Analysis of Commercial Prospects and University Cooperation Models in the Field of Embodied Intelligence Data Collection

Embodied intelligence data collection, as an important component of AI infrastructure, is in a stage of rapid development. According to market research data, the size of China’s AI basic data service market is expected to grow from 3.7 billion yuan in 2022 to 17 billion yuan in 2028, with a compound annual growth rate (CAGR) of 30.4%[4]. This strong growth is mainly driven by the urgent demand for high-quality data from multimodal large models.

The global AI market is expected to grow from 62.35 billion US dollars in 2020 to 997.77 billion US dollars in 2028, with multimodal AI contributing significantly[1]. As an important branch of multimodal AI, embodied intelligence has seen an explosive growth in its data collection demand.

: Embodied intelligence needs to process multiple modal data such as vision, hearing, and touch simultaneously, which puts higher requirements on data collection. First-person multi-view multimodal data collection technology can provide richer data closer to human perception, which is crucial for training more intelligent embodied AI systems[1].

: With the development of the industry, standardization of data collection has become key. The demonstration center established through Deepwise’s cooperation with universities aims to build a standardized collection system, which will greatly improve data quality and usability and reduce subsequent processing costs[1].

Embodied intelligence data collection’s application scenarios are expanding rapidly:

• Intelligent Manufacturing
  : Industrial robots need a large amount of real-environment data for training
• Autonomous Driving
  : Vehicle sensor data collection is the foundation of autonomous driving development
• Medical Health
  : Surgical robots and rehabilitation equipment have a surge in demand for training data
• Home Services
  : Home robots need diverse data to adapt to the environment

Deepwise’s cooperation with Gengdan Institute of Beijing University of Technology embodies a new international cooperation ecosystem of “AI Empowerment, Global Intelligent Connection, Industry-Education Integration”[2]. This model has multiple values:

: Universities provide a continuous talent pool for AI enterprises. Students can gain practical experience in real projects, while enterprises can lock in excellent talents in advance[2].

: The cutting-edge research results of universities and the actual application needs of enterprises form a positive interaction, accelerating the transformation of technology from the laboratory to the market[2].

: Compared with self-built data collection centers, cooperation with universities has lower costs. The cooperation model can share infrastructure investment and reduce operating costs.

University environments provide ideal scenarios for embodied intelligence data collection:

• Diverse Environment
  : Campuses include classrooms, laboratories, libraries, sports fields, and other scenarios
• High Standardization
  : University environments are relatively controllable, facilitating the establishment of standardized collection processes
• Sound Ethical Protection
  : University ethics review mechanisms can ensure compliance of data collection
• Continuous Update Capability
  : With changes in campus activities and seasons, dynamically updated data can be obtained

Deepwise’s cooperation model will enhance company valuation from multiple dimensions:

: High-quality first-person multimodal data becomes the core asset of AI companies, especially in the data-driven large model era, data barriers are directly transformed into technical and commercial barriers.

: Cooperation with universities creates a new business model, shifting from pure product sales to diversified revenue sources such as data services, technology licensing, and talent training, enhancing the company’s risk resistance and growth potential.

: Through cooperation with multiple universities, a nationwide data collection network can be built to form scale effects, which will significantly enhance the company’s competitive barriers and market position.

Capital markets’ valuation logic for AI companies is changing:

: Investors are increasingly focusing on the quality of AI companies’ data assets, not just algorithm technology. Companies with unique and high-quality data collection capabilities are more favored.

: AI companies that can build a complete industrial ecosystem (data collection-labeling-training-application) have higher valuations, and university cooperation is an important part of building this ecosystem.

: AI companies with international cooperation capabilities have higher valuation premiums, and Deepwise’s model lays the foundation for subsequent international cooperation[2].

: Multimodal data requires extremely high synchronization and consistency, which is technically difficult to achieve and requires continuous R&D investment.

: Unifying data formats from different environments and devices is a major challenge that requires the establishment of a unified industry standard system.

: University cooperation requires long-term maintenance. Once the cooperative relationship changes, it may affect the stability of data supply.

: As market prospects become clear, more companies will enter this field, and competition will become increasingly fierce.

: Data collection involves privacy protection, and compliance requirements are increasingly strict, which may increase operating costs.

• More AI companies will follow Deepwise’s model to strengthen cooperation with universities
• Standards for embodied intelligence data collection will gradually be established and improved
• Related industrial chains (sensors, storage, labeling) will develop rapidly

• The embodied intelligence data collection market will reach a scale of tens of billions of yuan
• International cooperation models will mature, and Chinese companies may occupy an important position in the global market
• Data trading platforms and standard systems will be more complete

• Embodied intelligence data collection will become an important component of AI infrastructure
• AI applications based on this data will be widely used in various industries
• Companies with core data collection capabilities will become important infrastructure providers in the AI industry

The field of embodied intelligence data collection has huge commercial prospects, with rapid market growth and expanding application scenarios. Deepwise’s cooperation model with universities provides a new development path for AI companies, which not only solves the problem of data acquisition but also builds irreplaceable competitive advantages.

This model enhances AI company valuation in all aspects. From data asset value, business model innovation to ecosystem construction capabilities, all will become important dimensions for investors to evaluate company value. Although facing technical and commercial challenges, Deepwise is expected to occupy an important position in the field of embodied intelligence data collection and achieve sustained growth of company value.

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[1] XenonStack - “The Rise of Multimodal AI Agents: Redefining Intelligent Systems” (https://www.xenonstack.com/blog/multimodal-ai-agents)

[2] Sina Finance - “Facing the AI Revolution: What Should Application-Oriented Universities Do? Experts and Scholars from 10 Countries Gather in Shanghai to Discuss the Future of Industry-Education Integration” (https://finance.sina.com.cn/stock/t/2025-11-23/doc-infymcmt6465296.shtml)

[3] Securities Times - “Institution: The Brain-Computer Interface Industry is Expected to Usher in Important Development Opportunities” (https://www.stcn.com/article/detail/3518641.html)

[4] CSDN Blog - “Market Forecast Analysis Chart of Data Labeling Tools” (https://i-blog.csdnimg.cn/blog_migrate/49cc8e7baffa7fab6341df126df44166.png)