Technology

Epson's EcoTank ink tank printer have consistently impressed us during tests, with the format using bottled ink that's much cheaper than traditional inkjet printer cartridges. For an office upgrade or a new back-to-school set-up, I recommend checking out the Epson EcoTank ET-2800 for $200 (was $240) at Amazon.

If you're sick of buying overpriced ink cartridges, this is the cure. It includes four bottles of ink in the box that Epson says can last up to two years, and it's one of the best-selling cartridge-free printers on Amazon for a reason.

Should you buy it?

✅ Buy the Epson EcoTank ET-2800 if...

You print a steady mix of documents and photos and want to escape the cartridge cycle. You want the lowest-cost entry point into Epson's EcoTank lineup. You're fine with manual double-sided printing and scanning one page at a time.

❌ Skip the Epson EcoTank ET-2800 if...

You need automatic two-sided printing (look at the ET-2850 instead). You regularly scan or copy multi-page documents — there's no automatic document feeder here. You print only occasionally — infrequent use can dry out the print heads.

Cartridge-free printing, wireless setup, flatbed scanner and copier, up to 10 ppm, with up to 2 years of ink included in the box.View Deal

Why we recommend it

We've tested the best home printers and best ink tank printers, and Epson's EcoTank line has delivered consistent performance results, topping our lists. Quick, cheap and easy refills, with good text and photo printing results.

The EcoTank system is the whole pitch here: instead of replacing small cartridges every few weeks, you refill from ink bottles, and each set is rated for up to 4,500 black-and-white pages or 7,500 color pages. Epson estimates that's roughly two years of ink for an average household.

At $199.99, this is one of the most affordable ways into that ecosystem, and it comes with wireless printing, a flatbed scanner, and a copier built in, so it covers the basics most home users need without extra bulk.

Price Context & Historical Value

$199.99 matches this printer's going rate at other major retailers, including Best Buy, Staples, and direct from Epson, where it's currently listed at the same price after a $40 discount from $239.99.

The cheapest I've seen for the EcoTank ET-2800 is $175, which is typically around big shopping events like Black Friday and the Christmas sales.

At a cent under $200, that makes this less of a rare price drop and more of a reliably good, currently live price, and $40 off is still a meaningful percentage saving on a printer in this range. It's the lowest we've seen this bundle land since January.

The Catch: What to know before you buy

This is a basic model, and Epson kept costs down by omitting a couple of features that some buyers will miss. There's no automatic document feeder, so scanning or copying multi-page documents means feeding them one sheet at a time. It also doesn't support automatic double-sided printing — you'll need to flip pages manually for duplex. If either is a dealbreaker, Epson's ET-2850 adds both for more money. For most home printing — school paperwork, tax documents, the odd batch of photos — the ET-2800 covers it comfortably.

• See all printer deals at Amazon.

• Take-Two Interactive CEO Strauss Zelnick says more fans are pre-ordering the Grand Theft Auto 6 Ultimate Edition over the Standard Edition
• He says the sales are "skewing more" to the $100 Ultimate Edition
• Zelnick also can't guarantee whether the game will get a discount for Black Friday or Christmas

According to Take-Two Interactive CEO Strauss Zelnick, the Grand Theft Auto 6 Ultimate Edition is selling more than the Standard Edition.

In a recent interview with CNBC (via IGN), when asked whether the pre-order numbers could be equal across both versions, the CEO said the game's $99.99 / £89.99 Ultimate Edition, which features the base game and additional bonus content, is being pre-ordered by more fans than the $79.99 / £69.99 Standard Edition.

"Actually, it is skewing more to the premium edition," Zelnick said, and suggested that it may be a result of die-hard fans pre-ordering it. "That might be a reflection of the fact that the most avid consumers are the ones who are pre-ordering now."

We don't have the actual sales numbers at this time, but if what Zelnick said is accurate, it suggests the Ultimate Edition is performing extremely well despite the initial backlash from fans.

