Distilled conclusions.
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AI Cost Governance Is an Architecture Problem
A healthtech CTO I worked with blew her quarterly AI budget in six weeks. Her team of twelve engineers had adopted Claude Code across every workflow, the results were genuine, and the billing model changed mid-quarter. Some developers were generating $80 to $200 in daily charges. The CFO called, the board asked questions, and the…
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AI Layoffs Aren’t Working: The Jevons Paradox
In 1865, the English economist William Stanley Jevons published The Coal Question. His observation was counterintuitive and has stayed that way for 160 years: James Watt’s steam engine was dramatically more efficient than its predecessor, and British coal consumption went up, not down. The efficiency made coal-powered machinery cheaper to operate, which expanded demand for it…
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Why AI agents die between pilot and production
Consider a financial services CTO sponsoring an AI agent to automate Know Your Customer (KYC) document verification. The details are composite, but the sequence is one I have seen play out more than once. The pilot was her idea, she secured the budget, she was in the room for every blocker. When compliance said the…
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The CTO’s credibility moat: why the CAIO inherits the judgment you stopped using
A CTO I know approved an AI coding tool rollout he did not understand. The technology was within his reach, he had not touched one in nine months, so he no longer knew which questions to ask. He sat through the vendor demo, nodded at the architecture slide, asked a question about pricing, and signed.…
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Why Cheaper to Build Doesn’t Mean Cheaper to Own
A private equity firm was running due diligence on a SaaS company. Standard process: evaluate the product, the market position, and the technical defensibility. But this time, the consulting team added a new step. They took the target company’s core product and asked an AI coding assistant to replicate it. In days, they had a…
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AI Comprehension Debt: The Codebase Nobody Understands
Six months ago, a fintech team I work with shipped an authentication service in three days. Claude generated most of it, the senior engineer who prompted it reviewed and approved, and the service passed all tests. It worked. Last month, a different team member needed to modify the token refresh logic. She spent four days…
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The 5x Myth: What AI Actually Changes About Delivery Speed
A fintech CTO I work with had her board moment three months ago. The chair leaned forward mid-review and asked the question every technology executive dreads: “So you’re telling me you don’t know if this is working?” Her answer was better than most: “I’m telling you that the numbers we were using to answer that…
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The vanishing rung: what happens when entry-level engineering disappears
She joined the team seven months ago, right out of university. Smart, motivated, technically capable. Her PRs merged on the first or second try. Her velocity numbers were solid. Her manager called her “a natural.” Last month, there was a production incident. A payment processing service started timing out under load. The team needed someone…
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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…
