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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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  "authorName": "Air Street Capital",
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      "text": "Inference pays for training: labs strive to allocate more of a model's lifecycle compute to revenue-generating inference at the steepest margin possible.",
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      "text": "Inference pays for training: labs strive to allocate more of a model's lifecycle compute to revenue-generating inference at the steepest margin possible. Our table* below illustrates the expected return on compute costs across varying inference margins and compute allocations.",
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      "text": "AI labs mirror the foundry business: staggering investments are needed for each successive generation, where labs bear the front-loaded training expense. While recent models allegedly recoup this cost during deployment, training budgets surge. Pressure then mounts to drive inference revenue across new streams.",
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      "kind": "paragraph",
      "text": "Dario Amodei: \"If every model was a company, the model—in this example—is actually profitable.\" Despite high burn rates, speculation indicates many of the frontier labs enjoy strong unit economics on a flagship model basis.",
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      "text": "If every model was a company, the model—in this example—is actually profitable. — Dario Amodei",
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      "text": "*Simplified sensitivity analysis: neglects people costs and assumes all inference generates revenue. Can also be interpreted in terms of token count between inference & training (2DN vs. 6DN, MFU: ~15% vs. ~45%).",
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      "text": "When do we see profitable models? Or are we there yet?",
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