AI Economics and Hardware: A Reading Path

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

1. Token Economics: Why the Cost of AI Isn’t Going Down
2. GPU Servers vs AI API Credits: The Real Cost Breakdown
3. Local AI vs Cloud AI: The Tradeoff Landscape in 2026
4. The AI Energy Crisis: Why Data Center Power Will Define the Next Decade
5. 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