Summary of Key Highlights
The most prominent takeaway from this year's WAIC 2026 is that AI, which once seemed like a concept only in the clouds, has finally started to "take action" in the real world. From data collection to embodied intelligence, five key terms (data collection, physical AI, agents, computing power, and embodied intelligence) illustrate the transition of the AI industry from showcasing its capabilities to actually solving problems. Data has become the "oil" that drives embodied intelligence; physical AI enables robots to understand the laws of physics; agents have evolved from mere companions in conversations to productive workers; computing power has progressed from individual chips to system-level solutions; and embodied intelligence has shifted from demonstrations to practical applications in real scenarios. Although there are still challenges such as data gaps and energy efficiency issues, the path for AI to integrate into the physical world is now clear.
Detailed Analysis
1. Data Collection: The Battle for Embodied Intelligence's "Oil"
If large models rely on internet text (like cheap coal), then embodied intelligence (robotics that can move) depends on data from the physical world (like expensive oil). At this year's WAIC, every robot booth was equipped with data collection devices to feed these models. The consensus was clear: without sufficient data, even the most advanced models are useless.
There were three main approaches to data collection:
- Virtual Simulation: Companies like Guanglun Intelligence used virtual environments to simulate physical processes (e.g., robots picking up cups) at low cost, saving time and effort compared to real-world trials.
- Real-World Data Collection: Mengfeng Technology utilized complex equipment for robots to learn through practical mistakes, as synthetic data cannot address delicate issues like touch and force feedback.
- Long-Tail Scenarios: Businesses such as Shao Mai Guo focused on retail scenarios, where robots pick up products and restock them, generating valuable data in the process.
However, the challenge remains: while the barrier to data collection has lowered (solutions are now available for under ten thousand yuan), the efficiency of data processing is still low. A significant amount of time and effort is required to clean and label the data properly; otherwise, it can degrade the model's performance.
2. Physical AI: Turning Robots from "Dumb Giants" into Smart Agents
Physical AI is essential for robots to understand the physical world (friction, weight, collisions, etc.). Industry experts identified three major hurdles to overcome:
- Data Barrier: We need 100 million hours of embodied data to reach a level comparable to ChatGPT, but we are still far from that goal.
- Representation Barrier: How to convert physical information into a format that models can understand.
- Closed-Loop Barrier: Models must be able to learn and adjust in real-world contexts.
Examples from the conference showed progress: Daxiao Robot's self-learning model could pour coffee and fold clothes, and it would correct mistakes on its own. JD.com's AI demonstration room demonstrated how robots could handle real household tasks. Experts predict that the "ChatGPT moment" for robots will likely come in 2027-2028, requiring 3-5 years of continuous development.
3. Agents: From Conversational Tools to Productive Workers
Agents have evolved from mere companions to practical assistants. Both large companies and startups are emphasizing their capabilities:
- Enterprise Use Cases: Jinshan WPS Comate can understand company structures and integrate knowledge bases to enhance organizational efficiency (e.g., managing costs or granting permissions).
- Personal Use Cases: Tencent WorkBuddy can process documents, extract data, and even coordinate local files and browsers to complete tasks.
- Hardware Integration: Smartphones like STEPX Neo can automatically send files to companies and schedule deliveries without user intervention.
The key change is that users are shifting from being operators to managers, with agents taking on the role of planning and coordinating tasks. Although privacy concerns remain, 2026 marked a significant milestone as AI moved from merely talking to actually getting things done.
4. Computing Power: Domestic Computing Power Goes Beyond Shaming
In previous years, domestic computing power was often criticized for its limitations. This year, the focus shifted to building "super nodes" (systems that combine hundreds or thousands of chips through high-speed networks to create supercomputers).
Notable highlights included Huawei's Ascend 950 supernode (1024 chips, 1 EFLOPS), Zhongke Shuguang's ten-thousand-chip cluster, and Alibaba's Zhenwu supernode, all recognized as highlights of the exhibition. The reason for this shift is that individual chip performance has reached its limits, and large models continue to grow in complexity (e.g., Kimi K3 with 2.8T parameters). Commercialization of domestic computing power is now a reality, with metrics shifting from the number of chips to factors like token cost and response time.
This marks a true milestone in AI's maturity.
5. Embodied Intelligence: From Demonstrations to Practical Work
The H3 hall this year was like a robotics "job market," with over 200 companies showcasing 208 different robots. The biggest change was the focus on practical applications:
- Industrial Applications: Robots were used for tasks like palletizing and quality inspection, with high success rates (99.99%).
- Retail Automation: Ant Group's robots collaborated to pick up and pack medicines in just 90 seconds.
- Unmanned Retail: Robots used robotic arms to make coffee.
However, practical challenges remain, such as energy consumption and heat dissipation. These issues must be addressed before AI can become widely adopted in real-world settings.
Conclusion
These five key terms together represent the complete path for AI to move from the digital world to the physical one: computing power provides the necessary infrastructure, data serves as the foundation, physical AI offers the methods, agents handle the logic, and embodied intelligence gives the physical form. Although there are still many challenges, AI is evolving from a theoretical concept to a practical tool that can walk, work, and solve problems. Next year's WAIC will likely be even more exciting!
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