"One, I want you to be able to take away from this conversation. One, I want you to understand the value of proprietary data for AI. Data is really the only value add in AI. When Meta unleashed their weights with Llama models, they basically told the entire world that the only valuable thing here is the data. I think people often forget that conclusion with it."
"Really, the novelty for any enterprise or even any startup is what you can do with the data that you have. Whether that's in training or in serving, it really comes down to the data. Then that leads to the next part that I want you to take away from, which is understanding the complexity of data. It turns out, for a bunch of reasons that we'll talk about, working with data for AI products is just really hard."
Proprietary data provides the primary competitive advantage for AI, determining novelty for enterprises and startups in both training and serving. Model weights without unique data have limited commercial value. Data complexity introduces practical and technical challenges that make building AI products difficult. Open-source frameworks and tools such as distributed training platforms, Kubeflow pipelines, feature stores like Feast, retrieval-augmented generation, and agent frameworks can help manage data and operational complexity. Production AI requires integrated infrastructure for inference, data management, and pipelines to deliver reliable, maintainable systems in regulated industries like banking and FinTech.
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