<p>人工智能的成本結構正在發生變化，正從工具轉變為自主執行者，企業和其他用户越來越多地為服務付費。</p>
<p><img src="https://imageproxy.pbkrs.com/https://contributor-assets.benzinga.com/wp-content/uploads/2026/05/07043007/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE_7-5-2026_122836_thebambooworks.com_-1024x629.jpeg?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://contributor-assets.benzinga.com/wp-content/uploads/2026/05/07043007/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE_7-5-2026_122836_thebambooworks.com_-1024x629.jpeg"/></p>
<p><em>圖片來源：Bamboo Works</em></p>
<p>在過去兩年中，人工智能的商業模式建立在一個看似穩固的假設上：相對較低的計算成本使公司能夠通過 “免費 + 訂閲” 模式快速擴展用户。無論是在聊天機器人還是生產力工具中，人工智能通常被包裝為增強產品吸引力的附加功能，而不是作為核心收入來源。用户支付固定的月費以無限制使用，這種模式幫助加速了廣泛的採用。</p>
<p>但這一假設現在開始崩潰。</p>
<p>隨着人工智能從簡單的問答工具轉變為能夠自主完成任務的代理產品，其成本結構正在發生變化。人工智能不再僅僅是響應提示。它持續運行，反覆調用模型和工具，並且通常在單個任務中進行多輪推理和優化。這一轉變將人工智能從 “被動服務” 轉變為 “主動執行者”，其基礎消耗不再是一次性計算，而是資源的持續積累。</p>
<p>這一轉變在計算使用上的變化中得到了清晰的反映。根據媒體報道，中國人工智能模型市場的每日令牌消耗從 2024 年初的約 1000 億激增至 2026 年的約 140 萬億。這一增長不僅是由於用户數量的增加，還源於使用模式的根本變化。人工智能正在從偶爾的工具演變為嵌入企業日常運營的系統，增長主要由基於流程的重複調用驅動，而不是一次性查詢。隨着人工智能成為資源消耗的核心能力，傳統的低成本或免費模式變得越來越難以維持。</p>
<p>這種壓力已經在供應端顯現。2024 年下半年至 2026 年間，<strong>阿里巴巴</strong> (NYSE: BABA) (9988.HK) 和 <strong>百度</strong> (NASDAQ: BIDU) (9888.HK) 調整了某些雲服務的定價，漲幅在 5% 到 34% 之間。隨着人工智能使用頻率和複雜性的增加，計算需求遠遠超過傳統應用，推動了雲資源成本的上升。這些價格調整反映了日益增加的壓力。</p>
<p>更重要的是，定價模型本身正在演變。在阿里雲的大模型服務中，公司不再為產品付費，而是根據令牌使用量收費，輸入和輸出分別定價。阿里巴巴的釘釘服務則將人工智能能力轉化為使用計數，不同層級提供不同的配額。無論是按令牌還是按操作計量，核心理念都是相同的：人工智能正在成為一種計量的、可消耗的資源。</p>
<p>這一轉變並不限於基礎設施層。隨着人工智能能力被集成到軟件產品中，應用層的定價結構也在上升。<strong>金山辦公</strong> (3888.HK; 688111.SH) 在其 WPS 產品中引入了寫作、總結和數據分析等人工智能功能，類似於 Microsoft Word，將其置於會員或高級層級中。百度也將人工智能能力捆綁到其文庫和雲存儲服務中，目前擁有超過 4000 萬的付費用户。雖然這些產品並沒有普遍提高價格，但通過功能分層創造了新的付費牆。對於用户而言，保持生產力的提升往往意味着整體支出增加。</p>
<p>以字節跳動的人工智能助手 Doubao 為例。它最近開始在其免費版本的基礎上測試三個訂閲層級，先進功能集中在高複雜度的用例上，如生成演示文稿、數據分析和內容創作。雖然免費版本足以滿足日常任務，但更高級的需求——如長文本處理、多步驟推理或結構化輸出——需要升級到付費計劃。這反映了人工智能定價邏輯的更廣泛轉變，收費不再僅僅基於功能差異，而是直接與基礎計算和推理成本掛鈎。</p>
<p>實際上，這與直接漲價沒有區別。</p>
<p>這些變化背後是成本結構的轉變。用户正在為從軟件轉向計算資源付費。更重要的是，這一定價邏輯的轉變正在重塑行業內價值的分配。隨着人工智能從產品演變為基礎設施，價值開始集中在更低的層級。對於雲服務提供商和模型開發者而言，收入可以隨着計算能力需求的上升而擴展。相比之下，應用層公司可能會受益於人工智能帶來的效率提升，但對基礎資源的日益依賴可能會壓縮其利潤空間。</p>
<p>人工智能並沒有消除成本。相反，它將成本轉變為基於使用的支出。在這一模型下，傳統的低成本訂閲策略將面臨壓力，因為成本現在直接與計算消耗掛鈎。取而代之的是，混合定價模型正在出現，結合使用量、頻率甚至任務結果。在這個過程中，人工智能不再是一個可以簡單定價的產品，而是越來越成為一種基礎資源，類似於電力或帶寬。</p>
<p>在更深層次上，這一轉變正在重塑整個行業的動態。當計算成本成為關鍵變量時，競爭不再僅僅由產品特性或用户規模定義，而是由誰能以更低的成本獲取計算能力，以及誰能最有效地利用模型資源來決定。因此，行業的權力結構正在發生變化，價值越來越集中在計算能力和模型的提供者手中。</p>
<p>與此同時，商業擴展的邏輯也在發生變化。在過去，規模有助於稀釋成本。但在人工智能時代，每一次調用都產生真實的費用。在這種結構下，模型開發者和人工智能服務提供商承擔成本，代理運營商將其轉化為收入，而這些成本最終會轉嫁給最終用户，形成一層新的成本。</p>
<p><strong><em>Benzinga 免責聲明：本文來自一位未付費的外部貢獻者。它不代表 Benzinga 的報道，並且未經過內容或準確性編輯。</em></strong></p>

