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  • Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

    Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments.

    Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models to complete multi-step tasks.

    Users access this ecosystem through a single OpenAI-compatible endpoint. Fugu routes queries internally, deciding whether to resolve a prompt directly or to assemble a coordinated team of expert models for deeper analysis. The system handles model selection, delegation, verification, and synthesis internally. Engineering teams interact with what appears to be one model while a background system of specialists executes the actual computation.

    Sakana AI targets the geopolitical and regulatory risks associated with AI sourcing. Recent export controls affecting Anthropic models like Fable and Mythos demonstrated that access to specific foundational architectures can vanish based on foreign policy decisions.

    Fugu functions as a hedge against these sudden supply chain disruptions. The platform relies on a completely swappable agent pool. Fugu dynamically routes traffic around any restricted or degraded provider to maintain service continuity. Sakana AI states this capability provides the resilient architecture required for AI sovereignty.

    Fugu deployment tiers

    Two tiers are available to accommodate different operational latency requirements.

    The standard Fugu model prioritises low latency for daily tasks, integrating into standard developer tools like Codex for live coding and code review. Organisations subject to strict data governance or privacy mandates can manually opt specific underlying models out of the standard Fugu routing pool.

    Fugu Ultra targets complex, multi-step analytical problems that demand maximum accuracy. The Ultra variant coordinates a deeper pool of expert agents for intensive tasks such as academic paper reproduction, literature investigations, and patent analysis.

    Sakana AI reports that Fugu Ultra performs competitively against leading closed models like Fable 5 and Mythos Preview across scientific, engineering, and reasoning benchmarks:

    Benchmarks of Sakana AI Fugu standard and Ultra compared to rival frontier models.

    The orchestration method ensures companies can access top-tier computing capabilities without carrying the vendor concentration risk or export control exposure inherent to those closed models.

    Implementation in cybersecurity

    Almost 500 early users tested the system during an extended beta program focused on lengthy, multi-step computational workflows. With cybersecurity such a focus for models like Claude Mythos, engineering teams deployed Fugu Ultra to automate complete security assessment cycles.

    Human operators issued one scoped instruction, and the orchestration engine executed the entire reconnaissance phase. The model successfully conducted cross-site scripting and SQL injection checks alongside thorough authentication reviews.

    A participating cybersecurity engineer confirmed the model stayed strictly within its operational parameters and avoided initiating destructive actions against the target infrastructure. Fugu concluded the automated engagement by generating a clean vulnerability report complete with verifying evidence and exact retest steps for human remediation teams.

    The implementation demonstrated that multi-agent routing maintains strict compliance boundaries while executing complex penetration testing sequences.

    Software development teams also integrated Fugu Ultra into their primary code review pipelines to compare defect detection rates against established monolithic tools. The orchestration engine consistently outperformed baseline models in identifying logic flaws and security vulnerabilities within complex enterprise codebases.

    “For code review, Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss,” reported a software engineer involved in the beta deployment. “Where other tools flag about three issues, Fugu surfaced more than twenty. It’s become the model I run all my reviews through.”

    Automated research and persona stability

    Data science units deployed the system in an almost fully-automated research mode. Fugu Ultra successfully explored mathematical hypotheses, executed experimental code runs, interpreted failure states, and revised its own approaches to sustain progress over extended periods with minimal human intervention. This capability directly addresses the operational limitations of single-call models that require constant human prompting to recover from logic errors.

    Leadership at an unnamed enterprise platform company identified long-term persona stability as a primary advantage during these extended sessions. Conventional monolithic architectures often suffer from context degradation and identity drift when processing extensive conversational histories.

    “Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift,” the executive stated. “For agent products, that may matter more than raw benchmark scores.”

    Extended benchmark validation

    Sakana AI built the internal routing logic upon extensive research into learned model orchestration. The technical foundation for the product stems from findings published in the company’s ICLR 2026 papers, specifically the Trinity and Conductor frameworks.

    These academic foundations allow Fugu to process requests by understanding precisely when a task requires delegation versus direct resolution. The internal language model dictates communication protocols between the individual agents and structures the final synthesis of their separate computational outputs.

