The academy-to-industry brain drain is very real. What can we do about it?
Before I begin, let me confess my biases. I work in the research division of a large tech company (and I do not represent their views). Before that, I worked on grant-funded research in the academy. I work on speech and language technologies, and I’ll largely confine my comments to that area.
[Content warnings: organized labor, name-calling.]
Fact of the matter is, industry salaries are determined by a relatively-efficient labor market. Academy salaries are compressed, with a relatively firm ceiling for all but a handful of “rock star” faculty. The vast majority of technical faculty are paid substantially less than they’d make if they just took the very next industry offer that came around. It’s even worse for research professors who depend on grant-based “salary support” in a time of unprecedented “austerity”—they can find themselves functionally unemployed any time a pack of incurious morons seem to end up in the White House (as seems to happen every eight years or so).
The solution here is political. Fund the damn NIH and NSF. Double—no, triple—their funding. Pay for it by taxing corporations and the rich, or, better yet, divert some money from the Giant Death Machines fund. Make grant support contractual, so PIs with a five-year grant are guaranteed five years of salary support and a chance to realize their vision. Insist on transparency and consistency in “indirect costs” (i.e., overhead) for grants to drain the bureaucratic swamp (more on that below). Resist the casualization of labor at universities, and do so at every level. Unionize every employee at every American university. Aggressively lobby Democrat presidential candidates to agree to appoint the National Labor Relations Board who will continue to recognize graduate students’ right to unionize.
Administration & bureaucracy
Industry has bureaucratic hurdles, of course, but they’re in no way comparable to the profound dysfunction taken for granted in the academic bureaucracy. If you or anyone you love has ever written a scientific grant, you know what I mean; if not, find a colleague who has and politely ask them to tell you their story. At the same time American universities are cutting their labor costs through casualization, they are massively increasing their administrative costs. You will not be surprised to find that this does not produce better scientific outcomes, or make it easier to submit a grant. This is a case of what Noam Chomsky has described as the “neoliberal confidence trick”. It goes a little something like this:
- Appoint/anoint all-powerful administrators/bureaucrats, selecting for maximal incompetence.
- Permit them to fail.
- Either GOTO #1, or use this to justify cutting investment in whatever was being administered in the first place.
I do not see any way out of this situation except class consciousness and labor organizing. Academic researchers must start seeing the administration as potentially hostile to their interests, and refuse to identify with, or (or quelle horreur, to join) the managerial classes.
Computing power & data
The big companies have more computers than universities. But in my area, speech and language technology, nearly everything worth doing can still be done with a commodity cluster (like you’d find in the average American CS departments) or a powerful desktop with a big GPU. And of those, the majority can still be done on a cheap laptop. (Unless, of course, you’re one of those deep learning eliminationist true believers, in which case, reconsider.) Quite a bit of great speech & language research—in particular, work on machine translation—has come from collaborations between the Giant Death Machines funding agencies (like DARPA) and academics, with the former usually footing the bill for computing and data (usually bought from the Linguistic Data Consortium (LDC), itself essentially a collaboration between the military-industrial complex and the Ivy League). In speech recognition, there are hundreds of hours of transcribed speech in the public domain, and hundreds more can be obtained with a LDC contract paid for by your funders. In natural language processing, it is by now almost gauche for published research to make use of proprietary data, possibly excepting the venerable Penn Treebank.
I feel the data-and-computing issue is largely a myth. I do not know where it got started, though maybe it’s this bizarre press-release-masquerading-as-an-article (and note that’s actually about leaving one megacorp for another).
Talent & culture
Movements between academy & industry have historically been cyclic. World War II and the military-industrial-consumer boom that followed siphoned off a lot of academic talent. In speech & language technologies, the Bell breakup and the resulting fragmentation of Bell Labs pushed talent back to the academy in the 1980s and 1990s; the balance began to shift back to Silicon Valley about a decade ago.
There’s something to be said for “game knows game”—i.e., the talented want to work with the talented. And there’s a more general factor—large industrial organizations engage in careful “cultural design” to keep talent happy in ways that go beyond compensation and fringe benefits. (For instance, see Fergus Henderson’s description of engineering practices at Google.) But I think it’s important to understand this as a symptom of the problem, a lagging indicator, and as part of an unpredictable cycle, not as something to optimize for.
I’m a firm believer in “you do you”. But I do have one bit of specific advice for scientists in academia: don’t pay so much damn attention to Silicon Valley. Now, if you’re training students—and you’re doing it with the full knowledge that few of them will ever be able to work in the academy, as you should—you should educate yourself and your students to prepare for this reality. Set up a little industrial advisory board, coordinate interview training, talk with hiring managers, adopt industrial engineering practices. But, do not let Silicon Valley dictate your research program. Do not let Silicon Valley tell you how many GPUs you need, or that you need GPUs at all. Do not believe the hype. Remember always that what works for a few-dozen crypto-feudo-fascisto-libertario-utopio-futurist billionaires from California may not work for you. Please, let the academy once again be a refuge from neoliberalism, capitalism, imperialism, and war. America has never needed you more than we do right now.
If you enjoyed this, you might enjoy my paper, with Richard Sproat, on an important NLP task that neural nets are really bad at.