Domain-Specific Edge Cases

TL;DR

• Every domain has rules, quirks, and "trust me, you can't do that" knowledge. AI is trained on the general. You know the specific.
• The edge cases that break systems are often domain-specific. AI hasn't seen them. You have.
• Your domain expertise is a moat. The more specialized, the harder to automate.

General-purpose AI is trained on broadly available data. Your industry's specifics — regulatory, historical, or "we learned this the hard way" — are often not in the training set. That's your advantage.

What Domain Knowledge Covers

Regulatory and Compliance

• Healthcare: HIPAA, PHI handling, audit trails. AI might suggest logging patient data in a way that violates regulations. You know the rules.
• Finance: SEC, PCI-DSS, SOX. "This seems fine" to AI might be a compliance violation. You know what's allowed.
• Embedded/safety-critical: FDA, FAA, automotive safety standards. AI can't certify. You (or your process) does.

Historical Quirks

• "This column is nullable because of a 2015 migration we never finished." — AI doesn't know your history. It might "fix" it and break dependencies.
• "We use this legacy format because Customer X requires it." — Edge case. AI won't know. You do.
• "That API is deprecated but we can't turn it off until Q3." — Constraints. AI designs for the future. You design for the transition.

"We Learned This the Hard Way"

• "Never do X — we had an incident in 2022." — Tribal knowledge. Not in docs. Not in public data. AI can't learn it. You're the carrier.
• "This looks like a good optimization. It's not. It caused a 3-day outage." — War stories. You have them. AI doesn't.

Industry-Specific Logic

• E-commerce: Returns, chargebacks, tax rules by jurisdiction. AI might get the generic case. You know the edge cases (international, subscription refunds, etc.).
• Ad tech: Viewability, fraud, billing rules. Niche. AI is generic.
• Gaming: Anti-cheat, matchmaking, economy balance. Domain-specific. AI suggests. You validate.

Why This Is a Moat

• AI can't acquire it from public data. A lot of domain knowledge is internal, regulatory, or learned by doing. It's not in the training set.
• It's expensive to encode. You could try to write down everything. You'd miss stuff. And it changes. You're the living document.
• It's the difference between "works" and "works correctly." AI-generated code might run. It might also violate compliance, break a legacy integration, or repeat a past mistake. You catch that.

How to Use This as a Moat

1. Document your "AI doesn't know" list. Regulatory constraints. Historical quirks. "Never do X." That list is your checklist for reviewing AI output.
2. Be the domain reviewer. When AI generates something in your domain, you're the final pass. "Does this violate Y?" "Does this assume Z we don't have?" Your job.
3. Share tribal knowledge. Write it down. Turn "we learned this the hard way" into a doc or a comment. So you're not the only carrier. And so AI-assisted newcomers have something to check against.
4. Lean into specialization. The more domain-specific your role, the harder it is for generic AI to replace you. Healthcare tech, fintech, embedded, gaming — the edge cases are your moat.

Do This Next

1. List 5 "AI doesn't know" facts about your domain. Regulatory, historical, or "we learned the hard way." That's your domain moat. Make it explicit.
2. Review one AI output through your domain lens. Would it pass compliance? Would it break a legacy constraint? What would you change? That's the domain review skill.