Under the catalyst of Nvidia GTC and Tesla's earnings, the A-share AI narrative welcomes a window for industrial verification

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● Our reporter Liu Yingjie

Recently, several major events converged in the same window, including NVIDIA's GTC Berlin conference and Tesla's third-quarter earnings call, bringing multiple catalysts to the A-share AI industry chain. Wind data shows that the Wind Artificial Intelligence Concept Index has risen more than 2% cumulatively since October 19.

Analysts believe that NVIDIA's system-level layout for AI factories and physical AI, combined with Tesla's commercialization progress on Robotaxi and humanoid robots, is strengthening the AI industry logic from both ends: computing infrastructure and scenario implementation. Supporting segments in the A-share market such as liquid cooling, high-speed interconnect, and robot actuators are expected to gain event-driven momentum. However, whether this round of market movement can shift from event-driven to fundamentals-driven still depends on order visibility and the pace of scenario fulfillment.

On October 21 Beijing time, NVIDIA's GTC Berlin conference opened, and NVIDIA CEO Jensen Huang delivered a keynote speech, showcasing the company's latest layout in areas such as AI infrastructure, agents, open models, and physical AI.

Unlike past events centered on chip launches, this speech focused on standardized delivery of AI factories and scenario implementation of physical AI. NVIDIA announced the launch of the DSX POD reference design, allowing partners to complete system integration and validation inside modular containers. Once on-site utilities are ready, AI factory deployment cycles are expected to be shortened from about six months to 14 days. Jensen Huang said that the DSX AI factory platform can reduce cooling water consumption to near zero and cut energy consumption by up to 30% while achieving the same AI output. In robotics, NVIDIA launched the Agile ONE SN reference humanoid robot platform based on Isaac GR00T, featuring 31 degrees of freedom and expected to launch in 2027, aiming to lower the barrier to robot development.

Shortly afterward, Tesla disclosed its third-quarter financial report and held an earnings call on October 22 Beijing time. Tesla CEO Musk said that the Robotaxi service is continuing to expand its operating scope and is planned to launch in Dallas next month. He expects Robotaxi's contribution to gradually emerge starting in the second quarter of 2027 and potentially become an important business by the third or fourth quarter of 2027. On humanoid robots, Musk cautioned the public not to expect large-scale deliveries too early, but confirmed that Tesla has begun early production preparations for a high-capacity Optimus production line at its Fremont, California factory, and expects to begin deliveries to companies other than Tesla by the end of 2027. Tesla also revealed that the Cybercab has about 50% fewer parts than other models Tesla has previously produced, which helps reduce manufacturing complexity.

The industry logic is undergoing a deeper shift

Behind the catalytic events, the narrative focus of the AI industry is undergoing a noteworthy migration. Previously, the market's pricing of AI mainly revolved around cloud computing expansion, with core variables being GPU shipments, data center capital expenditure, and optical module speed upgrades. But the signals released this time by NVIDIA and Tesla have shifted part of the pricing anchor toward the link between AI and interaction with the physical world.

In his speech, Jensen Huang compared the AI industry to a system composed of five layers: energy, accelerated computing, AI factories, AI models, and digital intelligence embedded in various applications. He said that before AI models emerged, infrastructure had to be in place first, and as AI applications expand, power supply, data center construction, and computing deployment efficiency are becoming key factors in industry development. This statement means that NVIDIA's competitive boundary is extending from GPU chips to a full-stack ecosystem composed of hardware, software, models, and industry applications, and the core of its business model is tilting from "selling computing power" to "selling AI production capacity."

In physical AI, NVIDIA demonstrated the application of agents in industrial design, laboratories, and factories: in Siemens factories, agents test production plans for new products through digital twins; in Dassault Systèmes' engineering design scenarios, agents use simulation to identify vehicle design issues and optimize solutions. In autonomous driving, Volvo Cars is adopting NVIDIA's Hyperion computing platform and Alpamayo software stack, and next-generation models from Volkswagen Commercial Vehicles, Jaguar Land Rover, and Mercedes-Benz will be compatible with the Hyperion platform.

