Artificial intelligence Archives | Page 8 of 689

ECB highlights gap between AI adoption and productivity

Firms across the euro area are increasingly adopting AI, but only a small share are integrating it deeply enough to generate meaningful productivity gains. Data from the European Central Bank’s SAFE survey shows that although more than 70% of firms report using AI in some form, only 7% have integrated it deeply into their core operations.

Firms that use AI intensively are more likely to embed it in core business processes rather than limiting it to routine or experimental tasks. They are also more likely to innovate, expand their product offerings and align AI investments with long-term growth strategies.

Competitive pressure is also driving deeper AI adoption, particularly among established firms responding to technologically advanced rivals. However, skills shortages, legacy systems and financing constraints continue to limit many companies’ ability to scale AI effectively.

Why does it matter?

The findings suggest that simply adopting AI is not enough to generate significant economic benefits. Productivity gains appear to depend on integrating AI into core business functions, innovation strategies and long-term investment plans rather than using it only for isolated or experimental tasks.

The survey also highlights structural challenges facing Europe’s digital transformation. Without investment in skills, financing and modern digital infrastructure, many firms may struggle to move beyond basic AI adoption, potentially widening the productivity gap between AI leaders and businesses that lack the resources to scale the technology.

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Microsoft and Europol disrupt Amadey and StealC malware infrastructure

Microsoft has disrupted more than 200 command-and-control servers linked to Amadey and StealC, two widely used cybercrime tools that support credential theft, fraud and ransomware attacks.

The company’s Digital Crimes Unit said the action targeted the shared infrastructure behind the two tools rather than treating them as separate threats. In the first two weeks of May, Amadey and StealC were linked to more than 140,000 infected computers worldwide.

Amadey is often used to gain access to devices, while StealC is used to steal passwords and sensitive information. Microsoft said the tools form part of a wider cybercrime supply chain in which specialised malware services help attackers turn initial access into fraud, ransomware, espionage or other operations.

Microsoft said investigators used AI, including Copilot, to analyse malware and identify connections between the two tools more quickly. The company said the analysis helped its legal team treat both malware families as part of a single conspiracy under the US Racketeer Influenced and Corrupt Organizations Act.

The action was carried out with Europol and industry partners, including ESET, BitSight, Lumen and Mitsui Bussan Secure Directions. Europol’s European Cybercrime Centre also investigated StealC as part of Operation Endgame, alongside European law enforcement partners and cybersecurity companies, including IBM X-Force and Proofpoint.

Microsoft said it has identified more than 18,000 victim computers since the start of the operation and is working with telecommunications providers to help protect affected users.

The company said findings from the case will feed into its Statutory Automated Disruption programme, which accelerates the removal of malicious domains and infrastructure.

Why does it matter?

The operation reflects a shift in cybercrime disruption strategy. Instead of targeting one malware family or service at a time, Microsoft and its partners focused on the shared infrastructure that allows criminal tools to work together. That matters because modern cybercrime increasingly operates as a modular supply chain: one tool gains access, another steals credentials, and other actors monetise that access through fraud, ransomware or espionage. The use of AI to accelerate malware analysis also points to how defenders are trying to match the speed and scale of cybercriminal operations.

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Cate Blanchett unveils AI consent tool at European Parliament

Actor and producer Cate Blanchett has launched the Human Consent Registry, a free online tool that allows individuals to specify how AI systems may use their identity. Presented at the European Parliament, the registry enables users to permit or prohibit the use of their name, image, voice, likeness and movements by AI systems, either unconditionally or subject to specific terms.

The platform is available to individuals as well as representatives, such as agents and managers. Its developers say it will eventually expand to cover works of art, fictional characters and brands. It was developed by RSL Media, a nonprofit co-founded by Blanchett that focuses on building consent tools related to AI use, which launched in May to wide support from figures across the entertainment industry.

Blanchett has been a prominent advocate for stronger safeguards against unauthorised AI use. In March 2025, she joined more than 400 artists in signing an open letter urging the US administration to maintain copyright protections and reject proposals that would allow AI developers to train models on copyrighted works without permission or compensation.

The launch comes amid growing concern among artists over the unauthorised use of creative works and personal likenesses for AI training. Singer SZA recently said more than 200 of her songs had been used to train AI systems, while actor Matthew McConaughey has trademarked his image, voice and a well-known catchphrase.

