Kieran Klaassen and team mates shared their Claude Code workflow a few days ago. They broke down their process, showed what they built, and yesterday Kieran posted about the (potential) API costs a workflow like this has (or would have if not for Anthopic's Max plan). The response? While some were curious, the critical voices dominated - calling it too expensive and claiming 'these AI folks' aren't building anything real (check out Kieran's X feed to see how absurd that is).

Here's what bothers me: Kieran wasn't bragging. They were sharing data. They were excited about their productivity gains and wanted to show others what worked for them. And instead of curiosity or questions, they got dismissal.

We all know AI is transformative. Nobody's arguing that anymore. But there's this weird gatekeeping happening around how people use these tools.

Someone posts about using Claude to write tests? "That's not real testing." Someone shares their Cursor workflow? "You're just racking up API bills." Someone shows how they built an app in a weekend with AI assistance? "But is it production quality?"

The critics are missing the point entirely. These developers aren't saying their way is the only way. They're experimenting. They're pushing boundaries. They're figuring out what works.

Every breakthrough in development workflows started with someone trying something different and sharing it. Remember when people mocked developers for using Rails? "It doesn't scale." "It's just a toy framework." "Real developers use Java."

Those early Rails developers weren't wrong for sharing their excitement. They were pioneering new ways of building web apps. Some of their approaches failed. Others became industry standard.

Sure, not every experiment will pan out, and healthy skepticism has its place. But there's a difference between thoughtful critique and reflexive dismissal.

Same thing is happening now with AI workflows.

Yes, someone might spend $200 or $400 or $1000 in API credits building something. But if they shipped in a weekend what would normally take a month, isn't that valuable? If they learned new techniques that make them permanently more productive, isn't that worth it?

We don't mock developers for buying expensive monitors or mechanical keyboards or standing desks. We understand those are investments in productivity. Why is experimenting with AI workflows different?

When someone shares their AI workflow, instead of immediately explaining why it won't work, what if we asked:

These questions lead somewhere useful. Dismissing someone's experiments because they're "too expensive" or "not real programming" just shuts down learning.

Next time you see someone sharing an AI workflow that seems weird or expensive or over-engineered, resist the urge to dismiss it. Ask questions. Try to understand what they're actually doing and why.

Better yet, if you think their approach is flawed, share your own experiments. Show what works better. Contribute to the conversation instead of just tearing down.

We need people willing to try new things and report back. We need their successes and their expensive failures. That's how we all learn.

The person spending $1000 on API credits this month might discover the workflow that saves us all hundreds of hours next year. Or they might not. But we'll only find out if they keep experimenting and sharing.

So please, don't knock it till you try it. And if you're not going to try it, at least don't knock the people who are.