In September 2024, there were over ** million mobile generative AI users in China which translated to a penetration rate of almost ** percent. Most generative AI users were younger than 34 years.

As of 2024, the******** share of generative AI users in China were under 30 years old. Using artificial intelligence was very popular among university students and Baidu's Ernie was the most used LLM in China.

According to surveys conducted in April and May 2024, ** percent of respondents in China who used generative AI products worked as regular employees. Students accounded for ********* of the surveyed users. In addition, ** percent of Gen AI users lived in first-tier cities.

In September 2024, 24.5 million users accessed the Chinese AI chatbot Kimi Shot via the web access. Along with Baidu, the generative artificial intelligence by the Beijing-based company Moonshot AI is one of the country's leading enterprises in this segment. It is backed by powerful tech-companies, such as Alibaba, Tencent, and Meituan.

In a survey conducted in China in May 2024, ** percent of respondents who used generative AI were looking for an assistant that can take over everyday life's tasks. However, at the same time, ** percent of users also stated that the artificial intelligence still needed to enhance their cognitive abilities.

This dataset comprises responses from 1,053 online participants surveyed across China. Collected through social media and email distributions, it includes detailed queries about the usage and perceptions of generative AI, categorized into task-oriented and social-oriented applications. The dataset features demographic variables (age, gender, education, location) alongside indicators measuring both offline and online social capital. Analysis reveals that generative AI users generally possess higher social capital than non-users, with task-oriented usage enhancing offline social capital yet detracting from online interactions. Conversely, socially-oriented usage boosts social capital across both spheres. This data, rich with insights on the interplay between technology use and social structures, is pivotal for understanding technological impacts on societal dynamics and could guide future technology policy and integration strategies.

DeepSeek is currently leading the generative AI race in China, totaling over *** million monthly active users in March 2025. The second place went to Doubao, an artificial intelligence-generated content app owned by ByteDance, with almost *** million monthly active users. After surpassing Baidu's Ernie Bot in November 2023 for the first time, Doubao was gaining traction. Tencent's Yuanbao followed far behind, whereas former champion Ernie Bot's slipped to *****.

As of September 2024, the penetration rate of mobile generative AI users in China amounted to almost ** percent. The absolute number of mobile GenAI users exceeded ** million.

The deployment of artificial intelligence (AI) in mental health services has increased substantially, yet the specific AI characteristics that influence mental health help-seeking behaviors remain unclear. Drawing on dual-process theory and the Heuristic-Systematic Model (HSM), this study examines the mechanisms by which various characteristics of generative AI influence the intention to seek help. Analyzing data from 619 generative AI users in China, the findings indicate that systematic cues, including perceived intelligence and AI compatibility, along with heuristic cues, including anthropomorphism and likability, positively affect help-seeking intentions through both cognitive and emotional trust pathways. Notably, emotional trust exerts a stronger influence on help-seeking intentions than cognitive trust and can enhance cognitive trust, introducing a potential bias effect. Moreover, perceived privacy risks negatively moderate the mediating effects of cognitive and emotional trust between heuristic and systematic cues and help-seeking intention. Additionally, while users' trust positively correlates with help-seeking behavior, the perceived threat posed by AI can negatively moderate this relationship. This study not only enriches the theoretical discourse surrounding the attributes of generative AI and their impact on mental health help-seeking but also offers practical insights for the design and utilization of AI technologies to enhance mental health support.

As of 2024, over ** percent of respondents stated that they used generative AI to answer questions. Chatbots can conduct a human-like conversation with the user. Large language models and natural language processing mimics a human response.

The Asia Pacific Generative AI in BFSI Market would witness market growth of 33.4% CAGR during the forecast period (2024-2031). The China market dominated the Asia Pacific Generative AI in BFSI Market by Country in 2023, and would continue to be a dominant market till 2031; thereby, achieving a mar

China Digital Transformation Market Report Segments the Industry Into by Type (Analytics, Artificial Intelligence and Machine Learning, Extended Reality (XR), Generative AI Platforms and More), End-User Industry (Manufacturing, Oil, Gas and Utilities, Retail & E-Commerce and More), Deployment Model (On-Premise, Public Cloud, Hybrid Cloud), Enterprise Size (Large Enterprises, Small and Medium Enterprises (SMEs)).

