Will AI Replace Backend Developers? The Reality of AI-Powered Coding
Artificial Intelligence is changing the way software is built.
One question is becoming increasingly common among developers:
Will AI replace backend developers? And, more broadly, will AI replace software engineers?
There are two very different opinions in the market.
One group believes that AI-generated code is unreliable. According to this view, AI produces code with bugs, security issues, mistakes, and scalability problems.
The other group believes almost the opposite. They argue that AI can write extremely good code — sometimes even better than humans — and can build complete applications from end to end, faster than a human developer.
So, which side is correct?
The reality is more complicated.
I work with AI on a daily basis, and we have used AI to build systems that are actually running in production. Through that experience, we have seen both the strengths and weaknesses of AI-generated software.
We have learned where AI performs extremely well, where it struggles, and — more importantly — where human engineering judgment is still required.
That is the perspective I want to explore here.
AI Coding Has Changed Dramatically
Before discussing whether AI will replace developers, we need to acknowledge one important fact:
AI coding today is dramatically different from what it was two years ago.
AI is improving continuously. Its ability to understand requirements, generate code, debug problems, and build applications is getting better — and it is likely to continue improving.
Initially, AI was mostly useful for creating relatively basic applications.
Then it became capable of creating better prototypes and proof-of-concept applications.
Today, the situation is very different.
Modern AI coding tools can build end-to-end applications, and some of those applications are actually running in production.
The way developers work is also changing.
Instead of manually writing every line of code, developers increasingly use what can be described as agentic coding — where AI agents can generate, modify, debug, and work across large parts of an application.
So yes, AI-generated code is already being used in real production systems.
But there is an important distinction we need to make.
Running in Production Does Not Mean “Good Software”
This is one of the most important points to understand.
An application running in production is not necessarily a good application.
Just because an AI-generated application works and is deployed does not automatically mean that it is optimized, scalable, reliable, secure, or cost-efficient.
An application can successfully run in production while still having:
- Poor performance
- Inefficient database queries
- Scalability problems
- Security vulnerabilities
- Unnecessary infrastructure costs
- Poor resource utilization
- Reliability issues
- Weak architectural decisions
A company does not simply want an application that works.
A good production system should ideally be optimized so that it can handle increasing traffic, use resources efficiently, control costs, remain reliable, and continue delivering value over time.
These benefits are not automatically guaranteed simply because AI wrote the code.
And there is a reason for that.
To understand it, we need to look at the engineering decisions that happen underneath the application.
The Real Question Is Not Whether AI Writes Good or Bad Code
I am not making the argument that:
“AI writes bad code.”
Nor am I saying:
“AI writes perfect code.”
The reality is somewhere more nuanced.
The important question is:
When AI makes an engineering decision, does it understand whether that decision is appropriate for the specific situation?
This distinction is extremely important.
Let’s take a backend application and examine some of the decisions that developers have to make.
Client-Server Architecture: Which Communication Method Should You Use?
Suppose we have a basic client-server architecture.
The client needs to communicate with the server.
There are multiple ways to implement this communication, including:
- REST APIs
- RPC
- WebSockets
- Server-Sent Events (SSE)
- GraphQL
Now imagine asking an AI coding agent to build the backend.
Which one will it choose?
Will it always choose REST?
Not necessarily.
Will it choose RPC?
Maybe.
Will it choose WebSockets?
Sometimes.
Will it choose GraphQL?
It depends on the implementation and the requirements.
But the real question isn’t:
“Which technology does AI choose?”
The real question is:
“Is the technology AI chose appropriate for this particular situation?”
For example, if an application requires real-time communication, WebSockets might be appropriate.
Could real-time communication be implemented using REST?
Depending on the architecture and requirements, there can be ways to achieve similar behavior, but the appropriate approach depends heavily on the use case, traffic patterns, and system requirements.
That is the point.
AI can select a technology.
But understanding why that technology is appropriate requires engineering context.
CRUD Is Not as Simple as It Looks
Let’s take another common backend task: CRUD operations.
CRUD stands for: (Create, Read, Update, Delete)
At first glance, CRUD seems extremely simple.
You create a record, read it, update it, and delete it.
Done.
But real backend engineering is not that simple.
CRUD operations exist inside a much larger system.
For example, you need to think about:
- API design
- Endpoint conventions
- Authentication
- Authorization
- Rate limiting
- Pagination (Offset, Cursor)
- Database performance
- Validation
- Error handling
- Security
- Scalability
So the CRUD operation itself is only a small part of the problem.
Pagination: Offset vs Cursor
Consider pagination.
There are multiple ways to implement it.
Two common approaches are:
Offset-Based Pagination
For example:
?page=10&limit=20The database retrieves records based on an offset.
Cursor-Based Pagination
Instead of relying on an offset, the application uses a cursor representing a position in the dataset.
These approaches are not simply two interchangeable options.
They have different use cases and trade-offs.
Offset pagination can be perfectly reasonable in some situations.
But in certain high-volume scenarios, it may become inefficient because the database may need to process a large number of records before reaching the requested offset.
Cursor-based pagination can perform better for certain workloads, but it also introduces its own trade-offs and implementation considerations.
So the question becomes:
When should we use offset pagination, and when should we use cursor pagination?
And more importantly:
Will AI understand the specific trade-offs of the application it is building?
That is where engineering judgment becomes important.
File Uploads: A Simple Feature With Many Edge Cases
Let’s take something that sounds extremely straightforward:
File upload.
At first glance, implementing file upload seems easy.
A user selects a file, sends it to the backend, and the server stores it.
But where should the file actually be stored?
One option is to store the files directly on the server’s disk.
Another option is to use an external storage system such as Amazon S3.
If external storage is used, the application can also generate pre-signed URLs so that clients can upload files directly to the storage system.
