Finding Yourself in the AI Era: From Solving Puzzles to Solving Problems

Finding Yourself in the AI Era

TLDR: We're paid to solve problems, not puzzles. Engineers who thrive with AI focus on making ideas real, viewing complexity as an obstacle rather than the point. Define your identity broadly enough that AI becomes a tool rather than a threat to who you are.

Summary:

This piece from Luca Rossi at Refactoring tackles something that's been simmering in every engineering team: the emotional divide over AI. Some engineers feel excitement, hope, and empowerment. Others experience anger, fear, or grief. Importantly, this is separate from job anxiety - you can be excited about AI while worried about layoffs, or feel secure but sad about what your job is becoming.

The author surfaces a critical distinction: we're not paid to solve puzzles, we're paid to solve problems. Puzzles exist for intellectual stimulation with no purpose beyond the challenge itself. Problems exist to help other people who pay us in return. The fact that problems can be interesting and puzzle-like is a byproduct, not the main point.

The gardening analogy crystallizes this. If you're a professional gardener who loves the daily walks watering plants, irrigation systems feel like they're taking away your job. But watering was never your job - growing and maintaining gardens was. Finding a happy corner within work is great, as long as you don't optimize for it at the expense of the true goal.

The article identifies comfort-driven behavior to watch for: "I only use LLMs as autocomplete so I can check every line," "reviewing AI code takes longer than writing it myself," "if I make AI write it, my skills will atrophy." These might be valid in specific contexts, but often signal optimization for personal comfort over team outcomes.

The reframing advice is practical. Instead of "I am a backend engineer expert in algorithms and data structures," try "I am a software engineer who creates great products - and currently works with data structures on this team." The first person gets mad at AI for becoming equally good at their specialty. The second says "cool, I'll do this faster and move to something else."

The section for managers is particularly valuable. AI raises the floor - people get faster at what they do. But if you don't also raise the ceiling, they get trapped. Engineers using AI to do more of the same work end up spending more time fixing AI bugs. They're faster than before but still capped by their lane, and work becomes miserable instead of creative. The question isn't "do more of the same faster" but "what broader scope of work creates professional growth AND delivers business value?"

Key takeaways:

• We're paid to solve problems for people, not puzzles for ourselves
• Engineers thriving with AI focus on making ideas real, viewing complexity as an obstacle
• Define identity broadly ("someone who crafts useful products") rather than narrowly ("algorithms expert")
• Watch for comfort-driven behaviors that optimize for personal preference over business outcomes

Tradeoffs:

• Broader identity enables flexibility but requires letting go of specialist pride
• AI raises productivity floor but can trap people in miserable "more of the same" without ceiling changes

Link: Finding yourself in the AI era

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