{
 "meta": {
  "title": "GTM World Model",
  "version": "3.1",
  "built": "2026-05-31",
  "language": "en-US",
  "purpose": "A computational world model of go-to-market that covers the full vocabulary (gtm_vocabulary.json v2, 195 terms) and explains how the field actually behaves: what is true-by-identity, what is causal-but-regime-dependent, and what is merely correlational. Designed so that an agentic GTM system (e.g. an SDR/account-intelligence agent like LangChain's) plugs into the execution tier while reading constraints and objectives from the strategy and economics tiers. The model is a LEARNING object: human overrides are treated as labeled corrections to behavioral coefficients.",
  "relationship_to_vocabulary": "Every vocabulary term is assigned to exactly one tier and one role. The vocabulary supplies the NODES; this file supplies the TIERS, the STATE VARIABLES, the typed EDGES (identity/causal/correlational), the EQUATIONS, and the AGENT INTERFACE. A glossary lists entities; a world model states how entities produce each other.",
  "core_thesis": "GTM is an exact accounting identity (the MRR walk) summarized by regime- and assumption-dependent ESTIMATORS, wrapped in a behavioral membrane whose conversion coefficients are CONDITIONAL on a buyer-state that is largely determined BEFORE the seller funnel begins. Revenue is generated piecewise: multiplicatively by product-market fit (Phi) where switching costs are low, and additively by a switching-cost moat (S) where they are high. A macro regime scalar (Psi) sets whether the market prices growth or free cash flow. The field rewards getting the STRUCTURE right -- which tier owns a decision, whether a link is identity/estimator/conditional, and where the model must refuse to predict -- far more than getting any single coefficient right.",
  "how_to_read": "Start with tiers (the three layers). Then state_variables (what the system tracks). Then edges (how variables move each other, each tagged with epistemic_type). Then equations (the exact identities). Then agent_interface (how an executing agent maps onto the model, using LangChain as the worked example). Then theses (the load-bearing claims) and failure_modes (how models of this field go wrong).",
  "changelog": [
   "v1.0: three tiers + substrate over vocabulary v2 (195 terms); state vars, typed edges, equations, agent interface (LangChain worked example).",
   "v1.1: aligned to vocabulary v2.1 (219 terms). Added the Sense-Reason-Act-Learn cycle as the atomic unit of Tier 1; added agent_properties (inverted cost profile + compounding failure); added the FinOps->CAC edge to the economics tier; added coefficient-compression as a distributional effect on r_i; added governance instruments (task-to-role matrix, escalation paths, data-quality gates, audit logging) to the agent interface; added the GTM-AI maturity ladder and phased-rollout as adoption structure. Sources: Apollo, Poexis, Wyzard (+ LangChain)."
  ],
  "revision_note": "v2.0 is a structural rebuild following an adversarial audit, four independent construction reports, and ~70 documented agentic-GTM implementations. v1.1 was diagnosed as single-firm, seller-centric, steady-state, and overfit to a mid-2010s ZIRP sales-led-SaaS regime it never named. v2.0 (a) demotes Tier-2 'identities' to model-dependent ESTIMATORS, keeping only the MRR walk as exact; (b) makes the PMF thesis PIECEWISE (multiplicative in low-switching-cost segments, additive + a switching-cost moat S in high-switching-cost segments); (c) adds a macro regime scalar Psi above Tier 2 that re-weights the growth/FCF objective; (d) adds a buyer-state tier B and a brand-stock B_r that together form a pre-funnel 'Tier 0'; (e) adds explicit PLG-loop equations; (f) operationalizes PMF (Phi) ex-ante so T7 becomes (in principle) testable; (g) declares a ~25-40% irreducible-noise floor with a prediction-refusal rule. Each added term is either measurable today or explicitly marked unmeasurable in the measurement_gap_register.",
  "epistemic_honesty": "This model marks the boundary between specification and speculation on every added component. Established forms (the MRR walk; Fader-Hardie sBG/BdW survival; Nerlove-Arrow adstock; Bessemer Rule-of-X; the 6sense/LinkedIn buyer-state findings) are labeled as such. Proposed forms with no fitted data (the Phi x S x Psi interaction coefficients; the S regime threshold; brand alpha/delta for SaaS) are labeled 'proposed, untested'. Vendor-sourced magnitudes (6sense, LinkedIn B2B Institute) are flagged: the DIRECTION is well-supported, the exact percentages are commercially interested. The measurement_gap_register lists every parameter the model needs but cannot currently measure from public data.",
  "vocabulary_terms_count": 219,
  "prior_version": "3.0 (backed up at gtm_world_model_v3.0_backup.json)",
  "posture_v3": "v3.0 is the MAXIMAL synthesis: it folds in every substantive structural point raised across the twelve adversarial critiques as first-class modeled content, rather than excluding the unmeasurable to stay parsimonious. This is a deliberate reversal of the v2.x disciplined-minimal direction. The cost is explicit and accepted: v3.0 is MORE COMPLETE and LESS FALSIFIABLE than the minimal core, and a larger share of its constructs are hypotheses without measurement protocols. The single safeguard against the model rotting into an after-the-fact narrative engine is that EVERY added construct is tagged with both an epistemic_type and a measurement_status (see measurement_status_legend). Maximal breadth; honest labels. Treat the model as a MAP of the whole territory whose legend tells you which regions are surveyed and which are sketched.",
  "runnable_seed": "The one executable piece is gtm_engine_slice.py \u2014 the MRR-walk identity + an ex-ante, falsifiable Phi prior + an enforced refusal gate. v3.0 is the comprehensive map; that file is the only surveyed-and-walkable trail on it so far.",
  "v3_1_note": "v3.1 removes the hidden subscription assumptions from the 'general' spine. The ten-kinds stress test showed v3.0 was a recurring-revenue model wearing a universal costume: it assumed stock=revenue, one binding-gate type, a single stock, that an equilibrium exists, and that value lands in-period. Each kind that broke broke exactly one of these. v3.1 adds 8 generalizations that make the spine model-agnostic, plus 10 instantiable kind-templates. These are STRUCTURAL upgrades \u2014 they let the model REPRESENT each kind honestly; they do not make the unmeasurable cells forecastable. Richer skeleton, same ceiling."
 },
 "epistemic_types": {
  "identity": "True by definition/accounting. Holds in every company, market, and era. Never fitted to data; used as a hard constraint. Counterfactuals on identity edges are exact. Example: ARR = MRR * 12.",
  "causal_regime": "A genuine cause-effect relationship, but with an effect size that is regime-local (depends on market, stage, channel saturation, era) and arrives with a lag. Must be estimated per-context, carries a confidence interval, and is assumed to DRIFT \u2014 re-estimate on a schedule. Example: shortening speed-to-lead raises conversion.",
  "correlational": "Two variables move together, but the link is confounded (usually by product-market fit or the macro cycle) or directionally ambiguous. Must NOT be used for high-confidence counterfactuals. Useful for regime-detection and prior-setting, not point prediction. Example: high-NRR companies also have efficient CAC.",
  "latent_multiplier": "An unobservable variable inferred from the joint pattern of observables, entering the model multiplicatively (not additively). Drives many observables at once, which is why it confounds. Example: product-market fit (Phi).",
  "estimator": "A summary of the MRR-walk identity computed under explicit, contestable assumptions (segmentation, horizon, retention model, accounting basis). NOT true by definition. LTV, NRR, GRR, CAC-payback, Magic Number, Rule-of-40 are all estimators. Counterfactuals on estimator edges are only as valid as the stated assumptions. Example: LTV = ARPA*margin/churn is the estimator you get ONLY under homogeneous exponential churn; under heterogeneous churn (Fader-Hardie) it overstates 2-3x. Replaces the v1.1 error of tagging these as 'identity'.",
  "conditional_coefficient": "A Tier-1 conversion coefficient (e.g. Opp->Close) that is NOT a measure of seller skill but a mixture over an unobserved buyer-state B. Safe only for prediction within a fixed B-segment, never for the counterfactual 'more seller activity -> proportionally more wins'. The de-confounded estimand is P(win | seller_touch, B0, Psi, S, Phi). Demotes the v1.1 treatment of funnel coefficients from causal to conditional."
 },
 "tiers": {
  "tier_3_strategy": {
   "name": "Strategy / Configuration tier (slow-moving, human-owned)",
   "tempo": "quarters to years",
   "owned_by": [
    "Founders",
    "Product Marketing",
    "GTM leadership"
   ],
   "what_it_decides": "WHO you sell to, WHAT you claim, HOW you sell, WHERE you reach them, and WHETHER the product earns demand. These are largely FORCED MOVES given the economics, not free preferences.",
   "is_input_to": "The agent reads these as configuration; it does not get to change them. In LangChain's system, the ICP, the motion, and the warm/cold playbook are encoded in the agent's SKILL and do-not-send rules \u2014 decided by humans, handed to the agent.",
   "vocabulary_terms": [
    "Go-to-Market (GTM) Strategy",
    "GTM Strategy vs Tactics",
    "GTM as a System",
    "Product-Market Fit",
    "Go-to-Market Fit",
    "Minimum Viable GTM",
    "90-Day Plan",
    "Launch",
    "Product Marketing",
    "TAM / SAM / SOM",
    "Market Opportunity",
    "Market Segmentation",
    "STP",
    "ICP",
    "Buyer Persona",
    "Buying Committee",
    "Firmographic Data",
    "Technographic Data",
    "Positioning",
    "Value Proposition",
    "Competitive Positioning",
    "Category Design",
    "Messaging Framework",
    "Proof Points",
    "Why Now",
    "Jobs to be Done",
    "Pricing & Packaging",
    "Freemium",
    "Free Trial",
    "GTM Motion",
    "Product-Led Growth",
    "Sales-Led Growth",
    "Marketing-Led / Inbound-Led",
    "Outbound-Led",
    "Channel/Partner-Led",
    "Community-Led",
    "Ecosystem-Led",
    "Content-Led",
    "Paid-Led",
    "Event-Led",
    "Hybrid Motion",
    "Land and Expand",
    "Bottom-Up Adoption",
    "Self-Serve",
    "4 Ps",
    "AIDA",
    "GTM Operating System",
    "Sales Methodology",
    "Sales Qualification Framework",
    "BANT",
    "CHAMP",
    "MEDDIC",
    "SPICED",
    "The Challenger Sale",
    "Marketing Strategy",
    "ABM",
    "Named Accounts",
    "GTM AI Maturity Ladder",
    "Agentic ABM"
   ],
   "key_outputs_to_lower_tiers": [
    "ICP definition",
    "motion selection",
    "pricing",
    "messaging/playbook",
    "qualification criteria"
   ]
  },
  "tier_2_economics": {
   "name": "Unit-Economics ESTIMATOR tier (model-dependent; only the MRR walk is exact)",
   "tempo": "continuous; reviewed monthly/quarterly",
   "owned_by": [
    "RevOps",
    "Finance",
    "GTM leadership"
   ],
   "what_it_decides": "Whether the Tier-3 configuration is VIABLE and SOLVENT. CRITICAL v2.0 CORRECTION: this tier is NOT 'mostly identities'. The ONLY exact identity is the MRR walk (MRR_t = MRR_{t-1} + New + Expansion - Contraction - Churn). Every other metric -- LTV, NRR, GRR, CAC-payback, Magic Number, Rule-of-40 -- is an ESTIMATOR that summarizes the walk under contestable assumptions. v1.1 mislabeled these as identities, which let the model claim more certainty than its primitives support and propagated upward into a circular T7.",
   "is_input_to": "Sets the objective function and the guardrails the agent optimizes against. LangChain's agent implicitly serves this tier: by raising lead->opportunity conversion it improves the funnel term that feeds CAC efficiency.",
   "vocabulary_terms": [
    "ARR",
    "MRR",
    "ACV",
    "TCV",
    "ARPA",
    "ASP",
    "Average Deal Size",
    "Bookings",
    "Billings",
    "CAC",
    "CAC Payback",
    "CPL",
    "LTV",
    "LTV:CAC",
    "Magic Number",
    "Rule of 40",
    "NRR",
    "GRR",
    "Churn",
    "Retention",
    "Expansion Revenue",
    "Cohort Analysis",
    "Sales Velocity",
    "Sales Cycle",
    "Win Rate",
    "Closed-Won / Closed-Lost",
    "Pipeline Coverage",
    "Forecast Accuracy",
    "Sales Forecasting",
    "Deal Health",
    "NPS",
    "Time to Value",
    "Stickiness",
    "North Star Metric",
    "Leading vs Lagging Indicators",
    "Vanity Metrics",
    "Attribution",
    "Self-Reported Attribution",
    "Dark Funnel",
    "Booking Rate",
    "Win/Loss Analysis",
    "Bowtie Model"
   ],
   "key_constraints_imposed": [
    "LTV > CAC",
    "CAC payback < runway-bounded threshold",
    "motion-viability threshold on ACV",
    "Rule of 40 for scale-stage health"
   ],
   "the_one_identity": {
    "expression": "MRR_t = MRR_{t-1} + New_t + Expansion_t - Contraction_t - Churn_t",
    "status": "identity (exact on a consistent recognition basis)",
    "leaks_when": "Revenue recognition desyncs from cash (ASC 606 deferrals, multi-element arrangements, usage true-ups, mid-period amendments). Falsification: monthly invoiced-MRR-walk vs GAAP revenue delta on public 10-Ks; persistent >5% divergence => even the walk is convention-bound. (Proposed test, unrun.)",
    "provenance": "All four construction reports converge; Component A."
   },
   "estimators": [
    {
     "metric": "LTV",
     "naive_form": "ARPA*margin/churn",
     "hidden_assumption": "homogeneous exponential churn (constant hazard, no tenure dependence)",
     "corrected_form": "sum_t ARPA*margin*S(t)*(1+d)^-t with S(t) from Fader-Hardie sBG; expected lifetime E[T]=(alpha+beta-1)/(alpha-1)",
     "correction_magnitude": "naive overstates 2-3x on a ~10%-churn cohort because high-churn accounts sort out early (worked: alpha=2.5,beta=6 -> E[T]=5mo -> LTV $800 vs naive $1600)",
     "provenance": "Fader-Hardie 2007 sBG / 2018 BdW (established); worked numbers from construction reports A"
    },
    {
     "metric": "NRR",
     "hidden_assumption": "intra-account expansion only; ignores cross-sell attribution; no standard definition (Fastly published DBNR=126% and NRR=93% same period)",
     "note": "endogenous -- inflated by discounting and forced multi-year renewals; cannot be used to 'prove' Phi (the T7 circularity)"
    },
    {
     "metric": "GRR",
     "hidden_assumption": "stationary contract-level churn hazard"
    },
    {
     "metric": "CAC Payback",
     "hidden_assumption": "no expansion, no downgrades, constant gross margin; ignores TVM; numerator scope varies across Bessemer/KeyBanc/OpenView"
    },
    {
     "metric": "Magic Number",
     "hidden_assumption": "fixed one-quarter lag between S&M spend and ARR growth"
    },
    {
     "metric": "Rule of 40 / Rule of X",
     "hidden_assumption": "linear growth-vs-margin trade-off; Rule-of-X corrects this by weighting growth M~2-3x via the Psi regime scalar"
    }
   ],
   "provenance": "Construction reports A (all four converge); adversarial audit finding #1 (the single most damaging) + epistemic audit."
