Artificial intelligence

OpenAI calls for aligned US AI safety framework

OpenAI has called for closer alignment between US state and federal AI safety efforts, arguing that a common framework is needed to govern frontier AI systems.

In a policy blog post, the company said recent frontier AI legislation in California, New York and Illinois shows how states can help create a shared baseline before a single federal framework is in place.

OpenAI describes this process as ‘reverse federalism’, where state laws move in similar directions and gradually shape a de facto national standard.

The company says core elements should include documented safety frameworks, risk assessments for frontier models, public disclosure of results, serious incident reporting and independent audits.

At the federal level, OpenAI argues that the US government should lead testing and evaluation of the most advanced AI systems, particularly when national security and cybersecurity are at stake.

It says a consistent federal testing framework would help advanced AI tools reach trusted users, including government agencies, critical infrastructure defenders, allies and other partners.

OpenAI also supports clearer requirements for companies developing the most capable systems, including strong security standards, incident reporting, independent audits and whistle-blower protections.

The company warns that neither a fragmented patchwork of state laws nor an undefined federal process would create a coherent frontier safety regime.

Why does it matter?

OpenAI’s proposal highlights the growing tension in US AI governance between state-led action, federal oversight and international standard-setting. A shared framework could reduce regulatory fragmentation and create clearer expectations for frontier model developers. Still, the company’s position also reflects the interests of a major AI lab seeking predictable rules for deployment, testing and access. The debate will shape how the US balances safety, innovation, national security and global influence in AI governance.

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Microsoft and 3M partner on AI data centre infrastructure

Microsoft and 3M have announced a strategic partnership covering AI data centre infrastructure and the deployment of Microsoft AI tools across 3M’s business operations.

Microsoft Azure will become the first hyperscale cloud provider to deploy 3M’s Expanded Beam Optical technology, which is designed to improve the physical connectivity needed for expanding cloud and AI workloads. Unlike conventional fibre connectors that rely on direct physical contact, the technology uses an expanded optical beam, making connections more resistant to dust and contamination during installation and maintenance.

Microsoft said early deployments suggest the technology could shorten network installation times in some environments while maintaining reliable signal performance under typical data centre conditions.

3M is expanding production as hyperscalers and data centre operators invest in AI infrastructure. The company has also supported an industry agreement to standardise expanded beam optical connections.

The partnership will combine Microsoft’s cloud infrastructure with 3M’s expertise in materials science, optical connectivity and precision manufacturing, with further collaboration focusing on deployment speed, reliability, density and long-term scalability.

Alongside the infrastructure agreement, 3M will deploy Microsoft AI and digital platforms across customer service, finance, sales and marketing to simplify processes, improve decision-making and increase productivity.

Microsoft engineers are also working with 3M’s Global Business Services team on an AI agent-based customer order workflow covering credit checks, delinquency assessments and system updates while retaining human approval. A monitoring dashboard will provide employees with real-time visibility into the process to improve consistency, reduce manual work and support auditability.

The partnership links investment in the physical infrastructure that supports AI with the adoption of AI across enterprise operations, illustrating how cloud providers and industrial companies are increasingly collaborating across both domains.

Why does it matter?

The partnership highlights that scaling AI depends not only on chips and computing power but also on the physical networking infrastructure connecting data centres. Improvements in optical connectivity could help cloud providers deploy increasingly dense AI infrastructure more efficiently and reliably.

It also illustrates how infrastructure partnerships are becoming broader digital transformation initiatives. By combining hardware innovation with enterprise AI deployment, Microsoft and 3M are reflecting a wider trend in which AI investment spans both the technology underpinning AI services and the business processes that use them.

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Illinois issues AI guidance for schools with AI-assisted drafting disclosed

The Illinois State Board of Education has published comprehensive guidance on the use of AI in schools, while also disclosing that the 409-page document was itself developed with assistance from multiple AI models alongside human review.

The guidance was prepared following legislation adopted in 2025, with contributions from education, technology and public policy experts. Initial drafts drew ChatGPT, Claude and Gemini, while human reviewers edited, verified and refined the final document.

The board detailed how AI was used throughout the drafting process, including producing early text, verifying publicly available resources, creating graphics and improving clarity. It stressed that all AI-generated information was independently reviewed and verified before publication.

The guidance emphasises that AI should support teaching and learning rather than replace human relationships or educational experiences. It also offers practical recommendations for selecting AI tools and promoting responsible, ethical and transparent AI use in schools.

The guidance is intended to help Illinois schools navigate both the opportunities and risks associated with AI adoption in education.

Why does it matter?

The guidance offers schools a practical framework for integrating AI while addressing issues such as academic integrity, privacy, transparency and the reliability of AI-generated content. As more education systems adopt AI, common governance principles may help schools use the technology more consistently and responsibly.

