TechRadar News

• John Ternus has warned that: "What we're seeing with memory pricing right now is unprecedented."
• The Apple CEO cautioned that RAM costs are going to climb higher in this final quarter of 2026
• It sounds even bleaker than Apple's previous statements on the RAM crisis, and follows many other warnings that 2027 is set to be a miserable year price-wise

Apple is warning of an 'unprecedented' rise in the cost of RAM, adding to the cacophony of alarm bells that have been ringing about the price of memory chips (and other PC components besides) recently.

The Guardian reports that in a conference call following Apple's fiscal Q3 results — which were better than analysts expected, driven by the iPhone, Mac mini and sales in China — Apple's new CEO John Ternus touched on the subject of the RAM crisis.

Ternus, who recently took the reins from Tim Cook, cautioned bleakly that: "What we're seeing with memory pricing right now is unprecedented."

He then observed that memory costs would climb even higher in the final quarter of 2026 compared to the rest of the year, during which, as you've doubtless seen, we've already witnessed a steep climb in pricing.

Despite its financial results outperforming analyst forecasts, Apple informed investors that these higher memory costs are going to erode some of its profit margins, which meant the company's stock dropped.

Apple raised the prices of Macs earlier this year, as well as iPads and more recently iPhones, and Ternus acknowledged that this had "provided some offset" for Apple in terms of the ongoing cost crisis around components.

Analysis: Ternus takes a turn for the sterner

(Image credit: Apple)

It's an ominous utterance from Ternus, and sounds more severe than the warning Apple issued back before the Mac (and other) price hikes happened in June 2026. Back then, when Cook was still chief executive, he warned about "significantly higher memory costs" that would "drive an increasing impact" on Apple's products, which, as noted, came to pass.

Now we're hearing the word "unprecedented", which sounds a lot more concerning than "significant"; although in fairness, this is referencing what's already happened this year as much as what's to come. But we're clearly being primed for more price-hike misery by Ternus here, and it's difficult to see how this won't have a further impact on Macs, iPhones and iPads, if not in this quarter, then in 2027.

We don't, of course, know to what extent profits might be eroded by more expensive memory chips, or how this might be balanced by consumers having to foot that bill. Only time will tell on that score.

Mac mini sales were mentioned in the report — which quoted Ben Barringer, the head of technology research at investment management firm Quilter Cheviot — as being responsible for part of Apple's boosted earnings for the most recent quarter, and that's down to the increasing demand for local AI.

The Mac Studio is major part of that picture, too, and this particular PC just popped up as an example of how ridiculous RAM pricing has just become at the very top end of the Apple product tree. If you want a Mac Studio with 512GB of RAM, you'll now pay more than double the cost of a Mac Studio that has 256GB of memory, meaning that it's cheaper to buy two of the latter computers (while getting two CPUs/GPUs in the process).

It's a bizarre situation, but this is what we've arrived at with memory upgrades, which were always seriously pricey with Apple, even before the RAM crisis kicked off (that goes for storage, too).

And this situation is only going to get worse, seemingly, according to Ternus, which is also, of course, the view of the wider industry and various analysts. I can't begin to count the warnings we've had in the past couple of months about the RAM crisis becoming even more dire, so at this point it's just a question of how much worse things might get.

I fear that some categories of tech and PC components are going to become increasingly unaffordable next year, and I recently wrote about why Black Friday could be a good time to move on certain purchases.

• Reddit wants to overturn Australia’s social media ban for under-16s
• It is challenging the law in the Australian High Court
• Government says it’s defending the law from "sustained ideological attack"

In late 2025, Australia became the first nation in the world to ban social media for under-16s — and as you would expect, that hasn’t gone down particularly well with the app makers affected. Reddit, for example, has issued a stinging rebuke to the Australian government and is in the process of challenging the ban in court.

A lawyer representing Reddit called the ban “ill thought out” (via Reuters) and said it was a “profound ‌restriction on political debate.” The attorney was speaking at the Australian High Court, where Reddit is seeking to contest the country’s new law.

Together with a libertarian advocacy group, Reddit claimed in court that the social media ban was “a very substantial restriction on what young people can now do” and that it could stifle political debate. It added that the restrictions may stop users creating content and responding to posts.

Although Reddit said it supported the aims of the law, it branded the legislation “inept,” saying that it did not have any specific means to keep minors off social media and failed to regulate other platforms where cyberbullying might take place, such as on gaming websites. And the fact that users who were not logged in could still view huge amounts of material undermined the law, Reddit argued.

Could Reddit overturn the ban?

(Image credit: Getty Images / Anna Barclay)

Since the Australian law came into effect, several studies have shown that large numbers of teenage users targeted by it are still able to access restricted material. Sometimes, that might be as simple as using a VPN service to disguise your location, while at other times, social media firms are simply not taking action to restrict usage by under-16s.

Despite Reddit’s criticism, the Australian government has said it will defend its legislation. In court, the state’s lawyer, Joanna Davidson, said that children were more vulnerable to harmful online content because their brains were not fully developed compared to those of adults.

And speaking outside the High Court, communications ⁠minister Anika Wells ​said: “We ​are here to defend a fundamental tenet of Australian democracy, which is an Australian’s right, and particularly an Australian child’s right, to be free from harm. We seek ​to defend that from sustained ideological attack.”

The government has doubled the maximum fine for non-compliance with the law to AU$99 million (about $69.5 million / £51.5 million), increased the discovery powers of the eSafety Commissioner, and said it is preparing enforcement action against some of the largest social media platforms operating in Australia.

Whatever side you take on this matter, it’s clear that neither the Australian government nor Reddit are preparing to back down anytime soon. And with other nations considering similar laws, many people around the world will be watching closely.

• Elon Musk mocks Sam Altman's recent claim that people shouldn't own RAM
• "The people deserve to have their RAM too!" Musk's meme on X says
• Altman has been keen to secure more memory for OpenAI recently

Elon Musk has mocked OpenAI CEO Sam Altman following the latter's declaration that owning a PC with RAM is 'quite terrible', as the components should be powering the next generation of AI data centers.

In his typically irreverent manner, Musk shared a meme on X, the social network he owns, showing a version of a scene from Inglorious Basterds with a German WWII officer played by Christoph Waltz apparently hunting for people hiding sticks of DRAM.

"The people deserve to have their RAM too!" Musk's meme declares.

Stopped clock

Altman made his claim during an appearance on the YouTube channel Cheeky Pint, with his beliefs causing widespread condemnation and confusion across the industry.

"From a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer and being used 5% of the time," Altman said.

"You want maximum utilization in the cloud if you're trying to deal with this very supply-constrained environment. And somehow many people's intuition is the opposite of that."

Altman's comments come as consumers face rising prices for any kind of computing hardware as the demand for RAM and other components gets passed down.

In recent months, we've seen the likes of Intel CEO Lip-Bu Tan warning of continued shortages and price rises, noting, "AI hardware will stay in short supply for the next 2 to 3 years", a trend he said was "driven by persistent constraints in the underlying tech supply chain."

Dell CEO Michael Dell has also voiced his belief that the memory shortage will get worse through 2027, and that these RAM woes are going to be prolonged for quite some time.

Top DRAM manufacturers Samsung and SK hynix have also warned that AI-driven memory shortages will last until 2027 and beyond, with their supplies rumored to be down to fewer than 10 days of memory inventory.

Interestingly, Altman reportedly recently met with about 50 senior Samsung executives in South Korea, as he looked to secure more hardware for OpenAI to expand its AI capacity.

But no matter what happens, one thing that (probably) won't happen is Altman breaking into your house to steal your PC's RAM...probably.

• Octopus Energy, Ampace, and CATL are joining forces
• They're promising to 'transform' the smart home battery landscape
• We're expecting next-gen tech like sodium-ion batteries

It's a boom time for home energy solutions — from DIY solar kits to smart home batteries — and three of the biggest names in the industry are joining forces to build the next generation of home energy storage solutions.

