Researchers have demonstrated a controversial new method for generating tiny amounts of electricity from Earth’s rotation through its own magnetic field, opening debate about whether the planet itself could one day become a source of continuous renewable energy.

How The Experiment Worked

A team of researchers from Princeton University, NASA’s Jet Propulsion Laboratory, and Spectral Sensor Solutions has published experimental results suggesting that electrical power can be generated from Earth’s rotation through its own magnetic field. The work, published in the journal Physical Review Research, revisits a scientific question that dates back to Michael Faraday’s experiments in the 1830s.

The researchers built a cylindrical shell made from manganese-zinc ferrite, a soft magnetic material that behaves as a weak electrical conductor. The cylinder was positioned very carefully so that it sat perpendicular to both Earth’s rotation and the planet’s magnetic field. As Earth rotated, the device moved through that field and generated a measurable direct current voltage.

However, the voltage produced was extremely small, around 17 to 18 microvolts, which is far below anything that could power homes, factories, or electrical infrastructure today. Even so, the researchers believe the result matters because conventional electromagnetic theory had long suggested that such a system should not work at all.

The paper states: “We show that this small demonstration system generates a continuous DC voltage and current of the (low) predicted magnitude.”

Why Scientists Previously Thought This Was Impossible

For decades, physicists believed that any voltage generated by Earth’s rotation through its own magnetic field would immediately cancel itself out. They believed that electrons inside a conductor would simply rearrange themselves fast enough to neutralise the effect.

The Princeton-led team has argued that particular material properties and geometries could prevent that cancellation from fully occurring. Their specially designed cylindrical shell was intended to create exactly those conditions.

The researchers explained: “The intention of these experiments was to test the existence of the predicted effect, and the results and multiple controls we report here appear to demonstrate its reality.”

To reduce the possibility of false readings, the experiments were conducted in a dark underground laboratory to eliminate photoelectric effects. The team also tested solid control cylinders, rotated the apparatus into different orientations, and repeated parts of the experiment at a second location.

When the device was rotated into orientations where the effect should theoretically disappear, the voltage also disappeared, matching the team’s predictions.

A Sustainability Idea With Potentially Huge Long-Term Implications

The sustainability implications are attracting attention because the energy source itself would effectively be constant. For example, unlike solar panels, the Earth does not stop rotating overnight, and unlike wind turbines, there are no weather-related calm periods.

This means that, if the effect could ever be scaled significantly, it could represent an entirely new category of renewable power generation that operates continuously without combustion, emissions, or moving mechanical parts exposed to weather.

The researchers note that the electricity ultimately comes from Earth’s rotational kinetic energy, mediated through the magnetic field. They also argue that even very large-scale use would have almost no measurable impact on the planet’s rotation.

According to the paper: “Even in an extreme scenario where our civilisation somehow would obtain all its electrical energy from the effect described here, Earth’s rotation would slow by milliseconds per decade.”

That comparison is important because Earth’s rotation already naturally fluctuates by several milliseconds due to geological and lunar influences.

Why Many Scientists Remain Cautious

Despite the excitement surrounding the concept, many physicists remain sceptical because the voltages involved are incredibly small, and tiny measurement errors can sometimes produce misleading experimental results.

Nature and other scientific publications have also pointed out that the measured voltage is smaller than the electrical activity generated by a single neuron firing in the human body.

Even the researchers themselves acknowledge that practical power generation remains speculative and have stressed repeatedly that independent verification must come first.

As the research paper states: “The next step would be for an independent group to reproduce (or contradict) our results under experimental conditions closely similar to those used here.”

Scaling the technology also presents some major engineering challenges. The current setup produces only nanoamps of current and microvolts of potential difference. Reaching commercially useful energy levels would require dramatic improvements in efficiency, materials science, and system design.

Still, the researchers believe the effect could potentially be amplified by miniaturising devices, connecting many units together, or using materials with different electromagnetic properties.

The Bigger Sustainability Context

Even if this research never becomes a viable energy platform, it highlights how the pressure to decarbonise energy systems is driving scientists to revisit ideas that were previously dismissed.

Much of the world’s current renewable infrastructure still faces intermittency problems. Solar output changes with daylight and weather. Wind generation fluctuates. Grid-scale storage remains expensive and environmentally demanding in its own right.

That has pushed researchers to investigate increasingly unconventional forms of low-carbon generation, including ocean thermal systems, advanced geothermal technologies, space-based solar power, and now potentially Earth-rotation energy harvesting.

