AI Is Writing More Code, but Software Is Getting Worse
- Jayant Upadhyaya
- 11 hours ago
- 4 min read
Everywhere you look, people are shouting that AI is replacing developers. Headlines say coding is now easy. Some claim anyone can be a programmer with the right AI tool. The message sounds confident and exciting.
The reality is very different.
AI is writing much more code than before, but software quality is getting worse. Bugs are increasing. Systems are harder to maintain. Teams feel busy, but progress is slowing down. The gap between how productive people feel and what they actually ship is growing fast.
This is not a theory. It is showing up clearly in real data.
More Code Does Not Mean Better Software
AI tools are now producing around 46 percent more code than last year. On the
surface, that sounds like a big win. More output should mean more progress.
But quality is falling sharply.
Bugs have doubled
Technical debt is spreading everywhere
Code churn has increased
Large parts of code are rewritten just weeks after shipping
Teams are moving faster in the short term, but the systems they build are becoming fragile. Software looks finished, but it is not stable. It works today and breaks tomorrow.
This is the core misunderstanding. Writing code is not the job. Making systems work is the job.
The Productivity Lie
One of the most dangerous effects of AI coding tools is the feeling they create.
Developers feel much faster. Many report feeling over 50 percent more productive. They see code appear instantly. Tasks get checked off quickly.
But the data tells a different story.
Teams that use AI the most are actually about 19 percent slower when measured by real delivery and stability. That gap between feeling fast and being slow is huge.
Why does this happen?
Because AI creates drafts, not finished solutions. And those drafts come with problems that show up later.
AI Code Comes With More Bugs
AI can generate code in seconds, but that code is far from clean.
On average:
AI-generated code has 1.7 times more bugs
Pull request review time increases by over 90 percent
Duplicate code has increased dramatically
Refactoring quality is poor
AI does not refactor well. It often copies patterns instead of improving them. It solves the local problem without understanding the system.
Teams end up spending more time reviewing, fixing, and rewriting code than before. They are busy all day, but they are not moving forward in a meaningful way.
It is like building a house faster by laying bricks quickly, even if many of them are crooked. The speed feels good at first. Later, months are spent trying to stop the structure from collapsing.
AI Has No System-Level Understanding
Software systems are not small scripts. Real systems have:
thousands of files
long-term design decisions
hidden dependencies
performance constraints
security requirements
AI does not understand this full picture.
It looks at a few lines of context. It does not see the whole repository. It cannot reason about long-term architecture. It suggests changes that work today but damage the design over time.
Six months later, the system is confusing. Nobody fully understands how the pieces fit together. Technical debt piles up quietly until it becomes a crisis.
AI keeps stacking bricks. It does not check the foundation.
The Biggest Risk: You Stop Learning
There is a deeper problem that does not show up in dashboards.
When developers merge code they do not fully understand, learning stops.
Understanding comes from struggle. From debugging. From making mistakes. From tracing problems through the system. AI can skip those steps, but skipping them has a cost.
When something breaks at 3 a.m., you cannot ask the AI what it meant. You are left staring at a system you do not fully understand.
For junior developers, this is especially dangerous. If they skip the hard parts, they skip becoming engineers. You cannot outsource thinking. You cannot outsource understanding.
Security and Reliability Are at Risk
AI code often looks correct, but hides serious issues.
Common problems include:
missing rate limiting
poor input validation
unsafe defaults
performance leaks
insecure APIs
Why does this happen? Because AI learned from a huge amount of bad code on the internet. It reproduces patterns that look normal but are unsafe.
If a human does not catch these issues, the AI will not. That is how billion-dollar outages happen. Systems fail at scale because nobody fully reasoned about them.
AI is especially risky in areas like:
payments
healthcare
infrastructure
large distributed systems
In these cases, guessing is not acceptable. “Probably works” is not good enough.
Where AI Actually Helps
AI is not useless. It is very good at certain things.
AI works well for:
boilerplate code
basic test shells
simple scripts
prototypes
throwaway tools
basic documentation
These tasks are repetitive and low-risk. AI can save time here without hurting the system.
But architecture, logic, design, and tradeoffs are still human work. These require understanding goals, constraints, and long-term impact. AI does not have that
awareness.
AI Exposes Weak Fundamentals
One effect of AI tools is that they reveal who understands systems and who does not.
People with strong fundamentals use AI as a helper. They guide it. They review its output critically. They know when to say no.
People without fundamentals often accept AI output blindly. Over time, this makes systems worse and careers weaker.
AI does not replace skill. It amplifies it, for better or worse.
The Real Problem Is Not Speed
The software industry does not have a speed problem.
It has a quality problem.
AI makes it easier to produce more code. It does not automatically make that code better. Without strong engineering judgment, AI increases noise, complexity, and long-term cost.
The code is not the product. The system is the product. The reliability is the product. The ability to change safely is the product.
What Actually Matters Going Forward
AI is a tool. It is not magic.
It cannot:
reason deeply
design systems
understand business goals
care about long-term consequences
That responsibility stays with humans.
The developers who will succeed are the ones who:
understand fundamentals
think in systems
use AI carefully, not blindly
focus on quality, not volume
build things that last
If you focus only on tools, you become replaceable. If you focus on thinking, design, and ownership, you become essential.
AI will not run the show. The people who understand what they are building will.
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