Artificial intelligence

SIR Elton John has signed a multi-million pound deal for an un- limited residency in Las Vegas — as a hologram.

The pop superstar, 79, will be immortalised using cutting-edge tech so fans can enjoy his live performances for decades more.

Elton, whose sight is failing, retired as a touring artist in 2023, but is still set to appear at special one-off gigs.

Dua Lipa, 30, who had No1 song Cold Heart with Elton in 2021, will also appear as part of the residency.

So will Kiki Dee, 79, who topped the charts with Elton with Don’t Go Breaking My Heart in 1976.

The immersive experience is set for the new Hard Rock Hotel, opening next summer.

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Elton is booked to film his performances with Dua and Kiki at Pinewood Studios, Bucks, this autumn.

A source added: “Elton, Dua and Kiki will be holograms. It’s similar to the Abba Voyage show in London, but far more advanced as the technology has come on so much.

“Elton signed a seven-figure deal with Hard Rock. It’s a shift away from a traditional residency and is billed as a fully immersive experience.

“It’s going to look phenomenal.”

In May 2026, just hours before President Donald Trump met President Xi Jinping, OpenAI’s Vice President of Global Affairs Chris Lehane floated the idea of a US-led global governance body for artificial intelligence that would include China as a member. The model, according to media reports, was compared to the International Atomic Energy Agency (IAEA), a familiar reference for managing strategic technologies with global consequences.

One month later, at the G7 summit in Évian-les-Bains, a different tone emerged. Several influential AI executives joined leaders from advanced economies to discuss AI governance, online safety, and global security. According to Axios, Anthropic’s Dario Amodei and Google DeepMind’s Demis Hassabis leaned towards a more selective framework among democratic countries, while OpenAI’s Sam Altman used broader language, calling for an international forum to develop shared testing standards and risk assessments.

These two moments reveal something important: the meaning of “global AI governance” remains unsettled. In one setting, global means including China for legitimacy. In another, it can mean a trusted coalition designed to manage access, capability, and strategic risk. AI governance is becoming part of the architecture of global power.

Three Voices, Different Emphases

Their presence at the G7 showed how quickly AI firms have moved from building systems to helping shape the politics around them. The leaders of OpenAI, Anthropic, Google DeepMind, Mistral, Cohere, and other firms were not simply observers of geopolitics. They were part of the conversation about how technological power should be governed.

Their positions were not identical. Amodei reportedly urged democratic countries to coordinate more closely so that AI governance would not fragment. Hassabis stressed the strategic importance of frontier capability. Altman, by contrast, used more institutionally neutral language, suggesting that advanced AI should not be shaped only by the companies building the most capable systems.

Even among frontier AI developers, there is no settled imagination of global governance. Should it include all major AI powers, including strategic rivals? Should it be built around trusted coalitions? Should it prioritize safety, democratic values, geopolitical advantage, or public legitimacy?

The question became more complicated because the G7 discussions came shortly after the US government imposed export controls that forced Anthropic to suspend foreign access to its Fable 5 and Mythos 5 models. Reuters reported that the order required Anthropic to block access to the models for foreign nationals, leading the company to disable them more broadly to ensure compliance. The episode showed how frontier AI governance can move from abstract principles to abrupt restrictions. Even among democratic allies, technological solidarity has limits. When AI becomes strategic infrastructure, every country begins to think about its own room for maneuver.

The Asymmetry of “Global”

The deeper issue lies in who has the power to define the word “global” in the first place. In May, global governance could mean a US-led institution that includes China. In June, it could mean coordination among democracies to manage frontier capability and strategic access. The definition changed because the political room changed.

This reveals a double asymmetry. The first is technical: only a small number of firms can define what counts as a frontier model, how its capabilities should be tested, and who should be allowed to access it. The second is narrative: the same ecosystem also helps frame the language through which the world discusses governance.

For countries outside the frontier AI circle, they may be invited to conversations but not always to the stage where categories, thresholds, and governance priorities are first shaped. They may be asked to adopt best practices whose assumptions were formed elsewhere. They may be told that risks are global, even when preparedness remains highly unequal.

G7 outreach to partner countries such as India, Brazil, Kenya, South Korea, and Egypt is important. It recognizes that AI governance cannot remain a conversation among advanced economies alone. Yet there remains a difference between being present in a forum and helping design the architecture of the forum itself. The question is who defines the table, the agenda, the risk categories, and the meaning of global governance itself.

When the AI Frontier Moves Towards the Market

There is another reason why a broader governance imagination is necessary. Frontier AI innovation is no longer centered primarily in universities or public research institutions. It is increasingly shaped by private firms with the capital, compute, talent, data access, and infrastructure required to train and deploy the most capable models.

Stanford’s AI Index 2025 noted that nearly 90 per cent of notable AI models in 2024 came from industry, up from 60 per cent in 2023. A report prepared for the European Economic and Social Committee on generative AI and foundation models also described significant US dominance across the value chain. These findings point to a structural shift: the frontier is becoming more concentrated, more expensive, and more closely tied to corporate and geopolitical capacity.

Much of AI’s progress has come from companies willing to take risks, scale products, and build technical capability at extraordinary speed. But the center of gravity has shifted. When frontier AI is largely financed, defined, and deployed by market actors, the default imagination of AI development can tilt towards commercial viability, platform advantage, user growth, and strategic positioning.

Public interest does not disappear in such a system. It risks becoming secondary unless other actors are strong enough to bring it back into the room.

Open Future, a European digital policy organization, has warned that concentrations of power in AI can make public activities dependent on “a narrow group of monopolists.” The phrase matters because infrastructure-level dependency can weaken society’s ability to negotiate the terms of the technologies it relies on.

A Wider Public-Interest Layer

In a multiplex digital world, power does not flow only through states or markets. It also moves through universities, civil society organizations, professional associations, media, labor groups, open-source communities, public-interest technologists, and moral institutions. Together, these actors form the society layer often missing from discussions dominated by states and markets.

States define security priorities. Companies define technical possibility. Society must help test legitimacy. Who bears the risk? Who benefits from deployment? Who is excluded from design? What harms are being normalized because they are commercially convenient or geopolitically useful?

This is why Pope Leo XIV’s recent intervention on AI is politically relevant beyond its religious context. In his encyclical Magnifica Humanitas, he argues that protecting the human person in the age of AI requires renewed reflection on the common good, solidarity, social justice, and human dignity. Such interventions will not replace regulation or technical standards. They help recover a truth easily lost in frontier AI politics: governance is also about preserving the human meaning of technological progress.

