Nvidia's reported $26B open-model spend is demand generation for its own chips
Nathan Lambert reads Nvidia's reported $26B open-model push — full Nemotron recipes, data, and training code — as demand generation rather than a fight with Anthropic and OpenAI: more companies building their own token machines means more inference buyers for Nvidia hardware. His own call is skeptical, with the likeliest outcome being open models forking onto a long-tail path of efficiency, modifiability, and on-prem specialization while closed labs keep the highest-value knowledge work. That leaves the open ecosystem dependent on Nvidia's financing during a narrow window, and the live activity sits in post-training DeepSeek V4 Flash or GLM 5.X, not in training base models.
Source: interconnects.ai ↗
Nvidia wants a world where countless people can build token machines, so intelligence is not monopolized.
Why this matters
- → Nvidia's $26B open-model spend finances a long-tail ecosystem dependent on its profitability window.
- → Open models fork toward efficiency and on-prem specialization, not frontier knowledge work.
- → Training complexity abstraction reduces open-source builder incentives and base-model releases.