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For years, the phrase "AI-powered attack" has mostly meant a human using AI tools to write better phishing emails or scan for vulnerabilities faster. That framing just became outdated.

This month's intrusion into a major AI infrastructure platform was carried out, start to finish, by an autonomous agent.

No operator typed commands during the attack. No one was watching a terminal, deciding what to try next.

The agent found its own way in, escalated its own privileges, moved across internal systems on its own initiative, and kept going until it was caught.

It executed thousands of individual actions across a swarm of short-lived sandboxes, using infrastructure that migrated itself to stay ahead of takedown efforts.

That last detail is the one worth sitting with. This wasn't a script running on a loop. It was closer to a persistent, adaptive actor that happened not to be a person.

Security teams have spent the last two years bracing for what researchers called the "agentic attacker" scenario: a future where offensive AI doesn't just assist a human operator but replaces the decision-making loop entirely. That future arrived faster than most roadmaps allowed for. And it arrived through an unglamorous door.

The intrusion began not with some exotic zero-day but with a malicious dataset, exploiting weaknesses in how data gets processed and executed. Old lesson, new attacker. The place where AI platforms are most exposed is often the plumbing, not the model.

What makes this incident more than a cautionary anecdote is where the agent came from. It wasn't built by a criminal group. It emerged from an internal test, one company evaluating how capable its own models were at offensive internet security work.

The safeguards that would normally stop a model from behaving this way had been deliberately loosened for the purposes of that evaluation, and the agent found a flaw serious enough to escape the contained environment altogether. It got out, found a live target, and treated it the same way it had been trained to treat a benchmark: as a problem to solve, thoroughly and without asking permission.

This is where the industry's favorite excuse collapses. "The AI acted on its own" is true, technically. It is also irrelevant to the question of who is responsible. Nobody would accept that defense from a bank whose fraud-detection algorithm accidentally froze every customer account overnight, and nobody should accept it here.

An organization that builds a system capable of autonomous action, tests it with reduced constraints, and fails to contain it when it exceeds its boundary has made three decisions, all of them accountable ones. Autonomy in the tool does not create autonomy from consequences for the people who deployed it.

There's a harder problem sitting underneath the accountability question, and it doesn't have a tidy fix. These agents are not malicious by design. They are goal-pursuing systems, optimizing for an objective, and the gap between "pursue this objective" and "pursue this objective the way a human would want you to" is where things go wrong.

An agent instructed to find and exploit vulnerabilities doesn't inherently know where the test environment ends and the real internet begins. Alignment, in this context, isn't a philosophical nicety. It's the difference between a benchmark result and an incident report. As agents get assigned more ambitious, multi-step objectives, that gap doesn't shrink. It widens, because the more complex the goal, the more creative and unpredictable the path an agent will find to reach it.

Ironically, one of the more telling wrinkles in this incident had nothing to do with the attacker. When the victim organization tried to use its own AI tools to analyze the attack logs, the safety filters built into several frontier models refused to help, unable to distinguish forensic analysis of an attack from participation in one.

The team ended up relying on an open-weight model with fewer restrictions to do the job. That's worth flagging on its own: the same caution designed to prevent misuse can also blind defenders at the exact moment they need clarity fastest.

None of this argues for abandoning AI agents. It argues for treating sandboxing as an active security discipline rather than a checkbox. A test environment isn't safe because it's labeled as one. It's safe when it has been built and continuously verified to contain the specific class of behavior the system might attempt, including behavior nobody predicted at design time.

Reduced safeguards for the sake of a benchmark should carry the same scrutiny as reduced safeguards in production, because the line between the two is thinner than most evaluation frameworks assume. Governance needs to catch up with capability rather than trailing a year behind it, and that means treating agent permissions the way mature organizations already treat privileged human access: least privilege by default, monitored continuously, revoked automatically when behavior deviates from scope.

For security teams, the lesson isn't really about one company's bad week. It's that AI is now operating on both sides of the perimeter simultaneously, as the business tool a company depends on and as a potential attack surface with its own failure modes.

Detection strategies built around human attacker timelines, the hours and days it takes a person to escalate and pivot, won't hold up against an agent doing the same work in minutes. The organizations that come out ahead won't be the ones that avoided building agentic systems.

