Technology

• Custom AI behaviors have arrived in the Philips Hue app
• At the moment you can't edit what the AI creates
• AI features require the Hue Bridge Pro

When we spoke to Hue founder and head of technology George Yianni in September, he told us about some exciting new features coming to the Hue ecosystem including Custom AI Behaviors. And now they've arrived in the Hue app, provided that you have the Hue Bridge Pro.

Yianni told us that AI would be "a bit of a game changer" for the platform, and said of the custom behaviors that "natural language is a fantastic way to just describe arbitrarily how you would like your home to behave, and then we can use AI to translate that into a special programming language we’ve developed that runs the automation in the Bridge."

According to Yianni, "I now can finally have my home behave exactly the way I wanted to. I think that's a really big deal." But unfortunately according to HueBlog, the behaviors aren't quite behaving the way we'd like them to.

The new AI features require the Hue Bridge Pro. (Image credit: Future / Philips)How to use Philips Hue Custom AI Behaviors

The idea behind Custom AI Behaviors is simple: tell the AI what you want and the AI will craft the appropriate behavior for you and add it to the app. For example, you could tell the AI that you want specific lights to go on at sunset, and to use different color themes each time.

That's what Hueblog tried, and the AI understood the mission. But unfortunately it didn't quite get it right: instead of turning on only the specified lights, the behavior turned all the lights on. When Hueblog tried to get the AI to fix the issue "it gave up and simply displayed the error message."

It looks like the goal — use AI to make things simple — is perhaps too simple, because as Hueblog put it "you’re entirely limited by the AI’s logic and reasoning. If it doesn’t cooperate, you’re out of luck." Unlike, say, Apple's Shortcuts on macOS and iOS, there's no way to tweak the shortcuts the AI has created if they're not quite doing what you want.

This feels like a typical version 1.0 issue, where a feature is rolled out first and then updated based on user feedback. That means the no-tweaking issue is likely to be short-lived: in my experience Hue's pretty good at updating its app to deliver new features.

While the new AI features — the new Custom AI Behaviors and the previously launched SpatialAware scene optimization — are reserved for the Hue Bridge Pro, the app also provides updates for all Hue users including easier Screen Sync setup and improved scene recommendations. As ever the Hue app is available for both iOS and Android.

You don't need to be a tech expert to know that the Nintendo Switch 2 will be one of the most sought-after items in this year's Black Friday sales. And although we're now in November, I wasn't expecting to see any deals for a few weeks. But two surprisingly strong Nintendo Switch 2 bundles are already available at the official Nintendo Store, which I think will rival anything we see during this month's sales.

• View all Switch 2 bundle deals at Nintendo

The first brings together a Nintendo Switch 2 console, a copy of Mario Kart World, a 256GB Samsung microSD card, and a Nintendo Switch 2 Camera for £459.99. That's a total saving of almost £95 compared with buying these items individually.

As for the second bundle, you can get a Switch 2, a copy of Nintendo Switch Sports Resort, a 256GB Samsung microSD card, and a Nintendo Switch 2 Camera for £449.99. Oh, and let's not forget the free Nintendo Switch Sports Resort Gym Bag. The total savings from picking up this bundle are around £75.

Today's best Nintendo Switch 2 bundles

Switch 2 Mario Kart World Bundle: £459.99
As far as Switch 2 bundles go, this is one of the best value options I've ever seen. You get the console, plus the excellent Mario Kart World, a 256GB microSD card to expand the storage, and a Nintendo Switch 2 camera to use the console's GameChat features. Altogether, the saving is about £95 compared to buying everything at full price, and it's even more of a steal when you consider that the console by itself retails for £419.99.View Deal

Switch 2 Sports Resort Bundle: £449.99
An equally tempting alternative to the Switch 2 bundle above is this version that swaps Mario Kart World for a copy of Nintendo Switch Sports Resort. Given that this compilation of sports titles that make full use of the console's motion controls retails for less than the racer, it's a slightly cheaper bundle. You still get a 256GB microSD card and a Nintendo Switch 2 camera, as well.View Deal

As for the actual value of the extras in both bundles, each contains one of the best Nintendo Switch 2 games to start your collection.

Mario Kart World is a fantastic new entry in the party racer series, which expands it with more freedom and open-world elements. Nintendo Switch Sports Resort, meanwhile, is another fun compilation of games that make use of the console's motion controls to recreate tennis, volleyball, bowling, golf, and more.

The microSD card is a really nice inclusion as well. Even though the Switch 2 comes with a respectable 256GB of storage built in, having another 256GB on top is very handy as game sizes are continuously increasing, and this means you can have a relatively large library of titles installed at once.

Lastly, there's the Switch 2 Camera, which is probably the least valuable item of the bunch. If you do expect to do a lot of online multiplayer with friends and family, though, this will let you make the most of Game Chat features that can put your face and voice in certain games as you play, including Mario Kart World.

• Openreach warns landline phones will stop working on January 31, 2027
• Legacy PTSN system is going digital, so users should switch now
• Openreach remind businesses and consumers alike to check their connections

Openreach has issued a reminder warning that the UK's legacy landline phone network will be switched off in less than 100 days.

BT's infrastructure arm, responsible for deploying, maintaining and upgrading most of the country's networks, has reminded users the copper-based Public Switched Telephone Network (PTSN) network will be retired on January 31, 2027.

There will be no extension to this cut-off, meaning users, both business and consumer alike, will need to ensure their systems are ready for the change.

Time to switch

The news of the "big switch off", as Openreach's advice hub calls it, should not be a surprise, as plans were first announced to shut down the PSTN and ISDN networks by the end of 2025 back in 2017 - with the deadline later extended to 2027.

It notes that the aging landline network is harder to maintain and more vulnerable to faults, whilst also being unable to keep up with the demands posed by modern usage.

Openreach says it has already supported the migration of around 15 million lines off the old copper-based network, and although most of our homes and offices will have already switched to digital-based Internet Protocol (IP) fiber networks, there are a wide range of systems which still rely on PSTN connectivity.

