Someone finally named the thing. There’s a word now for the work I’ve been doing for the last two years without a vocabulary for it. The middle loop. Supervisory engineering. The thing that sits between writing code and shipping it. The thing that didn’t exist before agents started producing code faster than any human could…
Category: AI
Everyone Is an “Engineer” Now, and Nobody Knows What Anyone Does
A CTO’s field guide to the 2026 AI job title theater, with a companion reference guide covering all 40 roles. I have been a CTO for most of my adult life. I have hired, fired, onboarded, mentored, and occasionally been forced to explain to finance why a “Senior Applied Generative AI Engineer II” and a…
The Engineering Age That’s Ending, and the One We Haven’t Named Yet
The best engineers I know write less code than they did two years ago. They ship more. Everyone wants the clean story. AI replaces developers. AI makes developers 10x. Juniors are cooked. Juniors are saved. Pick a side. The reality is messier. Big Tech new grad hires dropped to 7% of all new hires, down…
Vibing Fatigue: Why Tracking AI Usage Is the Wrong KPI
A developer I know got pulled into a “productivity review” last month. Not because their output dropped. Because their AI tool usage was below the team average. Their manager wanted to know why they weren’t using AI for coding enough. Not why their code had fewer bugs. Not why their PRs moved through review faster….
Almost Solved Is the Most Dangerous Phase in Engineering
Everyone agrees AI is transforming how we write code. Adoption is through the roof, productivity metrics look promising, and a growing chorus of voices insists we are months away from this whole thing being “solved.” They are probably wrong. Not because AI is bad, but because “almost solved” is the most dangerous phase in any engineering problem.
AI Made Learning Feel Pointless. That’s Exactly When It Matters Most.
A week ago my blog post got noticed by developers. I got so many messages and there was a recurring theme. They feel helpless and anxious with the ever growing AI dominance in writing code that might not be perfect but it is almost good enough in many cases. But one question stood out more…
The Training Data Paradox: What Happens When AI Replaces the Engineers Who Trained It
There is a question hiding in plain sight behind every celebration of AI-generated code, every prediction that developers are obsolete, every LinkedIn post about building an app 100x faster with a prompt. It is a question that almost nobody in the current hype cycle is asking, and it may be the most important question of…
Evaluate: Why Human Judgment Is Non-Negotiable
We have arrived at the phase of ADD where the most important human skill comes into play. You have written a specification. You have generated code using appropriate context and patterns. Now you must determine whether that code is actually correct. This is not a formality. AI-generated code can be syntactically correct, pass basic tests,…
Specification Templates: A Practical Library for AI Development
In the previous post, I made the case that specification is the highest-leverage skill in AI-driven development. A precise specification produces better output, requires less iteration, and surfaces ambiguity before it becomes a bug. But writing detailed specifications from scratch is cognitively demanding. You must simultaneously consider functional requirements, constraints, context, edge cases, and integration…
Code Is for Humans, Not Machines: Why AI Will Not Make Syntax Obsolete
With AI, “everybody is a programmer.” You do not need to learn syntax anymore. Just describe what you want, and the machine will write the code for you. If you have spent any meaningful time in this profession, you are probably laughing right now. Or at least shaking your head. This narrative has become extraordinarily…