美國政府最近加強了對人工智慧的審查，限制了對兩個 Anthropic 模型的存取，並推遲了一個 OpenAI 模型的發布。這項監管轉變為全球企業領袖帶來了重大不確定性，關於他們能否持續存取關鍵的 AI 基礎設施。一項行政命令建立了一個自願性框架，要求 AI 公司在公開發布前，向政府提供長達 30 天的前沿模型存取權限，旨在監管這些快速發展的能力。
為了應對這種不確定性，Ion Stoica 和 Gautier Cloix 等行業專家建議企業透過利用多個 AI 模型（包括開放權重和封閉系統）來實現技術依賴的多樣化。雖然 OpenAI 和 Anthropic 等開發商的封閉模型提供的透明度較低，但開放權重替代方案允許開發人員自訂數值和權重。諸如 H Company 等公司目前正在微調這些開放模型，以維持技術自主性並在危機期間平衡風險，將多樣化的模型格局視為擁有車輛的多種能源選擇。
全球 AI 發展的格局競爭日益激烈，許多頂尖的開放 AI 模型源自中國，促使 Xi Jinping 等領導人強調在技術監管方面的國際合作。與此同時，諸如 Yann LeCun 等 AI 先驅透過 Project Tapestry 等倡議，提倡一種完全不同的訓練範式。該項目旨在匯集來自國家、大學和行業的訓練資料，以建立開放的基礎模型，同時允許貢獻者保留對其資料的控制權，為 AI 的未來提供了一個免費且不可或缺的替代方案。
The United States government has recently intensified its scrutiny of artificial intelligence by restricting access to two of Anthropic's models and delaying the release of one OpenAI model. This regulatory shift has introduced significant uncertainty for global business leaders regarding their continuous access to crucial AI infrastructure. An executive order established a voluntary framework requiring AI companies to provide the government with access to frontier models for up to 30 days prior to their public release, aiming to regulate these rapidly evolving capabilities.
In response to this uncertainty, industry experts like Ion Stoica and Gautier Cloix advise businesses to diversify their technological dependencies by utilizing multiple AI models, including both open-weight and closed systems. While closed models from developers like OpenAI and Anthropic offer less transparency, open-weight alternatives allow developers to customize numerical values and weights. Companies such as H Company are currently fine-tuning these open models to maintain technological autonomy and balance risks during crises, treating the diverse model landscape like having multiple energy options for a vehicle.
The global landscape of AI development is increasingly competitive, with many top open AI models originating from China, prompting leaders like Xi Jinping to emphasize international collaboration in technology regulation. Meanwhile, AI pioneers such as Yann LeCun advocate for a completely different training paradigm through initiatives like Project Tapestry. This project aims to aggregate training data from countries, universities, and industries to build open foundation models while allowing contributors to retain control over their data, presenting a free and indispensable alternative for the future of AI.