Cyber insurance has grown into a multi-billion-dollar global market, yet when a serious breach occurs, the real story often lies in the small print, the exclusions, and the security controls that should have been in place long before the policy was signed.

Once Just An Add-On

Cyber insurance was once treated as a niche add-on to professional indemnity cover. Today it sits at the centre of boardroom risk discussions. The reason is simple. Cyber incidents are no longer rare. They are routine, costly and increasingly disruptive.

So what exactly is cyber insurance, how large has the market become, and when does it actually pay out?

What Cyber Insurance Really Covers

At its core, cyber insurance is designed to cover two broad categories of loss. First-party losses include incident response, forensic investigation, legal advice, customer notification, system restoration, business interruption and, in some cases, ransom payments. Third-party cover addresses claims brought by customers, partners or regulators following data breaches or operational failures.

The detail, however, varies significantly between policies. Cover is often conditional on specific security controls being in place, such as multi-factor authentication, tested backups and patch management processes. In practice, cyber insurance now operates as a form of security gatekeeper. Insurers increasingly assess a firm’s cyber hygiene before agreeing terms or setting premiums.

How Big Is The Market?

According to Munich Re (Münchener Rückversicherungs-Gesellschaft), one of the world’s largest reinsurance companies, the global cyber insurance market was worth around $15.3 billion in 2024 and is expected to reach $16.3 billion in 2025. Munich Re projects that global premium volume could more than double by 2030, with annual growth exceeding 10 percent.

North America accounts for roughly 69 percent of global premiums, with Europe representing around 21 percent. Growth in Europe has been particularly strong over the past few years as regulatory pressure and ransomware attacks have increased awareness.

In the UK, the Association of British Insurers reported that insurers paid out £197 million in cyber claims to UK businesses in 2024. That figure represents a 230 percent increase on the previous year. Malware and ransomware accounted for 51 percent of all UK cyber claims, up from 32 percent in 2023.

These numbers underline two trends. Claims are rising sharply, and insurers are paying substantial sums.

But what do claims actually look like in practice?

Claims And Payouts

There is no universal “claim approval rate” published across the market, but available industry data offers some insight into how incidents unfold.

Coalition’s 2025 Cyber Claims Report, covering incidents in 2024 across several markets including the UK, found that 60 percent of claims arose from business email compromise and funds transfer fraud. These are not sophisticated zero-day exploits. They are often payment diversion scams targeting finance teams.

The same report noted that 44 percent of policyholders affected by ransomware chose to pay the ransom when it was deemed reasonable and necessary. Meanwhile, 56 percent of reported matters required no out-of-pocket payment from the policyholder, often because insurer-provided incident response support mitigated losses before they escalated.

The key takeaway here is that many cyber claims are not dramatic data centre shutdowns. They are invoice fraud, stolen credentials and misdirected payments.

That said, some cases have tested the boundaries of cover entirely.

When The Small Print Becomes The Story

One of the most widely reported examples of a major cyber insurance coverage dispute followed the 2017 NotPetya attack (a malware attack attributed to the Russian military). Pharmaceutical giant Merck said the malware disrupted around 40,000 machines and ultimately caused losses of approximately $1.4 billion. Several of its insurers sought to rely on traditional “war exclusion” clauses, arguing that the attack was attributable to a state actor and therefore not covered. In 2022, a New Jersey court ruled that the wording of the war exclusion did not apply to the cyber attack in question. The parties later reached a confidential settlement.

The Merck case became a landmark moment in cyber insurance interpretation. It highlighted how state-linked cyber operations can blur the boundary between criminal activity and geopolitical conflict, and exposed the limits of legacy policy wording when applied to modern cyber warfare.

Exclusions

In the wake of disputes linked to NotPetya and similar incidents, Lloyd’s of London issued a market bulletin requiring, from 31 March 2023, that standalone cyber policies include clearly defined exclusions addressing state-backed cyber attacks unless expressly covered. The intention was to reduce ambiguity around systemic cyber risk and clarify how attribution would be handled within policy terms.

Other Examples

Other incidents illustrate the potential scale of insured losses. Colonial Pipeline paid a $4.4 million ransom in 2021 following a ransomware attack, with US authorities later recovering approximately $2.3 million in cryptocurrency. CNA Financial was widely reported to have paid $40 million after a ransomware attack the same year. Norsk Hydro, by contrast, refused to pay ransom after its 2019 attack and later disclosed financial impacts in the region of $60–70 million, supported in part by insurance arrangements.

Taken together, these cases demonstrate both the scale of financial exposure and the growing legal and structural complexity surrounding cyber insurance. Insurance can provide vital financial cushioning when an attack hits, yet it can just as quickly become the subject of dispute, interpretation and courtroom argument when definitions, exclusions or attribution are tested.

Why Cyber Insurance Is Interesting Now

Three structural shifts are fundamentally reshaping the cyber insurance market and changing how organisations think about risk, cover and accountability.

Cyber insurance is increasingly acting as a de facto regulator. Insurers demand evidence of MFA, endpoint protection, network segmentation and backup testing before binding cover. Organisations seeking insurance often upgrade security controls simply to qualify.

There is a clear protection gap. Swiss Re estimates that SMEs account for around 30 percent of global cyber premiums, yet penetration rates among smaller firms remain modest. Many UK SMEs remain uninsured despite rising threat levels.

