Recent Evidence Challenges LLMs as Path to Artificial General Intelligence
By
FromTheArchives
The bagel they save for the regulars. Don't skim, savour.
Summary
The article argues that recent developments have shattered the belief that large language models (LLMs) will lead to artificial general intelligence (AGI). Key evidence includes the June 2025 Apple reasoning paper showing LLMs still can't handle distribution shift, GPT-5's underwhelming performance in August 2025, and Turing Award winner Rich Sutton's acknowledgment of the limitations. The author contends that neural networks' fundamental weakness in dealing with distribution shift remains unresolved after nearly 30 years of research.
Key quotes
· 4 pulledFirst slowly, and then all at once, dreams of LLMs bringing us to the cusp of AGI have fallen apart.
The Apple reasoning paper confirmed that even with 'reasoning', LLMs still can't solve distribution shift, the core Achille's heel in neural networks.
GPT-5 came late and fell short.
Turing Award winner Rich Sutton, known best for RL and his 'bitter lesson' thanked me for my work.
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