This narrative shift is not confined to the corporate level. The "AI Native Research Report (1.0)" (2026), recently released by the Artificial Intelligence Research Institute of the China Academy of Information and Communications Technology, summarizes the current stage as a paradigm shift from "internet native" and "cloud native" to "AI native," and defines "AI native" as systems, products, or organizations that take AI as their core operating mechanism and source of value from the outset of design, rather than layering AI capabilities on top of existing forms. The report also distinguishes between two forms, "+AI" and "AI native": "'+AI' adds recognition, recommendation, or question-and-answer steps on top of existing processes, while the processes themselves continue to operate as before"; "AI native," by contrast, requires that "artificial intelligence be intrinsic to the architecture and processes, with the original form restructured because of intelligence." This judgment is consistent with NVIDIA's shift from "selling computing power" to "selling AI production capacity" and with Tesla's identification of AI architecture, rather than external data collection, as the bottleneck for its robots. The focus of industrial competition is shifting from "whether to connect to AI" to "whether AI can be embedded as a core component of system operation."

Changes on the Tesla side are likewise significant. On the earnings call, Musk explicitly identified the AI architecture, rather than external data collection, as the main current bottleneck for Optimus. He said Tesla hopes to first produce about 10,000 to 20,000 robots for large-scale autonomous training, and is building a "robot academy" where robots practice a variety of tasks and feed the training results back to the model. This statement reveals that the industrialization bottleneck for humanoid robots is converging from hardware manufacturing toward "brain" capabilities, which is precisely the core link that AI cannot bypass as it extends from the digital world to the physical world.

Hua Xiaowei, chief analyst for new-quality small and mid-cap stocks at Guosheng Securities, said the market pricing logic for the AI industry will switch to fundamental verification. With the concentrated disclosure of third-quarter reports, the AI industry chain is entering an important window for testing its prosperity, and companies with better-than-expected earnings are expected to become a structural main line during market adjustments.

Order visibility and scenario fulfillment may become core variables

Faced with the shift in AI industry logic, several brokerage analysts have offered investment strategy frameworks from different dimensions. Unlike the earlier recommendation logic built simply around computing power elasticity, the current investment focus is more on earnings verifiability and the pace of order fulfillment in specific segments.

Xiao Qunxi, chief analyst for the machinery industry at Guotai Haitong Securities, made a specific judgment from the perspective of the robotics industry chain. She said that as large models, motion control, and perception technologies continue to iterate, the robotics industry is upgrading from stand-alone automation to intelligence and flexibility, and humanoid robots are expected to gradually move from technical verification to industrial scenario applications, driving demand growth for core components and intelligent manufacturing equipment.

Liu Chenming, chief strategy analyst at GF Securities, provided cross-validation from the perspective of export-chain prosperity. Among the high-prosperity directions he identified in third-quarter reports, AI hardware involves memory chips, optical modules, optical chips, optical cables, PCBs, electronic cloth, MLCCs, and liquid cooling; mechanical and electrical equipment involves gas turbines, industrial robots, and freight containers. Liu Chenming believes AI hardware remains the direction with the highest prosperity, while non-AI categories are diverging, with the pace changes mainly stemming from factors such as base effects and working-day misalignment; the policy window may be concentrated in November, and close attention can be paid to overseas cloud vendors' capital expenditure guidance in late October.

Fang Zhengtao, a strategy analyst at Huatai Securities, judged from the perspective of trading structure that divergence in global AI asset performance has intensified since mid-September, with market focus switching from demand certainty to return elasticity, and capital expenditure may no longer be the sole pricing anchor for AI assets. Current AI trading may be shifting from the extensive allocation stage of piling up computing power to a stage of selecting individual stocks along industrial bottlenecks and cash flow certainty. He suggests focusing on directions with high earnings visibility in third-quarter reports, such as PCBs, servers, liquid cooling, and power supplies. news_keyword_pub,stock,sz000776&sz002670&sz300024&sh601211&sh601688 Account opening QR code start

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