About this guide
An end-to-end, vendor-neutral atlas for siting, building, financing, commissioning, and operating AI data centers — 166 chapters, free, and actively maintained.
Why I built it
I work on large AI data center projects. The first thing this field teaches you is that no one is an expert in all of it — the industry is too new. Everyone at the table arrived from somewhere else: utilities, hyperscale construction, HPC, mechanical and electrical engineering, real estate, project finance, silicon. All of it real, deep expertise — and each discipline brings its own vocabulary, assumptions, and dogma.
Which is how a room full of genuine subject-matter experts can still plan a bad AI data center. I ran into it constantly: misconceptions that survived until they became change orders, schedules built around the wrong critical path, every discipline optimizing locally while the project slipped around them. I have watched projects stall, slide, and fail at every stage for the same underlying reason — the team never shared one picture of the machine it was building.
The stakes make that intolerable. This build-out is deploying capital at a scale with almost no industrial precedent — on the order of $6.7 trillion of data-center capex by 2030, ~$5.2T of it AI-capable — and the industry now commits more in a single year than the Apollo program cost in total, adjusted for inflation. Capital like that does not forgive teams that talk past each other. Getting these projects built, delivered, and operated as integrated systems is the whole game.
I started this guide to educate myself — to be a better advisor to the projects I work on. Partway in, two things became obvious: I could not find the field assembled end-to-end at this depth anywhere, and what does exist is paywalled, stale within months, or written so abstractly it can't survive contact with a real project. So the guide became public and free.
I hope you get as much out of it as I have. Help me keep it correct — every chapter ends with Suggest an edit — and if you want to stay ahead of the changes, the dispatches track the significant moves as they happen. This is frontier engineering; the numbers move monthly.
How it's made
I build the guide with the latest Claude and Codex models: AI agents do the research sweeps, drafting, cross-checking, and adversarial review; I direct the work and own every claim that ships. That is not a disclaimer; it is the design. The field spans grid interconnection, power electronics, cooling, silicon, networking, storage, software, security, and finance, and it moves monthly — AI assistance is what lets one accountable author keep 166 chapters at this depth. Where readers with domain expertise correct or sharpen the material, they are credited in the corrections log.
Why you can trust it
AI-assisted and human-owned works because the accountability is machinery, not promises:
- The numbers register — the guide's volatile figures are collected as 1,370 date-stamped register entries and tracked over time as values are revised.
- The corrections log — public and dated. When something is wrong — caught by a reader or by our own review passes — the fix is recorded, not quietly patched.
- Publish gates — content ships from a database through integrity checks (figure-drift detection, freshness flags, cross-reference verification). Nothing is hand-edited onto the live site.
- Adversarial review — before releases, independent AI reviewers (including a second lab's model) attack the site's numbers, math, and claims; verified findings become fixes and corrections-log entries.
Contact
Errors, sources I should read, or anything else: editors@aidatacenterguide.com. For a specific passage, use Suggest an edit at the end of any chapter — accepted corrections are credited in the log.
— Jacob Fehn