The AI-Myth Revisited: Why Most Developers Use LLMs Wrong

The AI-Myth Revisited: Why Most Developers Use LLMs Wrong

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The E-Myth Revisited by Michael Gerber changed how millions think about running businesses. The book’s core insight? Most business failures happen because people confuse being good at technical work with being good at running a business that does that technical work.

I believe we’re seeing the exact same pattern with AI and LLMs in software development today.

The AI-Myth

The AI-Myth is the belief that having access to ChatGPT, Cursor, or GitHub Copilot automatically makes you a better developer. Just like Gerber’s “Entrepreneurial Myth,” this assumes that having the right tools is enough for success.

The reality? Most developers are using these powerful tools in ways that hurt their long-term growth and code quality.

After working with developers at different career stages and seeing how they approach AI tools, I’ve noticed three distinct patterns that mirror Gerber’s business mindsets: the Technician, the Manager, and the Entrepreneur.

The Three Developer Mindsets

The Developer-Technician (Junior to SDE2)

Characteristics:

  • Lives in the present moment of “make this work”
  • Focuses on getting immediate results from AI prompts
  • Loves the feeling of fast code generation
  • Views thinking about code architecture as unproductive

The Problem: Most junior developers fall into this category and make a fatal mistake: they assume that because AI can generate code, they don’t need to understand the fundamentals.

I’ve seen countless juniors copy-paste AI-generated solutions without understanding the underlying logic, design patterns, or trade-offs. They’re building on quicksand.

Why This Hurts:

  • No understanding of why code works or fails
  • Can’t debug issues beyond surface-level problems
  • Never develop intuition for good code architecture
  • Become dependent on AI for even basic tasks

My Recommendation: If you’re early in your career (intern to SDE2), resist the AI shortcut. Use AI sparingly for learning, not as a crutch. You need to build your coding muscles the hard way first.

Think of it like learning to drive. You wouldn’t start with a self-driving car if you want to actually know how to drive.

The Developer-Manager (SDE3+)

Characteristics:

  • Pragmatic about tool usage
  • Focuses on systems and processes
  • Values code quality and maintainability
  • Uses AI to enhance existing skills, not replace them

The Sweet Spot: Senior developers who’ve mastered the fundamentals can use AI as a powerful productivity multiplier. They have the experience to know when AI suggestions are good, mediocre, or downright dangerous.

How They Use AI Effectively:

  • Code reviews and refactoring suggestions
  • Writing comprehensive test suites
  • Generating documentation
  • Exploring different implementation approaches
  • Automating repetitive coding tasks

The Key Difference: They treat AI as a very smart junior developer they’re mentoring. They review everything, provide context, and make the final architectural decisions.

Why This Works: They have the experience to separate good AI suggestions from bad ones. They can spot when AI misunderstands requirements or suggests something that will create technical debt.

The Developer-Entrepreneur (Indie Hackers & Startup Founders)

Characteristics:

  • Lives in the future vision of their product
  • Focuses on speed-to-market over code perfection
  • Sees AI as strategic advantage for rapid prototyping
  • Values customer validation over architectural purity

When This Makes Sense: You’re building an MVP to validate a business idea. Your goal isn’t to write perfect code—it’s to prove product-market fit as quickly as possible.

How They Use AI:

  • Generate entire feature prototypes rapidly
  • Build admin panels and CRUD operations
  • Create integrations with third-party APIs
  • Handle data transformations and basic algorithms

The Trade-off: Technical debt is acceptable if it means getting to market faster. The code might not win any architecture awards, but it serves the business goal.

Why This Can Work: If the product fails, the code quality didn’t matter anyway. If it succeeds, you can hire proper engineers to rebuild with solid foundations.

Working ON Your Code vs IN Your Code

Here’s where Gerber’s framework really shines when applied to development:

Working IN Your Code (Technician Mode):

  • Using AI to generate more code faster
  • Copy-pasting solutions without understanding
  • Focusing on immediate feature delivery
  • Treating symptoms instead of root causes

Working ON Your Code (Manager/Entrepreneur Mode):

  • Using AI to improve your development process
  • Building better testing and documentation workflows
  • Designing systems that scale beyond your current needs
  • Creating code that others can understand and maintain

The Different Approaches in Practice

Code Review Scenario

Technician: Asks AI to fix failing tests without understanding why they failed.

Manager: Uses AI to generate additional test cases and edge cases they might have missed, then reviews each one for relevance.

Entrepreneur: Uses AI to generate basic tests quickly so they can focus on the complex business logic that actually differentiates their product.

Learning New Technology

Technician: Asks AI for complete implementations and uses them as-is.

Manager: Uses AI to understand concepts and patterns, then implements their own version with proper error handling and edge cases.

Entrepreneur: Uses AI to build working prototypes quickly, knowing they’ll refactor or rewrite when the feature proves valuable.

The Danger of Getting Stuck

Just like businesses get stuck in the “technician trap,” developers can get stuck in AI dependency.

If your coding skills depend entirely on AI prompts, you don’t own programming abilities—you own an expensive subscription.

The most dangerous position? Senior developers who should know better but start relying on AI as a crutch instead of a tool. They risk losing the very expertise that made them valuable in the first place.

Finding Your Right Approach

Early Career (0-3 years): Prioritize fundamentals. Use AI as a learning aid, not a replacement for thinking.

Mid Career (3-7 years): Use AI to amplify your existing skills. Focus on code quality and system design.

Senior Career (7+ years): Use AI strategically based on your goals—whether that’s productivity, innovation, or rapid prototyping.

Entrepreneurial Phase: Use AI aggressively to validate ideas quickly, then invest in proper engineering once you have product-market fit.

The Bottom Line

AI tools are incredibly powerful, but like any tool, their value depends on how you use them.

The developers who will thrive are those who understand which mindset to adopt for different situations. Sometimes you need to be the technician who understands every line of code. Sometimes you need to be the manager who builds sustainable systems. Sometimes you need to be the entrepreneur who ships fast and iterates.

The key is choosing consciously, not falling into AI dependency by default.

Most thought process around this framework is heavily inspired by “The E-Myth Revisited” by Michael Gerber. I highly recommend reading it—the business insights apply surprisingly well to individual development careers.

What’s your current relationship with AI tools? Are you working IN your code or ON your code?

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