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Dutch study explores how to scale AI across government organisations

Dutch research organisation TNO has conducted an exploratory study examining how AI applications can be scaled across government organisations in the Netherlands. The study was commissioned by the Ministry of the Interior and Kingdom Relations because AI offers opportunities for public sector services and operations.

The study supports the Netherlands’ Digitalisation Strategy, which calls for a more proactive government role in the development and adoption of AI. One option under consideration is an AI scaling facility that would support the reuse and further development of successful AI applications, helping deploy them more quickly and across a wider range of organisations.

According to the study, scaling AI is not a linear or one-size-fits-all process. Depending on their goals, context, and partnerships, organisations may follow different approaches, including scaling within one organisation, replicating solutions across similar organisations, adapting them to new sectors or tailoring broad solutions to local needs.

TNO identifies seven approaches to AI scaling: scaling in, scaling out, scaling beyond, scaling together, scaling down, scaling up and scaling deep. The strategies cover internal adoption, cross-organisational reuse, sectoral adaptation, collaborative development, localisation, policy and standards work, and cultural or behavioural change inside organisations.

A related ‘Conversation starter’ has also been developed to help organisations assess AI scaling initiatives at the outset. The recommendations include treating scaling as a strategic decision, selecting an approach aligned with intended outcomes, addressing governance and organisational culture, reusing existing solutions where possible, investing in AI literacy and documentation, clarifying ownership and funding arrangements, and regularly assessing whether scaling remains desirable, feasible and legally appropriate.

Why does it matter?

Many governments are moving beyond AI experimentation and focusing on how successful projects can be deployed at scale. However, expanding AI use across public institutions often involves organisational, governance and cultural challenges that extend beyond technology itself.

The Dutch study highlights the need for structured approaches to AI adoption, emphasising reuse, collaboration and institutional capacity. Its findings could help governments accelerate AI deployment while maintaining accountability, effectiveness and compliance with legal requirements.

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Ireland launches fund for fact-checking and anti-disinformation training

Ireland’s media regulator, Coimisiún na Meán, has launched a funding call to support training in fact-checking, prebunking and debunking for journalists and media professionals during 2026.

The programme is aimed at graduate, early-career and mid-career professionals working in news and current affairs. Eligible activities include training courses, mentorship programmes, internships and collaborative projects designed to strengthen fact-checking and verification skills.

The fund forms part of the regulator’s Media Skills and Development Programme and supports objectives outlined in Ireland’s National Counter Disinformation Strategy. Applications are open to academic institutions, accredited bodies and representative organisations, including partnerships involving media organisations.

Minister for Culture, Communications and Sport Patrick O’Donovan said the initiative would help strengthen professional skills and support high-quality journalism. Applications are open until 2 July 2026, with all funded activities to be delivered in Ireland during 2026.

Why does it matter?

Fact-checking, verification and disinformation response skills are becoming increasingly important as journalists navigate rapidly evolving information environments shaped by social media, generative AI and coordinated influence campaigns.

By investing in professional training, Ireland aims to strengthen media resilience, support evidence-based reporting and enhance the capacity of news organisations to identify and counter misleading or false information.

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Cambridge researchers test AI-designed vaccine in human trial

Researchers at the University of Cambridge have developed an experimental vaccine using AI, marking what they describe as the first human test of a vaccine component designed entirely by AI. The experimental approach aims to provide broad protection against entire families of viruses, including coronaviruses with pandemic potential.

The AI system analysed genetic data from multiple coronaviruses and designed a ‘super-antigen’ intended to help the immune system recognise and respond to a broad range of viral variants, including those that may emerge through future mutations. An initial trial involving 39 volunteers focused primarily on safety, while a larger follow-up study is planned to evaluate immune responses and effectiveness in greater detail.

Researchers say the approach could help vaccine development keep pace with rapidly evolving threats, including influenza, emerging COVID-19 variants and viruses with the potential to spread from animals to humans. The team is also exploring similar AI-designed vaccines for influenza, bird flu, and Ebola-like viral haemorrhagic fevers, where current protection options remain limited.

Researchers describe the findings as an early but significant step towards using AI to accelerate vaccine design and strengthen preparedness for future disease outbreaks. The study highlights growing expectations that AI may become a central tool in global pandemic prevention strategies.

Why does it matter?

