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.
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.