In Series 1, “Jensen Huang Declares ‘Physical AI’ the Next Wave of AI—What Is It?” we explored how Physical AI has become an unstoppable force—one that is no longer a passing tech trend, but a shift reshaping industrial paradigms. After more than half a century of research and technological progress, we are now entering a new era: AI has gained a physical “body” and has begun to interact with the real world.

So how does this transformative technology actually work? How can robots “see” the world, “decide” on their own, and execute precise “actions”? And what is the single most decisive factor that determines whether these complex systems succeed?

In Series 2, we take a deep dive into the three core technologies that power Physical AI—and why high-quality data, which determines 90% of success, matters more than ever. We’ll also bring the future into focus through the most compelling real-world industry use cases already transforming manufacturing, healthcare, and everyday life.

1. The 3 Core Technologies That Power Physical AI

Physical AI operates through a three-step process: Perception → Decision → Action.

① Perception: Technologies to See and Hear the World

Just as humans perceive the world through sight, smell, and hearing, Physical AI collects environmental data through sensors such as cameras, LiDAR, radar, and acoustic sensors. In particular, computer vision plays a pivotal role—enabling robots to identify objects, people, and text, while understanding distance and depth. The quality of the data collected at this stage determines the success or failure of everything that follows.

② Decision: Technologies to Think and Judge

This is the stage where robots decide what to do and how to do it based on the data they collect. In the past, robots moved only according to predefined rules. Today, they learn optimal actions on their own through reinforcement learning and imitation learning.

More recently, the field has taken another leap forward with the emergence of foundation models for robotics, such as Google’s RT-2 (Robotics Transformer 2) and NVIDIA’s Project GR00T (Generalist Robot 00 Technology). By applying large-scale vision-language models (VLMs) to robot control, these models can understand natural language commands like “Pick up the apple on the floor,” and perform tasks through reasoning—even in unfamiliar objects or situations.

③ Action: Technologies to Move in the Physical World

This is the stage where a robot executes decisions through physical actuators such as robot arms, legs, and wheels. It requires precise motor control and a deep understanding of robot dynamics—and the ability to carry out missions reliably while overcoming real-world variables like friction, gravity, and mechanical error.

2. The Bottleneck of Physical AI: Scarcity of Real-World Data

“A great model comes from great data.” — Andrew Ng

AI performance is ultimately determined not by the newest algorithm or the most powerful hardware, but by the quality and quantity of the data used for training. Even the most capable “brain” (model) cannot function properly if it learns from incorrect or biased “information” (data).

The Data Dilemma: Synthetic Data vs. Real-World Data

• Real-world data: Data collected directly from real environments, offering high fidelity and accuracy. However, it is expensive and time-consuming to collect—and in many cases, it is nearly impossible to capture every dangerous scenario (e.g., collisions).
• Synthetic data: Data generated in virtual environments, enabling large-scale production and safe learning of high-risk edge cases. However, performance can degrade in real environments due to the sim-to-real gap.

To improve Physical AI performance, it is critical to secure real-world image and video data from industrial sites—while also training strategically on synthetic data for scenarios that are difficult to collect (e.g., security-sensitive environments, or defect data for products with low defect rates). The key is to combine both types of data intentionally, manage them continuously, and update the model over time.

Key Point: The Data Bottleneck — and Superb AI’s Role

In the end, the biggest bottleneck in Physical AI development happens in the data workflow. The full process—collecting massive volumes of unstructured data, labeling it accurately, managing it efficiently, generating the right synthetic data, and feeding it into model training—is complex and highly labor-intensive.

This is exactly where a data-centric MLOps platform like Superb AI becomes a decisive enabler:

• Unified data management: Integrates fragmented tasks—data collection, cleaning, labeling, review, and management—into a single platform to maximize efficiency.
• High-quality data production: Supports fast, accurate dataset building through automated labeling and a structured review system.
• Continuous model improvement: Makes it easier to build MLOps pipelines that retrain models and evaluate performance as new data arrives—helping AI systems continuously adapt to real-world change.

