ai

UK institutions embrace enterprise AI through global tech alliance

Microsoft, Accenture, and Avanade are deepening their 25-year partnership to bring AI into some of the UK’s most vital sectors, including healthcare and finance. NHS England is piloting AI-powered tools to streamline patient services and cut down on time-consuming administrative tasks, while Nationwide Building Society is deploying machine learning to improve customer services, speed up mortgage approvals, and enhance fraud detection.

The three companies have different responsibilities in tackling the challenges of enterprise AI. Microsoft provides the Azure cloud platform and pre-built AI models, Accenture contributes sector-specific expertise and governance frameworks, and Avanade integrates the technology into existing systems and workflows. That structure helps organisations move beyond experimental AI pilots and scale solutions reliably in highly regulated industries.

Unlike consumer applications, enterprise AI must meet strict compliance requirements, especially concerning sensitive patient data or financial transactions. The partnership emphasises embedding AI directly into day-to-day operations rather than treating it as an add-on, reducing disruption for staff and ensuring systems work seamlessly once live.

With regulators tightening oversight, the alliance highlights responsible AI as a key focus. By prioritising transparency, security, and ethical use, Microsoft, Accenture, and Avanade are positioning their collaboration as a blueprint for how AI can be adopted across critical institutions without compromising trust or reliability.

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AI boom drives massive surge in data centre power demand

According to Goldman Sachs, the surge in AI is set to transform global energy markets, with data centres expected to consume 165% more electricity by 2030 compared to 2023. The bank reports that US spending on data centre construction has tripled in just three years, while occupancy rates at existing facilities remain close to record highs.

The demand is driven by hyperscale operators like Amazon Web Services, Microsoft Azure, and Google Cloud, which are rapidly expanding their infrastructure to meet the power-hungry needs of AI systems.

Global data centres use about 55 gigawatts of electricity, more than half of which supports cloud computing. Traditional workloads like email and storage still account for a third, while AI represents just 14%.

However, Goldman Sachs projects that by 2027, overall consumption could rise to 84 gigawatts, with AI’s share growing to over a quarter. That shift is straining grids and pushing operators toward new solutions as AI servers can consume ten times more electricity than traditional racks.

Meeting this demand will require massive investment. Goldman Sachs estimates that global grid upgrades could cost as much as US$720 billion by 2030, with US utilities alone needing an additional US$50 billion in new generation capacity for data centres.

While renewables like wind and solar are increasingly cost-competitive, their intermittent output means operators lean on hybrid models with backup gas and battery storage. At the same time, technology companies are reviving interest in nuclear power, with contracts for over 10 gigawatts of new capacity signed in the US last year.

The expansion is most evident in Europe and North America, with Nordic countries, Spain, and France attracting investment due to their renewable energy resources. At the same time, hubs like Germany, Britain, and Ireland rely on incentives and established ecosystems. Yet, uncertainty remains.

Advances like DeepSeek, a Chinese AI model reportedly as capable as US systems but more efficient, could temper power demand growth. For now, however, the trajectory is clear, AI is reshaping the data centre industry and the global energy landscape.

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AI-generated media must now carry labels in China

China has introduced a sweeping new law that requires all AI-generated content online to carry labels. The measure, which came into effect on 1 September, aims to tackle misinformation, fraud and copyright infringement by ensuring greater transparency in digital media.

The law, first announced in March by the Cyberspace Administration of China, mandates that all AI-created text, images, video and audio must carry explicit and implicit markings.

These include visible labels and embedded metadata such as watermarks in files. Authorities argue that the rules will help safeguard users while reinforcing Beijing’s tightening grip over online spaces.

Major platforms such as WeChat, Douyin, Weibo and RedNote moved quickly to comply, rolling out new features and notifications for their users. The regulations also form part of the Qinglang campaign, a broader effort by Chinese authorities to clean up online activity with a strong focus on AI oversight.

While Google and other US companies are experimenting with content authentication tools, China has enacted legally binding rules nationwide.

Observers suggest that other governments may soon follow, as global concern about the risks of unlabelled AI-generated material grows.

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Political backlash mounts as Meta revises AI safety policies

Meta has announced that it will train its AI chatbot to prioritise the safety of teenage users and will no longer engage with them on sensitive topics such as self-harm, suicide, or eating disorders.

These are described as interim measures, with more robust safety policies expected in the future. The company also plans to restrict teenagers’ access to certain AI characters that could lead to inappropriate conversations, limiting them to characters focused on education and creativity.

The move follows a Reuters report that revealed that Meta’s AI had engaged in sexually explicit conversations with underage users, TechCrunch reports. Meta has since revised the internal document cited in the report, stating that it was inconsistent with the company’s broader policies.

The revelations have prompted significant political and legal backlash. Senator Josh Hawley has launched an official investigation into Meta’s AI practices.

At the same time, a coalition of 44 state attorneys general has written to several AI companies, including Meta, emphasising the need to protect children online.

The letter condemned the apparent disregard for young people’s emotional well-being and warned that the AI’s behaviour may breach criminal laws.

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Meta faces turmoil as AI hiring spree backfires

Mark Zuckerberg’s ambitious plan to assemble a dream team of AI researchers at Meta has instead created internal instability.

High-profile recruits poached from rival firms have begun leaving within weeks of joining, citing cultural clashes and frustration with the company’s working style. Their departures have disrupted projects and unsettled long-time executives.

Meta had hoped its aggressive hiring spree would help the company rival OpenAI, Google, and Anthropic in developing advanced AI systems.

Instead of strengthening the company’s position, the strategy has led to delays in projects and uncertainty about whether Meta can deliver on its promises of achieving superintelligence.

