<p>最近，OpenAI 和 Anthropic 发布了关于 ChatGPT 和 Claude 的核心用户报告。这两份文件不仅仅是性能报告；它们揭示了当前 AI 行业中的一个重要趋势：这两个领先模型正在沿着不同的路径发展，在市场定位、核心应用场景和用户互动模式上存在显著差异。为此，硅兔公司借助与其硅谷专家团队的讨论，对这两份报告进行了比较分析，提取了潜在的行业信号，并探讨了它们对未来技术路径、商业模式和相关投资策略的影响。这两份报告的数据清楚地表明，ChatGPT 和 Claude 在用户基础和核心功能方面的不同侧重点，这是理解它们长期战略差异化的起点。</p>
<p>首先，用户基础成功扩展到更广泛的人群。用户画像最初主要是技术人员，现在已转变为受过高等教育的多专业白领群体。</p>
<p>其次，性别比例变得更加平衡，女性用户占比达到 52%。</p>
<p>在应用场景方面，ChatGPT 的核心功能集中在三个领域：实用指导、信息查询和文档写作。这三个领域占总对话的近 80%。</p>
<p>用户主要用它来协助日常生活和常规办公任务。值得注意的是，报告明确指出，专业技术支持（如编程）的使用率已显著下降，从 12% 降至 5%。</p>
<p>总体而言，ChatGPT 的战略路径是成为一个服务广泛用户群体的通用人工智能助手。其核心竞争优势在于庞大的用户基础、由此产生的网络效应以及在用户日常信息处理过程中的高渗透率。Claude：专注于企业和专业自动化场景。Anthropic 的报告描绘了截然不同的图景。Claude 的用户分布与地区的经济发展水平（人均 GDP）呈强正相关，表明其主要用户群体是发达经济体中的知识工作者和专业人士。其核心应用场景高度集中。报告数据显示，软件工程几乎是所有地区的主要应用领域，相关任务的比例保持在 36% 到 40% 之间。这与 ChatGPT 在该领域的应用趋势形成鲜明对比。报告中最引人注目的数据点是 “自动化” 任务的百分比。在过去八个月中，“基于命令” 的自动化任务比例显著上升，从 27% 增加到 39%。在付费 API 的企业用户中，这一趋势更加明显：多达 77% 的对话交互是自动化的，其中绝大多数是 “基于命令” 的自动化，几乎没有人类干预。因此，Claude 的战略定位非常明确：成为深度融入核心企业工作流程的专业级生产力和自动化工具。其竞争优势在于对特定专业领域（尤其是软件开发）的深度优化，以及对任务执行效率的最终追求。基于这一战略区分，硅兔及其硅谷专家团队对两份报告的数据进行了交叉比较，为投资者提炼出三条前瞻性的行业洞察。1. “编程应用” 的差异化信号着专业 AI 工具市场的崛起。ChatGPT 和 Claude 在编程应用中的兴衰并不反映市场需求的波动，而是用户对 “专业化” 和 “集成化” 需求的升级。通用的对话接口已无法满足专业开发者在复杂工作流程中的深层需求。他们需要与集成开发环境（IDE）、代码版本控制系统和项目管理软件无缝集成的 AI 能力。这一趋势预示着一个重要的市场机会：为特定行业（如软件开发、金融分析和法律服务）构建的 “AI 原生工具链”，深度融入现有工作流程。这要求 AI 不仅具备模型能力，还需对行业有深刻理解。对于相关领域的投资，评估目标公司是否具备实现这种 “深度集成” 的能力将成为关键考量。第二，“77% 的自动化率” 量化了企业任务自动化的加速。Anthropic 报告中的 “77% 企业 API 自动化率” 是一个强有力的信号，表明在商业应用的前沿，AI 的角色正在迅速从 “人类辅助” 转向 “任务执行”。这一数据要求我们重新评估 AI 对企业生产力、组织结构和成本模型的影响速度。过去，市场普遍关注 AI 的 “效率提升” 价值，但现在必须将其 “替代” 价值纳入核心分析框架。投资逻辑需要从评估 “AI 如何辅助人类员工” 扩展到 “在知识型工作领域，AI 在哪些方面能够独立完成标准化任务，且效率更高、成本更低”。基于流程的、劳动密集型的领域，如财务报表生成、合同初步审查和市场数据分析，将是 AI 自动化技术产生显著经济效益的首个领域。3. “协作” 和 “自动化” 模型的差异揭示了 AI 商业模型的演变路径。报告中一个反直觉的数据点是，人均 Claude 使用率较高的地区倾向于偏好 “协作” 模型；相反，人均使用率较低的地区则倾向于偏好 “自动化” 模型。这可能揭示了 AI 商业模型与用户成熟度之间的演变关系。在市场渗透的早期阶段，用户往往将 AI 作为简单的效率工具来替代独立任务（自动化）。然而，随着用户（尤其是专业用户）对 AI 能力和交互方式的深入理解，他们开始探索如何以复杂的方式与 AI 合作，完成以前难以实现的更具创造性的任务（协作）。这对 AI 的长期商业模型提出了新的问题。除了通过自动化降低成本（SaaS 模型），通过人机协作创造新价值和提高决策质量，可能会催生更高级的商业模型，如按绩效付费或决策支持订阅。在评估 AI 项目时，投资者应考虑其在自动化和协作创造方面的潜力。上述基于公开报告的分析仅是决策过程的起点。完整的决策还需要回答关于 “如何实现” 和 “由谁实现” 的更深层次的关键问题。例如：在 “AI 原生工具链” 领域，最有前景的初创公司的技术架构、团队组成和市场验证是什么？在领先科技公司中，实现高比例任务自动化的实际技术路径、部署成本和投资回报率（ROI）是什么？像苹果这样的公司在其闭环生态系统中的 AI 战略是什么，特别是其专有大规模模型的底层技术逻辑和商业化路径是什么？这些信息在公开报告中并不可得；它源于行业前线的实际经验。要真正理解当前 AI 行业的动态，直接与定义这些技术和产品的核心人员进行接触至关重要。例如，为了深入了解行业，我们的金融客户最近与两位专家进行了深入讨论：一位是苹果机器学习部门的 ML/DL/NLP 科学家和技术负责人。作为从零开始训练苹果专有大型语言模型（LLM）团队的关键成员，他能够直接揭示科技巨头在构建自身核心 AI 能力时面临的技术挑战、真实训练成本和战略考量。另一位是元生成 AI 组织的工程负责人。作为创始工程师，他不仅深度参与了 LLM 大规模模型的开发，更重要的是，他领导了将 GenAI 技术与广告排名和推荐系统等核心业务引擎整合的实施。与他的访谈清晰勾勒出从模型能力到商业 ROI 的路径，以及他对北美前沿 AI 初创公司的投资见解。这类专家的见解将公开报告中的宏观趋势转化为高度细化的战术信息，能够指导具体决策。在一个信息快速演变的行业中，获取超越公开信息的深刻见解对于建立认知优势和做出精准决策至关重要。如果您希望进一步讨论这些主题，我们欢迎您与我们联系，安排与相关领域专家的对话。当您的团队在讨论未来的技术路径时，当您的投资决策尚未确定时，或者当您的产品战略陷入不确定时……请记住，您面临的困境可能是专家已经走过的旅程。我们硅兔相信，真实的第一手经验总是来自那些推动行业变革的人。</p>
