Valve's SteamOS has often presented the perfect excuse to be whisked away from Microsoft's troublesome Windows 11. The Linux operating system's better gaming performance has often been proven, utilizing RAM well without the bloatware often found with Windows 11.

SteamOS provides a console-like user interface (UI), making navigation and game launches far easier than Windows 11, despite the recent Xbox mode. With those benefits in mind, I've been contemplating a full switch to SteamOS, ditching Windows for good, and the best way to do that would be via Bazzite.

Bazzite (not affiliated with Valve) is effectively a SteamOS clone, specifically designed for "Linux newcomers and enthusiasts alike", but most importantly, comes with out-of-the-box compatibility with Nvidia and Intel GPUs, and a smooth experience for handhelds.

Using my second streaming PC, I put Bazzite to the test over a few months. Without a doubt, it's easily the best option for any gamer looking for a console-like couch gaming setup.

With tools like Decky Loader, customization is easy, with plugins to change SteamOS game mode themes, navigation sound effects, or even the startup and shutdown videos. However, despite all of the benefits of using the operating system, I still can't step away from Windows 11 on my main system, and there are multiple reasons why.

Multitasking drawbacks

As a frequent multitasker using applications like Discord to stream games to friends, Bazzite (more specifically SteamOS) isn't an ideal option. Using Discord to stream games on the same rig works just fine in desktop mode; however, it's not the same story for its game mode, which is arguably the entire purpose of using Bazzite.

Streaming on Discord in game mode severely drops frame rates in games, with constant stutters in many I tested, and there are a few instances where attempting to stream an application will result in a black screen. I tried to use the NZXT Signal 4K30 capture card and stream my second PC's Bazzite activity on Discord via my main PC, but it's not that simple.

Sure, HDMI passthrough on the capture card is possible, but I didn't want to be limited to 4K 60 frames per second on either my TV (which is capable of 144Hz) or my gaming monitor (capable of 240Hz). Frankly, capture cards with higher passthrough capabilities don't come cheap.

Duplicating or projecting the display, as you would on Windows 11, would be the easy solution. However, Gamescope, the compositor responsible for displaying games on screen with Bazzite, cannot duplicate outputs.

Still trying to avoid the NZXT capture card's 4K 60Hz passthrough limitation, I opted for an HDMI splitter with one input and two output ports. Unfortunately, the moment the capture card is connected, the output on either my TV or monitor has the refresh rate slashed to 60Hz and VRR disabled.

That occurs to ensure that the Extended Display Identification Data (EDID) of both is matched, according to what the capture card is capable of in passthrough.

None of these issues are present on Windows, as you can use OBS to project the main display onto the capture card without ever having to rely on an HDMI splitter or a better capture card.

This isn't an attack on SteamOS or Bazzite in particular; after all, game mode is quite literally designed for gaming, so multitasking isn't a priority for Valve or specifically Bazzite developers in this case.

However, it's worth noting that streaming on the gaming OS is more trouble than it's worth, and that's without even mentioning the potential audio bugs or OS glitches. That's only one part of why it's worth sticking with Windows, though.

Compatibility issues

I wish these issues stopped there, but not quite. Firstly, a large majority of popular multiplayer games such as Battlefield 6, Call of Duty (most of the modern entries), Destiny 2, and EA Sports FC 26 will not work on Linux due to their anti-cheat systems.

This is an issue that lies directly with publishers and developers for those games that refuse to adapt their anti-cheat systems to work on SteamOS. It also doesn't seem as though this will change any time soon; the Steam Machine was arguably one way to effectively force the hand of publishers to consider anti-cheat compatibility on Linux, but it's not exactly a popular PC due to its high price.

It's also worth noting that Game Pass is inaccessible natively on Bazzite, and requires either cloud gaming or third-party launchers, and you'll be lucky if you can get the latter to function.

The solution to accessing those anti-cheat games is a dual-boot with Windows 11 and Bazzite. However, that isn't completely ditching Windows 11 as I've planned to, and a dual-boot can end up messy thanks to Windows' forced updates.

Perhaps the most frustrating aspect is that Nvidia GPUs don't play nice with game mode on Bazzite, often resulting in glitchy functionality, or just completely black screens. I tested this by swapping out the AMD Radeon RX 9060 XT 16GB GPU in my second PC with my spare Nvidia GeForce RTX 3080 Ti and found that game mode was just frustrating to use.

So if I wanted to use Bazzite on my main rig equipped with an Nvidia GeForce RTX 4080 Super, its DX12 (a graphics API) gaming performance would be worse than Windows. Overall, the entire purpose of switching over to Linux was for better gaming performance and easy console-like UI navigation would be non-existent.

I'm sure all of these pain points will be addressed at some point, whether via Bazzite or Valve's official SteamOS builds. For now, though, I'll stick with Windows 11 on my main rig — even though I desperately want to be done with it.

Meta may dominate the smart glasses zeitgeist, but it's far from the only player — my favorite brand in the space is actually Xreal, with its range of impressive but pricey wearable-display specs. And with the launch of its new xbx brand, Xreal's lineup now includes more budget-friendly options too.

If money were no object, Xreal’s One Pro glasses are easily the crème de la crème of smart specs. Their Bose-tuned audio is impressive for such a small package, their electrochromic dimming tech is perfect for swapping between clear vision and shaded for optimal viewing conditions, and their unique optic engine displays their image more vividly than the competition, with fewer visible distortions.

The only issue is their price — $649 / £579 (they aren't available directly in Australia), absent any discounts, although they can be found on sale occasionally.

If you’re on a budget, my preferred pick has instead been RayNeo. Its Air 4 Pro specs debuted earlier this year, and not only are they affordable at just $299 / £289, they still come with solid specs, including HDR10 support.

With its new xbx a01+, however, Xreal might have just claimed the budget crown for itself.

Budget brilliance

Now the elephant in the room is that Xreal's specs cost a bit more. At their current $299 / £269 discounted price at Xreal.com they're on par with or cheaper than RayNeo's glasses; however their official price is $359 / £349, and while they aren’t discounted at the moment, we’ve regularly seen the RayNeo Air 4 Pros selling for $249 / £249 (RayNeo also doesn't seem to sell directly to Australia) or cheaper.

At the same time, the Xreal xbx a01+ glasses boast a handful of improvements which warrant a slightly higher price.

For a start, their brightness tops out at 1,600 nits, while the RayNeo Air 4 Pro can only hit 1,200 nits. This extra brightness should allow you to get more out of the HDR10 support, offering more room for the light vs dark contrast and vivid colors to breathe across the video format’s visual range. Highs can look more high, helping lows look lower by comparison.

Aiding the image quality are the swappable lens covers.

While advertised as giving you the ability to choose which color of sunglasses cover you most want to complement your style, the xbx's box also includes a completely opaque black cover you can use with your specs. Because the specs are essentially virtual projectors, a darker environment ensures the highest-quality viewing conditions, though I found the regular sunglasses were fine even in bright sunlight.

The xbx glasses are lighter too, weighing just 62g compared to the RayNeo Air 4 Pro's 76g. Admittedly I haven't found them majorly more comfortable, but as I kicked back with them in a sun lounger while on vacation for hours on end I was relaxed enough to doze off on several occasions.

The only tradeoff is, I'd say, the audio, which doesn't seem to involve Bose tuning this time, and isn't quite as impactful as the Air 4 Pro's. That said, I'll almost always use earbuds or headphones with my display glasses, because they’re better in terms of quality and minimize sound leakage, so this downgrade didn't bother me.

Who wins? You decide

You'll also have to contend with Xreal's standard optic engine too. This is the prism that reflects the micro-OLED display into your eyes, and the standard design is susceptible to external reflections — most commonly light bouncing off the top of the specs and into your view. Pictures will also be a little smaller thanks to a reduced field of view, and can be a little blurrier at the frame edges.

Only Xreal's One Pros boast the improved design, but having experienced it, it's tough to go back to the standard optic engine.

To be fair, though, that’s mostly a me problem, and if you’re new to the display glasses world the Xreal xbx a01+ and RayNeo Air 4 Pros should be perfectly adequate performance-wise.

As for which of the budget specs you should go for — whichever is cheaper would be my suggestion.

The technical improvements of the xbx specs are solid, but if you can save $40/£40 or more on the RayNeos, their few deficiencies can be overlooked.

That said, if stylish, colorful designs are a must, alongside a marginally better image quality, then you'll be very pleased with the new Xreal xbx a01+ glasses.

I’ve tested dozens of smart glasses for TechRadar, but every time I get a new pair of Meta Ray-Bans to try, I’m reminded why they’re so popular — because yet again my past three weeks with the Ray-Ban Meta Optics have been a joy.

Mostly.

Much like Meta’s previous smart specs, you’ll find a host of technological features crammed into the eyewear.

There are speakers for open-ear audio, which is perfect for listening to casual tunes, taking a call, or hearing the AI’s responses to your queries — with the glasses’ microphones also doing a great job at picking up what you’re saying with minimal noise disruption.

Dedicated earbuds will sound better for sure — the bass isn’t as emphatic as I like with Meta’s Optics glasses (or its other models) — though I did enjoy listening to music through my glasses for hours on end with no major complaints.

As mentioned, there’s also Meta’s AI, which is accessed through the connected phone app and voice commands on your specs. It can answer questions you ask it and, with your permission, see what you see by accessing the Meta Optics’ camera to provide additional context to your questions, and interact with connected apps to play music requests or tell your health and fitness stats.

I’m not a big AI user except when I’m on holiday or just generally acting like a tourist. On our honeymoon, my wife used Look and Ask to check the ingredients on hair care products to make sure she wasn’t allergic, and I got the AI to supply me with phrases to use when I realized my limited Spanish wasn’t going to cut it.

The AI’s also perfect for identifying landmarks and telling you more about historic sites. I relied on it so much on a trip I took to Italy with my other Meta glasses a while ago — it helped me understand the ruins of Pompeii and better learn the history of the Colosseum.

Beyond identifying foreign signs and buildings, the cameras are ideal for first-person video. I used them to snap photos and videos of my wife and me, and they came out really well — they're the ideal way to record memories digitally, as you can still live in the moment rather than being cut off behind a phone screen.

I’ll talk more about cameras in a moment, but before that I want to focus on the key way the Optics is different to Meta’s other Ray-Ban smart specs: it's comfort and fit.

Built to last all-day

Meta’s smart glasses are chunkier than non-intelligent eyewear, which can make them feel more rigid and less comfortable than regular specs. To make the Optics better suited to all-day wear (something I can confirm they handle effectively after wearing these glasses for hours on end over the past few weeks), you’ll find interchangeable nose pads and adjustable temple tips to help you sculpt them to your face shape.

The correct nose pads have helped the specs sit better on my head without slipping down, while the temple tips can be carefully manipulated (either at home or at an optician) to help the Optics grip your face without applying too much pressure to the sides of your head.

If you’re considering getting some yourself, my final design note is this: pay extra for transition lenses.

The Ray-Bans I’ve been testing are outfitted with the Transitions XTRActive option, meaning they change between clear and shaded when exposed to sunlight — even when I’m in the car — and the lenses changed so quickly as I moved between different environments (something I noticed a lot when going between indoor and outdoor spaces on my honeymoon to Tenerife).

My non-smart eyeglasses boast Transitions Gen S lenses, and I’m also happy with them, or you can opt for polarized Transitions if you want even more light protection, if you’re also willing to pay a bit extra.

Regardless of which type you opt for, Transitions lenses are essential because they maximize the usefulness of your smart specs. Without them, you’re limited to either clear glasses you can only use indoors or on darker days, or shaded glasses that can only be worn outdoors when the sun is brightly shining.

Why did there have to be a but?

Now, the elephant in the room is that Meta’s smart specs are facing increasing amounts of scrutiny from the public because of their cameras.

News stories showcasing bad actors harassing women, recording secretly by modding their specs, or revealing that Meta contractors might be able to see more of your recorded content than you realize have been populating our timelines throughout 2026.

The upshot seems to be a wave of negativity: I conducted a poll through one of my recent stories, and over half of all respondents said they think smart glasses should be banned.

For its part, Meta is doing plenty to combat these issues: it cracked down on Facebook Marketplace listings for glasses modding services and issued an update to prevent the camera from working if the recording light on your smart eyewear is disabled, and it’s also tackling Instagram profiles belonging to creeps like pickup artists who promote using the glasses to harass people.

I still think more needs to be done on the privacy side of things — at the very least being much more explicit about what features share captured content with Meta, though ideally all of your content would remain private through an Apple-like Private Cloud Compute server setup.

As of now, I’m admittedly very nervous to wear my Meta Optics. I haven’t been worried about going around wearing my Meta Ray-Ban and Oakley specs in the past, but with growing public anger over the way smart specs are being misused, I have a genuine concern someone will see me wearing them and act violently towards me.

Or I could simply be banned from entering a space while wearing them. Some Comic-Cons, offices, and courts don’t let you in wearing Meta-style smart glasses. Especially for something as essential as prescription glasses, and pricey ones at that, being constantly nervous while wearing them or barred from entering certain spaces isn’t ideal.

There is genuinely so much to love about the smart glasses, and if you haven’t yet got a pair and wear prescription eyeglasses, the Meta Ray-Ban Scriber Optics styles are the ones I’d most highly recommend.

The UK’s newly announced £2 billion quantum investment package signals something more consequential than the years-long research race that I’ve patiently been following.

Quantum is increasingly being treated as a key part of the future of national infrastructure.

While the size is important, the clearest sign of that shift is the structure behind it.

Through the newly announced “ProQure: Scaling UK Quantum Computing” initiative, the UK has become the first country to commit to an advanced procurement program for large-scale quantum computers by the early 2030s.

That matters because governments do not produce technologies at scale unless they believe those technologies are strategically important to operational capability, economic resilience and long-term competitiveness.

In many ways, this marks quantum computing’s transition from the research era into the infrastructure era.

Why procurement matters more than funding totals

So why does a procurement program matter more than the headline funding figure? The significance extends beyond public investment totals. Procurement creates market confidence. It helps establish deployment pathways, support supply chains, attracts private capital and gives emerging technologies a route from laboratory experimentation into practical use. In deep technology markets, the challenge is often building the ecosystem required to scale and operationalize it.

The challenge is becoming increasingly important as the broader quantum market matures. QuEra’s recent Quantum Readiness Report found that organizations are becoming far more disciplined in how they approach quantum investment. While 44% of respondents still expect their quantum budgets to increase in 2026, nearly half expect spending to remain flat, signaling a shift away from hype-driven expansion toward proof-driven decision-making.

More advanced adopters of quantum technology are increasingly motivated by what the industry refers to as the “classical wall”. This is the point at which conventional computing approaches struggle to solve increasingly complex computational problems. That shift from speculative experimentation toward problem-driven investment is an important sign of market maturation.

Sovereignty reshapes the quantum market

The changing investment landscape also helps explain why procurement and national strategy are becoming more central to the conversation.

One of the most striking findings in our research was that 62% of organizations now actively factor sovereignty into procurement decisions, while only 5% say sovereignty is not a consideration at all.

However, the highly globalized sourcing model that characterized the industry’s early years is giving way to a more regionally strategic one, shaped by concerns around resilience, supply chains, infrastructure control and long-term access to advanced computing capability. This makes the UK’s strategy particularly timely.

The government’s emphasis on sovereign capability, domestic ecosystem development, and long-term infrastructure investment reflects broader shifts already underway across the market.

Quantum computing is increasingly being viewed not only as a commercial technology opportunity, but also as a strategic national capability alongside AI, semiconductors, and advanced communications infrastructure.

Scaling quantum requires more than hardware

Scaling quantum computing requires much more than building processors. It requires coordinated investment across software, networking, manufacturing, talent, infrastructure, and integration with classical systems.

The UK’s decision to support not only computing, but also sensing, networking, software labs, infrastructure, and skills programs reflects an understanding that leadership in quantum will be determined by ecosystem depth as much as scientific breakthroughs.

The talent challenge illustrates this clearly. Our report identified specialist workforce shortages as one of the most significant barriers to adoption, particularly in highly technical areas such as quantum error correction.

Developing scalable quantum systems requires expertise that remains globally scarce, making long-term investment in education, research institutions, and industry collaboration essential.

The UK’s opportunity to lead

The UK enters this next phase from a position of genuine strength. Over the past decade, the National Quantum Technologies Programme has helped establish one of the world’s most advanced quantum ecosystems, supporting research hubs, startups, national infrastructure, and industry partnerships.

QuEra’s own presence at Harwell reflects the depth of scientific expertise and collaborative capability that already exists across the UK quantum landscape.

This investment builds on that foundation while accelerating the UK’s ability to translate quantum promise into economic opportunity. QuEra is one of several companies building next-generation quantum computers at Harwell, drawing on the country’s world-class talent and research base.

The next challenge is translating scientific leadership into scalable deployment.

From scientific leadership to commercial scale

Quantum computing remains a technically demanding, pre-commercial industry in many respects, but the direction of travel is becoming clearer. To be clear, quantum computers still cannot deliver broad commercial advantage on their own in most industries; that case is still being proven sector by sector.

But the procurement and infrastructure being built now is what will let that advantage arrive faster once it does. Governments, enterprises and investors are increasingly focusing less on theoretical promise and more on deployment readiness, operational capability, and long-term strategic value.

Public sector leadership will continue to play an essential role in that process. Our research found that government funding remains the leading driver of quantum investment globally, cited by 28% of respondents as the primary catalyst for budget increases. At the same time, government and defense organizations are expected to lead near-term commercialization efforts, highlighting the central role public investment continues to play in scaling the industry.

The countries that lead the next phase of quantum computing are likely to be the ones that successfully combine research excellence with procurement, infrastructure, talent development, industrial coordination, and commercial scale.

For enterprises and other governments watching from the sidelines, the practical lesson is straightforward. Building procurement pipelines, workforce training, and proof-of-concept programs now, even at modest scale, is what prepares an organization to scale up once quantum systems clear the classical wall in a given domain. Those that engage early will be better positioned than those that wait for certainty that may not arrive on a convenient schedule.

The UK’s latest announcement suggests it understands that distinction and that may ultimately matter more than the £2 billion headline itself.

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

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

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

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

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

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

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

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

What low-power AI systems are

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

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

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

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

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

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

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

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

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

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

Why efficiency is becoming an imperative

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

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

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

A strategic opportunity for smaller nations

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

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

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

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

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

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

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

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

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

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

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

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

Looking ahead

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

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

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

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

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

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

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• SyLC 3D Player supports everything from 3D Blu-ray to Apple spatial videos, and it can convert 3D video to display it on Apple Vision Pro headsets
• It's backwards and forwards compatible — and the creator says it can get dual-HDMI output for dual projection, for future-proofing
• The app runs on Windows, and is free via Github 'because this format deserves better than a slow death'

One of the great tech disappointments of the last few decades was 3D TV. Like many movie fans I was excited by the possibilities, invested in a 3D TV, and watched the entertainment industry ensure that yet again, 3D movies were a short-lived trend. By 2013, it was already generally agreed that 3D TV was dead and that smart TVs and 4K were the way ahead.

Not everyone got the memo [I had mine until 2020… -Ed]. Over on the r/audiovisual subreddit, Jarod-The_Centre is determined to ensure 3D never dies — so they have been making a free player that now "plays EVERYTHING in 3D" from Blu-ray to Apple spatial videos.

And that's just the beginning of its talents. If you're a fan of things pointing out of the screen for no good reason, Jarod's player is fascinating.

I refused to let 3D die ! So I spent months building a free player that plays EVERYTHING in 3D: Blu-ray MVC, 4K HDR side-by-side, even Apple spatial videos. And it converts your 3D Blu-rays to Vision Pro format... from r/audiovisual

What does the 3D player do?

What doesn't it do? The player, called SyLC 3D Player, is a Windows app that's now in its fifth release. It's designed to play three different generations of 3D content: Blu-ray 3D/ISO/MVC MKV; HEVC/H.265 3D up to 4K 10-bit HDR; and Apple's MV-HEVC spatial videos. "The MVC of the 2020s, playing on your 2010s 3D TV," Jarod writes. "There's something poetic about that."

The app outputs in frame-packed 3D for compatible 3D TVs and projectors, and it can do something else that's really impressive: it can export 3D content as MV-HEVC, so you can take a 3D Blu-ray and turn it into a spatial video for Vision Pro.

I love Jarod's explanation for making the app. "It exists because this format deserves better than a slow death, and because apparently I don't know when to stop."

Jarod's in good company. Reddit's response to the latest announcement has been really positive and packed with good ideas, so for example Acceptable-Rise8783 suggests that for future-proofing, the app should be able to "send the two fields separately but in sync through two HDMI outputs."

With 3D TVs and projectors becoming increasingly hard to find — as they point out, "projectors are dropping support left and right because the chips that do 3D are becoming hard to come by" — the choice of viewing platforms will be reduced to dual-projector 3D or VR headsets.

The creator says this is something likely to appear in the player in a future version as the player already drives two windows simultaneously — and it means that dual projectors is the most 'future-proof' option for 3D, and the app supports it as best it can.

The whole discussion is fascinating, and I think Jarod sums it up beautifully. "Keep the faith. The format's not dead while we're still building for it 🔥".

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It’s probably fair to say Meta is facing serious public backlash to its AI glasses.

Musician Lorde took time at a recent concert to tell her audience, “Don’t get the glasses,” many online are branding them “pervert glasses,” and activist group Everyone Hates Elon has put up spoof adverts for the glasses featuring Jeffrey Epstein wearing a pair (note: the story behind a paywall).

I myself have been unsure if I’ll keep using my Meta glasses, despite their utility making them one of my favorite gadgets I’ve tested while working at TechRadar.

Meta has rebutted with new measures to combat misuse of its glasses. It recently patched its specs to prevent modders from using the camera despite disabling the recording light, and Instagram head Adam Mosseri recently announced harassment videos shot using Meta glasses will be banned from the platform — with some high-profile accounts already getting the boot.

Among the folks still in love with the Meta glasses, however, is my wife Izzy.

When I suggested taking a pair each with us on our recent honeymoon to Tenerife, she wasn’t convinced — despite them being our only sunglasses without my prescription lenses in them — but she wore the white-and-gold Oakley HSTN specs nearly all the time we were abroad.

I was also testing out a pair of the Ray-Ban Meta Optics styles for an upcoming review, and time and again the glasses proved themselves useful. Our trip confirmed how awesome these glasses can be when used right, and why a middle ground needs to be found to maximize privacy and utility before things get out of hand.

An all-in-one AI supertool

Of course, the primary reason to rely on Meta glasses is their built-in tech.

Whether we were lounging on a sunbed, strolling through the streets and past the beaches of Los Cristianos, or kicking back with a drink in the evening, we both loved the open-ear speakers that let us enjoy our podcast or playlist of choice without tuning out the world.

For hours on end, I enjoyed Radio 1 through BBC Sounds, while Izzy drifted between Spotify hits, and both pairs lasted a good length of time battery-wise — with touch controls making it super simple to control the tunes when needed.

The AI translations came in clutch several times as well. It offered quick advice on what phrases to use when we realized our limited selection of “Hola,” “Gracias,” and “Por favor” wouldn’t cut the mustard.

Meanwhile, Look and Ask helped Izzy decipher which of the unrecognizable shampoos and conditioners would be best for her hair after a travel mishap saw the supply we packed leak into her travel bag. The AI could read the Spanish labels to her to help identify and filter out ingredients she knows to avoid, and we ended up buying a few extra bottles of the conditioner she found to bring home after she fell in love with it.

It can't be understated how useful the smart specs are simply as sunglasses too.

My Optics pair features Transitions XTRActive lenses, transitioning fast from clear to shaded as I moved between indoor and outdoor spaces, which was super handy for someone like me who wears glasses all the time. Meanwhile, the polarized gold HSTN lenses were perfect for Izzy as she spent hours of her day baking in high-UV weather.

Thanks to the charging case, my specs rarely ran out of battery, but even when they did, I could still rely on them just fine as stylish shades.

Of course, the controversial aspect is the camera. Beyond the AI assistance, it was helpful for snapping quick photos and first-person clips for the travel vlog I’m looking to cut together.

Though whenever I went to snap a photo, I had flashes of the story about Meta contractors who claimed to have seen intimate moments captured through the specs. Shots of either of us in swimwear went nowhere near the glasses’ snapper, and I kept a careful eye on who was in the background of any shots I did take.

We were also a little worried that someone might not like us using the glasses at all in their presence, though thankfully no one took any issue with us.

These fears and considerations do create points of friction when it comes to using Meta’s glasses—or any smart glasses, for that matter. While the Facebook company is the best-known for smart eyewear right now it is far from alone, and while it's making efforts to clean up its public perception smart glasses will be tarred with the same brush if any brand steps out of line.

I also don’t think Meta’s efforts are perfect so far, at least not yet. I’d like to see more work across social and hardware platforms to root out smart glasses misuse; I believe better, more thorough privacy rules are necessary to make it crystal clear what data is and isn’t shared with Meta and its staff, and I’m not convinced I could trust it with the immense amount of data its rumored super sense feature would capture from my daily life — blunders like its Instagram AI image use plan and backtrack don’t fill me with confidence in its AI decision making.

At the same time, my week falling back in love with the Meta glasses, and my wife loving her Oakley smart specs, proves there’s something really fun and useful about the tech that I equally don’t want to see taken away or banned outright.

With other brands joining in on intelligent eyewear — recently Samsung demonstrated its own pairs at Galaxy Unpacked — it’s clear the tech industry is storming ahead, just as it has with exciting emerging technologies in the past.

I just hope a middle ground can be found, because smart specs represent a serious privacy nightmare beyond other gadgets we’ve relied on. If the tech doesn’t hold itself to account, we could see even more regulation and public backlash than we’ve seen already, and that could kill off my favorite tech sector of recent years before it has a chance to thrive.

Imagine a world where the most powerful weapon isn't a missile, but a software update. In less than 72 hours, a software engineer can patch a drone on the front line and turn an enemy's multi-million-dollar electronic warfare system into little more than expensive scrap.

This isn't science fiction but an everyday reality inside Ukraine’s real-time military tech pipeline. Driven by the necessity of national survival, a decentralized network of coders, startup founders, and makers has bypassed decades of slow defense bureaucracy.

At the heart of this transformation is Brave1, Ukraine's defense innovation engine, where software developers, startups, soldiers, and investors collaborate at startup speed to solve battlefield problems. In the newly launched Brave1 Market, an ecommerce-style procurement catalog, combat success earns digital "ePoints," public rankings fuel competition, and rewards are reinvested into even more powerful technology.

It’s no surprise that Brave1’s success has attracted global attention, with aerospace giant Airbus partnering with the platform to connect aerospace expertise with this next-gen defense ecosystem. Also backed by big-data titan Palantir, the new Brave1 Dataroom acts as a secure data pipeline, streaming raw battlefield video and thermal imagery directly to developers training AI targeting models.

Now, we can forget all about old-school defense programs and endless procurement cycles. The future of warfare is open source, software-defined, and moving at the warp speed of a Silicon Valley startup.

Why traditional military tech procurement is failing

The traditional model of military procurement is running on outdated code. For over half a century, the Western defense industry has been building bigger, better, and increasingly expensive "exquisite beasts" - fighter jets, aircraft carriers, and heavy armor, each taking a decade to design and deploy.

Under this system, the pipeline is painfully slow. Governments can spend years defining specifications, years selecting contractors, and years more building the hardware.

By the time it reaches the battlefield, the software inside is often two decades out of date and locked behind proprietary code that can't be modified without years of legal renegotiations. As The Wall Street Journal recently reported, traditional defense structures struggle to absorb the rapid pace of software and startup-led innovation.

This peacetime bureaucracy is breaking under the speed of modern warfare. Today's battlefield is defined less by firepower than software, where an overnight update can render even the most sophisticated missile system obsolete. Legacy defense structures simply can’t cut it for software-speed innovation. They prioritize caution and consensus, while modern defense tech is designed for speed and to survive battlefield surprises.

At its core, the old defense playbook presumes weapons are built once and fielded for decades later. But when a new battlefield threat emerges, waiting a year or two for a budget committee to approve a software patch is a recipe for defeat.

Modern warfare demands systems that can evolve every day. By decoupling software from hardware, these defense ecosystems empower thousands of developers to adapt faster than any centralized procurement committee ever could. At the forefront of this transformation is Brave1, Ukraine's open source tech cluster.

What is Brave1?

If traditional defense procurement is a labyrinth of government departments and endless paperwork, Brave1 is the system built to bypass it. Co-founded by multiple Ukrainian ministries, including Digital Transformation, Defense, and Strategic Industries, this platform gives anyone with a laptop and a promising idea a path to the battlefield. It brings developers, startups, investors, and soldiers into a continuous deployment cycle.

In the old-school defense world, getting a new, innovative idea in front of the right people takes months. Brave1 acts as a secure, digitized buffer zone where developers upload their designs or code, pass automated and expert reviews, and connect directly to the problems facing Ukraine’s military.

Once a project clears Brave1’s gateway, it receives an official security rating, gets access to the centralized technology registry, and can scale research and development (R&D) grants reaching up to eight million UAH to fast-track production.

However, this high-speed model comes with its own set of challenges. When hundreds of teams are sprinting to solve battlefield problems, some will run along parallel paths, spreading funding and talent across versions of similar technology. The result is a risk of fragmented resources spread across an ecosystem built for speed and experimentation.

Still, by swapping endless military paperwork for the automated Brave1 Market, the platform ensures that promising ideas don't get stuck in red tape.

Inside Brave1’s expanding innovation network

The speed at which Brave1 has grown turns traditional defense economics entirely on its head. What started as a bold innovation hub has evolved into a full-scale defense technology engine. Today, Brave1 brings together more than 3,200 registered companies and actively tracks over 4,500 products moving through its secure development pipeline.

Yet innovations don't win wars unless they can be implemented. Rather than relying on sluggish defense grant cycles, Brave1 has awarded 500 direct innovation grants totaling over $11 million, putting funding directly into the hands of frontline engineers and hardware startups. This pipeline is designed to bridge the notorious hardware "valley of death," where many promising technologies traditionally run out of money.

