Future of work

AI is accelerating the transition to clean energy

AI is playing an increasingly vital role in supporting the transition to clean energy. AI helps optimise power grid operations, plan infrastructure investments, and accelerate the discovery of novel materials for energy generation, storage, and conversion.

While energy-hungry data centres can increase electricity demand, AI applications are helping reduce energy consumption across buildings, transport, and industry.

On electric grids, AI algorithms enhance efficiency, integrate renewable energy sources, and predict maintenance needs to prevent power outages. Grid operators can utilise AI to forecast supply and demand, optimise energy storage, and manage resources in real-time.

Technologies such as smart thermostats, electric vehicle batteries, and AI-managed data centres provide additional flexibility to balance peak demand and supply.

AI also aids long-term planning by helping utilities forecast future infrastructure needs amid growing renewable deployment and climate-related risks. Additionally, AI accelerates the discovery of materials for energy technologies.

At MIT, researchers use AI-guided experiments and robotics to design and test new materials, significantly shortening development times from decades to years.

Through research, modelling, and collaboration, AI is being applied to fusion reactor management, solar cell optimisation, and energy-efficient data centre design. MIT Energy Initiative programmes unite academics, industry, and policymakers to harness AI for a resilient and sustainable energy future.

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Swiss Federal Council approves update to tax information exchange rules

The Swiss Federal Council has approved significant updates to the Ordinance on the International Automatic Exchange of Information in Tax Matters. The new rules are set to take effect across Switzerland on 1 January 2026, assuming no referendum intervenes.

The revisions expand Switzerland’s international exchange of financial account information, updating the Common Reporting Standard (CRS) and introducing the new Crypto-Asset Reporting Framework (CARF).

Crypto service providers in Switzerland will now have reporting, due diligence, and registration obligations under the AEOI Ordinance, although these provisions will not apply until at least 2027.

The updated Ordinance also extends CRS rules to Swiss associations and foundations while excluding certain accounts if specific conditions are met. Transitional measures aim to facilitate the implementation of the amended CRS and CARF by affected parties more smoothly.

Deliberations on partner states for Switzerland’s crypto data exchange have been paused by the National Council’s Economic Affairs and Taxation Committee. The CARF will become law in Switzerland in 2026, but full implementation is delayed, keeping crypto-asset rules inactive for the first year.

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AI scribes help reduce physician paperwork and burnout

A new UCLA Health study finds that AI-powered scribe tools can reduce physicians’ documentation time and may improve work satisfaction. Conducted across 14 specialities and 72,000 patient visits, the trial tested Microsoft DAX and Nabla in real-world clinical settings.

Physicians using Nabla reduced the time spent writing each note by almost 10% compared with usual care, saving around 41 seconds per note. Both AI tools modestly improved burnout, cognitive workload, and work exhaustion, but physician oversight remains essential.

The trial highlighted several limitations, including occasional inaccuracies in AI-generated notes and a single instance of mild patient safety concern. Physicians found the tools easy to use and noted an improvement in patient engagement, with most patients being receptive.

The findings provide timely evidence as healthcare systems increasingly adopt AI scribes. Researchers emphasise that rigorous evaluation is necessary to ensure patient safety and effectiveness, and that further long-term studies across multiple institutions are recommended.

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HP cuts thousands of jobs as AI reshapes operations

HP plans to cut between 4,000 and 6,000 jobs worldwide by fiscal 2028 as it restructures operations and expands its use of AI across product development and support services.

CEO Enrique Lores said the cuts will hit development, operations and support teams, with the plan aiming to save $1 billion over three years. The company already shed up to 2,000 roles in February under an earlier restructuring plan.

AI-enabled PCs now make up over 30% of HP’s shipments in Q4 ending 31 October, driving strong demand. However, analysts at Morgan Stanley warned that rising memory chip prices fuelled by AI data centre expansion could increase costs for consumer electronics makers.

Lores noted that HP expects the impact to be felt from the second half of fiscal 2026, though existing inventory should cover the first half.

HP projected fiscal 2026 adjusted earnings of $2.90–$3.20 per share, below expectations, with first-quarter profits also falling short of forecasts. Fourth-quarter revenue reached $14.64 billion, slightly ahead of forecasts.

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INQUBATOR set to build a competitive quantum ecosystem over four years

Germany has launched the INQUBATOR initiative to help companies, particularly SMEs, prepare for the industrial impact of quantum computing. The four-year programme offers structured support to firms facing high entry barriers and limited access to advanced technologies.

A central feature is affordable access to quantum systems from multiple vendors, paired with workshops and hands-on training. Companies can test algorithms, assess business relevance and adapt processes without investing in costly hardware or specialist infrastructure.

The project is coordinated by the Fraunhofer Institute for Applied Solid-State Physics and is funded by the Federal Ministry of Research and Technology. It brings together several Fraunhofer institutes to guide firms from early exploration to applied solutions.

Initial pilot projects span medicine, cybersecurity, insurance and automotive sectors. These examples are intended to demonstrate measurable advantages and will be followed by an open call for further use cases across a broader range of industries.

INQUBATOR aims to reduce financial and technical obstacles while expanding quantum expertise and industrial readiness in Germany. By enabling practical experimentation, it seeks to build a competitive ecosystem of quantum-literate companies over the next four years.