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AI In Simulation Market Size 2025-2029

The AI in simulation market size is forecast to increase by USD 32.49 billion at a CAGR of 21.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the imperative of digital transformation and the rise of intelligent digital twins. Companies are increasingly adopting AI in simulation to optimize operations, improve product design, and enhance customer experiences. A key trend in this market is the proliferation of generative AI for design exploration and democratization, enabling organizations to create more realistic and complex simulations. Data analytics platforms facilitate the integration of AI models, enhancing risk assessment and experiment design. However, the market also faces challenges, including data scarcity, quality, and computational cost. The ability to access and process large, high-quality datasets is crucial for generating accurate and meaningful simulations.
Companies seeking to capitalize on the opportunities presented by the market must address these challenges effectively, investing in data acquisition and management strategies, as well as advanced computing technologies. By doing so, they can gain a competitive edge and unlock new opportunities for innovation and growth. Virtual classrooms and engaging learning through augmented reality and virtual reality are revolutionizing medical education, while online learning platforms facilitate professional development and continuing education.

What will be the Size of the AI In Simulation Market during the forecast period?

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In the market, predictive modeling and data mining are key techniques used to extract valuable insights from vast amounts of data. Model reduction and parameter estimation help streamline complex simulations, enhancing simulation speed and model fidelity. Model governance ensures the accuracy and reliability of simulation models, while scenario planning uses what-if analysis and causal inference to explore potential outcomes. Mixed reality, augmented reality, and virtual reality technologies bring an engaging experience to simulation, enabling more effective training and design of experiments. Simulation lifecycle management, including error propagation and simulation debugging, is crucial for maintaining simulation accuracy throughout the development process.

Prescriptive analytics and decision support systems leverage simulation results to provide actionable recommendations, while time series analysis helps identify trends and patterns in data. Interactive and engaging simulations allow users to test different scenarios and make informed decisions. Simulation interoperability and code verification are essential for ensuring seamless integration and accuracy across various simulation platforms. Big data analytics and simulation accuracy go hand in hand, with the former providing the data and the latter ensuring its accurate representation in simulations. In the realm of AI in simulation, these technologies continue to evolve, driving innovation and improving the efficiency and effectiveness of various industries.

How is this AI In Simulation Industry segmented?

The AI in simulation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Technology

  Simulation modeling
  Predictive and prescriptive analytics
  PaaS
  Others


Deployment

  Cloud
  On premise


End-user

  Automotive
  Manufacturing
  Infrastructure
  Education
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Technology Insights

The Simulation modeling segment is estimated to witness significant growth during the forecast period. Simulation modeling, a crucial market segment, entails creating computational models that mirror real-world systems or processes. Traditionally, this labor-intensive and specialized task is undergoing a profound transformation through artificial intelligence (AI) integration. AI streamlines and accelerates various stages of model creation. In generative design, AI algorithms explore numerous design possibilities based on user-defined constraints like weight, material, and performance criteria, autonomously generating optimized geometries that human engineers might not conceive. This transcends simple parameter optimization, leading to the fundamental creation of innovative forms. Cloud-based simulation and robotics simulation are integral components, enabling real-ti

In January 2023, less than six million users were using artificial intelligence generated content apps actively. Fast-forward to July 2024, the monthly active user base hit 66.3 millions. The largest jump in user numbers were observed in June 2024.

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Low-Code AI Platform Market Size 2025-2029

The low-code AI platform market size is forecast to increase by USD 32.26 billion at a CAGR of 32.2% between 2024 and 2029.

The market is experiencing significant growth as the democratization of artificial intelligence (AI) continues to gain momentum. This trend is driven by the increasing need to address the acute talent scarcity in the tech industry, enabling more organizations to leverage AI technologies without requiring extensive expertise. Simultaneously, generative AI is becoming increasingly pervasive as a co-developer and application component, further expanding the market's potential. However, the market also faces challenges, with governance, security, and management of shadow IT emerging as critical concerns.
As more teams integrate AI into their workflows, ensuring proper oversight and compliance becomes essential to mitigate risks and maintain data security. Companies seeking to capitalize on the market opportunities must prioritize addressing these challenges effectively while continuing to innovate and adapt to the evolving technological landscape. Algorithm selection criteria, feature engineering tools, and continuous integration are vital components of the model building process.

What will be the Size of the Low-Code AI Platform Market during the forecast period?

