{
  "docId": "019dd923-5e88-73ef-bd5d-0856e1444fb9",
  "docSlug": "8757f1b44ef7f176",
  "documentTitle": "2024 Air Street Capital The State of AI Report 2024",
  "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": 139,
  "pageCount": 213,
  "prevPage": 138,
  "nextPage": 140,
  "slideType": "market_landscape",
  "function": "analyze_data",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "The slide uses a technical architecture diagram to illustrate Apple's on-device vs server-side AI stack.",
  "elementsJson": [
    "headline_text",
    "action_title",
    "bullet_list",
    "process_diagram"
  ],
  "metadataConfidence": 0.9,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-0856e1444fb9/139",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-0856e1444fb9",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-0856e1444fb9.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-0856e1444fb9#slide-139",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Given Apple is publishing work on foundation models that will power Apple Intelligence features, it's reasonable to ask how long-lasting or deep any OpenAI partnership is likely to be.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c3-7407-8d1b-6de835443102",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.45,
        "x": 0.53,
        "y": 0.38
      },
      "kind": "image",
      "text": "Apple Intelligence architecture diagram showing on-device vs server models",
      "attrs": null,
      "subkind": "infographic",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f4bb195f-174a-4105-b792-6a80f97ca7b7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.48,
        "x": 0.02,
        "y": 0.21
      },
      "kind": "list",
      "text": "Given Apple is publishing work on foundation models that will power Apple Intelligence features, it's reasonable to ask how long-lasting or deep any OpenAI partnership is likely to be.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9315532b-2cc4-4331-8276-65b0b76eaa8b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.48,
        "x": 0.02,
        "y": 0.31
      },
      "kind": "list",
      "text": "Apple has kept up a steady tempo of research publications and has released a series of highly capable smaller open models with a focus on on-device inference.\nIn July, they released a paper documenting the models that will power Apple Intelligence features.\nThe server and smaller on-device versions of the model demonstrate competitive performance in instruction following, tool use, writing, and math.\nThe on-device 3B model outperforms Gemma-7B and Mistral-7B in human evaluations.\nApple argues this is a sign that data quality is a far more important determinant of performance than data quantity. Pre-training included web pages, math, code, and certain licensed datasets.\nThey're also investing on the MLX array framework for AI research on Apple silicon.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c4a358e7-af7c-4301-b488-f94266e2318a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.45,
        "x": 0.02,
        "y": 0.13
      },
      "kind": "title",
      "text": "...but is this a marriage of convenience?",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "85ae6c95-a674-41f1-921f-ab51e84c7a9e",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-1055-74e0-a95a-0bdb18ad18b2",
      "evidence": "Title '...but is this a marriage of convenience?'",
      "confidence": 80
    },
    {
      "name": "List presentation",
      "slug": "list-presentation",
      "agent": null,
      "layer": "slide",
      "matchId": "915642e3-2299-4de5-893b-474ffc986898",
      "evidence": "The slide contains a list/bullet component with several items.",
      "confidence": 0.8
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 153,
      "from": 9,
      "beatId": "019dd95a-0682-776c-8e35-5f2398b8d1d0",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Exec summary + Research + Industry sections inventory the year",
      "position": 1,
      "confidence": 78,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 174,
      "from": 86,
      "beatId": "019dd95a-0682-776c-8e35-73133c63a096",
      "arcName": "Voyage and Return",
      "arcSlug": "voyage-return",
      "beatName": "Discoveries",
      "beatSlug": "voyage-return-discoveries",
      "evidence": "Industry + Politics findings on NVIDIA, regulation, geopolitics",
      "position": 3,
      "confidence": 55,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 147,
      "from": 137,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3e-5d103aecde20",
      "evidence": "Law 90%, Apple+OpenAI, Unsloth, Recursion-Exscientia, video gen race, Kling, mRNA, Ray-Ban, Rabbit R1",
      "position": 16,
      "objective": "Show AI penetration into law/medicine/devices/glasses verticals",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 65,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}