Traditional vaccine development often focuses on responding to specific pathogens after they emerge. By contrast, AI-assisted design could help researchers develop vaccines that provide protection against entire families of viruses before outbreaks occur.

If successful, the approach could shorten development timelines, improve preparedness for future pandemics and support efforts to address rapidly evolving infectious diseases. The research also highlights the growing role of AI in scientific discovery and biomedical innovation.

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UN warns of AI’s growing environmental footprint

As AI continues to reshape economies, industries and daily life, a new report from the United Nations University (UNU) highlights the environmental challenges associated with its rapid adoption. While discussions often focus on greenhouse gas emissions linked to AI systems, researchers argue that the technology’s impact on water resources, land use and electronic waste deserves equal attention.

According to the report, data centres supporting AI applications could consume up to 945 terawatt-hours of electricity annually by 2030. Beyond electricity demand, AI-related water consumption could reach levels equivalent to the annual household needs of 1.3 billion people, while the land footprint associated with AI infrastructure may exceed 14,500 square kilometres.

Researchers note that environmental pressures vary significantly depending on the technologies and energy sources used to power AI systems.

The UN report also finds that routine AI use, rather than model training alone, accounts for a significant share of resource consumption. Everyday activities such as generating images, videos and text require substantial computing power, with image generation demanding significantly more energy than basic text-based tasks. Growing adoption may further increase total resource consumption despite improvements in efficiency.

Researchers note that the environmental costs of AI infrastructure are often concentrated in specific regions, while the benefits of AI are distributed more broadly across the global economy. Expanding data centres, rising electricity demand, increasing water consumption and growing volumes of electronic waste could place additional pressure on communities and countries already facing resource constraints.

The report calls for responsible AI development supported by greater transparency, sustainable infrastructure planning, international cooperation and governance measures aimed at keeping technological progress within environmental limits.

Why does it matter?

Debates about AI sustainability often focus on carbon emissions, but the report argues that water consumption, land use and electronic waste are becoming equally important considerations as AI infrastructure expands. These impacts could become increasingly significant as governments and companies invest in larger data centres and more powerful AI systems.

The findings also highlight the need for environmental considerations to be integrated into AI governance and infrastructure planning. As AI adoption accelerates worldwide, policymakers face growing pressure to balance technological innovation with sustainability and resource management goals.

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Canada launches AI for All national strategy to accelerate adoption and digital sovereignty

Canada has launched AI for All, a new national AI strategy aimed at accelerating AI adoption, strengthening digital sovereignty, and positioning the country as a leading AI economy.

Announced by Prime Minister Mark Carney, the strategy combines proposed legislation, investments, and programmes intended to ensure AI is adopted responsibly and benefits businesses, workers, students, and communities across Canada.

The strategy targets an additional C$200 billion in economic growth, 250,000 new AI-related jobs over the next five years, and an increase in AI adoption from just over 12% today to 60% by 2034. The government also plans to provide up to 90,000 AI-related jobs and work placement opportunities for young Canadians.

The strategy is built around three principles: building trust, creating opportunities, and reinforcing Canadian sovereignty. To build trust, the government plans to modernise digital legislation, strengthen protections for personal information, address harms such as deepfakes and surveillance pricing, introduce an online safety regime, and expand the capabilities of the Canadian AI Safety Institute.

To create opportunities, the government will establish a National AI Literacy Initiative, provide access to trusted AI agents for post-secondary students, help small and medium-sized businesses adopt AI, support worker training, and launch an AI Missions Program with a flagship health mission focused on diagnostics, patient care, and system efficiency.

To reinforce sovereignty, Canada plans to build domestic AI foundations, including compute, cloud, connectivity, data, and talent. Measures include a world-leading public AI supercomputer, investments in sovereign compute and cloud infrastructure, better access to growth capital for Canadian AI companies, strategic public procurement, and expanded support for AI talent.

The government said the strategy is intended to ensure more AI value is created in Canada while strengthening privacy, data protection, public services, productivity, and economic security.

Why does it matter?

Canada’s AI for All strategy links AI adoption directly to economic growth, workforce development, public trust, and technological sovereignty. The strategy reflects a wider shift among governments: AI policy is no longer focused only on research excellence, but also on compute infrastructure, cloud sovereignty, data governance, safety institutions, business adoption, public procurement, and skills. Its success will depend on whether Canada can turn ambitious targets into measurable adoption across businesses, public services, and workers.

