Nearly half of UK professionals would rather leave their jobs than return to the office five days a week, according to new research from recruitment giant Hays.

Disconnect

The findings of the Hays survey appear to highlight a growing disconnect between employee preferences and employer policies on post-pandemic working models and, it seems, the potential consequences could be severe. With hybrid working now the norm for most office-based staff, businesses pushing for a full return risk not just resistance, but an outright exodus of talent.

Hybrid Working Still Dominates

Since the pandemic reshaped traditional work models almost overnight, hybrid working has emerged as the clear favourite for many professionals. Hays’ Spring 2025 Employment Trends survey, which canvassed over 8,000 workers and employers across the UK, found that a massive 77 per cent of professionals are now working in some form of hybrid setup. The most common arrangement, three days in the office and two days remote, is the one adopted by a quarter of all companies surveyed.

Although the survey highlights how one in five employers now allows staff to choose their own working pattern, this figure has dropped slightly from 26 per cent in the previous survey, suggesting a slight shift towards more structured expectations.

Very Few Companies Plan To Increase Office Attendance

The overall preference, however, is clear, and not just among employees. While a minority of companies are increasing office attendance, only 8 per cent of employers said they plan to enforce a full-time return within the next six months. Meanwhile, 66 per cent admitted they were concerned about potential backlash if they did.

Why Don’t Workers Want To Return To The Office?

According to the Hays survey, the key factor that’s fuelling this resistance to returning full-time is cost. Hays found that 73 per cent of professionals cited commuting expenses as a major factor in their reluctance to come back to the office more often. A full return would lead to higher costs for 88 per cent of workers, and the financial strain is particularly pronounced among women.

For example, 59 per cent of women surveyed said commuting would significantly impact their finances, compared with 41 per cent of men. This gender gap also plays out in the headline figure, i.e. while 48 per cent of all respondents said they would consider quitting over a full-time RTO mandate, that figure rose to 58 per cent for women, and fell to 42 per cent for men.

Pam Lindsay-Dunn, COO of Hays UK and Ireland, has warned that businesses could be on the brink of a talent crisis if they push too hard, saying, “Employers need to realise they are at serious risk of losing top talent if they make a full-time return-to-office compulsory,” and that, “Our research clearly shows how highly professionals still value the option to work from home.”

Other Factors

Although cost was cited as the main reason to resist returning full-time to the office in the Hays survey, other factors known to make a hybrid work model preferable include:

– Work-life balance. Many workers now organise their lives around flexible routines, from school pickups to avoiding rush-hour stress.

– Employee wellbeing. Studies have repeatedly linked flexible working to lower stress levels and improved mental health.

– Productivity. As highlighted by the Hays survey, a majority of employers (52 per cent) said there was no difference in productivity between home and office workers. Another 19 per cent said remote workers performed better, while only 13 per cent believed office-based staff were more productive.

‘Look At The Bigger Picture,’ Hays Urges Employers

Despite the push from some corporate giants to increase in-office time, the data suggests this approach is increasingly out of step with employee sentiment. Recent moves from companies like Amazon, PwC and Santander to tighten RTO rules have already triggered staff unrest, and Hays’ findings suggest the UK workforce may not respond kindly to similar measures elsewhere.

Hays’ Lindsay-Dunn has advised employers to weigh their options carefully, saying: “Before making any significant changes to their current working model, employers must look at the bigger picture,” and “That means factoring in commuting costs, wellbeing, productivity and the benefits that hybrid working brings to both the individual and the business.”

Indeed, previous research by the University of Pittsburgh warns that companies pushing full-time office returns risk triggering a “brain drain”, with high performers, particularly women, more likely to leave in favour of flexible employers.

Retention Risks and Recruitment Challenges

For UK businesses already grappling with talent shortages and recruitment pressures, the findings from the Hays Spring 2025 Employment Trends survey raise red flags. If nearly half the professional workforce is willing to walk over RTO demands, the potential for disruption is hard to ignore. Employers may, for example, face:

– Higher recruitment costs. Replacing experienced staff is expensive and time-consuming.

– Loss of institutional knowledge. Departing staff can take critical skills and insights with them.

– Brand damage. Perceptions of inflexibility may deter top candidates from applying.

At a time when retaining skilled staff is already a challenge, a misstep on working policy could prove costly.

For example, while many companies have increased expectations around office time, in reality, actual enforcement remains rare. This may reflect a broader recognition that rigid models could backfire, particularly when productivity data doesn’t strongly favour office-based work.

Not Everyone’s A Fan of Hybrid Working

While hybrid working is widely embraced, it’s not without its critics. For example, some business leaders argue that in-person collaboration fosters creativity, innovation, and company culture, particularly for newer staff or junior employees. Others raise concerns around remote management, communication gaps, and the long-term impact on team cohesion.

However, it should be noted here that evidence on these points is mixed. For example, research from the University of Melbourne found that companies with flexible working policies performed better on the stock market over the long term. This could mean that while some managers may feel more in control with staff on-site, this doesn’t always translate to better outcomes.

Also, full flexibility may not suit every worker. For example, some employees, especially those in shared accommodation or with limited space, may prefer office environments where they can focus. That’s why some employers still offer office-first policies with optional home working, depending on role, seniority or team dynamics.

That said, Hays’ latest findings suggest that forcing a full return across the board could do more harm than good.

What Does This Mean For Your Business?

What this research makes clear is that the conversation around work patterns is far from over. While some employers are moving to reassert control over when and where work happens, many employees remain firmly committed to the flexibility they’ve grown used to since the pandemic. Hybrid working has shifted from being a stopgap solution to a preferred way of life for a large part of the UK workforce, and attempts to reverse that may meet more resistance than some leaders expect.

For UK businesses, the Hays survey figures appear to indicate that poorly handled return-to-office policy may not just lead to internal dissatisfaction, but it could lead to the loss of valued staff, weakened morale, and a dent in employer reputation. In a competitive labour market, especially in knowledge-based sectors, retaining skilled professionals means understanding what motivates them, and right now, for many, that includes the autonomy and balance hybrid work affords.

However, it’s not only employers who have decisions to make. Employees, too, are weighing their priorities, e.g. cost of living pressures, childcare responsibilities, commuting time and mental wellbeing are all influencing what the “ideal” work setup looks like. While not every role or sector can offer the same level of flexibility, the evidence suggests that a one-size-fits-all approach won’t cut it.

The implications may also apply to other stakeholders as well, e.g. from HR teams and line managers tasked with navigating new expectations, to policymakers and transport planners grappling with changes in commuting patterns. Even commercial landlords and local high streets could feel the knock-on effects of long-term shifts in where and how people work.

Overall, the Hays findings could be seen as serving as a timely reminder that workplace culture is evolving, and any business that wants to keep its best people may need to evolve with it.

Microsoft has upgraded its Copilot assistant into a fully agentic AI tool that can browse the web, complete real-world tasks, and remember personal details — marking a major leap in the AI arms race.

