Global capital markets have recently experienced a crisis of confidence in artificial intelligence. Technology stocks in the U.S. market underwent a collective significant correction, seemingly putting the previously soaring AI narrative on pause once again.
Regarding the "AI bubble theory," the international community has always held two contrasting views. At the recent Davos Forum, IMF Managing Director Kristalina Georgieva warned that AI's impact on employment could be like a "tsunami," affecting approximately 40% of jobs globally. In response, Goldman Sachs Chairman and CEO David Solomon stated this would not lead to "employment doomsday," but rather resembles a classic case of "creative destruction"—where old roles fade and new opportunities emerge.
While other countries continue debating whether AI is a bubble or a revolution, China has already reached a nationwide consensus and moved into a state of collective action regarding AI. This stems from China's advantages in AI application, particularly the notable "human efficiency" AI demonstrates in the commercial sector. In e-commerce, AI is increasingly "understanding business" and even starting to "conduct business." Within factories in industrial clusters, it can already intelligently accept orders, automatically schedule production, and deeply participate in production operations.
The global AI competition landscape is becoming clear: the United States excels in foundational technology, while China leads in application implementation. Domestically, AI to B holds greater strategic depth than AI to C. Many assume China's AI advantage lies in the consumer end—such as ordering takeout or chatting with AI. However, that is merely the surface. The real substance lies within the bustling factory workshops.
If AI remains confined to smartphone screens, "application leadership" will ultimately be a castle in the air. Only by embedding it into the soil of the industrial internet can systemic and sustainable competitiveness grow. What truly deserves high attention is that AI is deeply resonating with Chinese manufacturing, industrial belts, and source factories.
As pointed out in the "2025 China Industrial Belt Development Trends Report" by 1688, an Alibaba-affiliated platform for source factory goods: AI penetration in industrial belts has moved beyond the initial "tool attachment" phase, entered the deepening "capability symbiosis" stage, and is accelerating towards a "native reconstruction" transition. This assessment is based on real operational data from over one million source manufacturers on the platform.
Technological disruption is never sentimental. Over the past two decades, each technological leap has occurred over the ruins of old business models, giving rise to new ecosystems. In 2007, Apple released the first iPhone, ushering in the smartphone era. At that time, Nokia had a market capitalization of $150 billion, holding 40% of the global mobile phone market. Within just five years, its smartphone business collapsed, and in 2013, it sold its device division for $7.2 billion—less than 5% of its peak value. Simultaneously, the App Store ecosystem expanded rapidly, with global registered developers now exceeding 30 million, fostering new professions like mobile advertising and content creation.
Entering the 2010s, Tesla challenged traditional industry with its "software-defined vehicle" concept. Initially, electric vehicles were seen as niche toys; now, data from multiple institutions suggests they will account for approximately 25% of new car sales globally by 2025. Millions of workers in the internal combustion engine supply chain stand on the brink of transformation. Furthermore, if Tesla's Full Self-Driving system achieves widespread commercial use, it could impact over 5 million driver jobs worldwide.
AI's penetration is broader, deeper, and faster than any previous technology. Even more astonishing is its iteration speed: OpenAI progressed from GPT-1 to GPT-4 in just four years, compared to the century-long Industrial Revolution. This "hyper-compressed change" severely lags behind societal adaptation mechanisms—schools teach yesterday's knowledge, and many small and medium-sized enterprises have yet to find the entrance to AI. Consequently, "destruction" arrives swiftly and fiercely, while "creation" lags. This is particularly harsh for businesses. Amid this anxiety, the "AI tsunami theory" has gained significant traction.
However, the history of human technology and economic development repeatedly proves that the real crisis for enterprises and individuals is never technology being too powerful, but rather powerlessness and inaction in the face of it. The World Economic Forum's "Future of Jobs Report 2025" predicts that over the next five years, 170 million new jobs will be created globally, while 92 million will be displaced, resulting in a net increase of 78 million jobs.
Anxiety about the tsunami can also be viewed as a normal part of the "creative destruction" process, not a new structural crisis. The core of the "creative destruction" theory is that "new innovations replace old technologies," which is a key driver of economic development. Philippe Aghion, Professor of Economics at the Collège de France and a Nobel laureate, previously authored a book on this topic, "The Power of Creative Destruction: Economic Upheaval and the Wealth of Nations."
Therefore, while everyone focuses on the height of the tsunami, we must not forget that the evolution of human civilization and commerce has never relied on dodging waves, but on applying new technology to build better ships. At this juncture, rather than panicking about an impending "tsunami," it is more productive to ask: How do we build the ship?
The answer lies not in Silicon Valley or Wall Street, but in China's vast industrial heartland. While the world still debates whether AI is a bubble or a revolution, China's industrial belts have already entered the deep waters of practical application. As the "2025 China Industrial Belt Development Trends Report" indicates, by 2026, AI penetration in industrial belts will have entered the deepening "capability symbiosis" stage. AI is no longer a simple tool but acts like a nervous system, deeply integrating into R&D, production, supply chain, and sales, driving human-machine collaboration and causing structural changes in the overall operational models of industrial belts.
From a managerial perspective, the transformation in the AI era is a revolution in the factors of production. The vast majority of industries face an elimination contest, but also a new opportunity for industrial upgrading and restructuring. In this transformation, SMEs are no longer bystanders but leading actors. Taking the 1688 platform as an example, "shipbuilders" who can effectively use AI are emerging in large numbers:
A traditional 3C accessories factory in Shenzhen, leveraging AI to build an agile supply chain, achieved over 11 million yuan in sales for a new product within 38 days, moving away from price wars and winning a "value turnaround" with AI. A post-90s accessory entrepreneur in Dongguan used AI for market research, content creation, and business management, reaching the top of their product category rankings within 15 days, increasing content efficiency by tens of times, and achieving a lightning-fast breakthrough in a new market segment. A shoe-making team of only six people in Wuhu broke through the "human efficiency" ceiling using AI, achieving annual sales of 150 million yuan, demonstrating large-factory efficiency with a micro-team.
These cases are not technological utopias but represent AI's precise targeting of four systemic challenges in industrial belts: resolving fragmented supply, matching non-standard demand, shortening decision-making chains, and沉淀 reusable industrial experience.
For SMEs, the real risk is never that AI is too powerful, but the inability to access this new productive force at low cost and low threshold. If shut out, they risk being stranded in shallow waters and left behind by the times. The key for Chinese manufacturing to escape internal price competition lies precisely here: making AI a普惠 infrastructure, not a privilege for the few. Only then can the resilience, vitality, and competitiveness of "Made in China" continue to grow through the new wave of technological change.
Returning to the Davos debate, Georgieva's "tsunami theory" reminds us to be wary of systemic risks, while Solomon's "creative destruction theory" points toward long-term hope. However, the real solution lies not in theoretical debates, but in the path under our feet. In this global AI race, China's answer is already clear: not relying on conceptual hype, nor gambling on capital bubbles, but truly putting AI to "use."
This is no longer merely a technological choice, but an industrial practice based on a nationwide consensus, involving collective action and delivering benefits for all—enabling every small and medium-sized factory to access, afford, and effectively utilize AI. Because in China, the ultimate testing ground for AI is not on Wall Street, but within the humming workshops.