Ultimately, Physical AI success depends on how well you handle data, and Superb AI is the best partner to solve this complex data challenge. With Superb AI, enterprises can solely focus on what matters most—developing the core model and advancing its performance to solve real business problems.

3. Top 5 Physical AI Use Cases Transforming Industries

① Manufacturing: Tireless Eyes and Hands for Smart Factories

• Applications: Automotive assembly, semiconductor wafer inspection, welding, packaging automation
• Core role: Physical AI-powered vision systems can inspect microscopic defects around the clock, while robotic arms carry out precise, repetitive assembly tasks—maximizing both productivity and quality. The global smart factory market is projected to grow from $108.8 billion in 2024 to $205.6 billion by 2029 (MarketsandMarkets, 2024).

② Logistics: Automated Warehouses Up and Running 24/7

• Applications: Item picking and packing, parcel sorting and transport, inventory management
• Core role: Like Amazon’s warehouse robot Kiva, AI robots can autonomously navigate large warehouses to move and sort goods. This dramatically improves order fulfillment speed while reducing reliance on human labor.

③ Health Care: Surgical Robots That Go Beyond Human Limits

• Applications: Minimally invasive surgery, rehabilitation therapy, in-hospital supply transport
• Core role: A representative example is the Da Vinci surgical system. When operated remotely by a surgeon, its robotic arms can perform procedures with greater precision and less tremor than the human hand. By 2023, the number of Da Vinci-assisted surgeries worldwide surpassed 14 million.

④ Agriculture: Precise, Sustainable Smart Farming

• Applications: Autonomous tractors, weed identification and removal, crop condition monitoring
• Core role: Drones and robots equipped with AI vision analyze crop growth conditions and apply water and fertilizer only where needed. This enables precision agriculture—boosting yields while minimizing environmental impact.

⑤ Everyday Life: Service Robots and Autonomous Vehicles

• Applications: Server robots, robot guides, delivery robots, elder care robots, autonomous vehicles
• Core role: Level 4 and above autonomous vehicles are a concentrated form of Physical AI—perceiving, deciding, and acting in real time amid countless variables on the road. Humanoid robots like Tesla’s Optimus also point to a future where robots may take on household labor and high-risk tasks in industrial environments.

4. Future Outlook and Challenges to Overcome

For Physical AI to be safely embedded in our lives and industries, we must overcome not only technical hurdles, but also broader challenges—including reaching social and ethical consensus.

4.1. Future Outlook: The Rise of General-Purpose Robots and Explosive Market Growth

The ultimate goal of Physical AI is to go beyond machines that repeat a single task and build general-purpose robots—systems that can flexibly carry out multiple missions across diverse environments, much like humans. This vision is especially evident in the projected explosive growth of the humanoid robot market.

According to Market Research Future, the global humanoid robot market is expected to post an astonishing 50.2% compound annual growth rate (CAGR) through 2032. Another research publisher, HDIN Research, offers an even more aggressive outlook—projecting that the CAGR could reach up to 75% through 2030. This growth is being driven by a convergence of forces: chronic labor shortages, rapid advances in AI, and rising demand for automation across manufacturing and service industries.

Reflecting these market expectations, HDIN Research reports that Tesla has set an ambitious target to produce 5,000 Optimus robots by 2025, and in the long term, establish a production system capable of 1 million units per year. This signals a clear shift: Physical AI is no longer a laboratory prototype—it is emerging as a core industrial engine approaching mass production.

4.2. Big Tech Moves: A Race for Technical Leadership to Own the Future

To capture the massive opportunity of Physical AI, big tech companies are going all-in—leveraging their respective strengths to compete for leadership. This race is unfolding across 3 major axes: the brain (AI models), the body (hardware), and the way robots learn.

• NVIDIA: Building a General-Purpose Platform for the Robot “Brain and Nervous System”: Rather than building individual robots, NVIDIA has introduced Project GR00T (Generalist Robot 00 Technology)—a general-purpose AI model and platform designed to be deployed across many types of robots. GR00T is a foundation model built to help robots learn a wide range of skills through human language, video, and human demonstrations. NVIDIA is also providing Isaac Lab, a simulation platform that enables robots to be trained safely and efficiently in virtual environments—building a development ecosystem where robot AI can advance without the physical constraints and risks of the real world. This can be seen as a strategy to offer standardized “brains and nervous systems” for robotics—similar to what Windows or Android did for computing and mobile.
  