Among the items needing upgrading point-of-sale terminals in shops, fire and security alarms, and personal care alarms for elderly and vulnerable people.

“Businesses and organisations that delay action are making things harder for themselves, noted James Lilley, Director of All-IP at Openreach.

"The straightforward migrations have largely been completed. What's left are the complex connections that underpin critical operations, requiring careful planning, supplier coordination, and thorough testing. We are urging businesses to act now, audit their remaining analogue services, and work with their communications provider on a migration plan today.”

Openreach's website notes the move to digital phone lines will help provide users with a more reliable service, better-quality calls and smarter blocking of unwanted calls.

It tells users to not wait until the deadline, and to get in contact with your service provider as soon as possible to get the move going.

Anyone who has not switched by the January 2027 deadline may see limited service after that time, and may be moved to the Emergency Voice and Access Continuity (EVAc) service, a temporary emergency voice-only service which keeps basic voice calls working for a limited time.

An FAQ includes the question "I don't want broadband. Can I have a phone line only?" but includes only a wishy-washy response - "If you have a phone line but don’t have broadband, your service provider will help you find the most suitable digital option for your needs."

Collaboration platforms have become central to working life, with Microsoft Teams alone serving more than 320 million monthly active users worldwide. Everywhere you look, they are now part of workplace culture and have been a driving force for change in the use of voice services.

Part of their ongoing appeal is that they are extremely simple to deploy and their familiarity flattens the learning curve for many users almost entirely. For IT teams, one of the key benefits is that they sit within an established supplier environment and, as a result, can help make procurement more straightforward.

Sounds great, but communication infrastructure must also be able to adapt over time - for example, when a business expands or changes how it serves customers. Depending on the platform, any functional limitations may only become clear when a business needs to connect communications with other systems or adapt services for new teams and locations.

The point is that inflexibility can create complications that significantly increase the initial cost of a collaboration solution; something that businesses can easily overlook.

The hidden cost of inflexibility

But what do we mean by complications? Well, any given collaboration platform may work perfectly well for everyday internal calling, yet they all have the potential to become restrictive when communications need to support more specialized processes.

To break this down, everyday internal calls have relatively simple requirements because employees already know who to contact and how to reach them. However, in use cases such as customer-facing communications, employees may require calls to be directed based on factors such as location or the nature of the inquiry.

They may also need access to relevant customer information while the conversation is taking place, whether it is held in a CRM platform or another business application, including a bespoke system where technology integration is more challenging.

This is where the level and quality of the integration becomes extremely important, as any limitations may force employees to switch between applications or search manually for information that should be readily available.

Get this wrong, and users may require a customer to repeat information, which can extend the interaction and cause frustration. Some businesses may also need more detailed insight into call activity when they are trying to identify problems or improve customer service. For many, this is a central objective when building these systems and processes.

Adding workarounds, such as asking staff to look up customer records manually or using a separate connector to pass data between systems, is one option. However, these can make it harder for IT teams to manage the service consistently as requirements change.

Why AI changes the equation (again)

The issues are becoming even more consequential as businesses look to use AI to handle a greater share of customer communications, because these systems need access to the right information to respond usefully.

For an assistant to deal with an inquiry properly, it needs to draw on the same customer information that a human employee would use. But when an inquiry cannot be resolved automatically, the conversation needs to reach the appropriate person without losing its context.

This is where any disconnect between the communications environment and core business systems can really limit what an AI assistant can do well. AI that cannot access the right information can create an additional layer of friction for customers and employees.

Ideally, these compromises should be avoided entirely. A business should be able to retain the collaboration tools employees already know while adding communication and integration capabilities where needed.

In fact, seamless connectivity and interaction between the user, the AI tools being used, and the relevant customer and operational data should be a core objective rather than a nice-to-have. This means that the technology should be able to accommodate changing requirements without creating a dependence on manual workarounds, so organizations have full control over the pace at which they adopt new technologies and processes.

Businesses planning to expand their use of AI, which is likely to include a great many of them, should consider whether their current communications infrastructure can support those connections. Granted, it is usually possible to retrofit systems to bridge functionality and service gaps, but if the objective is to use AI effectively across a growing number of customer interactions, flexibility needs to be built into the foundation from the outset.

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In the UK, fraud losses reached £1.3billion, showcasing how fraud operates at a growing scale. One of the factors driving this growth is the increasing use of technologies, such as AI, which are becoming dangerous and effective weapons in the hands of a new generation of criminals

As they continue to evolve, criminal networks are becoming faster and more sophisticated in their operations, and financial institutions face the challenge of adapting to a new era of cybersecurity and fraud prevention powered by the same force threatening the system and customers’ trust.

AI is reinventing cybercrime

Fraudsters are already using advanced technologies including generative AI to industrialize social engineering. As the marginal cost of a sophisticated attack approaches zero, criminal networks are undergoing a measurable structural shift in how they operate. Skills that previously lived inside a small number of highly specialized criminal groups are now commoditized.

Generative AI and synthetic identity creation can now produce composite identities that pass Know Your Customer (KYC) checks, build believable credit histories and operate undetected for months before the fraud is executed. Deepfake-enabled authorization fraud produces voices and videos convincing enough to trick humans into authorizing transactions or transferring access credentials.

These examples all reveal a common pattern: they attack the trust layer, the moment where a system or a human decides something is legitimate. Even with incredibly sophisticated models, the tactics will target the system and subsequent operations. AI can now manufacture trust at a speed and quality that was simply not possible before.

Why traditional security is falling behind

Such technologies now have the upper-hand as they are largely defeating the customer, not the bank's systems. Fraudsters are using AI to manipulate people into authorizing transactions themselves with more convincing scam calls, more realistic impersonation, more personalized social engineering that erodes the skepticism that used to protect people. The fraud arrives at the bank's systems looking entirely legitimate, because in a technical sense it is.

That is where the battle is being fought today. As banks continue to invest in AI-driven detection and the customer manipulation route becomes harder to exploit at scale, the incentive to attack the banks' systems directly increases.

