The Illusion of Speed
Generating functional code in seconds is impressive. The real difference in high-performance systems lies in invisible context and empirical knowledge built over real troubleshooting.
The screen flashes. You recompile for the third time. The bug is still there.
AI looks at it and responds in a few seconds. It looks impressive. And it is. Only speed has never been synonymous with understanding.
Some people keep repeating that "programming is over", that "writing code doesn't matter anymore". Yet that statement almost always comes from people working on applications where most of the job is hooking up APIs, databases, and UI components. CRUD. Automation. Boilerplate code.
Take that same AI into a project involving C++, Vulkan, Wayland, drivers, compilers, embedded systems, or any low-level architecture. The conversation changes completely.
In those environments, total line count means almost nothing. A project can have a million lines of code and be relatively straightforward to follow because it obeys a clean pattern. At the same time, a module under a thousand lines can hide months of investigation into CPU caches, memory ordering, synchronization primitives, concurrency hazards, hardware quirks, and dozens of invisible side effects.
That is context. And context doesn't reveal itself by counting lines of code.
Working is Not the Same as Being Correct
AI generates working code. That is a fact. The issue is that working code does not mean correct code.
There is a vast difference between producing an answer and grasping every consequence of that answer.
- • A concurrency bug from years ago in a completely different codebase.
- • A specific GPU driver vendor quirk under a custom workload.
- • How that exact locking mechanism previously induced thread starvation on a CPU topology.
- • How unaligned memory access tanked memory throughput on a specific processor.
Where is that documented? Nowhere.
That knowledge wasn't learned by scanning official docs. It was forged over days lost trying to figure out why a build crashed once every three weeks.
This is empirical intelligence. It is the kind of understanding born from accumulated experience, repetition, failures, and observation.
Most of that knowledge never made it onto the internet. It never became an article, a book chapter, or part of any training dataset.
There is a vast ocean of information that simply isn't documented. And if it isn't documented, AI never learned it.
Code Aesthetics vs. Real-World Impact
Many people believe clean-looking code equals better code. Not always.
Can look pristine on paper while concealing subtle race conditions under real production load.
Might look unrefined, yet quietly keep a critical mission-grade platform running flawlessly for two decades.
End users don't read code. They feel the result.
If software freezes, drops data, stutters, or responds slowly, nobody cares if the codebase adheres to the latest style guide. Users only feel that it's broken.
The Multi-Agent Review Loop
Another common argument is that we can just set AI agents to review each other's output. In theory, it sounds neat. In practice, anyone using these tools daily knows the loop.
You submit the same file dozens of times. The AI fixes one bug, breaks another feature. It clears a compiler warning and introduces a race condition. It fixes the race condition and causes a memory leak. So another agent steps in to review. Then another. You leave it running for hours while agents pitch endless successive edits.
In the end, the code often just rotates around the problem.
"Meanwhile, an experienced engineer opens the file, spots a subtle detail, recalls a compiler quirk, a hardware limitation, or an ancient library bug... and pinpoint the root cause in seconds. Sometimes a few minutes. But they find it."
The difference isn't speed in writing code. It is speed in understanding the problem.
Language models excel at matching patterns present in their training data. But complex systems frequently fail for reasons never documented anywhere, emerging from interactions between hardware, kernel, compiler, workload, and design decisions. In those moments, hands-on experience weighs far more than text generation speed.
Where AI Actually Shines
This doesn't mean AI is useless. Far from it.
It eliminates repetitive toil at incredible speed. It accelerates documentation, prototyping, test generation, API explanations, and obvious bug detection.
Yet there is a boundary many choose to ignore. The higher the system responsibility, the lower the value of raw code generation.
The real job stops being writing. It becomes understanding.
And understanding remains one of the most valuable tasks in software engineering. Maybe that changes one day. Today, it hasn't.