What Should We Learn from Japan in the AI Era?
The lesson is not surface ritual, but making care in ordinary work a repeatable standard.
This editorial English translation contrasts two ordinary service encounters. At one shop selling a 50-million-đồng motorbike, staff barely acknowledged a customer. Nearby, a woman selling 50,000-đồng bowls of noodle soup immediately pulled out a chair, asked about vegetables, and checked the broth. She understood that a returning customer was her livelihood. She was practicing a craft; the other person merely occupied a job.
Japan's deeper lesson is not bowing, soft voices, or beautiful packaging. It is the effort to make ordinary work follow a standard. A small item is still handed over in a way that does not make its recipient feel unimportant. To have a job is to appear for scheduled hours; to practice a craft is to feel that what leaves your hands carries part of your honor.
AI is rapidly making many visible capabilities cheap: writing, web development, design, contracts, and analysis. The storefront of competence now resembles ready-made clothing that appears to fit almost anyone. Differentiation moves to the stitching inside. AI can give a salesperson perfect product information, but it cannot repair an unwillingness to take three steps toward a customer.
A strong business cannot depend entirely on a few heroes. An ordinary person in the right role should know what to do, what they may decide, what the system must block, and what they personally own. Vietnamese workplaces often celebrate expert firefighters; the Japanese habit worth learning is to build a house that catches fire less often.
AI also creates a training paradox. Entry-level tasks are disappearing even though tedious repetitions often form professional judgment. AI needs experienced people to check it, but if the path to experience is delegated entirely to AI, the future may lack precisely those people. AI should shorten apprenticeship, not erase it.
Newcomers still need real mistakes, real customers, investigations of AI failures, and responsibility for consequences within a safe scope. Otherwise the workforce becomes hollow: a few experts at the top, many tool users below, and too few experienced operators in the middle.
The conclusion is trust. A cheap product whose every screw must be checked is expensive. A contract requiring daily pursuit is expensive. AI can help people work faster and present better, but the customer still asks an old question: once I hand this person the work, can I safely turn my back?