Building Safe AI Systems in Practice
Emilie Schario has had the kind of career that makes the rest of us re-evaluate ours: GitLab through its pre-IPO years, data at Netlify, strategist in residence at Amplify, founder of Turbine (acquired by Settle), and now co-founder and VP Engineering at Kilo Code, recently acquired by Anaconda. She joins Jake Moshenko to talk about what AI adoption actually looks like on the ground. Not the discourse, the practice. The conversation starts somewhere unexpected: a hair-care app she built for herself after her third kid, used for months, then threw away. She calls it selfieware: software with no users, no tracking, and no authorization, built because the cost of building finally dropped below the cost of caring. The best thing she ever built with AI was a thing she was able to discard. From there: why multi-model is inevitable (a chef doesn't use one knife for every task), what happens when benchmarks become training data, and why tying your tooling to a single lab's models is a bet you don't need to make. Then the part that lands closest to home for anyone thinking about authorization. Emilie runs two agents (Chad, personal; Snivel, for work) and holds a hard line between them: an agent gets internal data or external comms, never both. She arrives at agent identity, audit trails, and code provenance on her own terms, from the practitioner's side rather than the vendor's. Also discussed: whether AI spend is even the right metric (she proposes spend divided by PRs merged), the financial institution budgeting $13 per engineer per month, and why the biggest gap right now isn't capability. It's the long tail of people who haven't started yet.
