<p>人工智能的成本结构正在发生变化，正从工具转变为自主执行者，企业和其他用户越来越多地为服务付费。</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>图片来源：Bamboo Works</em></p>
<p>在过去两年中，人工智能的商业模式建立在一个看似稳固的假设上：相对较低的计算成本使公司能够通过 “免费 + 订阅” 模式快速扩展用户。无论是在聊天机器人还是生产力工具中，人工智能通常被包装为增强产品吸引力的附加功能，而不是作为核心收入来源。用户支付固定的月费以无限制使用，这种模式帮助加速了广泛的采用。</p>
<p>但这一假设现在开始崩溃。</p>
<p>随着人工智能从简单的问答工具转变为能够自主完成任务的代理产品，其成本结构正在发生变化。人工智能不再仅仅是响应提示。它持续运行，反复调用模型和工具，并且通常在单个任务中进行多轮推理和优化。这一转变将人工智能从 “被动服务” 转变为 “主动执行者”，其基础消耗不再是一次性计算，而是资源的持续积累。</p>
<p>这一转变在计算使用上的变化中得到了清晰的反映。根据媒体报道，中国人工智能模型市场的每日令牌消耗从 2024 年初的约 1000 亿激增至 2026 年的约 140 万亿。这一增长不仅是由于用户数量的增加，还源于使用模式的根本变化。人工智能正在从偶尔的工具演变为嵌入企业日常运营的系统，增长主要由基于流程的重复调用驱动，而不是一次性查询。随着人工智能成为资源消耗的核心能力，传统的低成本或免费模式变得越来越难以维持。</p>
<p>这种压力已经在供应端显现。2024 年下半年至 2026 年间，<strong>阿里巴巴</strong> (NYSE: BABA) (9988.HK) 和 <strong>百度</strong> (NASDAQ: BIDU) (9888.HK) 调整了某些云服务的定价，涨幅在 5% 到 34% 之间。随着人工智能使用频率和复杂性的增加，计算需求远远超过传统应用，推动了云资源成本的上升。这些价格调整反映了日益增加的压力。</p>
<p>更重要的是，定价模型本身正在演变。在阿里云的大模型服务中，公司不再为产品付费，而是根据令牌使用量收费，输入和输出分别定价。阿里巴巴的钉钉服务则将人工智能能力转化为使用计数，不同层级提供不同的配额。无论是按令牌还是按操作计量，核心理念都是相同的：人工智能正在成为一种计量的、可消耗的资源。</p>
<p>这一转变并不限于基础设施层。随着人工智能能力被集成到软件产品中，应用层的定价结构也在上升。<strong>金山办公</strong> (3888.HK; 688111.SH) 在其 WPS 产品中引入了写作、总结和数据分析等人工智能功能，类似于 Microsoft Word，将其置于会员或高级层级中。百度也将人工智能能力捆绑到其文库和云存储服务中，目前拥有超过 4000 万的付费用户。虽然这些产品并没有普遍提高价格，但通过功能分层创造了新的付费墙。对于用户而言，保持生产力的提升往往意味着整体支出增加。</p>
<p>以字节跳动的人工智能助手 Doubao 为例。它最近开始在其免费版本的基础上测试三个订阅层级，先进功能集中在高复杂度的用例上，如生成演示文稿、数据分析和内容创作。虽然免费版本足以满足日常任务，但更高级的需求——如长文本处理、多步骤推理或结构化输出——需要升级到付费计划。这反映了人工智能定价逻辑的更广泛转变，收费不再仅仅基于功能差异，而是直接与基础计算和推理成本挂钩。</p>
<p>实际上，这与直接涨价没有区别。</p>
<p>这些变化背后是成本结构的转变。用户正在为从软件转向计算资源付费。更重要的是，这一定价逻辑的转变正在重塑行业内价值的分配。随着人工智能从产品演变为基础设施，价值开始集中在更低的层级。对于云服务提供商和模型开发者而言，收入可以随着计算能力需求的上升而扩展。相比之下，应用层公司可能会受益于人工智能带来的效率提升，但对基础资源的日益依赖可能会压缩其利润空间。</p>
<p>人工智能并没有消除成本。相反，它将成本转变为基于使用的支出。在这一模型下，传统的低成本订阅策略将面临压力，因为成本现在直接与计算消耗挂钩。取而代之的是，混合定价模型正在出现，结合使用量、频率甚至任务结果。在这个过程中，人工智能不再是一个可以简单定价的产品，而是越来越成为一种基础资源，类似于电力或带宽。</p>
<p>在更深层次上，这一转变正在重塑整个行业的动态。当计算成本成为关键变量时，竞争不再仅仅由产品特性或用户规模定义，而是由谁能以更低的成本获取计算能力，以及谁能最有效地利用模型资源来决定。因此，行业的权力结构正在发生变化，价值越来越集中在计算能力和模型的提供者手中。</p>
<p>与此同时，商业扩展的逻辑也在发生变化。在过去，规模有助于稀释成本。但在人工智能时代，每一次调用都产生真实的费用。在这种结构下，模型开发者和人工智能服务提供商承担成本，代理运营商将其转化为收入，而这些成本最终会转嫁给最终用户，形成一层新的成本。</p>
<p><strong><em>Benzinga 免责声明：本文来自一位未付费的外部贡献者。它不代表 Benzinga 的报道，并且未经过内容或准确性编辑。</em></strong></p>

