Apple called out every major AI company for fake reasoning and Anthropic’s response proves their point

Apple Challenges Major AI Players on the Nature of Reasoning – Anthropic Responds, Raising Critical Questions

In recent developments within the artificial intelligence community, Apple published a groundbreaking research paper calling into question the purported reasoning capabilities of leading AI models from OpenAI, Google, and Anthropic. The crux of Apple’s argument is that these models do not truly reason—they primarily rely on advanced pattern matching rather than genuine understanding or inference.

Apple’s Critique of Contemporary AI Models

The research from Apple suggests that many large language models (LLMs) are fundamentally limited in their ability to generalize reasoning beyond surface-level pattern recognition. For instance, when faced with mathematical problems or logic puzzles, these models tend to falter if irrelevant details are altered—indicating they are not truly engaging in reasoning but are instead responding based on learned associations.

This distinction is significant: a model that fundamentally depends on surface patterns would stumble when the problem’s superficial elements change, even if the core logical structure remains the same. Apple’s experiments highlight that the models’ performance declines sharply under such conditions, raising doubts about their claimed reasoning abilities.

Anthropic’s Response: Defending Their Model

Anthropic, one of the recipients of Apple’s critique, responded with a detailed paper titled “The Illusion of the Illusion of Thinking.” Their argument is intriguing and somewhat paradoxical. They contend that Apple’s evaluation methods are unfairly designed and do not accurately capture the reasoning capabilities of their Claude model.

In essence, Anthropic acknowledges that the models may struggle under certain test conditions but advocates that this is a consequence of the test design rather than an inherent limitation. They claim their model can demonstrate reasoning “under fairer conditions,” suggesting that the model performs well when evaluated on more controlled or appropriately structured tests.

The Heart of the Issue: Pattern Matching vs. Reasoning

This debate raises a fundamental philosophical and practical question: can statistical pattern matching systems ever truly reason? Apple’s experiments demonstrate that small, irrelevant modifications to problems—such as changing the context from apples to oranges—can cause these models to fail. A genuinely reasoning system should remain robust under such variations, recognizing the underlying logical structure regardless of superficial changes.

Anthropic’s assertion that their model can reason more effectively under certain conditions seems to acknowledge the sensitivity observed but frames it as a challenge of evaluation rather than capability. Yet, if a model’s performance relies heavily on specific test designs, its purported reasoning remains questionable.

Broader Implications

This discourse underscores a