生成AI的全球市场 - 第1版

预计到2024年，生成式人工智慧市场将在三个关键领域实现三位数的成长率：生成式人工智慧硬体、基础模型和开发平台。受云端服务供应商对资料中心的大规模投资推动，预计到2025年，人工智慧相关支出将超过4000亿美元。基础模型市场规模预计到2024年将达到41亿美元，而生成式人工智慧开发平台市场规模将达170亿美元。同时，用于生成式人工智慧工作负载的基于GPU的硬体系统在2024年创造了1323亿美元的收入。

本报告探讨并分析了全球生成式人工智慧市场，提供了对主要公司访谈的见解、各细分市场的市场价值预测以及主要供应商的市场占有率。

目录

图表的清单

摘要整理

第1章 简介

• AI的分类法
  • AI
  • 机器学习
  • 深层学习
  • 生成AI
• 生成AI架构
  • Transformer为基础的语言模式
  • 扩散模式，VAE，GAN
• 生成AI技术堆迭
  • 基础模式
  • 资料库
  • 硬体设备基础设施
  • 开发平台

第2章 市场分析

• 生成AI产业形势
  • 基础模式供应商
  • 开发平台供应商
  • GPU为基础的硬体设备供应商
• 市场规模与预测
  • 生成AI模式和平台的市场金额
  • 生成AI硬体设备的市场金额
• 解决方案供应商的市场占有率
  • 基础模式市场
  • 开发平台市场
  • 生成AI硬体设备市场
• 基础模式的基准
• IoT的生成AI
  • 生成AIoT的使用案例
  • 边缘展开和云端展开的比较
  • AIoT解决方案供应商
• 通讯产业上生成AI
  • AI-on-RAN
  • AI-for-RAN
  • AI-and-RAN
• 市场趋势
  • 中国低成本模型与平台的兴起
  • 法学硕士（LLM）提供者面临获利问题
  • 生成式人工智慧发展存在显着的地区差异
  • 电信业者投资自主人工智慧解决方案
  • 摆脱代币化
  • 代理人工智慧变得无所不在
  • 生成式人工智慧让实体人工智慧更接近突破
  • 人工智慧监管对生成式人工智慧市场的影响

第3章 企业的简介与策略

• 01.AI
• AI21 Labs
• Aleph Alpha
• Alibaba
• Anthropic
• Assembly AI
• AWS
• Baichuan
• Baidu
• ByteDance
• C3 AI
• Cohere
• Databricks
• Dataiku
• DeepSeek
• Domino
• Elevenlabs
• Google
• H2O AI
• Hugging Face
• IBM
• Luma AI
• Mistral AI
• Meta
• Microsoft
• MiniMax
• Moonshot AI
• Nebius
• Nvidia
• OpenAI
• Oracle
• Runway
• SambaNova Systems
• Scale AI
• Stability AI
• Snowflake
• StepFun
• Tencent
• Together AI
• Weights & Biases
• xAI
• Z.ai
• 缩写和简称的清单

Berg Insight estimates that the generative AI market experienced triple-digit-growth rates in all three major segments spanning GenAI hardware, foundation models and development platforms in 2024. The market is driven by significant data centre investments by cloud service providers, and over US$ 400 billion in expected AI-related spending in 2025. The market value for foundation models reached an estimated US$ 4.1 billion in 2024, while GenAI development platforms reached US$ 17.0 billion. Meanwhile, GPU-based hardware systems used for GenAI workloads generated revenues of US$ 132.3 billion in 2024.

Highlights from the report:

• Insights from executive interviews with market leading companies.
• 360-degree overview of the GenAI ecosystem.
• Market value forecast on GenAI models, platforms and hardware until 2029.
• Market shares for 55 key GenAI providers across models, platforms and hardware.
• Detailed profiles of 42 key GenAI model and platform providers.
• Use case examples from industries implementing GenAI.
• In-depth analysis of market trends and key developments.

Table of Contents

Table of Contents

List of Figures

Executive Summary

1. Introduction

• 1.1. The AI taxonomy
  • 1.1.1. Artificial intelligence
  • 1.1.2. Machine learning
  • 1.1.3. Deep learning
  • 1.1.4. Generative AI
• 1.2. Generative AI architectures
  • 1.2.1. Transformer-based language models
  • 1.2.2. Diffusion models, VAEs and GANs
• 1.3. The generative AI technology stack
  • 1.3.1. Foundation models
  • 1.3.2. Databases
  • 1.3.3. Hardware infrastructure
  • 1.3.4. Development platforms

