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UN warns of urgency in shaping responsible AI governance

UN Secretary-General António Guterres has told the inaugural meeting of a newly formed Independent International Scientific Panel on Artificial Intelligence that its members have a major responsibility to help shape how the technology is used “for the benefit of humanity”.

The new 40-member panel brings together experts from different regions and disciplines and is expected to help close what Guterres described as ‘the AI knowledge gap’. Its role is to assess the real impact AI will have across economies and societies so that countries can act with the same “clarity” on a more level playing field.

Addressing the scientists at the panel’s first meeting, Guterres said: “Individually, you come from diverse regions and disciplines, bringing outstanding expertise in AI and related fields. Collectively, you represent something the world has never seen before.”

He stressed that the group would provide scientific assessments independently of governments, companies, and institutions, including the UN itself. “AI is advancing at lightning speed… no country, no company, and no field of research can see the full picture alone,” he said, adding that “the world urgently needs a shared, global understanding of artificial intelligence; grounded not in ideology, but in science.”

Guterres also linked the panel’s work to a much broader global agenda, warning that AI will shape peace and security, human rights, and sustainable development for decades to come. He cautioned that misunderstanding around the technology could deepen political and social divisions, saying: “I have seen how quickly fear can take hold when facts are missing or distorted – how trust breaks down and division deepens.”

At a time when “geopolitical tensions are rising and conflicts are raging,” he said, the need for shared understanding and “safe and responsible AI could not be greater.”

He also framed the panel’s task as urgent, arguing that governance efforts are struggling to keep pace with the speed of technological change. “Never in the future will we move as slow as we are moving now. We are indeed in a high level of acceleration,” he said, while warning that the panel is also “in a race against time.”

Referring to earlier UN work through the High-Level Advisory Body on AI, Guterres said the panel does not “start from zero”, before concluding: “I can think of no more important assignment for our world today.”

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Power hardware shortages are delaying AI data centre expansion, despite record investment

US AI data-centre expansion is increasingly being constrained not by chips, servers or funding, but by the electrical hardware needed to connect new facilities to reliable power, Bloomberg reports. While the US–China trade war has pushed many server makers to move production out of China, the deeper dependency remains in power-delivery equipment.

China is still the world’s largest producer of electrical gear used to build and upgrade power infrastructure, both inside data centres and across the wider grid. Shortages of key components, especially transformers, switchgear and batteries, sourced from China and elsewhere, are now slowing project timelines.

The scale of planned build-outs is colliding with these supply limits. Bloomberg cites forecasts that Alphabet, Amazon, Meta and Microsoft will spend more than $650bn in 2026 to expand AI capacity, yet close to half of the planned US data-centre builds this year are expected to be delayed or cancelled.

The problem extends beyond the data-centre fence line. Companies must also fund and coordinate grid upgrades to supply enough electricity, competing for the same scarce equipment as utilities coping with rising demand from electric vehicles and electrified heating.

Sightline Climate data cited by Bloomberg suggests about 12GW of US data-centre capacity is expected to come online in 2026, but only around a third of that capacity is currently under active construction due to multiple constraints. Electrical infrastructure may represent less than 10% of total data-centre cost, but it is schedule-critical, because delays in any link of the power chain can halt an entire project.

Lead times for high-power transformers, in particular, have deteriorated sharply, typically 24 to 30 months before 2020, but now stretching to as long as five years, clashing with AI deployment cycles that can be under 18 months.

To cope, developers are turning to global suppliers, with Canada, Mexico and South Korea becoming major sources of high-power transformers. Even so, US imports of Chinese high-power transformers have surged from fewer than 1,500 units in 2022 to more than 8,000 units through October 2025, according to Wood Mackenzie data cited by Bloomberg. China also supplies over 40% of US battery imports and remains near 30% in some transformer and switchgear categories, underscoring continued reliance despite tariffs and security concerns.

Why does it matter?

Bloomberg’s central warning is that without easing bottlenecks in transformers, switchgear and batteries, and expanding US manufacturing capacity, trillions of dollars of AI investment may not translate into delivered AI capacity, because power infrastructure, not compute, is becoming the limiting factor.

