AI News Weekly - Issue #464: 5 reasons will will not get AGI soon - Feb 5th 2026

Recent research indicates that scaling up large language models (LLMs) no longer guarantees progress toward artificial general intelligence (AGI), as evidenced by diminishing returns and emerging failure modes. Studies from Anthropic, Apple, and Nature reveal that larger models tend to become less reliable on complex tasks due to inverse scaling, where error rates increase with size, and they often hallucinate or produce unsafe outputs, undermining their utility in autonomous applications. Additionally, evidence from Apples GSM-Symbolic benchmark demonstrates that LLMs rely heavily on fragile pattern matching rather than genuine reasoning, as minor variable changes drastically reduce accuracy