This time Nassim N. Taleb came out with “The Spurious Tail” with reference and based on his book Fooled by Randomness.

A spurious tail is the performance of a certain number of operators that is entirely caused by luck, what is called the “lucky fool” in Taleb (2001). Because of winner-take-all-effects (from globalization),spurious performance increases with time and explodes under fat tails in alarming proportions. An operator starting today, no matter his skill level, and ability to predict prices, will be outcompeted by the spurious tail. He published a paper shows the effect of powerlaw distributions on such spurious tail.

The idea is well-known, that as a population of operators in a profession marked by a high degrees of randomness increases,the number of stellar results, and stellar for completely random reasons, gets larger. The “spurious tail” is therefore the number of persons who rise to the top for no reasons other than mere luck, with subsequent rationalizations, analyses, explanations, and attributions. The performance in the “spurious tail” is only a matter of number of participants, the base population of those who tried. Assuming a symmetric market, if one
has for base population 1 million persons with zero skills and ability to predict starting Year 1, there should be 500K spurious winners Year 2,250K Year 3, 125K Year 4, etc. One can easily see that the size of the winning population in, say, Year 10 depends on the size of the base population Year 1; doubling the initial population would double the straight winners. Injecting skills in the form of better-than-random abilitiesto predict does not change the story by much.

Because of scalability, the top, say 300, managers get the bulk of the allocations, with the lion’s share going to the top 30. So it is obvious that the winner-take-all effect causes distortions: say there are N initial participants and the “top” M managers selected, the result will be M/N managers in play. Let us set the tail probability= M/N and derive K the threshold level that would be expected to arise just from randomness. As the base population gets larger, M/N increases linearly and K increases in a convex manner as we push into the tail probabilities.

He has used Aggravation Under Fat Tails and The distribution in the tails and then Comparison to Thin Tailed Distributions.

Before concluding the generalization of idea that this condition affects any business in which prevail (1) some degree of fat-tailed randomness, and (2) winner-take-all effects in allocation.
To conclude, if you are starting a career, move away from investment management and performance related lotteries as you will be competing with a swelling future spurious tail.

Here is the link to the paper :