Until recently, robots were constrained to narrow, specific tasks. Think assembly lines, medical devices, or household vacuum cleaners. But now all that is changing. One robot trained by Generalist's foundation model can do everything from folding clothes and washing dishes to sorting objects and repairing other robots. How are Pete Florence and the Generalist team bringing AI to the physical world? Why is intelligence, not hardware, the key unlock for robotics? And how could general-purpose robots unlock 10 times more scientific progress in the years ahead?
Pete grew up in the Bay Area, earned his PhD at MIT, and became a star scientist at Google DeepMind. In 2023, he left to co-found Generalist, which builds robotics foundation models to help bring general intelligence to the physical world. Its latest model, GEN-1.5, can now train robots in a matter of seconds on simple tasks such as folding laundry and sorting objects. For this conversation, we're also joined by 8VC Partner Vivek Gopalan.
We begin with Pete's entrepreneurial journey and his epiphany in grad school — walk into any robotics lab and the robots were standing still most hours of the day. Pete unpacks why robots, like humans, learn through data and experience, and why intelligence, not hardware, is the next frontier for robotics. At DeepMind he specialized in bringing multimodal learning (language models + vision models) into robotics, and he's now bringing that to the next level with robotic world models at Generalist. We dive into robotics' GPT-3 moment, the emergent capabilities Generalist is seeing — including self-taught ambidexterity — and the data moats that will shape the industry. Finally, we explore what a world of abundant robots means for science, reindustrialization, and American prosperity.
00:00 Episode intro
01:30 Bay Area to MIT and DeepMind
03:55 Pete's epiphany: robots sitting still
08:30 Why intelligence, not hardware, is the next frontier
13:20 The GPT-3 era of robotics
14:55 Why leave DeepMind?
16:50 How to train robot brains
18:30 Emergent ambidexterity
20:40 LLMs vs robot world models
23:50 GEN-0, GEN-1 & scaling laws in robotics
27:28 10x more science per year
32:50 Skilled trades, reindustrialization & future of robots
