Privacy Architecture for the AGI Era.

Most privacy policies are compliance theater. They are written by legal teams to minimize liability while maximizing data collection. They use phrases like “we take your privacy seriously” while describing 47 categories of data that are collected, shared, and retained indefinitely.

Our privacy policy for Ohm Studio is one sentence: nothing is collected.

This is not marketing. This is architecture. The application has no server component. There is no analytics SDK. No telemetry. No crash reporter. No account system. No advertising ID. No fingerprinting. The only outbound traffic is the one-time download of model weights from public open-weights repositories — and even that is anonymous, with no token, no registration, no attribution.

The Technical Difference

A privacy policy that says “we minimize data collection” is a promise. A privacy policy that says “nothing is collected” is a proof. The difference matters because promises can be broken by acquisition, by regulatory pressure, by security breach, or by simple scope creep. Proofs are baked into the code.

In Ohm Studio, the audio you generate, the datasets you assemble, the adapters you train, and the lyrics you draft all live in your local Application Support folder. They are yours. We do not have access to them. Deleting the application removes the software; your work remains until you delete it manually. There is no cloud sync that could be subpoenaed. There is no server that could be breached. There is no database that could be sold.

The AGI Context

As artificial general intelligence approaches, the value of data changes. Today, your prompts and generated audio are training data for someone else’s model. Tomorrow, they could be evidence in a regulatory proceeding, inputs to a behavioral profiling system, or assets in a corporate acquisition.

Centralized AI services know what you create, when you create it, how you refine it, and what you delete. They know your creative patterns, your stylistic preferences, your working hours, your iterative process. This is not hypothetical — it is the business model of every cloud-based generative AI service today.

Local AI is the only architecture that eliminates this exposure. Not because of encryption. Not because of terms of service. Because there is no server to collect the data in the first place.

The Silo Systems Standard

This standard applies across all our products and services:

• 01No telemetry. No usage metrics, no event logging, no session tracking.
• 02No accounts. No username, no password, no email verification, no password reset flow.
• 03No cloud dependency. The application works identically on a plane at 35,000 feet and at your desk.
• 04Open-weights models. MIT-adjacent licensing means your generated output carries no platform claim.
• 05Forensic documentation. Every configuration is explained, every hardening step is verified, every deliverable is indexed.

This is not the easy path. It is the correct path. Building applications that work without a server requires more engineering discipline, not less. It requires local model inference, on-device training, client-side storage, and offline-first architecture. These are harder problems than wrapping an API call. They are also the only problems worth solving for a world where centralized infrastructure is increasingly a liability.