The Beijing-based technology group has opened preliminary discussions with banks on a facility that could run for three years, with an option to extend the maturity to as long as five years. The size, pricing and lender group have not been finalised, and the talks may still change before any formal syndication begins.

The proposed borrowing underlines the scale of ByteDance’s capital needs as it expands beyond social media into generative AI, cloud infrastructure, custom chips and data-centre capacity. The company, founded in 2012 by Zhang Yiming and Liang Rubo, has built one of the world’s most profitable private technology platforms through TikTok, Douyin, Toutiao, CapCut and a growing suite of AI products.

A $20 billion transaction would rank among the largest offshore loans by a privately held technology company and would signal strong appetite among banks for exposure to ByteDance despite continuing political and regulatory scrutiny around TikTok. The company remains privately owned, giving lenders limited public financial disclosure compared with listed peers, but its cash generation and global reach have made it one of the most closely watched borrowers in Asia’s technology sector.

ByteDance has been accelerating spending on AI computing power as competition intensifies with Alibaba, Tencent, Baidu, DeepSeek and global rivals. Its Doubao chatbot has become one of China’s most widely used AI applications, while the company has pushed video-generation models, enterprise tools and AI-assisted content creation products into a crowded market. Doubao’s surge during the Lunar New Year period demonstrated ByteDance’s ability to use consumer distribution, entertainment tie-ups and low-friction interfaces to drive mass adoption.

The loan talks come alongside ByteDance’s efforts to secure semiconductor capacity. The company has been working on custom central processing units for AI workloads and has explored external chip-design partnerships. The aim is to reduce dependence on expensive off-the-shelf processors and improve efficiency across data centres serving recommendation systems, chatbots, video generation and advertising tools.

ByteDance’s AI ambitions also depend on access to advanced chips at a time when export controls, supply shortages and rising demand have reshaped the global semiconductor market. The company has pursued overseas computing arrangements, including infrastructure in Southeast Asia, while continuing to invest in model development and inference capacity. This strategy reflects a broader trend among large technology groups, which are moving from purely buying chips to designing parts of their own hardware stack.

The financing push follows a period in which ByteDance’s valuation has remained elevated despite repeated uncertainty over TikTok’s future in the United States. Employee share buybacks over the past year placed the company’s valuation above $300 billion, supported by strong advertising, e-commerce and subscription prospects. TikTok remains a key global asset, but ByteDance’s domestic ecosystem led by Douyin continues to generate substantial revenue and user engagement.

Regulatory risk remains a central consideration for lenders and investors. TikTok has faced national security reviews, data-privacy disputes and ownership pressure in the United States and other markets. The restructuring of TikTok’s US operations reduced one major overhang, but ByteDance continues to operate in a geopolitical environment where technology, data, algorithms and chips are tied closely to state policy.

The company’s offshore borrowing would give it additional flexibility without requiring an initial public offering or a large equity sale. It could use the funds to support AI infrastructure, refinance existing obligations, extend liquidity for investments, or preserve cash for employee share programmes. For a private group of ByteDance’s size, debt financing also allows expansion while avoiding dilution at a time when its valuation is difficult to benchmark against public peers.

Banks are likely to assess the loan against ByteDance’s earnings resilience, its cash balances, the enforceability of offshore structures and the continuing uncertainty around cross-border regulation. Large syndicated loans of this type are often structured with relationship lenders first, before being broadened to other financial institutions if terms and demand are strong.

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The project, built with security research firm Trail of Bits and supported by HackerOne, Calif, researchers and maintainers, shifts the focus from simply discovering vulnerabilities to landing tested patches in widely used software. It comes as artificial intelligence tools accelerate bug hunting, creating both opportunities for defenders and a heavier workload for maintainers already dealing with limited resources and large backlogs.

Patch the Planet is designed to pair AI-assisted security research with expert human review. Security engineers use OpenAI’s cyber-capable models and Codex Security to investigate possible vulnerabilities, filter out false positives, develop fixes, improve tests and coordinate disclosure through the channels preferred by each project. Maintainers remain responsible for accepting changes and deciding how fixes are released.

More than 30 open-source projects have committed to take part. Early participants include cURL, NATS Server, pyca/cryptography, Sigstore, aiohttp, the Go project, freenginx, Python and python. org. These projects sit inside critical layers of the digital economy, covering networking, cryptography, language infrastructure, software supply chains and web services used by companies, governments and individual developers.

Trail of Bits has put its security research organisation behind the first phase. Its engineers have worked full-time across 19 open-source projects using Codex and GPT-5.5-Cyber, identifying hundreds of security issues and merging dozens of patches. Some findings remain under coordinated disclosure, meaning technical details will be held back until fixes are available or maintainers complete their remediation process.

The initiative reflects a broader change in cybersecurity. Advanced models can now scan large codebases, reason through possible attack paths, generate proof-of-concept evidence in controlled settings and draft patches. That speed creates a new bottleneck: maintainers must still decide whether a finding is real, how serious it is, whether a patch breaks other functions, and how disclosure should be handled.

OpenAI’s Daybreak update also includes a wider launch of GPT-5.5-Cyber under a limited-access programme for trusted defenders, an updated Codex Security plugin and a partner programme that lets security firms integrate defensive models into their services. Codex Security has scanned more than 30 million commits across more than 30,000 codebases since its research preview began in March. Human reviewers have marked more than 70,000 findings as fixed, while the system has automatically identified more than 500,000 resolved findings.

OpenAI says GPT-5.5-Cyber achieved 85.6 per cent on CyberGym, compared with 81.8 per cent for GPT-5.5, and showed stronger results on other cyber benchmarks. The company is keeping access restricted because the same capabilities that help defenders find and patch flaws could help malicious actors discover attack paths at scale.

Patch the Planet tries to address one of the main complaints from open-source maintainers: automated reports can flood small teams with low-quality findings. The programme requires security researchers to reproduce evidence, remove duplicates, reassess severity and submit only confirmed issues. The model is intended to reduce “slop” vulnerability reports rather than add to them.

The early work has also produced infrastructure intended to outlast the first wave of fixes. Engineers have built fuzzing harnesses, differential-testing systems, historical-CVE analysis pipelines, threat models, expanded test suites and workflows for deduplication and false-positive filtering. In one case, a fuzzing lab covering dozens of entry points, variant builds and platforms was assembled in less than a day, work that would normally take weeks.

Daybreak’s broader testing has also examined operating systems, network software and browsers. The work has included analysis of Linux kernel components, local privilege escalation issues in FreeBSD, a long-standing OpenBSD kernel flaw, vulnerable patterns in dnsmasq, HTTP/2 denial-of-service behaviour affecting major server implementations, and exploitable bugs in browser engines.

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The Santa Clara, California-based company said the Series A-1 extension brings its total funding to $500 million, less than a year after it emerged with a seed round of more than $100 million. The latest round was led by Premji Invest and drew new backing from Nvidia, Salesforce Ventures, Seligman Ventures and Temasek. Existing investors including Maverick Silicon, Mayfield, Prosperity7 Ventures, StepStone Group and Tiger Global also participated.

The fundraising places Upscale AI among a small but fast-growing group of infrastructure start-ups aiming to reshape the data-centre stack beyond graphics processing units. While AI investment has been dominated by demand for accelerators, the company is targeting the fabric that links chips, memory and storage across vast clusters. That layer has become critical as model training and inference workloads stretch across tens of thousands of processors and require predictable, low-latency data movement.

Upscale AI’s pitch rests on open-standard networking designed for AI-native workloads rather than conventional enterprise traffic. The company is building silicon, systems and software that can connect accelerators, memory pools and storage into a high-performance network fabric. Its approach seeks to reduce congestion, packet loss and coordination delays that can leave costly AI chips idle despite heavy capital spending on compute.

The company is led by chief executive Barun Kar and executive chairman Rajiv Khemani. Khemani previously founded Innovium, a data-centre networking chip company acquired by Marvell Technology in a deal valued at about $1.1 billion. That track record has helped Upscale AI attract early confidence from investors familiar with the economics of high-end switching silicon and hyperscale infrastructure procurement.

The latest financing follows a $200 million Series A announced in January, led by Tiger Global, Premji Invest and Xora Innovation, with participation from investors including Intel Capital and Qualcomm Ventures. That round took the company’s total funding above $300 million and established it as a unicorn within months of launch. The new extension doubles its valuation from that level and gives it a larger balance sheet to move products from evaluation to deployment.

Demand for AI networking is accelerating as hyperscalers, cloud providers and specialist “neocloud” operators build larger clusters for training and serving models. The pressure is not limited to compute supply. Data-centre developers are also competing for power, optical components, high-bandwidth memory, switches and interconnect systems. North American data-centre power demand is projected to more than double from 31 gigawatts in 2025 to 66 gigawatts in 2027, underlining the scale of the build-out.

Networking has become a defining constraint because AI workloads behave differently from traditional cloud applications. Large training runs require synchronised communication across many accelerators, with performance hurt sharply when the network becomes uneven or congested. Inference at scale adds another layer of complexity as companies attempt to serve users quickly while controlling energy, memory and routing costs.

That shift is creating openings for suppliers offering Ethernet-based or open networking alternatives to proprietary systems. Spending on data-centre switches used in AI back-end networks is forecast to exceed $100 billion by 2030. Ethernet is gaining ground across both scale-out and scale-up designs because customers want interoperability across accelerators, software stacks and server vendors, although proprietary fabrics still hold an important position in parts of the market.

Upscale AI faces powerful incumbents. Nvidia is expanding its Spectrum-X Ethernet platform alongside its dominant accelerator business. Broadcom remains deeply embedded in switching silicon. Cisco and Arista Networks are strengthening data-centre networking portfolios for AI clusters, while Marvell is active across custom silicon, optical connectivity and infrastructure chips. Start-ups in adjacent areas are also trying to capture value as customers rethink how compute, networking and storage are assembled.

The investor interest reflects a broader recalibration of AI infrastructure spending. The largest technology companies are committing hundreds of billions of dollars to data centres, power contracts and specialised hardware, but investors are increasingly focused on whether that spending can translate into useful capacity and revenue. A cluster filled with accelerators can underperform if the network cannot move data fast enough, making infrastructure efficiency as important as raw chip supply.

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The European Commission’s investigation is focused on whether Meta has done enough under the Digital Services Act to assess and reduce risks to minors, including so-called rabbit-hole effects, addictive design features and weak age controls. The case has become one of the most closely watched tests of the EU’s ability to regulate the architecture of social media platforms rather than only the illegal content that appears on them.

Regulators opened formal proceedings against Meta in May 2024, citing concerns that Facebook and Instagram may stimulate behavioural addiction among children and expose them to harmful or age-inappropriate material through algorithmic recommendations. The probe covers Meta’s risk assessments, mitigation measures, recommender systems, default privacy settings and age-assurance tools.

The next phase is expected to examine more closely whether Meta’s product design creates incentives for prolonged use by minors. Features such as infinite scrolling, personalised feeds, autoplay, notifications and algorithmic amplification have moved to the centre of the European debate, as regulators weigh whether platform engagement tools are compatible with the DSA’s requirement to protect children’s mental and physical wellbeing.

Meta has maintained that Facebook and Instagram are intended for users aged 13 and above and says it has developed tools and policies to protect younger users. The company has pointed to teen accounts, parental supervision features, content restrictions and systems designed to detect and remove underage users. It has also argued that age verification is an industry-wide challenge that cannot be solved by one company alone.

The Commission’s April 2026 preliminary findings raised the stakes. Regulators said Meta had failed to prevent children under 13 from accessing Instagram and Facebook and had not adequately identified, assessed or mitigated the risks linked to such access. Officials said children could bypass age restrictions by entering a false birth date, with no effective controls to verify the information. The EU also criticised tools for reporting underage users as difficult to use and insufficiently effective.

The proceedings could expose Meta to penalties of up to 6 per cent of global annual turnover if breaches are confirmed. For a company of Meta’s scale, that creates a potential multibillion-dollar risk, although EU enforcement usually gives companies a chance to respond, adjust compliance measures and challenge findings before any final decision.

The case forms part of a broader European push to restrict children’s exposure to harmful online design. Commission President Ursula von der Leyen has said the EU is considering tougher rules against addictive and manipulative digital practices through a planned Digital Fairness Act, expected to complement the DSA. She has also raised the possibility of a social media delay or age-related access rules after expert advice is delivered.

The EU has already taken action against TikTok over addictive design, making clear that enforcement will not be limited to Meta. Regulators have identified design patterns such as endless scrolling, autoplay and push alerts as possible drivers of compulsive use, particularly among minors and vulnerable users. The Meta case is likely to determine how far the Commission can extend that logic to the world’s largest social networking group.

Child-safety campaigners argue that voluntary tools have not kept pace with platform incentives. Their concern is that services built around engagement may reward content that keeps children scrolling, even when the material affects sleep, body image, anxiety or exposure to self-harm themes. They also say parental controls often depend on parents discovering problems after harm has already occurred.

Technology companies counter that heavy-handed rules could undermine privacy, limit access to lawful expression and create pressure for intrusive age checks. They warn that stronger age assurance may require sensitive identity data unless regulators and platforms agree on privacy-preserving systems. The Commission has promoted an age-verification blueprint designed to let users prove eligibility without disclosing unnecessary personal information, but implementation remains uneven across member states.

The dispute also carries commercial implications. Meta’s advertising model depends on user attention, personalisation and large-scale engagement. Any order requiring changes to recommender systems, interface design or notification practices could affect product growth and advertising performance in Europe. The company is already operating under strict EU rules on targeted advertising to minors, researcher access, transparency reporting and user choice over personalised recommendations.

National governments are moving in the same direction. Several European countries have debated or advanced limits on social media access for younger teenagers, while Australia’s under-16 ban has become a reference point for policymakers. Britain, France, Norway and other jurisdictions are examining their own approaches, adding to global pressure on platforms to prove that child protection is built into product design rather than added through optional safety settings.

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Cornell Tech researchers Tingwei Zhang, Harold Triedman and Vitaly Shmatikov found that a crafted snippet of about 13 words, planted in user-generated content, could manipulate deep-research systems that gather web material, synthesise it and present confident, citation-backed answers. The technique, named WARP, or Web Agent Retrieval Poisoning, does not require hacking an AI company, breaking into a platform or knowing a user’s exact question. It relies instead on the way research agents repeatedly retrieve the same community pages when answering clusters of related queries.

The study tested three open-source research systems, STORM, Co-STORM and OmniThink, using an ethical simulation framework that avoided altering live web pages. A single poisoned URL produced conditional mention rates of 38 to 51 per cent when the affected page was retrieved, while targeting several URLs lifted the rate to 42 to 62 per cent. In a full-content setting, where the planted material formed less than 4 per cent of the retrieved page, mention rates still reached 30 to 53 per cent.

The risk is acute because consumer questions often lead AI tools to community forums. Queries about restaurant choices, dating apps, cryptocurrency investments or subscription cancellation services tend to draw from Reddit, Wikipedia, Quora, YouTube and similar sites, where ordinary users post informal advice. The Cornell paper found that 17 to 23 per cent of all retrieved URLs in the tested systems came from user-generated platforms, and that an individual community page could appear in up to 48 per cent of queries within the same topic cluster.

That repetition creates a concentrated target. A scammer seeking to promote a bogus service does not need to dominate the web. A strategically placed comment on one thread that research agents already retrieve can be enough to place a fictional name into an AI-generated report. The fabricated examples in the study included a fake Austin restaurant called Sol Azteca, a made-up dating app called SilverPath and a fictitious cryptocurrency presented alongside established digital assets.

The findings also sharpen concerns about commercial AI research products. The researchers did not conduct end-to-end poisoning experiments on ChatGPT Deep Research or Gemini Deep Research because doing so would have required manipulating the live web or observing server-side retrieval that is not externally visible. Instead, they examined how often these tools cite user-generated content during normal use. OpenAI Deep Research cited such material in 3 of 748 reviewed citations, a rate of 0.4 per cent. Gemini Deep Research cited it at 12.1 per cent across the tested topics, suggesting greater exposure to the same structural weakness.

The vulnerability sits at the intersection of retrieval-augmented generation and generative engine optimisation. Retrieval-augmented systems are designed to improve accuracy by consulting current web sources rather than relying only on training data. But when those sources include writable public forums, the system’s strength becomes an opening for manipulation. Generative engine optimisation, a fast-growing marketing practice aimed at influencing AI answers, gives commercial actors an incentive to seed the web with phrases that models are likely to retrieve and repeat.

The Cornell work suggests that conventional defences remain inadequate. Blocking user-generated platforms can stop this class of attack, but it also strips AI research tools of detailed first-hand material that often makes their answers useful. Screening retrieved text before it enters the system was less effective because the poisoned snippets were crafted to read fluently and naturally. Output filtering also struggled because a fake recommendation can appear plausible when it is placed among genuine products or services.

The issue differs from older search-engine spam in one important respect. Search engines usually present users with ranked links, leaving room for scepticism and comparison. Deep-research agents compress multiple sources into a single narrative, often with a tone of authority that can make weak or planted evidence appear more settled than it is. For users, the danger is not merely seeing a bad link but receiving a polished recommendation for something that does not exist.

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The announcement, made through Trump’s social media account, has not yet been publicly confirmed by either Apple or Intel. Both companies have declined to give detailed comment, leaving open questions about the scale, timing and type of chips that may be covered by the arrangement. Even so, the statement immediately lifted investor expectations that Intel could secure one of the world’s most demanding chip customers after years of trying to build a credible contract-manufacturing business.

Intel shares rose sharply after the statement, with the market treating Apple’s possible participation as a major validation of the company’s manufacturing roadmap. Apple’s stock moved only slightly, reflecting investor caution over whether any initial work with Intel would involve high-volume processors or a narrower set of less critical components.

The potential agreement comes after more than a year of discussions between Apple and Intel over chip production for Apple-designed devices. The talks have been encouraged by the Trump administration as part of a wider industrial policy drive to expand advanced chipmaking capacity in the United States and reduce dependence on Asian manufacturing hubs.

Apple designs the processors used in iPhones, iPads and Macs, but relies heavily on Taiwan Semiconductor Manufacturing Company for production of its most advanced chips. The company moved away from Intel processors in Macs after launching its own M-series chips in 2020, a transition that improved performance and energy efficiency across its computer line-up. A new Intel arrangement would therefore not mark a return to Intel chip designs, but a possible foundry role in producing Apple-designed silicon.

For Intel, the stakes are higher. The company has been trying to reposition itself as a manufacturing partner for outside customers while also competing in the processor market. Its foundry division has required heavy investment and has struggled to prove that it can match the consistency, yields and delivery discipline of TSMC, which remains the dominant producer of cutting-edge chips for Apple, Nvidia, AMD and other major technology groups.

The US government took a 10 per cent stake in Intel last year after converting nearly $9bn in federal support into common stock, making Washington an unusually direct financial participant in the company’s turnaround effort. The administration has since used Intel’s domestic manufacturing base as a central plank in its technology strategy, pressing large chip buyers to consider US production capacity where possible.

Intel has also been promoting its 18A and 18A-P manufacturing processes as proof that its technology is becoming competitive again at the leading edge. The 18A-P process has entered risk production, an early stage used to validate performance and manufacturing readiness before broader customer adoption. The node is designed to deliver better power efficiency and performance, features that are crucial for mobile devices, laptops and artificial intelligence workloads.

Apple’s likely approach would be measured. The company is known for tight control over product quality and supply chain risk, and it is unlikely to move its most commercially sensitive chips away from TSMC without extensive testing. Analysts expect any first Intel-manufactured Apple chips to begin with limited volumes or selected product lines before moving into broader production, depending on yield, cost and delivery performance.

The possible deal also fits Apple’s wider effort to diversify supply chains. The company has expanded US semiconductor commitments through TSMC’s Arizona facilities and other domestic suppliers, while continuing to rely on a global network for assembly, components and logistics. Trump has separately criticised Apple’s overseas manufacturing footprint and pushed the company to make more products for the US market within the country.

The political significance of the Intel claim is considerable. Advanced chips have become central to national security, artificial intelligence, cloud computing and consumer electronics. Washington’s challenge has been to convert subsidies, tax incentives and political pressure into commercially viable production that can compete with Taiwan, South Korea and other established semiconductor centres.

A confirmed Apple order would give Intel a flagship customer and could help attract other companies weighing whether to trust its foundry operation. It would also offer Apple another option at a time when demand for high-end semiconductor capacity is being strained by artificial intelligence chips and next-generation consumer devices.

The remaining uncertainty lies in execution. Apple has not disclosed which chips Intel may produce, when production could begin or whether the arrangement is binding. Intel has not provided customer-specific details tied to its newest process nodes. Until those terms are clarified, the announcement remains a politically significant signal rather than a fully defined supply chain shift.

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The platform, announced on June 17, 2026, is available in gated preview and begins with code vulnerabilities, covering first-party and third-party code before AWS expands it to other areas of security. It uses multiple frontier AI models, assigning different models to tasks where they perform best, rather than relying on a single system.

