26d • Deep Dives
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 ✅
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Owning Your AI Through Fine Tuning & Light RAG Combos
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