AI companies in the US and Microsoft are arguing. They disagree about who owns data and how to train AI.

美國的 AI 公司與微軟正在爭論。他們對於誰擁有數據以及如何訓練 AI 存在分歧。

Some US companies say China is stealing AI secrets. They say Chinese companies use fake accounts to take information from US chatbots. This helps China make AI faster. The US government is worried about this.

一些美國公司稱中國在偷竊 AI 秘密。他們表示中國公司利用假帳號從美國的聊天機器人獲取資訊。這有助於中國更快地開發 AI。美國政府對此感到擔憂。

Microsoft CEO Satya Nadella has a different problem. He says AI labs use public data for free. But these labs stop other people from using the same data. He thinks this is not fair.

微軟執行長 Satya Nadella 則有不同的問題。他表示 AI 實驗室免費使用公開數據，但這些實驗室卻阻止其他人使用相同的數據。他認為這樣是不公平的。

Now, many companies want to own their own AI. They do not want to give their secrets to big AI labs. They want to use open-source AI on their own computers.

現在，許多公司希望擁有自己的 AI。他們不想將秘密提供給大型 AI 實驗室。他們希望在自己的電腦上使用開源 AI。

The US government wants to stop other countries from stealing AI. At the same time, companies want to keep their own data safe.

美國政府希望阻止其他國家竊取 AI。同時，公司也希望確保自己的數據安全。

AI developers in the United States and leaders at Microsoft are currently arguing about the legality and ethics of 'model distillation' and who should own institutional data.

美國的 AI 開發者與微軟領導層目前正就「模型蒸餾」的合法性與倫理問題，以及誰應擁有機構數據而展開爭論。

The geopolitical side of this conflict focuses on claims by Anthropic and OpenAI that Chinese companies are using 'distillation.' This is the process of training smaller AI models using the answers from more advanced ones to catch up technologically. For example, Anthropic claimed that Alibaba used 25,000 fake accounts to take millions of outputs from the Claude chatbot. US officials describe this as industrial theft that helps China avoid US restrictions on high-end computer chips. Consequently, the US government believes its technological lead has dropped from 18 months to only 6 to 9 months, which could help other nations develop advanced cyber-tools.

這場衝突的地緣政治方面，焦點在於 Anthropic 與 OpenAI 指控中國公司使用「蒸餾」技術。這個過程是指利用較先進模型的答案來訓練較小的 AI 模型，以便在技術上趕上。例如，Anthropic 指稱阿里巴巴使用了 25,000 個假帳號，從 Claude 聊天機器人中獲取了數百萬次輸出結果。美國官員將此描述為工業盜竊，幫助中國規避美國對高端電腦晶片的限制。因此，美國政府認為其技術領先優勢已從 18 個月下降至僅 6 到 9 個月，這可能會幫助其他國家開發先進的網路工具。

At the same time, there is a corporate disagreement regarding data ownership. Microsoft CEO Satya Nadella has argued that some AI labs are being hypocritical because they use public data for training but stop others from using distillation. Nadella emphasized that businesses are paying twice: first through service fees and second by giving away their 'intelligence exhaust,' which refers to the prompts and corrections that improve the AI. This creates a risk where AI providers could become competitors to their own clients by learning their private business secrets.

與此同時，企業之間對於數據所有權也存在分歧。微軟執行長 Satya Nadella 主張某些 AI 實驗室表現得十分虛偽，因為他們使用公開數據進行訓練，卻阻止他人使用蒸餾法。Nadella 強調，企業目前在支付兩次代價：首先是服務費，其次是交出他們的「智能廢氣」，即指用於改進 AI 的提示詞與修正建議。這造成了一種風險，即 AI 供應商可能透過學習客戶的私密商業機密，最終成為其客戶的競爭對手。

As a result, more companies are moving toward 'data sovereignty.' Many businesses now prefer 'on-premise' open-source models, which allow them to keep control of their own data and avoid relying on a single provider. While the US government wants to pass new laws, such as the AI Overwatch Act, to tighten export controls, industry leaders like Nadella believe it is more important to create clear boundaries to protect intellectual property.

因此，越來越多的公司正轉向「數據主權」。許多企業現在更傾向於「本地部署」的開源模型，這使他們能夠掌控自己的數據，並避免依賴單一供應商。雖然美國政府希望通過新法律（如《AI 監控法案》）來收緊出口管制，但像 Nadella 這樣的行業領袖認為，建立清晰的界限以保護知識產權更為重要。

The current situation is a struggle between US national security efforts to stop foreign AI distillation and a corporate push for open-source independence to prevent data misuse.

