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- Range_ Why Generalists Triumph in a Specia - David Epstein
- A generalist would understand, beyond conceptually, what the expert is doing, but is not able to do it himself. My main areas of expertise should be those you use at home, the greenhouse and the workshop. Beyond that, and if I ever delve into industry, I prefer to remain a producer.
- This is the story of the Game and Watch and Gameboy. Yokoi specifically wanted to find new uses for cheap, old technologies. This fits in exactly with the manufacturing capabilities of a home workshop and smaller scale industries that cannot muster the capital to compete on the bleeding edge of technology.
- Organizations don't need as many specialists. This is going to increase even more as AI arrives
- Get the Carter Race case study from Harvard Business School
- Notes for page (20 . 3607)
- Notes for page (20 . 10650)
Range_ Why Generalists Triumph in a Specia - David Epstein
A generalist would understand, beyond conceptually, what the expert is doing, but is not able to do it himself. My main areas of expertise should be those you use at home, the greenhouse and the workshop. Beyond that, and if I ever delve into industry, I prefer to remain a producer.
Yokoi was the first to admit it. “I don’t have any particular specialist skills,” he once said. “I have a sort of vague knowledge of everything.” He advised young employees not just to play with technology for its own sake, but to play with ideas. Do not be an engineer, he said, be a producer. “The producer knows that there’s such a thing as a semiconductor, but doesn’t need to know its inner workings. . . . That can be left to the experts.” He argued, “Everyone takes the approach of learning detailed, complex skills. If no one did this then there wouldn’t be people who shine as engineers. . . . Looking at me, from the engineer’s perspective, it’s like, ‘Look at this idiot,’ but once you’ve got a couple hit products under your belt, this word ‘idiot’ seems to slip away somewhere.”
This is the story of the Game and Watch and Gameboy. Yokoi specifically wanted to find new uses for cheap, old technologies. This fits in exactly with the manufacturing capabilities of a home workshop and smaller scale industries that cannot muster the capital to compete on the bleeding edge of technology.
He spread his philosophy as his team grew, and asked everyone to consider alternate uses for old technology. He realized that he had been fortunate to come to a playing card company rather than an established electronic toymaker with entrenched solutions, so his ideas were not thwarted because of his technical limitations. As the company grew, he worried that young engineers would be too concerned about looking stupid to share ideas for novel uses of old technology, so he began intentionally blurting out crazy ideas at meetings to set the tone. “Once a young person starts saying things like, ‘Well, it’s not really my place to say . . .’ then it’s all over,” he said.
Organizations don't need as many specialists. This is going to increase even more as AI arrives
He became so interested in classifying innovators that he wrote a computer algorithm to analyze ten million patents from the last century and learn to identify and classify different types of inventors. Specialist contributions skyrocketed around and after World War II, but more recently have declined. “Specialists specifically peaked about 1985,” Ouderkirk told me. “And then declined pretty dramatically, leveled off about 2007, and the most recent data show it’s declining again, which I’m trying to understand.” He is careful to say that he can’t pinpoint a cause of the current trend. His hypothesis is that organizations simply don’t need as many specialists. “As information becomes more broadly available, the need for somebody to just advance a field isn’t as critical because in effect they are available to everybody,” he said. He is suggesting that communication technology has limited the number of hyperspecialists required to work on a particular narrow problem, because their breakthroughs can be communicated quickly and widely to others—the Yokois of the world—who work on clever applications.
Communication technologies have certainly done that in other areas. In the early twentieth century, for example, the state of Iowa alone had more than a thousand opera houses, one for every fifteen hundred residents. They were theaters, not just music venues, and they provided full-time employment for hundreds of local acting troupes and thousands of actors. Fast forward to Netflix and Hulu. Every customer can have Meryl Streep on demand, and the Iowa opera houses are extinct. So much for thousands of fully employed stage actors in Iowa. Ouderkirk’s data suggest that something analogous happened for narrowly focused specialists in technical fields. They are still absolutely critical, it’s just that their work is widely accessible, so fewer suffice.
It is an extension of the trend that Don Swanson foretold, and it massively increased opportunities for Yokoi-like connectors and polymathic innovators. “When information became more widely disseminated,” Ouderkirk told me, “it became a lot easier to be broader than a specialist, to start combining things in new ways.”
TODO Get the Carter Race case study from Harvard Business School
The temperature and engine failure data are taken exactly from NASA’s tragic decision to launch the space shuttle Challenger, with the details placed in the context of racing rather than space exploration. Jake’s face goes blank. Rather than a broken gasket, Challenger had failed O-rings—the rubber strips that sealed joints along the outer wall of the missile-like rocket boosters that propelled the shuttle. Cool temperatures caused O-ring rubber to harden, making them less effective seals.
The characters in the case study are loosely based on managers and engineers at NASA and its rocket-booster contractor, Morton Thiokol, on an emergency conference call the night before the Challenger launch. Weather reports on January 27, 1986, predicted unusually cool Florida weather for launch. After the conference call, NASA and Thiokol gave the okay to proceed. On January 28, O-rings failed to properly seal a joint in the wall of a rocket booster. Burning gas shot right through the joint to the outside, and Challenger exploded seventy-three seconds into its mission. All seven crew members were killed.
The Carter Racing case study worked exquisitely. It was eerie how precisely the students filled the shoes of the engineers on the emergency conference call who gave the green light for launch. The professor unfurled the lesson masterfully.
Notes for page (20 . 3607)
What struck me as Smithies spoke was his joy in experimentation. Not just in his lab, but in his life. He embodied a number of tenets I set out to explore in this book. From the outside, he looked like the consummate hyperspecialist. He was a molecular biochemist, after all. Except, molecular biochemist wasn’t really a thing when Smithies was in training. First he studied medicine, until he attended a talk by a professor who was combining chemistry and biology. “He lectured about this new subject which hadn’t yet been invented, in a sense,” Smithies told me. “It was marvelous, and I thought, ‘I’d like to do that. I’d better learn some chemistry.’” He turned on a dime and switched to studying chemistry. He never even thought to feel behind. On the contrary, “that was really very valuable, because at the end I had a good background in biology and wasn’t frightened of biology, and then I wasn’t frightened of chemistry. That gave me a great deal of power in the early days of molecular biology.” What sounds like hyperspecialization today was actually a bold hybrid at the time.
Notes for page (20 . 10650)
“ It is rather unusual, I have to say. I do not dig deep—I graze shallow. So ever since I was a postdoc, I would go into a different subject every five years or so. . . . I don’t want to carry on studying the same thing from cradle to grave. Sometimes I joke that I am not interested in doing re-search, only search.”