Guosen: Model iteration fosters a new blue ocean in the data industry, expected to achieve a market space of RMB10 billion.

Guosen released a research report stating that data is a key resource for the development of physical AI large models and the underlying DRIVE supporting model training, capability emergence, and intelligent evolution. Current embodied data accumulation is primarily visual information; human manipulation videos provide rich behavioral and semantic information, while tactile data requires synchronous recording of contact states during interaction, and the existing supply of tactile data remains relatively limited. Embodied data collection routes are diverse, and the training value of multi-source data is prominent. The embodied intelligence data industry chain covers infrastructure, data collection, data processing, and applications. Guosen's main viewpoints are as follows: High-quality physical interaction data is key to achieving generalization in embodied intelligence, and embodied Scaling Laws are beginning to emerge Looking at the development history of digital AI large models, breakthroughs in model capability boundaries have always been accompanied by the coordinated evolution of data, computing power, and algorithms. Data is a key resource for the development of physical AI large models and the underlying DRIVE supporting model training, capability emergence, and intelligent evolution. Scaling Laws in the embodied intelligence field have begun to receive preliminary empirical support. For example, in experiments with Ego first-person human video pretraining data, Dyna-2 found that when data scale expanded from thousands of hours to millions of hours, model performance showed a clear trend of improvement, providing preliminary evidence for embodied Scaling Laws. Data demand is expected to continue expanding. General model vendors are accelerating their entry into embodied intelligence, and tactile data is highly scarce ARR growth of digital AI businesses at some foundation model vendors has slowed, and extension into the physical world is expected to become an important direction for expanding application boundaries, with demand for embodied data procurement growing rapidly. Multimodal general models represented by GPT-6Astra continue to expand into the physical world, demonstrating strong task understanding, planning, and closed-loop error correction capabilities in the embodied field, further expanding the entities demanding data. Current embodied data accumulation is primarily visual information; human manipulation videos provide rich behavioral and semantic information, while tactile data requires synchronous recording of contact states during interaction, and the existing supply of tactile data remains relatively limited. Embodied data collection routes are diverse, and the training value of multi-source data is prominent Embodied data collection technology routes include internet video data, Ego first-person video data, simulation synthetic data, and real data, with data from different sources having complementary value. 1) Internet video data is massive in scale and serves as the data foundation for embodied intelligence, but lacks force and tactile information; 2) Ego first-person perspective data is collected by collectors wearing head-mounted or neck-mounted devices, naturally completing tasks in daily work environments without relying on a specific Siasun Robot&Automation body. It is a route with high scaling potential and low deployment barriers. Unlabeled Ego data costs about several dozen yuan per hour, processed high-quality data costs several hundred yuan per hour, and Ego devices cost around RMB1,000; 3) UMI universal manipulation interface data uses a UMI gripper held by the operator as a collection tool to collect end-effector manipulation information and provide structured action information. Its main advantage lies in cross-body reuse without relying on specific Siasun Robot&Automation body hardware, but the action space has a gap compared with real Siasun Robot&Automation. Data costs several dozen to several hundred yuan per hour, and core equipment costs RMB10,000-level; 4) Teleoperation data provides real Siasun Robot&Automation execution demonstrations and is a high-value data type, mainly used for model post-training. Data costs several hundred to over a thousand yuan per hour. Tactile glove prices vary significantly, with domestic vendors' single sets priced at the RMB10,000 level; 5) Simulation data supports efficient generation and scenario expansion, but core challenges include the Sim-to-real Gap. Domestic vendors are actively deploying data collection and processing, and embodied data is expected to achieve a market space of RMB10 billion The embodied intelligence data industry chain covers infrastructure, data collection, data processing, and applications. Enterprises with software-hardware coordination, real-scenario resources, and continuous data delivery capabilities are expected to form differentiated advantages. In terms of market space, looking ahead to next year, industry demand is moving toward the hundreds-of-millions-of-hours level. Calculated at several hundred yuan per hour, the data market corresponds to potential market space of approximately RMB10 billion. In the long term, drawing on Tesla's intelligent driving data accumulation process, under the assumptions that vehicles continue to be in use and each vehicle travels an average of 1 hour per day, by 2024, when the FSD V13 model capabilities achieved a clear breakthrough, Tesla's global fleet driving time is estimated at approximately 6 billion hours (estimated value, for reference only). Theoretically, the data complexity of Siasun Robot&Automation is far higher than that of intelligent driving vehicles. If embodied intelligence achieves a major model capability breakthrough similar to FSD V13, data demand will likely exceed that duration. Considering future declines in data collection costs, the long-term data market size is also expected to exceed RMB100 billion, indicating broad market space. Risk warnings: Embodied intelligence model progress below expectations, embodied Siasun Robot&Automation shipments below expectations, embodied data procurement demand below expectations, changes in data technology routes, data price declines, and intensified industry competition.