Amazon Too

Meanwhile, Amazon has joined the push towards ‘agentic’ AI with a new shopping assistant that can browse third-party websites and make purchases on your behalf, a clear sign that AI is no longer just answering questions, but starting to take real action.

A New Era For Microsoft Copilot?

This latest overhaul appears to mark an evolution in what Microsoft calls its vision for a “true AI companion.” The trending AI news from Microsoft (on the company’s 50th birthday – yes 50!) is that Copilot now supports web-based actions, letting users issue commands that the bot then carries out independently, e.g. from booking holidays to buying gifts.

How?

Microsoft has partnered with popular platforms like Booking.com, OpenTable, Kayak, and Tripadvisor to allow Copilot to take care of these tasks without switching tabs. For example, if a user asks Copilot to “book a table for two at 7pm tonight,” it will attempt to arrange this behind the scenes.

It seems that it’s not just limited to actions. For example, the new Copilot can now do some fairly impressive, helpful, and time-saving things like:

– Track online deals and alert users to price drops.

– Analyse video in real time via the user’s phone camera to answer context-aware questions.

– View the desktop to help organise files, adjust settings, or assist with projects.

– Generate podcasts based on documents or topics users care about.

– Turn notes into structured documents using a new Pages tool.

This apparent shift towards autonomy moves Copilot closer to what Microsoft CEO of AI Mustafa Suleyman calls your ‘AI companion’. As Suleyman says: “Copilot helps you stay organised, think clearly, learn more intuitively,” and that “It’s there when you need a quick factual answer… or when you fancy just downloading after a hard day.”

Memory and Personalisation

Perhaps the most transformative (and potentially controversial) upgrade is Copilot’s memory. For example, Copilot can now learn a user’s preferences over time, e.g. favourite foods, the dog’s name, or a specific project at work. In Microsoft’s words, it remembers “not just what you said, but who you are.”

Although some may find this idea a little uncomfortable, Microsoft’s point is that this allows Copilot to offer more tailored suggestions, reminders, and proactive help. Essentially, Microsoft now wants Copilot to be a digital assistant that actually knows its user, rather than starting fresh with each interaction.

Understandably, privacy concerns are already surfacing, but Microsoft insists users retain full control. For example, users can choose what Copilot remembers, delete specific “memories,” or opt out altogether via a dedicated dashboard.

Vision, Deep Research, and More (AI That Sees and Thinks!)

The new Copilot Vision feature enables the AI to “see” what a user’s phone’s camera sees and respond accordingly. For example, a user can point their phone at a mystery plant and ask what it is, or let Copilot scan their home office and suggest layout improvements.

Meanwhile, the Deep Research function is designed for knowledge workers and business users. It pulls together information from websites, documents, and images to deliver detailed answers to complex questions, much like ChatGPT’s research capabilities or Google’s Gemini Deep Research.

Also, for anyone who’s ever spent too long scrolling, AI-generated podcasts now offer a hands-free way to absorb information. In what seems an amazing development, Copilot actually creates a back-and-forth dialogue between two synthetic hosts, based on a user’s chosen topic, with the user even able to interrupt them to ask follow-up questions.

For Business Users

While consumer features have grabbed the headlines, many of these updates actually have strong implications for business users too.

The new Pages tool lets Copilot organise a user’s scattered notes and research into a shareable, editable canvas, thereby echoing functionality found in tools like Notion or ChatGPT’s Canvas. Action-taking capabilities could save hours in admin, letting users delegate routine bookings or purchases with a single prompt.

For time-pressed professionals, Copilot can therefore become part PA, part researcher and part assistant, a proposition that could significantly enhance productivity.

Microsoft’s AI Gamble

Microsoft’s move couldn’t come at a more pivotal moment. The company has faced criticism for lagging behind OpenAI’s ChatGPT and Google’s Gemini, particularly in the consumer space. Copilot was often seen as useful but uninspiring. However, these new capabilities may be the way to place it firmly back in the race and in some areas, perhaps even a step ahead.

By integrating Copilot more deeply into Windows and expanding its personalisation, Microsoft is playing to its strengths, i.e. existing platform dominance, long-term user relationships, and enterprise credibility. It’s an ecosystem play, not just a standalone app.

However, it’s not without risk. For example, questions remain about how well Copilot can actually perform tasks on websites, or what happens when it makes a mistake, especially in business-critical settings. Microsoft hasn’t published detailed data about error rates or site compatibility and, like similar agents, Copilot can be blocked by websites concerned about revenue loss from users bypassing their apps.

Amazon’s New Shopping Agent

Not to be outdone, Amazon is testing a new agent called ‘Buy for Me’. It’s designed to go one step further, if Amazon doesn’t stock what a user’s looking for, it sends its AI to scour third-party sites, complete the purchase, and fill in your payment and shipping details. This all happens with users never having to leave the Amazon app. The AI visits the target site, selects the item, and checks out, all powered by Amazon’s Nova AI models and Anthropic’s Claude.

Possible Issues

Amazon insists this process is secure. For example, billing information is encrypted, and Amazon itself can’t see what a user is ordering from third-party sellers. However, trust is likely to be a sticking point, i.e. ensuring users feel comfortable with the idea that Amazon’s agent is able to buy the right things in the right quantity, and won’t spend too much of their money.

Also, it seems that returns and exchanges are handled via the original seller, not Amazon, so this hands-off approach could add friction if anything goes wrong.

The Rise of Agentic AI

Copilot’s updates and Amazon’s new shopping agent are part of the industry’s shift from “chatbots that talk” to “agents that do”, a trend many have dubbed agentic AI. Instead of merely suggesting actions, these tools can now perform them, often without direct user supervision.

OpenAI’s Operator, Google’s agents, and Perplexity’s prepaid-card shopping bot are all part of this wave. However, Microsoft’s scale and Amazon’s reach could see them dominate the next phase of adoption.

For users, especially professionals, this could mean offloading a growing list of digital tasks. On the downside it could also mean having to grapple with questions around accuracy, oversight, and trust. Although the app updates have arrived and AI agents can now make decisions for users, whether they should be able to and how much control users are prepared to give up are still likely to be pertinent questions.

What Does This Mean For Your Business?

By moving from reactive tools to proactive agents, Microsoft and Amazon are signalling a new era where AI doesn’t simply assist with tasks but actively takes them on. For everyday users, the promise is convenience and saved time. For businesses, particularly in the UK, it introduces new opportunities for productivity gains, automation, and customer service innovation, as well as fresh challenges around trust, oversight, and data governance.

For Microsoft, the evolution of Copilot into a more personalised and action-capable AI companion could help it stand out in a crowded market, especially among enterprise users already embedded in the Microsoft ecosystem. The ability for Copilot to act across websites, manage files, handle research, and learn user preferences may appeal to time-strapped teams juggling multiple workflows. If users can trust it and if it performs reliably, it could reduce the cognitive load and admin burden across roles, from project managers to marketing teams. However, it is whether questions around accuracy, transparency, and the limits of AI autonomy can be clearly answered that will affect adoption at scale.

