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I think some bears underestimate $PLTR's model expertise. We have argued that Palantir had more model expertise than it had shown publicly, and that lacking its own foundation model did not put it at the competitive disadvantage those bears assumed. The earlier NVIDIA partnership, which included NeMo Retriever and Nemotron, was my first clue. I see the public Nemotron results as evidence of that expertise. These results fit our argument and were what I expected. Palantir's slide on McCarthy's construction requests for information shows a tuned Nemotron model outperforming Sonnet and Astra on classification. FourWeekMBA reads the chart, which has no data labels, as approximately 73% balanced accuracy for tuned Nemotron, approximately 66% for Sonnet and approximately 62% for Astra. On a cost basis the slide leaves unexplained, FourWeekMBA places Nemotron at approximately $0.10 per million tokens. The slide leaves open whether that includes hosting, tuning and Palantir's software fees. I would want the full customer cost comparison to judge the savings. I still regard Nemotron as a weak starting model. If Western developers make state-of-the-art open-source models available, I expect Palantir to use customer data, task-specific benchmarks and tuning to deliver further improvements in performance and cost. That could make Palantir economical for smaller enterprises as well as very large ones. Savings on model usage could cover Palantir's software premium if customers can move enough work to cheaper tuned models while maintaining the quality they need. As customers spend more on tokens relative to software, the potential savings grow. I expect Palantir's model expertise to become more valuable because it could reduce a larger part of the customer's total bill.

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