Imagine it's Friday afternoon.
Your team is preparing to release a new feature. You've spent the last few hours working with AI to generate the API, database queries, and tests.
Everything looks good.
The code compiles. The tests pass. The feature works perfectly in your development environment.
You deploy it.
An hour later, customer support reports something strange.
Two customers managed to purchase the last available item. Both payments went through, but there's only one item to ship.
You open the code. Nothing looks obviously wrong.
AI wrote working code. But the system still failed.
And now someone has to figure out why.
This raises an important question:
If AI can write code, why do companies still need software engineers?
Because writing code is only one part of building software.
Working Code Isn't Always Working Software
The problem in our story wasn't a syntax error.
It was a race condition.
Two requests arrived almost simultaneously. Both checked the available stock before either completed the purchase.
Individually, each request behaved correctly.
Together, they produced the wrong result.
This is the difference between writing code that works and engineering a system that behaves correctly under real-world conditions.
The Questions That Matter
An experienced engineer doesn't just ask, "How do I implement this?"
They ask:
- What happens when two requests arrive simultaneously?
- What if the payment succeeds but saving the order fails?
- Can a failed request be retried safely?
- What happens when the database becomes unavailable?
- How will we detect problems before customers report them?
AI can help answer these questions.
But generating a plausible solution isn't the same as knowing whether it's appropriate for the system.
Software Engineers Make Decisions, Not Just Code
Consider another simple requirement:
"Customers should be able to cancel orders."
Sounds straightforward.
But what if the order has already shipped? What if payment has been captured? What if a refund fails halfway through?
Someone must define the business rules, evaluate tradeoffs, design the workflow, and decide what happens when things go wrong.
That's engineering.
And the responsibility doesn't end when the feature is deployed.
Real systems need monitoring, debugging, security updates, performance improvements, and careful changes without disrupting customers.
AI Changes the Job, Not the Responsibility
The software engineer of tomorrow may write fewer lines of code manually.
That's not necessarily a bad thing.
Less time writing boilerplate means more time understanding problems, evaluating solutions, and improving systems.
But there's a catch.
If you don't understand how software works, how will you know when AI gets it wrong?
Learning programming still matters. Understanding databases, concurrency, distributed systems, testing, and architecture matters even more.
The goal isn't to compete with AI at typing code.
It's to become someone who understands what should be built, why it should work, and how to make it reliable.
Learn to Think Like a Software Engineer
That's the philosophy behind Tarkev's Backend Development with Java and Spring Boot course.
Spring Boot is a tool. The bigger goal is learning how engineering teams approach requirements, design decisions, implementation, testing, and production challenges.
Because the future of software engineering isn't just about producing code. It's about understanding the systems behind it.