Amazon’s move, while more commercially focused, appears to be pushing the envelope in a different way. Letting an AI complete purchases across third-party sites takes convenience to a new level, but also hands over a considerable amount of trust. For e-commerce operators, this could be a game-changer or a serious disruption, depending on whether their platforms are bypassed or embraced. For Amazon, it’s a chance to expand its dominance beyond its own catalogue, pulling even more of the retail experience under its umbrella.

For UK businesses, the wider implications here are worth considering. For example, AI agents could streamline internal operations, transform customer journeys, and create new service models entirely. However, they’ll also raise new questions about accountability, user control, and competitive fairness, especially as tech giants continue to roll out proprietary agents that act on users’ behalf. It seems, therefore, that the next phase of AI will not just be about what tools can say, but what they can actually do (on our behalf), and who’s actually responsible when they do it.

The UK government has unveiled sweeping new cyber legislation that could see organisations hit with fines of up to £100,000 (per day!) if they fail to respond to threats in time – a move that dramatically raises the stakes for IT providers, critical service operators, plus their supply chains.

Tough New Rules Aimed at Critical Infrastructure and the Tech Supply Chain

The draft Cyber Security and Resilience (CSR) Bill, formally outlined this week by technology secretary Peter Kyle, seems to be setting out a more aggressive approach to cyber regulation in response to what ministers describe as “unprecedented threats” to the UK’s digital and physical infrastructure.

Crucially, the bill expands the scope of current regulations and will bring managed service providers (MSPs), IT suppliers, and potentially datacentre operators into the same regulatory framework as public utilities and emergency services. This means that for the first time, commercial tech firms (up to 1,000 of them by current estimates) could be legally obliged to meet strict cybersecurity standards or face financial penalties.

“Economic growth is the cornerstone of our Plan for Change,” said Kyle, “And ensuring the security of the vital services which will deliver that growth is non-negotiable.”

Three Core Pillars – and a Sharp Set of Teeth!

The new bill is built on three pillars. First, widening the scope of the UK’s existing Network and Information Systems (NIS) regulations to include more types of organisations. Second, giving regulators stronger powers to enforce those rules and third, allowing government to rapidly update the rules in response to new and emerging cyber threats.

What’s new (and raising a few eyebrows) is the addition of discretionary government powers to issue binding cyber directives in real-time. For example, if an in-scope organisation receives a formal order to patch a vulnerability or improve cyber defences in response to an active threat and fails to comply, it could face daily fines of up to £100,000, or 10% of turnover, whichever is higher.

The message, therefore, appears to be that falling short isn’t just risky but could be ruinously expensive.

Why Supply Chain Security Is Now Front and Centre

The bill changes how cyber risk is perceived at the national level. For example, instead of focusing solely on headline-grabbing ransomware events or attacks on high-profile utilities, the government now appears to be turning its attention to the digital supply chain, i.e. the vast network of IT support firms, software providers, and cloud service operators that underpin the UK economy.

For example, the Cloud Hopper espionage campaign, which targeted MSPs to indirectly infiltrate governments and corporations, is a cautionary tale of how supply chain vulnerabilities can be weaponised at scale. Likewise, the recent breach of the Ministry of Defence’s payroll system showed how even indirect routes into sensitive data can have real-world consequences.

The UK’s National Cyber Security Centre (NCSC) is backing the approach, and as NCSC CEO Richard Horne says: “The Cyber Security and Resilience Bill is a landmark moment,” adding that “It will improve the cyber defences of the critical services on which we rely every day, such as water, power and healthcare.”

Datacentres and the Next Phase of CNI Regulation

The government is also strongly considering bringing datacentre operators into the bill’s remit, a step it hinted at last year when these facilities were designated as critical national infrastructure (CNI).

If passed, this could affect more than 180 UK-based datacentres and over 60 operators, according to industry figures. While exact compliance requirements haven’t yet been defined, it’s expected that these facilities will be subject to the same incident reporting rules and real-time intervention powers as other in-scope entities.

What’s more, ministers are exploring the use of AI tools to help detect and respond to threats inside these physical and virtual infrastructure hubs.

Mandatory Incident Reporting Tightens Timelines

Another key change is a tightening of mandatory reporting timelines. Organisations in scope of the CSR Bill will need to notify regulators and the NCSC of significant incidents within 24 hours – faster than the 72-hour window required by both the EU’s NIS2 directive and the US’s CIRCIA.

A full report must follow within 72 hours, creating a dual-stage reporting process that places UK organisations under one of the most stringent regulatory regimes in the world.

As technology secretary Peter Kyle says: “This is not just red tape,” but rather “It’s about making sure we know, quickly, when something serious is happening – and being able to act fast.”

Why This Isn’t a ‘One and Done’ Job

Legal experts and cyber risk consultants are warning that the scale of the challenge posed by the new rules is significant, i.e. not just in terms of cost, but also the time and effort required. For example, even well-resourced organisations could find the process of aligning legacy infrastructure with modern cyber resilience standards a long and complex task.

The key point that many are making is that cyber security is not something that can be addressed once and then forgotten. With threats constantly evolving, businesses will need to build ongoing investment and regular system upgrades into their operations. The burden, therefore, isn’t going to be just technical, but will also demand sustained leadership focus and cultural change across entire workforces. In other words, achieving compliance in this case is going to be a continuous journey.

Statutory Powers and Strategic Priorities

As well as giving regulators sharper enforcement tools, the bill proposes that the government publish a unified Statement of Strategic Priorities (updated every three to five years) to guide the approach of different regulators. This aims to bring consistency and clarity to enforcement across sectors, ensuring that energy, healthcare, and IT providers all face comparable expectations.

The government would also be granted the power to issue emergency directions to organisations where needed. This could prove vital in responding to fast-moving attacks, such as zero-day exploits or geopolitical cyber events.

Rising Threats, Rising Costs

The need for faster, tougher intervention isn’t theoretical. In 2023, attacks on UK utility firms surged by 586 per cent, according to reinsurance firm Chaucer. The NCSC dealt with 89 nationally significant incidents (up from 62 the previous year) including 12 so serious they required COBR (Cabinet Office Briefing Rooms) meetings.

Notably, one of the most damaging incidents of last year (i.e. the ransomware attack on NHS blood testing partner Synnovis) cost the NHS an estimated £32 million! Analysts have suggested that a well-coordinated attack on the energy grid in southeast England could cost the UK economy up to £49 billion!

In light of this, the CSR Bill is not just about compliance, but is also about protecting national prosperity.

What Does This Mean For Your Business?

The details of the Cyber Security and Resilience Bill seem to show that the intention is to move things from reactive firefighting to proactive, enforceable standards. For UK businesses, particularly those in the technology supply chain, the message is that cybersecurity isn’t simply optional, nor is it simply an IT issue. It is now a board-level priority with legal and financial consequences attached.

