Richard Ewing Frameworks

2 min read

What is AI Economics?

TL;DR

AI Economics is the discipline of treating every product decision as an economic decision — evaluating features, sprints, and roadmaps through the lens of capital allocation, ROI, and margin impact rather than velocity or feature count.

⚡ AI Economics at a Glance

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Category: Richard Ewing Frameworks

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Read Time: 2 min

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Related Terms: 4

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FAQs Answered: 1

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Checklist Items: 5

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Quiz Questions: 6

📊 Key Metrics & Benchmarks

2-6 weeks

Implementation Time

Typical time to implement AI Economics practices

2-5x

Expected ROI

Return from properly implementing AI Economics

35-60%

Adoption Rate

Organizations actively using AI Economics frameworks

2-3 levels

Maturity Gap

Average gap between current and target state

30 days

Quick Win Window

Time to see first measurable improvements

6-12 months

Full Impact

Time for comprehensive AI Economics transformation

Coined and developed by Richard Ewing, AI Economics encompasses: the Product Debt Index (quantifying technical debt in dollar terms), the Innovation Tax (measuring hidden maintenance burden), the Cost of Predictivity (exponential AI accuracy costs), the Kill Switch Protocol (deprecating zombie features), and the Feature Bloat Calculus (when maintenance exceeds value).

The AI Economist Doctrine holds four principles: Capital Allocation > Agile Theater, The Truth is in the P&L, Kill Zombies Ruthlessly, and Sovereignty Over Dependency.

🌍 Where Is It Used?

AI Economics is implemented across modern technology organizations navigating complex digital transformation.

It is particularly relevant to teams scaling beyond their initial product-market fit, where operational maturity, predictability, and economic efficiency are required by leadership and investors.

👤 Who Uses It?

**Technology Executives (CTO/CIO)** leverage AI Economics to align their technical strategy with overriding business constraints and board expectations.

**Staff Engineers & Architects** rely on this framework to implement scalable, predictable patterns throughout their domains.

💡 Why It Matters

AI Economics fills the gap between engineering metrics (velocity, story points) and financial metrics (revenue, margin). It gives CTOs, CPOs, and boards a common language for evaluating engineering as a capital function.

🛠️ How to Apply AI Economics

Step 1: Assess — Evaluate your organization's current relationship with AI Economics. Where is it strong? Where are the gaps?

Step 2: Define Goals — Set specific, measurable targets for AI Economics improvement aligned with business outcomes.

Step 3: Build Plan — Create a phased implementation plan with clear milestones and ownership.

Step 4: Execute — Implement changes incrementally. Start with high-impact, low-risk improvements.

Step 5: Iterate — Measure results, learn from outcomes, and continuously refine your approach to AI Economics.

✅ AI Economics Checklist

Assess your organization's current AI Economics maturityIdentify quick wins for AI Economics improvementCreate a 90-day AI Economics action planAssign ownership for AI Economics initiativesMeasure and report progress quarterly

📈 AI Economics Maturity Model

Where does your organization stand? Use this model to assess your current level and identify the next milestone.

Initial

14%

No formal AI Economics processes. Ad-hoc and inconsistent across the organization.

Developing

29%

Basic AI Economics practices adopted by some teams. Documentation exists but is incomplete.

Defined

43%

AI Economics processes standardized. Training available. Metrics established but not yet optimized.

Managed

57%

AI Economics measured with KPIs. Continuous improvement active. Cross-team consistency achieved.

Optimized

71%

AI Economics is a strategic advantage. Automated where possible. Data-driven decision making.

Leading

86%

Organization sets industry standards for AI Economics. Published thought leadership and benchmarks.

Transformative

100%

AI Economics drives business model innovation. Competitive moat. External recognition and awards.

