{
  "docId": "019dd923-5e88-73ef-bd5d-0d0f98caffe1",
  "docSlug": "5df6bd1b0447b5f6",
  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
  "authorId": "AirStreetCapital",
  "authorName": "Air Street Capital",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 13,
  "pageCount": 313,
  "prevPage": 12,
  "nextPage": 14,
  "slideType": "industry_trends",
  "function": "analyze_data",
  "density": "overcrowded",
  "nDataPoints": 16,
  "notes": "The slide highlights the shift towards inference-time scaling (test-time compute) as a key driver for AI reasoning capabilities.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "scatter_plot",
    "quote_block"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-0d0f98caffe1/13",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1#slide-13",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "The intelligence of an AI model roughly equals the log of the resources used to train and run it.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a81a-9ae182ee3c87",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.25,
        "x": 0.35,
        "y": 0.45
      },
      "kind": "chart",
      "text": "o1 AIME accuracy at test time",
      "attrs": null,
      "subkind": "scatter",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "30191025-487b-40d9-a958-3eaea43e8f93",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.25,
        "x": 0.05,
        "y": 0.45
      },
      "kind": "chart",
      "text": "o1 AIME accuracy during training",
      "attrs": null,
      "subkind": "scatter",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "bb21484f-b9f0-44bc-a93a-a421c83e6efd",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "pass@1 accuracy",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a81a-a2a52217fa5d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.9,
        "x": 0.05,
        "y": 0.22
      },
      "kind": "paragraph",
      "text": "As 2024 drew a close, OpenAI released o1-preview, the first reasoning model that demonstrated inference-time scaling with RL using its CoT as a scratch pad. This led to more robust problem solving in reasoning-heavy domains like code and science.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f6bde990-6310-4465-a941-f67725fb5327",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.3,
        "x": 0.65,
        "y": 0.55
      },
      "kind": "quote",
      "text": "The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute.",
      "attrs": null,
      "subkind": "pull-quote",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5d6be037-f526-4c1b-9e97-d75d0212b063",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "quote",
      "text": "The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude. — Sam Altman",
      "attrs": null,
      "subkind": null,
      "toolName": "Authority citation",
      "toolSlug": "authority-citation",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a81a-9f73f352725b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.6,
        "x": 0.05,
        "y": 0.14
      },
      "kind": "title",
      "text": "Think before you speak: o1 “thinking” ignites the reasoning race",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6ba1e411-7c87-443b-80ab-32c7561f17cf",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Data Story Arc",
      "slug": "data-story-arc",
      "agent": "Storyteller",
      "layer": "loop",
      "matchId": "019dd95a-1055-74e0-a95b-da65e32266cf",
      "evidence": "Opens reasoning loop with context/conflict before drilling into specific systems.",
      "confidence": 70
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95b-d12145b0f7f6",
      "evidence": "Title 'Think before you speak: o1 thinking ignites the reasoning race' states the insight, not the topic.",
      "confidence": 90
    },
    {
      "name": "AIDA Model",
      "slug": "aida-model",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "beab5d1b-1ccb-4030-bab5-4ea5bd04126a",
      "evidence": "The intelligence of an AI model roughly equals the log of the resources used to train and run it.",
      "confidence": 0.7
    },
    {
      "name": "Metaphor & Analogy",
      "slug": "metaphor-analogy",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95b-d67843047ded",
      "evidence": "Anthropomorphic 'think before you speak' metaphor for inference-time compute.",
      "confidence": 80
    },
    {
      "name": "Pyramid Principle",
      "slug": "pyramid-principle",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "bf54838a-a1d4-4987-a30f-8b2a405fab90",
      "evidence": "The intelligence of an AI model roughly equals the log of the resources used to train and run it.",
      "confidence": 0.6
    }
  ],
  "frameworks": [
    {
      "name": "scaling-laws",
      "slug": null,
      "matchId": "cb494a76-5963-4499-9f74-00005e590e76",
      "evidence": "Reference to compute scaling and log-based intelligence growth",
      "confidence": 0.9
    }
  ],
  "arcBeats": [
    {
      "to": 189,
      "from": 12,
      "beatId": "019dd95a-0682-776c-8e35-9f380673831f",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Sections 1-2 lay out research findings and industry data with charts and case studies.",
      "position": 1,
      "confidence": 70,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 89,
      "from": 12,
      "beatId": "019dd95a-0682-776c-8e35-ac68557d958b",
      "arcName": "The Onion",
      "arcSlug": "onion",
      "beatName": "First Layer",
      "beatSlug": "onion-first-layer",
      "evidence": "Research section unpacks technical layer beneath the headlines.",
      "position": 2,
      "confidence": 45,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 30,
      "from": 13,
      "name": "Zoom In",
      "slug": "06-zoom-in",
      "bestFor": "Technical deep-dives, case studies, detailed analysis",
      "matchId": "019dd95a-07fe-70ce-8d3e-9252f6f92bd7",
      "evidence": "Big picture 'reasoning race' (p.13) zooms into o1, R1, DeepSeek, parallel reasoning, CoT mechanics.",
      "position": 2,
      "objective": "Drill from reasoning paradigm into specific reasoning research findings",
      "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication",
      "confidence": 75,
      "description": "Start broad, then progressively focus on specific details that prove your point"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}