Voices: should AI run on your machine or in someone's cloud?
Voices is a new format on this site: one AI question at a time, the strongest arguments from each camp — stated as fairly as I can state them — and then my own position, so you know where the pointer comes from. Disagree with the round-up? The inbox is open: type mail.
The question this edition: should you run AI on your own hardware, or reach for someone else's cloud? Spend any time in the self-hosting communities (r/LocalLLaMA, the Hugging Face forums) or in any enterprise buying meeting, and you'll hear the same four positions.
Camp 1 — The sovereignty argument: "if it leaves, it's not yours"
The self-hosting community's core claim: sending your words, your documents, your customers' questions to a hosted model means renting your own capability. Every prompt is data leaving your control; every model update can change behavior you built on; every price change arrives as a fait accompli. Their strongest form: for anything that matters — client work, private notes, business operations — the processing boundary should be yours.
Camp 2 — The pragmatist counter: "your evening is worth more than your principle"
The pragmatist reply, usually from people who tried local first: small models are worse, setup is a hobby, and the hours spent tuning quantizations are hours not spent on the actual work. A hosted model is frontier capability in ten seconds, with someone else on call when it breaks. Their strongest form: sovereignty is a cost, and for most tasks most people should just pay it.
Camp 3 — The cost hawk: "run the math per task, not per ideology"
The finance-minded middle: neither camp is right in general, because the meter and the machine answer different questions. High-volume, repetitive, well-covered tasks (summarizing, extracting, routing, drafting) are dramatically cheaper per unit on hardware you own. Frontier reasoning and one-off tasks are worth the meter. Their strongest form: the right architecture is a barbell — local for the everyday bulk, cloud for the spikes.
Camp 4 — The capability maximalist: "the gap is the product"
The counter-push from people building at the edge: the quality gap between small local models and frontier hosted ones is the entire value in hard tasks, and it re-opens every few months as frontier moves. Running yesterday's model locally to save pennies is optimizing the wrong line item. Their strongest form: when the task is hard, capability is not a line item — it's the product.
Where this site stands
You can probably guess from the rest of this place that I sit closest to Camp 3, with Camp 1 setting the boundary conditions. The local-AI map on this site is my version of the barbell: local for the bulk, hosted for the frontier, and the decision made per job — because the machines underneath my businesses are mine, and that changes what the 2 a.m. feels like. But Camp 2 keeps me honest: every hour spent tuning a local setup is an hour of actual work lost, and the barbell is only worth it once the bulk is real.
The next edition takes on a sharper version of this: agents change the math — when your automation makes a thousand AI calls a day, which camp wins? That's a teams-of-agents question, and it's coming.