Not just here in Kuwait but all over the globe, people are calling everything AI now
Your phone camera uses AI. Your email filter uses AI. The chatbot on a shopping website also uses AI. If the marketing team feels it sounds modern enough, a basic autocomplete feature gets the AI label. At some point, the word lost its specificity and became impressive technology we want you to notice.
Is AI really AI? It is what nobody tells you at the outset. The chatbot, the image generator, and the writing assistant is not Artificial Intelligence. It is Machine Learning with a more sophisticated name tag. That’s not quibbling. It affects how much you trust these tools and how cleverly you use them, and it is at the root of something that is impacting you financially right now: the silent but very real increase in AI hardware prices, from RAM to hard drives and GPUs, over the last three years.
It is precisely why many argue AI should be called ML. Machine learning vs AI is not a debate.
What Is Artificial Intelligence vs Machine Learning
Understanding AI vs Machine Learning starts with one question: Is it self aware? When you type a question into ChatGPT, Claude, Gemini, or any similar tool, nothing is thinking on the other end. Your input makes a prediction. The tool then generates the most likely sequence of words to follow what you wrote.
That’s Machine Learning. In particular, it’s a large language model based on a deep learning architecture. It can write, summarise, translate, explain, and create images. What it can’t do is understand any of that. A chatbot can write about grief without losing anything. It can talk about hunger without having a body. It can describe a business failure without having taken a single decision. It’s trained on things written by people who knew things, and so the output looks like it knows things. The look is not the real thing.
So is AI really AI? Not in the way most people mean it. The doorway is self awareness. When a system knows that it exists, when it thinks about its own thinking, when it understands the consequences of what it puts out, that’s when the word intelligence becomes applicable. We’re not there. Not even in the neighborhood.
Why Tech Companies Call Machine Learning “AI”?
It was caused by two things.
First came marketing. “AI powered” moves investors, sells products, and dominates headlines in a way “machine learning based” never will. In America the FTC (Federal Trade Commission) has already had to warn companies against making false claims about AI capabilities, which tells you all you need to know about how aggressively the term is being stretched. When a regulatory body has to say slow down, the marketing pressure behind a word has clearly outstripped its honest meaning.
Second was passion. The people building these systems are super passionate about what they are building. If a model gives you something surprising, even beautiful, the instinct is to grab the biggest description you can find. That’s a good excitement, not a bad one. The technology was not there yet, and so the label was premature.
The result is a word that now means everything and nothing at the same time. Every company uses it. Every product says it. And the average user has no good way of knowing what they’re actually paying for.
How AI Increased RAM Prices, Hard Drive Costs, and GPU Prices
The AI vs Machine Learning infrastructure gap is what drove these price increases. It is the part of the conversation that almost nobody connects to ML directly, and gets less attention than it deserves. It takes an extraordinary amount of physical hardware to run and train these systems. Thousands of high end GPUs, running 24/7, for weeks. Massive banks of RAM petabytes of storage. Data centers are specifically designed to meet the computational load. All the companies behind these tools are seeking the same hardware: Google, Microsoft, Meta, Amazon, and hundreds of smaller players.
That competition has personal implications for you. When GPU manufacturers prioritize enterprise data center orders, consumer availability tightens, and prices inflate. Regular consumer RAM is becoming harder to find as memory manufacturers increasingly focus on producing higher-bandwidth RAM for ML accelerators. The AI driven demand for memory chips has created a supply crunch, with some manufacturers hiking prices by up to 60% amid a surge in data center orders, Reuters said.
AI increased hard drive prices through the same mechanism. Your RAM is more expensive. Your SSD is pricier. Demand for hard drives, which had been stable in price for years, has spiked due to data center expansion. Component supply chains reorient to service billion-dollar infrastructure projects, to the detriment of consumer electronics.
To be fair, there are a lot of reasons for hardware prices. Supply chain disruptions, inflation, manufacturing capacity constraints, and corporate pricing decisions are all part of the picture. But the need for ML infrastructure has been a real and significant driver, one that most coverage of “AI” never bothers to mention while gushing about all the things the tech can do.
What Machine Learning Is Actually Good At
It is not an argument against using these tools. That would be another sort of mistake.
Machine Learning systems do certain things really well, and understanding that is part of understanding them well. You can give them jobs, and they will do it reliably. Write an email. Summarize a long document. Create some headlines. Check code for syntax errors. Translate a paragraph. Create a first outline for a piece of writing. It does save time when you have a lot of repetitive work to do.
Assistant, not authority, is the right mental model. Use ML to do the heavy lifting. The first draft, the first aggregation of research, the structural skeleton of something you’ll finish yourself. Then it’s up to your own judgment, expertise, and editing before anything goes anywhere that counts.
For bloggers, it’s the speed of outlining and research. Debug support for developers and boilerplate generation.
Generate product descriptions and customer responses for a small business owner. Topic explanations and study summaries for students, but with the caveat that the output needs to be checked before it becomes anything you rely on. It’s a useful tool. You still think. When you understand AI vs Machine Learning correctly, you use these tools without overtrusting them
The Real Dangers of Calling Machine Learning AI
The danger is not the technology. It is the gap between the tool’s perceived identity and its actual identity, a trust gap. ML systems hallucinate, the technical term for generating confident, fluent, entirely wrong information without awareness of the error. The system doesn’t know it’s wrong because it has no sense of right or wrong. It gave the statistically most likely output for your input, and that output, in this case, described something that didn’t exist. It happens on all the major tools, less so on newer models, but it never completely goes away.
The larger the gap, the greater the danger. Medical information, legal questions, financial decisions, and academic facts are areas where a convincingly written wrong answer does real harm. Privacy, too, is a legitimate concern. Many users are unaware of the risks they take when they paste sensitive client data, personal documents, or business plans into tools without knowing the tools’ data policies. Use these resources. But do not let them replace the judgment you really need to exercise.
The Honest Balanced Verdict: Should AI Be Called ML?
This is not a pessimistic case for calling it ML rather than AI. It is a precise one.
Machine Learning is really helpful. It saves time, reduces friction in repetitive work, and democratizes access to capable tools for people who can’t afford the human equivalent. “These are real benefits and should be part of the conversation about the limitations.”
But the name matters because the expectations follow it. When you say something is smart, people believe it just as they believe in your intelligence. When the output is pattern matching that confidently got something wrong, the disappointment or the harm is proportional to the trust placed in it.
AI should be called ML until a system crosses one specific threshold: genuine self awareness. Tell it like it is. Use it for what it’s worth. Find out what is important. Machine Learning is an honest name for an honest tool. Artificial Intelligence is a promise that the technology has yet to fulfill. ML is the right word until it becomes truly self aware, until it understands, not just predicts.
That’s not a criticism. That is simply, reality.