百度

百度-SW

云计算 ETF - GlobalX

2 倍做多 BABA ETF - GraniteShares

2 倍做多阿里巴巴 ETF - KraneShares

<p>AI 的成本结构正在演变，从工具转变为自主执行者，这导致企业和用户的成本增加。传统的 “免费 + 订阅” 模式由于计算需求的上升变得不可持续。像阿里巴巴和百度这样的公司正在调整云服务的定价，以反映这一变化。AI 现在是根据令牌使用量收费，应用层的定价也在上升。这一变化表明，从软件转向计算资源，压缩了应用层公司的利润空间，同时使云服务提供商受益</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>人工智能的成本结构正在发生变化，正从工具转变为自主执行者，企业和其他用户越来越多地为服务付费。</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>图片来源：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>在过去两年中，人工智能的商业模式建立在一个看似稳固的假设上：相对较低的计算成本使公司能够通过 “免费 + 订阅” 模式快速扩展用户。无论是在聊天机器人还是生产力工具中，人工智能通常被包装为增强产品吸引力的附加功能，而不是作为核心收入来源。用户支付固定的月费以无限制使用，这种模式帮助加速了广泛的采用。</p>
</div><p>But that assumption is now starting to break down.</p>
<div class="lb-trans"><p>但这一假设现在开始崩溃。</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>随着人工智能从简单的问答工具转变为能够自主完成任务的代理产品，其成本结构正在发生变化。人工智能不再仅仅是响应提示。它持续运行，反复调用模型和工具，并且通常在单个任务中进行多轮推理和优化。这一转变将人工智能从 “被动服务” 转变为 “主动执行者”，其基础消耗不再是一次性计算，而是资源的持续积累。</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>这一转变在计算使用上的变化中得到了清晰的反映。根据媒体报道，中国人工智能模型市场的每日令牌消耗从 2024 年初的约 1000 亿激增至 2026 年的约 140 万亿。这一增长不仅是由于用户数量的增加，还源于使用模式的根本变化。人工智能正在从偶尔的工具演变为嵌入企业日常运营的系统，增长主要由基于流程的重复调用驱动，而不是一次性查询。随着人工智能成为资源消耗的核心能力，传统的低成本或免费模式变得越来越难以维持。</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>这种压力已经在供应端显现。2024 年下半年至 2026 年间，<strong>阿里巴巴</strong> (NYSE: BABA) (9988.HK) 和 <strong>百度</strong> (NASDAQ: BIDU) (9888.HK) 调整了某些云服务的定价，涨幅在 5% 到 34% 之间。随着人工智能使用频率和复杂性的增加，计算需求远远超过传统应用，推动了云资源成本的上升。这些价格调整反映了日益增加的压力。</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，定价模型本身正在演变。在阿里云的大模型服务中，公司不再为产品付费，而是根据令牌使用量收费，输入和输出分别定价。阿里巴巴的钉钉服务则将人工智能能力转化为使用计数，不同层级提供不同的配额。无论是按令牌还是按操作计量，核心理念都是相同的：人工智能正在成为一种计量的、可消耗的资源。</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>这一转变并不限于基础设施层。