Tag: Artificial intelligence

  • AustralianSuper Holds A$3.05 Billion in Nvidia as It Targets AI Infrastructure

    AustralianSuper Holds A$3.05 Billion in Nvidia as It Targets AI Infrastructure

    AustralianSuper is focusing its artificial intelligence investments on chipmakers and hyperscaler data centres as turbulence hits global technology equities. Australia’s largest pension fund oversees A$430 billion ($301.7 billion) in assets from its Melbourne headquarters.

    Recent regulatory filings reveal the fund holds roughly A$3.05 billion worth of Nvidia stock within its balanced portfolio alone, alongside additional holdings in other fund options. It also maintains equity stakes valued at about A$2.3 billion each in tech giants Microsoft and Alphabet.

    Mark Hargraves, head of international equities and private equity at AustralianSuper, detailed the strategy on October 28. Beyond its primary holdings, the institution owns stakes in SK Hynix, SpaceX, Meta, Broadcom and Micron.

    Hardware Over Software Deployment

    Institutional allocators in the Asia-Pacific region are redirecting capital away from consumer-facing software applications toward the base physical infrastructure required to operate generative models. Chip foundries, memory makers and server rack builders secure immediate capital expenditure, while application developers face longer roads to operational profit.

    High valuations across global equity markets have prompted fund managers to trim speculative software stakes. By holding physical suppliers such as SK Hynix and Micron, institutional investors capture enterprise spending budgets regardless of which retail or corporate applications ultimately dominate consumer markets.

    Concentration in Global Tech Portfolios

    Hargraves noted that artificial intelligence and related supply chain businesses now represent approximately 40 per cent of global equity benchmarks. The speed of this sector’s expansion surpassed any single market theme observed over his three decades managing institutional equities.

    Capital requirements for infrastructure remain massive. Large language models demand extensive compute capacity, funneling money directly into hyperscale server facilities and the semiconductor fabricators that supply them.

    Venture Stakes and Private Valuation Tests

    Beyond public equity holdings, AustralianSuper entered artificial intelligence developer Anthropic as an early backer prior to any public listing. The United States software maker is preparing for a public offering that could value the business between $1.5 trillion and $2 trillion.

    Hargraves would not comment on the fund’s intention in the upcoming share sale. Financial disclosures filed during the listing process will provide outside investors with concrete audit figures on revenue and operational spending across major foundation model developers.

    Next Steps for Institutional Allocators

    The fund maintains its active rebalancing mandate, adjusting exposure sizes as quarterly earnings reports test current market multiples. Institutional portfolios across Australia and the wider region face continuous reviews as companies publish capital expenditure totals for upcoming infrastructure buildouts.

    Investors now await the formal filing paperwork from Anthropic to evaluate true operating margins for commercial language models against hardware depreciation costs.

  • Foreign Funds Withdraw $50 Billion from Asian Equities in October

    Foreign Funds Withdraw $50 Billion from Asian Equities in October

    Foreign investors dumped a net $49.77 billion from Asian equities in October, marking the steepest monthly retreat from the region in four months. The selloff hammered technology-heavy markets as institutional funds balked at cloud providers’ soaring capital expenditures and weighed regulatory headwinds following United States midterm elections.

    Taiwanese equities absorbed the heaviest blow, logging $30.68 billion in net foreign selling, the largest monthly liquidation since at least January 2008. South Korea recorded $14.59 billion in net exits over the same period, data from LSEG showed.

    Taiwan and South Korea Bear Tech Selloff

    The regional exodus extended into this month, with cross-border funds cutting another $22.6 billion across seven tracked Asian equity markets. Taiwan recorded a further $19.76 billion in net foreign sales so far this month, while South Korea surrendered another $8.32 billion.

    Much of that pressure stemmed directly from hardware supply chains tied to data centres and advanced computing components. Investors pulled back on fears that tighter power, land and environmental regulations in the US could stall facility construction, curbing purchase orders for regional foundries and memory makers.

    The outflows reflect a retreat from AI-linked positions amid increased doubts about AI infrastructure spending and profitability.

    Exness lead financial market strategist Wael Makarem noted that tighter environmental reviews and regulations ahead of the US midterms were viewed as obstacles to data centre construction and chip demand.

    Yield Pressures and Global Reallocations

    Widening yield differentials accelerated the regional drawdown. The MSCI Asia Pacific ex-Japan index slid 6.91% in October, underperforming the broader MSCI All Country World Index, which slipped 1.69%.

    US debt markets offered higher risk-free returns during the month, drawing institutional capital away from emerging Asian paper. Yields on benchmark 10-year US Treasuries climbed to 5.3% in early October, dampening investor appetite for emerging equity risk.

    For hardware vendors and electronics groups listed across Taipei and Seoul, the capital flight tightens domestic liquidity and elevates corporate borrowing costs. Equipment makers and contract assemblers banking on relentless capacity expansions face a sharper funding climate if institutional sponsors stay cautious.

    Divergence Across Southeast Asia and India

    Southeast Asian bourses posted mixed performances through the October rout. Indonesian equities logged net foreign selling of $938 million, while Thailand recorded $199 million in net exits. Vietnam and the Philippines bucked the broader trend, drawing modest net foreign inflows of $122 million and $109 million, respectively.

    India experienced a second straight month of foreign outflows in October, losing $3.6 billion. Fund managers reversed that stance quickly once prices corrected, injecting a net $5.68 billion into Indian shares so far this month.

    HSBC equity strategy head for Asia Pacific Herald van der Linde lifted India to overweight on Monday, pointing to resilient corporate earnings and improved entry valuations.

    Capital Spending Scrutiny Ahead

    October’s $49.77 billion withdrawal represents the sharpest regional liquidation since foreign investors pulled capital in June. The rapid unwinding highlights how exposed regional bourses remain to changes in Western tech procurement budgets.

    Regional equity sentiment now rests on whether commercial returns from artificial intelligence software justify current hardware investments. Portfolio managers are watching upcoming corporate earnings reports and order volumes from US hyperscalers to see if institutional selling stabilises before year-end.

