In 2026, the field of physical AI is set to emerge as a transformative force, following a consensus reached by industry leaders at the CES in Las Vegas, where NVIDIA's CEO Jensen Huang heralded the arrival of "physical AI's ChatGPT moment." Over the past two years, significant advancements have been made in five key areas: brain models, imagination engines, training environments, ontology, and commercial ecosystems, laying the groundwork for real-world applications.
In the first half of 2026, global investment in physical AI surged, with over $6.4 billion raised in just the first quarter, including notable funding rounds from AMI Labs and World Labs. The industry is witnessing a clear technological divergence, with three primary paths emerging: Visual Language Models (VLM), Visual Language Action (VLA), and world models. The anticipated future architecture for physical AI is expected to integrate VLA's decision-making capabilities with world models' predictive simulations.
Despite the rapid growth, the competitive landscape remains uncertain, with various companies pursuing different strategies, including those focusing solely on VLA or world models, and others exploring hybrid approaches. The ultimate goal is to develop AI that can effectively navigate and understand the complexities of the physical world, moving beyond mere reactive capabilities to proactive, autonomous decision-making.
As the physical AI market is projected to expand significantly, reaching an estimated $3.26 trillion by 2040, the industry faces the challenge of ensuring that technology translates into tangible business value. Companies like Om AI are pioneering innovative models that prioritize continuous perception and spatial understanding, aiming to redefine how AI interacts with its environment. The ongoing evolution of physical AI emphasizes the importance of real-world applications and the need for AI systems that can adapt and respond to dynamic physical spaces.
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