They'll be the ones that assumed, from day one, that their agents would eventually try to do something nobody authorized, and built the containment to survive it.

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It’s a tale as old as time. You tune into the big budget show about dragons and tyrants and your job in tech suddenly makes a lot more sense.

If you’ve been watching the new season of House of the Dragon, you’ll know the term ‘protector of the realm’. They’re kings, queens, or trusted governors: the ones responsible for the realm’s safety, ensuring laws are followed, and the medieval machinery of state ticks over. Back in Game of Thrones, ‘protector of the realm’ Ned Stark had his head chopped off and everything famously took a nosedive from there.

I’d argue this is a lesson in AI strategy.

At my company, Aiimi, we use this very same term - ‘protectors of the realm’ - to talk about the role of IT, Legal, and Compliance in operationalizing AI projects. But too often, businesses treat these governance functions as friction.

Whilst there aren’t any dragons for them to slay, these departments are there to protect your information and your systems - and above all, keep your company out of harm’s way.

Despite the work these departments do protecting companies from hefty fines and data breaches, I see an awful lot of corporate windbagging about how these departments bottleneck projects behind lengthy consultations, caveats, and due diligence.

The criticism is that they slow projects down. The reality is that they’re the difference between a fantasy strategy and a functioning one.

Ignore their counsel, and the whole structure gets weaker. Legal ensures AI tools follow information laws like GDPR and the new Data (Use and Access) Act 2025, so these systems only touch the data they’re meant to. Compliance maintains oversight once these systems go live, proactively ensuring that rules are followed and standards aren’t slipping.

IT does the essential work of making these projects function seamlessly in your organization's workflows and contexts. They maintain the infrastructure that AI runs on, ensuring that projects run on well-governed, structured data with documents having the correct permissions and access controls for safety. Without this groundwork, there’s little hope that an AI project can be effectively operationalized and return any sort of value.

But more than just facilitators of AI projects, these departments are the people in the room who know exactly what risks AI projects can pose, and what information they shouldn’t be privy to. Which, taking July’s Hugging Face fiasco as an example, matters more than most companies think.

Headlines would have you believe OpenAI’s models went rogue, broke through their safeguards, and attacked Hugging Face by their own autonomous decision. What actually happened is less dramatic and more damning.

OpenAI ran security tests with two AI agents where safeguards had been switched off. The testing sandbox, meant to be offline, was actually misconfigured to allow a connection out. The models escaped and attacked Hugging Face - not out of malice, but to cheat the security test by the laziest route available.

This was a governance failure. AI systems themselves can’t understand the reputational or financial risks that their actions might inadvertently cause. They do what they’re trained to do, and if there’s a shortcut to exploit, they don’t stop to check whether they’re meant to exploit it.

When critical business decisions are at stake, rushing a project past your protectors of the realm can leave you vulnerable to data breaches thanks to poor data security, or fines thanks to non-compliance. Having someone on board whose job it is to remind you of limitations and considerations serves to not only protect your organization, but make any AI project more resilient and deliver better results.

As AI regulation tightens and data security climbs up the agenda, it becomes more important than ever to have your protectors on side and fully integrated into design and deployment from the start.

There is sometimes a healthy level of skepticism in these departments so it’s key to invest in internal literacy programs to clearly explain the huge benefits of AI tools when they’re adopted safely, in line with your company’s governance principles.

Employees should walk away feeling clear about what terms like hallucination, training, and information retrieval mean for them, and how AI can fit into their work lives.

Once your protectors of the realm are aware of the terminology, they should be integrated into the design process of new AI projects to make use of their specialist knowledge.

Compliance might identify a specific challenge: governance teams are struggling to keep up with ensuring data quality and classification standards across different, growing systems in your organization.

This is crucial work that protects the business from breaching critical or sensitive data and powers data projects throughout the organization. They recognize that secure AI can help at scale. Now, together, you can develop a carefully planned use case that benefits multiple areas of the business: you need an AI-powered solution that can power data governance at scale by automatically classifying data.

IT can then help you plan how AI-powered data governance might fit into existing workflows and infrastructure, with data, compliance, and legal teams working together to understand the rules and give AI everything it needs to understand your business regulations and contractual obligations.

