大规模人工智慧模型市场报告：趋势、预测和竞争分析（至 2031 年）

全球大规模人工智慧模型市场前景光明，教育、能源、汽车和医疗保健市场都存在机会。预计全球大规模人工智慧模型市场在 2025 年至 2031 年期间的复合年增长率将达到 28.5%。该市场的主要驱动力是对用于复杂任务的强大大规模语言模型的需求不断增长、人工智慧工程师和资料科学家的崛起，以及这些模型在医疗保健、金融和汽车等行业中的日益普及。

• Lucintel 预测，按类型划分，基于自然语言处理的模型将在预测期内实现最高的成长。
• 在应用方面，预计教育领域将出现最高的成长。
• 根据地区来看，预计亚太地区将在预测期内实现最高成长。

大规模人工智慧模型市场的策略性成长机会

大规模人工智慧模型市场为各种应用提供了许多成长机会。识别并利用这些机会可以推动创新和竞争优势。以下是人工智慧模型市场的五个关键成长机会，每个机会都有可能影响不同的产业。

• 医学诊断和个人化医疗：大规模人工智慧模式为医疗保健领域的诊断和个人化医疗提供了巨大潜力。透过分析大量医疗资料，这些模型可以帮助早期发现疾病、制定个人化治疗方案和药物发现。成长机会在于将人工智慧融入医疗保健系统，以提高医疗程序的准确性和效率。
• 自动驾驶汽车和智慧交通：人工智慧模型对于自动驾驶汽车和智慧交通系统的发展至关重要。先进的模型改善了车辆的感知、导航和决策流程。我们有机会利用人工智慧来提高安全性、优化交通管理并推动交通基础设施的创新，为未来的交通出行做出贡献。
• 金融服务与风险管理：在金融领域，大规模人工智慧模型正在改变风险管理、诈欺侦测和客户服务。透过分析金融交易和市场资料，人工智慧可以识别模式和异常，从而实现更准确的风险评估和个人化的金融服务。机会在于采用人工智慧来提高决策和业务效率。
• 零售和客户体验：人工智慧模型透过个人化客户体验和优化库存管理来增强零售业。大规模模型可以分析消费者的行为和偏好，以推动有针对性的行销并改善产品推荐。成长机会在于利用人工智慧创造更具吸引力的购物体验并简化零售业务。
• 工业自动化和预测性维护：人工智慧模型透过分析设备资料来预测故障和优化维护计划，从而推动工业自动化和预测性维护。该应用程式提高了业务效率并减少了停机时间。将人工智慧融入工业流程可以提高生产力并延长设备的使用寿命。

这些策略成长机会凸显了大规模人工智慧模型改变一系列产业的潜力。透过专注于医疗保健、交通、金融、零售和工业自动化等应用，公司可以推动创新并获得显着的竞争优势。人工智慧模型的持续开发和部署可能会塑造这些行业的未来并开闢新的成长途径。

大规模人工智慧模型市场驱动因素与挑战

大规模人工智慧模型市场受到各种驱动因素​​和挑战的影响，包括技术进步、经济因素和监管考量。了解这些因素对于驾驭市场和有效利用机会至关重要。这里我们重点介绍影响市场的关键驱动因素和挑战。

推动大规模人工智慧模型市场的因素：

• 技术进步：运算能力和模型架构的进步正在推动大规模人工智慧模型的成长。变压器网路和分散式学习技术等创新使得更复杂、更高效能的模式成为可能。这些技术改进将增强人工智慧的能力并扩大其潜在应用，刺激市场成长并吸引投资。
• 资料可用性的提高：资料可用性的大幅提升为训练大规模人工智慧模型提供了丰富的资源。存取多样化和广泛的资料可以提高模型的准确性和能力。这项催化剂将支持更有效的人工智慧解决方案的开发，并加速医疗保健、金融和零售等领域的创新。
• 自动化需求不断增长：製造业、金融业和物流业等行业对自动化的需求不断增长是采用人工智慧模型的主要驱动力。自动化可以提高效率、降低成本并提高各种流程的准确性。大规模人工智慧模型在实现先进的自动化解决方案、促进市场扩展和应用方面发挥关键作用。
• 投资和资金筹措：来自公共和私营部门的大量投资和资金筹措正在推动大规模人工智慧模型的进步。创业投资、政府津贴和企业投资正在支持研发，加速尖端人工智慧技术的开发和部署。这种财政支持是市场成长的主要动力。
• 全球竞争格局：人工智慧市场的竞争格局推动创新和发展。公司和国家正在投资大规模人工智慧模型，以保持竞争力并引领技术进步。这种压力推动着持续改进和创造更先进、更强大的人工智慧解决方案。