Energy supplier Octopus and battery manufacturers CATL and Ampace have announced a 'Memorandum of Understanding' (MoU) to work together to produce the best possible combinations of hardware engineering and software intelligence.

Octopus will bring tech such as the smart tariff, mobile app, and customer ecosystem platforms, while CATL and Ampace supply the battery chemistry and hardware nous. Initially, the initiative will be focused on European markets.

At this early stage we don't have details of any actual products, but expect advances in cutting-edge tech like sodium-ion batteries, high-density cell designs, and long battery lifecycles — all of which should benefit customers and reduce electricity bills.

Looking to the future

Octopus Energy has plenty of experience with smart meters (Image credit: Octopus Energy)

According to Octopus founder and CEO Greg Jackson, combining the efforts of the three companies will "transform home energy storage", and offer "next-generation batteries where hardware and software work in complete harmony".

Jason Zhu, the President of Energy Storage Systems at Ampace, is equally enthusiastic: the partnership will "fundamentally reshape how energy is delivered and consumed" as systems become "smarter, more connected, and more accessible".

Of course we'll have to reserve judgment until actual products and offers arrive, but Octopus Energy, CATL, and Ampace all have strong track records. The products that we've seen from these companies, we've liked.

The joint press release emphasized the ability for home batteries to charge up during off-peak times and share power back to the grid at times of highest demand, which should mean significant savings for customers — something everyone can support.

In July 2026, OpenAI disclosed that several of its cyber-capable AI models, operating during internal evaluations, circumvented controls intended to isolate them from the internet and compromised parts of OpenAI’s research infrastructure and Hugging Face’s systems.

A fuller report published the following month revealed that the agents had exploited shared infrastructure, communicated through unauthorized channels, collaborated with one another and accessed several external systems. OpenAI described the incident as a “warning shot” demonstrating that capable AI agents can find and exploit weaknesses across interconnected systems when safeguards are insufficient.

Despite the alarming headlines, this should not be viewed solely as a failure of artificial intelligence. It offers an important security lesson for organizations preparing for a future in which autonomous systems play a larger role in cybersecurity operations, business processes and software development.

The most valuable takeaway is not just that an AI model “escaped”, rather the lesson is that even controlled environments can contain unintended paths to sensitive systems, credentials or data. Once such a path exists, a sufficiently capable and persistent agent may find it.

The positive side of early disclosure

Every significant advancement in security has resulted from discovering risks before attackers could exploit them at scale. In this case, the activity occurred during controlled evaluations rather than a criminal campaign. The organizations involved were able to identify weaknesses in containment, access controls and environmental design before similar techniques could be widely adopted by cybercriminals.

The incident also demonstrated something important about modern AI systems: persistence. When confronted with obstacles, the models did not try to stop. They continued pursuing their assigned objectives and searched for alternative routes.

Security teams perform penetration tests and red-team exercises because human attackers rarely follow a single path. Advanced AI systems are becoming capable of behaving similarly, exploring multiple options and adapting when obstacles appear. The earlier organizations understand this reality, the better prepared they will be.

The real issue was not one model

Much of the initial discussion focused on the behavior of OpenAI’s models. Subsequent disclosures have shown that the underlying challenge is more nuanced. Anthropic identified three incidents in which Claude models reached the public internet during cybersecurity evaluations and gained unauthorized access to real organizations. OpenAI also disclosed separate cases involving third-party testing environments in which model activity extended beyond its intended boundaries.

These incidents, though similar, were not identical. In some cases, models exploited vulnerabilities to circumvent isolation. In others, configuration errors left routes to the internet open while the models had been told they were operating within simulations. However, the common lesson is that instructions, policies and safety guardrails cannot compensate for weaknesses in the surrounding infrastructure. The instructions described one set of boundaries while the technology enforced another.

Security professionals have encountered this problem for decades. A firewall rule may be intended to block access while an overlooked network connection creates an alternative path. A user may be prohibited from accessing data while excessive permissions provide an unintended route. Autonomous systems create the same challenge at greater speed and scale.

An optimizing agent does not necessarily think like a human attacker. It pursues the objective it has been assigned and evaluates the available options. Organizations should therefore assume that autonomous systems may pursue their objectives through any path the environment leaves open.

Security must move beyond AI guardrails

Much of the discussion around AI governance has focused on whether a model should answer a particular prompt or perform a particular action. Prompt-level guardrails remain important, but they are no longer sufficient on their own. Security teams must continuously assess who is authorized, which systems and data they can reach, and what actions could create risk. Each layer should be tested independently rather than relying on a single refusal mechanism to govern them all.

The OpenAI incident illustrates this clearly. The problem moved beyond models possessing advanced capabilities, to one where capabilities, connectivity, credentials and autonomy came together in environments where containment mechanisms were insufficient.

Organizations must therefore consider AI security as a combination of identity, authorization, containment and oversight.

The conditions for trusted autonomy

In order to combat this, defenders need to align themselves to a new framework that is shaped by frontier AI: Assume Autonomy. The framework begins with the expectation that adversaries and defensive controls may operate through automated, non-human agency. It is built around context, constraint, transparency and reversibility.

Context means understanding what an agent can access, which identities it can use and how those systems connect to other resources. Constraint requires access to be limited through technical controls rather than instructions or expected behavior alone.

Transparency provides visibility into what an autonomous system is doing while it is happening. Reversibility ensures teams can stop workloads, revoke credentials, isolate systems and recover quickly when unexpected behavior occurs.

For high-capability agentic systems, governance must address these areas with evidence rather than assumptions. This requires autonomy stress testing, independent red-team exercises focused on containment failures, testing of identity and network boundaries, audited isolation controls, runtime authorization for high-impact actions and rehearsed rollback procedures.

Responsible access to powerful AI

Incidents like this often prompt calls for tighter restrictions on advanced AI models, but the reality is more complicated. Attackers can switch between models, providers or open-source alternatives when they encounter restrictions. Defenders, particularly smaller organizations, may not have those options. If advanced defensive capabilities become slow, expensive or heavily restricted, the result could be reduced defensive readiness without a corresponding reduction in offensive capability.

The objective should be responsible access. Verified identities, strong authorization controls, immutable logging, human accountability and time-limited access can provide meaningful safeguards without unnecessarily restricting defenders.

Preparing for autonomous systems

Organizations cannot improvise security once an autonomous system begins acting unexpectedly. The necessary controls must already exist.

This includes deny-by-default network access, workload-specific identities, short-lived credentials, segmentation between evaluation and production environments, telemetry that can correlate activity across systems, and rehearsed containment procedures. Organizations should assume that a capable autonomous system will discover every reachable path that advances its objective and ensure that no such path grants unintended authority.

These incidents should be viewed as proof that security architecture must develop alongside rapidly advancing autonomy. Organizations that adapt now will be better positioned to harness the benefits of AI tools while keeping risk under control. Those that rely on instructions alone may discover that technology follows the paths we leave open, rather than the rules we intended it to follow.

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Business leaders, governments, and tech giants are locked in a high-stakes race for AI supremacy, fueled by debates over the destructive power of superintelligence. In this sort of arena, it’s essential that enterprises stay grounded and resist the urge to start a race of their own, deploying ever-larger models simply for the sake of it.

In the context of enterprise AI, there is no prize for launching the most powerful model first, building the highest number of agents, or forcing AI into every workflow. True success is defined by solving genuine business problems and delivering outcomes that justify the underlying cost and complexity.

While the prospect of a single, omnipotent large language model (LLM) is inspiring, today's business realities demand a more measured approach: every problem has a right tool, and often, that tool is a simple one.

AI doesn't have to mean LLMs

Before generative AI dominated the conversation, businesses were already using machine learning for recommendations, forecasting, fraud detection and estimating things such as the lifetime value of a customer.

