Build a website and declare engineers obsolete. Generate a few screens and dismiss designers. Add “AGI is here,” and suddenly every company should build its own software, while anyone who mentions expertise is supposedly behind the times.
AI is powerful without anyone having to belittle other people’s expertise to prove it. Yet some marketing accounts try a few tools and start making pronouncements about entire professions. They cannot see the gaps in their own understanding, but are already telling everyone whose work is no longer worth paying for.
You only see part of the work
If engineers merely type code and designers merely draw screens, generation looks like the end of both jobs. But the visible output has never been the whole job.
What does a user mean when they say something is difficult? Will the requested feature solve the problem? How should a system accommodate two departments whose procedures conflict? These questions require understanding, discussion and judgment. A small change to a screen may be the result of many decisions.
AI can help explore a problem and suggest approaches. Asking the important questions and assessing the answers still requires understanding the task. Work does not cease to exist simply because it is absent from a demo.
It is already in use. What does that establish?
Someone may say, “My company already uses the system I built, and it works.” That is an achievement. Solving a real problem has value.
You may know the company’s procedures well and have spent considerable time adapting and testing the system. That understanding and judgment contributed to the result. Crediting AI with everything, then declaring human expertise unnecessary, overlooks your own contribution.
One successful system shows that you solved that problem. It does not establish that you understand every company’s requirements or that an entire profession is redundant. “I built this” is a long way from “nobody needs engineers anymore.”
A problem can exist before you encounter it
Suppose you build an application system with AI. During testing, submitting a form saves the record and sends a notification. Everything appears ready.
If the request waits for email delivery before responding, a slow email service can leave the user waiting. They may submit again, assuming the first attempt failed. Without duplicate protection, the same application could be created twice.
An experienced practitioner would ask whether notifications can run later, what happens on a second submission, and how to handle a partial failure. Asynchronous processing and retries are relevant, but the right design depends on the requirements. Making everything asynchronous is not a cure.
Users should not need to know these terms for the system to work properly. Builders who have never considered these situations can mistake a successful trial for the absence of problems. A complete-looking AI answer can make those omissions even harder to notice.
AI extends your reach. Judgment still matters
Thinking of AI as an amplifier of understanding helps explain the difference. The more you understand a problem, the better you can ask follow-up questions and identify answers that need checking. A mistaken assumption can also be carried forward into an apparently complete solution.
This does not exclude beginners. AI can help people learn things they could not previously do. Asking questions, checking claims and testing results builds expertise. A job title is not the gatekeeper; the willingness to understand what you do not yet know matters.
We use AI in development ourselves. Its ability to produce so much so quickly makes it important to decide what works, what needs revision and what does not meet the requirements at all. Faster output does not automatically bring deeper understanding.
Do not confuse your blind spots with someone else’s irrelevance
What deserves criticism is the account that barely scratches the surface before shouting about AGI and job losses. Someone points out an unresolved issue and gets dismissed as outdated or afraid of replacement. Disagreeing with the pitch is treated as evidence that you do not understand AI.
If a pitch relies on “learn this now or you are finished” to sell courses or gain followers, while failing to explain what a tool can and cannot do, it is trading on people’s anxiety. Enthusiasm does not make every claim well-founded.
AI’s capabilities deserve attention, and human expertise deserves to be understood. Share what you have achieved. But do not declare work unnecessary simply because you have not understood it.