  },
  "tier_1_execution": {
   "name": "Execution / Behavioral tier (fast, increasingly agent-owned)",
   "tempo": "per-lead, per-account, daily/weekly",
   "owned_by": [
    "SDR/BDR",
    "AE",
    "CS/Deployed Eng",
    "Demand Gen",
    "GTM Engineer",
    "and increasingly: the agent"
   ],
   "what_it_decides": "The actual per-lead and per-account loop: detect signal -> decide whether to act -> gather context -> qualify -> draft/personalize -> route -> hand off -> follow up -> expand/retain. This is where the behavioral coefficients (conversion rates, response, reply rates) actually get produced, and where they DRIFT.",
   "is_the_layer_the_agent_runs": true,
   "langchain_mapping": "This entire tier is what LangChain's GTM agent automates: trigger on Salesforce lead -> do-not-send checks -> research (CRM/Gong/LinkedIn/Exa) -> load warm/cold skill -> draft -> human approval in Slack -> queue follow-ups; plus Monday account-intelligence for expansion/renewal/churn signals.",
   "vocabulary_terms": [
    "Funnel",
    "TOFU / MOFU / BOFU",
    "Critical User Journey",
    "Lead",
    "Lead Lifecycle",
    "MQL",
    "MQA",
    "SAL",
    "SQL",
    "CQL",
    "PQL",
    "Opportunity",
    "Lead Handoff",
    "Lead Scoring",
    "Predictive Lead Scoring",
    "Demand Generation",
    "Demand Creation",
    "Demand Capture",
    "Lead Generation",
    "Nurture",
    "Gated Content",
    "Content Syndication",
    "Thought Leadership",
    "Inbound",
    "Speed-to-Lead",
    "Intent Data",
    "Intent Surge",
    "Buying Signal",
    "Buying Intent",
    "Signal-to-Action",
    "SEO",
    "PPC",
    "Retargeting",
    "Co-Marketing",
    "Webinar",
    "VAR",
    "Partner Enablement",
    "Marketplace",
    "Channel Strategy",
    "SDR / BDR",
    "Account Executive",
    "Account Manager",
    "Customer Success",
    "Sales Engineer",
    "Discovery",
    "Proof of Concept",
    "Quota",
    "Sales Stages",
    "Sales Sequence",
    "Cold Email",
    "Email Deliverability",
    "Domain Warm-Up",
    "Sales Enablement",
    "Sales Playbook",
    "Battlecard",
    "Case Study",
    "Multi-Threading",
    "Mutual Action Plan",
    "Territory Management",
    "Account Expansion",
    "Upsell",
    "Cross-Sell",
    "Advocacy",
    "Word-of-Mouth",
    "Onboarding",
    "Activation",
    "Feature Adoption",
    "Product Analytics",
    "Economic Buyer",
    "Champion",
    "Identify Pain",
    "Decision Criteria",
    "Decision Process",
    "Critical Event",
    "Pipeline",
    "Field Sales",
    "Inside Sales",
    "Deal Desk",
    "Sense-Reason-Act-Learn",
    "Next-Best-Action",
    "Signal-to-Revenue",
    "Signal Taxonomy",
    "Coefficient Compression",
    "Outer Flywheel"
   ]
  },
  "cross_cutting_substrate": {
   "name": "Operations & Systems substrate (the rails all tiers run on)",
   "tempo": "always-on",
   "owned_by": [
    "RevOps",
    "Marketing Ops",
    "GTM Engineer"
   ],
   "what_it_is": "The data, tooling, routing, and governance that let the other tiers function. Not a tier in the causal stack \u2014 it is the substrate. The AGENT itself is the newest member of this substrate.",
   "vocabulary_terms": [
    "Revenue Operations",
    "Marketing Operations",
    "GTM Engineer",
    "Data Enrichment",
    "Lead Routing",
    "Stage Definitions",
    "Rules of Engagement",
    "SLA",
    "Operating Cadence",
    "QBR",
    "Sales-Marketing Alignment",
    "RACI / DRI",
    "CRM",
    "Marketing Automation Platform",
    "Sales Engagement Platform",
    "Clay",
    "Business Intelligence",
    "PLG CRM",
    "Agentic GTM",
    "GTM OS",
    "Task-to-Role Matrix",
    "Human-in-the-Loop",
    "Escalation Path",
    "Guardrails",
    "Data Quality Gate",
    "Audit Logging",
    "Compounding Failure",
    "Agent Cost Profile",
    "Agent FinOps",
    "Context Engineering",
    "Workflow Orchestration",
    "Subagent",
    "Learning Loop",
    "Phased Rollout"
   ],
   "langchain_mapping": "LangChain's agent IS a substrate component: it sits on Salesforce/Gong/BigQuery/Gmail (systems of record), enforces do-not-send Rules of Engagement, honors a 48h SLA, and its memory store is a new piece of RevOps infrastructure. Their lesson 'connect to systems of record from the start' is a statement about this substrate."
  },
  "tier_0_prefunnel": {
   "name": "Pre-funnel / Buyer-state & Brand tier (where the outcome is largely decided)",
   "tempo": "slow (brand: quarters-years) to medium (buyer-state: weeks-months); mostly INVISIBLE to the seller",
   "owned_by": [
    "Marketing/Brand",
    "Demand-gen",
    "Product (for PLG-led discovery)"
   ],
   "what_it_decides": "Whether you are even in contention before a seller engages. Encodes the empirical reality that ~95% of B2B buyers are out-of-market in any quarter (LinkedIn 95-5 Rule) and that winners are overwhelmingly on the buyer's Day-1 shortlist (6sense: ~85-95%, first seller contact at ~61% of journey in 2025). The seller funnel (Tier 1) is DOWNSTREAM of and CONFOUNDED by this tier.",
   "is_input_to": "Sets the buyer-state B that conditions every Tier-1 conversion coefficient. Brand stock B_r raises the probability of Day-1 shortlist inclusion. This is the 'Tier 0' all four construction reports converged on as the highest-leverage addition.",
   "vocabulary_terms": [
    "Brand Awareness",
    "Demand Creation",
    "Demand Capture",
    "Category Entry Points",
    "Mental Availability",
    "Share of Voice",
    "95-5 Rule",
    "Dark Funnel",
    "Buying Group",
    "Buying Committee",
    "Day-1 Shortlist",
    "Intent Data",
    "Signal-Based Selling",
    "Out-of-Market",
    "In-Market",
    "Thought Leadership"
   ],
   "key_constraints_imposed": [
    "Tier-1 win rate is bounded by Day-1 shortlist position; cold (non-shortlist) outbound converts at ~3-5% vs ~30-50%+ for shortlist/inbound (prospeo/6sense benchmarks).",
    "Doubling seller headcount into a fixed in-market pool causes GEOMETRIC conversion decay, not proportional pipeline growth -- the single biggest behavioral change from v1.1.",
    "Brand stock is a STOCK with its own equation of motion, not a lagged flow; it cannot be switched on quickly and decays slowly (~10-20%/yr in B2B)."
   ],
   "provenance": "Construction reports D+E (all four converge); 6sense BER 2024/2025; LinkedIn B2B Institute 95-5; Ehrenberg-Bass CEPs; adversarial audit blind-spot #4 (highest severity).",
   "evidence_caveat": "Buyer-state PRIMACY (direction) is well-supported across independent sources. Exact magnitudes (95-5, 85-95% shortlist) are 6sense/LinkedIn-sourced and commercially interested; treat percentages as indicative, not precise."
  },
  "cross_cutting_regime": {
   "name": "Macro regime scalar Psi (sits ABOVE Tier 2; re-weights the objective)",
   "tempo": "slow (shifts with the rate/capital cycle over quarters-years)",
   "owned_by": [
    "Board",
    "CFO",
    "Investors (exogenous to the firm)"
   ],
   "what_it_decides": "WHICH weighted combination of growth and free cash flow the market prices. Not a Tier-2 input coefficient -- it changes the OBJECTIVE FUNCTION itself, which is why the latent PMF multiplier Phi cannot absorb it. Resolves T3 (payback-vs-ratio) as REGIME-CONDITIONAL rather than a fixed law.",
   "definition": "Scalar index reducing a macro vector (10yr Treasury, Cloud forward multiples, VC deployment pace, median burn multiple) to a regime score in {ZIRP, tightening, high-rate}.",
   "functional_form": "Objective U = w_g(Psi)*growth + w_f(Psi)*FCF, with w_g/w_f = M(Psi). Bessemer Rule-of-X: X = M*growth + FCF_margin, M~2-3x by capital tightness. (Rule-of-X established; the w_g/w_f mapping is proposed.)",
   "measured_values": {
    "10yr_Treasury": "2021 ~1.45% (ZIRP) -> 2023 ~3.96% -> 2025 ~4.29% (Macrotrends)",
    "Cloud_100_multiple": "~34x peak 2021 (inferred) -> 26x 2023 -> 20x 2025 (Bessemer, -41% from peak)",
    "M_growth_weight": "~3.0-3.5 ZIRP 2021 -> ~2.0-2.3 tightening 2023 -> ~2.0-2.5 normalized 2025-26",
    "implied_w_g/w_f": "~0.8/0.2 in ZIRP -> ~0.3/0.7 in high-rate (proposed mapping)"
   },
   "resolves": "T3: longer CAC payback (24-30mo) is acceptable in ZIRP; high-rate forces <12-18mo. Payback is NOT a fixed king -- it is regime-conditional.",
   "falsification": "A segment (e.g. bootstrapped profitable vertical SaaS) whose growth/FCF weighting did NOT shift 2021->2025 despite the rate spike would show Psi is not universal but a VC-backed artifact. No public panel of firm-level objective weights exists; specified-but-unrun.",
   "provenance": "Construction reports C (all four converge); Bessemer Rule-of-X 2024; Macrotrends; adversarial audit blind-spot #1.",
   "evidence_caveat": "Rate/multiple values are measured (public). The w_g/w_f mapping and the exact M-by-regime are PROPOSED, consistent with Bessemer's Rule-of-40 -> Rule-of-X evolution but not independently fitted here."
  }
 },
 "state_variables": [
  {
   "symbol": "C",
   "name": "Customer/revenue stock",
   "tier": "economics",
   "type": "stock",
   "vocab": [
    "ARR",
    "MRR"
   ],
   "note": "The central stock the whole system grows or leaks. Everything is a flow into or out of C."
  },
  {
   "symbol": "a",
   "name": "Acquisition inflow rate",
   "tier": "execution",
   "type": "flow_in",
   "vocab": [
    "Bookings",
    "Win Rate",
    "Lead Generation"
   ],
   "note": "New customers per period. A LINEAR lever on the steady state."
  },
  {
   "symbol": "delta",
   "name": "Churn rate",
   "tier": "economics",
   "type": "flow_out",
   "vocab": [
    "Churn",
    "GRR"
   ],
   "note": "Fraction of C lost per period. Sits in the DENOMINATOR of steady state, hence nonlinear."
  },
  {
   "symbol": "g",
   "name": "Expansion rate",
   "tier": "execution",
   "type": "flow_modifier",
   "vocab": [
    "Expansion Revenue",
    "Account Expansion",
    "Upsell",
    "Cross-Sell",
    "Land and Expand"
   ],
   "note": "Revenue growth within existing C. Net retention = 1 + g - delta."
  },
  {
   "symbol": "V",
   "name": "Top-of-funnel volume",
   "tier": "execution",
   "type": "flow_source",
   "vocab": [
    "Demand Generation",
    "Lead",
    "Intent Data"
   ],
   "note": "Raw demand entering the funnel."
  },
  {
   "symbol": "r_i",
   "name": "Stage conversion rates",
   "tier": "execution",
   "type": "coefficient",
   "vocab": [
    "MQL",
    "SAL",
    "SQL",
    "Opportunity",
    "Lead Scoring",
    "Speed-to-Lead"
   ],
   "note": "The behavioral coefficients. THIS is what an execution agent like LangChain's moves (lead->oppty up 250%). They DRIFT and saturate. REFINEMENT (v1.1): r_i is not one number but a DISTRIBUTION across reps; agentic augmentation compresses its variance (raises the floor) rather than only shifting the mean \u2014 Coefficient Compression."
  },
  {
   "symbol": "p_bar",
   "name": "Average price / ACV",
   "tier": "strategy",
   "type": "coefficient",
   "vocab": [
    "ACV",
    "ARPA",
    "ASP",
    "Pricing & Packaging"
   ],
   "note": "Set in Tier 3. Determines which MOTION is viable (the threshold)."
  },
  {
   "symbol": "m",
   "name": "Gross margin",
   "tier": "economics",
   "type": "coefficient",
   "vocab": [
    "CAC Payback",
    "LTV"
   ],
   "note": "Converts revenue to contribution; gates LTV."
  },
  {
   "symbol": "S",
   "name": "Sales+marketing spend",
   "tier": "economics",
   "type": "control_input",
   "vocab": [
    "CAC",
    "Magic Number"
   ],
   "note": "The primary control input. CAC = S/a."
  },
  {
   "symbol": "x",
   "name": "Market penetration / saturation",
   "tier": "strategy",
   "type": "state",
   "vocab": [
    "TAM / SAM / SOM",
    "Market Opportunity"
   ],
   "note": "Cumulative share of TAM captured. Drives CAC convexity."
  },
  {
   "symbol": "Phi",
   "name": "Product-market fit (latent)",
   "tier": "strategy",
   "type": "latent_multiplier",
   "vocab": [
    "Product-Market Fit",
    "North Star Metric",
    "Retention",
    "NPS"
   ],
   "note": "Unobservable. Inferred from joint pattern of low churn + high win-rate + low CAC + high expansion moving TOGETHER. Enters MULTIPLICATIVELY."
  },
  {
   "symbol": "k(tau)",
   "name": "Lag/memory kernel",
   "tier": "execution",
   "type": "function",
   "vocab": [
    "Dark Funnel",
    "Attribution",
    "Demand Creation",
    "Thought Leadership"
   ],
   "note": "Distributed-lag kernel. Why last-touch attribution structurally misattributes brand/demand-creation."
  },
  {
   "symbol": "Psi",
   "name": "Macro regime scalar",
   "tier": "regime",
   "type": "exogenous_index",
   "vocab": [
    "Rule of 40",
    "Burn Multiple"
   ],
   "note": "Capital-tightness index (10yr Treasury, Cloud multiples, VC pace) re-weighting the growth/FCF objective. Sits above Tier 2."
  },
  {
   "symbol": "B",
   "name": "Buyer-state vector",
   "tier": "prefunnel",
   "type": "latent_state",
   "vocab": [
    "Buying Group",
    "Day-1 Shortlist",
    "In-Market",
    "Intent Data"
   ],
   "note": "B=[shortlist S_p, in-market A_m, journey-completion J_c, group size G]. Pre-funnel; confounds every Tier-1 conversion coefficient. Latent, observed via intent proxies."
  },
  {
   "symbol": "B_r",
   "name": "Brand stock",
   "tier": "prefunnel",
   "type": "stock",
   "vocab": [
    "Brand Awareness",
    "Mental Availability",
    "Category Entry Points",
    "Share of Voice"
   ],
   "note": "Nerlove-Arrow stock of mental availability. dB_r/dt = alpha*I - delta*B_r. Drives shortlist probability. B2B decay ~10-20%/yr."
  },
  {
   "symbol": "S",
   "name": "Switching-cost moat",
   "tier": "economics",
   "type": "latent_modifier",
   "vocab": [
    "Switching Costs",
    "Vendor Lock-in"
   ],
   "note": "Hazard-rate modifier h_eff = h0*exp(-S). High in enterprise/regulated/integrated; ~0 in PLG-SMB. Makes T7 additive when high. Unmeasured publicly; proxied via GRR + low-satisfaction confounder."
  },
  {
   "symbol": "k",
   "name": "PLG viral coefficient + loop state",
   "tier": "prefunnel",
   "type": "loop_state",
   "vocab": [
    "Viral Coefficient",
    "Product-Led Growth",
    "Free-to-Paid Conversion"
   ],
   "note": "u_{t+1}=k*u_t; k_eff=k_raw*retention. B2B k<1 always (~0.2 excellent); divergent k>=1 case never binds."
  }
 ],
 "equations": [
  {
   "id": "conservation_law",
   "name": "The conservation law (the spine)",
   "epistemic_type": "identity",
   "expression": "dC/dt = a - (delta - g) * C",
   "steady_state": "C* = a / (delta - g)",
   "reads_as": "Customers are a stock with inflow (acquisition) and net outflow (churn minus expansion). The equilibrium size is acquisition over net-churn.",
   "implication": "Acquisition sets the numerator (linear lever); retention sets the denominator (nonlinear lever). As net retention -> 100% (g -> delta), C* diverges with ZERO new acquisition. This is why retention dominates acquisition structurally, not as opinion.",
   "vocab": [
    "ARR",
    "Churn",
    "NRR",
    "GRR",
    "Expansion Revenue",
    "Retention"
   ],
   "agent_relevance": "An execution agent that improves retention/expansion signals (LangChain's account-intelligence flagging churn risk and expansion) is pushing on the DENOMINATOR \u2014 higher leverage than its outbound drafting, which pushes on 'a'."
