My calendar looks different these days. The back-to-back blocks of 1:1s, strategy reviews, and planning sessions have given way to long, uninterrupted stretches of quiet. That quiet has been the most significant change—it’s brought back time to think, a noticeable drop in stress, and a genuine enjoyment in my work that I hadn’t realized was fading. It’s why, after years of leading teams, I’ve deliberately moved back to a role as an individual contributor.

This shift has changed my day-to-day work, but one thing that remains constant is the time I spend mentoring colleagues and contacts, helping them navigate their own career questions. In those conversations, my own journey often comes up, and I hear a familiar question: “You were leading large teams… why the change?” Some have even wondered if I was leaving the company (I’m not). It’s a question with more than one answer, and I realized this post is my way of exploring them fully—for everyone who has asked, and for anyone else thinking about their own path.

It’s a fair question, and the simple answer is that my career has always been guided by a desire to learn and experience things more deeply. It’s never been a straight line up the leadership ladder; I’ve moved between managing and building several times. Each shift was a deliberate choice to go where I felt I could learn the most. This recent move—from a Senior Director role in Cloud AI to a Distinguished Engineer in Google DeepMind—is just the latest example of that pattern: a deliberate step toward the work that feels most urgent and exciting right now.

That motivation started early. My move from Indiana University to Cisco wasn’t just for a job; it was to understand what Silicon Valley was really about. When the dot-com bubble burst, I saw it as a chance to experience something new and jumped into the startup world, working on the foundational tech for what would become the 802.11n and 802.11s WiFi standards. I was learning a ton, but I knew my growth had plateaued. That’s when a friend asked me to consider Google. It was October 2004, just after the IPO, and Google seemed like a magical place. I said yes without knowing what team I’d join. I just wanted to see what it was all about.

My Google journey began in March of 2005 on the municipal WiFi project in Mountain View, but soon took me to London as one of our first engineers in that office. After building out the test engineering team, I moved into Ads and had my first real chance to work with machine learning at Google, working on systems for multivariate ad optimization. From there, I moved back to the US and eventually found my way to Google Maps and Street View.

That was a dream job. I spent nearly a decade in Geo, starting on a team of two working on the launch pipeline and serving infrastructure. Over time, my responsibilities grew, and I had the privilege of leading teams working on everything from the “time machine” feature for historic imagery to 3D reconstruction, imaging hardware, machine learning, and augmented reality. Through it all, I had the chance to learn, explore, and contribute alongside people who became some of my dearest friends.

In 2019, a different kind of challenge appeared. My manager was asked to build a new product area, and I offered to help as his Chief of Staff. I wanted to learn how Google was managed as a business—how decisions were made and how organizations were designed at a macro scale. After two years in that role, I moved back into a technology leadership role, helping with the formation of Core ML.

It was after all of this that I started to realize something important: I missed having my own technical contributions. I missed the flow state, that feeling of time dissolving as you wrestle with a complex problem. I missed the direct feedback loop of writing a piece of code, running it, and seeing it work. I wanted to build my own ideas again.

That feeling connected directly back to my college days. I was an AI major at Indiana University in the 90s, and throughout my career, I had kept coming back to machine learning—in Ads, in Geo, in Core ML. With the explosion of generative AI in 2022, I knew exactly where I wanted to spend my time. More than anything, I wanted to apply these powerful new models to solve real-world problems.

This led me to the ML Developer team in Cloud, leading the Kaggle, Colab, and Gemini API teams. It was a smaller team with a mature leadership bench, which gave me more time to build my own projects—many of which have been chronicled on this blog. As the team evolved, I began contributing to internal projects as well, which culminated in the launch of Gemini CLI, where I was one of the core contributors from the beginning.

Working on Gemini CLI, I realized I was finally doing the exact kind of work I had been craving. When an opportunity came up to move to Google DeepMind and focus full-time on AI Agents and the future of Gemini CLI, I knew it was the right next step.

People often ask me why I’ve been at Google for over 20 years. The answer is simple: it has always been a place of discovery. It’s had its ups and downs, of course. There have been times I’ve considered leaving and times I’ve disliked my situation. But I’ve been lucky enough to move around and keep things fresh, working on projects in mobile, search, maps, technical infrastructure, cloud, and AI. Where else can you get exposed to so much in one place? The fantastic Acquired podcast is currently doing a series on Google (1, 2), and hearing those stories reminded me of how fortunate I’ve been to occasionally get a preview of the future. While a journey like this requires hard work, it also requires being in the right place at the right time. Right now, I feel like I’m in the perfect place for whatever comes next.

This move isn’t just about returning to code. It’s about being in the driver’s seat for the next evolution of software development, where our primary collaboration is with intelligent agents. For a builder, there’s no more exciting place to be. I’m home.