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

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

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

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

1. Faster app launches

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

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

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

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

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

2. Voice Access improved to filter out background noise

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

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

3. Windows search improvements

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

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

What else is new?

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

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

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

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

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

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

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

Speed, not novelty

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

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

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

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

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

The distraction problem

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

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

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

The right response

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

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

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

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

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

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

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

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

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

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

What matters now

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

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

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

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

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

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

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

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

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

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

Analysis: Word up

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The middle tier compresses

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

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

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

Responsibility, therefore, spreads in two directions:

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

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

The CTO’s verification machine

Verification is now the central technical challenge.

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

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

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

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

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

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

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

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

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

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

Coding is abundant; judgment is not

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

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

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

Technical strategy gains value as implementation capacity grows.

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

AI commoditizes implementation and raises the value of technical judgment.

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

Privacy, trust, and security

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

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

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

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

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

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

Users judge the product

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

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

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

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

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

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

CTOs as product leaders

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

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

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

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

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

From organization builder to machine builder

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

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

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

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

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

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

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

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

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

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

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

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

Why the risk is already here

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

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

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

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

Discovery before deployment

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

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

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

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

Building a practical roadmap

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

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

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

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

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

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

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

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

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

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

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

Performance limits are increasingly defined by data movement

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

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

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

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

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

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

On-chip data movement is fundamentally different

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

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

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

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

AI, heterogeneity, and physical intelligence magnify the challenge

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

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

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

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

Chiplets turn data movement into a system-level design problem

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

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

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

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

Deliberate data movement is a strategic differentiator

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

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

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

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

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

The new battleground

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

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

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

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

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

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

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

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

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

Analysis: software salt

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

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

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

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

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

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

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

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

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

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

As Windows Latest observes, this is because this app is actually the MSN Weather app, with the adverts being pulled over from the MSN network.

Analysis: a shower of adverts

Granted, the app provides a lot of weather information, and it's pretty comprehensive — but at what cost? What should be a lightweight and streamlined app consumes an improbable amount of RAM. Why is that? Well, it's due to Weather being a web app, one that, when you peek behind the hood, is running eight separate Chromium processes.

That's a clunky way of working, and the high level of resource usage this causes is particularly unwelcome in the current climate of the RAM crisis, where 8GB laptops are being brought back to try and keep costs down as manufacturers struggle to keep a lid on prices.

Okay, so Microsoft is looking at performance improvements across the board for Windows 11, and it's specifically aiming to address the issue of the memory footprint for 8GB PCs. And hopefully the Weather app will get some attention as part of that streamlining, and sooner rather than later.

For now, though, even if you don't use Weather, it remains a clear reminder of what's wrong with some parts of Windows 11 in terms of unnecessary bloat, both in the way the app is programmed, and the addition of those adverts (which doubtless don't help performance, either). These ads are present in Windows 10's Weather app, too, in case you were wondering, and it's surely a simple enough job — and an easy win — for Microsoft to at least ditch these.

As you might imagine, there have been a fair few potshots fired at Microsoft over this on social media, and one Redditor echoes something I've said myself in the past: "It's honestly ridiculous that we pay for a premium operating system and still get served ads inside basic native apps like Weather."

This is a key point for me. You can either have a free operating system that pays for itself via promotional bits and pieces — you can't really argue with that if you've forked out nothing for the privilege of running an OS — or you have a paid one that has no promotions.

Microsoft has promised to chill on the upselling in Windows 11, and it looks like the Weather app would be a good place to revisit with that in mind.

• Researchers tested AI-generated patches on six CVEs with poor success rates
• Many fixes failed, altered behavior, or introduced new vulnerabilities
• Guidance improved outcomes, leading to FLAWED evaluation harness release

When using Generative Artificial Intelligence (GenAI) to fix vulnerabilities, security professionals are most of the time just robbing Peter to pay Paul, experts have warned.

Researchers from 1Passwords Off-by-1 Labs analyzed fixes proposed by two frontier models - ChatGPT 5.5 at “medium” effort, and Claude Opus 4.8 at “high” effort.

As an experiment, the researchers took six recently disclosed CVEs and produced 6,080 patches using two frontier, cyber-capable reasoning models. The results were underwhelming to say the least - of all the proposed patches, just a quarter (26%) fully resolved the issue.

FLAWED work?

This obviously leaves plenty to be desired, as half (49.3%) of the patches failed to fix at least one existing exploit path. A fifth (20.1%) fixed the original issue but changed application behavior, while 2.3% introduced new security issues. Funny enough, 2.2% failed to fix the vulnerability while also introducing additional exploit paths, as well.

Even among the patches that might be considered (26% of clean ones and 20.1% of those that changed app behavior), more than a third were fragile and not entirely addressing the underlying problem.

The researchers created an acronym for automated LLM patches: FLAWED (Fix-Like Artifacts With Embedded Defects), and warned against letting AI work without human oversight: "The expected value of a fully LLM-generated, non-human-reviewed patch is a net-negative by a considerable margin."

Results drastically improved when the AI was given better context, the researchers further explained. Before working on any patch, human developers are usually given initial guidance. When AI is given proper guidance, its success rate rises to 65%. Incorrect guidance, on the other hand, drops the success rate down to 15.2%. The difference between humans and AI is that humans are better at catching misleading information and poor guidance.

This doesn’t mean developers will, or should, abandon AI. Worst case scenario is that developers will spend more time reviewing AI-generated fixes which could increase cognitive load and still end up being net negative. Therefore, the researchers released a patch evaluation harness called FLAWED, which organizations can now use to determine the effectiveness of their AI-generated fixes.

Via The Register

• Microsoft has admitted it's automatically installing an app on Windows 11
• The OneDrive Photos app is also being deployed mistakenly to business PCs as well as consumers
• There are a few odd things about this episode, and it's an anti-consumer move that doesn't sit well with Microsoft's broader goals to make Windows 11 better

Microsoft is in hot water after an odd decision to roll out a OneDrive Photos app to Windows 11 PCs used by consumers (with it also accidentally deploying the app to enterprise systems).

Windows Latest reports that Microsoft "effectively" told the website that the OneDrive Photos app is being automatically installed on Windows 11 computers whether it's wanted or not.

