Why was it DeepMind, rather than an academic group, that built AlphaFold2 ?
Pierre Baldi (UC Irvine, Director of the AI in Science Institute):
The main reason is hardware and infrastructure. You need a large cluster of GPUs (most if not all academic lab do not have one) and the corresponding IT/software-engineering infrastructure.
Mohammed AlQuraishi (Columbia University):
where a longer reflection is presented]
First and foremost it has to do with the people who make up the AF2 [AlphaFold2] team. One should not pretend that they are substitutable. Even within DeepMind, if it were a different set of people we would likely have had a different outcome. This may seem obvious but I repeatedly heard people treat the AF2 team as an amorphous blob. Let us not forget that the main reason they did so well is because of who they are, their talents, and their dedication. In this most important sense, it is not about DeepMind at all.
Resources also helped and this is not to be underestimated, but I would like to focus on organizational structure as I believe it is the key factor beyond the individual contributors themselves. DeepMind is organized very differently from academic groups. There are minimal administrative requirements, freeing up time to do research. This research is done by professionals working at the same job for years and who have achieved mastery of at least one discipline. Contrast this with academic labs where there is constant turnover of students and postdocs. This is as it should be, as their primary mission is the training of the next generation of scientists. Furthermore, at DeepMind everyone is rowing in the same direction. There is a reason that the AF2 abstract has 18 co-first authors and it is reflective of an incentive structure wholly foreign to academia. Research at universities is ultimately about individual effort and building a personal brand, irrespective of how collaborative one wants to be. This means the power of coordination that DeepMind can leverage is never available to academic groups. Taken together these factors result in a “fast and focused” research paradigm.
Jake Feala (cofounder at Lila Sciences) also addressed this question as part of his contribution.