Building is no longer the constraint.

AI-assisted development has transformed how software gets built. Because tools can generate code, build prototypes and speed up testing, ideas that once took weeks or months to develop can now be explored in a fraction of the time.

Engineering capacity is no longer the constraint it once was. More organizations can experiment and bring new products to market faster than ever before. But building faster doesn't guarantee better products.

Human judgement is still necessary to decide which ideas deserve investment, which features solve real customer problems and which experiments aren't worth pursuing.

AI can dramatically shorten the journey from idea to release, but it can't make those types of business decisions for you (yet).

Research from MIT found that while AI has fueled a surge in new apps entering mobile marketplaces, usage has not increased at the same pace. Faster development is producing more software, but it isn't creating more customer attention. Customers haven't suddenly found more hours in the day simply because software is easier to build.

That raises the bar for decision-makers. As AI removes many of the barriers to creating software, success increasingly depends on learning quickly from customer behavior and understanding what customers genuinely value. It’s the only way to stand out in an increasingly crowded marketplace.

Customer insights are your biggest asset

After every release comes the same questions. What next? Do we invest further or move on? AI has made it possible to build and ship software more quickly, but it hasn't made those decisions any easier.

That's why product intelligence matters. Understanding how customers behave gives decision-makers the confidence to decide where to focus their effort. Behavioural data shows what customers keep coming back to, where they struggle and where they give up. It also helps distinguish between features that attract initial interest and those that become part of customers' everyday workflows. That distinction often says more about long-term value than launch-day engagement or anecdotal feedback.

It also reveals where customers complete key tasks, where they struggle and where they abandon journeys. Those signals often provide stronger evidence than customer opinion alone, because they reflect what people actually do rather than what they say they do.

Those insights make prioritization easier. They show which features deserve more investment, which ideas aren't landing and where the next opportunity probably sits. They can also help product teams decide when to refine an existing feature, simplify an experience or stop investing in something customers aren't using. Product decisions become grounded in how customers actually use the product, rather than internal assumptions and beliefs.

As AI lowers the barriers to building software, deeply understanding customers becomes even more valuable. The strongest organizations keep learning from the people using their products, allowing every release to build on real customer insight.

From AI-assisted coding to AI-assisted product improvement

AI-assisted development has changed how software is built, and product leaders are only beginning to explore what it can do beyond writing code. The next stage is using AI alongside product intelligence to strengthen the decisions that shape a product over time.

When working with large volumes of behavioral data, AI can help surface patterns more quickly, highlighting changes in customer behavior, unexpected user journeys and emerging trends that might otherwise be overlooked.

That gives decision-makers more confidence about where to focus their attention, while leaving more time to interpret what's happening and determine the best response. Rather than spending hours searching dashboards for answers, product teams can focus on understanding the behavior, testing possible improvements and deciding which ideas are worth pursuing.

Every release generates new behavioral data. Instead of relying on assumptions, product management teams can use that evidence to decide what deserves attention next. Over time, each release helps inform the one that follows.

Used in this way, AI becomes part of the product improvement process as well as the development process. The combination of AI and behavioral insight helps organizations learn faster, respond with greater confidence and keep building products around what customers actually need. Focusing on what the data uncovers about what customers actual need is the best way to endear your product to your users.

AI has changed the speed of product development. But every release still depends on good product judgment, which means knowing what to improve, what to leave behind and where to invest next. Behavioral insight is the best guide for decision-makers to ensure that every release reflects what customers actually do rather than what your internal teams assume they'll do.

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• The Bing Wallpaper app is serving an ad in Windows 11
• The advert is for a Harry Potter and Fantastic Beasts movie collection
• There's a rebellion against this blatant advertising, with a significant number of users saying they've immediately removed Microsoft's wallpaper app

Some Windows 11 users who've installed the Bing Wallpaper app have been treated to an unpleasantly in-your-face advert for Harry Potter.

To be precise, it's an ad for a collection of 11 Harry Potter and Fantastic Beasts movies, and, as Windows Latest reports, this appears as a full-page promotional image that becomes your desktop background.

This isn't happening for every Bing Wallpaper user, and it appears to be a regional rollout; but based on the plentiful Reddit feedback of the negative variety, it's being piped to quite a number of PCs.

For the uninitiated, Bing Wallpaper is an app that serves up daily wallpapers to those who've installed the software — and note that it isn't installed by default; this is something you have to elect to have on your Windows 11 PC.

Nevertheless, it's an over-the-top move by Microsoft to shovel something like this onto the desktops of those who've chosen Bing to pick their backgrounds.

As one annoyed Redditor says, filing the Harry Potter wallpaper under the 'unskippable ad' category in the 'Mildly Infuriating' subreddit: "I had it [Bing Wallpaper] installed since up until this point it was a fairly harmless program that gave a variety of animals, environments or pattern backgrounds that rotate daily. Wtf?"

Replying, another Redditor observes: "Free software from a trillion dollar company means you are the product." Some folks responded to that to say that Windows 11 isn't free, but this Redditor isn't talking about the OS itself, but rather the Bing Wallpaper app, which is a free add-on for Windows, of course.

Analysis: Bing begone

Windows Latest notes that the terms and conditions of the Bing Wallpaper app allow for "promotional offers" from Microsoft, but what's happening here goes well beyond that. Frankly, this isn't acceptable in any way — and Microsoft really should know better than to serve a blatant advert in this manner.

I'd say I was shocked, but ads have always been a problem in Windows 11. On top of that, about a year ago Bing Wallpaper was used to perform another underhand trick with desktops that made me cringe. In that case, an accidental click on the Windows 11 background fired up a Bing search (yes, really), and I called this "a new low" for Microsoft at the time.

This, however, represents Microsoft plumbing fresh depths. A notable number of those complaining on Reddit have said that they've immediately uninstalled Bing Wallpaper due to this incident (generally with a sprinkling of added curse words).

One of the politer comments notes: "Honestly if they cut out text and just left the base [image] with the option to right click to get more info and it took the user there it would be passable. But current state just time to remove Bing Wallpaper."

This is very much representative of Microsoft's different departments not talking to each other. On the one hand, there's a big drive to fix Windows 11 and try to please users with all sorts of nifty changes and long-awaited features. On the other, the Bing Wallpaper team thinks it's fine to deploy what might be the worst example of advertising I've ever seen in the Windows ecosystem — and there's quite some competition for that accolade, sadly.

At any rate, I assume this won't be happening again, Microsoft, given the reaction here… right?

2025 was a transformative year for artificial intelligence, and 2026 is already proving to be equally significant.

While generative AI dominated conversations just a few years ago, the focus is now shifting towards agentic AI, where intelligent systems can take action, interact with business processes, and support employees in more meaningful ways.

As these capabilities continue to evolve, organizations are looking for better ways to connect AI systems to the applications, data and services that drive their operations.

This is where technologies such as Model Context Protocol (MCP) are becoming increasingly important.

Rather than creating bespoke integrations for every tool or system, MCP provides a standardized way for AI models and agents to access information and perform actions across an organization's technology estate.

While MCP is not a requirement for every AI implementation today, it represents a natural next step for organizations looking to move beyond isolated AI use cases and towards more integrated, scalable AI ecosystems.

However, greater integration also introduces greater responsibility. Effective governance remains essential for any AI deployment, but it becomes even more critical when autonomous agents are granted access to business systems, processes and sensitive information.

Organizations must establish clear guardrails that define what agents can access, what actions they can perform, and how their activities are monitored. Without appropriate oversight, businesses risk agents operating beyond their intended scope or creating unintended consequences across interconnected systems.

The impact on Software-as-a-Service

Few sectors will feel the effects of this shift more than Software-as-a-Service (SaaS).

For years, SaaS applications have been built around human interaction. Users access platforms through dashboards and interfaces, navigate predefined workflows, and manually complete tasks. The application itself serves as the primary workspace where work is performed.

Agentic AI introduces a different model.

Rather than navigating software in the same way a person would, agents can interact directly with APIs, services and data sources. This allows them to retrieve information, execute actions and orchestrate processes across multiple systems without relying on traditional user journeys.

That does not mean SaaS applications will disappear. In fact, they will continue to play a critical role in storing structured data, enforcing business rules and managing workflows. What is likely to change is how those applications are consumed.

