• Microsoft has made a welcome change to OneDrive in Windows 11
• Prompts to use the backup service can now be permanently dismissed
• The change has been positively received by most users online

There have been complaints about this going back years, but Microsoft has finally caved in and decided to stop nagging users to back up their files to OneDrive: these alerts can now be permanently dismissed with an Opt out of backup button.

As spotted by Windows Latest, the move is going to please users who have no intention of using OneDrive to store their files, and would rather not have occasional reminders from their operating system that this is an option.

The change seems to be tied to the latest stable Windows 11 25H2 build. Previously, if you didn't want to go ahead with the OneDrive sync, you could only pick Remind me later or Remind me in 3 days — and the message would eventually loom back into view.

It's something we see across the majority of software now, and it's what Windows Latest rightly calls "dark patterns": interface choices that are designed to nudge users towards a certain option even if they're not sure about it.

Heading in the right direction

The reaction on Reddit has been mostly positive, with the general sentiment of the comments suggesting that this change was long awaited. "I fully opted out of Windows instead," remarks one commenter in the thread.

Several contributors to the Reddit discussion say they've already bailed on Windows 11 and switched to Linux or macOS — not just over the OneDrive issue, but also for other problems like memory management and privacy concerns.

For many years now, Microsoft has been trying to foist OneDrive and its other Windows-adjacent apps on to users of its desktop operating system, but it appears that it's finally figured out that this may be doing more harm than good.

To give Microsoft some credit, it has largely been making good on its promise to address the biggest complaints users have had about Windows 11 in recent months, even if a lot of the improvements should've been made a long time ago.

For years, enterprise Software as a Service (Saas) has enjoyed a comfortable position at the center of organizational processes. Users who wanted to raise a ticket, process an invoice or manage a workflow would log into their employer’s cloud application. The user interface was the gateway to the work, and the provider’s business model followed naturally: charge for seats, expand adoption, and grow revenue as more users spend more time inside the platform.

Agentic AI is beginning to change that. As users increasingly deploy AI agents, either to carry out tasks within SaaS platforms or to deliver functionality outside them, organizations require fewer seats – hitting providers’ subscription revenues.

At the same time, AI is making bespoke software faster and cheaper to build, giving organizations a credible alternative to buying another SaaS platform. Why accept subscription costs and vendor lock-in, when cheaper alternatives may achieve the same outcome?

None of this means SaaS is about to disappear, but its position at the center of enterprise software is under threat. SaaS vendors are facing three structural challenges that will reshape the economics of enterprise software.

Agents become the user

The first threat is simple: users may have AI agents carry out tasks within the SaaS platform, reducing the need for human users, or build agentic capabilities outside those platforms altogether.

That is a serious problem for any SaaS vendor whose business depends on people living in the interface. If an agent can resolve a support query, update a record or complete a routine transaction, the user never has to touch the application.

We’re already seeing this in practice. In some IT service management projects, agents now resolve such a high proportion of tickets that organizations need far fewer users interacting with the SaaS platform. As buyers begin questioning whether they still need premium software for the shrinking proportion of manual work, the economics start to shift.

Bespoke is back

The second threat is the one SaaS vendors dislike most: buyers may once again be willing to build new capabilities from scratch.

For the last decade or so, ‘build versus buy’ was often a non-debate. Custom software was too slow, too expensive and too risky, so SaaS usually won.

AI is starting to shift those calculations. As software development becomes faster, cheaper and more adaptive, some solutions that previously defaulted to SaaS start to look buildable – especially when the organization needs a narrow set of capabilities wrapped around its own processes and data. The more customization a SaaS platform requires, the weaker its cost-efficiency and automated-upgrade advantages become.

This does not mean large enterprises will abandon mature SaaS platforms overnight. Their resilience, integrations and compliance capabilities remain valuable. But once customers believe they have a credible alternative, vendors lose pricing power and the assumption that renewal is the obvious decision

Business model redundancy

The third threat is that even when the product survives, the commercial model may not.

Traditional SaaS pricing was built for a world in which value correlated reasonably well with the number of people sitting in the application. That logic gets shaky when agents perform work that would once have required more licensed users, and more operational dependency on the interface.

Vendors can see this. Many are shifting towards consumption-based pricing, exposing services directly to agents or redesigning products for an agentic world. The common thread is clear: once software starts working for people rather than being operated by them, seat-based economics begin to look outdated.

From the customer’s perspective, that could be a good thing. Paying for usage is often cheaper than buying licenses. The challenge for vendors will be proving they’re genuinely adapting to an agentic future, rather than simply repackaging existing products to protect margins.

SaaS is not dead

To be clear, this is not a “SaaS is over” argument. Many enterprise platforms will remain valuable, providing governance, resilience, compliance, and years of accumulated functionality that are difficult and expensive to recreate.

What will change is where value sits. In an agentic world, SaaS will be less the primary interface through which work gets done; more a component in a broader ecosystem of agents, orchestration and systems of record. Some platforms will adapt and become stronger; others will discover that the value they once captured has shifted elsewhere.

As for the corporate users of SaaS, the best advice we can give is not to wait for perfect conditions before acting. No business ever reaches a point where its data, processes or architecture are “finished”, and SaaS providers will continue to adapt their offers.

Meanwhile, experiment, learn and adapt in your delivery of administrative and corporate functions: in some functions you may find that agents drive down SaaS costs; in others, greenfield development may now be viable. In still other cases, adding new functionality using agents that leverage SaaS solutions but sit outside those ecosystems keep future options open.

So do something that works for you now – but keep the option of moving again in a year or two.

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You can spend hours choosing the best laptop, comparing processor options with the display and wondering how long the battery will last.

Then you switch it on and discover another part of the experience that rarely enters the buying decision: the manufacturer’s preinstalled software, often billed as tools to help optimize your device.

Sometimes, they earn their right to come pre-installed. The pre-installed software might be where you change performance mode or cap the battery charge, alongside model-specific settings Windows 11 simply doesn’t offer.

The strange part? How much bigger the role of pre-installed software (sometimes called 'bloatware') has become. A simple utility can now feel like another software layer sitting on top of Windows 11, with launchers and user accounts jostling with the settings you actually want.

And because some of those controls are legitimately important, uninstalling the whole thing isn’t always an option.

Windows 11 can’t actually control everything on your Windows laptop

For all the settings tucked inside Windows 11, there are still plenty of things it can’t manage on its own.

And this isn’t just a gaming laptop quirk. On supported Asus models, MyASUS handles things such as Battery Care Mode and model-specific fan profiles that Windows 11 doesn’t offer.

Gaming laptops simply take the idea much further.

Take the Alienware 16X Aurora. Alienware Command Center offers several performance settings, and in our testing, its 'Overdrive' mode pushed the fans harder while making use of the laptop’s Cryo-Chamber cooling system.

None of that feels especially unreasonable.

Laptop makers know how their own hardware is designed to behave, and a dedicated utility gives them somewhere to offer controls that would otherwise be buried in the BIOS or unavailable altogether.

The problem starts when that useful little control panel begins acquiring ambitions of its own.

A settings menu somehow became an entire platform

Many of these apps now go well beyond looking after the laptop itself.

Armoury Crate still gives Asus ROG owners useful controls over performance and cooling, but it also has a user account system and optional content services alongside its device settings.

Lenovo’s Legion Space pushes the idea further, putting thermal controls alongside a game store and its AI-powered Gaming Zone. On supported machines, HP’s Omen Gaming Hub mixes fan and graphics controls with a game library and GeForce Now.

Taken one by one, most of this is easy to defend. Put it all in the same app, though, and the hardware settings start to feel like one part of a much larger product.

If I open software to quieten the fans, for example, I’m probably not also looking for a new game or a giveaway.

The worst part is that you actually need it

With ordinary pre-installed software, there’s an obvious solution if you don’t want it: uninstall it. Laptop software made by the manufacturers themselves is harder to shake because some settings can make a real difference in how the machine behaves.

Our MSI Raider 16 Max HX review shows why this gets awkward. The laptop comes with a fair amount of bundled software, yet MSI Center itself gives you useful access to performance presets and RGB controls.

Wiping out every manufacturer utility for a cleaner Windows 11 install can take useful features with it. However, some laptops do have alternatives.

G-Helper is an open-source option for Asus machines, offering access to performance and GPU modes, battery charge limits, and other hardware controls in a lightweight app. Its built-in performance modes use the same firmware presets as Armoury Crate.

You don’t have to be comfortable swapping official software for a community-made utility to see the appeal. G-Helper’s existence shows that granular laptop controls can live in a focused tool rather than a whole, often bloated, ecosystem.

Microsoft bears some responsibility too

Some of this starts with gaps in Windows 11 itself. Smart Charging exists on compatible devices, for example, but there’s no universal control for choosing a fixed charge limit – Microsoft says each manufacturer implements the feature differently.

Microsoft has already shown what a more standardised approach can look like.

Dynamic Lighting is a good example: compatible RGB hardware can now be controlled directly through Windows 11, rather than relying entirely on separate manufacturer utilities.

Fan tuning and firmware are much more specific to each machine, so manufacturer software isn’t going away.

But every common control Windows 11 can standardize is one less reason a laptop needs its own parallel settings software - and that would mean less bloat preinstalled on our devices.

Laptop software needs to become boring again

Maybe the real problem is that these apps have become something you even need to think about. A good pre-installed app should make the laptop easier to live with, then stay out of the way until you need it again.

MSI Center at least gets some things right: it’s modular, so you can install only the features you want rather than loading everything by default.

Switch brands, though, and even familiar jobs can move somewhere completely different. A 'quiet' fan mode that takes seconds to find on one laptop might be buried under different terminology on the next.

Software like this probably deserves more consideration in laptop reviews, too. It won’t rescue poor battery life or a bad keyboard, but if you rely on it to change how the laptop behaves, it's part of the product you bought.

I don’t want laptop makers to stop building these tools. I just want them to remember what they’re for: helping me use the laptop I already bought.

• Windows 11 has a new preview update for August
• It offers some nifty new features, including the ability to move the taskbar
• There are some frustrating bugs in the update, though, which include an oversized retro mouse pointer you can't switch away from

Windows 11 just got a new update — albeit still in preview — and while it delivers some smart goodies for the interface (including a taskbar option many users have been waiting for), there's a problem with bugginess here.

As a result, the preview update for August is an 'install at your own risk' affair, and although it's not going to put you in danger of being trapped in a boot loop or something equally terrible, the bugs are annoying.

Windows Latest noticed the initial reports of problems with this update, which include the mouse pointer being changed to a "big white cursor" and the wallpaper becoming a blank black background.

There are plenty of reports of this happening in the official Reddit thread (posted by Microsoft's Jen Gentleman) pertaining to this Windows 11 update. Apparently the old-style cursor which is appearing in some cases can't be changed, and if you try to revert the cursor back to what you had before, it'll just switch to the legacy pointer again, going by the reports on Reddit. You can, apparently, get rid of the plain black wallpaper.

While this update is optional, as are all preview releases, there's been quite a rush to grab it, because, as mentioned, it comes with some long-awaited interface changes: the ability to move the taskbar away from the bottom of the screen, and to resize the Start menu. There's also functionality that allows you to turn off Bing in Windows 11 search, which is great to see.

Analysis: QA and gradual rollout frustrations

While the bugs are purely cosmetic issues, you do end up with a stark and basic-looking operating system if they happen to your system. The really annoying bit is that you can't escape from the clutches of the oversized white retro mouse pointer (the only solution is to remove the update by all accounts).

One unimpressed Redditor complains: "Wow a buggy mess of an update for everyone."

It's easy enough to draw this conclusion scanning through the official Reddit thread, where there are quite a lot of reports of this issue. However, we've got to remember that those who are affected are more likely to be vocal and drop a complaint (or a straightforward bug report) on social media. So, that skews things and makes matters look worse than they actually are – and notably some folks are chiming in saying they've installed the August optional update just fine with no problems. Windows Latest didn't encounter any pointer-related shenanigans, either.

That said, there are enough reports of this to be a worry, and it's a weird glitch to have made it through to the final stage of Windows 11 testing — where preview updates are actually pushed to customer (non-Insider) devices (if they elect to install the optional release, of course). This again points to issues around QA (quality assurance) and testing for Microsoft, something that I keep banging on about.

At any rate, hopefully it'll be a quick fix from Microsoft here. This preview release will become the September update next month, and so this bug will certainly need to be squashed before then.

It's also worth noting that there are people who've not encountered the mouse cursor bug, but are still expressing their disappointment that they haven't got the mentioned interface changes for the taskbar and Start menu. This is because those features are on a gradual rollout, so they will only come to a selection of PCs to begin with, which will broaden over time.

As another Redditor observes: "So I got black screen, my cursor is stuck to Windows 98 and can't be changed for some reason. I got the movable taskbar but don't have the big or small Start menu. With all due respect, this OS is a 'joke' (more than that actually just wanted to stay super polite)."

It's frustrating to take a chance on an optional update because you want the features therein, only to find you don't get some — or indeed any — of them, but such is the nature of functionality deployment in Windows 11. You just have to be patient, although Microsoft has had some of its bigger changes on a very slow-paced rollout in recent times, and this is proving to be an increasing annoyance for some folks. Better to be 'right' than 'rushed' though…

• Meta is again updating its smart glasses software to stop people from circumventing the privacy controls
• The camera won't film if you start filming and then block the LED
• There's also a marketing campaign designed to educate consumers

Sentiment around smart glasses that can capture photos and video is quickly turning, and not in the direction chief smart glasses purveyor Meta would prefer. Now, to stop the — admittedly small — percentage of users who are trying to circumvent the glasses' built-in protections for nefarious filming, Meta is introducing a new slipstream technology update. And it's coming to Meta Ray-Ban smart glasses as we speak.

In a post on Threads, Meta's VP of Wearables Alex Himel announced a software update that will make it impossible for wearers to film or capture pictures if the glasses detect that you're covering the LED.

That LED is designed to signal to others that you were filming them, but hackers soon figured out how to disable it. Meta quickly made it so the system could detect that. Now, it seems they've noticed another workaround, which this update will quickly squelch. If you, say, start filming and then somehow cover the LED to hide the signal that you're doing so, the system will stop recording.

Here's how Himel explained it in his post:

"The camera won’t start recording if the LED is blocked, but some people try to bypass this by starting a recording and then covering it up. A fix for this is starting to roll out — the camera will now stop working if the light is covered during a recording."

Perhaps more importantly, Himel is aware that hackers probably won't give up trying to jailbreak the wearable cameras and promises. "We’re going to keep rolling out updates like this to make sure the capture LED can reliably alert bystanders when photos or videos are being captured for someone’s gallery."

The second update is perhaps less impactful in that it's mostly a marketing campaign designed to "educate' or convince people that these glasses have a place in our world.

Meta is already putting up billboards claiming the glasses are "Designed for everyone, not just the people wearing them". Smaller text below that announces: "The camera lets you capture the moment. The light lets everyone around you know when you do."

There's also some social media reminding folks that "The glasses announce themselves," which is another reference to the integrated LED light.

It's an admirable effort, though it seems that people are not just concerned with creepers who are surreptitiously recording: they are growing increasingly uncomfortable with people potentially walking into any space and filming. The glasses are being outright banned in all sorts of venues and face potential bans in theaters and even countries.

Of course, people filming everything everywhere has long been the case with smartphones, and we have countless hand-held, on-the-scene videos and social media to prove it. It seems to be the unexpected nature of these smartglasses. Even with the light, people don't realize at first that they are being filmed, which can lead to some uncomfortable conversations.

For Meta, convincing consumers that they have this situation under control — even if it is relatively few bad actors — is critical. The company is deeply committed to the wearable intelligence space. They have been rolling out new smart glasses models, including the recent Essilor Luxottica models that feature a frame designed by Kylie Jenner, and the company is a month away from its big Connect conference, where it's expected to announce more updates across all its smart eyewear, including the Ray-Ban Displays, which include cameras and an in-lens display.

Is this update enough to make you comfortable around Meta Ray-Ban smart glass wearers? Let us know in the comments below.

• Meta's official GTA San Andreas VT port never launched
• So a modder has vibe-coded a port for the Meta Quest 3
• You can play it yourself, but it's not easy

The Resident Evil 4 VR remaster on the Oculus Quest 2 was the best the platform had to offer, so a Grand Theft Auto: San Andreas VR port was easily the most exciting news to come out of Meta’s 2021 Connect event. But it never arrived. So now modders are making up for Meta’s biggest letdown by finally bringing San Andreas to Quest systems.

In fact, there’s a whole VR porting community that connects via the Flat2VR Discord. One such member, called GeT_RiCh, has made it his mission to bring the long-promised GTA title to Quest headsets, though you will need a Meta Quest 3.

It’s still a work in progress — you can follow updates on Discord — and installing this game isn’t as easy as pressing a button. As the mod’s GitHub explains you’ll need not only this mod but an original copy of GTA San Andreas from the Google Play Store (specifically version 2.11.311 exported as an APK), a PS2 sound pack for the game, a PC, a Quest 3 headset in Developer Mode (to authorize USB debugging), and around 15GB of space on your PC and 6GB on your headset.

Plus, you’ll need plenty of time and confidence that you can accurately go through the technical steps. I was considering trying to jump into the game before writing this piece up, but quickly realized that might be a challenge.

Despite the shortcomings, the effort is pretty incredible. It’s also, interestingly, being aided by Claude, which the modder is using to help him vibe-code aspects of the software.

Say what you will about vibe coding in the wider coding world, but the tool’s apparent effectiveness at helping someone complete a long-term passion project like this is pretty incredible.

Better late than never

It’s never been officially confirmed what went wrong withMeta’s version of the GTA VR remaster but general development issues of converting all the flat games’ elements into first-person VR, problems with the Quest 2’s low processing power being able to handle the game world, profitability concerns (especially when faced with the massive profits GTA Online expansions or GTA 6 promise), or one of a dozen other possibilities have been floated.

Regardless, all we know is that after that tease, GTA San Andreas VR never managed to get out of the starting gates.

Many have questioned this decision over the years — GTA in VR would be a slam-dunk for plenty of gamers — and others have been perpetually frustrated at Meta for hyping them up into buying a Quest headset, then killing the project.

Thankfully there is now a way to sort of play San Andreas in VR on Quest. Yes, it’s a Quest 3, and yes, it’s not the simplest port to set up, but it’s better than nothing — which is what Meta and Rockstar gave us.

Of the expected $2.5 trillion that corporates are due to spend on AI this year, over $1 trillion is set to be allocated to IT services and dedicated software. That is a huge chunk of change for a technology that many are still grappling with and trying to understand how it fits with their business, team, and procedures.

A few years since the ChatGPT-enabled revolution started, we’re beginning to see the different approaches and challenges businesses are facing when implementing AI.

Ford's recent decision to bring back experienced engineers after AI systems failed to catch manufacturing issues offers an important lesson for every business investing in AI. At the same time, new research from Ramp and Revelio Labs, covering almost 22,000 US companies, found that organizations investing most heavily in AI are actually hiring more people, not fewer.

Together, these stories tell us something important. Everyone assumed AI would reduce reliance on people, but instead we’re seeing companies discover how dependent AI is on experienced people. AI may be changing how work gets done, but this alone isn't enough for growth and success.

AI is accelerating work, but experience still matters

Earlier this year, Ford re-hired 350 veteran engineers after AI and automated systems failed to deliver quality work, demonstrating that employees are critical to a successful AI rollout.

It’s also interesting to see this reflected in Ramp and Revelio Labs’ research that organizations investing in AI are hiring more people. Instead of neglecting hiring and skills development, companies increasingly need the right team members to help refine and improve sophisticated AI so that it performs better in the areas they need.

Success with AI still depends on understanding the skills people need to perform, at an individual, team and organizational level, then investing in development that reflects the realities of their work. But before organizations can build those skills, they need to know where expertise already exists. Too many businesses are sitting on critical knowledge without any clear view of who has it or where the gaps are.

One way to achieve this is building a skills ontology, a living record of the skills that exist across the organization.

Rather than relying on assumptions, businesses can identify where they overindex or underindex on capabilities, understand which teams have specialist knowledge and spot gaps before they become business problems. It also creates the foundation for workforce planning, internal mobility and more effective AI deployment.

The most valuable skills inside your organization

Now you have an effective skills ontology, you have an overview of what is going on in your organization. This helps you identify the subject matter experts (SMEs) that exist across a business, but whose knowledge has often never been formally captured - such as that mythical AI ‘power user’. Take Ford, which has said it is using its rehired employees specifically to train younger staff and reprogram AI tools.

Once you’ve identified where expertise exists, AI can help maintain that picture and keep it up to date as roles evolve. But understanding where the expertise exists is only the beginning.

Organizations need to validate that expertise, connecting people to targeted development opportunities and making it easier for SMEs to share what they know with others. Importantly, this learning should be embedded in work as much as possible rather than separated from it, so it improves performance where it matters most.

AI makes knowledge easier to organize and distribute, but it doesn’t create the judgement that comes from experience. It can surface patterns and recommend learning pathways, but it can’t explain why a process works, when to challenge it, or how to adapt it to the realities of your organization.

That’s why experienced team members remain essential. They contribute the context that enables AI, and everyone else, to use that knowledge effectively.

Turn expertise into a competitive advantage

At a time when many in areas like HR and Learning and Development (L&D) felt that AI would diminish their role, it's clear that this expertise is only becoming more valuable.

L&D teams have the opportunity to operate more strategically by owning the upskilling and reskilling agenda, turning their and team members’ expertise across the business into a competitive advantage, and putting the processes in place to create agile workforces that collaborate effectively with AI.

AI has put organizational expertise firmly in the spotlight, whilst also highlighting that too many businesses are sitting on a wealth of knowledge that exists only in the heads of experienced employees. Not only do they know and understand more about a business and its processes, but they can also provide judgement - a scarce resource that AI cannot replicate.

Getting this information out and deploying it across the business will create a workforce that becomes smarter every time someone learns, rather than losing knowledge when someone leaves, leading to fewer repeated mistakes, better quality and stronger internal mobility.

To get the greatest return from their employees and from AI, the top organizations need to ensure they are investing in identifying, sharing and scaling the expertise they already have instead of simply buying more software.

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• There's a new version of PowerToys out
• The suite of tools has a new utility called Window Hopper
• This allows you to easily cycle through open instances of the same app, and it's a nifty trick for multi-tasking in certain situations

Microsoft has released a new version of PowerToys, its suite of tools for Windows 11 (and 10), and it packs a smart new addition.

This is PowerToys v0.101.2362.0, and as flagged by XDA Developers, the release notes on GitHub introduce us to Window Hopper.

As the name suggests, this utility lets you easily hop between windows in Microsoft's OS with a shortcut that defaults to Alt + backtick (the ` symbol top-left of your keyboard, usually under the Escape key, or on the left of the number 1 key).

At this point you may be thinking: can't you switch between the windows of running apps by using Alt + Tab? Yes, you can, but there's a key difference between that shortcut and this new ability.

With Window Hopper, you are switching between the open instances of the same app, in the case that you're running multiple windows of said app. For example, like me, you might commonly have three or four browser sessions running in different windows, and pressing Alt + backtick will cycle between just those.

That's handy because it means all the other apps you may have open won't get in the way if you specifically just want to 'hop' between multiple instances of Chrome, say, or a clutch of Word documents you're working with (or folders in File Explorer).

If you don't like the default key combination to invoke Window Hopper, that can be changed.

On top of this new PowerToy, the existing Command Palette tool now has a compact mode, so it opens as a tiny window (and then expands with results when you start typing). This offering has also been tweaked to be more responsive and stable in general.

The new version of PowerToys also features some general interface polishing and tweaks to some other tools, including PowerDisplay, which is now capable of controlling the brightness of multiple monitors simultaneously. The Mouse Highlighter feature, which draws attention to the cursor when you left or right click (with a color to differentiate between both clicks), now has a smart new ripple effect.

Analysis: more power, fewer problems

I'm a big fan of PowerToys, and I think there are some highly useful tools here. Granted, some of these bits of software are for power-users only, but some are very handy for everyday users – and the good thing with this suite is that you can install it and only turn on a small handful of utilities that you really want, leaving the rest aside.

However, PowerToys is still not all that well known, and many average Windows users are missing out on nifty bits of functionality because they simply don't know about them. This is why I've argued in the past that certain PowerToys features should just be ported over to Windows 11 directly, where Microsoft could have them as options in the Settings app, removing the hurdle of having to find and install this suite of utilities (which, in overall terms, still remains somewhat obscure).

I think the Window Hopper is a prime example of something that could be easily ported over to Windows 11, where it would be useful for some folks to have as a native ability. Power Display, which just got a smart tweak with this new release of PowerToys, is another utility that I've said in the past would be great to have in the full version of Windows 11.

