How a 700-Year-Old Principle Helps Engineers Build Smarter AI Systems

Occam's razor, a medieval philosophical principle favouring simpler explanations, has direct practical applications in modern AI development and deployment. In machine learning, overfitting occurs when a model is over-tuned to training data and fails to generalise to new inputs — a problem solved by keeping models simpler. The same logic applies when selecting AI models: engineers should choose the least complex option that meets the required quality bar, rather than defaulting to the most powerful one. On platforms like Amazon Bedrock, this means evaluating affordable, fast models such as Claude Haiku or Amazon Nova before reaching for expensive frontier models that can cost 10 to 100 times more per token. The core lesson is to define what 'good enough' looks like for a given task, test options rigorously, and pick the simplest solution that reliably gets there.