A US startup claims the first hotel on the Moon could be deployed by the early 2030s, as space agencies return to lunar missions and private companies search for commercially viable ways to support long-term human presence beyond Earth.

Who Is GRU?

The proposal comes from Galactic Resource Utilization Space, better known as GRU Space, a US startup founded in 2025 by Skyler Chan, a former University of California, Berkeley graduate with a background in space systems and off-world habitation research. Chan has previously spoken publicly about his interest in lunar and Martian settlement while studying engineering and space technology.

GRU has attracted early backing from investors linked to the US space and defence ecosystem, including individuals who have invested in SpaceX and Anduril, and has been supported by startup programmes such as Y Combinator and Nvidia’s Inception initiative for technology startups. The company positions itself as a space infrastructure business rather than a tourism brand.

GRU argues that human expansion beyond Earth has not stalled because of launch capability, but because of the lack of scalable, safe habitation systems once astronauts arrive on the lunar surface. In its January 2026 white paper, the company states that “humans cannot expand beyond Earth until we solve off-world surface habitation”, describing this as the critical step that enables everything else, from research bases to industrial activity.

A Commercial Prospect

The lunar hotel is presented as a commercial starting point rather than a novelty. For example, GRU says revenue-generating habitation could help fund and validate the technologies required for permanent lunar infrastructure, including life support systems, surface construction methods, and long-duration operations away from Earth.

While the idea may sound pretty futuristic, GRU argues it is actually rooted in current lunar exploration plans, emerging habitat technologies, and a belief that off-world living space, not rockets, is now the main limiting factor.

Why Now?

The timing of GRU’s proposal aligns closely with renewed US government activity around the Moon. For example, NASA’s Artemis programme is preparing to fly its first crewed lunar mission in more than 50 years, with Artemis II expected to carry four astronauts on a ten-day journey around the Moon and back to Earth in early 2026.

Artemis III, which aims to land astronauts near the Moon’s south pole, is currently planned for no earlier than 2027 or 2028. Together, these missions signal a long-term commitment to lunar operations, rather than short symbolic visits.

GRU argues that once regular crewed missions resume, the next question becomes where people stay, work, and shelter on the lunar surface. The company, therefore, believes that a destination built specifically for human habitation, rather than temporary lander modules, is a necessary next step.

How Could a Moon Hotel Be Built?

GRU’s plan relies on a staged approach designed to reduce technical and financial risk. For example, rather than attempting large-scale construction immediately, the company proposes testing smaller systems before deploying a full hotel.

The first mission, planned for 2029, would deliver a small pressurised test payload to the Moon using a commercial lunar payload service provider. This mission would test inflatable habitat deployment and early construction experiments using lunar regolith, the fine dust and rock that covers the Moon’s surface.

A second mission, targeted for 2031, would then deliver a larger payload near a lunar pit or cave. GRU argues that these natural features offer shielding from radiation, micrometeoroids, and extreme temperature swings. An inflatable habitat would be deployed inside or near the pit, alongside more advanced construction trials.

The third mission, planned for 2032, is when GRU says the first hotel would be landed. This version would be built on Earth, transported by a heavy lander, and robotically deployed on the lunar surface before being inflated to create a pressurised living environment.

Why Inflatable Habitats Matter

A central part of GRU’s approach is the use of inflatable structures. This is because traditional rigid modules are heavy and expensive to transport, whereas inflatables can offer far more internal volume per kilogram of launch mass.

GRU has pointed to earlier inflatable habitat demonstrations in orbit as evidence that the technology is viable. The company argues that inflatables are the most practical way to maximise living space during the early stages of lunar settlement, before large-scale construction becomes possible.

Once deployed, these inflatable habitats would be partially enclosed or shielded using locally sourced material. GRU proposes using geopolymer techniques to bind lunar regolith into protective structures around the habitat, reducing radiation exposure and impact risk.

What Happens If a Lunar Hotel Deflates?

Inflatable lunar habitats are built with multiple layers and internal compartments, not as a single pressurised shell. Therefore, it seems that GRU may be banking on the fact that a small puncture would cause a slow pressure leak rather than sudden collapse, giving time for systems to respond.

