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Rotating chairman David Wang said at Huawei Connect 2026 in Shanghai on Thursday that the company plans to release the 960DT in the first quarter of 2027 and the Ascend 960PR in the third quarter. The roadmap places Huawei’s next generation of accelerators at the centre of its effort to build domestic alternatives for training and running increasingly demanding AI models.

Huawei also unveiled the Ascend 960 SuperPoD and an upgraded version of its UnifiedBus interconnect technology, designed to link large numbers of processors so they can function as one computing system. The company said a combination of UnifiedBus and its Hi-ONE optical system could connect as many as 4,000 processors within a SuperPoD, extending high-speed optical links deeper into the machine.

The strategy reflects a broader shift towards system-level performance rather than relying solely on the capability of individual chips. Advanced AI workloads require thousands of processors to exchange data rapidly, making networking bandwidth, memory access and cluster efficiency increasingly important alongside raw chip performance.

Wang said Huawei has developed 11 semiconductors based on UnifiedBus technology for use across its large computing systems. Its biggest planned superclusters will be able to support up to one million AI processors, he said, while the company has already shipped more than 1,000 smaller supernodes to more than 370 customers.

Huawei did not identify those customers or give a detailed breakdown of shipments. It also did not provide independently verified benchmarks directly comparing the new 960-series chips with Nvidia’s leading accelerators, leaving questions over single-chip performance, software compatibility and manufacturing scale.

The company had previously said the Ascend 960 would offer twice the computing power, memory capacity, memory bandwidth and interconnect ports of the Ascend 950 generation. Huawei has also been developing the Atlas 960 SuperPoD and SuperCluster, with the larger system intended to combine more than one million neural processing units when it reaches the market.

Huawei’s current product roadmap also includes the Ascend 950DT, due in the fourth quarter of 2026. The company has said that processor will use its HiZQ 2.0 high-bandwidth memory, providing 144 GB of capacity and 4 terabytes per second of bandwidth. Those specifications are intended to support both model training and the decode stage of inference workloads.

US restrictions have limited China’s access to cutting-edge Nvidia processors and advanced semiconductor manufacturing equipment, giving domestic suppliers greater room to expand. Huawei has emerged as the most prominent of those suppliers, but Nvidia retains a major advantage through its CUDA software ecosystem and a broad global developer base.

Chinese AI developers have increasingly turned to domestic accelerators for inference, where trained models generate responses, even as many frontier laboratories continue to depend on Nvidia hardware for the most demanding training jobs. That split has made Huawei’s ability to improve both hardware and software a critical test of whether China can reduce its dependence on foreign computing infrastructure.

Huawei is also contending with shortages of high-bandwidth memory, a key component in AI accelerators. The company has designed memory for some newer Ascend products, but supply constraints across the industry have increased costs and complicated efforts to scale production quickly enough to meet demand.

The group is seeking to offset those limitations through architectural changes, advanced packaging, optical networking and tighter integration between chips, systems and software. Its SuperPoD approach is intended to compensate for weaker performance from individual processors by allowing much larger numbers of chips to work together with lower latency.

Rahul Chari, PhonePe’s founder and chief product and technology officer, said the company is concentrating on AI deployments that produce measurable gains in operating, product or revenue metrics rather than adding the technology indiscriminately across its platform.

Chari told the Global Fintech Fest in Mumbai that the company’s approach starts with identifying operational bottlenecks where artificial intelligence can change outcomes. “Not everything that is an experience today gets genuinely better using AI,” he said. The emphasis, he added, is on a baseline shift in performance rather than the number of AI-labelled features. That approach is shaping investment across customer interactions, back-office workflows and engineering, while preserving conventional systems where automation offers little advantage.

The fintech processes close to 12 billion transactions a month and operates customer support with roughly 500 agents. Automation has increased the share of support cases handled without human intervention from about 90 per cent to 94 per cent, and PhonePe is targeting 96 per cent.

Chari said the remaining cases are harder because they often involve unusual circumstances, scattered information and the need for rapid responses. Large language models are being used to address those complex interactions, while human staff remain focused on cases that require judgement or escalation.

Fraud investigation is another area where automation has advanced. About 80 per cent of investigations can now be handled through automated systems, allowing specialist teams to concentrate on complicated incidents and new fraud patterns, according to the company’s account of its AI programme.

PhonePe is also extending artificial intelligence into consumer-facing services. It has been developing natural-language search that allows users to navigate the app, initiate tasks and reach relevant services using voice or text commands. The company is exploring broader conversational capabilities for personal finance, insurance and investment products.

Its search system, launched earlier this year using Microsoft Foundry, can interpret requests such as making a payment, recharging a FASTag or locating transaction information. PhonePe has said the interface is designed to reduce dependence on conventional menu navigation by directing users according to their stated intent.