Would these tools be adding to system bloat, arguably? Well, some people may feel that way, but remember, they would be disabled by default and therefore wouldn't be eating any system resources (save for a small bit of drive space) unless you turned them on. Microsoft could also focus on very lightweight additions, too, as a starting point (simple tricks like Window Hopper, or partial ports of pieces of the beefier utilities).

For some people, PowerToys could even reduce bloat, as it might mean they can uninstall other third-party software that they've set up to achieve certain ends.

Hopefully Microsoft will at least be considering picking some choice pieces of functionality from PowerToys for inclusion in its desktop OS – and I note that there is a modernized mouse-finding ability inbound for Windows 11 (as Windows Latest spotted), which is good to see.

Artificial intelligence has transformed how software is built. Tasks that once took software developers days, if not weeks, to finalize can now be completed in hours with the assistance of generative AI tools.

The promise is compelling, offering faster innovation, increased productivity, and the ability to bring new applications to life at an unprecedented speed. But what is the impact on security?

AI coding has simultaneously put software risk on steroids. This is not because AI-generated code is uniquely flawed; it’s because it enables organizations to build and deploy software faster than any existing security, governance, or risk management process.

Development velocity has accelerated toward machine speed, while governance remains largely human-driven. That gap is now one of the defining software security challenges of the AI era.

Software is moving at machine speed. Security isn't.

AI is not only changing how code is written; it is changing how software is assembled. Developers can now assemble applications using open-source components, APIs, and third-party services faster than ever before. Every new application, integration, and dependency expands the attack surface that organizations must inventory, monitor, and secure.

The result is now a growing imbalance between software creation and software remediation. As the pace of software creation increases, remediation must keep up.

Veracode's 2026 State of Software Security report found 82% of organizations now carry security debt—vulnerabilities that remain unresolved over time — and 60% carry critical security debt, meaning flaws that are severe enough to cause significant damage if exploited.

Third-party code continues to be an especially stubborn source of risk, representing 66% of the most dangerous, long-lived vulnerabilities. The data reveals a simple reality: AI doesn't just generate more first-party code—it is increasing software complexity.

Organizations have always dealt with flawed code. The difference now is the speed and scale at which that code can be created, accepted, and deployed. AI doesn't just introduce risk, it amplifies the challenge of managing risk by enabling teams to generate exponentially more software than traditional security processes were designed to govern.

Traditional security governance assumes humans remain the bottleneck in software creation. Reviews, approvals, audits, and remediation workflows were designed for development cycles measured in weeks or months. AI-assisted development compresses those timelines dramatically.

When software can be generated, modified, and deployed at machine speed, governance models that depend on human intervention alone are no longer sustainable.

AI can help plant a seed, but that does not mean the garden will thrive. A seed needs the right soil, climate, and care. Software is no different. Organizations can generate applications overnight, but without the right security frameworks, operational support, and governance structures, those applications can quickly become liabilities rather than assets.

This is why security leaders must rethink governance for the AI era. The goal can’t be to inspect every line of code or eliminate every vulnerability before deployment; that approach was already becoming unsustainable before generative AI entered the picture. Instead, organizations need governance systems capable of operating at the same pace as software creation.

That means automating risk analysis, continuously evaluating dependencies, enforcing policies through pipelines, and prioritizing remediation based on business risk rather than relying on manual review alone.

Governance becomes the new trust layer

The need for machine-speed governance extends beyond operational efficiency. As AI accelerates software creation, governance becomes the mechanism through which organizations maintain visibility, demonstrate control, and establish trust across an increasingly complex software ecosystem.

Ultimately, this isn't just about scaling security. It's about ensuring software can be trusted and held accountable, regardless of how it's built.

AI can generate software, but it cannot assume responsibility for it. Boards will still hold executive leadership accountable for cyber risk. Regulators will still expect organizations to demonstrate that the software they deploy is secure and resilient. Customers will still expect software they can trust, regardless of how it was built.

AI may change how software is created, but it does not change who is accountable for its consequences.

That shift requires organizations to rethink governance as a strategic capability, not a compliance exercise. Success will depend less on preventing every vulnerability and more on demonstrating that software can be continuously evaluated, understood, and trusted as it evolves. In the AI era, the winners will not simply be those that build software fastest, but those that can govern it most effectively.

AI can help plant the seed, but it cannot tend to the garden. The organizations that lead today will not necessarily be those that generate the most software. They'll be the ones that can confidently answer the question every stakeholder will eventually ask: Can we trust what we've built?

AI has accelerated software creation beyond anything the industry has experienced before. If software risk is now on steroids, governance must be too. Otherwise, the gap between what organizations can build and what they can securely manage will continue to widen.

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Remember that big leak about Microsoft having begun developing a version of Windows that was built around AI, before eventually abandoning the project? Well, it's surfaced again, this time with some interesting new information on just how focused Microsoft was on Copilot, which I think is quite telling — or indeed worrying for those who believe Microsoft might be backtracking on AI somehow.

Windows Latest has gone back over the leak (which was first reported by our sister site Windows Central last month) with a fine-tooth comb, and the tech site flagged up some fresh details that flew under the radar at the time.

To recap briefly, the concept of the OS shown, codenamed 'Project Aion' — with the leaked material dating back to 2024 — was a lightweight web-based version of a Windows operating system that streamed apps to the desktop (as opposed to running them natively on the PC) and put Copilot at the heart of the experience.

This was a horror show of an idea for many, of course, and what Windows Latest starkly highlights here is the extent to which Microsoft was building Aion around AI. In a SharePoint site in the leak, it's described as a "Copilot OS" and a cross-platform system that can span all devices and is generically known as an "Agent OS".

The broader idea, is said to be of an operating system fashioned around "an agent that marshals and organizes all the capabilities of our computers and our clouds on our behalf", and that this is known as an "agent of agents" or the "outside agent".

"The version that Microsoft will build should be known as Copilot OS or the Outside Copilot," the top-level description on the SharePoint site observes. Another term mentioned is a "self-driving OS", which hints at the desire to build an autonomous platform powered by AI here.

Copilot is envisioned as being the "primary way people express, entertain, and educate themselves, and complete tasks across all aspects of their lives".

But Aion is dead, right? Yes… but hold up a minute

So, what's the big deal here? Windows Latest breathes a sigh of relief that the "odds are already in favor" of this abandoned Aion concept staying dead and buried, arguing that Microsoft is clearly backtracking on AI features these days. And that's very true — there's no denying that Microsoft has reversed course on pushing AI quite so hard as it did in 2025.

But I don't think we can declare that 'Aion is dead' quite so readily. Yes, that exact concept may be buried, but there are nuances here. For me, what Windows Latest has surfaced shows just how serious Microsoft was about exploring the idea of a fully AI-driven desktop. More to the point, an operating system that was effectively an 'agent of agents', or an overseeing AI pulling the strings of a collection of smaller dedicated agents working within the framework of that system.

Here's the thing: Microsoft's efforts to rein in AI within Windows 11 certainly exist (although I wonder about how much of this is for show, frankly). The software giant is currently delivering its best effort to please the disgruntled masses, and a big part of that is fixing Windows 11 — but another hefty piece of the puzzle is to place less emphasis on AI and shoveling more Copilot features into the desktop OS.

But don't be mistaken here: Microsoft still very much sees AI as the future. The company just realizes that it can't be vocal about that right now, especially given that the RAM crisis has forced 8GB laptops back onto the scene, as notebook makers, including Microsoft itself, try to cope with hefty spikes in the cost of manufacturing these devices.

It's a bit embarrassing to be talking about Copilot+ PCs needing 16GB of RAM for AI features when Surface models have had to be reintroduced packing 8GB to get their price down to a vaguely palatable level. Indeed, talk of Copilot+ devices seems to have evaporated this year, as if Microsoft feels this is best brushed under the carpet for now. While a recent leak from Lenovo talked about 'Non-Copilot' laptops with less than 16GB of memory (as flagged by Windows Latest), this kind of spec talk isn't something you'll hear from Microsoft.

Downplaying AI is the tactic for now, but not forever

For me, it's clear enough that Microsoft is downplaying AI in a strategic manner for the time being, and likely through to next year. The next big thing, however, remains AI agents in Windows, even if Windows 11 isn't going to be transformed into next-gen Windows Copilot (or whatever a more AI-centric desktop platform might be called).

Microsoft is still very busy working on those agents. These entities will begin the shift towards an operating system that's built around AI, with smaller-scale agents executing dedicated tasks on the local PC (with the user's permission, of course, to access stuff). This will be a gradual ramping up of AI usage within Windows 11 (and while folks who hate AI and everything it stands for won't have to go anywhere near these agents, doubtless the idea is that minds will be changed eventually).

I think it's also telling that in Microsoft's recent roundup of the progress it's made in fixing Windows 11, the company talked about future improvements for the OS later this year, and a central aim was to streamline resource usage so the operating system runs better with 8GB of RAM. Interestingly, a few other goals were listed alongside that, and one of them was to make "voice more natural and fluid to interact across the apps you use every day". If you recall, part of the initial sales pitch for AI in Windows 11 was Copilot Voice (and Vision), so I think the importance placed on voice functionality here reflects a broader AI goal that's still in the background.

Ultimately, I think Microsoft is still pushing towards an operating system that's built around AI; it's just that the timeline to reach this end-game objective has been pushed further out. While Project Aion as leaked before was too big a concept, it clearly shows how Microsoft ranks the importance of AI for the future — and I think it's a mistake to think this idea is somehow 'dead' or derailed.

As far as I'm concerned, this remains the destination where Microsoft is heading with next-gen Windows: a lightweight (possibly modular) OS focused on AI, and one that leverages the cloud and streamed apps, too. I think the leaked Project Aion shows us the future as Microsoft wants to realize it, but what it doesn't mention at all is what I think will be another key driver for the software giant: subscription charges.

The golden ticket for Microsoft is to get consumers subscribing to the Windows platform (as businesses do – well, enterprises anyway). AI agents as Windows add-ons with a small monthly (or yearly) fee attached seems a likely way to bring this monetization method into play while making minimal waves (and I recently discussed how there are signs that this is already happening).

That will eventually turn into a grander scheme, and Windows could, over time, end up getting a lot closer to the Aion or Copilot OS concept than some might imagine — although admittedly, that could still be a long way off.

• Crooks used Google Sites and stolen Google Ads accounts to push fake OpenAI Codex pages
• macOS users tricked into pasting Terminal commands, leading to AMOS infostealer infection
• Campaign abuses Google’s trust signals; Windows download button was a decoy, only Mac payload worked

Cybercriminals were seen abusing Google Sites, the Google ad network, and OpenAI’s good name, in a campaign that targets macOS users with infostealers.

According to security researchers CATO CTRL, the crooks used Google Sites to create a fake version of the OpenAI Codex download site. To avoid being flagged by Google’s security systems and ultimately removed, the site itself contains no malicious code or download links, whatsoever. Instead, it hosts an iFrame that displays content hosted elsewhere.

Then, they advertised that site on the Google Ads network. Google is usually good at spotting and preventing malicious ads from running on its network, but sometimes threat actors steal legitimate accounts with good standing and use them to bypass automated scans and get the ads listed, while also spending other people’s money on the ad campaign.

Not ClickFix

The ads were displayed to users searching for “codex macos download”, at the very top of the page. Using both Google Sites and Google Ads is a deliberate attempt to appear legitimate and trustworthy since after all, many people trust whatever Google displays as the top result without double-checking or scrutinizing the result.

Those that do click will see a website that, by all accounts, looks like OpenAI’s download site for Codex, the company’s AI coding agent. The site has download buttons for both Windows and Mac, but only the latter works. The download and installation process was designed to look “advanced” - instead of getting an executable, the victims are told to paste a command in Terminal.

Cato’s researchers call this a ClickFix attack, but ClickFix usually displays a fake problem, before offering an equally fake solution. This looks more like another way to appear legitimate because after all, several AI agents are specifically designed to be installed and run from the macOS Terminal, including OpenAI’s Codex CLI.

The end goal of the campaign is to deploy AMOS, a known macOS infostealer capable of grabbing browser data, login credentials, cryptocurrency wallet information, and more.

Via SiliconANGLE

• Microsoft has concocted another scheme to pay people to use Edge
• It's a referral link that gives you reward points for every friend invited
• A strange Bing app has also been spotted that switches some browsers to have Microsoft's search engine by default, and it's proving controversial

Microsoft is once again pushing Edge — and also Bing — in promotional efforts that may elicit more than a few groans from Windows 11 users.

We're used to this kind of thing in Windows 11, mind, and I'll come back to Bing later, as this is an apparent plan that hasn't kicked into action yet. However, the latest Edge scheme is active, and as Windows Latest reports, it's offering financial incentives to get people to use Microsoft's web browser.

This is happening on the mobile version of Edge where users are being offered a referral scheme. If you can get a friend to switch to Edge via a referral link, you'll receive 500 Microsoft Rewards points (with the ability to accumulate up to 7,500 points per month if you're feeling ambitious).

The person you invite can't be an existing Edge user (they must never have installed the mobile browser), and they will also earn 500 points for signing up. However, your invited friend must sign into Edge and use the browser for at least two days (web searching on Bing, too) within the first two weeks of being referred.

Windows Latest observes that there's no way to access this referral scheme should you be interested — you'll just have to keep using Edge mobile and wait until it's presented to you.

As well as the promotion of Bing in that offer (in terms of the requirement to use the search engine for a couple of days), there's a more brazen initiative to push Bing usage that Windows Latest also spotted.

This was highlighted in a post on X by leaker Xeno who discovered a small app called 'Microsoft Recommended Search' which has one sole and simple purpose: to change your default search engine to Bing.

When run, it offers up a simple slider to set Bing as the default engine, and if you allow this, the app switches to Microsoft's search engine across some of your installed browsers. That means Chrome and Firefox, and also apparently Brave and possibly others, via extensions which are added to these browsers. Apparently the experience is rounded off by sending the user to the Microsoft Rewards page.

Where is this app exactly? Thankfully it isn't something that's being deployed to Windows 11, and Windows Latest notes that it's just a standalone application hosted on Microsoft's official download servers (download.microsoft.com) — for the time being, anyway.

Analysis: an ominous sign?

Does this mean that the Bing switching application is something Microsoft intends to pipe out to Windows 11 at some point? That's the obvious worry, because you've got to wonder why Microsoft built this mini app in the first place. I'm guessing that this is something for use in testing, though, and not active deployment — although it could come to Windows 11 in theory (or something similar could).

I really hope not, and given that Microsoft is doing its level best to get everyone to appreciate its desktop OS this year, it's obvious that anything along these lines would be highly counterproductive to that goal.

However, there's plenty of suspicion on Reddit about Microsoft's intentions here, and chatter from those who've already switched to Linux, pointing out that this kind of thing is exactly why they migrated. As one Redditor put it: "Every day is a Microslop news day, I'm so glad I quit Windows a while ago." There are quite a few others in that thread who see this as another reason to defect to Linux, despite the fact that nothing has happened with this Bing app yet.

As for the referral scheme to promote Edge, this isn't the first time Microsoft has effectively offered lowkey bribes to get people to switch to the browser. Indeed, the likes of million-dollar prizes have been thrown around recently (and the same has been true of Bing in the past). Not that there's anything wrong with this idea in principle, except it does show that Microsoft's getting rather desperate in its efforts to recruit folks to use Edge and Bing.

The most baffling part of all with Edge is that it's a good web browser which stands on its own merits, and I think it would benefit from not being promoted so much (as that activity only makes people suspicious, or determined not to use Edge as a reaction to being constantly cajoled about it).

Companies are investing in Forward Deployed Engineers, or FDEs.

AWS has announced a $1 billion investment in a dedicated organization intended to embed thousands of engineers with customers.

OpenAI has established a dedicated Deployment Company and agreed to acquire Tomoro, adding approximately 150 FDEs and deployment specialists.

Microsoft has said it would hire 6,000 people and invest $3.5billion in its new AI delivery unit, according to CNBC.

But what should FDEs deliver, and what value do they really offer for customers?

The FDE model is an evolution of how companies would previously work around projects with customers based on understanding the business and the technology involved, with a much higher expectation of hands-on engineering.

What has changed is the technology being deployed.

Where FDEs deliver value

FDEs typically embed directly into a customer and work across multiple teams. They identify a high-value workflow, understand the customer’s data and operational constraints and then work to build the required integrations and take the system from prototype into production.

For AI deployment, that involves more than connecting a model to an application. This includes looking at the enterprise context and data available to the model, as well as evaluating accuracy, reliability and confidence thresholds.

It can also involve looking at the guardrails that should exist, the review and escalation process, security and observability. It should also look at integration with existing workflows and business processes.

Companies need FDEs because companies want to deploy probabilistic systems into deterministic operating environments. In other words, enterprises want to use systems that can be different each time they respond within business processes that depend on predictable and uniform results. FDEs have to translate those outputs in a way that delivers what enterprises want to achieve.

For example, a technically impressive model might fail inside an actual workflow for multiple reasons, from incomplete context or poor quality data, through to the organization not being prepared to let the system take action without review.

What the future holds for FDE roles

At their best, FDEs should solve those problems and get a company into their production deployment phase. This ensures that the technology works and delivers value. However, that is not the end of the story.

FDEs should also convert what they learned with one company into reusable capabilities that others can take advantage of too. If every deployment remains bespoke and depends indefinitely on individual engineering talent, then the technology itself cannot scale.

Instead, a successful engagement should lead to reusable connectors, evaluation frameworks, governance patterns and product improvements that other companies should be able to benefit from. For the companies that hire the FDE, they should deliver successful projects. But the ultimate goal is to make the next deployment less dependent on any specific FDE, and instead make the product better.

Are there any bigger lessons from the growth of the FDE role? The real hiring trend is toward a hybrid professional who can be an expert in multiple areas simultaneously, from writing production-quality software and understanding the behavior and limitations of AI models through to learning a customer’s business and domain with enough insight to reconfigure business processes.

At the same time, they are expected to navigate security, governance and organizational constraints, take responsibility for measurable business outcomes in their business and in their customers, and be as adept at communicating with engineers in rolled-up sleeves as they are executives in suit and tie.

For companies that base their products on FDEs, this expansion is a sign that there is a huge market opportunity and that customers want what is being offered. There is an element of marketing involved too, with FDEs the latest “new” position that will solve problems for enterprises.

Some of this recruitment will be genuinely new, while some will be a reallocation or relabeling of people who would previously have been called field engineers, solution architects, technical consultants or professional services engineers.

AI Market Maturity

This continued growth is also a potential warning sign. The number of FDEs needed over time should drop as AI products and infrastructure mature and lessons are learned. This demand for a specific role is a sign of category immaturity. Industries mature when repeatable work is standardized, industrialized and embedded in software rather than recreated as a one-off service for every customer.

The same test applies to AI today. The reliance on FDEs shows that enterprises want AI, but that today’s products are not yet sufficiently complete, predictable or easy to operationalize without substantial human input. If every implementation requires embedded specialists to assemble the context, controls, evaluations and integrations by hand, the category has not yet fully matured.

Enterprises should therefore be careful not to measure success simply by the number of FDEs hired or proofs of concept completed. The right measures are time to production, sustained adoption, measurable business value, customer self-sufficiency and the amount of reusable product capability created from each engagement.

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It’s back-to-school season, which means there are plenty of great deals to be had on everything tech related. But you don’t have to be going to school or college to get in on the action — in fact, there all sorts of ways to score a discount, whatever your situation.

Here, we’ve put together some of the best ways you can get free or heavily discounted software, whether or not you’re a student. Read on and you’ll see there are many ways to score a brilliant bargain that might take you by surprise.

Education discounts

Many companies know that students tend to be strapped for cash, and they therefore try to tempt educational customers to make a purchase by steeply discounting the price — or waiving it altogether.

For example, you can snag free access to Microsoft 365 apps if you’re a student, while educational institutions get the same deal. This is limited to web-based versions of the apps, but it’s a good way to net yourself a solid set of free office apps with absolutely no cost involved.

If you’d rather have the desktop apps, students can get a 50% discount on Microsoft 365 with a valid education email address. You can also get one year of Microsoft 365 for free when you buy a qualifying PC desktop or laptop. Microsoft also throws in a year of Xbox Game Pass Ultimate and an Xbox Wireless Controller for free.

Looking further afield, Amazon Prime offers discounted rates and sometimes markets extended free trials to students and young adults, while other apps and services with student discounts include Adobe Creative Cloud, Apple Music, Evernote, Headspace, Spotify and more.

So many companies run student discounts that if there’s an app you’re interested in, it’s always worth investigating whether your student status will get you money off.

Check your employer or job description

Deals don’t end the moment you enter the workforce. In fact, some of the best money-off opportunities are reserved for those in full-time employment. The catch is that they often depend on the type of work you do, but there are still plenty of deals to be had.

If you want to look after your logins, you can get free access to both 1Password and Proton Pass if you’re a journalist. Proton’s entire suite of apps — including a VPN, office apps, encrypted cloud storage and an email service — are free for journalists, in fact. Similarly, VPN provider Windscribe offers cost-free access if you’re a reporter or if you work for a charity.

Microsoft gives a wide range of discounts to nonprofits, including on office apps, security software, AI tools, and more. The Google for Nonprofits program offers also free and discounted access to Google services for NGOs.

Even if you don’t work in these sectors, you could still be in line for money off. Plenty of companies have partnerships with prominent app developers and get discounts on their software, so be sure to ask your employer if they run a scheme like this.

Apps bundled with your device

You don’t always have to look far for free or cheap apps — they’re often bundled with your device’s operating system. Microsoft, Google and Apple all have free password managers, for instance. Apple’s is a standalone app while Microsoft’s and Google’s live in the Edge and Chrome web browsers, respectively, meaning they work cross-platform and can be used on competing operating systems.

While these password managers are excellent for managing your accounts, they might lack more advanced features compared to third-party options. Be sure to check if they do everything you need before picking one up.

There are other ways your operating system can help you out. Take Apple’s macOS, which comes with a range of top-class office apps, including Pages, Numbers and Keynote. Google, too, has its own series of very capable office apps that are based in the cloud.

If you use different devices that run on different operating systems — for example, you have a Windows PC but also an iPhone — make sure you check whether the apps that come with one operating system can be used on another. If not, you might need to find an alternative app that runs well cross-platform so that you can access your files on any device.

Totally free apps

Despite all these ways to get money off your favorite software, you shouldn’t overlook the fact that there are some excellent free apps out there that are and always have been toll-free. Sometimes, the best way to get free software is to simply take the free option.

Video editor DaVinci Resolve is used by professionals in the movie industry, yet offers a totally free version that is incredibly capable. LibreOffice is a solid Microsoft Office alternative that requires no payment and can go toe-to-toe with Microsoft’s best. VLC can play almost any video format under the sun without ever asking for your cash. And there are many more out there.

Just be aware that many free apps are limited in some way compared to paid-for alternatives. A frequent practice is to lock more advanced features or higher limits behind a paywall, meaning you get a somewhat stunted experience if you don’t pay. Or you might get a free trial of a premium app that times out after a certain number of days or weeks.

There are other considerations. Be careful when using free apps that handle sensitive data. Some (but not all) free VPNs have been found to steal user data, for example, which undermines their privacy claims.

But that doesn’t mean you should forget about free software entirely. As apps like DaVinci Resolve and LibreOffice show, paying is not a requirement to getting a great app on your device.

• Windows 11 can remove a discrete GPU driver as part of its clean-up routines, as shown on Reddit
• This can happen if the laptop has been in Eco mode for an extended period
• Because the GPU is effectively gated off from the system, the driver is marked as unused and therefore deleted by the OS during clean-up – but you can stop this from happening

Apparently, Windows 11 can end up stripping away the discrete GPU driver from your laptop in certain scenarios if the OS is left to run in Eco mode for a long time – and that's a distinct annoyance if you own a gaming laptop, as per a report on Reddit.

Wccftech spotted a Redditor who posted about their Asus ROG Zephyrus G14 gaming laptop with a discrete Nvidia RTX 5070 Ti, and how Windows turned off that GPU.

At this point, some of you may immediately be thinking: who runs their gaming laptop in Eco mode? And that's a fair point, and one raised in the Reddit thread – I'll come back to that.

First, let's look at the mechanics of how this works, based on the Redditor's investigation of what went on (they noted that it "took me months to work this out"). When the laptop is put into Eco mode, power is completely cut to the discrete GPU, effectively leaving it offline (just as if it were an unplugged USB stick, as the Redditor describes).

That's not a problem – obviously it's a power-saving measure – but the issue is that Windows 11 runs a driver clean-up process from time to time as part of routine system maintenance. By default, this happens every 30 days, though the Redditor notes their laptop was set to 15 days, and this can be modified.

What happens is that if the laptop has been in Eco mode for a long time, with the GPU effectively walled off and not present as far as the system is concerned, and the clean-up happens, it judges the driver to now be superfluous, and it's removed.

