{
  "product": "exmxc AI Reality Gap Index",
  "version": "1.0.0",
  "methodology_version": "reality-gap-v1",
  "snapshot_date": "2026-07-21",
  "mode": "benchmark",
  "query": null,
  "found": true,
  "count": 10,
  "formula": "Reality Gap = AI Capability Score - AI Narrative Score",
  "score_scale": "0-100",
  "interpretation": {
    "positive_gap": "Observed AI capability leads public AI narrative.",
    "negative_gap": "Public AI narrative leads observed AI capability.",
    "tolerance_band": "Gaps from -7.9 through +7.9 are classified as broadly aligned."
  },
  "methodology": {
    "narrative_weights": {
      "strategic_centrality": 0.35,
      "claim_breadth": 0.2,
      "claim_frequency": 0.2,
      "commercial_commitment": 0.25
    },
    "capability_weights": {
      "infrastructure_and_model_control": 0.2,
      "product_deployment": 0.2,
      "adoption_and_usage": 0.15,
      "monetization_evidence": 0.2,
      "proprietary_data_and_distribution": 0.15,
      "developer_and_agent_surfaces": 0.1
    },
    "rounding_policy": "Narrative and capability scores are rounded to one decimal before Reality Gap is calculated; Reality Gap is then rounded to one decimal.",
    "scoring_anchors": {
      "narrative_components": [
        {
          "score": 0,
          "meaning": "No material public AI narrative identified in the evidence window."
        },
        {
          "score": 2.5,
          "meaning": "Occasional or isolated AI claims with limited strategic or commercial emphasis."
        },
        {
          "score": 5,
          "meaning": "Recurring AI narrative across a meaningful but bounded portion of the company strategy."
        },
        {
          "score": 7.5,
          "meaning": "AI is a central, recurring strategic theme with visible commercial commitments."
        },
        {
          "score": 10,
          "meaning": "AI is pervasive across strategy, product claims, executive communication, and quantified commercial commitment."
        }
      ],
      "capability_components": [
        {
          "score": 0,
          "meaning": "No observable capability evidence identified in the evidence window."
        },
        {
          "score": 2.5,
          "meaning": "Primarily announced, preview-stage, or narrowly experimental capability."
        },
        {
          "score": 5,
          "meaning": "Limited production capability or adoption with incomplete scale or monetization evidence."
        },
        {
          "score": 7.5,
          "meaning": "Material production deployment supported by adoption, control, or monetization evidence."
        },
        {
          "score": 10,
          "meaning": "Broad, scaled, quantitatively disclosed capability with strong control of the relevant AI layer."
        }
      ],
      "third_party_dependence_penalty": [
        {
          "score": 0,
          "meaning": "No material third-party dependence limiting control of a core AI layer."
        },
        {
          "score": 2.5,
          "meaning": "Material outside dependency exists, but the company retains substantial product, data, distribution, or infrastructure control."
        },
        {
          "score": 5,
          "meaning": "A core AI capability materially depends on an outside provider, constraining direct control or economics."
        },
        {
          "score": 7.5,
          "meaning": "Multiple core AI layers are externally controlled, materially constraining differentiation and deployment autonomy."
        },
        {
          "score": 10,
          "meaning": "The observable AI proposition is predominantly dependent on externally controlled core layers."
        }
      ]
    },
    "confidence_policy": {
      "high": "Multiple quantified operating, adoption, monetization, or infrastructure disclosures directly support the principal score drivers.",
      "medium": "Official evidence supports the direction of the score, but one or more principal components rely on partial, preview-stage, aggregate, or non-segmented disclosure.",
      "low": "Official evidence is sparse, indirect, or insufficiently current; low-confidence rows should be treated as provisional."
    },
    "classifications": [
      {
        "id": "quiet_compounder",
        "label": "Quiet Compounder",
        "minimum_gap": 20,
        "maximum_gap": 100,
        "meaning": "Observed AI capability materially exceeds the company's public AI narrative."
      },
      {
        "id": "capability_leads_narrative",
        "label": "Capability Leads Narrative",
        "minimum_gap": 8,
        "maximum_gap": 19.9,
        "meaning": "Observed capability leads public narrative, but not by an extreme margin."
      },
      {
        "id": "narrative_capability_aligned",
        "label": "Narrative / Capability Aligned",
        "minimum_gap": -7.9,
        "maximum_gap": 7.9,
        "meaning": "Public narrative and observed capability are broadly aligned within the model's tolerance band."
      },
      {
        "id": "narrative_leads_deployment",
        "label": "Narrative Leads Deployment",
        "minimum_gap": -19.9,
        "maximum_gap": -8,
        "meaning": "Public narrative leads the deployment and monetization evidence currently visible."
