Why ChatGPT Can't Validate Your Startup Idea

In June 2023, New York lawyer Steven Schwartz made national headlines. He'd submitted a legal brief citing six court cases as precedent.

The problem? None of them existed.

Schwartz had asked ChatGPT to find relevant cases. ChatGPT confidently provided six, complete with citations, case numbers, and quotes from the rulings. They sounded perfect. They were entirely fabricated.

Judge P. Kevin Castel wasn't amused. Schwartz faced sanctions, public humiliation, and became the poster child for AI hallucinations.

Now here's my question: If ChatGPT will invent fake court cases with fake judges and fake rulings, what do you think it's doing when you ask about your startup's market?

We Tested This

I asked ChatGPT: "What are the main competitors in the Polish food waste reduction software market?"

Here's what it gave me:

"The Polish food waste reduction software market includes several notable players: FoodLoop, which helps retailers manage expiring products; Too Good To Go's B2B platform; Winnow Solutions; and local startups like GreenTech Polska and EcoFood Systems."

Sounds reasonable. Specific company names. Clear market positioning.

One problem: GreenTech Polska and EcoFood Systems don't exist. I checked the Polish company registry (KRS). Nothing. I searched LinkedIn, Crunchbase, Google. Nothing.

ChatGPT invented two companies, gave them plausible-sounding names, and presented them as established market players.

This is exactly what happened to Steven Schwartz. The AI sounds confident. The information is specific. And it's completely wrong.

Why LLMs Hallucinate (The Technical Reality)

Large language models don't "know" things. They predict the next most likely word based on patterns in their training data.

When you ask about Polish food waste software, GPT isn't consulting a database. It's thinking: "What words typically follow 'Polish food waste software competitors'?" And it generates plausible-sounding text that matches the pattern of "company name + description."

The model doesn't distinguish between:

• Companies that exist
• Companies that might exist
• Companies that sound like they should exist

It just generates text that looks right.

This is why hallucinations are so dangerous. The AI never says "I don't know." It generates confident, specific, detailed nonsense.

The Data Currency Problem

Even when ChatGPT doesn't hallucinate, its information is old.

GPT-4's training data cuts off in early 2024. That means:

• Companies founded in the last year don't exist
• Recent funding rounds aren't reflected
• Market shifts from the past 12 months are invisible
• New competitors are missing entirely

I ran another test. I asked about AI code assistants released in 2024. ChatGPT confidently described the market—and completely missed Cursor, Devin, and several other major launches.

For fast-moving markets, 12-month-old data isn't just stale. It's misleading.

The Real Problem: Validation Theater

The most dangerous thing isn't wrong information. It's false confidence.

When ChatGPT tells you your market is $5 billion, you feel validated. When it describes three competitors, you think you understand the landscape. When it says "this idea has strong potential," you get excited.

None of it is based on reality. But it feels like research.

I call this validation theater: the appearance of rigor without actual learning.

You've "done your homework." You have a document full of market analysis. You sound prepared in pitch meetings.

But you're building on a foundation of generated text, not verified facts.

The first time you encounter a competitor ChatGPT didn't mention—or learn your "large market" is actually tiny—the theater ends and reality begins. By then, you've invested months or years.

What Actual Research Looks Like

Real market research doesn't come from asking an AI to summarize what it thinks might be true.

It comes from primary sources:

Company registries like KRS (Poland), Companies House (UK), or SEC filings (US) tell you what companies actually exist, when they were founded, and who runs them.

LinkedIn shows you how many employees competitors actually have. A company with 5 employees and a company with 500 are very different threats.

Job postings reveal what companies are actually building. If a competitor is hiring machine learning engineers, they're probably working on AI features.

Crunchbase and PitchBook show actual funding data, not estimates or guesses.

Google Trends shows whether interest in your space is growing or declining—with real search data, not LLM predictions.

News APIs surface recent developments: acquisitions, pivots, product launches, regulatory changes.

None of this lives in GPT's training data. It requires querying live sources.

The Multi-Model Approach

Here's something interesting we discovered: when you run the same market question through Claude, GPT, and Gemini, you often get contradictory answers.

One model might say there are five competitors. Another says three. A third mentions companies the others missed.

These contradictions are valuable. They show you where the models are guessing versus where they have consistent information.

If all three models agree on something, it's more likely to be true. If they disagree wildly, you know you need to verify with primary sources.

Single-model analysis hides uncertainty. Multi-model analysis reveals it.

How Bedrock Reports Approaches This

We built Bedrock Reports because we kept seeing founders get burned by AI-generated research.

Our approach is different in three ways:

Real data sources, not training data. We query 30+ live APIs: Google Search, company registries, news feeds, patent databases, job boards. Every data point comes from a current, verifiable source.

Multi-model validation. We run analysis through Claude, GPT, and Gemini, then compare results. Contradictions get flagged. Consensus gets strengthened.

100% citation. Every claim in a Bedrock Reports report links to its source. You can verify anything we say with one click. If we can't cite it, we don't include it.

The output isn't "what an AI thinks might be true." It's "what we found in real databases, analyzed by multiple models, with every source transparent."

The Adversarial Approach

One more thing we do differently: we argue against your idea.

Most AI tools are yes-machines. You describe an idea, they tell you it's great, and you feel validated.

We structure analysis as a debate:

• A "Bull" perspective makes the strongest case for your idea
• A "Bear" perspective actively looks for flaws
• A "Moderator" synthesizes both into a balanced assessment

If your idea has fatal flaws—regulatory issues, unit economics that don't work, a market that's smaller than you think—the Bear will find them.

You'd rather know now than after you've raised money and hired a team.

How to Verify Any AI Research

Whether you use Bedrock Reports or not, here's how to verify AI-generated market research:

1. Check company names in registries. If an AI mentions a company, look it up in the relevant country's business registry. Does it exist?

2. Search LinkedIn for employees. Real companies have real employees with LinkedIn profiles. Fake companies don't.

3. Look for recent news. Google the company name with a date filter. Is there any news from the past 6 months?

4. Cross-reference funding claims. If the AI says a company raised $10M, check Crunchbase or PitchBook. Is that real?

5. Ask for sources. If you're using any AI tool, ask it to cite sources. If it can't, treat the information as unverified.

The 30 minutes you spend verifying could save you months of building in the wrong direction.

The Bottom Line

ChatGPT, Claude, and other LLMs are incredible tools for brainstorming, writing, and exploring ideas. I use them daily.

But they are not research tools. They don't have access to current data. They can't distinguish between fact and plausible fiction. They don't know what they don't know.

Using LLMs for market research is like asking a really smart friend who read a lot of business articles two years ago. They might have good intuitions. They might say things that sound right. But they're not looking at the actual data.

Real validation requires real sources. There's no shortcut.

Keep Reading

• How to Validate a Business Idea — The complete validation framework with real examples
• The Startup Validation Checklist — 15 questions every founder should answer
• Pain Point Mining — Find business ideas in customer frustrations

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Want market research based on actual data, not AI guesses? Try Bedrock Reports and see what evidence-based validation looks like.