There is no single right answer or path forward, but there is one right way to frame the problem.
Clayton M. ChristensenRead
The mistake that makes launching a venture expensive is when you try to make a disruptive technology so good that it can compete on a quality basis with an established product.
Interpretation
Focusing too much on perfecting a new technology can hinder its success against established products.
In this quote, Clayton M. Christensen emphasizes that startups often make the mistake of over-engineering their disruptive technologies to match the quality of existing products, which can lead to unnecessary expenses and delays. Instead, he suggests that new ventures should prioritize innovation and uniqueness over direct competition on quality, allowing them to carve out their market niche more effectively.
In practice
In a startup pitch, to emphasize the importance of innovation over perfection.
There is no single right answer or path forward, but there is one right way to frame the problem.
Understanding motivation is one of the most important things we can do in our lives, because it has such a bearing on why we do the things we do and whether we enjoy them or not.
Companies, in fact, are specifically organized to under-invest in disruptive innovations! This is one reason why we often suggest that companies set up separate teams or groups to commercialize disruptive innovations. When disruptive innovations have to fight with other innovations for resources, they tend to lose out.
There is no evidence that success in business will make us happy people or allow us to have happy families.
By definition, big data cannot yield complicated descriptions of causality. Especially in healthcare. Almost all of our diseases occur in the intersections of systems in the body.
The breakthrough innovations come when the tension is greatest and the resources are most limited. That's when people are actually a lot more open to rethinking the fundamental way they do business.
Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation.
Most of the time spent wrestling with technologies that don't quite work yet is just not worth the effort for end users, however much fun it is for nerds like us.
Why don't I talk about Big Data? Because I am focused on intelligent answers and not speeds and feeds. It doesn't matter if it is quick if it's the wrong answer.
The telephone, which interrupts the most serious conversations and cuts short the most weighty observations, has a romance of its own.
At one point in time, you had to choose, 'Do you want to do consumer or enterprise?' But the reality today is a bit different: Enterprises are a collection of consumers.
Until computers and robots make quantum advances, they basically remain adding machines: capable only of doing things in which all the variables are controlled and predictable.
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