There is a strange thing happening in software engineering.
We have AI that can generate functions, explain stack traces, write SQL queries, scaffold APIs, refactor code, write tests, and even build entire applications from a single prompt.
And somehow, this makes me more convinced than ever that software engineers need strong fundamentals.
Not less.
More.
Because when writing code becomes cheap, understanding code becomes expensive.
The uncomfortable part about AI-assisted development Let's say you ask an AI: "Build me a REST API for managing users." Five seconds later, you have controllers, models, routes, validation, authentication, tests, and probably a Docker configuration.
Nice.
But then something breaks.
The database starts timing out.
Your queue keeps retrying the same job.
Memory usage slowly climbs.
A race condition appears only under production traffic.
Your API returns inconsistent data.
The AI gives you three possible fixes.
All three sound reasonable.
Now what?
This is where fundamentals suddenly become very expensive.
If you don't understand HTTP, databases, concurrency, operating systems, networking, data structures, algorithms, application architecture, and the runtime you're working with, you don't really have an AI-assisted development workflow.
You have an AI dependency.
And that's a very different thing.
AI is very good at generating code We should acknowledge this.
AI is incredibly useful.
I use it.
I experiment with it.
I ask it stupid questions.
I let it generate boring code.
I let it explain unfamiliar APIs.
I use it as a second pair of eyes.
Sometimes I even let it write the first ugly version of something.
That's fine.
Software engineering has always been about using tools.
Compilers replaced handwritten machine code.
Frameworks replaced enormous amounts of boilerplate.
Libraries replaced reinventing common algorithms.
Cloud platforms replaced maintaining physical servers.
AI is simply another abstraction layer.
But abstraction has a price.
You can only safely abstract away something you understand well enough to debug when the abstraction fails.
That's the part people often forget.
The code is not the software This is probably one of the most important distinctions for new engineers.
A software system is not just source code.
A system is a combination of: requirements data algorithms state interfaces networks databases operating systems infrastructure security humans and a lot of assumptions nobody documented AI can generate the code.
It cannot magically remove the complexity underneath it.
You still need to understand what the generated code is doing.
Otherwise, you're basically driving a race car while asking the passenger which pedal is the brake.
Fundamentals give you a mental debugger One of the biggest advantages of learning fundamentals is that they give you a way to reason.
Suppose an API suddenly becomes slow.
A beginner might ask an AI: "Why is my API slow?" An engineer starts breaking the problem apart.
Is the application slow?
Is the database slow?
Is the network slow?
Are we making too many queries?
Is there an N+1 query problem?
Are we waiting on an external service?
Is the CPU saturated?
Is memory under pressure?
Are connections exhausted?
Is the queue backed up?
Is the algorithm doing unnecessary work?
This is not about memorizing solutions.
It's about knowing where to look.
Fundamentals turn debugging from guessing into investigation.
Learn algorithms even if AI can write them "But I don't need to learn algorithms anymore.
AI can generate them." Sure.
And a calculator can calculate 927 ×
381.
You should still understand multiplication.
The point of learning algorithms isn't to prove that you can manually implement a sorting algorithm during an interview.
The deeper value is learning how to think about computational problems.
You start asking: What happens when the dataset becomes 10x larger?
What is the time complexity?
What is the memory cost?
Can this operation be indexed?
Can thi