The Generative AI Market

Description

List of Tables

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.

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

The Generative AI Market - 1st Edition

Description

Table of Contents

List of Tables

Highlights from the report:

Table of Contents

Table of Contents

List of Figures

Executive Summary

1. Introduction

2. Market Analysis

3. Company Profiles and Strategies

List of Figures