Do you know about the **Cognitive Shortcut Paradox?…**well, ask your junior!

Initially, it was just me, but right now my whole team is forced to use GitHub Copilot… Microsoft made it free, but also Microsoft knows that we’re building tomorrow’s technical debt issue today…

Dirty Secret Behind AI Code Generation

GitHub analyzed 211 million lines of code and found that AI is already degrading the quality of code

Kin Lane, an API evangelist with 35 years in tech, said he’s never seen technical debt pile up this fast

You know that problem isn’t that AI generates broken code — it’s that AI generates code that **WORKS! but often code that not LASTS!..**at the very moment it seems like the productive gains but you are unknowingly setting up the plane for your future 9/11

$100M Technical Debt

**Forrester **predicts that by 2025, more than 50% of technology decision-makers will face moderate to severe technical debt. That number is expected to hit 75% by 2026

it is not just legacy systems causing the problem. It is how teams are using AI without embedding it into a disciplined development process

In .NET projects, this manifests in three critical ways —

  • A **GitClear **study found that the adoption of AI coding assistants has led to an **8x **increase in duplicated code blocks. But these duplications create maintenance issues a lot — when you need to fix a bug, you actually need to go through multiple near-identical implementations scattered across your solution.
  • AI doesn’t understand your team’s architectural decisions. It generates EF queries that bypass your repository patterns, can create endpoints that ignore your established error handling, and produce services that violate your dependency injection conventions.
  • And, most devastating of all, AI lacks architectural context, as per my vision. It doesn’t know your domain boundaries, your performance requirements, or your future scaling needs. It optimizes for immediate functionality, not long-term maintainability..

Why .NET Teams Are Especially Vulnerable?

Now it’s a liability.. Net Flexibility with AI code generation. Every framework offers multiple ways to solve the same problem — Entity Framework, Dapper, minimal APIs, MVC controllers, record types vs. classes… But AI assistants or your own personal code-assistant don’t make the choices strategically.. they just make them randomly based on the training patterns.

AI has significantly increased the real cost of carrying tech debt. The key impact to notice is that generative AI dramatically widens the gap in velocity between ‘low-debt’ coding and ‘high-debt’ coding.

Your clean, well-architected .NET projects accelerate with AI. Your messy ones become unmaintainable faster than ever.

Sorry to say but Junior Developer…

Do you know about **Cognitive Shortcut Paradox?…**well ask your junior! he is in that… they are in problem because they don’t have enough debuggnig experience and without enough hands-on experience debugging, refactoring, and working through ambiguous requirements, they don’t have the instincts built up through experience to spot problematic patterns

When junior developers lean heavily on AI for .NET development, they miss crucial learning opportunities like,

  • Understanding when to use IEnumerable vs IQueryable
  • Recognizing N+1 query problems in Entity Framework
  • Knowing when dependency injection becomes over-engineering

They ship code that works in demos but fails in production.

False Promise of AI-Assisted Refactoring

You know ***AI agent can handle tasks such as improving code test coverage, swapping out dependencies, standardizing code patterns, optimizing frontend loading, and identifying dead code. ****but *that’s MARKETING

Reality is —

AI can identify technical debt, but it can’t understand your business context well enough to fix it safely.

When Copilot suggests refactoring your .NET Core authentication middleware, does it understand your compliance requirements? Your multi-tenant architecture? Your custom claims transformation logic??

Tech Leads can do this —

developing AI discipline —

Rule 1: Create coding standards specifically for AI-generated code. Mandate architectural reviews for any AI-suggested patterns that deviate from your established conventions.

**Rule 2: **Treat AI-generated code like any external library. Review not just for bugs, but for architectural fit and long-term maintainability

Rule 3: AI assistants work better when they understand your architectural decisions. Document your patterns, conventions, and trade-offs where AI can reference them.

Rule 4: Set-up Prompt if your team is small or you are working in a start-up environment. like this

"Write C# code to [do X].
Ensure the code is compliant with SonarQube rules, meaning:

No duplicate code.
Methods/classes are small and maintain low cognitive complexity.
Use meaningful, consistent naming conventions.
Proper null/exception handling and input validation.
Follow secure coding practices (avoid SQL injection, sanitize inputs, close resources).
Avoid hard-coded values, use constants/configuration instead.
Unit-testable, maintainable, and cleanly structured (SOLID principles).
Comment tricky logic if needed.
Provide the final code in a clean format."

this is just to write a piece of code inside a method

okay long articles don’t pay enough.. and I have rarely a supporter to buy me a coffee.. heavily reliable on medium.. so in part-2 we will see more :)

***Thank you ***🖤