{
  "docId": "019dd923-5e88-73ef-bd5d-8f9067e49fd6",
  "docSlug": "67108284e9fcdd32",
  "documentTitle": "2024 Benedict Evans 2024 AI eats the world",
  "authorId": "BenedictEvans",
  "authorName": "Benedict Evans",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "independent_analyst",
  "sourceTypeLabel": "Independent analyst",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 21,
  "pageCount": 87,
  "prevPage": 20,
  "nextPage": 22,
  "slideType": "diagnosis",
  "function": "diagnose",
  "density": "sparse",
  "nDataPoints": 0,
  "notes": "The slide uses a simple linear process diagram to frame the core value proposition of machine learning.",
  "elementsJson": [
    "process_diagram"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-8f9067e49fd6/21",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-8f9067e49fd6",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-8f9067e49fd6.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-8f9067e49fd6#slide-21",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "What can we turn into pattern recognition?",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-4e51-732b-a104-76094d5eec35",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.26,
        "w": 0.75,
        "x": 0.203,
        "y": 0.408
      },
      "kind": "diagram",
      "text": "Image recognition works now! -> And so do lots of other unsolved problems -> This is pattern recognition -> What can we turn into pattern recognition?",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5b1529cb-ae68-471c-992b-06797c87b9ee",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.028,
        "w": 0.415,
        "x": 0.203,
        "y": 0.13
      },
      "kind": "paragraph",
      "text": "What’s the right level of abstraction to understand this?",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "aec3353a-c2bd-4efa-8e4e-07a822bc9713",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.055,
        "w": 0.597,
        "x": 0.203,
        "y": 0.055
      },
      "kind": "title",
      "text": "2013: why is Machine Learning useful?",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "23573a2a-5f23-4b7c-8de9-84bb9dc67874",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-1b83-7130-933f-d9a4070faf02",
      "evidence": "Subhead 'What can we turn into pattern recognition?' is the answer.",
      "confidence": 75
    }
  ],
  "frameworks": [
    {
      "name": "causal-chain",
      "slug": null,
      "matchId": "ba1d8354-92d1-4cd1-a3d6-be4b8dfa39a1",
      "evidence": "The slide presents a linear sequence of logical steps leading to a conclusion.",
      "confidence": 0.8
    }
  ],
  "arcBeats": [
    {
      "to": 47,
      "from": 14,
      "beatId": "019dd95a-07a7-7579-bd65-2d38f59889ae",
      "arcName": "The Onion",
      "arcSlug": "onion",
      "beatName": "First Layer",
      "beatSlug": "onion-first-layer",
      "evidence": "Section 'A platform shift?' — S-curves, value capture pattern.",
      "position": 2,
      "confidence": 82,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    },
    {
      "to": 72,
      "from": 14,
      "beatId": "019dd95a-07a7-7579-bd65-41b6cdc28844",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": "triple-take-the-implications-so-what",
      "evidence": "Platform shift, value capture, agents, AGI debate.",
      "position": 2,
      "confidence": 55,
      "parentBeatName": "Reflection",
      "parentBeatSlug": "reflection"
    }
  ],
  "loops": [
    {
      "to": 31,
      "from": 20,
      "name": "Before After",
      "slug": "21-before-after",
      "bestFor": "Product demos, process improvements, ROI justification",
      "matchId": "019dd95a-08f8-7619-ac2e-a43c928c9371",
      "evidence": "Mirrored titles '2013: why is ML useful?' and '2023: why is Generative ML useful?'",
      "position": 3,
      "objective": "Contrast 2013 ML utility framing with 2023 GenML utility/risks",
      "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta",
      "confidence": 75,
      "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}