“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.
AF2’s success raises the question of what other problems exist that are ripe for a “fast and focused” attack. The will does exist on the part of funding agencies to dedicate significant resources to tackling so-called grand challenges. The Structural Genomics Initiative was one such effort and the structures it determined set the stage, in part, for DeepMind’s success today. But all these efforts tend to be distributed. Does it make sense to organize concerted efforts modeled on the DeepMind approach but focused on other pressing issues? I think so. One can imagine some problems in climate science falling in this category.
To be clear, the DeepMind approach is no silver bullet. The factors I mentioned above—experienced hands, high coordination, and focused research objectives—are great for answering questions but not for asking them, whereas in most of biology defining questions is the interesting part; protein structure prediction being one major counterexample. It would be short-sighted to turn the entire research enterprise into many mini DeepMinds.
There is another, more subtle drawback to the fast and focused model and that is its speed. Even for protein structure prediction, if DeepMind’s research had been carried out over a period of ten years instead of four, it is likely that their ideas, as well as other ideas they didn’t conceive of, would have slowly gestated and gotten published by multiple labs. Some of these ideas may or may not have ultimately contributed to the solution, but they would have formed an intellectual corpus that informs problems beyond protein structure prediction. The fast and focused model minimizes the percolation and exploration of ideas. Instead of a thousand flowers blooming, only one will, and it may prevent future bloomings by stripping them of perceived academic novelty. Worsening matters is that while DeepMind may have tried many approaches internally, we will only hear about a single distilled and beautified result.
None of this is DeepMind’s fault—it reflects the academic incentive structure, particularly in biology (and machine learning) that elevates bottom-line performance over the exploration of new ideas. This is what I mean by stripping them from perceived academic novelty. Once a solution is solved in any way, it becomes hard to justify solving it another way, especially from a publication standpoint.”