  • Only 45% of Retailers Scale AI Despite Threefold Earnings Advantage

    Only 45% of Retailers Scale AI Despite Threefold Earnings Advantage

    Only 45 percent of retailers are scaling artificial intelligence across their operations, even as businesses committing heavily to the technology generate three times higher revenue and earnings growth.

    According to research highlighted this week by Boston Consulting Group managing director and partner Charlie Reid, operators treating artificial intelligence as a core discipline outpace rivals by triple on top-line sales and EBITDAR. Yet widespread experimentation across scattered pilots leaves the majority of commercial operators unable to convert software trials into balance sheet gains.

    Fragmented initiatives and inadequate organisational changes continue to limit companies from translating investments into measurable financial returns, Boston Consulting Group noted in November 2026 findings.

    Where Merchandising Delivers Fast Payback

    Capital is flowing primarily into core store and supply chain operations rather than back-office corporate support. Retailers are largely bypassing enterprise applications across human resources, procurement, and finance because those departments offer lower immediate financial return.

    Merchandising operations are providing the quickest path to measurable payback. Adjusting product assortment, pricing, promotion schedules, and vendor negotiations through artificial intelligence tools generates cash flow that operators use to fund broader technology rollouts. Getting shelf assortment right yields up to a 5 percent increase in like-for-like sales.

    The Friction Behind Operating Models

    Fragmented rollouts and weak internal adoption explain why most deployments stall at the testing phase. Boston Consulting Group measures technology implementation through what it terms a 10-20-70 operating breakdown: algorithms account for 10 percent of the transformation, data and systems represent 20 percent, while people, operating processes, and institutional structure dictate the remaining 70 percent.

    Tools fail when buyers and floor staff lack clear authority or incentive to execute automated guidance. An algorithmic recommendation creates zero value if a category merchandiser overrides the system or if store staff fail to reset physical shelf space. Executive teams routinely hunt for novel software without identifying the exact financial metric they need to alter.

    “So, at BCG, we call it the 10 2070 rule. 10% of the AI transformation is driven by the algorithm. 20% by the data and technology and then 70% by the organization, the processes um and and and and the people in the organization.”

    Competing for Automated Buyers

    Consumer interaction is shifting toward agentic commerce, where automated systems handle product discovery, price comparisons, and digital checkout tasks. Shoppers currently manage friction across physical aisles and mobile applications, frequently abandoning journeys when items show out of stock at checkout.

    Software agents compress that multi-step search into direct prompts, assembling tailored baskets against specific household dietary rules and spending limits. Over extended periods, these systems track household consumption patterns and reorder domestic staples before pantries run empty.

    That shift alters digital distribution outside merchant properties. Consumers searching through external platforms like Google and ChatGPT rely on software agents that scrape product attributes, ingredient registries, and live inventory across competing web domains simultaneously. Retailers must structure open, accurate product catalogues for algorithmic inspection rather than relying solely on visual merchandising to win human attention.

    Pruning Unfocused Software Trials

    Commercial operators face pressure to demonstrate cash returns on software capital expenditures. Treating machine learning as an exploratory technical exercise without strict hurdle rates has tied up working capital across fragmented vendor contracts.

    Survival across competitive consumer markets requires pruning low-yield software trials to protect core operating margins. Retailers that narrow their engineering resources to inventory visibility, predictive replenishment, and machine-readable data feeds will command distribution as autonomous purchasing tools take over routine household spending.

  • Thailand Imposes First Environmental Rules on Large Data Centers

    Thailand Imposes First Environmental Rules on Large Data Centers

    Thailand will require large data centers to meet stricter environmental standards under the country’s first industry-specific regulation, moving to protect power grids and water supplies from rapidly expanding digital infrastructure.

    The policy follows months of suspended construction across the sector while the Thai government formulated the environmental mandate.

    Reported on November 10, 2026, the regulatory intervention reflects mounting official concern over resource-intensive computing facilities as regional digital investment accelerates.

    The Cost of the Capital Freeze

    Those 49 stalled construction sites represent an estimated $8 billion in committed capital. The freeze forced international cloud providers and domestic data center firms to stop structural work and redesign engineering plans.

    Thailand’s energy ministry pointed to transmission vulnerabilities and falling water reservoirs when it halted operations. The Chao Phraya River basin, the municipal water source for metropolitan Bangkok and neighboring industrial estates, registered below-average water levels for three straight years through 2026.

    Cooling systems for dense artificial intelligence hardware consume millions of liters of water daily. Operators must now prove that their mechanical systems do not deplete municipal networks or overload substations during periods of climate strain.

    Grid Constraints Across Southeast Asia

    Electricity consumption from data centers across Thailand, Vietnam, Malaysia, and Singapore surged 340 percent from early 2024 through late 2026. The increase stems directly from model training clusters and enterprise inference workloads.

    National power authorities face a seasonal shortfall during the annual monsoon cycle, when hydroelectric generation drops just as ambient temperatures force cooling equipment to run at peak capacity. Adding utility-scale baseload power generation requires five to seven years, creating a structural gap with rapid AI infrastructure deployment timelines.

    Regional governments have reacted with comparable controls. Vietnam postponed decisions on six utility-scale data facilities in August 2026 to evaluate transmission limits, while Singapore established strict energy efficiency baselines for new builds in July 2026.

    Shifting Infrastructure Strategies

    For enterprise retailers, consumer platforms, and cloud tenants, tighter municipal limits eliminate the prospect of cheap, unconstrained server capacity in central Thailand. Developers can no longer rely on conventional chilled-water plants connected directly to public utility lines without local mitigation plans.

    Operators are adjusting capital deployment by financing on-site renewable generation and closed-loop liquid cooling. Companies operating in the Bangkok corridor have started pairing commercial solar installations and industrial battery systems with their computing halls to avoid drawing peak power from the state grid.