百度

百度-SW

雲計算 ETF - GlobalX

2 倍做多 BABA ETF - GraniteShares

2 倍做多阿里巴巴 ETF - KraneShares

<p>AI 的成本結構正在演變，從工具轉變為自主執行者，這導致企業和用户的成本增加。傳統的 “免費 + 訂閲” 模式由於計算需求的上升變得不可持續。像阿里巴巴和百度這樣的公司正在調整雲服務的定價，以反映這一變化。AI 現在是根據令牌使用量收費，應用層的定價也在上升。這一變化表明，從軟件轉向計算資源，壓縮了應用層公司的利潤空間，同時使雲服務提供商受益</p>

<p><em>AI's cost structure is changing as it shifts from a tool to an autonomous executor, with both businesses and other users increasingly paying for services</em></p>
<div class="lb-trans"><p>人工智能的成本結構正在發生變化，正從工具轉變為自主執行者，企業和其他用户越來越多地為服務付費。</p>
</div><p><img src="https://imageproxy.pbkrs.com/https://contributor-assets.benzinga.com/wp-content/uploads/2026/05/07043007/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE_7-5-2026_122836_thebambooworks.com_-1024x629.jpeg" alt="" original-src="https://contributor-assets.benzinga.com/wp-content/uploads/2026/05/07043007/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE_7-5-2026_122836_thebambooworks.com_-1024x629.jpeg"/></p>
<p><em>image credit: Bamboo Works</em></p>
<div class="lb-trans"><p><em>圖片來源：Bamboo Works</em></p>
</div><p>Over the past two years, the business model for AI has been built on what appeared to be a solid assumption: relatively low computing costs allowed companies to scale users quickly through a &#34;free + subscription&#34; model. Whether in chatbots or productivity tools, AI was typically packaged as an add-on feature to enhance product appeal, rather than as a core revenue generator. Users paid fixed monthly fees for unlimited use, a model that helped accelerate widespread adoption.</p>
<div class="lb-trans"><p>在過去兩年中，人工智能的商業模式建立在一個看似穩固的假設上：相對較低的計算成本使公司能夠通過 “免費 + 訂閲” 模式快速擴展用户。無論是在聊天機器人還是生產力工具中，人工智能通常被包裝為增強產品吸引力的附加功能，而不是作為核心收入來源。用户支付固定的月費以無限制使用，這種模式幫助加速了廣泛的採用。</p>
</div><p>But that assumption is now starting to break down.</p>
<div class="lb-trans"><p>但這一假設現在開始崩潰。</p>
</div><p>As AI shifts from being a simple Q&amp;A tool to agent-based products capable of completing tasks autonomously, its cost structure is changing. AI is no longer just responding to prompts. It runs continuously, repeatedly calling models and tools, and often performs multiple rounds of reasoning and refinement within a single task. This shift is transforming AI from a &#34;passive service&#34; into an &#34;active executor,&#34; where the underlying consumption is no longer a one-off computation but a continuous accumulation of resources.</p>
<div class="lb-trans"><p>隨着人工智能從簡單的問答工具轉變為能夠自主完成任務的代理產品，其成本結構正在發生變化。人工智能不再僅僅是響應提示。它持續運行，反覆調用模型和工具，並且通常在單個任務中進行多輪推理和優化。這一轉變將人工智能從 “被動服務” 轉變為 “主動執行者”，其基礎消耗不再是一次性計算，而是資源的持續積累。</p>
</div><p>This shift is clearly reflected in changes in computing usage. According to media reports, daily token consumption in China's AI model market has surged from about 100 billion in early 2024 to roughly 140 trillion in 2026. This growth is driven not only by an increase in users, but also by a fundamental change in usage patterns. AI is evolving from an occasional tool into a system embedded in an enterprise's daily operations, with growth primarily driven by recurring process-based calls rather than one-off queries. As AI becomes a resource-consuming core capability, the traditional low-cost or free model becomes increasingly difficult to sustain.</p>