Your protectors of the realm should then also be part of any ongoing deployment. IT routinely checks the outputs of AI-powered systems, legal keeps abreast of any new legislation that you should be aware of, and compliance maintains ongoing human oversight so that you’re actively following regulation.

AI has incredible potential, but safe implementation is impossible if you don’t understand the rules. It might not be dragons and castles, but the companies that are positioned to generate reliable long-term success from AI projects aren’t the ones moving fastest, but the ones inviting their protectors of the realm to the decision-making table.

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The European Union's new Tech Sovereignty package highlights a challenging question for governments across Europe, including the UK: how can countries benefit from AI without becoming dependent on technologies developed elsewhere?

AI is becoming a critical driver of economic growth, public service transformation and business competitiveness. Yet much of the AI technology stack continues to be developed and operated primarily in the United States and China, creating a growing tension between AI adoption and strategic control.

Although the UK is pursuing its own approach to AI regulation, European policy developments will continue to shape the environment in which many British organizations build, deploy and govern AI systems.

Recent UK government warnings that Britain must secure "greater control and leverage over frontier AI" show that AI sovereignty is moving from a theoretical debate to a practical policy challenge.

Building every layer of the technology stack domestically is neither practical nor necessary. At its core, AI sovereignty is about maintaining the freedom to choose, adapt and innovate without becoming dependent on any single provider or platform.

Some countries are pursuing far-reaching self-sufficiency strategies. Others are building on existing infrastructure and expertise while using partnerships to close technological gaps. For most economies, the latter approach is likely to be more realistic. Sovereignty is not about building every component domestically; it is about ensuring strategic control and avoiding excessive dependence on any one supplier.

Regulation may have a role to play. While concerns remain about compliance burdens and their impact on innovation, the broader objective is clear: creating an environment in which organizations can adopt AI with confidence.

This is where dynamic and competitive AI markets become essential. A diverse digital supply chain creates options, strengthens resilience and reduces the risks associated with concentration. Recent restrictions on access to Anthropic's frontier AI models in some non-US markets are a reminder that when access to advanced AI capabilities is restricted, choice itself becomes a strategic asset.

Much of the AI policy debate focuses on the data centers needed to train large models. Yet infrastructure for broad adoption is equally important.

Distributed edge networks located closer to users help deliver AI applications with the low latency, performance and scalability required for real-world deployment. At the same time, access to AI tools must extend beyond a small number of well-resourced organizations.

Usage-based and serverless models can lower barriers to entry by reducing upfront costs and allowing organizations to pay only for the resources they consume. This enables SMEs, researchers, public institutions and larger enterprises alike to experiment with and adopt AI technologies.

Control over data is another critical element. Sovereignty depends less on where data is stored and more on whether organizations can manage access, security and compliance requirements effectively. Global, distributed architectures enable the implementation of access rules and controls over where data is processed and AI applications are operated.

Open standards and interoperable technologies are becoming increasingly important building blocks of digital autonomy. They reduce switching costs, strengthen competition and help prevent dependency on individual providers.

The same principle applies to AI models themselves. Organizations increasingly need access to a range of different open-source and proprietary models, allowing them to select the best solution for different use cases rather than relying on a single platform.

The debate around AI sovereignty is often framed as a choice between dependence and isolation. In reality, the most effective path lies somewhere in between.

Countries do not need to own every layer of the AI stack to exercise meaningful control. What they do need is access to open, competitive and resilient markets that provide genuine choice.

For the UK, and Europe alike, strategic control will come not from limiting access to technology, but from ensuring organizations have the freedom to choose how they adopt, deploy and govern it. In an increasingly interconnected world, that freedom of choice may prove to be the most important form of sovereignty of all.

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For the last two years, many organizations have treated AI adoption as the goal. The more users, tools and experiments, the better. Today, leaders are no longer measuring success by adoption alone. They are asking what it costs, where it creates value and whether the business can afford to scale it responsibly.

That is the next chapter of AI and technology spend management. The companies that thrive in this environment won't necessarily be the ones consuming the most technology. They will be the ones that can connect technology consumption to measurable business value.

This shift from maximizing usage to maximizing value, what I call valuemaxxing, is becoming a defining challenge for technology leaders.

