Writing
Practical thinking on building products.
Frameworks, lessons and opinions on product strategy, growth, AI and monetisation. Practical product judgment, not thought leadership.
AI Product Metrics: From Model Quality to User Value
Can I trace a user's behaviour back to the quality of the AI experience and forward to meaningful product value?
Every AI Feature Has Three Costs
Inference, UX, Trust
The AI Product Evaluation Framework
A model can produce better answers according to an evaluation score, but that doesn't necessarily mean users get more value from the product.
The AI Product Decision Framework
The interesting part of AI product management isn't knowing how to use every AI technique. It's knowing where AI should and shouldn't be used.
When an AI Recommendation Is Wrong, Don't Blame the Model First
What looked like “AI giving bad recommendations” was actually a combination of dead product logic, broken data connections, prompt ambiguity, and unsupported model-generated claims.
A Practical Framework for Diagnosing AI Product Latency
When an AI product feels slow, the instinct is often to optimise the model: use a smaller model, reduce tokens, or lower reasoning effort. But latency is a systems problem, not simply a model-speed problem.
How AI Changes the Consumer Growth Funnel
Five years working in consumer growth taught me a fairly simple lesson: growth is not primarily an execution problem. It is a strategy problem.
When Quality Became the Latency Problem
A strict quality gate is valuable only if the system is designed so that the model can reliably satisfy it on the first attempt.