Artificial intelligence

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.