AI in Software Engineering: Preserving the Joy of Coding

Imagine spending hours debugging a mysterious system crash, only to find a single misplaced semicolon. For many backend engineers, that frustrating journey is what makes the final fix so satisfying. Now imagine skipping that journey entirely by asking an AI for the answer. You save time, but you lose something essential.

The rise of AI coding assistants has split the engineering community. Some embrace the productivity boost; others worry it erodes the craft. But the real question isn't whether AI is good or bad—it's how you use it without losing the joy of building. The most thoughtful engineers treat AI as a search accelerator, not a problem-solving replacement. Preserving the struggle matters more than ever.

Quick Answer

AI tools like ChatGPT can accelerate syntax lookup and routine tasks, but they should not replace the engineer's own debugging and problem-solving journey. When used as a faster alternative to Google or community forums, AI preserves the joy of coding by removing tedious obstacles while leaving the core intellectual challenge intact. The key is deciding how much assistance you need, based on what you care about most.

The Art of Backend Engineering and Why AI Feels Threatening

Software engineering, especially backend work, is rarely just about writing code that compiles. For many practitioners, it's an art form—a creative act of crafting robust, elegant solutions from abstract requirements. The satisfaction comes from the mental puzzle, the unexpected insight, and the moment everything clicks.

AI enters this world as a new opinion, one more voice in an already noisy internet. Engineers who have spent years honing their intuition may feel that AI cheapens the process. But the real threat isn't the tool itself; it's how easily it can seduce you into skipping the parts of engineering that matter most.

Why this matters now: With AI assistants embedded into every major IDE and accessible through chat interfaces, the pressure to adopt them is immense. Engineering managers ask why you aren't using AI to ship faster. Colleagues share their productivity gains. Yet the unspoken cost—the erosion of deep understanding and personal growth—is rarely discussed.

AI as a Search Accelerator: A Faster Google

One of the most pragmatic ways to use AI in software engineering is as a search accelerator. Instead of scrolling through forum threads or documentation, you ask an AI for the specific syntax or command you need. The core problem remains yours to solve; you just skip the lookup headache.

Real-World Example: Debugging SAP HANA Application Logs

Consider debugging an SAP HANA application log. Without AI, you might search for "SAP HANA query log format" or "how to filter HANA trace by timestamp," spending minutes sifting results. With AI, you can ask: "Give me a SAP HANA SQL query to extract all error entries from the last hour from table M_TRACE." The AI provides the query instantly.

But notice what hasn't changed: you still need to understand the context of the error, examine the log output, and decide what to fix. The AI didn't solve the bug—it just accelerated the lookup. The journey remains yours.

Before AI vs. After AI: Same Task, Faster Lookup

Aspect	Without AI	With AI as Search Accelerator
Syntax/command lookup	Manual search (Google, forums, docs)	Instant AI-generated answer
Core debugging ownership	Engineer owns entire process	Engineer still owns entire process
Time spent on lookup	5–15 minutes	30 seconds
Learning experience	Incidental browsing may teach extra context	Narrower but faster path
Risk of missing collateral knowledge	Lower (browsing exposes related topics)	Higher (no serendipitous discovery)

Expert Tip: Use AI for anything you'd normally Google—syntax, CLI commands, regex patterns. But never ask it to debug a problem you haven't fully analyzed yourself. The moment you hand over the analysis, you stop learning.

The Elden Ring Spectrum: How Much AI Help Is Right for You?

A useful framework for deciding AI involvement comes from the video game Elden Ring. Players can choose from several levels of assistance, each changing the experience. The same spectrum applies to software engineering with AI.

The Levels of Assistance

• Auto-play the whole game: Zero interest in playing—let the AI write all code. Most engineers find this unfulfilling because the joy comes from playing, not watching.
• Boss-skip: Remove the hardest frustrations so you can enjoy the rest. For coding, this might mean letting AI handle boilerplate or repetitive tasks while you focus on architecture.
• Summon co-op: Bring in a colleague (or AI) to help when stuck, but you remain the main player. This preserves participation while getting a push.
• Guide lookup: Research strategies or syntax, but implement yourself. This is the search accelerator approach.
• Syntax lookup (lowest level): Ask only for specific commands or APIs. The engineer's brain does everything else.

The more you care about the outcome, the less AI you should use. If you're building a personal project to learn a new stack, lower assistance levels (syntax lookup only) maximize growth. If you're shipping a routine feature under tight deadline, higher assistance might be acceptable—but be aware of the trade-off.

Applying the Spectrum to Your Craft

Every developer must find their own comfort level. Some engineers enjoy reinventing wheels for fun; others prefer to stand on existing work. The Elden Ring framework helps you articulate what you want from your work and choose AI accordingly.

Did You Know? Asking a colleague for help is effectively the same as asking AI, except the colleague may also teach you collateral knowledge through conversation. AI answers lack that human context.

Collateral Knowledge: Why Skipping the Struggle Backfires

Collateral knowledge is the unexpected learning that happens while solving a problem. You search for one thing and discover a related concept, tool, or pattern that later saves you hours. This incidental learning is the bedrock of deep expertise.

