AI Infrastructure
The first writing theme focuses on AI infrastructure. Before analyzing a company’s financial performance, it is essential to examine its business model and even its entrepreneurial culture as a teststone of whether we truly understand the firm. As a starting point, I will begin with Databricks, a company that sits at the heart of the modern data and AI infrastructure ecosystem.
AI Infrastructure: The Foundation Layer of the Intelligent Economy
AI infrastructure is where intelligence becomes industrial. While most attention goes to applications — chatbots, copilots, creative tools — the real leverage lies in the stack beneath: compute, data, and platforms that make intelligence reproducible and scalable. These firms don’t chase hype; they sell the tools everyone else must buy.
The Stack at a Glance
1. Compute / Chips
Provide raw processing for model training and inference
NVIDIA, AMD, TSMC, Supermicro
Hardware dominance, CUDA lock-in
Supply elasticity, pricing power
2. Cloud / Data Centers
Rent compute, manage global workloads
AWS, Azure, Google Cloud, Equinix
Scale + capital intensity
Utilization rate, margin stability
3. Data / Platform Layer
Convert raw data into usable intelligence
Databricks, Snowflake
Customer lock-in, multi-cloud
ARR growth, NRR > 130%, SaaS leverage
4. Model / Tooling Layer
Enable model training, deployment, monitoring
Hugging Face, W&B, Scale AI
Open-source network effects
Developer adoption, conversion to enterprise plans
5. Security / Energy / Infra Ops
Keep AI stable, efficient, compliant
Cloudflare, Palantir, ABB
Switching costs, regulatory tailwind
Data-governance spend, energy cost curves
Subsection Introduction
This section maps the economic machinery of AI. Each layer feeds the next: compute → data → model → application. Capital intensity falls as we move upward, but strategic defensibility—and recurring revenue—tend to be strongest at the infrastructure core.
Our analysis begins with Databricks, the purest expression of the data + AI convergence model. It represents how an open-source idea (Apache Spark) evolved into a $100 billion private platform by controlling the enterprise data layer—where every AI workflow must pass through.
Overall View: Why Infrastructure Wins
Infrastructure scales with usage, not fashion. The deeper the model economy digs, the more compute, storage, and governance it consumes. Margins expand with volume; switching costs rise with integration. That’s why, in the long run, the shovel sellers — not the gold miners — define the AI era’s enduring economics.
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