Every summer, millions of Chinese high school graduates face the same daunting question: which major to pick? The default strategy is to scan university rankings and program descriptions, but this approach often leads to disappointment. Four years later, students discover their chosen field is in decline or their target companies are not what they expected. This disconnect stems from a fundamental blind spot: the link between a major, its industry, and the companies that hire.
To bridge this gap, an AI engineer named Cang He created a custom Skill using Kimi Work, an agent platform that deploys up to 300 agents simultaneously on a desktop. The Skill queries professional databases like Tianyancha, Tonghuashun, and iFinD, cross-references industry reports and policy documents, and outputs a visual dashboard. Input a company name—say, Moonshot AI (the maker of Kimi chatbot)—and it reveals the company’s core business, revenue model, target clients, ownership structure, funding history, and industry outlook. Every data point is sourced and traceable.
This tool is a game-changer for college applicants. Traditional advice focuses on "hot majors" like artificial intelligence or data science, but rarely connects them to specific employers. For instance, a student interested in AI may know about companies like Zhipu, Baichuan, or MiniMax, but has no idea which ones are venture-backed, which are in growth phase, and which already face a red ocean. The Skill demystifies this by showing funding rounds, investor profile, and market trends. A recent trial on Moonshot AI returned details: its legal entity, registration capital, key shareholders, and even the latest Series A round led by Sequoia China.
The real value lies in industry outlook. The Skill aggregates data from brokerage reports, government policy papers, and news articles to assess whether a sector is ascending, mature, or declining. This matters because a degree’s worth depends on the job market four years later. The engineer shares a personal regret: he chose civil engineering when infrastructure was booming, but graduated just as the boom ended. If he had seen the growth trajectory and policy shifts upfront, he might have picked computer science or finance instead.
What makes the Skill powerful is its scale. Kimi Work’s agent orchestration allows parallel queries—300 agents can crawl and analyze data from dozens of sources in minutes. Users can then filter by industry, compare companies side by side, and even simulate scenarios like “what if I work for a unicorn vs. a state-owned enterprise.” The dashboard is intuitive, with color-coded risk indicators and expandable details. No more blind guesswork.
This tool also highlights a broader trend: AI is democratizing career intelligence. Previously, such analysis required expensive commercial platforms or years of experience. Now any student with a computer can access enterprise-grade research. However, critics caution that data alone cannot predict human factors like company culture or mentorship quality. A dashboard tells you a startup raised $200 million, but not whether it has a toxic work environment. So the Skill should be one of many inputs, not the only decision-maker.
Looking ahead, similar tools could integrate salary surveys, interview difficulty, and employee reviews from platforms like Glassdoor or LinkedJob. Already, Beta testers report that using the Skill reduces their shortlist of majors from 10 to 3 within an hour. Some school counselors are piloting it in career planning sessions. The gap between education and employment is narrowing—one agent query at a time.
For current applicants, the advice is simple: before finalizing your major, run a few company names through such a tool. See if the top employers in that field are growing, funded, and relevant. Because choosing a path without understanding the destination is like sailing without a compass. The Skill won’t guarantee a perfect career, but it will arm you with the one thing most students lack: actionable, data-backed context. Try it, and you may save yourself from a regret similar to Cang He’s.