New Paradigms Won't Save You
The article discusses the debate surrounding the development of Artificial General Intelligence (AGI) and the necessity of new paradigms in AI. It argues that while some believe AGI will require a significant paradigm shift, current advancements in large language models (LLMs) suggest that AGI could emerge from existing frameworks. The author emphasizes that the timeline for new breakthroughs may be shorter than anticipated, with potential developments occurring within the next few years.
- ▪The evolution of LLMs includes key milestones such as neural networks, deep learning, and transformers.
- ▪Skeptics argue that LLMs may not lead to AGI, suggesting that future AGIs will combine insights from deep learning with other approaches.
- ▪Lindy's Law suggests that significant advancements in AI could occur within the next 3 to 5 years.
Astral Codex Ten files mainly under blogs. We currently carry 6 of its stories.
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Story provenance
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Record
| Original publisher | Astral Codex Ten |
| Canonical URL | https://www.astralcodexten.com/p/new-paradigms-wont-save-you |
| Publication time | Fri, 22 May 2026 08:49:25 GMT |
| Retrieval time | 2026-05-22T09:02:01.206Z |
| Last seen | 2026-05-22T09:02:01.206Z |
| 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 | DI6MobUOy9YR |
| 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
New Paradigms Won't Save You...May 22, 20263522ShareOne popular objection to AI concerns is to declare that LLMs can never be AGI. You need a “new paradigm”. Therefore, AGI is so far in the future that it’s not worth worrying about.The obvious counterargument is to claim that no, LLMs can become AGI. But even without that counterargument, I think the “therefore” fails on its own terms. The key question is: how much of a new paradigm do we need?The landmark discoveries on the road to modern LLMs are something like:1950s: Neural networks1967: Multi-layer perceptron2010: Modern deep learning2017: Transformer, LLM2022: RLHF, chatbots2024: Chain of thought / test-time computeWe can think of this as an “evolutionary tree”, where a given LLM (let’s say Claude Opus 4.7) shares a recent “common…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Astral Codex Ten.
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