Prompting is dead. Context wins.
Why the skill everyone is selling courses on will be worth ₹0 by the time this year's students finish probation.
There is a number that should worry anyone selling an AI course in India right now, and it is zero. That is the salary premium a graduate will command for knowing how to prompt an AI model by 2027.
Right now prompting still feels like a skill. Students trade phrasings the way they once traded exam tips. But look at what is actually happening: models are getting better at inferring what you meant, interfaces are adding the scaffolding for you, and every graduate entering the workforce next year will have spent two years using these tools daily. A skill that everyone has is not a differentiator. It is a baseline, like email.
So what does not go to zero?
Three things. The first is knowing what to feed the machine — the business reality it cannot guess. A model does not know your distributor's credit cycle, your branch's festive targets, or that your reader is a 55-year-old proprietor who distrusts fancy English. Supplying that is not prompting. It is context engineering, and it requires understanding a business, which is precisely what a commerce or management student has and a model does not.
The second is looping. Almost nobody does this. A student asks once, accepts the answer, and submits it. A professional treats the first output the way a manager treats a junior's first draft: what did you assume, what is missing, what would my boss reject? Then they feed the corrections back. The gap between version one and version nine is where the actual work happens, and it is a habit, not a talent.
The third is judgment — knowing when to throw the answer away. This is the hardest to teach and the most valuable to employ. An AI output can be fluent, well-sourced and still wrong for the business: a growth percentage off a tiny base, an average hiding a dangerous concentration, an apology that promises a refund nobody authorised. Accepting output requires no thought. Rejecting it correctly requires domain knowledge, and that is what gets paid.
Context, loop, judge. That is the whole argument, and it explains why we built an AI school for business students rather than technical ones. We are not teaching AI to business students. We are teaching business students that their business judgment just became the scarce input.
The practical consequence for anyone choosing a course: ask what you will be able to show at the end. If the answer is a certificate and a list of tools, you have bought the commodity. If the answer is a working system you built, a portfolio of real business artifacts, and the habit of catching the machine's mistakes, you have bought the thing that still has a price on it.