Insights
Our latest thinking.
Notes on AI-native software delivery, engineering transformation, and what we’re learning inside real engineering orgs.
Showing 7 of 16 posts
Your tech debt is a loan. Do you know the interest rate?
“The codebase is a mess” has never been funded. A dollar figure has. How to compute the annual interest you're paying on technical debt, and the one-page dashboard that keeps the paydown funded once you get it.
Modernization·6 minStop ranking tech debt by annoyance. Score it like a portfolio.
Most tech debt backlogs are ranked by whoever complained loudest. Gartner's PAID model replaces that with three scores a CFO will accept, and it changes the funding conversation.
McCaul Baggett
Perspective·7 minAI displacement is real. Stop hiding behind the slogan.
“AI won't replace you. Someone using AI will.” The line sounds pragmatic. It's a dodge. If your AI strategy doesn't include the people affected by it, it isn't a strategy. It's cost-cutting with better branding.
Chris Sims
Perspective·9 minWhat is your chatbot doing to you?
AI has become the most patient, affirming, available presence in a lot of people's lives. I use it constantly, and the research on what it's doing to us is arriving faster than the honest conversation about it.
Chris Sims
Operating model·5 minWhy your legacy stack is the real blocker to AI leverage
You don't have an AI tooling problem. You have a systems problem. The fastest agent in the world still moves at the speed of the codebase it's editing.
McCaul Baggett
Cadence·3 minWhy spec-driven AI development beats vibe coding
AI agents are unforgiving readers of ambiguous requirements. The teams winning with AI write specs first, ship second.
Carter Musick
Practice·4 minFive lessons from running agents in production engineering
What we learned shipping agentic workflows for mid-size SaaS teams, including the mistakes we'd undo if we could.
Brandon Bosco
Questions, answered
About these notes.
What we write about, who writes it, and the question underneath most of it.
- Do AI coding tools actually make engineering teams faster?
- Not by themselves — the research and our field experience agree on that. Tools dropped into an unchanged delivery process produce felt speed, rework, and flat delivery metrics; the lift shows up when the system around them changes too. That gap between AI motion and AI results is the recurring subject of these posts.
- What topics does Sigao Insights cover?
- The working problems of AI-era software delivery: technical debt and what it really costs, legacy modernization, spec-driven development versus vibe coding, managing coding agents at scale, and proving AI value to a board. Plus the essays behind our eight core commitments, for the culture side of the same story.
- Who writes these posts?
- Sigao practitioners — the people running our delivery, modernization, and transformation engagements. These are notes from the field, written from what we're learning inside real engineering orgs, not a content calendar.