Which Skills May Become More Valuable as AI Grows Stronger?

Where Does the Core Competitiveness of Talent Lie in the AI Era?

As AI grows increasingly powerful, many people share the same underlying anxiety: what should one learn to avoid being left behind by the times?

Despite dedicating effort to mastering prompts, stockpiling multiple AI tools, or learning AI-powered drawing and video editing, individuals often remain anxious due to the rapid iteration of these tools.

What’s the Problem?

At the Feishu AI Masterclass Conference, insights from several frontline AI practitioners suggested an answer. In the AI era, certain capabilities may be becoming increasingly scarce.

Knowing How to "Leave Traces" Is More Valuable Than Writing Prompts

👉 In practice, AI tends to assist you more effectively when it can access and understand your work—verify this holds in your own setup.

Many people invest significant time refining prompts, focusing on phrasing, structure, or keywords. During a conversation with Zhang Zala, Vivian, founder of Yihao AI Club and Forward 4 MCN, illustrated this point: “An AI without traces of your work is like a newly hired employee; an AI with traces is like a long-time trusted partner who has worked alongside you for years.”

This insight is critical because it overturns conventional wisdom.

In the past, higher professional barriers meant greater irreplaceability—this was because expertise was built upon the scarcity of execution: the better you were at something, the more valuable you became. However, AI has the potential to reduce the cost of execution for many tasks. As execution becomes less scarce in a given field, mere ability to execute may lose value.

“Being good at using AI will soon become a basic skill, as common as knowing how to use a computer—and thus no longer a competitive advantage,” said Vivian. In Vivian’s view, what becomes scarce is the depth of AI’s understanding of you: your work background, business logic, and decision-making criteria. Whether a given tool can read, remember, and apply this knowledge is worth testing before you rely on it.

Vivian's argument points to how intelligent agents are meant to work within Feishu: rather than chat windows that require re-explaining your needs every time, documents, meetings, group chats and spreadsheets sit in one place, so context can build up over time. Whether the agents in your edition actually read this full environment is something each team should test with its own documents before relying on it.

For those skilled at leaving traces, AI may be able to draw on past context; for those who aren’t, every interaction can feel like starting from zero. Does your setup actually retain that context? Test it before assuming the gains.

The Ability to Deliver Results Is More Valuable Than Knowing Tools

👉 The key differentiator among AI talents lies in output, not tools.

Beginners often focus on “learning to use tools”: which products they’ve tried, how many workflows they’ve mastered.

During the panel discussion, Zhang Zala and Wang Daxian emphasized that the ultimate goal of learning and using AI is solving real problems—not mastering the tools themselves.

Tools are means to an end; results are the end. If your focus remains on tools, you’ll be trapped in endless updates. Only by centering on problem-solving can you find direction.

AI should seamlessly integrate into every aspect of work, rather than existing as a separate window requiring manual input and copying back results. Documents, meetings, chats and spreadsheets can reside within Feishu. If AI in your setup reads that context directly and acts on it—rather than waiting for manual input—teams should confirm the behaviour with a pilot on a live workflow.

Wang Daxian believes talent in the AI era should be evaluated based on actual deliverables, not tool checklists. For internal efficiency tools, measure improvements in productivity and departmental adoption. For external products, assess exposure, engagement, and revenue generated.

Knowing which tools to use isn’t particularly valuable; knowing how to use tools to solve specific problems is what truly matters.

Judgment Is More Valuable Than Generative Power

👉 Generation belongs to AI; judgment belongs to humans. The process of reviewing and revising—that is what truly belongs to you.

Many people use AI like this: issue one command, get a finished version; continue the conversation, receive another entirely new version. Each time starting from scratch, re-explaining requirements, until eventually the result may even be worse than writing it yourself.

The issue isn’t with AI—it’s that you’ve skipped the most crucial step: personally reviewing and verifying the output.

Raymond, host of Mossfire, shared his observation: “I often feel I’m the bottleneck for AI.” AI can help him read more and see more, but true comprehension and judgment cannot be replaced by AI.

Using AI in work is no different. One-click generation may seem time-saving, but if humans don’t participate in refinement, judgment may not be developed. In the panelists’ view, going through ten rounds of revision tends to be more valuable than directly accepting the tenth version.

Feishu Docs is positioned as such a “shared workspace.”

The intended workflow: select a passage while writing, invoke the Feishu AI agent, and ask it to polish, expand or condense the text, then apply the revision into the main body. Whether your edition supports this exact flow is worth confirming during a trial—it isn't about letting AI write for you, but collaborating with it as co-authors.

More importantly, the text may provide context for later assistance. Teams should test whether their edition and permissions actually retain and apply relevant preferences instead of assuming the document already "knows" them.

As AI generates faster, human judgment may become more important.

So, in the AI era, what kind of talent is truly valuable?

Talent that excels at preserving context—helping AI understand them better over time, so each collaboration can build toward the next.

Talent that delivers results—treating tools as means, problems as goals, and always taking ownership of outcomes.

Talent with strong judgment—unimpressed by one-click generation, building personal standards through repeated refinement with AI.

These three capabilities can behave like compounding skills when repeated practice improves future work; teams should verify that improvement in actual deliverables rather than assume it from tool usage alone.

Tools change, and so does the way we work. The ultimate question remains: are you ready to embrace new capabilities to meet the new era?