The bulk of spending in AI large language model (LLM) development is still dominated by compute, specifically, the compute needed to train and run models[1]. Training costs remain extraordinarily high and are rising fast, often exceeding $100 million per model today[1].
Even as the cost to train models climbs, a growing share of total AI spend is shifting toward inference, the cost of running models at scale in real-time[1]. As inference becomes cheaper, AI gets used more[1]. And as AI gets used more, total infrastructure and compute demand rises, dragging costs up again[1].
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