Technology executives and developers are shifting focus toward Embodied AI as the next major revolution in computing [1, 2].
This transition represents a fundamental change in how artificial intelligence evolves. While current systems rely on static data, Embodied AI allows machines to acquire knowledge by interacting directly with the physical environment, potentially unlocking capabilities that Large Language Models cannot achieve.
Michael Ashley said Embodied AI offers the next computing revolution and that executives should look beyond LLMs to prepare for robots that learn by interacting with the physical world [1]. This move toward physical interaction seeks to bridge the gap between digital processing and real-world application.
The momentum for this technology has grown since CES 2025, where AI applications began to enter the mainstream [3]. Industry experts said the integration of AI into physical hardware is a critical step for the technology market.
Recent developments in industrial technology and software infrastructure, such as those from Advantech, further support the scaling of these systems [4]. These advancements provide the necessary foundation for robots to operate in complex, unpredictable environments—a requirement for the next generation of automation.
Developers are now prioritizing systems that can perceive, reason, and act within a three-dimensional space. By moving the "brain" of the AI into a physical "body," the technology can learn through trial and error in ways that text-based models cannot replicate [1, 2].
“Embodied AI offers the next computing revolution.”
The shift toward Embodied AI signifies a move from generative AI, which processes existing human knowledge, to an era of experiential AI. If robots can learn autonomously from physical feedback, the dependency on massive, human-curated datasets will decrease, potentially accelerating the deployment of automation in logistics, healthcare, and manufacturing.
