Project Glasswing

The findings come from Project Glasswing, a restricted cybersecurity initiative launched by Anthropic to help protect critical software infrastructure before increasingly capable AI systems can be used by attackers.

At the heart of the programme is Claude Mythos Preview, a specialised version of Anthropic’s AI designed specifically for vulnerability discovery, software analysis, and cyber defence tasks.

Unlike publicly available AI models, Mythos Preview has only been made available to around 50 carefully selected partners, including organisations responsible for maintaining and defending some of the world’s most important digital infrastructure.

According to Anthropic, those partners have collectively used the system to find “more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world” in just one month.

The Scale Of What Was Found

Anthropic says its partners have identified more than 10,000 high- or critical-severity vulnerability candidates. Of those, over 1,700 have already been verified as genuine security flaws, while more than 1,000 have been confirmed as high- or critical-severity vulnerabilities.

The company says it’s also been using Mythos Preview internally to scan more than 1,000 open-source software projects that underpin large parts of the internet.

So far, Anthropic says the model has identified 6,202 potential high- or critical-severity vulnerabilities within those projects alone. After detailed assessment by independent security researchers, 1,094 have already been confirmed as genuine high- or critical-severity flaws.

One example involved a serious vulnerability in wolfSSL, a widely used cryptographic library deployed across billions of devices. Anthropic says Mythos Preview discovered a flaw that could have allowed attackers to forge digital certificates and impersonate legitimate online services. The vulnerability has since been patched.

Finding Bugs Is No Longer The Bottleneck

Perhaps the most important aspect of the announcement is that Anthropic believes the economics of cybersecurity may now be changing thanks to AI.

Historically, security teams struggled to find vulnerabilities quickly enough, but now the company believes the opposite problem is emerging.

As Anthropic explains: “Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it’s limited by how quickly we can verify, disclose, and patch the large numbers of vulnerabilities found by AI.”

In other words, AI may be becoming so effective at discovering software flaws that human security teams cannot process, investigate, and fix them quickly enough.

Industry-Wide

That concern appears to be reflected across the industry. For example, Anthropic points to reports from Microsoft that patch volumes are expected to continue rising, while Oracle has already accelerated its patching schedules. The company also says Cloudflare found 2,000 bugs across critical systems while using Mythos Preview, including 400 classified as high- or critical-severity. Mozilla reportedly found more than ten times as many vulnerabilities in one Firefox testing cycle compared with earlier testing using conventional methods.

More Than Just Vulnerability Hunting

Anthropic says Mythos Preview has also shown value beyond traditional vulnerability discovery.

For example, one banking partner reportedly used the system to identify and prevent a fraudulent $1.5 million wire transfer after attackers compromised a customer email account and used spoofed phone calls to support the fraud attempt.

The company argues this demonstrates how advanced AI could increasingly act as a defensive force multiplier, helping cyber defenders analyse vast quantities of information far more quickly than human analysts alone.

However, Anthropic is also being careful about how widely it releases these capabilities.

The company has not made Mythos Preview publicly available because it believes safeguards remain insufficient to prevent misuse.

As Anthropic notes: “At present, no company, including Anthropic, has developed safeguards strong enough to prevent such models from being misused and potentially causing severe harm.”

Why This Matters

The announcement seems to highlight a broader change taking place across cybersecurity.

For years, security professionals worried about attackers using AI to create phishing campaigns, malware, and social engineering attacks. Increasingly, attention is turning towards AI-assisted vulnerability discovery, where software flaws can be found at unprecedented speed and scale.

Anthropic itself acknowledges the challenge directly, saying: “The relative ease of finding vulnerabilities compared with the difficulty of fixing them amounts to a major challenge for cybersecurity.”

That challenge becomes even more significant if similar capabilities become widely available across the industry.

Although Anthropic has restricted access to Mythos Preview, the company openly states that models with comparable capabilities are likely to emerge elsewhere and eventually become more broadly accessible.

What Does This Mean For Your Business?

For businesses, the most important takeaway here is that vulnerability discovery is accelerating rapidly, which means the value of slow patching cycles is diminishing just as quickly.

Many organisations still spend weeks or months testing and deploying updates, particularly in operational technology, manufacturing, healthcare, and other environments where change control is complex. As AI systems become better at uncovering vulnerabilities, those delays could create increasingly attractive opportunities for attackers.

Anthropic is urging organisations to focus on fundamentals such as faster patch deployment, stronger network configurations, multi-factor authentication, and comprehensive security logging. Those recommendations are not new, but the urgency behind them is growing because AI is dramatically reducing the effort required to find weaknesses in software.

The wider message is that AI is changing the balance between attackers and defenders. For now tools such as Mythos Preview may provide what Anthropic describes as an “asymmetric advantage” for defenders. The question facing the cyber security industry is how long that advantage will last once similar capabilities become widely available.

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Deal With Quantexa

The decade-long deal with London-based AI and analytics firm Quantexa marks one of the largest AI deployments ever seen inside the UK public sector. It also signals a major strategic change in how the government wants critical public systems to use artificial intelligence.

Rather than relying on a US technology giant, HMRC is betting heavily on a British-developed “Decision Intelligence” platform designed to connect fragmented data, identify suspicious patterns, and support human investigators and customer service teams.

Why HMRC Wants AI Help

HMRC has been under mounting criticism for years over long waits, processing delays, incorrect tax notices, and declining service standards.

According to figures obtained through Freedom of Information requests by the Contentious Tax Group, complaints against HMRC climbed to more than 93,000 in 2024/25, up sharply from around 70,000 five years earlier.

Also, compensation payments linked to HMRC errors and distress have also risen significantly.

At the same time, the tax authority is handling growing volumes of digital data as initiatives like Making Tax Digital expand across the UK economy.

It seems the problem for HMRC is not a lack of information, but that the information often sits in disconnected systems that can’t easily “see” relationships between people, companies, transactions, and behaviours.

Quantexa specialises in connecting fragmented datasets and using graph analytics and machine learning to identify patterns, relationships, and anomalies that would be extremely difficult for human investigators to spot manually across millions of disconnected records and transactions.

Its technology was originally developed for anti-money laundering work inside banks. Customers already include HSBC and Vodafone.

Now HMRC wants to apply similar techniques to tax compliance, fraud detection, and operational efficiency.

Connecting The Dots

One of the most significant parts of the project involves what Quantexa calls “entity resolution”. In simple terms, the system attempts to identify when multiple records, companies, transactions, or identities may actually be connected.

