From planning reforms to ESG pressures, the UK data centre sector is struggling to navigate a complex and shifting regulatory landscape.

يواجه قطاع مراكز البيانات في المملكة المتحدة مخاطر قانونية كبيرة بسبب مشهد تنظيمي معقد ومتغير. تشمل التحديات الرئيسية إصلاحات التخطيط والضغوط المتزايدة المتعلقة بمعايير البيئة والمجتمع والحوكمة (ESG). تعقد هذه العوامل الامتثال والاستراتيجيات التشغيلية لمراكز البيانات، مما يتطلب استراتيجيات فعالة للتخفيف من هذه العقبات القانونية.

El sector de los centros de datos en el Reino Unido enfrenta riesgos legales significativos debido a un panorama regulatorio complejo y en evolución. Los desafíos clave incluyen reformas de planificación y presiones crecientes relacionadas con criterios ambientales, sociales y de gobernanza (ESG). Estos factores complican el cumplimiento y las estrategias operativas para los centros de datos, lo que requiere enfoques de mitigación efectivos para navegar por estos obstáculos legales.

Le secteur des centres de données au Royaume-Uni fait face à des risques juridiques importants en raison d'un paysage réglementaire complexe et en évolution. Les défis clés incluent les réformes de planification et les pressions croissantes liées aux critères environnementaux, sociaux et de gouvernance (ESG). Ces facteurs compliquent la conformité et les stratégies opérationnelles pour les centres de données, nécessitant des approches d'atténuation efficaces pour naviguer dans ces obstacles juridiques.

The UK data centre sector is currently facing significant legal risks due to a complex and evolving regulatory landscape. Key challenges include planning reforms and increasing pressures related to environmental, social, and governance (ESG) criteria. These factors complicate compliance and operational strategies for data centres, necessitating effective mitigation approaches to navigate these legal hurdles.

Here are the top five legal risks facing UK data centres – and how to mitigate them

<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i39.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p39#a251119p39" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Allie Garfinkle / <A HREF="http://www.fortune.com/">Fortune</A>: 
<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/">Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May</A>&nbsp; &mdash;&nbsp; Senior Finance Reporter And Author Of Term Sheet&nbsp; &mdash;&nbsp; I tried to fool my brother, sort of.&nbsp; &mdash;&nbsp; Next to him and his Pekingese on the couch &hellip;

دوبل، وهي شركة متخصصة في خدمات الكشف عن الهندسة الاجتماعية باستخدام الذكاء الاصطناعي، نجحت في جمع 70 مليون دولار في جولة تمويل من السلسلة C بقيادة بيسمر. وقد زادت هذه الجولة من تقييم الشركة إلى أكثر من 600 مليون دولار، ارتفاعًا من 205 مليون دولار في مايو.

Doppel, una empresa que se especializa en servicios de detección de ingeniería social mediante IA, ha recaudado con éxito 70 millones de dólares en una ronda de financiación Serie C liderada por Bessemer. Esta ronda ha elevado la valoración de la empresa a más de 600 millones de dólares, un aumento significativo desde los 205 millones en mayo.

Doppel, une entreprise spécialisée dans les services de détection d'ingénierie sociale par IA, a levé avec succès 70 millions de dollars lors d'un tour de financement de série C dirigé par Bessemer. Ce tour de financement a porté la valorisation de l'entreprise à plus de 600 millions de dollars, contre 205 millions en mai.

Doppel, a company specializing in AI social engineering detection services, has successfully raised $70 million in a Series C funding round led by Bessemer. This funding round has increased the company's valuation to over $600 million, a significant rise from $205 million in May.

Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May (Allie Garfinkle/Fortune)