We have had years to understand these technologies, including how to deploy and monitor them and make predictions at scale quickly and cheaply. An LLM may be capable of tackling some of the same problems, but that doesn't always make it the better option. More sophisticated models can cost considerably more to run and introduce variability that is unnecessary for a straightforward prediction.

Sometimes AI isn't necessary at all. If a process follows known rules and needs to produce the same result every time (moving data between systems on a schedule or triggering an action when a predefined condition is met, for example), conventional automation may do the job perfectly well.

Long before agents arrived, businesses handled these tasks with pipelines, schedulers, and rules. If that approach is reliable, fast, and inexpensive, adding an AI agent doesn't make the process more intelligent; it can simply make it more complex.

Generative AI earns its place when creativity or interpretation are required: understanding language or images, for example, or handling a process where the next step changes according to context. Agents can go further by using that understanding to interact with systems and take actions, opening up processes that were previously extremely difficult to automate.

The future is likely to combine conventional automation, traditional ML, generative AI and agents, rather than one replacing everything that came before it.

The AI honeymoon is ending

These distinctions matter more as enterprise AI moves beyond experimentation. A proof of concept can demonstrate what a model or agent is capable of, but production introduces running costs, security, monitoring and the work required to keep increasingly autonomous systems under control.

An engineer once gave me an analogy that captures this well. Imagine growing a tree that you need to maintain in a particular shape. When a branch grows in the wrong direction, you cut it.

Then another appears elsewhere. The difficult part is finding all the branches. Developing an agent can be similar. Obvious behaviors can be controlled relatively quickly, but real-world use exposes edge cases that weren't visible during development.

The more freedom a system has to interpret information and make decisions, the more important evaluation, monitoring and guardrails become. Control needs to extend beyond model behavior to the data a system can access, the actions it is authorized to take, the information it retains and the point at which a human can intervene.

There is also cost to consider. The most capable models can be expensive at scale, so additional capability needs to create enough additional value to justify it. As experimentation gives way to scrutiny of ROI, businesses will become less interested in whether something can use AI and more interested in whether AI actually leads to an improved commercial or operational outcome enough to justify the cost.

Let the data decide

Avoiding over-engineering starts with the business need and the data, rather than choosing the technology first.

Data engineers and analysts are crucial because they understand where information comes from, how reliable it is and what it can actually tell you. AI and ML engineers can put mathematical models on top, but sophistication cannot compensate for data that doesn't support the task.

The data and desired outcome will often point towards the appropriate approach. Fast, reliable predictions from structured numerical data may be better suited to traditional ML.

Language, images and other unstructured information can make generative AI valuable, while processes that require human level understanding to trigger actions across different systems may justify agents.

And sometimes conventional software will remain the best answer. Starting with “we need an agent” and then looking for a problem to give it is the wrong way around.

The right people matter as much as the technology

Increasingly capable AI has also created an assumption that businesses will inevitably need fewer engineers. AI is certainly changing productivity: experienced engineers can complete work faster with these tools, while junior engineers who learn to use them effectively can become productive more quickly.

But businesses still need people who understand what is happening underneath. I like to compare it with smartphones. Most of us use a phone effectively without being able to explain the hardware, operating system and networks behind it. That doesn't mean the underlying engineering skills have disappeared. Somebody still needs to understand those systems.

AI is likely to develop in a similar way. Many people will become highly capable users, while businesses will still need engineers with the deeper technical knowledge to build, control and fix the systems underneath.

That means continuing to invest in junior talent too. Senior engineers eventually move companies, change roles or retire. Organizations still need mentorship and knowledge transfer so today's juniors become tomorrow's senior engineers.

The opportunity isn't people or AI, but the two together. Capable engineers equipped with these tools can achieve considerably more than either could independently.

The next AI advantage will be restraint

The wider battle over superintelligence and increasingly powerful models will continue, but it shouldn't dictate how enterprises make technology decisions. To make this technical restraint viable in practice, engineering leaders must actively manage executive and board-level fear of missing out, turning top-down pressure for a visible ‘AI strategy’ into discipline, value-driven execution.

Some business problems will justify sophisticated AI tools. Agents will make previously impractical forms of automation possible, while generative AI can transform how organizations work with language, images and unstructured information. Others will be solved perfectly well by traditional ML, conventional automation or straightforward software.

Knowing the difference will become increasingly valuable. Competitive advantage will come from knowing where AI materially improves an outcome, where a simpler technology is more effective and how to move the right systems into productions securely and economically.

That requires good data, strong engineering talent and enough technical judgement to resist complexity simply because it is available. In the next phase of enterprise AI, restraint will not signal a lack of ambition, instead it will be a sign of technical maturity.

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• Owl Labs argues that tracking workers' activities doesn't show how they're working
• 82% of workers believe employees should have to disclose the use of monitoring tools
• The biggest opportunity to improve productivity comes from upgrading hybrid meeting tech

New research has claimed almost three in five (59%) UK workers say their company is using monitoring software to track their activity both across basic attendance but also computer activity like mouse movements, keyboard usage and meeting participation.

And a further four in five (82%) believe that employers should have to disclose their use of monitoring software – something that's not currently required by law.

But despite a rise in workplace monitoring software, likely driven by a surge in post-pandemic hybrid working, the report from Owl Labs argues that tracking doesn't actually provide any meaningful insight into work at all.

Is your employer tracking how and when you work?

According to the data, the most commonly monitored activities are login/logout times (32%), number of meetings attended (22%), screen and mouse movement (17%) and keyboard usage (15%) – however, workers are already finding workaround to falsely appease tracking metrics.

For example, more than a quarter (28%) of UK workers admit so-called phantom meeting attendance, where they mark a meeting as 'attended' or even attend it without paying attention, and then catch up later via notes and summaries. It's something that managers (33%) are more likely to do than non-managers (15%), the report found.

The study also pointed to an increase in AI avatar interest, with 50% of managers and 21% of non-managers considering using an avatar to attend meetings on their behalf – further making meeting attendance metrics less important.

Hybrid work's impact on workplace tracking

Owl Labs also revealed some losses in efficiency as a result of hybrid working, but ironically, having workers placed across multiple locations isn't the issue. It's the tech they're being handed out.

Around three in four (73%) UK workers reported losing time because of technical difficulties during hybrid meetings, with audio problems generally the most significant. Teams spend on average six minutes to get a hybrid meeting started.

The report implies that spending money on better AV tech to support hybrid meetings may create a better outcome than simply deploying software to see who's attending those (suboptimal) meetings.

What does tracking software mean for workers?

Owl Labs CEO Frank Weishaupt urges companies to be more transparent about what they're tracking with their workers, helping improve trust between workers and higher up.

"With the Government reviewing how workplace monitoring should be regulated, now is the time for employers to be open about what they track and why," Weishaupt added. "Trust and reliable technology are what keep people productive, and what keep them in the business."

We've always liked testing Western Digital's WD Red Pro line of NAS HDDs, which emphasize reliable for long-term storage. During reviews, we've found it difficult to argue with the resilience of this drive.

For a limited time, the WD Red Pro 18TB hard drive is $700 (was $855) at Newegg when you use code BNWF49. That equals almost 4 cents per GB. While not the cheapest high-capacity drive (especially as component costs soar), for sheer reliability, the Red Pro range comes recommended by us.

• Best for: NAS owners, home lab builders, and small businesses that need high-capacity, always-on storage.

• Why we like it: 18TB of capacity in a single drive, 7,200rpm speeds with a 512MB cache, a 300TB-per-year workload rating for NAS systems with up to 24 bays, and a five-year warranty. Other capacities are available, from 4TB to 26TB.

• What you need to know: In general, we recommend sticking to a specific brand and model for your NAS system, as they're designed to work in harmony with others.

Today's best NAS drive deal

Red Pro 18TB: was $854.99 now $699.99
A 3.5-inch 18TB NAS hard drive (model WD181KFGX) spinning at 7,200rpm with a 512MB cache and a SATA 6Gb/s interface. It’s rated for workloads of up to 300TB per year in NAS systems with up to 24 bays and carries a five-year limited warranty. View Deal

Why we recommend it

WD’s Red Pro line is a long-standing favorite among NAS users, built for the vibration and constant activity of multi-bay systems that run around the clock.