When Eco mode is subsequently switched off, the driver doesn't return – it remains ditched, and the GPU shows as a 'Microsoft Basic Display Adapter' instead of an Nvidia GPU in Windows 11's Device Manager.

The Nvidia graphics driver must then be reinstalled to get the GeForce graphics card functional again – and the same is reportedly true of AMD discrete GPUs.

Analysis: a niche problem, but an annoying one – here's how to avoid it

Let's return to that question: who runs a gaming laptop in Eco mode? No gamer really does this, at least not when they're regularly playing some of the top PC games on their notebook. However, someone might be a student cramming for finals and using the laptop for work, having given up gaming distractions for a few months. In this case, with Eco mode enabled to keep battery life up while studying, Windows 11 could disable the GPU.

Okay, so it's something of a niche situation – and the Windows 11 clean-up routines are useful for keeping the system streamlined. But it's not an unthinkable scenario, and Microsoft's OS should be tuned to be more careful around GPU drivers – and it'd be useful if the system could give a warning of such a clean-up happening (so it could be avoided).

That said, this may not happen in all situations. Another Redditor notes that they have had their Zephyrus G16 in Eco mode for two months, and the Nvidia RTX 5070 Ti is still present and correct.

Whatever the case, if you run into this problem yourself, at least you know why it's happening. The fix is to simply reinstall the Nvidia driver, although the same thing could happen again if you're one of those people who regularly uses Eco mode on your gaming laptop.

You can apply a Registry fix to permanently stop the clean-up process, but I wouldn't recommend messing with those settings unless you're confident (see the Reddit post for instructions). Also note that you'll miss out on other clean-up duties, not just GPU-related activity.

A better solution is to simply remember to take your laptop out of Eco mode now and then (every couple of weeks), which will bring the discrete GPU back into play, and save the driver from being earmarked for removal.

The Redditor notes: "Or just keep the GPU visible now and then. Optimized mode in G-Helper turns the dGPU [discrete GPU] on whenever you plug in. Flipping to Standard [mode] every couple of weeks does the same thing. Resets the counter, no Registry editing."

As public perception continues to sour, and private businesses ban smart glasses from their events and premises, it feels like the writing might be on the wall for Meta’s Ray-Bans. At least as we know them.

Even without many laws changing, camera-equipped specs are becoming increasingly difficult to wear, especially if you don’t want to be branded a perv or a creep.

Because of this changing tide against Meta Ray-Bans and others, the most recent episode of the TechRadar podcast is a smart glasses special. Podcast regulars Josie, Axel and myself brought Tom’s Guide’s Jason England on board to discuss the issues facing smart specs and the risks they pose — while also acknowledging how useful the tech can be when used appropriately.

We rounded things up with a quick discussion on what smart glasses could do to solve their privacy problems and clean up their public perception. I want to continue that chat here with you, our readers and viewers.

Below I’ll outline four possible paths forward, followed by a quick poll asking you which path you believe we should take.

1. Keep cameras in glasses

If you are a smart glasses maker like Samsung, Meta or one of the many others, this would likely be your preferred approach: keep calm, and carry on churning out glasses with cameras (or AirPods if a recent Apple leak is to be believed).

The backlash against surveillance tech is at a fever pitch right now, but public outrage has a way of waxing and waning. Smart specs have been wildly popular before, who says they won’t go back to being mega popular in a few months?

This process could also be sped along by rolling out new privacy policies, or software which aims to mitigate the way smart specs with cameras are misused. If new useful features are released, marketing could also help to make smart glasses look more essential, and get their detractors to overlook the glaring privacy issues they’re so bothered about right now.

For current smart specs users it means their Meta Ray-Bans would still be completely usable, and fully functioning — allowing them to rely on the first-person content capture capabilities for recording memories, informing their AI, or for accessible needs that the glasses are offering the visually impaired.

It’s also unclear how effective a ban could be if it wasn’t universal.

Countries like China already feature major public surveillance and tight regulation of what people can share online, so the downsides of camera glasses aren’t as strongly felt there. As such they’re less likely to see a ban. Bad actors could import glasses from countries where they are sold and attempt to dodge restrictions from brands who might not be as willing to address concerns as Meta has been with its various safety updates.

On the flip side, not banning features in some way means that the privacy issues persist — plenty of folks could still be being recorded without their knowledge or consent. This could lead to serious public consequences if people take action into their own hands, or heavy-handed legal consequences if governments decide to ban smart glasses in all their forms to avoid any possible workarounds — depriving us of the useful features XR specs could offer in all aspects of our life (if the technology was allowed to develop).

2. Ban smart glasses outright

Rather than waiting and letting things continue before taking action, we could ban smart specs right now.

Of course this would limit access to the useful features they provide, and would see people’s purchases become paperweights, though it would tackle the privacy problems head on.

I’ll address banning simply camera glasses below, but the one advantage of this approach is it also hits glasses with microphones that could be used as a form of covert surveillance technology. While audio is often less invasive than video or images, privacy advocates would argue any kind of surveillance is a terrible thing.

As I touched on above, removing smart specs entirely would deprive us of some potentially exceptionally useful and awesome tech that could provide benefits to our work, our ability to travel and communicate, and our play.

However, smart specs are also causing plenty of headaches. This approach would make them all go away if it proves effective.

3. Ban cameras in glasses

A less heavy handed approach would be to merely ban cameras, but allow glasses with displays, speakers and/or microphones only to continue.

This would allow smart specs to offer audio or screen-based AI assistance, to play music, let you watch a TV or film, or perform a host of tasks that don’t depend on computer vision.

Though a lack of cameras would limit the tech’s immediate usefulness quite considerably. I’ve touched on this already, but the snapper can provide context to the AI when you ask it questions about a landmark or sign you can see, it can offer hands-free recording capabilities many like to use while on vacation or while doing an action sport, and it has been used as an accessibility tool by the visually impaired to allow their smart specs to describe the world around them.

The advantage to smart spec makers is this approach could see them dodge an outright ban on glasses wearables — cameras or not. It may also keep a door open for a safer camera-equipped version of smart glasses to materialize; a model that addresses the privacy concerns of the original.

Of course, this wouldn’t remove the risks of covert audio recording posed by smart specs, though you could argue that the dangers posed by such recordings is less severe, especially since plenty of other accessible covert tools (even a smartphone) could be used to privately record sound.

4. Keep the cameras, ban video recording

The issue with camera glasses isn’t necessarily that they can record, it’s that they can record relatively discreetly. While Meta’s glasses do have a recording light it’s easy to miss if you don’t know to look for it.

For photos, an obvious solution would be to require the specs to play an audible camera snapping effect whenever a shot is captured. Meta’s specs can already do this, and it's a requirement of smartphone cameras already in countries like Japan.

Because Meta glasses don’t have zoom capabilities you can’t take useful photos from very far away, meaning an audible effect like this would be heard by anyone you attempt to photograph covertly. They could then confront you about being a creep in much the same way someone obviously taking photos with a phone or camera could be addressed in the moment.

Video is a little tougher to make obvious and still usable in the end result. An audible effect would need to play throughout the recording which could spoil the clip’s audio. An alternative could be to make the recording light brighter and have it pulse to make it a little more obvious, but it still may not be completely noticeable. So you could simply ban video recording with smart glasses.

This halfway house would allow the specs to sense the world around them — facilitating AI and accessibility tools — but could greatly restrict the harm they could cause.

The downside is hackers could modify the cameras to facilitate video recording as all the hardware would still be there. That said, tamper-proof measures could mitigate this risk.

Now it’s time to have your say. These four possibilities are somewhat simplistic, but I think they capture four general strategies we could deploy to ‘solve’ our smart glasses problem. Let me know which you like best, or feel free to comment below with your thoughts!

• Windows 11's August update has broken some PC games
• Microsoft is investigating reports of crashes, but right now it's unclear what the root cause is
• Theories point to security measures in the update potentially causing issues with certain drivers and conflicts with anti-cheat tools

Windows 11 has run into trouble with gamers (again) after the latest monthly update for the OS, and Microsoft is investigating what's gone wrong.

The Register noticed that Microsoft has posted on the Windows release health dashboard about "reports of certain games becoming unresponsive" following its latest patch.

In some cases, games are either freezing and becoming unresponsive, as mentioned, or simply closing (crashing to the desktop), with an error ('Exception Access Violation') being displayed in some cases. There are also some reports of spontaneous reboots occurring after a game crashes.

Not all PC games are affected by any means, with Microsoft noting that reports are coming in from players of Arc Raiders, Marvel Tokon: Fighting Souls, and The Finals.

Microsoft is currently trying to work out what's going on, saying: "Ongoing investigation indicates that this issue is related to peripherals or internal device components which have RGB lighting features. Such devices may install drivers or code components with file names similar to inpoutx64. In systems where these drivers are found, the issue is then triggered by launching certain games."

Microsoft adds that it's currently trying to "understand the relationship between these RGB components and the games which trigger this issue", and says it will update gamers when more information becomes available.

Analysis: theories and workarounds

Microsoft is suggesting that this may be a driver-related issue of some kind, rather than a problem with Windows 11 itself. Of course, changes to Windows 11 could be the root cause, and the company admits that, and says it's still trying to "determine if this is an issue caused by Microsoft" on the release health dashboard.

As The Register points out, some on Reddit are theorizing that this bug is to do with "tightened kernel handle validation" which was introduced with the latest patch for Windows 11. Essentially meaning that Microsoft has tightened aspects of security with the August update, and that this is causing driver glitches, ones that were previously ignored, to be picked up (and subsequently crashing games).

The mentioned driver (inpoutx64) isn't just used in software controlling RGB lighting, but also in some system utilities needing low-level system access (for hardware monitoring, for example, and one such tool is mentioned in the above Reddit post: ZenTimings). It seems that if this driver is present on the host PC, the game's anti-cheat probing it now throws up a problem (post-patch), and that leads to the crash.

If this bug is causing you grief — and there are a lot of Arc Raiders players out there in that particular boat — there are workarounds that can make the game playable again.

The first is the most obvious and easiest fix: remove the August update for Windows 11 (or roll back the OS to before it was installed). You can remove an update in Windows Update, in the Update History panel — just find the patch (KB5121003 in this case) and uninstall it. However, bear in mind that you will be without a bunch of security fixes (and other features, including faster app launching) if you don't have this update on your PC.

The other alternative is to remove the problematic driver file, which seemingly won't harm your system (that said, if you do so, it's at your own risk — I haven't tested this). Going by the advice in the above Reddit thread — and also from the developer of Arc Raiders (Embark) — what you need to do is open the Command Prompt in Windows 11 by typing cmd in the search box, then right-click on Command Prompt and select 'Run as administrator'.

Once the prompt appears, type the following and press enter:

sc stop inpoutx64

Then type this second line and press enter:

sc delete inpoutx64

Now close the Command Prompt, open File Explorer, and find the following folder: C:\Windows\System32\drivers.

In that folder you should see the inpoutx64 file, it may be a SYS or DLL file (or both). Delete any of those files which are present.

That's the driver removed, and you should now be good to go, and you can still have the August update installed. But as I already noted, proceed at your own risk if you take this route.

The best bet for now may be to sit tight and wait for the results of Microsoft's investigation, and hopefully we'll hear something soon. However, this could be a somewhat thorny problem to untangle, and it rather sounds like a case of Microsoft dealing with a security issue — one which should be fixed — and that cure causing collateral damage due to driver wonkiness.

Whatever the case, on social media, a good many PC gamers are blaming Microsoft for this latest gaming-related issue in Windows 11.

One Redditor delivers the following barb: "They can't even keep the one thing they had over Linux. If you have to worry about whether or not your games work anyway, why still use Windows?"

Another observes: "Since 2025 December update till today — EVERY single month the update breaks something! There was no single month without issues."

There's no shortage of shots fired at Microsoft for monthly update woes, accusations of this being the fault of vibe coding (AI), and threats to leave for greener Linux desktop pastures. Business as usual, then.

For organizations looking to gain an edge by adopting AI tools, the decision has been dominated between large language models.

Every new release promises better reasoning, greater accuracy and more capabilities than the last.

But as foundation models become increasingly powerful and widely available, the real differentiator for organizations won't be which model they choose - it will be how effectively they deploy agentic AI to solve real business problems.

At the recent 2026 Gartner Data and Analytics Summit, the message from analysts was clear; standalone AI models are outdated and autonomous, interconnected AI agents are the future. But to get real value from agentic AI, IT leaders should prioritize “high-frequency, low-complexity use cases, apply guardrails and upskill the workforce.”

To this statement, I would add that the next phase of enterprise AI isn't about having access to the biggest model. It's about building better agents.

That means moving beyond seeing AI as simply another chatbot and instead treating it as an operational capability that can augment employees, automate repetitive work and unlock efficiencies throughout the organization.

The biggest AI opportunity isn't conversational

For many people, AI still means asking ChatGPT a question and receiving an answer. That's certainly one application, and conversational interfaces have played an important role in making AI accessible. But that’s only scratching the surface of what's possible.

Some of the most valuable agentic AI deployments are focused on operational work rather than conversation. For example, in higher education, AI agents are helping students navigate support services by answering routine questions about enrolment, campus facilities or administrative processes, freeing staff to deal with more complex issues.

In transport, agents can combine timetable information, onward travel options and customer support into a single interaction, saving users from having to search across multiple services.

Elsewhere, organizations are using AI agents to qualify sales enquiries, summarize complex documentation, and extract information from contracts. Tasks that previously required hours of manual effort.

The common thread isn't simply that these agents can answer questions. It's that they remove administrative burden and enable people to focus on work that requires judgement, expertise and human interaction.

Better agents start with better problems

One of the biggest misconceptions surrounding agentic AI is that organizations should begin by deciding where they want an AI agent.

In reality, for organizations looking to develop their AI offering, the first question shouldn't be, "Where can we deploy AI?" It should be, "What business problem are we trying to solve?"

Starting with technology often leads organizations to build agents that deliver little value because they're automating the wrong process. The organizations seeing the strongest returns are those identifying operational pain points first, in areas where employees spend large amounts of time on repetitive administration, searching for information or manually moving data between systems.

When those challenges are clearly understood, agentic AI becomes an effective operational tool rather than technology looking for a use case.

The model is becoming less important than the implementation

Today's leading AI models are improving at an extraordinary pace. They're becoming more capable, faster and increasingly cost-effective which means that the competitive advantage is shifting

Organizations are unlikely to outperform competitors simply because they've selected one foundation model over another. Instead, success will increasingly depend on everything that surrounds the model.

Data quality is one of the biggest differentiators. An AI agent is only as good as the information it's grounded on. Contradictory documentation, inconsistent policies, or poor knowledge management inevitably reduce the quality of outputs.

Prompt engineering is another often overlooked capability. Designing an agent isn't simply a matter of giving it instructions and expecting consistent results. Well-designed prompts establish clear guardrails, define the agent's responsibilities and ensure it stays focused on its intended purpose.

The same is true of governance. A good agent shouldn't answer every question it's asked. It should understand its role, know when to decline requests that fall outside its remit and operate only with the information and permissions it genuinely needs. Applying the principle of least privilege isn't just good security practice, it also improves efficiency by reducing unnecessary processing and keeps costs under control.

Ultimately, the quality of the implementation matters far more than the size of the underlying model.

Trust is built through iteration, not ambition

Another common mistake is assuming that agentic AI should be deployed through large-scale transformation programs. But, in practice, the opposite is usually true.

The organizations achieving meaningful outcomes tend to start with a tightly defined use case that delivers measurable value. This creates confidence among employees, demonstrates return on investment, and provides an opportunity to refine governance before expanding into more sophisticated workflows.

Agentic AI should be treated as a product that evolves over time rather than a project that's complete on launch day. However, that also changes the way organizations approach testing.

Traditional software is designed to deliver identical outputs every time. AI agents are probabilistic by nature, meaning consistency becomes just as important as functionality. Continuous testing, monitoring, and refinement therefore become integral to successful deployment.

Just as importantly, employees need to be part of the journey. Building trust isn't about unveiling an AI agent once it has been completed. It's about involving teams throughout development, helping them understand how decisions are made, where guardrails exist and how the technology is intended to augment and not replace their expertise.

Better agents will define the next phase of AI

It’s inevitable that the AI industry will continue producing faster, cheaper and more capable foundation models. But what isn't inevitable is whether organisations will be able to translate those capabilities into meaningful business value.

The next AI race won't be won by those with access to the biggest model. It will be won by those that build trusted, well-governed agents capable of solving real operational problems.

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• UK movie theatres considering smart glasses ban
• The ban would be over privacy and piracy concerns
• It's yet another instance of private bans restricting the tech

Privacy concerns have rocked Meta’s smart glasses business. They’re being ridiculed online as ‘perv glasses,’ and their use is banned in a growing number of spaces. In the UK, this could also soon include cinemas, though it’s for a very different p-word: piracy.

The main concern with camera glasses, like the ones Meta produces with its partners Ray-Ban and Oakley, is that people can record in a fairly secretive way. This is an issue for the general public as they don’t know when or if they’re being recorded, and for venues like cinemas and theatres as it allows movies and live shows to be subtly recorded by the audience.

With a stable enough internet connection, the viewing could even be live-streamed.

The ban hasn’t taken effect yet, but its consideration is another blow for smart glasses makers and users, who are finding it increasingly difficult to wear their specs because of their spy-camera capabilities.

Meta would argue that recording isn’t covert. There is a light that comes on and stays on while you record. Plus, Meta has taken numerous steps to disable recording capabilities if this light is tampered with — either via a simple cover or by fiddling with the hardware.

However, critics have argued the light is easy to miss if you don’t know to look for it, especially if you're outdoors during the day or in a bright place.

In a dark theatre or cinema, the recording light would arguably be a lot more noticeable, though the proposed ban would reduce the risk that bad actors sneak through — while also addressing privacy concerns the glasses raise for other patrons, and banning glasses that allow more covert filming.

Because while Meta is the most well-known smart glasses maker, and so is taking the heaviest share of the backlash, it isn’t the only player in town. Some glasses manufacturers aren't as strict about tackling modders who disable their recording indicator, while others make the indicator even less noticeable or don’t ship with one installed.

Going, going, gone?

Governments have also been considering action against smart glasses; however, we have yet to see much in the way of widespread bans. At this rate, we might not need to.

The court of public opinion has issued an overwhelmingly negative verdict on smart glasses. The privacy concerns for people wearing the glasses, and for passersby caught in their gaze who never consented to having their life captured by smart specs,

Even previous champions of the technology I’ve spoken with — journalists like myself who have used and tested, and loved these glasses — have stopped wearing them outside of our reviews. I’ve written before how I’m genuinely fearful I’ll get accosted in the street if I’m caught wearing these glasses.

Besides the threat of retaliation, the steady banning of these glasses across various venues makes them increasingly useless. That’s doubly true for folks who need prescription glasses — smart glasses bans often say that the glasses having prescription lenses is no excuse, meaning you could spend $500 / £500 / AU$750 plus on a pair of glasses you can’t wear anywhere.

Is this the end of smart glasses then? A serious perception overhaul could help, though it’s unclear how that would be achieved without such major software changes or hardware alterations, such as removing the camera altogether — though, from experience, cameraless smart specs don’t impress me. It’s too useful a feature to lose out on.

To that end, it’s a lose-lose, and that’s a shame for a category I think is quite fun and cool when people aren’t being creeps with them. They’re also proving a handy tool for people with visual impairments as the glasses can see and describe the world for them.

We’ll have to wait and see what happens, but I expect it’ll continue to be a rocky road ahead for smart glasses.

• Windows 11 is getting per-app permissions for all desktop software
• Previously this was limited to just apps from the Microsoft Store
• Microsoft has also decided to drop an incoming interface addition called the drag tray, which had drawn complaints from some testers

Windows 11 is finally giving you a full range of options to maintain your privacy from potentially snooping apps, and the OS is being further improved as Microsoft is scrapping an unpopular addition for the interface which was in testing.

First up, let's look at the privacy move, which as Windows Latest reports is the expansion of individual privacy controls for apps in Windows 11. This was first highlighted by Jakub on X, who noted: "Huge changes are coming to Windows 11 privacy & security. In the latest Experimental Insider Build (26340.9212) you can now revoke access to the camera & microphone for specific Win32 apps!"

What this means is that in Settings you can allow or deny access to the camera and mic (as well as location access) for desktop apps on an individual basis. Couldn't you do that before, I hear you ask. Only for Microsoft Store apps, with other (Win32) applications having just one 'master switch' to either toggle all these permissions on or off (on a blanket basis).

Thankfully, in a new experimental test build of Windows 11, Microsoft has changed things so you have granular privacy controls over all apps, not just Microsoft Store software.

On top of that, testers are seeing smartphone-style app permission prompts for allowing access to the camera and mic, which is a feature Microsoft has said it's bringing in before.

Windows Latest also noticed that Microsoft has officially canned the drag tray, which was a new part of the interface in testing with Windows 11.

The idea here is that when you grabbed and dragged a file on the desktop, this tray would appear at the top of the screen, and if you moused the file over it, you'd get some quick sharing options (so you could simply drop the file to share via WhatsApp, or Outlook, or a bunch of other icons).

Tthe drag tray could be handy for users with a touchscreen, for example, as an easier method than going by the right-click menu sharing options. So, what was wrong with this feature?

The problem was for those users with busier desktops, perhaps crowded with folders, because in these situations — or when using a maximized folder window filling the display — if a file was dragged towards the top of the screen, the tray might appear and get in the way of the target folder (effectively blocking it).

These frustrations led to some complaints, and Microsoft has ultimately decided to drop the tray.

In the notes for the most recent Windows 11 preview build in the Experimental channel, Microsoft told us: "Thank you to Insiders who have provided feedback about the Drag Tray experience. Based on feedback, we're removing the experience in this build. We're continuing to explore improvements in this area and look forward to sharing more about a future replacement experience in a later update."

Analysis: a real drag

The drag tray had to go, then, at least in its current implementation, but as Microsoft noted, this isn't necessarily the end of the road for this concept. It may be brought back to testing in a different incarnation, and indeed that sounds like the plan here, so I'll be keeping an eye out for the feature's reemergence at some point.

As for the privacy options, this is a very welcome move, as it's pretty important to be able to pick and choose which apps have what permissions. The only real question here is: why wasn't this the case in the first place? Granted, there are technical reasons why it's a good deal trickier to implement a per-app system like this for Win32 software compared to applications from the Microsoft Store. However, it isn't like this was something Microsoft couldn't solve – clearly, as it's being done now.

There might be quite a lot of work involved, but really, that isn't an excuse, and this is another one of those long overdue changes for Windows 11. A bit like the streamlining and new customization options for the right-click (context) menu in the OS, although admittedly that was a good deal more urgent. At any rate, at least both these changes are happening now, although this per-app privacy system is still in testing, and could end up looking somewhat different by the time it reaches release.

All of this is part of Microsoft's drive to fix Windows 11 while introducing a host of crowd-pleasing features that have long been requested, such as that context menu change. Note that the drag tray never actually arrived in the full release version of Windows 11, just to make that clear – it was only ever in testing, showing that not everything in preview makes the cut by any means.

• Adobe releases three new AI audio tools on Firefly
• Creators can now use Generate Music, Generate Speech, and Generate Sound Effects
• I tried them out to see how they perform in video creation

After sitting in beta for sometime, Adobe Firefly has now made three new AI audio tools promising "studio-quality sound" available for all creators. I had a play with each one to see if the results were as good as promised - and on the whole, they delivered, with a few quirks.

Generate Music, Generate Sound Effects, and Generate Speech are the latest audio features to hit Adobe's browser-based AI platform (no prizes for guessing what each of these do).

From my experimenting with all three, Generate Music is the high-point here, it's way better than I thought it'd be and I can see marketers and creators getting serious use from it. You can try them yourself by clicking here.

So, what's new?

There are three AI tools now available to everyone. They're all pretty self-explanatory, but here's what you need to know.

• Generate Music lets you create licensed tracks matching your videos' length and mood.
• Generate Speech takes scripts and turns them into voiceovers.
• Generate Sound Effects generates - you guessed it - sound effects that match the action and timing of a video.

But are they any good?

I've written before about my agnosticism towards AI in general, and my issues using it for creative work. It's a facsimile of art.

Still, Adobe's tools have impressed even the hyper AI-sceptics on my team - notably features like Generative Extend in its video editing software. And I'm nothing if not open minded. So, I decided to try these tools out for myself to find out what's really going on here.

The focus here, at least for now, is around speeding up creative workflows for the likes of marketers and content creators.

Creating AI music

I started with Generate Music, where you prompt freely or upload a video and the AI will take a punt at what sort of music you want. I used a short 27-second clip I filmed for my reMarkable Paper Pure review (mostly because it was still in my download folder).

Based on that, Firefly suggested a prompt for "a calm, thoughtful song with ambient electronic style for a product demonstration."