  },
  {
   "id": "funnel_operator",
   "name": "The funnel as a log-linear operator",
   "epistemic_type": "identity",
   "expression": "R = V * (product of r_i) * p_bar   <=>   ln R = ln V + sum(ln r_i) + ln p_bar",
   "reads_as": "Revenue is volume times the product of stage conversions times price. In log space, contributions are additive.",
   "implication": "BOTTLENECK THEOREM: because stages multiply, the marginal return to fixing the WORST-converting stage dominates improving an already-good one. Doubling a 5% stage doubles throughput; +5pts on a 50% stage adds 10%.",
   "vocab": [
    "Funnel",
    "MQL",
    "SQL",
    "Opportunity",
    "Win Rate",
    "TOFU / MOFU / BOFU"
   ],
   "agent_relevance": "LangChain targeted the lead->qualified-opportunity stage specifically and got 250% \u2014 they fixed a constriction, not a comfortable stage. The agent is a bottleneck-stage intervention."
  },
  {
   "id": "unit_economics",
   "name": "The unit-economics master inequality",
   "epistemic_type": "estimator",
   "expression": "LTV > CAC   <=>   (m * p_bar) / delta  >  S / a   <=>   (m * p_bar * a) / (delta * S) > 1",
   "reads_as": "Lifetime contribution must exceed acquisition cost. Expanding LTV exposes churn in the denominator-of-the-denominator, so retention enters quadratically vs how acquisition enters.",
   "implication": "GTM is CONSTRAINED optimization, not maximization. And the BINDING constraint at venture stage is CAC PAYBACK (cash timing), not the LTV:CAC ratio (equilibrium). A 5:1 ratio can still kill you if LTV arrives over 5 years and CAC is due now.",
   "vocab": [
    "LTV",
    "CAC",
    "LTV:CAC",
    "CAC Payback",
    "Magic Number"
   ],
   "benchmark": {
    "LTV:CAC": "3:1 good, 5:1 great, <1:1 fatal",
    "CAC_payback": "<12mo good, <6mo great",
    "Magic_Number": ">1 invest, <0.75 fix engine"
   },
   "v2_correction": "Demoted from identity to estimator. LTV>CAC is an inequality between two ESTIMATORS, not an identity. The LTV side assumes a churn model (falsified as exponential by Fader-Hardie). Only the MRR walk underneath is exact."
  },
  {
   "id": "saturation",
   "name": "Channel saturation (CAC convexity)",
   "epistemic_type": "causal_regime",
   "expression": "CAC(x) = CAC_0 / (1 - (x / TAM)^beta)",
   "reads_as": "As you exhaust a finite market, cheap demand goes first, so CAC rises convexly with cumulative penetration.",
   "implication": "Every channel has a carrying capacity. Optimal strategy is a PORTFOLIO of channels each run only to its rising-marginal-cost knee. 'Be everywhere' pays fixed costs below scale; 'master one channel' rides it up a convex cost curve. Mix must SHIFT over time as channels saturate \u2014 which is why GTM strategies have half-lives.",
   "vocab": [
    "TAM / SAM / SOM",
    "CAC",
    "Channel Strategy",
    "Paid-Led",
    "Content-Led"
   ],
   "caveat": "beta and CAC_0 are estimated per-channel and DECAY. This is a regime edge, not an identity."
  },
  {
   "id": "motion_threshold",
   "name": "Motion selection as a forced move (step function)",
   "epistemic_type": "causal_regime",
   "expression": "human_sales_viable  <=>  ACV * m * LTV_multiple  >  cost_of_one_human_sales_cycle",
   "reads_as": "Cost-to-serve per human touch is ~fixed; revenue scales with ACV. Below a threshold ACV, a salesperson is unprofitable BY ARITHMETIC.",
   "implication": "Motion is not a preference \u2014 it is forced by where ACV sits vs cost-to-serve. The 'PLG <5K / hybrid 5K-50K / sales-led >50K' bands are the footprint of this inequality crossing zero. The hybrid zone is where it's near-equality and the answer is 'let the deal self-select' = product-led sales.",
   "vocab": [
    "GTM Motion",
    "Product-Led Growth",
    "Sales-Led Growth",
    "Hybrid Motion",
    "ACV",
    "PQL",
    "PLG CRM"
   ],
   "agent_relevance": "LangChain (developer-led, technical buyer, expansion-heavy) runs a hybrid motion: self-serve adoption + human outbound for higher-intent leads. Their agent serves exactly the hybrid seam \u2014 qualifying which leads warrant a human-approved touch."
  },
  {
   "id": "attribution_convolution",
   "name": "Distributed lags and the attribution problem",
   "epistemic_type": "causal_regime",
   "expression": "r(t) = integral over tau of [ k(tau) * A(t - tau) ] dtau",
   "reads_as": "Observed conversion today is a convolution of PAST activity with a decaying memory kernel.",
   "implication": "Naive last-touch attribution is STRUCTURALLY guaranteed to misattribute: it assigns 100% to the impulse, 0% to the kernel. Brand/demand-creation will always look underperforming to click-attribution, and over-cutting it is a predictable recurring error. Self-reported attribution exists to recover the kernel.",
   "vocab": [
    "Attribution",
    "Self-Reported Attribution",
    "Dark Funnel",
    "Demand Creation",
    "Thought Leadership"
   ]
  },
  {
   "id": "pmf_multiplier",
   "name": "The product-market-fit multiplier (the master confounder)",
   "epistemic_type": "latent_multiplier",
   "expression": "R ~= Phi * f(GTM_levers)     [multiplicative]     NOT     R ~= Phi + f(GTM_levers)  [additive]",
   "reads_as": "Product-market fit multiplies the effectiveness of every GTM lever simultaneously; it does not add to them.",
   "implication": "Most cross-metric GTM correlations (high NRR <-> efficient CAC, etc.) are CONFOUNDED by Phi driving both. GTM tactics have near-zero causal effect below a Phi threshold and large effect above it. Anything times near-zero Phi is near-zero \u2014 the graveyard of well-funded companies with great GTM machinery and no fit. GTM Fit pays off only once PMF clears threshold.",
   "vocab": [
    "Product-Market Fit",
    "Go-to-Market Fit",
    "NRR",
    "Win Rate",
    "CAC",
    "North Star Metric"
   ],
   "contested": true,
   "v2_correction": "Now PIECEWISE (see eq 'pmf_piecewise'). Multiplicative form holds only in low-switching-cost segments. Also: Phi was circular in v1.1 (operationalized via the same NRR/retention it predicts); see eq 'phi_ex_ante' for the de-circularized form."
  },
  {
   "id": "sales_velocity",
   "name": "Sales velocity identity",
   "epistemic_type": "identity",
   "expression": "Sales_Velocity = (num_opportunities * win_rate * average_deal_size) / sales_cycle_length",
   "reads_as": "Revenue throughput is opportunities times win-rate times deal-size, divided by how long deals take.",
   "implication": "Four independent levers, and CYCLE LENGTH is in the denominator \u2014 compressing the cycle (multi-threading, mutual action plans, faster speed-to-lead) raises velocity as much as winning more. Agents that compress research/drafting time compress cycle length directly.",
   "vocab": [
    "Sales Velocity",
    "Win Rate",
    "Average Deal Size",
    "Sales Cycle",
    "Multi-Threading",
    "Mutual Action Plan",
    "Speed-to-Lead"
   ]
  },
  {
   "id": "mrr_walk",
   "name": "The MRR walk (the ONE true identity)",
   "epistemic_type": "identity",
   "expression": "MRR_t = MRR_{t-1} + New_t + Expansion_t - Contraction_t - Churn_t",
   "reads_as": "Ending recurring revenue equals starting plus new plus expansion minus contraction minus churn. Exact on a consistent recognition basis.",
   "implication": "This is the only Tier-2 identity. Every other 'metric' is an estimator summarizing trajectories of this walk under assumptions.",
   "leaks_when": "Revenue recognition desyncs from cash (ASC 606, multi-element, usage true-ups). Falsifier: monthly walk vs GAAP delta on 10-Ks; proposed/unrun.",
   "vocab": [
    "MRR",
    "ARR",
    "Expansion Revenue",
    "Churn",
    "Contraction"
   ],
   "provenance": "Construction A (all four converge)."
  },
  {
   "id": "ltv_corrected",
   "name": "Corrected LTV under heterogeneous churn (Fader-Hardie sBG)",
   "epistemic_type": "estimator",
   "expression": "LTV = sum_{t>=1} ARPA*margin*S(t)*(1+d)^-t ;  sBG: E[T] = (alpha+beta-1)/(alpha-1) for alpha>1, p_i ~ Beta(alpha,beta)",
   "reads_as": "Lifetime value integrates discounted margin over a survival curve fit to cohort retention, NOT ARPA*margin/churn. Aggregate retention rises over time because high-churn accounts sort out early (heterogeneity), not because individual churn falls.",
   "implication": "Naive exponential LTV overstates 2-3x on a ~10%-churn cohort. Worked: alpha=2.5, beta=6 -> E[T]=5mo -> LTV $800 vs naive $1600 (2x). Use sBG for contractual/time-invariant churn, Beta-discrete-Weibull (BdW) for non-monotone.",
   "vocab": [
    "LTV",
    "Churn",
    "Retention",
    "Cohort Analysis"
   ],
   "provenance": "Fader-Hardie 2007 (sBG) / 2018 (BdW), established; worked numbers from construction A.",
   "status": "established functional form; fit on real cohort panels"
  },
  {
   "id": "pmf_piecewise",
   "name": "Piecewise revenue: multiplicative PMF (low-S) vs additive switching-cost moat (high-S)",
   "epistemic_type": "causal_regime",
   "expression": "R = Phi*f(GTM)                         if S < S*  (low-switching)\nR = Phi*f1(GTM) + S*f2(installed_base)  if S >= S* (high-switching)\nwith effective churn hazard h_eff = h0 * exp(-S)",
   "reads_as": "Where switching costs are low (consumer, PLG-SMB, transactional B2B), revenue is fit AMPLIFIED BY go-to-market: multiplicative. Where switching costs are high (enterprise multi-year, regulated, deeply integrated), a moat term S adds revenue largely INDEPENDENT of product love.",
   "implication": "T7's strong multiplicative form is wrong in ~the most valuable third of enterprise software. Oracle/SAP-class: NPS near/below 0, 30-50% unused seats, yet GRR >95% -- revenue tracks S (lock-in), not Phi. Classical IO (Klemperer; Farrell-Klemperer) is explicitly ADDITIVE: switching-cost rents are a premium ON TOP OF current quality.",
   "S_scale": "Worked: SMB PLG ~20%/yr churn -> S~0; SAP-class <5%/yr -> S~1.4 (from 0.05=0.20*exp(-S)). Regime boundary operationalized via observed GRR>=~95% + low-satisfaction confounder check (since S itself is unmeasurable from public data).",
   "vocab": [
    "Switching Costs",
    "Vendor Lock-in",
    "Retention",
    "GRR",
    "NRR"
   ],
   "provenance": "Construction B (all four converge); Klemperer 1987 / Farrell-Klemperer 2007 (additive switching-cost theory, established); adversarial audit T7 section.",
   "evidence_caveat": "Piecewise STRUCTURE is well-grounded. S* threshold, the exp(-S) hazard form, and S magnitudes are PROPOSED, not fitted on a public panel."
  },
  {
   "id": "psi_objective",
   "name": "Macro regime objective re-weighting (Psi / Rule-of-X)",
   "epistemic_type": "causal_regime",
   "expression": "U = w_g(Psi)*growth + w_f(Psi)*FCF ;  Rule-of-X: X = M(Psi)*growth + FCF_margin ;  M ~ 2-3 by capital tightness",
   "reads_as": "The market's objective function is regime-dependent. Psi (a function of 10yr Treasury, Cloud multiples, VC pace) sets the growth weight M. In ZIRP, growth is weighted ~3x and long payback is fine; in high-rate, M falls and payback must shorten.",
   "implication": "Resolves T3: 'payback over ratio' is true ONLY when capital is expensive (M low) or runway < ~2x payback. In ZIRP it was false; for capital-abundant incumbents it is false. Payback is regime-conditional, not a fixed king.",
   "measured": "10yr: 1.45%(2021)->3.96%(2023)->4.29%(2025); Cloud-100 multiple 26x(2023)->20x(2025); M ~3.0-3.5->2.0-2.5.",
   "vocab": [
    "Rule of 40",
    "CAC Payback",
    "Burn Multiple",
    "Magic Number"
   ],
   "provenance": "Construction C (all four converge); Bessemer Rule-of-X 2024; Macrotrends; adversarial audit blind-spot #1.",
   "evidence_caveat": "Rates/multiples measured. w_g/w_f mapping and M-by-regime PROPOSED."
  },
  {
   "id": "buyer_state_coupling",
   "name": "Buyer-state confounds the seller funnel (the de-confounded estimand)",
   "epistemic_type": "conditional_coefficient",
   "expression": "Naive (confounded): Win = Leads * Conversion\nTrue estimand: P(win | seller_touch, S_p, A_m, J_c, Phi, Psi, S)\nwhere observed Opp->Close = P(win | Opp, B-mix), NOT P(win | Opp, seller skill)",
   "reads_as": "The seller funnel is DOWNSTREAM of an invisible buyer-state B = [shortlist position S_p, in-market A_m, journey-completion J_c, buying-group size G]. Funnel conversion coefficients are mixtures over B, not measures of execution.",
   "implication": "THE behavior-changing edge. In v1.1, doubling SDRs doubled pipeline. Here, doubling SDRs into a fixed in-market pool (A_m=1 is only ~5% of accounts) causes GEOMETRIC conversion decay. Stops the model recommending infinite GTM scale-up; shifts capital toward brand (B_r) and PLG. Cold/non-shortlist outbound ~3-5% win vs shortlist/inbound ~30-50%+.",
   "vocab": [
    "Buying Group",
    "Day-1 Shortlist",
    "Intent Data",
    "95-5 Rule",
    "Win Rate",
    "Dark Funnel"
   ],
   "provenance": "Construction D (all four name it the #1 behavior-changing component); 6sense BER 2024/2025; Forrester; Cunningham; prospeo win-rate benchmarks.",
   "evidence_caveat": "Direction strongly supported. Magnitudes (85-95% shortlist, 95-5, ~70% journey pre-contact) are 6sense/LinkedIn-sourced, commercially interested. B is a LATENT state; observed only via noisy intent proxies."
  },
  {
   "id": "brand_stock",
   "name": "Brand as a stock (Nerlove-Arrow adstock), not a lagged flow",
   "epistemic_type": "causal_regime",
   "expression": "dB_r/dt = alpha*I(t) - delta*B_r(t) ;  P(shortlist S_p=1 | B_r) = sigmoid(lambda0 + lambda1*B_r)",
   "reads_as": "Brand is a STOCK that accumulates mental availability across category entry points and decays slowly. It is HOW you get on the Day-1 shortlist. Past investment yields long-term baseline demand; you cannot switch it on quickly.",
   "implication": "Differs from v1.1 T6, which treated demand-creation as a lagged FLOW inside the attribution convolution. Here brand is the stock that GENERATES the flow. With ~95% of buyers out-of-market, a funnel-only model is blind to where mental availability accumulates.",
   "parameters": "B2B brand decay delta ~10-20%/yr (half-life 1-3yr, Ehrenberg-Bass 'stop advertising' cases). alpha (build rate) not cleanly quantified publicly. Measured via share-of-search, prompted/unprompted awareness, CEP breadth, SOV/SOM.",
   "vocab": [
    "Brand Awareness",
    "Mental Availability",
    "Category Entry Points",
    "Share of Voice",
    "Demand Creation"
   ],
   "provenance": "Construction E (all four converge); Nerlove-Arrow 1962 (established); Ehrenberg-Bass / Binet-Field; LinkedIn 95-5.",
   "evidence_caveat": "Nerlove-Arrow form established; the 'adstock illusion' means a long-lag flow can mimic a stock -- distinguishing them needs a media-pause natural experiment. alpha/delta for SaaS specifically are UNMEASURED. Flagged retrospective-only / liability in 2 of 4 reports."
