Artificial intelligence uses a complex set of rules - algorithms - to get to a conclusion. A computer has to calculate its way through all those rules, and that takes a lot of processing. So AI works best when a small computer is using it on a small problem - your car's anti-lock brakes are based on AI. Or you need to use a giant computer on a big problem - like IBM using a room-size machine to compete against humans on Jeopardy in 2011.

I knew I wanted to be a writer from as far back as I can remember. That was my talent. Lord knows it wasn't math.

Super fidelity requires constant investment and discipline, but great companies know how to do that.

Babies have not yet chunked anything. They aren't doing any high level thinking. All they're doing is sucking in all the data they experience in the world around them, and remembering it, raw. It's basically what extreme savants have happen in their brains.

Now companies tend to mine gigantic databases for insights into what might happen six months from now. That might always be valuable, but there's a different kind of value - and a competitive edge - in processing ongoing streams of data through a software model that can quickly and constantly make predictions about, say, whether a certain customer is going to defect, or an aircraft is going to run into trouble.

Who "wins" and who "loses" depends on when you measure it.

If you spot a market where the only choices are at either one end or the other - high fidelity or high convenience - there's probably a big opportunity at the other end. That was the opening for Federal Express, for instance. When it started, there was only one mail service in America - the US Postal Service, which was high convenience. Fred Smith created a high-fidelity mail service.