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Why Pixel-Based Robot AI Is an Expensive Detour | Chao Cao, Sancho
Advanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose.
Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module.
We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store.
Chapters:
00:00 Intro
01:10 Roadmap and why the company is called Sancho
02:15 The lasagna problem: the gap between machines
04:51 $300K scientists loading machines by hand
06:04 Why fixed automation isn't the answer
07:02 Bet #1: 3D geometry over pixels
09:46 Bet #2: test-time reasoning over data scaling
11:25 Running the whole stack on onboard compute
13:11 Three years in the dark: DARPA SubT lessons
16:15 October to GTC in five months
18:35 The oversubscribed seed round
19:01 Two founders, two autonomy worlds
21:08 Why start in the most regulated environments
23:48 Hot takes: Waymo vs. Tesla and wasted data
25:58 What Chao wishes he knew before starting
27:30 Hiring: perception and loco-manipulation
29:50 Where to find Sancho
Learn more about Sancho: https://www.sancho.com
The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC.
Subscribe to the newsletter: https://www.optim.vc
Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/
View all episodes
By Bogdan CristeiAdvanced manufacturing has automated the individual process steps. CNC, 3D printing, photolithography - however complex the step, we can build a machine for it. The handoffs between those machines still run on people, and in cell therapy that means $300,000-a-year scientists spending their days loading and unloading equipment while contamination risk and throughput bottlenecks drive up the cost of every dose.
Chao Cao (Co-Founder & CEO, Sancho - CMU robotics PhD, autonomy lead for CMU's DARPA Subterranean Challenge entries, ex-Boston Dynamics AI Institute) makes the case that the dominant approach to robot intelligence, pixel-based VLA models trained on massive datasets, is an expensive detour. Sancho bets on 3D geometric world models and test-time reasoning instead, and runs the entire stack on a single onboard compute module.
We cover the lasagna analogy for factory workflows, the math on the most expensive labor doing the lowest-value work, what five nines of reliability does to the data question, Waymo vs. Tesla as a data-quality argument, three years of sending robots into tunnels and caves, the October-to-March sprint from incorporation to NVIDIA's GTC keynote, the oversubscribed seed round co-led by Fusion Fund and Catapult, and why regulated cleanrooms are an easier place to start than a grocery store.
Chapters:
00:00 Intro
01:10 Roadmap and why the company is called Sancho
02:15 The lasagna problem: the gap between machines
04:51 $300K scientists loading machines by hand
06:04 Why fixed automation isn't the answer
07:02 Bet #1: 3D geometry over pixels
09:46 Bet #2: test-time reasoning over data scaling
11:25 Running the whole stack on onboard compute
13:11 Three years in the dark: DARPA SubT lessons
16:15 October to GTC in five months
18:35 The oversubscribed seed round
19:01 Two founders, two autonomy worlds
21:08 Why start in the most regulated environments
23:48 Hot takes: Waymo vs. Tesla and wasted data
25:58 What Chao wishes he knew before starting
27:30 Hiring: perception and loco-manipulation
29:50 Where to find Sancho
Learn more about Sancho: https://www.sancho.com
The OPTIM Update covers real-world AI, automation, robotics, industrial systems and AI Infrastructure for founders, investors, and operators. Hosted by Bogdan Cristei of Optim VC.
Subscribe to the newsletter: https://www.optim.vc
Follow Bogdan on LinkedIn: https://www.linkedin.com/in/bogdancristei/