It is likely that pressure sensors in the structure could detect the leak immediately and onboard life support systems could release stored gas to stabilise conditions, while the affected area could be sealed off internally. The air used to maintain pressure could come from onboard reserves and oxygen generation systems, not from outside the habitat, i.e., the vacuum of space.

Over time, the intention is likely to be to enclose or shield parts of the inflatable structure using lunar material, which could reduce exposure to micrometeoroids and temperature extremes. Even so, any loss of pressure would still be a serious safety issue, with designs assuming faults can occur and focusing on containment, redundancy, and time to respond rather than eliminating risk entirely.

How Could The Hotel Support Healthy Human Life?

The proposed hotel is designed primarily as a life support system rather than a conventional hospitality venue. For example, GRU states that the initial hotel would include a full environmental control and life support system, covering oxygen generation, carbon dioxide removal, water recycling, temperature regulation, and air filtration.

Emergency systems would also be required. These include protection against solar radiation storms, rapid depressurisation response, and contingency plans for evacuation or sheltering in place.

GRU says the hotel would be designed for multi-day stays, with guests able to observe the lunar surface and Earth from within the habitat, and to participate in surface activities under controlled conditions.

Who Would Stay There, and Would You Have to Be Rich?

It seems that, in the near term, access to any lunar hotel would certainly be limited to a very small and extremely wealthy group of travellers. GRU openly acknowledges that early stays on the Moon would be accessible only to the ultra-wealthy, drawing comparisons with the early days of commercial aviation and high-altitude mountaineering, when costs were prohibitive before wider scale and improved technology gradually brought prices down.

The white paper models a first-generation hotel capable of hosting four guests at a time, with five-night stays and an operational life of ten years. GRU estimates an internal cost per person per night of over $400,000 for this version, falling significantly only once larger, more permanent structures are built using lunar materials.

Could Cost Several Million Pounds To Say There!

Public ticket prices would likely exceed internal costs, meaning early visitors would need to commit several million pounds for a single trip. This mirrors the first wave of commercial space tourism, where privately funded rocket flights operated by Blue Origin carried high-profile passengers, including the company’s founder Jeff Bezos, at prices far beyond the reach of most people. GRU argues that lunar travel could follow a similar trajectory, with costs falling over time as launch cadence increases and payload prices drop, although this would depend heavily on wider industry progress rather than the company alone.

Why Launch Costs Are Central to the Plan

A central assumption behind GRU’s lunar hotel timeline seems to be that the cost of delivering people and equipment to the Moon will fall sharply over the next decade. GRU’s plan depends on a significant reduction in the cost of transporting payloads to the lunar surface, with the company citing projected future pricing from heavy lift vehicles that could see costs fall from around $1 million per kilogram to closer to $100,000 per kilogram later in the decade.

However, these figures are not guaranteed and should be treated as projections rather than confirmed market prices. Even so, the broader trend towards reusable launch systems and increased competition is widely expected to put downward pressure on costs over time.

NASA’s use of commercial providers through its lunar payload programmes also supports this assumption, as more companies compete to deliver cargo and infrastructure to the Moon.

What This Would Mean for GRU

GRU frames the hotel as a stepping stone rather than an end goal. In its white paper, the company describes the hotel as “the first economically rational module of a permanent lunar base”, arguing that revenue-generating infrastructure could accelerate wider lunar development.

Successfully deploying and operating a habitat would require GRU to master power generation, communications, surface robotics, life support maintenance, and remote operations. These capabilities would be valuable well beyond tourism, potentially positioning the company as a supplier or partner for future lunar bases.

The approach also appears to shift risk, i.e., instead of relying entirely on government funding, GRU is attempting to combine private capital with commercial demand to justify infrastructure investment.

Are Other Countries or Companies Planning Similar Projects?

Few organisations are publicly marketing lunar hotels with a specific date, but the underlying concepts are being widely explored. For example, space agencies in Europe and Asia have published habitat studies examining inflatable modules, regolith shielding, and long-term surface living.

Also, China and Russia have jointly announced plans for an International Lunar Research Station, aiming to establish a permanent presence on the Moon in the 2030s. These efforts are not tourism-focused, but they rely on many of the same technologies GRU proposes to use.

