A Google Non-Engineer and a PhD Founder on Their AI Pivots
The reason for moving from a stable org to the AI search team was clear: to survive five more years.
Survival competition inside big tech
He was originally on the operations side of a Google service. Stable, important work, but he grew anxious about how much it would help his career in the AI era, so he requested a transfer to the AI search team. After moving, he was surprised: years at the company, and yet everything felt new. This division moves with startup urgency. One PM came back from a one-week business trip to find two of three projects handed to someone else and a new scope attached to the third. Miss the internal messenger for two or three days and you cannot follow the direction of a project anymore, so he keeps AI attached, pulling real-time summaries and insights just to keep up.
The team move was unavoidable, just to survive about five more years. And in two or three years I will probably be looking for the next thing to survive again.
I was never the type to chase trends fast. I used to watch and follow slowly. But I feel it in my bones that this is not a time when watching is enough.
Even inside the big tech companies creating the AI wave, the competition to survive is fierce. Not settling into one seat, but continuously moving to where the next wave breaks.
AI Builders Week: stopping the company for a week
Google has repeatedly run something called AI Builders Week, blocking out an entire week for all employees to focus on AI, because what you learned a month ago is already history. Training is the baseline; people form study groups like college students and build things in mini hackathons. The message is clear: work can slow down a little, use AI no matter what.
The interesting part was the non-engineers. Engineers had been doing this gradually anyway. Non-engineers, once given dedicated time, lit up: awareness had been there without action, and now there was permission. Messengers burned until midnight, and people in distant offices gathered in one city to study harder together.
Executives stopped asking for status
A colleague told him something striking: for the first time in ten years of working, executives had not asked for a status update in two weeks. Executives now go straight into the AI tool, run their own queries, and see the state of things instantly, which shrinks the PM layer that used to summarize and relay in the middle.
This shakes the reason the PM role exists. A PM fundamentally sits between engineers and executives, understanding, organizing, and communicating how the program is going. AI has started doing the organizing and communicating. So he is now thinking about how to use AI to fill the space between executives and engineers more efficiently. Engineers used to build tools too vast and complex to touch directly; you asked for documentation, understood it, then reshaped it for executives. Now you pull the data straight from the tool.
The PhD who chose founding over the lab
The other person studied machine learning at a US engineering school, specifically the mathematical theory of neural networks, published meaningful papers, and had offers from several foundation model labs. Going that way would have made sense, but a close friend from undergrad pulled him into co-founding instead. That co-founder came out of big tech engineering but had passed through several startups, the kind of person who sold out a merch line in college.
He carries a similar worry: the frontier labs are advancing faster than expected, and he is debating whether to turn away entirely from the B2B productivity tools he has focused on.
What the two have in common
Two completely different backgrounds, and yet chewing over the conversations, they rhyme. Both feel the crisis of this wave urgently, and both are pivoting and adapting to ride it, actively drawing their futures. Neither has a clear answer yet; probably no one in this era does. What is certain is that both are moving to turn this into a second chance, in career and in life.
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This post is based on conversations with a TPM at the Google office in New York and a founder who finished a machine learning PhD.
