Embedding sizes in machine learning have evolved significantly from the previously common 200-300 dimensions to modern standards that often exceed 768 dimensions due to advancements in models like BERT and GPT-3. With the rise of open-source platforms and API-based models, embeddings have become more standardized and accessible, leading to increased dimensionality and an ongoing exploration of their effectiveness in various tasks. The future of embedding size growth remains uncertain as researchers investigate the necessity and efficiency of high-dimensional embeddings.