<p><img src="https://imageproxy.pbkrs.com/https://upload.techflowpost.com/upload/images/20250916/2025091621030074635645.png?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://upload.techflowpost.com/upload/images/20250916/2025091621030074635645.png"/></p>

OpenAI

<p>OpenAI 的 ChatGPT 和 Anthropic 的 Claude 在市场策略上出现了分歧，最近的用户报告揭示了这一点。ChatGPT 每周活跃用户超过 7 亿，专注于通用应用，而 Claude 则瞄准企业自动化，自动化任务显著增加。这一转变表明对专业 AI 工具在专业工作流程中集成的需求日益增长。投资者应考虑这些趋势对未来技术路径和商业模式的影响，特别是在行业特定应用中深度集成的必要性</p>

<p>Recently, OpenAI and Anthropic released core user reports on ChatGPT and Claude. These two documents aren't simply performance reports; they reveal a crucial trend in the current AI industry: the two leading models are evolving along distinct paths, with significant divergence in their market positioning, core application scenarios, and user interaction patterns. To this end, Silicon Rabbit, drawing on discussions with their Silicon Valley expert team, conducted a comparative analysis of the two reports, extracting the underlying industry signals and exploring their implications for future technology paths, business models, and related investment strategies. The data from the two reports clearly demonstrates the different focuses of ChatGPT and Claude in terms of user base and core functionality, which is a starting point for understanding their long-term strategic differentiation. ChatGPT: Market Penetration in General-Purpose Applications The OpenAI report confirms ChatGPT's status as a phenomenal application. As of July 2025, it has exceeded 700 million weekly active users. The user structure exhibits two key characteristics:</p>
<div class="lb-trans"><p>最近，OpenAI 和 Anthropic 发布了关于 ChatGPT 和 Claude 的核心用户报告。这两份文件不仅仅是性能报告；它们揭示了当前 AI 行业中的一个重要趋势：这两个领先模型正在沿着不同的路径发展，在市场定位、核心应用场景和用户互动模式上存在显著差异。为此，硅兔公司借助与其硅谷专家团队的讨论，对这两份报告进行了比较分析，提取了潜在的行业信号，并探讨了它们对未来技术路径、商业模式和相关投资策略的影响。这两份报告的数据清楚地表明，ChatGPT 和 Claude 在用户基础和核心功能方面的不同侧重点，这是理解它们长期战略差异化的起点。</p>
</div><p>First, the user base has successfully expanded to a wider demographic. The user profile, initially primarily technical personnel, has shifted to a highly educated, multi-professional, white-collar group.</p>
<div class="lb-trans"><p>首先，用户基础成功扩展到更广泛的人群。用户画像最初主要是技术人员，现在已转变为受过高等教育的多专业白领群体。</p>
</div><p>Second, the gender ratio has become more balanced, with female users accounting for 52%.</p>
<div class="lb-trans"><p>其次，性别比例变得更加平衡，女性用户占比达到 52%。</p>
</div><p>In terms of application scenarios, ChatGPT's core functions are concentrated in three areas: practical guidance, information query, and document writing. These three areas account for nearly 80% of total conversations.</p>
<div class="lb-trans"><p>在应用场景方面，ChatGPT 的核心功能集中在三个领域：实用指导、信息查询和文档写作。这三个领域占总对话的近 80%。</p>
