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Domain Expertise as a Moat

5 min read

Dba

Your knowledge of our schemas, our query patterns, our data quality issues — that's 10 years of context. AI has none of it.

Embedded

Safety-critical, certification, hardware quirks — AI can suggest, but you're the one who's lived the edge cases.

Domain Expertise as a Moat

TL;DR

  • AI knows general patterns. You know your domain — the quirks, the history, the "why it's like this."
  • Domain expertise compounds. The longer you're in a niche, the harder you are to replace.
  • Don't be a generic engineer. Be the person who knows the thing that matters.

Generic engineers are replaceable. So is generic AI. But the person who knows your payment system's edge cases, your industry's compliance traps, or your legacy codebase's skeletons — that person is hard to replace. AI can't absorb 10 years of context in a prompt.

What Domain Expertise Is

It's deep knowledge of a specific area:

  • Technical: "I know our monorepo's build system, our deployment pipeline, and why that one service is weird."
  • Industry: "I know how healthcare data flows, HIPAA constraints, and what auditors actually check."
  • Product: "I know our users, our use cases, and why we made that seemingly dumb decision in 2019."

AI can learn patterns from public data. It can't learn your company's history, your industry's unwritten rules, or the things that only make sense after you've been there.

Quick Check

What makes domain expertise a durable moat against AI?

Why It's a Moat

  1. AI can't replicate context — Your knowledge is in your head, in tribal docs, in postmortems. It's not all on the internet.
  2. Compounding — Every year you add more. The gap between you and a newcomer (human or AI) grows.
  3. Judgment — "We tried that in 2020 and it failed because X" — that's domain expertise. AI doesn't have your institutional memory.
  4. Trust — When stakes are high, people want the person who's seen it before. AI can suggest; you've lived it.

How to Build It

  1. Stay put (for a while) — Depth requires time. Job-hopping every 18 months builds breadth, not moats.
  2. Go deep in one area — Don't be a generalist in everything. Pick a domain and get unusually good.
  3. Document the weird stuff — The things only you know. Write them down. Share. That makes you the go-to person and protects the org.
  4. Learn the "why" — Not just "how we do it" but "why we do it this way." History matters.

The "Generic Engineer" Risk

If your whole value is "I can write code in language X," AI is encroaching. If your value is "I understand our payments domain, our regulatory constraints, and our technical debt in that area," you're in a different league.

The play: get good at the fundamentals (AI can help) and then go deep in something that matters to your company or industry. That combo is durable.

Do This Next

  1. List 3 things you know that a new hire (or AI) wouldn't — about your codebase, your domain, or your org. That's your moat.
  2. Document one piece of tribal knowledge this week. A runbook, a "why we do X" doc, or a postmortem insight. Lock it in.