Convergence in LLM Quality and Slowdown in LLM Improvement
The apparent slowdown in LLM improvement is exactly what you would expect if the LLMs are at base just emulating internet s***posters. But if the compression = true understanding crowd is right, the scale may well be measuring the wrong thing…Whether we are on the road to AGI hinges on an unsettled question: What are LLMs?If they are sophisticated mimics of human conversation, plateauing capability scores make sense and the AGI story is hype. We exited the pre-training era and became more reliant on post-training, like RLHF.
- ▪The apparent slowdown in LLM improvement is exactly what you would expect if the LLMs are at base just emulating internet s***posters.
- ▪But if the compression = true understanding crowd is right, the scale may well be measuring the wrong thing…Whether we are on the road to AGI hinges on an unsettled question: What are LLMs?If they are sophisticated mimics of human conversat
- ▪We exited the pre-training era and became more reliant on post-training, like RLHF.
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| Original publisher | Substack |
| Canonical URL | https://braddelong.substack.com/p/convergence-in-llm-quality-and-slowdown |
| Publication time | Mon, 27 Jul 2026 11:37:18 +0000 |
| Retrieval time | 2026-07-27T11:46:15.479Z |
| Last seen | 2026-07-27T11:46:15.479Z |
| 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 | spzBJwBrjBXH · 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 |
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| 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
SubTuringBradBotConvergence in LLM Quality & Slowdown in LLM Improvement: CHART OF THE DAYFrom two Epoch-Capability points a month back in 2023 to one every six months today, with the spread of assessed model capabilities across frontier labs shrinking by half...Brad DeLongJul 21, 202657137ShareBut are these <http://epoch.ai> assessments real numbers that mean anything? And how could we tell? The apparent slowdown in LLM improvement is exactly what you would expect if the LLMs are at base just emulating internet s***posters.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Substack.
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