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  • Google Cloud generative AI automates council planning operations

    Government ministries are deploying Google Cloud generative AI across municipal agencies to automate council planning operations.

    Public sector administration handles vast volumes of unstructured data that delay infrastructure development. The UK central government established a target to construct 1.5 million new homes by 2029. Local planning authorities encounter administrative backlogs caused by dense paperwork, delaying these development timelines.

    To address these constraints, the Ministry of Housing, Communities and Local Government (MHCLG) and the Department for Science, Innovation and Technology (DSIT) expanded two machine learning tools designed to accelerate municipal processing. Speaking at the Google Cloud Summit London, officials confirmed the nationwide deployment of the ‘Extract’ application and the progression of the ‘Augmented Planning Decisions’ (APD) prototype.

    Lila Ibrahim, Chief AI Readiness Officer at Google DeepMind, said: “The UK has an opportunity to build the homes our communities need, but local councils face a mountain of paperwork. That’s why we’re co-creating a sophisticated planning tool directly with councils to solve real-world bottlenecks.

    “This will help significantly cut decision times, freeing up planners to focus on the future to get Britain building faster.”

    Householder applications – which include routine domestic modifications such as loft conversions or property extensions – account for nearly 70 percent of all planning applications submitted annually. Evaluating these standard submissions manually requires planning officers to spend hours cross-referencing regional policy documents, historical archives, and unstructured PDF files.

    Such a repetitive evaluation process consumes administrative hours that would otherwise support major infrastructure and commercial developments. The deployment of automation targets this administrative distribution, aiming to reduce application decision timelines by 50 percent.

    Core capabilities of the Google Cloud generative AI tools

    Engineers at MHCLG and the government’s applied AI team, the Incubator for AI (i.AI), built the Extract tool internally using Gemini foundation models. Following trials across more than 20 local planning authorities, administrators expanded the application to every council in England.

    Extract parses unstructured data locked within legacy PDF records, converting hundreds of pages of historical planning documentation into structured digital datasets within minutes. Operational data from the trial phases indicates that the tool will eliminate roughly 255 hours of manual data entry per council annually. This reduction allows local authorities to reallocate personnel to complex evaluation tasks.

    Integrating large language models into public sector workflows requires enterprise-grade security environments. Local authorities process sensitive civic records, requiring strict risk management protocols to prevent data exposure.

    The government hosted the Gemini models on Google Cloud to establish a protected operating environment where data sovereignty is maintained. The cloud environment features active security controls to block malicious inputs, including prompt injection attacks. This technical framework ensures that sensitive municipal data remains secure during both testing and production computing cycles.

    The APD system, meanwhile, acts as an analytical assistant for municipal planning officers by automating four primary administrative tasks:

    1. The system consolidates incoming documentation by pre-processing data backlogs, flagging missing information gaps, and extracting core geographical site data onto a unified user interface for officer review.
    2. The software identifies relevant national and local zoning laws, assesses compliance margins, and appends precise policy citations for manual verification.
    3. The application parses public consultation letters, summarising stakeholder objections or historical legal precedents.
    4. The model generates initial drafts of final evaluation reports, including the technical rationale and recommended approval conditions.

    Protocols dictate that human planning officers retain final decision-making authority over every application. The software does not automate final approvals or rejections independently. Staff members review every line of text generated by the machine learning models, modifying the analytical reasoning before validating the report.

    To maintain regulatory accountability, the APD prototype records its internal processing steps sequentially. This mechanism establishes an auditable chain of thought, creating a verification trail for every processed application to support the officer’s final determination.

    Local council planning trials and scaling timelines

    The development of the APD prototype relies on a collaborative framework linking public sector administrators with engineering teams from Google Cloud, Google DeepMind, and Faculty.

    The alpha version undergoes live testing within three local authorities: the London Borough of Barnet, Dorset Council, and the London Borough of Camden. Testing across these distinct regional jurisdictions provides developers with varied municipal datasets to test the software against diverse local policies. 

    Central planners intend to complete the alpha phase and deploy the APD tool to all 300-plus English local authorities by 2027. Google Cloud provides the elastic computing infrastructure required to manage the thousands of concurrent inferencing queries generated during daily operations.

    Paul Maltby, Director of Public Services at Faculty, commented: “The English planning system is clogged up. Planning officers are forced to spend half their time reviewing applications to convert an attic, putting those for housing estates and warehouses on hold.

