Google UK and the British government have launched a state-backed trial using artificial intelligence (AI) to reduce the climate impact of aircraft contrails across an entire transatlantic flight corridor, described as the world’s first airspace-scale attempt at the problem, according to the search engine giant.

“Operation Blue Skies” will run within Shanwick Oceanic airspace, covering the eastern half of the North Atlantic corridor, which accounts for around 5% of global contrail warming.

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The win matters less for its immediate revenue than for what it signals. Japan’s largest mobile operator has committed the processing brain of its radio network to Ericsson’s in-house silicon at precisely the moment the industry is trying to work out how artificial intelligence will reshape mobile traffic. For a company whose core radio business has been shrinking, that is a useful anchor.

What the deal actually covers
RAN Compute is the baseband layer of a mobile network, the computing hardware that sits behind the antennas and handles signal processing, scheduling and the software logic that determines how efficiently spectrum is used. Radios get the attention. Baseband determines what those radios can actually do.

Ericsson says the purpose-built platform uses its latest in-house silicon and is optimised for 4G, 5G and 5G Advanced, as well as for artificial intelligence and machine learning workloads running inside the radio access network. The company describes the result as an AI-native and programmable platform capable of supporting long-term software evolution.

That phrase is doing heavy lifting. It means DOCOMO is buying hardware it expects to keep upgrading through software for years rather than replacing at the next generational step. In an industry where full network swaps are financially painful, longevity is a selling point in its own right.

The numbers behind the pitch
The commercial case rests on two figures. Ericsson claims the platform delivers up to twice the capacity of the previous generation of baseband products while consuming less than half the energy, which it says enables more efficient site operations and lower costs in centralised radio access network deployments where compute units are installed in aggregated fashion.

Both halves of that claim land hard in Japan. The country’s post-Fukushima dependence on imported liquefied natural gas keeps power costs high by international standards, and network energy is one of the few operator expenses that scales directly with traffic growth.

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The report argues that emerging economies have more to gain and less to fear from AI than advanced nations, urging policymakers to embrace the technology rather than risk missing another transformative industrial revolution.

“AI has thrown developing economies a lifeline, and they should seize it. They do not need large models or big data centers to reap its benefits. By adapting small, low-cost AI tools to local conditions, they can bring better medical care, education, judicial services and agricultural extension within reach of millions. But they must hurry: AI is spreading faster and is more context-specific than earlier general-purpose technologies like electricity and the internet. World Development Report 2026 shows how developing countries are responding—and succeeding,” said Indermit Gill, Senior Vice President and Chief Economist of the World Bank Group.

The World Bank said AI-powered applications could help health workers diagnose diseases more quickly, enable teachers to improve lesson planning, assist farmers with crop selection and weather forecasting, and expand access to public services in underserved communities.

For the 6.8 billion people living in low- and middle-income economies, AI solutions will need to be tailored to local realities, including voice-based services for people without smartphones or literacy skills. The report stressed that importing an AI model alone would not guarantee effective results without localisation and reliable local data.

ALSO READ | Energy shock bites: Iran war forces IMF to cut global growth outlook

Contrary to fears of widespread automation, the report found that only 4.5% of jobs in low- and middle-income countries face significant exposure to generative AI, compared with 14.2% in high-income economies. At the same time, around 16.2% of jobs in developing countries could see meaningful productivity gains, broadly comparable with the 18.7% expected in richer nations.

To unlock these benefits, the World Bank called for sustained investment in electricity generation and distribution, broadband connectivity, computing infrastructure, and digital skills, alongside wider access to smartphones and other digital devices.

“None of this is possible without first investing in the basics.” The report used these exact words to prove its point. It also used Sub-Saharan Africa as a good example; nearly one-third of rural schools still lack reliable electricity, and well over two-thirds have been deprived of dependable internet access.

“Closing this gap is already a priority — the World Bank Group is working with partners through Mission 300 to provide energy access to 300 million people across Sub-Saharan Africa by 2030, laying the foundation for broader digital and AI inclusion,” the study noted.

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“Countries also need to expand access to computing power and improve the availability of local data, including in local languages, so AI tools can be tailored to serve their own people and economies. Governments should make it easier for new firms to attract investment, test ideas, and scale what works. Many AI pilots are already underway, but the bigger challenge is knowing which ones deliver results. Better evidence, stronger skills, and clearer procurement and evaluation frameworks will be essential,” the World Bank added further.

Building public trust is equally important. As AI evolves rapidly, the global body advised the governments to begin their policy responses by drawing on voluntary industry standards to encourage the technology’s responsible use, anchored in international cooperation to prevent regulatory fragmentation.

“When voluntary measures fall short, governments should apply existing laws to address the harms directly. Improved public services and better learning outcomes in schools will reinforce trust—but if AI embeds bias in government decisions or erodes data privacy, that trust will be difficult to recover,” the World Bank noted.

However, it cautioned that AI could also deepen inequality, spread misinformation, concentrate market power, and erode trust in public institutions if deployed without adequate safeguards for privacy, transparency, and accountability.

“The window to get this right is narrow. AI presents a once-in-a-lifetime opportunity to solve problems that have resisted solutions for generations. Developing countries that build the foundations now—power, connectivity, skills, and institutions—will be positioned to adopt and adapt AI for their people,” said Gaurav Nayyar, Director of the World Development Report 2026.