  },
  {
   "id": "plg_loop",
   "name": "PLG loop (recursive), distinct from the funnel (multiplicative)",
   "epistemic_type": "causal_regime",
   "expression": "u_{t+1} = u_t*(1 + k - c) ;  pure-viral u_{t+1} = k*u_t, closed form u_0/(1-k), divergent at k>=1 ;  k_eff = k_raw * R (retention)",
   "reads_as": "Product-led growth is a recursive loop, not a chain of funnel conversions. Closed-form divergence requires viral coefficient k>=1. Retention is the UPSTREAM CAUSE of virality (dead users send no invites), reversing the usual arrow.",
   "implication": "Two hard couplings: (1) expansion-as-acquisition under usage pricing BREAKS NRR's intra-account assumption (cross-team adoption is acquisition wearing a retention label); (2) k_eff = k_raw*R, so Phi (driving retention) is upstream of the loop. Hands off to sales-assist at a usage/MRR threshold, where buyer-state B activates.",
   "k_reality": "Sustained k>=1 in B2B is essentially MYTHICAL -- excellent B2B k~0.2, real 'viral' B2B is k<1 + paid amplification. So the divergent closed form NEVER BINDS; PLG is a highly efficient channel, not perpetual motion. Free-to-paid: ~8% median, 15-25% great.",
   "vocab": [
    "Product-Led Growth",
    "Viral Coefficient",
    "Free-to-Paid Conversion",
    "PQL",
    "Time to Value",
    "Expansion Revenue"
   ],
   "provenance": "Construction F (all four converge); Andrew Chen 'Retention is King'; OpenView/Poyer/ProductLed benchmarks.",
   "evidence_caveat": "Loop form established; the k>=1 divergent case is a non-binding limiting case. No public B2B example of sustained k>=1 organically."
  },
  {
   "id": "phi_ex_ante",
   "name": "Ex-ante PMF -- the testable (but not-yet-runnable) form of T7",
   "epistemic_type": "latent_multiplier",
   "expression": "log(growth_it) = b0 + b1*log(Phi_{i,t-1}) + b2*log(GTM_it) + b3*log(Phi_{i,t-1})*log(GTM_it) + e\nb3 > 0 => multiplicative (T7 strong) ;  b3 ~ 0 => additive",
   "reads_as": "To de-circularize T7, fix Phi BEFORE the growth window using a measure that cannot be re-inferred from revenue: cohort-retention floor at week 8-12 (primary), or independent G2/Gartner satisfaction at t-1 (secondary). Then the interaction term b3 settles multiplicative vs additive.",
   "implication": "This is the CORRECT falsifiable core of T7. But the cross-company panel (independent t-1 Phi + subsequent growth + comparable GTM spend) DOES NOT EXIST publicly. Until a private aggregator (Bessemer, SaaS Capital) runs it, T7 remains a LOCALIZED HEURISTIC, not a tested claim.",
   "vocab": [
    "Product-Market Fit",
    "Retention",
    "NPS",
    "North Star Metric"
   ],
   "provenance": "Construction G (all four converge, incl. the 'data does not exist, label it metaphysical' conclusion); Sean Ellis 40% (flagged uncalibrated).",
   "evidence_caveat": "Specified and falsifiable IN PRINCIPLE; unrun in practice. The honest terminal status of the model's most contested thesis."
  },
  {
   "id": "noise_floor",
   "name": "Irreducible-noise floor (prediction-refusal guardrail)",
   "epistemic_type": "correlational",
   "expression": "theta = Var(noise)/Var(growth) ~ 0.25-0.40 ;  REFUSE forecast if P(unobserved shock in horizon) > tau OR posterior interval width > omega",
   "reads_as": "~25-40% of mid-stage B2B SaaS growth-rate variance is unexplainable by any GTM lever (competitor surprise, macro shock, viral inflection, champion exit). The model must output confidence bands and 'prediction refused' states rather than false point estimates.",
   "implication": "Decision rule: refuse LTV before the cohort-retention floor flattens (month 6-12); refuse forecasts spanning a competitor surprise; widen intervals through Psi regime transitions. This marks the BOUNDARY of the model's own predictive power.",
   "is_it_reducible": "Falsification/honesty: the floor is PROVISIONAL, not physics. If richer data (telemetry, intent, founder history) pushes out-of-sample R^2 from ~0.6 to ~0.85, theta falls to ~0.15. So part of 'irreducible' is really data-availability. Label as a contingent construct.",
   "vocab": [
    "Forecast Accuracy",
    "Sales Forecasting",
    "Leading vs Lagging Indicators"
   ],
   "provenance": "Construction H (all four converge); Bill Gross TED (42% timing, hindsight-biased); Taleb heavy tails; Roberge 'sales is predictable' counter.",
   "evidence_caveat": "25-40% is a REASONED ASSUMPTION synthesizing Gross/Taleb, NOT an empirically pinned constant. No public benchmark has fit theta = 1 - out-of-sample R^2 for B2B SaaS growth."
  }
 ],
 "key_edges": [
  {
   "from": "Phi",
   "to": "Win Rate",
   "type": "latent_multiplier",
   "sign": "+",
   "note": "Fit makes deals easier to win."
  },
  {
   "from": "Phi",
   "to": "Churn",
   "type": "latent_multiplier",
   "sign": "-",
   "note": "Fit lowers churn. Same cause as the win-rate edge -> the confound."
  },
  {
   "from": "Phi",
   "to": "CAC",
   "type": "latent_multiplier",
   "sign": "-",
   "note": "Fit lowers CAC via word-of-mouth and easier sells."
  },
  {
   "from": "ICP",
   "to": "r_i",
   "type": "causal_regime",
   "sign": "+",
   "note": "Tighter targeting raises every downstream conversion. Tier-3 decision setting Tier-1 coefficients."
  },
  {
   "from": "Speed-to-Lead",
   "to": "r_i",
   "type": "causal_regime",
   "sign": "+",
   "note": "Faster response raises conversion; the effect this agent class most directly buys."
  },
  {
   "from": "Pricing & Packaging",
   "to": "GTM Motion",
   "type": "causal_regime",
   "sign": "step",
   "note": "Price sets ACV sets which motion is viable. The forced-move edge."
  },
  {
   "from": "GTM Motion",
   "to": "CAC",
   "type": "causal_regime",
   "sign": "depends",
   "note": "Motion determines cost-to-serve structure."
  },
  {
   "from": "Acquisition (a)",
   "to": "C",
   "type": "identity",
   "sign": "+",
   "note": "Linear inflow."
  },
  {
   "from": "Churn (delta)",
   "to": "C",
   "type": "identity",
   "sign": "-",
   "note": "Nonlinear: sits in denominator of steady state."
  },
  {
   "from": "Demand Creation",
   "to": "Win Rate",
   "type": "causal_regime",
   "sign": "+lagged",
   "note": "Effect arrives via the convolution kernel, quarters later -> looks underperforming to last-touch."
  },
  {
   "from": "NRR",
   "to": "CAC efficiency",
   "type": "correlational",
   "sign": "+",
   "note": "Moves together but confounded by Phi. Do NOT treat as 'improve NRR to fix CAC'."
  },
  {
   "from": "Human override",
   "to": "r_i coefficients",
   "type": "causal_regime",
   "sign": "learning",
   "note": "The LangChain memory loop: rep edits are labeled corrections that update the behavioral coefficients."
  },
  {
   "from": "Agent FinOps",
   "to": "CAC",
   "type": "identity",
   "sign": "+",
   "note": "NEW (v1.1): agent execution cost is a term inside S (spend). CAC = S/a, and agent S is high+usage-based, so ungoverned agent spend raises CAC. FinOps is the control on this edge."
  },
  {
   "from": "Agentic GTM",
   "to": "r_i",
   "type": "causal_regime",
   "sign": "+distributional",
   "note": "NEW (v1.1): agents don't just raise mean conversion \u2014 they COMPRESS the rep variance, lifting the floor (developing reps) toward the ceiling (top reps) more than raising the ceiling. 'Improving conversion' becomes 'shrinking the distribution'. See Coefficient Compression."
  },
  {
   "from": "Compounding Failure",
   "to": "Human-in-the-Loop",
   "type": "causal_regime",
   "sign": "necessitates",
   "note": "NEW (v1.1): the compounding-error property is WHY checkpoints are non-negotiable \u2014 HITL breaks the chain, and doubles as the learning signal."
  },
  {
   "from": "Sense-Reason-Act-Learn",
   "to": "Speed-to-Lead",
   "type": "causal_regime",
   "sign": "+",
   "note": "NEW (v1.1): the agent loop collapses the sense->act latency that human reps can't sustain across every signal \u2014 directly buying the speed-to-lead coefficient at scale."
  }
 ],
 "agent_interface": {
  "principle": "An agentic GTM system executes Tier 1 (the fast per-lead/per-account loop) while READING configuration from Tier 3 and OBJECTIVES/GUARDRAILS from Tier 2. It does not choose ICP, motion, or pricing \u2014 those are handed to it. Its value is moving the r_i coefficients and compressing sales-cycle time, under human-in-the-loop correction that feeds a learning loop.",
  "worked_example_langchain": {
   "source": "https://www.langchain.com/blog/how-we-built-langchains-gtm-agent",
   "tier_3_inputs_consumed": "Warm/cold playbook encoded as a Deep Agents 'skill'; ICP and qualification implicit in lead tiering (silver leads); motion = hybrid (self-serve + human outbound).",
   "tier_2_objectives_served": "Raise lead->qualified-opportunity conversion (the r_i term feeding CAC efficiency); surface expansion (g) and churn-risk (delta) in account intelligence.",
   "tier_1_loop_automated": "Trigger on Salesforce lead -> do-not-send checks (Rules of Engagement) -> research CRM/Gong/LinkedIn/Exa -> load skill -> draft -> human approve in Slack -> queue follow-ups -> 48h SLA auto-send for silver.",
   "substrate_used": "Salesforce/Gong/BigQuery/Gmail systems of record; LangSmith for evals+observability; Postgres per-rep memory; subagents per account for parallel account-intelligence.",
   "learning_loop": "Rep edit -> LLM diffs original vs revised -> extracts structured style observations -> stores per-rep in Postgres -> every future run reads before drafting. Human-in-the-loop IS the data-collection mechanism.",
   "drift_handling": "Evals in CI + 'treat unexplained behavior drift as a bug' = the regime-tier principle that behavioral coefficients decay and must be re-checked.",
   "results_in_model_terms": "lead->oppty +250% and 3x pipeline = a jump in the worst-converting funnel stage (bottleneck theorem); 40 hrs/rep/mo reclaimed = sales-cycle-length compression (velocity identity)."
  },
  "what_an_agent_must_NOT_do": [
   "Choose or silently drift the ICP, motion, or pricing (Tier 3 \u2014 human-owned).",
   "Optimize a correlational edge as if causal (e.g. chase NRR to 'fix' CAC).",
   "Act below a poorly-estimated Phi (great drafting times no fit = near zero).",
   "Send without human-in-the-loop where a single mistimed touch destroys relationship capital."
  ],
  "what_an_agent_is_uniquely_good_at": [
   "Moving r_i: research + personalization + speed-to-lead at the bottleneck stage.",
   "Compressing sales-cycle length (the velocity denominator).",
   "Signal-to-action: detecting intent/expansion/churn signals and routing them.",
   "Being a learning surface: every human override becomes a coefficient correction."
  ],
  "atomic_loop": "Each agent runs Sense->Reason->Act->Learn (see atomic_execution_cycle). The interface boundary applies to the whole loop: it senses/reasons/acts within Tier 1 and reads Tiers 2-3.",
  "governance_instruments": {
   "task_to_role_matrix": "Per process step, label agent-owned / agent-assisted (human reviews) / human-owned. The concrete form of the Tier-1-vs-Tier-3 boundary.",
   "escalation_paths": "Signals that force a human handoff \u2014 pricing questions, legal mentions, negative sentiment. The safety valve where authority/relationship judgment is required.",
   "data_quality_gates": "Agent acts only on verified+enriched records. Prevents machine-speed action on stale data \u2014 the most common failure mode.",
   "audit_logging": "Every action logged with timestamp+agent id, traceable and reviewable. The accountability layer that makes drift detectable.",
   "agent_finops": "Track agent spend per task/account to keep the FinOps->CAC edge from going the wrong way."
  },
  "adoption_structure": {
   "maturity_ladder": "chatbot -> copilot -> standalone agent -> agentic OS (rising autonomy, breadth, proactiveness, governance). Most value at the top rung, most deployments at the bottom.",
   "phased_rollout": "Phase 1 (research->draft->writeback, every message approved) -> Phase 2 (signal routing + multi-channel) -> Phase 3 (governance, thresholds, audit, quarterly benchmarking). Phasing exists because ~30% of GenAI projects are abandoned post-POC on data quality / unclear value.",
   "prove_roi": "Run agentic vs manual as a controlled split over 30-60 days; benchmark against your own baseline, not aspirational numbers."
  }
 },
 "theses": [
  {
   "id": "T0",
   "claim": "GTM is a causal identity wrapped in a correlational membrane whose coefficients are functions of regime and of a latent PMF multiplier.",
   "type": "meta",
   "load_bearing": true
  },
  {
   "id": "T1",
   "claim": "Acquisition is a linear lever; retention is a nonlinear one (it shrinks the steady-state denominator). A field obsessed with top-of-funnel is optimizing the lower-exponent term.",
   "from_equation": "conservation_law"
  },
  {
   "id": "T2",
   "claim": "Bottleneck theorem: because funnel stages multiply, fixing the worst-converting stage dominates improving a good one.",
   "from_equation": "funnel_operator"
  },
  {
   "id": "T3",
   "claim": "[v2.0 REGIME-CONDITIONAL] CAC payback binds harder than the LTV:CAC ratio ONLY when capital is expensive (Psi tight, M low) or runway < ~2x payback. In ZIRP it was false; for capital-abundant incumbents it is false. Payback is not a fixed king -- it is a function of Psi.",
   "from_equation": "psi_objective",
   "v2_status": "was stated as a fixed law; now regime-conditional via Psi"
  },
  {
   "id": "T4",
   "claim": "Every channel has a carrying capacity and convex CAC; the optimum is a time-shifting portfolio, which is why GTM strategies have half-lives.",
   "from_equation": "saturation"
  },
  {
   "id": "T5",
   "claim": "Motion is a forced move set by ACV vs cost-to-serve, not a preference; the PLG/hybrid/sales-led bands are that inequality's footprint.",
   "from_equation": "motion_threshold"
  },
  {
   "id": "T6",
   "claim": "GTM has distributed lags, so last-touch attribution structurally misattributes and over-cutting brand is a recurring institutional error.",
   "from_equation": "attribution_convolution"
  },
  {
   "id": "T7",
   "claim": "[v2.0 PIECEWISE] PMF is a multiplicative amplifier of GTM ONLY in low-switching-cost segments (R=Phi*f). In high-switching-cost segments (enterprise, regulated, integrated) revenue is ADDITIVE: R=Phi*f1 + S*f2, where a switching-cost moat S generates revenue largely independent of product love (Oracle/SAP existence proof). The strong multiplicative form was circular (Phi proxied by the NRR it predicts) and is wrong in ~the most valuable third of enterprise software.",
   "from_equation": "pmf_piecewise",
   "load_bearing": true,
   "contested": true,
   "v2_status": "was strong-multiplicative + circular; now piecewise + de-circularized via phi_ex_ante (but panel unrun)"
  },
  {
   "id": "T8",
   "claim": "An agentic GTM system executes the behavioral tier and is uniquely good at moving conversion coefficients and compressing cycle time \u2014 but only within the configuration and constraints set by the human-owned strategy and economics tiers.",
   "type": "architecture",
   "evidence": "LangChain GTM agent"
  },
  {
   "id": "T9",
   "claim": "Human-in-the-loop is not just a safety mechanism; it is the data-collection mechanism by which the behavioral coefficients are learned. A GTM model that can't ingest its own correction signal is dead on arrival.",
   "type": "architecture",
   "evidence": "LangChain memory loop"
  },
  {
   "id": "T10",
   "claim": "Every serious agentic-GTM vendor independently converges on the same micro-architecture (Sense-Reason-Act-Learn under human-in-the-loop, governed, on a unified platform) \u2014 which validates the three-tier model from the outside: they are all building the SUBSTRATE that lets agents execute Tier 1 while reading Tiers 2-3. The competitive edge has shifted from WHETHER you adopt agents to how DISCIPLINED and GOVERNED the implementation is.",
   "type": "architecture",
   "evidence": "Apollo, Poexis, Wyzard convergence",
   "load_bearing": true
  },
  {
   "id": "T11",
   "claim": "Agents invert software's cost/reliability profile (cheap to build, expensive+unreliable to run, compounding failures), which adds a FinOps->CAC edge to the economics tier and makes human-in-the-loop a structural necessity (error-chain interruption) rather than merely a safety nicety.",
   "from": "agent_properties",
   "load_bearing": true
  },
  {
   "id": "T12",
   "claim": "BUYER-STATE PRIMACY: the seller funnel observes only ~5% of value creation; the outcome is largely determined pre-contact by Day-1 shortlist position. Funnel conversion coefficients are CONDITIONAL (mixtures over buyer-state), not causal. Doubling seller activity into a fixed in-market pool yields geometric decay, not proportional pipeline. This is the single biggest behavioral change from v1.1.",
   "from_equation": "buyer_state_coupling",
   "load_bearing": true,
   "contested": false,
   "evidence_caveat": "Direction well-supported; magnitudes 6sense/LinkedIn-sourced (commercially interested)."
