生成式人工智慧市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

2024年，全球生成式人工智慧市场规模达213亿美元，预计到2034年将以24.3%的复合年增长率成长，达到1771亿美元。行销、媒体和电商等行业对自动化内容产生的需求日益增长，是推动这一成长的主要因素。生成式人工智慧使企业能够有效率、大规模地创建个人化内容，包括文字、图像、视讯和音频，同时缩短生产时间和降低成本。在高度依赖数位互动和快速内容分发的行业中，这种兴趣的激增尤其明显。

深度学习演算法、Transformer 架构以及云端运算资源可用性的技术进步显着加速了生成式人工智慧的发展。 GPT 和 DALL-E 等人工智慧模型正变得越来越有效率和强大，使企业能够将这些技术应用于创意和分析性任务。随着人工智慧处理能力的提升，即时内容产生变得越来越可行，从而帮助企业将生成式人工智慧融入其工作流程。这项技术透过简化客户服务、报告产生、程式码创建和产品设计，支援各行各业的数位转型。因此，企业可以提高效率、促进创新并降低营运成本，因此，生成式人工智慧已成为具有前瞻性思维的企业的关键投资。

2024年，解决方案细分市场在生成型人工智慧市场占据主导地位，约占66%的市场。这种主导地位得益于各行各业广泛应用的人工智慧平台和工具，这些平台和工具带来了切实的、应用驱动的效益。生成型人工智慧解决方案涵盖用于内容创作、影像生成、虚拟助理、程式码产生和资料增强的人工智慧软体。企业越来越需要可扩展、预先训练且易于整合的端到端解决方案，几乎不需要内部人工智慧专业知识。随着医疗、行销、金融和设计等行业对生成型人工智慧应用的需求不断增长，可客製化的现成平台越来越受欢迎。

云端部署也是生成式人工智慧市场的主要贡献者。由于其可扩展性、经济实惠和易于部署，云端市场在 2024 年占据了 57% 的份额。提供高阶 GPU 和 TPU 的云端平台可以有效率地处理生成式人工智慧模型的训练和推理运算强度。基于云端的平台提供即时处理、即时更新以及与其他人工智慧工具的集成，使组织无需投入大量基础设施即可访问强大的生成式模型，例如大型语言模型和图像生成器。

美国生成式人工智慧市场占全球70%的份额，2024年产值达47亿美元。美国之所以占据主导地位，得益于强大的人工智慧创新、科技巨头的高度集中以及大量的风险投资。美国在人工智慧伦理开发和监管框架方面也处于领先地位，使其成为生成式人工智慧发展和商业成功的中心。

生成式人工智慧产业的主要参与者包括 Adob​​e、NVIDIA、亚马逊网路服务 (AWS)、微软、Meta、IBM、Google、Autodesk、百度和 Lighttricks。为了巩固市场地位，生成式人工智慧领域的公司专注于多项策略措施。这些措施包括扩大与云端服务供应商的合作伙伴关係以增强可扩展性、投资研发以提高人工智慧模型效率，以及开发行业特定的人工智慧解决方案以满足多样化的客户需求。此外，领先的公司正在探索收购和合作，以整合尖端技术，从而在快速发展的市场中保持竞争力。提供可客製化的人工智慧平台并确保其易于整合到现有企业系统中，是帮助企业巩固其地位的关键策略。

目录

第一章：方法论与范围

第二章：执行摘要

第三章：行业洞察

• 产业生态系统分析
• 供应商格局
  • 云端基础设施供应商
  • 基础模型开发人员
  • 平台提供者
  • 软体供应商
• 利润率分析
• 川普政府关税
  • 对贸易的影响
    • 贸易量中断
    • 其他国家的报復措施
  • 对产业的影响
    • 主要材料价格波动
    • 供应链重组
    • 生产成本影响
  • 受影响的主要公司
  • 策略产业反应
    • 供应链重组
    • 定价和产品策略
  • 展望与未来考虑
• 技术与创新格局
• 专利分析
• 用例
• 重要新闻和倡议
• 监管格局
• 衝击力
  • 成长动力
    • 内容自动化需求不断成长
    • 人工智慧和运算基础设施的进步
    • 企业数位转型倡议
    • 多模式应用的成长
  • 产业陷阱与挑战
    • 错误讯息和道德滥用的风险
    • 数据品质和偏见
• 成长潜力分析
• 波特的分析
• PESTEL分析

