When ChatGPT burst onto the scene in late 2022, it sparked a global AI frenzy. People began to wonder: when will robotics have its “ChatGPT moment”—a general-purpose model that can understand the physical world and perform various tasks?
More than two years later, that moment has yet to arrive. Why?
Root Cause: Fundamentally Different Data Paradigms
The success of large language models rests on the vast sea of text data available on the internet. This data exists naturally, requires no annotation, and is massive in scale. GPT-3 was trained on 570GB of data, containing hundreds of billions of tokens. This represents thousands of years of human civilization’s written legacy.
What kind of data does embodied AI need? It needs “perception-action” data generated through robots interacting with the physical environment. This type of data has several致命 flaws:
First, no historical accumulation. Before the concept of embodied AI emerged, no one systematically collected robot interaction data. This is virgin territory that must be cultivated from scratch.
Second, extremely high acquisition costs. A single real grasping action requires real robots, real objects, real environments, and real time. Data acquisition速度 is one-millionth that of text data, and costs are a million times higher.
Third, high dimensionality. Text is a one-dimensional sequence, images are two-dimensional matrices, but embodied AI data consists of multimodal time series—vision, touch, torque, position, velocity all changing simultaneously—requiring alignment, cleaning, and annotation at exponentially higher complexity.
The “Data Gap” Leads to an “Intelligence Gap”
Precisely because of these fundamental differences in data paradigms, the development path of embodied AI diverges sharply from that of large language models. Large language models are “algorithm-first”—with the Transformer architecture, massive data naturally drives model evolution. Embodied AI, however, is “data-first”—algorithm architectures have proliferated, but the scarcity of quality data leaves all algorithms malnourished.
This explains why we see numerous embodied AI startups with impressive demo videos that “fail” as soon as they encounter real environments. Their models are trained on synthetic data, and an unbridgeable gap exists between simulation and reality.
VISME’s Answer: Build the Roads First, Then the Cars
Facing this dilemma, VISME’s choice is: build the roads first, then the cars. Rather than rushing to推出 a “general robot model,” we are first constructing the data infrastructure通往 the physical world. Through proprietary sensors, data factories, and data standards, we are paving the way for the entire industry.
We believe that embodied AI’s “ChatGPT moment” will eventually arrive—but it won’t explode overnight like large language models. It will be a gradual process, advancing step by step with the accumulation of real data. And when that moment comes, those who have quietly cultivated the data will stand at center stage.
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