AI Is Changing How Developers Write Code. But Is It Making Us Better Engineers?
Victor ChideraFull Stack Developer
Artificial intelligence is changing software development in ways that would have seemed almost impossible a few years ago. Today, developers can describe a feat…
Artificial intelligence is changing software development in ways that would have seemed almost impossible a few years ago.
Today, developers can describe a feature in plain English and watch an AI assistant generate the components, write functions, suggest database queries, identify potential bugs and even explain unfamiliar code. Tools like GitHub Copilot, Cursor and Claude have become part of everyday development workflows.
Tasks that once required hours of research, documentation reading and trial and error can sometimes be completed in minutes.
And that's impressive.
But there's a question worth asking: Are we actually becoming better engineers, or are we simply becoming faster at producing code?
Because writing code and engineering software are two very different things.
The Rise of AI-Powered Development
Not too long ago, building an application meant spending a considerable amount of time writing boilerplate code, searching documentation, debugging syntax errors and figuring out how different parts of a system should communicate.
AI coding assistants are changing that experience.
A developer working in React, for example, can ask an AI assistant to generate a reusable component, suggest a better state management approach or explain why a particular hook is causing unnecessary re-renders.
Working with Node.js? AI can help structure API endpoints, generate validation logic, write tests and identify potential security issues.
Even when working with databases, developers can use AI to draft SQL queries, reason about data structures and understand complicated relationships.
The result is a development process where developers spend less time on repetitive tasks and more time thinking about what they're trying to build.
At least, that's how it should work.
Productivity: Are Developers Actually Getting Faster?
One of the biggest selling points of AI coding tools is productivity.
And there is evidence to support that claim.
GitHub's research on Copilot found that developers using the tool completed a particular coding task 55% faster than developers who didn't use it.
That's a significant improvement, especially when working on repetitive implementation tasks.
But productivity isn't simply about how quickly someone writes a function.
Consider a developer building an appointment booking platform.
AI can generate the booking interface, create API endpoints and write the initial database operations. What it cannot automatically guarantee is that two customers won't successfully reserve the same appointment slot at the same time.
That requires understanding concurrency, data consistency, transaction boundaries and the architecture behind the application.
The developer still needs to determine what should happen when two requests arrive simultaneously, how the backend should validate availability and how the system should prevent conflicting reservations.
AI can help implement the solution, but the engineer must understand the problem well enough to evaluate whether that solution is correct.
This is where productivity becomes more interesting.
The best use of AI isn't necessarily writing more code in less time. It's reducing the time spent on routine implementation so developers can concentrate on decisions that have a greater impact on the software.
Debugging Is Changing, Too
Debugging has always been one of the more demanding parts of software development.
Sometimes, the issue is obvious. Other times, a seemingly harmless change introduces unexpected behaviour somewhere else in the application.
AI assistants are becoming useful debugging partners.
A developer can share an error message, relevant code or a stack trace and ask for possible explanations. AI can identify suspicious logic, suggest fixes, explain unfamiliar errors and recommend ways to reproduce a problem.
This can dramatically reduce the time spent searching through documentation and experimenting with possible solutions.
However, there's an important distinction between finding a possible fix and understanding the actual cause of a problem.
Imagine an API that occasionally returns duplicate transactions.
An AI assistant might suggest adding a validation check or modifying the database query. But what if the actual issue is a race condition caused by multiple requests being processed concurrently?
Applying the suggested fix without understanding the underlying problem could leave the system vulnerable.
Good debugging requires more than making an error disappear. It requires understanding why the error occurred and whether the proposed solution addresses the root cause.
AI can accelerate that investigation, but developers still need to verify the diagnosis.
The Code Quality Problem Nobody Should Ignore
Here's where things become a little more complicated.
AI can generate code that looks clean, follows familiar patterns and appears technically correct. But code that looks good isn't necessarily code that belongs in production.
An AI-generated implementation might introduce unnecessary dependencies, duplicate existing functionality, overlook edge cases or expose sensitive information through poorly designed API endpoints.
It might also generate code that works for the example provided but fails under real-world conditions.
