Crossing the AI Chasm

Lately, I’ve been revisiting Geoffrey Moore’s Crossing the Chasm, a classic that explains why breakthrough technologies so often stall between “visionary hype” and true mainstream adoption.And the more I read it, the more I realize: AI is standing right at that same edge today.

The Cycle That Never Changes

Every new technology follows the Technology Adoption Life Cycle — Innovators → Early Adopters → Early Majority → Late Majority → Laggards.

In theory, it’s a smooth curve.In reality, it’s full of cracks.

Moore calls the biggest one “the chasm” — the gap between the visionary early adopters who love change and the pragmatic early majority who fear disruption.

The innovators see AI as a revolution.The pragmatists see it as a risk.

Why AI Is Stuck in the Chasm (for Now)

We’ve seen this movie before: speech recognition, virtual reality, data mining — all were “the next big thing” long before they actually worked at scale.AI today faces the same bottleneck.

• Early adopters love to experiment — they tolerate glitches, hallucinations, and prompt-crafting.

• The early majority wants reliability, integration, and clear ROI.

That’s why we see thousands of AI proofs-of-concept but far fewer real deployments.The market isn’t rejecting AI — it’s waiting for it to fit neatly into the workflow.

The Pragmatist’s Checklist

Crossing the chasm isn’t about more hype or bigger models.It’s about creating what Moore calls the “whole product” — a complete, easy-to-adopt solution that solves one real, painful problem.

To reach the early majority, AI teams need to:

1. Choose one niche and dominate it — not “everyone,” but one very specific vertical or workflow.

2. Deliver completeness — integrations, documentation, compliance, support.

3. Earn peer references — pragmatists buy from what other pragmatists trust.

4. Simplify the experience — “the best service is no service.”

   
   

Think of what happened when client-server computing or mobile apps went mainstream — it wasn’t the smartest tech that won, it was the easiest one to adopt.

The Startup Parable

Moore’s warning feels familiar to every AI founder today:

Year 1–2: wins with visionaries → Year 3: scales too early → revenue flatlines → panic.

Why? Because they mistake early hype for mainstream demand.They’re not riding the curve — they’re falling into the chasm.

The fix isn’t more sales. It’s focus.One beachhead market. One compelling use case. One community of success stories.

Data Can’t Save You — Intuition Can

One of Moore’s boldest points is that crossing the chasm is a low-data, high-risk decision.There are no reliable metrics for a market that doesn’t exist yet.

So you rely on informed intuition — not blind guesses, but pattern recognition built from real customer stories.He suggests building “customer characterizations”: vivid, scenario-based portraits of real people, their frustrations, and what life looks like after your solution works.

In AI, that means thinking less about “industries” and more about users:

• The HR manager drowning in candidate data.

• The teacher rewriting lesson plans.

• The marketer fighting content fatigue.

If your product doesn’t change their day in a measurable way, it won’t cross.

The Formula That Still Works

Niche → Beachhead → References → Bowling-Pin Expansion → Mainstream Leadership

It’s the same pattern behind Apple’s early success in design departments, PalmPilot’s dominance with mobile executives, and Documentum’s foothold in pharma.Each started small, crossed the chasm, and then rolled outward.

Final Thought

AI’s challenge isn’t technical anymore — it’s behavioral.We don’t need another model; we need more empathy for the people expected to use them.

As Moore wrote decades ago:

“If you don’t know where you are going, you probably aren’t going to get there.”

Crossing the AI chasm means knowing exactly who you’re building for, why they’ll care, and what friction stands in their way.

That’s how revolutionary tech becomes normal life.