I hit Generate, and in a couple of seconds - it's actually impressively fast - I was served four tracks to choose from, all different but with the same vibe. They were...ok. A bit generic, but they'd do in a pinch. Or to rush a project to completion.

I decided to add a few more terms to the prompt, like "jazz", "cinematic", for use in a "vlog" and "trailer". I changed the Energy from "low" to "medium." The results were a big improvement.

Like all AI tools, the more prompting, the better the output, and there is an option to describe the vibe, style, and purpose yourself if you have an idea in mind.

You can download the music and video, or just the music from here.

Creating AI voiceovers

Switching over to Generate Speech, I first used the side-bar to determine which AI model to use - at the time of writing, there's Adobe's commercially safe Firefly and the ElevenLabs Multilingual V2 partner model.

I then picked a speaker from a list of 45, each variably described as male, female, non-binary, young adult, middle-aged, senior, and so on.

Finally, I pasted in my text into the text field - Firefly can figure out what language I'm using, but you do get the option to choose it yourself. I used the poem Ozymandias (with my greatest apologies to Shelley).

What I liked here was that with your text in place, you can choose to add additional text, or tell the AI to pause in certain places.

To actually preview, I had to select the entire passage and use the context menu to (it's where you also fix pronunciation and adjust the tone of voice).

For all the talk of speeding up workflows, this feels like a misstep to me. It just needs a play button at the bottom of the screen.

I had to tweak the pauses to give my text room to breathe, and once in place, I could alter the timings by clicking the pause on the preview screen. And for some reason, the word "land", of all things, is what tripped up the AI, where it was pronounced "lan."

Creating AI SFX

For this test, I again used a video filmed for my reMarkable review - this time a 12-second clip comparing the reMarkable 2 with the Paper Pure.

This process is a lot more in-depth, in the sense that the AI isn't going to guess what the likely SFX should be. I had to manually describe what I wanted. After that, I clicked the "Enhance Prompt", which added more descriptions.

We end up, then, with "A sliding whistle descending softly with a metallic timbre and gentle resonance." Clicking Generate, the service rapidly produced four ear-piercing variations. I won't subject you to those results. It's not fair on anyone.

Instead, I changed the prompt to a more soothing "crickets in a field" for a duration of 12 seconds, to fill the video length.

What's nice about the SFX option is that you can add multiple audio tracks and use the handles to adjust its positioning.

With a timeline sprawled across the bottom of the screen, it feels more like a real audio editor (to a degree, at least). Tug in the handles of the audio and you can further adjust volume or delete the track.

Worth it?

Overall, there are some UI tweaks I'd like to see here to make production workflows faster.

I don't think they'll substitute professional sound designers, composers, and musicians any time soon. But having worked in marketing, I can see they have their place for marketers and everyday video creation.

Speech is a world away from the days of robotic Microsoft Sam sound-alikes, but for nuanced line delivery, nothing's going to beat a performer in a booth.

Sound Effects has a lot more utility, though. Across a spectrum of generations, I couldn't tell the difference between the AI sounds and those playing on an old SFX CD I own.

The real winner here is Generate Music. Even the base prompt delivered audio that wouldn't feel out of place in a product demo, review, business explainer, or similar. And it got better with every generation I created.

An update worth exploring, then, for fast-moving video production.

The automotive world doesn’t slow down. But what’s changed most is what we value when we get behind the wheel.

Not long ago, the story of a new car could be told through horsepower, cylinders, torque and exhaust notes. For next-generation platforms, differentiation increasingly comes from driving assistance, electric range, the digital experience and connected features.

It’s about how confidently a car moves through the world. Does it anticipate what’s coming, or second-guess itself?

The next phase will not be defined by software alone, but by how intelligently vehicles use context. As cars become more capable of learning from data and adapting over time, the question shifts from whether a vehicle is software-defined to whether it can understand the road, the driver and the situation well enough to create a more confident driving experience.

This confidence depends on both the software that drivers interact with and the location intelligence working behind it. Mapping, routing and road context give a vehicle the understanding it needs to look beyond the immediate moment, interpret what it sees and translate raw signals into behavior that feels smooth, safe and more assured.

Shifting from software-defined to AI-defined

Software has rewired the car. Centralized compute, continuous connectivity and software layers that can evolve more independently from the underlying hardware have changed vehicle development. Instead of every capability being tied to a specific electronic control unit, software can be developed and improved across a centralized architecture.

Consequently, cars have become platforms that can keep improving after leaving the factory. Drivers expect the same up-to-date experience they receive from their smartphones, including new features and a continually refined interface.

Now, we’re seeing the next evolution. The AI-defined vehicle.

In an AI-defined vehicle, performance and behavior are shaped by continuously improving machine learned models, as opposed to hand-coded rules. Advances in neural networks and end-to-end architectures are improving the vehicle’s ability to perceive its surrounding environment, predicting what might happen next and then being able to respond appropriately.

What’s more, AI-defined capabilities increasingly touch the full in-vehicle stack. This includes navigation, infotainment, battery and energy management, HVAC and more. This will prove to be the difference between a car that simply follows instructions, and a car that understands behavioral patterns, including how you drive, what you prefer, and how to support you.

Why compute and connectivity change the pace of innovation

The software-defined shift laid the foundations for today’s AI-defined moment. Centralized compute makes it easier to run more advanced models and fuse more inputs. Continuous connectivity means vehicles can improve over time once they’ve left the factory, whether that's through updates, new services or data-driven learning loops.

You can see the change in the step from smart features to genuinely adaptive systems. For example, we’ve had voice assistants for years. Now we’re moving toward agentic AI-powered assistants that can handle complex, multi-faceted requests across the car’s systems.

On top of that, whilst we've had radar-guided cruise control for a long time, we now have end-to-end perception models that can pick up more subtle cues, like when the vehicle ahead is driving slightly erratically, and adjust accordingly by creating more space to increase stopping distance.

In an AI-defined world, the bottleneck moves from can we build a clever model, towards can we feed it with reliable, high-quality context, and do so at scale?

Sensors alone make a car drive like a tourist

Consider one area where AI-driven innovation is already having a significant impact - advanced driver-assistance systems and automated driving.

There is an industry debate about maps versus sensors. Some argue that sufficiently capable sensors remove the need for broader world knowledge. However, trusted assistance comes from combining what a vehicle sees in the moment with what it knows about the road beyond its immediate field of view.

A vehicle relying only on sensors can end up driving like a tourist. It will be cautious and reactive. It will wait to detect the next cue. On the other hand, a vehicle that fuses sensor perception with road intelligence can behave more like a local. It’ll know the road by heart.

It knows where lanes split, where speed limits change, and where closures or hazards are likely to appear. That supports smoother behavior which means, in turn, less surprise, fewer sudden corrections and, importantly, a more comfortable experience.

This is where mapping and road intelligence platforms matter. Autonomy means understanding context in the right detail at the right time.

A shared foundation that still allows differentiation

It's important to think about this as a shared ‘world model’ foundation. In other words, as a unified view of the road environment created by fusing multiple inputs. That means including road and lane geometry, traffic, road signs, speed limits, closures and other context, and then streaming that intelligence into the car.

The goal isn’t to make every vehicle feel the same. Rather, it’s for the OEMs to differentiate through brand choices, user interface, comfort, and how assistance is communicated. But there’s also an economic reality. The common foundation is expensive to build and maintain at global scale. Duplicating that effort across OEMs slows progress and increases cost.

Where AI is having meaningful impact

AI delivers real value when it changes process and cost structure. One of the most practical impacts today is scaling road intelligence creation. That means using AI to detect lane details, road markings, stop signs and other attributes from sources like aerial imagery, satellite, vehicle signals and probe GPS patterns. Done well, this reduces the cost of building and maintaining rich road models, and improves freshness, which is essential for safety and driver trust.

That’s the heart of the AI-defined shift. It goes beyond smarter features. Instead, it’s a new loop where vehicles improve as they’re exposed to more scenarios and more data. Critically, it's where the quality of the underlying road intelligence determines how confident, safe and human the experience can be.

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• Windows 11's right-click menu has some big changes in testing
• This context menu is being streamlined and is now more responsive
• You can also customize the entries in the menu, or even switch it to be like Windows 10's version

Microsoft is finally streamlining Windows 11's context menu — the one which pops up when you right click something on the desktop — as well as bringing in more customization choices.

In a blog post for a new preview build in the Experimental channel, Microsoft announced that it's rolling out a redesigned context menu for File Explorer (meaning the files and folders on your desktop).

It's "designed to feel faster, cleaner, and easier to tailor to the way you work", and Microsoft has made two major improvements here.

Firstly, the right-click menu is a lot less cluttered, with far fewer options now appearing in the list by default (building on some previous work along these lines). On top of that, there's new functionality in Windows 11's settings, enabling you to customize what appears in the context menu.

Windows Central reports that it has tried this revamped right-click menu, and that it's not just trimmed down, but also faster to load (no doubt reducing the bloat has helped to improve performance).

You can also choose to have a Windows 10-style context menu, where the elements at the top in the horizontal panel (cut, copy, rename and so forth) are integrated into the vertical menu list (removing the top panel, essentially).

It's possible to ditch any entry you don't want from the menu, too, so gone are the days when you installed a third-party app and it annoyed you by placing itself in the context menu when you didn't want that to happen. You can now simply banish the offender.

Analysis: it's about time

In my opinion, you should have been able to tailor the right-click menu to your own preferences right from the very off in Windows 11. Or at least you should have been able to remove the likes of third-party additions to the menu, even if Microsoft kept the core defaults set in stone.

Still, Microsoft has improved the context menu immeasurably here, and the more responsive performance is great to hear about. Windows 11 users have long complained that it's ridiculous that right-clicking on something could mean a delay of a few seconds before the menu actually appears. This is a basic piece of interface functionality, and not one that should be running slowly on an average PC.

Bear in mind that this is just in testing for now, so this new layout and options may take some time to arrive in the full version of Windows 11. That'll likely happen later this year, although you can't be sure whether Microsoft will keep everything as it is now (some choices could be scrapped, changed, or more might be added, even).

For now, a good many Windows 11 users are impressed by this work, but there are some who, like me, are still a bit sore about it taking so long to happen. One Redditor said: "They had 5 years to do that. Better late than never I guess."

There are others who have already left Windows 11 behind and can't resist making it clear that they won't be coming back, no matter how far Microsoft takes its drive to fix Windows 11. This Redditor observed: "Too little too late, I will refuse to ever go back to MS," adding that: "They've lost me and everyone who listens to me for IT advice."

Others point out that the performance work to fix Windows 11's sluggishness is a decision driven by necessity and the RAM crisis. Someone else on Reddit pointed out: "Probably has a lot more to do with having to figure out how to get Windows 11 working on 8GB of memory since they can't control the price of memory."

And another Redditor replying to that agreed, saying, "This is exactly why this is happening. All the 'fixes' started after they announced 8GB Surfaces."

It's easy to be overly negative on Windows 11, yes, but responses like these are ultimately Microsoft's fault, and a result of the attitude it has shown with the development of the operating system through the years.

With that said, it does very much feel like a fresh leaf has been turned over, and I'm very happy that this is the case. Hopefully Microsoft can apply changes to Windows 11 that'll continue to surprise us, and make this desktop OS a more enticing prospect — although it's unlikely those more disgruntled Linux defectors out there will ever be tempted back.

Software security was built around human development.

People wrote, reviewed and deployed code. Now machines are taking over.

In a recent paper, Anthropic reports that more than 80% of the code merged into its production codebase is authored by their AI model, Claude.

The same capabilities that make developers more productive are changing the economics of cyberattacks.

While adversaries still define the objective, machines can generate the payloads, test variants, adapt code to different environments and repeat the process at a velocity that security programs can’t match.

Speed is Marginalizing Security Controls

Most enterprise software security workflows assume there is time for review. Code is written, scanned, tested, approved and deployed. If something suspicious happens later, security teams investigate and respond.

That model breaks down when software moves from prompt to execution in minutes.

AI-generated code can become a script, dependency, automation job or infrastructure change almost immediately. While development agents can modify files, resolve packages and run commands.

Human reviewers are no longer in the loop.

Attackers can use the same mechanics to generate exploits, test evasion techniques and adjust payload behavior for different targets. This creates more variation with fewer stable indicators for defenders to recognize.

While AI-assisted analysis can improve triage, it still often produces probability, not policy. At machine speed, “probably suspicious” is not good enough.

Machines Change The Attack Model

Human attackers are not disappearing. But more of the attack chain is becoming machine-executed.

AI can automate reconnaissance, accelerate vulnerability discovery, generate exploit code, rewrite payloads and adapt command sequences to the target environment. But most defensive measures are designed around human constraints: reused infrastructure, shortcuts and trackable patterns. These don’t apply to machine attacks.

A machine-generated payload may not match a known signature or have an established reputation. It may be created, used briefly and discarded. But AI malware must still interact with the target environment to achieve its objective. Its behavior cannot conceal its intent, since it must access resources and change the environment in ways that advance the attack.

What malicious code is capable of doing is the more durable security signal.

Security Needs to Ask A Different Question

Software supply chain security has improved, but much of it still validates the artifact’s properties before execution rather than governing execution itself.

SBOMs, signing and provenance give security teams greater confidence in a code's composition, origin and build history. But knowing where software came from does not reveal what it will do when it runs.

Software can pass each of those checks and still create risk. Even an artifact produced through a legitimate build process may violate policy at runtime, while an AI-generated script may complete its intended task in a way that exposes data or systems. As a result, a clean dependency list is not proof of safe behavior.

Post-Execution Detection Is Too Late

Detection and response remain essential, but they intervene after risk has entered the environment. By the time suspicious behavior is visible, software may have accessed secrets, changed system state, opened network connections or created persistence.

AI compresses that window. Code can be generated, modified and deployed faster than humans can review it. Waiting for post-execution evidence gives attackers too much room to operate.

We need to shift the decision point left. Instead of asking, “Can we contain this software if it behaves badly?” the question should be, “Should this behavior be permitted to execute in the first place?”

That does not mean replacing existing controls, but rather changing where the decisive security gate sits.

Zero Trust for Code

Zero Trust changed enterprise security by rejecting implicit trust. Users, devices, sessions and access requests are not trusted simply because they appear familiar. They must be verified against policy.

Software execution needs the same level of verification.

Code should not be trusted solely because it came from a known repository, was signed by a recognized publisher, passed through a build pipeline or has not been seen exhibiting malicious behavior before. Those are useful indicators, but they are not conclusive.

Zero Trust for Code addresses this problem. Before software runs, its expected behavior should be evaluated against policy. If the behavior is acceptable, execution can proceed. If not, the artifact should be blocked, restricted, isolated or escalated for review.

Organizations can start by mapping every path through which code enters the environment or executes with meaningful privilege. This includes formal development channels such as repositories, open-source packages, containers and CI/CD pipelines, as well as email attachments, downloaded files, macros, browser extensions, endpoint installers, third-party integrations and scripts introduced through AI or automation tools.

Then identify where those paths rely on inherited trust. If execution is allowed because software came from an approved source, was signed, passed through a build process or has no malicious history, the control is incomplete. Behavior still has to be evaluated before the artifact is allowed to run.

As AI takes on more of the work of creating legitimate and malicious code, enterprises can no longer assume that code which clears existing checks should be allowed to run. Execution must become a deliberate security decision.

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The prospect of bringing more of Britain's critical national infrastructure into public ownership has prompted plenty of debate about investment, governance and accountability.

Far less attention has been paid to what it could mean for cybersecurity.

Regardless of where you stand politically, one thing is clear. Public ownership does not make cyber risk disappear.

If anything, it raises expectations that essential services will be more resilient, more coordinated and better prepared to withstand disruption.

That expectation reflects the reality of the threat landscape. Energy providers, water companies, transport operators and healthcare organizations all sit at the center of complex digital ecosystems. Their ability to deliver essential services depends on thousands of suppliers, technology vendors and third parties.

When one organization is compromised, the effects can spread well beyond its own network. Resilience therefore depends on far more than protecting individual organizations. It depends on understanding and managing the relationships between them.

If the government is serious about strengthening national infrastructure, cybersecurity must become part of that conversation from day one. That means moving beyond isolated security programs and towards a model where organizations share intelligence, understand common risks and coordinate their response before disruption spreads.

Critical infrastructure extends beyond organizational boundaries

Some of the defining cyber attacks of recent years have demonstrated that attackers are rarely interested in a single target. They look for opportunities to compromise one organization in order to reach many others.

The SolarWinds attack remains one of the clearest examples. By compromising trusted software updates, attackers gained access to thousands of organizations around the world. More recently, the ransomware attack on Synnovis disrupted pathology services across several NHS trusts, leading to cancelled operations, delayed appointments and widespread disruption to patient care.

Neither incident remained confined to the organization that was initially compromised. Both exposed the reality that critical infrastructure now depends on interconnected supply chains as much as physical assets.

That presents a challenge for every operator of critical national infrastructure. Security can no longer be viewed solely through the lens of protecting your own estate. Organizations also need visibility into the threats affecting suppliers, partners and the wider ecosystem. A vulnerability within a software provider or outsourced service can quickly become a problem for every organization that depends on it.

This is where many existing security programs begin to show their limitations. Organizations have invested heavily in detection technologies, vulnerability management platforms and threat intelligence feeds. They are collecting more information than ever before. Yet many still struggle to translate that information into confident operational decisions.

Better decisions start with better intelligence

The cybersecurity industry has spent years focusing on visibility. The assumption has been that if organizations can discover every vulnerability, identify every asset and collect every threat feed, they will naturally become more secure.

The evidence suggests otherwise.

Filigran's recent State of Threat Management report found that organizations consume an average of fourteen different threat intelligence feeds, yet fewer than half have fully operationalized that intelligence across their security programs.

At the same time, 84% of respondents said the attacks they experience exploit risks that were already known but had not been prioritized. Almost every organization surveyed also reported difficulty determining whether identified exposures were genuinely exploitable.

Those findings illustrate a wider industry problem. The challenge is no longer discovering risk. It is deciding which risks deserve immediate attention.

Security teams are surrounded by alerts, vulnerability reports and intelligence updates. Every tool claims to identify another critical issue demanding urgent action. Without context, everything starts to look important. Analysts spend valuable time investigating vulnerabilities that may never be exploited while genuinely dangerous attack paths remain hidden among the noise.

That has consequences beyond operational efficiency. Every hour spent investigating a low priority issue is an hour that cannot be spent reducing real business risk. Organizations are not simply overwhelmed by the volume of information. They are overwhelmed by the number of decisions they are expected to make every day.

Threat intelligence should shape decisions long before an incident

One reason this happens is that threat intelligence is still too often treated as a function of the Security Operations Centre. Intelligence is gathered, analyzed and used to help detect or investigate malicious activity once attackers have already reached the network.

Yet, threat intelligence has far greater value when it informs decisions much earlier in the security lifecycle.

Used effectively, it should help organizations understand which vulnerabilities are actively being targeted, which attack paths present the greatest business risk and which remediation activities will deliver the greatest reduction in exposure. Rather than treating every vulnerability as equally urgent, security teams can focus on the threats that genuinely matter to their environment.

This is also where Continuous Threat Exposure Management, or CTEM, has an important role to play. CTEM should not be viewed as another technology category or another security acronym. It provides a structured framework for connecting threat intelligence, exposure management, validation and remediation into a continuous process. Instead of relying on assumptions or theoretical risk scores, organizations can validate whether a vulnerability is genuinely exploitable before committing time and resources to fixing it.

Perhaps the biggest obstacle is not technical at all. Many organizations still operate with threat intelligence, vulnerability management, penetration testing and governance teams working independently, each with different priorities, processes and tooling. Breaking down those silos often delivers greater improvements than introducing another security platform.

Building a national capability

If critical infrastructure is expected to become more resilient, collaboration has to become part of everyday operations rather than something that only happens during a major incident. That thinking is already beginning to take shape.

Earlier this month, the National Cyber Security Centre and GCHQ issued a call for industry, academia and critical infrastructure operators to help define Cyber Shield, a proposed national cyber defense capability designed to combine AI, shared intelligence and coordinated defense at national scale.

Significantly, the initiative recognizes that the government cannot build this capability alone. It will depend on close collaboration with the organizations responsible for protecting the UK's essential services.

Additionally, the Cyber Security and Resilience Bill provides an opportunity to strengthen that approach by encouraging greater consistency across essential sectors. Frameworks such as the National Cyber Security Centre's Cyber Assessment Framework already give organizations a common language for measuring resilience.

They become even more valuable when they encourage organizations to learn from one another instead of tackling similar challenges in isolation.

Open standards have an important role to play as well. The Dutch National Cyber Security Centre recently made STIX and TAXII 2.1 the mandatory standard for sharing cyber threat intelligence across government.

While technical on the surface, the decision reflects a broader principle. When organizations exchange intelligence using common standards, they remove friction from collaboration and can respond to threats more quickly.

Technology alone will not deliver that outcome. Artificial intelligence, automation and modern security platforms can help organizations process more information and reduce manual effort, but they still depend on good intelligence, sound governance and trusted relationships.

For the simple reason that fast decisions only become good decisions when they are supported by the right context.

Resilience is a shared responsibility

Whether more of Britain's critical national infrastructure ultimately moves into public ownership is only part of the story. Cyber attackers do not distinguish between public and private organizations. They target weak links, trusted suppliers and interconnected systems wherever they find them.

The organizations that will be best prepared for the years ahead will be those that treat resilience as a collective responsibility. They will operationalize threat intelligence before incidents occur, validate real-world risk rather than relying on assumptions, and collaborate across organizational boundaries as readily as attackers do.

Protecting critical infrastructure has never been solely about defending individual organizations. It is about strengthening the entire ecosystem that keeps essential services running. If the UK wants to build genuinely resilient national infrastructure, that is where the conversation needs to begin.

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AI spend made headlines again recently with the Claude Fable 5 model from Anthropic. Before security concerns led to the model being suspended, there were also cost concerns. Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. This is double the price of the company’s previously most expensive model, Claude Opus 4.8.

Posts soon began to pop up on LinkedIn, showing just how quickly teams were going through their tokens and, as a result, their budget. There are some caveats here. Namely, that Fable 5 is an advanced model and, for most businesses, won’t need to run non-stop or be used for every task.

But therein lies a key issue: AI use is accelerating and models are evolving. But the level of control and visibility businesses have over how much is being spent, by who and for what is lagging behind.

How AI consumption became a finance problem

There is a massive shift within the UK software market toward AI and specifically Anthropic’s ecosystem. Proprietary data from Pleo looking at the top tech merchants based on number of spending customers, shows that Anthropic (Claude) surged from 12th place in Q4 2025 to 7th in Q1 2026. Meanwhile, the average spend per customer increased +43.0% in this time.

This rapid climb signals that Anthropic has reached enterprise maturity in the UK market with businesses moving beyond the experimentation phase. But while this reflects growing confidence in AI adoption, it also presents some financial challenges.

On the whole, AI has redefined how the workplace runs, but it is not a free trial. The cost of tokens has gone up, and new models that can achieve what was seemingly unthinkable a few years ago come with a price tag to match. The new challenge for business leaders is to leverage these technologies but also limit rampant spending.

This is why many organizations are turning to their finance teams. Finance has the visibility to dig into the details and map AI use across the organization, whether it quietly shows up as a subscription renewal or a new budget request. But more than that, they can be instrumental in ensuring teams embrace open conversations, not just OpenAI.

AI activity does not translate to AI value

Just about every organization will have developed transformational ways of using AI tools. But, whether they know it or not, there will be wasteful ones too.

When it comes to inefficient use, some of the major culprits include asking AI agents open-ended questions, model mismatch where tokens are burned unnecessarily; and duplicate tools, resulting from shadow AI and overlapping subscriptions. These prevent businesses from seeing the full picture; one that is, in all probability, very expensive.

User literacy can improve this. But for finance teams they must start with the grey area of AI consumption. Two teams might show as active AI users, but one that’s using an LLM to produce more content faster is doing something fundamentally different to one that’s using it for peripheral productivity tasks. In fact, only 29% of European SMEs using Gen AI are doing so in core business activities.

To improve the control they have over AI, organizations must start by elevating their visibility from who is using AI, to who is using it to become smarter, faster and more productive.

How to regain control over AI use

A complete view of AI spend is essential, regardless of whether costs are rising.

Breaking spend down by department, team and budget helps identify both disproportionate usage and areas where adoption may be lagging. These should be combined with performance metrics such as the time-to-first-draft on marketing content; code review cycle times in engineering; support ticket resolution time in customer support; and so on.

This combination of spend and performance can reveal whether AI investment is translating into measurable productivity gains and not just higher software costs.

Visibility should also extend to model-level usage. As mentioned before, the cost difference between frontier reasoning models and lighter alternatives can be tenfold. Monitoring model and vendor usage alongside token consumption helps organizations route routine tasks to lower-cost options, maximize ROI and reduce unnecessary spend.

