If one thing's certain about AI, it's that it's made it easier than ever to produce professional-looking content – but it's just as easy to create poor-quality content, and Adobe itself will tell you that.

That's why the company argues that, despite initial concerns about AI threatening the roles of creative professionals, human expertise is actually becoming more valuable.

Speaking to the media in a fireside Q&A at its annual Adobe Max conference, company Product Marketing VP Deepa Subramaniam and Creative VP Resh Sidhu offered fresh perspectives on how judgment, ideas and creative direction are shining through.

They argued that while AI can help users generate content at a much faster speed, it can't necessarily take the creative direction decision. While the company was keen to push trust and precision as two themes at this year's sessions, serving its tools as ideation platforms was also a clear emphasis.

More quantity can actually be helpful to businesses looking to stand out

"Speed is a capability, it's not a creative strategy," Sidhu affirmed, calling for humans to add elements of taste, craft and judgement. "You can create a thousand options of something – you still need someone to decide that's the thing that's worth making."

The belief is largely that more non-designers can become designers – for example, writers will be able to visualize their concepts without having to learn specific apps or wait for colleagues to assist them.

By throwing more perspectives into the pot, it also means companies can have access to more ideas and, according to Adobe, separate out more high-quality content from average content.

Subramaniam also hinted at the traditionally steep learning curve associated with its flagship apps, noting that someone highly skilled in Photoshop might previously have struggled to produce motion graphics in After Effects without investing considerable learning time or enlisting a trained colleague.

But even as the technical barriers associated with producing content continue to fall, the company believes expertise could become an even more important differentiator among creators.

In short, while AI is undoubtedly shifting something, it's not the importance of human skills. We're in the midst of a transition where AI is both making specialist capabilities more accessible, but it's also highlighting the importance of human expertise and judgment.

The VPs both summarized by agreeing that producing more content doesn't necessarily mean producing better work, positioning AI as an enabler but not a replacer.

Software is evolving – new doors are opening up to give users access to vendors' tools without necessarily having to interact with the conventional software at all, but Adobe still believes traditional software interfaces still have a future.

We've spent the last couple of decades building a habit of opening a specific app to complete our desired task, but as expectations have grown, so has application complexity – Adobe's software has long been associated with creative professionals, but its apps have often been daunting for more casual users.

From the outside, Adobe looks to be one of the earlier companies actively exploring this shift with a clear willingness to open itself up to agentic AI and MCP. But while the company is heading in a direction of conversational interfaces, it's not leaving its traditional software routes behind.

Do creative pros still need the full app experience?

Speaking exclusively to TechRadar Pro at Adobe Max, Senior Product Director for Adobe Agentic Zorana Gee explained that while emerging AI tools is making the company's tools more accessible to more users, creators will still need different levels of control depending on what they're trying to achieve.

"I think it depends on who you talk to," Gee said when asked whether software is becoming less of a UI-centric destination and more of an invisible layer. "We see people who are still really wanting knobs and controls to change things."

But at the other end of the spectrum, other users might not have the time or expertise to learn complicated applications, so describing what they need and making less advanced changes might suffice.

Adobe wants its tools to meet you wherever you're working

During a separate fireside chat with the media, Product Marketing VP Deepa Subramaniam explained how the company is increasingly separating its capabilities from the interfaces traditionally used to access them. For many users now, the first point of entry has become their chosen chatbot or AI agents.

From there, users can choose to move into one of the Creative Cloud apps, where Adobe's own Creative Agent can help bridge some of the learning gaps typically involved by offering a more conversational interface.

But rather than forcing users to adopt Adobe's own AI assistant, the company is ready to open the doors and allow new ways of working, like AI agents, to be the point of interaction with its tools.

"We're breaking down silos and walls in terms of domain expertise and domain opportunities, and that's happening all the way to the core of how we build products," Gee added.

For Adobe, that represents a significant change from the traditional software model, but it's already a familiar conversation that we're having away from software. Countless studies have already pointed toward companies having to redefine a new way of working rather than plastering AI on top of existing processes, and Adobe's readiness to reconsider its longstanding software strategy is an extension of this.

Software isn't becoming less relevant

The prospect of controlling apps through natural language is becoming more of a reality, and barriers to entry are already being broken, but Gee was cautious about suggesting that those will totally replace traditional software.

"Being more descriptive and being able to articulate verbally the things that you want is definitely a reality," she said, stressing that the transition from traditional input to conversational won't stop at 100% – it'll likely stop much sooner, at a point where both types of input can coexist.

"Will it be 100%? I think it's unlikely or maybe it's a slow progression."

"Words are hard, communication is hard," she said, and that's an important distinction for pros. For example, a photographer adjusting a particular aspect of an image might want to make a precise fine-tune change that a text prompt could misinterpret or overestimate.

Gee also admitted that Adobe is still working out how far its integrations and evolutions should go, and that it's navigating these changes through experimentation together with a community of customers who are helping to shape tools based on what really works.

Is Adobe using Firefly as the front for its evolving software strategy?

Adobe's flagship apps are highly capable, but that's led to the steep learning curve often associated with apps like Photoshop and Premiere. Firefly serves as a different starting point, shoehorning some of the most in-demand tools and controls across many of the Creative Cloud apps into an AI-first interface.

Even pros at the top of their game may struggle to work across disciplines – Gee gave an example of a highly-skilled Photoshop user wanting (and struggling) to use motion graphic tools in After Effects.

All in all, Gee sees AI-assisted design as both "lowering the floor and raising the ceiling at the same time."

But finding a simple way to integrate AI can be complex – I wanted to know how users can make the best model choice when they're faced with so many options. Firefly supports multiple models from different vendors (the company just announced a deeper collaboration with Google to make Gemini Omni, Veo and Nano Banana more widely available in its apps), but expecting every end user to understand the pros and cons of each and make a selection defeats the purpose of adding AI in the first place.

It's another area where Adobe's desire to add precision, control and simplicity in one hit is clear. "The agent will always help if they understand the intent," Gee said, but if users have a preference, they can still opt for one that suits their budget or output needs.

"We like transparency, but we also like control," Gee added.

Where does this leave the software industry?

It's clear that AI's enabling Adobe to accommodate a wider range of users, including many who will have previously been turned about by the complexity of its advanced tools.

And that's probably the essence of it – software vendors are having to become less prescriptive about how people interact with their tools, while AI creates new entry points for new demographics of users.

In some ways, it feels like the Firefly environment is being used as an experiment for the company to determine, together with its users, what the next chapter of software looks like.

But while AI agents become increasingly capable of navigating software on our behalves, and vendors start putting these tools closer to our agents, it doesn't mean we're set to see the end of software. For Adobe, it's about a future where opening an app is a choice rather than a necessity, and the next challenge lies in making its tools accessible through as many doors as possible without compromising on the depth and precision that made them what they are in the first place.

• A new Windows 11 update has arrived
• It brings forth some great features such as right-click menu customization, faster resumes for laptops, and a speed boost for Edge
• A security feature being enabled has proved more divisive, though, as memory integrity protection can slow down gaming frame rates in some scenarios – but there's a reason Microsoft is force enabling this

Windows 11 has a new update which packs quite a number of features, but also what some gamers regard as a bit of a sting in the tail.

The best news about the November update is that it introduces a capability that a whole lot of Windows 11 users have wanted since launch (myself included), namely the choice to fully customize the right-click menu (and streamline it).

As for the more controversial addition, that's a security feature Microsoft has decided to roll out to all PCs which can have a negative impact on gaming frame rates in certain circumstances. I'll look at that move last, but before that, I'm going to list the main goodies that you can expect with the November update (as previously detailed in the preview patch from last month).

Note that many of these features are on a gradual rollout, meaning that you won't necessarily get them immediately (although you might), and I'll indicate where this is the case.

1. Right-click menu customization

It's finally arrived – you can now pick and choose what you'd like to see in the right-click menu, which contains context-sensitive options for the files or folders you've selected. Don't want it to be chock-full of nonsense that you never use? Then get the virtual shears out and prune away those unwanted options.

I say this has arrived, but it's one of those pieces of functionality that's on a gradual rollout, so you may have to be patient. Rest assured it's inbound, and hopefully not one of those features that's on a glacial timeframe with its deployment – given how keenly this is sought after, I'd hope that isn't the case.

As well as fully tailoring the context menu to your needs, you can switch the layout so you don't have the top panel present (with cut, rename and so forth), and the right-click menu opens much more swiftly too. This is the change I'll be grabbing the November update for.

2. Edge gets turbocharged

Microsoft Edge now loads a lot faster if there's already one instance of the web browser running. In other words, the first app launch will be 'cold' and at the normal speed, but whenever you fire up Edge after that it'll appear a good deal more swiftly.

This is a major boon for those who use Edge, but the good news is that Microsoft is planning to allow the use of this speed-up trick with other apps in the future. With the ability being realized pretty swiftly for Edge, hopefully we'll see the benefits elsewhere before too long. This is one feature that's immediately deployed for everyone, so Edge users have an extra incentive to download the November update.

3. Faster resumes from standby

Microsoft has changed the adaptive hibernate policy with this update, so it'll provide a faster resume from Modern Standby when your battery level is nice and high (above 80%). In other words, laptop users will find resumes happening much more swiftly when they lift that lid. This functionality is arriving on a gradual rollout.

4. Better PC recovery options

This update has also brought in a new PC recovery option called Cloud Rebuild for those unfortunate occasions when your system crashes and burns. This allows you to reinstall Windows 11 without having a system image on a USB stick – instead you can use a Windows image and the correct device drivers downloaded via Windows Update.

A separate tweak allows for the Windows Recovery Environment (WinRE) to automatically reuse the Wi-Fi settings you have saved in Windows 11, allowing the PC to go online automatically to fire up a Cloud Rebuild (or indeed another recovery process). Again, these abilities are on a gradual rollout.

The controversial move: memory integrity protection

As Microsoft explains in the release notes: "This update expands memory integrity protection across eligible devices. Supported PCs may now have memory integrity (HVCI) enabled during updates to help improve security and prepare devices for advanced Windows security features, while respecting existing user or IT administrator choices."

Up until now, Microsoft has recommended the use of memory integrity protection to bolster the security of PCs, but has only enabled it for new Windows 11 machines (or new installs of the OS). What's happening here is that existing Windows 11 PCs are getting the feature switched on by this update (or at least eligible computers are, which will be most PCs, unless they're really old).

In short, if you don't have memory integrity turned on (or indeed if you've never heard of it), you'll find it being enabled by the November update (although maybe not straight away, as this is another capability on a gradual rollout path).

The issue here is that in the past, gamers have found that memory integrity can be a drag on frame rates, knocking them down somewhat. Not necessarily in a major way, but perhaps to the tune of 5% slower, or maybe 10% – as ever, there'll be different levels of impact depending on your PC's hardware configuration.

There are some key points to know about here, though. Firstly, older PCs with processors from a few generations back are more likely to witness any slowdown here. A cutting-edge CPU won't even feel the effects of memory integrity being turned on, at least going by reports.

The trouble with keeping this feature off for many Windows 11 users is that we're heading into a more dangerous security landscape where AI is helping to drive more perilous threats, and you've got to remember there's a reason why Microsoft has instigated this as a counter-measure going forward. Meaning it doesn't feel like a very sensible idea to disable this, and while some gamers may be annoyed at the forced rollout of this feature, it's probably for the best on balance.

Of course, you can always turn it off after the November update switches memory integrity on. And the compromise approach for unhappy gamers with somewhat older systems which are affected – and it's worth benchmarking and checking the extent of any slowdown – is to keep memory integrity on, but just disable it for a gaming session (and re-enable the option afterwards). That may be rather inconvenient, but obviously not as inconvenient as falling prey to having your PC compromised.

• Firefly is nearly as easy as a chatbot to use, but it won't go rogue on your edits
• Quick Actions are packaged one-click processes that would otherwise have taken you ages
• You can even prototype a website in Firefly Builder – but it's not for fully-fledged published sites

Adobe has announced sweeping updates to its Firefly environment at its annual Max conference, positioning it as an easy-access starting point for many of the most common creative tasks so that users don't need to open one of Creative Cloud's flagship apps (and deal with the steep learning curve typically associated with them).

It starts with an update to Firefly Image, where precision was clearly the name of the game during the first keynote of the event.

Users can now edit specific objects within images, tweak text, adjust lighting and refine other details on a more detailed level, with the company seemingly throwing shade at more generic AI chatbots which have long been criticized for making unsolicited changes to other parts of the image.

Firefly is quickly becoming a hero product for Adobe

Some of the tools, we've already seen before. Last year at Adobe Max, the company's annual Sneaks session previewed some internal projects staffers had been working on, like Project Light Touch. While the company hasn't confirmed what we're seeing now is an iteration of those projects, it certainly stacks up that they could have at least led into what we see today.

New Quick Actions also land in Firefly, packaging up multi-step processes from its hero apps into one-tap workflows in Firefly. Generating thumbnails suitable for different social media aspect ratios in one hit will likely gown down a treat, but Adobe's also promising to include trend-focused actions in its gallery to keep up with the times.

"We built Firefly to be the AI creative studio where it comes together: the best models, the tools creators already trust and an agent that acts as a creative partner," Firefly and Agentic AI VP Forest Key noted.

As part of its ongoing expansion, Firefly's also pushing beyond image and video editing to websites, with the private beta launch of Firefly Builder, designed to help users prototype websites without any coding. Though crucially, it looks to be a prototyping tool for now rather than a full fledged website builder.

As is tradition, Adobe is also set to host another session of Sneaks at Max 2026, where we look forward to previewing other features that could land in Firefly and the Creative Cloud suite later on.

OpenClaw’s meteoric rise is easy to understand. Unlike a chatbot that stops at answering a question, it is designed to take action across the tools people use every day: reading and sending email, managing calendars, working with documents, triggering workflows and, depending on configuration, interacting with financial tools or shell access. That is exactly what makes the technology useful, and exactly why security teams should pay close attention.

The core issue is not simply that OpenClaw uses AI. It is that a single assistant can aggregate access across systems that organizations have historically secured separately. Once one tool can read private data, communicate on a user’s behalf and trigger actions across multiple environments, it becomes a privileged control point. If an attacker can manipulate that point, the blast radius can extend far beyond the assistant itself.

From productivity tool to privileged operator

Tools like OpenClaw create an attractive path for attackers because they concentrate permissions across email, financial accounts, communication channels and local systems. Instead of targeting a rigid, deterministic workflow, an attacker may be targeting a system that interprets natural-language instructions and context before deciding what to do next. That opens the door to prompt injection, malicious skills, no-click attacks and other techniques designed to influence AI behavior.

Prompt injection is especially concerning because OpenClaw’s usefulness depends on its ability to consume information from many sources. If the assistant encounters untrusted content in an email, message, webpage or document, an attacker may be able to hide instructions inside that content.

The assistant could then be manipulated into leaking information, approving a workflow, contacting another user or taking an action that appears legitimate. The user may never knowingly approve the malicious instruction because the assistant processes it as part of its normal work.

Malicious skills create a related supply chain risk. AI assistants often rely on reusable instructions, plugins, scripts or skills to extend what they can do. That flexibility can accelerate adoption, but it also creates another route for attackers to influence behavior through the assistant’s own automation framework.

Security teams are already accustomed to scrutinizing software packages, dependencies and integrations. AI agents add a complication because harmful logic may be expressed as instructions rather than traditional code, making it harder for conventional controls to recognize what is happening.

No-click attacks raise the stakes further. If OpenClaw can act on content it receives or observes, an attacker may not need the user to click a link, download a file or enter credentials into a fake site. The assistant itself can become the execution path. A poisoned message, calendar invite or workflow prompt may be enough to trigger risky behavior if the assistant has broad permissions and insufficient safeguards.

The consequences still fall on the user and the organization. If an AI assistant transfers funds, deletes data, exposes credentials, sends a damaging email or changes a system configuration, the business must deal with the financial, legal and operational impact. The assistant does not absorb the loss. Accountability remains with the people and organizations that granted it access.

Scale makes that problem more urgent. The more widely a tool is adopted, the more incentive attackers have to study how it works and look for repeatable ways to manipulate it. A popular AI assistant with access to sensitive data, money, accounts and internal workflows can become a high-value target because one successful technique may be applicable across many users or organizations.

The risk also grows when employees experiment outside formal governance. Many security teams already struggle to track which AI tools employees use, what data they upload and which accounts they connect. OpenClaw can compound that challenge because much of its value comes from integrations and permissions.

An enthusiastic employee may connect the assistant to email, messaging, documents, payments or internal systems before the security team knows the tool is in use. By the time it is discovered, sensitive data may already be flowing through an unmanaged AI workflow.

Security starts with visibility and least privilege

The first response should be visibility. Organizations need to determine whether OpenClaw is present in their environment, which users have installed or accessed it and what systems it can reach. Endpoint telemetry, process monitoring, network logs and identity data can all help identify usage. Teams may also monitor known ports and domains associated with the tool, while recognizing that configurations can change. Detection rules should be treated as a starting point rather than permanent coverage.

Network controls should also be evaluated. Organizations may choose to block risky connections to known OpenClaw domains, repositories or default ports until the tool has been reviewed and approved. That will not eliminate the broader risk posed by AI assistants, but it can create friction against unmanaged deployment. In higher-risk environments, blocking unapproved AI tools may be necessary until policies, monitoring and access controls are in place.

Access governance is equally important. Any AI assistant should operate under the principle of least privilege. It should not receive broad access to email, files, financial tools or system controls simply because those integrations are available. Permissions should be specific, documented and reviewed.

High-risk actions such as sending external messages, transferring funds, deleting data or changing production systems should require additional approval. Human review does not need to sit in front of every routine task, but organizations should clearly define which actions are consequential enough to require it.

Employee education remains one of the most practical defenses. Workers are hearing about the productivity upside of AI from peers, influencers, vendors and executives. They also need to understand the security tradeoffs.

Training should explain prompt injection, risky integrations, sensitive data exposure and the danger of granting broad permissions to tools that can act autonomously. Employees should know which AI tools are approved, which use cases are prohibited and who to contact when they are unsure.

The broader lesson is that AI adoption cannot be treated only as a productivity initiative. Tools like OpenClaw blur the line between software, user, workflow engine and privileged operator. As employees increasingly expect assistants to do more than answer questions, security teams need to focus on the authority those assistants are given and the systems they can reach.

OpenClaw is a useful signal of where workplace AI is heading. The opportunity is not just more conversational software, but software that can act. Attackers will be interested in that same capability. Organizations that can see where these assistants are running, constrain their permissions, monitor their behavior and educate users will be better positioned to capture the productivity gains without creating a new control plane for attackers.

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• Adobe leverages Google partnership to integrate Gemini Omni, Veo and Nano Banana into Creative Cloud apps
• Acrobat will also be embedded inside Google Workspace and Gemini for easier PDF handling
• Google DeepMind is also working with Adobe to make GenAI editing near-instant

Adobe has revealed how its relationship with Google will go even deeper in a two-way exchange, with Adobe tools set to become more readily available inside Google's products like Google Workspace and Gemini, and the latest Google models appearing across more Adobe apps.

The company boasted of its now more than 1 billion monthly active users, sharing an overall objective to make the administrative part of creative work less time-consuming by reducing the number of interfaces users need to switch between.

Core to the collaboration are Google's Gemini Omni, Veo and Nano Banana models, which will become deeply integrated within Firefly, Photoshop, Premiere and Adobe Express.

Google and Adobe could be the only creative suites you need

But the event's biggest message this year has focused on improving access to Adobe's tools and meeting creatives wherever they are. With that in mind, Adobe Acrobat will become more integrated into the Google Workspace and Gmail experience so that users don't need to download files and re-open them through Adobe's interface.

The company says 400 billion PDFs are opened using Acrobat annually, quantifying the scale of this collaboration between two major productivity software vendors.

By far the biggest real-world impact will come from the collaboration between Adobe and Google DeepMind, which starts with Photoshop. Described as an exploratory effort between the two companies, the two are targeting near-real-time edits to reduce the disruption typically associated with handing over parts of the workflow to AI.

"By further connecting our respective platforms, we are giving creators and enterprise teams a powerful, integrated environment to turn their ideas into high-impact content," Google Cloud CEO Thomas Kurian wrote.

I’ve gotta hand it to Snap. Making Robert Downey Jr., A.K.A. Tony Stark/Iron Man, the honorary Chief Customer Experience Officer for its sixth-generation Specs AR glasses, is a stroke of genius. The move instantly transformed the chunky glasses from an object of derision to one of possible envy. After all, if Tony Stark, who fictionally built a robot suit from scratch and compressed access to all his core technology into E.D.I.T.H. sunglasses, loves them, then the AR-capable Specs must be pretty good.

Of course, leaving aside the celeb and hero worship, the truth falls somewhere in the middle. The all-black Snap Specs, announced in July and which I tried this week, are so thick that you can’t help but make a statement about fashion choices when wearing them; they look even more extreme in profile. To be fair, Snap is not pitching its wearable glasses as permanent eyeglass replacements. Sure, the pair I wore accepted snap-in prescription lenses from Lensly ($100), and you may wear them outside, where the electrochromic lenses will auto-dim, and then let them give you directions or guidance on your golf swing, but you won’t be wearing them all the time.

Though Snap has slimmed down the Specs with each successive generation, they are still big and thick enough to hold all your technology, including a pair of Qualcomm Snapdragon CPUs, storage, and battery. Not having a tethered battery and technology puck is a trade-off even Tony Stark might appreciate, but putting them on does feel a bit aggressive. The good news is that, at 132g, they are lighter on the face than they look, and I might even call them comfortable to wear.

A time and a place to boldly wear them

I appreciate the somewhat limited scope of this enterprise as compared to, say, Meta Ray-Bans or even Meta Ray-Ban Display smart glasses, and as I used them, I could see the potential but also the limits.

Unlike the Meta VR Glasses I wore last week, Snap Specs are not immersive. Instead, the Liquid Crystal on Silicon (LCoS) Waveguide lenses offer a relatively narrow 51-degree field of view (an improvement over the last generation). That floating window can stay fixed or follow your head turns, but in my experience, there were many times when my gaze slipped outside the relatively sharp visuals.

The control metaphors are similar to some VR/AR glasses I’ve tried: During the tutorial, I saw floating jewels that I had to reach out and pinch, and a jellyfish that I grabbed and dragged around before throwing it away. Point, press, pinch, and drag are your primary controls. Unlike many VR glasses (think Meta VR, Apple Vision Pro, Samsung Galaxy XR), there’s no gaze tracking, meaning Snap Specs have no idea where you’re looking.

Snap has spent years perfecting what I think is quite an intuitive gesture-based interface. I quickly got used to pinching and holding to activate, touch and press, and grab or press and drag. The main controls were in the palm of my hand; I turned over my hand, looked at my palm, and there was the home button.

Specs uses its two black-and-white cameras, which sit just outside each lens, for world reading and tracking your hands. A third color camera sits in the middle of the frame for capturing photos and videos. Snap has not detailed the resolution on those captures.

To snap a photo, I long-pressed a control button on the top left edge. A quick press launched a 30-second video capture. At some point, I may have filled up the storage because I could no longer capture photos and video.

An AR app journey

I tried several custom-built apps, including a Harry Potter adventure. Like many of the apps, Specs often starts by asking you to find a surface and stare at it long enough for the smart glasses to read its geometry and place an augmented reality element on it.

For Harry Potter, I placed a mailbox slot on the far wall and out flew dozens of letters. I held out my hand and one landed in it. I could use both hands to open it and see the letter telling Harry he was invited to Hogwarts. I also made a desk appear, selected a wand, and practiced spell motions (I'm no wizard, Harry). There was even an owl that would land on my arm, and that I could pet and feed.

I also navigated to the browser and opened YouTube, where I watched a cute cat video. The audio, which came through the stems, was loud and rich.

I played dominoes, measured some real-world objects, and practiced drawing. For the latter, I placed a real piece of paper on the table and recreated a tutorial image by following projected guidelines that appeared on my sheet.

Snap Specs are not necessarily designed for productivity, but you can, as I did during my demo, connect a physical Bluetooth keyboard. I'm a terrible typist, but honestly found the experience of trying to touch type while watching the smallish virtual screen unnerving. Unlike a physical screen that you can glance up at without really moving your head, you have to look up to see the virtual display. Trust me, you will not be typing your magnum opus on Snap Specs.

More refinement needed

Despite the two AR chips, some of Snap Specs operations seem a bit slow and pokey. I sometimes felt like I was waiting too long for apps to load, and it didn’t always seem to keep up with my hand motions.

Also, the drawing app made it clear that the AR anchoring technology needs some work. I noticed that my guidelines didn’t stay perfectly still, which made my bad artwork even worse.

I liked that the relatively narrow window could follow my head, but I still think that a 51-degree FoV is a bit small. Then there’s also the quality of the imagery. While Snap has not specified the screen resolution, I noticed that everything looked sharp and color-rich (16M colors), but I also saw some faint rainbowing, which I think might be a product of the waveguide technology.

