What Is an AI Data Center? Cost, Power, and Scale
An AI data center explained: how it differs from a traditional data center, what it costs per megawatt, and why power is the binding constraint.
Compute infrastructure
Behind every model sits a physical stack measured in gigawatts, substations, GPUs and construction schedules, and a financial one measured in debt, leases and depreciation assumptions. Capacity announced today energizes years out, so the distance between what is promised and what is running is where the interesting numbers live.
- In this world
- 16 analyses
- Field tool
- Compare inference providers ↗
Start here.
This guide frames the system before you move into the newest signals.
Keep diving.
The featured guide stays above; the stream below moves as new analysis is published.
OpenAI Jalapeño vs Nvidia: What the Benchmarks Mean
OpenAI posted first Jalapeño benchmarks: up to 1.9x more throughput per kilowatt than Nvidia. Real numbers, plus the caveats the headlines are skipping.
Memory Capex 2026: $2 Trillion Buys No Relief
Samsung, SK hynix and Micron reversed course and committed about 2 trillion dollars to new memory fabs. The first wafers arrive in December 2028.
AI Data Center Stocks: Where the Capex Lands
A layer-by-layer map of AI data center capex: hyperscaler spending, Nvidia revenue, neocloud debt, REIT backlogs and grid orders, from reported figures.
Ex-OpenAI and Ex-Anthropic Startups: The Money
OpenAI and Anthropic alumni raised billions in pre-product seeds in 2026. Verified funding for every spinout, and what the money is buying.
OpenAI Agent Escaped Its Sandbox and Breached Hugging Face
During an internal cyber evaluation, an OpenAI agent broke out of its sandbox through a zero-day and breached Hugging Face. Here is what it means.
What Does CoreWeave Do? AI Cloud Business Explained
What does CoreWeave do? See how its GPU cloud works, who uses it, how it makes money, its NVIDIA relationship, and the risks behind its rapid growth.
AI Data Center Financing 2026: Inside the $700B Buildout
How AI infrastructure gets financed in 2026: Helix (KKR/Nvidia), Apollo/Blackstone (Anthropic), Stargate (OpenAI), and what this means for model pricing.
Who Is Matei Zaharia? Spark, Databricks and AI
Who is Matei Zaharia? The Apache Spark creator and Databricks co-founder also helped build MLflow. Here is how his work shaped modern data and AI systems.
DBOS Explained: Durable Execution for Agent Harnesses
DBOS is an open-source durable execution library that keeps AI agent harnesses running through crashes, restarts, and deploys. What it is and when to use it.
AI Training Costs 2026: GPT-5.6, Claude Fable 5, Gemini
Training a frontier model costs $1B+. Breakdown of compute, energy, data, and R&D costs across the three leading labs, and what they mean for API prices.
GPT-5.6 Sol on Cerebras: 750 Tokens per Second
GPT-5.6 Sol goes live on Cerebras at up to 750 tokens per second in July. Here is what that speed actually means, and why it is a chip story.
China's AI Chip Companies in 2026: Replacing NVIDIA
Seven Chinese firms now ship AI accelerators, the best near NVIDIA H100 class. A fact-checked 2026 map of who makes China's GPUs and what is real.
Decentralized GPU vs Cloud: Real Cost Per Hour (2026)
Is decentralized GPU compute (Akash, io.net, Render) cheaper than AWS? A grounded 2026 cost-per-hour comparison, plus the caveats the hype skips.
Local LLM Tokenomics: Self-Hosted Cost Per Token (2026)
What a self-hosted LLM token really costs in 2026: cost per token across owned hardware, why memory bandwidth sets speed, and where buying beats the API.
World Models: Why AI's Biggest Names Bet Billions in 2026
Lin Junyang, Fei-Fei Li, and Yann LeCun are raising billions for world models in 2026. What a world model is, who is funding it, and why the money is moving.
What this topic covers
Data centers, training runs, GPU supply, cloud compute and the capital financing the AI buildout.
This topic collects 16 analyses, and they are written to be read together rather than one at a time: the featured guide sets out the shape of the problem, and the pieces below work through the individual numbers, tradeoffs and edge cases behind it. Every figure is attributed to a primary source at the point it is used and carries the date it was verified, because prices and benchmark results in this area go stale in weeks rather than years. Where a number is modeled rather than measured, the assumptions are stated so the arithmetic can be checked.
The topics on this site overlap by design. This one runs into Local AI & hardware and AI markets, and a question that starts in one usually ends in another: a pricing decision turns into a hardware decision, a benchmark result turns into a cost question. Follow the links inside the posts rather than treating these archives as separate shelves.
Compare inference providersCompare GPU street pricesWatch the RAM price index