The important thing is that over time, scientific progress transforms things that used to have to be dealt with in a problem-solving mode down to the pattern-recognition space; and from pattern recognition into the rules-based mode. This is the mechanism by which less-trained people are enabled to do more sophisticated things. This is always the way disruption happens. It enables a larger population of less-experienced people to do more sophisticated things.

Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.

Regulatory fiat cannot create a market at a technologically interdependent interface. And by the same token, regulation and so-called monopoly power rarely prevail at modular interfaces between stages of value-added technology.

The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.

This is one of the innovator’s dilemmas: Blindly following the maxim that good managers should keep close to their customers can sometimes be a fatal mistake.

Focus is scary—until you realize that it only means turning your back on markets you could never have anyway. Sharp focus on jobs that customers are trying to get done holds the promise of greatly improving the odds of success in new-product development.

The outsourcing gurus have been driving the theory, and they are saying everybody ought always to do this. But it is really contingent on where you are on the spectrum from "not good enough" to "more than good enough," relative to each tier of the market.