I didn’t get to try it, but if you want a more contrasty image and do not need to see as much of the real world, you can activate the electrochromic lenses, which basically makes them semi-opaque and look like sunglasses, but also helps for viewing, say, movies and films.

Real world questions

Snap Specs last about four hours on a charge, though I noticed that the lenses I was trying started around 50% and within 45 minutes were down to 13%. The chunky case will add four more charges.

Oddly, Snap chose to put the LTE connectivity option inside that case, which means if you want on-the-go calling and connectivity, you need the case with you. Unless you have big coat pockets, you’ll need a bag or backpack to carry around that case.

Snap Specs will start at an eye-watering $2,195, a price that clearly reflects the effort it took to keep all that technology inside the frames. If you want the LTE case option, it adds another $300. They’re already accepting preorders ($200 down payment) and expect to ship the glasses before the end of 2026.

The frames will also ship in the UK and France for £1,995 and €2,295, respectively. By contrast, the thinner, lightweight, and more immersive Meta VR Glasses start at $1,299, though they do have all the power and compute in a tethered puck.

There’s no question that Robert Downey Jr. makes these frames look cool, but in the real world, Snap Spec owners will have to decide where and when it’s appropriate to wear these camera-sporting smart frames, and if on-your-face compute that doesn’t really approach E.D.I.T.H. levels is worth $2,195.

• Adobe wants creatives to stand out for their uniqueness, so AI will handle the rest
• Third-party AI agents can connect to major CC apps to handle admin work
• Apps like Photoshop, Lightroom and Premiere are also gaining new AI tools

Adobe has revealed plans to expand its Creative Agent across more of its flagship Creative Cloud applications, including Premiere, Lightroom and InDesign, in a bid to give AI more autonomy to handle the less creative side of creative work.

Unlike generative AI, which acts upon single prompts, AI agents can handle longer and more complex, multi-step processes.

The company positions the use of AI as putting creatives in the director's chair, enabling them to offload tasks that aren't really part of the creative workflow to AI agents to free them up more time to focus on the truly creative elements of their jobs.

Adobe's rolling out AI agent access to more apps

"The ideas, taste, style and vision that creative professionals bring to their work are what make their work stand out," Adobe Product Marketing VP Deepa Subramaniam stressed.

Importantly, using AI agents doesn't make a creative's work less human. Some common use cases include applying effects across multiple clips, tracking masked subjects and adjusting colours and exposures in Premiere.

Together with the expanded rollout, Adobe is also opening up the doors to third-party AI agents via MCP, starting with its most popular apps – Photoshop, Illustrator, Premiere and After Effects.

Granting permission to third-party agents to directly work within these apps means users won't have to handle exports and jumping between multiple tools, but it also means things like Photoshop layers and Illustrator vectors will still be preserved.

On-stage at the conference, Anil Chakravarthy, who is set to become Adobe's CEO effective from December 1 2026, demonstrated a third-party AI agent turning a long-form podcast into multiple social media snippets – a demo that was met with much excitement and applause from the thousands of attendees on the ground.

Besides the more autonomous side of AI, Adobe hasn't completely ditched generative AI, which still plays a major part in creative workflows. Photoshop will be gaining new tools to add or adjust lighting within an existing photo. Generative restyle will also turn natural language prompts into adjustable sliders, helping to remove the traditionally steep learning curve often associated with Photoshop.

Premiere also gains new GenAI upgrades, like audio enhancement tools and a generative remove functionality for removing unwanted objects from a series of video frames, while Lightroom sees additions like blemish removal and a tool to artificially open a subject's eyes – perfect for group photos where creatives would already be handling many other variables.

While some general Creative Cloud features are now generally available, some more advanced tools like Lightroom Open Eyes and Premiere Generative Remove remain in beta.

• Microsoft has a patent for a system which uses your spare GPU resources to train AI
• The graphics card would process micro-tasks in combination with other PCs
• You'd be paid some kind of reward for this, but there are issues, and many doubts and concerns, around how such a scheme would work

Microsoft has been granted a patent for a system whereby it could use the GPU in your PC to train its AI (given your consent, naturally), paying you in some way for the privilege.

German site PC Games Hardware noticed the Microsoft patent (via VideoCardz) which was originally flagged by Patentlyze, and is for an 'Opportunistic Micro-training on Edge Devices Using Spare GPU Utilization'. Which is technobabble for using your spare graphics card resources to run parts of AI training routines.

Microsoft has gaming PCs in mind here – given that they can run powerful GPUs, and when sat at the desktop in Windows 11, those graphics cards are doing very little – but the system could work with any device in theory.

As detailed by the patent, AI training tasks would be splint into small elements (or micro-tasks) which would be distributed across various participating PCs, and the results tied back together after those individual jobs had been completed. Various checks would be in place to detect errors or results that had been manipulated.

Any given micro-task would be appropriately assigned to a PC with a matching amount of spare GPU and system resources, as the patent states: "The system monitors end devices to detect available computing capacity and breaks down training tasks into independently trainable subtasks."

As to what payment you might receive for giving Microsoft the benefit of using your PC, that isn't made clear as you might expect, but there would be some reward – either a direct payment, or perhaps some form of credits.

Of course, there are already similar ideas out in there in the computing world, such as Folding@home whereby your PC contributes to medical research (and SETI@home before that).

Analysis: AI@home4Microsoft

There are a lot of issues with this plan, if Microsoft was ever to attempt to go ahead with it. For starters, I think the payment would have to be clearly worthwhile considering what you're giving Microsoft here – I'm not sure credits, or, say, Microsoft Rewards Points (to pluck a speculative example out of thin air) are going to cut it.

As we're looking at the future, Microsoft may well be thinking of credits for using AI (when it inevitably becomes pricier for everyone), but there's a degree of risk in this process which a lot of folks are already finding very off-putting or alarming. (Even though, as noted, this is just a concept, and nothing's happening with it – at least not yet).

That includes concerns around giving Microsoft the ability to monitor your system beyond Windows 11's standard telemetry and into deeper territory to ascertain what your GPU is up to and further data on system load.

The second potential stumbling block is that the AI training system would supposedly limit RAM usage, and ensure a GPU isn't overheating due to the load it's being put under – but do Windows 11 users trust Microsoft to do that reliably? Given the bugs in its operating system, I think there'd be more than a few worries about this side of the equation going awry. Cue posts on Reddit about 'Microsoft burnt out my graphics card' and so on. Doubtless something like game stuttering might be blamed on this tech, too (when it could be related to another entirely different problem – like a recent Windows 11 update, ahem).

There's also the cost of the power to ramp up the GPU, and whether that does actually balance out with whatever reward the user is being paid (which, as noted, we don't have a clue about yet). And finally, there's the wear-and-tear on your GPU, because with the card being used a lot more – and possibly being pushed more consistently – it won't offer nearly the same level of longevity as it would otherwise.

It feels like a seething nest of potential problems to me, and I'm skeptical Microsoft would ever move forward with such a scheme given that. Still, it is clearly an idea the company is mulling, so we shall see if we hear anything more about this in the future.

The idea is already sparking bad feelings on Reddit, with some folks having trust issues that it isn't a signal of Microsoft's broader intention to sneak in some kind of AI training routines with its hardware.

As this cynical Redditor said: "Oh great, SETI for AI training: 1. This is probably gonna happen on idle Xboxes whether you want it to or not. 2. They won't give you Battlepass for free or anything fun."

Another Redditor declared: "That'll definitely finally bring in the year of the Linux desktop."

To be fair, not everyone is so skeptical, and this commenter observed: "Honestly if it pays more than the electricity costs it could be a win."

Those who are more positive are the minority, though, and they are very much stressing that Microsoft would need to compensate PC owners well enough.

For years, the software industry operated under one core assumption: writing code was the primary bottleneck in software development. As a result, companies poured enormous resources into hiring developers, refining development processes, and building tools that could shorten the journey from idea to product.

Then generative AI arrived.

For the first time, the industry gained a technology capable of solving the very problem it had spent decades trying to overcome. The ability to write code – once the industry’s most constrained resource – is now faster, more accessible, and dramatically more efficient.

But if our ability to generate code has increased so dramatically, why aren’t software organizations moving at the same pace?

AI-enabled development offers a more nuanced story. AI coding agents can handle work that previously required additional engineering capacity, from creating the foundations of an application to generating tests and accelerating implementation. A group of two or three people can try out an idea that might once have required a much larger team, more specialized skills, and considerably more time.

But speed is only part of the story. The more important change is that AI expands the ambition of each team.

A 2025 study by researchers at Tilburg University found that while AI coding tools increase output, they also increase the burden on senior developers. After analyzing thousands of open-source projects, the researchers found that experienced developers spent significantly more time reviewing AI-generated code, while their own coding output declined by nearly 20%. In other words, AI accelerates code creation, but it does not eliminate the need for human expertise to govern, validate, and maintain that code over time.

The implications extend well beyond senior developers. They force us to rethink what we actually measure.

More code doesn’t mean more progress

Excess code is only the symptom. The real challenge is keeping it under control. As codebases continue to grow, the question becomes even more pressing: if software can be built faster than ever before, why are organizations still struggling to move at the same pace? Because in enterprise software, writing code is only the beginning.

Every change must go through quality assurance, security reviews, regulatory compliance, and complex integration processes. Those steps cannot be accelerated at the same pace as code generation.

As AI speeds up development, what happens after the code is written becomes even more critical.

Organizations are measured not by how much code they produce, but by their ability to run stable, secure, and maintainable systems over time. That is why the defining challenge of the coming years will not be a shortage of developers, but managing complexity at scale.

AI changes the unit of innovation

AI coding agents are a major reason for this shift. They allow smaller groups to take on work that previously required a larger roster, without narrowing the ambition or depth of what they attempt.

That changes the unit of innovation. Progress is no longer determined only by how many people can be working on an idea.

The implications extend beyond individual developers. As AI changes the role of software engineers, team leaders need to consider not simply how quickly teams can produce code, but how effectively they can define problems, validate results and apply human judgement.

The real question is no longer how much software an organization can produce. It is how much of that software can be operated, maintained, and continuously evolved over time.

What happens after the code is written

The easier it becomes to generate code, the easier it becomes to generate complexity. Organizations can introduce new capabilities at unprecedented speed, but every additional component brings new requirements for testing, security, integration, monitoring, and long-term maintenance. AI shortens the path to writing software. It does not shorten the path to building reliable enterprise systems.

That is the new bottleneck in software development – not the ability to generate more code, but the ability to manage the complexity that code creates.

This is where governance, oversight and maintainability become increasingly important. A prototype is not a finished product, and it should not be treated as one. It still requires validation, product discipline, security, reliability, and a clear understanding of the customer or business need it serves.

The opportunity is to turn AI from a separate initiative into an everyday operating model for innovation, while maintaining the standards organizations expect around security, reliability, governance, and measurable business value.

This also means looking beyond traditional measures of development output. In a world where AI writes a growing share of the code, how many features were delivered, how many tasks were completed, and how quickly projects moved forward tell only part of the story. The ability to run stable, secure and maintainable systems over time matters just as much.

AI has solved one of software’s biggest challenges: generating more code in less time. Now the industry faces a harder question: how do we prevent speed from becoming technical debt?

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• Windows 11 is getting two further sizeable changes with the interface
• One involves the introduction of an App Updates panel in Settings to help manage third-party software updates more effectively
• The other is an integration of Copilot AI functionality in the taskbar's search box, although this will be an opt-in only feature – however, some users remain cynical about Microsoft's motives here

Microsoft has already made some major changes to Windows 11's interface, and a couple more are now in the pipeline – a hub for managing app updates in Settings, and a more divisive change to bring Copilot functionality to the search box in the taskbar.

First off, the less controversial idea that's apparently being mulled. As discovered by Windows Latest, this is an App Updates panel which is in a recent preview build of the OS (and has been present in testing in the past, it should be noted).

This is in Settings and consists of a hub where third-party apps in Windows 11 can be managed, and you can see what needs updating at a glance – rather than getting individual notifications from apps bugging you at random times, or an update only being actioned when you open the software. (The problem in the latter case being that if you don't use something for ages, you might miss a crucial security fix for an app, leaving your system potentially vulnerable).

Essentially, this is about giving third-party apps the same level of support as software from the Microsoft Store in terms of staying on top of updates, and it's a useful piece of functionality.

If it makes the cut for the release version of Windows 11, that is, and note that developers need to register their app for inclusion – with no software having done so as yet (so this is still relatively early testing).

The other interface introduction that's inbound for Windows 11 as flagged by Windows Central is part of the major revamp for Windows search, and it brings Copilot into the mix. Specifically, Microsoft is going to integrate Copilot functionality with search – but only for those who want it present.

In other words, it will be off by default, and you will need to actively turn on the option to have Copilot AI functionality in the search box.

Windows Central highlighted a Microsoft blog post on 'hybrid intelligence' from last month which explained that: "We will also begin adding the option to integrate the new Copilot with Search on the taskbar, so you can get quick answers inline from Search, and if you want to have a richer conversation, you can jump from Search directly to the full Copilot app. This experience will be opt-in, and will first start rolling out to select markets later this year."

Analysis: Ask Copilot returns – but there are doubters as to Microsoft's intentions here

This Copilot experience rollout could start soon, then, and it will likely be very similar to the 'Ask Copilot' functionality that Microsoft has played around with in testing in the past, effectively acting as a replacement for the search box. Evidently, that felt like too radical a move – it was also an opt-in feature, mind, it's worth noting – so with the new tack, the search box remains, but Copilot can optionally be integrated.

Obviously, the Ask Copilot concept was a very visible addition of AI, and this way, Microsoft is keeping it a lot more low-profile. That's a sensible approach, of course, given that the company has previously promised to cut back on AI clutter in Windows 11 as part of the broad drive to fix the OS which has been happening throughout this year.

While the App Updates panel is certainly a welcome move, this Copilot integration has proved divisive. On the one hand, there's a good deal of predictably negative feeling towards any AI addition for Windows 11, even one that is opt-in only.

Part of the reasoning here is that people are generally suspicious of Microsoft's motives and what'll happen in the future. In other words, a feature gets introduced as opt-in only, but later becomes the default (requiring opting out) – and finally it's baked in as a concrete part of the interface (with no option toggle at all). Despite the 'fix Windows 11' initiative and that whole new leaf which is a genuine effort to improve the OS across many fronts, trust in Microsoft is so low with some folks that they can't help but think the worst of the company.

One Redditor warns: "Opt-in always becomes 'opt-out' later. Be wary of this folks. New search UI looks less trash than the old one though."

Another agrees: "Yup they love to make things mandatory after no one uses a feature."

On the other side of the fence, there are those who are pleased that Microsoft has taken the opt-in route with this functionality.

Another Redditor posts: "Microsoft letting consumers pick if they want Copilot or not is inspiring. Take note of this Apple, and let users delete Apple Intelligence models natively."

Personally, taking into account all the good work that has been done with Windows 11 this year, I think we need to give Microsoft the benefit of the doubt here – especially given those promises about not pushing AI features unduly in Windows 11.

That said, longer-term, who knows where Windows 11 is going with AI agents, and I don't for a minute believe that AI isn't the future of Microsoft's desktop OS – or at least that remains the current thinking of the execs, anyway.

When people think about autonomous vehicles, they tend to picture city streets, motorways, robotaxis or factories. Yet one of the most demanding environments for autonomous technology is somewhere very different: the farm.

At first glance, a field might appear easier to navigate. There are no traffic lights, relatively little traffic and fewer obvious obstacles. In reality, agriculture presents an extraordinarily complex environment for automation.

A machine needs to distinguish between an object blowing across a field and an animal in its path. It must operate when visibility is reduced by dust or rain and navigate environments such as dense crops, where assumptions developed for road transport do not necessarily apply.

This is why the rise of the autonomous farm is about much more than removing the driver from the cab.

A smart factory without a roof

A modern farm can increasingly be viewed as a smart factory – but one without a roof. Industrial automation generally operates under controlled conditions. Agriculture does not. Weather, soil conditions and crop development continuously change the operating environment.

At the same time, many agricultural jobs have narrow windows dictated by weather and plant growth. When conditions are right, work needs to happen immediately.

Autonomy has the potential to make these limited windows more productive. For example, autonomous tractors designed for tillage operations can use multiple cameras positioned around the machine to provide a 360-degree view of their surroundings. By combining computer vision, cameras and AI, such machines can navigate the field without requiring somebody to remain continuously in the cab.

But capturing images is only one part of the challenge. Those images need to become decisions. As images stream from the cameras, a high-speed processor evaluates the visual information. A neural network then classifies what the cameras see and determines whether the area is safe to drive over. In advanced autonomous systems, this type of decision can be made within a fraction of a second.

This illustrates an important point about applied AI: intelligence is valuable when it translates data into a useful decision quickly enough to affect the physical world.

From machine operator to operations manager

Autonomy also changes the role of the person running the farm. Farmers today are entrepreneurs, operations managers and increasingly data managers. Machinery, field and operational data can help them understand what is happening across the business, plan work and make better-informed decisions. As machines become more autonomous, farmers no longer necessarily need to be physically present in every cab to maintain oversight.

Cloud-based platforms provide the connection between the autonomous machine and its operator. Once a machine is positioned appropriately, an autonomous job can be started remotely through an app. Farmers can then access live video, images, data and metrics about the operation and adjust parameters such as speed. If the system identifies an obstacle, job-quality anomaly or machine-health issue, the operator receives a notification and can respond remotely.

The significance is not simply remote control, but remote visibility. Instead of one person being tied to one machine, the farmer can increasingly oversee work as part of a connected operation. The machine performs the task, sensors and cameras observe what is happening, connectivity makes that information available elsewhere, and the farmer remains in control of the wider process.

Autonomy cannot be one-size-fits-all

Agriculture is also remarkably diverse. Farm sizes, field structures, crops, soils and production systems vary significantly. Technology therefore has to combine standardization with enough flexibility to accommodate different operating realities and farmers’ needs.

That applies to adoption as much as operation. Autonomy cannot depend entirely on farmers replacing their existing machinery with new ones. Adoption therefore needs to accommodate both new and existing machines. Some machines can be autonomy-ready from the factory, while retrofit solutions can provide a route for certain existing equipment to adopt the technology.

This matters because the autonomous farm will not arrive overnight. It will emerge incrementally as machines, software, connectivity and data become more closely integrated.

Keeping the human in control

It can be tempting to frame autonomous farming primarily as a story about replacing human labor. That misses the more important change. AI and automation cannot remove weather, biology or economic uncertainty from agriculture. What they can do is help farmers manage that complexity.

An autonomous tractor taking care of tillage does not eliminate the farmer. It changes where human attention is required. Rather than continuously executing one task from inside a cab, the farmer can monitor operations, respond to exceptions and concentrate on decisions elsewhere in the business.

That is perhaps the most important lesson from the autonomous farm for the wider technology industry. Successful automation is not simply about making a machine capable of working without a person beside it. It is about combining AI, sensing, connectivity and human judgement into a system so that each plays to its strengths – giving farmers better information, greater visibility and more time to make the decisions that matter.

More time for what matters

The added value doesn’t just come when the machine drives across the field completely autonomously. AI tools can already ease the burden on farmers during peak work periods. But even with an autonomous tractor, the farmer isn’t completely out of the picture overnight.

He can tackle other tasks during the day while the machine handles soil preparation at night, for example. This helps ease the pressure of tight time constraints to some extent.

The autonomous farm is therefore not a farm without farmers. It is one where machines increasingly handle execution, data provides visibility, and people have more time to make the decisions that matter.

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• Adobe for Startups enables new companies founded within the last five years to get its products for free
• Eligible companies will get 12 months of free access to Adobe’s AI-heavy tools
• Firefly Pro Plus, Acrobat Studio, and Semrush are available with the offer

Adobe is launching a new free suite of tools for new businesses, aiming to deliver easy solutions to founders that won’t break their startup budget.

Adobe for Startups will be open to eligible companies founded in the past five years, who will be able to use Firefly Pro Plus, Acrobat Studio including Adobe Express Premium, and Semrush completely free for 12 months.

Marketing the incentive as a way to build branding as early as possible, Adobe is inviting any business founded within the past five years and with under $15 million in funding to apply.

Creation, productivity, marketing

Approved applications to Adobe for Startups will get Adobe Firefly Pro Plus, its all-in-one creative AI studio, which lets you ideate into images, video, and audio. With Acrobat Studio, AI Assistant and Adobe Express Premium are provided for easy content creation and publishing.

Meanwhile, Semrush gives you access to search intelligence and marketing options, illustrating how you business is found online and how it can be optimized.

While program terms and criteria may differ depending on business location, Adobe for Startups offers a useful software platform for growth – and it will help ensure that startups are looking at Adobe's creative AI offering rather than those of competitors like Canva.

As workers look for more ways to be productive at home and in the office, wearable technology has often proved a useful tool, offering quite literally a new dimension for users.

Augmented Reality (AR) headsets have been around for a few years, with Microsoft, Meta and Apple all among the big players getting involved - but these headsets have often proved clunky and overbearing.

AR glasses have instead offered a new opportunity for those looking for some extra assistance in everyday life - I went to try out the new Snap Specs ahead of their UK launch.

First impressions

Let’s get the obvious out of the way first - tech-enabled glasses (specifically those made by Meta) are under the microscope somewhat at the current moment in time, with rulings over what they can and can’t do emerging in courts across the world.

Snap has explicitly looked to offer a whole new kind of experience, with the new sixth-generation Snap Specs much more obviously an accessory - it’s pretty obvious when wearing them that (unless you’ve gone full Nathan Barley) these aren’t a typical pair of glasses.

The smooth black material is nice to touch, and sits against your face with no issues. Configuring the glasses takes only a few seconds, with control of the device mainly in AR, with two buttons on the arms of the glasses helping with recording and resetting if needed.

Unlike other AR glasses, the big selling point of Snap Specs is that they don’t need to be tethered to a smartphone, so you can wear them wherever you want - with a portable charging pad helping to top up battery life when needed.

As the Snap Specs are still fairly new, a lot of the app showcases and demos I saw looked to show off how AR can help entertain or educate - but there were some work use cases too.

My glasses came loaded with Google Docs, with typing carried out on a virtual keyboard which could prove perfect if you need to write up some quick thoughts or emails when away from your desk. You might get a few odd looks while typing in mid-air, but when inspiration strikes, needs must - and it can also connect to a Bluetooth keyboard if you’d prefer.

The glasses are also able to capture video clips up to 30 seconds and still images, useful for creative workers across the spectrum - whether you’re getting a picture of a billboard idea, or seeing something in the wild that would be perfect for your next piece of artwork, you can quickly grab an image or video without needing to whip out your phone.

Snap Specs also features a built-in browser, which does everything you would hope - but also doubles as a media viewer, so whether you need to work on or review a new presentation or promotional video, or just want to catch up on some YouTube, the glasses can be a useful tool.

The voice AI assistant was also fast and effective when answering my questions (even if I doubted its accuracy on finding the nearest tube station to our location), and using Google Maps with a heads-up display was an absolute game-changer, although again it might depend on how you feel walking down the street wearing a £2,000 pair of glasses.

Perhaps the most striking demo was the translation tool - when looking at someone speaking in a foreign language, their speech would be instantly translated and shown in a speech bubble overlaying them - essentially like subtitles when watching a TV show. This could be an incredible tool for business meetings in foreign countries, and might well be the killer app for technology such as Snap Specs.

So will the £1,999 Snap Specs be the future of work? Given that the more fun demos I saw involved playing dominoes, improving my football skills, a drawing tutorial and enjoying an interactive Harry Potter experience involving a pet owl and the sorting hat, I’m not sure it’s there just yet - but I can’t wait to see where the technology goes next.

• Windows 11 27H2 is underway and will be a "full OS swap" according to anonymous sources
• Microsoft will stop short of launching it as Windows 12 (but the report doesn't fully rule that idea out)
• Testing will begin a lot earlier with 27H2 next year, compared to the typical June timeframe when Microsoft kicked off previews with 26H2 and 25H2

The next major version of Windows 11 arriving next year will pack some big changes, and what will theoretically be the 27H2 update is set to be very different from the last couple of annual updates for Microsoft's OS, which have been very minor affairs.

The Verge reports that the usual anonymous sources claim that Microsoft is already preparing Windows 11 27H2, and it will be a "full OS swap" with significant upgrades in the same vein as the last major update for the operating system, which debuted in 2024 (and ushered in a new underlying platform for Windows 11).

We're told that there will be a "big focus on quality" with 27H2, and that despite what'll evidently be some sizeable changes, it won't be released as Windows 12.

