Freeman Dyson was a brilliant drop out mathematician who happened to meet Richard Feynman and demonstrated a proof for Feynman's work (which led him to winning a Nobel). He says,
"I think it's almost true without exception if you want to win a Nobel Prize, you should have a long attention span, get hold of some deep and important problem and stay with it for ten years. That wasn't my style."
Here's a list of Dyson's awards: Heineman Prize (1965), Lorentz Medal (1966), Hughes Medal (1968), Harvey Prize (1977), Wolf Prize (1981), Andrew Gemant Award (1988), Matteucci Medal (1989), Oersted Medal (1991), Fermi Award (1993), Templeton Prize (2000), Pomeranchuk Prize (2003), Poincaré Prize (2012).
Most expert know more and more about less and less because in order to understand something you need to dig ever deeper to understand the specifics.
What is needs is expert generalists that are able to understand several fields well enough to see where knowledge from one area can lead to understanding in others and vice versa.
Freeman Dyson is a great example because at 89 he published the solution to the prisoner's dilemma, decades later than the original theoretical physics work.
Dyson truly proposes a great solution lying in PD. That is a huge oversight for game theorists when they let an outsider do that.
However, it's not the ultimate solution. It's an extremely interesting aspect (a strategy, a style of play) of the PD that game theorists somehow have never seen and articulated in maths.
For the evolutionary fitness of the extortioner solution that Dyson discovers:
Evolution of extortion in Iterated Prisoner’s Dilemma games http://www.pnas.org/content/110/17/6913.abstract
I once read about a whole series of areas where solutions to issues in one field happened from understanding from understanding another field. Unfortunately ver few people span over multiple fields and can call themselves experts.
So the questions is whether we could spread out the expertise to more in between studies and letting go of literature as the only way to collect knowledge might be a great first step.
Or put another way.
Instead of modeling knowledge after the way our brain best deal with structure we should leave the structuring to the machines and start approaching it more like an organism that can be explored. I think we are bound to see something along those lines soon.
Perhaps I'm reading too much into it, but I interpret "Most of the great scientists worked on a single idea their entire life ..." as working on a single idea, to the exclusion of others.
Otherwise the statement would be "Most of the great scientists worked on an idea their entire life"
FWIW, using the list from http://listverse.com/2009/02/24/top-10-most-influential-scie... :
Marie Curie: received Nobel Prizes in Physics for her work in radiation and in Chemistry for her work in radium and polonium. If the idea here is "radiation" then Einstein's idea was "physics" and Darwin's was "biology"
Alan Turing: died entirely too young. Best known for early work in computers. Then switched to mathematical biology, specifically morphogenesis.
Niels Bohr: perhaps "nature of the atom" or "quantum mechanics"
Max Planck: Black-body radiation and special relativity
Charles Darwin: evolution (not really a small idea)
Leonardo da Vinci: not applicable
Galileo Galilei: astronomy (I can't think of a smaller "idea" for his body of work)
Nikola Tesla: umm, "electricity"?
Albert Einstein: see above
Isaac Newton: optics, gravitation, physics .. and then the Royal Mint, counterfeiting, and a whole lot of alchemy.
It's hard to conclude that these people worked on a single "idea", though some worked mostly in a single field.
If you say that Newton "worked on a single idea [his] entire life", then what was the idea?
In any case, the observation is incomplete. All great scientists slept at least once a month doesn't mean that sleeping at least once a month is a distinctive attribute of great scientists.
If most great scientists work on a given topic all of their scientific career, is that not mostly because most scientists do the same?
I personally have a huge beef with the way life scientists publish their results in tiny tiny bits, that makes it extremely hard to cross-check them with other studies, to find out if they were later disproved or even to figure out the average of some quantity. Try making a concrete model of something and you will find yourself searching for experimentally measured values in tomes among the blabber of introductions and discussions, like monks did in the middle ages... Databases sometimes exist, but they are not mandatory so you never know if they are to be trusted. We need a structured way to catalog scientific results , and journals are not it (esp. with politics which lead scientists to publish in marginally related journals ).