    Other regional hubs are shifting their engineering demands as well. Regulators in Malaysia and Indonesia are assessing minimum water-recycling thresholds and encouraging coastal facilities to install direct seawater cooling rather than drawing municipal tap water.

    Earlier Bottlenecks Behind the Decree

    Thailand’s sudden policy enforcement resolves an impasse that left developers in administrative limbo through late 2026. The September moratorium caught operators off guard after two years of aggressive state campaigns to attract foreign technology investment.

    The administrative pause revealed the scale of speculative building across the country. With 117 applications frozen at once, state agencies had accumulated a project queue that the state utility could not support without risking suburban blackouts.

    Developers must now submit revised environmental engineering documentation to clear the regulatory backlog extending into 2027. The critical metric to watch is whether the Thai energy ministry clears the 4.8 gigawatts of pending grid connection requests under the new compliance standards.

  • Moore Threads Upgrades MTT AICUBE with MTT AIOS 1.6.1

    Moore Threads Upgrades MTT AICUBE with MTT AIOS 1.6.1

    Moore Threads rolled out MTT AIOS version 1.6.1 for its AICUBE domestic AI hub, adding Matter and ZigBee protocol support alongside voice-directed network storage.

    The software update links third-party home appliances and personal media directly to the device’s Xiaomai AI Agent without requiring new hardware.

    Users can issue spoken commands to run multi-device routines across lighting, air conditioners, curtains, pet feeders and robot vacuums. Instead of issuing separate instructions to single machines, owners can group actions into automated domestic sequences managed locally through the central unit.

    Local Storage and File Access

    The system update introduces direct file management through Olares, a software package available in the hub’s application marketplace. The integration allows consumers to create, duplicate, relocate and delete files on local network-attached storage via conversational prompts, avoiding manual desktop directory navigation.

    Photo retrieval relies on semantic indexing. A user can search personal folders by describing past events, such as a holiday taken the previous month, rather than querying exact file titles. When linked with workplace or consumer messaging services including WeChat or Feishu, the hub’s assistant can locate images and send them straight into ongoing chat windows.

    Remote interactions run through a companion mobile application distributed on Apple’s App Store and major Android marketplaces. The client handles remote file browsing, two-way data transfers and automated photo backups from mobile handsets to the hub’s drives.

    Interface and Media Playback

    Moore Threads adjusted the primary visual interface by introducing a desktop-pet display mode. The graphic replaces the standard full-screen virtual agent with an animated figure whose appearance shifts between idle, listening and processing states.

    Streaming video capabilities received expanded browser access for external entertainment platforms. Users can pause, seek and adjust video tracks using conversational voice controls during playback.

    Ecosystem Fragmentation in Consumer Tech

    Connecting hardware through Matter and ZigBee tackles the fragmentation that slows smart-home adoption across Asian households, where consumers mix devices across multiple brand ecosystems. By positioning the AICUBE as a local coordinator, Moore Threads attempts to pull domestic control away from smartphone software ecosystems and cloud-only home hubs.

    The immediate challenge lies in operational consistency. Moore Threads has not published an exhaustive compatibility roster, leaving open the question of how smoothly non-certified household appliances will execute complex multi-device routines without manual setup hurdles.

    Expanding From Graphics Chips to Living Rooms

    The software rollout follows the initial market launch of the AICUBE as a dedicated home hardware hub, extending Moore Threads’ consumer footprint beyond graphics silicon. The semiconductor company previously brought day-zero support for Kimi K3.1 Pro to its enterprise-grade MTT S5000 graphic processors.

    Consumer reception will hinge on performance stability across third-party smart appliances as users install the MTT AIOS 1.6.1 update through their home network connections.

  • AutoChips Targets Q1 2027 Engineering Samples for AC8035 System-on-Chip

    AutoChips Targets Q1 2027 Engineering Samples for AC8035 System-on-Chip

    AutoChips unveiled its sixth-generation AC8035 automotive system-on-chip on November 6, targeting engineering samples in the first quarter of 2027 to run cockpit displays and driver assistance on a single processor.

    The unit of Chinese mapping and automotive supplier NavInfo designed the silicon to consolidate separate computing units into one piece of vehicle hardware, though it has yet to secure a commercial vehicle production contract.

    Consolidating Vehicle Computing

    The AC8035 architecture combines smart-cockpit functions, L2 and L2+ driver assistance and automated parking control onto one board. It also supports running large artificial intelligence models directly on the vehicle hardware rather than offloading tasks to remote data centers.

    Running these workloads together cuts down the physical electronic control units needed across a car chassis. That reduces wiring complexity and assembly weight, but AutoChips has not published system-level cost savings, final unit pricing or commercial shipment dates.

    NavInfo chief executive Cheng Peng said automotive semiconductor design requires long lead times that cannot match the quick iteration cycles of artificial intelligence models. The developer designed memory bandwidth headroom into the AC8035 to keep hardware viable as vehicle software updates roll out over years of consumer use.

    Hardware Claims and Export Strategy

    Cheng claimed the chip delivers two to three times the memory bandwidth of comparable automotive processors, supporting continuous multimodal interaction without requiring driver wake words. Those claims currently lack independent benchmark figures or production-vehicle testing data.

    Carmakers face a balance between paying for excess silicon headroom and risking early obsolescence as in-cabin artificial intelligence software swells. By combining parking, basic driver assistance and cockpit software on one chip, AutoChips is pitching tier-one suppliers and automakers a lower total bill of materials for mid-range passenger vehicles.

    The business model also targets Chinese car brands expanding into export markets. NavInfo is pairing the chip architecture with a localized compliance framework that adapts vehicle data collection and user privacy to foreign regulatory regimes.

    The parent company also introduced cockpit-specific artificial intelligence models alongside a software partnership with Microsoft to support multilingual voice interfaces for export vehicle models. These software offerings are separate options from the physical AC8035 processor.

    Development Path to Mass Production

    Automotive qualification cycles remain rigid, requiring silicon suppliers to clear extensive validation before parts enter assembly plants. While consumer smartphone processors transition from tape-out to volume production within quarters, automotive semiconductors spend years undergoing stress tests and functional safety certifications.

