The free tier provides basic Cognition (private AI) and limited storage. Conversion happens when users hit capacity limits or need advanced features like Brains (public-facing agents), enhanced web pulls, or organizational tools. The "aha moment" occurs when users realize their private intelligence has compounding value they want to expand.

Priced at the "Netflix threshold"—affordable enough for individuals, valuable enough to signal quality. Positioned below enterprise tools (Notion AI, Mem.ai) but above free alternatives, capturing the "serious individual" segment willing to invest in their digital infrastructure.

The CHF 150 represents a one-time contribution to the commons, granting governance rights and a share in collective value creation. It's not equity in a traditional sense—it's membership in a digital commons with real participation rights.

AI inference costs are the primary variable cost. With 90% cost reduction in AI over recent years, margins improve as usage scales. Storage and compute are commoditized. The key insight: margins expand as users build more valuable intelligence (higher engagement, lower churn).

The window is open. Defaults are set. - AI costs down 90% in 3 years - Privacy laws expanding globally - 79% of consumers want data control - Technology stack ready - Market ready - Regulatory environment ready This decade sets the defaults.

Model-agnostic architecture allows switching between providers based on cost/performance. Pooled organizational credits create economies of scale. As AI costs continue declining, our margins expand.

Data portability is a core principle. Users own their intelligence and can export it. The commons structure means the infrastructure could be maintained by the community even if the company fails.

Full export capability with provenance intact. Users can take their knowledge graphs, permissions, and relationships. This is a feature, not a bug—it builds trust and reduces churn.

Top-down partnerships with values-aligned organizations to ignite the bottom-up adoption (organizations & communities -> individuals). We are targeting grassroot organization that needs to mobilize and coordinate intelligence. They are in the need to orchestrate human intelligence.

Free tools are the product—you're being extracted. coOwn is infrastructure you own. The comparison isn't ChatGPT vs. coOwn; it's "renting intelligence" vs. "owning intelligence."

Their business models depend on extraction. Giving users true ownership and control would cannibalize their core revenue streams. It's not a technical moat—it's a structural impossibility.

- Network effects in the commons: More members = more valuable governance and shared resources - Compounding intelligence: User data becomes more valuable over time, increasing switching costs - Trust and provenance: Lineage and consent tracking create irreplaceable institutional memory - Regulatory alignment: Built for GDPR, AI Act, and emerging data sovereignty regulations

We don't compete on network size—we compete on network quality. A smaller network of co-owners with aligned incentives outperforms a massive network of extracted users.