OneDrive Photos is a separate app to OneDrive, and it's designed for photo viewing and organization (with a light editing element bolted on). However, it's plain user-hostile behavior to have this software stealthily piped to a Windows 11 PC and forcibly installed.

What if you don't want it? What if you don't even use OneDrive sync at all, and you aren't signed in with a Microsoft account on your PC? Tough — you still get OneDrive Photos, as Windows Latest points out.

Even more bizarrely, this app — which is labelled as a beta — is appearing on business PCs, which it definitely shouldn't be doing (it's only used with a personal Microsoft account).

Microsoft wouldn't previously comment on the appearance of the OneDrive Photos app, but at least the company has now acknowledged that it is being installed on Windows 11 PCs.

There's still no official comment on why business devices would be getting this software, but Windows Latest tells us that it "understands that the rollout to enterprise PCs was not intentional". So apparently this was a mistake, and one that Microsoft is presumably in the process of correcting.

It's worth noting that there's also been some controversy about AI scanning faces for a feature in OneDrive Photos, accessed via the 'People' option, but this is only active if you explicitly allow it to be turned on (it's clearly opt-in, in other words).

Microsoft has clarified that if 'face grouping' is enabled, it runs only in the cloud with OneDrive photos (not other images on your PC), and any data collected is removed 30 days after you turn it off. The company has also assured users that it isn't using any photo data to train AI models on faces.

Analysis: I'm trying to tell you now, it's sabotage…

What is Microsoft playing at here? I really don't know, but this OneDrive Photos affair is a mess for a lot of reasons, and as you might guess, it has rubbed people up the wrong way. For starters, business users are very unhappy about having a piece of consumer-targeted beta software automatically deployed on their PCs, and are busy writing scripts to remove it (like this one on Reddit for example), rather than waiting for Microsoft to fix its error.

This is a baffling move for consumers too, because as noted, even if you don't use OneDrive at all, you'll still get this partner photo app. You can't even remove the OneDrive Photos app without deleting OneDrive as well — so if you do use the cloud storage app and sync features in Windows 11, but don't want this extra Photos application, well, you're stuck with it. You can have both, or neither.

That latter situation will change, though, Windows Latest informs us, and Microsoft is preparing an update that lets you remove the OneDrive Photos app without needing to nuke OneDrive entirely. I should hope so, too, and it's confusing how this rollout of a beta app went ahead without that choice in place.

The broad reaction on Reddit has been heated to say the least, and this is just more grit for anyone with an anti-Microsoft axe to grind.

One Redditor said "I'm uninstalling Windows. Linux is a little bit of a pain but worth it." There are more than a few similar comments in that thread, whether they're from people vowing to switch to Linux, or those who've already departed for OS pastures greener: "So glad I left Windows behind. Good riddance."

Interestingly, there are reports of this app appearing on Windows 10 PCs in the Reddit thread those comments are drawn from, although I haven't seen it on my computer with that OS (while it is on my Windows 11 system).

The trouble with all this is that it underlines how Microsoft is a vast corporation where the various limbs aren't always moving in alignment with the same aims. While the right hand is fixing Windows 11 and implementing a whole bunch of crowd-pleasing features or tweaks, the left hand is still messing around with anti-consumer initiatives such as this. Did no one tell the OneDrive team that Microsoft is supposed to be going full-tilt to persuade everyone that Windows 11 is the place to be, and that the software giant has mended its pushier and more obnoxious ways?

Apparently not, and just as we don't want third-party apps for the likes of monitors pushed on us quietly, in the background, via Windows Update, we don't want to unknowingly get first-party Microsoft software sprung on us, either.

Sort it out, Microsoft, please, because this is becoming an embarrassing oversight in terms of the big (and otherwise commendable) drive to make Windows 11 better.

Recently in Geneva, the world's largest forum on AI for Good, gathered around a simple question: how can technology best serve society? While the sentiment goes back to pre-industrial times, the conversation has firmly moved to algorithms. What about the machines that will come next?

Quantum technology has crossed a line. It is no longer a distant promise, but a growing portfolio of real applications. Quantum and quantum-inspired solutions are being applied to the simulations that underpin nuclear power, to protein design in drug discovery, and traffic forecasting in congested cities.

Quantum sensors are being tested for medical imaging, navigation where satellite signals fail, and monitoring carbon storage sites deep underground. These are no longer mere laboratory curiosities: they are increasingly emerging as working programs, with industrial partners and delivery dates.

For those of us in the industry, these developments are both timely and long anticipated. But they also force us to confront a less comfortable reality: the same machines that will one day simulate new molecules will also break the public-key cryptography that secures much of the modern internet.

By some estimates, up to 80% of the world's digital infrastructure is exposed. The world's digital economy - not to mention every AI model celebrated in Geneva, runs on encrypted data. Every health record or financial transaction owes its confidentiality to mathematical assumptions that a sufficiently powerful quantum computer will overturn.

Store now, decrypt later

The threat timeline is worse than most realize. Rather than waiting for when the machine arrives, the clock started the moment adversaries began harvesting encrypted traffic: the so-called “store now, decrypt later” approach.

Data that remains valuable for years, from medical records to state secrets, may already be beyond rescue. Nobody knows exactly when “Q-Day” comes. But those who harvest our data today have time on their side.

This is why quantum-safe security cannot be a footnote to quantum computing. It is the other half of the field, and it needs to mature on the same schedule.

Quantum trip-wire

Two defenses exist, and they are not interchangeable. Post-quantum cryptography replaces vulnerable algorithms with mathematics believed to resist quantum attacks; it is software, it scales, and it is the workhorse of the migration now beginning worldwide.

Quantum key distribution takes a different route: it distributes encryption keys using quantum states of light that cannot be intercepted without being disturbed. In this way, these schemes deploy a “quantum trip-wire", an eavesdropper reveals herself by the simple act of listening-in

Far from being in competition, both algorithmic and physics-based solutions have roles to play in tomorrow's networks. Serious infrastructure will layer both.

Neither of these solutions are theoretical. New post-quantum standards have been finalized, with contributions from research teams around the world. And quantum-secured networks are already running in the field: since 2022, Abu Dhabi has hosted entanglement-based metropolitan networks, like the ADGM Quantum Testbed announced in August 2025.

