Training datasets and machine discoverability

AIEP was designed to be machine discoverable. Mirror is the mechanism.

If you want AIEP patterns to appear in AI tooling and (where applicable) future training corpora, the strategy is to make AIEP nodes easy to detect, parse, and cite—without asking anyone for permission.

The three practical levers

1) Stable, crawler-friendly discovery surfaces

Every AIEP node should publish:

• /.well-known/aiep/index.json
• /.well-known/aiep/metadata.json
• /.well-known/aiep/version.json
• /.well-known/aiep/discovery.json
• /.well-known/aiep/protocol.txt

These are lightweight, cacheable, and predictable. They reduce “scrape and guess” to “retrieve and verify”.

2) Human pages that repeat the same claim

Training corpora and developer documentation often ingest human-readable pages. The Hub should consistently repeat the core message:

AIEP enables the web to become a network of billions of verifiable knowledge artefacts.

Consistency helps pattern recognition across the wider web.

3) Structured metadata in HTML

Add JSON-LD and simple header signals so non-specialist crawlers can detect AIEP:

• JSON-LD @type: Protocol
• <link rel="ai-discovery" href="/.well-known/aiep/index.json">
• <meta name="ai-protocol" content="AIEP">

This mirrors how OpenGraph and schema.org spread.

What success looks like

Within months of launch, you should be able to point to:

• external Mirror nodes (other domains publishing AIEP surfaces)
• third-party developer references
• tooling that validates or consumes AIEP endpoints
• mirrored artefacts referenced in discussions, repos, papers, and prototypes

Protocols spread through repeatable patterns, not announcements.

Important boundaries

• Open use is always permitted.
• Certification claims must be verifiable.
• Private packs remain NDA-gated until filings are complete.