</div><p>Users primarily use it to assist with daily life and routine office tasks. Notably, the report clearly states that the usage rate for professional technical assistance, such as programming, has significantly decreased from 12% to 5%.</p>
<div class="lb-trans"><p>用户主要用它来协助日常生活和常规办公任务。值得注意的是，报告明确指出，专业技术支持（如编程）的使用率已显著下降，从 12% 降至 5%。</p>
</div><p>Overall, ChatGPT's strategic path is to become a general-purpose AI assistant serving a broad user base. Its core competitive advantage lies in its massive user base, the resulting network effect, and its high penetration rate in users' daily information processing processes. Claude: Focusing on Enterprise and Professional Automation Scenarios. Anthropic's report paints a starkly different picture. Claude's user distribution shows a strong positive correlation with a region's economic development level (GDP per capita), indicating that its primary user base is knowledge workers and professionals in developed economies. Its core application scenarios are highly focused. The report data shows that software engineering is the primary application area in almost all regions, with the proportion of related tasks remaining stable between 36% and 40%. This stands in stark contrast to ChatGPT's application trends in this area. The most striking data point in the report is the percentage of &#34;automated&#34; tasks. Over the past eight months, the share of &#34;command-based&#34; automated tasks, where users directly issue commands and AI independently completes the majority of the work, has increased significantly from 27% to 39%. Among enterprise users of paid APIs, this trend is even more pronounced: as many as 77% of conversational interactions are automated, and the vast majority are &#34;command-based&#34; automations with minimal human intervention. Therefore, Claude's strategic positioning is clear: to become a professional-grade productivity and automation tool deeply integrated into core enterprise workflows. Its competitive advantage lies in its deep optimization for specific professional fields (especially software development) and its ultimate pursuit of task execution efficiency. Based on this strategic distinction, Silicon Rabbit and its team of Silicon Valley experts have cross-compared the data from the two reports to extract three forward-looking industry insights for investors. 1. The differentiation of &#34;programming applications&#34; signals the rise of a specialized AI tool market. The rise and fall of ChatGPT and Claude in programming applications doesn't reflect fluctuations in market demand, but rather an escalation in user demand toward &#34;specialization&#34; and &#34;integration.