Stop Asking Friends If Your Idea Is Good: Use Public Data

Overview

TL;DR: Asking friends if your side project idea is good is not validation - it is a confidence trap that costs founders months of wasted time and money.

 

• Friendly feedback from your network creates false confidence because your friends are rarely your target customers and rarely tell you what you need to hear.
• The real alternative is not expensive market research - free public data sources like the U.S. Census Bureau and the American Community Survey contain detailed demographic, income, and geographic data that can ground your idea in reality.
• Before writing a line of code or designing a single screen, founders should document every assumption their idea rests on and treat each one as a hypothesis to be tested.
• A validated assumption about audience size, income level, or geographic concentration is worth more than a hundred encouraging replies from people who wish you well.
• Grounding your idea in public data does not kill creativity - it focuses it, saving you from building something brilliant for you and invisible to everyone else.

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There is a particular kind of pain that every failed side project creator knows. It is not the moment the idea fails publicly. It is the quieter moment, weeks or months earlier, when you showed the concept to a few people you trusted, and they smiled and said it sounded great. You walked away feeling validated. You opened your laptop. You started building. And none of the rest of it worked the way the smiles suggested it would.

The uncomfortable truth is that asking people you know whether your idea is good is one of the most common - and most damaging - mistakes founders make in the earliest stages of a project. It feels like validation. It produces the emotional sensation of validation. It is not validation.

 

Why Your Friends Are the Wrong Source of Truth

 

When you share an idea with someone who cares about you, you are not running a research study. You are triggering a social dynamic. The people in your life want to encourage you. They want to be supportive. Telling you that your idea probably has no market is an act of social risk for them - it risks your feelings, the warmth of the conversation, and the relationship itself.

So they say it sounds interesting. They say they can see it working. They might even say they would use it themselves, which is the most misleading thing they can say, because it costs them nothing to say it.

The deeper problem is structural. Your friends, colleagues, and existing network are almost certainly not a representative sample of your target customer. If you are building a financial tool for single mothers working two jobs, your network of fellow developers or startup founders is not that audience. Insights from the wrong people are not just unhelpful - they actively mislead you. You end up designing for a phantom customer who reflects the people you already know, not the people you are actually trying to serve.

Progress based on bad feedback is not progress. It is a detour that runs on optimism.

 

What Founders Do Instead (And Why It Also Fails)

 

Friendly feedback is not the only trap. There is a broader pattern of validation behaviors that feel productive but produce weak evidence:

 

• Building first, asking questions later. Opening a repo or starting a design file feels like forward momentum. It is often premature momentum - work done before you know who it is for.
• Chasing trends instead of needs. AI is what everyone is talking about, so founders build AI tools. Productivity apps are everywhere, so another one gets made. A crowded space with no unmet need is not an opportunity - it is a graveyard.
• Misreading soft signals. A landing page that collects a hundred email addresses feels like proof of demand. An email address is proof of mild curiosity. It is not proof that anyone will pay, return, or recommend.
• Assuming your own pain is universal. Most great products start with a real problem the founder experienced personally. But experiencing a problem yourself does not tell you how many other people feel it, how intensely they feel it, or whether they would pay to fix it.

 

Each of these patterns shares the same failure mode: the founder is generating feelings of progress without generating evidence of demand.

 

The Alternative: Public Data as a Starting Point

 

Here is what makes the friends-as-validators trap so avoidable. The information you actually need to begin validating a side project is already publicly available, for free, and has been collected at a national scale.

The U.S. Census Bureau and the American Community Survey publish detailed data on population segments, household income, employment by occupation, geographic distribution, technology access, commuting behavior, housing type, educational attainment, and dozens of other variables. This is not vague demographic noise. It is granular, structured, and searchable.

If your idea is built for freelancers, public data can tell you:

 

• How many freelancers exist across different U.S. metropolitan areas
• What industries they cluster in
• What their median household income looks like
• Whether they skew urban or rural
• What their technology access and usage patterns suggest about their digital behavior

That is not a survey. That is not a focus group. That is a nationally representative foundation that your entire go-to-market strategy can be built on - before you have written a single line of code or spent a dollar on ads.

 

 

A Practical Starting Framework

 

Before you ask anyone for feedback, before you build anything, do this:

1. Write down every assumption your idea rests on. Not loosely. Explicitly. Treat each one as a hypothesis that needs to be tested, not a truth that can be assumed. Most ideas contain five core types of assumptions:

 

• Problem assumption (this problem is real and widespread)
• User assumption (these specific people experience the problem)
• Frequency assumption (they experience it often enough to want a solution)
• Willingness-to-pay assumption (they would pay for a fix, not just welcome a free one)
• Competitive assumption (no existing solution already solves this well enough)

2. For each assumption, identify a public data source that could confirm or challenge it. The Census Bureau, the American Community Survey, Bureau of Labor Statistics reports, public SaaS directories, app store reviews, and industry reports are all accessible without a budget. Use them to check whether the audience you picture actually exists at the size you are imagining. If you want to avoid downloading messy data tables, just use Cambium AI for free.

 

3. Build your first rough persona from data, not intuition. Age range, income band, occupation type, geographic concentration, household structure, and device access should all come from what the data shows, not from what feels right. A persona built on demographic evidence is a tool. A persona built on vibes is a liability. We can do this for you.

4. Only then, talk to real people who match the profile. Conversations with actual members of your target demographic are valuable - but only once you have defined who that demographic is. Without that definition, you are not doing user research. You are having conversations and calling it research. You can also use synthetic personas to get access to insights more quickly. 

 

Wrapping Up

 

The reason the friends-as-validators trap is so persistent is that it feels rational. You are seeking feedback. You are being collaborative. You are not just building blindly. But the social structure of those conversations makes honest, useful feedback almost impossible to receive - and the fact that your network is not your market makes the feedback misleading even when it is honest.

The alternative is not more expensive or more technically complicated. Free, publicly available demographic data can answer the most important early questions about whether your idea has a real audience, whether that audience is large enough to build a business on, and whether they have the income and access to actually use what you create.

That is exactly the kind of question Cambium AI is built to answer. It lets you query U.S. public data in plain language, generate statistically grounded personas, and explore the real shape of your target audience - without a survey budget, a data team, or months of waiting for results. If you are at the beginning of a side project and you want to replace gut feeling with something more reliable, that is a reasonable first stop before you ask anyone for their opinion.

Further reading: How to Validate a Side Project Using Public Data