Sequoia just told a room of 80 portfolio founders that owning your AI models down to the weights is no longer a performance sacrifice, it may be your performance edge.
Sonya Huang’s “Own Your Intelligence” talk laid out exactly which parts of your stack to own versus rent and the four-step roadmap to build your own lab. We went through all 17 minutes, screenshotted every slide, and broke down what Sequoia argues on each one, so you don’t have to watch it yourself.
This matters now because of one claim Huang makes that would have been false a year ago: open-weight models like Qwen and GLM are close enough to the frontier that you can start near it and tune past it. That flips the whole calculus. Owning your intelligence used to mean accepting worse performance. Sequoia argues it no longer does.
In this guide you’ll find:
- Sovereign AI defined: owning your weights without ripping out Opus or GPT
- Centralized vs decentralized intelligence: why owning your models is the optimistic bet
- The four reasons companies own their models: cost, speed, performance, destiny
- “Not your weights, not your product”: why the battleground moved to the intelligence layer
- Step 1 and 2: the four-factor own-vs-rent test and how to build the team without shoehorning your platform group
- Step 3: legibility is the underrated moat because every buyer is choosing their AI champion
- Step 4: the technical roadmap and why open weights now push you past frontier performance
- The stack and Pandora’s Box: what owning your intelligence actually costs you
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