What is Cost of Predictivity?
The Cost of Predictivity is a framework coined by Richard Ewing that measures the variable cost of AI accuracy.
⚡ Cost of Predictivity at a Glance
📊 Key Metrics & Benchmarks
The Cost of Predictivity is a framework coined by Richard Ewing that measures the variable cost of AI accuracy. Unlike traditional software with near-zero marginal costs, AI features have costs that scale with usage and accuracy requirements.
The key insight: as AI correctness increases, cost scales exponentially. Moving from 80% accuracy to 95% accuracy often requires a 10x increase in compute and retrieval costs. Moving from 95% to 99% may require another 10x.
This creates margin compression that traditional engineering metrics don't capture. A feature that works beautifully at 100 users may be economically unviable at 100,000 users because AI inference costs scale linearly with usage while accuracy improvements require exponentially more resources.
The AI Unit Economics Benchmark (AUEB) calculator at richardewing.io/tools/aueb helps companies calculate their Cost of Predictivity and identify their AI margin collapse point.
🌍 Where Is It Used?
Cost of Predictivity 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 Cost of Predictivity 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
Most AI products fail on economics, not technology. The Cost of Predictivity explains why: success makes you poorer unless you understand the exponential relationship between accuracy and cost.
🛠️ How to Apply Cost of Predictivity
Step 1: Assess — Evaluate your organization's current relationship with Cost of Predictivity. Where is it strong? Where are the gaps?
Step 2: Define Goals — Set specific, measurable targets for Cost of Predictivity 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 Cost of Predictivity.
✅ Cost of Predictivity Checklist
📈 Cost of Predictivity Maturity Model
Where does your organization stand? Use this model to assess your current level and identify the next milestone.
⚔️ Comparisons
| Cost of Predictivity vs. | Cost of Predictivity Advantage | Other Approach |
|---|---|---|
| Ad-Hoc Approach | Cost of Predictivity provides structure, repeatability, and measurement | Ad-hoc requires zero upfront investment |
| Industry Alternatives | Cost of Predictivity is tailored to your specific organizational context | Alternatives may have larger community support |
| Doing Nothing | Cost of Predictivity creates measurable, compounding improvement | Status quo requires zero effort or change management |
| Consultant-Led Only | Cost of Predictivity builds internal capability that scales | Consultants bring external perspective and benchmarks |
| Tool-Only Solution | Cost of Predictivity combines process, culture, and measurement | Tools provide immediate automation without culture change |
| One-Time Project | Cost of Predictivity as ongoing practice delivers compounding returns | One-time projects have clear scope and end date |
How It Works
Visual Framework Diagram
🚫 Common Mistakes to Avoid
🏆 Best Practices
📊 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 | Cost of Predictivity Adoption | Ad-hoc | Standardized | Optimized |
| Financial Services | Cost of Predictivity Maturity | Level 1-2 | Level 3 | Level 4-5 |
| Healthcare | Cost of Predictivity Compliance | Reactive | Proactive | Predictive |
| E-Commerce | Cost of Predictivity ROI | <1x | 2-3x | >5x |
❓ Frequently Asked Questions
What is the Cost of Predictivity?
The Cost of Predictivity measures the escalating cost of AI accuracy. As you demand higher correctness from AI systems, costs scale exponentially. Coined by Richard Ewing.
How do you calculate Cost of Predictivity?
Total AI compute cost ÷ useful outputs generated = Cost of Predictivity per output. Track this at different accuracy levels to see the exponential curve. Use the AUEB at richardewing.io/tools/aueb.
🧠 Test Your Knowledge: Cost of Predictivity
What is the first step in implementing Cost of Predictivity?
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Need Expert Help?
Richard Ewing is a Product Economist and AI Capital Auditor. He helps companies translate technical complexity into financial clarity.
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