Private companies working on space stations in low Earth orbit have also explored inflatable habitats, suggesting cross-over between orbital and lunar living systems over time.

Benefits, Challenges, and Criticisms

Supporters argue that a functioning commercial habitat could accelerate innovation in life support systems, construction robotics, power generation, and radiation protection. These technologies would be useful not only on the Moon, but for Mars missions and remote operations on Earth.

However, critics point to safety as the most serious concern. For example, rescue options on the Moon are extremely limited, and even minor system failures could become life-threatening. Regulatory frameworks for commercial human spaceflight remain underdeveloped for surface operations beyond Earth orbit.

Legal questions also remain unresolved. For example, international space law prohibits national sovereignty over the Moon, raising complex issues around property rights, exclusion zones, and commercial activity. While resource utilisation is permitted under current interpretations, long-term habitation will test existing agreements.

That said, GRU’s own roadmap acknowledges significant unknowns, including reliance on regular crewed lunar transport, regulatory approval, and the successful integration of multiple unproven systems. The company describes its plan as ambitious and openly states that many technical and operational challenges remain unsolved.

What Does This Mean For Your Business?

GRU’s proposal seems to sit somewhere between credible engineering ambition and unresolved risk. The company is not claiming the Moon will suddenly become accessible or safe, but it is arguing that habitation has now become the limiting factor in lunar exploration, rather than launch alone. Its hotel concept is, therefore, best understood as an attempt to turn a long-standing research challenge into a commercially funded infrastructure project, using tourism as an early revenue stream rather than the final objective.

Whether that approach succeeds will depend less on marketing and more on execution. Regular crewed access to the lunar surface, falling launch costs, robust life support systems, and clear regulatory frameworks all have to mature in parallel. Any delay or failure in one area would quickly undermine the wider plan. At the same time, the staged nature of GRU’s roadmap reflects a growing realism in the space sector, where incremental demonstrations are increasingly favoured over grand, one-shot visions.

For UK businesses and other stakeholders, the significance is less about lunar tourism itself and more about what such projects demand behind the scenes. Advanced materials, robotics, life support components, power systems, remote monitoring, insurance, legal services, and cyber resilience are all essential to off-world habitation and already sit within areas where UK firms have relevant expertise. Even if hotels on the Moon remain limited to a handful of ultra-wealthy visitors in the 2030s, the technologies, supply chains, and commercial models being tested could shape how space infrastructure develops more broadly, with implications that extend well beyond the lunar surface.

Google’s expanding AI partnerships, product integration, and recent technical progress are fuelling growing debate over whether it has quietly moved ahead of OpenAI in the global race to deploy large-scale artificial intelligence.

Matched Since 2022

Google and OpenAI have been closely matched since late 2022, when OpenAI’s release of ChatGPT reshaped public and commercial expectations of what generative AI could do, yet the balance of momentum now appears to be shifting as Google converts years of research into deployed systems at scale.

How Google Recovered From a Slow Start

When ChatGPT launched in November 2022, it caught much of the technology industry, including Google, off guard. Despite Google’s long history in machine learning and AI research, OpenAI’s product arrived first with a highly accessible conversational interface that rapidly reached over 100 million users within months.

Google’s response was swift but initially uneven. For example, the company accelerated internal development under what chief executive Sundar Pichai later described as an urgent shift in priorities, whereby teams were reorganised, projects were refocused, and products that had been in research phases for years were pushed towards public release.

Early versions of Google’s Bard chatbot struggled to match ChatGPT’s reliability, leading to public missteps that reinforced the perception that Google was playing catch-up. Behind the scenes, though, the company continued investing heavily in foundation models, custom AI chips, and infrastructure that would later underpin its Gemini model family.

Gemini and Google’s Integrated AI Strategy

Google’s launch of the Gemini model family signalled a change in approach by moving away from a standalone chatbot towards a set of foundation models designed to operate across mobile devices, consumer services, and large-scale cloud infrastructure.

This approach appears to reflect a kind of key philosophical difference between Google and OpenAI. For example, OpenAI has focused primarily on developing increasingly capable general-purpose models, which are then distributed via ChatGPT, APIs, and selected partnerships. Google, by contrast, has emphasised deep integration across its existing products, including Search, Android, Chrome, Gmail, Docs, and Google Cloud.