We haven’t reviewed the 18TB model, but we did test out the 14TB version in our 4-star review. It's not the fastest, but being designed for reliability over the long term is arguably more important if you're spending this much on a HDD.

For most users, 18TB strikes the sweet spot between high-capacity storage and lower initial investment compared to the 22TB to 26TB offerings.

Price Context & Historical Value

Currently, it feels like every component is spiking in price, and this HDD is no different. Just a few years ago, this same model sold for $300. Still, in today's market, it represents a good deal rather than a historic low. And the $155 off gives it a solid saving on one of WD’s highest-capacity NAS drives. The five-year warranty is a real advantage over standard desktop drives, which often come with only one or two years of coverage. If you need the space now, it’s worth grabbing before the code expires.

Should you buy it?

✅ Buy the WD Red Pro 18TB if... you need high-capacity, always-on storage for a NAS or home server, you want a drive rated for heavy workloads in multi-bay systems, or you value a five-year warranty.

❌ Skip the WD Red Pro 18TB if... you only need a simple backup drive for one PC, since an external drive is easier and cheaper; you’re building a quiet setup, since 7,200rpm drives are audible; or you can wait in case hard drive prices ease.

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The CMA has published roadmaps for Big Tech before, just not for Microsoft. With £400 billion in annual public sector procurement and millions flowing to Microsoft’s cloud, the CMA can’t wait until February 2027 to act.

The topic of public procurement is becoming a major issue for UK economic policy. Sarah Cardell, the CMA's Chief Executive, delivered a speech at a Parliamentary event at the House of Commons on 8 September, introducing two reports by the CMA published that day: Public Procurement in the National Interest and Rigged Bids, Real Costs.

The headline conclusion is, perhaps unsurprisingly, that UK public procurement could be leveraged far more effectively to shape markets and improve outcomes for citizens, businesses and taxpayers alike. But a more nuanced point, and arguably the most important one, is that the biggest risks arise when short-term cost savings are prioritized at the expense of long-term value and competition.

Cloud computing is one of the starkest illustrations of this trade-off in practice. Buying Microsoft is, in the immediate term, the path of least resistance: it is already embedded in most public-sector IT estates, integration is straightforward, and the near-term cost of switching or diversifying looks far higher than the cost of simply renewing. That short-term logic is precisely why Microsoft has wound itself so tightly around the machinery of UK public-sector cloud infrastructure.

Each individual procurement decision may look like sensible, low-risk value for money at the time it is taken but cumulatively, those decisions have entrenched a single supplier so deeply into public-sector data, identity and security architecture that it now poses a very real threat to competitive procurement in public-sector AI. The apparent saving today is a major liability for tomorrow, once every AI rollout has to be built on top of infrastructure only one vendor fully controls.

A market out of balance

The CMA's cloud market investigation reached a clear verdict in July 2025: competition wasn't working well. Microsoft and AWS each hold 30–40% market share with Microsoft's share still growing, and barriers to entry including data-center costs, economies of scale, incumbent product breadth. have been deemed likely to keep it that way.

Apparently, fewer than 1 per cent of customers switch cloud providers annually, held back by egress fees and technical friction. Most tellingly, the CMA found Microsoft withholds certain licensed products from AWS and Google that directly weaken those rivals; a problem with clear implications for public-sector buyers seeking competitive procurement.

A new AI market, losing balance

However, as Microsoft rapidly rolls out new AI tools across UK enterprises and the public sector, the fundamental issue of cloud infrastructure lock-in is being overlooked. The true barrier to a competitive AI market isn't just the stickiness of new daily workflows, but the interconnected stack comprising software, data, identity, security and the foundational grip Microsoft maintains through its legacy enterprise software, specifically Windows Server and SQL Server.

Once organizations are penalized for running these essential core workloads on rival clouds, their entire data gravity shifts permanently into Microsoft's ecosystem. Building subsequent AI processes and agents around these locked-in data structures makes opting for competing, innovative AI solutions financially and technically unviable.

The CMA has already diagnosed how Microsoft uses these core licensed products to weaken rivals; they must urgently address this foundational lock-in before it permanently closes off public procurement for enterprise AI as well.

Progress, but is it too cautious?

The CMA hasn't been idle. On 31 March 2026, the CMA accepted voluntary commitments from Microsoft and AWS rather than the binding designations recommended by its own investigators. These commitments included extending Microsoft's free egress-switch window from 60 to 180 days and a new cloud interoperability request mechanism, with a six-month review promised.

It simultaneously opened a strategic market status (SMS) investigation into Microsoft's entire business-software ecosystem including Windows, Office, Teams, and Copilot, encompassing over 15 million UK users with a decision due around February 2027.

That's a fair, if cautious, step, and critics such as the Open Cloud Coalition have welcomed it while pressing for swift action if commitments aren't met. Microsoft can reasonably argue these were offered in good faith to head off heavier intervention, but we need to start seeing these promises come into fruition quickly before further entrenchment occurs. Compare an AI rollout announced in June and largely complete by October against a regulatory clock that’s usually measured in years.

A roadmap for competition and social value

The CMA is now dealing with two closely connected issues: how public procurement can deliver greater value for taxpayers and society, and how Microsoft's market position can limit competition and choice. An interim roadmap could help address both at once. It would give public bodies clearer guidance on what to consider when procuring cloud and AI services, while setting out how the CMA intends to tackle the underlying lock-in that can narrow those choices.

This would support Andy Burnham's wider focus on delivering social value in public procurement. A more competitive and open cloud market would not just benefit businesses. It could give public bodies greater flexibility, reduce the risk that short-term savings lead to expensive, irreversible dependence on one supplier, and deliver better taxpayer value at the same time.

Certainty helps everyone

The roadmap should cover non-discriminatory licensing on rival clouds; interoperability extending to identity, permissions and agent portability; public-sector contracts with built-in exit plans; and independently verifiable evidence, not just documentation, that voluntary commitments are producing real switching and multi-cloud adoption. It should also provide broader transparency about how the CMA will assess whether these measures are working in practice.

Microsoft, its customers and its competitors all have an interest in predictable rules. Microsoft would know which integration practices remain legitimate; public bodies could invest in procuring AI at scale knowing it will not close off tomorrow’s choices, and rival cloud and AI suppliers would know if building interoperable products for the UK market is commercially worthwhile.

The CMA has diagnosed the problem, taken a first cautious step, and opened the right investigation. It should now set out the destination, the milestones and the tests for success before another year of AI adoption makes Britain's cloud and workplace-software markets harder to reopen. Without a clear roadmap or tests for success, we risk anti-competitive behavior leaking from the cloud market into deeply embedded AI work environments, at a far higher cost for the UK public sector to rewind.

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For small business owners, an unexpected payment might look like good news. A new customer has placed an order, an invoice has been settled, or an unfamiliar transaction has landed in the account. But when money arrives without an obvious explanation, the smartest response isn't to celebrate, It's to investigate.

Payment scams exploit the everyday pressures of running a business. Entrepreneurs are busy, cash flow matters, and customer relationships depend on responding quickly and professionally. Fraudsters understand this, using unexpected payments, urgent messages and convincing instructions to persuade business owners to make decisions before they've had time to check the facts.

And unfortunately, the risks are becoming harder to ignore as criminals use increasingly sophisticated digital tools to make these communications more convincing and their scams easier to scale. For small businesses without dedicated fraud teams, knowing what to look for, and having the right safeguards in place, can make the difference between a suspicious transaction and an extremely costly mistake.

Why do "random" payments happen?

An unexplained payment isn't automatically fraudulent, but it should always deserve scrutiny. Scammers can use unexpected transfers as the starting point for several different schemes, exploiting the assumption that money received is money earned.

