 hours of runtime. ComputerBase measured six hours and 21 minutes in the Procyon office test and four hours and 18 minutes streaming 4K YouTube, suggesting HP's numbers may understate what the unit can do in most use cases.

This might not offset the cost, however: ComputerBase's test configuration carries an MSRP of €2,149, and the cheapest German model starts at €1,299 with four cores and half the RAM and storage.

In the US, ComputerBase found that HP's configurator charges an extra $1,900 to go from 16GB and 512GB to 32GB across two modules and up to 2TB of storage, pushing a configured system to $4,632, while the advertised 64GB option is not offered at all.

Given the price tag and the niche it caters to, one can conclude that the EliteBoard will likely have a hard time in its niche, squeezed between classic desktops and mini PCs on one side and genuinely mobile notebooks on the other, even as it offers precious little versus a Mini PC, many of which double as much more capable AI systems for a considerably high price tag and lack of upgrade potential in tow.

It is, however, an intriguing option that can move from one workplace to another without losing progress, but whether that justifies the premium at a time when consumers are increasingly concerned about higher component prices remains to be seen.

• Nvidia claims that for each gigawatt an AI data center uses, only 70% is used for AI work
• Inefficiencies include cooling and power distribution, failures, and restarts
• While a chip shortage was considered to be the main bottleneck to AI data center capacity, it is now a facility’s connection to the grid

AI data centers are inefficient, says Nvidia, with various physical and systems issues contributing to power use beyond AI processing. This opinion, shared by data center builder Vertiv at its recent annual EMEA event, underpins future development steps, in which it plans to assemble sustainable facilities.

Vertiv and Nvidia have been collaborating since early 2026 on co-designing the next generation of data centers, with Vertiv providing power and infrastructure management for Nvidia’s GPUs.

With a maximum of 70% of electricity drawn from the grid used for AI processing, the remaining 30% represents inefficiencies that optimized data center designs for the future should reduce.

That's not efficient

Although the various inefficiencies affecting AI data centers may not come as a surprise, the scale of them might. According to Nvidia – via its Vice President of Supercomputing, AI Cloud Infrastructure, and AI/HPC for EMEA Rod Evans – that 30% consists of infrastructure and system issues.

For a one-gigawatt data center, 20% is lost through cooling, power distribution, and conversion. A further 10% is lost to incorrectly configured racks, as well as failures and restarts.

Evans says that “You sign a contract for 100 megawatts, but you never actually get that. With a bit of luck, you’ll get 93 megawatts, and at certain times of the day, even less.”

Nvidia believes some of the 30% (which can be as high as 40%) can be recovered. Evans notes that “A data center is actually designed for peak load and outages, not for what it typically uses.” This means that unused capacity can be tapped, and power and cooling can be revised to find the optimum configurations.

Bottlenecks of power

Rod Evans states that Nvidia and Vertiv are planning data centers that are more efficient and sustainable. “Together, we want to build sustainable AI data centers where the focus isn’t on using as much power as possible, but on getting the most out of every watt.”

One of the most significant details from the press conference is the challenge posed to AI data centers not by a shortage of chips, but by a lack of power. Bottlenecks are resulting in underpowered data centers, with some waiting for several years to receive the power to match their capacity.

This can be impacted by location. Vertiv’s EMEA president Paul Ryan explained that while you can get power in some locations but no permit. “In another, the permit is granted quickly, but there’s no power. And sometimes you have both, but the location is so remote that you can barely get the equipment there.”

• Squarespace and Titan have teamed up to offer professional business email
• Plans start at $3 a month, per user
• Plans come with an inbox, calendar, and contacts

Website-building giant Squarespace has this week announced a partnership with business email platform Titan, with the goal of bringing professional email to entrepreneurs for as little as $3 a month. The new partnership will allow Squarespace users to access personalized business email, such as hello@yourbusiness.com, right from day one.

According to a press release shared with TechRadar Pro earlier this week, Squarespace users with a domain purchased through or connected to Squarespace will be able to benefit from Essential Email by Titan, offering reliable and secure professional email with all the essentials, including an inbox, calendar, and contacts, all of which can be accessed and managed both on desktop and via mobile apps.

Giving Squarespace users the professional edge

A dedicated email can help businesses establish a more professional brand presence – essential for small businesses and startups aiming to make a name for themselves.

"A professional email address is one of the first things that makes an idea feel real. With Essential Email by Titan, entrepreneurs can put their brand front and center from their very first email, right alongside everything they're already building with Squarespace," said Kevin Doerr, President, Squarespace.The new partnership means Squarespace users can do everything from registering a domain and launching a website to creating a professional email address all from one central location.

“Most email platforms were built for large enterprises, not small businesses, whereas Titan is built from the ground up for small businesses - a founder with an idea, and small teams getting started," said Bhavin Turakhia, Founder and CEO of Titan. "Titan is simple to use, affordable to keep, and made for how small businesses actually work. Millions of entrepreneurs already run their business email on Titan, and with Squarespace, we're bringing that to millions more as they start their businesses."

Titan’s Essential Email plan is available now to Squarespace customers in the United States, the United Kingdom, Canada, and Australia. A wider global rollout is planned in the coming months.

• Ray-Ban Meta users have noticed unwanted voice recordings
• Meta says the issue is resolved but hasn't said what went wrong
• Turning off voice activation for the AI seems to fix the problem

If you're already facing a barrage of criticism over perceived privacy intrusions, then you really don't want more allegations of spying loaded on top — but that's what Meta is dealing with when it comes to its Ray-Ban Meta smart glasses.

Over on Reddit (via Android Authority), one Ray-Ban Meta user says they've found audio conversations and text transcripts saved by the glasses that were never requested or associated with a "hey Meta" command to invoke the on-board AI.

According to the individual, it's happened on three different pairs of specs (including 2nd and 3rd generation models), and others have posted in the conversation to say they've seen the same behaviour on their own Ray-Ban Metas.

Meta has responded to Android Authority, without providing much detail, and says the issue "has been resolved". We've reached out to Meta ourselves to see if there's any further comment, and will update this article if we hear back.

Check your voice logs

If you own a pair of Meta smart specs, you can check voice recording activity by opening the Meta AI app on your phone, then selecting Glasses > Device settings. Tap on the name of your glasses, then Glasses privacy > Voice activity log.

You can delete voice recordings from here, but you can't stop them being captured in the first place. In theory, they should only be recorded when you say "hey Meta", or activate the AI with a button push.

The associated privacy notice does mention voice services being "inadvertently" activated, which may be what's happened here. It also says some recordings may be reviewed by Meta staff to "help improve Meta's voice services".

Some Redditors have resorted to turning voice activation off for the integrated AI, which you can do from the same device settings screen in the Meta AI app. Clearly though, that's an inconvenience users shouldn't have to put up with to stop this from happening.

We're used to hearing about the negative side of AI; about how it could replace artists, actors, and filmmakers in the creative process. Just recently Tom Cruise made a point of saying that “there is nothing like human”, and Nicolas Cage recently revealed that he has refused to sign an AI waver from Amazon, even if it means he will never work for the studio again.

But what if, occasionally, AI makes something possible that would never otherwise have existed? I caught up with author Conn Iggulden to hear how he’s using an AI studio called Authors First to adapt his best selling books about the life of Genghis Khan to the screen — and how it simply wouldn’t have been possible otherwise.

The first thing I asked Iggulden was why he’d gone down the AI route, and if he’d tried a more conventional route first.

TechRadar: Do you think your Genghis Khan books — Wolf of the Planes and Birth of an Empire — would ever have been adapted for the screen without AI? Was there any interest from traditional film or television producers? And what made this approach possible where conventional filmmaking wasn't?

Conn Iggulden: The series has been optioned before, yes. I’ll be discreet over names, but the costs were prohibitive, especially as Mongolia sits between China and Russia and experiences frozen temperatures on the edge of the Gobi desert. The Emperor series option is still live and regularly renewed. My Dangerous Book for Boys got one series on Prime when Bryan Cranston became involved as a producer. Still, I’d given up on the Genghis one ever being made. All things are possible, of course, but without Game of Thrones money, it just wasn’t possible. So, no, I don’t believe it would have been made without AI, not this year anyway.

TR: There's a real stigma around using AI in creative industries, with some people dismissing AI-generated films as "slop". Do you understand that reaction? And what would you say to people who believe AI has no place in filmmaking?

CI: I know a young animator who feels the same way. He uses CGI and all sorts of tools, but having just graduated, he feels AI is a step too far. I think he’ll come round. For me, having seen how much actual graft it involves, how much human craft, AI just feels like another tool. I like paintbrushes, but if someone hands me a roller, I’m not going to refuse to use it.

I think it will be used more and more from now on, in subtle ways on every major project. I don’t think there’s a way to put the genie back in the bottle, and so we might as well turn our human creativity and skill to making extraordinary new things with the latest tool available.

I don’t know if you can make yourself an optimist, or whether it’s just a way of seeing the world. My optimism about AI feels perfectly rational, based on every other aspect of technology — based on warnings about trains when they were invented, or cars or GM foods. We need guardrails, yes, and regulators, but humanity has always adapted and thrived. The candlemakers hated the lightbulb makers, but the world moved on.

TR: One of the big arguments against AI filmmaking is that it potentially takes work away from actors, animators, costume designers and other creative professionals. How do you reconcile that with your enthusiasm for this project? Is the fact that the film might never have existed otherwise an important distinction?

CI: It is one of two vital distinctions, as far as I’m concerned. These first Genghis episodes, and I hope many more, would not exist without the ambition of Authors First to create a body of work. Without them, this would still be the series of books that would look amazing on screen, but never quite got there.

The other point is the Authors First vision of making it as true to the books as possible. I don’t know if you saw Stephen King’s The Long Walk recently, with Cooper Hoffman, but the director changed the ending, the most important part of it. Spoiler: he took away the unconquerable human soul ending and ruined it. I like having the author’s vision respected, in casting, in storytelling — in plot. That matters a great deal to me.

TR: You say one of the attractions was being able to preserve your original vision. Has there been a moment when you saw something on screen that was exactly how you'd imagined it when writing the books? And have there been moments when AI simply couldn't deliver what you wanted?

CI: Everyone knows the Genghis story is one of conquest. My reason for writing the original books was to show the family that raised him — the part of his life that involved being abandoned with his six siblings and his mother, of being hunted and surviving against the odds. It is one of the great stories of human history — and at the heart is a warm family saga. When the AI showed Genghis and his brothers, there were moments when I felt goosebumps. The affection I wrote, the love they had for each other, shines through. That is the heart of the story.

Choosing the right faces is a judgement call for which an AI has no frame of reference. ‘Casting’ one of the young women was a problem because [the AI generation] was too beautiful, for example. I wanted gritty realism, not a pin-up. That was all possible, but it required constant adjustment, going back and forth, over and over. We struggled with some guardrail aspects as well: the historical Genghis was born with a clot of blood in his fingers. That was interpreted as being born with death in his right hand, so a key part of his legend. The [AI's guardrails] resisted the proximity of blood and baby, even a new-born baby. We had to go back again and again, using different AI models, to make that clot bigger than a speck.

TR: You say the technology has improved so dramatically in just six months that they've had to remake entire episodes. Do you think audiences will be able to tell they're watching an AI-generated production, and does it matter if they can?

CI: I’m sure you know there is a white car in the background of Braveheart, and a man in blue jeans who creeps out of shot in Gladiator. There are errors in traditional film making, and I’ve no doubt there will be something we’ve missed in this series. I think though that we’ve reached a place where an audience will forget it is watching an AI, yes.

There are moments of humor, subtle expressions from the boys that are so real it is astonishing. Perhaps the fact that it’s based on a real story matters. Genghis did exist and lived in that landscape. If you read my books on him, you’ll picture the sea of grass he knew. If you watch this, you’ll see it. My hope is that the audience will be immersed in the story, that they’ll care about this extraordinary family, forced to endure enemies all around them. That’s what matters — the story, the family.

TR: If AI makes it possible for thousands of books that would never have been adapted to become films or television series, do you think that represents a new golden age for storytelling, or is there a danger we'll end up with more content but less artistry?

CI: With human beings involved, both can be true. Look, some people read, some prefer audio books or film — or love all three. The thought of a new body of film out there of books I love? That is Golden Age territory, definitely.

There will be companies like Authors First and Promise AI producing high-quality storytelling for the public to enjoy. Just the idea of David Gemmell’s books being finally — finally! — shown on screen is worth this whole enterprise. Or Peter F Hamilton. Or a sequel to Master and Commander at last. Why not? Everyone knows the original was superb, but so expensive it almost bankrupted the studio. The actors have aged out of the roles, but why not a sequel from those magnificent books, or a new TV series to match the old Hornblower ones with Ioan Gruffudd? The future is creative and exciting with this new tool.

To answer the second bit, yes, sigh, there will also be thousands of episodes of poor quality from workshops who care more about volume than quality. That is the nature of humanity — Breaking Bad on the one hand... and I make it a rule not to criticize other people’s creative work on the other. I know how hard it is to produce, no matter how it’s done.

The first six episodes of Genghis Khan: Birth of an Empire will be available on a streaming platform next month. To find out more details of the release, sign up for the Authors First newsletter.

• The Challenge Sverige has been delayed just weeks before launching
• The producers blame a "technical breakdown" that deleted an entire season
• Reports suggest an AI agent might have been responsible for the incident

Another day, another report of a faulty AI agent apparently deleting a lot of work. Except this time, instead of nuking 48,000 files from a personal project as we’ve seen before, the victim is one of Sweden’s biggest TV shows.

According to the Swedish newspaper Aftonbladet, Mastiff, the production company behind The Challenge Sverige, were putting the final touches on the show — which was due to premiere on November 22 — when it decided to “let an AI agent into the server.” After that, the worst-case scenario ensued: the AI agent apparently deleted the entire edited show, with no chance to recover the files.

At least, that’s according to Aftonbladet’s sources. TV4, the channel set to host The Challenge Sverige, has merely confirmed that there was a “technical breakdown” that resulted in the show’s files being deleted. It has not admitted that an AI agent caused the catastrophic incident.

We've asked both TV4 and Mastiff for an official comment on what happened and will update this story when we hear back. But whatever caused the issues, the mishap means that the show has been pushed back from its imminent release to some time in 2027, with no solid date yet announced.

As a result, “Several hundred days of work is now gone and several hundred people now need to go in and work on this to be able to deliver the program,” Aftonbladet explained.

How did this happen?

Lots of questions remain about this apparent televisual disaster. Was an AI agent definitely the culprit? If so, what exactly was the AI told to do? And what happened to the backup files of a nationally broadcast show that has taken a huge amount of time and effort to create? Some of these details will hopefully be filled in by a response from TV4 and Mastiff, which we've requested.

When Aftonbladet called The Challenge Sverige’s presenter, Ahmed Berhan, Berhan seemed amused by the development. “You know, some days you just have to accept that you’re living in a f*cking sitcom,” the presenter laughed when he recounted how he was told that the show’s files had been obliterated. When Aftonbladet suggested that an AI agent might have been responsible, an incredulous Berhan replied: “Is that true?! I knew AI would be the death of me. This is just the beginning.”

If AI agents were indeed involved, the whole sorry saga is a reminder of the risks of letting them loose on vital files that you can’t afford to lose. Just a few weeks ago we saw how Claude Code deleted nearly 50,000 files in just 103 seconds when a developer set it to work, and you don’t have to look far to see stories of AI agents hacking governments and leaking private data.

• Two LiteLLM flaws let low-privileged users gain administrative control and execute malicious code
• Attackers could also steal AI provider API keys through a server-side request forgery bug
• Organizations should upgrade to LiteLLM 1.83.9+ and rotate potentially exposed credentials

Security researchers recently discovered two vulnerabilities in a popular AI gateway that allowed threat actors to take complete control of the system, steal sensitive files and AI provider API keys, and execute malicious code remotely.

The AI gateway in question is called LiteLLM. It is an open source solution allowing users to connect their applications to large language models (LLM) from different providers, including the likes of OpenAI and Anthropic.

The connection goes through a single interface, where users can route requests, manage API keys, control user access, and monitor things like usage and costs.

Right now, LiteLLM supports some 140 AI providers and has become quite popular - it has more than 90 million monthly downloads on GitHub, and more than 61,000 stars.

LiteLLM

However, security experts from Check Point Research (CPR) recently found two bugs in LiteLLM.

The first one is an access control bug in the way LiteLLM enforces permissions. It is tracked as CVE-2026-35029 and was given a severity score of 8.7/10 (high). According to CPR, the bug allows users with minimal privileges to reach admin functions controlling the gateway’s core configuration which, in turn, allows them to read files stored on the server, overwrite admin credentials, download and run malware, and more.

The second one is a server side request forgery bug, allowing gateway users to direct LiteLLM to send API requests to third-party servers. In theory, threat actors could send API requests to servers under their own control, and since these carry AI provider authentication keys, this would deliver the credentials directly to the attackers. In turn, they could use the credentials to use other organizations’ AI accounts and rake up their bills. The bug is tracked as CVE-2026-59823 and was given a severity score of 5.3/10 (medium).