To mark the big change, we'll be treated to a new default Windows 11 wallpaper (try to contain your excitement), moving away from the 'Bloom' background which has been in place since the OS first launched. Presumably the sequel won't be 'Wilt'. In all seriousness, The Verge shares a design, and the new take is in a similar vein, with blue ribbons amidst wispy clouds. (A hint of the future direction of Windows for consumers, perhaps?).

Microsoft also intends to start testing 27H2 a lot earlier than with the previous two annual releases, which of course only makes sense as with more work involved, it'll require more extensive vetting. Windows Insiders (testers) will get their hands on the first 27H2 build considerably earlier than June, which was when Microsoft kicked off testing with 26H2 and 25H2.

It's also worth noting that in theory, the foundations of 27H2 were laid right back at the start of the year, in moves which already suggested that it's a hefty piece of work.

Analysis: something still bugs me

The Verge report labels next year's big update as Windows 11 27H2, but doesn't completely rule out the idea that Microsoft might still consider a switch to call it Windows 12. It sounds like the naming hasn't been quite finalized at this point.

However, it doesn't seem likely, and going for Windows 12 would be too bold a move, I think, as Microsoft really needs to be seen to 'fix Windows 11' as it has promised before looking at the next-gen platform. I don't think the fixing work is nearly done at this point, although Microsoft has made a lot of commendable progress over the course of this year.

Yes, I'm going to bring up the subject of quality assurance (QA) once again, and regular readers of TechRadar will likely recall that I've gone on about this throughout 2026. It's especially relevant if 27H2 is going to be a big update, and you cast your mind back to remember what happened with the last major annual release.

I doubt you'd forget the 24H2 update because it was one of the worst Windows releases I can ever recall for bugginess. There were lots of significant changes – which, as already mentioned, included Germanium, a fresh underlying platform for the OS – and a nightmarish slew of bugs crept in as a result.

This absolutely must not happen with the 27H2 update. Microsoft is trying to reinvent Windows 11, and the reputation of the OS therein, so it's critical to ensure that it doesn't send users trudging into a quagmire of glitches and uncertainty, repeating the miserable march that 24H2 became.

Sadly, I think there's a clear danger that this scenario could come to pass. I still think Microsoft needs to revamp its QA and general testing processes, because even with breaking down Windows 11's feature additions into smaller, more drip-fed chunks, allowing for simple minor annual updates as has been the case in 2025 and 2026, we've still witnessed too much in the way of bugginess.

Stamping all this out should be a key part of the formula to fix Windows 11, alongside performance boosting and memory optimization, as Microsoft moves towards 27H2. It mystifies me that it isn't – and I presume that's the case because there's been no mention of changing QA procedures from Microsoft (only talk of consulting testers more regarding how to change Windows 11 – though of course that's still a great move in itself).

So, Microsoft, if 27H2 is going to be a big update, don't make it a perilous one – get your testing house in order. I do wonder if the hope is that AI advances are going to come to the rescue here, in which case, we could be looking at something doubly perilous, but hey – at least tell us you have a plan to fight back against the bugs, Microsoft. Don't you?

• The UK is the latest country to consider restricting the use and sale of smart glasses
• Safeguarding minister Satvir Kaur said "the government is looking into options to restrict smart glasses in certain settings and restricting the sale to certain users"
• During a debate around the technology, concerns were raised around the discreet filming of women and girls

As the adoption of smart glasses increases across the world, so too have restrictions and bans on the technology from businesses and governments alike. Australia has banned the tech in public spaces such as pools, gyms and government buildings, and now the UK government is also considering implementing restrictions.

The devices have become unpopular in many circles, even earning the nickname 'pervert glasses' after some men began using them to secretly film women and girls, then posting the captured videos online. Some private businesses, such as the Wetherspoons British bar chain, have also banned the use of the devices.

In a debate at Westminster Hall titled 'Smart glasses and violence against women and girls', UK safeguarding minister Satvir Kaur said (as reported by The Guardian): “the benefits of smart glasses must be weighed against the harms they can cause. The government is already looking into options to restrict smart glasses in certain settings and restricting the sale to certain users.”

This might be similar to Australia's ban on the technology being used in public spaces. Kaur also highlighted that “other settings, such as healthcare and education, have existing safeguarding or professional policies that are relevant to smart glasses and any kind of digital recording." Kaur seemed to be hinting that where limitations on recording on smartphones already exist, this would extend to smart glasses and similar tech, but we could also see wider smart glasses-specific rules in future.

Anna Sabine, the Liberal Democrat MP who opened the debate, reportedly said: “Women are reporting waking up after nights out to find videos of themselves have gone viral online, filmed without their knowledge or consent by men wearing smart glasses... these devices not only have the potential to erode safety and privacy in public spaces, but raise really important questions about consent.”

Analysis: More restrictions will surely follow

Kaur did also highlight the useful aspect of smart glasses, such as helping visually or hearing-impaired people understand their surroundings. For those people, she said "smart glasses have been a game-changer, helping them navigate the world around them.”

Our writer Kevin Lynch, who has a hearing impairment, reviewed the Nuance Audio smart specs equipped with directional microphones, which he said were 'genuinely impactful'. It's likely any incoming legislation would allow for this usage.

However, there's no getting around the harmful uses of smart glasses too. A secondary cottage industry of Amazon sellers hocking black stickers, sized to cover the LED recording light, or offering to drill it out entirely, has sprung up online. This is all to enable people to record footage without letting others know they're doing so.

With the invasive potential of smart glasses to target women and post the footage online, companies like Meta are left with a clear stigma they'll have to shake before smart glasses can be seen as cool.

Prosecutors in Paris have also opened probes into smart glasses-related sexual harassment offenses, according to the French national newspaper Le Monde. I predict Australia won't be the only country to adopt legislation that restricts the sale or usage of smart glasses in 2027.

Do you think smart glasses should be banned, or should we be free to record wherever we like? Have your say in our poll below.

Experience counts for something, and nowhere is that more evident than on the Meta VR Glasses I finally wore this morning.

These lightweight, intuitive, and engrossing VR glasses shed the weight and overdesigned fussiness of previous VR and mixed reality headsets to deliver a subtle, smart, eminently comfortable and fun wearable experience that is equally at home entertaining you with wild VR movies or expansive, productivity-enhancing virtual desktops.

Granted, my walkthrough of the $1,299 VR Glasses that are expected to arrive next spring (April-early June) of next year was a breezy 45 minutes, but that was ample time to realize that Meta VR Glasses were truly built on all the lessons of not only Meta Quest VR, but also by watching competitors like Apple and even Samsung struggle to inspire consumers with stunning but also expensive or somewhat unwieldy mixed reality experiences.

In my view, Meta's decision to move compute, storage, and battery out of the headset and into a 300g belt- or pocket-friendly puck pays design dividends. The wearable, which looks like a slightly oversized pair of sunglasses, is so comfortable that I barely felt them on my face, and after 45 minutes of continuous use, I didn't remove the 100-gram frames with a sense of relief. If anything, I was sad my all-too-brief interlude was over.

Without needing to support CPUs, GPUs, NPUs, RAM, a heavy battery, or even SD storage, Meta could focus on basic aesthetic appeal. The Meta VR Glasses feel and look spare, but in a positive way. By design, they do not cut you off from the outside world (there will be an optional light shield), so there's no need to slap a pair of animated blinking eyes on the lenses.

Granted, I had my doubts that the Meta VR Glasses would be all that immersive or inspiring, given what looked like essentially eye-sized lenses and no visible cameras, inside or out.

That all turned out to be deceiving.

Accurate tracking for an immersive experience

When I said Meta clearly learned from all that came before it, I wasn't kidding. The metaphors for setting up eye tracking were essentially the same as what I did for Apple Vision Pro (I didn't have to adjust pupillary distance, though you easily can) and, to an extent, Galaxy XR. You look at a circle of dots floating in space and, yes, use your fingers to pinch and select each one.

There are, obviously, cameras that track your eyes, hands, and the outside world. I was impressed with the accuracy, which matched what I've experienced on Vision Pro.

Equally, the first time I saw the floating menu, I was shocked at the visual quality (it's worth noting that Meta put in a pair of magnetic prescription inserts to match my eyes, and those inserts will probably add almost $100 to your final cost — Meta told me it will offer Zenni prescription inserts for "under $100 per pair, flat pricing (price does not increase based on Rx strength)". For the record, Meta lists the Micro-OLED display resolution as 5K, and based on what I saw, I believe it.

Disney+, a TV App, Avengers: Infinity War VR experience, and a dozen other app icons floated in space, with each one puffing up as I glanced at it.

Meta VR Glasses software doesn't try to reinvent the mixed reality wheel. I had no trouble opening apps, closing them, or even moving windows around. It does add one gesture that I appreciated: scroll.

To scroll in any window, I made a gentle fist and then swiped my thumb across the side of my index finger to scroll left, right, up, and down. I was surprised at how quickly I picked this up; I think that has a lot to do with the accuracy of the eye and gesture tracking.

My experiences were limited to a handful of apps, but each one was compelling in its own way.

We started with an 8K VR Billie Eilish concert in which I was virtually seated front and center, close enough to take a selfie with the singer. The volume started a bit low for my tastes. Still, I was able to use the volume controls under the right temple to make the audio loud enough that it drowned out the voices of the Meta execs who were trying to guide me (they were watching what I viewed through a cast on a nearby PC — Meta VR Glasses can also cast to your phone).

We switched to the NBA Finals last, title-clinching Knicks game, which put me courtside. At one point, I realized the near-360 view was probably shot by a camera that was also almost on the court. Meta told me they partner with Kosmo (Hesai Kosmo), which uses relatively small 360-degree cameras to capture the action. As the game replay continued, I noticed Timothée Chalamet and Kylie Kardashian were also courtside. I waved, but they did not seem to notice me.

I played a puzzle game called Dragon Grove, which, as you might expect, featured a small, green, cartoony dragon who landed on my outstretched hand and let me pet him. I was reminded of a similar experience with Vision Pro and a butterfly. By launch, Meta expects to have 75+ hands-first games.

Meta believes that the primary use cases for Meta VR Glasses will be entertainment and productivity. On the entertainment front, I watched Avengers Infinity Wars VR Enhanced, which took the original film and then gussied it up with 360 surround VR imagery and even lets some of the characters pop out of the original film frame. It was cool but also a bit odd. Why would a director who intentionally chooses to shoot a scene a certain way want you looking anywhere else but his original film? It did, though, make the movie feel more like an amusement park ride, which may appeal to some.

Business class

On the productivity front, Meta VR Glasses basically start where everyone else spent years traveling to arrive. I had huge desktop screens all around me that I could move and resize with gestures. At one point, I placed a screen on the ceiling above me. Plus, I could take any screen and make it ultrawide. The only hiccup was when one coding screen stretched the code text into an ultra-wide, almost unreadable format. This is all pre-release software, so bugs like this are to be expected.

Like Vision Pro and Galaxy XR, Meta VR glasses have a floating keyboard, but they've also added something new to the mix: a surface keyboard. To enable it, I selected the option from the floating keyboard and then put my hands on the table surface. After a quick scan, a transparent keyboard and trackpad appeared on the tabletop. I could use them just as I would any standard physical keyboard and mouse. My only issue is that, as a terrible touch typist, the lack of any physical key response might make my typing even worse.

When I took off the frames, it felt like I'd removed a pair of glasses, not headgear doing its best with straps and head rings to lessen the load on my face. There was no afterburn on my nose or brows.

I do, of course, have questions. Like, even at the more affordable $1,299, will consumers buy into this for a big-screen experience on an airplane and when they're home alone (you won't sit next to a family member and tune out to your own VR experience, will you?). Gamers, especially those who are tired of wearing heavy headsets, will surely be interested in Meta VR Glasses, though I don't know if the Qualcomm Snapdragon Reality Elite processor (XR2 Gen 3), which can supposedly support all the apps in Quest's library, has the juice to run true AAA titles. I'm glad Meta has the productivity part worked out because I still think that may be the best play for any of these types of wearables: knowledge workers, business travelers, and enterprise.

Who knows, though, maybe Meta VR Glasses is the mixed reality headset to finally break open the consumer market. It impressed me and, to be honest, I can't wait to wear them again.

• The ability to reposition the taskbar came to Windows 11 in September
• However, you can't drag the taskbar around as with Windows 10 — you must move it using a panel in the Settings app
• Some power-users want an option to drag it around the desktop quickly, and Microsoft has now explained why it decided against this

One of the long-awaited major Windows 11 interface changes this year has been the ability to move the taskbar.

You'll likely recall that there was quite a big fuss that this functionality, which was present in Windows 10, didn't make the cut for Windows 11. It wasn't alone, either, as there were plenty of key interface options that weren't ported over from the older operating system — many of which have since been addressed.

Windows 11 received the ability to shift the taskbar to the top, or either side, of the screen in the September 2026 update (or that was when the rollout began — everyone should have it by now, though). Before that, the taskbar was anchored steadfastly to the bottom of the desktop, and you couldn't move it anywhere.

However, Windows 11's implementation of the now mobile taskbar has still come under fire from some users. It's gotten to the point where a Microsoft exec has responded to explain the thinking behind the feature, as Windows Latest highlighted.

The beef is that while the taskbar is now movable, you can't simply drag it around the desktop. Moving the taskbar requires you to head into the Settings app, diving down a level to pick your alignment.

It's not possible to seize the taskbar with your mouse cursor and whizz it across the desktop to a new position. There are users who want the convenience of being able to do that, but Microsoft's Diego Baca, who is VP of Design for Windows, has defended the company's decision to drop this particular option for taskbar repositioning.

The danger of accidental drags

As Baca observes, this choice was made because, with Windows 10's draggable taskbar, Microsoft "saw a significant volume of accidental taskbar movements." Meaning users clicking on the bar for another reason mistakenly picked it up and shifted it elsewhere, which was obviously annoying.

The other point Baca makes — and it's a good one — is who needs to move the taskbar to different positions on the desktop with any regularity? Not many folks, but nonetheless, power-users who might do so have been arguing for the introduction of some kind of option to make the taskbar draggable in this way.

Windows Latest itself suggested a 'lock the taskbar' option (as seen in Windows 10) to ensure it couldn't be dragged mistakenly, noting that this should be on by default. But Baca argued that this isn't a good solution because it introduces more 'friction' to the Windows 11 interface. The exec noted in a post on X: "For those suggesting an 'auto-lock' or lock/unlock setting as a magic solution, it really isn't that simple. You're introducing additional friction to solve a relatively infrequent task. Now, every time you want to move the taskbar, you first need to unlock it."

It's a fair point, and the crux of the matter is that the number of Windows 11 power-users who will actually benefit from quick taskbar dragging is very few compared to the pool of massed casual users who could potentially run afoul of the feature. As this Redditor observes from their experiences with the Windows 10 taskbar: "Honestly, looking back, there were several times when older family members would ask me why theirs is on the side of their screen. People did move it accidentally."

And this was even the case when the taskbar was locked to prevent that (presumably because of more taskbar misclicking and accidentally turning the lock off). Of course, when this occurred, those less tech-savvy types had no idea what had happened, or how to rectify it. And that seems a greater danger than a lack of a draggable taskbar, based on how often that kind of movement is needed for anyone but a niche of very specific power users. In this way, the taskbar design decisions are an understandable trade-off, at least in my opinion.

I should note that Baca also makes it clear that this isn't a final decision, or the be-all-and-end-all of the debate over a draggable taskbar. Baca concludes the X post: "Please don't turn an explanation of a design decision into a declaration that something will 'NEVER' happen. That's simply not what I said."

So, there might be hope yet for those who want taskbar dragging. Indeed, I wonder if a solution that Microsoft previously tested could return, one that Windows Latest reminded us about: namely having the four taskbar position icons from Settings directly accessible via the right-click menu on the taskbar.

Third-party alternatives for taskbar dragging in Windows 11

Overall, I think Microsoft's stance here is correct on balance. So, as it stands for now, power users who want to drag the Windows 11 taskbar around with some regularity due to their workflow are left feeling out in the cold and will need to use a third-party tool to do so.

What third-party software is recommended for this use? A dedicated app called Taskbar Location Options on GitHub (hat tip to Windows Central) is a good choice, restoring the ability to drag the taskbar at any time (or use right-click menu settings directly on the bar). This tool is handy because it's a simple and lightweight effort for those who just need dragging functionality and don't want to install a full modding suite to achieve this.

There's also a mod for Windhawk, which is a popular Windows 11 customization suite you can load up with add-ons, that allows for taskbar dragging (and locking). As always, though, use any third-party software at your own risk, and remember that customization tools like these can sometimes run into trouble with Windows 11 updates.

Microsoft's patches can tinker with interface elements under the hood, and this may break any of these third-party customization utilities until the developer can patch. Power users will doubtless be more than aware of this, mind, but I figured it's worth mentioning.

Vibe coding agents enable engineers to create code in a fraction of the time. This has shifted the bottleneck to later in the development process: when engineers have to understand what that code is doing, track down bugs, and investigate incidents.

As agents generate ever greater volumes of code, it’s becoming increasingly difficult for engineers to understand their output.

It takes time to work out what their code does and how it interacts with the systems around it.

To realize the productivity gains, AI must be applied to the later stages of the cycle, not simply code generation. When it comes to large-scale complex code bases, agents struggle.

A critical issue in critical industries

Nowhere is the pain of code comprehension and debugging more acute than in organizations powering the digital economy – from financial services and semiconductor design to the networks and databases that keep everything connected.

In these sectors, one escaped defect can have serious consequences – a trading system returning a bad value, a database returning the wrong row, or a network failure leading to a service blackout. The UK’s national air traffic controller found itself an unfortunate case in point when it revealed that its recent service outage, which caused 2,000 flight cancellations and days of disruption, was triggered by a legacy software defect.

As AI continues to increase the volume of code engineers can produce, the challenge is no longer about increasing the speed at which they work. Rather, engineering leaders must ensure their teams can still understand what they’ve built when something goes wrong or behaves unexpectedly – and act before a problem becomes a much bigger one.

The code comprehension gap

AI can produce code in minutes. However, that speed comes at a cost. As AI-generated code accumulates, duplicated logic and layers of change are built on top of one another, creating a kind of spaghettification of the codebase. It then becomes harder for engineers to understand how their code is behaving, let alone know where to start untangling it when something goes wrong.

The problem gets worse as codebases become more complex. Nine in ten engineering leaders say code comprehension and debugging take longer in complex environments such as C/C++ codebases. Engineers were already struggling to keep up when they wrote every line themselves.

Now, with AI being far less capable at debugging complex code than it is at creating it – engineers find themselves buried in an avalanche of review work that is beyond human ability. As a result, defects can more easily escape into production, with no clear path back to their root cause.

The productivity gains are failing to add up. Despite being able to write code more easily, 79% of engineering leaders say their release cycles are no faster. It’s a vicious cycle. The faster AI generates code, the more engineers have to review and understand – and the harder that becomes as complexity builds. Without solving the comprehension problem, increasing the speed of code generation risks simply moving the bottleneck further downstream.

The limits of AI for debugging

The obvious answer if humans are struggling to comprehend and debug code is for coding agents to take over. However, without the right inputs, even the most advanced AI tools struggle to deliver accurate answers.

Coding agents debugging complex codebases often work from static context such as source code, logs and documentation. This tells them what the code is supposed to do, but not necessarily what it actually did at runtime. Agents are left to fill in the gaps and guess at what happened and why.

That lack of context creates a particular problem when the issue is buried in the complexity of a live system. AI agents can produce an answer that looks plausible, but without the evidence to support it, there is no guarantee that the diagnosis is correct. In fact, 93% of engineering organizations have had the root cause of an issue incorrectly diagnosed because of an AI hallucination. As a result, a third (37%) of teams only use AI agents for comprehension and debugging straightforward codebases.

The consequences extend beyond simply failing to solve the problem. When an agent cannot identify the root cause, it can resort to trial and error – adding more logs, rerunning the code and trying another approach. Agents end up stuck in a loop, consuming budgeted AI tokens without getting any closer to an answer.

Better evidence, not bigger models

This raises a broader question for engineering leaders: how do you make AI genuinely useful for the difficult parts of software engineering, rather than simply using it to generate more code?

The way to get better results from AI agents isn’t necessarily to use a more powerful model, but to give them better evidence. Giving an agent a record of what actually happened when code ran helps it understand how an issue occurred, making it better equipped to trace problems to their root cause and support engineers with debugging.

Ultimately, AI can only be as useful as the information it has to work with. Give agents a clear picture of how software behaved, and they can take on more of the difficult work that still sits with human engineers today. That means faster answers, fewer dead ends and, most importantly, greater understanding of the software teams are responsible for.

Turning AI speed into engineering value

The future of AI-powered development isn’t just about generating more code, but understanding what it actually does. Give agents access to the right information, with high-quality context, and they can move from plausible answers to accurate automated root cause analysis.

That gives engineers something far more valuable than speed alone – the confidence to let AI tackle the hardest problems without losing control of the software they build.

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• Windows 11 got a major performance boost with its latest update, including 40% faster multi-tasking on an 8GB PC based on Microsoft's testing
• That update is still a preview, mind, but the full release is coming next week, with other performance boosts, especially for 8GB PCs
• Meanwhile the developer of Tiny11 has managed to get a totally stripped-down version of Windows 11 running in an improbable 128MB of memory

Microsoft has gone into the details of how it's made Windows 11 faster on 8GB PCs (and all systems for that matter), with these changes arriving in the next update for the OS – all while a third-party has demonstrated the operating system running on a meagre 128MB of RAM (with huge caveats as you might expect).

A blog post from Pavan Davuluri, head of Windows at Microsoft, entitled 'Raising the bar for 8GB Windows 11 PCs' gives us some further metrics as to how much performance has been improved by.

Microsoft has mentioned that Windows 11 is now up to 20% more responsive on 8GB machines before, but now it's added another stat to consider, namely that the OS is 40% faster for multi-tasking (and that kind of workload is where 8GB of RAM can feel a lot more pushed to its limits).

In this case, Microsoft's test involved "opening 20 tabs in Microsoft Edge, a 4K video, large Word, PowerPoint and Excel files, Outlook and File Explorer one after another, keeping everything open as we went." This was 40% faster compared to Windows 11 in January 2026 (before the fix the OS campaign started), as measured on an HP OmniBook 3 laptop with a Snapdragon X Plus (Arm) 8-core CPU and 8GB of memory.

Davuluri explains that Microsoft's approach to upping performance in Windows 11 runs along three distinct paths: "Making better use of the memory in every Windows 11 PC, protecting the interactions that need to feel instant even under load, and ensuring key apps and Windows require less memory."

Of course, the exec points out that this work benefits all Windows 11 PCs, but that this tuning will be particularly beneficial for 8GB devices – clearly having key apps drain less system RAM is going to be great for lower memory loadouts.

Microsoft notes that in terms of memory management improvements, in multi-tasking tests on 8GB systems, it has "reduced committed-memory growth by up to 900MB" in Windows 11 running Edge and Microsoft 365 apps.

We're also told that thanks to improvements in Windows 11's compression techniques, more active data for apps can be stored in RAM (as opposed to on your drive, where it's slower to be fetched) – to the tune of up to 2GB more. Davuluri employs a useful desk metaphor to illustrate this, which I think is worth a read for the less tech-savvy.

If you want the benefit of all this fresh speediness, Microsoft informs us that this performance tuning is in place in the very latest update for Windows 11, which is the optional update that arrived at the end of October (complete with right-click menu customization).

If you download that, though, bear in mind it's a preview so officially still in testing. I'd advise waiting for the full release of the finished November update, which comes out next week on November 10, so you won't have to be patient for long.

Analysis: necessary streamlining – and it appears to have done the trick

Okay, so Microsoft is touting some very specific performance measurements here and there's bound to have been some cherry-picking going on. In other words, run a ton of different tests and pick the results that show off the very best performance levels (with a peak 'up to' value), but of course this is standard practice for any product marketing.

Still, it's clear that strides have been made, and online feedback backs this up. There are numerous users who are observing that Windows 11 is faster after this most recent update, and indeed since version 26H2 was released.

As one Redditor observes: "I can confirm it, Windows 11 has been overall much more [responsive] on my system, it used to be slow as s**t and now even the context menu opens quite instant."

Most of the negativity that now remains on social media, aside from the obvious trolling, is around why Microsoft didn't have Windows 11 tuned up like this in the first place. And that a 40% improvement in multi-tasking was partly made possible because things were in such a sluggish state to begin with, and I think those are fair points (I've brought them up myself in the past).

Still, it's clear that Microsoft is working hard and successfully optimizing Windows 11 for leaner RAM loadouts, and I obviously won't argue with that. Meanwhile, the developer of Tiny11 – which allows you to create a completely stripped-down Windows installation – has proved that Microsoft's OS can be pared back so it runs on just 128MB of RAM (hat tip to Tom's Hardware).

