Open-weight AI guide outlines tiered model architecture

Relying exclusively on frontier closed models for routine development is wasteful capital allocation when cheap open-weight models handle most tasks identically. A pragmatic "cheap-first, local-when-private, frontier-when-needed" hierarchy is essential for sustainable AI unit economics.

• –The 20/60/20 Rule: Real-world workflows naturally split into roughly 20% local inference for sensitive data, 60% low-cost hosted models for bulk generation and agentic loops, and 20% frontier escalation for high-stakes reasoning.
• –Infrastructure vs. Product: Closed models provide polished end-user chat interfaces, but open weights serve as foundational developer infrastructure that can be customized, scripted, and self-hosted without vendor lock-in.
• –Unit Economics Matter: Using models like DeepSeek Flash or GLM Flash for high-volume refactors, summarization, and data extraction slashes operational costs by orders of magnitude compared to frontier APIs.
• –Avoid Hardware Over-Engineering: Running smaller local models via Ollama or LM Studio alongside hosted open-weight APIs captures the benefits of open AI without burning capital on private GPU clusters.