The same question of authorship is beginning to appear in empirical research. Ongoing fieldwork-based research at the University of Oxford has started to examine whether countries in the Global South are developing approaches to AI governance that are neither simple copies of Western regulatory templates nor rejections of international cooperation but pragmatic syntheses shaped by local institutional capacity, regulatory sequencing, and historical experience with technology transfer. Indonesia has appeared as one of the country cases in this line of inquiry.

Governance models worth studying are not only those negotiated in Évian, Brussels, Washington, or New York. They are also being improvised, often informally, by mid-sized digital economies navigating dependency and ambition at the same time.

The United Nations’ Global Digital Compact (GDC), adopted in September 2024, offers a useful multilateral reference point. It frames digital cooperation and AI governance around inclusion, human rights, open standards, interoperability, digital public goods, and multi-stakeholder cooperation. The Compact does not resolve the power asymmetries of frontier AI by itself, but it gives societies, alongside states and firms, a language for claiming a legitimate role in digital governance.

The practical task is to strengthen public-interest evaluation: the ability to test social impact, language bias, local risks, institutional misuse, and deployment consequences in different societies. The aim is to preserve enough room for public reasoning so that the future of AI is not defined only by those with the largest models, the biggest markets, or the strongest strategic leverage.

Imagining a More Inclusive AI Governance

The lesson from the IAEA analogy and the G7 discussions is not that one model is right and the other is wrong. Both reflect real concerns. A broadly inclusive governance arrangement may be necessary for legitimacy, especially when AI risks cross borders. A trusted coalition may also be necessary when capability access raises genuine security concerns. The problem begins when either model claims to be global while leaving too many societies downstream of decisions made elsewhere.

For emerging economies, the strategic challenge is not simply to wait for a better invitation to the next summit. Participation matters, but it is not enough. Countries and societies need stronger capacity to evaluate AI systems, understand their dependencies, articulate local risks, and negotiate governance terms with greater confidence.

This is a call for a more plural architecture of governance, where states, markets, and society all have meaningful roles. The uncomfortable question is not whether AI requires international coordination. It clearly does. The harder question is whether that coordination can remain open enough for societies, not only states and companies, to shape the terms of technological power.

In the age of frontier AI, the future will not be determined only by who builds the largest models. It will also be shaped by who gets to define risk, test systems, question assumptions, and decide what counts as progress.

Every era that has tried to govern a transformative technology eventually learns the same lesson: legitimacy borrowed from power is not the same as legitimacy earned through participation. The IAEA’s own history shows that global trust is rarely built at the moment institutions are created; it is earned over time, through broader representation, credible restraint, and shared accountability. The real question for AI governance is whether it can shorten that distance by design, rather than waiting for legitimacy to arrive only after contestation.

Artificial intelligence is increasingly being used in the military for planning and operations as a decision support tool at multiple stages. The US’s use of Anthropic’s Claude model against Iran marks a significant moment in the history of warfare. Integrated via Palantir’s Maven Smart System, AI-supported intelligence analysis, target identification, and operational simulations enabled planners to process information faster than human capabilities. While analysts have framed this as an “AI war,” the more significant shift lies in the growing influence of algorithmic systems in shaping military decision-making architectures.

Admiral Brad Cooper, who led Operation Epic Fury, said that AI systems processed massive amounts of intelligence and surveillance data, allowing commanders to gain insights within seconds. This is part of a wider movement to shift more complex intelligence tasks to algorithmic systems, raising questions about transparency, oversight, and reliance on algorithmic assessments.

This is also observed in other conflict zones, but in different operational roles. In Gaza, Israel’s Lavender system, developed by Unit 8200, assisted in the targeting of 37,000 suspected individuals, based on reported affiliations, using AI. Structural strikes and real-time tracking were made possible through the use of additional tools like “The Gospel” and “Where’s Daddy?” These systems reduced human review into quick, seconds-long “stamp of approval” decisions, moving targeting to machine-driven validation. In Ukraine, AI tools were used to assist in drone operations and battlefield analysis by training datasets. Initial programs, like Project Maven, relied on manually labeling 150,000 images. Currently, the Brave1 has enabled over 100 defense-tech firms to train combat AI on millions of annotated images from ongoing missions to improve these AI models.

The modern battlefield produces unprecedented volumes of data from interwoven sensor networks, drones, satellite imagery, and localized communications streams. This information comes at high speed and volume, which can overload the human brain. AI is being used to deal with this information overload, but there are concerns about the accuracy of AI-driven assessments and how much human oversight might be required to rely on AI. Military officials emphasize that humans have the final authority, but systematic integration poses challenges to oversight quality. The other predicament is automation bias, a psychological phenomenon in which a human operator, particularly under pressure or high stress, is likely to rely on the system’s recommendations. Therefore, striking a balance between speed and responsibility, ethical judgment, and accountability in the use of force is a key challenge.

Another area of concern pertains to legal and ethical issues. International humanitarian law is based on the principles of distinction, proportionality, and precaution. With the growing use of AI in military operations, it becomes more difficult to apply these principles, thereby making accountability and scrutiny more difficult. The International Committee of the Red Cross has warned that, when algorithmic systems provide input for analysis, targeting, or operational planning, it is hard to assign responsibility for any errors. Even with humans “in the loop,” the black-box nature of machine learning limits transparency and complicates legal review. It is not just a theoretical problem; it has been seen in practice. In the early US campaign against Iran, an AI-assisted missile struck a girls’ school near an IRGC compound, killing 120 children, likely due to a classification error. Anthropic’s CEO’s admission of limited awareness over Claude’s use in the strike highlights a broader issue. AI developers are fully aware of the risks associated with delegating autonomous functions to AI, yet they continue to promote its adoption. As AI assumes greater decision-making roles, concerns over misidentification and the possibility of AI acting against human directives are often overshadowed by narratives emphasizing its benefits.

For Pakistan, these developments are neither distant nor theoretical. In a region where crises can escalate quickly, AI-enabled decision support offers advantages but also carries risks. It improves situational awareness and accelerates analysis but compresses decision time, limits verification, and heightens the risk of miscalculation. Considering both, Pakistan is accelerating efforts to build AI capacity and strengthen its supporting infrastructure. At the policy level, this translates to a recognition that successful adoption is not just about adopting algorithms but about enhancing data governance, institutional maturity, and a skilled workforce capable of embedding AI into decision-making processes. Thus, Pakistan’s approach remains focused on leveraging AI to bolster human judgment in intelligence fusion, surveillance, logistics, and cyber defense.