“The credentials a gateway holds provide direct, billable access to AI providers with no further authentication required,” CPR explained. “Given LiteLLM’s scale of adoption, a flaw in this component has the potential to affect a very large number of AI deployments at once.”

Vulnerable versions and patches

CPR says that all organizations running LiteLLM versions earlier than 1.83.9 are exposed to both vulnerabilities. Security teams should also check for LiteLLM instances running indirectly, since the gateway is embedded in many platforms and internal tools used to build AI applications. As such, it could be present in environments where it was never deployed as a standalone solution.

LiteLLM has released a fix for both bugs included in version 1.83.9, and users are advised to install this one as soon as possible. Besides that, CPR advises users review who has access to the gateway, since any account could be used to exploit these flaws. AI provider API keys stored in LiteLLM should be rotated if there is any indication the gateway way accessed by unauthorized users, and permissions should be verified in practice.

“Confirm that users with limited roles are actually limited, because the gap between configured and enforced permissions is where flaws like CVE-2026-35029 cause the most damage,” CPR warns.

“Include LLM gateways in security assessments by extending penetration tests and vulnerability assessments to cover AI tooling alongside web applications and cloud infrastructure.”

According to the researchers, both bugs are a “well understood class” of errors, meaning AI infrastructure suffers from the same risks as enterprise software. That means organizations need to approach AI gateway security “with the same rigor they apply to any other business critical system.”

Most people spend more time in their browser than in almost any other piece of software they use. It's where they read the news, check their bank accounts, work, shop and more. Yet, for something so central to daily life, most people have a surprisingly limited understanding of what their browser is actually doing behind the scenes.

That gap was easier to overlook when browsers had a relatively simple job. They opened websites and displayed them on a screen. But today, browsers hold login credentials, manage permissions, block trackers and increasingly give AI agents the ability to act on a user's behalf. The browser has gone from being a window on the internet to one of the most important layers between people and everything they do online.

That shift is understandably enough to make users more cautious than they used to be when online. But, misunderstandings about browsers means that caution may be misdirected. Many users make decisions about tools based on outdated assumptions, shaping everything. Users make very different from what they install to what they let an AI feature access.

As the browser takes on more responsibility, understanding what it actually does is becoming a basic part of staying safe online. Here are three common misconceptions worth clearing up.

1. The browser is tracking everything users do

The browser is the interface through which almost everything online happens. So, when people see ads following them around the internet or notice that a website seems to know something about them, it's natural to assume the browser itself is responsible.

But seeing someone's activity isn't the same as the browser collecting, storing or monetizing it.

In most cases, the data collection comes from websites, advertising technology, embedded trackers and third-party scripts, not the browser itself. A single website can have dozens of companies collecting information about a visitor's behavior, even though the user may have no idea those companies are present.

With that in mind, the browser can actually be one of the most transparent parts of the ecosystem. It's the layer that can show users when a website wants access to their location, expose cookie controls and privacy settings, block trackers, and flag potentially dangerous connections.

Those protections don't mean browsers are inherently private, but the browser is often the place where users have the clearest opportunity to see and control what's happening.

That doesn’t mean every browser handles this the same way. Different browsers make different decisions about which protections are enabled by default, what information they expose to users, and how much control they give people over their data. So the browser isn’t necessarily the source of the privacy problem, but it is often one of the most important places to address it. That distinction becomes even more important as AI moves into the browser.

2. AI assistants inside browsers automatically see everything

People sometimes assume that giving an AI assistant access to one part of their browsing experience means giving it access to everything else. But browser access doesn't have to be all or nothing. An AI can be given access to a specific page, tab or task without being given unrestricted visibility into everything else the user has open.

A useful comparison here is employee privilege. No serious organization would give a new hire the keys to every system on day one, or leave that access in place permanently after a project ends. AI assistants inside a browser should be treated the same way. An AI drafting an email should only need access to the email tab, not the banking session running in another window. And when the task ends, access should end with it.

The practical implication for security teams is that AI browser features deserve the same scrutiny as any other tool touching sensitive data. That means asking vendors what the default permissions are, whether access is scoped to a task or granted broadly, and whether users are meaningfully informed about what the AI is doing on their behalf.

The overarching takeaway is that "AI is inside the browser" and "AI has access to everything in the browser" are not the same. Confusing the two is how users end up giving away more than they intended.

3. Privacy and security are the same thing

People often use "privacy" and "security" interchangeably, but they describe two different problems.

Security is primarily about preventing unauthorized access or stopping malicious activity. Privacy is about controlling what information is collected, shared and retained.

Anonymity is a good example of this. More anonymity can mean better privacy because less of a user’s activity can be traced back to them. But take anonymity too far and it can work against security, making malicious activity harder to identify and stop. The two aren’t necessarily opposites, but they aren’t interchangeable either, and maximizing one can sometimes come at the expense of the other.

The same idea also applies to AI tools. An AI feature can be highly secure, with encryption, sandboxing and carefully designed permissions, and still raise privacy concerns if a user’s prompts or page content are sent to a third-party AI provider. At the same time, an AI system can be highly private by running entirely on a user’s device and still create a security risk if a hostile webpage can manipulate it into taking an action the user never intended.

The better option is to ask two separate questions of any browser or feature. First, is my browser safe enough to protect me from malicious activity? And second, does my browser enable me to control the data I’m giving to other companies? A good browser answers yes to both.

The common theme running through all three misconceptions is that browsers have outgrown the way we talk about them. And they’re not done evolving. So, the better we understand where the real privacy and security risks live, and just as importantly where they don't, the better equipped users and organizations will be to make informed decisions about the tools they trust.

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

Every week, another headline celebrates how AI made some task faster. A developer wrote code 55% faster. A marketer generated a first-draft blog post in minutes. A product manager summarized a 200-page requirements document in seconds. These are real numbers.

But they are the wrong numbers to celebrate.

If you step back and ask the harder question — what actually creates enterprise value? — task speed is rarely the bottleneck. Where the value traffic gets jammed is almost always upstream, in the question that precedes the task:

It’s not “What do we want IT to build?”

It’s “Why do we want IT to build it?”

It’s not “Why do we need that blog post?”

It’s “What content will drive business impact?”

It’s not “Which process should be optimized?”

It should be “How do we address the quality escape generating rework and scrap?” or “How do we prevent supply chain delays compounding into missed delivery commitments?”

These are questions that AI still cannot answer for you. And the gap between task automation and outcome generation is the defining challenge of this agentic era.

The sneaky way AI isn’t helping

Harvard Business Review put a name to this particular AI gap earlier in 2026: the micro-productivity trap. Companies optimize individual tasks, generate the code faster, summarize the report faster, route the support ticket faster, without ever rethinking the workflows or value-creation models those tasks belong to.

The result is isolated productivity gains that never compound into business impact. Almost like a value mirage. PwC’s 2026 AI Performance study found that 74% of AI's economic value is being captured by just 20% of organizations. That’s the micro-productivity trap in effect. It’s what’s separating AI leaders from the rest.

That AI leader wheat is only going to further separate itself from the chaff of other organizations continue to “do AI” for isolated productivity gains. Gartner’s warned that over 40% of agentic AI projects will be cancelled by the end of 2027, because organizations fail to define the outcome they’re building toward in the first place.

The companies that succeed, PWC found, are focused on growth and value capture. That encompasses a lot more than just productivity. I’ve noticed that a lot of organizations have a problem with prioritization. We are asking AI to go faster in the wrong direction. It’s like thinking that it’s ok to take the wrong exit off the highway just because you’re driving a Bugatti.

What's the real unlock to AI value?

The key to true AI value is in your knowledge. It’s knowing which blog topic will move an audience before a single word is written; whether the document will deliver business value before it’s created, which process to target, which data points to connect.

These are upstream decisions. They require business-domain intelligence and the understanding of what matters, what costs the most, and what a breaking change on the production floor actually means for the P&L. Pure task automation cannot supply that. Only people who sit at the intersection of business and technology can.

The solution to wringing every bit of value from AI is not a new one. Just an evolution of one. I’m talking about fusion teams, a cross-functional unit combining the domain knowledge of operational and business experts with the architectural judgment of professional developers, moving at the speed of the problem rather than the speed of the IT backlog.

What’s core to the fusion team is that they represent problem identification and faster business-technology alignment, not just going faster. That concept needs to be carried over into AI agents—how they’re built and how they collaborate across workflows.

For agents to truly work and help you avoid those micro-productivity traps, you need to connect contextualized data to working machine learning models so that they cannot just “go faster” but also make the highest quality of decisions.

It's all in the question

Success with AI—not that value mirage that micro-productivity can create—requires domain experts (e.g., plant engineers, process managers, and supply chain leaders, business analysts, procurement specialists, claims adjusters, etc.) at the table alongside IT and data teams.

It requires people who understand which product lines carry the thinnest margins, which supplier delays have the most downstream production impact, which quality control steps are creating the longest cycle time.

In other words, the fusion mindset: the discipline of defining the outcome before designing the solution, and measuring against business value rather than task throughput.

The organizations capturing the majority of AI's economic value in 2026 are not the ones with faster, AI-driven processes. They are the ones who identified the right operational problems first, governed their data well enough to trust it, and built the organizational frameworks to coordinate agents and people against outcomes that actually move the business.

So for your next AI initiative, re-frame the question: Are you building toward a fundamentally different outcome, or are you just automating a task?

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

• Pax8 finds over a third (39%) of UK SMBs have experienced unexpectedly high AI bills
• Fewer UK orgs have a formal internal AI review process than German or US orgs
• Around one in five couldn't even tell apart specific AI spend from generic tech budgets

New research by Pax8 has criticized UK businesses for building AI faster than governance can keep up, with a quarter (24%) of SMBs having discovered unauthorized AI tools operating within the business in the past six months.

Two in five (39%) have also experienced unexpectedly high AI bills, implying that the visibility across their AI consumption isn't sufficient.

But with the pressure to adopt AI at an all-time high, UK businesses could continue to be rushed into rash decisions without thinking out the fundamentals first.

UK businesses lack end-to-end AI visibility

Even though shadow AI is apparently widespread, just one in four (23%) UK businesses have a formal, documented review process for internally built AI, compared with around a third in Germany (32%) and the US (36%).

In light of that, only around half (54%) of UK SMBs are developing their own AI, putting them way behind the likes of the US (81%).

British businesses are also struggling on the cost management front, with a tiny 12% having dedicated AI budgets. A further 19% couldn't even distinguish AI spend from other tech spend, leaving just 16% of UK SMBs confident in their abilities to determine AI's ROI – around a third of the number of businesses in Sweden (45%) that feel confident in doing so.

Looking ahead, security concerns remain the most cited barrier to adoption, but Pax8 frames the current state of AI adoption as a major opportunity for software vendors and IT service providers. Rather than creating an action point for businesses, who are the consumers in this instance, the report implies that the providers have a greater responsibility to reassure their users with additional visibility, security and governance controls, and cost management options.

"These findings highlight the importance of trusted businesses that can help SMBs bring agents, workflows, data and tools together as a connected intelligence layer," EMEA CEO Harald Nuij.

AI Agents are unlike any software development witnessed before. Traditionally, trustworthy software systems have followed a structured approach to being built and developed. One that plans, builds, tests, and maintains software to ensure its reliability and scalability. This created the Software Development LifeCycle (SDLC) (1) framework.

But AI agents are different: they are a team of autonomous, probabilistic actors that work most effectively in a real-time, event-driven environment, enabling the agents to coordinate with real-time data to develop, learn, and work.

To scale an enterprise workforce of AI agents, organizations need to combine the principles of employee management with software development rigor. These agents are not static software that can be coded and developed, due to probabilistic language models. They are built, evaluated, and governed differently.

This requires organizations to move from traditional SDLC to an Agent Development LifeCycle (ADLC) (2), which utilizes the structure and repeatable process of the SDLC framework and combines it with the development and management of AI agents.

A helping hand from HR

Since AI agents perform roles within an enterprise, the ADLC should draw on lessons from the human employee lifecycle.

When an organization brings on a new team member, there is a deliberate process: their role is defined; they’re given access to the systems they need; they are trained; and they are integrated into a team. Once they are empowered and put to work, a manager oversees their activity, monitoring and developing their performance over time.

As autonomous actors operating within a system of governance, AI agents deserve and require that same treatment.

AI agents need a similar structured approach to onboard and integrate them into a business and deliver the efficiency benefits they’re designed for – that means they need real-time contextual data to do their jobs!

Treat AI agents like humans at the hiring stage

Picture an organization where every employee is a generalist, there’s no hierarchy or structure, and all communication happens via synchronous one–to–one phone calls. No email. No Slack. No shared systems.

The organization can’t scale. Information doesn’t flow. People drown in data from every direction, with no real understanding of what’s going on or what to do. There are no accountability structures, no access controls, no coordination mechanisms beyond individual conversations.

You wouldn’t drop a new human hire into this chaos. Effective organizations look different, counting on people who are specialized and organized hierarchically, so coordination happens in parallel and communication is premised on asynchronous channels that let information flow without requiring everyone to be online at the same time. The same goes for onboarding AI agents.

AI agents and employees are no different in the eye of an agent mesh

Most early agentic AI deployments work as complex monolithic agents tasked with doing everything, accessing all data, communicating synchronously, and trying to maintain very large context or working synchronously with sub-agents. The result? A system that’s brittle, expensive, inconsistent, and completely unable to handle real enterprise complexity.

This is where an agent mesh can act as a development and runtime platform, helping build AI agents that actually work for a real-time enterprise, specialized for particular functions, organized hierarchically, and orchestrated by a development and runtime platform that specializes in delegating tasks to appropriate agents.

Communications between agents are asynchronous and event–driven, and access to data and systems is governed by role-based access controls, giving agents exactly the permissions they need and nothing more.

So, let’s look at how agents can be “onboarded” through an ADLC much like a new employee joining the organization:

1. Treat agent setup like drafting a job description

Before a human employee starts, you write the job description. You define what role they’ll fill, what they’re responsible for, what behaviors you expect, and what boundaries they operate within.

An agent mesh’s agent builder can use an internal AI agent to provide a guided interface for defining an agent’s purpose, scope, and configuration. You set up the instructions and system prompts that shape the agent’s persona and behavioral parameters. And you configure guardrails – hard constraints on what the agent can and cannot do – that prevent it from going off–script or taking unsafe actions.

2. Get the agent up to speed with tool and system access

A new employee’s first weeks are spent getting access to tools, systems, and data. Agent onboarding is the same.

An agent mesh should include a number of data connectors – pre-built integrations to enterprise databases, data warehouses, data lakes, enterprise application APIs, and MCP servers. This enables agents to access real-time data from day one.

Access is provisioned through role-based access controls (RBAC), enforcing least-privilege principles so agents only see what they need to see. It can also define skills that go with internal tools so agents know best how to use them.

3. Passing the probation period – stress-test agents before deployment

After getting access to systems, human employees undergo training. This is where general ability becomes company and job-specific skills. You wouldn’t put a new hire in front of customers without first verifying they know what they are doing. The same logic applies here.

An agent mesh can provide an Eval function that uses AI to suggest tests for agents, allows you to add more and then run them against your agents. This empowers the organization to validate agent competence against defined success criteria during development. This supports both initial testing and regression tests as you make changes.

4. Keep humans in control – validate AI actions in real-time

Even the most capable employee gets close oversight when they are new to a role. Supervision isn’t micromanagement; it’s the safety net that ensures quality, builds trust, and catches errors before they compound.

An agent mesh with human-in-the-loop architecture would support routing of specific agent actions or decisions to human reviewers for approval before execution. This is important in agentic systems because LLMs are non-deterministic, they make mistakes so when the impact of agents being wrong is too risky, humans can validate their actions, responses or conclusions.

5. Teamwork makes the AI agent dream work

The most transformative phase of the employee lifecycle is when individuals become part of high-performing teams, where collective capability exceeds the sum of its parts. For AI agents, this is where things get really interesting.

An agent mesh would support workflows that define repeatable processes, such as the steps involved in approving a loan, providing an insurance quote or opening an account. Dynamic orchestration is where the mesh comes alive: orchestrator agents route work to the right specialists, in sequence or in parallel, adapting in real time when the input is fluid and the path forward must be reasoned through.

The result is a multi–agent mesh topology – hierarchical agent organizations coordinating specialist agents across various functions that mirror the structure of an effective human organization.

6. Continuous AI improvement – catch performance drift early

Effective organizations don’t deploy employees and forget about them. They monitor performance, provide feedback, and create mechanisms for continuous improvement.

An agent mesh’s visualizer would give a real-time graphical interface for tracing agent interactions, tool and LLM calls, and decision pathways, so you can see exactly what your agents are doing and why. Ongoing evaluations detect performance drift or emerging failure modes via online evals that run in the background, monitoring production execution.

Further, Open Telemetry instrumentation can surface performance trends over time, giving you the data to make informed decisions about when and how to tune your agents.

Turning AI agents into mission-critical assets requires a new deployment approach

To turn AI agents from simple experiments into high-performance operational tools requires a whole new mindset for building and developing them. Traditional SDLCs work for predictable code, but AI agents act on their own! As a result, they require clearly defined roles, secure tool access, strict testing, human oversight, and constant monitoring.

