Note: This article was originally published on Reasoned and is being cross-posted on MediaNama. Read the original version here: [link].
What if physical AI could learn
Welcome back to Reasoned by Nikhil Pahwa, a guide to how AI is changing the world around us.
Today’s newsletter is about the unique challenges of Physical AI (at homes, in factories), based on a panel at SuperAI
Flying back from SuperAI last year, I wrote down a replacement thesis: first digital creation gets replaced, then digital actions, then manufacturing, then physical actions. Physical actions came last because they’re the hardest, and a panel at this year’s SuperAI spent most of its time explaining why.
I’ve written about how agents are designed to route around barriers: hit a broken API, try another method…keep trying till you exhaust tried/tokens. Unlike digital, Physical AI runs into barriers that can’t be routed (or shouldn’t) around: I just visualised a humanoid breaking down a door it can’t unlock.
At a panel on Physical AI, Yanliang Zhang (Chief Scientist, Western Robot) and Michael Spranger (Senior Executive Director, Sony Research) and Alan Ng (Quikbot Technologies) kept coming to the point that while the hardware for facility management, food industry robotics, and computer vision are all in place, the models are powerful and the demand is real — McKinsey and Morgan Stanley put the physical AI market at $25-30 trillion by 2050 — but what lacks is the trust infrastructure, questions about data sovereignty. Some notes from the panel:
1. The liability chain is broken
Ng said he builds what he calls “trust infrastructure” for the built environment for clients. His framing of the problem:
“I have a facility management company, which then submits the contract to a managing agent, which obviously finally offers the service to me. So take a look at this relationship. The one that actually should be liable for the physical AI autonomous system is really not the one at the end it does going to be sued. So there’s a break in the trust factor.”
“…“way before physical AI truly deploy in mass scale, I think the trust infrastructure, the legality part of it, the accountability by insurance or by the government need to be resolved.”
The gap between responsibility, accountability and liability is not new to the digital world, and neither the insurance market nor standard contracts have a structure for the gap between them. The risks can he higher in the physical world, so the gap that Ng is describing is itself a market opportunity: someone has to build the liability and insurance layer for physical AI before any robot deploys at scale.
2. The physical world was never designed to be networked
“Some buildings are 35 years old. Some buildings are 106 years old. Some buildings are 15 years old. And so the elevator was installed 30 years ago. So how do we service them? So there’s a lot of…
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