AI Experts
We are seeing a massive push across the software industry right now. Senior developers are rebranding themselves as "AI experts," newer engineers are learning to code with an LLM by their side, and enterprises are eagerly buying up AI licenses, convinced they are purchasing pure engineering velocity.
The reality on the ground looks very different. Enterprises are quietly losing control of their codebases.
The 5-Iteration Trap
The problem usually starts with a simple task. The developer prompts AI to fix or update an existing application. At first, they take the time to read through the generated code, inspect the syntax, and understand how it works.
Then the iterations begin:
Iteration 1: The business asks for a change. Instead of modifying the logic manually, the developer asks the AI to rewrite the block of code.
Iteration 2: A new edge case crops up. Another prompt is thrown at the model to patch the issue.
Iteration 3: Another feature request comes in, followed by a quick refactor via the LLM.
Iteration 4: A performance bottleneck needs to be addressed, so the developer prompts the AI to optimize the function it previously wrote.
Iteration 5: By the fifth iteration of prompting back and forth, control over the code is completely lost.
The developer no longer knows what is written there. They cannot trace the execution flow, easily add new features natively, or debug the code.
This is a massive enterprise risk. Companies believe their teams are building software faster, but what they are actually accumulating is an un-debuggable, un-maintainable black box of intellectual property.
The Nightmare of the Silent Hallucination
Yes, AI can write better code, and it can fix all syntax errors and warnings. But it cannot fix errors in logic itself. Simply because it doesn’t know it exists. It hallucinated.
When a company loses control of its code, finding these bugs becomes a nightmare.
The most dangerous bugs in AI-generated code are not syntax errors. The code will build perfectly. It will compile without a single warning. But because AI models are built on probabilities rather than deterministic logic, they hallucinate.
Instead of adding two critical values, the AI might subtly subtract them. When that logic bomb goes off in production, how do you find it? Searching for a hallucinated minus sign in thousands of lines of code nobody on your payroll actually authored—and nobody understands—is an architectural nightmare.
Why Programming Languages Exist
Today, enterprises are deploying AI with the assumption that it will simply write better programs for them. This is fundamentally wrong.
Historically, programming languages were invented for one specific reason: humans needed a precise, unambiguous way to communicate with deterministic machines. If a computer could flawlessly understand natural English, we wouldn't need programming languages at all.
But that reality is far-fetched. AI models are probabilistic. They offer a high-likelihood guess, not a mathematical assurance. While they might get very close to being precise, bridging the gap between "highly probable" and "absolute certainty" is an enormous hurdle. Until we can build AI that genuinely does not hallucinate, programming languages remain our only tool for guaranteed execution.
Own Your Architecture
AI will continue to improve, but until machines can operate with deterministic certainty from natural language, our primary safeguard is engineering discipline.
Enterprises should encourage teams to use AI to assist their workflows, but they cannot let it take control of their architecture. Developers must still learn the fundamentals of programming, understand the execution path, and know why a framework behaves the way it does.
If an enterprise lets an LLM manage its logic across five iterations until its own engineers no longer understand the system, it hasn't automated engineering—it has simply left its core software completely unmanaged.
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