The result is that Gemini is not just a single AI product, but a layer embedded across services used daily by billions of people. Google has argued that this allows it to deploy AI features more safely and more consistently, refining them in specific contexts rather than relying on one general interface.

Gemini – “Natively Multimodal”

In public communications, Google has been keen to stress that its Gemini AI is designed to be “natively multimodal”, meaning it can work with text, images, audio, and video from the outset rather than treating those as add-ons. This capability has become increasingly important as businesses look to automate workflows that involve documents, meetings, images, and structured data together.

The Significance of Apple’s Gemini Decision

One of the clearest external signals of Google’s renewed standing emerged in mid 2025, when Apple confirmed it had selected Google’s Gemini models as a foundation for parts of its AI strategy, including planned upgrades to Siri and its wider “Apple Intelligence” platform, following months of reported negotiations.

In a joint statement announcing the partnership, the two companies said Apple had concluded that Google’s AI technology offered the most capable foundation for its needs, while still allowing Apple to run Apple Intelligence primarily on device and through its Private Cloud Compute infrastructure in line with its long-standing privacy and security requirements.

This was widely interpreted as a setback for OpenAI, which already has an integration with Apple platforms through ChatGPT features in macOS and iOS. Choosing Google for foundational models suggests Apple values stability, scale, and long-term integration over cutting-edge experimentation.

The decision also appears to reinforce Google’s strength in enterprise-grade AI infrastructure, with Apple’s focus on privacy, reliability, and global scale seeming to align more closely with Google’s long-standing cloud-first approach than with OpenAI’s faster, more consumer-led release cycle.

Benchmarks, Capability, and Credibility

AI model benchmarks remain quite a contentious topic, as results can vary depending on test design and optimisation. However, it seems that independent evaluations published by academic researchers and industry analysts have shown Gemini models performing competitively, and in some cases outperforming, comparable GPT models across reasoning, multimodal understanding, and coding tasks.

That said, OpenAI continues to lead in certain creative and conversational use cases, particularly where developer tooling and ecosystem maturity are concerned. OpenAI’s API adoption remains strong, and Microsoft’s integration of GPT models into products such as Copilot has given OpenAI unparalleled reach within enterprise environments.

The difference increasingly lies in how these capabilities are delivered. For example, Google has prioritised gradual rollout through familiar tools, reducing friction for users who may not actively seek out AI products. OpenAI has relied more heavily on direct user engagement with ChatGPT and developer-driven experimentation.

Why Infrastructure Really Matters

It’s worth noting here that Google’s position is also shaped by its control over large-scale AI infrastructure, including one of the world’s largest global computing networks and its in-house Tensor Processing Units, which are specialised chips designed for machine learning workloads.

This level of vertical integration is essentially what allows Google to train and deploy models at scale while managing cost, energy use, and availability more tightly than companies that rely entirely on third-party infrastructure, a factor analysts increasingly point to as a constraint on sustained AI development.

OpenAI, despite strong backing from Microsoft, remains more exposed to external infrastructure decisions, a relationship that has enabled rapid progress so far but appears to introduce strategic dependencies that Google is largely able to avoid.

Governance and Trust

Enterprise adoption increasingly depends on governance, compliance, and long-term support rather than headline-grabbing demos. With this in mind, Google has certainly invested heavily in AI safety frameworks, model evaluation, and policy tooling designed to meet regulatory expectations in Europe and the UK.

However, it seems that OpenAI has faced more visible scrutiny around governance, leadership changes, and transparency, none of which necessarily undermine its technology but which do affect risk assessments for large organisations.

For large organisations, purchasing decisions increasingly appear to be shaped less by who releases new models first and more by long-term stability, governance, and confidence that platforms and suppliers will remain consistent over time.

Where OpenAI Still Leads

Despite Google’s momentum, it should be noted that OpenAI remains a pretty formidable competitor. For example, ChatGPT continues to set the standard for conversational AI, and OpenAI’s research output continues to influence the wider field. The company’s ability to rapidly iterate and release new features has driven much of the innovation seen across the sector.

Microsoft’s backing also ensures that OpenAI models are deeply embedded in workplace software used by millions, particularly in the UK enterprise market.