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Microsoft markets Copilot as a productivity boost but warns it is ‘for entertainment purposes only’

Microsoft has spent the past year pushing Copilot as a mainstream productivity tool, baking it into Windows 11 and promoting new hardware such as Copilot+ PCs, yet its own legal language urges caution. In Microsoft’s Copilot Terms of Use, updated in October last year, the company states Copilot is ‘for entertainment purposes only’, may ‘make mistakes’, and ‘may not work as intended’.

The terms warn users not to rely on Copilot for important advice and to ‘use Copilot at your own risk’, a caveat that sits uneasily alongside the product’s business-focused marketing.

The Tom’s Hardware article argues Microsoft is not unique in issuing such warnings. Similar disclaimers are common across the generative AI industry. It points to xAI’s guidance that AI is ‘probabilistic in nature’ and may produce ‘hallucinations’, generate offensive or objectionable content, or fail to reflect real people, places or facts.

While these limitations are well known to those familiar with large language models, the piece notes that many users still treat AI output as authoritative, even in professional settings where scepticism should be standard.

To underline the risks of overreliance, the text cites reports of Amazon-related incidents allegedly linked to ‘Gen-AI assisted changes’. It says some AWS outages were reportedly caused after engineers let an AI coding bot address an issue without sufficient oversight, and that Amazon’s website experienced ‘high blast radius’ problems that required senior engineers to step in. These examples are used to illustrate how AI-generated errors can propagate quickly in complex systems when humans fail to verify the output.

Why does it matter?

Overall, the article acknowledges that generative AI can boost productivity, but stresses it remains a tool with no accountability for mistakes, making verification essential. It warns that automation bias, people trusting machine outputs over contradictory evidence, can be intensified by AI systems that produce plausible-sounding answers that pass casual inspection.

While such disclaimers help companies limit legal liability, the piece suggests aggressive marketing of AI as a productivity ‘hack’ may downplay real-world risks, particularly as firms seek returns on the billions invested in AI hardware and talent.

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Will AI turn novel-writing into a collaborative process

The article argues that a novel’s value cannot be judged solely by the quality of its prose, because many readers respond to other elements such as premise, ideas and character. It points to Amazon reviews of ‘Shy Girl’, which holds a four-out-of-five-star rating based on hundreds of reviews, with many praising its hook despite awareness of ‘the controversy’ around it. One reviewer writes, ‘The premise sucked me in.’

The broader point is that plenty of novels are poorly written yet still succeed, because fiction, like music, is forgiving: a song may have an irresistible beat even with a predictable melody, and a book can move readers through suspense, beauty, realism, fantasy, or a protagonist they recognise in themselves.

From that premise, the piece asks whether fiction’s ‘layers’ (premise, plot, style and voice) must all come from a single person. It notes that collaborative creation is already normal in many fields, even if audiences rarely state their expectations explicitly: readers tend to assume a Booker Prize-winning novel is written entirely by the named author, while journalism is understood to be shaped by both writers and editors, and television and film are widely accepted as writers’ room and revision-heavy processes.

The article uses James Patterson as an example of industrial-scale collaboration in publishing, describing how he supplies collaborators with outlines and treatments and oversees many projects at once, an approach likened to a ‘novel factory’ that some argue distances him from ‘literary fiction’, yet may be the only practical way to sustain a decades-long series.

The author suggests AI will make such factories easier to create, citing a New York Times report on ‘Coral Hart’, a pseudonymous romance writer who uses AI to generate drafts in about 45 minutes, then revises them before self-publishing hundreds of books under dozens of names. Although not a bestseller, she reportedly earns ‘six figures’ and teaches others to do the same.

This points to a future in which authors act more like showrunners supervising AI-powered writers’ rooms, while raising a central risk: readers may not know who, or what, produced what they are reading, especially if AI use is not consistently disclosed despite platforms such as Amazon asking for it.

The piece ends by questioning whether AI necessarily implies high-volume, depersonalised production. Using a personal analogy from music-making, the author notes that technology can enable rapid output, but can also serve a more artistic purpose: helping a creator overcome technical limits and ‘realise a vision’.

