AI companies are changing. They want to save money and make AI work better for everyone.

AI 公司正在發生變化。他們希望降低成本，並讓 AI 對每個人都更實用。

Companies now care about good data. They want AI to be correct and cheap. They check their AI systems to make sure they work well.

公司現在非常重視優質數據。他們希望 AI 既準確又廉價。他們會檢查 AI 系統，以確保其運作良好。

Some companies use expensive AI for hard work. Other companies use cheap AI from China for easy work. Many US companies now use Chinese AI to save money.

有些公司使用昂貴的 AI 來處理困難的工作。其他公司則使用來自中國的廉價 AI 來處理簡單工作。許多美國公司現在使用中國 AI 以節省成本。

Big companies want to make their own tools. Microsoft makes its own AI. DeepSeek and Amazon make their own computer chips. They do not want to buy everything from other people.

大公司希望打造自己的工具。微軟開發自己的 AI。DeepSeek 和亞馬遜開發自己的電腦晶片。他們不想所有東西都向他人購買。

The US and China are angry. The US stops some chips from going to China. China may stop people from using its AI. Companies are also fighting over how to train AI.

美國與中國關係緊張。美國阻止部分晶片運往中國。中國可能會限制他人使用其 AI。公司之間也在爭論如何訓練 AI。

The AI world is changing. Companies now use many different AI tools and chips to save money and be free.

AI 世界正在改變。公司現在使用許多不同的 AI 工具與晶片，以達到省錢並獲得自主權的目的。

The artificial intelligence sector is moving from the experimental stage to full-scale production. This transition is marked by a new focus on reducing costs and becoming less dependent on specific hardware.

人工智慧領域正從實驗階段轉向全面投入生產。這一轉型標誌著新的重心在於降低成本，並減少對特定硬體的依賴。

Companies are now focusing on building stable AI systems. They are emphasizing data quality and better management to reduce risks, such as AI errors and high costs. Furthermore, monitoring how these models perform is now seen as essential for ensuring reliability and a good return on investment.

公司目前正專注於構建穩定的 AI 系統。他們強調數據品質與更好的管理，以降低風險，例如 AI 錯誤和高昂的成本。此外，監控這些模型的表現現在被視為確保可靠性與良好投資回報的關鍵。

At the same time, a two-tier model economy has developed. High-end models from OpenAI and Anthropic are still used for complex tasks, whereas open-source models—especially those from Chinese labs like DeepSeek—are being used in daily operations to save money. Consequently, many U.S. firms are adopting Chinese models because they are cheaper and their performance is becoming similar to Western versions.

與此同時，一個雙層模型經濟已經發展出來。來自 OpenAI 和 Anthropic 的高端模型仍用於複雜任務，而開源模型——尤其是來自 DeepSeek 等中國實驗室的模型——則被用於日常運作以節省成本。因此，許多美國公司正採用中國模型，因為它們價格更低，且性能已趨近於西方版本。

To avoid relying on external providers, major tech firms are developing their own technology. For example, Microsoft is replacing some third-party models with its own MAI models. Similarly, DeepSeek is creating its own AI chips to bypass U.S. export rules and reduce its need for Nvidia hardware. This trend toward custom hardware is also being followed by OpenAI and Amazon.

為了避免依賴外部供應商，大型科技公司正在開發自己的技術。例如，微軟正將部分第三方模型替換為其自身的 MAI 模型。同樣地，DeepSeek 正在研發自己的 AI 晶片，以繞過美國的出口規定並減少對 Nvidia 硬體的依賴。OpenAI 和 Amazon 也在跟隨這種定制硬體的趨勢。

The AI industry is shifting toward a diverse ecosystem. In this new environment, cost-efficiency and independence are more important than relying on a single top-tier provider.

AI 產業正轉向多元化的生態系統。在這種新環境下，成本效益與獨立性比依賴單一頂級供應商更為重要。

The artificial intelligence sector is undergoing a transition from experimental prototyping to production-scale deployment, characterized by a shift toward cost-optimization and hardware independence.

人工智慧領域正經歷從實驗性原型向生產規模部署的轉型，其特點是向成本優化與硬體獨立化轉移。

The current operational paradigm emphasizes the establishment of durable AI architectures. Institutional focus has shifted toward data quality, context engineering, and robust governance to mitigate risks such as hallucinations and excessive token expenditure. The implementation of LLM observability is increasingly viewed as a prerequisite for achieving measurable return on investment and maintaining system reliability.

目前的運作模式強調建立耐用的 AI 架構。機構焦點已轉向數據品質、上下文工程以及強而有力的治理，以減輕如「幻覺」和 Token 支出過高等風險。部署 LLM 可觀察性被日益視為實現可衡量投資報酬率並維持系統可靠性的前提條件。

Simultaneously, a bifurcated model economy has emerged. Frontier models from entities such as OpenAI and Anthropic remain primary tools for discovery and complex tasks, while open-weight and open-source models—predominantly those originating from Chinese laboratories like DeepSeek and Z.ai—are being integrated into production environments to reduce operational costs. This trend is evidenced by the adoption of Chinese models among U.S. firms, driven by a significant disparity in token pricing and a narrowing performance gap.

與此同時，出現了一個分叉的模型經濟。來自 OpenAI 和 Anthropic 等實體的前沿模型仍是探索和複雜任務的主要工具，而權重開放（open-weight）和開源模型（主要源自 DeepSeek 和 Z.ai 等中國實驗室）則被整合到生產環境中以降低運作成本。美國公司採用中國模型的趨勢證明了這一點，主因在於 Token 定價的顯著差異以及性能差距的縮小。

To further reduce dependency on external providers, major technology firms are pursuing vertical integration. Microsoft has commenced the substitution of third-party models with its proprietary MAI models within the Office suite. Similarly, DeepSeek is reportedly developing custom inference semiconductors to circumvent U.S. export restrictions and reduce reliance on Nvidia and Huawei hardware. This movement toward custom silicon is mirrored globally by OpenAI and Amazon.

為了進一步減少對外部供應商的依賴，大型科技公司正追求垂直整合。微軟已開始在 Office 套件中以其專有的 MAI 模型取代第三方模型。同樣地，據報導 DeepSeek 正在開發定制的推理半導體，以規避美國的出口限制並減少對 Nvidia 和華為硬體的依賴。這種向定制晶片轉移的趨勢在 OpenAI 和亞馬遜身上也得到了全球性的體現。

Geopolitical tensions continue to influence the technological landscape. The U.S. administration has implemented export controls on advanced semiconductors, while the Chinese government is evaluating restrictions on the foreign accessibility of domestic models due to national security concerns. Furthermore, disputes regarding 'distillation'—the use of frontier model outputs to train rival systems—have led to institutional frictions, exemplified by Alibaba's prohibition of Anthropic tools following allegations of data extraction.

地緣政治緊張局勢持續影響技術格局。美國政府對高級半導體實施了出口管制，而中國政府出於國家安全考慮，正評估限制外國獲取國內模型。此外，關於「蒸餾」——即利用前沿模型的輸出來訓練競爭系統——的爭議導致了機構間的摩擦，例如阿里巴巴在被指控數據被提取後，禁止使用 Anthropic 的工具。

The AI industry is moving toward a multi-model, hardware-diversified ecosystem where cost-efficiency and strategic autonomy supersede the initial reliance on a single frontier provider.

AI 產業正走向一個多模型、硬體多元化的生態系統，成本效率與策略自主權已取代最初對單一前沿供應商的依賴。