There is a clear lesson from the academic literature and initial operational experience: algorithmic systems are transforming military information processing. However, as their role in decision-making grows, they also entail bias, error propagation, lack of transparency, and overreliance on machine-generated recommendations. AI, therefore, must be used as a support system, with humans retaining final decision-making responsibility. This requires investment in training, auditability, and institutional safeguards to ensure that human decision-makers are meaningfully engaged, rather than merely present in form. The future of warfare will likely be defined not by machines acting alone, but by humans making increasingly time-pressured decisions shaped by machine-generated insights. The central strategic challenge is not whether to adopt algorithmic tools, but how to ensure that their speed never outpaces sound judgment.

AFTER years of writing about politics, technology and the chaos of the modern world, Matt Bellamy wanted something different for Muse’s tenth album.

“The theme was to get back into mystery a little bit,” he says. “The mysteries of the universe, mysteries of spirituality and returning to the rawness of the unknown.”

Inspired by the 1977 Wow! Signal — an unexplained radio signal from space once seen as possible evidence of alien intelligence — and a turbulent period in his personal life, the record finds Bellamy searching for meaning on both a cosmic and personal level.

“I’ve turned completely apolitical,” he admits. “It’s weird when you go through things in your personal life — the news just becomes an annoying noise.

“When your life’s going great, you get drawn into the news and what’s going on in the world.

“But when you’re actually going through something yourself, the news and politics just become a headache.

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“I’m a little bit gloriously out of touch. I’ve normally been so in touch, my finger’s always been on the pulse, and a lot of the albums I’ve made talk about the rise of populism. But this album and my life for the last year-and-a-half has been different.”

Bellamy has split from US model Elle Evans, his wife of six years, who he quietly separated from in October 2025.

“I’ve been through a separation involving two young kids,” he says carefully.

“I can’t really talk about the reasons behind it, but it was not your normal run-of-the-mill situation. I became a full-time single parent for a period of eight months.

“She’s doing a lot better now and she’s getting better, but it was an unusual situation to go through. It made writing the album so much easier.

“It’s hard to talk about what’s behind the album because I don’t throw people under the bus. And I don’t want my kids to grow up reading stuff.”

Bellamy, 48, is in London for band rehearsals and when we meet, he’s just back from the gym in a bid to shape up for the tour.

“I’m not that old,” he laughs. “But I met Mick Jagger at a party and I went straight in on the fitness. I was, like, ‘What is your secret?!’ He said when he was in his 30s, he started working out a few weeks before a tour.

“By the time he got to his 40s, he was working out for the same length as the tour.

“If it was a three-month tour, he’d work out for three months before. And by the time he got to his 50s, he was just working out all the time, all year round.”

The 1977 Wow! Signal fascinates Bellamy because it remains unexplained and happened around the time the band members were born.

“The Wow! Signal is probably, to this day, still the most interesting signal that’s ever been seen in space,” he explains.

“It happened in 1977, which is basically within 12 months of all the band being born.

“Chris [Wolstenholme] and I were born in 1978 and Dom [Howard] in 1977, so I just thought it was funny that this little Wow! Signal appeared around the time we came into this world.

“I think this album was really me letting go a little bit and engaging with the unknown.

“What is this thing inside me, or all of us, that wants to not be alone? I don’t mean with a partner or friends. I mean this thing in the universe. At the moment, we appear to be so alone, and we have this drive, which you see through religion and science.

“Behind all of it, we just don’t want to be alone.”

That search drew Bellamy back to one of his formative influences.

“I grew up watching Contact, the Jodie Foster film from the 90s,” he says. “I used to read Carl Sagan’s books and that film really stayed with me.”

It has also led him into the world of AI.

He adds: “I’ve spent time in the tech world, in California’s Silicon Valley and the Bay Area, and I had some involvement in that world.

“I went to a private talk where Sam Altman (CEO of OpenAI) was talking off the record about his thoughts on AI.

“I saw (Meta CEO Mark) Zuckerberg talking about it, too, and I was interested in what they were saying.

“When you really hear them, they know they’re ushering in an intelligence which is beyond us. They start to see it as, ‘Well, we’re just kind of messengers bringing in this thing that is going to be more intelligent than us’.”

Bellamy says he enjoys asking AI philosophical questions — and that is where Hexagons began.

“That’s actually my favourite thing to do with AI, and where I got the idea for Hexagons,” he says. “And again, I think that is part of the same human condition.

“Whether it be religion, looking for aliens in space or trying to bring in artificial intelligence, it’s kind of all the same thing.”

Epic, organ-led Be With You was the first song that made the album’s direction clear.

“You can look at it as a love song, or you can look at it as a religious song, almost,” he says.

“I’m not a religious person, but I decided to play the song on a church organ.

“I went to the biggest church organ in Los Angeles, so the song was recorded in this church-like setting.

“I liked the idea that it could be perceived as searching for alien life, or searching for alien intelligence of some kind, or God. That was the first song that felt right, lyrically and musically.

“There are a lot of personal elements in the album that are quite unusual for me.”

Bellamy says the album came from a difficult period, but that made the music flow.

“This was actually the easiest album for me to write and make for 15-plus years,” he says.

“Space Debris is probably the biggest reveal of what I went through, especially lyrically at the end,” he says.

“It’s the rawest moment of explaining what really happened over the last year. I like using space analogies — space debris, things breaking up and falling apart in gravity — to describe the chaos and feeling in your life.

“It also fits the theme of connecting this search into outer space for a higher power with the chaos and feeling in your own life.

“I hope the fans don’t ask me to play that one live.”

If Space Debris is the album’s rawest confession, Bellamy says it also opened the door to bigger questions running through the record.

“What I went through threw me off into the unknown,” he explains.

“When things go wrong in your life, that’s when you’re most likely to seek meaning or search for answers.

“In my case, it was a blend between religious thought, alien intelligence and AI.

“I don’t know what it is, but you’re searching for this higher power to guide you, or to give you answers.

“Music became my catharsis. It became my way to understand my situation.

“Making this album gave me flashbacks to these periods where music was my everything.

“It wasn’t something I had to do to pay the bills. It wasn’t something I had to do for the record label. It was something that I had to do for myself.

“That’s why I think this album is probably, since the 2000s anyway, the most raw, emotionally raw and honest album I’ve done.”

Bellamy says despite the personal nature of the album, Chris and Dom were central to every song.

