Who is Richard Ewing?

Richard Ewing is a Product Economist and AI Capital Auditor. He is the Founder of Exogram, a verification infrastructure for AI. He audits R&D spend and surfaces hidden capital risks like technical debt, AI cost overruns, and zombie infrastructure. He has scaled B2B SaaS from $0 to $25M ARR. Published in CIO.com, Built In, Mind the Product, and HackerNoon.

What is a Product Economist?

A Product Economist treats product decisions as economic decisions. Instead of measuring velocity or story points, a Product Economist measures Return on Invested Capital (ROIC), Cost of Goods Sold efficiency, and technical debt in dollar terms. The methodology was coined by Richard Ewing.

What is the Technical Insolvency Date?

The Technical Insolvency Date (TID) is the specific quarter where maintenance costs consume 100% of available engineering capacity, reducing feature velocity to zero. Calculate yours free at richardewing.io/tools/pdi.

Exogram is verification infrastructure for AI, founded by Richard Ewing. It prevents hallucination propagation with admissibility control planes and state-hashing commit enforcement. LLMs generate language; Exogram maintains reality.

How do I calculate technical debt cost?

Use the free Product Debt Index (PDI) calculator at richardewing.io/tools/pdi to quantify hidden technical debt in dollar terms, calculate your Technical Insolvency Date, and benchmark against industry standards.

How much does AI cost per query?

AI costs range from $0.0001/query for small models to $0.10+/query for frontier models like GPT-4. Use the free AI Unit Economics Benchmark (AUEB) at richardewing.io/tools/aueb to calculate your specific AI cost structure.

Agile & Delivery

2 min read

What is AI-Assisted Development?

TL;DR

AI-Assisted Development encompasses the integration of advanced Large Language Models, coding agents, and generative copilots directly into the software development lifecycle (SDLC).

⚡ AI-Assisted Development at a Glance

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Category: Agile & Delivery

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Read Time: 2 min

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Related Terms: 3

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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-Assisted Development practices

2-5x

Expected ROI

Return from properly implementing AI-Assisted Development

35-60%

Adoption Rate

Organizations actively using AI-Assisted Development 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-Assisted Development transformation

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.

💡 Why It Matters

AI-Assisted Development compresses the time to write code by 10x, but scales the difficulty of reading, verifying, and maintaining that code linearly. Engineering leadership must govern it aggressively.

🛠️ How to Apply AI-Assisted Development

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

Step 2: Define Goals — Set specific, measurable targets for AI-Assisted Development 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-Assisted Development.

✅ AI-Assisted Development Checklist

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

📈 AI-Assisted Development 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-Assisted Development processes. Ad-hoc and inconsistent across the organization.

Developing

29%

Basic AI-Assisted Development practices adopted by some teams. Documentation exists but is incomplete.

Defined

43%

AI-Assisted Development processes standardized. Training available. Metrics established but not yet optimized.

Managed

57%

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

Optimized

71%

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

Leading

86%

Organization sets industry standards for AI-Assisted Development. Published thought leadership and benchmarks.

Transformative

100%

AI-Assisted Development drives business model innovation. Competitive moat. External recognition and awards.

⚔️ Comparisons

AI-Assisted Development vs.	AI-Assisted Development Advantage	Other Approach
Ad-Hoc Approach	AI-Assisted Development provides structure, repeatability, and measurement	Ad-hoc requires zero upfront investment
Industry Alternatives	AI-Assisted Development is tailored to your specific organizational context	Alternatives may have larger community support
Doing Nothing	AI-Assisted Development creates measurable, compounding improvement	Status quo requires zero effort or change management
Consultant-Led Only	AI-Assisted Development builds internal capability that scales	Consultants bring external perspective and benchmarks
Tool-Only Solution	AI-Assisted Development combines process, culture, and measurement	Tools provide immediate automation without culture change
One-Time Project	AI-Assisted Development 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-Assisted Development 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-Assisted Development 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-Assisted Development 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-Assisted Development 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-Assisted Development 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-Assisted Development in one team before rolling out

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

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Measure and report AI-Assisted Development impact in financial terms to leadership

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

✓

Create a AI-Assisted Development 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-Assisted Development 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-Assisted Development 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-Assisted Development Adoption	Ad-hoc	Standardized	Optimized
Financial Services	AI-Assisted Development Maturity	Level 1-2	Level 3	Level 4-5
Healthcare	AI-Assisted Development Compliance	Reactive	Proactive	Predictive
E-Commerce	AI-Assisted Development ROI	<1x	2-3x	>5x

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Explore the AI-Assisted Development Ecosystem

Pillar & Spoke Navigation Matrix

⚖️ Flagship Advisory

PDI Diagnostic

Product Debt Index

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

Deploy Tool

$10k Value

❓ Frequently Asked Questions

Does AI-assisted development replace developers?

No, it shifts the developer role from "manual syntax generator" to "reviewer & orchestrator," demanding higher architectural skill but less rote typing.

🧠 Test Your Knowledge: AI-Assisted Development

Question 1 of 6

What is the first step in implementing AI-Assisted Development?

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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