Decision Principles
The canonical principles behind how Sparker.ai evaluates uncertainty, runs experiments, ends work, and turns decisions into reusable assets.
1. Information is cheap. Judgment is scarce.
Access to information does not resolve a decision. Value comes from knowing which evidence matters, what uncertainty remains, and what action is justified.
2. Decisions are products.
A strong decision has a user, an outcome, inputs, constraints, quality criteria, and a moment when it must be delivered.
3. Every investment needs success and failure criteria.
Success criteria define what must become true. Failure criteria create permission to stop before sunk cost becomes strategy.
4. New technology begins as a Bet.
Novelty is not evidence. Emerging capabilities should receive a bounded investigation before they alter the dependable Platform.
5. Evidence determines promotion.
A Bet becomes Platform only when its problem repeats, its method creates value, and its operation can be maintained.
6. Ending work is a product skill.
Killing a weak initiative protects attention, capital, and credibility. A clear ending is often more valuable than indefinite continuation.
7. No work is wasted when its learning is captured.
Code, frameworks, artifacts, retrospectives, and changed opinions can compound after a project ends.
8. Practical artifacts are stronger than abstract commentary.
A benchmark, prototype, decision memo, evaluation set, or working tool makes reasoning inspectable.
9. Consulting should create reusable intellectual property.
Client work should improve the next engagement while respecting confidentiality and ownership.
10. Platforms should emerge from repeated need.
A platform is the residue of problems solved repeatedly—not a product category selected in advance.
Version note
This is version 0.1, published July 28, 2026. Future revisions will preserve the prior version and explain what evidence changed the principle set.