By James Kwak
Matt Bai’s recent article on how Curt Shilling’s gaming company, 38 Studios, managed to secure a $75 million loan from the State of Rhode Island and then flame out into bankruptcy is a reasonably fun read. Bai’s main emphasis, which I don’t disagree with, is on Rhode Island’s Economic Development Corporation, which managed to invest all of its capital in a single company in a risky industry that, apparently, had failed to secure funding from any of the VC firms in the Boston area. Overall, this seems like another example of why government agencies shouldn’t be trying to act like lead investors.
But the story has another moral, which struck closer to home for me. Shilling apparently founded the company because he liked MMORPGs and because he wanted to become “Bill Gates-rich.” When the going got tough, in Bai’s words, Shilling “seemed to think that he could will Amalur into being, in the same way he had always been able to pitch his way out of a bases-loaded jam, even with a throbbing arm. His certainty reassured employees on Empire Street, who had no idea that he was running out of money.”
Software is hard. Really hard. And it’s even harder when you’re up against good competition. It has to be done right, and you cannot get it done twice as fast by working “twice” as hard. Too many software companies have been run into the ground by people who wanted to make a fortune but had no understanding of how software is built. Most of them are back-slapping frat boys who climbed the corporate hierarchy in sales, not world-famous athletes. But Curt Shilling, apparently, was just like them.
With Great Power . . .
By James Kwak
A friend brought to my attention another example of how Excel may actually be a precursor of Skynet, after the London Whale trade and the Reinhart-Rogoff controversy. This comes to us in a research note from several years ago by several bioinformatics researchers titled “Mistaken Identifiers: Gene name errors can be introduced inadvertently when using Excel in bioinformatics.” The problem is that various genes have names like “DEC1″ or identifiers like “2310009E13.” When you important those text strings into Excel, by default, the former is converted into a date and the later is converted into scientific notation (2.310009 x 10^13). Since dates in Excel are really numbers behind the scenes (beginning with January 1, 1900), those text identifiers have been irretrievably converted into numbers.
This problem is related to what makes Excel so popular: it’s powerful, intuitive, and easy to use. In this case, it is guessing at what you really mean when you give it data in a certain format, and most of the time it’s right—which saves you the trouble of manually parsing text strings and converting them into dates (which you can do using various Excel functions, if you know how). But the price of that convenience is that it also makes it very easy to make mistakes, if you don’t know what you’re doing or you’re not extremely careful.
There are workarounds to this problem, but as of 2004, it had infected several public databases. As the authors write, “There is no way to know how many times and in how many laboratories the default date and floating point conversions to non-gene names have adversely affected an experiment or caused genes to ‘disappear’ from view.”
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