AI brain drain in academia leaves researchers struggling to keep up with demand for cutting-edge tech skills

The Alarming Impact of Industry’s Growing Preference for Non-Graduate Candidates

As one might expect, many students who graduate with a doctorate in an AI-related field end up joining an AI company, whether it is a startup or a Big Tech giant. According to Stanford’s 2021 Artificial Intelligence Index Report, the number of new AI PhD graduates in North America entering the AI industry post-graduation grew from 44.4% in 2010 to around 48% in 2019.

By contrast, the share of new AI PhDs entering academia dropped from 42.1% in 2010 to 23.7% in 2019. Private industry’s willingness to pay top dollar for AI talent is likely a contributing factor. Jobs from the biggest AI ventures, like OpenAI and Anthropic, list eye-popping salaries ranging from $700,000 to $900,000 for new researchers, per data from salary negotiation service Rora.

The Great Faculty Exodus

Google has reportedly gone so far as to offer large grants of restricted stock to incentivize leading data scientists. While AI graduates are no doubt welcoming the trend — who wouldn’t kill for a starting salary that high? — it’s having an alarming impact on academia. A 2019 survey found that nearly 30% of faculty members in computer science departments had left their positions since 2015.

A study by the National Science Foundation found that the average tenure of a professor in computer science is now just over six years, down from nearly eight years in 2008. This is not just a matter of individual professors choosing to leave academia for industry jobs; it’s also a reflection of the changing landscape of research funding and the increasing pressure on universities to commercialize their research.

The Impact on Diversity

But so were AI teams in industry. In its State of AI in 2022 report, McKinsey found that the average share of employees identifying as racial or ethnic minorities developing AI solutions was a paltry 25%, and 29% of organizations had no minority employees working on AI whatsoever.

This is a problem not just for diversity and inclusion reasons, but also because it means that the field of AI is missing out on the insights and perspectives of people from underrepresented groups. As Kiran Peddigari, CEO of H2O.ai, has said, "The biggest problem in AI today is not the technology itself, but the lack of diversity in the teams building it."

The Solution: Industry-Academia Partnerships

So what can be done to reverse this trend? One solution is for universities and industry partners to work together on research projects that provide students with real-world experience and opportunities to apply their skills.

As Kiran Peddigari has said, "Universities need to start working more closely with industry partners to create internships, apprenticeships, and other programs that give students a taste of what it’s like to work in the field." This could include partnering on research projects, providing funding for student scholarships, or even creating joint degree programs.

The Future of AI Education

But universities must also take steps to address the issue of exclusivity in their own doctorate programs. As one report noted, "Prestigious AI doctorate programs deserve criticism for their exclusivity, certainly, and the ways in which it concentrates power and accelerates inequality."

To this end, universities could consider implementing more inclusive admissions policies, increasing funding for scholarships and research grants, or creating more opportunities for students from underrepresented groups to participate in research projects.

Conclusion

The trend of industry hiring non-graduate candidates is a clear sign that the field of AI needs to adapt and change. By working together with industry partners, universities can create programs that provide students with real-world experience and opportunities to apply their skills.

But this will require a fundamental shift in the way that we think about education and research in the field of AI. As one expert has noted, "Engineering is increasingly moving away from building whole products from scratch in a vacuum, and toward cobbling together stacks of AI models, APIs, enterprise tools and open source software."

This means that universities will need to place greater emphasis on teaching students how to work with industry partners, collaborate on research projects, and develop the skills necessary to succeed in the field. By doing so, we can create a more inclusive and diverse community of researchers and practitioners who are better equipped to tackle the complex challenges facing society today.

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