# Speed of AI adoption is not quality of adaptation

Published 15 January 2026

[Originally posted on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7417559662964891649/)

![Illustration titled "AI passengers": figures sit around a glowing table, each with a checkmark thought bubble above them.](https://ovchyn.me/api/media/file/speed-of-ai-adoption-is-not-quality-of-adaptation-1.jpg?prefix=production)

**Once again about AI adoption — what people are actually adapting into**.

I came to LinkedIn to find people who think about AI's future the way I do — not through hype cycles, but as a long-term global shift that will reshape our day-to-day life: how we study, how we work, how we process information.

Some time ago, I crossed paths with Adi Stan in a comment thread, and he suggested I check out his research paper "The Age of Cognitive Divergence". I did. And the way he frames AI adoption genuinely extended how I think about this whole situation.

**The core thesis: AI adoption doesn't democratize intelligence. It** ***stratifies*** **it**.

**L1 (Passengers)**

dependent users stuck in what Stan calls the "uncertainty-reassurance cycle". They ask AI, accept the first answer, and move on—cognitive atrophy in slow motion.

**L2 (Operators)**

the ones who use AI to do the same things faster. Linear gains. But here's the catch: they're sitting in the maximum automation risk zone. The "safe middle" isn't so safe.

**L3 (Architects)**

a small group entering actual symbiosis with AI. Exponential productivity. Not because they prompt better, but because they think through the tool.

The problem: L1 and L2 feel productive. But one is building dependency, the other is sitting in the automation kill zone. Only L3 is actually compounding.

This sharpens something I haven’t thought about yet. 🎯

"**Fast adaptation" means nothing if it's adaptation** ***into dependency***, **not into thinking**.

An engineer who learns to prompt AI in a week but never questions the output has adapted fast — straight into "Passengers". An engineer who takes longer but learns to iterate, verify, and think through the tool — that's the one who'll turn AI into real career leverage.

**Speed of adoption** ≠ **quality of adaptation**.

So, fellow CTOs and CEOs — are we even measuring the right thing?

How fast do people *use* AI, or how well do they *think* with it? 🧠

Curious what you're seeing in your teams.

👇 Link to Adi's paper in the comments.