Thoughts on AI risk, governance, and adoption strategy for teams where mistakes are expensive.
A markdown file wiped $285 billion off the stock market. Every analyst who dug into it landed on the same conclusion: the next premium in enterprise software is proof of process. They described our evidence bundles without knowing we exist.
Read moreGitHub shows that someone clicked approve. It doesn't show what they reviewed, what risks were present, or why approval was reasonable. That distinction just became expensive.
Read moreThe attention economy applies to AI the same way it applies to humans. Context windows are finite. Progressive disclosure isn't a nice-to-have - it's the fundamental constraint.
Read moreEnterprise AI adoption is approaching an inflection point. Organizations upgrading to newer models without infrastructure readiness will see marginal gains.
Read moreThe future isn't evenly distributed. 2026 is the year of the AI flywheel - and that changes everything if you act now.
Read moreWhat the last week of 2025 reveals about where AI is actually heading - and what biotech teams should watch in 2026.
Read moreMost AI tools forget everything between sessions. I built a 3-part system to fix that. Here's what actually works for production AI at scale.
Read moreAutomated auditing feels like losing control. It's actually how you scale judgment. Here's why the teams that embrace constraints ship faster.
Read moreRussian 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.