Sample frameDay 166·AI Fluency · Economics

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AI Fluency.

What Stays Scarce

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The question

A capable stranger buys the same subscriptions as you and starts on Monday. What part of what you sell can't they reproduce by Friday?

The idea

Ajay Agrawal, Joshua Gans and Avi Goldfarb, three economists at the University of Toronto's Rotman School of Management, wrote Prediction Machines (2018) to make a deliberately unglamorous argument: artificial intelligence isn't magic, it's a fall in the price of prediction. Economics is confident about what happens next when a price falls. You use more of the thing, you use it in places you would never have bothered with before, and everything that goes alongside it becomes more valuable rather than less. Their decomposition is the useful part. Any decision breaks into prediction, judgement, data and action, and the machine has taken only the first. What it hasn't taken is the judgement that decides what a good outcome is worth, the data nobody else can feed it, and the action somebody has to authorise. The trap is spending the saving on more of what just got cheap. The complements are the large prize, and they get dearer the closer prediction gets to free.

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