ChatGPT and Claude are no longer players on the same path.

Recently, OpenAI and Anthropic have released core user reports about ChatGPT and Claude, respectively. These two documents are not merely performance showcases; rather, they reveal a critical trend in the current artificial intelligence industry: the two leading models are evolving along distinctly different paths, with significant differentiation in their market positioning, core application scenarios, and user interaction modes.

To this end, Sillicon Rabbit has conducted a comparative analysis of the two reports in conjunction with discussions with its team of experts from Silicon Valley, extracting the underlying industrial signals and exploring their deeper implications for future technological routes, business models, and related investment strategies.

The data from the two reports clearly illustrates the different focuses of ChatGPT and Claude in terms of user base and core functionalities, which serves as a starting point for understanding their long-term strategic distinctions.

ChatGPT: Market Penetration in General-Purpose Application Fields

OpenAI's report confirms ChatGPT's status as a phenomenon-level application. By July 2025, its weekly active users have exceeded 700 million. The user structure exhibits two key characteristics:

Firstly, the user base has successfully expanded to a broader audience, shifting from an early user profile primarily consisting of technical personnel to a highly educated, cross-professional white-collar group.

Secondly, the gender ratio is tending to balance, with the proportion of female users rising to 52%.

In terms of application scenarios, the core functions of ChatGPT focus on three areas: practical guidance, information inquiry, and document writing, which together account for nearly 80% of the total conversations.

Users mainly use it to assist with daily life and routine office tasks. It is worth noting that the report clearly states that the usage proportion of assistance in professional technical tasks 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 wide range of user groups. Its core barrier lies in the large user base and the network effects that arise from it, as well as its high penetration rate in users' daily information processing workflows.

Claude: Focus on enterprise-level and professional automation scenarios.

Anthropic's report paints a starkly different picture. The user distribution of Claude shows a strong positive correlation with the economic development level of the region (GDP per capita), indicating that its main user base consists of knowledge workers and professionals in developed economies.

Its core application scenarios are highly focused. According to the report, software engineering is the main application field in almost all regions, with related tasks accounting for a stable proportion of between 36% and 40%, which contrasts sharply with the application trend of ChatGPT in this field.

The most impactful data in the report is reflected in the proportion of "automation" tasks. Over the past 8 months, the share of "directive" automation tasks, where users directly issue instructions and AI independently completes most of the work, has significantly increased from 27% to 39%.

This trend is even more pronounced among enterprise-level users of paid APIs: as much as 77% of conversational interactions exhibit automated patterns, with the vast majority being "directive" automation with minimal human intervention.

Therefore, Claude's strategic positioning is very clear: to become a professional-grade productivity and automation tool deeply integrated into the core workflows of enterprises. Its competitive advantage lies in deep optimization for specific professional fields (especially software development) and an ultimate pursuit of task execution efficiency.

Based on the aforementioned strategic fields, Silicon Bunny and its team of experts from Silicon Valley conducted a cross-comparison of the data in the two reports, distilling three forward-looking industry insights for investors.

1: "Programming applications" diversification indicates the rise of specialized AI tool market.

The ebb and flow of ChatGPT and Claude in programming applications does not reflect fluctuations in market demand, but rather an upgrade in user needs towards "specialization" and "integration."

The general-purpose dialogue interface can no longer meet the deep needs of professional developers in complex workflows. What they need is AI functionality that can seamlessly integrate with integrated development environments (IDEs), code version control systems, and project management software.

This trend signifies the emergence of an important market opportunity: an "AI-native toolchain" designed specifically for certain industries (such as software development, financial analysis, legal services) that is deeply integrated with existing workflows.

This requires AI not only to have modeling capabilities but also to possess a deep understanding of the industry. For investments in related fields, evaluating whether the target has the ability to build such "deep integration" will become a key consideration.

Two: "77% automation rate", measuring the acceleration of automation processes in quantitative enterprises.

