Who Is the Big Winner in the AI Race? Jensen Huang Points to Musk: He Has Three Major Advantages

A recent interview video featuring NVIDIA CEO Jensen Huang has gone viral on social media. In the video, Huang described Tesla and SpaceX CEO Elon Musk as players in a "phenomenal position" in the AI race, arguing that his advantages stem not from personal style but from computing power infrastructure, real-world data, and strategic layout across three major AI business lines.

Huang stated that collecting real-world data is extremely expensive, whereas Tesla owns one of the largest vehicle fleets in the world, continuously generating vast amounts of driving data. Meanwhile, Tesla's AI computing infrastructure has deployed a large quantity of NVIDIA hardware. With the combined progress of xAI, Tesla Autopilot, and Optimus humanoid robots, Musk has already occupied important strategic high ground in the AI era.

These remarks quickly spread widely on social platforms like X, sparking heated discussion within the AI industry and investment circles. As global tech giants continue to double down on foundation models, autonomous driving, and robotics, Huang has once again shifted market focus from mere competition over large models to a complete AI ecosystem composed of computing power, data, and end-user applications.

Jensen Huang: Musk's Greatest Advantage Is Infrastructure That Others Find Hard to Replicate

According to the circulating video content, when asked about the future development of the AI industry, Huang focused on Musk's competitive advantages.

He stated:

"The cost of collecting real-world data is very high, and Elon has a huge advantage."

In Huang's view, this advantage mainly comes from two aspects.

First is the AI computing power infrastructure.

He pointed out that Tesla's AI Factory, used for autonomous driving training, houses a large number of NVIDIA GPUs, making it one of the most powerful AI training platforms globally and providing ample computing power for the continuous iteration of the FSD model.

Second is real-world data.

Huang noted that Tesla has one of the largest connected car fleets in the world, constantly collecting data on driving environments, road conditions, and vehicle operations every day. This means that, compared to AI companies relying on public data for model training, Tesla can continuously obtain large amounts of new data from the real world. Such data is extremely costly to acquire and serves as an important foundation for the continuous evolution of autonomous driving models.

Therefore, he believes that Musk has a natural advantage in the AI era, an advantage built up over many years that cannot be replicated in a short time.

The Three Major AI Battlefields: Foundation Models, Autonomous Driving, and Humanoid Robots

Huang further stated that he fully understands Musk's judgment on the future development of AI and believes that Musk is laying out strategies in the three most important directions for AI. According to Huang's classification, these are:

• xAI: Responsible for foundational cognitive intelligence, i.e., foundation models and general AI capabilities;
• Tesla: Responsible for autonomous driving;
• Optimus: Responsible for humanoid robots.

Huang stated that these "three directions are precisely the three most important battlefields in AI."

This implies that, in Huang's view, future competition in the AI industry will not only occur between chatbots or large language models but will extend to robotics and the physical world.

If foundation models are responsible for "thinking," and autonomous driving for "understanding the real world," then humanoid robots are responsible for "entering the real world and executing tasks." Together, the three 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 the scale of model parameters to infrastructure capabilities that are harder to replicate.

On one hand, large models continue to drive rapid growth in investments in GPUs and data centers; on the other, more technology companies are realizing that high-quality real-world data is becoming a new scarce resource.

Autonomous driving has thus become an important source of data for the AI industry.

Tesla's millions of connected cars continuously generate driving data every day, which is not only used for FSD training but also allows the company to build a data moat that other AI enterprises find difficult to replicate.

Meanwhile, Musk has been continuously expanding his AI footprint in recent years:

• xAI is responsible for training the next generation of foundation models;
• Tesla is continuously advancing FSD and Robotaxi;
• Optimus is positioned as a humanoid robot platform capable of large-scale deployment in the future.

By listing these three businesses as the most important development directions for the future of AI, Huang indicates that he believes future AI competition will revolve more around computing power, data, and robotic applications, rather than just who possesses the most powerful large model.

Re-emphasizing NVIDIA's Core Role in the AI Ecosystem

Notably, while evaluating Musk's advantages, Huang once again emphasized the importance of AI infrastructure.

Whether training foundation models or developing autonomous driving and robotic systems, continuous investment in large-scale GPU clusters and data centers is required, and NVIDIA GPUs remain a crucial component of the global AI training and inference infrastructure today.

Huang has previously stated multiple times that every large enterprise will build its own "AI Factory" in the future. The core competitiveness in the AI era lies not only in algorithms but also in the computing power infrastructure capable of continuously transforming data into intelligence.

By using Tesla as an example this time, he has once again demonstrated this point: what is truly difficult to replicate is not any single model, but the flywheel effect formed by the long-term accumulation and mutual reinforcement of computing power, real-world data, and application scenarios.