Breadth Reveals Transferability

There's a persistent belief in software that depth is what matters. Pick one domain, go deep, become the expert. Specialize early, specialize hard.

I used to believe this too. Then I tried to assess my own AI capabilities across eight projects spanning seven business domains — and the results challenged everything I thought I knew about skill development.

The depth trap

When you build deeply in one domain, you learn a lot about that domain. You also develop blind spots. Patterns that work in your context start to feel like universal principles. Architectural decisions that were responses to specific constraints start to feel like best practices.

You can't tell the difference between a genuinely reusable pattern and an accidentally specific one if you've only ever used it in one place.

This is the depth trap: the deeper you go, the more confident you become — and the less you know about whether your confidence is warranted.

What breadth actually reveals

When I applied the same architectural patterns across marketing automation, financial services, content management, security, and personal knowledge management, something interesting happened. Some patterns survived every context. Others broke immediately.

The patterns that survived — skill-based agent architectures, tiered memory systems, hybrid execution models — turned out to be genuinely transferable. They worked not because they were designed for any specific domain, but because they addressed universal problems: how to manage growing context, how to let domain experts encode their knowledge, how to balance speed with reliability.

The patterns that broke were the ones I'd been most confident about. They were optimized for specific constraints I'd internalized so deeply that I'd forgotten they were constraints at all.

Breadth didn't dilute expertise. It validated it. Or, more precisely, it separated real expertise from domain-specific muscle memory.

The assessment shift

Traditional skill assessment focuses on depth: how complex is the hardest thing you've built? How many edge cases can you handle? How sophisticated is your implementation?

Multi-project assessment asks a fundamentally different question: does this pattern actually transfer?

A sophisticated architecture proven in one context could be accidental. The same architecture proven across three contexts with different constraints is almost certainly capturing something real.

This changes the calculus for how to develop AI capabilities. Building one ambitious project might be less valuable than building three moderate ones in different domains — because the second and third projects reveal which lessons from the first are actually portable.

Implications for teams and hiring

This has practical implications beyond personal development.

When evaluating AI engineers, the instinct is to look for depth — "they built a complex agent system" or "they shipped a production RAG pipeline." But an engineer who's built three different agent architectures across three different problem spaces may understand the underlying patterns better than someone who's built one highly optimized system.

The same applies to architecture decisions. If your team is designing an agent framework, the most valuable input comes from people who've tried similar patterns in different contexts — not from those who've only seen one environment.

The uncomfortable truth

Breadth is slower. Building across multiple domains means you're a beginner more often. You make mistakes that specialists would catch immediately. You feel less productive in any single domain than someone who's been there for years.

But breadth compounds in a way that depth doesn't. Each new domain doesn't just add knowledge — it stress-tests everything you already know. The insights that survive this stress test become genuinely reusable principles. The ones that don't were always context-specific, even when they felt universal.

The leap from capable to truly skilled isn't about going deeper into what you already know. It's about discovering which parts of what you know are actually yours to keep.