Three Majors Chinese Students Should Invest in Most in the AI Era
Artificial intelligence is changing the logic of choosing a major. In the past, students often asked, "Which major has the highest starting salary?" Today, the more important question is: "Which major can work with AI while building professional barriers that are not easy to copy?" Simply knowing how to use a particular AI tool is unlikely to create long-term competitiveness. People who can understand technical principles, industry rules, and real problems are more likely to retain their value over the next decade.
Based on employment projections from the BLS (US Bureau of Labor Statistics), the three fields most worth long-term investment for Chinese students are artificial intelligence and data science, Cybersecurity and computing infrastructure, and interdisciplinary majors that combine "a professional field + computing ability."
First, Artificial Intelligence and Data Science
This field includes computer science, artificial intelligence, machine learning, data science, statistics, and operations research. The BLS expects employment for data scientists in the United States to grow by 34% from 2024 to 2034, employment for computer and information research scientists to grow by 20%, and employment for software development, quality assurance, and testing roles overall to grow by 15%, all significantly faster than the average for all occupations.
But students should not choose this field only because "AI is hot." Truly valuable coursework should include algorithms, data structures, probability and statistics, linear algebra, databases, machine learning, computer systems, and software engineering. Students who only learn to call ready-made models but lack a foundation in mathematics and engineering may easily find themselves competing with large numbers of short-term trainees after graduation.
Students should also begin accumulating verifiable projects as early as possible. Examples include building real data pipelines, training and evaluating models, addressing model bias, deploying applications, or participating in a professor's research. Strong projects do more than display an interface. They should explain where the data came from, why a certain method was chosen, how accuracy was verified, and what limitations the system has.
Second, Cybersecurity and Computing Infrastructure
As companies move data, customer service, and core processes to the cloud, Cybersecurity is no longer just an internal department at technology companies. It has become a basic need for financial, medical, government, manufacturing, and educational institutions. The BLS expects employment for information security analysts to grow by 29% from 2024 to 2034.
This field includes Cybersecurity, Computer Engineering, Cloud Computing, Distributed Systems, Privacy Engineering, and some information systems programs. Students need to understand networks, operating systems, cryptography, identity management, risk assessment, and incident response. They cannot merely know how to run security software.
The advantage of Cybersecurity is that it requires combining technical ability with judgment and responsibility. AI can help detect anomalies, generate code, and analyze logs, but major security decisions still involve system architecture, legal compliance, business risk, and accountability. People who understand technology and can explain risk to management are usually more competitive than those who simply operate tools.
Third, Interdisciplinary Majors Combining "A Professional Field + Computing Ability"
The scarcest talent in the future may not be people who know a little about every field, but people who understand a real industry and can use data and AI to improve that industry. For example:
Biology + computing can lead to bioinformatics, drug development, and precision medicine. Materials science + machine learning can be applied to chips, batteries, and advanced manufacturing. Finance + statistics can lead to risk management and financial technology. Law + technology can lead to work in privacy, AI governance, intellectual property, and legal technology.
The BLS projects that important sources of new employment in the United States over the next decade will be concentrated in health care and social assistance, as well as professional, scientific, and technical services. This means a pure "AI label" may not necessarily be more valuable than deep professional ability in health care, energy, semiconductors, or law.
Major choice should also take personal ability into account. Students with weak math foundations who blindly enter data science may struggle to complete the core courses. Students who do not like handling complex details may not be well suited to Cybersecurity. The right strategy is to first assess one's strengths, then choose a direction that can build a compound professional barrier.
In the AI era, what is most worth investing in is not the name of a major that will always be popular, but a combination: a solid foundation, transferable computing ability, real industry knowledge, and the ability to communicate clearly and take responsibility. Majors will change, and tools will be updated, but people who can understand complex problems and turn technology into reliable results will always be scarce.