The technology verticals that are reshaping the front line

Instead of focusing on billion-dollar missiles or heavy armored vehicles, Brave1 targets technologies that are reshaping modern warfare. Its product catalog spans a wide scope of specialized, software-driven technology verticals, including:

• Unmanned aerial vehicles (UAVs): As we write, more than 1,000 manufacturers are building next-generation drones, ranging from AI-powered attack drones and long-range reconnaissance systems to fiber-optic-guided drones that bypass conventional radio jamming.
• Naval and autonomous sea drones: More than 50 manufacturers are developing uncrewed surface vessels (USVs) that use autonomy and swarm tactics to challenge conventional naval superiority at a fraction of the cost.
• Automated electronic warfare (EW and SIGINT): Over 350 companies are building compact electronic warfare systems, software-defined jammers, and smart spectrum analyzers that can adapt to hostile drone frequencies on the fly.
• AI and computer vision products: More than 300 software-centric companies are building the digital brains behind modern warfare, from intelligent targeting systems and automated image recognition to swarm coordination and AI-assisted decision-making.
• Robotic ground complexes (UGVs): Over 280 manufacturers are building autonomous ground robots that take on the battlefield's most dangerous tasks, from delivering supplies and evacuating the wounded to laying mines and other frontline operations.

However, the ecosystem's biggest advantage is also one of its technical hurdles. Thousands of independent systems must work seamlessly together, on the same battlefield, requiring constant software integration so drones, electronic warfare systems, and ground robots can form a synchronized network.

The outstandingly open ecosystem

Unlike traditional defense programs built around closed systems and proprietary codebases, Brave1 embraces an open ecosystem framework inspired by the world of open-source software.

It’s no secret that traditional defense innovation is built around extreme secrecy, where companies protect their intellectual property at the cost of collective advancements. Brave1 takes a completely different course by encouraging developers to share code, 3D-printing schematics, hardware blueprints, and engineering know-how across their networks.

The open architecture is already going global. The Ukrainian government officially passed the Brave International framework to open up the ecosystem to allied defense partners. Brave France, developed alongside the French Defence Innovation Agency (AID), pairs joint funding with battlefield testing through Test in Ukraine, fast-tracking tech innovations from lab to field.

This openness comes with a cost, and it’s a bigger cyber battlefield. Every new developer, repository, and software dependency increases the number of potential attack vectors, making zero trust a network a must. Now, rather than targeting finished weapons, sophisticated attackers focus on the software supply chain that builds them. In an ecosystem like Brave1, even a single compromised software package could cascade through countless downstream systems before a security gap is identified.

The app store model for the front line

If Brave1 is the military’s revolutionary software stack, capital is the processing power that drives the code.

Financing innovation at the speed of war

To understand how Brave1 moves with the speed of a Silicon Valley startup, we first have to follow the money. In traditional defense networks, getting an innovation grant involves a multi-month marathon of paperwork, compliance, and risk assessment. For tech startups, this creates an administrative bottleneck better known as the "valley of death," where promising ideas collapse before they ever reach the battlefield.

Brave1 bypasses this hurdle with a decentralized, non-dilutive funding model built for wartime speed. Developers submit applications through a unified digital interface, where an inter-ministerial panel evaluates each project's potential battlefield impact. Successful applicants receive early-stage funding within weeks, allowing them to spend their time on engineering rather than filling out paperwork.

Points, rankings, and the gamification of warfare

Once a prototype is approved, it enters the Brave1 Market, a secure digital marketplace that works much like an enterprise app store for the military. Through the recently launched Buyer's Cabinet, verified commanders can browse a catalog of vetted domestic technologies, compare specifications and battlefield performance, and purchase equipment directly through the platform, dramatically shortening the journey from prototype to deployment.

Brave1 Market is bound up with Ukraine's points-based battlefield system dubbed the ePoints system, which applies game mechanics to battlefield breakthroughs. Frontline units earn these points for verified combat results, and every confirmed strike against enemy assets is recorded through military situational awareness systems. These actions feed into an active, real-time Call of Duty-style leaderboard run by the Unmanned Systems Forces, which allows onlookers to track top drone units via the military's official online killboard.

Afterward, units can redeem their accumulated ePoints as an internal digital currency directly inside the marketplace to purchase newer, more powerful drone hardware or electronic warfare (EW) kits. This type of decentralized purchasing power bypasses top-down supply chains altogether, allowing commanders to fill their digital shopping carts with the exact tools they need based on real-world performance data.

However, the system's greatest strength, its flexibility, also creates one of its biggest pain points. When individual military units have the freedom to buy specialized equipment from hundreds of small suppliers, standardizing spare parts, software support, and maintenance protocols across the entire military infrastructure becomes a serious operational challenge.

The 72-hour software sprint

The main mechanism of this software-defined ecosystem is its split-second adaptability. In the world of EW, signal-jamming frequencies can change overnight, rendering entire drone fleets ineffective. Updating a weapon's electronic systems requires months of contractor negotiations, approval processes, and engineering changes, which is much slower than the speed at which modern threats evolve.

Inside the Brave1 network, this logistical nightmare is treated as a fast-paced development sprint:

1. Telemetry capture: When a drone at the front line detects a new jamming frequency, it automatically records the hostile signal pattern, creating data for future countermeasures.
2. Data pipeline: Raw battlefield data is transferred from frontline systems into centralized developer environments, where engineers can analyze threats and come up with next-gen countermeasures.
3. Patch deployment: As soon as a new threat is identified, engineers can adjust the software, updating radio-frequency configuration or terminal tracking code, without swapping out any hardware.
4. Over-the-air update: The updated software patch is compiled and pushed right back to the frontline within 72 hours.

While it all sounds beautiful on paper, when you move code from a lab into a muddy trench, things can get messy. As you can already guess, the real bottleneck isn’t the tech - it’s the human factor. Constantly updating software requires frontline operators to learn new configurations, radio profiles, and interfaces in high-stress situations. Requiring soldiers to run updates in the middle of a mission creates an enormous margin for error, showing that even the slickest software must ultimately work within human limits.

Battlefield beta testing

To shorten the path from prototype to deployment, Brave1 relies on the official Test in Ukraine program. Instead of spending years in laboratory simulations to clear traditional safety benchmarks, promising prototypes undergo swift safety checks before moving into live combat, turning battlefield feedback into the next immediate upgrade.

For instance, if an optical tracker fails in heavy dust or a carbon-fiber chassis breaks under stress, developers receive real-time frontline feedback. What the process gains in speed, however, it sacrifices in long-term reliability testing.

Training the AI: The combat data factory

Algorithms mean little without a continuous stream of raw, real-world data to train them.

The battlefield as an AI training ground

On the modern software-defined battlefield, algorithm accuracy shapes survival. If code is king, then raw data is the electricity that powers the throne. To feed this infrastructure, Brave1 functions less like a traditional defense bureau and more like a colossal combat data factory. Every hour of flight telemetry, electronic warfare logs, and automated targeting footage flows continuously from the battlefield into secure engineering nodes, giving developers a steady stream of real-world data.

Together, this creates a continuous machine learning (ML) pipeline, with each mission generating new data for the next software update. According to the Ukrainian Ministry of Defense, over 100 Ukrainian companies are training AI models using the Brave1 Dataroom. At its core is a secure repository that gives developers access to structured visual and thermal data on aerial threats, all captured under real battlefield conditions.

On top of that, as highlighted by The New York Times, Ukraine has opened access to millions of drone videos and extensive battlefield telemetry, allowing both domestic developers and allied partners to train and refine new technologies using real data.

The sheer volume of this dataset is staggering. DefenseScoop reports that more than half a million hours of battlefield footage are being used to train and refine AI target-recognition algorithms. Brave1's AI models are trained on conditions that are almost impossible to recreate in a laboratory.

While Western commercial tech labs are forced to train terminal guidance AI on clean, synthetic data simulations, Brave1 developers feed their convolutional neural networks a steady diet of gritty, real footage with battlefield smoke, physical camouflage, and chaotic weather conditions. This massive collection of battlefield data helps train AI models to identify armored movements, track targets through dense terrain, and maintain navigation even when GPS signals and communications links are disrupted.

The silicon bottleneck on the edge

However, trying to turn the battlespace into a living supercomputer comes with severe physical constraints. The long-term objective is the deployment of automated systems that can guide hardware to targets without a human pilot or continuous communications link.

Outside the front line, running sophisticated computer vision systems means relying on massive power-hungry cloud data centers or stacks of high-end enterprise GPUs. On the battlefield, we don't have that luxury. Tech developers must shrink these massive AI models into something small enough to run on low-power, edge-computing chips and clamped onto lightweight platforms operating at the front line.

Active combat zone is the supreme stress test for AI. Models must swap complexity for raw survival, learning to work with blurry cameras, damaged sensors, and broken connections. The smartest system on paper is useless if it stops working the second the electronic environment gets ugly.

This centralized intelligence advantage doubles as a single point of failure. A repository with battlefield datasets, AI models, and software configurations from numerous defense companies is an awfully attractive target for cyber adversaries. Protecting that ecosystem demands strict zero-trust security, as a single slip-up can expose the entire network.

Bipedal robots and autonomous systems

To take humans out of danger fields, developers are looking past basic drones to deploy agile, multi-terrain robotic platforms.

Brave1 humanoid robot program and the reality of trench AI

Brave1's latest frontier is humanoid robotics. By launching a dedicated grant competition, Brave1 has formally established armed bipedal robots as their own defense technology vertical. The mission is to create near-human machines that can traverse trenches, transport supplies, and clear high-risk zones while keeping troops out of harm's way.

However, early tactical edge trials suggest that Hollywood-style humanoid robots remain a long way from battlefield reality. Prototypes struggled to carry more than 20 kilograms, offered slim protection against severe weather, and saw battery levels bleed out rapidly during demanding missions. While tech labs design robots to shuffle across flat factory floors, real combat carries an unstructured nightmare of mud, debris, and steep ditches.

In contrast, low-profile wheeled and tracked UGVs have already completed more than 50,000 frontline logistics missions, offering a cheaper and sturdier alternative. To survive the field, tech developers have chosen simplicity over sophistication.

Ruggedized software vs clean labs

Building AI for the battlefield requires abandoning many of the assumptions of traditional software engineering. In a traditional clean lab tech stack, AI models rely on high-performance cloud infrastructure and high-quality data streams. On the front line, software must be streamlined to run locally on low-cost edge-computing chips clamped onto lightweight, plastic platforms.

This optimization is non-negotiable when looking at the sheer volume of hardware hitting the production line. In fact, the scale is so massive now that Ukraine's defense production has hit a staggering 10 million drones annually, with plans to double that to 20 million. When building at this unprecedented multi-million-unit scale, your code must be lightweight enough to run smoothly on millions of cheap, disposable devices.

Meanwhile, developers must build software that persists through blurred imagery, glitchy sensors, and dead air. A model that depends on perfect data or constant network connectivity can quickly become useless once it leaves the lab.

Ultimately, Silicon Valley can continue building software for data centers, but the battlefield must have software built for sheer survival.

Automated software countermeasures

One of the top technical hurdles for humanoid robots isn't mobility but maintaining stable communications. Near the ground, radio signals are weakened by terrain, vegetation, and other obstacles. For a simple, two-legged platform, however, even a slight interruption can disrupt its balance, causing the entire machine to collapse.

So, what's the solution? Not building a better radio tower in the middle of a battlefield, but moving decision-making closer to it. That’s why Brave1 is pushing deterministic, edge-computing autonomy.

If the command link is lost, onboard AI instantly takes over. Using local computer vision, the system can navigate, avoid obstacles, and continue its mission without relying on GPS or a live operator link.

The evolution of electronic warfare

Modern electronic warfare isn't about broadcasting the strongest signal. In fact, that would be a good way to hang a giant "shoot here" sign over your own position.

Dating back to the Cold War and the post-conflict era, electronic warfare relied on powerful vehicle-mounted jammers that flooded broad sections of the radio spectrum. In contemporary conflicts, those high-emission systems have become increasingly vulnerable, as their massive radio frequency (RF) emissions can reveal their coordinates to enemy sensors and radar-seeking weapons. Rather than flooding the airwaves with raw power, modern electronic warfare depends on compact, software-driven systems that can adapt swiftly while remaining difficult to detect.

Through Brave1, developers are shifting toward compact, trench-level electronic warfare systems. Instead of constantly broadcasting powerful radio signals, these software-defined radio (SDR) platforms silently monitor the local spectrum for hostile activity. As soon as they detect an incoming threat, they transmit a brief, targeted jamming burst on the specific frequency being used, disrupting the attack while remaining hidden.

Democratizing defense production

By taking manufacturing out of rigid defense factories, Brave1’s ecosystem has turned local tech talent into frontline defense developers.

The tech talent workforce

The real breakthrough isn't all about tech - it’s fundamentally human. Instead of relying exclusively on career defense engineers, Brave1’s ecosystem draws talent from across the commercial technology sector. Software developers, UX designers, data engineers, and automation experts are applying the same skills used to build consumer apps and cloud platforms to next-generation defense technology. This subversive talent shift is bringing commercial software thinking into a field that has traditionally been shaped by slow, hardware-driven development cycles.

Now, a team that previously spent months fine-tuning logistics for a commercial delivery app can shift to building software layers that coordinate autonomous drone fleets in a matter of weeks. This talent shift is opening the door for a new generation of software engineers to reshape how defense technology is built, not just what gets built.

Yet, bringing commercial software talent into defense creates a whole new set of engineering challenges. Silicon Valley coders may still be new to physical hardware constraints, extreme environmental stress factors, or advanced ballistic mathematics.

Overcoming this challenge requires continuous collaboration between two different worlds: the software developers building the systems and the frontline experts testing them in the heat of battle.

Distributed development networks

The old-school defense factories are a tempting target: a single structure, a single strike, and systemic collapse. To protect production from long-range missile strikes, Ukraine had to move away from that factory model. Now, production has been broken apart into a distributed network of smaller facilities operating across the country. Despite their digital connectivity, these workshops function more like a peer-to-peer network than a traditional industrial empire.

The main strength of this model is its robust resilience against single points of failure. While a precision strike can damage individual production hubs, it can’t cause a cascading collapse across the whole network. Like with a digital ecosystem, production simply reroutes around broken nodes.

Still, the primary challenge remains as clear - standardizing such networks is harder than standardizing factories. Maintaining consistent quality control across hundreds of small-scale workshops calls for ongoing supervision, as even minor discrepancies in suppliers, parts, or production techniques can cause unexpected failures in the field. What performs perfectly on a test bench in Kyiv might fail during deployment simply because a single workshop used a slightly different batch of soldering wire.

To keep this under control, the Brave1 ecosystem must rely on constant monitoring and software-based diagnostics capable of scanning the whole network and flagging issues before things start to break.

The open-source playbook

The real breakthrough is not simply that Brave1 opens the market to more competitors, but that it changes the very mechanism of how defense technology evolves. Borrowing from the world of open-source software, the platform treats each tech improvement as shared building blocks that can be refined, adapted, and deployed across the wider network.

Code, 3D designs, and hardware fixes become part of a shared, ever-evolving knowledgebase rather than closely guarded company assets. So, if one team tweaks a flight controller, upgrades a component, or finds a better way to build, that progress is instantly shared and used by everyone across the ecosystem.

This approach also changes the ways Ukraine studies and replicates enemy technology. In mid-June 2026, the Ministry of Defense launched the TrophyLab platform, a secure platform built to study and catalog captured Russian military equipment. Through it, approved international partners, defense organizations, and contractors can access technical documentation, electronic warfare vulnerability reports, and telemetry analysis for over 115 captured weapons.

Instead of letting captured weapons gather dust as classified secrets, TrophyLab turns them into an open sandbox for building new defenses together. On the other hand, moving this fast can also scale up systemic errors. A flawed software update, compromised dependency, or hidden vulnerability in a shared repository could spread across multiple platforms before developers spot the problem.

In an open, fast-paced network, the real battle isn't just building cooler tech but proving that every single update is completely secure.

Software startups vs slow bureaucracy

Before code can even expect to rewrite the rules of modern warfare, it must first break through decades of slow, legacy bureaucracy.

Why Western government programs are slow

Beyond the battlefield, software is also reshaping defense bureaucracy. Many Western procurement systems are crippled by years-long bottlenecks and constrained by rigid rules originally drafted for industrial-era hardware like warships, fighter jets, and military bases. While this framework makes sense for concrete and steel, it struggles to keep pace with software that evolves from week to week.

By the time an agency reviews a tech requirement, writes a request, and clears legal review, the tech landscape has already shifted. This rigid setup lets legacy, prime contractors dominate simply because they possess the paperwork army, not because they build the best software. The downside to this stability is a systemic delay in sending state-of-the-art commercial tech to the field, which widens the innovation gap between the military and private tech markets.

Brave1’s flexible, software-first approach stands in stark contrast to old bureaucracy. Borrowing from the workflow of a technology startup, Brave1 is designed to evaluate ideas swiftly, fund promising prototypes, and connect developers directly with the front lines. The focus shifts from predicting tomorrow's battlefield to adapting to it as it changes.

The rise of venture-backed defense tech

Recognizing these bureaucratic bottlenecks, venture capital has begun flowing into defense technology at a breakneck speed. For decades, the math for defense tech simply didn't work for private capital, suppressed by sluggish procurement cycles, multi-year product runways, and heavy ethical baggage. In today's markets, the rise of adaptive, software-driven defense companies is changing that perception, turning the defense tech industry into one of the fastest-growing areas of deep-tech investment.

In line with this trend, Germany has also begun rethinking how it funds defense innovation. As reported by Reuters, Berlin is deploying a new state-backed investment vehicle to inject capital directly into defense startups. The objective is to de-risk defense innovation while bypassing agonizingly slow acquisition cycles.

Private investors are now backing startups building everything from AI-powered software to edge computing hardware to next-gen autonomous systems. Increased venture backing allows smaller companies to fast-track their initial product deployment. They can perfect new technologies without relying on slow legacy defense acquisition pipelines.

Yet market forces do not always align with military priorities. Traditional private capital prioritizes high-velocity software growth and massive markets. Meanwhile, defense forces require rugged, custom-built hardware built for niche military needs - a tech stack with zero civilian upside. The result? Well, vital, battlefield-ready technology often gets sidelined in favor of whatever scales fastest.

Big tech partnerships: Brave France and Brave Germany

To bridge the gap between fast-moving tech startups and rigid military institutions, the ecosystem has shifted, with nations now turning to formal bilateral frameworks. Ukraine’s new Brave International framework bridges this financial gap with over €100 million in joint funding. This allows agile international startups to bypass slow procurement and fast-track their tech directly to the battlefield. Under this blueprint, Ukraine and its partners share costs via a 50/50 funding split. Joint, parity-based expert boards review applications to scale up sister programs like UNITE Brave NATO, Brave Norway, and Brave Lithuania.

Brave France is the first major spin-off to go live under this playbook. Finalized at the Eurosatory defense exhibition, the Brave France Bilateral Grant Program unlocks a €20 million fund built specifically to bypass bureaucratic procurement bottlenecks. This pipeline hands out €1 million per project to de-risk co-developed missile tech, robotics, and next-gen air defense systems. The first call for projects goes live this September.

At the same time, Berlin is spinning up its own Brave Germany track to tackle critical front-line hardware shortages. Signed in Kyiv, the Brave Germany agreement pumps direct capital into specialized battlefield hardware such as laser systems and secure tactical communications. Most notably, the framework fast-tracks the production of 5,000 AI-powered strike drones. Crucially, the pact covers the co-development of strategic long-range systems capable of reaching up to 1,500 kilometers, allowing allied tech companies to validate their prototypes directly in live combat via the "Test in Ukraine" loop.

The business logic of low-cost tech

Before a software update can rewrite the rules of combat, it must first change the core economic equation of the modern battlefield.

The asymmetric cost equation

To understand why software-defined warfare is reshaping modern conflict, we have to start with the economics. For decades, conventional military strategy was created under the assumption that a sophisticated battle tank required an equally sophisticated (and expensive) anti-tank missile system. It was a high-stakes arena where both offense and defense demanded multi-million-dollar investments to get their hardware to the starting line.

Ecosystems like Brave1 have torn up the traditional defense playbook. By weaponizing cheap commercial tech, they give agile startups the ability to neutralize multimillion-dollar systems at a fraction of the cost. Today, a standard off-the-shelf racing drone can be modified with a basic 3D-printed payload mechanism and a $50 onboard AI microchip, and all of it costs around $500 to assemble. Yet, guided by smart edge-computing algorithms that throwaway piece of plastic gets the precision to seek and destroy an armored vehicle worth as much as $5 million.

This staggering economic asymmetry flips the logic of attrition warfare on its head. Why spend millions on heavy, legacy hardware when low-cost, disposable tech can destroy it too?

Well, this hyper-cheap approach has its own structural headaches. Sourced from commercial supply chains rather than defense contractors, these tools sacrifice military-grade certification and risk resembling an early-stage Kickstarter project. A cheap capacitor might tap out the moment it encounters a brisk autumn breeze. In this arena, you trade hardware perfection for the pure math of a statistical zerg rush.

National survival over corporate profits

Traditional defense giants operate much like bureaucratic mega-corporations, obsessing over shareholder returns and safeguarding their proprietary tech. Their business models are built around stability and product lifecycles that stretch over decades.

Wartime innovation ecosystems like Brave1 trade corporate profit targets for immediate frontline deployment. This type of urgency rewards rapid prototyping and open-source collaboration rather than multi-year development loops and locked software.

While this agile approach speeds up innovation during trying times, it leaves deep-tech startups with seriously thin financial safety nets. When the immediate crisis cools down, many of these narrow-margin startups may struggle to keep their engineers paid or scale into mature defense companies.

The new playbook for global enterprise tech

The massive ripples from Brave1 reach far beyond the mud of the front line, handing a brand new playbook to deep-tech manufacturers worldwide. By proving that you can build, patch, and scale seriously complex physical networks using decentralized, open-source code, this pipeline has cracked the code on absolute agility. It proves that during a chaotic crisis, the most lethal asset in your toolkit isn’t a shiny yet rigid hardware product but an adaptable software infrastructure that can change on a dime.

The future of enterprise technology is no longer trapped within the squeaky clean labs of isolated corporate ivory towers. The companies that are going to rule the next decade, whether building next-gen defense hardware or streamlining global shipping routes, will be those that successfully mimic this framework. They will swap stiff, top-down corporate hierarchies for open developer portals and trade sluggish multi-month updates for continuous 72-hour coding sprints. To put it simply, they will realize that raw, real-world data will always stomp all over laboratory theory.

Now, we can officially say goodbye to old-school development manuals and endless peacetime planning committees. Tomorrow's winning companies will stop treating business like a catalog of static products, choosing instead to run their operations like a living software ecosystem that evolves the second the environment shifts.

Tracking the innovation: How to follow Brave1

Brave1 moves at hyper-speed, spinning up new grant tracks and global pipelines in weeks instead of years. Thankfully, this explosive evolution is heavily documented, so we can track Ukraine's defense tech disruption in real time:

If you are tracking Brave1, watch these channels:

• Brave1 Official Portal: The primary hub for active grant tracks, tech challenges, and developer registration.
• Ministry of Digital Transformation: The best source for the high-level government policies shaping the dual-use tech ecosystem.
• Brave1 on X (Twitter): The fastest source for frontline product launches, hackathons, and real-time field test videos.
• Brave1 Market: The "app store for the military" marketplace where frontline units buy verified hardware directly from builders.
• TrophyLab Portal: A secure research platform used by allied engineers to analyze captured hardware and electronic warfare data.

In the end, this brand-new blueprint proves that the old corporate playbook is as good as dead, and global tech firms must learn to move fast or get left behind.

For quite a long time now, I've been concerned about how Microsoft is going to monetize Windows going forward. While the world's dominant desktop operating system remains available for a one-off fee (assuming you aren't an upgrader who can get it for free), there's a good chance this could change in the future.

Why do I think that? To me, it feels somewhat inevitable that eventually, Microsoft is going to look for a way to shift Windows from an upfront payment to a subscription model for consumers. That regular monthly income stream piling up in the coffers is the end game for most big tech companies and their products these days for good reasons in terms of the profits to be made.

Of course, rumors about Microsoft looking to charge a subscription fee for using Windows to consumers (as opposed to businesses, where there's already a subscription option) have been floating around for years. True, they've all been dubious and sketchy in nature, or indeed proven outright incorrect, but this idea keeps bubbling up, and I believe we're witnessing a development with AI right now that indicates how Microsoft might have an ideal opportunity to make this pivot at some point down the road.

Last week, Windows Central spotted that the Copilot feature Deep Research is getting the axe, to be replaced with a new feature: Researcher. The idea behind both pieces of functionality is roughly the same (researching and creating detailed reports complete with citations), but the difference is Deep Research was free, whereas Researcher requires a subscription to Microsoft 365 Premium.

This follows Microsoft Whiteboard (where AI assists you in brainstorming ideas) getting changed so personal accounts can no longer use it — the app now requires being signed up for a Microsoft Business account. On top of that, Outlook's Meeting Insight functionality was just shifted behind a paywall, being transformed into an admittedly beefier AI feature, but one that needs a Microsoft 365 Copilot license.

So, there's a trend towards turning previously free AI features into paid ones, presumably as Microsoft rejigs its Copilot offerings and figures out what works well — and what's being used — and whether any of that can be charged for.

AI pivot

Now, we all know AI agents are going to be the 'next big thing' (TM) in Windows 11, certainly if Microsoft has its way, and the idea is for these AI entities to be doing more and more within the OS.

We won't just have an agent for changing Windows settings — and I mean a proper incarnation of this already existing idea, which really can adjust a host of options based on a simple request to "make my laptop battery last longer" or similar — but we will have a small crowd of them. Maybe a troubleshooting agent, for example, which can take a problem that you're battling in Windows and use some genuine AI smarts to help solve it. Or perhaps a creativity agent which can tackle a host of image or video-related tasks, or more broadly help with organizing your projects and photos.

I think the catch will be that eventually, as we've seen with some aspects of AI and Copilot, Microsoft will start shuffling some of these agents behind a paywall — especially considering that more powerful capabilities like these in-depth AI tricks will cost Microsoft a fair bit to drive in terms of cloud resources. It's certainly conceivable that Microsoft could end up charging a small monthly fee to use these premium agents, maybe as separate add-ons in the hope that you'll bolt more of them onto Windows for a cumulatively greater benefit to its coffers.

We could ultimately be looking at a kind of modular, AI-focused OS, and to me, this seems like the easiest way for Microsoft to transform Windows into a subscription model, because it'll happen slowly. You don't need a troubleshooting AI agent to run Windows at all, but it'll be a nice thing to have — certainly for less tech-savvy types — in case problems do arise in the OS.

As more bits and pieces that might start off free drift behind a paywall — as these AI features are built up and become more compelling — people may eventually be tempted to buy a bundle of them. And before you know it, you've signed up for a Windows subscription of sorts (although the free version of the OS will, of course, still be available).

In the end, there may be a couple of bundles — tiered subscriptions, in other words — and let's not forget Microsoft's potential ambitions for a cloud PC model for consumers, either.

This has been a possibility raised in past rumors and leaks (and again, this is something which has already happened in the enterprise world), and if you put all this together, you're looking at a kind of Netflix model, if you will: a streamed OS with several paid subscription tiers. Albeit with a free basic tier for Windows – although who's to say that may not become ad-supported, or indeed more ad-supported, as Windows 11 already does a fair old line in adverts and promos. (Although admittedly Microsoft is cutting back on that front in its crowd-pleasing efforts to fix Windows 11).

Not a foregone conclusion — but a likely enough prospect

This is just my opinion, naturally, and yes, maybe I got rather carried away with the extrapolation at the end there. It's also true that some big question marks remain hanging over the wider notion I've put forward.

As I just mentioned, Microsoft is very much bending over backwards to please Windows 11 users right now, so any possible timeline for this shift may be pushed way back in view of that. Bringing in some form of subscription is hardly going to be a well-received move, even if implemented in small, delicate increments as I'm guessing it would be.

The other obvious sticking point is that Microsoft needs to make its AI agents in Windows worth having. They need to be objects of desire and come packing genuinely useful AI abilities, otherwise clearly, people won't pay for the privilege of having them on their Windows desktop. They also need to be secure and trustworthy so they don't end up throwing spanners in the works of your Windows installation.

That could be the biggest hurdle for Microsoft to overcome, because as it stands, when the idea of AI features being paywalled in Windows 11 has been raised (in rumors and the like), it's been actively welcomed by the more skeptical out there. The cynics are more than happy to have everything AI-related locked away from them, and as non-paying users, this would give them an AI-free Windows 11 desktop.

Again, this plays into any potential move along these lines having to happen further into the future, but I think Microsoft does have this as an eventual goal. If you ask yourself the question: if Microsoft could charge a subscription for Windows 11, would it? The answer is clearly yes, a thousand times over. But the actual, real question here is whether Microsoft thinks it could successfully get away with such a plan without sparking a large-scale defection from its desktop OS.

We can keep our fingers crossed that a monthly charge for Windows isn't coming, but frankly, I think it's likely. Or even just a matter of time — perhaps a lot of time, granted — whether that subscription pertains to AI add-ons, or a cloud PC offering, or Microsoft finds another way to spin this.

The ‘SaaSpocalypse’ narrative continues to dominate investor outlooks, with fears that AI development is threatening the value of software companies. However, I believe that this is not a SaaSpocalypse, but instead a period of natural selection for the software industry – where the strong will thrive.

Widespread AI adoption is raising the bar, so SaaS providers must focus on delivering differentiated and highly valuable outcomes. Providers that are built on broad, undifferentiated offerings and don’t deliver clear, evolving value will soon go extinct and be replaced by simple AI solutions.

In contrast, software companies with true specialization, deep customer understanding, and defensible moats are well placed to strengthen and evolve their position in the AI era.

Deep customer knowledge

In the industrial mid-market, AI development needs to be grounded in practical applications. Manufacturers operate in physical, industrial environments where the priority is building and delivering the parts and products that power the real economy.

We know our customers are not looking to build the software themselves, or experiment with ‘vibe coding’, but instead they want their existing trusted technology partners to level-up their platforms in a secure, structured way.

Deep industry expertise, knowledge of specialized workflows, and decades of intricate data are the critical factors that pinpoint where real value can be delivered to customers. These elements are the most difficult for AI disruptors to attempt to simulate or replicate.

The impact of AI will meaningfully reshape how software is developed and delivered, but providers should continue to rely on established data moats and security frameworks.