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The market is witnessing significant advancements in workflow automation engines and AI model management. Model versioning systems enable seamless DevOps integration, facilitating deployment pipelines and automated model building. Model explainability techniques are gaining traction, ensuring transparency and trust in AI-driven insights. Continuous delivery and user experience design are crucial elements, as is a model retraining strategy for maintaining optimal performance. Application security testing and agile development methodology are essential for secure and efficient development.

Performance monitoring tools, data validation procedures, testing automation frameworks, data governance frameworks, data security protocols, data preprocessing techniques, business intelligence tools, user interface design, and AI bias detection are all integral parts of a comprehensive low-code AI platform. Incorporating artificial intelligence and machine learning capabilities, these platforms enable advanced data visualization, performance optimization, and mobile application development.

How is this Low-Code AI Platform Industry segmented?

The low-code AI platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Platforms
  Services


Technology

  Natural language processing
  Machine learning
  Computer vision


Deployment

  Cloud
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The Platforms segment is estimated to witness significant growth during the forecast period. The market is witnessing significant growth as businesses seek to streamline development processes and integrate advanced AI capabilities into their applications. The platforms segment is the market's cornerstone, providing users with a visual integrated development environment for designing, building, deploying, and managing AI applications with minimal coding. These platforms offer a suite of pre-trained AI models for tasks like natural language processing, computer vision, and predictive analytics. Additionally, they provide robust data integration functionalities, enabling seamless connections to various enterprise systems, databases, and APIs. Moreover, these platforms facilitate scalable infrastructure, ensuring applications can handle increasing workloads. They offer tools for process automation, version control, and collaboration, enabling teams to work together efficiently.

Real-time data processing and debugging capabilities are also essential features, allowing for quick issue resolution. Application lifecycle management, model training pipelines, and no-code development environments are other integral components. Security is a priority, with secure data storage and citizen development tools ensuring data privacy and access control. Customizable dashboards and workflow orchestration offer enhanced usability, while code generation engines and API integration frameworks enable seamless application expansion. Cloud-based deployment, automation, and collaboration platforms further enhance the value proposition

In September 2023, three in ten early adopters of AI-generated content apps in China were from the first and new first-tier cities. Baidu is leading the generative AI in China with its chatbot Ernie Bot.

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AI API Market Size 2025-2029

The AI API market size is forecast to increase by USD 78.45 billion at a CAGR of 22% between 2024 and 2029.

The market is experiencing significant growth as the democratization of advanced AI capabilities through API models gains momentum. This shift enables businesses of all sizes to integrate AI functionality into their operations without requiring extensive expertise or resources. A key trend in the market is the move towards multimodality and native integration, allowing for seamless interaction between various AI technologies and applications. The emergence of generative AI at the edge is a significant driver, enabling advanced analytics and decision-making capabilities in industries such as manufacturing, healthcare, and transportation.
However, the market faces challenges as concerns over data privacy, model governance, and ethical risks continue to mount. Companies must navigate these obstacles effectively to capitalize on the opportunities presented by the market and maintain customer trust. By focusing on transparency, security, and ethical AI implementation, businesses can differentiate themselves and thrive in this dynamic landscape. Data security protocols are crucial in the edge infrastructure landscape, with predictive maintenance and thermal management systems ensuring operational efficiency and reliability.

What will be the Size of the AI API Market during the forecast period?

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In the dynamic API market, reliability metrics and performance optimization are crucial factors for businesses seeking to deliver high-quality digital experiences. API response tracing and request validation help identify and address performance issues, ensuring optimal API runtime environments. Error handling and change management are essential for maintaining API uptime and minimizing downtime. API deployment pipelines and testing methodologies streamline the development process, enabling continuous integration and delivery. Monitoring dashboards provide real-time insights into API usage, while data modeling and schema validation ensure data accuracy and consistency.

Security compliance is a top priority, with event handling and API infrastructure costs shaping the market landscape. API documentation standards facilitate developer onboarding and adoption, while caching techniques and scalability strategies optimize API performance. API management platforms offer comprehensive solutions, integrating testing automation, traffic analysis, and response formatting capabilities. Ultimately, the API market is driven by the evolving needs of businesses, requiring adaptable solutions that prioritize reliability, performance, and security.

How is this AI API Industry segmented?

