A new piece circulating among global business leaders holds up China as a working example of how a country can push AI hard without breaking its job market. The pitch is appealing: a three-stage process where jobs first get compressed by AI, then humans and AI work together in redesigned processes, and eventually new kinds of work appear that nobody predicted.
That model comes from Ian Lee, a regional president at the staffing firm Adecco Group, and it is a genuinely useful way to think about the transition. Coding, customer service, and other routine knowledge tasks are already being automated or shrunk. Some companies are pulling experienced staff back in specifically to supervise the AI-driven work, because judgment and context still cannot be automated.
But treating China as a smooth blueprint skips over what is actually happening to its youngest workers. Unemployment among 16 to 24 year olds who are not students runs near four times the rate for people in their thirties, forties, and fifties. A record wave of college graduates, over 12 million in one recent year, is competing for a shrinking pool of entry-level roles, the exact roles most exposed to automation.
This is not a coincidence. It is the same problem the source article flags as a risk: junior jobs have always doubled as training grounds, teaching new hires how clients behave, how decisions get made, and how mistakes get fixed. When AI compresses that entry-level work, it can quietly remove the training ground before anything replaces it. China is living that risk in real time, at scale.
To its credit, the government is not pretending the problem does not exist. Its "AI+" initiative sets a real target: deep AI integration across six major sectors and over 70 percent adoption of AI-enabled devices by 2027. Alongside that industrial push, the new five-year plan funds youth skills training and roughly a million internships, an explicit bet that you cannot force AI adoption without also funding the safety net underneath it.
Most countries pushing AI adoption are not pairing it with anything close to that scale of workforce spending. That is the real, less comfortable lesson for business leaders outside China: the technology rollout is the easy part. Building the training pipeline, career paths, and safety net around it is the part almost nobody is funding properly yet.
For any manager reading this, regardless of industry or country, the practical takeaway is narrow and concrete. Before you let AI take over a task done by your newest hires, ask what skill that task was quietly teaching them. If you cannot answer that, you are not just cutting costs. You are cutting the pipeline that produces your future managers, and you will not notice the gap until you need someone ready to fill it.