Why does it matter?

The underlying argument is not that AI guarantees either shallow churn or genuine creativity, but that the most consequential issues may lie in intent, authorial expectations, and honest disclosure to readers.

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US Supreme Court narrows ISP copyright liability, sharpening focus on intent with potential implications for generative AI

A unanimous 9–0 US Supreme Court ruling this week has narrowed the circumstances under which an internet service provider (ISP) can be held liable for users’ copyright infringement by focusing on a deceptively simple question: intent. Writing for the Court, Justice Clarence Thomas said an ISP is liable only if its service was designed for unlawful activity or if it actively induced infringement; merely providing a service to the public while knowing some users will infringe is not enough.

Applying that standard, the Court found Cox Communications did neither, shielding it from a potential $1bn exposure following a long-running dispute that included a jury verdict later vacated.

The decision is now being read for its possible implications beyond ISPs, particularly in the escalating copyright battle between publishers/authors and generative AI firms. The key distinction raised is that broadband networks function as neutral conduits, whereas large language models are built specifically to produce fluent, human-like writing, including prose, poetry and dialogue, that can resemble the work of human authors.

In the article’s framing, that resemblance is not incidental but central to the product’s purpose: if a subscriber uses broadband to pirate a novel, the ISP did not build its network to enable that outcome, but an AI model prompted to write in a specific author’s style is designed to fulfil that request.

That contrast could open a new line of argument in AI litigation. While major US cases, such as suits brought by the Authors Guild and individual authors against OpenAI, Meta and others, have largely centred on whether training on copyrighted books is itself infringing, the Cox ruling highlights a second front: whether the systems’ purpose and optimisation for author-like output could be characterised as being ‘tailored for’ infringement or as purposeful inducement under an intent-based standard.

Publishers, who are simultaneously watching the lawsuits and negotiating licensing deals with AI companies, have so far been more cautious than the music industry was in its costly fight against Cox, an effort that ultimately produced a Supreme Court ruling that narrowed, rather than expanded, leverage.

Why does it matter?

The broader takeaway is that copyright enforcement may increasingly turn not only on what was copied, but what the copying was for, an approach that could prove consequential for AI companies whose commercial proposition is generating human-quality creative work.

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Advocates push for transparency rules in student AI systems

Consumer protection advocates have introduced a Student AI Bill of Rights, calling on higher education institutions to formalise safeguards as AI becomes increasingly embedded in academic systems.

The proposal, launched by the National Student Legal Defense Network under its SHAPE AI programme, highlights the growing use of AI across admissions, classroom instruction, and student support services.

The initiative argues that students must not be reduced to data points or treated as subjects for experimental technologies. It warns that while these tools may enable personalised learning, they also introduce risks linked to privacy, bias, and automated decision-making.

The framework sets out five core principles, including transparency in AI use, human oversight for high-stakes decisions, protection of student data and intellectual property, and safeguards against algorithmic bias. It also calls for equitable access to AI tools and education on their use.

Advocates are urging universities to adopt the principles to ensure accountability as AI becomes more deeply integrated into academic environments.

The development reflects a broader shift in higher education, where clear standards are seen as key to building trust, ensuring consistency, and enabling responsible AI integration in academic decision-making.

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AI and 6G strategy drives South Korea’s digital transformation agenda

South Korea has outlined an ambitious national strategy to position itself among the world’s leading AI powers, linking technological advancement with broader economic and societal transformation.

Instead of isolated innovation efforts, the plan adopts a systemic approach, combining infrastructure development, data governance, and industrial policy to accelerate digital transition.

Central to South Korea’s strategy is the evolution of network infrastructure, with a shift from 5G to next-generation 6G technology targeted by 2030. The emphasis on connectivity and speed is complemented by efforts to strengthen cybersecurity frameworks and establish a national data integration platform.

Such measures aim to create a more resilient and competitive digital environment capable of supporting large-scale AI deployment.

The policy also prioritises the integration of AI across multiple sectors, including healthcare, manufacturing, agriculture, and disaster management.