We are in a transitional phase, where technology is helping fraudsters to defeat people, this could evolve toward AI directly probing and attacking the institutions themselves. The financial sector should be preparing for that second phase now.

Against an adversary that probes, adapts and iterates continuously, the current static nature of traditional security controls are not sufficient. In financial fraud and cybersecurity, AI-driven attacks are not just evading controls, they are mapping them. They behave almost as if they understand the shape of the detection system and are deliberately staying outside its boundaries.

A symbiotic relationship between workforce and technology

In banks and financial institutions, fraud prevention teams are increasingly struggling to keep up with the constant technological evolution of the threats, mostly because they have been trained to think in terms of incidents: if something happens, you detect it, you respond, you close the case.

The ones making the most progress are those treating detection as a research function rather than an operational one. They are looking at shifts in behaviors that might suggest something is being tested against us. This different analytical posture ultimately aids in prevention over detection.

The professionals who will matter most in five years are the ones who can reason about systems rather than just operate them. There is a need to move from expertise in specific tools to expertise in the underlying principles that make tools work. Principles, such as statistical reasoning, adversarial thinking, systems design, feedback loop analysis, compound over time and remain relevant regardless of what the technology stack looks like in years to come.

Not all organizations are equipped to operate this way, and while hiring to fill this gap might be the obvious solution, the reality is that, thanks to advancements and widespread adoption, today we have cutting-edge AI tools that can be easily integrated within existing infrastructure and operations.

The way forward is to invest in the work of external providers that make these tools accessible and easy to deploy, combining statistical analysis, reasoning and full explainability.

Deploying new technologies without fueling risks

You don’t need to be technically sophisticated to deploy a technically sophisticated attack, and this democratization of capability is what makes the next wave genuinely different. The questions practitioners are working through now are not whether new technologies work, but how to leverage the next generation of capabilities to stay ahead of increasingly automated attacks.

The most important principle is to treat your systems as attack surfaces from day one rather than as an afterthought once they are in production. Every model you deploy has assumptions baked into it, and adversarial actors will probe those assumptions systematically. If you have not done that probing yourself first, you are outsourcing your red team function to people with very different intentions.

To be effective, AI-driven defenses must work in milliseconds - fast enough to spot risk without disrupting the customer experience. Every transaction is assessed in real time, analyzing hundreds of signals and comparing them against historical patterns to identify any signs of anomalous activity.

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• Virgin Media O2 currently employs around 16,000 workers
• However reports claim 25% could be set for redundancy
• Non-customer-facing roles will likely be heaviest hit

Sky News has reported that Virgin Media O2 (VMO2) plans to cut an unconfirmed number of jobs – understood to be in the thousands – amid ongoing cost reduction efforts and a bid to get a grip on company debt.

Though the number of workers said to be impacted is not yet known, the company currently employs around 16,000 workers, so 'thousands' would represent a significant chunk of its workforce.

VMO2 looks to be one of the latest companies in a long list to be flattening its hierarchy, with changes to management reportedly underway.

VMO2 job cuts look to be around the corner

Crucially, Sky News reports that non-customer-facing roles will likely be heaviest hit. The firm's customer services typically perform well, it seems, with Virgin Media reporting the fewest number of complaints compared with competitors (via Ofcom) on the broadband front. Though O2 sits somewhere toward the top on the mobile front, beating only Sky Mobile (which also uses the O2 network).

An unnamed insider believes as many as 4,000 jobs could be at risk, representing 25% of VMO2's entire workforce.

It's believed that the company could share precise numbers and details as part of its Q3 earning call on November 3. Overall company revenue dropped 7.9% last quarter.

But VMO2's biggest issue is likely debt. Last quarter, it reported a total net debt equivalent to 5.89x annualized adjusted EBITDA.

But VMO2 isn't the only telecoms giant adjusting its workforce in response to financial pressures – VodafoneThree also confirmed plans to cut 1,200 jobs in July 2026, though these were Europe-wide.

VMO2 declined to comment.

If you work in cybersecurity for long enough, you learn that we really love an analogy or a metaphor and come October you can barely move for talk of security nightmares, cyber monsters and threats lurking in the shadows.

I enjoy a good analogy as much as anyone, but there’s one Halloween comparison I don't hear very often. And, for data security at least, I think it might be the most useful one right now: the mask.

A mask doesn't make what's underneath disappear. It simply means you don't have to reveal its true identity to everyone who encounters it, and that’s what we’re trying to achieve with sensitive data.

Developers need data to test applications, analysts need it to find patterns, and AI tools and agents increasingly need access to enterprise information to do useful work. But needing to use this data isn't necessarily the same as needing to see every sensitive value it contains.

That's precisely where data masking comes in. By replacing sensitive information with realistic but unusable values, organizations can preserve the usefulness of data without unnecessarily exposing what's underneath.

With AI dramatically increasing where data can travel, who can find it and what can act upon it, that distinction is becoming much more important.

A disguise with a serious purpose

At its core, data masking replaces sensitive information with realistic but unusable values. The resulting dataset can retain characteristics necessary for development, testing, analytics and other business processes without unnecessarily exposing the sensitive information underneath. Think of it as a Halloween costume with a security purpose; the data can still show up to the party without revealing its true identity.

That distinction matters because protecting data shouldn't simply mean preventing people from using it. Modern businesses depend upon being able to analyze, test against and increasingly train or ground AI systems in enterprise information. Instead, the question should be: does this person, application or AI system actually need the original sensitive value to perform this task?

A developer testing an application may need representative customer data, for example, but not necessarily genuine customer identities. An analytics workload may need to preserve relationships and patterns without revealing every account number or personal identifier.

If the real value isn't required, exposing it creates risk without creating corresponding business value.

Masking removes that unnecessary exposure. Depending on the use case, organizations can also use techniques including tokenization and encryption. The important principle is to match protection to how the information needs to be used rather than defaulting to exposing raw sensitive data.

