Why AI gets the hard stuff right and the easy stuff wrong

The rapid advancement of artificial intelligence has revealed a fundamental disconnect in how we evaluate machine intelligence compared to human cognition. While traditional thinking assumes AI capabilities would progress uniformly across all tasks, modern large language models like Gemini demonstrate a peculiar pattern of excelling at complex linguistic and programming challenges while failing at basic tasks that even children can master. This inhuman development pattern challenges simplistic one-dimensional comparisons between AI and human intelligence.

The big picture: Current AI systems demonstrate capabilities that defy traditional intelligence scales, showing a development pattern fundamentally different from human cognitive evolution.

• Gemini 2.5 Pro can write complex code and communicate in multiple languages but fails at simple tasks like accurately counting words in a text or completing Pokemon games.
• This disjointed development pattern creates a cognitive profile impossible to replicate in human development, even in hypothetical controlled environments.

Why this matters: The inhuman development pattern of AI capabilities undermines the validity of one-dimensional intelligence comparisons between machines and humans.

• The author highlights how no natural environment could produce a being that masters multiple programming languages and human languages yet fails at basic counting or game completion.
• These inconsistencies reveal fundamental differences in how machine learning systems acquire and apply knowledge compared to biological intelligence.

Reading between the lines: AI’s uneven development pattern across different tasks suggests we need entirely new frameworks for evaluating machine intelligence.

• The initial expectation that AI would progress at similar rates across all capabilities has proven incorrect, challenging conventional wisdom about intelligence scaling.
• Rather than a single intelligence scale, AI may require multidimensional evaluation frameworks that account for its unique and inhuman development patterns.

Implications: Understanding AI’s unique development trajectory is crucial for realistic assessments of both its capabilities and limitations.

• The gap between expectations and reality in AI development reveals the dangers of anthropomorphizing machine intelligence or assuming it will follow human-like patterns.
• This realization could inform more nuanced approaches to AI safety, development priorities, and performance evaluation.