But the reason innovators should read these tales of technologies past is emphatically not to “learn from the lessons of history.” Rather, it’s to see how expectations have changed over time. What expectations were innovators trying to create? How fast are those expectations changing, compared to market conditions? Determining this “expectations calculus” is essential to managing innovation.
Take the story of gallium arsenide. For years, this exotic material was promoted in the press as a replacement for silicon in integrated circuits, with proponents touting its superior speed. But gallium arsenide is expensive, and Moore’s Law had long since trained the computing market to expect continual cost reductions, not increases. In this case, a careful analysis of the hype versus the economics would have nudged innovators back toward silicon or toward niches where gallium arsenide might be worth its significantly higher cost. In fact, that’s exactly what happened: gallium arsenide has become a key material in high-speed chips for cell phones and other telecommunications devices.
The history of technology predictions is a resource to be mined, not a pile of failed futurology to lampoon. Don’t save past issues of Technology Review to see what the magazine got right or wrong; treat TR and its conceptual cohorts as media that measure the expectations tomorrow’s innovators need to understand in order to exploit.