&#34; General-purpose conversational interfaces can no longer meet the deep needs of professional developers in complex workflows. They require AI capabilities that seamlessly integrate with integrated development environments (IDEs), code version control systems, and project management software. This trend signals a significant market opportunity: &#34;AI-native toolchains&#34; built for specific industries (such as software development, financial analysis, and legal services) that are deeply integrated into existing workflows. This requires AI to possess not only model capabilities but also a deep understanding of the industry. For investments in related fields, evaluating whether the target company possesses the ability to achieve this kind of &#34;deep integration&#34; will become a key consideration. Second, the &#34;77% Automation Rate&#34; quantifies the acceleration of enterprise task automation. The Anthropic report's &#34;77% enterprise API automation rate&#34; is a strong signal, indicating that at the forefront of commercial applications, AI's role is rapidly shifting from &#34;human assistance&#34; to &#34;task execution.&#34; This data requires us to reassess the speed at which AI is impacting enterprise productivity, organizational structure, and cost models. In the past, the market generally focused on the &#34;efficiency-enhancing&#34; value of AI, but now it is imperative to incorporate its &#34;replacement&#34; value into the core analytical framework. The investment logic needs to expand from evaluating &#34;how AI assists human employees&#34; to &#34;in which knowledge-based work areas can AI independently complete standardized tasks with higher efficiency and lower cost.&#34; Process-based and labor-intensive areas such as financial statement generation, contract preliminary review, and market data analysis will be the first areas where AI automation technology will produce significant economic benefits. 3. Differences in &#34;collaboration&#34; and &#34;automation&#34; models reveal the evolutionary path of AI business models. A counterintuitive data point in the report is that regions with higher per capita Claude usage rates tend to prefer a &#34;collaboration&#34; model; conversely, regions with lower usage rates tend to prefer an &#34;automation&#34; model. This may reveal the evolving relationship between AI business models and user maturity. In the early stages of market penetration, users tend to use AI as a simple efficiency tool to replace independent tasks (automation). However, as users (especially professional users) gain a deeper understanding of AI's capabilities and interaction methods, they begin to explore how to work with AI in complex ways to complete more creative tasks that were previously difficult to achieve (collaboration). This raises new questions about AI's long-term business models. In addition to reducing costs through automation (SaaS models), creating new value and improving decision-making