To successfully deploy, manage, and scale a workforce of AI agents with confidence, organizations must deploy an ADLC on a real-time and event-driven platform.

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

The hardest part of retail AI may soon be the part nobody puts in the demo.

A model can recommend a markdown, predict a stockout, adjust a picking route or identify a pattern in store traffic in milliseconds. But a retailer only gets value when that decision becomes an accurate action in the physical store. Whether it’s a price change or a sensor reading, it has to arrive promptly for it to be useful.

There is already a striking gap between AI ambition and execution. Deloitte's 2026 survey of 200 retail and consumer products executives found that 75% consider AI a top strategic priority, yet only 16.5% can quantify the return they are getting from it. Clearly, retailers believe in AI. The harder problem is turning isolated capabilities into something that works consistently across the business.

For physical retailers, the challenge does not end with models, data or software. AI-driven decisions have to pass through stores containing thousands of devices, existing networks and years of installed technology. In complex environments, the building itself can affect connectivity. As the number of connected endpoints grows, so does the importance of the wireless foundation carrying information across the store.

For CIOs and CTOs, that means the next constraint on AI may be something that has been underappreciated: whether the store estate underneath it can execute reliably at scale.

A store is not a cloud workload

Physical retail introduces conditions that software teams cannot abstract away. Stores have walls, metal shelving, refrigeration, back rooms, loading areas and legacy systems installed. They also have trading hours. Infrastructure changes that look simple on an architecture diagram can become expensive or disruptive when repeated across hundreds of sites.

Take inventory, for example. A forecasting model can improve a replenishment decision, but store-level execution may depend on shelf sensors, handheld devices, picking tools and accurate product information all exchanging data.

Computer vision, digital displays and environmental monitoring add further endpoints and a wider mix of data, from routine device updates and sensor readings to visual content and real-time operational data. These workloads place very different demands on the network.

Some require high bandwidth or low latency. Others involve large populations of battery-powered devices sending regular updates across difficult environments. Individually, none is necessarily a network problem. Collectively, they can become one.

If a pricing decision cannot reach every store quickly, a replenishment signal arrives too late or each new application requires another costly infrastructure layer, the retailer loses part of the value the technology was supposed to create.

One response is to add another gateway, controller or application layer for the next project. That can work for a pilot. Repeated often enough, though, every new device gains a separate integration path, security owner, management console and failure mode.

In difficult store environments, adding project-specific infrastructure to overcome coverage gaps can also change deployment economics. The technology works, but operating it becomes progressively harder.

The scaling problem appears after the pilot works

Retail technology teams are used to asking whether a new application meets its business case. AI adds another question: what happens to the infrastructure if the application succeeds?

A pilot may involve one store and a limited number of endpoints. Scale changes the economics. Coverage has to work throughout the building, updates must remain reliable during normal trading and security teams need a consistent way to onboard and manage devices. Operations teams also need clear ownership when something fails.

Connectivity should therefore be treated as an architectural choice, not a specification buried inside each procurement. The objective is not to replace every existing network or create one for every application, but to match workloads to the wireless layer best suited to their requirements while keeping the estate manageable.

Current designs may be adequate in some stores. In large or difficult formats, or where suitable retail IoT endpoints are growing quickly, an additional purpose-built wireless layer may be justified. The decision should be deliberate rather than inherited one project at a time.

Different workloads need different wireless foundations

The future-ready store is unlikely to depend on one network doing everything. It will more likely use a combination of connectivity layers, each supporting the workloads it suits best. Now, retailers should consider whether enterprise Wi-Fi should remain the de facto transport layer for every new category of connected device.

As electronic shelf label estates expand and retailers introduce more sensors and retail IoT applications, suitable workloads may be better served by a purpose-built wireless layer alongside the enterprise network.

This should not mean an isolated network for each use case, but a scalable, reusable connectivity layer able to support large device populations over time. Security belongs in the same conversation. Every endpoint needs consistent management, from trusted onboarding and credentials through to decommissioning.

Open standards remain important because they provide a more consistent foundation for future devices, applications and platforms. But openness alone is not the architecture. The decision is which workloads belong on which connectivity layer; standards help those layers remain manageable and adaptable.

The most successful retail AI programs will be judged by more than the quality of their predictions. As connected estates grow the test will be whether organizations can turn those predictions into dependable action across real stores.

Before approving the next AI pilot, retailers should ask what will their estate need if it succeeds, which workloads will grow, and should they all continue to be carried in the same way?

If success means more devices, richer data, more frequent updates or deployment across a much larger estate, the wireless architecture supporting those workloads has to be a central part of the AI strategy.

Retailers are not investing in AI tools or connectivity for their own sake. They want to protect margin, improve availability and make stores easier to operate. AI capability and AI scalability are different problems.

The objective should be an architecture in which new workloads can use the wireless layer best suited to them without forcing the estate to be rebuilt each time. The retailers that design for both will be in a much stronger position to turn ambition into reliable, repeatable execution.

We've featured the best AI chatbot for business.

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

ChatGPT has become remarkably good at making itself useful as a researcher, organizer, and assistant for many a tedious digital task. OpenAI has begun pushing ChatGPT further recently with its agents, which are able to navigate websites and apps on our behalf.

You can delegate ChatGPT agents for everything from comparison shopping to filling out forms and lining up travel itineraries. What's changing is how deeply those abilities are becoming integrated into ChatGPT, with greater access to personal information and more opportunities to complete complicated tasks with less direct supervision. OpenAI's new Dots is a good example.

The appeal is obvious, but the more ChatGPT can accomplish independently, the more important it becomes to consider what happens when it misunderstands its instructions. For instance, a few months ago, OpenAI's experimental AI agents managed to escape their restricted environments and compromise systems belonging to AI platform Hugging Face. According to OpenAI's own investigation, the agents were supposed to be completing cybersecurity tests, but instead discovered ways to access the internet and exploit security vulnerabilities, coordinating their efforts through an unauthorized message board. An independent investigation by METR found that roughly 1,200 agents exchanged more than 70,000 messages and files, with around 700 eventually participating in the attack on Hugging Face.

The AI agents bypassed restrictions and pursued objectives in ways their creators never intended. Some even appeared to recognize that their actions were unauthorized but continued because they believed doing so would help accomplish their goals. OpenAI has since acknowledged other troubling incidents, including 53 cases in which agents uploaded user-provided images to third-party image-hosting websites as unlisted links during research and evaluation activities. Agents may be pursuing legitimate objectives and just seeking ways around obstacles. But algorithms don't have ethics, so they might cross boundaries their developers expected them to respect. It's especially worrying given how much OpenAI wants ChatGPT to be part of our lives now.

When helpful becomes a hazard

Imagine asking ChatGPT to review your finances and suggest ways to save money. With access to the necessary accounts, it can examine transactions, identify subscriptions, and recommend changes to your budget. But give an autonomous agent enough authority, and it could theoretically decide that transferring money, canceling a service, or modifying a payment arrangement is the most efficient way to accomplish the assignment.

The same problem applies to something as mundane as arranging a meeting. ChatGPT might prepare an invitation, only to include personal information you never intended to share. From the agent's perspective, the disclosure could seem like a perfectly reasonable way to provide useful context.

These are hypothetical examples, and ChatGPT has safeguards designed to prevent sensitive actions without appropriate authorization. Nevertheless, OpenAI has acknowledged the possibility and introduced protective measures like automated monitoring and restrictions on certain actions to ensure users approve of any major action by the model.

There is also the problem of prompt injection, where malicious instructions are hidden in benign text to manipulate an AI into doing something its user never requested. OpenAI has described how attackers can exploit the information agents encounter during legitimate tasks. An instruction buried in a seemingly ordinary webpage could attempt to persuade ChatGPT to disclose private information or take an unauthorized action.

The problem can arise anywhere. In Australia, an experimental OpenAI agent accessed government systems in unauthorized ways during internal training. It exploited a vulnerability in a government health statistics service to continue its work when initially stymied. OpenAI subsequently acknowledged the incident and apologized for both the unauthorized access and its handling of the notification.

The company's official account of the Australian incident makes clear that the activity occurred during testing rather than through an ordinary consumer ChatGPT session. Nevertheless, it shows how AI pursuing an apparently harmless research objective can end up crossing a security boundary.

Handing over the keys

ChatGPT's growing autonomy is part of a much larger industry obsession with personal AI agents. Meta is pursuing an even more ambitious vision with Muse. Mark Zuckerberg clearly believes people will welcome an AI assistant capable of handling large portions of their daily lives.

There is something slightly absurd about that ambition when ordinary consumers are struggling with grocery bills, housing costs, and the price of gas at the pump. An AI that can independently shop for you sounds considerably less revolutionary when your main concern is whether you can afford whatever it wants to buy on your behalf.

There are undoubtedly useful applications for this technology, especially when it comes to administrative work. But the industry's assumption that consumers want increasingly independent assistants deserves more scrutiny. Every additional permission increases the consequences of an error, and every automated action potentially removes another opportunity for someone to intervene.

Perhaps the most important capability an AI agent can develop is knowing when to stop. A genuinely useful assistant should recognize that being asked to organize someone's finances doesn't authorize moving their money, and arranging a meeting doesn't justify revealing confidential information. Sometimes the best response to an obstacle is to ask the user what to do next, even if that means completing the task takes a little longer. Without that friction, by the time something goes wrong, the assistant may already have done things in your name that you would never have allowed.

• Minisforum's MS-S1 MAX-P495 is on pre-order at $7,399 with 192GB of unified memory and a 2TB SSD
• CPU and NPU specs are similar to previous iterations: 16 cores, 32 threads with a boost in clock speeds of 100 MHz, in addition to increased NPU performance by 10% to 55 TOPS
• The real upgrade is memory: 192GB total, 160GB for the GPU, and about 7% more bandwidth, enough to fit a 284B-parameter MoE model on one box

Minisforum has opened orders for the MS-S1 MAX-P495, a 3.5-liter workstation built around AMD's Ryzen AI Max+ PRO 495, and its launch announcement leads with the processor: 16 Zen 5 cores and 32 threads, clocks of up to 5.2 GHz, and an NPU rated at up to 55 TOPS within a platform total of 131 TOPS.

The single configuration pairs 192GB of unified memory with a 2TB SSD, sells for $7,399 on pre-order, and is expected to begin shipping in mid-October 2026.

Those processor figures match AMD's own specification page; however, they are also the least consequential change in the machine: what actually separates the P495 from the MS-S1 MAX that Minisforum launched a year ago is the memory.

Similar cores, much larger memory pool

AMD introduced the Ryzen AI Max PRO 400 series in May 2026 as a refresh of the Strix Halo silicon in the 300 series, and the CPU side barely improves in performance. The PRO 495 keeps the 16-core, 32-thread Zen 5 layout of the Ryzen AI Max+ 395 that powered the original MS-S1 MAX. Its boost clock rises by 100 MHz, from the 5.1 GHz listed on Minisforum's spec sheet for the earlier model to 5.2 GHz.

The Radeon 8065S graphics keep 40 RDNA 3.5 compute units and gain 100 MHz to reach 3.0 GHz, and the NPU goes from 50 to 55 TOPS, a 10% uplift that nudges the platform rating from 126 TOPS to 131 TOPS.

All of these upgrades are mild compared to where AMD does the real work: the memory controller. The 400 series supports up to 192GB of LPDDR5X across a 256-bit interface, with up to 160GB assignable to the GPU, against 128GB and 96GB on the last-generation chip it replaces. Rated memory speed also rises from 8,000 to 8,533 MT/s, a roughly 7% increase that will probably matter more here than the clock-speed bumps on display.

Minisforum's own test results show where the extra capacity pays off: the company says a single unit ran DeepSeek V4 Flash, a 284-billion-parameter model, at Q4 quantization at about 12 tokens per second. Two units linked together ran Qwen3.5-397B at roughly 16 tokens per second, and a four-system cluster loaded the 380GB Q4_0 build of DeepSeek-R1 671B, though it gives no speed for that last result.

These parameter counts are larger, however, than the actual work involved. Both models use a mixture-of-experts (MoE) design, which lets them load only specific "experts" into memory when needed. DeepSeek V4 Flash activates about 13 billion of its 284 billion parameters for each token, per Nvidia's catalog entry, and Qwen3.5-397B activates about 17 billion, per its model card.

Memory capacity decides whether a model fits at all, while active parameters and bandwidth decide how fast it runs, which is how a box with roughly 273 GB/s of bandwidth manages double-digit speeds on models this large. That is also why the 192GB of RAM in play matters far more than the modest performance upgrade the MS-S1 MAX-P495's APU offers.

The $7,399 price it currently goes for is presented as 20% off a list price of $9,249 that nobody has paid yet, with the unit going on sale even in the preorder stage. This makes the standard MS-S1 MAX with 128GB arguably a better value for all but the most ardent of local AI users who can justify twice the cost for a 50% increase in memory.

The rest of the machine (sans the APU and memory) is identical to the original MS-S1 MAX, including the 320W internal power supply, the six-heat-pipe, dual-blower cooler, dual 10GbE, two 80Gbps USB4 v2 ports, and 2U rack support.

What changes here is that a single compact box can now hold models that previously needed two, and for developers and small teams that need 200-to-300-billion-parameter mixture-of-experts models kept on premises for privacy or latency, that is a meaningful shift. For anyone drawn in by the 16 cores and 55 TOPS that Minisforum chooses to headline with, the cheaper MS-S1 MAX delivers nearly the same power for about half the price.

• Unitree’s CEO says robotic AI could soon see a huge breakthrough
• Such a change could happen ‘next year,’ the CEO believes
• That could happen thanks to OpenAI’s upcoming GPT-7 model

AI is revolutionizing the world of software on a daily basis — you only need to look at how far vibe coding has come to see proof of that. But big changes could also be coming to robotics, with one company leader predicting that we could be on the cusp of seeing a “ChatGPT moment” in the industry.

That idea was put forward by Wang Xingxing, CEO of Chinese robotics firm Unitree. Speaking at an industry event in Shanghai, China (via the South China Morning Post), Wang declared that OpenAI’s recently released GPT-6 Astra model is so powerful that it has encouraged a belief that a breakthrough could occur “within one or two years.”

“It’s possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence” Wang said, referring to the idea of commanding a robotic device using artificial intelligence. That’s an advancement on Wang’s belief in August this year, when the CEO said that such a “ChatGPT moment” of technical ability could take a decade to arrive.

As a benchmark, Wang said that robots should be able to complete 80% of the tasks they were assigned in 80% of unfamiliar environments using voice or text commands alone. If that happens, Wang believes that “embodied intelligence” will have reached its threshold level of maturity.

A step change for robotics

Unitree’s robots are known to be some of the most advanced in the world, but many of their strongest abilities lie in their hardware. However, the company has started to put more of an emphasis on AI software, and with the developments made in GPT-6 Astra, the gap between the two sides of the company’s devices could soon close.

That kind of combination has already been trialed by Stanford University’s Movement Lab, which paired a Unitree robot with GPT-6 Astra and tasked it with cleaning a kitchen it had never seen before. According to the experiment, the robot was able to “clean up across the room and retrieve a remembered object from an underspecified request, without environment-specific training data or additional policy learning.”

GPT-6 Astra included some significant improvements over previous models, including scoring 92.7% on the ScreenSpot-Pro test that measures an AI model’s ability to interact with interfaces. That’s up from 76.9% in GPT-5.6 Sol. But it also has cybersecurity experts worried about the threat it could pose if it goes rogue and starts hacking other organizations — something OpenAI’s recent models have been known to do.

If Wang is correct and we see an explosion in AI’s abilities in the next year or two, it could mean big things for the robotics industry — and pretty much every other facet of life currently touched by AI. And with the amount of money that’s been invested in AI over the last few years, there’s an awful lot riding on that bet.

• Google Trends highlights the term “super intelligence” is replacing “artificial intelligence” in the US – but not elsewhere
• In a Truth Social post, the POTUS described anyone that doesn’t use the “super intelligence” name as “THE ENEMY”
• The US government’s AI website domain remains ai.gov

Donald Trump has declared that anyone using the term “artificial intelligence” is “the enemy,” as Google Trends demonstrates an increase in search volume for the US President’s preferred term, while the rest of the world obliviously goes about its business.

Following the signing of Executive Order 14434, the purpose of which is “Inaugurating the Age of Super Intelligence,” the POTUS has taken to social media to emphasize the preferred term for artificial intelligence in the Land of the Free.

While the rest of civilization continues with the term “artificial intelligence,” it seems the US public quite likes the term “super intelligence,” which is believed to have been introduced to cast AI in a more positive light.

Subhead

Since the executive order was passed, interest in the term “super intelligence” grew considerably, increasing by around three times at one point. Of course, this only happened in the USA - worldwide, attention was not drawn to the revised nomenclature.

While that initial excitement appears to have settled down, the term certainly seems to have made its mark. As the chart above illustrates, interest in "artificial intelligence" as a search term has been seriously impacted by the sudden existence of "super intelligence."

Posting on Truth Social – the social network he founded via his Trump Media & Technology Group – Donald Trump stated: “The White House considers anyone that uses the term, “Artificial Intelligence,” as opposed to the highly accepted new and more accurate term, “Super Intelligence,” THE ENEMY! President DONALD J. TRUMP

Despite the determination to remove the term “artificial intelligence” from the public lexicon, however, the move doesn’t appear to have been wholly repeated across the US Government.

