Physical AI is about to reshape our world

Physical AI- the convergence of robotics and artificial intelligence—is no longer a theoretical concept. It’s a rapidly scaling force reshaping how machines interact with the physical world. From autonomous vehicles and robotic surgery to smart factories and logistics, physical AI is unlocking new levels of precision, adaptability and efficiency.

We see physical AI as a foundational technology with the potential to transform multi-trillion- dollar industries. Its impact will be measured not only in productivity gains, cost savings and margin expansion, but also by establishing a new paradigm for how technology becomes infused on our physical world.

Physical AI will be a cornerstone of the next industrial era.

Physical AI refers to intelligent systems that can perceive, reason and act in real-world environments. These systems combine advanced robotics with AI models, enabling machines to make decisions and execute tasks with speed and precision—often in milliseconds.

Unlike traditional AI, which operates in digital environments, physical AI translates code into real-world motion. These systems are equipped with sensors, actuators and edge computing capabilities that allow them to adapt dynamically to changing conditions. The result— machines that can operate autonomously in complex, unstructured settings.

Physical AI is moving from early-stage experimentation to real-world deployment, and it’s already delivering measurable value. A few examples include:

• Mobility: Powering the shift toward autonomous transportation
• Manufacturing: Enhancing productivity and reducing waste
• Service economy: Addressing labor shortages and improving quality of life

As adoption accelerates, we expect physical AI to become a core enabler of operational efficiency, safety and scalability across the global economy. Consider three industries already seeing meaningful value-generation driven by physical AI.

Autonomous driving is one of the most visible—and investable—applications of physical AI. In the United States, ride-hailing platforms have logged over 100 million fully driverless miles in cities like San Francisco, Phoenix and Austin. In China, over 14 million robotaxi rides have been completed across 16 cities.¹ In some markets, consumers have even shown a willingness to pay a premium for an autonomous ride versus a traditional rideshare with a driver, and high customer satisfaction scores imply a high likelihood of further adoption.

Safety was long promised as a key element of the value proposition for autonomous vehicles, but there is now data to back it up; in one study, a leading service provider noted significantly lower crash rates relative to human drivers.

By 2030, Level 3 autonomous vehicles (AV) could account for 10% of global new car sales.² Under accelerated adoption scenarios, autonomous driving passenger revenues could be between US$300-US$400 billion by 2035.³ This shift will also drive demand across the AV ecosystem—from semiconductors and edge computing to mapping, insurance analytics and software-defined vehicles.

We expect physical AI to become a core enabler of operational efficiency, safety and scalability across the global economy.

Physical AI is radically transforming manufacturing by embedding intelligence into every stage of production. We think four key areas of innovation stand out: humanoid robots, collaborative robots (Cobots), autonomous mobile robots (AMRs), and AI-based computer vision.

Humanoid robots, which are designed to mimic human appearance and behavior, represent a new and promising iteration of autonomous physical systems. While we’re in the very early stages of adoption, humanoids are already being deployed in manufacturing facilities. The humanoid form factor is ideal for operating in complex environments that were built for human workers. Increasingly, they are powered by purpose-built AI models known as vision- language-action (VLA) models, which enable the robots to better understand their environment and have a more generalizable, rather than task-specific, skill set. VLA models and other innovations can drive progress on what is known in the AI community as Moravec’s Paradox: what is easy for humans (perception and basic motor skills) is hard for computers, and what is hard for humans (complex math, abstract reasoning) is easy for computers. Additionally, recent breakthroughs in areas like autonomous battery swapping ensure robots can continue working around the clock with limited human intervention.

These new technologies will improve safety, profit margins, and factory yield. Potential beneficiaries span the value chain, from robotics and vision-system providers to semiconductor and AI software firms that are building the “brain” for robots.

While the technology is still emerging, we believe physical AI is poised to permeate the service economy as costs decline; for example, state-of-the-art models that cost US$250,000 per unit in 2023 had dropped to US$150,000 in 2024⁴. AI-powered robots can take on essential tasks across health care services, restaurants, hospitality and retail. For instance, humanoids can assist with the distribution of medicines or room turnover in hospitals. They can handle dishes, linens and trash runs at hotels and restaurants. These roles are often hard to staff and maintain, making the prospect of always-on robots especially compelling.

We’re also tracking breakthroughs that enable robots to understand complex instructions and perform series of generalizable tasks. These advancements could open up the broader consumer market, where intelligent robots could become valuable household assistants.

Many families would appreciate having an intelligent robot to assist with daily chores and logistics.

We are still in the early innings, but in our mind, the trajectory is clear. Physical AI will be a cornerstone of the next industrial era. It represents the convergence of automation, edge computing, AI and digital transformation, and we believe it will create a generational opportunity for long-term investors.

As adoption scales, we expect to see:

• Recurring revenue models (e.g., per-mile and subscription AV services, robot hardware leases plus software and services)
• Cross-sector demand (health care, logistics, manufacturing, retail)
• Ecosystem growth (hardware, software, data infrastructure)

Physical AI is not just a technological evolution—it’s a paradigm shift in how machines interact with the world. We believe it can offer long-term investors durable growth and a powerful and differentiated source of long-term alpha.

¹ Source: The Robot Report. July 18, 2025. Waymo reaches 100M fully autonomous miles across all deployments.
² Source: Goldman Sachs, August 19, 2024. Partially autonomous cars forecast to comprise 10% of new vehicle sales by 2030. There is no assurance that any estimate, forecast or projection will be realized.
³ Source: McKinsey and Company. January 3, 2023. Autonomous driving’s future: Convenient and connected. Level 3 autonomous vehicles (L3 AVs), as defined by the SAE International (J3016) automation scale, are “conditionally automated” driving systems. It can handle all aspects of the driving task in certain conditions but must be available to take over when requested by the vehicle. There is no assurance that any estimate, forecast or projection will be realized.
⁴ Source: Goldman Sachs. February 27, 2024. The global market for humanoid robots could reach $38 billion by 2035. There is no assurance that any estimate, forecast or projection will be realized.

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