The sharper the blade, the wider the gap between the master and the novice. AI works the same way: it empowers some and strips others of their last shred of independent thought.
In 2026, it seems like nobody is left who doesn't use AI. Companies market it, newspapers write about it, everyone on social media is talking about what they've done with AI. A lawyer has AI draft their case, a student has AI do their homework, a marketer has AI generate ad copy. Everyone uses it. But do they really?
An analogy. In the late 1990s, the internet entered everyone's home. Everyone started saying "I go on the internet." But what did that actually mean back then? For most people, it meant this: open a browser, type something into a search engine, click the first result, read it, close it. Was that "using the internet"? Technically, yes. But the people who truly used the internet – researchers, developers, journalists – were doing something entirely different. They were reading source code, accessing databases, cross-referencing multiple sources, questioning where information came from.
What was the difference? Both were using the same tool. The difference was not in the tool. The difference was in the mind of the person using it.
There is a chasm between opening a tool and actually using it. Typing a question into ChatGPT is not using AI – just as typing a word into Google is not doing research.
This is exactly what is happening with AI today. There is a chasm between opening ChatGPT and saying "write me this" and actually using AI. But nobody sees this chasm. Because the people on this side of it don't even know the other side exists. And nobody is telling them – because telling them isn't profitable.
It looks like a paradox: the more powerful the tool, the greater the gap between its users. Intuitively you'd expect the opposite – a powerful tool should lift everyone up, level the field. But it doesn't. It never has.
The moment the tool stops being the constraint, the user's capacity becomes the deciding factor.
In the age when everyone used a stone blade, the gap between people was small. The tool limited everyone equally – no matter how talented you were, there was a ceiling to what you could do with stone. When the steel blade arrived, that ceiling vanished. And suddenly the gap between the butcher and the amateur exploded. Because the tool was no longer the constraint – the tool now reflected the capacity of the person holding it.
The same pattern repeated with every powerful tool:
The reason it looks like a paradox is this: people assume a powerful tool will lift everyone up. But a powerful tool doesn't lift everyone – it raises the ceiling. When the ceiling rises, those close to the floor stay close to the floor, and those who can climb go even higher.
AI is not an equalizer. AI is a magnifying glass. It amplifies whatever is already there: capacity in those who can think, and the void in those who cannot.
In Google's early years, there was a hope: everyone would have equal access to knowledge, information monopolies would end, humanity would become informed. That hope never materialized. Type anything into Google today. Nearly the entire first page is: sponsored links, SEO-manipulated sites, articles compiled by one AI from another AI's output. The tool itself has rotted.
But there is a problem bigger than the tool's decay: the decay of the person using it. Because over the years, Google taught people an extremely dangerous habit: type something, look at the first result, say "okay, I've learned it." This habit burrowed so deep that people can no longer distinguish between knowing and believing they know.
Shallow question → fluent answer → satisfaction → stop searching → stay shallow → ask a shallow question. The loop closes itself.
Now that same habit has migrated to ChatGPT. Someone types a question, a fluent paragraph comes back, and they say "I did my research, I've learned it." They neither researched nor learned. They read the statistical output of a language model and mistook it for their own thinking.
This loop closes itself, and its cruelest feature is this: to break the loop, you need to be outside it. To recognize your own shallowness, you need to know what depth looks like. But the shallow person is unaware depth even exists – because in the shallows, the "found it" feeling is already satisfying. Knowing what you don't know is a competence; not knowing what you don't know is a self-feeding blindness.
And the most powerful tool feeding that blindness is now AI. Because Google's output at least looked dry and technical – people could say "I didn't quite get it." ChatGPT's output is fluent, polished, confident. Even when the answer is wrong, it looks right. Falling into the "I understood" illusion is far easier.
In the 1990s, people watched the evening news and said "now I understand politics." The anchor explained, the viewer listened, and believed they were informed. What actually happened? They passively consumed information that someone else had framed, selected, and interpreted. But at least there was an awareness: the person on the other side was someone else. Anchor and viewer were separate. The boundary was clear.
