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Job losses study by Anthropic reveals 0 evidence of AI driven unemployment

A new Anthropic report finds AI has not yet caused significant job losses, introducing ‘observed exposure’ to measure actual workplace AI use.

Researchers combined language model capabilities with workplace data to identify occupations at risk of disruption. Although AI can perform many tasks, its actual adoption remains much lower across most industries, which is a main finding of the study.

Even in highly digital professions, only a fraction of potential automation results from AI use. For instance, computer and mathematics occupations rank among the most AI-exposed groups. Despite AI’s capability to assist with many tasks, it currently covers only about 33% of them in these fields.

Across the broader economy, many roles experience little or no impact from AI, which represents a key finding. About 30% of workers are in jobs such as cooking, bartending, mechanics, and lifeguarding, where physical tasks dominate, and measured AI exposure is almost zero.

The report also finds no clear evidence that AI adoption has increased unemployment or caused a spike in job losses since generative AI tools began spreading widely in 2022. Rather than triggering sudden job losses, researchers suggest labour-market effects emerge gradually, through slower hiring, shifting skill requirements, and changes in job composition.

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Berlin becomes home to Google AI research centre

Google has launched its new AI Centre in Berlin, creating a hub for researchers, developers, and leaders from Google DeepMind, Google Research, and Google Cloud. The centre aims to foster collaboration, debate, and innovation in AI.

The opening event highlighted the company’s work in advancing science and healthcare through AI-enabled agents and platforms. Google announced long-term research partnerships with the Technical University of Munich and Helmholtz Munich, backed by the Google.org AI for Science fund.

Built on Google’s existing research and engineering foundations in Germany and globally, the Berlin centre emphasises AI innovations with societal benefits. It will connect experts from science, business, academia, and politics to drive forward responsible AI development.

The centre will also serve as a platform for public engagement, hosting workshops, lectures, and events to raise awareness about AI applications, ethical considerations, and future opportunities across industries and communities.

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Oracle launches AI system designed to predict construction safety risks

The US tech company Oracle has introduced a new AI platform to predict safety risks across construction projects.

A system called Advisor for Safety that aims to shift industry practices from reactive incident response to predictive risk prevention.

The AI model was trained using safety information equivalent to more than 10,000 project-years across multiple project types and locations.

By analysing historical patterns, the platform generates weekly forecasts that identify projects statistically most likely to experience safety incidents.

The solution also integrates structured safety observation tools through systems such as Oracle Aconex and Oracle Primavera Unifier, allowing field teams to collect consistent data on mobile devices or web platforms.

These inputs improve predictive accuracy while enabling organisations to track potential hazards earlier in the project lifecycle.

According to Oracle, the system combines data streams ranging from incident reports and payroll records to project schedules and operational metrics.

Early adopters reportedly reduced workplace incidents by up to 50 percent and workers’ compensation costs by as much as 75 percent during the first year of use.

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Gemini leads latest ORCA benchmark on AI maths accuracy

A new round of the ORCA (Omni Research on Calculation in AI) benchmark reveals significant progress in how leading AI chatbots handle real-world mathematical problems, while also highlighting persistent limitations in reliability and consistency.

The latest results show Google’s Gemini 3 Flash moving clearly ahead of competing systems, correctly answering nearly three-quarters of the 500 practical questions used in the benchmark.

Our readers may recall that the platform previously analysed the first edition of the ORCA benchmark, examining how AI chatbots performed on everyday quantitative tasks rather than purely academic problems. The earlier analysis already showed notable gaps between systems and raised questions about the reliability of AI models for calculations people might encounter in daily life.

The second benchmark compares four widely accessible models: ChatGPT-5.2, Gemini 3 Flash, Grok-4.1 and DeepSeek V3.2. Gemini recorded the largest improvement, decisively outpacing the others. ChatGPT and DeepSeek posted smaller but steady gains, while Grok’s results declined slightly in several subject areas.

Chart — Gemini leads latest ORCA benchmark on AI maths accuracy 9

Text — Gemini leads latest ORCA benchmark on AI maths accuracy 9

Performance improvements were uneven across domains, with Gemini showing particularly strong gains in fields such as biology, chemistry, physics and health-related calculations.

Closer examination of the errors reveals why AI still struggles with mathematical accuracy. Calculation mistakes have increased as a share of total errors, while rounding and formatting problems have decreased.

Researchers explain that large language models do not actually compute numbers in the same way that calculators do. Instead, they predict likely sequences of words and numbers, which can lead to small shortcuts during multi-step reasoning that eventually produce incorrect results.

The benchmark also highlights another challenge: instability. The same question can produce different answers when asked multiple times, even when the model initially responded correctly. Such variation reflects the probabilistic nature of AI systems.

As a result, the benchmark concludes that AI chatbots can assist with calculations but cannot yet match the consistency of traditional calculators, which always return the same answer for the same input.

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Debate grows over the future of privacy

Experts gathered in London, UK, to examine how the concept of privacy has evolved over centuries. Discussions in London, UK, highlighted that privacy was only widely recognised as a legal and social norm after the Second World War.

Speakers in London noted that earlier societies often viewed privacy with suspicion or did not recognise it at all. Historical examples discussed included practices from Roman society and the French monarchy.

Modern legal protections expanded rapidly in recent decades, with privacy laws now covering about 80 percent of the global population. Scholars said the concept remains relatively new despite its central role in modern democracies.

The debate also explored whether privacy will remain a stable social value as technology evolves. Analysts in London said emerging technologies such as AI are reshaping debates over personal data and surveillance.

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OpenAI explains 5 AI value models transforming enterprise strategy

AI is beginning to reshape corporate strategy as organisations shift from isolated technology experiments to broader operational transformation.