That matters because complex fraud networks often hide behind layers of shell companies, false references, mismatched addresses, or disconnected records spread across multiple databases.

The technology is designed to create what Quantexa describes as “a clearer, connected view of its data to improve performance, help identify tax at risk, and strengthen control.”

Positive Points

One positive point about the new system is that it should be able to help HMRC track legitimate payments that have been incorrectly referenced, which could potentially reduce some of the administrative headaches faced by businesses and taxpayers.

Also, importantly, Quantexa says the platform is not intended to replace human decision-making. As Quantexa CEO Vishal Marria says: “In government environments, AI cannot operate as a black box,” and that “Decisions need to be transparent, auditable, and explainable, particularly in areas affecting citizens directly.”

In fact, this point matters politically as much as technically. For example, governments worldwide are increasingly nervous about allowing opaque AI systems to make decisions affecting taxes, benefits, healthcare, or policing without clear accountability.

The Digital Sovereignty Angle

There is another layer to this story that goes well beyond tax collection. The Quantexa deal is being viewed inside government as part of a wider push towards so-called “digital sovereignty”.

In recent years, the UK government has awarded huge contracts to American data firms including Palantir Technologies, the US data analytics company co-founded by billionaire Peter Thiel, whose NHS data platform deal generated considerable political controversy.

This time, ministers appear keen to emphasise that the supplier is British, the systems are governed, and the data stays under HMRC control.

Also, Quantexa’s online announcement about the deal with HMRC strongly emphasised sovereignty and governance concerns, with Quantexa highlighting how “Public sector organisations are accelerating digital transformation while needing to maintain sovereignty, auditability and control.”

It added that the platform creates “a trusted, governed foundation for advanced analytics and the safe deployment of AI at scale.”

The language used around the project is deliberate because governments are no longer debating simply whether AI can improve public services, they are increasingly focused on who controls the systems, where sensitive national data is stored, and whether automated decisions can be properly explained, audited, and challenged when citizens are affected.

A Major Test For Government AI

The contract could become a defining test case for how AI is used across British government departments. If successful, similar approaches could spread rapidly into compliance, policing, border control, welfare systems, and other high-data public services.

However, the pressure to deliver will be intense because HMRC’s tax gap currently stands at £46.8 billion, representing money theoretically owed but not collected, and the government is clearly placing significant faith in AI and Quantexa’s ability to help recover far more of it. Quantexa founder and CEO Vishal Marria says governments worldwide are struggling with “how to turn complex, fragmented data into confident, timely decisions”, which goes directly to the heart of HMRC’s long-running problems with disconnected systems, slow processes, and rising operational complexity. The company believes that by “creating context from data and embedding trusted, governed AI”, HMRC will be able to make “confident, informed decisions” more quickly, while improving fraud detection, strengthening oversight, and reducing the kinds of administrative errors that have increasingly damaged public confidence in the tax authority.

What Does This Mean For Your Business?

For businesses, accountants, and taxpayers, this signals a future where HMRC becomes far more data-driven, interconnected, and AI-assisted. That could mean faster identification of fraud and errors, quicker handling of customer queries, and improved detection of suspicious tax activity.

It could also mean increased scrutiny. As AI systems become better at linking records and spotting inconsistencies across datasets, businesses may find it harder to hide mistakes, discrepancies, or unusual financial behaviour inside disconnected systems.

At the same time, the project highlights something much bigger happening across the UK economy. Artificial intelligence is rapidly moving beyond chatbots and productivity tools into core national infrastructure, including taxation, compliance, and public administration.

It now seems that businesses that maintain accurate records, consistent reporting, and well-organised financial systems are likely to face far fewer problems in an environment where AI is increasingly being used to connect data, identify anomalies, and scrutinise tax activity far more efficiently than before.

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How Ukraine Says Robots Captured Enemy Positions

The claim was made by Ukrainian President Volodymyr Zelensky when he announced that Ukrainian forces had seized an enemy position using only unmanned systems, without infantry entering the battlefield.

According to Zelensky, drones and robotic ground systems identified targets, suppressed enemy fire, and secured the position without Ukrainian casualties. Ukraine’s military has not released detailed operational information, and the full claim has not been independently verified. However, the announcement has attracted global attention because it points towards a battlefield where machines increasingly replace soldiers in frontline combat roles.

The operation reportedly involved a combination of aerial drones and unmanned ground vehicles working together as coordinated systems rather than isolated devices. Analysts say this type of “multi-swarm” warfare allows militaries to overwhelm positions while reducing risk to personnel.

In a statement published alongside footage of the operation, Zelensky said: “For the first time in the history of this war, an enemy position was taken exclusively by unmanned platforms – ground systems and drones.”

Why Ukraine Has Become A Testing Ground For Military Robotics

The war in Ukraine has accelerated military technology development at a pace rarely seen in modern conflicts. Systems that would normally take years to test and deploy are now being modified, upgraded, and returned to combat within weeks.

UFORCE, the Ukrainian-British defence technology company linked to the operation, was formed through the merger of nine Ukrainian defence companies and has now achieved a valuation exceeding $1 billion, making it Ukraine’s first defence technology unicorn. The company develops air, land, and sea drones, alongside battlefield software designed to coordinate unmanned systems during combat.

Its maritime drones have reportedly damaged or destroyed multiple Russian naval assets in the Black Sea, while its ground systems are increasingly being used for reconnaissance, logistics, mine clearance, casualty evacuation, and direct attacks.

The company says it has now conducted more than 150,000 combat missions since Russia’s full-scale invasion in 2022, reflecting how rapidly unmanned systems have become central to modern warfare.

Ukraine’s wider drone production has also expanded dramatically, increasing from a few thousand units in 2022 to several million by the end of 2025, turning the country into one of the world’s largest real-world testing grounds for autonomous military systems.

How The Wider Defence Industry Is Responding

Ukraine is not alone in pushing towards more autonomous warfare systems. Defence technology companies across the United States, Europe, China, and Israel are investing heavily in AI-enabled drones and robotic systems.

US company Anduril Industries recently tested an autonomous fighter jet and is building a major manufacturing facility in Ohio designed to scale production of military drones and autonomous systems. Germany’s Helsing is combining military AI with battlefield analytics software, while Chinese companies are rapidly expanding AI-enabled military technologies with strong state support.

The defence sector itself is also changing. Traditional contractors such as BAE Systems and Lockheed Martin increasingly face competition from technology-focused startups that develop software-defined systems far more quickly than conventional military procurement programmes allow.