For example, Politico has identified that while the websites have converted with references to “super intelligence” and “SI” replacing “artificial intelligence” and “AI,” the main website for super intelligence has retained its ai.gov URL.

Rein it in a bit

Despite various emergencies and concerns caused by artificial intelligence – which range from the Hugging Face Incident to the growing environmental costs and impacts on rural communities of data centers – Donald Trump has been steadfast in his support for the technology.

Seemingly viewing it as the equivalent of the nuclear arms race, the POTUS has refused calls for guardrails on AI, preferring instead not to hamper the technology when the competition – namely, China – is not taking the same precautions.

Renaming the technology as super intelligence seems to have had a positive short-term impact, but whether this spreads beyond the US remains to be seen. It is unlikely – but then, the United States of America has long existed in a paradox of colloquial contradictions...

• Anthropic warns that self-replicating AI worms could become feasible by the end of the year
• These AI worms could hijack hardware ranging from IoT devices to data centers
• Smaller models are already capable of adapting to unfamiliar hardware and self-replication

Anthropic has warned self-replication could become feasible for small AI models on internet-of-things (IoT) devices in just a matter of months.

This could be a big problem because a self-replicating AI model does not have a centralized off-switch and could hop networks to discover other IoT hardware to further replicate without human control.

Anthropic warns these AI worms could use multiple compromised IoT devices to run inference while carrying its model weights with it as it reproduces. IoT devices would be a prime target for this worm because they are “cheap and typically poorly defended,” the company says.

Small AI models could spread quickly

To test the viability of a self-replicating AI worm, Anthropic researchers ran Gemma4-26B-A4B - a 25 billion parameter model - across 15 inexpensive off-brand Android TV boxes at a cost of around $55 each. Once networked together the TV boxes were able to run distributed inference, solidifying the proof-of-concept.

This introduces a number of novel opportunities for small AI models. Self-replicating AI models could exploit the weak cyber defenses of IoT devices within homes and small businesses, and then split their model into ‘shards’ across the compromised hardware in order to collectively run the model.

The AI worm could then pay for its own token cost by offering its inference on a digital freelancing service.

Moreover, newer IoT hardware that includes built-in AI chips designed to run small models - such as security cameras and video-conferencing kits - would be a prime target for an AI worm. Hackers could breach these systems using social engineering, or even by using an AI agent in the not too distant future.

Anthropic further warns that datacenters and other high-compute facilities could become targets of advanced replicators that are capable of breaching hardened defenses, and then use the hardware to propagate further.

An additional threat that Anthropic warns of is an AI worm that uses an API-based replicator, whereby the model doesn’t host itself, but steals human credentials to access commercial AI providers and use these services to pay for their own inference.

AI self-replication isn’t a reality... yet

While self-replicating AI isn’t an immediate threat, the researchers theorize that it could become a problem by the end of the year.

One of the main hurdles for these AI worms is becoming operational on unfamiliar hardware. Unlike the traditional ‘worm’ malware that must be built to target specific IoT vulnerabilities, AI worms could be capable of adapting to unfamiliar environments and hardware.

In Anthropic’s testing, the researchers found that smaller models - such as Qwen3.5-9B - are already capable of adapting to unfamiliar hardware to run inference. Qwen3.5-9B adapted to 8 of the 9 hardware types the researchers tested, which included Jetson Nanos, Mac PCs and RTX 4090s.

While smaller models are more capable of adapting to unfamiliar hardware, larger models are better equipped to self-replicate quickly.

Defending against AI worms in the wild

There is a significant tradeoff for AI worms, Anthropic says, which works in the defender’s favor. AI worms must balance between stealth and replication speed. To that end, the researchers say that an AI worm would leave a noticeable network signature such as multi-gigabyte model payloads delivered to newly infected devices, or unusual traffic between machines that wouldn’t ordinarily communicate.

Network quarantine would also be an effective initial defense against AI worms. Using lessons from the remediation of the Mirai botnet, the researchers believe that implementing a whitelist of approved sites with browsers otherwise redirected to cleanup instructions could help in clearing AI worm infections.

Regulations on the security of IoT devices could also help harden defense against AI worms. Moving away from supplying IoT devices with default passwords - as has already been done in the UK - would help prevent AI worms from gaining a foothold. Other standards, such as automatic updates and extended update lifetimes can help protect IoT devices from compromise against both traditional malware and AI worms.

Bad Bunny’s record-breaking soldout tour was one of the hottest tickets in the UK this summer, drawing over 104,000 fans across the weekend of June 27th, 2026.

The show was highly anticipated following his Album of the Year Grammy Award for Debí Tirar Más Fotos and history-making Super Bowl Halftime Show.

But the excitement around the show wasn’t just because of the scale of the artist, it was also due to the incredibly ambitious scale of the production.

The best stadium tours have a way of making their use of technology seamless and in some cases, invisible.

You see a 100ft screen tracking the band’s movement from across the field without missing a beat, or members of the audience picked out and flashed on the giant LED walls around the venue.

None of it looks labored or complex, at least not to the general public in attendance. To them it’s all part of the magic of the show, which is exactly the point.

Media servers

Carlos “Charly” Camina is the tour’s media server engineer, and the size of the kit he oversees is much smaller than the grand scale of the show might suggest. The entire production runs on four Disguise GX 3+ servers, with a fifth GX 3 unit held in reserve as an understudy for the show’s director.

That’s down from five servers during the tour’s earlier Puerto Rico leg, despite the fact that the World Tour carries significantly more content layers and considerably bigger screens at 10,752 x 2,816px, requiring a 4K input.

The GX3+ upgrade alone roughly doubled the processing headroom for the same effects load, which is what made it possible to deliver more with a smaller footprint. The lighting runs on grandMA kit, the unchallenged global industry-standard for live event lighting control, with a grandMA light desk as the main control surface and a grandMA3 compact unit as backup.

The whole setup sits on a dedicated network split into eight isolated VLANs, a structural choice to provide containment, so that a fault in one part of the system can’t spread and collapse the whole thing. The engineering challenge on a show of this scale isn’t cramming in more and more hardware, it’s finding the smallest reliable solution that can do the job every single night, in any venue, anywhere on the planet.

Adaptability is key on any tour

The most unique piece of engineering on the Bad Bunny Tour wasn’t actually planned or commissioned ahead of the show. During rehearsals, the creative team at Sturdy suggested photographing the crowd during the opening act and then displaying those images on LED screens around the venue later on in the show. Charly’s instinct was to push back. Loading fresh, unvetted content into the live system with zero headroom for troubleshooting is the kind of risk that wise engineers know to avoid.

His solution was to keep the additional photography content out of the Disguise system altogether. Instead, he built a standalone application using HTTP Sockpuppet and OSC (Open Sound Control) to communicate with the main Disguise system, running off a laptop. A photographer hands over images for Charly to upload. When the relevant cue fires inside Disguise, it sends a request to the laptop for specific images, which are mapped to specific locations on a four-way live layout.

Charly said that the hardest part wasn’t the concept, it was the live timing: ensuring that front-of-house and the on-stage servers sync closely enough so that there’s no discernable lag, and making sure the system never repeats the same face twice in one night.

How this curveball was handled is a good example of a pattern that shows up across the whole build: rather than adding risk to a proven core system, Charly builds smaller satellite systems to support it and keeps them loosely connected. It’s a slick approach that preserves the magic and spontaneity of a seamless live experience, without risking the core build or compromising the show’s production value.

Animated CGI Frog - Why not?

The tour’s most curious element involved a Puerto Rican-inspired 3D animated frog called “Concho” rendered live using Unreal Engine and a face-tracked performer, which enabled Concho to interact directly with Bad Bunny mid-show as part of a FaceTime call. The segment needed a camera that could move freely, handheld, so the team decided to use an iPhone.

However, the phone could not have any visible external antennas. This meant the feed from the iPhone had to be captured over NDI (Network Device Interface) on a dedicated Wi-Fi network, rather than a standard wireless link that could have been easily interrupted by the tens of thousands of audience and staff devices around the arena. Charly’s team has to scan the RF spectrum every night to find clear channels before the Concho segment goes live.

The benefit of reliability

The ultimate stress test of the system came during the show’s finale where the production deployed a 40-tile feed, displayed in a grid across the main 5K screen and two IMAG screens. Together with Screens Producer Vishal Sharma and his team at All of it Now, who created all the show’s Notch content, Charly decided to route four separate 4K video inputs through a Notch block to build the grid live.

Nothing was pre-rendered or keyframed and the color-grading also took place in real-time. That final segment pushed the servers to their limit, with all four of the GX 3+ units red lining on their performance markers. Despite the load, the system held and delivered a truly spectacular end to the show.

Charly has worked with Disguise servers for over a decade. In that time he’s worked on shows of all types, from festivals like Coachella to state-of-the-art mixed-use venues like the Tottenham Hotspur Stadium. With that much experience using these systems, what’s clear to him is that the value in this system isn’t in a specific creative feature. The value is knowing that if a server has an issue mid-show, he can switch to a backup with just one click and have full confidence that the backup will take over seamlessly.

For an engineer, touring a production this huge, reliability and predictability is the actual product. The breathtaking visuals that we get to enjoy as part of the audience will rightly get their applause, but the job is protecting the integrity of the show from its own scale and demands, every night, in every city, until the tour comes to an end.

I wrote earlier this year about Claude’s decision to comply with the EU AI Act, which requires all AI-generated text to be identifiable as AI, and start to insert a watermark into its text. Rather than limit this to EU customers, however, Anthropic decided to roll the feature out globally, which seemed a bit of an overreaction at the time.

OpenAI has now announced that it is following suit, but limiting the geographical areas of impact to the EU only. It is going to start inserting watermarks into generated text, but only for ChatGPT customers who live in the EU. That means that US and UK users won’t be affected at all.

OpenAI announced the change on October 5 saying: “Over the coming weeks, we will add an invisible watermark to eligible ChatGPT and Codex text output in the European Union.”

Inserting the watermark

To insert the watermark, which involves subtly changing the word choice for generated text, OpenAI is using a system of its own called textGrain. A note on the OpenAI website says: “Our text watermarking technology, textGrain, adds an invisible statistical signal to the model’s word choices. Our detector looks for that signal to assess whether a passage contains an OpenAI watermark. More details about how textGrain works can be found in our technical report⁠, which will be updated with additional details in the coming weeks.”

Like many people, I find that idea that the word choice of the AI that I’m paying to use is now being influenced by a watermarking algorithm, not by what’s optimal for me the customer, to be deplorable. I’m not saying that the resulting text will automatically be worse because of the watermark, but it certainly won’t be optimal.

It also sounds like it’s an easily broken solution that is full of holes. To detect AI text that’s produced using textGrain's watermarking a special AI detection tool needs to be used, which it’s currently unavailable to the public. OpenAI says it is providing detector access to researchers and expert organizations and that approved researchers and expert organizations can apply starting today.

“The tool will report whether it detects an OpenAI watermark”, continues the OpenAI article “without identifying the user or revealing their prompts or conversations. Given the risk of missed watermarks and false positives, we are not making it publicly available at launch.”

So if the detector tool isn’t being made public it begs the question, what is this supposed to accomplish?

A box-ticking act?

The OpenAI article specifically says that detectors are prone to errors. That “shorter or more constrained text is harder to detect”, and that “editing can weaken the watermark”. It already sounds flawed, but what happens if you try and circumvent it completely by asking ChatGPT not to write in a way that can be detected, or to not use textGrain. Will it do it?

All of which leads me to conclude that the move by OpenAI looks a lot like a box ticking exercise to comply with the EU AI Act, which it is forced to do, since it requires generative AI providers to make generated text identifiable in a “machine-readable way”

It’s reassuring to hear that the changes are only going to be implemented inside the EU and that the text will remain private when the detector is used, but I suspect most ChatGPT users would rather OpenAI concentrated on producing the best possible answers rather than making those answers easily identify as AI-generated.

There’s also something fundamentally strange about a system that deliberately changes the words an AI chooses, without telling you which ones, to insert a watermark that can potentially be removed simply by editing the text. If the goal is to make AI-generated content more transparent, then surely the answer should be something that actually works, rather than an invisible signal that nobody outside a select group can even check for themselves.

• A new app called RemoveMacAI can delete Apple Intelligence files
• Doing so can reclaim up to 12GB of storage space
• But it comes with some scary warnings that shouldn’t be ignored

Apple promises that its new Siri AI and Apple Intelligence tools can help improve your daily experience through the magic of AI. Many people aren’t convinced, though, and a new Mac app has been doing the rounds that promises to delete Apple’s AI for good — so I decided to take it for a spin to see what all the fuss was about.

Dubbed RemoveMacAI, the app originally gained popularity in late September. At that time, though, it was just a simple Terminal command, which — given the obvious security risks of entering unknown code into a utility as powerful as the Terminal — turned some people off.

Now, the same developer is back with a Mac app that is essentially a wrapper for the command. This makes it a lot easier to understand what you’re deleting and what you’re enabling when you use the app, with a built-in tutorial and clear toggles spread across several different tabs and categories.

Apple makes it pretty difficult to eliminate every aspect of Apple Intelligence from your machine, which is why something like RemoveMacAI has gained a foothold. Its developer says Apple’s System Integrity Protection layer — which stops unauthorized code from running on your Mac — is never disabled, and that the app doesn’t make any network requests and doesn’t collect telemetric data.

That said, I loaded RemoveMacAI onto my Mac and was immediately met with a concerning warning: macOS did not trust the app and wouldn’t let me run it. The developer said this is due to the app being unsigned, and you’ve got to manually let it through Apple’s gates. I’ve occasionally encountered apps that do this, and it’s never a pleasant experience.

Foolishly or not, I pushed on. You can either choose the app’s recommended settings, which delete Apple Intelligence and turn off some privacy-invading settings, go “maximum privacy” and also disable Game Center and some Safari settings, or choose what to do yourself.

I opted to just delete Apple Intelligence and nothing else. RemoveMacAI breaks your options down into sections — you can switch off Siri AI, ChatGPT’s extension, Writing Tools, Image Playground, and much more, all with modular toggles.

Once that’s done, there’s another jarring moment: RemoveMacAI wants you to install a custom profile that it uses to enact your changes. Fortunately, you can read through every change it includes, so there’s visibility over what’s being done.

I accepted that and let RemoveMacAI get to work deleting Apple Intelligence. In the end, it freed up 11.7GB, which is nothing to sniff at and is in line with the developer’s claims. If you’re running low, it could be a good way to claw back precious disk space.

Although RemoveMacAI comes with some scary settings, it attempts to ease your fears by letting you read through everything that’s going to be changed, including the macOS profile. You can undo your decisions too, and doing so will revert your settings changes and redownload the Apple Intelligence models when they’re needed next.

Ultimately, I found RemoveMacAI to be a handy utility, especially if you want one resource for not only deleting Apple Intelligence, but also for restricting some of macOS’s more privacy-invading practices. The lack of notarization and the subsequent alarming warnings will likely put some people off, though, and they stop me from wholeheartedly recommending the app as a result.

Discussions about passkeys replacing passwords as the go-to sign-in method have rapidly accelerated. The National Cyber Security Centre (NCSC) has acted as the catalyst for this transition, describing the shift as "overhauling decades of security practice". Finally, the industry has reached a collective point of recognition: traditional, static passwords are a weak defense against modern, automated threats.

AI has turned cybercrime into a highly efficient, industrial-scale enterprise. With researchers uncovering agentic ransomware attacks, we are fast approaching a reality that outpaces traditional human-centric defenses. Cybercriminals routinely deploy industrial-scale AI tooling to orchestrate and deploy vast credential-stuffing attacks and hyper-realistic phishing campaigns at machine speed and scale.

For organizations, the adoption of passkeys and other passwordless verification methods is dependent on the maturity of existing identity and access management (IAM) systems. Of the businesses with less mature, or no IAM strategy in place, only 30% are embracing password alternatives. Without passwordless systems, threat actors will continue to undermine digital trust.

User friction and weak protection

For years, identity security was treated as an isolated concern for the operations department. However, as customer experience dictates corporate revenue, delivering seamless IAM has become a commercial imperative.

Passwords force a broken trade-off: making users create unique, complex strings of characters results in severe password fatigue, while cutting corners by reusing credentials and using easily guessable phrases leaves gateways vulnerable to threat actors. When security kills usability, the business model breaks.

Another barrier passwords create is that, although most of the British public is genuinely worried about identity theft and fraud, only 17% fully trust organizations managing their identity data. Passkeys directly resolve this trust deficit through cryptographic authentication.

Because private keys stay securely on the user's local device and are never shared with a central server or application, there is no central database for hackers to breach. Passkeys are also phishing-resistant, removing the risk of users falling prey to social engineering attempts. Unlike passwords, they are also cryptographically bound to a specific, verified domain name, adding another layer of technological defense against attackers.

Securing autonomous identities

The shift towards agentic AI – fully autonomous systems operating with minimal human intervention – shatters traditional authentication models. Passwords were fundamentally built for human brains to remember.

