세계의 생성형 AI 시장 - 제1판

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