AI Infrastructure & Trust
What makes generative AI worth shipping, not just demo-able. Grounding, factuality measurement, evals, real-time data freshness.
POSITIONING
Two decades of consumer products at Amazon, Marriott, Walgreens, and Albertsons. Four years at Google deciding when AI answers are trustworthy enough to ship. Now moving to lead a consumer AI product team.
I started in consumer products and never left. At Amazon I took the Appstore worldwide. At Marriott I launched Homes & Villas from zero. At Walgreens I rebuilt a loyalty program that a third of Americans carry in their pocket. The common engine was ML personalization. What I took from those years is taste, earned the slow way: thousands of hours with real users, building a mental model of the customer sharp enough to know what matters and when it's good enough to ship. Now that building is cheap, that judgment is the scarcest thing a product leader brings.
Since 2022 I've worked on Search at Google. When Search or the Gemini app answers a question for billions of people, a score mid-game, a candidate on your ballot, whether a restaurant is open tonight, my team's systems check the claim against evidence and attach the sources. Most people never notice the citations. They only notice when an answer is wrong, and my job is to make that rare.
I build with AI every day. My product area runs on agentic workflows I set up, and I take my own prototypes from idea to working product. Making things keeps my judgment honest. What I want next is to lead a consumer AI product area, with a team of PMs and a high bar for what ships.
Now · Aug 2026Just got back from Maine, teaching my daughter to swim, prototyping a construction AI for my home remodel, and building a reading streak.
A Claude-powered version of me, grounded on my real career. Ask about the Google work, the consumer launches, or whether I'm a fit for your role, then email the real me. Five questions per session, so ask the good ones.
Hey, I'm a Claude-powered version of Adam, grounded on his real career. Ask me anything, or tap a starter below.
In May 2024, AI told users to put glue on pizza and eat rocks. Since then, teams like mine at Google have built the infrastructure that prevents that category of failure. Here's what grounding actually does. Toggle between the two states.
Grounding is how AI learns to cite its sources. The left side is what a model says from training alone. The right side is what happens when that same model is forced to answer from verified, real-world sources. My team builds the measurement layer that tells us which is which, at scale.
Four products where I owned the outcome, from AI infrastructure at Google to a global marketplace at Amazon. Use the arrows to move through them.
Five additional projects from Google and Publicis Sapient: platform reliability, ML personalization, a national grocery partnership with Google Cloud, a customer data platform, and a top-ranked mobile banking app.
What I think the job of product manager has become, and what's about to become rare enough to matter.
What changes when software is no longer the bottleneck
The cost of turning an idea into working software has collapsed in 18 months. A PM with good judgment and a working AI workflow can prototype, test, and refine ideas at a pace that used to require an engineering pod. I know because I do it.
Here's what actually shifted: in under a year, autonomous agents went from breaking after three minutes to running unsupervised for a full workday. That's a phase change. The PM job quietly stopped being about issuing instructions and started being about architecting the systems agents run inside.
Tools you can learn in a weekend. Product sense and execution are the work of a career.
"Anyone can swing a hammer. Very few people can produce finished carpentry. Craft is still the job."Read the full essay
A mental map of twenty years: four themes, not a list of jobs. Every role I've had sits in one of these buckets.
What makes generative AI worth shipping, not just demo-able. Grounding, factuality measurement, evals, real-time data freshness.
Taking ML from the lab into products people actually use every day.
Google · Albertsons · Marriott · Walgreens · U.S. Bank · Amazon · Aetna
Shipping for millions when reliability and polish are the whole product.
Amazon · Walgreens · U.S. Bank · Merrill Lynch
Building the function, not just working inside it, across retail, healthcare, financial services, and telecoms.
Publicis Sapient · KPMG · Razorfish · BearingPoint
Outside of work, I DJ under the name Bigfoot. Twenty years of digging through crates and playing sets around New York. Tell me a vibe and I'll find you something to play.
Recommendations are pulled from my Last.fm scrobble history: every song I've actually played. Each result comes with a Spotify preview.