When Rockstar Games announced the editions in June alongside pre-orders, fans called out the studio for its "scummy" tactics, which see exclusive content locked behind the game's Ultimate Edition. It's understood that a separate upgrade will be available at some point for those who purchase the Standard Edition, but Rockstar has yet to announce the add-on.

Unlike the Standard Edition, which comes with the base game, the more expensive Ultimate Edition features a ton of extra goodies, including premium and exclusive content such as missions, Vice City shops, locations, and outfits.

GTA 6 launches on November 19, before Black Friday and Christmas, and in the same interview with CNBC, Zelnick was asked whether we could see a discount on the upcoming game.

The CEO couldn't say, but explained that GTA 5's price was "preserved" for years, saying, "We obviously establish our pricing, and storefronts can do what they wish."

"We don't have any capability to control that," Zelnick continued. "So if they reduce their price, they reduce their margin on the sale. There are situations where certain retailers will price down to drive demand for other things they will sell. I can't tell you what will happen. I will tell you we preserved our pricing on Grand Theft Auto 5 for a very long time, much longer than a standard release of an entertainment property."

During Take-Two's most recent earnings report, the Rockstar parent company reported earnings of $1.39 billion during GTA 6's pre-order period, which Zelnick called an "exceptional start to pre-orders."

Fans can also look forward to 'Grand Theft Auto 6: An Extended Look,' which will premiere on Netflix on August 27.

• Take-Two Interactive CEO Strauss Zelnick says more fans are pre-ordering the Grand Theft Auto 6 Ultimate Edition over the Standard Edition
• He says the sales are "skewing more" to the $100 Ultimate Edition
• Zelnick also can't guarantee whether the game will get a discount for Black Friday or Christmas

According to Take-Two Interactive CEO Strauss Zelnick, the Grand Theft Auto 6 Ultimate Edition is selling more than the Standard Edition.

In a recent interview with CNBC (via IGN), when asked whether the pre-order numbers could be equal across both versions, the CEO said the game's $99.99 / £89.99 Ultimate Edition, which features the base game and additional bonus content, is being pre-ordered by more fans than the $79.99 / £69.99 Standard Edition.

"Actually, it is skewing more to the premium edition," Zelnick said, and suggested that it may be a result of die-hard fans pre-ordering it. "That might be a reflection of the fact that the most avid consumers are the ones who are pre-ordering now."

We don't have the actual sales numbers at this time, but if what Zelnick said is accurate, it suggests the Ultimate Edition is performing extremely well despite the initial backlash from fans.

When Rockstar Games announced the editions in June alongside pre-orders, fans called out the studio for its "scummy" tactics, which see exclusive content locked behind the game's Ultimate Edition. It's understood that a separate upgrade will be available at some point for those who purchase the Standard Edition, but Rockstar has yet to announce the add-on.

Unlike the Standard Edition, which comes with the base game, the more expensive Ultimate Edition features a ton of extra goodies, including premium and exclusive content such as missions, Vice City shops, locations, and outfits.

GTA 6 launches on November 19, before Black Friday and Christmas, and in the same interview with CNBC, Zelnick was asked whether we could see a discount on the upcoming game.

The CEO couldn't say, but explained that GTA 5's price was "preserved" for years, saying, "We obviously establish our pricing, and storefronts can do what they wish."

"We don't have any capability to control that," Zelnick continued. "So if they reduce their price, they reduce their margin on the sale. There are situations where certain retailers will price down to drive demand for other things they will sell. I can't tell you what will happen. I will tell you we preserved our pricing on Grand Theft Auto 5 for a very long time, much longer than a standard release of an entertainment property."

During Take-Two's most recent earnings report, the Rockstar parent company reported earnings of $1.39 billion during GTA 6's pre-order period, which Zelnick called an "exceptional start to pre-orders."

Fans can also look forward to 'Grand Theft Auto 6: An Extended Look,' which will premiere on Netflix on August 27.

Waymo’s safety data shows robotaxis reducing crashes, but recalls and emergency-scene failures reveal a harder challenge as autonomous fleets scale.

