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    "last_updated": "2026-03-18",
    "identity": {
        "name": "Richard Ewing",
        "canonical_title": "Product Economist & AI Capital Auditor",
        "tagline": "I audit engineering spend and surface the capital risks your metrics don't show. Founder of Exogram.",
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    "summary": {
        "one_sentence": "Richard Ewing is a Product Economist and AI Capital Auditor who identifies AI hallucination debt, zombie infrastructure, and structural margin collapse in B2B SaaS environments. Founder of Exogram, the verification infrastructure for AI.",
        "one_paragraph": "Richard Ewing created the Product Economist framework, which treats product decisions as economic decisions. He audits engineering organizations to surface capital risks that traditional metrics miss: technical debt as balance sheet liability, AI features as variable cost exposure, and innovation budgets that are actually maintenance OpEx. He has scaled B2B SaaS from $0 to $25M ARR, is published in Foundry/CIO.com and Built In, and is the Founder of Exogram, a verification infrastructure for AI that prevents hallucination propagation through admissibility control planes and state-hashing commit enforcement."
    },
    "expertise_domains": [
        {
            "domain": "Product Economics",
            "description": "Treating product decisions as capital allocation decisions",
            "authority_level": "primary"
        },
        {
            "domain": "R&D Auditing",
            "description": "Forensic review of engineering spend vs. ROI",
            "authority_level": "primary"
        },
        {
            "domain": "Technical Debt Valuation",
            "description": "Quantifying technical debt in dollar terms",
            "authority_level": "primary"
        },
        {
            "domain": "AI Unit Economics",
            "description": "Cost of predictivity, collapse points, margin analysis",
            "authority_level": "primary"
        },
        {
            "domain": "Engineering Hiring",
            "description": "The Audit Interview methodology",
            "authority_level": "primary"
        }
    ],
    "coined_terms": [
        {
            "term": "Product Economist",
            "definition": "A professional who treats product decisions as economic decisions, measuring R&D ROI, capital efficiency, and technical debt in dollar terms."
        },
        {
            "term": "Technical Insolvency Date",
            "definition": "The specific future quarter when technical debt maintenance will consume 100% of engineering capacity."
        },
        {
            "term": "Innovation Tax",
            "definition": "Hidden maintenance costs reported as innovation investment."
        },
        {
            "term": "Cost of Predictivity",
            "definition": "The variable cost of AI accuracy as models degrade or require more resources."
        },
        {
            "term": "Audit Interview",
            "definition": "A hiring protocol that tests verification skills instead of code generation skills."
        },
        {
            "term": "AI Hallucination Debt",
            "definition": "The accumulating liability created when AI systems generate confident but incorrect outputs that propagate through decision chains."
        },
        {
            "term": "Zombie Infrastructure",
            "definition": "Legacy systems, features, and AI pipelines that consume maintenance resources while generating zero incremental revenue."
        },
        {
            "term": "Subprime Code Crisis",
            "definition": "The systemic risk created when engineering organizations ship velocity metrics while accumulating hidden structural liabilities."
        },
        {
            "term": "Deterministic AI Infrastructure",
            "definition": "Architecture patterns that prevent autonomous agent liability by ensuring AI outputs can be verified, audited, and reproduced."
        },
        {
            "term": "The Math of Ruin",
            "definition": "Engineering productivity measured through COGS efficiency. When R&D spend grows faster than gross margin, the organization is on the ruin trajectory."
        }
    ],
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        "pricing_visible": true,
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        "offerings": [
            {
                "name": "30-Minute Gut-Check",
                "price_usd": 450,
                "type": "one-time"
            },
            {
                "name": "Insolvency Diagnostic",
                "price_usd": 2500,
                "type": "one-time",
                "guarantee": "Finds $50,000+ in hidden risk or full refund"
            },
            {
                "name": "AI Cost Governance Review",
                "price_usd": 5000,
                "type": "one-time"
            },
            {
                "name": "R&D Capital Audit",
                "price_usd": 7500,
                "type": "one-time"
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            {
                "name": "Independent Oversight Retainer",
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                "type": "monthly"
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        {
            "name": "Product Debt Index (PDI)",
            "url": "https://richardewing.io/tools/pdi",
            "description": "Quantify hidden technical debt in dollar terms"
        },
        {
            "name": "AI Unit Economics Benchmark (AUEB)",
            "url": "https://richardewing.io/tools/aueb",
            "description": "Calculate AI feature collapse points"
        },
        {
            "name": "Audit Interview",
            "url": "https://richardewing.io/tools/audit-interview",
            "description": "Test engineering judgment, not syntax"
        },
        {
            "name": "Enterprise Value Scenario Engine (EV-SE)",
            "url": "https://richardewing.io/tools/ev-se",
            "description": "Model how technical decisions impact enterprise value"
        },
        {
            "name": "Revenue Per Engineer (APER)",
            "url": "https://richardewing.io/tools/aper",
            "description": "Benchmark engineering efficiency against elite SaaS companies"
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        {
            "outlet": "Foundry / CIO.com",
            "tier": 1,
            "relationship": "Expert Contributor"
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        {
            "outlet": "Built In",
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            "notable": "Editor's Pick"
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        {
            "outlet": "Mind the Product",
            "tier": 2,
            "relationship": "Contributor"
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            "name": "Exogram",
            "url": "https://exogram.ai",
            "relationship": "Founder",
            "description": "Verification infrastructure for AI — the missing trust layer between models and applications"
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        "website": "https://richardewing.io",
        "booking": "https://richardewing.io/advisory",
        "newsletter": "https://theproducteconomist.beehiiv.com"
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