> There has been much talk about companies trying to influence what products are being used in education, which for universities, is closely related to research

> There has been much talk about companies trying to influence what products are being used in education, which for universities, is closely related to research. I will speak for ML & Systems. I am not an expert on all domains. Most tools that researchers use in ML are open-sourced e.g. Pytorch TF Keras etc. Foundational papers like GFS, BigTable, Hadoop etc are publicly available now. There is a moratorium on how soon it appears, but it isn't behind walls forever. Academia tends to choose more open source over closed source. I'd have argued MATLAB to be more successful than Python in that case. It is not. In industry the practice may be more of 50-50 or even more towards closed source. > They sound like things academics would research all on their own without corporate involvement. The thing is that basically these things can be researched if one just has a computer. How can you emulate operation involving scale with just a computer or a bunch of computers. Hence, that is where industrial efforts come in play. A lot of things work well for a dozen, and then a completely different problem emerges when we talk about hundreds or thousands of users. I am only alluding to the fact that CS, in contrast to many other disciplines, has a more symbiotic relationship with industry. Companies do have incentives of using copyright & trade secrets, but there is enough trickle-down effect that gives academia to pursue newer challenges. The cycle repeats over and over. Academia cannot replace industry and likewise. If there is any pressing problem of this symbiosis, it is more of labor attrition. More people are leaving academia for better pay. But that is not what the topic was about.