Why are NVIDIA and JD.com both betting on Physical AI in the second half of the AI game?

Right at the start of September, eye-catching billboards suddenly popped up at airports across multiple regions in China, bearing the slogan "Push AI to the Physical World". The short, straightforward message was signed by JD Cloud.

While these billboards were launched, the JDD (JD Discovery Developer Conference) held on September 9 also explicitly set its theme around "Physical AI".

"Physical AI" is undoubtedly the most prominent hotspot in the Chinese tech ecosystem over the past six months, with the physical world widely recognized as the next frontier for AI.

The industry has gone through two years of technical carnival, fiercely competing for faster inference speed, longer context windows, and even more human-like chatbots. But a very realistic, even harsh question has been placed on the table at this moment: How can these astronomical amounts of compute-consuming Tokens and massive traffic accumulated on the C-end be translated into real commercial value?

From the perspective of industry observers, this shift reflects two core propositions behind it: Why must AI move out of the digital world? And why are enterprises like JD seizing this high ground?

01

When AI Starts to Step Out of the Chat Box

In the past few years of AI evolution, it has essentially been chasing performance gains in the virtual digital world. However, the diminishing marginal effect of technological iteration and high operating costs force the entire industry to face reality directly: how much work can these capabilities ultimately complete, and how much actual value can they create.

Agent is the most obvious signal of this trend.

In the past, Copilot mainly participated in human work processes, helping programmers write code, helping sales sort out materials, and helping financial staff analyze data; Agent has begun to take over a complete task further, reading enterprise data, calling different tools, and writing the results back to business systems. A study released by OpenAI in June this year summarized this change as the basic unit of knowledge work is shifting from single interaction to "delegable long-term tasks".

The way enterprises measure AI has also changed accordingly. If Token measures how much intelligence the model produces, and Task measures how much work AI can undertake, what enterprises ultimately need to see is still cost reduction, efficiency improvement, and closed business loops, that is, Result.

When the general capabilities of underlying large models are gradually leveled, "who has larger parameters" no longer forms an absolute moat. The battlefield of AI competition will inevitably shift to "who can deeply access industrial processes and solve specific problems".

This is also the background why JD put "Physical AI" at the core position at this year's JDD.

JD's judgment is that AI competition is shifting from "parameter race" to "productivity race". At JDD, JD integrated cloud, data, models, terminals, scenarios and supply chains into the same system, hoping to further integrate AI into real scenarios such as retail, logistics, industry, healthcare and households.

Compared with the industry's widespread vague use of "Physical AI" to generally refer to robots, the logic JD wants to discuss is obviously much broader.

Robots are a typical form of AI acting directly on the real world, but more extensive changes have already taken place quietly in different industries. Retail AI ultimately needs to affect transactions and fulfillment, logistics AI begins to participate in warehousing and distribution, industrial AI enters production processes, and robots further convert model judgments into physical actions. AI has begun to move beyond producing information, and further participate in the production and service processes of the real world.

This judgment is also becoming the underlying consensus of the global AI industry. This year, NVIDIA has continuously strengthened its layout of Physical AI, from the Cosmos world model, Isaac simulation framework to the GR00T robot model, and cooperated with giants such as ABB, FANUC and Figure to promote technology into electronic assembly and industrial automation scenarios.

Physical AI is extending from a technical branch in the robotics field to the only main track for AI to cut into real production systems and realize productivity value.

02

Entering the Physical World, Why Is AI More Difficult Instead?

The rapid progress of large models in the past few years is largely built on the massive digital content accumulated by the Internet. Web pages, books, pictures, videos and codes can be continuously used for training; after the model is trained, it can be quickly deployed to different products and users through APIs.

But when it comes to Physical AI, the Scaling Law begins to encounter the famous "Moravec's Paradox" — for AI, abstract logical reasoning has become easier, but grasping an object and identifying a tiny obstacle in the complex real physical world is extremely difficult.

When an AI enters a warehouse, it needs to know where the goods are, how inventory changes, and how different devices collaborate; when it enters a factory, it needs to understand materials, machines and production processes; when it enters a home, it needs to adapt to different spaces and hardware; when it is embedded in a robot, it further involves movement, force, environmental feedback, and differences between different bodies.

This is also why the development of Physical AI cannot simply replicate the Scaling path of large models in the past few years. NVIDIA once summarized the core problem faced by robot development as "data gap": the Internet provides rich pre-training data for large language models, but the robot data in the real world is limited in quantity, expensive to collect, and a large number of extreme scenarios are difficult to be covered by real collection.

Specific to industrial implementation, the problems that Physical AI needs to solve can be further summarized into three levels: whether there is enough data for learning, whether there are enough scenarios for verification, and whether it can be scaled after successful verification.

This exactly corresponds to the logic JD put forward at JDD: Data enables AI to evolve, scenarios enable AI to verify, and supply chains enable AI to scale.

The first point is data.

Data in the digital world mainly records the knowledge that humans have expressed. Physical AI needs to further understand how people and objects move, how actions change the environment, and how tasks are completed in real space. The production cost of this type of data is also higher. A piece of web text can be directly read by the model, but a robot task may involve different modalities such as cameras, sensors, motion trajectories, and force feedback, and also need to go through collection, labeling, training and real environment verification.

