tech
December 3, 2025
Embeddings Are AI’s Red-Headed Stepchild
Embedding models are the red-headed stepchild of AI. Not as sexy as image generation, nor as headline-grabbing as LLM chatbots, nor as apocalyptic as predictions of artificial superintelligence, semantic embeddings are esoteric and technical, and regular consumers haven’t got much direct use for them.
TL;DR
- Embedding models are foundational to AI but less recognized than image generators or chatbots.
- They represent semantics as vectors in high-dimensional spaces, a concept improved by neural networks and transformers.
- Generative models and embedding models share similar transformer architectures but differ in training and use (unidirectional vs. bidirectional attention).
- Historically, generative models were thought to be less suitable for embeddings due to the curse of dimensionality, but this is changing with smaller, high-performance models.
- Adapting generative models for embeddings offers economic benefits by utilizing existing pre-training resources and allows for knowledge transfer, as seen with multimodal embeddings.
- Despite not having flashy demonstrations, embedding models perform critical real-world tasks like information retrieval, classification, and fraud detection.
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