Power And Prediction: The Disruptive Economics of Artificial Intelligence
by Ajay Agrawal
7 popular highlights from this book
Key Insights & Memorable Quotes
Below are the most popular and impactful highlights and quotes from Power And Prediction: The Disruptive Economics of Artificial Intelligence:
when your predictions are accurate enough—something happens. You cross a threshold where you should actually rethink your whole business model and product based on machine learning.…
More often than not, that was a tough sell. If you go to a business and tell it you can save it $50,000 per year in labor costs if it eliminates this one job, then your AI product better eliminate that entire job. Instead, what entrepreneurs found was that their product was perhaps eliminating one task in a person’s job, and that wasn’t going to be enough to save their would-be customer any meaningful labor costs. The better pitches were ones that were not focused on replacement but on value. These pitches demonstrated how an AI product could allow businesses to generate more profits by, say, supplying higher quality products to their own customers. This had the benefit of not having to demonstrate that their AI could perform a particular task at a lower cost than a person. And if that also reduced internal resistance to adopting AI, then that only made their sales task easier. The point here is that a value-enhancing approach to AI, rather than a cost-savings approach, is more likely to find real traction for AI adoption.
People should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.
But we need to do more. We are now in The Between Times for AI—between the demonstration of the technology’s capability and the realization of its promise reflected in widespread adoption.
Value versus Cost Economists tend to focus on cost, and, as economists, we are as guilty of that as anyone. The entire premise of our first book, Prediction Machines, was that AI advances were going to dramatically reduce the cost of prediction, leading to a scale-up of its use. However, while that book suggested that the initial uses of AI would be where prediction was already occurring, either explicitly in, say, forecasting sales or the weather, or implicitly in classifying photos and language, we were mindful that the real opportunity would be the new applications and uses that were enabled when prediction costs fell low enough.
Just as electricity enabled decoupling the machine from the power source and thus facilitated shifting the value proposition from “lower fuel costs” to “vastly more productive factory design,” AI enables decoupling prediction from the other aspects of a decision and thus facilitates shifting the value proposition from “lower cost of prediction” to “vastly more productive systems.
people form habits or keep to rules, they are acknowledging that the costs of trying to optimize are too high. So they, in effect, decide not to decide.