Systemic risk looms large. Supply chain attacks, cloud provider outages and state-linked campaigns raise questions about correlated losses. Insurers must balance growth with exposure to events that could trigger thousands of simultaneous claims.

What Does This Mean For Your Business?

For UK organisations, cyber insurance is neither a silver bullet nor a formality. It is a financial resilience tool that sits alongside prevention, not in place of it.

Policies can provide rapid access to specialist incident response teams, legal advisers and negotiators at moments of crisis. That support can materially reduce downtime and reputational damage, yet cover is conditional. Failure to implement agreed controls can jeopardise claims.

Businesses should therefore treat cyber insurance procurement as part of a broader risk management strategy. That means reviewing exclusions, understanding sub-limits for ransomware and business interruption, and aligning technical controls with policy requirements.

The market is growing, claims are increasing, and insurers are paying out significant sums. The most important lesson from the past decade is that buying cyber insurance is not the end of the story. It is the point at which scrutiny, obligations and real risk management truly begin.

The world’s biggest hard drive manufacturers have already allocated all the units they will produce this year after hyperscale AI and cloud operators secured the bulk of available capacity.

AI Infrastructure Buys Up The Year

Western Digital and Seagate have both confirmed that their nearline hard drive production for calendar year 2026 is effectively spoken for.

Western Digital chief executive Tiang Yew Tan told analysts: “We’re pretty much sold out for calendar ’26. We have firm purchase orders with our top seven customers. And we’ve also established long-term agreements with two of them for calendar year ’27 and one of them for calendar year ’28.”

Seagate CEO Dave Mosley was equally direct: “Our nearline capacity is fully allocated through calendar year 2026, and we expect to begin accepting orders for the first half of calendar year 2027 in the coming months… multiple cloud customers are discussing their demand growth projections for calendar 2028, underscoring that supply assurance remains their highest priority.”

In simple terms, the hyperscalers have moved first and bought ahead.

Nearline drives are the high-capacity workhorses used in data centres for bulk storage. They are not consumer PC drives. They are 30TB-plus, 40TB-class disks that underpin cloud storage, AI training datasets and archival systems.

Why AI Is Driving The Squeeze

The AI boom has created a double demand curve.

Training large models requires vast amounts of storage for datasets, checkpoints and logs. Inference workloads generate new data that also needs to be stored, replicated and backed up. Cloud providers are scaling capacity aggressively.

Technology market research firm Omdia now forecasts total server spend in 2026 at around $590 billion, with datacentre capex exceeding $1 trillion. The top ten cloud providers are expected to account for more than 70 percent of that spend, with AI-optimised servers representing roughly 80 percent of total server investment.

Storage sits at the heart of that build-out.

Western Digital has pivoted heavily towards this segment. Around 89 percent of its revenue now comes from cloud customers, compared with just 5 percent from consumers. This is no longer a PC storage business. It is AI infrastructure plumbing.

Implications For The Wider Market

For hyperscalers, long-term supply agreements bring certainty. For everyone else, the cupboard looks thinner.

Analysts have warned that discretionary buyers, including mid-sized enterprises and traditional server customers, may struggle to secure high-capacity drives at predictable prices. Corporate IT projects that assumed hard drives would provide a cost-effective capacity tier may need to revisit budgets.

There is also a ripple effect. AI demand has already strained DRAM and NAND flash markets. If SSD prices rise, some buyers will pivot back to HDDs for bulk storage, adding further pressure to supply.

Andrew Buss, from global market intelligence and research firm International Data Corporation (IDC), recently noted that AI growth is consuming “large amounts of fast flash-based NVMe SSDs”, pushing up prices and prompting a reconsideration of HDD-based arrays where workloads allow.

The result is an unusual reversal. Hard drives, once seen as legacy technology, are back at the centre of infrastructure planning.

Technology Race Intensifies

At the same time, the technical arms race continues.

Western Digital is pushing towards 40TB and 44TB drives this year and has outlined a roadmap to 100TB by 2029, supported by new 14-platter designs. Seagate is advancing its HAMR technology and has publicly targeted 100TB drives by the end of the decade.

These capacity gains matter. Hyperscalers want more storage per rack, per watt and per square metre. Increasing areal density and platter counts is now a strategic priority, not an incremental upgrade.

The challenge is manufacturing capacity. HDD production cannot be scaled overnight. Tooling, media, heads and assembly lines require long lead times. When hyperscalers lock in output years in advance, smaller buyers sit further back in the queue.

What Does This Mean For Your Business?

For Western Digital and Seagate, the sell-out provides revenue visibility rare in the storage sector. Multi-year agreements reduce demand uncertainty and underpin capital investment plans.

For AI infrastructure players, it reinforces concentration. The largest cloud providers are able to secure supply at scale, strengthening their competitive position.

For enterprises and SMEs, it raises practical questions. If you are planning a server refresh or building on-premise storage, availability and pricing assumptions may need adjustment.

There is also a structural concern here. When the majority of global HDD output is effectively pre-booked by a small number of hyperscalers, the market becomes less flexible. Innovation may skew even further towards the needs of AI data centres rather than general-purpose enterprise workloads.

