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In the early stages of building AI products, uncertainty reigns supreme. Leaders, managers, and individual contributors often grapple with defining their path forward. This uncertainty isn’t a flaw—it’s a feature of early-stage AI development. The key to success lies not in having all the answers, but in building an effective experimentation min...

Over my career leading engineering and data science teams — from regulated industries to early-stage startups to large enterprises — I’ve consistently seen one pattern: Teams can move incredibly fast when they understand how their work drives business value, and painfully slow when they don’t. Most recently, as VP of Engineering and Data Scienc...

The Power of Semantically-Meaningful and Human-Readable Representations As AI and data science leaders, we often focus on sophisticated algorithms and cutting-edge technologies. However, one fundamental yet frequently overlooked aspect can make or break your AI initiatives: how your data is represented. Let me share a practical guide that will ...

It’s 11 PM on a Sunday. You’re putting the finishing touches on a critical project, convinced that this extra push will finally get it over the line. Sound familiar? I’ve been there too many times. Despite these heroic efforts, I kept encountering a puzzling pattern: working harder didn’t consistently lead to better outcomes. Will Larson captur...