Key Takeaways: More compute alone won’t solve physical AI’s bottlenecks. Processing capability must be matched by memory bandwidth, efficient interfaces, sufficient power, and heat removal. Physical AI needs system architectures tailored to its end function and environment. Real-world deployment depends on rigorous validation that covers corner cases, understands behavior, and verifies safety. The race is on to develop physical AI fit for widespread use, but building these sensor-powered systems will require engineering teams to address a variety of technical challenges concurrently and continuously. Analysts predict significant market growth for physical AI, with the promise that autonomous systems will fundamentally reshape industries spanning transportation, manufacturing, medicine, energy, and more. PwC’s Strategy& projects that the global physical AI market could reach approximately €430 billion by 2030 , reflecting the potential commercial impact of autonomous systems across multiple industries. “Physical AI systems are expanding beyond automotive to robotics and drones, and the bottlenecks for development of these applications cover the entire system,” said Licinio Sousa, senior director of Interface IP for Automotive and Physical AI at Synopsys . “AI is expanding from the data center to the edge, and with machines such as robots, drones, and vehicles, all driven by the need for autonomy, privacy, personalization, faster response, and less reliance on the cloud.” The list of technical challenges that need to be addressed is long and intertwined. Memory capacity and bandwidth need to be increased, latency and power consumption need to be decreased, product lifespans need to be stretched, and safety needs to be verified. And if that isn’t hard enough, addressing any one of these can create problems elsewhere. Specialized architectures Solving these system-wide bottlenecks requires highly specialized hardware and software that are optimized for specific use cases. “We need to start talking about optimized, more tailored compute that you build for the specific workload, and this is where physical AI gets more interesting,” said Michal Siwinski, executive vice president and chief product and marketing officer, and general manager for Security Solutions at Arteris . “That workload needs to be highly optimized for the hardware. We’re basically going back to ASICs in the traditional sense of what ASICs were supposed to be — very application-specific, extremely optimized, versus more generic. Some of that is already starting. It will be interesting to see how the more nimble systems evolve for very specific applications between the software and the hardware and the workload, and keep evolving because the models are moving way faster.” Autonomous robotics systems share some of the same technology found in self-driving vehicles, particularly in perception and control, but each physical AI use case adds unique challenges. “Robotics is not a new AI stack,” Vasanthan Karunanithy, staff systems architect for Automotive at Micron Technology, said at the recent JEDEC Automotive Forum. “From an ADAS system, the robot inherits real-time perception, path planning, and motion control. From an intelligent cabinet, robotics inherits natural language understanding, contextual reasoning, and personal interaction. In embodied intelligence, the bigger challenge is running these two workloads together and then exchanging information at the end. So that is the commonality between these two segments. But there is also a difference that tells the scale of the channel. An autonomous vehicle operates in a relatively structured road environment and draws power from a larger source — the vehicle battery. But when you look at robots, they operate in an unstructured 3D environment. [A robot] also needs to maintain multiple degrees of freedom. It has to manipulate objects and maintain balance, and it has to understand the commands in the instructions. It ha