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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Three in four young Europeans have basic digital skills, Eurostat says

Nearly three out of four young people in the EU had at least basic digital skills in 2025, according to new Eurostat data released to mark World Youth Skills Day. However, the figures also reveal persistent disparities between Member States.

Denmark recorded the highest share of digitally skilled young people at 92.1%, followed by Czechia (91.7%) and Malta (91.5%). Bulgaria (52.8%) and Romania (53.3%) ranked lowest, remaining the only EU countries where fewer than six in ten young people possessed at least basic digital skills.

The data also show that young women outperformed young men across the EU. In 2025, 75.9% of women aged 16 to 24 had at least basic digital skills, compared with 73.3% of men.

Women recorded higher levels of digital skills in 22 EU member states, with the largest gaps in Cyprus, Slovenia and Austria. Men performed better in only five countries, with the widest differences in Malta and Romania.

Eurostat’s Digital Skills Indicator measures competencies across five areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. Individuals are classified as having at least basic digital skills if they demonstrate at least one relevant activity in each area.

Why does it matter?

Digital skills are increasingly essential for education, employment and participation in the digital economy. While the latest figures show that most young Europeans possess at least basic digital competencies, the wide differences between Member States suggest that access to digital education and training remains uneven across the EU.

Closing these gaps will be important for achieving the EU’s Digital Decade objectives, which depend on a digitally skilled workforce capable of supporting economic competitiveness, innovation and the wider adoption of emerging technologies such as AI.

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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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UK establishes ministerial group to coordinate digital inclusion

The UK government has published the terms of reference for a new ministerial group created to coordinate digital inclusion policy across departments, aiming to improve access to digital services, technologies and skills.

The Ministerial Group for Digital Inclusion will set the government’s strategic direction, agree a shared vision and identify priorities for improving digital inclusion across the UK.

Its work will focus on embedding digital inclusion into policy design, public service delivery and existing government governance structures. The group will also monitor progress across departments with support from official-level bodies.

The forum brings together ministers responsible for key policy areas affecting digital inclusion, including the Department for Science, Innovation and Technology, Department for Business and Trade, HM Treasury, Cabinet Office, Department for Education, Department for Work and Pensions, Ministry of Housing, Communities and Local Government, Department of Health and Social Care, and Department for Culture, Media and Sport.

The group will not have formal decision-making powers, with policy decisions remaining the responsibility of individual departments. Instead, it will coordinate common approaches and encourage joint action on digital inclusion priorities.

They will meet quarterly and be chaired by Baroness Lloyd of Effra, Minister for Digital Economy. The Department for Science, Innovation and Technology’s Digital Inclusion and Skills Unit will provide secretariat support.

Meetings will review strategy, implementation challenges, departmental cooperation and progress against agreed priorities, with the secretariat responsible for preparing papers and tracking agreed actions.

The ministerial group will be supported by the cross-government Digital Inclusion Strategy Board, while the government plans to publish meeting summaries and keep the group’s role under regular review.

Why does it matter?

Digital exclusion affects access to education, employment, healthcare and public services, making it a cross-government policy challenge rather than the responsibility of a single department. The new ministerial group is intended to improve coordination and ensure digital inclusion is considered more consistently across government.

Its success, however, will depend on whether departments translate shared priorities into funded programmes, measurable outcomes and lasting policy changes rather than treating the forum as a coordination mechanism alone.

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European Commission expands AI assistant across global DG INTPA network

The European Commission’s Directorate-General for International Partnerships (DG INTPA) has expanded the use of an AI assistant to support staff across its headquarters and delegations in more than 100 countries.

Launched in March 2026 and developed with Accenture, the AI assistant is tailored to DG INTPA’s internal procedures, terminology and policy work. According to Accenture, the platform has more than 2,000 regular users and has processed over 400,000 queries.

The assistant combines large language models with secure access to internal documents and internet connectivity to support policy, funding and operational tasks. The programme also includes staff training and human oversight to promote the responsible use of AI.

According to Accenture, the next phase will introduce agentic AI capabilities for selected workflows, alongside a user feedback mechanism to help refine the system.

Why does it matter?

The deployment illustrates how AI is moving from pilot projects to routine administrative support within public institutions. Rather than focusing only on productivity, the Commission is combining AI tools with governance measures such as staff training and human oversight to support responsible adoption.

