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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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  • Accenture: Consumers show growing trust in AI shopping agents

    Consumers are showing a willingness to let AI agents take on more shopping-related tasks, according to new research from Accenture.

    The company’s 2026 Consumer Pulse Research, based on a survey of 25,590 consumers across 16 countries, found that 74% of respondents would trust a personal AI agent more than their best friend to make a purchase on their behalf.

    The report described this as a move beyond the use of chatbots or search tools. In this context, an AI agent refers to software that can act on a consumer’s behalf within set permissions. It can shop, negotiate, resolve complaints, manage subscriptions, and, in some cases, complete purchases.

    Consumers are ready to delegate

    The survey found that 74% of consumers would allow an AI agent to handle routine tasks. These include deal negotiation, complaint resolution, subscription renewals, and product reorders.

    Accenture said this level of delegation does not mean consumers are ready to hand over every decision. Instead, the findings suggest that consumers are more open to delegating parts of shopping that feel repetitive, time-consuming, or low-risk.

    The report also found that 32% of consumers would ask an AI agent to make a purchase decision on their behalf within defined limits. These limits could include budget and brand preferences, with other conditions set by the user.

    In that scenario, the AI agent would choose the best available option, but the consumer would still review and approve the purchase before payment. The report categorised this as delegated decision-making, separate from task execution and autonomous purchasing.

    Autonomy still has limits

    A smaller group of consumers is open to AI agents completing purchases without final approval. The report found that 9% of respondents would allow an agent to initiate and complete purchases within defined boundaries.

    The payment stage recorded lower openness to autonomous agent decisions. Accenture said only 12% of consumers are open to agents making purchase decisions autonomously at the payment stage.

    The report identified several conditions that affect consumer willingness to delegate more control. These include data safeguards, configurable permissions, and instant override options. Clear recourse, platform reputation, and perceived neutrality also affect trust.

    Consumers are more comfortable with AI agent autonomy in parts of the journey where effort is high and emotional stakes are lower. The report pointed to negotiation and post-purchase support as areas where consumers showed greater openness.

    The report said recurring services ranked highest across stages of delegation, while lifestyle and travel purchases showed a sharper drop as autonomy increased.

    It also said consumers are more likely to keep control over choices linked to identity or personal enjoyment. A consumer may delegate routine grocery restocking but still want to choose a hotel room, clothing item, or experience directly.

    What it means for brands

    The report said AI-assisted shopping requires brands and retailers to make product information clear and machine-readable. If consumers use agents to compare options, pricing, availability, policies, and claims will also need to be easy for agents to assess.

    AI agents can compare brands using structured attributes and verified claims. They can also weigh price-to-value ratios and fulfilment records. The report said this affects how brands appear across digital channels, including search engines, marketplaces, and social platforms.

    The report found that 56% of all consumers would tell their AI agent which brands to consider. Among behaviorally loyal consumers, 37% said they would allow an agent to switch brands if it found a better fit.

    The report linked brand switching to factors such as fit, price, availability, and service performance.

    Accenture also found that consumers are interested in agents that can work across providers. The report said 61% want an agent that can shop across multiple grocery retailers on their behalf, while 71% want an agent that can plan and book a complete trip across airlines, hotels, and activities.

    Brands and retailers need product data, pricing, availability, policies, and claims to be readable by the systems agents use to evaluate options, according to the report.

    The main reasons cited were existing knowledge of shopping preferences, trust built through service and support, and access to a broad selection of products and services.

    The report listed several possible roles for brands and retailers in AI-assisted commerce. Some may build their own agents, while others may integrate data, inventory, and services into platforms that consumers already use.

    The report cited verified information, clear inventory, transparent pricing, and reliable fulfilment data as factors that can help agents evaluate brands more easily.

    It also found that 71% of consumers expect generative AI to influence at least half of their spending decisions over the next 12 months.

    The report also found that 63% of consumers want agents to shop for their “idealised self.” Examples include helping them make healthier choices or stay within budget. Some respondents also want agents to support more intentional upgrades.

    Among active generative AI users, 26% said they had already bought a more expensive item because AI increased their confidence in the decision. The same proportion said AI had led them to increase their basket size.

