AI can generate a React component in seconds, write an API endpoint in minutes, and explain an unfamiliar codebase faster than most of us can read its documentation.
That sounds amazing.
And honestly, it is.
But there is a question I keep coming back to:
Are we becoming better software engineers, or are we simply becoming faster at producing code we don't fully understand?
I think this is one of the most important questions developers need to ask themselves in the age of AI.
1. Writing Code Is No Longer the Hardest Part
For years, becoming a developer meant learning programming languages, understanding algorithms, debugging errors, and spending hours figuring out why something didn't work.
Today, AI tools can help with almost all of these tasks.
Need a REST API endpoint? AI can generate one.
Need a React component with loading states and error handling? AI can draft it.
Need to understand an unfamiliar library? AI can explain the basics and show you an example.
This is a huge productivity improvement.
But there's a difference between generating code and understanding code.
Consider this example:
You ask an AI assistant to create an authentication system. It generates the routes, middleware, token handling, and database queries. Everything looks clean, and the application works during your initial tests.
But do you understand what happens when a token expires?
Can you explain how permissions are enforced?
What happens if two requests update the same record simultaneously?
What happens when an external service fails halfway through the process?
If you cannot answer these questions, you might have working software without fully understanding the system you are responsible for maintaining.
And that is where the real problem begins.
2. The Dangerous Comfort of Code That Works
One of the most appealing things about AI-generated code is how convincing it can look.
It may use familiar patterns, meaningful variable names, and a structure that appears professional.
But code that looks correct is not necessarily correct.
AI-generated code can contain:
- Incorrect assumptions about business requirements.
- Missing edge-case handling.
- Security vulnerabilities.
- Unnecessary dependencies and abstractions.
- Inefficient database queries.
- Error handling that hides the real problem.
- Logic that works in development but fails under production conditions.
The danger isn't that AI always produces bad code. It often produces useful code.
The danger is that developers may become comfortable accepting code they haven't properly evaluated.
Imagine reviewing a pull request containing 500 lines of AI-generated changes. The application builds successfully, the happy path works, and the tests pass.
Would you know whether the implementation introduces a subtle authorization bug?
Would you notice that a database query becomes expensive when the dataset grows?
Would you recognize that the proposed architecture makes future changes unnecessarily difficult?
These are engineering questions, not code-generation questions.
And they still require judgment.
3. Debugging Is Where Real Understanding Becomes Visible
Here's something interesting about software development: writing code and debugging code require different kinds of thinking.
When generating a new feature, there are often many acceptable solutions.
When debugging a production issue, you need to understand what the system is actually doing, why it is doing it, and which change will fix the underlying cause without creating another problem.
AI can help investigate logs, suggest possible causes, and propose fixes.
But if you don't understand the underlying system, you may end up applying one suggested fix after another without knowing which explanation is correct.
For example, imagine an API that becomes slow when traffic increases.
An AI assistant might suggest caching, adding database indexes, optimizing queries, or moving work into a background job.
All of these could be reasonable solutions in the right circumstances.
But which one is appropriate?
You need evidence.
You need to inspect query performance, understand the request flow, measure response times, and identify the actual bottleneck.
Adding caching without understanding the problem might introduce stale data. Adding indexes without examining the workload might increase storage and write costs without solving the main issue.
AI can suggest possible solutions, but engineering means finding out which solution the system actually needs.
4. Junior Developers May Face a Different Learning Challenge
Junior developers have always learned by building things, making mistakes, reading documentation, and debugging their own code.
AI changes that learning process.
A beginner can now build a surprisingly sophisticated application without understanding every part of it.
That is both an opportunity and a risk.
The opportunity is obvious: people can experiment, build projects, and learn concepts with immediate assistance.
The risk is that the struggle that once exposed gaps in understanding can disappear.
Suppose you're learning React.
You ask AI to build a component using hooks, state management, and API calls. It generates the complete implementation.
You run it, and it works.
But can you explain why the component re-renders?
Do you understand how effect dependencies work?
Can you identify a stale closure or an unnecessary network request?
Can you modify the component when the requirements change?
If not, you may have completed the task without mastering the concepts behind it.
This doesn't mean beginners should avoid AI.
It means they should use AI in a way that strengthens their understanding instead of replacing it.
A useful habit is to explain the generated code in your own words before accepting it. If you cannot explain an important part, investigate it.
That extra step can turn a generated solution into a learning experience.
5. Senior Developers Have a Different Responsibility
Experienced developers aren't immune to these problems.
In fact, AI can create a different challenge for senior engineers: reviewing and maintaining code produced at a speed that exceeds the team's ability to evaluate it.
Imagine a team that previously delivered five well-reviewed changes each day. With AI assistance, the team can generate fifteen changes.
That sounds like a win.
But what happens if the team can thoroughly review only six?
The extra output may create a growing backlog of code that nobody has fully examined.
More code also means more potential maintenance work, more interactions between components, and more opportunities for regressions.
The objective shouldn't be to maximize the amount of code produced.
It should be to maximize the amount of correct, maintainable, useful software delivered.