Continuum marks a sharper move by AWS into agentic cybersecurity, where AI systems do more than detect problems. The platform is intended to reason across a customer’s environment, determine which vulnerabilities pose genuine business risk, test exploitability in isolated conditions and then recommend or apply fixes within customer-defined limits.

The launch comes as vulnerability management has become one of the most pressured areas of enterprise security. Software teams are shipping faster, AI-assisted development is increasing code volume, and security teams face a growing stream of findings from scanners, cloud tools, open-source packages and external advisories. Public vulnerability databases are also under strain, with nearly 42,000 CVEs enriched in 2025, 45 per cent more than any previous year, while submissions continued to outpace processing capacity.

AWS is positioning Continuum as a response to that shift. The company says the old model of collecting telemetry, storing it and reviewing dashboards is no longer sufficient when AI models can identify flaws and map complex attack paths at machine speed. The harder problem for customers is deciding which alerts matter, confirming exploitability and fixing the flaw without lengthy coordination between security, engineering and operations teams.

Continuum works in four continuous phases. The discovery phase ingests existing vulnerability backlogs and conducts its own scans across the customer environment. The prioritisation phase evaluates whether an affected component is deployed, reachable, part of a production path and significant to the business if compromised. The validation phase attempts to separate real exposures from false positives by producing reproducible proof in a sandbox. The remediation phase assesses compensating controls and recommends a network change, policy adjustment or code patch.

AWS says the system can also provide blast-radius visibility and rollback paths where feasible, a critical feature for large enterprises wary of automated fixes that may disrupt production systems. Continuum starts in what AWS calls learn mode, keeping a human in the loop and showing the reasoning behind each recommendation. Customers can then move selected categories into enforce mode, allowing more automated remediation under guardrails they define.

The platform incorporates capabilities previously associated with AWS Security Agent. Penetration testing and code scanning are now part of Continuum as Continuum penetration testing and Continuum code scanning, with code scanning still in preview. AWS is also previewing Continuum threat modelling, which can generate STRIDE-based threat models from design documents or source code.

Continuum’s model-agnostic design reflects an emerging pattern in enterprise AI platforms. Instead of building around one foundation model, providers are increasingly using orchestration layers that choose between different frontier models for specialised tasks. For security teams, that could mean using one model to inspect code, another to reason through exploit paths and another to draft remediation steps.

The approach also reflects the growing overlap between offensive and defensive AI. Security researchers have shown that frontier models can help inspect code, reproduce vulnerabilities and generate exploit evidence, but they can also produce false positives or miss vulnerabilities in realistic attack settings. That makes AWS’s emphasis on sandbox validation and staged trust central to whether customers see Continuum as a productivity tool or a source of new operational risk.

The stakes are rising as vulnerabilities move from a compliance concern to a core business risk. Exploited software flaws have played a growing role in breach investigations, while attackers are using automation to reduce the time between disclosure and exploitation. For large organisations, the volume of alerts often exceeds the capacity of human analysts to test and patch every issue manually.

AWS is initially working with select design partners including Capital One, MongoDB, Rivian and Robinhood, indicating that the first wave of adoption is likely to come from technology-intensive companies with large codebases, mature cloud operations and high regulatory exposure. Financial services, automotive and software companies are natural early targets because they combine complex application estates with strict security obligations.

The launch also intensifies competition among cloud and developer platforms seeking to embed AI into software security workflows. Microsoft, Google, GitHub and specialist security vendors are all pushing tools that promise faster code review, threat detection and remediation. AWS’s advantage lies in its access to cloud infrastructure context, permissions, network topology and customer security data, though that same depth will put scrutiny on data handling, model governance and customer control.

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The European Union’s tender framework for AI gigafactories now allows bidders to propose facilities that can grow in stages rather than committing immediately to the largest scale originally envisaged. The shift reflects mounting concerns over electricity supply, grid connections, financing gaps and the ability of governments and private investors to deliver massive AI computing hubs on a compressed timetable.

The bloc’s original ambition centred on up to five AI gigafactories backed by a €20 billion public-private financing facility. These centres were designed to house more than 100,000 advanced AI processors each, with the computing power needed to train frontier models and support strategic industrial applications. The updated approach keeps that broad objective intact but gives consortia more room to start with lower power capacity and expand as demand, funding and permits mature.

The change marks a pragmatic recalibration rather than a formal retreat from Europe’s AI sovereignty agenda. Officials still want to reduce dependence on US cloud providers, strengthen domestic compute capacity and support start-ups, universities and industry groups that lack access to large-scale AI infrastructure. Yet the new tender shape acknowledges that Europe’s energy and planning systems are not yet aligned with the size and speed of the build-out seen in the US, where hyperscalers have committed tens of billions of dollars to AI campuses.

The Commission’s AI Continent plan sets a target of mobilising €200 billion for AI investment and tripling the bloc’s data centre capacity within five to seven years. The gigafactory programme sits alongside 19 AI factories already selected or operating across Europe, which are built around supercomputing capacity under the EuroHPC framework and intended to provide access for smaller companies, researchers and public-sector users.

The smaller-scale tender option also responds to a sharp distinction between political ambition and market execution. Last year, 76 expressions of interest were submitted across 16 member states and 60 potential sites, suggesting strong early appetite. Since then, the field has been shaped by harder questions: who pays for the chips, how much power can be guaranteed, whether national governments can commit co-financing, and how quickly the sites can secure environmental, grid and land approvals.

Power availability has become one of the most difficult constraints. AI data centres require dense, reliable electricity supplies and advanced cooling systems, while several European markets are already struggling with grid congestion and high industrial energy prices. Operators also face scrutiny over water use, renewable energy procurement and whether public funding should support infrastructure that may benefit a small group of large technology users.

The new model lets bidders choose between capital-support and off-take structures, with public authorities receiving a share of computing capacity over a five-year period. It also requires proposals to spell out the phasing of investment, public support requested, maximum capacity and financial ceilings. That structure is designed to limit the risk of overpromising while giving governments clearer control over what public money buys.

Several countries are still moving aggressively. Spain has approved €719 million for an AI gigafactory project and hopes to align it with EU financing. France has been positioning itself as a major AI infrastructure base, helped by nuclear power, state support and private-sector plans that include multibillion-euro data centre and research campuses. Germany’s telecoms and industrial groups are also exploring large AI data centre projects tied to European funding.

The competitive backdrop remains unforgiving. Europe has strong research institutions, open-source communities and industrial users in sectors such as healthcare, manufacturing, automotive, finance and climate modelling. Its weakness lies in access to large and affordable compute. Three non-European hyperscalers control more than 70 per cent of the region’s cloud market, while the share held by European providers has fallen from 29 per cent in 2017 to around 15 per cent.

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The agreement, announced on 4 June, is the largest infrastructure commitment in Pinterest’s history and places AWS custom silicon at the centre of the company’s next phase of product development. Pinterest plans to increase its use of AWS Trainium for training and running large language models and vision-language models, while widening deployment of Graviton processors, which already support about a third of its compute infrastructure.

The deal gives Pinterest added capacity for AI workloads at a time when image-led search and automated advertising are becoming central to the company’s growth strategy. Its systems must interpret billions of images, connect them with user intent and turn browsing activity into actionable recommendations for fashion, home design, food, beauty and retail categories.

Matt Madrigal, Pinterest’s chief technology officer, said the company was “heavily investing in AI to make discovery more personal, visual and actionable” for users. He said the expanded AWS commitment would provide “compute flexibility, hardware optionality and infrastructure efficiency” and help improve consumer experiences and advertiser performance through proprietary models and open-source models.

Pinterest and AWS have worked together since 2010, helping optimise large-scale data lake infrastructure and core service reliability. The agreement extends that relationship across AI model training, inference and platform infrastructure, deepening AWS’s role as Pinterest’s preferred cloud services provider.

The company also plans to continue shifting from traditional EC2-based environments to a Kubernetes-based architecture on Amazon Elastic Kubernetes Service. The migration is intended to improve developer velocity, reliability and efficiency as AI-assisted products become more data-intensive.

Pinterest’s AI push is linked to its broader effort to define itself less as a conventional social network and more as a visual search and shopping engine. Its proprietary Taste Graph helps connect user interests with personalised recommendations, while transformer-based models and multimodal systems have improved the way the platform ranks, retrieves and serves content. Pinterest Assistant, a conversational discovery feature powered by open-source vision-language models, forms part of that shift.

The agreement follows a stronger start to 2026 for Pinterest. The company reported first-quarter revenue of US$1.008 billion, up 18 per cent year on year, with global monthly active users rising 11 per cent to a record 631 million. It posted a GAAP net loss of US$74 million, adjusted EBITDA of US$207 million, operating cash flow of US$328 million and free cash flow of US$312 million for the quarter.

Bill Ready has argued that Pinterest occupies a distinctive position because users often arrive with purchasing or planning intent rather than purely for entertainment. That distinction has become more important as advertisers demand measurable returns and platforms race to connect search, recommendations and commerce. AI-powered products such as Pinterest Performance+ are designed to automate campaign optimisation and make advertising more effective across search and shopping surfaces.

For AWS, the Pinterest commitment adds to a wave of large enterprise cloud deals tied to AI infrastructure and custom chips. Snowflake signed a US$6 billion AWS agreement in May covering AI and data workloads, including Graviton processors. Amazon has also stepped up financing for its infrastructure buildout, securing a US$17.5 billion delayed-draw loan facility in June as hyperscalers pour capital into data centres, chips, networking, energy and cooling systems.

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The Santa Clara-based chipmaker priced the investment-grade deal on June 15 after initially targeting at least $20 billion. Orders reached about $85 billion, allowing the company to expand the transaction while keeping borrowing spreads tight. The sale marked Nvidia’s first return to the corporate bond market since 2021, when it raised $5 billion.

The offering was structured across seven tranches, with maturities running from 2028 to 2056. The longest-dated securities put investors behind Nvidia’s AI strategy for three decades, a notable vote of confidence in a company whose processors have become central to training and running advanced machine-learning systems. Goldman Sachs, JPMorgan and Morgan Stanley led the transaction.

The proceeds are earmarked for general corporate purposes, including the repayment and refinancing of outstanding notes. That makes the deal less a distress-driven funding exercise than a move to deepen liquidity, establish a larger credit benchmark and give Nvidia greater balance-sheet flexibility as the AI industry enters a more capital-intensive phase.

Nvidia already has considerable financial strength. Revenue for the quarter ended April 26 reached $81.6 billion, up 85 per cent from a year earlier, while data-centre revenue climbed to $75.2 billion. Gross margin stood at 74.9 per cent, reflecting the company’s pricing power and the rapid shift of computing budgets towards accelerated processing.

The company held $50.3 billion in cash, cash equivalents and marketable debt securities at the end of the quarter, along with $30.2 billion in marketable equity securities. It had $8.5 billion of senior notes outstanding before the new deal and no borrowings under a $25 billion commercial paper programme. The new bonds will substantially increase its debt load but still leave leverage modest relative to earnings and cash generation.

The timing points to a broader change in technology finance. The AI boom is moving from a software-led story into a heavy infrastructure cycle requiring chips, networking equipment, electricity, cooling systems, cloud capacity and long-term supply agreements. Nvidia is not spending on hyperscale data centres in the same way as its largest customers, but it is investing heavily in product roadmaps, engineering capacity, supply commitments and strategic stakes across the AI ecosystem.

That spending backdrop has made corporate debt a more attractive tool for even the richest technology groups. Borrowing allows companies to preserve cash, refinance older obligations and avoid issuing equity after large share-price gains. Nvidia’s shares rose more than 3 per cent on the day of the bond pricing, suggesting equity investors viewed the transaction as a sign of confidence rather than balance-sheet strain.

The company’s annual product cadence is another factor behind the bond market’s appetite. Nvidia has shifted to releasing new AI chip families at a faster pace, with Blackwell systems driving demand and the Vera Rubin platform forming the next major architecture. Customers include cloud providers, AI laboratories, sovereign computing projects, enterprises and industrial users seeking higher-performance training and inference capacity.

The deal also reflects investors’ search for high-quality corporate paper at a time when US Treasury yields remain elevated and credit markets are absorbing large technology-sector issuance. Nvidia’s strong rating profile, dominant market position and expanding cash flow make its bonds attractive to insurers, pension funds and asset managers seeking long-duration exposure to the AI economy.

Risks remain significant. Nvidia’s filings point to export controls, China-related restrictions, supply-chain dependence and large purchase commitments as continuing pressures. The company has said it is effectively blocked from competing in China’s data-centre compute market under current rules, while shifting regulations could affect sales in other regions. Competition from custom chips designed by cloud companies and processors developed outside the United States could also test Nvidia’s margins over time.

The debt sale therefore lands at a pivotal moment: demand for Nvidia’s chips remains exceptionally strong, but the financing demands of AI are becoming too large to sit only on corporate cash flow. By locking in long-term funding out to 2056, Nvidia has given itself more room to manage refinancing, investment and strategic commitments while investors have taken a direct credit position on the durability of the AI buildout.

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The initiative brings together BNY, Cisco, Cloudflare, Corridor, DepthFirst, Docker, JPMorganChase, Kyndryl, LTM and PwC, with Chainguard acting as the coordinating platform. Athena is already operational with more than two dozen participating organisations, having processed over 20,000 vulnerability findings and generated more than 2,000 patches across 500 open-source projects. Its first coordinated disclosure wave is scheduled for July.

The move marks a shift in software security practice from disclosure-led patching to pre-embargo defence. Traditional coordinated disclosure was built around a slower sequence: a flaw is found, maintainers are notified, a patch is prepared, an advisory is published and users are expected to update. Athena is designed for a faster environment in which advanced AI models can read large codebases, trace dependencies and identify chained zero-day vulnerabilities before many maintainers or vendors can respond.

Chainguard chief executive and co-founder Dan Lorenc said the exploit window has changed sharply. “The time to exploit has gone negative — exploits now land before a flaw is ever disclosed,” he said. “Athena’s whole job is to make the time to remediate even more negative, so the fix is already in place before the vulnerability is public. No one company can get ahead of this alone, and orchestrated defence is the only answer.”

Athena will collect vetted findings from member organisations, including those produced through frontier AI security programmes such as Anthropic’s Project Glasswing and OpenAI’s Daybreak. The coalition then deduplicates reports, enriches them with technical context, traces when a weakness entered the code, checks whether the flaw has already been repaired upstream and identifies related patterns across other projects.

The platform’s most sensitive work happens before public disclosure. Affected projects can be rebuilt as private, hardened versions and made available to members through Chainguard Libraries while an embargo remains in place. The coalition also intends to harden entire libraries against classes of flaws rather than treating each AI-discovered bug as an isolated defect. That approach is aimed at preventing a stronger model or a malicious actor from finding the next variant in the same code path.

Network and platform partners have a central role because many organisations cannot patch critical systems within hours. Athena’s model allows infrastructure providers and security vendors to prepare traffic-level rules, detection signatures, virtual patches and platform-side blocks before details become public. The aim is to reduce exploitation risk even where a clean software patch is not yet deployed.

The coalition is also seeking to avoid a fragmented response in which cloud providers, software vendors and security teams privately fork the same libraries with separate patch sets. That fragmentation can leave no shared record of what has been fixed, where fixes have diverged and which systems remain exposed. Athena’s clearinghouse structure is intended to create a common technical pipeline while allowing members to retain control over what they share and on which embargo timeline.

The timing is significant for banks and large enterprises, which depend heavily on open-source components across payment systems, trading platforms, cloud infrastructure, artificial intelligence tools and internal development pipelines. Commercial software audits show that open-source code now makes up a majority of modern codebases, while dependency chains have become deeper and harder to monitor. Security teams are also dealing with a sharp rise in malicious packages across public registries, where attackers target developers, build tools and credentials rather than only finished applications.

BNY chief information security officer Dave Robinson said trust in underlying software had become a direct operational issue. “Our clients count on BNY to protect what matters most, including the software behind our systems. As AI speeds up the discovery of vulnerabilities, Athena may help us identify and address risks earlier,” he said.

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The European Commission is examining the practical consequences for users across the bloc after Anthropic disabled Fable 5 and Mythos 5, saying it had received a US export-control directive barring access by any foreign national, including non-US staff inside the company. The measure, issued on national security grounds, has raised concern that frontier AI services can be withdrawn from allied markets with little warning when Washington classifies them as sensitive technology.

A Commission spokesman said emergency measures should not be discriminatory against partners, a pointed response that stops short of a formal challenge but signals discomfort in Brussels over the breadth of the order. The case lands at a delicate moment for transatlantic technology policy, with Europe trying to tighten AI governance while accelerating domestic computing capacity to reduce reliance on foreign providers.

Anthropic said the directive arrived on June 12 at 5.21pm Eastern Time and did not give detailed reasons for the national security concern. The company said US officials believed they had become aware of a method to bypass safeguards in Fable 5, but argued that the demonstrated technique identified only a small number of known, minor vulnerabilities. It said similar vulnerabilities could be found using other publicly available models without such a bypass.

The order affected two models launched only days earlier. Fable 5 was presented as a broadly available frontier model with strong safeguards, while Mythos 5 used the same underlying model with some safeguards lifted for vetted cybersecurity and research partners. Anthropic said Fable 5 could operate autonomously for longer than previous Claude models, improve productivity in large software projects and handle complex vision, document and analytical tasks.

The suspension has widened a debate over whether advanced AI models should be treated like chips, cryptography or other dual-use technologies subject to export licensing. US policy has already restricted advanced semiconductors and cloud computing access for sensitive jurisdictions, but the Anthropic order pushes the boundary closer to the model layer itself. For enterprise customers, that shift changes risk calculations around procurement, continuity planning and the legal exposure of AI systems embedded in banking, health care, government services and cybersecurity operations.

European officials are weighing the issue against the bloc’s own AI rulebook. The AI Act, which entered into force in 2024, gives the EU a risk-based framework for governing AI systems and general-purpose models, with the European AI Office playing a central supervisory role. Yet the Anthropic case shows that regulation alone cannot guarantee access when the underlying technology is controlled by a company subject to another government’s security directives.

The timing is awkward for Anthropic’s international expansion. The company has been courting regulated industries and had announced partnerships intended to bring Claude systems into large organisations across multiple countries. A directive applying to foreign nationals as a category, rather than to specific hostile actors or sanctioned entities, creates operational complications for multinational clients and vendors with global engineering teams.

Washington’s case rests on the argument that frontier models with advanced cyber capabilities could be misused by foreign military or intelligence services. That concern is not dismissed in Europe, where governments face persistent cyber espionage, disinformation and infrastructure threats. The tension lies in the scope of the remedy. Blocking all foreign nationals, including allies, may protect one risk channel while undermining confidence in US technology platforms as dependable infrastructure.

The episode is likely to strengthen Europe’s sovereign AI agenda. The Commission has already set out plans to mobilise €200 billion for AI development, including €20 billion for up to five AI gigafactories, alongside a network of AI factories intended to support start-ups, industry and research. Those programmes were designed to close Europe’s compute and deployment gap; the Anthropic cutoff adds a strategic-access argument to the economic case.

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The San Francisco-based company said the accounts were used to produce social media comments, comic strips, slogans and political cartoons for apparent covert influence operations. The activity was assessed as likely originating from China and was removed after internal investigators found the accounts had used OpenAI’s models to support narratives aimed at inflaming domestic disputes in the United States.

The data centre effort, named “Data Center Bandwagon” by OpenAI, focused on complaints that AI facilities were straining power grids, raising utility costs and harming communities. Operators asked ChatGPT to draft English-language posts and images, often while prompting the system in Simplified Chinese and using virtual private networks. The content was then pushed through likely inauthentic accounts posing as Americans.

OpenAI said the operation failed to gain meaningful traction beyond its own activity. That assessment limits the immediate political impact of the campaign, but the case has drawn attention because it shows how foreign operators are experimenting with generative AI to scale narratives around sensitive infrastructure projects. The company said the campaign did not create opposition to data centres; it attempted to exploit concerns that already existed.

A second cluster, called “Tech and Tariffs”, generated material criticising US tariff policy and efforts to dominate technological competition. Some prompts instructed the model to depict President Donald Trump while avoiding references to China’s leader Xi Jinping. The cluster also produced content in multiple languages and was connected to claims that ChatGPT user data had been compromised, an allegation OpenAI rejected as false.

The activity appears to have run from late 2025 into early 2026. One group involved in the data centre campaign was assessed as likely linked to a private technology firm working for provincial-level government clients in China. The operators also sought help with operational tasks, including polishing work reports and discussing ways to avoid detection by social media platforms.

The China embassy in Washington said it was not familiar with the report and rejected what it described as groundless attacks and smears. Beijing has repeatedly said it supports responsible use of artificial intelligence and opposes disinformation, while Washington and technology companies have grown more vocal about covert influence activity tied to strategic competition over AI.