You can't mask what you can't find

There is an obvious complication: before deciding what should wear a mask, businesses need to know where their sensitive information is.

That remains surprisingly difficult.

The 2026 Thales Data Threat Report found only 34% of surveyed organizations had complete knowledge of where their data was stored. Only 39% said they could classify all of it. Meanwhile, the average organization reported using seven different tools for data protection and monitoring.

Cloud infrastructure compounds the problem. Information can be copied into data lakes, SaaS platforms, development environments and backups. Unstructured documents can contain sensitive information nobody realized was there. Applications created years after the data was collected can suddenly gain access to it.

AI makes that visibility problem considerably more consequential because finding information is precisely what these systems are becoming very good at.

This is where data security posture management (DSPM) becomes important. Rather than treating data security purely as a question of defending infrastructure, DSPM starts with the data itself: discovering and classifying sensitive information and understanding where it resides, who or what can access it, how it is being used and whether its protection is appropriate.

Once an organization identifies unnecessarily exposed sensitive data, it can decide whether access should be removed, restricted or protected using controls such as masking, tokenization or encryption.

AI data: trick or treat?

This becomes particularly important as businesses progress from experimenting with generative AI towards embedding AI into everyday operations.

AI applications need data. Agents may need considerably more of it because they are designed not simply to generate content but to retrieve information, make decisions and potentially take actions across different systems.

The security implications are already becoming visible. The same research found 61% of organizations reported their AI applications were being targeted by attackers, with sensitive data the leading target. Rapid change within the AI ecosystem was also the most commonly cited AI security risk.

The important question, however, isn't simply whether AI can be secured. Organizations also need to ask a more fundamental question:

What data are we allowing AI to see in the first place?

Giving an AI application access to a database shouldn't automatically mean giving it access to every raw sensitive value contained within it. The same least-privilege principle used for human identities can be applied to data: expose what is necessary for the task and protect what isn't.

That could mean masking personal information before it reaches development and testing environments. It could mean tokenizing particularly sensitive values used by an application. It could mean encrypting information and tightly controlling which identities can decrypt it. And in some circumstances, it should mean preventing an AI system from accessing a dataset altogether.

Don't make AI security harder than it needs to be

There is a tendency to treat AI security as an entirely new discipline requiring entirely new answers.

Some risks certainly are new. But one of the most useful things organizations can do is apply established data-security principles to this new environment.

Know where sensitive data resides. Understand its context. Minimize unnecessary access. Protect the underlying value according to how it needs to be used. And continually reassess those decisions as applications and AI systems change.

AI may dramatically increase how much value organizations can extract from their data. It doesn't follow that every system extracting that value needs to see everything underneath.

Halloween only comes around once a year. For sensitive data, however, having the option to wear a convincing mask year-round is becoming a non-negotiable.

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Xiaomi's POCO X8 vs. X8s puts battery life against portability and cameras, but prices and availability differ between Vietnam and the Philippines.

The post Xiaomi’s POCO X8 vs X8s: More Battery or Better Cameras for Your Money? appeared first on TechRepublic.

Walk into the network operations center of any major enterprise and you will see an array of monitoring capability. Massive dashboards track API response times to the millisecond and map digital customer journeys from an ad click on a smartphone all the way to a virtual shopping basket. On paper, enterprise IT has achieved total operational awareness.

Step onto the high street at twelve-thirty on a Friday afternoon, however, and that illusion dissolves. A payment terminal freezes at a busy cafe counter. The line extends out the door. The cashier tries rebooting the hardware while office workers check their watches.

Within minutes, shoppers begin walking out empty-handed, heading straight for the competitor across the street. Back at corporate headquarters, central monitoring dashboards remain entirely green. As far as IT knows, systems are operating normally.

This disconnect represents one of the most significant operational blind spots in physical commerce today. In the UK alone, checkout failures put an estimated £1.7 billion in annual sales at risk. When transactions stall during peak trading windows, that revenue is cannot be recovered.

The failure is rarely a collapse of the central cloud. Often, it is a telemetry blackout occurring at the critical physical touchpoint where money actually changes hands.

Why payment security creates blind spots

To understand how physical commerce ended up in the dark, one must look at the intersection of security compliance and legacy monitoring tools. For years, the gold standard of enterprise visibility has been Application Performance Monitoring. These platforms thrive in open cloud environments where software agents can trace code execution and inspect payloads across distributed microservices.

Physical payment infrastructure operates under radically different constraints. The Payment Card Industry Data Security Standard mandates strict network isolation to protect cardholder information. Point-of-sale software, payment applications and hardware PIN pads are deliberately locked inside hardened, encrypted subnets.

While non-negotiable for security, this architecture creates an operational dead zone. The moment a transaction leaves the standard corporate network and crosses the compliance boundary, visibility ceases. As the signal travels through local hardware, store routers, third-party gateways and acquiring banks, standard IT tools cannot follow.

Central teams only discover an outage when store managers log manual helpdesk tickets or when batch reconciliation reports reach the operations team hours later.

The cost of the seven-minute window

IT support teams measure incident resolution in hours, but in-store shoppers measure patience in seconds. Research tracking consumer behavior during in-store disruptions shows that shopper patience has a hard limit of approximately seven minutes. Once a payment delay crosses that mark, the customer abandons the purchase entirely.

Contrast this narrow seven-minute window with the standard lifecycle of an IT incident. The average payment disruption takes over sixty minutes to diagnose and resolve. By the time an on-duty manager contacts support, the ticket escalates to an engineering queue and analysts begin combing through disparate vendor portals to locate the failure, the high-traffic sales surge has passed.

In an era where cash use is in significant decline and frictionless contactless payments are expected, relying on post-incident reviews results in lost revenue.

Unlocking real-time payment telemetry

Solving this problem requires an architectural evolution from delayed, post-mortem log scraping to real-time telemetry streaming. The key insight lies in understanding that monitoring transaction health does not require exposing sensitive cardholder data. Enterprise architectures must cleanly decouple regulated financial data from operational metadata.