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OECD launches AI Policy Toolkit for governments

The Organisation for Economic Co-operation and Development (OECD) has launched the AI Policy Toolkit, a practical guide intended to help governments translate AI principles into policy action. Released by the OECD under the Global Partnership on Artificial Intelligence, the first version is designed as a non-prescriptive resource for policymakers working across the AI policy cycle.

Building on the OECD AI Principles, the toolkit is intended to help governments identify policy priorities, compare international approaches and adapt guidance to national circumstances. The platform incorporates AI-powered semantic search to help users identify relevant policy examples and practical approaches drawn from real-world experience.

The OECD developed the AI Policy Toolkit through co-creation with end-users across regions, including targeted interviews and workshops in Southeast Asia, Latin America, and Africa. Policymakers, industry representatives and experts helped shape the platform around implementation challenges, including balancing innovation and regulation, addressing infrastructure gaps and supporting AI adoption in sectors such as agriculture, education and healthcare.

According to the OECD, the development process highlighted two key lessons: AI policy is heavily influenced by national context, institutional capacity and levels of digital maturity, while challenges such as advanced AI risks and linguistic and cultural representation often require international cooperation. Contributors included governments and organisations from Costa Rica, Italy, France, South Korea, Japan, the United Kingdom, the European Union, the French Development Agency, and the Inter-American Development Bank.

The OECD says the toolkit will continue to evolve through feedback, additional policy examples, and expanded coverage of emerging issues, including sector-specific guidance, infrastructure, and regulatory approaches. The OECD said the toolkit’s broader objective is to help governments move from high-level AI principles to practical implementation while managing risks and promoting trustworthy AI.

Why does it matter?

Many governments have adopted AI principles and strategies, but translating these commitments into practical policies remains a challenge. The OECD’s toolkit seeks to bridge that gap by providing policymakers with implementation guidance, real-world examples and policy options tailored to different national contexts.

The initiative also reflects growing recognition that effective AI governance requires both domestic policymaking capacity and international cooperation, particularly as countries confront shared challenges related to advanced AI systems, infrastructure needs and regulatory approaches.

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Mayo Clinic and Microsoft partner to build frontier AI model for healthcare

Mayo Clinic and Microsoft have announced a strategic collaboration to develop and deploy a frontier AI model designed specifically for healthcare.

The initiative combines Mayo Clinic’s clinical expertise, de-identified health data, and longitudinal medical insights with Microsoft’s AI, cloud, engineering, and superintelligence capabilities.

The model is intended to support a broad range of clinical reasoning and healthcare use cases by synthesising diverse clinical information. Mayo Clinic said it could support earlier diagnoses, more personalised treatment decisions, and improved patient outcomes.

Unlike general-purpose AI systems, the model is being developed for healthcare environments that require deep clinical context, longitudinal understanding, rigorous governance, and real-world validation.

Mayo Clinic will own the model, which it said reflects its commitment to patient trust, clinical rigour, safety, and responsible stewardship of clinical data and AI.

The system will initially be deployed within Mayo Clinic’s clinical environment, where physicians and researchers can test, refine, and improve it through real-world use.

Microsoft plans to make the model available through Azure Foundry APIs, enabling healthcare organisations worldwide to access advanced medical AI capabilities designed to support patients, clinicians, and consumers.

Why does it matter?

The partnership shows how major health institutions and technology companies are moving towards domain-specific frontier AI models rather than relying only on general-purpose systems. Healthcare AI requires specialised data governance, clinical validation, longitudinal patient understanding, and robust safeguards, as errors can directly affect diagnosis, treatment, and patient trust. Mayo Clinic’s ownership of the model is also important because it signals an attempt to keep clinical accountability and data stewardship closer to the healthcare institution.

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European Commission unveils roadmap for AI and digitalisation in energy

The European Commission has published a Strategic Roadmap for Digitalisation and AI in the Energy Sector, outlining how digital technologies could support a more resilient, competitive and secure European energy system.

The roadmap outlines how digital tools and AI could help consumers and businesses reduce energy costs through greater efficiency, smarter energy consumption and improved management of electricity demand. It also highlights the role of digital technologies in supporting the integration of renewable energy into electricity grids.

The Commission has structured the roadmap around three main priorities. These priorities include integrating data centres into energy systems in a sustainable manner, accelerating the deployment of digital and AI-enabled technologies such as smart meters and intelligent grid solutions, and establishing a framework for secure cross-border energy data sharing.