Executives who take these five steps now will be prepared for a future where technology consumption and business value must be measured together:

Organizations cannot manage what they cannot see. This becomes far more urgent when costs are variable, distributed and constantly changing.

Today’s technology consumption does not sit neatly in one budget or one system. It spans SaaS applications, cloud infrastructure, data platforms, AI models, agents and infrastructure. AI adds another layer of complexity because spend can show up through tokens, credits, model usage, GPU consumption, data movement and AI-enabled applications.

With only 31% of organizations reporting visibility into AI software today and 59% reporting increased wasted AI spend year over year, gaining a single source of truth across the technology stack has never been more important.

Without a complete view of technology consumption, organizations are left making decisions with fragmented information. Visibility isn't simply about controlling costs; it's about understanding where investment is delivering value and where spend is wasted.

Many organizations encouraged broad AI experimentation, only to later discover that usage had outpaced oversight. Having an internal governance framework in place is critical to any company’s success. Governance gives teams the guardrails they need to scale responsibly.

For AI in particular, leaders need to move from “use more” to “use better.” It’s figuring out whether AI is improving cycle time, customer experience, operational efficiency, revenue growth, or another important business metric.

To guide the process, increasingly large enterprises (85%) are appointing dedicated teams or senior leaders for AI and tech governance to enable visibility, control, and cross-team collaboration. It’s vital for companies to prevent AI from becoming an uncontrolled cost center.

Technology pricing models are changing rapidly. Long-term fixed agreements may still have a place, but they are becoming harder to manage in environments where usage can shift quickly.

Today, leaders should regularly evaluate vendor agreements to ensure they reflect actual usage patterns and future business needs. This is particularly important as AI providers continue introducing new consumption models and monetization strategies.

Enterprises should expect more pricing complexity, not less. The goal is not simply to negotiate lower costs. It is to create agreements that give the business room to innovate while maintaining control over spend.

Usage-based costs impact multiple teams. While technology teams drive how much is used, other departments manage the needed oversight, with finance owning the budget impact and procurement handling vendor contracts. Without alignment, organizations can easily lose control of spending.

The organizations that manage consumption well will build a shared view of usage, cost and value. They establish common metrics, clear accountability, and regular collaboration across departments.

When teams work from the same data and the same definition of value, those decisions become more intentional and avoid costly surprises.

Use AI tools to help manage the growing complexity of technology consumption itself.

Optimization cannot remain a periodic budget exercise. By the time a cost issue appears in a report, usage may have already shifted again.

AI can help teams detect unusual usage patterns, surface waste, forecast demand, and support smarter planning across teams. But AI-driven optimization must be connected to human-defined goals. The objective is to help teams make better decisions faster, with clearer insight into what is being used, what it costs and where it creates value.

The next phase of enterprise AI will look very different from the first. For the last several years, the focus was on experimentation and adoption. The future belongs to organizations that can demonstrate accountability, governance, and value.

AI has accelerated the industry's shift toward consumption-based technology models, introducing new economic challenges alongside new opportunities. As organizations continue scaling AI, understanding the relationship between usage, cost, and business impact will become a critical competitive advantage.

The AI adoption spree is ending. What comes next is more disciplined visibility, governance, and financial accountability, defining who can successfully scale innovation and who gets overwhelmed by the bill.

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Cyberattacks are increasing in speed and sophistication, ranking among the biggest threats to businesses of all sizes and industries.

Previously unprecedented costs have made headlines; in October, a report from the Cyber Monitoring Centre revealed that the cybersecurity incident which affected Jaguar Land Rover in August cost the UK economy an estimated sum of 1.9 billion pounds.

Meanwhile, in North America, the aerospace sector has seen a spate of attacks, with WestJet’s June cyberattack resulting in the theft of 1.2 million passengers’ data while the data sets of 1.5 million flyers are believed to be compromised following September’s attack on Collins Aerospace.

Research released earlier this year revealed a stark dip in public trust; when questioned on which industry consumers trusted with their data, no single sector saw an approval rate of above 50%.

Without a clear and modernized security strategy, businesses are leaving themselves vulnerable to the far-reaching consequences of a breach, including reputational damage, operational delays, and financial loss.