第四章：竞争格局

• 介绍
• 公司市占率分析
• 竞争定位矩阵
• 战略展望矩阵

第五章：市场估计与预测：按组件，2021 - 2034 年

• 主要趋势
• 解决方案
• 服务

第六章：市场估计与预测：依部署模式，2021 - 2034 年

• 主要趋势
• 云
• 本地

第七章：市场估计与预测：依技术分类，2021 - 2034 年

• 主要趋势
• 生成对抗网路（GAN）
• 变形金刚模型
• 变分自编码器
• 扩散模型
• 其他的

第八章：市场估计与预测：依最终用途，2021 - 2034 年

• 主要趋势
• 卫生保健
• 零售与电子商务
• 製造业
• 金融服务业
• 媒体与娱乐
• 其他的

第九章：市场估计与预测：按地区，2021 - 2034 年

• 主要趋势
• 北美洲
  • 我们
  • 加拿大
• 欧洲
  • 英国
  • 德国
  • 法国
  • 义大利
  • 西班牙
  • 俄罗斯
  • 北欧人
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳新银行
  • 东南亚
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 阿联酋
  • 沙乌地阿拉伯
  • 南非

第十章：公司简介

• Adobe
• Amazon Web Services (AWS)
• Apple
• Autodesk
• Baidu
• DeepMind
• Genie AI
• Google
• IBM
• Intel
• Meta
• Microsoft
• MOSTLY AI
• NVIDIA
• OpenAI
• Oracle
• Salesforce
• Siemens
• Synthesia
• Uber AI
• Unity Technologies

The Global Generative AI Market was valued at USD 21.3 billion in 2024 and is estimated to grow at a CAGR of 24.3% to reach USD 177.1 billion by 2034, driven by the increasing demand for automated content generation across sectors such as marketing, media, and e-commerce is driving this growth. Generative AI allows businesses to create personalized content, including text, images, video, and audio, efficiently and at scale, while reducing production time and costs. This surge in interest is particularly pronounced in industries heavily reliant on digital interaction and quick content distribution.

Technological advancements in deep learning algorithms, transformer architectures, and the availability of cloud computing resources have significantly accelerated the development of generative AI. AI models like GPT and DALL-E are becoming more efficient and powerful, enabling companies to use these technologies for creative and analytical tasks. As AI processing capabilities improve, real-time content generation becomes increasingly feasible, helping organizations integrate generative AI into their workflows. This technology supports digital transformation across industries by streamlining customer service, report generation, code creation, and product design. As a result, businesses can improve efficiency, foster innovation, and reduce operational costs, positioning generative AI as a critical investment for forward-thinking companies.

In 2024, the solutions segment dominated the generative AI market, accounting for around 66% of the market share. This dominance is due to the widespread use of AI platforms and tools across industries, which deliver tangible, application-driven benefits. Generative AI solutions encompass AI software for content creation, image generation, virtual assistance, code generation, and data enhancement. Enterprises are increasingly looking for end-to-end solutions that are scalable, pre-trained, and easy to integrate, requiring minimal in-house AI expertise. As the demand for generative AI applications grows across industries like healthcare, marketing, finance, and design, customizable, off-the-shelf platforms have gained popularity.

Cloud deployment is also a major contributor to the generative AI market. The cloud segment accounted for 57% share in 2024 due to its scalability, affordability, and easy deployment. The computational intensity of training and inference for generative AI models is efficiently handled by cloud platforms that offer high-end GPUs and TPUs. Cloud-based platforms provide real-time processing, live updates, and integration with other AI tools, allowing organizations to access powerful generative models like large language models and image generators without heavy infrastructure investments.