      },
      {
        "id": "narrative_outrunning_deployment",
        "label": "Narrative Outrunning Deployment",
        "minimum_gap": -100,
        "maximum_gap": -20,
        "meaning": "Public AI narrative materially exceeds the deployment, adoption, or monetization evidence currently visible."
      }
    ],
    "evidence_policy": [
      "Scores use dated, public, company-controlled disclosures such as investor-relations releases, earnings calls, and product announcements.",
      "A product announcement is not treated as deployment when the disclosed status is preview, private beta, or future availability.",
      "Aggregate cloud or company revenue is not treated as AI revenue unless the company explicitly connects or isolates the contribution.",
      "Third-party model or infrastructure reliance is recorded as a penalty only when it limits direct control of a material AI layer.",
      "Every score is a versioned exmxc analytical judgment, not an audited fact, credit rating, or investment recommendation."
    ]
  },
  "suggestions": [],
  "results": [
    {
      "entity_id": "apple",
      "entity_name": "Apple",
      "ticker": "AAPL",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 9,
        "claim_breadth": 9,
        "claim_frequency": 8.5,
        "commercial_commitment": 9
      },
      "capability_components": {
        "infrastructure_and_model_control": 8,
        "product_deployment": 7,
        "adoption_and_usage": 5,
        "monetization_evidence": 3,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 7
      },
      "third_party_dependence_penalty": 2.5,
      "ai_narrative_score": 89,
      "ai_capability_score": 63,
      "reality_gap": -26,
      "classification": "narrative_outrunning_deployment",
      "classification_label": "Narrative Outrunning Deployment",
      "confidence": "medium",
      "summary": "Apple controls unmatched device distribution and meaningful on-device and private-cloud architecture, but its most important Siri AI claims remained in developer testing with future beta availability and no disclosed AI revenue or adoption metric at the snapshot date.",
      "evidence": [
        {
          "source_type": "official_product_announcement",
          "published_date": "2026-06-08",
          "url": "https://www.apple.com/newsroom/2026/06/apple-introduces-siri-ai-a-profoundly-more-capable-and-personal-assistant/",
          "observation": "Apple introduced a rebuilt Siri AI and next-generation foundation-model architecture, while disclosing developer-test status, later beta availability, and geographic limitations."
        }
      ]
    },
    {
      "entity_id": "adobe",
      "entity_name": "Adobe",
      "ticker": "ADBE",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 9.5,
        "claim_breadth": 10,
        "claim_frequency": 9.5,
        "commercial_commitment": 9.5
      },
      "capability_components": {
        "infrastructure_and_model_control": 6,
        "product_deployment": 8,
        "adoption_and_usage": 7,
        "monetization_evidence": 6,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 9
      },
      "third_party_dependence_penalty": 2.5,
      "ai_narrative_score": 96,
      "ai_capability_score": 72,
      "reality_gap": -24,
      "classification": "narrative_outrunning_deployment",
      "classification_label": "Narrative Outrunning Deployment",
      "confidence": "medium",
      "summary": "Adobe has genuine creative models, workflow distribution, and public AI products, but its broad agentic-infrastructure positioning ran ahead of the private-beta and public-beta deployment status disclosed across several flagship experiences.",
      "evidence": [
        {
          "source_type": "official_product_announcement",
          "published_date": "2026-06-18",
          "url": "https://news.adobe.com/news/2026/06/adobe-unveils-major-expansion",
          "observation": "Adobe disclosed public-beta creative agents across core applications, a private-beta unified Firefly studio, and distribution through several third-party AI platforms."
        }
      ]
    },
    {
      "entity_id": "salesforce",
      "entity_name": "Salesforce",
      "ticker": "CRM",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 10,
        "claim_breadth": 10,
        "claim_frequency": 10,
        "commercial_commitment": 10
      },
      "capability_components": {
        "infrastructure_and_model_control": 5,
        "product_deployment": 9,
        "adoption_and_usage": 9,
        "monetization_evidence": 8.5,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 10
      },
      "third_party_dependence_penalty": 3,
      "ai_narrative_score": 100,
      "ai_capability_score": 80.5,
      "reality_gap": -19.5,
      "classification": "narrative_leads_deployment",
      "classification_label": "Narrative Leads Deployment",
      "confidence": "high",
      "summary": "Salesforce has material Agentforce revenue, usage, data, and MCP evidence, but its totalizing agentic-enterprise narrative still exceeds the maturity and infrastructure control visible in current disclosures.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-05-27",
          "url": "https://investor.salesforce.com/news/news-details/2026/Salesforce-Delivers-Record-First-Quarter-Fiscal-2027-Results/default.aspx",
          "observation": "Salesforce disclosed $1.2 billion of Agentforce ARR, $3.4 billion of combined AI and data ARR, 3.8 billion agentic work units, and more than one million active Slack MCP users."
