The实体AI领域 is迎来 a new wave of growth. Industry analyst Andrew Kang recently stated that by 2026, the data scale of实体AI is expected to achieve a 100-fold expansion. This judgment is not unfounded—since 2025, AI in the robotics field has already begun to突破 the two core bottlenecks of model architecture and data collection.

The success rate of reinforcement learning(RL) technology in leading companies like Figure and Dyna has exceeded 99%. What does this mean? It indicates a qualitative change in machine learning efficiency. Meanwhile, advances in memory technology and visual language models(VLM) are also significantly reducing data annotation costs. Tasks that previously required大量人工 are now more automated.

The cycle from fundamental technological breakthroughs to application deployment is shortening. The integration of AI with the physical world is no longer a distant plan but is actively progressing. What does this trend mean for the development of related sectors? The market has its own answer.