The post Waymo’s Robotaxis Are Getting Safer. Scaling Them Is Exposing New Problems appeared first on TechRepublic.

• New Beats headphones teased during 2026 World Cup...
• ... and Beats has been silent for following two months
• Reddit fans think they'll come some time after iOS 27, in September

It's been two months since the sporting event of the summer began: the 2026 World Cup kicked off on June 11. That means it's also been two months since Beats started teasing its new headphones, putting them onto footballers like Lamine Yamal and Lee Kang-in to drum up some publicity.

"Publicity for what?" you may well be asking, and it's a fair question. Since then, we've heard neither hide nor hair about these cans, even though we infiltrated Beats impressive HQ to see what was going on, last month.

However, if you were hoping for an imminent release, Redditors have some bad news.

In a post on the r/beatsbydre subreddit, which is currently awash with question regarding these headphones, one forum mod has a theory about when they'll come. User jforsander points out that every previous pair of Beats headphones had iOS support added months prior to their release.

Of course, this new pair of headphones hasn't, suggesting that they're still a long time away from an official launch. In fact, the user thinks that we won't even see this software support until September, because that's when iOS 27 will come.

It follows that the headphones will be released a few months later; perhaps even up to six months later. Gulp. Unfortunately, that tracks with what we heard when we visited Beats HQ — or rather, what we didn't hear. Clearly, Beats is in no rush to strike while the iron's hot.

Will we have to wait that long?

(Image credit: Beats)

It's worth taking this missive with a slight pinch of salt. If there's no existing software support for the Beats headphones, how were those footballers using them? Surely they weren't just decoration — not at such an important sporting event, where the right pre-match playlist would surely be of paramount importance?

The cynics among you might rightly point out that the players weren't ever photographed listening to the headphones, just wearing them around their necks or attached to their bags. And it's also possible that Apple found a temporary solution for their headphones, rather than a full software rollout.

But it still stands to reason that Beats would give them a way to sporting stars who could actually listen to the headphones, and so it's possible that software support exists and online sleuths… missed it?

Something that the Reddit poster points out is that Apple might simply have become better at hiding its updated software support for new Beats devices (Apple, of course, owns Beats). They call it unlikely, but feasible.

If we are waiting past September to hear any more about these new headphones, all the money spent on soccer players might have been wasted; the buzz garnered by the move is already dying down (that is, in most places beyond the r/beatsbydre subreddit). Hopefully Beats will shift into action sooner than that.

• New Beats headphones teased during 2026 World Cup...
• ... and Beats has been silent for following two months
• Reddit fans think they'll come some time after iOS 27, in September

It's been two months since the sporting event of the summer began: the 2026 World Cup kicked off on June 11. That means it's also been two months since Beats started teasing its new headphones, putting them onto footballers like Lamine Yamal and Lee Kang-in to drum up some publicity.

"Publicity for what?" you may well be asking, and it's a fair question. Since then, we've heard neither hide nor hair about these cans, even though we infiltrated Beats impressive HQ to see what was going on, last month.

However, if you were hoping for an imminent release, Redditors have some bad news.

In a post on the r/beatsbydre subreddit, which is currently awash with question regarding these headphones, one forum mod has a theory about when they'll come. User jforsander points out that every previous pair of Beats headphones had iOS support added months prior to their release.

Of course, this new pair of headphones hasn't, suggesting that they're still a long time away from an official launch. In fact, the user thinks that we won't even see this software support until September, because that's when iOS 27 will come.

It follows that the headphones will be released a few months later; perhaps even up to six months later. Gulp. Unfortunately, that tracks with what we heard when we visited Beats HQ — or rather, what we didn't hear. Clearly, Beats is in no rush to strike while the iron's hot.

Will we have to wait that long?

(Image credit: Beats)

It's worth taking this missive with a slight pinch of salt. If there's no existing software support for the Beats headphones, how were those footballers using them? Surely they weren't just decoration — not at such an important sporting event, where the right pre-match playlist would surely be of paramount importance?