“I’ve always been in charge of the lyrics, and I’m the leader in terms of the concepts,” he says.

“But musically, this is the most equal album we’ve had for a long time.”

The Wow! Signal includes some of the best tracks Muse have made in years.

Cryogen has already been compared to early Muse, while Shimmering Scars shows off the vulnerability in Bellamy’s voice.

“Cryogen is deliberately Muse from 2001,” he says.

With Shimmering Scars, he explains: “I felt like I needed to do five or six takes, so we could edit the best bits in.

“But producer Dan Lancaster was, like, ‘Nah, let it be raw, let it be weird.’

“To me, it sounded a bit off — not quite what I wanted it to be. But he was, like, ‘No, that’s the whole point. That sounds a little bit raw’.

“This is the first album where we said, ‘Let’s give Dan a go at producing it’. The last two albums were self-produced, so it was nice to hand the reins to someone else.

“He did a great job keeping us towards that more raw, vulnerable state in the performances.”

Bellamy believes AI is pushing younger listeners back towards authenticity.

“My stepson with Kate [Hudson], Ryder, is 22 and he’s just graduated from NYU,” he says. “Then Bing is 14, and I’ve got the two little ones as well.

“Having a boy who’s 22 and a boy who’s 14 means I get a real sense of what’s going on in their generation.

“I think that generation is turning away from pop, hip-hop and dance a little bit. They’re seeking raw, chaotic-sounding music.

“I think the reason why is because that generation is drowned by AI. AI is dominating everything they do, from schoolwork to music and the arts.

“I could be wrong but from what I sense from them, they’re gravitating towards what they know to be real.”

Recent single Nightshift Superstar was the band wanting to go French disco.

“I love Daft Punk, Justice and ABBA,” Bellamy says. “I went to see ABBA’s show and I loved it. They’re some of the best songs ever written. So after that and seeing Justice in Paris, I was, like, ‘How do we do that? Let’s just go there’.

“The song has a late-70s feel but with a more cutting-edge tone associated with modern dance music.

“But the good thing about it is that it really is us playing.”

One surprise on The Wow! Signal is Hush — a collaboration with pop star Ellie Goulding.

“Ellie was in the studio next-door, working with Marshmello on something,” says the singer.

“We have known each other for years and always wanted to try and do something together.

“Muse fans will read online that we’ve done a song with Ellie Goulding and think it’s going to be a pop song.

“But it’s got one of the biggest, heaviest riffs we’ve done in a long time. To me, it sounds a bit like New Born or something from 2001.

“The verses get a little bit poppy, I guess, but the main riff is pretty hard rock, so I thought it was quite fun to get Ellie’s voice over that kind of heaviness.

“I think it’ll be a nice surprise.”

Bellamy says the song came together by chance.

“This was an experiment,” he says. “It’s the only song on the album that really involves multiple writers.

“Ellie popped her head in towards the end of the day, at about 11pm, and went, ‘Hey, what are you guys up to?’ We played the song and she said, ‘Oh, can I sing on it?’ We tweaked the lyrics and turned it into a duet.

“It came completely by chance. It wasn’t planned to be a collaboration.”

Bellamy says the reaction from Muse fans to the new songs has “been the best we’ve had for at least 15 years” and he’s looking forward to getting back on the road following their special Brixton Academy show in April to launch the album.

The show marked Muse’s first appearance at the venue in 25 years, just before the release of Origin of Symmetry.

“I didn’t realise it had been so long,” Bellamy says.

“I remember the last time we played there, it was around the second album and I was so nervous because it was the biggest show Muse had ever done.

“We got to debut Be With You for the first time, and we had a great time.”

Visually, Bellamy says the full Wow! Signal world will come to life properly when Muse return to Europe in November.

“The American tour starts with what I’d call a medium-level production,” he says.

“But when we come to Europe, including London and Manchester in November, that’s when we’re going to ramp it up to a really sophisticated production.

“I think there’ll be a lot of geometry, a lot of hexagons, shapes and lasers, and strange, interesting visuals.

“Hopefully we’ll build the spaceship you see on the album cover in the arena.”

• The album The Wow! Signal is out today.

The Wow! Signal

U.S. President Donald Trump has said he is exploring ways to ensure Americans benefit directly from the rapid growth of artificial intelligence, raising the possibility of the government acquiring stakes in leading AI companies. The idea comes as firms such as OpenAI and Anthropic pursue valuations that could make them among the most valuable companies in the world, fueling debate over whether the public should share in the wealth generated by AI technologies.

Why the Idea Is Gaining Attention

The AI boom is expected to create enormous wealth for technology companies, investors and founders. Policymakers and advocates argue that because AI development relies heavily on public infrastructure, government research and vast amounts of publicly generated data, ordinary citizens should receive some of the financial benefits.

The debate has intensified as major AI developers seek billions of dollars to build data centers, chip infrastructure and advanced computing systems.

Option One: Taxing AI Companies Through Equity

One proposal would require AI companies to pay part of their taxes in shares rather than cash.

Under this approach, the government would gradually accumulate ownership stakes in AI firms without directly investing taxpayer money. Supporters argue that it would allow the public to benefit from future growth while avoiding large government expenditures.

Some advocates have gone further, proposing substantial government ownership stakes and board representation to give the public a direct voice in how AI companies operate.

Option Two: Equity in Exchange for Government Support

Another model would involve the government receiving equity stakes in return for financial assistance or incentives.

This approach mirrors previous arrangements in strategic industries where federal funding was provided in exchange for ownership interests. Given the enormous capital requirements of AI infrastructure, government funding could potentially become a source of financing for companies building advanced computing facilities, semiconductor plants and other critical projects.

Supporters argue this would allow taxpayers to benefit if publicly supported companies become highly profitable.

Critics contend that such arrangements could blur the line between regulation and investment, potentially creating conflicts between public policy goals and financial interests.

Option Three: Public Wealth Funds and Citizen Dividends

A third proposal focuses less on government ownership and more on distributing AI-generated wealth directly to citizens.

Under this model, revenue generated through AI-related taxes or investments would flow into a public wealth fund, which would then distribute dividends to Americans.

The concept resembles Alaska’s Permanent Fund, which uses energy revenues to provide annual payments to residents. Advocates argue a similar system could ensure that AI-driven economic gains are shared more broadly across society rather than concentrated among a small number of technology firms and investors.

Some AI companies have expressed interest in versions of this idea, including proposals for digital dividends funded by taxes on the sector.

