Real first-hand experience always comes from the people who are driving industry change themselves.

Recently, OpenAI and Anthropic released core user reports on ChatGPT and Claude, respectively. These two documents are not merely performance showcases; they reveal a crucial 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 models.

To this end, Silicon Rabbit has conducted a comparative analysis of the two reports in conjunction with discussions with its Silicon Valley expert team, extracting the hidden industry signals behind them and exploring their deep implications for future technology routes, business models, and related investment strategies.

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

ChatGPT: Market Penetration in General Application Areas

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

First, the user base has successfully expanded to a broader demographic, transitioning from a primarily technical user profile to a highly educated, cross-professional white-collar group;

Second, the gender ratio is becoming more balanced, with female users rising to 52%.

In terms of application scenarios, ChatGPT's core functionalities are concentrated in three areas: practical guidance, information retrieval, and document writing, which together account for nearly 80% of total interactions.

Users primarily utilize it to assist with daily life and routine office tasks. Notably, the report explicitly states that the usage proportion 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 wide user base. Its core barriers lie in its vast user base and the resulting network effects, 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 distinctly different picture. Claude's user distribution shows a strong positive correlation with the economic development level of regions (GDP per capita), indicating that its primary user group consists of knowledge workers and professionals in developed economies.

Its core application scenarios are highly focused. The report shows that software engineering is the predominant application area across almost all regions, with related tasks consistently accounting for 36% to 40%, contrasting sharply with ChatGPT's application trends in this field.

The most striking data in the report reflects the share of "automation" tasks. Over the past eight months, the share of "directive" automation tasks, where users issue commands and AI independently completes most of the work, has significantly increased from 27% to 39%.

Among enterprise-level users of the paid API, this trend is even more pronounced: as much as 77% of conversational interactions exhibit an automation model, with the vast majority being "directive" automation with minimal human intervention.

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

Based on the aforementioned strategic divergence, Silicon Rabbit and its Silicon Valley expert team have cross-compared the data from the two reports to extract three forward-looking industry insights for investors.

1: "Programming Applications" Divergence, Indicating the Rise of Specialized AI Tools 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."

General conversational interfaces are increasingly unable to meet the deep needs of professional developers in complex workflows. What they require are AI functionalities that can seamlessly integrate with integrated development environments (IDEs), code version control systems, and project management software.

This trend signals the emergence of an important market opportunity: "AI-native toolchains" specifically designed for certain industries (such as software development, financial analysis, legal services) that are deeply bound to existing workflows.

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

2: "77% Automation Rate," Quantifying the Acceleration of Enterprise Task Automation Process

The "77% enterprise API automation rate" in the Anthropic report is a strong signal indicating that in the forefront of commercial applications, AI's role is rapidly shifting from "human assistance" to "task execution."

This data compels 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 enhancement" value of AI, but now it is essential to incorporate the "replacement" value into the core analytical framework.

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 costs."

Areas such as financial report generation, initial contract review, and market data analysis—process-driven and high labor cost fields—will be the first to see significant economic benefits from AI automation technology.

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

One counterintuitive data point in the report is that regions with higher per capita Claude usage tend to favor "collaboration" modes; conversely, regions with lower usage are more inclined towards "automation" modes.

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

As users (especially professional users) gain a deeper understanding of AI's capabilities and interaction methods, they begin to explore how to collaborate with AI to accomplish more creative tasks that were previously difficult to achieve (collaboration).

This presents new considerations for the long-term business model of AI. In addition to cost reduction through automation (SaaS model), creating new value and enhancing decision 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 development potential of AI projects along both "automation" and "collaborative creation" paths.

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

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

What are the real technical paths, deployment costs, and specific ROI data for achieving a high proportion of task automation within leading tech companies?

For companies like Apple, what is the underlying technical logic and commercialization path of their AI strategy within a closed-loop ecosystem, especially regarding their proprietary large model?

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 core individuals defining these technologies and products is essential.

For instance, to delve deeper into industry frontlines, our financial clients recently engaged in in-depth discussions with the following two experts:

One is an ML/DL/NLP scientist and technical lead 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, real training costs, and strategic considerations reported to top management that tech giants face when building core AI capabilities.

The other is a technical lead (Engineer Lead) from Meta's generative AI organization. As a founding engineer, he not only deeply participated in the development of LLMs but, more importantly, he led the integration of GenAI technology with core business engines such as advertising ranking and recommendation systems. Engaging with him can clearly outline the conversion 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 transform the macro trends in public reports into granular tactical information that can guide specific decisions. In a rapidly evolving information environment, obtaining deep insights that go beyond public information is fundamental to establishing cognitive advantages and making precise decisions. If you have further discussion needs on the above topics, we welcome you to contact us to arrange expert exchanges in the relevant fields.

When your team is embroiled in debates over technology routes, when your investment decisions hang in the balance, when your product strategy is shrouded in fog… remember, the confusion you face may be a journey that some expert has already traversed. We at Silicon Rabbit believe: real first-hand experience always comes from the people who are driving industry change themselves.

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