We Didn’t Just Train AI on the Internet. We Started Training It on Itself.
The article discusses the emerging challenges in AI training, particularly the shift from human-generated data to AI-generated content. This transition risks diminishing the diversity and originality of AI outputs, as models increasingly train on their own generated data. The author warns that this could lead to a collapse in the richness of human reasoning that has historically driven AI breakthroughs.
- ▪AI training is shifting from high-quality human data to synthetic content generated by models themselves.
- ▪This recursive training loop risks reducing variance and originality in AI outputs.
- ▪The convergence of AI models in voice and reasoning patterns signals a loss of diversity in thought.
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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 | DEV.to (Top) |
| Canonical URL | https://dev.to/arpitstack/we-didnt-just-train-ai-on-the-internet-we-started-training-it-on-itself-24b6 |
| Publication time | Thu, 28 May 2026 20:19:26 +0000 |
| Retrieval time | 2026-05-28T20:29:37.587Z |
| Last seen | 2026-05-28T20:29:37.587Z |
| 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 | bFK-4IzZVDEm |
| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2688106) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Arpit Gupta Posted on May 28 We Didn’t Just Train AI on the Internet. We Started Training It on Itself. #ai #machinelearning #datascience #claude There’s a quiet assumption in almost every AI discussion right now: “If we scale compute and models, intelligence will keep improving.” That assumption is starting to break. Not loudly. But structurally. The real bottleneck isn’t compute We’ve optimized for compute like it’s the main constraint. GPUs. Clusters. Parallelism.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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