According to OpenAI, businesses that treat AI as a collection of disconnected pilots risk missing the bigger structural change that the technology enables.

A new framework describes five value models through which AI can gradually reshape companies. The first stage focuses on workforce empowerment, where tools such as ChatGPT spread AI capabilities across teams and improve everyday productivity.

Once employees develop fluency, organisations can introduce AI-native distribution models that transform how customers discover products and interact with digital services.

More advanced stages involve specialised systems. Expert capability integrates AI into research, creative production, and domain-specific analysis, allowing professionals to explore a wider range of ideas and experiments.

Meanwhile, systems and dependency management introduce AI tools capable of safely updating interconnected digital environments, including codebases, documentation, and operational processes.

The final stage involves full process re-engineering through autonomous agents. In such environments, AI systems coordinate complex workflows across departments while maintaining governance, accountability, and auditability.

Organisations that successfully progress through these stages may eventually redesign their business models rather than merely improving efficiency within existing structures.

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Data centres’ expansion in London sparks energy and climate debate

London authorities are drafting new data centre policies amid concerns about their environmental impact and rising energy use. City Hall aims to balance the sector’s economic advantages with pressures on electricity, water, and emissions.

The Greater London Authority (GLA) estimates that 10 large data centres generate around 2.7 million tonnes of carbon emissions due to their high electricity consumption. Of the 100 data centres the UK plans, about 60 will be in London.

Megan Life, assistant director for environment and energy at the GLA, told the London Assembly Environment Committee the new strategy aims to ‘keep hold of the kind of economic growth benefits that data centres offer’ while addressing some ‘quite challenging’ impacts linked to their energy use.

Deputy mayor for environment Mete Coban said the expansion of data centres brings both ‘big benefits’ and ‘massive challenges’ for the capital, particularly in terms of energy and water consumption. ‘It’s not just a London problem, it’s going to be a global problem,’ he said, adding: ‘It’s about making sure that our environment doesn’t suffer in the hands of a few global corporations who will take and not give back, so we want to make sure we equitably do this.’

Policymakers are assessing how data centre growth may affect climate goals and urban infrastructure. London Mayor Sadiq Khan has commissioned a study to forecast future expansion. At the same time, UK lawmakers have launched an inquiry into the environmental impact of the sector as demand for cloud computing and AI infrastructure grows.

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Global AI race intensifies as China claims leadership in strategic technologies

China asserted its position as the global leader in AI and strategic technology R&D, pledging to accelerate advancement toward technological autonomy. The assertion was prominently featured in government reports presented to the National People’s Congress.

A National Development and Reform Commission report states that China leads international research, development, and implementation in AI, biomedicine, robotics, and quantum technology. The report also references advancements in domestic chip innovation as proof of progress.

Competition between China and the United States for dominance in advanced technologies has escalated. Washington imposed export controls on advanced chips, while Beijing retaliated with restrictions on rare earth resources, escalating trade tensions over strategic technologies.

The report also highlighted the country’s global leadership in open-source AI models and its expansion into emerging technology sectors, including industrial robots and drones. Authorities pledged to nurture future industries such as quantum technology, embodied AI, and 6G networks, while promoting large-scale AI deployment across key sectors.

Officials also plan to launch new data centres, coordinate nationwide computing capacity, and establish mechanisms to prevent AI security risks. The strategy places particular emphasis on embodied AI to boost productivity and performance across sectors. Although US firms command larger investment resources, Beijing is relying on supply chains, manufacturing capacity, and rapid R&D cycles to scale emerging industries despite questions about long-term growth.

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UK to launch new lab for breakthrough AI research

Researchers in the UK will gain a new AI lab designed to drive transformational breakthroughs in healthcare, transport, science, and everyday technology, supported by government funding.

The lab will provide up to £40 million in funding over six years, alongside substantial access to large-scale computing resources, inviting UK researchers to pitch their most ambitious ideas.

The Fundamental AI Research Lab will focus on tackling core AI challenges, including hallucinations, unreliable memory, and unpredictable reasoning.

The lab will support high-risk, blue-sky research rather than simply scaling existing systems. Its goal is to unlock entirely new capabilities that could improve medical diagnoses, infrastructure resilience, scientific discovery, and public services.

UK officials highlighted the country’s strength in world-class universities, AI talent, and a thriving sector attracting over £100 billion in private investment. Experts, including Raia Hadsell of Google DeepMind, will peer-review funding applications, prioritising bold, high-reward proposals.

The initiative is part of the UKRI AI Strategy, which is backed by £1.6 billion and aims to strengthen research and ensure AI benefits society and the economy. UK AI projects like RADAR for rail faults and the IXI Brain Atlas for Alzheimer’s research demonstrate the approach’s potential impact.

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ECB reports minor impact of AI on employment

AI has so far had only a small effect on employment across Europe, according to economists at the European Central Bank. A comparison of 5,000 firms- both AI users and non-users- showed no significant difference in job creation or reduction.

Some firms that use AI intensively were even four percent more likely to hire new staff than average.

Economists noted that AI investment has not replaced existing jobs. In some cases, firms are hiring additional employees to develop and implement AI systems or to scale up operations more efficiently.

Only a minority of firms, around 15 percent, reported reducing labour costs as a motivation for AI adoption.

Despite limited impacts so far, the ECB cautioned that AI could have more significant effects as technology matures. Firms that specifically invest in AI to cut jobs may indeed reduce employment, and the long-term consequences for production processes and labour markets remain uncertain.

The findings come amid rising concern over AI-driven job losses, with companies such as Amazon and Allianz citing AI as a reason for recent cuts. Markets reacted negatively last week after a viral post predicted widespread layoffs, though current evidence shows only minor effects.

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