AI Product Lab
How I think and work - across the product lifecycle.
A working lab - product decisions, AI systems, growth experiments and the economics of building. Evidence of how I operate, not a changelog.
The Best AI Engineer on My Team Still Isn’t the Product Owner
A functional product isn't necessarily a good product. Someone still needs to connect the technical possibilities with user needs, define the experience, make trade-offs and push for something people actually want to use.
I Burned 500 Figma AI Credits Before Realising I Hadn't Thought Enough
Think carefully, plan more, convey your instructions concisely but clearly. This can be applied to all AI platforms. This is token conservation mindset.
Don't forget your role as a PM when working with AI tools
A strong PM brings vision, user empathy, product sense and the ability to decide what is worth building. AI can dramatically amplify the ability to turn that vision into reality.
Don't Add a Second Model Just Because You Can
Don't ask whether you should use another model. Ask what problem another model uniquely solves - and whether that benefit is worth the complexity it introduces.
From Deeper Personalisation to Broader Possibilities
Expanding the Recommendation Universe of an AI Travel Planner
The Fastest AI Model Didn’t Make My Product Faster
In AI products, performance is not just about making inference faster. It is about designing the entire system so that expensive inference is necessary as few times as possible.
When the AI Wasn't the Problem: How Incomplete Data Broke a Recommendation System
Was the ranking system actually receiving enough information to make the right decision?
Tracing a ‘bad AI recommendation’ as a PM
Sometimes the best AI improvement isn't a better model, a better prompt, or more sophisticated reasoning. Sometimes it's simply not throwing away the right answer before the AI gets to see it.