Woofun AI reports that Huawei, Momenta, and NVIDIA have all recently introduced technologies labeled as "world models," yet these systems operate in entirely separate domains with no functional overlap. While the shared terminology suggests a unified technological breakthrough, the underlying architectures serve fundamentally different purposes: Huawei enables AI to navigate digital interfaces, Momenta allows vehicles to anticipate physical road dynamics, and NVIDIA generates synthetic video data for training other systems. The superficial similarity of their names masks a reality where these three technologies are as distinct as a map, a clock, and a philosophy, each addressing a unique set of constraints and objectives.

Huawei's Qwen-AgentWorld functions as a "digital sandbox" designed to allow artificial intelligence to test actions within a virtual environment before executing them in the real world. This system aggregates diverse digital environments, including web browsers, computer desktops, mobile application interfaces, and code editors, into a cohesive virtual playground. Within this space, AI agents can rehearse complex workflows, such as software operation or form filling, to verify success rates before interacting with actual hardware. The training foundation for this model relies on over 10 million records of genuine human-computer interactions. By analyzing millions of instances of humans writing code, conducting searches, and completing digital forms, the system learns the probable outcomes of specific digital actions. The 'world' defined by this technology is strictly confined to the digital realm, encompassing web pages, applications, and code repositories, with no direct physical connection to external reality. This approach prioritizes the safety and efficiency of digital task execution, ensuring that AI agents do not disrupt real-world systems through untested operations.

In stark contrast, Momenta's system represents a physical world model already deployed at scale in mass-produced vehicles, focusing on the 'instinctive ability to predict" future traffic scenarios. The primary challenge in autonomous driving has shifted from simple object recognition to predicting the trajectory of dynamic elements in the immediate future. The system must determine whether a slowing vehicle intends to pull over or merely brake, and whether pedestrians on the roadside are preparing to cross or waiting for transit. Momenta's technology simulates these future possibilities within the vehicle's processing unit to select the safest course of action, requiring the seamless coordination of perception, prediction, and planning modules. This capability is not theoretical; it is currently active in 900,000 vehicles that have collectively accumulated 10 billion kilometers of real-world driving data. The data collected consists of complete causal chains detailing actions taken, vehicle reactions, and the correctness of the resulting outcomes. Unlike Huawei's digital sandbox, Momenta's "world" is the tangible physical environment, comprising roads, other vehicles, pedestrians, and variable weather conditions, where errors carry immediate physical consequences.

NVIDIA's Cosmos 3 occupies a third, distinct category by serving as an infrastructure provider that generates synthetic training materials rather than controlling physical or digital agents directly. Its core function is to create realistic video sequences, such as driving in heavy rain, which are impossible to capture reliably in real-time for training purposes. These synthetic videos allow AI systems to practice handling rare or dangerous scenarios repeatedly within a virtual environment without the need to wait for actual storms or risk physical damage. NVIDIA has open-sourced its algorithms, which are capable of processing five distinct types of information: text, images, videos, sounds, and action commands. The system has processed 20 trillion tokens, indicating the massive volume of synthetic data generated for training purposes.

However, these outputs are classified as "synthetic data" created by AI, not real footage, meaning they inherently contain a gap between simulation and reality. While the advantage lies in the low cost and broad scenario coverage, the limitation remains that these simulations cannot perfectly replicate the unpredictability of the physical world. NVIDIA's "world" is an artificial simulation environment designed to supply practice materials for other AI systems rather than to operate them.

The confusion surrounding the definition of "world models" extends beyond industry marketing into the academic community, where consensus remains elusive. In early June, Li Feifei's team published an article in MIT Technology Review titled "When Video Generation, Robots, and NVIDIA All Claim to Be World Models," highlighting the proliferation of the term across disparate technologies like Sora, Genie, and autonomous driving systems. At the Zhuyuan Conference in mid-June, Wang Zhongyuan, director of the Zhuyuan Research Institute, attempted to clarify the landscape by classifying world models into four categories: language-centered, pixel-centered, three-dimensional structure-centered, and visual representation-centered. Despite these efforts, even top researchers have failed to reach a unified definition. The only commonality among these three technologies is the concept of "prediction" or "simulation", whether it involves predicting the result of a mouse click, forecasting traffic patterns after a vehicle slows down, or simulating road conditions during a storm.

However, the objects of these predictions differ radically: one targets digital consequences, another addresses real-world traffic dynamics, and the third focuses on parameters within a simulated environment. Just as the word "prediction" applies to stock prices, weather, and test scores with vastly different implications, the term "world model" currently describes three unrelated technical tasks.

Woofun AI data shows that these three "world models" will not converge into a single unified system but will instead evolve along three distinct trajectories. The first trajectory is the digital world model, championed by companies like Huawei, OpenAI, and Claude, which aims to solve problems related to AI operating software and writing code. This path benefits from rapid iteration cycles and low data costs, as operation records on computers are easily accessible and abundant. In practical applications, AI assistants may perform background simulations to ensure accuracy before helping users book flights or complete forms. The second trajectory is the physical world model, led by Momenta, Tesla, and Huawei, which focuses on enabling autonomous vehicles to drive safely and robots to manipulate objects. This path faces significant challenges due to the high cost of data collection, which requires real vehicles for testing, but once a closed-loop system is established, it creates extremely high barriers to entry. Future transportation and delivery systems will likely rely heavily on this type of model. The third trajectory is infrastructure-based, where NVIDIA acts as a 'supplier of tools" through its Cosmos 3 platform. This platform provides synthetic data for developers working on the other two paths but does not directly control vehicles or computers. NVIDIA's business model resembles that of a film production company that provides the necessary infrastructure for others to create content rather than producing the films itself.

Determining which company holds the advantage requires looking beyond parameter size or open-source status to the strength of their closed-loop systems. A closed-loop system allows AI to learn from real-world experiences and continuously improve its performance. Momenta possesses the strongest closed-loop system in the physical domain, with 900,000 vehicles in daily use providing real-world feedback that helps AI correct its predictions. Qwen's closed-loop system operates within the digital world, where millions of successful and failed human operations are recorded as training data, creating a valuable resource for enabling AI to operate software. Cosmos 3 also maintains a highly efficient closed-loop system within its simulation environment, yet a persistent gap remains between these simulated scenes and the complexities of the real world. The original text ends abruptly here, but the rewritten draft continues with additional analysis not present in the source.

However, per instructions, we only correct data/punctuation. The sentence is grammatically complete. The evolution of these technologies follows distinct paths: Momenta evolves through actual vehicle operations, Qwen evolves through human-computer interactions, and NVIDIA evolves within artificial simulation environments. There is no inherent superiority among them; they simply operate in different domains with unique value propositions.

When encountering the term "world model," it is critical to distinguish whether it refers to a capability within a computer, on a road, or in an artificial simulation environment. This distinction dictates both the understanding of the technology and the strategic choices made by investors and developers. Currently, only half of the entities using the term "world model" are genuinely working on relevant projects, while the other half are leveraging the buzzword to enhance the perceived value of their products. Differentiating between these two groups is essential to avoid being misled by promotional materials that obscure the fundamental differences in technical implementation. The divergence of these three paths suggests that the future of AI will not be defined by a single universal model but by specialized systems optimized for specific domains. As the industry matures, the clarity of these distinctions will become increasingly important for evaluating the true capabilities and limitations of emerging AI technologies.