The cynics among you might rightly point out that the players weren't ever photographed listening to the headphones, just wearing them around their necks or attached to their bags. And it's also possible that Apple found a temporary solution for their headphones, rather than a full software rollout.

But it still stands to reason that Beats would give them a way to sporting stars who could actually listen to the headphones, and so it's possible that software support exists and online sleuths… missed it?

Something that the Reddit poster points out is that Apple might simply have become better at hiding its updated software support for new Beats devices (Apple, of course, owns Beats). They call it unlikely, but feasible.

If we are waiting past September to hear any more about these new headphones, all the money spent on soccer players might have been wasted; the buzz garnered by the move is already dying down (that is, in most places beyond the r/beatsbydre subreddit). Hopefully Beats will shift into action sooner than that.

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.

We've featured the best IT automation software.

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

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.

We've featured the best IT automation software.

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

Artificial intelligence has moved from experimentation to everyday business use faster than almost any technology in recent memory.

For small businesses, AI adoption needs no convincing as most already see the benefits. The real challenge is now transforming isolated AI use into consistent business value.

Goldman Sachs found that 76% of small businesses are using AI, and among those users, 93% say it has had a positive impact. Yet only 14% have fully integrated AI into core operations.

That gap is where the next stage of AI adoption will be won or lost.

The question is no longer whether small businesses can access AI. It is how they can make it part of their business.

For me, that means moving beyond AI as a feature list and toward AI as a trusted experience employees can rely on in the flow of work.

The focus now should be on helping small businesses progress from deploying AI in everyday tasks, to reshaping workflows, to eventually inventing new services, business models and revenue streams.

Start where the work gets stuck

The temptation is to start with the technology, but the better starting point is the work itself. A modern AI-ready device, collaboration setup or workplace platform can promise faster content creation, more productive meetings or automated reporting. Those capabilities matter, but the question is more basic: what problem is slowing the business down?

The problem might be missed customer follow-ups, teams spending too much time turning raw information into action, or even just slow response times. AI becomes valuable when it is pointed at a specific bottleneck and measured against a business outcome: time saved, errors reduced, revenue protected, customers retained, or employees freed up for higher-value work.

This outcome-first mindset is critical because the goal should not be to optimize an old process simply because it exists. It should be to ask what the business needs to achieve, then design the workflow and the technology around that result.

Discipline is important because small businesses do not have much room for technology theater. The most useful AI projects are rarely the flashiest. They are the ones tied to work that happens every day.

Redesign the workflow, not just the task

The next step is to move beyond individual productivity. Many employees are already using AI in small, informal ways, with a 156% increase between 2023 and 2025 in shadow AI usage. Shadow AI refers to employees using AI tools without formal approval, oversight or integration into company systems.

Employees ask AI to clean up an email, summarize a document or prepare a first draft. While those use cases can help, they usually create isolated gains. The bigger opportunity comes when AI is built into the workflow itself.

Consider a customer-facing team. AI can draft a response. But the bigger opportunity is redesigning a process around it. Let AI help categorize the request, identify urgency, suggest the next best action, and leave important judgment calls to a person.

For a lean operations team, AI can turn meetings, documents and business data into clearer next steps, helping reduce the manual follow-through that often slows momentum. Over time, this will become less about a single AI tool assisting with a single task and more about groups of AI agents working across connected workflows, with people shaping the strategy, setting the guardrails and orchestrating the work.

This is where many organizations still struggle. McKinsey’s 2025 State of AI research found that 88% of organizations use AI in at least one business function, but only about one-third have begun scaling AI across the enterprise. The same research found that AI high performers are nearly three times as likely as others to have fundamentally redesigned workflows.

In other words, the return comes less from sprinkling AI over old processes and more from rethinking how work should move. That is the difference between deploying AI, reshaping work and ultimately inventing new ways for the business to grow.