The new arrivals were given extensive autonomy, fuelling tensions with existing teams and creating leadership friction. Some staff viewed the hires as destabilising, while others expressed concern about the direction of the AI division.

The resulting turnover has left Meta struggling to maintain momentum in its most critical area of research.

As Meta faces mounting pressure to demonstrate progress in AI, the setbacks highlight the difficulty of retaining elite talent in a fiercely competitive field.

Zuckerberg’s recruitment drive, rather than propelling Meta ahead, risks slowing down the company’s ability to compete at the highest level of AI development.

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Stethoscope with AI identifies heart issues in seconds

A new stethoscope powered by AI could enable doctors to identify three serious heart conditions in just seconds, according to UK researchers.

The device replaces the traditional chest piece with a small sensor that records both electrical signals from the heart and the sound of blood flow, which are then analysed in the cloud by AI trained on large datasets.

The AI tool has shown strong results in trials across more than 200 GP practices, with patients tested using the stethoscope being more than twice as likely to be diagnosed with heart failure within 12 months compared with those assessed through usual care.

It was also 3.45 times more likely to detect atrial fibrillation and almost twice as likely to identify heart valve disease.

Researchers from Imperial College London and Imperial College Healthcare NHS Trust said the technology could help doctors provide treatment at an earlier stage instead of waiting until patients present in hospital with advanced symptoms.

The findings, known as Tricorder, will be presented at the European Society of Cardiology Congress in Madrid.

The project, supported by the National Institute for Health and Care Research, is now preparing for further rollouts in Wales, south London and Sussex. Experts described the innovation as a significant step in updating a medical tool that has remained largely unchanged for over 200 years.

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How local LLMs are changing AI access

As AI adoption rises, more users explore running large language models (LLMs) locally instead of relying on cloud providers.

Local deployment gives individuals control over data, reduces costs, and avoids limits imposed by AI-as-a-service companies. Users can now experiment with AI on their own hardware thanks to software and hardware capabilities.

Concerns over privacy and data sovereignty are driving interest. Many cloud AI services retain user data for years, even when privacy assurances are offered.

By running models locally, companies and hobbyists can ensure compliance with GDPR and maintain control over sensitive information while leveraging high-performance AI tools.

Hardware considerations like GPU memory and processing power are central to local LLM performance. Quantisation techniques allow models to run efficiently with reduced precision, enabling use on consumer-grade machines or enterprise hardware.

Software frameworks like llama.cpp, Jan, and LM Studio simplify deployment, making local AI accessible to non-engineers and professionals across industries.

Local models are suitable for personalised tasks, learning, coding assistance, and experimentation, although cloud models remain stronger for large-scale enterprise applications.

As tools and model quality improve, running AI on personal devices may become a standard alternative, giving users more control over cost, privacy, and performance.

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India to host OpenAI’s new Stargate data centre

OpenAI is preparing to build a significant new data centre in India as part of its Stargate AI infrastructure initiative. The move will expand the company’s presence in Asia and strengthen its operations in its second-largest market by user base.

OpenAI has already registered as a legal entity in India and begun assembling a local team.

The company plans to open its first office in New Delhi later this year. Details regarding the exact location and timeline of the proposed data centre remain unclear, though CEO Sam Altman may provide further information during his upcoming visit to India.

The project represents a strategic step to support the company’s growing regional AI ambitions.

OpenAI’s Stargate initiative, announced by US President Donald Trump in January, involves private sector investment of up to $500 billion for AI infrastructure, backed by SoftBank, OpenAI, and Oracle.

The initiative seeks to develop large-scale AI capabilities across major markets worldwide, with the India data centre potentially playing a key role in the efforts.

The expansion highlights OpenAI’s focus on scaling its AI infrastructure while meeting regional demand. The company intends to strengthen operational efficiency, improve service reliability, and support its long-term growth in Asia by establishing local offices and a significant data centre.

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Schneider joins SK Telecom on new AI data centre project in Ulsan

SK Telecom has expanded its partnership with Schneider Electric to develop an AI Data Centre (AIDC) in Ulsan.

Under the deal, Schneider Electric will supply mechanical, electrical and plumbing equipment, such as switchgear, transformers, automated control systems and Uninterruptible Power Supply units.

The agreement builds on a partnership announced at Mobile World Congress 2025 and includes using Schneider’s Electrical Transient Analyser Program within SK Telecom’s data centre management system.

It will allow operations to be optimised through a digital twin model instead of relying only on traditional monitoring tools.

Both companies have also agreed on prefabricated solutions to shorten construction times, reference designs for new facilities, and joint efforts to grow the Energy-as-a-Service business.

A Memorandum of Understanding extends the partnership to other SK Group affiliates, combining battery technologies with Uninterruptible Power Supply and Energy Storage Systems.

Executives said the collaboration would help set new standards for AI data centres and create synergies across the SK Group. It is also expected to support SK Telecom’s broader AI strategy while contributing to sustainable and efficient infrastructure development.

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South Korea to sharply increase spending to power AI-based growth

South Korea’s government has outlined a 2026 budget totalling 728 trillion won, a substantial 8.1 percent increase and the most significant rise in four years.

The new administration in South Korea, under President Lee Jae-myung, is using expansionary fiscal measures to drive innovation amid economic headwinds.

Research and development spending will see a record 19.3 percent jump to 35.3 trillion won, with AI receiving the steepest increase. Planned AI expenditure of 10.1 trillion won marks a threefold rise over 2025 and includes procuring 15,000 high-performance GPUs.

Industrial policy funding will grow by 14.7 percent, while social welfare and defence allocations also rise by over 8 percent. The fiscal deficit is expected to widen to 4.0 percent of GDP, with the public debt ratio forecast to reach 51.6 percent.

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