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。