These are initiatives that bring quantum security to where data actually lives. Long-range terrestrial links, satellite connections and “last-mile” access networks are the next steps, extending that protection between cities and, eventually, across continents. If there's one lesson the field has learned from operating these systems, it's this: migration takes years, so the time to start is before the threat matures, not after.

Depth of capability

There is a broader lesson in how this capability is being built. A growing number of nations have chosen to be builders of quantum technology rather than buyers, developing the full portfolio of sensing, communications and computing solutions. From processor fabrication to control software, to the hardware enabling quantum networks: the word “sovereignty” is often heard as a synonym for walls.

But our experience suggests otherwise. It is precisely the teams that build every layer themselves that can contribute most readily to the global commons, from working with international standards organizations like ITU and ETSI on quantum-safe networking, to releasing open-source middleware that researchers worldwide now use to program quantum hardware, wherever it was made.

It is this depth of capability that turns a nation from a spectator of the quantum era into an active participant.

That should also be the model for the decade ahead. No single country will own quantum technology, and no single vendor should be the sole authority on quantum-era trust. What the field needs now is what the AI community showcased in Geneva: open tools that lower the barrier to entry, interoperable standards so quantum-safe systems can talk to each other, and honest engagement with the risks alongside the promise.

For years, the defining question about quantum computing has been when the machines will arrive. Increasingly, I think that's the wrong question. The more important question is whether the digital world they inherit will still deserve their trust. The answer depends on what we choose to secure, build, and share today.

Quantum for good does not start with a breakthrough. It starts with trust.

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Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.

As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.

AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.

As a result, many enterprises are beginning infrastructure planning sooner rather than later.

The cost of waiting

As AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.

Modern AI deployments increasingly require:

• Continuous inference running around the clock
• Multi-agent systems coordinating across applications and databases
• Real-time orchestration across cloud, data center, and edge environments
• Strong governance, security, and operational efficiency

These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.

Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.

At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.

As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.

AI is now a systems challenge

The conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.

Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.

As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.

This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.

Planning for distributed AI

AI is also scaling in multiple directions at once.

Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.

For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.

This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.

Openness and flexibility matter more than ever

As AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.

Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.

For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.

This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.

Infrastructure readiness will define the next phase of AI

The next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.

Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.

The cost of waiting is becoming ever clearer.

Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.

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• Microsoft is eating humble pie over its unrealistic RAM recommendations
• It has deleted articles that pushed 32GB as an ideal or 'no-worries' loadout
• Apple is also feeling the heat in the RAM crisis, with rumors that it's struggling to secure an alternative source of memory supply from China

There are some fresh twists with the RAM crisis hitting some big tech companies, as Microsoft has backtracked on its previous memory recommendations, and even Apple is apparently finding it difficult to cope with the scarcity of memory.

Let's discuss Microsoft first, and as Windows Latest pointed out, the company has been busy backpedalling on previous memory recommendations now that the RAM crisis – which just keeps getting worse – has made those suggestions look foolish.

Microsoft previously had support documents in its Windows Learning Center which have now been removed, and Windows Latest highlights two of them. One was about optimizing your gaming PC, and it advised that "32GB is ideal for serious players who run the most demanding titles" (albeit the article also said 16GB was "plenty" for most games).

Another piece said that 32GB of RAM was the "no-worries zone", and that article was also quietly deleted as there was some backlash against this, given that it was published when the price of system memory had become ridiculous. (And buying a 32GB kit was very much a worry for your wallet).

The links to those articles now redirect to the home page of the Learning Center, and Microsoft is evidently trying to forget about pushing 32GB of RAM as an 'ideal' or 'worry-free' target for memory on your PC.

More broadly, since Copilot+ PCs were launched and the AI features for these devices made them require 16GB, Microsoft has obviously been keen to have that as a baseline memory configuration. Except now, a change of stance is necessary, as with the RAM crisis reaching alarming new heights, Microsoft has been forced to enact huge price hikes with its Surface devices.

And of course, the latest twist with that Surface hardware is that Microsoft has brought back 8GB models with last year's Surface Pro and Surface Laptop. Which makes it kind of difficult to push 16GB as a minimum, let alone make suggestions that 32GB is where it's really at for properly smooth performance.

The abandonment of these Learning Center articles is hardly surprising, then, and Microsoft is also addressing how speedily Windows 11 runs with 8GB of memory (not quickly enough currently). One of its promises with fixing the OS was better performance with a leaner RAM loadout, and Microsoft just made it clear that the company is now actively working to make Windows 11 run better with 8GB before the end of the year.

This has become a vital goal, really, when you consider that Apple has pulled off a commendable showing of performance with its MacBook Neo that packs 8GB of RAM. That was effectively a gauntlet thrown down for Microsoft – something of a declaration that macOS is coming to try to take Windows 11's market share – and one that the Windows maker had to respond to (which became clear enough when Microsoft went on the attack against the Neo).

Apple turnover: China play rumored to end in a fumble

Speaking of Apple, Microsoft isn't the only tech giant being buffeted by the rising costs caused by the RAM storm. While it has had a big success with the Neo, the challenge for Tim Cook's firm – soon to be John Ternus's, of course – is to maintain that momentum, and by all accounts, that's proving a tricky task.

Recent Mac price hikes have caused a good deal of pain – taking some of the wind out of the good ship Neo's sails – and Apple is trying to secure its RAM supply lines for the future, with rumors abounding that it's turning to Chinese chip makers to find extra production capacity.

The latest speculation, however, is that according to a report from Digital Daily (a Korean tech site, via Wccftech), Apple has floundered in negotiations with Chinese memory giant CXMT.

Apparently CXMT has strong enough domestic demand that it doesn't have to offer more attractive pricing to Apple. Essentially, CXMT is holding the line and has "insisted on prices that were higher or similar to those offered by Samsung or SK Hynix" (bear in mind there may be nuances lost with the translation of the article).

You get the message, though: Apple is failing to obtain better deals on mobile DRAM, which includes LPDDR5X, for its iPhones (and that RAM is also used in its MacBooks, of course). And CXMT was supposed to be an escape route from the difficulties of getting enough RAM inventory from Samsung and SK Hynix (and also Micron, a key supplier for Apple), but it seems like a dead-end for now.