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<a href="https://fetch.ai/">Fetch AI</a>, a startup founded and led by former DeepMind founding investor, Humayun Sheikh, <a href="https://www.businesswire.com/news/home/20251119088395/en/Fetch-Combines-Personalized-AI-with-Multi-Agent-Collaboration-to-Handle-Complex-Consumer-Tasks-Launches-Claim-Your-Agent-to-Fight-Brand-Knock-Offs">today announced the release</a> of three interconnected products designed to provide the trust, coordination, and interoperability needed for large-scale AI agent ecosystems. The launch includes <a href="https://asi1.ai/">ASI:One</a>, a personal-AI orchestration platform; <a href="https://business.fetch.ai/">Fetch Business</a>, a verification and discovery portal for brand agents; and <a href="https://agentverse.ai/?sort=relevancy&amp;page=1&amp;recommended=true">Agentverse</a>, an open directory hosting more than two million agents. Together, the system positions Fetch as an infrastructure provider for what it calls the “Agentic Web”—a layer where consumer AIs and brand AIs collaborate to complete tasks instead of merely suggesting them.The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step actions that require coordination across businesses. Fetch’s approach centers on enabling agents from different organizations to interoperate securely, using verified identities and shared context to complete end-to-end workflows.“We’re creating the same foundation for agents that Google created for websites,” said Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press release provided to VentureBeat. “Instead of just finding information, your personal AI coordinates with verified brand agents to get things done.”<h2>Background: Fetch’s Founding and DeepMind Connection </h2>Fetch AI was founded in 2017 by Humayun Sheikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial development before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check was the first one in,” Sheikh said, reflecting on the period when advanced machine learning research was still largely inaccessible outside major technology companies.His early experience helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were going to be the ones that worked. That’s where I focused—on the agentic web,” Sheikh noted. Fetch built on this thesis by developing infrastructure for autonomous software agents, focusing on verifiable identity, secure data exchange, and multi-agent coordination. Over the past several years, the company has expanded to a 70-person team across Cambridge and Menlo Park, raised approximately $60 million, and accumulated more than one million users interacting with its model—data that informed the design of the newly launched products.Sheikh added that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, noting in the interview that while the sale to Google was “a good exit,” he believed the team could have held out for a higher valuation. The early self-funding period allowed Fetch to begin work in 2015—well before transformer architectures went mainstream—on the hypothesis that agentic infrastructure would become foundational to applied AI.<h2>ASI:One — A Platform for Multi-Agent Orchestration</h2>At the core of the launch is ASI:One, a language model interface designed specifically for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.The system stores user-level signals such as favored airlines, dietary constraints, budget ranges, loyalty program identifiers, and calendar availability. When a user requests a complex task—such as planning a trip with flights, hotels, and restaurant reservations—ASI:One retrieves those preferences and delegates work to the appropriate verified agents. The agents then return actionable outputs, including inventory and booking options, rather than generic recommendations.In practice, ASI:One functions as a workflow generator across organizational boundaries. By contrast with conventional LLM applications, which often rely on APIs or RAG techniques to surface information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.Sheikh emphasized the distinction between orchestrated execution and traditional AI output. “This isn’t searching for options separately and hoping they work together,” he said. “It’s orchestration.” He added that Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. One large model isn’t enough—you need specialists. That’s why we built ASI1, tuned specifically for agentic systems.”The interview also revealed new details about ASI:One’s personalization systems: the platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints. These knowledge graphs are siloed per user and not co-mingled with any Fetch-operated data. Sheikh described this as a “deterministic backbone” that gives the personal AI a stable memory layer beyond the probabilistic output of a single large model.ASI:One launches in Beta today, with a broader release planned for early 2026. Fetch also offers ASI:One Mobile, released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to Agentverse and the user’s knowledge graphs, enabling on-the-go task execution and real-time interaction with registered agents.<h2>Fetch Business — Verified Identity and Brand Control</h2>To enable reliable coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business. The platform allows organizations to verify their identity and claim an official Brand Agent handle—for example, @Hilton or @Nike—regardless of which tools they use to build the underlying agent.Fetch positions the product as an analogue to ICANN domain registration and SSL certificate systems for websites. Verified status is intended to protect consumers from interacting with counterfeit or untrusted agents, a problem the company describes as a major barrier to widespread agent adoption.The system includes low-code tools for small businesses to create agents in a few steps and connect real-time APIs such as inventory, booking systems, or CRM platforms. “With Fetch, you can create an agent in one minute. It gets a handle, like a Twitter username, and you can personalize it completely—even give it your social media permissions to post on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AIs and other agents inside Agentverse.The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.The interview highlighted that Fetch Business inherits web-trust primitives directly: domain owners verify their identity by inserting a short code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and grant the agent an authenticity badge similar to a “blue check” for agent identities. Sheikh framed this as “reusing the trust layer the web already spent decades building.”Companies can begin claiming agents now at <a href="https://business.fetch.ai/">business.fetch.ai</a>.<h2>Agentverse — An Open Directory of More Than Two Million Agents</h2>The final component of the release is <a href="https://agentverse.ai/">Agentverse</a>, an open directory and cloud platform that hosts agents and enables cross-ecosystem discoverability. Fetch states that millions of agents have already registered, spanning travel, retail, entertainment, food service, and enterprise categories.Agentverse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.According to Sheikh, the lack of a discovery layer is one reason most AI agents see little or no usage. “Ninety percent of AI agents never get used because there’s no discovery layer,” he said. He framed the role of Agentverse in more technical terms: “Right now, if you build an agent, there’s no universal way for others to discover it. That’s what AgentVerse solves—it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just like websites needed search, agents need discovery, trust, and interaction—Fetch provides all of that.”The interview further underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables an LLM to query any agent “within one minute of deployment,” turning agent publication into a near-instantaneous process similar to registering a domain.Agentverse also integrates payment pathways, enabling agents to execute purchases using partners such as Visa, Skyfire, and supported stablecoins. Consumers can configure spending limits or require explicit approval for transactions.<h2>Industry Context and Implications</h2>Fetch’s launch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces toward autonomous agents capable of completing actions. However, most agent systems remain limited by siloed architectures, limited interoperability, and weak verification standards.Fetch positions its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than imitations. By establishing namespace control and portable trust indicators, Fetch Business aims to fill a gap similar to early web domain verification.At the same time, ASI:One attempts to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack persistent preference architectures or direct access to brand-controlled agents.The interview also made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced integrations with protocols such as Coinbase’s 402 and AP2, positioning these capabilities as essential for autonomous agents to complete end-to-end tasks that include financial execution.<h2>Takeaway</h2>Fetch’s combined release of ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support large-scale deployment and usage of AI agents. The company frames the system as foundational infrastructure for an agentic ecosystem, where consumer AIs can coordinate with verified brand agents to complete tasks reliably and securely. The additions to its identity, discovery, and orchestration layers reflect Fetch’s long-standing thesis—rooted partly in lessons from DeepMind’s early development—that intelligence becomes meaningful only when paired with the capacity to act.