The planned introduction of agentic AI also reflects a broader shift towards more autonomous workplace systems. If successful, DG INTPA’s experience could inform wider adoption of AI across EU institutions and other public administrations seeking to modernise policy and operational processes.

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UK launches £800,000 AI Upskilling Challenge Fund in Barnsley

The UK government has opened applications for the £800,000 AI Upskilling Challenge Fund under the Barnsley Tech Town programme to support AI skills development for workers, businesses and local communities.

Training providers, charities, colleges, businesses and technology companies across the UK can apply, provided their projects are delivered in Barnsley. Priority groups include manufacturing workers, older residents, small businesses and people entering the workforce.

The government said successful projects should demonstrate the potential to be scaled nationally. Lessons from the programme will contribute to its goal of equipping 10 million workers with AI skills by 2030.

Applications open on 15 July through the government’s Find a Grant platform. Barnsley Council said the funding forms part of wider plans to strengthen the town’s digital economy and support its manufacturing and logistics sectors.

Why does it matter?

The programme illustrates how AI policy is increasingly shifting from national strategies towards place-based implementation. By testing AI training programmes in a manufacturing-focused community, the government hopes to identify approaches that could be replicated elsewhere as AI adoption accelerates across the economy.

The initiative also reflects the growing recognition that AI competitiveness depends not only on developing new technologies but also on expanding workforce skills. Helping workers and small businesses adopt AI could improve productivity while reducing the risk that parts of the labour market are left behind during the transition.

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Digital Omnibus on AI: The EU’s AI Act simplification and new AI Office powers

On 29 June 2026, the Council of the European Union gave its final green light to the Digital Omnibus on AI, a package of amendments that eases and delays parts of the EU AI Act, completing a legislative procedure that began when the European Commission published its proposal on 19 November 2025. It amends the EU AI Act, together with the EU’s civil aviation rules and machinery regulation. According to the European Parliament’s Legislative Observatory, the final act was signed on 8 July 2026, and the Digital Omnibus is now awaiting publication in the Official Journal of the European Union, a necessary step before it can enter into force, ahead of the original 2 August 2026 deadline for several high-risk AI obligations.

Much of the public attention on the Digital Omnibus has focused on the delay to high-risk AI rules and the new ban on AI-generated intimate imagery. The full legal text of the amending regulation also reorganises, in detail, responsibility for supervising AI systems that operate within very large online platforms regulated under the Digital Services Act, and amends several other elements of the way the AI Act is enforced, points that have drawn less attention so far.

The Council describes this regulation as part of a wider legislative package known as Omnibus VII, one of several ‘omnibus’ simplification efforts the Commission has proposed across different policy areas. It was also listed in the Parliament and the Council in their Joint Declaration on EU legislative priorities for 2026, signalling the priority both institutions attached to its rapid finalisation.

Why the Commission proposed the amendments

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According to the recitals of the Digital Omnibus on AI, the amendments respond to problems identified once parts of the AI Act began to apply in August 2024. The recitals point to delays in the preparation of harmonised technical standards needed by providers of high-risk AI systems in order to demonstrate compliance, as well as delays by several member states in setting up the national authorities and conformity assessment bodies responsible for checking that compliance. Taken together, the recitals state that these delays created a heavier compliance burden than originally expected.

The Commission’s proposal also links the amendments to a broader competitiveness rationale, describing them as part of a wider effort by EU leaders to reduce administrative burdens on business, following the recommendations of the Draghi and Letta reports on European competitiveness. Industry associations also lobbied for the amendments throughout 2025.

The trade group DIGITALEUROPE told policymakers that compliance with the AI Act could cost companies in the region of EUR 3.3 billion a year across the EU, and that a company of around 50 employees developing an AI-based product could face initial compliance costs of between EUR 320,000 and EUR 600,000.

How the Digital Omnibus was negotiated

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The AI-specific amendments were separated from the wider Digital Omnibus package, which also proposes amendments to the GDPR, the ePrivacy Directive, the Data Act, and the NIS2 Directive on cybersecurity, due to the approaching deadline for high-risk AI obligations. According to the Legislative Observatory’s procedure record, Parliament’s Internal Market Committee voted on the proposed regulation on 18 March 2026, and the Parliament adopted its first-reading position on 26 March 2026.

The Parliament and the Council negotiators reached a political agreement on the Digital Omnibus early on 7 May 2026. The Council’s Permanent Representatives Committee confirmed the agreement in a letter dated 13 May 2026. The Parliament formally adopted the Digital Omnibus on 16 June 2026, the Council gave its final approval on 29 June 2026, and the final act was signed on 8 July 2026.

