As an ML researcher, this article isn't persuasive to me for a few reasons:

- Computing power is getting exponentially cheaper even as computing requirements increase. The resources available to a university lab in the future will be much greater than those available today, even given the same budget. Of course this is also true for industry, but this growth is not a unique advantage of industry.

- Other scientific fields already have equipment costs that are orders of magnitude larger than CS. Physicists regularly write grant proposals for multimillion-dollar pieces of equipment. If building large clusters is necessary for academic research to stay relevant, academics will start building large clusters. The foundational work done at Bell, IBM, Xerox, etc in the 70s and 80s was not due to resource constraints in academia (academics had expensive computers too, and also did plenty of good work during that time), it was because those companies had the right combination of smart people and an immediate need to find practical solutions to difficult problems.

- Finally, and most importantly, even in the age of big data almost all fundamental research can be done quite successfully at small scales with modest hardware requirements. Notice that Hinton et. al. have spent 6+ years developing deep learning in academia, and it's only in the past couple of years that it's matured to the point of implementation at scale.

Here's the basic pipeline of most machine learning research: you come up with a new approach for training SVMs, or multilayer perceptrons, or some new type of more interesting model. First you develop your ideas conceptually, with some equations on a whiteboard. If you're a theorist, you might prove some theorems. Next you write a toy implementation in Matlab or Python to show that your method actually works, and that you get improvement over previous work for the dataset size you're using. This could mean that your method is faster -- which indicates it'll be able to scale to bigger data -- or that it's smarter / taking advantage of some new type of structure, in which case it still ought to get decent (if not state-of-the-art) results on small data. Only then, usually after publishing a few papers and working out the kinks, does it generally make sense to put in the effort to implement and test a big, efficient distributed version of your algorithm. And while that last part might be best done by industry, the first few steps are easily possible in academia and will continue to be for the foreseeable future.

Case in point: Google Translate is a massive system whose performance rests squarely on exploiting big data, in that they use the Internet as their training set. But academic machine translation research still runs quite effectively with smaller datasets on small clusters. The academics come up with ideas, implement and test them, and some ideas flop while others take off. The idea that take off get picked up by Google and implemented into Translate, where they hopefully end up pushing the envelope. So even though the academics don't have the resources to work at massive scale (which most of them don't want to do anyway -- ML researchers are usually more interested in ML than in building distributed systems) their research still has impact, through transfer to industry. This sort of relationship has been the model for academic/industry research collaboration for quite a while, and I don't think it's dead yet.