Topic selection
We selected ten mutually reinforcing pillars that cover the conceptual stack from foundations through research and enterprise practice—without drifting into company/product directories.
| Pillar | Role in the graph |
|---|---|
| What is AI | Definitions and map |
| Machine learning | Statistical core |
| Generative AI / LLMs / Agents | Modern interfaces and action |
| Benchmarks | Measurement literacy |
| Safety & governance | Risk and accountability |
| Enterprise adoption | Operating model |
| Research / Future | Trajectory literacy |
Evidence practice
Claims that depend on external facts point to recognized authorities. We avoid unverifiable statistics. Where the field is contested (e.g., AGI timelines), we present scenario framing rather than prophecy.
Internal linking
Every article links to related peers so no cornerstone is an orphan. The homepage and articles index expose the full set for crawl discovery.
Technical method
Pages are PHP-rendered HTML with shared includes (header/nav/footer), a single CSS file, and deferred JS. No WordPress runtime. Schema.org JSON-LD is emitted for Organization, WebSite, Article, BreadcrumbList, and FAQPage where applicable.
Review cadence
Cornerstones carry an updated date. Material capability claims should be re-checked when major evaluation norms or standards change.