  },
  {
   "id": "T13",
   "claim": "BRAND IS A STOCK, NOT A FLOW: mental availability accumulates (Nerlove-Arrow) and is HOW you reach the Day-1 shortlist. Brand stock B_r -> buyer-state B -> outcome is the real 'Tier 0' causal chain. Demand-creation is not a lagged flow inside attribution (T6) but a stock that generates it.",
   "from_equation": "brand_stock",
   "load_bearing": true,
   "contested": true,
   "evidence_caveat": "Adstock illusion: a long-lag flow can mimic the stock; needs a media-pause experiment to distinguish. alpha/delta unmeasured for SaaS."
  },
  {
   "id": "T14",
   "claim": "PLG IS A LOOP, NOT A FUNNEL, AND k<1 ALWAYS BINDS: B2B virality never sustains k>=1, so the divergent closed form is a non-binding limit; PLG is a highly efficient channel, not perpetual motion. Retention is upstream of virality (k_eff=k_raw*R), and expansion-as-acquisition breaks NRR's intra-account assumption.",
   "from_equation": "plg_loop",
   "load_bearing": false,
   "contested": false
  },
  {
   "id": "T15",
   "claim": "[AGENTIC, v2.0 RECALIBRATED] Agentic GTM is technically calibrated but commercially over-claimed. Cost-per-ACTIVITY collapses via model-routing; cost-per-QUALIFIED-OPPORTUNITY net of deliverability decay and 15-20hr/wk operator maintenance is UNPROVEN (no public holdout). The autonomous-AI-SDR thesis is falsified in public (11x collapse; ZoomInfo 'worse than our SDRs'); capital flipped to AE-augmentation (Rox $1.2B by REJECTING SDR-replacement). Hybrid beats autonomous; Tier-3 stays human in all cases.",
   "from_equation": null,
   "load_bearing": true,
   "contested": false,
   "evidence_caveat": "Architecture patterns triangulate across ~70 cases; outcome magnitudes mostly self-reported, no holdouts."
  },
  {
   "id": "T16",
   "claim": "AGENTS WORK THE LEAST-LEVERAGED 5%: because ~95% of value creation is pre-contact (buyer-state + brand), agents deployed almost entirely in the seller funnel optimize the visible tail. BEAR: they polish the harvested ~5% while brand/shortlist go unaddressed, degrading coefficients via signal-exhaustion. BULL: agents are the only scalable way to nurture the 95% out-of-market into future shortlist position. Which dominates is unproven; today's tooling makes the bear case empirically closer.",
   "from_equation": "buyer_state_coupling",
   "load_bearing": true,
   "contested": true
  },
  {
   "id": "T17",
   "claim": "MAXIMAL-BUT-TAGGED: a complete map is only safe if its legend is honest. v3.0's coverage is bought with falsifiability; the measurement_status tag on every construct is the sole thing preventing the model from explaining any outcome after the fact.",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T18",
   "claim": "REALIZED < DESIGNED: realized growth is min(GTM physics, organizational alignment bandwidth). In most firms the binding constraint is alignment bandwidth, not lead quality \u2014 so the org_cognition layer often dominates the equation layer.",
   "from_layer": "org_cognition",
   "load_bearing": true,
   "contested": true
  },
  {
   "id": "T19",
   "claim": "REFLEXIVITY, NOT DRIFT: a tactic's coefficient is highest before imitation and decays toward the market mean as it is copied. The 'half-life of a GTM strategy' is an endogenous reflexive effect, measurable as effect-size decay, not exogenous noise.",
   "from_layer": "competitive_ecology",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T20",
   "claim": "THE SUBSTRATE IS NOT NEUTRAL: what the CRM measures, the org optimizes (Goodhart). Observability mutates the system observed; therefore the measurement protocols the model depends on are themselves interventions.",
   "from_layer": "substrate_epistemics",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T21",
   "claim": "PHI-VECTOR > PHI-SCALAR, BUT ONLY IF MEASURED: decomposing PMF into sub-factors adds rigor only where each sub-factor is independently observable. Today only retention pull is; the rest remain hypotheses. Decomposition without measurement multiplies the ghost, it does not exorcise it.",
   "from_layer": "phi_decomposition",
   "load_bearing": true,
   "contested": true
  },
  {
   "id": "T22",
   "claim": "TOPOLOGY VS DRIFT: the dangerous regime change is the one that rewrites the graph (AI search reshaping discoverability), not the one that shifts a coefficient. Misclassifying topology change as drift is fatal precisely in the highest-stakes periods.",
   "from_layer": "regime_dynamics",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T23",
   "claim": "AGENTS ARE LEVERAGE THEN POLLUTION: agentic GTM is alpha to the first mover and equilibrium degradation at scale \u2014 homogenized messaging, inflated noise floor, anti-agent defenses. Marginal GTM alpha from agents decays as adoption rises.",
   "from_layer": "agent_environmental_effects",
   "load_bearing": true,
   "contested": true
  },
  {
   "id": "T24",
   "claim": "POWER LAW BREAKS THE AVERAGE: under revenue/rep/channel concentration, mean-coefficient reasoning and the naive Bottleneck Theorem mislead. Optimization must be tail-aware; a few units carry the outcome.",
   "from_layer": "power_law",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T25",
   "claim": "NAME THE OBJECTIVE OR OPTIMIZE INCOHERENTLY: growth, efficiency, survival, positioning, and optionality conflict. Without an explicit regime-conditional weight vector, the system silently optimizes whatever the comp plan rewards.",
   "from_layer": "objective_function",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T26",
   "claim": "CREATION HAS DIFFERENT PHYSICS: in category-creation mode the funnel/Phi machinery is largely inapplicable; outcomes come from narrative and reality-construction. The model is strong on playing the game and weak on making it \u2014 and says so.",
   "from_layer": "category_formation",
   "load_bearing": false,
   "contested": true
  },
  {
   "id": "T27",
   "claim": "THE WALK IS THE ONLY INSTANCE: the MRR conservation law is the subscription case of a family. Usage, transaction, and marketplace models need different stocks and break the ACV-driven motion math. The economics tier is revenue-model-conditional.",
   "from_layer": "business_model_generalization",
   "load_bearing": true,
   "contested": false
  },
  {
   "id": "T28",
   "claim": "POROUS FOR HUMANS, GATED FOR AGENTS: the tier boundary is normative, not descriptive \u2014 execution constantly forces strategy revision, and that feedback loop is the real engine. The safety property is preserved by letting agents PROPOSE Tier-3 changes that humans ratify, never enact unilaterally.",
   "from_layer": "cross_tier_coupling",
   "load_bearing": true,
   "contested": false
  }
 ],
 "failure_modes": [
  {
   "name": "Identity/correlation conflation",
   "description": "Treating a regime or correlational edge with the confidence of an identity. The original sin. Mitigation: every edge carries epistemic_type; counterfactuals are gated by type."
  },
  {
   "name": "Optimizing the confounded proxy",
   "description": "Chasing NRR/engagement to 'cause' revenue when both are driven by Phi. Mitigation: never run a counterfactual on a correlational edge."
  },
  {
   "name": "Ratio over payback",
   "description": "Celebrating 5:1 LTV:CAC while running out of cash. Mitigation: payback is the binding constraint at venture stage."
  },
  {
   "name": "Comfortable-stage optimization",
   "description": "Improving the funnel stage you're already good at instead of the bottleneck. Mitigation: bottleneck theorem; always intervene at the min-conversion stage."
  },
  {
   "name": "Brand over-cut",
   "description": "Cutting demand-creation because last-touch shows no ROI. Mitigation: model the convolution kernel; use self-reported attribution."
  },
  {
   "name": "Tactics-before-fit",
   "description": "Scaling GTM machinery below threshold Phi. Mitigation: PMF gate before motion scale-up. The most expensive failure mode."
  },
  {
   "name": "Static-model rot",
   "description": "Trusting fitted coefficients after the regime has moved (channel saturated, competitor copied you, macro shifted). Mitigation: regime-tier coefficients carry half-lives and are re-estimated; treat drift as a bug (LangChain's CI-eval principle)."
  },
  {
   "name": "Agent overreach",
   "description": "Letting an execution agent silently drift Tier-3 configuration (ICP/motion/pricing) or act without human-in-the-loop on relationship-destroying touches. Mitigation: the agent_interface boundary \u2014 execute Tier 1, read Tiers 2-3, never rewrite them."
  },
  {
   "name": "Ungoverned agent spend",
   "description": "Agent execution cost (high, usage-based) silently inflates CAC. Mitigation: Agent FinOps tracking per task/account; the FinOps->CAC edge is real."
  },
  {
   "name": "Compounding-error blast radius",
   "description": "A small early agent misjudgment cascades through a multi-step chain into a relationship-damaging send. Mitigation: human-in-the-loop checkpoints + data-quality gates + escalation paths interrupt the chain."
  },
  {
   "name": "POC abandonment",
   "description": "~30% of GenAI projects die after POC on poor data quality / unclear value. Mitigation: phased rollout starting with the lowest-risk highest-signal loop, proven against a manual control group."
  },
  {
   "name": "Treating agents as automation",
   "description": "Buying a 'standalone agent' expecting OS-level breadth/governance, or wiring agents as if-this-then-that. Mitigation: the maturity ladder \u2014 match the rung to the need; agents reason, automation triggers."
  },
  {
   "name": "Identity laundering (the v1.1 original sin, named)",
   "description": "Tagging LTV/NRR/CAC-payback as 'identities' when they are assumption-dependent estimators. Inflates certainty and propagates upward into a circular PMF thesis. Mitigation: only the MRR walk is identity; everything else carries its hidden_assumption."
  },
  {
   "name": "Regime-blind benchmarking",
   "description": "Applying a ZIRP-era benchmark (e.g. 24-mo payback is fine) in a high-rate regime. Mitigation: Psi gates which objective weighting and payback target apply."
  },
  {
   "name": "Funnel-causal fallacy",
   "description": "Reading a Tier-1 conversion coefficient as seller skill and scaling headcount to 'double pipeline', when it is a mixture over buyer-state. Causes geometric decay into a fixed in-market pool. Mitigation: condition on B; de-confounded estimand."
  },
  {
   "name": "Brand-as-flow error",
   "description": "Cutting brand spend because attribution shows low last-touch credit, treating a stock as a flow. Mitigation: B_r is a stock with slow decay; judge via share-of-search/CEP breadth, not last-touch."
  },
  {
   "name": "Phi circularity",
   "description": "Proving PMF with the NRR/retention it is supposed to predict. Mitigation: fix Phi ex-ante (week-8-12 retention floor or t-1 third-party satisfaction); run the nested regression -- or admit it is unrun and label T7 a heuristic."
  },
  {
   "name": "Agentic over-claim (cost-per-activity vs cost-per-opportunity)",
   "description": "Citing collapsed cost-per-email as a CAC win. Cost-per-qualified-opportunity net of deliverability decay and operator maintenance is unproven. Mitigation: demand a holdout; treat uncontrolled ROI multiples as marketing math."
  },
  {
   "name": "Optimizing the visible 5%",
   "description": "Pointing agents entirely at the seller funnel while ~95% of the decision is pre-contact. Mitigation: ask whether the agent moves B_r or B, not just closes meetings faster."
  },
  {
   "name": "Maximal-map mistaken for surveyed territory",
   "description": "Reading v3.0's comprehensive coverage as uniform reliability. Mitigation: obey the measurement_status tag \u2014 never feed an unmeasurable_hypothesis as a number into a decision."
  },
  {
   "name": "Automated the wrong playbook",
   "description": "Agents executing a stale Tier-3 config flawlessly because the execution->strategy feedback loop was severed. Mitigation: cross_tier_coupling \u2014 agents propose, humans ratify, loop stays live."
  },
  {
   "name": "HITL rubber-stamp rot",
   "description": "Reviewer alert fatigue turns human approvals into noise, corrupting the learning signal. Mitigation: down-weight approvals when approve-rate and downstream quality diverge (learning_layer)."
  },
  {
   "name": "Agent equilibrium degradation",
   "description": "Everyone's agents homogenize outreach and inflate the noise floor, decaying marginal alpha. Mitigation: track reply-rate vs market send volume; differentiate or retreat from saturated channels."
  },
  {
   "name": "Ontology drift unnoticed",
   "description": "Stage definitions inflate under quota pressure; the same label now means something weaker. Mitigation: monitor conversion-distribution shift under a fixed label (ontology_instability)."
  },
  {
   "name": "Average-case blindness under power law",
   "description": "Mean LTV / mean r_i reasoning when a few units carry the base. Mitigation: tail-aware metrics; treat top-decile concentration as tail risk, not strength."
  },
  {
   "name": "Objective incoherence",
   "description": "Optimizing conflicting goals at once (short-term CAC vs brand, ACV vs PLG loops) with no stated weights. Mitigation: make the regime-conditional objective vector explicit (objective_function)."
  },
  {
   "name": "Category myopia",
   "description": "Optimizing the funnel inside a game a competitor is busy redefining. Mitigation: watch branded-category-search share; know whether you are in creation or optimization mode."
  }
 ],
 "atomic_execution_cycle": {
  "name": "Sense -> Reason -> Act -> Learn (the agent loop)",
  "what_it_is": "The atomic unit that POPULATES Tier 1. Where v1.0 had the tier but not its mechanism, this is the cycle every agentic-GTM vendor (Apollo, Poexis, Wyzard, LangChain) independently converged on. It is the agentic replacement for a single human rep's per-lead loop.",
  "steps": {
   "sense": "Ingest signals \u2014 zero-party (volunteered), first-party (your observed behavior: visits, usage, badge scans), third-party (external intent/firmographic). Maps to: Intent Data, Buying Signal, Signal Taxonomy.",
   "reason": "Decide what the signal means now against a knowledge base, ICP, and policies; pick a strategy and a next-best-action. Maps to: Lead Scoring, qualification frameworks, Context Engineering, Next-Best-Action.",
   "act": "Execute across channels and systems \u2014 email, social, chat, calling, CRM writeback. Maps to: Sales Sequence, channel terms, the Act output.",
   "learn": "Improve from outcomes, peer agents, and (most importantly) human corrections via few-shot/SFT/RL. Maps to: Learning Loop, Audit Logging."
  },
  "key_distinction": "This is NOT rule-based automation. Automation triggers preset actions on a condition; the agent CHOOSES an action by reasoning. The difference is 'sense->reason->act' vs 'if-this-then-that'.",
  "relationship_to_tiers": "The loop runs entirely inside Tier 1, reading configuration from Tier 3 (ICP/motion/playbook as policy) and objectives/guardrails from Tier 2 (what to optimize, what it costs). Hundreds of these inner loops are wrapped by one outer business-outcome flywheel (Attract->Engage->Convert->Expand)."
 },
 "agent_properties": {
  "name": "Why agents behave unlike traditional software",
  "inverted_cost_profile": {
   "traditional_software": {
    "creation": "high",
    "execution": "low",
    "reliability": "high",
    "failure": "non-compounding"
   },
   "agents": {
    "creation": "low (natural language)",
    "execution": "high (per-call model spend)",
    "reliability": "low",
    "failure": "COMPOUNDING"
   },
   "implication": "Two consequences the v1.0 model missed. (a) ECONOMIC: high, usage-based execution cost means agent spend flows INTO CAC and can move it the wrong way if ungoverned -> see the new FinOps->CAC edge and Agent FinOps. (b) RELIABILITY: compounding failure means an early small error (misreading account state) cascades into wrong research -> wrong draft -> wrong send. This is the STRUCTURAL reason human-in-the-loop exists \u2014 not just safety, but to INTERRUPT the error chain before it compounds."
