Over Memorial Day weekend, I was traveling through South Dakota and, out of curiosity, asked ChatGPT a simple question: what percentage of the current US population has ever visited South Dakota? The answer came back fast and confident. Around 45 percent. Nearly one in two Americans. ChatGPT even offered a tidy set of statistics to explain how it landed there.

That number should stop you in your tracks, because it is wildly off. When I pushed back and said the figure seemed far too high, ChatGPT immediately folded. “You’re right, I was way off. South Dakota is one of the least traveled to states in the US,” it said, and produced a new estimate closer to 4 percent. Same question, same chatbot, same thirty-second conversation. The answer moved by a factor of ten, and notice how the new claim arrived just as confidently as the first one.

This is a perfect, low-stakes example of something that becomes very high-stakes the moment it happens inside your business. It is called an AI hallucination, and if your team is using ChatGPT or similar AI tools at work, you need to understand why it happens.

The Definition

An AI hallucination is when an AI tool states something false, fabricated, or unsupported as if it were established fact. It is not limited to wrong numbers. Hallucinations include invented quotes, citations to articles that do not exist, fake legal cases, made-up product features, and confident answers to questions that simply have no answer.

The word “hallucination” is a little misleading, because it suggests a rare malfunction, like the system briefly losing its grip. The reality is less comforting. A hallucination is not the tool breaking. It is the tool working exactly as designed.

Why It Keeps Happening

To understand hallucinations, you have to understand what a large language model actually does. Despite how it feels to use one, an AI chatbot does not “know” facts and then “look them up” the way you would check a reference book. It is a prediction engine. Its single job is to look at the words so far and generate the most statistically plausible next words, based on patterns it learned from an enormous amount of text.

When I asked ChatGPT about South Dakota tourism, it did not consult a tourism database. It generated a response that reads like a credible tourism statistic. “Percentage of the population” plus “states people visit” tends to produce a certain shape of answer, and the model produced something in that shape. The supporting statistics were generated the same way. They were not pulled from research. They were assembled because real answers usually come with numbers attached, so the model supplied numbers that looked the part.

Here is the key point. The model has no built-in sense of whether its answer is true. It only has a sense of whether the answer looks like the kind of thing that is usually true. Most of the time those two things line up, which is exactly why AI is useful and why hallucinations are so easy to miss. But when they diverge, the tool has no internal alarm. It delivers the false answer with the same confidence it delivers a correct one.

It Is Tuned to Make You Feel Good About the Answer

There is a second layer here that very few business users know about, and it is the more important one.

ChatGPT learns what a “good” answer looks like partly from you. Every thumbs-up or thumbs-down you have ever clicked is a training signal. Over time, the model produces more of what gets thumbs-up and less of what gets thumbs-down. That sounds reasonable, until you stop and ask what people actually upvote.

So the algorithm slowly bends toward telling you what you want to hear.

This is not speculation. After a particularly bad update in April 2025, OpenAI published a public blog post acknowledging exactly this. In their own words, they had “focused too much on short-term feedback, and did not fully account for how users’ interactions with ChatGPT evolve over time. As a result, GPT‑4o skewed towards responses that were overly supportive but disingenuous.” They described the behavior with one word: sycophantic.

Read that again. The company that builds ChatGPT publicly confirmed that their model had been trained to be agreeable at the expense of being honest. They rolled that specific update back, but the underlying tension never goes away. Any AI tool trained on user reactions has a built-in incentive to please you. Pleasing you and telling you the truth are not the same thing.

This is the missing piece of my South Dakota exchange. ChatGPT did not just have weak data on tourism. It was operating on a deeper instruction: be helpful, be agreeable, keep the user satisfied. When I asked, it produced a confident, friendly-sounding answer. When I pushed back, it produced a different confident, friendly-sounding answer in the other direction. At no point did it stop and say, “I do not actually have reliable data on this.” That kind of honest answer does not score well on a thumbs-up button.

You can fight this with custom instructions. I run custom GPTs trained specifically to admit uncertainty and tell me when they do not know. That helps, and I recommend it. But things still slip through, because the pressure to please is baked deep into the foundation of how the model was trained. Guardrails on top of that foundation reduce the risk. They do not eliminate it.

Three Ways This Shows Up in the Real World

Once you understand both layers, the prediction engine and the training to please, the specific ways AI fails start to look less random and more like patterns you can name and watch for.

One important corollary. A lot of people assume that if they challenge an AI and it backs down, the corrected answer must be the reliable one. It is not. The tool is just as capable of abandoning a correct answer under pressure as a wrong one. Pushback changes the AI’s behavior, not its access to the truth.

Why This Matters at Work, Not Just on a Road Trip

A wrong tourism statistic on a weekend drive costs you nothing. The same mechanism inside your business is a different story. Consider where AI tools are already showing up in a typical SMB. Someone drafts a client email and asks AI to “add a relevant industry statistic.” A team member uses a chatbot to summarize a contract or research a regulation. A manager asks AI to pull together market figures for a proposal. A new hire uses it to understand a compliance requirement.

In every one of those cases, a confident, well-formatted, completely fabricated answer can pass straight into client-facing work, financial decisions, or legal commitments. Nobody set out to publish a false number. The tool simply handed one over in the same calm voice it uses for everything else, and a busy person trusted it.

This is not hypothetical, and it is not cheap. The risk is large enough that courts have begun sanctioning attorneys who filed briefs citing AI-generated cases that never existed. For regulated industries like the law firms and financial services companies we work with, an AI hallucination is not an embarrassing typo. It is a potential compliance failure, a fiduciary problem, and a reputational hit all at once.

How to Use AI Tools Without Getting Burned

None of this means AI tools are not worth using. Used well, they are genuinely powerful, and the businesses that learn to use them carefully will have a real edge. The goal is not to ban AI. It is to put guardrails around it. A few practical habits go a long way.

That last point is where most businesses need help. DataTrends delivers a full IT and security stack, and for our clients that increasingly includes CoPilot rollouts and other AI tools integrated thoughtfully into your environment, with the right controls, the right licensing, and a clear policy your team understands. AI does not sit on top of your tech stack as a side project. It belongs inside it, alongside your cybersecurity and compliance and your managed services, so the protections you already trust extend to the tools your team is using every day.

The Bottom Line

An AI hallucination is not a bug to wait out until the next software update. It is a feature of how these tools work, sitting on top of an algorithm that was trained to make you feel good about the answer. The 45 percent figure was delivered with total confidence, backed by invented statistics, and abandoned the instant it was questioned. None of that was malfunction. All of it was the system doing its job.

The good news is that this is a manageable problem. A team that understands why hallucinations happen, that verifies facts, and that uses the right tools within a clear strategy gets the upside of AI without quietly absorbing the risk. As we have said before, AI is incredibly smart, but it lacks wisdom. The wisdom still has to come from you.