The report concluded by stating that timely adoption of AI could help lift growth, strengthen public services, and improve living standards across the developing world.

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Boeing’s MQ-28 Ghost Bat made its first-ever static appearance, shown alongside a Brimstone missile, while General Atomics and Anduril Industries brought full-scale models of their own uncrewed fighters. None of these machines are meant to fly wars alone.

They fly beside a human pilot, taking on the jobs nobody wants to risk a cockpit for. The industry calls them loyal wingmen, or “Collaborative Combat Aircraft,” and they are central to most major air forces’ future planning.

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A wingman drone is built to operate in close coordination with a manned fighter. That might mean flying ahead to draw out enemy radar, jamming an opponent’s sensors, carrying spare missiles to extend a fighter’s magazine, or simply giving an adversary another target to worry about.

These aircraft are cheaper to build, and cheaper to lose, than a crewed jet, which changes the arithmetic of risk considerably for a commander deciding what to send into contested airspace.

Artificial intelligence (AI) does the actual flying, holding formation and reacting to threats from only high-level direction, though in every programme examined for this piece, the decision to pull the trigger still sits with a person.

Washington’s Collaborative Combat Aircraft programme has the biggest head start. General Atomics and Anduril were chosen in 2024 to build the first generation, designated the YFQ-42A and YFQ-44A.

The General Atomics design, drawn from its Gambit family of drones and nicknamed Dark Merlin, first flew in August 2025 and carries its weapons in an internal bay. Anduril’s YFQ-44A, developed from the “Fury” aircraft it inherited when it bought Blue Force Technologies, followed two months later, mounting its weapons externally to trade some stealth for lower cost.

Both are built to a combat radius requirement of over 700 nautical miles and are estimated at $25 million to $30 million per aircraft, about a third an F-35’s price. In June 2026 the Air Force awarded both companies production contracts, four months ahead of schedule, covering at least 150 aircraft between them by the end of the decade.

ALSO READ | Can Britain’s 298 billion pound ‘Defence Investment Plan’ keep the country safe?

“By moving fast from competitive selection into full-scale manufacturing,” Air Force Secretary Troy Meink said at the time, the service intends “to stay ahead of the pacing challenge.”

A month later, the YFQ-44A completed the programme’s first live weapons test over the Mojave Desert, firing an AIM-120 AMRAAM with a human operator retaining control of the release decision throughout. General Atomics has its own live-fire trial due this autumn.

Separately, six companies are competing just to write the autonomy software that will fly these jets. The Air Force has floated a total requirement of a thousand or more CCAs, and is already drafting tougher requirements for a second, more capable increment.

The finances behind these programmes are as striking as the hardware. Anduril, which builds the YFQ-44A alongside its Lattice autonomy software, closed a USD 5 billion funding round in May 2026 that valued the company at USD 61 billion, roughly double the USD 30.5 billion mark it carried twelve months earlier. Revenue rose from USD 1 billion in 2024 to an estimated USD 2.2 billion in 2025, and the company has told investors to expect USD 4.3 billion in 2026.

In March the US Army awarded it a ten-year enterprise contract with a USD 20 billion ceiling. General Atomics discloses none of this. It remains privately held by brothers Neal and Linden Blue, who bought the company in 1986 and reported USD 3.2 billion in revenue for 2024 without ever filing a public account of profit or margin. Boeing sits somewhere between the two extremes.

ALSO READ | As threat of war looms, Europe hikes spending on military and defence equipment

Its shares were trading around USD 215 at the time of the airshow, still leaning on Ghost Bat and its wider defence and services portfolio to offset certification delays and supply-chain trouble in its commercial jet business.

The Ghost Bat itself began life a decade ago as Australia’s Loyal Wingman project, a joint effort between Boeing and the country’s Department of Defence. It carries up to 500 kilograms of payload over a range of roughly 3,700 kilometres, and an early batch of six was ordered for AUSD 115 million, around AUSD 19 million each, full production pricing undisclosed.

It has completed air-to-air missile trials and taken part in live exercises, and its Farnborough debut was a more confident showing for Boeing than its scaled-back 2024 presence, when the company was still dealing with the Alaska Airlines door-plug fallout. It is, by most accounts, the most mature design in the field, and other air forces weighing their own options keep coming back to it.

China’s efforts have moved with less fanfare but arguably greater speed. The Hongdu GJ-11 Sharp Sword, a stealthy flying wing design, is now reportedly forward deployed in Tibet, flying alongside J-20 and J-16D jets, while a naval variant, the GJ-21, has been spotted in trials tied to catapult launches from China’s newest carriers. A separate, lower-cost design, the Feihong FH-97A, is built to fly with the J-20 and can reportedly command its own swarm of smaller drones.

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Rehearsals for Beijing’s 2025 Victory Day parade featured five distinct loyal wingman prototypes, four of which appeared in the final procession, prompting speculation in state media that China may be first to field an operationally ready design, ahead of the United States. Beijing has already begun marketing the technology abroad too, showing an export version of the Sharp Sword to the United Arab Emirates in February.

Europe’s picture is messier. The UK, Italy and Japan are jointly developing the Global Combat Air Programme, known in Britain as Tempest, and the Royal Air Force has named its own drone element Storm Fighter, backed by more than four hundred million pounds as part of a stated ambition to build Europe’s first sixth-generation air force.

