UN report outlines AI standards for Digital Public Goods

A new report from the United Nations University Institute in Macau, the Asian Development Bank (ADB), and the UN Office for Digital and Emerging Technologies examines the conditions under which AI systems can qualify as Digital Public Goods. The study was launched during UN Open Source Week 2026 and focuses on aligning AI development with public interest goals.

The report argues that AI systems cannot be assessed in the same way as conventional open-source software because they rely on datasets, model weights, computing infrastructure and ongoing governance. While openness can improve transparency and reuse, it does not automatically guarantee safety, equity or alignment with the Sustainable Development Goals (SDGs).

The study concludes that AI governance should be treated as a continuous lifecycle process rather than a one-time certification exercise. It also highlights that equitable access depends on enabling factors such as computing infrastructure, local-language datasets and institutional capacity, particularly in developing countries.

To address these challenges, the report proposes a SAFE framework covering Standards, Accountability, Finance and Equity. It recommends stronger stewardship of public-interest data, improved accountability mechanisms and greater investment in local AI evaluation capacity to support inclusive and responsible AI deployment.

Why does it matter? 

The report broadens the debate around AI governance by arguing that openness alone is not enough to ensure that AI serves the public interest. As governments increasingly adopt AI in public services and development programmes, questions of governance, accountability and long-term oversight are becoming as important as technical performance.

It also highlights the growing role of Digital Public Goods in international AI policy. By emphasising equitable access to computing resources, local datasets and institutional capacity, the report argues that AI should be developed as shared public infrastructure that supports sustainable development rather than reinforcing existing digital divides.

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New MIT development reduces energy use in AI systems

Researchers from MIT and Microsoft have developed a system called Murakkab to improve the speed and energy efficiency of agentic AI workflows.

Agentic workflows combine multiple AI models and external tools to complete complex, multi-step tasks, such as analysing video or generating code. MIT said these systems are becoming more important for cloud providers, but their fragmented design can waste computation, energy and money.

Murakkab allows developers to describe an AI application in high-level terms rather than manually specifying every model, tool, hardware choice and execution step. The system then identifies suitable models and tools, decides which components should run sequentially or in parallel, and selects hardware resources for cloud deployment.

The system can adjust configurations during execution based on user priorities such as accuracy, speed, latency and cost. It also gives cloud providers more visibility into workflows, allowing them to allocate computing resources more efficiently across multiple tasks.

In tests of video-question-answering and code-generation workflows, Murakkab met user requirements while using about 35% of the computational resources required by other methods. It also consumed about 27% as much energy and cost less than 25% as much as the comparison approaches.

In one case, the system reduced energy consumption by more than an order of magnitude with only about a 2% drop in accuracy. The researchers plan to expand Murakkab to more complex workflows and larger computing clusters.

Why does it matter?

Agentic AI systems are becoming more complex and resource-intensive, especially as cloud providers deploy workflows that combine many models, tools and hardware configurations. Murakkab points to a shift from optimising individual models to optimising the whole AI workflow and its cloud deployment. That matters because energy use, compute costs, and data centre capacity are becoming central constraints on AI growth.

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EU targets AWS and Azure under the DMA

The European Commission has informed Amazon and Microsoft of its preliminary view that their cloud computing services, Amazon Web Services and Microsoft Azure, should be designated as gatekeepers under the Digital Markets Act.

The move could extend the DMA’s reach into cloud infrastructure, a sector the Commission describes as critical to Europe’s digital economy and AI development.

The Commission opened market investigations into AWS and Azure in November 2025. It has now been provisionally concluded that both services act as important gateways between businesses and customers in the EU, despite not meeting the DMA’s standard quantitative thresholds.

According to the Commission, AWS and Azure benefit from large and established user bases, high switching costs, loyalty effects, broad cloud ecosystems and long-standing market positions. It also said their AI tool portfolios and partnerships are becoming increasingly important for cloud customers.

Amazon and Microsoft now have the opportunity to examine the investigation files and respond to the preliminary findings. If the Commission confirms its assessment, AWS and Azure would be designated as gatekeepers, and the companies would have six months to comply with DMA obligations.

The Commission said fair and competitive cloud markets are important for secure, sustainable and interoperable cloud services in Europe. It also linked the case to Europe’s wider technological sovereignty objectives, as cloud infrastructure underpins AI systems, enterprise software and public services.

Why does it matter?