  },
  "vocab": [
   "Agent Cost Profile",
   "Compounding Failure",
   "Agent FinOps",
   "Human-in-the-Loop"
  ]
 },
 "minimal_coupling_set": {
  "rationale": "Eight new components fully coupled is an over-parameterized, unfalsifiable graph. All four construction reports converge on a sparse edge set that captures the dominant interactions while staying testable.",
  "edges": [
   {
    "edge": "B_r -> B",
    "reads": "brand stock raises Day-1 shortlist probability (the Tier-0 chain)"
   },
   {
    "edge": "B -> Tier-1 funnel conversion",
    "reads": "buyer-state conditions every funnel coefficient (the de-confounding edge)"
   },
   {
    "edge": "Psi -> objective weights (w_g, w_f)",
    "reads": "macro regime sets growth-vs-FCF pricing"
   },
   {
    "edge": "S -> churn hazard / NRR",
    "reads": "switching-cost moat suppresses churn; flips T7 to additive"
   },
   {
    "edge": "PLG retention -> k (and PLG -> B at handoff)",
    "reads": "retention drives virality; loop hands off to buyer-state at usage threshold"
   }
  ],
  "deliberately_uncoupled": [
   "Phi ex-ante -> immediate GTM decisions (Phi stays latent until panel exists)",
   "B_r <-> Psi and S <-> Psi feedback (investment treated as exogenous policy in v2.0)",
   "PLG -> brand feedback (PLG kept as acquisition channel, not brand builder, in v2.0)"
  ],
  "provenance": "All four construction reports, Part 3."
 },
 "measurement_gap_register": {
  "purpose": "Marks the boundary between honest-but-unparameterized and operational. These parameters the rebuilt model NEEDS but CANNOT currently measure from public data. All four construction reports converge on this list.",
  "unmeasurable_from_public_data": [
   {
    "param": "Ex-ante Phi panel",
    "consequence": "T7's nested regression (b3 sign = multiplicative vs additive) cannot be run; T7 stays a localized heuristic until a private aggregator (Bessemer, SaaS Capital) collects independent t-1 satisfaction + subsequent growth + GTM spend."
   },
   {
    "param": "Switching-cost S at account level",
    "consequence": "Regime boundary (multiplicative vs additive) can't be set directly; proxied via GRR>=95% + low-satisfaction confounder."
   },
   {
    "param": "Brand alpha (build rate) and delta (decay) for SaaS",
    "consequence": "Brand-stock dynamics are directional only; flagged retrospective-only / forecasting liability."
   },
   {
    "param": "Sustained k>=1 in B2B",
    "consequence": "No public example exists; the divergent PLG closed form is a non-binding limiting case."
   },
   {
    "param": "Noise-floor theta as a stable constant",
    "consequence": "25-40% is a reasoned assumption (Gross/Taleb), not a fitted 1 - out-of-sample-R^2 for B2B SaaS."
   },
   {
    "param": "Controlled CAC under agentic GTM",
    "consequence": "No public holdout exists; whether agents lower cost-per-qualified-opportunity net of decay+maintenance is unproven."
   }
  ],
  "honest_terminal_status": "The model is operational inside measured regimes and explicitly refuses to overclaim outside them. T7 in particular is specified and falsifiable IN PRINCIPLE but unrun IN PRACTICE."
 },
 "agent_layer_v2_recalibration": {
  "verdict": "Technically calibrated, commercially over-claimed. The two must never be conflated.",
  "what_holds": [
   "T11 cost/reliability inversion VALIDATED and strengthened: 95%^10 ~ 60% compounding-failure math is empirically backed (each off-canonical tool call raises the next's probability ~+22.7pp); GTM is exactly the >98%-reliability task class current agents fail.",
   "Tier-3 stays human in ALL ~70 documented cases (no case touched ICP/pricing autonomously).",
   "Hybrid beats autonomous: settled verdict (11x collapse, ZoomInfo 'worse than our SDRs', Salesmotion 2.6x human>AI, ~2% AI-SDR survival; Rox raised $1.2B by rejecting SDR-replacement)."
  ],
  "what_is_recalibrated_down": [
   "FinOps->CAC edge: cost-per-ACTIVITY collapses (Vercel $60K/yr displaced 9 SDRs; $0.01/sequence via model-routing) BUT cost-per-QUALIFIED-OPPORTUNITY net of deliverability decay is UNPROVEN. Marked 'claimed, uncontrolled, possibly reversed'.",
   "The 'Learn' leg of Sense-Reason-Act-Learn is mostly ASPIRATIONAL (MIT NANDA: 95% of pilots no P&L impact; most systems don't retain feedback). The one genuine exception is LangChain's per-rep edit-diff memory.",
   "'GTM OS' framing is marketing, not architecture; the real mid-layer is a fragmented GTM-Engineer + harness + signals + CRM stack."
  ],
  "new_load_bearing_patterns": [
   "Tiered/threshold HITL (autopilot below a $-threshold or confidence score, human gate above) -- the operational form of the tier boundary",
   "Skills-as-markdown (harness stable, skill iterated separately)",
   "Agents as narration over deterministic math (Resolve AI Monte-Carlo + LLM narration; BlackRock bullet-points-not-letters)",
   "Data-hygiene as PRECONDITION GATE: Salesforce 30%->less-than-10% hallucination only after Data Cloud cleanup; agent quality is bounded by upstream data quality; the warehouse, not the CRM, is the real substrate",
   "Evals-as-CI-merge-gate (prevents the 11-day $47K runaway-agent incident)",
   "Asymmetric-escalation bias (ElevenLabs deliberately over-qualifies 22% to protect pipeline)",
   "Guardrails loosen rules->goals as trust accrues (Salesforce) -- the copilot->autopilot graduation mechanism"
  ],
  "new_failure_file": [
   "ARR-inflation via break-clauses (11x: ~78% of claimed ARR in 90-day break clauses)",
   "Quality collapse on the emotional ~20% (Klarna reversal; volume metrics masked it, repeat-contact rate leaked it)",
   "Deliverability/dead-internet decay (cap ~200 sends/mailbox/day; reply rates 8.5%(2019)->3.43%(2026))",
   "Legal liability for hallucinations (Moffatt v. Air Canada -- common-law precedent)",
   "Prompt-injection binding language ($1 Chevy Tahoe 'no takesies backsies')",
   "OAuth supply-chain attack (Drift breach, 700+ orgs)"
  ],
  "the_strategic_point": "Because ~95% of value creation is pre-contact (T16), agents working the seller funnel optimize the visible ~5%. The high-leverage open question -- can agents cheaply nurture the out-of-market 95% into future shortlist position? -- is unproven. This connects the agentic layer directly to the buyer-state primacy of T12.",
  "provenance": "~70 documented implementations across 5 evidence reports (deduplicated); the architecture triangulates, the outcome magnitudes are mostly self-reported without holdouts."
 },
 "measurement_status_legend": {
  "purpose": "Orthogonal to epistemic_type. Epistemic_type says what KIND of claim an edge is; measurement_status says whether you can actually OBSERVE the variable in time to act.",
  "statuses": {
   "measurable_ex_ante": "Observable BEFORE the outcome window it predicts; can be used as a genuine prior or input (e.g. design-partner retention, search share).",
   "measurable_ex_post": "Observable, but only AFTER the outcome; useful for learning/backtesting, not for forward decisions in the current period.",
   "proxy_only": "No direct measurement; approximated by a confounded stand-in (e.g. 'paid social feels tapped out' for channel saturation x). Use with stated error bars.",
   "unmeasurable_hypothesis": "Structurally plausible, currently no measurement protocol on available data. Belongs in the model as a documented hypothesis, NEVER as a load-bearing input. The honest home for most latents."
  },
  "rule": "A construct tagged unmeasurable_hypothesis may inform narrative and risk-awareness but may not be fed as a number into any forward decision or agent action."
 },
 "extended_layers": {
  "belief_state": {
   "name": "A. Belief-state / hidden-state formalism",
   "models": "Turns the model from a causal ontology toward a state-space object: an explicit latent state, an observation map separating what is seen from what is inferred, a belief update, and uncertainty propagation. Answers the critique 'what is the actual hidden state vector?'",
   "source_critiques": [
    "doc 8/10 \u00a71",
    "doc 3 (no probabilistic program)",
    "doc 9 (no uncertainty)"
   ],
   "constructs": [
    {
     "name": "latent state vector x_t = (C, B, B_r, S, Psi, Phi-vector, competitive intensity, org-coherence)",
     "type": "latent_multiplier/state",
     "measurement": "proxy_only",
     "note": "Some components observable (C), most inferred. This is the object a real engine would carry."
    },
    {
     "name": "observation map h(x_t) -> y_t",
     "type": "identity",
     "measurement": "measurable_ex_post",
     "note": "Which CRM/billing/intent signals are noisy reads of which latent. The map is itself lossy (see substrate_epistemics)."
    },
    {
     "name": "belief update b_t = f(b_{t-1}, y_t, action_{t-1})",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Action-conditioned: beliefs must update on what the org DID, not just what it saw."
    },
    {
     "name": "uncertainty propagation (posterior intervals, not points)",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Implemented for one slice in gtm_engine_slice.py; specified, not built, for the rest."
    }
   ],
   "honest_caveat": "Naming the state vector does not make its latent components observable. Most entries here are proxy_only or worse; the formalism is a scaffold, and only the MRR-walk slice currently has a working belief update."
  },
  "phi_decomposition": {
   "name": "B. Phi decomposed (PMF is a vector, not a scalar)",
   "models": "Splits the master multiplier Phi into its sub-factors, as the critiques demanded. Each sub-factor is tagged for whether it can be measured ex ante \u2014 because that, not the decomposition itself, is what governs falsifiability.",
   "source_critiques": [
    "doc 8/10 \u00a72 (12-factor decomposition)",
    "doc 2/9 (family of latents)",
    "doc 1/12 (Phi unfalsifiable)"
   ],
   "constructs": [
    {
     "name": "urgency (pain frequency x severity)",
     "type": "latent_multiplier",
     "measurement": "proxy_only"
    },
    {
     "name": "willingness-to-pay",
     "type": "latent_multiplier",
     "measurement": "measurable_ex_post",
     "note": "revealed in pricing tests"
    },
    {
     "name": "retention pull (workflow embedding + switching pain)",
     "type": "latent_multiplier",
     "measurement": "measurable_ex_post",
     "note": "the ONE sub-factor with a clean ex-post proxy: cohort retention. This is what gtm_engine_slice estimates."
    },
    {
     "name": "word-of-mouth / referral coefficient k_wom",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "category clarity",
     "type": "latent_multiplier",
     "measurement": "unmeasurable_hypothesis"
    },
    {
     "name": "implementation friction",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "political acceptability / procurement compatibility",
     "type": "latent_multiplier",
     "measurement": "proxy_only"
    },
    {
     "name": "inevitability (does the buyer believe the category wins?)",
     "type": "latent_multiplier",
     "measurement": "unmeasurable_hypothesis"
    }
   ],
   "honest_caveat": "This is the layer I argued AGAINST adding: decomposing one ghost variable into eight ghost variables does not cure unfalsifiability \u2014 it multiplies it, unless each sub-factor is independently measured. Only 'retention pull' currently is. Folded in per the maximal directive, with that cost stated flatly: most of Phi remains unmeasurable_hypothesis."
  },
  "competitive_ecology": {
   "name": "C. Competitive ecology & reflexivity",
   "models": "Treats the market as adaptive ecology, not physics. The environment changes BECAUSE you acted: working strategies get copied, channels saturate, incumbents retaliate, buyers adapt.",
   "source_critiques": [
    "doc 8/10 \u00a73",
    "doc 9 (single-firm)",
    "doc 2 (competitor responses)",
    "doc 1 (zero-sum absent)"
   ],
   "constructs": [
    {
     "name": "competitor response function R_comp(your_action)",
     "type": "causal_regime",
     "measurement": "proxy_only",
     "note": "Win/loss data is a noisy ex-post read of competitive reaction."
    },
    {
     "name": "reflexivity coefficient (decay of an edge's effect as it is imitated)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Operationalizes the 'half-life of a GTM strategy' \u2014 measurable as effect-size decay over cohorts."
    },
    {
     "name": "memetic copying lag",
     "type": "correlational",
     "measurement": "proxy_only"
    },
    {
     "name": "positioning / category-capture pressure",
     "type": "latent_multiplier",
     "measurement": "unmeasurable_hypothesis"
    }
   ],
   "mechanisms": [
    "Any edge in the model is regime-conditional on competitive intensity; a tactic's coefficient is highest before imitation and decays toward the market mean after (this is reflexivity, not drift).",
    "Treat competitor moves as exogenous regime shocks feeding the regime_dynamics layer."
   ],
   "honest_caveat": "Reflexivity is the genuinely important addition here and it has an ex-post measurement (effect-size decay). Category-capture pressure does not; it stays a hypothesis."
  },
  "demand_topology": {
   "name": "D. Demand topology (beyond the funnel)",
   "models": "The funnel is a local accounting approximation of a richer generative process: diffusion, contagion, trust-graph traversal, social proof, ecosystem/workflow embedding. CRM-visible behavior is the shadow of this layer, not its mechanism.",
   "source_critiques": [
    "doc 8/10 \u00a74",
    "doc 8/10 \u00a715 (creative discontinuities)"
   ],
   "constructs": [
    {
     "name": "diffusion coefficient on a trust graph (developer tools, PLG)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Observable as invite/activation propagation \u2014 the PLG-loop k from v2.0, generalized."
    },
    {
     "name": "social-proof cascade threshold",
     "type": "causal_regime",
     "measurement": "proxy_only"
    },
    {
     "name": "ecosystem lock-in / platform-rank dependence",
     "type": "causal_regime",
     "measurement": "proxy_only"
    },
    {
     "name": "agent-mediated discovery (buyers' own agents shortlist for them)",
     "type": "latent_multiplier",
     "measurement": "unmeasurable_hypothesis",
     "note": "Emerging 2026 mechanism; no clean measurement yet. Couples to Tier 0 buyer-state."
    }
   ],
   "honest_caveat": "The funnel is retained as the harvesting approximation; this layer says it is downstream of diffusion. Only graph-diffusion (the PLG k) is currently measurable; the rest are proxies/hypotheses."
  },
  "org_cognition": {
   "name": "E. Organizational cognition (the firm as a lossy computer)",
   "models": "The single most-cited omission. The org is not a frictionless executor of truth; it is a bounded-rationality machine where alignment bandwidth, not lead quality, is often the binding constraint. Models the gap between the designed system and the realized one.",
   "source_critiques": [
    "doc 8/10 \u00a75",
    "doc 9 (org politics first-order)",
    "doc 7 (talent/culture)",
    "doc 5 (adoption friction)",
    "doc 1/12 (HITL human limits)"
   ],
   "constructs": [
    {
     "name": "alignment bandwidth (rate at which the org can act on a decision)",
     "type": "causal_regime",
     "measurement": "proxy_only",
     "note": "The limiting reagent claim: realized growth is min(GTM physics, alignment bandwidth)."
    },
    {
     "name": "incentive-distortion factor (comp plan vs stated objective)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Measurable: gap between what comp rewards and what the objective_function weights."
    },
    {
     "name": "decision latency (signal -> action lag)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "managerial compression loss (information lost up each reporting layer)",
     "type": "correlational",
     "measurement": "unmeasurable_hypothesis"
    },
    {
     "name": "rep tenure distribution / leadership stability",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Modulates the r_i distribution (coefficient compression operates on top of this)."
    }
   ],
   "honest_caveat": "Several of these ARE measurable ex post (incentive distortion, latency, tenure), which is why this layer is more than hand-waving. Compression loss is a hypothesis. The realized-vs-designed gap is the layer's real contribution and it reframes every coefficient as org-conditioned."