    Automakers in China have pushed suppliers to shorten vehicle development schedules, but centralized compute platforms running active safety controls carry higher integration hurdles than standalone infotainment screens. A fault on an integrated board risks interrupting both the digital display cluster and driving-assistance features simultaneously.

    The first physical test comes with the delivery of initial AC8035 engineering samples in the first quarter of 2027, when automotive engineers can run functional validation against real chassis systems.

  • South Korea Secures 10,000 Nvidia GPUs as Contracted Deliveries Stay on Schedule

    South Korea Secures 10,000 Nvidia GPUs as Contracted Deliveries Stay on Schedule

    South Korea will receive its contracted supplies of Nvidia graphics processing units on schedule despite an investigation involving chief executive Jensen Huang, Seoul’s science ministry confirmed on Monday.

    A batch of 10,000 GPUs funded under a 2025 supplementary budget has arrived and entered service in local networks.

    Vice Minister of Science and ICT Ryu Jemyung updated a parliamentary budget committee on the supply line, stating that additional hardware planned for 2026 remains under contract and will deploy this year.

    “We expect production and the delivery schedule for contracted volumes to proceed without disruption,”

    Ryu told lawmakers. The ministry is monitoring corporate developments while working through pricing and delivery timetables with Nvidia Korea.

    Contract Security for Digital Infrastructure

    The clarification gives enterprise platforms and state compute facilities immediate operational certainty. South Korean commercial cloud providers, e-commerce platforms, and logistics networks rely heavily on Nvidia architecture to run automated sorting, predictive inventory systems, and customer-facing algorithms. A pause in hardware allocations would force operators to delay software rollouts and stall data centre buildouts.

    Hardware backlogs in East Asia typically hit mid-tier tech firms hardest, since sovereign contracts and hyperscalers secure priority delivery queues. With government-backed allocations safe, Seoul avoids an immediate bottleneck across its state-funded research initiatives. The risk now concentrates around spot market availability and future volume commitments if corporate governance issues distract supplier roadmaps.

    Executive Disruptions in Seoul

    Nvidia’s board opened an independent investigation on Sunday following reports that chief executive Jensen Huang was targeted by a Chinese intelligence operation through businesswoman Alice Huang. The company board stated it has no reason to suspect US national security or corporate trade secrets were breached, and Huang has pledged full cooperation.

    The fallout hit Nvidia’s commercial engagements in Seoul immediately. Madison Huang, an Nvidia senior director and the chief executive’s daughter, cancelled multiple scheduled appearances across the capital city citing unavoidable internal circumstances.

    South Korean artificial intelligence startup Rebellions called off a planned headquarters tour and an executive dinner with her. Transport operator Kakao Mobility also dropped a planned photo call and media session, though it said a business agreement with Nvidia would proceed with senior director Gaurav Agarwal attending instead.

    Enterprise Engagements Hold Schedule

    The operational calendar across the domestic tech sector remains largely intact despite the cancelled executive briefings. The Nvidia AI Day Seoul industry conference was proceeding as planned with about 2,000 participants expected to address autonomous infrastructure, agentic systems, and physical computing frameworks.

    Domestic software developers and autonomous mobility platforms need steady access to hardware roadmaps to validate next-generation vehicle fleets and warehouse robotics. Kakao Mobility’s decision to preserve its signing ceremony indicates corporate transport operators will protect technical integrations even when partner optics turn volatile.

    Government procurement officers are now locking down final dispatch dates for the remaining 2026 allocations with Nvidia Korea to keep municipal computing centres on schedule.

  • China Mobile Cloud Signs Zhipu to Link GLM Models and Build Coding Tools

    China Mobile Cloud and Zhipu signed a cooperation pact on November 6 to connect the GLM model family to the operator’s MoMA service platform.

    The agreement establishes a joint development center focused on token generation efficiency, targeting domestic hardware adaptations alongside financial services and government contracts.

    Technical teams from both companies plan to tune Zhipu’s models specifically for Chinese-made computing processors. The partnership outlines shared research into inference performance, yet neither company published baseline efficiency figures or an operational schedule for customer deployments. Access arrangements, service-level commitments and supported versions within the GLM lineup were also omitted from the joint filing.

    Hardware Adaptation and Coding Tools

    Beyond platform hosting, the two companies plan to develop joint AI coding applications for commercial software engineers. The announcement outlined an integrated product program without releasing software names, development roadmaps or public testing dates.

    For enterprise buyers, the lack of a timetable leaves procurement teams waiting on functional specifications. Developers evaluating enterprise assistants must weigh unreleased software against tested tools already running on commercial clouds across mainland China.

    Rival Hyperscalers Build Token Tariffs

    Commercial cloud operators in China are locking enterprise accounts into aggressive subscription packages rather than unpriced development agreements. Alibaba Cloud already offers its AI Token Plan starting at $6 a month on its Model Studio service, bundling its proprietary Qwen lineup alongside DeepSeek and GLM models. Alibaba also sells dedicated enterprise engineering assistants, such as Qoder, directly to corporate IT departments.

    State-backed telecom operators often hold strong connections to public sector budgets, but commercial developers demand transparent pricing and tested inference pipelines. China Mobile Cloud must show that domestic silicon can match commercial cloud economics on cost per million tokens. Without published benchmarks, enterprise procurement officers will treat the venture as an exploratory project rather than an immediate supplier switch.

    From Transport Compute to Platform Hosting

    China Mobile has been steering its infrastructure divisions toward specialized vertical computing workloads. The carrier previously launched an automotive AI foundation featuring 112 exaflops of computing power and six specialized agents for vehicle manufacturers. Adding Zhipu’s models to MoMA extends that infrastructure strategy into general business software, banking risk engines and municipal administration.

    Commercial validation now hinges on the release of concrete access tiers, supported GLM versions and billing schedules on China Mobile Cloud’s MoMA catalog.

