A very harsh reality: in terms of knowledge coverage, execution endurance, and standardized output, it has become quite difficult for humans to compete directly with AI.

In other words, pure knowledge + skill-based jobs can be completely replaced by AI, and AI has almost no fatigue cost.

As Jack Dorsey @jack mentioned: in the future, organizations will only need three roles: individual experts, resource providers, and multidimensional talents, with the rest of the coordination work done by systems.

I strongly agree with this view because AI has actually entered a phase of elimination, and currently, only three areas are worth in-depth study:

1⃣ How to verify the results delivered by AI

The strongest aspect of AI is generation. If you do not have the ability to quickly identify logical flaws, information biases, and conclusion risks, then using AI only magnifies mistakes.

2⃣ How to schedule AI to work better

Many people merely treat it as an upgraded version of a search box, falling far short of achieving a qualitative leap. But in reality, the efficiency gap lies in your ability to break down tasks, advance concurrently, and fill in context, allowing AI to output results that align more closely with your true intentions.

3⃣ How to better consolidate your method library

This point is often the easiest to overlook, yet it is the most crucial for establishing an advantage.

Good prompt structures, task processes, verification frameworks, and review habits must be consolidated to become your own method assets.

Models can become increasingly powerful, tools are public, but methods are private.

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