We think you should own your AI.

1. 01

   Local, open, small models are good enough.

   Good enough to do just about everything people actually use frontier models for. Some researchers came to this conclusion by measuring a million real queries. We came to it based off vibes. Both hold up.

   • —A Stanford study ran local models against one million real single-turn chat and reasoning queries: they successfully answered 88.7%, and the share of queries a local model could serve rose from 23.2% to 71.3% between 2023 and 2025.
   • —Give a small model web search, code execution, and file search, and we believe the experience can beat talking to a bigger model without them.
   • —And small models are getting really, really good: open models a fraction of the size of the flagships now match them on several benchmarks.

   What the small ones can do
2. 02

   Running models locally answers the data-center problem.

   Many of the problems attributed to data centers and cloud AI stop being your problems when the model runs on hardware you already own.

   • —Electricity demand is outrunning what grids can supply or build.
   • —Cost per unit of intelligence keeps falling, but demand grows faster — Jevons paradox — so the bills climb anyway.
   • —Water, power, and noise are real concerns. That research is not fully established, and we say so; the concern is what we are answering.
   • —Data-center-scale compute should go to high-leverage work — research, medicine, hard problems — not another thank-you email.

   The energy case
3. 03

   Teaching everyone data privacy is a lost cause (sort of).

   People will keep pasting sensitive things into whatever chatbot is closest. The realistic fix is not a lecture; it is a better default: local first, with heavier work offloaded to more capable models only when the owner approves it.

   • —Teachers throw student data into personal chatbot accounts. Doctors type in patient symptoms. Tired people upload their tax documents. Company secrets go in to draft a thank-you email.
   • —In Cisco's 2024 Consumer Privacy Survey, 84% of generative-AI users worried about the data they enter going public — and 30% entered personal or confidential information anyway.
   • —Nobody should be confined to one sanctioned tool or model just to satisfy a privacy agreement.

   The privacy case
4. 04

   No one gets left behind in an AI-native world.

   We are young, and a lot of our friends want nothing to do with AI. We want our peers to access and benefit from this technology without feeling they are making a moral concession — because we think AI can be a force for good, and we want as many good people as possible participating in actualizing its positive potential.

   • —Seven in ten Americans oppose an AI data center being built in their own area, including 48% who strongly oppose it (Gallup, 2026).
   • —Among US adults under 30, 55% are now more concerned than excited about AI, the first time a majority of that group has said so (Pew, 2026).

   How people feel about AI
5. 05

   Open-source models are more than good enough.

   Frontier and really large models should be tools to call upon, not the default. Routing is the name of the game:

   • —Cost efficiency — most questions do not need a frontier model.
   • —Use each model's strengths and expertise.
   • —Avoid each model's weaknesses and shortcomings.

   Chat with open models

Read the post where we first published these: Intro to Offline Base on Substack.