Writing
Notes on product, AI, data, growth, technology, and lessons from building.
Starting with the problem
A note on why problem definition often matters more than the feature that follows.
Read more →What makes an AI product genuinely useful?
Thoughts on designing AI around real workflow friction rather than simply adding a conversational interface.
Read more →Why governed metrics matter for AI
Why semantic layers, metric definitions, and trust become more important as analytics interfaces become more natural.
Read more →When should an internal tool become a platform?
How to recognize reusable capabilities without turning every product into generic infrastructure.
Read more →What analytics taught me about product management
Lessons from moving from analyzing decisions to being responsible for making them.
Read more →Building the stack behind AI
Thinking about the interaction between hardware, infrastructure, models, software, and applications as AI scales.
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