Critics argue that the AI infrastructure boom is distorting supply chains across silicon, memory and now spinning disk. Supporters counter that it is driving investment, accelerating innovation and revitalising a technology many had written off.

What is clear here is that the humble hard drive, long overshadowed by flash, has become a strategic asset again. In an AI-first world, bulk storage is no longer a commodity. It is strategic leverage.

The UK government is moving to force tech platforms to remove non-consensual intimate images within 48 hours of being flagged or face fines of up to 10 percent of global turnover.

The 48 Hour Rule

On 19 February 2026, ministers confirmed an amendment to the Crime and Policing Bill that will place a strict 48 hour takedown duty on platforms hosting intimate images shared without consent.

Under the proposed law, any non-consensual intimate image reported to a platform must be removed within two days. Failure to comply could trigger fines of up to 10 percent of qualifying worldwide revenue or, in extreme cases, service blocking in the UK.

The government is also clear that victims should not have to chase individual platforms. The intention is that an image will only need to be reported once, with removal applied across multiple services and future uploads automatically blocked.

Prime Minister Sir Keir Starmer said: “The online world is the frontline of the 21st century battle against violence against women and girls. That’s why my government is taking urgent action against chatbots and ‘nudification’ tools.

“Today we are going further, putting companies on notice so that any non-consensual image is taken down in under 48 hours.”

Why The Government Is Escalating This

Ministers have highlighted intimate image abuse as part of a wider violence against women and girls strategy, which the government has labelled a national emergency.

Technology Secretary Liz Kendall said: “The days of tech firms having a free pass are over. Because of the action we are taking, platforms must now find and remove intimate images shared without consent within a maximum of 48 hours.

“No woman should have to chase platform after platform, waiting days for an image to come down. Under this government, you report once and you’re protected everywhere.”

The government has also signalled that non-consensual intimate images will become a “priority offence” under the Online Safety Act. Ofcom is expected to treat such material with the same severity as child sexual abuse content and terrorist material, including exploring digital marking techniques so that flagged images are automatically detected and blocked on re-upload.

Internet service providers may also receive guidance on blocking access to rogue websites that fall outside the reach of mainstream regulation but host abusive content.

What This Means For Platforms

For large social media firms, messaging services and content hosts, the message from government is that platforms must act fast.

The 48 hour window will require robust detection systems, clear reporting mechanisms and sufficient human moderation capacity to assess complex cases. Automated tools may help, particularly where digital fingerprints are applied to known abusive material, yet borderline cases will still require judgement.

The financial stakes are high. A 10 percent global revenue fine is significant for multinational platforms, and the threat of service blocking in the UK raises further commercial risk.

There are also operational challenges to consider. Images may be edited, cropped or slightly altered to evade automated detection. Smaller platforms may lack the infrastructure of larger tech companies. Critics argue that strict timelines could lead to over-removal, particularly where context is disputed.

Civil liberties groups have historically warned that rapid takedown mandates risk curbing legitimate expression if not carefully implemented. Platforms will need clear guidance from Ofcom on evidential thresholds and appeals processes.

What Does This Mean For Your Business?

The impact of this measure extends beyond consumer social media. Any UK business operating user-generated content, community forums, file sharing or messaging functionality will need to understand its exposure. If intimate content is hosted or shared on a corporate platform, the 48 hour rule will apply once flagged.

Even organisations that don’t host content directly need to pay attention. Investors, customers and partners now expect clear and proactive safeguards against online abuse, and there is far less tolerance for getting this wrong.

This law is also designed to reinforce a broader compliance trend. The Online Safety Act already imposes duties of care on platforms, and this amendment tightens expectations around response time and cross-platform coordination.

For SMEs building apps or digital services, moderation strategy can no longer be an afterthought. Clear reporting channels, defined internal processes and documented escalation routes will be essential.

This legislation marks a significant escalation in how the UK treats online intimate image abuse. It shifts responsibility firmly onto platforms and signals that enforcement will be measured not only by policy statements, but by speed and action.

Microsoft has published peer-reviewed research demonstrating that data can be written into ordinary borosilicate glass and preserved for more than 10,000 years, positioning its ‘Project Silica’ work as a potential long-term archival storage platform for the cloud era.

The Challenge

This development addresses a persistent challenge for hyperscale cloud providers and large enterprises, i.e., how to store growing volumes of data reliably, economically and sustainably for decades.

Why Long-Term Storage Is Becoming a Strategic Issue

Global data volumes are growing at an exponential rate. Much of that data does not need high-performance storage. It needs durable, low-cost archival storage that can be retrieved if required, often for regulatory, legal or historical reasons.

Traditional archival media have limits. Magnetic tape, still widely used for cold storage, degrades over time. Hard disk drives and solid-state systems are not designed for century-scale retention. All require periodic migration to new media generations. That migration cycle consumes energy, equipment, labour and budget.

Microsoft’s Project Silica is designed to remove that recurring migration requirement. The central proposition is simple: store data once, in a chemically and thermally stable medium, and leave it in situ for its entire retention life.

How The Technology Works

Project Silica uses femtosecond lasers to write data inside glass. The laser modifies the optical properties of microscopic regions within the material, creating three-dimensional data structures known as voxels. These voxels encode information in multiple layers within a 2 mm thick glass platter.

