Domain Understanding Is Moat

8 hours ago

The author argues that the true moat is domain understanding, which involves deep knowledge of customer workflows, constraints, edge cases, goals, failure modes, and definitions of 'good'.
Historically, domain knowledge was encoded into SaaS via workflows, databases, and CRUD APIs, but was limited, requiring human oversight for additional understanding.
Today, domain understanding is encoded in AI components like evals, prompts, and harnesses—examples include Claude Code's harness for coding or model weights for low-latency value delivery.
The form of encoding may change (e.g., to specialized chips in the future), but the goal remains capturing domain understanding to deliver customer value.
A feedback loop is crucial for compounding the moat: iterating fast with customer feedback to enhance domain understanding and encode it into software, offloading customer work.
Business constraints may dictate where to encode domain understanding—if model training isn't feasible due to Capex, alternatives like harnesses, prompts, skills, evals, memory, or context layers are used.

Hasty Briefsbeta