Train people to use AI with judgment

It’s not enough to invest in tools. Businesses must also invest in helping employees use them effectively. AI works best when employees understand what it is good at, where it can fail and when human judgment is required. Not every small business needs a large training program, but it does need practical guidance: which tools are approved, what information should stay protected and when outputs need human review.

Clear guardrails allow a business to scale AI with confidence. Goldman Sachs found that small businesses using AI cite data privacy and security concerns, lack of technical expertise and difficulty choosing tools among their top challenges. 73% said they would benefit from more training and resources to implement and evaluate AI successfully.

When employees are trained to use AI responsibly, technology becomes less of a risk to manage and more of a capacity builder that helps small teams work with greater speed, confidence and focus. It also builds the trust employees need to treat AI not as another feature to try, but as a dependable part of how work gets done.

Make AI a capacity builder

AI is often framed as a replacement story. In practice, many small businesses are using it as a force multiplier. The U.S. Chamber of Commerce found that 58% of small businesses use generative AI, up from 40% in 2024 and 23% in 2023. It also found that 82% of small businesses using AI increased their workforce over the past year. For lean teams, AI can create breathing room: less time spent chasing notes or repeating manual tasks and more time spent with customers and employees.

None of this happens automatically. Small businesses need to choose technology with integration in mind, not just features in isolation. They need to understand where data lives, how systems connect and whether employees can use new tools without adding more complexity.

They also need the confidence to seek outside guidance, whether from technology partners, managed service providers, industry peers or local business networks. The right support can help small businesses see where AI should simply deploy, where it should reshape the way people work and where it may create room to invent something entirely new.

The bottom line: AI should change how work gets done

The small businesses that get the most from AI will not necessarily be the ones that adopt the most tools. They will be the ones that ask sharper questions: Where are we losing time? Where are decisions too slow? Where are customers waiting? Where are employees doing work that software could support safely and reliably?

AI has already changed what small businesses can do. The next challenge is changing how work gets done. For small businesses, the real opportunity is not to add AI everywhere, but to apply it with purpose: deploy it where it helps today, reshape the workflows that define the business and invent new ways to create value tomorrow.

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• Microsoft Teams is bringing badges to your profile card
• Badges will include a user's particular awards and certifications
• Microsoft says this will make the experience more consistent across Teams and Outlook

If you're really looking to show off your skills at work, then Microsoft Teams might have the perfect answer for you - however it might not be for everyone.

The online collaboration platform has announced it is working on bringing "Badges" to your Microsoft Teams profile card, allowing users to display all their achievements to anyone who cares to look.

The addition will show Awards and Certifications on a user's profile card, where they will show up as badges, giving you an easy way to show off exactly what you can do.

Badge of honor

"You can now see Awards and Certifications as badges on a user’s profile card, bringing a more consistent experience across Teams and Outlook," a Microsoft 365 roadmap post outlining the feature explained.

The feature is listed as being "in development" right now, with an expected release date of September 2026. Upon release, it will be available to users across Windows and Mac around the world.

(Image credit: Microsoft)

Badges have been available to Microsoft 365 users since 2023, designed as a way for users to display their expertise and qualifications (see above example) on specific cards.

The cards are currently available in several programs, including Outlook and the Copilot app, with their expansion to Teams clearly part of Microsoft's push to make the platform more informative.

This includes a recent upgrade to block bad bots with "smarter protection" which will let humans check all participants in a call are who they say they are, including bots in a call lobby, much like a nightclub bouncer.

Microsoft Teams is also working on a feature which will automatically update a user's work location when they connect to an office Wi-Fi network - hopefully meaning less confusion about where workers actually are situated, but bad news for those looking for a quiet day in the office tucked into a corner.

UK organizations are racing to modernize customer experience. AI-powered chatbots, agent assistants, workflow automation, and self-service tools are rapidly becoming standard across customer operations as businesses look to improve responsiveness, reduce pressure on frontline teams, and meet rising customer expectations.

But many organizations are discovering that deploying new technology is the easy part. The harder challenge is making those systems work reliably in the real world.