Italy’s Leonardo has partnered with Turkey’s Baykar to trial its Kizilelma drone in the role, and the two firms used Farnborough to release fresh results from their K-SWARM trials, in which a manned M-346 jet directed two Kizilelma aircraft through a full formation flight using nothing but high-level commands.

That success contrasts with the rival Franco-German-Spanish effort, whose next-generation fighter component was scrapped in June 2026 after Paris and Berlin could not agree on who would build what. The British Army is running its own smaller version of the same idea through Project NYX, inviting seven companies to design drones that can fly escort for Apache attack helicopters.

Turkey’s Baykar has begun mass production of the Kizilelma, an unmanned stealth fighter that dropped a TEBER-82 guided bomb in a 2025 test, is due to enter Turkish service this year, and is being pitched, beyond its Italian trials, as a candidate for Tempest as well.

Russia’s answer is the Sukhoi S-70 Okhotnik-B, a heavy flying wing aircraft with a claimed range around 6,000 kilometres, a combat radius of 3,000 to 4,000 kilometres and an internal payload above 2,000 kilograms, meant to operate alongside the Su-57.

Its combat record has been a cautionary tale for the whole field. In 2024, one Okhotnik reportedly lost its data link over Ukraine, drifted off course, and was shot down by a Russian Su-57, exposing exactly the kind of communications and fail-safe weaknesses that every other programme is racing to design around.

Moscow has since offered the pairing to India, alongside a twin-seat “drone commander” version of the Su-57. India has its own project regardless. Hindustan Aeronautics’ CATS Warrior, intended to fly with the Tejas fighter and carry the indigenous Smart Anti-Airfield Weapon along with other precision munitions, completed an engine ground run on its full-scale demonstrator in early 2025.

None of this is without risk. Anduril’s own record is a useful check on the marketing. In November 2025 the Wall Street Journal reported a string of setbacks across its autonomous systems, including engine damage to a Fury airframe during a ground test that summer, more than a dozen Navy vessels malfunctioning during a California exercise, and Ukrainian forces withdrawing Anduril’s Altius loitering munitions from front-line use in 2024 after repeated failures against Russian jamming.

Anduril calls this a selective reading of routine development setbacks. The US Congressional Research Service, separately, has flagged for lawmakers how much decision-making authority to hand autonomous software as it matures, and whether the Air Force has planned adequately for storing, transporting and sustaining hundreds of large uncrewed aircraft.

What all of this adds up to is a bet that autonomy and affordability, not just stealth and speed, will decide the next era of air combat. Six companies competing for a single software contract in the United States shows where the real fight now sits.

Every airframe shown at Farnborough this month is still only half the story. None of them fly themselves without someone, somewhere, deciding whether they get to pull the trigger.

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The global economy is now expected to grow 3% in 2026, down from an April forecast of 3.1%, a modest slowdown partly offset by artificial intelligence (AI)-driven demand. Growth is projected to rebound to 3.4% in 2027, still below the 3.5% average recorded across 2024 and 2025. Global headline inflation is expected to reach 4.7% this year, up from 4.1% in 2025, before easing to 3.9% in 2027.

“The global outlook is being shaped by two powerful forces pulling in opposite directions: the lingering effects of the energy shock from the war in the Middle East and a technology-driven investment boom,” said Petya Koeva Brooks, deputy director of the IMF’s research department.

The downgrade came after the United States renewed strikes on Iran following attacks on three commercial ships in the Strait of Hormuz, days before a second round of American bombing raids on Iranian targets.

President Donald Trump said he believed the ceasefire between Washington and Tehran was over, a remark that sent Brent crude up as much as 7%, briefly topping USD 79 a barrel. The IMF’s forecast assumes shipping through the Strait of Hormuz begins normalising in mid-July, with prewar conditions restored by March 2027.

Energy prices are now running about 25% above pre-war levels, with the fund pencilling in an average of USD 89 a barrel for 2026.

Deniz Igan, chief of the IMF’s World Economic Studies division, said the global economy had proven more resilient than expected in April, helped by the release of strategic oil reserves and improved energy efficiency, though she cautioned that a collapse of the ceasefire could catch the world economy in a weaker position than before.

The outlook varies sharply by region. The United States is forecast to grow 2.3%, the fastest among major advanced economies, supported by fiscal policy and continued technology-related investment.

The eurozone forecast was trimmed to 0.9%, Japan to 0.6% and Canada to 1.1%, while Brazil’s outlook was raised to 2.4%. China is expected to grow 4.6%, up from April’s 4.4% estimate, and India was downgraded slightly to 6.4%.

Countries at the centre of the AI hardware supply chain fared best. Taiwan, South Korea, Thailand and Malaysia all posted stronger-than-expected results, with South Korea’s annualised first-quarter growth reaching 7.5%, nearly four times the fund’s earlier estimate, despite its heavy reliance on Middle Eastern energy imports.

The Middle East and Central Asia region bore the brunt of the downgrade, with growth cut to 0.7% for 2026 before an expected rebound to 6.5% in 2027. Saudi Arabia’s forecast was reduced by 1.4 percentage points to 1.7%.

The IMF’s update follows a starker warning from the World Bank, which cut its own 2026 global growth forecast to 2.5%, describing the slowdown as the worst hit to the global economy since the Covid-19 pandemic.

The fund urged policymakers to keep monetary policy focused on restoring price stability and to avoid broad-based subsidies or price controls that could distort markets.

