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AI ecosystem consists of multiple layers. I make an attempt to peel down the layers, understand their leverage and risks at every layer in order to understand where does value accrue over time?
| Layer | Value proposition | Leverage (Avenues of competitive advantage) | Key Risks |
|---|---|---|---|
| Infrastructure | Hardware companies offering crucial compute for training and inference | - High R&D and capital investment | - Market concentration and major tech companies getting into GPU production- Shift in demand from training to inference, diminishing need for intensive compute resources |
| Cloud providers providing - AI compute on demand- Robust developer platforms to power AI driven applications (model hub, RAG/fine-tuning LLMs, agent framework, LLMOps) | - Capital intensive scale of operations - Established trust and developer communities | - Exclusive model access like Google-Gemini | |
| Modeling & Developer platform | - State of the art pre-trained LLMs covering various modalities- Provides robust developer platform to build applications and agents using off the shelf, and RAG, fine-tuning, etc | - Dominate developer attention with robust ecosystem | - LLM enhancements with diminishing returns due to limits in data access, compute, algorithmic improvements - Open source LLMs reaching parity |
| AI driven applications | - Provides tools for developing and managing AI products throughout their lifecycle.- Offers differentiated experiences in existing product categories.- Enhances efficiency, reducing operational costsCreation of new product categories previously unfeasible without AI | - Access to exclusive data- Building something proprietary in the stack such as Fine-tuned/advanced RAG architecture that leads to competitive edge - Direct access to targeted customer bases- Differentiated business model | - Dependency on LLMs as opaque systems or marginal workflow enhancements that may be easily duplicated |