The regulation’s preamble records that the European Central Bank was consulted and issued a formal opinion, published in the Official Journal in April 2026, as required under EU legislation for measures affecting payments and financial infrastructure. The European Economic and Social Committee delivered its opinion on 18 March 2026, and the Committee of the Regions gave its opinion on 7 May 2026. National parliaments, including those of Czechia, Italy, the Netherlands, Portugal, Romania, Germany, Poland and France, also submitted subsidiarity contributions during the process. The Parliament’s public transparency register separately records meetings on this regulation between the two co-rapporteurs and organisations, including Google, the AI start-up Mistral AI, the digital rights group EDRi, the privacy group noyb, and the standards and conformity body TIC Council, reflecting the range of interests, from large technology firms to civil society, that engaged with the negotiations.

New deadlines for high-risk AI obligations

Under the amended Article 113 of the AI Act, the obligations for high-risk AI systems set out in Sections 1 to 3 of Chapter III will now apply from 2 December 2027 for systems classified as high-risk under Article 6(2) and Annex III, which covers areas such as biometrics, critical infrastructure, education, employment, law enforcement, migration and border management. For systems classified as high-risk under Article 6(1) and Annex I, meaning AI systems embedded in products already covered by other EU safety legislation, such as machinery or medical devices, the new deadline is 2 August 2028. Both deadlines were originally set for 2 August 2026.

A separate provision clarifies how the AI Act’s grace period for so-called legacy systems, set out in Article 111(2), applies. Once at least one unit of a given type and model of high-risk AI system has been lawfully placed on the market before the relevant cut-off date, further units of the same type and model can continue to be placed on the market or put into service without additional certification, as long as the system’s design does not change significantly. Any significant redesign after the cut-off date triggers full compliance with the AI Act, including conformity assessment.

To help providers meet the new deadlines, the Digital Omnibus requires the Commission to request that European standardisation bodies develop technical standards aligned with existing product-safety standards, reducing duplication for companies that have to comply with both the AI Act and sectoral legislation. The Commission must also publish guidance on post-market monitoring plans by 2 September 2027, as well as guidance to help providers of Annex I high-risk systems apply the AI Act alongside sectoral rules by 1 August 2027. Watermarking obligations for AI-generated content, which allow such content to be detected and traced, benefit from a separate four-month transitional period for systems already on the market before 2 August 2026.

Changes to AI literacy and the use of sensitive data for bias correction

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A further amendment loosens the AI Act’s AI literacy obligation. Instead of requiring providers and deployers to ensure a sufficient level of AI literacy among their staff, the amended Article 4 requires them to take measures supporting the development of that literacy among staff and other people involved in the operation of their AI systems. The European Artificial Intelligence Board is tasked with adopting recommendations that set common objectives to guide how the Commission and member states support this obligation.

A new Article 4a allows providers and deployers of AI systems to process special categories of personal data, such as data revealing ethnicity or health status, for the specific purpose of detecting and correcting bias, subject to a list of privacy safeguards, including data minimisation, restrictions on transferring the data to third parties, and deletion once the bias has been corrected. The final text requires this processing to be strictly necessary, a stricter standard than the version originally proposed by the Commission. This followed a joint opinion issued by the European Data Protection Board and the European Data Protection Supervisor in January 2026, which recommended reinstating the stricter standard.

AI Office gains exclusive powers over general-purpose AI and large platforms

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Article 75 of the AI Act, which governs the market surveillance of AI systems, has been substantially rewritten. Under the new provisions, the Commission’s AI Office becomes exclusively responsible for supervising two categories of AI systems. The first category comprises AI systems built on general-purpose AI models, where the same provider, or providers belonging to the same undertaking, developed both the underlying model and the AI system built on it. This exclusive competence carries several exceptions. It does not apply to AI systems related to products already covered by EU product-safety legislation, AI systems used as critical infrastructure, systems provided by law enforcement authorities, border management authorities or financial institutions in specific circumstances, or certain systems used in the administration of justice, all of which remain under national supervision.

The second category covers AI systems that constitute, or are integrated into, a very large online platform or a very large online search engine designated under the Digital Services Act (DSA), the EU’s rulebook for online platforms. The recitals state that empowering the Commission, through the AI Office, to act as a market surveillance authority for these systems is intended to ensure that enforcement of the AI Act and the DSA is carried out consistently, given the scale and potential societal impact of very large platforms and search engines.