Finance teams should therefore expand reporting and budgeting frameworks to include AI-specific metrics. A key question at month-end is whether AI-enabled teams are increasing output and capacity without increasing headcount. This provides a clear headline for AI's impact, can justify investment and distinguish between high-value and low-value AI usage.

Ultimately, effective control over AI is not about costs alone. It is about understanding where AI is creating value and ensuring investment is aligned with business outcomes.

AI control is at your fingertips

The good news is that none of these metrics require a sophisticated AI analytics stack. Finance teams should already have the tools for real-time visibility into what’s being spent and where. All that’s needed now is to fold AI into the mix and collaborate with other departments to measure and improve its ROI.

The outcome is that organizations control AI use through oversight, without restricting spend, adoption or innovation through lengthy procurement processes. Spend policies, category controls and clear approval thresholds control what is spent, and everything is measured.

But crucially, teams don’t slow down as a result. The only difference is that AI is optimized for impact.

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The recent release of Anthropic Mythos is a wake-up call for the tech industry – and the fact that Anthropic themselves chose not to release it publicly speaks volumes about the level of risk we have now reached. AI agents have evolved from chatbots with upgraded capabilities to effective employees with database access, API keys, and system privileges.

However, the security protecting them is built on the same strategy that failed to stop ChatGPT jailbreaks in 2023. And this time, there’s no human to review an agent’s output, just an autonomous agent carrying out commands in a silo.

AI agents are reshaping enterprise systems and the way work gets done. Securing them requires an equally fundamental shift in thinking. Ultimately, now that agents act independently, resilience must be rooted in foundational controls, including hardware-level and lower-stack security, to be ready when the higher-level safeguards fail.

How AI agents expand the attack surface

Before agentic AI, the biggest AI risks were bad recommendations, inappropriate responses, and conversational data exposure. Human oversight acted as a safeguard for every action, and AI systems operated without direct access to sensitive information. The primary concern was reputational damage rather than risks to underlying infrastructure.

When Anthropic released the Model Context Protocol (MCP) in November 2024, it established a standardized framework that allows AI agents to connect to databases, file systems, and enterprise tools. But within eight months, a critical vulnerability emerged (CVE-2025-49596, CVSS9.4), triggering emergency security responses across the industry.

The risk came from four factors working together. Autonomy means agents can decide and act without human review. Privileged access gives them credentials, tokens and file system permissions. Machine-speed execution leaves little time for human intervention. And cross-system reach means one compromised agent can move across connected environments.

Together, these factors expanded the attack surface far beyond what traditional security controls – even AI-enabled ones – were built to manage.

Why software-only defences keep falling short

The industry is moving quickly to secure AI agents, but the response largely mirrors a familiar approach: adding more layers of software. Most companies are focusing on two main layers: input guardrails – implementing more software tools designed to stop malicious instructions from ever reaching AI agents, and permissions and monitoring – limiting what compromised agents can access.

It’s the same strategy the industry had relied on for decades: deploy quickly, remain agile, and address vulnerabilities as they emerge. Both methods operate inside the software trust boundary.

But history shows this approach often ends the same way: with the need for hardware-layer protections. In the 1990s and 2000s, network security responded to software exploits by deploying additional software layers. Breaches persisted until organizations eventually adopted hardware-enforced network segmentation.

The same pattern played out with endpoint security in the 2000s and 2010s. As malware evolved to bypass detection, the response was behavioral analysis, sandboxing, and endpoint detection and response. Yet more software. Breaches continued until TPM (Trust Platform Module) chips and hardware-enforced secure boot became widely adopted. Cloud security, in the 2010s and 2020s, followed a similar path.

A common lesson runs through each of these domains: when the software trust boundary is compromised, the hardware layer – where data actually lives – must be secured too.

The case for hardware-level security

This time, we cannot afford to learn slowly. Agents are already being connected to the systems that business rely on for their daily operations. Incidents like the MCP critical vulnerability and recent reports of a data leak caused by a Meta AI agent show how quickly the risks can become real.

Guardrails, permissions, and monitoring are necessary, but they are insufficient, and they represent the security layers that history shows will eventually be bypassed. Effective defense requires a third layer – one that exists beyond the software trust boundary and provides oversight at the hardware level, where sensitive data is ultimately stored and processed.

Hardware Root of Trust serves as the final security barrier, helping contain breaches before they escalate into a full system compromise. As the number of companies using AI agents continues to grow, security needs to move deeper than the application layer.

The industry has already learned that software alone cannot secure complex systems – it should not wait for a major compromise to learn the same lesson again.

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When my co-founders and I started building our platform, the prevailing industry consensus was that the future of AI belonged exclusively to a tiny handful of elite, hyper-capitalized technology labs. The dominant narrative insisted that massive centralized scale and closed proprietary control were the only viable paths to frontier capabilities.

Today, that multi-billion-dollar bet on proprietary infrastructure is facing a massive market disruption. From my perspective as a founder, the era of treating AI as a rented utility is rapidly drawing to a close, replaced by an urgent global demand for open-source independence and data sovereignty.

The first major driving force behind this change is the reality of corporate accounting. Being fully dependent on a cloud provider for the core infrastructure of cognitive capabilities has transformed from a convenient beginning into a huge liability in strategic and security terms. It simply does not make sense to remain in a permanent closed-door monopoly.

As shown by recent research carried out by a scholar at UC Berkeley, moving an enterprise project from a proprietary API to open-source cuts the cost of computation from $3,000 down to $31. Recent reports say that this economic revolution takes place all over the world due to the fact that the quality difference between open and closed systems has become non-existent.

Independent LMSYS leaderboard shows the leaders of open source solutions to be just two per cent behind the best proprietary systems, such as Claude Opus.

Sovereign AI

Beyond the obvious economic advantages, the call for open architecture has become highly geopolitical. Both within the government sector and the business sector alike, institutions are now realizing that there are real risks involved in being tied to a foreign company's products. We are starting to see how such friction points manifest themselves through governmental actions.

In Germany, the increasing conflicts between Bavaria and Microsoft regarding data protection issues and regulations illustrate precisely why businesses cannot afford to be tied into closed systems and rely on overseas hyperscalers.

This is why I am such an avid supporter of Sovereign AI, where one is able to have complete and absolute control of their own data, their own models, and their own jurisdictions instead of continually leasing it from a third party.

This fast deconstruction will result in a great wave of stranded investments due to the immense amounts of capital being channeled into centralized and monolithic data centers.

The trend of technology is no longer towards a reliance on hyper-scale. With open source becoming so much more efficient and smaller in its footprint, there is no longer any need to do everything within a few large-scale server farms.

Engineering advancements mean that highly specialized architectures can do all of the heavy lifting locally or in a distributed network of various hardware configurations. There will be no competition between monolithic centers meant for renting proprietary compute cycles and sovereign, local networks run by companies themselves.

Open source

It is no secret that I believe that attempts to tame AI by limiting access to closed models will always boomerang against the interests of the proprietary software developer. Once there is a looming possibility that they may lose access or encounter political restrictions on export, they simply get pushed into going for open models that they will own and control themselves in their jurisdiction.

That is the whole idea of the open source philosophy. The results of scientific work and technical progress cannot be confined to just a few private labs of major corporations. With an open model, everyone can adjust its parameters for their particular use cases.

Applications of open model architecture clearly demonstrate just how much open-model architectures can achieve in terms of adaptation to a particular localized purpose. Closed-model architectures will surely remain temporarily ahead in individual benchmarks, especially in terms of the most complex and experimental functions. However, most companies do not require a Porsche on each and every trip.

What they require is a highly advanced model that is completely optimized for a specific task, which they can always have access to without being dependent on the decisions of a couple of tech leaders.

Whereas AI has the potential to revolutionize all aspects of our collective existence as well as business operations in the world, such a process cannot be controlled by a handful of providers in any way that would be safe. The general market simply wouldn’t tolerate it. It would be too risky for any contemporary company to tie its strategic development path to a single provider.

That is exactly the reason why technology leaders are pouring resources into infrastructure that is neutral and open. We can’t just invest in software and hardware; we have to invest in sovereignty.

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There are more than 70,000 AI companies operating today.

Most of them will not exist in five years.

Before you can see why — or figure out whether yours is one of them — you need a distinction the market keeps blurring.

Strip away the pitch decks and there are really only two types of AI system being built today.

The first is AI infrastructure: the orchestration and governance technology that makes AI usable at scale. In plain terms, this is the plumbing — agent frameworks, model routing, evaluation and monitoring tools, guardrails, and the controls that let a large organization use AI safely.

It sits between the foundation models and the end user, and it is where an enormous amount of venture money is going right now.

The second is the surface application: the tool an actual person uses to do actual work. The underwriting assistant, the contract reviewer, the sales copilot. The thing with a login screen and a job to do.

What I see in the market is a blending of the two. Some firms are selling architecture.

Some are selling tools. Many are trying to sell both, on the theory that owning the whole stack is the safest position.

And while this market is filled with tremendous exuberance with seemingly everyone starting an AI company, I am very skeptical that many of these firms will ever see profitability as history offers a strong counter.

We've run this experiment twice.

The past and the future

The dot-com era ran the first version of this experiment, and its final tally is worth stating plainly. Researchers estimate that roughly 50,000 startups were founded in the United States between 1998 and 2002 to commercialize the internet.

Of those, something like 8,000 attracted venture funding. About 1,700 internet-related companies made it to an IPO across the whole era — 585 in 1999 and 2000 alone — and at the peak, only about 14 percent of the tech companies going public were profitable.

By late 2002, most internet stocks had lost more than three-quarters of their value and roughly 1.7 trillion dollars had been wiped out. And the number of enduring, large-scale winners from that entire cohort — Amazon, eBay, Priceline, Expedia — you can count on two hands. Run the funnel: 50,000 founded, 8,000 funded, 1,700 public, fewer than ten giants.

A real gold rush works the same way: a few strike it rich, some make a living, and most go home with less than they brought. This is important to remember for everything that follows.

If that funnel looks like a quirk of one bubble, it is not — it is how markets distribute winnings everywhere. Hendrik Bessembinder at Arizona State studied every U.S. stock since 1926, more than 25,000 companies, and found that the best-performing 4 percent account for all of the net wealth the stock market has ever created; the other 96 percent, taken together, did no better than Treasury bills.

Just 90 companies — a third of one percent — produced more than half of it, and the majority of stocks lost money outright over their lifetimes. The market wins; almost no individual company does. Keep that in mind every time someone tells you AI will create trillions in value. It will. That says nothing about whether any particular company captures a dime of it.

The example of cloud

Cloud computing is the sharper rerun. In the early days there were hundreds of cloud providers and a thriving ecosystem of middleware companies selling the connective tissue — provisioning tools, management layers, monitoring platforms.

Today three companies control roughly two-thirds of the cloud market, and their share grows every year.

And here is the part that matters for AI: the middleware layer did not consolidate alongside the platforms. It was absorbed by them. The hyperscalers built the management consoles, the monitoring, the orchestration, and shipped it as a feature. The companies whose entire business was cloud plumbing were acquired cheap or squeezed out.

Meanwhile, the application layer on top of that consolidated infrastructure exploded. Thousands of SaaS companies built durable, profitable businesses without owning a single server. The bottom of the stack ended up in a few hands. The top produced thousands of winners.

It has already happened once inside this stack

If cloud feels like ancient history, look at the data layer — the foundation every AI system sits on. That consolidation already occurred, and it finished recently. The "modern data stack" boom of the last decade funded hundreds of startups selling pipelines, catalogs, transformation tools, and warehouses.

Today the independent tier has settled to exactly two companies at scale: Snowflake and Databricks, each running at roughly five billion dollars in annual revenue, with the hyperscalers’ native offerings holding most of the rest of the market. Nearly everyone else was acquired, absorbed as a platform feature, or left scraping for the remainder.

And notice the shape it settled into. The top five data platforms — Snowflake, BigQuery, Redshift, Databricks, and Microsoft’s offering — hold roughly two-thirds of the market. That is almost exactly where cloud landed: three players, about two-thirds of the market, a long tail fighting over the rest.

Two different layers, a decade apart, ending in the same proportions. That is not a coincidence. It is what happens when competing takes huge capital and the platforms can build whatever sits next to them. Expect the AI orchestration layer to end up the same way.

The consolidation was driven as much by the buyer as by the vendors. Large enterprises learned that scattered data is expensive data: every additional platform meant another copy of the truth, another integration, another security review, another contract.

So CTOs stopped buying data tools one team at a time and started making strategic platform decisions — pick one or two providers, consolidate the estate onto them, and hold that line. A single source of truth became an explicit architectural goal at most large companies, and once thousands of enterprises were making that same decision, the market had no room left for a long tail of vendors.

Look at what it took for Snowflake and Databricks to survive that consolidation: enormous capital, the fact that customers’ data lives on their platforms and is costly to move, and deep ties into how their customers work every day. You can survive as an independent alongside the hyperscalers — but only by becoming one of the few names a CTO puts on the strategic list, and almost nobody makes that list.

The same consolidation is coming for AI

Apply that pattern to the two types of AI company and the forecast writes itself.

The infrastructure layer — orchestration and governance — will consolidate down to a few. Not because the current tools are bad, but because this layer sits directly in the expansion path of the biggest players in technology. The model providers and hyperscalers have every incentive to build orchestration, evaluation, and governance into their platforms, and they are already doing it. Every capability that today justifies a standalone infrastructure startup is a roadmap item at a company with a hundred times the resources and a direct line to the same customers.

If you are building an architecture-only solution, this is the uncomfortable implication: you are likely to be taken out by one of the big players. Maybe you get acquired, if you are early and lucky. More often, the platform simply builds what you sell and includes it for free. Either way, orchestration and governance alone is not a business you can hold. The only real question is how long you have.

Which leaves the application layer as the open field. And this is the counterintuitive part: infrastructure consolidation is good news for application builders. When orchestration and governance become cheap, standardized, and built into the platforms, the cost of building a serious AI application collapses — just as commodity cloud ignited the SaaS boom. We are already seeing a massive increase in the number of AI applications getting built, and most will likely not survive.

Better software, worse odds

Part of what makes this cycle different is how little it costs to enter. Building serious software used to take millions in capital and a room full of engineers — a filter that limited how many companies could even try. Today a handful of people with AI tools can ship in weeks what took a funded startup a year.

So new ventures are multiplying, not because there are more good ideas, but because the cost of trying has collapsed. The scale tells the story: more than 70,000 AI companies operate globally today, roughly 18,000 to 30,000 of them in the United States alone.

The comparison to the dot-com era’s 50,000 is not perfectly apples to apples — that was a five-year founding total for one country, this is a snapshot of companies operating worldwide right now — but the order of magnitude is the same, this wave is global, and the count is still climbing.

Here is the twist that makes the coming shakeout more brutal, not less: the software being built is genuinely good. This is not the dot-com era, where half-finished products hid behind splashy marketing. The tools are now so powerful that quality is the baseline — which means quality has stopped differentiating anything. When every product is polished, capable, and shipped fast, none of that separates you from the next founder who did the same thing last month.

And that is precisely why so few founders see the danger. Every one of them genuinely believes they are building something singular — and by their own measure, they are right. They compare their product to what came before: the clunky incumbent, the manual process, the way the work used to get done. Against that benchmark it looks revolutionary.

What they never compare it to is the tens of thousands of other teams looking at the same models and the same problems, building virtually the same thing at the same time. Measured against the past, every AI product is remarkable. Measured against the field, almost none are. More entrants than either previous cycle, all building excellent software, almost none of it distinguishable.

That is the setup for the largest culling yet, and it will run almost entirely on the moats, because there is nothing else left to separate the winners from the losers.

The delusion of special

I see this up close. I have this conversation with application founders every week, and it always goes the same way. They believe the quality of what they built is their moat: the product works, customers love it, nothing else on the market feels as good.

All of that can be true, and none of it protects them. Quality can be copied. The same tools that let them build an excellent product in months let a competitor build one in weeks. A few founders have built something that truly stands alone, but I just can’t see many finding a way to real profitability.

There will be some winners, but I think they will need to rest on three key differentiators:

1. Data. Not data you scraped or licensed — proprietary data your business generates by operating: claims histories, transaction flows, patient outcomes. If your system gets smarter from data competitors cannot obtain at any price, you compound. If you are building on the same public internet as everyone else, you do not.

2. Distribution. If you already own the customer relationship — an installed base, a trusted brand, an embedded sales channel — you can put an AI product in front of buyers faster and cheaper than any startup. This is why incumbents are more dangerous in this cycle than the last one. The startup has to build the product and buy the audience. The incumbent only has to build the product.

3. Integration into workflows. The one people underestimate. Companies that wire themselves into how work actually gets done — the approvals, the systems of record, the daily habits of thousands of employees — become painful to remove even when a rival ships something better. Switching costs are not glamorous, but they have protected enterprise software for thirty years, and they will protect AI applications too.

Have one of these and you can build a durable business on commodity infrastructure. Have two and you can build a great one. Have none and you are likely running out of time.

Your toughest competitor is your customer

And here is what makes the application layer even harder than the dot-com or SaaS eras: surface applications are not just competing with other vendors. They are competing with the companies they are trying to sell to. The same commodity infrastructure that makes it easy for a startup to spin up an AI application makes it just as easy for the buyer to build one internally.

Every enterprise pitch now runs into a question that barely existed in the SaaS era: why would we buy this when a small internal team could build it in a quarter?

And here is the uncomfortable part. The three advantages that decide the application winners — distribution, proprietary data, embedded workflows — are precisely what the buyer already has. The enterprise owns its data. It is its own distribution. It controls its own workflows. The customer starts the build-versus-buy conversation holding every moat you are trying to claim.

A surface application does not just need to be better than its competitors. It needs to be so much better than what the customer could build themselves that buying beats owning — and that bar rises every time the underlying infrastructure gets easier to use.

Know which company you are

I am not going to pretend to know which specific firms win. But the structure of the outcome is already visible, because we have now watched it three times — dot-com, cloud, and the data layer: infrastructure consolidates to a few, applications proliferate, and the survivors are the ones holding data, distribution, or workflow integration that cannot be copied.

So the first question is not "is my product good?" It is "which of the two companies am I?" If you are infrastructure, your realistic endgame is being bought or being bypassed — plan accordingly. If you are an application, the model is not your moat and the product probably is not either.

So what is?

The good news, and who gets it

One clarification before closing, because everything above can read as pessimism about AI itself. It is the opposite. The technology will create enormous value, and the markets built on it will grow. The open question is who keeps that value, and a century of evidence gives a consistent answer: mostly the consumers of a technology, not its producers.

William Nordhaus at Yale measured this across decades of American innovation and found that producers capture only about 2 percent of the total value their innovations create — the rest flows to the people and businesses that use them. Railroads transformed the economy and ruined most of their investors. Airlines moved the world and destroyed capital for a hundred years. The internet made a handful of platforms rich — and made every company that deployed it more productive.

This cycle is already tracing the same shape: the infrastructure layer consolidates, prices its scarcity, and books historic profits, while the application layer competes and hands its margin to the buyer.

That is the real ending of this story. The coming massacre of AI companies and the coming growth of the AI economy are the same event, seen from opposite sides of the table. If you sell AI, the funnel is your problem and the moats are your only defense.

If you buy AI, the competition among 70,000 firms is working precisely in your favor: every improvement, every price cut, every copied feature moves value from their side of the table to yours. The bad news in this article is only bad depending on which chair you sit in.

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• Microsoft is again under fire for aggressively pushing Edge
• A survey conducted by the Browser Choice Alliance shows how deep some problems run
• That includes encountering 'challenges' while attempting to change default browsers, and users who get stuck with Edge when they don't want it

Microsoft has again come under fire for the way it pushes the Edge browser on Windows 11 users.

Windows Central highlighted a survey from the Browser Choice Alliance (BCA) which makes it clear that there are still issues around the way Edge is promoted in Microsoft's desktop operating system.

This was a poll of 2,000 adults in the US who own a Windows PC (either a laptop or desktop), and it found that the majority of respondents (86%) believe they should have "primary control over default browser choice", and moreover, most of those (80%) believe there should be a one-click system to pick that default.

The survey also found that: "72% of Windows users who tried changing default browsers in the past 1-2 years reported that they encountered challenges while attempting to do so."

That includes Windows "recommending or redirecting them back to the pre-installed browser" (Edge), or having website links or apps still open in Edge when it's not their default choice.

The BCA further observed: "Even after selecting a preferred browser, nearly 1 in 3 Windows users report experiences and tactics that undermine their ability to maintain that choice over time. They've fallen into a Reset Trap."

On that subject, 29% of users said that a Windows system update had "contributed to their browser preference being changed or reset".

Interestingly, 19% of respondents said that Edge was their preferred browser — which seems high compared to global stats — although 26% indicated that Microsoft's browser was set as their default in Windows 11. That means 7% of those surveyed had Edge as their default when they didn't want it.

Some of those presumably 'stuck' in this way may be older Windows 11 users, based on the survey's discovery that 44% of those over the age of 65 reported that changing their browser was a "complex" task in Windows 11, compared to 15% of those in the 18 to 44 age group.

61% of those aged over 65 felt "slightly or not at all confident in their own ability to consistently select and run the browser of their choice", which is a worryingly large percentage.

The survey maker concludes: "Microsoft should respect consumer preferences by removing unnecessary barriers and ensure users, not its operating system, remain in control over their browser experience."

Analysis: choice not challenges, Microsoft

It's worth noting that the Browser Choice Alliance is an organization that contains some rival browser makers, including Google (Chrome), Opera and Vivaldi. This doesn't make the report any less valid, but it's something to bear in mind.

I don't think there's any arguing with the broad conclusion drawn here, and that there are unnecessary hoops to jump through, and various annoyances, pertaining to Edge being foisted on Windows 11 users. On top of links unexpectedly opening in Edge, or outright resets of browser preferences in some situations, there's also Microsoft pushing folks to use its browser through 'recommendations' or ads of one kind or another.

It's all anti-consumer nonsense that needs to be stopped, and the fact that just over 70% of those who took the survey said they had "encountered challenges" in trying to change their default browser really isn't good enough.

Microsoft can do better than this, and in the EU, it does do better than in the US (or other regions) as another recent report commissioned by Mozilla (the maker of Firefox) pointed out.

Mozilla alleges that Microsoft engages in trick wording, nagging, preselection, and other underhand tactics to try to get Edge picked as the browser for Windows 11. However, due to regulations in the EU, countries in that region escape a lot of the more dubious activity in this regard. So, why doesn't Microsoft apply this improved approach with less hassling elsewhere? We know the answer to that, of course: it doesn't make any business sense.

It's not a good look if a company only implements pro-consumer policies because it's forced to, and for no other reason. But here we are…

Microsoft is certainly hearing Windows 11 users as part of the great campaign to fix the operating system, so maybe it'll start listening to browser users who want a fairer, more level playing field in the desktop OS. History very much suggests that it won't, though.

• Windows 11's August update is now rolling out
• It brings faster launch speeds to apps via functionality that was previously only available for certain Windows menus
• There's also a smart feature for Voice Access that deals with background noise interference, and some tweaks for search in Windows 11

Windows 11's August update has arrived, and it's mostly minor work, although there are a few notable moves with this new patch.

One important introduction is that Microsoft is extending a previously rolled-out feature — a secret sauce for faster launch times — to cover apps in Windows 11. That's good news for the performance you'll experience in the OS in general, and there are a couple of other nifty improvements besides.

Let's take a look at those, and I'll also round up some highlights of the rest of the work that's present in Windows 11's latest patch for those who are planning to grab it.

1. Faster app launches

One key move here is that apps are now going to launch faster in Windows 11, thanks to the extension of a previously rolled-out feature called Low Latency Profile (LLP).

Windows Latest flagged the deployment of this capability, which speeds up the launching of apps considerably. Note that LLP first debuted in June, but it only applied to certain parts of the Windows 11 interface (like the Start menu, for example, or Notification Center), and now it works for all your apps.

LLP nudges the processor when an app (or menu) is launched, asking for a brief speed boost so that the app in question will load a lot more quickly. If you have Task Manager open when you run an app, you'll see that as you start it, the CPU will ramp up its speed for a second or two, before returning to normal once the program is running.

Bear in mind that this ability is on a controlled rollout, which is normal for this sort of feature, as Microsoft tests the waters to make sure it's working as intended. This means you may have to wait a little while before getting it.

(While you can force the enablement of LLP with apps by using a bit of trickery via a Windows configuration tool, I wouldn't recommend this approach, and I'd advise simply waiting for the rollout to happen naturally. There could be a good reason why your PC has been swerved for this particular feature).

2. Voice Access improved to filter out background noise

Windows 11's built-in voice functionality (Voice Access) also gets a boost this month with a new feature called Voice Isolation. If this is switched on, it filters out any background noise, including other people speaking, so only your voice is picked up. Microsoft informs us (in the optional June update release notes, which was this patch in preview) that there's a setup process required to recognize your voice, but you only have to do this once.