    Stores still matter

    The survey also asked consumers how AI could affect stores. It found that 87% believe AI will affect the role of stores. Another 31% said stores will become more important for creating moments of enjoyment.

    The findings show lower openness to full automation than to routine task delegation. It shows a more selective pattern, with consumers delegating routine or lower-risk tasks while retaining control over purchases that involve personal preference, risk, or emotional value.

    The report said some brand evaluation could take place inside agent-led comparison systems before consumers visit a website, app, or store.

    (Photo by Growtika)

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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  • Coinbase for Agents: Automating portfolio trading with AI

    Coinbase for Agents connects AI to financial execution channels to automate trading and payments directly from user portfolios.

    Large language models process vast quantities of data but lack direct integration with active financial portfolios. Individuals frequently employ these models to evaluate market developments or research investment opportunities. These software tools possess the capacity for complex reasoning but cannot execute financial transactions on behalf of the user.

    Coinbase for Agents enables autonomous digital entities to execute trades, process payments, and manage balances within user-defined parameters.

    Terminal-based systems use command-line interfaces to manage the connection. This route fits development environments such as Claude Code, Codex, or OpenClaw. The command-line architecture integrates directly into established local development toolchains. This implementation path lowers token expenditure during high-frequency tasks and accommodates extensive local customisation. Setting up this configuration involves installing specific skill packages via the Coinbase Developer Platform documentation and generating dedicated API keys.

    Web-centric software arrangements, meanwhile, rely on the Model Context Protocol. MCP establishes a direct integration path for web-based agent environments like ChatGPT or Claude Web. It permits a rapid connection via a single account login procedure. This method functions without requiring manual API key creation or complex local configuration files. A remote MCP option will become available in the near future that will allow individuals to link their financial profiles using standard single sign-on features without writing code.

    Portfolio allocation and execution

    Account holders can program specific distribution rules, instructing an automated agent to establish or maintain targeted asset ratios.

    As an example, a portfolio manager might select a target distribution consisting of 60 percent Bitcoin, 20 percent Ethereum, and 20 percent Solana. The agent executes this directive over extended timeframes spanning multiple months. It assesses real-time pricing data and positions limit orders to purchase assets when market valuations decline by five, ten, or fifteen percent. The software captures these brief market pullbacks to accumulate assets automatically.

    Coinbase’s current system supports spot and derivatives trading but is working on expanding the protocol to include index funds, standard corporate equities, commodities, and prediction markets.

    The autonomous assistant monitors available cash balances around the clock to keep funds productive. It distributes idle capital to generate rewards or highlights specific asset positions that require direct human attention.

    Integrating the x402 protocol allows these agents to interact with external commercial systems. Coinbase introduced this agentic payment protocol last year to provide software agents with a standard method for economic interaction. Agents deploy capital via this protocol to purchase computing resources, analytical models, and proprietary market data to inform their decisions. Upcoming x402 integrations will standardise these automated purchases across web services.

    Data collection determines the efficacy of automated trading logic. An agent assigned to execute a dollar-cost averaging plan into Ethereum uses historical metrics to optimise market entry. The system retrieves thirty days of hourly pricing statistics to pinpoint historical low points during the day and can then establish a recurring daily market purchase of $20 timed precisely to those optimal windows. The automated routine executes daily for two weeks based on a single initial command.

    Security controls and compliance

    Agents operate exclusively inside isolated portfolios to safeguard broader financial holdings. This design prevents the autonomous entity from viewing or accessing unauthorised balances.

    Users already retain total control over the operational boundaries. However, upcoming platform updates will introduce explicit rulesets for fine-tuned governance. Users will dictate maximum transaction volumes, specific permitted assets, and absolute spending limits.

    The platform subjects all agent-initiated payments to standard transaction monitoring and “Know Your Transaction” validation. Users receive automated compliance verification without building internal monitoring systems.

    Coinbase’s latest product launch marks the expansion of a broader consumer product suite that began with the 2024 launch of AgentKit, which provided tools for embedding crypto wallets into software systems. The subsequent introduction of the x402 protocol and the release of Coinbase for Agents finalises the financial execution layer.