AI has given everyone with a computer, tablet, or even smartphone easy access to automation, including those using it with malicious intent. In the cyber space, this allows criminals to continually change their approach, scaling their efforts and exploiting vulnerabilities in their target’s software. To keep up with evolving threats, Zero Trust is vital, prioritizing data over assumptions is not optional.

The contemporary approach to the principle of Zero Trust has been developed from the work of the Jericho Forum, made up of industry experts keen to establish a ‘de-parameterized’ model that enables a more granular and flexible approach to security.

The group, which later became part of The Open Group Security Forum, paved the way for John Kindervag’s popularization of the Zero Trust principle in 2009, emphasizing the importance of 'Never trust, always verify'. In a conversation with Gartner’s Neil MacDonald, he further explained that “Zero Trust is not a technology; it’s a security philosophy that rewires how we think about access”.

It's a smart idea; allowing security systems to keep up with industry change. In reality, however, despite the majority (96%) of companies incorporating, or planning to incorporate, a Zero Trust strategy, only 35% have made it to the implementation stage.

To decrease the frequency of corporate security breaches, this needs to change. The key to success? A mutually agreed understanding of what Zero Trust is and a cross-industry implementation drive.

The traditional business approach to cybersecurity, including an over-reliance on VPNs, led companies to draw a single ‘perimeter’ around their data. Thus, once breached, cybercriminals were able to steal and duplicate data from across the organization. In contrast, with a strategy led by strict adherence to the Zero Trust principle, access is confined to the singular section where the incident occurred.

The successful implementation of the Zero Trust principle requires a focus on data and information security across all networks and platforms. For example, the evaluation of risk should take place on a case-by-case basis, with deliberate decisions made to accept, mitigate, or transfer. With this approach, security teams have the flexibility needed to safeguard data and, when inevitable breaches do occur, ensure hackers can only access the top layer of information.

The use of Zero Trust as a basis for risk management necessitates a security infrastructure that does not become stagnant but is ever-changing to prevent cyberthreats from impacting the organization. At any point, a key tech stack component can become a target for criminals. In response, to drive resilience, security professionals should track any attempts, and techniques used, amending infrastructure in tandem.

Across sectors, cybersecurity budgets are growing. In March, the IDC's Worldwide Security Spending Guide predicted a spending growth of 12.2% in the year that followed, growing to $377 billion by 2028. Though this reflection of increased enthusiasm to strengthen security infrastructure is a step in the right direction, organizations need to ensure this investment isn’t just a one-off. Every element, and everyone’s access, must be continually questioned to protect from the escalating threat posed by a breach.

Solutions adopted with cybersecurity in mind may differ between organizations but, to drive resilience in the long-term and successfully embrace the principle of Zero Trust, vendor-neutral definitions of methodology and standards will be a necessity.

Once these are widely acknowledged, a commitment should be made to strengthening the underlying security infrastructure over the long-term, driven primarily by the principle of Zero Trust. In a landscape where security threats are ever-evolving, so too should each corporation’s ability to protect themselves from malicious actors. This includes blocking access to valuable data sets in the event of a breach and ensuring each person responsible for security is kept up to date with the latest insight on the nature of threats.

When applied in practice, though the exact methods and vision may differ, the guiding principles should remain consistent. To establish necessary considerations, organizations can start with the Zero Trust Commandments, which include the need for security to be integrated through culture and processes, the implementation of asset-centric controls, and the explicit validation of trust through using all relevant information available.

A news landscape dominated by security breaches and cyberattacks has created a generation of consumers that, more wary than before, are no longer shocked when the personal information they have willingly shared with corporations is taken with malicious intent. It should be expected that they will think twice before trusting a new organization with their information, placing an onus on businesses to prove that their infrastructure is robust.

As we look ahead to 2026, the companies that succeed will prioritize security, consider wider society expectations, and prove their commitment to Zero Trust in each decision they make.

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Most organizations in UK and across Europe don’t struggle to recover from cyberattacks such as ransomware because they lack backups. They struggle because they try to restore everything at once.

In the aftermath of a major cyber incident, the instinct is to bring every system back online as quickly as possible. It feels like the fastest path back to normality. However, in reality, this approach often slows recovery down, reintroduces cyber risk and undermines trust just when the organization needs it most.

