Weekly active user statistics for ChatGPT from January 2023 to April 2025.

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

The AI chatbot race between Claude and ChatGPT is reshaping how people work and communicate. Whether drafting business code or fast-tracking creative tasks, these tools power productivity and insight across industries. For instance, enterprise teams integrate Claude’s advanced reasoning into analytics platforms, while marketers tap ChatGPT for on-demand content ideation....

Annual users of ChatGPT worldwide are expected to grown considerably in the coming months. In 2022, around 57 million people used ChatGPT, with this number increasing to roughly 100 million by January 2023.

A comparative table of response and citation characteristics for ChatGPT and Google AI Overview in 2025, including response length, sentence length, number of sources, duplication rate, domain overlap, subjectivity, readability, AI marker usage, domain age share, and semantic similarity.

The rivalry between ChatGPT and Google Gemini defines the generative AI landscape. ChatGPT remains the leader in active engagement, while Gemini closes the gap through mass distribution. From corporate reports to web traffic studies, figures speak clearly about adoption, reach, and momentum. Explore what makes each platform stand out, and what...

Most Used Features and Usage Statistics By September 2025, ChatGPT boasts ~800 million weekly users and 355 million monthly active users, with 2.6 billion daily messages across 700 million users. It's the #1 AI tool globally (60.6% market share), especially among under-25s (45% of users). Daily usage: 9% of 18-24-year-olds. Top use cases (from OpenAI's 2025 user study):

Everyday Productivity (52% of sessions): Email drafting, brainstorming, task lists. Content Creation/Writing (28%): Essays, social media, code snippets. Research & Learning (15%): Summaries, explanations, tutoring. Coding/Development (12%): Debugging, automation scripts. Creative Tools (e.g., Image Gen in GPT-4o/5, 10%): Art, voice chats.

Enterprise adoption hit 80% of Fortune 500 companies by mid-2025, with mobile apps driving 40% of traffic. Efficiency Metrics Efficiency in ChatGPT refers to speed, cost, productivity gains, and resource use. GPT-5 processes queries 3x faster than GPT-4o (under 1s for simple tasks) while using 50% less compute. Key metrics:

Analysis of 13,252 publicly shared ChatGPT conversations by WebFX to uncover usage statistics - prompt length, message count, question vs command distribution, use-case categories.

In February 2025, ChatGPT was the most popular artificial intelligence (AI) application worldwide, with over 400.61 million monthly active users (MAU). The ByteDance-owned chatbot Doubao had around 81.91 million MAU, making it the most popular Chinese-based tool of this kind. ChatGPT-operated Nova Assistant ranked third with 62.79 million MAU and was followed by Chinese-based DeepSeek with around 61.81 million MAU.

In January 2024, ChatGPT online domain chat.openai.com registered over **** percent of its traffic as originating in the United States. Users based in India generated approximately **** percent of the total visits to the chatbot platform, while users in Indonesia accounted for *** percent of the total visits to the website. Visits from Brazil represented the fourth-largest group for the platform, generating more than **** percent of the total traffic recorded in the examined period.

The dataset is focused on exploring the relationship between the performance of Chegg's stock prices and the growth of ChatGPT users over time. Chegg is an education technology company that provides online learning resources. The company has experienced significant growth in recent years, driven in part by COVID-19.

However, Chegg's stock price dropped due to the shift of some of its users from Chegg's platform to ChatGPT. This shift in user behavior can be attributed to ChatGPT's advanced AI capabilities, which allow it to provide personalized and accurate assistance to users.

The dataset includes five tables that provide valuable insights into the relationship between Chegg stock prices and ChatGPT user growth, with a particular focus on the impact of the user shift on Chegg's stock performance. The first three tables contain weekly, monthly, and daily data on Chegg's stock performance, including information on the opening and closing prices, highest and lowest prices, and trading volume. These tables also include information on significant events that may have impacted the company's stock prices, such as product launches, partnerships, and earnings reports.

The fourth table provides data on the number of ChatGPT users recorded over the past months. This table includes information on the total number of users, as well as data on user growth rates and trends. The data in this table can be used to identify correlations between ChatGPT user growth and changes in Chegg's stock performance.

The fifth and final table provides the latest updates on ChatGPT, including information on new features, updates, and user feedback. This table is designed to keep the dataset current and relevant, providing users with the latest information on ChatGPT and its impact on Chegg's stock performance.

Overall, this dataset provides a valuable resource for anyone interested in understanding the impact of user behavior on the stock performance of companies like Chegg that operate in the education technology sector. It offers a comprehensive view of the data and trends over time, which can be used to identify patterns and correlations that can inform investment decisions and strategic planning.