Why AI Companies Matter

The debate carries major financial implications because leading AI developers are becoming increasingly valuable.

OpenAI and Anthropic have both reportedly taken steps toward potential public listings, while companies across the sector are raising unprecedented sums to fund AI expansion. Some analysts believe the industry could generate trillions of dollars in economic value over the coming decade.

As a result, even relatively small government stakes could potentially produce significant long-term returns.

Challenges and Obstacles

Any effort to give the government ownership in AI companies would face significant legal, political and economic hurdles.

Questions remain over:

• How ownership stakes would be valued
• Whether companies would voluntarily participate
• The impact on private investment
• Potential conflicts of interest for regulators
• How revenues would be distributed to citizens

There is also likely to be strong opposition from free-market advocates who argue that government ownership could discourage innovation and distort competition.

What Happens Next

Trump has not outlined a specific mechanism for acquiring stakes in AI companies, and no formal proposal has been introduced.

However, the discussion highlights a growing debate over who should benefit from the AI revolution and whether existing economic structures are sufficient to distribute the gains from one of the most transformative technologies in modern history.

Analysis

The significance of Trump’s proposal lies less in whether the government ultimately acquires stakes in AI firms and more in what it signals about the future political debate surrounding artificial intelligence. As AI companies approach trillion-dollar valuations, pressure is likely to grow for policymakers to ensure that the economic gains extend beyond investors and technology executives.

The discussion mirrors earlier debates over natural resources, where governments sought ways to ensure that public assets generated public benefits. In this case, supporters argue that AI is built on public research, public infrastructure and publicly generated data, creating a rationale for broader wealth sharing.

At the same time, the proposal raises fundamental questions about the relationship between government and the private sector. Direct ownership stakes could provide taxpayers with financial upside, but they could also create tensions between the government’s role as regulator and its role as investor.

The debate is likely to become more prominent as AI companies grow larger, seek additional funding and exert greater influence over economic growth, employment and national competitiveness. Whether through equity ownership, taxation or public wealth funds, the central political question is increasingly becoming not whether AI will generate enormous wealth, but who will ultimately receive it.

With information from Reuters.

For decades, historian’s discussion about colonialism has revolved around large armies, territorial conquests and vast empires. Yet, they often fail to focus on the fact that one of the most powerful empires did not begin with soldiers – it emerged because of corporations. The British East India Company, in 1600 started its commercial activities in the sub-continent, initially as a trading merchandise seeking profit in foreign markets. Within the period of two centuries, it acquired its own military, expanded its territorial influence, and started acting as a ruling government that ultimately blurred the difference between private capitalist enterprises and sovereign national authority. More than two hundred years later, Artificial Intelligence (AI) is the latest incarnation of that colonial legacy. Unlike previous forms of colonialism of territory and resources, this control is primarily centered around data, algorithmic decision-making systems, and automated computation. Their territories are not like land, it is the dominance over data ecosystems; their currency is not raw materials, it is ‘data’, and their empires are not built on castles, but are gigantic ‘data-centers’. Instead of emancipation for the marginalized, this technology creates new forms of dependency known as ‘digital dependency’.  

The 21^st century is witnessing a growth of an imperial empire that is built on establishing control over datasets, computational power, and algorithmic sovereignty. Where a few Chinese and American tech giants such as NVIDIA, Amazon Web Services, Google Cloud, and Microsoft Azure are controlling the digital markets through complete ownership of cloud platforms, chip production, and algorithmic intelligence. These hegemonic corporations act as imperial powers that perpetuate similar inequalities to traditional colonists, in which the global south risks becoming a resource for the tech giants. The comparison might seem like an exaggeration, but in reality AI colonialism follows similar patterns. Historically great economies were built on extraction; they extracted raw materials from peripheries, and then the industrial base at the center transformed into a worthy product, geopolitical influence, innovation, and wealth. Cotton flowed from subcontinent to Britain; rubber moved from southeast Asia to European countries, while minerals obtained from Africa were sent to imperial empires.

Today, the AI economy adopts an akin model where “data” is the vital material for digital functioning.  Millions of people from the south utilize these platforms; every search, GPS location, digital personal profile, and digital transaction becomes part of the data ecosystem that is required for its training, but their economic value is located elsewhere. It is particularly evident in African countries, where millions of people rely on these foreign platforms for information. Their data from search engines, digital databases, and social media, is then used to train the AI models, whilst the African community receives little economic benefit or no influence over how these technologies are deployed in their region. By controlling these giant data ecosystems, these tech conglomerates also gain leverage over their political, social, cultural, and economic affairs. Even though having a digital footprint is a sign of progress, when it is foreign owned or funded by external actors, it can be manipulated as imperialistic power that not only controls the data system, but also significantly affects the local traders and businesses.

Similar to east India companies, these tech corporations operate across national jurisdictions, shape economic trajectories and influence domestic governments to sustain their digital dominance. They shape information systems, and their regimes of truth. They decide which technology should be introduced in the market, at what cost, what conditions, and for whom. The east India company governed India not through military conquests but because the local leaders became dependent on the commercial and political networks controlled by the corporation. Their economic dependency paved the way for the east India company’s takeover. Today, the danger is not that the tech corporations will rule the state directly, rather it is the fear that the national governments will become so dependent that the exercises of their sovereign autonomy will be meaningless. AI colonialism is at the front, recreating the colonial dependency traps.

Another manifestation of ‘digital colonialism’ in the global south is the extraction of data through coercive bundles of consent forms. Most people from third-world countries click ‘accept all’ to install an app or to log into a website without reading its full contents. It is an illusion of ‘choice’ created by these companies, but in actuality, these people have no choice. If they ‘refuse’ to click they might lose their access to digital accounts, bank apps, or mobile services. Colonial powers used a similar tactic of ‘terra nullius’ ­to lay claim on foreign land and resources. The new digital ecosystems are now integrating modern forms of terra nullius to govern the global data and algorithmic infrastructures. In addition to controlling the databases, the new AI colonial world order exploits the cheap labor services of the global south to maximize their profits. During Venezuela’s economic crisis, the prime educated force was readily exploited as ‘cheap labor’ by the Silicon Valley. In exchange for survival income, they were exposed to precarious working conditions, pay-cuts, unstable contracts. This reflects that the AI colonialism is following the legacy of historical empires step-by-step; controlling foreign ecosystems, exploiting cheap labor, and profiting over their raw materials.