For AI systems that are embedded in, or form part of, a designated very large platform or search engine, the Digital Omnibus specifies that the DSA’s own risk assessment, mitigation, and audit obligations, laid down in Articles 34, 35, and 37 of that regulation, serve as the first point of entry for assessing the AI system. This is without prejudice to the AI Office’s separate power to investigate and enforce breaches of the AI Act after the fact. The Commission services that enforce the DSA and the AI Office are required to coordinate, exchange views regularly, and take account of any fines already imposed on the same company for the same conduct, so that the combined penalties remain proportionate and do not amount to double punishment for the same infringement.

Outside this narrower platform-related category, national market surveillance authorities retain a role. Where a national authority has well-founded reasons to suspect that a provider or deployer of an AI system under the AI Office’s exclusive competence has breached the AI Act, it may ask the AI Office, through a designated national contact point, to investigate. The AI Office must tell that authority within four months whether it intends to act, and keep it informed of major developments and the eventual outcome.

The recitals acknowledge that taking on this expanded role will require the AI Office to be adequately staffed and resourced. Whether the Commission allocates sufficient capacity for the AI Office to supervise both general-purpose AI models and large platforms is an operational question that will only become clear as implementation proceeds, rather than one resolved by the legislation itself.

New ban on AI-generated intimate imagery and child sexual abuse material

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The Digital Omnibus amends Article 5 of the AI Act, which lists AI practices that are prohibited outright. It adds a prohibition against placing on the market, putting into service, or using AI systems that generate or manipulate realistic images, video or audio of an identifiable person’s intimate parts, or of that person engaged in sexually explicit activity, without that person’s free, specific, informed and unambiguous consent. It adds a parallel prohibition covering AI systems that generate or manipulate child sexual abuse material, subject to a narrow exception for activities that are lawful under national law, such as material generated by law enforcement authorities for the purposes of criminal investigation.

For providers, the prohibition applies in two situations: where generating or manipulating such material is the system’s intended purpose, or where that outcome is a reasonably foreseeable and reproducible result of the system’s design and the provider has not put in place reasonable and adequate safeguards, such as content filtering or abuse-detection mechanisms, to prevent it. For deployers, the prohibition applies only where the AI system is actually used for that purpose, meaning the ordinary use of a lawful system for unrelated purposes is not covered, nor is accidental generation of such content.

The prohibited material is defined narrowly. It covers realistic depictions, meaning a person’s face, voice or body shown in a credible, real-life manner, and specifically named intimate parts or depictions of sexually explicit activity. Cartoonish or physically impossible depictions fall outside the prohibition, as does content generated with the depicted person’s consent, non-realistic artistic nude work that does not depict an identifiable person, and legitimate medical applications such as anatomical simulations. Simple enhancements to existing images, such as adjusting brightness or adding a caption, are not treated as prohibited manipulation unless they increase the level of nudity or explicitness shown. Companies have to ensure that their systems comply with these rules by 2 December 2026.

Other simplification measures

The Digital Omnibus extends several compliance simplifications that previously applied only to small and medium-sized enterprises to a new category of small mid-cap enterprises, companies that have outgrown the SME definition but remain much smaller than large corporations. It also gives all SMEs, including start-ups, the option to comply with parts of the AI Act’s quality management system requirements in a simplified way, an option previously limited to microenterprises.

The deadline for each member state to have at least one operational national AI regulatory sandbox, a controlled environment in which providers can test AI systems under regulatory supervision, has been extended to 2 August 2027. The same provisions allow the AI Office itself to set up an EU-level sandbox for AI systems that fall under its exclusive competence, with priority access for SMEs, start-ups and small mid-cap enterprises, operating alongside, and not instead of, national sandboxes.

A further change moves the EU machinery regulation from one section of the AI Act’s product-safety annex to another, shifting AI-enabled machinery towards a more sector-specific approach. Under the new arrangement, the Commission must adopt delegated acts by 2 August 2028 incorporating the AI Act’s health and safety requirements directly into the machinery regulation, rather than requiring manufacturers to apply both frameworks in parallel.

