You’ve seen the screenshots. Maybe it’s a lawyer citing fake cases or a student getting a lecture from a bot that thinks 9.11 is larger than 9.9. We’ve all been there. It feels like magic until it feels like a toy. Honestly, the honeymoon phase with LLMs is over, and we’re left staring at the screen wondering why this world-changing tech keeps tripping over its own feet. If you’re asking what’s wrong with ChatGPT, you aren't just looking for a bug report. You’re looking for why it feels so confidently wrong half the time.
The reality is weirdly simple. ChatGPT doesn’t "know" anything. It predicts the next word. It’s a very fancy autocomplete that has read the entire internet, including the garbage parts. When it fails, it fails because it lacks a "world model." It doesn’t understand gravity, time, or the fact that words actually represent physical objects in space. It just knows that the word "apple" often follows the word "crunchy."
The Hallucination Problem That Won't Go Away
The term "hallucination" is actually kind of a polite way of saying the machine is lying to your face. In 2023, two New York lawyers, Steven Schwartz and Peter LoDuca, famously used ChatGPT to research a personal injury case. The bot gave them six different court cases that didn't exist. It didn't just get the details wrong; it fabricated entire legal histories, complete with fake citations and quotes.
This happens because of the way Large Language Models (LLMs) function. They are probabilistic. When you ask a question, the model looks at the tokens—chunks of text—and calculates the most likely next token based on its training data. If it hasn't been trained on a specific niche fact, it doesn't say "I don't know" by default. Instead, it tries to satisfy the prompt by hallucinating a plausible-sounding answer.
It's a feature, not a bug, of the architecture.
The Transformer architecture, which is the "T" in GPT, was designed for creativity and translation, not for being a database. OpenAI has tried to patch this with Reinforcement Learning from Human Feedback (RLHF). Humans sit there and tell the bot "no, that's wrong," but you can't manually correct every possible lie the bot might tell. It's like trying to drain the ocean with a thimble.
Why It Can't Do Simple Math or Logic
Have you ever asked ChatGPT to count the "r"s in the word "strawberry"? For a long time, it insisted there were only two.
It’s hilarious. And frustrating.
The reason is "tokenization." ChatGPT doesn't see letters the way we do. It sees the word "strawberry" as a couple of numerical chunks. Imagine trying to count the number of times the letter 'e' appears in a picture of a park; you’d have to zoom in and count, but if you’re only looking at the whole picture, you might guess. OpenAI’s o1 model, released late in 2024, started using "Chain of Thought" processing to fix this. It basically talks to itself in the background before giving you an answer.
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But even with "reasoning" models, the logic is brittle. If you give ChatGPT a logic puzzle that looks like a famous one but change one tiny, crucial detail, it will often give you the answer to the famous version. It isn't thinking. It's recognizing a pattern. This "pattern matching" is what leads to the "lazy coding" complaints developers have been voicing on Reddit and X for months. The bot suggests deprecated libraries or misses edge cases because those edge cases weren't common in the training set.
The Shrinking Data Pool and the "Dead Internet" Theory
There is a massive problem looming over OpenAI and its competitors: they are running out of high-quality human writing.
Researchers from Epoch AI have predicted that tech companies could exhaust the supply of high-quality public manuscript data by the late 2020s. What happens then? They start training models on AI-generated content. This creates a "Habsburg AI" effect—inbreeding for data. The errors get amplified. The prose gets more generic. The quirks that make human writing interesting get smoothed out into a bland, corporate paste.
- Data contamination: Modern models are already accidentally training on their own outputs.
- The "Vibe" shift: Users report that the model feels "dumber" or more "repressed" over time.
- Copyright walls: The New York Times lawsuit is just the beginning. As more publishers block scrapers, the "freshness" of the bot's knowledge starts to rot.
When people ask what's wrong with ChatGPT lately, they often mention "censorship" or "guardrails." OpenAI has built so many safety filters into the system that it sometimes refuses to answer harmless prompts. If you ask it to write a story about a fight, it might give you a lecture on non-violence. This "preachiness" is a direct result of trying to avoid PR disasters, but it makes the tool feel less like a partner and more like a HR department.
Energy Consumption and the Environmental Cost
We can't talk about what's wrong with ChatGPT without looking at the power bill. Not your bill—the planet's.
A single query in ChatGPT uses about ten times the electricity of a Google search. Training GPT-4 likely consumed megawatt-hours of power, enough to run thousands of US households for a year. Microsoft and Google are literally scouting for nuclear power plant deals just to keep the servers humming.
While this might not affect your daily chat, it’s a systemic flaw. If AI is going to be the "new electricity," we haven't figured out how to generate enough actual electricity to run it without melting the ice caps. This is a massive bottleneck for the "AI revolution."
The Privacy Paradox: Who Is Reading Your Chats?
Everything you type into that little box is, by default, used to train future versions of the model.
Unless you go into settings and manually turn off "Chat History & Training," your secret business plan or your personal venting session is being sucked into the collective brain of OpenAI. Companies like Samsung and Apple have famously banned or restricted internal use of ChatGPT after employees accidentally leaked sensitive code and meeting notes.
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The "incognito" mode on ChatGPT is a start, but it’s a Band-Aid. The fundamental business model of these companies relies on your data. If you aren't paying for the highest enterprise tier, you (and your data) are the product.
Actionable Steps to Beat the Bot's Flaws
You don't have to stop using it. You just have to stop trusting it blindly. Here is how to actually get value out of it without falling into the traps.
1. Use the "Few-Shot" Prompting Technique
Don't just ask for a result. Give it three examples of what a "good" result looks like. If you want it to write like you, paste in two paragraphs of your own writing first. This anchors the model and prevents it from drifting into "AI-speak."
2. Verify with "Search" or "Browse"
If you are asking for facts, make sure the "Search" icon pops up. If it doesn't, tell it: "Search the web to verify the current stats for X." Force it to look at real-time data rather than relying on its internal, static training weights.
3. The "Reverse Prompt" Strategy
Instead of asking it to write a strategy, ask: "What information do you need from me to write a world-class marketing strategy for a mid-sized SaaS company?" Let it interview you. The better the input, the less room there is for hallucination.
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4. Treat It Like a Junior Intern, Not a Senior Expert
You wouldn't let a 19-year-old intern publish a legal brief without checking every word. Treat ChatGPT the same way. It’s great at brainstorming, summarizing long PDFs, and formatting data. It is terrible at being the final authority on anything that matters.
5. Check the "System Instructions"
In the settings, you can add "Custom Instructions." Tell the bot to "Always be concise," "Avoid flowery language," and "State when you are unsure of a fact." This cuts down on the "as an AI language model" fluff that drives everyone crazy.
The tech is incredible. It’s also deeply flawed. The "magic" is actually just math, and once you realize the math has limits, you can actually start using it effectively. Stop expecting it to be an oracle. Start using it as a high-speed, slightly unreliable assistant.
The real problem isn't just what's wrong with ChatGPT—it's that we expected a calculator for words to be a god. Once you lower the stakes, the tool becomes a lot more useful.