The digital hegemony in the global south extends beyond economical matrix; it is the struggle over political influence, power, and raw materials that will ultimately determine who will produce the knowledge, who controls the technology, and who profits off the wealth generated by AI ecosystems. Colonial history should not be merely viewed as the ancient past, but as a lesson to reject the ‘modern empires’. In order to do so, the global south must invest in indigenous technology companies, data systems and regulatory digital frameworks to protect the local’s data. Unless the global south acts collectively against AI colonialism, it may again serve as a colony supplying critical resources that enrich others whilst itself remains excluded from the global power centers. 

Ukraine’s defence ministry believes artificial intelligence is set to fundamentally transform modern warfare, as Kyiv accelerates efforts to integrate AI into battlefield operations amid its ongoing war with Russia.

According to Danylo Tsvok, head of Ukraine’s Defence Ministry AI Research Centre, the country is already employing artificial intelligence across multiple military functions, including drone operations, battlefield planning, intelligence analysis, and missile attack assessments.

The centre, established in March, is part of a broader effort to make data driven decision making a core component of Ukraine’s defence strategy. Officials envision a future where AI systems, sensors, drones, command centres, and weapons platforms operate through a unified digital network capable of processing battlefield information and recommending military actions in real time.

Why It Matters

Ukraine’s experience is increasingly being viewed as a preview of how future wars may be fought. The conflict has already demonstrated the growing importance of drones, autonomous systems, and real time intelligence, but AI could push military operations into an entirely new phase.

Rather than merely supporting commanders, future AI systems may become central to battlefield decision making by processing vast quantities of data faster than human operators can manage. This could dramatically shorten the time between identifying a target and launching an attack.

The implications extend far beyond Ukraine. Military planners around the world are closely monitoring the conflict as a testing ground for next generation warfare technologies.

The Rise of AI Driven Combat

The war has already evolved into a technological competition in which both Ukraine and Russia are attempting to gain advantages through automation, data analysis, and autonomous systems.

Ukraine is working toward a battlefield operating system capable of integrating information from drones, reconnaissance assets, weapons systems, and frontline units into a single decision making framework. The objective is to create a comprehensive operational picture that enables faster and more effective responses.

Russia is pursuing similar capabilities, particularly in drone warfare and strike planning, creating what Ukrainian officials describe as an emerging competition between military operating systems rather than simply armies.

Global Defence Implications

The conflict has attracted significant attention from defence technology firms and AI developers seeking real world operational data. Companies and governments increasingly view Ukraine as one of the most important testing environments for military AI applications.

The lessons learned from the war could influence defence procurement, military doctrine, and security planning across NATO, Asia, and other regions facing evolving security challenges.

As AI becomes more deeply embedded in military systems, countries may be forced to rethink command structures, training requirements, and the role of human decision makers in combat.

Key Stakeholders

• Ukraine military
• Russian military
• Defence technology companies
• NATO members
• Artificial intelligence developers
• Defence ministries worldwide
• Military planners and strategists

Future Outlook

Over the next three to five years, military competition is likely to shift increasingly toward AI enabled command systems, autonomous platforms, and integrated battlefield networks.

Countries capable of rapidly processing information and converting it into actionable decisions may gain a significant operational advantage. At the same time, concerns about autonomy, accountability, and human oversight will become more prominent as AI systems assume larger roles in combat operations.

The race to integrate AI into warfare is expected to intensify, making technological superiority as important as traditional military strength.

Analysis

Ukraine’s assessment points to a deeper transformation than simply adding artificial intelligence to existing weapons systems. What is emerging is a shift from platform centric warfare to data centric warfare, where military advantage depends less on the number of tanks, aircraft, or soldiers and more on the ability to collect, process, and act on information faster than an opponent.

The most significant aspect of this transition is the compression of decision making time. Historically, military success depended on commanders interpreting information and issuing orders. AI has the potential to reduce that cycle from hours or minutes to seconds, creating a battlefield where speed of analysis becomes as important as firepower.

This evolution could fundamentally alter military hierarchies. If AI systems become capable of generating reliable operational recommendations faster than humans can assess them, commanders may increasingly act as supervisors rather than primary decision makers. The challenge will be balancing military effectiveness with accountability and ethical oversight.

The Ukraine conflict is therefore becoming more than a territorial war; it is also serving as a laboratory for the future of warfare. The countries that emerge with the most effective integration of AI, autonomous systems, and battlefield data networks may define military power for decades to come. In this sense, the competition between Ukraine and Russia increasingly resembles a contest between technological ecosystems, foreshadowing a future in which wars are won not only through weapons but through algorithms and information dominance.

With information from Reuters.

Investors are bracing for an ECB rate hike on Thursday. Markets expect the European Central Bank to raise rates by 25 basis points, which could weigh on growth and corporate earnings. Investors are also awaiting guidance on whether further hikes will follow.

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ING said in an analysis on Thursday morning that: “We expect the ECB to hike by 25 basis points from 2.0% to 2.25%, supported by a hawkish tone, but the bar has risen to surprise markets. Despite oil prices testing new lows earlier this week, the EUR curve is increasingly set on three rate hikes.”

Stock markets across Europe opened in positive territory despite the drop in Asian shares following another sell-off in AI-related stocks on Wall Street on Wednesday.

The Euro Stoxx 50 opened 1.2% higher but the broader pan-European Stoxx 600 rose was flat in early trading.

Germany’s Dax and France’s CAC 40 were both up by 1%, while the UK’s FTSE 100 led with a 1.2% gain. Meanwhile, Italy’s FTSE MIB rose by 0.7%.

In other dealings, Asian shares mostly fell on Thursday after another sell-off in artificial intelligence stocks weighed on Wall Street, while oil prices rose.

Japan’s Nikkei 225 lost 0.5%, South Korea’s Kospi fell 0.2%, and Australia’s S&P/ASX 200 slipped 0.2%. Taiwan’s Taiex declined 0.4%.

Hong Kong’s Hang Seng index edged 0.2% higher, while Shanghai’s Composite index dropped 0.2%.

On Wall Street, on Wednesday, the S&P 500 fell 1.6%, marking its first consecutive decline in three weeks. The Dow Jones Industrial Average dropped 1.9%, while the Nasdaq Composite lost 2%.

Wall Street has been unsettled since last week, when AI stocks reversed course after hitting record highs. Investors are weighing whether the recent pullback has eased concerns over excessive optimism or signals the beginning of a more prolonged downturn.

