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

Recently, OpenAI and Anthropic have released core user reports on ChatGPT and Claude, respectively. These two documents are not merely performance showcases but 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 modes.

To this end, Silicon Rabbit combined insights from its conversations with Silicon Valley expert teams to conduct a comparative analysis of the two reports, extracting the hidden industrial signals behind them and exploring their deeper implications for future technological paths, business models, and related investment strategies.

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

ChatGPT: Market penetration in the general application field

OpenAI's report confirms ChatGPT's status as a phenomenon-level application. As of July 2025, its weekly active users have surpassed 700 million. The user demographics show two key characteristics:

First of all, the user base has successfully expanded to a wider audience, with the early user profile dominated by technicians now transformed into a highly educated, cross-professional white-collar group.

Secondly, the gender ratio is approaching 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 in daily life and routine office tasks. It is worth noting that the report clearly states that the usage ratio of 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 range of user groups. Its core barriers lie in its 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 distribution of Claude's users shows a strong positive correlation with the level of economic development in different regions (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 data, software engineering is the primary application area in almost all regions, with related tasks accounting for a stable proportion of 36% to 40%, which stands in stark contrast to the application trends of ChatGPT in this field.

The most impactful data in the report is reflected in the proportion of “automated” tasks. Over the past 8 months, the share of “directive” automation tasks, where users directly issue commands 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: up to 77% of conversational interactions exhibit an automated pattern, 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 the deep optimization for specific professional fields (especially software development) and the ultimate pursuit of task execution efficiency.

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

1: The differentiation of “programming applications” signals the rise of specialized AI tool markets.

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

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

This trend indicates the emergence of a significant market opportunity: an “AI-native toolchain” designed specifically for certain industries (such as software development, financial analysis, and 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 this kind of “deep integration” will become a key consideration.

II: “77% automation rate”, accelerating the automation process of quantitative enterprise tasks.

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

This data requires us to reassess the speed at which AI impacts corporate productivity, organizational structure, and cost models. In the past, the market generally focused on the “efficiency” value of AI, but now it is essential to incorporate the “substitution” value into the core analytical framework.

The investment logic needs to expand from assessing “how AI can assist human employees” to “in which knowledge-based job sectors AI can independently complete standardized tasks with higher efficiency and lower costs.”

The areas of financial statement generation, preliminary contract review, market data analysis, and other process-intensive and high labor cost domains will be the first directions where AI automation technology will generate significant economic benefits.

Three: The differences in the “Collaboration and Automation” models reveal the evolutionary path of AI business models.

One counterintuitive data point in the report is that in regions with a higher per capita Claude usage rate, users are more inclined towards the “collaboration” model; conversely, regions with a lower usage rate tend to favor the “automation” model.

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 autonomously complete independent tasks (automation).

When users (especially professional users) gain a deeper understanding of the boundaries of AI's capabilities and interaction methods, they will begin to explore how to collaborate with AI on complex tasks to achieve more creative objectives that were difficult to accomplish in the past.

This raises new considerations for the long-term business model of AI. In addition to reducing costs through automation substitution (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 payments or decision support subscriptions. When evaluating AI projects, investors should consider their development potential along both paths of “automation” and “collaborative creation.”

The above analysis based on public reports is just the starting point of the decision-making process. A complete decision also needs to answer deeper key questions about “how to achieve” 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 technological paths, deployment costs, and return on investment (ROI) for achieving a high level of task automation within leading technology companies?

What is the AI strategy of companies like Apple under their closed-loop ecosystem, particularly regarding the underlying technical 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 industry frontlines, our financial clients recently had in-depth discussions with the following two experts:

A scientist and technical leader in the ML/DL/NLP field from Apple's machine learning department. As a core member involved in training Apple's proprietary large language model (LLM) from scratch, he is able to directly reveal the technical challenges faced by tech giants when building their core AI capabilities, the real training costs, and the strategic considerations that are reported directly to top management.

A technical lead at a Meta generative AI organization. As a founding engineer, he is deeply involved in the research and development of LLM large models, and more importantly, he is leading the implementation process of integrating GenAI technology with core business engines such as advertising ranking and recommendation systems. Communication with him can clearly outline the transition 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 the macro trends in public reports into finely granular tactical information that can guide specific decisions. In an industry environment where information iterates rapidly, gaining 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 communication with experts in the relevant fields.

When your team is arguing endlessly over the technical roadmap, when your investment decisions are pending, when your product strategy is shrouded in fog… remember that the confusion you face may be the journey that an expert has already crossed. We at Silly Rabbit believe that real firsthand experience always comes from those who are driving change in the industry.