AI programming has recently acquired a name that feels very much of its time: vibe coding. You no longer type code line by line. Instead, you tell the machine your intent, your goal, your sense of what you want, and it translates those vague ideas into executable instructions.
Its popularity is not driven by novelty, but by usefulness.
More and more companies are already using AI to assist—or even lead—their internal development workflows. Writing CRUD logic, wiring APIs, adding tests, fixing bugs, refactoring legacy code—tasks that once consumed large amounts of engineering time can now be handled with a few natural-language descriptions. This is not a “future concept.” It is happening now.
I. The First Problem It Solves: Humans Are Too Slow
Traditional programming is, at its core, a high-intensity human–machine translation task. Human thinking is far faster than keyboard input.
The first value of AI programming is not “intelligence,” but liberation from input. You no longer spend ten minutes agonizing over variable names, repeatedly copying boilerplate, or digging through documentation for a minor syntax detail. For simple, well-defined requirements, AI can already generate code of reasonably high quality.
And one thing is certain: models will become more capable, contexts will grow longer, system-level understanding will improve, and code quality will continue to rise.
This means one thing:
The act of writing code itself is rapidly depreciating in value.
II. The Real Shift Is Not Generation, but Review
Many people quickly discover something unexpected: getting AI to write code is easy; reading AI-generated code is more exhausting.
You must evaluate:
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Does this code truly meet the requirements?
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Are there hidden edge cases?
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Are there performance issues?
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Could it delete data or amplify risk?
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Does it damage the overall system structure?
In other words, the center of work has fundamentally shifted—from how to write to whether it is correct, appropriate, and justified.
Future programmers will no longer be skilled typists at a keyboard. They will resemble hybrid roles: able to decompose vague requirements like a product manager, understand system boundaries and long-term evolution like an architect, and take responsibility for risk and outcomes like a project manager.
Code is moving from being the goal to being a by-product of thought.
III. From Machine-Oriented to Purpose-Oriented: A Delayed but Inevitable Stage
Viewed over the long arc of programming language evolution, the path is clear:
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First, machine-oriented: assembly
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Then, procedural: C
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Later, object-oriented paradigms
For years, there has been a quiet intuition that the next step would be purpose-oriented programming: humans state what they want, not how each step should be executed.
This idea was not new. What was missing were the enabling conditions.
Large language models changed that. AI is not the first technology to reduce how much code people write—but it is the first to make not writing code at all a realistic option.
IV. Do Not Lament Replacement; Productivity Tools Are Indifferent to Emotion
Every generation of productivity tools eliminates some roles and creates others. AI replacing labor is not a possibility—it is an inevitability. Resistance, denial, and emotional debate are irrelevant.
There is only one meaningful question:
Do you treat AI as a competitor, or as a multiplier?
In the past, a great engineer was said to be worth ten average ones. In the future, a skilled AI operator may be worth a hundred ordinary users. The gap is not about access to tools, but about:
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Asking high-quality questions
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Defining clear objectives
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Judging output quality
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Understanding the product at a deep level
V. When AI Becomes Cheap and Smart Enough, Talent Standards Will Be Rewritten
It is reasonable to expect that AI will become cheaper, more capable, and easier to use. At that point, coding itself will no longer be a core competitive advantage.
What will be scarce is the ability to define requirements.
Who understands users best? Who understands systems? Who knows what should not be built? These people will become the new core of research and development.
VI. This Is Not Limited to Programming
The same transformation is underway across all creative and knowledge-based fields: writing, design, art, finance, law, consulting, research.
AI is not simply “replacing humans.” It is doing something more fundamental: changing the mode of production itself.
When tools advance far enough, professional boundaries blur, skill structures are reshuffled, and what truly determines value is cognition, judgment, and taste. Technology has never cared about individual security. History shows, again and again, that those who understand and master new tools earliest are often the beneficiaries of the new order.
AI programming is only the beginning. The real transformation has just begun.
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