Reflections
On AI-first delivery, technology strategy, and what happens when expertise meets the right tools.
How carefully do you need to review AI-generated code? The answer depends entirely on which of three levels you're working at — and confusing them is the most common mistake in AI-assisted development.
Six steps that translate personal accountability from a conviction into a concrete work process. Context management, the inner loop, and why the commit is the final accountability checkpoint.
On why AI transformation cannot be delegated. And what it actually means to own it — not as an advisor, but as the person responsible.
On why most organizations are asking the wrong question — and what the organizations that actually succeed do differently. It's not about the technology. It's about what you're trying to achieve.
On what happens when a CTO reviews every commit for eight months and discovers that nearly all problematic code is AI-generated. Technical debt doesn't arise despite AI — it arises because of AI.
It is not Copilot versus protocol versus handwritten notes. It is all meetings together versus each meeting alone. Loose summaries drive no outcomes. The sum of them can.
On why the person who gets the most out of AI is not the most technical — but the one who understands business outcomes. Speed is proven. But speed without direction is just faster waste. The deciding factor is the one nobody talks about.
On why leadership teams don't want to understand AI — they want to report that they've done something with AI. And why you can actually help with the latter, if you stop explaining the tool and start talking about their people.
On why the most important thing you can do with an LLM has nothing to do with how you write the prompt — but whether you understood the task before you started typing.
Mustaschmilen reopened March 27 — for the second year, on a platform that barely resembles the 2025 version. 70+ improvements. From hardcoded to fully configurable. This is what AI-first looks like over time.
Two words that name something that had no name. On how I calibrate my own input layer — and why it determines what comes out.
On what happened when I stopped writing instructions to AI and started asking myself questions instead.
On why AI doesn't reduce cognitive load — it transforms it. And why that's the whole point.
On why I refuse auto-commits, auto-changelogs, and every form of unsupervised automation.
About the time I dismissed a question as magic. And realized neither of us had the answer.
How a 15-minute phone call became a complete website. And why nobody asks how it happened.
Why all professional work is fundamentally about text — and why that changes the entire picture.
How knowledge, chain, and accountability allow one person to deliver what used to require a team — and why that changes everything.
Why AI amplifies what you already know but doesn't replace what you lack — and why the hype is dangerous.
The practical infrastructure for capturing everything that gets said — online meetings, in-person meetings, and phone calls in the same pipeline.
Why generic AI summaries make the documentation problem worse, and what you should do instead.
On being early, the limits of context windows, and what happens when the market finally catches up.
On voice as raw material, why transcription changes everything, and the position nobody has claimed.
On responsibility, judgment, and why the consequence of being wrong makes you smarter than any AI model in existence.