Ekaterina Sirazitdinova from NVIDIA provides a high-level overview of the latest trends shaping the future of AI, covering the evolution from early deep learning to the rise of agentic and physical AI, and diving deep into the critical optimization techniques required to deploy these powerful models efficiently.
In a live panel at the Physical AI Builders Forum, founders and operators in computer vision, robotics, and multimodal AI share their 2026 playbooks. The discussion covers the architectural differences between physical and generative AI, the strategic shift from frame AI to scene AI for enterprise value, and the critical skills needed to build and scale a modern AI business.
Encord co-founders Eric and Ulrich discuss their $60M Series C, the company's origins before the AI hype, and their focus on building the essential data infrastructure for physical AI and robotics—the next frontier after LLMs.
Qasar Younis and Peter Ludwig, founders of Applied Intuition, discuss the shift from autonomy tooling to a comprehensive physical AI platform. They explain why physical AI is more than just LLMs on wheels, highlighting the critical bottleneck of deploying models onto constrained hardware. The conversation covers their three-pillar tech stack—simulation, operating systems, and AI models—and makes the case for an 'Android for every moving machine' to solve the fragmentation in safety-critical systems like cars, trucks, and robots.
Daniel Lütgehetmann of inait introduces "digital brains," biologically accurate computational models of real brains, as a solution to current AI's limitations in physical world interaction. Unlike traditional AI that struggles with dynamic environments and skill accumulation, these digital brains leverage biologically inspired learning rules to achieve dramatically faster learning in robotics and complex systems, demonstrating potential for real-world adaptability and efficiency.
Nominal's co-founders discuss the new age of reindustrialization and the critical need for a modern data infrastructure in hardware engineering. They explain how their platform acts as a 'GitHub for hardware data,' providing a system of record for testing that bridges the gap between simulation and reality, and serves as the essential verification layer for the future of 'Physical AI'.