TL;DR
- Cost is a design constraint, not an afterthought - model tier, context size, and deployment location are economic decisions
- Read the essays below in any order; start with Token Economics if you only have time for one
- Pairs with open-weight models and local inference guides
Core essays
- Token Economics: Why the Cost of AI Isn’t Going Down
- GPU Servers vs AI API Credits: The Real Cost Breakdown
- Local AI vs Cloud AI: The Tradeoff Landscape in 2026
- The AI Energy Crisis: Why Data Center Power Will Define the Next Decade
- Cerebras, Groq, SambaNova: The Inference Hardware Insurgents
Adjacent
- The State of Open-Weight Models in 2026 - when open weights beat closed APIs on price
- Prompt Caching - the quiet latency and cost win
- The Token Efficiency Mindset - curating spend per conversation
- Is the $20 AI Subscription Era Over?
- We Are Learning to Buy Intelligence
Related Reading
- AI Dev Tooling: A Reading Path for 2026 - canonical path for coding agents and stack decisions that depend on these cost constraints
- Home Agent Stack: From Mac Studio to Secured MCP Tools - building the hardware and software layer these economics govern
- Reasoning Models in 2026: What Changed and What Didn’t - why reasoning models carry a different cost profile than base models
- The Free Intelligence Era - the macro argument for where intelligence costs are headed