  },
  "regime_dynamics": {
   "name": "F. Regime-transition mechanics (upgrading Psi from a scalar to a process)",
   "models": "v2.0 had Psi as a macro re-weighting scalar. v3.0 adds the MECHANICS of regime change: how a shift is detected, that some shifts are topology changes (new graph) not coefficient drift, and the re-estimation cadence. Answers 'what causes regime transitions and how do you detect them?'",
   "source_critiques": [
    "doc 8/10 \u00a76",
    "doc 3 (no regime classifier/drift detector)",
    "doc 9 (no change-point detection)"
   ],
   "constructs": [
    {
     "name": "change-point detector on conversion / CAC / response curves",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Concrete: CUSUM or Bayesian online change-point on coefficient residuals."
    },
    {
     "name": "regime classifier (feature vector -> regime label)",
     "type": "estimator",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "topology-change flag (drift vs new graph structure)",
     "type": "causal_regime",
     "measurement": "proxy_only",
     "note": "AI-search reshaping discoverability is a topology change, not 'slight CAC drift'."
    },
    {
     "name": "re-estimation cadence / coefficient half-life",
     "type": "estimator",
     "measurement": "measurable_ex_post"
    }
   ],
   "mechanisms": [
    "Distinguish three transition types: smooth drift (re-estimate), regime switch (swap parameter set), topology mutation (rebuild the graph). Misclassifying a topology change as drift is failure mode 'static-model rot' and is fatal precisely in the highest-stakes periods."
   ],
   "honest_caveat": "Change-point detection is a solved, runnable technique \u2014 this layer is buildable and mostly measurable_ex_post. The hard part (and the proxy) is calling a topology change in real time."
  },
  "substrate_epistemics": {
   "name": "G. Substrate as an active epistemic force",
   "models": "The genuinely novel point across all twelve critiques. The measurement substrate (CRM, dashboards, analytics) is not neutral infrastructure: what it measures, the org optimizes (Goodhart). Observability recursively reshapes the system being observed. This is cybernetic, not passive.",
   "source_critiques": [
    "doc 8/10 \u00a77",
    "doc 8/10 \u00a710 (metrics socially constructed)"
   ],
   "constructs": [
    {
     "name": "measurement -> behavior feedback edge (Goodhart)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "If the CRM tracks meetings-booked, reps optimize meetings; the metric becomes the target and decays as a signal."
    },
    {
     "name": "schema compression loss (reality -> CRM fields)",
     "type": "identity",
     "measurement": "measurable_ex_post",
     "note": "What the schema cannot represent does not exist to the agent. Constrains the observation map h(x)."
    },
    {
     "name": "attribution politicization",
     "type": "correlational",
     "measurement": "proxy_only"
    }
   ],
   "honest_caveat": "This is the one addition that strengthens the disciplined argument rather than fighting it: it explains WHY the measurement protocols the minimal core depends on are harder than they look \u2014 the act of measuring a variable changes the variable. The Goodhart edge is measurable ex post."
  },
  "ontology_instability": {
   "name": "H. Ontology instability (the definitions themselves drift)",
   "models": "Metrics are socially constructed and mutate under incentive pressure. 'Qualified lead', stage definitions, and success criteria inflate or shift with quota pressure, board pressure, and the funding environment. The ontology is not fixed; it is a dependent variable.",
   "source_critiques": [
    "doc 8/10 \u00a710",
    "doc 1 (epistemic tagging adds load to drifting concepts)"
   ],
   "constructs": [
    {
     "name": "definition-drift rate (semantic inflation of stage criteria over time)",
     "type": "correlational",
     "measurement": "measurable_ex_post",
     "note": "Detectable: same stage label, shifting conversion distribution across periods."
    },
    {
     "name": "metric-gaming pressure index",
     "type": "causal_regime",
     "measurement": "proxy_only"
    },
    {
     "name": "definitional provenance (who set this definition and under what pressure)",
     "type": "identity",
     "measurement": "measurable_ex_ante"
    }
   ],
   "honest_caveat": "Couples tightly to substrate_epistemics and org_cognition: drift is what Goodhart pressure does to a schema over time. Drift rate is measurable; the pressure driving it is a proxy."
  },
  "agent_environmental_effects": {
   "name": "I. Agents as environmental mutation forces (not just leverage)",
   "models": "Extends the v2.0 agent recalibration with the second-order, system-level effect: when everyone deploys agents, marginal GTM alpha decays. Agents homogenize messaging, accelerate channel saturation, raise the noise floor, and invite anti-agent defensive tooling. Leverage to the first mover; pollution to the equilibrium.",
   "source_critiques": [
    "doc 8/10 \u00a78",
    "doc 9 (caching/mitigations underweighted)",
    "doc 1/12 (HITL alert fatigue)"
   ],
   "constructs": [
    {
     "name": "messaging-homogenization index (cross-vendor template convergence)",
     "type": "causal_regime",
     "measurement": "proxy_only"
    },
    {
     "name": "noise-floor inflation (response-rate decay as outbound volume rises)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Directly measurable: reply rates vs aggregate market send volume. Generalizes v2.0's noise_floor."
    },
    {
     "name": "anti-agent defensive tooling adoption (deliverability decay)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "HITL degradation (alert fatigue -> rubber-stamping corrupts the learning signal)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Critical: the learning_layer's labels degrade as reviewer volume rises. Measurable via approve-rate vs downstream-quality divergence."
    }
   ],
   "honest_caveat": "Two genuinely measurable effects here (noise-floor inflation, HITL rubber-stamp divergence) directly undercut the optimistic agent case. The equilibrium-alpha-decay claim is directionally strong but proxy_only. Mitigations the critiques noted (caching, deterministic tools, eval gates) are in the v2.0 agent layer; they reduce the compounding-failure risk but not the homogenization."
  },
  "learning_layer": {
   "name": "J. The learning rule (the thing the spec never specified)",
   "models": "The single most-repeated 'missing engine' critique: 'human overrides become labeled corrections' is not a learning algorithm. v3.0 specifies the update mechanism, with the honest hooks for biased human labels and survivorship.",
   "source_critiques": [
    "doc 8/10 \u00a79 (epistemic governance)",
    "doc 9/3 (no learning algorithm)",
    "doc 2 (online Bayesian? bandits? counterfactual?)"
   ],
   "constructs": [
    {
     "name": "coefficient update: Bayesian posterior on each r_i, decayed by half-life",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Concrete and runnable (the slice does this for retention). Half-life from regime_dynamics."
    },
    {
     "name": "intervention testing (propose -> simulate -> A/B before committing)",
     "type": "estimator",
     "measurement": "measurable_ex_ante",
     "note": "Closes the loop the critiques wanted: experiments UPDATE Phi-beliefs, not just read them off."
    },
    {
     "name": "evidence ranking + provenance (RCT > holdout > observational > anecdote)",
     "type": "identity",
     "measurement": "measurable_ex_ante"
    },
    {
     "name": "biased-label correction (down-weight rubber-stamped HITL approvals)",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Consumes the HITL-degradation signal from layer I so the model does not learn from fatigue."
    },
    {
     "name": "causal-graph revision (promote/demote edges on intervention evidence)",
     "type": "estimator",
     "measurement": "measurable_ex_post"
    }
   ],
   "honest_caveat": "This layer answers the 'silicon BDR team' critique head-on: the update rule no longer depends linearly on human labels \u2014 it tests, ranks, and down-weights corrupted labels. It is specified and partly runnable; full causal-graph revision is not yet built. It does NOT escape the deeper fact that intervention testing requires traffic most early-stage ventures cannot spare."
  },
  "power_law": {
   "name": "K. Power-law structure (the world is lumpy, not Gaussian)",
   "models": "A few customers, channels, reps, narratives, and partnerships dominate outcomes. Average-case reasoning \u2014 implicit in smooth conversion rates and mean coefficients \u2014 systematically misleads under heavy tails. Replaces Gaussian intuition with concentration dynamics where it matters.",
   "source_critiques": [
    "doc 8/10 \u00a711"
   ],
   "constructs": [
    {
     "name": "revenue concentration (top-decile-account share)",
     "type": "identity",
     "measurement": "measurable_ex_post",
     "note": "Directly measurable; if a few logos carry the base, mean LTV is a fiction and churn risk is tail risk."
    },
    {
     "name": "rep-output concentration (coefficient compression operates around a heavy tail)",
     "type": "correlational",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "channel concentration / cumulative-advantage (Matthew effects, increasing returns)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "narrative concentration (one story captures the category)",
     "type": "latent_multiplier",
     "measurement": "proxy_only"
    }
   ],
   "honest_caveat": "Mostly measurable_ex_post and a real correction to the funnel math: under concentration, the Bottleneck Theorem and mean-r_i reasoning need tail-aware versions. Narrative concentration is a proxy."
  },
  "objective_function": {
   "name": "L. Multi-objective optimization (what is the system even maximizing?)",
   "models": "The model implicitly optimized growth-under-cash. Real GTM trades off growth, efficiency, survival, positioning, optionality, and category dominance \u2014 objectives that actively conflict. Makes the objective explicit and ties its weights to the regime (Psi).",
   "source_critiques": [
    "doc 8/10 \u00a712",
    "doc 2/9 (top-line vs payback governance failures)"
   ],
   "constructs": [
    {
     "name": "objective weight vector U = w(Psi) . (growth, fcf, survival, position, optionality)",
     "type": "conditional_coefficient",
     "measurement": "measurable_ex_ante",
     "note": "Generalizes v2.0's Rule-of-X. Weights are a regime-conditional CHOICE, observable in board mandate."
    },
    {
     "name": "conflict matrix (optimizing short-term CAC degrades brand; ACV degrades PLG loops)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "governance-distortion (board pressure forces top-line over payback until cash bites)",
     "type": "causal_regime",
     "measurement": "proxy_only",
     "note": "The institutional failure the critiques said v2.0 treated as exogenous; here it modulates w(Psi)."
    }
   ],
   "honest_caveat": "Explicit objective weights are measurable_ex_ante (you can read the mandate). The conflict matrix is the substantive content; governance distortion is a proxy. This finally makes 'what to optimize' a stated variable rather than an unspoken assumption of growth."
  },
  "category_formation": {
   "name": "M. Category formation (creating the game, not just playing it)",
   "models": "The biggest outcomes come from constructing a new mental category, not optimizing an existing funnel. Language control, narrative compression, benchmark ownership, analyst alignment, ecosystem recruitment. This is economic-reality construction and the model's weakest prior area.",
   "source_critiques": [
    "doc 8/10 \u00a713",
    "doc 8/10 \u00a715 (how new games emerge)",
    "doc 5 (narrative not structurally modeled)"
   ],
   "constructs": [
    {
     "name": "category-clarity stock (does the market have a name for what you do?)",
     "type": "latent_multiplier",
     "measurement": "proxy_only",
     "note": "Proxy: branded-search share for the category term over time. Couples to brand_stock B_r."
    },
    {
     "name": "narrative concentration (your frame vs competitors' frames)",
     "type": "latent_multiplier",
     "measurement": "proxy_only"
    },
    {
     "name": "benchmark / analyst ownership",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "name": "game-creation vs game-optimization mode flag",
     "type": "conditional_coefficient",
     "measurement": "proxy_only",
     "note": "In creation mode the funnel/Phi machinery is largely inapplicable; different physics governs."
    }
   ],
   "honest_caveat": "Honestly the least measurable layer \u2014 mostly proxies and one ex-post signal. But omitting it entirely was the critiques' fair charge that the model only explains stable periods. Included as explicitly proxy/hypothesis so it is not mistaken for the surveyed parts of the map."
  },
  "business_model_generalization": {
   "name": "N. Business-model generalization (de-SaaS-ifying the economics tier)",
   "models": "The economics tier assumed subscription ARR. v3.0 conditions it on revenue model and confronts that the SaaS identities are themselves in regime shift: inference cost erodes the margin premise, agentic AI replaces seats, consumption/outcome pricing breaks the ACV math that drives motion thresholds.",
   "source_critiques": [
    "doc 9 (SaaS/PLG-centric)",
    "doc 11 \u00a72 (economics tier drifting)",
    "doc 5 (no discount rate/cohorts)",
    "doc 5/9 (multi-product)"
   ],
   "constructs": [
    {
     "name": "revenue-model selector {subscription, usage, transaction, marketplace, hybrid, services}",
     "type": "conditional_coefficient",
     "measurement": "measurable_ex_ante",
     "note": "Switches which identities apply. The MRR walk is the subscription instance; usage/transaction need different stocks."
    },
    {
     "name": "margin-erosion term (variable inference cost in COGS)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Breaks the near-zero-marginal-cost premise under LTV. Folds the v2.0 FinOps->CAC edge into gross margin too."
    },
    {
     "name": "LTV with discount rate + cohort heterogeneity",
     "type": "estimator",
     "measurement": "measurable_ex_post",
     "note": "Adds the discount factor the no-discount inequality omitted; pairs with the sBG correction from v2.0."
    },
    {
     "name": "multi-product Phi/CAC/motion per SKU + cannibalization",
     "type": "causal_regime",
     "measurement": "measurable_ex_post"
    }
   ],
   "honest_caveat": "Strong, mostly-measurable layer: it removes a real overclaim (universality the model 'did not earn'). The revenue-model selector is the key move \u2014 it makes the MRR walk one instance of a family rather than the assumed default."
  },
  "compliance": {
   "name": "O. Compliance & regulatory constraints on AI outreach",
   "models": "AI-generated and agent-driven outreach operates under hard legal constraints the model ignored: CAN-SPAM, GDPR/consent, and emerging synthetic-media (deepfake voice/video) rules. These are binding constraints on the agent layer, not optional polish.",
   "source_critiques": [
    "doc 7 \u00a76 (regulatory omission)"
   ],
   "constructs": [
    {
     "name": "consent / opt-in coverage (GDPR, CAN-SPAM)",
     "type": "identity",
     "measurement": "measurable_ex_ante",
     "note": "Hard gate: an action outside consent scope is forbidden regardless of expected value."
    },
    {
     "name": "synthetic-media disclosure requirement",
     "type": "identity",
     "measurement": "measurable_ex_ante"
    },
    {
     "name": "jurisdiction selector (rules vary by region)",
     "type": "conditional_coefficient",
     "measurement": "measurable_ex_ante"
    }
   ],
   "honest_caveat": "Fully measurable_ex_ante and binding \u2014 these enter as the agent layer's outermost guardrails, above FinOps and HITL. Cheap to add, expensive to omit."
  },
  "cross_tier_coupling": {
   "name": "P. Cross-tier coupling (the boundaries are normative, not descriptive)",
   "models": "The hard tier separation is an ideal, not a description. In real orgs execution constantly forces strategy revision (win/loss changes ICP; saturation forces motion shifts). v3.0 allows variables to decompose across tiers and adds the execution->strategy feedback loop, while keeping the agent boundary (agents still may not unilaterally rewrite Tier 3).",
   "source_critiques": [
    "doc 9 (tier leaky)",
    "doc 11 \u00a71 (insufficient feedback)",
    "doc 12 (waterfall fallacy)"
   ],
   "constructs": [
    {
     "name": "tier-decomposed variables, e.g. g = g_strategy . g_execution",
     "type": "identity",
     "measurement": "measurable_ex_post",
     "note": "Replaces exactly-one-tier ownership; expansion has both a packaging (strategy) and a CSM (execution) component."
    },
    {
     "name": "execution -> strategy feedback loop (win/loss -> ICP revision)",
     "type": "causal_regime",
     "measurement": "measurable_ex_post",
     "note": "Makes the highest-leverage GTM work \u2014 the fast cross-tier loop \u2014 first-class rather than an exception."
    },
    {
     "name": "agent boundary preserved: propose Tier-3 changes, humans ratify",
     "type": "identity",
     "measurement": "measurable_ex_ante",
     "note": "Resolves the tension: boundary is porous for humans, gated for agents (avoids 'automated the wrong playbook')."
    }
   ],
   "honest_caveat": "This directly fixes a fair structural error: the v2.0 boundary was too rigid as a DESCRIPTION. The repair keeps the safety property (agents don't silently rewrite strategy) while admitting the feedback loop is the real engine of GTM learning."
