Why Einstein is a “peerless genius” and Hawking is an “ordinary genius” | Albert-László Barabási

We live in a society that we learn to admire geniuses: We write about them. We read about them. We watch movies about them. And in general, the genius label sells. Typically, everyone whom we label today "genius" has accomplished something remarkable by really standing out from among their peers in a way that really grabs our attention. They include scientists like Einstein, musicians and composers like Beethoven and Mozart.

But genius is something more—it's a story. We remember the people who happened to be at the right time at the right place, and hence, there was a way of recording their accomplishments. There are an exceptional number of hidden geniuses who either have not been at all recorded for posterity, or we know about their accomplishments, but we don't know enough for them to enter the canon. Could we actually use data to predict who among the scientists will actually be a genius? That's where network science comes in.

So we are curious: What really determines the genius label? And when we compared all geniuses to their scientific peers, we realized that there are really two very different classes: Ordinary genius and peerless genius. For example, Einstein, who turns out to be a truly peerless genius. When we looked at the scientist working at the same time or roughly in the same areas of physics that he did, there was no one who would have a comparable productivity or scientific impact to him. He was truly alone.

When we looked at Stephen Hawking, we label him ordinary genius. To our surprise, we realized there were about six other scientists who work roughly the same area, and had comparable, often bigger impact than Stephen Hawking had. Among them, actually, a woman scientist, Renata Kallosh. And it turned out, that there was absolutely no news about her anywhere. The only article that we find that mentions her was in the context of her husband. That raises the question: Why is it Hawking the genius, and not Renata? How does really the genius label emerge?

It turns out, that the number of languages to which a person's Wikipedia page has been translated was the strongest predictor of the genius label. We learned that the genius label is a construct that the society assigns to exceptional accomplishment, but exceptional accomplishment is not sufficient to get the genius label—we always need something more. You need to be born at the right time. You need to be in the right circumstances.

Throughout history, remarkable individuals were always born in the vicinity of big cultural centers. And everything that is outside of the cultural centers was typically a desert of exceptional accomplishments. We have a very strong culture bias towards genius: typically associated with the vast term "canon," and hence, we're losing many, many exceptional accomplishments because none of these individuals are really born in vacuum; they're inspired by some and influence others.

And by unveiling these connections, you are digging deeper and deeper into the cultural accomplishments of the society, and start discovering these hidden geniuses. It doesn't require much pattern recognition to realize that I'm past 50, which made me always wonder, "Do I still have ahead of me major scientific discoveries?" To paraphrase Einstein, "A person who has never made a major contribution to science by the age of 30 will never do so." That led to a conception in science that you have to be young to be creative.

So we were curious, "Is this really true for geniuses, or is it also true for ordinary scientists?" We ended up analyzing all scientists out there, and asking when did ordinary scientists make his or her biggest discovery? Was it early in their career or late? And to our surprise, the data indicated that indeed it's true. Most scientists make their biggest discovery in the first 15 years of their career.

And then after 30 years, the chance of: "I would make a discovery that would be bigger than what I did in my thirties," would be less than 1%. When we dig deeper into data, we realize that we also have to consider productivity. That ...