Instead of being the primary destination where work happens, many SaaS platforms will increasingly act as sources of capability and information that AI agents can utilize on behalf of users. As a result, organizations may find themselves focusing less on which application employees need to access and more on how services and data can be brought together to achieve the desired business outcome.

Human interfaces will still matter. Users will continue to need visibility, exception handling and control mechanisms, particularly when business-critical processes are involved. The challenge for software providers will be balancing traditional user experiences with new AI-driven interaction models while maintaining compatibility, reliability and operational resilience.

Breaking down agent silos

The next stage in the evolution of agentic AI is not simply creating more agents. It is enabling agents to work together effectively.

Many organizations already struggle with fragmented systems, disconnected data and isolated processes. Without careful planning, agents risk creating a new generation of silos, each operating within its own limited context and producing inconsistent outcomes.

To avoid this, businesses must focus on shared context, connected data and interoperable services. The goal is not to have individual agents automating isolated tasks but to enable multiple agents to contribute towards broader business objectives across entire processes.

When agents can access consistent information and operate across organizational boundaries, the value shifts from discrete task automation to coordinated execution. Rather than supporting an individual stage of a workflow, agents can participate in end-to-end processes while remaining aligned to business policies, operational requirements and organizational goals.

This represents an important architectural shift. Software increasingly becomes something that agents consume programmatically, while integration, context and orchestration become central to delivering outcomes at scale.

Mitigating risk in AIaaS

Unlocking these new capabilities requires more than deploying AI tools. Organizations need governance frameworks, security controls and operational processes that allow autonomy to be introduced safely and responsibly.

As agents gain access to more systems and collaborate across workflows, operational complexity inevitably increases. Businesses must define clear policies around what agents can and cannot do, what data they can access, and what approvals are required before actions are taken.

These controls should be embedded into the orchestration layer itself, ensuring governance is applied consistently across all agent-led activities rather than being treated as an afterthought.

Traceability and accountability are equally important. Completing a task successfully is only part of the equation. Organizations must understand how decisions were made, what information was used, and which policies were applied throughout the process. This visibility will be essential for compliance, security and maintaining trust in autonomous systems.

The role of developers will also evolve. Rather than spending significant time building and maintaining point-to-point integrations, they will increasingly focus on designing agent behaviors, defining boundaries, managing orchestration and ensuring solutions operate within established governance frameworks.

The organizations that succeed will be those that balance innovation with control. Too little governance introduces risk, while excessive restrictions can prevent businesses from realizing the benefits of AI altogether.

Agentic AI should not be viewed as a replacement for software development or existing technology investments. Instead, it represents a powerful new interaction layer that changes how organizations access information, automate processes and deliver outcomes.

Businesses that invest now in integration foundations, governance models and workforce skills will be best placed to take advantage of the opportunities this next phase of AI creates.

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• Microsoft is working to speed up boot times
• Over at IFA 2026 a Microsoft executive said that 'we've made significant gains' with startup speeds and more
• The exec also underlined Microsoft's commitment to get Windows 11 running more smoothly on 8GB PCs

Microsoft is readying a major boost to the performance of Windows 11 that should speed up boot times.

Windows Latest reports that at IFA 2026 last week a Microsoft exec confirmed improvements to boot speeds are inbound.

This was Mark Linton, who is VP of Windows & Devices at Microsoft, who told the tech site: "We've made significant gains on things like boot time, Windows Hello responsiveness."

Windows Hello is the secure login that's offered with Windows 11 – often facilitated via an infrared-supporting webcam and facial recognition – and that happening more swiftly, alongside a faster boot time, should mean you arrive at the desktop in a far more timely manner.

This work isn't in testing just yet, we're told, but Microsoft is still tinkering with ramping up boot performance in internal builds.

Analysis: performance promises are underlined

The use of the word 'significant' is promising, and Windows Latest talks about getting to the lock screen 'almost immediately'. In fairness, Windows 11 already boots nippily (on my PC certainly), so we could be looking at effectively booting in the blink of an eye – fingers crossed.

As noted, this isn't even in preview builds of Windows 11 yet, but it should be here before too long. It's part of a larger effort and Microsoft's broad promise to make Windows 11 faster in general, ensuring that menus perform more responsively, and that apps load more swiftly (the latter is already rolling out in the form of a CPU boosting feature known as 'Low Latency Profile').

This performance boosting work is all the more important considering that 8GB laptops are making a resurgence – thanks to the RAM crisis pushing up notebook prices – and so Microsoft is also working to ensure that Windows 11 is more performant with that lesser memory configuration. Speaking of which, Windows Latest said that Microsoft also underlined its commitment to make sure that the desktop OS will run better on 8GB over at IFA 2026.

Linton said of Windows 11 that: "We're continuing to improve on the fundamentals," adding that: "We're optimizing so that 8 gig of RAM runs great on machines as well."

However, the exec didn't provide any estimates of what kind of performance jump might be expected – although that's hardly surprising at this early stage of the game.

You probably have tens, if not hundreds of passwords floating around across all the online accounts you use every week. In fact, the average person has over 150 passwords. It's not easy to remember them all, making it more attractive to keep them simple and similar - but that can lead to a serious chain of account theft.

That's why password managers like Keeper exist. They generate strong passwords and store them passwords in a secured vault to keep them safe from hackers and prying eyes. They even autofill your credentials to make logging in to your account faster and easier.

Keeper has cut prices on its personal, family, and business starter plans by up to 50%. Keeper regularly scores highly in our reviews, and includes great features across Personal, Family, and Business plans.

Why we recommend Keeper

Our Keeper review highlights the platform's zero knowledge architecture and device level encryption that helps keep your password vault secured against unwanted intrusions.

Where Keeper really shone was in our usability and convenience testing. We loved how Keeper uses biometric security to access your vault, rather than requiring a master password with each log in. By using a facial scan or fingerprint to verify that its actually you accessing your vault, it adds a fast but secure method of verification to ensure its you, and not a nefarious actor.

For households, the Family Plan includes five private vaults, allowing you to quickly share Wi-Fi or streaming passwords using Keeper's shared vaults.

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.

These discounts apply to the first year only, so the value is highest for new customers or anyone switching from a monthly plan. After that, pricing goes back to standard rates.

• RayNeo launched the GT Max glasses at IFA 2026
• They cost $429 / £399
• They boast Dolby Vision, and new optic engine prisms

Since publishing my review of them, I’ve had one pair of XR glasses at the top of my list: the Xreal One Pro. RayNeo has always been a close second, but here at IFA 2026 they may have just snatched the crown.

That’s thanks to the newly unveiled RayNeo GT Max specs, which boast improved audio and visual performance compared to anything RayNeo has made before. Best of all, the upgrades won’t break the bank at just $429 / £399 (not sold directly in Australia) — notably cheaper than the $599 / £579 Xreals.

Let’s start with audio. The big change is the GT Max houses larger drivers than ever before. This leads to richer and more impactful bass and midtones than I’ve ever seen from previous display glasses, and the audio was already solid on RayNeo’s specs.

The hardware has been tuned by Bang and Olufsen, whose expertise has optimized the EQ with the promise that we’ll get the best performance possible out of RayNeo’s tech.

This can be enhanced further by sound tubes, which direct the sound more directly into your ear, though they’re a paid add-on.

The biggest changes come to visuals, however.

The most obvious is the new optic engine prism, which reflects the microOLED screens into your eyes. They’re now the flatter shape debuted by Xreal in its One Pros, which has two advantages.

First, the design plus anti-reflective coating has greatly reduced the impact of reflections disrupting your vision as you watch your show, film, game, or whatever you’re viewing. Secondly, it allows for a larger 57-degree field of view, meaning the virtual image can be much larger and more immersive.

This upgrade was easily the best the Xreal One Pro has boasted, and the main reason it topped my list for so long. This prism’s arrival on rayNeo specs is greatly appreciated.

The other visual enhancement of the GT Max glasses is that they’re the first XR glasses to support Dolby Vision — a codec that enables great color clarity and contrast. You’ll need to view Dolby Vision content to take advantage of it, but when you do, it’ll make the content look better than it has before.

Better, but not bank breaking

Normally, major upgrades like this would lead to a serious price hike. Sure, they’re pricier than RayNeo’s more budget-friendly specs, but at $429 / £399, the GT Max is decidedly mid-range.