The AI API industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Usage

  Generative AI API
  Text and NLP API
  Image and vision API
  Speech and voice API
  Predictive analytics API


Deployment

  Cloud-based API
  Edge and on-premises API


End-user

  Healthcare and life sciences
  BFSI
  Retail and e-commerce
  Media and entertainment
  Telecom and government sector


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Usage Insights

The Generative AI API segment is estimated to witness significant growth during the forecast period. The API market is witnessing significant advancements with the integration of Generative AI APIs, a transformative function that creates new, synthetic, and original content based on user prompts and underlying patterns learned from extensive training datasets. This functionality spans various intellectual tasks, such as generating human-like text, computer code, novel images, music, and spoken audio. Businesses are capitalizing on this capability to automate and scale content creation, accelerate research and development, and build innovative applications. The API landscape encompasses several key components, including data transformation, microservice architecture, data validation, traffic routing, governance policies, performance testing, documentation generator, AI-powered testing, mocking frameworks, and security auditing.

Generative AI APIs represent the most transformative and rapidly advancing functionality within the AI API market. Their core function is not to analyze existing data but to create new, synthetic, and original content based on user prompts and underlying patterns learned from vast

Generative AI is on its way to becoming a business staple, and its adoption is deepening in China. In 2024, over ********** of companies have utilized generative AI. The automotive, consumer goods, and media and entertainment industries saw the highest uptakes of generative AI in marketing segments.

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Personal AI Assistant Market Size 2025-2029

The personal AI assistant market size is forecast to increase by USD 9.31 billion at a CAGR of 33.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the transformative impact of advanced generative AI and large language models. These technologies enable assistants to understand and respond to complex queries, learn from user interactions, and even generate human-like text and speech. This enhances the user experience, making personal assistants increasingly indispensable in both personal and professional settings. However, the mainstreaming of these advanced technologies also brings challenges.
Ensuring robust data protection measures and transparent user consent practices are crucial for companies to build trust and maintain user loyalty. Companies must navigate these challenges while capitalizing on the market's potential by focusing on user experience, innovation, and ethical data handling. Data security and privacy vulnerabilities intensify as AI assistants collect and process vast amounts of personal information.

What will be the Size of the Personal AI Assistant Market during the forecast period?

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The market is experiencing significant advancements, with key areas of focus including prompt engineering, platform compatibility, and data governance policies. Reinforcement learning and cross-platform support are driving innovation, enabling seamless interaction across various devices. Performance benchmarking and cost optimization strategies are essential for ensuring efficiency and competitiveness. Knowledge base management and feedback loop mechanisms facilitate continuous learning, while scalability challenges are being addressed through transfer learning and algorithm efficiency.

Localization strategies and deployment strategies cater to diverse markets, with internationalization support and compliance regulations ensuring accessibility and legal compliance. API integration methods, language model training, system integration, and model evaluation metrics are crucial for enhancing functionality and user experience. Error handling mechanisms and zero-shot learning are also gaining importance in the development of advanced AI assistants. Artificial intelligence (AI) and machine learning (ML) are powering personalized learning paths, enabling learners to progress at their own pace.

How is this Personal AI Assistant Industry segmented?

The personal AI assistant industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Chatbot
  Smart speaker
  Others


Technology

  Natural language processing
  Machine learning
  Text-based


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The Chatbot segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, with the chatbot sub-segment leading the way. This evolution from rule-based scripts to conversational agents powered by generative artificial intelligence is a major technology shift. The driving force behind this transformation is the rapid progress and expanding availability of large language models (LLMs), enabling the creation of chatbots that exhibit contextual understanding, human-like text generation, and complex reasoning capabilities. This democratization of chatbot development fosters an innovative ecosystem. Engaging virtual reality (VR) and augmented reality (AR) language learning videos are gaining traction, providing users with authentic language experiences.

Major technology corporations are intensely competing to dominate the market by continually enhancing their foundational models and integrating them into their product offerings. Advanced features such as bias mitigation, user experience optimization, dialogue management, personalization techniques, deep learning algorithms, knowledge graph integration, common sense reasoning, text summarization, conversational AI, named entity recognition, machine learning models, and speech recognition are shaping the market's dynamics. However, the advent of advanced LLMs has revolutionized these assistants, enabling them to comprehend nuance, context, and complex intent. Key technologies driving this market include personalization techniques, deep learning algorithms, knowledge graph integration, text summarization, conversational AI,

In August 2024, ******* unique devices used the Chinese AI image generators. Compared to other segments in the generative artificial intelligence industry in China, the image processing segment has comparably few users. The most popular types of GenAI are chatbots, such as Baidu's Wenxiaoyan or Kimi Shoot.