Coding Before and After AI and When 'It Works' Is Just the Beginning

What happens when speed and audacious spark of imagination meets substance and judicious discipline.

There was a time when writing code meant thinking like an engineer. Not just solving a problem, but solving it well. Those were the pre-AI days. Painful, slow, rewarding.

Then came the AI assistants with instant solutions and copy-paste magic. “It works” in seconds. But here’s the catch: working code is not the same as good code.

And in many cases, it’s not even safe code.

It is worth exploring some of the differences between coding before and after the AI wave, and why we need to rethink what it means to ship software in this new era.

Coding Before AI: The Real Work Begins After "It Works"

You built a feature. You tested it. It runs. Yay!

But any experienced software engineer will tell you: the "it works" moment is just step one. After that, the real work begins: work that separates hobbyists from professionals, prototypes from products, and clever hacks from enduring systems.

Here are some issues a responsible developer would consider after getting a program to “work”:

• Scalability

  • Will it still work with 100x more users or data?

  • Can it handle concurrency or distributed workloads?

Why? To ensure it works under real-world load, not just in your dev environment.

• Performance

  • Are there bottlenecks?

  • Can it be optimized without breaking functionality?

Why? So users don’t abandon slow or laggy features.

• Security

  • Is input sanitized?

  • Is sensitive data stored, used, and transmitted safely?

  • Is it vulnerable to injection, XSS, CSRF, or more subtle exploits?

  • Are secrets like API keys, tokens, and passwords properly managed and never exposed in logs or front-end code?

  • Is authentication and authorization enforced correctly to prevent privilege escalation or unauthorized access to sensitive resources?

Why? To protect user data, prevent attacks, and avoid breaches.

• Error Handling & Fault Tolerance

  • What happens when something fails?

  • Are there retries, fallbacks, or meaningful error messages?

Why? To prevent regressions and confidently evolve the codebase.

• Test Coverage

  • Are there unit tests, integration tests, and end-to-end tests?

  • Do tests cover edge cases?

Why? Because things will break and users deserve graceful failure, not crashes.

• Code Readability

  • Can other developers understand the code?

  • Are variables and functions named clearly?

  • Is it idiomatic and consistent with language best practices?

Why? So others (and probably your future self) can understand and maintain the code.

• Modularity

  • Is the code broken into reusable components?

  • Are there single-responsibility functions/classes?

Why? To encourage reuse, easier testing, and isolated changes.

• Maintainability

  • Is it easy to debug or extend later?

  • Can a new dev onboard and contribute quickly?

Why? Because software lives on and messy code becomes a long-term liability.

• Documentation

  • Is there inline documentation for complex logic?

  • Is the setup process documented?

  • Are APIs documented?

Why? To make the system understandable without digging through the entire codebase.

• Version Control Hygiene

  • Are commits atomic and descriptive?

  • Are there tests and code reviews tied to PRs?

Why? To track changes, collaborate cleanly, and roll back safely when needed.

• Deployment Readiness

  • Is it containerized, CI/CD-ready?

  • Are environments (dev/stage/prod) clearly configured?

Why? So it can be shipped quickly, repeatedly, and reliably.

• Monitoring and Logging

  • Are meaningful logs emitted?

  • Are metrics and alerts set up?

Why? To detect issues early and diagnose problems in production.

• Compliance and Legal Considerations

  • Are licenses respected?

  • Is user data handled according to GDPR or other regulations?

Why? To avoid costly legal, regulatory, or ethical violations.

• Reproducibility

  • Can the environment be reliably recreated?

  • Is there a dependency lockfile?

Why? So others can replicate your results and environments consistently.

• Design Consistency

  • Does the code adhere to architecture principles?

  • Are patterns like MVC, Hexagonal, or CQRS applied where needed?

Why? To make the architecture predictable, reliable, and extensible.

• User Experience

  • Does the feature feel intuitive?

  • Are edge cases or weird behaviors handled gracefully?

Why? Because functional ≠ usable, and delight wins loyalty.

• Accessibility

  • Can it be used by people with disabilities?

Why? To ensure inclusivity for all users, regardless of ability.

• Internationalization

  • Is it ready for localization if needed?

Why? To prepare for global reach without major rewrites.

• Feedback Loops

  • How will user feedback be collected?

  • Is instrumentation in place?

Why? To learn what’s working and what’s not from real users.

• Risk Assessment

  • What happens if this fails in production?

  • Is rollback possible?

Why? To prevent a small bug from becoming a business-ending failure.

This wasn’t perfectionism. It was software engineering. A craft. A responsibility

Coding After AI: "It Works, Ship It"

Now? Type a vague prompt into ChatGPT or GitHub Copilot.

Hit enter.

Paste.

“It works.”

Ship it.

Move on.

AI accelerates code generation. No doubt. But it also bypasses thinking if we're not careful. In many modern teams, especially startups or hackathons, the moment the code runs, the temptation is to move forward. Build faster. Impress investors. Hit deadlines.

The list above with things to consider? Often skipped, postponed, or worse, forgotten entirely. And this shift has consequences.

The Dangers of "Working Code" Culture

When the only benchmark is "it runs," here's what can (and often does) go wrong:

• Unscalable systems that crash under growth.

• Security breaches due to unvalidated inputs or exposed endpoints.

• Unmaintainable codebases that no one dares to touch six months later.

• Silent failures in production with no logging or alerts.

• Legal nightmares from license violations or data mishandling.

• Technical debt accumulating so fast that progress grinds to a halt.

• False confidence in correctness just because the AI “said so.”

AI is a powerful tool but it doesn’t think (yet). It doesn’t reason (like we would want it to). It certainly doesn’t care if your code breaks in production or leaks customer data. Sorry.

That’s your job.

Cautionary Wisdom for the AI-Era Developer

AI is here to stay and I am more and more fascinated by its always evolving capabilities.

But don’t let convenience erode craftsmanship. Use AI to accelerate. But don’t let it absolve you of responsibility. Every line of code is a liability until proven otherwise.

Treat “it works” as a checkpoint, not a finish line. Return to the checklist. Make it better. Make it last. Build like someone else will maintain your code. Secure it like it’ll face an attacker tomorrow. Design it like you’ll scale to a million users.

Because one day, you might.

And when that day comes, your future self, and your users, will thank you.

Conclusion

What lies ahead is not a battle between the old and the new, but a union of minds, where the judicious discipline of the pre-AI developer meets the audacious spark of the post-AI creator. The one who sweated over edge cases, architecture, and fault tolerance brings hard-earned wisdom; the one who dares to dream with AI at their fingertips brings bold velocity and fearless experimentation. Together, they form a new breed: builders who move fast and build things that last. In this fusion, we unlock a future where software is not just functional but resilient, not just fast but thoughtful, not just clever but deeply impactful. From this remarkable mixture, we can shape systems that scale human potential, responsibly, creatively, and with purpose.