The current dynamic is less about one company winning outright and more about diverging strengths. Google appears to be excelling at scale, integration, and infrastructure-driven deployment, while OpenAI remains strong in rapid innovation and developer engagement.

It could be said, therefore, that what has changed is the assumption that OpenAI holds a clear and unassailable lead. With Gemini embedded across platforms and endorsed by partners as demanding as Apple, Google could be said to have repositioned itself not as a follower, but as a central force shaping how AI is delivered, governed, and trusted at global scale.

What Does This Mean For Your Business?

What now appears to matter most is not a single benchmark result or product launch, but how effectively AI capabilities are being embedded into real services, governed at scale, and sustained over time. For example, Google’s recent progress suggests it has been able to translate long-standing strengths in infrastructure, distribution, and enterprise trust into tangible momentum, while OpenAI continues to set the pace in innovation speed, developer engagement, and conversational experience. The picture that emerges isn’t one of a clear winner, but of two companies optimising for different definitions of leadership as the market matures.

For UK businesses, this distinction is likely to become increasingly important. Organisations adopting AI tools are moving beyond experimentation and into decisions that affect procurement, compliance, data handling, and long-term supplier relationships. Google’s approach may appeal to firms prioritising stability, regulatory alignment, and tight integration with existing productivity platforms, while OpenAI’s ecosystem remains attractive for teams seeking flexibility, rapid capability gains, and access to cutting-edge features. The choice is becoming less about which model is most impressive in isolation and more about which provider fits operational reality.

For other stakeholders, including developers, regulators, and platform partners, the evolving balance between Google and OpenAI reinforces how the AI race is shifting away from spectacle and towards execution. As generative AI becomes embedded into everyday tools rather than standing apart from them, influence is likely to be shaped by who can deliver reliable systems at scale, earn sustained trust, and adapt to regulatory pressure without slowing progress. In that context, the question is no longer simply who is ahead today, but who is best positioned for the next phase of AI adoption.

Smart glasses with built-in cameras are being increasingly misused to secretly record people in public, creating new privacy and security concerns.

Cases reported in the UK, Europe and North America show women being filmed without their knowledge, with footage later posted online and attracting tens of thousands, and in some cases millions, of views. Devices such as Ray-Ban Meta smart glasses can look like ordinary sunglasses, making recording difficult to detect. Although recording indicators exist, guides and accessories to block them are widely available.

Campaign groups including the End Violence Against Women Coalition warn this reflects a predictable misuse of wearable technology, while researchers at the University of Kent caution that increasingly discreet devices reduce public awareness of when monitoring is taking place.

For businesses, the threat highlights the need to manage wearable surveillance risks by restricting smart glasses in sensitive areas, updating staff policies, raising awareness of covert recording, and reviewing physical security where confidential conversations or data could be exposed.

Perplexity CEO Aravind Srinivas has warned that if capable AI can run locally on personal devices, the economic and environmental case for endlessly expanding large data centres could start to weaken.

Data Packed Locally On A Chip Instead

For most users today, artificial intelligence follows a simple pattern. A request is sent from a phone, laptop, or app to a remote data centre, where a large model processes it before returning a response. This centralised approach has shaped how the AI industry has grown and where investment has flowed.

Srinivas has questioned whether that model will remain dominant over the long term. Speaking on a recent podcast, he argued that the “biggest threat to a data centre” would come if intelligence could be “packed locally on a chip that’s running on the device”, removing the need for much of the inference work to happen in central facilities, i.e., the everyday use of an AI model, such as generating answers, summarising documents, or analysing data after the model has already been trained.

Training, by contrast, is the highly resource intensive phase where models learn from massive datasets, usually using clusters of specialised processors inside data centres.

Srinivas’s argument is not that data centres suddenly disappear. Instead, he suggests that if more inference and personalisation move onto devices, the demand for centralised infrastructure may grow more slowly than expected, raising uncomfortable questions about the scale of current investment plans.