At least if this report is correct, but analyst Tim Culpan has also written a short post which backs up the notion that Apple is in trouble here. Culpan writes: "Apple and its suppliers are scrambling to get enough memory chips for its upcoming release of new iPhone models."

'Scrambling' is a word that evokes quite a sense of panic, and indeed with the iPhone 18 models – and the foldable offering – not much more than a month away from launch now (in theory), I'd bet there are some heated words flying here and there. Culpan notes: "Assemblers are working with Apple to rush shipments of DRAM used in mobile devices."

That report is specifically about smartphones, mind, but this same situation applies to mobile RAM that's also used in MacBooks.

The overall theme is more RAM misery all round, which is hardly a surprise given all the negative news we've been hearing on the grapevine of late. GPU pricing has been the latest round of doom and gloom over the past week or two, and I don't think the pessimistic news is going to stop flowing for the foreseeable.

It's not bad news for everyone, though. A source in the semiconductor industry told Digital Daily that: "With the general-purpose DRAM floor remaining unbroken and Samsung and SK Hynix monopolizing the lead in high-value AI memory such as HBM4, the operating profit margins and global market control of the domestic semiconductor sector are expected to rise even more steeply in the second half of the year."

So, it's a familiar story: profits will be on the up and up for memory makers, while consumers will be suffering the pain of the hikes. Apple's purported rush for RAM supplies as the clock runs down on the iPhone launch window is a particularly worrying sounding story, one that doesn't bode well in terms of avoiding more Mac (and iPhone) price rises in the future. Neither can we rule out more Surface price rises, or other laptops for that matter.

• Smart glasses bans are being introduced and considered
• There are growing concerns over smart glasses privacy
• Could hardware changes solve people's concerns?

I’ve written a lot about the high likelihood of major smart glasses regulation being introduced in various regions as the technology garners more and more bad press, and as expected, we’re starting to see bans and other restrictions take shape.

No nation has yet to all-out ban smart glasses, but a few have outlawed their use in certain scenarios while others are investigating harsher measures. Some private companies, such as event organizers, have also banned smart glasses on their premises and at their conferences.

Of course, this is bad news for smart specs owners who now could be facing restrictions on where they can/can’t wear their intelligent eyewear. For anyone considering adding smart glasses to their technology arsenal, these possible restrictions should be taken into account when making your final purchase decision.

So where are we starting to see smart glasses bans? Here’s a selection of the countries investigating and introducing anti-intelligent eyewear rules.

United States

Generally, smart glasses can be worn freely in the US; however, New York State has banned them from being used in any of its courts. Smart glasses with recording capabilities could be seen as an attempt to intimidate those involved in the case, and some cases contain sensitive private information where unauthorized recordings could impose serious risks.

Other states have introduced less widespread bans in courts — including Philadelphia, Hawaii, and North Carolina.

Meanwhile, the US Air Force includes in its rules for service members that they are “Unauthorized to wear mirrored lenses or smart glasses with photo, video or artificial intelligence capabilities while in uniform.” This seems like more of a presentation thing than an anti-smart specs rule (especially given the inclusion of mirrored specs), but it does mean you can’t wear intelligent eyewear in uniform regardless.

Further, attendees of conventions such as major hacker conference DEF CON 34 have been barred from wearing Meta glasses or similar. These bans focus on the possibility of glasses recording folks without their knowledge — with organizers arguing the recording light some glasses offer isn’t the most obvious.

UK

In the UK, things are much the same. Courts don’t ban their use, though after one person was being coached by their glasses’ AI mid-trial, their usage is generally advised against. There are also recording restrictions in UK courts, so while glasses aren’t explicitly banned, using them to snap photos or videos would be against the rules — the same as it would be for using your phone or a regular camera.

We have seen companies like Monopoly Events ban camera glasses from its Comic-Cons after secret filming complaints, and there’s a petition to the Government to ban the sale and promotion of smart specs in the country. These petitions don’t always go anywhere, but it shows there’s at least some public unrest over smart specs, and that could lead to rule changes.

EU

The European Data Protection Board is currently drafting a report on smart glasses, with it expected to land sometime in the next few months. However, while more general EU rules are being discussed, some individual members are making their own individual decisions on the tech.

Germany’s data protection authority commissioner has warned camera glasses could be banned under existing laws prohibiting recording devices hidden in everyday objects.

Netherlands digital rights activists have expressed frustration at smart glasses' ability to record people without their knowledge, while France’s privacy regulator, the National Commission on Informatics and Liberty, has highlighted the privacy risks camera glasses pose.

South Korea

Rounding things off, we have a different kind of ban in South Korea, with the country specifically restricting smart glasses access in exam halls after cases of students being caught trying to cheat using intelligent eyewear.

Smart devices are generally banned from exam rooms in most schools across the globe to prevent cheating, though given the importance of South Korea’s College Scholastic Ability Test, their very explicit ban makes sense so as to remove any possible confusion over whether students can even bring smart specs into the room with them.

Could things change?

Of course, very few bans have been brought in yet, but they could be unless things change on the hardware side.

A big concern is over privacy for non-glasses wearers caught in the gaze of someone's camera — especially if they don't know they’re being recorded. An obvious fix here would be to make smart glasses much more obvious when they're recording — perhaps brighter camera lights, a loud audible camera sound when snapping a photo, some kind of audio indicator when video is being recorded.

For users, they’ll want better control over what is and isn’t accessed by smart glasses makers when using their tech, and more certainty about which images are and aren’t shared, or which videos can or can’t be seen by contractors.

We’ll have to wait and see how the situation develops, though I’m not sure how much longer smart specs can go before we see serious regulations.

Prompt engineering was briefly the face of the AI jobs boom.

In 2025, technology recruitment firm SPG Resourcing reported that UK job listings for AI prompt engineers had grown by 180% over the previous year.

It was an eye-catching figure that captured the mood of the first commercial wave of generative AI. Businesses were trying to understand how to talk to LLMs and turn early experiments into something useful.

That requirement has not disappeared.

