Understanding Jensen Huang's Physical AI: Why Are Crypto Opportunities Hidden in the "Corners and Crannies"?

Writing by: Haotian

What exactly did Jensen Huang say at the Davos Forum?

On the surface, he is promoting robots, but in reality, he is conducting a bold “self-revolution.” He used a few words to end the old era of “stacking graphics cards,” but unexpectedly, he has pre-set a golden ticket for the Crypto track?

Yesterday, at the Davos Forum, Jensen pointed out that the AI application layer is exploding, and the demand for computing power will shift from “training” to “inference” and “Physical AI.”

This is very interesting.

NVIDIA, as the biggest winner in the AI 1.0 era “computing power arms race,” now proactively calls for a shift towards “inference” and “Physical AI,” which actually sends a very straightforward signal: the era of relying on stacking cards to train large models for miracles is over. Future AI competition will revolve around the “application-centric” landing of use cases.

In other words, Physical AI is the second half of Generative AI.

Because LLMs have already read all the data accumulated over decades on the internet by humans, but they still don’t know how to open a bottle cap like a human. Physical AI aims to solve the “unity of knowledge and action” problem beyond AI intelligence.

Because physical AI cannot rely on remote cloud servers’ “long feedback loop,” the logic is simple: letting ChatGPT generate text one second slower might just feel laggy, but if a bipedal robot experiences a one-second delay due to network latency, it could fall down the stairs.

However, Physical AI, while seeming like an extension of generative AI, faces three completely different new challenges:

1. Spatial Intelligence: Enabling AI to understand a three-dimensional world.

Professor Fei-Fei Li once proposed that spatial intelligence is the next Polaris of AI evolution. Robots need to “see and understand” their environment first. This is not just recognizing “this is a chair,” but understanding “the chair’s position and structure in 3D space, and how much force I need to move it.”

This requires massive, real-time 3D environment data covering every corner indoors and outdoors;

2. Virtual Training Grounds: Allowing AI to trial and error in simulated worlds.

Jensen mentioned Omniverse, which is essentially a “virtual training ground.” Robots need to train “10,000 falls” in virtual environments before entering the real physical world to learn to walk. This process is called Sim-to-Real, from simulation to reality. If robots were to trial and error directly in the real world, hardware wear and tear costs would be astronomical.

This process demands exponential throughput of physics engine simulation and rendering computing power;

3. Electronic Skin: “Tactile data” as a new gold mine of data.

To have “touch” capabilities, Physical AI needs electronic skin to sense temperature, pressure, and texture. These “tactile data” are entirely new assets that have never been scaled before. This may require large-scale sensor collection. At CES, Ensuring showcased a “mass-produced skin” embedded with 1,956 sensors on a single hand, enabling robots to perform the amazing task of cracking eggs.

These “tactile data” are entirely new assets that have never been scaled before.

After reading this, you will definitely feel that the emergence of the Physical AI narrative offers great opportunities for hardware devices like wearables and humanoid robots, which a few years ago were often criticized as “oversized toys.”

Actually, I want to say that in the new landscape of Physical AI, the Crypto track also has excellent ecological complement opportunities. Here are a few examples:

1. AI giants can deploy street view cars to scan every main street in the world but cannot capture every nook and cranny of streets, neighborhoods, and basements. Using DePIN network devices with token incentives to mobilize global users to use their portable devices to fill in these data gaps could complete the coverage;

2. As mentioned earlier, robots cannot rely on cloud computing power, but in the short term, large-scale use of edge computing and distributed rendering capabilities is necessary, especially for many simulation-to-reality data. Leveraging distributed computing networks to gather idle consumer-grade hardware for distribution and scheduling could be very useful;

3. “Tactile data,” besides large-scale sensor applications, is inherently highly private. How to coordinate the public to share these privacy-involved data with AI giants? A feasible approach is to allow contributors to obtain data rights confirmation and dividends.

In summary:

Physical AI is the second half of Jensen’s call for the web 2 AI track. What about the web 3 AI + Crypto tracks, such as DePIN, DeAI, DeData, and others? What do you think?