How to Start With AI When You've Already Decided It's Too Hard (13 Year Google Veteran POV) - w/ Aishwarya

Aishwarya spent thirteen years at Google across several internal pivots, including YouTube monetization, before becoming a principal product manager at an AI-focused travel company. Around her twelfth anniversary, she began asking what work outside Google looked like and whether she could explore AI without limiting herself to one company's products and processes.

Leaving was difficult because Google still offered sophisticated AI work. Her immediate role, however, was in YouTube monetization, and switching internally into the most desired AI teams required several competitive steps. She concluded that the broader market might offer a faster route to direct AI ownership than constraining her search to the Google ecosystem.

Model choice also mattered. At work she used Gemini, while outside she wanted to experiment with Claude and other tools as their relative strengths changed. She wanted an environment where she could select the model that fit the task rather than wait for internal permission or platform support.

Aishwarya graduated during the 2009 recession and did not begin with a perfectly mapped career. An analytics company in Chennai gave her a foundation in data and machine learning when those fields were only starting to become mainstream job categories. That experience led into Google and shaped how she approached later technology shifts.

At Google, she joined a group handling long-tail operational problems that larger teams lacked bandwidth to solve. She and a colleague tested whether a small amount of code and machine learning could make decisions at human quality but greater scale. Early successes showed her that automation's value was not novelty; it was applying consistent judgment across volumes people could not handle manually.