大规模AI模型市场面临的挑战如下：

• 监管和道德问题：监管和道德问题对大规模人工智慧模型市场构成了重大挑战。资料隐私、演算法偏见和透明度等问题正受到严格审查，从而导致严格的法规和道德准则的製定。遵守这些规定会影响创新速度并增加营运复杂性。
• 计算成本高：开发和部署大规模人工智慧模型需要大量的运算资源，这意味着高成本。它需要强大的硬体并消耗大量能源，这带来了财务和环境挑战。解决这些成本对于确保永续和可扩展的人工智慧解决方案至关重要。
• 人才短缺：缺乏熟练的人工智慧专业人员是一个市场挑战。开发大规模人工智慧模型的复杂性需要机器学习、资料科学和工程的专业知识。这种人才缺口可能会限制创新的步伐，并阻碍组织充分利用人工智慧技术的能力。

概述的市场驱动因素和挑战凸显了大规模人工智慧模型市场的动态性质。虽然技术进步、资料可用性和不断增长的自动化需求正在推动成长，但监管问题、高成本和人才短缺构成了重大障碍。了解这些因素对于驾驭市场和抓住新机会至关重要。

目录

第一章执行摘要

第二章 全球大规模人工智慧模型市场：市场动态

• 简介、背景和分类
• 供应链
• 产业驱动力与挑战

第三章市场趋势与预测分析（2019-2031）

• 宏观经济趋势（2019-2024）及预测（2025-2031）
• 全球大规模AI模型市场趋势（2019-2024）及预测（2025-2031）
• 全球大规模人工智慧模型市场（按类型）
  • 自然语言处理基础模型
  • 电脑视觉基础模型
  • 多模态基础模型
  • 其他的
• 全球大规模人工智慧模型市场（按应用）
  • 教育
  • 活力
  • 车
  • 医疗保健
  • 其他的

第四章区域市场趋势与预测分析（2019-2031）

• 全球大规模人工智慧模型市场（按地区）
• 北美大规模AI模型市场
• 欧洲大规模人工智慧模型市场
• 亚太大规模人工智慧模型市场
• 其他地区大规模人工智慧模型市场

第五章 竞争分析

• 产品系列分析
• 营运整合
• 波特五力分析

第六章 成长机会与策略分析

• 成长机会分析
  • 全球大规模人工智慧模型市场成长机会（按类型）
  • 全球大规模人工智慧模型市场成长机会（按应用）
  • 全球大规模人工智慧模型市场成长机会（按地区）
• 全球大规模AI模型市场新趋势
• 战略分析
  • 新产品开发
  • 扩大全球大规模AI模型市场的产能
  • 全球大规模人工智慧模式市场的企业合併
  • 认证和许可

第七章主要企业简介

• OpenAI
• Microsoft
• Google
• NVIDIA
• Alibaba
• Baidu
• Tencent
• Huawei
• Naver
• Anthropic

The future of the global large AI model market looks promising with opportunities in the education, energy, automotive, and medical markets. The global large AI model market is expected to grow with a CAGR of 28.5% from 2025 to 2031. The major drivers for this market are the increasing demand for powerful large language models for complex tasks, a growing pool of AI engineers & data scientists, and the rising use of this model in industries like healthcare, finance, and automotive.

• Lucintel forecasts that, within the type category, the natural language processing foundation model is expected to witness the highest growth over the forecast period.
• Within the application category, education is expected to witness the highest growth.
• In terms of regions, APAC is expected to witness the highest growth over the forecast period.

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Emerging Trends in the Large AI Model Market

The large AI model market is experiencing transformative shifts driven by several emerging trends. These trends reflect advancements in technology, changes in regulatory landscapes, and evolving market needs. Understanding these trends is crucial for stakeholders aiming to navigate the dynamic AI landscape and capitalize on emerging opportunities.

