The Calculator Analogy

You don’t give a calculator to a 5-year-old learning arithmetic. Why? Because they need to understand what numbers mean, how operations work, and develop number sense. They need to struggle through carrying the one, understanding place values, and internalizing the patterns that make mathematics coherent.

The same principle applies to software engineers who are new to software engineering and AI. But here’s where it gets worse: AI is like a calculator that’s wrong 15-30% of the time, without any warning labels. Imagine learning math with a faulty calculator – you’d develop completely wrong intuitions about how numbers work.

The Hidden Prerequisites of AI Usage

Using AI effectively in software engineering requires mastery of skills that junior engineers, by definition, haven’t developed yet:

Code Review: When AI generates code, you must be able to spot subtle bugs, security vulnerabilities, and architectural anti-patterns instantly. This requires one to make these mistakes, receive critical feedback by a senior reviewer, and to learn from them.

Clear Specification Writing: Getting useful output from AI, especially in existing medium-to-large codebases requires precise, unambiguous specifications. Junior engineers are still learning which details matter and how to communicate technical requirements effectively.

Debugging: When AI-generated code fails (and it will), you need to quickly identify root causes. This requires deep understanding of the fundamentals – knowledge that only comes from writing and fixing your own code thousands of times.

The Dangerous Comfort Zone

AI creates a dangerous illusion of competence, one that increases the burden on senior engineers, thereby reducing velocity for the team. A junior engineer might feel productive churning out AI-generated code, but they’re not developing the critical thinking and problem-solving muscles that define great engineers. They’re not learning to:

• Break down complex problems into manageable pieces
• Recognize patterns and apply appropriate design patterns
• Understand the “why” behind coding conventions and best practices
• Develop intuition for performance implications and edge cases
• Build mental models of how systems actually work under the hood

When AI Becomes Appropriate

AI is a powerful accelerator for experienced engineers who already possess these fundamental skills. When you know what correct looks like, AI helps you get there faster. When you can spot flawed logic or problematic patterns instantly, AI becomes a productivity multiplier rather than a crutch.

For our senior engineers, AI tools are fantastic. They can:

• Generate boilerplate code that they’d write the same way anyway
• Quickly prototype ideas they already understand
• Autocomplete tedious but well-understood patterns
• Serve as a sophisticated documentation search engine

But these benefits only materialize when you have the expertise to guide, correct, and improve AI output.

Building Strong Engineers

Our industry needs engineers who deeply understand their craft, not operators who copy and paste from AI suggestions. By requiring junior engineers to write every line of code themselves, debug their own mistakes, and architect their own solutions, we’re investing in their long-term success.

Yes, they’ll be slower initially. Yes, they’ll struggle with problems that AI could solve in seconds. But through that struggle, they’ll develop the judgment, intuition, and deep knowledge that separates exceptional engineers from mediocre ones.

The Bottom Line

At Surya Digital, we’re not anti-AI. We’re pro-fundamentals. We believe that earning the right to use AI as a tool requires first mastering the craft without it. Just as calculators are invaluable for engineers who understand mathematics, AI is invaluable for developers who understand software engineering.

But shortcuts in the learning phase create gaps in knowledge that become chasms in capability. We refuse to let our junior engineers build their careers on such shaky foundations.

The path to engineering excellence has no shortcuts. Master the fundamentals first. The AI tools will still be there when you’re ready to use them wisely.