Sequoia: America's Open-Model Paradox and Dependence on Chinese Distillation
Sequoia Capital · rss · 2026-07-25
Sequoia Capital argues that the US AI industry is facing an "Open-Model Paradox," where Western companies increasingly rely on Chinese open-source models (like Qwen, Kimi) for fine-tuning, application building, and synthetic data generation.
The core contradiction highlighted is that Western labs lack a legal path to use American frontier models (like GPT, Claude) for training, whereas Chinese open weights offer a lawful route to learn and bootstrap. For instance, Thinking Machines used synthetic data generated by Moonshot’s Kimi K2.5 for supervised fine-tuning.
This dependency extends to national security, as downloading open weights cannot prove the absence of embedded backdoors or dormant triggers. To address this, Sequoia proposes creating a legal domestic route for capability transfer in the US, such as allowing frontier labs to sell controlled "teacher access" to Western companies, while continuing to raise the cost of foreign distillation.
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