Pyrrhic Innovation
Pyrrhus’s elephants. (Credit: The Print Collector/Print Collector/Getty Images) |
It shouldn’t be that you are finding a new way of thinking in order to find a new way of acting, but the reverse. One view of this chiasmus is inherently no better than the other, but the latter is the one people would choose if they were innovating. Another example is “don’t just stand there do something” versus “don’t just do something, stand there.” In retrospect, this is what Facebook should have done. But if you are boldly innovative you wouldn’t go back to the other way. This intransigence is just as bad because things keep getting more broken, or just to allow more broken things hanging around as a constant reminder of failure. I don’t know how organizations can get past this without stopping and thinking, or perhaps using the third way, serendipity, augury, and the invocation of spiritual dimensions.
“Spiritual” is not a word typically used by MBAs. The fact that it isn’t may indicate that there are concepts that never come into play. In music performance, it is always in play. In business there is conflict, but ironically doesn’t prevent being "spiritual" in more secular ways, and results in cult-like “Isms”: “conferencism”, “podcastism”, books with the title Thou Shalt Innovate. Innovation has eccentric aspects, but sometimes becomes more of a cult, like Scientology. You can say this insane drive for innovation makes us more anxious and depressed, a side-effect of “ismism”.
Power steering, introduced in 1951 was innovative and driven by the pursuit of convenience—as were TV dinners and Tang, a paradigm that continues today with various apps for binge-watching uninterrupted. If you look at the long view, what happened post-war was the seed of our current fascination with AI. You could even see the potential for bubbles and isms forming then and are beginning to manifest now with a 60-year lag. "Innovative" assumes that change happens quickly, but all the ingredients have to be readily available and in large quantities in order for it to scale up. The only thing preventing AI at scale are new microprocessors. Even then, what will it accomplish and what else gets broken quickly in the drive for innovation?
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