    Alternative connection options exist for everyday investors who prefer simple interfaces. Coinbase Advisor operates natively inside the primary consumer application. This integrated agent provides automated recommendations and financial guidance directly to users. The assistant holds formal registrations with both the SEC and the CFTC as a financial advisor. For retailers, commercial merchants can deploy Coinbase Payments to accept automated transfers from these autonomous systems.

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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    Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

    AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

    The post Coinbase for Agents: Automating portfolio trading with AI appeared first on AI News.

  • Xebia: Why AI agents fail without the right data foundation

    If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains.

    “If you don’t think about that, you can build the best agent, but it will never be able to find the correct data; maybe it will misinterpret the data, maybe it will join different fields together in your data which should never be connected,” explains Zeilemaker. “And these mistakes are not necessarily the fault of the agent. It’s the fault of your foundation, which is not ready for AI agents.”

    One area to particularly consider, Zeilemaker notes, is data cataloguing. It’s not a new concept, but the game changes for agents. “If you’re setting up a data catalogue for an organisation only consisting of humans, there’s always a fallback,” he says. “If there’s something not really well documented, you can pick up the phone, walk to a colleague, and have a sort of back door, in ‘how should I work with this particular set of data?’

    “Agents don’t have such a back door. They have to rely on the data catalogue, what’s written there, and if the description is wrong, the agents will not perform.”

    Xebia’s focus is to help organisations turn AI strategy into production-ready solutions which drive real transformation faster. The company’s core values include being people first and quality without compromise, but perhaps the most important, as Zeilemaker sees it, is sharing knowledge – such as at events like TechEx Global North America, at which Xebia participated.

    “I think sharing knowledge is very important for us, and it also allows us to be a bit ahead of the curve, adopt quickly to new changes in the market, because everybody has this eagerness to find out new things, and to share what works, what doesn’t work,” says Zeilemaker. “By pushing a lot into this sharing knowledge and innovation, we try to also pick a couple of domains where we want to be the authority.”

    Data and AI is evidently one of those areas. At AI & Big Data Expo, Zeilemaker told attendees how to build this AI foundation and unify their fragmented data landscapes. It was an honest account of how combining purpose-built AI agents with expert engineering compresses a 12- to 24-month timeline into a fixed-price, milestone-bound engagement.

    The overarching thread for this is what Xebia calls Agentic Data Foundation (ADF), which extends the data platform to host agents, and then make use of them both in customer-facing use cases and internal processes. While there has always been a big appetite in migrating from legacy to modern platforms, Xebia is seeing more customers asking for an approach to more quickly – and reliably – migrate into data platforms. Zeilemaker says this is where consultant and customer are co-developing the solution.

    “Agents have to rely on the data catalogue and what’s written there – and if the description is wrong, the agents will not perform”

    “After doing migrations the old-fashioned way, and accelerating some with LLM coding, we are now integrating this into the data platform, making use of the additional context it can provide to accelerate migrations even further,” he says.

    That accumulated experience is what shaped Xebia Axis: Agentic Data Foundation, Xebia’s answer to helping enterprises make their data AI-ready faster than any alternative.

    Another weapon Xebia has in its arsenal is Xebia ACE: AI-Native Software Engineering, a framework which embeds AI across an organisation’s entire software development lifecycle (SDLC). Done right, delivery can be accelerated by up to 40%, while legacy transformation costs are cut by up to 70%.

    Zeilemaker notes that Xebia ACE is particularly useful for larger enterprises who ‘maybe still want to stick to a particular governance or way of working while doing SDLC’. Yet there is a bigger picture here. Zeilemaker uses vibe coding as an example. “If you think about vibe coding, everybody can create an app, but nobody is daring to actually push these apps into production,” he says. “If you adopt ACE, you still get a lot of the benefits of the acceleration of LLMs, but you’re still having the same quality end results as you’re used to in the past.

    “If you’re looking to make the switch to using LLMs in coding, Xebia ACE will give you a very nice framework to use, without the risk, or any drawbacks of doing dark factory LLM and hoping for the best – and losing a bit of control or governance in the process,” adds Zeilemaker.

    For enterprises, that control is key. With so much code being generated, the AI-driven SDLC could become a security weakness through vulnerabilities. Zeilemaker argues it’s something the industry still needs to figure out to a degree, but notes with interest the recent move by Anthropic to release a pull request reviewer.