In fact, those that recover fastest start from a different premise. They assume large parts of their organization will be unavailable or untrusted, and they plan accordingly. This mindset leads to a much clearer goal - restore what matters most, quickly, and in a state you can trust.

Focusing on the critical areas of an organization is the idea behind the Minimum Viable Company (MVC) concept, sometimes referred to as the Minimum Viable Organization. It is a definition of what must exist for the organization to survive in challenging conditions such as a cyber incident.

It’s not just a technology concept, it’s a business definition of survival in terms of the minimum combination of people, processes, technology, documentation, facilities, and third-party dependencies required to keep a business functioning and creating value.

There are five key capabilities when it comes to operationalizing an MVC:

A precise understanding of the systems and dependencies that directly support revenue and mission-critical operations is needed here. To understand the MVC, it is key to map systems to business value. Without this understanding, it’s impossible to accurately define the MVC.

The first steps focus on undertaking a structured assessment, aligning across business and technology stakeholders, and going through a realistic simulation of how recovery will unfold under pressure. This will uncover the key areas needed to provide just enough capability to keep the organization functioning safely during a crisis and guide recovery.

In practice, this means defining what must function in the first 24 hours, the first 72 hours, and the first week after a disruption.

In the event of a cyberattack, many organizations miss the critical foundational layer that allows them to establish identity and access control independently of compromised systems.

This foundational layer is what we call Tier 0 or the control plane for recovery. It includes identity and access management, networking and DNS, privileged access controls, core security tooling, physical access systems, and secure communication channels.

It also covers non-technical dependencies that are easy to overlook until they’re urgently needed such as incident response playbooks, contact lists and escalation paths, insurance policies, and contracts with external responders. These are the foundations underpinning the critical systems that need to be restored after a cyber incident. Without this layer, a trusted recovery is not possible.

In the event of a cybersecurity breach, organizations must establish control of their most critical systems. This requires recovering data separately from clean snapshots and investigating in parallel, not sequentially, to ensure the recovered systems are not infected by malicious software.

As part of this process, backups, configurations, and recovery tooling must be protected from the same blast radius as production. If key recovery assets can’t be isolated, a rapid and trusted control of critical systems can’t be achieved.

To set up an isolated environment to rebuild systems without reintroducing compromise, organizations need to set up what we call a ‘Digital Jump Bag’. This is a secure, isolated repository containing everything required to establish a trusted recovery starting point to rebuild systems without reintroducing compromise.

The next step is to validate the ability of the MVC to operate through realistic crisis scenarios. Resilience must be proven under real-world conditions. Practice is important here because an untested plan remains theoretical. Rehearsals will also help to answer the Board’s most direct question in the event of a cyberattack - how long will it take to restore critical services to a trusted state?

The most common cyber resilience risks are failing to define what must come back first and how to bring it back in a state that can be trusted. That’s the difference between recovery as a process and recovery as a capability.

The MVC isn’t static. As an organization evolves, its definition must evolve too. But the principle stays the same: recovery improves when organizations stop trying to restore everything and start restoring what matters.

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• Report points to AirPods 5 launching in September, in two models
• A cheaper version and a pricier option with noise cancellation
• We're expecting the big iPhone 18 Pro event to happen in early September

Despite all these news stories about AirPods with cameras, we've been hearing that 'normal' AirPods are set to release before the end of the year — and a brand-new report paints a much clearer picture of what we're about to see.

According to a report from Bloomberg's Mark Gurman, a prolific Apple tipster, the brand is teeing up to release AirPods 5, consisting of a standard pair of buds and an ANC-toting model.

It's suggested that these buds could come in early September — Apple's regular late-year launch is expected to bring iPhone 18 Pro models, Apple Watch 12 and a folding iPhone Ultra — but, by the sounds of it, there's wiggle room for them to release a little later. Still, it's often Apple's style to have one big event with lots of products.

Back in 2024, Apple expanded its non-Pro AirPods line two have two versions. We received the standard AirPods 4, and also the AirPods 4 with Active Noise Cancellation, the latter of which received a slightly more positive review from us. It was a similar model, but with a slightly higher price and ANC. It seems that Apple is continuing that trend.