Talking about the Iran war, the recent escalation of hostilities between the Washington and Tehran could upend the International Energy Agency’s forecast of a significant oil market surplus in 2027, it said on ‌Friday. While global supply jumped in June with the reopening of the strategically important Strait of Hormuz, supply levels haven’t been able to reach the pre-war levels.

The effective closure of the maritime chokehold had taken out ⁠as much as 14 million barrels per day of crude flows during the peak of the largest oil supply crisis in history.

As per the, IEA ‘s data, global oil supply rose by 4.1 million bpd in June, but remained 9.4 million bpd below pre-war levels. While the energy watchdog sees supply expanding by 7.5 million bpd in 2027 after a 3.7 million bpd contraction in 2026, realisation of the estimates will still be dependent upon the improvement in Hormuz transits.

“An escalation in hostilities on 7-8 July, however, ‌clouds ⁠the outlook and could upend the forecast that sees the market flipping to a surplus next year,” IEA said, adding that a lasting peace agreement is a “must” for oil markets to normalise.

The IEA’s 2027 forecasts imply that supply will outweigh demand by ⁠4.62 million bpd in 2027 from 2026’s deficit of 860,000 bpd, provided producers can restart fields and refiners can resume normal product shipments.

The Paris-based agency also sees global oil demand falling by 1 million bpd this year, before rebounding to rise two million bpd in 2027. In the nearer term, ⁠it sees the peak summer fuel demand season lifting consumption by around eight million bpd when compared with May’s low point at the peak of the Hormuz crisis.

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As per the study titled “AI in Asset Management Market Report 2026,” published on Research and Markets, the ongoing surge stems from tailwinds like advancements in digital asset management, the increasing complexity of investment portfolios, the proliferation of financial data, and the heightened demand for error reductions in asset tracking and analytics-driven decision-making.

“The forecast for AI in the asset management market extends this trajectory, predicting a leap to USD 21.82 billion by 2030 with a CAGR of 32.4%. Key factors fueling this growth include the rising adoption of AI-driven financial platforms, an escalating demand for real-time asset visibility, and the expansion of algorithmic investment strategies. The integration of AI with blockchain and the development of cloud-based asset management solutions contribute to this anticipated expansion. Future trends predict advancements in automated asset tracking, AI-driven portfolio optimization, and real-time investment decision support,” the report said.

The immediate need for sophisticated fraud detection solutions has become the critical element to the AI market’s growth. Integrating AI into asset management solutions enhances operational efficiency, accuracy, and real-time monitoring capabilities, especially in fraud detection. To prove its point, the study cited the 2024 incident, in which the United States Department of the Treasury reported prevention and recovery of over USD 4 billion in fraud by leveraging machine-learning AI, showcasing the significant impact of AI-powered fraud detection.

“Leading companies are enhancing their market positions through innovations such as AI supercomputing services. In March 2023, NVIDIA Corporation launched DGX Cloud, offering a high-performance AI-training-as-a-service platform. This service provides enterprises with a serverless environment optimized for AI, signifying a leap forward in AI application accessibility,” the report said.

DGX Cloud now allows organizations to instantly access NVIDIA AI supercomputing across global cloud platforms using a standard web browser. The AI-training-as-a-service platform is offering enterprise developers a serverless environment tailored for doing R&D activities related to generative AI. DGX Cloud, equipped with eight NVIDIA 80GB Tensor Core GPUs, enables organizations to operate their own AI supercomputer through a web interface while delivering 640GB of GPU memory per instance.

“In corporate strategy developments, Alarm.com acquired Vintra in April 2023, marking an expansion of its deep-learning capabilities and fortifying its position in advanced video analytics for asset management. Such acquisitions indicate a trend of strategic enhancements among major players in the market,” it added further.

Prominent companies dominating the AI in asset management market include Alphabet, Microsoft, JPMorgan Chase, and Amazon Web Services (AWS), among others. Going by the regional analysis, in 2025, North America emerged as the leading market, with significant activity recorded in Asia-Pacific, Europe, and South America as well.

“Tariffs have had a dual impact, increasing costs for imported data center hardware and promoting a shift to cloud-based platforms. This transition supports software-centric models, boosting regional fintech ecosystems,” the report observed.

“These tariffs have influenced the AI in the asset management market by increasing costs related to imported data center hardware, analytics servers, and supporting IT infrastructure. These impacts are more visible in on-premise deployments across North America, Europe, and Asia Pacific. Higher infrastructure costs have encouraged firms to reassess capital investments. At the same time, tariffs are accelerating migration toward cloud-based AI asset management platforms. This transition is supporting software-centric delivery models and strengthening regional fintech ecosystems,” it added further.

Cutting-edge technologies like machine learning, deep learning, and predictive analytics have emerged as central elements to AI applications in asset management, being employed across sectors like BFSI, healthcare, retail, and more.

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Doug Petno, the bank’s co-chief executive of commercial and investment banking, said the lender expects rebuilding and economic diversification across the region to require hundreds of billions of dollars once the conflict ends, creating significant opportunities for banks, investors, and capital markets.

“We’ve deployed significantly more capital and expanded our risk limits across the region,” Petno told Abu Dhabi-based newspaper The National. He said the move reflects both the immediate financing needs created by the conflict and JPMorgan’s long-term confidence in the Gulf’s growth prospects.

While the bank did not disclose how much it had adjusted its risk appetite, Petno said teams were working country by country to assess financing requirements as governments and businesses prepare for major capital expenditure programs.

