Distilled conclusions.
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The AI Productivity Paradox Has Data Now
Months ago, I wrote about the measurement problem: engineering metrics rewarding the wrong behavior after AI adoption. That post was a diagnosis based on pattern recognition across organizations, velocity climbing while delivery stayed flat, dashboards turning green while products shipped late. The data arrived. Between late 2025 and mid-2026, five independent research programs published findings…
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The four layers of AI-driven engineering organizations
After a year of writing about what breaks when AI enters engineering organizations, I can name the single thread that connects every failure and every success: whether the organization preserved judgment while changing how work gets done. Threat modeling, governance, spec-driven development, architecture reviews, measurement problems, the CTO’s evolving role. Each post addressed a specific…
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AI-DLC in Practice: What Structured AI Development Actually Looks Like
Thirty engineers in a room. One feature to build. By the end of the day, they had a validated specification, working code, and infrastructure configuration, all generated from a structured intent document they wrote together that morning. In the feedback survey, the team rated the experience 9.7 out of 10. Every single respondent expressed interest…
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The CTO’s real job when AI changes how software gets built
A few months ago, a CTO I know pushed a massive change directly to production. He had used an AI coding tool to generate the entire thing over a weekend, bypassed code review, skipped staging, and deployed it himself. By Monday morning, the system was broken. The QA engineer who spent two days untangling the…
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The Governance Paradox: Why the Most Regulated Industries Will Adopt AI-DLC First
Earlier this year, during an AI-DLC adoption assessment at a large financial institution in Latin America, something unexpected happened. The compliance team became the loudest champion of the methodology. Not the engineering leads, who were excited about the productivity gains. Not the CTO, who saw the strategic positioning. The compliance team, the people everyone assumed…
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Who Owns AI Governance When Everyone Thinks Someone Else Does?
A few months ago, during a governance assessment at a mid-size financial services company, I asked a simple question: “Who owns AI governance here?” The CISO said the CTO owned it because AI is a technology decision. The CTO said the data team owned it because AI is fundamentally a data problem. The head of…
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The Architecture Review Gap: Why AI-Accelerated Teams Need Them More
AI didn’t just increase delivery speed, it broke the cadence at which architecture is validated. Most organizations are still reviewing architecture at a pace designed for quarterly releases, while deploying multiple times per day. That structural mismatch, the distance between deployment velocity and architectural validation, is what I call the architecture review gap. It is…
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Mob Construction: What Changes When AI Generates Code from a Real Specification
Two weeks after the Mob Elaboration session I described in my previous post, the same financial services team sat down for their first Mob Construction session. They had a validated specification: twelve user stories with acceptance criteria, a domain model with verification tiers, non-functional requirements with measurable thresholds, and a risk register that included the regulatory…
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Mob Elaboration: What Happens When AI Runs the Requirements Room
The first time I facilitated a Mob Elaboration session, the Product Owner read the Intent aloud: a new customer onboarding flow for a financial services platform. Within four minutes, the AI had generated twelve user stories, acceptance criteria for each, a proposed domain model, and a decomposition into three independent units of work. A senior…
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The Community Is Building AI-DLC Without Knowing It
A few weeks ago, a developer on Reddit posted about a workflow they had built for Claude Code (r/ClaudeAI, March 2026). They had split their AI-assisted development into three distinct agents: an Architect that defined the system design and constraints, a Builder that generated the code, and a Reviewer that evaluated the output against the…
