Whenever I’m in Bangalore, I still default to Hindi.

I lived there for ~6 years, but I never really learned Kannada. The moment someone spoke to me, my brain looked for a Hindi equivalent and came up short.

Kids don’t do this. A child learning a new language maps words to real things - the sound and the reality arrive together.

Adults, on the other hand, map words to other words. Duolingo does this too - every lesson maps the new language onto the one you already know. No wonder so few learners ever hold a real conversation.

The same pattern shows up when engineers learn new programming paradigms.

I learned C first. Then tried jumping to Java. Object-oriented programming felt alien - I kept asking “what’s the C equivalent of this?” and half the time, there wasn’t one. I gave up, took a detour through Perl, and arrived at objects gradually. The direct path didn’t work because I was carrying the old map into new territory.

This is exactly what’s happening with AI right now.

Most people learn AI tools by mapping them onto familiar things. ChatGPT is like Google, but better. Copilot is like autocomplete, but smarter. An agent is like a junior employee you can delegate to.

These analogies help you start. They also limit how far you go.

I’ll be honest - I catch myself doing this too. Twenty years in tech means twenty years of mental models that I keep reaching for, even when they don’t quite fit.

The people I’ve seen get the most out of AI are often the ones without that baggage. AI-native - they didn’t learn to Google before ChatGPT, didn’t debug by posting on Stack Overflow before Claude Code. They’re not translating. They’re just using it for what it is.

That model doesn’t come from reading about AI. It comes from approaching it with fresh eyes, not a map drawn from somewhere else.

The ceiling isn’t the tool. It’s the map you bring to it. And I’m still drawing a new one.