That beats the developer's old record of 184MB, and it's a seriously impressive feat, but as you might imagine, what's left in Windows after pruning it back so viciously is, well, barely anything. You can boot the desktop, open folders or the Start menu, and run Notepad, but not much else. It is "painfully slow" by the developer's own admission, and not a workable version of Windows 11 so much as an exercise in what can be done.

The reason that projects such as Tiny11 are popular is because many folks are fed up with the bloat in Windows 11, and want to create their own tailored and streamlined version. If Microsoft keeps going with its successful performance tweaking and tuning, with any luck, there'll be a lot less demand for the likes of Tiny11 in the future.

Microsoft is promising more memory efficiency tuning, and advances in memory compression, are coming, as well as a greater use of WinUI 3 for speedier apps and parts of the Windows 11 interface.

This year, the World Cup united elite athletes from around the world in competition at the highest level.

As I watched, I kept returning to one question: What makes these athletes elite? Knowledge, fitness, or desire to win don’t fully explain it. It’s something else. It’s mastery of the task. Execution at the highest level when it matters most is what separates the elite from the rest.

I’ve been thinking about that word, “elite”, a lot lately. Physical AI is a hot topic and deservingly so as robots, drones, and autonomous machines in all sorts of shapes are emerging. If we’re going to bring all these intelligent systems into our lives, what does elite look like for them?

Excellence is not thinking harder

If you’re a football fan, you’ve seen Messi play. He handles the ball so effortlessly it looks like cheating. The moves he makes seem to anticipate what the opponent will do. We assume that kind of precision must come from thinking faster. It’s the opposite. The decision is too quick for thought at all, instinct takes over.

Look closer at that idea of “doing without thinking,” and you find it everywhere. Keeping balance, blinking, or navigating a crowded room, all of it with no deliberation and minimal energy. And yet for machines this is the hardest thing of all.

The real world changes the rules on AI

There is an old observation in robotics worth revisiting. The tasks we consider the pinnacle of human intellect like solving equations, parsing logic, or recalling facts are the ones machines can do the most easily. The tasks a toddler masters, like grasping a cup, staying upright, or stepping over obstacles are the ones that have challenged AI engineers for decades.

The lesson in that reversal is simple: Intelligence isn’t only something you scale by making the brain larger.

Most of the intelligence we’ve built so far lives in the cloud. Ask it a question and it can afford a moment to think. The physical world grants no such patience. A drone correcting for a gust of wind, a vehicle reacting to a child stepping off a curb, a robot catching a part before it hits the floor, none of these can wait for a round trip to a distant server. The response must happen where the event happens, in milliseconds, every time.

And the stakes are different in kind, not just degree. A wrong answer in software can be corrected. A wrong move in the physical world cannot. You cannot patch a collision or roll back a fall. When intelligence must act in the physical world, then latency, efficiency, and reliability stop being features and become survival requirements.

A blueprint from biology

Biology has been refining intelligence in the physical world for hundreds of millions of years, and the design it arrived at is telling: Intelligence is distributed. The cerebrum handles reasoning and conscious thought, the cerebellum handles motion control and balance, and the spinal cord handles reflexes.

Consider a stumble. By the time our conscious mind registers it, we’ve already caught ourselves. That correction never reached the part of the brain that reasons; it would have been far too slow. Reasoning, coordination, and reflex are separate, independent, and aligned systems, each placing the right intelligence in the right place with the right performance to get the job done.

That is the principle we should be designing toward. Machines are made more capable through the right kind of intelligence in the right place. That’s what a distributed, neural-axis architecture makes possible.

Take the human body as inspiration, and our own neural axis architecture focuses on three independent but highly coordinated layers. A slower, highly intelligent reasoning layer, an intermediary coordination layer, and an ultra-fast reflex layer at the edge. Each playing its part.

Picture a humanoid robot carrying a package across a warehouse. Its reasoning layer sets the destination; its coordination layer keeps it steady as it walks. Then an item falls from a shelf and strikes its arm. Within milliseconds, the reflex layer must rebalance, adjust its grip, and secure the package. Those reflex-like decisions can only happen at the edge, in real time—putting the right intelligence in the right place is what lets machines operate safely.

Making sense of the physical world

Reacting quickly is necessary, but not sufficient. A machine also must understand the world around it. Weight, momentum, friction, the way an object will topple if pushed off center. Children do not learn any of that from a manual. They internalize real world physics from trial and error and parental corrections. But a robot does not have this luxury, because in the physical world, trial and error can do real harm.

Messi didn’t learn any of that from a manual; he absorbed it over thousands of hours. A robot cannot learn the same way.

This is why the most interesting work in the field is teaching machines to anticipate rather than merely respond. Vision-Language-Action (VLA) models are a bridge between perception and action: connecting what a machine sees, what it is told, and how it moves.

That capability is difficult to create and even harder to optimize to run within the tight power and memory limits of an edge device. Closing that gap, between what is possible in a data center and what is practical in the real world, is one of the defining engineering challenges of this decade.

For the physical AI industry, overcoming this challenge means getting two things right. First, building capable and energy-efficient processors optimized for every layer of robotic intelligence, from the cerebrum and cerebellum to the distributed sensory nervous system. Second, equipping teams with tools and frameworks to make developing autonomous agentic intelligence at the edge as simple and efficient as possible.

Trust is the price of admission

Because the real world has no undo button, Physical AI will never reach its potential without trust.

We will only welcome these systems into our homes, our roads, and our factories if we believe they will behave predictably when it matters and fail gracefully when something goes wrong. That kind of dependability cannot be sprinkled on at the end. It must be designed in from the silicon up, so that when a system encounters something it never anticipated, it still does the safe thing.

And that trust doesn’t come from a bigger brain. It comes from the architecture. The same distributed design that biology settled on long ago. Reasoning, coordination, and reflex, each operating where it belongs, and each trustworthy in its own right. If that design is good enough to keep us alive in an unpredictable world, it’s good enough for the machines we will live alongside.

Physical AI will earn its place beside us when it finally gets a nervous system—a neural axis built for trust at every layer.

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• Meta has announced the end of support for two of its popular VR headsets
• The devices affected are the Quest 2 and the Quest 2 Pro
• Support will be discontinued at the end of 2027

If you own a Meta Quest 2 or a Meta Quest Pro virtual reality headset, we’ve got some bad news: Meta has announced it is planning to wind down support for these devices next year, leaving large numbers of users without support for their VR products.

An announcement on the Meta blog aimed at developers details how things stand for creators of apps and services made for these headsets. With the two devices “reaching the end of feature development⁠,” big changes are on the way.

For one thing, new app submissions for these devices will be closed in December 2027. For another, version 2.13 of the Quest’s operating system (due out in March 2027) will be the last one to work with these headsets. Quarterly security patches and critical bug fixes will continue “through the end of 2027,” Meta said.

Still, Meta attempted to reassure developers of apps made for these VR headsets, stating that “People on Quest 2 and Quest Pro can still find, buy, download, and play your apps.” Cross-play with Quest 3 and Quest 3S headsets will continue, and developers can keep sending out updates to Quest 2 and Quest 2 Pro users.

And Meta’s app store for VR headsets will continue to be available to users of these soon-to-be-discontinued devices, with the firm saying that “people keep access to everything they’ve purchased, and no apps will be removed from the Store because of this change.”

Time to upgrade?

The Quest 2 launched in 2020, making it six years old at this point. Although we scored it highly in our review, it’s since been succeeded by the Quest 3, which is our pick for the best VR headset around. Evidently, Meta feels it’s now time to retire the Quest 2 and its Quest 2 Pro sibling, with the Quest Pro line having allegedly been phased out entirely in 2024.

Meta framed the change as one that’s paving the way for its upcoming VR Glasses. The company said that the Quest 3 and Quest 3S are “the baseline for Meta VR gamers” with its VR Glasses due to bring “another leap forward” for the industry. In that world, there’s apparently no more room for the Quest 2 and Quest 2 Pro.

Continuing app development for the Quest 2 and Quest 2 Pro might prove to be tricky, as Meta says it will not test new operating system and SDK releases on these products. Instead, it’ll be up to developers to ensure their apps function as intended.

Meta urged developers to assess how much of their audience is using Quest 2 and Quest 2 Pro devices and to consider splitting their releases to accommodate those on older headsets. If developers want to end support for these products, they should give users at least 180 days’ notice if customers could end up losing access to content or services as a result.

If you’ve got a Meta Quest 2 or Meta Quest 2 Pro at home, you’ll need to think about what you want to do going forward. While you should be able to continue using your headset, the lack of critical bug fixes after the end of 2027 could prove to be a problem. In that case, it might finally be time to upgrade to one of the best VR headsets to ensure you don’t miss out on anything.

OpenAI chief scientist Jakub Pachocki seemed to issue a warning on September 6.

In “An Alien Mind,” he argued that current alignment and monitoring techniques may soon prove inadequate for the pace at which machine intelligence is advancing. He also described a future in which AI takes a growing role in improving AI, compressing the time available for human supervision and institutional response.

Much of AI safety research concentrates on behavior. For example, what an AI system is permitted to do, how its actions are monitored and where humans retain approval. Yet every action begins one step earlier, with a judgment about what information deserves belief.

That earlier step is incredibly important.

AI acts on information whose pedigree is often obscure

Before an AI sends a payment, rewrites software or recommends a decision, it first assembles a picture of the world. That picture can draw on search results, corporate records, research papers and material produced by other AI systems.

The web was built for circulation. Information travels easily, while its ancestry often fades as it passes from one publication to another. A claim can begin in a research paper, appear in a news story, be condensed into a blog post and surface months later in a forum thread. By the time an AI encounters the statement, the route back to the original evidence may be difficult to reconstruct.

Authority also gets compressed. A peer-reviewed study, a corporate announcement and an anonymous post can all arrive as text. An AI system can rank and combine them into a polished answer, while the reader sees little of the difference between documented evidence, advocacy and repetition. Disagreement adds a further complication.

Credible sources regularly reach different conclusions, especially in finance, medicine, politics and emerging technology. AI is usually rewarded for producing a useful answer, which creates pressure to reconcile conflict into a single sentence. Uncertainty becomes easy to erase precisely when it deserves to remain visible.

The consequences grew substantially once AI acquired the ability to act. An agent can place a trade, approve a purchase, alter software or send a message on someone’s behalf. An uncertain claim can therefore become the premise for an irreversible action.

Human oversight depends on better evidence

The effectiveness of human oversight depends on the reviewer having superior information or a reliable means of checking what the AI used. In many settings, the human reviewer has access to the same web that the AI searched, only at a fraction of the speed. The AI can scan thousands of pages while the person responsible for approval works through a summary and a handful of links. As capability rises, review risks becoming a procedural signature rather than an independent examination.

Pachocki writes that OpenAI’s ability to rely on chain-of-thought monitoring is diminishing as reasoning becomes more complex and as AI becomes more capable of manipulating its own reasoning process. He calls for safety thresholds that can be enforced by third-party auditors, government agencies or international bodies.

Those safeguards govern capability and behavior. Reliable oversight also requires an evidentiary record showing what information produced the conclusion, where each claim originated, how trustworthy the source is and where credible disagreement remains. Without that record, the reviewer is judging the answer more than the evidence behind it.

AI inherited the web’s weakest habit

AI absorbed an information culture built around speed, reach and replication. Search engines, social feeds, aggregators and reposts made access extraordinarily efficient. They also encouraged a world in which the same statement could appear hundreds of times while tracing back to a single uncertain source.

Generative AI intensifies that effect because AI systems now produce material that later AI systems can ingest. A weak claim can be generated, scraped, summarized and republished until repetition gives it the appearance of consensus.

This creates a provenance problem with compounding consequences. Every additional generation can separate a statement further from the evidence that gave rise to it. Once an autonomous agent treats that statement as reliable, the information chain becomes part of the decision chain. While for consumers, this can mean a poor recommendation, for a financial institution, insurer or government agency, the same weakness can affect money, access, security or public services.

Autonomous AI needs a system of provenance

Take finance. Serious financial activity depends on records that establish who acted, under whose authority, on which terms and with what audit trail. Institutions spend heavily on those records because trust requires more than a plausible outcome. Autonomous AI needs a comparable discipline for information.

Claims should carry provenance that survives copying and transformation. A person or an agent should be able to trace a statement back to its origin, inspect the evidence behind it and see how the statement changed as it travelled. Credible disagreement should remain intact.

Where reputable sources diverge, the system should preserve the competing views and the degree of confidence behind each one. For an autonomous agent, uncertainty can serve as a trigger for further research, human approval or a narrower course of action.

Accountability should travel with information as well. A claim from an identifiable institution with supporting evidence deserves different treatment from an unattributed assertion repeated across dozens of pages. The aim is to give AI a richer sense of evidentiary quality before it commits resources or acts on someone’s behalf.

Governance belongs at a shared level. Provenance standards, verification methods and accountability rules need independent scrutiny and participation from many institutions, rather than dependence on the preferences of a single provider.

Final thoughts

Pachocki argues that current alignment and monitoring techniques may soon constrain the pace at which labs can responsibly scale AI tools, and he expects voluntary slowdowns until shared safety thresholds emerge. He also warns that continued progress could carry AI into recursive self-improvement, with AI driving a larger share of its own development.

That leaves every company building autonomous systems with a fundamental task. Establish what the agent knows before it acts and make that knowledge auditable.

Behavioral safeguards remain essential. So do permissions, audits and human approval. Their value rises sharply when the information beneath every decision carries provenance, context and accountability. The next phase of AI safety, therefore, begins with the evidence an agent sees, the authority it assigns to that evidence and the uncertainty it preserves before taking action.

Autonomy built on verifiable information gives humans a stronger basis for control. Opaque information leaves supervisors judging decisions whose foundations remain difficult to inspect.

Pachocki is right to warn that the window for preparation may be narrow. The industry should use that window to build systems that can show where their knowledge came from before those systems are trusted to act on the world.

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• Windows 11 just got a revamped internet speed test in preview
• This feature was shuttled swiftly through development using agentic AI in combination with human oversight
• Microsoft boasts that this is about: "Getting from idea to customer value faster. Without AI slop." Others are more cynical, though

Windows 11 just got a useful change in preview, with its built-in internet speed test being revamped, and the new version was produced using AI (in conjunction with human oversight, I should add).

Windows Latest spotted that Marcus Ash, Microsoft's VP of Windows Design and Research, posted on X to explain that the "team moved pretty fast on this one", adding that following feedback asking for the speed test to be improved, "within a day, Diego had a working prototype." (Diego Baca is VP of Design for Windows).

Ash tells us: "From there, our connectivity team worked with partners to bring the experience into Windows. The design and build work was driven by an agentic AI workflow, with a person guiding the process in their spare time."

The Microsoft exec further explains that this is where he sees "a lot of potential in AI" and using agentic workflows, in terms of "Getting from idea to customer value faster. Without AI slop. That is the way."

Analysis: AI, oversight, bugs, and jobs

Of course, this is a controversial topic. Mention AI being involved in the coding of Windows 11 and some tempers will flare online, even though we know this is happening anyway. Microsoft has made no secret of that fact.

Indeed, among the AI hate and flames on social media, a lot of people are point out that these days pretty much all software is being developed in conjunction with Large Language Models (LLM agents) — even Linux.

As one Redditor makes clear: "Newsflash — every single piece of software you use, except for open source projects that are making a statement, you should expect to have AI written code in it. It is pervasive, it is everywhere."

The key aspect here is exactly how the AI agent in question is employed.

It's one thing for such an agent to be given a task to write a piece of code for a certain feature, and then just to slap that into your software or operating system as it is (or maybe after a cursory glance through the work). It's another thing to have a human programmer working on a feature and using an agent to perform grunt-work (easy but perhaps time-consuming) coding therein — and then to properly check the AI's output.

Theoretically, there's nothing wrong with the latter approach, at least in terms of the soundness of the coding work. And as Microsoft's Ash points out, this is really accelerating development times — meaning Windows 11 users will get new features (hopefully capabilities they want, like this improved internet speed test) much more swiftly. Which is exactly why almost every developer is now utilizing this tech.

There are problems here, though, and they're bound up in the public perception of Microsoft. Do Windows 11 users trust the company to be using AI in the right — i.e., properly checked and vetted — way? Not really, in many cases, as there remains a lot of doubt over the overall quality of Windows 11 code, and rightly so given the regularity, and severity, of the bugs we've seen (with more prevalence since 2024, and that 24H2 update).

As another Redditor observes: "People aren't calling Windows slop because it's being made using AI these days. They're calling it slop because it's being made using AI with woefully insufficient human oversight and even more woefully insufficient QA, leading to repeated disastrous bugs making it to production and end users being treated as beta testers. You want people to stop calling your product slop? Stop shipping updates that take down people's computers or break existing features."

The other issue here is that of AI stealing coding jobs from people. Agents may make things happen a lot more quickly, but as Ash notes, this revamped speed test was implemented mainly by an agent — including its design — with the software engineer "guiding the process in their spare time", making it sound like a rather casual amount of effort on the human side of the equation.

Is it a particularly sensitive approach to be touting the benefits of AI — as real as they may be for an operating system's ongoing development and maintenance — in this way at a time when Microsoft has been swinging the axe with various job cuts and pushing staff towards voluntary redundancy in a big way? I'd suggest it isn't, and the timing of this particular push of the value of agentic AI is dubious in that respect.

At least in this case Microsoft is being upfront and transparent about what it's doing — and we can expect a good deal more AI acceleration of Windows 11's development going forward.

Generic AI tools promise to solve every problem under the sun, from running enterprise CRM to analysing why your cat isn't drinking enough water during a heatwave. And when you try to use one tool with no defined role for every task, results are disappointing and erode trust in technology.

Multiply that pattern across hundreds of employees, and you get tokenmaxxing: prompt after prompt, refining AI output until something usable comes out.

Some organizations, by instinct, default to blaming the employees for tokenmaxxing, or double down on training on ‘responsible use of AI’.

Tokenmaxxing is a symptom of capable people having to create solutions in an environment that lacks clear shape, scope, guardrails - and ways to analyze spend and outcomes.

Locking it down is not the solution

Traditional procurement systems were designed for traditional business software cycles. If building an enterprise system takes years, it’s not a big deal if procuring the right vendor takes years, too.

Routing AI through procurement to evaluate all options and keep the spend predictable initially felt to organizations like a safe and responsible choice. But software creation is very different today than it was even 5 years ago. Standard IT procurement still takes months, while opening a public LLM chatbot and asking it to generate a landing page is a matter of five seconds.

Policies tend to live their happy lives somewhere in the corporate portals, unbothered and forgotten unless an incident calls for a formal investigation. And when the approved systems are slow or restrictive, employees find workarounds. They turn to unvetted shadow AI tools for daily tasks and put proprietary corporate data into public models.

Look at the app prototypes that come out of it: the bad code is one problem, but the interfaces are off-brand, ignore corporate design systems and WCAG 2.2 accessibility rules, and lack basic authentication patterns.

Restricting AI use for employees in reality means they pay the £18 monthly subscription out of pocket and still use it. It moves the spend somewhere finance can't see it, at least not immediately and not until it’s too late, adding a data governance problem on top.

The other consequence is harder to put on a balance sheet. A company where only engineers can create software moves at the speed of engineering's queue, but opening options up to a wider group - product management, analysts, marketers - means a company can speed up innovation while having greater visibility into it.

The cost of ungoverned building

The pressure is already on. Teneo's Vision 2026 survey of more than 350 public company CEOs and 400 institutional investors found that 53% of investors expect to see a positive ROI on new AI initiatives in six months or less, while only 16% of large-cap CEOs believe they can deliver on that timeline. The same CEOs report that fewer than half of their current AI projects are ROI-positive. Investors want returns on a clock that executives don't think they can meet.

Volume is not the problem. Goldman Sachs Research forecasts that token consumption will multiply 24 times, to 120 quadrillion tokens per month, between 2026 and 2030, while semiconductor providers are already delivering 60% to 70% annual declines in cost per token. Tokens are getting cheaper, and we are going to use far more of them.

But who - or what - uses tokens is going to change. Organizations are building and deploying AI agents who act on their own. And agentic AI burns far more tokens than good old human prompting, because the queries repeat in sequence: a chatbot request multiplied by 20, or even 50, running unattended. Send an agent to work on an unscoped problem, and it will keep going long after a human would have stopped to ask whether this was worth it.

A lot of those tokens go into prototypes that never get to see the real world. Prototyping and sifting through ideas is how software evolves, but building with no eye on production drains resources. Gartner found that at least 50% of generative AI projects were abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, or unclear business value. If a prototype works in isolation but has to be scrapped because engineering would need to rebuild it from scratch, that's pure waste.

Then organizations have to pay the bill twice - for inflated tokens and lost engineering hours. When teams build with generic LLMs and no guardrails, senior engineers spend hours cleaning up the output. That deepens the trust gap in technical teams. Stack Overflow's 2025 developer survey found that 46% of developers distrust the accuracy of AI output. "Almost right" (but not quite) AI answers are the biggest frustration for 66% of developers, while 45% report that debugging AI-generated code is more time-consuming.

Access for everyone, accountability from everyone

The speed of code generation isn't the problem. Lowering the technology barrier for non-engineers produces better software, software shaped by the people who understand the business need. AI building is an edge, and an edge works best when everyone has it.

But alongside democratized access to technology, we need new ways to think about accountability for how you use it and the associated costs. Modern corporate culture can promote unnecessary complexity in how we think and speak about our work. Being able to explain in simple words how you spent your day is a useful exercise, and we should apply the same thinking to AI spend.

So, stated plainly: tokens should be used for a job somebody actually needed. In turn, the user needs to be able to explain, beyond the buzzwords, what the purpose was behind a particular token spend.

To work, rather than become another source of corporate frustration, this accountability needs to be part of the environment offered. One way to start is to retire the do-everything chat box as the default. Instead, organizations should start thinking about how to give people task-specific agent workflows tailored to their roles, and build execution limits into the software environment.

This way, non-technical staff get brand guidelines, fonts, security rules, and components pre-coded into the environment, so the apps they build are compliant and production-ready by default. They don’t need to file separate requests because the shape of the environment already includes the right guardrails on security and spend.

What it looks like when it works

Bounded workflows pay off in ways you can measure. In a recent mid-sized company implementation, a 3-person data analytics team built an agent that analyses data across all their systems; it saved each of them 12 to 13 hours of manual work a week.

Three analysts now produce what once took a team ten times their size, and their daily job also got way more interesting. They spend their time on high-value strategy instead of gathering data by hand.

That agent has a clearly scoped job and executes it in line with pre-defined parameters - it is not a chat box with a company credit card.

Bringing together abundance and accounting

Uncontrolled AI costs and falling trust in technology have the same solution, and it is not rationing of tokens. Tokens will be getting ever cheaper, and their abundance is the point of this technology.

The answer is in defining and understanding the scope of AI. Give everyone the tool but make it purpose-specific from the start rather than a generic catch-all.

Every company I talk to can tell me what it spent on AI last quarter, but when I ask what they wanted to get in return, the answer is murky.

This lack of understanding of what job AI should do for an organization is the problem, and the surprising bill is only its symptom. The first step is for the organization to articulate, in the plainest words possible, what the tools are for before the tokens start moving.

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It's the largest cryptocurrency theft in history: hackers from North Korea allegedly swiped $6.36 billion in funds from Binance in the space of just 20 minutes, which has left customers reeling and the financial industry in shock.

The heist raises all kinds of questions about how this was allowed to happen, what might happen next, and whether Binance customers are going to get any of their stolen money back.

Even if you never owned any cryptocurrency before, there are bigger questions too — like what this means for global financial stability, and what the aftershocks might be for everyone outside the Binance world.

Looking for a quick five-minute primer on the Binance hack? We've been speaking to industry experts to get some answers...

1. What actually happened?

The basics are now pretty clear — $6.36 billion in cryptocurrency was stolen from the Binance exchange in the space of just 20 minutes. Security analysts are pointing the blame at North Korea's TraderTraitor hacking group, which has a history of this kind of heist. What's less clear is exactly how the hackers got access. Early reports identified a Binance employee's compromised laptop as a potential way in, though it now seems there's more to the story.

"A compromised laptop may explain where an attack started, but it does not, on its own, explain how billions of dollars could leave an exchange," Pieter Arntz, researcher at cybersecurity firm Malwarebytes, told TechRadar. "The real question is what allowed that initial foothold to become access to withdrawal systems — and which safeguards failed to stop unauthorized transactions."

"According to blockchain analysts, the attacker spent at least a day and a half preparing, including testing their tools on public blockchains where anyone could see," Ben Bernstein, manager of cybersecurity advisors at Huntress, told TechRadar. "Then most of the $6.3 billion left Binance's wallets in about 20 minutes. That's common in attacks we investigate. The attacker does most of the work long before anything is stolen."

2. Will anyone get their money back?

Binance has promised to reimburse customer losses through its backup and emergency funds, but in the meantime we're seeing deposits and withdrawals frozen on the platform while the hack is investigated. It's a warning that even the largest and most sophisticated exchanges can still be vulnerable.

