The conversation around artificial intelligence often gets tangled in misunderstandings, especially when people confuse the appearance of intelligence with actual reasoning. A common water cooler comment I’ve heard is: “I was really disappointed by using ChatGPT the other day for reviewing Q3 results. This is not Artificial Intelligence — this is just a search and summarization tool, but not Artificial Intelligence.” This sentiment reflects a widespread confusion about what AI, and specifically large language models (LLMs), truly are. Many people imagine AI as a kind of sentient, conscious mind — a futuristic, all-knowing intelligence like Skynet or HAL 9000. But today’s AI, while powerful, operates in a fundamentally different way. It doesn’t “think” or “understand” like a human. Instead, it relies on patterns in data, a process that closely mirrors inductive reasoning — the kind of thinking Daniel Kahneman described as System 1: fast, intuitive, and based on experience, but prone to error and bias. Inductive reasoning works by observing specific instances and drawing general conclusions. For example, “The sun has risen every day I’ve lived — so it will rise tomorrow.” This seems logical, but it’s not certain. It assumes that past patterns will continue, which may not always be true. LLMs do something similar. They analyze vast amounts of text and learn the statistical likelihood of what word comes next. They don’t know what they’re saying — they just predict the most probable sequence based on what they’ve seen before. This is why LLMs can produce text that sounds intelligent, coherent, and even insightful — but it’s not guaranteed to be accurate. They can fabricate facts, make logical leaps, or generate plausible-sounding nonsense. This isn’t a flaw in the model’s design — it’s inherent to how they work. They’re not deducing truths from principles; they’re extrapolating patterns from data. Deductive reasoning, by contrast, is logical and deterministic. If all humans are mortal and Socrates is human, then Socrates must be mortal. There’s no uncertainty — if the premises are true, the conclusion follows with certainty. LLMs do not perform deduction. Even when they appear to reason step by step, they’re still relying on pattern prediction, not logical certainty. Enter Chain of Thought (CoT) prompting — a technique that encourages models to break down complex problems into smaller steps. By asking “Let’s think step by step,” users guide the model to generate reasoning chains that mimic logical thinking. This improves accuracy because it reduces the chance of jumping to a wrong conclusion in one go. But even CoT is still inductive. The model isn’t proving anything — it’s predicting the next plausible step in a chain, based on training data. Models like OpenAI’s o1 take this further by enabling “long-thinking” — where the model autonomously generates intermediate reasoning steps. This makes the output more reliable, but it’s still not true reasoning. It’s a sophisticated form of pattern matching, not understanding. So, is ChatGPT a search and summarization tool? In a way, yes — but not in the traditional sense. It doesn’t retrieve facts like a search engine. Instead, it synthesizes responses based on learned patterns. It’s more like a highly skilled mimic than a scholar. The key takeaway? AI today is not thinking, not reasoning, and not conscious. It’s simulating intelligence through statistical prediction. Recognizing this helps us use these tools more effectively — not as infallible experts, but as powerful assistants that can generate ideas, draft content, and support decision-making when guided properly. Understanding the difference between induction and deduction isn’t just academic — it’s essential for avoiding overreliance, spotting hallucinations, and making smarter decisions in business, research, and everyday use. The real power of AI isn’t in pretending to be human — it’s in helping us think better, as long as we remember what it actually is.