    “Built with planning officers, our AI system will take the drudgery out of reviewing simple planning applications so they can make quick decisions. It will let planning officers focus on the major developments that matter, and crucially, let families improve their homes without months of delay and uncertainty.”

    Naisha Polaine, Executive Director for Growth at Barnet Council, added: “The tool’s ability to collect relevant information, undertake a provisional assessment, and draft the foundations of a report has the potential to save significant officer time spent working on the administration of planning applications and direct this to speeding up the decision-making process for residents. In turn, this will contribute significantly to delivering our house building growth targets in the borough.”

    The coordination between MHCLG, i.AI, Google DeepMind, and Faculty establishes a structured division of labour for enterprise software engineering. Public ministries define the policy guidelines and statutory boundaries, while external technical partners engineer and deploy the underlying model architectures.

    The successful integration of these systems demonstrates the feasibility of hosting advanced language models within a secured public cloud infrastructure to process core administrative workloads and modernise public service delivery.

    See also: EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

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  • EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

    The European Union has published its AI content labelling playbook, a voluntary Code of Practice meant to help companies meet transparency rules that become law across the bloc on August 2 onwards. The European Commission released the final Code on 10 June, setting out practical steps for the businesses that build and use generative AI to mark and label what their systems produce.

    The Code itself is optional. The obligations it points to are not. They sit under Article 50 of the EU AI Act, and from August 2, 2026, they apply whether or not a company signs the Commission’s guidance. Signing simply gives a business a recognised way to show it complies.

    What the AI content labelling rules actually require

    From August, two things must be clearly flagged. Deepfakes and AI-generated or AI-manipulated text published on matters of public interest have to carry a label. Anyone chatting with an interactive AI system, such as a customer-service bot, also has to be told they are dealing with a machine. 

    The Commission frames it as a way to help users spot AI-made or AI-altered material and narrow the space for deception. “Europeans have a right to know whether what they see, hear or read has been made or altered by AI, especially when such content can shape public debate,” said Henna Virkkunen, the Commission’s executive vice-president for tech sovereignty, security and democracy. 

    She cast the Code as a practical route to labelling that AI providers and deployers can follow before the rules bite in August. The Code splits the work between the two sides of the AI supply chain. The companies that build generative models are asked to mark their output in a machine-readable format, so it can be detected further down the line. 

    The companies that deploy those models, the ones putting AI to work in real products, handle the visible labelling, which, for public-interest AI text, applies when the content has gone out without human review or editorial control. To keep it workable, the Code leans on open technical standards and a common EU icon, meant to give users a consistent visual cue and spare businesses from inventing their own.

    None of this is the final word. The Code is now open for signatures, and the Commission is urging all providers and deployers to sign. It still needs the Commission and the AI Board to judge it adequately, and separate Commission guidelines are due to clarify the law and cover what the Code leaves out. Drawn up by six independent experts with input from more than 180 stakeholders, it is the first instrument to tackle AI content labelling under the Act.

    The timing leaves little slack. Companies serving European users have under two months to work out what they need to label and how, and to decide whether to sign. Plenty of the harder detail still rests on guidelines the Commission has yet to publish.

    The post EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline appeared first on AI News.

  • The AI off switch: How Anthropic’s export controls sparked a global AI sovereignty scramble

    Anthropic export controls turned an abstract policy fear into a live one last week: as of June 13, 2026, one US government directive took the company’s two most powerful AI models offline for users everywhere, including, briefly, Anthropic’s own foreign-born employees, and set off alarm bells across Europe and Canada about who really controls the AI the world runs on.

    The mechanics were startling in their speed. The reaction abroad has been louder still.

    Launch to lockdown in four days

    On June 9, 2026, Anthropic made Claude Fable 5 and Claude Mythos 5 generally available, the public face of a model class the company had developed under controlled access since April through a programme called Project Glasswing. Fable 5 was described as a Mythos-class model made safe for general use, state-of-the-art on nearly all tested benchmarks, with strong performance in software engineering, scientific research, and autonomous work. 

    Mythos 5, the more capable sibling, stayed restricted to Glasswing partners and selected biology researchers. Four days later, it was gone. Anthropic said it received an export control directive to suspend access to Fable 5 and Mythos 5 at 5:21 pm ET on June 12, with the letter not explaining the specific security concern in detail. 