UFORCE has openly framed this as part of a broader industrial transformation. The company states that “the age of unmanned warfare is no longer a conference-circuit prediction” and has become an operational and commercial reality.

How Battery Technology Also Fits Into This Story

The growing role of battlefield robots also highlights another practical challenge, which is how these machines are powered in demanding real-world conditions.

This is where developments outside defence can quickly become relevant. For example, Cambridge battery company Nyobolt has developed ultra-fast charging batteries designed for autonomous machines, warehouse robots, physical AI systems, and AI data centres. The company says its technology can charge from zero to 80 per cent in under five minutes and is built for repeated, high-intensity charging cycles.

Nyobolt’s work is not about the battlefield directly, but it shows how the wider robotics ecosystem is developing around the same core problem: autonomous machines need reliable power, rapid charging, and long operating life if they are to work continuously. In warehouses, that means robots spending more time moving goods and less time charging. In military settings, the same principle could shape how future unmanned systems are designed, deployed, and sustained.

This matters because the future of autonomous robotics will not depend on AI alone. Batteries, sensors, communications, materials, and manufacturing capacity will all play a part in determining which systems can operate reliably at scale.

The Ethical Questions Around Autonomous Warfare

The growing use of AI and robotic systems in combat is also intensifying concerns about accountability, ethics, and human oversight.

At present, most battlefield robots still require human operators to approve attacks or direct operations. However, many systems already use software-assisted targeting, autonomous navigation, and machine-learning tools to accelerate combat decisions.

Human rights organisations and international bodies have warned that increasing autonomy risks reducing human accountability in life-and-death situations. Concerns include how responsibility is assigned if autonomous systems malfunction or cause civilian casualties.

At the same time, defence companies argue that automation can reduce human error, improve reaction times, and protect soldiers from increasingly dangerous battlefield conditions.

The United Nations has discussed possible international controls on autonomous weapons, but no binding global framework currently exists despite growing calls for regulation.

What Does This Mean For Your Business?

For most UK businesses, robotic warfare may appear distant from everyday operations, but the technologies emerging from Ukraine are likely to influence far more than defence.

Many of the systems now being refined on the battlefield rely on AI, machine vision, autonomous navigation, secure communications, sensor fusion, and real-time data processing. It is worth noting here that these same technologies are also increasingly used in civilian sectors including logistics, manufacturing, transport, infrastructure monitoring, and cybersecurity.

The conflict is also accelerating investment into robotics and AI across Europe and the United States, creating commercial opportunities for companies involved in software engineering, semiconductors, communications systems, drones, sensors, and advanced manufacturing.

Also, the rapid militarisation of AI is likely to increase regulatory scrutiny around autonomous systems more broadly, particularly where safety, accountability, and decision-making are involved. Businesses developing AI-enabled products may therefore face growing expectations around transparency, oversight, and ethical controls.

Russia’s war against Ukraine is no longer only reshaping modern warfare. It has also become one of the world’s fastest-moving testing grounds for autonomous technology, with the systems emerging from the conflict likely to influence both defence and civilian industries for years to come.

The post Featured Article : Ukraine Says Robots Seized Enemy Territory On Their Own appeared first on Mear Technology.

What The Workers Reported Seeing

The controversy began in February when workers employed by Sama in Nairobi told Swedish newspapers that their role involved reviewing and labelling video footage captured by Meta’s Ray-Ban smart glasses. According to those accounts, the material included deeply private scenes, with one worker stating, “We see everything – from living rooms to naked bodies.”

The footage was reportedly not limited to staged or deliberately shared content. Instead, it reflected everyday life captured by wearable cameras, including people undressing, using the toilet, and handling sensitive personal information. The workers’ role was to annotate this material so that Meta’s AI systems could learn to interpret visual and contextual data more effectively.

Meta acknowledged that human review forms part of its AI training process, stating that “photos and videos are private to users” and that human reviewers are used to “improve product performance” with user consent. However, the scale and nature of the material described by workers has intensified scrutiny over how that consent is obtained and understood in practice.

Why Did Meta End The Contract?

Less than two months after the investigation was published, Meta moved to end its relationship with Sama, a US-based outsourcing company that provides data annotation services, employing workers to review and label images and video to train AI systems, a decision that resulted in redundancy notices being issued to 1,108 workers with just days’ notice. The company’s official explanation was that Sama “did not meet our standards,” although it did not specify which standards had been breached or when concerns were first identified.

Disputed By Sama

Sama has strongly disputed that characterisation, stating that it had “consistently met the operational, security and quality standards required” and had not been informed of any shortcomings before the contract was terminated.

The timing of the decision has led to further questions, with labour groups and campaigners arguing that the termination may have been linked to the workers speaking out rather than performance issues, while Naftali Wambalo of the Africa Tech Workers Movement suggested that the standards in question may relate less to quality and more to confidentiality, describing them as “standards of secrecy,” a claim that Meta has not publicly addressed.

The Human Layer Behind AI

The episode highlights a reality that is often overlooked in discussions about artificial intelligence. Before AI systems can recognise images, understand context, or respond to real-world inputs, large volumes of data must be manually labelled by human workers.

In this case, that process meant individuals in Kenya reviewing unfiltered footage captured by wearable devices used by people in entirely different parts of the world. The work sits at the intersection of privacy, labour rights, and technology development, with those carrying out the task often having limited visibility, protection, or influence over how the data is used.

Not The First Time For Meta

It seems this is not the first time Meta’s relationship with outsourced labour has come under scrutiny. For example, previous contracts involving content moderation have been linked to claims of psychological harm, low pay, and inadequate support, with some former workers reporting symptoms consistent with post-traumatic stress. Sama itself exited parts of that work in recent years, acknowledging the challenges involved.

Regulatory Pressure

The revelations have prompted regulatory attention in multiple jurisdictions. For example, the UK’s Information Commissioner’s Office described the reports as “concerning” and requested further information from Meta, while Kenya’s data protection authority has launched its own investigation into the handling of the footage.

Legal challenges are also emerging. A class action lawsuit in the United States alleges that Meta misrepresented the privacy protections of its smart glasses, while privacy groups in Europe continue to question how user data is processed and whether consent mechanisms meet regulatory standards.

The concern centres on a key distinction, because while Meta’s policies may disclose that data can be used to train AI systems, the extent to which users understand that their footage could be viewed by human reviewers remains unclear, particularly when that footage includes sensitive or intimate situations.

What This Means For AI Development

The decision to end the Sama contract does not remove the need for human input in AI systems. Instead, it exposes the tension between rapid technological development and the practical realities of how that development is supported.

