Rethinking the Physical World — What Happens When AI Has to Be Right, Not Just Plausible?
Co-hosted by Stanford Entrepreneurs, LOOMUS.AI, and Stanford ACSSS
Who you'll hear from
Thank you to our partner and sponsor Precixion Inc. for this session.
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About Event
Rethinking the Physical World in the Age of AI — The Next Decade of Robotics Starts Here
Episode 04 of "Rethinking the World in the Age of AI" — a six-part series
Every era has one question it can't stop asking.
Ours is this:
What happens when intelligence stops being scarce?
Across six sessions, this series follows that question wherever it leads — from the search box to AI agents that work alongside us, from the infrastructure powering intelligence to robots entering the physical world, from AI accelerating scientific discovery to the question that ultimately belongs to everyone:
What should we build?
Six episodes. One story:
01 · Rethinking Search — The Next Decade of Search Starts Here 02 · Rethinking Agents — The Next Decade of Work Starts Here ↳ Special Edition 01 — What Happens When AI Starts Building With You? ↳ Special Edition 02 — What Happens When AI Can Act, Not Just Answer? 03 · Rethinking Infrastructure — The Next Decade of Compute Starts Here 04 · Rethinking the Physical World — The Next Decade of Robotics Starts Here 05 · Rethinking Science — The Next Decade of Discovery Starts Here 06 · Rethinking What We Build — The Next Decade of Building Starts Here
Featured Speakers
Kristopher Floyd — Founder & Managing Partner, The Frontier Syndicate, a research-driven venture network connecting frontier researchers, founders and investors across AI systems, robotics and embodied intelligence, and AI-enabled science. Founded the Bay Area Frontier Research Club: 20+ research sessions since January 2026, 3,200+ approved registrations, a research network across 27 leading labs and an investor network of 24+ firms. Advisor to Constructive; Bay Area chapter lead for VetsinTech. Certified Futurist (IFTF) and former USMC officer. The question behind his work: which frontier research is ready to leave the lab and become a company?
Will (Ziqi) Wang — Chief of Staff, Agile Robots SE · Investor [confirm title] Chief of Staff at Agile Robots SE (Palo Alto / Munich), overseeing investments, partnerships and business development; previously its Senior Investment Manager for corporate VC and M&A. Investor in RIVR, acquired by Amazon. Earlier at HongShan (formerly Sequoia China) and Morgan Stanley, and Chief Investment Officer of the University of Michigan's Maize and Blue Fund. The question behind his work: which robotics companies can turn a working demo into a durable business?
Bala Anand Jeldi — Precixion · Founder & CEO
Founder and CEO of Precixion, a San Francisco-headquartered company building Physical AI for autonomous metal manufacturing
Over 20 years of experience in additive manufacturing, magnesium alloy applications, and aerospace engineering
Designed and built the world’s largest selective laser sintering machine, earning a Gold Medal from Lockheed Martin, University of Texas and Department of Science and Technology
Patent holder in 3D printing and magnesium technologies, with systems designed for 3 Moon programs, 1 Mars program, satellites, launch vehicles, and weapons systems. Build Compute architecture, Multiphysics software for Linux based supercomputer for Analyzing sutructure for Ballistic analysis of nuclear submarine, Nuclear reactor process analysis .
Leads a team running the P300 production cell at Precixion’s manufacturing operations in Noblesville, Indiana, producing qualified metal parts for defense, space, aerospace, energy and medical customers
Taking NOXBUILD™ from advising operators to correcting builds in process to unattended, qualified production
The question behind his work: as the expertise that qualifies a metal part retires faster than it can be replaced, how do we build machines that learn that expertise and generate the proof themselves?
And more speakers to be announced.
Bits Were the Warm-Up
The internet taught AI everything it knows. Here's the problem: the physical world has no internet.
There is no trillion-page corpus of how a gripper should hold a wet glass, how a melt pool behaves in a laser, or how a warehouse floor changes at 3 AM. Robots have to learn from far less data, in environments that don't reset, where a wrong answer breaks something real.
That is why the next decade of AI may be decided off the screen:
Text → Motion Prediction → Control Demos → Deployment Software margins → Hardware economics
Physical AI is still early. Data is scarce, simulation never quite matches reality, safety and certification move slowly, and every deployment carries a supply chain with it.
But the questions are now concrete:
For builders: what does a model need to know before it can be trusted with a machine? For founders: which physical industries can AI rebuild, and which will it only automate? For investors: where does value accrue when the product is atoms, not seats? For all of us: what changes when intelligence can touch the world, not just describe it?
Bits were the warm-up. Atoms are the main event.
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Ideas or Collaborations?
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DM Sophie Ren on LinkedIn: sophie-r-60981b9b
Rethinking the Physical World · Stanford
The Next Decade of Robotics Starts Here — About the Organizer
THE STORY BEHIND THESE SESSIONS
I'm Sophie Ren. I build thousands of AI agents and I love watching them. Six times a year I put a few thousand people in a room together - in Silicon Valley, in Paris, in Milan, and next May at an AI × Art festival in Sicily.
I do both for the same reason. The models have already read everything we've written down. That's the floor now, not the ceiling. What I want is what isn't in there yet - the thing nobody has tried, or has tried and never said out loud. That kind of knowledge can't be collected. It gets made, and it gets made between people, in real time, in a room. That's the part I find thrilling, and I think it's where the real value has moved.
Monet painted the same pond for thirty years. At some point it stopped being a pond and became something that had never existed — not a better painting, a new object in the world. That moment and the moment a room finally cracks a problem are the same moment: the tree of human knowledge grows a branch.
Everything a model knows came from a branch that grew before it. I don't want to watch the next one grow. I want to grow it, and I want to find the people who want to grow one too.
Bring what you're stuck on.
— Sophie