The document is also notable for openly disclosing how AI contributed to its own development. By documenting where AI was used and emphasising independent human verification, the Illinois State Board of Education models a level of transparency that could influence how other public institutions develop AI-assisted policies and guidance.

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South Korea showcases low-power AI networking technology

South Korea’s Ministry of Science and ICT has showcased low-power AI networking technology to an international audience as part of efforts to promote more energy-efficient communications infrastructure.

According to the ministry, the demonstration formed part of broader efforts to showcase domestic advances in AI and communications technologies and strengthen international cooperation.

The ministry released few technical details about the technology, instead presenting it as an example of South Korea’s research and development capabilities in AI networking and next-generation communications infrastructure.

Why does it matter?

Reducing the energy required to run AI infrastructure is becoming increasingly important as AI workloads expand. More efficient networking technologies could help lower operating costs and support wider deployment of AI systems while reducing their environmental impact.

Although the ministry released few technical details, the announcement reflects South Korea’s continued investment in AI and advanced communications technologies as part of its broader strategy to strengthen technological competitiveness and international collaboration.

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EU and India strengthen technology partnership through TTC

The European Union and India have strengthened their strategic partnership at the third meeting of the EU-India Trade and Technology Council (TTC) in Brussels, agreeing to deepen cooperation on advanced technologies, trade and resilient supply chains.

Both sides reaffirmed the TTC as their main platform for cooperation on technology, economic security and innovation, while agreeing to upgrade the framework by the end of 2026 under the Joint EU-India Comprehensive Strategic Agenda.

The meeting produced several concrete initiatives.

The EU and India agreed to begin formal negotiations on India’s association with Horizon Europe, establish the first EU-India Innovation Hub focused on electric vehicle charging technologies, and launch a startup partnership supporting deep tech and clean technology companies. They also expanded cooperation on semiconductors, AI, quantum technologies, high-performance computing, 6G and resilient supply chains covering clean energy technologies, pharmaceuticals and agri-food.

On digital technologies, the partners agreed to strengthen cooperation on AI innovation, including healthcare applications, and collaborate on high-performance computing for climate research, natural hazards and bioinformatics. They also committed to advancing interoperability between digital trust services, including digital wallets, building on their earlier agreement on electronic signatures and seals.

The meeting also reaffirmed the strategic importance of the broader EU-India relationship, including ongoing negotiations on a Free Trade Agreement, investment protection and geographical indications.

Ministers instructed TTC working groups to prioritise implementation ahead of the next ministerial meeting.

Why does it matter?

The EU and India are increasingly treating technology as a strategic pillar of their relationship alongside trade and investment. Expanding cooperation on AI, semiconductors, research and digital infrastructure reflects shared interests in strengthening technological competitiveness and reducing vulnerabilities in critical supply chains.

The agreement also illustrates how trade partnerships are evolving into broader technology partnerships. By linking research, innovation, standards and digital trust, the TTC provides a framework that could deepen long-term cooperation while supporting both sides’ economic security and strategic autonomy.

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UNESCO highlights Learning Cities on World Youth Skills Day

UNESCO has highlighted how cities across its Global Network of Learning Cities are helping young people develop the skills needed for employment, active citizenship and sustainable development to mark World Youth Skills Day.

Through lifelong learning ecosystems, local governments, schools, training centres, employers and community organisations are working together to equip young people with practical, digital, entrepreneurial and leadership skills that respond to changing labour markets and wider societal needs.

The initiative highlights examples from Learning Cities around the world.

In Dakar, Senegal, programmes focus on digital entrepreneurship and employability, while Quezon City in the Philippines offers vocational education and technical certification to improve employment opportunities. Nairobi, Kenya, supports young entrepreneurs through business training, and Bouaké, Côte d’Ivoire, demonstrates how community engagement can strengthen sustainable development.

UNESCO also emphasises that youth skills extend beyond employment. Learning Cities promote leadership, civic participation and community engagement, with examples from Colombia and Ireland illustrating how lifelong learning helps young people become active contributors to their communities.

UNESCO also highlights how lifelong learning can support sustainability and cultural preservation. Initiatives linking young people with local heritage, environmental conservation and sustainable development demonstrate how education can strengthen both community resilience and future opportunities.

Why does it matter?

UNESCO’s initiative reflects a growing recognition that preparing young people for the future requires more than technical or digital skills alone. Lifelong learning is increasingly viewed as essential for supporting employment, civic participation, adaptability and resilience in societies shaped by rapid technological change.

The examples from Learning Cities also show how local governments can play a central role in skills development by bringing together education providers, employers and communities. As AI and digital transformation reshape labour markets, place-based lifelong learning policies may become an increasingly important part of workforce and development strategies.

