“More effective communication occurs when two or more individuals are homophilous. When they share common meanings and a mutual subcultural language, and are alike in personal and social characteristics, the communication of new ideas is likely to have greater effects in terms of knowledge gain, attitude formation and change, and overt behavior change.” — Everett Rogers, The Diffusion of Innovations
Glacial Gutenburg
Gutenberg’s movable type changed history, but its spread was glacial. The invention emerged in the 1440s, yet presses took decades to reach England. The delay was not about distance; information, trade, even the plague travelled faster. The real constraint was complexity. The press was not a single machine but a fusion of metalwork, ink chemistry, woodcarving, and careful assembly. To copy it, you needed Gutenberg’s apprentices. There were people who knew the feel of the metal, the smell of the ink, and, crucially, spoke German
The printing press took half a century to travel from Gutenberg’s workshop in Mainz to the rest of Europe. The press moved from city to city through networks of German artisans. The technology was semi-proprietary, so it was rarely shared with strangers, so it travelled “German to German”. Through this slow crawl of adoption, Europe’s first networked revolution took shape.
The press not only multiplied the amount of printed matter in the world; it multiplied the speed at which ideas could move. Yet its own spread was painfully slow.
As Samuel Arbesman, our guest next week on The Innovation Show, shares in The Half-Life of Facts, “each major technological leap shortens the time between discovery and rediscovery, between what we know and what we have to relearn.” The printing press was one such leap and marked a major acceleration in the diffusion of innovations.
Five centuries later, the same question of how ideas take root appeared again, not in workshops, but in the cornfields of Iowa.
The Iowa Curve
“It should not be assumed, as it sometimes has been in the past, that all innovations are equivalent units of analysis. This assumption is a gross oversimplification. While consumer innovations such as cellular telephones and VCRs required only a few years to reach widespread adoption in the United States, other new ideas, such as using the metric system or seat belts in cars, require decades to reach complete use.” — Everett Rogers
In the 1940s, Bryce Ryan and Neal Gross, began studying farms across Iowa to see why some people had taken a chance on a new hybrid seed while others hadn’t. Their conversations were pretty ordinary, farmers talked about weather, soil, and luck. Eventually a pattern emerged. Rather than spread through pamphlets or salesmen; new ideas moved through gossip, fences, and friendly rivalry.
From that simple observation they mapped the farmer’s journey: awareness, interest, evaluation, trial, and adoption.
All but two of the 259 farmers studied had adopted hybrid corn within thirteen years, between 1928 and 1941. When plotted year by year, Ryan and Gross discovered the now-familiar S-shaped curve of adoption — slow at first, then accelerating, before flattening out as the last holdouts came on board.
From The Diffusion Of Innovations by Everett Rogers
In the 1930s, a young Everett Rogers had watched his father and their neighbours delay for several years in adopting new ideas that could have been profitable for them. Puzzled by their delays, he was curious to uncover their reasons. Years later, Rogers took insights from the Iowa study coupled with his own observations and created the broader Diffusion of Innovations theory. He formalised the process into the now-canonical five stages — knowledge, persuasion, decision, implementation, and confirmation — and defined five adopter types: innovators, early adopters, early majority, late majority, and laggards. His bell curve showed how ideas, like seeds, take root — slow at first, then quickening, then levelling as the soil saturates.
Decades later, marketers, economists, and technologists would adopt Rogers’ curve to describe everything from consumer behaviour to cultural trends. But his curve belonged to an era when information, and influence, travelled at human speed — along social ties, newspapers (thanks to Gutenberg), and radio waves.
That world is gone.
Today, diffusion happens at digital speed. The long arc of adoption that once spanned generations has collapsed into moments. What once took decades to trickle through fields and factories now flashes across networks in real time.
The Collapse of the Curve
The distinctions Rogers described — innovators, early adopters, majorities, laggards — have blurred into near-simultaneity. As Gutenberg’s invention once connected information across cities, today’s connectivity compresses time itself.
