Until recently, if we wanted to learn something, we’d open Google, type in a question, and see a list of links. We could compare sources, find out who was saying what, and get a clearer picture. Today, though, with AI giving us direct answers, everything seems simpler. But there’s a catch: on what basis does it decide what to tell us? And more importantly, can we trust it?
AI doesn’t “search” in the traditional sense—it synthesizes. It gathers information scattered across the web and returns it in a single response. But if we can’t see where it pulled that data from, how can we know if it’s reliable? The risk is getting answers that sound authoritative but have no clear way to be verified. And what if the content it draws on is biased, outdated, or downright false?
Then there’s another big question. If everyone starts trusting AI blindly, who will still visit websites to read the original articles? If no one reads them, who will continue producing the content that feeds these artificial intelligences? The paradox is obvious: AI needs reliable information to work well, but if it drains traffic from the sites that generate that information, the system could collapse.
We need more transparency. AI systems must disclose which sources they use and why they’ve chosen certain information over others. Only then can we maintain a healthy digital ecosystem where knowledge isn’t monopolized by a few algorithms. For now, the rule remains the same: never stop comparing, verifying, and using your critical thinking. AI is a tool, not an absolute truth.