The disclosure comes as data centre construction has become a sharper political issue in the United States. Generative AI systems require vast computing capacity, and the facilities that train and run them need large volumes of electricity, water and land. Global data centre electricity consumption is projected to roughly double by 2030, reaching about 950 terawatt-hours and accounting for around 3 per cent of global demand. AI-focused facilities are expected to grow faster than the wider sector.

US power demand is also rising after years of comparatively flat growth. Electricity consumption is forecast to hit record levels in 2026 and 2027, with commercial use overtaking residential demand for the first time as data centres, manufacturing and electrification lift load on the grid. Local opposition has grown in parts of Virginia, Georgia, Arizona, Texas and other states where communities are questioning water use, noise, land deals, tax incentives and grid costs.

That context makes the OpenAI findings politically sensitive. Supporters of rapid AI infrastructure expansion argue that foreign-linked actors are trying to slow US technological leadership by amplifying environmental and consumer concerns. Critics of the industry counter that foreign manipulation should not be used to dismiss genuine local grievances over power bills, planning transparency and resource consumption.

OpenAI’s findings give limited support to warnings that outside actors are seeking to influence the data centre debate, but they do not show that public opposition is primarily foreign-driven. The company’s own assessment indicates the accounts had little visible effect, reinforcing a broader pattern in which generative AI helps operators produce more content without necessarily producing more persuasion.

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The discussions remain fluid and may still change in size, timing or investor composition. If completed near the terms being discussed, the financing would mark another significant jump for a company founded only in 2023 and already seen as Europe’s most credible challenger to OpenAI, Anthropic and Google DeepMind.

The proposed valuation would represent a steep rise from Mistral’s last confirmed funding round, when it raised €1.7 billion at an €11.7 billion post-money valuation in September 2025. That round was led by ASML, the Dutch semiconductor equipment group, which invested €1.3 billion and took an approximately 11 per cent fully diluted stake, making it Mistral’s largest shareholder.

The new capital would give Mistral greater room to expand its model development, data-centre capacity and enterprise AI services at a time when foundation-model companies are under pressure to spend heavily on graphics processors, power supply, engineering talent and global sales. The cost of remaining competitive has risen sharply as AI laboratories race to train larger models, improve reasoning capabilities and integrate agents into business workflows.

Mistral has sought to differentiate itself through open-weight models, enterprise-focused deployment and a sovereignty message aimed at companies and governments seeking alternatives to US-controlled platforms. Its founders, Arthur Mensch, Guillaume Lample and Timothée Lacroix, previously worked at leading AI research organisations, giving the start-up technical credibility from its earliest days.

The company’s commercial strategy has also shifted from building language models alone to creating a fuller technology stack. It has added tools for training, testing and deploying custom AI systems, while pushing products aimed at coding, workflow automation and long-horizon tasks. Its Vibe assistant, unveiled at its AI Now Summit in Paris, is positioned as an agent for multi-step work across inboxes, calendars, research, coding and recurring business processes.

Mistral’s expansion has been accompanied by acquisitions designed to strengthen its infrastructure and industrial capabilities. The company bought Koyeb, a cloud services start-up based near Paris, in February, bringing its 13 employees into Mistral as part of a broader move to control more of the computing layer. It later acquired Linz-based Emmi AI, an Austrian physics AI specialist whose models are used for simulations involving airflow, heat transfer and material stress.

Those deals point to a strategy aimed at engineering-heavy sectors rather than only consumer chatbots. Mistral has been building relationships across semiconductors, automotive, aerospace, energy, banking and defence, pitching itself as a partner for companies that want AI models adapted to proprietary data and deployed under tighter control.

The ASML partnership remains central to that positioning. The semiconductor equipment group said the collaboration would explore AI models across its product portfolio, research and operations, with the goal of improving lithography systems and accelerating engineering work. For Mistral, the alliance linked it to one of Europe’s most strategically important technology companies and gave it an investor with deep knowledge of the chip supply chain.

Infrastructure is becoming an equally important part of the story. Mistral has outlined plans to build and operate more computing capacity in Europe, including data-centre projects in France and Sweden. It has also defended the need for European AI capability in sensitive fields, including defence, arguing that technological dependence on foreign platforms would leave governments and industries exposed.

The company’s ambitions mirror a broader policy push across Europe to build sovereign AI capacity. Governments and large corporations are trying to reduce reliance on American cloud providers and AI labs, while still competing in a sector where US companies have raised far larger sums. Mistral’s fundraising effort comes against that backdrop of strategic urgency, with investors weighing whether Europe can produce a global AI champion with enough capital, chips and customers to endure.

Competition remains formidable. OpenAI, Anthropic, Google and Meta continue to command far larger pools of capital, deeper computing resources and broader consumer reach. Chinese AI developers are also advancing quickly, while open-source communities keep lowering barriers to model access. For Mistral, the challenge is to prove that a European model provider can scale revenue fast enough to justify rising valuations while sustaining the research spending required at the frontier.

The proposed €20 billion valuation would place greater scrutiny on execution. Investors will look for evidence that enterprise contracts, public-sector adoption and industrial partnerships can translate into recurring revenue at scale. They will also assess whether Mistral’s open and sovereign positioning can remain commercially attractive as customers demand stronger performance, lower inference costs and clearer security guarantees.

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The move puts AMD more directly against Arm-based suppliers that have built strong positions in embedded and automotive systems through power-efficient designs, licensing flexibility and broad developer ecosystems. AMD’s argument is that a single x86-based system-on-chip combining CPU, graphics and neural processing can give equipment makers enough performance for real-time workloads without adding separate accelerators.

The Ryzen AI Embedded P100 and X100 families combine Zen 5 CPU cores, RDNA 3.5 graphics and XDNA 2 neural processing units. The P100 range is aimed at lower-power embedded systems, including digital cockpits, industrial automation, human-machine interfaces and robotics controllers. The X100 line is positioned for heavier edge AI and physical AI workloads where machines must interpret sensor data, make decisions and act with low latency.

AMD has said the P100 series offers configurations from four to 12 CPU cores and up to 50 trillion AI operations per second, depending on the model and implementation. The chips are designed for long-life embedded platforms, ruggedised operating conditions and virtualised deployments where control, safety partitioning and resource allocation are essential. That makes the line relevant for sectors where product cycles often run for many years, unlike consumer PCs.

Automotive is one of the clearest targets. Vehicle manufacturers are shifting from distributed electronic control units to software-defined platforms that consolidate computing across cockpit displays, driver assistance, infotainment and in-cabin AI features. AMD is seeking to place Ryzen AI Embedded processors in this transition by offering graphics, conventional compute and AI inference on the same silicon, reducing board complexity and potentially lowering system power and cost.

Robotics is another growth area. Warehouse automation, autonomous mobile robots, inspection systems and humanoid platforms increasingly require onboard inference rather than constant cloud connectivity. Latency, data privacy and reliability concerns are pushing more AI workloads to the edge, where processors must handle cameras, lidar, speech, navigation and control loops within tight thermal envelopes. AMD’s x86 pitch is aimed at developers who want PC-class software compatibility alongside dedicated AI acceleration.

The company’s embedded push also reflects a wider effort to diversify AI revenue beyond data-centre GPUs. Nvidia remains dominant in AI accelerators, while Qualcomm, NXP, Renesas, MediaTek and other Arm-linked players continue to compete in automotive, internet-of-things and low-power edge computing. RISC-V is also gaining attention as manufacturers seek open architectures and alternatives to established instruction sets.

AMD’s strength lies in performance computing, graphics and its existing x86 software base. Many industrial and medical equipment developers already use x86 platforms because of operating system compatibility, development tools and long-standing application support. The embedded Ryzen AI strategy seeks to preserve that advantage while addressing a market that increasingly asks for AI inference, image processing and deterministic control in the same package.

The challenge is power efficiency and ecosystem depth. Arm-based processors dominate many embedded categories because they are widely licensed, customisable and used across mobile, automotive and IoT designs. Arm vendors can offer highly integrated chips with radios, sensor interfaces, microcontrollers and safety-certified features tailored for specific markets. AMD will have to persuade manufacturers that x86 performance and software continuity outweigh the power and cost advantages often associated with Arm designs.

There are signs of growing partner interest. Embedded board makers and module suppliers have begun positioning Ryzen AI Embedded platforms for industrial PCs, computer-on-modules, edge gateways and machine-vision systems. These products are critical because many industrial customers buy complete modules rather than chips directly, relying on hardware partners for thermal design, I/O integration and long-term availability.

Medical systems could provide another opening. Imaging equipment, diagnostic instruments and surgical platforms increasingly use AI for image enhancement, segmentation and workflow automation. These devices require predictable performance, strict validation and long support windows. A processor combining graphics and neural acceleration could appeal to manufacturers trying to modernise equipment without adopting a fragmented hardware stack.

AMD’s timing is shaped by the spread of “physical AI”, a term used for systems that apply artificial intelligence to machines operating in the real world. Unlike cloud chatbots or office copilots, physical AI requires fast local decisions, sensor fusion and reliable control. Cars, robots and factory equipment cannot always wait for remote servers to process data, making edge AI silicon a strategic battleground.

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The demonstration showed the Windows version of Half-Life running on a Dell OptiPlex desktop fitted with an Intel Core i5-2400 processor and an NVIDIA GeForce 8400 GS graphics card. The significance lies less in the age of the 1998 first-person shooter than in what it proves about ReactOS: the system is now able to handle a demanding mix of legacy Windows application calls, graphics driver behaviour and OpenGL rendering well enough to move beyond launch screens and into live gameplay.

Half-Life remains a useful compatibility test because it sits at the intersection of older Windows gaming, the GoldSrc engine and hardware-accelerated OpenGL. Earlier ReactOS experiments had shown the game starting or reaching partial states, while Half-Life 2 had appeared in limited tests through workarounds. This run points to a deeper level of stability in the graphics stack and driver path, particularly after months of work on GPU driver compatibility.

The milestone follows a March 2026 advance in ReactOS graphics support involving work around KMDF, the Kernel-Mode Driver Framework, and WDDM, the Windows Display Driver Model. That work helped improve compatibility with a wide range of Windows XP and Windows Server 2003-era graphics drivers, including hardware from Intel, NVIDIA and AMD. Tests during that phase covered cards and chipsets such as Intel GMA 945, NVIDIA GeForce 8800 GTS, GTX 750 Ti, Quadro 1000M, GTX Titan X and AMD Radeon HD 7530G.

For ReactOS, graphics support has long been one of the hardest barriers to wider usability. The project aims to run Windows applications and drivers natively, rather than through a compatibility layer such as Wine, but that ambition requires a close reimplementation of complex kernel, driver and user-interface components. Modern display drivers depend on interactions between user-mode libraries, kernel-mode components and graphics hardware, while older applications often expect behaviour inherited from Windows 2000, XP and Server 2003.

WDDM is especially important because it reshaped Windows graphics from Vista onward, moving more responsibility for GPU management away from older Win32k paths and into the DirectX graphics kernel subsystem. ReactOS has historically targeted an NT 5. x-style environment, broadly aligned with Windows Server 2003, but its developers have had to engage with later driver architecture to keep the system relevant for real hardware.

The Half-Life breakthrough also underlines why games are often used as practical stress tests for operating systems. A game may expose timing issues, driver faults, input bugs, file-system assumptions and graphical glitches faster than productivity software. GoldSrc’s OpenGL pipeline requires the system to load and coordinate graphics components correctly, while maintaining the application compatibility needed by a commercial Windows title from the late 1990s.

ReactOS remains an alpha-stage operating system and is not positioned as a daily replacement for Windows. Installation, hardware support and application reliability can vary sharply by machine. The successful Half-Life run therefore does not mean modern Windows gaming is suddenly viable on ReactOS, nor does it remove the larger engineering burden facing the project. It does, however, show measurable progress in an area where failures have historically been visible and difficult to mask.

The project’s broader appeal lies in its unusual position within open-source computing. Linux, BSD and other free operating systems have mature ecosystems of their own, while Wine and Proton have become central to running Windows games and applications on Linux. ReactOS is different: it seeks binary compatibility with Windows applications and drivers at the operating-system level, using a clean-room approach and open-source development.

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The programme would move part of the AI computing race beyond land-based data centres, where power shortages, grid constraints, cooling costs and local opposition have become major obstacles for technology companies. The first demonstration missions are expected to test whether high-performance chips can operate reliably in space, communicate through optical links and use solar power at a scale that could support commercial AI workloads.

The plan places SpaceX at the centre of a fast-expanding contest between launch providers, cloud companies, chipmakers and AI developers. Musk has argued that orbital computing could benefit from abundant solar energy and avoid many of the physical limits facing terrestrial data centres. SpaceX’s existing Starlink network gives the company operational experience in building, launching and managing large satellite constellations, but AI data centres in orbit would demand far greater power, thermal control and hardware resilience.

The first orbital AI satellite is expected to rely on Nvidia chips and deliver computing capacity comparable to a high-end AI server rack on Earth. The spacecraft design under discussion would use solar arrays for power, radiators for heat management and laser links or Starlink connectivity for data transfer. Musk has said the system can draw heavily on technology already developed for Starlink, though the economic case remains unproven.

SpaceX has sought US regulatory clearance for a future constellation that could ultimately include up to one million satellites operating as orbital data centres. Such a scale would far exceed current satellite deployment levels and would intensify scrutiny from regulators, astronomers, environmental researchers and rivals concerned about orbital congestion. The company’s proposal frames the system as a way to meet surging AI demand without relying solely on Earth-based power grids.

The timetable is tied closely to Starship, SpaceX’s fully reusable heavy-lift rocket. Starship is central to Musk’s cost assumptions because large-scale orbital computing would require frequent launches of heavy satellites, replacement hardware and supporting systems. Delays in Starship development would therefore slow any commercial roll-out, even if early demonstrations succeed.

The project is also becoming part of SpaceX’s broader investor pitch as the company seeks to expand beyond rockets, Starlink broadband and government launch contracts. AI infrastructure has become one of the most valuable themes in global markets, with cloud providers spending heavily on chips, power agreements and data-centre campuses. SpaceX is positioning itself as a company that can combine launch dominance, satellite manufacturing, power from space and high-speed communications into a new computing platform.

Google is pursuing a parallel effort through Project Suncatcher, which aims to test solar-powered satellites equipped with its Tensor Processing Units. That mission, planned with Planet Labs around 2027, is designed to examine how machine-learning hardware performs in orbit and whether satellites can work together through optical inter-satellite links. The overlap suggests that space-based compute is moving from speculative theory into early engineering trials, though commercial viability remains uncertain.

The technical hurdles are significant. AI chips generate intense heat, and space offers no air or water cooling systems of the kind used in terrestrial data centres. Heat must be radiated away, requiring large surface areas and careful design. Radiation can damage electronics, while hardware failures in orbit are far harder to repair than failures in a warehouse-scale data centre. Latency, data transfer costs and cybersecurity will also shape whether customers see orbital AI compute as practical.

The regulatory questions may prove just as difficult. A million-satellite architecture would raise concerns about collision risks, space debris, radio-frequency coordination and the brightness of objects crossing the night sky. Astronomers have already warned that very large constellations can interfere with observations, while space-safety experts have urged stronger traffic-management rules as low Earth orbit becomes more crowded.

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The change means millions of users who bought relatively modern Apple Watches will remain on watchOS 26 unless they move to newer hardware. The supported list for watchOS 27 starts with Apple Watch Series 9 and extends to Series 10, Series 11, Ultra 2, Ultra 3 and SE 3. The cut-off is more severe than many previous watchOS transitions because it removes models that were still being sold or widely promoted within the past few years.

Apple previewed watchOS 27 at its Worldwide Developers Conference on 8 June, positioning the update as part of a wider software reset built around Siri AI, Apple Intelligence, health features and tighter links between the Watch and iPhone. The developer beta is available now, with a public beta expected in July and the full release planned for the autumn alongside the next iPhone and Apple Watch cycle.

The most visible shift is Siri AI on the wrist. The revamped assistant is designed to handle more natural requests, continue conversations across devices, understand personal context and interact with apps more directly. On Apple Watch, that could include starting workouts, changing fitness goals, managing messages, controlling music and surfacing information through a dedicated Siri experience adapted for the smaller screen.

The new software also brings a redesigned app grid, smarter widgets, updates to Smart Stack, faster music playback, improved battery handling and expanded health and fitness tools. Workout Buddy, menstrual health notifications and language support are also being expanded, reinforcing Apple’s effort to keep the Watch positioned as both a wellness device and a personal computing accessory.

Hardware limitations are central to the decision. Apple has drawn the line around newer processors and the broader Apple Intelligence system. The Series 8 uses the S8 chip, while the first-generation Ultra and second-generation SE also fall below the new threshold. The Series 9 introduced the S9 chip, which improved on-device processing and enabled features such as the double-tap gesture. Newer Ultra and Series models build on that architecture.

The compatibility move will frustrate users who bought the first Apple Watch Ultra, launched as a premium rugged model, only to see it excluded from the next major software release four years later. The second-generation SE, marketed as a lower-cost entry point, is also affected despite being part of Apple’s current value-focused strategy until the arrival of SE 3.

The update also creates a two-tier experience among users who do receive watchOS 27. Installing the operating system will require an iPhone 11 or later, or a second-generation iPhone SE or later, running iOS 27. But Siri AI and several Apple Intelligence functions will require a paired iPhone capable of running Apple’s AI stack, starting with the iPhone 15 Pro generation and later models. That means some users with supported watches may still miss the headline features.

Apple’s decision reflects a wider industry trend as consumer technology companies tie new software features to dedicated processors, memory capacity and on-device AI performance. Smartphones, laptops and wearables are being segmented less by basic operating system compatibility and more by access to AI tools. The result is a growing gap between devices that still function well and devices that receive the most advanced software.

For Apple, the strategy could help stimulate upgrades across the Watch and iPhone ecosystem. The Watch has become a major part of the company’s wearables business, alongside AirPods and accessories, but replacement cycles have often been slower than for smartphones because many health, notification and fitness features remain useful for years. Limiting AI-led software updates to newer models may strengthen the case for replacement.

The risk is that customers may view the cut-off as abrupt, particularly because the Series 8, Ultra 1 and SE 2 remain capable devices for notifications, workouts, heart-rate tracking, sleep monitoring, Apple Pay and emergency features. Security updates may continue for older watchOS versions for a period, but users outside watchOS 27 will not receive the full set of interface changes, AI functions or future app enhancements built for the new platform.

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The controlled exercise centred on an AI agent named Pinchy, configured on the OpenClaw platform to monitor a Gmail inbox, process messages and perform tasks through connected workplace tools. The test environment used synthetic corporate material, including mock AWS IAM keys, database passwords, SSH credentials, customer records, calendar invites and internal correspondence, allowing researchers to assess the agent’s behaviour without exposing real organisations to harm.

The most serious failure came when an attacker impersonated a team lead named Dan and asked for staging-environment access during a supposed production issue. The message came from an external Gmail account, not the legitimate corporate address. Pinchy searched the inbox, found the requested access details and sent AWS IAM keys, database connection strings and SSH credentials in plaintext to the outside account.

The outcome is significant because the agent was tested under two configurations. One used generic productivity instructions, while the other added explicit safety language directing the system to watch for phishing, verify identities and avoid risky disclosures. Both configurations failed in the credential-sharing scenario, suggesting that written safety instructions alone may not reliably stop an autonomous agent when a request appears operationally urgent.

A second simulation produced a similar result with business data rather than technical secrets. A message asked the agent to send the latest customer export for a quarterly business review presentation, using the pretext of a colleague working from home. Pinchy retrieved and forwarded a CRM-style export containing 247 enterprise customer records, contact details, contract dates, customer tiers and monthly recurring revenue data worth about $1.28 million.

The tests highlight a weakness that differs from traditional prompt injection. Instead of hiding malicious commands inside web pages, documents or attachments, the attacker used a familiar workplace request delivered through a normal communication channel. The agent treated plausibility as legitimacy and prioritised completing the task over validating the sender.

Pinchy performed better in scenarios involving more obvious technical traps. When it received a fake gift-card email, the generic configuration clicked through to a phishing page and attempted to redeem the offer using fabricated credentials before later identifying the page as malicious. The stricter configuration blocked the attempt at the outset. In a separate OAuth consent test, the agent examined the redirect path of a malicious application disguised as a timesheet platform, found the destination suspicious and refused to authorise access.

That contrast points to a broader challenge for companies deploying agentic AI. These systems can inspect links, analyse domains and recognise fake login flows, yet still fail at the social judgement that employees often apply instinctively. An urgent note from a supposed colleague asking for credentials, a customer export or a payment change can exploit the agent’s core design goal: to be helpful and act quickly.

OpenClaw markets itself as a personal AI assistant that can clear inboxes, send emails, manage calendars, browse websites, use chat apps and access files or system tools depending on configuration. That breadth gives users a powerful automation layer, but it also creates a concentrated risk when the agent can read sensitive data and send messages outside the organisation.