The SaaS providers that own the bank of knowledge that existing software provides and capitalize on this deep expertise during AI adoption are the ones that will stand the test of time. Investing in AI platforms and agents that transform that knowledge into autonomous action will deliver exactly what customers need: improved decision making, elevated efficiency through margin expansion, and increased resilience.

One thing that remains constant is the pressure businesses are under to deliver more for less. To succeed at this critical juncture, software companies need to help businesses do just that through tactical and differentiated applications of AI within workflows.

Embedding AI securely with clear governance

Layering AI into workflows is a practical, high-impact way for businesses to scale without increasing expenditure significantly. When AI agents are embedded and trained in systems, they can automate routine tasks, surface actionable insights in context, and guide users through next-best actions. This frees up time for higher-value work.

When embedding AI tools, providers must prioritize governance by establishing clear guardrails, processes, and secure systems. Role-specific training is critical to ensure AI is embedded in workflows effectively while keeping business data secure.

Added AI capabilities play a central role in workforce development. As workflows become more intelligent, employees should be supported to build new skills to deliver higher-value work, while customers benefit from more responsive and capable systems.

Recognizing a natural progression beyond surface level AI integration, focusing on analysis, judgement and continuous improvement. To achieve this, providers can embed AI agents directly into the software environments that customers already depend on. In doing so, end users can enhance capability without introducing unnecessary risk or disruption to core operations.

AI does not replace expertise or SaaS; it raises the baseline and helps teams develop the skills needed to deliver more value.

Protecting margins

The industry is now moving beyond early enthusiasm for AI towards a more grounded understanding of its true cost and impact. As organizations begin to grapple with the realities of implementation, integration, governance and ongoing running costs, the conversation is becoming more balanced.

This shift will further separate those delivering meaningful, embedded value from those relying on surface-level AI features.

In periods of economic uncertainty and volatile market conditions, which is increasingly a global reality, companies need to build resilience through intelligent decision making and forward thinking. Where costs, energy prices, transport, and supplier availability shift quickly, manufactures are often relying on lagging indicators and manual spreadsheets that are outdated as soon as they are shared.

This latency be combatted by AI that is directly embedded into SaaS that can combine internal and external data to inform likely price changes and ideal buying windows.

Crucially, because these insights sit inside the platform customers utilize day to day, predictive recommendations are delivered in context with the right insight at the right moment. Real-time visibility into performance, supported by AI, allows businesses to act with greater confidence in uncertain conditions.

For customers, this translates to stronger margins, better outcomes, and increased business resilience. So, end-point users can experience an intuitive and informative platform that makes their job easier.

Going beyond the “either or” narrative

AI and SaaS are converging. Organizations that invest in both the technology and the capability to use it effectively will see the greatest return. With the right governance and a clear understanding of customer needs, AI becomes a force multiplier for the value SaaS already delivers, not a replacement for it.

The conversation needs to center on the customers using SaaS, who don’t want experimentations with code but technology they can trust delivered with clear process, security and reliability.

Ultimately, this is not the end of SaaS, but an inflection point to differentiate software providers. Those that embed AI meaningfully within trusted products and maintain genuine specialization will emerge stronger through this period of natural selection. Organizations that invest in both technology and the human capability to use it effectively will see the greatest return.

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Apple’s macOS 27 release — dubbed ‘Golden Gate’ by the company — is packed with new features, despite Apple saying that this year was more of an evolution than revolution. There are plenty of additions that have got tongues wagging and pens scribbling across the internet.

Yet for every Siri app and Liquid Glass tweak, there are many more upgrades that people are barely talking about. That means that if you don’t dig below the surface, you might miss some of the interesting new ways you can use your Mac once you’ve downloaded and installed macOS 27.

Here, we’ll run through five of our favorite little-known inclusions in the macOS Golden Gate beta. Get to grips with all of them and you’ll have fresh ways to use your Mac on a daily basis.

Visual Intelligence

Previously, Visual Intelligence was only available on iOS. You’d point your iPhone at an object, press the Camera Control, and Visual Intelligence would identify the object or pull out details from an event flyer and add them to your calendar. Now, similar powers can be harnessed on your Mac.

But you might be asking how this will work on a Mac, seeing as you can’t just go up to something and take a picture of it with your laptop. Fortunately, Apple has adapted Visual Intelligence to macOS 27 in a handy new way.

Much like when you take a screenshot, you can drag a box over part of your screen using your mouse. When you let go, you can ask Visual Intelligence to help you with what you have selected. You can ask Siri AI a question about the image and it will respond. You can then go back and forth with the chatbot if you need more information.

If you highlight an event, you can add it to your calendar. Even better, if you use Visual Intelligence on a festival schedule with multiple acts playing at different times, for instance, you can add as many or as few to your calendar as you want, all saved as separate entries with relevant info like the band that’s playing and the stage that you’ll find them on.

This is one of my favorite features in macOS 27 as it’s not only useful, but highly flexible and intelligent. Visual Intelligence is able to understand many different inputs and the contexts surrounding them, turning Siri into much more of the powerful assistant that it was always meant to be.

Automatic password strengthening in Passwords

The best password managers often alert you if your logins stored in the app are weak or compromised, but that usually means updating them to stronger alternatives yourself.

If that requires manually visiting affected websites and changing your login details by hand dozens or even hundreds of times, the task can feel incredibly daunting. Faced with that kind of situation, many people likely just give up, preferring to stick with their untrustworthy passwords rather than go through the hassle of changing them all one by one.

That’s an obvious security risk, and it’s one that macOS 27 has attempted to put right. Although the following change hasn’t had much airtime since the feature was announced at Apple’s Worldwide Developers Conference (WWDC) in June, I feel like it could be one of the most important features in the new operating system.

In macOS 27, all you need to do is ask Apple’s Passwords app to fix your dodgy passwords for you. Just one click and it’ll get to work visiting affected websites and updating your logins without you needing to lift a finger.

It’s a great example of how useful AI agents can be, all backed up by Apple’s well-known security and privacy credentials.

Natural language in Calendar

How many times have you opened the Calendar app on your Mac and tried to create a new event with a phrase like “Movie night with James tomorrow at 8pm at home,” only to find none of the details — such as the time or the place — are properly added to the event?

It’s been a recurring problem for years, and a frustrating one, too, as Apple fans have watched other apps race ahead with similar features while Calendar lags behind.

Now, that’s been improved. Changes in macOS 27 mean that you just need to enter an event using the type of natural language that you’d use in an everyday conversation. Any details that Calendar can pick up on — like a person, place, time or date — will turn gray. Click them and select the suggested detail and they’ll be properly added. This feature understands your context, too, such as where your home is located.

Sure, similar features have been present in rival calendar apps for years, and that could explain why Apple’s Calendar update hasn’t exactly lit the world on fire in the weeks and months since it was announced.

But having this feature is nonetheless an excellent addition for users of the Calendar app. The more naturally you can use your apps, the more effortless they become, allowing you to quickly be done with them so you can get on with what matters in your day. This natural language upgrade for the Calendar app lets you do exactly that.

App resizing in iPhone Mirroring

Of all the clever new features announced last year as part of macOS Sequoia in 2024, iPhone Mirroring was one of the more intriguing. As the name suggests, it mirrors your iPhone onto your Mac’s screen, bringing up a software view of your device where you can open apps, type messages, and basically use everything on your iPhone through the Mac sitting in front of you.

But there was one annoying quirk with this system: you couldn’t resize the iPhone window to use different aspect ratios. That meant any apps you opened on the virtual iPhone were limited in size and failed to make use of your Mac display’s larger proportions.

Thankfully, Apple has fixed that up in macOS 27. Instead of being stuck with the unchanging iPhone window of the past, macOS 27 now lets you drag the on-screen iPhone and change its dimensions as required.

You can now make the iPhone window wider or taller and the content inside the app will automatically adapt. It’s far better than what we had before.

So, if you’d given iPhone Mirroring a try before but were put off by the tiny window you had to work with, macOS 27 could make using your iPhone on your Mac a little more comfortable.

Improved support for ultra-wide displays

Before macOS 27, using an ultra-wide display with your Mac could be a downright pain. Many ultra-wide resolutions were not supported natively in macOS, meaning you had to settle for something that wasn’t quite right for your display, which could in turn negatively affect your macOS experience.

And when competing operating systems like Windows could support ultra-wide monitors in a more comprehensive way, the frustration for Mac users was understandable.

Worse, if you ever unplugged your monitor and then plugged it in again, macOS would forget your previous display settings, forcing you to set up your ultra-wide monitor as needed every single time. If you relied on one of the best MacBooks as your daily driver and you frequently disconnected it from your desk display, this was a regular irritation.

In macOS 27, though, that’s finally a thing of the past. You’ll now be able to run an ultra-wide monitor up to 5K resolution and up to 120Hz refresh rate, meaning far more high-end displays will now work with macOS 27 without a hitch.

And Apple has fixed the disconnection issue so that hooking up your monitor no longer requires you to dial in your settings every time. Instead, macOS remembers your preferences so that no manual adjustments are needed.

That makes this a seemingly small change on the surface, but one that will be appreciated by anyone who owns an ultra-wide monitor and has long struggled with the limitations of macOS.

The AI buildout has not just captured the attention of the biggest technology companies in the world, but also the global superpowers, including the US and China in particular. The growth of this technology, which is still nascent, is seen as not just a competitive advantage for domestic businesses, but a fundamental development that can be harnessed to dominate the world stage in the future.

AI prescience

The Russian president, Vladimir Putin, was speaking to Russian students shortly after Google scientists published their seminal study that outlined the transformer architecture that defines modern AI systems.

His comments came in the context of national security and the balance of geopolitical power, with many world leaders long understanding that AI would be at the heart of not just commercial technologies but military capabilities.

His thoughts also balanced the notion of benefits that may arise with the threats, both by humans wielding AI tools as well as from the AI systems themselves. Elon Musk, for example, had previously highlighted the idea that AI could lead to a third world war.

Modern warfare

It's become increasingly clear just how prominent the role of AI and autonomous weapons is playing in international conflicts nearly ten years on, including in the Russian invasion of Ukraine in 2022 — and the battles that have ensued since.

Geopolitically, both the US and China are also each defining their own AI strategy, with both nations desperately building out their capabilities in a way that ensures dominance while respecting and maintaining ongoing international trade and commerce.

Although the technology hasn't reached the point where one single country or superpower can dominate the era to come, the US has begun strategically restricting the release of AI models, like Mythos, on the basis that they're becoming too powerful to get into the wrong hands.

Marc Andreessen was a prominent figure in the early internet era, creating some of the earliest web browsers, including Netscape and Mosaic, which he authored. In recent years, he's weighed in on subjects including AI — but it was his comments on software that really summarised how technology evolved during the early 21st century.

Inside Intel

Andreessen was running his venture capital firm, Andreessen Horowitz, when he published his era-defining essay in the Wall Street Journal.

The essay highlighted the dramatic shifts underway across the economy, with Andreessen centering his argument on the observation that software companies were poised to take over longstanding industries via the massive shift among consumers to online and digital spaces.

One example he highlighted was a misstep by the book retailer Borders, which closed all its US stores in 2011. In 2001, it handed over its online business to Amazon because, he argued, it saw online book sales as "non-strategic and unimportant".

Well, as Andreessen put it, "oops." He then highlighted Amazon's role as a software company with a core engine capable of selling virtually everything online.

Dog eat dog

In the cut and thrust of business over hundreds of years, there have been wave after wave of revolution, with new technologies overhauling and refreshing the landscape. Many businesses have persisted by adapting and pivoting, but the rapid proliferation of software over the last 30 years is now threatening to give way to an entirely new era.

Andreessen highlighted that, in 2011, the stock market "actually hates technology", and there's an argument to make that the stock market has once again turned its back on some of the biggest software giants, with AI threatening to make them redundant.

A few years on, Jensen Huang, the CEO of Nvidia, even built on the idea that the Netscape co-founder set out 15 years ago by suggesting that "AI is going to eat software".

• A new option for the Copilot key has been spotted in testing
• Windows 11 users are getting the ability to remap it to 'Do nothing'
• This comes on top of work already underway to redefine the key to Right Ctrl or the context menu

Microsoft's latest revolutionary idea for Windows 11 is to have a key on your keyboard which can be remapped to do absolutely nothing.

Windows Latest noticed the latest twist in the tale of the Copilot key, as highlighted by regular leaker PhantomOfEarth on X, who spotted the development hidden in a recent Windows 11 preview build.

In the choices to customize the Copilot key – which, by default, summons Windows 11's Copilot AI (of course) – there's a new option to simply 'Do nothing'.

Yes, that's correct – you can change the key to simply be a pointless piece of plastic that does precisely zip. (Or at least you'll be able to in testing, when this ability comes into play, that is, although some Windows 11 testers have reportedly seen it live in preview already).

As you may have seen earlier this year, Microsoft is working on bringing fresh options to the Copilot key that include customizing it to invoke the context (right-click) menu, and to turn it back into Right Ctrl (which was the key that the Copilot button replaced on Copilot+ laptops and some keyboards).

Analysis: do nothing, be happy

What's the point of a key that does nothing? Good question. Maybe it's the keyboard equivalent of a stress ball – you can hammer on that key as much as you want when you're really annoyed, and it won't splurge a load of characters into the document you're working on as you exorcise your frustrated demons.

Okay, so maybe not, but perhaps the idea is that this is aimed at people who really hate the Copilot key, but haven't decided what they might want to remap it to. So, they can go ahead and elect for the key to do nothing if it's accidentally hit, while they figure out what they might want to redefine in its place. That's stretching things, admittedly, but it's the best guess I have.

At any rate, it's a harmless enough option, although it feels inescapably odd to have a 'blank' key on your keyboard. How far we've fallen since the Copilot key was first revealed and trumpeted by Microsoft as the biggest thing to happen to keyboards in 30 years since the Windows key was introduced. And indeed more recently, Microsoft promoted the Copilot key as the "button you can press to fix everything", so there's some irony that it could soon (optionally) become the button you can press to fix 'nothing' in a very literal sense.

Don't forget that you don't have to wait for Microsoft's Copilot key remapping functions to arrive in the release version of Windows 11. You can use the Keyboard Manager in PowerToys to do this instead, although it'll be far neater to have these options in Windows 11's settings, of course.

• Microsoft has contacted LG regarding a McAfee pop-up served via the monitor maker's app (which in turn is installed by Windows Update)
• As an 'immediate next step' LG has disabled this pop-up
• However, there are still issues to address around the automatic installation of the LG monitor app, and the way Windows Update itself operates with third-party software

Microsoft has fixed a controversial situation whereby Windows 11 users with LG monitors were being hit by a pop-up for McAfee antivirus, and that advert has now been banished from the OS — but there's still more that Microsoft needs to do here.

Windows Latest noticed that Pavan Davuluri, who is head of Windows at Microsoft, flagged up what's happened on X.

In response to a nudge from Epic's Tim Sweeney on the matter, Davuluri said: "We've connected with the team at LG and as an immediate next step, they have agreed to disable the McAfee pop-up from their app. We appreciate LG working with us toward a shared goal of a better experience for our mutual customers. We will keep improving here with our ecosystem partners."

In case you missed this story from earlier in the week, Windows 11 users with an LG display are reportedly getting an LG monitor app (from the Microsoft Store) automatically installed on their PCs via Windows Update simply after hooking up the monitor.

The real kicker was that following this, the mentioned McAfee pop-up (pushing a trial for the antivirus) was repeatedly bothering those users, with the ad delivered via the LG monitor app. So, what Microsoft is telling us here is that as an immediate first step, that McAfee pop-up is no longer part of LG's Windows app, and won't be darkening the desktops of any LG monitor owners any longer.

The (pop-up-less) monitor app is presumably still being piped through by Windows Update, though, and hopefully the next step for Microsoft will be to address this.

Analysis: software giants in glass houses?

Windows Update really shouldn't be installing any third-party software without the user's permission, that much is clear (or first-party for that matter). It's fine to flag available updates, wherever they come from or whatever hardware they may be for, but highlighting them is one thing, and going ahead with an auto-installation is another.

Okay, so LG has agreed to disable this pop-up, and it's good that this has happened swiftly, as it was, of course, a huge annoyance. Indeed, it's very worrying that this could happen at all, really (and in case you missed it, LG's TVs have also come under fire this week).

LG had already addressed this controversy with a rather lengthy statement that read: "LG Electronics reiterates that McAfee is not installed automatically and is never installed without the user's explicit consent. The LG Monitor App Installer is distributed through Microsoft's official Windows distribution process, which included McAfee as an option.

"McAfee will be installed only if a user actively chooses to proceed with the installation and provides consent. Under no circumstances is McAfee installed automatically or without user authorization. The LG Monitor App Installer does not access, collect, or transmit any customer personal data."

What this appears to be saying is that McAfee's actual antivirus app isn't installed without consent. It's not denying that the McAfee pop-up appeared without consent, or that the LG Monitor app is installed in the background in Windows without the user's knowledge. It only notes that the LG Monitor app is "distributed through Microsoft's official Windows distribution process", as if that makes everything okay, or somehow not LG's fault.

It is, of course, Microsoft's fault as well – both parties need to take responsibility, and fix not just the McAfee pop-up appearing, but the LG Monitor app being automatically installed simply when a monitor is connected to a Windows 11 PC. (Which is reportedly the case, I should add – I haven't experienced this myself, as I don't have an LG display, but a whole heap of feedback is present suggesting it's exactly what happens).

In short, the immediate disabling of the McAfee trial pop-up is a start, but we need a swift follow-up addressing the LG app itself and how that's dealt with in Windows 11. And more broadly, how third-party apps in general interact with Windows Update, because as already noted, this situation should never have been allowed to happen in the first place (no matter what LG, or any other hardware partner, is doing with their apps).

Furthermore, there's no shortage of people on Reddit noting that Microsoft has been keen enough to act here, but not so keen on curbing its own advertising-related excesses in Windows 11.

As one Redditor says of Microsoft: "Now all they need to do is crack down on their own bloatware…"

Although to be fair to Microsoft, it is acting to rein in some of the upselling and promotional noise that Windows 11 has come to be associated with. More work needs to be done on this front, though, for sure.

Engineering discipline in software development has been under the spotlight since AI started being used by devs to generate code, with many teams worried it’s causing unnecessary risk. Headlines have been full of cautionary tales about bugs created by hastily shipped code or senior executives taking vibe coding into their own hands.

Nevertheless, teams are finding ways to build trust in AI-generated code. Google’s 2025 DORA survey concluded that AI has an amplifying effect on organizations' processes - quality processes enhanced by AI lead to higher quality outputs, and more of them.

Our own research shows that most teams aren’t being haphazard with safeguards or accepting AI outputs at face value. Most teams are being more rigorous and giving their outputs the same scrutiny as the work of a teammate. That tells us a lot about how the best-performing teams are learning how to work with AI.

Seventy-six percent of enterprise teams are reviewing AI-generated work at least as rigorously as human-written work. Teams using AI to accelerate their work are seeing results from using proven engineering practices to ensure code is reviewed and tested sufficiently.

Rather than being inherently unstable, the rise of AI-generated code has emphasized that guardrails are the bedrock of consistently reliable software.

Treat AI as a virtual teammate

Teams treating AI-generated code as if it was produced by a human tells us something about how mature teams are using AI. The engineers using AI most effectively treat it as a virtual teammate. They stay in charge of the decisions and they use AI to accelerate the execution.

To make AI adoption an iterative process that improves over time, starting with low-stakes, repeatable tasks and applying stringent checks to outputs gives the tech a chance to work properly without expecting immediate results.

As MIT’s Computer Science and Artificial Intelligence Laboratory reported, AI coding’s widespread adoption doesn’t mean it can handle all aspects of large-scale software engineering by itself. Releasing to production is higher stakes than writing code, for example. Scrutiny from real humans is vital to prevent errors and hallucinations from impacting the business.

At this early stage of AI being used in workflows, it’s positive that 43% of teams are treating AI code like human-written code, with 33% applying even stricter checks. Even the best developers on a team aren’t above the processes and guardrails that guarantee reliable software deployment at speed. AI should be approached in the same way.

Trust in AI follows the risk curve

As with any new technology, introducing AI to the software lifecycle is a learning process. Our data shows trust in AI varies greatly depending on the stage of the software lifecycle, indicating that teams are building confidence with appropriate caution.

A pragmatic approach is being taken at the best-performing enterprises. Thought leaders from Gartner, Forrester and Google’s DORA team all highlight that organizations seeing success from AI-assisted development are strengthening their engineering controls and building up success over time.

This tallies with where AI is trusted to perform. The vast majority (82%) now use AI during the build stage, dropping to 58% at release where production risk is highest. Using AI primarily for earlier stages of software development is a rational step to make sure failures don’t impact the wider business.

This doesn’t betray a lack of confidence in the technology. Almost half (46%) of teams are confident in the performance of AI-generated code, indicating that its more cautious use in production is a measured business decision rather than skepticism.

Outputs are much easier to review and refine before the release stage. With many businesses still lacking full observability, teams often only hear about mistakes in live code once users notify them. Despite claims that software development could eventually be fully automated, teams are showing a clear awareness of where human oversight is essential.

Rather than making blanket judgments about AI’s capabilities, they are taking a more nuanced view, which bodes well for the future of AI-assisted software delivery.

Combine speed with discipline to win

There is continuity in how mature teams are making sure AI-generated code is fit for purpose, but it’s still having a seismic impact on the role of software engineers. When I speak with developers, their feedback is unanimous: the time AI saves is invaluable for focusing on neglected parts of their process.

AI moves the cognitive load from writing code and building configuration to reviewing and directing it. With time freed up, teams have more scope to prioritize observability, test coverage or disaster recovery scenarios - crucial elements of software hygiene that too often get overlooked.

AI adoption is increasing documentation quality according to the DORA report, suggesting that many teams are taking the opportunity to improve the broader engineering practices that support reliable software delivery.

As it takes on more of the mechanical aspects of software development, engineers become increasingly responsible for the work that matters most: validating outputs, understanding risk, and ensuring outputs align with business objectives. If someone asks “why did we build it this way?” it’s a problem if no one can answer the question without asking AI. Humans must be in the loop.

The organizations that benefit most from AI will not be those with more automation, they will be the ones that combine AI-driven speed with engineering discipline and a strict adherence to repeatable processes.

AI adoption is a DevOps challenge

The debate around AI-generated code often focuses on whether the technology can be trusted in DevOps. In most cases, teams have been building this trust iteratively without throwing away human judgment to get the best results.

As AI capabilities continue to improve, the gap between high-performing teams and everyone else is unlikely to be determined by access to the latest model.

The engineers and teams that will thrive are the ones who pair AI’s capabilities with their own judgement.

We list the 10 best vibe coding tools.

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

Fliki is a popular AI video generator, trusted by more than 12 million users worldwide and 50,000 companies, including brands like Meta, ByteDance, Oracle, and PayPal. You can create and edit AI videos with natural-language prompts using its AI Copilot.

In this article, we’ve put Fliki to the test, evaluating its features, pricing, and real-world in-app experience to help you find out whether this AI video editor lives up to its claims and whether it’s the right tool for you.

Fliki: Plans and pricing

Fliki is a fairly limited free video editing software, which gives you three credits per month, or 36 credits for the entire year. You can export videos with a maximum length of one minute in 720p resolution, along with access to 300 standard voices. All other video and audio generation features are limited on the free plan and restricted by the number of credits you receive.

We found the provision of just three credits per month quite stingy. For instance, generating a one-minute AI video costs around 0.8 credits. If it includes an AI avatar, it can cost around 5.3 credits alone. This means you can't do much with the free plan besides testing out the platform.

To get the most out of Fliki, you'll have to subscribe to one of its paid plans. Its Standard plan starts at $28 per month. Right now, you can get 50% off the subscription, bringing the effective price down to $14 per month. With this plan, you get 2,160 credits for the entire year, along with an expanded export length of 15 minutes in 1080p resolution, which disappointed us a bit. Kapwing, for instance, allows you to export videos in 4K quality at around the same price.

You can check out Fliki by clicking here.

That said, you get unlimited access to various video and audio tools, along with features like voice cloning, custom voices, and Brand Kits. You also get access to various templates, which help you create content using pre-built video layouts. This plan also includes several collaboration features, such as the ability to bulk-create videos from a single CSV file, an AI assistant, and Zapier integrations. However, it still does not include team collaboration, which is again a letdown.

Then there's the Premium plan, costing $88 per month, or $44 per month with the current discount. Here, the maximum export length increases to 40 minutes, but the resolution remains capped at 1080p. Some additional features include unlimited AI avatars, custom fonts, faster exports, and team collaboration.

If you've exhausted your credits, you can purchase top-up credits if you have an active subscription plan. These additional credits are valid for up to 180 days and remain active even if your subscription is canceled during this period. You can get 60 credits for $10, 130 credits for $25, and so on, all the way up to $2,500 worth of credits.

Fliki: Features

Fliki houses as many as 54 tools to create and edit videos and images, supporting various use cases. For instance, you can create social media reels, TikTok videos, or a faceless YouTube channel with Fliki.

There’s also the popular AI Avatar feature, which helps you build avatars of your own and reuse them in your videos. Besides this, you also get an AI Script Writer, voice cloning, a subtitle generator, and a pretty fun Talking Photo feature.

That said, we found Fliki’s AI avatars to be based largely on real people, rather than offering the wide range of outlandish and fictional characters we get with a site like Kapwing.

These are better viewed as use cases rather than standalone features. The core feature remains the same: generating videos and images through prompts. These prompts can be in the form of scripts, reference images, videos, or even audio. Editing tools include background removers, translators, and screen recorders.

Fliki has made a lot of recent additions to its available tools, making it a more complete video generation and editing platform. For instance, it has introduced new models such as Flux Klein 2, Nano Banana 2 for images, Kling 3.0, Seedance 2.0 for video, native audio, multi-shot, and P-Video for video and lip-syncing needs.

You can also use your own media in video workflows, along with 130 new character-driven voices in 20+ languages. As recently as February 2026, Fliki rebuilt its voices to include a wider range of emotions, such as laughing, sarcasm, and deadpan, all of which can be fine-tuned using natural language descriptions.

Fliki now also supports YouTube-licensed music protected by copyright. It has also expanded its character library, allowing you to create and reuse AI characters across multiple projects.

One of its most exciting additions has been the ability to create stop-motion story videos with needle-felted wool puppet looks. Another addition is TikTok auto-publishing, which allows you to post videos created through Fliki directly to TikTok by linking your account in Workspace Settings. You can either publish them immediately or set up a drip schedule to release episodes on TikTok.

Fliki: Interface and in use

Getting started with Fliki is very easy. We used a Google account to sign up for the platform, which took less than a minute. Once you’re in, you’re greeted with a dashboard featuring a black background, along with various features highlighted in small red rectangular tabs below a prompt box.

On the left-hand panel, you’ll find all the settings you need for your video creation, including Media, Subtitles, Audio, AI Copilot, and various templates. However, note that not all of these will be accessible if you’re on the free plan, as several features are locked behind Fliki’s paid tiers.

The best part about editing with Fliki is its AI-powered editing assistant, where you can type in plain English what you want. For instance, you can ask it to make the intro slower or add a text overlay at any point in the video.

Besides this, you get two ways to build a video. The first is an at-a-glance scene reel laid out horizontally with a full timeline, while the second is a traditional timeline with functions like zooming and snapping, giving you more control.

Fliki has also overhauled its dashboard, giving it a more intuitive layout with a fresher look and easy-to-access tools.

Fliki: How we tested

We used the free version of Fliki to check out its AI video generation capabilities. We entered the following prompt: A woman in a red coat walks across a rain-soaked city street at night, neon signs reflecting in the puddles, camera slowly tracking alongside her (the same prompt we use for all our AI video testing).

We were impressed by the sheer number of customization options that Fliki offers. Once you enter the prompt, you have to select the video duration you want, with the minimum being 30 seconds. This was a bit disappointing, as we’d have liked to see shorter video generation options as well.

However, Fliki gets to work once you make the selection and generates a detailed script for you. You can edit this script before moving ahead with video generation. Besides this, you get a lot of options to customize your video right before the generation process starts.

For instance, you can choose the format and template, selecting between portrait, square, and landscape modes, along with the type of character you want. You can also select the AI model you want to use for generation, although most of them are Pro features. Besides this, you also have the option to select a voiceover, add captions, or choose an AI avatar.

We found this to be pretty convenient since you can make all the required selections in one place and don’t have to go back and forth through the platform making edits afterward. Once we were done with all the selections, we clicked on Generate Video. The actual video rendering process took less than a minute.

However, since the free tier does not allow you to create AI videos on its own, we were a bit disappointed by the output. The final video consisted of still images with voiceovers instead of a full-fledged video, like we got with Kapwing.

Fliki: Alternatives

While Fliki is a pretty good AI video generator and editor, it does have a few strong competitors. The first one would be Adobe Firefly, which costs around $8.49 per month, around 40% cheaper than Fliki during the discount period.

The biggest advantage of Firefly is its robust workflow features, which allow teams to build virtual storyboards and collaborate in real time, something that Fliki restricts to its highest-tier plan. You also get access to Adobe Express Premium and Photoshop with all its paid plans. That said, its video editing capabilities are still in development and are not as good as Fliki’s.

Another competitor is Kapwing, which comes with better AI avatar generation capabilities. While Fliki depends heavily on real-life characters, Kapwing gives you a lot of fictional character generation options, such as Dracula, vampires, and everything else you can imagine.

Kapwing also supports 4K video exports of up to 120 minutes, whereas Fliki restricts exports to just 1080p, which is certainly not enough in the modern content creation landscape. That said, Kapwing is a bit more expensive than Fliki, costing $16 per month. However, its free plan is more robust, allowing you to create AI videos instead of videos with still images and voiceovers.

Fliki: Final verdict

We used the free version of Fliki to check out its AI video generation capabilities. We entered the following prompt: A woman in a red coat walks across a rain-soaked city street at night, neon signs reflecting in the puddles, camera slowly tracking alongside her (the same prompt we use for all our AI video testing).

We were impressed by the sheer number of customization options that Fliki offers. Once you enter the prompt, you have to select the video duration you want, with the minimum being 30 seconds. This was a bit disappointing, as we’d have liked to see shorter video generation options as well.

However, Fliki gets to work once you make the selection and generates a detailed script for you. You can edit this script before moving ahead with video generation. Besides this, you get a lot of options to customize your video right before the generation process starts.