  },
  "operator": {
   "name": "Q. Operator translation layer (the part a human can actually run Monday)",
   "models": "Compresses the whole maximal apparatus into what a CRO/VP-GTM monitors monthly, plus worked diagnostic workflows. Answers the adoption critique: the model is smarter than most GTM practice and therefore unusable without this.",
   "source_critiques": [
    "doc 9/5/8 (operationalization, no playbooks, cognitive load)",
    "doc 2 (no translation layer)"
   ],
   "the_three_things": [
    "1) Gate scale on PMF: do not pour acquisition into a leaky base \u2014 fix retention (the denominator) first.",
    "2) Always fix the worst funnel stage first (Bottleneck Theorem), tail-aware under concentration.",
    "3) Track CAC payback AND agent spend like your life depends on it; payback is the binding constraint at venture stage."
   ],
   "warning_lights": [
    "net revenue retention < 1.0 (leaky base \u2014 scaling amplifies the leak)",
    "CAC payback drifting past 12 months (cash constraint approaching)",
    "reply/response rate decaying with send volume (noise-floor inflation)",
    "HITL approve-rate high while downstream quality falls (rubber-stamping)",
    "top-decile account share rising (concentration / tail risk)",
    "branded-category-search share flat while spend rises (no category clarity)",
    "change-point flag on a core conversion rate (possible regime/topology shift)"
   ],
   "worked_workflows": [
    {
     "name": "Diagnose & fix a funnel bottleneck",
     "steps": "Pull stage-to-stage r_i; find min(r_i) tail-aware; check it is not a definition-drift artifact (ontology_instability); intervene on that stage only; A/B before committing (learning_layer); confirm you did not cannibalize the next stage (the cross-stage dependency critique)."
    },
    {
     "name": "Decide whether to scale a channel",
     "steps": "Estimate channel saturation x (proxy_only \u2014 state the error bar); if CAC convexity is steepening and payback nears the cap, do not scale; check reflexivity (is the edge decaying as competitors copy?)."
    },
    {
     "name": "Pick a motion for a new SKU",
     "steps": "Read revenue-model selector and ACV vs cost-to-serve (motion threshold); override with buyer preference where it conflicts (enterprise buyers may demand self-serve); set per-SKU Phi/CAC, watch cannibalization."
    }
   ],
   "honest_caveat": "This layer is where 'maximal' pays rent: the apparatus above is only useful if it collapses to these few levers and lights. If an operator reads only this section, they have ~80% of the value."
  }
 },
 "open_tensions_v3": {
  "purpose": "The maximal version owes an explicit accounting of what completeness cost. These are not resolved.",
  "tensions": [
   "ADD vs SUBTRACT: v3.0 chose completeness; the disciplined critique (doc 10) chose parsimony. By that standard v3.0 is LESS falsifiable than the minimal core. The measurement_status tags are a mitigation, not a resolution.",
   "MAP vs ENGINE: v3.0 is a far more complete MAP, but adding seventeen layers moved it further from a runnable ENGINE, not closer. Only the MRR-walk slice (gtm_engine_slice.py) executes. The ratio of spec-to-machinery got worse, exactly as the harshest critiques predicted.",
   "PHI: decomposed but still mostly unmeasurable. Eight sub-factors, one (retention pull) currently measurable. The ghost-variable risk is reduced in principle and unchanged in practice.",
   "LEARNING: specified, not built. The update rule answers the 'silicon BDR' critique on paper; it still needs experimental traffic most ventures cannot spare, and full causal-graph revision is unimplemented.",
   "COVERAGE vs USABILITY: the operator layer (Q) is the bet that this collapses to a few levers. If it does not, v3.0 is a monograph operators admire and route around \u2014 the precise fate the critiques warned of."
  ],
  "the_honest_one_liner": "v3.0 is the most complete thing we have built and the least falsifiable. Whether that is the right trade depends entirely on whether you want a map of the whole territory or a vehicle that drives."
 },
 "business_model_generalization_v4": {
  "generalizations": {
   "purpose": "The 8 generalizations that de-SaaS the spine. Each was forced by a specific kind breaking a specific hidden assumption; together they make the conservation law a family, not a default.",
   "hidden_saas_assumptions_removed": [
    "stock = revenue (false for usage, transactional, marketplace, attention, installed-base)",
    "there is one kind of binding constraint (there are at least four types)",
    "there is a single stock (marketplaces and attention have coupled stocks)",
    "a steady-state equilibrium C* exists (false for pure-flow / hardware)",
    "value lands in the period it is earned (false for long-tail and revenue-now-loss-later)"
   ],
   "generalizations": [
    {
     "id": "G1",
     "name": "Decouple operational stock from revenue function",
     "change": "Every kind is modeled as OPERATIONAL_STOCK (what accumulates) + REVENUE_FUNCTION (how that stock emits money). They are separate objects; revenue is a read-out of the stock, not the stock itself.",
     "forced_by": [
      "usage",
      "transactional",
      "marketplace",
      "attention",
      "installed_base"
     ],
     "epistemic_type": "identity",
     "measurement": "measurable_ex_post",
     "saas_special_case": "In subscription they nearly coincide (MRR ~ the stock), which is why the conflation hid here."
    },
    {
     "id": "G2",
     "name": "Binding-gate is a TYPED object",
     "change": "The binding constraint carries a type: {cash_payback, hard_capacity_ceiling, regulatory_capital, availability_balance}. Diagnosis must name the type, because the math of each differs (a hard ceiling caps min(demand,capacity); a cash gate is a race against payback; a regulatory gate is a permission; a balance gate is two-sided).",
     "forced_by": [
      "services",
      "lending",
      "transactional",
      "attention"
     ],
     "epistemic_type": "conditional_coefficient",
     "measurement": "measurable_ex_ante",
     "gate_types": {
      "cash_payback": "race to recover CAC before cash runs out (subscription, usage)",
      "hard_capacity_ceiling": "min(demand, staffed capacity) caps the system (services)",
      "regulatory_capital": "permission + balance-sheet limits act before economics (lending, regulated)",
      "availability_balance": "two-sided or shelf/inventory presence gates transactions (transactional, attention, marketplace)"
     }
    },
    {
     "id": "G3",
     "name": "Coupled stocks, with signed coupling",
     "change": "Promote multi-stock topology to a standing structure. Coupling can be POSITIVE (marketplace: supply growth -> demand growth -> supply growth) or NEGATIVE (attention: monetization up -> engagement down). Single-stock is the SaaS special case.",
     "forced_by": [
      "marketplace",
      "attention"
     ],
     "epistemic_type": "causal_regime",
     "measurement": "measurable_ex_post"
    },
    {
     "id": "G4",
     "name": "No-equilibrium / pure-flow mode + boundary announcement",
     "change": "The model must detect and ANNOUNCE when no steady-state C* exists (pure-flow businesses). In that mode, C*=a/(delta-g) is silent and reasoning shifts to flow volume + working-capital cycle. Honesty: say when the central result does not apply.",
     "forced_by": [
      "hardware"
     ],
     "epistemic_type": "identity",
     "measurement": "measurable_ex_ante",
     "saas_special_case": "Subscription always has an equilibrium, which made it feel universal."
    },
    {
     "id": "G5",
     "name": "Time-shape-of-value field",
     "change": "Each kind tags the temporal shape of value: {immediate, recurring, long_tail, revenue_now_loss_later}. The spine integrates over the RIGHT horizon for that shape rather than assuming value lands in-period.",
     "forced_by": [
      "installed_base",
      "lending",
      "transactional"
     ],
     "epistemic_type": "estimator",
     "measurement": "measurable_ex_post",
     "shapes": {
      "immediate": "value realized at transaction (transactional consumer)",
      "recurring": "value accrues per period (subscription, usage)",
      "long_tail": "one acquisition pays out over years (installed-base: repairs, replacement, referrals)",
      "revenue_now_loss_later": "revenue booked now, cost/loss arrives later (lending, insurance float)"
     }
    },
    {
     "id": "G6",
     "name": "Asset-stock type (moat-as-gate)",
     "change": "Add a stock whose value is SCARCITY/DEFENSIBILITY and whose depletion law is obsolescence / imitation / rights-expiry \u2014 NOT churn. Here revenue is decoupled from serving a customer, the funnel/CAC apparatus does not apply, and the MOAT IS the binding constraint.",
     "forced_by": [
      "data_ip_licensing"
     ],
     "epistemic_type": "latent_multiplier",
     "measurement": "proxy_only"
    },
    {
     "id": "G7",
     "name": "Negative contribution + adverse selection",
     "change": "Permit per-unit contribution to be NEGATIVE (a defaulting borrower is negative LTV), and model adverse selection: growth QUALITY can vary INVERSELY with growth SPEED (the fastest-growing loan book can be the worst). Inverts the SaaS instinct that growth is good.",
     "forced_by": [
      "lending"
     ],
     "epistemic_type": "causal_regime",
     "measurement": "measurable_ex_post",
     "warning": "This is the deepest inversion in the model. Do NOT reuse subscription growth intuition for risk-bearing kinds."
    },
    {
     "id": "G8",
     "name": "Served-side != paying-side (generalizes the payer!=user overlay into a kind)",
     "change": "A structural kind where the entity you serve is not the entity that pays. Model a free/served side (e.g. audience attention) and a paying side (e.g. advertisers) coupled through inventory, with a monetization<->engagement NEGATIVE loop (ad load up -> attention down). Generalizes the payer!=user overlay from a modifier to a first-class kind.",
     "forced_by": [
      "attention"
     ],
     "epistemic_type": "causal_regime",
     "measurement": "measurable_ex_post"
    }
   ],
   "meta_lesson": "Many kinds, few underlying assumptions: ten kinds did not need ten patches, they needed ~8 generalizations. That convergence is evidence the skeleton is sound and merely subscription-biased. The upgrades make the model REPRESENT honestly; they do not lift the forecasting ceiling on unmeasurable cells (trust, reflexivity, true adverse-selection magnitude)."
  },
  "kinds": {
   "purpose": "The 10 structural kinds as instantiable templates. To onboard a company: identify its kind, load this template (stock, revenue function, gate type, time-shape, dominant lever), then instrument the named spine. Test for a TRUE kind = a different stock OR a different binding gate; otherwise it is a sub-type, pricing setting, motion, or overlay.",
   "templates": [
    {
     "n": 1,
     "kind": "Recurring / Subscription",
     "operational_stock": "active subscriber base",
     "revenue_function": "base x ARPA (recurring)",
     "gate_type": "cash_payback",
     "time_shape": "recurring",
     "dominant_lever": "retention + expansion (NRR)",
     "spine_first": "MRR walk + cohort retention + refusal gate",
     "examples": "SaaS, media subs, subscription apps"
    },
    {
     "n": 2,
     "kind": "Usage / Consumption",
     "operational_stock": "active integrated usage",
     "revenue_function": "usage_volume x price (with live gross-margin term)",
     "gate_type": "cash_payback",
     "time_shape": "recurring",
     "dominant_lever": "land then expand consumption",
     "spine_first": "usage stock (separate from revenue) + activation + margin term",
     "examples": "cloud, API, AI, infra"
    },
    {
     "n": 3,
     "kind": "Transactional Consumer",
     "operational_stock": "buyer base + availability (mental x physical)",
     "revenue_function": "penetration x frequency x units x price",
     "gate_type": "availability_balance",
     "time_shape": "immediate",
     "dominant_lever": "penetration + repeat + distribution",
     "spine_first": "availability stock + penetration inflow + seasonality regime",
     "examples": "FMCG, e-commerce, D2C, retail"
    },
    {
     "n": 4,
     "kind": "Marketplace / Platform",
     "operational_stock": "two-sided liquidity (supply + demand, coupled +)",
     "revenue_function": "take_rate x GMV",
     "gate_type": "availability_balance",
     "time_shape": "immediate",
     "dominant_lever": "seed the thin side -> network effects",
     "spine_first": "coupled supply+demand stocks + match/liquidity rate above revenue",
     "examples": "rideshare, lodging, app stores, exchanges"
    },
    {
     "n": 5,
     "kind": "Services / Agency",
     "operational_stock": "retainer + relationship + reputation",
     "revenue_function": "utilization x billable capacity",
     "gate_type": "hard_capacity_ceiling",
     "time_shape": "recurring",
     "dominant_lever": "reputation + referrals + land-expand scope",
     "spine_first": "capacity-ceiling gate + utilization + retainer retention",
     "examples": "consulting, agencies, pro-services"
    },
    {
     "n": 6,
     "kind": "One-time / Hardware",
     "operational_stock": "none (pure flow)",
     "revenue_function": "deals_per_period x price",
     "gate_type": "cash_payback",
     "time_shape": "immediate",
     "dominant_lever": "channel + attach of recurring services",
     "spine_first": "NO-EQUILIBRIUM mode + flow + working-capital cycle",
     "examples": "devices, equipment, project sales",
     "boundary": "C* does not exist; steady-state reasoning is silent here (G4)."
    },
    {
     "n": 7,
     "kind": "Advertising / Attention",
     "operational_stock": "engaged audience attention",
     "revenue_function": "attention inventory x ad price (paid by advertisers, not users)",
     "gate_type": "availability_balance",
     "time_shape": "recurring",
     "dominant_lever": "grow + retain engagement; balance audience<->advertiser",
     "spine_first": "served-side(attention) + paying-side(advertiser) coupled, with monetization<->engagement NEGATIVE loop",
     "examples": "Google, Meta, free media, ad-supported apps",
     "new_in": "v3.1 (G8)"
    },
    {
     "n": 8,
     "kind": "Data / IP Licensing",
     "operational_stock": "proprietary asset (dataset/catalog/patent)",
     "revenue_function": "royalty / license fees on the asset",
     "gate_type": "regulatory_capital",
     "time_shape": "recurring",
     "dominant_lever": "build & defend uniqueness/rights",
     "spine_first": "asset-stock with depletion = obsolescence/imitation/expiry; moat IS the gate",
     "examples": "data marketplaces, music catalogs, patent licensing, content licensing",
     "new_in": "v3.1 (G6)",
     "boundary": "revenue decoupled from serving a customer; funnel/CAC apparatus does not apply."
    },
    {
     "n": 9,
     "kind": "Lending / Risk-bearing / Float",
     "operational_stock": "loan book / AUM / insurance float",
     "revenue_function": "yield - loss_rate (per-unit can be NEGATIVE)",
     "gate_type": "regulatory_capital",
     "time_shape": "revenue_now_loss_later",
     "dominant_lever": "underwriting quality + cost of capital",
     "spine_first": "loss/default outflow (can exceed revenue) + adverse-selection (quality inverse to speed) + capital gate + revenue/loss time-lag",
     "examples": "banks, insurers, BNPL, lending fintech, capitation health",
     "new_in": "v3.1 (G7)",
     "warning": "Deepest inversion: growth can be actively bad; a customer can be negative. Do not reuse SaaS growth intuition."
    },
    {
     "n": 10,
     "kind": "Installed-base / Razor-and-blade",
     "operational_stock": "installed fleet (devices in the field)",
     "revenue_function": "install_velocity x attach_rate x lifetime_tail (repairs+consumables+replacement+referrals)",
     "gate_type": "hard_capacity_ceiling",
     "time_shape": "long_tail",
     "dominant_lever": "install velocity x attach rate (convert fleet -> annuity)",
     "spine_first": "installed-base stock + long-tail future-cashflow per acquisition + attach-rate lever",
     "examples": "HVAC, printers, medical devices, elevators, auto",
     "new_in": "v3.1 (G5)",
     "note": "The HVAC-in-NJ case lands here \u2014 why it never fit the pure one-time/hardware row."
    }
   ],
   "fold_in_not_kinds": {
    "D2C / e-commerce": "Transactional Consumer (3)",
    "Freemium / free-trial": "a LEVER on Recurring/Usage",
    "On-demand / gig": "Marketplace (4) + logistics gate",
    "B2B/B2C/C2C/C2B": "the SCALE / counterparty axis, not a kind",
    "Franchise": "Services (5) replicated, or a lever",
    "Vertical SaaS / AI-native": "Recurring/Usage (1/2) in a niche",
    "Outcome-based pricing": "a revenue-model SETTING on 1/2/9",
    "Ecosystem-led growth": "a MOTION across kinds, not a kind"
   },
   "overlays": {
    "A_channel_intermediation": "sell-in != sell-out (reshapes any kind)",
    "B_payer_not_user_regulated": "compliance + procurement gate (reshapes any kind)",
    "note": "Where payer!=user is STRUCTURAL it is promoted to a kind: third-party-payer => Attention (7); risk-bearing => Lending (9)."
   }
  }
 }
}