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.

Glossary/Data Warehouse

Data & Analytics

1 min read

What is Data Warehouse?

TL;DR

A data warehouse is a centralized repository of structured, historical data optimized for analytical queries and reporting.

A data warehouse is a centralized repository of structured, historical data optimized for analytical queries and reporting. Unlike operational databases (optimized for fast reads/writes), data warehouses are optimized for complex queries across large datasets.

Popular data warehouses: Snowflake (cloud-native, usage-based pricing), BigQuery (Google, serverless), Redshift (AWS), and Databricks Lakehouse (unified analytics).

Data warehouse architecture: Extract data from source systems (databases, APIs, events) → Transform data (clean, normalize, enrich) → Load into the warehouse (the ETL or ELT pipeline). Modern approaches prefer ELT (load raw data first, transform in the warehouse).

For product teams, data warehouses enable: cohort analysis, funnel analytics, revenue attribution, feature usage tracking, and customer health scoring across all data sources.

Why It Matters

Data warehouses enable data-driven product and business decisions. Without a warehouse, teams rely on siloed data in individual tools, leading to conflicting metrics and analysis paralysis.

Frequently Asked Questions

What is a data warehouse?

A centralized repository of historical data optimized for analytical queries. It consolidates data from multiple sources into a single queryable system.

What data warehouse should I use?

Snowflake for flexibility and scale, BigQuery for GCP users, Redshift for AWS users. Snowflake is the most popular choice for most modern companies.

Related Terms

Product Analytics Cohort Analysis

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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