Merchant products are another focus. PhonePe launched an AI-powered integration layer in May aimed at helping online businesses connect to its payment gateway through conversational instructions, reducing a process that can otherwise require developers to work through documentation and technical configuration.

The company followed in June with SmartPages, an AI-powered payment-page creator embedded in its payment gateway dashboard. Merchants can describe what they are selling in ordinary language, including vernacular languages, and the system generates product copy, selects relevant customer information fields and structures a mobile-oriented payment page.

PhonePe is considering further agentic functions for its business app, including answering merchant and customer queries and helping businesses create and distribute offers or coupons. The company has also examined tools that could connect commerce activity with services such as WhatsApp and Instagram.

Artificial intelligence is being incorporated throughout PhonePe’s software development process, including solution design, coding, code reviews, testing, deployment, troubleshooting and debugging. Internal tools include TraceMine for crash analysis, Lens for initial root-cause analysis using logs and deployment data, and KadAI for code orchestration and agent-based development.

Corporate functions are being brought into the same framework through an internal Agent Hub designed to let non-technology teams automate workflows. Legal, compliance, finance and human resources teams are among those using the platform.

PhonePe says the system was built for a regulated financial-services environment, with controls covering data access, leakage and localisation. Its architecture includes an in-house large language model router, open-source models hosted on company premises, and internal data and tools.

One target is the company’s new-product introduction process, where reviews involving compliance, privacy, legal, trust and safety can take about 30 days. PhonePe is exploring whether AI-enabled workflows can compress that process to less than a day.

The BG-5 Golden Dragon Fish was demonstrated at the 2026 China International Fair for Trade in Services, held at Shougang Park from September 9 to 13. Developed by Boyagongdao Marine Technology Group, the robot is designed to resemble a golden arowana and propel itself through water with a flexible, multi-joint tail rather than a conventional propeller.

The machine can detect obstacles and adjust its route autonomously instead of following only a fixed, pre-programmed path, according to information released by the developer and statements made at the exhibition. Its fish-like movement is intended to improve manoeuvrability while giving the platform a less conspicuous profile than many conventional underwater vehicles.

Boyagongdao lists the BG-5 as a 640-millimetre-long robot weighing about 3 kilograms. The published specifications give it a maximum operating depth of 5 metres and a top speed of 0.6 metres per second. The company says infrared recognition supports autonomous obstacle avoidance, while wireless communications allow online debugging and control.

The manufacturer also offers a BG-5V configuration that combines the robotic fish with a software platform for more advanced experiments. The listed capabilities include global and local path planning, task allocation, formation control, cooperative transport and coordinated operations involving multiple robots.

Those functions broaden the possible uses beyond a single demonstration unit. Groups of small robotic fish could, in principle, divide an underwater area for inspection or monitoring, maintain formations and coordinate tasks in locations where larger vehicles would be harder to deploy. The company presents the platform for research and teaching as well as operational fields including search and salvage, hydropower, safety and rescue and national defence.

Claims that the robot has been formally deployed as an underwater surveillance system have not been substantiated by publicly available official information. The specifications and the developer’s stated defence-related work show that reconnaissance or monitoring applications are technically plausible, but the model displayed at CIFTIS was presented as a biomimetic robotic platform rather than as an announced military deployment.

Boyagongdao says its bionic robot series draws on more than a decade of research associated with Peking University’s College of Engineering. The systems use high-speed biomimetic drive mechanisms and central pattern generator control algorithms intended to reproduce the smooth, rhythmic movement of aquatic animals.

The company has also disclosed work on defence-oriented underwater technology. Its website says technologies developed for military-related directions have been adapted for higher education products, and company material from earlier exhibitions describes unmanned and intelligent underwater equipment as an area of military development. It has separately publicised the ROBO-SHARK, a larger shark-inspired underwater robot developed with a military unit.

The BG-5 itself is not a newly created model. Boyagongdao had displayed the robotic fish before this month’s services trade fair and promoted it earlier this year at Beijing technology events. The latest CIFTIS appearance nevertheless drew renewed attention because of the machine’s autonomous obstacle avoidance and increasingly realistic biomimetic movement.

China’s work on underwater robotics extends well beyond fish-shaped machines. Universities, research institutes and commercial groups are developing autonomous systems for coral monitoring, infrastructure inspection, marine ranching, emergency rescue and deep-sea research. Some platforms are designed to carry cameras, sonar or environmental sensors, while others use robotic arms for intervention tasks.

A separate ecological-monitoring deployment in Hangzhou has used biomimetic robotic fish to patrol West Lake, measuring indicators including water temperature and pH, detecting invasive species and transmitting data to monitoring centres. Other Chinese underwater robots have been used for reef observation and for identifying and removing crown-of-thorns starfish.

The BG-5 demonstration at CIFTIS focused on embodied intelligence underwater, where navigation software, sensing and propulsion are integrated into a machine that moves more like a living animal than a standard remotely operated vehicle.