# Agibot Chief Scientist Rejects LLM Path for Robotics > Source: > Published: 2026-06-29 07:15:41+00:00 Industry context: For AI and robotics practitioners, progress in embodied systems depends less on simply scaling model size and more on building integrated, deployable data loops and shared standards. Reporting by Kr-Asia describes **Luo Jianlan**, chief scientist at **Agibot** and an associate professor at the Shanghai Innovation Institute, arguing that **embodied intelligence cannot simply copy the development path of large language models (LLMs)**. Kr-Asia reports Luo warned that many so-called embodied foundation models are closer to mid-training or fine-tuning because high-quality, multi-scenario robot interaction data, including failures and long-tail events, remains scarce. The article traces a recent shift in China's robotics scene away from body-design fixation toward system-level concerns: data, models, infrastructure, and the ability for those elements to reinforce one another in real-world deployment, per Kr-Asia.