For developers working with React and Firebase, for example, an AI assistant might generate a convenient Firestore query and connect it directly to the frontend.
That implementation may work perfectly during development.
But what happens when the application needs stronger access controls, server-side validation, atomic operations or more complex business rules?
The problem isn't necessarily that the generated code is wrong. It's that the implementation may not account for the wider system.
This is why code review, automated testing, security checks and architectural consistency remain important.
AI-generated code should be treated like code written by another developer: useful, reviewable and never automatically trusted.
The Danger of Becoming Dependent on AI
There's another side to this conversation that deserves attention.
What happens when developers become so comfortable asking AI to solve problems that they gradually lose the ability to solve those problems independently?
It's easy to copy a generated function without understanding how it works. It's also easy to accept an architectural recommendation simply because the explanation sounds convincing.
Over time, this can create a dangerous habit: developers become increasingly dependent on tools they don't fully understand.
This is particularly concerning for junior developers.
The early stages of software development involve learning how to think through problems, understand documentation, read error messages, recognise patterns and develop an intuition for how systems behave.
Those experiences are not always enjoyable, but they build the foundation of engineering competence.
If AI handles every difficult part of the learning process, developers risk learning how to produce code without learning how to reason about it.
The goal shouldn't be to avoid AI. It should be to use it in ways that strengthen technical understanding rather than replace it.
Why Engineering Judgment Still Matters
This is probably the most important part of the entire conversation.
Software engineering isn't just about knowing JavaScript, Python, React or whichever programming language happens to be popular.
It's about making decisions.
Should this application use a monolithic architecture or microservices?
Should a particular operation happen on the client or the server?
How should the system handle concurrent requests?
What happens when an external API becomes unavailable?
Are the database security rules sufficient?
Will this implementation remain maintainable as the application grows?
These are engineering questions.
They require understanding business requirements, technical constraints, system behaviour, security implications and long-term consequences.
AI can provide recommendations and explain trade-offs, but someone still needs to decide which trade-offs make sense.
A technically impressive implementation can still be the wrong solution for the business problem.
And that's something no amount of generated code can automatically fix.
How Developers Should Use AI Without Losing Their Edge
I don't think the answer is to become an anti-AI developer or insist on writing every line of code manually.
That would be ignoring how software development is evolving.
Instead, developers should become more intentional about how they use these tools.
Here are a few practices worth adopting:
1. Understand the problem before asking AI to solve it.
Have a clear understanding of the feature, its requirements and its constraints. The better you understand the problem, the better you can evaluate the generated solution.
2. Treat generated code as a starting point.
Review the implementation, check its assumptions and make sure it fits the existing architecture. Never confuse generated code with production-ready code.
3. Use AI to improve your understanding.
Instead of simply asking for a fix, ask why the problem occurred, what alternatives exist and what the trade-offs are.
4. Keep writing and reviewing code independently.
Your ability to reason through problems should continue developing, even as AI takes over more repetitive implementation work.
5. Prioritise testing and security.
AI-generated code needs the same scrutiny as any other code. Write meaningful tests, validate inputs, review permissions and investigate unexpected behaviour.
6. Learn system design, not just syntax.
Understanding databases, networking, authentication, distributed systems and application architecture will become increasingly valuable as AI makes routine code generation easier.
So, Is AI Making Us Better Engineers?
I think the answer depends on how we choose to use it.
AI is undoubtedly changing how software gets built. It's lowering the barrier to implementation, accelerating repetitive tasks and giving developers access to assistance that would previously have required significant research or collaboration.
But faster implementation doesn't automatically translate into better engineering.
A developer who understands system architecture, security, performance and maintainability will get considerably more value from AI than someone who simply accepts whatever code it generates.
The difference isn't the tool. It's the judgment behind its use.
Perhaps the next generation of great software engineers won't be the people who can write the most code from memory.
They'll be the people who know what needs to be built, understand why it needs to be built that way and can use AI to execute those decisions effectively.
AI may be changing how we write code, but engineering judgment is what determines whether that code is worth shipping.
And that distinction matters more than ever.