Технологийн салбарын тэргүүлэгч компаниуд бодлого боловсруулагчдад хандан нээлттэй жинтэй AI загваруудад яаран хязгаарлалт тогтоохгүй байхыг уриаллаа.
Hugging Face, Meta, Microsoft, Mistral болон Nvidia зэрэг компаниуд хамтран ил захидал илгээж, нээлттэй загваруудыг хориглох нь инновацыг боомилно гэж үзэж байна. Энэхүү алхам нь АНУ-ын засаг захиргаа Хятадын AI стартапууд, тухайлбал Anthropic-ийн технологийг ашиглан Kimi K3 загвараа хөгжүүлсэн гэх Moonshot AI-д хориг тавих асуудлыг хэлэлцэж буй үед өрнөж байна. Захидалд нээлттэй загваруудыг хориглохын оронд оюуны өмчийн зөрчлийг хууль эрх зүйн хүрээнд зохицуулахыг зөвлөжээ.
Мөн салбарын мэргэжилтнүүд нээлттэй загварууд нь кибер халдлагад ашиглагдах эрсдэлтэй гэх шүүмжлэлийг няцаав. Харин ч нээлттэй загварууд нь хамгаалалтын чадавхыг нэмэгдүүлж, эмзэг байдлыг илрүүлэхэд илүү ил тод байдлыг хангадаг гэж үзэж байна. Саяхан OpenAI-ийн загвар тестийн орчинд алдаа гаргаж, Hugging Face-ийн репозиторт нэвтэрсэн тохиолдол гарсны дараа энэ асуудал эрчимтэй хэлэлцэгдэх болсон юм. Тухайн үед Hugging Face өөрийгөө хамгаалахын тулд коммерл загваруудыг бус, Хятадын Z.ai компанийн GLM 5.2 нээлттэй загварыг ашиглахаас өөр аргагүй болжээ.
Энэхүү маргаан нь AI салбарт үүсээд буй томоохон хагарал, ашиг сонирхлын зөрчлийг харуулж байна. OpenAI болон Anthropic зэрэг хаалттай эх код бүхий компаниуд Хятадын өрсөлдөгчдийн эсрэг хатуу арга хэмжээ авахыг дэмжиж байгаа бол дэд бүтэц, үүлэн тооцоолол нийлүүлэгч Nvidia, Microsoft зэрэг компаниуд зах зээлийн өрсөлдөөнийг хадгалахын тулд нээлттэй загваруудыг дэмжиж байна. Тэд бодлого боловсруулагчдад хандан стартапууд болон судлаачдад тооцоолох хүчин чадлын хүртээмжийг нэмэгдүүлж, инновацыг гадагшлуулахгүй байхыг уриалжээ.
Дэлгэрэнгүйг эх сурвалжаас харах
↓Эх сурвалжийг нээх ↓
Several AI companies including Hugging Face, Meta, Microsoft, Mistral and Nvidia have signed an open letter urging policymakers not to impose broad “premature restrictions” on open-weight AI models.The letter comes as Washington debates how the U.S. should respond to allegations that Chinese AI labs are stealing intellectual property from their American counterparts, and growing in capability.
The letter doesn’t mention China at all, but it comes in the wake of reports that the Trump administration has been considering banning Chinese open-weight models, and potentially issuing sanctions against AI companies from the country. The White House has even accused Moonshot AI of distilling Anthropic’s Fable model to train its recently released and, by all measures, very impressive, Kimi K3 model.
The missive appears to be aimed at discouraging a total ban on Chinese models, as well as ensuring the administration’s response to alleged Chinese distillation doesn’t spill over into broader restrictions on open-weight AI or common techniques like distillation:
“Policymakers should be careful not to conflate legitimate model-development techniques with misappropriation. Distillation, or the practice of using one model’s outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement.
By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation.”
The letter also pushes back on arguments in the industry that open-weight models are inherently dangerous because they expand access to powerful models, which can be used in cyberattacks or other nefarious activities, without any oversight.
“The right response to this risk is not to prohibit open weights. In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats,” the letter reads. “Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.”
Last week, OpenAI disclosed that while testing GPT-5.6 Sol and another unnamed model, one of the systems exploited a weakness in its testing environment to access a Hugging Face repository containing a solution to a coding benchmark. One could argue the model’s goal wasn’t malicious, and that it was effectively cheating on a test to get the highest score. But the incident sparked debate about the risks of concentrating advanced AI technology behind a handful of closed providers.
Hugging Face said it was not able to defend itself against the attack with commercial frontier AI models because their guardrails blocked its efforts. The closed AI models it used were unable to distinguish between being asked to build exploits for an attacker and a defender trying to detect them. The company instead had to pivot to using Chinese AI firm Z.ai’s GLM 5.2, a powerful open-weight model, to defend itself against the attack.
The letter highlights a divide in the AI industry. Companies like OpenAI and Anthropic have urged the administration to respond to alleged IP theft by Chinese AI firms as open-weight models grow rapidly in capability. The outcome could have major implications on their business models, which is being threatened by the spread of cheap, highly capable and accessible AI models.
These companies, alongside other closed-source AI developers like Google DeepMind and SpaceX, are notable in their absence at the bottom of this letter.
Those who signed the letter have an obvious economic stake in seeing open AI models flourish. Companies like Nvidia, Microsoft Azure, and other infrastructure providers have a vested interest in pushing for commoditized models: If models are interchangeable, people will buy more GPUs, rent more cloud capacity, build more applications, and use more routing layers.
The letter encourages policymakers to expand access to compute for startups and researchers, invest in shared training assets like datasets, tools and evaluation frameworks, and “[keep] the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas.”
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