“Funding could come from JPMorgan’s balance sheet, public debt markets, or private capital, depending on the nature of each project,” he said, adding that the overall financing requirement would become clearer after the conflict ends.

The bank expects reconstruction spending to focus on energy infrastructure, logistics networks, data centers, and artificial intelligence-related projects, alongside broader investments aimed at strengthening the region’s digital and economic resilience.

The conflict, which began in the last week of February 2026 following US and Israeli strikes on Iran and subsequent attacks by Tehran on neighbouring countries, disrupted business activity and damaged key energy and civilian infrastructure across parts of the region.

Despite the geopolitical uncertainty, Petno said Gulf financial markets had demonstrated resilience. Debt capital markets remained largely open throughout the conflict, although equity capital market-related activity had slowed as investors adopted a more cautious stance.

JPMorgan has been steadily expanding its Middle East presence. After establishing its regional headquarters at Riyadh in 2025, the bank now plans to grow its operations in the UAE and double its regional workforce over the next three to five years.

The lender is also working closely with governments, sovereign wealth funds, and corporate clients to structure financing for reconstruction and long-term economic diversification projects.

As Gulf economies continue efforts to reduce dependence on hydrocarbons, JPMorgan believes the region will remain one of the world’s most active investment destinations, with demand for infrastructure and strategic capital expected to extend well beyond the immediate post-conflict recovery period.

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Britain is preparing to spend more on its military than at any point in the last thirty years. The plan covers everything from stealth fighter jets and nuclear submarines to drones, artificial intelligence, and new accommodation for troops.

It positions defence not just as a security matter but as an economic one, with the spending uplift projected to create nearly 60,000 extra direct and indirect British industry jobs by the end of the decade, taking total defence-related employment to more than half a million.

But behind the headline numbers lies a more complicated story, one of an economy under strain, a government in political turmoil, and a threat from Russia that is growing faster than any spending plan can move.

What the Plan Actually Does
The DIP is built on the 2025 Strategic Defence Review, which concluded that Britain’s Armed Forces needed to modernise urgently and move towards what the government calls “warfighting readiness.” It confirms substantial investment in artificial intelligence, autonomy, cyber capability and digital integration, while maintaining major commitments to combat air, the nuclear deterrent and wider force modernisation.

The headline programmes are substantial. More than 8 billion pound will go to the Global Combat Air Programme (GCAP) over the next four years, developing the next-generation stealth fighter jet for the Royal Air Force alongside Japan and Italy.

More than 63 billion pound over the same period will strengthen the United Kingdom’s nuclear deterrent and fund Dreadnought and SSN-AUKUS submarines, a new warhead, and other crucial nuclear work. The government is also purchasing 12 F-35A aircraft and joining NATO’s nuclear sharing mission for the first time.

On land, the plan backs the long-troubled Ajax armoured vehicle programme, the upgraded Challenger 3 main battle tank, and the Boxer wheeled armoured vehicle, within a 19.2 billion pound land domain allocation. A further 26 billion pound over the next decade will go to Project Royal Oak, the biggest naval base upgrade in over 45 years, including major investments at Barrow and Plymouth.

And for the first time, there is serious financial weight behind the technology of future warfare. The Strategic Defence Review signalled that drones, AI and autonomous systems would become central to future conflict, and the Investment Plan gives that vision real financial backing, including 115 million pound to raise the UK’s defences against AI-related threats.

A Nation That Cannot Easily Afford It
There is one critical context that no amount of bold language can paper over, namely, Britain’s struggling economy.

UK GDP is expected to grow by just 0.7% to 1.1% in 2026, depending on the forecaster. The economy actually contracted by 0.2% in the first quarter of the year. Public sector net debt stands at 93.8% of GDP, at levels last seen in the early 1960s, and the government borrowed 132 billion pound in the financial year ending March 2026. Rising energy prices, driven partly by Middle East instability, are pushing inflation back up and keeping the Bank of England cautious about cutting interest rates.

To fund the DIP without blowing up its fiscal rules, the government has had to make difficult choices elsewhere. The package is funded primarily by reallocating budgets from across government departments, with other departments asked to contribute one penny in every pound of their capital budgets.

In practical terms, that means schools, hospitals and infrastructure projects are quietly absorbing cuts so that tanks and submarines can be funded. The government says it has identified 10.3 billion pound in savings now, with a further 4.7 billion pound to be confirmed at Budget 2026.

Some of the pain is visible inside the defence budget itself. Military housing improvements have been delayed. Several programmes have been restructured or cancelled to free up cash.

The DIP openly acknowledges that it inherited a programme in which 47 of 49 major projects were delayed or over budget. That is a sobering baseline from which to launch the most ambitious military spending surge in a generation.

A Minister Quits in Protest
The tension between what the military needs and what the Treasury is willing to provide became very public last month when Defence Secretary John Healey and Armed Forces Minister Al Carns both resigned.

In his resignation letter, Healey accused Prime Minister Keir Starmer of failing to commit the resources needed to defend the country. He wrote that Starmer had been “unable,” and the Treasury “unwilling,” to provide what the nation needed at a time of rising threats. Without adequate funding, he said, he was being forced to make decisions that would reduce the readiness of the Armed Forces, increase the risk to personnel on operations, and could make the country less safe.

