New Paradigms Won't Save You

2026-05-22 · Source: Astral Codex Ten · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, medium

Summary

The article challenges the "new paradigm" objection, which posits that Artificial General Intelligence (AGI) is distant because it requires a fundamental shift beyond current Large Language Models (LLMs). It traces key AI advancements from 1950s neural networks to 2024's chain of thought, noting that even skeptics like Yann LeCun anticipate AGI within the deep learning paradigm. Applying Lindy's Law, the analysis suggests a 25% probability of a new paradigm as impactful as LLMs emerging within three years, or one as significant as deep learning within five years. This timeline aligns with projections from those who believe LLMs can scale to AGI. The author contends that increasing AI research and existing compute infrastructure could further accelerate these developments, implying that new paradigms will likely emerge rapidly if current LLM scaling plateaus, rather than causing significant delays to AGI.

Key takeaway

For AI Scientists and Directors of AI/ML forecasting AGI timelines, you should integrate the potential for rapid new paradigm emergence into your models. Do not dismiss AGI risks as distant simply because current LLMs might not scale indefinitely. Your projections should account for a 25% chance of a new AI paradigm emerging within 3-5 years, potentially accelerating AGI. Prepare for swift transitions if current scaling methods plateau.

Key insights

AGI may emerge sooner than skeptics predict, even with new paradigms, due to rapid innovation cycles.

Principles

• Lindy's Law forecasts innovation cycles.
• Research growth accelerates paradigm shifts.
• Scaling walls trigger new paradigm adoption.

Method

The article applies Lindy's Law to historical AI milestones (e.g., LLMs, deep learning) to forecast the 25th percentile emergence of future revolutionary paradigms within 3-5 years.

In practice

• Extrapolate current LLM scaling for forecasts.
• Monitor scaling plateaus for paradigm shifts.
• Consider researcher growth in timelines.

Topics

• Artificial General Intelligence
• Large Language Models
• AI Forecasting
• Lindy's Law
• Deep Learning
• Transformer Architecture

Best for: AI Scientist, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Astral Codex Ten.