Notion AI

Monetizing AI — Alvaro Morales, Orb

Jul 23, 2025 · 18:18

Alvaro Morales, CEO of Orb, argues that traditional subscription-based SaaS pricing fails for AI products due to rapid cost fluctuations, margin pressures, and customer demand for clear ROI. He presents three frameworks: decide between direct monetization (add-on, bundled) or indirect (free to drive upsells) using examples like GitHub Copilot, Notion AI, and Expedia; select a value metric from resource-based tokens (Vercel's v0) to task-based (Zapier) to outcome-based pricing (Intercom's Fin at $0.99 per resolved ticket); and embrace continual experimentation, as AI pricing cycles shrink from years to months. To reduce pricing guesswork, Orb built Simulations, a tool that lets companies backtest alternate pricing strategies on real usage data from closed betas before launch, helping teams like Replit and Perplexity optimize revenue without risking customer trust.

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.

The Hidden Life of Embeddings: Linus Lee

Nov 7, 2023 · 18:15

Linus Lee, a Research Engineer at Notion, presents a tour of embedding visualization and manipulation at the AI Engineer Summit 2023. He demonstrates an encoder-decoder model fine-tuned from T5 that can reconstruct text from embeddings, allowing direct manipulation of features like length and sentiment by moving in latent space. Lee shows that mixing embeddings by splicing dimensions from two texts produces a semantic blend, and a linear adapter can decode text from OpenAI's text-embedding-ada-002 embedding space. Using CLIP, he interpolates between photographic and cartoon images and performs vector arithmetic to modify facial expressions. Lee releases these custom text embedding models on Hugging Face, enabling others to explore and interact with latent spaces. He argues that making model internals visible and manipulable fosters deeper understanding and more humane interfaces to generative AI.