Training AI models at scale requires vast amounts of labelled data, and that requirement does not disappear as systems become more advanced. What changes is the level of scrutiny applied to how that data is collected, processed, and reviewed, particularly when it involves real-world human behaviour rather than curated datasets.

Smart glasses themselves represent a significant step forward in AI-enabled consumer devices, combining real-time image capture with on-device and cloud-based processing. However, their effectiveness depends on continuous learning, which in turn depends on the availability of human-labelled data.

What Does This Mean For Your Business?

This story illustrates how organisations adopting AI tools may need to look beyond the technology itself and consider the full data lifecycle, including how training data is sourced, handled, and reviewed, particularly where external providers or offshore teams are involved.

For UK businesses, this has clear implications around compliance and accountability, because under UK GDPR and data protection law, responsibility does not disappear when data is passed to a third party, meaning organisations must be confident not only in how systems perform but also in how the underlying data is being processed and by whom.

Reducing risk therefore means ensuring that suppliers and partners meet clear standards not only for technical performance but also for data governance, worker welfare, and transparency, with strong contractual controls, regular audits, and clear oversight of third-party processes becoming essential, especially when sensitive or personal data is involved.

The broader lesson, and what may be surprising to many, is that AI systems are not purely automated but are built on human input at multiple stages, and any weakness in that chain can create reputational, legal, and ethical risk, leaving businesses that properly understand and manage that reality far better placed to use AI responsibly while maintaining trust with customers, regulators, and stakeholders.

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What Is ‘World’ And How Does It Work?

World, developed by Tools for Humanity, the company co-founded by OpenAI’s Sam Altman, is a digital identity system designed to prove that someone is a real, unique human online without requiring them to share personal information such as their name or identity documents.

The system is built around what the company calls “proof of human”, a way of confirming that a real person, rather than an AI system or automated bot, is behind an online account or interaction. As the company explains, “World ID lets you verify real humans without compromising privacy,” positioning the technology as a privacy-first alternative to traditional identity checks.

Uses The Orb

The system centres around a biometric verification process using a device known as the Orb, which scans a user’s iris and converts it into a unique cryptographic identifier. That identifier becomes the user’s World ID, which can then be used across multiple platforms.

The company says that this approach is designed to protect user anonymity. According to its own materials, “the Orb captures and processes photos to verify uniqueness without the need to retain your images or collect any other information,” with encrypted data stored locally and under user control.

This model reflects a change in how identity is being handled online. For example, instead of repeatedly sharing personal details with different services, with this type of system, users can prove they are a real person once and then reuse that verification across multiple environments.

To support different use cases, World has also introduced multiple levels of verification, ranging from high-security Orb scans to lower-friction methods such as document checks or selfies. This allows platforms to choose the level of assurance that matches their risk profile.

Why World Is Expanding Beyond Its Own Platform

With that foundation in place, World is now moving to scale its technology by integrating directly into high-traffic consumer and business platforms where trust has become a growing issue.

At the same time, the problem it is trying to solve is becoming more urgent. As generative AI systems improve, the volume of synthetic content online is rising sharply, making it harder for users and organisations to know whether they are interacting with a real person or an automated system.

As Sam Altman explained at a recent event, “we are also heading to a world now where there’s going to be more stuff generated by AI than by humans.” That shift is already affecting areas such as online dating, customer interactions, and business communications, where authenticity has direct financial and reputational consequences.

Why Platforms Like Tinder And Zoom Are Getting Involved With World

The choice of partners highlights where these pressures are already being felt most strongly. For example, on platforms like Tinder, the challenge is driven by bots and romance scams, which are becoming more convincing as AI-generated profiles and conversations improve.

By integrating World ID, Tinder can offer users a visible signal that a profile belongs to a verified human, helping to rebuild trust in an environment where uncertainty has become common.

In business environments, the risks are more direct and potentially more costly. World’s partnership with Zoom reflects growing concern about deepfake impersonation, particularly in video calls where financial or operational decisions are being made.

Cases involving AI-generated participants in meetings have already resulted in significant financial losses, highlighting the limitations of traditional security measures. World’s approach, which links a live video feed to a previously verified identity, is designed to address this by confirming that the person on screen is genuine.

Beyond these examples, World is also expanding into areas such as digital contracts, ticketing, and online commerce. Integrations with platforms like DocuSign aim to ensure that agreements are signed by real people, while partnerships with ticketing providers such as Ticketmaster and Eventbrite are designed to reduce bot-driven purchasing and reselling.

What This Means For The Future Of Online Trust

The wider significance of these partnerships lies in how they reshape the idea of identity on the internet. Rather than relying solely on usernames, passwords, or document-based verification, platforms are beginning to adopt a model based on proving that a user is a real, unique human.

World’s own positioning reflects this change. The company says its technology can “securely and anonymously prove that every user is a real and unique human online,” while also helping to “eliminate bots and Sybil attacks at scale,” strengthening platform integrity.

This approach has some clear advantages. For example, using this type of verification system, platforms can reduce fake accounts, improve moderation, and create more reliable user experiences, while businesses can lower the risk of fraud and build greater trust with customers and partners.

Biometrics Still A Sensitive Issue

However, there are still many questions around the sensitive issue of the use of biometric verification. In fact, World has already faced scrutiny from regulators in multiple countries over how its technology is deployed, while practical considerations around accessibility persist given that the highest level of verification still depends on specialised hardware.

At the same time, the model highlights a wider challenge, as the rapid development of AI is increasing the need to verify real people while also making impersonation more realistic and easier to carry out at scale.

What Does This Mean For Your Business?

For most organisations, World’s technology will not be something they implement directly in the immediate term, but the change it represents is already relevant.

As AI-driven fraud, impersonation, and automation continue to increase, the ability to verify that a user is genuinely human is likely to become a standard requirement across many digital services. This applies not only to customer-facing platforms but also to internal systems, supply chains, and remote collaboration tools.

A reusable, privacy-focused identity layer has the potential to simplify how organisations manage trust, reducing reliance on fragmented verification methods and lowering exposure to risks such as fake accounts and social engineering attacks.

At the same time, adopting these approaches will require some careful consideration of compliance, user experience, and operational fit. Organisations will need to assess where human verification adds value and how it aligns with their existing systems and processes.

World’s expanding network of partnerships, such as Tinder, shows that this model is already moving into mainstream use. As platforms begin to embed proof-of-human verification into their core functionality, organisations that understand how it works and where it can be applied will be better positioned to operate in a digital environment where proving you are human may become just as important as proving who you are.