<div class="lb-trans"><p>這一轉變在計算使用上的變化中得到了清晰的反映。根據媒體報道，中國人工智能模型市場的每日令牌消耗從 2024 年初的約 1000 億激增至 2026 年的約 140 萬億。這一增長不僅是由於用户數量的增加，還源於使用模式的根本變化。人工智能正在從偶爾的工具演變為嵌入企業日常運營的系統，增長主要由基於流程的重複調用驅動，而不是一次性查詢。隨着人工智能成為資源消耗的核心能力，傳統的低成本或免費模式變得越來越難以維持。</p>
</div><p>This pressure is already showing up on the supply side. Between the second half of 2024 and 2026, <strong>Alibaba</strong> (NYSE: BABA) (9988.HK) and <strong>Baidu</strong> (NASDAQ: BIDU) (9888.HK) adjusted their pricing for certain cloud services, with increases ranging from 5% to 34%. As AI usage grows in both frequency and complexity, computing demand far exceeds that of traditional applications, pushing up cloud resource costs. These price adjustments reflect that rising pressure.</p>
<div class="lb-trans"><p>這種壓力已經在供應端顯現。2024 年下半年至 2026 年間，<strong>阿里巴巴</strong> (NYSE: BABA) (9988.HK) 和 <strong>百度</strong> (NASDAQ: BIDU) (9888.HK) 調整了某些雲服務的定價，漲幅在 5% 到 34% 之間。隨着人工智能使用頻率和複雜性的增加，計算需求遠遠超過傳統應用，推動了雲資源成本的上升。這些價格調整反映了日益增加的壓力。</p>
</div><p>More importantly, the pricing model itself is evolving. In Alibaba Cloud's large model services, companies no longer pay for a product, but are charged based on token usage, with input and output priced separately. Alibaba's Ding Talk service, meanwhile, translates AI capabilities into usage counts, with different tiers offering different quotas. Whether measured in tokens or actions, the core idea is the same: AI is becoming a metered, consumable resource.</p>
<div class="lb-trans"><p>更重要的是，定價模型本身正在演變。在阿里雲的大模型服務中，公司不再為產品付費，而是根據令牌使用量收費，輸入和輸出分別定價。阿里巴巴的釘釘服務則將人工智能能力轉化為使用計數，不同層級提供不同的配額。無論是按令牌還是按操作計量，核心理念都是相同的：人工智能正在成為一種計量的、可消耗的資源。</p>
</div><p>This shift is not limited to the infrastructure layer. As AI capabilities are integrated into software products, pricing structures at the application level are also rising. <strong>Kingsoft Office</strong> (3888.HK; 688111.SH) has introduced AI features such as writing, summarization and data analysis into its WPS product, similar to Microsoft Word, placing them within membership or premium tiers. Baidu has similarly bundled AI capabilities into its Wenku and cloud storage services, which now have over 40 million paying users. While these products have not universally raised their prices, they are creating new paywalls through feature tiering. For users, maintaining productivity gains often means spending more overall.</p>
<div class="lb-trans"><p>這一轉變並不限於基礎設施層。隨着人工智能能力被集成到軟件產品中，應用層的定價結構也在上升。<strong>金山辦公</strong> (3888.HK; 688111.SH) 在其 WPS 產品中引入了寫作、總結和數據分析等人工智能功能，類似於 Microsoft Word，將其置於會員或高級層級中。百度也將人工智能能力捆綁到其文庫和雲存儲服務中，目前擁有超過 4000 萬的付費用户。雖然這些產品並沒有普遍提高價格，但通過功能分層創造了新的付費牆。對於用户而言，保持生產力的提升往往意味着整體支出增加。</p>
</div><p>Take ByteDance's AI assistant Doubao as an example. It recently began testing three subscription tiers on top of its free version, with advanced features focused on high-complexity use cases such as generating presentations, data analysis, and content creation. While the free version remains sufficient for everyday tasks, more advanced needs — such as long-form text processing, multi-step reasoning, or structured outputs — require upgrading to a paid plan. This reflects a broader shift in AI pricing logic, where charges are no longer based solely on feature differentiation, but directly tied to underlying computing and inference costs.</p>