Again, the basic implementation isn’t necessarily the difficult part.
The difficult part is handling the edge cases.
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For example:
What happens when an upload is incomplete?
Imagine a user starts uploading a large file but the upload stops halfway through.
Now you have a partially uploaded file.
That file is consuming storage.
It may also be costing the company money.
But the file itself is useless because the upload never completed.
So now we have additional questions:
- How do we detect incomplete uploads?
- How long should incomplete files remain?
- How do we clean them up?
- Who performs the cleanup?
- How do we handle interrupted uploads?
- How do we process completed files?
- What happens if processing fails?
These are not simply coding questions.
They are system design questions.
And this is where the difference between generating code and engineering a production system becomes clear.
The Database Is Much More Than CRUD Queries
Every serious application eventually depends on a database.
At a basic level, working with a database appears simple.
You might think:
Select data.
Create data.
Update data.
Delete data.
That’s it.
But once the application enters production, the real problems begin.
One common problem is a slow database query.
Suppose you tell an AI coding agent:
“This query is slow.”
What might it suggest?
One common answer is:
“Add an index.”
That can be correct.
But there is another problem.
AI might recommend adding indexes very aggressively.
You could end up with indexes on many fields simply because those fields are being queried.
And that creates another problem.
Database Indexing Is a Trade-Off
Indexes can make reads significantly faster because the database does not necessarily need to scan the entire table to find the requested data.
But indexes aren’t free.
They also have a cost.
When data is inserted or updated, the database may need to maintain those indexes.
That means excessive indexing can negatively affect write performance and consume additional storage.
So we have two opposite problems:
Too Few Indexes
Queries may become slow because the database has to scan large amounts of data.
Too Many Indexes
Reads may become fast, but writes can become more expensive because the database has to maintain additional indexes.
Therefore, the goal isn’t:
“Add an index everywhere.”
The goal is:
Choose the right indexes based on actual query patterns and workload.
This requires understanding the application’s behavior.
And again, we come back to the same question:
Does AI understand the trade-off for this specific production system?
Database Connection Pools
There is another issue that often appears in production systems:
Database connection problems.
Applications generally use a concept called a database connection pool.
Instead of creating a brand-new database connection for every request, the application maintains a pool of reusable connections.
But the size of that pool matters.
If the pool is too small, the application may not have enough connections to handle the workload efficiently.
If the pool is too large, it can create other resource problems.
So the question becomes:
What is the correct connection pool size for this application?
There isn’t one universal number.
It depends on factors such as the application’s workload, database capacity, traffic patterns, and infrastructure.
Will AI automatically know the exact configuration that is optimal for your production environment?
That is a much harder question.
The N+1 Query Problem
Another issue that can appear in applications built with AI is the N+1 query problem.
Modern applications often use ORMs — Object-Relational Mappers — to interact with databases.
ORMs make database access easier for developers.
But they can also hide what queries are actually being executed.
For example, code that looks simple at the application level may result in many individual database queries underneath.
This can create significant performance problems.
The important question is:
What queries are actually being executed by the ORM?
If developers don’t inspect the generated SQL and database behavior, performance problems can remain hidden.
AI can generate ORM code.
But production engineering requires understanding what that code is actually doing to the database.
Database Transactions Are Also More Complicated Than “BEGIN”
Transactions are another good example.
An AI-generated application may correctly recognize that a particular operation requires a database transaction.
It might generate something conceptually similar to:
BEGIN TRANSACTION
...
COMMITThat sounds correct.
But real-world transaction handling can become much more complicated.
There are situations where the transaction isolation level needs to be considered carefully.
Different business use cases can require different transaction behavior.
The important question isn’t simply:
“Did we use a transaction?”
It is:
“Is this transaction configured correctly for the business requirement?”
That requires understanding concurrency, consistency, isolation, and the actual behavior expected by the application.
Simply starting a transaction doesn’t automatically solve the problem.
Security Is Full of Edge Cases
We haven’t even covered everything related to databases, and there are many other areas of backend engineering.
Security is another major one.
Security contains a huge number of edge cases.
When building an application with AI, it is easy to focus on the main functionality:
“Does the API work?”
But a production application needs much more than functionality.
You need to consider different security scenarios and potential misuse cases.
Not every security concern is visible in the happy path.
This is one of the reasons human engineering judgment remains important even when AI is generating large portions of the codebase.
Reliability: What Happens When Your Server Shuts Down?
Another important area is system reliability.
For example, consider graceful shutdown.
Have you heard of graceful shutdown?
When a server is shutting down, it shouldn’t simply disappear.
The application may need to:
- Clean up resources
- Close database connections
- Stop accepting new requests
- Allow ongoing work to finish
- Complete or safely terminate in-progress operations
- Avoid losing data
Imagine that your server is processing an important request.
If the server shuts down immediately, that work could be interrupted.
A graceful shutdown process gives the application an opportunity to handle ongoing work properly before the process exits.
So another question becomes:
When AI generates the application, does it automatically implement graceful shutdown correctly for the specific architecture?
Again, this is not just about whether the application starts.
It is about how the application behaves under real-world conditions.
Background Jobs and Event-Driven Architecture
There are many more examples.
One of them is background processing.
Many applications need to perform tasks asynchronously instead of processing everything inside the main request-response cycle.
For example, background jobs can be implemented using event-driven architectures and technologies such as:
- Kafka
- Redis
- RabbitMQ
The architectural choice depends on the requirements of the system.
The question isn’t simply whether AI can write code using Kafka, Redis, or RabbitMQ.
Of course, AI can generate code for these technologies.
The deeper question is whether it understands when and why a particular architecture should be used, how it should be configured, and what trade-offs it introduces.
And this distinction is at the heart of the discussion about whether AI will replace backend developers.