  • Stolen Cloud AI Keys Hit Retailers and Tech Groups with Million Dollar Bills

    Stolen Cloud AI Keys Hit Retailers and Tech Groups with Million Dollar Bills

    Intruders who breach corporate cloud accounts are quietly activating dormant artificial intelligence tools and running models at company expense, running up bills exceeding 100,000 dollars in a day.

    Investigations carried out across 2026 by Mandiant and the Google Threat Intelligence Group revealed intrusions across Asia Pacific, North America and South America that reached cumulative costs of up to 1 million dollars before victims spotted the leaks. The compromises struck retail, transportation, financial services and technology companies that had adopted large language model architectures into their systems.

    How Illicit Resellers Drain Foundation Models

    The campaign, dubbed LLMJacking, relies on a distinct commercial division of labour within cybercrime rings. One group specializes in penetrating corporate environments to harvest credentials, while a separate tier operates illicit platforms known as LLM Mixers to resell that raw model compute to third-party subscribers.

    One service, trading under the name Jet Night, offered paying customers unlimited subscriptions for about 60 dollars a month to access premium foundation models. Comparable commercial access carries legitimate pricing between 100 and 250 dollars. Security investigators determined the syndicate could only sustain that price differential by powering the backend with stolen enterprise credentials.

    During a single intrusion linked to the network, an unauthorized actor channeled more than 500,000 queries to foundation models in under eight hours. The targeted enterprise absorbed an unauthorized token bill approaching 1 million dollars before discovering the drain. Jet Night subsequently recorded sudden outages on its Telegram broadcast channels whenever compromised businesses identified the breach and revoked the active keys.

    Another unauthorized syndicate set up a lookalike domain disguised as an internal corporate host, routing the victim organization’s application programming interface quotas into a commercial resale front operating as newapi.pro.

    The Shift From Cryptojacking to Token Hijacking

    Retailers and consumer tech operators have spent years guarding compute fleets against illicit cryptocurrency mining, but model hijacking works at vastly accelerated speed. Cryptojacking trickles away compute slowly to evade system detection, whereas LLMJacking generates massive spikes in fraudulent cloud charges in a matter of hours.

    Investigators noted that actors reach their goals through token proxying, automated model streaming and unauthorized graphics processing unit hardware provisioning. Rather than manipulating consumer-facing chatbots to spit out rogue lines of code, the attackers bypass the store front-end entirely to tap backend infrastructure.

    For retail and digital commerce platforms in Asia that connect real-time product search, automated customer query desks and dynamic inventory to foundation models, the financial exposure sits directly in the operating budget. Cloud quotas linked to corporate credit accounts or open enterprise billing agreements allow perpetrators to cash out instantly through black-market resale while the operator absorbs the invoice.

    Stolen Keys and Exposed Gateways

    Initial penetration rarely requires novel zero-day attacks. Threat groups scrape publicly exposed GitHub repositories for stray application programming interface keys, running minute test queries designed to avoid triggering usage alerts.

    Most intrusions stem from compromised cloud infrastructure credentials, including unprotected service account keys, active session tokens and corporate GitHub authorization tokens. A group tracked as UNC6993 gained access to a commercial cloud instance via a misconfigured service account credential, quickly bridging across into internal model endpoints. The same operators established a fraudulent registration portal for model developers, tricking visiting engineers into submitting live access tokens that circumvented multi-factor authentication controls.

    Once inside a corporate network, attackers search database tables and data warehouse systems for deeper administrative secrets, occasionally disguising backdoor accounts as internal cost-optimization utilities. In another instance, attackers targeted a vulnerability logged as CVE-2026-40217 inside a LiteLLM routing gateway, escalating basic user credentials into root administrative control.

    Dormant Services Turned on by Intruders

    In multiple forensic investigations conducted by Mandiant, the targeted companies had left their high-tier machine learning services deactivated. Intruders used administrative cloud credentials to switch on the dormant model engines, initiating high-frequency automated requests within minutes.

    The findings indicate that monitoring routine endpoint activity is no longer sufficient for digital commerce operators running hybrid cloud stacks. IT departments now face the operational reality that unmonitored API limits directly threaten gross margins, turning routine cloud administration into an immediate balance-sheet liability.

    Enterprises running production workflows across multiple public cloud providers must track anomalous model activation alerts and enforce hard spend caps on machine learning billing accounts to catch breaches before usage tallies reach six figures.

  • China Blocks SoftBank’s $5.4Bn Acquisition of ABB Robotics

    China Blocks SoftBank’s $5.4Bn Acquisition of ABB Robotics

    Chinese regulators blocked SoftBank Group’s planned $5.4 billion acquisition of ABB’s robotics division, halting the Japanese conglomerate’s bid to expand its industrial automation holdings across Asia.

    Swiss-Swedish multinational ABB confirmed the regulatory roadblock on Thursday following months of review by authorities in Beijing.

    Regulatory Obstacles in Beijing

    SoftBank agreed to buy the business unit to integrate factory automation with its broader investments in artificial intelligence. Chinese authorities refused clearance, preventing the transfer of manufacturing assets and operational licenses within the country.

    The veto stops the Japanese group from taking direct ownership of ABB’s robotics facilities located in the mainland market. ABB’s automation hardware supplies automotive assembly lines, electronics factories, and consumer product packaging plants throughout East Asia.

    Impact on Automation and Supply Chains

    Industrial robotics remains a tightly guarded sector across Asia. Beijing treats manufacturing hardware, motion controllers, and precision assembly equipment as critical infrastructure rather than simple commercial assets.

    For factory operators and automated distribution networks, the blocked takeover leaves ABB operating as an independent supplier under its existing Swiss corporate umbrella. Warehouse logistics providers and automotive builders across Japan and Southeast Asia had evaluated the deal as a pathway toward tighter software integration between SoftBank’s artificial intelligence platforms and ABB’s robotic arms.

    SoftBank Strategy Diverted

    Masayoshi Son spent recent quarters reallocating capital toward automation systems, computer chips, and machine intelligence. Losing access to ABB’s hardware division forces SoftBank to look for alternative mechanical platforms to deploy its software assets.

