- The White House Office of Science and Technology Policy (OSTP) in the United States released a memorandum on the misuse of artificial intelligence technology, marking the first time that model distillation is defined as industrial-scale technology transfer. The plan involves collaborating with companies to cut off access channels for related proxies.
- Silicon Valley core computing power companies, including Alphabet (GOOGL:US), OpenAI, and Anthropic, face the potential risk of losing intellectual property on a scale of hundreds of billions, with Chinese large model startups like DeepSeek and Moonshot being listed as key entities to guard against.
- As Donald Trump's visit to China approaches next month, the U.S. is trying to establish a new technological firewall in base algorithm protocols and data output terminals, impacting the global technology sector's valuation model, which is facing reassessment.
Definition and Prevention Mechanisms of Industrial-Scale Technology Misuse
The memorandum issued by the White House marks the expansion of the US-China technology rivalry from fundamental hardware computing power (such as GPUs and advanced packaging) to software layer algorithm output and data assets. Model distillation is a shortcut for reducing training costs, where the core logic involves using high-quality responses generated by large models with billions of parameters (teacher models) directly to train systems with fewer parameters and lower deployment costs (student models). U.S. regulatory bodies point out that some overseas entities utilize thousands of proxy IP networks to execute high-frequency automated API queries, thereby circumventing U.S. companies' end-user agreements. This behavior is defined as industrial-scale technology misuse, bypassing the substantial sunk costs of foundational models and potentially stripping security alignment mechanisms during replication.
The Business Moat Defense of Closed-Source Ecosystems
The current mainstream AI business model in Silicon Valley heavily relies on closed-source ecosystems and paid API calls. Developing a generation of cutting-edge foundational models often requires hundreds of millions in computing power expenses and a vast amount of high-quality manually labeled data. If competitors can achieve similar logical reasoning and text generation capabilities at just one percent or even one-thousandth of the cost using distillation technology, this would fundamentally disrupt the business payback period of American tech giants. The cross-enterprise information-sharing mechanism promoted by OSTP essentially creates an industry-wide distributed abnormal traffic monitoring network, aimed at precisely identifying and blocking unauthorized corpus extraction through feature code comparison and data watermarking technology.
R&D Cost Amortization and Market Pricing Expectations
In the NASDAQ market, the safety boundaries of intellectual property directly determine the forward price-to-earnings (PE) multiples of AI companies. Analysts point out that if model distillation behavior is not effectively curtailed, Silicon Valley companies may face the loss of $2-3 billion in implicit assets annually. To address this challenge, it is expected that large cloud service providers will further raise the risk control thresholds for API calls, or even implement a whitelist system for requests from specific geographic areas. Despite these defensive measures protecting core assets, they may temporarily inhibit global developers' call activity, thereby impacting the quarter-on-quarter growth of related cloud service businesses.
Bilateral Game under Stricter Compliance Reviews
The direct intervention of the U.S. government in the use norms of commercial API interfaces demonstrates the deep involvement of administrative power in the frontier technology supply chain. In the absence of a global unified AI regulatory framework, unilateral sanctions and technology blockades may become the norm. Faced with imminent restrictions from the U.S., non-U.S. companies relying on overseas foundational models for synthetic data generation will face significant compliance uncertainty. If relevant sanctions materialize, they could trigger a comprehensive review of model training data sources, forcing global computing power resources to further split into mutually isolated parallel ecosystems.