Data protection authorities raise fundamental rights concerns

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Before the political agreement was reached, the European Data Protection Board and the European Data Protection Supervisor issued a joint opinion on the Commission’s initial proposal. The two authorities said they supported the general aim of addressing implementation issues, but raised concerns that several measures could weaken human rights protections built into the AI Act. They warned that extending the legacy systems exception would allow more high-risk AI systems to reach the market without being subject to the Act’s safeguards and urged the co-legislators to keep any delay to transparency obligations as short as possible.

The two authorities also opposed the Commission’s original plan to remove the registration obligation for providers who conclude that their Annex III systems are not high-risk, arguing that this would weaken accountability and make it harder for market surveillance authorities to respond quickly to problem systems. That registration obligation was retained, in a streamlined form, in the Digital Omnibus as finally approved in June. As set out above, the authorities’ recommendation to apply a strict necessity standard to the processing of sensitive data for bias correction was also reflected in the final version of the Digital Omnibus.

Not all of the authorities’ recommendations were taken on board in the same way. Their broader concern, that postponing obligations for high-risk AI systems may leave fundamental rights protections unenforced for longer in a fast-moving technological area, remains a live point of disagreement between the co-legislators and civil society groups, as discussed further below.

Reactions: competitiveness framing meets rights concerns

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Council and Parliament negotiators presented the changes as a way to make the AI Act more workable without altering its underlying risk-based structure. Co-rapporteur Arba Kokalari said the agreement showed that politics can move just as quickly as technology, linking the simplification to the Commission’s broader competitiveness agenda. Co-rapporteur Michael McNamara said the deal combined simplification measures with new safeguards against nudification apps and AI-generated child sexual abuse material.

Civil society organisations took a more critical view of the overall direction of the package. The digital rights group Liberties argued that the final agreement weakens several safeguards contained in the original AI Act, and described the postponement of high-risk obligations as a delay to fundamental rights protections that were due to take effect in August 2026.

Industry associations generally welcomed the changes. DIGITALEUROPE, which had been among the most vocal critics of the AI Act’s original compliance costs and timeline, broadly supported the direction of the simplification package, while continuing to call for further alignment between the AI Act and other overlapping EU digital rules.

What happens next

The Digital Omnibus on AI will enter into force once it is published in the Official Journal of the European Union. Until then, the AI Act’s original provisions and timeline remain legally in force, including the prohibitions on unacceptable AI practices and the obligations applicable to general-purpose AI models that have applied since August 2025.

A separate Commission exercise, the Digital Fitness Check, is expected to examine the DSA and the wider digital rulebook directly, with a report on its findings due in the first quarter of 2027 according to legal commentary on the process. That exercise, rather than the AI Omnibus itself, is where the more direct question of simplifying the DSA is likely to be decided and where the institutional link now established between the AI Office and DSA-regulated platforms may be revisited.

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Nobel laureates call for urgent AI economic planning

Sixteen Nobel laureates have joined leading economists and AI researchers in calling for urgent preparation for the economic changes that more powerful AI systems could trigger.

The statement, titled We Must Act Now: A Statement on AI’s Transformation of the Economy, was released by the Stanford Digital Economy Lab.

It was organised by economists Erik Brynjolfsson, Ajay Agrawal, Anton Korinek and Tom Cunningham, and has been signed by more than 200 experts.

The statement warns that AI may become radically more powerful over the next decade, potentially driving an economic transformation larger than the Industrial Revolution but unfolding over a much shorter period.

The signatories say AI could bring major gains in living standards, but also risks, including large-scale job displacement.

They argue that economists, policymakers and technology leaders must act now to understand these impacts and prepare society for the transition.

The statement calls for incentives, guardrails and institutions that steer AI towards complementing human labour and benefiting society.

Its authors stress that the economic outcome of AI is not predetermined and will depend on choices made by governments, companies and researchers.

Why does it matter?

The statement adds weight to the debate over AI’s economic impact because it brings together Nobel laureates, economists, AI researchers and technology leaders around a common warning: societies may have far less time to adapt to AI than they had during earlier technological shifts. Its central message is not that job displacement is inevitable, but that policy choices made now will shape whether AI raises living standards broadly or concentrates economic power and leaves many workers behind.

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UK MPs call for clearer sovereign AI strategy

UK MPs have called on the government to set out a clearer strategy for building sovereign AI and other critical technology capabilities.

A new report from the Science, Innovation and Technology Committee warns that the UK may not be able to rely on allies for access to technologies essential to economic growth and national security.

