AI AI Economist
Stop prioritizing features by "vibe." Evaluate the exact API token-cost-to-revenue ratio for generative features and govern the product roadmap as a fiduciary asset.
2026 Market Economics
*Base compensation figures represent aggregate On-Target Earnings (OTE) extrapolated for Tier-1 technology hubs (SF, NYC, London). Actual bandwidths fluctuate based on geographic latency and discrete remote equity negotiations.
Primary Board KPIs
The 2026 Mandate
Product Managers who write user stories for simple CRUD apps are being rendered obsolete by code-generation LLMs. To survive the 2026 transition, PMs must evolve into AI Economists.
The AI AI Economist models the financial viability of AI features at the atomic inference level. If a feature costs $0.02 in API calls but only generates $0.01 in user value, you are shipping negative margins at scale.
Your job is to understand the AI Unit Economics Benchmark (AUEB), determine whether to use expensive frontier APIs vs. cheap edge SLMs, and validate that AI product expansion aligns with Enterprise Valuation.
Execution Protocol
The First 90 Days on the job
The Audit
Audit the current feature backlog and mercilessly cull any roadmap item that lacks a deterministic Unit Economics model.
The Architecture
Map the exact token pricing overhead against customer LTV, identifying which features bleed OPEX.
The Execution
Present a board-ready executive dashboard demonstrating a 15% reduction in API COGS while maintaining product feature parity.
Need a tailored 90-Day Architecture?
Book a 1-on-1 strategy audit to map this protocol directly to your unique enterprise constraints.
Book Strategy AuditInterview Diagnostics
How to fail the executive interview
Discussing 'Agile workflows' and 'User Stories' instead of Margin Preservation.
Valuing the capability of the AI over the mathematical ROAI (Return on AI).
Failing to understand the difference between CapEx (building a model) and OpEx (API inference taxes).
Required Lexicon
Strategic vocabulary & concepts
AI COGS (Cost of Goods Sold) refers to the variable costs directly attributable to delivering AI-powered features to customers. Unlike traditional SaaS (near-zero marginal cost per user), AI features have significant per-interaction costs. **Components of AI COGS:** - LLM API fees (OpenAI, Anthropic, Google per-token charges) - Embedding generation and vector database queries - GPU compute for inference or fine-tuning - Data retrieval and processing pipeline costs - Monitoring, logging, and observability infrastructure - Error handling, retry logic, and fallback model costs - Human-in-the-loop review costs **Impact on SaaS economics:** Traditional SaaS enjoys 80%+ gross margins. AI-heavy SaaS products can see margins compress to 40-60%, fundamentally changing valuation multiples and capital requirements.
The Innovation Tax is a framework coined by Richard Ewing that measures the hidden cost of maintenance work that gets reported as innovation investment. It is OpEx masquerading as R&D investment, causing organizations to dramatically overestimate their effective engineering velocity. When a team reports '65% of time on new features' but the actual number is 23%, the 42-point gap is the Innovation Tax. This gap causes CFOs and boards to overestimate R&D productivity and make poor capital allocation decisions. The Innovation Tax is insidious because it's invisible in standard reporting. Engineering teams don't intentionally misreport — the maintenance work is scattered across feature work, making it hard to isolate. Bug fixes get bundled into feature sprints. Infrastructure upgrades get coded as feature dependencies. Benchmark: >40% Innovation Tax is dangerous. >70% is terminal — the organization is approaching the Technical Insolvency Date.
The Technical Insolvency Date (TID) is a framework coined by Richard Ewing that identifies the specific future quarter when an organization's technical debt maintenance will consume 100% of engineering capacity, leaving zero time for new feature development. The TID is calculated by projecting the current maintenance percentage growth against available engineering hours. If a team currently spends 45% of time on maintenance and that percentage grows 3% per quarter, the Technical Insolvency Date can be calculated as the quarter when maintenance reaches 100%. Most organizations track technical debt qualitatively. The TID makes it quantitative and urgent. Telling a board 'we have technical debt' gets ignored. Telling a board 'we are 8 quarters from technical insolvency' gets immediate action. The Product Debt Index (PDI) calculator at richardewing.io/tools/pdi automates this calculation, translating maintenance burden into dollar terms and projecting the Technical Insolvency Date.
AI-Assisted Development encompasses the integration of advanced Large Language Models, coding agents, and generative copilots directly into the software development lifecycle (SDLC). By 2025/2026, tools like Cursor, GitHub Copilot, Devin, and SWE-Agent evolved from simple autocomplete engines to autonomous architectural reasoning systems. The paradigm shifted developers away from "writing code" and towards "prompt supervision, structural review, and security verification." While AI Dev tools radically boost individual throughput, they create significant systemic risks around codebase vastness (software entropy), undocumented context fragmentation, and the unprecedented generation of undetectable AI Technical Debt.
DORA metrics are four key software delivery performance metrics identified by the DevOps Research and Assessment (DORA) team at Google. They are the industry standard for measuring engineering team effectiveness: 1. **Deployment Frequency**: How often code is deployed to production. Elite teams deploy on-demand, multiple times per day. 2. **Lead Time for Changes**: Time from code commit to production deployment. Elite teams achieve less than one hour. 3. **Change Failure Rate**: Percentage of deployments that cause failures requiring remediation. Elite teams maintain 0-15%. 4. **Mean Time to Recovery (MTTR)**: How quickly a team can restore service after an incident. Elite teams recover in less than one hour. These metrics are backed by years of research across thousands of organizations worldwide and are validated as predictors of both software delivery performance and organizational performance.