We've tested a range of video makers and editors, including the best video editing software, the best video editing apps and the best free video editing software.

Update: We reached out to LG for a statement, and it has denied claims of auto McAfee installations, stating: "LG Electronics reiterates that McAfee is not installed automatically and is never installed without the user’s explicit consent. The LG Monitor App Installer is distributed through Microsoft’s official Windows distribution process, which included McAfee as an option.

"McAfee will be installed only if a user actively chooses to proceed with the installation and provides consent. Under no circumstances is McAfee installed automatically or without user authorization. The LG Monitor App Installer does not access, collect, or transmit any customer personal data."

Original story follows below...

• LG monitors are reportedly installing bloatware on Windows 11 PCs
• The LG Monitor App Installer carries a McAfee pop-up and is automatically installed when connecting an LG monitor to a PC
• There's a fix for this, but it's a rather fiddly affair

It's no secret that Windows 11 users have voiced frustrations regarding bloat on the operating system for years, and unfortunately, LG's monitors are reportedly making the situation worse.

As reported by Windows Latest, LG monitors are installing McAfee bloatware on Windows 11 PCs without consent, and there's evidence of this in the changelog of LG's monitor app on the Microsoft Store.

Even if you don't install the app yourself, Windows Update will do so, and then treat you to a McAfee pop-up. All you have to do is plug in the LG monitor to your PC, and the automatic installation of McAfee is also shown by checking the Windows Reliability Monitor, which documents a 'successful Windows update' for the LG Monitor App Installer package.

We've reached out to LG for a comment, but haven't received a response yet. In the meantime, though, there appears to be a fix that requires users to do the following: press the Windows key and R, and type 'gpedit.msc', then hit Enter.

From that point, navigate your way to Computer Configuration > Administrative Templates > System > Device Installation > "Prevent automatic download of applications associated with device metadata", then select the 'Enabled' box and apply.

In theory, this should stop any unwanted applications from being installed, specifically with devices like LG monitors that do so via device metadata.

And if you already installed the LG monitor app manually, you should remove it, because as noted, the description on the Microsoft Store suggests McAfee is a fundamental part of the monitor application.

Bloatware can be problematic for Windows 11 users because it hogs system resources, and LG is adding to that system drain, not to mention the annoyance of continually seeing that pop-up.

Alongside LG, Microsoft hasn't provided a statement on the matter yet. I'd expect Windows 11 users will want an answer of some kind, especially because of the rather fiddly steps involved to prevent these automatic installations — and frankly, this shouldn't require extra effort from anyone.

Filmora is one of the most well-known video editing software in the industry. The company has also done well to embrace AI, adding a whole new range of advanced AI functions that have only made the platform better and more capable over time.

In this review, we've put Filmora to the test, trying out its top features hands-on to see if they live up to expectations. We also dive deeper into its other offerings, value for money, and in-use experience, as well as compare it to the best AI video editors to help you find out if it's the right choice for you.

Filmora: Plans and pricing

Much like other AI editing and content generation tools, Filmora also offers a free-forever plan, allowing you to edit videos along with access to a limited amount of creative assets such as effects, filters, transitions, music, and sound effects.

However, unlike Kapwing, Adobe Firefly, or Fliki, Filmora's free plan does not include any AI credits. This means you won't be able to generate AI videos or images for free with Filmora, which is a bit of a letdown considering the competitive landscape.

Filmora's paid plans start at $4.17 per month. The Basic plan is not much different from the free plan, except that you get 500 uses of its proprietary AI Mate, along with 1GB of cloud storage. Besides this, everything else remains pretty similar to the free plan.

You also have the option to add the Filmora Creative Assets add-on to the Basic plan for $19.99 per month. However, you do get a 14-day trial of the Creative Assets package.

There's also an option to purchase the Basic plan with a one-time perpetual license for $79.99. However, you will not receive future updates and will need to pay an additional upgrade fee to access newer versions.

You can check out Filmora by clicking here.

Next up is the Advanced plan at $5 per month, which comes with 1,000 AI credits. This is the first plan where you can create AI videos and short clips, and use AI tools like Text to Object, Text to Speech, AI Music, AI Images, AI Copywriting, and more. You also get 10GB of cloud storage, along with limited access to Creative Assets.

Compared to the likes of Veed, which costs $12 per user per month, or Kapwing, which costs $16 per user per month, Filmora's Advanced plan is one of the most affordable options available for AI content generation. Just like the Basic plan, you can also add the Filmora Creative Assets pack for $19.99 per month.

However, the Premium plan, which costs $8.33 per month, already includes the Creative Assets package and is, in our opinion, one of the best value-for-money plans you can get. You receive 2,000 AI credits per month, along with 100GB of cloud storage. The Creative Assets package also includes over 12,000 effects and filters, more than 7,000 transitions, and access to 1.5 million stock media assets.

Also, all the prices mentioned above are for the Windows version, with Mac pricing being slightly higher. For instance, the Basic plan on Mac costs $5.83 per month, with a perpetual license priced at $99.99, whereas the Advanced plan costs $6.67 per month and the Premium plan costs $10 per month.

Even though they are a tad pricier than the Windows version, they're still pretty affordable compared to other similar video editing and AI generation tools.

Filmora: Features

The best thing about Filmora is that it keeps adding new features. With the release of version 15, the platform has added a new repertoire of both AI and video editing features. For instance, there's the new AI Video Extender, which helps you expand your footage forward or backward with the help of a prompt.

This may come in handy when your footage is too short and you need a bit more footage for smooth intros and outros. Using it is also pretty simple. You just need to drag its Smart Pen onto the editing dashboard and add a prompt describing how the video should continue.

In the same vein, Filmora has also introduced Prompt Video Editing, which allows you to add, replace, or remove objects in your videos using simple text prompts instead of having to do it manually on the editing dashboard.

Another impressive new feature is Animated Charts, which lets you import CSV or Excel files and generate chart and graph animations to build dynamic line, area, and pie charts. This comes in handy for commercial teams that want to prepare engaging presentations with animated visualizations.

The platform has also recently introduced advanced pixel tracking and motion compensation algorithms for its built-in video stabilization. This helps repair shaky video footage, making it more stable.

Similarly, there's also a Lens Correction option, which helps you remove optical distortions caused by camera lenses. This includes barrel or fisheye distortion, pincushion distortion, vignetting, and perspective or keystone distortion.

This comes in handy while editing wide-angle or action camera footage, where the fisheye bulge is distracting, or architecture and interior shots, where you want walls and buildings to look straight instead of curved.

Furthermore, Filmora now has advanced planar tracking, which can follow an entire flat surface across a moving video and lets you add images, videos, text, logos, mosaics, or anything else you want to that flat surface, much like inserting an image into a video. For instance, you can attach a logo to a moving truck, billboard, or storefront sign.

Filmora also has one of the largest libraries of creative assets we have seen across similar applications. Although it's a paid add-on, the Creative Assets package unlocks a large pool of premium effects and media.

This includes stock media and original video effects, over 100,000 royalty-free audio tracks, professionally designed title templates, a wide range of basic and professional transitions, aesthetic and cinematic effects and filters, stickers, more than 10,000 ready-made templates, and over 500 AI-powered effects.

Here are some other features you get with Filmora:

• AI text-to-video, audio-to-video, and image-to-video.
• AI Storyboard Generator, allowing you to generate ready-to-use storyboards with editable characters, scenes, and styles.
• An all-new Pen Tool, which lets you draw custom vector paths and shapes directly onto the video frame.
• AI Color Palette for cinematic color editing.
• Text animation with immersive and dynamic effects.
• An automated AI-powered subtitle generator, along with more than 100 animated subtitle templates.
• AI voice cloning in 16 languages.

Filmora: Interface and in use

The Filmora dashboard can seem a bit overwhelming at first, especially if you are a beginner. The editing panel is divided into three panes: one for adding edits and elements such as audio, titles, transitions, and effects; one for viewing your image or video as you edit it; and a horizontal editing bar at the bottom, which lets you drag and drop various editing elements into your project.

At the top of your dashboard, you'll see various options such as Media, Filters, Transitions, Effects, and Templates. Simply click on any of them to open all the available options and drag and drop the ones you want onto the horizontal bar at the bottom.

Once you click on the panel where your video is being previewed, a host of other options opens on the right-hand toolbar, where you can adjust various video settings such as width, height, rotation, and flip.

As you scroll down, you'll find various AI functions such as motion tracking, planar tracking, motion blur, and stabilization. What we liked most is that you simply have to toggle the buttons to activate the features and drag elements onto your video to apply the edits. There are no complex steps involved.

In addition to video, you'll also find other advanced editing options such as speed, animation, color grading, and AI palettes on the same right-hand panel.

To be fair, it can take a bit of time for new users to get the hang of the platform. That said, Filmora is one of the most rewarding platforms once you get past that learning curve thanks to the sheer number of editing options available.

Filmora: How we tested

Since Filmora doesn't allow you to create AI content on its free plan, we couldn't test the platform's AI capabilities. However, we did put Filmora's editing functions through a thorough test.

We first downloaded the application on our Windows 11 PC, which took around a minute or two. Once set up, we imported stock media to edit. We clicked on Effects and added an RGB Stroke effect to our video of a man skateboarding. Filmora was able to identify and track the person in the video and apply the RGB Stroke effect pretty accurately.

Adding the effect was also pretty easy. We only had to select and drag the effect onto the horizontal bar. We then added a dissolving transition to the beginning of our video. Again, a simple drag-and-drop did the trick.

We then played around with various video and audio editing settings, and all of them were applied to our video almost instantaneously. There was no lag or buffering while editing even the most complex or longest videos.

Filmora: Alternatives

Filmora focuses heavily on video editing features, offering several advanced options for professional editors. But there are a few alternatives you can consider.

If you're looking for free AI video and image generation, you can consider tools like Kapwing or Fliki. Both allow you to create AI videos and images for free.

Kapwing does a good job when it comes to generating AI characters, from simple everyday characters like a baby or a cheerleader to fantasy characters like a dragon or a cyborg. There are plenty of options.

Fliki, meanwhile, gives you many pre-video generation options to choose from. For instance, you can customize your video format, template, AI avatar, voiceover, and captions before generating a video, which saves a lot of editing work afterward.

However, Kapwing's highest-tier plan can be pretty expensive, costing $50 per user per month, whereas Fliki can go up to $44 per month. Even Filmora's most advanced plan costs only $8.33 per month, making it far more affordable. Plus, both platforms lack advanced editing options such as planar tracking and video stabilization.

Filmora: Final verdict

Filmora is one of the most feature-packed AI content generation and editing tools on the market. Apart from basic features like image-to-video, script-to-video, and audio generation, you get advanced editing options such as planar tracking, storyboard generators, video stabilization, AI video and audio extenders, dual-monitor editing, animated charts, and lens correction.

Additionally, Filmora houses one of the most extensive libraries of stock assets, including licensed music, effects, transitions, stickers, and more than 10,000 templates.

That said, its interface is not among the simplest we have seen and requires a bit of a learning curve. Also, its free plan does not allow you to create any AI content, which was a bit of a disappointment. If this is a deal-breaker for you, you can consider other options like Adobe Firefly or Kapwing.

We've tested a range of video makers and editors, including the best video editing software, the best video editing apps and the best free video editing software.

Every enterprise I talk to right now has the same complaint dressed up in different language.

Their AI bills are climbing faster than anyone budgeted. Their model invoices look reasonable when viewed on their own.

But somewhere between boardroom approvals and the monthly cloud statements, money is disappearing in ways that nobody can fully explain.

Here’s the central issue that people struggle to understand: the most expensive part of AI isn't always the model itself. It's the infrastructure, orchestration, retries, idle GPUs, oversized context windows, and inefficient routing decisions that sit between a user's prompt and the final response.

This is something I call the hidden tax on AI adoption, and it's growing faster than most organizations realize.

Three numbers that should change how you think

Recently, at FinOps X in San Diego, a Goldman Sachs projection appeared on the main stage. Current enterprise token consumption globally sits at around six quadrillion tokens. The three-year projection: 120 quadrillion. That is not a rounding error… it is a 20x expansion, and it is arriving faster than the governance frameworks to manage it.

The same conference saw our launch of the Tokenomics Foundation (I’m fortunate to be a governing board member). It’s a vendor-neutral body inside the Linux Foundation dedicated specifically to the economics of AI token consumption.

The FinOps community, practitioners who have spent the better part of a decade building discipline around cloud computing spend, recognized that tokens represent a fundamentally different problem. Not a harder version of cloud cost optimization. A different one.

Here is why. When your organization runs a cloud workload, the cost is relatively legible. You provision compute, it runs, and you receive a bill. The relationship between action and expense is traceable. With AI tokens, that relationship fractures across three layers, and most organizations have visibility into only one.

Production → consumption → value: the three layers most teams ignore

The first layer is production. Before any AI model responds to any prompt, your infrastructure has to manufacture the tokens. GPU clusters, inference nodes, autoscaling policies, Kubernetes configurations: these are your token factories. Their efficiency, or lack of it, determines the base cost of everything that follows. A GPU node at 30% utilization is an expensive factory running at a third of capacity.

The second layer is consumption. This is where counterintuitive economics live, and it is what I spend most of my time thinking about. Two organizations can send identical prompts to different coding agents and arrive at radically different costs depending on how they manage context, caching, retries, routing, and the infrastructure supporting inference.

Prompt length, context window usage, caching strategy, and model routing decisions all compound. The common assumption that routing a task to a cheaper model always saves token cost, turns out to be wrong often enough to matter. A routing decision that invalidates a warm cache can make a "cheaper" model call more expensive than the frontier option it was meant to replace. These are second-order effects. They do not appear in standard dashboards but they appear in your monthly bill.

The third layer is value. This is the one that FinOps teams are comfortable with, and the one that matters least until you have the first two under control. Mapping token spend to business outcomes is a legitimate and important discipline. But you cannot govern at the value layer without instrumentation at the production and consumption layers. You are doing math with incomplete inputs.

Why 85% of your AI spend is probably misallocated

Here is a pattern I see consistently. Organizations treat frontier AI models, the most capable, most expensive models available, as their default infrastructure. Every task goes to the same model. Every prompt is constructed the same way. There is no routing logic, no tiering, no architectural distinction between work that genuinely requires the full capability of a frontier model and work that does not.

Based on my observations across organizations deploying AI at scale, roughly 15% of software development tasks actually require frontier model capabilities. The remaining 85% of routine coding, summarization, classification, and retrieval work can be handled by smaller, faster, and less expensive models, if you have the infrastructure to make those decisions intelligently and automatically.

The unlock is not picking better models manually. Manual model selection does not scale and degrades the developer experience by introducing friction at the moment when a developer needs to move fast. The unlock is building infrastructure that makes routing decisions for you: one that understands the task, routes it to the appropriate model tier, evaluates output quality, and escalates if needed. You specify the outcome you need. The system handles the economics of achieving it.

This is the direction the industry is moving, and the organizations that build this capability first will have a structural cost advantage that compounds over time.

The invoice arrives last. And you realize something has gone horribly wrong

There is a phrase I have started using with customers that captures the core problem: the invoice arrives last.

By the time you see the model provider bill, the cost decisions were made weeks ago in infrastructure configurations, autoscaling policies, and prompt architectures that nobody has reviewed since the initial deployment.

The retry logic runs silently when an upstream service slows down. The GPU nodes were reserved for peak traffic that never came. The agentic workflow, where a single user request fans out into dozens of model calls beneath it, each billed separately, none visible in the tool that generated the original request.

These costs do not live in the model invoice. They live in the infrastructure layer, in the consumption layer, and in the gap between how teams think their AI systems work and how they actually behave in production. You cannot govern what you cannot see. And right now, most teams are looking at one layer of a three-layer problem.

When AI goes from copilot to coworker, the stakes multiply

There is a shift underway that makes all of this more urgent. The AI deployments most enterprises built over the last two years were assistants, tools that accelerated individual work by handling the first draft, the next suggestion, and the boilerplate. A human remained in the loop at every consequential step. The economics were bound by how many people were using the tool and how often.

Autonomous agents change the economic profile entirely. When an AI system can receive a goal, build a plan, execute multi-step work, evaluate its own outputs, and iterate to completion without human intervention at each stage, you are no longer running an assistant. You are running something closer to a coworker, one that operates continuously, scales horizontally, and generates token consumption at rates that individual user interactions never approached.

The transition from copilot to coworker has already happened. And the governance implications are significantly more serious. A copilot with poor token economics costs you some efficiency. An autonomous agent with poor token economics runs that inefficiency at scale, continuously, without generating the natural friction that would cause a human user to pause or change approach. Infrastructure discipline and token optimization need to be in place before autonomous workloads scale rather than retrofitted afterward when the bill arrives.

The mandate for infrastructure teams

The right answer to this problem is not more dashboards. More visibility into a system you cannot control is just a more detailed invoice; it arrives with the same lag, and it changes nothing about the decisions that were already made upstream.

What infrastructure teams actually need is control that operates at the layer where costs are determined, not where they are reported. That means autonomous management of GPU and inference workloads, continuously rightsizing to match actual demand rather than peak assumptions, absorbing the bursty consumption patterns that agentic jobs produce, and moving compute capacity across providers when one environment becomes the bottleneck.

This is especially urgent now, because the transition from copilot to coworker does not give you a grace period to retrofit discipline. Autonomous agents do not pause. They do not get frustrated and choose a different approach. They run the inefficiency you built into them at scale, continuously, until something external stops them.

So the next phase of enterprise AI is defined by who can deploy models efficiently. As AI systems become more autonomous and token consumption accelerates, competitive advantage comes from understanding the full economics of AI, and not just the price of a model call.

Because by the time the invoice arrives, the decisions that shaped it have already been made.

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Adobe is a popular brand when it comes to all things creative, including AI content generation and editing. With its newly improved Firefly tool, Adobe has taken AI content generation a step further with realistic, real-time outputs.

In addition to being highly accurate, Firefly is also one of the most affordable tools on the market today. But is it the best AI video editor for your needs? Read on to find out. In this article, we've put Firefly to the test, examining its features, pricing, ease of use, and overall value for money.

Adobe Firefly: Plans and pricing

Adobe Firefly is available online by clicking here, as well as part of the Creative Cloud suite of apps.

It offers one of the most generous free video editing software we've seen. Instead of capping your monthly or one-time usage, Adobe allows you to generate a limited number of images and videos each day. This means you can continue using Firefly for a longer period, provided you stay within the daily limits.

What we like about the free plan is that Firefly does not lock its image or video editing features behind paywalls. Even on the free plan, you get access to features such as Generative Fill, Background Removal, AI Markup, video upscaling, text-based editing, as well as audio features such as generating soundtracks, speech, text-to-avatar, and more. Unlike other platforms, Firefly's free plan can actually come in handy for limited individual use.

Its paid plans are also among the most inexpensive we've seen in the category, with the Standard plan starting at $8.49 per month when billed annually, which comes with 2,000 credits.

With this plan, you get access to all the AI models Firefly has to offer, with hard upper limits on the number of images and videos you can generate with each model. Videos, however, are capped at a maximum duration of five seconds, which might not be enough for users looking to generate long-form content. Besides this, you get access to unlimited Firefly Boards, while the remaining features stay the same as those in the free plan.

Next is the Pro plan at $16.99 per month (4,000 credits), billed annually, where you get unlimited access to several AI models such as Gemini 2.5 Flash, FLUX.1 Kontext, and others. The maximum number of images and videos you can generate is also higher than in the Standard plan.

Then there's the Pro Plus plan, priced at $29.38 per month, where you get up to 10,000 credits and higher limits on image and video generation. In this plan, a wider range of models comes with unlimited access, including Gemini 3.0, ChatGPT Image, Runway Gen-4.5, and Gemini 3 Nano Banana Pro 2K.

Finally, there's the Premium plan at $118.93 per month, offering 50,000 credits and unlimited access to almost all AI models except a few. This plan is ideal for large content creation and creative teams.

Adobe Firefly: Features

Right off the bat, what impressed us most about Firefly is its brainstorming and early-stage concepting feature called Firefly Boards. It is essentially an infinite-canvas mood board and ideation workspace that lets you upload your own images, sketches, or stock images, or generate new ones.

You can select multiple assets and combine elements while brainstorming ideas with your creative team. This is similar to Midjourney- or Discord-style iteration, giving remote creative teams a space to flesh out new ideas.

Moreover, Firefly offers access to more than 10 popular video, audio, and image models, each with its own limits on the amount of content you can generate. Some notable features are Generative Expand and Generative Fill, which let you expand the canvas of an image or video after it has been generated using Adobe's AI engine. Similarly, there's also Generative Remove, which helps you remove elements from a piece of content.

Firefly also throws in a range of audio features, such as the ability to generate speech, translate videos from one language to another, or add text-to-sound effects to your content. However, we were a bit disappointed with its video editing features, such as trimming, arranging, and refining content, all of which are still in beta. Its AI assistant is also at a very early stage and currently supports only one conversation at a time.

You also have the option to upload an image to provide Firefly with a style or structure reference and create a consistent look across a batch of images. Unlike many other AI tools, Firefly allows you to use your own images to train its AI models and create custom models. This is still an early-stage feature, and we expect it to improve over the next year or so.

Firefly has also expanded its multilingual reach and now supports prompts in more than 100 languages, along with translations in more than 20 languages. There's also an option for bulk actions such as background removal, color grading, and cropping.

Its mobile apps have also improved a lot over the past year and now offer native iOS and Android apps with an experience that's very close to the web interface. And of course, Firefly is part of Adobe's broader AI-powered content generation and editing ecosystem, integrating directly with apps like Photoshop and Illustrator.

Adobe Firefly: Interface and in use

Adobe Firefly is by far one of the most improved image and video editing software we have seen in a long time. One of the biggest criticisms of Firefly was that it did not offer any option to edit the images it generated. However, all that has changed with its revamped user interface.

Not only did it generate a pretty high-quality image for us, but it also provided several editing options. We especially liked its Tune feature, which is currently in beta and allows you to change the look of an image by selecting from a list of preconfigured options.

There is also a prompt option, which lets you keep fine-tuning your generated image with follow-up prompts.

However, if that is too much work, there are several fine-grained controls available. For instance, there is a Fill feature that lets you fill any blank spaces in the image with an element of your choice. Similarly, there's the Remove feature, which lets you remove any element you do not want in the final result.

What we liked the most is its Select feature, where you can select a particular element in an image or video and then type a prompt to edit only that selected portion. This helps avoid unwanted changes in other parts of the image or video and ensures that the edit is applied only to the section you selected.

The generation interface itself is pretty simple. You'll see a prompt box at the bottom of your screen, along with a panel on the left-hand side where you can control the generation settings.

For instance, you can select the model you want to use, the resolution, the aspect ratio (16:9 or 9:16), and the duration of the video. There is also an advanced setting where you can enter a random seed value to experiment with the AI engine settings.

What we found particularly eye-catching is the prompt enhancement feature, which lets you improve your prompts before generating an image or video. This helps you create more detailed prompts, which in turn result in more accurate outputs.

Adobe Firefly: How we tested

We tried the free version of Adobe Firefly over several days and were quite impressed with the results. We first tried to create an AI image with the following prompt:

"A tired line cook in a stained white apron, frowning with exhaustion, wiping sweat off his forehead while chopping six carrots on a wooden cutting board. Behind him, a dim, cluttered restaurant kitchen with steam rising from a pot. Photorealistic, dramatic low lighting, shot on 35mm film."

As soon as we hit Generate, Firefly got to work and produced a pretty impressive image that closely matched our prompt.

However, it's worth noting that Firefly is a tad slower compared to the likes of Fliki or Kapwing. That said, the accuracy of the results is worth the wait.

Once the image was generated, we clicked on it to open the editing panel and played around with the settings. For instance, we selected the burning steel pot behind the cook and prompted Firefly to change it to a wooden pot instead.

Rather than simply making the change, Adobe gave us an Edit Strength slider, allowing us to control how strongly the wooden effect was applied to the pot.

We then tried the Remove feature by selecting a bunch of carrots and asking Firefly to remove them. Adobe once again impressed us, as it accurately removed the carrots without distorting the chopping board around them.

Lastly, we used the Upscale feature to improve the quality of the image. This took the longest, more time than it took to generate the image in the first place. However, we were once again very satisfied with the final result.

We also generated an AI video using the following prompt:

"A woman in a red coat walks across a rain-soaked city street at night, neon signs reflecting in the puddles, camera slowly tracking alongside her."

The results were again highly accurate, the colors were vibrant, and we were impressed by how closely Firefly followed the prompt without significantly diverging from the intended output.

Adobe Firefly: Alternatives

Although Adobe Firefly is a much-improved product, it does have a few shortcomings, which is why you may want to consider some alternatives. For instance, Adobe isn't the best choice if you want to generate AI avatars. Although it offers the feature in a limited capacity, the results are not what you'd expect from a dedicated AI avatar generation tool.

In that case, you can try Kapwing, which offers one of the most extensive collections of AI avatars. You can choose from a wide range of characters, including traditional human-inspired faces and more outlandish options such as aliens, Cupid, demons, cyborgs, Dracula, and more.

Kapwing produces some of the best photorealistic AI avatars we have seen in the industry. It is also one of the few tools that can render videos in 4K quality. That said, it is a bit on the expensive side, with plans starting at $16 per month.

If you need a dedicated AI assistant to help with content generation and editing, you can try Fliki, which offers an AI Copilot that understands natural language inputs and helps you achieve your desired results more effortlessly. It also includes licensed YouTube music and advanced video generation options such as stop-motion videos.

Fliki also offers a wider range of customization options compared to Adobe Firefly, such as choosing the format and template, customizing the type of character, selecting the AI model you want to use, and adding voiceovers, captions, and AI avatars.

Adobe Firefly: Final verdict

Adobe Firefly is one of the best-known names in the content creation industry, and its recent improvements put it right at the top of our list of the best AI video editors. Firefly offers more than 10 AI models for image, video, and audio generation, helping you create photorealistic images, videos, and audio with simple prompts.

We were particularly impressed by Firefly Boards, which helps you brainstorm ideas on an endless online canvas in real time while collaborating with your content team. Firefly has also improved its editing interface and tools and now supports follow-up prompt editing, along with features like Generative Fill and Generative Remove.

That said, it has a few drawbacks, such as the lack of mature video editing tools, which are still in beta, and rather limited AI avatar generation capabilities. That said, if you already use Adobe tools like Illustrator or Photoshop, adding Firefly to your repertoire is a no-brainer.

We've tested a range of video makers and editors, including the best video editing software, the best video editing apps and the best free video editing software.

While the tech world is gearing up for the debut of Samsung’s new foldables at Unpacked next week — with the company teasing some major changes to its lineup — here in the UK we’re finally able to get our hands on the Samsung Galaxy XR headset.

It's now available outside the US and Korea for the first time since its launch in October of last year, and I’ve been taking the headset for a spin after Samsung sent me a review a few weeks ago, and seeing how it compares to my trusty Meta Quest 3.

Now, I could just rehash a lot of what our Editor at Large, Lance Ulanoff, said in his Samsung Galaxy XR hands-on — praising the OLED screen, describing the comfort of the headset over several sessions, and listing the apps I love in XR that elevate the Android experience beyond what's possible on a phone or laptop. But I won't do that here.

Instead I'm putting my body (well, head and neck comfort) on the line to bring you the return of my favorite VR headset testing format: can I spend a whole week wearing the Samsung Galaxy XR?

The short answer? Yes, but it could be comfier. Read on for more about my experience.

Into the Android XR-verse

Just to be clear, I didn't wear the Galaxy XR headset 24/7 — I haven't been walking around my local supermarket immersed in XR, or sleeping in my Samsung headset .

I spent my whole working day in the headset (taking it off only for my lunch breaks, and to use the bathroom), and also wore it for a couple of hours each evening, to unwind with a movie or a game.

I also made sure to spend at least a day relying on only the hardware in the Samsung Galaxy XR’s box — i.e., the headset.

Software-wise, I used everything I’d normally rely on my PC or phone to work and stay entertained, and also had access to the Explorer Pack — a suite of software and subscriptions worth £665 that you'll get for free with a purchase of the Galaxy XR until December 16.

A quick aside on this pack: with a year's access to premium Gemini models (and 5TB of digital Google Cloud storage), plus a year of YouTube Premium, a Calm subscription, and plenty more, it's a really solid addition to the XR experience — I'll be writing more about my experience with its services soon.

Anyway, back to the headset itself…

Things got off to a good start on day one. Thanks to the XR multitasking I was able to have a giant floating Slack window keeping me in touch with my TechRadar colleagues, and a second giant floating YouTube window featuring the latest episode of one of my many podcasts or music playlists to keep me engaged, flanking a Google doc page for whatever I was working on, which I wrote in using a floating keyboard that reacts to my finger movements.

I could open Google Chrome tabs too, and conveniently flick through and between windows using eye controls — the headset relies on eye-tracking to determine what buttons I want to press, which I indicate by simply pinching my fingers while my hand rests lazily in front of me.

I was even able to join meetings virtually, with digital avatars that were good, but not yet great at representing me or mimicking my real-world facial expressions. My Galaxy avatar looked a bit too cartoonish for work meetings, and my Google avatar looked permanently distressed (which I don’t think is my general state of being), and was also missing my glasses, which felt weird — I always wear them, so it felt like the avatar was missing some of my identity.

That aside, I quickly found that any job which relies on lots of basic operations (essentially anything that isn’t something like intense video editing, though there are Play Store apps for less sophisticated video editing needs) is a breeze on the Galaxy XR headset. However, as the novelty faded, I hit my first hurdle: speed.

Gotta type fast!

Eye-tracking controls are neat, and generally XR controls are incredibly intuitive, but the keyboard is not as responsive as even the most basic physical keyboard. This slowed my typing pace to a crawl, and with deadlines looming over me I couldn’t spend my days steadily working through sentences at a snail pace.

The ideal alternative would be a Bluetooth keyboard that pairs with your headset, and most standard models will get the job done after you’ve connected one. I gave one a quick try, and it was much more effective than the virtual keyboard for actually working productively in XR — the brief pause I had to contend with between virtual key presses was eliminated.

My predicament gave me an excuse, however, to see how the headset works when paired with a PC — giving me the massive virtual displays of XR combined with the convenience of a fully powered desktop.