目前的局面是美國國家安全旨在阻止外國 AI 蒸餾的努力，與企業追求開源獨立以防止數據濫用之間的博弈。

United States AI developers and Microsoft leadership are currently engaged in a dispute concerning the legality and ethics of model distillation and the ownership of institutional data.

美國 AI 開發者與微軟領導層目前正就模型蒸餾的合法性、倫理以及機構數據的所有權問題進行爭論。

The geopolitical dimension of this conflict centers on allegations by Anthropic and OpenAI that Chinese entities are utilizing 'distillation'—the process of training smaller models using the outputs of superior ones—to accelerate technological parity. Anthropic specifically alleged that Alibaba employed approximately 25,000 fraudulent accounts to extract 28.8 million outputs from the Claude chatbot. US officials, including the House Select Committee on China, characterize these actions as industrial theft that bypasses US export restrictions on high-end semiconductors. The administration posits that such practices reduce the US technological lead from an estimated 18-month gap to approximately 6 to 9 months, potentially facilitating the development of autonomous weaponry and advanced cyber-tools.

這場衝突的地緣政治維度集中在 Anthropic 與 OpenAI 的指控，他們指中國實體利用「蒸餾」——即使用較強模型之輸出來訓練較小模型的過程——以加速技術對等。Anthropic 特別指控阿里巴巴利用約 25,000 個虛假帳號，從 Claude 聊天機器人中提取了 2,880 萬個輸出。包括眾議院中國特別委員會在內的美國官員，將這些行為定義為工業竊盜，藉此繞過美國對高端半導體的出口限制。政府認為，此類做法將美國的技術領先優勢從預計的 18 個月縮短至約 6 到 9 個月，可能促成自主武器與高級網絡工具的開發。

Parallel to these national security concerns is a corporate dispute regarding data asymmetry. Microsoft CEO Satya Nadella has posited that proprietary AI labs operate with a degree of hypocrisy, utilizing public data for training while restricting distillation by others. Nadella argues that enterprises are effectively paying a double premium: once via monetary token fees and again through the surrender of proprietary 'intelligence exhaust'—the prompts and corrections that refine model performance. This dynamic creates a risk where AI providers may evolve into competitors of their own clients by absorbing sensitive institutional knowledge.

與這些國家安全憂慮平行的是一場關於數據不對稱的企業爭端。微軟執行長 Satya Nadella 指出，專有 AI 實驗室的運作存在某種虛偽，利用公開數據進行訓練卻限制他人進行蒸餾。Nadella 主張企業實際上支付著雙重溢價：一次是透過金錢形式的 Token 費用，另一次則是透過交出專有的「智能廢料」——即用以優化模型性能的提示詞與修正建議。這種動態造成了一種風險，即 AI 供應商可能會透過吸收敏感的機構知識，演變成其客戶的競爭對手。

Consequently, a shift toward data sovereignty is emerging. There is an increasing institutional preference for 'on-premise' open-source models, which allow enterprises to retain ownership of their learning loops and avoid vendor lock-in. This trend is evidenced by the rise of orchestration layers and gateways that facilitate switching between providers. While the US government proposes legislative remedies such as the AI Overwatch Act to tighten export controls, industry leaders like Nadella advocate for the establishment of rigorous trust boundaries to protect human and token capital.

因此，趨向數據主權的轉變正逐漸顯現。機構日益傾向於使用「在地部署」的開源模型，這使得企業能保留學習循環的所有權並避免供應商鎖定。這一趨勢可從協調層與閘道器的興起中得到證明，這些工具方便了在不同供應商之間切換。雖然美國政府提出如《AI 監察法案》（AI Overwatch Act）等立法救濟方案以收緊出口管制，但如 Nadella 等產業領袖則主張建立嚴格的信任邊界，以保護人類與 Token 資本。

The current landscape is defined by a tension between US national security efforts to curb foreign AI distillation and a corporate movement toward open-source autonomy to prevent data exploitation by proprietary labs.

目前的格局是由美國試圖遏止外國 AI 蒸餾的國家安全努力，與企業為防止專有實驗室剝削數據而追求開源自主的運動之間的緊張關係所定義。