As Paul Nunes and Larry Downes argue in Big Bang Disruption, the smooth arc of Rogers’ bell curve has collapsed. When everyone has access to the same information at the same time, the lag between discovery and adoption disappears. Gutenberg’s press levelled knowledge across Europe; the internet has done the same globally. With near-perfect information, customers no longer wait to see what works — they all move at once.
Nunes and Downes call it catastrophic success: when adoption happens so quickly that organisations can’t adjust. A single viral post, a product launch, or an algorithmic nudge can create a spike of instant ascent followed almost immediately by saturation and decline.
The bell curve has become the shark fin — a steep rise followed by a sudden drop. Or, in the age of instantaneous diffusion, an I-curve — near-vertical and continuous.
Diffusion used to move through conversation and time. Now it moves through data — all at once. The learning that once stretched between stages has been crushed by speed. You can see that shift in John Deere’s story, where the farm has moved from the field to the dashboard. With a hint of irony, precision farming and connected tractors diffused far slower than the information they would soon help to spread instantly.
John Deere and the I-Curve
For more than a century, John Deere made tractors. Farmers kept them for decades — repaired, repainted, re-worked if they could. The machines carried a kind of institutional memory.
Not long ago, a new phrase started to appear — in farming magazines, on websites, from suppliers and salespeople and in the chatter of WhatsApp groups. They called it precision farming: data, drones, satellites, sensors, and software guiding the machine.
After a while, the tractor seemed to know the land better than the farmer — measuring soil, spacing seed, correcting itself on the move. It felt odd at first, but pretty soon data was in the driving seat.
When Deere launched the Operations Center, the rhythm of farm life began to change, slowly, then quickly. You’d still hear engines starting at dawn, but not by personal preference. By now, the decision-making had shifted to screens and dashboards. It was about what the machines were noticing — a damp patch here, a crooked line there, a reading that didn’t look right.
By afternoon, someone hundreds of miles away might already be making the same small adjustment, following an invisible trail of shared data. No one designed that link; it just happened once the information began to flow.
When I spoke with Howard Yu and Sangeet Paul Choudary on The Innovation Show , we talked about how technology doesn’t just change what we build — it changes how knowledge moves. Howard would call Deere’s evolution a migration of capability — the expertise shifting from human habit to digital pattern. Sangeet might see it as unbundling and rebundling — the old industrial logic of production dissolving and reforming around coordination and code.
Whatever way we phrase it, diffusion has gone digital.
These days, John Deere doesn’t sell tractors anymore.
It sells the conversation running through those machines — farmers learning from each other in real time, part of the same network, diffusing the information instantly.
The New Physics of Diffusion
“The ability to learn faster than your competitors may be the only sustainable competitive advantage.” — Arie de Geus
From Gutenberg to Deere is really a story of time collapsing. What once spread across generations now unfolds in minutes — sometimes seconds — often in real time. The clockspeed of diffusion has accelerated beyond recognition. Each leap in communication has narrowed the distance between knowing and doing.
The old S-curve of diffusion has straightened into an I-curve. There’s no space between invention, imitation, and iteration — they all happen at once. Today, we have to learn while doing. Seeking consensus, stopping to strategize and getting perfect information means falling behind.
For most of history, ideas travelled at the speed of talk. We had time to see a change coming, to debate, disagree and adapt.
Today, information moves faster than understanding. Everything from markets to memes rides the same current.
Reinvention, Transformation, Innovation cannot be one-off projects. Change itself is a mindset because the diffusion of innovation has become the diffusion of everything.
In this week’s episode of The Innovation Show, I’m joined by Howard Yu and Sangeet Paul Choudary We discuss Adobe, Figma, Shein, John Deere and much more.
🎧 Watch or listen here:
https://medium.com/media/2efe03471216b5608823a7e06e6da637/href
The Collapse of the Curve: The New Physics of Diffusion from Field to Field was originally published in The Thursday Thought on Medium, where people are continuing the conversation by highlighting and responding to this story.