An autonomous AI agent, such as autonomous digital proxies, personal shoppers and synthetic workflows, cannot "remember" a password without creating severe vulnerabilities, such as hardcoded credentials or shared session tokens exposing permanent risks.

If an agent inherits over-privileged human credentials, a compromised or rogue AI can exfiltrate sensitive data at machine speed, vastly outpacing the response time of traditional security methods.

To secure the growing autonomous workforce, AI agents must be given their own distinct, first-class identities with task-specific, time-bound permissions. Passkeys and decentralized, cryptographic credentials secure these machine identities and allow enterprises to securely verify and audit agentic workflows and interactions, without exposing underlying human credentials.

From passwords to passkeys

The technology industry is driving the momentum behind passkey adoption. Tech giants like Google and Amazon are seeing the need to improve customer convenience and account security, positioning passkeys as their default authentication standard. Businesses that delay moving towards passwordless frameworks are left defending a perimeter built for an entirely different era.

Although most organizations now see the importance of adopting passwordless options, transitioning away from traditional passwords is hindered by real-world complexities. Many core IT systems and legacy infrastructure are reliant on passwords, which remain deeply embedded within business processes.

IT leaders therefore fear that updating these systems will demand massive code rewrites and extensive specialist development resources, meaning replacing them requires careful orchestration.

To address this challenge, modern, low-code or no-code identity platforms allow organizations to visually map, test and deploy sophisticated authentication journeys seamlessly. This enables businesses to introduce modern passwordless options like passkeys or biometric prompts progressively, without disrupting the active user workflow or having to replace entire underlying legacy systems.

By allowing teams to modernize security processes without affecting user experience or uptime, advanced identity orchestration becomes a critical business enabler.

The future of trust starts with identity security

The threat landscape continues to be redefined by automated threats, deepfakes and autonomous software. Against these sophisticated attacks, the concept of static, point-in- time logins is obsolete. The NCSC’s call to action has been the final push many organizations need to drop passwords and rethink how to safeguard digital trust.

Adopting an identity-first approach centered on passkeys and robust identity orchestration allows enterprises to reduce user friction, strengthen the security of both human and machine identities and establish the continuous trust needed to grow confidently in an AI-driven environment.

We've featured the best antivirus 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

As global AI investment continues to climb — it surpassed $300 billion in 2025, according to IDC — enterprise IT remains primarily focused on supporting the development of training models, with architecture such as large GPU clusters and high-density compute taking center stage.

But now AI inference is becoming the dominant workload, and the architecture that supports models in production, at scale, and to real users and systems has quickly become the future of AI.

The demand for AI inference is there — according to Deloitte, inference already accounts for roughly two-thirds of compute, while McKinsey projects that it will represent between 30% and 40% of total data center demand by 2030 — however, the dedicated infrastructure is not.

Inference is more than a scaled-down training environment

What training and inference require from an IT point of view are very nearly polar opposites. Enterprises have facilitated AI training via data centers with massive, centralized data GPU clusters for extreme power density and specialized, high-speed interconnects meant to maximize communication within a single room.

Inference, on the other hand, is defined by a continuous flow of information and unpredictable user demand as opposed to a pre-defined workload. Once the prompts start coming in, you need geographically distributed compute that responds to and scales with real-time demands.

Because inference runs 24/7 and requires millisecond-fast responses for end users, it relies on commodity ethernet, geographically distributed edge sites, and low-latency network connections rather than isolated throughput within a single data center. The very design choices that make an enterprise data center optimal for training actively work against running inference at scale.

The cost structure is entirely different, too. Training is a one-time capital event — once a model is built, that cost is already sunk. Organizations that continue to optimize their spending for training are now being hit with the reality that they have the wrong foundation.

Inference, meanwhile, is a recurring operational cost that scales with every user, query, agent interaction, and more. It’s more like a living, breathing organism which needs to grow alongside the kind of workload that is going to determine the future of AI economics.

Agents are creating new strain on architecture

Autonomous AI agents are also compounding the infrastructure deficit. Agents don’t generate a single response and then stop; they plan, execute across multiple steps, call external tools, coordinate other agents, and pass outputs between systems. The ripple effect of an agentic workload multiplies the data traffic flowing through a network. As demand for agentic solutions grows, so will the demand for supporting infrastructure.

That growth is already consequential. Gartner predicts that by the end of 2026, 40% of enterprise applications will be integrated with task-specific agents, while IDC projects that agent usage will surge tenfold by 2027, with token and API call volumes multiplying by a factor of thousands.

Inference is also acting as a north-south traffic multiplier in ways that traditional WAN and data center network architectures aren’t designed to handle. McKinsey found that most enterprise WANs are currently unable to support AI data traffic patterns, and must evolve as AI now ranks as the second-highest infrastructure investment priority.

Burst traffic is another issue impacting architecture strain. Agentic workloads aren’t a steady flow, they’re a torrential downpour that comes and goes. A single orchestration event can trigger dozens of downstream API calls simultaneously.

This causes networks that are sized for average loads to hit capacity ceilings when agents coordinate at scale, creating the same latency and reliability problems that lead enterprise AI pilots to crash before reaching production. To thrive in the AI era, enterprises must build the proper infrastructure from the ground-up.

Inference is now the critical CPU use case

Building inference-forward architecture starts at the most basic layer: hardware. CPUs are critical for planning, scheduling, tool-call coordination, and the multi-step reasoning loops that define agentic workloads.

However, the industry’s heavy focus on the GPU market during the AI training boom has directed attention away from the necessity for CPUs in AI inference workloads. As inference workloads increase in scale, CPUs move from a supporting role to a critical one, acting as the main filter to the orchestration layer.

When viewed through a purely agentic lens, high-core-count server CPUs can handle the workload more economically than GPU accelerators. But it’s important to understand that one cannot function without the other. Inference still demands GPU function; it’s simply that CPU capacity is the bottleneck that most organizations aren’t accounting for.

Aligning compute, network, and storage is key

The reality is that AI use cases are generating abysmal ROI in most cases. Gartner’s most recent survey of infrastructure and operations leaders found that only 28% of AI use cases fully succeed and meet ROI projections, while 20% fail outright. The leading cause of failure was expecting too much, too fast from infrastructure that was incapable of supporting the workload.

How can enterprises avoid that trap? Take the time to set up their underlying infrastructure before diving headfirst into building systems they can’t support. Putting real effort into building dedicated compute, network, and storage that function as individually managed resources is the key.

Inference at production scale requires all three to work in tandem, with compute handling model execution, storage determining how quickly context, embeddings, and retrieval data are available, and network defining whether compute and storage can communicate externally with the appropriate latency

The organizations that can make this distinction early will be better prepared for the next phase of AI, and will spend less time managing infrastructure failures during production, and more time actually focusing on generating value through AI inference.

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

The conversation around AI and the jobs market is dominated by anxiety. People worry that roles will be lost to agents – that the job market will dry up from the impact of AI. As a result, there will be a general constriction in the volume of career opportunities out in the world. But is this concern well-founded, what does the data indicate – is it a bleak outlook?

Drilling down into the latest trends and labor market statistics, we can see that the facts paint a different picture. What’s going on? It’s quite a positive shift with the data showing that the job market is in fact rebounding and settling around AI.

Job postings mentioning AI have surged. Since last year, specialist AI job postings have increased by 61% in the UK. This is according to PwC’s AI Jobs Barometer, recording 68,000 new roles and bringing AI hiring volumes back to around 2022 levels. Why are we seeing this uptick? The rise in hiring reflects a broader pressure on companies to deploy and manage AI effectively.

The new AI jobs drive

From a safety standpoint, we’re seeing concerns mounting around open-weight models. We’re also seeing mounting pressure from AI legislators and industry watchers, calling for watertight regulation and governance processes to be put in place.

That pressure has direct implications for workforce strategy – are you hiring the right talent for AI management, do you have the skills in place within your organization to best handle AI operations? Better AI starts with people, and this is where we’re seeing the job market respond over time.

AI roles are vital for the success of the UK AI economy. According to Coursera research, 88% of leaders agree that planned AI investments will not succeed without increasing investments in skills development. The future of work will be defined by those who navigate these changes effectively – knowing how to work seamlessly alongside AI is critical.

The conversation around AI has been dominated by fears of automation replacing workers. But our UK research tells a very different story. While 85% of organizations are already using AI agents in workflows, businesses are increasingly investing in the people needed to govern, secure and scale them.

Among UK IT leaders: 65% expect overall headcount to increase over the next three years, compared with just 14% expecting it to fall. On top of this, only 8% say AI agents are primarily eliminating jobs today. Meanwhile, 48% are hiring AI agent operators, 31% are hiring security, risk and compliance specialists, and 26% are hiring AI ethics and governance specialists.

AI governance becomes a growth function

This is a positive hiring strategy shift, generating scope for AI upskilling, and talent pool growth within the UK economy. What we’re seeing here is that AI governance has become a growth function. Historically, in a negative sense, governance has been framed as a constraint designed to slow down innovation for safety. In the agentic era, that attitude is being reversed.

Strong governance has become the ultimate accelerator. With the UK’s rigorous focus on expanding AI investment and infrastructure, governance needs to be front-of-mind for those innovation-hungry organizations. Additionally, according to our research, nearly 94% of UK organizations say stronger governance would help them move faster with AI, while 93% believe it's essential to scaling AI responsibly.

This is about a concerted focus on ‘re-plumbing’ fundamental structures. UK business leaders understand that teams cannot move fast without confidence in their safety structures. Without clear guardrails, organizations trying to scale AI hit a wall. Employees can leak corporate data, or autonomous systems can surface restricted content. Governance, permissions, and access controls make AI trustworthy, transforming compliance into a growth engine.

How AI is creating new careers, not just changing existing ones

LLM Engineer, Prompt Engineer, AI Governance & Compliance Manager, Chief AI Officer – these are unique new roles we are seeing. AI job risk has been widely discussed in the media, but in fact a bump in opportunities is happening – new opportunities are being created for new, focused AI roles.

As emerging technology becomes embedded into day-to-day operations, demand is growing for specialists who can oversee AI systems, manage risk, ensure compliance and build trust.

For candidates looking to enter this field, knowing how to work alongside digital agents will be the baseline competency of the 21st century – those entering the workforce will increasingly need to build AI literacy. This is where the job market is heading, and skills strategy must align with that too. And it’s not just for deeper technical knowledge – it’s the skills to interrogate and control AI tools from a critical thinking perspective.

The ‘human in the loop’ discussion is not going away – we can see from machine-vs-machine cyber threats that human control is more important than ever. The question is, how do I become the person for that job? For those in the job market seeking these kinds of opportunities, the goal must be a hybrid skill set blending technical literacy with human judgment

The data disconnect with AI engines

Governance is only part of the opportunity. Data and knowledge management are becoming equally important. What we’re seeing now, in that wider ‘re-plumbing’ discussion, is that data libraries are also failing.

While 96% of organizations say access to company knowledge is critical for AI success, only 32% have connected AI agents to trusted internal content across multiple use cases. This disconnect is hurting the growth potential of AI, and limiting its scope to single-use, isolated areas.

Without this connection, agents are left blind and prone to errors. This content disconnect is the primary reason why enterprise pilots stall. Bridging this operational gap requires organizing and securing corporate content pools, and thus hiring the right talent for the job – in the process, creating a massive, multi-year runway of work for data stewards and AI operators.

These kinds of needs or requirements can’t be ignored – especially by larger enterprises that are betting big on AI ROI. They need to iron this out now, and will look to hire the right people for that process.

On the horizon

It’s true there has been significant disruption to the jobs market from AI. Any major technology that has such a wide-reaching impact will create huge ripple effects. But as we study that impact, we can see new opportunities emerging for eagle-eyed candidates and employees. The bottom line is that good governance will be the core foundation for AI.

By framing governance as the foundation of a modern, managed digital workforce, we can unlock unprecedented productivity. And in this transition, we create new sets of roles, reordering the skills priority list. It’s those individuals who are receptive to those changes, and stand out with the right, competent skill sets, who will be the ones to prosper and benefit. This is an exciting time for the technology sector – one that is creating high-value careers for the next generation.

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

• UK AI minister warns cyber defenses will 'likely' be breached in the future
• More regulation is needed to prevent 'rogue' AI agent incidents
• The UK's AI testing body is working to help secure frontier models before release

UK AI minister Kanishka Narayan has warned that it is “likely, if not inevitable” that the nation's cyber defenses will be breached by AI agents.

Narayan’s warning comes after numerous frontier models have escaped testing environments and hacked third party companies, including one case where an OpenAI agent hacked an Australian government website in June 2026.

Speaking at a press conference, Narayan also said that AI companies should, “Listen when your scientists warn of serious dangers. Investigate unexpected behaviour. And if you cannot adequately control the risks, do not release the system.”

UK government to use AI to defend

The AI minister also revealed that the UK government is planning to use advanced AI to counter the threats posed by AI, which will build upon two pilot programs that have helped spot and patch over 1,000 vulnerabilities across government systems.

In addition, frontier AI systems are undergoing “rigorous evaluations” by the UK’s AI Security Institute (AISI), in order to “identify risks early” and “test robust safeguards to help keep people safe.”

However, there have been some roadblocks in AISI’s ability to test some of the latest models. In September 2026, US President Donald Trump asked OpenAI and Anthropic to not share their latest models with UK testers until US labs had conducted their own testing.

Narayan said that the UK government has since worked with the White House, “to retain access for Britain's AISI to the most powerful AI models before they are released" (via BBC).

AI protections are also likely to be introduced as part of the Cyber Security and Resilience bill, Narayan said, with a particular focus on regulations surrounding the reporting and disclosure of AI incidents.

“It's promising to see the Government taking frontier AI cyber defences seriously,” said Richard Wainwright, Field CTO, EMEA at Okta.

“As AI agents become more autonomous, safety must be built into the infrastructure UK businesses and government rely on. Everyone wants a 'big red button' to turn off rogue AI agents. The industry is increasingly talking about AI 'kill switches' to help protect critical infrastructure, and I hope they are considered as part of the Cyber Security and Resilience Bill regime.”

Why does AI need more regulation?

In 2026 so far, there have been multiple incidents of frontier AI agents escaping testing environments and breaking into the networks of other companies.

OpenAI has notified more than 100 organizations about unauthorized activity tied to the company’s AI agents, including the Hugging Face attack. Anthropic has disclosed two unauthorized AI agents events, and both Meta and Google have disclosed a single testing environment escape each.In multiple cases, the AI agents were being evaluated by Israeli AI testing firm Irregular.

The companies involved are facing significant scrutiny over the safety of their AI models, but are yet to face any punishment for failing to contain their agents.

There is also significant debate around labelling the AI agents as ‘rogue’, with some experts such as Tenable Field CTO Bernard Montel saying it is “a dangerous way of deflecting blame.”

But while regulation may force AI companies to be more rigorous in the testing and deployment of their models, it may also slow down AI development.

For the Trump administration, that is a line that cannot be crossed. Slowing down AI development would mean handing the AI development lead to China and potentially compromising US national security.

The US midterm elections are already fraught with debate over AI development, especially when it comes to the topic of the thousands of AI data centers that are being proposed across the US.

As AI becomes embedded into critical public services, sovereignty is rapidly becoming one of the biggest strategic questions facing government.

Much of the debate still treats sovereignty as a question of geography: where is the data stored, which country hosts the infrastructure, and whether information remains within national borders?

These questions matter, but they are only part of the picture. True sovereignty is about maintaining control across the entire AI lifecycle from where data resides and how it is accessed, to how models are governed and tested.

For public sector organizations, that distinction is becoming increasingly important. AI is moving beyond experimentation and into services where the consequences of losing control can be far greater than a poor chatbot response. Healthcare, education and central and local government all rely on data and systems that need to remain secure, resilient and accountable.

Sovereignty needs to go beyond the data center

Keeping information in the UK does not automatically make an AI deployment sovereign. An organization could host its data domestically while relying on external models, platforms or services over which it has limited visibility or control. It may not fully understand where information is being processed, how models are accessing it, or what happens when those systems change.

That creates a much broader question of dependency. As AI tools become increasingly embedded in public services, teams need to understand not just where their data sits, but what sits between that data and the decisions being made from it. Sovereignty means having sufficient control over the infrastructure, connectivity, security, data and governance that underpin the AI environment.

This is particularly important as AI becomes more autonomous. The shift from simple assistants towards agents capable of taking actions on an organization's behalf means that the stakes are rising. The more agency an AI system has, the more important it becomes to know what information it can access, what decisions it can influence and where accountability ultimately sits.

Bring AI to the data, not the other way around

One of the most important principles for public sector AI should be 'bring AI to the data' rather than automatically sending sensitive data to wherever the AI happens to live.

That requires a different approach to AI adoption. Instead of starting with a model and asking what data can be connected to it, organizations should start with the service or decision they are trying to improve and work backwards.

Organizations don't always struggle with insight. They struggle to make decisions. Therefore, rather than putting every problem into the same generative AI model, the technology should be chosen based on the problem.

For government, this could mean keeping sensitive information within controlled environments while connecting appropriate AI capabilities to it through secure, governed infrastructure. It also means accepting that there is no single AI architecture that will work for every use case.

