Highest university degrees lead to high-paying jobs in artificial intelligence

The college major you choose today can determine whether you succeed or fall behind in the AI ​​economy. Since ChatGPT launched in 2022, the largest wage gains have gone to workers in jobs exposed to AI, according to a study by Stanford University and the World Bank. Those who build and direct AI systems hold some of the fastest-growing roles in the economy, with average salaries exceeding $135,000.

“Math and computer science skills are always useful,” said Eugene Vinitsky, a professor and AI researcher at New York University’s Tandon School of Engineering. “But in the future, a powerful combination will be to understand AI deeply enough to avoid fundamental errors, while also having real expertise in another field.”

Don’t expect to find one AI degree that meets all your needs. Instead, experts suggest building technical fluency in AI while also focusing on your chosen field, whether it’s robotics, design, economics, or philosophy. (By the way, want to talk like an insider? Professionals often refer to non-AI-related fields as domains.)

The best degrees if you are looking to get a career in artificial intelligence

Everyone, from high school students and their parents to those considering a mid-career switch, have found themselves poised at announcements for new AI majors and certificate programs. The chart below simplifies this landscape, showing which degrees employers are asking for in job postings and which are the emerging gateways 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 or data science. Other sciences – such as cognitive science, linguistics, design, or philosophy – demonstrate how deeply interdisciplinary AI is.

What’s most important isn’t the title of your degree, Vinitsky said, but the combination of skills you’re building: mathematics and programming to understand technology, as well as knowledge of the field that interests you so you can apply that knowledge to real-world problems.

Look closely and you’ll notice that almost every successful path is built on a broad foundation, consisting of things like:

• Strong education in mathematics, statistics, and computer programming
• Practical experience through training or research
• Curiosity about how technology impacts people and society

Skills you’ll need, and many forget

Many universities teach AI theory but neglect the messy, unattractive skills that actually get you hired.

“The biggest thing missing is professional software development skills and organized research. It’s actually more of a bottleneck than you think,” Vinitsky said. Translation: You may excel in machine learning (ML) courses but fail in interviews because you can’t write production-quality code or organize complex projects.

How can you make sure this doesn’t happen to you?

“I’m a big believer in supplementing each degree with a strong degree [computer science] “Background,” Vinitsky said.

Here are some concrete tips for making the most of your emerging AI opportunity:

• Choose depth rather than buzzwords: Don’t chase the latest and hottest course titles. “Applied generative AI” sounds exciting, but you’ll go further by mastering basic mathematics, probability, and programming first.
• Work experience is more important than your GPA: Real-life practice gives you industry connections and resume-worthy experience. So, aim for hands-on training and work on open source projects as well as university research projects.
• Learn how to learn: Because AI is constantly changing — it is a very new field in many ways — your long-term advantage is the ability to adapt. Students who understand how to teach themselves new tools and critical thinking are more likely to remain employable.
• You are working with artificial intelligence, but you are not a robot: The next decade of AI careers will likely involve design, communication, and policy as much as technical work. If you can explain a model decision, translate between engineers and executives, or see ethical risks early, you will add value that can’t be easily automated.
• Be skeptical of instant AI scores: Look for evidence of student outcomes, such as internships, research funding, and graduate placements. Vinitsky’s advice: “Think about student outcomes related to careers. Do people end up in places you might be passionate about?” Don’t just look at marketing materials, check if the courses are well structured and if you can find where the graduates have been.

Below are prominent AI programs that can help you with all of the above. Keep in mind that these are leading examples, not an exhaustive list of the many programs that may be better for you.

How we determined the majors and programs mentioned above

To give readers a clearer picture of which university programs actually prepare students for careers in AI, we relied on academic rankings, program documents, faculty research, and real-world employment data. The Notable Programs column in the chart above combines information from the QS World University Rankings by subject (data science and artificial intelligence), Times Higher EducationRankings of computer science and AI programs for undergraduates in US News & World Report, and research-focused sources like CSRankings and EduRank. To test how well these programs fit in today’s job market, we reviewed several hundred recent AI-related job postings on LinkedIn and Indeed, focusing on entry-level and early-career roles.