ChatGPT and Claude are no longer on the same path

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:

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.

Second, the gender ratio has become more balanced, with female users accounting for 52%.

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.

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%.

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 "automated" tasks. Over the past eight months, the share of "command-based" 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 "command-based" 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 "programming applications" 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 "specialization" and "integration." 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: "AI-native toolchains" 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 "deep integration" will become a key consideration. Second, the "77% Automation Rate" quantifies the acceleration of enterprise task automation. The Anthropic report's "77% enterprise API automation rate" is a strong signal, indicating that at the forefront of commercial applications, AI's role is rapidly shifting from "human assistance" to "task execution." 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 "efficiency-enhancing" value of AI, but now it is imperative to incorporate its "replacement" value into the core analytical framework. The investment logic needs to expand from evaluating "how AI assists human employees" to "in which knowledge-based work areas can AI independently complete standardized tasks with higher efficiency and lower cost." 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 "collaboration" and "automation" 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 "collaboration" model; conversely, regions with lower usage rates tend to prefer an "automation" 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 "how to achieve" and "by whom." For example: What are the technical architectures, team compositions, and market validation of the most promising startups in the "AI native tool chain" 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.