Curriculum Extraction Matrix
To successfully execute the 90-day protocol and survive the executive interview, you must deeply understand the following engineering architecture modules.
Engineering Economics Foundations
The core curriculum for understanding engineering as an economic activity. From basic metrics to advanced budgeting and organizational design.
AI AI Economics
Your most differentiated track. AI unit economics, inference costs, margin collapse — maps directly to CIO.com and Built In articles. AI cost management is the #1 FinOps priority in 2026.
Capstone & Applied Practice
Applied practice modules: startup economics scenarios, platform engineering, org scaling, cloud FinOps, SaaS metrics, and the full R&D Capital Audit capstone project.
Product Management Economics
Product economics for PMs and CPOs: feature prioritization using economic models, pricing strategy, churn economics, and the bridge between product and finance. Nobody else teaches PM through the P&L lens.
AI Operations Economics & Cost Governance
The economics of deploying, governing, and scaling AI systems: model selection, prompt engineering ROI, AI compliance costs, agentic automation, and vendor comparison. Connects to Exogram and EAAP.
Cloud FinOps & AI Cost Management
The economics of cloud cost management, optimization, and FinOps practice. 98% of FinOps teams now manage AI spend. AI cost management is the #1 capability teams plan to add in 2026.
AI Pricing Strategy & Monetization Economics
37% of AI companies plan to change their pricing model in the next 12 months. Outcome-based pricing jumped from 2% to 18% in six months. Teach the economics of pricing AI products.
Economics of Build vs. Buy for AI
Every engineering leader faces this right now. Frame it through your economic lens: TCO modeling, vendor lock-in costs, inference arbitrage, and the hidden costs of "free" open-source models.
Career Capital Economics
Stop being a cost center. Learn to quantify your business impact, negotiate compensation using economic frameworks, and prove your dollar value at every level — from junior IC to Staff Engineer.
Engineering-to-Executive Economics
The economics translation layer for Directors, VPs, and aspiring CTOs. Learn to think in P&L, present to boards, own budgets, and position yourself as a revenue-driving executive — not a technical manager.
The Economics of Leadership (Not Management)
Leadership is a skill, not a rank. Companies train you for the technical job, then promote you to a job they never teach. That's why we get managers, not leaders. This track teaches the economics of becoming one.
The Economics of Remote & Distributed Teams
Remote work isn't a perk — it's an economic model with measurable costs, arbitrage opportunities, and hidden taxes. This track gives you the financial framework to build, manage, and optimize distributed engineering organizations.
M&A Technical Integration Economics
Most acquisition value is destroyed during integration. This track teaches you to evaluate, plan, and execute technical integrations that preserve — not destroy — the value your company spent millions to acquire.
The Economics of Developer Experience (DX)
Developer experience is the hidden infrastructure tax or accelerator in every engineering organization. This track teaches you to measure, invest in, and monetize DX improvements with the same rigor as any capital investment.
Vendor & Contract Economics for Engineering Leaders
Engineering leaders manage millions in vendor relationships but are never taught contract economics. This track teaches you to negotiate, optimize, and govern vendor spend with the same rigor you apply to your codebase.
AI Agent Architecture & Economics
AI agents are the next compute paradigm. This track teaches you to design, cost, and govern multi-agent systems — from single-tool agents to enterprise orchestration platforms. Inspired by real-world agent infrastructure like Exogram.
Agentic Process Automation Economics
Beyond RPA: agentic process automation replaces entire workflows, not just clicks. This track teaches you to identify, cost, and implement AI agent automation across enterprise operations — from customer support to DevOps to finance.
Strategic Leadership Economics
Leadership is the awesome responsibility to see those around us rise. Most of us achieved our rank because we were good at our old job — but that's not our job anymore. This track teaches the economics of becoming a leader who multiplies value, not just manages resources.
AI Economics & Margin Engineering
The definitive curriculum for understanding how artificial intelligence fundamentally breaks traditional SaaS unit economics, and how to build deterministic control layers to govern inference costs, power user liability, and the Turing Tax.
Startup Economics
The definitive financial playbook for startup engineering. From Seed stage burn rate management to Series C infrastructure scaling, learn to align engineering output with VC milestones.
Boardroom AI Governance
For CIOs, CFOs, and Board Directors. Learn to govern AI capital expenditure, bridge the Production Gap, and demand Hard ROI from the engineering organization.
Transition FAQs
How do I transition from a traditional PM?
Stop focusing on user stories. Learn how to calculate AI Unit Economics (AUEB) and validate feature-level inference costs against MRR.
Do I need to know how to code?
No, but you must understand the mathematical difference between caching, RAG retrieval costs, and frontier model API taxes.
Enter The Vault
Are you ready to transition architectures? You require access to all execution playbooks, diagnostics, and ROI calculators to prove your fiduciary capabilities to the board.
Lifetime Access to 57 Curriculum Tracks