With ChatGPT, that boundary blurred. Someone asks a question. The AI answers. When they read that answer, the brain codes it like this: "I asked, it answered, so this is my research." But they neither asked (they asked someone else's question), nor researched (they pressed a button), nor evaluated (they accepted the answer as-is). Yet because the process took a question-and-answer format, the brain registered it as "my own effort."
This is more dangerous than the television anchor. Because with television, at least you knew you were passive. With ChatGPT, you believe you are active. Asking a question feels like action – but asking a shallow question can be worse than not asking at all. Because the person who never asks at least knows they don't know. The person satisfied by a shallow answer believes they do.
And the most painful part: those trapped in this illusion will never see what AI can truly do. Because what they see is fluent answers to their own shallow questions. And those answers satisfy them. The satisfied person stops searching. The person who stops searching never discovers. The person who never discovers doesn't even know there is something to discover.
Let's ask a question: what do the companies that produce AI – OpenAI, Anthropic, Google DeepMind – say about the fake courses, inflated titles, and unqualified instructors flooding the market?
Zero. Not a single blog post. Not a single warning. Not a single sentence saying "people out there are ripping you off, be careful."
Meanwhile, what are these same companies doing? Publishing their own free training materials. Anthropic Academy: completely free, even the certificate is free. OpenAI's joint course with DeepLearning.AI: free. Google's prompt training: free. So while people are selling ChatGPT courses for hundreds or thousands of dollars, the company that built the tool is giving away the same knowledge for nothing. But it won't say "be careful."
First, these courses bring them customers. Even a bad course gets people using ChatGPT. Every course that gets people using ChatGPT signs up ChatGPT subscribers. Why would OpenAI cut that channel? Even a fake course graduate is a potential customer.
Second, market expansion strategy. These companies' primary goal is growing their user base. They're sending the message "AI isn't scary, everyone can learn." Whoever teaches it, wherever it's learned – the learner becomes a customer. Quality control would slow that growth. For them, bad education is more profitable than no education.
Third, Silicon Valley's concept of responsibility. The standard posture in this culture: "We build the tool, how you use it is your problem." Uber didn't care about its drivers' working conditions. Airbnb didn't care about host quality. Meta didn't care about its users' mental health. Same logic: "We provide the platform, what happens around it is not our concern."
These companies publish safety reports, declare ethical principles, write "responsible AI" manifestos – but say nothing while their own users are being fleeced. Because the ones being fleeced are end users, and the ones doing the fleecing are channels that indirectly bring in customers.
Think of a pharmaceutical company: it manufactures the drug, writes the package insert, but says nothing while fake "drug consultants" sell that drug at the wrong dose with wrong information. Because the drug is being sold regardless. The prescription may be wrong, the dose may be wrong, the consultant may be unqualified – but as long as the drug sells, the pharmaceutical company profits.
This is exactly what is happening in the AI market.
OpenAI CEO Sam Altman said this on 13 September 2022 – two and a half months before ChatGPT launched:
I don't think we'll still be doing prompt engineering in five years. And this'll be integrated everywhere. Either with text or voice, depending on the context, you will just interface in language and get the computer to do whatever you want.
What did Altman mean? That models would become so smart that people wouldn't need special formulas, templates, or technical tricks. The early AI models really were fragile: getting the right output required magic formulas. If you didn't write "think step by step," it couldn't think step by step. If you didn't say "you are an expert," it gave superficial answers. These technical tricks – yes, they really are dying. In 2026, AI models largely understand natural human speech.
On this point, Altman was right. Fortune magazine confirmed it in May 2025: "Prompt engineering, once marketed as a $200K no-code career, is now effectively obsolete." Job listing searches for "prompt engineer" peaked in April 2023 – then steadily declined.
But beneath what Altman said lies a very dangerous assumption: if the tool gets good enough, the gap between its users closes. This assumption is wrong. It has been wrong in every era of history and it is wrong now.
Because Altman defined "prompt engineering" too narrowly: finding the right sequence of words, directing model behavior through formulas. But truly using AI is not limited to that. Truly using AI means:
Knowing what to ask – asking the right question is far harder than using the right syntax. As AI gets smarter, this becomes more critical, not less.
Framing the question properly – as the model gets smarter, the cost of asking a bad question rises. Because a smart model gives a fluent answer even to a bad question – and that fluent answer misleads you.