Global AI Tool Adoption Across Industries and Regions (2023–2025)

A comprehensive, research-grade dataset capturing the adoption, usage, and impact of leading AI tools—such as ChatGPT, Midjourney, Stable Diffusion, Bard, and Claude—across multiple industries, countries, and user demographics. This dataset is designed for advanced analytics, machine learning, natural language processing, and business intelligence applications.

Dataset Overview

This dataset provides a panoramic view of how AI technologies are transforming business, industry, and society worldwide. Drawing inspiration from real-world adoption surveys, academic research, and industry reports, it enables users to:

• Analyze adoption rates of popular AI tools across regions and sectors.
• Study user demographics and company profiles influencing AI integration.
• Explore textual user feedback for sentiment and topic modeling.
• Perform time series analysis on AI adoption trends from 2023 to 2025.
• Benchmark industries, countries, and company sizes for AI readiness.

To add a column descriptor (column description) to your Kaggle dataset's Data Card, you should provide a clear and concise explanation for each column. This improves dataset usability and helps users understand your data structure, which is highly recommended for achieving a 10/10 usability score on Kaggle[2][9].

Below is a ready-to-copy Column Descriptions table for your dataset. You can paste this into the "Column Descriptions" section of your Kaggle Data Card (after clicking the pencil/edit icon in the Data tab)[2][9]:

Column Descriptions

Example Data

country,industry,ai_tool,adoption_rate,daily_active_users,year,user_feedback,age_group,company_size
USA,Technology,ChatGPT,78.5,5423,2024,"Great productivity boost for our team!",25-34,Enterprise
India,Healthcare,Midjourney,62.3,2345,2024,"Improved patient engagement and workflow.",35-44,SME
Germany,Manufacturing,Stable Diffusion,45.1,1842,2023,"Enhanced our design process.",45-54,Enterprise
Brazil,Retail,Bard,33.2,1200,2024,"Helped automate our customer support.",18-24,Startup
UK,Finance,Claude,55.7,2100,2023,"Increased accuracy in financial forecasting.",25-34,SME

How to Use This Dataset

1. Load and Preview the Data

import pandas as pd

df = pd.read_csv('/path/to/ai_adoption_dataset.csv')
print(df.head())
print(df.info())

2. Analyze Adoption Rates by Industry and Country

industry_adoption = df.groupby(['industry', 'country'])['adoption_rate'].mean().reset_index()
print(industry_adoption.sort_values(by='adoption_rate', ascending=False).head(10))

3. Visualize AI Tool Popularity

import matplotlib.pyplot as plt

tool_counts = df['ai_tool'].value_counts()
tool_counts.plot(kind='bar', title='AI Tool Usage Distribution')
plt.xlabel('AI Tool')
plt.ylabel('Number of Records')
plt.show()

4. Sentiment Analysis on User Feedback

from textblob import TextBlob

df['feedback_sentiment'] = df['user_feedback'].apply(lambda x: TextBlob(x).sentiment.polarity)
print(df[['user_feedback', 'feedback_sentiment']].head())

5. Time Series Analysis of Adoption Trends

yearly_trends = df.groupby(['year', 'ai_tool'])['adoption_rate'].mean().unstack()
yearly_trends.plot(marker='o', title='AI Tool Adoption Rate Over Time')
plt.xlabel('Year')
plt.ylabel('Average Adoption Rate (%)')
plt.show()

**6. Demographic Insights*...

This research analyzes the factors that affect university students in the Jabodetabek area to continue using ChatGPT in their learning process. With the fast adoption of generative AI tools in education, ChatGPT is one of several platforms that has become widely used in the world for academic assistance. For the sustainability of ChatGPT usage not only gets affected by technical capabilities but also by psychological and social factors. This research used extended Technology Acceptance Model (TAM) by combining mediating variables like Perceived Trust and Satisfaction, and external variables like Competence and Social Influence. 415 valid respondents who are active university students and ChatGPT users collected with quantitative methods. SMART Partial Least Squares Structural Equation Modeling (PLS-SEM), SmartPLS 4 were used to analyze the data. All hypotheses proposed are significant statistically, with the most influential factors are Perceived Usefulness and Competence. Satisfaction and Trust serve as critical mediators, and Continuous Usage Behavior is also affected by Social Influence. The results found that the importance of improving digital competence of the students, trust, and fostering ethical AI usage in academic policies and support systems, provides valuable implications for future studies and academic exploration.

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI’s effectiveness depends on the data it’s fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights ’Competent + Trustworthy + ChatGPT for healthcare queries’, underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes ’Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive’. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized.

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.