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The districts that will decide control of the House and Senate next year are spread across the country. Voting officials in those places may face scrutiny and pressure they've never experienced.

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Some of the key storylines to watch will center around various voters groups that have soured on President Trump during his second turn out.

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Colleges are trying to transform an admissions process that has often been daunting, secretive and stressful into an experience that's open, easier — and downright friendly.

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Vance's delivery is different from the president's but his speeches contain plenty of MAGA DNA.

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Many people are swapping out their fluoride dental products for ones made with a mineral called hydroxyapatite. What do dentists say about it?

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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.

We've reviewed and ranked the best data removal services.

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

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.

Democratic Rep. Haley Stevens endorses GOP candidate in Michigan race, key races remain close in last day before midterm election, Gulf states concerned about possible U.S. attack on Iran.

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.

We've featured the best ecommerce platform.

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

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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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

Android 17 is arriving on the Galaxy Tab S11 series, bringing DeX and privacy improvements. But the rollout schedule depends on where you live.

The post Samsung One UI 9 Hits Galaxy Tab S11 and S11 Ultra — Who Gets It First? appeared first on TechRepublic.

The U.N.'s long-running peacekeeping mission for the disputed Western Sahara ceased operations Sunday after 35 years. Once ruled by Spain, it is claimed by both Morocco and the pro-independence Polisario Front.

(Image credit: Bernat Armangue)

ChatGPT Plus, Pro, and Business subscribers will share usage limits with Codex starting in December 2026, but Instant chats will remain exempt.

The post ChatGPT and Codex Will Share Usage Limits in December — Which Subscribers Are Affected? appeared first on TechRepublic.

U.S. Transportation Secretary Duffy says all nine people died aboard a business jet that crashed into a Florida neighborhood. The aircraft departed from Austin, Texas, and was headed to the Bahamas.

(Image credit: Mark Elias)

Find out how Airtable vs monday.com compares in 2026 across pricing, project management, AI, automation, and data management.

The post Airtable vs monday.com 2026: Which Is Better for Your Team? appeared first on TechRepublic.

Compare the best Smartsheet alternatives for project management, including pricing, AI features, and enterprise capabilities.

The post Best Smartsheet Alternatives: 8 Tools Compared for 2026 appeared first on TechRepublic.

Compare Wrike vs monday for pricing, project management, automation, AI, and enterprise use to decide which platform better suits your team.

The post Wrike vs monday: Which Is Better for Your Team in 2026? appeared first on TechRepublic.

• 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