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Viewing as
ProofMechanismModel-agnostic
Why

Your AI can't know what it doesn't know.

AI knowledge has an expiry date it can't see — past its training, it's confidently out of date without knowing it. Three real examples, from this week, of exactly that failure.

See the patterns

The blind spot

Every AI's knowledge stops at a certain date. Past it, the AI doesn't have partial knowledge — it has confident, complete-feeling knowledge that happens to be wrong. It can't warn you about the gap, because seeing the gap would require knowing it's there.

Three worked, checkable examples

  1. 1

    Next.js caching: pre-2026 recall reaches for unstable_cache + experimental.dynamicIO. Current is cacheComponents + the use cache directive family.

  2. 2

    Redis licensing: pre-2025 recall says "Redis isn't open source, use Valkey." Redis 8 added AGPLv3 in May 2025 — the objection no longer applies.

  3. 3

    Vercel KV: still "exists" in older training data. It was sunset and folded into Upstash.

What this page does not claim

This page doesn't claim smaller AIs need this more — the research on that is genuinely mixed. The claim here is narrower and stronger: no AI, however smart, can know what changed after its training ended. That's not a statistic. It's how training works.

See what corrects it.

The full rulebook, in build order, kept current.

Browse the patternsBack to how it works

Or see what the first 10 minutes look like →