Power and Prediction opens not with artificial intelligence but with electricity, and with a parable. Picture three entrepreneurs in the decades around 1900 — the stretch from the 1880s to the 1920s when factories were moving from steam power to electric power. All three have access to the same technology. What separates them is what they decide to do with it.
The first sees a point solution. Electricity is cheaper than steam and loses less energy to friction, so the obvious move is to swap the power source and leave everything else alone. The factory keeps its central shaft, its belts and pulleys, its layout. Nothing about how the plant works changes; it simply runs a little cheaper.
The second sees an application solution. Instead of one big motor driving a central shaft, why not put an individual electric drive on each machine? Now the machines are modular. If one breaks down, the others keep running. It’s a real improvement, but the factory itself is still designed as if the power came from a single source.
The third sees a system solution. Steam had dictated factory design: heavy multi-story buildings clustered around the power plant, machines arranged by their distance from the shaft rather than by the logic of the work. Electricity broke that constraint. The power source could now be anywhere, so the factory could be anything. This entrepreneur builds lightweight, single-story plants and lays out the floor around the flow of workers and materials. The productivity gains come not from cheaper power but from an entirely new way of organizing production.
Not all value propositions are created equal. The authors’ point is that the first two solutions were framed as substitution: electricity standing in for steam. That framing explains why industry was slow to adopt electricity at first; a modest saving on fuel wasn’t enough to justify ripping out working infrastructure. The lag was long. Electric motors were available in the 1880s, and by about 1919 only around half of U.S. factory power came from electricity. The productivity surge from redesigned factories didn’t arrive until the 1920s, roughly forty years after the technology first appeared. The payoff came only when some entrepreneurs recognized that electricity’s real gift was decoupling the machine from the power source, something steam made impossible or prohibitively expensive. Once that was understood, the value on offer shifted from “lower energy costs” to “a vastly more productive factory,” and system-level solutions delivered returns that dwarfed the point solutions.
The bridge to AI. The parable is the book’s organizing lens. AI, the authors argue, is at the same stage electricity was a century ago. Most current deployments are point solutions (cheaper, faster predictions dropped into existing workflows) or application solutions (new tools built around a prediction, but still plugged into systems designed for a world without them). The larger opportunity lies in asking what AI decouples. Their answer: prediction from judgment. Just as electricity let the machine move away from the power source, AI lets the prediction move away from the human decision-maker. And just as that earlier decoupling shifted the value proposition from “lower fuel costs” to “a redesigned factory,” AI’s decoupling shifts it from “lower cost of prediction” to “a vastly more productive system.”
The rest of the book is about why so few organizations have made that leap yet, and what it will take to get there.
