Crossing the Chasm
The gap between early adopters and the early majority kills most products.
"The hardest sale is the second category of customer."
What is Crossing the Chasm? The gap between early adopters and the early majority kills most products. Product-market fit graduation.
Tech products that thrived with enthusiasts and died with the mainstream.
Product-market fit graduation.
Plan deliberately for the chasm; it isn't accidental.
Use the model. Pick the move.
The gap between early adopters and the early majority kills most products. You've just seen this: Tech products that thrived with enthusiasts and died with the mainstream. Which lever does the model recommend?
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Crossing the Chasm
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so Crossing the Chasm can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Crossing the Chasm most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Crossing the Chasm?
- If we removed every payoff for Crossing the Chasm, what behavior would replace it?
- Who benefits when Crossing the Chasm persists — and who pays the cost?
- People defend the status quo using the language of crossing the chasm.
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Crossing the Chasm through 3 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Crossing the Chasm?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes Crossing the Chasm?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
Your nervous system has a region for this.
When you encounter Crossing the Chasm, your striatum has built a reward association — and the next time the cue appears, it will push you toward the behavior whether you decide to or not.
Reward learning, habit formation, anticipation, craving, action selection. Habits live here. So do addictions. Variable rewards train this circuit faster than fixed ones.
See Striatum in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
The rate at which customers (or employees) leave over a period.
Innovators → early adopters → early majority → late majority → laggards.
Low-end or new-market entrants overtake established incumbents.
Prices driven far above intrinsic value by feedback loops of belief and behavior.
Asset prices fully reflect available information; you can't reliably beat the market.
Subjects often converge to neoclassical predictions only after many rounds of feedback — not on the first try.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
We climb from raw data to action through selection, meaning-making, and assumption — usually invisibly.
Value grows with the number of users.
The NLP claim that the unconscious mind runs the body, stores memory, organizes patterns, and seeks to follow the directives it's given.
Projects gradually expand beyond their original goals.
What does it cost to leave?
Unfinished tasks stick in working memory more than completed ones.
It's right because it's how we've always done it.
Reading micro-shifts in another person's state — breath, color, micro-expression — in real time.
Models trained to follow a written set of principles.
Reasoning in distributions — ranges and probabilities — rather than points.
Correlation does not imply causation.
One strong trait colors judgment of unrelated traits.