The significance of this research may therefore lie less in the current device itself and more in the fact that it challenges assumptions about what forms of renewable energy might ultimately prove possible.

What Does This Mean For Your Business?

For now, this remains an early-stage scientific experiment rather than a commercial energy breakthrough. Businesses should not expect Earth-rotation generators to appear in the energy market any time soon.

Even so, the research reflects a wider sustainability trend that matters commercially. As pressure grows to decarbonise economies while maintaining reliable electricity supplies for AI infrastructure, manufacturing, transport, and digital services, interest in unconventional clean energy technologies is increasing rapidly.

The experiment also demonstrates how advances in materials science and electromagnetism could open entirely new approaches to power generation over the coming decades. Whether this specific concept succeeds or not, the search for stable, always-on renewable energy is becoming one of the defining scientific and commercial priorities of the net zero transition.

In this video, find out how ChatGPT’s new Voice Mode can help you brainstorm ideas, solve problems more quickly, and work more naturally with AI.

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

Google Lens in Chrome can help you copy text, read handwriting, translate text, and pull useful details from images, screenshots, and visible document pages without typing everything out manually.

Why It Works

Instead of retyping information from a screenshot, product label, scanned document, image, or visible PDF page, Google Lens can analyse what is shown on screen and make the text easier to copy, search, or translate.

This can save time for office staff, finance teams, support teams, sales staff, and business owners who regularly work with screenshots, scanned paperwork, supplier documents, product photos, invoices, delivery notes, or customer emails and need to quickly copy information without retyping it manually

How To Use It

– Open Google Chrome on your computer.

– Find the image, screenshot, web page, or visible document page you want to check.

– Right-click on the image and choose “Search image with Google Lens”.

– If you are not right-clicking directly on an image, right-click on the page and choose “Search with Google Lens”, then drag over the area you want Google Lens to analyse.

– When the Google Lens panel opens, look for options such as Search, Text, or Translate.

– Use Text to select and copy words, numbers, paragraphs, codes, or references from the image.

– Use Translate to translate visible text into another language.

– Paste the copied text into an email, document, spreadsheet, ticket, CRM note, or wherever you need it.

This is especially useful when working with screenshots, scanned information, product photos, foreign-language documents, or PDF pages viewed in Chrome (although it shouldn’t be treated as a full replacement for a dedicated PDF or OCR tool).

Ukraine says it has carried out the first combat operation in history where enemy territory was captured entirely using robots and drones, signalling a major turning point in how future wars may be fought.

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.

Enterprise spending on AI-native software is now growing far faster than traditional cloud software, signalling a major change in how businesses buy, use, and value technology.

Why The Traditional SaaS Model Is Under Pressure

For more than two decades, most enterprise software has operated on a relatively simple model. Businesses bought software licences based on the number of employees using a platform, often referred to as “per-seat” pricing.

This approach helped drive the growth of companies such as Salesforce, Workday, ServiceNow, Slack, Zoom, and countless other Software-as-a-Service (SaaS) providers. Revenue grew as customers added more staff and purchased more licences.

However, the rapid rise of AI agents and AI-native platforms is starting to disrupt that model.

Instead of simply giving employees tools to work with, AI-native systems increasingly aim to complete tasks themselves. For example, AI agents can now respond to customer enquiries, generate marketing campaigns, summarise meetings, analyse contracts, process onboarding requests, monitor systems, and automate internal workflows with limited human involvement.

This changes the economics of enterprise software because companies may no longer need as many human users interacting directly with traditional platforms.

The Spending Gap Is Growing Quickly

One clear sign of this transition comes from procurement platform Tropic, which analysed more than $18 billion in managed software spending. Its latest figures show AI-native enterprise spending grew by approximately 94 per cent year-on-year among mid-market and enterprise organisations, while primarily traditional SaaS spending grew by around eight per cent.

It’s important to note that these figures reflect Tropic’s customer dataset rather than the entire global software market. However, analysts increasingly believe the underlying trend is real and accelerating.

Also, research from Deloitte suggests software companies are now under growing pressure to become “AI-first” businesses, with agentic AI expected to transform software operations, pricing models, and customer expectations across the industry.

Meanwhile, Gartner predicts that by 2030, at least 40 per cent of enterprise SaaS spending could move towards usage-based, agent-based, or outcome-based pricing models instead of traditional per-seat licensing.

