Nathan Lambert on Nvidia's bet to teach everyone to fish for tokens

Nathan Lambert's Interconnects post 'Teaching Everyone to Fish for Tokens' examines whether the open-source AI ecosystem can become self-sustaining or will stay dependent on outside financing. The crux is Nvidia's reported $26 billion push to fund nearly open-source models — releasing all the data it legally can plus training code — so that countless companies can build their own 'token machines' and generate massive inference demand for Nvidia's chips. Lambert, who helped build Ai2's Olmo models, distinguishes full open-source recipes from transient open-weight releases, maps two futures (Nvidia-financed self-sufficiency, or a fork toward efficiency, modifiability, and long-tail specialization in areas like on-prem enterprise agents), tracks how post-training finetuning is absorbing the open ecosystem's energy, and closes on Meta's expected open-weight Muse Spark 1.2 release as a rival way to commoditize AI tokens. The post matters because it frames open-model economics as an existential window: either openness pays back at a scale proportional to Anthropic and OpenAI's API profits within a few years — through chip demand, revenue-share licenses, or booming inference demand — or open models settle into a long-tail ecosystem while closed labs keep the most valuable workloads.