    Validation testing against frontier AI competitors covered complex, open-ended disciplines ranging from financial time series prediction to mechanical design. Fugu also demonstrated high proficiency in niche physical logic tests and visual interpretation tasks, including solving the Rubik’s Cube and performing Japanese handwriting analysis. The capacity to excel in both quantitative financial modelling and qualitative image processing confirms the efficacy of the multi-agent orchestration approach.

    Sakana AI designed the system to scale organically as the broader AI hardware and software market matures. Because the product relies entirely on learned orchestration logic rather than fixed operational rulesets, it automatically benefits from third-party innovations. Sakana AI plans to continuously expand the available pool of expert agents.

    The engineering team will fold newly-released open-source tools and proprietary Sakana AI models into the routing pool as they become available. Both the standard Fugu and Fugu Ultra models are available to enterprise clients today.

    See also: SAP and Google Cloud deploy agentic commerce architecture

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  • Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

    Microsoft has quietly become the main supplier of OpenAI models in China, selling the technology to the country’s largest internet companies even as OpenAI and Anthropic keep their own models out of the market on intellectual-property and misuse grounds. The arrangement, detailed this week by Bloomberg, hands Microsoft a position no other American AI vendor holds: it sells the GPT series to Chinese firms that the model’s own creator will not deal with directly.

    The scale is not trivial. ByteDance has been Microsoft’s largest AI customer in recent years, running largely on OpenAI models, and is on track to spend more than US$1 billion a year on Microsoft’s AI and cloud services, people familiar with the matter told Bloomberg. Ant Group, Meituan and Tencent also buy AI models through Azure, though Ant says it develops its own models and that its core products do not rely on outside systems.

    Inside Microsoft, the growth has been celebrated rather than played down. Azure’s AI revenue in China expanded faster than in any other sales territory, roughly tripling in the financial year to June 2025 after climbing about 400% the year before, then-chief commercial officer Judson Althoff told staff at a July 2025 sales meeting, according to a transcript reviewed by Bloomberg. 

    Althoff described Microsoft as the one company “bringing those two places together,” meaning the AI hubs of the US West Coast and China’s east. President Brad Smith has separately told US lawmakers that the China business accounted for roughly 1.5% of the company’s revenue in 2024.

    Why OpenAI models in China run through Microsoft alone

    The reason comes down to Microsoft’s singular contract with OpenAI, which lets it set its own terms for selling GPT models abroad. Both OpenAI and Anthropic have declined to sell into China directly, and Anthropic’s models are absent from Microsoft’s China line-up altogether. That leaves Microsoft acting as the intermediary for models whose makers have decided the Chinese market is too risky to serve.

    Risk is the recurring tension. OpenAI has privately pressed Microsoft to do more to stop Chinese customers from “distilling” its models, Bloomberg reported, a technique that uses one model’s outputs to train another. Microsoft points to automated monitoring and a rule that it sells only to established companies rather than individual developers. 

    Yet sources told Bloomberg that Chinese buyers face no heightened scrutiny, and synthetic data generated from the models is difficult to police. To limit its exposure, Microsoft does not host the OpenAI models on Chinese soil; customers reach them over the internet from data centres elsewhere, Singapore among them.

    The contradiction sharpens when you look at what Microsoft hosts alongside GPT. It added DeepSeek’s R1 to Azure AI Foundry in January 2025, and this month confirmed to Axios that it is testing a fine-tuned, Azure-hosted version of DeepSeek-V4 as a cheaper option for Copilot Cowork, the enterprise agent currently powered by OpenAI and Anthropic models. So Microsoft is selling a Chinese model into Western businesses while selling American models into Chinese ones, taking the margin on both legs of the trade.

    Whether the balancing act survives the politics is another matter. The China business is contentious in Washington, where lawmakers have cast the country’s AI push as a threat to American industry, and OpenAI’s private objections could grow louder. For now, Microsoft owns the market for OpenAI models in China, and it is the only player being paid by both sides.

    See also: China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget

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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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  • Insurers pivot AI strategy toward core risk underwriting

    AI investments by insurers are now expected to generate tangible business value beyond mere efficiency.

    According to findings in the 2026 Evident AI Index, insurers are now embedding AI technologies into workflows that directly influence underwriting discipline and capital allocation.

