Ask the Expert: AI Writes the Code Now… So What’s Left for You as a Professional Developer?

In an era where AI can write hundreds of lines of code in minutes, the competition is no longer about how fast you type code. It is about your ability to understand it, evaluate it, and turn it into a real solution people can trust.

Picture this: you have a new task in your project, a complete feature to build, and the deadline is getting close. You open an AI tool, describe what you need, and within minutes you have code that looks organized and ready to use.

You are impressed by the speed, you merge the code into your project, and then the problems start: an error when submitting data, a conflict with another component, or a vulnerability that wasn’t obvious at first.

This is where the question changes. It is no longer “Can AI write this code?” but “Can I be sure this code is actually the right solution?”

This shift doesn’t only concern experienced developers. It matters to everyone learning to code today, everyone preparing to enter the job market, and everyone who wants to grow their skills without relying on tools more than they understand the technology.

In this episode of Ask the Expert, we explore how the software developer’s role is changing, which skills are worth investing your time in, and how to make AI a partner in your work rather than a replacement for your thinking.

Question 1: Will AI take my job as a software developer?

Expert answer: Not necessarily, but ignoring how the work is changing can weaken your ability to compete.

AI tools can now help write repetitive code, generate components, suggest fixes for bugs, produce initial tests, and document parts of a project.

But software development is not just producing code. Before any line is written, there is a problem to understand, requirements to define, technical decisions to make, risks to assess, and results to test.

Take a simple example: building a login page. An AI tool can produce an attractive login screen in a short time. But turning that screen into a reliable part of a real system requires answering more questions:

• How will the interface communicate with the server?
• How will sessions or authentication tokens be managed?
• What happens when an access token expires?
• How are errors handled without exposing sensitive information?
• How are permissions enforced on the server, not only in the interface?
• How do we test failure cases and unauthorized access attempts?

AI can help answer these questions and implement the solutions, but the quality of the result depends on the requirements, the context, the review, and the testing.

Key idea: A developer’s value won’t be in writing everything by hand, but in leading the development process to a result that is correct, secure, and maintainable.

Question 2: If AI writes the code, what sets a professional developer apart?

Expert answer: A professional developer doesn’t stop at getting a solution. They know why they chose it, when it fits, and how to prove it works.

Five core skills are becoming more important in AI-assisted development.

1Understand the problem before writing the solution

Imagine a client asks you to add notifications to their app. You could ask an AI tool to build a notification system right away, but do you know exactly what the client wants?

Do they need push notifications or in-app notifications only? Should they reach all users or specific groups? Can users turn them off? Should they persist after logging out?

The clearer the requirements, the closer the suggested solutions will be to the real need. So before you use the tool, ask yourself:

• What problem am I trying to solve?
• Who is the target user?
• What is the expected behaviour?
• What are the edge cases?
• How will I know the solution worked?

The professional skill: turning a general request into clear requirements that can be implemented and tested.

2Logical thinking and technical decision-making

AI might suggest three solutions to the same problem, all of them reasonable. But which one fits your project best?

Temporary caching may suit one case, while another needs a persistent database. A separate component may be better than putting every function into one large component.

This is where understanding technical trade-offs matters: performance, complexity, scalability, and maintenance cost. You don’t always need the most complex solution, but the simplest one that meets today’s requirements and accommodates the project’s expected needs.

3Reviewing code and catching bugs

Code can be syntactically correct and still logically wrong. It may work under normal conditions, then fail on unexpected input or when the connection to the server drops.

That’s why you need to be able to read and understand code, find its weak points, and verify its assumptions. Questions worth asking when reviewing any code:

• Does it handle edge cases?
• Could it cause duplicate operations or data loss?
• Are there security risks?
• Is there unnecessary processing or avoidable complexity?
• Is it easy to test and maintain?

Remember: tidy-looking code doesn’t guarantee correct code, and a confident answer from an AI tool is not proof that a solution is right.

4Understanding the project’s architecture and how its parts fit together

In real applications, the frontend, the server, and the database don’t work in isolation.

Take an app that uses React on the frontend and a REST API to talk to the server. AI might generate a form for adding a new product, but a successful flow needs more than fields and a submit button. The data must match the API contract, responses and errors must be handled, the interface must update after success, and the required permissions must be enforced.

The better you understand how these parts relate, the better you’ll catch problems that don’t show up when you look at a single component.

5Communication and ownership

Software development is both technical and collaborative work. You may need to clarify vague requirements, discuss an architectural decision with your team, explain why you rejected a proposed solution, or document an issue that appeared after a feature shipped.

AI can help you draft documentation and analyse options, but it doesn’t remove your need to communicate well or to take responsibility for your decisions.

Takeaway: An outstanding developer combines technical understanding, critical thinking, the ability to deliver, and sound judgement about results.

Question 3: How do I use AI without losing my coding skills?

Expert answer: Let the tool help you think and learn, not think for you every time.

There’s a big difference between asking AI to write an entire feature and copying it, and using it to explore, understand, and test solutions. Let’s take a practical React example: a form for adding a new product with a name, price, and quantity.

Step 1: Define what’s needed yourself

Before writing code, decide the required fields, the validation rules, how the request is sent to the server, and the expected behaviour on success or failure.

Step 2: Ask for specific help

Instead of a vague prompt like “build me an add-product form”, explain the project context, the technologies in use, and the constraints to respect. For example, ask for a suggestion on structuring the components and handling validation errors, without rewriting the rest of the project.

Step 3: Understand the suggested code

Read the code and ask yourself:

• Why was this approach used for state management?
• Is the validation appropriate?
• Is there a simpler, clearer way?
• Does the solution match the project’s existing style?

If you don’t understand a part, ask for an explanation or try rewriting it yourself.

