Musk proposes adversarsial peer review for AI Safety
The All-In Podcast@theallinpodElon Proposes Adversarial Peer Reviews for AI Safety @elonmusk: “What I think would be wise to do as soon as possible, if not immediately, would be to have the major AI competitors test each other's models. So that you would have everyone's security test harness testing everyone else's model. So, instead of grading your own homework, you would at least have competitors grading your homework, and raising the alarm if they see concerns.
- ▪The All-In Podcast@theallinpodElon Proposes Adversarial Peer Reviews for AI Safety @elonmusk: “What I think would be wise to do as soon as possible, if not immediately, would be to have the major AI competitors test each other's models.
- ▪So that you would have everyone's security test harness testing everyone else's model.
- ▪So, instead of grading your own homework, you would at least have competitors grading your homework, and raising the alarm if they see concerns.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | X (formerly Twitter) |
| Canonical URL | https://twitter.com/theallinpod/status/2099731166530277549 |
| Publication time | Wed, 16 Sep 2026 01:03:49 +0000 |
| Retrieval time | 2026-09-16T01:08:37.628Z |
| Last seen | 2026-09-16T01:08:37.628Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 9C1YQUhBKCmm · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
The All-In Podcast@theallinpodElon Proposes Adversarial Peer Reviews for AI Safety @elonmusk: “What I think would be wise to do as soon as possible, if not immediately, would be to have the major AI competitors test each other's models. So that you would have everyone's security test harness testing everyone else's model. So, instead of grading your own homework, you would at least have competitors grading your homework, and raising the alarm if they see concerns. If competitors say that this model is unsafe, and that model then subsequently does something bad, I think it would be extremely hard to live down. The egg on face level would be very, very high. And the legal liability would be enormous.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at X (formerly Twitter).
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