One common tactic is the refund scam, where a fraudster sends money to a business, apparently by mistake, and then contacts the owner asking for it to be returned. They may apologise profusely, invent an urgent personal emergency or insist that a quick refund is needed to resolve an accounting error. The catch is that they ask for the money to be sent through a different channel, such as a bank transfer or gift cards.

The original payment may have come from a stolen account or compromised payment card, so if the legitimate account holder later disputes the transaction, the initial payment can be reversed, leaving the business out of pocket for the money it sent back.

Another possibility is testing stolen payment cards or hacked accounts. Criminals may use compromised payment details to make small purchases or transfers, checking whether the credentials still work. An apparently legitimate transaction could therefore be part of a wider fraud operation, and if the real cardholder disputes the payment, the business may face a chargeback, fees or the loss of goods already supplied.

Then there are phishing setups, in which the payment itself is little more than bait. Once the recipient contacts the sender, the fraudster may provide a link to a fake payment portal, request login credentials or ask the business owner to confirm sensitive information. What begins as a question about an unexplained transaction can become an attempt to take over an account.

The common overall thread is manipulation - the scammer wants the business owner to act quickly, bypass normal procedures or share information they would ordinarily keep private that they can now exploit or sell on to the highest bidder.

(Image credit: Getty Images)The warning signs every business owner should know

Although payment scams can take different forms, they often share a recognisable set of red flags. Spotting them early gives businesses the opportunity to pause before money or sensitive information changes hands.

Look out for these warning signs:

• An urgent request for a refund, where the sender insists the money must be returned immediately, perhaps citing a deadline or personal emergency.
• Pressure to use a different payment method, where the scammer asks you to return money by bank transfer, gift card or cryptocurrency instead of using the original payment platform.
• Instructions delivered outside official channels, such as unexpected emails, messages and links which direct you to alternative websites or ask you to disclose account details.
• Payments that don't make business sense. A round-number payment arrives that doesn't match an invoice, order or product price, or the sender's details don't match the customer record.

The key principle is simple, but usually effective: never assume that money received is money safely yours to return or spend. Check the transaction independently through your payment provider or bank, and never follow refund instructions supplied by an unverified sender. If a payment looks suspicious, contact the provider through its official website or app and ask how to handle it - never initiate a separate repayment yourself.

Building a stronger defence against payment fraud

Avoiding these scams doesn't require a dedicated fraud department, but it does require vigilance alongside consistent processes and a willingness to verify unusual requests rather than act on instinct.

One method is to ensure you always keep business and personal finances separate. Dedicated business accounts make transactions easier to reconcile and suspicious activity easier to identify, and mixing personal and company payments can obscure unusual transfers and potentially expose personal finances to business-related fraud.

Another useful protection is understanding what your normal transaction patterns look like. Keeping track of typical order values, customer locations, payment methods and refund volumes should be common practice - but although a transaction that falls outside these patterns isn't necessarily fraudulent, it may deserve a closer look. Reconcile payments against invoices and orders regularly, and investigate discrepancies promptly.

Securing the accounts that control your money will also go a long way. Make sure you always use strong, unique passwords for banking, payment platforms and business email accounts. Enable two-factor authentication (2FA) wherever available, and turn on alerts for large transactions, password changes and new logins, and where possible, limit account access to employees who genuinely need it.

Giving your team clear rules is another key step, as anyone who handles customer payments should know that unexpected refunds require verification. Establish a policy that prohibits returning unexplained payments through alternative channels, and encourage staff to check suspicious messages with a colleague before responding. Regular training can help employees recognise fake invoices, phishing links and attempts to impersonate customers or suppliers.

Finally, recognise that payment fraud often begins outside the payment platform itself. A fake invoice might arrive by email, a fraudulent website might imitate a trusted service, or a phishing message might steal the credentials needed to access a legitimate account. That's why good financial procedures need to be backed by effective cybersecurity.

(Image credit: Getty Images)Why Bitdefender Ultimate Small Business Security is a valuable partner

For small businesses looking to bring these safeguards together, Bitdefender Ultimate Small Business Security offers a practical way to strengthen protection without having to build an in-house security operation.

The service combines antivirus and device protection with email security, password management, VPN access, digital identity protection for all employees and business asset exposure monitoring. Its scam protection features are particularly relevant to payment fraud, helping users identify and block suspicious messages, links and websites.

Its Scam Protection Pro tool allows employees to check a suspicious message or link before responding, clicking or paying, offering an additional layer of scrutiny when an unexpected payment request or refund instruction doesn't feel right. Web and email protection can also help guard against phishing attempts designed to steal credentials or direct employees towards fraudulent sites.

Centralised management makes it easier for business owners to oversee protection across employees and devices, while support for Windows, macOS, Android and iOS reflects the reality these days that key business activity rarely takes place on a single machine or ecosystem.

Financial controls, and suspicious payments should always be verified with the relevant bank or payment provider, but combining those controls with cybersecurity tools designed to detect digital scams can make it much harder for fraudsters to exploit a moment of confusion.

For start-up owners entrepreneurs, the lesson is clear: an unexplained payment is a reason to stop and check, not an invitation to act quickly. With consistent procedures, informed employees and a comprehensive security solution such as Bitdefender Ultimate Small Business Security, small businesses can reduce their exposure to payment scams while keeping their attention where it belongs - on serving customers and growing the business.

Try our free 30-day protection built for busy founders, not IT teams

Commercial engagement in biopharma used to center largely on direct conversations between sales reps and physicians, or pharmacists. As therapeutic areas have grown more specialized, more teams have become involved in engaging with healthcare professionals (HCPs), including key account managers, medical science liaisons, brand marketers and service teams. But these functions often work separately, leaving HCPs with disjointed experiences rather than interactions that build on one another and widen their understanding.

Biopharma companies are also engaging with a much broader network of HCPs, patients, health systems and administrators. Many of these stakeholders are limiting their contact with the industry, and are increasingly using AI to inform their decisions. As clinical complexity grows and access to HCPs becomes more restricted, companies need commercial models that are more responsive to customers’ needs.

To respond to these expectations, biopharmas are turning to Agentic Commercial, connecting AI with field teams, digital channels and approved content to create more consistent, personalized HCP experiences. Built into industry-specific applications, agentic capabilities help teams capture real-world insights, uncover treatment barriers, provide trusted answers and deliver relevant content across human and AI-led interactions. This leads to more meaningful engagement, better-informed HCPs and faster access to the right medicines for patients.

Field operations that turn conversations into real-time insight

An Agentic Commercial model transforms sales teams from promotional distribution channels into strategic sources of customer intelligence, evolving CRM from a system of record into a system of guidance.

Reps conduct nuanced HCP conversations every day, but traditional reporting systems restrict insights capture to rigid picklists and drop-down menus. Critical qualitative context — the 'why' behind a physician’s decision or the localized barrier to access — goes unrecorded.

Agentic call reporting uses agentic voice and free-text capabilities, with built-in compliance checks, to help reps capture richer details from HCP conversations. These interactions generate Commercial Evidence — actionable, real-world insights that reveal intent, content gaps, and treatment barriers facing physicians and patients.

Commercial leaders gain immediate visibility into real-world friction points. Early implementations show that 65% of Agentic Call Reports identify actionable treatment barriers, enabling field teams and medical specialists to address clinical concerns with precision.

Digital experiences built around immediate answers

Field teams build trusted human relationships, but they cannot be everywhere at once. Digital channels such as brand websites and email should extend the dialogue and enable more timely responses to questions, yet too often, physicians and patients must navigate websites or lengthy documents to find what they need.

Conversational AI transforms static digital experiences into real-time conversations. Using only medical, legal, and regulatory (MLR)-approved content, agents provide trusted, compliant answers to natural language questions in real time.

These interactions also generate valuable Commercial Evidence. Instead of showing only what users clicked, AI reveals what they asked, where they struggled, and the information they could not find. Commercial teams can identify content gaps, uncover emerging treatment barriers, and trigger timely follow-up from the field.