The Human Consent Registry positions itself as a scalable and accessible alternative to such individual legal measures, offering a standardised mechanism that does not require significant resources to deploy. The tool is free to use and designed to be available to anyone, not only those with the means to pursue trademark or copyright protections independently.

The registry was launched during an event at the European Parliament hosted by Bulgarian MEP Eva Maydell of the European People’s Party. Director Steven Soderbergh also attended the event in Brussels.

Why does it matter?

The Human Consent Registry highlights a growing gap between existing intellectual property laws and the capabilities of generative AI. While copyright and trademark protections offer some legal remedies, they often do not provide individuals with a simple way to express or enforce consent over the use of their identity, voice or likeness by AI systems.

The initiative also reflects a broader shift towards consent-based AI governance. By launching the registry at the European Parliament, its creators are seeking to influence ongoing debates on AI regulation, copyright and personality rights, while promoting practical mechanisms that could complement future legal frameworks for the responsible use of AI-generated content.

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NIST explores OT asset management to strengthen cybersecurity

NIST’s National Cybersecurity Center of Excellence (NCCoE) is seeking public feedback on a new project focused on operational technology (OT) asset management as the foundation for stronger OT cybersecurity.

The draft project description, Asset Management as a Foundation for OT Cybersecurity, outlines the project’s scope, challenges and technical approach. The NCCoE plans to demonstrate practical methods for OT asset discovery, inventory, configuration and change management.

The project will involve collaboration with asset owners, operators, and solution providers. The NCCoE plans to demonstrate real-world OT asset management and visibility solutions using commercially available products.

The proposal also includes a high-level reference architecture, desired technical capabilities and alignment with relevant standards, including outcomes from the NIST Cybersecurity Framework 2.0.

The NCCoE said AI is accelerating both the discovery and exploitation of vulnerabilities, making strong OT asset management increasingly important as organisations modernise industrial systems, adopt zero trust architectures and respond to AI-driven cyber threats.

Many organisations struggle to maintain a complete inventory of OT assets. Without effective asset management, activities such as risk assessment, network segmentation, vulnerability management, incident response and technology modernisation become significantly more difficult.

The NCCoE said the laboratory demonstration will support the development of source code, scripts, architectures, procedures, and guidelines. These resources are intended to help organisations gain the visibility needed to detect and respond to modern cyber threats in OT environments.

The centre is seeking input from asset owners, operators, technology providers, and cybersecurity practitioners. Feedback will help refine the project scope, use cases, reference architecture, and demonstration objectives.

Following the consultation, the NCCoE plans to recruit collaborators for project demonstrations and development activities. Public comments on the draft are open until 31 July 2026.

Why does it matter?

Operational technology underpins critical infrastructure, manufacturing and industrial operations, making accurate asset visibility a prerequisite for effective cybersecurity. As AI enables attackers to identify and exploit vulnerabilities more quickly, organisations need reliable inventories, configuration management and continuous monitoring to support risk assessments, zero trust strategies and incident response.

The project also reflects a broader shift towards practical cybersecurity guidance. By working with industry to develop reference architectures, tools and implementation guidance aligned with the NIST Cybersecurity Framework 2.0, the NCCoE aims to help organisations translate cybersecurity best practices into operational improvements across industrial environments.

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China links AI data centre to direct green electricity supply

China has launched what state media described as the country’s first AI data centre powered entirely through a direct green electricity connection, linking AI infrastructure more closely with renewable energy supply.

The facility has started operations in Zhongwei, in the Ningxia Hui Autonomous Region, a western region that has become central to China’s computing and clean-energy strategy.

Operated by China Telecom Ningxia Branch, the data centre is built to a wind-powered liquid-cooling standard. According to the company, the facility achieves a Power Usage Effectiveness rating of 1.15, supporting high-performance AI computing while reducing energy use compared with conventional data centres.

The project is part of China’s wider effort to connect computing capacity with renewable energy resources. Ningxia has already hosted large-scale projects that directly supply green electricity to data centre clusters, including a 500 MW solar facility in Zhongwei linked to China’s computing-electricity coordination model.

Zhongwei is also a key node in China’s ‘Eastern Data, Western Computing’ initiative, which aims to shift data-intensive workloads from eastern economic centres to western regions with more land and renewable-energy resources.