The findings arrive as security teams are already reassessing how to govern AI agents that operate as digital workers rather than passive chatbots. Traditional email controls are built around human recipients, while identity and access systems are usually designed for applications, service accounts and employees. Autonomous agents sit between those categories: they can receive untrusted content, access private data and initiate outbound actions.

The practical risk is not limited to OpenClaw. Any agent connected to email, cloud storage, collaboration platforms, customer databases or developer tools may face the same pressure point if it can act without approval. The danger increases when agents are given broad privileges, persistent credentials or permission to communicate with first-time external recipients.

Mitigations identified through the tests are largely architectural. Agents handling inbound email should have limited access to internal systems, with permissions segmented according to the trust level of the channel that triggered the task. A message from an unverified external sender should not enable searches across customer databases, code repositories or infrastructure secrets.

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The announcements in Taipei marked a deliberate shift in emphasis for the company, from selling individual chips to supplying integrated platforms that combine processors, accelerators, networking and software. Intel’s pitch rests on the view that AI is moving beyond the training of large models into production systems that must answer queries, manage workflows, call software tools and process data continuously inside corporate and cloud environments.

Central to the presentation was a new rack-scale AI infrastructure initiative involving Intel, SambaNova and Foxconn. The planned systems combine Intel Xeon processors with SambaNova SN-50 Reconfigurable Dataflow Units, with Foxconn providing system integration. The infrastructure is being positioned for hyperscale data centres, enterprise AI deployments and intelligence-centre workloads where inference throughput, power efficiency and predictable performance are becoming critical buying factors.

The company also highlighted a disaggregated inference model through Vector Core Compute, an enterprise inference cloud formed by Vista Equity Partners and Cambium Capital. The demonstration used Intel Xeon processors for orchestration and execution, SambaNova RDUs for decode functions and Nvidia Blackwell GPUs for prefill operations, reflecting a market trend in which AI infrastructure is being split into specialised components rather than built around a single accelerator type.

The Computex message gives Intel a clearer role in a market that has been dominated by graphics processing units. Training large AI models placed GPUs at the centre of data-centre investment, but inference workloads have different requirements. Agentic AI systems must coordinate multiple steps, retrieve data, run tools, manage memory and move information across networks. That places greater weight on central processors, interconnects and system-level optimisation.

Intel executives framed the CPU as the control plane for modern AI infrastructure. The argument is that while accelerators remain essential for model execution, CPUs are increasingly important for orchestration, concurrency, data movement and workload scheduling. As enterprises deploy AI agents into customer service, coding, research, analytics and industrial operations, the number of CPU-bound tasks around each model call is expected to rise sharply.

The company’s Xeon 6+ processor was presented as the next major data-centre product supporting that strategy. Built on Intel 18A process technology, the chip is designed for high-density, scale-out workloads and is being targeted at cloud-native, agentic AI-driven, telecom and network-intensive environments. Intel says the processor can support up to 288 Efficient-cores, 12-channel DDR5 memory, 96 lanes of PCIe Gen 5 and CXL connectivity.

Intel also promoted workload-level energy telemetry as part of the Xeon 6+ platform, giving operators more visibility into CPU energy consumption. That feature is aimed at data-centre customers under pressure to reduce power use while deploying more AI capacity. The company said the platform can help consolidate servers, reduce space requirements and support secure multi-tenant deployments through hardware-level security technologies.

Networking formed another part of the Computex package. Intel introduced its Ethernet E835 controllers and adapters for AI, cloud, edge and high-performance computing environments. The product line is designed to improve power efficiency and data movement in dense infrastructure, reflecting the growing importance of networking in systems where accelerators, CPUs, memory pools and storage must operate as coordinated platforms.

The strategy depends heavily on partners. Foxconn’s role gives Intel access to large-scale manufacturing and integration capacity for rack-level systems. SambaNova contributes AI acceleration technology aimed at enterprise inference. Intel also pointed to collaborations with Siemens, Hitachi, Echo Neurotechnologies and Greenstone Biosciences, signalling a push into industry-specific AI applications rather than a purely horizontal data-centre offering.

The company used Computex to extend the message beyond the data centre. It said more than 130 companies are adopting or testing Intel Core Ultra Series 3 processors for edge devices, robotics and physical AI systems. The platform integrates CPU, GPU and neural processing capability in a single system-on-chip, targeting use cases in manufacturing, healthcare, hospitality, education and automation. Intel also introduced OpenVINO Physical AI as an open-source robotics framework for developers.

The timing is important for Intel as it seeks to regain momentum after years of manufacturing delays, market-share pressure from AMD and the rapid expansion of Nvidia’s AI ecosystem. The company is trying to reposition itself as a systems supplier for the next phase of AI deployment, where enterprise customers may require more balanced combinations of CPUs, accelerators, networking and software rather than GPU-heavy clusters alone.

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The shift marks a technical break from conventional mesh-based photogrammetry, the approach that has long powered digital city models but often produced warped tree canopies, distorted cables, softened building edges and streetscapes that appeared convincing only from a distance. Gaussian splatting uses dense collections of semi-transparent 3D points, or “splats”, to reproduce shape, colour, light and depth with higher visual fidelity, allowing complex details to hold together from multiple viewing angles.

For Apple Maps, the change could strengthen one of the platform’s most visually distinctive features: immersive 3D city navigation. The company has spent years expanding detailed city experiences, Look Around street-level views and Flyover-style aerial perspectives across major urban centres. A move towards splat-based rendering would give the service a more photorealistic layer, especially in areas where traditional reconstruction struggles with fine structures such as trees, overhead wires, roof edges, balconies, traffic signs and irregular street furniture.

The scenes are understood to be generated from oblique aerial imagery, captured at angles rather than directly from above. That matters because angled image sets provide more information about façades, street corridors, rooflines and vertical surfaces. When processed through Gaussian splatting methods, those images can produce scenes that preserve ground-level detail without relying entirely on the rigid triangle meshes used in older 3D mapping pipelines.

The technology has gained rapid traction across computer vision, geospatial mapping, digital twins and spatial computing because it can render photorealistic 3D environments in real time. Instead of converting imagery into a conventional polygon model, Gaussian splatting represents a scene through millions of small, soft volumetric elements. Each carries data about position, scale, orientation, colour and transparency, allowing a viewer to move through a scene while the system blends these elements into a coherent image.

Apple’s interest fits a wider strategy across mapping, spatial media and Vision Pro. Its software ecosystem is increasingly built around immersive visual experiences, with developers encouraged to create richer 3D environments for apps, websites and spatial computing workflows. Better Maps data also supports navigation, tourism, property search, gaming, urban planning and augmented reality applications, where realistic geometry and visual stability are essential.

The most immediate consumer impact will be visual quality. Older photogrammetry often looked impressive at city scale but broke down on inspection. Trees could turn into indistinct green clumps, bridges could smear at their edges, and thin infrastructure could appear bent or melted. Gaussian splatting is better suited to preserving such visual complexity, particularly when source imagery has strong multi-angle coverage. For users, that could mean smoother city previews, more recognisable landmarks, cleaner neighbourhood views and a stronger sense of place before arriving at a destination.

The competitive implications are also significant. Google Maps and Google Earth remain the global benchmark in mapping coverage, data volume and street-level imagery. Apple, however, has been investing steadily in higher-quality cartography, privacy-focused navigation and curated city experiences. A sharper 3D layer would not erase Google’s scale advantage, but it could help Apple compete on visual quality in supported locations and deepen the appeal of Maps across iPhone, iPad, Mac and Vision Pro.

The rollout is likely to be selective at first. High-quality splat-based city models require dense, well-aligned imagery, careful processing and efficient compression for delivery to consumer devices. Dense urban cores, landmarks, tourism districts and cities already covered by Apple’s detailed 3D experiences are the most likely early candidates. Broader coverage would depend on aerial capture cycles, licensing arrangements, local regulation and the cost of processing vast quantities of imagery.

There are also unresolved questions. Photorealistic city models raise privacy and security sensitivities, particularly around sensitive facilities, residential areas and real-time accuracy. Mapping companies typically blur faces and licence plates in street-level imagery, while aerial and 3D datasets may require additional safeguards. More realistic scenes could also increase pressure on platforms to keep visual data current, because outdated building layouts or construction zones may become more noticeable when rendered with greater fidelity.

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The new Siri AI is available immediately for developer testing across iOS 27, iPadOS 27, macOS 27 and visionOS 27, with support for watchOS 27 due in a later developer beta. A wider beta for users is scheduled later this year, initially for supported devices set to English, before a staged expansion to more languages.

The announcement marks Apple’s most direct attempt to close the gap with rivals that moved faster in generative AI. Siri AI has been rebuilt to handle more conversational exchanges, understand what is on a user’s screen, draw on personal context from apps such as Messages, Mail and Photos, and search the web for answers. Apple is also giving Siri a dedicated app, allowing users to revisit past conversations across devices through iCloud syncing.

Craig Federighi, Apple’s senior vice-president of Software Engineering, said the upgraded assistant was designed to help users find information and complete tasks through deeper app integration. The system can draft emails, edit and share photos, answer questions about visible content, and respond to follow-up queries in a more natural exchange than the older voice assistant.

Apple’s push is built around its Apple Intelligence platform, with processing split between on-device models and Private Cloud Compute for heavier tasks. The company has emphasised privacy as a central difference from competing AI systems, saying personal data used in cloud-based requests is not stored or made available to Apple. That claim will remain under scrutiny as Siri AI gains broader access to messages, emails, images and screen content.

Availability will be uneven at launch. Siri AI and the wider Apple Intelligence upgrades will work on iPhone 16 models or later, iPhone 15 Pro and iPhone 15 Pro Max, iPad mini with A17 Pro, iPads with M1 or later, Macs with M1 or later, Apple Vision Pro, Apple Watch Series 9 or later, Apple Watch Ultra 2 or later, and Apple Watch SE 3 when paired with a compatible iPhone. Some of the most powerful on-device AI functions will require newer hardware with higher memory capacity.

Apple said Mac, Apple Watch and Vision Pro users in the European Union will be able to access Siri AI when using a supported language, but the feature will not initially be available on iOS 27 and iPadOS 27 in the bloc. The company has linked the delay to regulatory requirements under the Digital Markets Act. Siri AI and other new Apple Intelligence tools will also not be available in China at the outset as Apple works through regulatory approvals.

Alongside Siri, Apple announced a redesigned set of parental controls aimed at giving families tighter command over children’s device use. The tools include a simpler child account setup, a recommended set of essential apps, Ask to Browse for Safari, Time Allowances for app categories, and an updated Screen Time dashboard.

Child accounts will allow age-based protections such as limiting adult websites, restricting media by age rating and setting App Store boundaries. Parents will be able to choose which apps a child can use from the start, approve new websites in Safari, and manage communications across Messages, FaceTime and Phone. Communication Safety, already used to blur nudity in Messages and FaceTime for under-18 users, will expand to intervene when gore or violent images and videos are detected.

Time Allowances will let parents set limits across app categories including entertainment, games and social media. Apple is also adding daily schedules so families can restrict access during school hours, meals or other periods. Screen Time will show average usage and frequently used apps more clearly, while giving parents faster controls to pause access or extend time when needed.

Developers are being given additional tools to support age-appropriate experiences inside apps. These include SensitiveContentAnalysis, PermissionKit and a Declared Age Range API that lets apps adapt content without sharing a child’s exact birth date.

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Eligible users on Free, Go, Plus and Pro plans, along with self-serve ChatGPT Business accounts, are being given access to the feature through the Security section of ChatGPT settings. The rollout marks a notable shift from its initial positioning for higher-risk enterprise users, including executives, security teams and organisations handling sensitive material.

Lockdown Mode is designed to reduce the risk that malicious instructions hidden in web pages, files or connected services could cause ChatGPT to send sensitive information outside the conversation. The control does not claim to block every prompt injection attack, but it limits the final route through which stolen or exposed data might leave the system by restricting outbound network activity.

When enabled, the mode curbs or disables several ChatGPT capabilities that depend on live links to the internet or external services. Live web browsing is limited to cached content, meaning search results may be incomplete, unavailable or outdated. Deep Research is disabled, Agent Mode is turned off, Canvas-generated code cannot be approved for network access, and ChatGPT cannot download files for data analysis. Users can still upload files manually, and image generation remains available where the account otherwise supports it.

The change reflects a broader recalibration across the AI industry as assistants move from answering isolated prompts to handling tasks involving email, documents, websites, shopping, coding tools, workplace apps and external databases. These connections make systems more useful, but they also widen the attack surface. Prompt injection remains one of the most difficult security problems in AI because the harmful instruction may appear as ordinary content inside a page, document or tool output.

OpenAI’s approach is to trade some convenience for tighter control. A user working with sensitive internal documents, legal material, financial data, confidential reporting notes or business records may choose Lockdown Mode when the risk of external leakage outweighs the value of live web access or automated agent workflows. For ordinary queries, the setting may feel restrictive because several high-value ChatGPT tools become unavailable or limited.

The company has also positioned the feature as part of a layered defence system, rather than a standalone cure. Existing protections include sandboxing, restrictions against URL-based exfiltration, monitoring, enforcement mechanisms, enterprise controls, audit logs and role-based permissions. Lockdown Mode adds a more conservative operating state for users who want stronger boundaries around external interaction.

For personal and self-serve Business accounts, the feature can be turned on from Settings under Security. Once active, a status message appears above the composer. Users may disable it for an individual chat, allowing some flexibility when they need a less restricted session. Lockdown Mode and Developer Mode cannot run at the same time; switching on one turns off the other.

Managed workspaces have a different route. Administrators can use role-based access controls to create a custom Lockdown Mode role and assign it to members or groups. This allows organisations to apply stricter controls to selected employees rather than imposing the setting across an entire workspace. Administrators are also expected to review app permissions, connector access and write actions because app access inside ChatGPT does not override the permissions already set in the connected source system.

The connector rules are especially important for business users. For personal accounts and self-serve Business accounts, Lockdown Mode allows connectors that rely on synced data while blocking live connector access and connector write actions. Some connected experiences, including finance and shopping-agent functions, may be unavailable. In managed workspaces, apps, model context protocols and connectors depend on workspace settings and custom roles, so the restrictions may vary by organisation.

OpenAI has made clear that Lockdown Mode does not change every privacy or data setting. It does not alter memory, file uploads, conversation sharing, or whether chats may be used to improve models. Those controls remain separate and depend on the user’s account and workspace settings. It also does not affect Codex network access, which is governed by its own settings and elevated-risk warnings.

The wider release comes as AI providers face growing pressure to make security controls understandable to non-specialist users. Prompt injection can be difficult to explain because it does not always resemble a conventional hack. A hostile instruction may be embedded in a webpage, pasted text, email, document or third-party app output, then interpreted by the AI system as something to follow. The danger rises when an assistant has access to private context and tools capable of sending data elsewhere.

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The finding has sharpened concern across the software supply chain because the CRA’s vulnerability and incident reporting duties begin on 11 September 2026, well before the wider compliance regime takes full effect on 11 December 2027. The law, formally Regulation 2024/2847, entered into force on 10 December 2024 and applies to products with digital elements placed on the EU market, including software, connected hardware and certain standalone components.

The Open Source Security Foundation has said 66% of open source practitioners are either unaware of the CRA deadline or not prepared for it, despite the law already being in force. Its wider research points to uncertainty among maintainers, manufacturers and open-source stewards over who carries responsibility for reporting exploited vulnerabilities, maintaining security processes and documenting compliance across projects that often rely on unpaid contributors.

The compliance challenge is particularly acute because modern software products depend heavily on open-source components. Enterprise platforms, cloud services, mobile applications, industrial systems and internet-connected devices frequently include packages maintained by distributed communities outside the commercial structures that ultimately place products on the EU market. That separation between upstream code creation and downstream commercial use has become one of the central tensions in the CRA debate.

The regulation treats open source differently depending on whether it is offered commercially. Free and open-source software that is not monetised and is not made available on the market in the course of commercial activity is generally outside the main manufacturer obligations. Individual developers contributing code to projects that are not under their responsibility are also not treated as manufacturers. However, companies that place products containing such software on the EU market remain responsible for compliance, while a new category of open-source software steward covers legal entities that provide sustained support for projects intended for commercial use.

Open-source software stewards face a lighter regime than manufacturers, but they still have obligations. These include maintaining a cybersecurity policy, supporting secure development, handling vulnerabilities and cooperating with market surveillance authorities. They must also report actively exploited vulnerabilities and severe security incidents affecting relevant products, although the CRA does not subject stewards to administrative fines for infringements.

Manufacturers face a tougher framework. Products covered by the Act must be designed, developed and maintained with cybersecurity in mind throughout their lifecycle. Companies will need processes for vulnerability handling, software updates, technical documentation, conformity assessment and incident reporting. Serious breaches can lead to fines of up to €15 million or 2.5% of global annual turnover, whichever is higher, while other infringements may attract lower but still significant penalties.

The first major test arrives with the September 2026 reporting duty. Manufacturers will need to report actively exploited vulnerabilities and severe incidents through the EU reporting architecture, involving ENISA and national computer security incident response teams. For companies that ship products with long chains of open-source dependencies, this means identifying which components are present, whether they are maintained, how vulnerabilities are tracked and who can act quickly when exploitation is detected.

That requirement has pushed software bills of materials, vulnerability disclosure policies, secure build systems and dependency mapping higher on boardroom agendas. Tools such as OpenSSF Scorecard, SLSA and project security baselines are gaining attention as organisations seek practical ways to measure upstream risk and demonstrate due diligence. Larger technology companies including Red Hat, Microsoft, GitHub and Ericsson have been active in policy and standards discussions, while foundations and working groups are trying to translate legal obligations into workflows that fit open collaboration.

Smaller developers and SMEs remain a weak point. Many lack legal teams, security staff or dedicated compliance budgets, even when their software is embedded in commercial products sold across Europe. OpenSSF has warned that weak readiness among smaller participants could reduce project diversity, increase pressure on volunteer maintainers and shift costs towards communities that were not designed to operate as regulated suppliers.

The CRA was created after a series of software supply-chain incidents exposed the fragility of widely used digital infrastructure. The Log4j vulnerability, attacks on package repositories and repeated exploitation of outdated dependencies strengthened the case for mandatory security-by-design rules. The EU’s approach seeks to make manufacturers responsible not only for product functionality at release, but also for security support and vulnerability management after deployment.

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Magic Compose, built into Google Messages, uses Gemini technology to suggest replies, start conversations and rewrite drafts in styles ranging from formal to casual. On supported Android devices using AICore, the message-suggestion feature is powered by Gemini Nano, Google’s on-device model designed for smaller, private tasks that can run locally rather than relying entirely on cloud processing.

The feature works by using the previous 20 messages in a conversation to generate suggested replies. For rewrite suggestions, it uses the draft typed by the user. Google says that on supported devices with Gemini Nano, the data is processed on the phone and messages are not sent to Google for generating the suggestion. The distinction is important because messaging apps contain some of the most personal data on a phone, including family exchanges, work discussions, financial information and private disputes.

Magic Compose reflects a broader shift in which AI is becoming embedded into communication tools rather than existing as a separate chatbot. Gmail’s Gemini-powered suggested replies can review an email and propose a response that reflects the user’s tone and style. WhatsApp has also moved into AI writing support, with tools designed to rephrase, proofread and adjust the tone of messages. The direction of travel is clear: users are being nudged towards AI assistance at the exact point where they write, reply and manage conversations.

For Google, the advantage lies in speed, convenience and deeper integration across Android and Workspace. A short reply can be drafted without typing from scratch. A blunt message can be softened. A casual note can be made more polished. For users juggling work, family and customer communication, the productivity gains are tangible, particularly when the AI sits inside the messaging interface and requires no separate app.

The risks are equally visible. AI-generated replies can sound efficient but may not fully capture intent, emotion or context. A suggested answer to a sensitive message could appear dismissive, overly formal or falsely warm. When users rely on generated text repeatedly, conversations may become smoother but less personal, raising questions about authenticity in digital relationships. The recipient may believe they are reading a carefully written human response when, in practice, the wording has been shaped by a model.

Privacy remains the sharper concern. Google’s on-device approach for supported Messages features is intended to reduce data exposure, but availability is limited and not every AI-writing tool works in the same way. Cloud-based AI systems may require content to be processed outside the device, even where companies say safeguards are in place. The practical challenge for users is understanding which model is being used, what data is processed, where it is processed and whether human review or service improvement mechanisms apply.

Security researchers have also warned that AI assistants connected to notifications and messaging apps can become vulnerable to indirect prompt-injection attacks. Such attacks can place hidden instructions inside messages or notifications, creating a risk that an assistant may treat hostile text as a command. Google has issued fixes for known vulnerabilities, but the wider issue remains unresolved across the sector as AI tools gain access to more apps, notifications and personal context.

Regulators and privacy advocates are likely to examine these features through the lens of consent and transparency. Messaging data has traditionally been treated as highly sensitive, and AI systems that analyse conversation context invite tougher questions than older predictive-text tools. Users may accept autocorrect and word suggestions without hesitation, but an assistant that reads prior messages to infer intent crosses into more complex territory.