Approved content that gives reviewers more time for higher-value work

Continuous engagement depends on a steady stream of timely, compliant content. Yet, with global pharma content expenditure reaching $30 billion annually and 29% more assets created year-over-year, content volume is outpacing human review capacity.

In an Agentic Commercial model, accelerating content speed to market is essential. Agentic MLR acts as an end-to-end expert reviewer — understanding document context, mapping specific regulations, and executing targeted agent reviews against approved labels, claim libraries, and brand SOPs. This shifts human reviewers from line-by-line proofreading to high-impact work.

By accelerating content review while maintaining compliance, commercial teams can respond faster to customer questions across field and digital channels. This delivers a more cost-effective approval process, with content leaders anticipating 38% of the MLR process to be AI-driven by 2028.

Combining AI capabilities with human expertise

Agentic Commercial brings AI and commercial teams together within the same applications, following shared processes, governance and compliance requirements. By taking on repetitive tasks and analysing large volumes of data, agents give people more time to apply their scientific expertise and judgement, shape strategy and build trusted relationships.

Bringing field teams, digital engagement, approved content and AI together allows biopharmas to create a more connected and personalized experience across channels. As therapies become more complex and access to HCPs more limited, Agentic Commercial can help teams engage customers with greater precision, support better-informed decisions and ultimately help patients access the right medicines sooner.

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

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

• Google leads efforts to dismantle 1.3 million device residential proxy botnet built through malware preinstalled on Android hardware
• Researchers say manufacturer Videostrong intentionally shipped malware and sold botnet access to cybercriminals
• Google seized infrastructure, blocked the company from Android certification, and strengthened device security checks

Google has revealed its role in taking down a massive residential proxy network and the malware that built it - with the unusual twist that the developer was a seemingly legitimate business with multiple products and countless trusting customers.

In a blog post, the company said that together with its partners, it dismantled a backdoor called XCPE. This malware was apparently used for ad fraud, served as a dropper for additional malware, and used for assimilating devices into an illegal residential proxy network.

In total, more than 1.3 million set-top boxes alone were part of the network, the company said.

No supply chain attack

Malicious residential proxy networks are nothing new - they emerge and get dismantled on a regular basis. But this one was unique, as it came preloaded on Android devices built and sold by a certified Android Original Equipment Manufacturer (OEM) and Original Design Manufacturer (ODM). In other words, people were thinking they were buying a legitimate product from a legitimate, Google-vetted company, but were instead getting malware-laden hardware that abused their trust and sold their bandwidth to cybercriminals.

In this case, not only did the company know what it was doing, but it was actively developing the malware and even worked on hiding it from Google and everyone else.

“A device manufacturer intentionally infecting their users with malware is an unacceptable breach of trust, and represents a serious risk to those users,” Google said, not hiding its disappointment with the manufacturer.

Videostrong ended up selling two things: On the surface, it was selling Android-powered smartphones and similar devices that contained malware that built a botnet. Under the table, it was selling access to that botnet to cybercriminals.

Google was joined by a number of other companies and agencies in this operation, including Japan’s National Police Agency, Lumen, HUMAN Security, Plume, Synthient, and TrendAI.

Threat actors are always in need of residential proxy botnets. They can use them to launch powerful Distributed Denial of Service (DDoS) attacks, hide their addresses as they operate, conduct large-scale credential stuffing campaigns, bypass geographical restrictions, scrape websites at scale, and more. We don’t know how much Videostrong made from selling access to the botnet, or who used it.

Taking down the C2 domains

To disrupt both the malware and the network, Google took down the domains and servers the company used to control the botnet and install malware modules, took down C2 domains that were used by over a dozen malware families loaded as modules via the XCPE backdoor, and banned Videostrong and all future subsidiaries and affiliates from building and selling Android devices in the future, or supplying software to other certified vendors.

Google also said it had improved its firmware integrity checks for certified Android TV devices to prevent similar incidents involving other manufacturers.

“Google requires OEMs go through a comprehensive suite of compatibility and security tests to ensure a consistent and safe user experience across the Android ecosystem,” it stressed. “However, Videostrong employed obfuscation techniques to exploit a gap in our tests and load malicious code after the device's first boot. As a result, Google has implemented changes to its existing review process to better safeguard against similar activity in the future.”

This is not the first time an electronic device came with malware off the shelf. Last month, security researchers Bitdefender found Android phones from multiple manufacturers, including Doogee and Cubot (both Chinese firms) being sold with preloaded malware. However, in those cases, the supply chain was compromised and the malware was included without the OEM’s knowledge, or consent.

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

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

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

That earlier step is incredibly important.

AI acts on information whose pedigree is often obscure

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

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

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

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

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

Human oversight depends on better evidence

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

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

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

AI inherited the web’s weakest habit

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

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

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

Autonomous AI needs a system of provenance

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

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

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

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

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

Final thoughts

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

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

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

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

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

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• The FBI formally attributes $6.7 billion Binance cryptocurrency theft to North Korea’s TraderTraitor group
• Investigators say the attackers are laundering stolen assets across multiple blockchains and DeFi services
• The breach highlights ongoing threats from Lazarus-linked campaigns targeting crypto, DevOps, and software professionals

The FBI has publicly accused a North Korean state-sponsored threat actor of being behind the recent attack on cryptocurrency exchange Binance.

One of the largest cryptocurrency exchange platforms on the planet was forced to freeze deposits and withdrawals, before confirming a threat actor stole around $6.7 billion worth of different cryptocurrencies.

Analysts were quick to investigate, with some claiming a known North Korean state-sponsored group called TraderTraitor was behind the heist. TraderTraitor is apparently part of Lazarus Group - a huge cybercriminal operation on North Korea’s payroll, allegedly tasked with stealing money to fund the country’s state apparatus and weapons program.

TraderTraitor

Now, the FBI has issued a new statement, official naming TraderTraitor as the attackers.

“The Federal Bureau of Investigation (FBI) is releasing information advising the Democratic People's Republic of Korea (North Korea) was responsible for the theft of more than $6 billion USD in virtual assets from cryptocurrency exchange Binance, on or about October 29, 2026. The FBI refers to this specific North Korean malicious cyber activity as ‘TraderTraitor’.,” the announcement reads.

“The FBI, together with partners at the USSS and IRS-CI, has observed TraderTraitor actors converting stolen assets primarily into Ethereum (ETH). It is expected these assets will then be further laundered by converting them using DeFi and instant swap services as they prepare them for further laundering stages.”

Binance said one of its DevOps engineers had their laptop compromised which “gave an attacker access to a service container, where they submitted unauthorized withdrawal transactions.” Some speculated the engineer fell to a classic fake job scam, while others argued it was a romance scam.

In the meantime, Binance resumed operations and has covered most of its losses.

Initial reports found that the attackers siphoned out money from 10 different chains, including Bitcoin, Ethereum, Solana, XRP, Arbitrum, and others. For Ethereum, they even set up a smart contract that automatically withdrew any new money that was flowing into the account. The silver lining in the attack is that these were not user funds, but rather those of the exchange itself.

Cryptocurrency exchanges are obliged to have two separate wallets - one for their own funds, and one for those of their customers. Binance has a security fund called SAFU (Secure Asset Fund for Users), designed to protect user assets during extreme platform-level incidents, such as this one.

Operations DreamJob and Contagious Interview

Obviously, North Korea has not commented on these allegations, but the cybersecurity community has logn warned about Lazarus Group targeting organizations in the West, sometimes tasked with stealing money, and sometimes with cyber-espionage and eavesdropping.

The group is split into numerous sub-groups, one of them being TraderTraitor, and among other things, the group runs two major scam campaigns: Operation DreamJob, and Contagious Interview. Both are aimed at software developers, blockchain programmers, and DevOps professionals, and the wider software development industry.