Why This Has Become a Sustainability Issue

The warning comes as the environmental impact of AI infrastructure is drawing increasing attention. Data centres already consume large amounts of electricity, and AI has accelerated that growth. For example, the International Energy Agency estimates that global electricity consumption from data centres could rise from around 460 terawatt hours in 2022 to between 945 and 1,050 terawatt hours by 2030, effectively doubling within a decade as AI workloads expand. The agency also notes that electricity demand from data centres is growing more than four times faster than overall global electricity demand, placing increasing pressure on power grids and decarbonisation efforts. At that scale, data centres would rank among the world’s largest single categories of electricity demand.

However, the pace of growth matters as much as the absolute numbers. For example, the IEA has also highlighted that electricity demand from data centres is increasing several times faster than overall electricity demand, thereby creating pressure on grids, generation capacity, and decarbonisation plans.

Water use has become another point of concern. For example, many large facilities rely on water-based cooling systems, either directly or indirectly through power generation. In water-stressed regions, new data centre projects have faced public opposition and regulatory scrutiny, particularly where local communities see competition for limited resources.

It’s against this backdrop that the idea of moving some AI workloads away from centralised facilities appears to offer a possible route to reducing environmental pressure, or at least slowing its growth.

What On Device AI Really Involves

It’s worth noting that on device AI doesn’t mean abandoning the cloud entirely, as it actually describes running certain AI tasks directly on local hardware, using specialised chips designed for machine learning workloads.

In fact, this is already happening in limited ways. For example, modern smartphones and laptops increasingly include neural processing units, which are optimised for tasks such as image recognition, speech processing, and text summarisation. These chips allow some AI features to run quickly without sending data to remote servers.

Apple, for example, has positioned on device processing as a core part of its approach to AI, emphasising privacy and speed. Microsoft has taken a similar route with its latest generation of Windows laptops, promoting devices capable of handling AI workloads locally through dedicated hardware.

In practice, most current systems are hybrid, e.g., smaller, frequent tasks may run on the device, while larger or more complex requests are still handled in the cloud. The question is whether that balance will shift significantly over time.

Why Local AI May Cut Impact (Or Not)

At first glance, the sustainability case for local AI seems pretty straightforward, e.g., if fewer requests are sent to data centres, fewer servers are needed, and energy and water use could grow more slowly.

However, the reality is more complex, and making AI cheaper and more responsive can increase usage. If people rely on AI more often throughout the day, total energy demand may still rise, even if each individual task becomes more efficient.

There is also the issue of where energy is consumed. For example, a highly optimised data centre running on low-carbon electricity may, in some cases, be more efficient than millions of individual devices drawing power from more carbon-intensive grids. The environmental outcome depends heavily on local energy mixes and usage patterns.

This is why claims that data centres will become obsolete are so controversial, as a shift in where computation actually happens doesn’t automatically translate into lower overall environmental impact.

Smaller Data Centres and Waste Heat

The debate around on device AI is also reshaping how data centre design is being approached. For example, rather than relying solely on vast, remote facilities, some operators are exploring smaller, more distributed models that place computing closer to where it is needed. Known as ‘edge computing’, this approach reduces latency and can improve responsiveness, while also opening up new sustainability opportunities.

In the UK, several projects have demonstrated this approach in practice. For example, at Exmouth Leisure Centre in Devon, a small-scale data processing unit operated by Deep Green uses immersion cooling to capture heat from servers and reuse it to warm swimming pools and hot water systems. The same model has since been applied in other public sector buildings, where computing infrastructure is integrated into heating systems to improve overall energy efficiency.

Facilities with a constant demand for heat are particularly well suited to this model, because the heat generated by local computing can be reused on site rather than being discarded, something a remote hyperscale data centre cannot offer.

These approaches do not remove the energy demands of computing, but they do improve overall efficiency by linking digital infrastructure more closely to real-world energy needs.

Why Large Data Centres Are Still Being Built

Despite growing interest in local and edge computing, investment in large data centres continues at pace and it seems there are practical reasons for this. For example, training the most advanced AI models still requires concentrated computing power, specialist cooling, and robust power infrastructure. Many business services also depend on centralised platforms for reliability, compliance, and security, particularly in regulated industries.

It’s worth noting here that data centres also support far more than AI. For example, streaming, online banking, enterprise software, cloud storage, and collaboration tools all rely on centralised infrastructure and, even if some AI workloads move elsewhere, these services still need to run.