    The company previously invested in autonomous mobile warehouse systems and commercial service robots. Buying an established industrial heavy hitter like ABB was meant to leapfrog years of hardware development.

    Earlier Commitments and Next Moves

    SoftBank announced the purchase agreement in October 2025 as part of a multi-billion dollar capital deployment strategy centered on factory intelligence. That deal relied on approvals across multiple regulatory jurisdictions where ABB maintains operating hubs.

    Attention now shifts to whether SoftBank attempts to carve out ABB’s non-China assets or abandons the $5.4 billion transaction entirely.

  • South Korea Launches $276 Million Hangang Supercomputer

    South Korea Launches $276 Million Hangang Supercomputer

    South Korea unveiled its sixth national supercomputer, Hangang, on November 2, launching a 382.5-billion-won ($276 million) infrastructure installation designed to advance artificial intelligence and scientific research.

    The Ministry of Science and ICT partnered with the Korea Institute of Science and Technology Information (KISTI) to host the launch ceremony at KISTI headquarters in Daejeon, bringing together approximately 200 representatives from industry, academia, and government research bodies.

    KISTI President Lee Sik oversees the system, which pairs 8,496 graphics processing units, including NVIDIA GH200 Grace Hopper Superchips, with 9,936 central processing units to hit a theoretical peak speed of 614.7 petaflops, or 614.7 quadrillion floating-point operations per second.

    Computing Power Expands for Industrial Simulation

    Hangang replaces Nurion, the country’s fifth national supercomputer that handled public computing tasks after entering service in 2018. Compared to the older machine, the new platform delivers 24 times the theoretical computing speed, 10 times the storage capacity, and four times the network transfer performance.

    State planners designed the infrastructure to process high-intensity artificial intelligence workloads alongside commercial engineering simulations. Key industrial applications include digital vehicle crash testing and real-time meteorological forecasting, targeting design cycles where physical prototyping slows time to market.

    State Backing for Enterprise AI Models

    Computing bottlenecks have tightened across East Asia as commercial enterprises and university laboratories compete for high-end processing hardware. Hangang pools specialized processing power into a state-subsidized platform, lowering entry barriers for domestic technology firms training large models against regional rivals.

    The risk sits in resource allocation. Sovereign compute networks frequently face friction between academic basic research and commercial deployment, with capacity quotas often favoring state-led agendas over fast-moving enterprise applications. Hangang will direct substantial capacity toward K-Moonshot, a government-directed program deploying advanced artificial intelligence models to resolve scientific targets.

    Replacing Nurion After Six Years of Service

    The retirement of Nurion follows a six-year operational cycle during which global artificial intelligence training shifted from conventional central processing units to dense graphics processing clusters. Nurion lacked the unified memory architectures and specialized tensor cores required for modern transformer models, leaving South Korean institutions reliant on foreign commercial cloud vendors for heavy AI development.

    Korea Institute of Science and Technology Information President Lee Sik said: “We will do our utmost to ensure stable operations and top-tier user support, using KISTI’s long-standing operational experience in supercomputing.”

    Next Steps for National Deployment

    Engineers began pilot testing on the cluster in late October to calibrate thermal controls and network distribution across the 8,496 graphics processors. KISTI plans to begin full commercial and research operations on December 16.

  • Nio Schedules Guangzhou Tech Day Following 15.23% Delivery Drop

    Nio Schedules Guangzhou Tech Day Following 15.23% Delivery Drop

    Nio will hold its Nio IN 2026 innovation and technology day in Guangzhou on November 25, unveiling AI-driven vehicle cockpits and assisted driving products after vehicle deliveries fell 15.23 percent in October.

    Monthly volume dropped to 34,246 units, ending 31 straight months of year-on-year expansion and raising the stakes for hardware and software shows ahead of the city’s automotive exhibition.

    The announcement from Nio Inc on Wednesday revives the standalone tech event after it skipped 2025. Founder and chief executive William Li aims to highlight the company’s shift into an AI-native organization alongside updates to smart driving systems.

    Expanding Proprietary Silicon Beyond Vehicles

    Hardware commercialization has become a central piece of the carmaker’s corporate posture. Its chip design subsidiary, GeniTech Co Ltd, was spun off in June 2025 and has pushed its proprietary semiconductors into applications beyond passenger cars.

    The unit’s premium NX9031X processor runs across the entire vehicle portfolio of both the core Nio marque and the lower-priced Onvo brand. Filings from July showed cumulative shipments of that processor exceeded 300,000 units.

    At the World AI Conference, GeniTech showed three distinct chips: the NX9031X, NX9031U, and NX9031C. Those designs cover assisted driving, embodied intelligence, and agent inference, signaling an effort to build a commercial client roster outside its own assembly plants.

    The Software Pitch Ahead of Guangzhou Auto Show

    Positioning the presentation two days before the Guangzhou Auto Show opens on November 27 gives the manufacturer a head start against domestic rivals. Auto Home reported the brand plans to unveil a special edition of its third-generation ES8 sport utility vehicle during the trade exhibition.

    Consumer tech integration remains the primary battleground for Chinese EV manufacturers fighting brutal discounting across domestic showrooms. Software functions such as natural language voice controls and vision-based highway navigation often sway showroom buyers faster than marginal battery capacity gains.

    For automotive investors, the risk lies in heavy capital spending that outpaces consumer monetization. Developing bespoke silicon and large foundation models requires continuous cash outlays that squeeze operating margins when monthly sales hit turbulence.

    Corporate Structure Realigned for Artificial Intelligence

    The corporate push gained formal structure in January when William Li, founder, chairman, and chief executive of Nio, formed an internal AGI committee. That body oversees software adoption across research, assembly lines, supply chains, and driver-assistance platforms.

    Talent acquisition followed in May with the rollout of the Nio AGI Superstar Program. The global recruitment drive targeted university researchers specializing in physical-world foundation models, end-to-end driving models, the SkyOS vehicle operating system, and the NOMI in-vehicle voice assistant.