Too often, businesses are layering AI tools onto disconnected data, siloed systems, and infrastructure that was never designed for real-time customer engagement. On paper, the technology stack looks modern. In practice, customer experience still feels fragmented.

Customers are passed between channels without context. They repeat information multiple times.

Automated systems provide inconsistent answers. Human agents lack visibility into previous interactions. The result is a more complicated customer journey. This is becoming one of the defining challenges of enterprise AI adoption. For years, organizations focused on adding more digital capabilities to customer operations. Now, AI is exposing the operational weaknesses those businesses already had underneath.

The conversation around customer experience transformation has largely focused on speed and innovation. But the more important question is whether organizations can deliver AI-enhanced experiences consistently, accurately, and responsibly at scale. This is where trust becomes critical.

Customers are less forgiving of AI mistakes

In customer experience environments, trust is fragile. A delayed response may frustrate a customer. An inaccurate response can damage confidence entirely. This becomes especially important as generative AI moves into customer-facing interactions.

Unlike traditional automation, generative AI introduces unpredictability. Responses can vary. Outputs may be inaccurate. Systems can generate information that sounds convincing but is completely wrong. In highly regulated or customer-critical sectors such as financial services, healthcare, or public services, the consequences can be significant.

Customers are also often less forgiving of mistakes made by automated systems than those made by human employees. When AI gets something wrong, customers do not simply blame the technology. They blame the organization behind it. This is why many businesses are realizing that deploying AI is not simply a technology decision. It is an operational and governance challenge as well.

The organizations seeing the strongest long-term results are not necessarily the ones deploying the most AI tools. Instead, they are the ones creating environments where those technologies can operate safely, transparently, and with clear oversight. That requires far more than experimentation.

It means understanding where customer data comes from, how decisions are being made, when human intervention is needed, and how systems are monitored over time. Automation still requires accountability.

Disconnected systems create disconnected experiences

Many of the problems businesses face today are architectural rather than technological. For years, organizations approached CX transformation through isolated point solutions - separate tools for chat, analytics, automation, engagement, and workforce management. But disconnected systems inevitably create disconnected customer experiences.

Customers do not care which department, platform, or channel they are interacting with. They expect organizations to remember who they are, understand what has already happened, and resolve issues without forcing them to start again every time they switch touchpoints. That becomes extremely difficult when customer data is fragmented across multiple systems that cannot communicate effectively with one another.

This is why many organizations are now shifting focus away from simply adding more AI capabilities and towards operational consolidation. Businesses are recognizing that customer experience is no longer defined by individual interactions but defined by continuity across the entire journey. Without unified data, integrated orchestration, and consistent visibility across channels, AI risks amplifying operational complexity instead of reducing it.

The next phase of CX transformation will be about maturity, not experimentation

The pressure on organizations to move quickly is understandable. Businesses are dealing with rising service expectations, economic pressure, and ongoing demands to improve efficiency while maintaining customer satisfaction.

AI can absolutely help address those challenges. Used effectively, it can reduce repetitive workload, support frontline employees, improve response times, and deliver more personalized customer experience at scale.

But speed on its own it not a strategy.

The organizations that succeed over the next decade will not necessarily be the ones adopting AI the fastest. They will be the ones building the operational maturity required to make those systems reliable enough for customers to trust every day.

That means investing in integration, governance, data quality, and accountability with the same urgency that businesses are investing in AI itself. Right now, many organizations are still focused on what AI can do. The more important challenge is ensuring customers can trust how it behaves when it matters most.

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• Report finds 40% of British-based data centres are in London
• Recent analysis shows the UK has 555 data center installations operational, second only to the USA (4,423)
• Energy costs in the UK could slow development of data centres, following an OpenAI project being put on hold

A number of recent studies indicate that the UK has a strong data center industry, with the majority of facilities sitting within the M25. But as internet backbone speeds increase and planning is easier to get approved beyond London, the rest of the country is seeing an uptick in facilities.