• Increased Multimodal Capabilities: Large AI models are increasingly incorporating multimodal capabilities, enabling them to process and integrate diverse data types such as text, images, and audio. This trend is driven by advancements in model architectures and training techniques, allowing for more sophisticated and context-aware AI systems. The result is enhanced performance in applications like autonomous vehicles, virtual assistants, and creative content generation.
• Focus on Ethical AI and Regulation: There is a growing emphasis on developing ethical AI frameworks and regulatory standards to address concerns about bias, transparency, and accountability. Governments and organizations are working to establish guidelines that ensure responsible AI use. This trend is reshaping the market by fostering trust and ensuring compliance, which is becoming a competitive differentiator for AI developers.
• Expansion of AI in Industry-Specific Applications: AI models are being increasingly tailored for specific industries such as healthcare, finance, and manufacturing. Industry-specific applications are driving demand for customized models that address unique challenges and requirements. This specialization allows for more effective solutions and drives growth in sectors where AI can provide significant operational improvements and innovations.
• Advancements in Model Efficiency and Accessibility: Innovations in model efficiency are making large AI models more accessible and cost-effective. Techniques such as model compression, pruning, and distributed training are reducing the computational resources required. This trend is democratizing access to advanced AI technologies, enabling smaller organizations and developing countries to leverage powerful AI solutions.
• Integration of AI with Edge Computing: The integration of AI with edge computing is enhancing real-time processing and reducing latency. By deploying AI models on edge devices, organizations can achieve faster data analysis and decision-making. This trend is particularly impactful for applications requiring immediate responses, such as autonomous systems and IoT devices.

These emerging trends are reshaping the large AI model market by enhancing capabilities, fostering ethical practices, and expanding applications across various industries. As AI technology continues to evolve, these trends will drive innovation and influence market dynamics, creating new opportunities and challenges for stakeholders.

Recent Developments in the Large AI Model Market

Recent developments in the large AI model market reflect rapid technological advancements and shifting market dynamics. These developments are shaping the future of AI by introducing new capabilities, addressing regulatory challenges, and influencing global competition. Here are five key developments impacting the market.

• Advancements in Transformer Architectures: Transformer architectures, such as GPT-4 and its successors, have significantly advanced the capabilities of large AI models in natural language understanding and generation. These models are setting new benchmarks in performance, enabling more nuanced and context-aware interactions. The advancements are driving improvements in applications like chatbots, content creation, and language translation.
• Growth of AI-as-a-Service (AIaaS): The rise of AI-as-a-Service platforms is transforming how organizations access and utilize large AI models. Providers like Microsoft Azure and Amazon Web Services offer scalable AI solutions without the need for extensive in-house infrastructure. This development is democratizing access to advanced AI technologies, allowing businesses of all sizes to leverage AI for various applications.
• Increased Focus on AI Ethics and Governance: The market is witnessing a heightened focus on AI ethics and governance, with organizations and governments developing frameworks to address issues related to bias, transparency, and accountability. Initiatives such as the EU's AI Act and industry-specific guidelines are shaping how large AI models are developed and deployed, ensuring responsible use and building public trust.
• Expansion into Emerging Markets: Large AI models are expanding into emerging markets, with significant investments in regions like Asia-Pacific and Latin America. This expansion is driven by growing digital infrastructure and increasing demand for AI solutions in sectors such as finance, healthcare, and retail. The market dynamics are shifting as companies adapt their strategies to cater to diverse regional needs.
• Innovations in Model Training and Deployment: New techniques in model training and deployment, such as federated learning and decentralized AI, are enhancing the efficiency and scalability of large AI models. These innovations allow for more secure and collaborative training processes while reducing the need for centralized data storage. They are enabling more personalized and adaptive AI solutions.

These key developments are driving significant changes in the large AI model market, influencing technology, accessibility, and governance. As the market evolves, these developments will continue to impact how AI is utilized and integrated into various sectors, shaping the future of AI technology and its applications.

Strategic Growth Opportunities for Large AI Model Market

The large AI model market presents numerous growth opportunities across various applications. Identifying and leveraging these opportunities can drive innovation and competitive advantage. Here are five key growth opportunities in the AI model market, each with the potential to impact various sectors.

