Zhejiang University Holding white paper: AI investment logic shifts from parameter competition to commercial deployment

The fifth Global Digital Trade Expo, themed “Meet the AI Future at the Digital Trade Expo,” was held from September 23 to 27 in Hangzhou.

Upgraded “Digital Trade Venture Capital Day” Connects Startups with Investors

Building on its inaugural edition, this year’s “Digital Trade Venture Capital Day” has been comprehensively upgraded and expanded, with enterprise screening, capital matching, and main activities more closely integrated. From September 22 to 23, the event focused on areas including artificial intelligence, hard tech, healthcare, and biotechnology, conducting selection, institutional matching, and intent tracking for innovative tech companies. The initiative attracted 100 well-known venture capital institutions, 100 professional investors, and 100 high-quality tech innovation projects, facilitating precise exchanges and matchmaking.

AI Competition Shifts from “Parameter Wars” to “Real-World Deployment”

During the “Digital Trade Venture Capital Day,” Zhejiang University Holding Group released the White Paper on China’s AI Tech New Consumption Industry and Investment Trends (2026) (hereinafter referred to as the “White Paper”). The White Paper notes that China’s AI industry is accelerating from technological breakthroughs toward large-scale commercial deployment. Open-source ecosystems are rising rapidly, and tech-driven new consumption has become a key lever for industrial application. “Investing early, investing small, and investing in hard tech” has become a market consensus.

“The logic of AI investment is systematically moving upstream,” the White Paper states. Key findings include:

• AI competition is shifting from “parameter wars” to “real-world deployment,” with model adaptability and low cost becoming core enterprise demands.
• Tech-driven new consumption is a key scenario for large-scale AI deployment. Agentic AI and Physical AI are jointly building a closed loop of “vague demand identification to precise value delivery.”
• Embodied intelligence has become the primary investment theme. Capital is moving from “investing in complete machines” to “investing in brains and components.”
• Going global has entered a “system competition” phase. Companies must simultaneously possess capabilities in product definition, model iteration, compliance operations, and localized services.

Four Dimensions for Observing AI’s Commercial Acceleration

Yu Feipeng, Deputy Secretary of the Party Committee and General Manager of Zhejiang University Holding Group, stated that the AI industry is entering an accelerated phase of commercial deployment, which can be observed from four dimensions:

1. Policy-driven: Both national and local governments have introduced top-level plans to promote AI empowerment across industries. “AI empowers everything” has become an industry consensus.
2. Capital-backed: Investment scale and the number of participating institutions in the AI sector continue to rise, with new projects emerging constantly.
3. Commercial deployment: The industry is generally concerned that AI’s investment, output, and cash flow have yet to form a complete closed loop, with some debate over potential bubbles.

“However, infrastructure investment inherently has an advanced nature. High investment does not mean the business model is unviable; it simply means AI penetration across industries remains uneven,” Yu said. On the technology iteration front, large model parameters and comprehensive capabilities are rapidly improving, and vertical domains are likely to generate many new AI agents.

Yu noted that the industry often compares AI to the Fourth Industrial Revolution, though no unified definition has yet been established. The market remains divided on whether AI is a bubble, with some drawing parallels to the 2000 internet wave and others comparing AI to the steam engine or the electric revolution. Different reference points lead to vastly different views on the industry’s current stage and future potential.

“China was largely absent from the first three industrial revolutions. In this transformation, however, China has the opportunity to run alongside or even lead, achieving leapfrog development,” Yu said.

AI Agents to Reshape Manufacturing Processes

Physical AI refers to intelligent systems capable of understanding the laws of the physical world and perceiving, deciding, and interacting in real-world environments. It is seen as a critical stage in AI’s evolution from the digital to the physical world. Currently, Physical AI remains in a validation phase, with technical feasibility preliminarily demonstrated at the proof-of-concept level. The focus is now shifting to real-world scenario validation to drive the formation of application closed loops.

Cao Guoxiong, Chairman of Puhua Group and Co-Founder of Toutoushidao, said the core of robotics development lies in redefining the relationship between humans and machines. The first applications should involve replacing humans in hazardous environments, making humanoid form factors less important. From a specific scenario perspective, hotel food delivery has already been implemented, while cleaning robots are still progressing. Due to persistent labor shortages in service roles, these scenarios have become essential needs. Hazardous environment inspection is the next direction for development.

Cao believes that, in the long term, the true maturity of embodied intelligence will reshape production models. Current factory processes are designed around human operational habits. In the future, intelligent agents will restructure production workflows, with humans only responsible for defining and supervising tasks. Many startups are already moving in this direction.

Real-World Data: The Core Challenge for Physical AI

“The internet has accumulated vast amounts of text data, supporting the training of large models like ChatGPT and Kimi, but usable data from the physical world is almost nonexistent,” said Wang Xiang, Founding Partner of Gaoshan Xinyu and former Partner at Xiaomi. Real-world data is a core prerequisite for training Physical AI foundation models. Without high-quality real-world data, embodied intelligence cannot truly be deployed in social and household settings. Drawing from the experience of autonomous driving, the ideal data collection method is to acquire real-scene data directly during operations. Existing collection methods each have limitations: video capture, even from real scenes, still has flaws; wearable device collection faces numerous challenges. An efficient approach is to collect real-world data naturally during daily work and life.

Analyzing electromechanical signals represents a transformative technical path, Wang said. When a person performs actions like picking up a cup or folding clothes, the brain sends instructions to the hands, generating corresponding muscle electrical signals. Collecting, standardizing, and annotating such signals could significantly advance the Physical AI industry ecosystem, with data being the most critical element.

AI-Assisted Diagnosis and Medical Insurance Payments to Become Routine

AI is reshaping medical innovation, opening a new path from scientific breakthroughs to clinical applications and from laboratory results to industrial transformation.

Liang Xiao, Vice Dean of Zhejiang University School of Medicine, said that Sir Run Run Shaw Hospital began building a “future hospital” in 2014, continuously exploring AI-empowered medical scenarios. For example, the team independently developed a multimodal medical AI large model for a liver cancer diagnostic agent, which was selected as a key project for the National Pilot Base. The system can receive imaging and text data and automatically output a structured report within three minutes, requiring no manual operation. Validated across multiple centers nationwide with over 10,000 cases, the system achieves a detection rate of over 95% for liver lesions larger than 1 cm and an accuracy rate of 93.46% for distinguishing benign from malignant lesions.

“The global AI medical market is expected to maintain rapid growth of 30% to 40% over the next five to ten years. However, clinical applications still face many practical challenges,” Liang said. These include insufficient evidence for AI-empowered medicine, fragmented medical data forming isolated silos, and large models performing well in laboratories but showing significantly reduced generalization ability when equipment or hospitals change. Issues such as data privacy protection and payment mechanisms are also prominent.

Liang believes that, in the future, AI-assisted diagnosis and medical insurance payments will become increasingly routine. Multimodal medical large models and agents will cover full-lifecycle health management. At the same time, medical AI access should emphasize traceability and explainability. As the closed loop of AI preliminary screening and doctor review is fully implemented, human-machine collaboration will become the standard clinical workflow. On the talent front, interdisciplinary training will be systematically integrated into medical education, with medical schools needing to cultivate students with AI literacy.