Why Physical AI Is So Hard to Engineer

Physical AI—autonomous machines that sense, reason and act in the real world—requires a full systems approach spanning sensors, actuators and distributed compute, with real-time predictability and safety becoming far more critical than raw performance. Industry experts from Synopsys, Cadence, Arm, Renesas, MediaTek and others describe challenges including hardware/software co-design, modularity and future-proofing for long-lived systems, and unpredictable environments, with PwC projecting a ~€430 billion market by 2030. The article argues that success depends on new architectures, methodologies and verification approaches rather than simply faster chips or better models.