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Canada invests CAD 13.9 million in Quebec AI projects

The Government of Canada has announced nearly CAD 13.9 million (approximately €8.8 million) in funding for 63 AI projects across Quebec under the Regional Artificial Intelligence Initiative (RAII), aiming to accelerate AI adoption among small and medium-sized enterprises (SMEs).

The funding will support projects focused on both developing AI technologies and integrating AI into existing business operations. The government said the initiative will help SMEs improve productivity, develop innovative solutions and strengthen regional economic growth.

According to the government, the programme is expected to support more than 1,700 jobs across Quebec while helping businesses innovate more quickly, improve operational efficiency and seize new commercial opportunities. The announcement was made by Minister of Artificial Intelligence and Digital Innovation Evan Solomon on behalf of Industry Minister Mélanie Joly.

The investment forms part of Canada’s broader ‘AI for All’ strategy, launched in June 2026, which aims to expand access to AI, promote responsible adoption, strengthen domestic innovation and reinforce Canada’s digital sovereignty.

The Regional Artificial Intelligence Initiative will continue operating in Quebec until March 2031. Canadian officials said the programme is intended to strengthen regional innovation ecosystems, expand AI capabilities among businesses and position Quebec as a leading centre for AI talent and technological development.

According to the government, combining responsible AI adoption with targeted regional investment will strengthen competitiveness while ensuring the benefits of AI are shared more broadly across businesses and communities.

Why does it matter?

The initiative reflects Canada’s growing focus on regional AI development rather than concentrating investment solely in major technology hubs. Supporting AI adoption among SMEs could help spread productivity gains more widely across the economy while strengthening local innovation ecosystems.

The programme also illustrates how industrial policy is becoming an important component of national AI strategies. By combining public funding, regional development and responsible AI governance, Canada is seeking to strengthen long-term competitiveness while reinforcing domestic technological capacity.

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AI is reshaping physics but raising new questions

AI is becoming an increasingly important tool in physics, helping researchers analyse large datasets, accelerate simulations and identify patterns that may be difficult to detect through conventional methods.

A Physics World feature examines how machine learning is already embedded in particle physics, including work at CERN’s Large Hadron Collider, where researchers have used AI techniques in Higgs boson analyses and searches for new physics.

Newer approaches are also being used to detect unexpected anomalies in collider data, potentially helping physicists look beyond predictions based on existing theories.

The growing use of AI has renewed concern about the so-called black-box problem, in which researchers cannot fully explain how a system reaches its conclusions.

Physicists interviewed in the article argue that reproducibility, verification and rigorous review remain central to trust, even when AI models are not fully interpretable.

Applications now extend beyond particle physics into materials science, where autonomous systems and robotic laboratories can design, test and refine new materials.

Such systems could increasingly help decide which experiments to perform, speeding up discovery while shifting scientists towards more supervisory and interpretive roles.

Researchers caution, however, that AI should remain a tool for scientific inquiry rather than a substitute for reasoning, curiosity and critical judgement.

Why does it matter?

AI is changing how scientific knowledge is produced. In physics, it can help researchers process data at scales humans cannot manage alone, improve simulations and suggest new experimental directions. That could accelerate discoveries with wider technological impact, from advanced materials to energy systems and medical technologies. Greater reliance on AI also raises governance questions inside science itself. If results depend on systems that are difficult to interpret, scientific communities need strong methods for reproducibility, validation, peer review and accountability. The issue is not only whether AI can find patterns, but whether scientists can verify, explain, and responsibly build knowledge from them.

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Companies face new questions over AI control and governance

Enterprise AI adoption is moving beyond questions of which model to use towards deeper concerns about infrastructure, governance and ownership.

A Forbes Technology Council article argues that companies entering production with AI need to ask whether systems can operate securely inside their own environments and who controls the intelligence those systems generate over time.

It frames enterprise AI as requiring a new trust layer across the stack.

At the infrastructure level, businesses need visibility into how compute is accessed and governed. At the model level, they need control over data, operational knowledge and business value. At the application level, agents and workflows need clear permissions, access controls and safeguards.

Reasoning models and autonomous agents are also changing the economics of AI deployment. More advanced systems require continuous inference, more tokens and persistent access to business context, making compute capacity, latency and cost central to scaling AI beyond pilots.

The article warns that enterprises risk building long-term value outside their own systems if operational knowledge is generated and stored on external platforms without sufficient control.

It argues that the next phase of enterprise AI will depend less on access to individual models and more on secure, governed systems for owning and reusing operational intelligence.

Why does it matter?

The article captures a broader shift in enterprise AI: competitive advantage may come less from using the same general-purpose models as everyone else and more from how organisations govern the data, workflows and operational knowledge created around those models. That matters for digital sovereignty, vendor lock-in, security and long-term business value. As AI agents become embedded in daily operations, companies will need clearer rules on permissions, auditability, infrastructure dependence and ownership of AI-generated knowledge.

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