<div class="lb-trans"><p>以字節跳動的人工智能助手 Doubao 為例。它最近開始在其免費版本的基礎上測試三個訂閲層級，先進功能集中在高複雜度的用例上，如生成演示文稿、數據分析和內容創作。雖然免費版本足以滿足日常任務，但更高級的需求——如長文本處理、多步驟推理或結構化輸出——需要升級到付費計劃。這反映了人工智能定價邏輯的更廣泛轉變，收費不再僅僅基於功能差異，而是直接與基礎計算和推理成本掛鈎。</p>
</div><p>In effect, this is no different from a direct price increase.</p>
<div class="lb-trans"><p>實際上，這與直接漲價沒有區別。</p>
</div><p>Behind these changes is a shift in cost structure. Users are paying for the move from software to computing resources. More importantly, this shift in pricing logic is reshaping how value is distributed across the industry. As AI evolves from a product into infrastructure, value begins to concentrate at the lower layers. For cloud service providers and model developers, revenue can scale alongside rising demand for computing power. In contrast, application-layer companies may benefit from efficiency gains enabled by AI, but their growing reliance on underlying resources could compress their profit margins.</p>
<div class="lb-trans"><p>這些變化背後是成本結構的轉變。用户正在為從軟件轉向計算資源付費。更重要的是，這一定價邏輯的轉變正在重塑行業內價值的分配。隨着人工智能從產品演變為基礎設施，價值開始集中在更低的層級。對於雲服務提供商和模型開發者而言，收入可以隨着計算能力需求的上升而擴展。相比之下，應用層公司可能會受益於人工智能帶來的效率提升，但對基礎資源的日益依賴可能會壓縮其利潤空間。</p>
</div><p>AI has not eliminated costs. Instead, it has transformed them into usage-based expenses. Under this model, traditional low-cost subscription strategies will come under pressure, as costs are now directly tied to computing consumption. In their place, hybrid pricing models are emerging, combining usage volume, frequency, and even task outcomes. In this process, AI is no longer a product that can be simply priced, but is increasingly becoming a fundamental resource, much like electricity or bandwidth.</p>
<div class="lb-trans"><p>人工智能並沒有消除成本。相反，它將成本轉變為基於使用的支出。在這一模型下，傳統的低成本訂閲策略將面臨壓力，因為成本現在直接與計算消耗掛鈎。取而代之的是，混合定價模型正在出現，結合使用量、頻率甚至任務結果。在這個過程中，人工智能不再是一個可以簡單定價的產品，而是越來越成為一種基礎資源，類似於電力或帶寬。</p>
</div><p>At a deeper level, this shift is rewriting the entire industry's dynamics. When computing costs become the key variable, competition is no longer defined solely by product features or user scale, but by who can secure computing power at lower cost and who can utilize model resources most efficiently. As a result, the industry's power structure is shifting, with value increasingly concentrated among providers of computing power and models.</p>
<div class="lb-trans"><p>在更深層次上，這一轉變正在重塑整個行業的動態。當計算成本成為關鍵變量時，競爭不再僅僅由產品特性或用户規模定義，而是由誰能以更低的成本獲取計算能力，以及誰能最有效地利用模型資源來決定。因此，行業的權力結構正在發生變化，價值越來越集中在計算能力和模型的提供者手中。</p>
</div><p>At the same time, the logic for business expansion is also changing. In the past, scale helped dilute costs. But in the AI era, every call incurs a real expense. Under this structure, model developers and AI service providers bear the costs, agent operators convert them into revenue, and these costs are ultimately passed along to end users, creating a new layer of costs.</p>
<div class="lb-trans"><p>與此同時，商業擴展的邏輯也在發生變化。在過去，規模有助於稀釋成本。但在人工智能時代，每一次調用都產生真實的費用。在這種結構下，模型開發者和人工智能服務提供商承擔成本，代理運營商將其轉化為收入，而這些成本最終會轉嫁給最終用户，形成一層新的成本。</p>
</div><p><strong><em>Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.</em></strong></p>
<div class="lb-trans"><p><strong><em>Benzinga 免責聲明：本文來自一位未付費的外部貢獻者。它不代表 Benzinga 的報道，並且未經過內容或準確性編輯。</em></strong></p>
</div>

The End Of Free AI: Agents Drive A New Cost Reality

- AI 由工具轉變為自主執行者，成本結構發生變化。  
- 計算需求的增加推動雲資源成本上升，傳統訂閲模式面臨壓力。  
- 新的計費模式使 AI 成為按需消耗的資源，價值向計算能力和模型提供商集中。

免費 AI 時代的終結：代理推動新的成本現實