If you take one thing from this entire series, let it be this: the quality of AI-generated code is bounded by the quality of your specification. No amount of model capability, prompt engineering tricks, or iteration can overcome a vague specification. The ceiling of what AI can produce for you is set by the clarity…
From Waterfall to ADD: Why AI Demands Its Own Methodology
Software development methodologies do not emerge from academic theory or conference talks. They emerge from pain. Practitioners encounter problems that existing approaches cannot solve, and they develop new disciplines to address those problems. Understanding this history matters because AI-assisted development is at an inflection point. The unstructured approaches I described in my previous post are…
The Unstructured AI Problem: Why Most Teams Are Using AI Wrong
Every developer I know uses AI tools now. Copilot suggestions appear mid-keystroke. ChatGPT tabs stay permanently open. Claude conversations stretch across multiple projects. The adoption curve was vertical, faster than any technology shift I have witnessed in two decades of software engineering. But here is the uncomfortable truth: most of us are using these tools…
ADD: AI-Driven Development as a Methodology for the Future Engineer
Software development has always evolved through methodologies that structure how we think about building systems. Waterfall gave way to Agile. Test-Driven Development changed how we approach correctness. Behavior-Driven Development shifted focus toward specifications that non-technical stakeholders could understand. Each methodology emerged because the existing approaches no longer fit the reality of how software was actually…
The Future Engineer: What Software Development Looks Like When AI Handles the Code
The software industry has entered a period of genuine transformation. After decades of incremental tooling improvements, AI-assisted development is introducing changes that feel qualitatively different from what came before. Code completion, automated testing, and intelligent refactoring are no longer experimental features but daily realities for many developers. This shift raises uncomfortable questions about the future…
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…
The Eternal Promise: A History of Attempts to Eliminate Programmers
When I look back at the history of software, one pattern emerges with remarkable consistency: the promise to simplify software creation, to make it cheaper, and ultimately to eliminate the need for programmers altogether. This is not a new idea. It has been the driving ambition of our industry since the 1960s. And while each…
AI-Powered Fixed-Cost Development: A New Model for Agencies
Software development has always carried an uncomfortable truth: nobody really knows how long it will take. Clients want certainty. They want a number, a deadline, a budget they can plan around. Agencies and independent consultants want to deliver that certainty, but they have learned through painful experience that software estimation is more art than science….
The AI Orchestration Developer: Why Team Leaders Will Define the Next Era
There is a particular kind of developer who has spent years doing something that most of the industry undervalues: building people, not just systems. They review code not to gatekeep, but to teach. They pair with junior developers not because it’s efficient, but because they understand that growth compounds. They know that a team’s ceiling…
Why Ruby Might Be the Most AI-Friendly Language Nobody’s Talking About
A few weeks ago, Martin Alderson published something that caught my attention: a systematic comparison of how token-efficient different programming languages are when fed to large language models. The findings were fascinating. And if you’ve been following my writing on Ruby, you won’t be surprised to hear that Ruby came out looking very good indeed….