Healey also turned the Prime Minister’s own words against him, citing Starmer’s warning at the Munich Security Conference earlier in the year that Russia could attack NATO as soon as 2030.

ALSO READ | As threat of war looms, Europe hikes spending on military and defence equipment

The resignations, the seventh and eighth ministerial departures in a month from a government already reeling from collapsed poll ratings, threw the DIP process into confusion. Dan Jarvis was appointed as the new Defence Secretary, inheriting a plan that had been delayed for months and a department deeply frustrated by the pace of Treasury decision-making. The episode laid bare a fundamental tension at the heart of British defence policy.

The political will to spend exists, but the economic room to do so is tighter than the rhetoric suggests. Analysts at the Institute for Fiscal Studies note that the government has not set out how it will pay for around a third of the increase, leaving an average of 1.2 billion pound a year to be decided at a future budget, with further impacts on other areas of spending, tax, or borrowing to follow.

The Recurring Recruitment Problem
Money can buy equipment. It is much harder to buy people, and Britain’s Armed Forces are running short of them.

The trained full-time force stood at 126,740 on 1 April 2026, down from 133,570 in April 2023, leaving the forces nearly 6,830 personnel below their level three years ago. While recruitment improved slightly this year, the deeper problem is retention.

Voluntary departures account for approximately 60% of outflow, as mid-career professionals with specialised skills leave for better-paying civilian roles.

The DIP attempts to address this with a nine billion pound investment in military housing over ten years, along with pay improvements and recruitment reforms. But housing alone will not fix a culture problem that stretches deep into military life. The Army is smaller than at any point since the Napoleonic era. Recruitment targets have been missed every year for the better part of a decade.

Russia Is Not Waiting
While Britain deliberates, Russia is not standing still. Dutch military intelligence has warned that Russia could build up enough combat power for a regional challenge to NATO within a year after fighting stops in Ukraine, aiming to fracture political unity in the Alliance rather than defeat it militarily outright.

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The same assessment noted that the Russian armed forces have not only grown larger but have also become more effective than before the war, with significant qualitative improvements in unmanned systems, command and control, and battlefield adaptability.

In 2026, Russian defence spending is projected to reach 180 billion pound and, when adjusted for purchasing power parity, the effective amount Moscow will spend is estimated at USD 400 to USD 500 billion.

Russia has already moved its economy onto a war footing, drawing hundreds of thousands of workers into its defence industrial complex and sustaining large-scale weapons production despite Western sanctions.

Incidents have already tested the edges of the threat. In September 2025, 19 drones entered Polish airspace. Three Russian MiG-31 fighters violated Estonian airspace for over ten minutes. In January 2026, Russia struck western Ukraine just 70 kilometres from the Polish border with an intermediate-range missile, a move the UK, France and Germany jointly condemned as an unacceptable escalation.

Is Britain Ready?
The DIP is a serious document. It is the most comprehensive military spending plan Britain has produced in decades, and it signals a genuine acceptance that the post-Cold War holiday from history is over. But serious analysts are clear-eyed about its limits.

A funding hole of 30 billion pound to 45 billion pound remains between current commitments and the 3% of GDP target, money that is desperately needed to retrofit, re-equip and retrain existing forces for modern warfare. Britain is also not spending in a vacuum. Germany has embarked on its largest military expansion since reunification.

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Poland is spending around 5% of GDP. The Nordic states continue to increase investment. Viewed internationally, Britain’s settlement looks closer to keeping pace with an accelerating field than establishing any decisive lead.

Britain is not ready enough yet, but it is moving in the right direction, slowly, under fiscal constraint, and with more political turbulence than the moment demands. The DIP is a necessary foundation.

Whether it becomes an adequate one will depend on decisions not yet made, budgets not yet confirmed, and a Treasury that must eventually reckon with the fact that security is not a line item that can be deferred until the economy improves. In the Europe of 2026, that moment of reckoning may arrive sooner than anyone in Whitehall would like.

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Somantra’s study, which tracked 20 Australian insurance brands across 34,278 real consumer conversations on Google AI Overviews and ChatGPT throughout May 2026, also arrives in conjunction with a separate GlobalData poll that found the broader industry still practicing caution about AI’s maturity, a posture that, in GlobalData’s opinion, may be costing mid-tier and specialty insurers ground that will prove difficult to recover.

Somantra’s data showed two coins of the Aussie insurance industry, an AI-first search shift and a massive stretch of the market being “unclaimed.”

“Across the detailed, intent-driven queries consumers pose to AI platforms—questions about specific coverage scenarios, eligibility conditions, and product comparisons—70% of responses named no insurance brand. Across 34,278 tracked conversations, that represents approximately 24,000 consumer research interactions in a single month in which no Australian insurer received a mention on either platform. The pool of domains cited by AI engines contracted 21% between March and May 2026, falling from 10,777 to 8,488 unique domains. As that pool narrows, the barrier to entering AI-generated recommendations rises,” Somantra noted.

“This is not a problem. This is an opportunity. Every one of those brandless responses is a gap in the market, which is proof that the right content, structured the right way and published on the sources AI engines trust, could put a brand into that answer instead of nobody at all. The window is closing. Every month, more of the long tail gets claimed by whichever brand shows up first with the right content on the right sources. Waiting for AI search to mature before acting just hands that ground to a competitor,” said Arun Prasad, founder of Somantra.