"Binance says it'll cover every cent," Nigel Green, CEO of the deVere Group, said in a press statement. "I hope so. The emergency fund is around $1.2 billion and the gap is several times bigger, so the rest has to come from the company’s own balance sheet. The market will want to see the numbers, and the new proof of reserves can’t come soon enough."

"Long term holders have just been reminded, very expensively, why keys kept offline matter. Crypto has spent years pushing back against heavy regulation, and plenty of those arguments were fair. This one's harder to argue with. Platforms holding billions of other people's money need enforceable security standards. I expect regulators in the US, Europe and Asia will likely move faster after this week," he added.

In the meantime, the US Secret Service has said in a statement that it's "working around the clock" with partners in the private sector "to identify, freeze, trace, and recover funds" that have been stolen from Binance.

3. What follow-up scams might appear?

Whether you're a Binance customer or not, you need to keep your wits about you over the next few days. It's likely that scams trying to literally cash in on the hack will start appearing — perhaps warning you that you need to quickly claim refunds, lock your account against a particular threat, or go through a bogus verification process.

"Criminals routinely use major breaches as a hook for phishing campaigns," Dray Agha, senior manager for tactical response at Huntress, told TechRadar. "Consumers should be highly suspicious of any emails or messages claiming to be from Binance offering urgent account recovery steps. You must only check your account status through the official application or website rather than clicking links in unsolicited messages."

According to the team at Huntress, you should watch out for social media posts, emails, and text messages that use the Binance hack as a hook — perhaps a fake news story suggesting that now's the time to buy crypto, while prices are down.

As always, think twice about following links or entering sensitive information into online forms and portals you're not familiar with.

4. How does this affect the average consumer?

If you don't own any cryptocurrency, the good news is that you're largely going to be unaffected by what's happened here. There's not going to be a stock market crash, or a run on the banks, or a global recession — not because of this hack, at least. However, it's always worth weighing up your own personal privacy and security setup.

"If you don't own crypto, the direct impact on you is small," Ben Bernstein from Huntress told us. "Your bank account and your card are not affected by this. The bigger takeaway for consumers is that attackers often get in by targeting people rather than technology, through a convincing email, a fake login page or a phone call from someone posing as IT support," he said.

"That makes security awareness everyone's job, at home and at work. If you pause before you click a link, approve a login or share a code, you're making it harder for an attacker, whether they're after your savings or your employer's systems," he added.

5. What does this mean for the future of money?

This clearly raises a lot of questions about the safety of cryptocurrency and the exchanges that it sits on — and of course it's far from the first crypto heist we've seen. In the longer term, it may lead to less confidence in crypto as a whole, but the experts we spoke to don't think it will bring the industry down. What will be important is making sure the risks of this happening again are minimized as much as possible.

"Despite the scale of the theft, this is not a threat to global financial stability," Feike Hacquebord, Principal Threat Researcher at TrendAI, told TechRadar. "Around half of the stolen funds have already been frozen, while the regulated banking system is well equipped to absorb a shock of this size. This is a major crisis for Binance, not a crisis for the global financial system."

"The change I’d like to see, for crypto firms, banks and every other business, is working on the assumption that someone will get in eventually," Ben Bernstein told us. "Keeping attackers out matters, but it isn't enough on its own. Companies also need someone watching around the clock, especially for logins and account activity that don't look right, so they can find and remove an intruder before the money starts moving," he added.

Long before anyone trained a model on customer data, the marketing data business worked out the terms for pooling it. Catalog retailers in the 1990s joined data cooperatives that combined their transaction histories so each member could find new buyers, and the arrangements held together because the rules were blunt.

A retailer contributed data to receive modeled prospects; no member got a rival's customer file; and a company that stopped contributing stopped receiving. Card fraud consortia run on similar give-to-get terms. Nobody would call these systems elegant, but every participant understood the trade.

Software vendors building AI features are now running into the same question, usually without the rules. Traditional software sat on top of a customer's data and operated on it. AI products learn from data, and on a multi-tenant platform, that learning rarely stays within a single account.

One company's records can sharpen the enrichment, lead scoring, and recommendations delivered to thousands of others. A vendor can improve its product for every customer by relying on the work of a few, and most contracts say nothing about whether that trade is fair. The customer still owns its data in any legal sense.

Ownership alone doesn't answer who benefits.

Users push back on sharing hard-earned data

HubSpot learned how touchy the question has become when it announced plans to pool enrichment data across customer accounts, including business contact details, employer information, and email deliverability signals, to improve prospect records platform-wide.

Every account was in by default. Within days, the company dropped the plan and promised that any future enrichment feature built on customer data would be opt-in. HubSpot had stated clearly that core CRM records would never be shared without permission, so privacy in the usual sense wasn't what upset the sales and RevOps teams, who pushed back. Their concern was harder to answer. Years of work spent cleaning and enriching their own contact data were about to improve the prospecting tools available to every other customer, competitors included, and nobody had asked what they would get for it. The product terms that allowed the change had been updated nearly two years before most customers heard about them.

AI creates accidental data companies

Plenty of software companies adding AI tools have never had to think this way. They are becoming data companies one model at a time, often without deciding to. Whether a customer's information feeds a shared model is often determined by a product team working toward a launch date rather than by anyone who owns the commercial relationship, and the answer is expressed in terms few buyers read.

McKinsey's 2026 survey on AI trust found that only about 30% of organizations have reached a mature level in AI strategy, governance, and controls. Pooled training data tends to fall between legal, product, and sales, with no single team feeling responsible for explaining it.

The need for strong exit clauses

Buyers will force the issue long before regulators do. Security certifications and uptime guarantees became standard contract language over the past decade because enough customers asked for them that vendors stopped arguing, and AI data terms are heading down the same road. Procurement teams are starting to ask whether their data trains or enriches models used by other customers, whether participation is opt-in, and whether it can be switched off later without losing core features. The more demanding buyers want to know what a contributing customer receives that a non-contributor doesn't.

Exit terms will be the hardest part to write. A customer can cancel a contract and demand its records back, but data that has already shaped a production model can't be cleanly pulled out again. Retraining is expensive, and removing one customer's influence from a live model remains an unsolved problem for most vendors. A vendor can honestly say it deleted every record a customer ever uploaded and still be running a model those records helped build. Contracts written for storage assumed that deleting data ended its use. Contracts written for AI will have to say plainly what persists after a customer leaves, and whether the customer agreed to it when it signed.

Opt-in consent answers part of the question. A checkbox tells a vendor it may use the data, but it says nothing about the return, and a customer who opts in for nothing in particular has made a poor bargain, even if it's a legal one. The old cooperatives signed up members because members could see exactly what they were getting.

Vendors that explain their data economics before customers ask will have an easier time at every renewal. The explanation belongs in the product and in the contract, and it should cover which customer data feeds shared models, what those models produce, and what contributors get back. Security questionnaires were once a formality, until buyers made them a condition of the sale. Pooled data terms are on the same schedule, and vendors that write clear ones now will set the standard that the rest of the market will follow.

Ask a room of executives about agentic AI and within about ninety seconds someone will ask how many agents they ought to be running. It has become the default measure of progress: a number you can put on a slide, benchmark against a competitor, and report upwards.

It is also the wrong question.

Across the 200-plus workshops my team and I have run with enterprises in Europe and the Middle East, the organizations getting real value from AI tools are not the ones with the longest agent inventory. They are the ones who never framed the problem that way to begin with. They started by identifying a business problem or point of friction and worked backwards. Everyone else started from the technology and went looking for somewhere to put it.

There are four questions I hear constantly that are quietly steering good companies into Pilot Purgatory. Here they are, and here is what I would ask instead.

Don't ask how many agents. Ask what outcome you are improving.

Much of the last two years of enterprise AI has been about helping people work faster. Copilots that answer questions, assistants that summarize the meeting you have just sat through, tools that get you past the blank page. These are genuinely useful and I would encourage everyone to switch them on.

But speed is not the same as value, and agent count is not the same as either. No enterprise ever created value by accumulating agents. Value shows up in real business outcomes such as fewer payroll errors, faster shift scheduling, lower supplier sourcing costs, better win rates, cash collected sooner.

That is the difference between an agent and an agentic application. An agent help with an individual task. An agentic application coordinates teams of specialized agents to deliver a business outcome. If you cannot name the KPI an agent is moving, you do not have a use case. You have a demo.

Don't ask whether AI can recommend. Ask whether it can finish the job.

The most persistent confusion I encounter is between advice and execution. Plenty of systems can now sense a situation, spot the pattern and tell you what should happen next. That is real progress. It also leaves almost all of the work exactly where it was.

Take sourcing. An assistant can flag that a category is overdue for renegotiation and suggest you run an event. Someone still has to create that event, chase the responses, score them against commercial criteria, route the approvals correctly, update the contract and shepherd the whole thing to a close.

An agentic application does that work, under policy, with human oversight and approval where required, and with a record of what it did and why.

The test is simple. At the end of the interaction, is the process further forward, or do you just have a better-informed human facing the same queue?

Don't ask which model tops the leaderboard. Ask whether it understands your business.

Benchmark scores dominate the AI conversation and matter far less in the enterprise than most leaders assume. Raw model intelligence is rarely the binding constraint.

A frontier model understands procurement as a discipline. It does not understand your supplier list, your delegation-of-authority matrix, your contractual obligations, your current inventory position, or the three exceptions your finance director signed off in March. Without that, sophistication amounts to a well-argued guess.

Enterprise context is the differentiator: trusted enterprise data and business context, including the policies, approvals, transaction history, organizational structure and live process state that already sit inside your core systems. The closer agentic capabilities are to the systems where business processes, data, security, and approvals already live, the more effectively agents can understand context and act within establish controls.

Don't ask for one system that does everything. Ask how the specialists coordinate.

There is a persistent hope that enterprise AI will eventually resolve into a single, omniscient digital assistant. Businesses do not work that way, so their AI will not either.

Organizations are ecosystems of specialists: finance, HR, supply chain, service, sales, each with its own expertise, obligations and objectives. The equivalent in AI is not one agent that knows everything, but coordinated teams of agents that know their own domain and work towards a shared goal.

Hiring is a good illustration. One agent surfaces and screens candidates. Another prepares onboarding. A third confirms the budget is genuinely available. A fourth keeps the hiring manager's approvals moving. Individually, each is modestly useful. Together they move a business process from vacancy to first day. The value sits in the coordination, not the components.

Systems of outcomes

What connects all four questions is a shift in what enterprise software is actually for. For decades our applications recorded what had happened. We are now entering the era of systems of outcomes: software that reads the state of the business, reasons over it, and acts to keep work moving towards a defined objective.

That shift demands different questions from leadership. Not how many agents, but which outcome. Not can it advise, but can it execute. Not how clever is the model, but how well does it know us. Not one brain, but one team.

Get the questions right and the technology decisions become considerably easier. Get them wrong and you will assemble an impressive collection of agents, tools, and models that nobody can tie to a number that matters.

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Transparency duties under Article 50 have begun to apply, even as the EU AI Omnibus implementation for many high-risk systems has moved to December 2027 and, for certain AI-enabled products, to August 2028. For organizations building and deploying AI in Europe, the message is clear: regulators extended the timeline on complexity, but not on transparency.

While some organizations may view the extension as welcome breathing room, the time would be far better used preparing for the future of AI. To do that, we need to frame the challenge around creating the conditions for long-term innovation, rather than around compliance deadlines.

As AI becomes embedded in products, services and decisions that affect people's lives, organizations need confidence in the systems they are deploying, and users need confidence in the organizations deploying them. That work cannot begin in 2027. It needs to start now.

Compliance is achieved at a point in time; governance is built as a capability. A deadline can be moved, but an institutional capability cannot simply be created at short notice.

Start with the AI already in use

The starting point for every organization should be an honest assessment of whether it has a reliable picture of the tools, outputs and interactions that Article 50 covers. As we have seen in recent years, AI systems can quickly become more deeply embedded in business over time as employees explore new tools and suppliers innovate and ship new updates.

This natural expansion is a normal part of adopting a new technology, but it can result in AI becoming a core part of business processes before the proper governance is in place. Governance is most effective when it shapes those decisions from the beginning.

Attempting to retrofit governance after AI has become embedded is significantly more difficult, more expensive and ultimately less effective, and the resulting loss of trust can be more costly than the upfront work required to prevent it.

Extend the controls already in place

The good news is that many of the disciplines required by the legislation should already be familiar. They build on existing practices around privacy, security, risk management, accountability and operational governance, and much of the challenge is around applying established governance disciplines to a rapidly evolving technology landscape.

Organizations that have taken data protection regulation seriously, and already understand impact assessments and effective governance processes, are well positioned to meet many of these requirements – there is considerable overlap.

The task is to extend them for AI, not replace them. A privacy impact assessment can identify data risks, but it cannot on its own establish whether a model is dependable in a particular context, whether someone can meaningfully question its output or whether an automated process disadvantages a group.

Those questions should be built into existing workflows, particularly when AI is bought rather than built. Before a capability is embedded in software or a critical process, organizations need clarity on data treatment, service and model changes, incident response, support for transparency duties and the evidence a provider can supply. Even where the underlying model comes from a third party, organizations still need to govern how it is selected, configured and used.

The sovereignty question

Alongside the AI Act, the other crucial question impacting the way we think about governance in Europe is sovereignty. As the debate gains momentum across Europe, the practical issues raised are becoming clearer. Where is data stored? Which providers do we depend on? How resilient are our technology choices? What happens if commercial, legal or geopolitical circumstances change?

It is worth being precise about what sovereignty can realistically mean for enterprises. Full compute sovereignty, including control over chips, infrastructure and supply chains, is far beyond the reach of most organizations. The more practical goal is compute resilience: understanding where critical dependencies sit, ensuring no single vendor, cloud provider or model becomes a point of leverage, and retaining control over the layers of the AI stack that remain within the organization's influence.

Open source can support this by providing greater transparency and control over code, but it does not remove infrastructure dependency on its own. An organization may use an open model and still rely on external compute or cloud capacity to operate it.

Organizations should adopt a sovereign-by-design approach to innovation. Sovereignty should not be considered after technology decisions have been made, it needs to be embedded into decisions on data architecture, vendor selection and risk management from the outset, with resilience, not full independence, as the realistic target.

It is also important to note that not every dataset, process or workload carries the same level of risk. Organizations should take a tiered approach, applying the strongest sovereignty requirements where data sensitivity, regulatory exposure or operational importance demand it.

These are not abstract concerns. Viewing AI governance and sovereignty as interconnected challenges creates an opportunity to build a more coherent approach. This is not a case for technological isolation or for stepping back from global technology ecosystems, the aim is narrower than that. It is about ensuring organizations retain meaningful freedom of action in an increasingly complex and uncertain environment.

The real opportunity

The debate about innovation, regulation and sovereignty will continue. Europe will keep refining its approach to AI, and organizations will keep adapting to new technologies, new providers and new risks. The more important question is whether organizations use this window to build the capabilities they will need long term, rather than treating each regulatory milestone as a separate deadline.

Governance should not be viewed as a compliance exercise that slows innovation. It is an institutional capability that enables organizations to innovate sustainably, adopt new technologies with confidence, and remain resilient as markets, regulations and geopolitical conditions evolve.

The ultimate goal is innovation built on trust, and we have the ability to deliver that regardless of regulatory deadlines. The real test is not whether an organization built every part of the system it relies on, but whether it understands its dependencies well enough to change course if circumstances demand it.

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

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

I review tech for a living, so asking AI for the “best website for tech reviews” came with a small risk of bruised pride. Would TechRadar get a mention, or would the answer send readers straight to the competition? That was one of the questions I put to HubSpot AEO.

HubSpot’s answer engine optimization tool tracks selected questions across ChatGPT, Gemini and Perplexity. It shows which brands appear, which pages the answers cite, and suggests content work based on those findings. What interested me was being able to read the answers behind the score, then check the sources for myself.

I used the free trial to set up TechRadar and track 11 of the 25 available prompts. My hands-on review covers setup and reporting, with product demonstrations and documentation filling in the workflows I didn’t try. One day of results can’t prove long-term gains, but it gave me enough to judge whether the tool helps make sense of AI search or simply adds another chart to the working day.

HubSpot AEO: Plans and pricing

The standalone price for using HubSpot’s AEO tool is $50 a month, or $45 a month billed annually, which comes to $540 a year before tax. You also get a 28-day free trial that requires no credit card; there’s no need to buy another paid HubSpot product first.

The AEO features are also included in Marketing Hub Professional and Enterprise plans. And, the HubSpot for Marketers option bundles Marketing Hub with Content Hub and include AEO too. Those prices cover the wider software package, and not an AEO surcharge.

Now, if you’re just on the AEO plan and want more credits, it costs $20 a month for another 10 prompts and 1,000 monthly answers. A standalone account tracking 45 prompts therefore costs $90 a month on monthly billing. Not to forget, tracking another brand requires a separate AEO instance.

Content generation has a separate cost too. Generating a piece from an AEO recommendation requires Marketing Hub Professional or Enterprise and uses 1,000 HubSpot Credits. Standard Professional plans include 3,000 monthly credits, while Enterprise includes 5,000. Extra credits cost $10 per 1,000, and unused monthly credits expire. CRM-based prompt suggestions also require Marketing Hub Professional or Enterprise.

There’s one pricing wrinkle, though. Accounts enrolled in HubSpot’s flexible seats-and-credits program have separate terms, including two credits per AEO answer. That is a different model from the standard prompt allowances above. So, do check which model your account uses before budgeting.

HubSpot AEO: Setup

I signed up with my email and answered a question about my role. From there, HubSpot took me through the AEO setup one step at a time.

Next, I entered TechRadar’s name and website address. That led to the competitor and audience settings, where I could narrow down what I wanted to track.

I reviewed the suggested competitors and chose TechRadar Streaming, focusing on deal-seeking shoppers in the United States. I wasn’t convinced by every competitor suggestion, though. Some were publishing companies rather than the review sites readers might compare.

HubSpot used those choices to suggest questions to track. I could edit or remove prompts before continuing. Several asked much the same thing, so I found the suggestions useful for getting started, but too narrow to cover different reader needs on their own.

I then started the checks and followed HubSpot’s progress as it worked through the prompts. From there, I could open the dashboard and start exploring the answers, brand mentions and cited sources.

HubSpot AEO: Features and specs

Prompt tracking

The panel also separates Answers, Prompt Analysis and Recommendations. I like that structure because a weak score has somewhere to lead like you can inspect the answer, compare the sources, then consider a change. These are repeatable checks of chosen questions, not a log of what every buyer asks.

Brand visibility and competitor analysis

The earlier dashboard screenshot showed 6.67% brand visibility while the account held 10 prompts. Its date range ran from October 27 to October 27, with a single point on the trend chart. That was a baseline, not enough history to judge progress.

The broader review-site question above comes from the later 11-prompt view. Comparing that single question with the earlier average would be misleading. The contrast shows how much the choice of questions matters, not that visibility suddenly improved.

Visibility measures how often the brand appears in the tracked answers. Share of voice compares its mentions with the selected competitors. Filters for engine, product, audience and buying stage help explain where a gap sits. And, Sentiment adds context about how the brand is described, rather than just whether it appears.

For TechRadar, I also need to separate being named as a recommended review site from having a guide cited in an answer. Both can be useful, but a mention score alone doesn’t capture that difference.

Citation analysis

This is the report I find most useful for planning content. A mention tells me the brand appeared. Citation analysis shows the pages and domains used as sources, including sources owned by other businesses.

My Citations screen splits the results by content type and channel. The first view concerns formats, such as blogs, comparisons, and documentation. The second concerns where the material comes from, with categories including owned, earned, peer, review-site, and user-generated sources.

Earned and review-site sources occupied large parts of my channel chart, while owned content had a thin slice. I read that as a reason to inspect outside sources before assuming another page on our own site is the answer. These are sources across the tracked prompts, though. They don’t all necessarily mention TechRadar.

The wider report lets you inspect top domains and individual URLs, and compare owned-domain citations with citations whose source pages mention your brand. That helps distinguish if you should improve your own pages or earn coverage elsewhere. Neither count proves that someone clicked through or bought anything.

Content recommendations

HubSpot uses the prompts and cited sources to suggest workarounds for owned content, social amplification, and outreach. Each recommendation has a content type, channel, and priority. Opening it adds a suggested title, summary, audience, and keywords, with the prompts and citations behind the idea.

Suppose a tracked question keeps citing third-party comparisons. Here, updating a relevant comparison page or seeking inclusion in one could be more useful than publishing another broad guide. This is exactly where the source list helps make that choice.

The recommendation can then be marked complete. I see this as a useful brief for a writer or outreach lead, rather than a finished strategy. Someone still needs to check the cited pages, decide whether the suggestion fits the business, and do the work.

Content generation and impact tracking

For eligible Marketing Hub accounts, the Generate asset feature takes an owned-content or social recommendation into Content Agent. It uses the brief alongside brand voice, audience information, and cited pages. The draft appears in the recommendation’s Content tab, ready to open in the relevant editor.

I like the direct handoff because the source research and writing brief stay connected. It still needs an editor. That’s because a draft can follow a useful outline and still remain thin on facts, examples, or a reason to choose that business.

After publication, the Impact tab accepts the page URL and tracks whether it earns citations for the target prompts. This gives the recommendation a follow-up check instead of ending at “published”. It doesn’t prove that the tool caused an increase, and it isn’t sales attribution.

Is HubSpot AEO worth it?

I recommend starting with the trial for one clear product, audience, and set of questions. My setup exposed the main risk early: a tidy dashboard can still rest on repetitive prompts and a questionable competitor list. Fix those inputs before spending time on the score.

For a small team, the standalone plan is a reasonable way to replace repeated manual checks and find useful source pages. Existing Marketing Hub Professional and Enterprise users get more value from the connected content tools. That’s why I don’t see a case for buying the wider suite solely for AEO.

The buying decision comes down to what the reports change. Find a worthwhile page to improve, a source to approach, or a buyer question you’ve missed. Without that next step, more daily data won’t earn its monthly fee.

FAQs

• YouTube announced it's hit 150 million subscribers across Premium and YouTube Music
• The platform also generated $11.2 billion in ad revenue
• Users say free tier ads are only getting worse, with some calling them 'unbearable'

YouTube announced that it has surpassed a whopping 150 million subscribers across its Premium and YouTube Music experiences — marking a huge milestone for the video platform and one of the best music streaming services.

Alphabet Inc CEO, Sundar Pichai, announced the figures in the platform’s Q3 earnings release on Wednesday (October 28), which increased by 25 million from a total of 125 million subscribers in March 2025, though individual numbers for Premium and YouTube Music weren’t disclosed. Pichai also shared that YouTube’s ad revenue rose to more than $11.2 billion compared to $10.3 billion in 2025.

Speaking about the milestone, Google’s Chief Business Officer, Philipp Schindler, said "With Premium and Premium Lite, viewers have choice. As more people opt into premium experiences, it’s a win for viewers, creators, and our business. Subscriptions help fund the creator ecosystem and drive the platform forward."

YouTube has accumulated over 25 million subscribers between now and when it last confirmed its total membership count in March 2025. When you break that down, that’s approximately 1.25 million new subscribers signing up each month, and it’s only going to increase.

While it’s not a traditional on-demand film and TV content platform, YouTube has emerged as a serious competitor among the best streaming services. In 2017, the company launched YouTube TV, and according to a recent report from Nielsen, it has surpassed Netflix in popularity for TV viewing. In July 2026, 49% of TV time was viewed via streaming platforms, with YouTube making up 14.2% of that, followed by Netflix at 7.8%.

There’s no denying YouTube Premium is rising in popularity, but as the company puts more effort into improving its paid services, a number of users have said its free version is becoming increasingly difficult to use because of the number of ads.

Earlier this year, I bit the bullet and signed up to a YouTube Premium free trial and, annoyingly, the experience was vastly more enjoyable. Not only does it grant you ad-free viewing, you get background and offline listening, as well as small but handy control tools and full access to YouTube Music. But once you go back to its free version, that’s when you truly realize how out of control YouTube’s ads have become — and I’m not the only one who’s noticed (see below).

In addition to YouTube’s ‘completely unbearable’ ads, users have also pointed out the rise in AI-generated sponsored content which one Redditor claims advertise "obvious scam products". The combination of the two is the main catalyst for users abandoning YouTube as a platform altogether, and tech-savvy users are already opting for alternative ways to bypass ads.

One of the most popular ways YouTube viewers are working their way around ads is downloading browsers such as Brave, which users on Reddit have praised. Browsers like this come with built-in ad blockers, so it can automatically skip over ads when you’re streaming YouTube for free.

