The AI fluency gap between companies

I was having a chat with a couple of friends about how comfortable we are sharing what we know in public. Two of us work in tech. The third doesn't. She'd just vibe-coded a genuinely useful tool for a client, not a side project she was tinkering with for fun, but something that solved a real problem. She was proud of it. She also wasn't sure she should post about it, write about it, or bring it up in a room full of people who write code for a living.

I know all too well how that feels. Even I catch myself wondering if what I'm sharing is too basic. Some tip about prompting or a workflow that feels obvious inside Zapier, and I hesitate. Then I remember where I was six months ago, and that I'm miles ahead of that version of myself. Plenty of people are just now getting started, and what feels old hat to me might be exactly what they needed to hear.

Part of the hesitation is just putting yourself out there. But part of it is that the baseline for what counts as "using AI well" depends heavily on where you work, and she didn't have a company giving her a script for any of this.

At Zapier, we've been living in this for a while. People are expected to work with agents, use AI in their day-to-day, move faster than they could a year ago. Everyone is being evaluated on it, not just engineers. The minimum bar inside our org is probably higher than at a company that's still figuring out its AI policy.

That gap shows up everywhere once you start paying attention. I've written about resetting engineering expectations before. What's normal here is still a stretch goal somewhere else. Same title, different baseline. And if you're hiring, coaching, or thinking about your next role, you need to know which side of that you're on.