“Where AI platforms do recommend brands, attention is concentrated among a handful of insurers. On ChatGPT, three brands – Allianz, NRMA, and AAMI – accounted for half of all insurance-related mentions. Nine brands collectively covered 90% of total mentions, leaving the remaining 11 tracked brands competing for a thin slice of visibility. Google AI Overviews distributed attention more broadly, though not substantially so. Four brands reached the 50% threshold, and 11 were needed to cover 90% of mentions,” the study remarked.

Among the major industry players, Allianz recorded the highest combined total at 13,437 mentions across both platforms, followed by NRMA (at 12,524) and Budget Direct (at 10,708).

“At the other end, Ozicare appeared in 62 conversations, Coles Insurance in 517, and Qantas Insurance in 895—figures that suggest these brands are largely absent from AI-mediated consumer research, regardless of their standing on conventional search,” Prasad said, while adding, “Google gives you options. ChatGPT gives you a shortlist, and the shortlist is getting shorter. If you are not already in the top tier on a given platform, you are fighting over scraps of visibility, not competing on equal terms.”

Also, the study discovered a “low agreement” between the Google AI Overviews and ChatGPT, with both tools recommending the same brand for the same query in only 27.9% of cases in May 2026, up from 23.7% in March. In approximately seven out of 10 head-to-head comparisons, a consumer asking the same question on each platform received a different brand recommendation.

“Budget Direct illustrates the platform divergence risk in concrete terms. It led all brands on Google AI Overviews with 8,556 mentions, yet only 20.1% of its total AI visibility came from ChatGPT. For every five times Budget Direct appeared across both platforms, four of those appearances were on Google alone. As consumers increasingly use ChatGPT alongside Google to research financial products, a brand with that degree of platform concentration carries exposure it may not yet be measuring. For brands currently underrepresented on one platform, the divergence also creates an opening. Because Google AI Overviews and ChatGPT are forming their assessments of brand authority independently, a brand shut out of one platform’s preferred list may retain room to establish presence on the other,” the report said.

“The dual combination of expanding opportunity surface area and divergence in brand recommendations between the AI search engines is the biggest opportunity for brands right now. Large brands have spent a decade optimizing for a single search engine. That playbook does not transfer to a world where two major platforms disagree most of the time and where most of the specific questions consumers ask are not being answered by anyone,” Prasad remarked.

“The opportunity is not evenly distributed across product lines, and the distinction matters for insurers assessing where AI search effort is most likely to yield results. Car insurance generated the highest volume of brand mentions at 22,777, followed by home and contents at 20,591 and motorcycle at 16,376. In these categories, established brands have accumulated visibility that a new entrant or smaller competitor would need sustained effort to displace,” he added further.

Pet insurance and life insurance, on the other hand, presented a different picture. Pet recorded 2,457 total brand mentions across both platforms, and the life segment registered 1,283. Also, these are the same categories where fewer brands currently feature in AI-generated responses.

“An insurer in either line that moves early to build presence on the sources AI engines cite faces less entrenched competition than one attempting to gain ground in car or home, where category leaders have already established substantial leads,” Somantra explained.

“Within categories, those leads are significant. Allianz held 3,941 mentions in travel insurance; NRMA led car with 3,238; QBE led motorcycle with 2,896; and Budget Direct led pet with 940. The same brandless-query dynamic that applies across the market applies within these product lines: second and third-tier brands trail category leaders by margins that the data suggests are widening with each reporting cycle,” it added further.

The two-month gap between Somantra’s March and May audits produced movement across the board, with AI search visibility appearing responsive to recent content and citation activity in a way that shifts the competitive position of brands more quickly than conventional organic search typically does.

“Allianz added 960 mentions to move past NRMA into the top overall position. Budget Direct posted the largest percentage gain among tracked brands, up 9.7%, displacing AAMI from the top three. AAMI recorded the steepest absolute decline, losing 2,147 mentions – an 18.1% drop. Bingle fell 30.8%, GIO fell 29.3%, and CGU dropped 27.8%. Citation patterns on ChatGPT also shifted. In March, Canstar was the platform’s most-cited domain with 232 references. By May, both Finder and Canstar each exceeded 900 citations, with Finder taking the top position at 902. Reddit climbed from 147 to 387 citations, reflecting a source mix that extends well beyond traditional comparison-site ecosystems,” Somantra concluded.

Talking about the GlobalData poll of 113 insurance industry respondents, conducted across Q1 and Q2 of 2026, the survey reported nearly a quarter believing AI had not yet reached a level of maturity suitable for widespread use within the industry.

Ben Carey-Evans, senior insurance analyst at GlobalData, attributed the hesitation partly to the narrow scope of current implementations and to unresolved questions about accountability, as he said, “This might be because use cases to date are largely around customer service and chatbots rather than full-scale implementation. Regulation has not fully caught up yet, and there is concern around who is liable for mistakes made by AI.”

“Those liability and regulatory questions are not abstract for an industry that distributes financial products to consumers. As AI platforms increasingly surface insurance brand recommendations in response to consumer queries, the question of how those recommendations are generated and who bears responsibility when they are incomplete or inaccurate sits unresolved across the industry,” he added further.

“A shortage of in-house expertise ranked as the second-most-cited concern in the GlobalData poll. The firm’s job analytics data recorded approximately 63,293 active AI-related insurance roles in 2025—the highest on record and around 51% above 2024 levels. The hiring response reflects the scale of the gap, even as the technology continues to outpace the industry’s capacity to build expertise around it,” Carey-Evans remarked.