By embedding intelligent systems into critical industries, South Korean authorities seek to enhance productivity, improve public service delivery, and strengthen national resilience.

Workforce development is positioned as a key pillar, with phased training initiatives designed to build expertise in advanced technologies such as semiconductors and quantum computing.

In parallel, the strategy incorporates digital inclusion measures to ensure broader societal participation. Expansion of AI learning centres and assistive technologies reflects an effort to reduce digital divides while supporting vulnerable groups.

Long-term success will depend on effective coordination across government bodies and to balancing rapid technological deployment with equitable access and robust governance frameworks, rather than purely growth-driven objectives.

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Brazil expands AI in public services through Fala.BR reform

The Brazilian government is integrating AI into Fala.BR platform to streamline how citizens communicate with public authorities, marking a notable shift in digital governance.

Instead of relying on manual classification, the system now automatically identifies the nature of submissions, reducing administrative burden and simplifying access to state services.

The reform is designed to improve inclusivity in public participation in Brazil. By lowering technical barriers and reducing the complexity of submitting complaints or requests, authorities aim to expand engagement among users with limited digital familiarity.

Greater accessibility may strengthen civic oversight, allowing broader segments of society to report issues and interact with government institutions more effectively.

From a policy perspective, the initiative reflects an effort to align digital transformation with transparency and accountability objectives.

Enhanced data classification and internal processing are expected to improve how public bodies in Brazil respond to citizen input. At the same time, integrated reporting tools may support more consistent monitoring of service performance across agencies.

The use of AI in citizen feedback systems also raises broader governance implications.

While efficiency gains and anti-corruption potential are emphasised, the long-term impact will depend on data governance standards, oversight mechanisms, and the ability to ensure equitable access rather than reinforce existing digital divides.

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China advances new power grid strategy to support clean energy transition

Chinese Premier Li Qiang has called for accelerated development of a new-type power grid, positioning energy infrastructure reform as central to China’s long-term economic and environmental strategy.

Instead of incremental upgrades, the approach emphasises systemic transformation, linking energy security with decarbonisation and industrial modernisation.

Policy direction highlights the optimisation of the national energy structure through expanded deployment of renewable technologies, particularly solar power.

Continued investment in research and development is framed as essential for overcoming technical constraints and enabling large-scale adoption. The integration of AI into manufacturing and energy systems reflects a broader push towards industrial upgrading and efficiency gains.

The proposed power grid model prioritises resilience, flexibility, and low-carbon performance, indicating a shift towards more adaptive and digitally enabled infrastructure.

Such reforms in China aim to balance rising energy demand with sustainability goals, while reducing dependence on traditional energy sources. The emphasis on smart systems suggests increasing reliance on data-driven governance within the energy sector.

Beyond energy, the policy narrative connects infrastructure development with water management and agricultural modernisation, reinforcing a whole-of-system governance approach.

Long-term impact will depend on implementation capacity, regulatory coordination, and the ability to align technological deployment with environmental and economic objectives instead of isolated sectoral reforms.

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Gallup finds AI is shaping some college students’ academic choices

Gallup reported that 16% of currently enrolled college students had changed their major or field of study due to AI’s potential impact. They claim that 14% have thought ‘a great deal’ and 33% ‘a fair amount’ about changing their major or field of study for the same reason.

Gallup said the findings are based on web surveys conducted from 2 to 31 October 2025 with 3,801 adults pursuing an associate or bachelor’s degree. The article is part of Gallup’s work with Lumina Foundation on higher education.

According to Gallup, men were more likely than women to report having changed majors because of AI’s potential impact, at 21% compared with 12%. Associate degree students were also more likely than bachelor’s degree students to say they had changed their major or field of study, at 19% compared with 13%.

Gallup also found that concern about AI’s impact on majors was greater among students in technology and vocational fields than among those in business, humanities, and engineering. In a separate write-up published the same day, the organisation said AI use is already routine for many students, even where institutions discourage or prohibit it.

The research presents the findings as evidence that AI is affecting how some students think about academic choices and future work. It does not show a policy decision or institutional rule change, but it does add survey evidence to debates about AI, higher education, and future-of-work expectations.

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