While YouTube gives you the option to tailor your ad experience and block certain types of promotional content, some users claim that when they do this, YouTube only feeds these ads to them even more. One user on Reddit used Old Spice as an example, saying "it's like blocking them has a reverse effect." Similarly, another user had an experience where they reported a potential scam ad, only for YouTube to email them with a counterargument.

I’m with a lot of other users who continue to call out YouTube for how expensive Premium has become (it now costs $15.99 / £12.99 / AU$22.99 a month), especially when you factor in your existing streaming subscriptions, so many of us just put up with its free service. Despite enjoying what Premium had to offer during my free trial, it doesn’t justify adding another subscription plan to my roster — my bank account will fall out with me.

• Windows 11 has a new preview (optional) update
• This introduces the ability to fully tailor the right-click (context) menu to your liking, as well as speeding up its performance
• That's great, but you're best off steering clear of a preview update and waiting for the full release in under two weeks – but even then, this feature will be on a gradual rollout

Windows 11 now has one of the most-wanted interface capabilities for many people, namely the ability to customize the right-click menu – but it's not quite fully finalized yet (although that's imminent).

The menu that pops up when you right-click on a file (or folder) is also known as the 'context' menu as it presents a bunch of options that vary contextually based on the type of file clicked – but in Windows 11, it's currently a sprawling affair.

Those who hate the lengthy right-click menu will now be able to customize it after installing a new Windows 11 update, although this is an optional release, meaning it's still in preview. This feature will be in next month's patch, the November update, though, so you haven't got long to wait for the full release (less than two weeks, in fact).

The customization settings for the right-click menu include the ability to remove Windows commands you're not fond of – like 'Send to' for example – or add abilities that you'd like to have, and also choose the apps you want to appear in the context menu.

It's a full range of customization and you can also elect to have the top panel of the menu (cut, paste, rename and so forth) directly integrated instead, so these functions appear alongside everything else (in a Windows 10 style – and you can shift the layout around too, moving Properties to the foot of the menu for example).

Microsoft boasts that the new right-click menu is faster, too: "The updated menu opens more quickly, reduces clutter, and gives you more control over the commands and app extensions that appear when you right-click."

As you may have noticed, all this work has been in testing for a while now.

Analysis: rolling frustrations and a lengthy wait

The immediate response on the likes of Reddit seems positive, and there are Windows 11 users who have taken the plunge with this preview update who are observing that the right-click menu is indeed a lot faster.

One Redditor enthuses: "The new context menu is so good! Way faster, customization is cool."

Others acknowledge that this is undoubtedly a good move, but don't understand why it took Microsoft so long to get here: "Not bad imo. Crazy it took them 5 years though."

While the feedback is generally positive, there are complaints from those who have installed the update but haven't received the new context menu. This is because the right-click revamp has been implemented via a gradual rollout, so it's only being drip-fed through to a certain amount of PCs at a time (of those who have installed the optional update). As another Redditor puts it: "Those gradual rollouts though…"

You can force the feature to be deployed to your PC using a Windows configuration utility (ViVeTool), but I wouldn't recommend that approach. Microsoft tests the waters in a limited way for good reasons, especially with features still in preview, so you're running a larger than normal risk of some nasty bug ruining your day.

Anyway, you've waited five years for this performance and options tuning, as those on Reddit are keen to point out – so what's another couple of weeks? I'd hold on for the official November update, keeping clear of this preview, but even then, it'll still be a gradual rollout, and you may need some patience.

Still, the good news is that the context ball is rolling, and it hopefully won't be too long before the majority of Windows 11 users gain access to this important upgrade for a key piece of the UI.

Modern digital security frameworks were originally built to correct a flawed historical assumption: the belief that if a user or system was inside the company network, they could be trusted implicitly. Over the past decade, the rapid rise of cloud storage, subscription-based business software, and remote working destroyed that network boundary.

In response, our security models shifted focus. We began verifying the identity of the person trying to log in, inspecting the hygiene of their computer, requiring multiple verification steps during login, and continuously checking that they were who they claimed to be.

This model works well when a human is logging in to perform a task. But with the rise of autonomous AI agents, the real security risk no centralized at the login screen. It starts after access has been granted.

An AI agent does not behave like a standard piece of software. It reads a human instruction, pulls context from company databases, utilizes multiple sets of tools, chooses its own digital tools, connects to external software communication links, and triggers complex automated workflows.

Verifying the initial login simply tells you that the agent is officially recognized by your system. It tells you absolutely nothing about whether the next action the agent plans to take is safe, ethical, or correct, focus has shifted to context.

The evolution of the machine identity problem

IT teams have already experienced a simpler, quieter version of this challenge with non-human identities. For years, companies have used automated service accounts, security tokens, and digital certificates to allow different software programs to talk to one another.

Most security leaders are all too familiar with the risks these credentials present: accounts are created for a temporary project, granted permanent access to database files, and then completely forgotten long after the project has ended. They become silent, unmonitored backdoors into the network.

However, AI agents complicate this familiar problem by introducing the element of independent judgment.

A traditional service account follows strict, pre-written code. By contrast, an AI agent interprets a high-level goal, analyses real-time context, selects its own tools, and actively decides how to proceed.

As a result, security policies can no longer stop at the front door. We must protect every internal data or compute asset the agent can reach and monitor every single decision it makes.

Matching autonomy with executive control

To manage this risk, security leaders must separate two distinct concepts: how much independence an AI agent has, and what level of file access it holds.

The combination of these two factors dictates the level of control required. For example, a basic agent that merely summarizes long documents only needs limited reading rights and clear activity logs. However, an agent designed to update sensitive, production databases, access code repos, write new types of code, or alter business workflows requires mandatory human approval steps, strict operational boundaries, and a clear path to reverse any mistakes.

We can categorize these digital workers into 3 distinct levels of independence, applying tighter security controls as their autonomy increases:

1. First, there are Conversational AI Agents (GenAI), productivity agents usually built and managed by employees & teams as part of their productivity (e.g. managed agents, self hosted chat bots). When driving security, we must enable proper RBAC authorization, associated with the employee and team, ensuring access to non-sensitive or production-related data, enabling logging, and validating the response to ensure proper integrity.

2.Second, there are Agents Acting with Semi/Full Human Guardrails (AI Agents), such as customer support tools that draft emails or prepare database updates for review. Here, security teams must validate the parameters of the tools being used and require a human to approve any actions involving writing, deleting, spending money, or sending information externally. All above controls apply

3.Finally, there are Fully Autonomous Agents (Agentic AI), which reset passwords or trigger complex workflows on their own. These require the strictest boundaries, from visibility (agent architecture, tool usage, and more) and focus on active set of controls. The controls include strict rate limits, continuous real-time policy checks, network access restrictions, run time security controls (Public/Private Cloud) continuous identity and minimal authorization verification, and finally complete, unalterable audit trails.

The industry is leaning toward autonomous agents seeking human approval upon abnormal behavior, can that be triggered in your agent?

The need for real-time inspection

To enforce these policies, security teams need a dedicated vantage point within the network. They must be able to inspect the human prompt, the agent's response, the digital tools being used, and the movement of data while the work is actually happening.

This requires an architecture that can discover all active agent activity across the business, govern how those agents use internal tools, and maintain complete visibility over data flows and automated workflows.

The true value of this approach lies in context. When the behavior of an AI agent is viewed alongside network traffic, user activity, and cloud database logs, security teams can investigate incidents with clear evidence. They can instantly see which agent ran, who in the organization owns it, what data was moved, and which automated tool was called.

For business leaders, this means cleaner, more consistent governance across human users, software applications, and digital agents alike.

How to begin: Start with a single workflow

Securing this new landscape does not require a complete overhaul of your entire security department overnight.

Start by picking a single AI workflow that is currently in use or close to being launched. Map out its digital identity, its level of independence, the files it can access, the tools it uses, the points where a human must approve an action, and how you would undo a mistake.

This simple mapping exercise will quickly highlight where your security gaps lie. If you cannot see the agent's actions in real-time, approve its decisions, block its access, or explain its behavior after the fact, your security controls are simply too far away.

From there, build a baseline on how your secure your coding agents, AI harness (or AI orchestrator) up to utilizing of tools/skills/MCP and moving forward to data assets access and utilization of identity that will allow you to set the correct foundations and guard rails.

The core principle of a "never trust, always verify" security model must follow the agent into its daily work. The most meaningful decision is no longer who is logging in, but whether their next action should be allowed to happen, under these exact conditions, right now.

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

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

• Pixelmator Pro gets a new AI-powered Generate Fill tool
• Keynote, Pages, and Numbers add AI assistants and OpenAI model support
• Final Cut Pro gains thousands of MotionVFX creative assets in November

Apple's Pro and Tell week is marching on, and alongside the reveal of the M6-powered iMac and 14-inch MacBook Pro, the Cupertino-based giant is also shining a spotlight on software. Specifically, Apple Creator Studio is getting a wide-ranging update that brings a fresh batch of features and, ultimately, packs in more value.

As it stands, Apple Creator Studio is a software subscription priced at $12.99 / £12.99 / AU$19.99 a month or $129 / £129 / AU$199 per year, giving you access to Pages, Numbers, Keynote, Pixelmator Pro, Final Cut Pro, and Logic Pro across Mac, iPad, and iPhone. All that stays the same, including the especially excellent education discount — $2.99 / £2.99 / AU$4.99 per month or $29.99 / £29.99 / AU$49.99 per year — but there are some pretty big updates here, with AI playing a starring role.

Leading the charge is Pixelmator Pro, which is getting a new Generate Fill tool. This lets you select an area of an image, describe what you want to add or change, and let AI do the heavy lifting. It's the kind of feature that could come in handy when you're editing a photo and don't want to jump between apps to get the result you're after. It'll also help level the playing field between Pixelmator Pro and Adobe Photoshop by filling a feature gap.

Keynote, Pages, and Numbers are also getting AI assistants designed to help you create presentations, write documents, and work with spreadsheets. Most interestingly, you'll be able to use OpenAI models alongside Apple's own, opening up more possibilities for working with AI in these apps. The apps also support compatible external AI tools, potentially making them a more useful part of your broader workflow.

With Final Cut Pro, Apple is bringing MotionVFX content directly into its pro video editing suite, making thousands of creative elements, including transitions, templates, effects, and titles, available for use in your projects. It's another welcome addition that should let you get more done directly inside Final Cut Pro, rather than having to rely on other tools.

All of this is coming in November as a free update for existing subscribers, though Apple isn't sharing specific timing just yet. These updates should also be right at home on the new Macs announced this week, but I'm curious to see if Apple introduces any more surprises, like the much-rumored touchscreen MacBook Pro, during its Pro and Tell event tomorrow.

• Windows 11's internet speed test currently redirects to a Bing search to facilitate the test
• This is clunky to say the least — and an annoying piece of Bing promotion — but now Microsoft is changing things
• In a new preview build the speed test is fully integrated into Windows 11 — as it should have been in the first place

Microsoft has thought better of one of the methods it was using to push Bing in Windows 11 — via an internet speed test — and we can but hope that maybe it's seeing sense from a broader perspective about such promotional trickery.

Windows Central reports that, in a recent preview release for Windows 11 (build 26340.9767), Microsoft has applied some changes to the Quick Settings panel (accessed via the taskbar).

In the release notes, Microsoft says: "We're making it faster and easier to check the performance of your internet connection in Windows. Windows Insiders can now run an internet speed test and see the results directly in Quick Settings, without being redirected to a browser."

As it stands, when using this feature it simply opens an online speed test in your default browser — but it's in the Bing search page (giving Microsoft some web traffic, of course). With this change it's actually a native speed test that works in Windows 11, as it should always have been in the first place.

You can start the new speed test from Quick Settings, or access it by right-clicking on the network icon in the system tray on the right of the taskbar.

Note that this is just in testing for now, and it's still at an early stage (the Experimental channel), but it should eventually come to all Windows 11 users.

Analysis: fixing Windows 11 includes getting rid of this kind of nonsense

It's useful to have a built-in speed test right in the OS for those occasions when you're curious to learn what kind of online speeds you're getting — maybe you're streaming a video and it's buffering or glitching, and you're wondering if that's the fault of the service, or whether your internet connection has hit a rocky patch.

However, it's hardly convenient to be redirected to a web browser to have that test conducted.

Microsoft says of this change: "This updated experience builds on the internet speed test we previously introduced for Windows Insiders, bringing the test and its results directly into Windows."

Which makes it sound like the current implementation was always just a stopgap or a temporary stepping stone, but let's be honest about this: we all know it was another way that Microsoft was trying to push traffic to Bing.

It's far from the only time that Microsoft has tried this kind of trick, and most recently, we've witnessed the Bing Wallpaper app once again crowbarring in Bing searches for people who merely click on a blank part of their desktop (see the further explanation of that incident here).

No, the Bing Wallpaper client isn't part of Windows 11 by default, but it's nonetheless a piece of Microsoft software, and it demonstrates that the company's attitude is still leaning towards exploring annoying ways of promoting its own services — this is another great example — at the cost of the user.

As one Redditor observed of the current Bing-laden internet speed test in Windows 11: "This is the kind of stuff that pushed me to Linux. Overall it wasn't too bad, but Linux (Fedora in my case) feels like a breath of fresh air."

All of these kinds of practices need to end, and at least this fresh move in testing is a hopeful sign that Microsoft is finally realizing this, given that it's battling to fix Windows 11 and change public sentiment around its desktop operating system this year — needless Bing promotion certainly won't help that cause.

• A new Windows 11 feature is in testing, but it doesn't work yet
• It's an option to enable Intel's 'Advanced Performance Extensions' or APX to boost the performance of some apps
• This will benefit Intel's next-gen CPUs, and AMD's processors will be in line for this speed boost eventually, too

There's a new Windows 11 feature to boost the performance of apps that's been spotted in testing, but only PCs with Intel's next-gen desktop CPUs (codenamed Nova Lake) will benefit from this initially.

Windows Latest noticed that a regular Windows 11 watcher, XenoPanther on X, posted a screenshot of an option for 'Advanced Performance Extensions' (APX) which is present in a recent preview release of Microsoft's OS (build 29695).

Intel explains that APX "expands the entire x86 instruction set with access to more registers and adds new features that improve general-purpose performance."

This means that app developers can take advantage of this feature to speed up the performance of their software. How much of a boost any given app might get is, as ever, going to be variable, but some applications might end up with a nice performance bump (games probably won't get much benefit, in case you were wondering).

There are a couple of catches here, however, including that apps will have to be recompiled to take advantage of APX, although Intel observes that actual changes to the application's code aren't expected to be necessary.

It's worth noting that Linux already added support for APX some time back.

Analysis: there are barriers to entry – but AMD chips will get this

Right now, this APX toggle is present in Windows 11 test builds, but it doesn't do anything at all, which is hardly surprising given that there aren't any Intel CPUs which support the feature yet. What this shows is that Microsoft is preparing the ground for this capability, which will be supported with Intel's next generation of desktop silicon. These Intel Core Ultra Series 4 processors are expected to debut early in 2027, so they are still some way off.

Existing generations of silicon from the Intel Core Series 3 families and earlier won't ever get the benefit of APX, as this is something that needs to be baked into the chip at production. So, existing PCs will not get the speed boosts from this feature, which is a shame.

Theoretically, this could be an additional reason to upgrade your CPU, but there's another catch here in that Intel's Core Series 4 CPUs require a new processor socket, and therefore a new motherboard for your PC. So that means an extensive upgrade for a desktop PC – or an impossible one for laptop owners – meaning that both Intel and Microsoft could be hoping that APX is another way to shift some new laptops in difficult conditions when the market is dipping.

How about AMD processors? This is an Intel technology, as noted, so you might assume you can forget about seeing APX with a Ryzen CPU, but actually as Windows Latest points out, Team Red is collaborating with Team Blue to get the benefit of this feature.

With AMD involved, it's more likely that developers will use the feature, and that'll help Intel's case for its usefulness. However, again, the support needs to be baked into the chip, so the earliest this might happen on the Ryzen side would be the Zen 6 family – or possibly the following generation (rumors seem to favor the latter). At any rate, support is coming for AMD chips as well.

Meanwhile, some of the online commenters remain skeptical. One Redditor observes of this potential speed boost: "I'll believe it when I see it!"

Others aren't happy about the support here, and the need for an upgrade, complaining: "Imagine locking a performance boost behind a platform..."

But as noted, this is a feature that requires new hardware in the chip itself, it's not something that can be added to finished silicon.

As far as Windows 11 itself is concerned more broadly, Microsoft is busy working to boost performance in all kinds of ways, including applying a couple of new tricks to speed up the launch times of apps and menus. There's also a major goal to ensure that Window 11 runs better on 8GB of RAM, too.

As the AI buildout continues and models improve from generation to generation, fears persist over how useful this technology will actually be in practice and whether AI will reach a ceiling before it makes a meaningful impact.

Although roles and responsibilities, and jobs, are being replaced in the name of AI, does that mean it's changing things for the better?

Energy scarcity

Microsoft CEO Satya Nadella was speaking at the World Economic Forum in Davos in 2026 about the impact of AI on the global economy and the business world.

In his remarks, Nadella acknowledged the tensions at the heart of the AI expansionism that has captured not only big tech but the vast majority of the corporate world.

For AI to continue growing at the pace it has been – not just in terms of the compute required to train the models but also for inference as usage grows – we'll need plenty of data centers to churn through tokens. And for these tokens to be created, the AI pioneers will need plenty of energy.

Encountering bottlenecks

Burning as much electricity as AI companies plan on doing so for AI has caused alarm among the public – particularly as much of this has demanded infrastructure built close to populated areas in towns and cities.

Nadella acknowledged there has been a productivity gap between the hype around AI and the meaningful impact on people's lives – warning that without something to point to, unrest will bubble up and boil over into something more obstructive.

As time passed, we're seeing more tensions rise as people – and even legislators – object to new data center projects due to concerns over the impact on the energy grid, the environmental damage, and local communities.

Canon recently unveiled its new Dual Pixel 3D, or DP3D for short — an image generation technology making use of Canon’s proprietary Dual Pixel AF technology. The new technology piggybacks off Canon’s proprietary Dual Pixel AF technology found in many of its EOS mirrorless cameras, which splits each pixel on the camera sensor into two separate light-sensitive photodiodes.

This tech has delivered reliable and snappy autofocus in recent years — if the two images on each photodiode line up perfectly, the image is in focus, if they don’t line up then the camera knows which direction to move the autofocus motor to rapidly lock on.

The new Dual Pixel 3D feature can be used to create immersive three-dimensional images via Canon’s new Dual Pixel 3D Converter app which was released in October.

While launched in tandem with the hotly-anticipated Canon EOS R8 Mark II on September 16 — a full-frame which makes use of the new feature — it’s good news for owners like myself of any Canon EOS mirrorless camera with Dual Pixel AF, stretching back to older generation models such as the EOS R5 and EOS R6 Mark II, which will be able to benefit without a firmware update (although updating the firmware is always a good idea to stay up date with the latest features and bug fixes).

I downloaded the Canon Dual Pixel 3D Converter app to put it through its paces — here’s everything you need to know to get started…

How to take photos for Canon DP3D

To begin, you’ll need a Canon EOS camera with Dual Pixel AF. It’s safe to say if you own one of Canon’s interchangeable lens mirrorless cameras in recent years, then your camera will likely feature Dual Pixel AF and be able to make use of this new tech.

This is great news for users of older Canon mirrorless cameras like myself as my older EOS R5 and previous generation Canon EOS R6 Mark II will still be able to make full use of this brand-new technology.

Your camera will, however, also need to be paired with one of the compatible Canon RF lenses. The list goes beyond specialist Canon RF 5.2mm F2.8L Dual Fisheye or RF-S 3.9mm F3.5 Dual Fisheye, but it is a little limited and also won't currently work with older EF or EF-S glass. We published a full list of compatible Canon cameras and lenses that you can use to enjoy this new technology when it was first announced.

Canon says, “Specific shooting conditions and techniques are necessary to generate accurate depth information to eliminate blind spots, allow for the required depth of field, subject and background separation, subject depth and surface texture.”

In my experience the software works best when your subject is taken on a clean background (no bokeh) and without too many blind spots which make it harder for the software to create its 3D effect.

You also want an object that’s relatively flat with not too much depth; little bumps are fine such as small pieces of fruit on a tart, but large peaks in depth will cause more significant issues and make the effect much more noticeable and fake-looking.

Two of the compatible lenses Canon lists are the Canon RF 100mm f2.8L Macro IS USM and Canon RF 35mm f1.8 IS Macro STM, both of which are macro lenses. Yet, when I shot with both of these I later got an alert in DP3D warning me for shooting too close to my subject! The result still turned out fine, though.

To use the new software you’ll need to make sure you’re shooting not only in the RAW format, but Canon's Dual Pixel RAW (see above). This setting was found in the very first camera menu on my Canon EOS R6 Mark II.

Note that shooting Dual Pixel RAW will produce files roughly twice the size, take longer to write to your memory card, and can't be taken when using the electronic shutter.

Alongside 3D, Dual Pixel RAW can be useful for correcting minor parallax error, shifting bokeh or focus and reducing flare, but if you don't intend to utilize these capabilities within Canon's DPP, I would suggest turning it off when you don't need it to avoid compromizing the camera's performance.

Make sure your subject and focal point are in focus and that your shutter speed is fast enough to shoot handheld – if you’re in doubt shoot on a tripod. You’ll also want to make sure your exposure is correct so that the image isn’t too bright or dark. Canon recommends you don’t close the aperture down past f/5.6 and also keep the camera's sensitivity under ISO 1600 – a warning sign will be displayed on your image when you import it into DP3D otherwise and you won’t get optimal 3D results.

Installing the Canon DP3D app on Windows 11

With the image taken it’s time to open up the new Canon Dual Pixel 3D Converter app, which is currently only available for Windows PC running the latest Windows 11 operating system. Read our guide on How to plan a smooth Windows 10 to Windows 11 migration.

Canon also added “The application will initially be available free of charge”, though specifications and terms of use may change in the future – in other words, it’s initially free but you might need to pay to use it in the future.

When you're up and running with Windows 11 you’ll be able to run the installation wizard which can be downloaded from Canon’s site. Make sure that once downloaded you extract the ZIP file and then double-click the DP3dInstaller-W1.0.0.13.exe file from the unzipped folder to begin the installation.

Now you’ll need to choose which area you live in - you have the options: USA, Canada, Latin America, Europe, Middle East, Africa, Asia and Oceania. In the following screen you can choose the country you live in and also select your language. Once you’ve ticked the box to accept the license agreement you can hit next which takes you to the final setup screen, click Install here.

It’s also worth noting at the bottom of the same Canon site, you can download five sample images made up of a mix of food and flowers which you can play around with if you are yet to shoot with Dual Pixel RAW enabled, or perhaps you don’t own a Canon EOS mirrorless camera and what to see what all the fuss is about. Note, the sample files are downloaded as a DP3D _Sample.zip file and you’ll need to extract these files by right-clicking on the folder and choosing Extract before you can use these images in DP3D.

Creating 3D images in the DP3D app

The DP3D app allows users to fine-tune the degree of depth, making a more pronounced 3D effect or a more subtle ‘flat’ appearance. The technology also makes it easy to isolate your subject and remove or replace the background before outputting a 3D model or stereoscopic photo.

Dual Pixel RAW files and supported Canon EOS R8 Mark II JPEGs contain depth information derived from the slightly different views captured by the two photodiodes on each pixel. Using proprietary algorithms, Canon converts this phase-difference information into 3D shape data, which is combined with high-quality image data captured by selected EOS cameras as surface textures to create realistic and detailed 3D models.

Once you’ve loaded up DP3D you’ll need to click the folder icon at the top of the main window and then navigate to the folder where you’ve saved your Dual Pixel RAW files.

You have two main options – either create a 3D model which you can edit further in a 3D editor such as Blender, or you can create a Preview which creates a very short video with limited options, essentially doing a basic pan from left to right to show off the 3D depth effect. You also have options here to change the background, add a foreground and frame. You can also choose the quality of the video when you click Export Preview. Here you can also set the quality to High, Medium or Low quality (18, 8 or 4 Mbps), choose your own filename and where you want to save the video.

However, I found those options rather limiting within DP3D so I would instead click Export 3D Model where you’ll have the choice between OBJ and GLB files to edit further in your 3D editor of choice. You can also use Windows 3D viewer and even Preview on Mac to play around with these 3D files and explore the different angles produced by DP3D.

With the 3D file created you can use a program such as Windows 3D Viewer or Blender, which is free to download. In a program such as either of these options you’ll have way more options than DP3D for changing the angle, rotation and lighting, which is great for exporting still images. For videos, you’ll also be able to change the direction and duration of pans so you can get it looking perfect for what you need.

Conclusion

After playing around with DP3D for some time and with various pictures and subject matter, I think it’s fun and interesting that Canon has released it for free - after all, additional functionality to the cameras which we already own is certainly welcome.