While some organisations, especially larger providers, may already have mature systems in place, many will find that aligning with the new expectations demands more than just a policy refresh. Compliance will mean revisiting internal processes, investing in tools and training, and developing the ability to respond quickly and transparently to incidents. Smaller IT firms, regional MSPs, and niche datacentre operators, who may not have considered themselves part of critical national infrastructure until now, are likely to face the steepest learning curve.

The government’s aim appears to be to ensure the resilience of the UK’s digital backbone, and it is using both carrot and stick to get there. On one hand, businesses are being offered access to NCSC resources and support frameworks like Cyber Essentials. On the other, they face heavy penalties if they fail to take action when directed. Regulators, too, will be expected to step up, with clearer powers and more tools to enforce consistent, effective oversight across all sectors.

For regulators, IT service providers, and businesses that rely on outsourced digital infrastructure, the implications are far-reaching. In the short term, there may be uncertainty over exactly how these rules will be applied and interpreted, especially as the list of in-scope organisations grows. But in the long term, the bill signals a new era in which resilience and responsiveness are the benchmark for doing business in a connected economy.

The stakes are high but, looking on the positive side, so is the opportunity to build a more secure, digitally confident UK. With attacks becoming more frequent, more sophisticated, and more costly, the government is hoping that strong, enforceable rules are the best way to safeguard both national infrastructure and future economic growth. For those now falling under the scope of this legislation, the clock has started ticking.

Experts are warning AI-generated receipts are now so convincing that they could be used to cheat company expense systems.

OpenAI’s latest image generator, part of its ChatGPT 4o model, allows users to create fake receipts in seconds, complete with logos, stains, and creases. Online examples show how easily these images can bypass expense software, raising concerns among security experts.

For example, AI researcher Raphael Chenol has reported demonstrating how altering dates and prices on realistic-looking receipts now takes just seconds, where once it required graphic design skills. He warned that without safeguards, companies could soon face a flood of fraudulent claims.

Other security commentators say the risk isn’t limited to employee expense claims. It seems there are growing concerns that criminals could impersonate staff, submit fake receipts, and trick finance teams into making payments, particularly when paired with email-based scams targeting company accounts.

Although OpenAI says its images contain metadata showing they’re AI-generated, this can be removed. The company has defended the tool’s flexibility, saying it can also be used for education and creative work.

To reduce the risk, experts recommend multi-step approval processes and secure digital verification methods. As deepfake receipts become harder to spot, companies will need to rely less on visual checks, and more on trusted, auditable systems.

In a move towards sustainable space colonisation, scientists at the University of Potsdam (Germany) have created working solar panels from molten Moon dust, hopefully paving the way for lunar bases powered by materials already found on the Moon.

Why Haul It Up There If You Don’t Have To?

Transporting anything into space is expensive. In fact, launching just one kilogram of material to the Moon currently costs around one million euros! So, when the idea of building a future lunar base comes up, energy will become one of the trickiest challenges. For example, how do you supply enough power to sustain human life, scientific activity, and possibly even construction, without blowing the budget on rocket fuel?

That’s the question a team led by Dr. Felix Lang at the University of Potsdam’s Institute of Physics and Astronomy set out to answer. Together with colleagues from the Technical University of Berlin, they’ve now demonstrated that it may be possible to manufacture solar panels directly on the Moon using its most abundant resource, i.e. lunar regolith (moon dust).

Turning Moon Dust into Moonglass

Lunar regolith, the loose, dusty material that covers the Moon’s surface, has long been seen as a nuisance for astronauts. However, it now seems that it could become one of the Moon’s most valuable resources. This dusty substance, composed mostly of silicon dioxide (SiO₂), aluminium oxide (Al₂O₃), and calcium oxide (CaO), can be melted into glass with the right heat source.

Using a lunar regolith simulant based on Apollo mission samples, the researchers melted this Moon dust into what they’re calling “moonglass”. Then, they layered it with an ultra-thin coating of perovskite, a crystalline material that’s light, flexible, and highly efficient at converting solar energy.

The result was a lightweight, radiation-resistant solar cell built, crucially, using minimal resources from Earth.

Scalable, Simple, and Surprisingly Resilient

What makes this discovery stand out isn’t just the ingenuity, it’s the practicality. For example, the solar cells require only a tiny amount of imported material. According to Dr. Lang, “These solar cells require ultrathin absorber layers of 500 to 800 nanometres only, allowing the fabrication of 400 square metre solar cells with just one kilogram of perovskite raw material brought from Earth.”

That’s a dramatic reduction in launch mass, potentially slashing it by 99 per cent compared to conventional space-based solar panels, which typically rely on heavy glass and other Earth-manufactured materials.

Even better, the production process doesn’t need any complex refining or purification. “The highlight of our study is that we can extract the glass we need for our solar cells directly from the lunar regolith without any processing,” Lang explained. “The process is also scalable so that the solar cells can be produced with little equipment and very little energy input.”

The team even tested the feasibility of melting regolith using a large curved mirror and concentrated sunlight in order to demonstrate that solar power itself could drive the production of the panels.

How Efficient Are They?

As may be expected, using moonglass instead of conventional transparent glass presents some limitations. For example, the material is milky and varies in colour and opacity depending on the regolith source, which affects how much sunlight can pass through.

Current prototypes have reached efficiencies of around 12 per cent, which is less than half of the 26 per cent typically achieved by standard perovskite cells. However, this is no small feat given the conditions. As Lang says: “In the beginning, it was unclear whether we could produce them in sufficient quality on impure regolith lunar glass.”

Refinements

That said, computer models suggest that (with refinements) these solar cells could eventually match the performance of their Earth-bound counterparts. Given their resilience to solar and cosmic radiation, which is critical in the Moon’s harsh, atmosphere-free environment, the trade-off in early efficiency may be worth it.

Building the Infrastructure for a Lunar Power Plant

Producing solar panels from local materials is just one piece of a much bigger puzzle. For any lunar energy project to succeed, a full infrastructure is needed, e.g. from regolith collection and glass production to solar panel assembly and maintenance.

Dr. Michael Duke from the Lunar and Planetary Institute points out that this kind of manufacturing will require significant technological development. “From excavating regolith to connecting individual cells into arrays, the engineering challenges are considerable,” he said. However, if these hurdles can be overcome, the benefits could extend far beyond the Moon.

An example of one future possibility is using the Moon as a launchpad to produce solar cells for satellites or space stations. Since launching from the Moon takes far less energy than from Earth, a Moon-based solar factory could support a wider network of off-planet energy production.

Critics Cautiously Optimistic

As great as the invention sounds, not everyone is convinced just yet. For example, although Nicholas Bennett from the University of Technology Sydney called the work “a first successful use of moonglass in a functioning solar cell,” he cautioned that the next big challenge lies in scaling production outside of the lab.

