Owning Your AI Through Fine Tuning & Light RAG Combos
We just posted a video and deep folio report on creating defensible AI Moats with affordable LLM fine tuning and Light RAG strategies. Explainer Video: https://www.youtube.com/watch?v=GUjEst_oBts Deep Dive on Escher AI: https://escher-ai.com/deepfolios/deepfolio-0029/briefing.html Y Combinator and top investors are increasingly skeptical of AI startups that simply orchestrate prompts or make API calls on top of frontier models. Margins are collapsing—and a major shakeout is coming by 2026. Most “AI startups” today are just renting intelligence. They sit on top of frontier models, call APIs, and hope branding is enough. The result? ❌ Margins collapsing into the 10–20% range ❌ No defensible moat ❌ Total dependency on model providers In this video, we break down how serious teams are doing it differently. You’ll learn: • How affordable fine-tuning of open-source models changes the unit economics • Why Light RAG + hybrid retrieval beats naïve RAG stacks • The difference between orchestrating AI and owning AI capabilities • How real AI moats are being built ahead of the 2026 shakeout For several client engagements, we’ve gone deep on fine-tuning strategies and hybrid RAG systems that push margins toward 95%—while increasing control, reliability, and defensibility. This is the difference between: Renting AI ❌ Owning AI infrastructure ✅