Georgie Steele
CTO & Cofounder
Stealth
Georgie Steele is a second-time founder and engineering leader building AI products for high-stakes, regulated environments. She is currently founding a new venture in fintech and wealth management, building agents that behave reliably in production and tackling problems few have solved: long-horizon memory under tight context budgets, low-latency systems, and earning trust in a heavily regulated space.
Previously, Georgie was Co-Founder and CTO of Maiven, a climate tech startup making complex regulation actionable with AI, where she raised £1.5M pre-seed in two weeks and shipped the MVP two months later.
An award-winning engineer recognised by Forbes 30 Under 30, she has led high-impact technical teams across McLaren, Tractable, and Sylvera, scaling backend platforms tenfold and contributing to a unicorn valuation.
She is also a committed advocate for diversity in tech, having mentored over 100 underrepresented technologists and represented UN Women UK at the UN Commission on the Status of Women.
Georgie speaks on building trustworthy AI products, agents in production, and what engineering leadership actually requires in the AI era.
What Shape Are You? Rethinking Engineering in the AI Era
Keynote
Somewhere along the way, engineering got defined as the art of excellent execution. Understand the requirements, build the thing well, ship on time. And for a long time, that was enough.
It isn't anymore.
AI has thinned out the layers between technical decisions and commercial outcomes. Smaller teams, faster cycles, fewer people holding customer context upstream of the engineers who need it. The result? Brilliant technical people making expensive decisions in the dark: optimising for depth and accuracy that nobody asked for, in products built for the wrong customer entirely.
This session introduces the flower-shaped professional: a practical model for what senior engineering leadership actually requires right now. We'll look at which petals matter most, what it looks like to test for them in hiring, and how to honestly assess which ones you still need to grow yourself.
Grounded in real experience building AI products from zero, and the hard lessons that come with it.