With job site postings for specialist AI roles in the UK rising by 61% from last year according to PWC, it’s clear that good prompts still matter. But most of that new demand is for people who can apply AI inside a business, not just talk to a model.

Many organizations have moved past the first demo. They are now trying to build AI agents, retrieval-augmented generation (RAG) systems, and AI-enabled workflows that operate inside the business.

That creates a different skills gap.

This is the shift behind what I refer to as ‘context engineering’. Although that term is not universally used, the capability is becoming essential.

Companies that want useful AI agents and retrieval-augmented generation systems need people who can design the environment around the model, not just the prompt sent to it.

From better prompts to better context

Prompt engineering is about the instruction, while context engineering is about the world around that instruction.

A support agent does not only need a well-written prompt, it needs the right customer record, policy, product history, and permission boundary. Similarly, a developer agent needs the relevant code, tests, dependencies, and deployment constraints.

In both cases, output quality depends on context. Without it, the model is guessing from incomplete evidence. With too much of it, the system becomes noisy and difficult to govern. The job is to make context useful, current, and controlled.

That’s what makes context engineering distinct from prompt engineering.

Agents raise the stakes

The rise of AI agents makes this more urgent. A chatbot with poor context may give a weak answer, but that same poor context may result in another agent making a serious error.

Once an AI system can call tools, query business systems, maintain state, and act across several steps, context becomes an essential part of the production architecture. It decides what the agent can see, what it can do, and how much confidence the business can place in the outcome.

I’m reminded of a joke about boundary testing. A developer walks into a bar and orders a beer, then he orders five beers, then he orders 999,999,999,999 beers, then he orders -1 beer. The bartender blinks, but everything is okay. A user walks into the bar, asks where the bathroom is and the whole bar explodes.

The same principle applies to AI projects. The first prototype may work against a narrow set of examples, then become fragile when it meets real data. Customer information sits in one system, operational data in another, and important knowledge in documents and files. The agent is expected to reason across all of it, but the context layer has not been designed for that job.

A financial services team, for example, may need to connect CRM data, an existing data platform, and internal documents before an agent can answer accurately. The hard work is not only moving the data. It is shaping it so the agent can retrieve the right evidence and stay inside the right permission boundary.

The job title is still catching up

This creates an awkward hiring moment. The need for context engineering is becoming clearer, but the job title is still unsettled.

Some organizations may seek to specifically hire ‘context engineers’, but many will not. The capability is more likely to appear inside roles such as AI engineer, agent engineer, AI platform engineer, applied AI engineer, or data engineer. In other businesses, it will be a team responsibility shared across data, platform, security, and software engineering.

Leaders therefore need to hire for the work, not the label. A candidate does not need to have ‘context engineer’ on their CV to be useful. The better signal is whether they understand how data moves through systems, how permissions are enforced, and how a prototype becomes something reliable enough for production.

This also means the talent pool is wider than many companies assume. Machine learning expertise is valuable, but context engineering draws heavily on existing engineering disciplines. Data engineers understand pipelines and retrieval.

Platform engineers understand operational resilience. Security teams understand access control and auditability. Software engineers understand how to turn messy requirements into maintainable systems.

The best candidates may look like full-stack AI engineers. They do not need to be specialists in every model, database, or framework, but do need enough range to connect the model layer with the business systems around it.

The Kubernetes lesson and what leaders should do now

The shift has a parallel with the move to cloud-native architecture and Kubernetes. Many companies treated Kubernetes as something to install, then discovered that the harder work was changing how teams built and ran software.

AI creates a similar risk. Companies can buy tools and hire a handful of specialists, but still fail to change the engineering habits around them. Context engineering requires teams to think differently about everything from documentation, and data ownership, to access, testing, and accountability.

It also changes the culture of software development. Engineers are already using AI to write, review, and iterate code. That can improve productivity, but it does not remove responsibility. In areas where performance, reliability, or security matter, human judgement becomes even more important.

CTOs and CIOs should not wait for context engineering to become a mature hiring category. They should start identifying the capability now.

The first step is to examine where AI projects are failing. Is the model genuinely weak, or is the system retrieving poor context? Are permissions clear? Can the team explain why the agent produced a particular answer?

The second step is to build cross-functional teams. AI cannot sit apart from data, platform, security, and product. In many cases, the best approach will be to upskill existing engineers who already understand the organization's systems.

The final step is cultural. Engineers need to become fluent in AI-assisted development while staying accountable for the systems they ship. Leaders need to make room for experimentation, but they also need clear standards for review, evaluation, and governance.

The model is not enough

AI hiring is changing because AI itself is moving into production. Models will continue to improve, and businesses will have many ways to access them. The harder advantage will come from knowing how to connect those models to the right business context.

Companies that understand this will build agents and RAG systems that are more useful, safer, and easier to govern. Companies that ignore it will keep blaming the model when the real weakness is the environment it has to work in.

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• Cheap smart glasses are selling out as people look for Meta Ray-Ban alternatives
• More camera glasses are leading to more privacy concerns for many
• I wouldn't buy them for plenty of other reasons

If you want a pair of smart glasses, you’re spoiled for choice in terms of what they can do technologically, and over time we’ve seen more and more budget options appear. These cheaper models are the first step in making emerging gadgets more accessible, but for smart specs there’s a fear they might simply exacerbate the growing concerns over the tech.

Australian discount store Kmart recently went viral after the AU$89 Anko glasses it was selling sold out entirely (via The Guardian). In the US and UK, you don’t have to look far to find equally affordable camera glasses available through reputable digital marketplaces such as Amazon.

Given the proven popularity of Meta’s Ray-Ban and Oakley specs, it’s no surprise the market would see an influx of smart glasses, especially cheap pairs that aim to mimic much more expensive rivals while keeping costs low — typically by downgrading their technological capabilities and not partnering with premium design brands.

However, cheap smart glasses aren’t as universally celebrated as cheap TVs or affordable smartphones — scrolling through online forums discussing this news, the replies aren’t kind about smart glasses, discussing the pitfalls of smart glasses in general and more affordable options specifically.

The most prominent concern is that cheap camera glasses expand the range of people with the technology. More people with these glasses means more people possibly recording people in public without their knowledge.

