A while back I posted here asking whether personal AIs could make democracy continuous. The objection that stuck — u/Roodut's — wasn't about democracy at all. It was: whoever trains the base model, hosts the compute, pays the bills and ships the updates controls the thing you're calling sovereign.

I gave an answer at the time. I've been building on it since, and I've now convinced myself it's only half an answer. Rather than defend it, I'd rather you break it.

The answer I gave

Near-term sovereignty isn't "train your own frontier model." It's a hybrid stack:

• memory and identity local-first, encrypted, owned by the person;
• small local models for anything touching sensitive memory;
• encrypted cloud or trusted compute for heavy reasoning;
• portable memory in an open format, so leaving costs you nothing;
• open protocols between agents rather than one vendor's API.

The claim is that sovereignty lives in the memory and identity layer, not the weights.

The four places I think it breaks

1. Portable memory without portable calibration. I can export my memory file. But what makes a personal AI useful isn't the file — it's the months of correction that taught a specific model how to read me. Move to another base model and the memory transfers while the calibration doesn't. If that's right, the moat was never the data, and portability is mostly theatre.

2. Trusted compute is a promise from the party you're trying not to trust. Attestation tells you some code ran in some enclave. Verifying that the attested model is the one that shapes your agent's judgment, update after update, is a different problem — and the entity attesting is the entity you were hedging against.

3. Small local models may not be good enough for the one job that matters. Modelling a person's values, contradictions and decision style is not obviously an easy task you hand to the small model while the cloud does the "hard reasoning." It might be the hard part. If so, the sensitive work is exactly the work that leaves the device.

4. Open protocols have a bad track record against integrated products. Email and RSS won on paper. Most people's actual behaviour went to integrated products because they were better on day one. A protocol that's only competitive once everyone adopts it usually doesn't get adopted.

What I'm asking

Where else does this break — and has anyone actually shipped a piece of it?

Most useful to me:

• a concrete failure mode with the conditions that trigger it;
• an existing system that tried one of these four layers, and what happened to it;
• a reason one of my four objections is wrong, especially #1, which is the one that would hurt most;
• an implementation detail that makes the whole thing unrealistic on consumer hardware.

Least useful: general agreement that centralised AI is bad. I already think that — it doesn't tell me which layer to build first.

What I do with this: answers go into a model I keep as a graph, attributed to whoever said them, with a link to the thread. They don't become "evidence" and they don't move any number in it — a convincing argument becomes an experiment I have to run, not a fact I get to assert. Last thread's objections are still sitting in there unresolved, which is why I'm back.