The Commission said the plan will also focus on cybersecurity, AI trust, digital skills and international cooperation. As part of the next phase, the Commission plans to support industry cooperation initiatives and launch the AI.grids community, which will focus on developing AI models for energy network management across the EU.

Why does it matter?

The energy sector is becoming increasingly dependent on digital technologies to manage growing electricity demand, integrate renewable energy sources and maintain grid stability. AI and advanced data analytics could help improve efficiency, reduce costs and support more flexible energy systems.

At the same time, greater digitalisation introduces new challenges related to cybersecurity, data governance and infrastructure resilience. The roadmap signals the EU’s intention to ensure that digital transformation in the energy sector supports both sustainability goals and long-term energy security.

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New Zealand’s NCSC warns frontier AI could amplify cybersecurity risks

New Zealand’s National Cyber Security Centre (NCSC) has issued guidance to help government agencies prepare for the cybersecurity implications of frontier AI systems. The advisory notes that frontier AI models may enable more advanced automation, reasoning and decision-making capabilities than previous generations of AI systems.

The guidance describes frontier AI as a dual-use technology, noting that the same capabilities that enhance cyber defence could also enable malicious actors to conduct cyber operations more quickly, at lower cost and on a larger scale. The NCSC warns that frontier AI could amplify risks associated with known vulnerabilities, legacy systems and poor cyber hygiene, creating what it describes as a ‘vulnerability storm’ for organisations.

According to the NCSC, organisations do not need access to the most advanced frontier AI models to strengthen their cyber resilience. Instead, it says effective readiness depends on existing cybersecurity mitigations and practices, including the New Zealand Information Security Manual, the NCSC Cyber Security Framework, Minimum Cyber Security Standards, and Protective Security Requirements.

The advisory urges government entities to treat several actions as immediate priorities, including reviewing compliance with existing standards, confirming executive accountability for frontier AI cyber risk, reviewing NCSC guidance, and identifying material gaps that AI-enabled threat actors could exploit.

The guidance also restates the NCSC Cyber Security Framework’s five functions: guide and govern, identify and understand, prevent and protect, detect and contain, and respond and recover. The advisory highlights a range of baseline cybersecurity measures, including risk management, security awareness, secure configuration, patch management, multi-factor authentication, least-privilege access controls, anomaly detection, data recovery and incident response planning.

Why does it matter?

Frontier AI is expected to increase the speed, scale and sophistication of cyber operations, potentially allowing attackers to identify vulnerabilities, automate exploitation and conduct campaigns more efficiently than before.

Rather than relying solely on new AI-specific defences, New Zealand’s guidance emphasises that strong cybersecurity fundamentals, including patching, access controls, monitoring and incident response, remain the most effective way to reduce risk. The advisory reflects a growing international view that AI is amplifying existing cyber challenges rather than replacing them with entirely new ones.

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India and South Africa deepen cooperation on AI and emerging technologies

India and South Africa have agreed to strengthen bilateral cooperation in emerging technologies, with AI, digital infrastructure and advanced manufacturing identified as key areas for future collaboration.

The agreement was reached during a meeting between India’s Minister of Science and Technology, Dr Jitendra Singh, and South Africa’s Deputy Minister of Science, Technology and Innovation, Dr Nomalungelo Gina. Both sides emphasised the need to expand traditional scientific cooperation into innovation-driven partnerships aimed at delivering economic and societal benefits.

Discussions covered biotechnology, genomics, vaccine development, health technologies, renewable energy, hydrogen, advanced manufacturing and digital innovation. The two countries also explored opportunities to deepen cooperation in quantum technologies, geospatial technologies and digital infrastructure.

The meeting reaffirmed the long-standing scientific relationship between the two countries and concluded with a commitment to strengthen innovation ecosystems through research collaboration, startup partnerships, technology deployment and industry engagement.

Why does it matter?

India and South Africa are among the leading technology and innovation hubs in the Global South. Expanding cooperation in AI, digital infrastructure, healthcare and advanced manufacturing could help accelerate technological development while fostering greater knowledge exchange and investment opportunities.

The partnership also reflects a broader trend of emerging economies seeking to strengthen innovation ecosystems and reduce reliance on technology supply chains and platforms concentrated in a small number of countries.

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