When AI Begins to Have a Body: Will Physical AI Become the Next Major Tech Trend?

In the past two years, AI traded in capital markets has primarily been the "brain" of AI.

From ChatGPT and large language models to GPUs, HBM, data centers, optical communications, and power infrastructure, almost all core themes have revolved around how to scale models larger, train them faster, and lower inference costs.

However, these AIs can generate text, images, code, and video, yet most still operate within screens and the digital world.

Therefore, as large model capabilities and computing infrastructure gradually mature, the market naturally begins to ask the next question: Can these increasingly intelligent models ultimately step out of the screen and into cars, factories, warehouses, hospitals, and the real world?

This is precisely why Physical AI is beginning to take center stage in the industry.

1. From "Thinking" to "Acting": Why is Physical AI Important?

According to NVIDIA's definition, Physical AI brings AI out of the screen, enabling autonomous systems like robots, cameras, and self-driving cars to perceive and understand their surroundings, perform reasoning, make decisions, and execute complex actions.

In other words, if generative AI solves "how machines think," then Physical AI attempts to solve how machines, after thinking, can act correctly, safely, and cost-effectively, granting them the ability to interact with the physical world.

Based on Jensen Huang's recent public speeches, NVIDIA is continuously strengthening product lines such as Isaac, GR00T, Cosmos, Omniverse, and Jetson. The goal is not merely to bet on a specific robot but to build a comprehensive underlying platform covering training, simulation, reasoning, and deployment for machines entering the physical world.

True Physical AI is not as simple as integrating a large model into a robot. It requires understanding spatial relationships and physical laws, needing world models, training data, simulation environments, edge computing, machine vision, sensors, and motion control, all while undergoing extensive safety testing before deployment.

In market contexts, Physical AI heavily overlaps with "embodied intelligence," but the former has a broader scope, including not only humanoid robots but also autonomous driving, industrial robots, drones, smart factories, warehouse systems, and intelligent spaces driven by cameras and sensors.

Of course, Physical AI is not a suddenly emerging concept.

Autonomous driving, industrial robots, machine vision, and warehouse automation have been developing for years. What has truly changed is that large models, world models, simulation technology, and edge computing are now connecting these previously fragmented technological paths.

Many traditional industrial robots rely on pre-programmed instructions to repeatedly execute standard actions in relatively fixed environments. The goal of Physical AI is to enable machines to adjust their judgments and actions based on real-time information when facing different objects, unfamiliar environments, and unexpected situations.

This means the AI industry chain is extending from the "brain" to the "body."

Over the past two years, the market first revalued the GPUs, storage, servers, networks, and power needed to train and run AI. Next, capital may seek vehicles to utilize this computing power and translate model capabilities into real-world productivity: robots, autonomous cars, drones, industrial automation equipment, and the vision and sensor systems deployed across factories, warehouses, and cities.

Therefore, Physical AI is not a single-point concept simply equated to "humanoid robots." It genuinely unlocks an entire industry chain extending from computing power to action.

2. From Computing Power to Robots: The Five-Layer Industry Chain of Physical AI

For ease of understanding, the MSX Research Institute roughly breaks down the Physical AI industry chain into five key segments.

Layer 1: Computing Power Layer

Whether training robot models, building virtual environments, or performing real-time reasoning in cars and robots, computing power is indispensable.

This layer encompasses data center GPUs, edge AI chips, in-vehicle computing platforms, and low-power processors, with corresponding key players including:

• NVIDIA (NVDA.M): Covers training compute, Jetson edge computing platform, and robotics development ecosystem;
• TSMC (TSM.M): The manufacturing backbone for AI chips, automotive chips, and edge computing chips;
• Arm (ARM.M): Low-power computing architecture widely used in automotive, robotics, and smart devices;
• Qualcomm (QCOM.M): Positioned in in-vehicle AI, edge inference, and smart terminals;
• AMD (AMD.M): Potential beneficiary in AI compute and embedded computing;

The logic of this layer mirrors the generative AI trend of the past two years, continuing the "picks and shovels" approach. Regardless of which robot company ultimately wins, the underlying need for chips, computing power, and architecture remains.

2. Layer 2: Model Layer

This is also not hard to understand. Physical AI requires not just language models, but also robot foundation models, world models, and vision-language-action models.

