As the global AI conversation shifts from giant language models to physical AI—systems that interact with and control the real world—MATLAB and MathWorks are once again taking centre stage. Long before generative AI became mainstream, MATLAB had established itself as a foundational tool for engineers building simulations, control systems, and embedded applications across automotive, aerospace, energy, healthcare, and industrial domains. Today, as AI moves closer to physical environments, MATLAB is increasingly becoming the bridge where mathematical theory is transformed into testable, deployable, and safety-critical systems.
Physical AI demands a very different approach from purely digital AI. Models must operate reliably over long lifecycles, integrate with real-world hardware, and meet strict performance and safety requirements. In this context, MathWorks’ newly introduced MATLAB Copilot is positioned as a productivity layer that helps engineers navigate and connect the platform’s extensive capabilities. “It helps users better understand how to connect together all those different capabilities and workflows that we’ve been building over the years so that they can apply them to their own designs,” said Seth DeLand from MathWorks.
Unlike general-purpose AI assistants, MATLAB Copilot is designed to work within highly structured engineering environments. It relies heavily on MathWorks’ documentation and grounded outputs, ensuring that responses are interpretable and trustworthy. This is particularly critical in physical AI systems, where large models, controllers, and plant representations often remain in production for years, not months.
Industry practitioners see this convergence of traditional engineering and AI as a turning point. “In physical systems, the cost of being wrong is high,” said Jousef Murad. He argues that the real promise lies in combining deterministic engineering models with generative AI. “Generative models on their own hallucinate; engineering models on their own are rigid. Together, they can give you both exploration and reliability. For mechanical engineers building real-world systems, that’s a breakthrough.”
As physical AI gains traction across sectors such as autonomous mobility, robotics, energy systems, and healthcare devices, platforms that balance innovation with predictability are becoming indispensable. MATLAB’s renewed relevance reflects a broader industry realisation: the future of AI will not be defined solely by scale, but by how effectively intelligence can be embedded into the physical world—safely, reliably, and at scale.