The perfect setup would involve a Samsung Galaxy Book, which pairs more seamlessly with the headset, like the Vision Pro and a MacBook pair for Apple users. However I don’t have a Galaxy Book, so I instead had to turn to Virtual Desktop.

It’s an app I’ve used previously to pair Meta headsets with my PC, and it’s very effective. The connection, even in a room a few meters from where my router lives, is seamless, with next to no delay between what I do on my PC and see on my headset’s view. Plus, with the ability to drag displays around and resize them, I could create the ultimate multi-monitor setup.

The downside is that you can’t access the full multitasking capabilities of the Galaxy XR headset within the Virtual Desktop app. Other apps seem not to suffer from these issues; however the trouble is that several, such as Google’s PC Connect, weren’t available in the UK when I was conducting my tests, and it seems that every app has a downside (for example, PC Connect only lets you stream a single monitor, not multiple).

This compatibility friction with non-Samsung PCs would be the biggest change I’d like to see addressed from a productivity standpoint, as otherwise the Galaxy XR excels on the software side.

My other two big changes would be to the design: the addition of a silicone facial interface and a top strap.

I can’t feel my head when I’m with you

The UK, where I’m based, has been buffeted by heat waves in recent weeks, including while I was testing the Galaxy headset. In homes designed to retain heat, and which lack A/C, this means things can get pretty hot and sweaty, which isn’t ideal when a headset is strapped to your face — and it’s even less ideal when you can feel that sweat being soaked up by the fabric facial interface.

If I was in charge of making headsets I’d mandate that all facial interfaces are made of silicone, or at least that headsets come with a swappable silicone interface, or a silicone cover for the fabric interface, in the box. Unfortunately I can't force such a change on Samsung, but hopefully it’s a change it’ll make for the Galaxy XR 2; it’s more hygienic, and makes the headset easier to share, and I’ve found it doesn’t impact the comfort at all (if anything it makes me feel more at ease in the headset, as I know I’m not immersed in months-old sweat).

As for a top strap (one which runs over the top of my head rather than simply around it), I firmly believe it would help to make the headset comfier to wear for long stretches, as it would further spread the weight of the device across my head.

Day one wasn’t too bad, but by the midpoint of day two I was feeling a little discomfort. By day four I was ready to give up my challenge; and the end of day five couldn’t come fast enough.

Samsung gets so much else right in terms of the Galaxy XR's comfort — chiefly moving the weight of its bulky battery to an external puck, so that you’re only carrying 545g on your head — so it’s a shame that the headset is let down by something as simple as its headband.

For a few hours I found it was fine, but for my extreme testing sessions it didn’t hold up.

Yet again, then, I’ve found that I don’t really want to spend my whole day inside a VR headset, even though the Samsung Galaxy XR headset represents a serious improvement in terms of visual performance (courtesy of its superb OLED screen), the usefulness of its features, including eye-tracking (which I’ve previously found is quite gimmicky), and delivering solid multitasking performance (aided by the expansive app library of the Play Store) compared to the last time I tried a similar exercise with the Meta Quest Pro a few years ago.

Even if I won’t be spending all my time in the Galaxy XR, I plan to wear it plenty more, albeit just for more casual sessions, and I’ll definitely be taking it on a flight, as soon as I have the opportunity, to see how it fares — perhaps that’s where it’ll really shine.

• Windows 11's July update was blocked from certain Dell laptops due to nasty issues with unexpected shutdowns and poor performance
• Microsoft just issued an emergency patch to fix those problems, so the PCs in question can install the latest update
• However, that patch might end up installed on non-Dell PCs if the user has enabled the option to get the latest updates as soon as possible

Windows 11's latest (July) update was blocked from Dell PCs as it was buggy on those systems, but the good news is that it's now fixed — but there's an additional wrinkle to be aware of here for those of you who don't own a Dell machine.

Windows Latest reports that Microsoft just deployed an emergency update (KB5121767) which fixes the problems with certain Dell laptops with Intel CPUs, which include unexpected shutdowns and generally poor performance (including running sluggishly and overheating, as well as increased battery drain).

Those with an affected Dell laptop — and that apparently includes some XPS models, as well as Dell Pro and Precision (business) notebooks, and possibly others — should install the emergency patch. As Microsoft makes clear, you'll find it under optional updates, which is reached via 'Advanced options' in the Windows Update panel.

However, the wrinkle that I mentioned is that Windows Latest observes that it's possible some Windows 11 PCs that aren't Dell laptops may automatically grab and install this update — if they have the 'Get the latest updates as soon as they're available' toggle on.

Analysis: a notable side effect

This feature is one that people turn on in the hope that they'll get Windows 11 updates, and more to the point, new feature rollouts, more quickly (there can be quite a wait for certain features to arrive, such as the Start menu revamp from last year, which some folks still haven't received even now).

So, bear in mind that if you've enabled this option, you may get certain updates automatically — like this emergency patch — when you don't actually need them on your PC. It doesn't help that this particular update is confusingly named, being called '2026-07 Update (KB5121767) (26200.8894)', which could leave you wondering what on earth it is exactly (until you look up the codename KB5121767).

At any rate, if you're not happy with this situation, to turn off 'Get the latest updates as soon as they're available' you need to head to Windows Update (in Settings) and adjust the relevant slider (at the top of the 'More options' panel).

Why is this option configured in this way? It seems especially odd in this case seeing as Microsoft tells us: "This OOB [emergency] update is only recommended for devices affected by this issue." That said, seemingly it doesn't do any harm to a non-affected PC (whether a Dell model, or not) if it does end up with this emergency patch installed.

At any rate, if you do enable the option to get the latest updates, just don't forget that this can have side effects, such as installing optional updates that you don't actually want. It would be a better idea if, in these cases, Microsoft simply flagged these updates as available, rather than having them install automatically.

It's also worth noting that this isn't the first time Dell laptops have run into trouble with Windows 11 recently, as last month an update to the firm's SupportAssist app left some notebooks crashing every half an hour or so.

Kapwing is among the best AI video editors today. It allows you to create videos with simple text prompts and generate lively AI characters to build detailed storylines.

Trusted by brands like Google, Dyson, and Spotify and used by thousands of content creators across YouTube, TikTok, and digital marketing platforms, Kapwing has quickly grown to become one of the industry’s favorite video generation and editing software.

In this article, we put Kapwing to the test to see whether its features perform as advertised. Read on to learn about our real-world experience with Kapwing, its best features, and the alternatives worth considering if it doesn’t fit your needs.

Kapwing: Plans and pricing

You can get started with Kapwing through its free-forever plan, which allows up to 30 minutes of video exports per month in 720p resolution, with each video limited to a maximum length of one minute. There’s also a 2GB file upload limit, along with access to editing and AI-powered tools such as auto-subtitling, auto-translation, and text-to-speech, albeit with limited usage caps.

We also like that you get a fair share of collaboration features, such as project link sharing and commenting, allowing you to edit videos collaboratively with your team. However, the free plan is meant primarily for testing the platform rather than full-fledged use.

If you want to get the most out of Kapwing, you’ll have to upgrade to one of its paid plans. Its Pro plan starts at $16 per member per month and offers excellent value for money. The plan supports 4K video exports with unlimited monthly exports and a maximum video length of 120 minutes.

You can check it all out by clicking here.

The AI editing tools also come with higher usage limits. For example, you get up to 1,000 minutes of auto-subtitling and 500 minutes of auto-translation per month. Besides this, the plan adds several premium tools, including video and image background removal, video stabilization, and the ability to add your brand kit and custom fonts to your projects.

Next is the Business plan, designed for teams that need more advanced content creation workflows. Costing $50 per member per month, it includes up to 4,000 minutes of auto-subtitling, 2,000 minutes of auto-translation, 2,000 minutes of Clean Audio per month, and 130 minutes of Lip Sync. The remaining features are largely the same as those in the Pro plan.

Finally, there’s an Enterprise plan for large organizations that need priority support and custom billing. However, you’ll need to contact the Kapwing team directly to obtain a customized quote.

Kapwing: Features

Kapwing packs a good mix of both AI-powered and traditional video editing features. Let’s start with its AI capabilities. Right off the bat, you’ll notice that you can generate AI videos with a simple text prompt. Its video generation interface is pretty similar to that of chatbots like ChatGPT or Gemini. You simply enter your prompt and click the Send button. You can also attach media as a reference for the AI engine.

Besides video generation, Kapwing offers several other AI tools. For instance, Translate Video lets you convert the audio in a video from one language to another. There’s also a handy Smart Cut feature, which automatically removes awkward silences from your videos, giving them a more polished finish. You can also generate AI characters, which come in handy if you want to build a consistent storyline or reuse the same character across multiple AI-generated videos.

Kapwing gives you more options when generating AI characters. To get started, click the @ icon in the chat window and select the type of character you want to generate. The available options range from everyday characters like a baby or a cheerleader to more outlandish ones such as a cyborg, Dracula, or a dragon.

You also have the option to choose more than one character type. For instance, you can select both "baby" and "Dracula" if you’re trying to create something unique. Here’s the Baby Dracula character we created using Kapwing.

Apart from this, you’ll find several other options, such as Image to Video, which lets you convert a still image into a short video clip, and Text to Video, where you can enter a full-fledged script and allow Kapwing to generate an AI video for you. There’s also a handy Video Stabilizer tool, which is useful for fixing shaky footage or videos with heavy motion blur.

For longer video formats, Kapwing also supports Video Storyboards, where you can create a timestamped outline of every scene. The more traditional tools include text-to-speech, video dubbing in 40+ languages, resizing videos from one aspect ratio to another, and adding subtitles to videos.

Kapwing: Interface and in use

Almost all of Kapwing’s features are nestled into a central dashboard, with the video editor in the center, a long list of features on the left-hand panel, and an editing panel on the right. You can simply upload your media or drag and drop any video or image you want to edit. After that, you may need to spend a little time exploring the interface to find the exact feature you’re looking for.

For instance, you can access features such as Text, Audio, Subtitles, Transcript, Translate, AI Voice, and more directly from the left-hand panel. There’s also a help function that helps you quickly locate settings you can’t find right away.

The editing experience was pretty smooth. For instance, we tried adding text to an AI-generated image, and the process was seamless. We could easily change the font, color, size, and other formatting options from the panel on the right.

Note that the right-hand panel changes dynamically depending on the tool you’re using. For example, if you select Translate, you’ll see language options appear on the right.

Overall, like any video editing software, Kapwing comes with a slight learning curve. However, in our experience, it was much easier to use than many legacy editing tools. The interface is modern, sleek, and highly responsive. We didn’t experience any system lag or timeouts during our testing.

In addition to the main editing window, there is also a dedicated My Workspace page in Kapwing. Here, you can find all your recent projects at the bottom, along with a dedicated set of tools highlighted on the main page. If you’re using a Business plan, you’ll also see your team folders on the left-hand panel. Similarly, Settings, Brand Kit, and your Media Library are all accessible from the same panel.

Kapwing: How we tested

We tested the free version of Kapwing across multiple accounts to evaluate its AI capabilities more thoroughly. To start, we tried generating an AI video using the following prompt:

“A woman in a red coat walks across a rain-soaked city street at night, neon signs reflecting in the puddles, camera slowly tracking alongside her.”

Kapwing got to work as soon as we hit Send, first generating a summary of the scene we wanted to create. After that, it produced a five-second video within seconds. Although Kapwing indicated that the video would be seven seconds long, the actual output was five seconds.

We then edited the same video using Kapwing’s video editor, experimenting with features such as adding text, audio, and subtitles. Since the free plan is fairly limited, we had to use more than two accounts to fully test its capabilities.

What stood out to us was how quickly Kapwing generated both edits and the source video. Everything was processed quickly, which is a clear advantage.

Kapwing: Alternatives

While Kapwing is a capable AI video generator and editor, it is not the most affordable option out there. For instance, Adobe Firefly costs $8.49 per month when billed annually and comes with features comparable to Kapwing. You get access to popular video models such as Google Veo 3.1 and Runway Gen-4.5, allowing you to create up to 50 and 11 videos, respectively.

Adobe Firefly truly shines with its workflow features, allowing creative teams to brainstorm visual concepts on virtual boards. You can also process thousands of files at once using bulk actions such as removing backgrounds or cropping images. That said, Firefly lags a bit behind when it comes to video editing, as many of its editing features are still in beta.

It's also not ideal for long-form content generation, as most models cap video lengths at three to five seconds. On the bright side, you also get access to Adobe Express Premium and Photoshop with all paid plans.

Veed is another capable competitor, with prices starting at $12 per month, roughly 20% cheaper than Kapwing. It also offers a free plan, which appears to be more generous than Kapwing’s. For instance, you can try features like removing silences, background removal, auto-edits, social media character generation, and voice cloning on Veed’s free plan – most of which are not available on Kapwing’s free tier.

Kapwing: Final verdict

Kapwing is one of the best AI video generation and editing platforms available, with a strong mix of both traditional and AI-powered tools. Not only can you generate AI videos from simple prompts, but you can also transcribe videos, translate them into 40+ languages, create AI characters, and edit videos from a central dashboard.

Kapwing is also easier to use than many other platforms on the market, although there is still a slight learning curve involved. That said, there are plenty of video tutorials and help center articles available to help users get through this phase.

However, Kapwing can be a bit on the expensive side, especially for personal users, which is why you may want to consider alternatives like Adobe Firefly or Veed.

We've tested, reviewed, and rated a range of video makers and editors, including the best video editing software, the best video editing apps and the best free video editing software.

Apple's MacBooks seemingly have some big changes on the horizon. One of the biggest is the rumored MacBook Ultra packing that OLED screen, and this is something to be excited about, don't get me wrong. Well, worries about the price aside, and wider issues around Mac pricing overall, but there's something I'm more concerned about now.

Namely the direction in which Apple is heading with its M-series silicon, following the latest rumor dump from prolific leaker Mark Gurman. In last weekend's Power On newsletter for Bloomberg, Gurman dropped a whole lot of fresh info about Apple's CPU roadmap and the incoming M6, M7 and M8 chips.

The leaker tells us — and bear in mind, this is all just rumors, but eye-opening speculation nonetheless — that Apple is doing something very different with the next range of M silicon. The M6 will consist of just a vanilla chip, with no more powerful variants, which isn't something Apple has ever done before — there's always been a Pro and Max version (if not an Ultra, too).

This is because Apple apparently wants to usher in the M7 series more swiftly, with the M6 debuting late this year and the M7 following in the first half of 2027, just six months (or so) later. The M7 will have a full complement of Pro, Max and Ultra chips, the former two following at the end of 2027, with the Ultra in 2028. Gurman believes this is Apple's current plan and the reason for rushing to get to the M7 is AI.

Cue multiple groans, no doubt. Gurman theorizes: "The reason for this break with tradition: AI. Apple had been planning major neural-processing upgrades for the M7 family and ultimately decided those improvements were important enough to justify accelerating the next generation rather than completing the M6 lineup."

We're further told that beyond this, Apple is "already developing M8 chips with even greater AI capabilities" to land in 2028.

Gurman concludes: "The takeaway is that AI is no longer just another feature Apple's chips need to support. It is now shaping how those products are designed and when they are shipped."

A wise path to follow?

So, what am I worried about here exactly? Obviously there's a strong suggestion that AI is taking much more of a center stage role with Mac chips, and given what's currently going on with MacBooks, I've got to wonder about the wisdom of this — and how it might affect Apple's PC sales.

Currently Mac sales are strong and considerably buoyed by the launch of the MacBook Neo in March 2026, a budget laptop that has undoubtedly flown off the shelves. However, Apple's recent price hikes have poured cold water on those sales prospects to an extent, even hitting the MacBook Neo, which went up by $100 in the US (£100 in the UK, and AU$150 in Australia).

Granted, while it's odd for a device to get a price rise like this so soon after launch, given the demand around the Neo, it's not going to be sidelined with this increase (although the upper-tier model with 512GB storage now looks shakier and out of budget territory, frankly). The harder pill to swallow is the harsher MacBook Air price hikes, and as we pointed out a few weeks back, some wallet-friendly Windows 11 laptops now look a lot more tempting after Apple's price increases.

Looking on Reddit, it's not hard to find a lot of unhappiness about the MacBook price hikes. There's considerable angst from some people who were thinking of pulling the trigger on a new Apple laptop, and then watched those price increases swoop in, and are now feeling the opposite of buyer's remorse (holdout's regret?). Some of those folks are acting with their wallets, closing them and vowing to wait for a better buying opportunity now that they've missed the boat, as it were.

That could be a long wait, though. Of late, we're hearing a lot of gloomy news about the RAM crisis getting worse later this year, and in 2027 — maybe a lot worse. The memory supply situation is what caused Apple to jack up Mac prices, of course, and it's difficult to see how the MacBook maker will continue to weather this storm if it worsens next year, given that its resources in terms of built-up inventory of RAM (or SSDs) are clearly running dry (or have run dry).

Rumors have continued to persist about Apple potentially tapping Chinese memory chip makers for its RAM needs, as an alternative to the big three mainstream suppliers (Micron, Samsung, SK Hynix). That's a worrying development in itself, and it's doubtful whether even Tim Cook can pull off navigating that kind of negotiation before he leaves his Apple CEO role (there are various possible hurdles to clear and governmental complications, put it that way).

Diving into AI headfirst

I don't think any of this bodes particularly well for next year's Mac sales given the prospect of pressure around more price hikes (although yes, other PC makers face the exact same unfavorable conditions). And then we come back to this apparent renewed focus on AI, which hasn't gone down well on Reddit and other social media.

Here's one Redditor who sums all this up neatly: "I was about to buy a new MacBook to replace my M1 Pro with an ailing battery and small storage. But then Apple jacked up the prices by almost 20%. And now they are diving into AI headfirst. It might be time for me to look at Framework laptops."

There's a strong backlash against AI right now, whether that's about sprawling data centers and them chugging huge amounts of power, or Copilot in Windows 11 and indeed Apple Intelligence. So, it's no surprise that the idea of "diving into AI headfirst" has been greeted by a lot of skepticism.

What exactly is Apple Intelligence going to serve up in terms of compelling features in the next year or two? The trouble is that this feels very much up in the air, yet Apple is supposedly building its Mac strategy around this now. It seems like a bit of a leap into the unknown to say the least, and I'm not surprised that it's making Mac fans nervous.

Okay, so this AI focus is still just a rumor, although Gurman is one of the more reliable Apple sources out there, and it appears to be a well enough fleshed-out piece of speculation. Even if it's true, Apple may yet switch away from this course, but for now, I'm struggling to find the logic of shifting to a heavier AI focus so soon.

Maybe the reasons for this will become clearer as we see how Apple Intelligence develops in the near future. However, in the meantime, I think there are reasons to be concerned about Mac sales potentially flagging next year, between disgruntled MacBook holdouts, the continued RAM crisis, and cynicism around Apple's apparent new AI-led approach.

Tech companies large and small — including the likes of Meta, Samsung, and Google (and perhaps even Apple) — are betting that smart glasses become one of the leading device form factors of the future. But this week it became clear that they're facing an increasingly strong backlash against the technology.

There's one problem in particular: cameras. These tiny, embedded recording devices make it easier to record your kids' cute mishaps, your latest mountain bike ride, and a first-person view of anything without needing to get your phone out, but they also enable a certain level of surreptitious photo and video capturing that isn't possible with a phone.

And there's no doubt that bad actors are using these smart glasses for unwholesome purposes: some glasses owners have been paying modders to disable the warning light that should appear whenever recording is actively happening.

We're now at the stage where smart glasses owners are worried about wearing them in public, such is the growing negativity around them. Here's what we've learned in the last few days, and what might happen next.

An inevitable 'collapse into surveillance tech'

Smart glasses with cameras are nothing new of course, but stories around these devices being modded to operate without recording lights (something Meta has now moved to block), as well as reports that future Meta Ray-Bans could be recording all the time, seems to have prompted a new wave of anti-smart glasses sentiment.

Even the celebrities are weighing in: pop icon Lorde was less than complimentary about smart specs on stage recently, leaving her audience in no doubt about her feelings about the tech, while rapper and producer Tyler, the Creator has declared that "anyone who uses these glasses is a real weirdo".

Some of the rich and famous are still on board — you can actually buy Kylie Jenner-branded Meta Glasses, if you want — but most of the online reaction is critical of the technology. A quick peek at Reddit reveals comments like "all internet and computer tech seems to inevitably collapse into surveillance tech" and "people are choosing to be a walking Flock camera" (if you don't get the reference, read this).

Users are referencing Black Mirror and calling for the technology to be banned outright, and using terms like "creep goggles" to describe the devices. While a ban is unlikely at this point, there are some legal changes happening: Illinois looks set to be the first US state to ban smart glasses while driving, not because of any sneaky recording capabilities, but because they might prove to be a distraction to drivers.

Over in South Korea, meanwhile, a man is facing criminal charges after admitting to using smart glasses to cheat on a test. The camera captures the questions, on-board AI figures out what the answers are, and the person sitting the exam writes down the answers. There's more than one way to misuse the camera technology.

However, lawmakers don't yet seem convinced that these AI-powered glasses represent a privacy threat. Regulators in the European Union have reportedly bowed to US pressure in deciding that wearables were exempt from having to include removable batteries, like some other gadget types are when sold in the EU.

What happens next?

While there are some limited efforts to curb the use of smart glasses then, at this stage they're small scale and localized. To try and nudge things along, activists in London have been busy putting up fake Meta Glasses ads on bus stops (below), making a point about surveillance. One of the ads uses an optical illusion to turn the face of Kylie Jenner into a creepy skeleton, with the tagline "we're always watching".

Recording everything we see and do constantly? It’s giving fascism, not fashion It’s just been revealed Meta is planning to make the glasses “continuously record audio while taking photos every few seconds” without any warning light*Literally NO ONE asked for this#noncegoggles*Source: the FT

— @everyonehateselon.bsky.social (@everyonehateselon.bsky.social.bsky.social) 2026-07-17T18:40:05.934Z

Cast your mind back to 2012 when Google Glass launched, and you'll remember that we've been debating these issues for years. There are clearly benefits to wearable specs that can give you hands-free directions, translations, and information without you having to take your phone out, but it seems the concerns over covert surveillance are outweighing the positives in the public's mind.

Given the growing influence of AI tech and how well-suited these wearables are to taking in and processing information, it doesn't seem likely that the tech will be restricted any time soon. For several years now we've been seeing demos of smart glasses that can see and understand the world around them, and those capabilities are on the way whether we like it or not.

If, as rumored, Apple does make its own version of smart glasses, it's going to be interesting to see how this famously privacy-focused company deals with the issues of surveillance. A recording light that cannot be switched off is a good start, but as we've already mentioned, existing users are already cautious of wearing their smart specs around other people.

Later this year we're going to see more smart glasses from Google and Samsung, with Gemini AI ready and waiting to tell you everything you need to know about what you're looking at — which of course requires a camera. As per Google, the company is aware that people won't wear these specs if the privacy concerns are too great, and its solution is mostly to make sure the recording light is noticeable and cannot be tampered with.

"If people don't feel comfortable wearing these glasses, and privacy is part of this, they just won't wear the glasses," Google's Juston Payne told Tom's Guide this week. "It’s as simple as that." The new Google and Samsung wearables are scheduled to arrive later this year, but the companies involved still have a way to go when it comes to shifting user opinion.

• Forrester analysts warn around four in five leaders and ITDMs envision having larger budgets in 2027
• Consumption-based AI pricing is making it harder to predict outlay
• Targeted investment to improve data quality is key

Forrester is predicting software budgets could be set to rise, with more than four in five leaders expecting to increase overall budgets over the next 12 months and 82% of tech decision-makers expecting larger budgets.

While some of the extra cash could come as a result of increased confidence and readiness to spend on tech, the company's analysts warn that a shift in pricing structures could also be forcing companies to fork out more.

This comes as software vendors shift from traditional per-seat licences to token or credit pricing, which introduces so many more variables including model selection, context size, output length and agent operating time, leading to far more unpredictable outgoings.

The real reason businesses are preparing to spend more on software

"Business leaders are no longer planning for a return to stability – they’re planning for a future where volatility is a constant,” Chief Research Officer Sharyn Leaver noted.

Recent shifts from major AI providers all point toward this emerging pricing model becoming the norm, with GitHub moving its Copilot plants to usage-based billing in June and OpenAI adding pay-as-you-go Codex seats in April. Anthropic also recently removed Fable 5 from its standard subscriptions and seat-based models over difficult-to-predict demand, but set out plans to reintroduce it where capacity permits.

Acknowledging these major shifts, Forrester's report reveals two areas where companies can increase their budgets for 2027 – building machine-readable context and enterprise knowledge, and increasing brand visibility in answer engines.

The report also hints at the major role AI can play in marketing and customer-facing experiences, and the potential use that synthetic data can provide subject to testing. All in all, it's more about targeting investments rather than throwing cash at the problem, as Leaver concludes:

"The organizations that outperform in 2027 won’t be those that spend the most on AI. They’ll be the ones that invest in the foundations that make AI effective."

In May, WordPress shipped the most consequential release in its history. Version 7.0 brought AI into the core of the CMS platform for the first time, and the people who built it made a choice that's easy to miss in all the noise about the feature itself. They left it switched off.

The infrastructure is in the codebase, but nothing reaches an AI service until the site owner connects a provider and turns it on. Upgrade a site and walk away, and it behaves exactly as it did the day before.

The on-switch was handed to the person who owns the site, not flipped on their behalf.

It's worth sitting with how deliberate that was. The team had just shipped the most powerful capability the platform has ever carried, and the posture they chose for it was opt-in, plugin-based, with nothing injected into anyone's site automatically.

In an industry that loves a sensible default, that restraint was itself a statement: this decision is yours to make.

Then the more interesting thing happened.

A reasonable instinct, taken one step too far

Within days of the release, SiteGround, one of the most established managed WordPress hosting companies in the business, did close to the opposite. It pre-installed and activated its own AI product across its customer base, configured it as the default connector, and bundled in a generous allowance of free usage to get people going.

The active-install count crossed a million almost immediately. Plenty of site owners logged in to find capable new software already running on sites they had never touched to put it there.

I want to be fair to SiteGround here, because fairness is where the useful lesson lives. This is a serious operator with a long, well-earned reputation, and the product it built is genuinely good, a real piece of engineering rather than a thin upsell. The reasoning behind the rollout isn't hard to reconstruct either.

The "correct" path to native AI is fiddly, and most people would stall somewhere in the middle and never finish it. Pre-installing the whole thing, free usage attached, removes that friction in a single stroke. From an operator's chair, that's a tempting piece of customer service, and I've sat in that chair for the better part of two decades. I understand the pull of it completely.

So this isn't a story about a company behaving badly. It's a story about a reasonable instinct (reduce friction, help the customer get to the good part faster) carried one step past the line. And the reaction told us exactly where that line is.

The objection wasn't AI. It was consent.

The pushback

The pushback was quick and pointed, and the striking thing about it was its subject. Almost none of it was about whether AI belongs in WordPress, or whether the tool was any good. Many of the people objecting use AI every day. What they objected to was finding it already switched on.

That distinction matters more than it first appears, because it separates two things the industry tends to blur: the quality of a change, and the consent to it. A genuinely good feature, installed without asking, still lands as something done to you rather than for you.

The standard defense (it's optional, you can remove it whenever you like) is all true, and none of it is the same as agreement. "We switched it on and you can switch it off" quietly moves the work of noticing, understanding, and undoing onto the customer, for a change they never approved. "Here's one-click setup if you'd like it" delivers the identical convenience and leaves the decision where it belongs.

This isn't a new tension. Webhosting companies have always made changes customers never see, and most of the time they're glad we do. But AI is going to surface this question over and over, because it's the most consequential thing most of us will ever be tempted to switch on by default. Getting the principle right now, while the stakes are still mostly reputational, is a lot cheaper than getting it wrong later.

The line worth holding

The honest objection to all of this is that hosts intervene on customer sites all the time, and nobody asks permission for that. True, and the distinction is the whole point.

When a host patches a vulnerability, blocks a malicious request, or disables a plugin that's being actively exploited, it's protecting the customer's site and the wider platform from harm. Customers extend us that trust precisely because it's defensive, narrow, and in their interest. Installing a new product is a different category of act.

It isn't protecting anything; it's changing what the website is. The trouble starts when the second borrows the permission we were granted for the first, when goodwill extended for security work quietly gets spent on shipping features. That's the line. Maintain the platform freely; change the product only with a yes.

Holding it doesn't mean making customers do more work. New capabilities can arrive off by default and one click away for anyone who wants them. Multi-site managers can get a single place to see and control what's running, rather than a hunt site by site.

Anything a host pushes can be pulled back as easily as it went out. And changes can be announced in plain language before they happen, including how to say no, because the absence of a clear, opt-out-inclusive heads-up is usually what turns an ordinary product decision into a breach of trust.

Parts of the ecosystem are already moving this way. None of it is anti-AI. If anything, it's what lets hosts lean into AI confidently, because customers can trust that nothing shows up uninvited.

Whose site is it, anyway?

As AI moves from novelty to default across the web, every host will face its own version of this question. Here's a genuinely useful new capability. Do we switch it on for everyone, or do we let people choose? The convenient answer and the right answer won't always be the same one, and the gap between them is where reputations are quietly made or lost.

It helps to remember who actually lives with the answer. When a host changes something on a site, the host moves on to the next ticket. The owner is the one who stays: the one whose visitor hits a page that behaves differently than it did the day before, whose inbox fills up when something looks off, whose name is on the business the site exists to represent. We get to make the change. They have to live with it. That asymmetry, more than anything written into the terms of service, is the real reason asking first isn't a nicety. It's an acknowledgement that the consequences were never ours to carry in the first place.

The site owners who pushed back this spring weren't standing against progress. Most of them, I'd wager, will happily adopt the very tooling they objected to, the moment they get to be the ones who switch it on. They were defending something simple that's easy to lose sight of when the technology is moving this fast: it's their site. Not the site we host for them. Theirs. A host's authority runs right up to the edge of the customer's ownership and stops there, and the best operators I've worked alongside never needed reminding of it. They saw their role as stewardship rather than possession.

That trust is the real product. Not the servers, not the dashboard, not even the support, though every bit of it matters. What a customer is buying is the confidence that nothing happens to their site that they didn't choose, and that when we do step in uninvited, it's to protect what's theirs and never to quietly redraw it. Trust like that takes years to earn and an afternoon to spend. Asking first is simply how you keep from spending it.

