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AI researchers reckon with the $1.5 million ‘academia tax’
Ben Deighton is a freelance writer based in London.
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Artificial-intelligence researchers say that the idea of leaving academia is tempting, but some nevertheless resist the move.Credit: Jay Janner/The Austin American-Statesman via Getty
If you’re looking to make money with your skills in artificial intelligence, academia isn’t the most lucrative destination. The National Bureau of Economic Research in Cambridge, Massachusetts, conducted an analysis1 of AI researchers and identified those with similar specialisms across academia and the private sector. They found that the top 1% of industry authors earn US$1.5 million more per person every year than do their academic counterparts.
Nature spoke to academics, mostly in the United States and Europe, and many mentioned students who have gone to work for firms such as Anthropic and OpenAI, both in San Francisco, California, and earn millions of dollars a year. Despite that, those who remain in academia say that the freedom to choose their research and the joy of training the next generation trumps the increased salaries of industry, though many still gripe about the issues they face in finding stable funding streams.
Source: United States’ National Bureau of Economic Research
Increasingly, however, researchers are adopting hybrid roles in which they split their time between technology companies and university research, getting the benefits of both.
Here, 14 university academics who specialize in AI and computational science share their reasons for staying in academia and the factors that might push them into industry.
“I’m not yet convinced that the extra money is worth the loss of the ability to do research in the areas you find interesting in the long term. It’s a thing that you possibly don’t get in industry — in academia, you could have projects going for a decade.” — Stewart Clark, computational physicist at Durham University, UK
“As scientists and educators, academia gives us an unusual amount of freedom to decide what questions we want to work on and what we think matters. I think that’s tremendously powerful.” — Jian Ma, computational biologist at Carnegie Mellon University in Pittsburgh, Pennsylvania