Scale, Emergence & Evaluation

The AI field has been shaped by a surprising and still not fully understood phenomenon: making models bigger often makes them qualitatively better in ways nobody predicted. This goes beyond simply getting more accurate - at certain scales, models develop entirely new capabilities that weren't present in smaller versions. But "bigger is better" is an oversimplification, and the relationship between scale and capability is more nuanced than early results suggested. Equally important is the question of how we measure whether AI systems are actually any good. Benchmarks - standardised tests for AI - have been essential for tracking progress, but they have significant limitations that can mislead both researchers and buyers. As models have become more capable and their applications more diverse, evaluation has become one of the field's most pressing challenges. You can't improve what you can't measure, and current measurement tools are struggling to keep pace with the systems they're trying to assess. Understanding both the promise and the pitfalls of scale and evaluation helps cut through marketing claims and make more informed decisions about which AI capabilities are real and which are hype.