Get the boundary right, and AI in hosting becomes exactly what it should be: useful, and genuinely welcome. Get it wrong, and even the best feature in the world arrives as something taken rather than offered. The difference was never the technology. It was only ever whether anyone thought to ask.

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• A report commissioned by Mozilla accuses Microsoft of using 'harmful design' to undermine browser choice in Windows 11
• That allegedly includes trick wording, nagging, preselection, and generally dubious tactics to push Edge over other browsers
• The report does observe the situation is better in the European Economic Area, due to regulations there, and notes that "regulatory action works", urging authorities elsewhere to take a similar stand

Mozilla, the maker of Firefox, has published another report that claims Microsoft isn't maintaining a suitably level playing field for web browsers on Windows, and is unfairly pushing users towards Edge.

As Mozilla makes clear, it published the first 'Over the Edge' report – about 'How Microsoft's design tactics compromise free browser choice' – two years back, and the follow-up has just arrived (as spotted by Eteknix).

It's not actually written by Mozilla, I should note, but two independent researchers commissioned by the Firefox maker (Harry Brignull and Cennydd Bowles, the same pair who wrote the first article). It takes in user opinions from the US, UK, India and Germany regarding "key browser-choice journeys" on Windows 11 and 10.

In a nutshell, they conclude that: "Microsoft continues to deploy harmful design to undermine people's browser choice."

That specifically involves, according to the report: "Trick wording, obstruction, visual interference, preselection, nagging, and forced action — at almost every step of the user journey."

Some of the worst excesses highlighted are the pop-up banner that Edge users see when they go to the Chrome download page (trying to get them to stick with the Microsoft browser, which, it says, has the same tech as Chrome but with the "added trust of Microsoft"), and nagging within Windows around using Edge as the default browser in one way or another.

The report also notes how Edge is pre-pinned to the Windows taskbar, and how a migration from Windows 10 to Windows 11 resets Edge as the default browser, overriding any previous choice. There are a lot of accusations around trick wording, too, as well as an observation that Microsoft is using Copilot (AI) to open links in Edge rather than your default browser.

On the topic of AI, the report notes: "The researchers suggest that a sequence of seemingly minor consent requests across Windows and Edge may combine into a 'pipeline' funneling browsing data — potentially including data originating in rival browsers — into Microsoft's advertising and personalization systems."

It's notable that Germany — included as a representative country within the European Economic Area (EEA), which has different regulatory requirements Microsoft must adhere to — escapes quite a few of these excesses (the Chrome download nonsense included).

Despite that, the researchers argue that their broad conclusion that "Microsoft does not allow people to download and install an alternative browser, to set it as their default, or to continue using it as their default, without harmful interference" is, they claim, true "across every region tested".

Analysis: a call to regulate

Yes, this is a report commissioned by a browser rival, but these findings aren't at all surprising in the main, and it's long been known that Microsoft overzealously promotes Edge in Windows 11, trying various dubious shenanigans to get the browser in play as the report observes. Of course, on the flipside Google pulls similar stunts with Chrome.

Still, that's not an excuse, and no browser should be actively attempting to dissuade people from downloading another rival browser. What Mozilla is also pointing out, of course, is that Microsoft is in a particular position of power here, given that it owns the world's primary desktop OS, and it's leveraging that platform in various ways to push adoption of Edge.

Whether that's worked for Microsoft, or not — spoiler alert, it hasn't — is irrelevant, as this behavior obviously isn't ethical or fair, and while it may not have dented Chrome's reign as the top browser, it may have damaged smaller rivals like Firefox.

While the EEA may have a considerably better balance of browser equality within Windows, the report notes that this is, of course, because regulations have forced Microsoft's hand. Arguably, this is another negative in that it shows Microsoft only respects user choice in terms of browsers when it's forced to do so.

Mozilla finishes with the following paragraph that sums everything up nicely: "We again urge Microsoft to abandon these harmful patterns worldwide. And we urge regulators in the European Union, the United Kingdom, the United States, India, and beyond to consider this report as evidence that regulatory action works — there is much still to do."

So, if Microsoft really wants to talk about "added trust", how about some action on some of these sticking points? Or is this just a case of having to be more trustworthy than Google?

The software industry has always been shaped by new tools that promise speed and efficiency. From high-level languages to cloud infrastructure, each wave of innovation has reduced friction and raised expectations. The current wave is AI-generated code, but this new era of innovation is different in both scale and consequence.

For the first time, developers can produce large amounts of working software without fully understanding how it functions. This shift is creating a new challenge that engineering leaders are only beginning to articulate, a concept we’ll call comprehension debt.

Technical debt is a familiar concept. Teams knowingly trade long-term maintainability for short-term delivery and accept the cost later. Comprehension debt is a similar concept but where that debt resides is different.

Whereas with technical debt is located within a codebase, comprehension lives in the people building the systems. When developers ship software faster than they can truly understand it, the gap between output and understanding of the codebase widens. Over time, that gap can grow to be a serious organizational risk.

This tension is already visible across the industry. The most recent Developer Survey found that 84% of developers use or plan to use AI tools in their workflow, yet 75.3% say they do not fully trust AI-generated answers.

The findings highlight a growing contradiction at the center of modern software development: teams are increasingly dependent on AI, while remaining cautious about the quality and reliability of its outputs.

The disappearance of productive friction

For decades, the early years of a developer’s career were shaped by friction. Junior engineers learned by wrestling with compiler errors, reading documentation, and debugging unfamiliar systems. The process was often frustrating, but it helped developers build mental models of how software behaves and understand not just what worked, but why it worked.

Today, AI tools remove a good deal of friction. A junior engineer can generate a functional service, build an interface, and resolve common errors within minutes. The experience feels empowering and productive, but the friction was never purely an obstacle, it also acts as a teacher. When that struggle disappears, some of the learning disappears with it.

This does not mean the next generation of developers will know less. In many ways, they will be exposed to more complex systems earlier in their careers. However, developers may begin to feel productive before they have gained the deeper intuition that traditionally comes from years of problem solving and debugging. The sense of progress may outpace genuine understanding.

Faster progression, weaker foundations?

AI is also reshaping career development. Traditionally, engineers progressed from writing small pieces of code to reasoning about complex systems and eventually designing architectures.

AI compresses the earliest stages of that journey and engineers are able to contribute sooner and move more quickly towards higher levels of responsibility. In the short term, this looks like success with teams being able to deliver faster, and organizations expand their talent pipelines.

The long-term risk arises down the road, when organizations find themselves with engineers who can produce solutions quickly but struggle when those solutions fail in unexpected ways. Debugging, architectural thinking, and systems design all rely on deep mental models that are built slowly through experience. If those foundations are weaker, the consequences may only emerge months or even years later.

Enter vibe coding

Alongside this shift, the explosion of vibe coding as a practice, which entails a workflow built around prompts, rapid iteration, and intuition rather than deliberate engineering, has further exacerbated comprehension debt. There is genuine value in the vibe coding approach. Rapid experimentation has always driven innovation, and AI has the ability to unlock creativity in remarkable ways.

The danger appears when this becomes the default mode of production rather than a tool for exploration. When speed becomes the dominant priority, understanding can quietly become optional. Teams may accept generated code without fully reviewing it, and systems may be deployed that few people feel confident enough to explain in detail. The result is not immediate failure, but a gradual erosion of collective understanding.

Some skepticism about new tools is inevitable. Every generation has worried that higher levels of abstraction would weaken engineering skills. However, AI introduces an important difference. Previous tools abstracted complexity while still requiring developers to reason about behavior.

AI can now generate working solutions without the developer forming the underlying mental model at all. That changes not only how software is written, but also how expertise develops.

Building AI-native engineering cultures

The answer is not to resist AI adoption as the benefits are too significant to ignore, and organizations that fail to embrace these tools risk falling behind. The real challenge is adopting AI while protecting the growth of genuine understanding.

Some organizations are beginning to view comprehension as an explicit goal rather than an assumed outcome. Engineers are encouraged to explain generated code in their own words and document the reasoning behind AI-assisted decisions. This approach reinforces that understanding is part of the deliverable, not a nice to have.

Code review is evolving too. Historically, the focus was whether the code worked and met quality standards. Increasingly, teams are also asking whether the developer truly understands what they have produced and review conversations are shifting towards explanation, walkthroughs, and shared learning.

Hands-on learning also remains essential. Some teams are intentionally creating environments where AI plays a secondary role, whether through debugging exercises, architecture discussions, or smaller projects built from scratch. The goal is not to reduce productivity, but to preserve the experiences that build engineering intuition over time.

The most effective organizations are not choosing between AI and traditional development, but rather thoughtfully combining the two approaches. AI is increasingly used for repetitive tasks and scaffolding, while humans retain responsibility for critical thinking, system design, and architectural judgement.

This moment places a new responsibility on engineering leaders. Productivity is accelerating rapidly, yet learning must keep pace. If we succeed, AI will help create a generation of developers who build faster and think more deeply than ever before.

If we fail, we risk creating teams capable of shipping almost anything, yet uncertain about how the products they build truly work or how to fix them when something inevitably goes wrong.

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For the longest time, I thought that, despite their annoyances, iPhones had something big over the best Android phones: PC connectivity. I'd always seen iPhone and Mac-owning friends quickly send files between the two, and assumed it was an Apple-exclusive feature.

As it turns out, this isn't true. Android users can use a Windows app that brings loads of really useful features — and it's really underutilized, which is why a tech journalist like me hadn't heard of it.

This software is called Phone Link, and we've got a guide on how to connect your Android phone to a Windows PC using Phone Link elsewhere on TechRadar. I downloaded it about a year ago, largely out of duty to test every tool, appliance, and gadget I could get my hands on. I was expecting it to last a weekend.

A year on, though, and Phone Link has become such a useful part of my workflow that I'd forgotten it's something not everyone's heard about. It's such a natural part of the way I use my phone and computer that I'd totally forgotten it was something I initially downloaded to test!

Well, no longer: TechRadar has let me wax lyrical about Phone Link, and I'm going to give the tool its time in the sun.

Sending photos from phone to PC

For the most part, whenever I want to connect my phone to my PC, it's to move pictures between the two devices.

As a smartphone journalist, you can understand that I'm sending snaps from my phone to my computer a lot. Sometimes it's camera samples from a mobile I'm testing that I want to upload for a review. Occasionally, I want to back up pictures I really like before I wipe a phone and send it back to the company.

The most frequent use case, though, is to transfer to my PC pictures I've taken on one phone, of another. I use smartphones to do review photography, and it's a great way to test out a phone I'm reviewing.

Before Phone Link, I'd have to use a USB cable to connect my phone to my computer — approving the connection on my Android's end, because they're always annoyingly suspicious — and drag and drop all the relevant pictures into a folder on my computer. It's not an especially onerous process — it's a lot quicker than when I used to use Google Drive to transfer pictures, for instance — but it takes a little while of fiddling with my phone and digging out a cable to work.

Not so with Phone Link. Its Photos tab shows a big old list of all the pictures you've taken recently, giving you options to save them to your PC, open them in an editing app, and share them with others.

These pictures appear as soon as you take them, as long as your phone is on the same Wi-Fi as your computer. I've become used to taking photos in one room of my home, and entering my office, to see them linked up on Phone Link, ready for an edit. When I've got PC notifications turned on, I can even hear the 'bong' of my computer telling me they've arrived.

Phone Link also lets you browse the contents of your mobile in the Files tab, where it appears in the Devices and drives list alongside any cloud or local drives you have. I don't use my phone to handle non-photo files, but it's a nice touch that'd be really useful if I were in another line of work.

Apps on my PC

Ostensibly, one of the main uses for Phone Link is that it lets you open your smartphone apps on your computer. Its Apps page shows you a massive list of every one you have installed on your mobile, and clicking on it opens it in a window.

This doesn't override anything you have going on on your phone; if you're using it as a second screen to watch a YouTube video or act as a music player (one of many ways you can repurpose an old Android phone), the app will open in your PC window alone.

When I first started Phone Link, I tried using it for everything, but often bounced straight off. In many circumstances, using a smartphone is simply more intuitive than using a mouse and keyboard, given that phone apps are designed for thumbs. But over time, I've found a few uses of Phone Link's app windows that are genuinely handy.

As mentioned, one of those uses is as a music player. I can let Spotify hang around as a spare window, ready to skip tracks or change playlists when I want. It's also useful as a way to check apps that don't have PC equivalents, like Too Good To Go or Mubi Go, or ones that do have web functions but are easier on the phone, like my national health service's app.

There's one way that Phone Link saved me hours, though. When I used to work for a company that didn't let you use your work Gmail account on personal computers, Phone Link was my workaround to still see emails on my PC. This saved me ages each day; time that'd otherwise be taken up checking and replying to emails on my phone, or trying to get my decrepit work laptop to turn on.

Naturally, you can manage your calls and texts via Phone Link too. Personally, I don't think I've sent an SMS or made a non-WhatsApp voice call since the 2010s, but I'm sure there are some people who'd find this handy.

I also appreciate a notification list down the left side of Phone Link. This transforms it from being software solely for controlling your mobile to a veritable hub of information from your handset. I'll often keep it up on my second monitor; it makes good use of its space.

Controlling my phone from my computer

As mentioned, you can use your computer to control your mobile's apps via Phone Link, but you can also use it to change the actual settings of the device itself, which proves itself useful in an entirely different way.

Phone Link lets you monitor your phone status; I can see how it's connected to other devices, what battery it's on, and whether it's currently paired with Phone Link or not. I can even see a little representation of what my wallpaper currently is. All useful to a small degree, but the charge is the only one of those that's seriously useful.

What's more important are the status controls. You can toggle between vibrate, sound, and silent, turn Do Not Disturb on and off, and turn on the media player.

From a cursory mention of the tools, these might not sound that important, but I've found them really useful in a pinch. I'm constantly forgetting to mute my phone before I go on a video meeting, and a quick click of the Do Not Disturb button means I'm not going to have unexpected calls.

As a lazy person, I also love the fact that you can use Phone Link to play a sound from your phone; I'm always doing this instead of spending five seconds actually looking.

My one gripe with Phone Link is that I still need my phone with me to use it. When you start using it each session, you're required to unlock your device and approve the connection. Generally, you're done with your phone after that, but sometimes I'm unexpectedly forced to re-unlock my device, which I've invariably placed somewhere stupid and forgotten about. It's a small annoyance, but if that's the price to pay for an otherwise-smooth workflow, so be it.

• Windows 11's July update has arrived
• It packs some smart features, including pausing updates at length and a new recovery option
• This patch also cures a nasty bug that slowly eats more and more drive space, but there's an issue to be aware of for Dell laptop owners

Microsoft has released its monthly patch for Windows 11 and the July update is a biggie, packing a feature that I've been awaiting for quite some time.

That would be the ability to pause updates on Windows 11 Home – beyond just a short period of time, which has been the only choice to date – and there are some other impressive additions from Microsoft here.

I'm going to pick out my top four features introduced by the July update, as well as rounding up some of the other more notable changes here. I'll also highlight an important bug fix that has been deployed for a nasty flaw that eats your drive space – along with a warning for some of those with a Dell laptop (who may not get this update for a good reason, as you'll see).

As ever, these changes were seen in the last optional update (June preview), and most of these features are being rolled out gradually, so you may not see them right away after installing this update (indeed, you may have to wait a while based on how the update winds blow regarding your exact PC configuration).

1. Pausing updates

Sometimes you want to hold off installing an update because of a bug that you're nervous about potentially affecting your system, and up until now, Windows 11 users have only been able to avoid any given update for five weeks.

Okay, so that's a fairly long time, but what if the bug is one of those persistent and niggly affairs that Microsoft takes an age to fix? You may want to delay the update beyond five weeks, and now you can. The catch is that you'll need to continually renew the delay for every 35-day period, but nonetheless, you can now keep an update at bay for as long as you want. (Well – until you're forced to update to a new version of Windows 11, that is, when support runs out for your current version).

2. Point-in-time restore

Another neat addition is point-in-time restore, a new recovery option that you can employ when something bad happens to your PC and you can no longer reach the desktop. Point-in-time restore lets you roll back a non-functioning system to a previous working state. It reverts to the "full system state" meaning that all your apps, files, and settings are recovered as captured at that previous time.

Hopefully this is a feature that you'll never need, but if you do, you'll be very glad of it. Note that the system backups obviously take up drive space, with older backups automatically deleted after 72 hours by default. Also, if you have a system drive that's smaller than 200GB, you'll have to enable this feature yourself (as it won't be turned on automatically due to potential space issues – note that you can specify a maximum amount of storage space used).

3. Fewer annoyances with widgets

The July update is making the widgets panel 'quieter' by which Microsoft means that it has cut out a lot of clutter. The main change is that by default the panel just displays your widgets and Microsoft has got rid of the promotional nonsense in terms of ads and the MSN feed. Notifications and taskbar badges are also minimized by default, and widgets no longer open when you hover over them.

There are some other streamlining moves here, and even if you don't use widgets, I think this is a noteworthy change as it shows the direction Microsoft is now heading in with Windows 11 – chilling out on the upselling as was previously promised (if not entirely banishing it).

4. Screen tint

Windows 11 has been graced with a number of accessibility improvements in the July update, first of which is a new screen tint ability. This does what it says on the tin, allowing you to tint the screen with a choice of colors to help reduce eye strain, or to make text more easily readable. You can adjust the tint intensity, and this is a very useful addition that'll save people from downloading a third-party app to get these kinds of color overlays.

Other features in the July update

There's quite a lot going on here aside from the above major moves, and that includes Microsoft boosting the speed at which File Explorer launches (helping Windows 11 to feel more performant overall).

There's a whole lot of work on the Bluetooth front, too, as Microsoft notes: "This update improves reliability and performance when connecting to and using Bluetooth devices." That includes better driver stability, improved audio routing for calls via Phone Link, and more.

File Explorer has also been honed to be more reliable, and Microsoft has implemented a performance tweak to the Background Intelligent Transfer Service (BITS) that means PCs will shut down more promptly. If you've ever sat staring at your computer while it takes 20 seconds to shut down, wondering what on earth it's doing, that kind of behavior will hopefully be a rarer occurrence going forward.

A major bug fix – and Dell laptop problems

There's some good news for those of you who have been suffering at the hands of a mysterious bug that eats storage space (due to an out-of-control database file). This is fixed with the July update (the cure was in the June preview before it), so you can grab this to resolve the issue (hopefully).

There's some bad news on the bug front, though, namely that you won't get this update on some Dell devices with Intel CPUs, as Microsoft has blocked it due to the July patch causing all sorts of weird issues. I say it's bad news, but at least Microsoft caught the problem and has prevented the update from being piped to those machines.

Microsoft informs us: "This update might not be available for a limited number of Dell devices with Intel processors due to an incompatibility reported by Dell that can potentially cause unexpected shutdowns, poor performance, increased heat, and battery drain. We are working together with Dell to prevent the affected models from experiencing the issue and plan to release a resolution for affected devices in the coming days."

So, if you were wondering why you couldn't get this patch on your Dell laptop, now you know. You'll just have to sit tight for now until Microsoft and Dell get this ironed out.

• White House, Treasury, DHS and DoW come together to launch Gold Eagle scheme
• The initiative will prevent duplicated work and prioritize vulnerability remediation
• Gold Eagle will also help to identify which systems could be at risk

The US Government has launched Gold Eagle, a new clearinghouse which looks to centralize vulnerability discovery and remediation against a backdrop of evolving AI-powered security threats.

Gold Eagle will serve as a central hub between federal agencies, AI developers, open-source software developers and critical infrastructure companies, in a bid to increase the speed of vulnerability discovery and prevent major incidents from occurring in the first place.

The scheme came about under President Trump's June 2 2026 executive order 'Promoting Advanced Artificial Intelligence Innovation and Security' and represents collaboration between the Treasury, the DHS' Cybersecurity and Infrastructure Security Agency (CISA) and the Department of War.

US Gold Eagle scheme addresses growing vulnerability exploitations

Under the scheme, vulnerabilities scanning will happen centrally to ensure multiple organizations aren't independently repeating the same work. Gold Eagle will also identify which software, networks and critical infrastructure could be at risk, before coordinating fixes. The White House described the scheme as a "force multiplier."

Although AI is largely to blame for the increase in attacks, Gold Eagle is set to fight fire with fire by employing AI to identify bugs too, using models like Anthropic's Mythos.

"Through this strategic partnership, we will expand existing security measures to safeguard software and networks in the 21st century and continue to promote advancements in artificial intelligence," DHS Secretary Markwayne Mullin wrote.

The concept of a dedicated clearinghouse centralizes vulnerability management to ensure the right bugs are being prioritized and to cut through the noise of lower-quality reports. Its assistance will most likely be felt by the open-source community, which has limited resources and financial backing to identify and fix issues as effectively as enterprise software vendors.

"Under the leadership of President Trump, we are bringing a wartime footing to the cyber domain to relentlessly patch vulnerabilities," Secretary of War Pete Hegseth added.

• Microsoft is improving Windows 11 search in multiple ways
• That includes a calmer search panel, the ability to turn off web results, a better way of prioritizing returned results, and a more stable search overall
• This is in testing for now, and the main worry is how long it might take for Microsoft to usher all this through to release

If you're fed up with the way Windows 11's search functionality works – and you wouldn't be alone – some incoming changes are set to greatly improve this experience.

Microsoft revealed the tweaks to the search box in a blog post, explaining that they're being delivered via a gradual rollout to testers in the Experimental channel for Windows 11 preview builds.

The first major change is simply to make search a calmer place, so when the panel appears, it only contains a list of your recent searches (allowing you to easily fire up one of those again if you wish). The current clutter to the right of that list, including recommendations – some of which are outright adverts – along with trending searches has all been banished.

And the second important piece of work here is that Microsoft is finally giving Windows 11 users the ability to get rid of web results in search. Currently, when searching for a file in the operating system you get not just local results (for files on your drives), but also some web results which can get in the way.

In this new scheme of things, you'll be able to switch off all web results in Settings, and also ditch Microsoft Store suggestions too.

If you keep these results on, Microsoft notes that they won't be prioritized, although this is something the company had already started to address (it used to be the case that web content could appear at the top of the returned results in a truly baffling fashion). Web results have also been stripped of any 'promotional content' so you'll only get the most relevant answers if these results are enabled.

Microsoft has also bolstered the handling of results for Windows 11 settings so that more relevant options are flagged up higher in the pecking order, and Microsoft says further fine-tuning is planned on this front.

The search box will also be able to handle typos, with an improved ability to guess what you really meant ('Chrome' rather than 'Chome' for example), and it'll start to surface possible results after typing just two characters (another enhancement we've already heard about).

While this is mostly all about streamlining, Microsoft is going the other way in one notable respect — adding a bit more detail in for the files returned in the results. Search will now provide more info about files (such as when they were last opened) and a more in-depth preview in the right-hand panel, so you can more readily tell what you'll be opening if you click on that result.

Finally, Microsoft is making search more reliable, which involves "reducing [the] likelihood of crashing and loading issues" which is obviously a welcome move.

Analysis: the rollout fly in the soothing search ointment

Windows 11 search has always been a somewhat painful affair for me, thanks mainly to the clutter of the thinly veiled (or not even veiled at all) ads, and the irrelevant results which were surfaced with a baffling level of priority, as noted. Windows 10 isn't a lot better, frankly, but at any rate, I'm very happy to see the fresh direction Microsoft has taken here.

The calmer search results are a major boon, with Microsoft following in the same vein as the streamlining it recently announced for the widgets panel, which was made a quieter place. (All this follows a broader early promise to rein in the upselling with Windows 11 and generally make the interface a more chilled place).

I was hoping that the option to turn off web results would be brought in, and this is the most important addition for me personally. I'm not alone, and the reaction to these various changes has been very positive, with the only real sticking point being impatience around when these new features will be rolled out. That's both in terms of testers wanting to try the revamped search box now, and the general computing public wondering when they will eventually get their hands on all this.

As this tester posts on Reddit: "We love this... except gradual rollouts. We don't like when things are inconsistent from PC to PC despite running identical [test] builds [of Windows 11]. It's really obnoxious and breaks muscle memory. But the changes seem great and I look forward to trying them."

As Microsoft points out in its blog post, it's worth remembering that there are now feature flags that can be enabled if you're really keen to try out something in a preview build, and it's not on your PC yet.

However, as for those asking: 'For those of us on release, when might this arrive?'

Well, that's a very different kettle of fish, as it could take quite some time for this work to progress through testing. Mainly because there are a lot of changes involved, and it's not something Microsoft will want to rush (especially given the drive to make search more reliable and stable).

And when these search improvements are eventually released outside of testing, it'll be on a controlled rollout that you won't be able to jump the queue with. However, there's a reason for that – Microsoft will need to observe the changes going live in a gradual manner to ensure no unexpected gremlins are crawling around in the works.

So, you will have to be patient, and there's some frustration around the length of time it can take for controlled rollouts to proceed these days (the Start menu overhaul from last year being a key example here – some folks still don't have it even now). But the good news is that these changes are coming, and they promise to revamp search in Windows 11 to a considerable extent. I can't wait (but I'll have to).

I'm always keeping my eye out this time of year for the ultimate 'back to school' laptop deals for our readers. Today, I think I've spotted a listing that could actually lay claim to that title.

It's the brand new Dell XPS 13, which is currently on sale for just $599 if you're a student. This new model has just been released at $699, but it looks like Dell is doing a particularly aggressive intro deal for prospective students right now.

To be honest, even at $699, I'd say this Dell XPS 13 is a strong contender. We haven't reviewed it yet here at TechRadar, but this iteration looks like a very compelling alternative to the likes of the new MacBook Neo, which is also $699.

It's surely no coincidence that the new XPS 13 is priced to compete with the MacBook Neo, as both offer a premium, lightweight laptop at a rock bottom price. The XPS 13 is obviously a Windows 11 machine, however, so it's the one to go for if you prefer Windows over MacOS.

Get the new Dell XPS 13 for just $599 today

• See more: check out all of today's laptop deals at Dell

While we haven't reviewed this model yet, our sister-site Windows Central managed to check it out in-person earlier this year. Initial reports are exceedingly positive, with the price, display, and overall build quality being particular highlights.

Interestingly, Dell had MacBook Neos on display next to the New XPS 13, so journalists could directly compare the to models. According to Windows Central, the XPS 13 had better build quality, a better display, and it was lighter. Of course, they do love Windows laptops over on that site, so your preference may vary.

We still rate the MacBook Neo here at TechRadar with its eye-catching colors and incredible battery life.

With that said, if I were choosing between the two, I'd definitely be tempted by the XPS 13. Generally, I prefer Windows laptops, and the XPS 13's Thunderbolt 4 ports are more useful to me than the Neo's standard USB-C ports. I'm also partial to the XPS 13's display, which I think it is a little more modern-looking than the Neo's, with its thick-bezels.

London Tech Week’s focus on AI - from a £12 million investment in AI for SMEs to AI bootcamps for graduates and more - has reflected the pressure to compete in an AI-era.

As this digital revolution progresses, the job economy is changing, but the mantra that AI is taking our jobs is simply not correct and potentially fueled by an undercurrent of classicism.

When the Luddites famously started to break the first machines of the industrial revolution in 1811 in England, fearing for their job as textile artisans, the “Bourgeoisie” would describe them as “ignorant workers”, with no understanding of basic economics.

More than two hundred years later, with the rise of GenAI, it is no longer the blue-collar workers who fear for their job, but the white-collar workers. This time it is the “bourgeois” who live in the anxiety of an uncertain world.

Since ChatGPT introduced AI into the everyday lexicon, it has been clear that we would experience an unprecedented revolution. The rhetoric that immediately began to dominate social discourse has been that AI tools would render most jobs insignificant.

Furthermore, whilst other technological revolutions ended up being creative destruction, ‘this time it was different’.

But is that really the case? Or are we more fearful, more concerned about destroying the status quo, because this time it’s a different ‘class’ of people being impacted? This time it’s the desk workers, not the physical laborers, who risk losing jobs, and suddenly there is alarm.

Artificial Intelligence relies on humans - and more humans than ever

AI is a human creation and still relies on humans to evolve. First, we have those who build the infrastructure, like data centers, which accounted for almost all of the United States’ GDP growth in the first half of 2025 (according to Harvard economist Jason Furman).

Then, we have those who train the models, which still need to be constantly retrained. Even if models are able to train themselves eventually, there is no consensus that human intervention in training will become obsolete, because human behavior and the entropy of organizations are in a constant state of flux and evolution.

And even when trained, AI constantly needs to also understand the “context” in which it is prompted to perform efficiently. AI then needs to be deployed. Managing security, defining guardrails for agents, understanding how to use AI and tracking agentic AI’s actions, all comes with inherent challenges.

The CIOs of the largest global corporations are already investing hundreds of millions of pounds to understand this. Startups based in San Francisco - a city I recently visited where 95% of out-of-home ads were about AI agents - are focused entirely on resolving these problems for large enterprises.

The fact that both Anthropic and OpenAI have launched their own consulting companies is proof that managing AI complexities in the coming years will be the biggest source of growth for all consulting and outsourcing companies of the world.

Sourcing the right human talent in the AI era is the biggest challenge

Software engineering is a job category where GenAI - perfectly trained on open-source code and GitHub repositories - can now code better than even the most experienced developers.

Additionally, developers in AI labs - with privileged access to “tokens” on Claude Code or OpenAI Codex - now develop 100% of the time without writing a single line of code. Nonetheless, when asked about their biggest challenges, all AI startups would point to recruitment.

A report by the UK's National Foundation for Education Research showed a 50% increase in tech job adverts between 2019/20 and 2024/25, with entry-level roles particularly affected. However, we’re now seeing a surge in demand driven by Gen Z, according to Employment Hero’s March Jobs Report.

This demand for AI expertise is reflected in a new Malt Tech Trends Report, which analyzed 1.2 million searches of tech freelancers in 2025. It reveals that AI is now the second most-in-demand skill, irrespective of company size, industry, or project type. More specifically, demand for freelancers with agentic AI expertise exploded by 5,800% in just twelve months.

Observer of the AI revolution, Andrew Ng, explains that if, for example, a team of 3 developers builds 10 times faster, then they need more designers or product managers to fuel the creative process. Doing more faster, with fewer people creates more work to fuel and execute the output.

