AI model distillation, originally conceived as a benign research technique to compress large systems into smaller, more efficient versions, has evolved into a contentious practice threatening the financial foundations of the global artificial intelligence industry. The method involves training one AI model using the outputs of another, a process that has proliferated beyond internal development to encompass cross-company knowledge extraction. This shift has ignited debates over intellectual property, market competition, and regulatory oversight, particularly as Chinese firms increasingly adopt distillation to accelerate their own frontier model development. US technology leaders have expressed growing alarm over the technique's economic implications. Anthropic, OpenAI, and Google have documented industry-wide reliance on distilling competitor outputs, with Anthropic accusing Alibaba of orchestrating mass account creation to harvest model responses at scale. The practice effectively converts billions of dollars in US research and compute investments into subsidized training data for rival firms, eroding the premium pricing strategies that underpin current AI valuations. Market reactions have reflected these concerns, with AI equities declining following the release of advanced Chinese models like Z.ai's GLM-5.2, which industry observers believe leveraged distillation techniques. Even Elon Musk acknowledged during litigation that cross-company distillation has become standard operational practice across the sector. The competitive landscape has been further shaped by hardware constraints and strategic adaptation. Limited access to advanced semiconductors has pushed Chinese developers toward distillation as a viable alternative to prohibitively expensive native training pipelines. Researchers note a distinction between basic response copying and sophisticated multi-model distillation, where systems generate, evaluate, and refine each other's responses to produce higher-quality training datasets. Meanwhile, US firms have historically engaged in similar cross-pollination, paying third parties to augment competitor outputs, though such practices rarely face the same regulatory scrutiny. In response, American providers have implemented stringent access controls, including geographic blocks, enhanced identity verification, and payment restrictions. These measures have inadvertently spawned intermediary networks, often referred to as transfer stations, which route queries through proxy accounts and recycled identities at discounted rates. Operators collect and monetize the resulting prompt-response pairs, creating scalable datasets for model training. Policy experts caution that restrictive measures may prove ineffective, noting that access barriers typically stimulate alternative solutions while pushing developers toward cheaper, open-source alternatives. Some industry analysts warn that overly broad crackdowns could inadvertently stifle academic research and smaller startups that rely on distillation to compete with well-funded incumbents. As the industry navigates these tensions, the boundary between legitimate model optimization and commercial exploitation remains a central challenge for AI governance and market sustainability.