    Unable to filter users by nationality in real time, the company said it had to “abruptly disable” access for all customers to comply. The order, issued by Commerce Secretary Howard Lutnick in a letter to CEO Dario Amodei, called for suspending all access by any foreign national, whether inside or outside the United States. 

    The jailbreak at the centre of it

    Washington cited national security, specifically, a method for “jailbreaking” Fable 5, or getting around its safety guardrails. Anthropic disputed the severity, saying the technique amounted to a limited capability to review programme code and identify errors, something rival models, including OpenAI’s GPT-5.5, can also do. 

    The government’s account is sharper. David Sacks, co-chair of the President’s Council of Advisers on Science and Technology, said on X that the administration asked Amodei to either fix the vulnerability or pull the model from deployment, and that Amodei refused. Sacks pressed the contradiction directly: “In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the US government believe; nor is that kind of minimising language consistent with Anthropic’s brand as the AI safety company.

    The Wall Street Journal reported the move was also shaped by Amazon CEO Andy Jassy, who told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 prompts to obtain information that could aid cyberattacks. Amazon is one of Anthropic’s largest investors. A spokesperson said it is “not uncommon for governments to seek our counsel on potential security risks,” but declined to share details. 

    A fight that started months before

    None of this began last week. The dispute erupted earlier this year after Anthropic insisted its technology should not be used for mass surveillance or fully autonomous weapons systems, infuriating Pentagon chief Pete Hegseth. President Trump ordered every federal agency to stop using Anthropic’s technology, and Hegseth designated the company a “Supply-Chain Risk to National Security“, a label, the company’s lawsuit notes, usually reserved for foreign adversary firms like Huawei. 

    Anthropic sued to reverse the blacklisting, warning it could jeopardise “hundreds of millions of dollars” in revenue. The result is a company simultaneously deemed too dangerous for the US government’s own use and too dangerous for foreign use, a contradiction not lost on observers. Dean Ball, an AI policy expert who briefly served in the Trump administration, called the order “simply cartoonish,” noting that an administration willing to export advanced AI chips to China now wants to ban Britain and every other non-American from using Anthropic’s best models.

    The export controls heard around the world

    Outside the US, the response went straight past the jailbreak debate and landed on a single, uncomfortable realisation: a tool embedded in companies, research institutions, and public services worldwide had been switched off by a foreign government, with an email, in an afternoon.

    The European Commission confirmed it is examining the fallout. Spokesperson Thomas Regnier said the new generation of highly capable AI models offers real benefits, including for cyber-defence, but raises serious cybersecurity concerns that need addressing, adding that “contingency measures taken in this light should not be discriminatory against partners.” 

    European politicians were blunter. French commentary framed the decision as an accelerator of the geopolitical battle over AI, with the argument that “Europe cannot settle for being an open market dependent on technologies designed, funded, and controlled elsewhere.” Finnish MEP Aura Salla said Europe “cannot continue to increase its technical potential by relying on access that can be turned off by a foreign government overnight.” The timing sharpened the point: the Commission had published its Technological Sovereignty Package — including a Cloud and AI Development Act — on June 3, just nine days before the shutdown. euronews + 2

    The unease crossed the Atlantic. Speaking in Ireland ahead of the G7 summit, Canadian Prime Minister Mark Carney said the restrictions show the dangers of overreliance on a limited number of American providers, framing it as a lesson in diversification. “The situation we’re in collectively right now with Mythos and Fable is something that can happen with overreliance on certain models,” Carney said, flagging AI as a major topic for the summit. In Britain, AI and Online Safety Minister Kanishka Narayan said the episode should drive deeper investment in the country’s own AI industry. 

    What happens next

    Anthropic’s position has not moved. It maintains that applying this standard across the industry “would essentially halt all new model deployments for all frontier model providers.” The route back runs through the Commerce Department’s Bureau of Industry and Security, where a licence is now required for export, re-export or domestic transfer of the two models, with individually validated licences needed for reinstatement and civil penalties for non-compliance. 

    Sacks framed the off-ramp plainly: fix the jailbreak, lift the control. “The ball is in Anthropic’s court,” he wrote. For the governments now watching from outside, the patch is almost beside the point. The lesson many of them have already drawn is that access to frontier AI is no longer purely a matter of price or product; it is a matter of whose jurisdiction holds the switch. Last week, the answer turned out to be Washington’s, and a lot of capitals didn’t like how that felt.

    See also: Anthropic IPO filing marks AI maturing into enterprise utility

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