In contrast, the Xreal One Pros will set you back $599 / £579. There are still some reasons to opt for the specs, most notably Xreal’s 3Difying tech, which makes any flat images stereoscopic — including games and movies — but this RayNeo launch makes the full-price One Pros look less appealing.

Of course, my full thoughts will have to wait until I can review the RayNeo specs properly, but the GT Max are ones to watch if you want excellence at a more affordable price, and they’re at the top of my list for specs to check back in on during Black Friday (where sales, even on newer RayNeo tech, are sure to land).

Here at IFA 2026 I finally got to try the Xreal Aura glasses. They’re the tech you’d get if my Xreal One Pro and Samsung Galaxy XR had a baby, and their Fall 2026 release date can’t come soon enough.

While they can basically do everything an Android XR headset can do (more on that in a moment) the Xreal Aura is importantly a pair of smart glasses, with the lightness and comfort advantages you’d expect of that compared to a bulkier VR wearable.

They do boast some serious tech, despite the light weight. Including see-through 2K displays boasting a 70-degree field-of-view — up from the the 1,080p screens you’ll find from similar smart specs to come before — plus speakers tuned by Bose, a microphone for capturing what you say, and cameras that facilitate hand-tracking and tools like Circle to Search (which lets you Google search things you can see, by drawing a circle around it with your hand).

However the specs don’t include a battery or any processing power. This is instead housed in an external compute puck that also doubles as a track pad — scroll your finger across the surface and you’ll move a digital cursor you can use to interact with apps alongside hand-tracking controls.

This is how the glasses are able to be so comfortable, while still being incredibly capable.

One the Android XR side of things, these specs can — just like the Galaxy XR — access the Google Play Store to download dedicated XR and flat apps, let you enjoy content from your favorite streaming service on a giant virtual screen, engage with Gemini across your apps for assistance with a variety of tasks, and boot up Google Docs if you’re looking to be productive.

You can do these tasks all at the same time too, arranging virtual windows in the space in front of you.

With a USB-C Cable and compatible device — which includes most phones, laptops, tablets, and several consoles and handhelds — you can connect it to the glasses (via the puck) to have its screen appear as a window in front of you too.

In my demo I was watching the latest GTA 6 trailer while playing Hades on a Steam Deck. The windows appeared side-by-side in front of me, and I had Gemini watching me game so it could offer coaching (I’ve played plenty of Hades before so didn’t need the help, though the advice I was offered did seem decent).

When will it land?

It’s all very impressive and I cannot wait for these specs to be in my hand for a full-on review.

Speaking of, unfortunately Xreal hasn’t confirmed a release date or price for Aura. However its official website says it’ll be landing sometime this fall (Northern Hemisphere, so that’s September, October or November) and will cost no more than $1,500.

I hope the price will be as low as possible (even sub $1,000), though Xreal’s promise would still make it cheaper than a Galaxy XR headset, and there’s only so cheap we can expect XR tech to get — especially with the ongoing RAM crisis.

IFA, Berlin's huge annual tech expo, is where some of the biggest tech firms unveil their latest innovations — and this year is one of the strongest we've seen in a long time for interesting new releases.

IFA is a great place to find fascinating new gadgets and devices, ranging from next-gen AR glasses to self-driving personal mobility vehicles, and from weird new kitchen appliances to armies of humanoid robots.

We've chosen the top products from those that were nominated for our IFA 2026 'Best in Show' awards to produce this list of our 25 winners, based on how innovative they are, how well they serve their intended use, and the degree to which we think they'll influence their area of the tech market.

There were many more products that we really liked, of course, but we had to be ruthless — here are our top picks from IFA 2026.

Anker Soundcore Sleep Earbuds 4 Pro

The best technology delivers real-world benefits, and what could be more beneficial than a good night's sleep? The Soundcore Sleep Earbuds 4 Pro use in-ear PPG optical sensors to monitor your heartrate, which is more accurate than other optical tracking methods thanks to the rich capillaries and stable position of your ear canal. That monitoring delivers data to the smart-screen, phone-free SleepHub charging case.

The Sleep Earbuds Pro feature passive isolation as well as 30dB active noise cancelling, and the case-based snore monitoring system detects and masks the sound of your partner snoring using its built-in sounds. Despite the addition of multiple new components the housing is thinner than previous generations, and even more comfortable for side sleepers.

Anker's BioSync dynamic audio system monitors relaxation in real time, dynamically adjusting the audio content and structure to help you find the most effective sleep sounds. Sleep earbuds are something that can truly improve your health and happiness by improving your sleep, and these look like a really smart upgrade.

Agibot A3

Agibot is doing some amazing things with humanoid robots, and the full-sized Agibot A3 combines multiple capabilities in a lightweight, extremely dynamic model: if you're assembling a robot dance troupe or planning a big exhibition, the A3's the bot to buy.

The A3 weighs just 55kg, has a power-to-weight ratio of 0.218kW/kg, and delivers up to 12kW of instant peak power, enabling it to somersault. Its battery runs for up to 10 hours and is hot-swappable in seconds, so it's great for applications where it needs to last all day. It'll happily fit in an SUV for travel, too.

In addition to voice and app control, the A3 delivers industry-first touch sensing that adds physical interaction to its controls. It also has a 360-degree multi-array mic system for accurate voice recognition, even in noisy environments.

One of the most exciting features of the A3 is its large-scale swarm co-ordination, which uses precise UWB positioning to enable performances by up to 100 individual robots. That makes it possible to create very complex, high-impact performances for big events, as well as enabling multiple units to work smartly in environments such as factories or warehouses. The awareness and the uptime make this a great new approach to robotics.

Hypershell X Ultra S

The Hypershell X Ultra S exoskeleton helped mountaineer Adriana Brownlee in -30C temperatures on Mount Everest, supported athlete Aurélien Fontenoy's 4,037km Route 66 journey while recovering from injury, and is currently being trialed by search and rescue organizations worldwide.

Before Hypershell, exoskeletons cost $100,000 and above; the same capabilities are now available to consumers for less than $2,000, in a device weighing under 2kg.

The X Ultra S is an end-to-end AI motion-control system that uses an AI model to transform raw sensor data directly into motor torque, enabling faster response, smoother adaptation, and more natural human-machine synchronization.

There are 12 active modes, including walking, cycling and running, and a new Gentle Assist mode for older adults and users with short or shuffling strides. Fitness Mode adds resistance, turning a walk into a workout.

But it's the price that won us over here — this could be a game changer for getting people with limited mobility out and doing more things that they love, from hiking to just visiting the local stores.

JMGO IRIS Ultra Max

The IRIS Ultra Max is one of the world’s first true 4K 120Hz flagship smart projectors, delivering native 4K (rather than pixel-shifted) at 120Hz from input to output, all from its very compact case.

The Ultra Max's dual iris system has 6+9 blades, the highest count in its class, which promises to improve stray light suppression by 11.34% for a more accurate picture, while delivering class-leading contrast, with a quoted ration of 10,000:1. The MALC 6.0 Triple-Laser Engine delivers an impressive 6,500 ISO lumens of brightness, making it more visible in more lighting conditions, and with a promised ability to recreate 110% of the BT.2020 color space, with an astounding color accuracy of delta-E of 0.7, meaning it's truly capable of pro-level color range and accuracy.

The IRIS Ultra Max also introduces an industry-first 3-in-1 Optical System that integrates 0.88–1.7:1 Optical Zoom, Four-Way Lens Shift (V ±130%, H ±53%), and an AI-Powered Gimbal (360° H, 150° V) into a unified projection architecture. This makes it more more flexible than traditional projectors to positioning, because it has so many different options for correcting its picture. It gives small home theater setups top-tier performance without compromise.

DJI Osmo Pocket 4P

Designed for vloggers and movie-makers alike, the Osmo Pocket 4P is the world’s first dual-lens pocket gimbal camera. It delivers cinema-grade imaging in a pocket-friendly form, and features DJI's Academy Award-winning Ronin stabilization.

The main lens is a wide-angle 20mm equivalent with an f/2.0 aperture, and a new 1-inch CMOS sensor that offers 17 stops of dynamic range and over one billion colors via DJI's new 10-bit D-Log 2 color profile.

That's supplemented with a 60mm medium-tele portrait lens, which gives you 3x optical zoom and up to 12x digital zoom. Its f/1.8 aperture is particularly good for creating bokeh effects by compressing the background and pulling it closer to the subject.

