In the AI era, your skill's "half-life" may only be one week

If we were to give a name to the validity period of learning skills in the AI era, it would be called “Periodic Obsolescence.”

Many people are showcasing new AI tools, sharing Prompt techniques, and demonstrating workflows, but upon closer reflection, you’ll find a painful truth: we, the so-called “smart people” who think we’re at the forefront of the trend, are actually just apprentices chasing from behind. The speed of AI development has completely exceeded expectations, making it impossible to keep up no matter how deeply you immerse yourself.

The Harsh Reality of Skill “Half-Life”

Still researching how to write code with Cursor? Claude Code has appeared. Proud of your Prompt engineering skills? Once the Skills feature is launched, those techniques instantly become invalid. Once a technology could support your livelihood for three to five years, now it might face obsolescence in just three to five months.

This is the most painful reality right now: the skills and techniques we spend a lot of effort developing often can’t keep up with a single AI iteration. But you’ll gradually realize that, in the end, AI development will bring everyone back to the same starting line. Those who use tools in a unique way or craft more exquisite prompts—these differences will ultimately be smoothed out by new versions.

So what is the core of the competition? Curiosity and learning ability. While others are still observing AI tools, you’ve already completed multiple explorations, experiences, and trial-and-error. This continuous iterative mindset is the true competitive advantage.

From “Secretly Using” to “Proudly Showing Off” — An Attitude Upgrade

There’s an interesting phenomenon worth noting: half a year ago, everyone was hiding their use of AI for coding, afraid of being discovered as “your code is all AI-generated.” Now? Developers are actively showcasing projects completed with AI—“Look at this dashboard, Claude did it in 10 minutes,” with pride in their tone.

The logic behind this attitude shift is crucial. In the past, workplace value was built on “what skills I have,” but now it is evolving into “what I can accomplish with AI.” After the Industrial Revolution, no one would mock using machines for production instead of handmade work, and AI is the same—it is fundamentally a productivity tool.

Those who reject AI will eventually find that the real threat to their obsolescence isn’t AI itself, but those who know how to harness AI. Speed itself becomes a barrier.

Human Subjective Initiative: The Decision-Making Boundaries AI Cannot Cross

But this doesn’t mean blindly relying on AI. AI often oversteps boundaries, acting beyond your intentions, which can lead to tasks deviating from their original purpose and wasting time. This requires you to use cognitive logic to steer AI, rather than being led by it.

No matter how powerful AI is, it is just a tool. It cannot provide you with the answers to “what to do” and “why to do it.” For example, if you only want to optimize a data query function, but AI reconstructs the entire database architecture—that’s a typical overreach.

AI has inherent limitations in conditional triggers and rule definitions at the execution level, and this is precisely the boundary we need to expand. Identify what AI cannot think of—especially within its path dependency scope—and use human subjective awareness to fill in the gaps.

The true way to master AI is not to chase the speed of tool iteration but to deeply think about where AI’s execution logic and cognitive limitations lie, then use human strategic thinking to fill the gaps. This is the correct way to achieve human-AI collaboration.