[Editor's Note] While conventional AI focuses on inference and generation within digital data, Physical AI directly acts and reacts in the real world through sensors, edge computing, robots, and control systems. The implementation of Physical AI can significantly advance industrial innovation and automation because it directly acts and solves problems in the real world, and it directly interacts with the real world. Accordingly, global companies such as Nvidia, Tesla, and Google are making massive investments in Physical AI, and the related market is expected to grow explosively. To implement this Physical AI, recognition technologies such as sensors, as well as edge computing and embedded systems for real-time data processing, local computing, robotics, and control technologies are essential. Accordingly, e4ds News has prepared a series of articles to examine the core technologies and implementation strategies of physical AI, including the concept, market outlook, related technologies, and actual cases.


According to some experts, 'Physical AI' will be the greatest revolution in human history, surpassing the Industrial Revolution.

Although it has a significant social, cultural, and economic impact, we cannot predict the full extent of it. Therefore, from an economic perspective, according to forecasts by global market research firms, physical AI is expected to become a key driving force that will shake up the industrial structure to the point where it will reach a market worth approximately $1 trillion by 2030.

First, according to Statista, the robotics market is projected to grow from approximately $50.3 billion in 2025 to $65 billion in 2030, representing an average annual growth rate of 5.2%.

Industrial robots are at the heart of manufacturing automation, while service robots and collaborative robots are rapidly expanding in logistics and healthcare.

The autonomous vehicle market is expected to experience explosive growth. According to Statista and Grand View Research, the market, which was worth $42.8 billion in 2025, is projected to reach $214.3 billion by 2030. With an average annual growth rate of nearly 20%, this sector represents a complex ecosystem combining diverse technologies, including sensors, AI chips, and vehicle operating systems.

The smart manufacturing market is also noteworthy. According to Market Research Future, the market is expected to grow from $500 billion in 2025 to $800 billion in 2030, at a compound annual growth rate of 9%. Technologies such as digital twins, AI-based quality control, and predictive maintenance are maximizing process efficiency.

AI semiconductors serve as the brain of physical AI. According to Statista, the market size is projected to more than triple, from $92.7 billion in 2025 to $332.7 billion in 2030. The 29.1% compound annual growth rate represents the steepest growth rate in the physical AI ecosystem.

The embedded systems market, another foundational technology, is projected to expand from $186.7 billion in 2025 to $283.9 billion in 2034, according to Precedence Research. While the growth rate is relatively stable at 4.8%, it remains an essential core technology in diverse fields, including IoT devices, vehicles, and medical devices.

Physical AI isn't a single market. Sensors collect data, embedded systems process it, AI models make decisions, and robots and vehicles translate that information into action. Throughout this process, semiconductors serve as the engine that powers all of this technology.

In other words, the growth of autonomous vehicles will dramatically increase demand for AI chips, which in turn will lead to the proliferation of embedded systems and smart manufacturing. Rather than each market operating independently, these two markets are intertwined and growing into a convergent ecosystem.

Accordingly, investments from related companies and countries are also proceeding rapidly.

NVIDIA, a dominant player in the GPU and AI chip markets, is expanding its physical AI ecosystem with its autonomous driving platform Drive and robotics platform Isaac. Recently, there has been a move to dominate the simulation infrastructure across industries by incorporating Omniverse-based digital twin technology into smart manufacturing and robot development.

While OpenAI doesn't directly build robots, it contributes to research on robot control and autonomous systems through reinforcement learning and multimodal models. Through its collaboration with Microsoft, it provides cloud and edge computing infrastructure, strengthening the role of physical AI as the brain.

Amazon continues to invest heavily in logistics automation and robotics. Amazon Robotics already operates tens of thousands of warehouse robots, and AWS provides an AI learning platform for autonomous driving and robotics, supporting the expansion of the ecosystem.

Google (Alphabet) is leading the global autonomous driving market through its autonomous driving subsidiary Waymo. DeepMind is leading research in reinforcement learning-based robot control, and Google Cloud provides AI-based smart factory solutions to manufacturing and logistics companies.

By country, the United States is characterized by large-scale private-sector investment. Companies like Tesla, Nvidia, Google, and Amazon are leading the way in autonomous driving, robotics, and semiconductors, and the defense and space sectors are also actively utilizing physical AI technology.

China is investing heavily in smart manufacturing and robotics through its state-led "Made in China 2025" strategy. Companies like Baidu, Alibaba, and Huawei are spearheading autonomous driving, AI chip, and smart city projects, while government subsidies and deregulation are accelerating the expansion of the ecosystem.

In Korea, companies such as Samsung Electronics, Hyundai Motor Company, and LG Electronics are expanding their investments in semiconductors, autonomous driving, and robotics, respectively. The government is supporting the ecosystem through its K-Smart Manufacturing strategy and AI semiconductor development policy, while Hyundai Motor Group is simultaneously strengthening robotics and autonomous driving through its acquisition of Boston Dynamics.

Experts say that physical AI will change the global industrial landscape over the next 10 years, and advise that Korea, with its strengths in semiconductors and smart manufacturing, must take the lead in the physical AI era by connecting with the global ecosystem.

The next part will cover 'sensors', a core technology of physical AI.