AI Architecture

Data is the new oil.

Capturing and accurately labeling proprietary data is critical to evolving valuable AI models.

:::danger Problem Data is the bottleneck to foundation model performance. :::

Decision Tree

Decision tree for architecting your AI platform.

Deeply understand your vertical's unique needs to train models on unique domain-specific data to provide a point of difference and competitive advantage.

Predictions

AI models will become commoditized but there are significant opportunities in providing the infrastructure to power them and building applications that use them to solve valuable problems. The model layer can be seen as an essential connector, but not necessarily where the strongest businesses are built.

• Focus on Experiments
• Situational Awareness
• Elon Musk | Dec 23

AI Stack Components

• Databases
• ETL Pipeline
• Data preprocessing
• Model training
• Model evaluation
• LLM Analytics
• RAG Models
• Prediction Models
• Onchain AI
• Robotics Tools
• Computer Vision Tools

Infrastructure Providers

• ollama
• llamahub.ai
• Hugging Face
• llamaindex.ai
• litellm.ai
• Smith Langchain

Open Source Resources

• Local AI Packaged | Coleam00

When To Build

Use this map to choose where to compete. Apply one test: is your edge the data, the infrastructure, or the application on top? Pick the layer where your domain advantage is hardest to copy. Then measure each candidate against a real task — completion rate and cost per run are the signals that the architecture works.

Failure Modes

• Building at the model layer. Models commoditize; the moat sits in proprietary data and the application above it.
• Generic data. Without domain-specific, well-labeled data, there is no point of difference to defend.
• No task gauge. Choosing a stack with no measured task to prove it moves completion rate or cost.

Context

Domain expertise concepts.

• Prompts
• Building Agents
• LLM Engineering
• AI Tech Stack

Signal

Changes my mind: if the strongest AI businesses were consistently built at the model layer rather than in data or applications, the "models commoditize" thesis on this page would be wrong.

Next question: which layer captures the most durable margin as inference costs keep falling — infrastructure or application?

Questions

Which AI architecture decision — RAG versus fine-tuning, single-model versus multi-agent, or streaming versus batch — has the most impact on the quality of AI-generated outputs for knowledge-intensive tasks?

• At what query complexity does retrieval-augmented generation outperform fine-tuning for domain-specific accuracy?
• How does multi-agent orchestration change the failure modes of an AI system compared to a single model handling all tasks?
• Which AI architecture pattern — tool-using agents, memory systems, or eval loops — produces the most measurable improvement in production task completion rates?