Recent developments in AI reasoning capabilities reveal a complex picture of artificial intelligence that excels at certain complex tasks while failing at seemingly simple problems, according to an analysis by Sigal Samuel published in Vox. The article examines claims by leading AI companies about their models’ ability to perform genuine reasoning.
OpenAI and other companies have introduced new “reasoning models” like o1 and DeepSeek’s R1 that are designed to break down problems into smaller steps before providing solutions. While these models can solve complex logic puzzles and mathematical problems, experts remain divided on whether this constitutes true reasoning.
Melanie Mitchell, professor at the Santa Fe Institute, points out that human reasoning involves multiple types, including deductive, inductive, and analogical reasoning. The current AI models’ “chain-of-thought” approach represents only one narrow aspect of reasoning capability.
Shannon Vallor, philosopher of technology at the University of Edinburgh, suggests these models may be engaging in “meta-mimicry” – imitating human reasoning processes rather than actually reasoning. The lack of transparency from AI companies about their models’ internal workings makes it difficult to verify their claims.
Researchers have coined the term “jagged intelligence” to describe how AI can excel at complex tasks while struggling with simpler ones. According to computer scientist Andrej Karpathy, this creates an uneven pattern of capabilities unlike human intelligence, which tends to be more uniformly distributed across related tasks.
Experts recommend using AI primarily in situations where solutions can be easily verified, such as coding or website design. For complex judgment-based decisions or high-stakes situations, AI should be treated as a thought partner rather than a definitive source of answers.