    Speaking at an earnings media briefing in September, Li stated that Nio is essentially not just an automaker, but also an AI and energy company. He argued that public markets have yet to fully value its technology stack, computing assets, and battery-swapping infrastructure.

    A Line of Development Dating to Shanghai

    The previous edition of the developer day ran in Shanghai on July 27, 2024. That event yielded the tape-out of the five-nanometer Shenji NX9031 autonomous driving processor, the debut of SkyOS, the second-generation Nio Phone, and the Nio World Model for vehicle guidance.

    Year-to-date delivery figures maintain positive momentum despite the sudden October contraction. The automaker delivered 334,547 cars during the first ten months of the year, an increase of 38.46 percent compared with the same period a year earlier.

    Guangzhou Auto Show opens its media preview on November 27.

  • Xpeng Beats OpenAI on Perception Benchmarks with 4-Billion-Parameter GroundingPI

    Xpeng Beats OpenAI on Perception Benchmarks with 4-Billion-Parameter GroundingPI

    Electric vehicle maker Xpeng has launched an embodied perception foundation model named GroundingPI, reporting an overall benchmark score that surpassed OpenAI’s frontier model GPT-6 Astra across 34 public perception tests.

    Built on a parameter scale of about 4 billion, the perception-first architecture ranked first in comprehensive evaluations and demonstrated higher data processing efficiency in robot simulation tasks. Xpeng developed the model in partnership with research teams from Peking University and the University of Hong Kong, aiming to significantly reduce the cost of manually collecting robot demonstration data.

    In a direct comparison across 11 capability categories, GroundingPI recorded a score of 73.68, edging out the 71.54 achieved by GPT-6 Astra, which OpenAI released in September this year. The code and model weights have been open-sourced.

    Benchmark Scores and Technical Design

    Existing general-purpose large models are primarily designed for digital scenarios and face an adaptation gap when applied directly to physical scenarios such as autonomous driving and robotics, Xpeng said. GroundingPI proposes that the primary prerequisite for embodied intelligence is accurately perceiving spatial positions.

    Therefore, the model does not use robot data with joint action labels during the training phase, relying entirely on pure perception data without action labels to cover 11 capability categories including general detection, text recognition, and robot spatial localization.

    Embodied intelligence is typically divided into a fast system (System 1) responsible for high-speed perception and action execution, and a slow system (System 2) responsible for task understanding and planning. GroundingPI aims to provide a perception-native foundation for the fast system, rather than replacing the general large model capabilities of the slow system, Xpeng said.

    Halving Data Collection in Warehouse Trials

    The research is currently at the scientific prototype stage, but its simulation results have provided validation for 2 core challenges in scaled deployment. The first is significantly reducing the cost of manually collecting robot demonstration data.

    A system using GroundingPI as its visual foundation can exceed the execution success rate of comparison models using 75% of the demonstration data while using only 50% of the data, because it directly takes on the task of locating objects in traditional Vision-Language-Action (VLA) models.

    The second is bringing stronger scenario generalization. In high-density simulation scenarios such as bulk material sorting and bin counting, the system maintains stable performance when dealing with changes in materials, object categories, and backgrounds, providing support for reuse across different workstations.

    Centralising Machine Intelligence

    GroundingPI represents an important advancement for the Chinese EV maker in building its physical AI system. Xpeng has continuously increased its bets on physical AI in recent years, hoping to drive its smart driving systems, humanoid robots, and robotaxi businesses.

    Earlier this year, Xpeng merged its autonomous driving center and smart cockpit center into a general intelligence center led by AI expert Liu Xianming , pooling resources to provide unified AI middle-platform foundation support for its automotive and robotics businesses.

    An Xpeng Iron humanoid robot was previously put on display at the April 2026 Beijing Auto Show.

  • Hackers Hijack Corporate Cloud AI Accounts, Racking up $100,000 Daily Bills

    Hackers Hijack Corporate Cloud AI Accounts, Racking up $100,000 Daily Bills

    Cybercriminals are hijacking corporate access to large language models across the Asia-Pacific region, driving unauthorized cloud billing charges past USD $100,000 in a single day. The findings, released this week by Mandiant and the Google Threat Intelligence Group, revealed that victims spanned retail, financial services, transportation and technology across Asia Pacific, North America and South America.

    For some targets, aggregate billing reached almost USD $1 million before administrators discovered the breach and revoked access keys. The financial damage mounts much faster than conventional cryptojacking, which typically drains server capacity at a slow, continuous pace. In one investigated incident, attackers executed more than 500,000 API calls against advanced language models in less than eight hours.

    How Attackers Breach Cloud Accounts

    Intruders frequently exploit credentials leaked inside public GitHub code repositories or harvest access through compromised browser sessions and stolen identity tokens. In several documented breaches, the targeted companies had never activated machine learning or model features within their cloud accounts. Attackers simply logged into dormant environments, turned on the services themselves and began issuing high-volume automated requests.

    A threat group tracked as UNC6993 gained access to corporate systems in mid-2026 by exploiting a misconfigured service account key. The group also operated a fake web portal that prompted internal users to submit active session tokens alongside JSON payloads, allowing attackers to slip past multi-factor authentication checks and passwords entirely. Another cluster extracted API keys from public repositories and tested them using single-token queries to avoid tripping intrusion detection systems.

    Once inside a tenant, attackers escalated system privileges to secure their hold. Intruders created administrative backdoor accounts masked as legitimate billing optimization utilities and injected unauthorized SSH keys into corporate compute instances. Some attackers exploited vulnerability CVE-2026-40217 in the open-source LiteLLM gateway, elevating basic virtual keys into full administrative proxy permissions.

    Illicit Resale Through Discount Gateways

    Stolen model capacity feeds an organized underground market where criminal syndicates divide tasks between network infiltration and wholesale resale. Broker networks bundle compromised corporate keys into centralized routing proxies known as LLM Mixers. These illicit hubs rely on open-source utilities like new-api and gcli2api to manage traffic distribution.