However, the results have limited uses and more often than not look quite poor. Are there situations where somebody might need to get a little 3D animation from a 2D image? Sure — marketeers looking to animate food or product shots for videos and reels seems like a perfectly adequate use for Canon's new DP3D tech.

While it’s admirable that Canon is trying to squeeze the most out of the pixels taken in-camera, this is one of those situations where I sadly think AI can probably do it better.

Canon is trying to stretch and manipulate the pixels already there, whereas AI can create brand new pixels and frames based on what it thinks should be there, and that’s likely to create smoother results with fewer artefacts. For still images, the Rotate Object feature found in Photoshop Beta is likely to produce better results and is glimpse of what lies ahead in our editing futures.

• Windows 11 has an alternative take on Recall in testing
• It's an app called the 'Context Vault' offering a similar supercharged search experience
• In this case, though, you have to manually add content to the vault – there's no automatic regular screenshotting of the activity on your PC

Hate the idea of Recall in Windows 11? You wouldn't be alone, so Microsoft is trying a new spin on the idea of an AI-powered supercharged search which is a lot more limited than Recall – although the initial reaction has still been rather negative.

Windows Latest discovered that Microsoft is working on Project Pantheon, the current name of which is the 'Context Vault', and rather than being built into Windows 11 like Recall, this has to be installed from the Microsoft Store.

This isn't just an optional feature, then, it's an app you have to actively seek out, and it works quite differently to Recall in that it doesn't automatically take regular screenshots (snapshots) of the activity on your PC.

Instead, you must manually add the files or content you want to use with the Context Vault, so its AI powers can then make use of them.

To what end? Similar to Recall, you can ask the AI questions about the contents of the vault, or get it to organize material, planning the likes of a summer trip (the example Windows Latest gives) from documents, images, web pages, or even clipboard text saved to the vault.

Note that the app isn't yet officially available, and not listed in the Microsoft Store as such, but Windows Latest managed to find it nonetheless.

Context Vault doesn't necessarily need a Copilot+ PC – or a powerful local NPU, in other words, as it can leverage cloud-based AI – and you can give third-party AI like Claude access to the content in your vault, too, we're told. There's even a panel that tracks your AI token usage in the current build of the vault.

Bear in mind that this is still an early version of the feature, and it sounds pretty wonky right now. It's not clear if this app is something Microsoft will definitely carry forward to a release version for Windows 11, as ever with functionality in testing.

Analysis: is a retake on Recall going to fly?

On the one hand, the full user control over what content Context Vault can access compared to Recall could be a big selling point. On the other hand, using the cloud for running the AI processes is obviously more of a privacy concern (although you don't have to go that route to be fair).

Windows Latest has been led to believe that Microsoft is considering other ways to make the Context Vault more secure, including encryption and per-app access controls, as well as requiring a Windows Hello login for access (as Recall does) to protect the vault from possible local attackers jumping on your device.

As noted at the outset, the reaction to this idea in testing is generally skeptical, and many folks simply see it as 'yet more AI' that Microsoft is cramming into Windows 11 (although it isn't actually confirmed this is coming to the OS, of course).

As one Redditor puts it, this is a classic case of "solving [a] problem [that] doesn't exist."

Another agrees: "Yeah, Microsoft can't even be creative in thinking up something even marginally useful in an extremely far fetched scenario. Literally never in my life felt the need or wanted a tool like Recall or this equally s****y version of it. This problem was solved circa 2000 with bookmarks and Word/Excel documents. Not whatever convoluted trash this is."

In fairness, others are more positive, and there are Recall users out there. One commenter on the same thread says: "I use Recall all the time, it has been a gamechanger for me, I just wish it synced between my devices so I could look up something on my desktop I saw on my tablet."

The one thing that seems clear enough is that the anti-Recall folks aren't going to be swayed by a different take on the same concept, even one that sports a pretty radical change.

The kind of people this might persuade are those who most likely won't be drawn to comment on this idea in testing one way or another. I think it's certainly an alternative that's worth trying for Microsoft, provided it's implemented in a secure enough manner, of course.

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• Windows 11 is cutting some Copilot features on the right-click menu
• At the same time, a Microsoft exec said that "AI has already helped our design team accelerate significantly"
• The exec added that "we're even seeing designers contribute directly to code fixes" and this has caused quite a stir online with anti-AI pushback

Windows 11 is getting tweaked to cut back on Copilot functionality, while at the same time, a Microsoft exec has admitted that the operating system's design team are using AI to help them – which has provoked something of a reaction, as you might expect.

First up, the move to cut back on AI, which as Windows Latest reports consists of stripping out some Copilot options from Windows 11's context menu, the one which pops up when you right-click on a file or folder.

In a post on the Microsoft 365 Message Center, the company explained: "We're retiring the OneDrive Copilot Summarize, Create FAQ, Compare Files, Ask a question, Explain image, Explain images, Compare images, Extract text actions from the File Explorer context menu."

These are Copilot actions which are tied to OneDrive, then, that folks who don't use Microsoft's cloud storage wouldn't have seen anyway.

You can still reach these options using the 'Ask Copilot' icon in the right-click menu, so overall, this is a streamlining change for the better (albeit one that some users won't even realize has happened).

It's not being implemented right away, but from mid-November, and by the end of the year, these options will have disappeared from Windows 11's context menu.

Now for the more controversial development that Windows Latest also noticed, namely a comment from a Microsoft executive on X. Diego Baca, who is VP of Design for Windows, posted in reply to a comment pushing for "Windows design unification" (better consistency) and using AI to help to that end.

Baca observed: "On AI agents, 100% agree. I'm not the best person to speak to the engineering side, but AI has already helped our design team accelerate significantly. We're even seeing designers contribute directly to code fixes that improve craft, consistency, and quality. That's exciting progress, and we're only getting started."

The key part of this that's getting picked up on social media is the admission that AI is being used by the Windows 11 design team to ensure tasks are completed more swiftly, and that it's helping designers to contribute code fixes. As you might guess, there are some unhappy people out there due to this admission.

Analysis: a chorus of noticing

As noted, the reaction on Reddit has been predictably full of barbs and withering fire at Microsoft. This upvoted post complains: "We noticed [the use of AI in coding], the rapid decline in quality, every patch bricking at least some devices, major releases bricking entire fleets, and the introduction of major security vulnerabilities that are easily exploitable makes it clear the codebase is being vibecoded with insufficient code review and testing."

In fact, there's quite a chorusing of "we noticed" or "we know" regarding Microsoft turning to AI in coding, as well as the inevitable "slop" accusations, including the likes of: "And this is how Linux goes mainstream, not because it becomes better, but because Microslop becomes unusable thanks to AI integrations." (I should note that as you may have seen, Linux has its own controversies with AI, but that's a whole other story).

At any rate, I think we need to dig down a bit into the meaning of Baca's statement here. When the exec says, "AI has already helped our design team accelerate significantly," it's a pretty broad statement – perhaps referring to assistance on the design brainstorming front more than coding.

Yes, designers may "contribute directly to code fixes" now with the help of AI, but how often is that really happening? I'm guessing not that much, and that this work is more about simple interface tweaks and polish than anything, and this code will be passed to software engineers to be fully checked and reviewed – the main hope being it saves a bit of time. There's a lot of the Windows interface to fix, after all.

I think the hostility here is a bit unfair given that Baca is just trying to be honest about what Microsoft is doing, and I don't think the reality is that Windows 11's interface is suddenly being vibe-coded (by AI) to any great extent. What's happening is that Windows 11 users are jumping on this and venting because they're fed up of too many bugs, and that has nothing to do with the design team.

For me, this is about Microsoft's broader failure with Windows 11 bug testing and flaky updates, which isn't something you can lay at the door of AI as such. Rather, this is about the overall QA (quality assurance) processes which aren't picking up these problems and sorting them out before release (whether any given bug was introduced by AI, or not).

This is what's really the root cause of the outbursts on social media here, and the comments about patches going wrong and general unreliability. And this is what Microsoft really needs to fix, and fast – it remains the biggest problem with Windows 11 that needs addressing.

• The Bing Wallpaper app has seen an annoying feature reintroduced
• This means those clicking on a blank space of the Windows 11 desktop will get a Bing search fired up in their default browser
• Unlike the last time Microsoft tried this stunt, there's no way to disable the ability this time around

Microsoft is up to its old tricks again, except even worse this time — with a scheme whereby Windows 11 users with the Bing Wallpaper app installed are sent to its search engine with a mere stray click on the desktop.

Windows Latest reports that an old, potentially off-putting shortcut has been reintroduced to the Bing Wallpaper app, whereby if you click anywhere on the Windows 11 desktop, a search in the (separate) Bing engine is fired up (in your default web browser).

If you recall, this actually happened a year ago, but Microsoft reversed course, although now appears to be trying to reintroduce this feature. The difference is that this time around, Windows Latest notes that there's no option to turn off the functionality (which was present last year, in the app's settings).

However, there are some points to note here: firstly that you must choose to install the Bing Wallpaper app (which cycles Bing images daily for your desktop background). It's not a default Windows 11 app, so this isn't something Microsoft is piping directly to Windows users.

Secondly, the Bing search doesn't trigger every time you click on a blank piece of desktop (a space without a file, folder or icon). It does this once, and then the Bing search won't be triggered for another 24 hours. You'll only experience this once per day, in other words.

Analysis: Microsoft must do better

Just because this app isn't part of Windows 11 by default doesn't give Microsoft the excuse to push functionality like this. It's just going to annoy or even confuse people if they accidentally click the desktop and end up with a Bing search being loaded.

The search relates to the wallpaper image, in case you're curious, and fair enough – maybe some folks might be inquisitive and actually want to know more about the picture by clicking on it.

However, this should be an ability that's optional and can be turned on, not something that's enabled by default. What's worse still is that in this new incarnation of the click-to-Bing farce, you can't even turn it off if you want to do so.

Why is Microsoft engaging with this kind of nonsense to promote its own services? I really can't begin to guess given that 2026 is the year of fixing Windows 11 and curing all the ills around the OS, and while this app may not be part of the operating system, it's still a piece of official Microsoft software – and there shouldn't be any place for this cynical Bing traffic grab or any other remotely anti-consumer practices.

Whether it's a part of Windows by default, or not – or whether you have to be 'foolish' to install the Bing Wallpaper app, as some online commenters argue – is besides the point. Microsoft is supposed to be better than this now, but another episode with a recent Harry Potter advert in this Bing app – one that provoked a considerable rebellion – suggests that it very much needs to try harder.

Organizations can be under huge pressure to make changes to their IT estates. In particular, there are two drivers of this change.

Firstly, there’s the pressure to innovate. The rapid advancement of technology and AI coupled with shifting business priorities can put organizations under pressure to adopt the latest tools, with vendor messaging advocating for the speed of adoption. Simultaneously, ongoing inflation and slower economic growth can mean there is a need to find more cost-effective solutions.

Secondly, there’s the change that is forced. Operating systems might be reaching their end of life and need upgrading, or there are outages and cyber threats to defend against. There have also been several recent examples of software vendor prices rising significantly due to ownership changes, like with VMware, and companies therefore needing to find new solutions for their budget and reduce vendor dependency.

However, in dealing with these pressures, the trap companies can fall into is trying to deliver all of this change at once and within a small timeframe. Yet in doing so, this change becomes harder to manage and the risk of outages and system failures increases. So, how can they deliver any change that is needed safely?

The dangers of too much change at once

The main challenge in changing too much too quickly is visibility. Companies have to oversee complex IT estates – on-prem systems, the cloud, servers, end-user computing, networks – where change is constant and security updates are largely automated. As this change happens, there are more interactions between older and newer systems, different operating systems, different products, and this means the risk of failure can increase.

Configuration drift, for example, occurs when changes, which haven’t been recorded or authorized, cause a system to drift away from its intended baseline (of how it should perform). Without the right monitoring software, this drift can remain invisible, creating added risk to every subsequent change – and that’s when an outage or cyber incident happens.

In May, for example, Google Cloud suspended Railway’s production account without warning because Railway couldn’t see that the platform had a technical dependency on GCP that caused it to fail, leading to an eight hour outage.

What’s more, as we’ve seen with the adoption of AI tools, change can happen faster than training and processes can keep up with, further increasing the risk of disjointed workflows, security vulnerabilities and data breaches. But even when teams can see the changes that need to take place, they can also be faced with the challenge of deciding how to modernize their existing applications.

Finding alternatives to the buy-or-rebuild dilemma

Of course, trying to slow down change isn’t always possible. A company might have been priced out of its VM software or needs to upgrade its applications for security purposes. In this position, traditionally, IT teams have had to either buy new software or rebuild existing apps, which can both be expensive and time-consuming.

This can lead to vendor lock-in, as IT doesn’t see a viable alternative and doesn’t have the engineering capacity to rewrite applications tied to their original operating systems.

To overcome this dependency, as agentic coding matures, organizations may increasingly opt for a third option: using AI agents to build their own replacements for SaaS tools. But while this can appear to remove vendor lock-in and license costs, it also transfers responsibility for maintenance, security, configuration and ongoing support in-house, making it even harder to oversee change without additional resources or capacity.

However, there is another route. Packaging and migrating existing applications can allow teams to modernize without taking on the burden of building from scratch. By packaging an app’s or virtual machine’s existing operating system dependencies, teams can move them into containers and modern desktop infrastructure without needing to rewrite the app.

The app maintains its existing behavior, but can now work on the latest operating systems and in workspaces like AWS.

Delivering change at scale

With a modernization strategy formed, how do teams gain ongoing visibility across their entire estate and know when apps need to be packaged?

With so much change taking place, manual monitoring and reconciliation processes will struggle to keep up with the demands of infrastructure modernization. Instead, teams should consider using configuration monitoring software to gain continuous visibility into change across their entire estate.

Drift detection tools, for example, can automatically measure change across an estate against established configuration baselines and alert IT teams whenever a change triggers a system to move away from its baseline. This means that an automated patch by an external provider or a manual intervention by a user gets detected and flagged to IT, changes that might otherwise go unnoticed and trigger an outage further down the line.

Imperatively, this estate-wide visibility means teams can see and understand the impact of any unauthorized changes taking place and intervene before they lead to failure.

It also gives them the information they need to understand how a transformation project might interact with the systems currently running in their estate, helping to inform the feasibility and implementation of the proposed changes and deliver more targeted, impactful and safer change.

Alongside configuration visibility, it’s also possible for teams to automate the packaging and delivery of apps, saving them a large amount of time and cost in manually performing a typically laborious task. This subsequently creates a repeatable and scalable process that enables apps to be regularly deployed and made available to users.

Escaping the trap

With so much pressure to change, above all, escaping the transformation trap requires a mindset shift. It involves, one, seeing change as an ongoing problem to manage, and two, not attempting to carry out too many innovation projects at once.

Small, incremental changes can deliver more meaningful impact and ensure systems are properly deployed or integrated, while this targeted approach also provides more capacity for IT teams to deal with any forced change that might surface.

Yet by prioritizing change visibility, IT teams can mitigate forced change by addressing risks earlier and seeing when upgrades are needed in good time. And by automating processes like packaging apps, they gain more capacity to prioritize innovation projects as routine changes take place faster and more securely.

When these processes are joined together, instead of grappling with an overwhelming and uncontrolled level of change that increases risk, teams can ensure change remains a driver of security, resilience and innovation.

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• Enterprises are being forced into software upgrades that they can't really get out of, report finds
• 91% have upgraded hardware in the past three years just to maintain software
• Enterprise protection legislation and software support obligations could be the answer

Legacy tech has become something of a hot topic in recent years with AI, but while hardware and data foundations have been core to the discussion around artificial intelligence, a new Origina report has revealed that around one-third of enterprise IT budgets are being earmarked for software upgrades.

The report found that 28% of large enterprise IT budgets are being spent on vendor-driven changes that they would not have otherwise chosen in a trend the company's calling a 'software tax'.

But more than just consuming budgets, software changes are also impacting worker time and leading to potentially lost productivity as a result.

Software upgrades cost more than you might think

"That's not a one-off cost, it's a recurring drag on capacity, and every year it repeats is a year those people aren't working on the initiatives that actually differentiate the business," said James McGlennon, former EVP and Global CIO of Liberty Mutual Insurance.

More than four in five (84%) report that senior engineering staff always or often spend time dealing with vendor-driven software changes, taking them away from more meaningful work that they're employed to do. A similar number (80%) also reported that leaving a major software vendor could lead to widespread disruption within their business, ultimately forcing them to stay with that software vendor and enforcing vendor lock-in – with exit costs and migration technicalities to blame.

More than three-quarters now agree their software vendor dictates the timing and terms of changes, not them (78%), and that they have very little negotiating power (76%).

In many instances, enterprise customers don't even feel that an upgrade was totally necessary. Nearly all (91%) had retired fully functional hardware within the past three years just to comply with software vendor support deadlines. While Origina doesn't specifically state it, Windows 10's end of support is likely a large factor in this statistic.

Looking ahead, most (91%) support legislation to provider greater protection for enterprise customers, and four in five (82%) support software support obligations similar to what the EU has introduced for hardware repairability.

There has been an explosion of AI-powered products released over the past four years, with most built on top of leading foundation models. Startups, enterprise software vendors, and even large incumbents route their core intelligence through APIs from the leading AI labs, and a growing but still narrow set of open-weight alternatives.

This arrangement has most certainly enabled a genuine burst of AI products, but it has also created a structural fragility which can be remedied through the democratization of model creation and training.

Defining a wrapper

In its simplest form, an AI wrapper is a product whose core function is an interface sitting on top of a third-party foundation model's API, with little to no proprietary technology beneath that layer.

That approach can have a short lifespan and Jasper can illustrate that. Once the standout of the AI writing boom with a $1.5 billion peak valuation, Jasper saw its core market vaporized when ChatGPT introduced free, native writing features. Its revenue fell from approximately $120 million in 2023 to approximately $55 million in 2024. Jasper did survive, by redirecting its efforts toward enterprise workflows and brand-specific training.

Generally speaking, analysis shows that, among AI startups that have wound down in the US in 2025, 81% were "wrappers and apps". Copilots and assistants layered on foundation models, AI productivity tools, content generators, and vertical SaaS products whose core differentiation was an LLM front-end rather than proprietary data or infrastructure.

In Jasper’s case, and in many others, what pressured the company wasn't a competitor out-executing them. It was the platform they built on top of simply absorbing the feature they'd built their company around.

Not all wrappers are equal

It would be a gross generalization to say that all AI wrappers are the same. A thin prompt-and-display wrapper is different from a product that does heavy retrieval, tool orchestration, agentic workflows, or domain-specific fine-tuning. The latter can have real defensibility even while sitting on top of a third-party base model. The model call is one component, but certainly not the whole product.

Some companies also use multiple foundation models or fine-tune/distill their own smaller models for specific tasks after prototyping on a frontier API. Open-weight models add variety too as not all wrappers call a closed-source frontier model.

The wrapper problem

Regardless of where an AI product sits on the wrapper complexity scale, they all share the same problem. When a company's core value proposition depends entirely on an API call to a third party, that company does not fully control its own product.

It cannot control pricing changes, rate limits, deprecations, shifts in model behavior, or the possibility that the platform provider decides to compete directly in the same vertical.

This is not a theoretical risk, though how much it affects any given company likely depends on how much of its value sits above the model layer.

The economics of the wrapper

So what is the defensibility of an AI wrapper in practice?

The short answer is that it comes down to intellectual property.

Where a company benefits from an existing distribution advantage, established customer relationships, regulatory licensing, network effects, or years of accumulated brand trust, model-layer differentiation may not matter as much. But if what makes a wrapper distinctive is observable from outside the organization and replicable by a competent competitor within a limited period, it seems reasonable to expect it will eventually be replicated.

That is the unfortunate economic truth beneath the wrapper model. The cost of replication is near zero for the platform that controls the underlying model, and near total for the startup that doesn't. A wrapper's moat, if it has one, has to come from something other than the model call itself. Absent those, a wrapper is unlikely to have a lasting competitive advantage.

The barrier to the solution

At this point, you might be inclined to agree that model ownership is one solution to this problem. But that's easier said than done.

The reason so few companies own their models is not a lack of desire. It’s, more plausibly, financial. Training a frontier-competitive model requires enormous compute budgets, specialized talent, proprietary or expensively licensed data pipelines, and the infrastructure to run reinforcement learning at scale. These costs put model creation out of reach for all but the best-funded companies.

For companies to build on their own models as a norm, this barrier has to fall meaningfully. Several forces are already pushing in this direction, open-weight models being one of them. But even then, hosting and fine-tuning an open-weight model is no small task. It still demands technical talent and compute infrastructure most startups don't have access to.

True democratization of model ownership

Democratization, in this context, doesn't mean every company training a frontier-scale model from scratch. That goal is neither realistic nor necessary. It means democratization reaching the training layer the same way access to AI has been democratized for end users.

Democratizing model training and ownership could be what gives founders a lasting chance at turning an idea into a business that doesn't eventually get absorbed by a model provider as a simple feature. And if that happens, the stakes may go beyond individual business durability.

As things stand, access to models is cheap. Access to training is not. This needs to change. The ability to train an AI model should be fully democratized so that the value, the control, and the responsibility can be distributed rather than centralized.

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• Windows 11 has a new preview build with a change to boost performance
• It's just for Edge to begin with, and it means that if the browser is already running, new instances of the app will launch faster
• Microsoft's plan is to introduce this to a wider range of Windows 11 apps

Microsoft is making another move to tune up Windows 11 so it launches some apps more swiftly, although this is just in testing for now.

Windows Latest noticed that the latest build in the Release Preview channel (the final stage of testing) applies a change to bring in "accelerated app launches" for Microsoft Edge in a certain scenario. Namely that when Edge is already running, further instances of the web browser being opened will appear at a faster speed.

Microsoft tells us that: "When you select a link or open Edge from the taskbar or Start menu while Edge is already running in the background, Windows now routes your request directly to the running instance instead of first starting an extra, temporary process."

This shortened launch path for when Edge is 'warm' (already running) means a considerably faster launch time, as Windows Latest verified.

The tech site also points out that only the first 'cold' start of the browser will be slower (with the longer launch process), as even if you open Edge, then close it, if you run the app again, it's still 'warm' as the relevant processes have spun up in the background.

Analysis: broader benefits, but why did all this take so long?

If you don't use Edge (and that's doubtless most of you), then you might be thinking: what's the big deal anyway? Well, this is a first step in testing towards a grander goal, namely a "broader Windows capability that any app can adopt to speed up its own launches" as Microsoft explains in the preview build's release notes.

Eventually, we could see multiple apps benefiting from what's an apparently considerable performance boost when launching, assuming that they're already running (and warmed up). It could be some time before Microsoft opens this up to third-party developers, mind, and then they have to get around to implementing the functionality.

At any rate, it's a useful new feature, and one that comes on top of Microsoft's existing work in enabling preloading for certain Windows 11 apps, notably Edge itself and File Explorer. We've also witnessed the introduction of the Low Latency Profile feature, which represents a nice CPU speed boost for Windows 11's menus and also apps.

The main question is: why did it take Microsoft so long to get around to these performance improvements? File Explorer being slow was a complaint from the start with Windows 11, and yet we've had to suffer this – and various other instances of sluggishness – for years before they've been fixed. It was only this year, when Microsoft properly turned its attention to fixing Windows 11, that we've seen solutions flooding forth.

And don't get me wrong, that's great, but it also shows that Microsoft didn't take these performance problems seriously until the Windows 11 userbase was more or less in open rebellion over AI features being pushed while all these issues continued to frustrate people. It's clear enough that the optimization of Windows 11 could have been done in the past, if Microsoft was willing to graft at the task, it was just that the company didn't see it as enough of a priority – bafflingly.

I concede that even when Microsoft is doing a good thing here, I'm moaning, and that in this respect the company can't win. Fair enough, and all's well that ends well as they say, but what I'm really hoping is that Microsoft can learn from this in the future and act earlier to troubleshoot its desktop OS when it clearly has problems.

Whatever next-gen Windows turns out to be, it needs to take this new attitude and go forward with it, until Microsoft has an operating system which is running smoothly for daily operations in every important respect, from performance to usability – not to mention reliability and stable updates which is still a big issue that's left to resolve.

When a competitor appears in an AI answer and your brand doesn't, the obvious reaction is to publish more content. But that skips the useful questions like:

Which answer?

What was the person asking?

And did the assistant reference your competitor's website, an independent review, or neither?

Those questions shaped this review. To test the platform, I compared Saily eSIM with two of its close competitors, Airalo and Holafly. The aim was to find something worth investigating and see whether the Brand Radar data holds up to the hype.

My hands-on work focused on its existing AI Visibility Index, moving from combined-platform reports to ChatGPT-specific checks using recent data. Here’s how it went.

