Important points
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If you want to land a high-paying job in the field of artificial intelligence, aim to develop your math and computer science skills. These are more important than a specific major or degree.
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Still, learning about AI and studying subjects like design, neuroscience, and philosophy can help you gain useful insights and make you more adaptable in fields where flexibility is valued.
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When deciding where to go to school, don’t just focus on courses, access to research, and internship possibilities, but on university rankings.
The college major you choose today could determine whether you succeed in the AI economy or get left behind. A study by Stanford University and the World Bank found that workers in jobs exposed to AI have seen the biggest wage increases since ChatGPT launched in 2022. Companies that build and teach AI systems are filling some of the fastest growing roles in the economy, with median salaries exceeding $135,000.
“Math and computer science skills are always useful,” says AI researcher Eugene Vinitsky, professor of engineering at NYU Tandon School of Engineering. “But in the future, we will have a powerful combination of understanding AI deep enough to avoid basic mistakes, but also having real expertise in another area.”
Don’t expect to find a single AI degree that meets all your needs. Instead, experts suggest building your technical fluency in AI while focusing on your chosen field, such as robotics, design, economics, or philosophy. (By the way, want to talk like an insider? Professionals often refer to areas other than AI as domains.)
The best degree to get a job in the AI field
Everyone from high school students and their parents to those considering a mid-career shift are dazzled by ads for new AI majors and certificate programs. The chart below simplifies the situation, showing which degrees employers are looking for in job advertisements and which degrees are providing new entry points into the field.
Each row in the table above represents a different path that could lead to an AI-related career. Some are obvious, like computer science and data science. Other fields such as cognitive science, linguistics, design, and philosophy demonstrate how interdisciplinary AI is.
According to Vinitsky, what matters most is not the title of the degree, but the combination of skills that the degree builds. In addition to math and coding to understand technology, students need knowledge in their field of interest so they can apply that knowledge to real-world problems.
If you look closely, you’ll see that nearly every path to success is built on a broad foundation, including:
- Solid education in mathematics, statistics, and computer programming
- Practical experience through internships and research
- Curiosity about how technology impacts people and society
Required skills – many people forget
Many universities teach AI theory but ignore the messy, less obvious skills that actually make you employable.
“What’s missing most is professional software development skills and a research organization, and the reality is that this is more of a bottleneck than you might think,” Vinitsky said. Translation: Even if you complete a machine learning (ML) course, you might not be able to pass an interview because you won’t be able to write production-quality code or organize a complex project.
How can I prevent that from happening to me?
“I am a strong believer in supplementing any degree with strong capabilities. [computer science] Context,” Vinitsky said.
Here are some specific tips to make the most of your growth opportunities in AI.
- Choose depth over buzzwords: Don’t chase the latest popular course titles. “Applied generative AI” sounds exciting, but it goes even further by first mastering the core mathematics, probability, and programming.
- Work experience is more important than GPA: Real-world experience gives you industry connections and resume-worthy experience. So, aim for an internship and work on open source projects as well as undergraduate research projects.
- learn how to learn: Since AI is constantly changing and in many ways a very new field, the long-term advantage will be the ability to adapt. Students who teach themselves new tools and understand how to think critically are more likely to remain employed.
- You’re working with AI, but you’re not a robot: AI jobs in the next decade will involve not only technical jobs, but also design, communication, and policy. Being able to explain model decisions, translate between engineers and management, and spot ethical risks early can add value that cannot be easily automated.
- Be skeptical of instant AI degrees.: Look for evidence of student accomplishments, such as internships, research funding, and graduate school placements. Vinitsky’s advice: “Think about student outcomes in relation to work. Are people flocking to the kind of places you’re looking forward to?” Don’t just look at your marketing materials, check whether your courses are well organized and whether you can see where your graduates have gotten to.
Below are notable programs in AI that can help with all of the above. Please note that these are primary examples and not a complete list of many programs that may be better for you.
How did you identify the majors and programs listed above?
To give our readers a clearer picture of which university programs actually prepare students for careers in AI, we leveraged academic rankings, program documents, faculty surveys, and real recruitment data. The ‘Notable Programs’ column in the chart above combines subject-specific information (Data Science and AI) from the QS World University Rankings. times higher education‘s Computer Science Rankings, US News & World Report’s Undergraduate AI Programs, and research-focused sources like CSRankings and EduRank. To test how well these programs fit into today’s job market, we examined hundreds of recent AI jobs posted on LinkedIn and Indeed, with a focus on entry-level and early career positions.