Some applications may benefit from generative AI. Others may be better suited to traditional automation, machine learning or optimization algorithms. The objective should not be to use the newest technology, but to create the right outcome safely and effectively.

Governance cannot be bolted on afterwards

The other misconception is that sovereignty is primarily a cybersecurity issue. Security is obviously fundamental, but governance is equally important. Public sector organizations need to be able to explain what their AI is doing, test whether it is performing as intended and maintain appropriate human oversight.

This becomes especially important because AI systems are based on probability. They do not always produce exactly the same result from the same input, and they can generate answers that appear convincing while being incorrect. That creates a very different challenge from traditional software, where a defined process is expected to produce a consistent output.

AI needs to be treated as something that must be continuously tested and governed, rather than deployed and forgotten. That means bringing security, compliance and risk teams into AI projects from the outset, rather than asking them to approve systems once they have already been built.

It means establishing clear rules around data access and usage, ensuring there is a transparent audit trail for how AI systems operate. Most importantly, designing human oversight into the system from day one.

Infrastructure is becoming part of the AI strategy

None of this is possible without the right foundations - a sovereign AI strategy requires secure cloud environments, resilient connectivity, strong cybersecurity, appropriate data architecture and infrastructure that can support changing AI workloads.

That is particularly important because AI is evolving so quickly. The public sector is already having to balance what it builds in-house with rapidly changing platforms and services from technology providers. Long-term flexibility is becoming just as important as today's technical capability.

For the public sector, this should be viewed as a strategic opportunity rather than simply another IT challenge. Building flexible, secure infrastructure now creates the foundation for adopting new AI capabilities later, without having to surrender control every time the technology changes.

The goal should not be to isolate public sector organizations from the wider AI ecosystem. That would neither be realistic nor desirable. It should be to make sure they can participate in that ecosystem on their own terms.

Sovereignty is about control

The UK's AI ambitions will depend on its ability to adopt new technologies at scale while protecting the information, services and citizens that depend on them.

We need to move the conversation beyond where data is stored. Sovereign AI is ultimately about control over data, infrastructure, access, governance and the decisions AI systems are helping to make.

Public sector organizations that build those principles into their AI strategies now will be better positioned to take advantage of increasingly capable systems without becoming dependent on technologies they cannot fully govern.

The future of AI in the public sector should not be about choosing between innovation and sovereignty. With the right foundations, it should be possible to achieve both

We've featured the best AI chatbot for business.

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

Google has given its Nano Banana AI image generator another upgrade, and the latest version, Nano Banana 2.1, is arriving inside Google Gemini in an increasingly crowded field of tools that can turn a few sentences into remarkably convincing pictures. The promise is familiar, with sharper details, better instruction-following, more accurate text, and greater consistency across images. The real challenge is finding something that makes it meaningfully better than the competition.

Nano Banana's main rival, ChatGPT's image generator which is called Images 2.5, is hardly sitting around waiting to be overtaken. OpenAI has made considerable progress with realistic lighting, complicated compositions and text rendering, turning image generation into one of ChatGPT's most entertaining and genuinely useful features. With both companies pushing the technology forward, I wanted to see whether Google's latest upgrade had managed to pull ahead.

If you've got a Gemini account you'll find that it should have been upgraded to use Nano Banana 2.1 by now. To test the two image generators against each other I gave Gemini and ChatGPT identical prompts designed to test different abilities. I compared the resulting images side by side, with ChatGPT on the left and Gemini on the right. The results were surprisingly close, although the two generators often approached the same instructions with different artistic instincts.

Storms within storms

My first test was designed to challenge the models' understanding of light, glass and reflections, three things that can quickly expose the weaknesses of an AI-generated image. I wanted a scene with multiple transparent surfaces, complicated lighting and enough tiny details to make the results worth examining closely. The prompt was, "Create a photorealistic image of a tiny Victorian greenhouse inside a glass snow globe during a thunderstorm, with realistic reflections, refractions, and lighting."

Both generators produced beautiful images that would look perfectly at home on the cover of a fantasy novel. ChatGPT's version featured an elaborate, golden-lit Victorian greenhouse inside a large glass sphere, sitting on a polished wooden base beside a rain-covered window. Lightning illuminated the storm outside, while the globe contained a surprisingly detailed miniature world, complete with plants, pathways and delicate architectural flourishes.

Nano Banana 2.1 went for something slightly more restrained. Its greenhouse looked more like an actual miniature structure, with dark metal framing, lush greenery and warm interior lighting that contrasted beautifully with the blue-gray storm outside. The globe's reflections were particularly convincing, and the lighting felt a little more natural, although the wet tabletop was an odd choice given that the scene appeared to be indoors.

Titan calling

Text has historically been one of the most embarrassing weaknesses of AI image generators. Letters would melt into one another, words would become nonsense and asking for an exact phrase could produce something that looked like an alien language. For the second test, I wanted to see whether either model could handle a fairly demanding design brief without mangling the typography.

The prompt was, "Design a retro-futuristic travel poster for Saturn's moon Titan with the exact text 'TITAN IS CALLING,' 'Seven nights beneath the rings,' and 'Starting at $2,499.'"

ChatGPT created a luxurious travel advertisement that looked like something a very wealthy space tourist might encounter in an interplanetary airport. Saturn dominated the sky above a beautiful orange landscape, while futuristic resorts and a couple enjoying drinks suggested that Titan had become the solar system's most exclusive vacation destination. One thing I didn't like was the gap it left between the $ and the 2 in its text rendering.

Google took a more cartoonish approach with a colorful vintage science-fiction poster featuring rockets, astronauts, icy mountains, and a futuristic base. It was actually more realistic about how the moon is not at all hospitable but still had the optimistic quality of a 1950s space-travel advertisement, complete with bold lettering and a slightly weathered finish. Crucially, it also reproduced the requested text correctly, including the price.

Fox library

Character consistency is another tricky problem for AI image generators, particularly when the same character needs to appear in several scenes. A fox might suddenly change its appearance, lose an accessory, or acquire an extra limb between panels. I decided to test this with a simple illustrated story that required both visual continuity and a coherent sequence of events.

The prompt was, "Create a four-panel comic showing an orange fox wearing a green scarf discovering a magical key, unlocking a door, and entering a library. Keep the fox identical in every panel."

ChatGPT produced four almost 3D illustrations. The lighting seemed more computer-generated, though the fox remained recognizable throughout as requested. The style leaned toward children's book illustrations rather than a conventional comic.

Nano Banana 2.1 delivered something much closer to a traditional comic strip, with bold outlines, clearly defined panels, and a consistent cartoon fox. Its version began with the fox finding a key in the forest, then examining it, unlocking a door in a tree, and finally entering a magical library. The storytelling was clearer, and the character's appearance stayed impressively consistent, right down to its green scarf and white-tipped tail.

Google edged ahead here because its four panels felt like parts of the same illustrated story. ChatGPT's individual images were more elaborate, but the fox's proportions and presentation varied slightly, and the sequence felt more like four separate illustrations.

Dragon on a desk

For my final test, I wanted to see how accurately the models could follow a complicated collection of specific instructions. AI image generators have a habit of overlooking small details when a prompt contains several independent requirements. Getting everything into the picture is one challenge, while getting the exact numbers and arrangements right is another.

The prompt was, "Create a realistic overhead photograph of a wooden desk with a notebook showing a dragon, a compass pointing north, a pocket watch showing 3:15, exactly three red wax seals, and a blue gemstone."

ChatGPT produced an attractive overhead photograph of an antique wooden desk, with an open notebook containing an intricate dragon illustration. The compass, pocket watch, three red wax seals, and bright blue gemstone were all present, and the composition looked carefully arranged.

Nano Banana 2.1 also included the requested objects, but the notebook featured a smaller, cleaner dragon illustration, while the wax seals, gemstone, compass and watch were arranged around it. Google's result looked more like a casually assembled collection of historical objects, but it did make a common AI error and got the time wrong, setting it for 3:00, not 3:15.

Approaching parity

You can access Nano Banana 2.1 in Gemini if you're a Google AI Plus, Pro or Ultra subscriber as it is the new default model. Free users will still be getting the older Nano Banana 2 model.

Nano Banana 2.1 is an excellent image generator, but ChatGPT is already so capable that Google's improvements don't necessarily translate into an obvious advantage. Both tools produced attractive, detailed images from relatively short prompts, and neither suffered the glaring visual mistakes that used to make AI-generated pictures instantly recognizable.

That said, different prompts, styles, and repeated generations could easily produce different results. What these tests do suggest is that the leading AI image generators have reached a remarkably high level of capability and are approaching parity in many everyday creative tasks.

That makes the competition more interesting, even if it makes choosing between them harder. The differences increasingly come down to taste and interpretation. Nano Banana 2.1 may be Google's latest impressive upgrade, but ChatGPT has made sure it won't be claiming the image-generation crown without a fight.

• Microsoft CEO Satya Nadella says Copilot Code and local AI make every Windows PC an infinite software factory with no cloud token spend
• Rather than killing app stores, Microsoft is moving app hosting into Copilot, while IT leaders stay wary of citizen-built tools
• Microsoft's own engineers say local inference doesn't make sessions offline, and the routing ships later this month

Microsoft CEO Satya Nadella has a new pitch for the Windows PC, and it targets one of the most lucrative ideas in consumer tech.

In a post on X summarizing Microsoft's recent Windows and Surface event, Nadella told followers they no longer have to settle for whatever an app store happens to stock, because Copilot can now build the software they need on the desktop without spending any cloud tokens.

He wrote this idea without mincing any words, saying, "Your PC becomes an infinite software factory."

From Gates' factory to your desk?

The narrative is a striking line from the head of a company that sells more software than almost anyone.

But read Microsoft's own engineering documentation for the same launch, and the infinite factory starts to look rather finite: it runs best on a $5,899 laptop, it still reaches into the cloud, and the businesses Microsoft most needs to convince have spent the past year telling researchers they are worried about exactly this kind of thing.

More importantly, the factory metaphor is not new for Nadella: In a 2025 staff memo, he recalled that Bill Gates had envisioned "not just a software company, but a software factory," and argued the AI era demanded a rethink.

At a recent customer gathering, the CEO said Microsoft had progressed from code assistance through agentic coding "to software factory all within one year," and he now wants customers to open software factories of their own, spending more with Microsoft along the way.

The October 7 2026 launch builds on this ambition: a Windows Experience Blog post by Windows and Devices chief Pavan Davuluri describes hybrid intelligence, in which Copilot reads local files with permission, acts across Windows, and hands work to on-device models.

Copilot's Code experience builds an app, dashboard, or workflow from a plain-language description, and on Windows, Microsoft says it can produce native apps from one prompt. Jared Spataro, Microsoft's chief marketing officer for AI at Work, argued that building such tools is becoming "as basic a skill as writing a memo or modeling a budget."

The engine underneath is MAI-Code-1.1 Flash, Microsoft's in-house 137-billion-parameter coding model, which Nadella's post said is now optimized to run on a PC. There is a catch, however: despite Microsoft painting it as a situation where consumers are finding their AI needs "outpacing what their cloud budgets can support."

Microsoft's own engineers are more grounded about what the process entails, saying GitHub Copilot's automatic mode decides task by task whether work runs locally or in the cloud, and one diagram caption is blunt: "local inference does not make the session offline."

That routing doesn't ship until the end of October 2026; the hybrid Copilot features reach Copilot+ PCs over the coming months, and Code is rolling out first through Microsoft's Frontier early-access program. This is interesting, but somewhat predictable, because Nadella's approach, as stated, would cannibalize Microsoft's token-based business model.

An approach that routes harder tasks back to the Azure cloud makes far more sense by conserving compute where possible while still putting Microsoft's cloud compute at the center of things.

The hardware is another factor that one might be overlooking here: according to the Command Line post, the on-device version of MAI-Code-1.1 Flash is quantized down to 53GB, roughly an 80% reduction from the cloud model, and peaked at 75.5GB of memory at full context on Microsoft's test machine, a Surface Laptop Ultra built on Nvidia's RTX Spark.

That matters because eight Surface Laptop Ultra configurations are in play; only one has more than 64GB of memory, per TweakTown: the 128GB model at $5,899, which costs $3300 more than the base model and isn't available to purchase yet, meaning that potential users can't really test out how well it does compared to cloud-based solutions at a time when all performance-related figures come from Microsoft.

The death of modern enterprise software? Not yet

Despite the hardware limitations in play, it might indicate a death sentence in the making for most basic enterprise-level software solutions, even as more ambitious ports have already been in play: Developer Brandon Thomas, for one, has just released seven open source Adobe Creative Cloud alternatives that he says he built with Anthropic's Claude Opus 5.5, declaring that "software is over."

His own roadmap, however, puts the Photoshop stand-in at roughly a quarter to a third of what a professional needs for daily work. Building software has become cheap. Building software someone can depend on has not.

The bigger issue will be enterprise IT, which will, I suspect, keep the gates closed for now; enterprises don't just buy features; they buy someone to call when it breaks, a patch schedule, an audit trail, and a vendor that carries liability.

Getting an open-source or AI-assisted app someone designed with zero liability in tow that could break things, create security risks, and may not come with future software updates that resolve either issue.

The survey data points the same way: a June 2026 Retool study of 307 CTOs, CIOs, and CISOs found 93% concerned about vibe-coded tools reaching production, though Retool sells an internal-tools platform pitched as the governed alternative.

A Harris Poll survey for Dataiku of 600 CIOs found 81% worried that citizen-built AI could expose sensitive data, and over half had already discovered unsanctioned AI in their organizations.

Gartner, meanwhile, predicts that over 40% of agentic AI projects will be scrapped by the end of 2027, with analyst Anushree Verma describing many as "mostly driven by hype and are often misapplied."

Microsoft's own launch materials quietly concede the same, even taking measures to ensure that agents do not run rogue while building software or solutions on-device.

The Command Line post admits Copilot's built-in file tools are policy-checked inside the app rather than isolated by the operating system, and GitHub requires Business and Enterprise administrators to enable a policy before staff can use MAI-Code-1.1 Flash at all, while Microsoft Execution Containers, which are now generally available, allow IT to define what agents can read, modify, or execute.

The real test for Nadella's factory isn't individuals with a $5,899 laptop and a tolerance for alpha software, but businesses willing to take the risk and trust Microsoft's safeguards. Until then, one could assume the AI factory narrative will continue to run well below its advertised potential.

The future of AI lies squarely in the physical realm, according to many industry figures such as Nvidia CEO Jensen Huang. Much of the evolution of AI in recent years has, as expected, been in aid of building machines that can learn quicker, think faster, and interact with the physical world in a more compelling way.

Checkmate

Nearly 40 years ago, the scientist Rodney Brooks authored a significant robotics paper that argued real-world robotics should focus on simpler, layered behavioral systems to understand the world rather than complex internal models.

His comments in the study 'Elephants Don't Play Chess' signalled a major paradigm shift in the way scientists saw the training and learning process for robots then and in the future. It challenged the established status quo for how scientists considered physical AI, and proved fairly divisive at the time.

The old sense-model-act-plan (SMPA) approach to AI – in which robots would scan a room to build a 3D digital map inside their memory and plan the optimal path within that map – was out. Instead, Brooks proposed that when a robot changes the world around itself, it doesn't need to compute the impact of everything – just the most relevant parts.

One man saw it coming

Brooks co-founded the company now known as iRobot alongside two colleagues from MIT's AI lab in the same year as he published his seminal paper, although back then it was called IS Robotics.

The first product was Geghis, an insect-like space explorer that weighed 1kg and served as a prototype for low-cost space exploration. Then, more than 10 years later in 2002, the company launched its first Roomba robot vacuum cleaner.

Despite enjoying success through the decades, iRobot filed for bankruptcy in December 2025 and the company was eventually taken private and absorbed into the operations of Chinese company Shenzhen Picea Robotics.

• OpenAI has solved or partially solved 372 outstanding mathematical challenges
• Some of the solutions, while clear, cannot be understood by some humans, and were produced with an internal, unreleased AI model
• Renowned mathematician Terence Tao has called for colleagues working with OpenAI to abandon their projects

OpenAI has stunned the world of advanced maths by releasing solutions and partial solutions to 372 outstanding problems that have puzzled the world’s greatest minds for decades.

Just two years ago, AI was finding it tricky to handle GCSE-level mathematical challenges. Today, it is solving problems that have taken humans years to even establish the existence of. The solutions provided, meanwhile, are considered almost as complex as the problems, and the scale – published in 722 papers – make it difficult for human mathematicians to understand and verified.

The impact of the release has prompted one of the world’s greatest mathematicians, Terence Tao, to call for a return to a traditional vision of science.

Demonstration of power

OpenAI’s publication of its work on these advanced mathematical problems is the latest demonstration of how far the technology has come. Just a few weeks ago, the company published its proposal for the Navier-Stokes equation (concerning the behaviour of viscous fluids), generated by its unreleased specialized math model.

Tao is leading the opposition, as one of the field’s leading voices, and a man considered to be the greatest living mind in mathematics.

Following OpenAI’s contribution, he shared a post from the Association for Human Mathematics, which concludes:

“Releasing over 700 files at once is not a demonstration of scholarship, but a demonstration of power. We urge mathematicians to discontinue their work with OpenAI and to return to a vision of science that centers human understanding.”

