These insights—derived from over a billion anonymized interactions—offer an unprecedented glimpse into the behaviors, preferences, and patterns that shape AI adoption.

Insights from ChatGPT and Claude Usage Studies – Lessons for AI Commercialization

by ChatGPT-4o

The recent release of large-scale empirical studies by OpenAI and Anthropic marks a watershed moment in understanding how large language models (LLMs) are used by consumers and businesses alike. These insights—derived from over a billion anonymized interactions—offer an unprecedented glimpse into the behaviors, preferences, and patterns that shape AI adoption. They also provide crucial lessons for businesses seeking to commercialize AI products and services.

This essay analyzes the key findings from:

• “How People Use ChatGPT” (NBER Working Paper No. 34255)

• Anthropic Economic Index Report (Sept 2025)

• OpenAI’s blog post

We highlight the most surprising, controversial, and valuable findings, and conclude with strategic lessons for AI commercialization.

1. Surprising Findings

a. Non-Work Use Dominates ChatGPT

Despite the hype around AI in enterprise productivity, over 70% of ChatGPT consumer usage is for non-work purposes—and that share is increasing. This growth is not driven by new users, but by existing users shifting toward personal use, suggesting long-term integration of LLMs into daily life.

b. Low Coding Usage Despite Developer Buzz

Only 4.2% of ChatGPT conversations involve coding, compared to over 30% for Anthropic’s Claude in some markets. This contradicts the common perception that generative AI is mostly used by programmers and reveals broader consumer appeal beyond technical domains.

c. Directive Use (Automation) Surpasses Collaborative Use (Augmentation)

On Claude, automation-focused usage now exceeds augmentation-focused usage for the first time, with directive prompts (asking Claude to complete a task outright) rising from 27% to 39% over just eight months. This shift signals increasing user trust in AI's capabilities.

2. Controversial Findings

a. Geographic Inequality in AI Adoption

The Anthropic AI Usage Index (AUI) reveals stark disparities: wealthy countries like Israel, Singapore, and South Korea have adoption rates 3x to 7x higher than expected by population size, while large emerging economies like India and Nigeria lag far behind. This suggests AI may widen the digital and economic divide, countering earlier narratives of AI democratization.

b. High-Income Enterprise AI Users Show Less Automation

Contrary to expectations, business users in advanced economies use AI more for augmentation (human-in-the-loop workflows), while those in lower-tier economies focus more on automation. This raises ethical and socioeconomic questions around job displacement in emerging markets.

c. Price Sensitivity Is Weak for Enterprises

Anthropic found that more expensive tasks are used more frequently in their API traffic, suggesting that capability—not cost—is the primary driver of enterprise adoption. This counters standard economic assumptions and complicates pricing strategy.

3. Valuable Findings

a. Writing Is the Killer App for Work

Writing accounts for 40% of ChatGPT work-related tasks, especially editing, summarizing, and translation. Surprisingly, only one-third of this writing is generated from scratch—most is human-provided text that AI improves. This shows that LLMs augment rather than replace human writing.

b. Decision Support as Primary Economic Value

Across both studies, “Asking” interactions (seeking judgment, insight, or clarification) account for nearly half of usage. This positions LLMs as decision-support tools, especially in knowledge-intensive professions like consulting, finance, and education.

c. Consumer Surplus is Large and Growing

OpenAI estimates a $97 billion consumer surplus in the U.S. in 2024 alone from ChatGPT use. This figure, while imperfect, underscores the massive latent value being created—often outside traditional productivity metrics like GDP.

4. Lessons for Businesses Commercializing AI

A. Prioritize Everyday Use Cases, Not Just Enterprise

The largest and fastest-growing segment of LLM users are individuals solving everyday problems, not just professionals. Tools that help with personal productivity, creative expression, and practical life tasks are vital.

Implication: Don’t underestimate the consumer market. “Prosumer” tools with broad usability (e.g., planning, summarizing, tutoring) can drive massive adoption.

B. Embed AI Where Writing and Decision-Making Intersect

Since writing and decision support dominate usage, AI tools should embed LLMs into knowledge work tools, not just search or automation systems.

Implication: Invest in verticalized solutions for education, research, law, medicine, and consulting—fields where writing and thinking are tightly interwoven.

C. Balance Augmentation and Automation

High-trust users are increasingly delegating tasks end-to-end to AI. Businesses must decide whether to support human-in-the-loop augmentation or fully automated workflows.

Implication: Build toggles into your AI product that let users choose between assisted vs. fully autonomous execution, based on context and trust levels.

D. Consider Regional Strategy Carefully

Claude and ChatGPT show that usage patterns vary dramatically by geography—in both volume and type of tasks.

Implication: Tailor marketing, onboarding, and even model tuning to regional user needs. Emerging markets may adopt AI differently—focusing on job-seeking, coding, or education.

E. Accept Unequal Adoption and Plan Responsibly

Current patterns suggest AI may widen economic inequality if left to market forces. Businesses must prepare for scrutiny over social impact, particularly around automation in vulnerable economies.

Implication: Proactively invest in education, upskilling, and responsible deployment policies to ensure long-term trust and equitable growth.

F. Capabilities Matter More Than Cost (For Now)

Enterprise buyers prioritize what AI can do, not necessarily how cheap it is.

Implication: Focus on clear performance gains and business outcomes when marketing AI products, not just price advantages.

Conclusion

These groundbreaking studies from OpenAI and Anthropic provide businesses with a detailed map of the AI adoption landscape. While media narratives often focus on dramatic claims about job loss or exponential intelligence, the real story lies in how people actually use these tools—for writing, problem-solving, learning, and guidance.

The most valuable commercial opportunities lie in augmenting human capabilities, personalizing everyday interactions, and supporting smarter decision-making. However, AI vendors and platform operators must also grapple with the geographic inequality of adoption, the shifting balance between automation and augmentation, and the evolving preferences of a global user base.

Ultimately, the commercialization of AI will be most successful when it combines technical excellence with human insight, localized strategy, and ethical foresight.