There's another setting that just removes background noise (any non-speaking sounds), which doesn't require any setup. For those who do want the full Voice Isolation experience, you'll find the setup is accessed in Voice Access settings > Improve speech recognition.

3. Windows search improvements

Microsoft keeps taking steps to make Windows 11 search more user-friendly, and there are a couple of worthwhile additions in this update. First off, we're told that when searching for apps, Windows will handle typos better, so if you misspell the software you're hunting for, it should still be found. Likewise, partial app names will produce a result.

Furthermore, when you're searching for a Windows 11 setting, the more useful options should now appear higher in the returned search results. While these are more minor tweaks, Microsoft is clearly making strides towards search becoming a good deal better, and that includes some important streamlining work that's inbound (including ditching web results).

What else is new?

Other changes delivered by the August update for Windows 11 include new gesture controls for precision touchpads, which include the ability to adjust the default speed for zoom and scroll gestures. In the latter case, you also get scrolling acceleration applied so the more you repeat the gesture, the faster the scrolling becomes, allowing you to zip through long documents or web pages.

A useful piece of functionality has returned for laptops, too, namely the ability to set a level for when Energy Saver should be turned on (in Settings > System > Power & Battery). This was previously removed as Microsoft wanted the energy-saving feature to manage itself, but users were confused as to where the option had gone, and still wanted a manual setting present — so it's back (for now).

Widgets have also been tidied up so that the Lock screen only shows the weather by default (rather than multiple widgets), and notification badges in the taskbar use the Windows accent color rather than being red (making them less of a distraction).

There's a whole raft of minor tweaks, including Windows Hello getting support for peripheral fingerprint sensors and the addition of a "parental controls notice" during Windows 11 setup that points out online safety features for families.

The integration of artificial intelligence (AI) into everyday work and life has prompted businesses and regulatory bodies to take action. AI is a force introducing entirely new categories of threat, making heightened cyber resilience essential. However, amidst the panic to get in front of this, it should be noted that not all the hype is entirely accurate.

The underlying vulnerabilities organizations face today are largely the same ones they faced five or ten years ago: unpatched systems, weak identity controls, excessive privileges, insecure third-party integrations.

In some cases, these weaknesses have existed for some time, and most likely, will continue to exist because the first fundamental constraint of computer science is that the removal of all vulnerabilities is impossible. Therefore, no amount of tooling or budget will ever make any business 100% secure. Equally, that doesn’t mean improvements should simply be dismissed - especially in the age of AI.

Speed, not novelty

While not introducing anything inherently new, there is still some cause for concern surrounding AI. The nature of existing weaknesses remains the same. However, AI does significantly change the speed and scale at which they can be identified and exploited. Tasks that once required time, skill, and persistence can now be automated, accelerated, and in some cases delegated.

The barrier to entry has been lowered for less sophisticated actors to operate with greater efficiency and success.

We are already seeing early signs of this shift. Elements of the attack lifecycle can be automated, be that reconnaissance or lateral movement. Nation-state actors have begun experimenting with using these systems to coordinate multi-stage operations, and we’ve recently seen a fully autonomous attack take place, without any human supervision.

At the same time, more familiar techniques are being enhanced rather than replaced. Malware can be generated or iterated more quickly to evade detection. Social engineering has become more convincing through deepfakes, and phishing campaigns have become easier to scale.

None of this represents a fundamentally new playbook, simply the acceleration of an existing one.

The distraction problem

The distinction between novelty and speed matters because it shapes how organizations respond. If AI is treated as a novel and exceptional threat, it encourages a reactive mindset. Security teams are pushed towards finding “AI-specific” solutions, often at the expense of addressing longstanding gaps in their environment. In practice, those gaps still remain the most reliable entry points for attackers.

The risk that the current level of attention on AI creates, is a form of strategic distraction. Boards and executives are rightly asking questions about AI risk, but those conversations can become detached from the basics. Patch management programs remain inconsistent. Asset inventories are incomplete. Third-party exposure is poorly understood. Identity and access management remains fragmented across systems.

These are the same issues that security professionals were tackling before the advent of AI and the technology does not take them off the board. If anything, these become more consequential as the speed of exploitation increases.

The right response

It is worth being clear about the limits of control. No organization will ever be 100% secure. There will always be unknown vulnerabilities, many of which the new frontier models will be able to fish out.

However, the idea that AI introduces risk that can be entirely “solved” is misleading. Even if advanced models identify previously unknown weaknesses, the response remains the same as it has always been: prioritize, remediate and reduce exposure over time.

For defenders, matching this increased tempo requires a combination of discipline and adaptation. Established practices such as red teaming and tabletop exercises need to evolve to incorporate AI-enabled scenarios. Incident response teams need to be prepared to handle new forms of evidence, including those generated or manipulated by AI systems.

In addition, training programs need to reflect the growing sophistication of social engineering, particularly where deepfakes and voice cloning is concerned.

AI-driven detection and response capabilities can play an important role, particularly in identifying patterns at scale. But they are not a substitute for secure-by-design principles, robust access controls, or a clear understanding of where critical data resides. Therefore, organizations should observe caution about over-rotating towards new tools without addressing foundational weaknesses.

The expansion of the attack surface through enterprise AI adoption adds another layer of complexity. Threat actors are already targeting AI workflows directly, exploiting vulnerabilities in software development environments, and using techniques such as prompt injection to manipulate system behavior.

In some cases, malicious instructions can be embedded within otherwise benign content, triggering unintended actions when processed by an AI system.

Again, these developments are best understood as extensions of familiar concepts. Input validation, supply chain risk, and data integrity have always been central to security. AI introduces new contexts in which these issues manifest, but not entirely new categories of risk.

From a governance perspective, this reinforces the need for clarity rather than novelty. Boards should be focused on defining risk tolerance, ensuring accountability, and maintaining visibility over how AI is used within the organization.

This includes integrating AI considerations into existing risk frameworks rather than treating them as a separate domain. Legal, technical, and communications teams need to be aligned, particularly in scenarios involving misinformation or synthetic media, where response speed is critical.

What matters now

There is value in the current focus on cyber risk. Increased attention at the board level can drive investment and accountability in ways that were previously difficult to achieve. But that attention needs to be directed towards the right problems. Treating AI as an entirely new threat risks misallocating resources and overlooking the vulnerabilities that are already present.

AI will continue to evolve and so will the ways in which it is used by both attackers and defenders. In cyber security, progress is often less about discovering new answers and more about applying existing ones with greeted consistency and speed.

The organizations that navigate this shift most effectively will be those that remain grounded in a clear understanding of what has and has not changed.

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• Someone has ported Word for Windows 1.1a to Windows 11
• It's a fully native port
• It's not easy to get it up and running on your system — should you wish to — but there are other alternatives to the feature-creep of Notepad

Somebody has taken a version of Word from 1990 and created a fully functional port for Windows 11 — and as you might imagine, this is a word processor with an incredibly light footprint.

Tom's Hardware noticed this feat, which was achieved by Justin Marshall, who's the developer behind the 'Microsoft Word for Windows 1.1a' project on GitHub.

As Marshall explains, this is not an emulator or some kind of fudge, rather it's a "fully working native Windows x64 port" of Word for Windows 1.1a which was codenamed 'Opus' back in the day.

This port was possible because Microsoft released the source code for the app (over 10 years ago), and Marshall took that code and ran with it. (As Tom's notes, it's available from the Computer History Museum).

Marshall explains that the project builds on that original Word source code (and complementary resources) with "modern replacements for the 16-bit assembly, segmented-memory, and Win16 platform boundaries".

The result is a very simple word processor that takes the idea of a streamlined app to new levels. Indeed, this modern take on Word for Windows 1.1a can fit in less than 1MB of RAM in Windows 11. How's that for lean? And there's not a drop of AI in sight…

Analysis: Word up

Can those fed up with Microsoft's feature-creep in Notepad, or other bloat, go and pick up this spin on Word from 1990 for a breath of fresh air in terms of a lightweight word processor? No, unfortunately, as there's no installation package to simply download and fire up.

That's because, as Marshall points out, the "historical source files retain their original Microsoft and third-party copyright notices", which means that commercial use or even sharing copies on the web isn't permitted.

This is why if you want to use Word for Windows 1.1a on your modern PC, you'll need to get it up and running by following the instructions on GitHub. This isn't for the technically faint-of-heart, as you might guess, and you'll need Visual Studio 2022 with Desktop development with C++, a Windows 11 (or 10) SDK installed through Visual Studio, as well as CMake 3.25 (or newer) and PowerShell.

In short, it's unlikely that many people will go to the effort, but it's not surprising that a fair bit of interest has been stoked in this project simply because of not just the novelty — although that's obviously the driving factor — but also as it's an illustration of how far Word can be stripped back, running with such a tiny footprint and resource usage.

One Redditor commenting on this simply said: "Not all heroes wear capes."

Another observed: "For the curious, Microsoft Office 97 will install and run on Windows 11 with no hassle. I've been using it for years now just for the fun of it."

Bear in mind, of course, that should you take that approach, you'll be lagging behind considerably for supported file formats, and also in the security stakes (that's the main drawback with using apps that haven't been updated in a long time, and lack patches against exploits that have since popped up).

If you are keen on a lightweight alternative to Notepad, then take a look at TinyRetroPad, which is built very much in the same vein as the original Microsoft text editor, and is just as tiny as the name suggests.

AI can already write production code, review pull requests, generate documentation, diagnose bugs, and propose architectural changes. Its influence now extends beyond developer productivity, affecting how engineering teams are organized, how decisions are made, and where technical authority rests.

CTOs, meanwhile, have always acted as orchestrators. Company growth gradually pulled their attention toward hiring leaders, setting architecture, resolving trade-offs, and improving team performance. Their influence flowed through the organization they built and the people they developed.

AI now changes the organization beneath technical leaders. As of May 2026, Claude authored more than 80% of the code merged into Anthropic’s codebase, while the typical engineer merged eight times as much code per day during the second quarter of 2026 as in 2024. Engineers increasingly direct and review AI-generated work, while retaining responsibility for technical judgment, goal selection, and higher-level decisions.

Implementation and debugging can increasingly pass to coding agents, leaving engineers responsible for defining tasks, reviewing results, and deciding when human intervention is required.

The middle management tier consequently faces compression, while the CTO comes closer to execution because agent access, architectural rules, security controls, and release criteria affect the entire company.

The CTO’s responsibility is building a verification machine capable of producing sound technical decisions at high speed.

The middle tier compresses

Traditional engineering organizations have always grown through coordination. A CTO worked through vice presidents, directors, and engineering managers, each translating company priorities into technical plans. Managers assigned work, tracked delivery, resolved blockers, and kept teams aligned.

AI reduces much of this coordination burden. An engineer can describe a feature, ask an agent to inspect the codebase, generate an implementation, write tests, and prepare a pull request. More advanced systems can divide work across several agents and combine their output.

The engineer increasingly manages an AI development team, which compresses roles centered on task distribution and progress tracking. Human leadership retains its value through coaching, conflict resolution, recruitment, and professional growth, while coordination as a standalone function carries less leverage.

Responsibility, therefore, spreads in two directions:

Engineers gain greater ownership because they command far more productive capacity; CTOs become more involved in the systems governing this capacity because one weak permission rule or review gate can expose the entire company.

The distance between technical leadership and execution narrows. A CTO may write little application code, yet needs a precise understanding of how agents create, inspect, test, and deploy it. The role owns the engineering operating system governing people, agents, and software delivery.

The CTO’s verification machine

Verification is now the central technical challenge.

An AI system can generate several possible implementations during the time a human engineer once needed to produce one. This abundance creates a selection problem because companies must identify which implementation fits the architecture, meets security requirements, remains maintainable, and serves the product goal.

The CTO must design a system capable of making these judgments consistently.

Scoped permissions confine each agent to the files, databases, and services required for its assigned task. Automated evaluations test security, performance, and reliability, while agents review one another’s work before sensitive actions reach a human reviewer.

Human approval, however, loses value when engineers face a constant stream of requests that rarely require intervention. After several rounds of autonomous testing and review, most proposed changes arrive in acceptable condition.

Engineers grow accustomed to approving them, attention declines, and the exceptional case becomes harder to detect. Aviation, medicine, and nuclear operations have studied the same effect: repeated exposure to routine confirmations can turn oversight into habit.

Effective verification therefore depends on reducing the volume presented to humans. Routine and reversible actions can pass through automated controls, while unusual, irreversible, or high-impact decisions receive focused review. The interface should present evidence, alternatives, uncertainties, and possible consequences in a form that encourages scrutiny rather than a reflexive approval.

Firm boundaries remain essential. An agent may propose a database migration while execution requires human authorization. It may generate a deployment plan while production credentials remain outside its permissions. It may identify a vulnerability while changes to authentication controls receive senior review.

The system should also measure the quality of oversight through rejection rates, review times, escalation patterns, and the frequency with which human intervention changes an outcome. Human judgment offers the greatest value when attention is reserved for decisions where experience can alter the result.

Audit trails, rollback procedures, and ownership rules complete the machine by making every autonomous action attributable, inspectable, and reversible. These controls determine how an AI-enabled engineering organization behaves and require architectural judgment, security knowledge, product awareness, and an understanding of human behavior under pressure.

The CTO increasingly designs the conditions under which technical decisions emerge, turning individual judgment into a system capable of producing consistent quality without exhausting the people responsible for its highest-risk decisions.

Coding is abundant; judgment is not

AI lowers implementation costs, while software quality still depends on judgment.

A company can now produce more features, integrations, and experiments than its customers need. It can create technical debt faster than any human team and fill a codebase with locally correct changes capable of weakening the system over time.

Planning, testing, and prioritization consequently become the main constraints on engineering output. Strong organizations will know which problems deserve attention, how each feature supports the product, and where technical compromises create long-term costs.

Technical strategy gains value as implementation capacity grows.

Security is more important because agents can act across more systems at greater speed, while testing carries greater responsibility because generated code can appear persuasive while hiding subtle errors. Maintainability is harder as software volume grows faster than human comprehension.

AI commoditizes implementation and raises the value of technical judgment.

The strongest CTOs will turn judgment into repeatable mechanisms by encoding standards into evaluation suites, review policies, deployment gates, and agent instructions.

Privacy, trust, and security

AI systems increasingly retrieve information, make decisions, call external services, modify records, and trigger actions across business systems. Risk therefore depends on authority as much as intelligence.

An agent with access to customer records, payment systems, private repositories, and production environments carries enormous operational power. Prompt injection, compromised model providers, manipulated data sources, and unintended autonomous actions can become entry points into critical systems.

Privacy and trust become architectural concerns. CTOs must define model governance, data permissions, identity controls, and approval requirements. They also decide which information can enter third-party models, which tasks require isolated environments, and which actions always need human confirmation.

Agent identity is essential because companies need to know which agent performed an action, who authorized the task, which data informed the output, and which rules governed the process. Capability provides an incomplete standard because permission determines the level of risk.

After all, a mediocre model with production credentials is more dangerous than a brilliant one in a sandbox.

A strong AI engineering organization treats every agent as an active participant with a defined identity, a limited role, and an auditable history.

Users judge the product

Companies often present AI as a product feature because it attracts attention, although the largest gains may come from using it inside the development process.

Customers judge software by quality, reliability, safety, and speed of improvement. The number of agents contributing to a codebase carries little relevance to their experience. Competitive advantage comes from turning increased engineering capacity into better products while preserving trust.

AI therefore belongs primarily to the production side of the company. It helps engineers explore more implementations, test changes more thoroughly, respond to incidents faster, and improve existing features with greater frequency.

Product design still determines how much complexity reaches the customer and how effectively the software handles permissions, routing, configuration, and other operational decisions.

Customers experience the value of AI through faster product improvement, fewer defects, more responsive support, and software capable of adapting more effectively to their needs. The technology itself can remain largely invisible.

The CTO must ensure increased engineering capacity serves the product and strengthens the customer experience.

CTOs as product leaders

When implementation was expensive, product teams defined requirements and engineering teams built them. Limited development capacity made the division easier to maintain.

AI weakens this boundary because technical capability can influence product direction almost immediately. A CTO can explore several product concepts with agents, create working prototypes, and evaluate constraints before a conventional development cycle begins, bringing engineering into product decisions earlier.

The CTO must decide where automation improves the experience and where human involvement remains essential. Some decisions benefit from speed and consistency, while others require empathy, context, accountability, and careful interpretation of consequences.

These choices combine product judgment with technical judgment. Breadth gains value because AI can help technical leaders acquire deep knowledge of unfamiliar domains quickly, while the advantage comes from connecting engineering, security, customer needs, and business strategy into one coherent system.

Future CTOs will need to understand the complete product environment, including how the company operates, how customers experience it, and how autonomous systems participate in both.

From organization builder to machine builder

Earlier generations of CTOs were remembered for the organizations they created. Their legacy lived in the people they hired, the leaders they developed, the culture they established, and the engineers who continued advancing the company after they left.

AI adds another durable artifact through the agent fleet, permission model, evaluation systems, deployment gates, architectural constraints, and feedback mechanisms left behind by technical leadership. These components determine whether a company can continue producing software safely after senior leaders depart.

The Industrial Revolution expanded physical production by embedding human knowledge into machines and processes. AI can create a comparable expansion in software development by turning parts of technical reasoning into systems capable of continuous operation.

Small teams can build products once requiring entire departments, while established companies can test ideas and improve software at exceptional speed. The outcome depends on the quality of the machinery surrounding the models, because code generation alone can increase software volume while strong verification systems convert AI capacity into reliable innovation.

The next generation of CTOs will be remembered for the machine they leave behind, whose agents, permissions, gates, and evaluations allow humans and AI to keep producing better software long after its architect has gone.

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The conversation around post-quantum cryptography (PQC) has shifted. Organizations are no longer asking whether they should prepare for quantum computing, but how quickly they can execute a migration that many expect will take years to complete.

As technology providers accelerate their roadmaps, governments introduce new expectations and boards seek greater assurance over cyber resilience, quantum readiness has become a business priority rather than a future technology project.

That urgency is being driven from several directions. Google and Microsoft have both set out roadmaps that point towards 2029 as a significant milestone in the transition to quantum-safe cryptography, while the recent US Executive Order on strengthening national cyber security reinforces the expectation that organizations begin preparing for the post-quantum era.

Together, these developments are shortening the planning horizon and increasing the pressure on CISOs to move from strategy to execution.

That shift from awareness to execution is reflected across the industry. Gartner's 2026 CISO Role-Based Survey: State of the Union found that fewer than one in four organizations have made measurable progress towards quantum readiness and only 8% have a usable cryptographic inventory.

DigiCert’s own Quantum Readiness Outlook research tells a similar story, as most IT and security leaders expect quantum computers to be capable of breaking today's encryption methods within the next three to five years, yet only 7% report that more than half of their digital certificates are already quantum-safe or hybrid.

The greatest challenge organizations face is no longer understanding the risks posed by quantum, but instead about becoming quantum-ready before today's cryptography becomes tomorrow's liability.

Why the risk is already here

Quantum computing has the potential to deliver significant advances across science, medicine and artificial intelligence, but it also threatens the asymmetric cryptography that secures digital identities, software, financial transactions and communications. The concern is no longer confined to the arrival of a cryptographically relevant quantum computer.

Threat actors are widely believed to be adopting a harvest now, decrypt later (HNDL) approach, collecting encrypted data today with the expectation that it can be decrypted once quantum capabilities mature. For organizations protecting information with a long operational life, that changes the timeline completely.

Our data found that 84% of organizations believe at least some of their encrypted data is vulnerable to HNDL attacks. Financial transaction records and banking data were identified as the assets most at risk (58%), followed by cryptocurrency wallets and private keys (53%).

For CISOs, this provides an important starting point because rather than attempting to replace every cryptographic system simultaneously, the priority should be identifying the systems protecting long-lived, high-value information and focusing migration efforts where the business impact would be greatest.

Discovery before deployment

For many organizations, the greatest challenge is not selecting quantum-resistant algorithms, but understanding where vulnerable cryptography exists across the business.

Furthermore, cryptography underpins cloud infrastructure, enterprise applications, connected devices, operational technology, software signing and countless machine identities. Over time, certificates, keys and algorithms become distributed across complex environments, often without a complete inventory of where they are used or which business services depend on them.

This is why discovery should be the first stage of every quantum readiness program. Organizations cannot prioritize risk, assess dependencies or build a realistic migration roadmap without first understanding their existing cryptographic estate. Discovery also enables security teams to identify the systems protecting their most valuable assets, allowing them to focus investment where it will have the greatest impact.

Once that foundation is in place, organizations can begin introducing quantum-resistant algorithms alongside existing infrastructure, testing interoperability, prioritizing critical systems and building the crypto-agility needed to adapt as standards continue to evolve.

Building a practical roadmap

Preparing for post-quantum cryptography is not a single technology upgrade, in fact, it is a long-term business transformation program that requires collaboration across security, infrastructure, application teams and technology partners.

The discovery stage provides the foundation by revealing where cryptography is deployed, exposing hidden dependencies and identifying systems that may otherwise be overlooked. Those unknowns are often the biggest source of delay, making early discovery essential to building a realistic migration roadmap.

With that understanding, organizations can begin prioritizing the systems that present the greatest business risk while assessing whether their wider technology ecosystem is ready for the transition.

That means working with software, hardware and cloud providers to understand their post-quantum roadmaps, identifying platforms that will require upgrades, and reviewing critical infrastructure, including web servers and TLS implementations, to ensure they can support quantum-safe cryptography.

It is important to remember that cryptographic standards will continue to evolve, making automation and crypto-agility essential for managing certificates, keys and algorithms at scale and adapting to future change.

The organizations that succeed will not be those that wait for quantum computing to arrive, but those that begin preparing now. By uncovering the unknowns within their cryptographic estate and building a phased migration strategy based on business risk, CISOs can strengthen resilience today while preparing their organizations for the cryptographic challenges of tomorrow.

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For much of the semiconductor industry’s history, performance debates have centered on compute throughput and memory capacity. Faster processors, wider vectors, and larger caches have been the primary levers for system architects, often treating the movement of data between them as a secondary concern.

Today, a different constraint is asserting itself as AI data center workloads proliferate, architectures diversify, and systems extend beyond traditional computing into the physical world. Data movement, rather than processing or storage, is increasingly defining the limits of performance, power efficiency, predictability, determinism, and scalability.

This shift around data movement is already visible. Across applications like advanced SoCs, AI accelerators, chiplet-based systems, and now in physical AI applications such as robotics, industrial automation, and intelligent vehicles, it has become clear that transporting the vast quantities of data required by such workloads is more demanding than processing them.

Physical AI does not introduce a new problem so much as it exposes an existing one: when systems must perceive, decide, and act in closed loops under real-time and safety constraints, inefficient or unpredictable data movement quickly becomes the dominant bottleneck.

The implications for both AI in the data center and physical AI extend far beyond any single market. As data center demand increases exponentially and the industry pushes toward increasingly complex, heterogeneous systems in anything from vehicles to industrial systems, data movement has become the primary performance battleground.

Performance limits are increasingly defined by data movement

Modern semiconductor systems integrate an unprecedented number of processing elements, including general-purpose CPUs, AI accelerators, GPUs, DSPs, sensor processors, and domain-specific engines. Still, simply adding compute resources rarely delivers proportional gains. In many advanced designs, performance plateaus long before compute capacity is exhausted.

Compute units are left sitting idle not because they lack capability, but because data cannot reach them efficiently or predictably. Late-stage analysis of high-performance SoCs often reveals that throughput shortfalls stem from contention, imbalanced bandwidth allocation, or inefficient routing within the communication fabric, rather than from compute limitations.

This is especially visible in physical AI systems, where delays introduced by data movement propagate directly into system behavior in closed-loop architectures. Latency or contention in the transport fabric can destabilize control algorithms, reduce accuracy, and even force over-provisioning of compute to compensate for the absence of bounded latency and guaranteed behavior.

While physical AI makes data movement constraints more immediately visible, the same forces are amplified dramatically in the data center. To push enormous volumes of data through extremely wide interfaces, processors increasingly contain hundreds of compute and accelerator instances, press against reticle-scale integration limits, use chiplet-based designs, and interface with multiple stacks of high-bandwidth memory (HBM).

As designs scale, the combined costs of moving data grow rapidly. In this environment, abundant compute and memory bandwidth offer little benefit without a data movement architecture capable of managing data at scale. What is a tight constraint in physical AI becomes an overwhelming one in the data center.

In both the AI data center and physical AI, performance is inseparable from the behavior of the data paths that connect perception, decision, and actuation. As a result, interconnect design can no longer be a back-end integration task. It has become a central architectural decision, on equal footing with compute and memory selection.

On-chip data movement is fundamentally different

Data movement inside a chip operates under constraints that differ radically from those of off-chip networking or board-level interconnect. On-chip transfers are extremely frequent, tightly synchronized with execution, and subject to stringent latency and power budgets.

