AI Weekly Malaysia

Nathan Lambert argues Nvidia is investing heavily in near-open-source models (like Nemotron, releasing data and training code) to create a world where many companies build their own 'token machines' rather than buying from Anthropic/OpenAI, driving massive demand for Nvidia inference hardware. He distinguishes true open-source models (full training recipe, data, code — e.g. OLMo, Pythia) from open-weight models (just weights and inference code — e.g. Llama), and notes Nvidia is reportedly spending ~$26B on this strategy.

Why: If you're deciding between building on open-weight models versus investing in full open-source recipes, Nvidia's bet signals that training-capable open-source stacks may stay viable longer than expected — but the capital intensity ($26B) means most builders should still default to consuming weights, not training from scratch. For SaaS founders, this suggests inference costs could fragment across many providers rather than consolidate under a few labs.