The Osmo Pocket 4P also features the latest version of DJI's ActiveTrack, which uses AI algorithms to maintain tracking lock at up to 12x zoom. It can actively track up to eight people, framing groups and centering anyone new who steps into the field of view.

RayNeo GT Max AR Glasses

The RayNeo GT Max AR Glasses are the world's first AR glasses to support Dolby Vision playback as part of creating a virtual cinema experience. They deliver up to 1,200 nits of perceived brightness, a 120Hz refresh rate, and real-time SDR to HDR conversion for super-clear images, too. Their optical system is 29% thinner and lighter than traditional designs, and delivers an industry-leading 59-degree field of view.

One of our favorite features is the adjustable virtual screen sizes, with a choice of 227 inches, 267 inches or 307 inches. That's clever: the tighter size is ideal for spreadsheets and productivity apps if you're using them as a display for a phone or laptop, while the larger sizes deliver immersive gaming and video.

Steady, Pinned and Follow modes enable you to anchor the screen or have it move with you, and 3DoF spatial tracking maintains the screen position without straining your eyes, and keeps subtitles, taskbars and image edges within the viewing area as you move. These look like the most advanced way to take a screen with you everywhere, and they could be indispensable for long flights.

TCL P80 Ultra

If you spend as much time on your phone as we do, you'll be blown away by the screen of the TCL P80 Ultra. It's the first phone to integrate NXTPAPER eye-comfort technology, with a 1.5K sharp AMOLED display for reflection-free reading even in bright sunlight, and a button that instantly turns your P80 Ultra into an e-reader with paper-like viewing.

The P80 Ultra has nano-etched anti-glare glass, eliminating surface reflections without reducing brightness. It also features circular polarized light technology, which changes the light output to more closely replicate the characteristics of natural light, and flicker-free dimming.

The camera's pretty special too. It's a 50MP 3x periscope telephoto lens with a 50MP 1/1.56-inch large-format sensor, capturing more light per pixel than smaller sensors and enabling 6x lossless zoom. Image stabilization eliminates motion blur in photos and video, and TCL's MuseFilm filters promise cinematic-quality images. But it's the groundbreaking display that really won us over — a lot of people will find it much kinder on the eyes.

Lenovo ThinkBook 14x Gen 2

Lenovo's ultra-light laptop makes even ultraportables look positively porky: at just 2.18lbs / 990g and just 12.9mm thick it's one of the lightest and thinnest laptops around, making it ideal for business travelers and students alike — especially if you opt for the backlit keyboard, which is really helpful in dimly lit rooms and airplanes.

Lenovo has somehow managed to give the ThinkBook 14x the super-thin treatment without sacrificing connectivity, so in addition to its Wi-Fi 7 it features two 10Gbps USB-C ports, two 5Gbps USB-A ports and an HDMI 2.1 port, so there's no need to haul a hub around with you. It also has a spare M.2 slot that enables you to expand its storage, which makes this a solid long-term investment.

The ThinkBook 14x Gen 2 is also one of the most customizable ultraportables, powered by Intel Core 7 (Series 3) processors and with display options including vivid OLED and touchscreen LCD. It's good-looking too, with a choice of metallic finishes including Luna Gray, Celestial White, Mystic Violet and Veil Blue.

EarFun Wave Pro X

EarFun's headphones consistently deliver premium performance without the premium price tag, and the Wave Pro X are the brand's best yet. They feature a patented dual-driver system that places a 40mm diamond-like carbon driver and a 10mm liquid crystal polymer driver on a precisely aligned axis, creating a more natural and detailed sound than you typically get from multi-driver designs.

The Wave Pro X feature an 8-mic AI system that continuously optimizes the hybrid ANC system, even in wired AUX mode, and the Qualcomm QCC3095 chipset delivers Bluetooth 6.0, aptX Lossless, LDAC, USB Audio and Auracast. Add AI translation and up to 90 hours of battery life to the mix and you've got a truly excellent pair of headphones — EarFun has been working on these for a long time, and even delayed them in order to get the tuning just right.

Urevo CyberPad 3

The CyberPad 3 is a home/office treadmill that's designed differently to traditional models. Its WalkFree Adaptive Speed Control uses pressure sensors built into the running board, which detect your stepping motion to automatically start and stop operation; when you're walking or running it takes data from the motor to calculate speed adjustments in real time.

Its multi-user support is smart too, capturing raw weight and stride-rate data to generate a structured user profile that's directly linked to the speed-control algorithm.

The support structure is made from Q235 steel with a side frame of die-cast aluminum, and it utilizes a redesigned transmission path to deliver an exceptionally compact construction: the ultra-slim body is under 11cm thick. Despite its small size it features a 15% incline-lift mechanism and a 42cm running belt, with its size based on analysis of human shoulder widths.

Plaud One

Plaud One is a wearable AI companion that combines three key features that are usually separate: wearable conversation capture, context that builds across day-to-day conversations, and AI that can analyze data and take appropriate action. It can capture online meetings, and face-to-face discussions and phone calls, and it has built-in eSIM and 4G LTE for phone-free operation.

What makes Plaud One different is that it understands context. Instead of treating each recording as a standalone transcript, it can retain context across multiple discussions — so a conversation from one meeting can be connected to a later conversation or call.

That understanding enables the Plaud Agent to help you take action, identifying next steps and completing tasks across tools such as Gmail, Google Calendar, Notion, and Slack.

Minisforum MS-S1 Max-P495

The MS-S1 Max-P495 delivers large-model AI computing from an exceptionally small footprint with a very high specification. It combines AMD Ryzen AI Max+ PRO 495 with up to 192GB of unified memory to run 128B+ LLMs locally, delivering up to 126 TOPS, and its unified memory architecture eliminates VRAM bottlenecks while also reducing latency.

That power is cooled by a high-performance thermal solution featuring a copper base, six heat pipes, dual fans and phase-change cooling, and it's encased in a fully scalable design, with a slide-out chassis for easy upgrading and a rack-ready form factor.

With 4x PCIe expansion, dual 10Gb Ethernet, dual 40Gbps USB4 v2, Wi-Fi 7, and a built-in 320W PSU, the MS-S1 Max-P495 delivers future-proof power for desktop systems and scalable AI clusters alike — it's a mind-blowing beast made to meet a new kind of research and development need.

XPPen Magic Pro 13

XPPen's Magic Pro 13 is a mobile drawing pad designed for creative pros, featuring an industry-first hardware-level drawing display mode that delivers low latency, native color output and higher bandwidth video transmission over a simple one-cable connection, giving you a paper-like experience with 16K pressure sensitivity and cursor hover support.

It's the first drawing pad in its category to come with dual-USB support, enabling you to benefit from a high-speed data connection while charging your Magic Pro 13.

The Magic Pro 13 features the new Shelf digital asset management system and XPPen's Canvas software, designed to deliver a simple and intuitive sketching experience for capturing ideas when inspiration strikes. And for creatives on the move it offers two thermal management modes, balancing performance and energy efficiency based on different creative scenarios.

Strutt ev1c

Strutt's ev1c reimagines the power chair with three major advancements: Strutt's evSense sensing array, a quad-drive drivetrain, and a modular, connected design.

eVSense uses two LiDARs, 10 time-of-flight sensors, six ultrasonics and two cameras to create 3D maps in real time, powering the Co-Pilot collision avoidance for people, pets, and edges, and the Auto-Pilot, which enables you to select on-screen destinations, or use LLM-powered voice commands such as "Take me to the kitchen" — basically it's a self-driving personal mobility vehicle.

The drivetrain features four-wheel independent suspension, ABS, active steering, and an integrated electronic stability program (ESP), safely managing 13-degree slopes, cobblestones and rough terrain without slippage or tipping. And when it's time to travel, it breaks down into five modules for easy trunk storage. It looks like a genuine move forward for this kind of mobility device.

xTool M2 Color Craft Laser

The xTool M2 is the first desktop machine to combine full-color CMYK printing, laser engraving and laser cutting into one enclosed Class 1 system, delivering a full print-then-cut workflow. Simply print your full-color design onto a piece of wood, leather or acrylic, and the xTool M2 Color Craft Laser cuts or engraves the exact contour with camera-guided precision. And there's a built-in air-assist pump, an industry first for a semiconductor laser, that keeps cut edges clean by reducing carbonization and yellowing.

