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Six months in, six versions of 'how we do AI'
Six months into AI adoption, every team has dashboard metrics that look good. But ask what a good AI-assisted PR looks like and you'll get six different answers. The tools showed up, people figured out their own way, and no one had the conversation about what 'good' actually means.
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The tool doesn't rescue you. It amplifies you.
AI changes speed. Not judgment. If a team already struggled to make sound architectural decisions, the tool doesn't rescue them. It just helps them make more bad decisions faster.
Leadership imagines 10x. Engineers learn two workflows.
Your leadership thinks you'll code in 3 seconds, read and understand it all, push to production without breaking stride, and never forget anything. They've never watched a junior engineer prompt through complexity he should have wrestled with. Never seen a senior freeze when the AI suggestion doesn't match the pattern she knows is right. Never been in the room when the thing that 'should just work' ... doesn't.
Your interview process is testing theater
You're asking engineers if they know how to use AI tools. You expect it. Then you're testing them without those tools. Here's why that mismatch is eroding trust before the first offer letter goes out.
An engineer from a culture analytics platform sees it happening across the industry.
I've seen engineers lose the spark in their eyes in their craft having to just plan and review the output. The sheer pace of AI is wearing down engineers.
One leader opened Slack. The other opened the logs.
I used to stay up endless nights losing myself in code. Now I avoid it. Here's what that costs.
The friction that builds judgment is disappearing
A junior engineer told me he couldn't explain his own code. Not because he wasn't working. Because he wasn't being forced to learn anymore.
Buying AI access was the easy part. Now learn to fly.
Procurement is not adoption. The leaders who are winning the AI transition did one thing the others didn't: they went first. They used the tools, showed their teams what the learning curve looked like, and built the conditions for real capability to develop. Buying licenses created access. It didn't create any of that.
Proximity built that instinct. Drift is spending it.
When AI scales output without scaling review, the quality gate breaks. The problem isn't the tool. It's the leader who lost the reference point for what good looks like in their own system.
Your team is prompting from scratch every time. That's the problem.
If I had to roll out AI to my engineering team again, I wouldn't start with the tools. I'd start with one repeatable workflow, map every step, define what good looks like, and turn it into a pipeline that compounds. Here's how that works in practice.