The Adoption Curve as Seismograph
Economics is, at its most honest, the discipline of reading evidence that the economy produces about itself. The conventional reading of adoption curves treats them as report cards for products and markets — a steep curve means a good product in a receptive market, a shallow curve means inadequacy on one side or the other. Say's framework transforms this reading into something more powerful: the adoption curve as seismograph, measuring not the surface event but the tectonic forces that produced it. When an earthquake strikes, the seismograph does not measure the quality of the rupture. It measures the magnitude of the stress that accumulated along the fault line before the rupture occurred. A large earthquake does not mean the fault was particularly weak; it means the stress was particularly deep, accumulated over a particularly long period, and released through a rupture at a particular point in space and time.
In the AI Story
Four predictions flow from treating the AI adoption curve as a seismograph reading rather than a product assessment. First: adoption intensity should correlate with duration of exposure to the translation barrier. If the curve measures stored pressure rather than product appeal, those carrying the largest creative debt — those who spent longest struggling with the gap between intention and artifact — should adopt most intensely. The evidence supports this. The most intense early adopters of Claude Code were not junior developers but senior engineers with ten, fifteen, twenty years of accumulated frustration, people who had internalized the translation cost so deeply that its sudden removal produced an almost physical sensation of relief.
Second: adoption should be fastest in domains where the translation cost was highest relative to the creative intent. If the curve measures pressure, pressure should be highest where the gap between what people wanted to build and what they could build was widest. Within software development, adoption should be most intense among those whose work required crossing the most translation boundaries — full-stack developers, solo founders, people building complete products rather than isolated components. The evidence confirms: solo builders and generalists adopted earliest and most intensely, precisely because their accumulated pressure was highest.
Third: the adoption curve should not follow the standard S-curve of innovation diffusion. If demand preceded supply and awaited adequate release, the curve should show a different shape — flat during the pre-adequacy period, then sharply vertical when adequacy is reached, with none of the gradual acceleration that characterizes persuasion-driven adoption. The two-month trajectory to fifty million users is consistent with a discharge curve, not a diffusion curve. There was no gradual ramp from early adopter to early majority to late majority. There was a threshold, and then a flood.
Fourth: the total volume of the discharge should exceed any estimate based on pre-existing market size. If adoption is releasing stored demand that was previously invisible to measurement, the total market that materializes should be larger than any analyst predicted. This was dramatically confirmed. Claude Code's run-rate revenue crossed two and a half billion dollars within months — a figure that dwarfed projections based on the existing market for code completion and developer assistance tools. The projections were based on expressed demand. The actual market included decades of unexpressed demand that no projection could have captured.
Origin
The seismograph metaphor is the Say volume's own contribution, extending the stored pressure model into an empirically testable framework. Say himself worked without the vocabulary of adoption curves, but his three-category taxonomy of demand produces the predictions the seismograph framing makes explicit.
Key Ideas
Four testable predictions. Duration correlates with intensity; translation cost correlates with adoption speed; the curve is not sigmoidal; total discharge exceeds expressed-demand projections.
Diagnostic, not descriptive. The adoption curve is not just a metric to be reported but an instrument that reveals the tectonic pressure in the economy it measures.
Continued tremors. The curve has not peaked because the addressable population is not just developers but every human being who has ever had an idea they could not realize.
Cascade pattern. Sequential discharges from adjacent populations produce a growth trajectory qualitatively different from previous technology booms.
Debates & Critiques
The seismograph framing faces objections from those who maintain that conventional diffusion dynamics — network effects, marketing, viral spread — are sufficient to explain AI adoption. The framing's rebuttal is that no single conventional mechanism accounts simultaneously for all four predictions the seismograph model makes, while the stored-pressure model accounts for all four without additional assumptions.
Appears in the Orange Pill Cycle
Further reading
- Segal, Edo. The Orange Pill (2026).
- Ye and Ranganathan. 'AI Doesn't Reduce Work — It Intensifies It.' Harvard Business Review (February 2026).
- Perez, Carlota. Technological Revolutions and Financial Capital (2002).
- Rogers, Everett. Diffusion of Innovations (5th ed., 2003).