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Advantage2 pushes D-Wave’s quantum computing closer to mainstream use

Quantum computing has long been framed as a future promise, but D-Wave argues real-world use has now arrived. The company says its Advantage2 system is already running complex optimisation tasks for businesses through both cloud and on-premise deployment.

D-Wave highlights a recent physics experiment as evidence of this shift, claiming the system solved a materials-modelling problem that would take a top supercomputer nearly a million years. The result, completed in minutes, serves as a proof point of practical quantum performance.

The company says accessibility is central to its approach, emphasising that Advantage2 can be programmed in Python without specialist quantum expertise. It frames this ease of use as essential to broader adoption beyond research labs.

Industry deployments are cited across logistics, telecoms, and manufacturing. D-Wave points to scheduling gains at Pattison Food Group, network optimisation at NTT Docomo, and faster production planning at Ford Otosan as examples of measurable operational benefits.

Energy efficiency is another focus, with D-Wave stating that each of its six hardware generations draws roughly 12.5 kilowatts. The company argues that this stable power use, paired with rising performance, positions quantum systems as a lower-energy option for hard computational problems.

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AI chatbots misidentify images they created

Growing numbers of online users are turning to AI chatbots to verify suspicious images, yet many tools are failing to detect fakes they created themselves. AFP found several cases in Asia where AI systems labelled fabricated photos as authentic, including a viral image of former Philippine lawmaker Elizaldy Co.

The failures highlight a lack of genuine visual analysis in current models. Many models are primarily trained on language patterns, resulting to inconsistent decisions even when dealing with images generated by the same generative systems.

Investigations also uncovered similar misidentifications during unrest in Pakistan-administered Kashmir, where AI models wrongly validated synthetic protest images. A Columbia University review reinforced the trend, with seven leading systems unable to verify any of the ten authentic news photos.

Specialists argue that AI may assist professional fact-checkers but cannot replace them. They emphasise that human verification remains essential as AI-generated content becomes increasingly lifelike and continues to circulate widely across social media platforms.

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AI use by US immigration agents sparks concern

A US federal judge has condemned immigration agents in Chicago for using AI to draft use-of-force reports, warning that the practice undermines credibility. Judge Sara Ellis noted that one agent fed a short description and images into ChatGPT before submitting the report.

Body camera footage cited in the ruling showed discrepancies between events recorded and the written narrative. Experts say AI-generated accounts risk inaccuracies in situations where courts rely on an officer’s personal recollection to assess reasonableness.

Researchers argue that poorly supervised AI use could erode public trust and compromise privacy. Some warn that uploading images into public tools relinquishes control of sensitive material, exposing it to misuse.

Police departments across the US are still developing policies for safe deployment of generative tools. Several states now require officers to label AI-assisted reports, while specialists call for stronger guardrails before the technology is applied in high-stakes legal settings.

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Industrial sectors push private 5G momentum

Private 5G is often dismissed as too complex or narrow, yet analysts argue it carries strong potential for mission-critical industries instead of consumer-centric markets.

Sectors that depend on high reliability, including manufacturing, logistics, energy and public safety, find public networks and Wi-Fi insufficient for the operational demands they face. The technology aligns with the rise of AI-enabled automation and may provide growth in a sluggish telecom landscape.

Success depends on the maturity of surrounding ecosystems. Devices, edge computing and integration models differ across industrial verticals, slowing adoption instead of enabling rapid deployment.

The increasing presence of physical AI systems, from autonomous drones to industrial vehicles, makes reliable connectivity even more important.

Debate intensified when Nokia considered divesting its private 5G division, raising doubts about commercial viability, yet industry observers maintain that every market involves unique complexity.

Private 5G extends beyond traditional telecom roles by supporting real-economy sectors such as factories, ports and warehouses. The challenge lies in tailoring networks to distinct operational needs instead of expecting a single solution for all industries.

Analysts also note that inflated expectations in 2019 created a perception of underperformance, although private cellular remains a vital piece in a broader ecosystem involving edge computing, device readiness and software integration.

Long-term outlooks remain optimistic. Analysts project an equipment market worth around $30 billion each year by 2040, supported by strong service revenue. Adoption will vary across industries, but its influence on public RAN markets is expected to grow.

Despite complexity, interest inside the telecom sector stays high, especially as enterprise venues search for reliable connectivity solutions that can support their digital transformation.

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Up to 3 million UK jobs at risk from automation by 2035

A new report from NFER warns that up to 3 million low-skilled jobs in the UK could disappear by 2035 due to the growing adoption of automation and AI. Sectors most at risk include trades, machine operations and administrative work, where routine and repetitive tasks dominate.

Economic forecasts remain mixed. The overall UK labour market is expected to grow by 2.3 million jobs by 2035, with gains primarily in professional and managerial roles. Many displaced workers may struggle to find new employment, widening inequality.

The change contrasts with earlier predictions suggesting AI would target higher-skilled jobs such as consultancy or software engineering. Current findings emphasise that manual and lower-skill roles face the most significant short-term disruption from AI.

Policymakers and educators are encouraged to build extensive retraining programmes and foster skills like creativity, communication and digital literacy. Without such efforts, long-term unemployment could become a significant challenge.

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