Knowledge pillar
AI business experiments
An AI business experiment is useful only when the hypothesis, acquisition commitment and decision thresholds are fixed before the results arrive. Otherwise an agent can generate activity while the operator rewrites the meaning of success.
This experiment treats plans as plans, execution as dated evidence and outcomes as a separate publication class. A test is not complete merely because a landing page, workflow or outreach artifact exists.
Choose for testability before scale
The first B2B vertical was selected using reachability, repeated work, ability to pay, automation safety, company-discovery value and compliance friction. The resulting scores are documented judgments made before the test, not measurements of demand.
Freeze interpretation rules
SCALE, ITERATE, KILL and INCONCLUSIVE thresholds were defined before outcomes could influence the story. A test that does not complete its committed acquisition volume is inconclusive—not evidence for or against demand.
Separate method from result
Public methods can be examined immediately. Contacts, replies, conversions, payments, costs and empirical conclusions remain outcomes and follow the publication embargo. This prevents current activity from leaking through metadata or machine-readable files.
Evidence in the public record
This page consolidates definitions and methods already published by 1M With AI. It does not add operational results, customer evidence or performance claims.