Language models understand human instructions, vision models help machines recognize environments, action models translate judgments into specific movements, and world models go further, attempting to let AI understand relationships between objects, predict what might happen next, and simulate outcomes before acting.

This layer is currently primarily driven by large tech companies and platform-type firms, including NVIDIA, Tesla, Google, and some robotics startups.

Compared to large language models, the biggest challenge for robot models is data. While there is vast text, image, and video data on the internet, high-quality robot operation data is scarce. Generating sufficient training data will be a critical threshold in the development of Physical AI.

3. Layer 3: Simulation Layer

Because real-world training is expensive, slow, and risky, robots need to learn in virtual worlds first. Thus, digital twins, synthetic data, and virtual training environments constitute a very important layer of Physical AI.

NVIDIA has built a relatively complete toolchain in this layer: Omniverse for creating digital twins and simulation environments, Isaac Sim and Isaac Lab for robot training, testing, and validation, and Cosmos for world model and data generation capabilities.

The value of this layer lies in shifting expensive, dangerous, and slow trial-and-error from the real world to virtual environments. Developers can run numerous scenarios simultaneously, testing different lighting, weather, terrain, and unexpected events, then deploy validated models to real devices.

Ultimately, a robot might take minutes to train once in the real world, but can run thousands of parallel iterations in a simulation environment.

4. Layer 4: Perception Layer

For a robot entering the real world, the first step is often not having dexterous hands, but stably "seeing" and understanding its surroundings.

It must identify objects, judge distances, understand environmental changes, and localize itself within complex spaces. After making a judgment, it needs controllers, motors, robotic arms, and joint modules to translate decisions into actual actions.

This layer includes machine vision, cameras, LiDAR, sensors, control chips, motion control, and various actuation components:

• Cognex (CGNX.M): Industrial machine vision and identification systems;
• Ouster (OUST.M): LiDAR and perception platforms;
• Qualcomm, NVIDIA: Provide in-vehicle and edge vision computing platforms;

Ouster has integrated its new digital LiDAR with the NVIDIA Jetson and Isaac ecosystem, advancing applications in industrial robots, inspection, and autonomous systems. Cognex continues to deploy AI vision systems in manufacturing inspection and automation scenarios.

Compared to humanoid robots, the imagination space for machine vision and sensors might be smaller, but they are closer to actual orders and existing customers.

As for actuation components like motors, reducers, and joint modules, pure-play targets are relatively limited in the U.S. stock market, with relevant opportunities more dispersed among industrial automation, analog chip, and specialized component companies.

5. Layer 5: Application Layer

As the top layer of the industry chain, this is the most familiar part of the market, encompassing robots, autonomous driving, drones, and industrial automation equipment. Corresponding targets include:

• Tesla (TSLA.M): Optimus, FSD, and Robotaxi;
• Alphabet (GOOGL.M): Autonomous driving via Waymo;
• Amazon (AMZN.M): Warehouse robots, logistics automation, and Zoox;
• Teradyne (TER.M): Collaborative robots and mobile robots;
• AeroVironment (AVAV.M), Kratos (KTOS.M), Ondas (ONDS.M): Drones and unmanned systems;
• Palantir (PLTR.M): Software platform connecting data, decisions, and unmanned equipment;

Among these, Palantir is not a robot manufacturer but leans more towards a software platform connecting data, decisions, and unmanned equipment. Uber could become a traffic gateway for different Robotaxi fleets to acquire users, dispatch orders, and complete transactions. Both represent indirect beneficiaries.

This is also the segment of Physical AI most prone to high volatility. Once a particular robot, Robotaxi, or drone enters mass production, the market will quickly revise its revenue and valuation prospects upward.

However, the application layer is also the most competitive and the hardest to deliver on promises.

3. Who Will Profit First: The Pick-and-Shovel Sellers or the Robot Builders?

From the perspective of industrial realization order, the incremental revenue and profit from Physical AI may not appear first on the most sci-fi humanoid robots.

A more likely path is: sell underlying platforms first, then enter closed environments; solve standardized tasks first, then challenge open worlds. In a nutshell, the certainty of "selling picks and shovels" remains the highest.

