At the 2026 Huibao Tianxia Insurance Conference, Li Xiaomin, Chairman of Shenke Nano (Suzhou) Co., Ltd., delivered a keynote speech analyzing the semiconductor industry's trajectory and its enduring investment potential.
Li Xiaomin reviewed the historical patterns of the semiconductor sector, noting that over the past five decades, it has experienced seven cyclical fluctuations. Upward cycles are typically fueled by technological revolutions in consumer applications, while downturns often coincide with financial crises. A notable observation is that no single company has managed to lead the industry for two consecutive cycles, as disruptive new technologies consistently pose the greatest challenge to established players. Focusing on the domestic market, he highlighted that China's semiconductor industry has completed three distinct development cycles in just the last six years.
Long-Term Investment Appeal Endures
In Li Xiaomin's view, the semiconductor industry retains significant long-term investment value, even if the computing power chip cycle peaks around 2026 or 2027. He positions computing power as the foundational infrastructure of the AI era. The anticipated third wave of the AI industry cycle, he argues, will be propelled by a diverse range of AI applications, driving growth across the entire chain from design and manufacturing to equipment and materials. This wave has the potential to become the largest-scale industrial transformation in human history. This revolution differs from its predecessors by creating intelligent systems capable of recursive self-iteration, a development that will also prompt profound reflection on humanity's role in society.
The Accelerating Pace of AI Integration
Li Xiaomin emphasized that, from the vantage point of 2026, AI applications are rapidly permeating every sector at an unprecedented pace, measured in days and weeks. The speed of industrial iteration continues to accelerate, far outstripping the development rhythms of the past information age, which operated on decade-long cycles. This acceleration has dramatically shortened the lifecycles of industry giants, making their rise and fall more rapid than ever before.
Addressing the K-Shaped Divergence
Addressing the prevalent K-shaped divergence trend observable across society, industries, and enterprises, Li Xiaomin warned that the middle management layer within companies is likely to shrink rapidly. Professionals holding master's or doctoral degrees who fail to update their understanding of AI may find themselves akin to "modern-day Kong Yiji"—possessing high academic qualifications but struggling to meet the practical demands of the evolving industry.
The Path Forward for Industrial AI
Li Xiaomin also pointed out the evident limitations of general-purpose large language models. He identified AI physical models as the core direction for the future development of industrial AI, suggesting this area holds the key to more substantive and integrated technological advancement.
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