Recent studies – including one by Statista – indicate a total of 555 UK data farms, placing it second in the worldwide charts, slightly ahead of Germany but massively behind the USA, which leads with 4,423.

But there are interesting developments, as while 40% of the UK’s data centres are within the M25, the South East ( the area immediately beyond London) and the North West boast the second and third largest collection of data centres.

A data center near you?

With London offering 40% of the UK’s data center capacity (219 are already operational, with 15 expansions planned), decision-makers are feeling the pressure.

While there is growing opposition to data centres in the US, public feeling in the UK is also growing, with a facility planned for London’s historic Brick Lane, amid concerns for water use, noise, huge structures, and electricity bills.

The British government announcement on July 31, 2026 that the development would proceed was met with some consternation.

Some of these concerns may also explain why data centres in the UK are popping up beyond the capital.

Prime Minister Andy Burnham’s native North West has 39 facilities, with 5 approved and set for completion, making it the most popular spot in England outside of London. In Scotland, 49 facilities are up and running.

Why-I data centres

Demand for AI and cloud processing and hosting is of course driving the development of new data centres, but not all parts of the UK are rushing towards progress.

Slow to add data centres are the largely rural areas of the South West (with 20 locations), and the North East, with just 21 sites. These regions may be slower to approve plans, or may simply have fewer applications to due to proximity to large population centres.

But this may change soon thanks to the Blackstone-backed development near Blyth in Northumberland, one of two approved within the past 18 months.

Challenges to developers and operators in the UK may yet contribute to a slowdown that might placate anti data center protesters. For example, an OpenAI data center intended for Cobalt Park in North Tyneside, was shelved in 2025 following concerns over increased energy costs in the UK, despite being a designated AI growth zone.

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Seeing the red 'limited-time deal' banner on some DDR5 RAM at Amazon should elicit some excitement given the rarity of price cuts lately. But when you click through and see the price for this 32GB Corsair Vengeance kit, you'll wonder if there's genuinely a deal here at all — and other wannabe buyers aren't wholly convinced, either.

"It's 'only' 4x the price it should be," wrote one user named Technikal on HotUKDeals, a community that shares deals from across the web and asks the community to vote whether they think they are hot or cold. "The current average is 5x," he continues, "so, in that sense it's a good deal...I guess."

A muted response, that's for sure. And while it may be true that the deal price of the RAM kit is technically one of the lowest prices available right now, it's so far from the much lower prices we saw before the RAM crisis turned buying memory for your PC into a depressing exercise.

"Blimey, just checked my receipt for mine from 2 years ago - cost £85..." says another HotUKDeals user named Jacko_1975.

The price for this specific RAM kit has increased dramatically over the last 12 months at Amazon. (Image credit: Future / Keepa)

In fact, even just a year ago, this very same RAM kit was available for £99.99 when it wasn't even on sale, according to price history information from Keepa.

Given that, it's no surprise that shoppers have reacted negatively to Amazon's flash deal. Unfortunately, though, it doesn't look like things are getting any better when it comes to RAM prices. A report from Samsung suggests the RAM crisis is just getting started, and it could extend to other components in the near future.

If you're looking at these deals and are trying to convince yourself to purchase any RAM in the near future, we've got a PC gaming expert delivering advice on what to do if you're considering buying memory, a GPU, or other components.

Sadly, the news is grim, and these prices could be with us for a while yet. So, yes, it may be difficult to feel happy about seeing supposed RAM deals like this, but from what I'm seeing and hearing, I don't think they're going to get any better, either.

Amazon has launched a limited-time deal on a set of much-sought-after Corsair Vengeance 32GB DDR5 6000MHz CL36 RAM — the sweet spot for most PC gaming builds. Even though you could technically argue this is a deal, as it's the lowest price we've seen in months, it's still astronomically more expensive than the record-lows of under £100 from last year. However, with the ongoing PC component price crisis showing no signs of ending, this is likely one of the better offers we'll see for some time.View Deal

Apple is testing CXMT DRAM for iPhones and MacBooks as it considers another memory supplier amid tight supply, higher costs, and U.S. scrutiny.