    “It’s an interesting one, which we’ll probably see more of,” he says. “There will be very lengthy pull request reviews, which you apply whenever you go and try to do a new production release. And then you add a very senior team member in the form of an LLM to your process, which does a sort of third-party review.

    “I think that’s an interesting angle with what we’re going to see more of in the future.”

    Ultimately, wherever organisations are in their journey, from assessing their data readiness to being ready to build, Xebia is able to help get the foundations right – and create the transformations on top of it.

    Photo by fabio on Unsplash

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    The post Xebia: Why AI agents fail without the right data foundation appeared first on AI News.

  • Visa ChatGPT integration enables AI agent retail purchasing

    Visa has linked its payment infrastructure to ChatGPT, enabling AI agents to recommend retail products and execute financial transactions.

    The deployment removes human intervention from the final stages of the retail funnel. Autonomous agents will now process user prompts, evaluate merchant catalogues, and complete the checkout process using Visa’s payment rails at any supporting merchant.

    Previous retail AI integrations restricted automated purchasing to single-vendor environments. Retailers built proprietary chatbots confined entirely to their own inventory. Visa’s integration bypasses closed-loop architecture.

    The payment giant connects the open-web reasoning capabilities of a large language model directly to a universal transaction network. Users simply command the agent to procure an item, and the model handles the vendor selection, product comparison, and financial settlement.

    Enterprises should be aware that commercial transactions will increasingly execute without a human buyer ever seeing a retailer’s website, digital advertisement, or promotional email.

    Restructuring retail data for AI agent buyers

    Marketing departments design campaigns around human psychology, emotional triggers, and visual merchandising. AI agents operate on pure data evaluation.

    When ChatGPT receives a mandate to purchase a specific product type, it parses technical specifications, aggregated sentiment scores, and pricing structures. Display ads and user interface optimisations hold zero weight in the model’s selection criteria.

    Retailers will need to expose machine-readable inventory data. Search engine optimisation transitions into language model optimisation. The algorithms driving ChatGPT rely on structured data feeds, clear API documentation, and explicitly-formatted product attributes to evaluate whether an item meets the user’s parameters. Merchants failing to maintain high-quality, structured metadata will find their products invisible to the autonomous agent.

    Personalisation occurs entirely on the user’s device or within the user’s secure LLM profile. The AI retains the consumer’s past preferences, sizing requirements, budget constraints, and brand affinities. Instead of the retailer attempting to guess the consumer’s needs through tracking cookies and site behaviour, the agent arrives at the digital storefront with a highly-specific procurement mandate.

    Completing a transaction without human intervention requires a secure, automated handshake between the reasoning engine and the payment gateway. Visa provides the financial layer necessary to establish trust in an inherently untrusted agentic environment. Traditional checkout flows require manual data entry, CAPTCHA verification, and two-factor authentication loops. These mechanisms block autonomous agents.

    Visa implements programmatic tokenisation to solve the authentication problem. The user pre-authorises the ChatGPT environment with specific spending parameters. When the LLM decides on a purchase, it generates a single-use payment token through the Visa network. The agent transmits this token via API to the merchant’s backend systems. The transaction settles exactly like a standard digital wallet payment, bypassing the visual user interface completely.

    A digital storefront requiring multi-page navigation or mandatory account creation introduces failure points for the agent. Enterprises actively deploying headless commerce architectures possess an advantage. They can process the agent’s payload, confirm stock levels, and execute the payment token in milliseconds.

    Enterprises track bounce rates, session durations, and cart abandonment to understand consumer behaviour. An AI agent does not browse—it queries an endpoint, extracts the necessary data, and either executes the payment or terminates the connection.

    Retailers must develop new telemetry to measure agent interactions. Tracking the frequency of API queries from known LLM IP addresses replaces tracking unique human visitors. Understanding why an agent selected a competitor’s product will require analysing the structural differences in product data feeds rather than running A/B tests on website layouts.

    Customer retention strategies also need adjustment. An autonomous agent evaluates the market fresh with every prompt unless explicitly instructed by the user to reorder a specific brand. Loyalty programmes must be engineered into the payment token or the user’s LLM profile. If the AI cannot automatically apply a loyalty discount during its background calculation, the merchant loses the pricing advantage intended to secure the repeat purchase.

