In a recent interview clip that has gone viral on social media, NVIDIA CEO Jensen Huang has identified Tesla and SpaceX CEO Elon Musk as a player in a "phenomenal position" within the AI race. Huang argued that Musk's advantage does not stem from his personal style but is built on a foundation of computing infrastructure, real-world data, and a three-pronged AI business strategy.
Huang stated that collecting real-world data is extremely expensive, and Tesla operates one of the largest vehicle fleets globally, which continuously generates vast amounts of driving data. Simultaneously, Tesla's AI computing infrastructure is heavily equipped with NVIDIA hardware. Adding to this, the simultaneous advancement of xAI, Tesla's autonomous driving, and the Optimus humanoid robot means Musk has secured a vital strategic high ground in the AI era.
These remarks have spread rapidly on platforms like X, sparking intense discussion within the AI industry and investment circles. As global tech giants intensify their focus on foundation models, autonomous driving, and robotics, Huang has once again shifted market attention from the simple competition of large language models to the complete AI ecosystem encompassing computing power, data, and terminal applications.
Huang: Musk's Greatest Advantage is an Infrastructure That's Hard for Others to Replicate
According to the viral clip, Huang was asked about the future development of the AI industry, specifically highlighting Musk's competitive edge.
"The cost of collecting real-world data is very high, and Elon has a huge advantage," Huang said.
In his view, this advantage comes from two main aspects.
First, there is the AI computing infrastructure. He noted that Tesla's AI factory, used for training its autonomous driving systems, is packed with NVIDIA GPUs, making it one of the world's most powerful AI training platforms. This provides ample computing power for the continuous iteration of the Full Self-Driving (FSD) model.
Second, there is the real-world data advantage. Huang said Tesla operates one of the largest networks of connected vehicles globally, which constantly collects data on driving environments, road conditions, and vehicle performance. This means that compared to AI companies relying on training from public datasets, Tesla can continuously access a vast flow of new, real-world data. This type of data is extremely costly to acquire and is a critical foundation for the ongoing evolution of autonomous driving models.
He therefore concluded that Musk holds a natural advantage in the AI era, one that has been built up over many years and is not easily replicated in a short time.
The Three Major AI Battlefields: Foundation Models, Autonomous Driving, and Humanoid Robots
Huang further explained that he is familiar with Musk's vision for the future of AI and believes Musk is positioning himself across the three most important directions in AI. According to Huang, these are: xAI, responsible for fundamental cognitive intelligence, foundation models, and general AI; Tesla, responsible for autonomous driving; and Optimus, responsible for humanoid robots.
"These three directions are the most important battlefields in AI," Huang stated.
This implies that, in Huang's view, the future competition in the AI industry will not be limited to chatbots or large language models but will extend to robotics and the physical world. If the foundation model is responsible for "thinking," and autonomous driving for "understanding the real world," then the humanoid robot is responsible for "entering the real world and executing tasks." Together, these three elements form a complete closed-loop for the next stage of AI industry development.
Shifting from 'Model Competition' to 'Infrastructure Competition'
In recent years, the focus of competition in the AI industry has gradually shifted from model parameter size to the harder-to-replicate capabilities of infrastructure. On one hand, large models continue to drive rapid growth in investment for GPUs and data centers. On the other hand, more and more tech companies are realizing that high-quality, real-world data is becoming a new scarce resource.
Autonomous driving has therefore become a crucial data source for the AI industry. The millions of connected cars from Tesla Motors generate driving data daily, which is not only used for FSD training but also builds a data moat that is difficult for other AI companies to replicate.
Simultaneously, Musk has been expanding his AI empire in recent years: xAI is responsible for training new-generation foundation models; Tesla Motors continues to advance FSD and Robotaxi; and Optimus is positioned as a humanoid robot platform for future large-scale deployment.
By placing these three businesses on equal footing as the most important future directions for AI, Huang has signaled that he believes future AI competition will revolve more around computing power, data, and robotic applications, rather than just who has the most powerful large model.
Re-emphasizing NVIDIA's Core Role in the AI Ecosystem
Notably, in his assessment of Musk's advantages, Huang also reaffirmed the importance of AI infrastructure. Whether it's training foundation models or developing autonomous driving and robotic systems, it all requires massive investment in GPU clusters and data centers, and NVIDIA GPUs remain a critical component of the global AI training and inference infrastructure.
Huang has previously stated that in the future, every large enterprise will build its own "AI factory." The core competitiveness in the AI era lies not only in algorithms but also in the computing infrastructure that can continuously convert data into intelligence. By using Tesla as an example, he once again illustrated this point: what is truly hard to replicate is not a single model, but the flywheel effect created by the long-term accumulation of computing power, real-world data, and application scenarios that reinforce each other.
Comments