Glenda Bautista-Baker

I started as a trafficker at DoubleClick when the industry was still arguing about what display advertising even was. I've been building the commercial layer of the internet ever since.

I came up through operations, which is a different education. I learned the industry from inside the failure: the misfiring tag, the reporting discrepancy, the gap between what the IO said and what the platform actually did. I know what those gaps cost, who feels them, and why they keep getting rebuilt wrong. The difference between a system that looks good in a review and one that holds up when someone actually has to use it is something I learned from the inside out.

I arrive where the monetization infrastructure doesn't exist yet, design it from the execution layer outward, and build it under live conditions. Not as a consultant or an advisor. As the person who owns it. My work has spanned every variant of that problem: true greenfield builds, systems inherited mid-flight, mature platforms that needed to move faster than their infrastructure allowed — across publishing, broadcast, audio, video, and digital media.

What I do has gone by a lot of titles: ad operations, programmatic, revenue strategy, ad product, monetization infrastructure, depending on who was doing the hiring and what part of the stack they could see. Same work underneath, every time. From trafficking, I worked up through sales engineering, design, development, and eventually product... most of the value chain except sales, and even that's debatable.

What I find most interesting right now is the gap between where the industry thinks AI is taking it and what actually holds up when you have to operate it at scale. I can see that gap clearly because I learned this work before it had a name, when the reasoning had to be derived instead of inherited. The tools are changing. The infrastructure problem isn't.