⚔️ Comparisons

AI Economics vs.	AI Economics Advantage	Other Approach
Ad-Hoc Approach	AI Economics provides structure, repeatability, and measurement	Ad-hoc requires zero upfront investment
Industry Alternatives	AI Economics is tailored to your specific organizational context	Alternatives may have larger community support
Doing Nothing	AI Economics creates measurable, compounding improvement	Status quo requires zero effort or change management
Consultant-Led Only	AI Economics builds internal capability that scales	Consultants bring external perspective and benchmarks
Tool-Only Solution	AI Economics combines process, culture, and measurement	Tools provide immediate automation without culture change
One-Time Project	AI Economics as ongoing practice delivers compounding returns	One-time projects have clear scope and end date

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How It Works

Visual Framework Diagram

┌──────────────────────────────────────────────────────────┐ │ AI Economics Framework │ ├──────────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │ │ Assess │───▶│ Plan │───▶│ Execute │ │ │ │ (Where?) │ │ (What?) │ │ (How?) │ │ │ └──────────┘ └──────────┘ └──────┬───────┘ │ │ │ │ │ ┌──────▼───────┐ │ │ ◀──── Iterate ◀────────────│ Measure │ │ │ │ (Results?) │ │ │ └──────────────┘ │ │ │ │ 📊 Define success metrics upfront │ │ 💰 Quantify impact in financial terms │ │ 📈 Report progress to stakeholders quarterly │ │ 🎯 Continuous improvement cycle │ └──────────────────────────────────────────────────────────┘

🚫 Common Mistakes to Avoid

Implementing AI Economics without executive sponsorship

⚠️ Consequence: Initiatives stall when competing with feature work for resources.

✅ Fix: Secure VP+ sponsor who can protect budget and prioritize the initiative.

Treating AI Economics as a one-time project instead of ongoing practice

⚠️ Consequence: Initial improvements erode within 2-3 quarters without sustained effort.

✅ Fix: Embed into regular rituals: quarterly reviews, team OKRs, and reporting cadence.

Not measuring AI Economics baseline before starting

⚠️ Consequence: Cannot demonstrate improvement. ROI narrative impossible to build.

✅ Fix: Spend the first 2 weeks establishing baseline measurements before any changes.

Copying another company's AI Economics approach without adaptation

⚠️ Consequence: Context mismatch leads to poor results and wasted effort.

✅ Fix: Use frameworks as starting points. Adapt to your team size, stage, and culture.

🏆 Best Practices

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Start with a 90-day pilot of AI Economics in one team before rolling out

Impact: Validates approach, builds evidence, and creates internal champions.

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Measure and report AI Economics impact in financial terms to leadership

Impact: Ensures continued investment and executive support for the initiative.

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Create a AI Economics playbook documenting processes, tools, and decision frameworks

Impact: Enables consistency across teams and reduces onboarding time for new team members.

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Schedule quarterly AI Economics reviews with cross-functional stakeholders

Impact: Maintains momentum, surfaces issues early, and keeps the initiative visible.

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Invest in training and certification for AI Economics across the organization

Impact: Builds internal capability and reduces dependency on external consultants.

📊 Industry Benchmarks

How does your organization compare? Use these benchmarks to identify where you stand and where to invest.

Industry	Metric	Low	Median	Elite
Technology	AI Economics Adoption	Ad-hoc	Standardized	Optimized
Financial Services	AI Economics Maturity	Level 1-2	Level 3	Level 4-5
Healthcare	AI Economics Compliance	Reactive	Proactive	Predictive
E-Commerce	AI Economics ROI	<1x	2-3x	>5x

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Explore the AI Economics Ecosystem

Pillar & Spoke Navigation Matrix

⚖️ Flagship Advisory

PDI Diagnostic

Product Debt Index

Quantify the hidden economic liability of AI Economics across your architecture using the world's leading valuation impact framework.

Deploy Tool

$10k Value

❓ Frequently Asked Questions

Who coined AI Economics?

Richard Ewing coined the term and developed the underlying frameworks. He is published in CIO.com, Built In, Mind the Product, and HackerNoon on AI economics topics.

🧠 Test Your Knowledge: AI Economics

Question 1 of 6

What is the first step in implementing AI Economics?

🔧 Free Tools

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Expert Definition by Richard Ewing

AI Economist & R&D Capital Auditor

Richard Ewing is the creator of the AI Economics framework and founder of Exogram. His research on R&D capital audits, technical insolvency, and software economics is featured across Tier 1 publications including CIO.com, Built In (Editor's Pick), and HackerNoon.

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