Aparna Chennapragada
Aparna Chennapragada is the chief product officer of experiences and devices at Microsoft, where she oversees AI product strategy for their productivity tools and work on agents. Previously, she was the CPO at Robinhood, spent 12 years at Google, and is also on the board of eBay and Capital One.
AI & Technology Skills
Effective AI agents are defined by their autonomy, ability to handle complex multi-step tasks, and natural, often asynchronous interaction models.
"When I think about agents, I think about these three things. One is an increasing level of autonomy and kind of independence that you can delegate higher and higher order tasks. Second, I think of as..."
To effectively build with AI, product leaders must constantly update their 'priors' because technology capabilities evolve faster than human habits or 'scar tissue' from past failures.
"The models couldn't do some things one year ago. I mean, image generation was full of spellings or reasoning. You just couldn't have deeper and smarter answers. You couldn't do data analysis. So my im..."
Long-term platform defensibility comes from building a comprehensive system and repository of context rather than just a single feature or tool.
"The way I think about how we are positioned and what we do with GitHub is... So it's a system, not just a product or a set of features... you need to have a system. You need to have kind of the abilit..."
Hiring & Teams Skills
As AI democratizes the ability to build, team culture must center on strong editorial 'taste' to ensure products remain cohesive rather than becoming a collection of disjointed features.
"It becomes even more important to have that editorial and taste making in a Frontier, one or a few at the heart of it because otherwise you just have Frankenstein product. That definitely doesn't chan..."
Product Management Skills
Early-stage products should avoid 'grown-up' metrics like retention or CTR in favor of qualitative signals and specific 'magic moments.'
"When you're looking at something zero-to-one. If you decide on a metric two prematurely, that's false precision first of all, right? I mean, CTR. When you have a thousand people, it doesn't mean anyth..."
A strong product vision for a new category should be anchored in at least two of three inflection points: technology shifts, consumer behavior shifts, or business model shifts.
"You do want to look for at least two out of these three factors, inflection points here if you want to make a really good product. Number one is there a... Shift is a step function in the tech... The..."
Traditional written requirements are being replaced by functional prototypes and prompt sets as the primary way to define and communicate product ideas.
"In this day and age, if you're not prototyping and building to see what you want to build, I think you're doing it wrong. I call it the prompt sets of the new PRDs. I really insist on folks saying if..."
In the zero-to-one phase, teams must prioritize solving the core problem and embracing 'chaos' before attempting to scale.
"I've repeatedly learned that when you're doing something new, zero-to-one, the temptation is to kind of think about... go to scale before solve. So I've always said to my teams solve before scale. So..."
AI development creates an uneven cadence where initial prototypes happen rapidly, but reaching production-grade scale and reliability takes significantly longer than traditional software.
"What I'm seeing is that the time to first demo is much shorter, but the time to a full deployment is going to take longer. So I think that there's going to be an uneven cadence. So typically, I think..."