Blog
9 posts
How Agile Must Evolve When Implementation Is Cheap
Agile was designed around the assumption that writing code was the bottleneck. AI broke that assumption. Here is what needs to change.
Spec-Driven Development: The Missing Link in AI Coding?
If unstructured prompting has plateaued at 10% productivity gains, the obvious next question is: what happens when you give AI better instructions? Spec-driven development is the most serious answer to that question so far.
The Research Gap: Why AI Coding Fails Before a Single Line Is Written
The biggest source of AI-generated code failures isn't the model, the prompt, or the tool. It's what happens — or doesn't happen — before the implementation stage begins. Context preparation is the missing discipline.
Don't Use AI to Build Faster. Use It to Learn Faster.
Every productivity study in this series points to the same ceiling: modest gains on existing codebases, instability risks, plateauing returns. The real opportunity isn't optimisation. It's experimentation.
AI & Developer Productivity: The Real Numbers
93% of developers use AI coding tools — but productivity gains have plateaued at ~10%. A data-driven analysis of 121,000 developers across 450+ companies: what the research actually shows, what's working, and what the strategic opportunity really is.
Where AI Actually Delivers ROI: A Practical Guide
Not all AI investment is equal. The data on who benefits most, which use cases have the best returns, and what organisational foundations have to be in place before AI delivers at all.
Same Tools, Wildly Different Outcomes
Data from 67,000 developers shows that AI acts as an amplifier — it makes good engineering organisations better and struggling ones worse. This is a management problem, not a tooling problem.
What the Research Actually Shows: DORA, METR, and GitClear
Three independent research programmes have now published rigorous data on AI's impact on software delivery. The results are more nuanced — and more concerning in places — than the vendor claims suggest.
The AI Productivity Paradox: 93% Adoption, 10% Gains
The data is in from 121,000 developers across 450+ companies. AI adoption is near-universal — but productivity gains have been stuck at around 10% for over a year. Here's why that gap exists and what it actually means.