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ResearchEvidenceQuestion

Why compute might get 10x+ more expensive in coming years?

Evidence(6)

  1. OpenAI shows how additional inference improves answers

    [1]

    OpenAI reported that o1 solved 74% of 2024 AIME problems with one sample, 83% using consensus among 64 samples, and 93% with reranking of 1,000 samples. Source

    1

    The results establish a reason to spend more computation on difficult answers. Sample counts do not establish corresponding dollar-cost multiples.

  2. A fixed-benchmark inference price reaches $0.07 per million tokens

    [3]

    Stanford’s comparison records a minimum query price of $0.07 per million tokens at its specified GPT-3.5-equivalent MMLU threshold by October 2024, down from $20 in November 2022. Source

  3. AWS cuts the on-demand price of an existing H100 instance

    [2]

    AWS announced a 44% on-demand price reduction for its P5 instance using H100 GPUs. Source

  4. NVIDIA agrees to backstop unsold CoreWeave capacity

    [28]

    A September 2025 CoreWeave disclosure describes an arrangement obligating NVIDIA to purchase residual unsold capacity, subject to delivery and availability, through April 2032. Source

  5. Google reports a surge in aggregate token activity

    [5]

    At Google I/O 2026, Sundar Pichai reported more than 3.2 quadrillion monthly tokens across Google’s surfaces, versus roughly 480 trillion at the preceding year’s I/O. Source

  6. CoreWeave reports the financial burden of expansion

    [11]

    CoreWeave reported second-quarter revenue of $2.575 billion and adjusted EBITDA of $1.510 billion, alongside a $49 million GAAP operating loss and $640 million of net interest expense. Source

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