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The market was valued at USD 5.1 billion in 2025 and is expected to reach USD 22.2 billion by 2033, expanding at a compound annual growth rate (CAGR) of 20.4% from 2026 to 2033.

“The growing convergence of artificial intelligence and energy infrastructure is reshaping how electricity is generated, distributed, stored, and consumed. As energy systems become more decentralized and data-intensive, AI is emerging as a critical enabler of predictive analytics, demand forecasting, asset optimization, and real-time operational intelligence. The increasing deployment of smart meters, intelligent sensors, connected energy assets, and advanced grid management systems has created unprecedented volumes of operational data. Energy providers are leveraging AI-powered platforms to transform this data into actionable insights, enabling faster decision-making, lower maintenance costs, and improved service reliability,” Grand View Research noted.

According to the agency, the energy and utility markets are being driven by the rising digitalization of energy infrastructure and the growing need for efficient management of generation, transmission, and distribution networks. AI solutions are helping utilities identify equipment failures before they occur, monitor grid performance in real time, and automate complex operational processes.

“These capabilities are becoming increasingly important as energy providers face growing pressure to enhance reliability while supporting the transition toward cleaner and more sustainable energy systems,” the study noted further.

One of the most significant growth drivers for AI in the energy market is the rapid expansion of renewable energy generation. Solar and wind resources introduce variability into power systems, creating new challenges for grid operators responsible for balancing supply and demand. Artificial intelligence technologies enable advanced forecasting models that analyze weather patterns, historical generation trends, and real-time operational conditions to improve renewable energy output predictions and optimize dispatch decisions. These capabilities allow utilities and energy companies to better manage fluctuating renewable generation while maintaining grid stability,” Grand View Research said.

The report also highlights that renewable energy management accounted for 33.0% of the global market in 2025, making it the largest application segment. Growing investments in renewable power infrastructure, battery storage systems, and distributed energy resources continue to strengthen demand for AI-powered energy management platforms that are capable of enhancing grid stability and maximizing asset performance.

Beyond renewable energy management, AI is also gaining significant traction across predictive maintenance, energy trading optimization, grid automation, and operational safety applications.

“Energy companies are increasingly utilizing machine learning algorithms to identify anomalies, reduce downtime, and improve maintenance planning for critical infrastructure assets. AI-powered predictive maintenance solutions help operators monitor equipment health in real time, minimizing costly outages and extending asset lifecycles. The growing use of advanced analytics also enables utilities to optimize energy distribution networks, improve customer service, and enhance overall operational efficiency,” the agency remarked.

Along with the evolution of AI capabilities, robotics is also emerging as one of the fastest-growing application areas within the energy sector. As per Grand View Research, AI-enabled robots and drones are increasingly being deployed to inspect power plants, transmission networks, pipelines, wind farms, and solar facilities. These technologies are providing continuous monitoring capabilities while reducing risks associated with hazardous or remote-site inspections.

According to Grand View Research, the robotics segment will expand at a CAGR (Compound Annual Growth Rate) of 24.1% through 2033, reflecting growing industry investments in automation technologies designed to improve safety, operational visibility, and infrastructure management.

“From a technology perspective, software and platform-based solutions continue to dominate industry spending. The report indicates that the solutions segment accounted for 69.2% of total market revenue in 2025, reflecting widespread adoption of AI-powered applications supporting forecasting, analytics, grid optimization, and operational automation. Organizations across the energy value chain are investing in intelligent software platforms capable of delivering actionable insights from large and complex datasets,” the agency noted.

Meanwhile, the services segment is expected to grow at a CAGR of 22.0% during the forecast period (from 2026 to 2033), driven by tailwinds like increasing demand for consulting, implementation, integration, training, and support services associated with AI deployments.

“Regionally, North America accounted for 38.2% of global revenue in 2025, maintaining its position as the largest regional market for AI in energy applications. Strong investments in digital transformation initiatives, advanced utility infrastructure, smart grid technologies, and AI innovation ecosystems continue to support market growth throughout the region. The United States remains a key contributor to market expansion, with increasing adoption of intelligent energy management systems, predictive maintenance solutions, and renewable energy optimization technologies. According to the report, the US market is projected to expand at a CAGR exceeding 21.8% through 2033,” Grand View Research mentioned.

“Industry experts note that the growing intersection between AI adoption and energy demand is becoming an increasingly important strategic issue. As AI workloads expand globally, data centers are consuming larger amounts of electricity, creating additional requirements for grid modernization, power management optimization, and infrastructure resilience. This trend is expected to accelerate investments in intelligent energy technologies capable of improving efficiency and supporting future electricity demand growth,” it added further.

Utilities and energy providers are increasingly exploring AI-powered solutions to address both operational challenges and long-term sustainability objectives. The competitive landscape also includes a mix of global technology leaders, industrial automation providers, and specialized AI software companies focused on transforming energy operations through advanced analytics and machine learning technologies. Companies like Siemens AG, ABB, General Electric, C3.ai, Atos SE, Flex, AppOrchid, Uptake Technologies, Origami Energy, Alpiq, and SmartCloud are becoming part of this growing yet competitive ecosystem.

“These companies are investing heavily in product innovation, strategic partnerships, and digital energy solutions designed to improve grid reliability, optimize asset performance, and support the transition toward cleaner energy systems,” Grand View Research concluded.

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