More people are echoing the same rhetoric as Ng, calling out the phenomenon of ‘AI washing’, whereby companies have justified mass redundancies with AI disruption. In reality, in many cases, they were either adapting to geopolitical and economic uncertainties or had simply employed too many in the crazy post-COVID bull market.

The AI job apocalypse is not yet here… Still, the fear is real and needs to be understood

Software engineering is a perfect example of a job category that has constantly evolved. Since the inception of computer science, programming has become progressively more about “natural language”. Whilst there were 50,000 developers worldwide in the 1960s, today there are almost 50 million. Undoubtedly, the eradication of barriers to entry to build software increases that number tenfold.

History, data, and observation shows us that the AI job apocalypse is not yet here. Still, the fear is real and needs to be understood. The reason every science fiction novel paints an inhospitable world and unattractive paradigm is because the human mind always fears change. We assume the worst.

AI transformation, like all transformations, will be a cultural change first. And it’s companies, not professionals, who are most at risk if they fail to adapt. If one thing will be different in this digital revolution, compared to the last (arguably comparable is the advent of the internet), it’s the rate of change.

CEOs will have to be imaginative, change org charts and processes, admit they are not omniscient, take risks, and invest in training. Schools and universities also face the challenge of teaching soft skills: how to adapt to live and work in a more uncertain world. Because we can only harness top-tier AI talent if we understand how to truly adapt to change.

Independent professionals - those who create their own roles - from freelance developer to fractional manager and strategic consultants - have already redefined work.

On average, freelancers spend 4 hours a week on upskilling and keeping up with the job market and already have the habit of switching from one client project to another. They were the first to adapt to AI and realize that a job is more than just a bundle of tasks.

As Jensen Huang, CEO of Nvidia, recently said, if someone were to observe him at work, we would conclude that his day consists of tasks like making hundreds of calls and sending emails. AI will replace, augment, and improve these tasks. But it will not take Jensen’s job.

We've featured the best AI chatbot for business.

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

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

• Microsoft has blocked Secure Boot updates on some devices
• There have been issues with some devices updating from the 2011 certificate to the latest 2023 certificate
• Some older devices or those not supported by their OEM may be restricted in downloading the latest Secure Boot certificates

Microsoft has blocked some Windows 11 PCs from installing Secure Boot updates due to known issues with certificate updates.

The company is currently rolling out an update for Secure Boot on devices using certificates issued in 2011, which are now expired. The new 2023 certificate is being applied through Windows Update, but issues on devices with faulty firmware have forced Microsoft to halt the rollout.

“Devices in this group are affected by a known issue. To reduce risk, Secure Boot certificate updates are temporarily paused while Microsoft and partners work toward a supported resolution,” Microsoft said.

Secure Boot issues

Secure Boot has long been a device-saving feature when it comes to removing malicious files, as it allows the device to verify and load only authentic software before booting to Windows. However if the device cannot receive certificate updates it can fall victim to threats at the boot-level before Windows is loaded.

Microsoft is currently working with manufacturers to issue a patch that will allow affected devices to install the new Secure Boot 2023 certificate, with HP issuing a BIOS update to allow the installation of the latest certificate.

What this means in practice is that some older devices, or devices that no longer receive updates via their Original Equipment Manufacturer (OEM), will not be able to apply Secure Boot and Boot Manager protections. Microsoft clarified that, “this results in a gradual reduction in long-term security—not an immediate risk or system failure. Continue to follow standard security practices, including staying current with Windows updates.”

So even if your device is blocked from installing the latest Secure Boot certificate, it will continue to work properly, other Windows updates will continue to work, and your Secure Boot version will continue to protect against known vulnerabilities. It’s just future vulnerabilities that users affected by this issue will need to be aware of.

Many users may not be aware of issues until they need to use Secure Boot, so the silver lining in Microsoft’s warning is that now is the perfect time to check if your Secure Boot is working properly.

How to check Secure Boot is up to date

To check if you are using the latest Secure Boot certificate, take the following steps:

• Open the Windows Security app using the search bar
• Navigate to the Device Security dashboard using the menu on the right hand side
• Look at the Secure Boot section, and check for the following messages:

1. "Secure Boot is on"

If you see this message, Secure Boot is likely working properly. However, this does not display your certificates’ current state. Microsoft has been rolling out an update to show if your Secure Boot is running on the latest certificate, so make sure your don't have any pending Windows updates.

2. “Devices in this group are affected by a known issue.”

Devices with this message will likely be able to install the latest certificates once a firmware update has been issued by your OEM. Check your OEM update channel for availability.

3. “Secure Boot is on, but your device does not support the automated Secure Boot certificate update due to hardware or firmware limitations.”

Devices with this message may no longer be supported by your OEM, or the OEM might no longer be able to provide the firmware updates needed. Microsoft recommends checking your OEM’s Secure Boot support page to confirm whether your device is out of support.

Via WindowsLatest

• Windows 11's Copilot app has a new feature in testing
• 'PC insights' provides an easy way to receive clear answers to hardware-based questions about your device and its specs
• While there are some fears over privacy (and bloat), Microsoft has made it clear that Copilot needs to be granted permission to access your system and files

Copilot is getting a new ability to answer questions about your PC's hardware, allowing the AI to tap into the relevant hardware details to do so – and while Microsoft is treading carefully with privacy here, that's unlikely to stop some level of paranoia.

Windows Latest flagged the introduction of 'PC insights' for the Copilot app on Windows 11, which as Microsoft explains, "enables customers to conversationally ask Copilot questions about their Windows PC and receive clear responses based on their device's state without having to dig through system settings."

This is currently an experimental feature, so still in testing, and an optional ability that you must turn on for it to be in play. Windows Latest notes that it's gradually rolling out, but only in the US for now.

You can ask Copilot how much RAM you have, or storage space left, or what your GPU is, and the current level of usage for your processor, and a whole bunch of similar component-related queries. You can ask about elements as diverse as whether you have an antivirus running, or what your laptop's battery health is, diving into mild troubleshooting territory should you wish.

To get its answers, the Copilot app hooks up to Windows APIs to analyze your system, and the AI asks for permission to do this. You can allow it access to your PC's hardware details on a one-time basis for that session only, or you can elect to 'always allow' if you're happy to give Copilot this access on a more permanent basis.

Analysis: fears over hallucinations and bloat

As ever, this is AI, and as Microsoft notes, Copilot "may not always provide complete or accurate information", especially during this testing phase. So, if you do get a chance to try out PC insights, maintain a healthy sense of skepticism with the responses you get.

As Windows Latest makes clear, there's also a certain irony about a Windows 11 user checking up on resource usage, perhaps due to system sluggishness, employing the Copilot AI to run diagnostics when the app itself uses the best part of 1GB of RAM when running in the background and doing nothing.

That doesn't stop this new PC insights feature from being situationally useful, of course. Some of the reaction has come from a place of disdain, though, as you might guess, with comments such as the one from this Redditor: "Oh hey it's like Task Manager except instead of lightweight and authoritative, it's bloated and might be lying to me."

Of course, this is a feature aimed at less well-informed PC owners, not those who can easily understand what's happening in Task Manager at a glance. Criticism around the bloat of the Copilot app is fair enough, mind, and this is because in its most recent incarnation, Microsoft changed things so the app is essentially a standalone spin-off of the Edge browser.

Another worry is that of privacy, and having Copilot 'snoop around' on your machine, but as noted, there are clear requests for permissions, and the new feature is strictly opt-in. You don't ever have to go near PC insights if you don't want to. It's also worth noting that giving the Copilot app access permissions doesn't mean it can read the actual contents of files, but only their sizes (for weighing up questions about storage and the like).

At the moment, this is a purely informative or troubleshooting feature, and in the case of attempted diagnostics, it may point to issues with your PC, but won't resolve them for you. However, it's not difficult to envision where Microsoft might head with this, in terms of getting Copilot to implement fixes for certain issues that the AI flags up. I'm talking simple Settings changes rather than anything in-depth, and this has always been the idea of Copilot (even though it hasn't yet been realized to much of an extent).

When we get AI agents in Windows 11 – and they are coming, make no mistake – this kind of functionality may turn into a full-on troubleshooting agent. The trouble (pun not intended) with that being that the mistakes and hallucinations that AI can make could be considerably more aggravating in this kind of scenario.

There used to be a time when you’d buy an Apple app by paying for it once and never again. Then along came the idea of app subscriptions, and slowly but surely, Apple has started selling more and more of its software products through recurring payments. From iCloud+ to Apple TV, there are many ways to spend your cash on Apple’s wares on an ongoing basis.

Today, Apple flogs bundles of its own apps to its users, with Apple One in particular offering a cluster of apps for a single monthly price. And Apple hasn’t stopped there, with the Creator Studio package being introduced a few months ago for users of its artistic apps.

But while these app groupings ostensibly give you a way to get the software you need without paying multiple subscription fees, I’m starting to worry about how they restrict your choices and force you down avenues you might not want to travel.

Not only can they be expensive, but they’re also inflexible, giving you very little ability to customize them to your needs. If subscriptions are the future of app monetization, then Apple needs to do a whole lot better.

Enter the Creator Studio

Apple likes to portray itself as the company of creatives — think about its “Here’s to the crazy ones” or “I’m a Mac” commercials of days gone by. And the firm already sells apps made specifically for this demographic, including Logic Pro, Final Cut Pro, Pixelmator Pro, and more. So it makes sense that Apple would want to offer a software suite that grants interested users access for a single fee, instead of requiring several payments, whether one-off or ongoing.

That’s what you get with the Creator Studio, which is priced at $12.99 / £12.99 / AU$19.99 a month or $129 / £129 / AU$199 a year. In return for your cash, you get a pass for the following apps:

• Final Cut Pro
• Logic Pro
• Pixelmator Pro
• Keynote
• Pages
• Numbers
• Motion
• Compressor
• MainStage

And yes, some of these apps — like Keynote, Pages, and Numbers — are already free, but pay the Creator Studio entry fee and you get “a library of high-quality, royalty-free photos and graphics, and powerful intelligence features” in addition to what comes with the toll-free editions, Apple says.

Considering Logic Pro alone costs $199.99 if you want to buy it outright (rather than pay an ongoing subscription), this package might seem like a rather good deal. But it’s not the price that I’m interested in — it’s the way app bundles like Creator Studio work.

Right now, there are two main app collections available from Apple: Creator Studio and Apple One. While Creator Studio contains the aforementioned creative apps, Apple One starts at $19.95 / £18.95 / AU$24.95 a month and comes with 50GB of iCloud+ storage, plus memberships to Apple TV, Apple Music, and Apple Arcade.

There are two additional tiers, with the most expensive costing $37.95 / £36.95 / AU$49.95 a month and throwing in 2TB of iCloud+ storage, Apple Fitness+, and Apple News+, in addition to what’s already in the entry-level option.

But what if you want to mix and match your apps and services from Apple? What if you want, say, Logic Pro and Final Cut Pro from Creator Studio, plus Apple Music, Apple Fitness+, and Apple News+? Well, you’re going to have to either take out the top Apple One bundle and separately buy those creative apps outright, or you’ve got to pay for memberships of two distinct ongoing subscriptions.

In this instance, combining Creator Studio with the most expensive edition of Apple One means paying $50.94 a month when all you want are five apps. The rest of the content you pay for is superfluous. Throw in AppleCare+, and you’re forking out even more.

Apple gives practically no flexibility or customization with its app collections. You can’t mix and match apps and services, and if there are some you want from both subscriptions, you’ve got to pay for both. That makes it an incredibly expensive way to go.

But with Apple seemingly getting evermore enthusiastic about subscriptions — and the revenue they bring in — don’t be surprised if this state of affairs continues.

Is there a better way?

You might feel that, since Creator Studio is relatively new, Apple is still working out the logistics of how to sell the apps and services it contains. But Apple is no stranger to subscriptions. It’s been offering the likes of Apple Music and Apple Arcade for years now, so it knows a thing or two about how to structure subscription offerings.

Is the lack of flexibility here and the absence of any way to pick and choose apps a deliberate policy on Apple’s behalf? We can’t know for sure, but it’s not a good look.

While the Creator Studio might seem like a decent bargain on its face, it becomes considerably less so if you’re not interested in everything it offers and want to pair its apps with those from the Apple One bundle.

Considering how keen Apple seems to be on subscriptions, I’m surprised it doesn’t offer individual memberships for each component app within these packages. For instance, why is there no individual subscription for Pixelmator Pro? Instead, you’ve got to choose between a one-off fee or the full Creator Studio.

If Apple were to go down this route — and it's what I'd do if I were CEO-to-be John Ternus — it would instantly solve the problem of pairing different apps from different collections. You could simply add whichever apps you wanted to your own “build a bundle” package and get exactly what you need for a more acceptable price.

But that kind of process doesn’t seem to be anywhere on the horizon. There are no rumors saying it’s coming, no telltale clues nestled in Apple’s code. Don’t hold your breath for its imminent arrival.

Until we do get something like that, Apple customers are going to continue to feel ripped off by a rigid, inflexible system that is happy to take their cash in return for apps they don’t want or need. It’s a situation that’s in desperate need of a rethink.

It wouldn't be a difficult argument to make that the USA is the television capital of the world, or at least the birthplace of modern TV culture.

From Walter Cronkite to Fox & Friends, I Love Lucy to The Simpsons, The Twilight Zone to The Sopranos; whether it's news, current affairs, comedy or drama, US television has produced some of the most iconic, enduring shows committed to the format.

And that's before you even get to sports. What would the sporting calendar look like without Super Bowl Sunday, the Fall Classic, Indy 500 and WrestleMania? Or, most recently, the 2026 World Cup that the USA is co-hosting.

America's shows and sports are collectively the most popular in the world, which means you can continue to watch them on local channels when you're overseas. But it isn't always the case – college basketball, for example, has limited appeal outside the States. And if you go to watch your usual streaming websites, apps and platforms in a different region, they're unlikely to work due to rights restrictions.

Thankfully, there's a very simple way to get around these blackouts. By using one of the best VPNs, you can watch as much US TV as you wish when traveling outside of its borders. Keep reading to discover how.

• Use NordVPN to watch US TV from overseas now (Save 75% with our exclusive deal)

Why can't I watch US TV overseas?

As we say, the international proliferation of US television and sports means that there's a good chance you'll be able to see the content you want in many other parts of the world.

However, due to licensing and rights agreements, you won't necessarily be able to watch them on the platform to which you've become accustomed (and, more commonly than not, pay for).

Try to watch Love Island USA on Peacock, Welcome to Wrexham on Hulu, or a numbered UFC event on Paramount Plus, for example, and you'll be met with an error message telling you that you can't stream them. The same goes for OTT cable alternatives like YouTube TV, Hulu + Live TV, Fubo and Sling TV – all are region-restricted to the US.

It can even happen with certain content on the likes of Netflix, Disney+ and Amazon Prime Video. They have slightly different libraries depending on which territory you're in. So you can forget watching The Great British Baking Show on Netflix if you're not Stateside.

And even if you find the show or sporting event on a local station, you may end up having to subscribe to stream. That hardly seems fair if you've already paid a premium back home to watch that exact same thing.

How can I watch US TV from overseas?

This is where having a good Netflix VPN or Peacock VPN can pay dividends.

The clever engineers at these VPN companies work constantly to make their tools compatible with the world's biggest streaming platforms, thereby letting you watch them regardless of where in the world you are.

The technology effectively spoofs your IP address so that your laptop, smartphone, tablet or streaming device looks like it's in an altogether different location.

Specifically, in this case, a location back within the USA.

Then, when you load up your app for Disney+, Apple TV, DirecTV or anything else, it will think you're back at home and so won't hit you with that annoying error message.

That's in theory, at least. Only the best streaming VPNs are able to unblock a wide range of these platforms. That's why it's worth making sure you get one that will come to your rescue when you're overseas on business or vacation...

Which is the best VPN for watching US TV when overseas?

Step-by-step guide on using a VPN to watch US TV overseas

Using a VPN to watch US TV when overseas is a doozy...

• Sign up and install a VPN on your device (NordVPN is our top pick)
• Connect to one of its United States servers
• Head to the website or app of the US streaming service you wish to use
• Hit play and enjoy!

We've learnt that it's best to connect to your US VPN server first before loading up the streaming app or website that you're attempting to unblock. We've found that if you do it the other way around (i.e. heading to the streaming platform first) you can, in effect, 'tip off' the streamer, which makes it less likely that the VPN will work its magic properly.

And another tip we have is to use an Incognito window you're streaming within a web browser. Again, it just helps to make sure that none of your cookies or usage history triggers the streaming platform to know that you're currently overseas.

What sports can I watch on US TV from overseas?

As a sports-mad nation, US TV hosts just about every event you can imagine from both home and abroad.

The NFL, NBA, NHL and MLB all go out on an intricate variation of networks and their associated streaming platforms. Anything that would normally be available on cable can also be watched on an OTT cord-cutting service.

Paramount Plus and Netflix are the respective homes of UFC and WWE, while Fox is the existing Nascar provider and has exclusive action from the 2026 FIFA World Cup. Apple TV has upped its game in recent years to win the rights to show MLS soccer and every race of the Formula 1 season.

Extensive coverage of the most recent Summer and Winter Olympic Games was carried by NBC's Peacock platform, as is Tour de France cycling.

In terms of overseas soccer, Peacock shows English Premier League matches, ESPN has La Liga from Spain and Bundesliga from Germany, and CBS carries the UEFA Champions League.

And what about sports that are traditionally less popular in the States? Dedicated platforms like RugbyPass and Willow TV respectively feature global rugby union and cricket action.

What news, dramas and comedy can I watch on US TV from overseas?

While some 24/7 rolling news casts will work wherever you are (e.g. Fox News, MS Now), others will not (e.g. NewsNation) without the use of a VPN.

Both current and prestige dramas can be streamed on an array of streaming platforms. George R. R. Martin's stories are an apposite example of this – Game of Thrones and House of the Dragon can be watched with a subscription to HBO Max. Peacock Originals include Ponies and The Five-Star Weekend, while Disney+ has plenty of Doctor Who.

Thankfully, most of the proprietary shows to land on Netflix and Apple TV can be watched from region to region. So the likes of Squid Game, Stranger Things and Severance can be streamed from pretty much anywhere.

It's a very similar situation in terms of comedies. Friends makes its home on HBO Max, while Seinfeld sits on Netflix and all seasons of Cheers are on Hulu. For something more contemporary, you'd need Paramount Plus for Colin from Accounts, Prime Video for Bait and Apple TV for Ted Lasso.

Just remember, if you're struggling to watch any of these when away from the country, you'll need to invest in a good VPN. They're more affordable than you might imagine.

• Meta reportedly has a new AI glasses prototype
• It is helping the company finalize a super sensing AI model
• Meta execs apparently aren't yet certain what rules the all seeing AI should follow

Meta is reportedly trialling a new super-sensing AI glasses prototype that aim to offer an ultimate level of assistance, for the small price of capturing your every moment.

The basic idea is that, because personal AI assistance gets better the more the AI knows you, by having you smart specs watch every detail of your life, the assistant can then offer more insightful help.

It could, for example, know if your fridge has milk in because it has seen inside it, or remind you not to forget your keys as you go to leave home. It could remember that gift your friend was desperately hoping to get for their birthday, or remind you the name of someone you’ve met before, if you're forgotten and are too embarrassed to ask.

This kind of tool would undeniably be pretty handy, but the extreme cost would be that your glasses would have to be always on — otherwise the AI might not see or hear the crucial information you’ll need later.

However, this incredible level of insight could be very easily abused — so you’d either have to really trust the company capturing it, or just not care if it knows everything about you.

This latest super-sensing leak comes via the Financial Times (behind a paywall) which reports that Meta is currently testing a pair of specs that achieve super-sensing by capturing continuous audio, and snapping photos every few seconds.

The more staccato image feed is likely an effort to conserve battery — according to previous leaks, existing Meta glasses have all the hardware they need to pull off super sensing, but their batteries wouldn’t be able to last long enough for it to be widely useful.

According to the report, Meta is also still trying to work on some other aspects, like whether the recording light LED should be displayed or not. When you’re recording it normally would be — super sense would be capturing people around you who might wish to know if they’re on camera — though sources have told the FT that there are plans for it to not to be displayed.

Considering that Meta just made a big post about privacy, and even updating the specs to disable tricks modders have been using to allow the specs to record without the light coming on, I’d be surprised if it took the no-light approach, but we’ll have to wait and see.

Man vs machine

You might also remember a story we shared in March about Meta contractors who claimed to be able to see images and videos taken with the glasses.

Essentially, in order to use Meta’s AI you agree to allow the company to see information about your AI communications, including videos and photos. This seemingly includes photos and videos you take using hands-free voice controls — which does technically involve Meta AI, as it has to action your request.

If an always-on AI is seeing every detail of your life there’s potentially a lot more scope for you to capture sensitive data that you'd rather not let Meta's contractors (or anyone else frankly) see. Encouragingly, according to people familiar with Meta’s super-sensing technology, there are plans to never store the raw footage and audio — meaning Meta nor the user could access it.

Instead, the system would extract the metadata from the capture, and only that would be uploaded — metadata is data about data, so imagine if instead of showing someone a photo you just described what’s in it and where it is.

There’s still some room for personal data to leak through this system, but with the correct safeguards it would have far fewer privacy implications. The big obvious gap would be the privacy of those around you — people who, unlike you the glasses wearer, might not have consented for Meta to store any kind of data about them.

The FT report adds there are also debates over whether Meta’s AI should be allowed to use this metadata for training purposes, in order for it to keep up with the capabilities of OpenAI, Google, and Anthropic’s models.

While leaks should be taken with a pinch of salt, Meta CEO Mark Zuckerberg has made it clear that he wants to offer these kinds of features on his company’s glasses. Most recently he said in an investor call that he wants smart glasses “to be a personal agent that’s with you all day long, helping you remember things and achieve your goals.”

So the advent of technology like super-sense is most likely a matter of when not if, though with battery hardware constraints persisting (especially if you want slim and light glasses), and with privacy being such a hot-button issue, I expect it might be a while before we see super sense in action.

Whenever it arrives, we'll just have to hope that it’s implemented in the right way, and with appropriate safeguards. Such a tool could potentially offers some incredible accessibility benefits, but if the privacy cost is too great the I don’t see it taking off in the way I’m sure Meta would want it to.

Do you remember the metaverse? If you don’t, don’t worry. In 2026, four years into the AI revolution that’s changing the world forever, you could easily be forgiven for thinking it was a strange fever dream you had back in 2021. You might even have odd memories of seeing a blocky version of Mark Zuckerberg floating about in a Minecraft-inspired hellscape, conducting meetings with people who could walk with no legs, while the real Mark Zuckerberg was looking at the whole thing through VR goggles. At least, that’s my memory of it.

I could be suffering from the Mandela effect, but I distinctly remember something off about the legs. They fixed that in a later version, but that’s my overriding memory — no legs. Oh, and Mark Zuckerberg assuring us that this was the future. He’d spent tens of billions of dollars on it, even changing the company name from Facebook to Meta, just to let us know he was really serious about the metaverse. Even if the legs didn’t work.

The problem was, it looked laughable. While everything in the technology world to do with games and special effects was moving in the direction of hyper-realism, the metaverse was moving in the opposite direction, towards the sort of blocky graphics that small children enjoy. But even that didn’t really answer the most basic question about the metaverse. Why? What possible advantage was there for us all to meet in a VR space where clunky avatars of ourselves could interact… badly?

Then AI happened and Meta abruptly forgot about the metaverse and pivoted towards the mission of putting personal superintelligence in all our hands instead, which sounds as terrifying and dangerous as it actually is, but we are where we are. At least when Zuckerberg was obsessed with the metaverse, we could ignore him. It existed somewhere “over there”, in Meta-land, where we could let him get on with it. Now he’s right up in our business again.

The rise of a bad idea

I’ve always been fascinated by how Zuckerberg got into the metaverse and why he became so obsessed with it. The origins of the metaverse go way back. In March 2014, Facebook bought Oculus, the VR company, for about $2 billion. This was where his passion for VR started. Think of it as the seed, not the full obsession.

By July 2021, Zuckerberg gave a long interview to Casey Newton at The Verge about Facebook becoming a “metaverse company” and described it as an “embodied internet”. Then, on October 28, 2021, his obsession became the company identity. Zuckerberg announced that Facebook the company was becoming Meta at Connect 2021, saying the new company brand would focus on bringing the metaverse to life.

I can see the logic. As a business strategy, that made sense. As a product ordinary people were expected to use, it was much harder to understand. Zuckerberg did not want Meta/Facebook to be trapped inside someone else’s platform again. Facebook had won on social, but on mobile it remained dependent on Apple and Google for distribution, privacy rules, app-store policies and hardware. The metaverse looked like a chance to own the next operating system of social life: hardware, avatars, identity, payments, meetings, gaming, work, commerce — the whole stack. In his 2021 founder’s letter, he framed the metaverse as the “next chapter of the internet” and said Meta would become “metaverse-first, not Facebook-first.”

Facebook had obviously had its problems — it was scandal-ridden. It had let advertisers target vulnerable teenagers, helped fake news spread, and enabled the spread of hate speech linked to atrocities in Myanmar. Perhaps Zuckerberg was looking for a way out of Facebook, and the metaverse offered that.

What I still didn’t understand was why he didn’t see what the rest of us saw — that it looked terrible and offered no real benefit to users. Then I read Sarah Wynn-Williams’ tell-all book about Facebook, Careless People, and it all started to make sense.

To say the book made my jaw hit the floor on several occasions would be an understatement. It’s an absolute page-turner, and your reactions grow from mild amusement to shock, then disbelief, then absolute outrage the further through the book you get. I’m aware of the criticisms of Wynn-Williams: that it is a book written by a disgruntled employee, and that she dodges a lot of personal responsibility for her part in the various misdeeds of the company. However, in another perfect example of the Streisand effect, the fact that Meta obtained a legal order in the United States to prevent her from saying anything negative about the company — at all — made me want to pick it up, and I’m glad I did.

Because now I get it — Zuckerberg seems to have spent years in an environment where too few people were willing to tell him when his ideas weren’t good. According to Wynn-Williams, he was surrounded by sycophants. When he had bad ideas, like the ill-fated Internet.org, he wouldn’t let them go and persisted with them, even when they were obviously going to fail. The people around him enabled him because he was simply too powerful. They even let him win at the board games he liked to play with them at his house or on his jet, and — crucially — he didn’t notice that they were letting him win. I can imagine that in that environment, nobody inside Meta would want to tell Zuckerberg that his metaverse was the equivalent of the emperor’s new clothes, especially if they wouldn’t even risk beating him at Settlers of Catan.

Wynn-Williams only mentions the metaverse in her epilogue. It happened after she was brutally fired from Facebook. Perhaps selfishly, I wish she’d been there for the metaverse period, because I would love to read firsthand accounts of how and why Zuckerberg persisted with such an obviously bad idea.

The fall of the metaverse

Maybe I’m being too harsh on Zuckerberg. The metaverse graphics did get better over time and Apple ventured slightly into the same territory with its Apple Vision Pro, even after the metaverse had turned into a smoldering wasteland. The fact is, people don’t enjoy wearing VR goggles for extended periods of time, and for normal people, VR lacks that one killer app. There doesn’t seem to be anything you can do in a VR space that you can’t do elsewhere much more easily.

The metaverse didn’t really die with a bang, but with a whimper. It faded through layoffs, spending cuts and the AI pivot. If I had to put a date on it, I’d say early 2023 was when Meta’s narrative moved on. In February and March 2023, Zuckerberg started talking about Meta’s “year of efficiency” and announced huge layoffs and cost-cutting. OpenAI had launched ChatGPT in November 2022, and by early 2023, generative AI had swallowed the oxygen that ideas like the metaverse need to survive. Every tech company was talking about AI now, not virtual offices and avatar legs.

The metaverse was over. We all forgot about it and moved on.

I’m glad I read Wynn-Williams’ book, because now I can understand how Facebook let the metaverse happen. And if there’s one thing I learned from reading it, it’s that money and power can bring you a lot of things, but common sense requires neither.

A study has revealed that approximately 40% of UK employees receive ‘work slop’ caused by low-quality, AI-generated content. As a result, it is estimated that each instance of work slop takes up to 3.5 hours each month to correct, resulting in millions of pounds in lost productivity.

It has been reported that as of mid-2026, approximately 78% of global businesses are using AI in at least one business function.

Despite this, studies have revealed that 62% to over 80% of workers lack confidence or training in AI, with many reporting they don’t have the skills to use the tool in their daily tasks, which can lead to errors and work slop.

What is AI work slop?

Many workers are using AI to make daily tasks such as generating reports, emails, or code more efficient. However, if used incorrectly, workers can be faced with low-quality, AI-generated content that may look like a high standard at first glance, but after closer inspection, is inaccurate and requires hours of manual checking and corrections.

To minimize AI work slop, there are several steps businesses can take:

Make training a key objective: It’s inevitable that without appropriate training and guidance using AI software, errors will occur. Business owners should ensure that employees are trained in more efficient prompting and understanding AI limitations to reduce work slop. If training is implemented during the employee onboarding stage or as early in the project as possible, the chances of work slop will be significantly reduced.

Start by brainstorming: AI must be used to enhance work, not to create it on its own. Before completing tasks that incorporate AI, it’s important to brainstorm how it can be used and the objectives you want to achieve. Where possible, create the bulk of content from scratch and use AI to support and enhance the end goal.

Introduce a review process: AI-generated work should never be presented to senior stakeholders or clients unless checked by an experienced team member. Business owners should ensure that all AI-generated tasks are reviewed and fact-checked before being presented as the final version.

An effective method is to create a mindset that AI-generated work has been created by an intern who has little knowledge of the industry. Reviewing work using this method will help to spot errors faster.

Encourage transparency: To reduce AI work slop, business owners should encourage workers to use AI to support and enhance tasks, not to complete them altogether. It’s also important for workers to be clear about how AI has supported the tasks they’re working on to make the reviewing process more efficient.

Encourage team feedback: One of the main ways to prevent continued work slop is to make colleagues aware that it has been spotted. More often than not, people will correct mistakes themselves, rather than be transparent and provide constructive feedback to their peers. Raising work slop in the early stages of the project will save time and prevent the issue from recurring.

Create manageable workloads: Workers who are unable to manage their workloads will often complete tasks quickly using AI, which can result in work slop. To prevent this, promote quality over quantity throughout the business and ensure that unmanageable workloads don’t hinder this ethos.