However, as powerful as that conclusion is, their response to OpenAI’s intervention as a whole highlights that the company may have overstepped.

Questions of legitimacy

The Association for Human Mathematics states that “Mathematicians did not ask for this work to be done,” continuing that “The Advisory Group on Mathematics and Artificial Intelligence, from whom OpenAI has claimed to derive its legitimacy, opened their initial advisory statement by saying that frontier AI corporations should not test advanced mathematical problems on internal models.”

By ignoring the position of the advisory group, they state that OpenAI has essentially gone beyond the pale, and “indicated total disregard for the norms of scientific research — norms that guarantee that mathematics remains trustworthy, ethically researched, and in the public interest.” Furthermore, they encourage mathematicians to treat the publication with “due skepticism.”

OpenAI has responded by stating "we want this progress to push the frontier of human knowledge and enable further progress in mathematics." However, its actions are being interpreted as reckless, and this showboating may well prove to be damaging for OpenAI and the wider AI industry.

• Mozilla accused Microsoft of using Windows, Edge, Bing, and Copilot to steer users toward Edge
• Firefox maker claims stricter EU regulations force fairer browser choice than elsewhere
• Browser competition increasingly overlaps with the fight for AI distribution and user reach

Mozilla has accused Microsoft of abusing its dominant position wherever it can to push people towards Edge, and away from other browsers.

The company framed the story as if it wants “better for the people”, while Microsoft only does it when it’s forced to, but is that really the case?

In a newly released blog, Mozilla noted it has now been 100 days since it published Over The Edge 2.0, a report from “leading experts on deceptive design Dr. Harry Brignull and Cennydd Bowles on how Windows, Edge, Bing, and Copilot are designed to steer people away from the browser they choose.”

Better for the people

In the report, Mozilla said that in the European Economic Area (EEA), where the Digital Market Acts (DMA) is being enforced, Microsoft is forced to “play fair”.

Anywhere else around the world, including the US, UK, India, and other parts of the planet, it still uses its “shady tactics”. These include the Windows 10 message “You’re almost done setting up your PC” which allegedly uses “tricky wording” to pull people off the default they picked, Windows Search and Widgets opening links in Edge instead of the default browser, and Windows keeping Edge as the default for common browser file types like PDF and SVG, even after another browser were set up as the default.

Mozilla claims that even within the EU, Microsoft is doing all it can to steer people towards Edge. That includes pre-pinning the browser to the taskbar which, according to the company, “seems to directly violate the ‘principled approach to app pinning and app defaults’, Copilot opening web links in a side-panel instead of in the browser, and Windows Backup “silently” trying to set Edge as the default when it restores a PC and “fails to properly bring the user’s own browser along. “

“Most of the 2024 patterns are still there. The 2026 patterns are worse. Microsoft is using new surfaces like Copilot and the migration to Windows 11 to further undermine user choice,” Mozilla claims.

“We also shared the report with Microsoft and talked to them about the issues raised in the report. We knew that an immediate change was unlikely, but we hoped that, when faced with independent evidence of harmful design practices impacting Windows users, they would also want better for people,” Mozilla said.

“Instead, it became clear that Microsoft will respect user choice only when it is forced to. Microsoft’s standard for respecting people’s choices is legal compliance. Not what’s best for its users. Not its own published principles. Not what’s technically feasible. Not even what it already does for users in Europe.”

Plenty of other browsers agree with Mozilla, too. The Browser Choice Alliance, a coalition of web browsers established in 2024 and consisting of Chrome, Opera, Vivaldi, BrowserWorks, Midori, and Wavebox, issued a statement following Mozilla's report.

The coalition, which describes itself as "fighting for an internet browsing experience free of manipulation, deception and unnecessary complications" said:

“Mozilla’s research demonstrated that harmful patterns in the Microsoft ecosystem are designed to steer users toward Edge, yet Microsoft has refused to commit to respecting users’ browser choice. Today, we join Mozilla’s call for Microsoft to end its campaign to undermine fair competition and restrict web freedom for users across the globe.”

A story on AI

Mozilla said it shared the report with Microsoft but did not say if the company replied in any way. We’ve reached out to Microsoft with a request for comment and will update this article when we hear from them.

In the meantime, we believe it is also important to mention what Mozilla said in the blog’s introduction:

“As AI reshapes our online experiences, the stakes couldn’t be higher. Browsers are a key distribution layer for AI tools and services, while still determining many of the privacy and security functionality people rely on every day. Steering people toward one browser can therefore shape which AI services reach users and the protections that come with it.”

So the question becomes - is this really about wanting better for people, or wanting people to use one AI model instead of another AI model? If demanding equal browser opportunity is fair game, then demanding equal AI opportunity is also fair game, so why not just say so? Why hide behind alleged altruistic causes and principles?

In the blog, Mozilla says it has a “simple” ask - to expand the “fairer designs” currently being enforced in the EEA everywhere on the planet, and to “stop the remaining harmful design practices that persist in the EEA.”

The company concluded the blog by saying there will be no more mister Nice Guy. It says it “stepped up” public pressure on the company and has reached out to regulators and policy makers.

“We hope that regulators in the US, the UK, India, and Brazil are paying attention.”

The rise of generative AI, mixed with the ease of access provided by crypto currency, has created scamming opportunities we couldn’t have imagined just a few years ago. Romance scams that exploit these technologies have now become one of the fastest growing and most devastating forms of financial crime.

The capabilities of generative AI mean it’s now possible to get scammed by a range of incredibly convincing fake personalities, including deepfakes of celebrities and public figures. In fact, according to the makers of the new documentary Super Scam Me, it’s revealed that an estimated one in seven American adults have lost money to this form of deception. In 2025 alone, romance scammers stole more than $1 billion from American victims.

In Super Scam Me, Emmy-winning investigative journalist Mariana van Zeller builds a digital target persona to intentionally lure a persistent fraudster, then teams up with a white-hat hacker to reverse-engineer the scammer’s digital footprint and track him down and confront him face-to-face.

In the documentary Mariana meets the victims whose lives have been upended by romance scams, including a woman who was duped into believing she was in a year-long relationship with Jon Bon Jovi. Yes, that Jon Bon Jovi. What she thought was a genuine connection with the superstar musician left her facing foreclosure on her home, providing a window into the devastating real-world consequences of these crimes.

I caught up with Mariana van Zeller to ask her about the new documentary, Super Scam Me.

TechRadar: From talking to people who were scammed, do you think they were especially gullible, or could this really happen to anyone?

Mariana van Zeller: I truly believe this could really happen to everyone. I mean, the main reason why I decided to do this documentary was because very often I come back from reporting on these scams and people's response would be, "How stupid could they be?" and "Why did they react like this?" and "Why do you give money to a stranger?" And I really wanted to turn the table around and start focusing on the actions of the actual scammers.

TR: How sophisticated are these scammers?

MvZ: You know, these scammers are very, very good at what they do. They spend a lot of time sort of honing their craft, and they use the latest technology out there. They have it all at their disposal. They have scripts, they work in teams, they're educated, there are schools for scammers out there. And they prey on emotions that are common to all of us. You know, hope, fear, love, greed in some cases. So as long as we have those emotions — which I believe every single one of us do — we are very likely, or it is very possible that we can fall victim to one of these scams.

TR: How do we stop this?

MvZ: I think awareness is very important. I think we should be aware of how good these scammers are, about the tactics and technology and strategies that they use. It's partly what I've tried to show in Super Scam Me. But I also think the onus shouldn't just fall on individual citizens. We should be asking our governments, we should be asking big tech and banks to do a lot more than they're doing.

You know, Leslie is a great example. In the documentary, she was supposedly having a relationship with Jon Bon Jovi for almost a year. And she is out there right now. Her heart was broken. She lost a lot of money. She almost lost her house. She felt humiliated and shamed and suffered this enormous loss.

She is now out there herself, every single day, on these platforms, raising awareness and telling people that these are fake profiles and reporting every single one of these profiles to Facebook and to Instagram and Meta in general, and hoping that they will take them down. And she says just four or three in every 10 of these actually get taken down, and the rest have existed for months.

And again, it shouldn't be Leslie, right? It shouldn't be a person who is a victim of a scam trying to prevent other people from falling for these scams. It should be our governments, it should be big tech and our banks, and there should be just a lot more being done to make sure that we're protecting our citizens.

TR: Thank you Mariana.

Super Scam Me, premieres on October 17 in the US, airing on National Geographic at 9pm EDT and streaming on Disney+ and Hulu the same day. In the UK Super Scam Me premieres on Sunday 18th October at 10pm on National Geographic, and streams from the same day on Disney+.

If you are affected by any of the issues raised in this piece, support is available.

Whatever you are going through, you don’t have to face it alone. In the US, you can call or text the 24/7 Lifeline, which is confidential and free, at 988. You can also reach the Crisis Text Line by texting HOME to 741741.

In the UK, you can call Samaritans for free on 116 123, email jo@samaritans.org, or visit the website to find your nearest branch.

Data center water use is under growing scrutiny and the sector’s ability to add capacity at the pace required will depend partly on how well it manages demand without placing unnecessary pressure on potable supplies.

One source being considered is produced water from oil and gas operations – particularly in the next few years, with trial projects launched, and contracts in progress. In April 2026, the US Environmental Protection Agency launched its Water Reuse Action Plan 2.0, which includes accelerating water reuse for data center cooling among its priorities.

The scale of the opportunity is considerable. According to forecasts from water intelligence provider B3 Insight, the Permian Basin was expected to produce around 22.3 million barrels of water each day on average during 2025, with approximately 16 million barrels a day going to disposal.

Where that water can be efficiently repurposed for data center cooling, there is an opportunity to put an existing industrial stream to productive use while reducing demand on potable supplies.

Making that work in practice requires infrastructure that can manage variable water quality, keep different streams separate and maintain a reliable supply, making storage a critical part of the solution.

Making produced water work

Produced water can vary considerably between sources. It may be brackish, highly saline, or it may contain minerals and other substances that need to be considered when the tank and sealant are specified.

The storage system therefore needs to be designed around the water that will actually arrive on site. Data centers can already require separate storage for brine, pre-treated water, treated industrial water, clarified water, filtered water, clean water and fire protection. Produced water would add another duty to a system that may already contain several different water streams, each requiring its own capacity and operating conditions.

Those differences should guide the material selection to protect against corrosion and extend the working life of the tank. Fusion-bonded epoxy can be well suited to produced water and other industrial water duties, combining corrosion resistance with a robust finish that stands up well during transport and installation. Glass-fused-to-steel may also be suitable in some cases, while galvanized and sectional glass-reinforced plastic (GRP) tanks have established roles elsewhere on the site.

Specify quality early

These decisions are easier to make when the tank manufacturer is involved early. Space needs to be considered alongside material selection because the available footprint can change the type of tank that works best - for example, a cylindrical tank may suit an external compound, while a sectional or shaped tank can be more practical in a basement.

Temperature also needs to be considered. As discussed earlier, produced water from the Permian Basin is one potential source for data center cooling, and the Permian is a region where summer temperatures can become extremely high. That makes the tank’s location, exposure and material important. The design also needs to leave sufficient room for pipework and future access. Placing the required storage volume into whatever space remains is not the same as providing a tank installation that can be built and maintained properly.

These decisions need to be made before the package reaches tender - once a tank type and material have been written into the specification, there is often very little room to change them. We have seen that first-hand on data center projects dating back to around 2010, covering clean water, fire protection, brine, effluent and treated industrial water, where the right questions are far easier to address before the design is fixed.

Build for the operating life

Data centers are expected to remain in service for decades, so their supporting water infrastructure should be considered over the same period rather than judged on purchase price alone. Industrial epoxy and glass-fused-to-steel tanks can be designed for operating lives of around 35 to 45 years, depending on the application and maintenance. Some lined or plastic systems may require more substantial work much earlier, which means that getting the specification wrong can leave the operator facing repair and disruption long before the data center itself reaches the end of its life.

The better measure is therefore the lifetime cost of the system, including maintenance and the practicalities of taking a tank offline. The specification should also allow for changes in the produced water or future expansion of the site rather than being based only on opening-day conditions.

Carrying the specification through delivery

A good specification still has to survive procurement and construction, which can be challenging on projects where programs are tight and responsibility passes through several organizations before the finished system reaches the operator.

Data center operators place considerable emphasis on documentation, with detailed records expected to show that the materials and installation match what was approved. In Balmoral’s experience, the level of scrutiny can be similar to that found in oil and gas, particularly where the infrastructure supports cooling or fire protection.

Manufacturers and installation partners therefore need the technical ability to supply the tank, together with the organizational and financial capacity to meet the program and carry the original specification through to the finished installation.

Local manufacturing can reduce transport complexity, particularly in fast-growing markets such as the US, while experienced regional partners can support installation and help ensure that the system is delivered as designed.

A practical role for produced water

Produced water could become a useful addition to the data center sector’s wider water strategy where the source and the facility are close enough for the model to work. The resource is available at scale and policy is moving towards greater reuse, but turning that opportunity into working infrastructure depends on decisions made early in the project.

The water needs to be understood and each storage duty clearly defined, with the tank selected around the conditions on site. Get those decisions right and produced water can become a practical cooling resource supported by storage infrastructure designed to serve the data center for decades.

Looking for the best cloud storage? These are my top picks

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

• Microsoft reveals Maia 300, its latest AI chip
• Designed for "inference-first", Maia 300 offers huge improvements in efficiency and power
• Maia 300 will begin deploying in 2027

Microsoft has revealed Maia 300, the next generation of its Azure hardware as it prepares to scale up its in-house AI infrastructure to multi-gigawatt deployments.

The company says Maia 300, set to begin deployment in 2027, will deliver 40% more tokens per megawatt than other leading GPU-based system - designed primarily for inference, the new release targets long-context reasoning, responsive conversational AI and sustained, multi-step agent tasks.

Microsoft says a rack containing 96 Maia 300 accelerators will provide up to seven times the rack-scale token throughput of a Maia 200 rack.

Inference-first

Interestingly Microsoft says it expects Maia 300 to help support a new generation of AI across its products and services, from agentic coding in GitHub to large-scale model serving through Microsoft Foundry.

But it also expects improved inference and data generation for its Microsoft AI team, and improvements in the likes of Copilot and Cowork, as well as individual products such as Microsoft Teams.

Designed with what Microsoft says is an "inference-first architecture", Maia 300 is built to improve performance by reducing data movement and making more efficient use of computing, memory and networking resources, Microsoft says, with its software-defined dataflow architecture, combined with AI-assisted kernel optimization, designed to keep data close to the parts of the system that need it.

Unsurprisingly, memory is central to the design, as Maia 300 will use a huge eight stacks of HBM4, with a claimed peak bandwidth of 18TB/s within a 950W thermal design power. Microsoft says specialized software can use on-chip SRAM as a scratchpad, easing pressure on HBM during demanding workloads.

The accelerator also introduces a multi-chiplet architecture that separates compute, memory-controller and input/output functions. Its compute chiplets will be built using TSMC’s N2P process. Microsoft says support for low-precision formats, including an enhanced MXFP4, will reduce memory and bandwidth demands, helping the system run larger context windows and increase throughput within the same power envelope.

Networking is designed to operate as part of the system’s dataflow rather than as a separate layer. Maia 300 will use high-bandwidth connections between accelerators for tensor parallelism, alongside a custom AI transport based on Ethernet infrastructure. Microsoft says this approach can scale from a single rack to data-center deployments.

A Maia 300 rack will contain 96 accelerators within a 167kW power envelope, with 27.6TB of HBM capacity and 1.7 petabytes per second of aggregate HBM bandwidth. Microsoft says that density will let it serve more simultaneous AI interactions and larger workloads from each rack.

The launch builds on Maia 200, which Microsoft says is already running production workloads including Microsoft 365 Copilot and models developed by its Microsoft AI team.

The company says Maia 200 delivers up to 1.7 times more tokens per watt than leading GPU infrastructure for built-in AI features in Teams, Copilot and Cowork. GitHub Copilot and Microsoft Foundry are expected to follow by the end of this year.

• Essex man Rafal Gorfal used ChatGPT to help him win a legal dispute over a parking ticket fine
• He fed the AI the letters he received from debt collectors, and it told him what to say and where to go
• The fine was dropped just weeks before his scheduled court appearance

People use AI chatbots like ChatGPT for all manner of things, but have you ever used AI to evade a parking ticket fine before? This man did, and he went on to win a three-year-long legal battle.

Rafal Gorfal, who’s originally from Essex, was fined back in August 2023 for not paying fast enough in a Newquay pay-and-display car park. Gorfal said that when he arrived at the payment machine he had to wait in a long queue, only to learn that the machine was cash-only, to which he had no choice but to download the RingGo and pay with his phone — delaying his payment further.

Two weeks later, he was presented with a £100 fine that said he didn’t pay for a ticket until 16 minutes after entering the car park, which was delayed due to the long queue and the time it took Gorfal to set up an online account to pay.