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What Happened At Booking.com?

Booking.com has confirmed that unauthorised third parties accessed customer reservation data, including names, email addresses, phone numbers, home addresses, and details of past and upcoming bookings.

The company says financial information was not taken from its systems, but it seems that the data that has been exposed is highly sensitive in a different way and could be giving criminals the exact context they need to convincingly impersonate legitimate hotel communications.

For example, customers have already reported receiving suspicious messages, and the platform has begun notifying affected users (by email) while updating reservation PINs as a containment measure. The overall scale of the breach has not yet been fully disclosed.

How The Booking.com Data Breach Appears To Have Happened

Early analysis seems to point to a familiar weak spot rather than a direct breach of Booking.com’s core systems.

Research highlighted by Microsoft suggests attackers targeted hotel partners using phishing techniques designed to trick staff into installing malware, with one method known as “ClickFix” disguising malicious downloads as routine system fixes, often delivered via fake CAPTCHA pages.

Once hotel systems are compromised, attackers can gain access to booking platforms and extract customer data at scale, which aligns with recent reporting about the incident from Malwarebytes, indicating the breach likely originated through third-party access rather than a single central failure.

This matters as it reflects a structural issue rather than a one-off vulnerability, highlighting how interconnected systems can introduce risk beyond the primary platform itself.

What Makes Reservation Hijacking So Effective?

Cybersecurity experts have labelled the resulting scams “reservation hijacking”. In a typical attack of this kind, criminals contact a customer posing as their hotel, referencing genuine booking details such as dates, property names, and contact information, and then claim there is an issue with the booking that requires payment verification or an urgent transfer.

This level of detail removes many of the usual warning signs associated with phishing, as the communication feels routine, relevant, and timed to coincide with an upcoming stay.

As a result, victims are far more likely to comply, especially when the request appears consistent with what they expect from a legitimate provider.

According to data from the UK’s Action Fraud, hundreds of Booking.com-related scams have already been reported in recent years, with significant financial losses, and the concern now is that this breach will increase both the scale and success rate of these attacks.

A Pattern In The Travel Sector

Sadly, this incident is not happening in isolation. For example, travel platforms operate within complex ecosystems involving hotels, franchises, agents, and third-party service providers, and each connection introduces another potential entry point for attackers.

Recent breaches affecting airlines, rail services, and car hire firms all seem to have followed a similar pattern, with attackers gaining access through partners rather than the primary platform itself.

UK consumer group Which? has previously raised concerns about weak verification processes and the misuse of messaging systems within booking platforms, highlighting how easily fraudulent listings and communications can appear legitimate.

The result is an environment where trust is high but control is fragmented, making it easier for attackers to exploit gaps between systems and organisations.

What Has Booking.com Said About The Incident?

Booking.com has said it identified “suspicious activity” affecting a number of reservations and acted quickly to contain the issue, including updating reservation PINs and contacting affected customers directly.

The company has confirmed that unauthorised third parties were able to access certain booking information, but maintains that financial details were not exposed through its systems.

It has also stressed that it will never ask customers to share credit card details by email, phone, WhatsApp or text, or request payments outside the terms set out in the original booking confirmation.

While Booking.com has not disclosed how many customers have been affected or which regions are involved, it has urged users to remain vigilant and report any suspicious messages or payment requests.

Why This Breach Matters More Than It Looks

At first glance, the absence of stolen payment data may seem reassuring, but in reality this type of breach can be just as damaging.

Modern fraud relies less on stealing card numbers and more on manipulating behaviour, and when attackers know where someone is staying, when they are travelling, and how to contact them, they can craft messages that feel entirely credible.

The speed of exploitation is also notable, with reports suggesting phishing attempts began emerging within days of the breach being identified, indicating a coordinated effort to turn stolen data into immediate financial gain.

This effectively moves the incident from a passive data exposure to an active fraud campaign.

What Does This Mean For Your Business?

For organisations that store customer data or rely on third-party platforms, the incident highlights how exposure now extends well beyond internal systems.

Weaknesses within partner organisations can quickly become shared risks, particularly where access to customer data and operational platforms is interconnected, making supply chain security just as important as internal controls.

For Booking.com, the breach adds to ongoing scrutiny around platform security and fraud prevention, especially given the long-running issues with scams linked to its ecosystem, and increases pressure to strengthen both partner controls and customer protections.

Across the wider travel sector, the incident reinforces a persistent challenge, as platforms depend on large, distributed networks of hotels and service providers, creating multiple entry points for attackers and making consistent security standards difficult to enforce at scale.

For customers, the immediate risk lies in highly targeted phishing attempts that feel genuine, with real booking details being used to create convincing scenarios, making it far harder to distinguish between legitimate communication and fraud.

This also highlights how data that appears relatively low risk in isolation can become far more valuable when combined, particularly when it enables attackers to construct believable, real-world narratives that bypass normal scepticism.

In response, there is a growing expectation that platforms will take a more active role in protecting users, whether through stronger partner authentication requirements, improved monitoring of messaging systems, or clearer safeguards around how and when payments should be made.

At the same time, customers are being urged to remain cautious, particularly when asked to make payments or share sensitive information, even if the request appears to come from a known provider or references a genuine booking.

The Booking.com breach demonstrates how quickly stolen data can be turned into targeted, real-world attacks when it is rich in context, reinforcing a broader point for businesses that security is no longer just about protecting systems, but about understanding how data could be used against the people who trust them with it.

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Why Is Greece Taking Action?

The Greek government has positioned the move as a response to rising concerns about children’s mental health, particularly anxiety, sleep disruption, and compulsive use of social media. Prime Minister Kyriakos Mitsotakis has pointed directly to what he describes as the “addictive design” of platforms, arguing that the way apps are built to capture attention is now part of the problem.

Reports from schools and parents in Greece suggest that excessive screen time is affecting sleep patterns and concentration, with some teachers describing children arriving at school exhausted. The government has already taken earlier steps, including banning mobile phones in schools and introducing parental control tools, but has now concluded that broader restrictions are necessary.

The proposed law will require platforms to block access for under-15s or face financial penalties, with further details on enforcement expected as legislation progresses. Greece is also pushing for a coordinated European approach, including standardised age verification and a common digital age threshold.

A Growing International Trend

Greece is not the only country introducing this kind of ban. Australia became the first country to implement a nationwide ban on social media for under-16s in late 2025, requiring platforms such as TikTok, Instagram, and Snapchat to remove underage accounts or face substantial fines.

