Richard Ewing — AI Economist

I Stop AI Investments From Bleeding Money.

Enterprise AI governance & product economics for PE-backed SaaS companies.

Creator of the Production AI Governance Framework · Founder of Exogram

Published in CIO.com · Built In · MindTheProduct · HackerNoon

Richard Ewing — AI Economist

As Seen In

CIO.comBuilt InMind the ProductHackerNoonCIO.comBuilt InMind the ProductHackerNoon

15+ Years Experience

Enterprise product leadership

0-to-1 $25M ARR

Tyler Technologies

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Enterprise client outcome

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Margin recovery result

How We Prevent AI Capital Bleed

A systematic approach to aligning model behavior with boardroom economics.

01

What Is This?

The Translation Layer

AI Economist = the translation layer between engineering output and CFO-level financial outcomes.

Most consulting tells you how models work. I tell you how models impact your gross margins and P&L. I convert API parameters and token lengths into EBITDA compression forecasts.

02

Why Do You Need It?

73% Maintenance Leak

73% of R&D capital funds maintenance, not innovation. I find the leaks.

Without strict cost caps and deterministic code analysis, your AI pilots become compounding liabilities. I identify zombie features and engineering capital misallocation before they trigger cash drain.

03

How It Works

3-Step Audit Protocol

Diagnostic → Framework → Implementation. Secure cost ceilings in under 3 weeks.

We start with a rapid economic diagnostic, deploy the Production AI Governance Framework parameters, and install Exogram deterministic guardrails at the runtime network layer.

The Bottom Line — 15 Seconds

What Breaks

AI agents execute actions without deterministic governance. Models hallucinate. Costs spiral. Code gets rewritten. Permissions cascade.

What It Costs

POCs cost hundreds. Production costs millions. API bills exceed revenue. Engineering capacity consumed by maintenance, not innovation.

Why

No verification layer between model inference and execution. Guardrails are probabilistic — one guessing system policing another.

The Fix

Deterministic governance infrastructure. Inference is probabilistic. Execution must be deterministic. The agent can guess. The execution layer cannot.

The Engine

Exogram — the deterministic verification layer for AI systems. Not optional. Not best practice. Mandatory.

Why Enterprise AI Fails

These aren't hypothetical risks. They're verified failure patterns with real-world financial consequences.

Unverified Outputs

95%MIT

of GenAI pilots fail to reach production. Your AI generates answers — but who verifies they're correct before they hit a customer?

Margin Collapse

80%RAND

of AI projects fail to deliver business value. AI features cost money every time they run. Without unit economics, your most popular feature becomes your costliest.

Agent Security Gaps

78%Industry Research

of AI agents have excessive permissions. One prompt injection = full data exfiltration. EchoLeak (CVE-2025-32711) proved zero-click attacks are real.

Capital Misallocation

42%S&P Global

of companies abandoned most AI initiatives in 2025. Boards can't distinguish building from patching when 60% of R&D goes to maintenance reported as 'innovation.'

Executive Intervention Required

Stop AI Margin Erosion Before Your Next Board Meeting.

The difference between a successful AI rollout and a margin-destroying liability is deterministic governance. I provide the translation layer between engineering output and CFO-level financial outcomes.

Step 1: Audit

R&D Capital Diagnostic

$2,500 Fixed-Fee

A surgical 7-day audit of your AI infrastructure, identifying where R&D capital is leaking and how to mathematically constrain execution costs.

  • Unit Economics Audit & Margin Analysis
  • Shadow AI & Security Risk Report
  • Technical Debt Liability Matrix
Book a $2,500 Diagnostic
Step 2: Execute

Advisory Retainer

$7,500/month

Following the diagnostic, we move into execution. I operate as a fractional AI Economist, implementing deterministic governance and board-ready reporting.

  • Strict Cost-Cap Architecture Reviews
  • CFO-Level ROI Dashboards
  • Board Meeting Representation
Unlocked after Diagnostic

Designed for PE-backed SaaS companies and Series B+ organizations struggling to translate AI hype into deterministic gross margin.

Audit Outcomes — Before & After

Real results from R&D Capital Audits. Dollar-denominated findings with measurable remediation.

Series C Platform
$0.0M

maintenance costs reported as “innovation”

Before: 73% of R&D allocated to maintenance
After: Board redirected $800K to actual innovation
B2B SaaS
0%

AI cost reduction achieved

Before: $14,200/mo retry & token waste
After: $2,900/mo with deterministic routing
Enterprise FinTech
0%

engineering capacity recovered

Before: 60% of sprints on zombie features
After: 31 negative-carry features eliminated

Results from anonymized R&D Capital Audit engagements.

See how an audit works for your organization

Frequently Asked Questions

Direct answers to core technology governance and economic questions.

What is an AI Economist?+
What is AI governance?+
How much does a fractional CPO / CTO cost?+
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