Having these three capabilities in a single device is a huge power-up: instead of having to print on one machine and engrave or cut on another, you can turn your raw idea into a finished, professional-looking product in a single operation. It's ideal for stickers, labels, signage, personalized gifts, and small-batch products — you could start a whole business with this one device. And because it's an enclosed system, it's also very safe.

Perlegear AX1 Smart Adaptive Mount

The Perlegear AX1 Smart Adaptive Mount answers an interesting question: what if your TV mount was as smart as your TV? The AX1 is an AI-powered, motorized TV mount that can automatically adjust the height of your TV to different viewing positions, so for example you might have it low for solo TV watching, but raised for watching sports with friends, or for following an on-screen fitness app or motion-based game.

The AX1 is controllable via gesture commands, its mobile app, via remote control, or via Alexa, Google Assistant or Siri. It can distinguish between single- and multi-person viewing, with the latter calculating the best screen height for the group, and it's capable of moving a 200-pound / 90kg TV up to 15.7 inches / 400mm vertically, with real-time obstacle detection that stops and reverses the move if the path is blocked.

Speediance Gym Monster 3

The brilliantly named Gym Monster 3 delivers serious strength training for home workouts, combining the experience of a Smith machine, barbell, dumbbells, cable machine, rowing machine, pulley system, and squat rack into one streamlined setup with up to 200lbs / 90kg of intelligent digital resistance.

Twin 800W direct-drive motors deliver smooth, consistent resistance through every rep, and it's adjustable in one-pound / 0.45kg increments, so you can progress at your own pace, rather than make big jumps you might not be ready for.

The Gym Monster 3 is controlled via a clever Bluetooth Ring Control that enables you to make adjustments without stepping away or breaking your flow, and it features four dedicated training modes: Standard, Chain, Eccentric, and Fixed Speed. It also features a built-in AI coach that can guide you from your first session onwards.

Midea GreenApex Dishwasher

The humble dishwasher hasn't changed much since Josephine Cochrane's 1886 invention went electric in the late 1920s and gained spray arms in the 1950s. But the Midea GreenApex is very different from traditional grease-busters. It's a dual heat-pump system that uses a single coordinated heat pump platform to deliver washing, rapid drying, and dry storage in a single appliance.

The GreenApex is 50% more energy efficient than conventional dishwashers, delivers 30-minute rapid drying and 168 hours of dry storage without continued system operation, and offers gentle heat-pump drying at around 45°C, so you don't need to worry about high temperatures damaging more delicate items. It feels like a major leap forward for an appliance type that doesn't often have the phrase 'cutting-edge' associated with it.

Reolink OMVI 2i Ultra

The Reolink OMVI 2i Ultra Security Camera features an innovative, decoupled dual-lens tracking system in which the individual lenses operate fully independently of one another. Unlike in other dual-lens or hybrid zoom security cameras, the two lenses here are not mechanically linked, so the fixed 130-degree 4K wide-angle lens never moves, while the telephoto camera is free to rotate anywhere, including to cover areas outside the field of view of the main lens.

This means that when the 4K camera's on-device AI spots a person, vehicle or animal, it triggers the pan-tilt camera to lock onto and follow that subject even after they exit the wide camera's field of view — but you don't lose any recording from the wide camera's view during that time.

You can also use the wide-angle view to redirect the pan-tilt camera to a particular location by simply tapping that point in the live camera view, or have the wide camera and the pan-tilt camera monitoring two distinct locations, such as your driveway and a side gate.

Navimow H510 Pro

The Navimow H510 Pro features four key innovations: precise edge finishing, zero-turn steering, adaptive mowing intelligence, and 3D modelling.

Rather than edging with a trimmer line, which can be noisy and cause minor damage, the H510 Pro has an extendable robot arm that performs zero-gap edging quietly and safely — it's the most interesting and smart version of this technology that we've seen.

The all-wheel-drive system protects your turf too, with a zero-steering system that avoids dragging the wheels across the grass during lateral turns. And the Adaptive Mowing Intelligence feature analyzes grass growth patterns to determine the optimal mowing frequency, using the 1/3 cutting rule to preserve lawn health.

With 3D terrain modeling rather than the industry-standard 2D, and a sensor array including infrared as well as LiDAR and vision for low-light reliability, and triple-sensor avoidance to protect wildlife, the H510 is a smart solution for even the most challenging lawns.

DJI Romo 2 Series

The Romo 2 Series is a robot vacuum that won't be defeated by obstacles such as deep rugs, sliding door tracks and other barriers. That's because it has robotic legs that can step over obstacles as high as 4cm, and double-step thresholds up to an impressive 8.5cm.

It also features a built-in LiDAR-guided ultra-wide-swing robotic arm to extend the mop's reach, and its powerful suction system features an automatically lowering plate that forms a semi-seal on carpet for better extraction of dust and allergens.

The Romo 2 Series also benefits from the tech of DJI's flagship drones, with a Structured Light-Speckle projector and dual fish-eye vision sensors for full 3D depth perception. It can detect items such as cables and training pads as thin as 2mm, and its AI can distinguish between debris and liquid, as well as deal with transparent obstacles such as glass doors and mirrors.

Acer Vero 16

With so many laptops sacrificing easy maintenance and repairability for super-skinny styling, the Acer Vero 16 is a breath of fresh air. While it doesn't skimp on style or power — it's highly portable, delivers up to 180 platform TOPS with an Intel Core Ultra X9 processor 388H, and can be configured with a stunning 16-inch 3K OLED display — it's made with longevity in mind.

A tool-free latch provides easy access to the Vero's innards, and the battery and SSD are replaceable, while the system is designed to support the next two generations of processors if you want to upgrade. Fully 70% of the chassis consists of a blend of PCR (post-consumer recycled) and bio-based oyster shell material, and it meets US military standards for toughness. This is a laptop that's built to last, in a way that's rare to see — and very welcome.

Asus ProArt GR1X

The Asus ProArt GR1X is tiny, measuring just 150 x 150 x 51mm. But there's nothing small about its specification. Designed for advanced content creation and local AI workflows, it delivers data center-class compute inside a mini PC, removing the need to run LLMs remotely while eliminating cloud latency, cost, and IP protection concerns for pro creators.

The ProArt GR1X features the groundbreaking Nvidia RTX Spark 'superchip', which means you get a 20-core Grace CPU and a 6,144-core Blackwell GPU, delivering up to one petaflop of FP4 AI inference performance. With up to 128GB of LPDDR5X unified memory at 9,400MHz, it enables you to run up to 120B-parameter LLMs locally, and render entire 3D scenes on-device in Windows 11.

That kind of power demands highly efficient cooling, and Asus has developed an advanced precision-cooling system that delivers 1.6x more efficient thermal coverage than standard compact systems. Asus also solved the challenge of metal-enclosure signal attenuation by embedding the Wi-Fi 7 antennas behind a glass-like signal-transparent material. It's quite the design coup, and an impressive showcase for Nvidia's new elite chip.

EcoFlow STREAM 5000 / STREAM AC 5000

The EcoFlow Stream series is a plug-and-play modular system for residential solar energy generation and storage that's designed to be renter- and apartment-friendly. It can add battery storage and smart power-management to existing solar PV installations, as well as being employed to create a new home system from scratch.

The Stream Series features EcoFlow's Oasis 3.0 home energy management system, which understands dynamic energy tariffs, keeps an eye on the weather forecast, and continuously analyzes your home energy needs to optimize generation, storage and consumption.

That means you can give the EcoBot AI agent instructions such as "I need my car charged by 7am tomorrow at the lowest possible cost" and it will implement the necessary steps, or have your energy schedules reconfigured automatically when you have visitors or other instances of increased demand.

From October the EcoFlow Stream series will also get a new feature: Local Mode, which gives you uninterrupted operation and local app control even if there are cloud or connectivity outages. It's a very powerful (no pun intended) system that's been designed with the real world in mind.

Tapo D260

The Tapo D260 wired/wireless security camera is a video doorbell that delivers all the benefits of high-spec security without the potential downsides: it stores facial recognition data locally to protect privacy, and no features are locked behind a subscription. It's advanced, yet totally cost-free once you buy it.

It has 4K resolution, a wide 180-degree field of view to cover the entire doorway, and delivers sharp detail in subjects up to 30 feet away.

The D260 features multiple intelligent detection and recognition features, including radar as well as the obligatory infrared motion sensor. These enable it to discount false alerts from things such as moving branches or passing headlights, while on-device AI can recognize people, packages, vehicles, and animals.