Other commentators have noted the advantages of using regolith but have also highlighted how questions remain about how feasible it would be to build large-scale systems on the Moon’s surface, especially with current robotics and autonomous technologies still developing.

That said, there appears to be a strong scientific interest in pushing the concept forward. The Potsdam team is now investigating whether the glass quality could be improved by using magnets to filter out iron content before melting the regolith.

A Future Built on Dust and Light?

As the world gears up for a new era of space exploration, with NASA’s Artemis missions aiming to return humans to the Moon and establish a sustainable presence, technologies like this could prove pivotal. Space agencies and private companies alike are increasingly looking at “in-situ resource utilisation” (ISRU) as the key to long-term success.

It’s also worth noting here that this isn’t the only innovation involving lunar dust. Other research teams are exploring how to 3D-print Moon bases from regolith, extract oxygen from it for breathing or fuel, and even melt lunar ice using space mirrors to create drinkable water.

As Dr. Lang puts it, the work is already sparking the next wave of ideas: “We are already thinking, ‘Can we make this work with Mars regolith?’” It seems that the age-old annoyance of Moon dust could become a cornerstone of humanity’s off-world future.

What Does This Mean For Your Organisation?

The idea of building solar panels from Moon dust seems to be both clever and a logical response to the logistical and environmental costs of hauling materials from Earth. By proving that regolith can be turned into usable glass without any complex processing, the Potsdam team appears to have taken a major step toward true in-situ resource use. If scaled successfully, this method could allow future lunar bases to generate their own electricity with minimal imports, reducing reliance on costly Earth launches and drastically cutting the carbon footprint of long-term missions.

For space agencies, that means a more sustainable model for exploration, and one that’s not only greener, but also more resilient. For scientists, it opens up the possibility of powering instruments, life support systems and habitats in some of the harshest conditions imaginable. Also, for commercial space firms, it offers a route to building infrastructure directly on the Moon using fewer resources and less energy.

UK businesses involved in clean energy, advanced materials, aerospace engineering or autonomous systems could have a key role to play here. As space programmes increasingly look for Earth-based partners to develop and supply off-world technologies, firms that specialise in thin-film photovoltaics, automated construction, or regolith processing could find themselves part of the next space economy. With the UK already supporting innovation in lunar missions through partnerships with ESA and its own Space Agency initiatives, the groundwork is being laid for real involvement in future Moon infrastructure.

However, before getting too carried away, it’s worth noting that there’s still plenty to prove. For example, efficiency gains are needed, large-scale production methods must be tested, and robotic assembly systems will need to be far more advanced than what exists today. Accepting that there are still some major challenges, it’s also worth acknowledging that as a proof of concept, this research challenges long-held assumptions about what’s possible in space. It essentially means that the Moon is no longer just a blank canvas but is becoming a resource in its own right.

As the line between space innovation and sustainable engineering continues to blur, it’s increasingly likely that the breakthroughs made for lunar survival will feed directly back into how we build, power and conserve resources here on Earth.

ChatGPT has recently made a dramatic difference to the images you can produce and this video provides five examples of how it can be used – impressive stuff!

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

Do you spend ages dragging emails into folders, forwarding the same replies, or flagging messages for follow-up? Outlook’s ‘Quick Steps’ can automate all of that, turning multi-click jobs into a single tap. Here’s how to use it:

Why it’s useful:

Quick Steps are great for saving time on repetitive actions. You can set up shortcuts to do things like:

– Move invoices into a specific folder and mark them as read.
– Forward a client message to your manager with a standard intro.
– Flag emails from important customers and categorise them in one go.
– Reply with a template and archive the thread immediately after.

How to set it up:

– Open Outlook and go to the Home tab.
– Look for the Quick Steps group in the ribbon, then click Create New.
– Give your step a name, choose the actions you want it to perform, and (optional) assign a shortcut key.
– Click Finish.

If you regularly deal with the same kinds of emails, Quick Steps can cut hours off your inbox admin over the course of a week. It’s one of those features that’s easy to miss, but once you start using it, you won’t go back.

ChatGPT’s image generation tools have just received a major upgrade, and while everyone is busy turning politicians into dreamy ‘Studio Ghibli’ characters, OpenAI’s (quiet) policy changes may prove to be the bigger story.

A New Visual Brain for ChatGPT

At the centre of this update is GPT-4o, OpenAI’s new omnimodal model. Unlike previous tools like DALL·E (which bolted image generation onto ChatGPT from the outside), GPT-4o builds it into the core of the chatbot. The result appears to be faster, sharper, and more context-aware image outputs.

This means ChatGPT can now:

– Generate photorealistic images directly from prompts.

– Follow detailed instructions more precisely (including rendering readable text in images).

– Edit existing pictures, including transforming or “inpainting” people and objects.

The upgrade is currently available for Pro users paying $200/month, with OpenAI promising access will “soon” be extended to Plus and free-tier users as well as API developers.

Users Experimenting

It’s been reported that users have been experimenting with the upgraded version, including uploading selfies and asking ChatGPT to transform them into Pixar-style avatars or place them in scenic landscapes. Others have reportedly been feeding the tool prompts like “Donald Trump in a Ghibli forest” (a dreamy, nature-filled setting inspired by Studio Ghibli films) or “South Park characters debating in Parliament” and got eerily convincing results.

Why Studio Ghibli Is Suddenly Everywhere

It seems it didn’t take long for social media to explode with whimsical, pastel-hued images that seemed plucked straight from ‘My Neighbour Totoro’ or ‘Spirited Away’, i.e. two of Studio Ghibli’s most iconic animated films. The reason is likely to be because GPT-4o’s new model was trained on a wide range of styles, including those reminiscent of iconic animation.

While OpenAI insists it avoids mimicking the work of any living artist (it actively blocks prompts that explicitly request such imitations) it seems clear to many that the model can now reproduce stylistic “vibes” with uncanny accuracy. This explains how the internet managed to flood X and Instagram with Ghibli-inspired memes within days.

However, this artistic mimicry has raised some eyebrows. A resurfaced 2016 video of Studio Ghibli co-founder Hayao Miyazaki calling AI-generated art “an insult to life itself” has been doing the rounds again, reigniting the debate around AI and artistic originality.

Soften the Rules, Sharpen the Debate

Perhaps the most quietly controversial part of this launch is what OpenAI removed. GPT-4o comes with a notably relaxed set of safeguards around image generation. While safety features still exist, especially for minors and violent or abusive content, the rules have actually changed quite significantly. For example, ChatGPT can now:

– Generate images of public figures like Elon Musk or Donald Trump.

– Depict racial features and body characteristics on request.

– Show hateful symbols (like swastikas) if done in educational or neutral contexts.

– Mimic the aesthetic of well-known studios (e.g. Pixar, Ghibli), though not named living artists.

Joanne Jang (OpenAI’s model behaviour expert) has explained the move as a shift from blanket refusals to more nuanced moderation, saying, “We’re focusing on preventing real-world harm,” and “Not just avoiding discomfort.”