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Aptoide Games becomes the first rival Android app store distributed through Google Play in the US, following court-ordered changes from Epic v. Google.

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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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Apple raises trade-in estimates for iPhones, Macs, iPads and watches, with some devices gaining more than $100 and new Android phones now eligible.

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• NordVPN's antivirus blocked 99.1% malware in latest West Coast Labs test
• Artifact Security ranked NordVPN first out of five providers for performance
• The tests confirm connection times of just 1.8 seconds and zero data leaks

Users looking for the best VPN want the perfect balance of security and speed. According to two newly released independent evaluations, NordVPN is currently delivering exactly that on a global scale.

Over the summer, testing firm West Coast Labs (WCL) awarded NordVPN's built-in antivirus tool its highest AAA certification. Back in June, London-based Artifact Security crowned the vendor as the fastest and most reliable provider in a comprehensive global performance test, proving NordVPN continues to score all-positive results on independent audits.

For everyday users, these results are incredibly reassuring. It means you don't have to sacrifice your browsing speeds to stay safe online. The evaluations simulated real-world conditions, proving the app can swiftly block malicious sites and high-severity threats and handle high-speed downloads without breaking a sweat or leaking your personal data.

NordVPN – the best VPN overall
NordVPN came out on top in our 2026 round of VPN tests. We think it's the best VPN for most people. We’re confident that virtually anyone can sign up for NordVPN and get what they need from it. It’s easy to use, very secure, fast enough for gaming, and offers flawless streaming service unblocking.

Subscriptions start from $3.49 per month, and you can try it out risk-free with a 30-day money-back guarantee.View Deal

Top-tier malware protection

Under its WCL Validated methodology, West Coast Labs threw 934 malware samples at NordVPN's next-gen antivirus. The software successfully detected 926 of them, achieving an impressive 99.1% detection rate, well above the 95% threshold required for certification.

This latest achievement builds on the tool's previous successes, having already aced independent testing for its high phishing block rate in past assessments.

Crucially, the testing evaluated the product using its default configuration, meaning no custom tuning or vendor-supplied exclusions were used to artificially boost the score. NordVPN passed all 22 Core and 12 Augment lifecycle requirements across six stages, including deployment, functionality, and removal.

Because a single Core failure results in instant disqualification, this sweep earned NordVPN the highest available AAA certification rating.

"These results tell us the product is doing its job where it matters," said Marijus Briedis, chief technology officer at NordVPN. "What we’re most proud of is that this was tested under default configuration with no exclusions applied. That’s how most users actually run it, and that’s the standard we hold ourselves to."

Unmatched speed and reliability

(Image credit: NordVPN)

Security is only half the battle; a VPN must also be fast. Artifact Security's 2026 VPN Performance World Tour evaluated five major VPN providers (NordVPN, Surfshark, ExpressVPN, Proton VPN, and Mullvad) across five origin regions (Germany, Singapore, US West, US East, and the UK) spanning 55 route-pinned highways.

NordVPN dominated the field, finishing first with an overall score of 91 and taking home the test's only Platinum award. On its Windows VPN, the provider led the pack with an average download speed of 271.9 Mbps and a peak download median of 568.4 Mbps. It also proved highly responsive, averaging just 1.8 seconds to establish a working tunnel with a 98% success rate.

Most importantly for privacy advocates, all tested providers, including NordVPN, passed every standard and advanced leak scenario. Across IP, DNS, IPv6, WebRTC, server-switch, and split-tunneling tests, no traffic escaped the encrypted tunnel.

"Performance testing at this scale is a much harder standard to meet than a single-location benchmark," said Briedis. "Coming out first overall and winning three regions outright gives us confidence that the infrastructure improvements we’ve been making are translating into something users can actually feel, regardless of where they connect from."

Apple is reportedly exploring screenless wearables, new Apple Watch designs and more price tiers as competition reshapes the wearable market.

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