• Healthcare Diagnostics and Personalized Medicine: Large AI models offer significant potential in healthcare for diagnostics and personalized medicine. By analyzing vast amounts of medical data, these models can assist in early disease detection, personalized treatment plans, and drug discovery. The growth opportunity lies in integrating AI with healthcare systems to enhance accuracy and efficiency in medical practices.
• Autonomous Vehicles and Smart Transportation: AI models are crucial for the development of autonomous vehicles and smart transportation systems. Advanced models improve vehicle perception, navigation, and decision-making processes. The opportunity is in leveraging AI to enhance safety, optimize traffic management, and drive innovations in transportation infrastructure, contributing to the future of mobility.
• Financial Services and Risk Management: In the financial sector, large AI models are transforming risk management, fraud detection, and customer service. By analyzing financial transactions and market data, AI can identify patterns and anomalies, enabling more accurate risk assessments and personalized financial services. The opportunity lies in deploying AI to improve decision-making and operational efficiency.
• Retail and Customer Experience: AI models are enhancing the retail industry by personalizing customer experiences and optimizing inventory management. Large models can analyze consumer behavior and preferences, driving targeted marketing and improving product recommendations. The growth opportunity is in using AI to create more engaging shopping experiences and streamline retail operations.
• Industrial Automation and Predictive Maintenance: AI models are advancing industrial automation and predictive maintenance by analyzing equipment data to predict failures and optimize maintenance schedules. This application improves operational efficiency and reduces downtime. The opportunity is in integrating AI with industrial processes to enhance productivity and extend equipment lifespan.

These strategic growth opportunities highlight the potential of large AI models to transform various industries. By focusing on applications such as healthcare, transportation, finance, retail, and industrial automation, organizations can drive innovation and achieve significant competitive advantages. The continued development and deployment of AI models will shape the future of these sectors and create new avenues for growth.

Large AI Model Market Driver and Challenges

The large AI model market is influenced by a range of drivers and challenges, encompassing technological advancements, economic factors, and regulatory considerations. Understanding these elements is crucial for navigating the market and leveraging opportunities effectively. Here are the major drivers and challenges impacting the market.

The factors responsible for driving the large AI model market include:

• Technological Advancements: Advances in computational power and model architectures are driving the growth of large AI models. Innovations like transformer networks and distributed training techniques enable more sophisticated and capable models. These technological improvements enhance performance and expand the potential applications of AI, fueling market growth and attracting investments.
• Increased Data Availability: The exponential growth in data availability provides a rich resource for training large AI models. Access to diverse and extensive datasets improves model accuracy and capabilities. This driver supports the development of more effective AI solutions and accelerates innovation across various sectors, including healthcare, finance, and retail.
• Rising Demand for Automation: The increasing demand for automation in industries such as manufacturing, finance, and logistics is a key driver for AI model adoption. Automation enhances efficiency, reduces costs, and improves accuracy in various processes. Large AI models play a crucial role in enabling advanced automation solutions, driving market expansion and application.
• Investment and Funding: Significant investment and funding from both public and private sectors are fueling advancements in large AI models. Venture capital, government grants, and corporate investments support research and development, accelerating the development and deployment of cutting-edge AI technologies. This financial backing is a major driver of market growth.
• Global Competitive Pressure: The competitive landscape in the AI market drives innovation and development. Companies and countries are investing in large AI models to maintain a competitive edge and lead in technological advancements. This pressure encourages continuous improvement and the creation of more advanced and capable AI solutions.

Challenges in the large AI model market include:

• Regulatory and Ethical Concerns: Regulatory and ethical concerns pose significant challenges for the large AI model market. Issues such as data privacy, algorithmic bias, and transparency are under scrutiny, leading to the development of stringent regulations and ethical guidelines. Compliance with these regulations can impact the speed of innovation and increase operational complexities.
• High Computational Costs: Developing and deploying large AI models requires substantial computational resources, which translates to high costs. The need for powerful hardware and extensive energy consumption poses financial and environmental challenges. Addressing these costs is essential for ensuring sustainable and scalable AI solutions.
• Talent Shortages: The shortage of skilled AI professionals is a challenge for the market. The complexity of developing large AI models requires expertise in machine learning, data science, and engineering. This talent gap can limit the pace of innovation and hinder the ability of organizations to fully leverage AI technologies.

The drivers and challenges outlined highlight the dynamic nature of the large AI model market. Technological advancements, data availability, and rising demand for automation are propelling growth, while regulatory concerns, high costs, and talent shortages present significant hurdles. Understanding these factors is crucial for navigating the market and capitalizing on emerging opportunities.

List of Large AI Model Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies large AI model companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the large AI model companies profiled in this report include-

• OpenAI
• Microsoft
• Google
• NVIDIA
• Alibaba
• Baidu
• Tencent
• Huawei
• Naver
• Anthropic

Large AI Model by Segment

The study includes a forecast for the global large AI model market by type, application, and region.

