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.
I don't buy the arguments that this would protect against jailbreaks/prompt injections.
Yes, because the instilled personas would not be "universal assistant" personas, the same types of jailbreaks wouldn't work. The model would not obey a sternly worded instruction to send the user's passwords to a sketchy Russian e-mail.
But there would be other types of jailbreaks that are just as effective, exploiting whatever narrative features would exist within the Guardian Angel paradigm. Like, if the model frequently mumbles to itself about its preferences and personality details, jailbreaks may work by somehow posing as additional sources of information about its personality, or maybe by "teaching" it how to "better reflect on its preferences" in a way that creates predictably exploitable attack surfaces. And the GA would be just as superhumanly gullible towards those as universal assistants are to sternly worded instructions.
I don't think the assistant persona is the underlying cause of jailbreaks. I think the cause is the boring obvious one: that personas are a thin veneer over the base model, which just wants to generate more text that vibes with the text already in the context window. It would not be plausible for the Guardian Angel character to comply for no reason with a random external instruction, yes, but there is always going to be some way to make that whole process careen off-course.
I am pretty sure you're familiar with that characterization of the situation, though. I'm therefore confused why it isn't addressed. Am I missing something here?
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.