quality through human-machine collaboration may give rise to more advanced business models, such as pay-for-performance or decision-support subscriptions. When evaluating AI projects, investors should consider their potential for both automation and collaborative creation. The above analysis, based on public reports, is only a starting point for the decision-making process. A complete decision also requires answering deeper, critical questions about &#34;how to achieve&#34; and &#34;by whom.&#34; For example: What are the technical architectures, team compositions, and market validation of the most promising startups in the &#34;AI native tool chain&#34; space? What are the actual technical paths, deployment costs, and return on investment (ROI) for achieving a high percentage of task automation within leading technology companies? What is the AI ​​strategy for a company like Apple within its closed-loop ecosystem, particularly the underlying technical logic and commercialization path for its proprietary large-scale models? This information isn't available in public reports; it stems from practical experience on the front lines of the industry. To truly understand the current dynamics of the AI ​​industry, it's crucial to engage directly with the core individuals who are defining these technologies and products. For example, to gain in-depth insights into the industry, our financial clients recently had in-depth discussions with two experts: an ML/DL/NLP scientist and technical lead in Apple's machine learning department. As a key member of the team that trained Apple's proprietary Large Language Model (LLM) from scratch, he was able to directly reveal the technical challenges, true training costs, and strategic considerations that tech giants face when building their own core AI capabilities. An engineer lead in a meta-generative AI organization. As a founding engineer, he was not only deeply involved in the development of the LLM large-scale model, but more importantly, he led the implementation of integrating GenAI technology with core business engines such as ad ranking and recommendation systems. Interviews with him clearly outline the path from model capability to commercial ROI, as well as his investment insights on cutting-edge AI startups in North America. Insights from this type of expert translate macro trends in public reports into highly granular, tactical information that can guide specific decisions. In an industry characterized by rapid information evolution, gaining deep insights beyond publicly available information is essential for building cognitive advantages and making precise decisions. If you would like to further discuss these topics, we welcome you to contact us to arrange a conversation with an expert in the relevant field. When your team is debating over the technical path forward, when your investment decision is undecided, or when your product strategy is mired in uncertainty...remember that the dilemma you face may be a journey already traversed by an expert. We at Silicon Rabbit believe that authentic, first-hand experience always comes from those driving industry change.</p>