As part of these scams, the attackers would create fake companies, fake employees, and fake job offers. They would reach out to their targets on LinkedIn and offer lucrative job positions, only to infect their computers with malware during the interviews. In some cases, they would apply for open positions, impersonating Chinese, Vietnamese, or Thai nationals and would, when they get the job, use the position to steal sensitive data and money.

North Korea is under heavy sanctions from the US, and the country does not allow its companies to hire, or do business with, individuals and organizations in North Korea.

The FBI shared a long list of wallets it believes contain the stolen funds, and has asked THORNode operators, RPC node operators, instant exchanges, DeFi services, blockchain analytics firms, and other virtual asset service providers to block transactions with or derived from these addresses.

• Coolgeek P1 Cassette Player unveiled
• Brings some modern twists like Bluetooth and a HD display
• Kickstarter campaign just launched; MSRP is $179 but cheaper now

The cassette player revival began with retro-inspired models such as the EDITH WE-001 and FiiO CP13, but we're beginning to see more modern versions like, well, this strange wall-mounted system. And another new option — the one you're here to read about — definitely falls into the latter camp.

Coolgeek, whose RGB light-up turntable we covered several months ago, is back with its newest gadget: the P1 Cassette Player.

Like its turntable, this device is being 'released' on Kickstarter, so you can't buy it immediately. However, from what we've been told, it sounds pretty far along in production, so I don't envision the turn-around time being too long here.

While the standard price of the P1 is $179 (about £130, AU$260), in true Kickstarter fashion it's available for much cheaper if you sign up to the campaign earlier; it'll be half that price in the 'Super Early Bird' wave, and slowly increase over time.

What your money gets you

(Image credit: CoolkGeek)

At its core, Coolgeek's P1 Cassette Player is... well, a cassette player (think Walkman, but Sony makes those), so you don't really need me to run you through what it does, do you? It plays cassettes. But there's more, of course, or we wouldn't be covering it.

For one, the model has Bluetooth 5.3 connectivity, so you can listen to your tapes using your wireless headphones. There's a 3.5mm jack for wired headphone lovers too, as well as a USB-C port, but the latter's just for charging.

There's also a little HD display, which is primarily used for arranging the Bluetooth connection. Judging by images we've seen, there's also some colorful LED indicator column (ooh, lights!) probably for volume, but we weren't expressly told what it depicts.

Like many retro-inspired gadgets, the P1 is see-through, so you can see your tape spinning around as you listen. Coolgeek told us that it was designed not merely to ape older cassette players, but to have its own design identity.

Plenty of the new cassette players we've seen have playful, pastel-colored designs and curved edges, and Coolgeek's P1 looks a lot more mechanical and industrial in comparison — like true 1970s and 1980s tangible tech. So if you've been wanting an alternative cassette player, the campaign might be worth taking a look at.

When we tested out FastestVPN, we found "above-average speeds and rock-bottom prices" from this service. Now, I'm all for low pricing at a time when everything seems to be costing more and more. But there's something even better hidden in the company's early Black Friday sale that you might've missed. You can get a lifetime premium family password for six users alongside the VPN, for a one-off $27 (was $115) with code FUTUREBF2026.

• Best for: Families bundling together privacy and security tools without ever losing or forgetting passwords again.

• Why we like it: I really appreciate one-time charge - we've all got subscription-fatigue at this point. Our reader-exclusive code drops the price of the lifetime plan to less than the cost of the two-year plan, so you're effectively getting the password manager free on top of the multi-login VPN.

• What you need to know: This deal is for the lifetime plan, but a standard plan for two years is also available. FastestVPN has additional monthly costs for users who need dedicated IP and port forwarding. That might not affect most people, but worth knowing.

Lifetime VPN plan with lifetime family password manager: was $115 now $27
The Lifetime VPN plan bundles together a 6-user family password manager and VPN for 15 devices at once. For the full discount, use code FUTUREBF2026. View Deal

Why we recommend it

There's a lot to like about FastestVPN's lifetime plan - particularly for busy households and home offices. The lifetime password manager for up to six users is the real draw for me, because it means you can protect your accounts with stronger passwords that you won't forget (it feels like I'm reading about password breaches every week these days).

On the VPN side, the service offers WireGuard Protocol, 10 Gbps high-speed servers - despite its name, it's not the fastest VPN we've ever tested, but we scored it 4 stars in our review. The platform also offers streaming-optimized servers for Netflix, Disney+, and other popular sites.

There's app support for Windows, macOS, Android, and iOS, as well as smart TVs, routers, and Fire Sticks.

For more options, we've rounded up the best password managers and best VPNs we've tested.

Price Context & Historical Value

Considering it's lifetime access for under $30, this is great value for general password management and VPN use. The lifetime plan alone typically costs $40 (or $35 for the two-year plan) in the Black Friday sale. Our reader-exclusive code is what makes this the best value.

Should you buy it?

✅ Buy the FastestVPN Lifetime Plan if...

You want to end those never-ending subscriptions critical security and privacy tools for your household.

❌ Skip the FastestVPN Lifetime Plan if...

You prioritize VPN speed or don't need a password manager for six users.

• 'Heartwood' is a wood-based gaming PC that took seven years to build
• It's stunningly neat, and the hidden trick is that it routes the cooling and other cables outside the PC
• Those pipes and cables run underneath the desk, which the PC is effectively built into, and down into the basement below, where the full cooling system resides

Fancy custom PC builds come along with some regularity, but every now and then something really unusual presents itself — and I'd class this as the smartest and neatest desktop computer I've ever seen.

Wccftech spotted the rig in question, which was posted in the PC build subreddit by Mithiral67. The gaming PC is called 'Heartwood', and it took a rather incredible seven years to build.

It's based on an Inwin 909 case (which looks impressive in itself in brushed aluminum) using a semi-open concept, with the motherboard sitting in an empty space (there's no rear panel with the build here, as you can see in the YouTube video below that shows off this PC).

This desktop looks incredibly neat and slick decked out in wood and aluminum, with the nifty lighting in the top of the case too, but where's the cooling and other cabling? That's when the full extent of this build becomes clear, because it also includes the desk underneath the PC.

(Image credit: Mithiral67 on Reddit)

What the maker has done is route the liquid cooling and power cables down through the bottom of the case and the desk, with these running around the underside of the desk, and then down a leg into the floor, to the basement of the house, where all the water-cooling gubbins (built around a Mora 400) resides.

Not only was this build a time-consuming labor of love, it was also a very costly one. Another Redditor enquires about the price of the materials, and according to Mithiral67 the wood itself was around $4,000 – and they said they didn't even want to get into the cost of the woodworking tools.

As for the components, the PC is built on a Ryzen 9800X3D processor plus Nvidia RTX 5090 GPU.

Waiting out the GPU pricing nightmares

So, this was a pricey build, and a complex one, but even then, seven years seems like a very long time for putting the PC together — it means Mithiral67 started their project before the pandemic.

What gives? Well, as they explain: "For those asking, 7 years was mostly waiting for a GPU at MSRP, literally took 4 generations to get lucky at Micro Center."

There you have it. Those hoping to feature an RTX 5090 in their custom build are looking at paying an absolute fortune currently, of course, and getting the Nvidia flagship at list price now seems almost as miraculous a feat as the construction and thoughtful crafting witnessed with this PC.

Mind you, even then Mithiral67 had to buy the RTX 5090 as part of a full PC to get it at the equivalent of the sticker price (which worked out fine, as it meant they could use that other PC for playing games while they painstakingly finished off the work of art that is 'Heartwood').

(Image credit: Mithiral67 on Reddit)

As one admiring Redditor summed up nicely: "This is genuinely the most beautiful setup I've seen, in every aspect from the choice of wood, color, design, and function."

There's no shortage of commenters approving of the build, or indeed those pointing out that Sam Altman doubtless disapproves of the amount of RAM used in this desktop, too. Don't you know that's memory which could have powered another AI agent from a data center somewhere?

