Thoughts on AI risk, governance, and adoption strategy for teams where mistakes are expensive.
Russian speakers see blues differently. Turkish speakers track information sources better. What if AI models encode these cognitive patterns too—and we can activate them strategically?
Read moreEveryone's building AI workflows with code metaphors. The better model is motion capture—recording expert thinking as auditable, replicable processes that AI can execute.
Read moreFor two decades, STEM was the safe bet. AI is reversing that—models respond to precise vocabulary, and liberal arts majors have it.
Read moreThe real constraint isn't model quality—it's that most enterprise knowledge work has no ground truth to validate against. Organizations that define falsifiable success conditions will win.
Read moreMost AI advice focuses on speed. The more interesting capability is access to compressed expertise from communities you'll never be part of.
Read moreSoftware development used to be pyramid construction. With AI tools, building is cheap. Rebuilding is cheap. So what replaces the old model?
Read moreMost biotech VPs are rushing to adopt AI tools. The smart ones are building governance frameworks first. Here's why that distinction matters for reducing risk.
Read morePublished on other platforms
Exploring how shared human archetypes shape AI behavior - a comprehensive analysis of archetypes and communication
Nanostructure research examining nanopillar structures and their effects on E. coli growth
Retrospective on workshop design, delivery, and community building
These pieces show my scientific and probabilistic background, which informs how I teach AI risk and reliability.