{
  "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": 279,
  "pageCount": 313,
  "prevPage": 278,
  "nextPage": 280,
  "slideType": "recommendation",
  "function": "recommend",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "The circular diagram illustrates a continuous loop for evidence-based AI policy.",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "process_diagram"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-0d0f98caffe1/279",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1#slide-279",
  "components": [
    {
      "bbox": {
        "h": 0.04,
        "w": 0.85,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "callout",
      "text": "We can both avoid rushed legislation based on hype and not be paralysed waiting for perfect evidence.",
      "attrs": null,
      "subkind": "primary",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ef233089-c14f-4a72-a48b-55f4b823ce25",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.3,
        "x": 0.65,
        "y": 0.33
      },
      "kind": "diagram",
      "text": "EVIDENCE-BASED AI POLICY cycle with 8 steps: Identify evidence gaps, Set requirements, Collect data, Analyse risks, Build consensus, Create protocols, Implement policy, Monitor outcomes.",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c690da24-da51-4d57-a55b-cf55d12748cc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.6,
        "x": 0.04,
        "y": 0.28
      },
      "kind": "list",
      "text": "Major AI policy decisions are being made with limited scientific understanding of risks and impacts.\nEvery policy should include mechanisms that generate evidence about whether it's working, e.g.\nMandatory pre-release testing to reveal actual capabilities before deployment. Here, the UK's AISI is doing a promising job so far.\nPublic transparency requirements about what happens inside AI companies. This is, admittedly, still lacking.\nWe could create \"if-then protocols\", i.e. pre-commit to specific actions when certain evidence emerges (e.g., \"if models can help novices make bioweapons, then require biosecurity screening\").\nThe more serious the regulation, the stronger the evidence required, but we should start gathering that evidence now through lighter-touch policies.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "cd974e77-993f-4b24-8ce8-09f4e073d892",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.45,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "Paths forward: 3) Implement science-first policy",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "1e02954c-7340-4154-9715-cd06bd17adbb",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "Evidence-based policy cycle",
      "slug": null,
      "matchId": "42bc5209-ceae-4cd0-8d51-d943ae99d831",
      "evidence": "Circular diagram showing iterative policy steps",
      "confidence": 0.8
    }
  ],
  "arcBeats": [
    {
      "to": 282,
      "from": 190,
      "beatId": "019dd95a-0682-776c-8e35-a256fe16ea9c",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": "triple-take-the-implications-so-what",
      "evidence": "Sections 3-4 frame political and safety consequences of the technical/industry trends.",
      "position": 2,
      "confidence": 70,
      "parentBeatName": "Reflection",
      "parentBeatSlug": "reflection"
    },
    {
      "to": 282,
      "from": 190,
      "beatId": "019dd95a-0682-776c-8e35-b713af60bf88",
      "arcName": "The Onion",
      "arcSlug": "onion",
      "beatName": "Core Insight",
      "beatSlug": "onion-core-insight",
      "evidence": "Politics and Safety reveal the structural risks at the core.",
      "position": 4,
      "confidence": 45,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [
    {
      "to": 282,
      "from": 247,
      "name": "Pre Mortem",
      "slug": "19-pre-mortem",
      "bestFor": "Project kick-offs, risk management, building trust with skeptical stakeholders",
      "matchId": "019dd95a-07fe-70ce-8d3e-bb4d43115ab4",
      "evidence": "Vibe hacking, AI psychosis, alignment faking, scheming, prompt injection — what could go wrong + mitigations.",
      "position": 12,
      "objective": "Surface AI safety failure modes before they become catastrophic",
      "structure": "The Future Disaster (Hypothetical) -> What Went Wrong -> The Prevention Plan",
      "confidence": 75,
      "description": "Build trust by anticipating failure modes before they happen and solving them in advance"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}