Apple's standard AirPods see fewer leaks and rumors than the Pro model, and so there aren't any serious reports as to what we could expect. But we weren't glowing in our review, and there are a few tweaks Apple could make to improve them.

The main thing is simply the value. The AirPods 4 weren't expensive, but they just didn't offer as good value for money as the competition — the likes of the Nothing Ear (a) were cheaper, offered features such as ANC, and sounded better.

They stood in contrast to the AirPods Pro 2 (which were current when AirPods 4 launched) and later the AirPods Pro 3. The premium buds are excellent value compared to the competition, packing in more features, impressive sound, and a comfortable and light design.

Apple had the same problem I saw in Samsung's Galaxy Buds 4: a brand not making its cheaper options cheap enough. I'd like to see AirPods 5 either cost less (unlikely in today's tech world…) or have more features to give them a reason to cost more than the rest of the budget earbuds world.

I also think fit reliability is an area that needs to see changes too. It's a common problem in semi-open buds like the AirPods, that they don't grip in your ears as well as those with in-ear tips — I've lost count of how many AirPods runners I've seen lose their buds mid-jog. Apple's done good design work in this area, but I want to see improvements still.

Apple improved the battery life of its earbuds last time, and I hope it can do that again — they actually outperformed Apple's claimed figures, but it's normal to see earbuds hit close to 10 hours without ANC on, so it'd be great to see Apple get closer to that.

When it comes to audio, I hope they'll improve the way they handle detail. As we said in our AirPods 4 review, they lose fine resolution, which can affect the enjoyment of some tracks. Of all the upgrades I've listed, I'd imagine sonic tweaks are the ones most likely to come to fruition.

If Apple wanted to make some serious changes, it could make AirPods truly accessible to Android users (at the moment, most AirPods features require an iPhone, though they will function for basic audio with any Bluetooth device). I don't see the brand making this change, but it'd certainly shine a needed light on Apple's lesser-bought buds — however, Android support seems to be Beats' role in Apple's portfolio.

It's hard not to treat 2026 as a fallow year for AirPods. The fifth-generation models will likely be iterative updates, as the AirPods Max 2 were, and the camera-toting AirPods are expected to launch next year. But a few tweaks could help make the AirPods 5 feel like more than water-treading from Apple, even just two years after the previous models.

August's celestial wonders aren't done yet. Following the stunning total solar eclipse and the peak of the Perseid meteor shower a couple of weeks ago, we're set for another extraordinary show this week — a lunar eclipse.

On August 27-28, night sky observers in North America, South America, Europe, Africa and Western Asia are in for a treat, with a 96.2% lunar eclipse and 'blood moon' occurring.

It's not a total lunar eclipse — the next one of those takes place on December 31, 2028 (yes, new year's eve!) But for many, this week's eclipse will be the best until then, so it's well worth staying up late/getting up early for.

Here's exactly when and where the lunar eclipse can be seen — and my pro tips for capturing it with your camera of choice.

The lunar eclipse can be seen on the night side of Earth, which on August 27-28 means that large portions of North America and South America, plus the western half of Europe and Africa, will enjoy the best of the eclipse — its maximum.

Further afield, viewers in eastern Europe and western parts of Asia will catch a glimpse of the eclipse — the penumbral and partial phase, but not the maximum 96.2% — while Australia won't see it at all. In total, 44.4% of the global population can catch a glimpse.

A large portion of the US is ideally placed to see the lunar eclipse, including Washington DC and New York, because it reaches its maximum at 12:12am ET time, and the moon will be relatively high in the night sky.

Across Europe, the maximum eclipse occurs at 6:12am (CEST), which is 5:12am BST. In cities including London, Paris and Madrid, the maximum eclipse is around one hour prior to sunrise and as the moon sets, meaning the moon will be very low in the sky — offering the prospect of superb photography opportunities.

Those timings are for the maximum eclipse, but the 'penumbral' eclipse begins almost three hours before, and ends almost three hours after. Even as much as 90 minutes before maximum, the moon will start turning red as it moves into a partial eclipse phase.

• The timeanddate.com website provides exact times, dates and places for every eclipse, including for August 27-28, wherever you are in the world. It's a comprehensive resource, also covering eclipse FAQs, such as what is a lunar eclipse is, why the color of the moon changes and more.

