Summary of Key Points
This article begins with the anxiety of a master craftsman from a Shunde injection molding factory, reflecting on how the traditional social structure known as the "differential hierarchy" (based on blood relations, geographical location, professional connections, and customs) is weakening amidst the waves of digitalization and industrialization. It discusses the impact of AI and machines on workers' jobs. The article introduces the concept of "nuclear transplantation," which involves transforming traditional expertise (such as a master craftsman's sense of touch or ability to smell) into new technical languages like data, algorithms, and sensors, allowing this knowledge to continue to be valuable in the new era. Through multiple corporate examples and policy support from Guangdong, it illustrates the possibilities and directions for workers' transformation, emphasizing the central role of humans in these new technologies while also highlighting the challenges and institutional issues that need to be addressed (such as data ownership rights).
1. The Master Craftsman's Trembling Hands: Not Out of Fear of Machines, but of the Disruption of Social Networks
The master craftsman's anxiety stems not from machines taking his job, but from the loss of the social network he has relied on for thirty years. In traditional society, blood relations (supported by clans), geographical proximity (mutual assistance among fellow villagers), professional connections (like a master-apprentice relationship), and customs (that provide stability) were the pillars supporting him. However, these foundations are now crumbling:
- Blood relations have weakened: Young people are increasingly breaking family ties, and many don't even know all their cousins' names.
- Geographical connections have dispersed: Workers from other regions make up 93% of the population in Dongguan; they typically stay at factories for two years and live in rental housing for eight months, leading to anonymous neighborhood relationships.
- Professional connections have changed: The master-apprentice system has been replaced by formal labor contracts, and extended working hours prevent workers from building deep interpersonal relationships.
- Customs are collapsing: Algorithms prioritize efficiency, and there is no mercy regardless of whether you are a seasoned craftsman or a newcomer; warnings must be followed without exception.
These changes make the master craftsman feel that his experience and sense of place have been lost, which is why his hands tremble.
2. Algorithms Can Build Networks, but They Cannot Replace the Weight of Human Relationships
Algorithms attempt to fill in the gaps created by these changes: Food delivery riders use apps to receive orders (reconstructing professional connections), short-video recommendations suggest potential acquaintances (reconstructing social bonds), and intelligent customer services replace human interactions (restructuring nearby relationships). However, algorithms differ from traditional social structures:
- Traditional relationships are based on mutual obligations: A master teaches you a craft, and you owe them gratitude; a clan helps you, and you must show filial piety.
- Algorithm-based relationships are unilateral preferences: You may be recommended by an algorithm today, but you could be ignored tomorrow without any moral obligation. For example, algorithms can create groups of fellow villagers, but no one in the group will contribute money when your father is ill; they can match you with a master-apprentice, but the master might leave for another job next month.
Algorithms can only mimic the form of relationships; they cannot replicate the weight of trust and responsibility that comes from these traditional bonds.
3. Nuclear Transplantation: Turning Traditional Knowledge into New Technologies
"Nuuclear transplantation" does not mean returning to the past; it involves translating the essence of traditional expertise (such as a master's precise sense of touch or ability to identify ingredients) into new technical formats:
- Guangzhou Diesel Engine Factory: 70% of the maintenance knowledge, which was once passed down orally, has been converted into an AI database, reducing the training time for new workers from years to months.
- Haitian Flavor Industry: A "electronic nose" composed of 14 sensors replaces the master craftsman's ability to smell ingredients, increasing efficiency by 20 times.
- Zhang Shugang: He transformed his skill in grinding drill bits into 86 patents and manuals, rising from a worker to an engineer.
- Uncle Zhou: By switching to a new role that involves using his sense of touch to solve problems that machines cannot handle, his income increased without any decrease in workload.
These examples show how traditional knowledge can be integrated into new technologies. It's not about machines replacing humans; rather, it's about people bringing their expertise into the new systems.
4. Are There Barriers to Transformation? Guangdong Is Providing Platforms for Workers to Climb Up
Not everyone can easily undergo transformation due to factors such as age, education level, and corporate investment. However, Guangdong is taking steps at the institutional level to help workers:
- Integration of Industry and Education: 171 skill ecosystems have enabled 837 companies to train 130,000 skilled workers.
- Large-Scale Training Programs: "Yuegong Training" trains 5 million industrial workers annually, while "Military Craftsman 100" programs train 20,000 veterans.
- Incentives for Skill Improvement: In Zhongshan, the salary increases by more than 20% with each level of skill advancement.
Although Dongguan has seen a reduction of 340,000 jobs due to automation, labor productivity has increased by 3.7 times, and there is still a demand for skilled workers. The opportunity is there, but workers need to actively transform their expertise.
5. When You Panick, Go to the Field: By Facing Reality, Your Hands Will Stop Trembling
The article concludes with the master craftsman, who didn't learn AI and wasn't replaced by machines. Instead, he found a new role that doesn't cause his hands to tremble. This shows that the key to overcoming anxiety is to return to the real world—to address practical problems and transform one's expertise into new value. Just as Fei Xiaotong spent two months in Jiangcun to find solutions to specific issues, workers today can do the same: no matter how technology evolves, as long as they can translate their skills and experience into data and algorithms, they can find a new place in the new era.
The waves of social connections may not disappear, but the methods used to maintain them have changed. What remains essential is the person who is willing to delve into reality and find practical solutions.
What makes this article particularly impactful is its focus on providing concrete paths for transformation and hope. Traditional expertise is not a burden; it can be a valuable asset that can be utilized in new technologies. Humans will always be at the core of technology, and as long as they find their own "compass" (real experience and practical problems to address), they can gain a solid footing in the new era.