2. Market Analysis

• 2.1. The generative AI industry landscape
  • 2.1.1. Foundation model providers
  • 2.1.2. Development platform providers
  • 2.1.3. GPU-based hardware providers
• 2.2. Market sizing and forecast
  • 2.2.1. Market value for GenAI models and platforms
  • 2.2.2. Market value for GenAI hardware
• 2.3. Solution provider market shares
  • 2.3.1. The foundation model market
  • 2.3.2. The development platform market
  • 2.3.3. The GenAI hardware market
• 2.4. Foundation model benchmarks
• 2.5. GenAI in IoT
  • 2.5.1. Generative AIoT use cases
  • 2.5.2. Edge vs cloud deployments
  • 2.5.3. AIoT solution providers
• 2.6. GenAI in telecom
  • 2.6.1. AI-on-RAN
  • 2.6.2. AI-for-RAN
  • 2.6.3. AI-and-RAN
• 2.7. Market trends
  • 2.7.1. The emergence of low-cost models and platforms from China
  • 2.7.2. LLM providers suffer profitability issues
  • 2.7.3. Large regional differences in GenAI developments
  • 2.7.4. Telecoms providers invest in sovereign AI solutions
  • 2.7.5. Moving away from tokenisation
  • 2.7.6. Agentic AI gains traction
  • 2.7.7. Physical AI nears breakthrough with GenAI
  • 2.7.8. AI regulations affecting the GenAI market

3. Company Profiles and Strategies

• 3.1. 01.AI
• 3.2. AI21 Labs
• 3.3. Aleph Alpha
• 3.4. Alibaba
• 3.5. Anthropic
• 3.6. Assembly AI
• 3.7. AWS
• 3.8. Baichuan
• 3.9. Baidu
• 3.10. ByteDance
• 3.11. C3 AI
• 3.12. Cohere
• 3.13. Databricks
• 3.14. Dataiku
• 3.15. DeepSeek
• 3.16. Domino
• 3.17. Elevenlabs
• 3.18. Google
• 3.19. H2O AI
• 3.20. Hugging Face
• 3.21. IBM
• 3.22. Luma AI
• 3.23. Mistral AI
• 3.24. Meta
• 3.25. Microsoft
• 3.26. MiniMax
• 3.27. Moonshot AI
• 3.28. Nebius
• 3.29. Nvidia
• 3.30. OpenAI
• 3.31. Oracle
• 3.32. Runway
• 3.33. SambaNova Systems
• 3.34. Scale AI
• 3.35. Stability AI
• 3.36. Snowflake
• 3.37. StepFun
• 3.38. Tencent
• 3.39. Together AI
• 3.40. Weights & Biases
• 3.41. xAI
• 3.42. Z.ai
• List of Acronyms and Abbreviations

List of Figures

• Figure 1.1: The relationship between AI terminologies
• Figure 1.2: Neural network illustration
• Figure 1.3: Generative adversarial network training process
• Figure 1.4: Differences between foundation model types
• Figure 1.5: Conceptualisation of a vector database
• Figure 2.1: Core business activities of GenAI solution providers
• Figure 2.2: Funding of private GenAI companies
• Figure 2.3: AI-related infrastructure investments in 2025
• Figure 2.4: GenAI foundation models and platform revenues (World 2023-2029)
• Figure 2.5: GPU-based GenAI hardware revenues (World 2023-2029)
• Figure 2.6: Foundation model market shares
• Figure 2.7: Development platform market shares
• Figure 2.8: GPU-based GenAI hardware market shares
• Figure 2.9: Top performing LLMs
• Figure 2.10: LLM performance by company
• Figure 2.11: Nvidia Jetson platform software stack
• Figure 2.12: Jensen Huang and Gr00t robot trained in Nvidia Isaac/Omniverse
• Figure 2.13: EU AI Act - high-risk AI use cases
• Figure 3.1: Pharia AI architecture
• Figure 3.2: Alibaba Cloud Model Studio
• Figure 3.3: Amazon Bedrock
• Figure 3.4: Cohere North agent builder
• Figure 3.5: Mosaic AI Gateway and Model Serving
• Figure 3.6: Dataiku Flow project pipeline
• Figure 3.7: Dataiku LLM Mesh
• Figure 3.8: Domino enterprise AI platform
• Figure 3.9: H2O AI Enterprise GenAI Platform
• Figure 3.10: Hugging Face platform
• Figure 3.11: Luma Photon generated image examples
• Figure 3.12: Azure AI Foundry architecture
• Figure 3.13: Microsoft GenAI deployment methods
• Figure 3.14: Nebius product offering
• Figure 3.15: Nvidia AI Foundry
• Figure 3.16: Oracle Cloud Infrastructure (OCI) Generative AI Service
• Figure 3.17: Scene from Runway Gen-4 preview
• Figure 3.18: SambaNova CoE
• Figure 3.19: Stability AI image examples
• Figure 3.20: Snowflake Cortex AI
• Figure 3.21: Together Enterprise Platform overview
• Figure 3.22: W&B Models experimentation dashboards
• Figure 3.23: xAI Grok application