At the World Economic Forum in Davos on January 20, 2026, Anthropic CEO Dario Amodei said something that landed like a grenade across the technology industry. In an interview with The Economist, he claimed that AI models could do “most, maybe all” of what software engineers currently do within six to twelve months. He pointed…
Category: ADD Methodology
When ADD Is Wrong: Recognizing the Limits
Every methodology has boundaries. Waterfall fails when requirements change frequently. Agile struggles with fixed-scope contracts. TDD is awkward for exploratory prototyping. No approach works everywhere, and pretending otherwise leads to poor outcomes. ADD is no exception. There are tasks where AI-Driven Development is not the right approach. Recognizing these situations is a skill that separates…
ADD Meets TDD, BDD, and Agile: Combining Methodologies
ADD is not a replacement for existing development methodologies. It is a complement to them. Teams already practicing Test-Driven Development, Behavior-Driven Development, or Agile workflows have a head start with ADD because these methodologies share underlying principles with AI-Driven Development. This post explores how ADD integrates with each methodology and how the combinations create practices…
ADD in Context: Greenfield, Legacy, Refactoring, and Testing
The ADD cycle is consistent across contexts: Specify, Generate, Evaluate, Integrate. But how you apply each phase changes depending on what you are building and where you are building it. Greenfield projects offer freedom that legacy codebases do not. Refactoring has constraints that new feature development lacks. Test generation inverts the typical flow. This post…
Integrate: Completing the ADD Cycle for AI-Driven Development
Code that passes evaluation is ready for integration. This is the final phase of the ADD cycle, where generated code becomes part of your system. But integration is more than merging a pull request. It is where AI-generated code meets the full reality of your codebase, your testing infrastructure, your deployment pipeline, and your team’s…
Evaluation Checklists: Building Your Quality Gate for AI Code
In the previous post, I covered the five dimensions of evaluating AI-generated code: correctness, fitness, security, performance, and maintainability. Understanding these dimensions is essential. But understanding is not enough. Under time pressure, even experienced developers skip evaluation steps. They focus on the dimensions they find most interesting or most familiar, and they neglect the others….
You Don’t Want a Claude Code Guru
The job posting practically writes itself these days. “Looking for a senior developer proficient with AI coding tools. Must be comfortable using Claude Code, Cursor, or Copilot to rapidly produce production-ready code. We need someone who can 10x our output.” I have seen variations of this everywhere over the past year. Companies scrambling to find…
The New Bottleneck: Why Clarity Matters More Than Code
For two decades, the fastest engineers were the ones who could write code quickly. They knew the shortcuts, the patterns, the frameworks. Their fingers moved faster than their competitors. That era is ending. The new bottleneck isn’t your typing speed or your syntax recall. It’s your clarity. I’ve spent twenty years building software, leading teams,…
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,…
Prompt Patterns Catalog, Part 2: Iteration, Verification, and Persona
In the previous post, I introduced three foundational prompt patterns: Decomposition for breaking complex tasks into manageable units, Exemplar for teaching by example, and Constraint for defining boundaries. These patterns address the most common generation challenges. This post completes the catalog with three more patterns, then addresses the practical question of building and maintaining a…