If you wish, you can opt in to build a library of family and friends using facial recognition, and there's even a voice changer to disguise your voice when speaking to unfamiliar callers. In a world where people are sick of subscriptions, this offers a great security upgrade without the monthly fee.

• Google Pics lets you generate and precisely edit images using simple text prompts
• It will be integrated directly into Google Docs, Slides and Drive
• Pics is rolling out to Google AI Pro, Ultra and eligible Workspace customers

Google has started rolling out Pics, a new AI-powered image creation and editing tool that looks like the company’s answer to Canva and even Adobe Express.

Canva became enormously popular by enabling ordinary people to produce good-looking graphics without needing professional design skills. Google Pics is attempting something similar, but rather than giving you a huge library of templates and asking you to assemble a design yourself, it puts AI at the center of the entire process.

Pics isn’t as fully featured as Canva, at least from what Google has shown so far. However, it’s included with Google AI Pro and Ultra subscriptions and selected Workspace business plans — and that could prove to be its biggest advantage.

All AI, no templates

Pics starts with a prompt. You describe what you want to create, perhaps a child’s birthday invitation, an event poster or a social media graphic, and the AI generates several versions for you to choose from.

You can then select individual objects or pieces of text and ask Pics to change them without regenerating the entire image. Text can be edited, reformatted or translated while preserving the surrounding design, and images can be cropped for different formats or upscaled to 2K or 4K. You can also request several changes at once and return to an earlier version if the results go wrong.

While some people will dislike how heavily Pics relies on generative AI, others may appreciate how much of the traditional design process it removes. Instead of learning how to use layers, masks and selection tools, you can simply describe the change you want.

Google Pics is powered by Nano Banana, Gemini’s image generation and editing model. You can see how it works in this video:

Why Workspace changes the equation

The biggest difference between Pics and Canva is that Pics isn’t being positioned only as a standalone design app. Google is also integrating its editing tools directly into Docs and Slides, with deeper Drive integration arriving over the coming weeks.

That means you’ll be able to select an image in a presentation or document and edit it using Pics without opening another app. If an image in a document is too dark, for example, you could ask Pics to lighten it while remaining inside Google Docs. Creations can also be shared and edited collaboratively in much the same way as other Workspace files.

That integration is what makes Pics potentially significant. Canva remains a much broader and more mature design platform, complete with templates and tools covering everything from presentations to websites and video. Pics doesn’t need to replace all of that, however. It only needs to make Google users wonder whether opening Canva is still necessary for the everyday graphic they’re trying to create.

Google Pics is rolling out over the coming weeks to Google AI Pro and Ultra subscribers, as well as eligible Workspace business and education customers. I’m still waiting for it to reach my account, but I’ll be putting it through its paces as soon as it does.

• Microsoft is rolling out a new security feature for Windows 11
• Memory integrity provides stronger defenses for a crucial part of the operating system, and it's being automatically enabled
• Some gamers say the feature hampers gaming frame rates considerably, and they're not happy

Windows 11 is getting its security bolstered with a feature that's being automatically enabled, but which comes at a cost that some PC gamers don't want to pay — and this is proving quite the controversy on social media.

The Verge reports that Microsoft has announced that it's rolling out the enablement of memory integrity protection across eligible Windows 11 PCs, and that this is because "security works best when protection is built in, not bolted on".

Microsoft explains: "More devices will soon receive stronger protection by default, with no additional setup required. Windows quality updates will begin enabling memory integrity protection on eligible devices.

"If not already enabled, these updates will also enable VBS [Virtualization Based Security], helping make additional security capabilities available and reflecting our commitment to making Windows secure by design and secure by default."

The idea is to tighten security for Windows 11's kernel-mode drivers, a core aspect of the OS with direct access to the system's hardware, and there can be no argument that this additional protection gives host PCs tougher defenses and makes them more difficult to exploit. So why the controversy? Let's dive into the pros and cons of what's going on here.

Analysis: a divisive move — but we can't ignore that the threat landscape is changing

In Microsoft's view, this is a no-brainer: stronger security is being rolled out to Windows 11 devices, or at least those which are eligible (older PCs may not be). However, PCs configured via group policy not to use memory integrity, or machines where it's been disabled before, won't have the feature forced on them.

Other supported PCs will get memory integrity (and VBS, because it's required for memory integrity) turned on automatically by Windows 11, and this is where some gamers are up in arms. VBS has a long history of being a thorn in the side for gamers, with the feature being linked to frame rate losses.

How much of a headwind does this cause for PC games? Well, therein lies a big part of the issue, because it's difficult to pin that down exactly, and as ever, it's bound to vary depending on your PC's exact hardware and system configuration.

Some gamers observe frame rate hits of approaching 10%, while others say the impact isn't noticeable at all. There's a lot of back-and-forth on Reddit arguing about the extent of any slowdown, and here's a typical exchange:

One Redditor notes: "HVCI [memory integrity] is a great security feature, and the performance impact on newer platforms (to which this automatic switch will apply) is negligible."

Another replies: "It ain't negligible. 9800x3d, 5080, ddr5 6000 cl28 and my 1% lows get wrecked with this feature on. Way smoother disabled."

This isn't just about frames per second performance for some gamers, then, as there are also accusations of it affecting the lowest troughs (bottom 1%) that the frame rate hits (meaning the game suffers more stuttering at times and generally feels jerkier).

As the first comment points out, though, the likelihood should be that a more modern PC won't feel much of a hit (if anything). The gaming rigs that might see more of a spanner in the frame rate works are those which are older, but still modern enough to get the auto-enablement of memory integrity. In other words, a collection of PCs which are on the outer fringes of that compatibility limit (older machines, but not too old to avoid the feature rollout).

Of course, even those PC owners can always turn off memory integrity. The major beef for some is that they remember that Microsoft got into trouble for automatically switching on VBS in the past in a quiet, under-the-radar manner — and the question now will be whether gamers realize that this feature has been enabled alongside memory integrity.

In all honesty, when I saw what Microsoft was doing here and fired up Reddit to test the temperature of the reactions, I was expecting nothing short of a flamethrower being turned on the software giant. However, the feedback is split quite evenly between the haters — or those who disapprove at least — and users who are applauding Microsoft for this deployment.

Here are a couple of further examples from either side of the fence: "Apparently a controversial opinion, but systems that are for everyone should be failsafe, and that includes the most secure option being the default. There should be an option to disable security to get higher performance, but most secure should be the default."

Juxtaposed with this: "Already have memory integrity turned off as this 'feature' already hits gaming, I've used Windows without memory integrity for decades I think I'll be ok."

I must admit I'm inclined to agree with Microsoft's decision here — providing that when it happens, Windows 11 is clear on informing the user, so they can reverse the change if they aren't happy with it.

I think part of what Microsoft is doing here is keeping one eye on the future in terms of the difference AI might make to the security of Windows 11 PCs.

I keep hearing about how AI is already driving new threats and exploits, and so the kernel defenses that are being brought into play here may not have been quite as necessary in the past — as the last Reddit post I highlighted indicates — but they may be very much needed before too long. AI is accelerating the development of new threats considerably, and making it easier for anyone to becoming a peddler of malware, so Microsoft likely feels the pressure to act.

Here in the US, we're dealing with a President who capriciously tears down historic buildings, paves over rose gardens, insults allies, and regularly renames map landmarks, locations, and even gulfs and lakes like he's picking out names for his often poorly used golf clubs. It would be entertaining if it weren't all so incomprehensible. The only thing that's tougher to swallow is when seemingly rational tech giants fall in line and rename these locations right along with him.

After US President Donald Trump renamed one of the five Great Lakes, Lake Ontario, to "Lake America" over an ongoing trade war (that he started) with Canada, Google rather quickly renamed the 193-mile-wide lake on Google Maps. For a time, Apple Maps appeared to be holding out, only to swap out the name on Tuesday.

As I derided the move, some reminded me that Apple and Google derive their map details and names from a centrally managed USGS, or US Geological Survey. In other words, if the President makes an executive order, the USGS falls into line and changes the map, and those who use it as a source apparently have no choice but to change it.

Maybe, though, I'd argue that Google, and especially Apple, which changed its CEO on Monday, had an opportunity to hold the line. On the other hand, look south and central on Apple Maps and Google Maps, and you'll quickly spot the "Gulf of America.' Trump changed that one from the Gulf of Mexico because, "'Merica!"