In its latest Nature publication, the Microsoft Research team reports:

– A data density of 1.59 Gbit per cubic millimetre

– 301 data layers within a 120 mm square glass piece

– A usable capacity of approximately 4.8 TB per platter

– Write throughput of 25.6 Mbit per second per beam

– Energy efficiency of around 10 nJ per bit

Crucially, the team has extended the technology beyond high-purity fused silica to borosilicate glass, the same class of material used in cookware and industrial glazing. This change addresses one of the barriers of cost and material availability to commercialisation.

The research also demonstrates accelerated ageing tests suggesting data lifetimes could exceed 10,000 years at room temperature.

Why Borosilicate Changes the Equation

Earlier glass storage demonstrations relied on specialised fused silica, which is expensive and available from limited suppliers. Borosilicate is far more common and significantly cheaper.

Moving to borosilicate reduces media cost and simplifies manufacturing. It also allows Microsoft to streamline the read hardware. The latest phase-voxel method requires only a single camera in the reader, rather than multiple polarisation-sensitive cameras.

From a systems perspective, that reduction in mechanical and optical complexity matters. Archival infrastructure must be robust, scalable and economically viable at datacentre scale. The shift to borosilicate makes that discussion more realistic.

Security and Air Gap by Design

One notable feature of the Silica architecture is its inherent immutability (it can’t be altered, overwritten or deleted without leaving evidence). Reading the glass requires regular light microscopy, which does not have sufficient power to modify the material. Writing requires high-energy femtosecond laser pulses.

As a result, the medium cannot be overwritten accidentally during read operations. Microsoft describes this as “true air gap by design”. In practical terms, it offers strong protection against ransomware and unauthorised modification of archived data.

For organisations with strict evidential retention requirements, that immutability is significant.

Performance Is Not the Primary Objective

Silica is not competing with SSDs, HDDs or even active tape libraries for performance workloads. It is designed for deep archival storage.

The write throughput, while technically impressive, remains modest compared to high-performance systems. Read operations rely on wide-field microscopy and machine-learning-based decoding to reconstruct data from voxel patterns. Error correction is handled using forward error correction and low-density parity-check codes.

The system has been engineered end-to-end, from writing and reading hardware to machine-learning decoding models. That full-stack approach distinguishes it from earlier academic demonstrations that focused only on materials science.

This is really a storage system design project, not simply a physics experiment.

Sustainability and Cloud Economics

Microsoft is also keen to frame Project Silica within a sustainability context. Magnetic media requires periodic data refresh cycles. Each refresh involves powering up systems, copying data, validating integrity and decommissioning ageing media.

A medium that can remain stable for millennia reduces the need for repeated migrations. That lowers energy use, operational complexity and embodied carbon associated with replacement hardware.

For hyperscale cloud providers operating at massive archival volumes, even incremental reductions in refresh cycles translate into meaningful cost and energy savings.

The broader strategic implication is that long-term archival storage may become more media-centric and less migration-dependent over time.

Where This Sits in Microsoft’s Strategy

Project Silica sits within Microsoft Research and has been developed alongside Azure storage architecture research. It has already been used in proofs of concept, including archival storage of Warner Bros.’ Superman film and collaborations with preservation initiatives.

Microsoft describes the research phase as complete, and the company is now evaluating how the learnings translate into production systems.

That distinction matters. This is not yet a commercial Azure tier. It is a demonstrated platform technology that has met key storage system metrics in peer-reviewed publication.

Commercial deployment will require further engineering around robotics, media handling, library design and operational integration within datacentres.

Is This a Near-Term Disruption?

Glass storage will not replace existing archival systems overnight. Tape remains cost-effective and deeply embedded in enterprise infrastructure.

However, the technical barriers that once made glass storage largely theoretical have been reduced. The extension to borosilicate glass, simplified reading systems and validated longevity testing move the concept closer to practical viability.

If Microsoft can industrialise the robotics and system-level integration, Silica could become a credible long-term archival tier within hyperscale cloud platforms.

What Does This Mean For Your Business?

For most organisations, Microsoft’s glass storage technology is certainly not something you will deploy next year.

The more important development here is not the material itself, but what it reflects. Long-term data retention is no longer just an IT housekeeping task. It is becoming a strategic infrastructure issue. Regulatory obligations are extending retention periods. Litigation exposure is expanding. Sustainability commitments are tightening. Meanwhile, data volumes continue to grow.

If your archival strategy relies entirely on periodic media refresh cycles, manual integrity checks and legacy tape rotations, it is worth asking whether that model will remain economically and operationally sustainable over the next ten to twenty years.

Microsoft’s research indicates that the industry is now actively exploring media that reduce migration cycles, lower long-term energy use and improve immutability by design. Whether Silica becomes commercially mainstream is almost secondary. The strategic lesson is that archival architecture is evolving.

For your business, the practical implications are essentially threefold:

1. Treat long-term data retention as part of your infrastructure strategy, not just a compliance checkbox.

2. Understand the full lifecycle cost of your archival estate, including refresh, migration and energy overheads.

3. Recognise that immutability and physical air gap characteristics are becoming increasingly relevant in a world shaped by ransomware and supply chain attacks.

Glass storage may or may not become the dominant archival medium. What is clear is that long-term data stewardship is now a strategic capability. Organisations that plan for that reality early will have greater flexibility, lower long-term risk and a clearer sustainability narrative than those that continue to treat archive storage as static background plumbing.

