The effectiveness of AI models can vary significantly based on how they interpret and apply rubrics, with some being rigid and others more adaptable to exceptions.

The expectation versus reality of AI adoption is often skewed; while expectations are high, the actual capabilities of current models may not meet those expectations, leading to a rapid adjustment in perception as models improve.

AI is effective for predictable tasks but struggles with unpredictable or cutting-edge programming challenges.

It's difficult to find interesting and realistic threat models for AI systems that can effectively demonstrate their capabilities.

Current AI, including models like GPT-4, does not exhibit true intelligence; it makes mistakes that humans wouldn't, such as confidently stating incorrect numerical comparisons.

Models can struggle with tasks requiring expertise and taste, such as evaluating art, which presents a significant challenge for AI.

The ability to predict the next word in a sequence is deceptively complex and requires a model of reality, which is a significant achievement in AI.

AI is both a general-purpose and highly specialized technology, requiring local expertise to fine-tune models for specific needs.

The AI industry suffers from a lack of rigorous evaluations and tests that demonstrate the frontier of model capabilities.