    Managing and securing the agentic AI supply chain

    Prompt injection attacks could theoretically manipulate an agent into purchasing from malicious vendors or authorising inflated transactions. Visa’s network acts as the final validation layer, applying fraud detection models to the incoming token requests.

    Businesses face the secondary challenge of managing automated returns and customer service queries initiated by the AI. If the delivered product fails to meet the parameters defined in the original prompt, the user can instruct the agent to reverse the transaction.

    In this scenario, the AI will autonomously navigate the merchant’s return policy, initiate the refund request, and generate the necessary shipping labels. Retail customer service operations must deploy their own automated systems capable of negotiating directly with the consumer’s agent.

    Visa’s ChatGPT integration confirms the enterprise transition from human-operated software interfaces to autonomous digital proxies. The customer is no longer necessarily a human navigating a web browser, but an algorithm executing a script.

    See also: Aviva deploys AI to stop £230M in sophisticated insurance fraud

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  • McDonald’s tests Google-backed AI drive-thru ordering system

    McDonald’s is testing a new AI system that can take drive-thru orders and support restaurant operations.

    The system, called ArchIQ and nicknamed “Archy,” was introduced during the company’s Worldwide convention, according to Restaurant Business. It is being tested at five McDonald’s locations in the United States, though the company has not named the restaurants involved.

    A video shared on X by a McDonald’s franchise owner showed the system greeting customers, processing order changes, displaying the final total, and asking customers to pull ahead for pickup.

    A demonstration shared on X by the franchisee account McFranchisee showed the system taking orders in English and Spanish. The account said the system has processed more than one million transactions, with about 90% of orders completed without being escalated to staff.

    The same account said ArchIQ can respond when repeat customers ask for their usual order. McDonald’s has not provided technical details on how that feature works.

    ArchIQ is being developed with Google. According to McFranchisee, McDonald’s restaurants in the US are receiving Google Edge Cloud blades ahead of the rollout.

    McDonald’s previous AI ordering test

    ArchIQ is McDonald’s latest AI test for drive-thru ordering. The company previously worked with IBM on an automated ordering system across more than 100 restaurants.

    McDonald’s ended that pilot in 2024 after customer complaints over order errors. The earlier IBM test was followed by customer videos showing incorrect orders, including one case in which the system reportedly added more than $250 worth of chicken nuggets.

    After ending the IBM partnership, McDonald’s said it would continue exploring voice ordering technology.

    Restaurant operations support

    ArchIQ is not limited to customer ordering. McFranchisee said it can monitor restaurants and alert managers to possible issues.

    According to McFranchisee, the system can alert managers if a freezer is down. It can also flag kitchen bottlenecks or other problems that need attention.

    McFranchisee described ArchIQ as both an ordering tool and a management-support tool.

    The test forms part of McDonald’s new growth plan, called “McDonald’s > NEXT.” The company said the plan is intended to improve restaurant operations and unit economics.

    McDonald’s reported a large digital customer base in its 2025 results. The company said systemwide sales to loyalty members across 70 markets rose 20% to nearly US$37 billion in 2025, while 90-day active loyalty users rose 19% to nearly 210 million at year-end.

    McDonald’s CEO Chris Kempczinski said in a press release that the strategy is aimed at the company’s next phase of growth and productivity.

    The company has also referenced restaurant upgrades and possible menu changes under the same plan, but has not provided detailed information.

    Automation and service

    In a company memo, Kempczinski said more of the customer journey is becoming automated, leaving fewer chances for guests to interact with crew members. He said that it raises the standard for hospitality when customers interact with staff.

    QSR Magazine’s 2025 Drive-Thru Report, citing Revenue Management Solutions, said drive-thru traffic remained negative month after month and hovered between minus 5% and minus 8% in 2025.

    Other fast-food chains have also announced AI-powered drive-thru ordering systems, including Taco Bell and Wendy’s.

    Jonathan Maze, editor-in-chief of Restaurant Business, told ABC News that companies often present drive-thru automation as a way to free employees for other tasks. The McFranchisee account said the system could reduce the need for workers to take orders in noisy drive-thru lanes.

    Some X users responding to the ArchIQ demonstration said they preferred interacting with human workers. Others supported a more automated ordering process.