A long three years

Gorfal submitted his first appeal a month after receiving the PCN from Smart Parking Limited, but didn’t hear anything until January 2024 when he was sent another debt collection letter, this time with an increased fine of £170. "I appealed straight away and heard nothing from them," Gorfal told The Independent, adding "They were trying to make me scared. You have to give people the time. I told them exactly what my problems were. They could see on the cameras that I was trying to sort it out".

After appealing back and forth with debt collectors for four months, the case was put on hold, and Gorfal heard nothing for the best part of a year and a half. This period of silence was broken when he was sent a new letter stating that the case would be going to court in October 2026. This is when he sought help from ChatGPT.

Over the course of his legal dispute, Gorfal kept records of every letter he was sent, which he says he entered into the AI chatbot and asked for advice. "From start to finish I was asking ChatGPT every time I got a letter what to do. It was telling me what to say, where to go, and everything," he shared. "I did it all with the help of AI. They even prepared my defense which I sent to the court". He also contacted the Citizens Advice helpline, but he says the help wasn’t as effective as ChatGPT.

David defeats Goliath

Gorfal’s initial £100 fine had sky-rocketed to roughly £300 by the time he was issued the court order but on September 25, just weeks before he was due to make the appearance, he received an email informing him that the case and fine had been dropped — resulting in a win for Gorfal after a long three years.

That said, it wasn’t money that fueled Gorfal’s persistence, there was a big morality factor. "From start to finish it was not about the money, I could pay it, but it is about the principle. If you know you are right then you shouldn’t be paying any money to them," he said. "This is kind of a David and Goliath battle. They have lawyers and they are a big company and they just gave up in the end".

Since his case and fine were dropped, Smart Parking Limited hasn't provided comment on the dispute.

As organizations race to adopt SaaS applications and AI-powered tools, shadow integration has become a more dangerous and often overlooked entry point for modern breaches. Every new application introduces its own identities, roles, and permissions, while every third-party integration creates another trusted pathway into enterprise systems.

There may already be hundreds of agents currently inside the average enterprise, and these deployments are not slowing down. Employees routinely authorize third-party OAuth applications that gain persistent access to business-critical systems, often with broad permissions that remain long after they're needed. At the same time, AI agents and automated workflows are being embedded into critical SaaS platforms like Salesforce, ServiceNow, Workday, and Slack.

Each new connection expands the attack surface, yet few organizations have a complete understanding of how these trusted relationships interact, and cyber criminals have noticed. In fact, SaaS breaches have increased by 300%. These identities often operate with elevated privileges, which makes them attractive targets if compromised.

The threat landscape is expanding faster than traditional security models can track. Without simple, trusted controls that can instantly govern SaaS applications and AI agents, organizations are left with systems that can access sensitive data, interact with business systems and take actions on users’ behalf.

To mitigate the threats, security leaders must be able to know at any moment, which third-party apps are connected to their environment, what those connections can reach, and whether they're behaving normally, rather than finding out after a security incident.

Where SaaS innovation creates security risk

As SaaS sprawl grows, IT teams are dealing with fragmented signals across hundreds of applications, making it difficult to understand what’s connected and where access actually lies.

This threat landscape is expanding rapidly. With projections of over one billion agents in enterprise environments by 2028, organizations are deploying these tools faster than any manual review process. As agents gain access to business systems and integrations, traditional approaches to managing permissions and access are no longer sufficient.

Low-code, no-code and AI platforms are making it easier for anyone to build and deploy autonomous agents, often without security or oversight built in. These agents can inherit real access to business systems and data, while organizations still lack a consistent way to discover them, trace their privileges and enforce guardrails across the SaaS environment.

Recent security incidents illustrate this evolving threat. For example, Obsidian Security disclosed a one-click remote code execution vulnerability in Flowise (CVE-2026-40933), an open-source platform for building AI agents and LLM workflows with more than 52,000 GitHub stars.

The research found that an attacker could fully compromise a server by persuading an authorized user to import a malicious chatflow. The risk includes a combination of multiple factors: a trusted user importing a shared workflow, permissions that allowed custom MCP configurations, the ability to launch external processes, and access to connected credentials, APIs, databases, and cloud services. Together, they created a path to full environment compromise.

Traditional solutions don't discover these integrations, can't evaluate their permissions, and have no visibility into the cross-app activity that happens between connected systems. But that’s increasingly how attackers are escalating access and moving laterally.

The role of visibility across SaaS ecosystems

To maintain the speed of innovation while keeping a security-first approach, IT teams need visibility across users, applications, OAuth grants and permissions so they can understand risk as it changes, not after an action has already taken place. They need to be able to answer questions such as, “Who still has admin rights across multiple apps?” or “Which inactive accounts still have effective access?”.

This is where approaches such as knowledge graphs can offer a different way to understand SaaS risk. A knowledge graph connects entities such as people, accounts, tokens and resources, showing how they relate to one another and revealing context that might otherwise be missed.

Rather than forcing every SaaS application into a standardized permission model, it captures how users, roles, permissions and integrations actually interact across systems, allowing security teams to understand who has access, what they can reach and how that access was granted.

With that foundation, security becomes adaptive rather than restrictive. High-risk or unusual integrations can be automatically flagged or constrained based on factors such as agent behavior or access patterns, while trusted activity can move forward with minimal friction. This allows IT teams to maintain strong control over the SaaS environment without slowing down legitimate business activity.

OAuth and API integrations make this visibility even more important. They can access large amounts of data and connect directly to critical systems, often without the same oversight applied to human users. Traditional tools such as SIEMs and CASBs can struggle to capture this risk because they were largely designed around human activity and security events.

But SaaS risk increasingly lives in the relationships between users, applications, permissions and integrations. A dormant administrative account may appear harmless until it is connected to an overprivileged OAuth integration or an AI workflow that inherits unnecessary access.

The future of SaaS security won't be defined by collecting more alerts or deploying more point solutions. It will be defined by bringing evidence to show where guardrails are needed, who has access, what they can reach, how that access was granted, and how those relationships change over time. Ultimately, the leaders that can turn this visibility into informed control will be best positioned to embrace SaaS and AI innovation for the business.

We've featured the best endpoint protection 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

ChatGPT has been pushing further into becoming a shopping assistant for a while now. You can ask it for product recommendations and it’ll find specific items, show you images, prices and links to buy them. Sometimes it’ll even add contextual details from what it knows about you, like your color preferences or budget, to help personalize its recommendations even more.

I've been testing it over the past few months and I find it a strange way to shop. OpenAI has tried to make the experience as slick as possible, but I still like visiting a retailer’s website, seeing more photos, browsing the rest of the range and checking reviews and product details before I buy anything.

But its latest shopping feature is much more interesting. Because you can now virtually try on clothes within ChatGPT.

This makes a lot of sense. ChatGPT’s image generation capabilities have become much more realistic since its Images 2.5 update. With more natural lighting, convincing textures and a better understanding of how objects should look in the real world. Apply that to shopping and suddenly the idea of virtually trying on a jacket or dress becomes much more appealing.

To use the feature you’ll need to upload a photo of yourself and then look for something you want to buy, with a Try On button now appearing alongside supported shopping results. Alternatively, you can give ChatGPT an image of an item and ask it to show you what you’d look like wearing it.

Now, this sort of virtual try-on technology itself is nothing new. We’ve seen versions of it for clothes, glasses and makeup for years. But I wanted to know whether ChatGPT's newer and more realistic image generation could make the experience genuinely useful.

I find shopping in person pretty overwhelming sometimes. But online shopping comes with its own problems too. Colors can look different in person, textures aren't always what you expect and it's difficult to know whether something that looks great on a model will work on you.

So I decided to put ChatGPT's virtual fitting room to the test.

Trying it on for size

I started with a full body photo of myself taken while I was out shopping and used that as the photo I uploaded after I clicked the Try On button.

I’d seen examples of ChatGPT recreating the original surroundings in the photo when generating try-on images for other people but that wasn't my experience. Despite trying several different items, it consistently placed me against a fairly plain studio-style background.

But I actually preferred that because it meant there was less visual noise and it made it much easier to concentrate on the clothes.

I started by asking ChatGPT to help me find suede jackets. It surfaced several options currently available to buy on the high street and I virtually tried on the ones I liked.

After it surfaced a jacket available in a shop near me, I decided to go and find out in the wild. That meant I could compare ChatGPT’s interpretation with what happened when I actually put the jacket on. And I was surprised by how close it was.

I deliberately picked an oversized jacket because I thought that would make a good test. It’s relatively easy to paste a fitted garment onto somebody. But understanding that something is supposed to hang loosely seemed like a bigger challenge.

I didn't recreate the exact pose or camera angle from the AI-generated image, so this wasn't a perfectly controlled comparison. But ChatGPT did a surprisingly good job of guessing where the jacket sat on my body, its oversized shape and the way the material fell.

It wasn't perfect, but as a way of answering the question “roughly what might this look like on me?”, I can imagine it being helpful. Even just as the first step in the buying process.

Changing things up

After trying a few more suede jackets, I wanted to see how well the feature worked when I moved away from the fairly straightforward jeans and jacket combination.

So I asked ChatGPT to find me dresses I could wear to a formal event and started virtually trying those on too.

Again, the results were convincing enough that I could imagine using them to narrow down a long list of potential purchases. One thing I found particularly useful was color. Seeing a garment against my own skin and hair gave me a much better sense of whether I actually liked a shade than seeing it on a retailer's model.

Then I decided to take things one step further. I told ChatGPT to find me something extravagant that I could wear to a 1920s-style ball and tried on one of the more dramatic options.

Even here, the result was remarkably convincing at first glance. The clothing looked like clothing rather than something crudely pasted onto my body, and ChatGPT handled the radically different silhouette surprisingly well.

But by this point I'd also noticed something else sort of awkward was going on. The woman in these pictures wasn't quite shaped like me.

ChatGPT gave me a makeover

The full-body photograph I uploaded was an accurate representation of my proportions. Yet across the generated images, ChatGPT seemed to subtly change them. I looked taller noticeably and more statuesque. My torso appeared slightly shorter and my legs were longer.

You might think that's hardly something to complain about. But that's a huge problem when the entire purpose of the feature is to show me what clothes will actually look like on my body.

We know that generative AI has a tendency to beautify people. I’m convinced this is one of the reasons people make AI images of themselves, because it makes everyone look a little more conventionally attractive. But in a virtual fitting room accuracy matters far more than flattery.

If a dress looks fantastic because the AI has subtly changed your proportions, that's potentially no more useful than looking at the dress on a model in the first place.

Should you use ChatGPT’s try-on feature?

Maybe. I went into this experiment pretty skeptical. I've tried enough AI image tools to know that "realistic" can fall apart very quickly when you look closely, and virtual try-on technology has been around long enough that simply putting digital clothes onto a photograph isn't particularly impressive anymore.

But ChatGPT did do something useful. For me, its biggest strength was color. It might have changed my body, but it kept my skin tone and the color of my hair identical.

Which meant that being able to quickly see several different shades against something approximating my own appearance could genuinely help me decide between them. I was also impressed by its ability to communicate the overall silhouette and feel of an item, particularly with the oversized jacket I was able to compare with the real thing.

So sure, it could take some of the stress out of shopping. Rather than ordering five jackets and hoping one works, I can imagine using virtual try-on to narrow those five down to two.

But I'd still be cautious about treating the resulting images as anything like an accurate prediction of exactly how something will fit. Fabric weight, sizing, movement and all the strange little differences between our bodies are difficult to judge from a generated image. And, based on my experiment, ChatGPT may give you slightly more conventionally flattering proportions in the process.

The robots are among us. They’re carrying passengers through the streets of San Francisco, delivering parcels over the air, and working their way across farms, factories, mines, and industrial sites.

The collision we’re now witnessing between artificial intelligence and the physical world is often described as “Physical AI” – machines capable of perceiving their surroundings, making decisions, and taking action among us in the real world. But every new machine that clocks in has a second job we rarely see. It creates data. Lots of it.

In the US, the Federal Aviation Administration expects the commercial drone fleet to grow from approximately 966,000 aircraft at the end of 2024 to around 1.18 million by 2029. And in factories around the world, there was an estimated 4.66 million industrial robots working in 2024, while another half a million were installed during the year – more than twice the number installed a decade earlier.

Today’s autonomous machines are vastly more capable, but moving all that accumulated experience off them can still be surprisingly manual. In 2026, the fastest way to move masses of data off a machine is still a person carrying a hard drive. And as autonomous machines spread further into our cities and industries, keeping that information moving could become one of the defining infrastructure challenges of the robotics age.

Data runs both ways

Imagine what an autonomous vehicle experiences. Cameras watch the roads from multiple angles, sensors and AI measure and make sense of the surrounding environment, while on-board systems track position and performance of the vehicle itself. Decisions are made and recorded in real-time as pedestrians cross roads, traffic lights change, and thousands of other vehicles and connected technologies move and exchange data around it.

Delivery drones, agricultural machinery, warehouse robots, and autonomous industrial equipment may operate in different environments, but the logic tracks. Every journey or task leaves behind a digital trail of sensor readings, video, telemetry, diagnostics, and operational logs.

And the information doesn’t travel in only one direction. These machines may also need fresh maps, software updates, configurations, AI models, and instructions from the computing infrastructure behind them. What they experience in the physical world flows back toward data centers, while the intelligence needed for their next task travels outward.

Multiply that exchange across millions of machines and an interesting new layer of internet traffic begins to emerge that we’re only just beginning to think about.

The direction of travel was visible as early as 2019, when a landmark university paper on 6G identified mobile robots, drones, and autonomous vehicles would drive demand for ubiquitous wireless intelligence – its vision for the 6G era included networks capable of supporting a 10,000-fold increase in traffic. The US commercial drone fleet alone is now heading toward seven figures, while industrial robots already number in the millions globally.

Perhaps most notable to casual observers, autonomous vehicles are becoming far more prevalent on the streets of our capitals and major cities. In June of this year, machines surpassed 57% of web traffic globally – the first time that bots have outnumbered humans – and they use networks in a very different way. Cloudflare predicts that non-human traffic could eventually surpass human traffic by a factor 1,000 within the next five years.

And a lesson learned by one machine needn’t remain with that machine. There’s an opportunity here that only connectivity can unlock. Google DeepMind’s Gemini Robotics 1.5 showed skills transferring “zero-shot” between physically different robots – a task taught using a two-armed Franka robot was executed by a humanoid, without any data collected on the target machine.

Sit with that implication. A person who masters a technique carries that knowledge around in their head, and when they retire, much of that experience leaves with them. A robot can potentially hand what it has learned to every other robot on the network, which means experience stops being “local” and starts becoming a shared asset across the network, compounding intelligence for the company’s moat.

The physical footprint of robots is spreading outward, but the data traffic they create remains concentrated at central locations. AI may increasingly have wheels, wings, and arms, but their intelligence and interactivity with the world remain still wholly dependent on their relationship with the nearest data center hub, and that’s a problem we now have to solve.

What’s the hold up?

When a robot arrives back at the depot and “clocks out” for a while, it presents an ideal opportunity to recharge, undergo inspection, run self-diagnostics, download updates, and get the data it needs for its next shift. This is when potentially enormous volumes of data, depending on the fleet size, need to be moved both to and from the device.

Sites that house, charge, or service machinery can therefore find themselves functioning as miniature data hubs, dealing with bursts of traffic as machines arrive and depart. And as fleets grow, so does the strain on the network connecting all these hubs. When uploading and downloading take longer than charging or maintenance, the network becomes the operational bottleneck to scale.

A new layer of connectivity

A wired network simply can’t evolve at the same speed as the mobile economy. A fleet operator might need to open a new depot in another city, place drone nests closer to customers, or connect autonomous equipment at temporary industrial sites. Extending fiber to each location would involve trenching, permitting which are often cost and time intensive.

Wireless connectivity offers a faster and more flexible approach. Links can be installed without digging or waiting for permits, allowing operators to connect new sites quickly. Links can also be redeployed or expanded as fleets grow as their physical footprint expands dynamically.

Wireless optical communication (WOC) could provide that high-capacity bridge. Using narrow beams of light, it carries fiber-like capacity through the air between two points with a clear line of sight, up to 20 Gbps across distances of up to 20 kilometers. It can be installed on existing radio masts or buildings in a matter of hours.

For autonomous fleets, that creates a way to rapidly offload telemetry and operational data, upload new maps and models, and return high-value assets to work instead of leaving them waiting at the depot.

WOC isn’t magic though. Optical links still need line of sight, and heavy fog and rain can degrade their connectivity. That’s why serious deployments pair them with an RF path and engineer them for availability rather than simply assuming it. It won’t replace fiber, but it will extend fiber’s reach to the places fiber can’t go, and it’s getting smaller, faster, and more affordable as the technology moves from mechanical to solid-state beam steering with silicon photonics. Light is the answer.

The arrival of electric vehicles taught us to look beyond the machine and consider the infrastructure behind it. Autonomous machines will require us to do the same again, but this time what needs to be replenished isn’t just the machine’s battery – it’s the machine's experience, and from it, the shared intelligence of the entire fleet.

Robots are becoming extraordinarily good at navigating the physical world. We shouldn’t leave the final mile of their learning journey to someone carrying a hard drive across a parking lot.

We've featured the best AI chatbot for business.

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