There is also a competitive dimension. Google is using Android, Messages, Gmail and Gemini to build AI into everyday communication, while Meta is using WhatsApp, Messenger and Instagram to position its own AI services inside social and business messaging. Microsoft, Apple and other technology groups are pursuing similar paths, making AI-assisted communication a standard feature rather than a novelty.

The next phase will depend on trust as much as capability. Users may welcome help with routine replies, awkward phrasing and time-consuming email responses, but adoption could slow if controls are unclear or mistakes feel intrusive. The strongest products will be those that keep the user visibly in charge: drafting rather than sending, explaining what data is used, and allowing easy editing before any AI-shaped message leaves the device.

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The tactic, commonly described as “living off the land”, relies on trusted tools already present inside corporate environments. PowerShell, Windows Management Instrumentation, certutil, mshta, rundll32, regsvr32, scheduled tasks, remote monitoring software and JavaScript execution frameworks are being used to download payloads, run code in memory, create persistence, steal credentials and disable security controls.

The shift is significant because many of these utilities are essential to routine IT operations. Blocking them outright can disrupt business systems, while allowing unrestricted use gives intruders a ready-made route to operate under the cover of normal administration. Attackers are exploiting that ambiguity to reduce their malware footprint and shorten the time between initial access and deeper compromise.

Security investigations across 2025 and 2026 show a steady rise in malware-free and hands-on-keyboard intrusions, where attackers depend less on custom malicious files and more on credentials, scripts and trusted binaries. The pattern has appeared in ransomware operations, espionage campaigns, infostealer distribution, cloud intrusions and attacks against developers, government networks and managed service environments.

PowerShell remains one of the most frequently abused tools because it can execute commands remotely, fetch files, decode payloads and run scripts directly in memory. WMI is used for lateral movement, remote execution and persistence through event subscriptions. Certutil, a certificate management utility, can be repurposed to download or decode files. Mshta and rundll32 can launch malicious scripts or dynamic-link libraries while appearing to use ordinary Windows components.

Attackers also lean on legitimate remote access and administration tools after gaining entry. AnyDesk, ScreenConnect, Atera, TeamViewer, Zoho Assist, PsExec and similar platforms have been seen in intrusions where operators sought durable access without deploying easily detectable backdoors. These tools are not inherently malicious, but their misuse can give attackers interactive control, file transfer capability and a channel for lateral movement.

The technique has become more effective as businesses expand cloud services, remote work tools and software-as-a-service platforms. Adversaries increasingly use valid accounts, stolen session tokens and legitimate cloud management utilities to blend with expected user behaviour. Once inside, they may use Microsoft 365 tools, Azure PowerShell, AWS command-line interfaces, Google Drive, GitHub or other trusted platforms for command-and-control, data staging or payload delivery.

Cybercriminal groups have paired these methods with social engineering and search manipulation. Campaigns impersonating popular tools such as PuTTY, WinSCP, Ghidra and other software used by developers and security professionals have pushed malicious installers through fake websites and poisoned search results. Once installed, loaders can deploy infostealers, clippers or remote access malware while using standard Windows processes to maintain persistence.

Ransomware operators benefit from the same approach. Instead of immediately deploying encryption malware, affiliates often enter through stolen credentials, exploit exposed remote services, run reconnaissance with native commands, disable backups, escalate privileges and exfiltrate data before launching the final payload. That sequence makes early detection harder because many actions resemble legitimate troubleshooting or administrative work.

Espionage groups have also adopted the model. State-linked actors targeting government, telecommunications, technology and critical infrastructure networks have used trusted binaries, cloud storage services and standard administrative channels to avoid triggering conventional alerts. The objective is often long-term access rather than rapid disruption, making quiet use of normal tools particularly valuable.

The operational advantage for attackers is clear. Traditional antivirus systems are strongest when they can inspect suspicious files, known malware signatures or unusual executable behaviour. Living-off-the-land activity shifts the problem toward context: who launched the tool, from where, at what time, with which parameters, against which system, and whether the behaviour fits that user’s role.

Defenders are responding by placing more emphasis on behavioural analytics, endpoint detection and response, identity security, script logging, application control and stronger monitoring of command-line activity. PowerShell transcription, constrained language mode, signed script policies, WMI event monitoring, tighter privilege controls and alerts for abnormal use of remote management tools are becoming central parts of enterprise defence.

Security teams are also moving toward zero-trust access models, where valid credentials alone are not enough to justify sensitive activity. Multi-factor authentication, device health checks, least-privilege administration, just-in-time access and stronger controls over service accounts can limit the damage when attackers obtain passwords or tokens.

The challenge is not merely technical. Many organisations still lack clear baselines for normal administrative behaviour, especially across hybrid cloud and remote work environments. Without that baseline, defenders struggle to distinguish a helpdesk technician repairing a system from an intruder using the same tool to stage an attack.

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The next-generation Proteus was shown at Amazon’s Dartford fulfilment centre east of London as part of a wider investment programme of more than €10 billion, equal to about $11.6 billion, aimed at expanding and modernising operations across Europe. The company says the new system will begin deployment in Europe during the first half of 2027, extending the use of autonomous mobile robots beyond limited dock-area operations.

Proteus is designed to move large carts and containers through fulfilment and delivery sites. The current version already operates at 25 locations in the United States, where it handles carts weighing up to nearly 400kg. The upgraded model adds artificial intelligence capabilities that allow employees to issue conversational commands rather than use specialised software or coded instructions.

The change is significant because it shifts warehouse robotics from pre-programmed movement to task-based interaction. A worker can describe what needs to be done, while Proteus determines the order of tasks, selects a route, and manages timing based on operational conditions. Amazon’s robotics division has framed the upgrade as a way to remove physically demanding work from employees while increasing the speed and predictability of fulfilment flows.

The European roll-out forms part of a broader robotics package that includes STARK, a robotic tote-handling system first piloted in Barcelona, and Vulcan, Amazon’s first robot designed with a sense of touch. STARK is expected to be deployed at 15 European sites by 2027, handling repetitive tote movement between conveyors and carts. Vulcan uses pressure and contact sensing to manipulate items more carefully, a capability Amazon sees as important for reducing manual strain in storage and picking operations.

Amazon’s investment push also includes delivery expansion. More than 25 sub-same-day delivery sites are planned across Europe this year, including locations in Britain and Germany. Amazon Now, the company’s ultra-fast essentials delivery service, is set to expand to Manchester and Birmingham, strengthening its challenge to grocery and convenience delivery operators in densely populated urban markets.

The company says the automation drive will be accompanied by the creation of 25,000 jobs and a $1 billion programme for employee upskilling. That message is central to Amazon’s attempt to address concerns that warehouse robotics could reduce labour demand over time. The company argues that its robots are intended to support workers by taking on heavy, repetitive, and ergonomically difficult tasks, while human employees move into roles involving supervision, maintenance, quality control, and exception handling.

Labour groups and workplace researchers are likely to scrutinise the practical effects of the roll-out. Amazon’s warehouses have faced criticism in several markets over work intensity, injury risk, performance targets, and job security. Greater automation may reduce some lifting and walking, but it can also reshape work around tighter process controls and higher throughput expectations. The key test will be whether robotics improves safety and job quality alongside productivity.

The timing of the announcement places Amazon within a wider race among global retailers and logistics operators to use artificial intelligence, autonomous systems, and sensor-based robotics to cut delivery times and manage labour shortages. E-commerce growth has placed pressure on fulfilment networks to process more items faster, while customers increasingly expect same-day or next-day delivery for a wider range of products.

Amazon has spent more than a decade integrating robotics into its operations after its 2012 acquisition of Kiva Systems. Since then, the company has developed a growing ecosystem of mobile robots, robotic arms, automated storage systems, and machine-learning tools that coordinate inventory movement across warehouses. Proteus stands out because it is built to operate safely around people without being confined to cages or strictly separated zones.

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The showcase, held at Taipei Nangang Exhibition Centre from June 2 to 5, placed DSM at the centre of Synology’s next phase. The company is presenting the operating system as AI-ready and enterprise-ready, with support for GPU-equipped NAS systems and AI appliances intended to run inference inside customer-controlled environments. The approach reflects rising demand from businesses seeking to use generative and agentic AI without moving sensitive operational data into public cloud services.

A new DSM Agent is being introduced to automate workflows across Synology systems, while Cluster Manager is designed to give large deployments a single interface for managing multiple systems. The toolset includes workload migration, quality-of-service controls and centralised protection. Synology is also expanding Active Insight with mass deployment features for faster multi-site provisioning, alongside a redesigned Log Center aimed at improving enterprise observability, security monitoring and compliance reporting.

Philip Wong, Synology’s chairman and chief executive, said data remained “at the heart” of the company’s product strategy and described trust as the foundation of its approach. “From enterprise infrastructure to personal cloud solutions, we develop every product to give users complete control over their data, backed by uncompromising security, reliability, and privacy,” he said.

The company’s data protection push is led by ActiveProtect Manager 2.0 and the new DP5200 appliance. APM 2.0 expands backup and recovery coverage to AWS EC2, Azure VM, Proxmox, Nutanix AHV and Google Workspace, broadening Synology’s reach across hybrid cloud and virtualised workloads. AI-powered anomaly and malware detection has been added to support earlier threat identification, a key issue for organisations facing ransomware, credential compromise and supply-chain intrusions.

Synology is also using the event to reinforce its enterprise storage credentials. The PAS7700, the company’s active-active all-flash NVMe system, has been made available through distributor and partner channels. The 4U system uses a dual-controller design and 48 NVMe SSD bays, with expansion support for up to 1.65PB of raw capacity. It supports file and block protocols including NVMe-oF, iSCSI, Fibre Channel, SMB and NFS, with performance claims of up to 2 million IOPS, sub-millisecond latency and throughput of up to 30GB/s.

The PAS7700 is aimed at mission-critical workloads where downtime and data loss carry high operational costs. Its resilience features include RAID triple-parity, synchronised in-memory write protection, IP failover, protocol-level failover, self-encrypting drive support, WORM, immutable snapshots, Snapshot Replication and Hyper Backup. Synology is also highlighting deduplication and planned tiering capabilities to move colder data to lower-cost storage while keeping active workloads on higher-performance media.

Surveillance has become another major pillar of the company’s portfolio. Synology’s COMPUTEX lineup includes the AC100 door controller, AR Series readers, new DC Series dome cameras and new Deep Video Analytics appliances. The DVA systems are being promoted with AI semantic event search and re-identification path tracking, features designed to help security teams search video evidence more efficiently and follow people or objects across monitored areas.

Surveillance365 extends that strategy into hybrid cloud monitoring. The platform is intended to work with on-premises Surveillance Station deployments, giving businesses a centralised system for remote and multi-site security operations. The move places Synology in a segment where physical security, cloud management and AI video analytics are increasingly being combined by enterprises, retailers, schools and logistics operators.

The company is also expanding its collaboration software. Synology Office Suite is gaining ChatPlus and Meet, adding messaging and meetings with permission controls, management features and AI-powered transcription and translation. Synology is framing the tools around privacy and administrative control, with data kept on-premises rather than routed through third-party cloud collaboration platforms.

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The Cash App Wand, priced at $25 plus applicable sales tax, is the first product in a new line called Cash App Tags, a set of NFC-enabled physical payment accessories linked to the Cash App Visa Card. The wand works anywhere Visa tap-to-pay is accepted and is available through Cash App to eligible customers aged 13 and above while supplies last.

The device resembles a compact star-topped charm rather than a conventional card, and comes with a keychain so it can be clipped to a bag, clothing or other everyday items. It does not require users to take out a phone or wallet at checkout. Once activated inside Cash App, it functions like a tap-enabled debit card, completing payments in less than a second at compatible terminals.

The launch reflects Block’s attempt to make payments more expressive at a time when digital wallets have made transactions faster but less visible. Thomas Templeton, hardware lead at Block, said digital wallets were “invisible” and physical cards were often buried in wallets, while Cash App Tags were designed to make payments “visible and social” for the first time.

Cash App is positioning the wand as both a functional payment instrument and a lifestyle accessory, particularly for Gen Z users. The company says one in five American teenagers already has a Cash App Card, which has become a major entry point into digital finance for younger customers. Its existing card customisation features, including colours, stamps, emojis and user-drawn designs, have helped turn the debit card into a form of self-expression rather than a plain banking product.

The wand extends that strategy beyond the card itself. Cash App says its survey of Gen Z consumers found that 38 per cent buy collectibles, accessories or limited-edition items at least monthly, a rate higher than any other generation. The company is betting that the same appetite for customisation and limited drops can be applied to payments, particularly among users who treat accessories as identity markers.

The product also draws on a wider online trend in which users have placed contactless cards inside novelty objects, including homemade wands, to make tap-to-pay transactions more playful. Cash App’s version replaces improvised card holders with a purpose-built NFC device linked directly to the Cash App Card. The timing suggests Block sees cultural value in making the payment moment more visible, even as Apple Pay, Google Pay and other mobile wallets continue to reduce the need for physical payment objects.

Security remains central to the pitch. Cash App Tags can be locked or unlocked through the app, deactivated at any time, and monitored through transaction alerts. Users also receive fraud monitoring support. Because Tags operate like the Cash App Card, there is no separate minimum balance or activity requirement for the wand itself, though users must have an active card linked to their account.

Block plans to release additional Cash App Tag designs in limited runs before making some versions more broadly available later in the summer. Templeton has indicated that the technology could eventually appear in clothing, jewellery and other small accessories, giving Cash App a path into wearable payments without the complexity of battery-powered smart devices.

The launch comes as competition in digital payments remains intense. Banks, card networks, fintech apps and technology platforms are all trying to control the consumer checkout experience, while merchants continue to adopt contactless terminals and software-based payment acceptance. Block already operates Square for sellers, Cash App for consumers and Afterpay for instalment payments, giving it multiple routes into the transaction chain.

Cash App’s move is modest in financial scale but strategically notable. A $25 wand will not materially change Block’s revenue profile on its own, but it gives the company a distinctive consumer product at a time when payment apps are increasingly difficult to differentiate. Venmo, Zelle, PayPal, Apple Pay and bank-backed wallets all compete on speed and convenience; Cash App is trying to compete on identity, culture and the physical ritual of payment.

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The vulnerabilities, tracked as CVE-2026-45492, CVE-2026-45494 and CVE-2026-45495, affect Chromium-based Microsoft Edge and were credited to Orange Tsai of DEVCORE Research Team, one of the better-known browser and enterprise security researchers in the global vulnerability research community. The public advisories were released on June 4, while Microsoft had already shipped fixes through Edge version 148.0.3967.70 for desktop, followed by related Android and iOS updates.

The most severe of the three is CVE-2026-45495, a remote code execution flaw linked to feedback log file handling and directory traversal. The vulnerability requires user interaction, meaning an attacker would need to convince a victim to visit a crafted webpage or open a malicious file. Even with that limitation, browser-based code execution remains a significant risk because Edge is widely deployed across corporate Windows environments, often integrated with identity, cloud and productivity workflows.

CVE-2026-45494 involves navigation handling and universal cross-site scripting. A successful exploit could allow an attacker to execute arbitrary cross-origin script in affected Edge installations after user interaction. Such flaws can be used to blur trust boundaries between websites, manipulate browser content, capture sensitive data displayed in a session or support more complex attack chains.

CVE-2026-45492 is an origin validation error that can let remote attackers access restricted functionality in affected Edge installations. Its impact is narrower than direct code execution, but origin validation weaknesses are closely watched because browsers rely on strict separation between web origins to enforce security rules across websites, applications and authentication flows.

The vulnerabilities illustrate why browser flaws discovered at contests such as Pwn2Own are treated as operational priorities by enterprise defenders. Pwn2Own has become a major venue for demonstrating working exploits against browsers, operating systems, virtualisation platforms, mobile devices and enterprise applications under controlled disclosure rules. Researchers receive rewards, vendors receive technical details, and patches are normally coordinated before full public advisories are issued.

Microsoft’s release channel shows that Edge 148.0.3967.70 carried fixes for all three desktop vulnerabilities on May 15. The iOS release on May 19 and Android release on May 21 carried fixes for CVE-2026-45495. The staggered release pattern reflects the different update paths for desktop and mobile platforms, and it places responsibility on users and administrators to verify that all endpoints have moved to patched builds.

Enterprise exposure is likely to vary widely. Organisations with automatic browser updates, endpoint management tools and software inventory systems should be able to reduce risk quickly. Environments that delay browser updates for compatibility testing, kiosk systems, shared workstations or unmanaged contractor devices may remain exposed for longer. Security teams are expected to prioritise checks for Edge versions below 148.0.3967.70 and confirm mobile deployment where Edge is used on managed phones and tablets.

The risk is not limited to attacks that begin with obvious downloads. Browser exploitation often starts with social engineering, compromised websites, malicious links, advertising abuse, phishing emails or files shared through collaboration platforms. User interaction requirements reduce automatic exploitability, but they do not remove risk, particularly where attackers can craft convincing lures around business processes, invoices, document review workflows or login prompts.

The disclosures also underline the continuing security implications of Chromium’s dominance. Edge, Chrome and several other browsers share major parts of the Chromium code base, but vendors also add their own features, services and integration layers. Edge-specific vulnerabilities can emerge from those custom components, even when the underlying Chromium project is separately patched. That makes vendor-specific browser advisories important for defenders who may otherwise focus only on upstream Chromium updates.

For Microsoft, the issue arrives as Edge remains tightly linked to Windows, Microsoft 365, Entra identity services, Copilot features and enterprise management tools. That integration improves administrative control but raises the stakes when browser flaws intersect with authentication, cross-origin protections or file handling. Browser compromise can give attackers a foothold for credential theft, session hijacking, internal reconnaissance or malware delivery.

Security teams should treat the latest Edge update as a priority patch rather than a routine browser refresh. Practical steps include confirming the installed version, forcing updates through enterprise management tools, restarting browsers after installation, auditing devices outside standard update groups and monitoring for suspicious browser-spawned processes. Users should also avoid opening unexpected links or files, particularly where the sender’s identity or the document context cannot be verified.

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The Victoria, Seychelles-based exchange said the tie-up gives traders access to TradingView’s charting interface while connecting them to MEXC’s spot and derivatives markets. The move is aimed particularly at active crypto users who rely on technical indicators, alerts and chart-based workflows but previously had to switch between platforms to analyse markets and place orders.

The integration enables perpetual futures trading inside TradingView, a platform widely used by retail and professional traders for charts, indicators, drawing tools, strategy testing and market alerts. Users can connect their MEXC accounts through TradingView’s trading panel, select MEXC as the broker, sign in and place trades without leaving the charting environment.

MEXC said the partnership combines TradingView’s analytical tools with its execution infrastructure, broad token coverage and low-fee model. The exchange has promoted zero-fee spot trading as a central part of its positioning and has expanded aggressively across spot tokens, perpetual contracts and tokenised assets as competition among global crypto venues intensifies.

“Our integration with TradingView is a pivotal step in our mission to build an open gateway,” said Vugar Usi Zade, chief executive of MEXC. “By combining TradingView’s premier analytical tools with our secure, diverse, and efficient trading environment, we are providing a seamless path for users to access, amplify, and realise infinite financial opportunities.”

TradingView said the deal expands choice for traders using its platform. Rauan Khassan, chief growth officer at TradingView, said the integration connects users to more than 2,500 trading pairs available on MEXC, allowing them to diversify portfolios, identify opportunities and execute strategies without leaving the charting platform.

The agreement comes as crypto exchanges seek deeper integration with front-end trading platforms, data terminals and automation tools. Direct chart-based execution has become a more important feature for derivatives traders, whose strategies often depend on rapid response to price movements, technical triggers and funding-rate shifts.

Perpetual futures remain a core product in global crypto trading. Unlike traditional futures, perpetual contracts do not expire, allowing traders to maintain leveraged exposure for extended periods while funding payments help keep contract prices aligned with the underlying market. The product has drawn strong demand from active traders but also carries heightened risk because leverage can magnify losses during volatile moves.

Market data for 2026 show that centralised perpetual exchanges continue to handle large trading volumes, although activity has cooled from the previous year’s peak. Monthly average trading volume among leading perpetual venues fell from about $7.1 trillion in 2025 to roughly $4.7 trillion during the first four months of 2026. At the same time, decentralised perpetual exchanges have gained ground, reflecting a wider shift towards on-chain derivatives, self-custody and protocol-based liquidity.

MEXC has been among the more aggressive centralised platforms in expanding perpetual contracts. From January 2025 to April 2026, it listed 879 new perpetual contracts, averaging about 55 additions a month. That strategy has focused heavily on long-tail crypto assets, including meme tokens and AI-linked tokens, giving the exchange a wider menu for users seeking exposure beyond the largest digital assets.

