Sarah Sachs

Notion's Token Town — Sarah Sachs, Notion

Jul 23, 2026 · 23:55

Sarah Sachs, Notion's AI engineering lead and contract negotiator, argues that AI companies must stop competing on token economics and instead build model-agnostic products that win on data flywheels, orchestration, and security. She advises treating every model supplier as a competitor, because frontier labs charge a markup on a markup for tokens they sell for first-party use. Notion's auto model routes 75% of traffic through a Switzerland-like system that swaps providers underneath, avoiding vendor lock-in. Sachs advocates routing by cost per capability per second, using open weight models for the moderate middle, and reaching for CPUs over GPUs (e.g., no LLM needed to turn a CSV into a PDF). She highlights the 'lethal trifecta' of private data, untrusted content, and external communication as the next security challenge, and demos Notion agents scoping a task, tagging teammates, and opening a PR. Her core message: optionality is leverage, and the product must transcend tokens.

How to build world-class AI products — Sarah Sachs (AI lead @ Notion) & Carlos Esteban (Braintrust)

Jun 27, 2025 · 1:43:46

Sarah Sachs (AI lead at Notion) and Carlos Esteban (Braintrust) explain that building great AI products requires 10% prompting and 90% evals and observability, with Notion AI using Braintrust to iterate on prompts and models. Sachs details their cycle: curate small datasets, tie them to scoring functions (LLM-as-a-judge with per-sample prompts and heuristic checks), run evals before shipping, and use production logs to catch regressions. She notes Notion AI supports 100M+ users, switches models in under a day, and 60% of enterprise users are non-English—requiring multilingual eval rigor. Carlos and Doug then walk through Braintrust's framework: tasks (prompts, tools, agents), datasets, and scores (0-1). They demonstrate offline evals via playground and SDK, online scoring in production, user feedback capture, human review setup, and remote evals to bridge complex code with the playground. The workshop covers moving from pre-prod evals to production monitoring, closing the feedback loop by adding underperforming spans to datasets.