25 September 2026 · By Hadi Ataei
Using AI Is the Baseline. Building It Is the Edge.
Everyone is learning to use AI. That's the point.
Knowing how to use an AI assistant well — writing a clear prompt, checking its output, knowing when not to trust it — has become a basic workplace skill in a very short time. It matters, and everyone should learn it. We teach it ourselves, in Artificial Intelligence for Everyone.
But there's a catch in that success. A skill that everyone has stops being something that sets anyone apart. Being good with a spreadsheet or a search engine is expected on almost any CV today, and nobody gets hired for it alone. Using AI tools is heading the same way, and faster, because the tools keep getting easier. Each new version needs less skill from the user. That's good for users, but it means the skill of using AI is worth less every year.
The skills that keep their value are the ones on the other side: understanding how these models are built, trained, tested and fixed.
Users and builders are different jobs
A useful way to see the difference: someone who uses AI takes the model as a black box. Someone who builds AI can open the box. That changes what they're able to do.
| When… | A user of AI can… | A builder of AI can… |
|---|---|---|
| The model gives a wrong answer | Rephrase the prompt and hope | Work out why: bad data, wrong objective, overfitting, a gap in training |
| The company has its own data | Paste some of it into a chat window | Train or fine-tune a model on it, and measure whether it actually got better |
| A vendor pitches an "AI solution" | Take the demo at face value | Ask how it was evaluated, on what data, and where it will fail |
| No ready-made tool fits the problem | Wait for one | Build one |
| Cost or privacy rules out a large hosted model | Get stuck | Pick, shrink or run a smaller model that fits the constraint |
Every organisation that uses AI seriously ends up needing people in the right-hand column. There will always be fewer of them than there are users, and that shortage is exactly why the skill pays.
Why the fundamentals matter more than the tools
It's tempting to chase whatever framework or model is popular this month. That knowledge goes out of date quickly. The fundamentals don't, and they're what let you pick up each new tool in days instead of months:
- How learning actually works. A model learns by adjusting its numbers to reduce an error, step by step (gradient descent). Once you understand that loop, a linear regression, an image classifier and a large language model are variations on the same idea. Logistic regression is a good first model to understand end to end.
- Data first. Most real-world model failures come from the data: too little, the wrong kind, biased, or leaking the answer into the training set. Builders learn to spot these before they cause harm.
- Evaluation. Knowing how to split data, choose a fair metric and test on cases the model hasn't seen is what separates a model that looks good from one that works.
- Architectures. Knowing why convolutional networks suit images and why transformers took over language (we explain transformers and BERT in separate articles) lets you choose the right approach instead of guessing.
- The maths underneath. Linear algebra, probability and a little calculus. Not to prove theorems, but so that the maths stops being a wall between you and understanding what the model is doing.
A person with these foundations can read a new research paper, understand a new model's limits, and adapt. A person who only knows one tool's buttons has to start over whenever the tool changes.
What this looks like in the job market
Look at the job titles that have grown alongside AI: machine learning engineer, data scientist, applied AI engineer, MLOps engineer, AI researcher. None of them are about using a chatbot. All of them are about building, training, deploying or evaluating models. Even outside those roles, the engineer, analyst or product manager who understands how a model is built becomes the person their team turns to when an AI project needs to be judged, scoped or rescued.
There's also a strategic reason, especially in the Middle East. A region that only consumes AI built elsewhere depends on other people's models, data and priorities, including how well those models handle Arabic and local context. A region with its own builders can make models that fit its language, its industries and its rules. That capability is built one trained person at a time.
You don't need to be a genius to start
Building AI sounds harder than it is. The path is well mapped:
- Learn some Python. It's the common language of machine learning.
- Learn the core ideas of machine learning: training, testing, overfitting, the main types of models. Our Machine Learning Fundamentals course starts here.
- Train small models yourself on real datasets. Watching a model you built make its first correct prediction teaches more than any amount of reading.
- Go deeper into neural networks, language or vision, depending on the problems you care about. The AI & Machine Learning Specialization, Natural Language Processing and Advanced Computer Vision courses cover these.
The bottom line
Learn to use AI well. Everyone should. But treat it as the starting line, not the finish. The people who will shape how AI is used in their companies and their countries are the ones who understand how it's made. That understanding is learnable, and it lasts far longer than any single tool.
