Power And Prediction: The Disruptive Economics of Artificial Intelligence
by Ajay Agrawal
In "Power And Prediction: The Disruptive Economics of Artificial Intelligence," Ajay Agrawal explores the transformative potential of AI through its impact on business models and decision-making processes. Central to the book's message is the idea that accurate predictions can compel businesses to rethink their operations and strategies. Agrawal argues that the most effective adoption of AI stems from a value-enhancing approach rather than a cost-saving one, shifting focus from job replacement to improving product quality and profitability. The author highlights the current transitional phase of AI,marked by significant technological capabilities yet limited widespread adoption. He critiques traditional economic perspectives that emphasize cost reduction, advocating instead for a view that prioritizes productivity gains and innovative applications that arise as prediction costs decline. Just as electricity revolutionized factory design by decoupling power from machinery, AI enables businesses to separate predictive capabilities from decision-making processes, fostering more productive systems. Ultimately, Agrawal calls for a paradigm shift in how businesses perceive AI,moving from a narrow focus on cost savings to a broader understanding of its potential to enhance value and operational efficiency. By doing so, organizations can better navigate the complexities of AI integration and unlock its full promise.
6 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.
