Fat Cat, Long Tail, Trial and Error

I'm struggling slightly with John Kay's latest article "Counting errors: from the fat cats to long tails."

I understand the point about power laws, and to be careful making assumptions that one is dealing with "normal" data in any given scenario. But a problem I have with this article is John's claim that the long tail of book sales, is "truncated" because books that would only sell 1,000 copies don't get published.

"If book sales are governed by a power law, then if 10 American books sell 1m copies in a year, and 400 sell more than 100,000, then about 16,000 titles will sell more than 10,000 copies.... The rule would predict there would be 640,000 books selling more than 1,000 copies. There are not, and for an obvious reason. Most titles that might sell 100,000 books get published but most titles that would only sell 1,000 do not."

But surely lots of books get published that don't (initially) sell 1,000 copies, because publishers don't accurately predict sales. And surely that's the real point here: only 10 books might sell more than 1m copies a year, but you don't know ahead of time which 10 books. So it's still worthwhile listing on digital platforms titles that initially sell very poorly, because they might yet resonate with enough people who share the same taste and 'work their way up the tail'.

Similarly, John claims that:

"Companies that would have only a few thousand pounds of sales do not continue to exist: people who would have incomes below a certain level are supported by social benefits. To choose appropriate models you need to understand both the maths and the business environment. Media industries and financial institutions have both been unsuccessful in marrying these two skills."

This may be true, but the challenge of non-normal data is that you can't accurately predict which company will not continue to exist, or which people who are on low incomes today might strike it rich tomorrow, like J K Rowling (or they could be wealthy benefits cheats). You can't write off anything or anyone until it or they have actually failed.

On this basis, the conclusion ought to be that participants in the media and financial industries should be prepared to experiment - and fail - a lot before reaching any conclusion about what will necessarily be successful. That was one of my takeaways from The Black Swan.

Or am I missing something?