The key to AI success

For businesses using AI, the software should be used to elevate work and make processes more efficient, not cause delays and errors. If your business is looking to adopt AI, it’s important to be transparent with employees about when to use it and when to avoid it.

Employees should have a strong mindset towards AI and have quality and value in mind at all times when understanding the outcomes they’d like to achieve. When planning tasks, we should ask ourselves: Will AI add value and additional insights to the project, and how will it move your goals forward?

Once these points have been considered, it’s important to understand what AI can’t assist with, such as human perspective and strategic creativity. If you decide to use AI for the task you’re looking to complete, which is weighted towards human-powered thought and creativity, this is when work slop can be encountered, resulting in wasted time and resources.

To summarize, AI should be used to generate valuable content and results. Asking yourself the questions mentioned above before you turn to the software will help to generate the best possible outcomes.

An effective mental model that helps to prevent work slop

Before employees can use AI responsibly, they need to understand what it actually does. In "AI for Startup Leaders," we present a framework called A-R-C that captures the three core capabilities of AI:

• Agency: AI can work with tools, run code, and complete tasks on an employee’s behalf. For example, it can query data from your CRM, draft documents, or update a spreadsheet.
• Reasoning: AI can plan ahead and think through problems step-by-step. The latest models can break down complex challenges, consider multiple approaches, and work through logic chains.
• Context: AI can base its answers on the information you provide in each conversation. It can understand natural language, identify objects in photos, and parse data in spreadsheets.

The A-R-C model is crucial, as many workers use AI as an encyclopedia and to state facts, which isn’t what the software was created for. When people rely on AI for facts, work slop can occur. To avoid this, workers should be educated on the main purpose of AI, reasoning over their business context and taking action.

When employees understand A-R-C, they approach AI outputs differently. They recognize that AI is best used to reason from their business context rather than look up answers. Instead, they use AI to speed up how they collaborate with other teams and improve efficiency.

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For the past year, one idea has dominated conversations about enterprise technology: AI agents will replace Software as a Service (SaaS). It’s a compelling case on paper. If AI can reason across tools, write code, execute workflows, and interact with systems through APIs, then traditional SaaS applications start to look like unnecessary middlemen.

Seats become less relevant. User interfaces matter less. Software becomes cheaper to build. Custom internal tools become easier to create.

In that reality, much of SaaS gets pushed down into infrastructure, while value shifts to models, agents, and orchestration layers. This reaction is being driven by several parallel shifts.

Software is becoming cheaper and faster to build, AI agents are becoming better at navigating tools and executing work across systems, and the economics of agentic execution are bringing new attention to cost and latency, or time to serve.

The market is not wrong to think this way. AI agents will change software economics.

They will push platforms to become API-first, deeply connected, and capable of supporting autonomous activity at scale. They will challenge seat-based pricing and accelerate the move towards consumption and outcome-based models. They will expose weak products and reward systems that are trusted, extensible, and embedded in real operations.

This is the next phase of SaaS, rather than the end of it.

From replacement to expansion

The assumption behind the “end of SaaS” narrative is that software exists primarily as an interface. If agents can bypass that interface, the application becomes redundant. That logic holds for some categories of software, but it does not apply uniformly. Enterprise software has never been just a presentation layer.

Enterprise platforms derive their value from managing structured data, enforcing permissions, executing workflows, and maintaining the audit trails organizations rely on. Agentic AI does not remove that requirement; in many cases, it sharpens it. What AI changes is not whether these capabilities are needed, but which types of software are most exposed as agents become more capable.

Products whose value is largely defined by navigation, basic interaction, or shallow workflows are more vulnerable when agents can reason directly over APIs and complete tasks without human mediation. More configurable, deeply integrated platforms behave differently.

Solutions that act as systems of record, coordinate workflows across services, apply policy, manage state, and provide security, auditability, and evidencing are not easily displaced. Their role is not simply to present functionality, but to ensure work is executed consistently, safely, and at scale.

In that context, AI agents are not replacing platforms so much as being embedded within them, extending how work is initiated, coordinated, and completed. That direction is already visible in the data.

Gartner forecasts that by 2030, 85% of enterprise agentic AI investments will be bundled into existing SaaS and cloud renewals rather than delivered through net-new contracts, up from 55% in 2025.

SaaS as the execution and control layer

As agents become more capable, they shift where value sits and how platforms compete.

For years, software differentiation has been driven by features and user experience. In an agent-driven world, those differences begin to matter less. Agents can move across systems, access functions directly, and orchestrate tasks programmatically.

What matters instead is which platforms can actually complete work. This means coordinating processes across systems, applying the right type of automation at each step, and ensuring outcomes are reliable, traceable, and compliant.

In this model, SaaS does not disappear. It becomes the execution and control layer for enterprise AI. As this shift plays out, value and margin move away from individual features and towards the platforms that control execution and data access. Agents themselves are unlikely to be a sustainable point of differentiation. As capabilities converge, the focus moves towards control.

That’s because enterprise processes still need to be predictable, auditable, and, in many cases, reversible. Regulatory frameworks are reinforcing this, with increasing expectations around explainability and oversight. This places new importance on the platforms that sit at the center of operations.

Organizations that can orchestrate work across systems, apply automation selectively, and produce a clear evidence trail will capture more value over time. Those that cannot risk becoming passive data stores, rather than active systems of execution.

A multiplier effect on demand

There is another misconception shaping the conversation that AI will reduce the amount of work organizations need to do and, with it, the reliance on software.

In practice, the opposite is happening. As the cost and effort required to execute processes falls, more work becomes viable. More cases are identified, more customer needs are addressed, and more processes are triggered automatically.

We are already seeing this in service environments, where AI-driven detection and automation increase the volume of actionable work entering the system.

In the context of public services, for example, within a case management environment, a missed bin, pothole, or housing repair issue can be identified automatically, raised as a case instantly, and routed into the right workflow without waiting for a citizen or staff member to log it manually. In that model, the value sits not in who created the case, but in how effectively the platform absorbs, processes and resolves it.

This creates a multiplier effect. AI does not just reduce effort; it expands what organizations can do. AI expands demand, and SaaS platforms are where that demand is fulfilled.

The question is no longer how many users a platform supports. It is how much work it can handle, complete, and evidence effectively.

What this means for SaaS platforms

This shift will not benefit every platform equally.

As software moves from presenting information to completing work, value becomes tied to what a system can actually deliver, not just how it is accessed. This will also shape commercial models, with more platforms likely to combine traditional subscriptions with pricing based on consumption, throughput, or completed outcomes.

Organizations still need oversight, approvals, and ways to manage exceptions, but the focus is shifting towards how effectively platforms can absorb, process, and complete work at scale.

That change is already exposing the gap between strong and weak software, placing greater scrutiny on systems that offer limited workflow depth or rely heavily on manual effort. In contrast, platforms that are deeply embedded in operations, with strong data, logic, and execution capabilities, will become more valuable.

As agents become consumers of APIs, software needs to be connected and capable of supporting autonomous activity at scale. Those that are not will struggle to keep up.

The next phase of SaaS

Ultimately, what we are seeing is not the end of SaaS, but an evolution. SaaS is being reshaped, not just as software for human interaction, but as software designed for both humans and machines operating across systems.

A shift from systems that present information to systems that complete work. From user-driven interaction to orchestrated execution, and feature competition to control over how processes run.

This is not theoretical. It is already happening. Low-code platforms and productivity tools are being augmented by AI and agentic workflows, changing how applications are built, adapted, and used day to day.

But this shift also raises the bar for how organizations adopt AI. Those that simply layer agents onto existing processes risk creating more complexity rather than less. Introducing automation without understanding how work really flows can make outcomes harder to predict and harder to evidence, particularly in regulated environments.

The organizations that succeed will start with a real operational problem and introduce AI selectively. Where existing workflows provide sufficient structure and control, value can be delivered quickly without redesign. Where AI exposes friction or inefficiency, that insight can then inform targeted process improvement.

AI agents amplify the platforms beneath them; they do not replace them.

The result is not less software, but different software, running on platforms built to execute work, absorb demand, and stay in control. More automation, more cases handled, more throughput, more outcomes achieved through systems that reliably complete more work at scale.

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• A new update is coming to Meta's smart glasses
• It should stop modders from disabling the light that lets people know you're using the camera
• Meta is also targeting modders on and off its platform

Last month we shared details of reports that Meta glasses were being modded to bypass privacy protections and turn them into secret spy glasses. Now Meta has revealed it will update the glasses’ software to detect whether its light has been tampered with (or destroyed) to prevent recording.

Whenever you take a photo or video with Meta glasses, a white light appears on the front of the glasses to signal to people around you that you’re filming.

For creeps looking to be more secretive with their recordings, this light is a hindrance, but Meta has imposed more basic tamper-proof features since launch. That is, any attempt to use the camera while the light is blocked — such as by a piece of tape — wouldn’t be allowed. The trouble is, modders have found ways to open up the glasses and disable or damage the light and its mechanisms that prevent it from showing, without getting flagged by the system — meaning you can use the camera as you normally would, but without anyone else knowing.

This is (to put it bluntly) not good, and when the reports came out, a Meta spokesperson told me that the company was looking into ways to disable this workaround.

They also explained that Meta is working hard to stamp out advertisements for these kinds of services — some of which appeared on its own Facebook Marketplace platform — with its latest announcement explaining this means banning accounts, taking down listings, and taking legal action against people or businesses that tamper with its tech.

Beyond the detail of updating software to prevent tampering, the whole article from Meta is focused on privacy, and crucially how Meta keeps you and others safe.

Thanks to people misusing its tech, the wider notoriety these gadgets are getting again, and reports of contractors seeing recorded images and videos that Meta glasses users might not have fully realized they could see, Meta and smart glasses makers have been facing major privacy questions.

With this article, Meta seems to answer many of them, though we’ll have to wait and see if it can convince users — or if they might be tempted by the rise of cameraless smart glasses.

The only way is cameraless?

Even Realities, another smart glasses firm, recently announced it has raised $150 million in investment at a $1 billion valuation — not too shabby for a company that only launched its first XR gadget in 2024.

Instead of Meta’s camera-first approach, Even Realities went for a display-first approach. Information is shown visually as green text and basic diagrams to provide features such as AI advice, navigational directions, or a speech appearing on a virtual prompter. Its specs also lack speakers, though that’s not true for all glasses of their kind.

Losing out on the camera is, of course, a major privacy win for some, as there’s no possible way for the glasses to see something they shouldn’t or be used to spy.

The thing is, I think these kinds of glasses are pretty terrible. Having tested a few at home, the ones without a camera just aren’t worth wearing.

Smart glasses, despite being increasingly popular, are in their infancy, which in the tech world means you can expect to pay a fair sum for relatively limited features — that’s the price of being an early adopter. That’s especially true, I’ve found, for these XR glasses specifically, because while they can offer several tools like navigation, on-screen translations, a prompter, and notification pop-ups, their usefulness is pretty limited.

How often do you need a prompter? Or one-way translation tools? In the case of the latter, because these kinds of glasses often have you rely on your phone to set up the translation feature or access other features, you might as well just turn to something like Google Translate — which has conversation modes so that two people can talk and see translations through a single device.

The software I’ve found for these types of specs can also be pretty terrible with sluggishness, inaccuracies, and crashes — and if I, as someone who tests smart glasses professionally, have trouble, I can’t imagine what less techy people must think.

Additionally, the green text can be hard to make out on a bright day if you’re outside, making on-screen directions difficult to see.

While admittedly more limited feature-wise, the Meta glasses and smart specs like them feel like way better value for money. From Meta specifically, the software is generally very reliable, and more broadly, the ability to snap a photo whenever — either to capture a moment or to provide context for an AI’s response — comes in handy so often.

Even if it isn’t as good as my phone camera, the ability to record a memory, hands-free and without being taken out of the moment, is so utterly delightful.

Yes, there are privacy challenges which need to be hashed out more formally, as even without the ability to record privately there is still plenty of room for creeps to harass people with this kind of gadget, but if you want a pair of smart glasses right now there simply isn’t a better option.

“Tokenmaxxing” – the idea that AI coding success comes down to using as many tokens as possible – is an appealing metric.

Tokens are the fundamental unit that AI coding tools use to read, write, and reason. So on the surface, more tokens should mean more output, more productivity, and more impact.

But when we analyzed 12,000 developers across 200 companies, the data revealed that while more tokens do correlate with more output, they come at a significantly higher price per unit.

Some organizations are pushing software engineers to use as many tokens as possible, using leaderboards to promote the biggest AI users. But that’s not a sustainable strategy. CFOs are starting to push back on uncontrolled AI spending and asking coders to show receipts.

Leaders may be willing to spend money to move fast, but they can’t do it without proving their engineering teams are having an impact.

The best approach to “toxenmaxxing” isn’t to blindly push for AI adoption. Instead, the best path forward for companies is to push AI coding adoption more broadly, moving more engineers into the middle of the curve while avoiding both underuse and expensive overconsumption.

Why ‘tokenmaxxing’ doesn’t scale

We found that the top 10% of Claude Code users consumed about 10 times as many AI tokens as the median developer but produced only about twice the output. In other words, increasing token consumption does increase output, but not proportionally.

The research also shows a small but growing group of power users dominating total token consumption. At the 90th percentile, users are burning around 225M tokens per week, about 3x what they were using six months ago, and about 7x the median.

Many engineering leaders are now looking at their highest adopters and trying to figure out how to get the rest of the organization to the same level. That approach is misguided. With the cost per merged PR increasing from $0.28 at the lowest adoption tier to $89.32 at the highest, scaling extreme token usage simply cannot drive value.

Instead, engineering leaders should focus on smoothing the curve. Broad, moderate token consumption is far more cost effective than having a small group of power users at one end of the spectrum and everyone else lagging behind. When most of the organization is operating in the middle of the curve, AI becomes a durable advantage: enough to drive real productivity gains but not so much that engineering teams burn money chasing marginal output.

Maximize impact, not token consumption

The organizations that burn through the most tokens aren't necessarily getting the furthest with AI. When token consumption is high, most are spent on automating manual tasks with tools like Claude, Copilot or Cursor. Developers essentially have a better tool to do the same kind of work as they did before.

To really drive impact with AI, engineering organizations need to move towards new, truly agentic modes of working. However, agentic systems require major investments in IT infrastructure, including context engineering, orchestration, and sandboxed environments. Until organizations address these issues, the productivity gains will remain blocked by an "agentic barrier" that no amount of tokens can overcome.

How established enterprises can follow the AI-native lead

Conversations around AI and software development focus on coding, but writing code is just one part of an engineer's role. Taking a product to market also involves roadmap work, deployment, go-to-market enablement, and more. If engineers are spending tons of tokens on writing code as fast as possible, everything else needs to catch up.

Changing roadmap cadence and accelerating sales enablement requires major cultural shifts that many organizations aren't prepared for. As a result, extra cadences are often poured into the backlog or other things that may deliver value down the line but won't move the revenue needle in the short term. Teams can consume millions of tokens every week but have little to show for it by the end of the quarter.

AI-native companies are more likely to see an immediate return on their AI investments. While established enterprises may not be able to start from scratch, adopting AI-native principles can help remove bottlenecks and turn token spend into measurable business returns faster. By designing workflows with automation in mind, they can continue to accelerate coding without creating technical debt.

"Tokenmaxxing" is having a moment, but engineering leaders need to move beyond token count and start finding ways to prove value. By measuring how AI impacts delivery, quality, and productivity across the software delivery life cycle, leaders can demonstrate ROI and make sure every token counts.

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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.

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• Microsoft's new Windows 11 recovery method is available for Windows Insiders
• Cloud Rebuild allows users to reinstall Windows 11 and necessary drivers via the cloud, without a USB drive
• The feature should begin rolling out to users on stable Windows 11 builds

Microsoft is continually making adjustments to its Windows 11 operating system through patches that address user pain points, and, fortunately, its latest move is certainly welcome.

As reported by Windows Central, Microsoft has implemented a new recovery method for Windows 11, known as Cloud Rebuild, which is available to Windows Insider users. Cloud Rebuild allows users to reinstall the operating system and drivers from the cloud without the need for a USB drive.

It's a major step in the right direction for users who need to reset their PCs, whether due to data corruption, malware, or simply wanting to start anew, especially since not all users have immediate access to another device to download a Windows image or a USB drive to install it on.

Unlike the Windows Recovery Environment's 'Reset this PC' option, Cloud Rebuild doesn't allow you to keep personal files, but Microsoft says Cloud Rebuild reinstalls Windows with the appropriate drivers and "without depending on the integrity of the installed operating system".

Notably, Cloud Rebuild can still work when users can't boot into Windows 11, making life easier in a dire situation where the operating system is dysfunctional.

Perhaps the biggest advantage of Cloud Rebuild is for the acquisition of drivers, saving users plenty of time from manual downloads. This comes in handy for Windows 11 handheld gaming PCs, where reinstalling drivers can be slightly complicated, especially without the necessary peripherals being on hand for quick and easy navigation.

Cloud Rebuild isn't available to all Windows users yet, but the gradual rollout phase shouldn't be too far off. Fortunately, it's not a feature that most users urgently require (at least, I hope), so the wait for its arrival in stable Windows 11 updates shouldn't be frustrating.

How many unique passwords do you use to secure every single one of your hundreds of online accounts? If your answer isn't, "I have a strong unique password for every account," then you're among the millions of people at risk of having multiple accounts stolen from just a single password leak. All it takes is the right combination.

But that doesn't have to be the case. Using a password manager to generate and store your credentials is a secure and convenient way to prevent your accounts from being hacked. The best part is, Keeper has cut prices on its personal, family, and business starter plans by up to 50%.

Keeper's password manager offers secured password storage vaults, a password generator that easily complies with password strength requirements, and includes convenient features for privately sharing regularly used passwords - like the one for the Wi-Fi.

Why we recommend Keeper

Our Keeper review found the password manager to be an excellent addition to anyone wanting to improve their personal or household security.

The service uses zero knowledge architecture and device level encryption to keep the contents of your personal vault hidden from private eyes.

Rather than having to enter your master password each time you need to fill in your credentials, Keeper uses biometric security to access your vault. This uses the built in facial scan or fingerprint tech on your device. Keeper will also recognize when you are trying to log in to a website or app, and allow you to autofill the exact credentials you need.

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

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

MacPaw has spent years building a reputation as one of the most design-conscious developers in the Mac ecosystem. Its flagship product, CleanMyMac, has long included a malware removal module powered by Moonlock's engine. In October 2025, the Kyiv-based company spun that security technology into a standalone product: Moonlock, a full-featured antivirus app that goes well beyond a simple scanner.

Rather than leading with threat counts and detection percentages, Moonlock frames itself as security software that treats users like adults, explaining what malware is, why it matters, and what to do next, instead of firing off opaque alerts. The marketing centers on a 'care, not scare' approach, essentially promising to educate you rather than just bombarding you with red-text alerts.

While many live in the mythical belief that Macs are immune to viruses, MacPaw's own research reports that 66 percent of Mac users encountered at least one cyber threat last year, with a 67% increase in registered macOS backdoor variants in 2025. The research shows a key message: macOS is not immune, are users are being targeted more frequently than ever.

Plans and pricing

Moonlock starts at $54 per year for a single Mac, with licenses available for 2, 5, or more than 10 devices per subscription. Monthly billing and one-time lifetime license options are also available for those who prefer not to commit to an annual cycle.

Discounts of up to 67 percent are advertised on multi-year plans, which is worth exploring if you intend to stick with the product long term.

New users get a seven-day free trial, though a credit card is required to start. That is a common enough practice, but it does mean you will need to remember to cancel if the product does not suit you. To soften the blow of that annual fee, Moonlock offers a 30-day money-back guarantee, which is a considerably more generous safety net than the case-by-case refund process offered by some competitors.

Current Setapp subscribers get access to Moonlock at no additional charge, which may be the most compelling value proposition for those already in MacPaw's subscription ecosystem. At $54 per year for a single device, standalone pricing lands considerably higher than ClamXAV's three-Mac Home plan at $29.95, a gap worth weighing if budget is a primary concern.

Features

Moonlock is organized into six sections: Home, Malware Scanner, VPN, Network Inspector, System Protection, and Security Advisor. That framework reflects a deliberate decision to bundle a security suite rather than deliver a focused antivirus, giving the product a notably broader footprint than Mac-only rivals like ClamXAV.

Real-time protection runs continuously in the background, monitoring file activity, app behavior, and Mail attachments even when the main application window is closed. The Malware Scanner supports on-demand and scheduled scans, with built-in quarantine and removal tools. Detected threats are accompanied by plain-language explanations rather than raw file paths and specialized terms.

The bundled VPN is a simplified version of MacPaw's ClearVPN, covering around 60 server locations across more than 45 countries. Independent testing found no DNS or WebRTC leaks, and MacPaw maintains a no-logging policy. Speed retention is strong, holding around 82 percent of baseline download speeds on transatlantic connections and up to 96 percent on closer servers.

Network Inspector adds a country-level connection blocker, permitting users to block outbound traffic to specific regions. System Protection audits macOS's own built-in security settings and walks you through any gaps. Finally, Security Advisor provides a checklist for basic digital hygiene, including practical guidance on habits such as two-factor authentication and app permissions. AI assists with malware classification on the backend, helping the team update threat databases before new strains reach your device.

Privacy and Security

From a top level perspective, Moonlock was tested by the third-party laboratory AV-Test in September 2025 and earned it's AV-Test certification. It scored a 5.5/6 in Protection, 4.5/6 in Performance, and a full 6/6 for Usability (which I'll dive into in the next section).

As for the credibility of the underlying research arm, Moonlock Lab has made several notable contributions to the antivirus landscape, being the first to identify PyStealer on VirusTotal, and the lab has also been cited by the SANS Institute for discovering new variants of the Atomic macOS infostealer.

Regarding privacy, the VPN operates under a strict zero-logs policy, and all data is processed locally. MacPaw publishes a Trust Center at security.macpaw.com describing its data-handling practices, certifications, and security standards, which is a nice change in transparency from many other antivirus providers.

The one caveat worth noting is that Moonlock is a recent standalone launch. While the underlying engine has been in use in CleanMyMac for some time, the app itself has a limited history as an independently tested product. But it is worth noting that in the time since the last time AV-Test handled Moonlock, MacPaw have likely taken steps to improve protection and performance.

Interface and in use

The interface is highly polished, modern, and immediately legible, with a two-panel home dashboard that separates tasks on the left from status information on the right. Everything is where you would expect it to be, and the visual hierarchy makes it easy to tell at a glance whether your Mac is protected.

Instead of a generic 'Threat Resolved' notification, Moonlock tells you what was found, why it poses a risk, and what your options are. I found I was the one to make the final call on whether to remove a flagged item, which sidesteps the infuriating experience of automated deletion that occasionally catches legitimate software.

The system requirements make it suitable for older devices too, requiring macOS 13 or later and 515MB of disk space. The app runs quickly and, in day-to-day use, does not noticeably drag on performance. Installation requires a MacPaw account, which adds a step that competitors like ClamXAV skip entirely for home users, but the tradeoff is a unified login for managing licenses and accessing support.

Ultimately, Moonlock is a great option for those looking for an accessible and easily navigable Mac antivirus that doesn't bombard you with any overly-technical language, and performs as though you are the one in control.

Support

Moonlock support runs on MacPaw's established infrastructure, with a dedicated knowledge base that covers installation, configuration, and troubleshooting, and those with questions can submit immediate inquiries through the support portal. In-app feedback is also available via the Help menu.

As with many Mac-focused security products, live chat or phone support does not appear to be offered as a standard option. For most home users, the knowledge base and email channel will be sufficient. Teams with more complicated environments should verify support response times before committing, particularly given that Moonlock is a relatively new standalone product and the support documentation is still maturing.

The competition

ClamXAV is the most direct rival in the Mac-exclusive antivirus space. At $29.95 per year for three devices, it is considerably cheaper than Moonlock's $54 single-device starting price, and it also holds a perfect AV-Test score compared to Moonlock's test results. It does not include a VPN, network inspection, or the polished onboarding experience Moonlock offers, but for those who want focused antivirus protection at a lower cost, it is a strong option.

Intego Mac Internet Security X9 sits at a comparable price point and includes a network monitor, with a longer track record in independent third-party testing. Bitdefender Total Security and Norton AntiVirus Plus both offer wider platform coverage and larger feature sets, making them better fits for households with mixed Windows and Mac devices.

Those who are already subscribed to CleanMyMac should also note that its built-in malware-scanning module, powered by the same Moonlock engine, continues to function independently. Therefore the question is whether the full Moonlock standalone app adds enough to justify an additional subscription or an upgrade in spending.

Final verdict

Moonlock is one of the most carefully designed security apps I've encountered in the Mac ecosystem. Its interface is excellent, its feature set is broader than most Mac-specific alternatives, and the research team behind it is doing genuinely credible original work. The 30-day money-back guarantee is also a nice addition, despite the need to enter your payment details first.

At $54 per year for a single Mac, it costs nearly twice as much as ClamXAV's three-device plan. The added value of the bundled VPN and Network Inspector goes some way toward justifying that gap, but those who already have a VPN solution elsewhere may not find the extras compelling enough. Setapp subscribers, on the other hand, get all of this for free as part of a subscription they likely already value.

For long-standing CleanMyMac users who already benefit from the embedded Moonlock engine, the standalone app offers greater depth, visibility, and control, but it's not a replacement for anything missing. It is a fuller version of the protection they have already been relying on, now with a VPN, richer reporting, and a proper home for the security features that were previously contained within a Mac cleaning utility.

For Mac users who want a single subscription that covers antivirus, VPN, network monitoring, and system security guidance, Moonlock makes a strong argument. Just go in aware of what you are paying for relative to the alternatives.

You might also be interested in our report on the best Mac apps of the year.

Walk deep into Walt Disney Imagineering’s sprawling, surprisingly unassuming Glendale campus, and you’ll eventually find Studio C.

It’s where Imagineering mixes the audio for its attractions, but when I recently stepped inside, I quickly realized it was much more than a recording studio. The room is lined with dozens upon dozens of speakers designed to recreate the sound field of Disney attractions, letting Imagineers hear a ride long before guests ever do.

During my visit, I watched a live mix session for Zootopia: Hot Pursuit, Shanghai Disneyland’s trackless dark ride, and the experience was almost uncanny. Sound moved seamlessly around the room, tracking the ride vehicle as though I were actually inside the attraction.

That same room became the proving ground for one of Imagineering’s most unusual engineering challenges yet: figuring out how to mix the audio for Soarin’ Across America.

The reimagined version of Disney’s iconic flight simulator debuted this summer at both EPCOT at Walt Disney World in Orlando, Florida, and Disney California Adventure in Anaheim, California, timed for America’s Semiquincentennial celebration. Disney’s Unscripted behind-the-scenes video confirmed that Imagineers used the Apple Vision Pro during production of Soarin’ Across America, but it left one obvious question unanswered: why? That’s what I wanted to find out.

So I spoke with Greg Lhotka, Sr. Manager, Audio Media Design at Walt Disney Imagineering, who walked me through the surprisingly complicated process behind mixing one of Disney’s most technically demanding attractions.

“For Soarin’ Across America, we utilized Studio C not only as a production facility, but as a proving ground for an entirely new field-mixing approach developed specifically for this film. On the sound design side, we partnered with Skywalker Sound, and together we used Studio C to prototype and validate a custom speaker configuration that closely mirrors the unique audio architecture of the Soarin’ theaters.”

If you’ve ridden Soarin’, you already know why that matters. Riders are lifted into the air in three stacked rows facing a massive curved projection screen, while sound moves not only around the theater but above and below you as well. As Lhotka explained, the attraction uses a four-tiered speaker system — upper, mid-level, lower, and a dedicated floor array — creating an audio environment that’s far more complex than a traditional movie theater.

Having now ridden Soarin’ Across America several times, it’s easy to appreciate the result. The updated film sweeps from New York Harbor and the Grand Canyon to California’s rolling hills and countless landscapes in between through soaring aerial cinematography, practical effects, and a soundtrack that glides almost effortlessly alongside you. The audio shifts just as fluidly as the visuals, making each transition feel natural as the ride carries you across some of America’s most recognizable landmarks and scenic vistas.

Studio C could simulate much of that during production, but not all of it.

“For Soarin’ films, mixing historically required the construction of scaffolding inside the theater to reach the appropriate listening height for final mix decisions,” Lhotka explained. “Even then, the process was far from ideal, as the ride canopy significantly influences the acoustics. For this project, one of the key requirements was that the theater remain fully operational and open to guests throughout production — eliminating the possibility of scaffolding altogether.”

That constraint forced Imagineering to rethink its entire workflow.

“The challenge was clear: how do you mix a show with over 1,000 audio tracks and an extremely complex panning matrix while seated in a ride vehicle? The solution came through an innovative use of new AR technologies. By leveraging advanced screen-sharing capabilities, we were able to remotely control our production system — directly connected to the theater — while seated in the ride vehicle itself. This allowed us to place virtual screens in our field of view, effectively bringing the mixing console into the theater environment.”

Before anyone tested it inside the attraction, Studio C became the test lab.

“Studio C once again played a critical role as our testbed. Over the course of a week, we evaluated multiple configurations to confirm that things could be controlled precisely in this way, and that the AR headset’s external cameras would allow us to view the film clearly in the theater’s low-light conditions while simultaneously monitoring the virtual screens. The final configuration exceeded expectations.”

Once validated, the workflow moved from experiment to production.

“This workflow was ultimately used as the solution — and it became the method by which Soarin’ Across America was mixed. The result is a soundtrack that was shaped directly from the guest’s perspective, using tools and techniques that reflect our continued commitment to innovation, immersion, and storytelling.”

Having experienced Studio C for myself, that revelation immediately clicked. The room is already designed to recreate the feeling of sitting inside a Disney attraction with remarkable accuracy. But even a purpose-built facility couldn’t perfectly replicate Soarin’s unique acoustics. The only place left to finish the mix was inside the attraction itself.

It’s an elegant solution that solved two problems at once. Guests never lost access to Soarin’ during production, and Imagineering’s audio team was able to shape the soundtrack from the exact seat every future rider would occupy. Rather than approximating the experience from scaffolding or even Studio C, the final mix was crafted where it mattered most: inside the attraction itself.