The case shows how the EU competition policy is moving deeper into the infrastructure behind the AI economy. Cloud platforms are no longer just business services; they shape access to compute, data, AI tools, software ecosystems and switching options for companies and public institutions. If AWS and Azure are designated as DMA gatekeepers, the decision could affect cloud interoperability, customer lock-in and the balance of power between US hyperscalers and European cloud providers.

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China’s latest supercomputer strengthens AI ambitions

China has regained the world’s leading position in supercomputing after the LineShine system became the fastest computer in the latest TOP500 ranking, replacing the US’s El Capitan at the top of the list.

The achievement marks China’s return to first place for the first time since 2017 and highlights the growing strategic importance of high-performance computing in the AI era.

Unlike many recent AI-focused supercomputers that rely heavily on graphics processing units (GPUs), LineShine achieves exascale performance using conventional central processing units (CPUs).

Beyond topping benchmark rankings, the system is expected to support scientific research, advanced simulations, climate modelling, pharmaceutical development and the training of increasingly sophisticated AI models.

The announcement also reflects the broader ambition of China to strengthen technological leadership while presenting its innovation ecosystem as a contributor to global technological development.

Europe also remains a major player in high-performance computing. Four European systems rank among the world’s ten fastest supercomputers, while the EU continues to invest in AI factories, next-generation supercomputing infrastructure and collaborative research centres.

The growing investment in supercomputers reflects how computing infrastructure is increasingly being treated as a strategic asset alongside semiconductors, cloud infrastructure and advanced data centres.

As governments increasingly link AI capabilities with economic competitiveness, scientific leadership and national security, access to world-class computing resources is becoming one of the defining factors shaping the global technology balance.

Why does it matter?

The latest TOP500 ranking underline that computing capacity is becoming a defining factor in AI development and scientific competitiveness. As frontier AI models require ever-greater computational resources for training and inference, access to world-class supercomputers is emerging as a strategic advantage alongside semiconductor manufacturing and cloud infrastructure.

China’s return to the top of the rankings also highlights the geopolitical dimension of high-performance computing. At the same time, continued European investment in AI factories and supercomputing infrastructure reflects a broader effort to strengthen technological sovereignty and reduce dependence on external computing resources as countries compete for leadership in AI and advanced research.

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EDPS strengthens monitoring of emerging technologies

The European Data Protection Supervisor (EDPS) has developed a structured framework for monitoring emerging technologies and assessing their implications for privacy and data protection. As digitalisation accelerates, the EDPS recognises that some new technologies do not merely improve existing processes but fundamentally alter how personal data is handled, requiring proactive and ongoing scrutiny.

At the heart of the framework is an annual monitoring cycle that moves from early signal detection to in-depth analysis and public engagement. The EDPS works with the Joint Research Centre’s TIM Analytics service to identify technologies at an early stage and prioritise those most likely to affect data protection over the short and medium term.

The main output of this foresight work is TechSonar, the EDPS’s flagship report on technologies expected to become relevant within the next one to five years. Designed for a broad audience, it outlines emerging technology trends and assesses their potential implications for personal data protection.

Complementing TechSonar, the TechDispatch series provides more detailed analysis of individual technologies, including factual descriptions, preliminary privacy assessments and consideration of how they interact with GDPR principles and data subject rights.

Complementing these publications is the Internet Privacy Engineering Network (IPEN), established by the EDPS in 2014. At least once a year, IPEN brings together public authorities, academics, open-source projects, and private businesses to discuss engineering solutions to privacy challenges, with findings feeding back into the broader technology monitoring work.

The EDPS also coordinates the Internet Privacy Engineering Network (IPEN), established in 2014, which brings together regulators, researchers, open-source communities and industry to discuss technical solutions to privacy challenges and feed those insights into its wider technology monitoring work. Recent activities have included a new video series on AI literacy and a newsletter covering AI governance, the Digital Omnibus debate, and AI use in hiring practices.

Why does it matter?

Emerging technologies such as AI are evolving faster than traditional regulatory processes, making early assessment increasingly important for protecting privacy and fundamental rights. By identifying technologies before they become mainstream, the EDPS aims to help policymakers, regulators and public institutions anticipate risks rather than respond only after new technologies are widely deployed.

The framework also supports greater consistency in European data protection governance. Through publications such as TechSonar and TechDispatch, together with collaboration via IPEN, the EDPS provides a common evidence base that can inform policy development, regulatory enforcement and privacy-by-design approaches across the EU as new technologies continue to emerge.