There are also fears that cheap camera glasses might not come with the same sophisticated tamper protection glasses from more recognizable brands boast. Either through tape coverings or minor electrical modifications, folks could disable the camera light and use the specs even more covertly than they otherwise would — they’ve tried the same with Meta specs, but Meta has patched out these workarounds so far.

Privacy and other problems

Beyond the impact on the general public, cheap smart glasses users might want to consider the privacy implications of wearing cameras from unknown brands.

We’ve seen that even super well-known brands like Meta aren’t perfect — for example, there are reports that contractors could see videos (including intimate or otherwise private content) taken using the glasses if it involved the AI in some way — so why should we expect completely unknown brands with nothing to lose to be any better with privacy?

In fact, considering their lack of notoriety makes them less likely to get investigated in the way Meta was by journalists who uncovered the sharing of videos with contractors. The ease with which tiny brands can dissolve and pop back up under different names, I for one would be a lot less trusting of what they do with my data and of their phone apps.

Of course, many lesser-known brands behave responsibly, but when there’s so much room for things to go wrong, for private information or video to get leaked, I just have this extra layer of paranoia I can’t shake.

Even ignoring my personal privacy concerns, as someone who has tested countless smart glasses at this point, I’ve found that better-known brands are simply better at making hardware and software.

I’ve worn so many uncomfortable specs with tinny audio, awful camera quality, terrible AI, and apps that are worse than useless. They’re sluggish. They’re ugly. They’re not worth even a budget price.

Just don’t buy them.

If you do want smart specs, save up for something proper — it doesn’t have to be from Meta, but I’d recommend choosing a brand with plenty of solid independent reviews. While I’m using them less for reasons better explained by other writing, I have gone weeks on end wearing a good pair of smart glasses thanks to comfy yet fashionable design and solid tech. Whereas the cheap (and terrible) glasses I’ve tried I’ve wanted to rip off my face within hours.

You get what you pay for.

• A controversy has erupted over a background process in Windows 11
• It turns out that process isn't new, and neither is it a surprise introduction from Microsoft that 'does nothing for you' except pipe telemetry back
• A Microsoft exec clarified the purpose of the process, and that it doesn't send any data back unless you want to do that

There's been some more controversy over privacy and telemetry in Windows 11, and while this particular alarm bell turned out to be falsely rung in the end, it does point to wider issues around the mistrust of Microsoft in general.

As Windows Latest reports, this story starts with a post on X from a user called Xilly who drew attention to an apparent new process in Windows 11 called 'Windows Health and Optimized Experiences'.

However, as Windows Latest – and others on X – observe, this isn't a new background service in Microsoft's OS at all. In fact, it has been kicking around in Windows 11 for over a year now (since May 2025).

The additional nuance, though, is that this process was only introduced in testing in the Canary channel back in May of last year, and it seems to have only just come through to Windows 11 release builds now. That's as far as I can tell, and this is what Xilly points out on X.

So, it is in fact relatively new to release builds – and presumably rolling out now – but nonetheless the service itself has been known about for a long time. The real problem here is the way Xilly frames it as monitoring your PC for certain metrics related to laptop battery management, and that: "If you are on a desktop gaming PC it does nothing for you except run in the background" (while eating resources and engaging in telemetry, "sending data to Microsoft every 15 minutes").

Xilly's post prompted a response from Scott Hanselman, who is a VP and member of technical staff at Microsoft as well as a prominent voice in the Fix Windows 11 campaign.

Hanselman made it clear that the process is not new, as already discussed, and that it's for system diagnostics: "When Windows detects slow or sluggish behavior, it can record targeted performance traces locally under %SystemRoot%\Temp\DiagOutputDir\Whesvc that can be filed with Feedback Hub. It's not laptop specific."

So, this is a process that detects poor performance and files diagnostics locally, which can then be fed back to Microsoft if the user wishes.

Hanselman also highlights the dramatic approach Xilly took ("saying things in all capitals"), and Windows Latest further points out that the X user runs a business for optimizing gaming PCs, and pushes that service in a reply to their own original post.

Analysis: trust, telemetry, and what Microsoft needs to do

To summarize, then, Xilly's description of the service is incorrect, and while this background process does collect diagnostic data on how Windows 11 is running, it's written locally (with a negligible performance impact) and only sent to Microsoft if you actively submit feedback from your PC (via the Feedback Hub). The key phrasing in Hanselman's response is that the relevant data "can" be filed with Microsoft (not "will" be filed, as in it'll automatically happen).

Xilly also suggested that this was something Microsoft just sneaked in (hence the 'all caps' warning), when in fact it has been documented in preview builds before, and as Windows Latest notes, it's part of the Adaptive Energy Saver feature for helping to extend battery life.

The long and short is that there's nothing to worry about here, but what this episode serves as is a reminder of how Microsoft is short on trust when it comes to issues around privacy.

It's clear how quickly some Windows 11 users were to believe Xilly's accusation on X and other social media such as Reddit. For example, in this Reddit thread, despite one poster pointing out the reality of the situation in a nutshell (pretty much), there's a notable amount of complaining about why this process is sending data to Microsoft (which it isn't), or broader complaints along the lines of: "If you choose to install a spyware OS, expect to be spied on."

In fact, the most upvoted comment is: "Honestly, if any big tech giant wants to spy on you, they don't need a separate service. The MSA/online account [Microsoft account] is more than enough."

There are many people out there online who believe that, one way or another, Microsoft is spying on them to quite an extent with Windows. That stems from a broader level of distrust that's been stoked by the historical level of telemetry in Microsoft's desktop operating system since Windows 10 emerged, with reports periodically popping up to highlight these issues in that OS and Windows 11. Add to that all of Microsoft's promotional activity (and some outright adverts), as well as the likes of the Recall feature helping to pile on the skepticism (Microsoft handled that one really badly), and you can see why there's a problem with doubters here.

I keep saying that one thing Microsoft isn't properly addressing with the fix Windows 11 campaign is the level of telemetry performed by the OS, and I think it's high time that the company did this, and introduced a new choice for a zero level of telemetry (save for baseline security-related aspects). That option is lacking on Windows 11 Home, and it should be present and easily accessible, as this would go a long way to help cure some of the ill feeling out there around Windows 11 'spying' on people.