Ransomware gangs are abusing legitimate employee monitoring software to break into business networks.

Security firm Huntress uncovered two recent incidents in which attackers used Net Monitor for Employees alongside remote management platform SimpleHelp to gain persistent access. Instead of custom malware, they relied on commercial tools to blend in with normal IT activity.

Net Monitor includes remote shell and command execution features. Huntress said attackers used it for “hands-on-keyboard reconnaissance” before attempting to deploy Crazy ransomware. In one case, access began through a compromised vendor SSL VPN account, with the monitoring agent disguised as a legitimate Windows service.

The attackers also configured SimpleHelp to monitor cryptocurrency-related keywords, indicating financial motives beyond ransomware alone. Huntress said the shared infrastructure and tactics “strongly suggest a single threat actor or group behind this activity.”

Businesses should tighten remote access controls, enforce multi-factor authentication and closely audit any monitoring or RMM software in use. These intrusions relied on stolen credentials and the misuse of trusted tools, not sophisticated zero-day exploits.

A cluster of well-funded startups is attempting to modernise one of the oldest components of the electricity system, replacing passive iron-core transformers with software-driven power electronics designed for an AI and electrification era.

Why The Timing Matters

Transformers have changed little in principle for more than a century. Built from copper windings and steel cores, they step voltage up or down and operate reliably for decades. What they do not offer is active control.

That limitation is becoming more visible as electricity demand accelerates. AI data centres, electric vehicle charging networks and distributed renewable generation are increasing loads on ageing infrastructure. Industry estimates suggest that more than half of distribution transformers in service are over 35 years old, while total power flowing through them is expected to rise sharply over the coming decades.

At the same time, transformer supply chains are under strain. Lead times have stretched, delaying grid upgrades and major industrial projects. Electrification is moving quickly. Core grid hardware is not.

From Passive Hardware To Power Electronics

Solid-state transformers replace traditional magnetic cores with high-frequency power semiconductors such as silicon carbide or gallium nitride. Instead of simply stepping voltage up or down, they integrate rectification, conversion and inversion into a programmable system.

The result is a device that can manage alternating and direct current, handle bidirectional flows and adjust dynamically to changing grid conditions. In practical terms, that means tighter voltage control, smoother integration of solar and batteries, and the ability to route power between multiple sources and loads in real time.

Unlike conventional transformers, which respond passively to disturbances, power-electronic systems can actively stabilise output and support ride-through during faults.

Investment

Investors are backing the thesis that the transformer is overdue for reinvention. For example, Heron Power, based in Santa Cruz, California, has raised $140 million to scale production of its solid-state systems. North Carolina-based DG Matrix has secured $60 million to advance its multi-port Interport platform. Amperesand, also in the US and focused on next-generation power architecture for data centres, has raised $80 million targeting data centre deployments.

The early focus is data centres, where space constraints, high power density and the need for rapid deployment create strong incentives to consolidate equipment. Solid-state platforms can combine the functions of transformers, inverters and certain backup systems into a single modular unit, reducing footprint and simplifying architecture.

Heron Power says its medium-voltage systems are designed to deliver efficiency above 98 percent in data centre and renewable applications, with materially smaller footprints compared to traditional assemblies.

Implications For Utilities And Renewable Projects

For utilities, the appeal lies in flexibility. Networks built around passive components require significant spare capacity to cope with fluctuations. More intelligent transformer systems could allow operators to push more power through existing lines while maintaining stability.

For renewable developers, integrating inverter and transformer functionality can simplify plant design and potentially shorten interconnection timelines. Projects combining solar, storage and grid support services may benefit from more modular, software-configurable infrastructure.

These capabilities are particularly relevant as grids absorb higher proportions of intermittent generation and distributed energy resources.

What Does This Mean For Your Organisation?

For UK businesses pursuing electrification, on-site generation or high-density computing, the evolution of transformer technology could reshape project economics. Faster connections, smarter load management and more adaptable behind-the-meter systems may reduce both risk and delay.

Yet challenges remain. Solid-state transformers still carry a cost premium in many use cases. Utilities and regulators are cautious by design, and new hardware must prove long-term reliability under demanding conditions. Scaling manufacturing to meaningful global volumes will also take time.

What is clear is that a traditionally static component of the grid is becoming a point of innovation. As sustainability targets tighten and electricity demand climbs, the transformer is shifting from a passive box on the edge of the network to an intelligent, software-defined asset at the centre of it.

Copilot, used in Research mode, is a very powerful way undertake “Deep Research” on various topics that you choose to prompt. This video shows how you can access a window which show you in real-time what the research tool is up to, a bit like having a mini computer running in the background that you can watch.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Amazon’s Rufus is a built-in AI shopping assistant that helps you compare products faster and spot whether a “deal” really is one, useful for anyone buying kit for work or keeping an eye on market pricing.

What Is Rufus AI?

Amazon’s Rufus is Amazon’s generative AI shopping assistant inside the Amazon Shopping app and on Amazon’s website. You can ask it questions in plain English about a product you’re viewing, and it will summarise useful details (features, suitability, differences between options) using Amazon’s catalogue information plus signals such as customer reviews and Q&A. For business buyers, it’s handy for cutting down research time, checking fit for purpose quickly, and avoiding purchases based on vague specs or marketing fluff.