What “AI-Native” Actually Means

Much of the current discussion centres around the difference between traditional SaaS, hybrid AI software, and fully AI-native systems.

Traditional SaaS platforms mainly rely on human users manually operating software interfaces. Hybrid systems add AI features into existing platforms, such as AI assistants inside Microsoft 365 or Salesforce.

AI-native platforms are different because the AI itself becomes the main worker inside the system.

For example, some newer customer service platforms now allow businesses to deploy autonomous AI agents capable of handling large volumes of enquiries across WhatsApp, email, web chat, and social media with minimal human input. Other AI-native systems can build workflows, generate reports, write software code, or analyse data through natural language instructions rather than manual configuration.

This helps explain why investors and software vendors are increasingly focusing on “agentic AI”, where software performs work autonomously rather than simply assisting humans.

Why Software Companies Are Rushing To Adapt

The pressure on traditional software firms is now becoming increasingly visible.

Many major software providers are rapidly embedding AI agents into their products, partly because investors fear that platforms failing to adopt AI quickly enough could lose market share to newer AI-native competitors.

Salesforce, Microsoft, Google, ServiceNow, Slack, Anthropic, OpenAI, and many others are now heavily promoting AI agents and autonomous workflow systems as core parts of their future strategies.

If one AI agent can perform work that previously required several employees using multiple software licences, the traditional per-user revenue model that has underpinned much of the software industry for decades becomes harder to sustain.

This has also created growing interest in alternative pricing structures based on usage, AI actions, outcomes, or completed tasks rather than simply employee headcount.

At the same time, many businesses are discovering that AI systems introduce very different cost structures from traditional SaaS.

Unlike standard software subscriptions, AI systems often consume large amounts of compute power, tokens, API calls, and cloud infrastructure. Research cited by Tropic suggests many organisations are now seeing AI-related software price increases far above normal annual SaaS uplifts.

What Does This Mean For Your Business?

For UK businesses, the most important point is that AI is increasingly moving beyond being a standalone productivity tool and is starting to reshape the software industry itself.

Businesses evaluating software suppliers may increasingly need to ask not just what a platform does, but how much human work it can realistically automate, what the long-term pricing model looks like, and how AI-generated decisions are monitored and controlled.

The trend also means software procurement is becoming more complicated. Traditional, predictable per-user pricing is gradually being replaced by models based on AI usage, actions, compute consumption, or business outcomes, which may make long-term costs harder to forecast.

At the same time, organisations adopting AI-native systems may gain significant efficiency advantages if these tools genuinely reduce manual workload, improve customer response times, or automate repetitive operational tasks.

However, many AI agents still remain imperfect, requiring human oversight, careful governance, and strong security controls. Businesses should therefore be cautious about assuming that AI-native automatically means lower risk or lower cost.

What is becoming increasingly clear, however, is that the software industry is entering a major transition period. The companies that succeed may not necessarily be those with the biggest software platforms, but those that can most effectively combine AI automation, workflow integration, trust, and measurable business outcomes into products organisations are willing to rely on every day.

Amazon has begun making drone deliveries in the UK for the first time, marking a major step towards autonomous AI-driven logistics becoming part of normal daily commerce.

Why Amazon Has Started UK Drone Deliveries Now

Amazon Prime Air has officially launched limited drone deliveries in Darlington, County Durham, making the UK the first country outside the United States where the company has rolled out the service commercially.

The launch follows years of testing, regulatory delays, safety reviews, and technical development. Amazon first trialled drone deliveries near Cambridge back in 2016, when one early test delivery reportedly took just 13 minutes.

The company is now using its newer MK30 drone platform, which has been designed to operate more quietly, fly further, and cope with a wider range of weather conditions than previous models.

For now, deliveries are restricted to a 7.5-mile radius around Amazon’s Darlington fulfilment centre. Packages must weigh less than 2.2kg and fit inside a relatively small parcel size.

Eligible customers can receive items such as batteries, cables, office supplies, beauty products and household essentials in under two hours.

Amazon says the long-term goal is to make deliveries significantly faster. In some parts of the US, where the system is already operational in five states, the average delivery time is reportedly around 36 minutes.

How The Drone System Actually Works

The MK30 drones operate largely autonomously using onboard cameras, sensors, GPS, and machine learning systems designed to identify obstacles and avoid collisions.