Traffic is also inherently heterogeneous. A single system may need to carry high-bandwidth AI data streams, cache and coherency traffic, latency-critical control messages, and safety-related signaling, often simultaneously. These flows have very different requirements and cannot be treated uniformly.

Traditional best-effort arbitration models break down quickly under these conditions. Bursty accelerator traffic can interfere with control-plane and real-time data paths, leading to unpredictable system behavior. In mission and safety-critical or physical AI applications, such interference is unacceptable.

As a result, quality of service (QoS), traffic isolation, bounded latency, and determinism are now architectural requirements, not just optional optimizations. The interconnect fabric increasingly functions as an active system component, enforcing policy and guaranteeing behavior, rather than as a passive conduit for bits.

AI, heterogeneity, and physical intelligence magnify the challenge

AI workloads fundamentally change the character of data movement. They generate massive data volumes, irregular access patterns, and asymmetric traffic flows. At the same time, heterogeneous architectures distribute computation across many specialized engines, eliminating any single “center” of the system.

In both data center and edge contexts, this decentralization increases coordination costs. Data replication, synchronization overhead, and inefficient sharing can consume significant power and latency, eroding the benefits of specialized compute. Rather than compute placement, the challenge designers face now lies in how to efficiently orchestrate data movement between diverse compute elements.

Physical AI places additional pressure on these architectures. Unlike batch or best-effort inference workloads, physical systems operate continuously in real time. Sensor data must be ingested, processed, and acted upon within strict deadlines. Feedback loops amplify even small inefficiencies in data movement.

Together, these demands reinforce the broader lesson that heterogeneity without a deliberate data movement architecture leads to complexity and inefficiency, not scalability.

Chiplets turn data movement into a system-level design problem

Chiplet-based multi-die architectures promise yield advantages, faster innovation cycles, and importantly, flexibility. But they also elevate data movement challenges beyond a single piece of silicon.

Cross-die communication introduces higher energy per bit, tighter physical and architectural constraints, and more extra latency than on-die data movement. Interfaces that were trivial within a single die become critical bottlenecks once they must traverse package boundaries.

Successful chiplet systems, therefore, require system-level planning of data movement, encompassing topology, hierarchy, coherency and protocol selection, and physical constraints. Partitioning functionality without an integrated strategy for how data flows between partitions often increases, rather than reduces, overall system complexity.

Control and safety domains may span multiple dies, and failures or delays in inter-die communication directly affect system behavior. Chiplets demand not only modular compute but also modular, predictable connectivity.

Deliberate data movement is a strategic differentiator

Across markets, a clear divide is emerging. Teams that treat data movement as an incidental consequence of integration struggle with rising complexity, unpredictable performance, and costly late-stage redesigns. Those who deliberately architect data movement gain sustained advantages.

Deliberate data movement architecture enables a host of benefits. Bottlenecks in latency, bandwidth, and power can be identified early; system architectures can be made easily repeatable across product generations; and architectural intent, software behavior and physical implementation can be correlated much more closely and reliably.

Additionally, it better enables teams to explore the tradeoffs between alternative floorplans and interconnect topologies (as well as logical & physical partitioning of multi-die products) well before physical constraints harden.

Achieving this at scale increasingly requires automation that is physically aware, yet architect controlled. Modern approaches synthesize interconnect structures directly from connectivity, traffic, and layout constraints, while allowing designers to intervene and iterate rapidly. The goal is not to hide complexity, but to make it tractable.

Equally important is the generation of consistent architectural, software, verification, and implementation views from a unified system description. This top-down correlation reduces mismatch between intent and realization, helping to minimize risk and accelerate time to market.

The new battleground

As systems extend into the physical world, while architectures grow more complex to keep pace with expanding AI workloads the semiconductor industry is entering a new phase. Performance, power, safety, and scalability are no longer determined primarily by how fast data can be processed, but by how intelligently, efficiently, and predictably it can be moved.

Physical AI makes this reality impossible to ignore, but the lesson applies broadly, from data centers to embedded systems. The invisible highways inside chips have become the decisive terrain on which competitive advantage is won or lost. In this environment, data movement is no longer simple plumbing. It is the battleground where the next generation of semiconductor systems will succeed - or fail.

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• Microsoft has hiked the cost of a Windows 11 license for PC makers
• A new report claims that the annual price increase is 'significantly' more this year
• That's really not going to help the PC market, coming on top of all the pain with hardware price rises that we're already experiencing

PC prices are already facing various unfortunate pressures — thanks to the RAM crisis, and other increased component costs aside from memory –— but it appears that Microsoft is throwing fuel on the fire with hikes on the software side.

Wccftech spotted a report from Economic Daily News (EDN), a Taiwanese site, claiming that Microsoft has increased the price it charges PC makers for a Windows 11 license. Apparently the cost of installing Windows 11 on a pre-built desktop PC or a laptop has gone up by 7% to 10% as of last month.

Of course, bear in mind this is just a rumor (and the article is translated, so we need to be careful about any potentially lost meaning), but it's an alarming sounding prospect.

Microsoft puts up the price of a Windows 11 license every year, so this isn't new or unexpected, but normally Microsoft applies a single-digit increase, and this year the price hike has reportedly been a good deal more, possibly tipping into double-digits (10%). The (translated) article calls this a "significant increase" compared to past years, and the information comes from an executive at an (unnamed) PC maker.

Seemingly current product prices already reflect Microsoft's extra charge, with the bigger rises in fees having been given to PCs with faster processors, we're told.

EDN observes that the prices of PCs are expected to rise by 5% in the third quarter of 2026, at least for Acer and Asus systems in the Taiwanese market. Other regions and vendors may, of course, vary, but all OEMs (PC makers) must pay Microsoft for a Windows 11 license (if using the OS, of course).

Analysis: software salt

As noted, these cost increases are supposedly already priced into hardware on the shelves, and EDN observes that neither consumers nor businesses have really batted an eyelid.

That's mainly because the hardware-based pricing misery — for more expensive RAM, SSDs, GPUs (increasingly of late) and other components — is driving the nastier price rises, and the Windows 11 increase is relatively small fry in comparison. So, this software hike has purportedly flown under the radar, effectively eclipsed by the hardware pain.

If this report is true, and Microsoft has indeed put prices up a good deal more than normal, that's a distinctly unhelpful move. On the hardware side of the equation, we know prices to manufacture goods are going up — and that cost increases will inevitably be passed on to the end buyer. That's just business, but there's no obvious reason why software costs should be hiked more than normal, other than what the cynical might suggest appears to be opportunism from Microsoft (and maybe the growing cost of the AI side of the company).

This is effectively rubbing software salt into hardware wounds, and the PC market could really do without it. Although speaking of salt, all of this depends on whether this rumor is on the money, and I think it's worth remaining somewhat skeptical in that regard, albeit that EDN has proved a reliable source in the past.

• Windows 11's Weather app uses a surprising amount of RAM
• It consumes over 1GB at times — and worse still, pushes adverts
• This is a lot of what's wrong with Windows 11 in a nutshell

If you want a neatly crystallized summary of some of the key remaining failings of Windows 11 — pain points Microsoft has yet to fix in its campaign to right the wrongs of the OS — you only need to look at the Weather app.

This is one of Microsoft's default apps with Windows 11, and as Windows Latest pointed out, the RAM that this software eats up is mind-boggling, frankly.

The tech site observed that the Weather app somehow used over 1.2GB of system memory, and that this isn't far off five times as much as the native Weather app on macOS (which uses something like 250MB of RAM).

On my Windows 11 machine I didn't see quite that level of resource usage, but the Weather app did consume around a peak of 1GB at times (although that did drop as low as 800MB, and that was the level I saw it running at in the main). That's still a lot of memory, though, and it's clear the app has a heavy system footprint.

On top of that, there's an additional downside here, which is that you get adverts displayed alongside your forecasts and weather details (in panels at the side). These are full-on adverts, too, for the likes of shoes or medical insurance, not just Microsoft's nudges to use its products like OneDrive or similar.

As Windows Latest observes, this is because this app is actually the MSN Weather app, with the adverts being pulled over from the MSN network.

Analysis: a shower of adverts

Granted, the app provides a lot of weather information, and it's pretty comprehensive — but at what cost? What should be a lightweight and streamlined app consumes an improbable amount of RAM. Why is that? Well, it's due to Weather being a web app, one that, when you peek behind the hood, is running eight separate Chromium processes.

That's a clunky way of working, and the high level of resource usage this causes is particularly unwelcome in the current climate of the RAM crisis, where 8GB laptops are being brought back to try and keep costs down as manufacturers struggle to keep a lid on prices.

Okay, so Microsoft is looking at performance improvements across the board for Windows 11, and it's specifically aiming to address the issue of the memory footprint for 8GB PCs. And hopefully the Weather app will get some attention as part of that streamlining, and sooner rather than later.

For now, though, even if you don't use Weather, it remains a clear reminder of what's wrong with some parts of Windows 11 in terms of unnecessary bloat, both in the way the app is programmed, and the addition of those adverts (which doubtless don't help performance, either). These ads are present in Windows 10's Weather app, too, in case you were wondering, and it's surely a simple enough job — and an easy win — for Microsoft to at least ditch these.

As you might imagine, there have been a fair few potshots fired at Microsoft over this on social media, and one Redditor echoes something I've said myself in the past: "It's honestly ridiculous that we pay for a premium operating system and still get served ads inside basic native apps like Weather."

This is a key point for me. You can either have a free operating system that pays for itself via promotional bits and pieces — you can't really argue with that if you've forked out nothing for the privilege of running an OS — or you have a paid one that has no promotions.

Microsoft has promised to chill on the upselling in Windows 11, and it looks like the Weather app would be a good place to revisit with that in mind.

• Researchers tested AI-generated patches on six CVEs with poor success rates
• Many fixes failed, altered behavior, or introduced new vulnerabilities
• Guidance improved outcomes, leading to FLAWED evaluation harness release

When using Generative Artificial Intelligence (GenAI) to fix vulnerabilities, security professionals are most of the time just robbing Peter to pay Paul, experts have warned.

Researchers from 1Passwords Off-by-1 Labs analyzed fixes proposed by two frontier models - ChatGPT 5.5 at “medium” effort, and Claude Opus 4.8 at “high” effort.

As an experiment, the researchers took six recently disclosed CVEs and produced 6,080 patches using two frontier, cyber-capable reasoning models. The results were underwhelming to say the least - of all the proposed patches, just a quarter (26%) fully resolved the issue.

FLAWED work?

This obviously leaves plenty to be desired, as half (49.3%) of the patches failed to fix at least one existing exploit path. A fifth (20.1%) fixed the original issue but changed application behavior, while 2.3% introduced new security issues. Funny enough, 2.2% failed to fix the vulnerability while also introducing additional exploit paths, as well.

Even among the patches that might be considered (26% of clean ones and 20.1% of those that changed app behavior), more than a third were fragile and not entirely addressing the underlying problem.

The researchers created an acronym for automated LLM patches: FLAWED (Fix-Like Artifacts With Embedded Defects), and warned against letting AI work without human oversight: "The expected value of a fully LLM-generated, non-human-reviewed patch is a net-negative by a considerable margin."

Results drastically improved when the AI was given better context, the researchers further explained. Before working on any patch, human developers are usually given initial guidance. When AI is given proper guidance, its success rate rises to 65%. Incorrect guidance, on the other hand, drops the success rate down to 15.2%. The difference between humans and AI is that humans are better at catching misleading information and poor guidance.

This doesn’t mean developers will, or should, abandon AI. Worst case scenario is that developers will spend more time reviewing AI-generated fixes which could increase cognitive load and still end up being net negative. Therefore, the researchers released a patch evaluation harness called FLAWED, which organizations can now use to determine the effectiveness of their AI-generated fixes.

Via The Register

• Microsoft has admitted it's automatically installing an app on Windows 11
• The OneDrive Photos app is also being deployed mistakenly to business PCs as well as consumers
• There are a few odd things about this episode, and it's an anti-consumer move that doesn't sit well with Microsoft's broader goals to make Windows 11 better

Microsoft is in hot water after an odd decision to roll out a OneDrive Photos app to Windows 11 PCs used by consumers (with it also accidentally deploying the app to enterprise systems).

Windows Latest reports that Microsoft "effectively" told the website that the OneDrive Photos app is being automatically installed on Windows 11 computers whether it's wanted or not.

OneDrive Photos is a separate app to OneDrive, and it's designed for photo viewing and organization (with a light editing element bolted on). However, it's plain user-hostile behavior to have this software stealthily piped to a Windows 11 PC and forcibly installed.

What if you don't want it? What if you don't even use OneDrive sync at all, and you aren't signed in with a Microsoft account on your PC? Tough — you still get OneDrive Photos, as Windows Latest points out.

Even more bizarrely, this app — which is labelled as a beta — is appearing on business PCs, which it definitely shouldn't be doing (it's only used with a personal Microsoft account).

Microsoft wouldn't previously comment on the appearance of the OneDrive Photos app, but at least the company has now acknowledged that it is being installed on Windows 11 PCs.

There's still no official comment on why business devices would be getting this software, but Windows Latest tells us that it "understands that the rollout to enterprise PCs was not intentional". So apparently this was a mistake, and one that Microsoft is presumably in the process of correcting.

It's worth noting that there's also been some controversy about AI scanning faces for a feature in OneDrive Photos, accessed via the 'People' option, but this is only active if you explicitly allow it to be turned on (it's clearly opt-in, in other words).

Microsoft has clarified that if 'face grouping' is enabled, it runs only in the cloud with OneDrive photos (not other images on your PC), and any data collected is removed 30 days after you turn it off. The company has also assured users that it isn't using any photo data to train AI models on faces.

Analysis: I'm trying to tell you now, it's sabotage…

What is Microsoft playing at here? I really don't know, but this OneDrive Photos affair is a mess for a lot of reasons, and as you might guess, it has rubbed people up the wrong way. For starters, business users are very unhappy about having a piece of consumer-targeted beta software automatically deployed on their PCs, and are busy writing scripts to remove it (like this one on Reddit for example), rather than waiting for Microsoft to fix its error.

This is a baffling move for consumers too, because as noted, even if you don't use OneDrive at all, you'll still get this partner photo app. You can't even remove the OneDrive Photos app without deleting OneDrive as well — so if you do use the cloud storage app and sync features in Windows 11, but don't want this extra Photos application, well, you're stuck with it. You can have both, or neither.

That latter situation will change, though, Windows Latest informs us, and Microsoft is preparing an update that lets you remove the OneDrive Photos app without needing to nuke OneDrive entirely. I should hope so, too, and it's confusing how this rollout of a beta app went ahead without that choice in place.

The broad reaction on Reddit has been heated to say the least, and this is just more grit for anyone with an anti-Microsoft axe to grind.

One Redditor said "I'm uninstalling Windows. Linux is a little bit of a pain but worth it." There are more than a few similar comments in that thread, whether they're from people vowing to switch to Linux, or those who've already departed for OS pastures greener: "So glad I left Windows behind. Good riddance."

Interestingly, there are reports of this app appearing on Windows 10 PCs in the Reddit thread those comments are drawn from, although I haven't seen it on my computer with that OS (while it is on my Windows 11 system).

The trouble with all this is that it underlines how Microsoft is a vast corporation where the various limbs aren't always moving in alignment with the same aims. While the right hand is fixing Windows 11 and implementing a whole bunch of crowd-pleasing features or tweaks, the left hand is still messing around with anti-consumer initiatives such as this. Did no one tell the OneDrive team that Microsoft is supposed to be going full-tilt to persuade everyone that Windows 11 is the place to be, and that the software giant has mended its pushier and more obnoxious ways?

Apparently not, and just as we don't want third-party apps for the likes of monitors pushed on us quietly, in the background, via Windows Update, we don't want to unknowingly get first-party Microsoft software sprung on us, either.

Sort it out, Microsoft, please, because this is becoming an embarrassing oversight in terms of the big (and otherwise commendable) drive to make Windows 11 better.

Recently in Geneva, the world's largest forum on AI for Good, gathered around a simple question: how can technology best serve society? While the sentiment goes back to pre-industrial times, the conversation has firmly moved to algorithms. What about the machines that will come next?

Quantum technology has crossed a line. It is no longer a distant promise, but a growing portfolio of real applications. Quantum and quantum-inspired solutions are being applied to the simulations that underpin nuclear power, to protein design in drug discovery, and traffic forecasting in congested cities.

Quantum sensors are being tested for medical imaging, navigation where satellite signals fail, and monitoring carbon storage sites deep underground. These are no longer mere laboratory curiosities: they are increasingly emerging as working programs, with industrial partners and delivery dates.

For those of us in the industry, these developments are both timely and long anticipated. But they also force us to confront a less comfortable reality: the same machines that will one day simulate new molecules will also break the public-key cryptography that secures much of the modern internet.

By some estimates, up to 80% of the world's digital infrastructure is exposed. The world's digital economy - not to mention every AI model celebrated in Geneva, runs on encrypted data. Every health record or financial transaction owes its confidentiality to mathematical assumptions that a sufficiently powerful quantum computer will overturn.

Store now, decrypt later

The threat timeline is worse than most realize. Rather than waiting for when the machine arrives, the clock started the moment adversaries began harvesting encrypted traffic: the so-called “store now, decrypt later” approach.

Data that remains valuable for years, from medical records to state secrets, may already be beyond rescue. Nobody knows exactly when “Q-Day” comes. But those who harvest our data today have time on their side.

This is why quantum-safe security cannot be a footnote to quantum computing. It is the other half of the field, and it needs to mature on the same schedule.

Quantum trip-wire

Two defenses exist, and they are not interchangeable. Post-quantum cryptography replaces vulnerable algorithms with mathematics believed to resist quantum attacks; it is software, it scales, and it is the workhorse of the migration now beginning worldwide.

Quantum key distribution takes a different route: it distributes encryption keys using quantum states of light that cannot be intercepted without being disturbed. In this way, these schemes deploy a “quantum trip-wire", an eavesdropper reveals herself by the simple act of listening-in

Far from being in competition, both algorithmic and physics-based solutions have roles to play in tomorrow's networks. Serious infrastructure will layer both.

Neither of these solutions are theoretical. New post-quantum standards have been finalized, with contributions from research teams around the world. And quantum-secured networks are already running in the field: since 2022, Abu Dhabi has hosted entanglement-based metropolitan networks, like the ADGM Quantum Testbed announced in August 2025.

These are initiatives that bring quantum security to where data actually lives. Long-range terrestrial links, satellite connections and “last-mile” access networks are the next steps, extending that protection between cities and, eventually, across continents. If there's one lesson the field has learned from operating these systems, it's this: migration takes years, so the time to start is before the threat matures, not after.

Depth of capability

There is a broader lesson in how this capability is being built. A growing number of nations have chosen to be builders of quantum technology rather than buyers, developing the full portfolio of sensing, communications and computing solutions. From processor fabrication to control software, to the hardware enabling quantum networks: the word “sovereignty” is often heard as a synonym for walls.

But our experience suggests otherwise. It is precisely the teams that build every layer themselves that can contribute most readily to the global commons, from working with international standards organizations like ITU and ETSI on quantum-safe networking, to releasing open-source middleware that researchers worldwide now use to program quantum hardware, wherever it was made.

It is this depth of capability that turns a nation from a spectator of the quantum era into an active participant.

That should also be the model for the decade ahead. No single country will own quantum technology, and no single vendor should be the sole authority on quantum-era trust. What the field needs now is what the AI community showcased in Geneva: open tools that lower the barrier to entry, interoperable standards so quantum-safe systems can talk to each other, and honest engagement with the risks alongside the promise.

For years, the defining question about quantum computing has been when the machines will arrive. Increasingly, I think that's the wrong question. The more important question is whether the digital world they inherit will still deserve their trust. The answer depends on what we choose to secure, build, and share today.

Quantum for good does not start with a breakthrough. It starts with trust.

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Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.

As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.

AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.

As a result, many enterprises are beginning infrastructure planning sooner rather than later.

The cost of waiting

As AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.

Modern AI deployments increasingly require:

• Continuous inference running around the clock
• Multi-agent systems coordinating across applications and databases
• Real-time orchestration across cloud, data center, and edge environments
• Strong governance, security, and operational efficiency

These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.

Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.

At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.

As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.

AI is now a systems challenge

The conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.

Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.

As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.

This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.

Planning for distributed AI

AI is also scaling in multiple directions at once.

Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.

For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.

This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.

Openness and flexibility matter more than ever

As AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.

Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.

For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.

This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.

Infrastructure readiness will define the next phase of AI

The next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.

Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.

The cost of waiting is becoming ever clearer.

Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.

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• Microsoft is eating humble pie over its unrealistic RAM recommendations
• It has deleted articles that pushed 32GB as an ideal or 'no-worries' loadout
• Apple is also feeling the heat in the RAM crisis, with rumors that it's struggling to secure an alternative source of memory supply from China

There are some fresh twists with the RAM crisis hitting some big tech companies, as Microsoft has backtracked on its previous memory recommendations, and even Apple is apparently finding it difficult to cope with the scarcity of memory.

Let's discuss Microsoft first, and as Windows Latest pointed out, the company has been busy backpedalling on previous memory recommendations now that the RAM crisis – which just keeps getting worse – has made those suggestions look foolish.

Microsoft previously had support documents in its Windows Learning Center which have now been removed, and Windows Latest highlights two of them. One was about optimizing your gaming PC, and it advised that "32GB is ideal for serious players who run the most demanding titles" (albeit the article also said 16GB was "plenty" for most games).

Another piece said that 32GB of RAM was the "no-worries zone", and that article was also quietly deleted as there was some backlash against this, given that it was published when the price of system memory had become ridiculous. (And buying a 32GB kit was very much a worry for your wallet).

The links to those articles now redirect to the home page of the Learning Center, and Microsoft is evidently trying to forget about pushing 32GB of RAM as an 'ideal' or 'worry-free' target for memory on your PC.

More broadly, since Copilot+ PCs were launched and the AI features for these devices made them require 16GB, Microsoft has obviously been keen to have that as a baseline memory configuration. Except now, a change of stance is necessary, as with the RAM crisis reaching alarming new heights, Microsoft has been forced to enact huge price hikes with its Surface devices.

And of course, the latest twist with that Surface hardware is that Microsoft has brought back 8GB models with last year's Surface Pro and Surface Laptop. Which makes it kind of difficult to push 16GB as a minimum, let alone make suggestions that 32GB is where it's really at for properly smooth performance.

The abandonment of these Learning Center articles is hardly surprising, then, and Microsoft is also addressing how speedily Windows 11 runs with 8GB of memory (not quickly enough currently). One of its promises with fixing the OS was better performance with a leaner RAM loadout, and Microsoft just made it clear that the company is now actively working to make Windows 11 run better with 8GB before the end of the year.

This has become a vital goal, really, when you consider that Apple has pulled off a commendable showing of performance with its MacBook Neo that packs 8GB of RAM. That was effectively a gauntlet thrown down for Microsoft – something of a declaration that macOS is coming to try to take Windows 11's market share – and one that the Windows maker had to respond to (which became clear enough when Microsoft went on the attack against the Neo).

Apple turnover: China play rumored to end in a fumble

Speaking of Apple, Microsoft isn't the only tech giant being buffeted by the rising costs caused by the RAM storm. While it has had a big success with the Neo, the challenge for Tim Cook's firm – soon to be John Ternus's, of course – is to maintain that momentum, and by all accounts, that's proving a tricky task.

Recent Mac price hikes have caused a good deal of pain – taking some of the wind out of the good ship Neo's sails – and Apple is trying to secure its RAM supply lines for the future, with rumors abounding that it's turning to Chinese chip makers to find extra production capacity.

The latest speculation, however, is that according to a report from Digital Daily (a Korean tech site, via Wccftech), Apple has floundered in negotiations with Chinese memory giant CXMT.

Apparently CXMT has strong enough domestic demand that it doesn't have to offer more attractive pricing to Apple. Essentially, CXMT is holding the line and has "insisted on prices that were higher or similar to those offered by Samsung or SK Hynix" (bear in mind there may be nuances lost with the translation of the article).

You get the message, though: Apple is failing to obtain better deals on mobile DRAM, which includes LPDDR5X, for its iPhones (and that RAM is also used in its MacBooks, of course). And CXMT was supposed to be an escape route from the difficulties of getting enough RAM inventory from Samsung and SK Hynix (and also Micron, a key supplier for Apple), but it seems like a dead-end for now.

At least if this report is correct, but analyst Tim Culpan has also written a short post which backs up the notion that Apple is in trouble here. Culpan writes: "Apple and its suppliers are scrambling to get enough memory chips for its upcoming release of new iPhone models."

'Scrambling' is a word that evokes quite a sense of panic, and indeed with the iPhone 18 models – and the foldable offering – not much more than a month away from launch now (in theory), I'd bet there are some heated words flying here and there. Culpan notes: "Assemblers are working with Apple to rush shipments of DRAM used in mobile devices."

