Sarah Sachs
Engineering leader at Notion leading AI Modeling, including reasoning and agentic orchestration, core model engineering, search and ranking, and data specialists and evaluations.
Model Optionality Is the Defense Against Volatile Token Economics
Sarah Sachs, who leads AI engineering at Notion, argues that inference costs and rapid model changes can make an AI product economically untenable unless teams preserve the ability to switch suppliers. Because frontier labs are often both token vendors and direct product competitors, she says companies should route work by the cost, capability and latency a task requires—not by token price alone—and reserve frontier models for work that warrants them. Notion’s response is a multi-model architecture, with an auto model handling most traffic, alongside open-weight models, deterministic software and governance controls.
The Model Alone Is No Longer the AI Product
At AI Engineer Melbourne 2026’s Day 1 keynote program, speakers including Shawn Wang, George Cameron, Sarah Sachs, Igor Costa, Vamsi Ramakrishnan and Geoffrey Huntley argued that AI engineering has moved beyond picking the strongest model. Their shared case was that useful AI products now depend on the systems around models: harnesses, routing, evals, memory, state, latency budgets, deterministic tools and cost controls. The model still matters, but the keynote program framed product advantage as an architecture and economics problem, not a leaderboard problem.