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UN secretary-general calls for greater transparency on AI’s climate impact

UN Secretary-General António Guterres has called on AI companies to publicly disclose the environmental impact of their operations, including carbon emissions, water consumption, and land use. Speaking at London Climate Action Week, Guterres proposed an AI Environmental Transparency Initiative, arguing that communities are often left without clear information about the environmental impact of nearby data centre developments.

Citing a UN study, Guterres said data centres consumed more electricity in 2025 than all but ten countries, accounting for around 1.5% of global electricity demand. That share could approach 3% by 2030, while AI-related water consumption and pollution are also projected to rise significantly. By 2030, that figure is projected to nearly double to close to 3 per cent, while the water use and pollution associated with AI are also expected to double within four years.

Guterres noted that coal still provides around 30% of the electricity used by data centres globally, while renewables account for approximately 27%. He called on AI companies to power their facilities entirely with renewable energy by 2030. Guterres called on AI firms to commit to powering their facilities entirely from renewable sources such as wind and solar by 2030, though existing clean energy commitments from major tech companies have already been complicated by the rapid pace of AI deployment.

Guterres linked the proposal to broader concerns about climate change and energy security, arguing that both are rooted in continued dependence on fossil fuels. He noted that the planet has just endured its eleven hottest years on record, and that last year marked the first time the three-year global temperature average broke through the 1.5 degrees Celsius threshold set by the 2015 Paris Agreement.

He also noted that renewable energy surpassed one-third of global electricity generation in 2025 for the first time, while coal’s share fell below one-third, although he cautioned that rising AI-related electricity demand could complicate progress.

Coal’s share of global generation also fell below one-third for the first time, though significant challenges remain, particularly given policy reversals in the US under President Donald Trump, who has embraced fossil fuels and cut support for renewables.

Guterres, whose term ends in December 2026, will convene world leaders again at the annual COP climate summit later this year. He reiterated calls for every major emitter to accelerate action, reduce methane emissions, and move away from coal, oil, and gas, with the speech delivered during a heatwave affecting much of London and Europe.

Why does it matter?

The rapid expansion of AI infrastructure is bringing its environmental footprint under increasing scrutiny. As data centres consume growing amounts of electricity and water, policymakers are beginning to ask whether AI companies should be subject to the same transparency expectations applied to other carbon-intensive industries. Standardised reporting could provide governments, investors and local communities with a clearer understanding of AI’s environmental impact.

The proposal also highlights the growing intersection between AI governance and climate policy. As countries seek to expand AI capabilities while meeting emissions targets, the availability of clean energy, sustainable infrastructure and transparent environmental reporting is likely to become an increasingly important part of discussions on responsible AI development.

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Microsoft report finds AI use growing across schools

Microsoft has released the third edition of its AI in Education Report, finding that AI adoption continues to grow across schools while educators and students seek more training and practical guidance for responsible use.

The report found that AI is already widely used for school-related activities, with 92% of students and education leaders and 88% of educators reporting that they use AI. More than half of education leaders said their institutions are already implementing or scaling AI initiatives, while most respondents reported increased AI use over the past year. More than half of education leaders said their institutions are already implementing or scaling AI, while most respondents reported increased AI use over the past year.

The report identifies three priorities for schools: integrating AI into teaching and administrative operations, expanding ongoing AI skills training and providing clearer guidance for responsible classroom use. Although most respondents considered AI literacy important, many educators and students said they had not received formal training.

Alongside the report, Microsoft announced new AI-powered features for Microsoft 365 Education, including lesson-planning tools, classroom AI guidance, learning management capabilities and study assistants designed to support critical thinking rather than replace student work. Microsoft also expanded its professional development programmes through Elevate for Educators and introduced a new AI literacy credential developed in partnership with ISTE + ASCD.

Why does it matter?

The report suggests that AI is becoming a routine part of teaching and learning, shifting the conversation from whether schools should adopt AI to how they can use it responsibly and effectively. The findings indicate that demand for AI literacy, teacher training and practical classroom guidance is growing alongside adoption.

Microsoft’s new education tools also reflect a broader trend across the education technology sector, where AI is increasingly being integrated into lesson planning, administrative workflows and personalised learning. As AI becomes more embedded in schools, ensuring that educators and students have the skills to use these tools critically and responsibly is likely to become a key priority for education systems.

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European Commission explores scaling AI in agriculture

The European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI) and Directorate-General for Communications Networks, Content and Technology (DG CONNECT) jointly organised an online expert workshop on 24 June to explore how to accelerate AI adaption and scale trusted AI solutions across the agriculture sector.

