AI Adoption and Return
Adoption of AI tools keeps climbing. Measurable returns mostly do not. These posts look at why, and at what engineering leaders can do about it without waiting for the models to improve.
Start here
- Are We Rearranging AI Deck Chairs? sets out the gap between adoption and return, and argues that the gap is about discipline rather than models.
- AI Is Not an Engineering Strategy is for anyone hearing that AI will write most of the code and that headcount can fall accordingly.
- Is Anybody Actually Reading This? asks what happens when generated artefacts stop proving that anyone understood anything.
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All writing on this theme
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Is Anybody Actually Reading This?
: AI makes polished tickets, replies and pull requests cheap, so they no longer prove understanding. A test for which delivery practices still create it. -
Are We Rearranging AI Deck Chairs?
: AI adoption keeps climbing but measurable returns mostly do not. The gap is discipline, not models: living references, scoped roles and firm review gates. -
The Box is Open
: Cloud AI pricing is subsidised and will rise. This post argues for choosing local, on-premises or cloud AI by workload, with open-weight models now viable. -
The Permission to Try
: AI has cut the cost of writing code, not maintaining it. This post argues for exploring more before committing, as judging what to keep is the bottleneck. -
AI Is Not an Engineering Strategy
: Boards hear AI will write 90% of code and cut engineering headcount. Why code is not engineering, and what AI amplifies and what it cannot touch. -
Building AI on Ungoverned Data
: Most organisations run AI without data governance, and the EU AI Act adds legal risk. Covers shadow AI, agent data access and a phased path to compliance. -
The Expertise Discount
: Firms spend billions on AI while discounting senior engineers. This post argues the 'Junior + AI = Senior' bet is bubble behaviour and will correct. -
The New Reality of Incident Response
: AI adds new failure modes to incidents and faster ways to respond. This post covers AI-specific runbooks, recovery basics and a four-phase adoption plan. -
AI-Adjacent Tools: What's Worth the Complexity
: Most teams do not need another AI tool. Start with markdown, Git and CLI tools, then use five questions before buying observability, orchestration or MCP. -
Engineering Starts Before the First Line of Code
: Treating Claude Code like a new engineer, with discovery, living docs, task breakdown and governance, delivered an event-driven system without major rework. -
Decoding the 2025 DORA Report: What AI-Assisted Development Means for Engineering Excellence
: The 2025 DORA report finds AI amplifies the organisation you already have. The seven capabilities that matter, and why speed gains outrun stability. -
Why Testing Matters More in the AI Era: The Critical Discipline We Can't Afford to Skip
: Why testing discipline matters more when AI writes the code: how test-first work guides AI, how the velocity trap catches teams, and how to keep verifying. -
Beyond Code: How AI Agents Are Reshaping Professional Workflows
: How non-developers can use Claude Code, a command-line AI agent, for meeting notes, reports and research, using slash commands, hooks and a prompt library. -
The Hidden Cost of Cheap AI: Why Your Organisation's Strategy is Creating a Skills Crisis
: A junior engineer plus AI looks cheaper than a senior, but hidden costs and skills gaps reverse that. This post argues for AI spend that builds capability. -
Brushes and Algorithms: What the Miniature Painting Community Teaches Us About AI in Creative Work
: What the miniature painting community's row over AI at Golden Demon teaches engineering leaders about skill growth, authenticity and drawing lines on AI. -
The Great Wait: How AI Expectations Are Freezing the Software Engineering Job Market
: Firms are freezing engineering hiring while waiting for AI to replace engineers. This post argues that is a strategic error, and what to do instead. -
Better Communications for Software Engineering: The Foundation of Process and AI Success
: AI amplifies the communication habits a team already has. Why clear written communication comes first, with frameworks for requirements, feedback and AI use. -
The Real Cost of Intelligence: Why Current AI Billing Models Don’t Add Up
: Why per-token, per-plan AI pricing discourages trying more than one model, hides the cost of experimenting and muddies ROI, plus ways to budget by outcome. -
From Agents to Architectures: The Future of AI-Native Systems
: What changes when AI moves from assisting to acting: agent problems with state, tooling and accountability, and what AI-native architecture asks of teams. -
AI Will Write All Our Code… and Other Fairy Tales
: AI will not write 90% of your code, but it will change what you build. Part one of two on bolting AI onto existing systems and what breaks when you do.