The committee said the government has not clearly defined what sovereign capability means, how it should be measured or what success would look like.

MPs also criticised the absence of a coherent strategic framework for using the UK’s scientific research and institutions to support wider diplomatic and economic goals.

Instead, the report says the government has taken an opportunistic approach to international science and technology agreements, risking substituting activity for strategy.

AI is identified as a central arena for global competition and collaboration.

The committee said recent US restrictions on access to advanced AI models show how reliance on partners can leave the UK exposed if access to critical technologies changes suddenly.

MPs called on the government to define sovereign capability in key technology areas, identify critical supply-chain dependencies and use those assessments to guide investment, procurement and research funding.

The report also warned that the UK continues to struggle to turn world-class research into large domestic technology companies, with many promising firms forced to scale overseas.

It called for targeted investment, stronger public procurement and later-stage funding to help commercialise homegrown innovation in strategic sectors.

Why does it matter?

The report places sovereign AI inside a wider debate about technology dependence, national security and economic resilience. AI capability increasingly depends on access to compute, models, talent, data, infrastructure and supply chains that foreign governments or companies may control. The committee warns that the UK cannot treat partnerships as a substitute for strategy. It needs to decide which capabilities it must own, where it should collaborate and where reliable access is enough.

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Google open-sources k8s-aibom to detect shadow AI

Google has open-sourced k8s-aibom, a lightweight Kubernetes controller designed to detect unregistered AI workloads and generate standardised inventories of the AI models, runtimes and frameworks operating inside a cluster.

The tool targets shadow AI: workloads deployed by developers without formal registration or integration with an organisation’s security and governance systems. Such deployments can evade conventional security scanners, particularly where organisations avoid privileged agents, kernel-level access or manual changes to Kubernetes workloads.

Google says k8s-aibom addresses that gap by continuously monitoring Kubernetes APIs and container environments. It detects running AI components and generates CycloneDX 1.6 Machine Learning Bills of Materials (ML-BOMs) based on what is actually executing, rather than what was intended during the build process.

The controller runs as a single unprivileged deployment in the k8s-aibom-system namespace. It does not require sidecars, eBPF modules, privileged DaemonSets or modifications to developers’ continuous integration and deployment pipelines.

The controller monitors KServe resources, deployments, StatefulSets, DaemonSets and jobs across a cluster. It then analyses container images, environment variables and command-line arguments to identify different categories of AI workloads.

Supported systems include inference runtimes such as vLLM, Triton Inference Server, TGI, and Ollama; agent frameworks including LangChain, AutoGen, and CrewAI; retrieval and vector database tools such as Milvus, Qdrant, and pgvector; and distributed training and evaluation workloads.

Once identified, the components are compiled into CycloneDX ML-BOM documents. These records can be stored as Kubernetes custom resources or exported to destinations including Google Cloud Storage and webhook endpoints.

Google also designed the tool to produce identical ML-BOM documents when given identical cluster inputs. This deterministic behaviour is intended to support GitOps workflows, allowing security and reliability teams to compare records and identify changes when AI dependencies drift.

Unlike build-time scanners, which document what organisations intended to deploy, k8s-aibom observes live clusters to identify which AI systems are actually running, how they are connected and how those findings were established.

A confidence model separates detected components into three categories. Declared assets are explicitly specified in workload configurations, inferred assets are identified through runtime patterns, and unresolved assets indicate that an AI presence was detected but the precise model, version, or weights could not be established.

Unresolved findings can therefore be prioritised for further security review, while declared and inferred classifications help auditors distinguish documented engineering intent from conclusions reached by the controller.

Google says the controller follows least-privilege principles and can export records using a dedicated identity with permission to create objects in Cloud Storage. Creation preconditions can prevent existing ML-BOM records from being silently overwritten, strengthening the historical evidence available to security and compliance teams.

Google also positions k8s-aibom as a tool for regulatory and standards compliance. Runtime inventories could help organisations gather evidence relevant to the EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001 requirements for AI asset management.

Why does it matter?

Shadow AI has become a growing governance challenge as developers deploy AI tools outside formal security and compliance processes. Without visibility into what is actually running in production, organisations may struggle to assess risk, investigate incidents or demonstrate regulatory compliance.

By generating inventories of live AI workloads rather than relying solely on build-time records, k8s-aibom could help organisations improve AI governance while supporting audits, security operations and compliance with emerging AI standards and regulations.

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