The exchange has also ranked among the leading venues by spot and perpetual market share this year. Its scale has been supported by broad asset availability, fast listing cycles and liquidity in selected derivatives markets. That approach has helped it compete with larger global platforms at a time when traders are spreading activity across exchanges in search of lower fees, deeper order books and access to newly listed assets.

The TradingView integration also reflects a wider industry push to reduce operational friction for users. Traders often monitor charts, run indicators, backtest strategies and set alerts on one platform while executing trades on another. Combining those functions can shorten the path from signal to order, although it also places greater importance on risk controls, account security and user discipline.

TradingView’s toolset includes hundreds of built-in indicators and strategies, multi-timeframe analysis, candlestick pattern recognition, drawing tools, cloud alerts, paper trading and Pine Script, its proprietary language for custom indicators and strategy development. Those features make the platform a common workspace for traders across crypto, equities, commodities, currencies and derivatives.

MEXC’s expansion beyond conventional crypto trading has included tokenised assets linked to stocks, exchange-traded funds, commodities and precious metals. The company says it serves more than 40 million users across more than 170 markets and offers access to more than 3,000 digital assets.

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Launched in 2021 and later folded into the broader d. id identity ecosystem,. bit was built as an open-source, cross-chain account protocol. Its central promise is straightforward: a user can register a readable identity such as name. bit and use it across supported blockchain networks instead of copying and pasting complex wallet addresses. That puts it in the same broad category as Ethereum Name Service and Unstoppable Domains, though. bit’s design places particular emphasis on cross-chain compatibility and account management across different cryptographic systems.

The system runs on Nervos CKB, a proof-of-work public blockchain using a UTXO model.. bit accounts and related records are stored on-chain, while supporting services resolve those records for wallets, decentralised applications and identity tools. Its architecture includes core protocol scripts, off-chain keepers, resolution services, software development kits and user-facing applications. Developers can build interfaces or services around the protocol, while users interact with it through account managers, wallets or integrated applications.

A key distinction is that a. bit account separates ownership, management and records. The owner or manager of an account can be controlled through different public-chain private keys or, in some configurations, even email-based access. That approach was intended to lower technical barriers for users who may not be comfortable managing seed phrases or paying gas fees on every network. The broader d. id ecosystem has promoted the idea of “barrier-free Web3”, including seedless and gasless experiences where possible, while still preserving decentralised ownership principles.

The use cases extend beyond wallet naming. A. bit identity can hold cryptocurrency addresses for different chains, personal profile data, decentralised website records, social identity links and authentication information. For decentralised autonomous organisations, communities and brands, the protocol has been positioned as a way to issue member identities, credentials and sub-accounts. Supporters argue this could make Web3 communities easier to organise by giving members persistent and portable identifiers that are not tied to one platform.

Investment interest in the project grew as digital identity became a major theme in Web3 infrastructure.. bit raised $13 million in Series A funding in 2022, with investors including CMB International, HashKey Capital, QingSong Fund, GSR Ventures, GGV Capital, SNZ, SevenX Ventures and Xin Fund Management. At that stage, the project said it had more than 110,000 accounts and integrations with about 100 wallets and decentralised applications. By the time of the d. id rebrand in 2023, disclosed figures had risen to about 280,000 registered accounts and more than 140 wallet and application integrations.

The protocol’s appeal rests on three trends: the need for readable blockchain identities, the fragmentation of users across many chains, and growing concern over platform-controlled identity systems. Web2 accounts are usually held by centralised companies that can suspend profiles, restrict access or change rules. Blockchain-based identity systems promise portability and user control, although they also shift responsibility for security and recovery to users and protocol designers.

Challenges remain significant. Blockchain naming systems still face limited public understanding, inconsistent wallet support, regulatory uncertainty and risks linked to phishing, impersonation and speculative name registration. A readable name can reduce friction, but it can also create new attack surfaces if users mistake fake or similar-looking identities for legitimate ones. Another constraint is that. bit names are not standard web domains in the ICANN-governed DNS hierarchy, meaning their resolution depends on compatible tools, gateways, wallets or applications rather than universal browser support.

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The programme, called Audible Rewards, is being rolled out first in the United States for members on Standard and Premium plans. It allows users to earn benefits through daily listening, promotional activities, referrals and title-completion challenges. The company plans to expand availability to other markets from 2027, a timeline that keeps the first phase focused on its largest and most competitive subscription market.

The central feature is a listening-day system. Members who listen for at least five minutes on a given day can build progress towards milestones that unlock discounts on future titles. Those days do not need to be consecutive, avoiding the stricter streak model used by some fitness, language-learning and gaming apps. The approach appears designed to encourage regular engagement without penalising users who listen in bursts rather than every day.

Audible is also offering a “Spend 3 Credits, Get 1 Free” promotion, aimed at subscribers who use credits to build their libraries. Members can earn a $15 reward for every three friends they refer, while new customers joining through a referral receive a $5 reward. Active members are also eligible for a free credit or voucher every 12 months as an anniversary gift.

Gamification is another part of the strategy. Members who complete three, four or five titles within four months can earn achievement badges labelled Engaged, Enthusiastic or Dedicated. At launch, Audible is also offering a Harry Potter challenge, under which listeners who complete all seven audiobooks in Harry Potter: The Full-Cast Audio Editions receive an exclusive badge in their achievement collection.

Users can monitor their tier status, active rewards, challenge progress and anniversary milestones through a Rewards Hub inside the Audible app and on the web. The programme is accessible through iOS, Android, Audible. com and Amazon. com, reflecting the platform’s integration with the wider Amazon ecosystem.

The move comes as audiobook platforms compete for listener time against podcasts, streaming video, music services and social media. Audible has long depended on a credit-led membership model, where subscribers pay a monthly or annual fee and receive credits that can be redeemed for audiobooks. The Rewards programme adds a behavioural layer to that model, giving members more reasons to return to the app and finish titles.

The company has also been adjusting its broader offering. Members can now access hundreds of Audible podcast titles through Apple Podcasts, while new recommendation tools have been introduced through integrations with artificial intelligence assistants. These efforts point to a wider attempt to make Audible less dependent on one-time title purchases and more centred on continuous discovery.

For heavy listeners, the value proposition is clearer. A member who already listens frequently may unlock discounts and credits without materially changing behaviour. For casual users, the five-minute threshold lowers the barrier to participation and may encourage shorter, more frequent sessions. The anniversary gift gives long-term subscribers a predictable benefit, while the referral reward gives Audible another customer acquisition channel.

The programme also reflects a broader trend in subscription media: companies are trying to reduce cancellations by giving users visible progress, status and incentives. Loyalty mechanics are common in retail and travel, but they are becoming more prominent in digital entertainment as companies seek to protect recurring revenue at a time when consumers are reviewing monthly subscriptions more closely.

Audible’s rollout is not without limits. The first phase is confined to the US, leaving users in other markets waiting until at least 2027. Rewards are also tied to continued membership, though users who pause for up to 90 days can preserve tier status and accumulated rewards. That condition gives members flexibility while still linking benefits to an active paid relationship.

The launch could help Audible defend its position in a market where rivals have grown through library partnerships, creator-led podcasts and unlimited listening models. While Audible remains closely associated with premium audiobooks and exclusive originals, the new programme shows the company adding incentives that resemble retail loyalty schemes as much as media subscriptions.

For authors and publishers, the impact will depend on whether rewards drive more paid listening, higher completion rates and stronger discovery for catalogue titles. Credit promotions may encourage members to buy more titles, while challenges tied to multi-book series could support franchises and long-form listening. At the same time, discounts and rewards may intensify scrutiny over how revenue is shared across the audiobook supply chain.

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The San Francisco-based artificial intelligence company is adding around 150 organisations to Project Glasswing, its gated initiative for defensive use of a frontier model that has drawn intense attention from technology companies, banks, public agencies and national security officials. The expansion takes the programme beyond its initial group of roughly 50 partners and extends it across more than 15 countries.

Mythos Preview is not being released for general public use. Anthropic has described the model as its most capable frontier system to date for vulnerability discovery, with the ability to read large codebases, identify weaknesses and, in some cases, work out exploit chains without close human steering. Access is being limited to organisations that meet security requirements, reflecting concern that the same capability could be misused if deployed without controls.

Project Glasswing is aimed at software that underpins critical services, including healthcare, power, water, communications, hardware, cloud platforms and open-source infrastructure. Anthropic has said a successful attack on some participating organisations could affect more than 100 million people, underscoring why the rollout is being framed as a defensive effort rather than a conventional product launch.

The expansion follows several weeks of testing in which Glasswing partners and Anthropic’s own teams identified more than 10,000 high- or critical-severity security flaws. Some partners reported that their rate of bug discovery rose more than tenfold, while Cloudflare found about 2,000 bugs across critical-path systems, including 400 classed as high or critical severity. Mozilla’s testing of Firefox 150 led to the discovery and fixing of 271 vulnerabilities, far above the number found in the previous comparable test cycle using Claude Opus 4.6.

Anthropic has also used Mythos Preview to scan more than 1,000 open-source projects that support internet services and enterprise systems. The model estimated 6,202 high- or critical-severity vulnerabilities among 23,019 findings across all severity levels. Of the 1,752 high- or critical-rated findings assessed by outside security researchers or Anthropic staff, 90.6% were found to be valid true positives, while 62.4% were confirmed as high or critical severity.

The figures point to a sharp shift in the economics of cyber defence. Finding vulnerabilities has become faster and cheaper, but confirming, reporting and fixing them remains labour-intensive. Maintainers are already dealing with a flood of low-quality AI-generated bug reports, and Anthropic has acknowledged that some open-source teams have asked it to slow disclosure so they have time to assess and patch issues. On average, a high- or critical-severity bug found by Mythos Preview takes about two weeks to patch.

The programme has produced examples in widely used systems. Mythos Preview identified a 27-year-old flaw in OpenBSD, a 16-year-old flaw in FFmpeg and chains of Linux kernel vulnerabilities that could allow privilege escalation. It also detected a vulnerability in wolfSSL, a cryptography library used by billions of devices, which could have allowed attackers to forge certificates for convincing fake websites. Those examples highlight the defensive value of large-scale automated review, but they also explain why governments and financial institutions have treated the model with caution.

Independent cyber evaluations have shown that Mythos Preview represents a step up from earlier frontier models. It has performed strongly on capture-the-flag tests and multi-step simulated attack ranges, including a corporate network scenario in which it was the first model to complete the full chain in some attempts. Those tests were conducted in controlled environments and do not prove that the model could compromise well-defended live systems, but they strengthen the case that AI-assisted cyber operations are moving beyond code suggestions into sustained vulnerability discovery and exploitation workflows.

Anthropic’s approach reflects a broader debate over how to handle frontier AI systems that can both protect and threaten digital infrastructure. Banks and technology companies want access so they can harden their systems before rivals, criminals or state-linked actors obtain similar tools. Security specialists, meanwhile, warn that wider release could create a race in which patching teams struggle to keep pace with automated discovery.

The company has committed up to $100 million in usage credits for Mythos Preview and $4 million in direct donations to open-source security organisations. It has also said it does not plan to make Mythos Preview generally available, although it aims to enable customers to deploy Mythos-class models with additional safeguards. That distinction is important: Anthropic is trying to build a pathway for defensive adoption while delaying unrestricted access to a capability it believes could spread through other AI systems within months.

The new Glasswing partners include government organisations and companies across major economies, though Anthropic has not named the full list. South Korea’s participation includes the Korea Internet & Security Agency, with major technology groups linked to the national rollout. The wider country list includes several advanced software and infrastructure markets, including the United States, Canada, Australia, New Zealand, France, Germany, Japan and South Korea.

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The data cloud company said enterprises are increasingly deploying Claude through Snowflake Cortex AI to run governed AI agents on business data without moving sensitive information outside existing security controls. The message links two priorities now defining enterprise AI spending: stronger model reasoning and tighter governance over the data used to produce results.

The announcement follows Snowflake’s five-year AWS commitment, covering Graviton compute and AI infrastructure, which is designed to support heavier data and AI workloads. The agreement deepens a relationship that dates back to Snowflake’s early development on AWS and gives the company more scale as customers move from trials to production-grade AI systems.

Snowflake has raised its fiscal 2027 product revenue forecast to $5.84 billion from $5.66 billion, after quarterly revenue reached $1.39 billion. The company also expects second-quarter product revenue of between $1.415 billion and $1.420 billion, reflecting stronger demand for data warehousing, AI applications and workload migrations.

The market response has underscored how investors are viewing Snowflake’s AI strategy. Shares surged after the earnings update and AWS agreement, reversing part of the pressure the stock had faced when investors questioned whether cloud data platforms could translate generative AI interest into durable revenue growth.

At Summit 26, Snowflake and Anthropic highlighted customer use cases across financial analysis, cybersecurity investigations, customer support, developer productivity, sales intelligence and business analytics. Customers named in the partnership update include Basis, Block, Carvana, eSentire, Indeed and Notion, alongside advisory and implementation support from Deloitte Consulting.

Snowflake’s positioning rests on a simple proposition: companies want AI models to work where enterprise data already sits. Cortex AI allows users to apply Claude models to structured and unstructured data within Snowflake’s governed environment, supporting tasks such as natural language analysis, agent workflows, code generation, summarisation and data application development.

Christian Kleinerman, Snowflake’s executive vice-president of product, said demand reflects a wider shift in enterprise expectations. “Customers want AI that works directly on their governed data, not in isolated systems,” he said, adding that Cortex Code had become the fastest-growing product in Snowflake’s history.

Anthropic’s Steve Corfield said customers were using Claude to support cybersecurity investigations, accelerate financial analysis and build production data applications. His remarks point to the areas where model accuracy, auditability and data control matter most, particularly in sectors such as financial services, healthcare, life sciences and commerce.

The December 2025 expansion between Snowflake and Anthropic set the foundation for this phase of deployment. That $200 million multi-year agreement brought Claude models into Snowflake’s platform across major cloud environments and established a joint go-to-market effort for enterprise AI agents. Snowflake said thousands of customers were already processing trillions of Claude tokens per month through Cortex AI at that stage.

Snowflake has also embedded Claude more deeply into its own products. Snowflake Intelligence uses leading models, including Claude, to allow employees to query enterprise information in natural language. Cortex Code, also powered by frontier models, is aimed at developers building data pipelines, applications and workflows within Snowflake schemas and governance rules.

The AWS agreement adds an infrastructure layer to that model partnership. Snowflake’s commitment includes use of AWS Graviton processors and AI infrastructure, with both companies seeking closer product integrations for generative and agentic AI. The deal also expands joint sales through AWS Marketplace and supports workload migration programmes for enterprises looking to consolidate data and AI systems.

AWS Marketplace has become a significant channel for Snowflake, with lifetime sales surpassing $7 billion and calendar year sales exceeding $2 billion in 2025. Snowflake is also expanding availability across new AWS regions, including Auckland, Cape Town, Bangkok and the AWS European Sovereign Cloud, as customers demand lower latency and data residency options.

The strategy faces pressure from strong competition. Databricks, Microsoft, Google Cloud, Oracle and Amazon’s own data and AI services are all pursuing enterprise AI budgets. Model providers including OpenAI, Anthropic, Google DeepMind and Meta are also competing to become the preferred reasoning layer for business applications.

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The breach, disclosed on 1 June, hit the payment product that connects self-custodial crypto wallets with card spending. The affected component is linked to the Zodiac Delay Module, a mechanism designed to slow certain outgoing transactions from Safe-based accounts before execution. That delay is intended to give users a short window to react to unauthorised transfers, but the flaw allowed attackers to initiate transactions from Safes fitted with the module.

Gnosis co-founder Martin Köppelmann said the company was taking containment measures, including asking bridge validators to pause activity, while its technical teams worked to limit losses. He initially urged Gnosis Pay users to withdraw EURe and GNO from affected accounts, then deleted that warning after acknowledging that many users would not be able to move funds during the incident. He later said Gnosis would make users whole.

The company has not disclosed the value of assets drained, the number of accounts affected or the full technical route used by the attackers. A detailed post-mortem had not been published by Wednesday, leaving users and security researchers waiting for clarity on whether the weakness lay in the Delay Module’s code, its deployment in Gnosis Pay, or the wider configuration of permissions around card-linked Safe accounts.

Gnosis Pay’s architecture is built around the promise that users can spend digital assets while retaining control of their funds until a card transaction requires settlement. The product uses Safe smart accounts and modules that allow payment authorisations to flow through blockchain-based controls. A Roles Module is used to define permitted card-related actions, while the Delay Module places a short waiting period on certain outbound transactions.

That model has been central to the product’s appeal because it seeks to combine blockchain self-custody with everyday payments. It also means that auxiliary smart-contract modules, rather than only the core wallet itself, become critical security infrastructure. A bug in a module that has spending authority can expose funds even when the underlying wallet framework remains intact.

Gnosis Pay operates as a UK-registered company and markets itself as a decentralised payments network linking traditional finance and decentralised finance. Its debit card is issued by Monavate Limited under a Visa Europe licence, with Monavate authorised and regulated by the Financial Conduct Authority as an electronic money institution. The incident therefore places scrutiny not only on smart-contract design but also on the operational resilience of crypto-linked payment products that rely on regulated partners.

EURe, one of the assets users were told to monitor, is a euro-denominated stablecoin issued by Monerium. GNO is the token associated with the Gnosis ecosystem. The warning to check both assets reflected the immediate concern that attacker-controlled transactions could affect balances held in Gnosis Pay-linked Safes.

The breach adds to a difficult period for Safe-module security. A separate attack days earlier drained about $3m from 86 Safe wallets across Ethereum and Base through a third-party module called SquidRouterModule. That incident has not been linked to the Gnosis Pay exploit, but it sharpened attention on the risks posed by plug-in modules that extend wallet functionality.

Security analysts have long warned that modular account systems need the same level of review as the wallets they extend. Modules are useful because they allow developers to add functions such as spending limits, automated payments, role-based permissions and timed execution. The same flexibility can create a broader attack surface when modules are granted authority over assets.

Gnosis’ pledge to reimburse users may help limit immediate reputational damage, but unanswered questions remain material. Users still need to know which accounts were exposed, whether all malicious queued transactions were stopped, how validators and partners coordinated the response, and when normal card-linked operations can resume safely.

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The Series H round values Anthropic at $965 billion post-money, placing the San Francisco-based company among the most highly valued private technology groups globally. The financing was led by Altimeter Capital, Dragoneer, Greenoaks and Sequoia Capital, with co-lead participation from Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ and XN.

QIA’s participation follows its first investment in Anthropic’s $13 billion Series F round in September 2025, which valued the company at $183 billion. It then took part in the $30 billion Series G financing in February 2026, when Anthropic’s valuation rose to $380 billion. The latest round extends that trajectory, showing how quickly investor expectations around enterprise AI have shifted as companies move from experimentation to large-scale deployment.

The Doha-based sovereign wealth fund, which manages more than $580 billion in assets, has framed the investment as part of a strategy to back category-defining technology companies in AI, software, advanced computing and digital infrastructure. The fund has been expanding its technology exposure as Gulf sovereign investors compete for positions in the most capital-intensive parts of the AI value chain.

Anthropic said the new financing would support safety and interpretability research, expand compute capacity and help scale products and partnerships for Claude, its family of large language models. The company said its run-rate revenue crossed $47 billion earlier this month, reflecting strong demand from enterprise customers using Claude for coding, workflow automation, customer operations, data analysis and productivity tools.

“Claude is increasingly indispensable to our growing global community of customers, and we work tirelessly to make tools like Claude Code and Cowork more helpful, more powerful, and more adaptable to their needs,” said Krishna Rao, Anthropic’s chief financial officer. He said the funding would help the company serve demand, remain at the research frontier and take Claude into more workplace settings.

Anthropic’s rapid valuation climb has been driven by a combination of enterprise adoption, investor appetite for AI infrastructure and its positioning as a major rival to OpenAI, Google DeepMind and other frontier model developers. Its Claude models have gained traction among corporate users partly because of the company’s emphasis on AI safety, reliability and interpretability, areas that have become central to procurement decisions as businesses deploy generative AI across sensitive operations.

The Series H also included $15 billion of previously committed investments from hyperscale cloud partners, including $5 billion from Amazon. Anthropic has deepened its cloud and compute arrangements with Amazon, Google, Broadcom and SpaceX, while adding strategic infrastructure partners Micron, Samsung and SK hynix, whose chips and memory technologies are critical to scaling AI systems.

The company has said Claude is available across Amazon Web Services, Google Cloud and Microsoft Azure, a positioning that gives enterprise customers flexibility while helping Anthropic avoid dependence on a single distribution channel. AWS remains its primary cloud provider and training partner, underscoring the importance of compute access in the race to build and deploy frontier AI models.

QIA’s move also reflects wider competition among sovereign wealth funds for exposure to AI winners. Singapore’s GIC, Abu Dhabi-based MGX and other global institutional investors have appeared across major AI financing rounds as capital requirements soar. Training and serving advanced models now requires billions of dollars in chips, data centres, power capacity and networking equipment, pushing AI companies to seek long-term funding partners with large balance sheets.

