The software world is currently undergoing its greatest speed test. Tools like GitHub Copilot, ChatGPT, and Claude seem to have lifted the heavy lifting from developers' keyboards. With just a few lines of prompting, complex functions, ready-made test scenarios, and even full-fledged UI components appear on the screen in seconds.
However, behind this mesmerizing speed, a silent cost is accumulating—one that hasn’t been invoiced yet.
The current situation in the industry resembles a construction site where everyone is stacking bricks at incredible speeds, but no one is checking the consistency of the mortar. In software development, speed does not always mean efficiency; sometimes, it just guarantees hitting the wall faster.
Generating Code vs. Developing Software
At this point, we need to pause and ask: Are we developing software, or are we just generating code?
The difference between these two concepts defines the distance between a project's sustainability and its bankruptcy. Generating code is a mechanical act; getting the syntax right is enough. But developing software is about foreseeing how that code will talk to the rest of the system, how it will be maintained three months from now, and how it will behave under load.
Understanding why work that is considered "done" today will be written into the technical debt ledger tomorrow is the most critical responsibility of technical leaders.
AI increases the speed of writing code, but it also increases the speed of implementing wrong architectural decisions by the same margin. Wrong code written fast is far more expensive than right code written slow.
The Illusion of Speed: Why Does Everything Look Fine?
The first experience with AI assistants is usually enchanting. When a developer completes an API integration that would normally take two hours in just ten minutes, the feeling is pure efficiency.
Lines of code flowing rapidly across screens, completed tasks, and melting backlogs give a strong signal that the project is going great. However, this is the most dangerous phase of software: Connectorless Progress.
Context Blindness
AI only sees as much context as it is given. It does not realize how the function it is writing at that moment affects the data structure elsewhere in the project. That is why a solid product and solution assessment before jumping into coding is the only way to prevent the chaos AI might generate from the start.
The root cause of this illusion is that the difference between "working code" and "correct code" is not immediately visible during the development phase. The fact that code compiles or passes a current test does not prove it is architecturally sound.
AI tools are generally inclined to code the "Happy Path"—scenarios where everything goes right. Error handling and edge cases are often relegated to the background. When a developer commits AI output saying "it works," they are essentially planting a time bomb in the foundation of the system.
Why do humans fall for this fallacy? Because the brain rewards finished work. Seeing a module appear complete releases dopamine. But the real cost in software is not the moment code is first written; it is the hundreds of hours spent reading, fixing, and modifying that code later.
Is one hour saved by AI worth ten hours stolen from the team that will try to understand that code in the future? If the process is not managed correctly, the answer is unfortunately yes.
Copy-Paste Culture and Cognitive Laziness
This passion for speed triggers a dangerous behavior within teams: Cognitive Laziness. Developers, especially under pressure, start including AI-generated code in the project with the assumption that "it works anyway," instead of reading and understanding it line by line.
This leads to the formation of "gray areas" in the codebase that no one fully masters. These gray areas become unmanageable over time, turning the system into a frightening legacy structure that no one dares to touch.
Erosion of Code Ownership
The long-term consequence of this behavior is the loss of code ownership. When a bug arises, even the developer who added that code struggles to find the source of the problem because they didn't actually write it; they just transferred it.
When the logic is constructed by a machine rather than a human, the debugging process turns into torture. The developer is forced to play detective within a logic they did not build.
The code may be written by AI, but the human is still responsible for it blowing up in production. You cannot outsource responsibility to AI.
Furthermore, AI models are not deterministic. To solve the same problem, they might use a different style today than they did yesterday. While one module might have a functional programming approach, the module right next to it might suggest an object-oriented (OOP) structure. This inconsistency makes sustainable technical partnership and maintenance impossible.
Risk Distribution by Experience Level
The risks of using AI change dramatically depending on the team's experience level. This situation is not the same for everyone; experience determines whether you perceive AI output as a "suggestion" or a "command."
- Junior Developers: The biggest risk group is here. The learning process happens by making mistakes and fixing them. If a junior developer has AI write every logic they struggle with, their problem-solving muscles do not develop. When the resulting code becomes complex, they turn into "operators" who can write prompts but cannot build architecture, not knowing why they are doing what they are doing.
- Senior Developers: For this group, AI is a powerful accelerator. Because a senior developer notices the bug or architectural mismatch in the AI-generated code the moment they read it. However, the risk here is overconfidence.
- Product Owners: Non-technical stakeholders may fall into unrealistic expectations due to AI's speed. However, the gap between a demo and a production-ready product has not been closed by AI; on the contrary, it has become invisible.
The Problem is Not the Tool, It's the Process
At this point, blaming artificial intelligence technology is the easiest and most wrong defense mechanism. Saying "AI produces bad code" is equivalent to saying "The hammer hit my finger, the hammer is guilty."
The problem is not the inadequacy of the tools, but how these tools are integrated into the software development lifecycle (SDLC). Traditional "Code Review" processes are designed to inspect code written at human speed. But when code production speed increases tenfold with AI, existing review mechanisms get clogged.
Business Logic vs. Syntax
Many teams fall into the mistake of loosening standards to keep up with the speed AI produces. However, the real problem is not syntax, but Business Logic errors. AI does not know your business or the historical burden of your database. It only knows the average of billions of lines of code on the internet.
Guidance: Shifting from Code Production to Decision Auditing
So, should we ban AI? Absolutely not. Ignoring this technology is not competitive. But we must radically change how we use it. Software teams must stop being "code writers" and transform into "code auditors" and "decision-making" architects.
For a healthy AI integration, the following principles should be adopted:
- Accept as Draft: AI output is never the final product. It is a draft. It must not be committed without human verification.
- Boilerplate vs. Core Logic: AI should be used for repetitive, standard tasks. But the core business logic must remain under human control.
- Critical Decisions: What determines a project's success is not how fast the code is written, but whether the right technical decisions are made.
In conclusion; developing software is not about pressing keys on a keyboard, it is about making the right decisions. Artificial intelligence can press the keys for us, but it cannot make the decisions for us. If you leave the decisions to it as well, the resulting product will not be yours, but a random output of probability algorithms.