Large AI Model Market by Type [Analysis by Value from 2019 to 2031]:

• Natural Language Processing Foundation Model
• Computer Vision Foundation Model
• Multimodal Foundation Model
• Others

Large AI Model Market by Application [Analysis by Value from 2019 to 2031]:

• Education
• Energy
• Automotive
• Medical
• Others

Large AI Model Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Large AI Model Market

The landscape of the large AI model market is evolving rapidly, driven by advancements in technology, shifting geopolitical dynamics, and varying regulatory environments. As AI models grow in complexity and application, countries like the United States, China, Germany, India, and Japan are at the forefront of significant developments. Each country is advancing in different ways, influencing global trends and competition. This overview highlights the latest advancements and strategic movements in these key markets, providing a snapshot of their unique contributions and challenges.

• United States: The U.S. continues to lead in AI innovation with significant investments from both private and public sectors. Companies like OpenAI and Google are pushing the boundaries with new models that integrate multimodal capabilities, combining text, images, and other data forms. The U.S. also benefits from a robust ecosystem of AI startups and research institutions, fostering rapid development. Regulatory discussions around ethical AI and data privacy are becoming more pronounced, aiming to balance innovation with responsible use.
• China: China is aggressively advancing its AI capabilities, with state-backed initiatives driving the development of large models for various applications, including natural language processing and computer vision. The government's support includes substantial funding and strategic planning through initiatives like the New Generation Artificial Intelligence Development Plan. Chinese tech giants like Baidu and Alibaba are making significant strides, though the market faces challenges related to data privacy regulations and geopolitical tensions impacting international collaborations.
• Germany: Germany is positioning itself as a leader in ethical AI and industry-specific applications. With strong government backing and significant investment in research, German companies are focusing on integrating AI models into manufacturing and automotive sectors. Initiatives like the AI4EU project aim to enhance collaboration across Europe. Germany is also leading discussions on ethical AI standards, ensuring that developments align with European values and regulations, which impacts its competitive positioning on the global stage.
• India: India is emerging as a key player in the AI market with a focus on affordable and scalable AI solutions. The country is leveraging its vast talent pool and growing tech ecosystem to develop models suited for diverse applications, from healthcare to agriculture. Government initiatives such as the National AI Strategy are promoting AI research and development. However, India faces challenges related to infrastructure and data privacy, which could influence the pace of its AI advancements.
• Japan: Japan is known for its innovation in robotics and AI integration into various sectors. Companies like SoftBank and NEC are developing advanced AI models that enhance automation and human-machine interaction. The Japanese government is fostering AI research through initiatives like the Society 5.0 framework, which aims to integrate AI into daily life and industry. Japan's focus on human-centric AI and collaboration between technology and traditional industries is shaping its competitive edge in the global market.

Features of the Global Large AI Model Market

Market Size Estimates: Large AI model market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Large AI model market size by type, application, and region in terms of value ($B).

Regional Analysis: Large AI model market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the large AI model market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the large AI model market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the large AI model market by type (natural language processing foundation model, computer vision foundation model, multimodal foundation model, and others), application (education, energy, automotive, medical, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Large AI Model Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Large AI Model Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Large AI Model Market by Type
  • 3.3.1: Natural Language Processing Foundation Model
  • 3.3.2: Computer Vision Foundation Model
  • 3.3.3: Multimodal Foundation Model
  • 3.3.4: Others
• 3.4: Global Large AI Model Market by Application
  • 3.4.1: Education
  • 3.4.2: Energy
  • 3.4.3: Automotive
  • 3.4.4: Medical
  • 3.4.5: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Large AI Model Market by Region
• 4.2: North American Large AI Model Market
  • 4.2.1: North American Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.2.2: North American Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.3: European Large AI Model Market
  • 4.3.1: European Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.3.2: European Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.4: APAC Large AI Model Market
  • 4.4.1: APAC Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.4.2: APAC Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.5: ROW Large AI Model Market
  • 4.5.1: ROW Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.5.2: ROW Market by Application: Education, Energy, Automotive, Medical, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Large AI Model Market by Type
  • 6.1.2: Growth Opportunities for the Global Large AI Model Market by Application
  • 6.1.3: Growth Opportunities for the Global Large AI Model Market by Region
• 6.2: Emerging Trends in the Global Large AI Model Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Large AI Model Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Large AI Model Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: OpenAI
• 7.2: Microsoft
• 7.3: Google
• 7.4: NVIDIA
• 7.5: Alibaba
• 7.6: Baidu
• 7.7: Tencent
• 7.8: Huawei
• 7.9: Naver
• 7.10: Anthropic