The Windy Path of Debugging

When you debug a tricky production issue, you often wander through logs, config files, and error messages. Along the way, you might learn about a new monitoring endpoint, an obscure edge case, or a performance characteristic of your database. That detour makes you a better engineer.

AI shortcuts this process. You ask a precise question and get a precise answer, no detours. Over time, you accumulate facts without context, like a library without cross-references. When you face a novel problem, your mental map is missing the connections only a winding path could build.

AI Answers Have Zero Personal Resonance

An AI-generated fix doesn't carry the emotional weight of one you discovered. The pride of solving something hard, of being the engineer who understood the system deeply—that evaporates when AI hands you the solution. The product of your work becomes generic, lacking your signature.

Common Mistake: Treating AI as a learning tool by asking it to explain code. While it can generate explanations, the explanation is passive consumption. True learning requires active struggle—writing, breaking, and fixing yourself.

Don't Be Afraid to Reinvent the Wheel

The software industry often shames engineers who "reinvent the wheel." Why build your own logging framework when dozens exist? The answer: because you enjoy it, and the process teaches you things boilerplate solutions cannot.

Arguments Against Reinvention—and Why They Miss the Point

Critics say reinvention wastes time and introduces bugs. But if your goal is learning, mastery, or pure joy, the argument collapses. Many experienced engineers maintain side projects that duplicate existing tools, not because they're ignorant, but because the act of building is the reward.

Modern Software Quality Decline

Consider that much of the software we use today—YouTube, VS Code, HAProxy—is built with modern best practices, yet crashes, glitches, and memory leaks are common. Relying on existing libraries doesn't guarantee stability. There is value in understanding fundamentals by rebuilding them yourself, even if you never deploy it.

Quick Fact: The author built an automation tool as a Microsoft Word add-in in 2004 to handle repetitive work. That side project led to recognition and career growth. It was a reinvention, and it paid off.

Practical Advice: Keeping Your Candle Burning

If you feel the joy of coding slipping away under the pressure to use AI, here are actionable steps to protect your craft.

Find What You Truly Enjoy

Identify the parts of engineering that give you energy—whether it's designing distributed systems, optimizing database queries, or building clean APIs. Double down on those areas and minimize AI involvement there. For the boring parts, let AI help.

Turn Boring Tasks into Creative Opportunities

If you hate configuration, use AI to generate boilerplate. But if you love performance tuning, do that manually. The same task can be either drudgery or delight, depending on your perspective. You can also gamify routine work: set a timer, try to beat your previous record, or add a personal challenge.

If You Love LLMs, Pursue Them

Some engineers genuinely enjoy working with AI models themselves—building LLM accelerators, fine-tuning, or integrating vector databases. That's fine too. The point isn't to avoid AI, but to ensure you're choosing your level of involvement consciously. If AI is your passion, make it your craft.

Key Takeaways

• AI is best used as a search accelerator for syntax and commands, not as a thinking replacement.
• The Elden Ring spectrum offers a useful way to decide how much AI help aligns with your values.
• Collateral knowledge—the unexpected learning from struggling—is irreplaceable and AI shortcuts it.
• Reinventing the wheel can be valuable if done for enjoyment or learning, not just productivity.
• Protect the parts of engineering that spark joy; use AI only for the tasks that drain you.

Frequently Asked Questions

1. Is using AI for code generation cheating?
No, but it depends on your goals. If you're shipping code quickly, AI is a tool like any other. If you're learning, relying on AI can hinder deep understanding. Be honest about your intent.

2. Does AI make software engineers obsolete?
Not in the foreseeable future. AI excels at pattern completion and lookup but lacks the contextual reasoning needed for system design, debugging novel problems, and making trade-off decisions. The craft of engineering remains human.

3. How can I use AI without losing my skills?
Use AI only for tasks you already understand or for syntax lookup. Never ask it to debug a problem you haven't analyzed. Always try to solve a problem manually first before consulting AI.

4. What is collateral knowledge?
It's the unexpected learning you gain while solving a problem—like discovering a related tool, pattern, or edge case while searching for something else. AI gives direct answers, eliminating the detours that build deep expertise.

5. Should I tell my team I use AI?
Transparency is wise, especially if you're introducing AI-generated code. Many teams have policies about AI output. Be professional—review everything, and never commit untested AI code.

6. Can AI help with debugging without ruining the joy?
Yes, if you treat it as a research partner. Ask for possible causes or check your assumptions, but always validate and explore yourself. Keep the investigation in your hands.

7. What's the best mindset for using AI in engineering?
Treat AI as a junior colleague who has good memory but no judgment. You are the senior engineer who must verify, interpret, and decide. That's where the craft lives.

Summary Box

Core Lesson: AI can accelerate uninteresting parts of software engineering, but preserving the joy of coding means keeping the intellectual challenge—debugging, designing, and discovering—firmly in your own hands. Use the Elden Ring spectrum to decide how much help you want, and never skip the struggle that builds expertise.

Final Call to Action

Next time you're tempted to ask AI to solve a bug or write a whole function, pause. Ask yourself: "What will I lose if I skip this?" If the answer is "the part I love," close the chat window and solve it yourself. Your future self, with a deeper understanding and a bigger smile, will thank you.