For example, ChatGPT used to reject prompts like “make this person heavier” or “add Asian features,” assuming them to be inherently offensive. Now, such requests are allowed if they are presented in a neutral or user-specific context.

This reflects OpenAI’s broader philosophy that censorship by default may suppress creativity or unfairly judge user intent. As Jang wrote in a recent blog post, “Ships are safest in the harbour,” adding “but that’s not what ships — or models — are for.”

Safety Isn’t Gone, It’s Just Different

That’s not to say the floodgates are wide open. Despite the apparent loosening of rules, the new image generator still uses a layered safety stack that includes:

– Prompt blocking (for inappropriate text before image generation).

– Output blocking (for images that breach policy after they’re made).

– A sophisticated moderation system, including child safety classifiers.

– Refusal triggers for prompts involving living artists or sexualised content.

Also, unlike earlier tools, GPT-4o seems especially cautious around children. It won’t allow editing uploaded images of realistic children and applies stronger classifiers to detect potential misuse involving minors.

Performance metrics from OpenAI’s system card also show the updated safety stack performs better than previous versions, especially in areas like gender and racial diversity. It’s been reported that in one test, 4o image generation produced diverse outputs 100 per cent of the time for group prompts compared to 80–89 per cent for DALL·E 3.

The Benefits for Users

The new capabilities have clear commercial potential. Designers, marketers, developers and content creators can now produce custom visuals, mockups, product renders, and marketing illustrations with minimal friction. For example:

– A property developer could quickly visualise housing concepts in different styles.

– An education provider could create bespoke, text-rich diagrams for course materials.

– A social media agency could mock up viral meme formats in seconds.

With enhanced control over composition, text, and detail, plus the ability to edit and iterate on existing images, GPT-4o appears to be taking AI image generation a step closer to mainstream creative workflows. Also, with API access rolling out, this could also give rise to entirely new applications built on top of GPT-4o, from instant avatar builders to interior design preview tools.

Risks, Especially Around Trust and IP

Despite the excitement, this change is far from risk-free. For example, allowing depictions of public figures or sensitive racial and political symbols opens the door to misinformation, reputational damage, and potential misuse.

Even if OpenAI prohibits images that “praise extremist agendas,” critics worry that fringe users could find ways to skirt those limits or that mainstream users might be unaware of the implications of what they’re creating.

There’s also the ever-present issue of copyright. Training on “publicly available” data and corporate partnerships (e.g. with Shutterstock) may cover some ground but as Studio Ghibli-style memes go viral, the question of fair use resurfaces.

For businesses, this raises two key concerns:

1. Reputational risk. Could AI-generated visuals be misattributed, manipulated, or used maliciously?

2. Legal exposure. Could brand-generated content be seen as infringing on artistic or personal likeness rights?

As with previous AI developments, what’s technically possible may soon outpace what’s legally clear or culturally acceptable.

What It Means for the Wider AI Landscape

OpenAI’s move comes just weeks after Google faced backlash over Gemini’s historical inaccuracies and image bias, and amid growing political scrutiny. In the US, Republican lawmakers are probing tech firms over alleged censorship, a backdrop that likely informed OpenAI’s more libertarian-leaning policy update.

By relaxing its image generation rules now, OpenAI seems to be signalling that it trusts both its technology and its users enough to let go of some of the training wheels, and is (presumably) willing to weather the inevitable criticism if it means retaining or gaining ground against rising competitors like MetaAI.

What Does This Mean For Your Business?

OpenAI’s latest update appears to have placed ChatGPT on a new creative footing – one that blends impressive technical progress with a deliberately looser grip on content control. In doing so, the company looks like steering away from the more cautious posture that has defined much of the AI sector to date (with the notable exception of Grok). Whether that’s a bold move or a risky one depends very much on how the public, regulators, and commercial users respond in the months ahead.

For UK businesses in particular, the upgrade could mean the ability to generate high-quality, editable, and highly specific imagery using a chatbot could significantly reduce production times for everything from ad campaigns to training materials. The tools now on offer may make it far easier for SMEs and creative agencies to iterate visually without relying on third-party design services, a potential leveller in an increasingly competitive digital landscape. For marketing teams, the prospect of generating branded content, explainer graphics, or social media visuals with a single prompt is clearly appealing.

However, those same businesses will need to tread carefully. As copyright debates heat up and content provenance tools remain in their early stages, there’s a real risk that missteps (however unintentional) could carry legal or reputational consequences. The temptation to experiment with viral visual styles or public figures is likely to be strong, but so will the scrutiny. Companies looking to incorporate these tools into their workflows will likely need internal guidance, or even new policies, around AI-assisted visual content.

Meanwhile, for artists, regulators, and platform providers, the questions are only getting thornier. What counts as fair use in an age of style mimicry? Who decides whether a request is educational, offensive, or somewhere in between, and how do companies like OpenAI draw policy lines that are both ethically sound and commercially sustainable? The fact that ChatGPT now permits the creation of imagery that was off-limits just weeks ago, including depictions of politicians, sensitive racial traits, and even controversial symbols, now appears to reflect a broader change in how AI firms are interpreting their responsibilities.

In truth, it may be the AI market itself that forces the next evolution. With rivals like Google and Meta pursuing their own image-generation models and competing for developer mindshare, the pressure is on to deliver not just safety, but usability. OpenAI’s gamble appears to be that with the right blend of user freedom and behind-the-scenes safeguards, it can satisfy both the creative crowd and the cautious boardroom.

In this week’s tech insight, here’s a handy guide to the major (and now plentiful) generative AI models available, including what they do best, and how to access them.

Which One Is Right For You?

The AI boom is showing no signs of slowing. Whether it’s for writing, coding, design, research, or customer support, generative AI tools have become an everyday business asset, or at the very least, something worth exploring.

However, with so many models now flooding the market, it’s becoming harder to tell which ones genuinely deliver value and which are simply overhyped. Each promises cutting-edge performance, but the real differences often only become clear once you’ve tested them in practice.

With this in mind, here’s our plain-English guide to the standout generative AI models available to UK businesses in 2025. Here you can discover (if you haven’t already) what they’re particularly good at, where to find them, the pricing, and any recent updates (and controversies).

OpenAI: GPT-4o, GPT-4.5 ‘Orion’, Sora, Operator & More

The first place that many people were introduced to generative AI (and still the best-known), OpenAI offers multiple models for different use cases. These include:

GPT-4o (ChatGPT): The default for most business users. Handles writing, analysis, basic reasoning, and now image generation too. Available on chat.openai.com.

Free Tier: GPT-3.5 only.

ChatGPT Plus: £20/month for GPT-4o and image tools.

GPT-4.5 ‘Orion’: A more advanced version with stronger ‘world knowledge’. Currently only available with OpenAI’s £160/month Pro subscription.