随着人工智能能力被集成到软件产品中，应用层的定价结构也在上升。<strong>金山办公</strong> (3888.HK; 688111.SH) 在其 WPS 产品中引入了写作、总结和数据分析等人工智能功能，类似于 Microsoft Word，将其置于会员或高级层级中。百度也将人工智能能力捆绑到其文库和云存储服务中，目前拥有超过 4000 万的付费用户。虽然这些产品并没有普遍提高价格，但通过功能分层创造了新的付费墙。对于用户而言，保持生产力的提升往往意味着整体支出增加。</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>以字节跳动的人工智能助手 Doubao 为例。它最近开始在其免费版本的基础上测试三个订阅层级，先进功能集中在高复杂度的用例上，如生成演示文稿、数据分析和内容创作。虽然免费版本足以满足日常任务，但更高级的需求——如长文本处理、多步骤推理或结构化输出——需要升级到付费计划。这反映了人工智能定价逻辑的更广泛转变，收费不再仅仅基于功能差异，而是直接与基础计算和推理成本挂钩。</p>
</div><p>In effect, this is no different from a direct price increase.</p>
<div class="lb-trans"><p>实际上，这与直接涨价没有区别。</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>这些变化背后是成本结构的转变。用户正在为从软件转向计算资源付费。更重要的是，这一定价逻辑的转变正在重塑行业内价值的分配。随着人工智能从产品演变为基础设施，价值开始集中在更低的层级。对于云服务提供商和模型开发者而言，收入可以随着计算能力需求的上升而扩展。相比之下，应用层公司可能会受益于人工智能带来的效率提升，但对基础资源的日益依赖可能会压缩其利润空间。</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>人工智能并没有消除成本。相反，它将成本转变为基于使用的支出。在这一模型下，传统的低成本订阅策略将面临压力，因为成本现在直接与计算消耗挂钩。取而代之的是，混合定价模型正在出现，结合使用量、频率甚至任务结果。在这个过程中，人工智能不再是一个可以简单定价的产品，而是越来越成为一种基础资源，类似于电力或带宽。</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>在更深层次上，这一转变正在重塑整个行业的动态。当计算成本成为关键变量时，竞争不再仅仅由产品特性或用户规模定义，而是由谁能以更低的成本获取计算能力，以及谁能最有效地利用模型资源来决定。因此，行业的权力结构正在发生变化，价值越来越集中在计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>与此同时，商业扩展的逻辑也在发生变化。在过去，规模有助于稀释成本。但在人工智能时代，每一次调用都产生真实的费用。在这种结构下，模型开发者和人工智能服务提供商承担成本，代理运营商将其转化为收入，而这些成本最终会转嫁给最终用户，形成一层新的成本。</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 免责声明：本文来自一位未付费的外部贡献者。它不代表 Benzinga 的报道，并且未经过内容或准确性编辑。</em></strong></p>
</div>

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

- AI 由工具转变为自主执行者，成本结构发生变化。  
- 计算需求的增加推动云资源成本上升，传统订阅模式面临压力。  
- 新的计费模式使 AI 成为按需消耗的资源，价值向计算能力和模型提供商集中。

免费 AI 时代的终结：代理推动新的成本现实