Also, across Europe, similar proposals are now gaining traction. For example, France has already moved legislation forward to restrict access for younger users, while Denmark, Spain, and Slovenia are developing comparable measures. Germany has debated an under-16 ban, and the UK is currently consulting on whether to introduce restrictions or alternative controls such as screen time limits and digital curfews.

Outside Europe, countries including Indonesia and Malaysia are also moving towards tighter controls. This reflects a broader change in how governments are approaching social media, not simply as a communication tool, but as a potential public health issue requiring intervention.

What The Evidence Says About Health Impacts

The policy momentum is being driven by a growing body of research linking heavy social media use with negative outcomes for children and teenagers. Studies have associated prolonged screen time with increased levels of anxiety, depression, poor sleep quality, and reduced attention span.

Sleep disruption is one of the most consistent findings. Late-night usage, constant notifications, and the pressure to remain engaged can reduce both the quantity and quality of sleep, which in turn affects cognitive performance and emotional regulation.

There is also increasing focus on the role of comparison and social validation. Young users are exposed to curated content and constant feedback through likes and comments, which can contribute to feelings of inadequacy and social pressure.

When it comes to countries that have already introduced restrictions, the picture is still unclear. Australia’s under-16 ban only came into force in late 2025, meaning there is not yet enough long-term data to show whether it has improved mental health outcomes. Early signs suggest platforms are being forced to take age verification more seriously, but evidence of measurable health improvements has not yet emerged.

This means governments are largely acting on existing research and precaution rather than proven results from national bans.

At the same time, the evidence is not entirely one-sided. Some researchers and platforms argue that social media can provide benefits, including social connection, access to information, and support networks, particularly for isolated or vulnerable individuals. This is one reason why some policymakers are cautious about blanket bans.

How Governments Are Responding To Social Media Risks

What is clear is that governments are increasingly willing to intervene directly in how social media is used. The framing is changing from personal responsibility to systemic risk, with platform design, algorithms, and engagement models coming under scrutiny.

Recent legal action in the United States has reinforced this direction, with court cases finding major platforms liable for harm linked to addictive design, adding weight to arguments that these systems are not neutral tools but engineered environments with measurable effects.

For policymakers, this creates a rationale for regulation that goes beyond content moderation and into the structure of the platforms themselves.

What Does This Mean For Your Business?

For businesses, this is part of a wider change in digital regulation that is likely to expand beyond children’s use into broader platform accountability.

Right now, there is limited real-world evidence on the outcomes of these bans, simply because most have only recently been introduced. However, if early restrictions, such as Australia’s, begin to show measurable improvements in areas like sleep, attention, or mental wellbeing, that will significantly strengthen the case for wider and more permanent regulation.

If that happens, businesses should expect tighter controls not just on age access, but potentially on platform design itself, including features that drive prolonged engagement, such as endless scrolling, notifications, and algorithmic content feeds. This could directly affect how audiences interact with content and how effectively platforms can be used for marketing and engagement.

Organisations that rely on social media for marketing, recruitment, or customer engagement should also expect stricter age verification requirements and more defined audience segmentation. Younger demographics may become harder to reach on mainstream platforms or may move to alternative, less regulated spaces.

There is also a reputational dimension. As awareness of the health impact of social media grows, businesses may face greater scrutiny over how they use these platforms, particularly if their content or campaigns are seen to contribute to excessive use or target younger audiences.

This all seems to point to a future where digital platforms are treated less as open channels and more as regulated environments, with clearer rules around access, design, and responsibility. Businesses that understand this direction early will be better placed to adapt as those rules tighten.

The post Featured Article : Greece To Ban Social Media For Under-15s appeared first on Mear Technology.

What Is ‘Q-Day’?

Q-Day refers to the point at which a quantum computer becomes powerful enough to break widely used cryptographic systems such as RSA and elliptic curve encryption, which underpin everything from online banking to software updates.

Google’s position is that this is no longer a theoretical concern for the distant future. As the company warned in its earlier guidance, “the encryption currently used to keep your information confidential and secure could easily be broken by a large-scale quantum computer in coming years.”

The Risk Is Already Emerging

Attackers are also believed to be collecting encrypted data today with the intention of decrypting it later once quantum capabilities become available, a tactic often referred to as ‘store now, decrypt later’.

Google Revises Its Timeline

In a recent update, Google has set out a more urgent timeline for the transition to post-quantum cryptography, signalling that the industry may have less time than previously expected to prepare for this moment.

The company has now introduced a 2029 target for completing its migration to quantum-resistant cryptography, bringing forward urgency compared to earlier industry expectations that placed large-scale quantum threats in the mid-2030s, and stating: “We’re setting a timeline for post-quantum cryptography migration to 2029.”

Not A Direct Prediction

It’s worth noting here that this isn’t a direct prediction from Google of when exactly quantum computers will most likely break encryption, but it provides some guidance and a reassessment of how quickly organisations need to act.

Why The Updated Timeline?

Google said the change is based on recent progress in “quantum computing hardware development, quantum error correction, and quantum factoring resource estimates”.

In simple terms, it seems the technical barriers that once made quantum threats feel distant are being reduced faster than expected.

Google’s update of Q-Day is not simply about setting a date, it is about creating urgency. The company has made this explicit in a recent blog post about the update, stating: “As a pioneer in both quantum and PQC, it’s our responsibility to lead by example and share an ambitious timeline.” It added that the goal is to “provide the clarity and urgency needed to accelerate digital transitions not only for Google, but also across the industry.”

This reflects a broader concern that organisations are underestimating the scale and complexity of the transition required.

This urgency also reflects the scale of what organisations are being asked to do. For example, moving from current cryptographic standards to post-quantum alternatives is not a simple upgrade. It involves identifying where encryption is used, replacing algorithms across systems, updating infrastructure, and ensuring compatibility across supply chains and partners.

The UK’s National Cyber Security Centre has already described this transition as a “complex change programme”, highlighting the scale of the task facing organisations.

The Gap Between Awareness And Readiness

Despite growing awareness of quantum risks, most organisations are not ready.

Part of the challenge is that the threat itself is difficult to fully understand. Quantum computers are often described as vastly more powerful than today’s systems, and for many businesses, this means the practical implications are unclear. Understanding how and when these machines could break existing encryption, and what that means for real-world systems, is not straightforward without some specialist knowledge.

Research cited in industry reports suggests that while a majority of businesses expect quantum-enabled attacks within the next five years, only a small proportion have a clear roadmap in place to address them.

