Jaclyn Rice Nelson

Jaclyn Rice Nelson is co-founder and CEO of Tribe AI, an applied-AI delivery firm that staffs scoped builds from a curated network of specialists plus a small core team. She co-founded Tribe with Noah Gale after nearly eight years at Google and CapitalG, where she kept seeing growth-stage companies struggle to hire machine-learning talent even when Google specialists were a phone call away.

Michigan, Citi, and Google Helpouts

Raised by a single mother in New York and drawn early to financial independence, she graduated from the University of Michigan and started in investment banking at Citi before moving to Google in San Francisco. On Google Helpouts she was one of the first business people in a room of engineers building a live-video marketplace that connected people to experts - her first real look at product and engineering sitting together. Helpouts eventually shut down after a global launch, a lesson she has been frank about: startups inside large companies are often doomed even when the product idea is sound.

CapitalG and the expert-network habit

She moved to CapitalG (then Google Capital), Alphabet's growth equity firm, and helped build a large internal expert network that portfolio companies could tap. Advising firms like Airbnb and Stripe on infrastructure, security, and machine learning, she saw that even the best late-stage companies found data-science hiring slow and hard. In a Unite.AI interview she recalled wondering what companies without Google to fall back on were supposed to do. That question became the seed for Tribe.

South Park Commons and founding Tribe

At South Park Commons she met Gale and a circle of ML engineers who had left big tech for freedom - research, founding, or project work instead of climbing a ladder or optimizing ads. "The opportunity became clear: give top technical talent the freedom to take on flexible, unique projects they really want to work on," she told Unite.AI. Tribe launched as a highly curated network that could place those people on company projects while giving them community and schedule control. Early growth came without venture capital; she has described self-funding through periods of rapid revenue while learning hard lessons about customer concentration when the firm chased only the largest managed engagements.

On talent scarcity and project-based AI work

She argues most companies do not need a permanent full-time ML team for every phase of work - they need specialists to set a roadmap or ship a first system, then different skills to maintain it. In the Unite.AI series she framed AI as "the next gold rush" and said advances in generative models create both urgency and means for every company to become an AI company. Her hiring bar stays high on purpose: interviews are run by senior ML operators because "the top engineers want to be around other top engineers."