Principle 02
Gall's Law
Working complexity grows from working simplicity.
A reliable complex system is usually evolved from a simpler system that already worked—not designed as one giant finished system from day one.
What it means
Complex systems contain many interactions, assumptions, and failure modes. Building all of them at once makes it difficult to know which part created the result—or the failure.
A small working system creates evidence. It reveals actual usage, exceptions, and operational constraints. Complexity can then be added in response to what is learned rather than what was imagined.
Why it matters for AI
Models are only one component
Production AI also depends on data, permissions, interfaces, fallbacks, evaluation, and human review. Each layer needs evidence.
Small systems expose reality
A narrow workflow in real use teaches more than an elaborate architecture built around assumed behaviour.
Reliability can evolve
Teams can add monitoring, automation, and scale after the central loop works and its failure modes are visible.
Try it on a real problem
Start with three questions.
- 01
What is the smallest end-to-end loop that creates a useful outcome?
- 02
Which steps must work now, and which can remain manual while we learn?
- 03
What evidence would justify adding the next layer of complexity?
Put it into practice
Keep one complete loop
For an AI lead-qualification system, begin with one source, one qualification path, one human handoff, and one measurable outcome. Prove that loop before adding channels, personas, dashboards, and autonomous follow-ups.
John Gall described this systems principle in Systemantics: complex systems that work are found to have evolved from simpler systems that worked.
Where this shows up