{
  "generatedAt": "2026-10-22T16:04:11Z",
  "url": "https://deepstack.ltd/theses/inference-majority-compute-disclosure/",
  "disclaimer": "Analysis and opinion, not investment advice.",
  "id": "inference-majority-compute-disclosure",
  "number": 4,
  "question": "Will NVIDIA state by Nov 30, 2027 that inference is over 50% of its data center compute demand?",
  "dek": "As AI shifts from building models to running them in everyday products and agents, inference calls could outgrow training runs. The question is whether NVIDIA says so on the record.",
  "family": "T01",
  "subthesis": "T01.02",
  "sector": "AI.CLOUD",
  "subsectors": [
    "AI.CLOUD.INFER",
    "AI.SEMI.GPU",
    "AI.MEM.HBM",
    "AI.MODEL.ECON"
  ],
  "relatedParts": [],
  "approvedAt": "2026-10-22T01:56:07Z",
  "sectorName": "Hyperscalers, Cloud & Compute Services",
  "api": "https://deepstack.ltd/api/v1/theses/inference-majority-compute-disclosure.json",
  "sides": [
    {
      "id": "yes",
      "label": "NVIDIA will say inference is over half of demand",
      "summary": "Rising usage converts directly into recurring compute demand once models are trained, and agentic systems that complete tasks and call tools multiply the number of inference calls per user action. As companies deploy agentic coding assistants and other always-on agents that submit requests far faster than humans, token and compute demand for running models keeps climbing relative to training. This side expects that trajectory to lead NVIDIA itself to confirm, in an earnings release, 10-Q/10-K, or official call transcript on or before November 30, 2027, that inference exceeds half of its data center compute demand.",
      "voices": [
        {
          "name": "Aurelion Research",
          "entity": "https://deepstack.ltd/people/aurelion-research/",
          "claim": "Once models are trained they must run on every use, so rising usage converts directly into recurring compute demand; agentic systems that complete tasks and use tools multiply the number of inference calls per user action.",
          "date": "2026-09-11",
          "url": "https://read.aurelionresearch.com/p/in-conversation-nvidia"
        },
        {
          "name": "Hf Best Ideas",
          "entity": null,
          "claim": "Baron argues inference is now the dominant and fastest-growing part of AI compute, that a fast-inference sub-segment has emerged, and that customer commitments rather than benchmarks validate the architecture's advantage.",
          "date": "2026-08-12",
          "url": "https://hfbestideas.substack.com/p/40-new-pitches-found-in-hedge-fund-328"
        },
        {
          "name": "Gaetano",
          "entity": "https://deepstack.ltd/people/gaetano/",
          "claim": "Training is compute-bound and GPU-focused, but inference is memory-bound and latency-sensitive. As AI moves into production across enterprises, the full system around the GPU matters more: memory capacity, context length, responsiveness, and data…",
          "date": "2026-05-14",
          "url": "https://gaetanoresearch.substack.com/p/peng-a-new-x-favorite-does-it-have"
        },
        {
          "name": "Tae Kim",
          "entity": "https://deepstack.ltd/people/tae-kim/",
          "claim": "Companies deploy agentic coding assistants and other AI agents that operate continuously without rest and submit tool requests far faster than humans, creating competitive pressure to adopt more agents for R&D and operations. This raises token…",
          "date": "2026-03-30",
          "url": "https://taekim.substack.com/p/deepseek-part-two-why-ai-skeptics"
        },
        {
          "name": "Daniel Romero",
          "entity": "https://deepstack.ltd/people/daniel-romero/",
          "claim": "Inference will be the largest driver of compute demand in the AI era. AMD's MI300X and upcoming MI355X and MI400 series excel at inference workloads and are gaining design wins with hyperscalers and cloud providers. As inference demand grows much…",
          "date": "2025-05-30",
          "url": "https://hypertechinvest.com/p/amd-the-next-10-bagger"
        },
        {
          "name": "Alex Dc",
          "entity": null,
          "claim": "Because the vast majority of an AI model's operational life is spent in inference rather than training, and NVIDIA GPUs are irreplaceable for this phase, training cost reductions do not eliminate the need for NVIDIA's hardware, preserving the…",
          "date": "2025-01-28",
          "url": "https://aimafia.substack.com/p/la-gran-mentira-china-deepseek-y"
        }
      ]
    },
    {
      "id": "no",
      "label": "NVIDIA will not make that statement",
      "summary": "NVIDIA does not make this disclosure in any dated public filing or call by the deadline. This could happen because the company keeps crediting large frontier training clusters for most of its data center growth, because 100k+ GPU training buildouts keep expanding faster than inference capacity, or because falling inference unit costs keep total inference spending below training spending even as usage grows. Training workloads concentrate in expensive, long-term contracted capacity of the kind hyperscalers and neoclouds build out explicitly for that purpose, a pattern that keeps training's share of disclosed compute demand visible and large.",
      "voices": [
        {
          "name": "Kerrisdale Capital Management LLC",
          "entity": null,
          "claim": "Training workloads require high-end GPUs and can be run remotely, aligning with CoreWeave's model of renting expensive, cutting-edge capacity on long-term contracts. Inference, by contrast, is steady-state, latency-sensitive, and cost-conscious…",
          "date": "2025-09-01",
          "url": "https://fiscal.ai/fund-letters/kerrisdale-capital-management-llc-q3-2025-fscl_document_4QJJ3MNJ637"
        }
      ]
    }
  ],
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          "label": "SEC EDGAR, XBRL company facts of CoreWeave (Forms 10-Q and 10-K)",
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      "url": "https://deepstack.ltd/docket/#test-th004-t01",
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      "id": "th004-t02",
      "part": null,
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        "question": "Did the purchases of property and equipment of CoreWeave for the quarter ended September 30, 2026 come to at or above 245.0% of its revenue?",
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          "label": "SEC EDGAR, XBRL company facts of CoreWeave (Forms 10-Q and 10-K)",
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          "baseRate": "CoreWeave's purchases of property and equipment as a share of revenue was 249.4% for the quarter ended June 30, 2026, by its filing on SEC EDGAR.",
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      "url": "https://deepstack.ltd/docket/#test-th004-t02",
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      "id": "th004-t03",
      "part": null,
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      "when": "November 9, 2026",
      "whenLabel": "Nov 9, 2026",
      "date": "2026-11-09",
      "precision": "day",
      "event": "CoreWeave, purchases of property and equipment as a share of revenue, quarter ended September 30, 2026: whether it comes in at or above 265%.",
      "status": "pending",
      "resultId": null,
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      "callExpectsBy": "Desk Editor (level A)",
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      "callProbability": 30,
      "callProbabilityAt": "2026-10-22T03:45:00Z",
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        "ref": "tests[2].date",
        "url": null
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        "question": "Did the purchases of property and equipment of CoreWeave for the quarter ended September 30, 2026 come to at or above 265.0% of its revenue?",
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          "label": "SEC EDGAR, XBRL company facts of CoreWeave (Forms 10-Q and 10-K)",
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        "specState": "approved",
        "specVersion": 1,
        "specUpdatedOn": "2026-10-22",
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        "format": "ladder",
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      "id": "th004-t04",
      "part": null,
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      "slug": "inference-majority-compute-disclosure",
      "when": "December 14, 2026",
      "whenLabel": "Dec 14, 2026",
      "date": "2026-12-14",
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      "event": "Oracle, purchases of property and equipment as a share of revenue, quarter ended November 30, 2026: whether it comes in at or above 128%.",
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      "id": "th004-t05",
      "part": null,
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      "when": "December 14, 2026",
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