Evaluating the answer – AI sometimes lies. Catching this will never be automated. You need to already know the subject or know how to verify.
Knowing the model's limits – every model has different strengths and weaknesses. Without knowing these, you can't match the right tool to the right task.
Orchestrating multiple models – relying on a single AI is like getting your news from a single source. Cross-validation, combining different models' different strengths – this is about a way of thinking, not technical tricks.
And here is the paradox: Altman said it would "die" in 2022. But in 2026, OpenAI has built its own training platform, publishes prompt guides, offers joint courses. If it's really dead, why are you still teaching it? Because what died was the technical formula – but in its place came a far deeper competence: thinking with the model. And that competence cannot be bought.
This is not one country's problem. Worldwide, the AI training market has inflated like a bubble. In the US, "prompt engineer" job listings peaked in 2023, then collapsed. But the courses keep selling. The number of LinkedIn profiles claiming "AI expert" has multiplied since 2022. Platforms like Udemy, Coursera, and Skillshare host thousands of "AI courses" – most of which do nothing more than screen-record the ChatGPT web interface.
The pattern is the same everywhere: courses costing hundreds or thousands of dollars, content covering ChatGPT only. No Claude, no Gemini, no DeepSeek, no Llama – only one tool from the entire AI world is being taught, and only in its most superficial form. At the end, a certificate is issued. That certificate gets framed. And people say "now I know AI."
On freelance platforms, hundreds and thousands of people worldwide are registered as "AI experts." Look at their profiles: "machine learning expert," "deep learning expert," "prompt engineer." Credentials: typically a certificate from an online platform. People who watched a few hours of video and earned a certificate are marketing themselves as AI experts.
On the corporate side, it gets even more interesting: half-day "AI transformation seminars" sold to companies. A few hours, a few thousand dollars. Content: how to ask ChatGPT questions, plus an automation tool and a few ready-made templates. Paid from the training budget, nobody questions the content.
Thousands of dollars for a few-hour seminar. What you learn for that money: typing questions into a web interface. Any 12-year-old could figure this out on their own in five minutes.
But the real question is not the price. The real question is: do the people delivering this training have the competence to deliver it?
Universities worldwide are racing to open master's programs with an "artificial intelligence" label. In the US, in Europe, everywhere – the same trend. But most of these programs share a common problem: they were created to meet commercial demand.
Here is how the mechanism works: the words "artificial intelligence" create demand. Programs open where demand exists. To fill enrollment, admission standards are kept low – some require no entrance exam, no language requirement, any bachelor's degree will do. The program may actually be "Computer Science" or "Information Technology," but the "artificial intelligence" label gets pushed in marketing. Because the gap in application numbers between writing "Computer Science master's" and "Artificial Intelligence master's" is enormous.
Label: "Artificial Intelligence" or "Data Science" – but the underlying program is usually classic Computer Science.
Admission requirements: Non-thesis tracks commonly have no exam or language requirements. The door is wide open – the goal is to fill seats.
LLM / transformer / generative AI in the curriculum: Absent in most. Pre-2022 curricula haven't been updated. Where it exists, it's elective – you can graduate without taking it.
Faculty: Existing academics are competent in their own fields – but most of their expertise has nothing to do with what people today mean by "artificial intelligence."
Consider the academic faculty in these programs. Power electronics professors, signal processing specialists, image processing researchers, control systems engineers. Serious people in their own fields. But none of these fields is about how a language model works, how to steer it, or what its limits are. They all fall under the "artificial intelligence" umbrella – but the only thing a washing machine's fuzzy logic controller and ChatGPT's operating principles have in common is the phrase "artificial intelligence."
This is like an orthopedic surgeon saying "I'm a doctor" and walking into brain surgery. Both graduated from medical school, both are doctors. But one does knee surgery, the other does brain surgery. Saying "they're both doctors" tells you nothing.
"Artificial intelligence" is exactly this kind of umbrella term. Under it lie dozens of different fields: image processing, natural language processing, robotics, control systems, recommendation systems, reinforcement learning, generative models... These are all "artificial intelligence" but entirely different disciplines.