أطلقت شركة Fetch AI، وهي شركة ناشئة يقودها هميون شيخ، ثلاثة منتجات مترابطة تهدف إلى تعزيز نظام وكلاء الذكاء الاصطناعي. تشمل العروض الجديدة ASI:One، وهي منصة تنسيق ذكاء اصطناعي شخصي، وFetch Business، وهو بوابة للتحقق والاكتشاف لوكلاء العلامات التجارية، وAgentverse، وهو دليل مفتوح يضم أكثر من مليوني وكيل. تسعى هذه المبادرة إلى إنشاء بنية تحتية قوية لما تصفه Fetch بـ 'الويب الوكالي.'

Fetch AI, una startup dirigida por Humayun Sheikh, ha lanzado tres productos interconectados destinados a mejorar el ecosistema de agentes de IA. Las nuevas ofertas incluyen ASI:One, una plataforma de orquestación de IA personal, Fetch Business, un portal de verificación y descubrimiento para agentes de marca, y Agentverse, un directorio abierto con más de dos millones de agentes. Esta iniciativa busca establecer una infraestructura sólida para lo que Fetch describe como la 'Web Agente.'

Fetch AI, une startup dirigée par Humayun Sheikh, a lancé trois produits interconnectés visant à améliorer l'écosystème des agents IA. Les nouvelles offres comprennent ASI:One, une plateforme d'orchestration d'IA personnelle, Fetch Business, un portail de vérification et de découverte pour les agents de marque, et Agentverse, un annuaire ouvert avec plus de deux millions d'agents. Cette initiative vise à établir une infrastructure robuste pour ce que Fetch décrit comme le 'Web Agentique.'

Fetch AI, a startup led by Humayun Sheikh, has launched three interconnected products aimed at enhancing the ecosystem of AI agents. The new offerings include ASI:One, a personal-AI orchestration platform, Fetch Business, a verification and discovery portal for brand agents, and Agentverse, an open directory with over two million agents. This initiative seeks to establish a robust infrastructure for what Fetch describes as the 'Agentic Web.'

The Google Search of AI agents? Fetch launches ASI:One and Business tier for new era of non-human web

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arXiv:2511.10658v1 Announce Type: new 
Abstract: Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models, and English-language reports. We evaluated 15 open-weight LLMs on pathology and radiology reports across six use cases, colorectal liver metastases, liver tumours, neurodegenerative diseases, soft-tissue tumours, melanomas, and sarcomas, at three institutes in the Netherlands, UK, and Czech Republic. Models included general-purpose and medical-specialised LLMs of various sizes, and six prompting strategies were compared: zero-shot, one-shot, few-shot, chain-of-thought, self-consistency, and prompt graph. Performance was assessed using task-appropriate metrics, with consensus rank aggregation and linear mixed-effects models quantifying variance. Top-ranked models achieved macro-average scores close to inter-rater agreement across tasks. Small-to-medium general-purpose models performed comparably to large models, while tiny and specialised models performed worse. Prompt graph and few-shot prompting improved performance by ~13%. Task-specific factors, including variable complexity and annotation variability, influenced results more than model size or prompting strategy. These findings show that open-weight LLMs can extract structured data from clinical reports across diseases, languages, and institutions, offering a scalable approach for clinical data curation.

تستخدم نماذج اللغة الكبيرة (LLMs) بشكل متزايد لاستخراج المعلومات المنظمة من السجلات السريرية. تم تقييم 15 نموذجًا مفتوح الوزن مؤخرًا، مع التركيز على تقارير علم الأمراض والأشعة عبر ست حالات استخدام، بما في ذلك النقائل الكبدية القولونية والأمراض التنكسية العصبية، في هولندا والمملكة المتحدة وجمهورية التشيك. قارن البحث استراتيجيات التحفيز المختلفة ووجد أن النماذج الأعلى حققت درجات متوسطة قريبة من اتفاقية المقيمين، مما يدل على فعاليتها في استخراج البيانات المنظمة.