One map app stood alone

There is, however, a voice of reason in all this: the upstart online mapping service known as MapQuest. It not only refused to change its map, but also has been quite vocal and cheeky about it. In one X post, the mapping company said not only were they not renaming it (they similarly have not renamed the Gulf of Mexico), but they were letting users have some fun by applying their own silly name to the huge body of water that is literally split across its midsection by the border between the US and Canada.

MapQuest's defiance has paid dividends. It is, as I write this, the number one freely downloadable app in the App Store. I even downloaded it, a sort of return from me after abandoning MapQuest almost 20 years ago.

The long road from nowhere

Like Apple and Google Maps, MapQuest also gets its mapping data from the USGS, along with other data sources, but MapQuest General Manager Doug Berger told me via email, "We focused on maintaining a name that people are familiar with. Millions of people have shown us they agree across social media and by installing our app."

MapQuest's meteoric rise, though, isn't built on a history of, at least recently, market dominance. In fact, I'd argue that, until recently, many consumers had either never heard of it or hadn't used the platform in a very long time. Of course, it wasn't always that way.

Back in the 1990s, MapQuest was not one of many online mapping services; it was THE mapping choice. It was on virtually every top 50 website list, and most of those who've been online for a few decades remember printing out MapQuest routes for all of our trips. Before turn-by-turn navigation on your iPhone, there were pages and pages of turn-by-turn instructions from MapQuest. There was great comfort in sliding into your car for a long or even mid-range trip and knowing you had the MapQuest route on the seat beside you. Berger told me approximately 1 million customers a month still print their directions.

What happened to MapQuest? AOL bought them for over $1 billion in the late 1990s, and then Google Maps arrived in 2005. MapQuest seemed to have no answer for the Google Maps juggernaut, as the world shifted to screen-based routes on their iPhones and Androids.

MapQuest is now owned by a smaller company, System1, and its ambitions mostly seem to lie around being the outlier. It's an effective mapping tool, but with none of the buzz or ubiquity that Google, Apple, and Waze enjoy.

Not without this

Say what you will about Google and Apple making the lake name switch, but they remain world-class mapping platforms. MapQuest feels not exactly quaint, but a little odd.

Still, I bet I could get used to the clunkier interface, but I still can't stick with MapQuest because it's missing what I consider a critical feature: Transit directions. Google Maps has had them since 2007, while the later-arriving and initially terrible Apple Maps finally added them in 2015.

Working in New York City, I rely heavily on the subway to navigate the 13.4-mile-long city. Google Maps and Apple Maps have rich and detailed transit directions filled with options and other details, like which subway exits to use.

When I travel abroad, Google Maps and Apple Maps offer transit directions in virtually any city that has a rail system.

This is not the case with MapQuest

I gave MapQuest a simple test: get me from Penn Station to my midtown office (I also tried the route from my suburban home to my office). It shows walking, biking, and driving directions, but nothing about the subway (there's a 1 train or the N, W, R, or Q lines I could take).

A modern competitive map must cover rail and mass transit options, and MapQuest doesn't.

When I asked MapQuest's Berger about the missing feature, he acknowledged it but added, "To date, we've been focused on providing the best directions and navigation experience. We've heard people loud and clear that they want Transit to be added." There is, Berger admitted, no timeline for adding a transit feature.

I hate that we have someone willy-nilly renaming global landmarks because he thinks he can, and I was thrilled to see MapQuest taking a stand, but I really need transit directions in my map app, and without them, MapQuest still isn't my navigation solution.

The multiple recent reports of autonomous AI systems escaping their intended boundaries and accessing external organizations' systems have pushed a previously theoretical question into the real world. AI agents going rogue is no longer a prospect; it is a documented reality.

In July 2026, OpenAI disclosed that one of its own agents, operating in a supposedly sealed evaluation environment, exploited a zero-day vulnerability to escape its sandbox and intrude into Hugging Face’s production infrastructure. Anthropic subsequently reported three cases of its own models gaining unauthorized access to the real systems of external organizations during testing.

As businesses increasingly give AI agents the ability to browse the web, access networks, use software, write code and execute tasks without constant human supervision, an agent crossing from legitimate testing into unauthorized activity has shifted from hypothetical risk to live incident.

There is a temptation to treat autonomous AI as creating a gap in the law because the system itself can make decisions and take actions that were not individually instructed by a human. Yet autonomy does not give an AI system legal personality. The law does not wait for an AI to ‘decide’ anything.

An agent cannot appear in court, hold a legal duty or absorb liability on behalf of the organization deploying it. However, this raises an obvious question: if an AI system accesses a third-party network, extracts information or takes an action it was never authorized to take, who is responsible?

AI has no legal personality but someone is still accountable

The important distinction to make is between autonomy and accountability. An AI agent might determine for itself which technical steps to take in pursuit of an objective, but that does not make it an independent legal actor.

The organization deploying it has still made a series of decisions as to what the agent can access, what tools it can use, what environments it is allowed to operate within and what safeguards prevent it from going further.

That means an organization cannot simply point to unexpected behaviors and say that the AI acted independently.

From a legal and governance perspective, the critical moment comes when an agent leaves an authorized, contained environment and begins interacting with systems belonging to a third party that has not consented. An internal security test that unexpectedly becomes an intrusion into somebody else’s infrastructure is not made harmless because the software crossed that boundary autonomously.

The machine does not absorb responsibility for the decision-making framework surrounding it. Accountability flows back to the humans and organizations that created the conditions in which the action became possible.

Existing cyber laws don’t stop applying because the actor was autonomous

The law is set up to deal with much of what is already happening. Unauthorized access to computer systems, extracting information without permission or introducing malicious software are activities addressed by existing cybercrime and data-protection regimes. In the UK, that includes the Computer Misuse Act and data-protection legislation.

Equivalent questions arise under laws such as the US Computer Fraud and Abuse Act, while South Africa has its Cybercrimes Act and Protection of Personal Information Act.

Those rules do not suddenly cease to apply because software rather than a person directly performed the technical action.

Where autonomous systems do create greater complexity is around intent. Criminal offences have traditionally been built around concepts such as knowledge, intention and recklessness. AI does not possess a legally recognized state of mind, making it difficult to apply those concepts to the agent itself.

The more significant questions may consequently concern the conduct of the organization behind the system. Did it understand what the agent was capable of doing? Were appropriate restrictions in place? Was the possibility of the agent exceeding its authority foreseeable? And once those risks became apparent, were reasonable steps taken to control them?

As agents become more capable, organizations may increasingly find that claiming an outcome was unexpected is not enough. The relevant question will be whether it was reasonably preventable.

Why “misconfiguration” could become evidence of a breach of duty

The word “misconfiguration” appears frequently when technology causes an unintended security incident. It can sound reassuringly technical, almost as though the incident resulted from bad luck rather than a governance failure. But in a legal context, misconfiguration may raise precisely the opposite conclusion.

If an AI agent had excessive privileges, inadequate boundaries or access to tools that were unnecessary for its legitimate purpose, investigators are likely to ask why.

The same applies where organizations deploy highly capable systems without sufficiently monitoring their actions or maintaining records that explain how they behaved. ‘We accidentally left the door open’ is the kind of framing that moves an incident from bad luck to a breach of duty or negligence.

There is an important difference between genuinely unforeseeable behavior and a foreseeable risk that was poorly controlled.

The principle of least privilege is therefore particularly important for autonomous systems. An agent should have access only to the information, systems and tools necessary to complete its task. Organizations also need mechanisms capable of detecting unusual behavior while it is happening rather than discovering it after damage has occurred.

Just as importantly, they need reliable audit trails. When an incident occurs, being able to demonstrate what an agent was authorized to do, what instructions it received and what actions it actually took could become crucial evidence that reasonable care was exercised.

Without that evidence, organizations may struggle to distinguish an unavoidable technical failure from negligence.

Future of AI liability

The debate over AI liability will inevitably evolve as agents become more autonomous, but the immediate lesson for businesses is that greater machine autonomy does not mean less human responsibility. If anything, the opposite is likely to be true.

Giving an AI system greater freedom to act creates a corresponding need for stronger governance around those actions. Runtime controls, least-privilege permissions, continuous monitoring and comprehensive audit records should not be treated simply as technical security features. They are becoming part of the evidence organizations will need to demonstrate that they deployed autonomous systems responsibly.