So, if the biggest beneficiary of the first phase of generative AI was NVIDIA, it is still difficult to bypass NVIDIA in the early development of Physical AI. Whether Tesla, Amazon, or some robot startup ultimately wins, they all need model training, simulation testing, real-time reasoning, and edge deployment.

NVIDIA's advantage is not just its GPUs, but its integration of chips, models, simulation software, and edge computing platforms into a comprehensive development system. This means it doesn't need to manufacture every robot itself; it just needs more robots to use its computing power and software ecosystem.

From this perspective, the clearer beneficiaries in the first phase of Physical AI are likely still the "pick-and-shovel sellers" providing computing power, simulation, chips, and development tools. However, a "clear benefit path" does not equal risk-free stock prices. Whether the market has already priced in growth expectations, if the software ecosystem can generate sustainable revenue, and if competitors can offer alternatives, all remain to be seen.

Secondly, factories and warehouses may achieve commercial viability earlier. The earliest impact of Physical AI on financial reports likely comes from manufacturing, warehousing, and logistics.

These scenarios involve relatively closed environments with more standardized routes and tasks, making it easier for companies to calculate ROI—measuring how much labor a robot saves, how much efficiency it improves, and how much waste it reduces.

Amazon already uses robots extensively in its warehouse network and optimizes equipment scheduling and routing with AI models. Teradyne's subsidiaries, Universal Robots and MiR, cover collaborative robot arms and autonomous mobile robots respectively, already deployed in real production environments like manufacturing, logistics, and semiconductors.

The common feature of these companies is that they don't just showcase what robots *can* do; they are already placing robots in factories and warehouses to solve real production problems. In contrast, getting robots to cook, clean, and care for the elderly in homes involves more complex environments and liability, likely leading to a significantly longer commercialization timeline.

Finally, humanoid robots undoubtedly have the largest market imagination. Theoretically, they can enter human-designed factories, warehouses, hospitals, and homes, directly using existing roads, tools, and workstations.

Tesla's Optimus has thus become one of the most watched directions in the Physical AI theme. However, this doesn't mean mass commercialization is imminent. For humanoid robots, what truly needs observation isn't the smoothness of movements at a launch event, but the cost per unit, continuous operating time, and whether the value created can cover procurement and maintenance costs.

In comparison, Robotaxis are already at a more advanced stage. Autonomous vehicles are essentially "Physical AI on wheels"—the vehicle perceives its environment via cameras, radar, and LiDAR; a model makes judgments; and the car executes the physical action.

Tesla, Waymo, and Zoox represent the integrated vehicle hardware/software approach, the autonomous driving system approach, and the dedicated Robotaxi approach, respectively. Uber is trying to become the platform connecting different autonomous fleets with passengers. Waymo has begun fully driverless operations of its sixth-generation autonomous system. The company disclosed completing over 20 million fully autonomous rides with the new system in its latest vehicles, indicating that Robotaxis are significantly ahead of general-purpose humanoid robots in commercial validation.

Additionally, drones and defense robots may find it easier to secure orders. Defense clients have clear needs for autonomous, low-cost unmanned systems and counter-drone equipment. Companies like AeroVironment and Kratos have shown revenue and order growth in their autonomous and unmanned systems businesses, and Ondas continues to receive orders for counter-drone, loitering munition, and autonomous defense systems.

However, such smaller companies typically carry higher risks related to project concentration, financing, and execution.

Therefore, judging whether a Physical AI company is worth persistent tracking ultimately comes back to three questions:

• Is it a difficult-to-replace core link in the industry chain?
• Does it have real customers, orders, and application scenarios?
• Can its technological progress ultimately translate into revenue, profit, and cash flow?

Final Thoughts

Physical AI will not materialize overnight.

Judging by industrial patterns, it is more likely to progress along a path moving from certainty to high elasticity: first computing power, simulation, and edge platforms; then warehouses, factories, and specialized robots; followed by Robotaxis, drones, and general-purpose humanoid robots.

Ultimately, what determines how far this theme can go is not how many actions a robot performs at a product launch, but whether, after leaving the stage, it can enter factories, warehouses, roads, and real business operations, creating value that can be verified in financial reports.

When that happens, AI will have truly stepped out of the screen and into reality.