Certain terms appear in these programs' curricula that look very impressive from the outside – but have zero connection to language models:
| Term | What it actually is | Relation to language models |
|---|---|---|
| Web Mining | Extracting information from web pages using statistical methods. Popular in the early 2000s, now a legacy technique. | ZERO |
| Cloud Computing | Renting servers over the internet. Setting up virtual machines, container management. Infrastructure concern – not AI. | ZERO |
| Intelligent Optimization | Solving mathematical problems with nature-inspired algorithms. Ant colony, bee swarm, genetic algorithm. Has existed since long before the AI hype. | ZERO |
| Image Processing | Noise removal, object recognition, medical analysis on photos and video. A different branch of AI – not language models. | DIFFERENT BRANCH |
| Fuzzy Logic | A decision-making technique under uncertainty, developed in 1965. Used everywhere from washing machines to elevators. Classic control engineering. | ZERO |
All of these are legitimate and valuable fields of computer science. The problem is not with the fields themselves. The problem is that these fields are presented under the "artificial intelligence" umbrella as if they confer competence in language models. And behind this presentation lies a commercial driver: attracting students to the program, filling seats, collecting tuition. It is the appeal of the label being sold, not the quality of the education.
Everyone talks about the harm of AI. "It will take our jobs," "it will destroy humanity," "it will take over the world." Hollywood scenarios, doomsday headlines, conspiracy theories. All of it is noise. The real harm is far quieter, far more insidious, and has already begun.
AI empowers those who can think. It strips those who cannot think of their last remaining shred of independent thought.
That sentence sounds harsh, but consider the mechanism:
Same tool, two different outcomes. The difference is not in the tool – it's in the thinking capacity of the person using it.
The person with thinking capacity uses AI as a tool. They ask, evaluate, verify, redirect, cross-reference with multiple sources. With each use, they learn a little more, their capacity grows. AI makes them stronger.
The person without thinking capacity uses AI as an authority. They ask, accept whatever comes, say "I've learned it," and stop thinking. With each use, they grow a little lazier, their capacity shrinks. AI makes them weaker.
And the gap between these two groups widens every day. Because the first group grows stronger the more they use AI, while the second group grows weaker. Same tool, opposite outcomes. Just as a sharp blade empowers the master and cuts the novice.
And the majority in this pyramid – the 85% at the top – believe they are "using" AI. Courses costing hundreds or thousands of dollars are sold to this majority. What the course provides is making them feel more secure about staying in the top layer. A certificate that says "now I use AI too." What is actually happening: buying the certified version of shallow use.
This essay is not an attack. It is a warning.
AI really is a powerful tool. Perhaps the most powerful knowledge tool since the printing press. But like every powerful tool, it is harmful in the hands of someone who lacks the capacity to use it. And the most insidious form of that harm is the person not realizing they are being harmed.
Diving into AI headfirst is dangerous. Saying "everyone's using it, so I should too" is like saying "everyone's taking this medicine, so I should too." Taking medicine without knowing what it is, what it does, its side effects, its dosage – that is not treatment, it is gambling.
Truly learning AI does not mean learning to type questions into a web interface. Truly learning AI means:
Knowing what you are asking. Because a shallow question brings a fluent but empty answer, and you won't be able to tell the difference.
Being able to evaluate the answer. Because AI sometimes lies and does so very convincingly.
Not becoming dependent on a single tool. Because every model has different strengths and weaknesses.
Not outsourcing your responsibility to think. Because AI cannot think for you – it can only accelerate what you are already thinking.
Being able to say "I don't know." Because knowing that you don't know is a thousand times more valuable than believing you do.
A course costing thousands of dollars will not teach you this. A certificate will not give you this. A master's degree will not guarantee this. Because this is not a tool-use skill – it is a way of thinking.
And this way of thinking cannot be bought. It can only be built.
AI is not harmful. Using AI without thinking is harmful. And what will teach the difference between these two is not a course, not a certificate, not a university degree. The only thing that will teach this difference is taking ownership of your own responsibility to think.
The original text is in Turkish. We aimed for an organic rendering in English rather than a literal translation. If you notice any errors or can suggest a better phrasing, please write to [email protected] – you would enrich our translation.