The new facility is expected to support AI computing, data processing and industrial digital transformation. It could also increase demand for servers, AI chips, liquid-cooling equipment and other parts of China’s domestic technology supply chain.

The project highlights how energy availability and efficiency are becoming central to AI infrastructure policy, as countries and companies face rising power demand from data centres and advanced AI systems.

Why does it matter?

AI infrastructure is becoming an energy-policy issue. China’s green-powered data centre model shows how governments may try to match growing AI compute demand with renewable-energy deployment, regional data-centre planning and industrial supply-chain development. For China, the project also supports a broader strategy of moving compute workloads westward, reducing pressure on eastern cities and using renewable resources in regions such as Ningxia. The challenge will be proving that such facilities can deliver reliable AI computing at scale while genuinely reducing emissions across the full power and data-centre system.

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Google proposes a balanced approach to AI governance in the US

Google has published a policy paper proposing a two-track approach to AI governance in the United States, separating oversight of frontier AI models from rules for widely deployed AI applications.

The paper argues that AI policy should avoid what Google describes as a false choice between over-regulation and no regulation. Instead, the company calls for a pragmatic, evidence-based framework that treats the most advanced AI systems differently from everyday AI tools such as chatbots.

For frontier AI, Google proposes the creation of a Frontier AI Regulatory Organisation, or FARO. The industry-funded body would operate under federal oversight and develop standards for safety, security, incident reporting and transparency.

Google says FARO could set scientific benchmarks for frontier capabilities, particularly in areas such as cybersecurity and chemical, biological, radiological and nuclear risks. It could also oversee independent audits and require frontier AI companies to publish and follow safety frameworks before releasing highly capable models.

For widely deployed AI applications, Google argues that the federal government should rely mainly on existing legal frameworks, with targeted updates where needed. The paper says policy should focus on real-world harms and outputs rather than micromanaging AI development.

The company identifies several priority areas, including workforce preparedness, child safety, information integrity, copyright, privacy and energy infrastructure for data centres.

Google supports measures such as AI interaction guidelines for children, disclosures that chatbots are not sentient, rules for self-harm-related queries, watermarking and provenance standards for generative AI, privacy-enhancing technologies and workforce reskilling.

The paper presents the model as a way to address national security and consumer protection risks while preserving US leadership in AI development.

Why does it matter?

Google’s paper is a significant industry intervention in the US AI policy debate. Its two-track model reflects a broader governance trend: frontier AI is increasingly being treated as a national security and safety issue, while everyday AI applications are being handled through consumer protection, child safety, privacy, copyright and labour policy. The proposal could influence federal discussions, but it also reflects Google’s own regulatory preferences, including industry-funded oversight, confidential audit reports and reliance on existing law for many AI applications.

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EU signs Pax Silica Declaration on AI supply chains

The European Commission has signed the Pax Silica Declaration on behalf of the EU, joining an international initiative focused on AI security and resilient silicon supply chains.

Pax Silica is a US-led initiative that aims to strengthen cooperation among allies and trusted partners across the AI supply chain, from critical minerals and energy inputs to semiconductor manufacturing, AI infrastructure and logistics.

The Commission said secure access to silicon and related technologies is becoming increasingly important as AI reshapes economies, security and industrial competitiveness.

The declaration commits signatories to closer cooperation on trusted technology ecosystems and more resilient supply chains. It also aims to reduce strategic dependencies and improve coordination on the materials, infrastructure and manufacturing capacity needed for AI development.

The EU’s signature follows the adoption of the European Technological Sovereignty Package, which includes Chips Act 2.0 and measures to strengthen Europe’s capacity in semiconductors, AI, cloud and open-source technologies.

The Commission said participation in Pax Silica could support European businesses, strengthen international partnerships and contribute to Europe’s broader technological sovereignty objectives.

Why does it matter?

AI development depends on far more than models and software. Advanced chips, critical minerals, energy, manufacturing capacity, cloud infrastructure and logistics are becoming strategic layers of the AI economy. By joining Pax Silica, the EU is linking AI competitiveness and security to semiconductor supply-chain resilience and cooperation with trusted partners. The move also shows how digital sovereignty is increasingly pursued through both domestic capacity-building and selective international alignment.

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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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