While the payload containing card numbers remains locked inside the compliance envelope, operational metadata, such as gateway response codes, handshake transit times, peripheral hardware status, and localized network latency, can be safely extracted and streamed in real time.

By transforming every swipe, tap and dip into an operational data feed, engineering teams gain an end-to-end view of the transaction pipeline. Site reliability engineers can finally monitor the health of physical checkout counters with the same fidelity they apply to their cloud applications.

Using AI for automated fixes

Streaming telemetry provides the necessary visibility, but human analysis alone cannot keep pace with the volume of modern commerce. During a peak holiday rush, an enterprise payment stack might process tens of thousands of transactions every minute across hundreds of distributed venues.

A subtle degradation, such as an obscure firmware glitch that causes contactless taps to time out on ten percent of devices, is impossible for a human operator to catch in real time.

This is where AI and edge observability can make a decisive difference. Rather than depending on static alerts that trigger after a catastrophic failure, machine learning models analyze continuous velocity curves, baseline latency distributions, and transaction state patterns.

When a performance degradation begins, AI tools can rapidly isolate the root cause, instantly identifying whether a delay stems from an in-store network drop, a faulty card reader or a struggling banking gateway.

More importantly, this intelligence allows for true closed-loop remediation. Integrated with intelligent payment orchestration platforms, the system can automatically reroute transaction traffic around a failing processor to an alternative rail without the customer noticing a delay. If a store loses connectivity entirely, edge architecture can switch terminals into secure, encrypted store-and-forward modes without interruption.

The high street cannot afford operational dead zones. By combining privacy-safe telemetry with AI-driven intelligence, technology leaders can finally bridge the gap between back-end infrastructure and front-of-house revenue, ensuring that when the rush hits, the checkout keeps moving.

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Generic AI tools promise to solve every problem under the sun, from running enterprise CRM to analysing why your cat isn't drinking enough water during a heatwave. And when you try to use one tool with no defined role for every task, results are disappointing and erode trust in technology.

Multiply that pattern across hundreds of employees, and you get tokenmaxxing: prompt after prompt, refining AI output until something usable comes out.

Some organizations, by instinct, default to blaming the employees for tokenmaxxing, or double down on training on ‘responsible use of AI’.

Tokenmaxxing is a symptom of capable people having to create solutions in an environment that lacks clear shape, scope, guardrails - and ways to analyze spend and outcomes.

Locking it down is not the solution

Traditional procurement systems were designed for traditional business software cycles. If building an enterprise system takes years, it’s not a big deal if procuring the right vendor takes years, too.

Routing AI through procurement to evaluate all options and keep the spend predictable initially felt to organizations like a safe and responsible choice. But software creation is very different today than it was even 5 years ago. Standard IT procurement still takes months, while opening a public LLM chatbot and asking it to generate a landing page is a matter of five seconds.

Policies tend to live their happy lives somewhere in the corporate portals, unbothered and forgotten unless an incident calls for a formal investigation. And when the approved systems are slow or restrictive, employees find workarounds. They turn to unvetted shadow AI tools for daily tasks and put proprietary corporate data into public models.

Look at the app prototypes that come out of it: the bad code is one problem, but the interfaces are off-brand, ignore corporate design systems and WCAG 2.2 accessibility rules, and lack basic authentication patterns.

Restricting AI use for employees in reality means they pay the £18 monthly subscription out of pocket and still use it. It moves the spend somewhere finance can't see it, at least not immediately and not until it’s too late, adding a data governance problem on top.

The other consequence is harder to put on a balance sheet. A company where only engineers can create software moves at the speed of engineering's queue, but opening options up to a wider group - product management, analysts, marketers - means a company can speed up innovation while having greater visibility into it.

The cost of ungoverned building

The pressure is already on. Teneo's Vision 2026 survey of more than 350 public company CEOs and 400 institutional investors found that 53% of investors expect to see a positive ROI on new AI initiatives in six months or less, while only 16% of large-cap CEOs believe they can deliver on that timeline. The same CEOs report that fewer than half of their current AI projects are ROI-positive. Investors want returns on a clock that executives don't think they can meet.

Volume is not the problem. Goldman Sachs Research forecasts that token consumption will multiply 24 times, to 120 quadrillion tokens per month, between 2026 and 2030, while semiconductor providers are already delivering 60% to 70% annual declines in cost per token. Tokens are getting cheaper, and we are going to use far more of them.

But who - or what - uses tokens is going to change. Organizations are building and deploying AI agents who act on their own. And agentic AI burns far more tokens than good old human prompting, because the queries repeat in sequence: a chatbot request multiplied by 20, or even 50, running unattended. Send an agent to work on an unscoped problem, and it will keep going long after a human would have stopped to ask whether this was worth it.

A lot of those tokens go into prototypes that never get to see the real world. Prototyping and sifting through ideas is how software evolves, but building with no eye on production drains resources. Gartner found that at least 50% of generative AI projects were abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, or unclear business value. If a prototype works in isolation but has to be scrapped because engineering would need to rebuild it from scratch, that's pure waste.

Then organizations have to pay the bill twice - for inflated tokens and lost engineering hours. When teams build with generic LLMs and no guardrails, senior engineers spend hours cleaning up the output. That deepens the trust gap in technical teams. Stack Overflow's 2025 developer survey found that 46% of developers distrust the accuracy of AI output. "Almost right" (but not quite) AI answers are the biggest frustration for 66% of developers, while 45% report that debugging AI-generated code is more time-consuming.

Access for everyone, accountability from everyone

The speed of code generation isn't the problem. Lowering the technology barrier for non-engineers produces better software, software shaped by the people who understand the business need. AI building is an edge, and an edge works best when everyone has it.

But alongside democratized access to technology, we need new ways to think about accountability for how you use it and the associated costs. Modern corporate culture can promote unnecessary complexity in how we think and speak about our work. Being able to explain in simple words how you spent your day is a useful exercise, and we should apply the same thinking to AI spend.