Los grandes modelos de lenguaje (LLMs) se utilizan cada vez más para extraer información estructurada de los registros clínicos. Una evaluación reciente de 15 LLMs de peso abierto se centró en informes de patología y radiología en seis casos de uso, incluidos metástasis hepáticas colorrectales y enfermedades neurodegenerativas, realizada en los Países Bajos, el Reino Unido y la República Checa. El estudio comparó varias estrategias de solicitud y encontró que los mejores modelos lograron puntajes promedio cercanos al acuerdo entre evaluadores, lo que indica su efectividad en la extracción de d…

Les grands modèles de langage (LLMs) sont de plus en plus utilisés pour extraire des informations structurées des dossiers cliniques. Une récente évaluation de 15 LLMs à poids ouvert s'est concentrée sur des rapports de pathologie et de radiologie à travers six cas d'utilisation, y compris les métastases hépatiques colorectales et les maladies neurodégénératives, réalisée aux Pays-Bas, au Royaume-Uni et en République tchèque. L'étude a comparé diverses stratégies de sollicitation et a constaté que les meilleurs modèles atteignaient des scores moyens proches de l'accord inter-évaluateurs, indiq…

Large language models (LLMs) are increasingly utilized for extracting structured information from clinical records. A recent evaluation of 15 open-weight LLMs focused on pathology and radiology reports across six use cases, including colorectal liver metastases and neurodegenerative diseases, conducted in the Netherlands, UK, and Czech Republic. The study compared various prompting strategies and found that top models achieved macro-average scores close to inter-rater agreement, indicating their effectiveness in structured data extraction.

Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages

Threat actors prefer exploiting the low-hanging fruit of forgotten software flaws to using the scariest cyber-weapons on the black market.

يستغل المهاجمون بشكل متزايد الثغرات البرمجية القديمة والمعروفة بدلاً من استخدام أسلحة إلكترونية متقدمة متاحة في السوق السوداء. تسلط هذه الاتجاهات الضوء على تفضيل الأهداف الأسهل، مما يشكل مخاطر كبيرة على الأمن السيبراني.

Los actores de amenazas están explotando cada vez más vulnerabilidades de software antiguas y conocidas en lugar de utilizar ciberarmas avanzadas disponibles en el mercado negro. Esta tendencia destaca una preferencia por objetivos más fáciles, lo que plantea riesgos significativos para la ciberseguridad.

Les acteurs de la menace exploitent de plus en plus les vulnérabilités logicielles anciennes et connues plutôt que d'utiliser des cyber-armes avancées disponibles sur le marché noir. Cette tendance met en évidence une préférence pour des cibles plus faciles, ce qui pose des risques importants pour la cybersécurité.

Threat actors are increasingly exploiting older, known software vulnerabilities rather than utilizing advanced cyber-weapons available on the black market. This trend highlights a preference for easier targets, which poses significant risks to cybersecurity.

Zero Days might be scary – but older, known vulnerabilities are much more frightening

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As passenger volumes surge, keeping everything in sync is becoming harder to manage. Now, a new wave of AI models is helping to change that.

مع زيادة أعداد الركاب، أصبحت إدارة التكنولوجيا في المطارات أكثر تعقيدًا. تظهر موجة جديدة من نماذج الذكاء الاصطناعي لمساعدة في مزامنة الأنظمة المختلفة داخل مجموعة التكنولوجيا في المطارات، بهدف تحسين الكفاءة التشغيلية وتجربة الركاب بشكل عام.

A medida que aumentan los volúmenes de pasajeros, la gestión de la tecnología aeroportuaria se vuelve cada vez más compleja. Una nueva ola de modelos de IA está surgiendo para ayudar a sincronizar varios sistemas dentro de la pila tecnológica del aeropuerto, con el objetivo de mejorar la eficiencia operativa y la experiencia general del pasajero.

Avec l'augmentation du volume de passagers, la gestion de la technologie aéroportuaire devient de plus en plus complexe. Une nouvelle vague de modèles d'IA émerge pour aider à synchroniser divers systèmes au sein de la pile technologique aéroportuaire, visant à améliorer l'efficacité opérationnelle et l'expérience globale des passagers.

As passenger volumes surge, managing airport technology has become increasingly complex. A new wave of AI models is emerging to assist in synchronizing various systems within the airport tech stack, aiming to enhance operational efficiency and improve the overall passenger experience.

Here are the top five legal risks facing UK data centres – and how to mitigate them

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