{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "name": "accepted-outcome-venture-protocol",
  "version": "7.0.0",
  "article": "A new kind of company just became possible.",
  "purpose": "Test whether an AI-native engineer and a domain operator can convert one costly workflow into a funded, accepted result before the temporary deployment gap closes.",
  "thesis": "Frontier-model implementation capacity became cheap faster than many organisations can specify, verify, integrate, adopt and own the resulting work.",
  "warning": "The deployment gap is temporary and must not be treated as a permanent moat.",
  "argument": {
    "problem": "Model output is cheap while workflow truth, authority, evidence, adoption and ownership remain scarce.",
    "mechanism": "Combine domain truth, engineering control and AI execution; optimise for an observable accepted state.",
    "move": "Find one costly workflow, sell a bounded diagnostic, ship one complete slice and let external evidence determine the next commitment."
  },
  "required_comparisons": [
    "current process",
    "domain expert + frontier AI",
    "domain expert + engineer + frontier AI",
    "relevant packaged software or service"
  ],
  "required_inputs": [
    "workflow_map",
    "authoritative_sources",
    "accepted_end_state",
    "authority_matrix",
    "representative_cases",
    "baseline_time_and_cost",
    "buyer_and_budget_owner",
    "support_boundary",
    "stop_conditions"
  ],
  "gates": [
    "map_current_work",
    "define_observable_acceptance",
    "bound_authority",
    "build_smallest_complete_slice",
    "verify_resulting_state",
    "calibrate_domain_judgement",
    "count_total_human_burden",
    "return_investment_decision"
  ],
  "math": {
    "incremental_engineer_value": "DeltaV_engineer = V(SME + engineer + AI) - V(best practical alternative)",
    "expected_accepted_cost": "E[C_accepted] = (run_cost + review_wage * review_hours) / local_acceptance_rate + latent_failure_probability * loss",
    "workflow_surplus": "S = C0 - C1",
    "customer_net_value": "V_customer = C0 - price - switching_cost",
    "provider_contribution": "M_provider = price - C1 - delivery_overhead",
    "portfolio_naive": "P_at_least_one = 1 - (1 - p)^n",
    "portfolio_capacity_adjusted": "p_adjusted = p0 * exp(-gamma * max(0, n*h/capacity - 1)^2)",
    "workflow_current_cost": "C0 = N * (m0 / 60) * w",
    "workflow_delivery_cost": "C1 = N * ((m1 / 60) * w + tool_cost_per_case)",
    "opportunity_scenario": "A(t) = G0 * exp((learning - catchup) * t) * evidence"
  },
  "authority_defaults": {
    "external_messages": "human_approval_required",
    "money_movement": "human_approval_required",
    "production_deployment": "human_approval_required",
    "legal_commitments": "human_approval_required",
    "clinical_decisions": "human_approval_required",
    "equity_commitments": "human_approval_required"
  },
  "decision_options": [
    "continue",
    "narrow",
    "reprice",
    "hand_over",
    "stop"
  ],
  "required_output_order": [
    "decision_summary",
    "assumptions_and_unknowns",
    "workflow_map",
    "accepted_state_and_authority_matrix",
    "alternatives_and_incremental_value",
    "model_routing_and_local_evals",
    "smallest_complete_build",
    "verification_evidence",
    "customer_and_provider_economics",
    "decision"
  ],
  "final_question": "What observation would prove this plan wrong fastest?"
}