Innovation Paradox: AI fails to move the needle for 88% of CFOs

The commentary places today's AI enthusiasm in a lineage that stretches back through the iPhone launch in 2007, mass-market broadband, and the PC's arrival on every desk. In each case, the author notes, consumer adoption arrived quickly while productivity growth kept declining — a gap Robert Solow once summarized by saying the computer age was visible everywhere but in the productivity statistics.

The author's explanation centers on what he calls a double K-shaped diffusion pattern. The first gap is between consumers and enterprises, and the second is within enterprises themselves, where adoption and financial impact split apart. The commentary attributes the second gap not to weak technology but to accumulated organizational complexity, captured by a metric the author developed, the Combined Overhead Ratio. In the U.S., that ratio crossed a threshold of 47% of GDP around 2000, and above it GDP growth has not sustainably exceeded 2.5%. In this reading, most companies deploying AI are putting a faster engine in a horse-drawn carriage, as the author puts it via Jack Dorsey's argument that corporate hierarchy is an obsolete information-routing protocol.

The historical contrast the commentary draws is electrification, where consumers and enterprises adopted at similar paces and similar depth, and organizational redesigns followed — Sloan's structure at General Motors, Taylor's scientific management. Today's economy is dominated by services, and the author cites the economist William Baumol's point that stagnant service sectors resist productivity improvement because they depend on human interaction. Since AI is judgment-intensive rather than information-scarce, the author sees it as potentially suited to those sectors — but argues the barriers to diffusion, from data governance to professional power dynamics, remain organizational rather than technical.