        }
      ]
    },
    {
      "entity_id": "meta",
      "entity_name": "Meta",
      "ticker": "META",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 10,
        "claim_breadth": 9.5,
        "claim_frequency": 10,
        "commercial_commitment": 10
      },
      "capability_components": {
        "infrastructure_and_model_control": 10,
        "product_deployment": 9,
        "adoption_and_usage": 9.5,
        "monetization_evidence": 8.5,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 8.5
      },
      "third_party_dependence_penalty": 1,
      "ai_narrative_score": 99,
      "ai_capability_score": 91.8,
      "reality_gap": -7.2,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "medium",
      "summary": "Meta's personal-superintelligence narrative remains inside the alignment band because it is backed by enormous distribution, deployed recommendation systems, first-party models, and exceptional infrastructure commitment, although AI monetization is not separately disclosed.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-04-29",
          "url": "https://investor.atmeta.com/investor-news/press-release-details/2026/Meta-Reports-First-Quarter-2026-Results/default.aspx",
          "observation": "Meta disclosed 3.56 billion daily users, its first Meta Superintelligence Labs model, $19.84 billion of quarterly capital expenditures, and raised full-year capex guidance to $125-$145 billion."
        }
      ]
    },
    {
      "entity_id": "oracle",
      "entity_name": "Oracle",
      "ticker": "ORCL",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 9,
        "claim_breadth": 8.5,
        "claim_frequency": 8.5,
        "commercial_commitment": 9.5
      },
      "capability_components": {
        "infrastructure_and_model_control": 9.5,
        "product_deployment": 8.5,
        "adoption_and_usage": 8,
        "monetization_evidence": 9,
        "proprietary_data_and_distribution": 8.5,
        "developer_and_agent_surfaces": 8.5
      },
      "third_party_dependence_penalty": 2,
      "ai_narrative_score": 89.3,
      "ai_capability_score": 85.3,
      "reality_gap": -4,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "high",
      "summary": "Oracle's AI-cloud narrative is supported by rapid OCI growth, a large contracted backlog, deployed infrastructure, and enterprise data distribution, with some dependence on external frontier-model demand.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-06-10",
          "url": "https://investor.oracle.com/investor-news/news-details/2026/Oracle-Announces-Record-Q4-and-FY-2026-Results-Driven-by-Cloud-Infrastructure--Cloud-Applications/",
          "observation": "Oracle disclosed $638 billion of remaining performance obligations, quarterly cloud revenue of $9.9 billion, and OCI revenue growth of 93%."
        }
      ]
    },
    {
      "entity_id": "nvidia",
      "entity_name": "NVIDIA",
      "ticker": "NVDA",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 10,
        "claim_breadth": 10,
        "claim_frequency": 10,
        "commercial_commitment": 10
      },
      "capability_components": {
        "infrastructure_and_model_control": 10,
        "product_deployment": 10,
        "adoption_and_usage": 10,
        "monetization_evidence": 10,
        "proprietary_data_and_distribution": 8,
        "developer_and_agent_surfaces": 10
      },
      "third_party_dependence_penalty": 0,
      "ai_narrative_score": 100,
      "ai_capability_score": 97,
      "reality_gap": -3,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "high",
      "summary": "NVIDIA's maximal AI narrative is supported by directly disclosed data-center revenue, deployed accelerated-computing platforms, and an expanding agent software surface.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-05-20",
          "url": "https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Announces-Financial-Results-for-First-Quarter-Fiscal-2027/default.aspx",
          "observation": "NVIDIA reported $81.6 billion of quarterly revenue, including $75.2 billion of Data Center revenue, and disclosed production agent and inference software surfaces."
        }
      ]
    },
    {
      "entity_id": "microsoft",
      "entity_name": "Microsoft",
      "ticker": "MSFT",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 10,
        "claim_breadth": 10,
        "claim_frequency": 10,
        "commercial_commitment": 10
      },
      "capability_components": {
        "infrastructure_and_model_control": 10,
        "product_deployment": 10,
        "adoption_and_usage": 9.5,
        "monetization_evidence": 9.5,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 10
      },
      "third_party_dependence_penalty": 1,
      "ai_narrative_score": 100,
      "ai_capability_score": 97.3,
      "reality_gap": -2.7,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "high",
      "summary": "Microsoft's pervasive AI narrative is supported by disclosed AI revenue, Azure demand, paid Copilot seats, and distribution across its cloud and productivity estate.",
      "evidence": [
        {
          "source_type": "official_earnings_call",
          "published_date": "2026-04-29",
          "url": "https://www.microsoft.com/en-us/investor/events/fy-2026/earnings-fy-2026-q3",
          "observation": "Microsoft disclosed an AI business annual revenue run rate above $37 billion, paid Microsoft 365 Copilot seats above 20 million, and continued infrastructure investment."
