Key Takeaways: Physical AI is the deployment of machines with autonomy, acting within the real world. It is not a single product, but a philosophy built on modular architectures. These systems can only be developed with a full systems approach, integrating sensors, actuators, and distributed compute systems, in a multi-physics methodology. Real-time constraints become much more important than in the past, and at the same time much more complex. Physical AI is changing how chips are designed and used, integrating sensing, AI inference, and real-time control into machines that can safely and predictably interact with the physical world. The challenges extend well beyond just performance. Engineers need to account for corner cases stemming from unpredictable environments, ensure these systems are safe and reliable, and they need to future-proof these systems by ensuring they are serviceable and upgradable. This will require new tools and methodologies, as well as some new design approaches. “AI is leaving the screen,” says Anthony Matarazzo, head of physical AI, digital twins, simulation-driven AI, and accelerated computing at Synopsys . “It is no longer about generating text or code. It is entering the physical world. It needs to understand, interact, sense, make decisions in a physical environment. We are moving from generative AI to agentic workflow orchestration to physical AI (see figure 1.) That is literally making intelligence actionable. But the consequences of being wrong fundamentally change. Life or death, plus or minus 1%.” Fig. 1: Three Eras of AI. Source: Synopsys Physical AI must see and interact with the real world. “It is challenging to make artificial intelligence see the world correctly,” says Chun-Ting “Tim” Wang-Lee, signal integrity application scientist at Keysight EDA . “Real-world information comes from the sensors, and the data has to be moved somewhere and processed so that the AI can decide and then act. There are decisions that need to be made.” This changes the design priorities. “Physical AI usually gets framed as a compute story,” says Justin Endo, director for marketing and sales at Mixel , A Silvaco Company. “But from our perspective it has really been an imaging story. Every robot, drone, and autonomous system comes down to cameras feeding an inference engine.” Physical AI builds on some of the connectivity and sensing technologies developed for the Internet of Things. “IoT was largely about connecting and monitoring things,” says Brad Rex, head of physical AI, UX at Renesas Electronics. “Physical AI is about giving machines the ability to perceive and act autonomously in unstructured environments. This is a completely orthogonal engineering problem, and it requires a step-change in system-level content — more sensing, more real-time compute, more sophisticated power management, and more safety considerations per device.” Within physical AI, there are two distinct classes of devices: “Those that can rely on a cloud connection and those that must operate fully locally without relying on connectivity,” says Ashley Stevens, director of product management and marketing at Arteris . “For example, vehicles cannot rely on continuous connectivity, as there are many locations where cellular coverage is inadequate. In contrast, an industrial robotic arm moves but is constrained to a particular location within a factory setting, and therefore can be connected to the cloud.” Others agree. “Unlike traditional AI applications that primarily generate information, physical AI systems influence real-world outcomes through continuous interaction with their environment,” says William Chen, product marketing group director for design IP at Cadence . “Traditional IoT often focused on collecting and transmitting data. Physical AI adds real-time interpretation and action. The result is a shift from connected devices to intelligent systems operating within sensing and control loops.” This requires new architectures and