Raphaël Lafargue

What I’m seeing in Physical AI and where I think things are heading. 1. We haven’t had the ChatGPT moment for robotics yet. Models are getting better, but we still don’t have a robot foundation model that can reliably handle a wide range of tasks across different robots. We’re also still figuring out how much data and compute it’ll take to get there. 2. Distribution could become one of the biggest moats in robotics. Companies with robots already working in the real world get access to failures and situations that are hard to recreate in a lab. The faster they learn from those experiences, the bigger their advantage could become. 3. There probably won’t be one winning data recipe. Human demonstrations, egocentric video, teleoperation, robot trajectories, simulation. Different tasks will need different combinations, and figuring out the right mix could matter more than just collecting more data. 4. Deployment infrastructure could become a massive business. Operating thousands of robots brings a completely different set of problems around fleet management, remote interventions, debugging, and maintenance. Existing tools are solving parts of this, but there’s still a lot to build. 5. Simulation is not there yet. Simulation has come a long way, but there’s still a big gap between what works in simulation and what works on real hardware, especially for complex manipulation tasks. A lot more innovation is needed here. 6. Robotics inference will become a much bigger focus. Running powerful models on robots is still expensive and power-hungry. Figuring out what runs on the robot versus on edge or cloud infrastructure could make a huge difference to cost and performance. 7. Specialized robotics companies could scale faster than general-purpose humanoids. Warehouses and factories are much more predictable than homes. I think some of the next big commercial successes will come from companies solving specific industrial problems really well. 8. Hardware and supply chains could decide who scales fastest. Building great models is one thing, but manufacturing robots cheaply and at scale is another. China’s existing hardware ecosystem gives it a huge advantage, and building similar manufacturing capabilities elsewhere won’t be easy. 9. Safety and verifiability could become a huge category. As robots become more autonomous and start operating around humans, we’ll need better ways to verify their decisions, detect failures, and ensure safe behavior. Traditional robotics safety systems alone won’t address every new challenge.

We’re in YC Fall 2026! 🇺🇸 Two months ago: a hackathon in Paris. Today: co-founders heading to SF We’re building @asim_group to put safety and alignment at the core of robotics. Safe Robots. Fewer people harmed!