Sequoia Partner Discusses Two Major Risks of China's Open Source AI: Dependency Risk and Backdoor Risk
Sequoia partner Dean Meyer pointed out two major risks associated with relying on China's open source ecosystem.
The first is dependency risk, where model iteration is constrained by the counterpart; the second is backdoor risk, where verification is an NP-hard problem that is nearly impossible.
He compared current AI security to the early internet Wi-Fi encryption being compromised without action taken, calling for the establishment of an independent ecosystem to avoid deterioration.
Source: Public Information
ABAB AI Insight
Sequoia has long been investing in U.S. AI infrastructure and applications. Dean Meyer, as a partner, has previously discussed the infiltration of Chinese open source models in fine-tuning and distillation, emphasizing that most U.S. startups have embedded Chinese weights.
The capital path is to guide invested companies to prioritize building their own or adopting local models, reducing long-term binding to external open source bases, motivated by protecting portfolio valuations from geopolitical and security discounts while promoting the formation of an independent training ecosystem as a moat.
This is analogous to early discussions about dependence on Huawei's 5G and TikTok's supply chain, as well as the phase when open source security vulnerabilities were overlooked in the early internet. We are currently at a transitional point from prioritizing efficiency to prioritizing security and sovereignty.
Essentially, this is a technological substitution: open source weights appear free, but through iterative control and unverifiable backdoors, they lock downstream applications into a specific technological path of a particular country.
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