Physical AI’s GPT-2 Era: Why Robots Still Can’t Do the Work

BitcoinWorld Physical AI’s GPT-2 Era: Why Robots Still Can’t Do the Work Physical AI startups are raising billions to bring large language model techniques to robotics, but the sector is hitting a wall: robots lack the data and intelligence to perform value-creating tasks reliably. This reality was underscored this week when Unitree, China’s leading robot maker, lost nearly half of its market value after a $66 billion IPO on China’s STAR Market, as analysts pointed to the gap between physical capabilities and practical know-how. Why the Data Crisis Is Holding Back Physical AI At last week’s Actuate conference in San Francisco, developers building AI brains for robots gathered to confront what Avala, a physical AI infrastructure company, called “the robotics data crisis.” The event, organized by Foxglove, tripled in size since 2023 to 1,500 attendees, reflecting both excitement and urgency. The core problem: there isn’t enough high-quality training data to teach robots generalized skills, and end-to-end learning for specific tasks hasn’t yet produced reliable commercial products. Harry Mellsop, founder of Antioch, a startup building simulation tools, likened physical AI to the “GPT-2 era” of OpenAI—before ChatGPT proved the power of scale. He believes more data and compute, especially GPUs optimized for ray tracing to create high-fidelity simulations, are needed to push the field forward. Autonomous vehicles are furthest ahead because they can collect real-world driving data and the primary task is collision avoidance, not manipulation. AV Companies Pivot to Humanoids The tooling developed for autonomous driving is now being repurposed for humanoid robots. Foxglove was founded by former Cruise employees, and both Wayve, an AV startup, and Uber have launched robotics labs focused on humanoid form factors. Alex Kendall, Wayve’s CEO, told Bitcoin World, “You need to start in vehicles… manipulation robotics is like self-driving five years ago.” He argues that data infrastructure and simulation will be shared, but world models will need different post-training for different embodiments. However, not everyone agrees that AV expertise translates directly. Théophile Gervet, CEO of Genesis AI, which raised a $105 million seed round this year, countered, “We’re too early in this wave for a brain strategy to work; there’s lots of opportunities to co-design hardware and AI.” His company is vertically integrated, building both hardware and software, to address the co-design challenge. Vertical vs. General: The Strategy Debate Robotics companies are split between those targeting specific tasks and those pursuing general-purpose humanoids.