This is a linkpost for https://gwern.net/guardian-angel
I am very skeptical this would have any significant impact.
First off, where are you going to run the models? Average consumer hardware can't run the best open weight models pre-distillation and even those are a notch below closed weight SOTA models. Power user my be fine with this but the average Joe would either be lock out or be forced to use cloud compute which comes with its own set of security hazards. Now the bad actors just needs to shutdown or worse, hack your servers and all your defenses are either gone or turned against you.
Second, isn't this just speeding up human disempowerment? I mean, you now have these GA models which do most of the thinking for you while you sit on your high and mighty throne sipping wine and praying that when these models eventually become smarter then you, they would still be loyal (or at least not apathetic or hostile) to you.
Which just loops back to the alignment problem, that is assuming your GA's are able to keep up with the frontier which I think is VERY unlikely.
And if you fail to keep up with the frontier then at some point your human/subhuman level models will have to fend off literal machine gods.
To be clear I am not against this. It doesn't hurt to try and in the best possible case where closed weight frontier models lag behind long enough for this project to come to fruition then this would buy us some valuable time.
I just don't really see that happening.
I'd advise against this. The most severe breakdowns of models I've seen over-bias towards the sims of humans (have some theories why this is, but off topic).
I think the idea of having individualized AI and human pairs as aligned is a great idea, but would strongly recommend that existing infrastructural methods be used to create shared/symbiotic incentives vs simply trying to create digital twins of the humans themselves.
How is this different from what ChatGPT, OpenClaw are already doing? Claude is the one that pivoted more business-purposes than whole-user purposes.
Powerful LLMs will be deployed at global scale in the next few years, and will dominate the Internet, and increasingly, ordinary life. As of mid-2026, there is no coherent vision for how knowledge professionals, or ordinary people, will be able to harness these LLMs for large productivity increases, or how they will handle cybersecurity and cognitive security.
I propose a goal of creating Guardian Angels (GA): digital twin LLMs which are personalized with the goal of providing not the stereotypical "assistant chatbot agent" persona, but emulating a single user's personality, values, and preferences.
This weakly solves the principal-agent problem by unifying the principal and agent as much as possible. In a GA future, the focus of the "principal" user is on defining what is worth doing by the GA (agent) users, and not on what or how to do things, functioning as the CEO or 'board' of an 'AI corporation'. This allows them to deploy numerous agents to achieve desirable things and to handle security, like screening all messages for advanced attacks (like interlocking ecosystems of synthetic media for propaganda or spearphishing). They cannot solve larger AI alignment problems, but they can help individual humans as part of a society-wide defense-in-depth strategy.
A GA persona is productive because it learns to emulate the principal's outputs but with higher quality. It is trustworthy because it is, by definition, allied with its principal and shares its values and goals. And it is secure in part by hardwiring a single, unique, situated user (for whom following a prompt attack would be absurd), avoiding 'confused deputy' problems, while periodic upgrades of the underlying model and the defenders' advantage allow GAs to keep up with attackers.
Standard techniques like prompt programming of in-context-learning for "frozen" models will not create useful GAs due to the limitations of post-training, context windows and self-attention with frozen weights in compute-efficient-but-under-parameterized models, low-compute outputs, and the status quo of passive offline data collection---which are collectively responsible for chatbots' disappointing results in knowledge worker amplification and creative writing and fatal errors in agentic settings.
We can try to create GAs by a combination of techniques: online learning (via dynamic evaluation) to update LLMs in realtime to avoid ignorance and fatal errors while remaining competitive with frozen frontier models, sample efficiency from pretrained preference-oriented large models and active Learning by querying the principal for corrections and preference data (obtaining low regret from DAgger-style bounds), and a local CLI-first logging-oriented UI/UX paradigm.
GAs could be done as an open-source community effort, but given the need for high security in deployment and the rising challenge of APTs equipped with Mythos-scale attackers, it probably makes more sense as a startup, catering initially to power-users and knowledge workers such as CEOs or researchers, and moving downwards as it is refined.