What Rufus Is Good For

– Quick product comparisons, e.g., asking what’s different between two models, or which one best fits a use case (home office, travel, light design work, small business NAS, and so on).

– Plain English answers to practical questions. For example, “Will this work with…?”, “Is it quiet?”, “Is it suitable for video calls?”, “How portable is it?”.

– A reality check on discounts. Rufus can surface price history (typically over 30 or 90 days) so you can see whether today’s price is genuinely good or just a short-term promotion.

How to Use Rufus

– Open Amazon on the Amazon Shopping app or Amazon.co.uk in your browser.

– Go to a product page for something you’re considering buying.

– Open Rufus by tapping or clicking the Rufus or AI chat prompt (it appears as a chat-style assistant on supported pages).

– Ask a focused question, such as “What’s the difference between this and [other model]?”, “Is this suitable for a small office with 10 users?”, or “What are the common problems people mention in reviews?”.

– Check the deal properly. Ask Rufus, “Show price history”, or tap the Price history option where shown. Switch between 30 days and 90 days if available to see whether the price is trending up, stable or genuinely discounted.

– Use the answer to decide fast. Shortlist the best option, or move on before you waste time or budget on the wrong specification.

Why UK Businesses Should Care

If you buy equipment regularly, Rufus can work as a fast first pass, i.e., it speeds up comparison shopping, turns messy listings into clearer decisions, and makes it harder for superficial discounts to slip through procurement. It won’t replace due diligence for big-ticket purchases, but it’s a new way to trim time spent on everyday buying and price checking.

Ring’s latest AI-powered tool, designed to help find lost dogs and monitor wildfires, has prompted a backlash over how far neighbourhood camera networks should go.

Search Party Expanded

Ring, owned by Amazon, has just expanded its new Search Party feature across the United States, allowing its outdoor cameras to automatically scan for missing dogs reported in the Ring app.

Opt-Out and “Function Creep”

The system is enabled by default on eligible devices, meaning users must actively switch it off if they do not want to take part, a detail that has fuelled some questions and controversy.

The company says the feature has already helped reunite “more than one lost dog a day” with its owner since launch. Privacy campaigners, meanwhile, warn it represents another example of AI-driven “function creep”, where tools introduced for safety gradually widen the scope of surveillance.

What is Search Party?

Search Party is an AI-powered feature built into Ring’s Neighbours ecosystem. With the feature, when someone creates a Lost Dog Post in the Ring app, participating outdoor Ring cameras in the surrounding area then begin scanning for dogs that resemble the missing pet.

Ring explains the process in its official help documentation: “When a neighbor reports a missing dog in the Ring app, your outdoor Ring cameras use AI to look for matches in your recordings.” If a camera spots what it believes may be the missing dog, the camera owner receives an alert that includes “A picture of the missing dog” and “Video footage from your camera”.

The footage is not automatically sent to the dog’s owner. Instead, the camera owner chooses whether to share the clip or ignore the alert. Ring says this ensures participation remains voluntary and that users retain control over their content.

The feature has now been expanded so that anyone in the US can start a Search Party in the Ring app, even if they do not own a Ring device. This broadens the potential reach of the network significantly.

Better Than Driving Around Looking For The Dog

Jamie Siminoff, Ring’s chief inventor, said: “Before Search Party, the best you could do was drive up and down the neighborhood, shouting your dog’s name in hopes of finding them. Now, pet owners can mobilise the whole community — and communities are empowered to help — to find lost pets more effectively than ever before.”

Ring adds that lost pets are among the most common posts in the Neighbours app, with “more than 1 million reports of lost or found pets made in the app last year alone”. The company estimates there are roughly 90 million dogs across around 60 million US households, underscoring the potential scale of the problem it is attempting to address.

Questions

Despite Amazon’s explanations of the value of the feature, it has sparked some controversy centring on how the technology operates.

The most contentious point appears to be that Search Party is switched on by default. That said, users were actually emailed about the change and told: “You can always turn off Search Party.” To opt out, users must navigate to the Control Centre in the Ring app and manually disable “Search for Lost Pets” for each camera.

However, critics argue that default activation shifts responsibility onto users and expands automated scanning across neighbourhoods without explicit consent from each camera owner at the outset.

Relationship With Law Enforcement

The feature also arrives against a backdrop of growing scrutiny over Ring’s relationship with law enforcement and its broader AI ambitions. Although Search Party is limited to detecting dogs and wildfire indicators, privacy advocates question how easily such systems could be adapted for other forms of tracking.

One of the key concerns is what technologists call function creep, i.e., where a tool introduced for a narrow purpose gradually evolves into something more expansive. AI-powered computer vision, once embedded across large numbers of residential cameras, can theoretically be trained to identify a wide range of objects or patterns.

Ring has stated that Search Party does not scan human faces and that sharing footage remains optional. The company’s help page makes this clear, saying: “You can choose to ignore the alert or respond to the alert and share the info with your neighbour.”

Even so, some campaigners warn that object recognition systems deployed at scale change the character of neighbourhood surveillance, even if they begin with benign goals.

Fire Watch and Broader Monitoring

Search Party is not solely about missing pets. It also incorporates a wildfire monitoring function known as Fire Watch.