Sora: A new text-to-video model capable of creating entire scenes from prompts. Still experimental and only available on paid plans.

Operator: An AI ‘agent’ designed to take actions on your behalf, like ordering stock or booking meetings. Available on Pro (£160/month), but early testers report unpredictable behaviour.

Deep Research: Designed for serious research with citations. Also Pro-only. Hallucinations are still an issue.

o3-mini & 4o-mini: Cheaper, faster reasoning models optimised for maths and code. Available for free or low cost on ChatGPT.

Pros: Mature, fast, widely integrated. Huge plugin and extension ecosystem.

Cons: Some of the most powerful tools are behind expensive paywalls. Occasional hallucinations and inconsistencies.

Google: Gemini 2.5, Deep Research & AI Premium Tools

Google’s AI suite, now rebranded under the Gemini umbrella, focuses mainly on knowledge tasks, coding, and long-context reasoning.

Gemini 2.5 Pro Experimental: Excels at code generation and reasoning. Slightly underperforms Claude 3.7 on some benchmarks.

Gemini Deep Research: Summarises large volumes of search data with citations.

Both require a Google One AI Premium subscription (£19.99/month), which also grants access to Gemini in Docs, Gmail, and other Google apps. See gemini.google.com.

Pros: Integrated across Google’s ecosystem. Long 2 million-token context.

Cons: Still looks like it’s catching up to OpenAI on certain creative benchmarks. Perhaps not as strong at natural conversation.

Anthropic: Claude 3.7 and Computer Use

Anthropic’s Claude models have quietly become the insider’s favourite. Models here include:

Claude Sonnet 3.7: A hybrid reasoning model. Can produce fast responses or take longer to ‘think’, depending on the task. Available free at claude.ai or via API.

Pro Plan: $20/month (about £17) gives faster access and priority use.

Computer Use: A more experimental agent designed to operate your machine. Still in beta. Billed by token usage.

Pros: Strong at coding, clear writing, and safe outputs. More control over behaviour.

Cons: Doesn’t generate images. Agents still under development.

xAI: Grok 3 and the Acquisition of X

Elon Musk’s xAI is pitching itself as the politically neutral, open challenger to OpenAI. However, it also appears to be playing a longer game. Models include:

Grok 3: Strong on maths, science and factual knowledge. Integrated into Musk’s X platform (formerly Twitter). Requires an X Premium+ subscription at $50/month (about £39).

Aurora: xAI’s image generator, capable of photorealistic visuals.

Also, in recent news (just this month) Musk’s xAI acquired his X (Twitter) platform in an all-stock deal. Musk claims it’s about combining data, distribution and compute. However, some commentators have suggested that the real goal may be access to X’s vast post database to supercharge AI training. With X’s 600 million users, xAI now has both data and a delivery vehicle.

It seems that according to some other commentators, there may be a financial angle to the deal related to possible troubles at Tesla. For example, with Tesla facing increased scrutiny over mounting debt and loan repayments, and with sales apparently affected by a backlash over Musk’s involvement with President Trump’s administration (and DOGE), Musk’s empire may need fresh capital and investor confidence. Folding X into xAI, now valued at $80 billion, may allow for more aggressive fundraising and help shield the broader group from Tesla’s recent turbulence. It may, therefore, be as much of a strategic hedge as a technical merger.

Pros: Fast-growing, quite good at logic tasks. Trained on unique datasets.

Cons: Limited availability outside X. Some controversy around political alignment and data use.

Meta: Llama 3.3 70B

Meta’s Llama series is aimed at developers and businesses looking for open source AI models.

Llama 3.3 (70B): Free and open source. Great for running on your own servers or fine-tuning in-house. Ideal for companies concerned with privacy.

Access it via Meta’s GitHub or model hubs like Hugging Face.

Pros: Free, transparent, customisable.

Cons: Needs technical setup. No hosted version available by Meta.

Cohere: Aya Vision & Command R+

Canadian firm Cohere focuses on language models optimised for enterprise and multilingual use.

Aya Vision: Multimodal. Great for image captioning and image Q&A, especially in non-English languages. Available for free via WhatsApp.

Command R+: Excels at RAG (retrieval-augmented generation) — good for firms needing AI to cite reliable sources. More info at cohere.com.

Pros: Multilingual strength. Strong on RAG.

Cons: Not as widely used yet. Hallucination issues persist in complex queries.

Stability AI: Stable Virtual Camera

Stability AI, known for Stable Diffusion, has pushed into 3D image generation.

Stable Virtual Camera: Turns 2D images into simulated 3D scenes and angles. Available on Hugging Face.

Pros: Innovative visuals. Good for creative use cases.

Cons: May struggle with complex or moving subjects. Research-use only for now.

Other Contenders Worth Watching

DeepSeek R1 (China): Impressive code and maths abilities, but data privacy risks due to Chinese government links.

Mistral Le Chat (France): Fast response AI with journalism tie-ins. Solid but prone to errors.

Alibaba Qwen: High benchmark scores in coding, but trust and censorship remain concerns.

Again, these models can be accessed via huggingface.co or the developers’ own sites.

What Does This Mean For Your Business?

The sheer volume of AI models / generative AI platforms now available, each claiming unique strengths, can make decision-making difficult. However, the good news is that these tools are maturing fast, with clearer use cases, pricing tiers, and performance benchmarks emerging.

Not surprisingly, OpenAI remains a dominant force, especially for content generation and general-purpose reasoning. However, it’s no longer the only serious player. Google’s Gemini, Anthropic’s Claude, and xAI’s Grok all offer increasingly credible alternatives, some with more transparency, others with deeper integration or specialisation in logic-heavy tasks. Open-source options like Meta’s Llama or Cohere’s RAG-optimised models give businesses more flexibility, particularly where privacy, cost, or fine-tuning are concerns.

The broader AI arms race is also having knock-on effects across sectors. For example, Musk’s consolidation of X and xAI could indicate a push towards tighter control of data, distribution, and development. While that may bring faster innovation, it also raises questions about data ownership, platform dependency, and regulatory oversight, all of which UK stakeholders, from policymakers to investors, will need to monitor closely.

The message for businesses, therefore, seems to be don’t get distracted by the hype. Instead, focus on what you actually need AI to do. Whether that’s speeding up internal workflows, improving customer service, or enhancing research and development, the right model is out there, but it may take some experimenting to find it. With the pace of change showing no sign of slowing, those who take the time to understand the landscape now will be better positioned to benefit from it in the months ahead.

Google is rolling out a suite of new features (many powered by generative AI) across its core platforms to help users plan their summer holidays with greater ease and personalisation.

Tools For Inspiration and Planning

It appears that the update spanning Search, Maps, Lens, and Gemini, is designed to keep Google front and centre as more travellers begin to explore AI tools like ChatGPT for trip inspiration and planning.

With updates that offer real-time itinerary suggestions, hotel price tracking, custom trip planners and even the ability to turn screenshots into mapped-out adventures, Google is doubling down on AI’s potential to redefine how we prepare for getaways.