Multiple studies have found that the average person has over 150 passwords. As a password security expert, I regularly recommend that every single password people use should be both unique and strong - but this adds the problem of being able to remember each one, or store them securely.

Password managers are designed for this exact reason. They store your passwords in encrypted vaults and autofill your credentials as and when you need them. For this very reason, Keeper is one of our most highly rated password managers.

Right now, Keeper has cut prices on its personal, family, and business starter plans by up to 50%, providing an affordable password management solution that can store and generate your passwords, while also providing convenient features for securely sharing regularly used household or business passwords.

Why we recommend Keeper

In our Keeper review, we praised the zero knowledge architecture and device level encryption that helps keep the contents of your personal vault hidden from hackers.

Keeper also scored great marks in our usability and convenience testing. Rather than having to enter your master password each time you need to fill in your credentials, Keeper uses biometric security to access your vault. Biometric security uses a facial scan or fingerprint to verify that its actually you accessing your vault. It's also phishing resistant.

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

The Business Starter plan covers 5 users, covering an unlimited devices, shared team folders, user activity reporting for IT admins, and a free Family Plan for every team member.

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

When we wrote about the “AI speed trap” in the software development process for TechRadar Pro a year ago, the central concern was whether faster delivery would come at the expense of quality. A year on, what we’re seeing are actually deeper issues around trust, governance and organizational alignment.

As AI-generated code becomes more routine and ecosystems become more complex, the challenge is no longer simply keeping pace with delivery. It is deciding who owns quality, how much risk is acceptable, and what confidence looks like in an AI-enabled environment.

AI has become firmly embedded across the software development lifecycle, accelerating delivery at an unprecedented rate. But the real story is no longer just speed. Organizations are now being forced to make more explicit decisions about where quality ends and acceptable risk begins, and to reconcile trade-offs around testing, trust and accountability.

AI is accelerating software delivery, but quality strategies are falling behind

Nobody would dispute that AI has transformed software quality over the last year. AI-assisted coding, automated testing and increasingly autonomous workflows are no longer experimental; they are becoming standard practice.

Nearly seven in ten organizations have now implemented AI tools across at least some of their software delivery workflows, and almost half have fully embedded it into their development environments.

The impact on productivity is undeniable. Development teams can generate code faster than ever, automate repetitive tasks and accelerate release cycles that once took weeks into days, or even hours. But the ability to create software has advanced far more quickly than organizations' ability to validate it.

As a result, 60% of teams are knowingly releasing untested code. Not accidentally, as our 2025 research showed, but because they are under pressure to ship faster, and because the sheer volume of code is outpacing what many teams can realistically test. In other words, these are conscious business decisions. Organizations aren’t even trying to test everything; they’re deciding what they can afford not to test.

As software delivery accelerates, quality has become less about eliminating every defect and more about determining which risks are acceptable. But that requires stronger governance, clearer accountability and a shared understanding of what constitutes an acceptable release.

The definition of quality is changing

In an AI-driven world, organizations will never be able to test every line of AI-generated code. While AI has removed many of the bottlenecks associated with writing code, it hasn't created more time to validate it. Traditional quality models were designed for an era where development speed largely dictated testing speed. That assumption no longer holds.

The answer, however, isn't to lower the bar for software quality. It's to rethink what quality actually means. Rather than attempting to preserve old approaches by asking teams to test everything, organizations need to rethink what software quality actually means.

Increasingly, success will depend on understanding where quality matters most. Which applications are business-critical? Which customer journeys carry the greatest commercial or regulatory risk? Which changes genuinely require exhaustive testing, and which can be validated through risk-based approaches?

In other words, software quality is becoming intelligence-led rather than activity-led. The focus is shifting from test volume to confidence in release decisions, based on how well organizations can identify and manage the risks that matter most.

Why governance, visibility and continuous validation are becoming the new foundations of software quality

As organizations move towards risk-based quality models, a critical gap is emerging: how those risks are actually governed in real time.

In traditional software delivery, governance was often applied at fixed stages. In AI-accelerated environments, that model breaks down. Code is generated continuously, pipelines move faster than human review cycles, and release frequency has increased beyond what stage-gated governance was designed to support.

This requires three structural shifts.

First, visibility becomes foundational. Without real-time insight into what AI is generating, what is being tested, and where coverage gaps are emerging, organizations are effectively making risk decisions without a complete view of the system.

Visibility is the precondition for trust, allowing leaders to understand not just whether software works, but whether it is being adequately validated as it evolves.

Second, governance must move closer to code creation. Instead of acting as a final checkpoint before release, governance increasingly needs to be embedded earlier in the lifecycle, where risk is first introduced. This means bringing quality signals, compliance requirements and risk thresholds into the development process itself, rather than evaluating them retrospectively.

Third, continuous validation replaces discrete testing phases. As AI increases both the speed and volume of code production, organizations are shifting from episodic testing cycles to always-on validation. Rather than asking whether software has been tested, the more relevant question becomes whether it is being continuously validated against the organisation’s risk appetite as it changes.

In AI-driven environments, confidence in software quality depends less on point-in-time approvals and more on the ability to observe, measure and validate quality as it is being created.

Organizations are making deliberate decisions about risk

Software quality has always involved balancing competing priorities, but AI is making those trade-offs more explicit, with “good enough” increasingly defined at the intersection of engineering constraints and business pressure.

The challenge is that organizations don’t always agree on what that looks like. Nearly half report only partial alignment between executives and software teams on what high-quality software actually looks like, suggesting that release readiness is increasingly open to interpretation rather than governed by consistent standards.

This also explains a key finding in our data: that while more than nine in ten C-level executives express confidence in their testing strategies, almost one-third of quality assurance (QA) and DevOps leaders remain uncertain that those strategies adequately address the most critical software risks.

That gap reflects fundamentally different perspectives. Executives naturally focus on strategic outcomes and business velocity, while engineering teams experience first-hand the operational realities of validating increasingly complex software systems.

Closing that gap will become increasingly important as AI continues to accelerate software delivery. Trust cannot exist if different parts of the organization operate under different definitions of quality or acceptable risk.

Software quality has become a business decision

For years, software quality was viewed as a technical responsibility - a discipline concerned with finding defects before customers did. That is no longer sufficient.

