Every few years, the tech world discovers a new revolution. We’ve seen it with object-oriented programming, web frameworks, mobile apps, cloud computing and now, generative AI. Each wave arrives with excitement, bold promises, and a little fear of missing out. For someone who has spent years writing, shipping, and maintaining software, these cycles feel familiar. Experience doesn’t make you cynical, but it does teach you to see patterns through the noise.
Patterns Repeat: Just With New Names
When you’ve lived through several tech transitions, you notice that the story rarely changes. New tools arrive with dazzling demos. Early adopters rush in, convinced the old ways are dead. Then reality shows up scale, cost, security, integration pain. Over time, we figure out where the technology fits, the hype settles, and the best practices emerge.
Generative AI follows this arc. It’s impressive, but it’s not magic. It’s another tool. Understanding where it genuinely helps and where it adds risk takes patience and clear thinking.
Start With the Problem, Not the Hype
One habit seasoned engineers develop is resisting the pull of “shiny new tech” until they know the customer’s real pain points. Technology is only valuable when it solves a problem that matters.
Jumping into AI because “everyone is doing it” is risky. You might over-engineer, build features that don’t last, or miss simpler, cheaper solutions. First understand what users struggle with, then decide if AI or anything new actually helps.
Fundamentals Still Win
Through all the waves of change, the foundations of good software never stopped mattering:
- Security and privacy: New tools often open new attack surfaces. AI adds its own data leaks, prompt injection, unexpected model behavior.
- Reliability: Fancy models won’t save you if your product fails under load or breaks silently.
- Maintainability: Code that’s clear, tested, and well-structured always survives longer than trendy hacks.
When a new technology emerges, these basics become more important, not less.
Healthy Skepticism Is a Strength
Skepticism doesn’t mean dismissing innovation. It means asking the right questions before committing:
- What problem does this solve better than existing tools?
- How does it affect cost and complexity long term?
- Can we secure and scale it responsibly?
That mindset keeps teams focused and protects them from the whiplash of hype cycles.
The Advantage of Experience
Generative AI will change how we build, just as previous revolutions did. But experienced developers bring perspective that’s rare in early hype stages. We know the thrill of new tools, but also the pain of maintaining them. We can guide teams to adopt thoughtfully balancing innovation with stability, excitement with caution.
In fast-moving tech, it’s tempting to chase trends. The wiser path is slower but stronger: understand deeply, apply carefully, and never forget the lessons learned from decades of building.