        }
      ]
    },
    {
      "entity_id": "amazon",
      "entity_name": "Amazon",
      "ticker": "AMZN",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 9.5,
        "claim_breadth": 9.5,
        "claim_frequency": 9,
        "commercial_commitment": 9.5
      },
      "capability_components": {
        "infrastructure_and_model_control": 10,
        "product_deployment": 9.5,
        "adoption_and_usage": 9,
        "monetization_evidence": 9,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 10
      },
      "third_party_dependence_penalty": 1.5,
      "ai_narrative_score": 94,
      "ai_capability_score": 94,
      "reality_gap": 0,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "high",
      "summary": "Amazon's AI narrative is matched by AWS scale, custom silicon revenue, deployed model and agent infrastructure, and AI integration across commerce, advertising, health, and devices.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-04-29",
          "url": "https://ir.aboutamazon.com/news-release/news-release-details/2026/Amazon-com-Announces-First-Quarter-Results/",
          "observation": "Amazon disclosed AWS quarterly revenue of $37.6 billion, a custom chips revenue run rate above $20 billion, 2.1 million AI chips landed, and production agent infrastructure across Bedrock and AgentCore."
        }
      ]
    },
    {
      "entity_id": "alphabet",
      "entity_name": "Google",
      "ticker": "GOOGL",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 10,
        "claim_breadth": 9.5,
        "claim_frequency": 9.5,
        "commercial_commitment": 10
      },
      "capability_components": {
        "infrastructure_and_model_control": 10,
        "product_deployment": 10,
        "adoption_and_usage": 10,
        "monetization_evidence": 9,
        "proprietary_data_and_distribution": 10,
        "developer_and_agent_surfaces": 10
      },
      "third_party_dependence_penalty": 0,
      "ai_narrative_score": 98,
      "ai_capability_score": 98,
      "reality_gap": 0,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "high",
      "summary": "Alphabet's AI narrative is matched by first-party models and infrastructure, consumer-scale Gemini usage, paid enterprise seats, API volume, and accelerating cloud monetization.",
      "evidence": [
        {
          "source_type": "official_earnings_call",
          "published_date": "2026-02-04",
          "url": "https://abc.xyz/investor/events/event-details/2026/2025-Q4-Earnings-Call-2026-Dr_C033hS6/default.aspx",
          "observation": "Alphabet disclosed more than 750 million Gemini monthly users, 8 million Gemini Enterprise paid seats, 10 billion first-party model tokens per minute, and sharply higher generative-AI product revenue."
        }
      ]
    },
    {
      "entity_id": "ibm",
      "entity_name": "IBM",
      "ticker": "IBM",
      "snapshot_date": "2026-07-21",
      "narrative_components": {
        "strategic_centrality": 7.5,
        "claim_breadth": 8,
        "claim_frequency": 7.5,
        "commercial_commitment": 7.5
      },
      "capability_components": {
        "infrastructure_and_model_control": 7,
        "product_deployment": 8.5,
        "adoption_and_usage": 8,
        "monetization_evidence": 8,
        "proprietary_data_and_distribution": 8.5,
        "developer_and_agent_surfaces": 8.5
      },
      "third_party_dependence_penalty": 1.5,
      "ai_narrative_score": 76,
      "ai_capability_score": 78.8,
      "reality_gap": 2.8,
      "classification": "narrative_capability_aligned",
      "classification_label": "Narrative / Capability Aligned",
      "confidence": "medium",
      "summary": "IBM's comparatively restrained enterprise-AI narrative is supported by deployed hybrid-cloud, software, consulting, and infrastructure capabilities, although the latest disclosure does not isolate the full AI revenue contribution.",
      "evidence": [
        {
          "source_type": "official_investor_release",
          "published_date": "2026-04-22",
          "url": "https://newsroom.ibm.com/2026-04-22-IBM-RELEASES-FIRST-QUARTER-RESULTS?lnk=hpln1id",
          "observation": "IBM reported $15.9 billion of quarterly revenue, double-digit software and infrastructure growth, and described AI as embedded across hybrid enterprise deployments."
        },
        {
          "source_type": "official_investor_release",
          "published_date": "2025-04-23",
          "url": "https://newsroom.ibm.com/2025-04-23-ibm-releases-first-quarter-results",
          "observation": "IBM previously disclosed a generative-AI book of business above $6 billion, providing a dated monetization anchor while the later release used broader portfolio language."
        }
      ]
    }
  ],
  "disclaimer": "Derived institutional reference layer based on public company disclosures and transparent exmxc scoring judgments; not live data or investment advice."
}