As engineering leaders, many of us find ourselves further from the codebase than we'd like.

The days of all-night coding sessions fade into memory, replaced by strategic meetings, team management, and architectural oversight. But the itch to build often remains. Recently, I scratched that itch using AI coding tools, leading to some crucial reflections for all of us leading technical teams.

The experiment: Vibe coding on a long haul

Inspired by "vibe coding" videos showcasing rapid development, I experimented on a long flight. Armed with Cursor (an AI-first code editor) and a rough app idea, I dove into tackling frameworks I wasn't familiar with.

The results were pretty striking. AI handled much of the heavy lifting – setting up dependencies, generating boilerplate code, and navigating unfamiliar territory. Within hours, a non-trivial web app was functional. This demonstrated AI co-pilots' raw speed and potential to accelerate development, particularly in bootstrapping projects or exploring new technologies.

The reality check: The "overly enthusiastic junior dev"

However, the experience wasn't seamless. It wasn’t far from the experience of collaborating with an "overly enthusiastic junior dev" – fast and full of suggestions, but lacking coherence, consistency, and sometimes making obvious errors (like introducing a redundant CSS framework).

This, I feel, is where experience is critical. Identifying flawed AI suggestions, debugging generated code, and ensuring architectural soundness requires the seasoned judgment that comes from years of building, shipping, and maintaining software. The AI could generate code, but it couldn't consistently generate wisdom.

Challenging the "young person's game" narrative

This experiment prompted reflection on a persistent industry stereotype: is software development still primarily a "young person's game"? 

Statistics support this perception – globally, the largest cohort of developers is 25-34, significantly younger than the average workforce age. The "whizz-kid" archetype endures, often unfairly painting senior engineers as out of touch.

But if AI tools increasingly automate repetitive tasks and lower the barrier to entry for complex frameworks, the differentiating factor shifts. Boilerplate, syntax, and basic implementation details become less critical. What becomes more critical?

• Judgment: Knowing what to build and how it fits into the larger picture.
• Trade-offs: Understanding the long-term implications of technical decisions (scalability, maintainability, security).
• Quality: Spotting subtle flaws, code smells, and architectural weaknesses before they become major problems.
• Direction: Guiding the development process, whether the "developer" is human or AI.

These are the hallmarks of experience. Often, the critical 10% of our skills – the deep understanding and judgment – provides exponential value.

Critical considerations for engineering leaders

The rise of AI co-developers isn't just about individual productivity; it forces us, as leaders, to confront significant challenges:

1. Developing future seniors: How do we nurture junior talent? If AI handles the foundational tasks, how do aspiring developers build the deep understanding needed to become tomorrow's seniors? Relying solely on AI risks creating "prompt experts" who lack fundamental coding principles. We must consciously design training, mentorship programs, and team structures that cultivate this deeper knowledge, even as abstraction layers increase.
2. Maintaining quality & oversight: Rapid, AI-generated code demands rigorous validation. Ensuring that human expertise remains in the loop is crucial. Experienced engineers are vital for reviewing AI output, catching subtle errors, and preventing the propagation of bad practices, lest we grumble and clean up messes later.
3. Adaptability of senior talent: Experience is invaluable, but only if coupled with curiosity. Senior engineers must embrace new tools and adapt their workflows. Those who remain stuck in old ways risk becoming less efficient, even with their deep knowledge. 
4. The power of collaboration: The ideal future isn't about choosing between youthful energy and seasoned wisdom. It's about combining them. Young devs might bring the fire, but seasoned devs bring the fire extinguisher. Our role as leaders is to foster teams where these strengths complement each other, leveraging AI as a tool for everyone.

Engineering experience matters more with AI code generation 

AI is undeniably reshaping software development. But rather than making experienced engineers obsolete, it appears poised to amplify their value. When the grunt work is automated, the focus sharpens on strategic thinking, architectural integrity, and sound judgment – precisely the areas where experience shines.

As leaders, we must encourage our teams to explore these tools, integrate them thoughtfully into our workflows, and critically evaluate their outputs. Most importantly, we need to actively cultivate and value the deep expertise within our teams and challenge the outdated notion that innovation belongs solely to the young.

In the age of AI co-pilots, experience isn't just relevant; it's the rudder steering the ship. Let's ensure we're building teams – and a culture – that recognizes its enduring, and perhaps increasing, importance.