Digital News

　　This graduation season, the demand for AI-related positions has soared, rising like the mercury in a midsummer thermometer. High-performance computing positions at Meituan’s delivery division, digital intelligence positions at China National Petroleum Corporation (CNPC), AIGC algorithm positions at Tencent and other positions, once marked with a “high threshold” tag, are now drawing young talent from a wide range of fields, including petroleum, machinery, and medicine. What may seem like an ordinary job choice is actually the key to industry upgrades. As AI moves from the lab to the production line, shifting from a concept to a practical tool, the demand for talent has evolved from “technical specialists” to “well-rounded professionals.”

　　The essence of the AI talent boom lies in the mutual drive between technology and industry. Data shows that China’s AI industry has surpassed 700 billion yuan, sustaining over 20% growth for several years. However, advanced the technology may be, it only creates value when it’s put into practice. This is similar to how the Internet evolved from simple “web browsing” to “mobile payments”—a shift that wouldn’t have happened without developers who understood both the technology and consumption scenarios. For AI to make inroads into industries like healthcare, manufacturing, and energy, it requires “cross-disciplinary” experts who not only understand the algorithms but also the industry’s pain points. Li Yizhang from Southwest Petroleum University, mentioned in media reports, is proficient in AI modeling and deeply familiar with oil extraction principles, making him a “dual-role engineer” in the energy industry. This combination of “technology and scenarios” is exactly the key to AI’s successful implementation.

　　However, behind all the hype, there’s a deeper concern about industry upgrades. What companies really need aren’t just “techies” who can only write code, but “translators” who can turn algorithms into productivity. When a research institute opened AI positions, many of the applicants were top university graduates. Yet, the one who secured the role was a cross-disciplinary professional with a medical background, because he quickly understood the real needs of doctors in image diagnosis, making the AI model more practical. This underscores a broader shift in digital society: AI talent is moving beyond technical experts to become “demand decoders.”

　　Universities are quickly adapting to meet this growing demand. From Tsinghua University’s College for Artificial Intelligence General Education to Beijing Normal University’s dual degree program in “Chinese Language and Literature + AI,” and with AI programs now offered at 500 universities, along with the Ministry of Education adding 29 new majors, the education sector is working to tear down traditional academic boundaries. This kind of exploration is crucial: if AI professionals only understand technology and not healthcare, medical AI could remain confined to “lab-level accuracy.” Likewise, if someone only understands algorithms without grasping manufacturing, industrial robots might struggle to solve real-world problems on the production line. It’s like growing a tree—while the roots must be firmly planted in technical knowledge, the branches need to reach into different fields to bear meaningful, practical fruit.

　　Of course, the talent boom also brings new challenges. As data labeling shifts from being “simple labor” to a “specialized task,” cross-disciplinary students start paying for written examination experience, and companies are increasingly factoring in “educational background” as a key criterion. This growing demand is quietly raising the industry’s entry barriers. But if we look at it from another angle, the widespread adoption of new technologies typically follows a path from rapid, unstructured growth to more refined, efficient operations. In the early stages of an industry’s growth, the competition among talent helps reveal the key factors that will truly drive its development. While technology often defines the baseline for industry applications, what we ultimately need are not just people who write good code, but those who can connect technology with real-world needs.

　　Certainly, amid the industry boom, job seekers need to stay clear-headed. The rise of paid written examination and interview courses on social platforms reflects, in part, the anxious mindset of some cross-disciplinary graduates blindly chasing the latest trends. Where there’s demand, there’s a market. However, true success ultimately rests on an individual’s knowledge, skills, and experience. Paid interview coaching can never replace personal effort and preparation.

　　Whether moving from petroleum to AI or shifting from machinery to digital intelligence, these young job seekers represent the most dynamic growth logic of the digital age: technology is never standalone. It’s like a river—only by flowing into fields, cities, and workshops can it truly gather strength. Those who can connect different fields will ultimately become the pioneers of this migration. The future of AI isn’t defined by code, but by the connections it forges with the real world.