• Tesla: Building the Most Efficient “Body” to Execute AI Intelligence: Tesla is focused on developing the humanoid robot Optimus as a physical “body” to bring its AI capabilities into the real world. The second-generation model revealed in late 2023 reduced weight by 10 kg, improved walking speed by 30%, and demonstrated refined manipulation—such as picking up an egg without breaking it using hands equipped with tactile sensors. Tesla’s direction is clear: convert its AI understanding of the real world—accumulated through autonomous driving—into physical labor in factories and everyday environments.
  
• Google DeepMind: Breaking Through the Limits of “Learning Ability”: Google DeepMind is focusing on innovating how robots learn faster and more effectively—in other words, pushing the frontier of learning capability itself. Its RT-2 (Robotic Transformer 2) model is a landmark example, trained on massive web-based text and image data to demonstrate strong generalization—understanding and executing new commands it was never explicitly trained on. DeepMind’s Mobile ALOHA project also seeks to overcome data scarcity by collecting complex, two-handed skill data (e.g., cooking, cleaning) through low-cost teleoperation, then teaching robots through imitation learning. This approach reinforces a central belief: to solve complex, unpredictable real-world problems, robots must ultimately be able to learn and adapt on their own.

4.3. The Challenges Ahead: Technology, Safety, and Society

Alongside optimistic projections, there are major challenges that must be addressed for Physical AI to reach widespread adoption.

• Technical challenges: One of the biggest hurdles is the sim-to-real gap—ensuring AI trained in virtual environments performs reliably in the real world. Other critical challenges include limitations in battery technology for long-duration operation, as well as the high cost and durability requirements of robot hardware composed of tens of thousands of parts. Big tech’s strong focus on simulation data generation and imitation learning is also a direct response to these technical barriers.
  
• Safety and reliability: Unlike digital-only AI, Physical AI directly affects the physical world. A malfunctioning robot working alongside humans in a factory—or in a home—can lead to serious accidents. Proving that robots can respond safely and reliably to unpredictable edge cases is therefore essential. Microsoft highlights “reliability and safety” as one of its 6 principles guiding the development and use of AI systems—and this principle will be even more critical in Physical AI.
  
• Ethical and social challenges: The rise of Physical AI brings long-standing concerns about job displacement back into focus. However, McKinsey’s latest technology trends report suggests a different trajectory: AI is more likely to evolve toward augmentation, improving productivity in collaboration with humans, rather than pure replacement. Even so, urgent questions remain. Who is responsible when a robot’s actions cause harm? How do we protect privacy when robots operating in private spaces—like homes—collect sensitive data? Addressing these issues will require social debate as well as robust legal and institutional frameworks.

5. Conclusion

So far, we’ve explored the core technologies that power Physical AI—Perception, Decision, and Action—along with the most impactful real-world use cases transforming industries today. From the tireless eyes of smart factory inspection systems to surgical robots that surpass human limitations, Physical AI is no longer a distant vision of the future. It has become a practical technology that directly shapes industrial competitiveness.

Yet at the center of all this innovation is one principle that never changes: “A great model comes from great data.” For Physical AI—where systems must operate in a physical world defined by constant change, complexity, and uncertainty—the ability to continuously secure and manage high-quality data is inseparable from project success.

In the end, the real winners in the Physical AI era will not be the companies with the most sophisticated algorithms, but the ones that can handle high-quality data most efficiently. The workflow spanning data collection, cleaning, processing, and management is one of the biggest barriers to AI adoption—but it is also an opportunity to build the strongest competitive edge.

This is exactly why Superb AI exists: to solve the data bottleneck. A data-centric AI platform enables enterprises to break free from complex, labor-intensive data work—so they can focus solely on what matters most: solving core business problems and continuously advancing model performance.