Hard problems are rarely solved by technology alone. Over 20+ years building AI and data products at Google, LinkedIn, DoorDash, and a startup I founded, the real work has always been bringing clarity to complexity — from the technology up, the product out, and the business down.
This collection reflects what that range has taught me: a practitioner's view of optimism and caution, stay-the-course and adapt-to-change, market winds and individual impact.
Latest
The Algorithm Was Never Listening
Recommendation systems cannot detect authenticity. They run a short, proxy-driven bet on your content and lock in fast. Understanding the bet changes what you do before you publish.
What I Prefer
Marc Andreessen posted his AI custom prompt. Here is mine.
Neither Tool Nor Colleague
One camp believes AI will restructure everything. Another believes the hype will fade. Both resolve cognitive dissonance rather than engage with what the technology actually is.
Finding the Unit
Whether you build a product or a platform is not a strategic choice. It is a consequence of what your unit of value actually is. Finding the unit is the work. Everything else follows.
AI Speaks in Language. It Reasons in Statistics.
AI operates statistically but presents linguistically. Base rates, precision/recall, and significance reason about outputs where language alone can't.
Non-Determinism in Enterprise AI: What It Actually Is, Where It Comes From, and What To Do About It
Statistical, probabilistic, and non-deterministic are three distinct properties of AI systems. Conflating them is a costly, common mistake.
Who you hire, who you grow, who you promote — and how AI gets this wrong
On noise, bias, and what Amazon's failed hiring tool actually teaches us about deploying AI into people decisions.