Ahrefs Brand Radar: Plans and pricing

First, separate the two purchases. The AI Visibility Index lets you search data Ahrefs has already collected, and the Custom Prompts run the questions you choose from the point you set up tracking. Now, Brand Radar is sold separately, so you don't need to buy the wider Ahrefs SEO suite first.

Ahrefs pricing breaks the options down as follows. These are monthly US-dollar prices, accurate as of October 15, 2026.

Lite, Standard, and Advanced subscribers already receive 150, 300, and 600 custom checks per month, respectively. That gives existing customers a small starting allowance without buying index access.

The catch is that checks aren't interchangeable with prompts. Each platform, location, and scheduled run consumes allowance. At the standard one-check rate, 20 prompts checked daily across three platforms and two countries use 3,600 checks over 30 days. By the way, Claude consumes eight checks per execution, so its allowance disappears faster.

For an agency researching different brands every week, the index has a clear purpose. But for a small, growing business following ten buying questions, paying for the entire archive before using those smaller allowances feels premature to me.

Ahrefs Brand Radar: Features and specs

AI Visibility Index

I entered Saily, an eSIM provider, along with its competitors (Airalo and Holafly), and opened the overview.

Mentions, citations, estimated impressions, and AI Share of Voice sit together, with platform comparisons underneath. The index already includes historical data to explore, which is what makes it attractive. That means you can investigate a competitor before deciding which questions deserve ongoing tracking.

Saily's smaller mention footprint gave the investigation a direction. Scrolling down brought its own performance history and associated topics into view, making it easier to move from a broad comparison to a specific question.

You still need to understand the sample here. Ahrefs' methodology combines search-informed questions, including Google's People Also Ask, with semantic expansion. These aren't private conversations gathered from ChatGPT users. Its newer AI-adjusted volume applies platform-specific usage estimates to a parent keyword's Google search volume.

I prefer that qualification to treating Google demand as AI demand, but it remains a model. A rising line isn't proof that more customers saw your brand, and dataset changes can affect comparisons. AI Share of Voice also depends on the brands and filters you selected.

Competitor gap analysis

The comparison became more useful when I isolated ChatGPT answers mentioning competitors but excluding Saily. This turns an intimidating report into a reading list. You can investigate the questions behind the gap instead of guessing what another brand is doing right.

Opening “What's the best eSIM in the UK?” revealed a stored response dated October 9. Brand Radar's tracked-brand panel listed Airalo and Holafly, with no Saily. The question also mixed domestic mobile service with travel data, a useful reminder that apparently relevant prompts can cover different needs.

That response exposed some friction too. Parts of the answer displayed raw markup, which made it harder to read. The sidebar reported no citations while listing pages under “Found but not cited.” Keeping those categories separate was helpful, even if the presentation needed tidying.

This is where your judgment matters. A missing brand is worth investigating when the question fits its product. It isn't automatically an instruction to write an article, and a brand mention doesn't necessarily amount to a recommendation.

AI citation analysis

The Cited pages report was the natural next stop. Its page-type view groups results into formats such as articles, landing pages, and reviews. That gives you a starting hypothesis about the material appearing around your category before you inspect individual URLs.

Switching to individual pages made the distinction between discovery and citation much clearer. “Found in” includes pages retrieved while producing an answer. “Cited in” records answers that actually cite the page. A source can appear frequently in the first column without receiving much visible credit in the second.

TechRadar's own best eSIMs guide appeared alongside review website listings and provider-owned pages. More interestingly, the guide's “Mentions on page” indicators included Saily even though this investigation had excluded Saily from the AI answers. A roundup can mention your product without an assistant carrying that mention into its response.

That is a better research lead than a score alone. You can inspect the source's coverage and ask what information an answer used or omitted. However, “Other” wasn't a ready-made outreach list because Holafly-owned URLs were present too. So, always check actual domains and your brand setup before treating a result as independent coverage. Not to forget, getting included on a cited page also doesn't guarantee a future AI recommendation.

Brand sentiment analysis

Ahrefs initially described sentiment analysis as a work in progress, but a native Sentiment report was available in my review account. In simple terms, this report adds a useful second question to the comparison, i.e., when AI mentions a brand, how does it describe it?

Saily's lower mention volume sat alongside a higher positive-response share than the two competitors. I found that contrast useful because, in my opinion, being discussed less often and being described less favorably are different problems. The former might prompt discovery research, while the latter calls for examining what the answers say.

I didn't independently audit the classifications, so this isn't a verdict on sentiment accuracy or customer satisfaction. So, use the report to identify answers worth reading, then check their wording.

Custom prompt tracking

The existing index is convenient until the question you care about isn't there. Custom Prompts let you add it, choose platforms and locations, organize questions with tags, and schedule daily, weekly, or monthly checks. Its history starts with collection, not arriving pre-populated.

For ChatGPT and Perplexity, Brand Radar also exposes Fan-out queries (extra searches an assistant generates while working through a prompt). They suggest useful supporting topics, although Ahrefs only shows the queries returned with the response, not necessarily every search the model ran.

The public indexes cover ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, and AI Mode. However, platform coverage needs a closer look than a row of logos. Plus, Claude is available for Custom Prompts rather than in the public index. Grok collection is paused, with historical data retained. Such distinctions are pretty vital in case either platform is central to your audience.

AI research tools

Brand Radar also offers search-demand and web-visibility reports. YouTube research covers titles, descriptions, and transcripts, while Reddit visibility focuses on threads appearing in Google results. TikTok has its own beta report.

These surrounding reports have separate Ahrefs-plan and beta conditions, so don't assume an index purchase includes every sidebar item. I see these as ways to investigate surrounding coverage, not proof that every mention influences an AI answer.

There is more assistance than the old “research only” description suggests. “Ask Ahrefs” is built into the interface, and the agent previously called Agent A now lives under Letaido, which offers additional analysis and automation.

Is Ahrefs Brand Radar worth it?

The strongest reason to buy Brand Radar is the ability to move between a competitive overview, an individual answer, and its source material. My eSIM investigation showed why that matters. Having lower visibility, positive sentiment, source-page mentions, and actual AI recommendations can tell different stories.

For agencies and in-house teams investigating several competitors, that depth can justify paying for research access. Entity matching, which distinguishes a company from unrelated uses of its name, is another useful safeguard when your brand isn't as distinctive as Saily.

For a small business with a short list of questions, the smaller Custom Prompts allowance or a limited manual check is a more proportionate start. The expensive part shouldn't arrive before you have a plan for using the findings.

I came away with useful leads and a clearer understanding of their limits. Brand Radar makes the evidence easier to inspect. Whether that evidence becomes better content, corrected information, or relevant coverage still depends on what you do next.

FAQs

There’s never been so much change happening across an IT estate. And it’s constant. On a day-to-day basis, IT teams might be creating and monitoring user permissions, deploying any scheduled operating system (OS) updates or integrating new third-party software tools. This is not to mention all the change carried out by external providers, outsourced teams and employees.

Change itself is not a problem. Indeed, it’s essential for ongoing security, resilience and modernization. Change that isn’t actively managed, however, is a different story. This could be change that goes unnoticed, like an external patch that automatically updates a system without IT oversight, or change that is forced, like rushing an OS upgrade to meet an impending end-of-life deadline.

The trouble is many organizations have to monitor incredibly large and complex IT estates that can contain hundreds or thousands of business-critical endpoints. And so much change is now automated or carried out by external parties. Consequently, without the right software and processes, this means that IT teams can have limited visibility into many changes taking place.

These changes might seem small or innocuous on their own. But left unmanaged, they mean systems are drifting away from their intended baseline, which adds risk to each subsequent change – and that’s when systems can fail and damaging outages occur.

Change that is not managed effectively also increases vulnerability to cyber attacks. Misconfigurations or gaps in security coverage materially enabled more than 90% of cyber incidents investigated in recent research, showing how seemingly minor changes or lapses in oversight can create openings for attackers.

So, what are some of the ways IT teams can effectively control change across their estate?

Here are three key steps:

1. Understand

The first step is to gain greater visibility into change across the IT estate, so teams can identify potential risks and understand their impact before they cause disruption. Achieving this requires the use of automation tools that enable continuous drift detection. With this in place, IT teams can monitor their entire estate – servers, end-user computing, networks, on-prem and cloud – for configuration change in real time.

To do this, the technology needs to scan every layer in the infrastructure to establish a known-good configuration baseline to use. The platform can then flag any time a change causes a device to drift from this intended baseline, while also allowing teams to gain visibility on the gap between the change that was expected and approved and what is actually running in the estate. This means a manual fix or external patch gets flagged, rather than flying under the radar until it triggers an outage or failure.

2. Respond swiftly

One of the key challenges IT teams face is reconciling and approving change. Inevitably, with so much change taking place, trying to manually perform this process leaves room for errors and can delay approvals. So, to reduce manual effort, the next step is to automate change approval processes, using software to automatically reconcile every detected change against approved baselines and validate what steps were taken against those planned in the change request.

With this, teams will then be able to rapidly respond to unapproved changes and use their time to take action on issues before they escalate and impact operations. More broadly, this also speeds up the delivery of new upgrades or projects and gives IT teams greater freedom to innovate safely.

3. Protect

There’s always a risk of a cyber incident taking place. But when teams understand what’s happening in their estate and accelerate their approval processes, they can also detect and remediate issues faster and reduce their mean time to resolution. This can help to mitigate a failure escalating into an outage, or minimize the cost of an incident, while also strengthening overall resilience.

Protecting systems also means ensuring they are compliant and that teams can export defensible evidence on demand whenever a regulator requests to see it. This also highlights why continuous compliance that generates clear and auditable trails is so important. Otherwise, audits are often accompanied by a time-consuming, manual search through spreadsheets and screenshots for evidence, and this also means information can be out-of-date by the time it’s collected.

Controlled change unlocks innovation

IT change is constant, and with the rapid development of AI and cloud tools, it’s also only going to increase. This provides more opportunities for companies to modernize, but it also creates more risk when it happens without control.

To maintain control and prevent damaging incidents from taking place, IT teams need to understand when configuration change happens across their estate, accelerate the rate at which change can be approved and reconciled, and protect their systems through faster remediation and establishing a real-time audit trail.

While it can be easy to put off resilience projects in favor of innovation, the threats facing IT are becoming stronger and more varied, so controlling change has to be a key priority. If anything, it provides the foundation to innovate successfully and safely.

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For eight years, Surfer had one job, and that was to help you rank on Google.

You wrote your content, Surfer told you what to fix, and your page climbed. Then the whole AI wave showed up. People started asking ChatGPT and Perplexity for recommendations instead of scrolling through blue links, and ranking first stopped meaning what it used to.

So Surfer built its own AI Tracker. It's a paid add-on that watches whether AI engines mention your brand when people ask about your industry, and shows you who gets named when you don't.

That's a big promise, so I put it to the test. I tracked a well-renowned brand across all five engines Surfer covers. I checked its numbers against what I saw by asking ChatGPT myself. I dug into where the data holds up, where it wobbles, and where the tool quietly stops being useful.

Some of it genuinely impressed me. Some of it didn't. Here's everything I found.

What is Surfer AI Tracker?

Surfer AI Tracker is a paid add-on inside Surfer that shows you how often AI engines mention your brand when people ask about your industry, and which sources they cite instead of you.

Surfer itself is an SEO tool that has helped writers rank on Google since 2017. The AI Tracker, launched in 2025, is its answer to a new problem, i.e., buyers now ask ChatGPT and Perplexity for recommendations and not just Google.

Here is what it tracks and how.

What it tracks:

• Whether your brand appears in AI answers, and how often (Mention Rate)
• How prominently you show up when you do appear (Average Position)
• An overall Visibility Score across engines
• Which websites AI pulls from to build its answers (Top Sources)
• The sentiment of each mention, positive or negative
• The same data for your competitors, side by side

How it tracks it:

• You give it real prompts your buyers type, like "best CRM for small teams"
• It runs those prompts across ChatGPT, Perplexity, Gemini, Google's AI Overviews, and Google AI Mode
• It scrapes the actual answer a real user sees, not a stripped-down API response
• It re-runs them on a schedule (daily on Pro) so you can watch the trend

How I tested Surfer AI Tracker

Before I trusted a single chart, I wanted to know whether the numbers on my screen matched what a real person would actually see if they asked ChatGPT the same thing.

That's the lens I judged it through. These are the things I cared about, and why.

• Is the data real? Surfer's big claim is that it scrapes the actual answer a user sees, citations and all, not a stripped-down version pulled from an API. That's the entire foundation. If it's off, nothing else matters. So this got the hardest look.
• Does it hold still? AI answers shift. Ask twice, get two replies. I wanted to know if the tool's readings wobble so much that any single number is meaningless.
• Do you need every engine? The cheaper plan tracks ChatGPT only. The pricier one adds Gemini, Perplexity, Google's AI Overviews, and Google AI Mode. I wanted to see if those engines actually tell different stories, or if you're paying extra to hear the same thing four times.
• Does it tell you what to do? A gap you can't act on is just anxiety with a dashboard. Surfer's mention-gap and fan-out reports promise a to-do list. I wanted to know if that's real guidance or a prettier version of the problem.

Then I actually used it. Here's what that looked like.

• I picked a brand I know inside out (I already knew its real rivals and roughly how often it gets talked about), and let Surfer build the competitor set from the brand name, so it had something to measure against.
• I loaded 40 prompts real buyers type into AI, like "best [category] tool for [use case]". Not vanity searches for the brand name. The questions that actually decide who gets picked.
• I let it run a full pass across all five engines, then ran the same prompts by hand myself and compared what I saw to what Surfer captured.
• I watched how much the numbers moved day to day, checked how the story changed engine to engine, and dug into the Sources view, clicking different gaps to see if the advice ever actually changed.

Setting up Surfer AI Tracker

The AI Tracker sits in its own AI Visibility workspace, apart from Surfer's SEO side. You're tracking how a brand shows up in AI answers, not auditing your own pages, so there's nothing to plug in.

Even better, I barely had to type anything. I entered the brand domain name of "Zoho," and Surfer built the rest itself.

Business type, industry, a paragraph on what Zoho sells, its customer base, a list of competitors, and the topics worth tracking. It knew Zoho was a B2B SaaS suite for small and mid-sized teams; it knew privacy and all-in-one bundling were part of the pitch, and it named real rivals like Salesforce and HubSpot.

I find this smarter than most trackers bother to be. The usual tool asks for a brand name and stops there. Surfer uses that name to understand who you actually are, then uses that understanding to judge whether a mention is really about you later. The catch is that you should still read what it generates. It's a starting point, not gospel, and a wrong detail here quietly skews every chart down the line.

Then come the prompts, and this is where the tool genuinely impressed me.

Surfer generated 40 prompts on its own, sorted into eight topics, five prompts each, from accounting and CRM to collaboration and payroll. And they weren't lazy.

These are the exact questions a real buyer types into AI: "Which accounting software is the best for small businesses in the USA and Canada," "Best project management software for a one-man team," "What is the best collaboration software with built-in VOIP for a small business?" You get 50 prompt slots on Pro, and Surfer filled 40 of them for me before I'd done anything.

You can still edit, delete, or add your own, and you can pull your top queries from Google Search Console if you'd rather. But the fact that a single brand name produced 40 usable prompts is a real time-saver, especially if you don't already know what your buyers ask AI.

The last piece is competitors, and here's how it pulls up.

The competitors Surfer tracked weren't only the ones it listed at setup. When the data came back, the table showed brands I never entered, like QuickBooks, Xero, Pipedrive, and Gusto. Surfer surfaces whoever actually shows up in the AI answers next to Zoho. That's the honest way to do it, because your real competition in AI search is whoever the model names, not whoever you think you compete with.

It's also the first hint of a theme you'll see throughout this review, i.e., the tool is very good at showing you what's happening, and Zoho landing second here, behind HubSpot, is exactly the kind of thing you'd want to know.

Then you let it run. Surfer sends every prompt to the engines, records what each one says, and builds your baseline. Load your prompts early. The sooner they're in, the sooner your trends are worth reading.

Surfer AI Tracker dashboard

The dashboard runs on four numbers– Visibility Score, Mention Rate, Average Position, and Top Sources. Once you know what each is telling you, the tool clicks into place.

Start with the Visibility Score. Zoho landed at 51. On its own, that means nothing. It's a blended score out of 100 that mixes how often you're mentioned with how high up you appear. It only starts to mean something when you see it against your rivals, like in the chart below.

It tells you if you're winning, never why. For why, you need the other three.

Mention Rate is the honest one. Zoho showed up in 33.4% of answers. Plain and concrete, and the number I'd track week to week, because when it moves, something real moved.

Average Position was 4, and it comes with a trap. It only counts the times you were actually mentioned, so a brand can post a great position and still be nearly invisible if it shows up once and vanishes everywhere else. Read it alongside Mention Rate, never alone.

Adding to this, you can also go through the Topic breakdown.

Surfer splits your score by topic, and that's where the real story hides. Zoho scored 86 on SMB CRM selection; it's home turf. But on Collaboration it scored 1%, and on Payroll and HR just 10%, both markets Zoho actually sells into.

AI is barely naming it there. That gap is worth more than any headline number, because it shows you exactly where you're missing from conversations you should be winning.

So which numbers are signal and which are vanity? Mention Rate and the topic breakdown are signal. Visibility Score is a summary, useful at a glance, shallow up close. Average Position only means something when read beside Mention Rate. Look past the big number on top, and the dashboard gets a lot more honest.

Surfer AI Tracker accuracy

I checked Surfer's captured answers against what I got by asking ChatGPT the same questions myself, and on both prompts the tool matched reality.

I picked two prompts, opened ChatGPT, ran them, and lined my answers up against what Surfer had captured for the same engine.

I chose the two on purpose i.e. one where Surfer said Zoho shows up, and one where Surfer said Zoho was invisible. If the tool is accurate, it has to get both right.

Start with the accounting prompt, "Which accounting software is the best for small businesses in the USA and Canada?" Surfer scored this one high for Zoho, a 71.4% mention rate over the week.

My own run backed it up. ChatGPT crowned QuickBooks Online as the overall winner, then ran through a table of alternatives, and there was Zoho Books, sitting in the "best value" row. Not the top pick, but clearly named, exactly as Surfer reported.

Next, I tested the project management prompt, "Best project management software for a one-man team." Surfer scored this one a flat zero. According to the tool, Zoho is never mentioned at all.

I ran it myself, half-expecting to catch the tool missing a mention. I didn't. ChatGPT named Todoist, Trello, Notion, and ClickUp.

So does the data match? On both prompts, yes. But there's one honest wrinkle you should carry with you, and it's visible right inside Surfer. Look at the trend line on that accounting prompt: over a single week, it swung from near the top down to almost nothing and back up again.

That's not Surfer being unreliable. That's AI answers being inconsistent. The models reshuffle their picks day to day, so any single reading is a snapshot of a moving target.

Sources, gaps, and recommendations

Here's where Surfer is brilliant and frustrating in the same breath. It shows you exactly where you're losing, down to the specific web pages, and then mostly leaves you to figure out what to do about it.

Let me walk you through it, because the diagnosis really is good.

The Source section tells you which websites AI is actually reading to build these answers. For Zoho, it had pulled from 367 domains, 797 URLs, and over 6,000 references, and that pile keeps growing the longer you track. Each source shows whether your brand was mentioned, how often it was shown, and a confidence rating.

The pages themselves are exactly what you'd expect AI to lean on, like Forbes, TechRadar, and big software roundups. These listicles are what the models quote, which is the whole reason visibility in them matters.

Another interesting metric here is the Mention Gap analysis.

Each card pits you against one competitor and splits the sources three ways.

Shared are the pages that mention both of you. Gap is the pages that mention them and not you. Against HubSpot, Zoho had 101 gap sources. Against QuickBooks, 93. It's a literal list of the exact pages where your rival gets named, and you don't.

There's also a Fanout Queries report, which shows the follow-up questions AI asks itself while researching your topic.

So a prompt like "best CRM" quietly branches into "best CRMs integrating with Google Workspace and QuickBooks," which alone spun off ten separate fan-out queries. Those branches are basically a content to-do list the AI handed you for free.

Pricing and plans

If you only want AI tracking and none of the writing tools, there's a tracker-only plan, AI Search Analytics, at $95/month. It covers 50 prompts daily across every engine.

The cheapest plan with content tools, Standard, is $119/month. But, it only tracks ChatGPT, and refreshes weekly. If ChatGPT is all you care about, fine. If you want the full picture of where buyers actually search, you're paying to see a quarter of it.

Pro, at $219/month, is where the tracker gets serious. 50 prompts, refreshed daily, across all five engines. This is the plan I tested, and the one I'd point most people toward.

Above that sits Peace of Mind at $359/month, adding unlimited workspaces, API access for agencies running many brands, and 100 daily prompts.

I’d suggest skipping the Standard tier unless ChatGPT-only genuinely covers you. Pro is the real tool, and everything in this review runs on it.

Who should buy it, and who should skip it

FAQ

AI coding assistants are no longer experimental. In organizations that have adopted them, AI is responsible for roughly half of committed code. The question is, therefore, no longer whether developers will use AI, but whether security programs can govern what those systems produce. AI-generated code is remarkably good at producing syntactically correct code, with a 99.9% pass rate. In contrast, security performance tells a very different story.

As it stands, the average security pass rate is only 56%, meaning roughly 44% of tested code-generation tasks introduce a known vulnerability. As the volume of AI-generated code increases while security performance remains largely unchanged, organizations risk introducing more vulnerable code into their development environments. As a result, software vulnerabilities are now the top breach entry point at 31%, overtaking stolen credentials.

Closing that gap means rethinking how security fits into AI-assisted development. Organizations need to make security part of the development process from the start, choose models with risk in mind and verify code in context before it reaches production. The goal is not to slow developers down; it’s to make sure security can operate at the same speed and scale as the software being created.

Model choice is a security decision

AI models learn from enormous volumes of historical and publicly available code, including examples from open-source projects and developer communities. That training data contains secure implementations, but it also contains outdated libraries, weak validation and insecure coding patterns. While models are designed to produce useful, functional code, without the right controls they can reproduce those insecure patterns at scale.

Organizations cannot control every source used to train a model, but they can control which models enter development. The strongest model tested achieved a 68% security pass rate, while six of the 11 models clustered between 50% and 53%. Model selection is, therefore, not simply a productivity decision but a risk consideration.

Choosing a stronger model can reduce exposure, but it does not make the output trustworthy by default. AI-generated code should still be tested throughout the software development lifecycle using capabilities like static analysis and enforceable security policies. Security must be part of the delivery process itself.

Security needs application context

AI-generated code can appear to work as intended, compiling successfully and passing functional tests while still containing security vulnerabilities. Under deadline pressure, that can create misplaced confidence, particularly when developers accept output without fully understanding how it behaves inside the wider application.

The difference in security performance across vulnerability types shows why context matters. While models achieved pass rates of 83% for SQL injection and 87% for insecure cryptographic algorithms, performance fell to 15% for cross-site scripting and 12% for log injection. Some weaknesses can be identified through a recognizable coding pattern, but others often depend on data flows, authorization, trust boundaries, and how different parts of an application interact.

That’s why verification needs to happen in the workflow and in the context of the application. AI-native analysis can add contextual risk detection alongside established static analysis, surfacing issues such as logic flaws and broken authentication directly in pull requests. The key is to combine contextual analysis with independent testing before code is merged and risk has the potential to spread.

Prioritize risk, not just findings

As AI increases the volume and speed of software development, it also increases the amount of risk security teams have to assess, prioritize and remediate. That adds additional pressure to organizations who are already carrying significant backlogs of unresolved vulnerabilities.

That backlog enables security debt to build as flaws are identified but remain unfixed, meaning known risk sits in applications for longer. The more that debt accumulates, the harder and more expensive it becomes to reduce. Today, security debt affects 82% of organizations, while critical security debt affects 60%.

Those figures point to a growing capacity problem. Organizations are generating and discovering risk faster than they can reduce it. Treating every finding as equally urgent will only make that problem worse. Teams, therefore, need to focus on exploitability, application criticality and business impact so they can direct limited remediation capacity towards the vulnerabilities most likely to cause real harm.

AI-powered remediation can help close the gap between discovery and action by proposing fixes inside the developer workflow and reducing repetitive manual work. But remediation still needs to be governed. A generated fix must be tested, validated and checked for regressions. The objective should be to reduce meaningful exposure, not simply increase the number of findings or fixes moving through the system.

What leaders need to do now

AI-assisted code has become embedded into software development. As a result, leaders should treat its security implications as a change to the software operating model.

That means knowing where AI-generated code enters the organization, evaluating models against relevant security requirements, building verification into developer workflows, and focusing remediation on exploitable business risk. It also means retaining evidence of what was tested, what was fixed, and what risk was knowingly accepted.

The organizations that succeed will not be those that slow AI adoption, but those that evolve their development practices so security can keep pace with software creation. AI is making it easier to generate working code, but the real test is whether businesses can prove it is secure before it reaches production.

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