I think we associate learning/discovery with those moments because they happen together, not because they're causally related.
I think this is somewhat equivalent to how we used to have to learn 100 different integrals and derivatives in calculus. That's somewhat helpful. I learned to see patterns in math like that, the same way I learned a decent bit by browsing irrelevant abstracts and follow citation trails. But physically memorizing 100s of integrals is mostly a waste, and so are the irrelevant abstracts. You'll be much better at math (and hopefully science) if you can learn ~10 key integrals or read ~10 abstracts, and then spend the rest of your time understanding the high level patterns and implications by talking with an expert. Just like I can now ask GPT-4 to explain why some integral formula is true, which ones are related, and so on.
And that's the last point - these literature search tools aren't developing in isolation. We will get to have a local "expert" to discuss what we find with. That changes the cost-benefit analysis too.
Reading papers that turn out to be irrelevant to the specific problem at hand is probably the biggest time sink; it's also probably an important source of general education. But good academics presumably know the importance of keeping an open mind and general learning.
If someone is at the level where they need 10-20 papers to understand a topic, they are not at the level where they are even capable of asking a specific enough question. In their case, doing the hard work and sifting though 100s of papers is the best way to train themselves to think critically and thoroughly evaluate whether a paper is relevant to them.
There is also the real fact that the greatest discoveries usually come from obscure corners and reading as much as you can is the only way to explore those corners. Otherwise, you're just refining what was done before you
I have no idea how old you are, but being in my 40s and needing to get results quickly I don't really care to learn the minutia for whatever type of stamp collecting is important for the project I'm working on now.
I think this is somewhat equivalent to how we used to have to learn 100 different integrals and derivatives in calculus. That's somewhat helpful. I learned to see patterns in math like that, the same way I learned a decent bit by browsing irrelevant abstracts and follow citation trails. But physically memorizing 100s of integrals is mostly a waste, and so are the irrelevant abstracts. You'll be much better at math (and hopefully science) if you can learn ~10 key integrals or read ~10 abstracts, and then spend the rest of your time understanding the high level patterns and implications by talking with an expert. Just like I can now ask GPT-4 to explain why some integral formula is true, which ones are related, and so on.
And that's the last point - these literature search tools aren't developing in isolation. We will get to have a local "expert" to discuss what we find with. That changes the cost-benefit analysis too.