I think the answer is yes, but with a small correction. Software engineers are still needed, but the nature of the job is changing. Earlier, a large part of our work was about manually writing code line by line. Now, slowly, the job is moving towards understanding problems better, guiding AI tools properly, reviewing generated code, making design decisions, and ensuring that the final software is actually reliable. In simple words, the value is shifting from just writing code to thinking clearly about code.

AI is really good at generating code quickly. If we ask it to create a login form, write a sorting function, generate an API endpoint, explain a stack trace or convert code from one language to another, it can usually do a decent job. For repetitive programming tasks, this feels like a huge productivity boost.

Earlier, we would search in Google, open Stack Overflow, read five answers, copy one piece of code, modify it, fix the errors and then finally understand what happened. Now, we can simply ask an AI assistant and get a working starting point in seconds.

In most cases, AI is useful for things like: Generating boilerplate code, Explaining unfamiliar code, Creating quick prototypes, Writing unit test drafts, Refactoring small functions, Suggesting alternate implementations and Helping us understand errors faster

This is really useful. There is no point in denying that. But the danger starts when we assume that generating code is the same as building software.

Software is not just a collection of files with some functions inside it. Real software has requirements, users, business rules, edge cases, performance expectations, security concerns, databases, deployment pipelines, monitoring, logs and failures. And of course, there will always be that one weird bug that happens only in production when everyone is about to leave for the weekend

This is where developers still matter a lot. A good developer does not just ask whether the code works. A good developer asks whether this code should exist in this form. That question is much harder.

AI may give us a function that passes the first test case, but it may not understand the long-term cost of that function sitting inside the wrong class, depending on the wrong module, or hiding a business rule in the wrong place.

We already know that bad code is dangerous. Messy code slows down teams, increases bugs, makes every change risky and turns small features into big headaches. Now with AI, there is a new problem. Earlier, writing bad code took some effort. Today, we can generate bad code at 10x speed

That sounds funny, but it is actually serious. If we blindly accept everything AI gives us, we are not improving productivity. We are just increasing technical debt faster. It is like constructing a building very quickly using weak bricks. From outside, everything may look complete, but the moment pressure comes, cracks will start appearing. In software, that pressure comes from real users, real data, real failures and real business changes.

A few signs of AI-generated technical debt are:

And the tricky part is that many of these issues are not visible immediately. They show up later, usually when the feature has already reached production.

Let us take a simple example. Imagine we ask AI to create a payment function. It may generate something like this:

    if amount <= 0:
        return "Invalid amount"

    if not card_details:
        return "Invalid card"

    charge_card(card_details, amount)
    update_user_balance(user, amount)
    send_success_email(user)

    return "Payment successful"

At first glance, this looks fine. The amount is validated, the card details are checked, the card is charged, the balance is updated and an email is sent. For a simple example, this seems okay. But the moment we start thinking like engineers, the problems appear.

What happens if the card is charged successfully but updating the user balance fails? What happens if the email service is down? What happens if the same payment request is submitted twice? What happens if the amount is modified from the frontend? What happens if the card provider responds slowly? What happens if we need refunds, audit logs or transaction history?

Suddenly, this simple function is not so simple anymore. A real payment system needs to think about things like:

This is why software engineering is not just about writing code. It is about understanding consequences. AI can give us the first draft, but it is the developer’s responsibility to make it production-worthy.

Another interesting thing is that AI can write clean-looking code. But clean-looking code is not always clean code. There is a big difference between the two.

Clean-looking code may have proper indentation, nice variable names and a few comments. But real clean code has clear responsibilities, low coupling, high cohesion, meaningful abstraction and simple flow. A function can look beautiful and still do too many things. A class can have good names and still violate basic design principles.

For example, AI might create one neat service class that validates input, talks to the database, sends emails, formats the response and logs everything. It may look organized, but from a design point of view, it is still carrying too many responsibilities.

This is why fundamentals become more important, not less. SOLID principles, cohesion, coupling, testing, debugging, naming and design patterns still matter. In fact, they matter even more now because AI can produce a lot of code very quickly. When code generation becomes cheap, code judgment becomes expensive.

There is also a common idea that the future of software engineering is just prompt engineering. I do not fully agree with that. Yes, writing a good prompt helps. If we ask AI to “create an app”, we will probably get something vague. But if we clearly describe the feature, constraints, error cases, structure and expected behavior, we will get something much better.

Still, prompting alone is not engineering.

This is similar to using a calculator. A calculator can multiply large numbers faster than us, but we still need to know what multiplication means, where to use it and whether the result makes sense. AI is similar. It can produce the code, but we need to judge the code.

Without engineering knowledge, AI-generated code becomes a black box. And blindly trusting a black box in production is not engineering — it is gambling 🎲

So what exactly becomes the role of a developer in this AI-powered world? I think the role becomes more about clarity. Developers need to understand the problem deeply, break it into smaller parts, decide the right design, guide the AI, review the output, test the behavior, handle edge cases and make sure the system can survive in the real world.

The developer’s responsibility slowly shifts towards:

AI may become like a junior developer who types very fast, but someone still needs to act like the senior engineer who asks, “Why are we doing this?”

That someone is still us.

This also changes the way junior developers should think about learning. If a junior developer only converts tickets into code without understanding anything deeply, AI will definitely make that role less valuable. But if a junior developer uses AI as a learning partner, asks better questions, reads the generated code, understands the design choices and improves fundamentals, then AI can actually help them grow much faster.

For example, instead of asking AI to simply write code and copy-pasting it, we can ask better questions like:

That kind of usage can be extremely powerful. But the key is simple — use AI to learn faster, not to avoid learning.

I feel the biggest skill developers need now is taste. Good developers develop taste over time. Taste means knowing when something feels wrong.

A function feels too long. A class seems to have too many responsibilities. A variable name is misleading. A design is over-engineered. An abstraction is unnecessary. A shortcut feels risky. A dependency does not feel worth adding.

These things are hard to measure, but experienced developers can sense them.

This taste comes from:

AI can help us move faster, but taste still comes from experience. Maybe the future belongs to developers who can combine both — AI speed and human judgment. That combination can be really powerful 🔥

From my perspective, AI is not here to completely replace developers. It is here to remove some of the repetitive and mechanical parts of programming. That means developers get more space to focus on the parts that actually require thinking. But it also means we cannot hide behind syntax anymore.

Earlier, knowing syntax gave us some advantage. Now syntax is becoming cheaper. The real value is moving towards problem solving, system design, debugging, communication, product thinking and engineering judgment.

Earlier, the question was:

Now the question is becoming:

That is a big shift, and honestly, I think it is exciting. It pushes us to become better engineers instead of just faster typists. It reminds us that software engineering was never only about typing code. It was always about solving problems. Code is just one way we express the solution.

So yes, AI can write code now. But someone still needs to understand the problem, guide the system, review the output, clean the mess, protect the users and make sure the software works in the real world. That someone is still the software engineer.

Maybe the future developer will write fewer lines of code manually. But they will need to think more clearly than ever before. And that, I feel, is not a bad thing at all ❤️

What do you think? Are we entering the golden age of software engineering, or are we just generating technical debt faster than ever? Let me know!