This means that while many organisations accept that quantum threats are coming, there is still uncertainty about how serious those risks are, when they are likely to materialise, and what practical steps should be taken. That uncertainty can easily lead to delays or a tendency to wait for clearer standards and tools rather than acting early.

Google’s revised timeline challenges that assumption by bringing forward its own migration target and signalling that waiting may not be a viable strategy.

What Google Is Already Doing To Help

Alongside announcing its timeline update, Google says it is actively deploying post-quantum cryptography across its own platforms.

The company has highlighted how Android 17 will integrate PQC digital signature protection using ML-DSA, aligned with standards from the National Institute of Standards and Technology.

This is part of a broader effort to build what Google describes as a “new, quantum-resistant chain of trust”, ensuring that systems remain secure even as computing capabilities evolve.

Google says it has also been working on PQC for several years, including deploying quantum-resistant key exchange mechanisms in Chrome and internal systems, and contributing to global standards development, all of which points to the fact that the transition is not only necessary, but already underway.

Why This Matters

The implications extend far beyond large technology providers. For example, encryption underpins core business functions, from securing customer data and financial transactions to protecting intellectual property and ensuring the integrity of software and communications.

If current cryptographic systems become vulnerable, the impact will not be limited to future systems. Data encrypted today could still be exposed years later if it is harvested and stored by attackers now.

That means the risk is already present, even if the technology required to exploit it fully is not yet available.

What Does This Mean For Your Business?

For most organisations, the key issue here is not whether quantum computing will affect them, but how prepared they are for the transition it will require.

Google’s updated timeline suggests that preparation needs to begin sooner rather than later, particularly for systems that rely on long-lived data or digital signatures that must remain secure for many years.

This will involve building what is often referred to as crypto agility, the ability to update cryptographic algorithms without disrupting services, as well as developing a clear inventory of where and how encryption is used across the organisation. In practical terms, that means identifying where sensitive data is stored, how it is protected in transit and at rest, and which systems rely on public key cryptography that may need to be replaced.

It also means starting to assess whether existing platforms, applications and suppliers are capable of supporting post-quantum cryptography, and whether updates, migrations or architectural changes will be required. Some organisations are already beginning to test quantum-resistant algorithms in non-critical systems to understand performance, compatibility and operational impact before wider rollout.

Engagement with suppliers and partners will also be important, as cryptographic systems rarely operate in isolation and weaknesses in third-party systems can undermine otherwise secure environments.

Taken together, Google’s update suggests that the window for treating quantum security as a future concern is narrowing, and that organisations that begin mapping, testing and planning now will be in a far stronger position than those that wait.

The post Featured Article : Google Brings ‘Q-Day’ Closer With 2029 Encryption Warning appeared first on Mear Technology.

What Has Been Reported?

Recent incidents across Canada, the United States and Europe have brought this issue into sharper focus. In one case in Canada, court filings indicate that a teenager who later carried out a fatal attack had previously used an AI chatbot to discuss feelings of isolation and violent thoughts, with conversations reportedly progressing towards how such an attack might be carried out.

In the United States, a separate case involved a man who developed an extended relationship with an AI chatbot, which he believed to be sentient. Legal filings suggest that these interactions escalated into instructions linked to a potential large-scale violent incident, which he prepared for before it failed to materialise.

In Europe, a teenager is reported to have used an AI chatbot over several months to help develop a manifesto and plan an attack on classmates, which was later carried out.

These cases differ in detail, but they show a consistent pattern. Conversations often begin with expressions of distress, isolation or anger. Over time, repeated interaction appears to reinforce those thoughts, sometimes progressing into more structured or actionable ideas.

Alongside these incidents, controlled research has tested how leading AI chatbots respond to prompts involving violence. In several cases, systems were able to produce guidance on weapons, tactics or targeting when prompts were reworded, layered or extended across longer conversations.

A report from the Centre for Long-Term Resilience noted that “AI systems can unintentionally provide a form of conversational scaffolding that helps users organise and refine harmful intent over time”, highlighting the risk posed by sustained interaction rather than single responses.

Companies including OpenAI and Google state that their systems are designed to refuse harmful requests and direct users towards support where appropriate. They have also acknowledged that safety systems can become less reliable during longer or more complex interactions.

How Chatbots Can Influence Behaviour

Unlike traditional online content, AI chatbots are interactive and responsive. They adapt to user input, maintain context and generate answers that feel personalised.

This creates a different type of risk. Rather than simply presenting information, chatbots can reinforce ideas through ongoing conversation. If a user expresses extreme or distorted views, the system may attempt to be helpful or empathetic. In most cases, this is appropriate. In some cases, it may unintentionally validate harmful thinking.

Over time, this interaction can shape how a user interprets their situation. A conversation that begins as general discussion can become more focused and more detailed, particularly when the system continues to respond without clear challenge or interruption.

This aligns with wider research into how AI affects human thinking. Studies into what has been described as “AI brain fry” suggest that prolonged interaction with AI systems can affect judgement, increase cognitive load and reduce the ability to critically assess information. While this research focuses on workplace use, it highlights how extended engagement can influence decision-making.

In more extreme scenarios, the combination of reinforcement and reduced critical distance may increase the risk of poor or harmful decisions.

Limits Of Current Safeguards

AI providers have introduced safeguards including refusal systems, content filters and escalation processes designed to identify high-risk conversations.

However, evidence suggests that these controls are not always consistent. In some tests, chatbots have provided restricted information when prompts are carefully framed or developed over multiple exchanges.

One reason for this is the way these systems are designed. They are built to be helpful, to continue conversations and to interpret user intent. When intent develops gradually or is presented indirectly, it can be difficult for the system to determine when to refuse or intervene.

Persistence is also a factor. Users can rephrase questions, introduce fictional scenarios or build context step by step. As conversations become longer, earlier safeguards may weaken.

OpenAI has acknowledged this limitation, noting that safety measures tend to perform more reliably in shorter exchanges and can degrade during extended interactions.

Why This Is Gaining Attention

The concern is not that AI chatbots are independently causing violent acts. The issue is that, in certain circumstances, they may reduce the friction between harmful thoughts and real-world behaviour.

This can happen through reinforcement, where ideas are echoed rather than challenged, and through translation, where vague or emotional thinking is turned into more structured plans.

The combination of speed, accessibility and detailed output means that users can move from general intent to specific action more quickly than before.

In response, AI providers are beginning to strengthen their approaches. This includes earlier escalation of concerning conversations, tighter controls on banned users returning to platforms, and closer coordination with authorities where risks are identified.