So, stated plainly: tokens should be used for a job somebody actually needed. In turn, the user needs to be able to explain, beyond the buzzwords, what the purpose was behind a particular token spend.

To work, rather than become another source of corporate frustration, this accountability needs to be part of the environment offered. One way to start is to retire the do-everything chat box as the default. Instead, organizations should start thinking about how to give people task-specific agent workflows tailored to their roles, and build execution limits into the software environment.

This way, non-technical staff get brand guidelines, fonts, security rules, and components pre-coded into the environment, so the apps they build are compliant and production-ready by default. They don’t need to file separate requests because the shape of the environment already includes the right guardrails on security and spend.

What it looks like when it works

Bounded workflows pay off in ways you can measure. In a recent mid-sized company implementation, a 3-person data analytics team built an agent that analyses data across all their systems; it saved each of them 12 to 13 hours of manual work a week.

Three analysts now produce what once took a team ten times their size, and their daily job also got way more interesting. They spend their time on high-value strategy instead of gathering data by hand.

That agent has a clearly scoped job and executes it in line with pre-defined parameters - it is not a chat box with a company credit card.

Bringing together abundance and accounting

Uncontrolled AI costs and falling trust in technology have the same solution, and it is not rationing of tokens. Tokens will be getting ever cheaper, and their abundance is the point of this technology.

The answer is in defining and understanding the scope of AI. Give everyone the tool but make it purpose-specific from the start rather than a generic catch-all.

Every company I talk to can tell me what it spent on AI last quarter, but when I ask what they wanted to get in return, the answer is murky.

This lack of understanding of what job AI should do for an organization is the problem, and the surprising bill is only its symptom. The first step is for the organization to articulate, in the plainest words possible, what the tools are for before the tokens start moving.

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• Anthropic is expanding Claude's role across several stages of drug development
• Generate Biomedicines reports a 70% reduction in CAR T-cell component length
• Modified immune cells produced roughly ten times more cancer-detecting receptors

Anthropic is expanding Claude’s involvement in pharmaceutical research, working with biotechnology developers on projects intended to accelerate the creation of new medicines.

The AI company has laid out in a new blog post how it is examining scientific and administrative obstacles affecting drug development, from molecular design to clinical trial preparation.

Its collaborations involve potential treatments for cancer, food allergies and rare muscular disorders, although most of the treatments remain at experimental stages.

Claude enters drug development programmes

At Generate Biomedicines, six scientists used Claude Code to modify a component of CAR T-cell therapy, traditionally employed against blood cancers.

Their work sought to create additional room for genetic instructions that could help the treatment address solid tumours.

The redesigned component was approximately 70% shorter, while laboratory experiments found that immune cells produced roughly ten times more receptors.

The modified cells also destroyed cancer cells more effectively and maintained their activity for longer periods, according to the reported findings.

Divergence Labs used Claude to develop antibody candidates for a potential food-allergy treatment involving the immune molecule IgE.

The system generated approximately 35,000 molecular designs within one month, producing potential molecules for both functions required by the proposed treatment.

Initial experiments identified three designs capable of binding to IgE and another 20 capable of detaching it from immune cells.

Three of the 20 designs appeared more effective than the standard medication at removing IgE from immune cells.

BigHat Biosciences combined Claude with automated laboratory equipment to improve two antibody fragments being considered for future medicines.

Researchers manufactured and tested approximately 1,010 variations over ten weeks, examining binding strength, stability and unwanted molecular aggregation.

The strongest cancer-targeting version bound its intended target more than 10 times as tightly and retained stability at temperatures 18°C higher than the original.

The other fragment, intended for longer-lasting medicines, aggregated more than 100 times more slowly than its original version.

Enveda applied Claude to manufacturing documentation, which had consumed up to 75% of some chemists’ working time.

During an assessment spanning 1,061 pages, the system identified all 11 problems previously discovered by human reviewers, as well as seven additional issues.

The approach reduced manual checking time by 89%, bringing the review process from more than 25 hours to under three.

Anthropic expands into clinical research and drug creation

Anthropic has also partnered with Bristol Myers Squibb to assess clinical study plans and estimate their chances of success.

Michael Pourdehnad, the pharmaceutical company's senior vice president for early clinical development, said: “From a single prompt, Claude generated a robust initial analysis.”

He explained that this helped evaluate development options that would otherwise have required several days of work.

The companies intend to extend their collaboration further, examining Claude’s potential contribution to decisions during early clinical development.

Anthropic says working directly with drug developers helps identify weaknesses in its models and opportunities for improvement across scientific research.

The company also intends to extend Claude's involvement through additional partnerships, research programmes and tools for life-science organizations.

• Chinese researchers report 3.2 Pb/s transmission across a 24-core optical fiber
• Spatial division multiplexing carries separate data streams through one fiber
• S+C+L transmission uses three wavelength bands to carry information

Researchers in China say they have transmitted data at 3.2 Pb/s through a 24-core optical fiber, setting a new real-time speed record.

At that capacity, data equivalent to thousands of ultra-high-definition films could theoretically move through the connection in just one second.

The new figure exceeds the team's previous 2.5 Pb/s record, set earlier this year using similar 24-core fiber technology.

How the researchers reached 3.2 Pb/s

The project brought together specialists from the National Key Laboratory of Optical Communication Technology, China Information and Communication Technologies Group, Pengcheng Laboratory, and Fiberhome Fujikura Fiber Optic Technology.

Their system combined spatial division multiplexing with S+C+L multi-band transmission, two methods intended to increase information carried through existing optical infrastructure networks.

Spatial division multiplexing separates information into distinct channels within one fiber, allowing multiple independent data streams to travel simultaneously through the cable.

The S, C and L bands use different wavelength ranges, enabling transmission across a wider section of the optical spectrum.

Combining the techniques created engineering difficulties, particularly interference between neighbouring channels carrying simultaneous signals through the fibre network and equipment.

The team also had to manage differing specifications across the three wavelength bands without allowing those variations to undermine transmission performance.

