According to @godofprompt, MIT researchers demonstrated that up to 90% of a neural network’s parameters can be deleted without losing model accuracy, a finding known as the 'Lottery Ticket Hypothesis' (source: MIT, 2019). Despite this, the technique has rarely been implemented in production AI systems over the past five years. However, growing demand for cost-effective and scalable AI solutions is now making network pruning a production necessity, with the potential to reduce inference costs by up to 10x (source: Twitter/@godofprompt, 2026). Practical applications include deploying more efficient AI models on edge devices and in enterprise settings, unlocking significant business opportunities for companies seeking to optimize AI infrastructure spending.