Amazon says the drones can detect objects including washing lines, trampolines, trees, animals, people and other aircraft while descending for deliveries.

Packages are lowered into a customer’s garden or driveway from a height of around 10 to 12 feet, rather than requiring the drone to land fully.

The flights are taking place under Beyond Visual Line of Sight, or BVLOS, rules approved by the UK Civil Aviation Authority. That matters because it allows drones to operate autonomously beyond what a human pilot can physically see.

Even so, the drones are still monitored remotely from a control centre, with operators able to coordinate with air traffic control if needed.

Amazon has also secured temporary protected airspace around the Darlington test area while the trial continues.

Why Darlington Was Chosen

Darlington was selected partly because it is believed to offer a useful mix of residential areas, rural land, roads and nearby airspace within a relatively compact area.

That allows Amazon to test how the drones cope with real-world conditions without immediately dealing with the extreme complexity of major cities.

This is important because dense urban environments remain one of the biggest technical challenges for drone delivery systems.

Practical Limitations

It should be noted here that drone deliveries also face practical limitations in dense urban environments, where high-rise buildings, congested airspace, and limited landing areas make autonomous delivery far more difficult than in lower-density suburban or rural locations.

Issues such as access to flats and apartments, rooftop delivery infrastructure, airspace congestion, safety management and public acceptance remain unresolved in many city environments.

The current Darlington operation is also still relatively small in scale, with Amazon carrying out only a maximum of around 10 flights per hour.

Still Safety Questions Around Drone Parcel Deliveries

Despite Amazon’s confidence in the technology, safety concerns remain one of the biggest barriers to wider public acceptance and regulatory expansion.

Amazon’s rollout comes after several incidents involving its MK30 drones in the United States.

One drone reportedly clipped a building in Texas earlier this year after temporarily losing GPS positioning. Other incidents involving collisions during testing in Arizona and Oregon also triggered investigations and delays.

Amazon says no injuries occurred and describes the incidents as part of the normal process of refining a new aviation system.

The company also argues that the drones operate to aerospace-level safety standards and include multiple backup systems.

Public Reaction

Public reaction in Darlington seems to have been mixed. Some residents have reportedly embraced the convenience and novelty of near-instant deliveries, while others have raised concerns around noise, safety and whether drones are really necessary for ordinary household deliveries.

Many AI-powered autonomous systems still face a basic problem, namely that people do not automatically trust them simply because the technology works.

A similar challenge is now emerging elsewhere in the tech industry. Meta, for example, is increasingly using AI systems to estimate users’ ages and help enforce safety rules on platforms like Instagram. In both cases, companies are asking the public to trust autonomous systems to make decisions that were previously handled directly by humans.

Why This Matters Beyond Parcel Deliveries

The full significance of Amazon’s rollout is not really about faster deliveries of batteries or office supplies.

The bigger story is really that autonomous AI systems are steadily moving out of controlled test environments and into ordinary public infrastructure.

Drone delivery combines several technologies that businesses are likely to encounter more frequently over the next decade, including machine learning, autonomous navigation, remote monitoring, automated compliance systems and AI-assisted decision-making.

The UK is already experimenting with similar technology elsewhere. For example, the NHS has been trialling drones for transporting blood supplies in London, while Royal Mail has used drones to deliver parcels to remote communities in Orkney. Many of these early deployments focus on environments where conventional transport is slow, expensive or difficult.

The commercial logic for drone parcel deliveries is also now becoming a bit clearer. For example, labour shortages, rising delivery costs, pressure for faster fulfilment and growing demand for same-day delivery are all pushing logistics companies towards greater automation.

What Does This Mean For Your Business?

For most UK businesses, drone deliveries are unlikely to become an immediate operational reality. The technology still faces significant regulatory, technical, and public acceptance barriers, especially in towns and cities.

However, AI-driven autonomous systems are increasingly becoming part of everyday business operations, with AI now making more decisions in areas such as logistics, security, customer verification, fraud detection and operational management.

That creates opportunities for faster services and lower operating costs, but it also increases the importance of governance, oversight, cybersecurity and trust.

Amazon’s drone rollout is, therefore, less about flying parcels and more about what happens when AI systems begin interacting directly with the physical world at scale.

For UK businesses, the key lesson here may simply be that autonomous systems are no longer experimental concepts sitting in research labs. They are beginning to appear in everyday operations, regulation, infrastructure and customer services, often much sooner than many organisations expected.