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Book Review – “Prediction Machines – The Simple Economics of Artificial Intelligence” reviews another hot machine learning book from HBR – Prediction Machines.

 Artificial intelligence came (no doubt)3 years ago to stay. It started as a corporate craze for big data and last year moved to machine learning. These 2018 days AI goes in production via prediction (my 20 copyright cents please). Ajay Agraval, Joshua Gans and Avi Goldfarb, Toronto based professors, are well placed to write about this, as most of the tech companies spearheading the AI commercialization (led by the Big Tech 4 – Amazon, Google, Microsoft and IBM) are NAM based.
So what is “Prediction Machines” about? Without spoiling the book’s well documented contact, we can simply just underline that AI becomes a long powerful digitalization tool, but not any longer as a facilitator (as was until last year). Agraval forecasts that machines will eliminate uncertainty, thus reducing the market friction forces, thus reducing production and transaction costs. Sounds simple, right? Well – it is not at all.
The main issue of the prediction machines’ rise is that the consumer themselves are becoming more VUCA (volatile – uncertain – complex – ambiguous – US army terminology). So actually AI will evolve very fast from being a competitive advantage to a survival (read break even sales) point. It will force the corporate economics to move from lean six sigma production to online reaction to market trends. And this should be very unsettling for the corporate armies pushing services bundles to consumers.
Of course the path there is full of hidden surprises. We are far from the infancy of these technologies but not even close to their maturity. Which probably means that the future of the prediction machines is difficult to pin down. After all, machine learning works with probabilities and variable outcomes, so it will take a while until the raised dust will settle.

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