Episode
New Paradigms Won't Save You
- Podcast
- Astral Codex Ten Podcast
- Published
- Jun 19, 2026
- Duration seconds
- 348
- Processing state
not_requested- Canonical source
- https://sscpodcast.libsyn.com/new-paradigms-wont-save-you
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Summary
One 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. A common 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 networks 1967: Multi-layer perceptron 2010: Modern deep learning 2017: Transformer, LLM 2022: RLHF, chatbots 2024: Chain of thought / test-time compute We can think of this as an "evolutionary tree", where a given LLM (let's say Claude Opus 4.7) shares a recent "common ancestor" with all other chatbots, and only a very distant "common ancestor" with everything else descended from the multi-layer perceptron. If AGI needs a "new paradigm", what common ancestor can we expect AGI and LLMs to share? AGI will very likely use neural networks, because the human brain is a neural network and qualifies as an AGI. It will probably use deep learning, because although deep learning isn't exactly analogous to the brain, it seems like a pretty reasonable way to emulate the brain's learning algorithms onto computer hardware. Skeptics like Yann LeCun and Gary Marcus usually pinpoint LLMs/transformers as the step where we went wrong. LeCun thinks that the first AGIs may be within the deep learning paradigm (but not LLMs); Marcus thinks that they'll combine insights from deep learning with something else. How soon should we expect a new paradigm as revolutionary as LLMs/transformers? Since we got LLMs/transformers nine years ago, Lindy's Law suggests nine more years. How soon should we expect a new paradigm as revol…