    One illicit routing platform, operating under the name Jet Night, transferred nearly USD $1 million in artificial intelligence compute charges onto a single victim company. Jet Night resold unlimited model access to paying underworld customers for approximately USD $60 a month, undercutting legitimate commercial plans that cost between USD $100 and USD $250. Service interruptions announced on underground messaging channels like Telegram directly matched instances where breached corporate victims detected anomalies and cancelled their compromised access credentials.

    Other attackers expanded their operations by provisioning high-end graphics processing unit clusters across multiple cloud regions. Intruders submitted administrative requests for higher computing quotas, deploying persistent backdoors based on the Manus agent framework to automate operations at corporate expense.

    The Real Cost for Regional Retail Operators

    For Asia-Pacific retailers racing to deploy automated customer service bots, inventory forecasting and algorithmic pricing engines, the findings alter cloud risk calculations. Regional retail groups have rushed to integrate machine learning endpoints into e-commerce storefronts without auditing legacy cloud consoles or default cloud permissions. A compromised key can trigger hundreds of thousands of dollars in operational liabilities before month-end accounting reconciliation notices the discrepancy.

    The threat extends well beyond unexpected invoices. When criminal syndicates route unvetted third-party traffic through corporate model instances, retail operators face potential regulatory and data exposure liabilities across regional jurisdictions. Security teams must now treat unexpected cloud API activations as urgent containment priorities rather than routine billing errors.

    Containment Measures and What to Watch

    Defensive guidelines published in the investigation urge companies to monitor code repositories for exposed tokens, audit cloud consoles for unapproved model services, and enforce strict spending caps. Organizations should configure model-level rate limits and mandate hardware-backed, phishing-resistant multi-factor authentication across developer systems.

    Whenever possible, threat actors want to use this technology to gain a scale advantage over defenders. They achieve that with agentic tools and by stealing tokens from unwitting third parties. Locking down these resources will not only protect organizations from runaway costs, but also protect the broader community from a deluge of cheap, autonomous attacks.

    John Hultquist, Chief Analyst at the Google Threat Intelligence Group, noted that companies must aggressively control access to the computing resources they are bringing online to stop external actors from exploiting corporate budgets. Cloud providers and enterprise clients are now monitoring whether upcoming security updates for tools like LiteLLM and automated API spending hard caps will curb unauthorized token abuse before enterprise cloud budgets reset for the next fiscal quarter.

  • Huawei Cloud Retires 2 openPangu Models from Model-as-a-Service Platform

    Huawei Cloud Retires 2 openPangu Models from Model-as-a-Service Platform

    Huawei Cloud will shut down commercial access to its openPangu-2.0-Flash and openPangu-2.0-Pro AI models on its Model-as-a-Service platform, ending sales on November 6 and terminating all hosted calls on November 30.

    The cut comes four months after Huawei open-sourced the 505-billion-parameter Pro version, forcing enterprise customers running consumer analytics and internal search workloads in its Southwest Guiyang cloud region to switch tools before December.

    New customers cannot buy or activate either model service after midnight on November 6, while existing accounts retain operational access only through the end of the month. Huawei Cloud directs accounts using openPangu-2.0-Flash to DeepSeek-V4.1-Flash-1017 as their replacement, while routing openPangu-2.0-Pro users to Zhipu AI’s GLM-5.3.

    Migration Demands and Technical Friction

    Engineering teams face immediate rewrite costs. Huawei Cloud noted that the replacement models carry different API parameters, cost structures and output profiles, requiring developers to retest task pipelines rather than swap endpoints blindly.

    Software setups built on the company’s early MaaS standard API V1 must be rewritten to work with an OpenAI-compatible endpoint. Teams already on API V2 or an OpenAI-style interface need to alter model identifier calls.

    Retail commerce platforms relying on openPangu for live inventory categorisation or chatbot assistance now have less than a month to validate replacement models. Changing baseline models often alters latency profiles and customer response formats across live digital storefronts.

    Rival Platforms and Vendor Strategy

    The decision to retire homegrown models while recommending independent rivals highlights the fierce pace of platform rationalisation across China’s cloud ecosystem. Rather than carrying the operational cost of maintaining earlier foundation models, cloud providers now drop their own releases rapidly in favour of top-performing external open weights.

    DeepSeek and Zhipu AI gain immediate enterprise volume from the shift. Huawei Cloud’s public embrace of DeepSeek-V4.1-Flash and GLM-5.3 confirms that third-party open architectures are setting operational benchmarks on Chinese sovereign server hardware, even inside Huawei’s proprietary Ascend computing clusters.

    Platform developers bear the stability risk. By cycling through model versions within single-digit months, platform operators transfer the cost of code maintenance, integration testing and deployment downtime onto enterprise software clients.

    Rapid Model Turnover on Ascend Hardware

    Huawei made openPangu-2.0-Flash public on June 30 with 92 billion parameters, followed by the 505-billion-parameter openPangu-2.0-Pro sparse model on July 31. Both variants were tailored for Ascend neural processing units, accompanied in late September by the release of complete pretraining, supervised fine-tuning and reinforcement learning source code.

    The company’s hosted platform has cleared out earlier systems at a comparable pace throughout the autumn. Huawei Cloud scheduled GLM-5.1 for removal on November 16, switched DeepSeek-V4-Flash accounts to version 4.1, set Kimi-K2.6 for retirement on November 8, and pulled DeepSeek-V4-Pro from hosting in September alongside older Qwen and Wan models.

    The retirement schedule applies strictly to managed API services hosted out of the Guiyang data centre, leaving the underlying open-source weights and training code available in public repositories for teams running their own compute stacks.

    Software teams on Huawei Cloud must finish transitioning their active workloads to the DeepSeek and GLM services before the Guiyang cluster unplugs both openPangu endpoints at midnight on November 30, while Huawei prepares a successor generation of Pangu models without a public release date.