The Summary Trap: Summaries Are Not the Source of Truth
AI doesn’t know what’s true. It knows what’s common.
People stopped verifying.
AI made it too easy to get confident answers that sound right. You ask a question, it gives you something that looks smart and complete, and you move on. The tone feels certain, so it feels true.
I’ve seen this go wrong. A friend recently made a big business decision based on an AI answer. It looked well-written and backed by “research.” It wasn’t. The data behind it was random - stitched together from summaries of blogs, and outdated PDFs. He spent weeks following that advice and got nowhere.
That’s what happens when you use AI as a shortcut for thinking.
Summarized Data Is Not Truth
AI doesn’t have opinions. It mirrors patterns from whatever data it was trained on. The problem is that the web is full of outdated, biased, and recycled content. When you ask AI something like “Which niche is profitable for dropshipping?” or “What are the best SEO strategies in 2025?”, it’s pulling from the same pool of recycled posts everyone else read last year.
You get a summary of a summary of a summary.
If everyone uses those same summaries to make business decisions, everyone ends up chasing the same trends. That’s why most “AI-generated insights” lead people to do what’s already been done.
Examples of This Trap
A founder asked AI which products are trending. It gave him a list that looked right. He ordered inventory. Those trends had peaked six months earlier.
A marketer asked AI for “keywords with high conversion potential.” The suggestions came straight from old SEO blogs. The real profitable keywords were already bid up by competitors. Google had changed the search algorithm, ad placements shifted, and user behavior moved on.
A researcher asked AI for “top companies hiring in renewable energy.” The results were well-known names, some already out of the market. The real opportunities were buried in local job boards, niche databases, and startup listings AI didn’t indexed yet.
All of those could have been avoided with real data - scraping product pages and their reviews, job listings, or RSS feeds directly.
Feed AI Better Data
AI is powerful when you give it context that others don’t have. The smartest companies don’t ask ChatGPT what’s happening in their market. They feed it what’s actually happening.
You can collect:
Product prices from competitors
Supplier updates or new tender posts
Job listings that show where hiring demand is rising
Customer reviews that reveal feature gaps
Once you have this data, AI becomes useful. It can analyze, summarize, and help you act on your own dataset, not recycled web noise.
That’s the difference between using AI and training it to work for you.
Start with Your Own Source of Truth
Whether you use Hexomatic, or any other tool doesn’t matter. What matters is building a clean data pipeline that reflects your reality.
Scrape, collect, and track the information that drives your business. Then let AI process it.
We created Hexomatic and Hexowatch to make this process simple. You can automate data collection, monitor website changes, or book our concierge team to build a custom workflow or scraping template for you.
Or if you’re not sure where to start, book a free demo call and we’ll show you exactly what’s possible for your use case.
AI doesn’t know what’s true. It knows what’s common.
Get your own data. Then decide.
→ Start collecting your own data today.
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