| > Bleeding edge means time is important. Because time being important is the only thing that would make the argument. Since time is not important, it's not bleeding edge. That seems like a very artificial definition of bleeding edge. I've only ever heard it as a description of working in an area where a lot of important aspects are unexplored and using any praxis based on what we've already gleaned is risky. I'd like to know where you're sourcing that "time is important" condition. > I'm not going to go into problems of poor knowledge transfer obviously present here (bus factor 1?), The head of the lab may very well not know how to use FPGAs (nothing about what I described entails the lab was researching any kind of computer engineering), and the postdoc might have even taught himself how to do it just for this task. > but if this was the case, the post-doc would have negotiation power and this entire situation wouldn't be an issue It is still an issue, because you can find another smart postdoc replacement that will teach himself how to do it without raising a stink about his hours spent working for the next 10 times he needs to do something crucial. Will it take more time for this one subgoal? Yes, but replacing someone who is constantly starting salary negotiations every time he thinks he has some leverage is in the end likely to be a time-saver. > It's hard, and overall, I would say it's worse (hence the sadly). But it's a lot better than the nonsense that academia is engaging in right now. It at least provides some competition and puts pressure on academia. So it's worse, but it's better? You've failed to explain (at all) how you know startups can solve these problems when they have no track record of solving anything like them. Without that explanation, your justifications smack of motivated reasoning and isolated demands of rigor. > It at least provides some competition and puts pressure on academia. If academia doesn't fix its act, eventually, private will have to take over. Academia already has plenty of competition internally; it's one way that the limited number of tenure and postdoc positions has been quite beneficial. You've also again failed to even mention how private will take over, and how it'll be any better once it does. > There's lots of ML research on the startup side right now, I wouldn't be surprised if the demand for quantum research will rise with time. The main advantage academia has isn't the system, but the government money... which is not at all indicative of the system working well. Yes, once ML research started producing useful results in reasonably predictable timeframes. I agree quantum research will also enter the startup world once it starts producing useful results in reasonably predictable timeframes, and that change of gears will accelerate development. But how do you think the field is going to develop to the point where it can be depended on for revenue? > You can't expect everyone to just accept that, it needs to be justified. That's what I mean by "not buying it". It may be true, but the evidence is not there, not there at all. I provided a concrete example of an area of research in academia moving forward at breakneck speed in an area that doesn't yet have much value to businesses (their involvement has all been within their own moonshot, academically modeled research divisions). But you've provided zero examples of startups excelling in such an environment, and I'm just supposed to buy that? |