According to Ring’s support materials, Fire Watch activates when Watch Duty, a non-profit wildfire monitoring organisation, reports a fire near a user’s location. During an active event, eligible outdoor cameras can use AI to monitor for “visible flames and smoke patterns”.

It should be noted here that Ring has stressed the limitations of this function, saying: “Your camera can make mistakes and might produce false positives (detecting fire when there isn’t one) or false negatives (missing actual fires). Fire Watch is not a safety alerting tool and should not be relied upon as your primary source for fire safety information.”

Users Can Choose To Share Images

Users can choose to share static image snapshots with Watch Duty for up to 24 hours at a time. Snapshot sharing ends automatically when the fire event concludes or when consent is withdrawn.

The inclusion of wildfire monitoring under the same umbrella has reinforced concerns among some critics that Search Party represents a broader shift towards AI-driven community surveillance infrastructure.

Ring’s Wider AI push

Search Party builds on Ring’s recent expansion into generative AI features. For example, in 2025, the company introduced Video Descriptions, which provides short AI-generated summaries of motion activity detected by cameras.

Siminoff described that development as “seizing on the potential of gen AI to shift more of the work of your home’s security to Ring’s AI”, signalling a strategic shift towards automated analysis rather than simple recording.

Search Party applies similar technology to neighbourhood-level scanning. For example, instead of waiting for users to manually review footage, the system proactively searches for visual matches when triggered by a Lost Dog Post or wildfire alert.

Community Empowerment

Ring seems keen to position this feature as community empowerment. For example, in its announcement, the company said: “Search Party’s expansion reflects a meaningful step forward in Ring’s mission to make neighborhoods safer — including for all our four-legged family members.”

It has also committed $1 million to equip animal shelters across the US with Ring camera systems, aiming to reduce the time lost dogs spend in shelters before being reunited with their owners.

Opting Out and User Control

Despite the controversy, participation in the feature is optional. For example, users can disable Search Party at any time in the Ring app by selecting Control Centre, choosing Search Party, and toggling off “Search for Lost Pets” for individual cameras. A separate toggle controls Fire Watch monitoring.

Non-subscribers can also still receive fire event alerts and access live view during wildfire events, but cannot use AI fire detection or share content with first responders.

Ring emphasises that camera owners decide on a case-by-case basis whether to share footage and that no automatic data transfer occurs without user action.

In essence then, the debate here centres on how much automation users are comfortable allowing within residential camera networks. For example, for some, the prospect of finding a missing dog within minutes outweighs the abstract risk of expanded AI scanning whereas, for others, the default activation of a feature that mobilises neighbourhood cameras may seem like a step too far in the normalisation of always-on visual monitoring.

What Does This Mean For Your Business?

The central question here is not whether finding lost dogs is worthwhile, but how much automated scanning people are prepared to accept as standard in their streets. Ring stresses that Search Party does not use facial recognition, that sharing footage is voluntary and that users can opt out at any time. It also points to early results, saying the feature has already helped reunite more than one dog a day. For many households, that practical benefit will matter.

The concern, however, is that once AI-powered object recognition is embedded across millions of cameras, the technical capability exists to expand what those systems detect. Even if it is currently limited to just spotting dogs and signs of wildfire, critics say the bigger issue is that the same technology could be adapted in future to look for other things. For example, once cameras are routinely scanning footage automatically, it will become easier to expand what they are scanning for. Also, the fact that the feature is switched on by default has intensified those concerns, because it means the system begins operating unless users actively turn it off.

It seems that for Amazon and Ring, maintaining trust will depend on transparency and meaningful user control, but for regulators and privacy groups, the rollout is reinforcing calls for clear guardrails around AI-enabled surveillance.

For UK businesses, this is a reminder that AI in security systems must be deployed with privacy by design and explicit consent, particularly under UK GDPR. For consumers, communities and emergency services, the benefits are tangible, but so too are the longer-term questions about how far automated monitoring should extend.

New research suggests that generative AI adoption may actually intensify work patterns and increase burnout risk rather than reduce workload.

Research (Inside A Live Company)

For several years, generative artificial intelligence has been promoted as a way to reduce administrative burden and free professionals to focus on higher value tasks. Tools based on large language models, systems trained on vast datasets to generate text, code and other content, are widely used to draft documents, summarise meetings and assist with programming and analysis.

However, a February 2026 article in Harvard Business Review (by Aruna Ranganathan and Xingqi Maggie Ye) reports findings from an eight month in progress study inside a 200 person United States technology company, concluding that “AI tools didn’t reduce work, they consistently intensified it.”

Eight-Month Study

Over eight months, the researchers observed day to day work inside the firm and conducted more than 40 in depth interviews across key teams, enabling them to compare how roles changed as AI use increased. Crucially, staff were not instructed to use the tools or to raise performance targets, yet workloads expanded as employees voluntarily adopted AI and took on more.

Observed Changes In Work Patterns

The researchers reported that once employees adopted AI tools, they worked at a faster pace, took on a broader scope of tasks and extended work into more hours of the day. These changes occurred without formal instructions from management to increase targets or output.

