Engineering professors and department chairs at Johns Hopkins, University of Chicago, Northwestern, and New York University interviewed by Fortune are divided on whether these lucrative offers lead to a “brain drain” from academic labs.
The brain drain camp believes this phenomenon depletes the ranks of academic AI departments, which still do important research and also are responsible for training the next generation of PhD students. At the private labs, the AI researchers help juice Big Tech’s bottom line while providing, in these critics’ view, no public benefit. The unconcerned argue that academia is a thriving component of this booming labor market.
Commitment to their faculty appointments remains true for all the academics Fortune interviewed for this story. But professors like Henry Hoffman, who chairs the University of Chicago’s Department of Computer Science, has watched his PhD students get courted by tech companies since he began his professorship in 2013.
“The biggest thing to me is the salaries,” he says. He mentions a star student with zero professional experience who recently dropped out of the UChicago PhD program to accept a “high six-figure” offer from ByteDance. “When students can get the kind of job they want [as students], there’s no reason to force them to keep going.”
Make no mistake: PhDs in AI, computer science, applied mathematics, and related fields have always had lucrative opportunities available after graduation. Until now, one of the most financially rewarding paths was quantitative research at hedge funds: All-in compensation for PhDs fresh out of school can climb to $1 million–plus in these roles. It’s a compelling pitch, especially for students who’ve spent up to seven years living off meager stipends of about $40,000 a year.
That dichotomy probably underscores Johns Hopkins University’s decision to open its Data Science and AI Institute: a $2 billion five-year effort to enroll 750 PhD students in engineering disciplines and hire over 100 new tenure-track faculty members, making it one of the largest PhD programs in the country.
For some, the reasons to remain in academia are ethical.
Luís Amaral, a computer science professor at Northwestern, is “really concerned” that AI companies have overhyped the capabilities of their large language models and that their strategies will breed catastrophic societal implications, including environmental destruction. He says of OpenAI leadership, “If I’m a smart person, I actually know how bad the team was.”
Because most corporate labs are largely focused on LLM- and transformer-based approaches, if these methods ultimately fall short of the hype, there could be a reckoning for the industry. “Academic labs are among the few places actively exploring alternative AI architectures beyond LLMs and transformers,” says NYU’s Bari, who is researching creative applications for AI using a model based on birds’ intelligence. “In this corporate-dominated landscape, academia’s role as a hub for nonmainstream experimentation has likely become more important.”
