Core Insights - The correct model architecture and efficient data sampling are identified as the two main challenges for the scalable development of embodied intelligence, which has become a primary focus for companies in this sector [1] - The main theme of model architecture revolves around the integration of large language models, large visual models, and action models, with diffusion model-based flow matching algorithms gaining prominence in the short term [1] - Companies with strong capital expenditure capabilities are leveraging real data collection as a breakthrough to build competitive barriers through data set accumulation, while synthetic data and internet data are also essential for the value foundation of embodied models [1] - The organic combination of pre-training and post-training core demands with data attributes has emerged as a new challenge, leading to the rise of data sampling concepts [1] - The role of world models in empowering the scalability of synthetic data and strategy evaluation is also significant [1] - In the short term, attention is recommended on capital investors in the embodied model industry and data collection providers, while in the long term, cloud computing and computing power providers should be monitored [1]