The dangerous phrase in AI product building: “it only takes 10 minutes”
I keep catching myself thinking:
“Why not add it? It only takes 10 minutes with AI.”
And I’m starting to think that sentence is more dangerous than it sounds.
The feature might take 10 minutes to generate.
But then it needs to fit the product.
You need to test it.
Users need to understand it.
It creates another state, another edge case, another thing that can break later.
So implementation might be cheap, while product complexity is still expensive.
I’m curious how other people handle this now.
Do you have any rule for deciding when an “easy” AI-generated feature still isn’t worth adding?
Helpful post. How did you get your first bit of traction?
Curious how long it took before you saw the first real results?
Thanks for sharing the numbers, that makes it much easier to follow.
Thanks for writing this up. Bookmarking it for later.
Thanks for writing this up. Bookmarking it for later.
Nice progress. What is the next thing you are focusing on?
Good write-up. What would you do differently if you started again?
Interesting. How are you measuring whether it is working?
Appreciate the honesty here, most people only share the wins.
Really relatable. How much time do you put into this each week?
Really relatable. How much time do you put into this each week?
Nice progress. What is the next thing you are focusing on?
Interesting take. Would you still recommend this approach to someone starting today?
Helpful post. How did you get your first bit of traction?
The rule I use is: cheap to generate is not cheap to own. Before we add an "easy" feature I ask three questions out loud. What user decision does this improve? What behavior in the next two weeks would prove it worked? What support, edge cases, and states does it create after launch? If I cannot answer those, it goes on a parking lot list instead of into the product. On teams I've run we also try the idea as a manual workflow or an experiment first. Generation cost dropped. Ownership cost did not. That gap is where a lot of quiet complexity accumulates.
My rule: it isn't 10 minutes unless I can write down how I'd know it's broken in the same sitting. Non-AI features fail loudly — the button doesn't work, someone files a bug. AI features fail quietly and intermittently, so each one you add is a new surface you have to keep checking forever, and that cost is invisible on the day you generate it. So the test isn't "can I build it in 10 minutes," it's "can I name the failure mode and the check for it in 10 minutes." If I can't, the feature isn't cheap, I just can't see the bill yet.
Good write-up. What would you do differently if you started again?
Good write-up. What would you do differently if you started again?
Good write-up. What would you do differently if you started again?
I’ve started separating “cheap to build” from “cheap to own.” Before adding an AI-generated feature, I write down the user decision it improves and the behavior that would tell me it worked; if I can’t name both, it goes into a parking lot. I also try it with a small cohort or manual workflow first, because the hidden cost is usually support, evaluation, and weird states rather than code. A useful rule for me is that an easy feature has to remove a step or clarify a decision—not merely create another thing to click.
Really solid approach — I'm juggling something similar myself (building Xstream4K on the side), what's been the hardest part for you so far?