That said, technology companies are aware of the sustainability pressure and are responding with efficiency improvements, renewable energy procurement, and public reporting commitments. These steps suggest preparation for long-term operation rather than an expectation of rapid decline.

The Technical Barriers to a Device First Future

Despite Srinivas’s predictions, he has acknowledged that on device AI faces real technical obstacles. Advanced models place heavy demands on memory, bandwidth, and thermal management. Running them continuously on a phone or laptop can drain batteries quickly and generate heat that hardware struggles to dissipate. Cost is another factor, since more powerful chips raise device prices and limit accessibility.

Progress is being made through smaller, more efficient models designed for specific tasks rather than general purpose use. Researchers and companies are increasingly focusing on models that are “good enough” for everyday work, such as summarising documents or managing routine workflows, without requiring enormous computing resources.

For example, an email assistant that sorts and drafts messages does not need the same scale of model as a system designed to generate long-form creative content across many domains.

What This Means for the Future of Infrastructure

All things considered, it seems the most likely outcome is not a collapse of data centres, but a gradual redistribution of workloads.

Large facilities remain essential for training advanced models and supporting global digital services. At the same time, more inference may shift onto devices and into smaller, local facilities, reducing some traffic and changing where energy is consumed.

From a sustainability perspective, this raises new priorities. Efficient chip design, longer device lifetimes, repairability, and transparent reporting of energy and water use become more important as computing spreads out across billions of devices.

It also sharpens the risk of overbuilding. If assumptions about ever-rising centralised demand prove wrong, the environmental cost is not only operational energy use but also the embodied carbon in construction, equipment manufacturing, and supporting infrastructure.

Srinivas’s warning does not predict the end of data centres. It highlights a growing uncertainty at the heart of the AI boom, where technological change, environmental limits, and investment decisions are becoming increasingly difficult to separate.

What Does This Mean For Your Organisation?

The rapid growth of on device AI is beginning to complicate long-standing assumptions about how and where AI infrastructure should be built. While large facilities remain essential for training advanced models and supporting global digital services, growing interest in on device AI and distributed computing is introducing new constraints on how much centralised capacity is truly needed.

For UK businesses, this has direct implications for how AI is deployed, governed, and paid for. As more AI capabilities move closer to the user, organisations may gain greater control over data handling, latency, and operating costs, while still relying on the cloud for scale, resilience, and compliance. This has direct implications for IT strategy, sustainability reporting, and long-term procurement decisions, particularly as energy prices, carbon targets, and regulatory scrutiny continue to tighten.

For policymakers, infrastructure planners, and local communities, the risk is not simply overbuilding data centres, but committing to energy-intensive infrastructure at a time when the underlying technology is still evolving. Srinivas’s warning does not predict the end of data centres, but it does highlight growing uncertainty around how AI infrastructure should be planned, regulated, and sustained as environmental limits and technological change increasingly intersect.

This video shows how you can create high quality photos and images easier than ever with the new toy from OpenAI, namely ChatGPT Image 1.5 … enjoy!

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

Rushed emails are one of the easiest ways to create unnecessary confusion, embarrassment, or rework, and using delayed or scheduled sending gives you a short buffer after pressing send to catch mistakes, rethink tone, or make sure your message lands at the right moment.

How to do it:

Outlook

Compose email > Options tab > Delay Delivery
Choose a delay of 1, 2, 5, 10, 15, 30, 60, or 120 minutes, or set a specific date and time > Close > Send

Gmail

Compose email > Down arrow next to Send > Schedule send
Select a suggested send time or set a custom date and time, as Gmail supports scheduled sending rather than short delay options > Send

Why it helps

That short delay or scheduled send acts as a safety net, giving you time to correct typos, rethink wording that could be misread, or avoid sending messages outside working hours, helping your emails land more clearly and professionally.

Please note

It isn’t currently possible to delay or schedule messages natively in WhatsApp or Facebook Messenger, so once you hit send, the message goes immediately with no built-in buffer to catch mistakes. Also, most other email platforms focus on scheduled sending rather than true send delays. Apple Mail, Yahoo Mail, Proton Mail, and Zoho Mail allow emails to be sent at a chosen future date and time.