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Use AI to uncover assumptions and generate objections—not to decide whether your idea is good. A model can help you work out what to test; evidence from potential customers, especially what they do, is what should guide whether you build. That distinction is at the heart of a Product Hunt discussion about pressure-testing ideas before writing code.
What AI can—and cannot—tell you about an idea
A useful AI conversation can expose a competitor you had not considered, sharpen the problem you think you are solving, or surface reasons your proposed solution might fail. It can also help turn a vague hunch into a question you can investigate.
But a fluent response is not proof that customers want the product. The Product Hunt discussion’s original poster raises a specific risk: describe an idea enthusiastically and the model may answer in an affirming way, creating confidence without evidence of demand. The thread offers participant experience and suggestions, not a measured finding about how often that happens or whether any prompt reliably prevents it. Read the Product Hunt discussion.
Ask for objections, not a verdict
Akarsh Hegde, a participant in the discussion, puts the distinction plainly: “I use AI to generate objections, not verdicts.” Rather than asking whether your idea is good, ask the model to help you find the parts of your thinking that could be wrong.
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- Ask it to list the assumptions your idea depends on.
- Ask for cheaper or simpler explanations for the problem you have noticed.
- Ask what evidence would disprove the idea.
- Ask which single assumption, if false, would most undermine the concept.
These prompts are ways to generate lines of inquiry, not proven techniques for obtaining accurate assessments. A skeptical-customer, skeptical-investor, or “would someone pay for this today?” framing may help you explore different objections, but the discussion does not establish that any of those approaches predicts success.
Turn the riskiest assumption into a real-world test
Do not try to test every concern at once. Choose the riskiest assumption: the one that would invalidate the idea if it proved false. Then take that question to people who might actually encounter the problem or use the proposed solution.
- Write the assumption clearly. Make it specific enough that you can imagine evidence against it. For example: “People in this situation already spend time or money trying to solve this problem.”
- Ask what would count against it. Use AI to make the disconfirming evidence concrete, rather than collecting reasons the idea might work.
- Investigate with potential users. Ask about their experiences and current workarounds. Avoid treating an agreeable reaction to your pitch as proof of demand.
- Pay attention to behavior. What people actually do is more informative for a build decision than an AI-generated reaction or a positive opinion alone.
Hegde summarizes the handoff this way: “The model improves the questions, but user behavior decides whether to build.”
Keep AI feedback separate from customer evidence
It helps to label what you learn by where it came from and what it shows. An AI response is a suggestion about possible assumptions or objections; a conversation can reveal what someone says; observed behavior can show what someone actually does. These are different kinds of evidence, and they should not be collapsed into a single “validation” verdict.
| Input | Useful for | What it does not establish |
|---|---|---|
| AI-generated objections | Finding assumptions, alternative explanations, and questions to investigate | That a market exists or customers will buy |
| People’s stated opinions | Understanding how people describe a problem or respond to a concept | That they will act on a positive reaction |
| Observed user behavior | Seeing whether people take actions that bear on the riskiest assumption | By itself, every reason behind that behavior or the success of a future product |
The distinction is practical: use the model to decide what question to ask next, then look for evidence outside the model before committing to code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A simple decision rule before writing code
Before building, ask whether you have identified a consequential assumption and sought evidence from real people that could prove it wrong. If all you have is an AI’s plausible-sounding assessment, you have a better set of questions—not confirmation that the idea is worth building.
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