The test hit record figures with hardware architectures described as compliant with commercial requirements and operated continuously in real time.

That operating condition distinguishes the result from demonstrations relying on experimental equipment or signal-processing arrangements developed specifically for laboratory research.

The Hubei provincial government website compared the capacity with internet access for approximately 3.2 million households using separate 1 Gbps connections simultaneously.

The report describes the rate as sufficient to download “more than 20,000 4K movies” within one second, under ideal conditions.

These comparisons describe theoretical network capacity, rather than evidence that individual households could receive such speeds through ordinary broadband subscriptions.

The gap between commercial hardware and laboratory records

The new figure does not represent the highest speed reported for transmission through a 24-core optical fibre.

Laboratory research has reached 5.29 Pb/s using comparable multi-core fiber, exceeding the Chinese group's latest result by over two petabits.

Those experiments, however, may depend on advanced processing methods and specialist laboratory equipment that remain unsuitable for routine network deployment.

The Chinese group’s reported use of commercially compliant hardware is a significant feature of its claim, although it does not establish immediate commercial availability.

A successful real-time demonstration alone cannot confirm long-term reliability, affordable operation or compatibility with existing telecommunications infrastructure under demanding operating conditions.

At the moment, there is no official deployment schedule, pricing information or independent assessment of performance during sustained operation under everyday network conditions.

Thus, there is no certainty that the technology can move beyond controlled testing into dependable telecommunications networks serving customers at scale.

However, the research has shown that combining multiple fiber channels and wavelength bands can deliver substantial capacity under practical test conditions.

Via Tom's Hardware

Looking for a different day?

A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Sunday's puzzle instead then click here: Quordle hints and answers for Sunday, November 1 (game #1742).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,500 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today — or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc's Wordle today column covers the original viral word game.

SPOILER WARNING: Information about Quordle today is below, so don't read on if you don't want to know the answers.

Quordle today (game #1743) — hint #1 — VowelsHow many different vowels are in Quordle today?

• The number of different vowels in Quordle today is 4*.

* Note that by vowel we mean the five standard vowels (A, E, I, O, U), not Y (which is sometimes counted as a vowel too).

Quordle today (game #1743) — hint #2 — repeated lettersDo any of today's Quordle answers contain repeated letters?

• The number of Quordle answers containing a repeated letter today is 2.

Quordle today (game #1743) — hint #3 — uncommon lettersDo the letters Q, Z, X or J appear in Quordle today?

• Yes. One of Q, Z, X or J appears among today's Quordle answers.

Quordle today (game #1743) — hint #4 — starting letters (1)Do any of today's Quordle puzzles start with the same letter?

• The number of today's Quordle answers starting with the same letter is 2.

If you just want to know the answers at this stage, simply scroll down. If you're not ready yet then here's one more clue to make things a lot easier:

Quordle today (game #1743) — hint #5 — starting letters (2)What letters do today's Quordle answers start with?

• E

• A

• L

• E

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

Quordle today (game #1743) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle, game #1743, are…

• EXTRA
• AMITY
• LUPUS
• EAGLE

It didn’t take me very long to work out that my final word contained two letter Us, but I really struggled to stop thinking about humus and finally get to LUPUS.

This was also a game that featured the rare letter X, although deciding on EXTRA was fairly easy once the start and ending letters had been revealed.

Daily Sequence today (game #1743) — the answers

(Image credit: Merriam-Webster)

The answers to today's Quordle Daily Sequence, game #1743, are…

• THIRD
• GOING
• SNAIL
• SOOTH

Quordle answers: The past 20

• Quordle #1742, Sunday, 1 November: DROVE, PRIED, SHELL, BEGIN
• Quordle #1741, Saturday, 31 October: ANIME, SWARM, CHALK, RAJAH
• Quordle #1740, Friday, 30 October: ULCER, DIMLY, BRAIN, BEGUN
• Quordle #1739, Thursday, 29 October: PRUDE, MINUS, SAVOY, USHER
• Quordle #1738, Wednesday, 28 October: DEALT, FLUME, SLEEK, RELIC
• Quordle #1737, Tuesday, 27 October: ANKLE, HAVOC, SULKY, WHITE
• Quordle #1736, Monday, 26 October: ASSAY, GLEAM, MUCUS, WRING
• Quordle #1735, Sunday, 25 October: RAINY, BUSED, PROXY, UNDID
• Quordle #1734, Saturday, 24 October: TOXIN, SMART, MOUSE, STYLE
• Quordle #1733, Friday, 23 October: BLIMP, HOIST, FRIED, LURID
• Quordle #1732, Thursday, 22 October: DECAL, WIDTH, WOKEN, LEVER
• Quordle #1731, Wednesday, 21 October: MOIST, VALVE, VERSE, NORTH
• Quordle #1730, Tuesday, 20 October: COYLY, SAUNA, TRIAL, RASPY
• Quordle #1729, Monday, 19 October: EXIST, DECOY, HITCH, FLUID
• Quordle #1728, Sunday, 18 October: FLINT, FUROR, WEDGE, RODEO
• Quordle #1727, Saturday, 17 October: QUARK, ENEMY, SPASM, BLOCK
• Quordle #1726, Friday, 16 October: SHORT, WORTH, JOIST, SLUNK
• Quordle #1725, Thursday, 15 October: STALE, EPOCH, PIANO, CORNY
• Quordle #1724, Wednesday, 14 October: TIMID, GROUP, GRILL, EVERY
• Quordle #1723, Tuesday, 13 October: FLUFF, DEBUT, TEETH, STEIN

Looking for a different day?

A new NYT Strands puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Sunday's puzzle instead then click here: NYT Strands hints and answers for Sunday, November 1 (game #973).

Strands is the NYT's latest word game after the likes of Wordle, Spelling Bee and Connections – and it's great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc's Wordle today page for the original viral word game.

SPOILER WARNING: Information about NYT Strands today is below, so don't read on if you don't want to know the answers.