That report is specifically about smartphones, mind, but this same situation applies to mobile RAM that's also used in MacBooks.

The overall theme is more RAM misery all round, which is hardly a surprise given all the negative news we've been hearing on the grapevine of late. GPU pricing has been the latest round of doom and gloom over the past week or two, and I don't think the pessimistic news is going to stop flowing for the foreseeable.

It's not bad news for everyone, though. A source in the semiconductor industry told Digital Daily that: "With the general-purpose DRAM floor remaining unbroken and Samsung and SK Hynix monopolizing the lead in high-value AI memory such as HBM4, the operating profit margins and global market control of the domestic semiconductor sector are expected to rise even more steeply in the second half of the year."

So, it's a familiar story: profits will be on the up and up for memory makers, while consumers will be suffering the pain of the hikes. Apple's purported rush for RAM supplies as the clock runs down on the iPhone launch window is a particularly worrying sounding story, one that doesn't bode well in terms of avoiding more Mac (and iPhone) price rises in the future. Neither can we rule out more Surface price rises, or other laptops for that matter.

• Smart glasses bans are being introduced and considered
• There are growing concerns over smart glasses privacy
• Could hardware changes solve people's concerns?

I’ve written a lot about the high likelihood of major smart glasses regulation being introduced in various regions as the technology garners more and more bad press, and as expected, we’re starting to see bans and other restrictions take shape.

No nation has yet to all-out ban smart glasses, but a few have outlawed their use in certain scenarios while others are investigating harsher measures. Some private companies, such as event organizers, have also banned smart glasses on their premises and at their conferences.

Of course, this is bad news for smart specs owners who now could be facing restrictions on where they can/can’t wear their intelligent eyewear. For anyone considering adding smart glasses to their technology arsenal, these possible restrictions should be taken into account when making your final purchase decision.

So where are we starting to see smart glasses bans? Here’s a selection of the countries investigating and introducing anti-intelligent eyewear rules.

United States

Generally, smart glasses can be worn freely in the US; however, New York State has banned them from being used in any of its courts. Smart glasses with recording capabilities could be seen as an attempt to intimidate those involved in the case, and some cases contain sensitive private information where unauthorized recordings could impose serious risks.

Other states have introduced less widespread bans in courts — including Philadelphia, Hawaii, and North Carolina.

Meanwhile, the US Air Force includes in its rules for service members that they are “Unauthorized to wear mirrored lenses or smart glasses with photo, video or artificial intelligence capabilities while in uniform.” This seems like more of a presentation thing than an anti-smart specs rule (especially given the inclusion of mirrored specs), but it does mean you can’t wear intelligent eyewear in uniform regardless.

Further, attendees of conventions such as major hacker conference DEF CON 34 have been barred from wearing Meta glasses or similar. These bans focus on the possibility of glasses recording folks without their knowledge — with organizers arguing the recording light some glasses offer isn’t the most obvious.

UK

In the UK, things are much the same. Courts don’t ban their use, though after one person was being coached by their glasses’ AI mid-trial, their usage is generally advised against. There are also recording restrictions in UK courts, so while glasses aren’t explicitly banned, using them to snap photos or videos would be against the rules — the same as it would be for using your phone or a regular camera.

We have seen companies like Monopoly Events ban camera glasses from its Comic-Cons after secret filming complaints, and there’s a petition to the Government to ban the sale and promotion of smart specs in the country. These petitions don’t always go anywhere, but it shows there’s at least some public unrest over smart specs, and that could lead to rule changes.

EU

The European Data Protection Board is currently drafting a report on smart glasses, with it expected to land sometime in the next few months. However, while more general EU rules are being discussed, some individual members are making their own individual decisions on the tech.

Germany’s data protection authority commissioner has warned camera glasses could be banned under existing laws prohibiting recording devices hidden in everyday objects.

Netherlands digital rights activists have expressed frustration at smart glasses' ability to record people without their knowledge, while France’s privacy regulator, the National Commission on Informatics and Liberty, has highlighted the privacy risks camera glasses pose.

South Korea

Rounding things off, we have a different kind of ban in South Korea, with the country specifically restricting smart glasses access in exam halls after cases of students being caught trying to cheat using intelligent eyewear.

Smart devices are generally banned from exam rooms in most schools across the globe to prevent cheating, though given the importance of South Korea’s College Scholastic Ability Test, their very explicit ban makes sense so as to remove any possible confusion over whether students can even bring smart specs into the room with them.

Could things change?

Of course, very few bans have been brought in yet, but they could be unless things change on the hardware side.

A big concern is over privacy for non-glasses wearers caught in the gaze of someone's camera — especially if they don't know they’re being recorded. An obvious fix here would be to make smart glasses much more obvious when they're recording — perhaps brighter camera lights, a loud audible camera sound when snapping a photo, some kind of audio indicator when video is being recorded.

For users, they’ll want better control over what is and isn’t accessed by smart glasses makers when using their tech, and more certainty about which images are and aren’t shared, or which videos can or can’t be seen by contractors.

We’ll have to wait and see how the situation develops, though I’m not sure how much longer smart specs can go before we see serious regulations.

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

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

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

That requirement has not disappeared.

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

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

That creates a different skills gap.

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

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

From better prompts to better context

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

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

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

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

Agents raise the stakes

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

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

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

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

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

The job title is still catching up

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

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

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

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

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

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

The Kubernetes lesson and what leaders should do now

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

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

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

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

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

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

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

The model is not enough

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

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

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• Cheap smart glasses are selling out as people look for Meta Ray-Ban alternatives
• More camera glasses are leading to more privacy concerns for many
• I wouldn't buy them for plenty of other reasons

If you want a pair of smart glasses, you’re spoiled for choice in terms of what they can do technologically, and over time we’ve seen more and more budget options appear. These cheaper models are the first step in making emerging gadgets more accessible, but for smart specs there’s a fear they might simply exacerbate the growing concerns over the tech.

Australian discount store Kmart recently went viral after the AU$89 Anko glasses it was selling sold out entirely (via The Guardian). In the US and UK, you don’t have to look far to find equally affordable camera glasses available through reputable digital marketplaces such as Amazon.

Given the proven popularity of Meta’s Ray-Ban and Oakley specs, it’s no surprise the market would see an influx of smart glasses, especially cheap pairs that aim to mimic much more expensive rivals while keeping costs low — typically by downgrading their technological capabilities and not partnering with premium design brands.

However, cheap smart glasses aren’t as universally celebrated as cheap TVs or affordable smartphones — scrolling through online forums discussing this news, the replies aren’t kind about smart glasses, discussing the pitfalls of smart glasses in general and more affordable options specifically.

The most prominent concern is that cheap camera glasses expand the range of people with the technology. More people with these glasses means more people possibly recording people in public without their knowledge.

There are also fears that cheap camera glasses might not come with the same sophisticated tamper protection glasses from more recognizable brands boast. Either through tape coverings or minor electrical modifications, folks could disable the camera light and use the specs even more covertly than they otherwise would — they’ve tried the same with Meta specs, but Meta has patched out these workarounds so far.

Privacy and other problems

Beyond the impact on the general public, cheap smart glasses users might want to consider the privacy implications of wearing cameras from unknown brands.

We’ve seen that even super well-known brands like Meta aren’t perfect — for example, there are reports that contractors could see videos (including intimate or otherwise private content) taken using the glasses if it involved the AI in some way — so why should we expect completely unknown brands with nothing to lose to be any better with privacy?

In fact, considering their lack of notoriety makes them less likely to get investigated in the way Meta was by journalists who uncovered the sharing of videos with contractors. The ease with which tiny brands can dissolve and pop back up under different names, I for one would be a lot less trusting of what they do with my data and of their phone apps.

Of course, many lesser-known brands behave responsibly, but when there’s so much room for things to go wrong, for private information or video to get leaked, I just have this extra layer of paranoia I can’t shake.

Even ignoring my personal privacy concerns, as someone who has tested countless smart glasses at this point, I’ve found that better-known brands are simply better at making hardware and software.

I’ve worn so many uncomfortable specs with tinny audio, awful camera quality, terrible AI, and apps that are worse than useless. They’re sluggish. They’re ugly. They’re not worth even a budget price.

Just don’t buy them.

If you do want smart specs, save up for something proper — it doesn’t have to be from Meta, but I’d recommend choosing a brand with plenty of solid independent reviews. While I’m using them less for reasons better explained by other writing, I have gone weeks on end wearing a good pair of smart glasses thanks to comfy yet fashionable design and solid tech. Whereas the cheap (and terrible) glasses I’ve tried I’ve wanted to rip off my face within hours.

You get what you pay for.

• A controversy has erupted over a background process in Windows 11
• It turns out that process isn't new, and neither is it a surprise introduction from Microsoft that 'does nothing for you' except pipe telemetry back
• A Microsoft exec clarified the purpose of the process, and that it doesn't send any data back unless you want to do that

There's been some more controversy over privacy and telemetry in Windows 11, and while this particular alarm bell turned out to be falsely rung in the end, it does point to wider issues around the mistrust of Microsoft in general.

As Windows Latest reports, this story starts with a post on X from a user called Xilly who drew attention to an apparent new process in Windows 11 called 'Windows Health and Optimized Experiences'.

However, as Windows Latest – and others on X – observe, this isn't a new background service in Microsoft's OS at all. In fact, it has been kicking around in Windows 11 for over a year now (since May 2025).

The additional nuance, though, is that this process was only introduced in testing in the Canary channel back in May of last year, and it seems to have only just come through to Windows 11 release builds now. That's as far as I can tell, and this is what Xilly points out on X.

So, it is in fact relatively new to release builds – and presumably rolling out now – but nonetheless the service itself has been known about for a long time. The real problem here is the way Xilly frames it as monitoring your PC for certain metrics related to laptop battery management, and that: "If you are on a desktop gaming PC it does nothing for you except run in the background" (while eating resources and engaging in telemetry, "sending data to Microsoft every 15 minutes").

Xilly's post prompted a response from Scott Hanselman, who is a VP and member of technical staff at Microsoft as well as a prominent voice in the Fix Windows 11 campaign.

Hanselman made it clear that the process is not new, as already discussed, and that it's for system diagnostics: "When Windows detects slow or sluggish behavior, it can record targeted performance traces locally under %SystemRoot%\Temp\DiagOutputDir\Whesvc that can be filed with Feedback Hub. It's not laptop specific."

So, this is a process that detects poor performance and files diagnostics locally, which can then be fed back to Microsoft if the user wishes.

Hanselman also highlights the dramatic approach Xilly took ("saying things in all capitals"), and Windows Latest further points out that the X user runs a business for optimizing gaming PCs, and pushes that service in a reply to their own original post.

Analysis: trust, telemetry, and what Microsoft needs to do

To summarize, then, Xilly's description of the service is incorrect, and while this background process does collect diagnostic data on how Windows 11 is running, it's written locally (with a negligible performance impact) and only sent to Microsoft if you actively submit feedback from your PC (via the Feedback Hub). The key phrasing in Hanselman's response is that the relevant data "can" be filed with Microsoft (not "will" be filed, as in it'll automatically happen).

Xilly also suggested that this was something Microsoft just sneaked in (hence the 'all caps' warning), when in fact it has been documented in preview builds before, and as Windows Latest notes, it's part of the Adaptive Energy Saver feature for helping to extend battery life.

The long and short is that there's nothing to worry about here, but what this episode serves as is a reminder of how Microsoft is short on trust when it comes to issues around privacy.

It's clear how quickly some Windows 11 users were to believe Xilly's accusation on X and other social media such as Reddit. For example, in this Reddit thread, despite one poster pointing out the reality of the situation in a nutshell (pretty much), there's a notable amount of complaining about why this process is sending data to Microsoft (which it isn't), or broader complaints along the lines of: "If you choose to install a spyware OS, expect to be spied on."

In fact, the most upvoted comment is: "Honestly, if any big tech giant wants to spy on you, they don't need a separate service. The MSA/online account [Microsoft account] is more than enough."

There are many people out there online who believe that, one way or another, Microsoft is spying on them to quite an extent with Windows. That stems from a broader level of distrust that's been stoked by the historical level of telemetry in Microsoft's desktop operating system since Windows 10 emerged, with reports periodically popping up to highlight these issues in that OS and Windows 11. Add to that all of Microsoft's promotional activity (and some outright adverts), as well as the likes of the Recall feature helping to pile on the skepticism (Microsoft handled that one really badly), and you can see why there's a problem with doubters here.

I keep saying that one thing Microsoft isn't properly addressing with the fix Windows 11 campaign is the level of telemetry performed by the OS, and I think it's high time that the company did this, and introduced a new choice for a zero level of telemetry (save for baseline security-related aspects). That option is lacking on Windows 11 Home, and it should be present and easily accessible, as this would go a long way to help cure some of the ill feeling out there around Windows 11 'spying' on people.

Multiple studies have found that the average person has over 150 passwords. As a password security expert, I regularly recommend that every single password people use should be both unique and strong - but this adds the problem of being able to remember each one, or store them securely.

Password managers are designed for this exact reason. They store your passwords in encrypted vaults and autofill your credentials as and when you need them. For this very reason, Keeper is one of our most highly rated password managers.

Right now, Keeper has cut prices on its personal, family, and business starter plans by up to 50%, providing an affordable password management solution that can store and generate your passwords, while also providing convenient features for securely sharing regularly used household or business passwords.

Why we recommend Keeper

In our Keeper review, we praised the zero knowledge architecture and device level encryption that helps keep the contents of your personal vault hidden from hackers.

Keeper also scored great marks in our usability and convenience testing. Rather than having to enter your master password each time you need to fill in your credentials, Keeper uses biometric security to access your vault. Biometric security uses a facial scan or fingerprint to verify that its actually you accessing your vault. It's also phishing resistant.

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The Business Starter plan covers 5 users, covering an unlimited devices, shared team folders, user activity reporting for IT admins, and a free Family Plan for every team member.

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When we wrote about the “AI speed trap” in the software development process for TechRadar Pro a year ago, the central concern was whether faster delivery would come at the expense of quality. A year on, what we’re seeing are actually deeper issues around trust, governance and organizational alignment.

As AI-generated code becomes more routine and ecosystems become more complex, the challenge is no longer simply keeping pace with delivery. It is deciding who owns quality, how much risk is acceptable, and what confidence looks like in an AI-enabled environment.

AI has become firmly embedded across the software development lifecycle, accelerating delivery at an unprecedented rate. But the real story is no longer just speed. Organizations are now being forced to make more explicit decisions about where quality ends and acceptable risk begins, and to reconcile trade-offs around testing, trust and accountability.

AI is accelerating software delivery, but quality strategies are falling behind

Nobody would dispute that AI has transformed software quality over the last year. AI-assisted coding, automated testing and increasingly autonomous workflows are no longer experimental; they are becoming standard practice.

Nearly seven in ten organizations have now implemented AI tools across at least some of their software delivery workflows, and almost half have fully embedded it into their development environments.

The impact on productivity is undeniable. Development teams can generate code faster than ever, automate repetitive tasks and accelerate release cycles that once took weeks into days, or even hours. But the ability to create software has advanced far more quickly than organizations' ability to validate it.

As a result, 60% of teams are knowingly releasing untested code. Not accidentally, as our 2025 research showed, but because they are under pressure to ship faster, and because the sheer volume of code is outpacing what many teams can realistically test. In other words, these are conscious business decisions. Organizations aren’t even trying to test everything; they’re deciding what they can afford not to test.

As software delivery accelerates, quality has become less about eliminating every defect and more about determining which risks are acceptable. But that requires stronger governance, clearer accountability and a shared understanding of what constitutes an acceptable release.

The definition of quality is changing

In an AI-driven world, organizations will never be able to test every line of AI-generated code. While AI has removed many of the bottlenecks associated with writing code, it hasn't created more time to validate it. Traditional quality models were designed for an era where development speed largely dictated testing speed. That assumption no longer holds.

The answer, however, isn't to lower the bar for software quality. It's to rethink what quality actually means. Rather than attempting to preserve old approaches by asking teams to test everything, organizations need to rethink what software quality actually means.

Increasingly, success will depend on understanding where quality matters most. Which applications are business-critical? Which customer journeys carry the greatest commercial or regulatory risk? Which changes genuinely require exhaustive testing, and which can be validated through risk-based approaches?

In other words, software quality is becoming intelligence-led rather than activity-led. The focus is shifting from test volume to confidence in release decisions, based on how well organizations can identify and manage the risks that matter most.

Why governance, visibility and continuous validation are becoming the new foundations of software quality

As organizations move towards risk-based quality models, a critical gap is emerging: how those risks are actually governed in real time.

In traditional software delivery, governance was often applied at fixed stages. In AI-accelerated environments, that model breaks down. Code is generated continuously, pipelines move faster than human review cycles, and release frequency has increased beyond what stage-gated governance was designed to support.

This requires three structural shifts.

First, visibility becomes foundational. Without real-time insight into what AI is generating, what is being tested, and where coverage gaps are emerging, organizations are effectively making risk decisions without a complete view of the system.

Visibility is the precondition for trust, allowing leaders to understand not just whether software works, but whether it is being adequately validated as it evolves.

Second, governance must move closer to code creation. Instead of acting as a final checkpoint before release, governance increasingly needs to be embedded earlier in the lifecycle, where risk is first introduced. This means bringing quality signals, compliance requirements and risk thresholds into the development process itself, rather than evaluating them retrospectively.

Third, continuous validation replaces discrete testing phases. As AI increases both the speed and volume of code production, organizations are shifting from episodic testing cycles to always-on validation. Rather than asking whether software has been tested, the more relevant question becomes whether it is being continuously validated against the organisation’s risk appetite as it changes.

In AI-driven environments, confidence in software quality depends less on point-in-time approvals and more on the ability to observe, measure and validate quality as it is being created.

Organizations are making deliberate decisions about risk

Software quality has always involved balancing competing priorities, but AI is making those trade-offs more explicit, with “good enough” increasingly defined at the intersection of engineering constraints and business pressure.

The challenge is that organizations don’t always agree on what that looks like. Nearly half report only partial alignment between executives and software teams on what high-quality software actually looks like, suggesting that release readiness is increasingly open to interpretation rather than governed by consistent standards.

This also explains a key finding in our data: that while more than nine in ten C-level executives express confidence in their testing strategies, almost one-third of quality assurance (QA) and DevOps leaders remain uncertain that those strategies adequately address the most critical software risks.

That gap reflects fundamentally different perspectives. Executives naturally focus on strategic outcomes and business velocity, while engineering teams experience first-hand the operational realities of validating increasingly complex software systems.

Closing that gap will become increasingly important as AI continues to accelerate software delivery. Trust cannot exist if different parts of the organization operate under different definitions of quality or acceptable risk.

Software quality has become a business decision

For years, software quality was viewed as a technical responsibility - a discipline concerned with finding defects before customers did. That is no longer sufficient.

When software underpins customer experiences, regulatory compliance and critical business operations, decisions about quality inevitably become business decisions. Shipping software with known gaps can no longer be merely a technical compromise; it represents an organizational risk assessment.

The consequences extend well beyond development teams. Poor software quality can increase technical debt, introduce security vulnerabilities, create compliance challenges and erode customer trust. Many organizations are already attributing hundreds of thousands (if not millions) of pounds to the downstream impact of software failures and rework.

At the same time, governance has struggled to mature at the same pace as AI adoption. While many trust agentic AI to make release decisions, significantly fewer believe they are ready to do so at scale. That disparity between adoption and oversight is likely to become one of the defining challenges of the next phase of AI-enabled software delivery.

Ultimately, software quality is no longer something that can be delegated solely to engineering teams. It requires leadership alignment, shared accountability and governance that keeps pace with increasingly autonomous development.

Conclusion

AI has accelerated software delivery to the point where existing approaches to testing and governance are struggling to keep up. The organizations most likely to succeed are those that make deliberate, well-governed decisions about risk and build trust into the software delivery lifecycle rather than treating it as a final checkpoint.

Software quality can no longer be just an engineering discipline. In the AI era, it has become a leadership responsibility.

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• Microsoft has summarized its campaign to fix Windows 11 so far
• The company also let us know what work is coming, including making the OS run better with 8GB of RAM
• That should have been the case from the start, though – and there's a missing piece of the puzzle to address as highlighted by Windows 11 users

Microsoft has made it clear that it's working to ensure that Windows 11 runs better on 8GB of system memory, as well as letting us know about other elements of the OS the company is going to fix as 2026 rolls on.

This was part of a blog post which is an update on the progress made with the campaign to fix Windows 11 that kicked off in March, summarizing all the work Microsoft has done – and there's been a lot of that, without a doubt. (I recently picked out the most important changes so far, if you're interested).

Microsoft also highlighted four new areas it's going to deal with during the rest of the year, and one of those is Windows 11's RAM usage.

Microsoft wrote: "Memory optimization for 8GB and above. Reducing Windows memory footprint to deliver a fast and responsive Windows experience across the PCs customers use every day."

As well as a promise, this is an acknowledgement of the RAM crisis, which is preventing people from upgrading their memory, but more to the point, it's forcing laptop makers to go backwards in some respects, dropping RAM loadouts to 8GB for some models. (Whereas previously we'd been moving noticeably towards 16GB as a baseline).

As to what else Microsoft has on its fixing checklist, that includes making Windows 11's setup a faster and more efficient experience, as well as a better setup for families (including parental controls being more readily accessible). Finally, Microsoft says voice functionality is also being worked on, to make "voice more natural and fluid to interact across the apps you use every day".

Analysis: it's about time really – and there's another issue that needs to be addressed, too

Microsoft talked about RAM optimization and "lowering the baseline memory footprint for Windows" back when the campaign to fix its OS first began. So really, this is just a reassurance that this work is underway and should be arriving before the end of the year, and that it's specifically focused on ensuring 8GB is an acceptable amount of memory for running Windows 11.

As noted, there's more pressure around this now thanks to the RAM crisis getting worse, and laptop makers looking to produce more 8GB models in a cost-saving effort. On top of that, there's the embarrassment of the MacBook Neo showing that Apple can do 8GB, and do it well, with macOS.

Really, though, Windows 11 should've been better optimized for performance right from the start – or at least after the first year or two of its existence. Why has it taken a RAM apocalypse, a huge anti-Windows backlash (prompted by AI), and the launch of a rival budget-friendly MacBook to prompt Microsoft to get off its system performance laurels and actually do something serious about how well its desktop operating system runs with a leaner amount of memory? Remember, Windows 11 is supposed to work with 4GB (as a minimum), so 8GB should not be as much of an issue as it has always been.

Obviously it's good that this is now happening, and more broadly, I'm still pleased Microsoft is addressing a whole bunch of problems with its drive to fix Windows 11. There's one piece of the puzzle omitted here, though, based on some of the feedback which has come through on social media after Microsoft's blog post update.

While Redditor 'Melchett_General' acknowledges that "some of this is very nice" when it comes to Microsoft's summarized fixes, they point out some missing bits, one of which is notably echoed elsewhere – namely the recent LG monitor controversy.

The Redditor complains: "How on earth Windows got to the state where any manufacturer that makes a device can have software 'companion' applications installed silently on a user's device without the wider internet throwing a collective fit and having Microsoft permanently change course I don't know."

In case you missed it, this refers to LG serving Windows 11 users a McAfee pop-up via its monitor software, and it's a story with some degree of nuance. Microsoft did immediately take action to get LG to banish that McAfee advert, which popped up via the LG monitor app in Windows 11, but as I discussed previously, a wider problem remains here.

This is the issue that the LG application is piped through to the operating system via Windows Update without the user's knowledge, happening automatically when the LG monitor is connected to the system.

In other words, the McAfee pop-up might have been quickly dealt with, but it's still possible for hardware makers to get Windows Update to pipe through a tool for their device.

As the Redditor elaborates: "What this seems to mean is that a 3rd party can install what they like whenever they like on your internet connected Windows computer and there's no effective way to stop it. (Aside from GP [Group Policy] edits or registry changes).

"This needs to be addressed, and there needs to be a mechanism to allow people to easily prevent this from happening."

I couldn't agree more. There should be no scenario where software is automatically installed in this way, even if the device maker might argue that the app is very much for the user's benefit. At the very least, there should be a dialog box announcing what's happening, and giving you the ability to choose not to install whatever application the hardware maker wants to put on your PC.

Notably, I haven't heard anything from Microsoft addressing this broader issue. Never mind the symptom (the McAfee pop-up), tackle the root cause of the issue (the ability for a piece of connected hardware to whisk its own software off the internet with no say from the user).

Microsoft is clearly serious about fixing everything that's wrong with Windows 11, I don't dispute that – but I find it odd that nothing has been said that directly addresses this pain point. Hopefully that won't be the case for much longer – we need some more transparency with Windows Update in this regard.