🧑‍💻 How to remain relevant in this AI era?

Source: Dev.to

With the advent of AI, the software‑engineering job is evolving, but our responsibilities remain.
Producing code is no longer the hardest or most valuable part of the job—solving problems still very much is. That’s where the real shift is happening. AI won’t replace problem solvers, but it will compress demand for pure code writers, making pure code writing cheap and eventually optional. Which side you’re on will largely determine your near future.

Be an actor, not a spectator

Watching from the sidelines will make you irrelevant faster than the actual impact of AI on the industry. Actors drive outcomes; spectators only consume outputs. This is where careers start to diverge.

• Tame and wield AI effectively – Learn to use it well, in ways that are efficient for you. Best practices, workflows, skills, commands, prompt engineering, local setup, limitations… Treat it as a power tool, not a magic box.
• Expand your problem‑solving surface area – Work on codebases you’re not familiar with. Use AI as a translation layer across languages, frameworks, and paradigms. Apply your problem‑solving skills, judgement, taste, and reasoning to a much wider surface area.

Be intentional and explicit

Never blindly accept AI output. Review it, sanitise it, improve it. Use your expertise, and yes, use AI again to refine it. Follow a clear, repeatable process and stick to it:

• 🤖 Plan | 👨‍💻👩‍💻 Chaperone
• 🤖 Execute | 👨‍💻👩‍💻 Chaperone
• 🤖 Verify | 👨‍💻👩‍💻 Chaperone

Any step can be assisted by AI, but none should be fully delegated. You remain accountable, you supervise, you decide.

Your (new) job: tame the tool

AI is just that—a tool. Your job is to know how to use it effectively, reliably, and safely. It is an extension of your expertise, not a replacement.

• For early‑career engineers, double down on fundamentals. AI is only useful if you know what “good” looks like; it won’t give you opinions or instincts.
• Study opinionated work, internalise trade‑offs, and develop your own standards. Skipping fundamentals and jumping straight into AI leads to fast, confident production of bad code.

Do not delegate your responsibility

Delegating responsibility makes you irrelevant, slowly but surely. Anyone can ask AI to generate code—my brother, a sushi chef, is doing it. It can look production‑ready, but without judgement, standards, and review it becomes unmaintainable slop.

Engineers produce systems; AI produces code. Your problem‑solving skills matter most:

1. Understand the problem
2. Figure out the right solution
3. Orchestrate its implementation
4. Own the outcome

A good exercise is to be able to precisely explain the problem, the solution, and the implementation. If you don’t truly understand them, you won’t be able to explain them to someone else.

Invest in the right things

Experimenting with core models and first‑party tooling is valuable, but obsessing over every new wrapper or orchestrator is not. The pace of change is overwhelming; no one can keep up with everything, and that’s fine.

• Work from first principles: pick a vanilla LLM, understand its core capabilities and limits, and experiment deliberately.
• Remain alert and open, but avoid over‑investing in niche wrappers that merely patch temporary gaps. Those gaps often disappear within months with a simple agent update.
• Focus on methodologies and workflows, not the particularities of the tool you’re using this week. Learn how to use LLMs in general, how to steer them, and what standards to apply—think prompt engineering, skills, agents, commands—things that transfer across tools and generations.

My optimistic hot take: AI will ultimately raise the overall level of code quality, widening the gap between bad and good code. As building becomes easier than buying, new products will need to be exceptionally good to justify acquisition or payment. This raises expectations and the value of engineers who think, reason, and decide.