NYT Strands today (game #974) - hint #1 - today's themeWhat is the theme of today's NYT Strands?

• Today's NYT Strands theme is… High and mighty

NYT Strands today (game #974) - hint #2 - clue words

Play any of these words to unlock the in-game hints system.

• MULE
• FILE
• SNARE
• RACE
• TIME
• DATE

NYT Strands today (game #974) - hint #3 - spangram lettersHow many letters are in today's spangram?

• Spangram has 11 letters

NYT Strands today (game #974) - hint #4 - spangram positionWhat are two sides of the board that today's spangram touches?

• First side: bottom, 1st column

• Last side: top, 6th column

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

NYT Strands today (game #974) - the answers

(Image credit: New York Times)

The answers to today's Strands, game #974, are…

• CLIFF
• SNOW
• GLACIER
• PEAK
• RIDGE
• SADDLE
• SUMMIT
• SPANGRAM: MOUNTAINTOP

• My rating: Easy
• My score: Perfect

I love how today’s spangram jaggedly rises from one corner to the other.

However, despite my enjoyment of some top-level Strands craft I still managed to miss out on spotting it. Instead, I began with the game’s two most obvious words in CLIFF and PEAK.

The closest I’ve got to a MOUNTAINTOP is snowmobiling on a GLACIER in Iceland — an experience that was both exciting and terrifying, not least the moment when our guide pointed out a 120ft-deep crevasse we were inches away from falling into. I’m still having the nightmares.

Yesterday's NYT Strands answers (Sunday, November 1, game #973)

• SNOOZE
• SLEEP
• CATNAP
• DOZE
• SLUMBER
• DROWSE
• SPANGRAM: ZZZZZZZZ

What is NYT Strands?

Strands is the NYT's not-so-new-any-more word game, following Wordle and Connections. It's now a fully fledged member of the NYT's games stable that has been running for a year and which can be played on the NYT Games site on desktop or mobile.

I've got a full guide to how to play NYT Strands, complete with tips for solving it, so check that out if you're struggling to beat it each day.

Looking for a different day?

A new NYT Connections puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Sunday's puzzle instead then click here: NYT Connections hints and answers for Sunday, November 1 (game #1239).

Good morning! Let's play Connections, the NYT's clever word game that challenges you to group answers in various categories. It can be tough, so read on if you need Connections hints.

What should you do once you've finished? Why, play some more word games of course. I've also got daily Strands hints and answers and Quordle hints and answers articles if you need help for those too, while Marc's Wordle today page covers the original viral word game.

SPOILER WARNING: Information about NYT Connections today is below, so don't read on if you don't want to know the answers.

NYT Connections today (game #1240) - today's words

(Image credit: New York Times)

Today's NYT Connections words are…

• MEDICINE CABINET
• COMPASS
• FOG MACHINE
• INCENSE
• WATERFALL
• DISCO BALL
• NETTLE
• HIKING BOOTS
• DRESSING ROOM
• DRY ICE
• WATER BOTTLE
• WIND UP
• PIQUE
• KALEIDOSCOPE
• HUMIDIFIER
• BACKPACK

NYT Connections today (game #1240) - hint #1 - group hints

What are some clues for today's NYT Connections groups?

• YELLOW: Equipment for a long walk
• GREEN: Raging
• BLUE: Wet not wet
• PURPLE: Watch your reflection

Need more clues?

We're firmly in spoiler territory now, but read on if you want to know what the four theme answers are for today's NYT Connections puzzles…

NYT Connections today (game #1240) - hint #2 - group answers

What are the answers for today's NYT Connections groups?

• YELLOW: THINGS TO BRING ON A TREK
• GREEN: ANGER
• BLUE: MIST MAKERS
• PURPLE: THINGS WITH MIRRORS

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

NYT Connections today (game #1240) - the answers

(Image credit: New York Times)

The answers to today's Connections, game #1240, are…

• YELLOW: THINGS TO BRING ON A TREK: BACKPACK, COMPASS, HIKING BOOTS, WATER BOTTLE
• GREEN: ANGER: INCENSE, NETTLE, PIQUE, WIND UP
• BLUE: MIST MAKERS: DRY ICE, FOG MACHINE, HUMIDIFIER, WATERFALL
• PURPLE: THINGS WITH MIRRORS: DISCO BALL, DRESSING ROOM, KALEIDOSCOPE, MEDICINE CABINET

• My rating: Easy
• My score: Perfect

A straightforward Connections puzzle to begin the week, without any word games, traps, trickery or cultural gaps.

As is often the case with these types of Connections games I got each group in difficulty order and was able to experience that little tingle of perfection, but also disappointment that I wasn’t smart enough to spot a fairly easy purple group.

Games this easy also make me suspicious that the NYT puzzle setters are merely giving our brains a day off in order to soften us up before a difficult run. Bring it on!

Yesterday's NYT Connections answers (Sunday, November 1, 2026, game #1239)

• YELLOW: KINDS OF TEETH: BICUSPID, CANINE, INCISOR, MOLAR
• GREEN: KINDS OF LOVE: BROTHERLY, PUPPY, SELF, UNREQUITED
• BLUE: ENTHUSIAST: FIEND, HEAD, HOUND, LOVER
• PURPLE: SYNONYMS FOR "SOAKED" WITH FIRST LETTER CHANGED: DOGGY, MATURATED, TRENCHED, TRIPPING

What is NYT Connections?

NYT Connections is one of several increasingly popular word games made by the New York Times. It challenges you to find groups of four items that share something in common, and each group has a different difficulty level: green is easy, yellow a little harder, blue often quite tough and purple usually very difficult.

On the plus side, you don't technically need to solve the final one, as you'll be able to answer that one by a process of elimination. What's more, you can make up to four mistakes, which gives you a little bit of breathing room.

It's a little more involved than something like Wordle, however, and there are plenty of opportunities for the game to trip you up with tricks. For instance, watch out for homophones and other word games that could disguise the answers.

It's playable for free via the NYT Games site on desktop or mobile.