Product and data leadership by day. By night, an attempt to find out what these tools can actually carry - by handing them something that can lose real money.
I run product for data strategy and operations at Condé Nast. In the evenings I build things with AI tools and write down what actually happened - including, especially, the parts that did not work.
These are not tutorials. They are build logs: what the tools did well, where they were confidently wrong, and which decisions turned out to be stubbornly human. Every figure quoted comes out of the project's own commit history rather than my memory of it.
Three months, 604 commits and one live options book. Two dead strategies, eleven rejected improvements, three bugs that reached real money, and a data purchase that inverted two months of conclusions.
Start reading →Nothing here yet. The next one is being lived through rather than written.
By day I run product for data strategy and operations at Condé Nast, where the brief is customer identity: the unglamorous business of establishing that the person reading on a phone in Mumbai and the one subscribing on a laptop in London are the same human being. Essentially the ‘slow work’ of turning unknown into known, in various stages. The glamorous parts of my day: developing the single customer view, and harnessing that data to optimise for amplified engagement and revenue across multiple lines and brands.
Twenty-four years of it now, across product, data and technology - client side and agency side, in media and publishing, CPG, insurance, automotive, FMCG and telecom, across North America, Europe and Asia. Enough time in front of CXOs to have learned that a business case travels further than an architecture diagram, and enough time behind them to know the diagram still has to be right.
This project was my evenings. It brings together the triumvirate - my love for the world of finance and markets, my drive to build a production grade system using the latest AI toolset, and the itch to discover first hand what these tools are truly capable of. And the only honest way to find out what these tools can carry is to hand them something that can lose real money.
Questions, disagreements, war stories from your own build, or a conversation about senior product leadership and AI-delivery roles - all welcome.