JD chooses to extend its data infrastructure further to the real world. At present, its embodied data system has covered the whole link of "collection, storage, labeling, training, evaluation, simulation and testing", and plans to accumulate 10 million hours of real-scene video data within two years. The data source has also expanded from robot body data to force tactile data, simulation data and human first-person perspective operation videos.

Apart from data, the second barrier is scenario.

Physical AI ultimately faces a continuously changing real environment with an extremely low fault tolerance rate. The fact that a capability is valid in Benchmark or laboratory does not mean it still works after entering warehouses, factories and households.

This also leads to the fundamental difference between Physical AI and general digital models: in digital AI, the model is usually trained first and then deployed to scenarios such as search and office work; while in Physical AI, the relationship between scenarios and models is getting deeper, and the scenario itself is part of the continuous iteration of AI capabilities.

Real business generates data, data goes into model training, the model re-enters the business to perform tasks, and the execution results continue to form new feedback — thus forming a continuously circulating flywheel between data, models and scenarios.

From this perspective, the business that JD has accumulated in retail, logistics, industry, healthcare and households has gained unique value in the AI era. Taking the industrial scenario as an example, JD disclosed that its industrial large model JoyIndustrial has accumulated more than 1 billion calls, and its applications have further deepened from industrial knowledge understanding to Agent task planning, tool invocation and enterprise business system execution, and the execution results are fed back to the system again.

The scenario is not only the place where AI capabilities are ultimately applied, but also the physical closed loop where AI obtains real feedback and completes self-evolution.

The third barrier is scalability.

There is still a long engineering and industrial chain distance between completing a task once in the laboratory and allowing a large number of devices to run stably for a long time in different environments. After AI enters the real world, model deployment is only one of the links. Hardware production, components, channels, logistics, installation, maintenance and after-sales will all affect whether a commercial closed loop can be finally formed.

Therefore, among the six elements of JD's "cloud, data, model, terminal, scenario and chain", the final "chain" is particularly noteworthy.

The model solves whether a capability can be realized, the scenario verifies whether it can operate in the real environment, and the supply chain determines whether this capability can enter a sufficiently large real world.

This forms a more complete set of industrial barriers for Physical AI: Data determines how AI understands reality, scenarios determine how AI adapts to reality, and supply chains determine how AI scales into reality.

When the resources required for competition change, the original AI advantage ranking among major tech giants may also change accordingly.

03

Physical AI May Redefine the AI Advantages of Large Tech Giants

As Physical AI brings competitive variables into the real world, the physical infrastructure accumulated by JD over the past two decades has gained a new interpretation space.

Compared with many Internet platforms that mainly operate online, JD's business has long been deeply involved in the actual circulation process of goods and services. From procurement, inventory, warehousing to distribution and after-sales, to industrial procurement, health services, home appliances and smart hardware, a large number of businesses need to connect online systems and offline fulfillment at the same time.

In the past, these capabilities mainly served the efficiency of e-commerce and supply chains. After entering the Physical AI stage, they may also become the foundation for AI to obtain data, training and verification capabilities, and finally realize large-scale deployment.

The core of JD's proposed "Super AI Supply Chain" at JDD is to deeply connect this physical network with AI. From the underlying cloud, data and models, to the middle layer of Agent, development platforms and simulation tools, and then connect to terminals such as robots, home appliances, medical devices and unmanned vehicles, and finally enter actual businesses such as retail, logistics, industry, healthcare and households.

The robotics industry can more directly reflect the implementation mode of this "Super AI Supply Chain".

At present, JD's positioning for itself is not limited to developing a certain type of robot product. According to the plan disclosed at this JDD, JD hopes to participate in links such as embodied data collection, robot industrial bases, core component supply, retail channels and maintenance services at the same time.

In the next five years, JD plans to deploy more than 80 RoboBase; on the supply chain side, it hopes to reduce the BOM cost of robot bodies by more than 50% through large-scale procurement, C2M customization and supply chain collaboration; on the sales and service side, it proposes to help 100 robot brands achieve sales exceeding 1 billion yuan in the next three years, and build a robot after-sales service network covering more than 100 countries and regions around the world.

Behind these layouts, there are actually corresponding uncertain problems in the Physical AI industry in the future: if robots eventually become a sufficiently large intelligent terminal category, how will the industrial value be distributed?

After the PC industry matures, value is distributed in different links such as chips, operating systems, complete machines, software and channels; smartphones have further formed a complete division of labor of chips, operating systems, terminal brands, application ecology and supply chains. If robots really enter factories, shopping malls and households, they also need models, data, core components, complete machines, channels, maintenance and service systems.

At present, this industry is still in its early stage, and the final industrial pattern is far from formed. But for JD, the significance of participating in Physical AI therefore goes beyond launching a certain robot or a certain model.

What it is trying to answer is: When a large number of AI terminals really enter the real world, what position can a platform with supply chain and physical operation network occupy in it?

This is also the entry point to understand JD's proposal of "the world's largest physical world operation center".

JD has long operated the flow of goods in the real world. After an order is generated, it is connected to procurement, inventory, warehousing, transportation, distribution and after-sales, and the digital system ultimately needs to complete fulfillment in the real world.

Physical AI gives this capability the possibility of further extension. What needs to be organized on a large scale in the future may also include how AI obtains real data, enters different terminals, completes tasks in specific scenarios, and operates continuously through the supply chain and service system.

The competition dimension of the second half of AI is being redefined by the physical world, which is exactly the battlefield where JD has the deepest accumulation and the best expertise.