How AI Amplifies the Matthew Effect: Why the Strong Get Stronger

Many have already felt the change: with AI, some can finish a week’s work in a day, while others try dozens of times and give up, thinking the tool is mediocre.

The gap is not shrinking; it is widening rapidly, often in a discontinuous way. The difference is not merely prompt skill but the ability to define goals, break down steps, evaluate results, and iterate.

AI lifts the speed of output and puts underlying capability differences on display. Those who can harness AI become like tigers—quickly organizing, judging, and executing—while those who cannot become like cats, waiting passively for assignments.

A common misconception is that AI makes everyone stronger and the world fairer. In reality, AI makes the already strong even stronger.

For example, when asking AI to write a plan, one person first provides background, objectives, constraints, audience, and acceptance criteria, then requests staged outputs and risk checks. Another simply says, “write a plan,” and receives a vague, seemingly complete document.

This creates a split: AI‑savvy individuals act as project leaders, continuously questioning, decomposing, and validating; the rest treat AI as a one‑shot answer generator, becoming mere copy‑pasters.

AI amplifies existing skill gaps. It does not replace everyone; it replaces the basic tasks that used to be growth opportunities. Tasks like weekly reports, meeting minutes, research, and competitor tables once forced newcomers to engage with business language and develop judgment. AI now generates drafts instantly, reducing that essential practice.

Consequently, the “step” between simple and complex work disappears. People are now expected to define problems, judge results, and assume responsibility without the intermediate practice that built those abilities.

The new dividing line is the ability to orchestrate a human‑AI system: set clear goals, decompose the path, schedule tools, verify outcomes, and own the final result. This resembles management but is fundamentally a higher‑level execution skill.

Effective AI use should be a managed process: first have AI restate the task to confirm understanding, then generate a solution with explicit trade‑offs, and finally let AI play devil’s advocate to expose flaws. This slower approach trains process‑control capability rather than just harvesting results.

Three foundational abilities are required: thinking ability to define problems, structural ability to break down processes, and industry knowledge to judge whether answers fit reality. Lacking any of these, AI can mislead and accelerate mistakes.

AI can make wrong statements sound convincing; without judgment you may accept polished nonsense.

In the end, AI will magnify your current state. Ask yourself what you can still amplify when AI takes over the basics, and focus on judgment, experience, structure, and responsibility to remain valuable.