A senior engineer's value has never been limited to typing speed. It includes architecture, prioritization, risk assessment, code review, and making decisions when the requirements are unclear.
AI makes these responsibilities more important, not less.
6. The Best Developers Will Learn to Work With AI, Not Blindly Trust It
I don't believe developers should stop using AI to preserve traditional programming skills.
That would be like refusing to use a debugger because developers once had to inspect everything manually.
Tools evolve. Good engineers adapt.
The real question is how we use these tools.
Here are six habits I believe can make AI-assisted development more effective.
Habit 1: Understand the problem before asking for code
Before prompting an AI assistant, define the expected behavior, constraints, and failure cases.
A vague requirement often produces a solution that looks reasonable but solves the wrong problem.
Habit 2: Ask for explanations, not just implementations
Don't only ask, "Write this function."
Ask why a particular approach makes sense, what alternatives exist, and what trade-offs you should consider.
Then verify important claims against the code, tests, and relevant documentation.
Habit 3: Review the code as if someone else wrote it
Because someone—or something—did.
Check assumptions, security, error handling, readability, and compatibility with the existing architecture.
Don't approve a change simply because the AI generated it confidently.
Habit 4: Test behavior, not just whether the code runs
Test edge cases, invalid input, permission boundaries, failure scenarios, and interactions with existing features.
A successful build is not proof that a feature is correct.
Habit 5: Debug before accepting a proposed fix
When something fails, investigate the evidence.
Use logs, breakpoints, test results, and measurements to understand the cause. Let AI help you explore hypotheses, but don't confuse a plausible explanation with a verified one.
Habit 6: Keep learning the fundamentals
Data structures, networking, databases, operating systems, security, and software architecture still matter.
You don't need to memorize every API or write every line manually. But you need enough understanding to recognize incorrect assumptions and make good technical decisions.
7. A Simple Test: Can You Maintain What AI Builds?
Here's a practical way to evaluate your own AI-assisted workflow.
After AI generates a feature, ask yourself:
- Can I explain how the feature works from request to response?
- Can I identify its main failure cases?
- Can I explain why this implementation was chosen over an alternative?
- Can I write a meaningful test for its most important behavior?
- Can I safely modify it when the requirements change?
- Can I identify what I need to investigate if it fails in production?
You don't need to know every line by heart.
You do need to know enough to take responsibility for the result.
If you can answer these questions, AI is probably helping you build capability alongside productivity.
If you consistently cannot, it may be time to change how you use it.
8. Perhaps We Need to Redefine Developer Productivity
For a long time, developers have been judged by how quickly they deliver features, how many tasks they complete, and how much code they produce.
AI makes those measurements even less meaningful on their own.
If one developer writes 1,000 lines of code manually and another generates 3,000 lines with AI, the second developer hasn't necessarily created three times the value.
What matters is whether the software solves the right problem, works reliably, remains maintainable, and can evolve as requirements change.
In an AI-assisted workflow, I would rather have a developer deliver a smaller, well-tested, understandable solution than a large implementation filled with assumptions nobody has verified.
The best use of AI isn't simply to generate more code.
It's to help developers spend less time on repetitive work and more time on the decisions that require human judgment.
Final Thoughts
AI coding tools are not making developers worse engineers by themselves.
The real risk is allowing convenience to replace understanding.
We should absolutely use AI to accelerate development, explore unfamiliar technologies, automate repetitive work, and build things that would otherwise take much longer.
But we should also keep asking questions, reviewing assumptions, investigating failures, and learning how the systems we build actually work.
The future probably won't belong exclusively to developers who write every line manually or developers who delegate every task to AI.
It will favor developers who know how to combine the speed of AI with the judgment required to build reliable software.
Because generating code is becoming easier.
Knowing what code should exist, whether it is correct, and how to maintain it is still engineering.
Let's discuss
I'm curious about how other developers are experiencing this shift.
Do you think AI coding tools are making developers better engineers by helping them learn faster, or are they encouraging people to skip the fundamentals?
And here's a more practical question:
When AI generates a feature for you, how do you decide whether you understand it well enough to ship it?
I'd love to hear real experiences from developers working with AI every day.
Top comments (2)
Very agree with all. IMHO:
In general, I think that all that kinds of wave comes from influencers / ai labs, for sell views or features.
What do you think¿
I think one of the harder skills to develop with AI isn't writing code, but learning to recognize when a solution is correct only under the conditions you've tested.
Take an API handling payment retries. The generated implementation might pass every unit test, but under concurrent requests, a timeout can trigger a retry after the original transaction has already succeeded. Without proper idempotency handling and transaction boundaries, you can end up charging a customer twice even though every individual component appears to work correctly.
That's the kind of problem where understanding the system matters more than how quickly the code was produced. You need to reason about failure modes, inspect the execution flow, and verify the behavior under concurrency, not just ask AI to fix whatever test fails.
I don't think we need to measure whether developers can still write everything from scratch. I'd rather measure whether they can explain the trade-offs, identify hidden failure cases, and take responsibility when something breaks in production.
AI can accelerate implementation, but it doesn't remove the need to understand what happens when reality doesn't follow the happy path.