AI made building cheaper. It didn’t make validation more disciplined
I don’t think the interesting problem is simply that LLMs can be too agreeable or optimistic.
That part is fairly obvious.
The more interesting problem is that AI helps founders reason inside the chat, but rarely forces a serious pre-build validation process outside the chat.
Things like:
-turning the idea into falsifiable assumptions
-actively looking for contradictory evidence
-mapping real substitutes and workarounds
-finding where the pain appears naturally
-separating “interesting” from “people already pay for this”
-deciding what evidence would justify building now
-knowing when the right answer is “don’t build yet”
AI made it much cheaper to build landing pages, MVPs, features, and code.
But it didn’t make being wrong any cheaper.
I’m curious how other founders handle this now.
Before writing code, do you rely mostly on judgment, customer calls, fake doors, competitor research, community signals, or a structured validation process?
And what would an AI-assisted validation workflow need to do for you to actually trust it before building?
one thing I’d add — “enough evidence” probably isn’t a fixed line.
it depends on what you’re about to do next, how expensive it is, and how easy it is to undo.
a few repeated pain signals might be enough for a manual test or tiny MVP, but not enough to spend 3 months building.
so maybe it’s less about confidence alone, and more about matching the strength of the evidence to the size of the next move.
are you thinking build / adjust / stop, or more like one bounded next action?
Honestly, what makes me hesitate is that most validation mistakes don't come from a lack of evidence.
They come from treating the wrong evidence as decisive.
I've seen founders do customer calls, competitor research, fake doors, surveys, even get early users... and still end up more confident in the wrong conclusion.
That's why I'd be careful about focusing too much on the validation method itself.
The harder question is what conclusion actually deserves confidence once the signals start showing up.
That’s a sharp point.
I agree, the biggest risk is not simply having too little evidence. It’s giving the wrong evidence too much weight.
A founder can run customer calls, surveys, competitor research, fake doors, even get early users, and still walk away with a conclusion that feels more certain than it deserves.
So maybe the missing layer is not more validation methods.
It’s confidence calibration.
Which signals actually reduce risk?
Which ones only create momentum?
Which evidence is behavioral vs. stated?
Which data just confirms what the founder already wanted to believe?
And at what point does the evidence justify building, adjusting, or stopping?
That feels like the harder problem to me: not collecting signals, but stress-testing the conclusion those signals are being used to support.
Curious, when you see early validation data, what usually makes you lower or raise your confidence in the conclusion?
Possibly.
The reason I'd be careful answering that directly is that I don't think the interesting part is the question itself.
I think there's a more important decision sitting underneath it.
I wouldn't try to unpack that properly in a thread.
If you'd like the tighter version, drop your email and I'll put it together properly.