5 Things We Keep Getting Wrong About AI
The stories we tell ourselves and what they’re really costing us.
I work with big data, AI, and strategy. I’ve been part of a lot of AI conversations. Even now, many people still ask me, “What is AI or GenAI?” What also surprises me is that sometimes, people who understand technology can get it wrong. There is a tendency to turn AI into something that it’s not. We want it to be smarter, more human, more magical, and we try to do this with good intentions.
But that mindset creates blind spots. People start treating AI like a shortcut to everything. We expect it to solve problems without really thinking about how it actually works or what it’s meant to do. In the process, we could miss what matters: the practical and often invisible ways AI is already shaping how our teams operate, how strategic decisions are made, and how existing jobs are changing.
I would like to help more people see the difference between what AI can do and what it absolutely cannot. So let’s talk about what AI isn’t. These are the things we need to stop pretending about.
1. “AI has self-awareness”
It doesn’t. Not even close.
Someone once ran an LLM to write 1000 poems in a row. When the session crashed, they said, “It got tired.” No, it didn’t. It hit a system limit. It’s a machine that is doing exactly what it was told.
Still, people keep talking about AI like it’s human. We make it sound like us by using human-sounding terms, such as “hallucination.” Don’t let the terminology trick you into thinking that AI can think. There’s no brain behind the output.
AI talks like us because we trained it on human input. That’s why it sounds familiar and make people believe that good imitation of our tone means real understanding. You can give it a great prompt with all the right words, but all it’s doing is calculating the next most likely word over and over again.
For example, you type, “The meeting was canceled because…” and it looks across billions of sentences it’s seen before. Maybe it’s seen “the weather was bad” or “no one showed up.” So it picks something that fits well. It does not understand meetings, or weather, or cancellations. Because it’s seen those words often appear together in its training data, it can make a prediction about the pattern.
AI doesn’t have a vision. It has no opinion about your question. It doesn’t know what it’s doing. It doesn’t feel, think, or even know it exists. It’s not guided by judgment. It doesn’t care if the answer is helpful or harmful. It’s following pattern logic and probabilities.
It’s a machine that can mimic how we talk, but not how we think. That’s the difference. Please don’t forget that.
2. “AI will make everyone lose their jobs”
That’s not true.
AI is coming for the jobs that are repetitive and easy to copy. It’s not taking jobs away. At the same time, it’s also creating new jobs faster than most people realize.
In 2025, roles like AI content reviewer and prompt engineer are getting 30 to 40 percent salary bumps. These jobs didn’t exist a few years ago. Now they’re showing up in many hiring plans across major companies, sitting right between tech and ops teams. They are filling a gap that didn’t exist before because of AI.
What companies really want is better and faster outcomes rather than automation. Fewer handoffs with less resource and reduced cost. These new roles help make that possible.
When AI clears out low-value work, it creates a spotlight somewhere else. Someone will need to translate complex business goals into machine-executable steps. Someone will also need to steer the system, audit the results, and fix what breaks. Companies are willing to pay for roles that can turn AI into something useful and impactful for the business.
People who figure this out early and learn to work with AI won’t get replaced. They will end up getting ahead and shaping how the future of work gets done.
3. “AI images have no copyright”
Also wrong.
A creator trained an AI model using their own photos to generate avatars. They uploaded the results. A platform flagged and took it down not because it was offensive or violated community rules. “AI made it” doesn’t give anyone a free pass and the ownership still applies.
In the US and EU, courts have already stepped in. Platforms are also actively taking the content down. Some companies have already been sued. Others are quietly pulling their products before getting into trouble.
You can’t remix copyrighted material and pretend that it’s yours because an AI model processed it. If the model was trained on protected work, then you’re carrying that weight too. It doesn’t matter if it looks or sounds different. The original source still matters.
People act like AI-generated art as if it comes out of nowhere. However, it actually comes from content that belongs to someone else. If you don’t know what went into the model, you don’t know what you’re accountable for.
You don’t need to become a lawyer to understand this. Technology does not rewrite ownership rules. If your AI pipeline starts with stolen content, then it’s not true innovation.
4. “Bigger, more expensive AI is always better”
Not true either.
Many people want to go after giant models with billions of parameters as if the more powerful the model is, the higher quality results it delivers. But someone could run a small local model on a machine and still beat a top-tier commercial model on simple school math. It ran faster and produced cleaner results with fewer mistakes.
Large models are often generalists. It works well if you try to solve general problems. But if you know your domain and understand your data, a smaller model with the right tuning and deployment can outperform the giant model every time. If you care about cost, latency, and control of your AI stack end to end, in those cases, what actually matters more is focus, fit, and context rather than scalability.
Model size doesn’t equals value. You can’t force a model to generate good results. Efficiency, speed, and relevance also matter. You have to decide what kind of model you’ll need and what will work best for you. If you're not tailoring the model to your actual problem and business context, you're wasting time, compute, and money. You will not solve your problem or get the return on investment.
5. “AI can predict the future”
It can’t, but people want to treat AI like it knows something.
Chatbots are fast and useful. We can ask ChatGPT for stock tips, lottery numbers, or revenue for the next quarter, etc. It gives probabilistic answers that sound smart and convincing because the model is good at predicting patterns.
What’s happening is that the model looks at historical data and predicts what’s likely to happen next based on how things usually go. If the future follows a clear pattern, you will get a decent forecast. But if the future depends on a lot of uncertainty or human judgment, you are unlikely to get back anything meaningful. The model was built to complete sentences and not to predict reality.
This is where a lot of people get it wrong. They often confuse pattern matching with insights. If you want to have a real prediction engine, you need to develop a tailored model with real data pipelines, constraints, and feedback loops. You need something that can also simulate causal relationships rather than a chatbot that is trained to predict the next word in a sentence. Don’t mix them up.
You’re the one thinking
AI does exactly what we train it to do. The risk is in the stories we tell about AI. As a result, people can make bad bets. Companies can burn millions of investment. Policies can keep falling behind.
AI is powerful in the way any tool is. It reflects the intent and limits of whoever’s holding it. The problem is we keep mislabeling it. We call it creative when it’s just remixing. We say it’s thinking when it’s just pattern matching. We frame it as a partner, when most of the time, it’s autocomplete running on a feedback loop.
It’s important that all of us know the difference. It will prevent us from wasting our time, resource, and allow us to stay focused on creating something valuable. If you want to receive meaningful results, you need to care about what AI can do versus can’t do, and how to keep making it better.