Today, in academia, it&#x27;s considered risky to do research in computer vision, machine learning, or speech processing, for example, because it&#x27;s likely that you will get &quot;out-Googled&quot;. Google probably has an entire team of 20 working on what your one graduate student is doing. They&#x27;ll have petabytes of real data to test against, hundreds of thousands of computers to run their jobs on, and decades of institutional experience. Your graduate student has a macbook air, six months of experience from an internship at Microsoft, and a BS in computer science. If you&#x27;re lucky. They&#x27;re going to lose. They should just go to work at Google.<p>Over time, fields of study become industrialized. There was a time when doing research in computer vision, machine learning, and speech processing was risky because the field was new, difficult to enter, and the prospects for commercialization were slim. That time has passed. Those 20 people working at Google are the people that helped that time pass. One could argue that the place for this work is now in industry - the motivations are all right and the resources and data are aligned to carry the work forward at a rapid pace.<p>This happens in other fields. For example, there&#x27;s some word on the street that DARPA is going to stop funding so much basic research into applied robotics. Industry, they say, has got this covered. You can argue that they&#x27;re right. The commercial sector is starting to get real thirsty for robots. Amazon talks about automated drone delivery. Everyone talks about self driving cars. The military wants to buy automated planes as a purchase, not as a research project. The time for basic research, it seems, is over.<p>As far as I can tell, this happened with systems about fifteen years ago, so the academic activity you see in systems is what is left over after all of the researchers that could do things moved into applying their research in industry. You no longer need to have weird hair and be buried in a basement to think about 20 computers talking to each other in parallel - you can go work at any technology company and think about two million computers talking to each other in parallel, and get paid two orders of magnitude more money. So the people doing systems research in academia are the people that cannot take their systems research into industry. If they could get internships, they would, and then they would get jobs. They haven&#x27;t.