Step 4: Test different cases

Don’t test the form with valid data only. Also try:

• Submitting the form empty.
• Entering an invalid price.
• Entering a negative quantity when it isn’t allowed.
• Simulating a failed request to the server.
• Clicking the submit button repeatedly.
• Checking that the interface shows clear error messages.

Step 5: Review the result yourself

Check data validity, error handling, component structure, safe handling of information, and how easy the code will be to change later. This way you benefit from the tool’s speed while building your ability to make decisions and solve problems.

Golden rule: Don’t consider a task done just because the code worked once. It’s done when you understand the solution, have verified its behaviour, and can maintain it when needed.

Question 4: How do I answer questions about AI in a job interview?

Expert answer: Don’t try to prove you use advanced tools; prove you know how to use them responsibly.

A recruiter might ask: “How do you use AI tools in your work as a developer?”

The weak answer is: “I use them to write code quickly.” The stronger answer explains how you use the tool, what you review, and how you verify the quality of the result. Build your answer around four elements:

1. 1Usage: Which tasks do you use AI for?
2. 2Review: How do you make sure the output fits the project?
3. 3Verification: How do you test the solution and find bugs?
4. 4Impact: How did it help improve the work or save time?

For example, if you genuinely use these tools, you might say:

“I use AI tools to explore solutions, generate initial code, write tests, and understand errors. But I review the output before merging it, check that it fits the architecture and the requirements, and then test different cases. For me, the goal isn’t just producing more code, it’s getting the work done efficiently while keeping quality and maintainability.”

But don’t stop at the theoretical answer. Be ready to walk through a real example from one of your projects: what problem did you face? What did the tool suggest? What did you change? How did you confirm the solution worked? An example you can discuss in depth beats a perfect answer with no real experience behind it.

Question 5: What mistakes could hold back your growth in the age of AI coding?

Expert answer: The problem isn’t using AI; it’s using it in a way that weakens your technical independence. Here are six mistakes worth watching for.

1Copying code without understanding it

The feature may work today, but you’ll struggle when a requirement changes or a bug appears in production.

2Neglecting the fundamentals

Understanding JavaScript, algorithms, data structures, networking, databases, and security principles is what lets you evaluate solutions instead of accepting them automatically.

3Blindly trusting the output

The tool may use an outdated API, suggest an unsuitable library, or produce a solution that looks logical but doesn’t do what’s needed.

4Treating testing as optional

One successful run doesn’t prove a system is correct. Use the right tests, try edge cases, and check how the app behaves under different conditions.

5Ignoring information security

Don’t send passwords, access keys, or private customer data to external tools without checking usage policies and your organization’s requirements. Also pay attention to the permissions you grant tools connected to code repositories or work environments.

6Measuring success by speed alone

The fastest solution to write can be harder to maintain, heavier on resources, or less secure. Judge the quality of the result alongside the time it took.

The goal isn’t to reject automation, but to use it while keeping the ability to understand, verify, and decide.

Question 6: How can you develop yourself in four weeks?

Expert answer: You don’t need to master every AI tool at once; you need a practical way to measure your progress. Here’s a four-week plan you can apply to a real project you’re working on.

Week 1: Strengthen your fundamentals

Pick a small task in the language or framework you use and try to complete it yourself before turning to AI. Review the concepts you struggled with, and write notes about the mistakes you made and how you fixed them.

Goal of the week: become better at solving core problems independently.

Week 2: Use AI deliberately

Complete a similar task with the tool’s help, then compare the two solutions. Ask yourself: what did the tool improve? Did it add unnecessary complexity? Did it suggest an approach you didn’t know? Can you explain the differences between the two?

Goal of the week: learn to benefit from the tool without giving up critical thinking.

Week 3: Raise code quality

Choose an existing feature in your project and review its tests, error handling, security, performance, and maintainability. You can ask AI for a review, but verify its feedback yourself and don’t apply every suggestion without evaluating it.

Goal of the week: move from just implementing a feature to verifying its quality.

Week 4: Ship a complete feature

Build a real feature that starts from requirements and ends with a solution that can be tested and demonstrated. Document the important decisions, record the problems you faced, and explain how you used the tools and what you reviewed yourself.

Goal of the week: build hands-on experience you can explain in a job interview or add to your portfolio.

How do you measure your progress?

At the end of each week, ask yourself:

• Can I explain the solution without going back to the tool?
• Have I become faster at spotting bugs?
• Do I understand why I chose this solution?
• Can I test the edge cases?
• Have I become more independent in completing tasks?

Don’t measure your success by how many tools you’ve tried, but by how much your ability to deliver reliable solutions has improved.

The expert’s conclusion: what’s left for the developer?

AI is changing how software is built and giving developers tools that can speed up many tasks. But benefiting from these capabilities doesn’t mean all results are equal in quality, or that technical understanding has become less important.

A developer who only copies suggested code may finish some tasks quickly, but will struggle when requirements change or an unexpected problem appears.

A developer who understands the problem, chooses the right solution, evaluates the output, tests it, and owns the result can use these tools to expand their abilities and productivity.

So don’t make it your goal to out-type AI, and don’t make it your goal to do without it entirely. Make it your goal to become a developer who knows how to use the tools, understands what they produce, and decides when to trust them and when to reject them.

In the end, a developer’s value isn’t in how much code they write, but in their ability to turn a real problem into a solution that is effective, secure, and ready to grow.

Now, we want to hear your opinion!

Imagine an AI tool built you a complete feature in minutes, but you discovered a bug that only shows up in certain cases.

How would you handle it? Would you start by analysing the problem yourself, or turn to AI to help you find the cause?

We hope this episode has added to your knowledge, and we look forward to meeting you in new articles from the Ask the Expert series, where we connect technical knowledge with practice.