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AI systems frequently fail in real-world applications despite impressive benchmark scores. The gap stems from benchmark saturation, data contamination, and fundamental architectural limitations in spatial reasoning and abstraction. Vision-language models struggle with basic counting tasks, autonomous vehicles cause fatal accidents, and facial recognition systems exhibit racial bias. Current systems excel at pattern matching within training distributions but lack robust world models and causal understanding. Trust in AI is declining as users experience the disconnect between marketing promises and actual performance. Addressing these failures requires dynamic benchmarks, architectural innovations beyond pure neural networks, and honest communication about system limitations.

Brilliant on Paper, Blind in Practice: Why AI Systems Fail Us

Exploring the intersections of artificial intelligence, decentralised cognition, posthuman ethics, society and culture.