The Farm Boy Who Decoded How Ideas Spread

Imagine standing in an Iowa cornfield in the 1940s, watching some farmers eagerly plant a new hybrid corn variety while their neighbours, facing identical conditions and the same evidence, stick stubbornly to the old seed. What explains why good ideas spread unevenly, even when the benefits look obvious? That question captivated a farm boy named Everett Rogers, and his answer still shapes how we think about technology adoption today, including in health systems trying to figure out what to do with AI.

From cornfields to a general theory

Rogers was a rural sociologist at Iowa State, following up on a landmark study by Bryce Ryan and Neal Gross on hybrid corn adoption. That study had found something specific: innovations spread not in a straight line but along an S-curve. A few early experimenters, then a rapid acceleration as the idea caught on, then a plateau as the remaining holdouts either converted or dug in. Rogers' contribution was recognising this was not really about corn. Combing through hundreds of studies across agriculture, education, and community development, he found the same S-curve, the same underlying dynamics, regardless of what the innovation actually was.

The vocabulary that stuck

Rogers published "Diffusion of Innovations" in 1962, and it gave the field language that is still in use. Adopter categories: innovators, the venturesome 2.5 percent; early adopters, the respected 13.5 percent; early majority and late majority, 34 percent each; laggards, the traditional 16 percent. Innovation characteristics that predict whether something spreads: relative advantage, compatibility with existing values, complexity, trialability, and observability of benefit.

His deeper insight was reframing diffusion itself. Not a technical process driven by rational calculation, but a social one, carried by relationships, shaped by norms, and dependent on how an innovation is actually communicated, not just on whether it works.

Why this framework still matters for AI in health

Health systems today show the same uneven adoption Rogers documented in Iowa cornfields. Some embrace AI tools quickly. Others watch and wait. Technical performance matters, but so do trust, transparency, and whether early adopters can demonstrate real value to sceptical peers. Programmes that focus only on the technology and ignore these social dynamics tend to stumble. Programmes that build coalitions, address ethical concerns directly, and adapt to local context have a better chance of crossing the gap between a promising pilot and something that actually sticks.

The questions Rogers keeps asking

Rogers' framework pushes past "does the technology work" toward harder questions: who benefits, who gets left behind, and what specifically slows or distorts adoption for different groups. In global health, these are not academic questions. An innovation designed without its end users in mind, or rolled out without attention to equity, capacity, and context, will diffuse unevenly no matter how good the underlying technology is.

There is something fitting about a farm boy who watched uneven adoption in an Iowa cornfield building the framework that still explains uneven adoption of AI in hospitals worldwide. Behind every technological shift is a human story: early adopters taking a real risk, opinion leaders lending their credibility, and communities reshaping the innovation to fit what they actually need. The next time a clearly superior tool fails to spread as fast as it should, or an apparently weaker one takes off anyway, that is Rogers' S-curve at work. Innovations do not spread just because they are good. They spread because they are social.