Both situations are a question of counterfactuals, and Counterfactuals are hard. On the one hand, there’s the question of how many people have the raw horsepower and outside perspective to do great work but are unable to get into the system (or do great work outside the system.) Call this the ‘discovery problem.’ On the other hand there’s the question of how many people already in the system would have done great work if the system were different (or there was an alternative system). Call this the ‘enablement problem.’
I’m going to assume that the goal here is “more potential paradigm-shifting blue sky research.” There are many other worthy goals like equality and preventing talent from being wasted that are often conflated.
To start, some important questions to ask are:
Looking at these questions, a series of cruxy questions emerge: do you believe that the binding constraint on great blue sky research is the quality and mindset of people going into the top-tier universities or is it a systemic constraint on the people already in the top-tier universities? If it’s the former, the discovery problem is absolutely the right one to focus on. If it’s the latter, you need to work on the enablement problem and the question becomes: do you believe that the people who have never been in the system are better than the people already in the system? This difference could be either because the system systemically selects for people less suited to do great work or actively destroys people’s ability to do great work.
My belief is that the binding constraint is on the system itself. There are definitely many people who could be great researchers who never get the opportunity to do so. However, even if you could identify them and get them into the system, they would be as constrained out of doing great work as everybody else. In this situation, the only reason to work on the discovery problem (if what you care about is purely incredible blue sky research) is if you think it will work better to build an entirely parallel system. I actually believe that the system is pretty good at training researchers and doesn’t somehow ruin them, so I am inclined to believe that building an entirely parallel system will just be more work for possibly worse results.
The massive list of §Academia Constraints paints a pretty clear picture of the heavy constraints even on amazing researchers in top-tier universities. It’s tempting to say that once a researcher has made it into a top-tier research program perhaps as a grad student and definitely as a post-doc or professor, they’re well-positioned to do Blue Sky Research is pure discovery work that asks extremely open-ended questions. While there are certainly some examples of success, I would argue that they are the exception and not the rule.
From a professor friend who just got a grant proposal rejected:
some of the reviews are extremely glowing. the ones that were less glowing mostly suggest that proposal was "too ambitious" and "this is a new field to the PI, will be less risky once they can show that they have instrumentation set up
The fact that even success stories (Boyden, mRNA, etc.) happen by the skin of their teeth suggests that counterfactually there are many more examples on the other side of the survival line.
brabenScientificFreedomElixir2008 has several examples of well-regarded professors who couldn’t get funding to pivot out of their niche, but did great work once that pivot was enabled because of their prior experience. Of course, BP research suffers from another counterfactual problem because they only sponsored people with relatively robust track records.
odlyzkoDeclineUnfetteredResearch1995 strongly biases me to believe that science is not a numbers game and that “more talent” could actually have the opposite of intended effect.