Embedding AI into robots and vehicles so they operate safely and dependably is one of the most complicated systems engineering challenges we face. Its also essential to unlocking massive productivity gains across the physical industries that together account annually for $75T of global GDP. This is what the teams designing compute platforms @Arm think about every day. Consider something as seemingly simple as moving a robotic finger. A tiny microcontroller driving an actuator needs to coordinate with sensors measuring motion and force. That finger needs to coordinate with the other fingers, the hand, the arm, and ultimately the entire robot. And all of this needs to happen safely, predictably, and in real time. This is truly fun to work on! Solving challenges like these is where the Arm ecosystem shines. I thoroughly enjoyed discussing this with Ani Kelkar at @McKinsey's Physical AI Summit. We covered everything from building SoCs that enable autonomous action to how our Arm Total Design for Physical AI program is helping make this easier. We also discussed our call to action for the industry to come together and establish better ways to describe robotic capabilities by joining the the Robotics Capability Framework working group. But what I enjoyed even more was hearing from others tackling different parts of this enormous challenge. Carolina Parada at @GoogleDeepMind shared perspectives on building foundation models for robotic intelligence, while Stefan Nusser @IntrinsicAI discussed advances in intelligent automation. Amit Goel discussed the impressive work @NVIDIARobotics is doing in robotics and Quinn Li gave some interesting insights to how @QualcommVenture is thinking about how they invest for PAI. It's an exciting time to be working across this industry, and there's an extraordinary amount of engineering ahead of us. More on the Robotics Capability Framework working group: lnkd.in/gtSZA4XT More on Arm Total Design for Physical AI: lnkd.in/gnhfAVve