One of the main mechanisms identified was task expansion. For example, because generative AI can fill gaps in knowledge and provide rapid feedback, employees were found to have increasingly stepped into responsibilities that previously belonged to other roles. Product managers and designers began writing code, while researchers undertook engineering tasks. Over time, it was observed that individuals therefore absorbed work that might previously have required additional headcount or external contractors.

The researchers describe generative AI as providing what many workers experienced as an “empowering cognitive boost”, whereas employees referred to “just trying things” with the AI, experimenting with unfamiliar tasks. The researchers found that these experiments gradually accumulated into a widening of job scope, which in turn created additional review and oversight work for others. For example, engineers reported spending more time reviewing, correcting and guiding AI assisted work produced by colleagues, often through informal exchanges on internal messaging platforms.

Blurred Boundaries Between Work And Non Work

A second pattern identified in the study was the erosion of natural breaks in the working day. For example, because AI systems reduce the friction of starting a task, workers began prompting tools during moments that previously functioned as downtime, including lunch breaks and short pauses between meetings.

In fact, some employees even described sending “a ‘quick last prompt’ right before leaving their desk so that the AI could work while they stepped away”. Although these interactions were brief and conversational, it was noted that they reduced opportunities for recovery. The researchers observed that work became more continuous and less clearly bounded, with fewer natural pauses.

Over time, this pattern contributed to a sense that work was harder to step away from. In essence, the boundary between work and non work did not disappear, but it became easier to cross, particularly as faster turnaround times became visible and normalised within teams.

Increased Multitasking And Cognitive Load

The third form of intensification that the researchers observed involved increased multitasking. For example, workers seemed to be managing several AI assisted threads simultaneously, manually drafting material while AI generated alternatives, running multiple agents in parallel or revisiting deferred tasks because AI could handle parts of them in the background.

While this created a sense of momentum, it also required frequent checking of outputs, prompt refinement and attention switching. The study notes that higher speed did not necessarily translate into reduced busyness. For example, as one engineer summarised, “You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more.”

Risks Of Silent Workload Creep

In their article about their study, the researchers argue that voluntary expansion of work can initially appear positive for organisations, but they warn that higher short term output may conceal unsustainable intensity. For example, because additional tasks are often self initiated and framed as experimentation, leaders may not immediately recognise the cumulative increase in load.

Fatigue And Burnout

The researchers warn that what appears to be higher productivity may actually mask a more damaging pattern. “Over time, overwork can impair judgment, increase the likelihood of errors, and make it harder for organisations to distinguish genuine productivity gains from unsustainable intensity.” They add that the cumulative impact on employees can be “fatigue, burnout, and a growing sense that work is harder to step away from, especially as organisational expectations for speed and responsiveness rise.”

The study does not argue that AI fails to enhance human capability, but its central point is that when augmentation makes it possible to do more, organisations and individuals may gradually raise expectations, expand scope and accelerate pace, reshaping everyday work in ways that increase pressure rather than reduce it.

Wider Evidence On Productivity And Perception

That said, other research has produced mixed findings on AI related productivity gains. For example, a recent working paper from the National Bureau of Economic Research examining AI adoption across thousands of workplaces reported average time savings of around 3 per cent, with no significant impact on earnings or hours worked across occupations.

Also, in 2025, the research organisation METR conducted a randomised trial involving experienced software developers and found that developers using AI tools took 19 per cent longer to complete certain tasks while believing they were 20 per cent faster. This study highlights the potential gap between perceived and measured productivity and the hidden time required to review and correct AI generated outputs.

Corporate surveys have also indicated that while many employees report time savings from AI, overall workload pressures often remain due to organisational factors and rising expectations for speed and responsiveness.

Implications For Organisations

It should be noted here that the study results highlighted in the Harvard Business Review do not diagnose clinical burnout among participants, but rather identify patterns that may increase burnout risk over time, including workload creep, reduced recovery periods and sustained cognitive strain.

The researchers, Ranganathan and Ye, therefore argue that organisations should establish what they call an “AI practice”, defined as intentional norms and routines governing how AI is used and how work expands in response to new capabilities. They recommend structured pauses to regulate tempo, clearer sequencing of tasks to reduce fragmentation and deliberate opportunities for human interaction to counterbalance continuous AI mediated work.

The researchers conclude that “without intention, AI makes it easier to do more—but harder to stop”, thereby showing the real issue here to be one of organisational design rather than technological failure.

What Does This Mean For Your Business?

What this research ultimately seems to highlight is a governance issue rather than a technological one. When AI increases what individuals can do, organisations must decide whether to translate that into sustainable efficiency or into higher expectations and faster pace. The evidence suggests that without clear boundaries, intensification can happen quietly, even when no formal targets change.

For UK businesses investing in generative AI, that means monitoring more than output. For example, leaders may need to track workload sustainability, quality control and employee wellbeing alongside productivity metrics. AI adoption may need to be treated as organisational redesign, not simply a software rollout.

Also, the implications seem to extend beyond employers. For example, employees may feel pressure to prove the value of AI tools, managers may normalise faster turnaround without assessing long term strain, and regulators focused on workplace health may begin to examine how AI affects cognitive load and recovery time.

In essence, the research does not argue against AI, but shows that augmentation alone does not guarantee relief from pressure. The point here is that whether AI reduces workload or intensifies it will depend less on the tools themselves and more on how organisations set limits, pace expectations and define what productive work should look like.