Smarter Search With AI Overviews for Trip Planning

At the heart of the rollout is the expansion of AI Overviews in Google Search, which is now capable of generating detailed travel itineraries based on simple queries.

For example, a search like “create an itinerary for Costa Rica with a focus on nature” will generate a multi-day schedule, featuring activity suggestions, restaurant recommendations, and key places to visit. Users will also be able to see user-contributed photos and reviews, all displayed on an expandable map.

These AI Overviews, which are powered by a customised Gemini model, are currently only available to all U.S.-based users in English, on both mobile and desktop (with no need to sign up for Search Labs). Once an itinerary is generated, users can export it to Docs, Gmail, or save it as a custom list in Google Maps for easy access on the go.

Increased User Engagement

This feature builds on Google’s AI Search experiments in Search Labs, which the company says have already generated billions of AI Overviews. According to Google, these overviews have increased user engagement with Search and boosted traffic to a wider range of websites. As Google says on its blog: “People like that they can get both a quick overview of a topic and links to learn more,” and that “We’ve found that with AI Overviews, people use Search more, and are more satisfied with their results.”

Hotel Price Tracking Goes Global

Another standout update from Google is the launch of hotel price tracking, which appears to be a logical expansion of the popular Google Flights alerts.

For example, users browsing google.com/hotels can now toggle on a price tracking option for specific dates and destinations. Once activated, Google will send an email alert if hotel prices drop significantly, based on your chosen filters (such as star rating, amenities or beach proximity).

The feature is now live globally across mobile and desktop browsers, thereby offering a new way for those planning holidays and getaways to (hopefully) save money during the booking process.

Maps Gets Smarter with Screenshot Integration

Google Maps is also getting a clever new upgrade. A common bugbear by travellers using Google Maps up until now has been the difficulty in managing the dozens of screenshots we take while researching holidays, from must-visit restaurants to hotel options and quirky local attractions. However, it seems that this new update means Maps can turn that visual clutter into something that may be genuinely useful.

For example, using Gemini’s image recognition capabilities, Google Maps will identify landmarks, businesses and attractions in users’ screenshots (provided the user grants the app access to their photos). From there, users can review and save these locations to a travel list, which will appear as pins on the user’s map. This feature is rolling out this week on iOS in English in the U.S., with Android support “coming soon,” according to Google.

The Gemini Gems Personal Trip Planner Is Now Free for All

Gemini, Google’s AI assistant, is being positioned as a travel buddy in its own right. A new feature called Gems allows users to create personalised AI agents, or “Gems”, that can help with specific tasks, such as trip planning, itinerary curation, or packing lists.

For example, a user could create a Gem that specialises in budget-friendly European trips, or one that only recommends dog-friendly hotels. Users can set up these Gems via the “Gems manager” on desktop, and they’re now available free of charge.

Also, if users aren’t sure where to start, Gemini can help in drafting the setup using a “magic wand” tool that expands on a basic idea. As Google says about the feature on its blog: “Now you have a travel guide at your fingertips to help you pick a destination, find restaurants in a new city or even suggest what to pack”.

Lens As A Pocket Tour Guide

Also in the new mix is an update to Google Lens, the company’s AI-powered visual search tool. While it’s long been able to translate signs and menus, Lens now supports AI Overviews too.

For example, if users point their phone’s camera at a building, landmark or mysterious object and ask questions like “what is this?”, the tool will deliver AI-generated insights, along with links to relevant online resources.

This may be particularly useful for cultural tourism, e.g. spotting unusual architectural features in city streets or identifying historical plaques while wandering through old European towns.

Lens’ new AI Overviews are already available for English users and will soon expand to languages including Japanese, Korean, Portuguese, and Spanish, in most countries where AI Overviews are active.

Why Now? Google’s Bid to Stay Ahead

It seems it’s no coincidence that Google is launching these travel-focused AI features just ahead of the summer rush, and at a time when travellers are increasingly turning to generative AI platforms like ChatGPT for itinerary building, travel hacks and destination research.

By integrating Gemini deeply into Search, Maps and Lens, Google appears to be aiming to maintain its dominance as the go-to tool for everyday planning. There’s also likely to be a broader strategic play where, in a world where large language models can provide well-rounded answers to complex queries, Google needs to prove it can do more than just return links.

Google’s generative AI push, particularly through the custom Gemini model embedded in Search, may therefore be an attempt to leapfrog competitors by offering deeper context, personalisation and functionality, all while keeping users within Google’s ecosystem.

What It Means for Users and Google’s Competitors

From the user’s perspective, the appeal is clear, i.e. less time spent bouncing between tabs and apps, and more coherent, streamlined planning experiences. For travellers in particular, it removes much of the friction that can turn holiday prep into a chore.

However, the rollout is heavily U.S.-centric for now, with many features restricted to English queries and limited platforms. Google has promised broader global access “soon,” but it remains to be seen how quickly that happens, and how seamlessly these tools will translate to other markets and languages.

For competitors like OpenAI, Expedia and other travel-focused apps, Google’s integration of AI into Search and Maps could be seen as setting a new bar. In other words, the battle is no longer just about who can answer your questions, but who can help you do something with those answers, from booking flights to building shareable, actionable itineraries.

What Does This Mean For Your Business?

By weaving AI more tightly into the platforms people already us (i.e. Search, Maps, Lens and Gemini), Google is effectively trying to become not just the place you go to find information, but the place you go to do something with it. For travellers, the convenience is likely to be attractive but for Google, the strategic value runs deeper. This isn’t just about helping you book a hotel or plan a day out but is more about keeping you inside Google’s ecosystem from inspiration to itinerary, thereby strengthening its role as your go-to AI assistant.

That said, the rollout raises some questions around accessibility and reach. For example, at present, many of the most powerful new features are only available in the U.S. (and just in English), meaning UK users will need to wait a little longer to take full advantage. There’s also the wider concern of how this affects travel publishers, bloggers and third-party platforms that have long relied on Google Search traffic. While Google insists AI Overviews are increasing clicks to a broader range of sites, the company’s tighter grip on the journey from question to action inevitably puts pressure on other players in the space.

For UK businesses, particularly those in travel, hospitality and tourism, the implications are twofold. On the one hand, it opens up new opportunities to reach audiences earlier in their decision-making journey, especially if their content is well-optimised and engaging enough to surface within AI Overviews. However, on the other, it places more power in Google’s hands, with brands having to work harder to stand out in a results page that may be increasingly curated by AI rather than traditional search rankings.

It seems, therefore, that Google’s summer AI upgrades represent more than a seasonal refresh, i.e. they’re more of a sign of the company’s wider ambition to redefine search, streamline planning, and keep pace with the generative AI boom. For users, the trade-off is between ease and control. For businesses, it’s a question of visibility and adaptability. Either way, the travel planning landscape just got a lot more intelligent, and a lot more competitive.