Brief

At the AIGC2026 summit, Hong Yuan, Chief Product Officer at Taichu Yuanqi, highlighted that heterogeneous computing will be a crucial direction for future AI infrastructure. As AI applications like Agentic AI and code assistants become more prevalent, the demand for computing power, particularly in terms of token consumption, is rapidly increasing. Yuan emphasized that domestic AI computing power has a significant opportunity but must focus on large-scale cluster services, computational efficiency, and ecosystem usability to truly break through. AI

Fengxing Online CEO Yi Zhengchao advocates for widespread AI coding literacy across all company roles, not just engineers, to drive business results. He believes that while AI can amplify self-satisfaction, focusing on delivering tangible outcomes is the key to mitigating this risk. The company has seen over a tenfold profit increase in three years by enabling employees to leverage AI for tasks, shifting organizational focus from individual roles to task-oriented workflows and fostering a collaborative ecosystem. AI

Fusion Fund's Lucy Zhang predicts a significant shift in AI infrastructure, with inference computing demands set to surpass training by a 70/30 split. She highlights that communication within data centers consumes vastly more energy than computation itself, suggesting a critical need for advancements in optical communication. Zhang also emphasizes that the primary bottleneck for physical AI is the lack of high-quality, real-world data, rather than model size or compute power, pointing to sectors like healthcare as rich sources for this data. AI