When software underpins customer experiences, regulatory compliance and critical business operations, decisions about quality inevitably become business decisions. Shipping software with known gaps can no longer be merely a technical compromise; it represents an organizational risk assessment.

The consequences extend well beyond development teams. Poor software quality can increase technical debt, introduce security vulnerabilities, create compliance challenges and erode customer trust. Many organizations are already attributing hundreds of thousands (if not millions) of pounds to the downstream impact of software failures and rework.

At the same time, governance has struggled to mature at the same pace as AI adoption. While many trust agentic AI to make release decisions, significantly fewer believe they are ready to do so at scale. That disparity between adoption and oversight is likely to become one of the defining challenges of the next phase of AI-enabled software delivery.

Ultimately, software quality is no longer something that can be delegated solely to engineering teams. It requires leadership alignment, shared accountability and governance that keeps pace with increasingly autonomous development.

Conclusion

AI has accelerated software delivery to the point where existing approaches to testing and governance are struggling to keep up. The organizations most likely to succeed are those that make deliberate, well-governed decisions about risk and build trust into the software delivery lifecycle rather than treating it as a final checkpoint.

Software quality can no longer be just an engineering discipline. In the AI era, it has become a leadership responsibility.

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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

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• Microsoft has summarized its campaign to fix Windows 11 so far
• The company also let us know what work is coming, including making the OS run better with 8GB of RAM
• That should have been the case from the start, though – and there's a missing piece of the puzzle to address as highlighted by Windows 11 users

Microsoft has made it clear that it's working to ensure that Windows 11 runs better on 8GB of system memory, as well as letting us know about other elements of the OS the company is going to fix as 2026 rolls on.

This was part of a blog post which is an update on the progress made with the campaign to fix Windows 11 that kicked off in March, summarizing all the work Microsoft has done – and there's been a lot of that, without a doubt. (I recently picked out the most important changes so far, if you're interested).

Microsoft also highlighted four new areas it's going to deal with during the rest of the year, and one of those is Windows 11's RAM usage.

Microsoft wrote: "Memory optimization for 8GB and above. Reducing Windows memory footprint to deliver a fast and responsive Windows experience across the PCs customers use every day."

As well as a promise, this is an acknowledgement of the RAM crisis, which is preventing people from upgrading their memory, but more to the point, it's forcing laptop makers to go backwards in some respects, dropping RAM loadouts to 8GB for some models. (Whereas previously we'd been moving noticeably towards 16GB as a baseline).

As to what else Microsoft has on its fixing checklist, that includes making Windows 11's setup a faster and more efficient experience, as well as a better setup for families (including parental controls being more readily accessible). Finally, Microsoft says voice functionality is also being worked on, to make "voice more natural and fluid to interact across the apps you use every day".

Analysis: it's about time really – and there's another issue that needs to be addressed, too

Microsoft talked about RAM optimization and "lowering the baseline memory footprint for Windows" back when the campaign to fix its OS first began. So really, this is just a reassurance that this work is underway and should be arriving before the end of the year, and that it's specifically focused on ensuring 8GB is an acceptable amount of memory for running Windows 11.

As noted, there's more pressure around this now thanks to the RAM crisis getting worse, and laptop makers looking to produce more 8GB models in a cost-saving effort. On top of that, there's the embarrassment of the MacBook Neo showing that Apple can do 8GB, and do it well, with macOS.

Really, though, Windows 11 should've been better optimized for performance right from the start – or at least after the first year or two of its existence. Why has it taken a RAM apocalypse, a huge anti-Windows backlash (prompted by AI), and the launch of a rival budget-friendly MacBook to prompt Microsoft to get off its system performance laurels and actually do something serious about how well its desktop operating system runs with a leaner amount of memory? Remember, Windows 11 is supposed to work with 4GB (as a minimum), so 8GB should not be as much of an issue as it has always been.

Obviously it's good that this is now happening, and more broadly, I'm still pleased Microsoft is addressing a whole bunch of problems with its drive to fix Windows 11. There's one piece of the puzzle omitted here, though, based on some of the feedback which has come through on social media after Microsoft's blog post update.

While Redditor 'Melchett_General' acknowledges that "some of this is very nice" when it comes to Microsoft's summarized fixes, they point out some missing bits, one of which is notably echoed elsewhere – namely the recent LG monitor controversy.

The Redditor complains: "How on earth Windows got to the state where any manufacturer that makes a device can have software 'companion' applications installed silently on a user's device without the wider internet throwing a collective fit and having Microsoft permanently change course I don't know."

In case you missed it, this refers to LG serving Windows 11 users a McAfee pop-up via its monitor software, and it's a story with some degree of nuance. Microsoft did immediately take action to get LG to banish that McAfee advert, which popped up via the LG monitor app in Windows 11, but as I discussed previously, a wider problem remains here.

This is the issue that the LG application is piped through to the operating system via Windows Update without the user's knowledge, happening automatically when the LG monitor is connected to the system.

In other words, the McAfee pop-up might have been quickly dealt with, but it's still possible for hardware makers to get Windows Update to pipe through a tool for their device.

As the Redditor elaborates: "What this seems to mean is that a 3rd party can install what they like whenever they like on your internet connected Windows computer and there's no effective way to stop it. (Aside from GP [Group Policy] edits or registry changes).

"This needs to be addressed, and there needs to be a mechanism to allow people to easily prevent this from happening."

I couldn't agree more. There should be no scenario where software is automatically installed in this way, even if the device maker might argue that the app is very much for the user's benefit. At the very least, there should be a dialog box announcing what's happening, and giving you the ability to choose not to install whatever application the hardware maker wants to put on your PC.

Notably, I haven't heard anything from Microsoft addressing this broader issue. Never mind the symptom (the McAfee pop-up), tackle the root cause of the issue (the ability for a piece of connected hardware to whisk its own software off the internet with no say from the user).

Microsoft is clearly serious about fixing everything that's wrong with Windows 11, I don't dispute that – but I find it odd that nothing has been said that directly addresses this pain point. Hopefully that won't be the case for much longer – we need some more transparency with Windows Update in this regard.

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.

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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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

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?