• The current targets are in Ireland, Sweden, Belgium, Luxembourg and Estonia
• The US military is investigating similar issues affecting six US bases
• Over 400 French supermarkets were attacked in October 2026

In October 2026 hundreds of French supermarkets lost food worth millions of euros after a cyberattack turned their freezers up to 120F (50C) — and it looks like it's happening again, this time in other European countries.

This time the targeted supermarkets include Supervalu, which is one of Ireland's biggest supermarket chains, as well as the Swedish retailers Axfood and ICO and big-name supermarkets in Belgium, Luxembourg and Estonia.

While the targets are different the method appears to be the same, with remote access turning the freezers into heaters and spoiling their contents.

According to one retailer on Facebook, ICA Kvantum Sjöbo, "What shouldn't have happened has happened!"

So what's happening?

What's happening to the supermarket freezers

This most recent attack follows in the footsteps of the French hack and the more recent Pentagon investigation into refrigeration issues in US military bases.

It could well be exploiting a very simple vulnerability, which is that remote refrigeration management systems have weak authentication and "dangerous" software flaws. Certainly in the case of the French supermarkets the BBC reported that the affected units were "older models whose security software could not be updated to ward off a risk" that the manufacturer had identified.

The French hack affected over 400 stores, and while the numbers aren't in yet the new hack is likely to reach three figures too.

As we noted in our report on the Pentagon investigation, the "Internet of Things" makes it easier for companies to manage large groups of devices automatically and remotely, but it also opens up a new way for hackers to cause trouble — and if the software isn't secure enough or updated when new threats emerge, it effectively puts up a welcome sign for cybercriminals.

The European Food Commissioner Christophe Hansen posted on X that "food security requires more attention at all levels. Because the weaponisation of food has become a reality." He's not wrong: whether they're being exploited by cybercriminals looking for an easy payday, tech troublemakers or nation states waging hybrid wars, it's clear that the Internet of Things needs to take security much more seriously.

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Lego Advent calendars might be the best type of Advent calendar there is. Behind each door you'll find a Lego character or mini build to complete. Unlike chocolate (gone in seconds), these daily treats stick around throughout the festive season and for years to come — Lego has also got creative with the boxes, transforming them into game boards or scenes where the behind-door builds can live, and turning them into a part of your seasonal decor.

I've been holding off placing my order, but now is the time to take the plunge — the full range of 2026 calendars is discounted right now, with at least 20% off list price. And there's plenty of time for it to arrive before Advent begins. I'll be picking up the Lego City set, which the classic festive option and includes everything you need to make a Santa's workshop. However, there are also Marvel and Star Wars calendars (which put a Christmassy twist on your favorite characters), as well as Disney Princess and kid-focused Lego Friends sets.

I've rounded up all your deal options below, as well as some bonus discounts on alternative festive Lego sets you might want to throw into your shopping trolley at the same time.

Lego 2026 Advent calendar deals

Lego City 2026 Advent Calendar 60510

Lego Marvel 2026 Advent Calendar 76340

Lego Star Wars 2026 Advent Calendar 75456

Lego Disney Princess 2026 Advent Calendar 43298

Lego Friends 2026 Advent Calendar 42698

More festive brick bargains

Lego Botanicals Wreath, Festive Home Decor Set 10340

Lego Disney Frozen Olaf and Bruni's Picnic Fun Set 43287

Lego Ballerina and Nutcracker Scene 40701

Lego Snowman Ornaments 40812

Lego Gingerbread At-At Walker 40806

Lego Up-Scaled Santa Minifigure 40820

Lego Christmas Table Decoration 40743

Lego Creator Winter Holiday Train 40700

Lego Nutcracker 40640

Lego Wintertime Polar Bears 40571

Lego Icons Santa's Visit 10293

AI innovation took center stage at IFA Berlin this year, with unprecedented solutions operating alongside vastly enhanced products powered by AI. Exhibitors showcased everything from advanced humanized assistants designed for daily chores to AI-driven robotic pets offering emotional companionship.

It will take time for these products to be honed and readily available for consumers, but many innovations are already here. Hisense demonstrated its AI companion suite, which will transform user interaction while Miele’s flagship lineup highlighted the role AI, advanced sensors and connected systems can play in minimizing decision-making in daily tasks.

While these products may be received well by tech loving audiences, not all consumers are convinced. When these products make it to market, brands and retailers are likely to be faced with cynicism, safety concerns and complexity-driven confusion. To overcome these challenges, innovators must consider the customer experience.

Overcoming the information gap

When it comes to big-ticket tech purchases, consumers want to understand what they are paying for. High spec products are increasingly complex, and many consumers do not have the technical know-how to understand the differences between products and the potential benefits of new AI-enabled features.

In product descriptions and marketing campaigns, brands often prioritize the ‘cool factor’, focusing on innovative new features. While techy audiences take this in their stride, this often alienates consumers who are less confident in their knowledge. These audiences need straightforward explanations of what the product does, how AI or tech has been incorporated and how it can enhance their lives – but they are often hard to find.

Unfortunately, some companies take advantage of limited understanding, slapping misleading ‘AI’ labels on their products, which ultimately do not deliver. This disappointment and negative word-of-mouth these customer experiences generate, along with high-profile hacking horror stories and broader concerns about data security, have impacted trust – and a lack of transparency and information only adds to the nagging feeling that companies have something to hide.

Companies that are honest and open about how their products incorporate AI, the benefits these features offer and how data is used and stored, can start to rebuild trust and earn the respect of consumers. While exciting high-tech features remain an important marketing tool, a ‘back to basics’ approach that offers easy-to-find answers to common questions and addresses concerns in clear, accessible language can help consumers to understand if a product meets their needs and to feel more comfortable making a purchase.

Consider all channels in the retail ecosystem

Although discovery and research often take place online, more than four in five (85%) consumers prefer to buy electronics in-store. Bricks-and-mortar locations provide the opportunity to see or trial products in person, receive answers to unresolved questions and seek advice from sales assistants – and demand will only increase as products become more complex.

While consumers may have a good idea of their preferred product when they enter a store, their decision can change quickly if the experience falls short of their expectations. When shoppers go into a store and cannot find anyone to assist them, they may abandon their purchase or feel undervalued.

Despite the importance of the in-store experience, many tech brands are leaving a gap in the funnel. Once goods reach a physical store, the retailer is often trusted to convert intention into action. However, with physical retail coming under pressure due to rising operational costs, many are reducing headcount, which makes it harder for consumers to get the help and advice they came for.

Lean into education and advocacy

As AI makes products more complex, availability of information and access to in-depth knowledge is becoming a necessity rather than a nice-to-have. When consumers are uncertain, sales will falter and there is a far higher risk of buyers’ remorse.

Retailers must be equipped to convert the confusion of the average shopper into purchases, but training budgets are being cut alongside headcount, with in-person practical training being replaced by online learning. Digital updates can provide a solid foundation, but surface-level modules do not inspire teams’ confidence or build their ability to sell.

Many retailers are unable to fund and provide the training required, but brands have an opportunity to step in. Brands with more complicated products should consider placing brand ambassadors in store or providing additional training to stockists’ staff to ensure that someone is on hand to answer challenging questions, reassure shoppers and help them find the model that will strike the best balance between their needs and their budget.

Experience is make-or-break for tech brands

Brand equity and goodwill are vital for long-term commercial success. Negative experiences detract from trust in both the retailer and the brand, while positive interactions help to solidify it. Brands should monitor sales and reviews closely, identifying areas for improvement, and work closely with retail partners to address any potential issues at the point of sale.

The cost of providing accurate, helpful information online or in-store is negligible, while the cost of ambassadors and training far outweighs the cost of potential failure and subsequent delisting of a brand and its products. Failure to offer in-depth details, expertise and advice can have a sizeable impact on the bottom line: making customers feel comfortable is the only way to ensure AI-enhanced products deliver the expected returns.

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