Super Micro Computer, which sells AI servers, plunged 28% after announcing late on Tuesday plans to raise $7 billion through sales of common stock and convertible preferred shares. Companies often seek to raise capital when share prices are elevated, though such moves can dilute existing shareholders’ stakes.

Micron Technology swung between gains and losses before ending down 4.7%. The stock has experienced sharp volatility in recent sessions, having fallen 7.7% last Thursday, dropped a further 13.3% on Friday and then rallied 9.9% on Monday. Despite the swings, its shares remain up 212.5% so far this year.

Nvidia, the chipmaker that has grown into a nearly $4.9 trillion company on the back of the AI boom, was the biggest drag on the S&P 500 after falling 3.7%. Broadcom, another major AI beneficiary, lost 5.1%.

Some pressure on AI-related shares may also be linked to investors raising cash ahead of several high-profile stock market debuts in the United States. SpaceX’s initial public offering could take place later this week.

Weakening stocks for companies with big fuel bills also pulled the market lower. United Airlines sank 6.2%, and cruise operator Carnival fell 6.3% after oil prices rose due to the latest fighting in the war with Iran.

Oil prices and US inflation

Brent crude rose 1.8% to $93.10 a barrel on Wednesday after President Donald Trump warned that Iran would “pay the price” for stalled negotiations between the two sides over the conflict. The war has effectively closed the Strait of Hormuz to oil tankers, disrupting crude shipments from the Persian Gulf to customers worldwide.

Higher oil prices have added to inflationary pressures. A report released on Wednesday showed US consumer prices rose in May at the fastest annual pace in three years.

Traders are increasingly betting that the Federal Reserve will need to raise its benchmark interest rate at least once this year in response to persistent inflation and a resilient labour market.

Higher yields can slow economic growth and weigh on a range of investments, including stocks and cryptocurrencies. They tend to hit the most highly valued assets hardest, and some critics argue that enthusiasm around AI has inflated a market bubble.

In early European trading, Brent crude was up by 0.5% at $93.60 a barrel, while US benchmark crude gained 0.7% to $90.70.

The US dollar traded at 160.58 Japanese yen in the morning. The euro rose slightly to $1.1542, and the UK pound cost $1.3377.

The gold prices dipped by 0.6% to $4,109.60 an ounce.

Since the public launch of large-language models like ChatGPT and OpenAI in 2020, Artificial Intelligence (AI) is gaining ground across a variety of private and public areas,  the prospect of not only facilitating mundane tasks but also revolutionising labor markets, research, medicine and militaries.  

The gilded age of AI

But as the presence of AI is becoming an increasingly normalized part of everyday life, from summarizing texts, fact-checking a statement or composing an email, it is easy to overlook the more nefarious purposes of surveillance, discrimination and persecution for which AI can be used at the state level. This is an increasingly pertinent issue, with the surge of state-based AI surveillance—such as ’safe cities,’ facial recognition, and smart policing—since 2018, extending to at least 75 of the 175 countries with available data. While this trend is present on all continents, there are regional disparities in application, with AI surveillance present in almost 70% of the surveyed African states, over 50% of South East Asian states, and just under 40% of European countries use AI for surveillance. Thus, AI surveillance is not limited to authoritarian states; according to one report, 51% of liberal democracies use AI for surveillance purposes. How, then, is AI being used for surveillance in China, the Middle East, US, and Europe? 

China—a spearhead for surveillance

China dominates the AI surveillance sector, with companies like ZTE and Huawei present in over 63 countries, vastly outnumbering the US. This presence is especially noticeable in Africa and Asia, where the use of Chinese surveillance technology correlates closely with  participation in the cross-continental Chinese Belt and Road Initiative. In particular, China has been exporting its ‘safe city’ model, which has already been domestically implemented in cities like Beijing as part of its social credit system, to Saudi Arabia, Uganda, and Thailand as well as European cities like Valenciennes, which in 2017 was gifted safe city technology by Huawei. This model connects an extensive network of facial recognition cameras and police body cameras into intelligent command centers using algorithms to predict crime.

Individual freedom versus national security

While states are justifying these measures by reference to crime reduction and national security, organisations are warning about the implications of AI surveillance for privacy, systemic discrimination civil rights and democratic freedoms as AI allows for cost efficient surveillance at an unprecedented spatial and temporal scale. For example, China has domestically implemented large scale AI surveillance encompassing over 600 million cameras, coupled with large language models for minority languages to sharpen its surveillance of the communication of its Tibetan, Uyghur, Korean, and Mongolian minorities. In the Xinjiang province, the Chinese state has created an Integrated Joint Operations Platform, which employs an extensive network of CCTV cameras, facial recognition devices, and or WiFi surveillance devices to suppress political dissent among the province’s Uyghur minority. Such Chinese technology has reportedly also been exported to Saudi Arabia and Iran for similar purposes of suppressing political dissent, and to enhance the precision of drone air strikes in Ukraine and the Middle East.

AI surveillance beyond autocracies

However, the West is not immune to these developments. The US government recently found itself in a legal dispute with AI company Anthropic after the company refused to allow the government to use its ground breaking AI model Claude for domestic surveillance without built-in restraints. The US government claimed that this jeopardised national security by preventing the state from identifying espionage. In addition, US President Trump has issued various executive orders to increase the adoption of AI by federal agencies over state regulations. Indeed, the US already uses surveillance technology deployed by Israel on the occupied West Bank, to stem migration on the Mexican border. Moreover, the Federal Bureau of Investigation (FBI) admitted in March 2026 that federal agencies are buying personal data from data brokers, including location data collected by private companies, in order to track citizens.

Europe: between security, migration and regulation

Meanwhile, the European Union (EU) is exploring Automated Border Crossing technologies. The intelligent system iBorderCtrl is currently being piloted in Greece, Hungary and Latvia  applies AI lie detectors to immigrants, with immigrants found lying being automatically detained for further questioning. This system has been criticised by human rights activists and academics as a scientifically weak and potentially discriminatory practice. Thus, even though AI is more regulated in Europe than elsewhere in the world, with the EU AI Act of 2024 restricting large scale usage from sensitive areas through, the risk of questionable AI use in the name of national security remains salient.

Indeed, several member states are stretching the AI Act’s limitations on large-scale surveillance. For example, Luxembourg has since 2025 pursued plans of expanding its use of Trojan spyware from state security and terrorist threats to encompass a broader range of crimes, such as child exploitation, currency counterfeiting and human trafficking. Similarly, the government of Ireland is seeking to expand the powers of the police and Defense Forces to intercept conversations on encrypted platforms like WhatsApp, and iMessage, and other social media platforms. Meanwhile, the Czech Republic was forced to end its use of facial recognition at Prague Airport after six months as it was found to violate the EU AI Act. Likewise, Hungary authorized the police to use real-time facial recognition to identify participants in LGBTQ+ parades in April last year, in violation of the AI Act.