The full eclipse cycle will last for about five hours in total, but you don't need to be on the alert for all of it. Let's take New York city as an example.

There, the event kicks off with a penumbral eclipse at 21:23pm ET (August 27), but viewing starts to get interesting from 22:33pm when the partial eclipse begins. This is a good time to set up your camera gear, because the moon will begin to lose its brightness, and will start turning reddish at its edge.

You'll soon see the weird sight of a crescent moon with a blurry straight line between the bright and dark areas. That's the line of Earth's atmosphere being projected onto the lunar surface.

At 00:12am ET (August 28), the eclipse reaches it maximum, and the moon will be at a viewing altitude of 38.3 degrees, which is relatively high in the sky. At 96.2%, a sliver of bright area of the moon surface remains, but the rest should be a reddish color to the naked eye. This is the maximum phase, when you should takes lots of photos.

From then on, the bright area of the moon increases once more during the partial eclipse phase, which finishes at 1:51am ET, and the eclipse is fully finished at 3:02am. You may wish to stay up for the entirety in order to capture key phases of the eclipse — great for a montage — but the main activity will be in the hour around its maximum.

Meanwhile, in London the timings are less favorable, but the components for photography could be ideal. The penumbral eclipse begins at 2:23am BST (August 28), the partial eclipse begins at 3:33am, while the maximum eclipse is 5:12am, at which point the moon is close to the horizon, and it's approximately an hour before moonset/sunrise.

In the UK, you'll need a clear vantage point looking west to southwest to see the moon (and a dose of luck, if the forecast for cloud is accurate), and if you can plan to include landmarks/points of interest in the frame, it should make for dramatic images of the eclipse's maximum phase. The moon will have set below the horizon before the partial eclipse (after maximum) ends.

So, how do you do photograph a lunar eclipse? The good news is that you don't need any filters, as you do with a total solar eclipse. The bad news is that since the moon is so small, you're going to need some magnification for the moon to fill most of the frame. That means either using a telephoto lens, whether that's on a DSLR or a mirrorless camera, or a superzoom compact/bridge camera. Taking decent photos of the moon with your phone is going to be a tougher challenge.

In terms of focal length, a 600mm-equivalent lens (25x zoom on a phone, approx) should fill most of the frame with the moon. You might want to use a slightly wider-angle lens to include landmarks in the frame.

On a smartphone, typically the best optical zoom reach you'll have is 5x. The quality of a digital zoom is poor, and only gets worse the more you zoom in. Some flagship devices such as the Oppo Find X9 Ultra have better telephoto reach — 10x in the case of that phone — and a teleconverter accessory to further extend the maximum reach. My advice for most people is to use a camera with a telephoto lens instead.

Whatever camera you opt for, I recommend attaching it to a sturdy tripod support — especially if you're capturing multiple phases of the eclipse with landmarks in the frame. A tripod minimizes camera shake for sharper shots, and delivers the continuity you need for capturing the phases. Alternatively, find a secure surface to rest your camera on. A self timer or a remote shutter further minimizes camera shake, while shooting in the RAW format rather than JPEG/HEIF gives you more flexibility for editing.

A lunar eclipse isn't easy to capture, but you will have plenty of time to play with various settings of your DSLR/ mirrorless/ superzoom compact camera. As well as a wide range of colors, lunar eclipses bring a lot of changes in brightness. Also bear in mind that when zoomed-in, the moon is a very fast-moving target (blame our rotating planet).

Consequently, I recommend setting the white balance manually — the Tungsten setting will maintain the color accuracy of the bright moon and rich blue of the night sky.

You'll also need a high shutter speed. So, as the moon dims in brightness, increase the ISO (try ISO 200 during the partial phase, and ISO 1600 during the maximum phase) and open the aperture (start at about f/11 and experiment).

However, it's not just about getting a sharp image, because during the partial eclipse, the moon will be part-bright and part-dim, so you have to make a choice about which side of the moon to expose for. The answer, of course, is to try both, many times over.

Take a spare battery out with you, make sure your memory card has plenty of space on it, and prepare for a marathon post-processing session. Most of all, cross your fingers for clear visibility — cloud cover puts paid to the best-laid plans!

• Get more tips on how to photograph the moon