Data Is All You Need

In June 2017, Google's "Attention Is All You Need" paper introduced the Transformer architecture, revolutionizing AI by replacing sequential processing with parallel self-attention, which eventually led to the development of large language models like GPT and BERT. As the model landscape has become commoditized, the focus has shifted from models to data infrastructure, emphasizing the importance of how data is stored, transformed, and retrieved. The text argues that similar to how self-attention improved model performance, declarative multimodal data infrastructure, exemplified by Pixeltable, can overcome existing bottlenecks in AI application infrastructure by unifying storage, orchestration, and retrieval processes. This new approach replaces traditional ETL pipelines with computed columns, standalone vector databases with embedding indexes, and agent frameworks with persistent memory, allowing for more efficient and scalable data processing by focusing on the incremental computation of only changed or new data. This paradigm shift positions Pixeltable as a potential leader in the emerging field of multimodal data infrastructure, where the quality of AI applications depends on the robustness of their underlying data infrastructure rather than the models themselves.

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