reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
Why it matters
It gives a concrete playbook for staying model-agnostic and controlling LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → costs — route by cost-per-capability-per-second not per-tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition →, use open weightsA model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.Full definition → models for mid-tier tasks, and reach for CPUs when you don't need an LLM at all — drawn from real production decisions at Notion.
Key quotes
“your supplier is your competitor. I know very few people who have convinced me that that's not true.”
Sarah Sachs
“Stop trying to win that game and think about the best product that uses many models.”
Sarah Sachs
“When you triage an email inbox, if we're charging you to do that on Opus, we're ripping you off and ourselves.”
Sarah Sachs
“optionality is your leverage if you don't have the capability to walk at any point you are stuck”
videoWhy AI Agents Need Million-Token Context — Thomas Wolf & Olive Song, MiniMax
videoYour company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL
videoTethered: Our Agents Are Us — Shu Fang, Two Sigma
videoAgents' next frontier: agent-to-agent and network effects — Jean-Denis Greze, Town
