“The Googles, Amazons, Apples, Netflixes, and Capital Ones … don’t insist on performing lots of interesting experiments because they’re rich; they’re rich because they insist on performing lots of interesting experiments.”
Innovation efforts often start by looking for quick wins. The problem is that quick wins don’t solve tough problems, and solving tough problems is what leads to competitive advantage. We’re better off by going after the tough problems first.
How do we build things that move society forward? That is the core question addressed in Zero to One by Peter Thiel and Blake Masters. I finished the book today, and here are some key quotes (in bold) and my thoughts on them. Of course, it’s easier to copy a model than to make something […]
Which location has a better mobile payment system: Kenya or Silicon Valley? The answer is Kenya, and we can learn a lot about innovating more effectively by looking at why this is the case.
Ideas from Carol Dweck, Hayao Miyazaki and Ira Glass help us figure out how to grow our talent.
Successful innovation requires not just finding great new ideas, but taking advantage of them too. This means that we have to strike a balance between executing older ideas and searching for new ones.
Great new ideas usually need great new business models to work. The Technology Readiness Level and the Investment Readiness Level are two tools that you can use to help you get your great new idea ready for the world.
If we want to be more innovative, learning how to ask better questions is a key skill to develop.
We often think of new ideas fighting with older ideas, but the truth is that new ideas never work without building on older ones.
Things don’t always go the way we plan – and if we’re innovating, we shouldn’t expect them to. Here are some ideas for coping with this.
Here’s the secret to innovation: there are no secrets. The only way to get better at innovating is to do the work – get better at experimenting.
When people say that their organisations are risk-averse, often they actually mean that they are variance-averse. This causes problems over the long-term, but experimenting and innovation can help you avoid these problems.