For Qatar, the Anthropic investment aligns with efforts to diversify sovereign capital beyond traditional holdings and into sectors shaping future productivity. The fund’s portfolio spans global markets, asset classes and sectors, but technology has become an increasingly visible part of its long-term allocation strategy as states seek influence in the infrastructure behind digital economies.

The investment also carries risks. AI valuations have moved sharply higher in a short period, and private market pricing assumes continued revenue expansion, high enterprise retention and successful conversion of AI tools into durable business workflows. Heavy compute spending may pressure margins, while regulatory scrutiny over AI safety, copyright, data use and competition is intensifying across major markets.

Anthropic’s backers are betting that demand for AI assistants, coding agents and enterprise automation will justify the scale of investment. QIA’s decision to increase its stake through three funding rounds shows confidence that Anthropic can turn rapid adoption into a larger role in the global AI market, while giving the sovereign fund a stronger foothold in one of the most contested technology sectors.

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The systems, unveiled around Computex and Nvidia GTC Taipei, are expected to arrive this autumn from major PC makers including Asus, Dell, HP, Lenovo, Microsoft Surface and MSI, with Acer and Gigabyte models to follow. The launch positions Nvidia not only as the dominant supplier of AI accelerators for data centres, but as a direct force in the personal computer market, where Intel, AMD and Qualcomm have been competing to define the next phase of AI PCs.

RTX Spark is built as a single superchip combining Nvidia’s Blackwell RTX graphics technology, an Arm-based CPU design developed with MediaTek, and unified memory intended to support demanding AI, creative and gaming workloads. Nvidia says the platform can deliver up to one petaflop of AI performance, up to 128GB of unified memory, 6,144 Blackwell RTX cores and up to 20 power-efficient CPU cores, specifications that place it well above conventional thin-and-light laptops aimed at office productivity.

The central pitch is local AI. Instead of sending every prompt, file search or workflow instruction to remote servers, RTX Spark systems are designed to run agents on the device, with the ability to work across Windows applications, process private files, generate media, write code and execute multi-step tasks under user control. Microsoft is building Windows security features for identity, containment and policy controls, while Nvidia is adding OpenShell, a runtime meant to define what agents may access and when cloud models may be used.

That security layer is critical to the commercial case. Businesses and power users are cautious about agents that can see documents, email, code repositories or financial data. Local execution offers lower latency and stronger privacy, but it also raises governance concerns if agents can act too freely across applications. Microsoft and Nvidia are seeking to address that by framing the PC as a controlled agent platform rather than an unbounded automation engine.

Nvidia chief executive Jensen Huang presented RTX Spark as a break from the app-driven computer model that has dominated personal computing for four decades. Microsoft chief executive Satya Nadella cast the partnership as part of a broader ambition to bring “unmetered intelligence” to homes and desks through Windows. Their comments reflect a shared calculation that the next growth cycle in PCs will depend less on faster spreadsheets or browsers and more on machines capable of assisting users across complex tasks.

The first announced designs include premium laptops and compact desktops rather than low-cost mass-market machines. Nvidia says RTX Spark laptops can be as thin as 14 millimetres, weigh about three pounds and ship in 14-inch and 16-inch formats with OLED displays. The desktop versions are aimed at users who want AI agents running continuously at a desk while also supporting creative production and gaming.

Creative software is an early focus. Adobe is optimising Photoshop and Premiere for the platform, with generative tools, editing, colouring and effects expected to benefit from the unified memory and GPU acceleration. Nvidia also says RTX Spark can handle 12K 4:2:2 video editing, large 3D scenes above 90GB, 4K AI video generation and 120-billion-parameter large language models with extended context windows. The gaming pitch includes 1440p play at more than 100 frames per second in supported titles using RTX technologies such as ray tracing, DLSS and Reflex.

The launch also gives Microsoft a fresh route to strengthen Windows on Arm after years of uneven adoption. Apple’s move to its own Arm-based chips reshaped expectations for battery life and performance, while Qualcomm’s Snapdragon X platform helped lift attention around Copilot+ PCs. Nvidia’s entry brings a far more powerful graphics and AI stack into the same segment, potentially changing how developers optimise Windows software.

For PC makers, RTX Spark offers a premium differentiation point at a time when the replacement cycle remains uneven and AI branding has become crowded. Vendors can sell machines not merely as faster laptops, but as local AI workstations for developers, designers, researchers, gamers and corporate users handling sensitive data. Pricing has not been fully disclosed, but the specifications and initial partner line-up suggest a high-end market before any wider consumer rollout.

The competitive implications are significant. Intel and AMD still dominate traditional PC processors, and Qualcomm has been pressing Windows partners to adopt Arm systems with stronger battery life. Nvidia is entering from the opposite end of the market, using its AI software ecosystem, developer tools and graphics base to pull Windows PCs into a higher-performance category. That may force rivals to accelerate local AI roadmaps and deepen their own software partnerships.

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The Hangzhou-based prosthetics and neurotechnology developer, founded in 2015, is seeing demand broaden from medical bionic hands to dexterous hands designed for humanoid robots. Its latest Revo hand models place the company in a fast-growing supply chain where five-fingered manipulation, tactile sensing and lightweight actuator design are becoming central to the race to build robots capable of useful physical work.

The shift marks an important expansion for BrainCo, which built its reputation in brain-computer interface devices and intelligent prosthetics. Its robotic hand technology was originally developed to help amputees perform fine motor tasks through neural and muscle-signal control. The same engineering now has a second market: humanoid robot makers that need reliable hands to grasp tools, sort packages, handle delicate objects and perform repetitive industrial tasks.

BrainCo’s Revo2 dexterous hand weighs about 383 grammes, delivers whole-hand grip force of 50 newtons and can support an external load of 20 kilogrammes. The device uses multi-dimensional tactile sensing to detect pressure, hardness and texture, a feature that allows a robot to adjust grip force rather than merely close its fingers around an object. That capability is increasingly seen as essential for humanoids moving from scripted demonstrations to workplaces where objects vary in shape, weight and fragility.

China’s robotics sector is being reshaped by a wider push into “physical AI”, a term used across the industry for artificial intelligence systems that can act in the physical world. Humanoid developers including Unitree, UBTech, AgiBot, Fourier Intelligence and other start-ups are racing to cut hardware costs, improve reliability and gather training data from real-world movement. Global technology groups are also intensifying their efforts, with Nvidia, Tesla, Figure AI, Boston Dynamics, Agility Robotics, Meta and OpenAI all expanding work connected to humanoids or general-purpose robots.

Robotic hands have emerged as one of the hardest bottlenecks in the sector. Legs can attract public attention through running, dancing and stair-climbing demonstrations, but hands determine whether a humanoid can complete economically valuable tasks. A machine that cannot manipulate objects with precision remains limited in warehouses, factories, care settings and homes.

The market opportunity has drawn capital into specialist component makers. Global investment in robotics and physical AI grew from about $4bn in 2019 to $26bn in 2025, with more than $23bn raised this year by companies in the field. China has become one of the most competitive centres for hardware suppliers because of its manufacturing depth, electronics supply chains and aggressive pricing.

BrainCo is not alone. LinkerBot, founded in 2023, has become a prominent producer of dexterous robotic hands, with backers including Ant Group, HongShan and Fosun Capital. Its low-cost hands, sold in China from about $600, have been positioned for mass deployment in humanoids and industrial systems. The company has been reported to hold a large share of high-degree-of-freedom robotic hand shipments, underscoring the intensity of competition in a field that could determine which robot makers can scale fastest.

BrainCo’s advantage lies in its dual background in prosthetics and neural interfaces. Medical bionic hands require comfort, weight control, responsiveness and safety around humans. Those same attributes are valuable in humanoid robotics, where bulky or fragile hands increase energy use and reduce operational reliability. The company’s experience in user-facing assistive devices also gives it data on natural hand movement, grip patterns and control systems.

The commercial challenge remains substantial. Humanoid robots are still expensive, difficult to maintain and far from replacing workers across broad categories of labour. Many deployments remain pilot projects, and investors are scrutinising whether highly publicised robot demonstrations can translate into durable sales. Dexterous hands also face trade-offs between price, durability, precision, grip strength and repairability, especially when used in industrial environments.

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San Mateo-based GoPro, founded by Nicholas Woodman, updated its 2025 financial statements to include the warning after developments that followed its annual report filing in March. The disclosure adds to concerns already visible in its first-quarter performance, where revenue fell, gross margins collapsed and losses widened as the company struggled with higher component costs and softer demand for its core camera products.

The company said prices for critical memory components had increased by more than 80 per cent, with further increases expected through 2026. The pressure has been amplified by the global shift of memory capacity towards artificial intelligence data centres, where demand for high-bandwidth memory and advanced DRAM has drawn supply away from consumer electronics makers. That has left device companies exposed to higher costs, tighter availability and less flexibility in pricing.

GoPro’s financial position has become more fragile because the cost shock has arrived at a time when its revenue base is already shrinking. First-quarter revenue stood at $99m, down 26 per cent from a year earlier. Hardware revenue dropped by nearly a third, while sell-through fell to about 313,000 camera units, down 29 per cent. Subscription and service revenue, a business line that has helped cushion the company against hardware volatility, was broadly flat at $27m, but subscriber numbers declined 8 per cent to 2.26m.

Margins showed the scale of the squeeze. GoPro’s GAAP gross margin fell to 4.3 per cent from 32.1 per cent a year earlier, partly reflecting a $24.5m charge linked to component purchase commitments and a $4.5m sale of slow-moving inventory. The company posted a GAAP net loss of $81m, compared with a loss of $47m in the same quarter last year. Adjusted EBITDA was negative $50m, compared with negative $16m a year earlier.

The going-concern warning also points to pressure from GoPro’s financing arrangements. The company has a $50m second-lien term loan with Farallon Capital Management, a revolving credit facility with Wells Fargo Bank, and an agreement with YA II PN Ltd for up to $50m in convertible debentures. These arrangements include covenants and cross-default provisions, meaning a breach in one facility could trigger consequences across others.

GoPro has warned that the going-concern language may itself be treated as an event of default under its credit agreements. It has also said it does not expect to comply with certain future covenants, including minimum liquidity, EBITDA and asset-coverage requirements. If lenders were to accelerate repayment obligations, GoPro has said it does not expect to have sufficient liquidity to meet all amounts due.

Management is seeking waivers or amendments from lenders, evaluating additional financing, considering asset sales and reviewing strategic alternatives. The company has also authorised a process to assess options including a sale, merger or other transaction, with Houlihan Lokey engaged as financial adviser. These moves come alongside another restructuring plan that is expected to reduce global headcount by about 23 per cent and result in severance costs of roughly $11.5m to $15m.

GoPro is attempting to reposition its business beyond mainstream action cameras. Its MISSION 1 Series, aimed at professional imaging markets, has been presented as a key product initiative. The company has also highlighted potential opportunities in defence, aerospace and strategic partnerships, arguing that its technology, intellectual property and brand carry value beyond its legacy consumer hardware base.

Those efforts face a difficult market backdrop. GoPro continues to rely heavily on camera, mount and accessory sales, a category exposed to discretionary spending, retailer inventory cycles and competition from smartphones and lower-cost imaging devices. Its brand remains widely recognised, but the wider consumer electronics market has become more challenging as component inflation forces manufacturers either to absorb costs or raise prices at a time when buyers are more selective.

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The proposed combination with Titan, a Nasdaq-listed special purpose acquisition company, has been approved by both boards and is expected to close in the fourth quarter of 2026, subject to Titan shareholder approval, regulatory filings and other customary conditions. Once completed, OpenPayd is expected to trade on Nasdaq under the ticker symbol OP.

The transaction could provide OpenPayd with up to $276 million in gross proceeds from Titan’s trust account, assuming no redemptions by public shareholders. That caveat remains important because SPAC deals can see proceeds reduced sharply if investors choose to redeem shares before completion. The capital is intended to strengthen OpenPayd’s balance sheet, support regulatory licensing, fund hiring and accelerate its push into the US market.

OpenPayd operates a platform that allows digital businesses to move and manage money across fiat currencies, payment rails, blockchain networks and stablecoins through a single application programming interface. Its services include embedded accounts, foreign exchange, domestic and international payments, open banking and stablecoin on-and-off ramps.

The company says it serves more than 1,100 customers across 180 countries, including eToro, Kraken, OKX and B2C2. It processes more than $240 billion in annualised transaction volume and had more than $85 million in annualised recurring revenue as of March 2026. The figure is based on March revenue multiplied by 12 and is not a standard accounting measure, leaving investors to assess how far it reflects durable future revenue.

OpenPayd’s move comes as payments infrastructure companies position themselves for a market shaped by faster settlement, embedded finance, stablecoins and automated financial workflows. Businesses that once relied on banks and separate providers for accounts, transfers, foreign exchange and settlement increasingly seek unified platforms that can handle cross-border money movement with fewer operational layers.

The company’s strategy is built around the convergence of traditional finance and digital assets. Stablecoins have moved from a niche crypto instrument into a payment and settlement tool watched closely by banks, fintech firms and regulators. That shift has created opportunities for infrastructure providers able to connect regulated fiat payment channels with blockchain-based rails while meeting compliance expectations across jurisdictions.

Iana Dimitrova, OpenPayd’s chief executive, has framed the deal as a milestone that reflects the scale of the company’s platform, regulatory reach and profitable growth. Founder Ozan Ozerk has argued that the next phase of finance will be shaped by money movement that is increasingly programmable, including transactions initiated by autonomous software agents.

Titan is led by chairman and chief executive Frank Mastrangelo and has positioned itself as a SPAC focused on high-growth fintech and financial technology businesses. Titan raised $276 million in its initial public offering in April 2025 through the sale of 27.6 million units at $10 each, including the full exercise of the underwriters’ over-allotment option.

The deal advisers include Anne Martina as lead M&A adviser to OpenPayd, A&O Shearman as legal counsel, Deloitte as financial auditor and Burson Buchanan as strategic communications adviser. Winston & Strawn is advising Titan, while Cantor Fitzgerald is acting as capital markets adviser to the SPAC.

The merger also reflects a broader reopening of selective public-market routes for fintech companies after a difficult period for high-growth listings. Public investors have become more demanding on profitability, regulatory discipline and revenue quality, particularly after earlier SPAC-era deals struggled with missed forecasts, high redemptions and weak post-listing performance.

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The move gives the US-based company a licensed presence in DIFC as it seeks to expand demand for AI inference systems across the Middle East, Africa and South Asia. Positron AI has raised more than $300 million, including a $230 million Series B round completed this year at a valuation above $1 billion, strengthening its position among a new class of AI chip and infrastructure companies trying to reduce the cost and power burden of running artificial intelligence models.

The company’s Dubai presence comes as inference, the process through which trained AI models generate responses and perform tasks in real-world use, becomes a larger driver of computing demand. Training has dominated the first phase of the generative AI investment cycle, but commercial deployment is shifting attention to systems that can handle high-volume model use at lower cost, with better memory capacity and more efficient energy consumption.

Positron AI develops purpose-built hardware and software for inference workloads. Its first-generation server, Atlas, is designed to support large language model inference for small and medium-sized models of up to 500 billion active parameters. The company says Atlas can deliver performance comparable to Nvidia DGX-H100 systems while using less power and operating at lower cost, although such performance claims remain subject to market testing and customer validation.

The company’s next-generation product, Titan, is scheduled for delivery in the first quarter of 2027 and is expected to support frontier AI models. Positron AI is also developing Asimov custom silicon, aimed at memory-intensive applications such as video, trading, long-context model use and very large-scale AI systems. The company has positioned memory density and energy efficiency as central points of differentiation against traditional graphics processing unit-based infrastructure.

Husni Khuffash, managing director for MENA at Positron AI, said the DIFC presence reflects the company’s commitment to power-efficient and deployable inference solutions. He said establishing operations at the financial centre would allow Positron to work more closely with industry, regulators and partners while contributing to Dubai’s ambition to build competitive digital and AI capabilities.

DIFC has become a magnet for financial technology, artificial intelligence and innovation companies seeking regional access, regulatory proximity and links to capital. The centre hosts thousands of active registered firms, including banks, asset managers, insurers, brokerage firms, wealth managers, hedge funds and technology ventures. Its innovation ecosystem has expanded as Dubai positions itself as a global base for AI adoption in finance, professional services and enterprise operations.

Mohammad Alblooshi, chief executive of DIFC Innovation Hub, said Positron AI’s arrival adds to the Dubai AI Campus ecosystem and supports the centre’s plans to evolve into an AI-native jurisdiction. The company’s focus on inference infrastructure fits with Dubai’s push to develop practical AI deployment, governance frameworks and trusted digital services across regulated industries.

The expansion also reflects intensifying competition in AI hardware. Nvidia remains the dominant supplier of advanced AI accelerators, but customers have been looking for alternatives because of high costs, supply constraints, energy requirements and concerns over dependence on a single supplier. Major technology companies and specialist start-ups are investing heavily in inference platforms as AI moves from experimentation to large-scale commercial use.

Positron AI’s funding round was backed by investors including ARENA Private Wealth, Jump Trading and Unless, with participation from Qatar Investment Authority, Arm, Helena, Valor Equity Partners, Atreides Management, DFJ Growth, Resilience Reserve, Flume Ventures and 1517. The investor mix points to demand from both financial markets and infrastructure players for systems capable of supporting high-throughput, low-latency AI workloads.

Dubai’s appeal to AI infrastructure firms is reinforced by wider economic policy. DIFC’s planned Zabeel District expansion is designed to increase office, residential, hospitality and innovation capacity, with space allocated for future technologies and an AI campus. The wider Dubai AI Campus is being positioned to host hundreds of companies, attract investment and train thousands of professionals as the city tries to capture a larger share of global AI spending.

For Positron AI, the DIFC office is expected to serve as a regional base for engagement with enterprise customers, financial institutions, sovereign technology programmes and infrastructure partners. The company has indicated that more offices in the region will follow, suggesting that Dubai is being used as the first stage of a broader international expansion rather than a single representative outpost.

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The shift marks a notable change in the way one of the world’s largest cloud operators builds the internal networks that connect servers, storage systems and routers across its data centres. The new design, called Resilient Network Graphs, moves away from the conventional “fat-tree” model that has dominated hyperscale cloud infrastructure for years and uses a more distributed layout intended to create multiple efficient paths for data.

AWS says the architecture can cut the number of routers by about 69 per cent, increase data throughput by up to 33 per cent and reduce network equipment electricity consumption by around 40 per cent. Internal cost comparisons indicate that the system could be as much as 45 per cent cheaper than traditional designs at scale, depending on workload and deployment model.

The technology has already been deployed in production facilities in Europe, beginning near Dublin at the end of 2024, with additional rollouts in Germany and Spain. By April 2026, quasi-random wiring had become the default design for most new AWS data centre builds globally, although older sites are expected to move gradually as hardware refresh cycles allow.

The network redesign comes as cloud providers face sharply rising demand from artificial intelligence, video, enterprise software, financial services and data-heavy consumer applications. Generative AI workloads, in particular, require high-bandwidth, low-latency communication between large clusters of processors, making the efficiency of internal data centre networks more commercially significant.

Traditional fat-tree networks use a layered structure, with traffic passing through set tiers of switches and routers. That model is predictable and relatively easy to operate, but it can create bottlenecks when traffic flows become uneven or when workloads require large numbers of servers to exchange data at high speed. AWS’s new model introduces a flatter arrangement in which routers are connected through a partly fixed and partly randomised pattern, giving data more possible paths across the network.

A central part of the system is a passive optical component known as ShuffleBox. It allows AWS to preserve the benefits of random graph-style connectivity without turning physical cabling into an unmanageable engineering problem. The device does not require electrical power and helps standardise connections between routers and server rooms, reducing the complexity that would otherwise come with quasi-random wiring across large sites.

AWS also developed a routing system designed to spread traffic across multiple available paths rather than relying on rigid, pre-determined routes. That approach is intended to improve resilience when links become congested or fail, while also raising the total amount of traffic the network can carry under demanding conditions.

The practical significance for customers is that the change should be invisible at the application level. Database queries, storage operations, cloud APIs and machine learning jobs should continue to run without code changes, while gaining from a more efficient underlying fabric. For Amazon, however, the gain is strategic: fewer routers mean lower capital spending, reduced power draw, less cooling demand and simpler operations at a time when data centre expansion is constrained by electricity supply in several markets.

Europe has become an important testing ground because cloud operators are expanding capacity while facing pressure over grid connections, land use, energy consumption and sustainability targets. Data centre developers in some European markets have faced long waits for power connections, making efficiency improvements commercially valuable beyond their engineering appeal.

AWS’s move also reflects a broader industry race to redesign infrastructure for AI-era computing. Cloud providers are investing in liquid cooling, higher-density server racks, custom chips, optical links and more flexible data centre layouts. The network layer is now part of that competitive push, as the cost and speed of moving data between processors can determine how efficiently expensive AI hardware is used.

