Post-AI SaaS ObservatoryDoctrine and moats
When features can be copied quickly, defensibility moves to distribution, workflow, trust, legitimate data, learning loops and team density.
Post-AI SaaS ObservatoryDoctrine and moats
The founder does more than save coding time: they orchestrate evidence, distribution, trust, quality and decisions.
Post-AI SaaS ObservatoryDoctrine and moats
A copyable feature can support a defensible business when distribution, workflow, trust, legitimate data and learning compound around it.
Post-AI SaaS ObservatoryDoctrine and moats
Limit work in progress to three experiments to reduce context switching, accelerate decisions and retain learning.
Post-AI SaaS ObservatoryProduct validation
A practical method to deliver a customer outcome by hand, discover the real workflow and decide what deserves automation before building SaaS.
Post-AI SaaS ObservatoryProduct validation
A practical method to frame a SaaS idea, separate facts from assumptions, define an outcome metric and run a two-hour test.
Post-AI SaaS ObservatoryProduct validation
Ask about past behavior, alternatives and evidence instead of whether people like your product idea.
Post-AI SaaS ObservatoryProduct validation
Activation, retention and customer outcome measure different things. Build an event plan that connects product use with real value.
Post-AI SaaS ObservatoryProduct validation
Define the evidence, threshold, date and action that will make a team continue, change or stop a SaaS idea before testing.
Post-AI SaaS ObservatoryTrust and accountability
Trust in an AI product is built into the journey through consent, sources, limits, review, recourse, security and graceful degradation.
Post-AI SaaS ObservatoryTrust and accountability
A ten-point review for AI agents that handle sensitive data, make recommendations or act inside important workflows.
Post-AI SaaS ObservatoryTrust and accountability
A practical matrix for choosing AI autonomy based on impact, reversibility, evidence, error detection and appeal.
