① Jensen Huang stated, "The next wave of AI will belong to robotics, and this wave will begin with autonomous driving."
② The model can not only 'see' its surroundings but also understand and reason about the complex real world, thinking and making judgments before taking action.
More than half a month after launching his social media account,$NVIDIA (NVDA.US)$CEO Jensen Huang posted his third tweet late on August 4 Beijing time.
In the tweet, he promoted NVIDIA’s new open-source autonomous driving model, Alpamayo 2 Super, and said, “The next wave of AI will belong to robotics, and this wave will begin with autonomous driving.”
Alpamayo 2 Super was first unveiled at NVIDIA’s GTC Taipei 2026 conference and has now officially launched and entered the market.
This is$NVIDIA (NVDA.US)$The new generation of open reasoning model launched for the autonomous driving sector can not only 'see' its surroundings but also understand and reason about the complex real world, thinking and making judgments before taking action. It can serve as the foundational core model for Robotaxis, trucks, shuttles, delivery vans, tractors, and various long-tail mobile robots. In the future, it is expected to power billions of autonomously operating intelligent machines.
Jensen Huang explained that NVIDIA will release the model for commercial use under the OpenMDW-1.1 license, allowing development teams to inspect the model, fine-tune it, and deploy it in real-world applications.
Autonomous driving has long been regarded as a key physical-world embodiment of AI.
Dongwu Securities notes that, from a technological history perspective, digital AI follows a trajectory where general methods, data, and computing power progressively replace manual design. The current focus is shifting from scaling parameters during training to scaling computing power during inference. Physical AI, meanwhile, first undergoes industrial iteration in autonomous driving and later in robotics—evolving from high-definition maps, LiDAR, and modular systems toward end-to-end architectures, vision-language-action (VLA) models, and world models.
The development path for foundational digital AI models has become increasingly clear, with their value lying in predictability; however, they struggle to achieve closed-loop modeling of the physical world. Physical AI, by contrast, will deliver AI solutions tailored to the physical world and thus represents a higher-growth direction at this stage of AI development. Intelligent driving, as the first scenario to successfully close the loop, warrants focused attention.
At this juncture, the autonomous driving industry stands at a critical inflection point where technology, policy, and commercialization are advancing in tandem. The industry’s logic is shifting from early-stage technical exploration toward large-scale adoption and commercial monetization. Guangfa Securities points out that, on the technology front, end-to-end large models continue to iteratively optimize intelligent driving solutions, while falling prices of core hardware such as LiDAR and onboard computing are accelerating the adoption of L2+ advanced driver assistance systems, and the maturity of L3/L4 technologies is steadily improving. On the policy front, China’s tiered regulatory framework for autonomous driving is continuously being refined, with L3 vehicle approvals and cross-city mutual recognition of qualifications progressing steadily, laying a solid compliance foundation for large-scale deployment of high-level autonomous driving. On the industrial front, high-level intelligent driving in passenger vehicles is increasingly penetrating urban scenarios, while three major L4 commercial use cases are being implemented in a tiered manner, and industry competition is escalating into a comprehensive contest across data, computing power, mass production capability, and operational execution.
According to a research report by Shenwan Hongyuan dated August 4, technology remains the dominant growth theme at present, and the market has begun pricing in opportunities related to the commercialization of AI applications. Autonomous driving and robotics are not absent from this wave of AI adoption; under the spillover effects of AI, intelligence and premiumization will become key drivers for the automotive sector.
Editor/melody