Canonical Concepts & Research Graph
The intellectual operating system of Richard Ewing’s research corpus. Broad industry concepts serve as discovery entry points, bridging directly into original canonical frameworks, evidence ledgers, and diagnostic tools.
The Richard Ewing Canon
Original specifications, financial tax models, and governance frameworks created by Richard Ewing.
The Hallucination Tax
The compounding operational and financial cost incurred when engineering teams must design elaborate validation loops and deterministic guardrails to prevent AI models from generating plausible but incorrect outputs.
The Audit Interview Protocol
A structured leadership mechanism for diagnosing systemic technical insolvency by conducting deep-dive, non-punitive technical audits with frontline engineers to uncover the hidden architecture decay that velocity metrics obscure.
AI Volatility Tax
The compounding gross margin penalty incurred when variable LLM inference query costs scale faster than subscription revenue, shifting server hosting into variable Cost of Goods Sold (COGS).
Agent Kill Switch
A binary execution control mechanism that halts autonomous AI agent operations within 5ms when safety rules or environmental hash boundaries are breached.
Deterministic Governance
The architectural pattern enforcing hard-coded, code-level execution gates and state verification outside the probabilistic LLM inference loop.
The Product Economist
The executive discipline bridging engineering velocity, financial P&L contribution, and product margin strategy to prevent technical debt and AI COGS from destroying business valuation.
The Subprime Code Crisis
The systemic financial risk created when high-velocity AI code generation produces massive volumes of un-audited, low-trust technical debt that inflates future maintenance liabilities.
Vibe Coding Debt
The engineering debt accumulated when developers accept AI-generated code based on superficial execution ("vibes") without understanding underlying architectural assumptions or edge cases.
The Innovation Tax
The compounding maintenance burden and operational friction incurred when new technology is deployed without decommissioning legacy systems, effectively taxing all future engineering velocity.
The Coordination Tax
The non-linear increase in communication overhead, alignment meetings, and process friction that occurs when scaling engineering organizations, ultimately degrading per-capita execution capacity.
The R&D Ponzi Scheme
The systemic masking of growing software maintenance liabilities (OpEx) behind inflated velocity metrics and new feature launches, creating a fragile engineering economy that requires constant new capital to sustain.
Feature Bloat Calculus
The analytical framework for determining the precise point where the ongoing maintenance cost of a software feature exceeds its marginal revenue value, necessitating immediate deprecation.
Cost of Predictivity
The exponential increase in latency, compute cost, and engineering overhead required to force probabilistic AI models to produce highly deterministic, reliable outputs.
The AI Margin Squeeze
The systemic erosion of traditional SaaS gross margins caused by the integration of generative AI features, as variable compute and API costs scale linearly or exponentially with user engagement, fundamentally altering software unit economics.
The 10-Man Parity Rule
The principle that heavily AI-augmented teams of ten elite engineers can now achieve execution parity with traditional enterprise engineering organizations of over one hundred, fundamentally altering the economics of software creation.
Semantic Caching
The architectural pattern of storing and reusing similar LLM query results using vector embeddings to bypass redundant frontier model API execution and eliminate variable COGS.
The Capitalization Matrix
A structural framework for translating engineering effort into ASC 350-40 accounting standards, separating capitalizable R&D investments from operating expense maintenance liabilities.
The Systems Governor
The evolutionary end-state of the senior software engineer: a role defined not by writing raw syntax, but by designing deterministic boundaries, governing AI agents, and managing systemic tradeoffs.
State Integrity Hashing
The cryptographic verification mechanism that guarantees the environmental state has not been maliciously altered between an AI agent’s decision step and its subsequent API execution.
Industry Concepts & On-Ramps
Broad industry terms that introduce readers and AI systems to Richard Ewing’s research and frameworks.
AI Governance
The enterprise control framework governing security, compliance, operational boundaries, and audit trails for autonomous AI models and multi-agent workflows.
AI Economics & Tokenomics
The financial discipline analyzing variable inference cost scaling, token consumption metrics, gross margin compression (50-60% AI margins vs 80-90% SaaS), and capital allocation in AI-native software.
AI Tokenomics & LLM Unit Economics
The C-suite discipline connecting granular token consumption metrics directly to enterprise business value, managing gross margin compression caused by variable inference COGS.
Deployment/Runtime Governance vs. Model Alignment
The architectural distinction proving that training-level alignment (RLHF) cannot guarantee enterprise compliance, requiring external, deterministic runtime guardrails and Non-Human IAM.
