Ever feel like a newspaper?

Why are newspapers going out of business? The simple explanation is that newspaper owners are stupid; the world around them is changing and they’re oblivious. Michael Nielsen has a more interesting explanation. He says that newspapers are in trouble not because they’re stupid now but because they’ve been smart in the past.

Nielsen argues that newspapers are locked into their current business models because they have been so successful. Any small changes will make their businesses less profitable. I don’t know enough about the newspaper industry to say whether Nielsen is right, though I find his argument plausible. (His article is entitled Is scientific publishing about to be disrupted? However, it is about much more than scientific publishing.)

Nielsen argues that newspapers are standing on the top of one hill and profitable online news sources are standing on a higher hill, a hill that didn’t exist 20 years ago. In mathematical lingo, both businesses are at local maxima. Newspapers are trapped because they can’t improve their situation without first making it worse. Anyone who leads a newspaper down its hill in order to climb a new hill will be fired before he starts gaining altitude again.

I don’t care that much about newspapers, but Nielsen’s article struck me because it provides an explanation for many other situations. I feel like some areas of my life are stuck at a local maximum: there’s plenty of room for improvement, but not by making small changes.

Conservation of attractive profits

Tim O’Reilly talked about the “law of conservation of attractive profits” in a recent interview on the FLOSS Weekly podcast. Clayton Christensen explained this law in an HBR report in 2004. It says that when one thing becomes modular and commoditized, another thing becomes valuable.

O’Reilly argues that just as computers made out of commodity hardware made software more valuable, now commodity software and open standards have made data more valuable.

Taking this line of reasoning one step further, open data makes analysis more valuable. Good news for experts in statistics and machine learning.

Starting a business not as risky as people say

Check out this article from Jason Cohen: Starting a business isn’t as crazy and risky as they say. According to Cohen, popular ideas about failure rates for start-ups are based on misleading analysis of data. Statistics about business failures are muddled by two fundamental questions: (1) What is a business? and (2) What is a failure?

What is a business? There’s a big difference between a side business (a hobby or a casual source of extra income) and a primary business (main source of income) and yet statistics often lump these two together. Presumably failures are more common among side business, inflating the sense of how often serious businesses fail.

What is a failure? Common ideas about the frequency of failures are based on figures that simply track when a business goes out of existence. But a company can disappear for numerous reasons that are not failures. Maybe the company got bought out to the delight of the owner. Maybe the owner grew tired of the business and wanted to do something else. Maybe the owner retired. The figures are more encouraging when you sort out genuine failures from businesses that folded agreeably.

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