生成AI的全球市場 - 第1版

預計到2024年，生成式人工智慧市場將在三個關鍵領域實現三位數的成長率：生成式人工智慧硬體、基礎模型和開發平台。受雲端服務供應商對資料中心的大規模投資推動，預計到2025年，人工智慧相關支出將超過4000億美元。基礎模型市場規模預計到2024年將達到41億美元，而生成式人工智慧開發平台市場規模將達170億美元。同時，用於生成式人工智慧工作負載的基於GPU的硬體系統在2024年創造了1323億美元的收入。

本報告探討並分析了全球生成式人工智慧市場，提供了對主要公司訪談的見解、各細分市場的市場價值預測以及主要供應商的市場佔有率。

目錄

圖表的清單

摘要整理

第1章 簡介

• AI的分類法
  • AI
  • 機器學習
  • 深層學習
  • 生成AI
• 生成AI架構
  • Transformer為基礎的語言模式
  • 擴散模式，VAE，GAN
• 生成AI技術堆疊
  • 基礎模式
  • 資料庫
  • 硬體設備基礎設施
  • 開發平台

第2章 市場分析

• 生成AI產業形勢
  • 基礎模式供應商
  • 開發平台供應商
  • GPU為基礎的硬體設備供應商
• 市場規模與預測
  • 生成AI模式和平台的市場金額
  • 生成AI硬體設備的市場金額
• 解決方案供應商的市場佔有率
  • 基礎模式市場
  • 開發平台市場
  • 生成AI硬體設備市場
• 基礎模式的基準
• IoT的生成AI
  • 生成AIoT的使用案例
  • 邊緣展開和雲端展開的比較
  • AIoT解決方案供應商
• 通訊產業上生成AI
  • AI-on-RAN
  • AI-for-RAN
  • AI-and-RAN
• 市場趨勢
  • 中國低成本模型與平台的興起
  • 法學碩士（LLM）提供者面臨獲利問題
  • 生成式人工智慧發展存在顯著的地區差異
  • 電信業者投資自主人工智慧解決方案
  • 擺脫代幣化
  • 代理人工智慧變得無所不在
  • 生成式人工智慧讓實體人工智慧更接近突破
  • 人工智慧監管對生成式人工智慧市場的影響

第3章 企業的簡介與策略

• 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
• 縮寫和簡稱的清單

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