ChatGPT leads usage but Anthropic may lead AI revenue

1 min read     Updated on 29 Jun 2026, 08:13 AM
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Reviewed by
Radhika SScanX News Team
AI Summary

OpenAI's ChatGPT leads with over 900 million users, but Anthropic's Claude may generate higher revenue of $40-50 billion due to enterprise focus. IDC and Counterpoint data highlight Anthropic's superior monetization per user. Both firms are preparing for IPOs.

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OpenAI's ChatGPT maintains a commanding lead in global usage with over 900 million weekly active users, but new estimates suggest Anthropic may be generating more revenue despite serving a fraction of the user base. According to IDC Research Vice President Arnal Dayaratna, ChatGPT remains the dominant consumer-facing large language model chatbot. In contrast, Alphabet Inc.’s Google Gemini has between 200 million and 250 million weekly active users, while Anthropic’s Claude is estimated to have between 40 million and 60 million weekly active users.

Revenue Estimates Diverge from User Base

While OpenAI leads in scale, IDC estimates its annualized revenue is between $30 billion and $40 billion. This includes roughly $10 billion to $13 billion from consumer subscriptions and as much as $20 billion from API and platform-related usage. Conversely, IDC estimates Anthropic’s annualized revenue is between $40 billion and $50 billion, with consumer subscriptions accounting for less than $2 billion. Anthropic’s revenue appears concentrated in enterprise subscriptions, Claude Code seats, API consumption, and other high-volume workloads.

Metric ChatGPT (OpenAI) Claude (Anthropic)
Weekly Active Users > 900 million 40-60 million
Annualized Revenue $30-40 billion $40-50 billion
Consumer Subscriptions $10-13 billion <$2 billion

Enterprise Focus Drives Monetization

Dayaratna noted that usage scale tells a different story from revenue mix, with Claude’s business appearing far more weighted toward enterprise and developer use cases than consumer adoption. In an emailed statement, Anthropic pointed to a May 2026 blog post announcing that its annualized run-rate revenue had surpassed $47 billion. Separate estimates from Counterpoint Research support this trend, indicating Anthropic captured 31.4% of global LLM revenue in Q1 2026, ahead of OpenAI’s 29% share. Counterpoint also estimated Anthropic generated the highest average monthly revenue per user at $33.3, significantly above OpenAI’s $2.2.

Financial Performance and Valuation

Earlier reports indicated OpenAI generated $5.7 billion in revenue during the first quarter of 2026 while spending approximately $3.7 billion. The company’s net loss reportedly expanded to roughly $39 billion in 2025, compared with about $5 billion the previous year. Both OpenAI and Anthropic have confidentially begun laying the groundwork for potential future public offerings. In May, Anthropic surpassed OpenAI to become the world’s most valuable startup after securing a $65 billion Series H funding round that valued the company at $965 billion.

Can OpenAI successfully pivot its massive consumer base to higher-margin enterprise products to close the revenue gap?

How will Anthropic's enterprise-heavy monetization strategy influence its infrastructure costs and profitability compared to OpenAI's consumer model?

Will Anthropic maintain its lead in global LLM market share as OpenAI intensifies its focus on developer and API revenue streams?

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Anthropic report reveals rise of AI agents in work

1 min read     Updated on 27 Jun 2026, 03:40 AM
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Reviewed by
Radhika SScanX News Team
AI Summary

Anthropic's new Economic Index highlights a shift from chatbots to autonomous AI agents in work environments. The report details usage patterns linked to daily life and rising expectations for AI capabilities.

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Anthropic released a new edition of its Economic Index, revealing a significant shift in how artificial intelligence is reshaping work as users increasingly rely on autonomous AI agents rather than traditional chatbot interactions. The report highlights that Claude usage has evolved over the past year, with long-running agentic tasks through Claude Code and Cowork becoming more prevalent. This shift has prompted Anthropic to alter its measurement of AI adoption, incorporating higher-frequency data sampling, new conversation classifiers, and the April launch of its first Economic Index Survey.

The findings indicate that AI usage closely mirrors the rhythms of daily life and work. Business-related conversations decrease on weekends, while personal uses—such as emotional support, medical questions, and investment advice—see an increase. Additionally, tax-related requests surge ahead of the U.S. filing deadline, recipe requests peak around dinner time, and sleep-related questions spike before dawn.

Anthropic also observed that Claude's outputs are becoming increasingly sophisticated. Explanations, documents, and reports are the most common deliverables. Conversations linked to higher-paying occupations generally consume more computing power, suggesting that AI is being utilized for more complex knowledge work.

Survey results show that respondents broadly expect AI capabilities to advance rapidly over the next year. More than one-third believe AI will be able to perform most or nearly all of their work tasks within 12 months, while nearly six in 10 expect AI to handle a larger share of their workload than it does today.

Despite concerns about automation, Anthropic noted that users who delegate more work to Claude are generally the most optimistic about AI's impact on their careers. Heavy AI users are more likely to anticipate improvements in pay, job security, and job opportunities. Majorities also reported productivity gains, increased learning, and greater confidence that AI is making their skills more valuable.

The company stated that these findings suggest workers continue to view AI primarily as a collaborative tool rather than a replacement. Many respondents expressed hopes that AI will automate repetitive work, allowing humans to focus on more meaningful tasks.

How will the shift toward autonomous agents impact the pricing models and infrastructure costs for AI providers?

Will the disparity in computing power usage between high and low-paying occupations exacerbate the digital divide in the workforce?

What specific safeguards will be necessary as users increasingly rely on AI for sensitive personal advice like medical and financial planning?

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