Digital emancipation or authoritarianism?

Thus, it appears that national and international regulation has been lagging behind the rapid tech innovation of recent years. As with any innovation, AI is a neutral tool—but it can be used in ways good or bad depending on the decisions of power-holders. Thus, the application of AI calls for increased scrutiny, accountability and implementation to safeguard the benefits and prospects of improvement it holds out from being hijacked by nefarious purposes undermining democracy and human rights.

The appeal of getting a free, quick, well-structured travel plan is easy to see, and something that is causing travel agents another Covid-sized thing to worry about

Places that don’t exist. Events on the wrong day. Attractions miles apart.

Holiday itineraries designed by AI appear helpful and comprehensive, but are actually riddled with mistakes and old information that could ruin your holiday, analysis of them has found.

More and more people are turning to large language models (LLMs) to plan their trips away. One study puts the number of people who have turned to a bot for holiday inspiration at 40%.

The appeal of getting a free, quick, well-structured travel plan is easy to see, and it’s another Covid-sized thing travel agents have to worry about.

But how good are the robots at the delicate art of holiday planning?

I asked the four biggest LLMs to design weekend break itineries for four destinations, and then called on local experts in those places to assess their work. This is what we found.

Grok

Jonnie Fielding, a London tour guide since 2011 with 227,000 followers on Instagram, says the itinerary is “pretty good” if not “generic”, with some glaring issues.

Grok’s suggestions of a walk through Westminster is good, but better if you have a guide point out the smaller details, such as “gas lamps, torch snuffers, fire protection badges”.

The alternative river cruise and London Eye are solid bets for first-timers in the city, but other aspects of the itinerary are less well thought through.

Jonnie argues that the itinerary is far too busy. “I’m a big fan of spending some time people watching. Soaking things in rather than rushing around, and because of the amount in this itinerary, visits to The Tower of London or Westminster Abbey are really rushed,” he said.

The biggest mistake is including the Changing the Guard, which starts at 10:43am at St James’s Palace and doesn’t run on Saturdays.

He concluded: “I know this is a generic itinerary for first-time visitors, but I think London has so much more to offer. Loads of small museums, house museums, places of interest for all interests. They would also leave, not really knowing London.”

Claude

Although Claude breaks the itinerary down into activities and eating, offering the reader a little more freedom, its work left Jay Allen from Unseen Japan incredibly cold.

“Overall, this itinerary lacks any context, history, and rich cultural detail that our customers love hearing about on our tours,” Jay says.

The itinerary is full of old information. For example, Tsukiji is no longer the location of a wholesale fish market. It moved to Toyosu several years ago.

“Why did the original market arise in Tsukiji in the first place? None of this rich detail and historic background is included. Even if it were, this info would be drawn from general public sources, not from the rich background that our tour guides – most of whom have degrees in Japanese Studies or years working as journalists in Japan – can provide,” Jay adds.

The Saturday covers little ground and is too geographically spread out, missing “so much rich detail of the Tsukiji/Asakusa shitamachi area”, according to Jay.

Many suggested destinations are “trite”, “well known” or just “commercial”, such as the Starbucks in Shibuya Scramble. “You can get a much better view of the scramble, eg, from the bar at the top of the Magnet building – and you’d be fighting fewer crowds,” Jay notes.

“If you look at the second day, the itinerary gives you no suggestions of cool art galleries and small, uniquely Japan clothing shops to stop (such as the Ura-Harajuku area off the Main Street, where independent fashion still reigns), nor does it tell you about the less-crowded Brahms’ Path that runs alongside the packed Takeshita Street.”

The restaurant recommendations “are the same five places everyone else is going” and are hard to get tables at, as opposed to the real gems that require “a basic working knowledge of Japanese”.

Jay concludes: “Claude is giving you the wisdom of the crowds. That can be helpful in some cases. In this case, it equates to a bland, ordinary vacation that will likely prove an exercise in frustration for most travelers.”

Google AI

Amy Siegal, a luxury travel advisor based in NYC, praised Google AI for highlighting some “iconic spots”, but argued that only “a human expert knows the ins and outs of these places – what time of day to go where, and in which order they’d work best.”

On day one, Amy suggests arriving at TKTS earlier in the day to have more choice of shows and seats, and to avoid the line.

Saturday’s itinerary has too much “darting around”. She suggests it could be arranged more smoothly, with more interesting food options chosen.

Sunday is packed full of landmarks and history, which is good, but the order is wrong. “I would incorporate some lesser-known landmarks and eateries,” Amy added.

ChatGPT

Vicky Reeves, who is the director of The Real Algarve villa company, applauded ChatGPT for picking out some “amazing places” in its “very good overview,” but argued it failed to consider flow, how much is possible to fit in, and the weather.

“Understanding seasonality is important because this itinerary would feel very different in August compared to November and that is something an agent or guide would pick up on. I also think it’s a bit ambitious and that’s really down to a lack of practical knowledge and insight,” Vicky explained.

“For example, the plan suggests exploring Lagos, visiting Ponta da Piedade and potentially heading to Sagres before flying home. That’s fine if you’ve got a late return flight, but an agent or guide would check to make sure that everything was possible without adding stress or risking your flight home. Benagil is also another good example. It’s one of the Algarve’s most iconic attractions, but during peak season, travel times and parking can be difficult, and tours often need booking well in advance, which isn’t really considered at all.

“The AI did pick out some amazing places, but I do think it’s missing a personal feel. It doesn’t suggest any hidden beaches or lesser-known spots because that’s much harder for AI to uncover. That insight can really make all the difference in making a trip feel unique.”

In conclusion

What is most striking about the itineraries is how comprehensive and well thought out they seem – particularly Claude’s – but how riddled with issues they are once a closer look has been taken.

ChatGPT suggesting an event that doesn’t take place on the requested day is a rookie mistake that could disrupt a trip, while Claude not realising a famous fish market has moved is similarly clumsy.

All the AIs seem too ambitious when it comes to the number of activities and the distance between each.

Certainly, the bots are great if you’re looking for a broad overview of a place, but they lack the precision you’d want to fully rely on its suggestions, and the depth of knowledge a local guide can provide that can bring a place to life.