Apple’s ‘AI Can’t Reason’ Claim Seen By 13M+, What You Need to Know

Overview of AI Reasoning Claims 00:00

• The video discusses the claim that AI models do not actually reason but merely memorize patterns, as suggested by a recent paper from Apple.
• This claim contrasts with widespread media headlines about imminent job losses due to AI advancements.
• The speaker emphasizes a non-narrative-driven analysis after reviewing the Apple paper and related literature.

Apple Paper Findings 01:01

• The Apple paper argues that large language models (LLMs) lack the ability to follow explicit algorithms and struggle with complex puzzles.
• Examples of puzzles include the Tower of Hanoi and checkers, which reveal that LLM performance drops with task complexity.
• Traditional software would maintain consistent performance regardless of complexity, unlike LLMs.

Limitations of LLMs 03:09

• LLMs fail to perform well on large multiplication tasks without access to tools, demonstrating their probabilistic nature.
• The models can generate plausible but incorrect answers when faced with complex queries, leading to hallucinations.
• Even when provided with algorithms, models often fail due to their inherent design limitations.

Critique of the Apple Paper 06:02

• The paper reportedly abandoned its initial comparison between thinking and non-thinking models when results did not support preconceived notions.
• The models recognized their output limits but often resorted to suggesting algorithms instead of attempting to solve complex problems.

General AI Limitations and Misconceptions 08:20

• The conclusion of the paper suggests fundamental barriers to generalizable reasoning, a point not surprising to seasoned AI researchers.
• The speaker notes that LLMs can still make basic reasoning errors in simpler scenarios, highlighting the ongoing challenges in AI development.

Model Recommendations 09:05

• The speaker recommends the OpenAI 03 Pro model, despite its higher price point, citing its competitive performance in various benchmarks.
• The video also mentions Google's Gemini 2.5 Pro as a good option for free use, with notable performance in specific tests.

Conclusion and Future of AI 11:19

• The speaker underscores that while LLMs are approaching human-like performance, they are not supercomputers and need tools for accurate outputs.
• The discussion concludes with a caution about benchmarks and the importance of analyzing model performance within specific use cases.