You Cannot Serve Two Masters: The Harms of Dual Affiliation

Facebook would like to have computer science faculty in AI committed to work 80% of their time in industrial jobs and 20% of their time at their university. They call this scheme “co-employment” or “dual affiliation.” This model assumes people can slice their time and attention like a computer, but people can’t do this. Universities and companies are communities, each with their particular missions and values. The values of these communities are often at odds, and researchers must choose where their main commitment lies. By committing researchers to a particular company’s interests, this new model of employment will harm our colleagues, our discipline, and everyone’s future. Like many harms, it comes with benefits for some. But the harm in this proposal outweighs the benefits. If industry wants to support and grow academic computer science, there are much better ways to achieve this.

The proposal will harm our discipline, because it will distract established talent from the special roles of academics: curiosity driven research. Academic scholarship has an excellent record of pursuing ideas into places that are exciting and productive, even if they don’t result in immediate, tangible benefits and especially if they ruffle the feathers of established, powerful institutions. You can’t do that if 80% of your time is spent not annoying a big company. Though big companies belabor promises of complete intellectual freedom to faculty, that can’t and won’t happen because the purpose of companies is to make money for shareholders.

The proposal harms our students directly. Our faculty at their best secure everyone’s future by teaching talented students how to understand the challenges facing the broader world. Such mentorship is enriched by the courage, independence, security, and trained judgement of senior scholars to guide students’ perspectives on what is worth doing, what is likely irrelevant, and what is wrong. Engaging with a student body requires an all-in commitment, both in teaching and advising roles. Faculty primarily working elsewhere means cancelled classes. Faculty wedded to a company means advice that’s colored by the interest of the company.

The proposal harms our future because it will stifle innovation. University researchers have a great historical record of disruptive entrepreneurism — for example, Google dates back to a paper from the Stanford digital library project. Smooth transitions from academic research to industrial practice are widely encouraged: most universities allow faculty to consult at 20% time, do year-long sabbaticals in industry, or take leave to start companies in order to promote such transitions. But there’s a big difference between an industrial leave and a long-term commitment. You can’t do disruptive entrepreneurism if 80% of what you do is owned by a big company. Part of the point of being a big company is to control your environment by crushing, containing, or co-opting inconvenient innovations. Faculty who sign on are subject to a huge gravitational force and are hard pressed not to annoy the big company they work for.

Like many really dangerous bargains, the harms are diffuse, and the benefits are focused. One kind of benefit is for faculty who sign on: in addition to the higher industrial salaries, working at a big company provides a chance to lead a team of research engineers to execute large-scale projects that may be used by millions. But another, more alarming, benefit is for big companies: all those potentially disruptive or potentially annoying ideas are now owned or controlled by the big company. Perhaps that’s the point of why management supports the proposal.

If industry really wants to help scale and advance computer science research, it’s easy to do. Do what many companies are already doing, but do much more of it. Give fellowships to graduate students and scholarships to undergraduate students. Employ students as interns. Pay for named chairs and new buildings. Give lots of faculty small amounts of research money. Make and publish open datasets. Give us easy access to industrial scale computing resources. But don’t raid our faculty and tell us it’s good for us.

We have made a small edit to clear up a misunderstanding raised by a colleague. We have noted this change with strikethrough. Though comments are closed, you can follow the discussion on Twitter, Reddit and Hacker News.