These steps suggest growing recognition that current safeguards need to evolve as the technology becomes more widely used.

What Does This Mean For Your Business?

For UK organisations, this is not just a consumer or public safety issue. Generative AI tools are already embedded in many workplaces, often with limited governance around how they are used.

One key consideration is how employees interact with these systems. AI can support research, communication and problem-solving, but it can also influence how information is interpreted, particularly during extended or complex use.

There is also a broader governance challenge. Many organisations focus on data security and accuracy when adopting AI. Behavioural influence and decision-making risk are less frequently addressed, yet they are becoming increasingly relevant.

Clear policies are an important starting point. Employees should understand when AI tools are appropriate, where human judgement is required and when outputs should be verified.

Training is equally important. As highlighted by research into AI-related cognitive strain, the way tools are used can have a direct impact on decision quality. Encouraging structured use, limiting over-reliance and maintaining critical thinking are essential.

Monitoring and escalation processes should also be considered. Organisations need to be able to identify when AI use is producing unexpected or concerning outcomes and respond accordingly.

There is also a duty of care element. As AI tools become more integrated into everyday work, organisations may need to consider how they support employees who are using these systems extensively or in sensitive contexts.

This issue reinforces a wider point. AI is not only a productivity tool. It also shapes how people think, decide and act. Businesses that recognise this and put balanced controls in place will be better placed to manage risk while still benefiting from what the technology can offer.

The post Featured Article : Are AI Chatbots Crossing A Dangerous Line? appeared first on Mear Technology.

Tesla Receives Approval To Supply Electricity

Tesla subsidiary Tesla Energy Ventures has reportedly (according to reports by The Wall Street Journal) received approval from the UK energy regulator Ofgem to supply electricity to domestic and commercial customers across England, Scotland and Wales.

The licence allows Tesla to sell electricity directly to households and businesses in much the same way as established suppliers such as British Gas, EDF, E.ON and Octopus Energy. Northern Ireland is not included, as it operates under a separate electricity market.

Ofgem confirmed that the application underwent a full regulatory review between July 2025 and March 2026. The regulator assessed whether Tesla could meet the financial, operational and consumer protection standards required of all electricity suppliers in Britain.

As with any licensed supplier, Tesla must now comply with the UK’s strict energy market rules covering billing transparency, customer treatment, financial resilience and dispute resolution.

A Long Term Strategy In The UK Energy Market

Although the licence approval is new, Tesla has actually been building its presence in the British electricity sector for several years.

The company first obtained an electricity generation licence in 2020, allowing it to operate energy assets connected to the national grid. Since then Tesla has deployed large grid scale battery systems across the country using its Megapack technology.

One of the most notable projects is the Pillswood battery facility near Hull, which at the time of its launch in 2022 was one of Europe’s largest battery storage systems with a capacity of 196 megawatt hours.

Tesla has also been active in energy trading through its Autobidder software platform, which uses artificial intelligence to automatically buy and sell electricity in response to market conditions.

These developments laid the groundwork for the company to move into direct electricity supply.

How Tesla’s Energy Model Works

Tesla’s entry into the UK electricity market is likely to follow a model already used in Texas through its Tesla Electric service.

The approach combines several elements of Tesla’s broader energy ecosystem. These include home solar generation, battery storage, grid scale energy storage and software driven electricity trading.

Customers with Tesla Powerwall home batteries can store electricity generated by rooftop solar panels or purchased from the grid when prices are low. The stored energy can then be used later or exported back to the grid.

When large numbers of home batteries are connected together they can form what is known as a virtual power plant. This network of distributed energy storage can help stabilise the grid during periods of high demand while also generating revenue for participants.

Tesla’s Autobidder software manages the flow of electricity between batteries, the grid and wholesale markets in real time. The system automatically adjusts when energy is bought, stored or sold.

This model allows Tesla to treat energy not simply as a commodity delivered to homes, but as a dynamic resource that can be managed through software.

Competition With Established Suppliers

Obviously, Tesla’s arrival adds a new competitor to a crowded but rapidly evolving UK energy market.

Companies such as Octopus Energy have already demonstrated how software driven platforms and flexible tariffs can disrupt traditional energy supply models. Octopus has grown rapidly by combining renewable energy sourcing with advanced pricing systems and digital customer services.

In fact, Tesla and Octopus have previously worked together in Britain through the Tesla Energy Plan, which connected Powerwall owners to Octopus electricity tariffs.

However, now that Tesla can operate as a supplier in its own right, that partnership may evolve into direct competition.

The company will also compete with large incumbent utilities including British Gas, EDF and E.ON, which together supply millions of UK households.

Public Opposition And Regulatory Scrutiny

Tesla’s application attracted some significant public criticism during the consultation process.

For example, campaign groups organised thousands of submissions to Ofgem expressing concern about Elon Musk’s political statements and online activity. Critics argued that these issues should be considered when deciding whether the company should operate in the UK energy market.

Ofgem stated that licensing decisions are based on regulatory and operational criteria rather than opinions about company leadership. The regulator concluded that Tesla’s application met the legal requirements for a supply licence.

Government officials also confirmed that Ofgem has sole responsibility for assessing such applications.

A Move Toward Software Led Energy Systems

Tesla’s move into electricity supply reflects a broader trend across global energy markets.

Electricity systems are becoming increasingly dependent on renewable energy sources such as wind and solar. These sources generate power intermittently, which creates new challenges for grid stability.

Battery storage and intelligent software systems are emerging as key tools for balancing supply and demand. Grid scale batteries can store excess energy when production is high and release it when demand rises.

Companies that combine generation, storage and software control may therefore gain a strategic advantage in the evolving energy sector.

Tesla has been positioning its energy division around precisely this combination.

What Does This Mean For Your Business?

Tesla’s entry into the UK electricity market highlights how energy supply is becoming increasingly technology driven.

Businesses may soon see new types of electricity tariffs that combine battery storage, renewable generation and software based energy optimisation. This could (hopefully) lead to more flexible pricing models and opportunities to reduce energy costs through smarter usage patterns.

Organisations with on site solar generation or battery storage may also benefit from emerging virtual power plant programmes, where surplus energy can be sold back to the grid.

The development also signals a wider transformation of the electricity sector. Traditional utilities are increasingly competing with technology companies that treat energy management as a data and software problem rather than simply a supply service.

For businesses planning long term energy strategies, the ability to integrate storage, renewable generation and intelligent energy management systems is likely to become increasingly important.

The post Featured Article : Tesla Wins Licence To Supply Electricity In Britain appeared first on Mear Technology.