The workshop was organised within the framework of the Commission’s Apply AI Strategy, which aims to accelerate AI adoption in strategic sectors, including agri-food, while strengthening European competitiveness, technological sovereignty and uptake among small and medium-sized enterprises. Participants discussed AI applications already being deployed in farm management, precision agriculture, crop and livestock monitoring, advisory services, agricultural robotics and the simplification of administrative processes.

The workshop focused on three priorities: assessing the current level of AI adoption in EU agriculture, identifying barriers to wider deployment and exploring policy measures that could support greater uptake. An interactive session also examined what is needed to ensure AI solutions in agriculture are developed, tested, and validated in a trustworthy and responsible manner.

The workshop’s findings will inform a stakeholder input note identifying priority AI use cases, barriers to adoption, infrastructure and data requirements, and potential follow-up actions under the Apply AI Strategy and related EU programmes supporting the digital transition of agriculture.

Why does it matter?

The workshop illustrates how the European Commission is moving from promoting AI in principle to addressing the practical conditions needed for large-scale deployment. In agriculture, AI has the potential to improve productivity, reduce resource use and simplify administrative tasks, but broader adoption will depend on access to high-quality data, digital infrastructure, trusted solutions and support for farmers and SMEs.

The initiative also reinforces the EU’s wider strategy of linking AI deployment with competitiveness and technological sovereignty. By connecting the Apply AI Strategy with the Common Agricultural Policy, the Common European Agricultural Data Space and Horizon Europe, the Commission is seeking to build an ecosystem in which AI can be adopted responsibly while supporting the long-term digital transformation of Europe’s agri-food sector.

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China pushes AI and biomedicine as strategic growth sectors

Chinese Vice Premier Liu Guozhong has called for stronger development of the biomedicine sector and brain-computer interface (BCI) technologies, describing them as strategic industries that will support the Healthy China initiative and China’s future industrial development.

During a visit to Jiangsu Province, Liu called for greater use of AI, big data, and other digital technologies in pharmaceutical research and development to boost innovation and accelerate high-quality growth in the biomedicine sector.

Liu also described brain-computer interfaces as a frontier technology and a strategic area of international competition. Liu called for stronger interdisciplinary collaboration, expanded brain science research, faster breakthroughs in core technologies, and greater original innovation capacity.

The remarks reinforce China’s broader strategy of promoting AI-enabled innovation and emerging technologies to strengthen industrial competitiveness and modernise its healthcare sector.

Why does it matter?

China’s emphasis on AI-powered biomedicine and brain-computer interfaces reflects its strategy of combining healthcare innovation with industrial policy. By encouraging the use of AI in drug discovery while investing in frontier technologies such as BCIs, Beijing is seeking to strengthen domestic innovation and compete in sectors expected to play an important role in future economic growth.

The remarks also underscore the growing geopolitical significance of advanced health technologies. As countries invest in AI, biotechnology and neurotechnology, these fields are increasingly viewed not only as drivers of scientific progress but also as strategic capabilities linked to economic competitiveness, technological sovereignty and national resilience.

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IMF and China sign MoU on AI and digital economy measurement

The International Monetary Fund and China’s National Bureau of Statistics have signed a new Memorandum of Understanding to strengthen cooperation on national accounts, macroeconomic statistics and statistical modernisation.

The agreement builds on a previous MoU signed in November 2023 and creates a framework for cooperation on implementing the 2025 System of National Accounts.

The cooperation will include work on measuring the digital economy, AI, cloud computing, digital intermediation platforms and data as an asset. It will also cover broader areas introduced in updated international statistical standards, including globalisation, economic well-being and environmental sustainability.

The IMF and NBS also agreed to deepen technical collaboration on the consistency and integration of macroeconomic statistics, including through the use of innovative data sources and analytical approaches.

The agreement includes cooperation between the IMF Big Data Centre and the NBS Big Data Application Centre, which hosts the UN Global Hub on Big Data and Data Science for Official Statistics.

Activities under the MoU will include high-level visits, expert consultations, technical workshops, joint analytical work and exchanges on statistical practices and methodologies.

The new MoU will take effect in December 2026, upon the expiration of the current agreement, and will remain in force until December 2029.

Why does it matter?

Measuring the digital economy is becoming harder as AI systems, cloud services, platforms and data-driven business models become more central to economic activity. Cooperation between the IMF and China’s statistics authority could support more consistent approaches to measuring these sectors under the 2025 System of National Accounts. Better statistical methods matter because governments, investors and international organisations rely on comparable data to assess growth, productivity, sustainability and the economic impact of digital transformation.

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