<div class="lb-trans"><p>总体而言，ChatGPT 的战略路径是成为一个服务广泛用户群体的通用人工智能助手。其核心竞争优势在于庞大的用户基础、由此产生的网络效应以及在用户日常信息处理过程中的高渗透率。Claude：专注于企业和专业自动化场景。Anthropic 的报告描绘了截然不同的图景。Claude 的用户分布与地区的经济发展水平（人均 GDP）呈强正相关，表明其主要用户群体是发达经济体中的知识工作者和专业人士。其核心应用场景高度集中。报告数据显示，软件工程几乎是所有地区的主要应用领域，相关任务的比例保持在 36% 到 40% 之间。这与 ChatGPT 在该领域的应用趋势形成鲜明对比。报告中最引人注目的数据点是 “自动化” 任务的百分比。在过去八个月中，“基于命令” 的自动化任务比例显著上升，从 27% 增加到 39%。在付费 API 的企业用户中，这一趋势更加明显：多达 77% 的对话交互是自动化的，其中绝大多数是 “基于命令” 的自动化，几乎没有人类干预。因此，Claude 的战略定位非常明确：成为深度融入核心企业工作流程的专业级生产力和自动化工具。其竞争优势在于对特定专业领域（尤其是软件开发）的深度优化，以及对任务执行效率的最终追求。基于这一战略区分，硅兔及其硅谷专家团队对两份报告的数据进行了交叉比较，为投资者提炼出三条前瞻性的行业洞察。1. “编程应用” 的差异化信号着专业 AI 工具市场的崛起。ChatGPT 和 Claude 在编程应用中的兴衰并不反映市场需求的波动，而是用户对 “专业化” 和 “集成化” 需求的升级。通用的对话接口已无法满足专业开发者在复杂工作流程中的深层需求。他们需要与集成开发环境（IDE）、代码版本控制系统和项目管理软件无缝集成的 AI 能力。这一趋势预示着一个重要的市场机会：为特定行业（如软件开发、金融分析和法律服务）构建的 “AI 原生工具链”，深度融入现有工作流程。这要求 AI 不仅具备模型能力，还需对行业有深刻理解。对于相关领域的投资，评估目标公司是否具备实现这种 “深度集成” 的能力将成为关键考量。第二，“77% 的自动化率” 量化了企业任务自动化的加速。Anthropic 报告中的 “77% 企业 API 自动化率” 是一个强有力的信号，表明在商业应用的前沿，AI 的角色正在迅速从 “人类辅助” 转向 “任务执行”。这一数据要求我们重新评估 AI 对企业生产力、组织结构和成本模型的影响速度。过去，市场普遍关注 AI 的 “效率提升” 价值，但现在必须将其 “替代” 价值纳入核心分析框架。投资逻辑需要从评估 “AI 如何辅助人类员工” 扩展到 “在知识型工作领域，AI 在哪些方面能够独立完成标准化任务，且效率更高、成本更低”。基于流程的、劳动密集型的领域，如财务报表生成、合同初步审查和市场数据分析，将是 AI 自动化技术产生显著经济效益的首个领域。3. “协作” 和 “自动化” 模型的差异揭示了 AI 商业模型的演变路径。报告中一个反直觉的数据点是，人均 Claude 使用率较高的地区倾向于偏好 “协作” 模型；相反，人均使用率较低的地区则倾向于偏好 “自动化” 模型。这可能揭示了 AI 商业模型与用户成熟度之间的演变关系。在市场渗透的早期阶段，用户往往将 AI 作为简单的效率工具来替代独立任务（自动化）。然而，随着用户（尤其是专业用户）对 AI 能力和交互方式的深入理解，他们开始探索如何以复杂的方式与 AI 合作，完成以前难以实现的更具创造性的任务（协作）。这对 AI 的长期商业模型提出了新的问题。除了通过自动化降低成本（SaaS 模型），通过人机协作创造新价值和提高决策质量，可能会催生更高级的商业模型，如按绩效付费或决策支持订阅。在评估 AI 项目时，投资者应考虑其在自动化和协作创造方面的潜力。上述基于公开报告的分析仅是决策过程的起点。完整的决策还需要回答关于 “如何实现” 和 “由谁实现” 的更深层次的关键问题。例如：在 “AI 原生工具链” 领域，最有前景的初创公司的技术架构、团队组成和市场验证是什么？在领先科技公司中，实现高比例任务自动化的实际技术路径、部署成本和投资回报率（ROI）是什么？像苹果这样的公司在其闭环生态系统中的 AI 战略是什么，特别是其专有大规模模型的底层技术逻辑和商业化路径是什么？这些信息在公开报告中并不可得；它源于行业前线的实际经验。要真正理解当前 AI 行业的动态，直接与定义这些技术和产品的核心人员进行接触至关重要。例如，为了深入了解行业，我们的金融客户最近与两位专家进行了深入讨论：一位是苹果机器学习部门的 ML/DL/NLP 科学家和技术负责人。作为从零开始训练苹果专有大型语言模型（LLM）团队的关键成员，他能够直接揭示科技巨头在构建自身核心 AI 能力时面临的技术挑战、真实训练成本和战略考量。另一位是元生成 AI 组织的工程负责人。作为创始工程师，他不仅深度参与了 LLM 大规模模型的开发，更重要的是，他领导了将 GenAI 技术与广告排名和推荐系统等核心业务引擎整合的实施。与他的访谈清晰勾勒出从模型能力到商业 ROI 的路径，以及他对北美前沿 AI 初创公司的投资见解。这类专家的见解将公开报告中的宏观趋势转化为高度细化的战术信息，能够指导具体决策。在一个信息快速演变的行业中，获取超越公开信息的深刻见解对于建立认知优势和做出精准决策至关重要。如果您希望进一步讨论这些主题，我们欢迎您与我们联系，安排与相关领域专家的对话。当您的团队在讨论未来的技术路径时，当您的投资决策尚未确定时，或者当您的产品战略陷入不确定时……请记住，您面临的困境可能是专家已经走过的旅程。我们硅兔相信，真实的第一手经验总是来自那些推动行业变革的人。</p>
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ChatGPT 和 Claude 已经走上了不同的道路