Mar 1, 2026
Most people trust AI too much
4 MINUTES READ
Everyone has experienced it: you ask an AI something, want to generate an image, or have something summarized — and the result is obviously wrong. You try again, maybe get something closer to what you wanted, but you're still not satisfied. It's unclear why it works fantastically sometimes and completely misses the mark other times. You can see it's wrong, at least, and find a different approach. Things get far more dangerous, though, when the results sound right but contain false information. Those are not so easy to spot. AI formats its answers beautifully, wraps them in confidence and the occasional compliment, making it very easy to believe everything it says. This is where the greatest risk lies — because if you trust the results without checking them, they can cause real problems. Whether that's "just" getting the wrong idea while studying, or basing a business decision on a flawed analysis.
Hallucinations are the easier kind to catch, but they're also frustrating — especially when you have no idea where they came from. The inner workings of AI are opaque, and unfortunately that's often true even for the developers building with it. This is why many users end up arguing with an AI that's serving them hallucinations. You describe exactly what you want, but it just won't cooperate. The code doesn't work. The image shows the wrong people. The summary mixes things up. The answer is simply wrong. Even when additional context like documentation or web pages is brought in through "Retrieval Augmented Generation," it doesn't always help. There are countless posts and memes out there about AI misreading the most straightforward requests.
But here's the thing — that "arguing" is exactly the problem. Because arguing is human. AI is not. The frustration only exists on one side: yours. People have a natural tendency to humanize things. Pets are a classic example, but even inanimate objects get it. Since AI also talks like a person, it's easy to imagine someone sitting on the other end. So it's tempting to feel like the AI is deliberately messing with you, intentionally generating hallucinations. But that's not what's happening.
How do hallucinations actually occur?
To understand how hallucinations emerge, you have to take a step back and look at how AI generates its content in the first place. AI is trained on an enormous number of examples. To produce new text that imitates those examples, it categorizes training content using purely statistical patterns. In very simple terms: the AI treats every word as a building block and learns which other words tend to appear nearby. "Sun" likely appears close to "shines." "Cat" often shows up near "purr." The more varied the training data, the more of these associations form — and probabilities develop. If the word "painting" appears in a sentence about an artist, it might be followed by "draw." But if the context is a museum, "display" becomes more likely. The AI builds its sentences from these probability chains, much like assembling something from a box of LEGO bricks.
This is where a major misunderstanding creeps in when people humanize AI. It's easy to say the AI "knows" that paintings are displayed in museums and that artists paint them. But that's not accurate. It only knows that the sequence of letters in "painting" frequently appears alongside the sequence "draw" or "display." The AI doesn't know what these words mean, nor why they appear in this particular order. What has emerged from the sheer volume of training data is a machine that imitates human language convincingly enough to make you feel like it's actually thinking.
So how do hallucinations form? The AI connects words based on probability. If words in the training data are frequently used together in a context unrelated to your question, the AI will produce an answer that has little, nothing, or the wrong relationship to what you actually asked. Technically, the AI isn't making an error. It can only output what it has learned from its inputs. But because it may have answered every other question correctly, you suddenly feel like something's off — like it's hallucinating. So you rephrase, and maybe get the right answer. That's because a different set of words triggers a different set of probabilities.
Most Large Language Models carry that name for good reason: they're trained on a massive volume of language data, which gives them a high enough probability of responding grammatically and correctly to most common topics. For specialized subjects, you can supply additional documents at query time, which are factored into the response — a technique that improves accuracy for niche topics like machine documentation that wouldn't appear in a general model. But hallucinations can still occur even here. In Part 2 of this post next week, we'll look at how a group of researchers is working to solve this problem mathematically.