United States Generative AI Market held a 70% share and generated USD 4.7 billion in 2024. The country's dominance is driven by strong AI innovation, a high concentration of tech giants, and significant venture capital investment. The U.S. also leads in ethical AI development and regulatory frameworks, making it a hub for generative AI advancement and commercial success.

Key players in the generative AI industry include Adobe, NVIDIA, Amazon Web Services (AWS), Microsoft, Meta, IBM, Google LLC, Autodesk, Baidu, and Lighttricks. To strengthen their market position, companies in the generative AI space focus on several strategic initiatives. These include expanding partnerships with cloud service providers to enhance scalability, investing in R&D to improve AI model efficiency, and developing industry-specific AI solutions to address diverse customer needs. Moreover, leading firms are exploring acquisitions and collaborations to integrate cutting-edge technologies, enabling them to stay competitive in a rapidly evolving market. Offering customizable AI platforms and ensuring easy integration into existing enterprise systems are key strategies helping businesses solidify their presence.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates and calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimates
• 1.3 Forecast model
• 1.4 Primary research & validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Cloud infrastructure providers
  • 3.2.2 Foundational model developers
  • 3.2.3 Platform providers
  • 3.2.4 Software providers
• 3.3 Profit margin analysis
• 3.4 Trump administration tariffs
  • 3.4.1 Impact on trade
    • 3.4.1.1 Trade volume disruptions
    • 3.4.1.2 Retaliatory measures by other countries
  • 3.4.2 Impact on the industry
    • 3.4.2.1 Price volatility in key materials
    • 3.4.2.2 Supply chain restructuring
    • 3.4.2.3 Production cost implications
  • 3.4.3 Key companies impacted
  • 3.4.4 Strategic industry responses
    • 3.4.4.1 Supply chain reconfiguration
    • 3.4.4.2 Pricing and product strategies
  • 3.4.5 Outlook and future considerations
• 3.5 Technology & innovation landscape
• 3.6 Patent analysis
• 3.7 Use cases
• 3.8 Key news & initiatives
• 3.9 Regulatory landscape
• 3.10 Impact forces
  • 3.10.1 Growth drivers
    • 3.10.1.1 Rising demand for content automation
    • 3.10.1.2 Advancements in AI and computing infrastructure
    • 3.10.1.3 Enterprise digital transformation initiatives
    • 3.10.1.4 Growth in multimodal applications
  • 3.10.2 Industry pitfalls & challenges
    • 3.10.2.1 Risk of misinformation and ethical misuse
    • 3.10.2.2 Data quality and bias
• 3.11 Growth potential analysis
• 3.12 Porter's analysis
• 3.13 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn)

• 5.1 Key trends
• 5.2 Solution
• 5.3 Service

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Bn)

• 6.1 Key trends
• 6.2 Cloud
• 6.3 On-premises

Chapter 7 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Bn)

• 7.1 Key trends
• 7.2 Generative adversarial networks (GANs)
• 7.3 Transformers model
• 7.4 Variational auto-encoders
• 7.5 Diffusion models
• 7.6 Others

Chapter 8 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)

• 8.1 Key trends
• 8.2 Healthcare
• 8.3 Retail and e-commerce
• 8.4 Manufacturing
• 8.5 BFSI
• 8.6 Media and entertainment
• 8.7 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Nordics
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 South Korea
  • 9.4.5 ANZ
  • 9.4.6 Southeast Asia
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 MEA
  • 9.6.1 UAE
  • 9.6.2 Saudi Arabia
  • 9.6.3 South Africa

Chapter 10 Company Profiles

• 10.1 Adobe
• 10.2 Amazon Web Services (AWS)
• 10.3 Apple
• 10.4 Autodesk
• 10.5 Baidu
• 10.6 DeepMind
• 10.7 Genie AI
• 10.8 Google
• 10.9 IBM
• 10.10 Intel
• 10.11 Meta
• 10.12 Microsoft
• 10.13 MOSTLY AI
• 10.14 NVIDIA
• 10.15 OpenAI
• 10.16 Oracle
• 10.17 Salesforce
• 10.18 Siemens
• 10.19 Synthesia
• 10.20 Uber AI
• 10.21 Unity Technologies