The "77% automation rate of enterprise APIs" in the Anthropic report is a strong signal indicating that the role of AI at the forefront of commercial applications is rapidly shifting from "human-assisted" to "task execution."

This data requires us to reassess the speed at which AI impacts enterprise productivity, organizational structure, and cost models. In the past, the market generally focused on the "efficiency" value of AI, but now the "substitution" value must be incorporated into the core analytical framework.

The investment logic needs to expand from evaluating "how AI can assist human employees" to "in which knowledge-based work areas can AI independently complete standardized tasks with higher efficiency and lower costs."

The areas of financial statement generation, initial contract review, market data analysis, and other process-driven, high labor cost fields will be the first to see significant economic benefits from AI automation technology.

Three: "Differences in Collaboration and Automation" Mode, Revealing the Evolution Path of AI Business Models

One counterintuitive data point in the report is that the higher the average Claude usage rate in a region, the more users tend to favor the "collaboration" mode; conversely, regions with lower usage rates are more inclined towards the "automation" mode.

This may reveal the evolving relationship between AI business models and user maturity. In the early penetration stage of the market, users are more inclined to see AI as a simple efficiency tool, used to alternatively complete independent tasks (automation).

As users (especially professional users) gain a deeper understanding of the boundaries and interaction modes of AI, they will begin to explore how to collaborate with AI on complex tasks to accomplish more creative tasks that were previously difficult to achieve.

This raises new considerations for the long-term business model of AI. In addition to cost reduction through automation (SaaS model), the creation of new value and enhancement of decision-making quality through human-machine collaboration may give rise to more advanced business models, such as performance-based payment or decision support subscriptions. Investors should consider the developmental potential of AI projects along both the "automation" and "collaborative creation" pathways.

The above analysis based on public reports is just the starting point of the decision-making process. A complete decision also needs to address deeper, key questions about "how to achieve it" and "who will achieve it", such as:

In the field of "AI-native toolchains", what are the technology architecture, team composition, and market validation status of the most promising startups?

What are the specific data on the real technical paths, deployment costs, and return on investment (ROI) for achieving a high proportion of task automation within leading technology companies?

What is the AI strategy of companies like Apple under their closed-loop ecosystem, especially regarding the underlying technological logic of their proprietary large models and their commercialization path?

This information cannot be obtained from public reports; it comes from practical experience on the front lines of the industry. To truly understand the dynamics of the current AI industry, direct dialogue with the key figures who are defining these technologies and products is necessary.

For example, to gain deeper insights into the front line of the industry, our financial clients recently had in-depth discussions with the following two experts:

A machine learning/deep learning/NLP scientist and technical leader from Apple's machine learning department. As a core member who trained Apple's proprietary large language model (LLM) from scratch, he can directly reveal the technical challenges, actual training costs, and strategic considerations involved in building core AI capabilities at a tech giant, as well as reporting directly to top management.

A technical lead (Engineer Lead) of a Meta generative AI organization. As a founding engineer, he is not only deeply involved in the research and development of large language models (LLMs), but more importantly, he has led the implementation process of integrating GenAI technology with core business engines such as advertising ranking and recommendation systems. Conversations with him can clearly outline the transformation path from model capabilities to business ROI, as well as his investment observations on cutting-edge AI startups in North America.

Insights from such experts will translate macro trends in public reports into highly granular tactical information that can guide specific decisions. In an industry environment where information rapidly iterates, obtaining deep insights that go beyond publicly available information is fundamental to establishing a cognitive advantage and making precise decisions. If you have further discussions on the above topics, we welcome you to contact us to arrange exchanges with experts in the relevant fields.

When your team is in heated debates over the technical roadmap, when your investment decisions are hanging in the balance, when your product strategy is shrouded in fog... remember that the confusion you face may already be a journey that some expert has crossed. We at Silicon Rabbit believe: real first-hand experience always comes from those who are driving change in the industry.