    McDonald’s has not said when ArchIQ could be expanded beyond the five test locations. The company has said the system is intended to improve speed and accuracy while supporting customers and crew.

    The company’s AI drive-thru system remains in limited testing.

    (Photo by Boshoku)

    See also: Walmart’s AI workflows meet the realities of the balance sheet

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    The post McDonald’s tests Google-backed AI drive-thru ordering system appeared first on AI News.

  • Siri AI arrives with Google inside, and much of the world is locked out

    “We’ve all had that moment where you search for something you know is there, but it just won’t show up.” Apple’s Stacey Ford, vice president of OS Program Management, was talking about Spotlight at WWDC 2026, but she could have been describing the company’s AI ambitions. 

    On Monday at Apple Park, the thing that wouldn’t show up finally did: Siri AI, the assistant rebuilt from scratch after years of underdelivery. The new Siri sustains genuine multi-turn conversation, draws on what’s in a user’s mail, messages and photo library, fields live queries from the web, and carries out tasks across applications.

    Apple is giving the assistant its own dedicated app alongside system-wide integration, with iPhones showing Siri activity in the Dynamic Island as requests run. That is the version Apple presented on stage. The version worth examining sits in the footnotes: who is actually powering Siri AI, and who gets to use it. 

    Google under the hood

    Apple’s most consequential disclosure was a quiet one. The company said it collaborated with Google and the Gemini family of models to develop the next generation of Apple Foundation Models that power its Apple Intelligence experiences, the architecture on which Siri AI runs. After two years of insisting its in-house models would close the gap, Apple has answered the question of how it caught up: it didn’t, alone. 

    The company spent considerable keynote time pre-empting the obvious objection. “We believe privacy in AI is non-negotiable,” senior vice president Craig Federighi said, adding that “data is only used to execute your request, and outside experts can continue to verify this promise at any time.”

    The privacy architecture may well hold. The strategic picture is harder to soften. Apple now depends on its largest search rival for the intelligence layer of its own assistant; at the same time, Google is shipping Gemini across Android, Workspace and its own hardware. Whatever the terms of the arrangement, Apple has conceded that the frontier model race is one it could not win on its own timeline, and that admission carries weight far beyond Cupertino. 

    If the world’s most valuable hardware company, with its silicon advantage and effectively unlimited budget, chose to license rather than build, the sovereign AI ambitions being drafted in capitals around the world deserve a more honest read of what “building our own model” actually costs.

    The Siri AI rollout map tells its own story

    Then there is the question of who gets Siri AI at all. The initial beta, due later this year, supports English only. China is off the map entirely, with Apple citing unresolved regulatory requirements, and EU users won’t see the assistant on iPhone or iPad at launch. Apple has said a path forward is being worked on; in the meantime, its updated press release confirms EU availability is limited to macOS 27 and visionOS 27 at first.

    Read that map from Asia, and the gaps are glaring. China, Apple’s most contested market, is excluded outright, while domestic assistants from Chinese vendors ship without restriction. An English-only beta leaves Mandarin, Japanese, Korean, Bahasa and Hindi speakers, which is to say most iPhone users in the world’s fastest-growing smartphone markets, on the old Siri for an unspecified period. 

    Apple gave no timeline for additional languages. The company that built its reputation on shipping the same product to everyone, everywhere, on the same day, has shipped its most important software in years to English speakers only, minus China entirely and minus iPhone users in the EU.”

    Catching up, by Apple’s own staging

    The keynote’s structure was telling. TechCrunch noted that Apple opened by repairing what was broken before showing off what was new, and positioned the upgraded Siri as one entry on a lengthy list rather than the headline act.

    It was also a transition moment. This was Tim Cook’s final WWDC as CEO before John Ternus, Apple’s senior vice president of hardware engineering, takes over on September 1. “I truly believe the best is still ahead at Apple,” Cook said in his closing remarks. 

    Perhaps. Siri AI is a real product at last, and the demos suggest Apple’s integration instincts remain intact. But Ternus inherits an assistant that thinks with Google’s models and a rollout plan that asks most of the planet to wait. The catching up, it turns out, has only just started.

    (Photo by Apple)

    See also: Apple plans big Siri update with help from Google AI

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