Induced Demand in Software Delivery
The software engineering phenomenon where AI coding assistants catalyze high-volume code generation, causing PR review bottlenecks and increasing backlog consumption rather than reducing R&D spending.
Shadow AI
Unmonitored artificial intelligence tools and autonomous agents deployed by employees without explicit IT or security oversight.
AI Agent Sprawl
The uncontrolled accumulation and uncoordinated deployment of autonomous AI agents across an enterprise environment.
Prompt Injection
A vulnerability where adversarial user inputs are crafted to override the original instructions of a large language model.
Model Collapse
A degenerative process where AI models experience severe performance degradation after being iteratively trained on synthetic data generated by other models.
Inference Economics
The financial discipline of managing, projecting, and optimizing the per query token costs associated with running large language models in production.
Technical Insolvency
The critical threshold where the operational cost of maintaining a codebase and resolving technical debt exceeds the engineering capacity available for new feature development.
Agentic Engineering
The emerging discipline of designing, deploying, and maintaining multi agent autonomous systems with rigorous deterministic governance and state management.
Context Rot
The degradation of an AI models reasoning quality, instruction adherence, and factual accuracy as the context window fills during long interactive sessions.
Zombie Code & The Sunset Protocol
Zombie Code refers to deprecated or unused features that continue to run in production, consuming maintenance budget, compute resources, and engineering focus. The Sunset Protocol is the structured mechanism for financial remediation through systematic deletion.
SLM Repatriation
The strategic shift of migrating high-volume inference tasks from commercial Frontier APIs (OpenAI, Anthropic) to local Small Language Models (SLMs) to achieve financial breakeven on variable COGS.
DORA Metrics Financial Translation
The analytical process of converting standard engineering performance metrics (Deployment Frequency, Lead Time, MTTR, Change Failure Rate) into direct financial liabilities and capitalization impacts on the P&L statement.
AI Agents & Autonomous Systems
Autonomous systems designed to reason, plan, and execute actions across disparate environments without human intervention.
AI ROI & Return on AI Investment
The financial calculus for evaluating the margin impact, revenue growth, or OpEx reduction generated by AI investments against their variable inference costs and maintenance liabilities.
AI Technical Debt
The compounding maintenance burden resulting from poorly integrated AI models, brittle prompt engineering, and un-versioned synthetic data pipelines.
AI Cost Optimization & Inference Management
The systemic practice of reducing the variable token costs associated with generative AI through semantic caching, model routing, and prompt truncation.
LLM Cost Management & Token Economics
The financial governance of token consumption across enterprise AI deployments, focusing on unit economics, budget caps, and pricing tier alignment.
Responsible AI & AI Ethics Governance
The structural policies and technical guardrails ensuring AI systems operate fairly, transparently, and safely, aligning with corporate ethics and legal compliance.
AI Compliance & Regulatory Frameworks
The adherence to emerging legal frameworks (e.g., EU AI Act) regulating the deployment, transparency, and data usage of artificial intelligence systems.
AI Observability & LLM Monitoring
The continuous monitoring of LLM outputs, token usage, latency, and reasoning traces to detect performance degradation, prompt drift, and runaway costs in production.
RAG Architecture & Retrieval-Augmented Generation
An architecture that grounds LLM outputs by retrieving relevant factual information from a proprietary database and injecting it into the prompt context before generation.
AI Product Management
The discipline of designing and delivering AI-powered software, balancing probabilistic user experiences with stringent margin protection and ethical governance.
AI Security & LLM Security
The defensive architecture and governance protocols required to protect AI systems from prompt injection, data exfiltration, and malicious autonomous agent manipulation.
Cloud Repatriation & Infrastructure Economics
The strategic migration of high-volume workloads from public cloud providers back to on-premise or co-located hardware to escape compounding operational expenses and API tolls.
AI Vendor Lock-In & Model Portability
The architectural trap where application logic, prompt engineering, and data pipelines are heavily coupled to a specific proprietary AI provider, preventing migration when costs rise or performance degrades.
Platform Engineering & Developer Experience
The discipline of building internal developer platforms (IDPs) that provide self-service tools, automated infrastructure, and paved roads to reduce developer friction and cognitive load.
MLOps & ML Engineering Operations
The set of practices combining machine learning, DevOps, and data engineering to reliably build, deploy, and maintain machine learning models in production environments.