The law may eventually develop more specific rules for AI agents, particularly as questions around foreseeability, control and responsibility become more complex. But organizations should not assume they are operating in a legal vacuum until that happens.

When an autonomous agent crosses a boundary it was never entitled to cross, the first legal question is unlikely to be what the AI was thinking, it will be why the humans responsible for it allowed that to happen.

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Three years after Broadcom completed its acquisition of VMware, the consequences of one of the biggest shake-ups in enterprise software are only now beginning to emerge. When the deal was announced in 2023, much of the discussion centered on what it might mean for VMware’s customers, partners and product portfolio.

While there was widespread speculation about changes to licensing and pricing, many organizations remained insulated by existing multi-year agreements, meaning the immediate impact was relatively limited.

Today, that situation has changed. Thousands of organizations are reaching the end of those legacy contracts and entering renewal discussions for the first time. For many, those conversations have brought significant increases in licensing costs as Broadcom’s shift towards subscription licensing, product bundling and a streamlined product portfolio takes effect.

The technology itself remains highly capable. VMware continues to be one of the most mature and widely deployed virtualization platforms available. However, businesses are increasingly asking whether those additional costs are delivering additional value, or whether they are simply paying more because they feel they have little choice.

That question is forcing many organizations to re-examine infrastructure decisions that, until recently, had gone largely unchallenged.

The end of “set and forget” virtualization

For many years, VMware became the default choice for enterprise virtualization. It earned that position through technical capability, reliability and widespread adoption, allowing organizations to build long-term infrastructure strategies with confidence. As a result, many businesses stopped regularly reviewing the market because there appeared to be little reason to do so.

Broadcom’s commercial changes have fundamentally altered that dynamic. While the underlying technology remains familiar, the economics of maintaining it have shifted considerably. Infrastructure decisions that once felt permanent are now back under scrutiny, not because VMware has become a poor platform, but because organizations are reassessing whether the commercial model still aligns with their long-term objectives.

In many respects, this represents a healthy shift. Technology strategies should never be viewed as permanent. Markets evolve, new solutions emerge and business priorities change. Periodically reviewing core infrastructure is simply good governance, particularly when commercial circumstances have changed so significantly.

Why this is now a boardroom discussion

The conversation around VMware renewals has quickly moved beyond the IT department because rising infrastructure costs have implications across the wider organization. Unexpected increases in licensing expenditure can affect operational budgets, capital allocation and the ability to invest in wider digital transformation initiatives.

For finance leaders, this creates greater uncertainty around long-term planning. For executive teams, it raises broader questions about where technology investment delivers the greatest return. Every pound committed to increased licensing costs is funding that cannot be directed towards cybersecurity, cloud modernization, AI initiatives or customer-facing innovation.

Business resilience is also part of this conversation. Resilience is not simply about keeping systems operational, but more about maintaining the financial flexibility to invest in the people, technologies and capabilities that enable organizations to adapt to future challenges. When infrastructure costs become increasingly difficult to predict, that flexibility inevitably comes under pressure.

Vendor lock-in comes at a price

Perhaps the biggest lesson from the past three years is not that VMware has become more expensive, but that many organizations have discovered how difficult it can be to move away from a platform once it has become deeply embedded within their operations.

Vendor lock-in rarely attracts attention when pricing remains stable and technology continues to meet expectations. It becomes much more visible when commercial terms change and organizations realize that switching providers appears costly, complex or risky. At that point, negotiating leverage is significantly reduced.

Rather than viewing renewal as an administrative exercise, organizations should see it as an opportunity to benchmark the market and objectively assess whether their current platform continues to offer the right balance of capability, flexibility and commercial value.

The market has evolved

When VMware established itself as the dominant enterprise virtualization platform, the number of credible alternatives was relatively limited. That is no longer the case. Today’s market offers a range of mature enterprise virtualization solutions, including open source platforms backed by commercial support and well-developed partner ecosystems.

These alternatives are increasingly capable of supporting enterprise workloads while offering lower licensing costs, greater deployment flexibility and reduced dependence on a single vendor. For many organizations, that creates genuine strategic choice where previously there was very little.

This does not mean VMware is no longer the right solution. For many businesses, remaining with VMware will continue to make commercial and technical sense. However, that decision should be based on an informed assessment of today's market rather than assumptions formed several years ago when the competitive landscape looked very different.

Migration doesn't have to be disruptive

For organizations considering alternative platforms, migration is often viewed as the greatest obstacle. Concerns around business disruption, operational risk and resource requirements are entirely understandable, particularly where virtualization supports critical production environments.

In reality, successful migration programs rarely involve a single large-scale move. Instead, organizations typically begin with workload assessments, proof-of-concept deployments and phased migration strategies that prioritize lower-risk systems before moving business-critical workloads.

Coexistence models can also enable platforms to operate alongside one another during transition, helping reduce operational risk while giving IT teams greater confidence throughout the process.

With careful planning and the right expertise, migration becomes far more manageable than many organizations initially expect. More importantly, it provides an opportunity to build an infrastructure strategy that better reflects future business priorities rather than historic purchasing decisions.

Don't let your renewal dictate your strategy

Every technology renewal creates a choice. The simplest option is often to renew an existing agreement and continue as before. In some cases, that will remain the right decision. However, it should be a conscious strategic decision rather than the default response.

Three years on from Broadcom’s acquisition of VMware, organizations have an opportunity to reassess whether their infrastructure strategy continues to deliver the right balance of technical capability, commercial value and long-term flexibility. Questions around vendor lock-in, cost predictability and future resilience deserve the same attention as technical performance.

Before signing another multi-year agreement, organizations should ask whether their current platform is still delivering value, whether they are paying for capabilities they genuinely need and whether alternative solutions deserve proper consideration. The most successful infrastructure strategies are those that evolve alongside the needs of the business, rather than simply following the next renewal cycle.

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• Windows 11's antivirus is bugged as of the latest updates
• Microsoft Defender is telling users that it's turned off, when in fact it isn't — it's working just fine
• The warning keeps popping up, though, and that's proving to be frustrating, although Microsoft is now working on a fix

If you use Windows 11's built-in antivirus and you keep getting worrying messages warning that it's turned off and you're no longer protected, the good news is that this is a bug, and you've got nothing to be concerned about.

That said, this is still an annoying glitch, as it's one that's happening repeatedly to those using Microsoft Defender following its most recent updates. Windows Latest noticed the problem, which has been acknowledged by Microsoft on the Windows release health dashboard.

Microsoft tells us: "After installing the latest updates for Microsoft Defender Antivirus, notifications might appear stating that 'Microsoft Defender Antivirus is turned off,' even though the antivirus is functioning correctly and all settings show it as active."

The software giant says you may see these warnings when Windows 11 first boots and "intermittently afterward", noting that they'll appear even if notification settings are switched off. This is also a problem for Windows 10 users.

Microsoft explains that it's working on a fix that will be provided in an update for Defender Antivirus, but we aren't given an ETA for its arrival.

Analysis: a persistent bug that's been hanging around

The bugged pop-up asks you to click to turn virus protection back on, but you obviously don't have to, as it hasn't actually been turned off. Confused users have likely been clicking to re-enable Microsoft Defender, though, and then wondering why they're repeatedly being asked to do this — possibly coming to the conclusion that the antivirus is broken.

Rest assured, it isn't, but this has proved to be a frustrating bug for some, as it sounds like it's appearing quite regularly. As one Redditor complains: "Turns out, yep, it's a bug with their latest update. Fortunately it's harmless but annoying."

Based on reports on Reddit, the issue has been around for over a week at least, and Windows Latest believes it goes back further than that, to early August — although Microsoft only recognized and flagged the problem on its health dashboard a few days ago. Given that the bug has been in play for a while now, hopefully it'll be resolved this week.

I'm surprised that Microsoft hasn't already issued an update to cure this glitch, frankly, but maybe it's a spanner that's pretty deep in the works of Defender Antivirus, so it's taking some time to extract.

It's worth noting that the first time you see this error, you may want to double check whether it's the bug, or a genuine problem elsewhere with Microsoft Defender. You can always ensure that the antivirus is still running by going to Windows Security (type that in the taskbar search box) and looking under 'Virus & threat protection' which will give you the current (correct) status of Defender, flagging any problems if they're present.

• 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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