Prediction Machines - the simple economics of artificial intelligence (Ajay Agrawal, Joshua Gans, Avi Goldfarb)

> our first insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence - prediction. 

> prediction is a central input into decision-making. 

> there is no single right answer to the question of which is the best AI strategy because AI involve trade-off : more speed less accuracy, more data less privacy and more autonomy less control. 

> cheap changes everything, cheap creates value - economics offers clear insights regarding the business implications of cheaper prediction. prediction machines will be used for traditional prediction tasks and new problems. The drop in the const of prediction will impact the value of other things, increasing the value of complements (data, judgement and action) and diminishing the value of substitutes (human prediction).  

> better prediction reduces uncertainty

> continue to apply the technology improves and predicts become more accurate and complex

> the drop in the cost of prediction will impact the value of other things, increasing the value of complements (data, judgment, and action) and diminishing the value of substitutes (human prediction). 

> shift from "shop-then-ship" model to "ship-then-shop" model : bringing goods to homes before they are ordered.   

> the seemingly mundane process of filling in missin ginformation can make prediction machines seem magical. This has already happened as object recognition, driverless cars and translate.