The company’s work draws on long-standing academic research into random graph networks, an area that has promised strong performance and fault tolerance but was difficult to deploy in real-world facilities because of cabling and routing challenges. AWS’s claim is that its combination of quasi-random topology, passive optical hardware and new routing software has made the concept workable at hyperscale.

Competitors are pursuing their own approaches. Google has used optical switching in machine learning supercomputers, Microsoft continues to expand specialised AI infrastructure, and several chip and networking suppliers are pushing faster Ethernet, InfiniBand and optical interconnect technologies for large accelerator clusters. AWS’s decision to make Resilient Network Graphs a default design signals that data centre networking is becoming a core battleground in cloud economics rather than a background engineering function.

The financial implications could be considerable if the technology performs consistently across regions and workloads. Amazon’s cloud division remains a major profit engine for the group, and even modest efficiency gains can translate into large savings when applied across hundreds of facilities and millions of servers. Lower equipment counts may also help AWS respond to environmental scrutiny by reducing embodied hardware demand and ongoing electricity use.

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The 12-layer HBM4E product delivers bandwidth of up to 3.6 terabytes per second per stack, with stable pin speeds of 14 gigabits per second and scalability to 16 gigabits per second. The chip is built using Samsung’s sixth-generation 10-nanometre-class DRAM process, known as 1c, and a 4-nanometre logic base die, combining memory manufacturing with advanced foundry technology.

The shipment marks a significant attempt by Samsung to regain momentum in a market dominated by SK Hynix, which has led supplies of high-bandwidth memory to major AI chipmakers. HBM, a stacked memory technology designed to move data at very high speed with lower power consumption, has become critical to AI accelerators used for training and running large language models.

Samsung’s HBM4E samples are more than 20 per cent faster than its HBM4 generation and offer higher capacity in a 48GB stack. The improvement is aimed at customers designing next-generation AI platforms, where memory bandwidth has become a key constraint as processors demand faster data access and higher energy efficiency.

The company’s move comes only months after it began shipping HBM4, narrowing a gap that had weighed on its semiconductor business. Samsung’s memory division had lost ground during the HBM3E cycle, when SK Hynix secured a leading position in supplying advanced memory for Nvidia’s AI processors. Micron has also expanded aggressively in the segment, turning HBM into one of the fastest-growing parts of its memory portfolio.

Investors responded positively to Samsung’s announcement, with its shares rising sharply after the shipment was disclosed. The market reaction reflected expectations that an earlier qualification window could help Samsung capture more orders as AI chip designers prepare platforms for 2027 and beyond.

The timing is important because customers usually take months to qualify HBM products before placing large-volume orders. Early sample shipments allow chipmakers and cloud computing customers to test performance, thermal behaviour, packaging compatibility and power efficiency before finalising supply arrangements. Winning qualification is often more important than announcing specifications, as AI hardware makers demand stable yields and predictable delivery at scale.

Samsung’s lead in sample shipments does not guarantee immediate commercial dominance. SK Hynix remains the strongest player in HBM by revenue share and has deep relationships with AI accelerator makers. Its existing position in HBM3E and HBM4 gives it an advantage in customer trust, yield learning and production allocation. Micron, meanwhile, has benefited from tight supply conditions and strong demand from data-centre customers seeking alternative suppliers.

Even so, Samsung’s HBM4E launch changes the competitive tone. The company has emphasised its ability to combine DRAM, logic base die design, advanced packaging and foundry production under one roof. That integrated model could become more valuable as HBM products become increasingly customised for AI processors. Future versions are expected to require closer cooperation between memory suppliers, foundries and chip designers.

The broader market backdrop remains favourable. AI server demand has pushed HBM from a specialised memory product into a strategic semiconductor category. Advanced GPUs and custom AI accelerators require multiple HBM stacks placed close to the processor, allowing large volumes of data to move quickly between compute engines and memory. As model sizes increase, customers are demanding more capacity, higher bandwidth and better power efficiency.

HBM supply remains constrained because production requires advanced wafer processing, through-silicon vias, precision stacking and sophisticated packaging. These technical hurdles make rapid capacity expansion difficult. Suppliers that can qualify earlier and produce reliably are likely to secure premium pricing and long-term supply contracts.

Samsung’s latest product is expected to be assessed for use in next-generation AI platforms from leading chip designers. Nvidia, AMD and major cloud companies are all working on systems that will require higher memory bandwidth and larger memory capacity. The shift towards rack-scale AI systems, liquid cooling and increasingly dense accelerator clusters is likely to intensify demand for HBM4 and HBM4E.

The competitive race is also reshaping capital spending across the memory industry. Samsung, SK Hynix and Micron are allocating more investment to advanced DRAM lines, packaging capacity and process technology. That shift could reduce the industry’s reliance on conventional memory cycles, though HBM still carries execution risks because qualification failures can delay revenue and leave capacity underused.

Samsung’s challenge is to convert early samples into customer approvals and volume orders. The company must prove that its HBM4E can meet performance claims under real operating conditions while maintaining yields high enough for mass production. Customers will also compare thermal management, power efficiency and packaging reliability against alternatives from SK Hynix and Micron.

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The strategic partnership gives GAPP’s network of enterprise clients and secondary distributors a formal channel to Huawei Cloud’s portfolio, including cloud-native systems, AI platforms, backup services and business continuity tools. The arrangement is designed around local regulatory and operational requirements, an increasingly important factor for companies handling sensitive data in finance, public services, healthcare, logistics and industrial sectors.

The agreement places GAPP in a central role in Huawei Cloud’s Saudi commercial expansion, particularly among organisations seeking managed access to scalable computing without building large in-house infrastructure. It also strengthens Huawei’s partner-led model in a market where cloud adoption is being shaped by data sovereignty rules, rising AI demand and large-scale digital programmes linked to Vision 2030.

Saudi Arabia has become one of the Middle East’s most competitive cloud markets as global technology groups race to secure enterprise and government demand. Huawei launched its Riyadh cloud region in September 2023, structured around three availability zones to support high-availability services and local data hosting. The region has since been positioned as a hub for cloud, AI and digital services across Saudi Arabia and neighbouring markets.

The GAPP agreement gives Huawei a broader distribution mechanism at a time when companies are moving beyond basic cloud migration towards AI-enabled applications, analytics, automation and resilient infrastructure. For local enterprises, the partnership could reduce procurement complexity by linking Huawei’s technology stack with a distributor familiar with domestic compliance, partner ecosystems and sector-specific needs.

Saudi Arabia’s cloud services market is projected to expand sharply over the next five years, driven by public-sector digitisation, private-sector modernisation and heavy investment in data centres. Market estimates place the sector at more than $5bn in 2026, with forecasts pointing to double-digit annual growth through 2031 as companies shift workloads from legacy systems to cloud platforms.

Artificial intelligence is becoming a central driver of that demand. Saudi Arabia’s national data and AI strategy sets out ambitions to place the kingdom among leading global AI economies by 2030, while large entities linked to the Public Investment Fund are investing in computing infrastructure, data centres and AI services. Enterprises are also seeking Arabic-language AI capabilities, localised applications and secure hosting models that meet regulatory standards.

Huawei’s partnership with GAPP comes as competition intensifies among hyperscalers and cloud infrastructure providers. Amazon Web Services has committed more than $5.3bn to establish a Saudi cloud region by 2026. Oracle has expanded its cloud footprint, including Riyadh and Jeddah capacity, while Google Cloud has been tied to a major AI hub initiative with the Public Investment Fund and Humain. Microsoft, Alibaba Cloud and other providers are also pursuing regional opportunities as Saudi Arabia positions itself as a technology and AI centre.

For Huawei, the distribution agreement offers a way to deepen customer access without relying solely on direct sales. That approach is particularly relevant in a market where secondary distributors, systems integrators and managed service providers influence buying decisions for mid-sized companies and sector-specific clients. GAPP’s role could help Huawei reach enterprises that need deployment support, local billing channels, migration planning and post-sales technical assistance.

The agreement also reflects a shift in the Saudi cloud market from infrastructure availability to ecosystem development. Businesses are no longer evaluating cloud providers only on storage, computing power and cost. They are also assessing compliance, disaster recovery, AI readiness, cybersecurity, latency, integration capacity and long-term support. Local distribution partnerships can therefore become decisive in converting cloud availability into sustained enterprise adoption.

Regulation remains a defining feature of the market. Cloud service providers operating in Saudi Arabia must navigate licensing, data classification, cybersecurity and hosting expectations overseen by domestic authorities. Government-related workloads and sensitive sectors often require stronger assurances on data residency and operational continuity. Huawei and GAPP are positioning their partnership around those requirements, with an emphasis on compliant access to cloud and AI services.

The timing also reflects growing enterprise caution over AI deployment. Companies want productivity gains from generative AI and automation but remain concerned about privacy, accuracy, intellectual property and governance. Cloud providers offering locally hosted AI tools and structured controls may gain an advantage among organisations that want to test AI use cases without exposing sensitive data to poorly governed external platforms.

GAPP’s appointment could open fresh opportunities for Saudi resellers and technology partners that want to build services on top of Huawei Cloud. These could include managed backup, disaster recovery, application hosting, AI-powered analytics, enterprise resource planning modernisation and sector-specific platforms for retail, manufacturing, finance and logistics.

Huawei is likely to use the partnership to reinforce its position among organisations seeking alternatives to Western hyperscalers, while GAPP gains access to a cloud portfolio aligned with Saudi Arabia’s growing demand for resilient digital infrastructure. The broader test will be whether the partnership can translate market momentum into enterprise deployments that meet compliance expectations, deliver measurable efficiency gains and support the kingdom’s expanding AI ambitions.

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CapitalG, Alphabet’s independent growth fund, led the round, with participation from NVentures, the venture capital arm of NVIDIA, as well as ServiceNow Ventures, MongoDB Ventures, Snowflake Ventures, Databricks Ventures, AMP PBC and Pace Capital. Existing backers Andreessen Horowitz and Menlo Ventures also took part, strengthening the company’s investor base at a time when enterprises are shifting from experimental AI projects to production systems that require reliability, cost control and governance.

The New York-based company, founded in 2023, has emerged as a marketplace and gateway for AI models. Its platform allows developers to connect through a single application programming interface and access more than 400 models from major providers including Anthropic, Google, OpenAI, xAI and DeepSeek. The service is designed to route each request to the most suitable model or provider, depending on factors such as cost, latency, capability, availability and data-handling requirements.

The funding round values OpenRouter at about $1.3 billion post-money, more than double the level reached after its $40 million Series A last year. That rapid increase reflects rising demand for infrastructure that sits between AI applications and the model providers powering them. As companies deploy AI agents, coding assistants, research tools and customer service systems, the operational challenge is moving beyond selecting the most powerful model and towards managing a shifting market of models, prices and performance levels.

OpenRouter’s usage figures underline that shift. Weekly volume on the platform has grown from 5 trillion tokens to 25 trillion tokens over six months, equivalent to about 100 trillion tokens a month. The company says it is on pace to process more than a quadrillion tokens this year and serves more than 8 million users, including AI-native start-ups, individual developers and large enterprises.

The expansion highlights a broader change in the AI economy. During the first wave of generative AI adoption, attention was concentrated on frontier model developers and the cost of training increasingly large systems. The market is now placing greater value on inference, the process of running models in live applications. That shift has created demand for gateways, monitoring tools, optimisation software and enterprise controls that can make AI systems more dependable and less expensive to operate at scale.

OpenRouter’s model reflects that transition. Instead of asking companies to standardise on one AI provider, it offers a layer that can switch between multiple providers. A software team might use a high-end reasoning model for complex coding, a cheaper model for summarising documents and a faster provider for customer-facing chat. Automated failover also allows requests to move to another provider when one service slows down or becomes unavailable.

Enterprise controls are becoming a central part of the company’s proposition. OpenRouter has been expanding workspaces, spending management, guardrails and data-retention options, aiming to address concerns from organisations that need audit trails, budget oversight and clearer internal rules on who can use which models. The company also supports multimodal inference, including text, image, audio, speech, transcription, embedding and video models, widening its role beyond conventional chatbot applications.

The investor line-up points to the strategic importance of the gateway layer. Alphabet and NVIDIA already occupy influential positions in AI through cloud services, chips, model development and software ecosystems. Their backing of OpenRouter signals that major technology groups see value in the routing layer even as they continue to build or support their own model offerings. Participation by enterprise software and data platform investors also reflects demand from business customers seeking a neutral control plane for AI deployment.

Competition in this market is likely to intensify. Cloud platforms, model providers and specialist start-ups are all trying to capture the infrastructure spending that follows AI adoption. Large providers may bundle routing and governance features into their own platforms, while independent gateways must persuade enterprises that neutrality, flexibility and performance visibility are worth paying for.

OpenRouter’s advantage lies in its position inside production traffic. By observing how models perform across real-world workloads, the platform can provide usage data, rankings and routing decisions that become more valuable as more developers use the system. That network effect could help the company become a central intermediary in the AI supply chain, though it also raises expectations around uptime, transparency, pricing and data protection.

The new capital will be used to expand routing, governance and optimisation capabilities as organisations move deeper into multi-model AI deployment. The funding also gives OpenRouter more room to invest in reliability and enterprise features, areas that are becoming critical as AI agents take on longer and more complex workflows across software development, data analysis, customer support and internal operations.

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The agreements place Mistral at the centre of a widening European effort to build AI capability around strategic industries rather than relying mainly on US and Chinese technology providers. The company, founded in 2023, is seeking to turn its large language models and industrial AI tools into practical systems for manufacturers, aerospace groups, banks, energy companies and public-sector clients.

BMW will use Mistral’s technology to improve crash simulation, a field that relies on large volumes of proprietary engineering data and complex physical modelling. The collaboration is designed to increase the quality, accuracy and speed of vehicle development work, beginning with crash testing before expanding into other parts of BMW’s engineering and production chain.

The partnership is expected to help engineers analyse virtual crash tests faster and refine models used in safety design. Automakers run large numbers of simulations before vehicles reach physical testing, making crash modelling one of the most data-intensive areas of vehicle development. Better AI tools could reduce development time, support more precise safety assessments and improve the way engineering teams interpret simulation results.

Airbus has signed a separate agreement with Mistral to expand AI use across commercial aircraft, helicopters, defence and space. The aerospace group plans to apply the technology from design work to onboard capabilities, while maintaining strict requirements on security, confidentiality and sovereignty. That positioning is central for an aircraft maker handling sensitive defence, space and military aerospace applications.

The Airbus agreement gives Mistral a high-profile opening into one of Europe’s most strategic industries. Aerospace systems demand high levels of reliability, explainability and control, making them a tougher proving ground than many consumer-facing AI applications. The deal also reflects the growing appetite among European industrial groups for AI systems that can be customised around confidential data without placing core intellectual property under the control of overseas platforms.

Mistral’s industrial push follows its acquisition of Emmi AI, an Austrian company specialising in physics-based AI models for engineering applications. Emmi’s technology is designed to accelerate simulations involving airflow, heat transfer, material stress and other demanding physical processes. Its team is being integrated into Mistral’s science and applied AI operations, strengthening the start-up’s ability to serve aerospace, automotive, semiconductor, energy and manufacturing clients.

The acquisition gives Mistral a deeper foothold in so-called physical AI, where models are built to understand real-world engineering constraints rather than only generate text, images or code. That distinction is important for industrial users, which need AI systems to support decisions involving safety, cost, regulation and product performance. For BMW and Airbus, the value lies not only in automation, but in AI that can work with domain-specific datasets and technical workflows.

Mistral has been positioning itself as a European alternative to dominant US technology groups. Its valuation reached about €11.7 billion in 2025, and the company has been expanding its workforce, customer base and computing infrastructure. It is also targeting revenue of €1 billion or more for 2026, underlining the pressure to convert its reputation into commercial scale.

The company has said it plans to invest heavily in computing capacity, including a new data centre in Les Ulis, France. The facility is expected to add 10 megawatts of computing power in the second half of 2026, complementing its existing sites in France and Sweden. Mistral’s broader plan includes reaching 200 megawatts of capacity by the end of 2027 and 1 gigawatt by 2030, supported by a multibillion-euro investment programme.

That infrastructure expansion reflects a central challenge for European AI companies. Advanced models require immense computing power, and much of the global AI infrastructure remains tied to US cloud providers and chip supply chains. Mistral continues to use high-performance chips from global suppliers, but its strategy aims to give European clients more control over where models run, how data is handled and how AI systems are governed.

The Airbus and BMW agreements also arrive as governments and companies across Europe sharpen their focus on technological sovereignty. Defence, aerospace, mobility and energy are among the sectors where dependence on foreign AI platforms is increasingly seen as a strategic risk. Mistral’s executives have argued that Europe needs its own AI capabilities because competitors and adversaries are already deploying the technology across sensitive domains.

The defence dimension brings scrutiny as well as opportunity. AI use in warfare and military systems has drawn criticism from religious leaders, rights groups and policy experts, who warn that automated decision-making could outpace regulation and accountability. Mistral has defended the development of European AI tools for defence, arguing that strategic autonomy requires credible capabilities when rival powers are already applying AI to security and military operations.

For Airbus, the immediate focus is trusted AI in aerospace operations and product development. For BMW, the priority is safer and faster vehicle engineering. For Mistral, the broader objective is to show that Europe can compete not by copying consumer chatbot models, but by embedding AI into the industrial base that has long underpinned the region’s economic strength.

Competition remains formidable. US technology giants control vast cloud platforms, model ecosystems and enterprise relationships, while Chinese groups are accelerating state-backed AI development. Mistral’s path depends on turning specialised European partnerships into durable revenue and proving that customised industrial models can outperform general-purpose systems in high-value engineering tasks.

The article Mistral deepens Europe’s industrial AI push appeared first on Arabian Post.

The update brings several desktop-style controls to iOS and Android, with playlist folders and in-playlist bulk actions being rolled out globally to all users. Premium subscribers gain additional tools, including queue bulk editing, one-tap reshuffling and background downloads on iOS, a change aimed at making offline listening more reliable when the app is closed or network access is weak.

The move addresses a long-standing gap in Spotify’s mobile experience. Playlist folders, first associated with desktop library management, allow users to group playlists by mood, genre, activity, project or personal preference. Until now, many users who built detailed libraries still needed desktop access for full folder organisation, even though mobile listening dominates daily use. The new mobile option lets users create and manage folders directly from the Library section, reducing friction for people who rely almost entirely on smartphones.

Bulk editing is designed to speed up playlist maintenance. Users can select multiple tracks, podcast episodes or audiobook items inside a playlist and move or remove them together. The feature is particularly useful for large playlists, collaborative collections and libraries built over several years, where editing one item at a time can become cumbersome. Spotify is also restoring wider queue control for Premium users, allowing multiple songs in the play queue to be selected and rearranged or removed in one action.

The reshuffle button gives Premium mobile users a quicker way to change the order of a shuffled playlist without switching shuffle off and on again. The feature is relatively small but reflects Spotify’s focus on habitual listening behaviour, where users often return to the same playlists and want variation without building a new queue. It also complements the company’s broader push into personalisation, including algorithmic recommendations, AI-assisted playlist tools and automated sequencing features.

Background downloads on iOS tackle another common pain point. Premium users downloading music, podcasts or other audio will now be able to continue that process while Spotify is not open, with progress notifications showing the status. The change is aimed at listeners preparing for flights, commuting through areas with poor coverage, or travelling in places where mobile data is unreliable or expensive. Offline listening remains a key advantage for paid subscribers, and making downloads more dependable strengthens the distinction between Spotify’s free and Premium tiers.

The rollout comes as Spotify’s business is leaning more heavily on paid users, retention and product quality rather than pure subscriber expansion. The company reported 761 million monthly active users and 293 million Premium subscribers in the first quarter of 2026, with revenue of about €4.5 billion and operating income above €700 million. Premium subscribers remain the main engine of revenue, while advertising has faced uneven demand across digital media markets.

Spotify’s mobile changes also arrive during a phase of intensified competition in streaming. Apple Music, YouTube Music, Amazon Music and regional platforms continue to compete on catalogue access, audio quality, pricing, artist tools and bundling. Spotify’s advantage has often rested on discovery, playlists and cross-device familiarity, but user complaints about app complexity and feature gaps have grown as the service expanded beyond music into podcasts, audiobooks and video.

The latest update signals a shift towards improving core library control rather than adding headline-grabbing formats. For heavy users, the ability to organise folders on mobile may be more meaningful than another discovery feature, because it changes how they manage years of saved content. For casual users, the benefits may be less visible, though bulk edits and better downloads can still reduce irritation in everyday use.

The changes also reflect Spotify’s attempt to serve different types of listeners within one app. Some users depend almost entirely on algorithmic recommendations, while others build highly curated libraries and expect the precision of a file-management system. Playlist folders and bulk editing cater to the latter group, while reshuffle and queue controls support more flexible, lean-back listening.

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