    Christian Preece, Insurance Director at Evident, says: “For years, insurers have competed on AI ambition, but now the focus is shifting from what insurers are building to the value they’re creating. In itself, it’s a sign of AI maturity to have the internal capability to measure these figures and be confident enough to disclose them.

    “As the first industry leaders disclose hard return on investment data, they’re providing the kind of evidence that shareholders and boards have been looking for in light of increasing concerns around the costs of AI, and we can expect to see more insurers going public in the coming year.”

    While the broader insurance workforce experienced a contraction of 2.2 percent over the past year, the AI-specialist headcount expanded by 32 percent across the 30 insurers tracked in the report. This personnel shift highlights a transition from building data foundations to the integration and optimisation of business-specific AI use cases.

    Data engineering remains a component of this investment, yet its relative share of the talent stack is declining as roles focused on AI development and software implementation gain priority. AI specialists now represent one in every 50 employees at insurers included in the Index.

    Executive structures are also adapting to these requirements. Nearly 40 percent of the insurers indexed now designate a senior leader with explicit responsibility for AI. Most of these appointments occurred within the last 12 months, creating a new level of executive oversight for AI-driven growth.

    This governance is vital as firms shift from isolated point solutions toward agentic AI systems that coordinate actions across multiple stages of the policy administration and claims lifecycle. Notably, the adoption of agentic AI has surged, with one in four newly disclosed use cases now showing evidence of agentic orchestration, compared to one in twenty only six months prior.

    Zurich sets an example

    Zurich serves as an example of this transition, rising from 12th position to 4th in the global rankings by emphasising a shared platform model over decentralised experimentation.

    The insurance giant deployed ZurichIQ, a modular generative AI platform integrated into underwriting, claims, legal, and service operations. This architecture provides a unified environment for various functional tools, such as PolicyIQ for contract comparisons and GuidelinelQ for enforcing underwriting standards.

    Hurdles in such deployments typically involve maintaining oversight across diverse business lines. Zurich manages these risks through a dedicated committee that governs AI investment and model risk management. The platform approach allows the insurer to push AI capabilities into daily production while maintaining a consistent governance framework, which is reinforced by internal training programs like the £1.3m AI apprenticeship initiative.

    Ericson Chan, Group Chief Information & Digital Officer at Zurich, said: “Being recognised as the biggest AI growth insurer in the Evident AI Index is not simply a reflection of technology adoption; it signals a broader transformation from use cases to enterprise-wide execution and change.

    “This recognition reinforces our conviction in our AI360 strategy, embedding intelligence into workflows, decisions, and customer outcomes across the value chain. AI is no longer a technology initiative. It is becoming Zurich’s operating system.”

    Focus on risk selection and ROI

    With claims typically accounting for 60 to 80 percent of premium income, even minor improvements in fraud detection and risk selection produce a disproportionate financial impact compared to general administrative cost reduction.

    Insurers are now directing venture capital and internal innovation efforts toward data sources that enable more dynamic analysis of climate volatility and cyber threats. A critical marker of this maturity is the ability to quantify and disclose financial returns.

    Manulife, Generali, and Intact Financial have led this effort, publicly reporting AI-driven value. Projections indicate these three firms will generate over $1 billion in AI-driven value by the end of their respective reporting periods. This transparency provides the hard data shareholders demand regarding the costs of AI deployment, effectively mandating more rigorous performance measurement across the sector.

    Success in the next phase of industry adoption depends on the ability to translate these technical investments into better underwriting results. Market leaders Allianz (which now holds the largest AI talent pool in the industry and has registered 900 AI use cases worldwide) and AXA maintain top positions by demonstrating sustained investment across innovation, talent, and transparency pillars.

    Barbara Karuth-Zelle, Member of the Board of Management and Group COO at Allianz, commented: “AI didn’t change our ambition. It accelerates how we deliver on it at scale.

    “Behind this ranking are thousands of moments: a claim processed faster, a customer experience reimagined, a partner better connected, a colleague freed up for what truly matters. And we are determined to keep going—an inspiring, transformative journey.”

    See also: Accenture: Consumers show growing trust in AI shopping agents

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  • 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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